10832 lines
1.2 MiB
10832 lines
1.2 MiB
Save Path : ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844
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{'arch': 'GDAS_F1', 'data_path': '/home/dongxuanyi/.torch/cifar.python', 'dataset': 'cifar10', 'grad_clip': 5.0, 'init_channels': 36, 'layers': 20, 'manualSeed': 6844, 'model_config': './configs/nas-cifar-cos-cut.config', 'print_freq': 100, 'save_path': './output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844', 'workers': 6}
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Random Seed : 6844
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Python version : 3.6.8 |Anaconda, Inc.| (default, Dec 30 2018, 01:22:34) [GCC 7.3.0]
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Torch version : 1.0.1
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CUDA version : 9.2.148
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cuDNN version : 7301
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Num of GPUs : 1
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configuration : Configure(type='cosine', batch_size=96, epochs=600, momentum=0.9, decay=0.0003, LR=0.025, LR_MIN=0.0001, auxiliary=True, auxiliary_weight=0.4, grad_clip=5.0, cutout=16, drop_path_prob=0.2)
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genotype : Genotype(normal=[('skip_connect', 0, 0.16), ('skip_connect', 1, 0.13), ('skip_connect', 0, 0.17), ('sep_conv_3x3', 2, 0.15), ('skip_connect', 0, 0.17), ('sep_conv_3x3', 2, 0.15), ('skip_connect', 0, 0.16), ('sep_conv_3x3', 2, 0.15)], normal_concat=[2, 3, 4, 5], reduce=None, reduce_concat=[2, 3, 4, 5])
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-------------------------------------- main-procedure
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config : Configure(type='cosine', batch_size=96, epochs=600, momentum=0.9, decay=0.0003, LR=0.025, LR_MIN=0.0001, auxiliary=True, auxiliary_weight=0.4, grad_clip=5.0, cutout=16, drop_path_prob=0.2)
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genotype : Genotype(normal=[('skip_connect', 0, 0.16), ('skip_connect', 1, 0.13), ('skip_connect', 0, 0.17), ('sep_conv_3x3', 2, 0.15), ('skip_connect', 0, 0.17), ('sep_conv_3x3', 2, 0.15), ('skip_connect', 0, 0.16), ('sep_conv_3x3', 2, 0.15)], normal_concat=[2, 3, 4, 5], reduce=None, reduce_concat=[2, 3, 4, 5])
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init_channels : 36
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layers : 20
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class_num : 10
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Network =>
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NetworkCIFAR(
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(stem): Sequential(
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(0): Conv2d(3, 108, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
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(1): BatchNorm2d(108, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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)
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(cells): ModuleList(
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(0): Cell(
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(preprocess0): ReLUConvBN(
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(op): Sequential(
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(0): ReLU()
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(1): Conv2d(108, 36, kernel_size=(1, 1), stride=(1, 1), bias=False)
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(2): BatchNorm2d(36, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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)
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)
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(preprocess1): ReLUConvBN(
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(op): Sequential(
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(0): ReLU()
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(1): Conv2d(108, 36, kernel_size=(1, 1), stride=(1, 1), bias=False)
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(2): BatchNorm2d(36, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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)
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)
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(_ops): ModuleList(
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(0): Identity()
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(1): Identity()
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(2): Identity()
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(3): SepConv(
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(op): Sequential(
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(0): ReLU()
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(1): Conv2d(36, 36, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=36, bias=False)
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(2): Conv2d(36, 36, kernel_size=(1, 1), stride=(1, 1), bias=False)
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(3): BatchNorm2d(36, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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(4): ReLU()
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(5): Conv2d(36, 36, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=36, bias=False)
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(6): Conv2d(36, 36, kernel_size=(1, 1), stride=(1, 1), bias=False)
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(7): BatchNorm2d(36, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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)
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)
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(4): Identity()
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(5): SepConv(
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(op): Sequential(
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(0): ReLU()
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(1): Conv2d(36, 36, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=36, bias=False)
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(2): Conv2d(36, 36, kernel_size=(1, 1), stride=(1, 1), bias=False)
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(3): BatchNorm2d(36, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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(4): ReLU()
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(5): Conv2d(36, 36, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=36, bias=False)
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(6): Conv2d(36, 36, kernel_size=(1, 1), stride=(1, 1), bias=False)
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(7): BatchNorm2d(36, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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)
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)
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(6): Identity()
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(7): SepConv(
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(op): Sequential(
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(0): ReLU()
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(1): Conv2d(36, 36, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=36, bias=False)
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(2): Conv2d(36, 36, kernel_size=(1, 1), stride=(1, 1), bias=False)
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(3): BatchNorm2d(36, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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(4): ReLU()
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(5): Conv2d(36, 36, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=36, bias=False)
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(6): Conv2d(36, 36, kernel_size=(1, 1), stride=(1, 1), bias=False)
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(7): BatchNorm2d(36, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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)
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)
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)
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)
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(1): Cell(
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(preprocess0): ReLUConvBN(
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(op): Sequential(
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(0): ReLU()
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(1): Conv2d(108, 36, kernel_size=(1, 1), stride=(1, 1), bias=False)
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(2): BatchNorm2d(36, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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)
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)
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(preprocess1): ReLUConvBN(
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(op): Sequential(
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(0): ReLU()
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(1): Conv2d(144, 36, kernel_size=(1, 1), stride=(1, 1), bias=False)
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(2): BatchNorm2d(36, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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)
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)
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(_ops): ModuleList(
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(0): Identity()
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(1): Identity()
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(2): Identity()
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(3): SepConv(
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(op): Sequential(
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(0): ReLU()
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(1): Conv2d(36, 36, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=36, bias=False)
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(2): Conv2d(36, 36, kernel_size=(1, 1), stride=(1, 1), bias=False)
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(3): BatchNorm2d(36, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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(4): ReLU()
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(5): Conv2d(36, 36, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=36, bias=False)
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(6): Conv2d(36, 36, kernel_size=(1, 1), stride=(1, 1), bias=False)
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(7): BatchNorm2d(36, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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)
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)
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(4): Identity()
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(5): SepConv(
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(op): Sequential(
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(0): ReLU()
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(1): Conv2d(36, 36, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=36, bias=False)
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(2): Conv2d(36, 36, kernel_size=(1, 1), stride=(1, 1), bias=False)
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(3): BatchNorm2d(36, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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(4): ReLU()
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(5): Conv2d(36, 36, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=36, bias=False)
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(6): Conv2d(36, 36, kernel_size=(1, 1), stride=(1, 1), bias=False)
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(7): BatchNorm2d(36, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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)
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)
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(6): Identity()
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(7): SepConv(
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(op): Sequential(
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(0): ReLU()
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(1): Conv2d(36, 36, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=36, bias=False)
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(2): Conv2d(36, 36, kernel_size=(1, 1), stride=(1, 1), bias=False)
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(3): BatchNorm2d(36, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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(4): ReLU()
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(5): Conv2d(36, 36, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=36, bias=False)
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(6): Conv2d(36, 36, kernel_size=(1, 1), stride=(1, 1), bias=False)
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(7): BatchNorm2d(36, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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)
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)
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)
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)
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(2): Cell(
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(preprocess0): ReLUConvBN(
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(op): Sequential(
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(0): ReLU()
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(1): Conv2d(144, 36, kernel_size=(1, 1), stride=(1, 1), bias=False)
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(2): BatchNorm2d(36, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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)
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)
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(preprocess1): ReLUConvBN(
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(op): Sequential(
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(0): ReLU()
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(1): Conv2d(144, 36, kernel_size=(1, 1), stride=(1, 1), bias=False)
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(2): BatchNorm2d(36, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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)
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)
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(_ops): ModuleList(
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(0): Identity()
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(1): Identity()
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(2): Identity()
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(3): SepConv(
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(op): Sequential(
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(0): ReLU()
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(1): Conv2d(36, 36, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=36, bias=False)
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(2): Conv2d(36, 36, kernel_size=(1, 1), stride=(1, 1), bias=False)
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(3): BatchNorm2d(36, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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(4): ReLU()
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(5): Conv2d(36, 36, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=36, bias=False)
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(6): Conv2d(36, 36, kernel_size=(1, 1), stride=(1, 1), bias=False)
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(7): BatchNorm2d(36, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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)
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)
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(4): Identity()
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(5): SepConv(
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(op): Sequential(
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(0): ReLU()
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(1): Conv2d(36, 36, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=36, bias=False)
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(2): Conv2d(36, 36, kernel_size=(1, 1), stride=(1, 1), bias=False)
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(3): BatchNorm2d(36, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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(4): ReLU()
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(5): Conv2d(36, 36, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=36, bias=False)
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(6): Conv2d(36, 36, kernel_size=(1, 1), stride=(1, 1), bias=False)
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(7): BatchNorm2d(36, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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)
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)
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(6): Identity()
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(7): SepConv(
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(op): Sequential(
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(0): ReLU()
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(1): Conv2d(36, 36, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=36, bias=False)
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(2): Conv2d(36, 36, kernel_size=(1, 1), stride=(1, 1), bias=False)
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(3): BatchNorm2d(36, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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(4): ReLU()
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(5): Conv2d(36, 36, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=36, bias=False)
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(6): Conv2d(36, 36, kernel_size=(1, 1), stride=(1, 1), bias=False)
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(7): BatchNorm2d(36, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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)
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)
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)
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)
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(3): Cell(
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(preprocess0): ReLUConvBN(
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(op): Sequential(
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(0): ReLU()
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(1): Conv2d(144, 36, kernel_size=(1, 1), stride=(1, 1), bias=False)
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(2): BatchNorm2d(36, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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)
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)
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(preprocess1): ReLUConvBN(
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(op): Sequential(
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(0): ReLU()
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(1): Conv2d(144, 36, kernel_size=(1, 1), stride=(1, 1), bias=False)
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(2): BatchNorm2d(36, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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)
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)
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(_ops): ModuleList(
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(0): Identity()
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(1): Identity()
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(2): Identity()
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(3): SepConv(
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(op): Sequential(
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(0): ReLU()
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(1): Conv2d(36, 36, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=36, bias=False)
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(2): Conv2d(36, 36, kernel_size=(1, 1), stride=(1, 1), bias=False)
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(3): BatchNorm2d(36, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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(4): ReLU()
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(5): Conv2d(36, 36, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=36, bias=False)
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(6): Conv2d(36, 36, kernel_size=(1, 1), stride=(1, 1), bias=False)
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(7): BatchNorm2d(36, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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)
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)
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(4): Identity()
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(5): SepConv(
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(op): Sequential(
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(0): ReLU()
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(1): Conv2d(36, 36, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=36, bias=False)
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(2): Conv2d(36, 36, kernel_size=(1, 1), stride=(1, 1), bias=False)
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(3): BatchNorm2d(36, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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(4): ReLU()
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(5): Conv2d(36, 36, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=36, bias=False)
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(6): Conv2d(36, 36, kernel_size=(1, 1), stride=(1, 1), bias=False)
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(7): BatchNorm2d(36, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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)
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)
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(6): Identity()
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(7): SepConv(
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(op): Sequential(
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(0): ReLU()
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(1): Conv2d(36, 36, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=36, bias=False)
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(2): Conv2d(36, 36, kernel_size=(1, 1), stride=(1, 1), bias=False)
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(3): BatchNorm2d(36, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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(4): ReLU()
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(5): Conv2d(36, 36, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=36, bias=False)
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(6): Conv2d(36, 36, kernel_size=(1, 1), stride=(1, 1), bias=False)
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(7): BatchNorm2d(36, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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)
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)
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)
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)
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(4): Cell(
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(preprocess0): ReLUConvBN(
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(op): Sequential(
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(0): ReLU()
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(1): Conv2d(144, 36, kernel_size=(1, 1), stride=(1, 1), bias=False)
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(2): BatchNorm2d(36, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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)
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)
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(preprocess1): ReLUConvBN(
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(op): Sequential(
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(0): ReLU()
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(1): Conv2d(144, 36, kernel_size=(1, 1), stride=(1, 1), bias=False)
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(2): BatchNorm2d(36, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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)
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)
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(_ops): ModuleList(
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(0): Identity()
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|
(1): Identity()
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|
(2): Identity()
|
|
(3): SepConv(
|
|
(op): Sequential(
|
|
(0): ReLU()
|
|
(1): Conv2d(36, 36, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=36, bias=False)
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(2): Conv2d(36, 36, kernel_size=(1, 1), stride=(1, 1), bias=False)
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(3): BatchNorm2d(36, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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(4): ReLU()
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(5): Conv2d(36, 36, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=36, bias=False)
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(6): Conv2d(36, 36, kernel_size=(1, 1), stride=(1, 1), bias=False)
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(7): BatchNorm2d(36, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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)
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)
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(4): Identity()
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(5): SepConv(
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(op): Sequential(
|
|
(0): ReLU()
|
|
(1): Conv2d(36, 36, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=36, bias=False)
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(2): Conv2d(36, 36, kernel_size=(1, 1), stride=(1, 1), bias=False)
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(3): BatchNorm2d(36, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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(4): ReLU()
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|
(5): Conv2d(36, 36, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=36, bias=False)
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(6): Conv2d(36, 36, kernel_size=(1, 1), stride=(1, 1), bias=False)
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(7): BatchNorm2d(36, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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)
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)
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(6): Identity()
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|
(7): SepConv(
|
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(op): Sequential(
|
|
(0): ReLU()
|
|
(1): Conv2d(36, 36, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=36, bias=False)
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(2): Conv2d(36, 36, kernel_size=(1, 1), stride=(1, 1), bias=False)
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(3): BatchNorm2d(36, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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(4): ReLU()
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(5): Conv2d(36, 36, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=36, bias=False)
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(6): Conv2d(36, 36, kernel_size=(1, 1), stride=(1, 1), bias=False)
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(7): BatchNorm2d(36, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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)
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)
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)
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)
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|
(5): Cell(
|
|
(preprocess0): ReLUConvBN(
|
|
(op): Sequential(
|
|
(0): ReLU()
|
|
(1): Conv2d(144, 36, kernel_size=(1, 1), stride=(1, 1), bias=False)
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(2): BatchNorm2d(36, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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)
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)
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(preprocess1): ReLUConvBN(
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(op): Sequential(
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(0): ReLU()
|
|
(1): Conv2d(144, 36, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(2): BatchNorm2d(36, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
)
|
|
)
|
|
(_ops): ModuleList(
|
|
(0): Identity()
|
|
(1): Identity()
|
|
(2): Identity()
|
|
(3): SepConv(
|
|
(op): Sequential(
|
|
(0): ReLU()
|
|
(1): Conv2d(36, 36, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=36, bias=False)
|
|
(2): Conv2d(36, 36, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(3): BatchNorm2d(36, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
(4): ReLU()
|
|
(5): Conv2d(36, 36, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=36, bias=False)
|
|
(6): Conv2d(36, 36, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(7): BatchNorm2d(36, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
)
|
|
)
|
|
(4): Identity()
|
|
(5): SepConv(
|
|
(op): Sequential(
|
|
(0): ReLU()
|
|
(1): Conv2d(36, 36, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=36, bias=False)
|
|
(2): Conv2d(36, 36, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(3): BatchNorm2d(36, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
(4): ReLU()
|
|
(5): Conv2d(36, 36, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=36, bias=False)
|
|
(6): Conv2d(36, 36, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(7): BatchNorm2d(36, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
)
|
|
)
|
|
(6): Identity()
|
|
(7): SepConv(
|
|
(op): Sequential(
|
|
(0): ReLU()
|
|
(1): Conv2d(36, 36, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=36, bias=False)
|
|
(2): Conv2d(36, 36, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(3): BatchNorm2d(36, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
(4): ReLU()
|
|
(5): Conv2d(36, 36, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=36, bias=False)
|
|
(6): Conv2d(36, 36, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(7): BatchNorm2d(36, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
)
|
|
)
|
|
)
|
|
)
|
|
(6): Transition(
|
|
(preprocess0): ReLUConvBN(
|
|
(op): Sequential(
|
|
(0): ReLU()
|
|
(1): Conv2d(144, 72, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(2): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
)
|
|
)
|
|
(preprocess1): ReLUConvBN(
|
|
(op): Sequential(
|
|
(0): ReLU()
|
|
(1): Conv2d(144, 72, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(2): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
)
|
|
)
|
|
(ops1): ModuleList(
|
|
(0): Sequential(
|
|
(0): ReLU()
|
|
(1): Conv2d(72, 72, kernel_size=(1, 3), stride=(1, 2), padding=(0, 1), groups=8, bias=False)
|
|
(2): Conv2d(72, 72, kernel_size=(3, 1), stride=(2, 1), padding=(1, 0), groups=8, bias=False)
|
|
(3): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
(4): ReLU()
|
|
(5): Conv2d(72, 72, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(6): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
)
|
|
(1): Sequential(
|
|
(0): ReLU()
|
|
(1): Conv2d(72, 72, kernel_size=(1, 3), stride=(1, 2), padding=(0, 1), groups=8, bias=False)
|
|
(2): Conv2d(72, 72, kernel_size=(3, 1), stride=(2, 1), padding=(1, 0), groups=8, bias=False)
|
|
(3): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
(4): ReLU()
|
|
(5): Conv2d(72, 72, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(6): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
)
|
|
)
|
|
(ops2): ModuleList(
|
|
(0): Sequential(
|
|
(0): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
|
|
(1): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
)
|
|
(1): Sequential(
|
|
(0): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
|
|
(1): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
)
|
|
)
|
|
)
|
|
(7): Cell(
|
|
(preprocess0): FactorizedReduce(
|
|
(relu): ReLU()
|
|
(conv_1): Conv2d(144, 36, kernel_size=(1, 1), stride=(2, 2), bias=False)
|
|
(conv_2): Conv2d(144, 36, kernel_size=(1, 1), stride=(2, 2), bias=False)
|
|
(bn): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
(pad): ConstantPad2d(padding=(0, 1, 0, 1), value=0)
|
|
)
|
|
(preprocess1): ReLUConvBN(
|
|
(op): Sequential(
|
|
(0): ReLU()
|
|
(1): Conv2d(288, 72, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(2): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
)
|
|
)
|
|
(_ops): ModuleList(
|
|
(0): Identity()
|
|
(1): Identity()
|
|
(2): Identity()
|
|
(3): SepConv(
|
|
(op): Sequential(
|
|
(0): ReLU()
|
|
(1): Conv2d(72, 72, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=72, bias=False)
|
|
(2): Conv2d(72, 72, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(3): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
(4): ReLU()
|
|
(5): Conv2d(72, 72, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=72, bias=False)
|
|
(6): Conv2d(72, 72, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(7): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
)
|
|
)
|
|
(4): Identity()
|
|
(5): SepConv(
|
|
(op): Sequential(
|
|
(0): ReLU()
|
|
(1): Conv2d(72, 72, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=72, bias=False)
|
|
(2): Conv2d(72, 72, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(3): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
(4): ReLU()
|
|
(5): Conv2d(72, 72, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=72, bias=False)
|
|
(6): Conv2d(72, 72, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(7): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
)
|
|
)
|
|
(6): Identity()
|
|
(7): SepConv(
|
|
(op): Sequential(
|
|
(0): ReLU()
|
|
(1): Conv2d(72, 72, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=72, bias=False)
|
|
(2): Conv2d(72, 72, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(3): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
(4): ReLU()
|
|
(5): Conv2d(72, 72, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=72, bias=False)
|
|
(6): Conv2d(72, 72, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(7): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
)
|
|
)
|
|
)
|
|
)
|
|
(8): Cell(
|
|
(preprocess0): ReLUConvBN(
|
|
(op): Sequential(
|
|
(0): ReLU()
|
|
(1): Conv2d(288, 72, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(2): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
)
|
|
)
|
|
(preprocess1): ReLUConvBN(
|
|
(op): Sequential(
|
|
(0): ReLU()
|
|
(1): Conv2d(288, 72, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(2): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
)
|
|
)
|
|
(_ops): ModuleList(
|
|
(0): Identity()
|
|
(1): Identity()
|
|
(2): Identity()
|
|
(3): SepConv(
|
|
(op): Sequential(
|
|
(0): ReLU()
|
|
(1): Conv2d(72, 72, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=72, bias=False)
|
|
(2): Conv2d(72, 72, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(3): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
(4): ReLU()
|
|
(5): Conv2d(72, 72, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=72, bias=False)
|
|
(6): Conv2d(72, 72, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(7): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
)
|
|
)
|
|
(4): Identity()
|
|
(5): SepConv(
|
|
(op): Sequential(
|
|
(0): ReLU()
|
|
(1): Conv2d(72, 72, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=72, bias=False)
|
|
(2): Conv2d(72, 72, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(3): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
(4): ReLU()
|
|
(5): Conv2d(72, 72, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=72, bias=False)
|
|
(6): Conv2d(72, 72, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(7): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
)
|
|
)
|
|
(6): Identity()
|
|
(7): SepConv(
|
|
(op): Sequential(
|
|
(0): ReLU()
|
|
(1): Conv2d(72, 72, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=72, bias=False)
|
|
(2): Conv2d(72, 72, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(3): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
(4): ReLU()
|
|
(5): Conv2d(72, 72, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=72, bias=False)
|
|
(6): Conv2d(72, 72, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(7): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
)
|
|
)
|
|
)
|
|
)
|
|
(9): Cell(
|
|
(preprocess0): ReLUConvBN(
|
|
(op): Sequential(
|
|
(0): ReLU()
|
|
(1): Conv2d(288, 72, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(2): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
)
|
|
)
|
|
(preprocess1): ReLUConvBN(
|
|
(op): Sequential(
|
|
(0): ReLU()
|
|
(1): Conv2d(288, 72, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(2): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
)
|
|
)
|
|
(_ops): ModuleList(
|
|
(0): Identity()
|
|
(1): Identity()
|
|
(2): Identity()
|
|
(3): SepConv(
|
|
(op): Sequential(
|
|
(0): ReLU()
|
|
(1): Conv2d(72, 72, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=72, bias=False)
|
|
(2): Conv2d(72, 72, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(3): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
(4): ReLU()
|
|
(5): Conv2d(72, 72, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=72, bias=False)
|
|
(6): Conv2d(72, 72, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(7): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
)
|
|
)
|
|
(4): Identity()
|
|
(5): SepConv(
|
|
(op): Sequential(
|
|
(0): ReLU()
|
|
(1): Conv2d(72, 72, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=72, bias=False)
|
|
(2): Conv2d(72, 72, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(3): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
(4): ReLU()
|
|
(5): Conv2d(72, 72, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=72, bias=False)
|
|
(6): Conv2d(72, 72, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(7): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
)
|
|
)
|
|
(6): Identity()
|
|
(7): SepConv(
|
|
(op): Sequential(
|
|
(0): ReLU()
|
|
(1): Conv2d(72, 72, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=72, bias=False)
|
|
(2): Conv2d(72, 72, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(3): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
(4): ReLU()
|
|
(5): Conv2d(72, 72, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=72, bias=False)
|
|
(6): Conv2d(72, 72, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(7): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
)
|
|
)
|
|
)
|
|
)
|
|
(10): Cell(
|
|
(preprocess0): ReLUConvBN(
|
|
(op): Sequential(
|
|
(0): ReLU()
|
|
(1): Conv2d(288, 72, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(2): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
)
|
|
)
|
|
(preprocess1): ReLUConvBN(
|
|
(op): Sequential(
|
|
(0): ReLU()
|
|
(1): Conv2d(288, 72, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(2): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
)
|
|
)
|
|
(_ops): ModuleList(
|
|
(0): Identity()
|
|
(1): Identity()
|
|
(2): Identity()
|
|
(3): SepConv(
|
|
(op): Sequential(
|
|
(0): ReLU()
|
|
(1): Conv2d(72, 72, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=72, bias=False)
|
|
(2): Conv2d(72, 72, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(3): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
(4): ReLU()
|
|
(5): Conv2d(72, 72, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=72, bias=False)
|
|
(6): Conv2d(72, 72, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(7): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
)
|
|
)
|
|
(4): Identity()
|
|
(5): SepConv(
|
|
(op): Sequential(
|
|
(0): ReLU()
|
|
(1): Conv2d(72, 72, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=72, bias=False)
|
|
(2): Conv2d(72, 72, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(3): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
(4): ReLU()
|
|
(5): Conv2d(72, 72, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=72, bias=False)
|
|
(6): Conv2d(72, 72, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(7): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
)
|
|
)
|
|
(6): Identity()
|
|
(7): SepConv(
|
|
(op): Sequential(
|
|
(0): ReLU()
|
|
(1): Conv2d(72, 72, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=72, bias=False)
|
|
(2): Conv2d(72, 72, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(3): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
(4): ReLU()
|
|
(5): Conv2d(72, 72, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=72, bias=False)
|
|
(6): Conv2d(72, 72, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(7): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
)
|
|
)
|
|
)
|
|
)
|
|
(11): Cell(
|
|
(preprocess0): ReLUConvBN(
|
|
(op): Sequential(
|
|
(0): ReLU()
|
|
(1): Conv2d(288, 72, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(2): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
)
|
|
)
|
|
(preprocess1): ReLUConvBN(
|
|
(op): Sequential(
|
|
(0): ReLU()
|
|
(1): Conv2d(288, 72, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(2): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
)
|
|
)
|
|
(_ops): ModuleList(
|
|
(0): Identity()
|
|
(1): Identity()
|
|
(2): Identity()
|
|
(3): SepConv(
|
|
(op): Sequential(
|
|
(0): ReLU()
|
|
(1): Conv2d(72, 72, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=72, bias=False)
|
|
(2): Conv2d(72, 72, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(3): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
(4): ReLU()
|
|
(5): Conv2d(72, 72, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=72, bias=False)
|
|
(6): Conv2d(72, 72, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(7): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
)
|
|
)
|
|
(4): Identity()
|
|
(5): SepConv(
|
|
(op): Sequential(
|
|
(0): ReLU()
|
|
(1): Conv2d(72, 72, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=72, bias=False)
|
|
(2): Conv2d(72, 72, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(3): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
(4): ReLU()
|
|
(5): Conv2d(72, 72, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=72, bias=False)
|
|
(6): Conv2d(72, 72, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(7): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
)
|
|
)
|
|
(6): Identity()
|
|
(7): SepConv(
|
|
(op): Sequential(
|
|
(0): ReLU()
|
|
(1): Conv2d(72, 72, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=72, bias=False)
|
|
(2): Conv2d(72, 72, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(3): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
(4): ReLU()
|
|
(5): Conv2d(72, 72, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=72, bias=False)
|
|
(6): Conv2d(72, 72, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(7): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
)
|
|
)
|
|
)
|
|
)
|
|
(12): Cell(
|
|
(preprocess0): ReLUConvBN(
|
|
(op): Sequential(
|
|
(0): ReLU()
|
|
(1): Conv2d(288, 72, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(2): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
)
|
|
)
|
|
(preprocess1): ReLUConvBN(
|
|
(op): Sequential(
|
|
(0): ReLU()
|
|
(1): Conv2d(288, 72, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(2): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
)
|
|
)
|
|
(_ops): ModuleList(
|
|
(0): Identity()
|
|
(1): Identity()
|
|
(2): Identity()
|
|
(3): SepConv(
|
|
(op): Sequential(
|
|
(0): ReLU()
|
|
(1): Conv2d(72, 72, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=72, bias=False)
|
|
(2): Conv2d(72, 72, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(3): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
(4): ReLU()
|
|
(5): Conv2d(72, 72, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=72, bias=False)
|
|
(6): Conv2d(72, 72, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(7): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
)
|
|
)
|
|
(4): Identity()
|
|
(5): SepConv(
|
|
(op): Sequential(
|
|
(0): ReLU()
|
|
(1): Conv2d(72, 72, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=72, bias=False)
|
|
(2): Conv2d(72, 72, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(3): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
(4): ReLU()
|
|
(5): Conv2d(72, 72, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=72, bias=False)
|
|
(6): Conv2d(72, 72, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(7): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
)
|
|
)
|
|
(6): Identity()
|
|
(7): SepConv(
|
|
(op): Sequential(
|
|
(0): ReLU()
|
|
(1): Conv2d(72, 72, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=72, bias=False)
|
|
(2): Conv2d(72, 72, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(3): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
(4): ReLU()
|
|
(5): Conv2d(72, 72, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=72, bias=False)
|
|
(6): Conv2d(72, 72, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(7): BatchNorm2d(72, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
)
|
|
)
|
|
)
|
|
)
|
|
(13): Transition(
|
|
(preprocess0): ReLUConvBN(
|
|
(op): Sequential(
|
|
(0): ReLU()
|
|
(1): Conv2d(288, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(2): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
)
|
|
)
|
|
(preprocess1): ReLUConvBN(
|
|
(op): Sequential(
|
|
(0): ReLU()
|
|
(1): Conv2d(288, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(2): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
)
|
|
)
|
|
(ops1): ModuleList(
|
|
(0): Sequential(
|
|
(0): ReLU()
|
|
(1): Conv2d(144, 144, kernel_size=(1, 3), stride=(1, 2), padding=(0, 1), groups=8, bias=False)
|
|
(2): Conv2d(144, 144, kernel_size=(3, 1), stride=(2, 1), padding=(1, 0), groups=8, bias=False)
|
|
(3): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
(4): ReLU()
|
|
(5): Conv2d(144, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(6): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
)
|
|
(1): Sequential(
|
|
(0): ReLU()
|
|
(1): Conv2d(144, 144, kernel_size=(1, 3), stride=(1, 2), padding=(0, 1), groups=8, bias=False)
|
|
(2): Conv2d(144, 144, kernel_size=(3, 1), stride=(2, 1), padding=(1, 0), groups=8, bias=False)
|
|
(3): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
(4): ReLU()
|
|
(5): Conv2d(144, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(6): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
)
|
|
)
|
|
(ops2): ModuleList(
|
|
(0): Sequential(
|
|
(0): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
|
|
(1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
)
|
|
(1): Sequential(
|
|
(0): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
|
|
(1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
)
|
|
)
|
|
)
|
|
(14): Cell(
|
|
(preprocess0): FactorizedReduce(
|
|
(relu): ReLU()
|
|
(conv_1): Conv2d(288, 72, kernel_size=(1, 1), stride=(2, 2), bias=False)
|
|
(conv_2): Conv2d(288, 72, kernel_size=(1, 1), stride=(2, 2), bias=False)
|
|
(bn): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
(pad): ConstantPad2d(padding=(0, 1, 0, 1), value=0)
|
|
)
|
|
(preprocess1): ReLUConvBN(
|
|
(op): Sequential(
|
|
(0): ReLU()
|
|
(1): Conv2d(576, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(2): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
)
|
|
)
|
|
(_ops): ModuleList(
|
|
(0): Identity()
|
|
(1): Identity()
|
|
(2): Identity()
|
|
(3): SepConv(
|
|
(op): Sequential(
|
|
(0): ReLU()
|
|
(1): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)
|
|
(2): Conv2d(144, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(3): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
(4): ReLU()
|
|
(5): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)
|
|
(6): Conv2d(144, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(7): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
)
|
|
)
|
|
(4): Identity()
|
|
(5): SepConv(
|
|
(op): Sequential(
|
|
(0): ReLU()
|
|
(1): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)
|
|
(2): Conv2d(144, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(3): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
(4): ReLU()
|
|
(5): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)
|
|
(6): Conv2d(144, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(7): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
)
|
|
)
|
|
(6): Identity()
|
|
(7): SepConv(
|
|
(op): Sequential(
|
|
(0): ReLU()
|
|
(1): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)
|
|
(2): Conv2d(144, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(3): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
(4): ReLU()
|
|
(5): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)
|
|
(6): Conv2d(144, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(7): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
)
|
|
)
|
|
)
|
|
)
|
|
(15): Cell(
|
|
(preprocess0): ReLUConvBN(
|
|
(op): Sequential(
|
|
(0): ReLU()
|
|
(1): Conv2d(576, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(2): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
)
|
|
)
|
|
(preprocess1): ReLUConvBN(
|
|
(op): Sequential(
|
|
(0): ReLU()
|
|
(1): Conv2d(576, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(2): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
)
|
|
)
|
|
(_ops): ModuleList(
|
|
(0): Identity()
|
|
(1): Identity()
|
|
(2): Identity()
|
|
(3): SepConv(
|
|
(op): Sequential(
|
|
(0): ReLU()
|
|
(1): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)
|
|
(2): Conv2d(144, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(3): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
(4): ReLU()
|
|
(5): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)
|
|
(6): Conv2d(144, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(7): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
)
|
|
)
|
|
(4): Identity()
|
|
(5): SepConv(
|
|
(op): Sequential(
|
|
(0): ReLU()
|
|
(1): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)
|
|
(2): Conv2d(144, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(3): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
(4): ReLU()
|
|
(5): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)
|
|
(6): Conv2d(144, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(7): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
)
|
|
)
|
|
(6): Identity()
|
|
(7): SepConv(
|
|
(op): Sequential(
|
|
(0): ReLU()
|
|
(1): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)
|
|
(2): Conv2d(144, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(3): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
(4): ReLU()
|
|
(5): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)
|
|
(6): Conv2d(144, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(7): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
)
|
|
)
|
|
)
|
|
)
|
|
(16): Cell(
|
|
(preprocess0): ReLUConvBN(
|
|
(op): Sequential(
|
|
(0): ReLU()
|
|
(1): Conv2d(576, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(2): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
)
|
|
)
|
|
(preprocess1): ReLUConvBN(
|
|
(op): Sequential(
|
|
(0): ReLU()
|
|
(1): Conv2d(576, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(2): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
)
|
|
)
|
|
(_ops): ModuleList(
|
|
(0): Identity()
|
|
(1): Identity()
|
|
(2): Identity()
|
|
(3): SepConv(
|
|
(op): Sequential(
|
|
(0): ReLU()
|
|
(1): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)
|
|
(2): Conv2d(144, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(3): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
(4): ReLU()
|
|
(5): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)
|
|
(6): Conv2d(144, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(7): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
)
|
|
)
|
|
(4): Identity()
|
|
(5): SepConv(
|
|
(op): Sequential(
|
|
(0): ReLU()
|
|
(1): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)
|
|
(2): Conv2d(144, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(3): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
(4): ReLU()
|
|
(5): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)
|
|
(6): Conv2d(144, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(7): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
)
|
|
)
|
|
(6): Identity()
|
|
(7): SepConv(
|
|
(op): Sequential(
|
|
(0): ReLU()
|
|
(1): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)
|
|
(2): Conv2d(144, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(3): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
(4): ReLU()
|
|
(5): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)
|
|
(6): Conv2d(144, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(7): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
)
|
|
)
|
|
)
|
|
)
|
|
(17): Cell(
|
|
(preprocess0): ReLUConvBN(
|
|
(op): Sequential(
|
|
(0): ReLU()
|
|
(1): Conv2d(576, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(2): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
)
|
|
)
|
|
(preprocess1): ReLUConvBN(
|
|
(op): Sequential(
|
|
(0): ReLU()
|
|
(1): Conv2d(576, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
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(2): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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)
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)
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(_ops): ModuleList(
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(0): Identity()
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(1): Identity()
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(2): Identity()
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(3): SepConv(
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(op): Sequential(
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(0): ReLU()
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(1): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)
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(2): Conv2d(144, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
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(3): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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(4): ReLU()
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(5): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)
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(6): Conv2d(144, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
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(7): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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)
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)
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(4): Identity()
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(5): SepConv(
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(op): Sequential(
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(0): ReLU()
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(1): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)
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(2): Conv2d(144, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
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(3): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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(4): ReLU()
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(5): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)
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(6): Conv2d(144, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
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(7): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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)
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)
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(6): Identity()
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(7): SepConv(
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(op): Sequential(
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(0): ReLU()
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(1): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)
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(2): Conv2d(144, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
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(3): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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(4): ReLU()
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(5): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)
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(6): Conv2d(144, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
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(7): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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)
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)
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)
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)
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(18): Cell(
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(preprocess0): ReLUConvBN(
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(op): Sequential(
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(0): ReLU()
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(1): Conv2d(576, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
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(2): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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)
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)
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(preprocess1): ReLUConvBN(
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(op): Sequential(
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(0): ReLU()
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(1): Conv2d(576, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
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(2): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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)
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)
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(_ops): ModuleList(
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(0): Identity()
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(1): Identity()
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(2): Identity()
|
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(3): SepConv(
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(op): Sequential(
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(0): ReLU()
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(1): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)
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(2): Conv2d(144, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
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(3): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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(4): ReLU()
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(5): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)
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(6): Conv2d(144, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
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(7): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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)
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)
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(4): Identity()
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(5): SepConv(
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(op): Sequential(
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(0): ReLU()
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(1): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)
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(2): Conv2d(144, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
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(3): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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(4): ReLU()
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(5): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)
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(6): Conv2d(144, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
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(7): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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)
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)
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(6): Identity()
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(7): SepConv(
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(op): Sequential(
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(0): ReLU()
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(1): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)
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(2): Conv2d(144, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
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(3): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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(4): ReLU()
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(5): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)
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(6): Conv2d(144, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
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(7): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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)
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)
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)
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)
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(19): Cell(
|
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(preprocess0): ReLUConvBN(
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(op): Sequential(
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(0): ReLU()
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(1): Conv2d(576, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
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(2): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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)
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)
|
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(preprocess1): ReLUConvBN(
|
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(op): Sequential(
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(0): ReLU()
|
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(1): Conv2d(576, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
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(2): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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)
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)
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(_ops): ModuleList(
|
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(0): Identity()
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(1): Identity()
|
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(2): Identity()
|
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(3): SepConv(
|
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(op): Sequential(
|
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(0): ReLU()
|
|
(1): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)
|
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(2): Conv2d(144, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
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(3): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
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(4): ReLU()
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(5): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)
|
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(6): Conv2d(144, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
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(7): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
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)
|
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)
|
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(4): Identity()
|
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(5): SepConv(
|
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(op): Sequential(
|
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(0): ReLU()
|
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(1): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)
|
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(2): Conv2d(144, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
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(3): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
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(4): ReLU()
|
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(5): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)
|
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(6): Conv2d(144, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
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(7): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
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)
|
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)
|
|
(6): Identity()
|
|
(7): SepConv(
|
|
(op): Sequential(
|
|
(0): ReLU()
|
|
(1): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)
|
|
(2): Conv2d(144, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(3): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
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(4): ReLU()
|
|
(5): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)
|
|
(6): Conv2d(144, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
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(7): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
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)
|
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)
|
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)
|
|
)
|
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)
|
|
(auxiliary_head): AuxiliaryHeadCIFAR(
|
|
(features): Sequential(
|
|
(0): ReLU(inplace)
|
|
(1): AvgPool2d(kernel_size=5, stride=3, padding=0)
|
|
(2): Conv2d(576, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)
|
|
(3): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
(4): ReLU(inplace)
|
|
(5): Conv2d(128, 768, kernel_size=(2, 2), stride=(1, 1), bias=False)
|
|
(6): BatchNorm2d(768, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
|
|
(7): ReLU(inplace)
|
|
)
|
|
(classifier): Linear(in_features=768, out_features=10, bias=True)
|
|
)
|
|
(global_pooling): AdaptiveAvgPool2d(output_size=1)
|
|
(classifier): Linear(in_features=576, out_features=10, bias=True)
|
|
)
|
|
Parameters : 3.01944 - 0.476426 = 2.543 MB
|
|
config : Configure(type='cosine', batch_size=96, epochs=600, momentum=0.9, decay=0.0003, LR=0.025, LR_MIN=0.0001, auxiliary=True, auxiliary_weight=0.4, grad_clip=5.0, cutout=16, drop_path_prob=0.2)
|
|
genotype : Genotype(normal=[('skip_connect', 0, 0.16), ('skip_connect', 1, 0.13), ('skip_connect', 0, 0.17), ('sep_conv_3x3', 2, 0.15), ('skip_connect', 0, 0.17), ('sep_conv_3x3', 2, 0.15), ('skip_connect', 0, 0.16), ('sep_conv_3x3', 2, 0.15)], normal_concat=[2, 3, 4, 5], reduce=None, reduce_concat=[2, 3, 4, 5])
|
|
args : Namespace(arch='GDAS_F1', data_path='/home/dongxuanyi/.torch/cifar.python', dataset='cifar10', grad_clip=5.0, init_channels=36, layers=20, manualSeed=6844, model_config='./configs/nas-cifar-cos-cut.config', print_freq=100, save_path='./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844', workers=6)
|
|
Train-Dataset : Dataset CIFAR10
|
|
Number of datapoints: 50000
|
|
Split: train
|
|
Root Location: /home/dongxuanyi/.torch/cifar.python
|
|
Transforms (if any): Compose(
|
|
RandomHorizontalFlip(p=0.5)
|
|
RandomCrop(size=(32, 32), padding=4)
|
|
ToTensor()
|
|
Normalize(mean=[0.4913725490196078, 0.4823529411764706, 0.4466666666666667], std=[0.24705882352941178, 0.24352941176470588, 0.2615686274509804])
|
|
Cutout(length=16)
|
|
)
|
|
Target Transforms (if any): None
|
|
Train-Trans : Compose(
|
|
RandomHorizontalFlip(p=0.5)
|
|
RandomCrop(size=(32, 32), padding=4)
|
|
ToTensor()
|
|
Normalize(mean=[0.4913725490196078, 0.4823529411764706, 0.4466666666666667], std=[0.24705882352941178, 0.24352941176470588, 0.2615686274509804])
|
|
Cutout(length=16)
|
|
)
|
|
Test--Dataset : Dataset CIFAR10
|
|
Number of datapoints: 10000
|
|
Split: test
|
|
Root Location: /home/dongxuanyi/.torch/cifar.python
|
|
Transforms (if any): Compose(
|
|
ToTensor()
|
|
Normalize(mean=[0.4913725490196078, 0.4823529411764706, 0.4466666666666667], std=[0.24705882352941178, 0.24352941176470588, 0.2615686274509804])
|
|
)
|
|
Target Transforms (if any): None
|
|
Test--Trans : Compose(
|
|
ToTensor()
|
|
Normalize(mean=[0.4913725490196078, 0.4823529411764706, 0.4466666666666667], std=[0.24705882352941178, 0.24352941176470588, 0.2615686274509804])
|
|
)
|
|
Train model from scratch without pre-trained model or snapshot
|
|
|
|
==>>[2019-03-31-14:59:52] [Epoch=000/600] [Need: 00:00:00] LR=0.0250 ~ 0.0250, Batch=96
|
|
train[2019-03-31-14:59:58] Epoch: [000][000/521] Time 6.48 (6.48) Data 0.35 (0.35) Loss 3.224 (3.224) Prec@1 10.42 (10.42) Prec@5 61.46 (61.46)
|
|
train[2019-03-31-15:00:23] Epoch: [000][100/521] Time 0.22 (0.31) Data 0.00 (0.00) Loss 2.652 (2.798) Prec@1 33.33 (24.42) Prec@5 85.42 (77.02)
|
|
train[2019-03-31-15:00:46] Epoch: [000][200/521] Time 0.23 (0.27) Data 0.00 (0.00) Loss 2.189 (2.615) Prec@1 40.62 (29.34) Prec@5 90.62 (82.09)
|
|
train[2019-03-31-15:01:09] Epoch: [000][300/521] Time 0.23 (0.25) Data 0.00 (0.00) Loss 2.165 (2.478) Prec@1 44.79 (33.42) Prec@5 90.62 (84.85)
|
|
train[2019-03-31-15:01:31] Epoch: [000][400/521] Time 0.23 (0.25) Data 0.00 (0.00) Loss 2.050 (2.369) Prec@1 52.08 (36.66) Prec@5 87.50 (86.68)
|
|
train[2019-03-31-15:01:54] Epoch: [000][500/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 2.017 (2.278) Prec@1 51.04 (39.40) Prec@5 94.79 (87.97)
|
|
train[2019-03-31-15:02:03] Epoch: [000][520/521] Time 5.22 (0.25) Data 0.00 (0.00) Loss 1.817 (2.263) Prec@1 47.50 (39.86) Prec@5 95.00 (88.17)
|
|
[2019-03-31-15:02:04] **train** Prec@1 39.86 Prec@5 88.17 Error@1 60.14 Error@5 11.83 Loss:2.263
|
|
test [2019-03-31-15:02:04] Epoch: [000][000/105] Time 0.48 (0.48) Data 0.40 (0.40) Loss 1.348 (1.348) Prec@1 48.96 (48.96) Prec@5 94.79 (94.79)
|
|
test [2019-03-31-15:02:08] Epoch: [000][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 1.292 (1.460) Prec@1 55.21 (49.75) Prec@5 96.88 (94.07)
|
|
test [2019-03-31-15:02:08] Epoch: [000][104/105] Time 0.11 (0.05) Data 0.00 (0.00) Loss 0.978 (1.463) Prec@1 62.50 (49.65) Prec@5 93.75 (94.02)
|
|
[2019-03-31-15:02:08] **test** Prec@1 49.65 Prec@5 94.02 Error@1 50.35 Error@5 5.98 Loss:1.463
|
|
----> Best Accuracy : Acc@1=49.65, Acc@5=94.02, Error@1=50.35, Error@5=5.98
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-15:02:09] [Epoch=001/600] [Need: 22:43:40] LR=0.0250 ~ 0.0250, Batch=96
|
|
train[2019-03-31-15:02:12] Epoch: [001][000/521] Time 3.19 (3.19) Data 0.44 (0.44) Loss 2.103 (2.103) Prec@1 44.79 (44.79) Prec@5 90.62 (90.62)
|
|
train[2019-03-31-15:02:36] Epoch: [001][100/521] Time 0.24 (0.27) Data 0.00 (0.00) Loss 1.668 (1.779) Prec@1 52.08 (54.09) Prec@5 95.83 (94.30)
|
|
train[2019-03-31-15:03:00] Epoch: [001][200/521] Time 0.23 (0.25) Data 0.00 (0.00) Loss 1.739 (1.722) Prec@1 51.04 (55.86) Prec@5 93.75 (94.81)
|
|
train[2019-03-31-15:03:23] Epoch: [001][300/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 1.511 (1.682) Prec@1 55.21 (56.94) Prec@5 96.88 (95.17)
|
|
train[2019-03-31-15:03:47] Epoch: [001][400/521] Time 0.23 (0.25) Data 0.00 (0.00) Loss 1.565 (1.646) Prec@1 59.37 (57.95) Prec@5 95.83 (95.38)
|
|
train[2019-03-31-15:04:11] Epoch: [001][500/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 1.575 (1.623) Prec@1 60.42 (58.70) Prec@5 91.67 (95.51)
|
|
train[2019-03-31-15:04:16] Epoch: [001][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 1.780 (1.615) Prec@1 57.50 (58.87) Prec@5 96.25 (95.54)
|
|
[2019-03-31-15:04:16] **train** Prec@1 58.87 Prec@5 95.54 Error@1 41.13 Error@5 4.46 Loss:1.615
|
|
test [2019-03-31-15:04:16] Epoch: [001][000/105] Time 0.47 (0.47) Data 0.41 (0.41) Loss 1.343 (1.343) Prec@1 56.25 (56.25) Prec@5 93.75 (93.75)
|
|
test [2019-03-31-15:04:20] Epoch: [001][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 1.301 (1.243) Prec@1 65.62 (59.30) Prec@5 92.71 (95.60)
|
|
test [2019-03-31-15:04:20] Epoch: [001][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.931 (1.242) Prec@1 68.75 (59.34) Prec@5 100.00 (95.59)
|
|
[2019-03-31-15:04:20] **test** Prec@1 59.34 Prec@5 95.59 Error@1 40.66 Error@5 4.41 Loss:1.242
|
|
----> Best Accuracy : Acc@1=59.34, Acc@5=95.59, Error@1=40.66, Error@5=4.41
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-15:04:21] [Epoch=002/600] [Need: 21:56:37] LR=0.0250 ~ 0.0250, Batch=96
|
|
train[2019-03-31-15:04:21] Epoch: [002][000/521] Time 0.70 (0.70) Data 0.43 (0.43) Loss 1.607 (1.607) Prec@1 52.08 (52.08) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-15:04:45] Epoch: [002][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 1.387 (1.428) Prec@1 60.42 (64.66) Prec@5 98.96 (96.63)
|
|
train[2019-03-31-15:05:09] Epoch: [002][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 1.221 (1.408) Prec@1 63.54 (64.75) Prec@5 98.96 (96.71)
|
|
train[2019-03-31-15:05:32] Epoch: [002][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 1.246 (1.377) Prec@1 69.79 (65.48) Prec@5 97.92 (96.93)
|
|
train[2019-03-31-15:05:56] Epoch: [002][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 1.211 (1.356) Prec@1 71.88 (66.21) Prec@5 97.92 (97.05)
|
|
train[2019-03-31-15:06:20] Epoch: [002][500/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 1.093 (1.332) Prec@1 75.00 (66.77) Prec@5 98.96 (97.18)
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train[2019-03-31-15:06:24] Epoch: [002][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 1.269 (1.330) Prec@1 72.50 (66.89) Prec@5 95.00 (97.19)
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[2019-03-31-15:06:24] **train** Prec@1 66.89 Prec@5 97.19 Error@1 33.11 Error@5 2.81 Loss:1.330
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test [2019-03-31-15:06:25] Epoch: [002][000/105] Time 0.59 (0.59) Data 0.53 (0.53) Loss 1.227 (1.227) Prec@1 64.58 (64.58) Prec@5 93.75 (93.75)
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test [2019-03-31-15:06:29] Epoch: [002][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 1.055 (1.089) Prec@1 67.71 (67.21) Prec@5 91.67 (95.90)
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test [2019-03-31-15:06:29] Epoch: [002][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 1.409 (1.089) Prec@1 68.75 (67.11) Prec@5 100.00 (95.93)
|
|
[2019-03-31-15:06:29] **test** Prec@1 67.11 Prec@5 95.93 Error@1 32.89 Error@5 4.07 Loss:1.089
|
|
----> Best Accuracy : Acc@1=67.11, Acc@5=95.93, Error@1=32.89, Error@5=4.07
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-15:06:30] [Epoch=003/600] [Need: 21:22:19] LR=0.0250 ~ 0.0250, Batch=96
|
|
train[2019-03-31-15:06:30] Epoch: [003][000/521] Time 0.73 (0.73) Data 0.47 (0.47) Loss 1.233 (1.233) Prec@1 70.83 (70.83) Prec@5 98.96 (98.96)
|
|
train[2019-03-31-15:06:54] Epoch: [003][100/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 1.166 (1.200) Prec@1 71.88 (70.65) Prec@5 100.00 (97.82)
|
|
train[2019-03-31-15:07:18] Epoch: [003][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 1.249 (1.203) Prec@1 70.83 (70.50) Prec@5 97.92 (97.80)
|
|
train[2019-03-31-15:07:42] Epoch: [003][300/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 1.222 (1.179) Prec@1 78.12 (71.10) Prec@5 97.92 (97.89)
|
|
train[2019-03-31-15:08:05] Epoch: [003][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 1.137 (1.160) Prec@1 77.08 (71.62) Prec@5 97.92 (97.92)
|
|
train[2019-03-31-15:08:29] Epoch: [003][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.896 (1.151) Prec@1 79.17 (71.88) Prec@5 98.96 (97.97)
|
|
train[2019-03-31-15:08:34] Epoch: [003][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 1.211 (1.147) Prec@1 67.50 (71.97) Prec@5 98.75 (97.99)
|
|
[2019-03-31-15:08:34] **train** Prec@1 71.97 Prec@5 97.99 Error@1 28.03 Error@5 2.01 Loss:1.147
|
|
test [2019-03-31-15:08:35] Epoch: [003][000/105] Time 0.60 (0.60) Data 0.54 (0.54) Loss 0.709 (0.709) Prec@1 72.92 (72.92) Prec@5 98.96 (98.96)
|
|
test [2019-03-31-15:08:39] Epoch: [003][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.534 (0.714) Prec@1 80.21 (76.71) Prec@5 100.00 (98.29)
|
|
test [2019-03-31-15:08:39] Epoch: [003][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.404 (0.717) Prec@1 87.50 (76.57) Prec@5 100.00 (98.29)
|
|
[2019-03-31-15:08:39] **test** Prec@1 76.57 Prec@5 98.29 Error@1 23.43 Error@5 1.71 Loss:0.717
|
|
----> Best Accuracy : Acc@1=76.57, Acc@5=98.29, Error@1=23.43, Error@5=1.71
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-15:08:39] [Epoch=004/600] [Need: 21:28:37] LR=0.0250 ~ 0.0250, Batch=96
|
|
train[2019-03-31-15:08:40] Epoch: [004][000/521] Time 0.83 (0.83) Data 0.56 (0.56) Loss 0.819 (0.819) Prec@1 81.25 (81.25) Prec@5 98.96 (98.96)
|
|
train[2019-03-31-15:09:04] Epoch: [004][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.859 (1.058) Prec@1 80.21 (74.55) Prec@5 98.96 (98.21)
|
|
train[2019-03-31-15:09:28] Epoch: [004][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 1.055 (1.047) Prec@1 71.88 (74.72) Prec@5 100.00 (98.25)
|
|
train[2019-03-31-15:09:51] Epoch: [004][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 1.093 (1.045) Prec@1 70.83 (74.80) Prec@5 96.88 (98.32)
|
|
train[2019-03-31-15:10:15] Epoch: [004][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 1.221 (1.035) Prec@1 72.92 (75.04) Prec@5 98.96 (98.37)
|
|
train[2019-03-31-15:10:39] Epoch: [004][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 1.101 (1.033) Prec@1 77.08 (75.12) Prec@5 96.88 (98.35)
|
|
train[2019-03-31-15:10:44] Epoch: [004][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.823 (1.032) Prec@1 77.50 (75.17) Prec@5 98.75 (98.36)
|
|
[2019-03-31-15:10:44] **train** Prec@1 75.17 Prec@5 98.36 Error@1 24.83 Error@5 1.64 Loss:1.032
|
|
test [2019-03-31-15:10:45] Epoch: [004][000/105] Time 0.60 (0.60) Data 0.53 (0.53) Loss 0.919 (0.919) Prec@1 73.96 (73.96) Prec@5 95.83 (95.83)
|
|
test [2019-03-31-15:10:49] Epoch: [004][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.630 (0.718) Prec@1 78.12 (76.46) Prec@5 96.88 (98.14)
|
|
test [2019-03-31-15:10:49] Epoch: [004][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.737 (0.720) Prec@1 81.25 (76.38) Prec@5 100.00 (98.13)
|
|
[2019-03-31-15:10:49] **test** Prec@1 76.38 Prec@5 98.13 Error@1 23.62 Error@5 1.87 Loss:0.720
|
|
----> Best Accuracy : Acc@1=76.57, Acc@5=98.29, Error@1=23.43, Error@5=1.71
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-15:10:49] [Epoch=005/600] [Need: 21:27:50] LR=0.0250 ~ 0.0250, Batch=96
|
|
train[2019-03-31-15:10:50] Epoch: [005][000/521] Time 0.83 (0.83) Data 0.55 (0.55) Loss 0.755 (0.755) Prec@1 82.29 (82.29) Prec@5 98.96 (98.96)
|
|
train[2019-03-31-15:11:14] Epoch: [005][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 1.103 (0.978) Prec@1 75.00 (76.44) Prec@5 98.96 (98.67)
|
|
train[2019-03-31-15:11:37] Epoch: [005][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 1.143 (0.973) Prec@1 70.83 (76.23) Prec@5 97.92 (98.73)
|
|
train[2019-03-31-15:12:01] Epoch: [005][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 1.070 (0.960) Prec@1 73.96 (76.67) Prec@5 96.88 (98.68)
|
|
train[2019-03-31-15:12:25] Epoch: [005][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 1.255 (0.956) Prec@1 71.88 (76.88) Prec@5 97.92 (98.66)
|
|
train[2019-03-31-15:12:49] Epoch: [005][500/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.934 (0.946) Prec@1 78.12 (77.16) Prec@5 98.96 (98.70)
|
|
train[2019-03-31-15:12:53] Epoch: [005][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.895 (0.944) Prec@1 81.25 (77.24) Prec@5 100.00 (98.71)
|
|
[2019-03-31-15:12:54] **train** Prec@1 77.24 Prec@5 98.71 Error@1 22.76 Error@5 1.29 Loss:0.944
|
|
test [2019-03-31-15:12:54] Epoch: [005][000/105] Time 0.60 (0.60) Data 0.53 (0.53) Loss 0.638 (0.638) Prec@1 78.12 (78.12) Prec@5 97.92 (97.92)
|
|
test [2019-03-31-15:12:58] Epoch: [005][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.461 (0.611) Prec@1 87.50 (79.75) Prec@5 98.96 (98.93)
|
|
test [2019-03-31-15:12:58] Epoch: [005][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.531 (0.613) Prec@1 75.00 (79.64) Prec@5 100.00 (98.93)
|
|
[2019-03-31-15:12:58] **test** Prec@1 79.64 Prec@5 98.93 Error@1 20.36 Error@5 1.07 Loss:0.613
|
|
----> Best Accuracy : Acc@1=79.64, Acc@5=98.93, Error@1=20.36, Error@5=1.07
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-15:12:59] [Epoch=006/600] [Need: 21:22:26] LR=0.0250 ~ 0.0250, Batch=96
|
|
train[2019-03-31-15:13:00] Epoch: [006][000/521] Time 0.83 (0.83) Data 0.56 (0.56) Loss 1.002 (1.002) Prec@1 78.12 (78.12) Prec@5 97.92 (97.92)
|
|
train[2019-03-31-15:13:23] Epoch: [006][100/521] Time 0.23 (0.24) Data 0.00 (0.01) Loss 0.721 (0.885) Prec@1 82.29 (78.62) Prec@5 100.00 (98.65)
|
|
train[2019-03-31-15:13:47] Epoch: [006][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.813 (0.885) Prec@1 79.17 (78.77) Prec@5 98.96 (98.70)
|
|
train[2019-03-31-15:14:11] Epoch: [006][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.606 (0.882) Prec@1 87.50 (78.90) Prec@5 98.96 (98.72)
|
|
train[2019-03-31-15:14:35] Epoch: [006][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.650 (0.883) Prec@1 84.38 (78.95) Prec@5 100.00 (98.73)
|
|
train[2019-03-31-15:14:58] Epoch: [006][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 1.116 (0.883) Prec@1 72.92 (78.97) Prec@5 97.92 (98.73)
|
|
train[2019-03-31-15:15:03] Epoch: [006][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.997 (0.882) Prec@1 73.75 (78.98) Prec@5 97.50 (98.73)
|
|
[2019-03-31-15:15:03] **train** Prec@1 78.98 Prec@5 98.73 Error@1 21.02 Error@5 1.27 Loss:0.882
|
|
test [2019-03-31-15:15:04] Epoch: [006][000/105] Time 0.47 (0.47) Data 0.40 (0.40) Loss 0.470 (0.470) Prec@1 82.29 (82.29) Prec@5 97.92 (97.92)
|
|
test [2019-03-31-15:15:08] Epoch: [006][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.411 (0.549) Prec@1 84.38 (81.15) Prec@5 100.00 (99.11)
|
|
test [2019-03-31-15:15:08] Epoch: [006][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.503 (0.549) Prec@1 87.50 (81.06) Prec@5 100.00 (99.13)
|
|
[2019-03-31-15:15:08] **test** Prec@1 81.06 Prec@5 99.13 Error@1 18.94 Error@5 0.87 Loss:0.549
|
|
----> Best Accuracy : Acc@1=81.06, Acc@5=99.13, Error@1=18.94, Error@5=0.87
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-15:15:08] [Epoch=007/600] [Need: 21:18:38] LR=0.0250 ~ 0.0250, Batch=96
|
|
train[2019-03-31-15:15:09] Epoch: [007][000/521] Time 0.85 (0.85) Data 0.58 (0.58) Loss 0.953 (0.953) Prec@1 73.96 (73.96) Prec@5 96.88 (96.88)
|
|
train[2019-03-31-15:15:33] Epoch: [007][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.816 (0.844) Prec@1 76.04 (79.47) Prec@5 100.00 (98.93)
|
|
train[2019-03-31-15:15:58] Epoch: [007][200/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.939 (0.846) Prec@1 77.08 (79.63) Prec@5 98.96 (98.90)
|
|
train[2019-03-31-15:16:24] Epoch: [007][300/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.777 (0.843) Prec@1 79.17 (79.72) Prec@5 97.92 (98.93)
|
|
train[2019-03-31-15:16:51] Epoch: [007][400/521] Time 0.25 (0.26) Data 0.00 (0.00) Loss 0.741 (0.837) Prec@1 83.33 (79.90) Prec@5 97.92 (98.94)
|
|
train[2019-03-31-15:17:16] Epoch: [007][500/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.910 (0.834) Prec@1 80.21 (79.99) Prec@5 98.96 (98.98)
|
|
train[2019-03-31-15:17:20] Epoch: [007][520/521] Time 0.21 (0.25) Data 0.00 (0.00) Loss 0.795 (0.832) Prec@1 81.25 (80.02) Prec@5 98.75 (98.98)
|
|
[2019-03-31-15:17:21] **train** Prec@1 80.02 Prec@5 98.98 Error@1 19.98 Error@5 1.02 Loss:0.832
|
|
test [2019-03-31-15:17:21] Epoch: [007][000/105] Time 0.47 (0.47) Data 0.40 (0.40) Loss 0.485 (0.485) Prec@1 84.38 (84.38) Prec@5 98.96 (98.96)
|
|
test [2019-03-31-15:17:25] Epoch: [007][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.456 (0.520) Prec@1 85.42 (82.23) Prec@5 100.00 (99.23)
|
|
test [2019-03-31-15:17:25] Epoch: [007][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.517 (0.518) Prec@1 75.00 (82.21) Prec@5 100.00 (99.24)
|
|
[2019-03-31-15:17:25] **test** Prec@1 82.21 Prec@5 99.24 Error@1 17.79 Error@5 0.76 Loss:0.518
|
|
----> Best Accuracy : Acc@1=82.21, Acc@5=99.24, Error@1=17.79, Error@5=0.76
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-15:17:26] [Epoch=008/600] [Need: 22:37:00] LR=0.0250 ~ 0.0250, Batch=96
|
|
train[2019-03-31-15:17:26] Epoch: [008][000/521] Time 0.70 (0.70) Data 0.43 (0.43) Loss 0.871 (0.871) Prec@1 73.96 (73.96) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-15:17:50] Epoch: [008][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.919 (0.809) Prec@1 76.04 (81.13) Prec@5 97.92 (99.02)
|
|
train[2019-03-31-15:18:14] Epoch: [008][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.843 (0.797) Prec@1 85.42 (81.15) Prec@5 100.00 (99.10)
|
|
train[2019-03-31-15:18:37] Epoch: [008][300/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.583 (0.795) Prec@1 86.46 (81.11) Prec@5 100.00 (99.08)
|
|
train[2019-03-31-15:19:01] Epoch: [008][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.904 (0.785) Prec@1 78.12 (81.33) Prec@5 97.92 (99.11)
|
|
train[2019-03-31-15:19:25] Epoch: [008][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.951 (0.787) Prec@1 75.00 (81.15) Prec@5 97.92 (99.13)
|
|
train[2019-03-31-15:19:30] Epoch: [008][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.799 (0.786) Prec@1 78.75 (81.20) Prec@5 98.75 (99.12)
|
|
[2019-03-31-15:19:30] **train** Prec@1 81.20 Prec@5 99.12 Error@1 18.80 Error@5 0.88 Loss:0.786
|
|
test [2019-03-31-15:19:30] Epoch: [008][000/105] Time 0.47 (0.47) Data 0.41 (0.41) Loss 0.404 (0.404) Prec@1 84.38 (84.38) Prec@5 98.96 (98.96)
|
|
test [2019-03-31-15:19:34] Epoch: [008][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.429 (0.489) Prec@1 86.46 (83.58) Prec@5 100.00 (99.38)
|
|
test [2019-03-31-15:19:34] Epoch: [008][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.546 (0.490) Prec@1 81.25 (83.56) Prec@5 100.00 (99.40)
|
|
[2019-03-31-15:19:34] **test** Prec@1 83.56 Prec@5 99.40 Error@1 16.44 Error@5 0.60 Loss:0.490
|
|
----> Best Accuracy : Acc@1=83.56, Acc@5=99.40, Error@1=16.44, Error@5=0.60
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-15:19:35] [Epoch=009/600] [Need: 21:11:33] LR=0.0250 ~ 0.0250, Batch=96
|
|
train[2019-03-31-15:19:36] Epoch: [009][000/521] Time 0.84 (0.84) Data 0.57 (0.57) Loss 0.732 (0.732) Prec@1 85.42 (85.42) Prec@5 98.96 (98.96)
|
|
train[2019-03-31-15:19:59] Epoch: [009][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.684 (0.744) Prec@1 84.38 (82.45) Prec@5 100.00 (99.16)
|
|
train[2019-03-31-15:20:24] Epoch: [009][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.589 (0.753) Prec@1 84.38 (82.08) Prec@5 100.00 (99.14)
|
|
train[2019-03-31-15:20:47] Epoch: [009][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.764 (0.754) Prec@1 83.33 (82.09) Prec@5 97.92 (99.12)
|
|
train[2019-03-31-15:21:11] Epoch: [009][400/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.858 (0.759) Prec@1 80.21 (81.90) Prec@5 98.96 (99.09)
|
|
train[2019-03-31-15:21:35] Epoch: [009][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 1.065 (0.762) Prec@1 72.92 (81.78) Prec@5 100.00 (99.11)
|
|
train[2019-03-31-15:21:39] Epoch: [009][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.838 (0.762) Prec@1 77.50 (81.78) Prec@5 100.00 (99.12)
|
|
[2019-03-31-15:21:39] **train** Prec@1 81.78 Prec@5 99.12 Error@1 18.22 Error@5 0.88 Loss:0.762
|
|
test [2019-03-31-15:21:40] Epoch: [009][000/105] Time 0.61 (0.61) Data 0.54 (0.54) Loss 0.612 (0.612) Prec@1 80.21 (80.21) Prec@5 98.96 (98.96)
|
|
test [2019-03-31-15:21:44] Epoch: [009][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.262 (0.486) Prec@1 93.75 (83.93) Prec@5 98.96 (99.15)
|
|
test [2019-03-31-15:21:44] Epoch: [009][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.614 (0.486) Prec@1 75.00 (83.88) Prec@5 100.00 (99.16)
|
|
[2019-03-31-15:21:44] **test** Prec@1 83.88 Prec@5 99.16 Error@1 16.12 Error@5 0.84 Loss:0.486
|
|
----> Best Accuracy : Acc@1=83.88, Acc@5=99.16, Error@1=16.12, Error@5=0.84
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-15:21:45] [Epoch=010/600] [Need: 21:17:21] LR=0.0250 ~ 0.0250, Batch=96
|
|
train[2019-03-31-15:21:45] Epoch: [010][000/521] Time 0.83 (0.83) Data 0.56 (0.56) Loss 0.537 (0.537) Prec@1 90.62 (90.62) Prec@5 98.96 (98.96)
|
|
train[2019-03-31-15:22:09] Epoch: [010][100/521] Time 0.23 (0.24) Data 0.00 (0.01) Loss 0.800 (0.724) Prec@1 81.25 (82.99) Prec@5 96.88 (99.20)
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train[2019-03-31-15:22:33] Epoch: [010][200/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.516 (0.721) Prec@1 87.50 (82.97) Prec@5 100.00 (99.30)
|
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train[2019-03-31-15:22:57] Epoch: [010][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.793 (0.734) Prec@1 81.25 (82.63) Prec@5 100.00 (99.24)
|
|
train[2019-03-31-15:23:20] Epoch: [010][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.787 (0.731) Prec@1 82.29 (82.73) Prec@5 98.96 (99.20)
|
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train[2019-03-31-15:23:44] Epoch: [010][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.960 (0.732) Prec@1 78.12 (82.76) Prec@5 98.96 (99.19)
|
|
train[2019-03-31-15:23:49] Epoch: [010][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.599 (0.730) Prec@1 90.00 (82.83) Prec@5 98.75 (99.18)
|
|
[2019-03-31-15:23:49] **train** Prec@1 82.83 Prec@5 99.18 Error@1 17.17 Error@5 0.82 Loss:0.730
|
|
test [2019-03-31-15:23:50] Epoch: [010][000/105] Time 0.47 (0.47) Data 0.41 (0.41) Loss 0.607 (0.607) Prec@1 81.25 (81.25) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-15:23:54] Epoch: [010][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.375 (0.485) Prec@1 90.62 (83.94) Prec@5 100.00 (99.36)
|
|
test [2019-03-31-15:23:54] Epoch: [010][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.232 (0.484) Prec@1 93.75 (83.91) Prec@5 100.00 (99.37)
|
|
[2019-03-31-15:23:54] **test** Prec@1 83.91 Prec@5 99.37 Error@1 16.09 Error@5 0.63 Loss:0.484
|
|
----> Best Accuracy : Acc@1=83.91, Acc@5=99.37, Error@1=16.09, Error@5=0.63
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-15:23:54] [Epoch=011/600] [Need: 21:10:25] LR=0.0250 ~ 0.0250, Batch=96
|
|
train[2019-03-31-15:23:55] Epoch: [011][000/521] Time 0.71 (0.71) Data 0.43 (0.43) Loss 0.672 (0.672) Prec@1 83.33 (83.33) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-15:24:18] Epoch: [011][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.915 (0.718) Prec@1 78.12 (82.71) Prec@5 96.88 (99.38)
|
|
train[2019-03-31-15:24:42] Epoch: [011][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.666 (0.715) Prec@1 84.38 (82.78) Prec@5 98.96 (99.31)
|
|
train[2019-03-31-15:25:06] Epoch: [011][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.585 (0.708) Prec@1 85.42 (82.95) Prec@5 98.96 (99.30)
|
|
train[2019-03-31-15:25:30] Epoch: [011][400/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.608 (0.709) Prec@1 83.33 (82.94) Prec@5 100.00 (99.31)
|
|
train[2019-03-31-15:25:53] Epoch: [011][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.675 (0.706) Prec@1 86.46 (83.07) Prec@5 98.96 (99.30)
|
|
train[2019-03-31-15:25:58] Epoch: [011][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.538 (0.708) Prec@1 90.00 (83.03) Prec@5 98.75 (99.30)
|
|
[2019-03-31-15:25:58] **train** Prec@1 83.03 Prec@5 99.30 Error@1 16.97 Error@5 0.70 Loss:0.708
|
|
test [2019-03-31-15:25:58] Epoch: [011][000/105] Time 0.61 (0.61) Data 0.54 (0.54) Loss 0.544 (0.544) Prec@1 83.33 (83.33) Prec@5 98.96 (98.96)
|
|
test [2019-03-31-15:26:03] Epoch: [011][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.226 (0.442) Prec@1 93.75 (85.23) Prec@5 100.00 (99.40)
|
|
test [2019-03-31-15:26:03] Epoch: [011][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.346 (0.442) Prec@1 75.00 (85.18) Prec@5 100.00 (99.42)
|
|
[2019-03-31-15:26:03] **test** Prec@1 85.18 Prec@5 99.42 Error@1 14.82 Error@5 0.58 Loss:0.442
|
|
----> Best Accuracy : Acc@1=85.18, Acc@5=99.42, Error@1=14.82, Error@5=0.58
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-15:26:03] [Epoch=012/600] [Need: 21:03:43] LR=0.0250 ~ 0.0250, Batch=96
|
|
train[2019-03-31-15:26:04] Epoch: [012][000/521] Time 0.83 (0.83) Data 0.56 (0.56) Loss 0.753 (0.753) Prec@1 81.25 (81.25) Prec@5 98.96 (98.96)
|
|
train[2019-03-31-15:26:27] Epoch: [012][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.750 (0.645) Prec@1 78.12 (84.88) Prec@5 100.00 (99.43)
|
|
train[2019-03-31-15:26:51] Epoch: [012][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.924 (0.667) Prec@1 76.04 (84.32) Prec@5 98.96 (99.37)
|
|
train[2019-03-31-15:27:15] Epoch: [012][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.613 (0.670) Prec@1 86.46 (84.33) Prec@5 100.00 (99.33)
|
|
train[2019-03-31-15:27:39] Epoch: [012][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.766 (0.670) Prec@1 80.21 (84.28) Prec@5 100.00 (99.32)
|
|
train[2019-03-31-15:28:03] Epoch: [012][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.761 (0.678) Prec@1 80.21 (84.06) Prec@5 97.92 (99.30)
|
|
train[2019-03-31-15:28:07] Epoch: [012][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.708 (0.678) Prec@1 82.50 (84.05) Prec@5 98.75 (99.31)
|
|
[2019-03-31-15:28:07] **train** Prec@1 84.05 Prec@5 99.31 Error@1 15.95 Error@5 0.69 Loss:0.678
|
|
test [2019-03-31-15:28:08] Epoch: [012][000/105] Time 0.49 (0.49) Data 0.43 (0.43) Loss 0.351 (0.351) Prec@1 84.38 (84.38) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-15:28:12] Epoch: [012][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.293 (0.468) Prec@1 92.71 (84.44) Prec@5 100.00 (99.36)
|
|
test [2019-03-31-15:28:12] Epoch: [012][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.457 (0.469) Prec@1 75.00 (84.34) Prec@5 100.00 (99.38)
|
|
[2019-03-31-15:28:12] **test** Prec@1 84.34 Prec@5 99.38 Error@1 15.66 Error@5 0.62 Loss:0.469
|
|
----> Best Accuracy : Acc@1=85.18, Acc@5=99.42, Error@1=14.82, Error@5=0.58
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-15:28:12] [Epoch=013/600] [Need: 21:06:03] LR=0.0250 ~ 0.0250, Batch=96
|
|
train[2019-03-31-15:28:13] Epoch: [013][000/521] Time 0.83 (0.83) Data 0.56 (0.56) Loss 0.696 (0.696) Prec@1 84.38 (84.38) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-15:28:37] Epoch: [013][100/521] Time 0.26 (0.24) Data 0.00 (0.01) Loss 0.870 (0.647) Prec@1 79.17 (84.64) Prec@5 100.00 (99.41)
|
|
train[2019-03-31-15:29:01] Epoch: [013][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.630 (0.661) Prec@1 84.38 (84.29) Prec@5 100.00 (99.41)
|
|
train[2019-03-31-15:29:25] Epoch: [013][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.644 (0.657) Prec@1 83.33 (84.36) Prec@5 100.00 (99.40)
|
|
train[2019-03-31-15:29:49] Epoch: [013][400/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.558 (0.651) Prec@1 87.50 (84.44) Prec@5 100.00 (99.37)
|
|
train[2019-03-31-15:30:13] Epoch: [013][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.698 (0.658) Prec@1 80.21 (84.29) Prec@5 100.00 (99.36)
|
|
train[2019-03-31-15:30:17] Epoch: [013][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.647 (0.659) Prec@1 86.25 (84.25) Prec@5 97.50 (99.35)
|
|
[2019-03-31-15:30:18] **train** Prec@1 84.25 Prec@5 99.35 Error@1 15.75 Error@5 0.65 Loss:0.659
|
|
test [2019-03-31-15:30:18] Epoch: [013][000/105] Time 0.48 (0.48) Data 0.40 (0.40) Loss 0.334 (0.334) Prec@1 85.42 (85.42) Prec@5 98.96 (98.96)
|
|
test [2019-03-31-15:30:22] Epoch: [013][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.265 (0.402) Prec@1 86.46 (86.53) Prec@5 100.00 (99.48)
|
|
test [2019-03-31-15:30:22] Epoch: [013][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.493 (0.402) Prec@1 87.50 (86.54) Prec@5 100.00 (99.48)
|
|
[2019-03-31-15:30:22] **test** Prec@1 86.54 Prec@5 99.48 Error@1 13.46 Error@5 0.52 Loss:0.402
|
|
----> Best Accuracy : Acc@1=86.54, Acc@5=99.48, Error@1=13.46, Error@5=0.52
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-15:30:23] [Epoch=014/600] [Need: 21:11:42] LR=0.0250 ~ 0.0250, Batch=96
|
|
train[2019-03-31-15:30:23] Epoch: [014][000/521] Time 0.88 (0.88) Data 0.59 (0.59) Loss 0.519 (0.519) Prec@1 90.62 (90.62) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-15:30:47] Epoch: [014][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.453 (0.620) Prec@1 86.46 (85.68) Prec@5 100.00 (99.38)
|
|
train[2019-03-31-15:31:11] Epoch: [014][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.773 (0.635) Prec@1 82.29 (85.18) Prec@5 98.96 (99.31)
|
|
train[2019-03-31-15:31:35] Epoch: [014][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.753 (0.639) Prec@1 84.38 (85.16) Prec@5 100.00 (99.30)
|
|
train[2019-03-31-15:31:59] Epoch: [014][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.600 (0.645) Prec@1 88.54 (84.96) Prec@5 98.96 (99.32)
|
|
train[2019-03-31-15:32:22] Epoch: [014][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.621 (0.645) Prec@1 85.42 (84.87) Prec@5 98.96 (99.36)
|
|
train[2019-03-31-15:32:27] Epoch: [014][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.504 (0.644) Prec@1 90.00 (84.87) Prec@5 100.00 (99.36)
|
|
[2019-03-31-15:32:27] **train** Prec@1 84.87 Prec@5 99.36 Error@1 15.13 Error@5 0.64 Loss:0.644
|
|
test [2019-03-31-15:32:28] Epoch: [014][000/105] Time 0.46 (0.46) Data 0.39 (0.39) Loss 0.481 (0.481) Prec@1 83.33 (83.33) Prec@5 98.96 (98.96)
|
|
test [2019-03-31-15:32:32] Epoch: [014][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.337 (0.466) Prec@1 86.46 (85.25) Prec@5 100.00 (99.33)
|
|
test [2019-03-31-15:32:32] Epoch: [014][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.178 (0.465) Prec@1 93.75 (85.22) Prec@5 100.00 (99.33)
|
|
[2019-03-31-15:32:32] **test** Prec@1 85.22 Prec@5 99.33 Error@1 14.78 Error@5 0.67 Loss:0.465
|
|
----> Best Accuracy : Acc@1=86.54, Acc@5=99.48, Error@1=13.46, Error@5=0.52
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-15:32:32] [Epoch=015/600] [Need: 21:02:23] LR=0.0250 ~ 0.0250, Batch=96
|
|
train[2019-03-31-15:32:33] Epoch: [015][000/521] Time 0.71 (0.71) Data 0.43 (0.43) Loss 0.640 (0.640) Prec@1 83.33 (83.33) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-15:32:56] Epoch: [015][100/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.643 (0.633) Prec@1 83.33 (84.90) Prec@5 100.00 (99.43)
|
|
train[2019-03-31-15:33:20] Epoch: [015][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.939 (0.639) Prec@1 81.25 (84.97) Prec@5 97.92 (99.31)
|
|
train[2019-03-31-15:33:44] Epoch: [015][300/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.579 (0.630) Prec@1 86.46 (85.26) Prec@5 100.00 (99.35)
|
|
train[2019-03-31-15:34:07] Epoch: [015][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.713 (0.627) Prec@1 82.29 (85.24) Prec@5 100.00 (99.39)
|
|
train[2019-03-31-15:34:31] Epoch: [015][500/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.534 (0.631) Prec@1 86.46 (85.20) Prec@5 100.00 (99.39)
|
|
train[2019-03-31-15:34:36] Epoch: [015][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.737 (0.630) Prec@1 81.25 (85.24) Prec@5 100.00 (99.40)
|
|
[2019-03-31-15:34:36] **train** Prec@1 85.24 Prec@5 99.40 Error@1 14.76 Error@5 0.60 Loss:0.630
|
|
test [2019-03-31-15:34:36] Epoch: [015][000/105] Time 0.49 (0.49) Data 0.43 (0.43) Loss 0.436 (0.436) Prec@1 87.50 (87.50) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-15:34:40] Epoch: [015][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.225 (0.353) Prec@1 92.71 (87.98) Prec@5 100.00 (99.62)
|
|
test [2019-03-31-15:34:40] Epoch: [015][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.159 (0.353) Prec@1 93.75 (87.96) Prec@5 100.00 (99.63)
|
|
[2019-03-31-15:34:41] **test** Prec@1 87.96 Prec@5 99.63 Error@1 12.04 Error@5 0.37 Loss:0.353
|
|
----> Best Accuracy : Acc@1=87.96, Acc@5=99.63, Error@1=12.04, Error@5=0.37
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-15:34:41] [Epoch=016/600] [Need: 20:52:49] LR=0.0250 ~ 0.0250, Batch=96
|
|
train[2019-03-31-15:34:41] Epoch: [016][000/521] Time 0.71 (0.71) Data 0.44 (0.44) Loss 0.546 (0.546) Prec@1 87.50 (87.50) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-15:35:05] Epoch: [016][100/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.695 (0.591) Prec@1 82.29 (86.41) Prec@5 100.00 (99.59)
|
|
train[2019-03-31-15:35:29] Epoch: [016][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.532 (0.601) Prec@1 88.54 (85.88) Prec@5 98.96 (99.51)
|
|
train[2019-03-31-15:35:52] Epoch: [016][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.627 (0.607) Prec@1 86.46 (85.76) Prec@5 100.00 (99.49)
|
|
train[2019-03-31-15:36:16] Epoch: [016][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.632 (0.607) Prec@1 83.33 (85.77) Prec@5 100.00 (99.47)
|
|
train[2019-03-31-15:36:40] Epoch: [016][500/521] Time 0.26 (0.24) Data 0.00 (0.00) Loss 0.494 (0.610) Prec@1 89.58 (85.61) Prec@5 100.00 (99.46)
|
|
train[2019-03-31-15:36:45] Epoch: [016][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.713 (0.610) Prec@1 78.75 (85.62) Prec@5 98.75 (99.47)
|
|
[2019-03-31-15:36:45] **train** Prec@1 85.62 Prec@5 99.47 Error@1 14.38 Error@5 0.53 Loss:0.610
|
|
test [2019-03-31-15:36:45] Epoch: [016][000/105] Time 0.47 (0.47) Data 0.40 (0.40) Loss 0.365 (0.365) Prec@1 86.46 (86.46) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-15:36:49] Epoch: [016][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.200 (0.383) Prec@1 91.67 (87.18) Prec@5 100.00 (99.46)
|
|
test [2019-03-31-15:36:49] Epoch: [016][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.321 (0.382) Prec@1 93.75 (87.25) Prec@5 100.00 (99.47)
|
|
[2019-03-31-15:36:49] **test** Prec@1 87.25 Prec@5 99.47 Error@1 12.75 Error@5 0.53 Loss:0.382
|
|
----> Best Accuracy : Acc@1=87.96, Acc@5=99.63, Error@1=12.04, Error@5=0.37
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-15:36:50] [Epoch=017/600] [Need: 20:52:00] LR=0.0250 ~ 0.0250, Batch=96
|
|
train[2019-03-31-15:36:50] Epoch: [017][000/521] Time 0.72 (0.72) Data 0.45 (0.45) Loss 0.573 (0.573) Prec@1 89.58 (89.58) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-15:37:14] Epoch: [017][100/521] Time 0.23 (0.25) Data 0.00 (0.00) Loss 0.757 (0.600) Prec@1 85.42 (85.92) Prec@5 98.96 (99.46)
|
|
train[2019-03-31-15:37:38] Epoch: [017][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.699 (0.607) Prec@1 81.25 (85.75) Prec@5 98.96 (99.49)
|
|
train[2019-03-31-15:38:02] Epoch: [017][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.466 (0.604) Prec@1 85.42 (85.85) Prec@5 100.00 (99.46)
|
|
train[2019-03-31-15:38:25] Epoch: [017][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.596 (0.603) Prec@1 87.50 (85.91) Prec@5 100.00 (99.46)
|
|
train[2019-03-31-15:38:49] Epoch: [017][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.542 (0.606) Prec@1 89.58 (85.83) Prec@5 100.00 (99.46)
|
|
train[2019-03-31-15:38:54] Epoch: [017][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.492 (0.605) Prec@1 87.50 (85.84) Prec@5 100.00 (99.47)
|
|
[2019-03-31-15:38:54] **train** Prec@1 85.84 Prec@5 99.47 Error@1 14.16 Error@5 0.53 Loss:0.605
|
|
test [2019-03-31-15:38:55] Epoch: [017][000/105] Time 0.48 (0.48) Data 0.42 (0.42) Loss 0.378 (0.378) Prec@1 89.58 (89.58) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-15:38:59] Epoch: [017][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.238 (0.347) Prec@1 93.75 (88.75) Prec@5 98.96 (99.60)
|
|
test [2019-03-31-15:38:59] Epoch: [017][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.559 (0.347) Prec@1 87.50 (88.69) Prec@5 100.00 (99.61)
|
|
[2019-03-31-15:38:59] **test** Prec@1 88.69 Prec@5 99.61 Error@1 11.31 Error@5 0.39 Loss:0.347
|
|
----> Best Accuracy : Acc@1=88.69, Acc@5=99.61, Error@1=11.31, Error@5=0.39
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-15:38:59] [Epoch=018/600] [Need: 20:55:30] LR=0.0249 ~ 0.0249, Batch=96
|
|
train[2019-03-31-15:39:00] Epoch: [018][000/521] Time 0.70 (0.70) Data 0.43 (0.43) Loss 0.372 (0.372) Prec@1 92.71 (92.71) Prec@5 98.96 (98.96)
|
|
train[2019-03-31-15:39:23] Epoch: [018][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.524 (0.568) Prec@1 87.50 (86.98) Prec@5 100.00 (99.41)
|
|
train[2019-03-31-15:39:47] Epoch: [018][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.552 (0.577) Prec@1 89.58 (86.62) Prec@5 100.00 (99.48)
|
|
train[2019-03-31-15:40:11] Epoch: [018][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.550 (0.584) Prec@1 86.46 (86.36) Prec@5 98.96 (99.49)
|
|
train[2019-03-31-15:40:35] Epoch: [018][400/521] Time 0.29 (0.24) Data 0.00 (0.00) Loss 0.578 (0.588) Prec@1 88.54 (86.26) Prec@5 98.96 (99.49)
|
|
train[2019-03-31-15:40:59] Epoch: [018][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.539 (0.592) Prec@1 87.50 (86.23) Prec@5 98.96 (99.48)
|
|
train[2019-03-31-15:41:04] Epoch: [018][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.748 (0.593) Prec@1 85.00 (86.18) Prec@5 100.00 (99.48)
|
|
[2019-03-31-15:41:04] **train** Prec@1 86.18 Prec@5 99.48 Error@1 13.82 Error@5 0.52 Loss:0.593
|
|
test [2019-03-31-15:41:04] Epoch: [018][000/105] Time 0.47 (0.47) Data 0.42 (0.42) Loss 0.336 (0.336) Prec@1 88.54 (88.54) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-15:41:08] Epoch: [018][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.213 (0.360) Prec@1 93.75 (87.92) Prec@5 100.00 (99.64)
|
|
test [2019-03-31-15:41:08] Epoch: [018][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.490 (0.360) Prec@1 81.25 (87.87) Prec@5 100.00 (99.64)
|
|
[2019-03-31-15:41:09] **test** Prec@1 87.87 Prec@5 99.64 Error@1 12.13 Error@5 0.36 Loss:0.360
|
|
----> Best Accuracy : Acc@1=88.69, Acc@5=99.61, Error@1=11.31, Error@5=0.39
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-15:41:09] [Epoch=019/600] [Need: 20:55:30] LR=0.0249 ~ 0.0249, Batch=96
|
|
train[2019-03-31-15:41:10] Epoch: [019][000/521] Time 0.83 (0.83) Data 0.56 (0.56) Loss 0.709 (0.709) Prec@1 84.38 (84.38) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-15:41:33] Epoch: [019][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.498 (0.557) Prec@1 90.62 (86.78) Prec@5 98.96 (99.52)
|
|
train[2019-03-31-15:41:57] Epoch: [019][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.608 (0.569) Prec@1 86.46 (86.67) Prec@5 98.96 (99.52)
|
|
train[2019-03-31-15:42:21] Epoch: [019][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.599 (0.563) Prec@1 84.38 (86.86) Prec@5 100.00 (99.53)
|
|
train[2019-03-31-15:42:44] Epoch: [019][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.633 (0.567) Prec@1 82.29 (86.68) Prec@5 98.96 (99.53)
|
|
train[2019-03-31-15:43:08] Epoch: [019][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.358 (0.574) Prec@1 92.71 (86.51) Prec@5 100.00 (99.52)
|
|
train[2019-03-31-15:43:13] Epoch: [019][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.690 (0.575) Prec@1 86.25 (86.51) Prec@5 100.00 (99.52)
|
|
[2019-03-31-15:43:13] **train** Prec@1 86.51 Prec@5 99.52 Error@1 13.49 Error@5 0.48 Loss:0.575
|
|
test [2019-03-31-15:43:14] Epoch: [019][000/105] Time 0.60 (0.60) Data 0.53 (0.53) Loss 0.359 (0.359) Prec@1 86.46 (86.46) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-15:43:18] Epoch: [019][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.168 (0.338) Prec@1 93.75 (88.33) Prec@5 100.00 (99.61)
|
|
test [2019-03-31-15:43:18] Epoch: [019][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.284 (0.337) Prec@1 87.50 (88.33) Prec@5 100.00 (99.62)
|
|
[2019-03-31-15:43:18] **test** Prec@1 88.33 Prec@5 99.62 Error@1 11.67 Error@5 0.38 Loss:0.337
|
|
----> Best Accuracy : Acc@1=88.69, Acc@5=99.61, Error@1=11.31, Error@5=0.39
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-15:43:18] [Epoch=020/600] [Need: 20:49:56] LR=0.0249 ~ 0.0249, Batch=96
|
|
train[2019-03-31-15:43:19] Epoch: [020][000/521] Time 0.83 (0.83) Data 0.56 (0.56) Loss 0.701 (0.701) Prec@1 82.29 (82.29) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-15:43:43] Epoch: [020][100/521] Time 0.24 (0.25) Data 0.00 (0.01) Loss 0.581 (0.565) Prec@1 84.38 (86.50) Prec@5 100.00 (99.59)
|
|
train[2019-03-31-15:44:07] Epoch: [020][200/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.747 (0.574) Prec@1 85.42 (86.24) Prec@5 100.00 (99.58)
|
|
train[2019-03-31-15:44:32] Epoch: [020][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.664 (0.576) Prec@1 78.12 (86.19) Prec@5 97.92 (99.61)
|
|
train[2019-03-31-15:44:55] Epoch: [020][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.475 (0.573) Prec@1 90.62 (86.40) Prec@5 98.96 (99.57)
|
|
train[2019-03-31-15:45:20] Epoch: [020][500/521] Time 0.26 (0.24) Data 0.00 (0.00) Loss 0.641 (0.576) Prec@1 83.33 (86.42) Prec@5 98.96 (99.56)
|
|
train[2019-03-31-15:45:25] Epoch: [020][520/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.365 (0.575) Prec@1 88.75 (86.42) Prec@5 100.00 (99.56)
|
|
[2019-03-31-15:45:26] **train** Prec@1 86.42 Prec@5 99.56 Error@1 13.58 Error@5 0.44 Loss:0.575
|
|
test [2019-03-31-15:45:26] Epoch: [020][000/105] Time 0.51 (0.51) Data 0.45 (0.45) Loss 0.410 (0.410) Prec@1 89.58 (89.58) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-15:45:30] Epoch: [020][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.224 (0.389) Prec@1 93.75 (87.25) Prec@5 100.00 (99.69)
|
|
test [2019-03-31-15:45:31] Epoch: [020][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.120 (0.390) Prec@1 100.00 (87.27) Prec@5 100.00 (99.70)
|
|
[2019-03-31-15:45:31] **test** Prec@1 87.27 Prec@5 99.70 Error@1 12.73 Error@5 0.30 Loss:0.390
|
|
----> Best Accuracy : Acc@1=88.69, Acc@5=99.61, Error@1=11.31, Error@5=0.39
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-15:45:31] [Epoch=021/600] [Need: 21:21:20] LR=0.0249 ~ 0.0249, Batch=96
|
|
train[2019-03-31-15:45:32] Epoch: [021][000/521] Time 0.72 (0.72) Data 0.45 (0.45) Loss 0.566 (0.566) Prec@1 86.46 (86.46) Prec@5 98.96 (98.96)
|
|
train[2019-03-31-15:45:57] Epoch: [021][100/521] Time 0.25 (0.26) Data 0.00 (0.00) Loss 0.336 (0.556) Prec@1 92.71 (87.00) Prec@5 100.00 (99.60)
|
|
train[2019-03-31-15:46:22] Epoch: [021][200/521] Time 0.26 (0.26) Data 0.00 (0.00) Loss 0.475 (0.569) Prec@1 89.58 (86.73) Prec@5 100.00 (99.54)
|
|
train[2019-03-31-15:46:48] Epoch: [021][300/521] Time 0.26 (0.26) Data 0.00 (0.00) Loss 0.521 (0.564) Prec@1 91.67 (86.78) Prec@5 97.92 (99.52)
|
|
train[2019-03-31-15:47:14] Epoch: [021][400/521] Time 0.25 (0.26) Data 0.00 (0.00) Loss 0.481 (0.562) Prec@1 87.50 (86.84) Prec@5 100.00 (99.50)
|
|
train[2019-03-31-15:47:39] Epoch: [021][500/521] Time 0.25 (0.26) Data 0.00 (0.00) Loss 0.597 (0.564) Prec@1 86.46 (86.83) Prec@5 100.00 (99.49)
|
|
train[2019-03-31-15:47:45] Epoch: [021][520/521] Time 0.24 (0.26) Data 0.00 (0.00) Loss 0.560 (0.566) Prec@1 90.00 (86.76) Prec@5 100.00 (99.48)
|
|
[2019-03-31-15:47:45] **train** Prec@1 86.76 Prec@5 99.48 Error@1 13.24 Error@5 0.52 Loss:0.566
|
|
test [2019-03-31-15:47:45] Epoch: [021][000/105] Time 0.63 (0.63) Data 0.54 (0.54) Loss 0.281 (0.281) Prec@1 89.58 (89.58) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-15:47:50] Epoch: [021][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.221 (0.423) Prec@1 88.54 (86.25) Prec@5 100.00 (99.43)
|
|
test [2019-03-31-15:47:50] Epoch: [021][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.295 (0.422) Prec@1 81.25 (86.21) Prec@5 100.00 (99.45)
|
|
[2019-03-31-15:47:50] **test** Prec@1 86.21 Prec@5 99.45 Error@1 13.79 Error@5 0.55 Loss:0.422
|
|
----> Best Accuracy : Acc@1=88.69, Acc@5=99.61, Error@1=11.31, Error@5=0.39
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-15:47:50] [Epoch=022/600] [Need: 22:23:00] LR=0.0249 ~ 0.0249, Batch=96
|
|
train[2019-03-31-15:47:51] Epoch: [022][000/521] Time 0.89 (0.89) Data 0.60 (0.60) Loss 0.702 (0.702) Prec@1 83.33 (83.33) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-15:48:17] Epoch: [022][100/521] Time 0.25 (0.26) Data 0.00 (0.01) Loss 0.633 (0.557) Prec@1 83.33 (86.85) Prec@5 100.00 (99.61)
|
|
train[2019-03-31-15:48:42] Epoch: [022][200/521] Time 0.25 (0.26) Data 0.00 (0.00) Loss 0.520 (0.556) Prec@1 87.50 (86.97) Prec@5 100.00 (99.55)
|
|
train[2019-03-31-15:49:08] Epoch: [022][300/521] Time 0.25 (0.26) Data 0.00 (0.00) Loss 0.696 (0.558) Prec@1 83.33 (86.85) Prec@5 98.96 (99.56)
|
|
train[2019-03-31-15:49:33] Epoch: [022][400/521] Time 0.25 (0.26) Data 0.00 (0.00) Loss 0.561 (0.558) Prec@1 87.50 (86.96) Prec@5 98.96 (99.55)
|
|
train[2019-03-31-15:49:58] Epoch: [022][500/521] Time 0.25 (0.26) Data 0.00 (0.00) Loss 0.498 (0.560) Prec@1 90.62 (86.91) Prec@5 100.00 (99.51)
|
|
train[2019-03-31-15:50:03] Epoch: [022][520/521] Time 0.24 (0.26) Data 0.00 (0.00) Loss 0.502 (0.560) Prec@1 88.75 (86.90) Prec@5 97.50 (99.51)
|
|
[2019-03-31-15:50:03] **train** Prec@1 86.90 Prec@5 99.51 Error@1 13.10 Error@5 0.49 Loss:0.560
|
|
test [2019-03-31-15:50:04] Epoch: [022][000/105] Time 0.53 (0.53) Data 0.47 (0.47) Loss 0.356 (0.356) Prec@1 90.62 (90.62) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-15:50:08] Epoch: [022][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.234 (0.345) Prec@1 94.79 (88.27) Prec@5 100.00 (99.65)
|
|
test [2019-03-31-15:50:08] Epoch: [022][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.164 (0.345) Prec@1 93.75 (88.27) Prec@5 100.00 (99.66)
|
|
[2019-03-31-15:50:08] **test** Prec@1 88.27 Prec@5 99.66 Error@1 11.73 Error@5 0.34 Loss:0.345
|
|
----> Best Accuracy : Acc@1=88.69, Acc@5=99.61, Error@1=11.31, Error@5=0.39
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-15:50:08] [Epoch=023/600] [Need: 22:08:48] LR=0.0249 ~ 0.0249, Batch=96
|
|
train[2019-03-31-15:50:09] Epoch: [023][000/521] Time 0.74 (0.74) Data 0.46 (0.46) Loss 0.475 (0.475) Prec@1 89.58 (89.58) Prec@5 98.96 (98.96)
|
|
train[2019-03-31-15:50:34] Epoch: [023][100/521] Time 0.26 (0.26) Data 0.00 (0.00) Loss 0.610 (0.554) Prec@1 84.38 (87.09) Prec@5 98.96 (99.56)
|
|
train[2019-03-31-15:51:00] Epoch: [023][200/521] Time 0.28 (0.26) Data 0.00 (0.00) Loss 0.423 (0.553) Prec@1 90.62 (87.16) Prec@5 100.00 (99.54)
|
|
train[2019-03-31-15:51:25] Epoch: [023][300/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.697 (0.549) Prec@1 87.50 (87.21) Prec@5 97.92 (99.55)
|
|
train[2019-03-31-15:51:50] Epoch: [023][400/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.654 (0.553) Prec@1 84.38 (86.99) Prec@5 98.96 (99.54)
|
|
train[2019-03-31-15:52:14] Epoch: [023][500/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.519 (0.552) Prec@1 90.62 (87.07) Prec@5 100.00 (99.55)
|
|
train[2019-03-31-15:52:18] Epoch: [023][520/521] Time 0.21 (0.25) Data 0.00 (0.00) Loss 0.441 (0.552) Prec@1 92.50 (87.08) Prec@5 100.00 (99.55)
|
|
[2019-03-31-15:52:18] **train** Prec@1 87.08 Prec@5 99.55 Error@1 12.92 Error@5 0.45 Loss:0.552
|
|
test [2019-03-31-15:52:19] Epoch: [023][000/105] Time 0.60 (0.60) Data 0.53 (0.53) Loss 0.420 (0.420) Prec@1 87.50 (87.50) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-15:52:23] Epoch: [023][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.176 (0.455) Prec@1 94.79 (86.31) Prec@5 100.00 (99.63)
|
|
test [2019-03-31-15:52:23] Epoch: [023][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 1.202 (0.454) Prec@1 75.00 (86.32) Prec@5 100.00 (99.64)
|
|
[2019-03-31-15:52:23] **test** Prec@1 86.32 Prec@5 99.64 Error@1 13.68 Error@5 0.36 Loss:0.454
|
|
----> Best Accuracy : Acc@1=88.69, Acc@5=99.61, Error@1=11.31, Error@5=0.39
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-15:52:23] [Epoch=024/600] [Need: 21:37:02] LR=0.0249 ~ 0.0249, Batch=96
|
|
train[2019-03-31-15:52:24] Epoch: [024][000/521] Time 0.70 (0.70) Data 0.42 (0.42) Loss 0.416 (0.416) Prec@1 91.67 (91.67) Prec@5 98.96 (98.96)
|
|
train[2019-03-31-15:52:48] Epoch: [024][100/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.450 (0.535) Prec@1 88.54 (87.50) Prec@5 100.00 (99.64)
|
|
train[2019-03-31-15:53:12] Epoch: [024][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.474 (0.541) Prec@1 87.50 (87.37) Prec@5 100.00 (99.64)
|
|
train[2019-03-31-15:53:36] Epoch: [024][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.518 (0.540) Prec@1 89.58 (87.40) Prec@5 97.92 (99.63)
|
|
train[2019-03-31-15:54:00] Epoch: [024][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.629 (0.542) Prec@1 84.38 (87.34) Prec@5 98.96 (99.62)
|
|
train[2019-03-31-15:54:24] Epoch: [024][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.665 (0.546) Prec@1 85.42 (87.20) Prec@5 100.00 (99.59)
|
|
train[2019-03-31-15:54:29] Epoch: [024][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.678 (0.547) Prec@1 82.50 (87.17) Prec@5 100.00 (99.59)
|
|
[2019-03-31-15:54:29] **train** Prec@1 87.17 Prec@5 99.59 Error@1 12.83 Error@5 0.41 Loss:0.547
|
|
test [2019-03-31-15:54:29] Epoch: [024][000/105] Time 0.60 (0.60) Data 0.53 (0.53) Loss 0.525 (0.525) Prec@1 84.38 (84.38) Prec@5 97.92 (97.92)
|
|
test [2019-03-31-15:54:33] Epoch: [024][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.322 (0.442) Prec@1 90.62 (86.34) Prec@5 98.96 (99.29)
|
|
test [2019-03-31-15:54:33] Epoch: [024][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.412 (0.443) Prec@1 81.25 (86.31) Prec@5 100.00 (99.30)
|
|
[2019-03-31-15:54:34] **test** Prec@1 86.31 Prec@5 99.30 Error@1 13.69 Error@5 0.70 Loss:0.443
|
|
----> Best Accuracy : Acc@1=88.69, Acc@5=99.61, Error@1=11.31, Error@5=0.39
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-15:54:34] [Epoch=025/600] [Need: 20:47:47] LR=0.0249 ~ 0.0249, Batch=96
|
|
train[2019-03-31-15:54:35] Epoch: [025][000/521] Time 0.84 (0.84) Data 0.55 (0.55) Loss 0.730 (0.730) Prec@1 83.33 (83.33) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-15:54:58] Epoch: [025][100/521] Time 0.24 (0.25) Data 0.00 (0.01) Loss 0.432 (0.521) Prec@1 89.58 (87.89) Prec@5 100.00 (99.65)
|
|
train[2019-03-31-15:55:22] Epoch: [025][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.476 (0.530) Prec@1 85.42 (87.74) Prec@5 100.00 (99.59)
|
|
train[2019-03-31-15:55:46] Epoch: [025][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.524 (0.533) Prec@1 87.50 (87.57) Prec@5 97.92 (99.58)
|
|
train[2019-03-31-15:56:10] Epoch: [025][400/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.488 (0.536) Prec@1 85.42 (87.56) Prec@5 100.00 (99.56)
|
|
train[2019-03-31-15:56:34] Epoch: [025][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.538 (0.536) Prec@1 83.33 (87.48) Prec@5 100.00 (99.54)
|
|
train[2019-03-31-15:56:39] Epoch: [025][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.538 (0.534) Prec@1 85.00 (87.53) Prec@5 100.00 (99.55)
|
|
[2019-03-31-15:56:39] **train** Prec@1 87.53 Prec@5 99.55 Error@1 12.47 Error@5 0.45 Loss:0.534
|
|
test [2019-03-31-15:56:39] Epoch: [025][000/105] Time 0.60 (0.60) Data 0.53 (0.53) Loss 0.316 (0.316) Prec@1 86.46 (86.46) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-15:56:43] Epoch: [025][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.214 (0.342) Prec@1 90.62 (88.37) Prec@5 100.00 (99.66)
|
|
test [2019-03-31-15:56:43] Epoch: [025][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.561 (0.341) Prec@1 81.25 (88.33) Prec@5 100.00 (99.67)
|
|
[2019-03-31-15:56:44] **test** Prec@1 88.33 Prec@5 99.67 Error@1 11.67 Error@5 0.33 Loss:0.341
|
|
----> Best Accuracy : Acc@1=88.69, Acc@5=99.61, Error@1=11.31, Error@5=0.39
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-15:56:44] [Epoch=026/600] [Need: 20:43:52] LR=0.0249 ~ 0.0249, Batch=96
|
|
train[2019-03-31-15:56:45] Epoch: [026][000/521] Time 0.84 (0.84) Data 0.58 (0.58) Loss 0.584 (0.584) Prec@1 85.42 (85.42) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-15:57:08] Epoch: [026][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.459 (0.512) Prec@1 90.62 (87.90) Prec@5 100.00 (99.58)
|
|
train[2019-03-31-15:57:32] Epoch: [026][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.506 (0.530) Prec@1 87.50 (87.66) Prec@5 98.96 (99.54)
|
|
train[2019-03-31-15:57:56] Epoch: [026][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.575 (0.527) Prec@1 86.46 (87.71) Prec@5 98.96 (99.56)
|
|
train[2019-03-31-15:58:20] Epoch: [026][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.584 (0.536) Prec@1 86.46 (87.41) Prec@5 100.00 (99.55)
|
|
train[2019-03-31-15:58:44] Epoch: [026][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.699 (0.537) Prec@1 79.17 (87.39) Prec@5 100.00 (99.53)
|
|
train[2019-03-31-15:58:48] Epoch: [026][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.789 (0.538) Prec@1 83.75 (87.36) Prec@5 98.75 (99.54)
|
|
[2019-03-31-15:58:49] **train** Prec@1 87.36 Prec@5 99.54 Error@1 12.64 Error@5 0.46 Loss:0.538
|
|
test [2019-03-31-15:58:49] Epoch: [026][000/105] Time 0.60 (0.60) Data 0.54 (0.54) Loss 0.322 (0.322) Prec@1 86.46 (86.46) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-15:58:53] Epoch: [026][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.330 (0.467) Prec@1 85.42 (85.24) Prec@5 100.00 (99.56)
|
|
test [2019-03-31-15:58:53] Epoch: [026][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.678 (0.468) Prec@1 81.25 (85.17) Prec@5 100.00 (99.57)
|
|
[2019-03-31-15:58:53] **test** Prec@1 85.17 Prec@5 99.57 Error@1 14.83 Error@5 0.43 Loss:0.468
|
|
----> Best Accuracy : Acc@1=88.69, Acc@5=99.61, Error@1=11.31, Error@5=0.39
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-15:58:54] [Epoch=027/600] [Need: 20:40:40] LR=0.0249 ~ 0.0249, Batch=96
|
|
train[2019-03-31-15:58:54] Epoch: [027][000/521] Time 0.84 (0.84) Data 0.57 (0.57) Loss 0.627 (0.627) Prec@1 87.50 (87.50) Prec@5 98.96 (98.96)
|
|
train[2019-03-31-15:59:18] Epoch: [027][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.382 (0.525) Prec@1 93.75 (88.07) Prec@5 100.00 (99.69)
|
|
train[2019-03-31-15:59:42] Epoch: [027][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.663 (0.527) Prec@1 82.29 (87.82) Prec@5 98.96 (99.62)
|
|
train[2019-03-31-16:00:06] Epoch: [027][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.571 (0.526) Prec@1 85.42 (87.77) Prec@5 98.96 (99.63)
|
|
train[2019-03-31-16:00:30] Epoch: [027][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.408 (0.524) Prec@1 92.71 (87.77) Prec@5 100.00 (99.64)
|
|
train[2019-03-31-16:00:54] Epoch: [027][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.533 (0.527) Prec@1 86.46 (87.70) Prec@5 100.00 (99.63)
|
|
train[2019-03-31-16:00:59] Epoch: [027][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.583 (0.527) Prec@1 88.75 (87.70) Prec@5 100.00 (99.62)
|
|
[2019-03-31-16:00:59] **train** Prec@1 87.70 Prec@5 99.62 Error@1 12.30 Error@5 0.38 Loss:0.527
|
|
test [2019-03-31-16:00:59] Epoch: [027][000/105] Time 0.48 (0.48) Data 0.41 (0.41) Loss 0.317 (0.317) Prec@1 87.50 (87.50) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-16:01:03] Epoch: [027][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.258 (0.370) Prec@1 90.62 (88.05) Prec@5 100.00 (99.63)
|
|
test [2019-03-31-16:01:03] Epoch: [027][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.320 (0.372) Prec@1 87.50 (88.06) Prec@5 100.00 (99.64)
|
|
[2019-03-31-16:01:04] **test** Prec@1 88.06 Prec@5 99.64 Error@1 11.94 Error@5 0.36 Loss:0.372
|
|
----> Best Accuracy : Acc@1=88.69, Acc@5=99.61, Error@1=11.31, Error@5=0.39
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-16:01:04] [Epoch=028/600] [Need: 20:40:09] LR=0.0249 ~ 0.0249, Batch=96
|
|
train[2019-03-31-16:01:05] Epoch: [028][000/521] Time 0.84 (0.84) Data 0.57 (0.57) Loss 0.736 (0.736) Prec@1 83.33 (83.33) Prec@5 98.96 (98.96)
|
|
train[2019-03-31-16:01:28] Epoch: [028][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.533 (0.483) Prec@1 86.46 (88.71) Prec@5 100.00 (99.66)
|
|
train[2019-03-31-16:01:52] Epoch: [028][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.562 (0.503) Prec@1 84.38 (88.12) Prec@5 98.96 (99.67)
|
|
train[2019-03-31-16:02:16] Epoch: [028][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.525 (0.507) Prec@1 85.42 (87.96) Prec@5 100.00 (99.66)
|
|
train[2019-03-31-16:02:40] Epoch: [028][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.445 (0.511) Prec@1 86.46 (87.91) Prec@5 100.00 (99.59)
|
|
train[2019-03-31-16:03:04] Epoch: [028][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.729 (0.512) Prec@1 84.38 (87.96) Prec@5 98.96 (99.58)
|
|
train[2019-03-31-16:03:09] Epoch: [028][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.604 (0.512) Prec@1 86.25 (87.97) Prec@5 100.00 (99.58)
|
|
[2019-03-31-16:03:09] **train** Prec@1 87.97 Prec@5 99.58 Error@1 12.03 Error@5 0.42 Loss:0.512
|
|
test [2019-03-31-16:03:09] Epoch: [028][000/105] Time 0.61 (0.61) Data 0.54 (0.54) Loss 0.412 (0.412) Prec@1 87.50 (87.50) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-16:03:13] Epoch: [028][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.187 (0.325) Prec@1 92.71 (89.28) Prec@5 100.00 (99.62)
|
|
test [2019-03-31-16:03:14] Epoch: [028][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.484 (0.327) Prec@1 87.50 (89.20) Prec@5 100.00 (99.62)
|
|
[2019-03-31-16:03:14] **test** Prec@1 89.20 Prec@5 99.62 Error@1 10.80 Error@5 0.38 Loss:0.327
|
|
----> Best Accuracy : Acc@1=89.20, Acc@5=99.62, Error@1=10.80, Error@5=0.38
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-16:03:14] [Epoch=029/600] [Need: 20:38:53] LR=0.0249 ~ 0.0249, Batch=96
|
|
train[2019-03-31-16:03:15] Epoch: [029][000/521] Time 0.83 (0.83) Data 0.56 (0.56) Loss 0.415 (0.415) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-16:03:39] Epoch: [029][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.488 (0.502) Prec@1 87.50 (88.44) Prec@5 100.00 (99.66)
|
|
train[2019-03-31-16:04:03] Epoch: [029][200/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.309 (0.522) Prec@1 92.71 (87.79) Prec@5 100.00 (99.61)
|
|
train[2019-03-31-16:04:27] Epoch: [029][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.468 (0.522) Prec@1 86.46 (87.74) Prec@5 100.00 (99.63)
|
|
train[2019-03-31-16:04:51] Epoch: [029][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.618 (0.520) Prec@1 85.42 (87.89) Prec@5 98.96 (99.61)
|
|
train[2019-03-31-16:05:15] Epoch: [029][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.494 (0.522) Prec@1 89.58 (87.84) Prec@5 100.00 (99.61)
|
|
train[2019-03-31-16:05:19] Epoch: [029][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.324 (0.521) Prec@1 92.50 (87.86) Prec@5 100.00 (99.60)
|
|
[2019-03-31-16:05:19] **train** Prec@1 87.86 Prec@5 99.60 Error@1 12.14 Error@5 0.40 Loss:0.521
|
|
test [2019-03-31-16:05:20] Epoch: [029][000/105] Time 0.60 (0.60) Data 0.54 (0.54) Loss 0.222 (0.222) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-16:05:24] Epoch: [029][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.200 (0.326) Prec@1 93.75 (89.22) Prec@5 100.00 (99.70)
|
|
test [2019-03-31-16:05:24] Epoch: [029][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.335 (0.327) Prec@1 93.75 (89.21) Prec@5 100.00 (99.71)
|
|
[2019-03-31-16:05:24] **test** Prec@1 89.21 Prec@5 99.71 Error@1 10.79 Error@5 0.29 Loss:0.327
|
|
----> Best Accuracy : Acc@1=89.21, Acc@5=99.71, Error@1=10.79, Error@5=0.29
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-16:05:24] [Epoch=030/600] [Need: 20:40:00] LR=0.0248 ~ 0.0248, Batch=96
|
|
train[2019-03-31-16:05:25] Epoch: [030][000/521] Time 0.70 (0.70) Data 0.43 (0.43) Loss 0.538 (0.538) Prec@1 83.33 (83.33) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-16:05:49] Epoch: [030][100/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.715 (0.515) Prec@1 82.29 (87.48) Prec@5 98.96 (99.56)
|
|
train[2019-03-31-16:06:13] Epoch: [030][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.385 (0.515) Prec@1 90.62 (87.68) Prec@5 100.00 (99.58)
|
|
train[2019-03-31-16:06:37] Epoch: [030][300/521] Time 0.27 (0.24) Data 0.00 (0.00) Loss 0.523 (0.506) Prec@1 85.42 (87.98) Prec@5 100.00 (99.62)
|
|
train[2019-03-31-16:07:01] Epoch: [030][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.396 (0.512) Prec@1 90.62 (87.94) Prec@5 100.00 (99.58)
|
|
train[2019-03-31-16:07:25] Epoch: [030][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.648 (0.512) Prec@1 82.29 (87.92) Prec@5 100.00 (99.59)
|
|
train[2019-03-31-16:07:30] Epoch: [030][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.391 (0.514) Prec@1 92.50 (87.88) Prec@5 100.00 (99.59)
|
|
[2019-03-31-16:07:30] **train** Prec@1 87.88 Prec@5 99.59 Error@1 12.12 Error@5 0.41 Loss:0.514
|
|
test [2019-03-31-16:07:30] Epoch: [030][000/105] Time 0.48 (0.48) Data 0.42 (0.42) Loss 0.355 (0.355) Prec@1 86.46 (86.46) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-16:07:34] Epoch: [030][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.269 (0.345) Prec@1 89.58 (88.40) Prec@5 100.00 (99.71)
|
|
test [2019-03-31-16:07:34] Epoch: [030][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.160 (0.344) Prec@1 87.50 (88.40) Prec@5 100.00 (99.72)
|
|
[2019-03-31-16:07:34] **test** Prec@1 88.40 Prec@5 99.72 Error@1 11.60 Error@5 0.28 Loss:0.344
|
|
----> Best Accuracy : Acc@1=89.21, Acc@5=99.71, Error@1=10.79, Error@5=0.29
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-16:07:35] [Epoch=031/600] [Need: 20:34:28] LR=0.0248 ~ 0.0248, Batch=96
|
|
train[2019-03-31-16:07:35] Epoch: [031][000/521] Time 0.88 (0.88) Data 0.57 (0.57) Loss 0.640 (0.640) Prec@1 82.29 (82.29) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-16:07:59] Epoch: [031][100/521] Time 0.24 (0.25) Data 0.00 (0.01) Loss 0.746 (0.485) Prec@1 87.50 (88.90) Prec@5 98.96 (99.65)
|
|
train[2019-03-31-16:08:23] Epoch: [031][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.370 (0.497) Prec@1 89.58 (88.46) Prec@5 100.00 (99.66)
|
|
train[2019-03-31-16:08:47] Epoch: [031][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.393 (0.495) Prec@1 89.58 (88.47) Prec@5 100.00 (99.67)
|
|
train[2019-03-31-16:09:11] Epoch: [031][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.431 (0.499) Prec@1 88.54 (88.40) Prec@5 100.00 (99.66)
|
|
train[2019-03-31-16:09:35] Epoch: [031][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.696 (0.505) Prec@1 81.25 (88.18) Prec@5 98.96 (99.65)
|
|
train[2019-03-31-16:09:40] Epoch: [031][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.588 (0.505) Prec@1 83.75 (88.18) Prec@5 100.00 (99.65)
|
|
[2019-03-31-16:09:40] **train** Prec@1 88.18 Prec@5 99.65 Error@1 11.82 Error@5 0.35 Loss:0.505
|
|
test [2019-03-31-16:09:41] Epoch: [031][000/105] Time 0.62 (0.62) Data 0.56 (0.56) Loss 0.285 (0.285) Prec@1 85.42 (85.42) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-16:09:45] Epoch: [031][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.216 (0.337) Prec@1 92.71 (89.09) Prec@5 100.00 (99.61)
|
|
test [2019-03-31-16:09:45] Epoch: [031][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.277 (0.337) Prec@1 87.50 (89.09) Prec@5 100.00 (99.62)
|
|
[2019-03-31-16:09:45] **test** Prec@1 89.09 Prec@5 99.62 Error@1 10.91 Error@5 0.38 Loss:0.337
|
|
----> Best Accuracy : Acc@1=89.21, Acc@5=99.71, Error@1=10.79, Error@5=0.29
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-16:09:45] [Epoch=032/600] [Need: 20:36:48] LR=0.0248 ~ 0.0248, Batch=96
|
|
train[2019-03-31-16:09:46] Epoch: [032][000/521] Time 0.84 (0.84) Data 0.56 (0.56) Loss 0.332 (0.332) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-16:10:10] Epoch: [032][100/521] Time 0.24 (0.25) Data 0.00 (0.01) Loss 0.434 (0.470) Prec@1 89.58 (89.33) Prec@5 100.00 (99.76)
|
|
train[2019-03-31-16:10:34] Epoch: [032][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.435 (0.489) Prec@1 87.50 (88.70) Prec@5 100.00 (99.71)
|
|
train[2019-03-31-16:10:58] Epoch: [032][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.335 (0.494) Prec@1 93.75 (88.50) Prec@5 100.00 (99.66)
|
|
train[2019-03-31-16:11:22] Epoch: [032][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.568 (0.495) Prec@1 86.46 (88.49) Prec@5 100.00 (99.68)
|
|
train[2019-03-31-16:11:45] Epoch: [032][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.362 (0.497) Prec@1 92.71 (88.42) Prec@5 100.00 (99.68)
|
|
train[2019-03-31-16:11:50] Epoch: [032][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.457 (0.497) Prec@1 88.75 (88.44) Prec@5 100.00 (99.68)
|
|
[2019-03-31-16:11:50] **train** Prec@1 88.44 Prec@5 99.68 Error@1 11.56 Error@5 0.32 Loss:0.497
|
|
test [2019-03-31-16:11:51] Epoch: [032][000/105] Time 0.50 (0.50) Data 0.43 (0.43) Loss 0.447 (0.447) Prec@1 84.38 (84.38) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-16:11:55] Epoch: [032][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.237 (0.356) Prec@1 91.67 (88.27) Prec@5 100.00 (99.65)
|
|
test [2019-03-31-16:11:55] Epoch: [032][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.662 (0.356) Prec@1 81.25 (88.27) Prec@5 100.00 (99.65)
|
|
[2019-03-31-16:11:55] **test** Prec@1 88.27 Prec@5 99.65 Error@1 11.73 Error@5 0.35 Loss:0.356
|
|
----> Best Accuracy : Acc@1=89.21, Acc@5=99.71, Error@1=10.79, Error@5=0.29
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-16:11:55] [Epoch=033/600] [Need: 20:29:10] LR=0.0248 ~ 0.0248, Batch=96
|
|
train[2019-03-31-16:11:56] Epoch: [033][000/521] Time 0.71 (0.71) Data 0.43 (0.43) Loss 0.501 (0.501) Prec@1 87.50 (87.50) Prec@5 98.96 (98.96)
|
|
train[2019-03-31-16:12:20] Epoch: [033][100/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.615 (0.498) Prec@1 86.46 (88.72) Prec@5 97.92 (99.57)
|
|
train[2019-03-31-16:12:45] Epoch: [033][200/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.523 (0.494) Prec@1 90.62 (88.76) Prec@5 100.00 (99.59)
|
|
train[2019-03-31-16:13:09] Epoch: [033][300/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.428 (0.498) Prec@1 89.58 (88.55) Prec@5 100.00 (99.58)
|
|
train[2019-03-31-16:13:33] Epoch: [033][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.665 (0.502) Prec@1 85.42 (88.40) Prec@5 97.92 (99.56)
|
|
train[2019-03-31-16:13:57] Epoch: [033][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.713 (0.504) Prec@1 85.42 (88.26) Prec@5 98.96 (99.57)
|
|
train[2019-03-31-16:14:02] Epoch: [033][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.926 (0.505) Prec@1 80.00 (88.21) Prec@5 97.50 (99.56)
|
|
[2019-03-31-16:14:02] **train** Prec@1 88.21 Prec@5 99.56 Error@1 11.79 Error@5 0.44 Loss:0.505
|
|
test [2019-03-31-16:14:02] Epoch: [033][000/105] Time 0.68 (0.68) Data 0.58 (0.58) Loss 0.323 (0.323) Prec@1 88.54 (88.54) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-16:14:07] Epoch: [033][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.194 (0.346) Prec@1 90.62 (88.41) Prec@5 100.00 (99.69)
|
|
test [2019-03-31-16:14:07] Epoch: [033][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.455 (0.344) Prec@1 93.75 (88.45) Prec@5 100.00 (99.70)
|
|
[2019-03-31-16:14:07] **test** Prec@1 88.45 Prec@5 99.70 Error@1 11.55 Error@5 0.30 Loss:0.344
|
|
----> Best Accuracy : Acc@1=89.21, Acc@5=99.71, Error@1=10.79, Error@5=0.29
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-16:14:07] [Epoch=034/600] [Need: 20:43:34] LR=0.0248 ~ 0.0248, Batch=96
|
|
train[2019-03-31-16:14:08] Epoch: [034][000/521] Time 0.87 (0.87) Data 0.56 (0.56) Loss 0.545 (0.545) Prec@1 86.46 (86.46) Prec@5 98.96 (98.96)
|
|
train[2019-03-31-16:14:33] Epoch: [034][100/521] Time 0.24 (0.25) Data 0.00 (0.01) Loss 0.583 (0.477) Prec@1 85.42 (88.64) Prec@5 100.00 (99.75)
|
|
train[2019-03-31-16:14:56] Epoch: [034][200/521] Time 0.23 (0.25) Data 0.00 (0.00) Loss 0.638 (0.488) Prec@1 84.38 (88.45) Prec@5 100.00 (99.73)
|
|
train[2019-03-31-16:15:21] Epoch: [034][300/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.449 (0.488) Prec@1 91.67 (88.59) Prec@5 100.00 (99.67)
|
|
train[2019-03-31-16:15:45] Epoch: [034][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.480 (0.491) Prec@1 88.54 (88.53) Prec@5 98.96 (99.68)
|
|
train[2019-03-31-16:16:08] Epoch: [034][500/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.606 (0.494) Prec@1 85.42 (88.49) Prec@5 100.00 (99.66)
|
|
train[2019-03-31-16:16:13] Epoch: [034][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.570 (0.496) Prec@1 90.00 (88.45) Prec@5 98.75 (99.66)
|
|
[2019-03-31-16:16:13] **train** Prec@1 88.45 Prec@5 99.66 Error@1 11.55 Error@5 0.34 Loss:0.496
|
|
test [2019-03-31-16:16:14] Epoch: [034][000/105] Time 0.48 (0.48) Data 0.41 (0.41) Loss 0.281 (0.281) Prec@1 90.62 (90.62) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-16:16:18] Epoch: [034][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.281 (0.386) Prec@1 89.58 (87.35) Prec@5 100.00 (99.49)
|
|
test [2019-03-31-16:16:18] Epoch: [034][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.441 (0.386) Prec@1 81.25 (87.30) Prec@5 100.00 (99.51)
|
|
[2019-03-31-16:16:18] **test** Prec@1 87.30 Prec@5 99.51 Error@1 12.70 Error@5 0.49 Loss:0.386
|
|
----> Best Accuracy : Acc@1=89.21, Acc@5=99.71, Error@1=10.79, Error@5=0.29
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-16:16:18] [Epoch=035/600] [Need: 20:33:42] LR=0.0248 ~ 0.0248, Batch=96
|
|
train[2019-03-31-16:16:19] Epoch: [035][000/521] Time 1.09 (1.09) Data 0.76 (0.76) Loss 0.634 (0.634) Prec@1 86.46 (86.46) Prec@5 98.96 (98.96)
|
|
train[2019-03-31-16:16:44] Epoch: [035][100/521] Time 0.24 (0.26) Data 0.00 (0.01) Loss 0.483 (0.487) Prec@1 92.71 (89.17) Prec@5 100.00 (99.67)
|
|
train[2019-03-31-16:17:08] Epoch: [035][200/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.418 (0.483) Prec@1 92.71 (89.04) Prec@5 98.96 (99.65)
|
|
train[2019-03-31-16:17:33] Epoch: [035][300/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.540 (0.481) Prec@1 86.46 (88.89) Prec@5 100.00 (99.66)
|
|
train[2019-03-31-16:17:58] Epoch: [035][400/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.587 (0.489) Prec@1 88.54 (88.61) Prec@5 98.96 (99.64)
|
|
train[2019-03-31-16:18:23] Epoch: [035][500/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.544 (0.492) Prec@1 86.46 (88.55) Prec@5 100.00 (99.63)
|
|
train[2019-03-31-16:18:28] Epoch: [035][520/521] Time 0.23 (0.25) Data 0.00 (0.00) Loss 0.552 (0.493) Prec@1 87.50 (88.51) Prec@5 100.00 (99.63)
|
|
[2019-03-31-16:18:28] **train** Prec@1 88.51 Prec@5 99.63 Error@1 11.49 Error@5 0.37 Loss:0.493
|
|
test [2019-03-31-16:18:29] Epoch: [035][000/105] Time 0.54 (0.54) Data 0.45 (0.45) Loss 0.437 (0.437) Prec@1 88.54 (88.54) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-16:18:33] Epoch: [035][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.123 (0.291) Prec@1 94.79 (90.51) Prec@5 100.00 (99.76)
|
|
test [2019-03-31-16:18:33] Epoch: [035][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.200 (0.291) Prec@1 93.75 (90.54) Prec@5 100.00 (99.77)
|
|
[2019-03-31-16:18:33] **test** Prec@1 90.54 Prec@5 99.77 Error@1 9.46 Error@5 0.23 Loss:0.291
|
|
----> Best Accuracy : Acc@1=90.54, Acc@5=99.77, Error@1=9.46, Error@5=0.23
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-16:18:33] [Epoch=036/600] [Need: 21:10:44] LR=0.0248 ~ 0.0248, Batch=96
|
|
train[2019-03-31-16:18:34] Epoch: [036][000/521] Time 0.74 (0.74) Data 0.45 (0.45) Loss 0.599 (0.599) Prec@1 86.46 (86.46) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-16:18:59] Epoch: [036][100/521] Time 0.24 (0.26) Data 0.00 (0.00) Loss 0.543 (0.484) Prec@1 87.50 (88.48) Prec@5 100.00 (99.66)
|
|
train[2019-03-31-16:19:25] Epoch: [036][200/521] Time 0.24 (0.26) Data 0.00 (0.00) Loss 0.445 (0.484) Prec@1 91.67 (88.56) Prec@5 100.00 (99.69)
|
|
train[2019-03-31-16:19:50] Epoch: [036][300/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.479 (0.487) Prec@1 88.54 (88.57) Prec@5 100.00 (99.70)
|
|
train[2019-03-31-16:20:15] Epoch: [036][400/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.546 (0.485) Prec@1 87.50 (88.57) Prec@5 98.96 (99.70)
|
|
train[2019-03-31-16:20:40] Epoch: [036][500/521] Time 0.27 (0.25) Data 0.00 (0.00) Loss 0.652 (0.492) Prec@1 86.46 (88.35) Prec@5 98.96 (99.68)
|
|
train[2019-03-31-16:20:45] Epoch: [036][520/521] Time 0.23 (0.25) Data 0.00 (0.00) Loss 0.532 (0.492) Prec@1 90.00 (88.35) Prec@5 100.00 (99.68)
|
|
[2019-03-31-16:20:45] **train** Prec@1 88.35 Prec@5 99.68 Error@1 11.65 Error@5 0.32 Loss:0.492
|
|
test [2019-03-31-16:20:46] Epoch: [036][000/105] Time 0.49 (0.49) Data 0.43 (0.43) Loss 0.419 (0.419) Prec@1 86.46 (86.46) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-16:20:50] Epoch: [036][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.284 (0.422) Prec@1 91.67 (86.96) Prec@5 100.00 (99.52)
|
|
test [2019-03-31-16:20:50] Epoch: [036][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.280 (0.422) Prec@1 81.25 (86.93) Prec@5 100.00 (99.52)
|
|
[2019-03-31-16:20:50] **test** Prec@1 86.93 Prec@5 99.52 Error@1 13.07 Error@5 0.48 Loss:0.422
|
|
----> Best Accuracy : Acc@1=90.54, Acc@5=99.77, Error@1=9.46, Error@5=0.23
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-16:20:50] [Epoch=037/600] [Need: 21:26:58] LR=0.0248 ~ 0.0248, Batch=96
|
|
train[2019-03-31-16:20:51] Epoch: [037][000/521] Time 0.74 (0.74) Data 0.46 (0.46) Loss 0.547 (0.547) Prec@1 84.38 (84.38) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-16:21:16] Epoch: [037][100/521] Time 0.26 (0.25) Data 0.00 (0.00) Loss 0.403 (0.452) Prec@1 90.62 (89.84) Prec@5 100.00 (99.70)
|
|
train[2019-03-31-16:21:41] Epoch: [037][200/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.591 (0.466) Prec@1 87.50 (89.25) Prec@5 100.00 (99.70)
|
|
train[2019-03-31-16:22:06] Epoch: [037][300/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.556 (0.480) Prec@1 87.50 (88.88) Prec@5 98.96 (99.70)
|
|
train[2019-03-31-16:22:31] Epoch: [037][400/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.388 (0.485) Prec@1 93.75 (88.74) Prec@5 100.00 (99.69)
|
|
train[2019-03-31-16:22:57] Epoch: [037][500/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.383 (0.487) Prec@1 94.79 (88.73) Prec@5 100.00 (99.69)
|
|
train[2019-03-31-16:23:02] Epoch: [037][520/521] Time 0.22 (0.25) Data 0.00 (0.00) Loss 0.434 (0.485) Prec@1 88.75 (88.77) Prec@5 100.00 (99.68)
|
|
[2019-03-31-16:23:02] **train** Prec@1 88.77 Prec@5 99.68 Error@1 11.23 Error@5 0.32 Loss:0.485
|
|
test [2019-03-31-16:23:02] Epoch: [037][000/105] Time 0.52 (0.52) Data 0.45 (0.45) Loss 0.376 (0.376) Prec@1 86.46 (86.46) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-16:23:06] Epoch: [037][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.119 (0.340) Prec@1 97.92 (89.51) Prec@5 100.00 (99.46)
|
|
test [2019-03-31-16:23:07] Epoch: [037][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.124 (0.340) Prec@1 93.75 (89.47) Prec@5 100.00 (99.48)
|
|
[2019-03-31-16:23:07] **test** Prec@1 89.47 Prec@5 99.48 Error@1 10.53 Error@5 0.52 Loss:0.340
|
|
----> Best Accuracy : Acc@1=90.54, Acc@5=99.77, Error@1=9.46, Error@5=0.23
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-16:23:07] [Epoch=038/600] [Need: 21:16:49] LR=0.0248 ~ 0.0248, Batch=96
|
|
train[2019-03-31-16:23:08] Epoch: [038][000/521] Time 0.89 (0.89) Data 0.60 (0.60) Loss 0.284 (0.284) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-16:23:33] Epoch: [038][100/521] Time 0.28 (0.26) Data 0.00 (0.01) Loss 0.564 (0.474) Prec@1 82.29 (88.71) Prec@5 100.00 (99.69)
|
|
train[2019-03-31-16:23:58] Epoch: [038][200/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.339 (0.474) Prec@1 92.71 (88.76) Prec@5 100.00 (99.70)
|
|
train[2019-03-31-16:24:22] Epoch: [038][300/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.558 (0.482) Prec@1 82.29 (88.52) Prec@5 98.96 (99.71)
|
|
train[2019-03-31-16:24:46] Epoch: [038][400/521] Time 0.26 (0.25) Data 0.00 (0.00) Loss 0.547 (0.478) Prec@1 82.29 (88.68) Prec@5 98.96 (99.71)
|
|
train[2019-03-31-16:25:10] Epoch: [038][500/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.643 (0.485) Prec@1 85.42 (88.50) Prec@5 98.96 (99.69)
|
|
train[2019-03-31-16:25:14] Epoch: [038][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.686 (0.486) Prec@1 87.50 (88.49) Prec@5 98.75 (99.70)
|
|
[2019-03-31-16:25:14] **train** Prec@1 88.49 Prec@5 99.70 Error@1 11.51 Error@5 0.30 Loss:0.486
|
|
test [2019-03-31-16:25:15] Epoch: [038][000/105] Time 0.59 (0.59) Data 0.52 (0.52) Loss 0.361 (0.361) Prec@1 86.46 (86.46) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-16:25:19] Epoch: [038][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.179 (0.295) Prec@1 92.71 (90.09) Prec@5 98.96 (99.68)
|
|
test [2019-03-31-16:25:19] Epoch: [038][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.435 (0.295) Prec@1 93.75 (90.11) Prec@5 100.00 (99.69)
|
|
[2019-03-31-16:25:19] **test** Prec@1 90.11 Prec@5 99.69 Error@1 9.89 Error@5 0.31 Loss:0.295
|
|
----> Best Accuracy : Acc@1=90.54, Acc@5=99.77, Error@1=9.46, Error@5=0.23
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-16:25:19] [Epoch=039/600] [Need: 20:40:14] LR=0.0247 ~ 0.0247, Batch=96
|
|
train[2019-03-31-16:25:20] Epoch: [039][000/521] Time 0.84 (0.84) Data 0.56 (0.56) Loss 0.638 (0.638) Prec@1 83.33 (83.33) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-16:25:44] Epoch: [039][100/521] Time 0.24 (0.25) Data 0.00 (0.01) Loss 0.397 (0.450) Prec@1 92.71 (89.47) Prec@5 98.96 (99.72)
|
|
train[2019-03-31-16:26:08] Epoch: [039][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.380 (0.471) Prec@1 92.71 (88.99) Prec@5 100.00 (99.66)
|
|
train[2019-03-31-16:26:32] Epoch: [039][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.480 (0.474) Prec@1 88.54 (88.77) Prec@5 97.92 (99.69)
|
|
train[2019-03-31-16:26:55] Epoch: [039][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.823 (0.474) Prec@1 81.25 (88.79) Prec@5 98.96 (99.68)
|
|
train[2019-03-31-16:27:19] Epoch: [039][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.500 (0.476) Prec@1 87.50 (88.73) Prec@5 97.92 (99.67)
|
|
train[2019-03-31-16:27:24] Epoch: [039][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.582 (0.478) Prec@1 85.00 (88.72) Prec@5 100.00 (99.66)
|
|
[2019-03-31-16:27:24] **train** Prec@1 88.72 Prec@5 99.66 Error@1 11.28 Error@5 0.34 Loss:0.478
|
|
test [2019-03-31-16:27:24] Epoch: [039][000/105] Time 0.46 (0.46) Data 0.40 (0.40) Loss 0.345 (0.345) Prec@1 86.46 (86.46) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-16:27:29] Epoch: [039][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.229 (0.345) Prec@1 90.62 (88.71) Prec@5 100.00 (99.66)
|
|
test [2019-03-31-16:27:29] Epoch: [039][104/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.160 (0.345) Prec@1 93.75 (88.70) Prec@5 100.00 (99.67)
|
|
[2019-03-31-16:27:29] **test** Prec@1 88.70 Prec@5 99.67 Error@1 11.30 Error@5 0.33 Loss:0.345
|
|
----> Best Accuracy : Acc@1=90.54, Acc@5=99.77, Error@1=9.46, Error@5=0.23
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-16:27:29] [Epoch=040/600] [Need: 20:08:11] LR=0.0247 ~ 0.0247, Batch=96
|
|
train[2019-03-31-16:27:30] Epoch: [040][000/521] Time 0.74 (0.74) Data 0.43 (0.43) Loss 0.468 (0.468) Prec@1 88.54 (88.54) Prec@5 97.92 (97.92)
|
|
train[2019-03-31-16:27:53] Epoch: [040][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.543 (0.442) Prec@1 84.38 (89.51) Prec@5 100.00 (99.75)
|
|
train[2019-03-31-16:28:17] Epoch: [040][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.413 (0.466) Prec@1 90.62 (89.03) Prec@5 100.00 (99.72)
|
|
train[2019-03-31-16:28:41] Epoch: [040][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.544 (0.461) Prec@1 87.50 (89.24) Prec@5 100.00 (99.74)
|
|
train[2019-03-31-16:29:05] Epoch: [040][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.372 (0.466) Prec@1 91.67 (89.14) Prec@5 100.00 (99.74)
|
|
train[2019-03-31-16:29:29] Epoch: [040][500/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.365 (0.472) Prec@1 91.67 (89.07) Prec@5 100.00 (99.71)
|
|
train[2019-03-31-16:29:34] Epoch: [040][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.574 (0.473) Prec@1 88.75 (89.05) Prec@5 98.75 (99.70)
|
|
[2019-03-31-16:29:34] **train** Prec@1 89.05 Prec@5 99.70 Error@1 10.95 Error@5 0.30 Loss:0.473
|
|
test [2019-03-31-16:29:34] Epoch: [040][000/105] Time 0.47 (0.47) Data 0.41 (0.41) Loss 0.324 (0.324) Prec@1 91.67 (91.67) Prec@5 98.96 (98.96)
|
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test [2019-03-31-16:29:38] Epoch: [040][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.184 (0.322) Prec@1 93.75 (89.58) Prec@5 100.00 (99.70)
|
|
test [2019-03-31-16:29:39] Epoch: [040][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.188 (0.322) Prec@1 87.50 (89.57) Prec@5 100.00 (99.71)
|
|
[2019-03-31-16:29:39] **test** Prec@1 89.57 Prec@5 99.71 Error@1 10.43 Error@5 0.29 Loss:0.322
|
|
----> Best Accuracy : Acc@1=90.54, Acc@5=99.77, Error@1=9.46, Error@5=0.23
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-16:29:39] [Epoch=041/600] [Need: 20:10:20] LR=0.0247 ~ 0.0247, Batch=96
|
|
train[2019-03-31-16:29:40] Epoch: [041][000/521] Time 0.71 (0.71) Data 0.43 (0.43) Loss 0.389 (0.389) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-16:30:03] Epoch: [041][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.472 (0.471) Prec@1 91.67 (88.83) Prec@5 98.96 (99.76)
|
|
train[2019-03-31-16:30:27] Epoch: [041][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.439 (0.477) Prec@1 88.54 (88.57) Prec@5 100.00 (99.71)
|
|
train[2019-03-31-16:30:51] Epoch: [041][300/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.462 (0.476) Prec@1 89.58 (88.65) Prec@5 100.00 (99.72)
|
|
train[2019-03-31-16:31:14] Epoch: [041][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.410 (0.475) Prec@1 89.58 (88.70) Prec@5 100.00 (99.70)
|
|
train[2019-03-31-16:31:39] Epoch: [041][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.568 (0.477) Prec@1 87.50 (88.72) Prec@5 98.96 (99.69)
|
|
train[2019-03-31-16:31:43] Epoch: [041][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.418 (0.474) Prec@1 91.25 (88.78) Prec@5 100.00 (99.69)
|
|
[2019-03-31-16:31:43] **train** Prec@1 88.78 Prec@5 99.69 Error@1 11.22 Error@5 0.31 Loss:0.474
|
|
test [2019-03-31-16:31:44] Epoch: [041][000/105] Time 0.48 (0.48) Data 0.41 (0.41) Loss 0.223 (0.223) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-16:31:48] Epoch: [041][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.141 (0.371) Prec@1 93.75 (88.36) Prec@5 100.00 (99.71)
|
|
test [2019-03-31-16:31:48] Epoch: [041][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.341 (0.370) Prec@1 87.50 (88.36) Prec@5 100.00 (99.72)
|
|
[2019-03-31-16:31:48] **test** Prec@1 88.36 Prec@5 99.72 Error@1 11.64 Error@5 0.28 Loss:0.370
|
|
----> Best Accuracy : Acc@1=90.54, Acc@5=99.77, Error@1=9.46, Error@5=0.23
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-16:31:48] [Epoch=042/600] [Need: 20:03:30] LR=0.0247 ~ 0.0247, Batch=96
|
|
train[2019-03-31-16:31:49] Epoch: [042][000/521] Time 0.84 (0.84) Data 0.57 (0.57) Loss 0.464 (0.464) Prec@1 89.58 (89.58) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-16:32:13] Epoch: [042][100/521] Time 0.24 (0.25) Data 0.00 (0.01) Loss 0.602 (0.459) Prec@1 86.46 (88.96) Prec@5 100.00 (99.76)
|
|
train[2019-03-31-16:32:37] Epoch: [042][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.315 (0.459) Prec@1 94.79 (89.05) Prec@5 100.00 (99.70)
|
|
train[2019-03-31-16:33:01] Epoch: [042][300/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.398 (0.459) Prec@1 89.58 (89.05) Prec@5 100.00 (99.69)
|
|
train[2019-03-31-16:33:25] Epoch: [042][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.620 (0.461) Prec@1 86.46 (89.12) Prec@5 98.96 (99.69)
|
|
train[2019-03-31-16:33:49] Epoch: [042][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.599 (0.469) Prec@1 89.58 (88.91) Prec@5 100.00 (99.69)
|
|
train[2019-03-31-16:33:53] Epoch: [042][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.540 (0.470) Prec@1 87.50 (88.88) Prec@5 100.00 (99.70)
|
|
[2019-03-31-16:33:54] **train** Prec@1 88.88 Prec@5 99.70 Error@1 11.12 Error@5 0.30 Loss:0.470
|
|
test [2019-03-31-16:33:54] Epoch: [042][000/105] Time 0.47 (0.47) Data 0.41 (0.41) Loss 0.304 (0.304) Prec@1 90.62 (90.62) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-16:33:58] Epoch: [042][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.132 (0.297) Prec@1 95.83 (90.07) Prec@5 100.00 (99.74)
|
|
test [2019-03-31-16:33:58] Epoch: [042][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.317 (0.297) Prec@1 87.50 (90.10) Prec@5 100.00 (99.75)
|
|
[2019-03-31-16:33:58] **test** Prec@1 90.10 Prec@5 99.75 Error@1 9.90 Error@5 0.25 Loss:0.297
|
|
----> Best Accuracy : Acc@1=90.54, Acc@5=99.77, Error@1=9.46, Error@5=0.23
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-16:33:58] [Epoch=043/600] [Need: 20:09:20] LR=0.0247 ~ 0.0247, Batch=96
|
|
train[2019-03-31-16:33:59] Epoch: [043][000/521] Time 0.72 (0.72) Data 0.46 (0.46) Loss 0.438 (0.438) Prec@1 88.54 (88.54) Prec@5 98.96 (98.96)
|
|
train[2019-03-31-16:34:23] Epoch: [043][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.464 (0.444) Prec@1 89.58 (89.71) Prec@5 98.96 (99.73)
|
|
train[2019-03-31-16:34:47] Epoch: [043][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.353 (0.455) Prec@1 90.62 (89.45) Prec@5 100.00 (99.76)
|
|
train[2019-03-31-16:35:10] Epoch: [043][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.485 (0.456) Prec@1 86.46 (89.40) Prec@5 100.00 (99.71)
|
|
train[2019-03-31-16:35:34] Epoch: [043][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.326 (0.454) Prec@1 94.79 (89.45) Prec@5 100.00 (99.70)
|
|
train[2019-03-31-16:35:57] Epoch: [043][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.480 (0.463) Prec@1 90.62 (89.26) Prec@5 100.00 (99.68)
|
|
train[2019-03-31-16:36:02] Epoch: [043][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.515 (0.464) Prec@1 88.75 (89.23) Prec@5 100.00 (99.68)
|
|
[2019-03-31-16:36:02] **train** Prec@1 89.23 Prec@5 99.68 Error@1 10.77 Error@5 0.32 Loss:0.464
|
|
test [2019-03-31-16:36:03] Epoch: [043][000/105] Time 0.61 (0.61) Data 0.54 (0.54) Loss 0.322 (0.322) Prec@1 87.50 (87.50) Prec@5 98.96 (98.96)
|
|
test [2019-03-31-16:36:07] Epoch: [043][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.176 (0.329) Prec@1 92.71 (89.50) Prec@5 100.00 (99.58)
|
|
test [2019-03-31-16:36:07] Epoch: [043][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.563 (0.329) Prec@1 87.50 (89.48) Prec@5 100.00 (99.59)
|
|
[2019-03-31-16:36:07] **test** Prec@1 89.48 Prec@5 99.59 Error@1 10.52 Error@5 0.41 Loss:0.329
|
|
----> Best Accuracy : Acc@1=90.54, Acc@5=99.77, Error@1=9.46, Error@5=0.23
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-16:36:07] [Epoch=044/600] [Need: 19:53:43] LR=0.0247 ~ 0.0247, Batch=96
|
|
train[2019-03-31-16:36:08] Epoch: [044][000/521] Time 0.77 (0.77) Data 0.48 (0.48) Loss 0.327 (0.327) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-16:36:32] Epoch: [044][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.489 (0.449) Prec@1 87.50 (89.65) Prec@5 100.00 (99.74)
|
|
train[2019-03-31-16:36:56] Epoch: [044][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.634 (0.468) Prec@1 83.33 (89.07) Prec@5 100.00 (99.73)
|
|
train[2019-03-31-16:37:20] Epoch: [044][300/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.343 (0.468) Prec@1 91.67 (89.05) Prec@5 100.00 (99.71)
|
|
train[2019-03-31-16:37:43] Epoch: [044][400/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.264 (0.467) Prec@1 94.79 (89.07) Prec@5 100.00 (99.67)
|
|
train[2019-03-31-16:38:07] Epoch: [044][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.599 (0.467) Prec@1 88.54 (89.11) Prec@5 100.00 (99.68)
|
|
train[2019-03-31-16:38:12] Epoch: [044][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.357 (0.465) Prec@1 90.00 (89.19) Prec@5 100.00 (99.69)
|
|
[2019-03-31-16:38:12] **train** Prec@1 89.19 Prec@5 99.69 Error@1 10.81 Error@5 0.31 Loss:0.465
|
|
test [2019-03-31-16:38:12] Epoch: [044][000/105] Time 0.47 (0.47) Data 0.40 (0.40) Loss 0.274 (0.274) Prec@1 90.62 (90.62) Prec@5 98.96 (98.96)
|
|
test [2019-03-31-16:38:16] Epoch: [044][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.202 (0.304) Prec@1 90.62 (89.95) Prec@5 100.00 (99.64)
|
|
test [2019-03-31-16:38:16] Epoch: [044][104/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.089 (0.304) Prec@1 93.75 (89.98) Prec@5 100.00 (99.65)
|
|
[2019-03-31-16:38:17] **test** Prec@1 89.98 Prec@5 99.65 Error@1 10.02 Error@5 0.35 Loss:0.304
|
|
----> Best Accuracy : Acc@1=90.54, Acc@5=99.77, Error@1=9.46, Error@5=0.23
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-16:38:17] [Epoch=045/600] [Need: 19:56:38] LR=0.0247 ~ 0.0247, Batch=96
|
|
train[2019-03-31-16:38:17] Epoch: [045][000/521] Time 0.72 (0.72) Data 0.45 (0.45) Loss 0.407 (0.407) Prec@1 91.67 (91.67) Prec@5 98.96 (98.96)
|
|
train[2019-03-31-16:38:41] Epoch: [045][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.420 (0.437) Prec@1 87.50 (89.99) Prec@5 98.96 (99.72)
|
|
train[2019-03-31-16:39:05] Epoch: [045][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.534 (0.460) Prec@1 86.46 (89.33) Prec@5 97.92 (99.70)
|
|
train[2019-03-31-16:39:29] Epoch: [045][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.476 (0.459) Prec@1 90.62 (89.37) Prec@5 100.00 (99.71)
|
|
train[2019-03-31-16:39:52] Epoch: [045][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.331 (0.453) Prec@1 92.71 (89.48) Prec@5 100.00 (99.71)
|
|
train[2019-03-31-16:40:16] Epoch: [045][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.512 (0.459) Prec@1 87.50 (89.26) Prec@5 98.96 (99.69)
|
|
train[2019-03-31-16:40:21] Epoch: [045][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.729 (0.460) Prec@1 81.25 (89.26) Prec@5 100.00 (99.70)
|
|
[2019-03-31-16:40:21] **train** Prec@1 89.26 Prec@5 99.70 Error@1 10.74 Error@5 0.30 Loss:0.460
|
|
test [2019-03-31-16:40:22] Epoch: [045][000/105] Time 0.60 (0.60) Data 0.54 (0.54) Loss 0.367 (0.367) Prec@1 89.58 (89.58) Prec@5 98.96 (98.96)
|
|
test [2019-03-31-16:40:26] Epoch: [045][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.210 (0.315) Prec@1 92.71 (89.87) Prec@5 100.00 (99.68)
|
|
test [2019-03-31-16:40:26] Epoch: [045][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.293 (0.316) Prec@1 93.75 (89.86) Prec@5 100.00 (99.68)
|
|
[2019-03-31-16:40:26] **test** Prec@1 89.86 Prec@5 99.68 Error@1 10.14 Error@5 0.32 Loss:0.316
|
|
----> Best Accuracy : Acc@1=90.54, Acc@5=99.77, Error@1=9.46, Error@5=0.23
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-16:40:26] [Epoch=046/600] [Need: 19:54:17] LR=0.0246 ~ 0.0246, Batch=96
|
|
train[2019-03-31-16:40:27] Epoch: [046][000/521] Time 0.85 (0.85) Data 0.58 (0.58) Loss 0.423 (0.423) Prec@1 89.58 (89.58) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-16:40:51] Epoch: [046][100/521] Time 0.23 (0.24) Data 0.00 (0.01) Loss 0.521 (0.450) Prec@1 88.54 (89.95) Prec@5 100.00 (99.70)
|
|
train[2019-03-31-16:41:14] Epoch: [046][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.306 (0.460) Prec@1 92.71 (89.42) Prec@5 100.00 (99.64)
|
|
train[2019-03-31-16:41:38] Epoch: [046][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.491 (0.460) Prec@1 89.58 (89.40) Prec@5 98.96 (99.68)
|
|
train[2019-03-31-16:42:02] Epoch: [046][400/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.306 (0.459) Prec@1 92.71 (89.34) Prec@5 100.00 (99.69)
|
|
train[2019-03-31-16:42:25] Epoch: [046][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.487 (0.458) Prec@1 88.54 (89.35) Prec@5 100.00 (99.71)
|
|
train[2019-03-31-16:42:30] Epoch: [046][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.424 (0.459) Prec@1 87.50 (89.28) Prec@5 100.00 (99.71)
|
|
[2019-03-31-16:42:30] **train** Prec@1 89.28 Prec@5 99.71 Error@1 10.72 Error@5 0.29 Loss:0.459
|
|
test [2019-03-31-16:42:31] Epoch: [046][000/105] Time 0.61 (0.61) Data 0.55 (0.55) Loss 0.345 (0.345) Prec@1 86.46 (86.46) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-16:42:35] Epoch: [046][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.217 (0.288) Prec@1 93.75 (90.78) Prec@5 100.00 (99.71)
|
|
test [2019-03-31-16:42:35] Epoch: [046][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.082 (0.286) Prec@1 93.75 (90.77) Prec@5 100.00 (99.72)
|
|
[2019-03-31-16:42:35] **test** Prec@1 90.77 Prec@5 99.72 Error@1 9.23 Error@5 0.28 Loss:0.286
|
|
----> Best Accuracy : Acc@1=90.77, Acc@5=99.72, Error@1=9.23, Error@5=0.28
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-16:42:35] [Epoch=047/600] [Need: 19:49:09] LR=0.0246 ~ 0.0246, Batch=96
|
|
train[2019-03-31-16:42:36] Epoch: [047][000/521] Time 0.85 (0.85) Data 0.58 (0.58) Loss 0.357 (0.357) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-16:43:00] Epoch: [047][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.441 (0.455) Prec@1 89.58 (89.17) Prec@5 100.00 (99.74)
|
|
train[2019-03-31-16:43:23] Epoch: [047][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.461 (0.464) Prec@1 90.62 (89.06) Prec@5 100.00 (99.68)
|
|
train[2019-03-31-16:43:47] Epoch: [047][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.352 (0.457) Prec@1 90.62 (89.33) Prec@5 98.96 (99.71)
|
|
train[2019-03-31-16:44:11] Epoch: [047][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.858 (0.459) Prec@1 78.12 (89.27) Prec@5 100.00 (99.71)
|
|
train[2019-03-31-16:44:35] Epoch: [047][500/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.483 (0.462) Prec@1 88.54 (89.20) Prec@5 100.00 (99.72)
|
|
train[2019-03-31-16:44:39] Epoch: [047][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.279 (0.461) Prec@1 93.75 (89.22) Prec@5 100.00 (99.71)
|
|
[2019-03-31-16:44:39] **train** Prec@1 89.22 Prec@5 99.71 Error@1 10.78 Error@5 0.29 Loss:0.461
|
|
test [2019-03-31-16:44:40] Epoch: [047][000/105] Time 0.60 (0.60) Data 0.53 (0.53) Loss 0.413 (0.413) Prec@1 88.54 (88.54) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-16:44:44] Epoch: [047][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.203 (0.294) Prec@1 94.79 (90.43) Prec@5 100.00 (99.77)
|
|
test [2019-03-31-16:44:44] Epoch: [047][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.446 (0.295) Prec@1 81.25 (90.39) Prec@5 100.00 (99.78)
|
|
[2019-03-31-16:44:44] **test** Prec@1 90.39 Prec@5 99.78 Error@1 9.61 Error@5 0.22 Loss:0.295
|
|
----> Best Accuracy : Acc@1=90.77, Acc@5=99.72, Error@1=9.23, Error@5=0.28
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-16:44:44] [Epoch=048/600] [Need: 19:50:07] LR=0.0246 ~ 0.0246, Batch=96
|
|
train[2019-03-31-16:44:45] Epoch: [048][000/521] Time 0.71 (0.71) Data 0.44 (0.44) Loss 0.486 (0.486) Prec@1 87.50 (87.50) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-16:45:09] Epoch: [048][100/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.389 (0.428) Prec@1 91.67 (90.08) Prec@5 100.00 (99.81)
|
|
train[2019-03-31-16:45:33] Epoch: [048][200/521] Time 0.27 (0.24) Data 0.00 (0.00) Loss 0.600 (0.429) Prec@1 89.58 (89.97) Prec@5 100.00 (99.76)
|
|
train[2019-03-31-16:45:57] Epoch: [048][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.455 (0.440) Prec@1 87.50 (89.60) Prec@5 98.96 (99.74)
|
|
train[2019-03-31-16:46:21] Epoch: [048][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.599 (0.443) Prec@1 87.50 (89.55) Prec@5 100.00 (99.73)
|
|
train[2019-03-31-16:46:45] Epoch: [048][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.312 (0.449) Prec@1 94.79 (89.40) Prec@5 100.00 (99.76)
|
|
train[2019-03-31-16:46:50] Epoch: [048][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.487 (0.449) Prec@1 85.00 (89.38) Prec@5 97.50 (99.76)
|
|
[2019-03-31-16:46:50] **train** Prec@1 89.38 Prec@5 99.76 Error@1 10.62 Error@5 0.24 Loss:0.449
|
|
test [2019-03-31-16:46:50] Epoch: [048][000/105] Time 0.60 (0.60) Data 0.54 (0.54) Loss 0.306 (0.306) Prec@1 88.54 (88.54) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-16:46:54] Epoch: [048][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.213 (0.282) Prec@1 93.75 (90.90) Prec@5 100.00 (99.69)
|
|
test [2019-03-31-16:46:55] Epoch: [048][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.273 (0.282) Prec@1 93.75 (90.93) Prec@5 93.75 (99.69)
|
|
[2019-03-31-16:46:55] **test** Prec@1 90.93 Prec@5 99.69 Error@1 9.07 Error@5 0.31 Loss:0.282
|
|
----> Best Accuracy : Acc@1=90.93, Acc@5=99.69, Error@1=9.07, Error@5=0.31
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-16:46:55] [Epoch=049/600] [Need: 19:57:36] LR=0.0246 ~ 0.0246, Batch=96
|
|
train[2019-03-31-16:46:56] Epoch: [049][000/521] Time 0.83 (0.83) Data 0.55 (0.55) Loss 0.527 (0.527) Prec@1 88.54 (88.54) Prec@5 98.96 (98.96)
|
|
train[2019-03-31-16:47:19] Epoch: [049][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.312 (0.434) Prec@1 91.67 (90.01) Prec@5 100.00 (99.74)
|
|
train[2019-03-31-16:47:43] Epoch: [049][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.585 (0.436) Prec@1 89.58 (89.92) Prec@5 100.00 (99.78)
|
|
train[2019-03-31-16:48:07] Epoch: [049][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.497 (0.442) Prec@1 91.67 (89.75) Prec@5 100.00 (99.75)
|
|
train[2019-03-31-16:48:31] Epoch: [049][400/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.608 (0.443) Prec@1 86.46 (89.78) Prec@5 100.00 (99.73)
|
|
train[2019-03-31-16:48:54] Epoch: [049][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.477 (0.450) Prec@1 85.42 (89.61) Prec@5 100.00 (99.70)
|
|
train[2019-03-31-16:48:59] Epoch: [049][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.354 (0.451) Prec@1 91.25 (89.57) Prec@5 100.00 (99.70)
|
|
[2019-03-31-16:48:59] **train** Prec@1 89.57 Prec@5 99.70 Error@1 10.43 Error@5 0.30 Loss:0.451
|
|
test [2019-03-31-16:49:00] Epoch: [049][000/105] Time 0.47 (0.47) Data 0.41 (0.41) Loss 0.345 (0.345) Prec@1 84.38 (84.38) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-16:49:04] Epoch: [049][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.125 (0.348) Prec@1 94.79 (89.39) Prec@5 100.00 (99.56)
|
|
test [2019-03-31-16:49:04] Epoch: [049][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.286 (0.348) Prec@1 81.25 (89.35) Prec@5 100.00 (99.57)
|
|
[2019-03-31-16:49:04] **test** Prec@1 89.35 Prec@5 99.57 Error@1 10.65 Error@5 0.43 Loss:0.348
|
|
----> Best Accuracy : Acc@1=90.93, Acc@5=99.69, Error@1=9.07, Error@5=0.31
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-16:49:04] [Epoch=050/600] [Need: 19:46:01] LR=0.0246 ~ 0.0246, Batch=96
|
|
train[2019-03-31-16:49:05] Epoch: [050][000/521] Time 0.83 (0.83) Data 0.56 (0.56) Loss 0.410 (0.410) Prec@1 92.71 (92.71) Prec@5 97.92 (97.92)
|
|
train[2019-03-31-16:49:29] Epoch: [050][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.435 (0.433) Prec@1 87.50 (89.66) Prec@5 100.00 (99.77)
|
|
train[2019-03-31-16:49:53] Epoch: [050][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.450 (0.442) Prec@1 88.54 (89.48) Prec@5 98.96 (99.76)
|
|
train[2019-03-31-16:50:16] Epoch: [050][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.398 (0.440) Prec@1 89.58 (89.56) Prec@5 98.96 (99.78)
|
|
train[2019-03-31-16:50:40] Epoch: [050][400/521] Time 0.27 (0.24) Data 0.00 (0.00) Loss 0.733 (0.442) Prec@1 82.29 (89.58) Prec@5 100.00 (99.76)
|
|
train[2019-03-31-16:51:04] Epoch: [050][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.427 (0.442) Prec@1 88.54 (89.61) Prec@5 100.00 (99.76)
|
|
train[2019-03-31-16:51:09] Epoch: [050][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.412 (0.442) Prec@1 91.25 (89.61) Prec@5 98.75 (99.75)
|
|
[2019-03-31-16:51:09] **train** Prec@1 89.61 Prec@5 99.75 Error@1 10.39 Error@5 0.25 Loss:0.442
|
|
test [2019-03-31-16:51:09] Epoch: [050][000/105] Time 0.48 (0.48) Data 0.42 (0.42) Loss 0.218 (0.218) Prec@1 91.67 (91.67) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-16:51:14] Epoch: [050][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.116 (0.400) Prec@1 95.83 (88.05) Prec@5 100.00 (99.63)
|
|
test [2019-03-31-16:51:14] Epoch: [050][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.478 (0.401) Prec@1 87.50 (88.04) Prec@5 100.00 (99.63)
|
|
[2019-03-31-16:51:14] **test** Prec@1 88.04 Prec@5 99.63 Error@1 11.96 Error@5 0.37 Loss:0.401
|
|
----> Best Accuracy : Acc@1=90.93, Acc@5=99.69, Error@1=9.07, Error@5=0.31
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-16:51:14] [Epoch=051/600] [Need: 19:46:43] LR=0.0246 ~ 0.0246, Batch=96
|
|
train[2019-03-31-16:51:15] Epoch: [051][000/521] Time 0.82 (0.82) Data 0.55 (0.55) Loss 0.448 (0.448) Prec@1 89.58 (89.58) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-16:51:38] Epoch: [051][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.636 (0.447) Prec@1 85.42 (89.37) Prec@5 100.00 (99.75)
|
|
train[2019-03-31-16:52:03] Epoch: [051][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.530 (0.444) Prec@1 84.38 (89.50) Prec@5 98.96 (99.75)
|
|
train[2019-03-31-16:52:26] Epoch: [051][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.387 (0.443) Prec@1 93.75 (89.54) Prec@5 100.00 (99.77)
|
|
train[2019-03-31-16:52:50] Epoch: [051][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.594 (0.444) Prec@1 85.42 (89.58) Prec@5 100.00 (99.75)
|
|
train[2019-03-31-16:53:14] Epoch: [051][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.474 (0.447) Prec@1 87.50 (89.55) Prec@5 100.00 (99.75)
|
|
train[2019-03-31-16:53:18] Epoch: [051][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.369 (0.446) Prec@1 90.00 (89.60) Prec@5 100.00 (99.74)
|
|
[2019-03-31-16:53:19] **train** Prec@1 89.60 Prec@5 99.74 Error@1 10.40 Error@5 0.26 Loss:0.446
|
|
test [2019-03-31-16:53:19] Epoch: [051][000/105] Time 0.60 (0.60) Data 0.55 (0.55) Loss 0.305 (0.305) Prec@1 87.50 (87.50) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-16:53:23] Epoch: [051][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.173 (0.306) Prec@1 91.67 (89.80) Prec@5 100.00 (99.74)
|
|
test [2019-03-31-16:53:23] Epoch: [051][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.617 (0.308) Prec@1 87.50 (89.75) Prec@5 100.00 (99.72)
|
|
[2019-03-31-16:53:24] **test** Prec@1 89.75 Prec@5 99.72 Error@1 10.25 Error@5 0.28 Loss:0.308
|
|
----> Best Accuracy : Acc@1=90.93, Acc@5=99.69, Error@1=9.07, Error@5=0.31
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-16:53:24] [Epoch=052/600] [Need: 19:44:57] LR=0.0245 ~ 0.0245, Batch=96
|
|
train[2019-03-31-16:53:24] Epoch: [052][000/521] Time 0.83 (0.83) Data 0.56 (0.56) Loss 0.513 (0.513) Prec@1 86.46 (86.46) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-16:53:48] Epoch: [052][100/521] Time 0.25 (0.24) Data 0.00 (0.01) Loss 0.398 (0.431) Prec@1 92.71 (90.13) Prec@5 100.00 (99.76)
|
|
train[2019-03-31-16:54:12] Epoch: [052][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.561 (0.430) Prec@1 86.46 (89.87) Prec@5 98.96 (99.73)
|
|
train[2019-03-31-16:54:36] Epoch: [052][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.295 (0.427) Prec@1 91.67 (89.97) Prec@5 100.00 (99.76)
|
|
train[2019-03-31-16:54:59] Epoch: [052][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.355 (0.428) Prec@1 92.71 (89.97) Prec@5 100.00 (99.75)
|
|
train[2019-03-31-16:55:23] Epoch: [052][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.434 (0.435) Prec@1 87.50 (89.85) Prec@5 100.00 (99.73)
|
|
train[2019-03-31-16:55:28] Epoch: [052][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.432 (0.437) Prec@1 93.75 (89.82) Prec@5 100.00 (99.72)
|
|
[2019-03-31-16:55:28] **train** Prec@1 89.82 Prec@5 99.72 Error@1 10.18 Error@5 0.28 Loss:0.437
|
|
test [2019-03-31-16:55:28] Epoch: [052][000/105] Time 0.61 (0.61) Data 0.54 (0.54) Loss 0.372 (0.372) Prec@1 88.54 (88.54) Prec@5 98.96 (98.96)
|
|
test [2019-03-31-16:55:32] Epoch: [052][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.102 (0.301) Prec@1 95.83 (90.68) Prec@5 100.00 (99.63)
|
|
test [2019-03-31-16:55:33] Epoch: [052][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.226 (0.301) Prec@1 87.50 (90.66) Prec@5 100.00 (99.63)
|
|
[2019-03-31-16:55:33] **test** Prec@1 90.66 Prec@5 99.63 Error@1 9.34 Error@5 0.37 Loss:0.301
|
|
----> Best Accuracy : Acc@1=90.93, Acc@5=99.69, Error@1=9.07, Error@5=0.31
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-16:55:33] [Epoch=053/600] [Need: 19:37:36] LR=0.0245 ~ 0.0245, Batch=96
|
|
train[2019-03-31-16:55:34] Epoch: [053][000/521] Time 0.75 (0.75) Data 0.48 (0.48) Loss 0.191 (0.191) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-16:55:57] Epoch: [053][100/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.354 (0.412) Prec@1 91.67 (90.49) Prec@5 98.96 (99.75)
|
|
train[2019-03-31-16:56:21] Epoch: [053][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.330 (0.430) Prec@1 91.67 (89.93) Prec@5 100.00 (99.76)
|
|
train[2019-03-31-16:56:45] Epoch: [053][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.783 (0.437) Prec@1 82.29 (89.77) Prec@5 100.00 (99.74)
|
|
train[2019-03-31-16:57:09] Epoch: [053][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.663 (0.439) Prec@1 84.38 (89.70) Prec@5 100.00 (99.73)
|
|
train[2019-03-31-16:57:33] Epoch: [053][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.434 (0.442) Prec@1 91.67 (89.56) Prec@5 100.00 (99.71)
|
|
train[2019-03-31-16:57:38] Epoch: [053][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.526 (0.442) Prec@1 88.75 (89.57) Prec@5 98.75 (99.71)
|
|
[2019-03-31-16:57:38] **train** Prec@1 89.57 Prec@5 99.71 Error@1 10.43 Error@5 0.29 Loss:0.442
|
|
test [2019-03-31-16:57:39] Epoch: [053][000/105] Time 0.50 (0.50) Data 0.43 (0.43) Loss 0.349 (0.349) Prec@1 85.42 (85.42) Prec@5 98.96 (98.96)
|
|
test [2019-03-31-16:57:43] Epoch: [053][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.144 (0.331) Prec@1 94.79 (89.73) Prec@5 100.00 (99.63)
|
|
test [2019-03-31-16:57:43] Epoch: [053][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.450 (0.332) Prec@1 93.75 (89.72) Prec@5 100.00 (99.64)
|
|
[2019-03-31-16:57:43] **test** Prec@1 89.72 Prec@5 99.64 Error@1 10.28 Error@5 0.36 Loss:0.332
|
|
----> Best Accuracy : Acc@1=90.93, Acc@5=99.69, Error@1=9.07, Error@5=0.31
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-16:57:43] [Epoch=054/600] [Need: 19:47:31] LR=0.0245 ~ 0.0245, Batch=96
|
|
train[2019-03-31-16:57:44] Epoch: [054][000/521] Time 0.70 (0.70) Data 0.43 (0.43) Loss 0.325 (0.325) Prec@1 91.67 (91.67) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-16:58:08] Epoch: [054][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.571 (0.450) Prec@1 84.38 (89.33) Prec@5 100.00 (99.72)
|
|
train[2019-03-31-16:58:33] Epoch: [054][200/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.432 (0.445) Prec@1 87.50 (89.62) Prec@5 100.00 (99.71)
|
|
train[2019-03-31-16:58:58] Epoch: [054][300/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.519 (0.440) Prec@1 90.62 (89.79) Prec@5 100.00 (99.72)
|
|
train[2019-03-31-16:59:24] Epoch: [054][400/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.505 (0.434) Prec@1 89.58 (89.97) Prec@5 100.00 (99.73)
|
|
train[2019-03-31-16:59:49] Epoch: [054][500/521] Time 0.26 (0.25) Data 0.00 (0.00) Loss 0.421 (0.439) Prec@1 89.58 (89.88) Prec@5 96.88 (99.72)
|
|
train[2019-03-31-16:59:54] Epoch: [054][520/521] Time 0.23 (0.25) Data 0.00 (0.00) Loss 0.425 (0.440) Prec@1 90.00 (89.85) Prec@5 100.00 (99.72)
|
|
[2019-03-31-16:59:54] **train** Prec@1 89.85 Prec@5 99.72 Error@1 10.15 Error@5 0.28 Loss:0.440
|
|
test [2019-03-31-16:59:54] Epoch: [054][000/105] Time 0.50 (0.50) Data 0.43 (0.43) Loss 0.340 (0.340) Prec@1 87.50 (87.50) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-16:59:59] Epoch: [054][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.205 (0.309) Prec@1 91.67 (90.12) Prec@5 100.00 (99.60)
|
|
test [2019-03-31-16:59:59] Epoch: [054][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.562 (0.310) Prec@1 75.00 (90.12) Prec@5 100.00 (99.61)
|
|
[2019-03-31-16:59:59] **test** Prec@1 90.12 Prec@5 99.61 Error@1 9.88 Error@5 0.39 Loss:0.310
|
|
----> Best Accuracy : Acc@1=90.93, Acc@5=99.69, Error@1=9.07, Error@5=0.31
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-16:59:59] [Epoch=055/600] [Need: 20:32:11] LR=0.0245 ~ 0.0245, Batch=96
|
|
train[2019-03-31-17:00:00] Epoch: [055][000/521] Time 0.72 (0.72) Data 0.44 (0.44) Loss 0.533 (0.533) Prec@1 89.58 (89.58) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-17:00:25] Epoch: [055][100/521] Time 0.24 (0.26) Data 0.00 (0.00) Loss 0.468 (0.431) Prec@1 87.50 (90.26) Prec@5 100.00 (99.71)
|
|
train[2019-03-31-17:00:50] Epoch: [055][200/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.206 (0.437) Prec@1 94.79 (90.05) Prec@5 100.00 (99.75)
|
|
train[2019-03-31-17:01:15] Epoch: [055][300/521] Time 0.28 (0.25) Data 0.00 (0.00) Loss 0.530 (0.428) Prec@1 85.42 (90.23) Prec@5 100.00 (99.70)
|
|
train[2019-03-31-17:01:41] Epoch: [055][400/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.549 (0.433) Prec@1 90.62 (90.07) Prec@5 97.92 (99.70)
|
|
train[2019-03-31-17:02:06] Epoch: [055][500/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.472 (0.439) Prec@1 86.46 (89.84) Prec@5 100.00 (99.70)
|
|
train[2019-03-31-17:02:11] Epoch: [055][520/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.409 (0.438) Prec@1 90.00 (89.86) Prec@5 100.00 (99.71)
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|
[2019-03-31-17:02:12] **train** Prec@1 89.86 Prec@5 99.71 Error@1 10.14 Error@5 0.29 Loss:0.438
|
|
test [2019-03-31-17:02:12] Epoch: [055][000/105] Time 0.62 (0.62) Data 0.55 (0.55) Loss 0.272 (0.272) Prec@1 90.62 (90.62) Prec@5 98.96 (98.96)
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test [2019-03-31-17:02:16] Epoch: [055][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.166 (0.295) Prec@1 96.88 (90.39) Prec@5 100.00 (99.76)
|
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test [2019-03-31-17:02:17] Epoch: [055][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.213 (0.295) Prec@1 87.50 (90.38) Prec@5 100.00 (99.77)
|
|
[2019-03-31-17:02:17] **test** Prec@1 90.38 Prec@5 99.77 Error@1 9.62 Error@5 0.23 Loss:0.295
|
|
----> Best Accuracy : Acc@1=90.93, Acc@5=99.69, Error@1=9.07, Error@5=0.31
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-17:02:17] [Epoch=056/600] [Need: 20:49:55] LR=0.0245 ~ 0.0245, Batch=96
|
|
train[2019-03-31-17:02:18] Epoch: [056][000/521] Time 0.78 (0.78) Data 0.49 (0.49) Loss 0.430 (0.430) Prec@1 88.54 (88.54) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-17:02:43] Epoch: [056][100/521] Time 0.28 (0.26) Data 0.00 (0.01) Loss 0.587 (0.420) Prec@1 87.50 (90.24) Prec@5 98.96 (99.80)
|
|
train[2019-03-31-17:03:08] Epoch: [056][200/521] Time 0.25 (0.26) Data 0.00 (0.00) Loss 0.505 (0.422) Prec@1 87.50 (90.17) Prec@5 100.00 (99.79)
|
|
train[2019-03-31-17:03:34] Epoch: [056][300/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.454 (0.428) Prec@1 89.58 (90.05) Prec@5 98.96 (99.79)
|
|
train[2019-03-31-17:04:01] Epoch: [056][400/521] Time 0.25 (0.26) Data 0.00 (0.00) Loss 0.720 (0.430) Prec@1 88.54 (90.06) Prec@5 100.00 (99.77)
|
|
train[2019-03-31-17:04:26] Epoch: [056][500/521] Time 0.26 (0.26) Data 0.00 (0.00) Loss 0.391 (0.437) Prec@1 92.71 (89.92) Prec@5 98.96 (99.73)
|
|
train[2019-03-31-17:04:31] Epoch: [056][520/521] Time 0.22 (0.26) Data 0.00 (0.00) Loss 0.334 (0.436) Prec@1 92.50 (89.91) Prec@5 100.00 (99.73)
|
|
[2019-03-31-17:04:31] **train** Prec@1 89.91 Prec@5 99.73 Error@1 10.09 Error@5 0.27 Loss:0.436
|
|
test [2019-03-31-17:04:32] Epoch: [056][000/105] Time 0.65 (0.65) Data 0.58 (0.58) Loss 0.508 (0.508) Prec@1 88.54 (88.54) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-17:04:36] Epoch: [056][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.107 (0.298) Prec@1 95.83 (90.98) Prec@5 100.00 (99.63)
|
|
test [2019-03-31-17:04:36] Epoch: [056][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.370 (0.300) Prec@1 87.50 (90.91) Prec@5 100.00 (99.62)
|
|
[2019-03-31-17:04:36] **test** Prec@1 90.91 Prec@5 99.62 Error@1 9.09 Error@5 0.38 Loss:0.300
|
|
----> Best Accuracy : Acc@1=90.93, Acc@5=99.69, Error@1=9.07, Error@5=0.31
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-17:04:36] [Epoch=057/600] [Need: 21:03:11] LR=0.0244 ~ 0.0244, Batch=96
|
|
train[2019-03-31-17:04:37] Epoch: [057][000/521] Time 0.86 (0.86) Data 0.57 (0.57) Loss 0.281 (0.281) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-17:05:02] Epoch: [057][100/521] Time 0.25 (0.26) Data 0.00 (0.01) Loss 0.405 (0.414) Prec@1 88.54 (90.47) Prec@5 100.00 (99.75)
|
|
train[2019-03-31-17:05:27] Epoch: [057][200/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.479 (0.418) Prec@1 88.54 (90.35) Prec@5 98.96 (99.71)
|
|
train[2019-03-31-17:05:51] Epoch: [057][300/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.502 (0.424) Prec@1 88.54 (90.12) Prec@5 100.00 (99.70)
|
|
train[2019-03-31-17:06:15] Epoch: [057][400/521] Time 0.26 (0.25) Data 0.00 (0.00) Loss 0.352 (0.420) Prec@1 88.54 (90.17) Prec@5 98.96 (99.74)
|
|
train[2019-03-31-17:06:41] Epoch: [057][500/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.452 (0.429) Prec@1 91.67 (90.00) Prec@5 100.00 (99.73)
|
|
train[2019-03-31-17:06:45] Epoch: [057][520/521] Time 0.22 (0.25) Data 0.00 (0.00) Loss 0.457 (0.430) Prec@1 87.50 (89.97) Prec@5 100.00 (99.72)
|
|
[2019-03-31-17:06:45] **train** Prec@1 89.97 Prec@5 99.72 Error@1 10.03 Error@5 0.28 Loss:0.430
|
|
test [2019-03-31-17:06:46] Epoch: [057][000/105] Time 0.57 (0.57) Data 0.50 (0.50) Loss 0.315 (0.315) Prec@1 90.62 (90.62) Prec@5 98.96 (98.96)
|
|
test [2019-03-31-17:06:50] Epoch: [057][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.080 (0.290) Prec@1 96.88 (90.96) Prec@5 100.00 (99.72)
|
|
test [2019-03-31-17:06:50] Epoch: [057][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.060 (0.289) Prec@1 100.00 (90.92) Prec@5 100.00 (99.72)
|
|
[2019-03-31-17:06:50] **test** Prec@1 90.92 Prec@5 99.72 Error@1 9.08 Error@5 0.28 Loss:0.289
|
|
----> Best Accuracy : Acc@1=90.93, Acc@5=99.69, Error@1=9.07, Error@5=0.31
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-17:06:50] [Epoch=058/600] [Need: 20:10:29] LR=0.0244 ~ 0.0244, Batch=96
|
|
train[2019-03-31-17:06:51] Epoch: [058][000/521] Time 0.83 (0.83) Data 0.56 (0.56) Loss 0.274 (0.274) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-17:07:15] Epoch: [058][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.418 (0.415) Prec@1 90.62 (90.44) Prec@5 100.00 (99.74)
|
|
train[2019-03-31-17:07:40] Epoch: [058][200/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.404 (0.428) Prec@1 90.62 (90.09) Prec@5 98.96 (99.73)
|
|
train[2019-03-31-17:08:04] Epoch: [058][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.328 (0.422) Prec@1 90.62 (90.22) Prec@5 100.00 (99.71)
|
|
train[2019-03-31-17:08:28] Epoch: [058][400/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.444 (0.417) Prec@1 89.58 (90.36) Prec@5 100.00 (99.72)
|
|
train[2019-03-31-17:08:53] Epoch: [058][500/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.629 (0.427) Prec@1 86.46 (90.06) Prec@5 98.96 (99.70)
|
|
train[2019-03-31-17:08:58] Epoch: [058][520/521] Time 0.22 (0.25) Data 0.00 (0.00) Loss 0.365 (0.426) Prec@1 91.25 (90.11) Prec@5 100.00 (99.70)
|
|
[2019-03-31-17:08:58] **train** Prec@1 90.11 Prec@5 99.70 Error@1 9.89 Error@5 0.30 Loss:0.426
|
|
test [2019-03-31-17:08:59] Epoch: [058][000/105] Time 0.65 (0.65) Data 0.59 (0.59) Loss 0.386 (0.386) Prec@1 87.50 (87.50) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-17:09:03] Epoch: [058][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.084 (0.276) Prec@1 96.88 (91.12) Prec@5 100.00 (99.78)
|
|
test [2019-03-31-17:09:03] Epoch: [058][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.168 (0.275) Prec@1 93.75 (91.13) Prec@5 100.00 (99.79)
|
|
[2019-03-31-17:09:04] **test** Prec@1 91.13 Prec@5 99.79 Error@1 8.87 Error@5 0.21 Loss:0.275
|
|
----> Best Accuracy : Acc@1=91.13, Acc@5=99.79, Error@1=8.87, Error@5=0.21
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-17:09:04] [Epoch=059/600] [Need: 20:03:00] LR=0.0244 ~ 0.0244, Batch=96
|
|
train[2019-03-31-17:09:05] Epoch: [059][000/521] Time 0.79 (0.79) Data 0.46 (0.46) Loss 0.423 (0.423) Prec@1 89.58 (89.58) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-17:09:30] Epoch: [059][100/521] Time 0.24 (0.26) Data 0.00 (0.00) Loss 0.397 (0.416) Prec@1 89.58 (90.27) Prec@5 100.00 (99.79)
|
|
train[2019-03-31-17:09:54] Epoch: [059][200/521] Time 0.23 (0.25) Data 0.00 (0.00) Loss 0.380 (0.420) Prec@1 92.71 (90.29) Prec@5 98.96 (99.74)
|
|
train[2019-03-31-17:10:17] Epoch: [059][300/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.600 (0.418) Prec@1 87.50 (90.29) Prec@5 100.00 (99.74)
|
|
train[2019-03-31-17:10:42] Epoch: [059][400/521] Time 0.28 (0.24) Data 0.00 (0.00) Loss 0.294 (0.422) Prec@1 93.75 (90.19) Prec@5 100.00 (99.74)
|
|
train[2019-03-31-17:11:06] Epoch: [059][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.546 (0.427) Prec@1 83.33 (90.12) Prec@5 100.00 (99.75)
|
|
train[2019-03-31-17:11:11] Epoch: [059][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.327 (0.426) Prec@1 93.75 (90.13) Prec@5 100.00 (99.75)
|
|
[2019-03-31-17:11:11] **train** Prec@1 90.13 Prec@5 99.75 Error@1 9.87 Error@5 0.25 Loss:0.426
|
|
test [2019-03-31-17:11:11] Epoch: [059][000/105] Time 0.47 (0.47) Data 0.41 (0.41) Loss 0.297 (0.297) Prec@1 89.58 (89.58) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-17:11:15] Epoch: [059][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.112 (0.280) Prec@1 96.88 (91.03) Prec@5 100.00 (99.72)
|
|
test [2019-03-31-17:11:16] Epoch: [059][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.080 (0.278) Prec@1 100.00 (91.12) Prec@5 100.00 (99.72)
|
|
[2019-03-31-17:11:16] **test** Prec@1 91.12 Prec@5 99.72 Error@1 8.88 Error@5 0.28 Loss:0.278
|
|
----> Best Accuracy : Acc@1=91.13, Acc@5=99.79, Error@1=8.87, Error@5=0.21
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-17:11:16] [Epoch=060/600] [Need: 19:47:52] LR=0.0244 ~ 0.0244, Batch=96
|
|
train[2019-03-31-17:11:17] Epoch: [060][000/521] Time 0.83 (0.83) Data 0.56 (0.56) Loss 0.393 (0.393) Prec@1 89.58 (89.58) Prec@5 97.92 (97.92)
|
|
train[2019-03-31-17:11:43] Epoch: [060][100/521] Time 0.26 (0.27) Data 0.00 (0.01) Loss 0.530 (0.410) Prec@1 85.42 (90.08) Prec@5 98.96 (99.71)
|
|
train[2019-03-31-17:12:08] Epoch: [060][200/521] Time 0.27 (0.26) Data 0.00 (0.00) Loss 0.377 (0.410) Prec@1 91.67 (90.33) Prec@5 100.00 (99.76)
|
|
train[2019-03-31-17:12:33] Epoch: [060][300/521] Time 0.26 (0.26) Data 0.00 (0.00) Loss 0.368 (0.418) Prec@1 85.42 (90.12) Prec@5 100.00 (99.76)
|
|
train[2019-03-31-17:12:57] Epoch: [060][400/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.441 (0.416) Prec@1 87.50 (90.21) Prec@5 100.00 (99.76)
|
|
train[2019-03-31-17:13:21] Epoch: [060][500/521] Time 0.23 (0.25) Data 0.00 (0.00) Loss 0.385 (0.420) Prec@1 91.67 (90.11) Prec@5 100.00 (99.76)
|
|
train[2019-03-31-17:13:25] Epoch: [060][520/521] Time 0.22 (0.25) Data 0.00 (0.00) Loss 0.419 (0.420) Prec@1 91.25 (90.10) Prec@5 100.00 (99.75)
|
|
[2019-03-31-17:13:25] **train** Prec@1 90.10 Prec@5 99.75 Error@1 9.90 Error@5 0.25 Loss:0.420
|
|
test [2019-03-31-17:13:26] Epoch: [060][000/105] Time 0.48 (0.48) Data 0.43 (0.43) Loss 0.380 (0.380) Prec@1 85.42 (85.42) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-17:13:30] Epoch: [060][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.196 (0.340) Prec@1 93.75 (89.35) Prec@5 100.00 (99.60)
|
|
test [2019-03-31-17:13:30] Epoch: [060][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.179 (0.338) Prec@1 87.50 (89.40) Prec@5 100.00 (99.60)
|
|
[2019-03-31-17:13:30] **test** Prec@1 89.40 Prec@5 99.60 Error@1 10.60 Error@5 0.40 Loss:0.338
|
|
----> Best Accuracy : Acc@1=91.13, Acc@5=99.79, Error@1=8.87, Error@5=0.21
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-17:13:30] [Epoch=061/600] [Need: 20:07:45] LR=0.0244 ~ 0.0244, Batch=96
|
|
train[2019-03-31-17:13:31] Epoch: [061][000/521] Time 0.84 (0.84) Data 0.57 (0.57) Loss 0.563 (0.563) Prec@1 87.50 (87.50) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-17:13:55] Epoch: [061][100/521] Time 0.24 (0.25) Data 0.00 (0.01) Loss 0.470 (0.419) Prec@1 89.58 (90.31) Prec@5 100.00 (99.73)
|
|
train[2019-03-31-17:14:19] Epoch: [061][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.466 (0.411) Prec@1 88.54 (90.51) Prec@5 100.00 (99.74)
|
|
train[2019-03-31-17:14:43] Epoch: [061][300/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.443 (0.416) Prec@1 91.67 (90.36) Prec@5 100.00 (99.73)
|
|
train[2019-03-31-17:15:07] Epoch: [061][400/521] Time 0.27 (0.24) Data 0.00 (0.00) Loss 0.407 (0.416) Prec@1 91.67 (90.40) Prec@5 100.00 (99.72)
|
|
train[2019-03-31-17:15:32] Epoch: [061][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.449 (0.422) Prec@1 88.54 (90.21) Prec@5 100.00 (99.72)
|
|
train[2019-03-31-17:15:36] Epoch: [061][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.518 (0.425) Prec@1 86.25 (90.16) Prec@5 100.00 (99.72)
|
|
[2019-03-31-17:15:36] **train** Prec@1 90.16 Prec@5 99.72 Error@1 9.84 Error@5 0.28 Loss:0.425
|
|
test [2019-03-31-17:15:37] Epoch: [061][000/105] Time 0.60 (0.60) Data 0.54 (0.54) Loss 0.247 (0.247) Prec@1 90.62 (90.62) Prec@5 98.96 (98.96)
|
|
test [2019-03-31-17:15:41] Epoch: [061][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.153 (0.306) Prec@1 93.75 (90.16) Prec@5 100.00 (99.54)
|
|
test [2019-03-31-17:15:41] Epoch: [061][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.166 (0.304) Prec@1 93.75 (90.19) Prec@5 100.00 (99.55)
|
|
[2019-03-31-17:15:41] **test** Prec@1 90.19 Prec@5 99.55 Error@1 9.81 Error@5 0.45 Loss:0.304
|
|
----> Best Accuracy : Acc@1=91.13, Acc@5=99.79, Error@1=8.87, Error@5=0.21
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-17:15:41] [Epoch=062/600] [Need: 19:35:44] LR=0.0243 ~ 0.0243, Batch=96
|
|
train[2019-03-31-17:15:42] Epoch: [062][000/521] Time 0.85 (0.85) Data 0.58 (0.58) Loss 0.362 (0.362) Prec@1 90.62 (90.62) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-17:16:07] Epoch: [062][100/521] Time 0.25 (0.25) Data 0.00 (0.01) Loss 0.376 (0.392) Prec@1 89.58 (90.99) Prec@5 100.00 (99.71)
|
|
train[2019-03-31-17:16:32] Epoch: [062][200/521] Time 0.26 (0.25) Data 0.00 (0.00) Loss 0.308 (0.406) Prec@1 93.75 (90.52) Prec@5 100.00 (99.68)
|
|
train[2019-03-31-17:16:57] Epoch: [062][300/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.410 (0.411) Prec@1 93.75 (90.40) Prec@5 100.00 (99.70)
|
|
train[2019-03-31-17:17:24] Epoch: [062][400/521] Time 0.27 (0.25) Data 0.00 (0.00) Loss 0.483 (0.415) Prec@1 89.58 (90.32) Prec@5 100.00 (99.71)
|
|
train[2019-03-31-17:17:49] Epoch: [062][500/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.588 (0.414) Prec@1 88.54 (90.34) Prec@5 98.96 (99.71)
|
|
train[2019-03-31-17:17:54] Epoch: [062][520/521] Time 0.26 (0.25) Data 0.00 (0.00) Loss 0.329 (0.415) Prec@1 90.00 (90.32) Prec@5 100.00 (99.71)
|
|
[2019-03-31-17:17:54] **train** Prec@1 90.32 Prec@5 99.71 Error@1 9.68 Error@5 0.29 Loss:0.415
|
|
test [2019-03-31-17:17:54] Epoch: [062][000/105] Time 0.55 (0.55) Data 0.48 (0.48) Loss 0.278 (0.278) Prec@1 91.67 (91.67) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-17:18:00] Epoch: [062][100/105] Time 0.04 (0.06) Data 0.00 (0.01) Loss 0.114 (0.286) Prec@1 95.83 (90.56) Prec@5 100.00 (99.77)
|
|
test [2019-03-31-17:18:00] Epoch: [062][104/105] Time 0.04 (0.06) Data 0.00 (0.00) Loss 0.240 (0.285) Prec@1 87.50 (90.57) Prec@5 100.00 (99.78)
|
|
[2019-03-31-17:18:00] **test** Prec@1 90.57 Prec@5 99.78 Error@1 9.43 Error@5 0.22 Loss:0.285
|
|
----> Best Accuracy : Acc@1=91.13, Acc@5=99.79, Error@1=8.87, Error@5=0.21
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-17:18:01] [Epoch=063/600] [Need: 20:46:27] LR=0.0243 ~ 0.0243, Batch=96
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|
train[2019-03-31-17:18:02] Epoch: [063][000/521] Time 0.97 (0.97) Data 0.61 (0.61) Loss 0.405 (0.405) Prec@1 94.79 (94.79) Prec@5 98.96 (98.96)
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train[2019-03-31-17:18:27] Epoch: [063][100/521] Time 0.25 (0.26) Data 0.00 (0.01) Loss 0.418 (0.406) Prec@1 94.79 (90.70) Prec@5 98.96 (99.74)
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train[2019-03-31-17:18:52] Epoch: [063][200/521] Time 0.25 (0.26) Data 0.00 (0.00) Loss 0.511 (0.414) Prec@1 87.50 (90.51) Prec@5 100.00 (99.78)
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train[2019-03-31-17:19:17] Epoch: [063][300/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.348 (0.420) Prec@1 86.46 (90.39) Prec@5 100.00 (99.76)
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train[2019-03-31-17:19:42] Epoch: [063][400/521] Time 0.26 (0.25) Data 0.00 (0.00) Loss 0.454 (0.419) Prec@1 89.58 (90.44) Prec@5 97.92 (99.75)
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train[2019-03-31-17:20:07] Epoch: [063][500/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.329 (0.427) Prec@1 91.67 (90.21) Prec@5 100.00 (99.73)
|
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train[2019-03-31-17:20:12] Epoch: [063][520/521] Time 0.23 (0.25) Data 0.00 (0.00) Loss 0.360 (0.427) Prec@1 92.50 (90.19) Prec@5 100.00 (99.73)
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[2019-03-31-17:20:12] **train** Prec@1 90.19 Prec@5 99.73 Error@1 9.81 Error@5 0.27 Loss:0.427
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test [2019-03-31-17:20:13] Epoch: [063][000/105] Time 0.67 (0.67) Data 0.57 (0.57) Loss 0.466 (0.466) Prec@1 89.58 (89.58) Prec@5 100.00 (100.00)
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test [2019-03-31-17:20:17] Epoch: [063][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.122 (0.309) Prec@1 93.75 (90.12) Prec@5 100.00 (99.77)
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test [2019-03-31-17:20:17] Epoch: [063][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.237 (0.307) Prec@1 93.75 (90.15) Prec@5 100.00 (99.78)
|
|
[2019-03-31-17:20:17] **test** Prec@1 90.15 Prec@5 99.78 Error@1 9.85 Error@5 0.22 Loss:0.307
|
|
----> Best Accuracy : Acc@1=91.13, Acc@5=99.79, Error@1=8.87, Error@5=0.21
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
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|
|
==>>[2019-03-31-17:20:17] [Epoch=064/600] [Need: 20:20:02] LR=0.0243 ~ 0.0243, Batch=96
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|
train[2019-03-31-17:20:18] Epoch: [064][000/521] Time 0.91 (0.91) Data 0.63 (0.63) Loss 0.496 (0.496) Prec@1 89.58 (89.58) Prec@5 100.00 (100.00)
|
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train[2019-03-31-17:20:43] Epoch: [064][100/521] Time 0.25 (0.26) Data 0.00 (0.01) Loss 0.684 (0.408) Prec@1 86.46 (90.41) Prec@5 98.96 (99.78)
|
|
train[2019-03-31-17:21:08] Epoch: [064][200/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.348 (0.405) Prec@1 89.58 (90.45) Prec@5 100.00 (99.79)
|
|
train[2019-03-31-17:21:33] Epoch: [064][300/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.406 (0.406) Prec@1 91.67 (90.41) Prec@5 98.96 (99.78)
|
|
train[2019-03-31-17:21:58] Epoch: [064][400/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.396 (0.406) Prec@1 89.58 (90.51) Prec@5 100.00 (99.78)
|
|
train[2019-03-31-17:22:24] Epoch: [064][500/521] Time 0.26 (0.25) Data 0.00 (0.00) Loss 0.296 (0.411) Prec@1 94.79 (90.44) Prec@5 100.00 (99.78)
|
|
train[2019-03-31-17:22:28] Epoch: [064][520/521] Time 0.22 (0.25) Data 0.00 (0.00) Loss 0.309 (0.411) Prec@1 91.25 (90.42) Prec@5 100.00 (99.78)
|
|
[2019-03-31-17:22:29] **train** Prec@1 90.42 Prec@5 99.78 Error@1 9.58 Error@5 0.22 Loss:0.411
|
|
test [2019-03-31-17:22:29] Epoch: [064][000/105] Time 0.66 (0.66) Data 0.60 (0.60) Loss 0.277 (0.277) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-17:22:34] Epoch: [064][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.112 (0.289) Prec@1 96.88 (90.67) Prec@5 100.00 (99.73)
|
|
test [2019-03-31-17:22:34] Epoch: [064][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.102 (0.289) Prec@1 93.75 (90.67) Prec@5 100.00 (99.73)
|
|
[2019-03-31-17:22:34] **test** Prec@1 90.67 Prec@5 99.73 Error@1 9.33 Error@5 0.27 Loss:0.289
|
|
----> Best Accuracy : Acc@1=91.13, Acc@5=99.79, Error@1=8.87, Error@5=0.21
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-17:22:34] [Epoch=065/600] [Need: 20:19:50] LR=0.0243 ~ 0.0243, Batch=96
|
|
train[2019-03-31-17:22:35] Epoch: [065][000/521] Time 0.88 (0.88) Data 0.59 (0.59) Loss 0.282 (0.282) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-17:23:01] Epoch: [065][100/521] Time 0.28 (0.26) Data 0.00 (0.01) Loss 0.407 (0.397) Prec@1 92.71 (90.89) Prec@5 100.00 (99.87)
|
|
train[2019-03-31-17:23:26] Epoch: [065][200/521] Time 0.25 (0.26) Data 0.00 (0.00) Loss 0.358 (0.402) Prec@1 90.62 (90.68) Prec@5 100.00 (99.81)
|
|
train[2019-03-31-17:23:52] Epoch: [065][300/521] Time 0.25 (0.26) Data 0.00 (0.00) Loss 0.684 (0.409) Prec@1 86.46 (90.55) Prec@5 98.96 (99.75)
|
|
train[2019-03-31-17:24:17] Epoch: [065][400/521] Time 0.25 (0.26) Data 0.00 (0.00) Loss 0.510 (0.413) Prec@1 87.50 (90.42) Prec@5 100.00 (99.75)
|
|
train[2019-03-31-17:24:42] Epoch: [065][500/521] Time 0.25 (0.26) Data 0.00 (0.00) Loss 0.359 (0.415) Prec@1 88.54 (90.39) Prec@5 100.00 (99.75)
|
|
train[2019-03-31-17:24:47] Epoch: [065][520/521] Time 0.22 (0.26) Data 0.00 (0.00) Loss 0.503 (0.417) Prec@1 88.75 (90.36) Prec@5 98.75 (99.74)
|
|
[2019-03-31-17:24:47] **train** Prec@1 90.36 Prec@5 99.74 Error@1 9.64 Error@5 0.26 Loss:0.417
|
|
test [2019-03-31-17:24:48] Epoch: [065][000/105] Time 0.63 (0.63) Data 0.57 (0.57) Loss 0.262 (0.262) Prec@1 90.62 (90.62) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-17:24:52] Epoch: [065][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.132 (0.284) Prec@1 94.79 (91.01) Prec@5 100.00 (99.76)
|
|
test [2019-03-31-17:24:52] Epoch: [065][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.317 (0.283) Prec@1 87.50 (91.02) Prec@5 100.00 (99.76)
|
|
[2019-03-31-17:24:52] **test** Prec@1 91.02 Prec@5 99.76 Error@1 8.98 Error@5 0.24 Loss:0.283
|
|
----> Best Accuracy : Acc@1=91.13, Acc@5=99.79, Error@1=8.87, Error@5=0.21
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-17:24:53] [Epoch=066/600] [Need: 20:33:23] LR=0.0243 ~ 0.0243, Batch=96
|
|
train[2019-03-31-17:24:53] Epoch: [066][000/521] Time 0.77 (0.77) Data 0.49 (0.49) Loss 0.593 (0.593) Prec@1 87.50 (87.50) Prec@5 97.92 (97.92)
|
|
train[2019-03-31-17:25:19] Epoch: [066][100/521] Time 0.26 (0.26) Data 0.00 (0.01) Loss 0.423 (0.409) Prec@1 87.50 (90.41) Prec@5 98.96 (99.67)
|
|
train[2019-03-31-17:25:44] Epoch: [066][200/521] Time 0.25 (0.26) Data 0.00 (0.00) Loss 0.316 (0.412) Prec@1 92.71 (90.44) Prec@5 98.96 (99.66)
|
|
train[2019-03-31-17:26:09] Epoch: [066][300/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.313 (0.410) Prec@1 90.62 (90.48) Prec@5 100.00 (99.71)
|
|
train[2019-03-31-17:26:36] Epoch: [066][400/521] Time 0.34 (0.26) Data 0.00 (0.00) Loss 0.396 (0.412) Prec@1 89.58 (90.31) Prec@5 98.96 (99.70)
|
|
train[2019-03-31-17:27:02] Epoch: [066][500/521] Time 0.25 (0.26) Data 0.00 (0.00) Loss 0.486 (0.417) Prec@1 88.54 (90.19) Prec@5 98.96 (99.70)
|
|
train[2019-03-31-17:27:06] Epoch: [066][520/521] Time 0.22 (0.26) Data 0.00 (0.00) Loss 0.367 (0.417) Prec@1 92.50 (90.17) Prec@5 100.00 (99.70)
|
|
[2019-03-31-17:27:07] **train** Prec@1 90.17 Prec@5 99.70 Error@1 9.83 Error@5 0.30 Loss:0.417
|
|
test [2019-03-31-17:27:07] Epoch: [066][000/105] Time 0.67 (0.67) Data 0.60 (0.60) Loss 0.199 (0.199) Prec@1 90.62 (90.62) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-17:27:12] Epoch: [066][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.203 (0.295) Prec@1 92.71 (90.43) Prec@5 100.00 (99.78)
|
|
test [2019-03-31-17:27:12] Epoch: [066][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.701 (0.296) Prec@1 87.50 (90.42) Prec@5 100.00 (99.78)
|
|
[2019-03-31-17:27:12] **test** Prec@1 90.42 Prec@5 99.78 Error@1 9.58 Error@5 0.22 Loss:0.296
|
|
----> Best Accuracy : Acc@1=91.13, Acc@5=99.79, Error@1=8.87, Error@5=0.21
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-17:27:12] [Epoch=067/600] [Need: 20:37:58] LR=0.0242 ~ 0.0242, Batch=96
|
|
train[2019-03-31-17:27:13] Epoch: [067][000/521] Time 0.81 (0.81) Data 0.50 (0.50) Loss 0.504 (0.504) Prec@1 85.42 (85.42) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-17:27:38] Epoch: [067][100/521] Time 0.26 (0.26) Data 0.00 (0.01) Loss 0.338 (0.406) Prec@1 91.67 (90.25) Prec@5 100.00 (99.83)
|
|
train[2019-03-31-17:28:05] Epoch: [067][200/521] Time 0.25 (0.26) Data 0.00 (0.00) Loss 0.437 (0.406) Prec@1 88.54 (90.43) Prec@5 100.00 (99.81)
|
|
train[2019-03-31-17:28:30] Epoch: [067][300/521] Time 0.27 (0.26) Data 0.00 (0.00) Loss 0.374 (0.410) Prec@1 91.67 (90.40) Prec@5 100.00 (99.77)
|
|
train[2019-03-31-17:28:56] Epoch: [067][400/521] Time 0.26 (0.26) Data 0.00 (0.00) Loss 0.315 (0.408) Prec@1 90.62 (90.44) Prec@5 100.00 (99.77)
|
|
train[2019-03-31-17:29:21] Epoch: [067][500/521] Time 0.25 (0.26) Data 0.00 (0.00) Loss 0.331 (0.412) Prec@1 92.71 (90.39) Prec@5 100.00 (99.75)
|
|
train[2019-03-31-17:29:26] Epoch: [067][520/521] Time 0.24 (0.26) Data 0.00 (0.00) Loss 0.515 (0.411) Prec@1 85.00 (90.44) Prec@5 100.00 (99.75)
|
|
[2019-03-31-17:29:26] **train** Prec@1 90.44 Prec@5 99.75 Error@1 9.56 Error@5 0.25 Loss:0.411
|
|
test [2019-03-31-17:29:27] Epoch: [067][000/105] Time 0.49 (0.49) Data 0.42 (0.42) Loss 0.272 (0.272) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-17:29:31] Epoch: [067][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.100 (0.262) Prec@1 96.88 (91.70) Prec@5 100.00 (99.78)
|
|
test [2019-03-31-17:29:31] Epoch: [067][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.060 (0.262) Prec@1 100.00 (91.73) Prec@5 100.00 (99.79)
|
|
[2019-03-31-17:29:31] **test** Prec@1 91.73 Prec@5 99.79 Error@1 8.27 Error@5 0.21 Loss:0.262
|
|
----> Best Accuracy : Acc@1=91.73, Acc@5=99.79, Error@1=8.27, Error@5=0.21
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-17:29:32] [Epoch=068/600] [Need: 20:38:12] LR=0.0242 ~ 0.0242, Batch=96
|
|
train[2019-03-31-17:29:32] Epoch: [068][000/521] Time 0.74 (0.74) Data 0.46 (0.46) Loss 0.312 (0.312) Prec@1 92.71 (92.71) Prec@5 98.96 (98.96)
|
|
train[2019-03-31-17:29:57] Epoch: [068][100/521] Time 0.25 (0.26) Data 0.00 (0.00) Loss 0.458 (0.391) Prec@1 92.71 (90.79) Prec@5 100.00 (99.88)
|
|
train[2019-03-31-17:30:23] Epoch: [068][200/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.682 (0.402) Prec@1 84.38 (90.48) Prec@5 100.00 (99.81)
|
|
train[2019-03-31-17:30:48] Epoch: [068][300/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.418 (0.398) Prec@1 90.62 (90.59) Prec@5 100.00 (99.79)
|
|
train[2019-03-31-17:31:13] Epoch: [068][400/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.623 (0.399) Prec@1 86.46 (90.62) Prec@5 98.96 (99.78)
|
|
train[2019-03-31-17:31:39] Epoch: [068][500/521] Time 0.31 (0.25) Data 0.00 (0.00) Loss 0.445 (0.406) Prec@1 88.54 (90.48) Prec@5 100.00 (99.75)
|
|
train[2019-03-31-17:31:44] Epoch: [068][520/521] Time 0.26 (0.25) Data 0.00 (0.00) Loss 0.535 (0.407) Prec@1 86.25 (90.46) Prec@5 100.00 (99.75)
|
|
[2019-03-31-17:31:45] **train** Prec@1 90.46 Prec@5 99.75 Error@1 9.54 Error@5 0.25 Loss:0.407
|
|
test [2019-03-31-17:31:45] Epoch: [068][000/105] Time 0.52 (0.52) Data 0.45 (0.45) Loss 0.328 (0.328) Prec@1 91.67 (91.67) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-17:31:49] Epoch: [068][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.185 (0.323) Prec@1 90.62 (89.62) Prec@5 100.00 (99.64)
|
|
test [2019-03-31-17:31:49] Epoch: [068][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.347 (0.325) Prec@1 93.75 (89.57) Prec@5 100.00 (99.64)
|
|
[2019-03-31-17:31:50] **test** Prec@1 89.57 Prec@5 99.64 Error@1 10.43 Error@5 0.36 Loss:0.325
|
|
----> Best Accuracy : Acc@1=91.73, Acc@5=99.79, Error@1=8.27, Error@5=0.21
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-17:31:50] [Epoch=069/600] [Need: 20:23:00] LR=0.0242 ~ 0.0242, Batch=96
|
|
train[2019-03-31-17:31:51] Epoch: [069][000/521] Time 0.74 (0.74) Data 0.46 (0.46) Loss 0.456 (0.456) Prec@1 90.62 (90.62) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-17:32:19] Epoch: [069][100/521] Time 0.24 (0.28) Data 0.00 (0.00) Loss 0.303 (0.383) Prec@1 95.83 (91.05) Prec@5 100.00 (99.78)
|
|
train[2019-03-31-17:32:43] Epoch: [069][200/521] Time 0.24 (0.27) Data 0.00 (0.00) Loss 0.328 (0.397) Prec@1 90.62 (90.87) Prec@5 100.00 (99.77)
|
|
train[2019-03-31-17:33:10] Epoch: [069][300/521] Time 0.25 (0.27) Data 0.00 (0.00) Loss 0.439 (0.404) Prec@1 87.50 (90.67) Prec@5 100.00 (99.77)
|
|
train[2019-03-31-17:33:36] Epoch: [069][400/521] Time 0.25 (0.26) Data 0.00 (0.00) Loss 0.371 (0.407) Prec@1 90.62 (90.54) Prec@5 100.00 (99.77)
|
|
train[2019-03-31-17:34:01] Epoch: [069][500/521] Time 0.25 (0.26) Data 0.00 (0.00) Loss 0.297 (0.411) Prec@1 95.83 (90.42) Prec@5 100.00 (99.77)
|
|
train[2019-03-31-17:34:06] Epoch: [069][520/521] Time 0.23 (0.26) Data 0.00 (0.00) Loss 0.505 (0.412) Prec@1 91.25 (90.41) Prec@5 98.75 (99.77)
|
|
[2019-03-31-17:34:06] **train** Prec@1 90.41 Prec@5 99.77 Error@1 9.59 Error@5 0.23 Loss:0.412
|
|
test [2019-03-31-17:34:06] Epoch: [069][000/105] Time 0.64 (0.64) Data 0.58 (0.58) Loss 0.251 (0.251) Prec@1 89.58 (89.58) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-17:34:11] Epoch: [069][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.065 (0.262) Prec@1 97.92 (91.52) Prec@5 100.00 (99.73)
|
|
test [2019-03-31-17:34:11] Epoch: [069][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.285 (0.263) Prec@1 93.75 (91.50) Prec@5 100.00 (99.74)
|
|
[2019-03-31-17:34:11] **test** Prec@1 91.50 Prec@5 99.74 Error@1 8.50 Error@5 0.26 Loss:0.263
|
|
----> Best Accuracy : Acc@1=91.73, Acc@5=99.79, Error@1=8.27, Error@5=0.21
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-17:34:11] [Epoch=070/600] [Need: 20:47:54] LR=0.0242 ~ 0.0242, Batch=96
|
|
train[2019-03-31-17:34:12] Epoch: [070][000/521] Time 0.75 (0.75) Data 0.46 (0.46) Loss 0.527 (0.527) Prec@1 89.58 (89.58) Prec@5 98.96 (98.96)
|
|
train[2019-03-31-17:34:37] Epoch: [070][100/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.351 (0.386) Prec@1 91.67 (90.99) Prec@5 100.00 (99.78)
|
|
train[2019-03-31-17:35:01] Epoch: [070][200/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.513 (0.396) Prec@1 89.58 (90.96) Prec@5 100.00 (99.72)
|
|
train[2019-03-31-17:35:25] Epoch: [070][300/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.279 (0.400) Prec@1 94.79 (90.86) Prec@5 100.00 (99.73)
|
|
train[2019-03-31-17:35:49] Epoch: [070][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.253 (0.402) Prec@1 94.79 (90.84) Prec@5 100.00 (99.75)
|
|
train[2019-03-31-17:36:14] Epoch: [070][500/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.435 (0.400) Prec@1 89.58 (90.87) Prec@5 100.00 (99.74)
|
|
train[2019-03-31-17:36:18] Epoch: [070][520/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.389 (0.399) Prec@1 90.00 (90.86) Prec@5 100.00 (99.74)
|
|
[2019-03-31-17:36:19] **train** Prec@1 90.86 Prec@5 99.74 Error@1 9.14 Error@5 0.26 Loss:0.399
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|
test [2019-03-31-17:36:19] Epoch: [070][000/105] Time 0.63 (0.63) Data 0.57 (0.57) Loss 0.234 (0.234) Prec@1 91.67 (91.67) Prec@5 100.00 (100.00)
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test [2019-03-31-17:36:23] Epoch: [070][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.146 (0.326) Prec@1 91.67 (89.98) Prec@5 100.00 (99.68)
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test [2019-03-31-17:36:24] Epoch: [070][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.061 (0.326) Prec@1 100.00 (89.98) Prec@5 100.00 (99.69)
|
|
[2019-03-31-17:36:24] **test** Prec@1 89.98 Prec@5 99.69 Error@1 10.02 Error@5 0.31 Loss:0.326
|
|
----> Best Accuracy : Acc@1=91.73, Acc@5=99.79, Error@1=8.27, Error@5=0.21
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-17:36:24] [Epoch=071/600] [Need: 19:30:18] LR=0.0241 ~ 0.0241, Batch=96
|
|
train[2019-03-31-17:36:25] Epoch: [071][000/521] Time 0.88 (0.88) Data 0.60 (0.60) Loss 0.334 (0.334) Prec@1 91.67 (91.67) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-17:36:50] Epoch: [071][100/521] Time 0.25 (0.26) Data 0.00 (0.01) Loss 0.377 (0.386) Prec@1 91.67 (90.99) Prec@5 100.00 (99.79)
|
|
train[2019-03-31-17:37:16] Epoch: [071][200/521] Time 0.25 (0.26) Data 0.00 (0.00) Loss 0.593 (0.405) Prec@1 87.50 (90.44) Prec@5 97.92 (99.74)
|
|
train[2019-03-31-17:37:41] Epoch: [071][300/521] Time 0.27 (0.26) Data 0.00 (0.00) Loss 0.313 (0.397) Prec@1 92.71 (90.66) Prec@5 100.00 (99.74)
|
|
train[2019-03-31-17:38:06] Epoch: [071][400/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.621 (0.401) Prec@1 88.54 (90.51) Prec@5 100.00 (99.77)
|
|
train[2019-03-31-17:38:31] Epoch: [071][500/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.409 (0.405) Prec@1 90.62 (90.43) Prec@5 100.00 (99.76)
|
|
train[2019-03-31-17:38:36] Epoch: [071][520/521] Time 0.22 (0.25) Data 0.00 (0.00) Loss 0.348 (0.403) Prec@1 91.25 (90.48) Prec@5 100.00 (99.76)
|
|
[2019-03-31-17:38:36] **train** Prec@1 90.48 Prec@5 99.76 Error@1 9.52 Error@5 0.24 Loss:0.403
|
|
test [2019-03-31-17:38:37] Epoch: [071][000/105] Time 0.64 (0.64) Data 0.57 (0.57) Loss 0.391 (0.391) Prec@1 89.58 (89.58) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-17:38:41] Epoch: [071][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.214 (0.314) Prec@1 94.79 (90.27) Prec@5 100.00 (99.69)
|
|
test [2019-03-31-17:38:41] Epoch: [071][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.034 (0.313) Prec@1 100.00 (90.33) Prec@5 100.00 (99.69)
|
|
[2019-03-31-17:38:41] **test** Prec@1 90.33 Prec@5 99.69 Error@1 9.67 Error@5 0.31 Loss:0.313
|
|
----> Best Accuracy : Acc@1=91.73, Acc@5=99.79, Error@1=8.27, Error@5=0.21
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-17:38:42] [Epoch=072/600] [Need: 20:11:59] LR=0.0241 ~ 0.0241, Batch=96
|
|
train[2019-03-31-17:38:42] Epoch: [072][000/521] Time 0.76 (0.76) Data 0.46 (0.46) Loss 0.409 (0.409) Prec@1 89.58 (89.58) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-17:39:08] Epoch: [072][100/521] Time 0.25 (0.26) Data 0.00 (0.00) Loss 0.214 (0.385) Prec@1 95.83 (91.07) Prec@5 100.00 (99.87)
|
|
train[2019-03-31-17:39:33] Epoch: [072][200/521] Time 0.26 (0.25) Data 0.00 (0.00) Loss 0.483 (0.391) Prec@1 87.50 (90.94) Prec@5 100.00 (99.79)
|
|
train[2019-03-31-17:39:58] Epoch: [072][300/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.218 (0.388) Prec@1 95.83 (91.03) Prec@5 100.00 (99.80)
|
|
train[2019-03-31-17:40:24] Epoch: [072][400/521] Time 0.26 (0.25) Data 0.00 (0.00) Loss 0.339 (0.394) Prec@1 91.67 (90.90) Prec@5 100.00 (99.79)
|
|
train[2019-03-31-17:40:50] Epoch: [072][500/521] Time 0.27 (0.26) Data 0.00 (0.00) Loss 0.318 (0.399) Prec@1 91.67 (90.75) Prec@5 100.00 (99.77)
|
|
train[2019-03-31-17:40:55] Epoch: [072][520/521] Time 0.24 (0.26) Data 0.00 (0.00) Loss 0.258 (0.400) Prec@1 96.25 (90.73) Prec@5 100.00 (99.77)
|
|
[2019-03-31-17:40:55] **train** Prec@1 90.73 Prec@5 99.77 Error@1 9.27 Error@5 0.23 Loss:0.400
|
|
test [2019-03-31-17:40:55] Epoch: [072][000/105] Time 0.51 (0.51) Data 0.44 (0.44) Loss 0.270 (0.270) Prec@1 89.58 (89.58) Prec@5 98.96 (98.96)
|
|
test [2019-03-31-17:41:00] Epoch: [072][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.239 (0.298) Prec@1 92.71 (90.79) Prec@5 100.00 (99.70)
|
|
test [2019-03-31-17:41:00] Epoch: [072][104/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.139 (0.300) Prec@1 93.75 (90.81) Prec@5 100.00 (99.71)
|
|
[2019-03-31-17:41:00] **test** Prec@1 90.81 Prec@5 99.71 Error@1 9.19 Error@5 0.29 Loss:0.300
|
|
----> Best Accuracy : Acc@1=91.73, Acc@5=99.79, Error@1=8.27, Error@5=0.21
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-17:41:00] [Epoch=073/600] [Need: 20:16:12] LR=0.0241 ~ 0.0241, Batch=96
|
|
train[2019-03-31-17:41:01] Epoch: [073][000/521] Time 0.91 (0.91) Data 0.63 (0.63) Loss 0.521 (0.521) Prec@1 87.50 (87.50) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-17:41:26] Epoch: [073][100/521] Time 0.25 (0.26) Data 0.00 (0.01) Loss 0.391 (0.399) Prec@1 92.71 (90.60) Prec@5 100.00 (99.77)
|
|
train[2019-03-31-17:41:52] Epoch: [073][200/521] Time 0.25 (0.26) Data 0.00 (0.00) Loss 0.518 (0.398) Prec@1 89.58 (90.80) Prec@5 98.96 (99.80)
|
|
train[2019-03-31-17:42:17] Epoch: [073][300/521] Time 0.25 (0.26) Data 0.00 (0.00) Loss 0.379 (0.407) Prec@1 92.71 (90.67) Prec@5 100.00 (99.75)
|
|
train[2019-03-31-17:42:43] Epoch: [073][400/521] Time 0.24 (0.26) Data 0.00 (0.00) Loss 0.498 (0.409) Prec@1 90.62 (90.59) Prec@5 98.96 (99.76)
|
|
train[2019-03-31-17:43:08] Epoch: [073][500/521] Time 0.31 (0.26) Data 0.00 (0.00) Loss 0.492 (0.409) Prec@1 89.58 (90.56) Prec@5 100.00 (99.77)
|
|
train[2019-03-31-17:43:14] Epoch: [073][520/521] Time 0.22 (0.26) Data 0.00 (0.00) Loss 0.320 (0.408) Prec@1 96.25 (90.59) Prec@5 98.75 (99.77)
|
|
[2019-03-31-17:43:14] **train** Prec@1 90.59 Prec@5 99.77 Error@1 9.41 Error@5 0.23 Loss:0.408
|
|
test [2019-03-31-17:43:15] Epoch: [073][000/105] Time 0.52 (0.52) Data 0.45 (0.45) Loss 0.305 (0.305) Prec@1 89.58 (89.58) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-17:43:19] Epoch: [073][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.168 (0.296) Prec@1 92.71 (90.29) Prec@5 100.00 (99.67)
|
|
test [2019-03-31-17:43:19] Epoch: [073][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.147 (0.296) Prec@1 93.75 (90.34) Prec@5 100.00 (99.68)
|
|
[2019-03-31-17:43:19] **test** Prec@1 90.34 Prec@5 99.68 Error@1 9.66 Error@5 0.32 Loss:0.296
|
|
----> Best Accuracy : Acc@1=91.73, Acc@5=99.79, Error@1=8.27, Error@5=0.21
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-17:43:19] [Epoch=074/600] [Need: 20:21:24] LR=0.0241 ~ 0.0241, Batch=96
|
|
train[2019-03-31-17:43:20] Epoch: [074][000/521] Time 0.83 (0.83) Data 0.54 (0.54) Loss 0.597 (0.597) Prec@1 84.38 (84.38) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-17:43:45] Epoch: [074][100/521] Time 0.23 (0.26) Data 0.00 (0.01) Loss 0.252 (0.375) Prec@1 95.83 (91.31) Prec@5 100.00 (99.78)
|
|
train[2019-03-31-17:44:11] Epoch: [074][200/521] Time 0.24 (0.26) Data 0.00 (0.00) Loss 0.295 (0.384) Prec@1 93.75 (90.98) Prec@5 100.00 (99.80)
|
|
train[2019-03-31-17:44:36] Epoch: [074][300/521] Time 0.26 (0.26) Data 0.00 (0.00) Loss 0.499 (0.396) Prec@1 88.54 (90.76) Prec@5 100.00 (99.77)
|
|
train[2019-03-31-17:45:02] Epoch: [074][400/521] Time 0.24 (0.26) Data 0.00 (0.00) Loss 0.351 (0.399) Prec@1 89.58 (90.72) Prec@5 100.00 (99.77)
|
|
train[2019-03-31-17:45:27] Epoch: [074][500/521] Time 0.25 (0.26) Data 0.00 (0.00) Loss 0.344 (0.402) Prec@1 92.71 (90.63) Prec@5 100.00 (99.76)
|
|
train[2019-03-31-17:45:32] Epoch: [074][520/521] Time 0.23 (0.26) Data 0.00 (0.00) Loss 0.370 (0.402) Prec@1 92.50 (90.67) Prec@5 100.00 (99.76)
|
|
[2019-03-31-17:45:33] **train** Prec@1 90.67 Prec@5 99.76 Error@1 9.33 Error@5 0.24 Loss:0.402
|
|
test [2019-03-31-17:45:33] Epoch: [074][000/105] Time 0.54 (0.54) Data 0.46 (0.46) Loss 0.211 (0.211) Prec@1 90.62 (90.62) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-17:45:37] Epoch: [074][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.252 (0.293) Prec@1 91.67 (90.69) Prec@5 100.00 (99.69)
|
|
test [2019-03-31-17:45:38] Epoch: [074][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.502 (0.293) Prec@1 93.75 (90.71) Prec@5 100.00 (99.69)
|
|
[2019-03-31-17:45:38] **test** Prec@1 90.71 Prec@5 99.69 Error@1 9.29 Error@5 0.31 Loss:0.293
|
|
----> Best Accuracy : Acc@1=91.73, Acc@5=99.79, Error@1=8.27, Error@5=0.21
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-17:45:38] [Epoch=075/600] [Need: 20:12:06] LR=0.0241 ~ 0.0241, Batch=96
|
|
train[2019-03-31-17:45:39] Epoch: [075][000/521] Time 0.89 (0.89) Data 0.58 (0.58) Loss 0.300 (0.300) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-17:46:04] Epoch: [075][100/521] Time 0.25 (0.26) Data 0.00 (0.01) Loss 0.527 (0.384) Prec@1 86.46 (90.90) Prec@5 97.92 (99.80)
|
|
train[2019-03-31-17:46:30] Epoch: [075][200/521] Time 0.26 (0.26) Data 0.00 (0.00) Loss 0.442 (0.402) Prec@1 89.58 (90.51) Prec@5 100.00 (99.81)
|
|
train[2019-03-31-17:46:55] Epoch: [075][300/521] Time 0.27 (0.26) Data 0.00 (0.00) Loss 0.413 (0.400) Prec@1 90.62 (90.51) Prec@5 100.00 (99.83)
|
|
train[2019-03-31-17:47:20] Epoch: [075][400/521] Time 0.25 (0.26) Data 0.00 (0.00) Loss 0.470 (0.406) Prec@1 87.50 (90.44) Prec@5 100.00 (99.79)
|
|
train[2019-03-31-17:47:46] Epoch: [075][500/521] Time 0.24 (0.26) Data 0.00 (0.00) Loss 0.508 (0.407) Prec@1 83.33 (90.49) Prec@5 100.00 (99.78)
|
|
train[2019-03-31-17:47:51] Epoch: [075][520/521] Time 0.21 (0.26) Data 0.00 (0.00) Loss 0.267 (0.405) Prec@1 92.50 (90.53) Prec@5 100.00 (99.79)
|
|
[2019-03-31-17:47:51] **train** Prec@1 90.53 Prec@5 99.79 Error@1 9.47 Error@5 0.21 Loss:0.405
|
|
test [2019-03-31-17:47:52] Epoch: [075][000/105] Time 0.60 (0.60) Data 0.53 (0.53) Loss 0.302 (0.302) Prec@1 89.58 (89.58) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-17:47:56] Epoch: [075][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.195 (0.325) Prec@1 91.67 (90.08) Prec@5 100.00 (99.58)
|
|
test [2019-03-31-17:47:56] Epoch: [075][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.118 (0.325) Prec@1 100.00 (90.09) Prec@5 100.00 (99.58)
|
|
[2019-03-31-17:47:56] **test** Prec@1 90.09 Prec@5 99.58 Error@1 9.91 Error@5 0.42 Loss:0.325
|
|
----> Best Accuracy : Acc@1=91.73, Acc@5=99.79, Error@1=8.27, Error@5=0.21
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-17:47:56] [Epoch=076/600] [Need: 20:09:08] LR=0.0240 ~ 0.0240, Batch=96
|
|
train[2019-03-31-17:47:57] Epoch: [076][000/521] Time 0.83 (0.83) Data 0.56 (0.56) Loss 0.320 (0.320) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-17:48:22] Epoch: [076][100/521] Time 0.24 (0.25) Data 0.00 (0.01) Loss 0.273 (0.355) Prec@1 93.75 (91.82) Prec@5 100.00 (99.85)
|
|
train[2019-03-31-17:48:45] Epoch: [076][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.567 (0.371) Prec@1 83.33 (91.44) Prec@5 98.96 (99.83)
|
|
train[2019-03-31-17:49:09] Epoch: [076][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.601 (0.385) Prec@1 83.33 (91.08) Prec@5 100.00 (99.81)
|
|
train[2019-03-31-17:49:33] Epoch: [076][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.368 (0.389) Prec@1 91.67 (91.03) Prec@5 98.96 (99.81)
|
|
train[2019-03-31-17:49:58] Epoch: [076][500/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.563 (0.393) Prec@1 83.33 (90.89) Prec@5 98.96 (99.80)
|
|
train[2019-03-31-17:50:03] Epoch: [076][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.334 (0.395) Prec@1 90.00 (90.87) Prec@5 100.00 (99.79)
|
|
[2019-03-31-17:50:03] **train** Prec@1 90.87 Prec@5 99.79 Error@1 9.13 Error@5 0.21 Loss:0.395
|
|
test [2019-03-31-17:50:04] Epoch: [076][000/105] Time 0.52 (0.52) Data 0.45 (0.45) Loss 0.225 (0.225) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-17:50:08] Epoch: [076][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.157 (0.271) Prec@1 94.79 (91.29) Prec@5 100.00 (99.80)
|
|
test [2019-03-31-17:50:08] Epoch: [076][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.333 (0.270) Prec@1 93.75 (91.33) Prec@5 100.00 (99.81)
|
|
[2019-03-31-17:50:08] **test** Prec@1 91.33 Prec@5 99.81 Error@1 8.67 Error@5 0.19 Loss:0.270
|
|
----> Best Accuracy : Acc@1=91.73, Acc@5=99.79, Error@1=8.27, Error@5=0.21
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-17:50:09] [Epoch=077/600] [Need: 19:12:30] LR=0.0240 ~ 0.0240, Batch=96
|
|
train[2019-03-31-17:50:09] Epoch: [077][000/521] Time 0.87 (0.87) Data 0.57 (0.57) Loss 0.550 (0.550) Prec@1 87.50 (87.50) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-17:50:34] Epoch: [077][100/521] Time 0.24 (0.25) Data 0.00 (0.01) Loss 0.327 (0.401) Prec@1 90.62 (90.72) Prec@5 100.00 (99.71)
|
|
train[2019-03-31-17:50:59] Epoch: [077][200/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.223 (0.399) Prec@1 95.83 (90.81) Prec@5 100.00 (99.77)
|
|
train[2019-03-31-17:51:24] Epoch: [077][300/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.395 (0.399) Prec@1 90.62 (90.80) Prec@5 98.96 (99.78)
|
|
train[2019-03-31-17:51:48] Epoch: [077][400/521] Time 0.23 (0.25) Data 0.00 (0.00) Loss 0.482 (0.396) Prec@1 89.58 (90.79) Prec@5 100.00 (99.79)
|
|
train[2019-03-31-17:52:12] Epoch: [077][500/521] Time 0.23 (0.25) Data 0.00 (0.00) Loss 0.369 (0.398) Prec@1 92.71 (90.71) Prec@5 100.00 (99.79)
|
|
train[2019-03-31-17:52:17] Epoch: [077][520/521] Time 0.22 (0.25) Data 0.00 (0.00) Loss 0.586 (0.398) Prec@1 83.75 (90.70) Prec@5 100.00 (99.80)
|
|
[2019-03-31-17:52:17] **train** Prec@1 90.70 Prec@5 99.80 Error@1 9.30 Error@5 0.20 Loss:0.398
|
|
test [2019-03-31-17:52:18] Epoch: [077][000/105] Time 0.48 (0.48) Data 0.42 (0.42) Loss 0.297 (0.297) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-17:52:22] Epoch: [077][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.119 (0.255) Prec@1 96.88 (92.15) Prec@5 100.00 (99.73)
|
|
test [2019-03-31-17:52:22] Epoch: [077][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.030 (0.255) Prec@1 100.00 (92.16) Prec@5 100.00 (99.73)
|
|
[2019-03-31-17:52:22] **test** Prec@1 92.16 Prec@5 99.73 Error@1 7.84 Error@5 0.27 Loss:0.255
|
|
----> Best Accuracy : Acc@1=92.16, Acc@5=99.73, Error@1=7.84, Error@5=0.27
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-17:52:22] [Epoch=078/600] [Need: 19:23:43] LR=0.0240 ~ 0.0240, Batch=96
|
|
train[2019-03-31-17:52:23] Epoch: [078][000/521] Time 0.83 (0.83) Data 0.56 (0.56) Loss 0.416 (0.416) Prec@1 93.75 (93.75) Prec@5 98.96 (98.96)
|
|
train[2019-03-31-17:52:47] Epoch: [078][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.297 (0.388) Prec@1 94.79 (90.88) Prec@5 100.00 (99.81)
|
|
train[2019-03-31-17:53:11] Epoch: [078][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.300 (0.389) Prec@1 92.71 (90.80) Prec@5 100.00 (99.83)
|
|
train[2019-03-31-17:53:34] Epoch: [078][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.331 (0.387) Prec@1 92.71 (90.85) Prec@5 100.00 (99.81)
|
|
train[2019-03-31-17:53:58] Epoch: [078][400/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.374 (0.395) Prec@1 89.58 (90.72) Prec@5 100.00 (99.78)
|
|
train[2019-03-31-17:54:22] Epoch: [078][500/521] Time 0.26 (0.24) Data 0.00 (0.00) Loss 0.274 (0.396) Prec@1 95.83 (90.73) Prec@5 100.00 (99.78)
|
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train[2019-03-31-17:54:27] Epoch: [078][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.438 (0.398) Prec@1 87.50 (90.72) Prec@5 100.00 (99.79)
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[2019-03-31-17:54:27] **train** Prec@1 90.72 Prec@5 99.79 Error@1 9.28 Error@5 0.21 Loss:0.398
|
|
test [2019-03-31-17:54:28] Epoch: [078][000/105] Time 0.61 (0.61) Data 0.54 (0.54) Loss 0.286 (0.286) Prec@1 87.50 (87.50) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-17:54:32] Epoch: [078][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.210 (0.311) Prec@1 92.71 (90.48) Prec@5 100.00 (99.66)
|
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test [2019-03-31-17:54:32] Epoch: [078][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.280 (0.311) Prec@1 93.75 (90.45) Prec@5 100.00 (99.67)
|
|
[2019-03-31-17:54:32] **test** Prec@1 90.45 Prec@5 99.67 Error@1 9.55 Error@5 0.33 Loss:0.311
|
|
----> Best Accuracy : Acc@1=92.16, Acc@5=99.73, Error@1=7.84, Error@5=0.27
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-17:54:32] [Epoch=079/600] [Need: 18:48:31] LR=0.0239 ~ 0.0239, Batch=96
|
|
train[2019-03-31-17:54:33] Epoch: [079][000/521] Time 0.84 (0.84) Data 0.57 (0.57) Loss 0.493 (0.493) Prec@1 88.54 (88.54) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-17:54:57] Epoch: [079][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.305 (0.384) Prec@1 90.62 (91.14) Prec@5 100.00 (99.79)
|
|
train[2019-03-31-17:55:21] Epoch: [079][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.463 (0.394) Prec@1 86.46 (90.87) Prec@5 98.96 (99.77)
|
|
train[2019-03-31-17:55:45] Epoch: [079][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.441 (0.390) Prec@1 89.58 (90.97) Prec@5 100.00 (99.77)
|
|
train[2019-03-31-17:56:08] Epoch: [079][400/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.257 (0.390) Prec@1 90.62 (91.00) Prec@5 100.00 (99.76)
|
|
train[2019-03-31-17:56:32] Epoch: [079][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.376 (0.393) Prec@1 91.67 (90.91) Prec@5 98.96 (99.77)
|
|
train[2019-03-31-17:56:37] Epoch: [079][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.485 (0.393) Prec@1 86.25 (90.90) Prec@5 98.75 (99.77)
|
|
[2019-03-31-17:56:37] **train** Prec@1 90.90 Prec@5 99.77 Error@1 9.10 Error@5 0.23 Loss:0.393
|
|
test [2019-03-31-17:56:37] Epoch: [079][000/105] Time 0.60 (0.60) Data 0.54 (0.54) Loss 0.162 (0.162) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-17:56:41] Epoch: [079][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.146 (0.232) Prec@1 91.67 (92.49) Prec@5 100.00 (99.88)
|
|
test [2019-03-31-17:56:42] Epoch: [079][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.210 (0.234) Prec@1 93.75 (92.46) Prec@5 100.00 (99.88)
|
|
[2019-03-31-17:56:42] **test** Prec@1 92.46 Prec@5 99.88 Error@1 7.54 Error@5 0.12 Loss:0.234
|
|
----> Best Accuracy : Acc@1=92.46, Acc@5=99.88, Error@1=7.54, Error@5=0.12
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-17:56:42] [Epoch=080/600] [Need: 18:43:11] LR=0.0239 ~ 0.0239, Batch=96
|
|
train[2019-03-31-17:56:43] Epoch: [080][000/521] Time 0.83 (0.83) Data 0.56 (0.56) Loss 0.332 (0.332) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-17:57:06] Epoch: [080][100/521] Time 0.23 (0.24) Data 0.00 (0.01) Loss 0.361 (0.388) Prec@1 92.71 (91.10) Prec@5 100.00 (99.79)
|
|
train[2019-03-31-17:57:30] Epoch: [080][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.329 (0.390) Prec@1 91.67 (90.94) Prec@5 100.00 (99.78)
|
|
train[2019-03-31-17:57:54] Epoch: [080][300/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.304 (0.387) Prec@1 93.75 (91.01) Prec@5 100.00 (99.78)
|
|
train[2019-03-31-17:58:18] Epoch: [080][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.496 (0.391) Prec@1 85.42 (90.92) Prec@5 100.00 (99.78)
|
|
train[2019-03-31-17:58:41] Epoch: [080][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.397 (0.390) Prec@1 92.71 (90.90) Prec@5 100.00 (99.78)
|
|
train[2019-03-31-17:58:46] Epoch: [080][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.321 (0.390) Prec@1 91.25 (90.93) Prec@5 98.75 (99.78)
|
|
[2019-03-31-17:58:46] **train** Prec@1 90.93 Prec@5 99.78 Error@1 9.07 Error@5 0.22 Loss:0.390
|
|
test [2019-03-31-17:58:47] Epoch: [080][000/105] Time 0.46 (0.46) Data 0.40 (0.40) Loss 0.321 (0.321) Prec@1 88.54 (88.54) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-17:58:51] Epoch: [080][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.214 (0.280) Prec@1 92.71 (91.22) Prec@5 100.00 (99.76)
|
|
test [2019-03-31-17:58:51] Epoch: [080][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.147 (0.281) Prec@1 87.50 (91.20) Prec@5 100.00 (99.77)
|
|
[2019-03-31-17:58:51] **test** Prec@1 91.20 Prec@5 99.77 Error@1 8.80 Error@5 0.23 Loss:0.281
|
|
----> Best Accuracy : Acc@1=92.46, Acc@5=99.88, Error@1=7.54, Error@5=0.12
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-17:58:51] [Epoch=081/600] [Need: 18:39:06] LR=0.0239 ~ 0.0239, Batch=96
|
|
train[2019-03-31-17:58:52] Epoch: [081][000/521] Time 0.70 (0.70) Data 0.43 (0.43) Loss 0.475 (0.475) Prec@1 89.58 (89.58) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-17:59:17] Epoch: [081][100/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.350 (0.375) Prec@1 90.62 (91.36) Prec@5 100.00 (99.76)
|
|
train[2019-03-31-17:59:40] Epoch: [081][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.619 (0.374) Prec@1 85.42 (91.30) Prec@5 97.92 (99.78)
|
|
train[2019-03-31-18:00:04] Epoch: [081][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.255 (0.371) Prec@1 93.75 (91.39) Prec@5 100.00 (99.81)
|
|
train[2019-03-31-18:00:28] Epoch: [081][400/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.545 (0.385) Prec@1 86.46 (91.02) Prec@5 100.00 (99.80)
|
|
train[2019-03-31-18:00:52] Epoch: [081][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.377 (0.390) Prec@1 90.62 (90.89) Prec@5 100.00 (99.80)
|
|
train[2019-03-31-18:00:56] Epoch: [081][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.433 (0.390) Prec@1 90.00 (90.87) Prec@5 100.00 (99.80)
|
|
[2019-03-31-18:00:57] **train** Prec@1 90.87 Prec@5 99.80 Error@1 9.13 Error@5 0.20 Loss:0.390
|
|
test [2019-03-31-18:00:57] Epoch: [081][000/105] Time 0.53 (0.53) Data 0.47 (0.47) Loss 0.169 (0.169) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-18:01:01] Epoch: [081][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.177 (0.259) Prec@1 93.75 (91.78) Prec@5 100.00 (99.80)
|
|
test [2019-03-31-18:01:01] Epoch: [081][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.110 (0.257) Prec@1 100.00 (91.86) Prec@5 100.00 (99.81)
|
|
[2019-03-31-18:01:01] **test** Prec@1 91.86 Prec@5 99.81 Error@1 8.14 Error@5 0.19 Loss:0.257
|
|
----> Best Accuracy : Acc@1=92.46, Acc@5=99.88, Error@1=7.54, Error@5=0.12
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-18:01:02] [Epoch=082/600] [Need: 18:44:34] LR=0.0239 ~ 0.0239, Batch=96
|
|
train[2019-03-31-18:01:02] Epoch: [082][000/521] Time 0.70 (0.70) Data 0.42 (0.42) Loss 0.458 (0.458) Prec@1 88.54 (88.54) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-18:01:26] Epoch: [082][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.419 (0.388) Prec@1 92.71 (90.98) Prec@5 100.00 (99.80)
|
|
train[2019-03-31-18:01:50] Epoch: [082][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.452 (0.401) Prec@1 88.54 (90.47) Prec@5 100.00 (99.78)
|
|
train[2019-03-31-18:02:14] Epoch: [082][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.470 (0.395) Prec@1 86.46 (90.69) Prec@5 100.00 (99.78)
|
|
train[2019-03-31-18:02:38] Epoch: [082][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.288 (0.391) Prec@1 93.75 (90.80) Prec@5 100.00 (99.79)
|
|
train[2019-03-31-18:03:02] Epoch: [082][500/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.322 (0.393) Prec@1 93.75 (90.79) Prec@5 100.00 (99.79)
|
|
train[2019-03-31-18:03:07] Epoch: [082][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.457 (0.394) Prec@1 83.75 (90.76) Prec@5 100.00 (99.78)
|
|
[2019-03-31-18:03:07] **train** Prec@1 90.76 Prec@5 99.78 Error@1 9.24 Error@5 0.22 Loss:0.394
|
|
test [2019-03-31-18:03:07] Epoch: [082][000/105] Time 0.49 (0.49) Data 0.42 (0.42) Loss 0.339 (0.339) Prec@1 88.54 (88.54) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-18:03:12] Epoch: [082][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.091 (0.288) Prec@1 96.88 (90.69) Prec@5 100.00 (99.59)
|
|
test [2019-03-31-18:03:12] Epoch: [082][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.185 (0.289) Prec@1 93.75 (90.68) Prec@5 100.00 (99.60)
|
|
[2019-03-31-18:03:12] **test** Prec@1 90.68 Prec@5 99.60 Error@1 9.32 Error@5 0.40 Loss:0.289
|
|
----> Best Accuracy : Acc@1=92.46, Acc@5=99.88, Error@1=7.54, Error@5=0.12
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-18:03:12] [Epoch=083/600] [Need: 18:43:59] LR=0.0238 ~ 0.0238, Batch=96
|
|
train[2019-03-31-18:03:13] Epoch: [083][000/521] Time 0.70 (0.70) Data 0.43 (0.43) Loss 0.215 (0.215) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-18:03:38] Epoch: [083][100/521] Time 0.25 (0.26) Data 0.00 (0.00) Loss 0.410 (0.380) Prec@1 85.42 (91.30) Prec@5 100.00 (99.82)
|
|
train[2019-03-31-18:04:03] Epoch: [083][200/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.357 (0.373) Prec@1 91.67 (91.40) Prec@5 100.00 (99.82)
|
|
train[2019-03-31-18:04:28] Epoch: [083][300/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.454 (0.383) Prec@1 90.62 (91.10) Prec@5 98.96 (99.80)
|
|
train[2019-03-31-18:04:51] Epoch: [083][400/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.483 (0.383) Prec@1 88.54 (91.17) Prec@5 100.00 (99.81)
|
|
train[2019-03-31-18:05:15] Epoch: [083][500/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.460 (0.386) Prec@1 92.71 (91.08) Prec@5 98.96 (99.79)
|
|
train[2019-03-31-18:05:20] Epoch: [083][520/521] Time 0.22 (0.25) Data 0.00 (0.00) Loss 0.330 (0.387) Prec@1 91.25 (91.10) Prec@5 100.00 (99.79)
|
|
[2019-03-31-18:05:20] **train** Prec@1 91.10 Prec@5 99.79 Error@1 8.90 Error@5 0.21 Loss:0.387
|
|
test [2019-03-31-18:05:20] Epoch: [083][000/105] Time 0.62 (0.62) Data 0.55 (0.55) Loss 0.198 (0.198) Prec@1 91.67 (91.67) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-18:05:25] Epoch: [083][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.093 (0.269) Prec@1 95.83 (91.66) Prec@5 100.00 (99.63)
|
|
test [2019-03-31-18:05:25] Epoch: [083][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.184 (0.268) Prec@1 93.75 (91.68) Prec@5 100.00 (99.64)
|
|
[2019-03-31-18:05:25] **test** Prec@1 91.68 Prec@5 99.64 Error@1 8.32 Error@5 0.36 Loss:0.268
|
|
----> Best Accuracy : Acc@1=92.46, Acc@5=99.88, Error@1=7.54, Error@5=0.12
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-18:05:25] [Epoch=084/600] [Need: 19:03:37] LR=0.0238 ~ 0.0238, Batch=96
|
|
train[2019-03-31-18:05:26] Epoch: [084][000/521] Time 0.71 (0.71) Data 0.43 (0.43) Loss 0.359 (0.359) Prec@1 94.79 (94.79) Prec@5 98.96 (98.96)
|
|
train[2019-03-31-18:05:50] Epoch: [084][100/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.490 (0.370) Prec@1 87.50 (91.41) Prec@5 100.00 (99.80)
|
|
train[2019-03-31-18:06:14] Epoch: [084][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.442 (0.377) Prec@1 89.58 (91.40) Prec@5 98.96 (99.79)
|
|
train[2019-03-31-18:06:39] Epoch: [084][300/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.385 (0.379) Prec@1 90.62 (91.41) Prec@5 100.00 (99.81)
|
|
train[2019-03-31-18:07:04] Epoch: [084][400/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.269 (0.377) Prec@1 94.79 (91.44) Prec@5 100.00 (99.81)
|
|
train[2019-03-31-18:07:30] Epoch: [084][500/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.516 (0.382) Prec@1 89.58 (91.25) Prec@5 100.00 (99.79)
|
|
train[2019-03-31-18:07:35] Epoch: [084][520/521] Time 0.23 (0.25) Data 0.00 (0.00) Loss 0.363 (0.383) Prec@1 92.50 (91.21) Prec@5 100.00 (99.79)
|
|
[2019-03-31-18:07:35] **train** Prec@1 91.21 Prec@5 99.79 Error@1 8.79 Error@5 0.21 Loss:0.383
|
|
test [2019-03-31-18:07:35] Epoch: [084][000/105] Time 0.58 (0.58) Data 0.51 (0.51) Loss 0.308 (0.308) Prec@1 90.62 (90.62) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-18:07:40] Epoch: [084][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.148 (0.296) Prec@1 94.79 (90.58) Prec@5 100.00 (99.71)
|
|
test [2019-03-31-18:07:40] Epoch: [084][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.259 (0.296) Prec@1 93.75 (90.61) Prec@5 100.00 (99.72)
|
|
[2019-03-31-18:07:40] **test** Prec@1 90.61 Prec@5 99.72 Error@1 9.39 Error@5 0.28 Loss:0.296
|
|
----> Best Accuracy : Acc@1=92.46, Acc@5=99.88, Error@1=7.54, Error@5=0.12
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-18:07:40] [Epoch=085/600] [Need: 19:20:02] LR=0.0238 ~ 0.0238, Batch=96
|
|
train[2019-03-31-18:07:41] Epoch: [085][000/521] Time 0.89 (0.89) Data 0.60 (0.60) Loss 0.283 (0.283) Prec@1 90.62 (90.62) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-18:08:06] Epoch: [085][100/521] Time 0.25 (0.26) Data 0.00 (0.01) Loss 0.275 (0.377) Prec@1 95.83 (91.08) Prec@5 100.00 (99.70)
|
|
train[2019-03-31-18:08:31] Epoch: [085][200/521] Time 0.25 (0.26) Data 0.00 (0.00) Loss 0.466 (0.380) Prec@1 86.46 (91.06) Prec@5 98.96 (99.76)
|
|
train[2019-03-31-18:08:57] Epoch: [085][300/521] Time 0.30 (0.25) Data 0.00 (0.00) Loss 0.352 (0.388) Prec@1 91.67 (90.94) Prec@5 100.00 (99.76)
|
|
train[2019-03-31-18:09:22] Epoch: [085][400/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.334 (0.388) Prec@1 93.75 (90.93) Prec@5 100.00 (99.76)
|
|
train[2019-03-31-18:09:47] Epoch: [085][500/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.529 (0.387) Prec@1 87.50 (90.96) Prec@5 100.00 (99.77)
|
|
train[2019-03-31-18:09:52] Epoch: [085][520/521] Time 0.23 (0.25) Data 0.00 (0.00) Loss 0.405 (0.388) Prec@1 90.00 (90.94) Prec@5 100.00 (99.78)
|
|
[2019-03-31-18:09:52] **train** Prec@1 90.94 Prec@5 99.78 Error@1 9.06 Error@5 0.22 Loss:0.388
|
|
test [2019-03-31-18:09:52] Epoch: [085][000/105] Time 0.63 (0.63) Data 0.55 (0.55) Loss 0.370 (0.370) Prec@1 88.54 (88.54) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-18:09:57] Epoch: [085][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.130 (0.314) Prec@1 95.83 (90.59) Prec@5 100.00 (99.72)
|
|
test [2019-03-31-18:09:57] Epoch: [085][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.107 (0.314) Prec@1 100.00 (90.64) Prec@5 100.00 (99.72)
|
|
[2019-03-31-18:09:57] **test** Prec@1 90.64 Prec@5 99.72 Error@1 9.36 Error@5 0.28 Loss:0.314
|
|
----> Best Accuracy : Acc@1=92.46, Acc@5=99.88, Error@1=7.54, Error@5=0.12
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-18:09:57] [Epoch=086/600] [Need: 19:34:27] LR=0.0238 ~ 0.0238, Batch=96
|
|
train[2019-03-31-18:09:58] Epoch: [086][000/521] Time 0.75 (0.75) Data 0.46 (0.46) Loss 0.265 (0.265) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-18:10:23] Epoch: [086][100/521] Time 0.25 (0.26) Data 0.00 (0.00) Loss 0.436 (0.369) Prec@1 88.54 (91.47) Prec@5 100.00 (99.88)
|
|
train[2019-03-31-18:10:49] Epoch: [086][200/521] Time 0.30 (0.26) Data 0.00 (0.00) Loss 0.352 (0.372) Prec@1 90.62 (91.44) Prec@5 100.00 (99.85)
|
|
train[2019-03-31-18:11:14] Epoch: [086][300/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.409 (0.374) Prec@1 89.58 (91.29) Prec@5 100.00 (99.83)
|
|
train[2019-03-31-18:11:39] Epoch: [086][400/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.391 (0.376) Prec@1 91.67 (91.26) Prec@5 98.96 (99.81)
|
|
train[2019-03-31-18:12:04] Epoch: [086][500/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.569 (0.378) Prec@1 84.38 (91.23) Prec@5 98.96 (99.79)
|
|
train[2019-03-31-18:12:09] Epoch: [086][520/521] Time 0.26 (0.25) Data 0.00 (0.00) Loss 0.569 (0.379) Prec@1 85.00 (91.19) Prec@5 100.00 (99.79)
|
|
[2019-03-31-18:12:09] **train** Prec@1 91.19 Prec@5 99.79 Error@1 8.81 Error@5 0.21 Loss:0.379
|
|
test [2019-03-31-18:12:09] Epoch: [086][000/105] Time 0.52 (0.52) Data 0.45 (0.45) Loss 0.305 (0.305) Prec@1 87.50 (87.50) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-18:12:14] Epoch: [086][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.080 (0.252) Prec@1 96.88 (92.10) Prec@5 100.00 (99.75)
|
|
test [2019-03-31-18:12:14] Epoch: [086][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.381 (0.254) Prec@1 87.50 (92.07) Prec@5 100.00 (99.75)
|
|
[2019-03-31-18:12:14] **test** Prec@1 92.07 Prec@5 99.75 Error@1 7.93 Error@5 0.25 Loss:0.254
|
|
----> Best Accuracy : Acc@1=92.46, Acc@5=99.88, Error@1=7.54, Error@5=0.12
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-18:12:14] [Epoch=087/600] [Need: 19:30:27] LR=0.0237 ~ 0.0237, Batch=96
|
|
train[2019-03-31-18:12:15] Epoch: [087][000/521] Time 0.73 (0.73) Data 0.45 (0.45) Loss 0.261 (0.261) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-18:12:40] Epoch: [087][100/521] Time 0.25 (0.26) Data 0.00 (0.00) Loss 0.445 (0.377) Prec@1 87.50 (91.19) Prec@5 98.96 (99.83)
|
|
train[2019-03-31-18:13:05] Epoch: [087][200/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.315 (0.377) Prec@1 91.67 (91.24) Prec@5 100.00 (99.79)
|
|
train[2019-03-31-18:13:32] Epoch: [087][300/521] Time 0.25 (0.26) Data 0.00 (0.00) Loss 0.360 (0.377) Prec@1 92.71 (91.29) Prec@5 98.96 (99.79)
|
|
train[2019-03-31-18:13:57] Epoch: [087][400/521] Time 0.24 (0.26) Data 0.00 (0.00) Loss 0.516 (0.378) Prec@1 82.29 (91.15) Prec@5 98.96 (99.80)
|
|
train[2019-03-31-18:14:23] Epoch: [087][500/521] Time 0.25 (0.26) Data 0.00 (0.00) Loss 0.402 (0.384) Prec@1 88.54 (91.06) Prec@5 98.96 (99.80)
|
|
train[2019-03-31-18:14:28] Epoch: [087][520/521] Time 0.22 (0.26) Data 0.00 (0.00) Loss 0.258 (0.384) Prec@1 93.75 (91.05) Prec@5 100.00 (99.80)
|
|
[2019-03-31-18:14:28] **train** Prec@1 91.05 Prec@5 99.80 Error@1 8.95 Error@5 0.20 Loss:0.384
|
|
test [2019-03-31-18:14:29] Epoch: [087][000/105] Time 0.49 (0.49) Data 0.42 (0.42) Loss 0.359 (0.359) Prec@1 87.50 (87.50) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-18:14:33] Epoch: [087][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.207 (0.279) Prec@1 94.79 (91.73) Prec@5 100.00 (99.77)
|
|
test [2019-03-31-18:14:33] Epoch: [087][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.188 (0.277) Prec@1 93.75 (91.75) Prec@5 100.00 (99.78)
|
|
[2019-03-31-18:14:33] **test** Prec@1 91.75 Prec@5 99.78 Error@1 8.25 Error@5 0.22 Loss:0.277
|
|
----> Best Accuracy : Acc@1=92.46, Acc@5=99.88, Error@1=7.54, Error@5=0.12
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-18:14:33] [Epoch=088/600] [Need: 19:48:47] LR=0.0237 ~ 0.0237, Batch=96
|
|
train[2019-03-31-18:14:34] Epoch: [088][000/521] Time 0.76 (0.76) Data 0.47 (0.47) Loss 0.718 (0.718) Prec@1 85.42 (85.42) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-18:14:59] Epoch: [088][100/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.405 (0.376) Prec@1 89.58 (91.20) Prec@5 100.00 (99.83)
|
|
train[2019-03-31-18:15:24] Epoch: [088][200/521] Time 0.26 (0.25) Data 0.00 (0.00) Loss 0.434 (0.379) Prec@1 89.58 (91.20) Prec@5 100.00 (99.82)
|
|
train[2019-03-31-18:15:50] Epoch: [088][300/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.370 (0.376) Prec@1 93.75 (91.23) Prec@5 100.00 (99.80)
|
|
train[2019-03-31-18:16:15] Epoch: [088][400/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.549 (0.385) Prec@1 86.46 (91.01) Prec@5 100.00 (99.79)
|
|
train[2019-03-31-18:16:40] Epoch: [088][500/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.449 (0.384) Prec@1 90.62 (91.02) Prec@5 100.00 (99.79)
|
|
train[2019-03-31-18:16:45] Epoch: [088][520/521] Time 0.23 (0.25) Data 0.00 (0.00) Loss 0.549 (0.384) Prec@1 86.25 (91.00) Prec@5 100.00 (99.80)
|
|
[2019-03-31-18:16:45] **train** Prec@1 91.00 Prec@5 99.80 Error@1 9.00 Error@5 0.20 Loss:0.384
|
|
test [2019-03-31-18:16:46] Epoch: [088][000/105] Time 0.56 (0.56) Data 0.49 (0.49) Loss 0.387 (0.387) Prec@1 89.58 (89.58) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-18:16:50] Epoch: [088][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.089 (0.270) Prec@1 95.83 (91.49) Prec@5 100.00 (99.72)
|
|
test [2019-03-31-18:16:50] Epoch: [088][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.311 (0.270) Prec@1 87.50 (91.44) Prec@5 100.00 (99.73)
|
|
[2019-03-31-18:16:50] **test** Prec@1 91.44 Prec@5 99.73 Error@1 8.56 Error@5 0.27 Loss:0.270
|
|
----> Best Accuracy : Acc@1=92.46, Acc@5=99.88, Error@1=7.54, Error@5=0.12
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-18:16:51] [Epoch=089/600] [Need: 19:28:21] LR=0.0237 ~ 0.0237, Batch=96
|
|
train[2019-03-31-18:16:51] Epoch: [089][000/521] Time 0.74 (0.74) Data 0.45 (0.45) Loss 0.562 (0.562) Prec@1 88.54 (88.54) Prec@5 98.96 (98.96)
|
|
train[2019-03-31-18:17:16] Epoch: [089][100/521] Time 0.26 (0.26) Data 0.00 (0.00) Loss 0.229 (0.373) Prec@1 95.83 (91.40) Prec@5 100.00 (99.82)
|
|
train[2019-03-31-18:17:43] Epoch: [089][200/521] Time 0.24 (0.26) Data 0.00 (0.00) Loss 0.278 (0.384) Prec@1 94.79 (90.96) Prec@5 98.96 (99.83)
|
|
train[2019-03-31-18:18:06] Epoch: [089][300/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.616 (0.382) Prec@1 88.54 (90.93) Prec@5 98.96 (99.80)
|
|
train[2019-03-31-18:18:30] Epoch: [089][400/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.332 (0.379) Prec@1 91.67 (91.02) Prec@5 100.00 (99.81)
|
|
train[2019-03-31-18:18:55] Epoch: [089][500/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.396 (0.383) Prec@1 91.67 (91.01) Prec@5 100.00 (99.81)
|
|
train[2019-03-31-18:19:00] Epoch: [089][520/521] Time 0.23 (0.25) Data 0.00 (0.00) Loss 0.465 (0.384) Prec@1 92.50 (91.00) Prec@5 100.00 (99.81)
|
|
[2019-03-31-18:19:00] **train** Prec@1 91.00 Prec@5 99.81 Error@1 9.00 Error@5 0.19 Loss:0.384
|
|
test [2019-03-31-18:19:00] Epoch: [089][000/105] Time 0.48 (0.48) Data 0.41 (0.41) Loss 0.234 (0.234) Prec@1 87.50 (87.50) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-18:19:04] Epoch: [089][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.186 (0.289) Prec@1 93.75 (90.87) Prec@5 100.00 (99.79)
|
|
test [2019-03-31-18:19:05] Epoch: [089][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.743 (0.290) Prec@1 81.25 (90.93) Prec@5 100.00 (99.80)
|
|
[2019-03-31-18:19:05] **test** Prec@1 90.93 Prec@5 99.80 Error@1 9.07 Error@5 0.20 Loss:0.290
|
|
----> Best Accuracy : Acc@1=92.46, Acc@5=99.88, Error@1=7.54, Error@5=0.12
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-18:19:05] [Epoch=090/600] [Need: 19:00:41] LR=0.0236 ~ 0.0236, Batch=96
|
|
train[2019-03-31-18:19:06] Epoch: [090][000/521] Time 0.82 (0.82) Data 0.55 (0.55) Loss 0.359 (0.359) Prec@1 90.62 (90.62) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-18:19:29] Epoch: [090][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.274 (0.359) Prec@1 92.71 (91.64) Prec@5 100.00 (99.80)
|
|
train[2019-03-31-18:19:54] Epoch: [090][200/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.336 (0.368) Prec@1 94.79 (91.45) Prec@5 98.96 (99.80)
|
|
train[2019-03-31-18:20:19] Epoch: [090][300/521] Time 0.26 (0.25) Data 0.00 (0.00) Loss 0.408 (0.375) Prec@1 89.58 (91.31) Prec@5 100.00 (99.83)
|
|
train[2019-03-31-18:20:45] Epoch: [090][400/521] Time 0.26 (0.25) Data 0.00 (0.00) Loss 0.394 (0.377) Prec@1 90.62 (91.25) Prec@5 100.00 (99.81)
|
|
train[2019-03-31-18:21:10] Epoch: [090][500/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.560 (0.377) Prec@1 83.33 (91.25) Prec@5 100.00 (99.80)
|
|
train[2019-03-31-18:21:15] Epoch: [090][520/521] Time 0.22 (0.25) Data 0.00 (0.00) Loss 0.383 (0.377) Prec@1 90.00 (91.26) Prec@5 97.50 (99.79)
|
|
[2019-03-31-18:21:15] **train** Prec@1 91.26 Prec@5 99.79 Error@1 8.74 Error@5 0.21 Loss:0.377
|
|
test [2019-03-31-18:21:15] Epoch: [090][000/105] Time 0.50 (0.50) Data 0.42 (0.42) Loss 0.223 (0.223) Prec@1 91.67 (91.67) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-18:21:20] Epoch: [090][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.183 (0.250) Prec@1 91.67 (92.13) Prec@5 100.00 (99.80)
|
|
test [2019-03-31-18:21:20] Epoch: [090][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.058 (0.251) Prec@1 100.00 (92.14) Prec@5 100.00 (99.81)
|
|
[2019-03-31-18:21:20] **test** Prec@1 92.14 Prec@5 99.81 Error@1 7.86 Error@5 0.19 Loss:0.251
|
|
----> Best Accuracy : Acc@1=92.46, Acc@5=99.88, Error@1=7.54, Error@5=0.12
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-18:21:20] [Epoch=091/600] [Need: 19:08:09] LR=0.0236 ~ 0.0236, Batch=96
|
|
train[2019-03-31-18:21:21] Epoch: [091][000/521] Time 0.74 (0.74) Data 0.46 (0.46) Loss 0.453 (0.453) Prec@1 84.38 (84.38) Prec@5 98.96 (98.96)
|
|
train[2019-03-31-18:21:46] Epoch: [091][100/521] Time 0.25 (0.26) Data 0.00 (0.00) Loss 0.309 (0.368) Prec@1 92.71 (91.57) Prec@5 100.00 (99.78)
|
|
train[2019-03-31-18:22:11] Epoch: [091][200/521] Time 0.27 (0.25) Data 0.00 (0.00) Loss 0.263 (0.370) Prec@1 94.79 (91.53) Prec@5 100.00 (99.83)
|
|
train[2019-03-31-18:22:36] Epoch: [091][300/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.518 (0.367) Prec@1 86.46 (91.54) Prec@5 98.96 (99.84)
|
|
train[2019-03-31-18:23:01] Epoch: [091][400/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.529 (0.364) Prec@1 89.58 (91.61) Prec@5 100.00 (99.84)
|
|
train[2019-03-31-18:23:26] Epoch: [091][500/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.384 (0.368) Prec@1 92.71 (91.50) Prec@5 100.00 (99.83)
|
|
train[2019-03-31-18:23:31] Epoch: [091][520/521] Time 0.22 (0.25) Data 0.00 (0.00) Loss 0.485 (0.370) Prec@1 88.75 (91.46) Prec@5 100.00 (99.83)
|
|
[2019-03-31-18:23:31] **train** Prec@1 91.46 Prec@5 99.83 Error@1 8.54 Error@5 0.17 Loss:0.370
|
|
test [2019-03-31-18:23:32] Epoch: [091][000/105] Time 0.57 (0.57) Data 0.50 (0.50) Loss 0.450 (0.450) Prec@1 88.54 (88.54) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-18:23:36] Epoch: [091][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.126 (0.318) Prec@1 96.88 (90.35) Prec@5 100.00 (99.66)
|
|
test [2019-03-31-18:23:36] Epoch: [091][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.060 (0.318) Prec@1 100.00 (90.36) Prec@5 100.00 (99.67)
|
|
[2019-03-31-18:23:36] **test** Prec@1 90.36 Prec@5 99.67 Error@1 9.64 Error@5 0.33 Loss:0.318
|
|
----> Best Accuracy : Acc@1=92.46, Acc@5=99.88, Error@1=7.54, Error@5=0.12
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-18:23:37] [Epoch=092/600] [Need: 19:15:54] LR=0.0236 ~ 0.0236, Batch=96
|
|
train[2019-03-31-18:23:37] Epoch: [092][000/521] Time 0.75 (0.75) Data 0.46 (0.46) Loss 0.285 (0.285) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-18:24:02] Epoch: [092][100/521] Time 0.26 (0.26) Data 0.00 (0.00) Loss 0.368 (0.357) Prec@1 94.79 (91.72) Prec@5 98.96 (99.89)
|
|
train[2019-03-31-18:24:28] Epoch: [092][200/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.433 (0.364) Prec@1 89.58 (91.41) Prec@5 100.00 (99.86)
|
|
train[2019-03-31-18:24:53] Epoch: [092][300/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.499 (0.370) Prec@1 86.46 (91.22) Prec@5 100.00 (99.82)
|
|
train[2019-03-31-18:25:18] Epoch: [092][400/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.241 (0.374) Prec@1 93.75 (91.08) Prec@5 100.00 (99.82)
|
|
train[2019-03-31-18:25:43] Epoch: [092][500/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.430 (0.377) Prec@1 88.54 (91.11) Prec@5 100.00 (99.80)
|
|
train[2019-03-31-18:25:48] Epoch: [092][520/521] Time 0.23 (0.25) Data 0.00 (0.00) Loss 0.450 (0.377) Prec@1 95.00 (91.12) Prec@5 100.00 (99.79)
|
|
[2019-03-31-18:25:48] **train** Prec@1 91.12 Prec@5 99.79 Error@1 8.88 Error@5 0.21 Loss:0.377
|
|
test [2019-03-31-18:25:49] Epoch: [092][000/105] Time 0.50 (0.50) Data 0.43 (0.43) Loss 0.391 (0.391) Prec@1 87.50 (87.50) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-18:25:53] Epoch: [092][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.126 (0.254) Prec@1 95.83 (91.59) Prec@5 100.00 (99.83)
|
|
test [2019-03-31-18:25:53] Epoch: [092][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.135 (0.255) Prec@1 87.50 (91.58) Prec@5 100.00 (99.84)
|
|
[2019-03-31-18:25:53] **test** Prec@1 91.58 Prec@5 99.84 Error@1 8.42 Error@5 0.16 Loss:0.255
|
|
----> Best Accuracy : Acc@1=92.46, Acc@5=99.88, Error@1=7.54, Error@5=0.12
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-18:25:53] [Epoch=093/600] [Need: 19:14:16] LR=0.0236 ~ 0.0236, Batch=96
|
|
train[2019-03-31-18:25:54] Epoch: [093][000/521] Time 0.84 (0.84) Data 0.56 (0.56) Loss 0.243 (0.243) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-18:26:19] Epoch: [093][100/521] Time 0.25 (0.26) Data 0.00 (0.01) Loss 0.279 (0.352) Prec@1 94.79 (92.06) Prec@5 98.96 (99.81)
|
|
train[2019-03-31-18:26:44] Epoch: [093][200/521] Time 0.26 (0.25) Data 0.00 (0.00) Loss 0.492 (0.356) Prec@1 87.50 (91.80) Prec@5 98.96 (99.83)
|
|
train[2019-03-31-18:27:09] Epoch: [093][300/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.186 (0.356) Prec@1 96.88 (91.87) Prec@5 100.00 (99.83)
|
|
train[2019-03-31-18:27:35] Epoch: [093][400/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.359 (0.364) Prec@1 91.67 (91.72) Prec@5 100.00 (99.82)
|
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train[2019-03-31-18:28:00] Epoch: [093][500/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.426 (0.366) Prec@1 92.71 (91.63) Prec@5 100.00 (99.82)
|
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train[2019-03-31-18:28:05] Epoch: [093][520/521] Time 0.22 (0.25) Data 0.00 (0.00) Loss 0.376 (0.366) Prec@1 93.75 (91.60) Prec@5 100.00 (99.82)
|
|
[2019-03-31-18:28:05] **train** Prec@1 91.60 Prec@5 99.82 Error@1 8.40 Error@5 0.18 Loss:0.366
|
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test [2019-03-31-18:28:06] Epoch: [093][000/105] Time 0.64 (0.64) Data 0.58 (0.58) Loss 0.438 (0.438) Prec@1 86.46 (86.46) Prec@5 100.00 (100.00)
|
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test [2019-03-31-18:28:10] Epoch: [093][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.231 (0.312) Prec@1 89.58 (90.38) Prec@5 100.00 (99.80)
|
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test [2019-03-31-18:28:10] Epoch: [093][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.464 (0.311) Prec@1 87.50 (90.43) Prec@5 100.00 (99.81)
|
|
[2019-03-31-18:28:10] **test** Prec@1 90.43 Prec@5 99.81 Error@1 9.57 Error@5 0.19 Loss:0.311
|
|
----> Best Accuracy : Acc@1=92.46, Acc@5=99.88, Error@1=7.54, Error@5=0.12
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-18:28:10] [Epoch=094/600] [Need: 19:14:28] LR=0.0235 ~ 0.0235, Batch=96
|
|
train[2019-03-31-18:28:11] Epoch: [094][000/521] Time 0.89 (0.89) Data 0.60 (0.60) Loss 0.298 (0.298) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-18:28:36] Epoch: [094][100/521] Time 0.25 (0.26) Data 0.00 (0.01) Loss 0.224 (0.353) Prec@1 96.88 (92.16) Prec@5 100.00 (99.76)
|
|
train[2019-03-31-18:29:01] Epoch: [094][200/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.419 (0.360) Prec@1 91.67 (91.83) Prec@5 100.00 (99.78)
|
|
train[2019-03-31-18:29:26] Epoch: [094][300/521] Time 0.26 (0.25) Data 0.00 (0.00) Loss 0.506 (0.369) Prec@1 88.54 (91.46) Prec@5 100.00 (99.81)
|
|
train[2019-03-31-18:29:52] Epoch: [094][400/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.475 (0.376) Prec@1 86.46 (91.26) Prec@5 100.00 (99.80)
|
|
train[2019-03-31-18:30:17] Epoch: [094][500/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.317 (0.375) Prec@1 90.62 (91.28) Prec@5 100.00 (99.81)
|
|
train[2019-03-31-18:30:22] Epoch: [094][520/521] Time 0.22 (0.25) Data 0.00 (0.00) Loss 0.551 (0.377) Prec@1 88.75 (91.26) Prec@5 100.00 (99.81)
|
|
[2019-03-31-18:30:22] **train** Prec@1 91.26 Prec@5 99.81 Error@1 8.74 Error@5 0.19 Loss:0.377
|
|
test [2019-03-31-18:30:22] Epoch: [094][000/105] Time 0.64 (0.64) Data 0.58 (0.58) Loss 0.262 (0.262) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-18:30:27] Epoch: [094][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.215 (0.275) Prec@1 95.83 (91.59) Prec@5 100.00 (99.76)
|
|
test [2019-03-31-18:30:27] Epoch: [094][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.078 (0.274) Prec@1 93.75 (91.59) Prec@5 100.00 (99.77)
|
|
[2019-03-31-18:30:27] **test** Prec@1 91.59 Prec@5 99.77 Error@1 8.41 Error@5 0.23 Loss:0.274
|
|
----> Best Accuracy : Acc@1=92.46, Acc@5=99.88, Error@1=7.54, Error@5=0.12
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-18:30:27] [Epoch=095/600] [Need: 19:12:57] LR=0.0235 ~ 0.0235, Batch=96
|
|
train[2019-03-31-18:30:28] Epoch: [095][000/521] Time 0.89 (0.89) Data 0.60 (0.60) Loss 0.503 (0.503) Prec@1 87.50 (87.50) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-18:30:53] Epoch: [095][100/521] Time 0.24 (0.26) Data 0.00 (0.01) Loss 0.303 (0.358) Prec@1 91.67 (91.90) Prec@5 100.00 (99.70)
|
|
train[2019-03-31-18:31:18] Epoch: [095][200/521] Time 0.26 (0.25) Data 0.00 (0.00) Loss 0.422 (0.381) Prec@1 85.42 (91.12) Prec@5 100.00 (99.76)
|
|
train[2019-03-31-18:31:43] Epoch: [095][300/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.340 (0.379) Prec@1 93.75 (91.24) Prec@5 100.00 (99.76)
|
|
train[2019-03-31-18:32:09] Epoch: [095][400/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.546 (0.377) Prec@1 85.42 (91.29) Prec@5 100.00 (99.76)
|
|
train[2019-03-31-18:32:34] Epoch: [095][500/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.360 (0.378) Prec@1 89.58 (91.28) Prec@5 100.00 (99.78)
|
|
train[2019-03-31-18:32:39] Epoch: [095][520/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.468 (0.379) Prec@1 87.50 (91.27) Prec@5 100.00 (99.78)
|
|
[2019-03-31-18:32:39] **train** Prec@1 91.27 Prec@5 99.78 Error@1 8.73 Error@5 0.22 Loss:0.379
|
|
test [2019-03-31-18:32:40] Epoch: [095][000/105] Time 0.65 (0.65) Data 0.59 (0.59) Loss 0.309 (0.309) Prec@1 92.71 (92.71) Prec@5 98.96 (98.96)
|
|
test [2019-03-31-18:32:44] Epoch: [095][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.272 (0.300) Prec@1 90.62 (90.64) Prec@5 100.00 (99.71)
|
|
test [2019-03-31-18:32:44] Epoch: [095][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.178 (0.298) Prec@1 87.50 (90.69) Prec@5 100.00 (99.72)
|
|
[2019-03-31-18:32:44] **test** Prec@1 90.69 Prec@5 99.72 Error@1 9.31 Error@5 0.28 Loss:0.298
|
|
----> Best Accuracy : Acc@1=92.46, Acc@5=99.88, Error@1=7.54, Error@5=0.12
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-18:32:44] [Epoch=096/600] [Need: 19:13:22] LR=0.0235 ~ 0.0235, Batch=96
|
|
train[2019-03-31-18:32:45] Epoch: [096][000/521] Time 0.89 (0.89) Data 0.60 (0.60) Loss 0.372 (0.372) Prec@1 91.67 (91.67) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-18:33:10] Epoch: [096][100/521] Time 0.25 (0.26) Data 0.00 (0.01) Loss 0.373 (0.388) Prec@1 89.58 (91.04) Prec@5 100.00 (99.81)
|
|
train[2019-03-31-18:33:35] Epoch: [096][200/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.396 (0.380) Prec@1 90.62 (91.24) Prec@5 100.00 (99.84)
|
|
train[2019-03-31-18:34:01] Epoch: [096][300/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.276 (0.376) Prec@1 93.75 (91.38) Prec@5 100.00 (99.82)
|
|
train[2019-03-31-18:34:25] Epoch: [096][400/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.349 (0.374) Prec@1 92.71 (91.44) Prec@5 100.00 (99.81)
|
|
train[2019-03-31-18:34:50] Epoch: [096][500/521] Time 0.27 (0.25) Data 0.00 (0.00) Loss 0.308 (0.374) Prec@1 91.67 (91.46) Prec@5 100.00 (99.82)
|
|
train[2019-03-31-18:34:55] Epoch: [096][520/521] Time 0.27 (0.25) Data 0.00 (0.00) Loss 0.399 (0.374) Prec@1 87.50 (91.49) Prec@5 100.00 (99.82)
|
|
[2019-03-31-18:34:55] **train** Prec@1 91.49 Prec@5 99.82 Error@1 8.51 Error@5 0.18 Loss:0.374
|
|
test [2019-03-31-18:34:56] Epoch: [096][000/105] Time 0.51 (0.51) Data 0.45 (0.45) Loss 0.292 (0.292) Prec@1 87.50 (87.50) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-18:35:00] Epoch: [096][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.101 (0.243) Prec@1 94.79 (92.35) Prec@5 100.00 (99.80)
|
|
test [2019-03-31-18:35:00] Epoch: [096][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.174 (0.244) Prec@1 93.75 (92.34) Prec@5 100.00 (99.81)
|
|
[2019-03-31-18:35:01] **test** Prec@1 92.34 Prec@5 99.81 Error@1 7.66 Error@5 0.19 Loss:0.244
|
|
----> Best Accuracy : Acc@1=92.46, Acc@5=99.88, Error@1=7.54, Error@5=0.12
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-18:35:01] [Epoch=097/600] [Need: 19:02:31] LR=0.0234 ~ 0.0234, Batch=96
|
|
train[2019-03-31-18:35:02] Epoch: [097][000/521] Time 0.88 (0.88) Data 0.59 (0.59) Loss 0.279 (0.279) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-18:35:27] Epoch: [097][100/521] Time 0.25 (0.26) Data 0.00 (0.01) Loss 0.480 (0.344) Prec@1 90.62 (91.82) Prec@5 100.00 (99.88)
|
|
train[2019-03-31-18:35:52] Epoch: [097][200/521] Time 0.26 (0.25) Data 0.00 (0.00) Loss 0.408 (0.355) Prec@1 90.62 (91.77) Prec@5 100.00 (99.84)
|
|
train[2019-03-31-18:36:17] Epoch: [097][300/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.337 (0.364) Prec@1 92.71 (91.56) Prec@5 100.00 (99.81)
|
|
train[2019-03-31-18:36:42] Epoch: [097][400/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.399 (0.372) Prec@1 92.71 (91.39) Prec@5 100.00 (99.81)
|
|
train[2019-03-31-18:37:07] Epoch: [097][500/521] Time 0.26 (0.25) Data 0.00 (0.00) Loss 0.603 (0.373) Prec@1 85.42 (91.32) Prec@5 100.00 (99.82)
|
|
train[2019-03-31-18:37:12] Epoch: [097][520/521] Time 0.23 (0.25) Data 0.00 (0.00) Loss 0.320 (0.374) Prec@1 92.50 (91.31) Prec@5 100.00 (99.81)
|
|
[2019-03-31-18:37:12] **train** Prec@1 91.31 Prec@5 99.81 Error@1 8.69 Error@5 0.19 Loss:0.374
|
|
test [2019-03-31-18:37:13] Epoch: [097][000/105] Time 0.56 (0.56) Data 0.50 (0.50) Loss 0.329 (0.329) Prec@1 90.62 (90.62) Prec@5 98.96 (98.96)
|
|
test [2019-03-31-18:37:17] Epoch: [097][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.236 (0.279) Prec@1 90.62 (91.39) Prec@5 100.00 (99.73)
|
|
test [2019-03-31-18:37:17] Epoch: [097][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.121 (0.279) Prec@1 93.75 (91.36) Prec@5 100.00 (99.73)
|
|
[2019-03-31-18:37:17] **test** Prec@1 91.36 Prec@5 99.73 Error@1 8.64 Error@5 0.27 Loss:0.279
|
|
----> Best Accuracy : Acc@1=92.46, Acc@5=99.88, Error@1=7.54, Error@5=0.12
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-18:37:17] [Epoch=098/600] [Need: 19:03:28] LR=0.0234 ~ 0.0234, Batch=96
|
|
train[2019-03-31-18:37:18] Epoch: [098][000/521] Time 0.77 (0.77) Data 0.49 (0.49) Loss 0.344 (0.344) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-18:37:43] Epoch: [098][100/521] Time 0.24 (0.26) Data 0.00 (0.01) Loss 0.263 (0.365) Prec@1 94.79 (91.54) Prec@5 100.00 (99.79)
|
|
train[2019-03-31-18:38:08] Epoch: [098][200/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.328 (0.369) Prec@1 93.75 (91.44) Prec@5 100.00 (99.80)
|
|
train[2019-03-31-18:38:35] Epoch: [098][300/521] Time 0.29 (0.26) Data 0.00 (0.00) Loss 0.263 (0.370) Prec@1 94.79 (91.46) Prec@5 100.00 (99.80)
|
|
train[2019-03-31-18:39:01] Epoch: [098][400/521] Time 0.25 (0.26) Data 0.00 (0.00) Loss 0.381 (0.367) Prec@1 85.42 (91.49) Prec@5 100.00 (99.80)
|
|
train[2019-03-31-18:39:28] Epoch: [098][500/521] Time 0.29 (0.26) Data 0.00 (0.00) Loss 0.233 (0.368) Prec@1 96.88 (91.51) Prec@5 100.00 (99.80)
|
|
train[2019-03-31-18:39:33] Epoch: [098][520/521] Time 0.22 (0.26) Data 0.00 (0.00) Loss 0.245 (0.367) Prec@1 93.75 (91.49) Prec@5 100.00 (99.81)
|
|
[2019-03-31-18:39:33] **train** Prec@1 91.49 Prec@5 99.81 Error@1 8.51 Error@5 0.19 Loss:0.367
|
|
test [2019-03-31-18:39:33] Epoch: [098][000/105] Time 0.48 (0.48) Data 0.41 (0.41) Loss 0.329 (0.329) Prec@1 88.54 (88.54) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-18:39:37] Epoch: [098][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.115 (0.262) Prec@1 96.88 (91.62) Prec@5 100.00 (99.80)
|
|
test [2019-03-31-18:39:38] Epoch: [098][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.194 (0.260) Prec@1 93.75 (91.63) Prec@5 100.00 (99.81)
|
|
[2019-03-31-18:39:38] **test** Prec@1 91.63 Prec@5 99.81 Error@1 8.37 Error@5 0.19 Loss:0.260
|
|
----> Best Accuracy : Acc@1=92.46, Acc@5=99.88, Error@1=7.54, Error@5=0.12
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-18:39:38] [Epoch=099/600] [Need: 19:33:05] LR=0.0234 ~ 0.0234, Batch=96
|
|
train[2019-03-31-18:39:39] Epoch: [099][000/521] Time 0.72 (0.72) Data 0.46 (0.46) Loss 0.275 (0.275) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-18:40:02] Epoch: [099][100/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.383 (0.353) Prec@1 92.71 (92.10) Prec@5 100.00 (99.82)
|
|
train[2019-03-31-18:40:26] Epoch: [099][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.244 (0.372) Prec@1 95.83 (91.71) Prec@5 100.00 (99.83)
|
|
train[2019-03-31-18:40:50] Epoch: [099][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.408 (0.374) Prec@1 93.75 (91.41) Prec@5 100.00 (99.82)
|
|
train[2019-03-31-18:41:14] Epoch: [099][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.270 (0.373) Prec@1 92.71 (91.40) Prec@5 100.00 (99.82)
|
|
train[2019-03-31-18:41:38] Epoch: [099][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.362 (0.377) Prec@1 92.71 (91.32) Prec@5 100.00 (99.81)
|
|
train[2019-03-31-18:41:42] Epoch: [099][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.400 (0.375) Prec@1 91.25 (91.35) Prec@5 100.00 (99.82)
|
|
[2019-03-31-18:41:42] **train** Prec@1 91.35 Prec@5 99.82 Error@1 8.65 Error@5 0.18 Loss:0.375
|
|
test [2019-03-31-18:41:43] Epoch: [099][000/105] Time 0.50 (0.50) Data 0.43 (0.43) Loss 0.194 (0.194) Prec@1 91.67 (91.67) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-18:41:47] Epoch: [099][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.100 (0.235) Prec@1 94.79 (92.54) Prec@5 100.00 (99.83)
|
|
test [2019-03-31-18:41:47] Epoch: [099][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.083 (0.233) Prec@1 93.75 (92.58) Prec@5 100.00 (99.84)
|
|
[2019-03-31-18:41:47] **test** Prec@1 92.58 Prec@5 99.84 Error@1 7.42 Error@5 0.16 Loss:0.233
|
|
----> Best Accuracy : Acc@1=92.58, Acc@5=99.84, Error@1=7.42, Error@5=0.16
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-18:41:47] [Epoch=100/600] [Need: 17:59:32] LR=0.0233 ~ 0.0233, Batch=96
|
|
train[2019-03-31-18:41:48] Epoch: [100][000/521] Time 0.85 (0.85) Data 0.58 (0.58) Loss 0.470 (0.470) Prec@1 87.50 (87.50) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-18:42:14] Epoch: [100][100/521] Time 0.23 (0.26) Data 0.00 (0.01) Loss 0.277 (0.357) Prec@1 95.83 (91.75) Prec@5 100.00 (99.83)
|
|
train[2019-03-31-18:42:41] Epoch: [100][200/521] Time 0.30 (0.26) Data 0.00 (0.00) Loss 0.385 (0.364) Prec@1 90.62 (91.50) Prec@5 100.00 (99.82)
|
|
train[2019-03-31-18:43:07] Epoch: [100][300/521] Time 0.27 (0.27) Data 0.00 (0.00) Loss 0.477 (0.370) Prec@1 90.62 (91.25) Prec@5 100.00 (99.83)
|
|
train[2019-03-31-18:43:33] Epoch: [100][400/521] Time 0.24 (0.26) Data 0.00 (0.00) Loss 0.394 (0.368) Prec@1 90.62 (91.40) Prec@5 100.00 (99.81)
|
|
train[2019-03-31-18:43:57] Epoch: [100][500/521] Time 0.24 (0.26) Data 0.00 (0.00) Loss 0.285 (0.370) Prec@1 93.75 (91.35) Prec@5 100.00 (99.77)
|
|
train[2019-03-31-18:44:01] Epoch: [100][520/521] Time 0.22 (0.26) Data 0.00 (0.00) Loss 0.340 (0.372) Prec@1 88.75 (91.31) Prec@5 100.00 (99.77)
|
|
[2019-03-31-18:44:02] **train** Prec@1 91.31 Prec@5 99.77 Error@1 8.69 Error@5 0.23 Loss:0.372
|
|
test [2019-03-31-18:44:02] Epoch: [100][000/105] Time 0.47 (0.47) Data 0.40 (0.40) Loss 0.293 (0.293) Prec@1 88.54 (88.54) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-18:44:06] Epoch: [100][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.164 (0.328) Prec@1 94.79 (89.98) Prec@5 100.00 (99.72)
|
|
test [2019-03-31-18:44:06] Epoch: [100][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.309 (0.326) Prec@1 87.50 (90.05) Prec@5 100.00 (99.73)
|
|
[2019-03-31-18:44:06] **test** Prec@1 90.05 Prec@5 99.73 Error@1 9.95 Error@5 0.27 Loss:0.326
|
|
----> Best Accuracy : Acc@1=92.58, Acc@5=99.84, Error@1=7.42, Error@5=0.16
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-18:44:07] [Epoch=101/600] [Need: 19:16:50] LR=0.0233 ~ 0.0233, Batch=96
|
|
train[2019-03-31-18:44:07] Epoch: [101][000/521] Time 0.71 (0.71) Data 0.44 (0.44) Loss 0.406 (0.406) Prec@1 91.67 (91.67) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-18:44:31] Epoch: [101][100/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.265 (0.385) Prec@1 94.79 (90.83) Prec@5 100.00 (99.78)
|
|
train[2019-03-31-18:44:56] Epoch: [101][200/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.384 (0.376) Prec@1 89.58 (91.01) Prec@5 100.00 (99.82)
|
|
train[2019-03-31-18:45:22] Epoch: [101][300/521] Time 0.26 (0.25) Data 0.00 (0.00) Loss 0.580 (0.375) Prec@1 87.50 (91.17) Prec@5 100.00 (99.82)
|
|
train[2019-03-31-18:45:47] Epoch: [101][400/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.213 (0.372) Prec@1 94.79 (91.25) Prec@5 100.00 (99.80)
|
|
train[2019-03-31-18:46:12] Epoch: [101][500/521] Time 0.26 (0.25) Data 0.00 (0.00) Loss 0.446 (0.372) Prec@1 87.50 (91.31) Prec@5 98.96 (99.80)
|
|
train[2019-03-31-18:46:17] Epoch: [101][520/521] Time 0.23 (0.25) Data 0.00 (0.00) Loss 0.436 (0.372) Prec@1 92.50 (91.31) Prec@5 100.00 (99.81)
|
|
[2019-03-31-18:46:17] **train** Prec@1 91.31 Prec@5 99.81 Error@1 8.69 Error@5 0.19 Loss:0.372
|
|
test [2019-03-31-18:46:18] Epoch: [101][000/105] Time 0.65 (0.65) Data 0.58 (0.58) Loss 0.230 (0.230) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-18:46:22] Epoch: [101][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.176 (0.301) Prec@1 94.79 (90.47) Prec@5 100.00 (99.67)
|
|
test [2019-03-31-18:46:22] Epoch: [101][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.395 (0.300) Prec@1 93.75 (90.46) Prec@5 100.00 (99.68)
|
|
[2019-03-31-18:46:22] **test** Prec@1 90.46 Prec@5 99.68 Error@1 9.54 Error@5 0.32 Loss:0.300
|
|
----> Best Accuracy : Acc@1=92.58, Acc@5=99.84, Error@1=7.42, Error@5=0.16
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-18:46:22] [Epoch=102/600] [Need: 18:46:27] LR=0.0233 ~ 0.0233, Batch=96
|
|
train[2019-03-31-18:46:23] Epoch: [102][000/521] Time 0.77 (0.77) Data 0.45 (0.45) Loss 0.433 (0.433) Prec@1 91.67 (91.67) Prec@5 98.96 (98.96)
|
|
train[2019-03-31-18:46:48] Epoch: [102][100/521] Time 0.25 (0.26) Data 0.00 (0.00) Loss 0.224 (0.369) Prec@1 96.88 (91.67) Prec@5 100.00 (99.75)
|
|
train[2019-03-31-18:47:13] Epoch: [102][200/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.492 (0.365) Prec@1 88.54 (91.73) Prec@5 100.00 (99.76)
|
|
train[2019-03-31-18:47:38] Epoch: [102][300/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.446 (0.363) Prec@1 92.71 (91.75) Prec@5 100.00 (99.80)
|
|
train[2019-03-31-18:48:03] Epoch: [102][400/521] Time 0.26 (0.25) Data 0.00 (0.00) Loss 0.178 (0.367) Prec@1 96.88 (91.69) Prec@5 100.00 (99.80)
|
|
train[2019-03-31-18:48:28] Epoch: [102][500/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.432 (0.372) Prec@1 88.54 (91.56) Prec@5 98.96 (99.78)
|
|
train[2019-03-31-18:48:33] Epoch: [102][520/521] Time 0.23 (0.25) Data 0.00 (0.00) Loss 0.380 (0.371) Prec@1 91.25 (91.57) Prec@5 100.00 (99.78)
|
|
[2019-03-31-18:48:33] **train** Prec@1 91.57 Prec@5 99.78 Error@1 8.43 Error@5 0.22 Loss:0.371
|
|
test [2019-03-31-18:48:34] Epoch: [102][000/105] Time 0.62 (0.62) Data 0.55 (0.55) Loss 0.130 (0.130) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-18:48:38] Epoch: [102][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.212 (0.244) Prec@1 93.75 (92.41) Prec@5 100.00 (99.69)
|
|
test [2019-03-31-18:48:38] Epoch: [102][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.131 (0.244) Prec@1 100.00 (92.41) Prec@5 100.00 (99.69)
|
|
[2019-03-31-18:48:39] **test** Prec@1 92.41 Prec@5 99.69 Error@1 7.59 Error@5 0.31 Loss:0.244
|
|
----> Best Accuracy : Acc@1=92.58, Acc@5=99.84, Error@1=7.42, Error@5=0.16
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-18:48:39] [Epoch=103/600] [Need: 18:50:54] LR=0.0232 ~ 0.0232, Batch=96
|
|
train[2019-03-31-18:48:40] Epoch: [103][000/521] Time 0.87 (0.87) Data 0.59 (0.59) Loss 0.346 (0.346) Prec@1 93.75 (93.75) Prec@5 98.96 (98.96)
|
|
train[2019-03-31-18:49:05] Epoch: [103][100/521] Time 0.25 (0.26) Data 0.00 (0.01) Loss 0.288 (0.347) Prec@1 94.79 (92.39) Prec@5 100.00 (99.86)
|
|
train[2019-03-31-18:49:30] Epoch: [103][200/521] Time 0.25 (0.26) Data 0.00 (0.00) Loss 0.342 (0.361) Prec@1 90.62 (91.84) Prec@5 98.96 (99.80)
|
|
train[2019-03-31-18:49:56] Epoch: [103][300/521] Time 0.25 (0.26) Data 0.00 (0.00) Loss 0.359 (0.359) Prec@1 91.67 (91.81) Prec@5 100.00 (99.80)
|
|
train[2019-03-31-18:50:21] Epoch: [103][400/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.334 (0.362) Prec@1 90.62 (91.71) Prec@5 100.00 (99.79)
|
|
train[2019-03-31-18:50:46] Epoch: [103][500/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.449 (0.366) Prec@1 88.54 (91.55) Prec@5 100.00 (99.80)
|
|
train[2019-03-31-18:50:51] Epoch: [103][520/521] Time 0.23 (0.25) Data 0.00 (0.00) Loss 0.285 (0.366) Prec@1 91.25 (91.54) Prec@5 100.00 (99.80)
|
|
[2019-03-31-18:50:51] **train** Prec@1 91.54 Prec@5 99.80 Error@1 8.46 Error@5 0.20 Loss:0.366
|
|
test [2019-03-31-18:50:52] Epoch: [103][000/105] Time 0.63 (0.63) Data 0.56 (0.56) Loss 0.378 (0.378) Prec@1 88.54 (88.54) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-18:50:56] Epoch: [103][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.108 (0.257) Prec@1 96.88 (92.11) Prec@5 100.00 (99.76)
|
|
test [2019-03-31-18:50:56] Epoch: [103][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.151 (0.256) Prec@1 93.75 (92.09) Prec@5 100.00 (99.76)
|
|
[2019-03-31-18:50:56] **test** Prec@1 92.09 Prec@5 99.76 Error@1 7.91 Error@5 0.24 Loss:0.256
|
|
----> Best Accuracy : Acc@1=92.58, Acc@5=99.84, Error@1=7.42, Error@5=0.16
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-18:50:56] [Epoch=104/600] [Need: 18:56:35] LR=0.0232 ~ 0.0232, Batch=96
|
|
train[2019-03-31-18:50:57] Epoch: [104][000/521] Time 0.75 (0.75) Data 0.47 (0.47) Loss 0.454 (0.454) Prec@1 88.54 (88.54) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-18:51:22] Epoch: [104][100/521] Time 0.26 (0.25) Data 0.00 (0.00) Loss 0.367 (0.382) Prec@1 91.67 (91.20) Prec@5 100.00 (99.76)
|
|
train[2019-03-31-18:51:47] Epoch: [104][200/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.388 (0.370) Prec@1 92.71 (91.41) Prec@5 100.00 (99.78)
|
|
train[2019-03-31-18:52:13] Epoch: [104][300/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.213 (0.370) Prec@1 95.83 (91.38) Prec@5 100.00 (99.79)
|
|
train[2019-03-31-18:52:38] Epoch: [104][400/521] Time 0.26 (0.25) Data 0.00 (0.00) Loss 0.485 (0.372) Prec@1 89.58 (91.26) Prec@5 98.96 (99.81)
|
|
train[2019-03-31-18:53:03] Epoch: [104][500/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.299 (0.374) Prec@1 93.75 (91.24) Prec@5 100.00 (99.80)
|
|
train[2019-03-31-18:53:08] Epoch: [104][520/521] Time 0.23 (0.25) Data 0.00 (0.00) Loss 0.489 (0.374) Prec@1 91.25 (91.28) Prec@5 100.00 (99.81)
|
|
[2019-03-31-18:53:08] **train** Prec@1 91.28 Prec@5 99.81 Error@1 8.72 Error@5 0.19 Loss:0.374
|
|
test [2019-03-31-18:53:08] Epoch: [104][000/105] Time 0.55 (0.55) Data 0.48 (0.48) Loss 0.135 (0.135) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-18:53:13] Epoch: [104][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.077 (0.264) Prec@1 97.92 (92.11) Prec@5 100.00 (99.74)
|
|
test [2019-03-31-18:53:13] Epoch: [104][104/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.284 (0.264) Prec@1 93.75 (92.11) Prec@5 100.00 (99.75)
|
|
[2019-03-31-18:53:13] **test** Prec@1 92.11 Prec@5 99.75 Error@1 7.89 Error@5 0.25 Loss:0.264
|
|
----> Best Accuracy : Acc@1=92.58, Acc@5=99.84, Error@1=7.42, Error@5=0.16
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-18:53:13] [Epoch=105/600] [Need: 18:48:48] LR=0.0232 ~ 0.0232, Batch=96
|
|
train[2019-03-31-18:53:14] Epoch: [105][000/521] Time 0.76 (0.76) Data 0.47 (0.47) Loss 0.338 (0.338) Prec@1 90.62 (90.62) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-18:53:39] Epoch: [105][100/521] Time 0.25 (0.26) Data 0.00 (0.00) Loss 0.183 (0.355) Prec@1 97.92 (91.74) Prec@5 100.00 (99.73)
|
|
train[2019-03-31-18:54:04] Epoch: [105][200/521] Time 0.26 (0.25) Data 0.00 (0.00) Loss 0.240 (0.349) Prec@1 93.75 (91.94) Prec@5 100.00 (99.78)
|
|
train[2019-03-31-18:54:29] Epoch: [105][300/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.201 (0.355) Prec@1 95.83 (91.79) Prec@5 100.00 (99.79)
|
|
train[2019-03-31-18:54:54] Epoch: [105][400/521] Time 0.26 (0.25) Data 0.00 (0.00) Loss 0.433 (0.358) Prec@1 89.58 (91.73) Prec@5 100.00 (99.80)
|
|
train[2019-03-31-18:55:19] Epoch: [105][500/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.394 (0.361) Prec@1 89.58 (91.62) Prec@5 100.00 (99.79)
|
|
train[2019-03-31-18:55:24] Epoch: [105][520/521] Time 0.26 (0.25) Data 0.00 (0.00) Loss 0.333 (0.361) Prec@1 93.75 (91.61) Prec@5 100.00 (99.80)
|
|
[2019-03-31-18:55:25] **train** Prec@1 91.61 Prec@5 99.80 Error@1 8.39 Error@5 0.20 Loss:0.361
|
|
test [2019-03-31-18:55:25] Epoch: [105][000/105] Time 0.51 (0.51) Data 0.44 (0.44) Loss 0.283 (0.283) Prec@1 89.58 (89.58) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-18:55:29] Epoch: [105][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.133 (0.242) Prec@1 95.83 (92.25) Prec@5 100.00 (99.81)
|
|
test [2019-03-31-18:55:29] Epoch: [105][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.129 (0.241) Prec@1 93.75 (92.23) Prec@5 100.00 (99.82)
|
|
[2019-03-31-18:55:30] **test** Prec@1 92.23 Prec@5 99.82 Error@1 7.77 Error@5 0.18 Loss:0.241
|
|
----> Best Accuracy : Acc@1=92.58, Acc@5=99.84, Error@1=7.42, Error@5=0.16
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-18:55:30] [Epoch=106/600] [Need: 18:44:42] LR=0.0231 ~ 0.0231, Batch=96
|
|
train[2019-03-31-18:55:30] Epoch: [106][000/521] Time 0.80 (0.80) Data 0.50 (0.50) Loss 0.252 (0.252) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-18:55:55] Epoch: [106][100/521] Time 0.24 (0.25) Data 0.00 (0.01) Loss 0.475 (0.364) Prec@1 90.62 (91.64) Prec@5 98.96 (99.81)
|
|
train[2019-03-31-18:56:20] Epoch: [106][200/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.287 (0.356) Prec@1 94.79 (91.91) Prec@5 100.00 (99.82)
|
|
train[2019-03-31-18:56:45] Epoch: [106][300/521] Time 0.26 (0.25) Data 0.00 (0.00) Loss 0.385 (0.352) Prec@1 89.58 (91.99) Prec@5 98.96 (99.80)
|
|
train[2019-03-31-18:57:10] Epoch: [106][400/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.227 (0.356) Prec@1 92.71 (91.84) Prec@5 100.00 (99.81)
|
|
train[2019-03-31-18:57:35] Epoch: [106][500/521] Time 0.26 (0.25) Data 0.00 (0.00) Loss 0.420 (0.360) Prec@1 90.62 (91.78) Prec@5 98.96 (99.81)
|
|
train[2019-03-31-18:57:40] Epoch: [106][520/521] Time 0.23 (0.25) Data 0.00 (0.00) Loss 0.144 (0.360) Prec@1 100.00 (91.77) Prec@5 100.00 (99.81)
|
|
[2019-03-31-18:57:40] **train** Prec@1 91.77 Prec@5 99.81 Error@1 8.23 Error@5 0.19 Loss:0.360
|
|
test [2019-03-31-18:57:41] Epoch: [106][000/105] Time 0.59 (0.59) Data 0.52 (0.52) Loss 0.303 (0.303) Prec@1 91.67 (91.67) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-18:57:45] Epoch: [106][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.109 (0.270) Prec@1 92.71 (91.76) Prec@5 100.00 (99.80)
|
|
test [2019-03-31-18:57:45] Epoch: [106][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.378 (0.269) Prec@1 93.75 (91.77) Prec@5 100.00 (99.81)
|
|
[2019-03-31-18:57:45] **test** Prec@1 91.77 Prec@5 99.81 Error@1 8.23 Error@5 0.19 Loss:0.269
|
|
----> Best Accuracy : Acc@1=92.58, Acc@5=99.84, Error@1=7.42, Error@5=0.16
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-18:57:45] [Epoch=107/600] [Need: 18:34:58] LR=0.0231 ~ 0.0231, Batch=96
|
|
train[2019-03-31-18:57:46] Epoch: [107][000/521] Time 0.89 (0.89) Data 0.61 (0.61) Loss 0.475 (0.475) Prec@1 88.54 (88.54) Prec@5 98.96 (98.96)
|
|
train[2019-03-31-18:58:11] Epoch: [107][100/521] Time 0.24 (0.25) Data 0.00 (0.01) Loss 0.261 (0.349) Prec@1 94.79 (91.88) Prec@5 100.00 (99.83)
|
|
train[2019-03-31-18:58:36] Epoch: [107][200/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.465 (0.358) Prec@1 90.62 (91.71) Prec@5 100.00 (99.78)
|
|
train[2019-03-31-18:59:01] Epoch: [107][300/521] Time 0.26 (0.25) Data 0.00 (0.00) Loss 0.306 (0.359) Prec@1 91.67 (91.67) Prec@5 100.00 (99.79)
|
|
train[2019-03-31-18:59:27] Epoch: [107][400/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.352 (0.360) Prec@1 90.62 (91.67) Prec@5 100.00 (99.80)
|
|
train[2019-03-31-18:59:52] Epoch: [107][500/521] Time 0.27 (0.25) Data 0.00 (0.00) Loss 0.374 (0.366) Prec@1 90.62 (91.51) Prec@5 100.00 (99.79)
|
|
train[2019-03-31-18:59:57] Epoch: [107][520/521] Time 0.23 (0.25) Data 0.00 (0.00) Loss 0.330 (0.367) Prec@1 88.75 (91.52) Prec@5 100.00 (99.78)
|
|
[2019-03-31-18:59:57] **train** Prec@1 91.52 Prec@5 99.78 Error@1 8.48 Error@5 0.22 Loss:0.367
|
|
test [2019-03-31-18:59:57] Epoch: [107][000/105] Time 0.50 (0.50) Data 0.43 (0.43) Loss 0.213 (0.213) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-19:00:01] Epoch: [107][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.151 (0.228) Prec@1 94.79 (92.80) Prec@5 100.00 (99.83)
|
|
test [2019-03-31-19:00:02] Epoch: [107][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.024 (0.227) Prec@1 100.00 (92.82) Prec@5 100.00 (99.84)
|
|
[2019-03-31-19:00:02] **test** Prec@1 92.82 Prec@5 99.84 Error@1 7.18 Error@5 0.16 Loss:0.227
|
|
----> Best Accuracy : Acc@1=92.82, Acc@5=99.84, Error@1=7.18, Error@5=0.16
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-19:00:02] [Epoch=108/600] [Need: 18:39:35] LR=0.0231 ~ 0.0231, Batch=96
|
|
train[2019-03-31-19:00:03] Epoch: [108][000/521] Time 0.89 (0.89) Data 0.60 (0.60) Loss 0.502 (0.502) Prec@1 87.50 (87.50) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-19:00:28] Epoch: [108][100/521] Time 0.25 (0.26) Data 0.00 (0.01) Loss 0.375 (0.368) Prec@1 91.67 (91.49) Prec@5 100.00 (99.78)
|
|
train[2019-03-31-19:00:53] Epoch: [108][200/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.360 (0.368) Prec@1 90.62 (91.67) Prec@5 100.00 (99.75)
|
|
train[2019-03-31-19:01:18] Epoch: [108][300/521] Time 0.26 (0.25) Data 0.00 (0.00) Loss 0.523 (0.374) Prec@1 87.50 (91.41) Prec@5 100.00 (99.78)
|
|
train[2019-03-31-19:01:43] Epoch: [108][400/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.600 (0.371) Prec@1 87.50 (91.54) Prec@5 100.00 (99.79)
|
|
train[2019-03-31-19:02:08] Epoch: [108][500/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.340 (0.370) Prec@1 92.71 (91.52) Prec@5 100.00 (99.77)
|
|
train[2019-03-31-19:02:13] Epoch: [108][520/521] Time 0.23 (0.25) Data 0.00 (0.00) Loss 0.262 (0.369) Prec@1 93.75 (91.56) Prec@5 100.00 (99.77)
|
|
[2019-03-31-19:02:13] **train** Prec@1 91.56 Prec@5 99.77 Error@1 8.44 Error@5 0.23 Loss:0.369
|
|
test [2019-03-31-19:02:13] Epoch: [108][000/105] Time 0.51 (0.51) Data 0.44 (0.44) Loss 0.242 (0.242) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-19:02:18] Epoch: [108][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.162 (0.236) Prec@1 92.71 (92.67) Prec@5 100.00 (99.75)
|
|
test [2019-03-31-19:02:18] Epoch: [108][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.196 (0.236) Prec@1 93.75 (92.65) Prec@5 100.00 (99.76)
|
|
[2019-03-31-19:02:18] **test** Prec@1 92.65 Prec@5 99.76 Error@1 7.35 Error@5 0.24 Loss:0.236
|
|
----> Best Accuracy : Acc@1=92.82, Acc@5=99.84, Error@1=7.18, Error@5=0.16
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-19:02:18] [Epoch=109/600] [Need: 18:33:33] LR=0.0230 ~ 0.0230, Batch=96
|
|
train[2019-03-31-19:02:19] Epoch: [109][000/521] Time 0.71 (0.71) Data 0.44 (0.44) Loss 0.331 (0.331) Prec@1 89.58 (89.58) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-19:02:44] Epoch: [109][100/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.277 (0.345) Prec@1 94.79 (92.19) Prec@5 100.00 (99.83)
|
|
train[2019-03-31-19:03:09] Epoch: [109][200/521] Time 0.26 (0.25) Data 0.00 (0.00) Loss 0.389 (0.359) Prec@1 90.62 (91.69) Prec@5 100.00 (99.84)
|
|
train[2019-03-31-19:03:34] Epoch: [109][300/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.377 (0.360) Prec@1 88.54 (91.64) Prec@5 100.00 (99.84)
|
|
train[2019-03-31-19:03:59] Epoch: [109][400/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.623 (0.358) Prec@1 82.29 (91.72) Prec@5 100.00 (99.83)
|
|
train[2019-03-31-19:04:24] Epoch: [109][500/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.522 (0.365) Prec@1 86.46 (91.53) Prec@5 100.00 (99.82)
|
|
train[2019-03-31-19:04:29] Epoch: [109][520/521] Time 0.23 (0.25) Data 0.00 (0.00) Loss 0.226 (0.367) Prec@1 95.00 (91.48) Prec@5 100.00 (99.82)
|
|
[2019-03-31-19:04:29] **train** Prec@1 91.48 Prec@5 99.82 Error@1 8.52 Error@5 0.18 Loss:0.367
|
|
test [2019-03-31-19:04:30] Epoch: [109][000/105] Time 0.64 (0.64) Data 0.57 (0.57) Loss 0.219 (0.219) Prec@1 89.58 (89.58) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-19:04:34] Epoch: [109][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.104 (0.257) Prec@1 95.83 (92.05) Prec@5 100.00 (99.73)
|
|
test [2019-03-31-19:04:34] Epoch: [109][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.140 (0.254) Prec@1 87.50 (92.11) Prec@5 100.00 (99.74)
|
|
[2019-03-31-19:04:34] **test** Prec@1 92.11 Prec@5 99.74 Error@1 7.89 Error@5 0.26 Loss:0.254
|
|
----> Best Accuracy : Acc@1=92.82, Acc@5=99.84, Error@1=7.18, Error@5=0.16
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-19:04:34] [Epoch=110/600] [Need: 18:32:58] LR=0.0230 ~ 0.0230, Batch=96
|
|
train[2019-03-31-19:04:35] Epoch: [110][000/521] Time 0.74 (0.74) Data 0.46 (0.46) Loss 0.455 (0.455) Prec@1 90.62 (90.62) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-19:05:00] Epoch: [110][100/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.319 (0.352) Prec@1 91.67 (92.01) Prec@5 100.00 (99.81)
|
|
train[2019-03-31-19:05:25] Epoch: [110][200/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.327 (0.350) Prec@1 89.58 (91.88) Prec@5 100.00 (99.82)
|
|
train[2019-03-31-19:05:50] Epoch: [110][300/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.424 (0.351) Prec@1 89.58 (91.85) Prec@5 98.96 (99.84)
|
|
train[2019-03-31-19:06:15] Epoch: [110][400/521] Time 0.29 (0.25) Data 0.00 (0.00) Loss 0.668 (0.354) Prec@1 83.33 (91.87) Prec@5 98.96 (99.84)
|
|
train[2019-03-31-19:06:41] Epoch: [110][500/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.381 (0.356) Prec@1 90.62 (91.84) Prec@5 100.00 (99.83)
|
|
train[2019-03-31-19:06:46] Epoch: [110][520/521] Time 0.23 (0.25) Data 0.00 (0.00) Loss 0.602 (0.357) Prec@1 83.75 (91.83) Prec@5 100.00 (99.84)
|
|
[2019-03-31-19:06:46] **train** Prec@1 91.83 Prec@5 99.84 Error@1 8.17 Error@5 0.16 Loss:0.357
|
|
test [2019-03-31-19:06:46] Epoch: [110][000/105] Time 0.53 (0.53) Data 0.46 (0.46) Loss 0.246 (0.246) Prec@1 90.62 (90.62) Prec@5 98.96 (98.96)
|
|
test [2019-03-31-19:06:51] Epoch: [110][100/105] Time 0.05 (0.05) Data 0.00 (0.00) Loss 0.135 (0.257) Prec@1 94.79 (92.43) Prec@5 100.00 (99.71)
|
|
test [2019-03-31-19:06:51] Epoch: [110][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.238 (0.258) Prec@1 81.25 (92.36) Prec@5 100.00 (99.70)
|
|
[2019-03-31-19:06:51] **test** Prec@1 92.36 Prec@5 99.70 Error@1 7.64 Error@5 0.30 Loss:0.258
|
|
----> Best Accuracy : Acc@1=92.82, Acc@5=99.84, Error@1=7.18, Error@5=0.16
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-19:06:51] [Epoch=111/600] [Need: 18:34:18] LR=0.0230 ~ 0.0230, Batch=96
|
|
train[2019-03-31-19:06:52] Epoch: [111][000/521] Time 0.91 (0.91) Data 0.61 (0.61) Loss 0.258 (0.258) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-19:07:17] Epoch: [111][100/521] Time 0.25 (0.26) Data 0.00 (0.01) Loss 0.274 (0.343) Prec@1 92.71 (92.17) Prec@5 100.00 (99.81)
|
|
train[2019-03-31-19:07:43] Epoch: [111][200/521] Time 0.25 (0.26) Data 0.00 (0.00) Loss 0.388 (0.349) Prec@1 89.58 (91.92) Prec@5 100.00 (99.83)
|
|
train[2019-03-31-19:08:08] Epoch: [111][300/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.234 (0.350) Prec@1 94.79 (91.79) Prec@5 100.00 (99.83)
|
|
train[2019-03-31-19:08:33] Epoch: [111][400/521] Time 0.26 (0.25) Data 0.00 (0.00) Loss 0.406 (0.354) Prec@1 89.58 (91.74) Prec@5 100.00 (99.85)
|
|
train[2019-03-31-19:08:58] Epoch: [111][500/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.370 (0.358) Prec@1 92.71 (91.61) Prec@5 100.00 (99.85)
|
|
train[2019-03-31-19:09:03] Epoch: [111][520/521] Time 0.23 (0.25) Data 0.00 (0.00) Loss 0.406 (0.358) Prec@1 90.00 (91.64) Prec@5 100.00 (99.85)
|
|
[2019-03-31-19:09:03] **train** Prec@1 91.64 Prec@5 99.85 Error@1 8.36 Error@5 0.15 Loss:0.358
|
|
test [2019-03-31-19:09:04] Epoch: [111][000/105] Time 0.64 (0.64) Data 0.58 (0.58) Loss 0.180 (0.180) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-19:09:08] Epoch: [111][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.101 (0.244) Prec@1 96.88 (92.50) Prec@5 100.00 (99.86)
|
|
test [2019-03-31-19:09:08] Epoch: [111][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.194 (0.242) Prec@1 93.75 (92.52) Prec@5 100.00 (99.86)
|
|
[2019-03-31-19:09:08] **test** Prec@1 92.52 Prec@5 99.86 Error@1 7.48 Error@5 0.14 Loss:0.242
|
|
----> Best Accuracy : Acc@1=92.82, Acc@5=99.84, Error@1=7.18, Error@5=0.16
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-19:09:08] [Epoch=112/600] [Need: 18:37:09] LR=0.0229 ~ 0.0229, Batch=96
|
|
train[2019-03-31-19:09:09] Epoch: [112][000/521] Time 0.76 (0.76) Data 0.48 (0.48) Loss 0.435 (0.435) Prec@1 88.54 (88.54) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-19:09:34] Epoch: [112][100/521] Time 0.24 (0.26) Data 0.00 (0.00) Loss 0.344 (0.364) Prec@1 91.67 (91.46) Prec@5 100.00 (99.83)
|
|
train[2019-03-31-19:09:59] Epoch: [112][200/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.438 (0.362) Prec@1 89.58 (91.47) Prec@5 98.96 (99.83)
|
|
train[2019-03-31-19:10:24] Epoch: [112][300/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.460 (0.363) Prec@1 88.54 (91.49) Prec@5 100.00 (99.81)
|
|
train[2019-03-31-19:10:50] Epoch: [112][400/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.531 (0.363) Prec@1 87.50 (91.48) Prec@5 98.96 (99.81)
|
|
train[2019-03-31-19:11:15] Epoch: [112][500/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.490 (0.362) Prec@1 90.62 (91.52) Prec@5 100.00 (99.82)
|
|
train[2019-03-31-19:11:20] Epoch: [112][520/521] Time 0.23 (0.25) Data 0.00 (0.00) Loss 0.365 (0.361) Prec@1 92.50 (91.56) Prec@5 100.00 (99.83)
|
|
[2019-03-31-19:11:20] **train** Prec@1 91.56 Prec@5 99.83 Error@1 8.44 Error@5 0.17 Loss:0.361
|
|
test [2019-03-31-19:11:21] Epoch: [112][000/105] Time 0.53 (0.53) Data 0.45 (0.45) Loss 0.209 (0.209) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-19:11:25] Epoch: [112][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.201 (0.254) Prec@1 93.75 (91.93) Prec@5 100.00 (99.78)
|
|
test [2019-03-31-19:11:25] Epoch: [112][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.268 (0.254) Prec@1 81.25 (91.92) Prec@5 100.00 (99.79)
|
|
[2019-03-31-19:11:25] **test** Prec@1 91.92 Prec@5 99.79 Error@1 8.08 Error@5 0.21 Loss:0.254
|
|
----> Best Accuracy : Acc@1=92.82, Acc@5=99.84, Error@1=7.18, Error@5=0.16
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-19:11:25] [Epoch=113/600] [Need: 18:31:07] LR=0.0229 ~ 0.0229, Batch=96
|
|
train[2019-03-31-19:11:26] Epoch: [113][000/521] Time 0.88 (0.88) Data 0.59 (0.59) Loss 0.461 (0.461) Prec@1 86.46 (86.46) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-19:11:51] Epoch: [113][100/521] Time 0.27 (0.25) Data 0.00 (0.01) Loss 0.343 (0.351) Prec@1 90.62 (91.66) Prec@5 100.00 (99.81)
|
|
train[2019-03-31-19:12:17] Epoch: [113][200/521] Time 0.24 (0.26) Data 0.00 (0.00) Loss 0.477 (0.346) Prec@1 85.42 (91.80) Prec@5 100.00 (99.79)
|
|
train[2019-03-31-19:12:42] Epoch: [113][300/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.209 (0.350) Prec@1 94.79 (91.84) Prec@5 100.00 (99.79)
|
|
train[2019-03-31-19:13:07] Epoch: [113][400/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.389 (0.356) Prec@1 88.54 (91.66) Prec@5 100.00 (99.78)
|
|
train[2019-03-31-19:13:32] Epoch: [113][500/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.388 (0.359) Prec@1 89.58 (91.65) Prec@5 100.00 (99.78)
|
|
train[2019-03-31-19:13:37] Epoch: [113][520/521] Time 0.23 (0.25) Data 0.00 (0.00) Loss 0.376 (0.360) Prec@1 90.00 (91.63) Prec@5 98.75 (99.78)
|
|
[2019-03-31-19:13:37] **train** Prec@1 91.63 Prec@5 99.78 Error@1 8.37 Error@5 0.22 Loss:0.360
|
|
test [2019-03-31-19:13:38] Epoch: [113][000/105] Time 0.51 (0.51) Data 0.44 (0.44) Loss 0.220 (0.220) Prec@1 91.67 (91.67) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-19:13:42] Epoch: [113][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.153 (0.240) Prec@1 92.71 (92.56) Prec@5 100.00 (99.77)
|
|
test [2019-03-31-19:13:42] Epoch: [113][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.026 (0.240) Prec@1 100.00 (92.56) Prec@5 100.00 (99.78)
|
|
[2019-03-31-19:13:42] **test** Prec@1 92.56 Prec@5 99.78 Error@1 7.44 Error@5 0.22 Loss:0.240
|
|
----> Best Accuracy : Acc@1=92.82, Acc@5=99.84, Error@1=7.18, Error@5=0.16
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-19:13:42] [Epoch=114/600] [Need: 18:31:38] LR=0.0228 ~ 0.0228, Batch=96
|
|
train[2019-03-31-19:13:43] Epoch: [114][000/521] Time 0.84 (0.84) Data 0.54 (0.54) Loss 0.367 (0.367) Prec@1 91.67 (91.67) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-19:14:08] Epoch: [114][100/521] Time 0.25 (0.26) Data 0.00 (0.01) Loss 0.297 (0.350) Prec@1 93.75 (91.80) Prec@5 98.96 (99.85)
|
|
train[2019-03-31-19:14:33] Epoch: [114][200/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.381 (0.360) Prec@1 92.71 (91.68) Prec@5 100.00 (99.82)
|
|
train[2019-03-31-19:14:58] Epoch: [114][300/521] Time 0.26 (0.25) Data 0.00 (0.00) Loss 0.405 (0.357) Prec@1 89.58 (91.78) Prec@5 100.00 (99.82)
|
|
train[2019-03-31-19:15:24] Epoch: [114][400/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.317 (0.362) Prec@1 93.75 (91.73) Prec@5 98.96 (99.81)
|
|
train[2019-03-31-19:15:49] Epoch: [114][500/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.342 (0.365) Prec@1 91.67 (91.64) Prec@5 100.00 (99.80)
|
|
train[2019-03-31-19:15:54] Epoch: [114][520/521] Time 0.22 (0.25) Data 0.00 (0.00) Loss 0.253 (0.364) Prec@1 96.25 (91.67) Prec@5 100.00 (99.80)
|
|
[2019-03-31-19:15:54] **train** Prec@1 91.67 Prec@5 99.80 Error@1 8.33 Error@5 0.20 Loss:0.364
|
|
test [2019-03-31-19:15:54] Epoch: [114][000/105] Time 0.63 (0.63) Data 0.56 (0.56) Loss 0.204 (0.204) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-19:15:59] Epoch: [114][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.071 (0.227) Prec@1 97.92 (92.97) Prec@5 100.00 (99.72)
|
|
test [2019-03-31-19:15:59] Epoch: [114][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.121 (0.225) Prec@1 93.75 (92.96) Prec@5 100.00 (99.73)
|
|
[2019-03-31-19:15:59] **test** Prec@1 92.96 Prec@5 99.73 Error@1 7.04 Error@5 0.27 Loss:0.225
|
|
----> Best Accuracy : Acc@1=92.96, Acc@5=99.73, Error@1=7.04, Error@5=0.27
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-19:15:59] [Epoch=115/600] [Need: 18:24:04] LR=0.0228 ~ 0.0228, Batch=96
|
|
train[2019-03-31-19:16:00] Epoch: [115][000/521] Time 0.81 (0.81) Data 0.54 (0.54) Loss 0.261 (0.261) Prec@1 90.62 (90.62) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-19:16:25] Epoch: [115][100/521] Time 0.25 (0.25) Data 0.00 (0.01) Loss 0.509 (0.371) Prec@1 90.62 (91.23) Prec@5 98.96 (99.87)
|
|
train[2019-03-31-19:16:50] Epoch: [115][200/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.270 (0.361) Prec@1 94.79 (91.54) Prec@5 100.00 (99.85)
|
|
train[2019-03-31-19:17:15] Epoch: [115][300/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.274 (0.352) Prec@1 96.88 (91.92) Prec@5 100.00 (99.86)
|
|
train[2019-03-31-19:17:40] Epoch: [115][400/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.574 (0.355) Prec@1 86.46 (91.82) Prec@5 97.92 (99.83)
|
|
train[2019-03-31-19:18:05] Epoch: [115][500/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.458 (0.358) Prec@1 86.46 (91.71) Prec@5 100.00 (99.83)
|
|
train[2019-03-31-19:18:10] Epoch: [115][520/521] Time 0.23 (0.25) Data 0.00 (0.00) Loss 0.524 (0.359) Prec@1 88.75 (91.69) Prec@5 98.75 (99.82)
|
|
[2019-03-31-19:18:10] **train** Prec@1 91.69 Prec@5 99.82 Error@1 8.31 Error@5 0.18 Loss:0.359
|
|
test [2019-03-31-19:18:10] Epoch: [115][000/105] Time 0.64 (0.64) Data 0.57 (0.57) Loss 0.246 (0.246) Prec@1 91.67 (91.67) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-19:18:14] Epoch: [115][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.172 (0.260) Prec@1 95.83 (92.21) Prec@5 100.00 (99.80)
|
|
test [2019-03-31-19:18:15] Epoch: [115][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.095 (0.259) Prec@1 100.00 (92.22) Prec@5 100.00 (99.80)
|
|
[2019-03-31-19:18:15] **test** Prec@1 92.22 Prec@5 99.80 Error@1 7.78 Error@5 0.20 Loss:0.259
|
|
----> Best Accuracy : Acc@1=92.96, Acc@5=99.73, Error@1=7.04, Error@5=0.27
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-19:18:15] [Epoch=116/600] [Need: 18:15:41] LR=0.0228 ~ 0.0228, Batch=96
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|
train[2019-03-31-19:18:16] Epoch: [116][000/521] Time 0.75 (0.75) Data 0.45 (0.45) Loss 0.345 (0.345) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
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train[2019-03-31-19:18:41] Epoch: [116][100/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.318 (0.336) Prec@1 93.75 (92.48) Prec@5 100.00 (99.82)
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train[2019-03-31-19:19:08] Epoch: [116][200/521] Time 0.24 (0.26) Data 0.00 (0.00) Loss 0.477 (0.341) Prec@1 90.62 (92.24) Prec@5 100.00 (99.85)
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train[2019-03-31-19:19:32] Epoch: [116][300/521] Time 0.24 (0.26) Data 0.00 (0.00) Loss 0.546 (0.350) Prec@1 84.38 (91.86) Prec@5 100.00 (99.83)
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train[2019-03-31-19:19:58] Epoch: [116][400/521] Time 0.25 (0.26) Data 0.00 (0.00) Loss 0.347 (0.349) Prec@1 89.58 (91.90) Prec@5 100.00 (99.83)
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train[2019-03-31-19:20:23] Epoch: [116][500/521] Time 0.25 (0.26) Data 0.00 (0.00) Loss 0.352 (0.358) Prec@1 92.71 (91.73) Prec@5 100.00 (99.82)
|
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train[2019-03-31-19:20:28] Epoch: [116][520/521] Time 0.22 (0.26) Data 0.00 (0.00) Loss 0.392 (0.358) Prec@1 92.50 (91.73) Prec@5 100.00 (99.82)
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[2019-03-31-19:20:28] **train** Prec@1 91.73 Prec@5 99.82 Error@1 8.27 Error@5 0.18 Loss:0.358
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test [2019-03-31-19:20:29] Epoch: [116][000/105] Time 0.51 (0.51) Data 0.44 (0.44) Loss 0.210 (0.210) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
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test [2019-03-31-19:20:33] Epoch: [116][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.133 (0.248) Prec@1 93.75 (92.54) Prec@5 100.00 (99.80)
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test [2019-03-31-19:20:33] Epoch: [116][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.280 (0.248) Prec@1 93.75 (92.54) Prec@5 100.00 (99.81)
|
|
[2019-03-31-19:20:33] **test** Prec@1 92.54 Prec@5 99.81 Error@1 7.46 Error@5 0.19 Loss:0.248
|
|
----> Best Accuracy : Acc@1=92.96, Acc@5=99.73, Error@1=7.04, Error@5=0.27
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
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|
|
==>>[2019-03-31-19:20:34] [Epoch=117/600] [Need: 18:36:02] LR=0.0227 ~ 0.0227, Batch=96
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|
train[2019-03-31-19:20:34] Epoch: [117][000/521] Time 0.87 (0.87) Data 0.58 (0.58) Loss 0.272 (0.272) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-19:20:59] Epoch: [117][100/521] Time 0.24 (0.26) Data 0.00 (0.01) Loss 0.241 (0.332) Prec@1 95.83 (92.46) Prec@5 98.96 (99.89)
|
|
train[2019-03-31-19:21:25] Epoch: [117][200/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.331 (0.338) Prec@1 92.71 (92.37) Prec@5 100.00 (99.84)
|
|
train[2019-03-31-19:21:50] Epoch: [117][300/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.433 (0.343) Prec@1 89.58 (92.24) Prec@5 100.00 (99.84)
|
|
train[2019-03-31-19:22:15] Epoch: [117][400/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.491 (0.346) Prec@1 89.58 (92.12) Prec@5 100.00 (99.84)
|
|
train[2019-03-31-19:22:40] Epoch: [117][500/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.296 (0.350) Prec@1 92.71 (92.01) Prec@5 100.00 (99.84)
|
|
train[2019-03-31-19:22:45] Epoch: [117][520/521] Time 0.23 (0.25) Data 0.00 (0.00) Loss 0.509 (0.350) Prec@1 86.25 (92.01) Prec@5 98.75 (99.83)
|
|
[2019-03-31-19:22:45] **train** Prec@1 92.01 Prec@5 99.83 Error@1 7.99 Error@5 0.17 Loss:0.350
|
|
test [2019-03-31-19:22:46] Epoch: [117][000/105] Time 0.65 (0.65) Data 0.58 (0.58) Loss 0.315 (0.315) Prec@1 88.54 (88.54) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-19:22:50] Epoch: [117][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.143 (0.307) Prec@1 94.79 (90.81) Prec@5 100.00 (99.74)
|
|
test [2019-03-31-19:22:50] Epoch: [117][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.165 (0.306) Prec@1 87.50 (90.83) Prec@5 100.00 (99.74)
|
|
[2019-03-31-19:22:50] **test** Prec@1 90.83 Prec@5 99.74 Error@1 9.17 Error@5 0.26 Loss:0.306
|
|
----> Best Accuracy : Acc@1=92.96, Acc@5=99.73, Error@1=7.04, Error@5=0.27
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-19:22:50] [Epoch=118/600] [Need: 18:20:04] LR=0.0227 ~ 0.0227, Batch=96
|
|
train[2019-03-31-19:22:51] Epoch: [118][000/521] Time 0.80 (0.80) Data 0.51 (0.51) Loss 0.522 (0.522) Prec@1 88.54 (88.54) Prec@5 98.96 (98.96)
|
|
train[2019-03-31-19:23:17] Epoch: [118][100/521] Time 0.25 (0.26) Data 0.00 (0.01) Loss 0.261 (0.330) Prec@1 91.67 (92.34) Prec@5 100.00 (99.88)
|
|
train[2019-03-31-19:23:41] Epoch: [118][200/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.216 (0.342) Prec@1 94.79 (92.06) Prec@5 100.00 (99.87)
|
|
train[2019-03-31-19:24:06] Epoch: [118][300/521] Time 0.26 (0.25) Data 0.00 (0.00) Loss 0.188 (0.346) Prec@1 96.88 (91.99) Prec@5 100.00 (99.84)
|
|
train[2019-03-31-19:24:33] Epoch: [118][400/521] Time 0.24 (0.26) Data 0.00 (0.00) Loss 0.405 (0.349) Prec@1 91.67 (91.90) Prec@5 100.00 (99.84)
|
|
train[2019-03-31-19:24:59] Epoch: [118][500/521] Time 0.29 (0.26) Data 0.00 (0.00) Loss 0.333 (0.354) Prec@1 93.75 (91.80) Prec@5 100.00 (99.84)
|
|
train[2019-03-31-19:25:05] Epoch: [118][520/521] Time 0.26 (0.26) Data 0.00 (0.00) Loss 0.353 (0.354) Prec@1 93.75 (91.82) Prec@5 100.00 (99.83)
|
|
[2019-03-31-19:25:05] **train** Prec@1 91.82 Prec@5 99.83 Error@1 8.18 Error@5 0.17 Loss:0.354
|
|
test [2019-03-31-19:25:05] Epoch: [118][000/105] Time 0.71 (0.71) Data 0.64 (0.64) Loss 0.323 (0.323) Prec@1 91.67 (91.67) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-19:25:10] Epoch: [118][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.189 (0.251) Prec@1 91.67 (92.40) Prec@5 100.00 (99.83)
|
|
test [2019-03-31-19:25:10] Epoch: [118][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.406 (0.251) Prec@1 81.25 (92.37) Prec@5 100.00 (99.84)
|
|
[2019-03-31-19:25:10] **test** Prec@1 92.37 Prec@5 99.84 Error@1 7.63 Error@5 0.16 Loss:0.251
|
|
----> Best Accuracy : Acc@1=92.96, Acc@5=99.73, Error@1=7.04, Error@5=0.27
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-19:25:10] [Epoch=119/600] [Need: 18:40:47] LR=0.0227 ~ 0.0227, Batch=96
|
|
train[2019-03-31-19:25:11] Epoch: [119][000/521] Time 0.85 (0.85) Data 0.56 (0.56) Loss 0.487 (0.487) Prec@1 89.58 (89.58) Prec@5 98.96 (98.96)
|
|
train[2019-03-31-19:25:38] Epoch: [119][100/521] Time 0.26 (0.27) Data 0.00 (0.01) Loss 0.281 (0.331) Prec@1 91.67 (92.48) Prec@5 100.00 (99.82)
|
|
train[2019-03-31-19:26:04] Epoch: [119][200/521] Time 0.29 (0.27) Data 0.00 (0.00) Loss 0.310 (0.333) Prec@1 92.71 (92.43) Prec@5 100.00 (99.82)
|
|
train[2019-03-31-19:26:30] Epoch: [119][300/521] Time 0.26 (0.27) Data 0.00 (0.00) Loss 0.369 (0.340) Prec@1 93.75 (92.23) Prec@5 100.00 (99.81)
|
|
train[2019-03-31-19:26:57] Epoch: [119][400/521] Time 0.26 (0.27) Data 0.00 (0.00) Loss 0.331 (0.346) Prec@1 93.75 (92.09) Prec@5 98.96 (99.80)
|
|
train[2019-03-31-19:27:23] Epoch: [119][500/521] Time 0.27 (0.27) Data 0.00 (0.00) Loss 0.358 (0.351) Prec@1 90.62 (92.00) Prec@5 100.00 (99.81)
|
|
train[2019-03-31-19:27:29] Epoch: [119][520/521] Time 0.26 (0.27) Data 0.00 (0.00) Loss 0.548 (0.350) Prec@1 87.50 (92.04) Prec@5 100.00 (99.81)
|
|
[2019-03-31-19:27:29] **train** Prec@1 92.04 Prec@5 99.81 Error@1 7.96 Error@5 0.19 Loss:0.350
|
|
test [2019-03-31-19:27:30] Epoch: [119][000/105] Time 0.75 (0.75) Data 0.67 (0.67) Loss 0.204 (0.204) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-19:27:34] Epoch: [119][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.141 (0.216) Prec@1 92.71 (93.24) Prec@5 100.00 (99.81)
|
|
test [2019-03-31-19:27:34] Epoch: [119][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.104 (0.215) Prec@1 100.00 (93.29) Prec@5 100.00 (99.82)
|
|
[2019-03-31-19:27:34] **test** Prec@1 93.29 Prec@5 99.82 Error@1 6.71 Error@5 0.18 Loss:0.215
|
|
----> Best Accuracy : Acc@1=93.29, Acc@5=99.82, Error@1=6.71, Error@5=0.18
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-19:27:34] [Epoch=120/600] [Need: 19:12:41] LR=0.0226 ~ 0.0226, Batch=96
|
|
train[2019-03-31-19:27:35] Epoch: [120][000/521] Time 0.81 (0.81) Data 0.53 (0.53) Loss 0.424 (0.424) Prec@1 88.54 (88.54) Prec@5 98.96 (98.96)
|
|
train[2019-03-31-19:28:01] Epoch: [120][100/521] Time 0.26 (0.27) Data 0.00 (0.01) Loss 0.291 (0.338) Prec@1 94.79 (92.39) Prec@5 100.00 (99.81)
|
|
train[2019-03-31-19:28:28] Epoch: [120][200/521] Time 0.29 (0.27) Data 0.00 (0.00) Loss 0.563 (0.353) Prec@1 84.38 (92.07) Prec@5 100.00 (99.78)
|
|
train[2019-03-31-19:28:54] Epoch: [120][300/521] Time 0.27 (0.27) Data 0.00 (0.00) Loss 0.322 (0.350) Prec@1 92.71 (92.12) Prec@5 98.96 (99.79)
|
|
train[2019-03-31-19:29:21] Epoch: [120][400/521] Time 0.28 (0.27) Data 0.00 (0.00) Loss 0.302 (0.354) Prec@1 91.67 (91.99) Prec@5 100.00 (99.79)
|
|
train[2019-03-31-19:29:48] Epoch: [120][500/521] Time 0.25 (0.27) Data 0.00 (0.00) Loss 0.311 (0.357) Prec@1 91.67 (91.90) Prec@5 100.00 (99.80)
|
|
train[2019-03-31-19:29:53] Epoch: [120][520/521] Time 0.23 (0.27) Data 0.00 (0.00) Loss 0.300 (0.358) Prec@1 93.75 (91.87) Prec@5 100.00 (99.79)
|
|
[2019-03-31-19:29:53] **train** Prec@1 91.87 Prec@5 99.79 Error@1 8.13 Error@5 0.21 Loss:0.358
|
|
test [2019-03-31-19:29:54] Epoch: [120][000/105] Time 0.69 (0.69) Data 0.59 (0.59) Loss 0.218 (0.218) Prec@1 90.62 (90.62) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-19:29:58] Epoch: [120][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.173 (0.247) Prec@1 92.71 (92.63) Prec@5 100.00 (99.86)
|
|
test [2019-03-31-19:29:58] Epoch: [120][104/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.115 (0.248) Prec@1 93.75 (92.58) Prec@5 100.00 (99.86)
|
|
[2019-03-31-19:29:58] **test** Prec@1 92.58 Prec@5 99.86 Error@1 7.42 Error@5 0.14 Loss:0.248
|
|
----> Best Accuracy : Acc@1=93.29, Acc@5=99.82, Error@1=6.71, Error@5=0.18
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-19:29:58] [Epoch=121/600] [Need: 19:10:23] LR=0.0226 ~ 0.0226, Batch=96
|
|
train[2019-03-31-19:29:59] Epoch: [121][000/521] Time 0.88 (0.88) Data 0.56 (0.56) Loss 0.289 (0.289) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-19:30:26] Epoch: [121][100/521] Time 0.27 (0.27) Data 0.00 (0.01) Loss 0.367 (0.356) Prec@1 88.54 (91.86) Prec@5 100.00 (99.79)
|
|
train[2019-03-31-19:30:52] Epoch: [121][200/521] Time 0.24 (0.27) Data 0.00 (0.00) Loss 0.324 (0.349) Prec@1 92.71 (92.00) Prec@5 100.00 (99.82)
|
|
train[2019-03-31-19:31:16] Epoch: [121][300/521] Time 0.23 (0.26) Data 0.00 (0.00) Loss 0.229 (0.346) Prec@1 97.92 (92.09) Prec@5 100.00 (99.82)
|
|
train[2019-03-31-19:31:39] Epoch: [121][400/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.284 (0.351) Prec@1 93.75 (91.90) Prec@5 100.00 (99.82)
|
|
train[2019-03-31-19:32:03] Epoch: [121][500/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.283 (0.351) Prec@1 95.83 (91.88) Prec@5 100.00 (99.83)
|
|
train[2019-03-31-19:32:08] Epoch: [121][520/521] Time 0.22 (0.25) Data 0.00 (0.00) Loss 0.435 (0.350) Prec@1 88.75 (91.90) Prec@5 100.00 (99.83)
|
|
[2019-03-31-19:32:08] **train** Prec@1 91.90 Prec@5 99.83 Error@1 8.10 Error@5 0.17 Loss:0.350
|
|
test [2019-03-31-19:32:08] Epoch: [121][000/105] Time 0.47 (0.47) Data 0.41 (0.41) Loss 0.259 (0.259) Prec@1 90.62 (90.62) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-19:32:12] Epoch: [121][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.173 (0.236) Prec@1 93.75 (92.98) Prec@5 100.00 (99.76)
|
|
test [2019-03-31-19:32:13] Epoch: [121][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.179 (0.237) Prec@1 87.50 (92.95) Prec@5 100.00 (99.77)
|
|
[2019-03-31-19:32:13] **test** Prec@1 92.95 Prec@5 99.77 Error@1 7.05 Error@5 0.23 Loss:0.237
|
|
----> Best Accuracy : Acc@1=93.29, Acc@5=99.82, Error@1=6.71, Error@5=0.18
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-19:32:13] [Epoch=122/600] [Need: 17:49:56] LR=0.0225 ~ 0.0225, Batch=96
|
|
train[2019-03-31-19:32:14] Epoch: [122][000/521] Time 0.72 (0.72) Data 0.44 (0.44) Loss 0.450 (0.450) Prec@1 90.62 (90.62) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-19:32:37] Epoch: [122][100/521] Time 0.28 (0.24) Data 0.00 (0.00) Loss 0.296 (0.327) Prec@1 93.75 (92.40) Prec@5 100.00 (99.82)
|
|
train[2019-03-31-19:33:01] Epoch: [122][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.346 (0.341) Prec@1 90.62 (92.07) Prec@5 100.00 (99.82)
|
|
train[2019-03-31-19:33:25] Epoch: [122][300/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.242 (0.343) Prec@1 93.75 (92.08) Prec@5 100.00 (99.83)
|
|
train[2019-03-31-19:33:49] Epoch: [122][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.323 (0.351) Prec@1 93.75 (91.91) Prec@5 98.96 (99.81)
|
|
train[2019-03-31-19:34:13] Epoch: [122][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.580 (0.353) Prec@1 91.67 (91.89) Prec@5 100.00 (99.81)
|
|
train[2019-03-31-19:34:17] Epoch: [122][520/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.390 (0.353) Prec@1 92.50 (91.88) Prec@5 100.00 (99.81)
|
|
[2019-03-31-19:34:17] **train** Prec@1 91.88 Prec@5 99.81 Error@1 8.12 Error@5 0.19 Loss:0.353
|
|
test [2019-03-31-19:34:18] Epoch: [122][000/105] Time 0.47 (0.47) Data 0.41 (0.41) Loss 0.327 (0.327) Prec@1 89.58 (89.58) Prec@5 98.96 (98.96)
|
|
test [2019-03-31-19:34:22] Epoch: [122][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.154 (0.273) Prec@1 93.75 (91.75) Prec@5 98.96 (99.71)
|
|
test [2019-03-31-19:34:22] Epoch: [122][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.160 (0.272) Prec@1 93.75 (91.74) Prec@5 100.00 (99.71)
|
|
[2019-03-31-19:34:22] **test** Prec@1 91.74 Prec@5 99.71 Error@1 8.26 Error@5 0.29 Loss:0.272
|
|
----> Best Accuracy : Acc@1=93.29, Acc@5=99.82, Error@1=6.71, Error@5=0.18
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-19:34:22] [Epoch=123/600] [Need: 17:10:42] LR=0.0225 ~ 0.0225, Batch=96
|
|
train[2019-03-31-19:34:23] Epoch: [123][000/521] Time 0.84 (0.84) Data 0.57 (0.57) Loss 0.367 (0.367) Prec@1 91.67 (91.67) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-19:34:47] Epoch: [123][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.224 (0.347) Prec@1 94.79 (92.07) Prec@5 100.00 (99.79)
|
|
train[2019-03-31-19:35:11] Epoch: [123][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.296 (0.344) Prec@1 91.67 (92.16) Prec@5 100.00 (99.81)
|
|
train[2019-03-31-19:35:35] Epoch: [123][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.235 (0.343) Prec@1 95.83 (92.19) Prec@5 100.00 (99.80)
|
|
train[2019-03-31-19:35:58] Epoch: [123][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.349 (0.347) Prec@1 90.62 (92.12) Prec@5 100.00 (99.81)
|
|
train[2019-03-31-19:36:22] Epoch: [123][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.300 (0.348) Prec@1 92.71 (92.08) Prec@5 100.00 (99.82)
|
|
train[2019-03-31-19:36:27] Epoch: [123][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.298 (0.347) Prec@1 93.75 (92.09) Prec@5 98.75 (99.82)
|
|
[2019-03-31-19:36:27] **train** Prec@1 92.09 Prec@5 99.82 Error@1 7.91 Error@5 0.18 Loss:0.347
|
|
test [2019-03-31-19:36:27] Epoch: [123][000/105] Time 0.48 (0.48) Data 0.41 (0.41) Loss 0.326 (0.326) Prec@1 90.62 (90.62) Prec@5 98.96 (98.96)
|
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test [2019-03-31-19:36:31] Epoch: [123][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.048 (0.210) Prec@1 97.92 (93.08) Prec@5 100.00 (99.85)
|
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test [2019-03-31-19:36:32] Epoch: [123][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.298 (0.209) Prec@1 87.50 (93.11) Prec@5 100.00 (99.85)
|
|
[2019-03-31-19:36:32] **test** Prec@1 93.11 Prec@5 99.85 Error@1 6.89 Error@5 0.15 Loss:0.209
|
|
----> Best Accuracy : Acc@1=93.29, Acc@5=99.82, Error@1=6.71, Error@5=0.18
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-19:36:32] [Epoch=124/600] [Need: 17:06:27] LR=0.0225 ~ 0.0225, Batch=96
|
|
train[2019-03-31-19:36:33] Epoch: [124][000/521] Time 0.70 (0.70) Data 0.43 (0.43) Loss 0.329 (0.329) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-19:36:56] Epoch: [124][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.262 (0.331) Prec@1 94.79 (92.30) Prec@5 100.00 (99.77)
|
|
train[2019-03-31-19:37:20] Epoch: [124][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.456 (0.344) Prec@1 88.54 (92.06) Prec@5 100.00 (99.79)
|
|
train[2019-03-31-19:37:44] Epoch: [124][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.380 (0.348) Prec@1 88.54 (92.01) Prec@5 100.00 (99.82)
|
|
train[2019-03-31-19:38:08] Epoch: [124][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.401 (0.350) Prec@1 90.62 (91.88) Prec@5 100.00 (99.82)
|
|
train[2019-03-31-19:38:32] Epoch: [124][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.258 (0.351) Prec@1 94.79 (91.86) Prec@5 98.96 (99.81)
|
|
train[2019-03-31-19:38:36] Epoch: [124][520/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.165 (0.351) Prec@1 95.00 (91.85) Prec@5 100.00 (99.81)
|
|
[2019-03-31-19:38:36] **train** Prec@1 91.85 Prec@5 99.81 Error@1 8.15 Error@5 0.19 Loss:0.351
|
|
test [2019-03-31-19:38:37] Epoch: [124][000/105] Time 0.60 (0.60) Data 0.54 (0.54) Loss 0.319 (0.319) Prec@1 88.54 (88.54) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-19:38:41] Epoch: [124][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.172 (0.225) Prec@1 94.79 (93.01) Prec@5 100.00 (99.81)
|
|
test [2019-03-31-19:38:41] Epoch: [124][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.062 (0.225) Prec@1 100.00 (93.03) Prec@5 100.00 (99.81)
|
|
[2019-03-31-19:38:41] **test** Prec@1 93.03 Prec@5 99.81 Error@1 6.97 Error@5 0.19 Loss:0.225
|
|
----> Best Accuracy : Acc@1=93.29, Acc@5=99.82, Error@1=6.71, Error@5=0.18
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-19:38:42] [Epoch=125/600] [Need: 17:07:28] LR=0.0224 ~ 0.0224, Batch=96
|
|
train[2019-03-31-19:38:42] Epoch: [125][000/521] Time 0.85 (0.85) Data 0.57 (0.57) Loss 0.307 (0.307) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-19:39:06] Epoch: [125][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.457 (0.338) Prec@1 87.50 (92.12) Prec@5 100.00 (99.91)
|
|
train[2019-03-31-19:39:30] Epoch: [125][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.250 (0.344) Prec@1 95.83 (92.00) Prec@5 100.00 (99.82)
|
|
train[2019-03-31-19:39:54] Epoch: [125][300/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.225 (0.339) Prec@1 94.79 (92.22) Prec@5 100.00 (99.83)
|
|
train[2019-03-31-19:40:18] Epoch: [125][400/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.346 (0.341) Prec@1 93.75 (92.21) Prec@5 100.00 (99.83)
|
|
train[2019-03-31-19:40:41] Epoch: [125][500/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.174 (0.343) Prec@1 96.88 (92.15) Prec@5 100.00 (99.84)
|
|
train[2019-03-31-19:40:46] Epoch: [125][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.276 (0.345) Prec@1 96.25 (92.12) Prec@5 100.00 (99.84)
|
|
[2019-03-31-19:40:46] **train** Prec@1 92.12 Prec@5 99.84 Error@1 7.88 Error@5 0.16 Loss:0.345
|
|
test [2019-03-31-19:40:47] Epoch: [125][000/105] Time 0.61 (0.61) Data 0.55 (0.55) Loss 0.359 (0.359) Prec@1 91.67 (91.67) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-19:40:51] Epoch: [125][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.144 (0.244) Prec@1 95.83 (91.92) Prec@5 100.00 (99.91)
|
|
test [2019-03-31-19:40:51] Epoch: [125][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.286 (0.244) Prec@1 87.50 (91.92) Prec@5 100.00 (99.91)
|
|
[2019-03-31-19:40:51] **test** Prec@1 91.92 Prec@5 99.91 Error@1 8.08 Error@5 0.09 Loss:0.244
|
|
----> Best Accuracy : Acc@1=93.29, Acc@5=99.82, Error@1=6.71, Error@5=0.18
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-19:40:51] [Epoch=126/600] [Need: 17:04:35] LR=0.0224 ~ 0.0224, Batch=96
|
|
train[2019-03-31-19:40:52] Epoch: [126][000/521] Time 0.83 (0.83) Data 0.56 (0.56) Loss 0.454 (0.454) Prec@1 88.54 (88.54) Prec@5 98.96 (98.96)
|
|
train[2019-03-31-19:41:16] Epoch: [126][100/521] Time 0.23 (0.24) Data 0.00 (0.01) Loss 0.313 (0.344) Prec@1 91.67 (91.92) Prec@5 100.00 (99.80)
|
|
train[2019-03-31-19:41:40] Epoch: [126][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.339 (0.354) Prec@1 92.71 (91.69) Prec@5 100.00 (99.81)
|
|
train[2019-03-31-19:42:03] Epoch: [126][300/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.297 (0.348) Prec@1 94.79 (91.88) Prec@5 100.00 (99.81)
|
|
train[2019-03-31-19:42:26] Epoch: [126][400/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.283 (0.346) Prec@1 95.83 (91.98) Prec@5 100.00 (99.82)
|
|
train[2019-03-31-19:42:50] Epoch: [126][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.301 (0.348) Prec@1 95.83 (91.96) Prec@5 100.00 (99.83)
|
|
train[2019-03-31-19:42:55] Epoch: [126][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.389 (0.349) Prec@1 90.00 (91.98) Prec@5 100.00 (99.82)
|
|
[2019-03-31-19:42:55] **train** Prec@1 91.98 Prec@5 99.82 Error@1 8.02 Error@5 0.18 Loss:0.349
|
|
test [2019-03-31-19:42:55] Epoch: [126][000/105] Time 0.48 (0.48) Data 0.41 (0.41) Loss 0.200 (0.200) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-19:42:59] Epoch: [126][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.197 (0.258) Prec@1 95.83 (92.42) Prec@5 100.00 (99.83)
|
|
test [2019-03-31-19:42:59] Epoch: [126][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.695 (0.261) Prec@1 93.75 (92.40) Prec@5 100.00 (99.84)
|
|
[2019-03-31-19:42:59] **test** Prec@1 92.40 Prec@5 99.84 Error@1 7.60 Error@5 0.16 Loss:0.261
|
|
----> Best Accuracy : Acc@1=93.29, Acc@5=99.82, Error@1=6.71, Error@5=0.18
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-19:43:00] [Epoch=127/600] [Need: 16:51:24] LR=0.0223 ~ 0.0223, Batch=96
|
|
train[2019-03-31-19:43:00] Epoch: [127][000/521] Time 0.71 (0.71) Data 0.44 (0.44) Loss 0.357 (0.357) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-19:43:24] Epoch: [127][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.380 (0.332) Prec@1 90.62 (92.24) Prec@5 100.00 (99.92)
|
|
train[2019-03-31-19:43:48] Epoch: [127][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.447 (0.334) Prec@1 88.54 (92.23) Prec@5 100.00 (99.90)
|
|
train[2019-03-31-19:44:12] Epoch: [127][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.565 (0.336) Prec@1 87.50 (92.17) Prec@5 98.96 (99.87)
|
|
train[2019-03-31-19:44:35] Epoch: [127][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.309 (0.341) Prec@1 93.75 (92.12) Prec@5 100.00 (99.86)
|
|
train[2019-03-31-19:44:59] Epoch: [127][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.558 (0.343) Prec@1 85.42 (92.11) Prec@5 98.96 (99.85)
|
|
train[2019-03-31-19:45:04] Epoch: [127][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.490 (0.344) Prec@1 88.75 (92.08) Prec@5 100.00 (99.86)
|
|
[2019-03-31-19:45:04] **train** Prec@1 92.08 Prec@5 99.86 Error@1 7.92 Error@5 0.14 Loss:0.344
|
|
test [2019-03-31-19:45:04] Epoch: [127][000/105] Time 0.47 (0.47) Data 0.41 (0.41) Loss 0.317 (0.317) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-19:45:09] Epoch: [127][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.225 (0.253) Prec@1 95.83 (92.22) Prec@5 100.00 (99.85)
|
|
test [2019-03-31-19:45:09] Epoch: [127][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.246 (0.253) Prec@1 87.50 (92.20) Prec@5 100.00 (99.84)
|
|
[2019-03-31-19:45:09] **test** Prec@1 92.20 Prec@5 99.84 Error@1 7.80 Error@5 0.16 Loss:0.253
|
|
----> Best Accuracy : Acc@1=93.29, Acc@5=99.82, Error@1=6.71, Error@5=0.18
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-19:45:09] [Epoch=128/600] [Need: 16:57:26] LR=0.0223 ~ 0.0223, Batch=96
|
|
train[2019-03-31-19:45:10] Epoch: [128][000/521] Time 0.84 (0.84) Data 0.58 (0.58) Loss 0.345 (0.345) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-19:45:33] Epoch: [128][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.462 (0.338) Prec@1 86.46 (92.04) Prec@5 100.00 (99.79)
|
|
train[2019-03-31-19:45:57] Epoch: [128][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.467 (0.335) Prec@1 88.54 (92.24) Prec@5 98.96 (99.82)
|
|
train[2019-03-31-19:46:21] Epoch: [128][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.624 (0.340) Prec@1 86.46 (92.09) Prec@5 100.00 (99.83)
|
|
train[2019-03-31-19:46:45] Epoch: [128][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.280 (0.346) Prec@1 92.71 (91.99) Prec@5 100.00 (99.82)
|
|
train[2019-03-31-19:47:09] Epoch: [128][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.367 (0.347) Prec@1 92.71 (91.98) Prec@5 100.00 (99.83)
|
|
train[2019-03-31-19:47:14] Epoch: [128][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.212 (0.347) Prec@1 92.50 (91.98) Prec@5 100.00 (99.83)
|
|
[2019-03-31-19:47:14] **train** Prec@1 91.98 Prec@5 99.83 Error@1 8.02 Error@5 0.17 Loss:0.347
|
|
test [2019-03-31-19:47:14] Epoch: [128][000/105] Time 0.47 (0.47) Data 0.40 (0.40) Loss 0.257 (0.257) Prec@1 91.67 (91.67) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-19:47:18] Epoch: [128][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.133 (0.212) Prec@1 96.88 (93.34) Prec@5 100.00 (99.83)
|
|
test [2019-03-31-19:47:19] Epoch: [128][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.067 (0.212) Prec@1 93.75 (93.34) Prec@5 100.00 (99.84)
|
|
[2019-03-31-19:47:19] **test** Prec@1 93.34 Prec@5 99.84 Error@1 6.66 Error@5 0.16 Loss:0.212
|
|
----> Best Accuracy : Acc@1=93.34, Acc@5=99.84, Error@1=6.66, Error@5=0.16
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-19:47:19] [Epoch=129/600] [Need: 17:00:33] LR=0.0223 ~ 0.0223, Batch=96
|
|
train[2019-03-31-19:47:20] Epoch: [129][000/521] Time 0.70 (0.70) Data 0.43 (0.43) Loss 0.541 (0.541) Prec@1 87.50 (87.50) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-19:47:43] Epoch: [129][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.248 (0.352) Prec@1 94.79 (92.18) Prec@5 100.00 (99.86)
|
|
train[2019-03-31-19:48:08] Epoch: [129][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.328 (0.344) Prec@1 90.62 (92.15) Prec@5 100.00 (99.82)
|
|
train[2019-03-31-19:48:31] Epoch: [129][300/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.381 (0.339) Prec@1 90.62 (92.30) Prec@5 100.00 (99.82)
|
|
train[2019-03-31-19:48:55] Epoch: [129][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.345 (0.340) Prec@1 94.79 (92.28) Prec@5 100.00 (99.83)
|
|
train[2019-03-31-19:49:19] Epoch: [129][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.369 (0.351) Prec@1 91.67 (92.02) Prec@5 98.96 (99.81)
|
|
train[2019-03-31-19:49:24] Epoch: [129][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.194 (0.352) Prec@1 97.50 (92.00) Prec@5 100.00 (99.81)
|
|
[2019-03-31-19:49:24] **train** Prec@1 92.00 Prec@5 99.81 Error@1 8.00 Error@5 0.19 Loss:0.352
|
|
test [2019-03-31-19:49:24] Epoch: [129][000/105] Time 0.47 (0.47) Data 0.40 (0.40) Loss 0.330 (0.330) Prec@1 88.54 (88.54) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-19:49:28] Epoch: [129][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.154 (0.265) Prec@1 93.75 (91.81) Prec@5 100.00 (99.87)
|
|
test [2019-03-31-19:49:28] Epoch: [129][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.244 (0.264) Prec@1 81.25 (91.76) Prec@5 100.00 (99.87)
|
|
[2019-03-31-19:49:28] **test** Prec@1 91.76 Prec@5 99.87 Error@1 8.24 Error@5 0.13 Loss:0.264
|
|
----> Best Accuracy : Acc@1=93.34, Acc@5=99.84, Error@1=6.66, Error@5=0.16
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-19:49:29] [Epoch=130/600] [Need: 16:55:29] LR=0.0222 ~ 0.0222, Batch=96
|
|
train[2019-03-31-19:49:29] Epoch: [130][000/521] Time 0.71 (0.71) Data 0.43 (0.43) Loss 0.266 (0.266) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-19:49:53] Epoch: [130][100/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.306 (0.351) Prec@1 93.75 (91.72) Prec@5 100.00 (99.83)
|
|
train[2019-03-31-19:50:17] Epoch: [130][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.369 (0.339) Prec@1 92.71 (92.01) Prec@5 100.00 (99.84)
|
|
train[2019-03-31-19:50:40] Epoch: [130][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.197 (0.333) Prec@1 95.83 (92.26) Prec@5 100.00 (99.87)
|
|
train[2019-03-31-19:51:04] Epoch: [130][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.244 (0.335) Prec@1 95.83 (92.22) Prec@5 100.00 (99.85)
|
|
train[2019-03-31-19:51:30] Epoch: [130][500/521] Time 0.71 (0.24) Data 0.00 (0.00) Loss 0.389 (0.339) Prec@1 93.75 (92.13) Prec@5 100.00 (99.84)
|
|
train[2019-03-31-19:51:42] Epoch: [130][520/521] Time 0.35 (0.26) Data 0.00 (0.00) Loss 0.318 (0.340) Prec@1 90.00 (92.11) Prec@5 100.00 (99.84)
|
|
[2019-03-31-19:51:43] **train** Prec@1 92.11 Prec@5 99.84 Error@1 7.89 Error@5 0.16 Loss:0.340
|
|
test [2019-03-31-19:51:45] Epoch: [130][000/105] Time 1.91 (1.91) Data 1.72 (1.72) Loss 0.260 (0.260) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-19:51:57] Epoch: [130][100/105] Time 0.09 (0.15) Data 0.00 (0.02) Loss 0.087 (0.242) Prec@1 97.92 (92.32) Prec@5 100.00 (99.87)
|
|
test [2019-03-31-19:51:58] Epoch: [130][104/105] Time 0.11 (0.14) Data 0.00 (0.02) Loss 0.207 (0.242) Prec@1 87.50 (92.37) Prec@5 100.00 (99.86)
|
|
[2019-03-31-19:51:58] **test** Prec@1 92.37 Prec@5 99.86 Error@1 7.63 Error@5 0.14 Loss:0.242
|
|
----> Best Accuracy : Acc@1=93.34, Acc@5=99.84, Error@1=6.66, Error@5=0.16
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-19:51:59] [Epoch=131/600] [Need: 19:37:34] LR=0.0222 ~ 0.0222, Batch=96
|
|
train[2019-03-31-19:52:02] Epoch: [131][000/521] Time 2.37 (2.37) Data 1.56 (1.56) Loss 0.330 (0.330) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-19:53:01] Epoch: [131][100/521] Time 0.68 (0.62) Data 0.00 (0.02) Loss 0.346 (0.331) Prec@1 89.58 (92.08) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-19:53:41] Epoch: [131][200/521] Time 0.24 (0.50) Data 0.00 (0.01) Loss 0.176 (0.344) Prec@1 96.88 (92.02) Prec@5 100.00 (99.87)
|
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train[2019-03-31-19:54:04] Epoch: [131][300/521] Time 0.24 (0.42) Data 0.00 (0.01) Loss 0.439 (0.342) Prec@1 91.67 (92.11) Prec@5 100.00 (99.87)
|
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train[2019-03-31-19:54:28] Epoch: [131][400/521] Time 0.24 (0.37) Data 0.00 (0.01) Loss 0.243 (0.346) Prec@1 94.79 (92.09) Prec@5 100.00 (99.85)
|
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train[2019-03-31-19:54:52] Epoch: [131][500/521] Time 0.23 (0.35) Data 0.00 (0.00) Loss 0.624 (0.351) Prec@1 88.54 (91.97) Prec@5 98.96 (99.85)
|
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train[2019-03-31-19:54:57] Epoch: [131][520/521] Time 0.21 (0.34) Data 0.00 (0.00) Loss 0.305 (0.352) Prec@1 92.50 (91.95) Prec@5 98.75 (99.85)
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[2019-03-31-19:54:57] **train** Prec@1 91.95 Prec@5 99.85 Error@1 8.05 Error@5 0.15 Loss:0.352
|
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test [2019-03-31-19:54:57] Epoch: [131][000/105] Time 0.60 (0.60) Data 0.53 (0.53) Loss 0.151 (0.151) Prec@1 95.83 (95.83) Prec@5 98.96 (98.96)
|
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test [2019-03-31-19:55:02] Epoch: [131][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.073 (0.226) Prec@1 97.92 (93.17) Prec@5 100.00 (99.81)
|
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test [2019-03-31-19:55:02] Epoch: [131][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.310 (0.226) Prec@1 87.50 (93.18) Prec@5 100.00 (99.82)
|
|
[2019-03-31-19:55:02] **test** Prec@1 93.18 Prec@5 99.82 Error@1 6.82 Error@5 0.18 Loss:0.226
|
|
----> Best Accuracy : Acc@1=93.34, Acc@5=99.84, Error@1=6.66, Error@5=0.16
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-19:55:02] [Epoch=132/600] [Need: 23:45:04] LR=0.0221 ~ 0.0221, Batch=96
|
|
train[2019-03-31-19:55:03] Epoch: [132][000/521] Time 0.70 (0.70) Data 0.42 (0.42) Loss 0.325 (0.325) Prec@1 89.58 (89.58) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-19:55:46] Epoch: [132][100/521] Time 0.63 (0.43) Data 0.00 (0.01) Loss 0.300 (0.327) Prec@1 90.62 (92.46) Prec@5 100.00 (99.91)
|
|
train[2019-03-31-19:56:49] Epoch: [132][200/521] Time 0.82 (0.53) Data 0.00 (0.00) Loss 0.232 (0.329) Prec@1 95.83 (92.43) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-19:57:51] Epoch: [132][300/521] Time 0.61 (0.56) Data 0.00 (0.00) Loss 0.365 (0.332) Prec@1 91.67 (92.48) Prec@5 100.00 (99.85)
|
|
train[2019-03-31-19:58:53] Epoch: [132][400/521] Time 0.72 (0.58) Data 0.00 (0.00) Loss 0.214 (0.336) Prec@1 96.88 (92.41) Prec@5 98.96 (99.84)
|
|
train[2019-03-31-19:59:57] Epoch: [132][500/521] Time 0.71 (0.59) Data 0.00 (0.00) Loss 0.231 (0.338) Prec@1 93.75 (92.31) Prec@5 100.00 (99.84)
|
|
train[2019-03-31-20:00:09] Epoch: [132][520/521] Time 0.43 (0.59) Data 0.00 (0.00) Loss 0.275 (0.338) Prec@1 91.25 (92.31) Prec@5 100.00 (99.84)
|
|
[2019-03-31-20:00:09] **train** Prec@1 92.31 Prec@5 99.84 Error@1 7.69 Error@5 0.16 Loss:0.338
|
|
test [2019-03-31-20:00:11] Epoch: [132][000/105] Time 1.83 (1.83) Data 1.61 (1.61) Loss 0.177 (0.177) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-20:00:25] Epoch: [132][100/105] Time 0.15 (0.16) Data 0.00 (0.02) Loss 0.051 (0.222) Prec@1 97.92 (93.42) Prec@5 100.00 (99.82)
|
|
test [2019-03-31-20:00:26] Epoch: [132][104/105] Time 0.15 (0.16) Data 0.00 (0.02) Loss 0.496 (0.223) Prec@1 87.50 (93.36) Prec@5 100.00 (99.83)
|
|
[2019-03-31-20:00:27] **test** Prec@1 93.36 Prec@5 99.83 Error@1 6.64 Error@5 0.17 Loss:0.223
|
|
----> Best Accuracy : Acc@1=93.36, Acc@5=99.83, Error@1=6.64, Error@5=0.17
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-20:00:27] [Epoch=133/600] [Need: 42:12:34] LR=0.0221 ~ 0.0221, Batch=96
|
|
train[2019-03-31-20:00:31] Epoch: [133][000/521] Time 3.75 (3.75) Data 3.01 (3.01) Loss 0.393 (0.393) Prec@1 90.62 (90.62) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-20:01:04] Epoch: [133][100/521] Time 0.25 (0.36) Data 0.00 (0.03) Loss 0.465 (0.343) Prec@1 90.62 (91.82) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-20:01:28] Epoch: [133][200/521] Time 0.24 (0.30) Data 0.00 (0.02) Loss 0.444 (0.341) Prec@1 90.62 (92.14) Prec@5 100.00 (99.84)
|
|
train[2019-03-31-20:01:51] Epoch: [133][300/521] Time 0.24 (0.28) Data 0.00 (0.01) Loss 0.372 (0.343) Prec@1 89.58 (92.10) Prec@5 100.00 (99.86)
|
|
train[2019-03-31-20:02:15] Epoch: [133][400/521] Time 0.24 (0.27) Data 0.00 (0.01) Loss 0.287 (0.344) Prec@1 94.79 (92.00) Prec@5 100.00 (99.84)
|
|
train[2019-03-31-20:02:39] Epoch: [133][500/521] Time 0.24 (0.26) Data 0.00 (0.01) Loss 0.358 (0.349) Prec@1 92.71 (91.90) Prec@5 98.96 (99.83)
|
|
train[2019-03-31-20:02:44] Epoch: [133][520/521] Time 0.21 (0.26) Data 0.00 (0.01) Loss 0.418 (0.350) Prec@1 90.00 (91.88) Prec@5 100.00 (99.82)
|
|
[2019-03-31-20:02:44] **train** Prec@1 91.88 Prec@5 99.82 Error@1 8.12 Error@5 0.18 Loss:0.350
|
|
test [2019-03-31-20:02:44] Epoch: [133][000/105] Time 0.60 (0.60) Data 0.53 (0.53) Loss 0.171 (0.171) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-20:02:49] Epoch: [133][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.141 (0.246) Prec@1 92.71 (92.50) Prec@5 100.00 (99.77)
|
|
test [2019-03-31-20:02:49] Epoch: [133][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.275 (0.247) Prec@1 87.50 (92.45) Prec@5 100.00 (99.77)
|
|
[2019-03-31-20:02:49] **test** Prec@1 92.45 Prec@5 99.77 Error@1 7.55 Error@5 0.23 Loss:0.247
|
|
----> Best Accuracy : Acc@1=93.36, Acc@5=99.83, Error@1=6.64, Error@5=0.17
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-20:02:49] [Epoch=134/600] [Need: 18:20:02] LR=0.0221 ~ 0.0221, Batch=96
|
|
train[2019-03-31-20:02:50] Epoch: [134][000/521] Time 0.83 (0.83) Data 0.56 (0.56) Loss 0.380 (0.380) Prec@1 88.54 (88.54) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-20:03:14] Epoch: [134][100/521] Time 0.24 (0.25) Data 0.00 (0.01) Loss 0.261 (0.328) Prec@1 93.75 (92.89) Prec@5 100.00 (99.86)
|
|
train[2019-03-31-20:03:49] Epoch: [134][200/521] Time 0.24 (0.30) Data 0.00 (0.00) Loss 0.354 (0.337) Prec@1 89.58 (92.36) Prec@5 100.00 (99.84)
|
|
train[2019-03-31-20:04:13] Epoch: [134][300/521] Time 0.24 (0.28) Data 0.00 (0.00) Loss 0.297 (0.337) Prec@1 93.75 (92.35) Prec@5 100.00 (99.83)
|
|
train[2019-03-31-20:04:37] Epoch: [134][400/521] Time 0.24 (0.27) Data 0.00 (0.00) Loss 0.277 (0.339) Prec@1 94.79 (92.40) Prec@5 100.00 (99.84)
|
|
train[2019-03-31-20:05:01] Epoch: [134][500/521] Time 0.24 (0.26) Data 0.00 (0.00) Loss 0.514 (0.341) Prec@1 87.50 (92.30) Prec@5 100.00 (99.85)
|
|
train[2019-03-31-20:05:05] Epoch: [134][520/521] Time 0.22 (0.26) Data 0.00 (0.00) Loss 0.286 (0.341) Prec@1 95.00 (92.29) Prec@5 100.00 (99.85)
|
|
[2019-03-31-20:05:05] **train** Prec@1 92.29 Prec@5 99.85 Error@1 7.71 Error@5 0.15 Loss:0.341
|
|
test [2019-03-31-20:05:06] Epoch: [134][000/105] Time 0.60 (0.60) Data 0.54 (0.54) Loss 0.198 (0.198) Prec@1 89.58 (89.58) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-20:05:10] Epoch: [134][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.118 (0.249) Prec@1 97.92 (92.43) Prec@5 100.00 (99.78)
|
|
test [2019-03-31-20:05:10] Epoch: [134][104/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.345 (0.247) Prec@1 93.75 (92.44) Prec@5 100.00 (99.79)
|
|
[2019-03-31-20:05:10] **test** Prec@1 92.44 Prec@5 99.79 Error@1 7.56 Error@5 0.21 Loss:0.247
|
|
----> Best Accuracy : Acc@1=93.36, Acc@5=99.83, Error@1=6.64, Error@5=0.17
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-20:05:10] [Epoch=135/600] [Need: 18:16:39] LR=0.0220 ~ 0.0220, Batch=96
|
|
train[2019-03-31-20:05:11] Epoch: [135][000/521] Time 0.86 (0.86) Data 0.57 (0.57) Loss 0.365 (0.365) Prec@1 89.58 (89.58) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-20:05:35] Epoch: [135][100/521] Time 0.24 (0.25) Data 0.00 (0.01) Loss 0.196 (0.333) Prec@1 93.75 (92.11) Prec@5 100.00 (99.85)
|
|
train[2019-03-31-20:06:00] Epoch: [135][200/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.288 (0.327) Prec@1 95.83 (92.39) Prec@5 100.00 (99.84)
|
|
train[2019-03-31-20:06:24] Epoch: [135][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.335 (0.332) Prec@1 91.67 (92.26) Prec@5 100.00 (99.86)
|
|
train[2019-03-31-20:07:00] Epoch: [135][400/521] Time 0.47 (0.27) Data 0.00 (0.00) Loss 0.462 (0.337) Prec@1 91.67 (92.16) Prec@5 100.00 (99.85)
|
|
train[2019-03-31-20:07:40] Epoch: [135][500/521] Time 0.23 (0.30) Data 0.00 (0.00) Loss 0.205 (0.337) Prec@1 95.83 (92.23) Prec@5 100.00 (99.83)
|
|
train[2019-03-31-20:07:44] Epoch: [135][520/521] Time 0.21 (0.30) Data 0.00 (0.00) Loss 0.272 (0.338) Prec@1 96.25 (92.18) Prec@5 100.00 (99.84)
|
|
[2019-03-31-20:07:44] **train** Prec@1 92.18 Prec@5 99.84 Error@1 7.82 Error@5 0.16 Loss:0.338
|
|
test [2019-03-31-20:07:45] Epoch: [135][000/105] Time 0.60 (0.60) Data 0.54 (0.54) Loss 0.274 (0.274) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-20:07:49] Epoch: [135][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.227 (0.257) Prec@1 93.75 (92.67) Prec@5 100.00 (99.75)
|
|
test [2019-03-31-20:07:49] Epoch: [135][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.097 (0.254) Prec@1 93.75 (92.71) Prec@5 100.00 (99.75)
|
|
[2019-03-31-20:07:49] **test** Prec@1 92.71 Prec@5 99.75 Error@1 7.29 Error@5 0.25 Loss:0.254
|
|
----> Best Accuracy : Acc@1=93.36, Acc@5=99.83, Error@1=6.64, Error@5=0.17
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-20:07:50] [Epoch=136/600] [Need: 20:30:13] LR=0.0220 ~ 0.0220, Batch=96
|
|
train[2019-03-31-20:07:50] Epoch: [136][000/521] Time 0.70 (0.70) Data 0.43 (0.43) Loss 0.399 (0.399) Prec@1 90.62 (90.62) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-20:08:14] Epoch: [136][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.386 (0.339) Prec@1 89.58 (92.12) Prec@5 100.00 (99.87)
|
|
train[2019-03-31-20:08:38] Epoch: [136][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.284 (0.341) Prec@1 92.71 (92.06) Prec@5 100.00 (99.90)
|
|
train[2019-03-31-20:09:01] Epoch: [136][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.284 (0.342) Prec@1 92.71 (92.05) Prec@5 98.96 (99.86)
|
|
train[2019-03-31-20:09:26] Epoch: [136][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.439 (0.345) Prec@1 92.71 (92.05) Prec@5 100.00 (99.84)
|
|
train[2019-03-31-20:09:50] Epoch: [136][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.464 (0.347) Prec@1 89.58 (91.97) Prec@5 100.00 (99.83)
|
|
train[2019-03-31-20:09:55] Epoch: [136][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.314 (0.347) Prec@1 93.75 (91.99) Prec@5 100.00 (99.83)
|
|
[2019-03-31-20:09:55] **train** Prec@1 91.99 Prec@5 99.83 Error@1 8.01 Error@5 0.17 Loss:0.347
|
|
test [2019-03-31-20:09:56] Epoch: [136][000/105] Time 0.61 (0.61) Data 0.54 (0.54) Loss 0.273 (0.273) Prec@1 88.54 (88.54) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-20:10:00] Epoch: [136][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.055 (0.234) Prec@1 95.83 (93.01) Prec@5 100.00 (99.77)
|
|
test [2019-03-31-20:10:00] Epoch: [136][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.014 (0.233) Prec@1 100.00 (93.06) Prec@5 100.00 (99.78)
|
|
[2019-03-31-20:10:00] **test** Prec@1 93.06 Prec@5 99.78 Error@1 6.94 Error@5 0.22 Loss:0.233
|
|
----> Best Accuracy : Acc@1=93.36, Acc@5=99.83, Error@1=6.64, Error@5=0.17
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-20:10:00] [Epoch=137/600] [Need: 16:48:39] LR=0.0219 ~ 0.0219, Batch=96
|
|
train[2019-03-31-20:10:01] Epoch: [137][000/521] Time 0.86 (0.86) Data 0.58 (0.58) Loss 0.315 (0.315) Prec@1 91.67 (91.67) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-20:10:25] Epoch: [137][100/521] Time 0.25 (0.25) Data 0.00 (0.01) Loss 0.284 (0.320) Prec@1 94.79 (92.39) Prec@5 100.00 (99.94)
|
|
train[2019-03-31-20:11:01] Epoch: [137][200/521] Time 0.35 (0.30) Data 0.00 (0.00) Loss 0.357 (0.330) Prec@1 91.67 (92.20) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-20:11:26] Epoch: [137][300/521] Time 0.24 (0.28) Data 0.00 (0.00) Loss 0.378 (0.333) Prec@1 91.67 (92.25) Prec@5 100.00 (99.87)
|
|
train[2019-03-31-20:11:54] Epoch: [137][400/521] Time 0.29 (0.28) Data 0.00 (0.00) Loss 0.375 (0.329) Prec@1 93.75 (92.37) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-20:12:19] Epoch: [137][500/521] Time 0.24 (0.28) Data 0.00 (0.00) Loss 0.369 (0.335) Prec@1 93.75 (92.26) Prec@5 100.00 (99.87)
|
|
train[2019-03-31-20:12:24] Epoch: [137][520/521] Time 0.22 (0.28) Data 0.00 (0.00) Loss 0.236 (0.334) Prec@1 92.50 (92.30) Prec@5 100.00 (99.87)
|
|
[2019-03-31-20:12:24] **train** Prec@1 92.30 Prec@5 99.87 Error@1 7.70 Error@5 0.13 Loss:0.334
|
|
test [2019-03-31-20:12:25] Epoch: [137][000/105] Time 0.46 (0.46) Data 0.40 (0.40) Loss 0.190 (0.190) Prec@1 90.62 (90.62) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-20:12:29] Epoch: [137][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.070 (0.213) Prec@1 97.92 (93.55) Prec@5 100.00 (99.83)
|
|
test [2019-03-31-20:12:29] Epoch: [137][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.093 (0.215) Prec@1 93.75 (93.52) Prec@5 100.00 (99.84)
|
|
[2019-03-31-20:12:29] **test** Prec@1 93.52 Prec@5 99.84 Error@1 6.48 Error@5 0.16 Loss:0.215
|
|
----> Best Accuracy : Acc@1=93.52, Acc@5=99.84, Error@1=6.48, Error@5=0.16
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-20:12:29] [Epoch=138/600] [Need: 19:06:23] LR=0.0219 ~ 0.0219, Batch=96
|
|
train[2019-03-31-20:12:30] Epoch: [138][000/521] Time 0.84 (0.84) Data 0.56 (0.56) Loss 0.273 (0.273) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-20:12:54] Epoch: [138][100/521] Time 0.25 (0.25) Data 0.00 (0.01) Loss 0.370 (0.331) Prec@1 87.50 (92.39) Prec@5 100.00 (99.91)
|
|
train[2019-03-31-20:13:18] Epoch: [138][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.375 (0.334) Prec@1 90.62 (92.38) Prec@5 100.00 (99.85)
|
|
train[2019-03-31-20:14:34] Epoch: [138][300/521] Time 0.24 (0.42) Data 0.00 (0.00) Loss 0.313 (0.335) Prec@1 92.71 (92.16) Prec@5 100.00 (99.86)
|
|
train[2019-03-31-20:14:58] Epoch: [138][400/521] Time 0.23 (0.37) Data 0.00 (0.00) Loss 0.441 (0.340) Prec@1 91.67 (92.08) Prec@5 100.00 (99.83)
|
|
train[2019-03-31-20:15:22] Epoch: [138][500/521] Time 0.24 (0.35) Data 0.00 (0.00) Loss 0.272 (0.341) Prec@1 94.79 (92.10) Prec@5 100.00 (99.83)
|
|
train[2019-03-31-20:15:27] Epoch: [138][520/521] Time 0.22 (0.34) Data 0.00 (0.00) Loss 0.327 (0.342) Prec@1 93.75 (92.06) Prec@5 100.00 (99.84)
|
|
[2019-03-31-20:15:27] **train** Prec@1 92.06 Prec@5 99.84 Error@1 7.94 Error@5 0.16 Loss:0.342
|
|
test [2019-03-31-20:15:28] Epoch: [138][000/105] Time 0.62 (0.62) Data 0.55 (0.55) Loss 0.257 (0.257) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-20:15:32] Epoch: [138][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.093 (0.214) Prec@1 95.83 (93.21) Prec@5 100.00 (99.82)
|
|
test [2019-03-31-20:15:32] Epoch: [138][104/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.098 (0.213) Prec@1 93.75 (93.24) Prec@5 100.00 (99.82)
|
|
[2019-03-31-20:15:32] **test** Prec@1 93.24 Prec@5 99.82 Error@1 6.76 Error@5 0.18 Loss:0.213
|
|
----> Best Accuracy : Acc@1=93.52, Acc@5=99.84, Error@1=6.48, Error@5=0.16
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-20:15:32] [Epoch=139/600] [Need: 23:26:03] LR=0.0218 ~ 0.0218, Batch=96
|
|
train[2019-03-31-20:15:33] Epoch: [139][000/521] Time 0.72 (0.72) Data 0.44 (0.44) Loss 0.264 (0.264) Prec@1 91.67 (91.67) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-20:16:06] Epoch: [139][100/521] Time 0.58 (0.34) Data 0.00 (0.00) Loss 0.391 (0.328) Prec@1 89.58 (92.40) Prec@5 100.00 (99.87)
|
|
train[2019-03-31-20:16:50] Epoch: [139][200/521] Time 0.38 (0.39) Data 0.00 (0.00) Loss 0.299 (0.332) Prec@1 90.62 (92.38) Prec@5 100.00 (99.84)
|
|
train[2019-03-31-20:17:36] Epoch: [139][300/521] Time 0.44 (0.41) Data 0.00 (0.00) Loss 0.340 (0.337) Prec@1 92.71 (92.22) Prec@5 100.00 (99.85)
|
|
train[2019-03-31-20:18:17] Epoch: [139][400/521] Time 0.26 (0.41) Data 0.00 (0.00) Loss 0.355 (0.342) Prec@1 90.62 (92.06) Prec@5 100.00 (99.84)
|
|
train[2019-03-31-20:18:46] Epoch: [139][500/521] Time 0.36 (0.39) Data 0.00 (0.00) Loss 0.365 (0.343) Prec@1 89.58 (91.98) Prec@5 100.00 (99.85)
|
|
train[2019-03-31-20:18:51] Epoch: [139][520/521] Time 0.31 (0.38) Data 0.00 (0.00) Loss 0.516 (0.344) Prec@1 87.50 (91.95) Prec@5 100.00 (99.84)
|
|
[2019-03-31-20:18:51] **train** Prec@1 91.95 Prec@5 99.84 Error@1 8.05 Error@5 0.16 Loss:0.344
|
|
test [2019-03-31-20:18:52] Epoch: [139][000/105] Time 0.64 (0.64) Data 0.57 (0.57) Loss 0.313 (0.313) Prec@1 90.62 (90.62) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-20:18:56] Epoch: [139][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.119 (0.248) Prec@1 94.79 (92.39) Prec@5 100.00 (99.82)
|
|
test [2019-03-31-20:18:56] Epoch: [139][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.185 (0.247) Prec@1 93.75 (92.42) Prec@5 100.00 (99.83)
|
|
[2019-03-31-20:18:57] **test** Prec@1 92.42 Prec@5 99.83 Error@1 7.58 Error@5 0.17 Loss:0.247
|
|
----> Best Accuracy : Acc@1=93.52, Acc@5=99.84, Error@1=6.48, Error@5=0.16
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-20:18:57] [Epoch=140/600] [Need: 26:08:41] LR=0.0218 ~ 0.0218, Batch=96
|
|
train[2019-03-31-20:18:58] Epoch: [140][000/521] Time 0.86 (0.86) Data 0.59 (0.59) Loss 0.295 (0.295) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-20:19:21] Epoch: [140][100/521] Time 0.25 (0.24) Data 0.00 (0.01) Loss 0.436 (0.326) Prec@1 88.54 (92.58) Prec@5 98.96 (99.82)
|
|
train[2019-03-31-20:19:54] Epoch: [140][200/521] Time 0.47 (0.28) Data 0.00 (0.00) Loss 0.361 (0.333) Prec@1 89.58 (92.47) Prec@5 100.00 (99.83)
|
|
train[2019-03-31-20:20:43] Epoch: [140][300/521] Time 0.35 (0.35) Data 0.00 (0.00) Loss 0.327 (0.330) Prec@1 90.62 (92.57) Prec@5 100.00 (99.83)
|
|
train[2019-03-31-20:21:31] Epoch: [140][400/521] Time 0.25 (0.39) Data 0.00 (0.00) Loss 0.382 (0.331) Prec@1 90.62 (92.50) Prec@5 100.00 (99.83)
|
|
train[2019-03-31-20:21:55] Epoch: [140][500/521] Time 0.24 (0.36) Data 0.00 (0.00) Loss 0.484 (0.335) Prec@1 89.58 (92.43) Prec@5 98.96 (99.83)
|
|
train[2019-03-31-20:22:00] Epoch: [140][520/521] Time 0.22 (0.35) Data 0.00 (0.00) Loss 0.361 (0.337) Prec@1 91.25 (92.40) Prec@5 100.00 (99.83)
|
|
[2019-03-31-20:22:00] **train** Prec@1 92.40 Prec@5 99.83 Error@1 7.60 Error@5 0.17 Loss:0.337
|
|
test [2019-03-31-20:22:00] Epoch: [140][000/105] Time 0.47 (0.47) Data 0.41 (0.41) Loss 0.157 (0.157) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-20:22:04] Epoch: [140][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.119 (0.202) Prec@1 95.83 (93.47) Prec@5 100.00 (99.82)
|
|
test [2019-03-31-20:22:04] Epoch: [140][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.137 (0.202) Prec@1 93.75 (93.45) Prec@5 100.00 (99.83)
|
|
[2019-03-31-20:22:05] **test** Prec@1 93.45 Prec@5 99.83 Error@1 6.55 Error@5 0.17 Loss:0.202
|
|
----> Best Accuracy : Acc@1=93.52, Acc@5=99.84, Error@1=6.48, Error@5=0.16
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-20:22:05] [Epoch=141/600] [Need: 23:58:11] LR=0.0218 ~ 0.0218, Batch=96
|
|
train[2019-03-31-20:22:06] Epoch: [141][000/521] Time 0.80 (0.80) Data 0.53 (0.53) Loss 0.201 (0.201) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-20:22:29] Epoch: [141][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.393 (0.307) Prec@1 90.62 (93.08) Prec@5 100.00 (99.90)
|
|
train[2019-03-31-20:22:53] Epoch: [141][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.395 (0.313) Prec@1 89.58 (92.95) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-20:23:17] Epoch: [141][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.349 (0.320) Prec@1 89.58 (92.77) Prec@5 98.96 (99.85)
|
|
train[2019-03-31-20:23:41] Epoch: [141][400/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.299 (0.330) Prec@1 90.62 (92.48) Prec@5 100.00 (99.84)
|
|
train[2019-03-31-20:24:04] Epoch: [141][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.341 (0.338) Prec@1 91.67 (92.31) Prec@5 100.00 (99.83)
|
|
train[2019-03-31-20:24:09] Epoch: [141][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.346 (0.339) Prec@1 95.00 (92.28) Prec@5 100.00 (99.83)
|
|
[2019-03-31-20:24:09] **train** Prec@1 92.28 Prec@5 99.83 Error@1 7.72 Error@5 0.17 Loss:0.339
|
|
test [2019-03-31-20:24:09] Epoch: [141][000/105] Time 0.46 (0.46) Data 0.40 (0.40) Loss 0.231 (0.231) Prec@1 91.67 (91.67) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-20:24:14] Epoch: [141][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.090 (0.265) Prec@1 97.92 (91.95) Prec@5 100.00 (99.78)
|
|
test [2019-03-31-20:24:14] Epoch: [141][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.226 (0.263) Prec@1 93.75 (91.99) Prec@5 100.00 (99.79)
|
|
[2019-03-31-20:24:14] **test** Prec@1 91.99 Prec@5 99.79 Error@1 8.01 Error@5 0.21 Loss:0.263
|
|
----> Best Accuracy : Acc@1=93.52, Acc@5=99.84, Error@1=6.48, Error@5=0.16
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-20:24:14] [Epoch=142/600] [Need: 16:26:08] LR=0.0217 ~ 0.0217, Batch=96
|
|
train[2019-03-31-20:24:15] Epoch: [142][000/521] Time 0.71 (0.71) Data 0.43 (0.43) Loss 0.418 (0.418) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-20:24:38] Epoch: [142][100/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.387 (0.331) Prec@1 88.54 (92.49) Prec@5 100.00 (99.87)
|
|
train[2019-03-31-20:25:02] Epoch: [142][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.204 (0.337) Prec@1 94.79 (92.34) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-20:25:26] Epoch: [142][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.194 (0.336) Prec@1 94.79 (92.23) Prec@5 100.00 (99.86)
|
|
train[2019-03-31-20:25:49] Epoch: [142][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.407 (0.335) Prec@1 89.58 (92.27) Prec@5 98.96 (99.86)
|
|
train[2019-03-31-20:26:13] Epoch: [142][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.455 (0.339) Prec@1 90.62 (92.19) Prec@5 98.96 (99.84)
|
|
train[2019-03-31-20:26:18] Epoch: [142][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.409 (0.341) Prec@1 95.00 (92.17) Prec@5 100.00 (99.84)
|
|
[2019-03-31-20:26:18] **train** Prec@1 92.17 Prec@5 99.84 Error@1 7.83 Error@5 0.16 Loss:0.341
|
|
test [2019-03-31-20:26:18] Epoch: [142][000/105] Time 0.59 (0.59) Data 0.53 (0.53) Loss 0.328 (0.328) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-20:26:23] Epoch: [142][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.077 (0.234) Prec@1 96.88 (92.68) Prec@5 100.00 (99.82)
|
|
test [2019-03-31-20:26:23] Epoch: [142][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.232 (0.234) Prec@1 87.50 (92.62) Prec@5 100.00 (99.83)
|
|
[2019-03-31-20:26:23] **test** Prec@1 92.62 Prec@5 99.83 Error@1 7.38 Error@5 0.17 Loss:0.234
|
|
----> Best Accuracy : Acc@1=93.52, Acc@5=99.84, Error@1=6.48, Error@5=0.16
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-20:26:23] [Epoch=143/600] [Need: 16:22:41] LR=0.0217 ~ 0.0217, Batch=96
|
|
train[2019-03-31-20:26:24] Epoch: [143][000/521] Time 0.71 (0.71) Data 0.45 (0.45) Loss 0.465 (0.465) Prec@1 89.58 (89.58) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-20:26:47] Epoch: [143][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.390 (0.328) Prec@1 92.71 (92.41) Prec@5 100.00 (99.91)
|
|
train[2019-03-31-20:27:11] Epoch: [143][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.305 (0.327) Prec@1 91.67 (92.49) Prec@5 100.00 (99.90)
|
|
train[2019-03-31-20:27:35] Epoch: [143][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.340 (0.328) Prec@1 92.71 (92.57) Prec@5 100.00 (99.87)
|
|
train[2019-03-31-20:27:59] Epoch: [143][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.385 (0.327) Prec@1 93.75 (92.60) Prec@5 100.00 (99.86)
|
|
train[2019-03-31-20:28:22] Epoch: [143][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.269 (0.336) Prec@1 93.75 (92.31) Prec@5 100.00 (99.84)
|
|
train[2019-03-31-20:28:27] Epoch: [143][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.324 (0.337) Prec@1 93.75 (92.31) Prec@5 100.00 (99.84)
|
|
[2019-03-31-20:28:27] **train** Prec@1 92.31 Prec@5 99.84 Error@1 7.69 Error@5 0.16 Loss:0.337
|
|
test [2019-03-31-20:28:27] Epoch: [143][000/105] Time 0.46 (0.46) Data 0.40 (0.40) Loss 0.432 (0.432) Prec@1 87.50 (87.50) Prec@5 98.96 (98.96)
|
|
test [2019-03-31-20:28:32] Epoch: [143][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.196 (0.333) Prec@1 90.62 (90.23) Prec@5 100.00 (99.73)
|
|
test [2019-03-31-20:28:32] Epoch: [143][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.141 (0.332) Prec@1 93.75 (90.24) Prec@5 100.00 (99.74)
|
|
[2019-03-31-20:28:32] **test** Prec@1 90.24 Prec@5 99.74 Error@1 9.76 Error@5 0.26 Loss:0.332
|
|
----> Best Accuracy : Acc@1=93.52, Acc@5=99.84, Error@1=6.48, Error@5=0.16
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-20:28:32] [Epoch=144/600] [Need: 16:20:19] LR=0.0216 ~ 0.0216, Batch=96
|
|
train[2019-03-31-20:28:33] Epoch: [144][000/521] Time 0.71 (0.71) Data 0.43 (0.43) Loss 0.183 (0.183) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-20:28:56] Epoch: [144][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.304 (0.333) Prec@1 94.79 (92.34) Prec@5 100.00 (99.85)
|
|
train[2019-03-31-20:29:20] Epoch: [144][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.221 (0.333) Prec@1 94.79 (92.35) Prec@5 100.00 (99.86)
|
|
train[2019-03-31-20:29:44] Epoch: [144][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.398 (0.328) Prec@1 90.62 (92.42) Prec@5 100.00 (99.88)
|
|
train[2019-03-31-20:30:07] Epoch: [144][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.330 (0.332) Prec@1 91.67 (92.41) Prec@5 100.00 (99.88)
|
|
train[2019-03-31-20:30:31] Epoch: [144][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.318 (0.336) Prec@1 91.67 (92.28) Prec@5 100.00 (99.86)
|
|
train[2019-03-31-20:30:36] Epoch: [144][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.459 (0.336) Prec@1 86.25 (92.28) Prec@5 100.00 (99.87)
|
|
[2019-03-31-20:30:36] **train** Prec@1 92.28 Prec@5 99.87 Error@1 7.72 Error@5 0.13 Loss:0.336
|
|
test [2019-03-31-20:30:36] Epoch: [144][000/105] Time 0.47 (0.47) Data 0.41 (0.41) Loss 0.228 (0.228) Prec@1 90.62 (90.62) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-20:30:41] Epoch: [144][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.128 (0.265) Prec@1 94.79 (91.79) Prec@5 100.00 (99.73)
|
|
test [2019-03-31-20:30:41] Epoch: [144][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.092 (0.264) Prec@1 100.00 (91.87) Prec@5 100.00 (99.74)
|
|
[2019-03-31-20:30:41] **test** Prec@1 91.87 Prec@5 99.74 Error@1 8.13 Error@5 0.26 Loss:0.264
|
|
----> Best Accuracy : Acc@1=93.52, Acc@5=99.84, Error@1=6.48, Error@5=0.16
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-20:30:41] [Epoch=145/600] [Need: 16:18:20] LR=0.0216 ~ 0.0216, Batch=96
|
|
train[2019-03-31-20:30:42] Epoch: [145][000/521] Time 0.83 (0.83) Data 0.56 (0.56) Loss 0.330 (0.330) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-20:31:05] Epoch: [145][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.430 (0.315) Prec@1 87.50 (92.66) Prec@5 100.00 (99.90)
|
|
train[2019-03-31-20:31:29] Epoch: [145][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.454 (0.327) Prec@1 88.54 (92.39) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-20:31:53] Epoch: [145][300/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.376 (0.328) Prec@1 92.71 (92.43) Prec@5 98.96 (99.87)
|
|
train[2019-03-31-20:32:17] Epoch: [145][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.397 (0.332) Prec@1 91.67 (92.37) Prec@5 100.00 (99.86)
|
|
train[2019-03-31-20:32:40] Epoch: [145][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.362 (0.336) Prec@1 90.62 (92.29) Prec@5 100.00 (99.86)
|
|
train[2019-03-31-20:32:45] Epoch: [145][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.427 (0.337) Prec@1 90.00 (92.28) Prec@5 100.00 (99.86)
|
|
[2019-03-31-20:32:45] **train** Prec@1 92.28 Prec@5 99.86 Error@1 7.72 Error@5 0.14 Loss:0.337
|
|
test [2019-03-31-20:32:46] Epoch: [145][000/105] Time 0.47 (0.47) Data 0.41 (0.41) Loss 0.146 (0.146) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-20:32:50] Epoch: [145][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.104 (0.241) Prec@1 96.88 (92.78) Prec@5 100.00 (99.87)
|
|
test [2019-03-31-20:32:50] Epoch: [145][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.083 (0.240) Prec@1 93.75 (92.79) Prec@5 100.00 (99.87)
|
|
[2019-03-31-20:32:50] **test** Prec@1 92.79 Prec@5 99.87 Error@1 7.21 Error@5 0.13 Loss:0.240
|
|
----> Best Accuracy : Acc@1=93.52, Acc@5=99.84, Error@1=6.48, Error@5=0.16
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-20:32:50] [Epoch=146/600] [Need: 16:17:00] LR=0.0215 ~ 0.0215, Batch=96
|
|
train[2019-03-31-20:32:51] Epoch: [146][000/521] Time 0.71 (0.71) Data 0.44 (0.44) Loss 0.190 (0.190) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-20:33:15] Epoch: [146][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.174 (0.332) Prec@1 96.88 (92.00) Prec@5 100.00 (99.90)
|
|
train[2019-03-31-20:33:38] Epoch: [146][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.301 (0.337) Prec@1 93.75 (92.17) Prec@5 100.00 (99.88)
|
|
train[2019-03-31-20:34:02] Epoch: [146][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.422 (0.340) Prec@1 90.62 (92.21) Prec@5 100.00 (99.85)
|
|
train[2019-03-31-20:34:26] Epoch: [146][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.330 (0.336) Prec@1 92.71 (92.38) Prec@5 100.00 (99.85)
|
|
train[2019-03-31-20:34:49] Epoch: [146][500/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.299 (0.339) Prec@1 93.75 (92.27) Prec@5 100.00 (99.84)
|
|
train[2019-03-31-20:34:54] Epoch: [146][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.357 (0.338) Prec@1 92.50 (92.29) Prec@5 100.00 (99.85)
|
|
[2019-03-31-20:34:54] **train** Prec@1 92.29 Prec@5 99.85 Error@1 7.71 Error@5 0.15 Loss:0.338
|
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test [2019-03-31-20:34:55] Epoch: [146][000/105] Time 0.60 (0.60) Data 0.54 (0.54) Loss 0.256 (0.256) Prec@1 91.67 (91.67) Prec@5 100.00 (100.00)
|
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test [2019-03-31-20:34:59] Epoch: [146][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.101 (0.245) Prec@1 97.92 (92.34) Prec@5 100.00 (99.64)
|
|
test [2019-03-31-20:34:59] Epoch: [146][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.370 (0.244) Prec@1 87.50 (92.39) Prec@5 100.00 (99.64)
|
|
[2019-03-31-20:34:59] **test** Prec@1 92.39 Prec@5 99.64 Error@1 7.61 Error@5 0.36 Loss:0.244
|
|
----> Best Accuracy : Acc@1=93.52, Acc@5=99.84, Error@1=6.48, Error@5=0.16
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-20:34:59] [Epoch=147/600] [Need: 16:14:37] LR=0.0215 ~ 0.0215, Batch=96
|
|
train[2019-03-31-20:35:00] Epoch: [147][000/521] Time 0.84 (0.84) Data 0.57 (0.57) Loss 0.286 (0.286) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-20:35:24] Epoch: [147][100/521] Time 0.23 (0.24) Data 0.00 (0.01) Loss 0.336 (0.325) Prec@1 91.67 (92.53) Prec@5 100.00 (99.92)
|
|
train[2019-03-31-20:35:47] Epoch: [147][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.210 (0.329) Prec@1 95.83 (92.46) Prec@5 100.00 (99.88)
|
|
train[2019-03-31-20:36:11] Epoch: [147][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.369 (0.331) Prec@1 92.71 (92.33) Prec@5 98.96 (99.87)
|
|
train[2019-03-31-20:36:36] Epoch: [147][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.192 (0.333) Prec@1 96.88 (92.23) Prec@5 100.00 (99.87)
|
|
train[2019-03-31-20:36:59] Epoch: [147][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.297 (0.337) Prec@1 92.71 (92.08) Prec@5 100.00 (99.87)
|
|
train[2019-03-31-20:37:04] Epoch: [147][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.382 (0.336) Prec@1 95.00 (92.10) Prec@5 100.00 (99.87)
|
|
[2019-03-31-20:37:04] **train** Prec@1 92.10 Prec@5 99.87 Error@1 7.90 Error@5 0.13 Loss:0.336
|
|
test [2019-03-31-20:37:05] Epoch: [147][000/105] Time 0.52 (0.52) Data 0.46 (0.46) Loss 0.232 (0.232) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-20:37:09] Epoch: [147][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.187 (0.212) Prec@1 93.75 (93.39) Prec@5 100.00 (99.85)
|
|
test [2019-03-31-20:37:09] Epoch: [147][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.039 (0.213) Prec@1 100.00 (93.41) Prec@5 100.00 (99.85)
|
|
[2019-03-31-20:37:09] **test** Prec@1 93.41 Prec@5 99.85 Error@1 6.59 Error@5 0.15 Loss:0.213
|
|
----> Best Accuracy : Acc@1=93.52, Acc@5=99.84, Error@1=6.48, Error@5=0.16
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-20:37:09] [Epoch=148/600] [Need: 16:18:51] LR=0.0214 ~ 0.0214, Batch=96
|
|
train[2019-03-31-20:37:10] Epoch: [148][000/521] Time 0.71 (0.71) Data 0.44 (0.44) Loss 0.218 (0.218) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-20:37:34] Epoch: [148][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.394 (0.332) Prec@1 93.75 (92.40) Prec@5 97.92 (99.89)
|
|
train[2019-03-31-20:37:57] Epoch: [148][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.312 (0.323) Prec@1 90.62 (92.60) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-20:38:21] Epoch: [148][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.263 (0.322) Prec@1 94.79 (92.62) Prec@5 100.00 (99.85)
|
|
train[2019-03-31-20:38:44] Epoch: [148][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.429 (0.326) Prec@1 87.50 (92.52) Prec@5 100.00 (99.86)
|
|
train[2019-03-31-20:39:08] Epoch: [148][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.314 (0.330) Prec@1 91.67 (92.49) Prec@5 100.00 (99.84)
|
|
train[2019-03-31-20:39:13] Epoch: [148][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.374 (0.329) Prec@1 91.25 (92.50) Prec@5 98.75 (99.84)
|
|
[2019-03-31-20:39:13] **train** Prec@1 92.50 Prec@5 99.84 Error@1 7.50 Error@5 0.16 Loss:0.329
|
|
test [2019-03-31-20:39:13] Epoch: [148][000/105] Time 0.62 (0.62) Data 0.55 (0.55) Loss 0.220 (0.220) Prec@1 91.67 (91.67) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-20:39:17] Epoch: [148][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.158 (0.239) Prec@1 94.79 (92.58) Prec@5 100.00 (99.86)
|
|
test [2019-03-31-20:39:18] Epoch: [148][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.362 (0.239) Prec@1 87.50 (92.56) Prec@5 100.00 (99.86)
|
|
[2019-03-31-20:39:18] **test** Prec@1 92.56 Prec@5 99.86 Error@1 7.44 Error@5 0.14 Loss:0.239
|
|
----> Best Accuracy : Acc@1=93.52, Acc@5=99.84, Error@1=6.48, Error@5=0.16
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-20:39:18] [Epoch=149/600] [Need: 16:07:50] LR=0.0214 ~ 0.0214, Batch=96
|
|
train[2019-03-31-20:39:19] Epoch: [149][000/521] Time 0.71 (0.71) Data 0.44 (0.44) Loss 0.225 (0.225) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-20:39:42] Epoch: [149][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.205 (0.334) Prec@1 95.83 (92.65) Prec@5 100.00 (99.80)
|
|
train[2019-03-31-20:40:06] Epoch: [149][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.347 (0.337) Prec@1 92.71 (92.45) Prec@5 98.96 (99.82)
|
|
train[2019-03-31-20:40:30] Epoch: [149][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.295 (0.340) Prec@1 94.79 (92.41) Prec@5 100.00 (99.82)
|
|
train[2019-03-31-20:40:53] Epoch: [149][400/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.271 (0.334) Prec@1 94.79 (92.44) Prec@5 100.00 (99.85)
|
|
train[2019-03-31-20:41:17] Epoch: [149][500/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.344 (0.336) Prec@1 90.62 (92.34) Prec@5 100.00 (99.85)
|
|
train[2019-03-31-20:41:22] Epoch: [149][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.388 (0.336) Prec@1 92.50 (92.36) Prec@5 100.00 (99.85)
|
|
[2019-03-31-20:41:22] **train** Prec@1 92.36 Prec@5 99.85 Error@1 7.64 Error@5 0.15 Loss:0.336
|
|
test [2019-03-31-20:41:22] Epoch: [149][000/105] Time 0.52 (0.52) Data 0.46 (0.46) Loss 0.290 (0.290) Prec@1 90.62 (90.62) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-20:41:26] Epoch: [149][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.130 (0.235) Prec@1 95.83 (92.48) Prec@5 100.00 (99.77)
|
|
test [2019-03-31-20:41:26] Epoch: [149][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.505 (0.236) Prec@1 87.50 (92.49) Prec@5 100.00 (99.78)
|
|
[2019-03-31-20:41:27] **test** Prec@1 92.49 Prec@5 99.78 Error@1 7.51 Error@5 0.22 Loss:0.236
|
|
----> Best Accuracy : Acc@1=93.52, Acc@5=99.84, Error@1=6.48, Error@5=0.16
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-20:41:27] [Epoch=150/600] [Need: 16:06:15] LR=0.0214 ~ 0.0214, Batch=96
|
|
train[2019-03-31-20:41:28] Epoch: [150][000/521] Time 0.84 (0.84) Data 0.57 (0.57) Loss 0.310 (0.310) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-20:41:51] Epoch: [150][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.339 (0.320) Prec@1 92.71 (92.85) Prec@5 100.00 (99.85)
|
|
train[2019-03-31-20:42:15] Epoch: [150][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.302 (0.333) Prec@1 93.75 (92.46) Prec@5 100.00 (99.80)
|
|
train[2019-03-31-20:42:39] Epoch: [150][300/521] Time 0.26 (0.24) Data 0.00 (0.00) Loss 0.224 (0.340) Prec@1 96.88 (92.29) Prec@5 100.00 (99.81)
|
|
train[2019-03-31-20:43:02] Epoch: [150][400/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.399 (0.338) Prec@1 93.75 (92.32) Prec@5 100.00 (99.82)
|
|
train[2019-03-31-20:43:26] Epoch: [150][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.364 (0.337) Prec@1 88.54 (92.35) Prec@5 100.00 (99.82)
|
|
train[2019-03-31-20:43:31] Epoch: [150][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.222 (0.337) Prec@1 93.75 (92.34) Prec@5 100.00 (99.82)
|
|
[2019-03-31-20:43:31] **train** Prec@1 92.34 Prec@5 99.82 Error@1 7.66 Error@5 0.18 Loss:0.337
|
|
test [2019-03-31-20:43:31] Epoch: [150][000/105] Time 0.50 (0.50) Data 0.40 (0.40) Loss 0.266 (0.266) Prec@1 86.46 (86.46) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-20:43:35] Epoch: [150][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.106 (0.266) Prec@1 95.83 (92.15) Prec@5 100.00 (99.72)
|
|
test [2019-03-31-20:43:36] Epoch: [150][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.169 (0.265) Prec@1 93.75 (92.13) Prec@5 100.00 (99.73)
|
|
[2019-03-31-20:43:36] **test** Prec@1 92.13 Prec@5 99.73 Error@1 7.87 Error@5 0.27 Loss:0.265
|
|
----> Best Accuracy : Acc@1=93.52, Acc@5=99.84, Error@1=6.48, Error@5=0.16
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-20:43:36] [Epoch=151/600] [Need: 16:06:23] LR=0.0213 ~ 0.0213, Batch=96
|
|
train[2019-03-31-20:43:37] Epoch: [151][000/521] Time 0.80 (0.80) Data 0.53 (0.53) Loss 0.262 (0.262) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-20:44:00] Epoch: [151][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.349 (0.320) Prec@1 92.71 (92.47) Prec@5 100.00 (99.94)
|
|
train[2019-03-31-20:44:24] Epoch: [151][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.355 (0.333) Prec@1 92.71 (92.41) Prec@5 100.00 (99.84)
|
|
train[2019-03-31-20:44:49] Epoch: [151][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.254 (0.331) Prec@1 94.79 (92.45) Prec@5 100.00 (99.85)
|
|
train[2019-03-31-20:45:12] Epoch: [151][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.333 (0.334) Prec@1 91.67 (92.33) Prec@5 100.00 (99.86)
|
|
train[2019-03-31-20:45:36] Epoch: [151][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.294 (0.338) Prec@1 91.67 (92.15) Prec@5 100.00 (99.85)
|
|
train[2019-03-31-20:45:41] Epoch: [151][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.279 (0.337) Prec@1 96.25 (92.18) Prec@5 100.00 (99.86)
|
|
[2019-03-31-20:45:41] **train** Prec@1 92.18 Prec@5 99.86 Error@1 7.82 Error@5 0.14 Loss:0.337
|
|
test [2019-03-31-20:45:41] Epoch: [151][000/105] Time 0.46 (0.46) Data 0.40 (0.40) Loss 0.312 (0.312) Prec@1 88.54 (88.54) Prec@5 98.96 (98.96)
|
|
test [2019-03-31-20:45:45] Epoch: [151][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.129 (0.235) Prec@1 96.88 (92.67) Prec@5 100.00 (99.89)
|
|
test [2019-03-31-20:45:46] Epoch: [151][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.042 (0.235) Prec@1 100.00 (92.68) Prec@5 100.00 (99.89)
|
|
[2019-03-31-20:45:46] **test** Prec@1 92.68 Prec@5 99.89 Error@1 7.32 Error@5 0.11 Loss:0.235
|
|
----> Best Accuracy : Acc@1=93.52, Acc@5=99.84, Error@1=6.48, Error@5=0.16
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-20:45:46] [Epoch=152/600] [Need: 16:10:42] LR=0.0213 ~ 0.0213, Batch=96
|
|
train[2019-03-31-20:45:47] Epoch: [152][000/521] Time 0.70 (0.70) Data 0.43 (0.43) Loss 0.241 (0.241) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-20:46:10] Epoch: [152][100/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.229 (0.325) Prec@1 94.79 (92.61) Prec@5 100.00 (99.92)
|
|
train[2019-03-31-20:46:34] Epoch: [152][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.165 (0.325) Prec@1 96.88 (92.65) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-20:46:58] Epoch: [152][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.206 (0.319) Prec@1 96.88 (92.82) Prec@5 100.00 (99.88)
|
|
train[2019-03-31-20:47:21] Epoch: [152][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.493 (0.319) Prec@1 88.54 (92.82) Prec@5 100.00 (99.90)
|
|
train[2019-03-31-20:47:45] Epoch: [152][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.243 (0.328) Prec@1 94.79 (92.58) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-20:47:50] Epoch: [152][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.470 (0.329) Prec@1 87.50 (92.56) Prec@5 98.75 (99.88)
|
|
[2019-03-31-20:47:50] **train** Prec@1 92.56 Prec@5 99.88 Error@1 7.44 Error@5 0.12 Loss:0.329
|
|
test [2019-03-31-20:47:51] Epoch: [152][000/105] Time 0.47 (0.47) Data 0.40 (0.40) Loss 0.216 (0.216) Prec@1 91.67 (91.67) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-20:47:55] Epoch: [152][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.166 (0.254) Prec@1 93.75 (92.41) Prec@5 100.00 (99.86)
|
|
test [2019-03-31-20:47:55] Epoch: [152][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.034 (0.255) Prec@1 100.00 (92.39) Prec@5 100.00 (99.86)
|
|
[2019-03-31-20:47:55] **test** Prec@1 92.39 Prec@5 99.86 Error@1 7.61 Error@5 0.14 Loss:0.255
|
|
----> Best Accuracy : Acc@1=93.52, Acc@5=99.84, Error@1=6.48, Error@5=0.16
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-20:47:55] [Epoch=153/600] [Need: 16:02:52] LR=0.0212 ~ 0.0212, Batch=96
|
|
train[2019-03-31-20:47:56] Epoch: [153][000/521] Time 0.70 (0.70) Data 0.43 (0.43) Loss 0.281 (0.281) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-20:48:20] Epoch: [153][100/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.382 (0.317) Prec@1 90.62 (93.09) Prec@5 98.96 (99.90)
|
|
train[2019-03-31-20:48:44] Epoch: [153][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.288 (0.330) Prec@1 93.75 (92.71) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-20:49:08] Epoch: [153][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.314 (0.327) Prec@1 90.62 (92.70) Prec@5 98.96 (99.90)
|
|
train[2019-03-31-20:49:32] Epoch: [153][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.176 (0.334) Prec@1 96.88 (92.47) Prec@5 100.00 (99.87)
|
|
train[2019-03-31-20:49:56] Epoch: [153][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.373 (0.335) Prec@1 91.67 (92.41) Prec@5 100.00 (99.87)
|
|
train[2019-03-31-20:50:00] Epoch: [153][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.264 (0.335) Prec@1 95.00 (92.41) Prec@5 100.00 (99.87)
|
|
[2019-03-31-20:50:00] **train** Prec@1 92.41 Prec@5 99.87 Error@1 7.59 Error@5 0.13 Loss:0.335
|
|
test [2019-03-31-20:50:01] Epoch: [153][000/105] Time 0.60 (0.60) Data 0.53 (0.53) Loss 0.206 (0.206) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-20:50:05] Epoch: [153][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.072 (0.223) Prec@1 96.88 (93.10) Prec@5 100.00 (99.76)
|
|
test [2019-03-31-20:50:05] Epoch: [153][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.197 (0.224) Prec@1 87.50 (93.09) Prec@5 100.00 (99.76)
|
|
[2019-03-31-20:50:05] **test** Prec@1 93.09 Prec@5 99.76 Error@1 6.91 Error@5 0.24 Loss:0.224
|
|
----> Best Accuracy : Acc@1=93.52, Acc@5=99.84, Error@1=6.48, Error@5=0.16
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-20:50:05] [Epoch=154/600] [Need: 16:08:12] LR=0.0212 ~ 0.0212, Batch=96
|
|
train[2019-03-31-20:50:06] Epoch: [154][000/521] Time 0.84 (0.84) Data 0.56 (0.56) Loss 0.376 (0.376) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-20:50:30] Epoch: [154][100/521] Time 0.23 (0.24) Data 0.00 (0.01) Loss 0.360 (0.321) Prec@1 93.75 (92.89) Prec@5 100.00 (99.88)
|
|
train[2019-03-31-20:50:54] Epoch: [154][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.159 (0.325) Prec@1 98.96 (92.77) Prec@5 100.00 (99.85)
|
|
train[2019-03-31-20:51:17] Epoch: [154][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.324 (0.331) Prec@1 91.67 (92.51) Prec@5 100.00 (99.85)
|
|
train[2019-03-31-20:51:41] Epoch: [154][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.267 (0.328) Prec@1 93.75 (92.58) Prec@5 100.00 (99.85)
|
|
train[2019-03-31-20:52:05] Epoch: [154][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.395 (0.333) Prec@1 90.62 (92.47) Prec@5 100.00 (99.86)
|
|
train[2019-03-31-20:52:09] Epoch: [154][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.339 (0.332) Prec@1 92.50 (92.50) Prec@5 98.75 (99.86)
|
|
[2019-03-31-20:52:09] **train** Prec@1 92.50 Prec@5 99.86 Error@1 7.50 Error@5 0.14 Loss:0.332
|
|
test [2019-03-31-20:52:10] Epoch: [154][000/105] Time 0.47 (0.47) Data 0.40 (0.40) Loss 0.172 (0.172) Prec@1 94.79 (94.79) Prec@5 98.96 (98.96)
|
|
test [2019-03-31-20:52:14] Epoch: [154][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.059 (0.203) Prec@1 96.88 (93.72) Prec@5 100.00 (99.82)
|
|
test [2019-03-31-20:52:14] Epoch: [154][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.143 (0.204) Prec@1 93.75 (93.70) Prec@5 100.00 (99.83)
|
|
[2019-03-31-20:52:14] **test** Prec@1 93.70 Prec@5 99.83 Error@1 6.30 Error@5 0.17 Loss:0.204
|
|
----> Best Accuracy : Acc@1=93.70, Acc@5=99.83, Error@1=6.30, Error@5=0.17
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-20:52:14] [Epoch=155/600] [Need: 15:57:22] LR=0.0211 ~ 0.0211, Batch=96
|
|
train[2019-03-31-20:52:15] Epoch: [155][000/521] Time 0.70 (0.70) Data 0.43 (0.43) Loss 0.332 (0.332) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-20:52:39] Epoch: [155][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.377 (0.316) Prec@1 91.67 (92.46) Prec@5 98.96 (99.87)
|
|
train[2019-03-31-20:53:03] Epoch: [155][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.271 (0.328) Prec@1 92.71 (92.42) Prec@5 100.00 (99.86)
|
|
train[2019-03-31-20:53:26] Epoch: [155][300/521] Time 0.26 (0.24) Data 0.00 (0.00) Loss 0.295 (0.324) Prec@1 91.67 (92.52) Prec@5 98.96 (99.85)
|
|
train[2019-03-31-20:53:51] Epoch: [155][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.449 (0.325) Prec@1 87.50 (92.53) Prec@5 100.00 (99.84)
|
|
train[2019-03-31-20:54:14] Epoch: [155][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.390 (0.330) Prec@1 93.75 (92.42) Prec@5 100.00 (99.84)
|
|
train[2019-03-31-20:54:19] Epoch: [155][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.373 (0.331) Prec@1 93.75 (92.41) Prec@5 100.00 (99.84)
|
|
[2019-03-31-20:54:19] **train** Prec@1 92.41 Prec@5 99.84 Error@1 7.59 Error@5 0.16 Loss:0.331
|
|
test [2019-03-31-20:54:19] Epoch: [155][000/105] Time 0.47 (0.47) Data 0.40 (0.40) Loss 0.101 (0.101) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-20:54:23] Epoch: [155][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.111 (0.246) Prec@1 96.88 (92.48) Prec@5 100.00 (99.71)
|
|
test [2019-03-31-20:54:24] Epoch: [155][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.102 (0.247) Prec@1 93.75 (92.44) Prec@5 100.00 (99.72)
|
|
[2019-03-31-20:54:24] **test** Prec@1 92.44 Prec@5 99.72 Error@1 7.56 Error@5 0.28 Loss:0.247
|
|
----> Best Accuracy : Acc@1=93.70, Acc@5=99.83, Error@1=6.30, Error@5=0.17
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-20:54:24] [Epoch=156/600] [Need: 15:57:39] LR=0.0211 ~ 0.0211, Batch=96
|
|
train[2019-03-31-20:54:25] Epoch: [156][000/521] Time 0.83 (0.83) Data 0.56 (0.56) Loss 0.319 (0.319) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-20:54:48] Epoch: [156][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.217 (0.303) Prec@1 95.83 (92.91) Prec@5 100.00 (99.87)
|
|
train[2019-03-31-20:55:12] Epoch: [156][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.363 (0.316) Prec@1 91.67 (92.68) Prec@5 100.00 (99.87)
|
|
train[2019-03-31-20:55:36] Epoch: [156][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.342 (0.314) Prec@1 90.62 (92.80) Prec@5 100.00 (99.85)
|
|
train[2019-03-31-20:56:00] Epoch: [156][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.344 (0.322) Prec@1 92.71 (92.60) Prec@5 100.00 (99.86)
|
|
train[2019-03-31-20:56:24] Epoch: [156][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.461 (0.327) Prec@1 89.58 (92.50) Prec@5 100.00 (99.86)
|
|
train[2019-03-31-20:56:28] Epoch: [156][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.572 (0.328) Prec@1 87.50 (92.48) Prec@5 100.00 (99.85)
|
|
[2019-03-31-20:56:29] **train** Prec@1 92.48 Prec@5 99.85 Error@1 7.52 Error@5 0.15 Loss:0.328
|
|
test [2019-03-31-20:56:29] Epoch: [156][000/105] Time 0.48 (0.48) Data 0.41 (0.41) Loss 0.277 (0.277) Prec@1 90.62 (90.62) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-20:56:33] Epoch: [156][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.086 (0.234) Prec@1 95.83 (92.69) Prec@5 100.00 (99.82)
|
|
test [2019-03-31-20:56:33] Epoch: [156][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.105 (0.234) Prec@1 93.75 (92.66) Prec@5 100.00 (99.83)
|
|
[2019-03-31-20:56:33] **test** Prec@1 92.66 Prec@5 99.83 Error@1 7.34 Error@5 0.17 Loss:0.234
|
|
----> Best Accuracy : Acc@1=93.70, Acc@5=99.83, Error@1=6.30, Error@5=0.17
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-20:56:34] [Epoch=157/600] [Need: 15:57:32] LR=0.0210 ~ 0.0210, Batch=96
|
|
train[2019-03-31-20:56:34] Epoch: [157][000/521] Time 0.84 (0.84) Data 0.56 (0.56) Loss 0.216 (0.216) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-20:56:58] Epoch: [157][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.284 (0.323) Prec@1 91.67 (92.49) Prec@5 100.00 (99.86)
|
|
train[2019-03-31-20:57:22] Epoch: [157][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.339 (0.328) Prec@1 90.62 (92.44) Prec@5 100.00 (99.85)
|
|
train[2019-03-31-20:57:45] Epoch: [157][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.348 (0.325) Prec@1 94.79 (92.56) Prec@5 100.00 (99.84)
|
|
train[2019-03-31-20:58:09] Epoch: [157][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.473 (0.326) Prec@1 86.46 (92.54) Prec@5 100.00 (99.83)
|
|
train[2019-03-31-20:58:33] Epoch: [157][500/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.447 (0.328) Prec@1 92.71 (92.50) Prec@5 100.00 (99.83)
|
|
train[2019-03-31-20:58:37] Epoch: [157][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.433 (0.330) Prec@1 90.00 (92.46) Prec@5 100.00 (99.84)
|
|
[2019-03-31-20:58:38] **train** Prec@1 92.46 Prec@5 99.84 Error@1 7.54 Error@5 0.16 Loss:0.330
|
|
test [2019-03-31-20:58:38] Epoch: [157][000/105] Time 0.62 (0.62) Data 0.54 (0.54) Loss 0.200 (0.200) Prec@1 91.67 (91.67) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-20:58:42] Epoch: [157][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.043 (0.270) Prec@1 100.00 (92.03) Prec@5 100.00 (99.83)
|
|
test [2019-03-31-20:58:42] Epoch: [157][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.093 (0.269) Prec@1 93.75 (92.02) Prec@5 100.00 (99.84)
|
|
[2019-03-31-20:58:42] **test** Prec@1 92.02 Prec@5 99.84 Error@1 7.98 Error@5 0.16 Loss:0.269
|
|
----> Best Accuracy : Acc@1=93.70, Acc@5=99.83, Error@1=6.30, Error@5=0.17
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-20:58:43] [Epoch=158/600] [Need: 15:51:16] LR=0.0210 ~ 0.0210, Batch=96
|
|
train[2019-03-31-20:58:43] Epoch: [158][000/521] Time 0.70 (0.70) Data 0.43 (0.43) Loss 0.476 (0.476) Prec@1 88.54 (88.54) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-20:59:07] Epoch: [158][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.294 (0.306) Prec@1 95.83 (93.06) Prec@5 100.00 (99.95)
|
|
train[2019-03-31-20:59:31] Epoch: [158][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.333 (0.318) Prec@1 91.67 (92.74) Prec@5 100.00 (99.91)
|
|
train[2019-03-31-20:59:55] Epoch: [158][300/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.293 (0.322) Prec@1 93.75 (92.67) Prec@5 100.00 (99.88)
|
|
train[2019-03-31-21:00:19] Epoch: [158][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.393 (0.329) Prec@1 91.67 (92.50) Prec@5 100.00 (99.88)
|
|
train[2019-03-31-21:00:43] Epoch: [158][500/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.456 (0.332) Prec@1 87.50 (92.44) Prec@5 100.00 (99.86)
|
|
train[2019-03-31-21:00:47] Epoch: [158][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.379 (0.331) Prec@1 88.75 (92.47) Prec@5 100.00 (99.87)
|
|
[2019-03-31-21:00:47] **train** Prec@1 92.47 Prec@5 99.87 Error@1 7.53 Error@5 0.13 Loss:0.331
|
|
test [2019-03-31-21:00:48] Epoch: [158][000/105] Time 0.48 (0.48) Data 0.41 (0.41) Loss 0.215 (0.215) Prec@1 91.67 (91.67) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-21:00:52] Epoch: [158][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.084 (0.228) Prec@1 95.83 (92.95) Prec@5 100.00 (99.78)
|
|
test [2019-03-31-21:00:52] Epoch: [158][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.398 (0.226) Prec@1 87.50 (93.00) Prec@5 100.00 (99.79)
|
|
[2019-03-31-21:00:52] **test** Prec@1 93.00 Prec@5 99.79 Error@1 7.00 Error@5 0.21 Loss:0.226
|
|
----> Best Accuracy : Acc@1=93.70, Acc@5=99.83, Error@1=6.30, Error@5=0.17
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-21:00:52] [Epoch=159/600] [Need: 15:52:15] LR=0.0209 ~ 0.0209, Batch=96
|
|
train[2019-03-31-21:00:53] Epoch: [159][000/521] Time 0.84 (0.84) Data 0.56 (0.56) Loss 0.339 (0.339) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-21:01:16] Epoch: [159][100/521] Time 0.23 (0.24) Data 0.00 (0.01) Loss 0.454 (0.316) Prec@1 88.54 (92.81) Prec@5 100.00 (99.90)
|
|
train[2019-03-31-21:01:40] Epoch: [159][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.462 (0.313) Prec@1 90.62 (93.02) Prec@5 100.00 (99.87)
|
|
train[2019-03-31-21:02:03] Epoch: [159][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.418 (0.323) Prec@1 89.58 (92.69) Prec@5 100.00 (99.85)
|
|
train[2019-03-31-21:02:28] Epoch: [159][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.249 (0.323) Prec@1 93.75 (92.64) Prec@5 100.00 (99.85)
|
|
train[2019-03-31-21:02:52] Epoch: [159][500/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.377 (0.326) Prec@1 91.67 (92.58) Prec@5 100.00 (99.85)
|
|
train[2019-03-31-21:02:56] Epoch: [159][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.493 (0.326) Prec@1 91.25 (92.56) Prec@5 100.00 (99.85)
|
|
[2019-03-31-21:02:56] **train** Prec@1 92.56 Prec@5 99.85 Error@1 7.44 Error@5 0.15 Loss:0.326
|
|
test [2019-03-31-21:02:57] Epoch: [159][000/105] Time 0.46 (0.46) Data 0.40 (0.40) Loss 0.220 (0.220) Prec@1 91.67 (91.67) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-21:03:01] Epoch: [159][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.107 (0.235) Prec@1 95.83 (92.79) Prec@5 100.00 (99.83)
|
|
test [2019-03-31-21:03:01] Epoch: [159][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.024 (0.233) Prec@1 100.00 (92.79) Prec@5 100.00 (99.84)
|
|
[2019-03-31-21:03:01] **test** Prec@1 92.79 Prec@5 99.84 Error@1 7.21 Error@5 0.16 Loss:0.233
|
|
----> Best Accuracy : Acc@1=93.70, Acc@5=99.83, Error@1=6.30, Error@5=0.17
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-21:03:01] [Epoch=160/600] [Need: 15:46:59] LR=0.0209 ~ 0.0209, Batch=96
|
|
train[2019-03-31-21:03:02] Epoch: [160][000/521] Time 0.70 (0.70) Data 0.43 (0.43) Loss 0.472 (0.472) Prec@1 89.58 (89.58) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-21:03:26] Epoch: [160][100/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.352 (0.325) Prec@1 90.62 (92.80) Prec@5 100.00 (99.88)
|
|
train[2019-03-31-21:03:49] Epoch: [160][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.221 (0.324) Prec@1 93.75 (92.68) Prec@5 100.00 (99.87)
|
|
train[2019-03-31-21:04:13] Epoch: [160][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.269 (0.323) Prec@1 94.79 (92.58) Prec@5 100.00 (99.88)
|
|
train[2019-03-31-21:04:37] Epoch: [160][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.313 (0.327) Prec@1 93.75 (92.41) Prec@5 100.00 (99.88)
|
|
train[2019-03-31-21:05:00] Epoch: [160][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.309 (0.332) Prec@1 93.75 (92.33) Prec@5 100.00 (99.86)
|
|
train[2019-03-31-21:05:05] Epoch: [160][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.321 (0.332) Prec@1 91.25 (92.34) Prec@5 100.00 (99.87)
|
|
[2019-03-31-21:05:05] **train** Prec@1 92.34 Prec@5 99.87 Error@1 7.66 Error@5 0.13 Loss:0.332
|
|
test [2019-03-31-21:05:06] Epoch: [160][000/105] Time 0.61 (0.61) Data 0.55 (0.55) Loss 0.339 (0.339) Prec@1 90.62 (90.62) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-21:05:10] Epoch: [160][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.090 (0.232) Prec@1 96.88 (92.83) Prec@5 100.00 (99.78)
|
|
test [2019-03-31-21:05:10] Epoch: [160][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.093 (0.232) Prec@1 93.75 (92.84) Prec@5 100.00 (99.78)
|
|
[2019-03-31-21:05:10] **test** Prec@1 92.84 Prec@5 99.78 Error@1 7.16 Error@5 0.22 Loss:0.232
|
|
----> Best Accuracy : Acc@1=93.70, Acc@5=99.83, Error@1=6.30, Error@5=0.17
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-21:05:10] [Epoch=161/600] [Need: 15:43:20] LR=0.0208 ~ 0.0208, Batch=96
|
|
train[2019-03-31-21:05:11] Epoch: [161][000/521] Time 0.83 (0.83) Data 0.57 (0.57) Loss 0.349 (0.349) Prec@1 90.62 (90.62) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-21:05:35] Epoch: [161][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.170 (0.300) Prec@1 97.92 (93.16) Prec@5 100.00 (99.90)
|
|
train[2019-03-31-21:05:58] Epoch: [161][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.339 (0.318) Prec@1 92.71 (92.66) Prec@5 100.00 (99.85)
|
|
train[2019-03-31-21:06:22] Epoch: [161][300/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.246 (0.315) Prec@1 94.79 (92.75) Prec@5 100.00 (99.86)
|
|
train[2019-03-31-21:06:46] Epoch: [161][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.155 (0.316) Prec@1 95.83 (92.78) Prec@5 100.00 (99.86)
|
|
train[2019-03-31-21:07:09] Epoch: [161][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.496 (0.324) Prec@1 88.54 (92.61) Prec@5 100.00 (99.85)
|
|
train[2019-03-31-21:07:14] Epoch: [161][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.196 (0.324) Prec@1 96.25 (92.62) Prec@5 98.75 (99.85)
|
|
[2019-03-31-21:07:14] **train** Prec@1 92.62 Prec@5 99.85 Error@1 7.38 Error@5 0.15 Loss:0.324
|
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test [2019-03-31-21:07:15] Epoch: [161][000/105] Time 0.59 (0.59) Data 0.53 (0.53) Loss 0.083 (0.083) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
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test [2019-03-31-21:07:19] Epoch: [161][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.180 (0.211) Prec@1 91.67 (93.21) Prec@5 100.00 (99.85)
|
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test [2019-03-31-21:07:19] Epoch: [161][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.033 (0.210) Prec@1 100.00 (93.23) Prec@5 100.00 (99.85)
|
|
[2019-03-31-21:07:19] **test** Prec@1 93.23 Prec@5 99.85 Error@1 6.77 Error@5 0.15 Loss:0.210
|
|
----> Best Accuracy : Acc@1=93.70, Acc@5=99.83, Error@1=6.30, Error@5=0.17
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-21:07:19] [Epoch=162/600] [Need: 15:41:40] LR=0.0208 ~ 0.0208, Batch=96
|
|
train[2019-03-31-21:07:20] Epoch: [162][000/521] Time 0.71 (0.71) Data 0.44 (0.44) Loss 0.312 (0.312) Prec@1 90.62 (90.62) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-21:07:44] Epoch: [162][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.275 (0.312) Prec@1 94.79 (92.82) Prec@5 100.00 (99.93)
|
|
train[2019-03-31-21:08:07] Epoch: [162][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.554 (0.322) Prec@1 89.58 (92.64) Prec@5 98.96 (99.93)
|
|
train[2019-03-31-21:08:31] Epoch: [162][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.309 (0.324) Prec@1 90.62 (92.64) Prec@5 100.00 (99.91)
|
|
train[2019-03-31-21:08:55] Epoch: [162][400/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.290 (0.323) Prec@1 94.79 (92.63) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-21:09:19] Epoch: [162][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.460 (0.324) Prec@1 89.58 (92.64) Prec@5 100.00 (99.88)
|
|
train[2019-03-31-21:09:23] Epoch: [162][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.569 (0.324) Prec@1 88.75 (92.66) Prec@5 100.00 (99.88)
|
|
[2019-03-31-21:09:24] **train** Prec@1 92.66 Prec@5 99.88 Error@1 7.34 Error@5 0.12 Loss:0.324
|
|
test [2019-03-31-21:09:24] Epoch: [162][000/105] Time 0.47 (0.47) Data 0.40 (0.40) Loss 0.227 (0.227) Prec@1 91.67 (91.67) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-21:09:28] Epoch: [162][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.165 (0.236) Prec@1 93.75 (93.02) Prec@5 100.00 (99.85)
|
|
test [2019-03-31-21:09:28] Epoch: [162][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.156 (0.238) Prec@1 93.75 (93.03) Prec@5 100.00 (99.83)
|
|
[2019-03-31-21:09:28] **test** Prec@1 93.03 Prec@5 99.83 Error@1 6.97 Error@5 0.17 Loss:0.238
|
|
----> Best Accuracy : Acc@1=93.70, Acc@5=99.83, Error@1=6.30, Error@5=0.17
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-21:09:28] [Epoch=163/600] [Need: 15:41:08] LR=0.0207 ~ 0.0207, Batch=96
|
|
train[2019-03-31-21:09:29] Epoch: [163][000/521] Time 0.71 (0.71) Data 0.43 (0.43) Loss 0.424 (0.424) Prec@1 91.67 (91.67) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-21:09:53] Epoch: [163][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.373 (0.316) Prec@1 90.62 (92.86) Prec@5 100.00 (99.88)
|
|
train[2019-03-31-21:10:17] Epoch: [163][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.285 (0.323) Prec@1 91.67 (92.64) Prec@5 100.00 (99.85)
|
|
train[2019-03-31-21:10:41] Epoch: [163][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.202 (0.323) Prec@1 95.83 (92.58) Prec@5 100.00 (99.87)
|
|
train[2019-03-31-21:11:04] Epoch: [163][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.280 (0.325) Prec@1 93.75 (92.56) Prec@5 100.00 (99.87)
|
|
train[2019-03-31-21:11:28] Epoch: [163][500/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.209 (0.328) Prec@1 95.83 (92.43) Prec@5 100.00 (99.86)
|
|
train[2019-03-31-21:11:33] Epoch: [163][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.258 (0.329) Prec@1 95.00 (92.42) Prec@5 100.00 (99.86)
|
|
[2019-03-31-21:11:33] **train** Prec@1 92.42 Prec@5 99.86 Error@1 7.58 Error@5 0.14 Loss:0.329
|
|
test [2019-03-31-21:11:33] Epoch: [163][000/105] Time 0.48 (0.48) Data 0.41 (0.41) Loss 0.159 (0.159) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-21:11:37] Epoch: [163][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.078 (0.215) Prec@1 96.88 (93.46) Prec@5 100.00 (99.86)
|
|
test [2019-03-31-21:11:38] Epoch: [163][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.044 (0.215) Prec@1 100.00 (93.44) Prec@5 100.00 (99.86)
|
|
[2019-03-31-21:11:38] **test** Prec@1 93.44 Prec@5 99.86 Error@1 6.56 Error@5 0.14 Loss:0.215
|
|
----> Best Accuracy : Acc@1=93.70, Acc@5=99.83, Error@1=6.30, Error@5=0.17
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-21:11:38] [Epoch=164/600] [Need: 15:40:03] LR=0.0207 ~ 0.0207, Batch=96
|
|
train[2019-03-31-21:11:39] Epoch: [164][000/521] Time 0.74 (0.74) Data 0.47 (0.47) Loss 0.326 (0.326) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-21:12:02] Epoch: [164][100/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.172 (0.312) Prec@1 95.83 (92.70) Prec@5 100.00 (99.87)
|
|
train[2019-03-31-21:12:26] Epoch: [164][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.357 (0.310) Prec@1 91.67 (92.79) Prec@5 98.96 (99.89)
|
|
train[2019-03-31-21:12:50] Epoch: [164][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.351 (0.313) Prec@1 92.71 (92.79) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-21:13:13] Epoch: [164][400/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.366 (0.313) Prec@1 93.75 (92.77) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-21:13:37] Epoch: [164][500/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.211 (0.314) Prec@1 93.75 (92.72) Prec@5 100.00 (99.88)
|
|
train[2019-03-31-21:13:41] Epoch: [164][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.228 (0.314) Prec@1 96.25 (92.70) Prec@5 100.00 (99.88)
|
|
[2019-03-31-21:13:41] **train** Prec@1 92.70 Prec@5 99.88 Error@1 7.30 Error@5 0.12 Loss:0.314
|
|
test [2019-03-31-21:13:42] Epoch: [164][000/105] Time 0.61 (0.61) Data 0.54 (0.54) Loss 0.167 (0.167) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-21:13:46] Epoch: [164][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.071 (0.209) Prec@1 96.88 (93.40) Prec@5 100.00 (99.86)
|
|
test [2019-03-31-21:13:46] Epoch: [164][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.228 (0.209) Prec@1 93.75 (93.40) Prec@5 100.00 (99.86)
|
|
[2019-03-31-21:13:46] **test** Prec@1 93.40 Prec@5 99.86 Error@1 6.60 Error@5 0.14 Loss:0.209
|
|
----> Best Accuracy : Acc@1=93.70, Acc@5=99.83, Error@1=6.30, Error@5=0.17
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-21:13:47] [Epoch=165/600] [Need: 15:33:07] LR=0.0206 ~ 0.0206, Batch=96
|
|
train[2019-03-31-21:13:47] Epoch: [165][000/521] Time 0.70 (0.70) Data 0.43 (0.43) Loss 0.103 (0.103) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-21:14:11] Epoch: [165][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.309 (0.310) Prec@1 93.75 (92.89) Prec@5 100.00 (99.92)
|
|
train[2019-03-31-21:14:35] Epoch: [165][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.362 (0.323) Prec@1 90.62 (92.56) Prec@5 98.96 (99.86)
|
|
train[2019-03-31-21:14:59] Epoch: [165][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.400 (0.328) Prec@1 90.62 (92.51) Prec@5 100.00 (99.88)
|
|
train[2019-03-31-21:15:23] Epoch: [165][400/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.218 (0.327) Prec@1 95.83 (92.53) Prec@5 98.96 (99.87)
|
|
train[2019-03-31-21:15:47] Epoch: [165][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.255 (0.328) Prec@1 94.79 (92.54) Prec@5 100.00 (99.86)
|
|
train[2019-03-31-21:15:51] Epoch: [165][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.315 (0.327) Prec@1 93.75 (92.54) Prec@5 100.00 (99.86)
|
|
[2019-03-31-21:15:51] **train** Prec@1 92.54 Prec@5 99.86 Error@1 7.46 Error@5 0.14 Loss:0.327
|
|
test [2019-03-31-21:15:52] Epoch: [165][000/105] Time 0.61 (0.61) Data 0.55 (0.55) Loss 0.227 (0.227) Prec@1 91.67 (91.67) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-21:15:56] Epoch: [165][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.077 (0.194) Prec@1 97.92 (94.00) Prec@5 100.00 (99.82)
|
|
test [2019-03-31-21:15:56] Epoch: [165][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.192 (0.194) Prec@1 87.50 (94.01) Prec@5 100.00 (99.81)
|
|
[2019-03-31-21:15:56] **test** Prec@1 94.01 Prec@5 99.81 Error@1 5.99 Error@5 0.19 Loss:0.194
|
|
----> Best Accuracy : Acc@1=94.01, Acc@5=99.81, Error@1=5.99, Error@5=0.19
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-21:15:57] [Epoch=166/600] [Need: 15:40:31] LR=0.0206 ~ 0.0206, Batch=96
|
|
train[2019-03-31-21:15:57] Epoch: [166][000/521] Time 0.71 (0.71) Data 0.44 (0.44) Loss 0.285 (0.285) Prec@1 90.62 (90.62) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-21:16:21] Epoch: [166][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.331 (0.312) Prec@1 93.75 (92.98) Prec@5 100.00 (99.87)
|
|
train[2019-03-31-21:16:45] Epoch: [166][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.442 (0.324) Prec@1 91.67 (92.60) Prec@5 98.96 (99.87)
|
|
train[2019-03-31-21:17:09] Epoch: [166][300/521] Time 0.27 (0.24) Data 0.00 (0.00) Loss 0.226 (0.322) Prec@1 95.83 (92.58) Prec@5 100.00 (99.87)
|
|
train[2019-03-31-21:17:33] Epoch: [166][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.447 (0.325) Prec@1 89.58 (92.54) Prec@5 100.00 (99.86)
|
|
train[2019-03-31-21:17:56] Epoch: [166][500/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.352 (0.328) Prec@1 91.67 (92.43) Prec@5 100.00 (99.85)
|
|
train[2019-03-31-21:18:01] Epoch: [166][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.412 (0.328) Prec@1 88.75 (92.44) Prec@5 100.00 (99.85)
|
|
[2019-03-31-21:18:01] **train** Prec@1 92.44 Prec@5 99.85 Error@1 7.56 Error@5 0.15 Loss:0.328
|
|
test [2019-03-31-21:18:02] Epoch: [166][000/105] Time 0.60 (0.60) Data 0.53 (0.53) Loss 0.166 (0.166) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-21:18:06] Epoch: [166][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.220 (0.258) Prec@1 91.67 (92.07) Prec@5 100.00 (99.78)
|
|
test [2019-03-31-21:18:06] Epoch: [166][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.415 (0.257) Prec@1 87.50 (92.08) Prec@5 100.00 (99.79)
|
|
[2019-03-31-21:18:06] **test** Prec@1 92.08 Prec@5 99.79 Error@1 7.92 Error@5 0.21 Loss:0.257
|
|
----> Best Accuracy : Acc@1=94.01, Acc@5=99.81, Error@1=5.99, Error@5=0.19
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-21:18:06] [Epoch=167/600] [Need: 15:34:51] LR=0.0205 ~ 0.0205, Batch=96
|
|
train[2019-03-31-21:18:07] Epoch: [167][000/521] Time 0.71 (0.71) Data 0.44 (0.44) Loss 0.418 (0.418) Prec@1 89.58 (89.58) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-21:18:31] Epoch: [167][100/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.386 (0.321) Prec@1 89.58 (92.70) Prec@5 100.00 (99.87)
|
|
train[2019-03-31-21:18:55] Epoch: [167][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.282 (0.320) Prec@1 93.75 (92.69) Prec@5 100.00 (99.86)
|
|
train[2019-03-31-21:19:19] Epoch: [167][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.236 (0.319) Prec@1 94.79 (92.78) Prec@5 100.00 (99.87)
|
|
train[2019-03-31-21:19:43] Epoch: [167][400/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.370 (0.325) Prec@1 91.67 (92.69) Prec@5 98.96 (99.84)
|
|
train[2019-03-31-21:20:06] Epoch: [167][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.500 (0.325) Prec@1 87.50 (92.65) Prec@5 100.00 (99.85)
|
|
train[2019-03-31-21:20:11] Epoch: [167][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.297 (0.325) Prec@1 92.50 (92.65) Prec@5 100.00 (99.85)
|
|
[2019-03-31-21:20:11] **train** Prec@1 92.65 Prec@5 99.85 Error@1 7.35 Error@5 0.15 Loss:0.325
|
|
test [2019-03-31-21:20:11] Epoch: [167][000/105] Time 0.54 (0.54) Data 0.47 (0.47) Loss 0.263 (0.263) Prec@1 91.67 (91.67) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-21:20:15] Epoch: [167][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.118 (0.248) Prec@1 94.79 (92.88) Prec@5 100.00 (99.82)
|
|
test [2019-03-31-21:20:16] Epoch: [167][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.216 (0.248) Prec@1 93.75 (92.89) Prec@5 100.00 (99.83)
|
|
[2019-03-31-21:20:16] **test** Prec@1 92.89 Prec@5 99.83 Error@1 7.11 Error@5 0.17 Loss:0.248
|
|
----> Best Accuracy : Acc@1=94.01, Acc@5=99.81, Error@1=5.99, Error@5=0.19
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-21:20:16] [Epoch=168/600] [Need: 15:34:00] LR=0.0205 ~ 0.0205, Batch=96
|
|
train[2019-03-31-21:20:17] Epoch: [168][000/521] Time 0.71 (0.71) Data 0.44 (0.44) Loss 0.355 (0.355) Prec@1 91.67 (91.67) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-21:20:40] Epoch: [168][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.303 (0.300) Prec@1 90.62 (93.23) Prec@5 100.00 (99.93)
|
|
train[2019-03-31-21:21:04] Epoch: [168][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.215 (0.312) Prec@1 94.79 (93.05) Prec@5 100.00 (99.90)
|
|
train[2019-03-31-21:21:27] Epoch: [168][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.298 (0.320) Prec@1 96.88 (92.84) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-21:21:51] Epoch: [168][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.299 (0.326) Prec@1 94.79 (92.72) Prec@5 100.00 (99.88)
|
|
train[2019-03-31-21:22:15] Epoch: [168][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.255 (0.328) Prec@1 93.75 (92.66) Prec@5 100.00 (99.86)
|
|
train[2019-03-31-21:22:20] Epoch: [168][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.325 (0.328) Prec@1 95.00 (92.65) Prec@5 100.00 (99.86)
|
|
[2019-03-31-21:22:20] **train** Prec@1 92.65 Prec@5 99.86 Error@1 7.35 Error@5 0.14 Loss:0.328
|
|
test [2019-03-31-21:22:20] Epoch: [168][000/105] Time 0.47 (0.47) Data 0.41 (0.41) Loss 0.203 (0.203) Prec@1 90.62 (90.62) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-21:22:24] Epoch: [168][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.120 (0.209) Prec@1 95.83 (93.67) Prec@5 100.00 (99.88)
|
|
test [2019-03-31-21:22:24] Epoch: [168][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.243 (0.208) Prec@1 93.75 (93.65) Prec@5 100.00 (99.88)
|
|
[2019-03-31-21:22:24] **test** Prec@1 93.65 Prec@5 99.88 Error@1 6.35 Error@5 0.12 Loss:0.208
|
|
----> Best Accuracy : Acc@1=94.01, Acc@5=99.81, Error@1=5.99, Error@5=0.19
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-21:22:25] [Epoch=169/600] [Need: 15:24:46] LR=0.0204 ~ 0.0204, Batch=96
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|
train[2019-03-31-21:22:25] Epoch: [169][000/521] Time 0.70 (0.70) Data 0.43 (0.43) Loss 0.231 (0.231) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
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train[2019-03-31-21:22:49] Epoch: [169][100/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.302 (0.306) Prec@1 93.75 (93.22) Prec@5 100.00 (99.87)
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train[2019-03-31-21:23:13] Epoch: [169][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.258 (0.307) Prec@1 93.75 (93.21) Prec@5 100.00 (99.90)
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train[2019-03-31-21:23:36] Epoch: [169][300/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.181 (0.317) Prec@1 96.88 (92.87) Prec@5 100.00 (99.89)
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train[2019-03-31-21:24:00] Epoch: [169][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.296 (0.319) Prec@1 91.67 (92.77) Prec@5 100.00 (99.88)
|
|
train[2019-03-31-21:24:24] Epoch: [169][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.338 (0.323) Prec@1 89.58 (92.62) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-21:24:28] Epoch: [169][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.204 (0.323) Prec@1 95.00 (92.64) Prec@5 100.00 (99.89)
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[2019-03-31-21:24:29] **train** Prec@1 92.64 Prec@5 99.89 Error@1 7.36 Error@5 0.11 Loss:0.323
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test [2019-03-31-21:24:29] Epoch: [169][000/105] Time 0.48 (0.48) Data 0.42 (0.42) Loss 0.252 (0.252) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
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test [2019-03-31-21:24:33] Epoch: [169][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.098 (0.249) Prec@1 95.83 (92.48) Prec@5 100.00 (99.79)
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test [2019-03-31-21:24:33] Epoch: [169][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.381 (0.250) Prec@1 93.75 (92.46) Prec@5 100.00 (99.80)
|
|
[2019-03-31-21:24:33] **test** Prec@1 92.46 Prec@5 99.80 Error@1 7.54 Error@5 0.20 Loss:0.250
|
|
----> Best Accuracy : Acc@1=94.01, Acc@5=99.81, Error@1=5.99, Error@5=0.19
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
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|
|
==>>[2019-03-31-21:24:34] [Epoch=170/600] [Need: 15:23:56] LR=0.0204 ~ 0.0204, Batch=96
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|
train[2019-03-31-21:24:34] Epoch: [170][000/521] Time 0.84 (0.84) Data 0.56 (0.56) Loss 0.473 (0.473) Prec@1 86.46 (86.46) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-21:24:58] Epoch: [170][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.330 (0.321) Prec@1 92.71 (92.73) Prec@5 100.00 (99.80)
|
|
train[2019-03-31-21:25:22] Epoch: [170][200/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.371 (0.317) Prec@1 90.62 (92.73) Prec@5 100.00 (99.84)
|
|
train[2019-03-31-21:25:46] Epoch: [170][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.211 (0.312) Prec@1 97.92 (92.84) Prec@5 100.00 (99.86)
|
|
train[2019-03-31-21:26:09] Epoch: [170][400/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.395 (0.314) Prec@1 90.62 (92.76) Prec@5 98.96 (99.85)
|
|
train[2019-03-31-21:26:33] Epoch: [170][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.421 (0.318) Prec@1 93.75 (92.73) Prec@5 98.96 (99.85)
|
|
train[2019-03-31-21:26:38] Epoch: [170][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.182 (0.318) Prec@1 97.50 (92.73) Prec@5 100.00 (99.85)
|
|
[2019-03-31-21:26:38] **train** Prec@1 92.73 Prec@5 99.85 Error@1 7.27 Error@5 0.15 Loss:0.318
|
|
test [2019-03-31-21:26:38] Epoch: [170][000/105] Time 0.60 (0.60) Data 0.53 (0.53) Loss 0.215 (0.215) Prec@1 91.67 (91.67) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-21:26:42] Epoch: [170][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.109 (0.241) Prec@1 95.83 (92.74) Prec@5 100.00 (99.81)
|
|
test [2019-03-31-21:26:43] Epoch: [170][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.049 (0.239) Prec@1 100.00 (92.78) Prec@5 100.00 (99.82)
|
|
[2019-03-31-21:26:43] **test** Prec@1 92.78 Prec@5 99.82 Error@1 7.22 Error@5 0.18 Loss:0.239
|
|
----> Best Accuracy : Acc@1=94.01, Acc@5=99.81, Error@1=5.99, Error@5=0.19
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-21:26:43] [Epoch=171/600] [Need: 15:24:43] LR=0.0203 ~ 0.0203, Batch=96
|
|
train[2019-03-31-21:26:44] Epoch: [171][000/521] Time 0.84 (0.84) Data 0.57 (0.57) Loss 0.389 (0.389) Prec@1 90.62 (90.62) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-21:27:07] Epoch: [171][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.320 (0.330) Prec@1 89.58 (92.43) Prec@5 98.96 (99.86)
|
|
train[2019-03-31-21:27:32] Epoch: [171][200/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.508 (0.325) Prec@1 89.58 (92.45) Prec@5 98.96 (99.84)
|
|
train[2019-03-31-21:27:56] Epoch: [171][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.210 (0.324) Prec@1 94.79 (92.48) Prec@5 100.00 (99.87)
|
|
train[2019-03-31-21:28:20] Epoch: [171][400/521] Time 0.26 (0.24) Data 0.00 (0.00) Loss 0.318 (0.322) Prec@1 90.62 (92.56) Prec@5 100.00 (99.87)
|
|
train[2019-03-31-21:28:44] Epoch: [171][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.372 (0.327) Prec@1 92.71 (92.40) Prec@5 100.00 (99.87)
|
|
train[2019-03-31-21:28:48] Epoch: [171][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.401 (0.327) Prec@1 92.50 (92.42) Prec@5 100.00 (99.87)
|
|
[2019-03-31-21:28:48] **train** Prec@1 92.42 Prec@5 99.87 Error@1 7.58 Error@5 0.13 Loss:0.327
|
|
test [2019-03-31-21:28:49] Epoch: [171][000/105] Time 0.47 (0.47) Data 0.40 (0.40) Loss 0.319 (0.319) Prec@1 89.58 (89.58) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-21:28:53] Epoch: [171][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.092 (0.262) Prec@1 97.92 (92.26) Prec@5 100.00 (99.72)
|
|
test [2019-03-31-21:28:53] Epoch: [171][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.686 (0.261) Prec@1 87.50 (92.30) Prec@5 100.00 (99.72)
|
|
[2019-03-31-21:28:53] **test** Prec@1 92.30 Prec@5 99.72 Error@1 7.70 Error@5 0.28 Loss:0.261
|
|
----> Best Accuracy : Acc@1=94.01, Acc@5=99.81, Error@1=5.99, Error@5=0.19
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-21:28:53] [Epoch=172/600] [Need: 15:31:28] LR=0.0203 ~ 0.0203, Batch=96
|
|
train[2019-03-31-21:28:54] Epoch: [172][000/521] Time 0.84 (0.84) Data 0.57 (0.57) Loss 0.381 (0.381) Prec@1 91.67 (91.67) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-21:29:17] Epoch: [172][100/521] Time 0.23 (0.24) Data 0.00 (0.01) Loss 0.239 (0.319) Prec@1 96.88 (92.63) Prec@5 100.00 (99.90)
|
|
train[2019-03-31-21:29:41] Epoch: [172][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.470 (0.310) Prec@1 92.71 (92.87) Prec@5 100.00 (99.90)
|
|
train[2019-03-31-21:30:05] Epoch: [172][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.234 (0.312) Prec@1 94.79 (92.92) Prec@5 100.00 (99.90)
|
|
train[2019-03-31-21:30:29] Epoch: [172][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.244 (0.314) Prec@1 96.88 (92.82) Prec@5 98.96 (99.90)
|
|
train[2019-03-31-21:30:52] Epoch: [172][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.611 (0.322) Prec@1 84.38 (92.64) Prec@5 98.96 (99.88)
|
|
train[2019-03-31-21:30:57] Epoch: [172][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.214 (0.321) Prec@1 96.25 (92.67) Prec@5 100.00 (99.88)
|
|
[2019-03-31-21:30:57] **train** Prec@1 92.67 Prec@5 99.88 Error@1 7.33 Error@5 0.12 Loss:0.321
|
|
test [2019-03-31-21:30:58] Epoch: [172][000/105] Time 0.48 (0.48) Data 0.42 (0.42) Loss 0.170 (0.170) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-21:31:02] Epoch: [172][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.068 (0.204) Prec@1 97.92 (93.36) Prec@5 100.00 (99.81)
|
|
test [2019-03-31-21:31:02] Epoch: [172][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.263 (0.204) Prec@1 93.75 (93.34) Prec@5 100.00 (99.82)
|
|
[2019-03-31-21:31:02] **test** Prec@1 93.34 Prec@5 99.82 Error@1 6.66 Error@5 0.18 Loss:0.204
|
|
----> Best Accuracy : Acc@1=94.01, Acc@5=99.81, Error@1=5.99, Error@5=0.19
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-21:31:02] [Epoch=173/600] [Need: 15:15:55] LR=0.0202 ~ 0.0202, Batch=96
|
|
train[2019-03-31-21:31:03] Epoch: [173][000/521] Time 0.72 (0.72) Data 0.43 (0.43) Loss 0.385 (0.385) Prec@1 89.58 (89.58) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-21:31:27] Epoch: [173][100/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.256 (0.299) Prec@1 95.83 (93.09) Prec@5 100.00 (99.91)
|
|
train[2019-03-31-21:31:50] Epoch: [173][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.246 (0.310) Prec@1 93.75 (92.83) Prec@5 100.00 (99.88)
|
|
train[2019-03-31-21:32:14] Epoch: [173][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.407 (0.323) Prec@1 87.50 (92.48) Prec@5 100.00 (99.87)
|
|
train[2019-03-31-21:32:38] Epoch: [173][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.315 (0.322) Prec@1 92.71 (92.56) Prec@5 100.00 (99.87)
|
|
train[2019-03-31-21:33:02] Epoch: [173][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.459 (0.323) Prec@1 91.67 (92.59) Prec@5 100.00 (99.87)
|
|
train[2019-03-31-21:33:06] Epoch: [173][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.376 (0.323) Prec@1 92.50 (92.61) Prec@5 100.00 (99.87)
|
|
[2019-03-31-21:33:06] **train** Prec@1 92.61 Prec@5 99.87 Error@1 7.39 Error@5 0.13 Loss:0.323
|
|
test [2019-03-31-21:33:07] Epoch: [173][000/105] Time 0.47 (0.47) Data 0.41 (0.41) Loss 0.240 (0.240) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-21:33:11] Epoch: [173][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.098 (0.221) Prec@1 94.79 (93.38) Prec@5 100.00 (99.89)
|
|
test [2019-03-31-21:33:11] Epoch: [173][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.255 (0.221) Prec@1 87.50 (93.38) Prec@5 100.00 (99.89)
|
|
[2019-03-31-21:33:11] **test** Prec@1 93.38 Prec@5 99.89 Error@1 6.62 Error@5 0.11 Loss:0.221
|
|
----> Best Accuracy : Acc@1=94.01, Acc@5=99.81, Error@1=5.99, Error@5=0.19
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-21:33:11] [Epoch=174/600] [Need: 15:17:33] LR=0.0202 ~ 0.0202, Batch=96
|
|
train[2019-03-31-21:33:12] Epoch: [174][000/521] Time 0.72 (0.72) Data 0.44 (0.44) Loss 0.309 (0.309) Prec@1 90.62 (90.62) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-21:33:36] Epoch: [174][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.314 (0.293) Prec@1 93.75 (93.45) Prec@5 98.96 (99.92)
|
|
train[2019-03-31-21:33:59] Epoch: [174][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.268 (0.304) Prec@1 90.62 (93.06) Prec@5 100.00 (99.90)
|
|
train[2019-03-31-21:34:23] Epoch: [174][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.275 (0.307) Prec@1 93.75 (92.91) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-21:34:47] Epoch: [174][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.266 (0.311) Prec@1 93.75 (92.87) Prec@5 100.00 (99.88)
|
|
train[2019-03-31-21:35:10] Epoch: [174][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.377 (0.316) Prec@1 91.67 (92.75) Prec@5 98.96 (99.86)
|
|
train[2019-03-31-21:35:15] Epoch: [174][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.395 (0.317) Prec@1 91.25 (92.73) Prec@5 100.00 (99.87)
|
|
[2019-03-31-21:35:15] **train** Prec@1 92.73 Prec@5 99.87 Error@1 7.27 Error@5 0.13 Loss:0.317
|
|
test [2019-03-31-21:35:16] Epoch: [174][000/105] Time 0.48 (0.48) Data 0.40 (0.40) Loss 0.210 (0.210) Prec@1 90.62 (90.62) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-21:35:20] Epoch: [174][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.076 (0.253) Prec@1 96.88 (92.34) Prec@5 100.00 (99.79)
|
|
test [2019-03-31-21:35:20] Epoch: [174][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.265 (0.254) Prec@1 93.75 (92.32) Prec@5 100.00 (99.80)
|
|
[2019-03-31-21:35:20] **test** Prec@1 92.32 Prec@5 99.80 Error@1 7.68 Error@5 0.20 Loss:0.254
|
|
----> Best Accuracy : Acc@1=94.01, Acc@5=99.81, Error@1=5.99, Error@5=0.19
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-21:35:20] [Epoch=175/600] [Need: 15:11:19] LR=0.0201 ~ 0.0201, Batch=96
|
|
train[2019-03-31-21:35:21] Epoch: [175][000/521] Time 0.70 (0.70) Data 0.43 (0.43) Loss 0.334 (0.334) Prec@1 89.58 (89.58) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-21:35:45] Epoch: [175][100/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.206 (0.317) Prec@1 93.75 (92.67) Prec@5 100.00 (99.91)
|
|
train[2019-03-31-21:36:08] Epoch: [175][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.248 (0.321) Prec@1 96.88 (92.75) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-21:36:32] Epoch: [175][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.364 (0.325) Prec@1 91.67 (92.70) Prec@5 100.00 (99.86)
|
|
train[2019-03-31-21:36:56] Epoch: [175][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.308 (0.327) Prec@1 90.62 (92.63) Prec@5 98.96 (99.85)
|
|
train[2019-03-31-21:37:19] Epoch: [175][500/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.382 (0.327) Prec@1 90.62 (92.60) Prec@5 100.00 (99.85)
|
|
train[2019-03-31-21:37:24] Epoch: [175][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.221 (0.326) Prec@1 95.00 (92.63) Prec@5 100.00 (99.85)
|
|
[2019-03-31-21:37:24] **train** Prec@1 92.63 Prec@5 99.85 Error@1 7.37 Error@5 0.15 Loss:0.326
|
|
test [2019-03-31-21:37:25] Epoch: [175][000/105] Time 0.60 (0.60) Data 0.53 (0.53) Loss 0.211 (0.211) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-21:37:29] Epoch: [175][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.075 (0.217) Prec@1 97.92 (93.21) Prec@5 100.00 (99.87)
|
|
test [2019-03-31-21:37:29] Epoch: [175][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.233 (0.217) Prec@1 87.50 (93.16) Prec@5 100.00 (99.87)
|
|
[2019-03-31-21:37:29] **test** Prec@1 93.16 Prec@5 99.87 Error@1 6.84 Error@5 0.13 Loss:0.217
|
|
----> Best Accuracy : Acc@1=94.01, Acc@5=99.81, Error@1=5.99, Error@5=0.19
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-21:37:29] [Epoch=176/600] [Need: 15:11:11] LR=0.0201 ~ 0.0201, Batch=96
|
|
train[2019-03-31-21:37:30] Epoch: [176][000/521] Time 0.84 (0.84) Data 0.58 (0.58) Loss 0.404 (0.404) Prec@1 91.67 (91.67) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-21:37:53] Epoch: [176][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.263 (0.322) Prec@1 96.88 (92.69) Prec@5 100.00 (99.91)
|
|
train[2019-03-31-21:38:17] Epoch: [176][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.385 (0.327) Prec@1 92.71 (92.69) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-21:38:41] Epoch: [176][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.201 (0.318) Prec@1 95.83 (92.87) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-21:39:05] Epoch: [176][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.214 (0.320) Prec@1 94.79 (92.85) Prec@5 100.00 (99.86)
|
|
train[2019-03-31-21:39:28] Epoch: [176][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.397 (0.324) Prec@1 91.67 (92.75) Prec@5 98.96 (99.84)
|
|
train[2019-03-31-21:39:33] Epoch: [176][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.447 (0.326) Prec@1 90.00 (92.68) Prec@5 100.00 (99.85)
|
|
[2019-03-31-21:39:33] **train** Prec@1 92.68 Prec@5 99.85 Error@1 7.32 Error@5 0.15 Loss:0.326
|
|
test [2019-03-31-21:39:34] Epoch: [176][000/105] Time 0.63 (0.63) Data 0.57 (0.57) Loss 0.244 (0.244) Prec@1 87.50 (87.50) Prec@5 100.00 (100.00)
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|
test [2019-03-31-21:39:38] Epoch: [176][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.129 (0.210) Prec@1 93.75 (93.46) Prec@5 100.00 (99.87)
|
|
test [2019-03-31-21:39:38] Epoch: [176][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.231 (0.210) Prec@1 93.75 (93.48) Prec@5 100.00 (99.87)
|
|
[2019-03-31-21:39:38] **test** Prec@1 93.48 Prec@5 99.87 Error@1 6.52 Error@5 0.13 Loss:0.210
|
|
----> Best Accuracy : Acc@1=94.01, Acc@5=99.81, Error@1=5.99, Error@5=0.19
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-21:39:38] [Epoch=177/600] [Need: 15:12:07] LR=0.0200 ~ 0.0200, Batch=96
|
|
train[2019-03-31-21:39:39] Epoch: [177][000/521] Time 0.84 (0.84) Data 0.57 (0.57) Loss 0.411 (0.411) Prec@1 89.58 (89.58) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-21:40:03] Epoch: [177][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.270 (0.298) Prec@1 93.75 (93.05) Prec@5 100.00 (99.91)
|
|
train[2019-03-31-21:40:28] Epoch: [177][200/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.185 (0.313) Prec@1 96.88 (92.81) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-21:40:51] Epoch: [177][300/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.353 (0.310) Prec@1 91.67 (92.95) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-21:41:15] Epoch: [177][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.221 (0.313) Prec@1 93.75 (92.86) Prec@5 98.96 (99.87)
|
|
train[2019-03-31-21:41:39] Epoch: [177][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.239 (0.314) Prec@1 92.71 (92.86) Prec@5 100.00 (99.87)
|
|
train[2019-03-31-21:41:44] Epoch: [177][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.257 (0.314) Prec@1 95.00 (92.82) Prec@5 100.00 (99.87)
|
|
[2019-03-31-21:41:44] **train** Prec@1 92.82 Prec@5 99.87 Error@1 7.18 Error@5 0.13 Loss:0.314
|
|
test [2019-03-31-21:41:44] Epoch: [177][000/105] Time 0.47 (0.47) Data 0.40 (0.40) Loss 0.187 (0.187) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-21:41:48] Epoch: [177][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.100 (0.222) Prec@1 98.96 (93.42) Prec@5 100.00 (99.85)
|
|
test [2019-03-31-21:41:48] Epoch: [177][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.322 (0.222) Prec@1 87.50 (93.41) Prec@5 100.00 (99.85)
|
|
[2019-03-31-21:41:48] **test** Prec@1 93.41 Prec@5 99.85 Error@1 6.59 Error@5 0.15 Loss:0.222
|
|
----> Best Accuracy : Acc@1=94.01, Acc@5=99.81, Error@1=5.99, Error@5=0.19
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-21:41:49] [Epoch=178/600] [Need: 15:15:40] LR=0.0200 ~ 0.0200, Batch=96
|
|
train[2019-03-31-21:41:49] Epoch: [178][000/521] Time 0.84 (0.84) Data 0.58 (0.58) Loss 0.351 (0.351) Prec@1 93.75 (93.75) Prec@5 98.96 (98.96)
|
|
train[2019-03-31-21:42:13] Epoch: [178][100/521] Time 0.23 (0.24) Data 0.00 (0.01) Loss 0.281 (0.305) Prec@1 96.88 (93.19) Prec@5 100.00 (99.79)
|
|
train[2019-03-31-21:42:37] Epoch: [178][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.764 (0.311) Prec@1 80.21 (92.86) Prec@5 100.00 (99.83)
|
|
train[2019-03-31-21:43:01] Epoch: [178][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.251 (0.311) Prec@1 93.75 (92.86) Prec@5 100.00 (99.85)
|
|
train[2019-03-31-21:43:24] Epoch: [178][400/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.354 (0.314) Prec@1 91.67 (92.82) Prec@5 100.00 (99.85)
|
|
train[2019-03-31-21:43:48] Epoch: [178][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.365 (0.319) Prec@1 91.67 (92.72) Prec@5 100.00 (99.84)
|
|
train[2019-03-31-21:43:53] Epoch: [178][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.358 (0.319) Prec@1 90.00 (92.72) Prec@5 98.75 (99.84)
|
|
[2019-03-31-21:43:53] **train** Prec@1 92.72 Prec@5 99.84 Error@1 7.28 Error@5 0.16 Loss:0.319
|
|
test [2019-03-31-21:43:54] Epoch: [178][000/105] Time 0.60 (0.60) Data 0.53 (0.53) Loss 0.444 (0.444) Prec@1 83.33 (83.33) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-21:43:58] Epoch: [178][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.125 (0.330) Prec@1 92.71 (90.60) Prec@5 100.00 (99.76)
|
|
test [2019-03-31-21:43:58] Epoch: [178][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.032 (0.331) Prec@1 100.00 (90.61) Prec@5 100.00 (99.75)
|
|
[2019-03-31-21:43:58] **test** Prec@1 90.61 Prec@5 99.75 Error@1 9.39 Error@5 0.25 Loss:0.331
|
|
----> Best Accuracy : Acc@1=94.01, Acc@5=99.81, Error@1=5.99, Error@5=0.19
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-21:43:58] [Epoch=179/600] [Need: 15:08:36] LR=0.0199 ~ 0.0199, Batch=96
|
|
train[2019-03-31-21:43:59] Epoch: [179][000/521] Time 0.70 (0.70) Data 0.43 (0.43) Loss 0.304 (0.304) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-21:44:22] Epoch: [179][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.139 (0.298) Prec@1 95.83 (93.31) Prec@5 100.00 (99.92)
|
|
train[2019-03-31-21:44:46] Epoch: [179][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.264 (0.294) Prec@1 91.67 (93.42) Prec@5 100.00 (99.90)
|
|
train[2019-03-31-21:45:10] Epoch: [179][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.434 (0.305) Prec@1 89.58 (93.19) Prec@5 100.00 (99.90)
|
|
train[2019-03-31-21:45:33] Epoch: [179][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.494 (0.308) Prec@1 88.54 (93.11) Prec@5 98.96 (99.87)
|
|
train[2019-03-31-21:45:57] Epoch: [179][500/521] Time 0.27 (0.24) Data 0.00 (0.00) Loss 0.253 (0.310) Prec@1 94.79 (93.04) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-21:46:02] Epoch: [179][520/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.241 (0.311) Prec@1 92.50 (93.01) Prec@5 100.00 (99.88)
|
|
[2019-03-31-21:46:03] **train** Prec@1 93.01 Prec@5 99.88 Error@1 6.99 Error@5 0.12 Loss:0.311
|
|
test [2019-03-31-21:46:03] Epoch: [179][000/105] Time 0.50 (0.50) Data 0.43 (0.43) Loss 0.292 (0.292) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-21:46:07] Epoch: [179][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.128 (0.243) Prec@1 94.79 (92.85) Prec@5 100.00 (99.83)
|
|
test [2019-03-31-21:46:07] Epoch: [179][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.067 (0.240) Prec@1 100.00 (92.96) Prec@5 100.00 (99.84)
|
|
[2019-03-31-21:46:07] **test** Prec@1 92.96 Prec@5 99.84 Error@1 7.04 Error@5 0.16 Loss:0.240
|
|
----> Best Accuracy : Acc@1=94.01, Acc@5=99.81, Error@1=5.99, Error@5=0.19
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-21:46:08] [Epoch=180/600] [Need: 15:06:51] LR=0.0199 ~ 0.0199, Batch=96
|
|
train[2019-03-31-21:46:08] Epoch: [180][000/521] Time 0.83 (0.83) Data 0.55 (0.55) Loss 0.328 (0.328) Prec@1 88.54 (88.54) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-21:46:32] Epoch: [180][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.283 (0.298) Prec@1 94.79 (93.13) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-21:46:56] Epoch: [180][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.366 (0.311) Prec@1 88.54 (92.80) Prec@5 100.00 (99.84)
|
|
train[2019-03-31-21:47:19] Epoch: [180][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.274 (0.309) Prec@1 95.83 (92.92) Prec@5 100.00 (99.84)
|
|
train[2019-03-31-21:47:43] Epoch: [180][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.431 (0.312) Prec@1 93.75 (92.85) Prec@5 98.96 (99.84)
|
|
train[2019-03-31-21:48:07] Epoch: [180][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.422 (0.315) Prec@1 93.75 (92.83) Prec@5 100.00 (99.83)
|
|
train[2019-03-31-21:48:12] Epoch: [180][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.412 (0.316) Prec@1 91.25 (92.80) Prec@5 100.00 (99.82)
|
|
[2019-03-31-21:48:12] **train** Prec@1 92.80 Prec@5 99.82 Error@1 7.20 Error@5 0.18 Loss:0.316
|
|
test [2019-03-31-21:48:12] Epoch: [180][000/105] Time 0.63 (0.63) Data 0.57 (0.57) Loss 0.331 (0.331) Prec@1 87.50 (87.50) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-21:48:16] Epoch: [180][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.139 (0.241) Prec@1 95.83 (92.73) Prec@5 100.00 (99.82)
|
|
test [2019-03-31-21:48:17] Epoch: [180][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.089 (0.241) Prec@1 93.75 (92.72) Prec@5 100.00 (99.83)
|
|
[2019-03-31-21:48:17] **test** Prec@1 92.72 Prec@5 99.83 Error@1 7.28 Error@5 0.17 Loss:0.241
|
|
----> Best Accuracy : Acc@1=94.01, Acc@5=99.81, Error@1=5.99, Error@5=0.19
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-21:48:17] [Epoch=181/600] [Need: 15:02:17] LR=0.0198 ~ 0.0198, Batch=96
|
|
train[2019-03-31-21:48:18] Epoch: [181][000/521] Time 0.72 (0.72) Data 0.45 (0.45) Loss 0.222 (0.222) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-21:48:41] Epoch: [181][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.508 (0.303) Prec@1 86.46 (93.23) Prec@5 98.96 (99.87)
|
|
train[2019-03-31-21:49:05] Epoch: [181][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.344 (0.309) Prec@1 93.75 (93.09) Prec@5 100.00 (99.86)
|
|
train[2019-03-31-21:49:29] Epoch: [181][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.298 (0.314) Prec@1 93.75 (92.95) Prec@5 98.96 (99.87)
|
|
train[2019-03-31-21:49:52] Epoch: [181][400/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.208 (0.314) Prec@1 94.79 (92.89) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-21:50:16] Epoch: [181][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.341 (0.311) Prec@1 89.58 (92.92) Prec@5 100.00 (99.88)
|
|
train[2019-03-31-21:50:21] Epoch: [181][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.345 (0.312) Prec@1 93.75 (92.88) Prec@5 98.75 (99.88)
|
|
[2019-03-31-21:50:21] **train** Prec@1 92.88 Prec@5 99.88 Error@1 7.12 Error@5 0.12 Loss:0.312
|
|
test [2019-03-31-21:50:22] Epoch: [181][000/105] Time 0.46 (0.46) Data 0.40 (0.40) Loss 0.227 (0.227) Prec@1 91.67 (91.67) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-21:50:26] Epoch: [181][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.104 (0.234) Prec@1 95.83 (92.78) Prec@5 100.00 (99.83)
|
|
test [2019-03-31-21:50:26] Epoch: [181][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.061 (0.235) Prec@1 93.75 (92.79) Prec@5 100.00 (99.84)
|
|
[2019-03-31-21:50:26] **test** Prec@1 92.79 Prec@5 99.84 Error@1 7.21 Error@5 0.16 Loss:0.235
|
|
----> Best Accuracy : Acc@1=94.01, Acc@5=99.81, Error@1=5.99, Error@5=0.19
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-21:50:26] [Epoch=182/600] [Need: 15:00:11] LR=0.0198 ~ 0.0198, Batch=96
|
|
train[2019-03-31-21:50:27] Epoch: [182][000/521] Time 0.83 (0.83) Data 0.56 (0.56) Loss 0.301 (0.301) Prec@1 92.71 (92.71) Prec@5 98.96 (98.96)
|
|
train[2019-03-31-21:50:51] Epoch: [182][100/521] Time 0.24 (0.25) Data 0.00 (0.01) Loss 0.224 (0.300) Prec@1 94.79 (93.39) Prec@5 100.00 (99.85)
|
|
train[2019-03-31-21:51:15] Epoch: [182][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.239 (0.306) Prec@1 94.79 (93.22) Prec@5 100.00 (99.88)
|
|
train[2019-03-31-21:51:39] Epoch: [182][300/521] Time 0.28 (0.24) Data 0.00 (0.00) Loss 0.229 (0.307) Prec@1 95.83 (93.22) Prec@5 100.00 (99.87)
|
|
train[2019-03-31-21:52:02] Epoch: [182][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.315 (0.312) Prec@1 93.75 (93.01) Prec@5 100.00 (99.87)
|
|
train[2019-03-31-21:52:26] Epoch: [182][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.385 (0.314) Prec@1 94.79 (92.97) Prec@5 100.00 (99.87)
|
|
train[2019-03-31-21:52:31] Epoch: [182][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.317 (0.313) Prec@1 92.50 (92.99) Prec@5 100.00 (99.87)
|
|
[2019-03-31-21:52:31] **train** Prec@1 92.99 Prec@5 99.87 Error@1 7.01 Error@5 0.13 Loss:0.313
|
|
test [2019-03-31-21:52:31] Epoch: [182][000/105] Time 0.60 (0.60) Data 0.54 (0.54) Loss 0.234 (0.234) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-21:52:36] Epoch: [182][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.062 (0.239) Prec@1 97.92 (92.87) Prec@5 100.00 (99.80)
|
|
test [2019-03-31-21:52:36] Epoch: [182][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.037 (0.241) Prec@1 100.00 (92.85) Prec@5 100.00 (99.81)
|
|
[2019-03-31-21:52:36] **test** Prec@1 92.85 Prec@5 99.81 Error@1 7.15 Error@5 0.19 Loss:0.241
|
|
----> Best Accuracy : Acc@1=94.01, Acc@5=99.81, Error@1=5.99, Error@5=0.19
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-21:52:36] [Epoch=183/600] [Need: 15:03:08] LR=0.0197 ~ 0.0197, Batch=96
|
|
train[2019-03-31-21:52:37] Epoch: [183][000/521] Time 0.70 (0.70) Data 0.43 (0.43) Loss 0.170 (0.170) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-21:53:00] Epoch: [183][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.373 (0.296) Prec@1 92.71 (93.29) Prec@5 100.00 (99.91)
|
|
train[2019-03-31-21:53:24] Epoch: [183][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.265 (0.313) Prec@1 95.83 (92.86) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-21:53:48] Epoch: [183][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.304 (0.310) Prec@1 95.83 (92.96) Prec@5 100.00 (99.90)
|
|
train[2019-03-31-21:54:11] Epoch: [183][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.364 (0.309) Prec@1 93.75 (92.98) Prec@5 100.00 (99.90)
|
|
train[2019-03-31-21:54:36] Epoch: [183][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.406 (0.311) Prec@1 88.54 (92.94) Prec@5 98.96 (99.89)
|
|
train[2019-03-31-21:54:40] Epoch: [183][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.512 (0.312) Prec@1 87.50 (92.93) Prec@5 100.00 (99.89)
|
|
[2019-03-31-21:54:40] **train** Prec@1 92.93 Prec@5 99.89 Error@1 7.07 Error@5 0.11 Loss:0.312
|
|
test [2019-03-31-21:54:41] Epoch: [183][000/105] Time 0.62 (0.62) Data 0.55 (0.55) Loss 0.183 (0.183) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-21:54:45] Epoch: [183][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.083 (0.215) Prec@1 97.92 (93.41) Prec@5 100.00 (99.85)
|
|
test [2019-03-31-21:54:45] Epoch: [183][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.266 (0.216) Prec@1 87.50 (93.40) Prec@5 100.00 (99.84)
|
|
[2019-03-31-21:54:45] **test** Prec@1 93.40 Prec@5 99.84 Error@1 6.60 Error@5 0.16 Loss:0.216
|
|
----> Best Accuracy : Acc@1=94.01, Acc@5=99.81, Error@1=5.99, Error@5=0.19
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-21:54:45] [Epoch=184/600] [Need: 14:57:47] LR=0.0197 ~ 0.0197, Batch=96
|
|
train[2019-03-31-21:54:46] Epoch: [184][000/521] Time 0.73 (0.73) Data 0.46 (0.46) Loss 0.345 (0.345) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-21:55:10] Epoch: [184][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.309 (0.305) Prec@1 94.79 (93.07) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-21:55:34] Epoch: [184][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.468 (0.312) Prec@1 89.58 (92.80) Prec@5 100.00 (99.86)
|
|
train[2019-03-31-21:55:58] Epoch: [184][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.369 (0.314) Prec@1 89.58 (92.88) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-21:56:21] Epoch: [184][400/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.349 (0.318) Prec@1 90.62 (92.76) Prec@5 100.00 (99.88)
|
|
train[2019-03-31-21:56:45] Epoch: [184][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.390 (0.319) Prec@1 87.50 (92.72) Prec@5 100.00 (99.88)
|
|
train[2019-03-31-21:56:50] Epoch: [184][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.237 (0.319) Prec@1 93.75 (92.71) Prec@5 100.00 (99.88)
|
|
[2019-03-31-21:56:50] **train** Prec@1 92.71 Prec@5 99.88 Error@1 7.29 Error@5 0.12 Loss:0.319
|
|
test [2019-03-31-21:56:50] Epoch: [184][000/105] Time 0.48 (0.48) Data 0.41 (0.41) Loss 0.122 (0.122) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-21:56:54] Epoch: [184][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.070 (0.248) Prec@1 96.88 (92.69) Prec@5 100.00 (99.79)
|
|
test [2019-03-31-21:56:54] Epoch: [184][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.264 (0.249) Prec@1 93.75 (92.65) Prec@5 100.00 (99.80)
|
|
[2019-03-31-21:56:54] **test** Prec@1 92.65 Prec@5 99.80 Error@1 7.35 Error@5 0.20 Loss:0.249
|
|
----> Best Accuracy : Acc@1=94.01, Acc@5=99.81, Error@1=5.99, Error@5=0.19
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-21:56:54] [Epoch=185/600] [Need: 14:52:41] LR=0.0196 ~ 0.0196, Batch=96
|
|
train[2019-03-31-21:56:55] Epoch: [185][000/521] Time 0.71 (0.71) Data 0.43 (0.43) Loss 0.242 (0.242) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-21:57:19] Epoch: [185][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.328 (0.313) Prec@1 92.71 (92.84) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-21:57:43] Epoch: [185][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.367 (0.315) Prec@1 91.67 (92.88) Prec@5 100.00 (99.87)
|
|
train[2019-03-31-21:58:06] Epoch: [185][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.337 (0.308) Prec@1 94.79 (93.04) Prec@5 100.00 (99.88)
|
|
train[2019-03-31-21:58:30] Epoch: [185][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.258 (0.306) Prec@1 96.88 (93.18) Prec@5 100.00 (99.87)
|
|
train[2019-03-31-21:58:54] Epoch: [185][500/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.273 (0.307) Prec@1 92.71 (93.10) Prec@5 100.00 (99.88)
|
|
train[2019-03-31-21:58:58] Epoch: [185][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.226 (0.307) Prec@1 93.75 (93.13) Prec@5 100.00 (99.89)
|
|
[2019-03-31-21:58:58] **train** Prec@1 93.13 Prec@5 99.89 Error@1 6.87 Error@5 0.11 Loss:0.307
|
|
test [2019-03-31-21:58:59] Epoch: [185][000/105] Time 0.46 (0.46) Data 0.40 (0.40) Loss 0.245 (0.245) Prec@1 91.67 (91.67) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-21:59:03] Epoch: [185][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.123 (0.208) Prec@1 94.79 (93.48) Prec@5 100.00 (99.83)
|
|
test [2019-03-31-21:59:03] Epoch: [185][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.350 (0.207) Prec@1 93.75 (93.49) Prec@5 100.00 (99.84)
|
|
[2019-03-31-21:59:03] **test** Prec@1 93.49 Prec@5 99.84 Error@1 6.51 Error@5 0.16 Loss:0.207
|
|
----> Best Accuracy : Acc@1=94.01, Acc@5=99.81, Error@1=5.99, Error@5=0.19
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-21:59:03] [Epoch=186/600] [Need: 14:49:20] LR=0.0195 ~ 0.0195, Batch=96
|
|
train[2019-03-31-21:59:04] Epoch: [186][000/521] Time 0.83 (0.83) Data 0.56 (0.56) Loss 0.379 (0.379) Prec@1 89.58 (89.58) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-21:59:28] Epoch: [186][100/521] Time 0.23 (0.24) Data 0.00 (0.01) Loss 0.342 (0.313) Prec@1 89.58 (93.08) Prec@5 100.00 (99.83)
|
|
train[2019-03-31-21:59:51] Epoch: [186][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.385 (0.315) Prec@1 90.62 (92.78) Prec@5 100.00 (99.86)
|
|
train[2019-03-31-22:00:15] Epoch: [186][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.305 (0.313) Prec@1 94.79 (92.84) Prec@5 98.96 (99.86)
|
|
train[2019-03-31-22:00:39] Epoch: [186][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.272 (0.310) Prec@1 91.67 (92.91) Prec@5 100.00 (99.88)
|
|
train[2019-03-31-22:01:03] Epoch: [186][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.310 (0.312) Prec@1 90.62 (92.87) Prec@5 100.00 (99.86)
|
|
train[2019-03-31-22:01:08] Epoch: [186][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.410 (0.313) Prec@1 92.50 (92.86) Prec@5 100.00 (99.86)
|
|
[2019-03-31-22:01:08] **train** Prec@1 92.86 Prec@5 99.86 Error@1 7.14 Error@5 0.14 Loss:0.313
|
|
test [2019-03-31-22:01:08] Epoch: [186][000/105] Time 0.60 (0.60) Data 0.54 (0.54) Loss 0.333 (0.333) Prec@1 90.62 (90.62) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-22:01:12] Epoch: [186][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.142 (0.267) Prec@1 93.75 (91.99) Prec@5 100.00 (99.76)
|
|
test [2019-03-31-22:01:12] Epoch: [186][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.219 (0.266) Prec@1 93.75 (92.05) Prec@5 100.00 (99.77)
|
|
[2019-03-31-22:01:13] **test** Prec@1 92.05 Prec@5 99.77 Error@1 7.95 Error@5 0.23 Loss:0.266
|
|
----> Best Accuracy : Acc@1=94.01, Acc@5=99.81, Error@1=5.99, Error@5=0.19
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-22:01:13] [Epoch=187/600] [Need: 14:50:11] LR=0.0195 ~ 0.0195, Batch=96
|
|
train[2019-03-31-22:01:13] Epoch: [187][000/521] Time 0.70 (0.70) Data 0.42 (0.42) Loss 0.308 (0.308) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-22:01:37] Epoch: [187][100/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.377 (0.307) Prec@1 90.62 (92.91) Prec@5 100.00 (99.87)
|
|
train[2019-03-31-22:02:01] Epoch: [187][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.311 (0.316) Prec@1 90.62 (92.78) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-22:02:25] Epoch: [187][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.175 (0.317) Prec@1 96.88 (92.79) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-22:02:49] Epoch: [187][400/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.450 (0.317) Prec@1 91.67 (92.74) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-22:03:12] Epoch: [187][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.404 (0.320) Prec@1 91.67 (92.61) Prec@5 100.00 (99.88)
|
|
train[2019-03-31-22:03:17] Epoch: [187][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.235 (0.321) Prec@1 92.50 (92.60) Prec@5 100.00 (99.88)
|
|
[2019-03-31-22:03:17] **train** Prec@1 92.60 Prec@5 99.88 Error@1 7.40 Error@5 0.12 Loss:0.321
|
|
test [2019-03-31-22:03:18] Epoch: [187][000/105] Time 0.60 (0.60) Data 0.54 (0.54) Loss 0.334 (0.334) Prec@1 89.58 (89.58) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-22:03:22] Epoch: [187][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.208 (0.243) Prec@1 92.71 (92.59) Prec@5 100.00 (99.80)
|
|
test [2019-03-31-22:03:22] Epoch: [187][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.019 (0.242) Prec@1 100.00 (92.62) Prec@5 100.00 (99.81)
|
|
[2019-03-31-22:03:22] **test** Prec@1 92.62 Prec@5 99.81 Error@1 7.38 Error@5 0.19 Loss:0.242
|
|
----> Best Accuracy : Acc@1=94.01, Acc@5=99.81, Error@1=5.99, Error@5=0.19
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-22:03:22] [Epoch=188/600] [Need: 14:49:49] LR=0.0194 ~ 0.0194, Batch=96
|
|
train[2019-03-31-22:03:23] Epoch: [188][000/521] Time 0.83 (0.83) Data 0.56 (0.56) Loss 0.433 (0.433) Prec@1 89.58 (89.58) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-22:03:47] Epoch: [188][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.237 (0.288) Prec@1 94.79 (93.57) Prec@5 100.00 (99.92)
|
|
train[2019-03-31-22:04:11] Epoch: [188][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.247 (0.301) Prec@1 94.79 (93.26) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-22:04:34] Epoch: [188][300/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.178 (0.299) Prec@1 96.88 (93.25) Prec@5 98.96 (99.90)
|
|
train[2019-03-31-22:04:58] Epoch: [188][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.421 (0.303) Prec@1 90.62 (93.16) Prec@5 98.96 (99.88)
|
|
train[2019-03-31-22:05:22] Epoch: [188][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.494 (0.311) Prec@1 90.62 (92.98) Prec@5 98.96 (99.87)
|
|
train[2019-03-31-22:05:27] Epoch: [188][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.173 (0.311) Prec@1 98.75 (92.97) Prec@5 100.00 (99.87)
|
|
[2019-03-31-22:05:27] **train** Prec@1 92.97 Prec@5 99.87 Error@1 7.03 Error@5 0.13 Loss:0.311
|
|
test [2019-03-31-22:05:27] Epoch: [188][000/105] Time 0.48 (0.48) Data 0.41 (0.41) Loss 0.347 (0.347) Prec@1 92.71 (92.71) Prec@5 98.96 (98.96)
|
|
test [2019-03-31-22:05:31] Epoch: [188][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.156 (0.200) Prec@1 93.75 (93.73) Prec@5 100.00 (99.79)
|
|
test [2019-03-31-22:05:31] Epoch: [188][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.164 (0.200) Prec@1 87.50 (93.68) Prec@5 100.00 (99.80)
|
|
[2019-03-31-22:05:32] **test** Prec@1 93.68 Prec@5 99.80 Error@1 6.32 Error@5 0.20 Loss:0.200
|
|
----> Best Accuracy : Acc@1=94.01, Acc@5=99.81, Error@1=5.99, Error@5=0.19
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-22:05:32] [Epoch=189/600] [Need: 14:46:15] LR=0.0194 ~ 0.0194, Batch=96
|
|
train[2019-03-31-22:05:32] Epoch: [189][000/521] Time 0.71 (0.71) Data 0.44 (0.44) Loss 0.329 (0.329) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-22:05:56] Epoch: [189][100/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.167 (0.286) Prec@1 93.75 (93.69) Prec@5 100.00 (99.91)
|
|
train[2019-03-31-22:06:20] Epoch: [189][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.306 (0.299) Prec@1 91.67 (93.34) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-22:06:45] Epoch: [189][300/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.333 (0.299) Prec@1 91.67 (93.33) Prec@5 100.00 (99.90)
|
|
train[2019-03-31-22:07:08] Epoch: [189][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.191 (0.304) Prec@1 96.88 (93.21) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-22:07:32] Epoch: [189][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.449 (0.308) Prec@1 91.67 (93.12) Prec@5 98.96 (99.88)
|
|
train[2019-03-31-22:07:37] Epoch: [189][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.344 (0.308) Prec@1 90.00 (93.10) Prec@5 100.00 (99.88)
|
|
[2019-03-31-22:07:37] **train** Prec@1 93.10 Prec@5 99.88 Error@1 6.90 Error@5 0.12 Loss:0.308
|
|
test [2019-03-31-22:07:37] Epoch: [189][000/105] Time 0.47 (0.47) Data 0.40 (0.40) Loss 0.137 (0.137) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-22:07:41] Epoch: [189][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.089 (0.224) Prec@1 97.92 (93.45) Prec@5 100.00 (99.87)
|
|
test [2019-03-31-22:07:42] Epoch: [189][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.059 (0.224) Prec@1 93.75 (93.45) Prec@5 100.00 (99.86)
|
|
[2019-03-31-22:07:42] **test** Prec@1 93.45 Prec@5 99.86 Error@1 6.55 Error@5 0.14 Loss:0.224
|
|
----> Best Accuracy : Acc@1=94.01, Acc@5=99.81, Error@1=5.99, Error@5=0.19
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-22:07:42] [Epoch=190/600] [Need: 14:49:32] LR=0.0193 ~ 0.0193, Batch=96
|
|
train[2019-03-31-22:07:43] Epoch: [190][000/521] Time 0.85 (0.85) Data 0.58 (0.58) Loss 0.274 (0.274) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-22:08:06] Epoch: [190][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.299 (0.290) Prec@1 92.71 (93.54) Prec@5 100.00 (99.94)
|
|
train[2019-03-31-22:08:30] Epoch: [190][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.194 (0.291) Prec@1 95.83 (93.53) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-22:08:54] Epoch: [190][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.292 (0.294) Prec@1 92.71 (93.41) Prec@5 98.96 (99.89)
|
|
train[2019-03-31-22:09:18] Epoch: [190][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.332 (0.297) Prec@1 91.67 (93.34) Prec@5 100.00 (99.88)
|
|
train[2019-03-31-22:09:41] Epoch: [190][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.429 (0.304) Prec@1 90.62 (93.20) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-22:09:46] Epoch: [190][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.293 (0.303) Prec@1 91.25 (93.17) Prec@5 100.00 (99.89)
|
|
[2019-03-31-22:09:46] **train** Prec@1 93.17 Prec@5 99.89 Error@1 6.83 Error@5 0.11 Loss:0.303
|
|
test [2019-03-31-22:09:47] Epoch: [190][000/105] Time 0.47 (0.47) Data 0.40 (0.40) Loss 0.193 (0.193) Prec@1 91.67 (91.67) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-22:09:51] Epoch: [190][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.085 (0.218) Prec@1 96.88 (93.56) Prec@5 100.00 (99.85)
|
|
test [2019-03-31-22:09:51] Epoch: [190][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.548 (0.218) Prec@1 87.50 (93.58) Prec@5 100.00 (99.85)
|
|
[2019-03-31-22:09:51] **test** Prec@1 93.58 Prec@5 99.85 Error@1 6.42 Error@5 0.15 Loss:0.218
|
|
----> Best Accuracy : Acc@1=94.01, Acc@5=99.81, Error@1=5.99, Error@5=0.19
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-22:09:51] [Epoch=191/600] [Need: 14:40:54] LR=0.0193 ~ 0.0193, Batch=96
|
|
train[2019-03-31-22:09:52] Epoch: [191][000/521] Time 0.85 (0.85) Data 0.58 (0.58) Loss 0.201 (0.201) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-22:10:16] Epoch: [191][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.388 (0.303) Prec@1 92.71 (93.35) Prec@5 100.00 (99.88)
|
|
train[2019-03-31-22:10:39] Epoch: [191][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.242 (0.306) Prec@1 93.75 (93.01) Prec@5 100.00 (99.93)
|
|
train[2019-03-31-22:11:03] Epoch: [191][300/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.150 (0.302) Prec@1 96.88 (93.10) Prec@5 100.00 (99.91)
|
|
train[2019-03-31-22:11:27] Epoch: [191][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.330 (0.306) Prec@1 92.71 (93.07) Prec@5 100.00 (99.90)
|
|
train[2019-03-31-22:11:51] Epoch: [191][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.363 (0.309) Prec@1 90.62 (93.05) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-22:11:55] Epoch: [191][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.449 (0.308) Prec@1 88.75 (93.08) Prec@5 100.00 (99.89)
|
|
[2019-03-31-22:11:55] **train** Prec@1 93.08 Prec@5 99.89 Error@1 6.92 Error@5 0.11 Loss:0.308
|
|
test [2019-03-31-22:11:56] Epoch: [191][000/105] Time 0.60 (0.60) Data 0.54 (0.54) Loss 0.300 (0.300) Prec@1 88.54 (88.54) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-22:12:00] Epoch: [191][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.098 (0.225) Prec@1 96.88 (93.13) Prec@5 100.00 (99.82)
|
|
test [2019-03-31-22:12:00] Epoch: [191][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.036 (0.224) Prec@1 100.00 (93.15) Prec@5 100.00 (99.83)
|
|
[2019-03-31-22:12:00] **test** Prec@1 93.15 Prec@5 99.83 Error@1 6.85 Error@5 0.17 Loss:0.224
|
|
----> Best Accuracy : Acc@1=94.01, Acc@5=99.81, Error@1=5.99, Error@5=0.19
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-22:12:00] [Epoch=192/600] [Need: 14:39:49] LR=0.0192 ~ 0.0192, Batch=96
|
|
train[2019-03-31-22:12:01] Epoch: [192][000/521] Time 0.71 (0.71) Data 0.44 (0.44) Loss 0.284 (0.284) Prec@1 91.67 (91.67) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-22:12:25] Epoch: [192][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.230 (0.314) Prec@1 97.92 (93.28) Prec@5 100.00 (99.88)
|
|
train[2019-03-31-22:12:49] Epoch: [192][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.398 (0.319) Prec@1 90.62 (92.85) Prec@5 100.00 (99.85)
|
|
train[2019-03-31-22:13:12] Epoch: [192][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.266 (0.307) Prec@1 91.67 (93.11) Prec@5 100.00 (99.88)
|
|
train[2019-03-31-22:13:36] Epoch: [192][400/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.377 (0.308) Prec@1 93.75 (93.06) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-22:14:00] Epoch: [192][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.481 (0.313) Prec@1 88.54 (92.90) Prec@5 97.92 (99.88)
|
|
train[2019-03-31-22:14:05] Epoch: [192][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.300 (0.313) Prec@1 92.50 (92.92) Prec@5 100.00 (99.88)
|
|
[2019-03-31-22:14:05] **train** Prec@1 92.92 Prec@5 99.88 Error@1 7.08 Error@5 0.12 Loss:0.313
|
|
test [2019-03-31-22:14:05] Epoch: [192][000/105] Time 0.46 (0.46) Data 0.40 (0.40) Loss 0.169 (0.169) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-22:14:09] Epoch: [192][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.085 (0.203) Prec@1 97.92 (93.75) Prec@5 100.00 (99.81)
|
|
test [2019-03-31-22:14:09] Epoch: [192][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.109 (0.203) Prec@1 100.00 (93.76) Prec@5 100.00 (99.82)
|
|
[2019-03-31-22:14:10] **test** Prec@1 93.76 Prec@5 99.82 Error@1 6.24 Error@5 0.18 Loss:0.203
|
|
----> Best Accuracy : Acc@1=94.01, Acc@5=99.81, Error@1=5.99, Error@5=0.19
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-22:14:10] [Epoch=193/600] [Need: 14:36:49] LR=0.0192 ~ 0.0192, Batch=96
|
|
train[2019-03-31-22:14:10] Epoch: [193][000/521] Time 0.70 (0.70) Data 0.43 (0.43) Loss 0.332 (0.332) Prec@1 91.67 (91.67) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-22:14:34] Epoch: [193][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.275 (0.291) Prec@1 91.67 (93.73) Prec@5 100.00 (99.87)
|
|
train[2019-03-31-22:14:58] Epoch: [193][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.272 (0.296) Prec@1 92.71 (93.28) Prec@5 100.00 (99.88)
|
|
train[2019-03-31-22:15:21] Epoch: [193][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.257 (0.303) Prec@1 93.75 (93.12) Prec@5 100.00 (99.87)
|
|
train[2019-03-31-22:15:45] Epoch: [193][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.230 (0.308) Prec@1 93.75 (92.94) Prec@5 100.00 (99.87)
|
|
train[2019-03-31-22:16:10] Epoch: [193][500/521] Time 0.27 (0.24) Data 0.00 (0.00) Loss 0.409 (0.310) Prec@1 93.75 (92.87) Prec@5 98.96 (99.86)
|
|
train[2019-03-31-22:16:15] Epoch: [193][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.157 (0.311) Prec@1 97.50 (92.88) Prec@5 100.00 (99.86)
|
|
[2019-03-31-22:16:15] **train** Prec@1 92.88 Prec@5 99.86 Error@1 7.12 Error@5 0.14 Loss:0.311
|
|
test [2019-03-31-22:16:15] Epoch: [193][000/105] Time 0.60 (0.60) Data 0.54 (0.54) Loss 0.220 (0.220) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-22:16:19] Epoch: [193][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.113 (0.255) Prec@1 95.83 (92.28) Prec@5 100.00 (99.77)
|
|
test [2019-03-31-22:16:20] Epoch: [193][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.035 (0.252) Prec@1 100.00 (92.32) Prec@5 100.00 (99.78)
|
|
[2019-03-31-22:16:20] **test** Prec@1 92.32 Prec@5 99.78 Error@1 7.68 Error@5 0.22 Loss:0.252
|
|
----> Best Accuracy : Acc@1=94.01, Acc@5=99.81, Error@1=5.99, Error@5=0.19
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-22:16:20] [Epoch=194/600] [Need: 14:40:00] LR=0.0191 ~ 0.0191, Batch=96
|
|
train[2019-03-31-22:16:20] Epoch: [194][000/521] Time 0.70 (0.70) Data 0.43 (0.43) Loss 0.427 (0.427) Prec@1 92.71 (92.71) Prec@5 98.96 (98.96)
|
|
train[2019-03-31-22:16:45] Epoch: [194][100/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.275 (0.299) Prec@1 93.75 (93.18) Prec@5 100.00 (99.90)
|
|
train[2019-03-31-22:17:08] Epoch: [194][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.242 (0.297) Prec@1 94.79 (93.41) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-22:17:32] Epoch: [194][300/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.257 (0.296) Prec@1 92.71 (93.38) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-22:17:56] Epoch: [194][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.365 (0.303) Prec@1 93.75 (93.31) Prec@5 100.00 (99.88)
|
|
train[2019-03-31-22:18:19] Epoch: [194][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.229 (0.309) Prec@1 94.79 (93.14) Prec@5 100.00 (99.86)
|
|
train[2019-03-31-22:18:24] Epoch: [194][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.447 (0.310) Prec@1 87.50 (93.11) Prec@5 100.00 (99.87)
|
|
[2019-03-31-22:18:24] **train** Prec@1 93.11 Prec@5 99.87 Error@1 6.89 Error@5 0.13 Loss:0.310
|
|
test [2019-03-31-22:18:25] Epoch: [194][000/105] Time 0.47 (0.47) Data 0.40 (0.40) Loss 0.227 (0.227) Prec@1 90.62 (90.62) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-22:18:29] Epoch: [194][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.167 (0.242) Prec@1 93.75 (92.39) Prec@5 100.00 (99.88)
|
|
test [2019-03-31-22:18:29] Epoch: [194][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.450 (0.243) Prec@1 93.75 (92.37) Prec@5 100.00 (99.88)
|
|
[2019-03-31-22:18:29] **test** Prec@1 92.37 Prec@5 99.88 Error@1 7.63 Error@5 0.12 Loss:0.243
|
|
----> Best Accuracy : Acc@1=94.01, Acc@5=99.81, Error@1=5.99, Error@5=0.19
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-22:18:29] [Epoch=195/600] [Need: 14:33:21] LR=0.0191 ~ 0.0191, Batch=96
|
|
train[2019-03-31-22:18:30] Epoch: [195][000/521] Time 0.72 (0.72) Data 0.44 (0.44) Loss 0.394 (0.394) Prec@1 87.50 (87.50) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-22:18:54] Epoch: [195][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.264 (0.300) Prec@1 95.83 (93.43) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-22:19:17] Epoch: [195][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.240 (0.296) Prec@1 92.71 (93.43) Prec@5 100.00 (99.90)
|
|
train[2019-03-31-22:19:41] Epoch: [195][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.323 (0.300) Prec@1 91.67 (93.26) Prec@5 100.00 (99.91)
|
|
train[2019-03-31-22:20:05] Epoch: [195][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.400 (0.300) Prec@1 89.58 (93.26) Prec@5 100.00 (99.91)
|
|
train[2019-03-31-22:20:28] Epoch: [195][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.358 (0.309) Prec@1 91.67 (93.01) Prec@5 98.96 (99.90)
|
|
train[2019-03-31-22:20:33] Epoch: [195][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.318 (0.309) Prec@1 88.75 (92.99) Prec@5 100.00 (99.90)
|
|
[2019-03-31-22:20:33] **train** Prec@1 92.99 Prec@5 99.90 Error@1 7.01 Error@5 0.10 Loss:0.309
|
|
test [2019-03-31-22:20:34] Epoch: [195][000/105] Time 0.47 (0.47) Data 0.41 (0.41) Loss 0.184 (0.184) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-22:20:38] Epoch: [195][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.094 (0.233) Prec@1 95.83 (92.73) Prec@5 100.00 (99.88)
|
|
test [2019-03-31-22:20:38] Epoch: [195][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.341 (0.233) Prec@1 93.75 (92.73) Prec@5 100.00 (99.88)
|
|
[2019-03-31-22:20:38] **test** Prec@1 92.73 Prec@5 99.88 Error@1 7.27 Error@5 0.12 Loss:0.233
|
|
----> Best Accuracy : Acc@1=94.01, Acc@5=99.81, Error@1=5.99, Error@5=0.19
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-22:20:38] [Epoch=196/600] [Need: 14:28:30] LR=0.0190 ~ 0.0190, Batch=96
|
|
train[2019-03-31-22:20:39] Epoch: [196][000/521] Time 0.71 (0.71) Data 0.44 (0.44) Loss 0.174 (0.174) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-22:21:03] Epoch: [196][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.260 (0.296) Prec@1 92.71 (93.46) Prec@5 100.00 (99.87)
|
|
train[2019-03-31-22:21:26] Epoch: [196][200/521] Time 0.27 (0.24) Data 0.00 (0.00) Loss 0.470 (0.306) Prec@1 91.67 (93.28) Prec@5 100.00 (99.87)
|
|
train[2019-03-31-22:21:50] Epoch: [196][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.359 (0.305) Prec@1 90.62 (93.21) Prec@5 100.00 (99.88)
|
|
train[2019-03-31-22:22:14] Epoch: [196][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.319 (0.309) Prec@1 93.75 (93.11) Prec@5 98.96 (99.89)
|
|
train[2019-03-31-22:22:38] Epoch: [196][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.242 (0.307) Prec@1 94.79 (93.12) Prec@5 100.00 (99.88)
|
|
train[2019-03-31-22:22:42] Epoch: [196][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.293 (0.309) Prec@1 91.25 (93.06) Prec@5 100.00 (99.88)
|
|
[2019-03-31-22:22:42] **train** Prec@1 93.06 Prec@5 99.88 Error@1 6.94 Error@5 0.12 Loss:0.309
|
|
test [2019-03-31-22:22:43] Epoch: [196][000/105] Time 0.59 (0.59) Data 0.52 (0.52) Loss 0.203 (0.203) Prec@1 91.67 (91.67) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-22:22:47] Epoch: [196][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.179 (0.232) Prec@1 96.88 (92.86) Prec@5 100.00 (99.86)
|
|
test [2019-03-31-22:22:47] Epoch: [196][104/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.162 (0.230) Prec@1 87.50 (92.88) Prec@5 100.00 (99.86)
|
|
[2019-03-31-22:22:47] **test** Prec@1 92.88 Prec@5 99.86 Error@1 7.12 Error@5 0.14 Loss:0.230
|
|
----> Best Accuracy : Acc@1=94.01, Acc@5=99.81, Error@1=5.99, Error@5=0.19
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-22:22:47] [Epoch=197/600] [Need: 14:28:02] LR=0.0189 ~ 0.0189, Batch=96
|
|
train[2019-03-31-22:22:48] Epoch: [197][000/521] Time 0.70 (0.70) Data 0.42 (0.42) Loss 0.357 (0.357) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-22:23:12] Epoch: [197][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.245 (0.303) Prec@1 94.79 (93.30) Prec@5 98.96 (99.81)
|
|
train[2019-03-31-22:23:35] Epoch: [197][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.327 (0.298) Prec@1 91.67 (93.13) Prec@5 100.00 (99.84)
|
|
train[2019-03-31-22:23:59] Epoch: [197][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.390 (0.305) Prec@1 90.62 (93.00) Prec@5 100.00 (99.83)
|
|
train[2019-03-31-22:24:23] Epoch: [197][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.224 (0.309) Prec@1 94.79 (93.01) Prec@5 100.00 (99.84)
|
|
train[2019-03-31-22:24:47] Epoch: [197][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.214 (0.308) Prec@1 95.83 (93.01) Prec@5 100.00 (99.86)
|
|
train[2019-03-31-22:24:51] Epoch: [197][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.219 (0.309) Prec@1 96.25 (92.99) Prec@5 100.00 (99.86)
|
|
[2019-03-31-22:24:51] **train** Prec@1 92.99 Prec@5 99.86 Error@1 7.01 Error@5 0.14 Loss:0.309
|
|
test [2019-03-31-22:24:52] Epoch: [197][000/105] Time 0.46 (0.46) Data 0.40 (0.40) Loss 0.115 (0.115) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-22:24:56] Epoch: [197][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.087 (0.200) Prec@1 95.83 (94.00) Prec@5 100.00 (99.86)
|
|
test [2019-03-31-22:24:56] Epoch: [197][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.034 (0.199) Prec@1 100.00 (94.04) Prec@5 100.00 (99.85)
|
|
[2019-03-31-22:24:56] **test** Prec@1 94.04 Prec@5 99.85 Error@1 5.96 Error@5 0.15 Loss:0.199
|
|
----> Best Accuracy : Acc@1=94.04, Acc@5=99.85, Error@1=5.96, Error@5=0.15
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-22:24:56] [Epoch=198/600] [Need: 14:24:03] LR=0.0189 ~ 0.0189, Batch=96
|
|
train[2019-03-31-22:24:57] Epoch: [198][000/521] Time 0.85 (0.85) Data 0.58 (0.58) Loss 0.316 (0.316) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-22:25:21] Epoch: [198][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.201 (0.289) Prec@1 94.79 (93.47) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-22:25:45] Epoch: [198][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.355 (0.300) Prec@1 94.79 (93.25) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-22:26:08] Epoch: [198][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.271 (0.302) Prec@1 95.83 (93.34) Prec@5 98.96 (99.88)
|
|
train[2019-03-31-22:26:32] Epoch: [198][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.442 (0.303) Prec@1 90.62 (93.25) Prec@5 100.00 (99.88)
|
|
train[2019-03-31-22:26:56] Epoch: [198][500/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.144 (0.304) Prec@1 93.75 (93.18) Prec@5 100.00 (99.88)
|
|
train[2019-03-31-22:27:01] Epoch: [198][520/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.274 (0.304) Prec@1 96.25 (93.20) Prec@5 100.00 (99.88)
|
|
[2019-03-31-22:27:01] **train** Prec@1 93.20 Prec@5 99.88 Error@1 6.80 Error@5 0.12 Loss:0.304
|
|
test [2019-03-31-22:27:01] Epoch: [198][000/105] Time 0.46 (0.46) Data 0.40 (0.40) Loss 0.120 (0.120) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-22:27:05] Epoch: [198][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.069 (0.222) Prec@1 96.88 (93.33) Prec@5 100.00 (99.85)
|
|
test [2019-03-31-22:27:05] Epoch: [198][104/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.042 (0.223) Prec@1 100.00 (93.32) Prec@5 100.00 (99.85)
|
|
[2019-03-31-22:27:05] **test** Prec@1 93.32 Prec@5 99.85 Error@1 6.68 Error@5 0.15 Loss:0.223
|
|
----> Best Accuracy : Acc@1=94.04, Acc@5=99.85, Error@1=5.96, Error@5=0.15
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-22:27:05] [Epoch=199/600] [Need: 14:22:52] LR=0.0188 ~ 0.0188, Batch=96
|
|
train[2019-03-31-22:27:06] Epoch: [199][000/521] Time 0.73 (0.73) Data 0.43 (0.43) Loss 0.404 (0.404) Prec@1 92.71 (92.71) Prec@5 98.96 (98.96)
|
|
train[2019-03-31-22:27:31] Epoch: [199][100/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.210 (0.303) Prec@1 95.83 (93.05) Prec@5 100.00 (99.92)
|
|
train[2019-03-31-22:27:54] Epoch: [199][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.301 (0.300) Prec@1 93.75 (93.09) Prec@5 100.00 (99.91)
|
|
train[2019-03-31-22:28:18] Epoch: [199][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.343 (0.303) Prec@1 89.58 (93.04) Prec@5 100.00 (99.91)
|
|
train[2019-03-31-22:28:42] Epoch: [199][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.384 (0.308) Prec@1 91.67 (93.01) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-22:29:06] Epoch: [199][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.304 (0.309) Prec@1 89.58 (93.01) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-22:29:10] Epoch: [199][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.246 (0.308) Prec@1 96.25 (93.01) Prec@5 100.00 (99.89)
|
|
[2019-03-31-22:29:11] **train** Prec@1 93.01 Prec@5 99.89 Error@1 6.99 Error@5 0.11 Loss:0.308
|
|
test [2019-03-31-22:29:11] Epoch: [199][000/105] Time 0.46 (0.46) Data 0.39 (0.39) Loss 0.174 (0.174) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
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test [2019-03-31-22:29:15] Epoch: [199][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.092 (0.279) Prec@1 95.83 (91.80) Prec@5 100.00 (99.82)
|
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test [2019-03-31-22:29:15] Epoch: [199][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.248 (0.277) Prec@1 81.25 (91.84) Prec@5 100.00 (99.83)
|
|
[2019-03-31-22:29:15] **test** Prec@1 91.84 Prec@5 99.83 Error@1 8.16 Error@5 0.17 Loss:0.277
|
|
----> Best Accuracy : Acc@1=94.04, Acc@5=99.85, Error@1=5.96, Error@5=0.15
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-22:29:15] [Epoch=200/600] [Need: 14:26:31] LR=0.0188 ~ 0.0188, Batch=96
|
|
train[2019-03-31-22:29:16] Epoch: [200][000/521] Time 0.85 (0.85) Data 0.58 (0.58) Loss 0.373 (0.373) Prec@1 90.62 (90.62) Prec@5 98.96 (98.96)
|
|
train[2019-03-31-22:29:40] Epoch: [200][100/521] Time 0.23 (0.24) Data 0.00 (0.01) Loss 0.389 (0.298) Prec@1 94.79 (93.13) Prec@5 100.00 (99.92)
|
|
train[2019-03-31-22:30:04] Epoch: [200][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.230 (0.301) Prec@1 92.71 (92.98) Prec@5 100.00 (99.90)
|
|
train[2019-03-31-22:30:27] Epoch: [200][300/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.406 (0.304) Prec@1 91.67 (92.89) Prec@5 98.96 (99.86)
|
|
train[2019-03-31-22:30:51] Epoch: [200][400/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.320 (0.302) Prec@1 93.75 (92.96) Prec@5 100.00 (99.86)
|
|
train[2019-03-31-22:31:15] Epoch: [200][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.550 (0.310) Prec@1 87.50 (92.75) Prec@5 98.96 (99.87)
|
|
train[2019-03-31-22:31:20] Epoch: [200][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.231 (0.310) Prec@1 96.25 (92.77) Prec@5 100.00 (99.86)
|
|
[2019-03-31-22:31:20] **train** Prec@1 92.77 Prec@5 99.86 Error@1 7.23 Error@5 0.14 Loss:0.310
|
|
test [2019-03-31-22:31:20] Epoch: [200][000/105] Time 0.47 (0.47) Data 0.41 (0.41) Loss 0.261 (0.261) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-22:31:24] Epoch: [200][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.151 (0.229) Prec@1 94.79 (93.22) Prec@5 100.00 (99.86)
|
|
test [2019-03-31-22:31:24] Epoch: [200][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.188 (0.228) Prec@1 93.75 (93.21) Prec@5 100.00 (99.86)
|
|
[2019-03-31-22:31:24] **test** Prec@1 93.21 Prec@5 99.86 Error@1 6.79 Error@5 0.14 Loss:0.228
|
|
----> Best Accuracy : Acc@1=94.04, Acc@5=99.85, Error@1=5.96, Error@5=0.15
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-22:31:25] [Epoch=201/600] [Need: 14:18:47] LR=0.0187 ~ 0.0187, Batch=96
|
|
train[2019-03-31-22:31:25] Epoch: [201][000/521] Time 0.70 (0.70) Data 0.43 (0.43) Loss 0.295 (0.295) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-22:31:50] Epoch: [201][100/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.271 (0.298) Prec@1 92.71 (93.21) Prec@5 100.00 (99.88)
|
|
train[2019-03-31-22:32:13] Epoch: [201][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.180 (0.293) Prec@1 95.83 (93.39) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-22:32:37] Epoch: [201][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.220 (0.300) Prec@1 95.83 (93.19) Prec@5 100.00 (99.88)
|
|
train[2019-03-31-22:33:01] Epoch: [201][400/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.243 (0.304) Prec@1 92.71 (93.19) Prec@5 100.00 (99.87)
|
|
train[2019-03-31-22:33:24] Epoch: [201][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.357 (0.305) Prec@1 91.67 (93.14) Prec@5 98.96 (99.87)
|
|
train[2019-03-31-22:33:29] Epoch: [201][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.184 (0.305) Prec@1 97.50 (93.14) Prec@5 100.00 (99.87)
|
|
[2019-03-31-22:33:29] **train** Prec@1 93.14 Prec@5 99.87 Error@1 6.86 Error@5 0.13 Loss:0.305
|
|
test [2019-03-31-22:33:30] Epoch: [201][000/105] Time 0.48 (0.48) Data 0.42 (0.42) Loss 0.258 (0.258) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-22:33:34] Epoch: [201][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.171 (0.233) Prec@1 93.75 (93.10) Prec@5 100.00 (99.88)
|
|
test [2019-03-31-22:33:34] Epoch: [201][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.026 (0.232) Prec@1 100.00 (93.11) Prec@5 100.00 (99.88)
|
|
[2019-03-31-22:33:34] **test** Prec@1 93.11 Prec@5 99.88 Error@1 6.89 Error@5 0.12 Loss:0.232
|
|
----> Best Accuracy : Acc@1=94.04, Acc@5=99.85, Error@1=5.96, Error@5=0.15
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-22:33:34] [Epoch=202/600] [Need: 14:19:19] LR=0.0187 ~ 0.0187, Batch=96
|
|
train[2019-03-31-22:33:35] Epoch: [202][000/521] Time 0.70 (0.70) Data 0.43 (0.43) Loss 0.310 (0.310) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-22:33:58] Epoch: [202][100/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.286 (0.306) Prec@1 92.71 (93.36) Prec@5 100.00 (99.90)
|
|
train[2019-03-31-22:34:22] Epoch: [202][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.204 (0.300) Prec@1 93.75 (93.30) Prec@5 100.00 (99.91)
|
|
train[2019-03-31-22:34:46] Epoch: [202][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.256 (0.297) Prec@1 94.79 (93.43) Prec@5 100.00 (99.91)
|
|
train[2019-03-31-22:35:09] Epoch: [202][400/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.224 (0.297) Prec@1 93.75 (93.32) Prec@5 100.00 (99.92)
|
|
train[2019-03-31-22:35:33] Epoch: [202][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.410 (0.301) Prec@1 90.62 (93.23) Prec@5 100.00 (99.90)
|
|
train[2019-03-31-22:35:38] Epoch: [202][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.275 (0.302) Prec@1 92.50 (93.21) Prec@5 100.00 (99.89)
|
|
[2019-03-31-22:35:38] **train** Prec@1 93.21 Prec@5 99.89 Error@1 6.79 Error@5 0.11 Loss:0.302
|
|
test [2019-03-31-22:35:39] Epoch: [202][000/105] Time 0.60 (0.60) Data 0.54 (0.54) Loss 0.279 (0.279) Prec@1 87.50 (87.50) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-22:35:43] Epoch: [202][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.043 (0.216) Prec@1 98.96 (93.56) Prec@5 100.00 (99.83)
|
|
test [2019-03-31-22:35:43] Epoch: [202][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.387 (0.219) Prec@1 87.50 (93.49) Prec@5 100.00 (99.83)
|
|
[2019-03-31-22:35:43] **test** Prec@1 93.49 Prec@5 99.83 Error@1 6.51 Error@5 0.17 Loss:0.219
|
|
----> Best Accuracy : Acc@1=94.04, Acc@5=99.85, Error@1=5.96, Error@5=0.15
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-22:35:43] [Epoch=203/600] [Need: 14:13:38] LR=0.0186 ~ 0.0186, Batch=96
|
|
train[2019-03-31-22:35:44] Epoch: [203][000/521] Time 0.70 (0.70) Data 0.43 (0.43) Loss 0.223 (0.223) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-22:36:08] Epoch: [203][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.321 (0.301) Prec@1 93.75 (93.27) Prec@5 100.00 (99.87)
|
|
train[2019-03-31-22:36:31] Epoch: [203][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.393 (0.311) Prec@1 91.67 (92.90) Prec@5 100.00 (99.88)
|
|
train[2019-03-31-22:36:55] Epoch: [203][300/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.210 (0.309) Prec@1 97.92 (92.88) Prec@5 100.00 (99.90)
|
|
train[2019-03-31-22:37:19] Epoch: [203][400/521] Time 0.26 (0.24) Data 0.00 (0.00) Loss 0.266 (0.316) Prec@1 93.75 (92.68) Prec@5 100.00 (99.86)
|
|
train[2019-03-31-22:37:42] Epoch: [203][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.198 (0.315) Prec@1 96.88 (92.76) Prec@5 100.00 (99.86)
|
|
train[2019-03-31-22:37:47] Epoch: [203][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.323 (0.314) Prec@1 93.75 (92.76) Prec@5 100.00 (99.87)
|
|
[2019-03-31-22:37:47] **train** Prec@1 92.76 Prec@5 99.87 Error@1 7.24 Error@5 0.13 Loss:0.314
|
|
test [2019-03-31-22:37:48] Epoch: [203][000/105] Time 0.63 (0.63) Data 0.55 (0.55) Loss 0.178 (0.178) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-22:37:52] Epoch: [203][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.267 (0.239) Prec@1 95.83 (93.29) Prec@5 100.00 (99.87)
|
|
test [2019-03-31-22:37:52] Epoch: [203][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.098 (0.239) Prec@1 93.75 (93.25) Prec@5 100.00 (99.86)
|
|
[2019-03-31-22:37:52] **test** Prec@1 93.25 Prec@5 99.86 Error@1 6.75 Error@5 0.14 Loss:0.239
|
|
----> Best Accuracy : Acc@1=94.04, Acc@5=99.85, Error@1=5.96, Error@5=0.15
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-22:37:52] [Epoch=204/600] [Need: 14:12:38] LR=0.0185 ~ 0.0185, Batch=96
|
|
train[2019-03-31-22:37:53] Epoch: [204][000/521] Time 0.84 (0.84) Data 0.56 (0.56) Loss 0.269 (0.269) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-22:38:17] Epoch: [204][100/521] Time 0.23 (0.24) Data 0.00 (0.01) Loss 0.342 (0.291) Prec@1 93.75 (93.64) Prec@5 100.00 (99.88)
|
|
train[2019-03-31-22:38:41] Epoch: [204][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.214 (0.293) Prec@1 96.88 (93.49) Prec@5 100.00 (99.88)
|
|
train[2019-03-31-22:39:04] Epoch: [204][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.319 (0.293) Prec@1 91.67 (93.44) Prec@5 98.96 (99.87)
|
|
train[2019-03-31-22:39:28] Epoch: [204][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.277 (0.298) Prec@1 92.71 (93.30) Prec@5 100.00 (99.85)
|
|
train[2019-03-31-22:39:52] Epoch: [204][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.275 (0.298) Prec@1 95.83 (93.25) Prec@5 100.00 (99.85)
|
|
train[2019-03-31-22:39:57] Epoch: [204][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.459 (0.299) Prec@1 88.75 (93.24) Prec@5 100.00 (99.85)
|
|
[2019-03-31-22:39:57] **train** Prec@1 93.24 Prec@5 99.85 Error@1 6.76 Error@5 0.15 Loss:0.299
|
|
test [2019-03-31-22:39:57] Epoch: [204][000/105] Time 0.60 (0.60) Data 0.54 (0.54) Loss 0.126 (0.126) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-22:40:01] Epoch: [204][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.144 (0.227) Prec@1 94.79 (93.08) Prec@5 100.00 (99.75)
|
|
test [2019-03-31-22:40:02] Epoch: [204][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.018 (0.228) Prec@1 100.00 (93.08) Prec@5 100.00 (99.76)
|
|
[2019-03-31-22:40:02] **test** Prec@1 93.08 Prec@5 99.76 Error@1 6.92 Error@5 0.24 Loss:0.228
|
|
----> Best Accuracy : Acc@1=94.04, Acc@5=99.85, Error@1=5.96, Error@5=0.15
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-22:40:02] [Epoch=205/600] [Need: 14:12:18] LR=0.0185 ~ 0.0185, Batch=96
|
|
train[2019-03-31-22:40:03] Epoch: [205][000/521] Time 0.83 (0.83) Data 0.56 (0.56) Loss 0.275 (0.275) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-22:40:26] Epoch: [205][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.373 (0.300) Prec@1 89.58 (92.98) Prec@5 100.00 (99.92)
|
|
train[2019-03-31-22:40:50] Epoch: [205][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.288 (0.300) Prec@1 94.79 (93.01) Prec@5 98.96 (99.89)
|
|
train[2019-03-31-22:41:14] Epoch: [205][300/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.418 (0.296) Prec@1 90.62 (93.24) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-22:41:39] Epoch: [205][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.340 (0.297) Prec@1 89.58 (93.22) Prec@5 100.00 (99.88)
|
|
train[2019-03-31-22:42:03] Epoch: [205][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.368 (0.302) Prec@1 93.75 (93.12) Prec@5 100.00 (99.88)
|
|
train[2019-03-31-22:42:07] Epoch: [205][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.146 (0.301) Prec@1 98.75 (93.17) Prec@5 100.00 (99.88)
|
|
[2019-03-31-22:42:08] **train** Prec@1 93.17 Prec@5 99.88 Error@1 6.83 Error@5 0.12 Loss:0.301
|
|
test [2019-03-31-22:42:08] Epoch: [205][000/105] Time 0.61 (0.61) Data 0.53 (0.53) Loss 0.246 (0.246) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-22:42:12] Epoch: [205][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.048 (0.221) Prec@1 97.92 (93.55) Prec@5 100.00 (99.88)
|
|
test [2019-03-31-22:42:12] Epoch: [205][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.247 (0.221) Prec@1 93.75 (93.53) Prec@5 100.00 (99.87)
|
|
[2019-03-31-22:42:13] **test** Prec@1 93.53 Prec@5 99.87 Error@1 6.47 Error@5 0.13 Loss:0.221
|
|
----> Best Accuracy : Acc@1=94.04, Acc@5=99.85, Error@1=5.96, Error@5=0.15
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-22:42:13] [Epoch=206/600] [Need: 14:19:23] LR=0.0184 ~ 0.0184, Batch=96
|
|
train[2019-03-31-22:42:14] Epoch: [206][000/521] Time 0.87 (0.87) Data 0.60 (0.60) Loss 0.254 (0.254) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-22:42:37] Epoch: [206][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.352 (0.296) Prec@1 94.79 (93.35) Prec@5 100.00 (99.83)
|
|
train[2019-03-31-22:43:01] Epoch: [206][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.272 (0.294) Prec@1 93.75 (93.34) Prec@5 100.00 (99.87)
|
|
train[2019-03-31-22:43:25] Epoch: [206][300/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.360 (0.301) Prec@1 93.75 (93.30) Prec@5 100.00 (99.86)
|
|
train[2019-03-31-22:43:48] Epoch: [206][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.154 (0.303) Prec@1 95.83 (93.20) Prec@5 100.00 (99.86)
|
|
train[2019-03-31-22:44:12] Epoch: [206][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.219 (0.305) Prec@1 95.83 (93.18) Prec@5 100.00 (99.86)
|
|
train[2019-03-31-22:44:17] Epoch: [206][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.121 (0.305) Prec@1 98.75 (93.17) Prec@5 100.00 (99.86)
|
|
[2019-03-31-22:44:17] **train** Prec@1 93.17 Prec@5 99.86 Error@1 6.83 Error@5 0.14 Loss:0.305
|
|
test [2019-03-31-22:44:17] Epoch: [206][000/105] Time 0.47 (0.47) Data 0.40 (0.40) Loss 0.254 (0.254) Prec@1 90.62 (90.62) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-22:44:22] Epoch: [206][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.126 (0.247) Prec@1 96.88 (92.46) Prec@5 100.00 (99.86)
|
|
test [2019-03-31-22:44:22] Epoch: [206][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.203 (0.246) Prec@1 93.75 (92.50) Prec@5 100.00 (99.86)
|
|
[2019-03-31-22:44:22] **test** Prec@1 92.50 Prec@5 99.86 Error@1 7.50 Error@5 0.14 Loss:0.246
|
|
----> Best Accuracy : Acc@1=94.04, Acc@5=99.85, Error@1=5.96, Error@5=0.15
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-22:44:22] [Epoch=207/600] [Need: 14:06:34] LR=0.0184 ~ 0.0184, Batch=96
|
|
train[2019-03-31-22:44:23] Epoch: [207][000/521] Time 0.83 (0.83) Data 0.56 (0.56) Loss 0.229 (0.229) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-22:44:46] Epoch: [207][100/521] Time 0.23 (0.24) Data 0.00 (0.01) Loss 0.271 (0.293) Prec@1 93.75 (93.27) Prec@5 100.00 (99.86)
|
|
train[2019-03-31-22:45:10] Epoch: [207][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.305 (0.292) Prec@1 91.67 (93.26) Prec@5 100.00 (99.87)
|
|
train[2019-03-31-22:45:34] Epoch: [207][300/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.219 (0.293) Prec@1 95.83 (93.30) Prec@5 100.00 (99.87)
|
|
train[2019-03-31-22:45:58] Epoch: [207][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.359 (0.298) Prec@1 88.54 (93.15) Prec@5 100.00 (99.86)
|
|
train[2019-03-31-22:46:21] Epoch: [207][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.268 (0.300) Prec@1 96.88 (93.15) Prec@5 100.00 (99.86)
|
|
train[2019-03-31-22:46:26] Epoch: [207][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.288 (0.300) Prec@1 97.50 (93.15) Prec@5 100.00 (99.86)
|
|
[2019-03-31-22:46:26] **train** Prec@1 93.15 Prec@5 99.86 Error@1 6.85 Error@5 0.14 Loss:0.300
|
|
test [2019-03-31-22:46:27] Epoch: [207][000/105] Time 0.60 (0.60) Data 0.54 (0.54) Loss 0.202 (0.202) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-22:46:31] Epoch: [207][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.089 (0.220) Prec@1 95.83 (93.44) Prec@5 100.00 (99.83)
|
|
test [2019-03-31-22:46:31] Epoch: [207][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.618 (0.221) Prec@1 87.50 (93.44) Prec@5 100.00 (99.84)
|
|
[2019-03-31-22:46:31] **test** Prec@1 93.44 Prec@5 99.84 Error@1 6.56 Error@5 0.16 Loss:0.221
|
|
----> Best Accuracy : Acc@1=94.04, Acc@5=99.85, Error@1=5.96, Error@5=0.15
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-22:46:31] [Epoch=208/600] [Need: 14:05:14] LR=0.0183 ~ 0.0183, Batch=96
|
|
train[2019-03-31-22:46:32] Epoch: [208][000/521] Time 0.70 (0.70) Data 0.42 (0.42) Loss 0.328 (0.328) Prec@1 89.58 (89.58) Prec@5 98.96 (98.96)
|
|
train[2019-03-31-22:46:56] Epoch: [208][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.349 (0.289) Prec@1 91.67 (93.56) Prec@5 100.00 (99.82)
|
|
train[2019-03-31-22:47:19] Epoch: [208][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.429 (0.294) Prec@1 89.58 (93.43) Prec@5 100.00 (99.82)
|
|
train[2019-03-31-22:47:43] Epoch: [208][300/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.370 (0.295) Prec@1 90.62 (93.30) Prec@5 100.00 (99.84)
|
|
train[2019-03-31-22:48:07] Epoch: [208][400/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.227 (0.299) Prec@1 94.79 (93.26) Prec@5 98.96 (99.85)
|
|
train[2019-03-31-22:48:30] Epoch: [208][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.315 (0.299) Prec@1 92.71 (93.24) Prec@5 100.00 (99.85)
|
|
train[2019-03-31-22:48:35] Epoch: [208][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.339 (0.300) Prec@1 90.00 (93.22) Prec@5 100.00 (99.85)
|
|
[2019-03-31-22:48:35] **train** Prec@1 93.22 Prec@5 99.85 Error@1 6.78 Error@5 0.15 Loss:0.300
|
|
test [2019-03-31-22:48:36] Epoch: [208][000/105] Time 0.60 (0.60) Data 0.54 (0.54) Loss 0.140 (0.140) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-22:48:40] Epoch: [208][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.053 (0.208) Prec@1 97.92 (93.99) Prec@5 100.00 (99.86)
|
|
test [2019-03-31-22:48:40] Epoch: [208][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.240 (0.209) Prec@1 93.75 (93.91) Prec@5 100.00 (99.86)
|
|
[2019-03-31-22:48:40] **test** Prec@1 93.91 Prec@5 99.86 Error@1 6.09 Error@5 0.14 Loss:0.209
|
|
----> Best Accuracy : Acc@1=94.04, Acc@5=99.85, Error@1=5.96, Error@5=0.15
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-22:48:40] [Epoch=209/600] [Need: 13:59:35] LR=0.0183 ~ 0.0183, Batch=96
|
|
train[2019-03-31-22:48:41] Epoch: [209][000/521] Time 0.83 (0.83) Data 0.56 (0.56) Loss 0.282 (0.282) Prec@1 89.58 (89.58) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-22:49:05] Epoch: [209][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.247 (0.281) Prec@1 95.83 (93.72) Prec@5 100.00 (99.91)
|
|
train[2019-03-31-22:49:28] Epoch: [209][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.331 (0.292) Prec@1 94.79 (93.58) Prec@5 98.96 (99.89)
|
|
train[2019-03-31-22:49:52] Epoch: [209][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.311 (0.290) Prec@1 90.62 (93.55) Prec@5 100.00 (99.90)
|
|
train[2019-03-31-22:50:16] Epoch: [209][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.343 (0.297) Prec@1 91.67 (93.36) Prec@5 100.00 (99.90)
|
|
train[2019-03-31-22:50:40] Epoch: [209][500/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.271 (0.297) Prec@1 94.79 (93.37) Prec@5 100.00 (99.88)
|
|
train[2019-03-31-22:50:45] Epoch: [209][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.200 (0.298) Prec@1 96.25 (93.34) Prec@5 100.00 (99.88)
|
|
[2019-03-31-22:50:45] **train** Prec@1 93.34 Prec@5 99.88 Error@1 6.66 Error@5 0.12 Loss:0.298
|
|
test [2019-03-31-22:50:45] Epoch: [209][000/105] Time 0.47 (0.47) Data 0.40 (0.40) Loss 0.208 (0.208) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-22:50:49] Epoch: [209][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.077 (0.255) Prec@1 95.83 (92.55) Prec@5 100.00 (99.88)
|
|
test [2019-03-31-22:50:50] Epoch: [209][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.314 (0.256) Prec@1 93.75 (92.59) Prec@5 100.00 (99.88)
|
|
[2019-03-31-22:50:50] **test** Prec@1 92.59 Prec@5 99.88 Error@1 7.41 Error@5 0.12 Loss:0.256
|
|
----> Best Accuracy : Acc@1=94.04, Acc@5=99.85, Error@1=5.96, Error@5=0.15
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-22:50:50] [Epoch=210/600] [Need: 14:02:40] LR=0.0182 ~ 0.0182, Batch=96
|
|
train[2019-03-31-22:50:51] Epoch: [210][000/521] Time 0.72 (0.72) Data 0.45 (0.45) Loss 0.444 (0.444) Prec@1 88.54 (88.54) Prec@5 98.96 (98.96)
|
|
train[2019-03-31-22:51:14] Epoch: [210][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.294 (0.281) Prec@1 92.71 (93.76) Prec@5 100.00 (99.90)
|
|
train[2019-03-31-22:51:38] Epoch: [210][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.324 (0.289) Prec@1 91.67 (93.55) Prec@5 100.00 (99.90)
|
|
train[2019-03-31-22:52:02] Epoch: [210][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.245 (0.290) Prec@1 95.83 (93.56) Prec@5 98.96 (99.88)
|
|
train[2019-03-31-22:52:26] Epoch: [210][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.353 (0.294) Prec@1 93.75 (93.47) Prec@5 98.96 (99.89)
|
|
train[2019-03-31-22:52:49] Epoch: [210][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.290 (0.299) Prec@1 96.88 (93.35) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-22:52:54] Epoch: [210][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.257 (0.299) Prec@1 93.75 (93.34) Prec@5 100.00 (99.89)
|
|
[2019-03-31-22:52:54] **train** Prec@1 93.34 Prec@5 99.89 Error@1 6.66 Error@5 0.11 Loss:0.299
|
|
test [2019-03-31-22:52:54] Epoch: [210][000/105] Time 0.47 (0.47) Data 0.40 (0.40) Loss 0.134 (0.134) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-22:52:58] Epoch: [210][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.200 (0.217) Prec@1 95.83 (93.78) Prec@5 100.00 (99.80)
|
|
test [2019-03-31-22:52:59] Epoch: [210][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.188 (0.215) Prec@1 93.75 (93.81) Prec@5 100.00 (99.81)
|
|
[2019-03-31-22:52:59] **test** Prec@1 93.81 Prec@5 99.81 Error@1 6.19 Error@5 0.19 Loss:0.215
|
|
----> Best Accuracy : Acc@1=94.04, Acc@5=99.85, Error@1=5.96, Error@5=0.15
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-22:52:59] [Epoch=211/600] [Need: 13:57:01] LR=0.0181 ~ 0.0181, Batch=96
|
|
train[2019-03-31-22:53:00] Epoch: [211][000/521] Time 0.86 (0.86) Data 0.58 (0.58) Loss 0.281 (0.281) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-22:53:23] Epoch: [211][100/521] Time 0.23 (0.24) Data 0.00 (0.01) Loss 0.284 (0.292) Prec@1 89.58 (93.57) Prec@5 100.00 (99.87)
|
|
train[2019-03-31-22:53:47] Epoch: [211][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.296 (0.297) Prec@1 90.62 (93.16) Prec@5 100.00 (99.86)
|
|
train[2019-03-31-22:54:11] Epoch: [211][300/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.408 (0.297) Prec@1 87.50 (93.18) Prec@5 100.00 (99.87)
|
|
train[2019-03-31-22:54:34] Epoch: [211][400/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.305 (0.295) Prec@1 90.62 (93.19) Prec@5 100.00 (99.88)
|
|
train[2019-03-31-22:54:58] Epoch: [211][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.471 (0.301) Prec@1 90.62 (93.06) Prec@5 98.96 (99.88)
|
|
train[2019-03-31-22:55:03] Epoch: [211][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.415 (0.301) Prec@1 93.75 (93.03) Prec@5 98.75 (99.88)
|
|
[2019-03-31-22:55:03] **train** Prec@1 93.03 Prec@5 99.88 Error@1 6.97 Error@5 0.12 Loss:0.301
|
|
test [2019-03-31-22:55:03] Epoch: [211][000/105] Time 0.46 (0.46) Data 0.40 (0.40) Loss 0.158 (0.158) Prec@1 91.67 (91.67) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-22:55:07] Epoch: [211][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.106 (0.215) Prec@1 94.79 (93.38) Prec@5 100.00 (99.85)
|
|
test [2019-03-31-22:55:08] Epoch: [211][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.261 (0.216) Prec@1 93.75 (93.39) Prec@5 100.00 (99.84)
|
|
[2019-03-31-22:55:08] **test** Prec@1 93.39 Prec@5 99.84 Error@1 6.61 Error@5 0.16 Loss:0.216
|
|
----> Best Accuracy : Acc@1=94.04, Acc@5=99.85, Error@1=5.96, Error@5=0.15
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-22:55:08] [Epoch=212/600] [Need: 13:53:18] LR=0.0181 ~ 0.0181, Batch=96
|
|
train[2019-03-31-22:55:09] Epoch: [212][000/521] Time 0.88 (0.88) Data 0.58 (0.58) Loss 0.390 (0.390) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-22:55:32] Epoch: [212][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.321 (0.280) Prec@1 91.67 (93.72) Prec@5 100.00 (99.91)
|
|
train[2019-03-31-22:55:56] Epoch: [212][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.301 (0.292) Prec@1 90.62 (93.49) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-22:56:20] Epoch: [212][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.378 (0.293) Prec@1 89.58 (93.42) Prec@5 100.00 (99.88)
|
|
train[2019-03-31-22:56:43] Epoch: [212][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.447 (0.300) Prec@1 90.62 (93.33) Prec@5 100.00 (99.87)
|
|
train[2019-03-31-22:57:07] Epoch: [212][500/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.461 (0.304) Prec@1 92.71 (93.20) Prec@5 98.96 (99.88)
|
|
train[2019-03-31-22:57:12] Epoch: [212][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.233 (0.304) Prec@1 95.00 (93.21) Prec@5 100.00 (99.88)
|
|
[2019-03-31-22:57:12] **train** Prec@1 93.21 Prec@5 99.88 Error@1 6.79 Error@5 0.12 Loss:0.304
|
|
test [2019-03-31-22:57:13] Epoch: [212][000/105] Time 0.59 (0.59) Data 0.53 (0.53) Loss 0.150 (0.150) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-22:57:17] Epoch: [212][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.176 (0.236) Prec@1 91.67 (92.59) Prec@5 100.00 (99.87)
|
|
test [2019-03-31-22:57:17] Epoch: [212][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.122 (0.235) Prec@1 93.75 (92.62) Prec@5 100.00 (99.87)
|
|
[2019-03-31-22:57:17] **test** Prec@1 92.62 Prec@5 99.87 Error@1 7.38 Error@5 0.13 Loss:0.235
|
|
----> Best Accuracy : Acc@1=94.04, Acc@5=99.85, Error@1=5.96, Error@5=0.15
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-22:57:17] [Epoch=213/600] [Need: 13:54:08] LR=0.0180 ~ 0.0180, Batch=96
|
|
train[2019-03-31-22:57:18] Epoch: [213][000/521] Time 0.71 (0.71) Data 0.43 (0.43) Loss 0.324 (0.324) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-22:57:42] Epoch: [213][100/521] Time 0.27 (0.24) Data 0.00 (0.00) Loss 0.372 (0.291) Prec@1 90.62 (93.54) Prec@5 100.00 (99.86)
|
|
train[2019-03-31-22:58:06] Epoch: [213][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.223 (0.281) Prec@1 95.83 (93.78) Prec@5 100.00 (99.88)
|
|
train[2019-03-31-22:58:29] Epoch: [213][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.263 (0.282) Prec@1 93.75 (93.65) Prec@5 100.00 (99.90)
|
|
train[2019-03-31-22:58:53] Epoch: [213][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.387 (0.284) Prec@1 91.67 (93.62) Prec@5 100.00 (99.90)
|
|
train[2019-03-31-22:59:16] Epoch: [213][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.352 (0.292) Prec@1 91.67 (93.35) Prec@5 100.00 (99.90)
|
|
train[2019-03-31-22:59:21] Epoch: [213][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.192 (0.293) Prec@1 98.75 (93.35) Prec@5 100.00 (99.90)
|
|
[2019-03-31-22:59:21] **train** Prec@1 93.35 Prec@5 99.90 Error@1 6.65 Error@5 0.10 Loss:0.293
|
|
test [2019-03-31-22:59:22] Epoch: [213][000/105] Time 0.59 (0.59) Data 0.53 (0.53) Loss 0.181 (0.181) Prec@1 91.67 (91.67) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-22:59:26] Epoch: [213][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.051 (0.199) Prec@1 97.92 (93.77) Prec@5 100.00 (99.89)
|
|
test [2019-03-31-22:59:26] Epoch: [213][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.167 (0.199) Prec@1 87.50 (93.75) Prec@5 100.00 (99.89)
|
|
[2019-03-31-22:59:26] **test** Prec@1 93.75 Prec@5 99.89 Error@1 6.25 Error@5 0.11 Loss:0.199
|
|
----> Best Accuracy : Acc@1=94.04, Acc@5=99.85, Error@1=5.96, Error@5=0.15
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-22:59:26] [Epoch=214/600] [Need: 13:50:59] LR=0.0180 ~ 0.0180, Batch=96
|
|
train[2019-03-31-22:59:27] Epoch: [214][000/521] Time 0.84 (0.84) Data 0.56 (0.56) Loss 0.271 (0.271) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-22:59:51] Epoch: [214][100/521] Time 0.23 (0.24) Data 0.00 (0.01) Loss 0.217 (0.298) Prec@1 95.83 (93.47) Prec@5 100.00 (99.87)
|
|
train[2019-03-31-23:00:15] Epoch: [214][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.463 (0.299) Prec@1 90.62 (93.29) Prec@5 98.96 (99.85)
|
|
train[2019-03-31-23:00:38] Epoch: [214][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.341 (0.295) Prec@1 93.75 (93.46) Prec@5 100.00 (99.86)
|
|
train[2019-03-31-23:01:02] Epoch: [214][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.188 (0.295) Prec@1 97.92 (93.49) Prec@5 100.00 (99.85)
|
|
train[2019-03-31-23:01:26] Epoch: [214][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.298 (0.296) Prec@1 94.79 (93.48) Prec@5 100.00 (99.85)
|
|
train[2019-03-31-23:01:30] Epoch: [214][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.281 (0.296) Prec@1 95.00 (93.48) Prec@5 100.00 (99.85)
|
|
[2019-03-31-23:01:30] **train** Prec@1 93.48 Prec@5 99.85 Error@1 6.52 Error@5 0.15 Loss:0.296
|
|
test [2019-03-31-23:01:31] Epoch: [214][000/105] Time 0.47 (0.47) Data 0.41 (0.41) Loss 0.195 (0.195) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-23:01:35] Epoch: [214][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.114 (0.210) Prec@1 95.83 (93.71) Prec@5 100.00 (99.89)
|
|
test [2019-03-31-23:01:35] Epoch: [214][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.085 (0.210) Prec@1 93.75 (93.70) Prec@5 100.00 (99.89)
|
|
[2019-03-31-23:01:35] **test** Prec@1 93.70 Prec@5 99.89 Error@1 6.30 Error@5 0.11 Loss:0.210
|
|
----> Best Accuracy : Acc@1=94.04, Acc@5=99.85, Error@1=5.96, Error@5=0.15
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-23:01:35] [Epoch=215/600] [Need: 13:48:46] LR=0.0179 ~ 0.0179, Batch=96
|
|
train[2019-03-31-23:01:36] Epoch: [215][000/521] Time 0.83 (0.83) Data 0.56 (0.56) Loss 0.208 (0.208) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-23:02:00] Epoch: [215][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.316 (0.280) Prec@1 91.67 (93.78) Prec@5 100.00 (99.81)
|
|
train[2019-03-31-23:02:24] Epoch: [215][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.333 (0.289) Prec@1 91.67 (93.63) Prec@5 100.00 (99.85)
|
|
train[2019-03-31-23:02:47] Epoch: [215][300/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.376 (0.293) Prec@1 92.71 (93.42) Prec@5 100.00 (99.87)
|
|
train[2019-03-31-23:03:11] Epoch: [215][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.487 (0.297) Prec@1 86.46 (93.27) Prec@5 98.96 (99.85)
|
|
train[2019-03-31-23:03:35] Epoch: [215][500/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.224 (0.300) Prec@1 94.79 (93.18) Prec@5 100.00 (99.86)
|
|
train[2019-03-31-23:03:40] Epoch: [215][520/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.334 (0.301) Prec@1 92.50 (93.16) Prec@5 100.00 (99.86)
|
|
[2019-03-31-23:03:40] **train** Prec@1 93.16 Prec@5 99.86 Error@1 6.84 Error@5 0.14 Loss:0.301
|
|
test [2019-03-31-23:03:40] Epoch: [215][000/105] Time 0.47 (0.47) Data 0.40 (0.40) Loss 0.206 (0.206) Prec@1 91.67 (91.67) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-23:03:45] Epoch: [215][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.090 (0.251) Prec@1 94.79 (92.43) Prec@5 100.00 (99.90)
|
|
test [2019-03-31-23:03:45] Epoch: [215][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.174 (0.250) Prec@1 93.75 (92.46) Prec@5 100.00 (99.90)
|
|
[2019-03-31-23:03:45] **test** Prec@1 92.46 Prec@5 99.90 Error@1 7.54 Error@5 0.10 Loss:0.250
|
|
----> Best Accuracy : Acc@1=94.04, Acc@5=99.85, Error@1=5.96, Error@5=0.15
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-23:03:45] [Epoch=216/600] [Need: 13:48:52] LR=0.0179 ~ 0.0179, Batch=96
|
|
train[2019-03-31-23:03:46] Epoch: [216][000/521] Time 0.72 (0.72) Data 0.45 (0.45) Loss 0.611 (0.611) Prec@1 83.33 (83.33) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-23:04:09] Epoch: [216][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.543 (0.288) Prec@1 89.58 (93.46) Prec@5 100.00 (99.94)
|
|
train[2019-03-31-23:04:33] Epoch: [216][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.244 (0.299) Prec@1 94.79 (93.23) Prec@5 100.00 (99.91)
|
|
train[2019-03-31-23:04:57] Epoch: [216][300/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.234 (0.294) Prec@1 92.71 (93.32) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-23:05:20] Epoch: [216][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.312 (0.298) Prec@1 89.58 (93.20) Prec@5 100.00 (99.90)
|
|
train[2019-03-31-23:05:44] Epoch: [216][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.275 (0.297) Prec@1 92.71 (93.24) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-23:05:49] Epoch: [216][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.302 (0.297) Prec@1 92.50 (93.25) Prec@5 100.00 (99.89)
|
|
[2019-03-31-23:05:49] **train** Prec@1 93.25 Prec@5 99.89 Error@1 6.75 Error@5 0.11 Loss:0.297
|
|
test [2019-03-31-23:05:49] Epoch: [216][000/105] Time 0.61 (0.61) Data 0.54 (0.54) Loss 0.175 (0.175) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-23:05:53] Epoch: [216][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.097 (0.206) Prec@1 95.83 (94.04) Prec@5 100.00 (99.88)
|
|
test [2019-03-31-23:05:53] Epoch: [216][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.139 (0.206) Prec@1 93.75 (93.99) Prec@5 100.00 (99.88)
|
|
[2019-03-31-23:05:54] **test** Prec@1 93.99 Prec@5 99.88 Error@1 6.01 Error@5 0.12 Loss:0.206
|
|
----> Best Accuracy : Acc@1=94.04, Acc@5=99.85, Error@1=5.96, Error@5=0.15
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-23:05:54] [Epoch=217/600] [Need: 13:42:22] LR=0.0178 ~ 0.0178, Batch=96
|
|
train[2019-03-31-23:05:55] Epoch: [217][000/521] Time 0.83 (0.83) Data 0.56 (0.56) Loss 0.247 (0.247) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-23:06:18] Epoch: [217][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.333 (0.279) Prec@1 92.71 (93.71) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-23:06:42] Epoch: [217][200/521] Time 0.27 (0.24) Data 0.00 (0.00) Loss 0.308 (0.293) Prec@1 94.79 (93.45) Prec@5 100.00 (99.85)
|
|
train[2019-03-31-23:07:06] Epoch: [217][300/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.180 (0.291) Prec@1 97.92 (93.59) Prec@5 100.00 (99.85)
|
|
train[2019-03-31-23:07:30] Epoch: [217][400/521] Time 0.27 (0.24) Data 0.00 (0.00) Loss 0.552 (0.292) Prec@1 89.58 (93.52) Prec@5 100.00 (99.86)
|
|
train[2019-03-31-23:07:53] Epoch: [217][500/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.329 (0.295) Prec@1 92.71 (93.39) Prec@5 98.96 (99.86)
|
|
train[2019-03-31-23:07:58] Epoch: [217][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.500 (0.296) Prec@1 91.25 (93.37) Prec@5 98.75 (99.86)
|
|
[2019-03-31-23:07:58] **train** Prec@1 93.37 Prec@5 99.86 Error@1 6.63 Error@5 0.14 Loss:0.296
|
|
test [2019-03-31-23:07:59] Epoch: [217][000/105] Time 0.47 (0.47) Data 0.41 (0.41) Loss 0.087 (0.087) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-23:08:03] Epoch: [217][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.116 (0.205) Prec@1 96.88 (93.67) Prec@5 100.00 (99.92)
|
|
test [2019-03-31-23:08:03] Epoch: [217][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.142 (0.206) Prec@1 93.75 (93.70) Prec@5 100.00 (99.91)
|
|
[2019-03-31-23:08:03] **test** Prec@1 93.70 Prec@5 99.91 Error@1 6.30 Error@5 0.09 Loss:0.206
|
|
----> Best Accuracy : Acc@1=94.04, Acc@5=99.85, Error@1=5.96, Error@5=0.15
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-23:08:03] [Epoch=218/600] [Need: 13:43:30] LR=0.0177 ~ 0.0177, Batch=96
|
|
train[2019-03-31-23:08:04] Epoch: [218][000/521] Time 0.71 (0.71) Data 0.44 (0.44) Loss 0.238 (0.238) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-23:08:27] Epoch: [218][100/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.277 (0.290) Prec@1 92.71 (93.44) Prec@5 100.00 (99.92)
|
|
train[2019-03-31-23:08:51] Epoch: [218][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.233 (0.290) Prec@1 95.83 (93.40) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-23:09:14] Epoch: [218][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.336 (0.294) Prec@1 92.71 (93.24) Prec@5 100.00 (99.90)
|
|
train[2019-03-31-23:09:38] Epoch: [218][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.244 (0.296) Prec@1 93.75 (93.23) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-23:10:01] Epoch: [218][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.240 (0.299) Prec@1 94.79 (93.18) Prec@5 100.00 (99.88)
|
|
train[2019-03-31-23:10:06] Epoch: [218][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.312 (0.299) Prec@1 92.50 (93.18) Prec@5 100.00 (99.88)
|
|
[2019-03-31-23:10:06] **train** Prec@1 93.18 Prec@5 99.88 Error@1 6.82 Error@5 0.12 Loss:0.299
|
|
test [2019-03-31-23:10:07] Epoch: [218][000/105] Time 0.47 (0.47) Data 0.40 (0.40) Loss 0.267 (0.267) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-23:10:11] Epoch: [218][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.069 (0.212) Prec@1 95.83 (93.61) Prec@5 100.00 (99.89)
|
|
test [2019-03-31-23:10:11] Epoch: [218][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.209 (0.210) Prec@1 93.75 (93.66) Prec@5 100.00 (99.89)
|
|
[2019-03-31-23:10:11] **test** Prec@1 93.66 Prec@5 99.89 Error@1 6.34 Error@5 0.11 Loss:0.210
|
|
----> Best Accuracy : Acc@1=94.04, Acc@5=99.85, Error@1=5.96, Error@5=0.15
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-23:10:11] [Epoch=219/600] [Need: 13:32:56] LR=0.0177 ~ 0.0177, Batch=96
|
|
train[2019-03-31-23:10:12] Epoch: [219][000/521] Time 0.83 (0.83) Data 0.55 (0.55) Loss 0.197 (0.197) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-23:10:36] Epoch: [219][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.229 (0.271) Prec@1 95.83 (94.03) Prec@5 100.00 (99.90)
|
|
train[2019-03-31-23:10:59] Epoch: [219][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.279 (0.285) Prec@1 93.75 (93.68) Prec@5 100.00 (99.91)
|
|
train[2019-03-31-23:11:23] Epoch: [219][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.390 (0.292) Prec@1 94.79 (93.49) Prec@5 98.96 (99.89)
|
|
train[2019-03-31-23:11:47] Epoch: [219][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.270 (0.292) Prec@1 94.79 (93.55) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-23:12:10] Epoch: [219][500/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.224 (0.295) Prec@1 94.79 (93.45) Prec@5 100.00 (99.88)
|
|
train[2019-03-31-23:12:15] Epoch: [219][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.195 (0.296) Prec@1 96.25 (93.44) Prec@5 100.00 (99.88)
|
|
[2019-03-31-23:12:15] **train** Prec@1 93.44 Prec@5 99.88 Error@1 6.56 Error@5 0.12 Loss:0.296
|
|
test [2019-03-31-23:12:16] Epoch: [219][000/105] Time 0.60 (0.60) Data 0.53 (0.53) Loss 0.210 (0.210) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-23:12:20] Epoch: [219][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.047 (0.191) Prec@1 98.96 (94.06) Prec@5 100.00 (99.91)
|
|
test [2019-03-31-23:12:20] Epoch: [219][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.090 (0.192) Prec@1 93.75 (94.04) Prec@5 100.00 (99.91)
|
|
[2019-03-31-23:12:20] **test** Prec@1 94.04 Prec@5 99.91 Error@1 5.96 Error@5 0.09 Loss:0.192
|
|
----> Best Accuracy : Acc@1=94.04, Acc@5=99.85, Error@1=5.96, Error@5=0.15
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-23:12:20] [Epoch=220/600] [Need: 13:36:45] LR=0.0176 ~ 0.0176, Batch=96
|
|
train[2019-03-31-23:12:21] Epoch: [220][000/521] Time 0.83 (0.83) Data 0.56 (0.56) Loss 0.250 (0.250) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-23:12:45] Epoch: [220][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.338 (0.301) Prec@1 90.62 (93.18) Prec@5 100.00 (99.90)
|
|
train[2019-03-31-23:13:08] Epoch: [220][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.360 (0.289) Prec@1 92.71 (93.58) Prec@5 98.96 (99.89)
|
|
train[2019-03-31-23:13:32] Epoch: [220][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.365 (0.288) Prec@1 91.67 (93.53) Prec@5 100.00 (99.87)
|
|
train[2019-03-31-23:13:56] Epoch: [220][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.145 (0.287) Prec@1 97.92 (93.59) Prec@5 100.00 (99.88)
|
|
train[2019-03-31-23:14:20] Epoch: [220][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.400 (0.294) Prec@1 91.67 (93.36) Prec@5 100.00 (99.87)
|
|
train[2019-03-31-23:14:24] Epoch: [220][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.533 (0.295) Prec@1 90.00 (93.36) Prec@5 98.75 (99.87)
|
|
[2019-03-31-23:14:24] **train** Prec@1 93.36 Prec@5 99.87 Error@1 6.64 Error@5 0.13 Loss:0.295
|
|
test [2019-03-31-23:14:25] Epoch: [220][000/105] Time 0.47 (0.47) Data 0.41 (0.41) Loss 0.265 (0.265) Prec@1 91.67 (91.67) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-23:14:29] Epoch: [220][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.069 (0.202) Prec@1 96.88 (93.77) Prec@5 100.00 (99.92)
|
|
test [2019-03-31-23:14:29] Epoch: [220][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.105 (0.204) Prec@1 93.75 (93.76) Prec@5 100.00 (99.91)
|
|
[2019-03-31-23:14:29] **test** Prec@1 93.76 Prec@5 99.91 Error@1 6.24 Error@5 0.09 Loss:0.204
|
|
----> Best Accuracy : Acc@1=94.04, Acc@5=99.85, Error@1=5.96, Error@5=0.15
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-23:14:29] [Epoch=221/600] [Need: 13:35:57] LR=0.0176 ~ 0.0176, Batch=96
|
|
train[2019-03-31-23:14:30] Epoch: [221][000/521] Time 0.71 (0.71) Data 0.44 (0.44) Loss 0.191 (0.191) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-23:14:54] Epoch: [221][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.192 (0.288) Prec@1 96.88 (93.67) Prec@5 100.00 (99.88)
|
|
train[2019-03-31-23:15:17] Epoch: [221][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.206 (0.284) Prec@1 91.67 (93.67) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-23:15:41] Epoch: [221][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.360 (0.280) Prec@1 91.67 (93.74) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-23:16:05] Epoch: [221][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.291 (0.288) Prec@1 93.75 (93.57) Prec@5 100.00 (99.88)
|
|
train[2019-03-31-23:16:28] Epoch: [221][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.278 (0.292) Prec@1 95.83 (93.48) Prec@5 100.00 (99.86)
|
|
train[2019-03-31-23:16:33] Epoch: [221][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.226 (0.293) Prec@1 95.00 (93.48) Prec@5 100.00 (99.86)
|
|
[2019-03-31-23:16:33] **train** Prec@1 93.48 Prec@5 99.86 Error@1 6.52 Error@5 0.14 Loss:0.293
|
|
test [2019-03-31-23:16:34] Epoch: [221][000/105] Time 0.47 (0.47) Data 0.41 (0.41) Loss 0.095 (0.095) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-23:16:38] Epoch: [221][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.049 (0.202) Prec@1 97.92 (94.01) Prec@5 100.00 (99.91)
|
|
test [2019-03-31-23:16:38] Epoch: [221][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.030 (0.202) Prec@1 100.00 (93.98) Prec@5 100.00 (99.91)
|
|
[2019-03-31-23:16:38] **test** Prec@1 93.98 Prec@5 99.91 Error@1 6.02 Error@5 0.09 Loss:0.202
|
|
----> Best Accuracy : Acc@1=94.04, Acc@5=99.85, Error@1=5.96, Error@5=0.15
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-23:16:38] [Epoch=222/600] [Need: 13:32:29] LR=0.0175 ~ 0.0175, Batch=96
|
|
train[2019-03-31-23:16:39] Epoch: [222][000/521] Time 0.85 (0.85) Data 0.58 (0.58) Loss 0.384 (0.384) Prec@1 90.62 (90.62) Prec@5 98.96 (98.96)
|
|
train[2019-03-31-23:17:03] Epoch: [222][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.240 (0.279) Prec@1 94.79 (93.98) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-23:17:26] Epoch: [222][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.272 (0.290) Prec@1 92.71 (93.54) Prec@5 100.00 (99.86)
|
|
train[2019-03-31-23:17:50] Epoch: [222][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.375 (0.288) Prec@1 92.71 (93.54) Prec@5 100.00 (99.86)
|
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train[2019-03-31-23:18:14] Epoch: [222][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.260 (0.293) Prec@1 92.71 (93.46) Prec@5 100.00 (99.85)
|
|
train[2019-03-31-23:18:38] Epoch: [222][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.276 (0.297) Prec@1 96.88 (93.42) Prec@5 100.00 (99.86)
|
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train[2019-03-31-23:18:42] Epoch: [222][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.453 (0.298) Prec@1 92.50 (93.42) Prec@5 97.50 (99.86)
|
|
[2019-03-31-23:18:42] **train** Prec@1 93.42 Prec@5 99.86 Error@1 6.58 Error@5 0.14 Loss:0.298
|
|
test [2019-03-31-23:18:43] Epoch: [222][000/105] Time 0.60 (0.60) Data 0.54 (0.54) Loss 0.212 (0.212) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
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test [2019-03-31-23:18:47] Epoch: [222][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.075 (0.230) Prec@1 96.88 (93.16) Prec@5 100.00 (99.89)
|
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test [2019-03-31-23:18:47] Epoch: [222][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.014 (0.229) Prec@1 100.00 (93.18) Prec@5 100.00 (99.89)
|
|
[2019-03-31-23:18:47] **test** Prec@1 93.18 Prec@5 99.89 Error@1 6.82 Error@5 0.11 Loss:0.229
|
|
----> Best Accuracy : Acc@1=94.04, Acc@5=99.85, Error@1=5.96, Error@5=0.15
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
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|
|
==>>[2019-03-31-23:18:48] [Epoch=223/600] [Need: 13:32:28] LR=0.0174 ~ 0.0174, Batch=96
|
|
train[2019-03-31-23:18:48] Epoch: [223][000/521] Time 0.84 (0.84) Data 0.57 (0.57) Loss 0.215 (0.215) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-23:19:12] Epoch: [223][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.477 (0.300) Prec@1 89.58 (93.04) Prec@5 98.96 (99.93)
|
|
train[2019-03-31-23:19:36] Epoch: [223][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.253 (0.292) Prec@1 94.79 (93.37) Prec@5 100.00 (99.91)
|
|
train[2019-03-31-23:20:00] Epoch: [223][300/521] Time 0.27 (0.24) Data 0.00 (0.00) Loss 0.356 (0.290) Prec@1 93.75 (93.53) Prec@5 100.00 (99.91)
|
|
train[2019-03-31-23:20:24] Epoch: [223][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.282 (0.289) Prec@1 93.75 (93.61) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-23:20:47] Epoch: [223][500/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.238 (0.295) Prec@1 94.79 (93.47) Prec@5 100.00 (99.88)
|
|
train[2019-03-31-23:20:52] Epoch: [223][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.394 (0.295) Prec@1 88.75 (93.44) Prec@5 98.75 (99.88)
|
|
[2019-03-31-23:20:52] **train** Prec@1 93.44 Prec@5 99.88 Error@1 6.56 Error@5 0.12 Loss:0.295
|
|
test [2019-03-31-23:20:52] Epoch: [223][000/105] Time 0.47 (0.47) Data 0.40 (0.40) Loss 0.227 (0.227) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-23:20:57] Epoch: [223][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.166 (0.217) Prec@1 95.83 (93.37) Prec@5 100.00 (99.85)
|
|
test [2019-03-31-23:20:57] Epoch: [223][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.495 (0.217) Prec@1 93.75 (93.40) Prec@5 100.00 (99.85)
|
|
[2019-03-31-23:20:57] **test** Prec@1 93.40 Prec@5 99.85 Error@1 6.60 Error@5 0.15 Loss:0.217
|
|
----> Best Accuracy : Acc@1=94.04, Acc@5=99.85, Error@1=5.96, Error@5=0.15
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-23:20:57] [Epoch=224/600] [Need: 13:31:11] LR=0.0174 ~ 0.0174, Batch=96
|
|
train[2019-03-31-23:20:58] Epoch: [224][000/521] Time 0.70 (0.70) Data 0.43 (0.43) Loss 0.231 (0.231) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-23:21:21] Epoch: [224][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.148 (0.292) Prec@1 98.96 (93.44) Prec@5 100.00 (99.92)
|
|
train[2019-03-31-23:21:45] Epoch: [224][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.229 (0.292) Prec@1 96.88 (93.46) Prec@5 100.00 (99.88)
|
|
train[2019-03-31-23:22:08] Epoch: [224][300/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.221 (0.284) Prec@1 94.79 (93.64) Prec@5 100.00 (99.90)
|
|
train[2019-03-31-23:22:32] Epoch: [224][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.220 (0.285) Prec@1 94.79 (93.64) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-23:22:56] Epoch: [224][500/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.274 (0.290) Prec@1 92.71 (93.52) Prec@5 100.00 (99.87)
|
|
train[2019-03-31-23:23:01] Epoch: [224][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.215 (0.289) Prec@1 95.00 (93.51) Prec@5 100.00 (99.87)
|
|
[2019-03-31-23:23:01] **train** Prec@1 93.51 Prec@5 99.87 Error@1 6.49 Error@5 0.13 Loss:0.289
|
|
test [2019-03-31-23:23:01] Epoch: [224][000/105] Time 0.61 (0.61) Data 0.55 (0.55) Loss 0.142 (0.142) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-23:23:06] Epoch: [224][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.062 (0.187) Prec@1 98.96 (94.30) Prec@5 100.00 (99.86)
|
|
test [2019-03-31-23:23:06] Epoch: [224][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.056 (0.186) Prec@1 100.00 (94.30) Prec@5 100.00 (99.86)
|
|
[2019-03-31-23:23:06] **test** Prec@1 94.30 Prec@5 99.86 Error@1 5.70 Error@5 0.14 Loss:0.186
|
|
----> Best Accuracy : Acc@1=94.30, Acc@5=99.86, Error@1=5.70, Error@5=0.14
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-23:23:06] [Epoch=225/600] [Need: 13:26:19] LR=0.0173 ~ 0.0173, Batch=96
|
|
train[2019-03-31-23:23:07] Epoch: [225][000/521] Time 0.86 (0.86) Data 0.59 (0.59) Loss 0.261 (0.261) Prec@1 95.83 (95.83) Prec@5 98.96 (98.96)
|
|
train[2019-03-31-23:23:31] Epoch: [225][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.495 (0.285) Prec@1 89.58 (93.44) Prec@5 100.00 (99.91)
|
|
train[2019-03-31-23:23:54] Epoch: [225][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.295 (0.296) Prec@1 92.71 (93.24) Prec@5 100.00 (99.90)
|
|
train[2019-03-31-23:24:18] Epoch: [225][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.283 (0.292) Prec@1 95.83 (93.38) Prec@5 100.00 (99.90)
|
|
train[2019-03-31-23:24:42] Epoch: [225][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.386 (0.290) Prec@1 92.71 (93.49) Prec@5 98.96 (99.89)
|
|
train[2019-03-31-23:25:05] Epoch: [225][500/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.219 (0.293) Prec@1 95.83 (93.40) Prec@5 100.00 (99.88)
|
|
train[2019-03-31-23:25:10] Epoch: [225][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.301 (0.293) Prec@1 92.50 (93.41) Prec@5 100.00 (99.88)
|
|
[2019-03-31-23:25:10] **train** Prec@1 93.41 Prec@5 99.88 Error@1 6.59 Error@5 0.12 Loss:0.293
|
|
test [2019-03-31-23:25:11] Epoch: [225][000/105] Time 0.62 (0.62) Data 0.56 (0.56) Loss 0.219 (0.219) Prec@1 91.67 (91.67) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-23:25:15] Epoch: [225][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.083 (0.204) Prec@1 96.88 (93.77) Prec@5 100.00 (99.91)
|
|
test [2019-03-31-23:25:15] Epoch: [225][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.058 (0.203) Prec@1 93.75 (93.78) Prec@5 100.00 (99.91)
|
|
[2019-03-31-23:25:15] **test** Prec@1 93.78 Prec@5 99.91 Error@1 6.22 Error@5 0.09 Loss:0.203
|
|
----> Best Accuracy : Acc@1=94.30, Acc@5=99.86, Error@1=5.70, Error@5=0.14
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-23:25:15] [Epoch=226/600] [Need: 13:26:15] LR=0.0173 ~ 0.0173, Batch=96
|
|
train[2019-03-31-23:25:16] Epoch: [226][000/521] Time 0.74 (0.74) Data 0.47 (0.47) Loss 0.301 (0.301) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-23:25:40] Epoch: [226][100/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.353 (0.295) Prec@1 90.62 (93.11) Prec@5 100.00 (99.87)
|
|
train[2019-03-31-23:26:04] Epoch: [226][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.283 (0.298) Prec@1 93.75 (93.19) Prec@5 100.00 (99.88)
|
|
train[2019-03-31-23:26:27] Epoch: [226][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.340 (0.292) Prec@1 93.75 (93.36) Prec@5 100.00 (99.88)
|
|
train[2019-03-31-23:26:51] Epoch: [226][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.314 (0.294) Prec@1 94.79 (93.39) Prec@5 100.00 (99.88)
|
|
train[2019-03-31-23:27:14] Epoch: [226][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.232 (0.296) Prec@1 96.88 (93.33) Prec@5 100.00 (99.86)
|
|
train[2019-03-31-23:27:19] Epoch: [226][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.177 (0.295) Prec@1 97.50 (93.35) Prec@5 100.00 (99.86)
|
|
[2019-03-31-23:27:19] **train** Prec@1 93.35 Prec@5 99.86 Error@1 6.65 Error@5 0.14 Loss:0.295
|
|
test [2019-03-31-23:27:20] Epoch: [226][000/105] Time 0.60 (0.60) Data 0.54 (0.54) Loss 0.114 (0.114) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-23:27:24] Epoch: [226][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.088 (0.193) Prec@1 96.88 (94.26) Prec@5 100.00 (99.87)
|
|
test [2019-03-31-23:27:24] Epoch: [226][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.301 (0.193) Prec@1 93.75 (94.23) Prec@5 100.00 (99.87)
|
|
[2019-03-31-23:27:24] **test** Prec@1 94.23 Prec@5 99.87 Error@1 5.77 Error@5 0.13 Loss:0.193
|
|
----> Best Accuracy : Acc@1=94.30, Acc@5=99.86, Error@1=5.70, Error@5=0.14
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-23:27:24] [Epoch=227/600] [Need: 13:21:23] LR=0.0172 ~ 0.0172, Batch=96
|
|
train[2019-03-31-23:27:25] Epoch: [227][000/521] Time 0.73 (0.73) Data 0.46 (0.46) Loss 0.206 (0.206) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-23:27:49] Epoch: [227][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.337 (0.277) Prec@1 92.71 (93.87) Prec@5 100.00 (99.93)
|
|
train[2019-03-31-23:28:12] Epoch: [227][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.206 (0.285) Prec@1 95.83 (93.72) Prec@5 100.00 (99.91)
|
|
train[2019-03-31-23:28:36] Epoch: [227][300/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.123 (0.281) Prec@1 97.92 (93.75) Prec@5 100.00 (99.92)
|
|
train[2019-03-31-23:28:59] Epoch: [227][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.221 (0.283) Prec@1 94.79 (93.71) Prec@5 100.00 (99.91)
|
|
train[2019-03-31-23:29:23] Epoch: [227][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.292 (0.288) Prec@1 93.75 (93.60) Prec@5 100.00 (99.90)
|
|
train[2019-03-31-23:29:28] Epoch: [227][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.257 (0.288) Prec@1 91.25 (93.60) Prec@5 100.00 (99.90)
|
|
[2019-03-31-23:29:28] **train** Prec@1 93.60 Prec@5 99.90 Error@1 6.40 Error@5 0.10 Loss:0.288
|
|
test [2019-03-31-23:29:28] Epoch: [227][000/105] Time 0.60 (0.60) Data 0.53 (0.53) Loss 0.253 (0.253) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-23:29:32] Epoch: [227][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.062 (0.228) Prec@1 96.88 (93.45) Prec@5 100.00 (99.80)
|
|
test [2019-03-31-23:29:33] Epoch: [227][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.025 (0.228) Prec@1 100.00 (93.44) Prec@5 100.00 (99.81)
|
|
[2019-03-31-23:29:33] **test** Prec@1 93.44 Prec@5 99.81 Error@1 6.56 Error@5 0.19 Loss:0.228
|
|
----> Best Accuracy : Acc@1=94.30, Acc@5=99.86, Error@1=5.70, Error@5=0.14
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-23:29:33] [Epoch=228/600] [Need: 13:17:30] LR=0.0171 ~ 0.0171, Batch=96
|
|
train[2019-03-31-23:29:34] Epoch: [228][000/521] Time 0.72 (0.72) Data 0.45 (0.45) Loss 0.232 (0.232) Prec@1 94.79 (94.79) Prec@5 98.96 (98.96)
|
|
train[2019-03-31-23:29:57] Epoch: [228][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.514 (0.297) Prec@1 83.33 (93.24) Prec@5 100.00 (99.88)
|
|
train[2019-03-31-23:30:21] Epoch: [228][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.255 (0.293) Prec@1 95.83 (93.37) Prec@5 100.00 (99.87)
|
|
train[2019-03-31-23:30:45] Epoch: [228][300/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.362 (0.290) Prec@1 93.75 (93.47) Prec@5 100.00 (99.87)
|
|
train[2019-03-31-23:31:08] Epoch: [228][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.242 (0.289) Prec@1 92.71 (93.51) Prec@5 100.00 (99.87)
|
|
train[2019-03-31-23:31:32] Epoch: [228][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.323 (0.291) Prec@1 93.75 (93.44) Prec@5 100.00 (99.87)
|
|
train[2019-03-31-23:31:37] Epoch: [228][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.240 (0.290) Prec@1 95.00 (93.48) Prec@5 100.00 (99.88)
|
|
[2019-03-31-23:31:37] **train** Prec@1 93.48 Prec@5 99.88 Error@1 6.52 Error@5 0.12 Loss:0.290
|
|
test [2019-03-31-23:31:37] Epoch: [228][000/105] Time 0.47 (0.47) Data 0.41 (0.41) Loss 0.131 (0.131) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-23:31:41] Epoch: [228][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.099 (0.195) Prec@1 95.83 (94.30) Prec@5 100.00 (99.83)
|
|
test [2019-03-31-23:31:42] Epoch: [228][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.130 (0.196) Prec@1 93.75 (94.29) Prec@5 100.00 (99.84)
|
|
[2019-03-31-23:31:42] **test** Prec@1 94.29 Prec@5 99.84 Error@1 5.71 Error@5 0.16 Loss:0.196
|
|
----> Best Accuracy : Acc@1=94.30, Acc@5=99.86, Error@1=5.70, Error@5=0.14
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-23:31:42] [Epoch=229/600] [Need: 13:17:05] LR=0.0171 ~ 0.0171, Batch=96
|
|
train[2019-03-31-23:31:42] Epoch: [229][000/521] Time 0.70 (0.70) Data 0.42 (0.42) Loss 0.175 (0.175) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-23:32:06] Epoch: [229][100/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.319 (0.277) Prec@1 91.67 (93.98) Prec@5 100.00 (99.94)
|
|
train[2019-03-31-23:32:30] Epoch: [229][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.224 (0.277) Prec@1 95.83 (93.91) Prec@5 100.00 (99.94)
|
|
train[2019-03-31-23:32:54] Epoch: [229][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.524 (0.282) Prec@1 87.50 (93.68) Prec@5 100.00 (99.94)
|
|
train[2019-03-31-23:33:18] Epoch: [229][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.181 (0.279) Prec@1 96.88 (93.83) Prec@5 100.00 (99.92)
|
|
train[2019-03-31-23:33:42] Epoch: [229][500/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.367 (0.283) Prec@1 90.62 (93.66) Prec@5 100.00 (99.91)
|
|
train[2019-03-31-23:33:47] Epoch: [229][520/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.252 (0.284) Prec@1 92.50 (93.61) Prec@5 100.00 (99.91)
|
|
[2019-03-31-23:33:47] **train** Prec@1 93.61 Prec@5 99.91 Error@1 6.39 Error@5 0.09 Loss:0.284
|
|
test [2019-03-31-23:33:47] Epoch: [229][000/105] Time 0.49 (0.49) Data 0.41 (0.41) Loss 0.161 (0.161) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-23:33:51] Epoch: [229][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.120 (0.242) Prec@1 96.88 (92.85) Prec@5 100.00 (99.87)
|
|
test [2019-03-31-23:33:51] Epoch: [229][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.040 (0.244) Prec@1 100.00 (92.82) Prec@5 100.00 (99.87)
|
|
[2019-03-31-23:33:51] **test** Prec@1 92.82 Prec@5 99.87 Error@1 7.18 Error@5 0.13 Loss:0.244
|
|
----> Best Accuracy : Acc@1=94.30, Acc@5=99.86, Error@1=5.70, Error@5=0.14
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-23:33:52] [Epoch=230/600] [Need: 13:20:44] LR=0.0170 ~ 0.0170, Batch=96
|
|
train[2019-03-31-23:33:52] Epoch: [230][000/521] Time 0.71 (0.71) Data 0.43 (0.43) Loss 0.399 (0.399) Prec@1 91.67 (91.67) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-23:34:16] Epoch: [230][100/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.252 (0.286) Prec@1 94.79 (93.69) Prec@5 100.00 (99.91)
|
|
train[2019-03-31-23:34:40] Epoch: [230][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.213 (0.289) Prec@1 96.88 (93.49) Prec@5 100.00 (99.90)
|
|
train[2019-03-31-23:35:03] Epoch: [230][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.305 (0.284) Prec@1 91.67 (93.70) Prec@5 100.00 (99.90)
|
|
train[2019-03-31-23:35:27] Epoch: [230][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.338 (0.288) Prec@1 93.75 (93.64) Prec@5 100.00 (99.91)
|
|
train[2019-03-31-23:35:51] Epoch: [230][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.342 (0.293) Prec@1 90.62 (93.47) Prec@5 100.00 (99.90)
|
|
train[2019-03-31-23:35:56] Epoch: [230][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.229 (0.294) Prec@1 95.00 (93.46) Prec@5 100.00 (99.90)
|
|
[2019-03-31-23:35:56] **train** Prec@1 93.46 Prec@5 99.90 Error@1 6.54 Error@5 0.10 Loss:0.294
|
|
test [2019-03-31-23:35:56] Epoch: [230][000/105] Time 0.47 (0.47) Data 0.40 (0.40) Loss 0.144 (0.144) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-23:36:01] Epoch: [230][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.112 (0.251) Prec@1 94.79 (92.67) Prec@5 100.00 (99.74)
|
|
test [2019-03-31-23:36:01] Epoch: [230][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.030 (0.251) Prec@1 100.00 (92.68) Prec@5 100.00 (99.73)
|
|
[2019-03-31-23:36:01] **test** Prec@1 92.68 Prec@5 99.73 Error@1 7.32 Error@5 0.27 Loss:0.251
|
|
----> Best Accuracy : Acc@1=94.30, Acc@5=99.86, Error@1=5.70, Error@5=0.14
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-23:36:01] [Epoch=231/600] [Need: 13:15:07] LR=0.0170 ~ 0.0170, Batch=96
|
|
train[2019-03-31-23:36:02] Epoch: [231][000/521] Time 0.83 (0.83) Data 0.56 (0.56) Loss 0.184 (0.184) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-23:36:26] Epoch: [231][100/521] Time 0.27 (0.25) Data 0.00 (0.01) Loss 0.246 (0.277) Prec@1 91.67 (93.63) Prec@5 100.00 (99.91)
|
|
train[2019-03-31-23:36:50] Epoch: [231][200/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.145 (0.279) Prec@1 96.88 (93.85) Prec@5 100.00 (99.91)
|
|
train[2019-03-31-23:37:14] Epoch: [231][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.374 (0.281) Prec@1 92.71 (93.65) Prec@5 100.00 (99.87)
|
|
train[2019-03-31-23:37:38] Epoch: [231][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.431 (0.284) Prec@1 91.67 (93.57) Prec@5 100.00 (99.87)
|
|
train[2019-03-31-23:38:01] Epoch: [231][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.443 (0.287) Prec@1 87.50 (93.50) Prec@5 100.00 (99.87)
|
|
train[2019-03-31-23:38:06] Epoch: [231][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.139 (0.286) Prec@1 97.50 (93.53) Prec@5 100.00 (99.88)
|
|
[2019-03-31-23:38:06] **train** Prec@1 93.53 Prec@5 99.88 Error@1 6.47 Error@5 0.12 Loss:0.286
|
|
test [2019-03-31-23:38:07] Epoch: [231][000/105] Time 0.47 (0.47) Data 0.40 (0.40) Loss 0.156 (0.156) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-23:38:11] Epoch: [231][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.065 (0.226) Prec@1 97.92 (93.33) Prec@5 100.00 (99.88)
|
|
test [2019-03-31-23:38:11] Epoch: [231][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.032 (0.225) Prec@1 100.00 (93.31) Prec@5 100.00 (99.88)
|
|
[2019-03-31-23:38:11] **test** Prec@1 93.31 Prec@5 99.88 Error@1 6.69 Error@5 0.12 Loss:0.225
|
|
----> Best Accuracy : Acc@1=94.30, Acc@5=99.86, Error@1=5.70, Error@5=0.14
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-23:38:11] [Epoch=232/600] [Need: 13:18:27] LR=0.0169 ~ 0.0169, Batch=96
|
|
train[2019-03-31-23:38:12] Epoch: [232][000/521] Time 0.71 (0.71) Data 0.43 (0.43) Loss 0.227 (0.227) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-23:38:36] Epoch: [232][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.300 (0.280) Prec@1 90.62 (93.70) Prec@5 100.00 (99.87)
|
|
train[2019-03-31-23:38:59] Epoch: [232][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.417 (0.280) Prec@1 90.62 (93.77) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-23:39:23] Epoch: [232][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.251 (0.278) Prec@1 92.71 (93.75) Prec@5 100.00 (99.90)
|
|
train[2019-03-31-23:39:47] Epoch: [232][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.197 (0.279) Prec@1 96.88 (93.71) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-23:40:11] Epoch: [232][500/521] Time 0.27 (0.24) Data 0.00 (0.00) Loss 0.236 (0.282) Prec@1 95.83 (93.63) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-23:40:16] Epoch: [232][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.294 (0.283) Prec@1 91.25 (93.61) Prec@5 100.00 (99.89)
|
|
[2019-03-31-23:40:16] **train** Prec@1 93.61 Prec@5 99.89 Error@1 6.39 Error@5 0.11 Loss:0.283
|
|
test [2019-03-31-23:40:17] Epoch: [232][000/105] Time 0.48 (0.48) Data 0.42 (0.42) Loss 0.118 (0.118) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-23:40:21] Epoch: [232][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.044 (0.181) Prec@1 98.96 (94.11) Prec@5 100.00 (99.89)
|
|
test [2019-03-31-23:40:21] Epoch: [232][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.155 (0.182) Prec@1 93.75 (94.11) Prec@5 100.00 (99.89)
|
|
[2019-03-31-23:40:21] **test** Prec@1 94.11 Prec@5 99.89 Error@1 5.89 Error@5 0.11 Loss:0.182
|
|
----> Best Accuracy : Acc@1=94.30, Acc@5=99.86, Error@1=5.70, Error@5=0.14
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-23:40:21] [Epoch=233/600] [Need: 13:16:02] LR=0.0168 ~ 0.0168, Batch=96
|
|
train[2019-03-31-23:40:22] Epoch: [233][000/521] Time 0.70 (0.70) Data 0.42 (0.42) Loss 0.316 (0.316) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-23:40:46] Epoch: [233][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.330 (0.272) Prec@1 95.83 (94.19) Prec@5 98.96 (99.83)
|
|
train[2019-03-31-23:41:09] Epoch: [233][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.195 (0.279) Prec@1 95.83 (93.77) Prec@5 100.00 (99.85)
|
|
train[2019-03-31-23:41:33] Epoch: [233][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.236 (0.279) Prec@1 94.79 (93.83) Prec@5 100.00 (99.87)
|
|
train[2019-03-31-23:41:57] Epoch: [233][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.303 (0.281) Prec@1 93.75 (93.75) Prec@5 98.96 (99.88)
|
|
train[2019-03-31-23:42:21] Epoch: [233][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.216 (0.284) Prec@1 91.67 (93.63) Prec@5 100.00 (99.88)
|
|
train[2019-03-31-23:42:25] Epoch: [233][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.330 (0.285) Prec@1 92.50 (93.60) Prec@5 100.00 (99.87)
|
|
[2019-03-31-23:42:25] **train** Prec@1 93.60 Prec@5 99.87 Error@1 6.40 Error@5 0.13 Loss:0.285
|
|
test [2019-03-31-23:42:26] Epoch: [233][000/105] Time 0.47 (0.47) Data 0.41 (0.41) Loss 0.283 (0.283) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-23:42:30] Epoch: [233][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.045 (0.221) Prec@1 96.88 (93.57) Prec@5 100.00 (99.87)
|
|
test [2019-03-31-23:42:30] Epoch: [233][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.269 (0.222) Prec@1 93.75 (93.55) Prec@5 100.00 (99.87)
|
|
[2019-03-31-23:42:30] **test** Prec@1 93.55 Prec@5 99.87 Error@1 6.45 Error@5 0.13 Loss:0.222
|
|
----> Best Accuracy : Acc@1=94.30, Acc@5=99.86, Error@1=5.70, Error@5=0.14
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-23:42:30] [Epoch=234/600] [Need: 13:07:28] LR=0.0168 ~ 0.0168, Batch=96
|
|
train[2019-03-31-23:42:31] Epoch: [234][000/521] Time 0.84 (0.84) Data 0.57 (0.57) Loss 0.320 (0.320) Prec@1 91.67 (91.67) Prec@5 98.96 (98.96)
|
|
train[2019-03-31-23:42:55] Epoch: [234][100/521] Time 0.23 (0.25) Data 0.00 (0.01) Loss 0.323 (0.284) Prec@1 92.71 (93.67) Prec@5 100.00 (99.81)
|
|
train[2019-03-31-23:43:19] Epoch: [234][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.292 (0.286) Prec@1 89.58 (93.62) Prec@5 100.00 (99.86)
|
|
train[2019-03-31-23:43:43] Epoch: [234][300/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.282 (0.286) Prec@1 94.79 (93.60) Prec@5 100.00 (99.86)
|
|
train[2019-03-31-23:44:06] Epoch: [234][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.412 (0.287) Prec@1 89.58 (93.61) Prec@5 100.00 (99.87)
|
|
train[2019-03-31-23:44:30] Epoch: [234][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.188 (0.286) Prec@1 95.83 (93.64) Prec@5 100.00 (99.87)
|
|
train[2019-03-31-23:44:35] Epoch: [234][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.355 (0.285) Prec@1 93.75 (93.68) Prec@5 100.00 (99.87)
|
|
[2019-03-31-23:44:35] **train** Prec@1 93.68 Prec@5 99.87 Error@1 6.32 Error@5 0.13 Loss:0.285
|
|
test [2019-03-31-23:44:35] Epoch: [234][000/105] Time 0.46 (0.46) Data 0.40 (0.40) Loss 0.087 (0.087) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-23:44:40] Epoch: [234][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.060 (0.198) Prec@1 96.88 (93.78) Prec@5 100.00 (99.92)
|
|
test [2019-03-31-23:44:40] Epoch: [234][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.021 (0.197) Prec@1 100.00 (93.80) Prec@5 100.00 (99.92)
|
|
[2019-03-31-23:44:40] **test** Prec@1 93.80 Prec@5 99.92 Error@1 6.20 Error@5 0.08 Loss:0.197
|
|
----> Best Accuracy : Acc@1=94.30, Acc@5=99.86, Error@1=5.70, Error@5=0.14
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-23:44:40] [Epoch=235/600] [Need: 13:08:29] LR=0.0167 ~ 0.0167, Batch=96
|
|
train[2019-03-31-23:44:41] Epoch: [235][000/521] Time 0.83 (0.83) Data 0.56 (0.56) Loss 0.407 (0.407) Prec@1 89.58 (89.58) Prec@5 98.96 (98.96)
|
|
train[2019-03-31-23:45:05] Epoch: [235][100/521] Time 0.24 (0.25) Data 0.00 (0.01) Loss 0.295 (0.283) Prec@1 92.71 (93.65) Prec@5 100.00 (99.92)
|
|
train[2019-03-31-23:45:29] Epoch: [235][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.189 (0.284) Prec@1 96.88 (93.80) Prec@5 100.00 (99.92)
|
|
train[2019-03-31-23:45:52] Epoch: [235][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.186 (0.284) Prec@1 96.88 (93.68) Prec@5 100.00 (99.90)
|
|
train[2019-03-31-23:46:16] Epoch: [235][400/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.304 (0.287) Prec@1 95.83 (93.60) Prec@5 100.00 (99.90)
|
|
train[2019-03-31-23:46:40] Epoch: [235][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.277 (0.287) Prec@1 94.79 (93.62) Prec@5 100.00 (99.90)
|
|
train[2019-03-31-23:46:44] Epoch: [235][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.235 (0.287) Prec@1 96.25 (93.59) Prec@5 100.00 (99.90)
|
|
[2019-03-31-23:46:45] **train** Prec@1 93.59 Prec@5 99.90 Error@1 6.41 Error@5 0.10 Loss:0.287
|
|
test [2019-03-31-23:46:45] Epoch: [235][000/105] Time 0.60 (0.60) Data 0.54 (0.54) Loss 0.162 (0.162) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-23:46:49] Epoch: [235][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.212 (0.244) Prec@1 94.79 (92.66) Prec@5 100.00 (99.89)
|
|
test [2019-03-31-23:46:49] Epoch: [235][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.174 (0.243) Prec@1 93.75 (92.67) Prec@5 100.00 (99.89)
|
|
[2019-03-31-23:46:49] **test** Prec@1 92.67 Prec@5 99.89 Error@1 7.33 Error@5 0.11 Loss:0.243
|
|
----> Best Accuracy : Acc@1=94.30, Acc@5=99.86, Error@1=5.70, Error@5=0.14
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-23:46:50] [Epoch=236/600] [Need: 13:06:20] LR=0.0166 ~ 0.0166, Batch=96
|
|
train[2019-03-31-23:46:50] Epoch: [236][000/521] Time 0.84 (0.84) Data 0.57 (0.57) Loss 0.191 (0.191) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-23:47:14] Epoch: [236][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.210 (0.257) Prec@1 95.83 (94.30) Prec@5 100.00 (99.92)
|
|
train[2019-03-31-23:47:38] Epoch: [236][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.374 (0.263) Prec@1 91.67 (94.12) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-23:48:02] Epoch: [236][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.232 (0.269) Prec@1 95.83 (93.96) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-23:48:25] Epoch: [236][400/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.308 (0.279) Prec@1 93.75 (93.77) Prec@5 100.00 (99.90)
|
|
train[2019-03-31-23:48:49] Epoch: [236][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.323 (0.283) Prec@1 91.67 (93.71) Prec@5 100.00 (99.90)
|
|
train[2019-03-31-23:48:54] Epoch: [236][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.215 (0.283) Prec@1 96.25 (93.73) Prec@5 100.00 (99.90)
|
|
[2019-03-31-23:48:54] **train** Prec@1 93.73 Prec@5 99.90 Error@1 6.27 Error@5 0.10 Loss:0.283
|
|
test [2019-03-31-23:48:54] Epoch: [236][000/105] Time 0.62 (0.62) Data 0.54 (0.54) Loss 0.146 (0.146) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-23:48:58] Epoch: [236][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.068 (0.199) Prec@1 95.83 (93.76) Prec@5 100.00 (99.85)
|
|
test [2019-03-31-23:48:59] Epoch: [236][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.208 (0.196) Prec@1 93.75 (93.84) Prec@5 100.00 (99.85)
|
|
[2019-03-31-23:48:59] **test** Prec@1 93.84 Prec@5 99.85 Error@1 6.16 Error@5 0.15 Loss:0.196
|
|
----> Best Accuracy : Acc@1=94.30, Acc@5=99.86, Error@1=5.70, Error@5=0.14
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-23:48:59] [Epoch=237/600] [Need: 13:01:43] LR=0.0166 ~ 0.0166, Batch=96
|
|
train[2019-03-31-23:49:00] Epoch: [237][000/521] Time 0.84 (0.84) Data 0.56 (0.56) Loss 0.338 (0.338) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-23:49:23] Epoch: [237][100/521] Time 0.23 (0.24) Data 0.00 (0.01) Loss 0.176 (0.269) Prec@1 96.88 (93.99) Prec@5 100.00 (99.91)
|
|
train[2019-03-31-23:49:47] Epoch: [237][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.450 (0.283) Prec@1 90.62 (93.68) Prec@5 100.00 (99.90)
|
|
train[2019-03-31-23:50:11] Epoch: [237][300/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.183 (0.278) Prec@1 94.79 (93.89) Prec@5 100.00 (99.90)
|
|
train[2019-03-31-23:50:34] Epoch: [237][400/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.297 (0.280) Prec@1 94.79 (93.81) Prec@5 98.96 (99.89)
|
|
train[2019-03-31-23:50:58] Epoch: [237][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.274 (0.284) Prec@1 93.75 (93.64) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-23:51:03] Epoch: [237][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.229 (0.284) Prec@1 93.75 (93.66) Prec@5 100.00 (99.89)
|
|
[2019-03-31-23:51:03] **train** Prec@1 93.66 Prec@5 99.89 Error@1 6.34 Error@5 0.11 Loss:0.284
|
|
test [2019-03-31-23:51:03] Epoch: [237][000/105] Time 0.48 (0.48) Data 0.42 (0.42) Loss 0.240 (0.240) Prec@1 91.67 (91.67) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-23:51:07] Epoch: [237][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.121 (0.196) Prec@1 96.88 (94.20) Prec@5 100.00 (99.88)
|
|
test [2019-03-31-23:51:08] Epoch: [237][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.050 (0.196) Prec@1 100.00 (94.18) Prec@5 100.00 (99.88)
|
|
[2019-03-31-23:51:08] **test** Prec@1 94.18 Prec@5 99.88 Error@1 5.82 Error@5 0.12 Loss:0.196
|
|
----> Best Accuracy : Acc@1=94.30, Acc@5=99.86, Error@1=5.70, Error@5=0.14
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-23:51:08] [Epoch=238/600] [Need: 12:58:11] LR=0.0165 ~ 0.0165, Batch=96
|
|
train[2019-03-31-23:51:08] Epoch: [238][000/521] Time 0.71 (0.71) Data 0.43 (0.43) Loss 0.382 (0.382) Prec@1 90.62 (90.62) Prec@5 98.96 (98.96)
|
|
train[2019-03-31-23:51:32] Epoch: [238][100/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.407 (0.279) Prec@1 88.54 (93.75) Prec@5 100.00 (99.87)
|
|
train[2019-03-31-23:51:56] Epoch: [238][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.201 (0.283) Prec@1 96.88 (93.70) Prec@5 100.00 (99.88)
|
|
train[2019-03-31-23:52:19] Epoch: [238][300/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.181 (0.277) Prec@1 96.88 (93.78) Prec@5 100.00 (99.90)
|
|
train[2019-03-31-23:52:43] Epoch: [238][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.231 (0.281) Prec@1 97.92 (93.70) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-23:53:07] Epoch: [238][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.178 (0.285) Prec@1 95.83 (93.62) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-23:53:12] Epoch: [238][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.279 (0.285) Prec@1 92.50 (93.59) Prec@5 100.00 (99.89)
|
|
[2019-03-31-23:53:12] **train** Prec@1 93.59 Prec@5 99.89 Error@1 6.41 Error@5 0.11 Loss:0.285
|
|
test [2019-03-31-23:53:12] Epoch: [238][000/105] Time 0.60 (0.60) Data 0.53 (0.53) Loss 0.153 (0.153) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-23:53:16] Epoch: [238][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.043 (0.190) Prec@1 98.96 (94.27) Prec@5 100.00 (99.87)
|
|
test [2019-03-31-23:53:16] Epoch: [238][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.117 (0.188) Prec@1 93.75 (94.30) Prec@5 100.00 (99.87)
|
|
[2019-03-31-23:53:17] **test** Prec@1 94.30 Prec@5 99.87 Error@1 5.70 Error@5 0.13 Loss:0.188
|
|
----> Best Accuracy : Acc@1=94.30, Acc@5=99.87, Error@1=5.70, Error@5=0.13
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-23:53:17] [Epoch=239/600] [Need: 12:56:12] LR=0.0165 ~ 0.0165, Batch=96
|
|
train[2019-03-31-23:53:18] Epoch: [239][000/521] Time 0.85 (0.85) Data 0.58 (0.58) Loss 0.308 (0.308) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-23:53:41] Epoch: [239][100/521] Time 0.23 (0.24) Data 0.00 (0.01) Loss 0.263 (0.263) Prec@1 94.79 (94.17) Prec@5 100.00 (99.96)
|
|
train[2019-03-31-23:54:05] Epoch: [239][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.294 (0.274) Prec@1 93.75 (93.81) Prec@5 98.96 (99.92)
|
|
train[2019-03-31-23:54:29] Epoch: [239][300/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.218 (0.276) Prec@1 93.75 (93.81) Prec@5 100.00 (99.92)
|
|
train[2019-03-31-23:54:52] Epoch: [239][400/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.320 (0.282) Prec@1 93.75 (93.66) Prec@5 100.00 (99.90)
|
|
train[2019-03-31-23:55:16] Epoch: [239][500/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.392 (0.282) Prec@1 90.62 (93.66) Prec@5 100.00 (99.90)
|
|
train[2019-03-31-23:55:21] Epoch: [239][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.376 (0.281) Prec@1 91.25 (93.68) Prec@5 100.00 (99.90)
|
|
[2019-03-31-23:55:21] **train** Prec@1 93.68 Prec@5 99.90 Error@1 6.32 Error@5 0.10 Loss:0.281
|
|
test [2019-03-31-23:55:22] Epoch: [239][000/105] Time 0.60 (0.60) Data 0.53 (0.53) Loss 0.128 (0.128) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-23:55:26] Epoch: [239][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.059 (0.211) Prec@1 97.92 (93.69) Prec@5 100.00 (99.88)
|
|
test [2019-03-31-23:55:26] Epoch: [239][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.025 (0.212) Prec@1 100.00 (93.66) Prec@5 100.00 (99.88)
|
|
[2019-03-31-23:55:26] **test** Prec@1 93.66 Prec@5 99.88 Error@1 6.34 Error@5 0.12 Loss:0.212
|
|
----> Best Accuracy : Acc@1=94.30, Acc@5=99.87, Error@1=5.70, Error@5=0.13
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-23:55:26] [Epoch=240/600] [Need: 12:55:23] LR=0.0164 ~ 0.0164, Batch=96
|
|
train[2019-03-31-23:55:27] Epoch: [240][000/521] Time 0.84 (0.84) Data 0.58 (0.58) Loss 0.411 (0.411) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-23:55:51] Epoch: [240][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.234 (0.265) Prec@1 94.79 (94.28) Prec@5 100.00 (99.91)
|
|
train[2019-03-31-23:56:15] Epoch: [240][200/521] Time 0.27 (0.24) Data 0.00 (0.00) Loss 0.152 (0.280) Prec@1 95.83 (93.82) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-23:56:39] Epoch: [240][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.212 (0.279) Prec@1 94.79 (93.74) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-23:57:02] Epoch: [240][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.290 (0.277) Prec@1 92.71 (93.77) Prec@5 100.00 (99.89)
|
|
train[2019-03-31-23:57:26] Epoch: [240][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.300 (0.279) Prec@1 93.75 (93.78) Prec@5 100.00 (99.90)
|
|
train[2019-03-31-23:57:31] Epoch: [240][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.155 (0.279) Prec@1 98.75 (93.79) Prec@5 100.00 (99.90)
|
|
[2019-03-31-23:57:31] **train** Prec@1 93.79 Prec@5 99.90 Error@1 6.21 Error@5 0.10 Loss:0.279
|
|
test [2019-03-31-23:57:32] Epoch: [240][000/105] Time 0.60 (0.60) Data 0.53 (0.53) Loss 0.209 (0.209) Prec@1 90.62 (90.62) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-23:57:36] Epoch: [240][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.098 (0.217) Prec@1 96.88 (93.55) Prec@5 100.00 (99.86)
|
|
test [2019-03-31-23:57:36] Epoch: [240][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.045 (0.217) Prec@1 100.00 (93.53) Prec@5 100.00 (99.86)
|
|
[2019-03-31-23:57:36] **test** Prec@1 93.53 Prec@5 99.86 Error@1 6.47 Error@5 0.14 Loss:0.217
|
|
----> Best Accuracy : Acc@1=94.30, Acc@5=99.87, Error@1=5.70, Error@5=0.13
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-23:57:36] [Epoch=241/600] [Need: 12:57:37] LR=0.0163 ~ 0.0163, Batch=96
|
|
train[2019-03-31-23:57:37] Epoch: [241][000/521] Time 0.70 (0.70) Data 0.42 (0.42) Loss 0.198 (0.198) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
train[2019-03-31-23:58:00] Epoch: [241][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.182 (0.270) Prec@1 96.88 (94.05) Prec@5 100.00 (99.93)
|
|
train[2019-03-31-23:58:24] Epoch: [241][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.154 (0.276) Prec@1 97.92 (93.87) Prec@5 100.00 (99.91)
|
|
train[2019-03-31-23:58:48] Epoch: [241][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.220 (0.275) Prec@1 94.79 (93.87) Prec@5 100.00 (99.92)
|
|
train[2019-03-31-23:59:12] Epoch: [241][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.333 (0.279) Prec@1 92.71 (93.76) Prec@5 100.00 (99.90)
|
|
train[2019-03-31-23:59:35] Epoch: [241][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.547 (0.282) Prec@1 88.54 (93.70) Prec@5 98.96 (99.89)
|
|
train[2019-03-31-23:59:40] Epoch: [241][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.145 (0.283) Prec@1 95.00 (93.67) Prec@5 100.00 (99.88)
|
|
[2019-03-31-23:59:40] **train** Prec@1 93.67 Prec@5 99.88 Error@1 6.33 Error@5 0.12 Loss:0.283
|
|
test [2019-03-31-23:59:41] Epoch: [241][000/105] Time 0.61 (0.61) Data 0.54 (0.54) Loss 0.181 (0.181) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
test [2019-03-31-23:59:45] Epoch: [241][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.141 (0.226) Prec@1 95.83 (93.42) Prec@5 100.00 (99.90)
|
|
test [2019-03-31-23:59:45] Epoch: [241][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.045 (0.226) Prec@1 100.00 (93.39) Prec@5 100.00 (99.90)
|
|
[2019-03-31-23:59:45] **test** Prec@1 93.39 Prec@5 99.90 Error@1 6.61 Error@5 0.10 Loss:0.226
|
|
----> Best Accuracy : Acc@1=94.30, Acc@5=99.87, Error@1=5.70, Error@5=0.13
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-03-31-23:59:45] [Epoch=242/600] [Need: 12:52:01] LR=0.0163 ~ 0.0163, Batch=96
|
|
train[2019-03-31-23:59:46] Epoch: [242][000/521] Time 0.71 (0.71) Data 0.43 (0.43) Loss 0.206 (0.206) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-00:00:10] Epoch: [242][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.219 (0.271) Prec@1 96.88 (93.90) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-00:00:34] Epoch: [242][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.160 (0.277) Prec@1 96.88 (93.87) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-00:00:58] Epoch: [242][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.353 (0.274) Prec@1 90.62 (93.91) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-00:01:21] Epoch: [242][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.212 (0.281) Prec@1 96.88 (93.71) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-00:01:45] Epoch: [242][500/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.387 (0.283) Prec@1 92.71 (93.63) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-00:01:49] Epoch: [242][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.298 (0.284) Prec@1 93.75 (93.59) Prec@5 100.00 (99.91)
|
|
[2019-04-01-00:01:50] **train** Prec@1 93.59 Prec@5 99.91 Error@1 6.41 Error@5 0.09 Loss:0.284
|
|
test [2019-04-01-00:01:50] Epoch: [242][000/105] Time 0.60 (0.60) Data 0.53 (0.53) Loss 0.160 (0.160) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-00:01:54] Epoch: [242][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.060 (0.201) Prec@1 97.92 (93.82) Prec@5 100.00 (99.90)
|
|
test [2019-04-01-00:01:54] Epoch: [242][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.039 (0.202) Prec@1 100.00 (93.83) Prec@5 100.00 (99.88)
|
|
[2019-04-01-00:01:55] **test** Prec@1 93.83 Prec@5 99.88 Error@1 6.17 Error@5 0.12 Loss:0.202
|
|
----> Best Accuracy : Acc@1=94.30, Acc@5=99.87, Error@1=5.70, Error@5=0.13
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-00:01:55] [Epoch=243/600] [Need: 12:49:14] LR=0.0162 ~ 0.0162, Batch=96
|
|
train[2019-04-01-00:01:55] Epoch: [243][000/521] Time 0.70 (0.70) Data 0.43 (0.43) Loss 0.203 (0.203) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-00:02:19] Epoch: [243][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.525 (0.264) Prec@1 87.50 (94.26) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-00:02:43] Epoch: [243][200/521] Time 0.27 (0.24) Data 0.00 (0.00) Loss 0.330 (0.279) Prec@1 94.79 (93.91) Prec@5 100.00 (99.89)
|
|
train[2019-04-01-00:03:07] Epoch: [243][300/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.142 (0.275) Prec@1 96.88 (93.89) Prec@5 100.00 (99.90)
|
|
train[2019-04-01-00:03:30] Epoch: [243][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.249 (0.277) Prec@1 94.79 (93.80) Prec@5 100.00 (99.89)
|
|
train[2019-04-01-00:03:54] Epoch: [243][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.171 (0.281) Prec@1 94.79 (93.70) Prec@5 100.00 (99.89)
|
|
train[2019-04-01-00:03:59] Epoch: [243][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.224 (0.280) Prec@1 93.75 (93.72) Prec@5 100.00 (99.89)
|
|
[2019-04-01-00:03:59] **train** Prec@1 93.72 Prec@5 99.89 Error@1 6.28 Error@5 0.11 Loss:0.280
|
|
test [2019-04-01-00:03:59] Epoch: [243][000/105] Time 0.46 (0.46) Data 0.40 (0.40) Loss 0.246 (0.246) Prec@1 90.62 (90.62) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-00:04:03] Epoch: [243][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.100 (0.197) Prec@1 95.83 (94.08) Prec@5 100.00 (99.81)
|
|
test [2019-04-01-00:04:03] Epoch: [243][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.117 (0.197) Prec@1 93.75 (94.04) Prec@5 100.00 (99.81)
|
|
[2019-04-01-00:04:03] **test** Prec@1 94.04 Prec@5 99.81 Error@1 5.96 Error@5 0.19 Loss:0.197
|
|
----> Best Accuracy : Acc@1=94.30, Acc@5=99.87, Error@1=5.70, Error@5=0.13
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-00:04:04] [Epoch=244/600] [Need: 12:45:04] LR=0.0161 ~ 0.0161, Batch=96
|
|
train[2019-04-01-00:04:04] Epoch: [244][000/521] Time 0.83 (0.83) Data 0.56 (0.56) Loss 0.409 (0.409) Prec@1 91.67 (91.67) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-00:04:28] Epoch: [244][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.342 (0.281) Prec@1 90.62 (93.61) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-00:04:52] Epoch: [244][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.198 (0.281) Prec@1 95.83 (93.69) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-00:05:16] Epoch: [244][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.209 (0.279) Prec@1 94.79 (93.75) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-00:05:39] Epoch: [244][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.354 (0.277) Prec@1 91.67 (93.75) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-00:06:03] Epoch: [244][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.247 (0.279) Prec@1 96.88 (93.67) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-00:06:08] Epoch: [244][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.280 (0.279) Prec@1 92.50 (93.66) Prec@5 100.00 (99.91)
|
|
[2019-04-01-00:06:08] **train** Prec@1 93.66 Prec@5 99.91 Error@1 6.34 Error@5 0.09 Loss:0.279
|
|
test [2019-04-01-00:06:08] Epoch: [244][000/105] Time 0.46 (0.46) Data 0.40 (0.40) Loss 0.142 (0.142) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-00:06:12] Epoch: [244][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.123 (0.184) Prec@1 94.79 (94.66) Prec@5 100.00 (99.91)
|
|
test [2019-04-01-00:06:12] Epoch: [244][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.196 (0.183) Prec@1 93.75 (94.66) Prec@5 100.00 (99.91)
|
|
[2019-04-01-00:06:13] **test** Prec@1 94.66 Prec@5 99.91 Error@1 5.34 Error@5 0.09 Loss:0.183
|
|
----> Best Accuracy : Acc@1=94.66, Acc@5=99.91, Error@1=5.34, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-00:06:13] [Epoch=245/600] [Need: 12:43:51] LR=0.0161 ~ 0.0161, Batch=96
|
|
train[2019-04-01-00:06:14] Epoch: [245][000/521] Time 0.83 (0.83) Data 0.56 (0.56) Loss 0.264 (0.264) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-00:06:37] Epoch: [245][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.394 (0.265) Prec@1 93.75 (94.31) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-00:07:01] Epoch: [245][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.288 (0.270) Prec@1 95.83 (94.08) Prec@5 98.96 (99.92)
|
|
train[2019-04-01-00:07:24] Epoch: [245][300/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.228 (0.262) Prec@1 96.88 (94.26) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-00:07:48] Epoch: [245][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.349 (0.264) Prec@1 89.58 (94.21) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-00:08:12] Epoch: [245][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.187 (0.269) Prec@1 95.83 (94.09) Prec@5 100.00 (99.90)
|
|
train[2019-04-01-00:08:17] Epoch: [245][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.321 (0.270) Prec@1 91.25 (94.05) Prec@5 100.00 (99.89)
|
|
[2019-04-01-00:08:17] **train** Prec@1 94.05 Prec@5 99.89 Error@1 5.95 Error@5 0.11 Loss:0.270
|
|
test [2019-04-01-00:08:17] Epoch: [245][000/105] Time 0.48 (0.48) Data 0.42 (0.42) Loss 0.100 (0.100) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-00:08:21] Epoch: [245][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.066 (0.201) Prec@1 97.92 (93.99) Prec@5 100.00 (99.89)
|
|
test [2019-04-01-00:08:21] Epoch: [245][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.025 (0.200) Prec@1 100.00 (94.02) Prec@5 100.00 (99.88)
|
|
[2019-04-01-00:08:21] **test** Prec@1 94.02 Prec@5 99.88 Error@1 5.98 Error@5 0.12 Loss:0.200
|
|
----> Best Accuracy : Acc@1=94.66, Acc@5=99.91, Error@1=5.34, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-00:08:22] [Epoch=246/600] [Need: 12:40:23] LR=0.0160 ~ 0.0160, Batch=96
|
|
train[2019-04-01-00:08:22] Epoch: [246][000/521] Time 0.71 (0.71) Data 0.43 (0.43) Loss 0.351 (0.351) Prec@1 91.67 (91.67) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-00:08:46] Epoch: [246][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.164 (0.272) Prec@1 96.88 (93.96) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-00:09:10] Epoch: [246][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.247 (0.278) Prec@1 93.75 (93.85) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-00:09:33] Epoch: [246][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.260 (0.269) Prec@1 94.79 (94.06) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-00:09:57] Epoch: [246][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.310 (0.272) Prec@1 88.54 (93.98) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-00:10:21] Epoch: [246][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.220 (0.275) Prec@1 93.75 (93.87) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-00:10:26] Epoch: [246][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.257 (0.277) Prec@1 93.75 (93.83) Prec@5 100.00 (99.92)
|
|
[2019-04-01-00:10:26] **train** Prec@1 93.83 Prec@5 99.92 Error@1 6.17 Error@5 0.08 Loss:0.277
|
|
test [2019-04-01-00:10:26] Epoch: [246][000/105] Time 0.46 (0.46) Data 0.40 (0.40) Loss 0.216 (0.216) Prec@1 89.58 (89.58) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-00:10:30] Epoch: [246][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.113 (0.220) Prec@1 97.92 (93.67) Prec@5 100.00 (99.81)
|
|
test [2019-04-01-00:10:30] Epoch: [246][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.125 (0.219) Prec@1 93.75 (93.66) Prec@5 100.00 (99.81)
|
|
[2019-04-01-00:10:30] **test** Prec@1 93.66 Prec@5 99.81 Error@1 6.34 Error@5 0.19 Loss:0.219
|
|
----> Best Accuracy : Acc@1=94.66, Acc@5=99.91, Error@1=5.34, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-00:10:31] [Epoch=247/600] [Need: 12:39:01] LR=0.0160 ~ 0.0160, Batch=96
|
|
train[2019-04-01-00:10:31] Epoch: [247][000/521] Time 0.71 (0.71) Data 0.43 (0.43) Loss 0.268 (0.268) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-00:10:55] Epoch: [247][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.277 (0.258) Prec@1 94.79 (94.69) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-00:11:19] Epoch: [247][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.207 (0.267) Prec@1 96.88 (94.41) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-00:11:42] Epoch: [247][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.501 (0.268) Prec@1 84.38 (94.22) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-00:12:06] Epoch: [247][400/521] Time 0.27 (0.24) Data 0.00 (0.00) Loss 0.133 (0.271) Prec@1 98.96 (94.12) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-00:12:30] Epoch: [247][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.443 (0.275) Prec@1 89.58 (93.96) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-00:12:34] Epoch: [247][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.318 (0.276) Prec@1 91.25 (93.95) Prec@5 98.75 (99.93)
|
|
[2019-04-01-00:12:35] **train** Prec@1 93.95 Prec@5 99.93 Error@1 6.05 Error@5 0.07 Loss:0.276
|
|
test [2019-04-01-00:12:35] Epoch: [247][000/105] Time 0.62 (0.62) Data 0.55 (0.55) Loss 0.254 (0.254) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-00:12:39] Epoch: [247][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.210 (0.225) Prec@1 94.79 (93.58) Prec@5 100.00 (99.90)
|
|
test [2019-04-01-00:12:39] Epoch: [247][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.052 (0.224) Prec@1 100.00 (93.61) Prec@5 100.00 (99.90)
|
|
[2019-04-01-00:12:39] **test** Prec@1 93.61 Prec@5 99.90 Error@1 6.39 Error@5 0.10 Loss:0.224
|
|
----> Best Accuracy : Acc@1=94.66, Acc@5=99.91, Error@1=5.34, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-00:12:40] [Epoch=248/600] [Need: 12:36:49] LR=0.0159 ~ 0.0159, Batch=96
|
|
train[2019-04-01-00:12:40] Epoch: [248][000/521] Time 0.84 (0.84) Data 0.57 (0.57) Loss 0.243 (0.243) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-00:13:04] Epoch: [248][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.465 (0.279) Prec@1 89.58 (93.93) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-00:13:29] Epoch: [248][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.322 (0.282) Prec@1 89.58 (93.79) Prec@5 100.00 (99.89)
|
|
train[2019-04-01-00:13:52] Epoch: [248][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.193 (0.276) Prec@1 96.88 (93.90) Prec@5 100.00 (99.89)
|
|
train[2019-04-01-00:14:16] Epoch: [248][400/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.359 (0.277) Prec@1 90.62 (93.79) Prec@5 100.00 (99.89)
|
|
train[2019-04-01-00:14:40] Epoch: [248][500/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.294 (0.279) Prec@1 92.71 (93.78) Prec@5 100.00 (99.86)
|
|
train[2019-04-01-00:14:45] Epoch: [248][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.170 (0.280) Prec@1 98.75 (93.78) Prec@5 100.00 (99.86)
|
|
[2019-04-01-00:14:45] **train** Prec@1 93.78 Prec@5 99.86 Error@1 6.22 Error@5 0.14 Loss:0.280
|
|
test [2019-04-01-00:14:46] Epoch: [248][000/105] Time 0.60 (0.60) Data 0.53 (0.53) Loss 0.230 (0.230) Prec@1 90.62 (90.62) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-00:14:50] Epoch: [248][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.071 (0.201) Prec@1 95.83 (93.92) Prec@5 100.00 (99.88)
|
|
test [2019-04-01-00:14:50] Epoch: [248][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.011 (0.200) Prec@1 100.00 (93.92) Prec@5 100.00 (99.88)
|
|
[2019-04-01-00:14:50] **test** Prec@1 93.92 Prec@5 99.88 Error@1 6.08 Error@5 0.12 Loss:0.200
|
|
----> Best Accuracy : Acc@1=94.66, Acc@5=99.91, Error@1=5.34, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-00:14:50] [Epoch=249/600] [Need: 12:42:57] LR=0.0158 ~ 0.0158, Batch=96
|
|
train[2019-04-01-00:14:51] Epoch: [249][000/521] Time 0.83 (0.83) Data 0.56 (0.56) Loss 0.212 (0.212) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-00:15:14] Epoch: [249][100/521] Time 0.23 (0.24) Data 0.00 (0.01) Loss 0.302 (0.266) Prec@1 90.62 (93.98) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-00:15:38] Epoch: [249][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.377 (0.278) Prec@1 93.75 (93.68) Prec@5 100.00 (99.90)
|
|
train[2019-04-01-00:16:02] Epoch: [249][300/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.192 (0.279) Prec@1 95.83 (93.65) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-00:16:26] Epoch: [249][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.347 (0.281) Prec@1 91.67 (93.58) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-00:16:49] Epoch: [249][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.397 (0.284) Prec@1 89.58 (93.55) Prec@5 100.00 (99.90)
|
|
train[2019-04-01-00:16:54] Epoch: [249][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.201 (0.284) Prec@1 97.50 (93.55) Prec@5 100.00 (99.90)
|
|
[2019-04-01-00:16:54] **train** Prec@1 93.55 Prec@5 99.90 Error@1 6.45 Error@5 0.10 Loss:0.284
|
|
test [2019-04-01-00:16:55] Epoch: [249][000/105] Time 0.60 (0.60) Data 0.53 (0.53) Loss 0.273 (0.273) Prec@1 90.62 (90.62) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-00:16:59] Epoch: [249][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.137 (0.201) Prec@1 94.79 (93.97) Prec@5 100.00 (99.83)
|
|
test [2019-04-01-00:16:59] Epoch: [249][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.481 (0.203) Prec@1 87.50 (93.88) Prec@5 100.00 (99.84)
|
|
[2019-04-01-00:16:59] **test** Prec@1 93.88 Prec@5 99.84 Error@1 6.12 Error@5 0.16 Loss:0.203
|
|
----> Best Accuracy : Acc@1=94.66, Acc@5=99.91, Error@1=5.34, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-00:16:59] [Epoch=250/600] [Need: 12:33:09] LR=0.0158 ~ 0.0158, Batch=96
|
|
train[2019-04-01-00:17:00] Epoch: [250][000/521] Time 0.71 (0.71) Data 0.44 (0.44) Loss 0.226 (0.226) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-00:17:23] Epoch: [250][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.237 (0.269) Prec@1 95.83 (94.19) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-00:17:48] Epoch: [250][200/521] Time 0.27 (0.24) Data 0.00 (0.00) Loss 0.227 (0.269) Prec@1 92.71 (94.09) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-00:18:12] Epoch: [250][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.417 (0.269) Prec@1 89.58 (94.05) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-00:18:35] Epoch: [250][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.247 (0.272) Prec@1 94.79 (93.95) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-00:18:59] Epoch: [250][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.282 (0.276) Prec@1 92.71 (93.84) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-00:19:04] Epoch: [250][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.210 (0.276) Prec@1 93.75 (93.86) Prec@5 100.00 (99.92)
|
|
[2019-04-01-00:19:04] **train** Prec@1 93.86 Prec@5 99.92 Error@1 6.14 Error@5 0.08 Loss:0.276
|
|
test [2019-04-01-00:19:05] Epoch: [250][000/105] Time 0.47 (0.47) Data 0.41 (0.41) Loss 0.202 (0.202) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-00:19:09] Epoch: [250][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.043 (0.166) Prec@1 97.92 (94.87) Prec@5 100.00 (99.90)
|
|
test [2019-04-01-00:19:09] Epoch: [250][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.017 (0.166) Prec@1 100.00 (94.90) Prec@5 100.00 (99.90)
|
|
[2019-04-01-00:19:09] **test** Prec@1 94.90 Prec@5 99.90 Error@1 5.10 Error@5 0.10 Loss:0.166
|
|
----> Best Accuracy : Acc@1=94.90, Acc@5=99.90, Error@1=5.10, Error@5=0.10
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-00:19:09] [Epoch=251/600] [Need: 12:36:03] LR=0.0157 ~ 0.0157, Batch=96
|
|
train[2019-04-01-00:19:10] Epoch: [251][000/521] Time 0.71 (0.71) Data 0.43 (0.43) Loss 0.262 (0.262) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-00:19:34] Epoch: [251][100/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.134 (0.268) Prec@1 96.88 (94.05) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-00:19:57] Epoch: [251][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.387 (0.283) Prec@1 92.71 (93.75) Prec@5 98.96 (99.89)
|
|
train[2019-04-01-00:20:21] Epoch: [251][300/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.331 (0.275) Prec@1 91.67 (93.83) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-00:20:45] Epoch: [251][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.115 (0.279) Prec@1 97.92 (93.71) Prec@5 100.00 (99.90)
|
|
train[2019-04-01-00:21:08] Epoch: [251][500/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.189 (0.279) Prec@1 96.88 (93.70) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-00:21:13] Epoch: [251][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.217 (0.279) Prec@1 95.00 (93.69) Prec@5 100.00 (99.91)
|
|
[2019-04-01-00:21:13] **train** Prec@1 93.69 Prec@5 99.91 Error@1 6.31 Error@5 0.09 Loss:0.279
|
|
test [2019-04-01-00:21:14] Epoch: [251][000/105] Time 0.61 (0.61) Data 0.54 (0.54) Loss 0.377 (0.377) Prec@1 90.62 (90.62) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-00:21:18] Epoch: [251][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.112 (0.243) Prec@1 97.92 (93.09) Prec@5 100.00 (99.67)
|
|
test [2019-04-01-00:21:18] Epoch: [251][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.491 (0.245) Prec@1 93.75 (93.04) Prec@5 100.00 (99.68)
|
|
[2019-04-01-00:21:18] **test** Prec@1 93.04 Prec@5 99.68 Error@1 6.96 Error@5 0.32 Loss:0.245
|
|
----> Best Accuracy : Acc@1=94.90, Acc@5=99.90, Error@1=5.10, Error@5=0.10
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-00:21:18] [Epoch=252/600] [Need: 12:29:04] LR=0.0156 ~ 0.0156, Batch=96
|
|
train[2019-04-01-00:21:19] Epoch: [252][000/521] Time 0.86 (0.86) Data 0.56 (0.56) Loss 0.339 (0.339) Prec@1 90.62 (90.62) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-00:21:43] Epoch: [252][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.146 (0.259) Prec@1 95.83 (94.31) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-00:22:07] Epoch: [252][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.341 (0.265) Prec@1 91.67 (94.12) Prec@5 100.00 (99.90)
|
|
train[2019-04-01-00:22:30] Epoch: [252][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.260 (0.267) Prec@1 93.75 (94.13) Prec@5 100.00 (99.89)
|
|
train[2019-04-01-00:22:54] Epoch: [252][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.205 (0.267) Prec@1 95.83 (94.11) Prec@5 100.00 (99.89)
|
|
train[2019-04-01-00:23:18] Epoch: [252][500/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.213 (0.268) Prec@1 95.83 (94.08) Prec@5 100.00 (99.89)
|
|
train[2019-04-01-00:23:22] Epoch: [252][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.181 (0.269) Prec@1 95.00 (94.05) Prec@5 100.00 (99.89)
|
|
[2019-04-01-00:23:22] **train** Prec@1 94.05 Prec@5 99.89 Error@1 5.95 Error@5 0.11 Loss:0.269
|
|
test [2019-04-01-00:23:23] Epoch: [252][000/105] Time 0.47 (0.47) Data 0.41 (0.41) Loss 0.130 (0.130) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-00:23:27] Epoch: [252][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.052 (0.192) Prec@1 97.92 (94.33) Prec@5 100.00 (99.89)
|
|
test [2019-04-01-00:23:27] Epoch: [252][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.348 (0.191) Prec@1 87.50 (94.32) Prec@5 100.00 (99.89)
|
|
[2019-04-01-00:23:27] **test** Prec@1 94.32 Prec@5 99.89 Error@1 5.68 Error@5 0.11 Loss:0.191
|
|
----> Best Accuracy : Acc@1=94.90, Acc@5=99.90, Error@1=5.10, Error@5=0.10
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-00:23:27] [Epoch=253/600] [Need: 12:27:04] LR=0.0156 ~ 0.0156, Batch=96
|
|
train[2019-04-01-00:23:28] Epoch: [253][000/521] Time 0.71 (0.71) Data 0.44 (0.44) Loss 0.096 (0.096) Prec@1 100.00 (100.00) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-00:23:52] Epoch: [253][100/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.226 (0.264) Prec@1 93.75 (94.20) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-00:24:16] Epoch: [253][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.187 (0.268) Prec@1 94.79 (94.12) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-00:24:40] Epoch: [253][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.257 (0.271) Prec@1 89.58 (93.96) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-00:25:04] Epoch: [253][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.257 (0.277) Prec@1 94.79 (93.89) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-00:25:27] Epoch: [253][500/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.319 (0.275) Prec@1 92.71 (93.90) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-00:25:32] Epoch: [253][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.252 (0.274) Prec@1 93.75 (93.92) Prec@5 100.00 (99.91)
|
|
[2019-04-01-00:25:32] **train** Prec@1 93.92 Prec@5 99.91 Error@1 6.08 Error@5 0.09 Loss:0.274
|
|
test [2019-04-01-00:25:33] Epoch: [253][000/105] Time 0.61 (0.61) Data 0.55 (0.55) Loss 0.163 (0.163) Prec@1 90.62 (90.62) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-00:25:37] Epoch: [253][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.136 (0.216) Prec@1 95.83 (93.56) Prec@5 100.00 (99.87)
|
|
test [2019-04-01-00:25:37] Epoch: [253][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.045 (0.215) Prec@1 100.00 (93.58) Prec@5 100.00 (99.87)
|
|
[2019-04-01-00:25:37] **test** Prec@1 93.58 Prec@5 99.87 Error@1 6.42 Error@5 0.13 Loss:0.215
|
|
----> Best Accuracy : Acc@1=94.90, Acc@5=99.90, Error@1=5.10, Error@5=0.10
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-00:25:37] [Epoch=254/600] [Need: 12:27:48] LR=0.0155 ~ 0.0155, Batch=96
|
|
train[2019-04-01-00:25:38] Epoch: [254][000/521] Time 0.71 (0.71) Data 0.43 (0.43) Loss 0.265 (0.265) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-00:26:01] Epoch: [254][100/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.242 (0.277) Prec@1 93.75 (93.55) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-00:26:25] Epoch: [254][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.120 (0.273) Prec@1 97.92 (93.91) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-00:26:49] Epoch: [254][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.224 (0.271) Prec@1 95.83 (94.02) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-00:27:13] Epoch: [254][400/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.157 (0.270) Prec@1 96.88 (94.01) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-00:27:36] Epoch: [254][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.235 (0.272) Prec@1 94.79 (93.96) Prec@5 100.00 (99.89)
|
|
train[2019-04-01-00:27:41] Epoch: [254][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.172 (0.271) Prec@1 96.25 (93.98) Prec@5 100.00 (99.90)
|
|
[2019-04-01-00:27:41] **train** Prec@1 93.98 Prec@5 99.90 Error@1 6.02 Error@5 0.10 Loss:0.271
|
|
test [2019-04-01-00:27:42] Epoch: [254][000/105] Time 0.47 (0.47) Data 0.40 (0.40) Loss 0.133 (0.133) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-00:27:46] Epoch: [254][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.140 (0.191) Prec@1 94.79 (93.95) Prec@5 100.00 (99.86)
|
|
test [2019-04-01-00:27:46] Epoch: [254][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.305 (0.193) Prec@1 93.75 (93.93) Prec@5 100.00 (99.86)
|
|
[2019-04-01-00:27:46] **test** Prec@1 93.93 Prec@5 99.86 Error@1 6.07 Error@5 0.14 Loss:0.193
|
|
----> Best Accuracy : Acc@1=94.90, Acc@5=99.90, Error@1=5.10, Error@5=0.10
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-00:27:46] [Epoch=255/600] [Need: 12:21:55] LR=0.0155 ~ 0.0155, Batch=96
|
|
train[2019-04-01-00:27:47] Epoch: [255][000/521] Time 0.86 (0.86) Data 0.58 (0.58) Loss 0.158 (0.158) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-00:28:11] Epoch: [255][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.215 (0.258) Prec@1 93.75 (94.31) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-00:28:35] Epoch: [255][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.421 (0.271) Prec@1 89.58 (93.93) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-00:28:58] Epoch: [255][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.212 (0.277) Prec@1 95.83 (93.80) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-00:29:22] Epoch: [255][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.235 (0.279) Prec@1 92.71 (93.73) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-00:29:46] Epoch: [255][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.226 (0.281) Prec@1 93.75 (93.70) Prec@5 100.00 (99.90)
|
|
train[2019-04-01-00:29:50] Epoch: [255][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.205 (0.282) Prec@1 96.25 (93.68) Prec@5 100.00 (99.89)
|
|
[2019-04-01-00:29:50] **train** Prec@1 93.68 Prec@5 99.89 Error@1 6.32 Error@5 0.11 Loss:0.282
|
|
test [2019-04-01-00:29:51] Epoch: [255][000/105] Time 0.61 (0.61) Data 0.54 (0.54) Loss 0.198 (0.198) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-00:29:55] Epoch: [255][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.044 (0.208) Prec@1 97.92 (94.02) Prec@5 100.00 (99.82)
|
|
test [2019-04-01-00:29:55] Epoch: [255][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.024 (0.206) Prec@1 100.00 (94.03) Prec@5 100.00 (99.83)
|
|
[2019-04-01-00:29:55] **test** Prec@1 94.03 Prec@5 99.83 Error@1 5.97 Error@5 0.17 Loss:0.206
|
|
----> Best Accuracy : Acc@1=94.90, Acc@5=99.90, Error@1=5.10, Error@5=0.10
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-00:29:56] [Epoch=256/600] [Need: 12:21:52] LR=0.0154 ~ 0.0154, Batch=96
|
|
train[2019-04-01-00:29:56] Epoch: [256][000/521] Time 0.71 (0.71) Data 0.43 (0.43) Loss 0.248 (0.248) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-00:30:20] Epoch: [256][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.322 (0.267) Prec@1 91.67 (94.23) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-00:30:44] Epoch: [256][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.177 (0.265) Prec@1 98.96 (94.14) Prec@5 100.00 (99.89)
|
|
train[2019-04-01-00:31:07] Epoch: [256][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.241 (0.262) Prec@1 94.79 (94.17) Prec@5 98.96 (99.90)
|
|
train[2019-04-01-00:31:31] Epoch: [256][400/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.346 (0.262) Prec@1 91.67 (94.26) Prec@5 100.00 (99.89)
|
|
train[2019-04-01-00:31:54] Epoch: [256][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.424 (0.266) Prec@1 90.62 (94.16) Prec@5 98.96 (99.88)
|
|
train[2019-04-01-00:31:59] Epoch: [256][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.370 (0.267) Prec@1 91.25 (94.11) Prec@5 100.00 (99.88)
|
|
[2019-04-01-00:31:59] **train** Prec@1 94.11 Prec@5 99.88 Error@1 5.89 Error@5 0.12 Loss:0.267
|
|
test [2019-04-01-00:32:00] Epoch: [256][000/105] Time 0.47 (0.47) Data 0.40 (0.40) Loss 0.152 (0.152) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-00:32:04] Epoch: [256][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.050 (0.204) Prec@1 98.96 (94.00) Prec@5 100.00 (99.81)
|
|
test [2019-04-01-00:32:04] Epoch: [256][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.571 (0.205) Prec@1 81.25 (93.92) Prec@5 100.00 (99.82)
|
|
[2019-04-01-00:32:04] **test** Prec@1 93.92 Prec@5 99.82 Error@1 6.08 Error@5 0.18 Loss:0.205
|
|
----> Best Accuracy : Acc@1=94.90, Acc@5=99.90, Error@1=5.10, Error@5=0.10
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-00:32:04] [Epoch=257/600] [Need: 12:15:03] LR=0.0153 ~ 0.0153, Batch=96
|
|
train[2019-04-01-00:32:05] Epoch: [257][000/521] Time 0.84 (0.84) Data 0.55 (0.55) Loss 0.177 (0.177) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-00:32:29] Epoch: [257][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.163 (0.258) Prec@1 97.92 (94.35) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-00:32:52] Epoch: [257][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.127 (0.266) Prec@1 97.92 (93.99) Prec@5 100.00 (99.89)
|
|
train[2019-04-01-00:33:16] Epoch: [257][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.164 (0.269) Prec@1 97.92 (93.88) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-00:33:40] Epoch: [257][400/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.171 (0.274) Prec@1 95.83 (93.75) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-00:34:04] Epoch: [257][500/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.219 (0.278) Prec@1 93.75 (93.68) Prec@5 100.00 (99.89)
|
|
train[2019-04-01-00:34:08] Epoch: [257][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.392 (0.277) Prec@1 91.25 (93.69) Prec@5 100.00 (99.89)
|
|
[2019-04-01-00:34:08] **train** Prec@1 93.69 Prec@5 99.89 Error@1 6.31 Error@5 0.11 Loss:0.277
|
|
test [2019-04-01-00:34:09] Epoch: [257][000/105] Time 0.60 (0.60) Data 0.54 (0.54) Loss 0.127 (0.127) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-00:34:13] Epoch: [257][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.051 (0.182) Prec@1 97.92 (94.26) Prec@5 100.00 (99.86)
|
|
test [2019-04-01-00:34:13] Epoch: [257][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.133 (0.182) Prec@1 93.75 (94.28) Prec@5 100.00 (99.86)
|
|
[2019-04-01-00:34:13] **test** Prec@1 94.28 Prec@5 99.86 Error@1 5.72 Error@5 0.14 Loss:0.182
|
|
----> Best Accuracy : Acc@1=94.90, Acc@5=99.90, Error@1=5.10, Error@5=0.10
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-00:34:13] [Epoch=258/600] [Need: 12:16:50] LR=0.0153 ~ 0.0153, Batch=96
|
|
train[2019-04-01-00:34:14] Epoch: [258][000/521] Time 0.85 (0.85) Data 0.56 (0.56) Loss 0.231 (0.231) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-00:34:38] Epoch: [258][100/521] Time 0.23 (0.24) Data 0.00 (0.01) Loss 0.164 (0.256) Prec@1 95.83 (94.37) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-00:35:02] Epoch: [258][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.313 (0.263) Prec@1 90.62 (94.19) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-00:35:26] Epoch: [258][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.252 (0.265) Prec@1 95.83 (94.18) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-00:35:50] Epoch: [258][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.215 (0.265) Prec@1 97.92 (94.17) Prec@5 98.96 (99.91)
|
|
train[2019-04-01-00:36:13] Epoch: [258][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.232 (0.267) Prec@1 93.75 (94.13) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-00:36:18] Epoch: [258][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.284 (0.267) Prec@1 90.00 (94.11) Prec@5 100.00 (99.91)
|
|
[2019-04-01-00:36:18] **train** Prec@1 94.11 Prec@5 99.91 Error@1 5.89 Error@5 0.09 Loss:0.267
|
|
test [2019-04-01-00:36:18] Epoch: [258][000/105] Time 0.50 (0.50) Data 0.43 (0.43) Loss 0.140 (0.140) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-00:36:22] Epoch: [258][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.164 (0.205) Prec@1 94.79 (94.06) Prec@5 100.00 (99.85)
|
|
test [2019-04-01-00:36:23] Epoch: [258][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.408 (0.207) Prec@1 93.75 (94.00) Prec@5 100.00 (99.85)
|
|
[2019-04-01-00:36:23] **test** Prec@1 94.00 Prec@5 99.85 Error@1 6.00 Error@5 0.15 Loss:0.207
|
|
----> Best Accuracy : Acc@1=94.90, Acc@5=99.90, Error@1=5.10, Error@5=0.10
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-00:36:23] [Epoch=259/600] [Need: 12:15:13] LR=0.0152 ~ 0.0152, Batch=96
|
|
train[2019-04-01-00:36:23] Epoch: [259][000/521] Time 0.73 (0.73) Data 0.43 (0.43) Loss 0.373 (0.373) Prec@1 91.67 (91.67) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-00:36:48] Epoch: [259][100/521] Time 0.26 (0.25) Data 0.00 (0.00) Loss 0.241 (0.271) Prec@1 93.75 (93.81) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-00:37:12] Epoch: [259][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.200 (0.272) Prec@1 94.79 (93.82) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-00:37:36] Epoch: [259][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.202 (0.264) Prec@1 96.88 (94.10) Prec@5 98.96 (99.93)
|
|
train[2019-04-01-00:37:59] Epoch: [259][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.387 (0.266) Prec@1 89.58 (94.05) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-00:38:23] Epoch: [259][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.316 (0.273) Prec@1 93.75 (93.87) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-00:38:28] Epoch: [259][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.271 (0.273) Prec@1 93.75 (93.89) Prec@5 100.00 (99.93)
|
|
[2019-04-01-00:38:28] **train** Prec@1 93.89 Prec@5 99.93 Error@1 6.11 Error@5 0.07 Loss:0.273
|
|
test [2019-04-01-00:38:28] Epoch: [259][000/105] Time 0.47 (0.47) Data 0.41 (0.41) Loss 0.147 (0.147) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-00:38:32] Epoch: [259][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.142 (0.188) Prec@1 96.88 (94.46) Prec@5 100.00 (99.89)
|
|
test [2019-04-01-00:38:32] Epoch: [259][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.059 (0.188) Prec@1 93.75 (94.47) Prec@5 100.00 (99.87)
|
|
[2019-04-01-00:38:33] **test** Prec@1 94.47 Prec@5 99.87 Error@1 5.53 Error@5 0.13 Loss:0.188
|
|
----> Best Accuracy : Acc@1=94.90, Acc@5=99.90, Error@1=5.10, Error@5=0.10
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-00:38:33] [Epoch=260/600] [Need: 12:16:32] LR=0.0151 ~ 0.0151, Batch=96
|
|
train[2019-04-01-00:38:34] Epoch: [260][000/521] Time 0.84 (0.84) Data 0.57 (0.57) Loss 0.307 (0.307) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-00:38:57] Epoch: [260][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.218 (0.273) Prec@1 93.75 (94.20) Prec@5 100.00 (99.81)
|
|
train[2019-04-01-00:39:21] Epoch: [260][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.215 (0.279) Prec@1 92.71 (93.79) Prec@5 100.00 (99.87)
|
|
train[2019-04-01-00:39:45] Epoch: [260][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.307 (0.271) Prec@1 91.67 (93.95) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-00:40:08] Epoch: [260][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.472 (0.272) Prec@1 87.50 (93.92) Prec@5 100.00 (99.90)
|
|
train[2019-04-01-00:40:32] Epoch: [260][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.299 (0.275) Prec@1 94.79 (93.87) Prec@5 100.00 (99.90)
|
|
train[2019-04-01-00:40:37] Epoch: [260][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.320 (0.276) Prec@1 97.50 (93.86) Prec@5 100.00 (99.90)
|
|
[2019-04-01-00:40:37] **train** Prec@1 93.86 Prec@5 99.90 Error@1 6.14 Error@5 0.10 Loss:0.276
|
|
test [2019-04-01-00:40:37] Epoch: [260][000/105] Time 0.61 (0.61) Data 0.54 (0.54) Loss 0.170 (0.170) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-00:40:41] Epoch: [260][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.097 (0.222) Prec@1 96.88 (93.25) Prec@5 100.00 (99.83)
|
|
test [2019-04-01-00:40:41] Epoch: [260][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.038 (0.223) Prec@1 100.00 (93.20) Prec@5 100.00 (99.84)
|
|
[2019-04-01-00:40:42] **test** Prec@1 93.20 Prec@5 99.84 Error@1 6.80 Error@5 0.16 Loss:0.223
|
|
----> Best Accuracy : Acc@1=94.90, Acc@5=99.90, Error@1=5.10, Error@5=0.10
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-00:40:42] [Epoch=261/600] [Need: 12:08:41] LR=0.0151 ~ 0.0151, Batch=96
|
|
train[2019-04-01-00:40:42] Epoch: [261][000/521] Time 0.76 (0.76) Data 0.48 (0.48) Loss 0.265 (0.265) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-00:41:06] Epoch: [261][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.252 (0.256) Prec@1 94.79 (94.51) Prec@5 98.96 (99.89)
|
|
train[2019-04-01-00:41:30] Epoch: [261][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.331 (0.263) Prec@1 91.67 (94.25) Prec@5 100.00 (99.89)
|
|
train[2019-04-01-00:41:54] Epoch: [261][300/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.318 (0.261) Prec@1 92.71 (94.25) Prec@5 100.00 (99.89)
|
|
train[2019-04-01-00:42:17] Epoch: [261][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.227 (0.262) Prec@1 95.83 (94.26) Prec@5 100.00 (99.89)
|
|
train[2019-04-01-00:42:41] Epoch: [261][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.418 (0.267) Prec@1 90.62 (94.10) Prec@5 100.00 (99.90)
|
|
train[2019-04-01-00:42:46] Epoch: [261][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.228 (0.269) Prec@1 95.00 (94.07) Prec@5 100.00 (99.90)
|
|
[2019-04-01-00:42:46] **train** Prec@1 94.07 Prec@5 99.90 Error@1 5.93 Error@5 0.10 Loss:0.269
|
|
test [2019-04-01-00:42:46] Epoch: [261][000/105] Time 0.47 (0.47) Data 0.41 (0.41) Loss 0.256 (0.256) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-00:42:50] Epoch: [261][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.158 (0.261) Prec@1 96.88 (92.46) Prec@5 100.00 (99.77)
|
|
test [2019-04-01-00:42:51] Epoch: [261][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.091 (0.260) Prec@1 93.75 (92.45) Prec@5 100.00 (99.78)
|
|
[2019-04-01-00:42:51] **test** Prec@1 92.45 Prec@5 99.78 Error@1 7.55 Error@5 0.22 Loss:0.260
|
|
----> Best Accuracy : Acc@1=94.90, Acc@5=99.90, Error@1=5.10, Error@5=0.10
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-00:42:51] [Epoch=262/600] [Need: 12:07:02] LR=0.0150 ~ 0.0150, Batch=96
|
|
train[2019-04-01-00:42:51] Epoch: [262][000/521] Time 0.71 (0.71) Data 0.43 (0.43) Loss 0.350 (0.350) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-00:43:15] Epoch: [262][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.197 (0.276) Prec@1 95.83 (94.04) Prec@5 100.00 (99.85)
|
|
train[2019-04-01-00:43:39] Epoch: [262][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.134 (0.272) Prec@1 97.92 (93.93) Prec@5 100.00 (99.89)
|
|
train[2019-04-01-00:44:03] Epoch: [262][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.288 (0.270) Prec@1 94.79 (93.89) Prec@5 98.96 (99.90)
|
|
train[2019-04-01-00:44:26] Epoch: [262][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.223 (0.266) Prec@1 94.79 (94.03) Prec@5 100.00 (99.90)
|
|
train[2019-04-01-00:44:50] Epoch: [262][500/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.284 (0.267) Prec@1 93.75 (94.01) Prec@5 100.00 (99.90)
|
|
train[2019-04-01-00:44:55] Epoch: [262][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.121 (0.267) Prec@1 98.75 (94.01) Prec@5 100.00 (99.91)
|
|
[2019-04-01-00:44:55] **train** Prec@1 94.01 Prec@5 99.91 Error@1 5.99 Error@5 0.09 Loss:0.267
|
|
test [2019-04-01-00:44:55] Epoch: [262][000/105] Time 0.60 (0.60) Data 0.54 (0.54) Loss 0.215 (0.215) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-00:45:00] Epoch: [262][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.058 (0.214) Prec@1 98.96 (93.63) Prec@5 100.00 (99.88)
|
|
test [2019-04-01-00:45:00] Epoch: [262][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.045 (0.216) Prec@1 100.00 (93.62) Prec@5 100.00 (99.88)
|
|
[2019-04-01-00:45:00] **test** Prec@1 93.62 Prec@5 99.88 Error@1 6.38 Error@5 0.12 Loss:0.216
|
|
----> Best Accuracy : Acc@1=94.90, Acc@5=99.90, Error@1=5.10, Error@5=0.10
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-00:45:00] [Epoch=263/600] [Need: 12:05:22] LR=0.0149 ~ 0.0149, Batch=96
|
|
train[2019-04-01-00:45:01] Epoch: [263][000/521] Time 0.84 (0.84) Data 0.57 (0.57) Loss 0.211 (0.211) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-00:45:24] Epoch: [263][100/521] Time 0.23 (0.24) Data 0.00 (0.01) Loss 0.227 (0.259) Prec@1 94.79 (94.48) Prec@5 100.00 (99.88)
|
|
train[2019-04-01-00:45:48] Epoch: [263][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.296 (0.260) Prec@1 93.75 (94.20) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-00:46:12] Epoch: [263][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.293 (0.257) Prec@1 91.67 (94.32) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-00:46:36] Epoch: [263][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.116 (0.254) Prec@1 96.88 (94.38) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-00:46:59] Epoch: [263][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.279 (0.258) Prec@1 96.88 (94.29) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-00:47:04] Epoch: [263][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.231 (0.258) Prec@1 91.25 (94.29) Prec@5 100.00 (99.92)
|
|
[2019-04-01-00:47:04] **train** Prec@1 94.29 Prec@5 99.92 Error@1 5.71 Error@5 0.08 Loss:0.258
|
|
test [2019-04-01-00:47:05] Epoch: [263][000/105] Time 0.46 (0.46) Data 0.40 (0.40) Loss 0.171 (0.171) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-00:47:09] Epoch: [263][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.026 (0.209) Prec@1 98.96 (94.08) Prec@5 100.00 (99.82)
|
|
test [2019-04-01-00:47:09] Epoch: [263][104/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.009 (0.210) Prec@1 100.00 (94.11) Prec@5 100.00 (99.83)
|
|
[2019-04-01-00:47:09] **test** Prec@1 94.11 Prec@5 99.83 Error@1 5.89 Error@5 0.17 Loss:0.210
|
|
----> Best Accuracy : Acc@1=94.90, Acc@5=99.90, Error@1=5.10, Error@5=0.10
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-00:47:09] [Epoch=264/600] [Need: 12:03:33] LR=0.0149 ~ 0.0149, Batch=96
|
|
train[2019-04-01-00:47:10] Epoch: [264][000/521] Time 0.71 (0.71) Data 0.44 (0.44) Loss 0.333 (0.333) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-00:47:33] Epoch: [264][100/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.378 (0.261) Prec@1 93.75 (94.41) Prec@5 98.96 (99.85)
|
|
train[2019-04-01-00:47:57] Epoch: [264][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.316 (0.264) Prec@1 91.67 (94.30) Prec@5 100.00 (99.89)
|
|
train[2019-04-01-00:48:21] Epoch: [264][300/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.275 (0.262) Prec@1 95.83 (94.24) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-00:48:44] Epoch: [264][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.421 (0.262) Prec@1 89.58 (94.20) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-00:49:08] Epoch: [264][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.250 (0.268) Prec@1 94.79 (94.02) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-00:49:13] Epoch: [264][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.200 (0.269) Prec@1 95.00 (94.01) Prec@5 100.00 (99.91)
|
|
[2019-04-01-00:49:13] **train** Prec@1 94.01 Prec@5 99.91 Error@1 5.99 Error@5 0.09 Loss:0.269
|
|
test [2019-04-01-00:49:13] Epoch: [264][000/105] Time 0.47 (0.47) Data 0.41 (0.41) Loss 0.171 (0.171) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-00:49:18] Epoch: [264][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.078 (0.197) Prec@1 96.88 (94.18) Prec@5 100.00 (99.90)
|
|
test [2019-04-01-00:49:18] Epoch: [264][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.116 (0.199) Prec@1 93.75 (94.15) Prec@5 100.00 (99.89)
|
|
[2019-04-01-00:49:18] **test** Prec@1 94.15 Prec@5 99.89 Error@1 5.85 Error@5 0.11 Loss:0.199
|
|
----> Best Accuracy : Acc@1=94.90, Acc@5=99.90, Error@1=5.10, Error@5=0.10
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-00:49:18] [Epoch=265/600] [Need: 11:59:03] LR=0.0148 ~ 0.0148, Batch=96
|
|
train[2019-04-01-00:49:19] Epoch: [265][000/521] Time 0.71 (0.71) Data 0.44 (0.44) Loss 0.232 (0.232) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-00:49:43] Epoch: [265][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.411 (0.250) Prec@1 91.67 (94.74) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-00:50:07] Epoch: [265][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.363 (0.257) Prec@1 92.71 (94.39) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-00:50:30] Epoch: [265][300/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.306 (0.263) Prec@1 91.67 (94.15) Prec@5 100.00 (99.88)
|
|
train[2019-04-01-00:50:54] Epoch: [265][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.188 (0.262) Prec@1 96.88 (94.13) Prec@5 100.00 (99.88)
|
|
train[2019-04-01-00:51:18] Epoch: [265][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.312 (0.267) Prec@1 93.75 (94.03) Prec@5 98.96 (99.89)
|
|
train[2019-04-01-00:51:23] Epoch: [265][520/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.443 (0.268) Prec@1 92.50 (94.00) Prec@5 98.75 (99.89)
|
|
[2019-04-01-00:51:23] **train** Prec@1 94.00 Prec@5 99.89 Error@1 6.00 Error@5 0.11 Loss:0.268
|
|
test [2019-04-01-00:51:23] Epoch: [265][000/105] Time 0.48 (0.48) Data 0.43 (0.43) Loss 0.143 (0.143) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-00:51:27] Epoch: [265][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.113 (0.185) Prec@1 96.88 (94.46) Prec@5 100.00 (99.87)
|
|
test [2019-04-01-00:51:28] Epoch: [265][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.006 (0.185) Prec@1 100.00 (94.49) Prec@5 100.00 (99.87)
|
|
[2019-04-01-00:51:28] **test** Prec@1 94.49 Prec@5 99.87 Error@1 5.51 Error@5 0.13 Loss:0.185
|
|
----> Best Accuracy : Acc@1=94.90, Acc@5=99.90, Error@1=5.10, Error@5=0.10
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-00:51:28] [Epoch=266/600] [Need: 12:03:21] LR=0.0148 ~ 0.0148, Batch=96
|
|
train[2019-04-01-00:51:29] Epoch: [266][000/521] Time 0.84 (0.84) Data 0.57 (0.57) Loss 0.416 (0.416) Prec@1 90.62 (90.62) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-00:51:52] Epoch: [266][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.263 (0.253) Prec@1 94.79 (94.44) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-00:52:16] Epoch: [266][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.214 (0.266) Prec@1 91.67 (94.14) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-00:52:40] Epoch: [266][300/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.363 (0.264) Prec@1 92.71 (94.14) Prec@5 98.96 (99.90)
|
|
train[2019-04-01-00:53:03] Epoch: [266][400/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.167 (0.266) Prec@1 97.92 (94.05) Prec@5 100.00 (99.90)
|
|
train[2019-04-01-00:53:27] Epoch: [266][500/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.334 (0.267) Prec@1 92.71 (94.05) Prec@5 100.00 (99.90)
|
|
train[2019-04-01-00:53:32] Epoch: [266][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.223 (0.266) Prec@1 93.75 (94.06) Prec@5 100.00 (99.91)
|
|
[2019-04-01-00:53:32] **train** Prec@1 94.06 Prec@5 99.91 Error@1 5.94 Error@5 0.09 Loss:0.266
|
|
test [2019-04-01-00:53:32] Epoch: [266][000/105] Time 0.48 (0.48) Data 0.42 (0.42) Loss 0.171 (0.171) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-00:53:36] Epoch: [266][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.049 (0.178) Prec@1 96.88 (94.62) Prec@5 100.00 (99.86)
|
|
test [2019-04-01-00:53:36] Epoch: [266][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.240 (0.178) Prec@1 93.75 (94.67) Prec@5 100.00 (99.86)
|
|
[2019-04-01-00:53:36] **test** Prec@1 94.67 Prec@5 99.86 Error@1 5.33 Error@5 0.14 Loss:0.178
|
|
----> Best Accuracy : Acc@1=94.90, Acc@5=99.90, Error@1=5.10, Error@5=0.10
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-00:53:37] [Epoch=267/600] [Need: 11:54:22] LR=0.0147 ~ 0.0147, Batch=96
|
|
train[2019-04-01-00:53:37] Epoch: [267][000/521] Time 0.84 (0.84) Data 0.56 (0.56) Loss 0.233 (0.233) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-00:54:01] Epoch: [267][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.327 (0.269) Prec@1 92.71 (93.92) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-00:54:25] Epoch: [267][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.428 (0.278) Prec@1 91.67 (93.72) Prec@5 100.00 (99.89)
|
|
train[2019-04-01-00:54:49] Epoch: [267][300/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.268 (0.271) Prec@1 90.62 (93.96) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-00:55:12] Epoch: [267][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.168 (0.269) Prec@1 97.92 (94.03) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-00:55:36] Epoch: [267][500/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.165 (0.270) Prec@1 96.88 (93.97) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-00:55:41] Epoch: [267][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.185 (0.271) Prec@1 98.75 (93.97) Prec@5 100.00 (99.90)
|
|
[2019-04-01-00:55:41] **train** Prec@1 93.97 Prec@5 99.90 Error@1 6.03 Error@5 0.10 Loss:0.271
|
|
test [2019-04-01-00:55:41] Epoch: [267][000/105] Time 0.50 (0.50) Data 0.43 (0.43) Loss 0.105 (0.105) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-00:55:45] Epoch: [267][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.069 (0.216) Prec@1 95.83 (93.56) Prec@5 100.00 (99.74)
|
|
test [2019-04-01-00:55:46] Epoch: [267][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.211 (0.216) Prec@1 93.75 (93.53) Prec@5 100.00 (99.75)
|
|
[2019-04-01-00:55:46] **test** Prec@1 93.53 Prec@5 99.75 Error@1 6.47 Error@5 0.25 Loss:0.216
|
|
----> Best Accuracy : Acc@1=94.90, Acc@5=99.90, Error@1=5.10, Error@5=0.10
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-00:55:46] [Epoch=268/600] [Need: 11:55:24] LR=0.0146 ~ 0.0146, Batch=96
|
|
train[2019-04-01-00:55:47] Epoch: [268][000/521] Time 0.72 (0.72) Data 0.44 (0.44) Loss 0.326 (0.326) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-00:56:10] Epoch: [268][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.354 (0.255) Prec@1 89.58 (94.23) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-00:56:34] Epoch: [268][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.293 (0.263) Prec@1 94.79 (94.06) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-00:56:58] Epoch: [268][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.201 (0.261) Prec@1 95.83 (94.12) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-00:57:22] Epoch: [268][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.368 (0.259) Prec@1 91.67 (94.16) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-00:57:45] Epoch: [268][500/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.180 (0.269) Prec@1 98.96 (93.97) Prec@5 100.00 (99.90)
|
|
train[2019-04-01-00:57:50] Epoch: [268][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.199 (0.271) Prec@1 95.00 (93.91) Prec@5 100.00 (99.90)
|
|
[2019-04-01-00:57:50] **train** Prec@1 93.91 Prec@5 99.90 Error@1 6.09 Error@5 0.10 Loss:0.271
|
|
test [2019-04-01-00:57:51] Epoch: [268][000/105] Time 0.61 (0.61) Data 0.55 (0.55) Loss 0.151 (0.151) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-00:57:55] Epoch: [268][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.077 (0.217) Prec@1 98.96 (93.47) Prec@5 100.00 (99.83)
|
|
test [2019-04-01-00:57:55] Epoch: [268][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.084 (0.220) Prec@1 93.75 (93.42) Prec@5 100.00 (99.84)
|
|
[2019-04-01-00:57:55] **test** Prec@1 93.42 Prec@5 99.84 Error@1 6.58 Error@5 0.16 Loss:0.220
|
|
----> Best Accuracy : Acc@1=94.90, Acc@5=99.90, Error@1=5.10, Error@5=0.10
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-00:57:55] [Epoch=269/600] [Need: 11:52:45] LR=0.0146 ~ 0.0146, Batch=96
|
|
train[2019-04-01-00:57:56] Epoch: [269][000/521] Time 0.86 (0.86) Data 0.59 (0.59) Loss 0.177 (0.177) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-00:58:20] Epoch: [269][100/521] Time 0.24 (0.25) Data 0.00 (0.01) Loss 0.289 (0.258) Prec@1 91.67 (94.27) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-00:58:44] Epoch: [269][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.218 (0.254) Prec@1 92.71 (94.47) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-00:59:08] Epoch: [269][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.277 (0.259) Prec@1 94.79 (94.35) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-00:59:32] Epoch: [269][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.224 (0.261) Prec@1 95.83 (94.18) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-00:59:55] Epoch: [269][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.200 (0.263) Prec@1 95.83 (94.13) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-01:00:00] Epoch: [269][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.070 (0.262) Prec@1 100.00 (94.14) Prec@5 100.00 (99.94)
|
|
[2019-04-01-01:00:00] **train** Prec@1 94.14 Prec@5 99.94 Error@1 5.86 Error@5 0.06 Loss:0.262
|
|
test [2019-04-01-01:00:01] Epoch: [269][000/105] Time 0.49 (0.49) Data 0.43 (0.43) Loss 0.187 (0.187) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-01:00:05] Epoch: [269][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.103 (0.222) Prec@1 94.79 (93.76) Prec@5 100.00 (99.77)
|
|
test [2019-04-01-01:00:05] Epoch: [269][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.142 (0.222) Prec@1 93.75 (93.74) Prec@5 100.00 (99.77)
|
|
[2019-04-01-01:00:05] **test** Prec@1 93.74 Prec@5 99.77 Error@1 6.26 Error@5 0.23 Loss:0.222
|
|
----> Best Accuracy : Acc@1=94.90, Acc@5=99.90, Error@1=5.10, Error@5=0.10
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-01:00:05] [Epoch=270/600] [Need: 11:55:00] LR=0.0145 ~ 0.0145, Batch=96
|
|
train[2019-04-01-01:00:06] Epoch: [270][000/521] Time 0.71 (0.71) Data 0.44 (0.44) Loss 0.208 (0.208) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-01:00:30] Epoch: [270][100/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.326 (0.259) Prec@1 93.75 (94.23) Prec@5 98.96 (99.96)
|
|
train[2019-04-01-01:00:53] Epoch: [270][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.183 (0.260) Prec@1 95.83 (94.29) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-01:01:17] Epoch: [270][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.176 (0.260) Prec@1 96.88 (94.28) Prec@5 98.96 (99.92)
|
|
train[2019-04-01-01:01:41] Epoch: [270][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.252 (0.259) Prec@1 92.71 (94.26) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-01:02:04] Epoch: [270][500/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.260 (0.260) Prec@1 92.71 (94.24) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-01:02:09] Epoch: [270][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.186 (0.262) Prec@1 97.50 (94.19) Prec@5 100.00 (99.93)
|
|
[2019-04-01-01:02:09] **train** Prec@1 94.19 Prec@5 99.93 Error@1 5.81 Error@5 0.07 Loss:0.262
|
|
test [2019-04-01-01:02:10] Epoch: [270][000/105] Time 0.60 (0.60) Data 0.53 (0.53) Loss 0.183 (0.183) Prec@1 91.67 (91.67) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-01:02:14] Epoch: [270][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.126 (0.207) Prec@1 95.83 (93.84) Prec@5 100.00 (99.83)
|
|
test [2019-04-01-01:02:14] Epoch: [270][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.243 (0.207) Prec@1 87.50 (93.85) Prec@5 100.00 (99.84)
|
|
[2019-04-01-01:02:14] **test** Prec@1 93.85 Prec@5 99.84 Error@1 6.15 Error@5 0.16 Loss:0.207
|
|
----> Best Accuracy : Acc@1=94.90, Acc@5=99.90, Error@1=5.10, Error@5=0.10
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-01:02:14] [Epoch=271/600] [Need: 11:48:37] LR=0.0144 ~ 0.0144, Batch=96
|
|
train[2019-04-01-01:02:15] Epoch: [271][000/521] Time 0.83 (0.83) Data 0.56 (0.56) Loss 0.242 (0.242) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-01:02:39] Epoch: [271][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.251 (0.262) Prec@1 93.75 (94.10) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-01:03:02] Epoch: [271][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.174 (0.263) Prec@1 96.88 (94.08) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-01:03:26] Epoch: [271][300/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.286 (0.262) Prec@1 92.71 (94.15) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-01:03:50] Epoch: [271][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.255 (0.264) Prec@1 93.75 (94.11) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-01:04:14] Epoch: [271][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.127 (0.265) Prec@1 100.00 (94.10) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-01:04:19] Epoch: [271][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.255 (0.265) Prec@1 92.50 (94.09) Prec@5 100.00 (99.92)
|
|
[2019-04-01-01:04:19] **train** Prec@1 94.09 Prec@5 99.92 Error@1 5.91 Error@5 0.08 Loss:0.265
|
|
test [2019-04-01-01:04:20] Epoch: [271][000/105] Time 0.62 (0.62) Data 0.55 (0.55) Loss 0.154 (0.154) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-01:04:24] Epoch: [271][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.082 (0.184) Prec@1 95.83 (94.35) Prec@5 100.00 (99.83)
|
|
test [2019-04-01-01:04:24] Epoch: [271][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.425 (0.185) Prec@1 93.75 (94.31) Prec@5 100.00 (99.83)
|
|
[2019-04-01-01:04:24] **test** Prec@1 94.31 Prec@5 99.83 Error@1 5.69 Error@5 0.17 Loss:0.185
|
|
----> Best Accuracy : Acc@1=94.90, Acc@5=99.90, Error@1=5.10, Error@5=0.10
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-01:04:24] [Epoch=272/600] [Need: 11:48:54] LR=0.0144 ~ 0.0144, Batch=96
|
|
train[2019-04-01-01:04:25] Epoch: [272][000/521] Time 0.71 (0.71) Data 0.44 (0.44) Loss 0.199 (0.199) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-01:04:49] Epoch: [272][100/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.312 (0.248) Prec@1 94.79 (94.81) Prec@5 100.00 (99.87)
|
|
train[2019-04-01-01:05:12] Epoch: [272][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.163 (0.259) Prec@1 97.92 (94.41) Prec@5 100.00 (99.90)
|
|
train[2019-04-01-01:05:36] Epoch: [272][300/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.253 (0.258) Prec@1 96.88 (94.42) Prec@5 100.00 (99.90)
|
|
train[2019-04-01-01:06:00] Epoch: [272][400/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.382 (0.260) Prec@1 89.58 (94.35) Prec@5 98.96 (99.90)
|
|
train[2019-04-01-01:06:23] Epoch: [272][500/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.254 (0.261) Prec@1 94.79 (94.28) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-01:06:28] Epoch: [272][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.258 (0.260) Prec@1 93.75 (94.30) Prec@5 100.00 (99.91)
|
|
[2019-04-01-01:06:28] **train** Prec@1 94.30 Prec@5 99.91 Error@1 5.70 Error@5 0.09 Loss:0.260
|
|
test [2019-04-01-01:06:28] Epoch: [272][000/105] Time 0.47 (0.47) Data 0.40 (0.40) Loss 0.147 (0.147) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-01:06:33] Epoch: [272][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.094 (0.217) Prec@1 96.88 (93.78) Prec@5 100.00 (99.81)
|
|
test [2019-04-01-01:06:33] Epoch: [272][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.019 (0.217) Prec@1 100.00 (93.81) Prec@5 100.00 (99.82)
|
|
[2019-04-01-01:06:33] **test** Prec@1 93.81 Prec@5 99.82 Error@1 6.19 Error@5 0.18 Loss:0.217
|
|
----> Best Accuracy : Acc@1=94.90, Acc@5=99.90, Error@1=5.10, Error@5=0.10
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-01:06:33] [Epoch=273/600] [Need: 11:42:44] LR=0.0143 ~ 0.0143, Batch=96
|
|
train[2019-04-01-01:06:34] Epoch: [273][000/521] Time 0.69 (0.69) Data 0.42 (0.42) Loss 0.239 (0.239) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-01:06:58] Epoch: [273][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.173 (0.255) Prec@1 94.79 (94.56) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-01:07:21] Epoch: [273][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.180 (0.254) Prec@1 93.75 (94.67) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-01:07:45] Epoch: [273][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.251 (0.256) Prec@1 94.79 (94.55) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-01:08:10] Epoch: [273][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.227 (0.256) Prec@1 96.88 (94.52) Prec@5 100.00 (99.89)
|
|
train[2019-04-01-01:08:33] Epoch: [273][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.287 (0.261) Prec@1 91.67 (94.30) Prec@5 98.96 (99.89)
|
|
train[2019-04-01-01:08:38] Epoch: [273][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.200 (0.261) Prec@1 97.50 (94.29) Prec@5 100.00 (99.90)
|
|
[2019-04-01-01:08:38] **train** Prec@1 94.29 Prec@5 99.90 Error@1 5.71 Error@5 0.10 Loss:0.261
|
|
test [2019-04-01-01:08:39] Epoch: [273][000/105] Time 0.60 (0.60) Data 0.54 (0.54) Loss 0.107 (0.107) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-01:08:43] Epoch: [273][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.109 (0.189) Prec@1 96.88 (94.37) Prec@5 100.00 (99.81)
|
|
test [2019-04-01-01:08:43] Epoch: [273][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.085 (0.189) Prec@1 93.75 (94.34) Prec@5 100.00 (99.80)
|
|
[2019-04-01-01:08:43] **test** Prec@1 94.34 Prec@5 99.80 Error@1 5.66 Error@5 0.20 Loss:0.189
|
|
----> Best Accuracy : Acc@1=94.90, Acc@5=99.90, Error@1=5.10, Error@5=0.10
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-01:08:43] [Epoch=274/600] [Need: 11:47:30] LR=0.0142 ~ 0.0142, Batch=96
|
|
train[2019-04-01-01:08:44] Epoch: [274][000/521] Time 0.71 (0.71) Data 0.44 (0.44) Loss 0.226 (0.226) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-01:09:08] Epoch: [274][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.409 (0.244) Prec@1 90.62 (94.77) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-01:09:31] Epoch: [274][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.134 (0.254) Prec@1 97.92 (94.54) Prec@5 100.00 (99.89)
|
|
train[2019-04-01-01:09:55] Epoch: [274][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.284 (0.260) Prec@1 93.75 (94.46) Prec@5 100.00 (99.90)
|
|
train[2019-04-01-01:10:19] Epoch: [274][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.217 (0.255) Prec@1 93.75 (94.54) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-01:10:43] Epoch: [274][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.340 (0.263) Prec@1 93.75 (94.31) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-01:10:47] Epoch: [274][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.266 (0.264) Prec@1 92.50 (94.26) Prec@5 100.00 (99.91)
|
|
[2019-04-01-01:10:48] **train** Prec@1 94.26 Prec@5 99.91 Error@1 5.74 Error@5 0.09 Loss:0.264
|
|
test [2019-04-01-01:10:48] Epoch: [274][000/105] Time 0.46 (0.46) Data 0.40 (0.40) Loss 0.261 (0.261) Prec@1 90.62 (90.62) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-01:10:52] Epoch: [274][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.042 (0.189) Prec@1 97.92 (94.35) Prec@5 100.00 (99.82)
|
|
test [2019-04-01-01:10:52] Epoch: [274][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.024 (0.189) Prec@1 100.00 (94.33) Prec@5 100.00 (99.83)
|
|
[2019-04-01-01:10:52] **test** Prec@1 94.33 Prec@5 99.83 Error@1 5.67 Error@5 0.17 Loss:0.189
|
|
----> Best Accuracy : Acc@1=94.90, Acc@5=99.90, Error@1=5.10, Error@5=0.10
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-01:10:52] [Epoch=275/600] [Need: 11:40:19] LR=0.0142 ~ 0.0142, Batch=96
|
|
train[2019-04-01-01:10:53] Epoch: [275][000/521] Time 0.69 (0.69) Data 0.42 (0.42) Loss 0.158 (0.158) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-01:11:18] Epoch: [275][100/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.201 (0.262) Prec@1 95.83 (94.31) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-01:11:41] Epoch: [275][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.267 (0.258) Prec@1 97.92 (94.36) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-01:12:05] Epoch: [275][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.105 (0.253) Prec@1 97.92 (94.46) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-01:12:29] Epoch: [275][400/521] Time 0.27 (0.24) Data 0.00 (0.00) Loss 0.264 (0.255) Prec@1 95.83 (94.36) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-01:12:53] Epoch: [275][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.246 (0.257) Prec@1 93.75 (94.33) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-01:12:58] Epoch: [275][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.194 (0.258) Prec@1 96.25 (94.32) Prec@5 100.00 (99.91)
|
|
[2019-04-01-01:12:58] **train** Prec@1 94.32 Prec@5 99.91 Error@1 5.68 Error@5 0.09 Loss:0.258
|
|
test [2019-04-01-01:12:58] Epoch: [275][000/105] Time 0.47 (0.47) Data 0.41 (0.41) Loss 0.206 (0.206) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-01:13:02] Epoch: [275][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.056 (0.193) Prec@1 96.88 (94.14) Prec@5 100.00 (99.89)
|
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test [2019-04-01-01:13:02] Epoch: [275][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.065 (0.194) Prec@1 100.00 (94.13) Prec@5 100.00 (99.88)
|
|
[2019-04-01-01:13:03] **test** Prec@1 94.13 Prec@5 99.88 Error@1 5.87 Error@5 0.12 Loss:0.194
|
|
----> Best Accuracy : Acc@1=94.90, Acc@5=99.90, Error@1=5.10, Error@5=0.10
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-01:13:03] [Epoch=276/600] [Need: 11:43:29] LR=0.0141 ~ 0.0141, Batch=96
|
|
train[2019-04-01-01:13:03] Epoch: [276][000/521] Time 0.75 (0.75) Data 0.46 (0.46) Loss 0.274 (0.274) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-01:13:27] Epoch: [276][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.280 (0.247) Prec@1 91.67 (94.70) Prec@5 100.00 (99.88)
|
|
train[2019-04-01-01:13:51] Epoch: [276][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.161 (0.250) Prec@1 96.88 (94.56) Prec@5 100.00 (99.88)
|
|
train[2019-04-01-01:14:15] Epoch: [276][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.206 (0.249) Prec@1 95.83 (94.53) Prec@5 100.00 (99.90)
|
|
train[2019-04-01-01:14:39] Epoch: [276][400/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.266 (0.256) Prec@1 93.75 (94.34) Prec@5 100.00 (99.90)
|
|
train[2019-04-01-01:15:03] Epoch: [276][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.272 (0.257) Prec@1 92.71 (94.27) Prec@5 100.00 (99.90)
|
|
train[2019-04-01-01:15:07] Epoch: [276][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.211 (0.257) Prec@1 95.00 (94.30) Prec@5 100.00 (99.90)
|
|
[2019-04-01-01:15:08] **train** Prec@1 94.30 Prec@5 99.90 Error@1 5.70 Error@5 0.10 Loss:0.257
|
|
test [2019-04-01-01:15:08] Epoch: [276][000/105] Time 0.62 (0.62) Data 0.56 (0.56) Loss 0.206 (0.206) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-01:15:12] Epoch: [276][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.083 (0.185) Prec@1 95.83 (94.44) Prec@5 100.00 (99.91)
|
|
test [2019-04-01-01:15:12] Epoch: [276][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.061 (0.185) Prec@1 100.00 (94.45) Prec@5 100.00 (99.91)
|
|
[2019-04-01-01:15:12] **test** Prec@1 94.45 Prec@5 99.91 Error@1 5.55 Error@5 0.09 Loss:0.185
|
|
----> Best Accuracy : Acc@1=94.90, Acc@5=99.90, Error@1=5.10, Error@5=0.10
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-01:15:13] [Epoch=277/600] [Need: 11:39:08] LR=0.0140 ~ 0.0140, Batch=96
|
|
train[2019-04-01-01:15:13] Epoch: [277][000/521] Time 0.83 (0.83) Data 0.56 (0.56) Loss 0.280 (0.280) Prec@1 90.62 (90.62) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-01:15:37] Epoch: [277][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.162 (0.250) Prec@1 95.83 (94.46) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-01:16:01] Epoch: [277][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.263 (0.253) Prec@1 95.83 (94.42) Prec@5 98.96 (99.90)
|
|
train[2019-04-01-01:16:24] Epoch: [277][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.139 (0.254) Prec@1 97.92 (94.39) Prec@5 100.00 (99.90)
|
|
train[2019-04-01-01:16:48] Epoch: [277][400/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.200 (0.257) Prec@1 94.79 (94.35) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-01:17:12] Epoch: [277][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.277 (0.261) Prec@1 94.79 (94.29) Prec@5 100.00 (99.89)
|
|
train[2019-04-01-01:17:17] Epoch: [277][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.145 (0.262) Prec@1 97.50 (94.30) Prec@5 100.00 (99.89)
|
|
[2019-04-01-01:17:17] **train** Prec@1 94.30 Prec@5 99.89 Error@1 5.70 Error@5 0.11 Loss:0.262
|
|
test [2019-04-01-01:17:17] Epoch: [277][000/105] Time 0.47 (0.47) Data 0.40 (0.40) Loss 0.154 (0.154) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-01:17:21] Epoch: [277][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.070 (0.208) Prec@1 95.83 (94.08) Prec@5 100.00 (99.89)
|
|
test [2019-04-01-01:17:21] Epoch: [277][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.036 (0.209) Prec@1 100.00 (94.06) Prec@5 100.00 (99.89)
|
|
[2019-04-01-01:17:22] **test** Prec@1 94.06 Prec@5 99.89 Error@1 5.94 Error@5 0.11 Loss:0.209
|
|
----> Best Accuracy : Acc@1=94.90, Acc@5=99.90, Error@1=5.10, Error@5=0.10
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-01:17:22] [Epoch=278/600] [Need: 11:33:09] LR=0.0140 ~ 0.0140, Batch=96
|
|
train[2019-04-01-01:17:22] Epoch: [278][000/521] Time 0.71 (0.71) Data 0.44 (0.44) Loss 0.198 (0.198) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-01:17:46] Epoch: [278][100/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.235 (0.271) Prec@1 94.79 (94.03) Prec@5 100.00 (99.89)
|
|
train[2019-04-01-01:18:10] Epoch: [278][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.221 (0.259) Prec@1 94.79 (94.31) Prec@5 100.00 (99.88)
|
|
train[2019-04-01-01:18:34] Epoch: [278][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.201 (0.255) Prec@1 95.83 (94.44) Prec@5 100.00 (99.90)
|
|
train[2019-04-01-01:18:58] Epoch: [278][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.359 (0.258) Prec@1 90.62 (94.33) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-01:19:22] Epoch: [278][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.469 (0.260) Prec@1 88.54 (94.25) Prec@5 98.96 (99.90)
|
|
train[2019-04-01-01:19:26] Epoch: [278][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.356 (0.261) Prec@1 93.75 (94.27) Prec@5 100.00 (99.91)
|
|
[2019-04-01-01:19:26] **train** Prec@1 94.27 Prec@5 99.91 Error@1 5.73 Error@5 0.09 Loss:0.261
|
|
test [2019-04-01-01:19:27] Epoch: [278][000/105] Time 0.47 (0.47) Data 0.41 (0.41) Loss 0.147 (0.147) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-01:19:31] Epoch: [278][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.047 (0.212) Prec@1 98.96 (94.15) Prec@5 100.00 (99.89)
|
|
test [2019-04-01-01:19:31] Epoch: [278][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.071 (0.211) Prec@1 93.75 (94.11) Prec@5 100.00 (99.89)
|
|
[2019-04-01-01:19:31] **test** Prec@1 94.11 Prec@5 99.89 Error@1 5.89 Error@5 0.11 Loss:0.211
|
|
----> Best Accuracy : Acc@1=94.90, Acc@5=99.90, Error@1=5.10, Error@5=0.10
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-01:19:31] [Epoch=279/600] [Need: 11:33:43] LR=0.0139 ~ 0.0139, Batch=96
|
|
train[2019-04-01-01:19:32] Epoch: [279][000/521] Time 0.73 (0.73) Data 0.45 (0.45) Loss 0.261 (0.261) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-01:19:56] Epoch: [279][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.128 (0.240) Prec@1 97.92 (94.53) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-01:20:20] Epoch: [279][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.309 (0.257) Prec@1 91.67 (94.13) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-01:20:43] Epoch: [279][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.186 (0.257) Prec@1 94.79 (94.23) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-01:21:07] Epoch: [279][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.236 (0.258) Prec@1 94.79 (94.16) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-01:21:31] Epoch: [279][500/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.245 (0.261) Prec@1 93.75 (94.13) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-01:21:36] Epoch: [279][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.192 (0.261) Prec@1 93.75 (94.13) Prec@5 100.00 (99.92)
|
|
[2019-04-01-01:21:36] **train** Prec@1 94.13 Prec@5 99.92 Error@1 5.87 Error@5 0.08 Loss:0.261
|
|
test [2019-04-01-01:21:37] Epoch: [279][000/105] Time 0.59 (0.59) Data 0.53 (0.53) Loss 0.171 (0.171) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-01:21:41] Epoch: [279][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.082 (0.211) Prec@1 95.83 (93.83) Prec@5 100.00 (99.92)
|
|
test [2019-04-01-01:21:41] Epoch: [279][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.103 (0.212) Prec@1 93.75 (93.79) Prec@5 100.00 (99.91)
|
|
[2019-04-01-01:21:41] **test** Prec@1 93.79 Prec@5 99.91 Error@1 6.21 Error@5 0.09 Loss:0.212
|
|
----> Best Accuracy : Acc@1=94.90, Acc@5=99.90, Error@1=5.10, Error@5=0.10
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-01:21:41] [Epoch=280/600] [Need: 11:32:30] LR=0.0139 ~ 0.0139, Batch=96
|
|
train[2019-04-01-01:21:42] Epoch: [280][000/521] Time 0.71 (0.71) Data 0.43 (0.43) Loss 0.192 (0.192) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-01:22:06] Epoch: [280][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.209 (0.262) Prec@1 92.71 (94.07) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-01:22:30] Epoch: [280][200/521] Time 0.27 (0.24) Data 0.00 (0.00) Loss 0.268 (0.258) Prec@1 94.79 (94.17) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-01:22:54] Epoch: [280][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.407 (0.256) Prec@1 90.62 (94.26) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-01:23:18] Epoch: [280][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.227 (0.255) Prec@1 97.92 (94.24) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-01:23:42] Epoch: [280][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.427 (0.257) Prec@1 88.54 (94.21) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-01:23:46] Epoch: [280][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.367 (0.257) Prec@1 92.50 (94.24) Prec@5 100.00 (99.94)
|
|
[2019-04-01-01:23:47] **train** Prec@1 94.24 Prec@5 99.94 Error@1 5.76 Error@5 0.06 Loss:0.257
|
|
test [2019-04-01-01:23:47] Epoch: [280][000/105] Time 0.61 (0.61) Data 0.55 (0.55) Loss 0.172 (0.172) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-01:23:51] Epoch: [280][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.037 (0.207) Prec@1 97.92 (94.00) Prec@5 100.00 (99.86)
|
|
test [2019-04-01-01:23:51] Epoch: [280][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.006 (0.208) Prec@1 100.00 (93.96) Prec@5 100.00 (99.86)
|
|
[2019-04-01-01:23:51] **test** Prec@1 93.96 Prec@5 99.86 Error@1 6.04 Error@5 0.14 Loss:0.208
|
|
----> Best Accuracy : Acc@1=94.90, Acc@5=99.90, Error@1=5.10, Error@5=0.10
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-01:23:52] [Epoch=281/600] [Need: 11:33:08] LR=0.0138 ~ 0.0138, Batch=96
|
|
train[2019-04-01-01:23:52] Epoch: [281][000/521] Time 0.83 (0.83) Data 0.56 (0.56) Loss 0.191 (0.191) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-01:24:16] Epoch: [281][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.273 (0.262) Prec@1 91.67 (94.02) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-01:24:40] Epoch: [281][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.195 (0.262) Prec@1 95.83 (94.00) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-01:25:04] Epoch: [281][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.291 (0.259) Prec@1 93.75 (94.20) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-01:25:28] Epoch: [281][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.236 (0.259) Prec@1 94.79 (94.22) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-01:25:51] Epoch: [281][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.289 (0.262) Prec@1 90.62 (94.08) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-01:25:56] Epoch: [281][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.304 (0.262) Prec@1 93.75 (94.09) Prec@5 98.75 (99.90)
|
|
[2019-04-01-01:25:56] **train** Prec@1 94.09 Prec@5 99.90 Error@1 5.91 Error@5 0.10 Loss:0.262
|
|
test [2019-04-01-01:25:57] Epoch: [281][000/105] Time 0.47 (0.47) Data 0.41 (0.41) Loss 0.138 (0.138) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-01:26:01] Epoch: [281][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.081 (0.196) Prec@1 96.88 (94.18) Prec@5 100.00 (99.82)
|
|
test [2019-04-01-01:26:01] Epoch: [281][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.056 (0.196) Prec@1 100.00 (94.21) Prec@5 100.00 (99.83)
|
|
[2019-04-01-01:26:01] **test** Prec@1 94.21 Prec@5 99.83 Error@1 5.79 Error@5 0.17 Loss:0.196
|
|
----> Best Accuracy : Acc@1=94.90, Acc@5=99.90, Error@1=5.10, Error@5=0.10
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-01:26:01] [Epoch=282/600] [Need: 11:26:51] LR=0.0137 ~ 0.0137, Batch=96
|
|
train[2019-04-01-01:26:02] Epoch: [282][000/521] Time 0.71 (0.71) Data 0.43 (0.43) Loss 0.174 (0.174) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-01:26:26] Epoch: [282][100/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.291 (0.250) Prec@1 93.75 (94.36) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-01:26:49] Epoch: [282][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.172 (0.256) Prec@1 96.88 (94.41) Prec@5 100.00 (99.89)
|
|
train[2019-04-01-01:27:13] Epoch: [282][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.232 (0.253) Prec@1 93.75 (94.50) Prec@5 100.00 (99.90)
|
|
train[2019-04-01-01:27:37] Epoch: [282][400/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.320 (0.256) Prec@1 91.67 (94.34) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-01:28:01] Epoch: [282][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.348 (0.256) Prec@1 92.71 (94.31) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-01:28:06] Epoch: [282][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.308 (0.257) Prec@1 92.50 (94.28) Prec@5 100.00 (99.91)
|
|
[2019-04-01-01:28:06] **train** Prec@1 94.28 Prec@5 99.91 Error@1 5.72 Error@5 0.09 Loss:0.257
|
|
test [2019-04-01-01:28:06] Epoch: [282][000/105] Time 0.46 (0.46) Data 0.40 (0.40) Loss 0.202 (0.202) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-01:28:10] Epoch: [282][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.092 (0.204) Prec@1 95.83 (93.89) Prec@5 100.00 (99.82)
|
|
test [2019-04-01-01:28:10] Epoch: [282][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.185 (0.203) Prec@1 93.75 (93.92) Prec@5 100.00 (99.83)
|
|
[2019-04-01-01:28:11] **test** Prec@1 93.92 Prec@5 99.83 Error@1 6.08 Error@5 0.17 Loss:0.203
|
|
----> Best Accuracy : Acc@1=94.90, Acc@5=99.90, Error@1=5.10, Error@5=0.10
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-01:28:11] [Epoch=283/600] [Need: 11:23:54] LR=0.0137 ~ 0.0137, Batch=96
|
|
train[2019-04-01-01:28:11] Epoch: [283][000/521] Time 0.83 (0.83) Data 0.56 (0.56) Loss 0.184 (0.184) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-01:28:35] Epoch: [283][100/521] Time 0.23 (0.25) Data 0.00 (0.01) Loss 0.234 (0.260) Prec@1 94.79 (94.04) Prec@5 98.96 (99.88)
|
|
train[2019-04-01-01:28:59] Epoch: [283][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.219 (0.253) Prec@1 96.88 (94.33) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-01:29:23] Epoch: [283][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.231 (0.254) Prec@1 92.71 (94.44) Prec@5 100.00 (99.89)
|
|
train[2019-04-01-01:29:47] Epoch: [283][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.140 (0.253) Prec@1 98.96 (94.46) Prec@5 100.00 (99.89)
|
|
train[2019-04-01-01:30:10] Epoch: [283][500/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.322 (0.256) Prec@1 92.71 (94.41) Prec@5 100.00 (99.90)
|
|
train[2019-04-01-01:30:15] Epoch: [283][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.421 (0.257) Prec@1 92.50 (94.41) Prec@5 98.75 (99.90)
|
|
[2019-04-01-01:30:15] **train** Prec@1 94.41 Prec@5 99.90 Error@1 5.59 Error@5 0.10 Loss:0.257
|
|
test [2019-04-01-01:30:16] Epoch: [283][000/105] Time 0.60 (0.60) Data 0.54 (0.54) Loss 0.188 (0.188) Prec@1 91.67 (91.67) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-01:30:20] Epoch: [283][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.026 (0.198) Prec@1 98.96 (94.16) Prec@5 100.00 (99.83)
|
|
test [2019-04-01-01:30:20] Epoch: [283][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.046 (0.198) Prec@1 100.00 (94.18) Prec@5 100.00 (99.83)
|
|
[2019-04-01-01:30:20] **test** Prec@1 94.18 Prec@5 99.83 Error@1 5.82 Error@5 0.17 Loss:0.198
|
|
----> Best Accuracy : Acc@1=94.90, Acc@5=99.90, Error@1=5.10, Error@5=0.10
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-01:30:20] [Epoch=284/600] [Need: 11:21:51] LR=0.0136 ~ 0.0136, Batch=96
|
|
train[2019-04-01-01:30:21] Epoch: [284][000/521] Time 0.84 (0.84) Data 0.58 (0.58) Loss 0.147 (0.147) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-01:30:45] Epoch: [284][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.182 (0.240) Prec@1 96.88 (94.72) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-01:31:08] Epoch: [284][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.419 (0.250) Prec@1 89.58 (94.47) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-01:31:32] Epoch: [284][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.256 (0.254) Prec@1 93.75 (94.35) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-01:31:56] Epoch: [284][400/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.218 (0.254) Prec@1 95.83 (94.34) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-01:32:20] Epoch: [284][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.283 (0.259) Prec@1 94.79 (94.22) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-01:32:25] Epoch: [284][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.293 (0.260) Prec@1 92.50 (94.19) Prec@5 100.00 (99.92)
|
|
[2019-04-01-01:32:25] **train** Prec@1 94.19 Prec@5 99.92 Error@1 5.81 Error@5 0.08 Loss:0.260
|
|
test [2019-04-01-01:32:25] Epoch: [284][000/105] Time 0.47 (0.47) Data 0.41 (0.41) Loss 0.156 (0.156) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-01:32:29] Epoch: [284][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.063 (0.177) Prec@1 97.92 (94.87) Prec@5 100.00 (99.92)
|
|
test [2019-04-01-01:32:29] Epoch: [284][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.031 (0.176) Prec@1 100.00 (94.86) Prec@5 100.00 (99.92)
|
|
[2019-04-01-01:32:30] **test** Prec@1 94.86 Prec@5 99.92 Error@1 5.14 Error@5 0.08 Loss:0.176
|
|
----> Best Accuracy : Acc@1=94.90, Acc@5=99.90, Error@1=5.10, Error@5=0.10
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-01:32:30] [Epoch=285/600] [Need: 11:20:00] LR=0.0135 ~ 0.0135, Batch=96
|
|
train[2019-04-01-01:32:30] Epoch: [285][000/521] Time 0.70 (0.70) Data 0.43 (0.43) Loss 0.223 (0.223) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-01:32:54] Epoch: [285][100/521] Time 0.28 (0.24) Data 0.00 (0.00) Loss 0.305 (0.244) Prec@1 92.71 (94.37) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-01:33:18] Epoch: [285][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.361 (0.250) Prec@1 91.67 (94.41) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-01:33:41] Epoch: [285][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.325 (0.248) Prec@1 90.62 (94.49) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-01:34:05] Epoch: [285][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.152 (0.251) Prec@1 97.92 (94.43) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-01:34:29] Epoch: [285][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.250 (0.253) Prec@1 92.71 (94.39) Prec@5 100.00 (99.90)
|
|
train[2019-04-01-01:34:34] Epoch: [285][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.275 (0.252) Prec@1 92.50 (94.41) Prec@5 100.00 (99.90)
|
|
[2019-04-01-01:34:34] **train** Prec@1 94.41 Prec@5 99.90 Error@1 5.59 Error@5 0.10 Loss:0.252
|
|
test [2019-04-01-01:34:35] Epoch: [285][000/105] Time 0.60 (0.60) Data 0.54 (0.54) Loss 0.095 (0.095) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-01:34:39] Epoch: [285][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.060 (0.173) Prec@1 97.92 (95.01) Prec@5 100.00 (99.89)
|
|
test [2019-04-01-01:34:39] Epoch: [285][104/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.006 (0.173) Prec@1 100.00 (95.00) Prec@5 100.00 (99.89)
|
|
[2019-04-01-01:34:39] **test** Prec@1 95.00 Prec@5 99.89 Error@1 5.00 Error@5 0.11 Loss:0.173
|
|
----> Best Accuracy : Acc@1=95.00, Acc@5=99.89, Error@1=5.00, Error@5=0.11
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-01:34:39] [Epoch=286/600] [Need: 11:16:53] LR=0.0135 ~ 0.0135, Batch=96
|
|
train[2019-04-01-01:34:40] Epoch: [286][000/521] Time 0.74 (0.74) Data 0.43 (0.43) Loss 0.183 (0.183) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-01:35:04] Epoch: [286][100/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.286 (0.260) Prec@1 95.83 (94.41) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-01:35:28] Epoch: [286][200/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.153 (0.260) Prec@1 97.92 (94.30) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-01:35:52] Epoch: [286][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.294 (0.256) Prec@1 93.75 (94.32) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-01:36:16] Epoch: [286][400/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.251 (0.259) Prec@1 93.75 (94.21) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-01:36:40] Epoch: [286][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.205 (0.259) Prec@1 94.79 (94.22) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-01:36:44] Epoch: [286][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.236 (0.259) Prec@1 96.25 (94.21) Prec@5 100.00 (99.94)
|
|
[2019-04-01-01:36:44] **train** Prec@1 94.21 Prec@5 99.94 Error@1 5.79 Error@5 0.06 Loss:0.259
|
|
test [2019-04-01-01:36:45] Epoch: [286][000/105] Time 0.48 (0.48) Data 0.41 (0.41) Loss 0.138 (0.138) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-01:36:49] Epoch: [286][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.094 (0.225) Prec@1 96.88 (94.13) Prec@5 100.00 (99.81)
|
|
test [2019-04-01-01:36:49] Epoch: [286][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.067 (0.225) Prec@1 100.00 (94.15) Prec@5 100.00 (99.81)
|
|
[2019-04-01-01:36:49] **test** Prec@1 94.15 Prec@5 99.81 Error@1 5.85 Error@5 0.19 Loss:0.225
|
|
----> Best Accuracy : Acc@1=95.00, Acc@5=99.89, Error@1=5.00, Error@5=0.11
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-01:36:49] [Epoch=287/600] [Need: 11:19:31] LR=0.0134 ~ 0.0134, Batch=96
|
|
train[2019-04-01-01:36:50] Epoch: [287][000/521] Time 0.72 (0.72) Data 0.45 (0.45) Loss 0.224 (0.224) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-01:37:14] Epoch: [287][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.280 (0.229) Prec@1 94.79 (95.05) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-01:37:37] Epoch: [287][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.187 (0.241) Prec@1 96.88 (94.65) Prec@5 100.00 (99.90)
|
|
train[2019-04-01-01:38:01] Epoch: [287][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.228 (0.245) Prec@1 94.79 (94.54) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-01:38:25] Epoch: [287][400/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.337 (0.244) Prec@1 91.67 (94.56) Prec@5 98.96 (99.92)
|
|
train[2019-04-01-01:38:49] Epoch: [287][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.470 (0.247) Prec@1 89.58 (94.49) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-01:38:53] Epoch: [287][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.318 (0.249) Prec@1 92.50 (94.45) Prec@5 100.00 (99.92)
|
|
[2019-04-01-01:38:53] **train** Prec@1 94.45 Prec@5 99.92 Error@1 5.55 Error@5 0.08 Loss:0.249
|
|
test [2019-04-01-01:38:54] Epoch: [287][000/105] Time 0.59 (0.59) Data 0.53 (0.53) Loss 0.171 (0.171) Prec@1 91.67 (91.67) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-01:38:58] Epoch: [287][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.194 (0.217) Prec@1 95.83 (93.89) Prec@5 100.00 (99.81)
|
|
test [2019-04-01-01:38:58] Epoch: [287][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.131 (0.217) Prec@1 93.75 (93.86) Prec@5 100.00 (99.81)
|
|
[2019-04-01-01:38:58] **test** Prec@1 93.86 Prec@5 99.81 Error@1 6.14 Error@5 0.19 Loss:0.217
|
|
----> Best Accuracy : Acc@1=95.00, Acc@5=99.89, Error@1=5.00, Error@5=0.11
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-01:38:59] [Epoch=288/600] [Need: 11:12:05] LR=0.0133 ~ 0.0133, Batch=96
|
|
train[2019-04-01-01:38:59] Epoch: [288][000/521] Time 0.84 (0.84) Data 0.56 (0.56) Loss 0.179 (0.179) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-01:39:23] Epoch: [288][100/521] Time 0.23 (0.25) Data 0.00 (0.01) Loss 0.246 (0.253) Prec@1 94.79 (94.35) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-01:39:47] Epoch: [288][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.197 (0.248) Prec@1 95.83 (94.38) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-01:40:11] Epoch: [288][300/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.162 (0.250) Prec@1 96.88 (94.40) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-01:40:34] Epoch: [288][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.367 (0.253) Prec@1 92.71 (94.33) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-01:40:58] Epoch: [288][500/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.459 (0.254) Prec@1 91.67 (94.34) Prec@5 98.96 (99.93)
|
|
train[2019-04-01-01:41:03] Epoch: [288][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.138 (0.254) Prec@1 97.50 (94.34) Prec@5 100.00 (99.93)
|
|
[2019-04-01-01:41:03] **train** Prec@1 94.34 Prec@5 99.93 Error@1 5.66 Error@5 0.07 Loss:0.254
|
|
test [2019-04-01-01:41:03] Epoch: [288][000/105] Time 0.48 (0.48) Data 0.41 (0.41) Loss 0.193 (0.193) Prec@1 91.67 (91.67) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-01:41:07] Epoch: [288][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.136 (0.218) Prec@1 95.83 (93.61) Prec@5 100.00 (99.87)
|
|
test [2019-04-01-01:41:07] Epoch: [288][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.182 (0.218) Prec@1 93.75 (93.60) Prec@5 100.00 (99.87)
|
|
[2019-04-01-01:41:08] **test** Prec@1 93.60 Prec@5 99.87 Error@1 6.40 Error@5 0.13 Loss:0.218
|
|
----> Best Accuracy : Acc@1=95.00, Acc@5=99.89, Error@1=5.00, Error@5=0.11
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-01:41:08] [Epoch=289/600] [Need: 11:09:50] LR=0.0133 ~ 0.0133, Batch=96
|
|
train[2019-04-01-01:41:08] Epoch: [289][000/521] Time 0.71 (0.71) Data 0.43 (0.43) Loss 0.279 (0.279) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-01:41:32] Epoch: [289][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.223 (0.252) Prec@1 95.83 (94.72) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-01:41:56] Epoch: [289][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.188 (0.249) Prec@1 94.79 (94.64) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-01:42:20] Epoch: [289][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.177 (0.249) Prec@1 98.96 (94.62) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-01:42:43] Epoch: [289][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.215 (0.248) Prec@1 94.79 (94.57) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-01:43:07] Epoch: [289][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.309 (0.249) Prec@1 93.75 (94.55) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-01:43:12] Epoch: [289][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.279 (0.248) Prec@1 92.50 (94.56) Prec@5 100.00 (99.93)
|
|
[2019-04-01-01:43:12] **train** Prec@1 94.56 Prec@5 99.93 Error@1 5.44 Error@5 0.07 Loss:0.248
|
|
test [2019-04-01-01:43:13] Epoch: [289][000/105] Time 0.60 (0.60) Data 0.53 (0.53) Loss 0.134 (0.134) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-01:43:17] Epoch: [289][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.077 (0.184) Prec@1 97.92 (95.01) Prec@5 100.00 (99.87)
|
|
test [2019-04-01-01:43:17] Epoch: [289][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.043 (0.184) Prec@1 100.00 (94.99) Prec@5 100.00 (99.87)
|
|
[2019-04-01-01:43:17] **test** Prec@1 94.99 Prec@5 99.87 Error@1 5.01 Error@5 0.13 Loss:0.184
|
|
----> Best Accuracy : Acc@1=95.00, Acc@5=99.89, Error@1=5.00, Error@5=0.11
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-01:43:17] [Epoch=290/600] [Need: 11:07:49] LR=0.0132 ~ 0.0132, Batch=96
|
|
train[2019-04-01-01:43:18] Epoch: [290][000/521] Time 0.84 (0.84) Data 0.56 (0.56) Loss 0.288 (0.288) Prec@1 91.67 (91.67) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-01:43:41] Epoch: [290][100/521] Time 0.23 (0.24) Data 0.00 (0.01) Loss 0.268 (0.243) Prec@1 94.79 (94.85) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-01:44:05] Epoch: [290][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.146 (0.241) Prec@1 97.92 (94.76) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-01:44:29] Epoch: [290][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.391 (0.245) Prec@1 90.62 (94.63) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-01:44:53] Epoch: [290][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.218 (0.241) Prec@1 94.79 (94.70) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-01:45:16] Epoch: [290][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.146 (0.244) Prec@1 96.88 (94.65) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-01:45:21] Epoch: [290][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.191 (0.244) Prec@1 97.50 (94.66) Prec@5 100.00 (99.93)
|
|
[2019-04-01-01:45:21] **train** Prec@1 94.66 Prec@5 99.93 Error@1 5.34 Error@5 0.07 Loss:0.244
|
|
test [2019-04-01-01:45:22] Epoch: [290][000/105] Time 0.47 (0.47) Data 0.41 (0.41) Loss 0.135 (0.135) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-01:45:26] Epoch: [290][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.087 (0.202) Prec@1 97.92 (94.49) Prec@5 100.00 (99.88)
|
|
test [2019-04-01-01:45:26] Epoch: [290][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.371 (0.204) Prec@1 81.25 (94.39) Prec@5 93.75 (99.87)
|
|
[2019-04-01-01:45:26] **test** Prec@1 94.39 Prec@5 99.87 Error@1 5.61 Error@5 0.13 Loss:0.204
|
|
----> Best Accuracy : Acc@1=95.00, Acc@5=99.89, Error@1=5.00, Error@5=0.11
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-01:45:26] [Epoch=291/600] [Need: 11:04:14] LR=0.0131 ~ 0.0131, Batch=96
|
|
train[2019-04-01-01:45:27] Epoch: [291][000/521] Time 0.70 (0.70) Data 0.43 (0.43) Loss 0.161 (0.161) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-01:45:50] Epoch: [291][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.306 (0.245) Prec@1 92.71 (94.68) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-01:46:14] Epoch: [291][200/521] Time 0.27 (0.24) Data 0.00 (0.00) Loss 0.265 (0.242) Prec@1 93.75 (94.83) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-01:46:38] Epoch: [291][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.349 (0.247) Prec@1 94.79 (94.72) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-01:47:02] Epoch: [291][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.122 (0.247) Prec@1 97.92 (94.67) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-01:47:26] Epoch: [291][500/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.301 (0.247) Prec@1 91.67 (94.66) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-01:47:31] Epoch: [291][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.136 (0.248) Prec@1 98.75 (94.64) Prec@5 100.00 (99.93)
|
|
[2019-04-01-01:47:31] **train** Prec@1 94.64 Prec@5 99.93 Error@1 5.36 Error@5 0.07 Loss:0.248
|
|
test [2019-04-01-01:47:31] Epoch: [291][000/105] Time 0.46 (0.46) Data 0.40 (0.40) Loss 0.237 (0.237) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-01:47:35] Epoch: [291][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.028 (0.206) Prec@1 98.96 (94.14) Prec@5 100.00 (99.88)
|
|
test [2019-04-01-01:47:35] Epoch: [291][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.171 (0.206) Prec@1 93.75 (94.11) Prec@5 100.00 (99.88)
|
|
[2019-04-01-01:47:36] **test** Prec@1 94.11 Prec@5 99.88 Error@1 5.89 Error@5 0.12 Loss:0.206
|
|
----> Best Accuracy : Acc@1=95.00, Acc@5=99.89, Error@1=5.00, Error@5=0.11
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-01:47:36] [Epoch=292/600] [Need: 11:05:49] LR=0.0131 ~ 0.0131, Batch=96
|
|
train[2019-04-01-01:47:36] Epoch: [292][000/521] Time 0.72 (0.72) Data 0.46 (0.46) Loss 0.230 (0.230) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-01:48:00] Epoch: [292][100/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.316 (0.251) Prec@1 96.88 (94.33) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-01:48:24] Epoch: [292][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.167 (0.247) Prec@1 95.83 (94.45) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-01:48:48] Epoch: [292][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.292 (0.245) Prec@1 92.71 (94.48) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-01:49:12] Epoch: [292][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.363 (0.250) Prec@1 92.71 (94.36) Prec@5 98.96 (99.91)
|
|
train[2019-04-01-01:49:36] Epoch: [292][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.175 (0.250) Prec@1 95.83 (94.41) Prec@5 100.00 (99.90)
|
|
train[2019-04-01-01:49:40] Epoch: [292][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.339 (0.251) Prec@1 91.25 (94.37) Prec@5 100.00 (99.91)
|
|
[2019-04-01-01:49:41] **train** Prec@1 94.37 Prec@5 99.91 Error@1 5.63 Error@5 0.09 Loss:0.251
|
|
test [2019-04-01-01:49:41] Epoch: [292][000/105] Time 0.48 (0.48) Data 0.41 (0.41) Loss 0.301 (0.301) Prec@1 92.71 (92.71) Prec@5 98.96 (98.96)
|
|
test [2019-04-01-01:49:45] Epoch: [292][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.077 (0.200) Prec@1 97.92 (93.87) Prec@5 100.00 (99.88)
|
|
test [2019-04-01-01:49:45] Epoch: [292][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.067 (0.200) Prec@1 93.75 (93.89) Prec@5 100.00 (99.87)
|
|
[2019-04-01-01:49:45] **test** Prec@1 93.89 Prec@5 99.87 Error@1 6.11 Error@5 0.13 Loss:0.200
|
|
----> Best Accuracy : Acc@1=95.00, Acc@5=99.89, Error@1=5.00, Error@5=0.11
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-01:49:46] [Epoch=293/600] [Need: 11:04:20] LR=0.0130 ~ 0.0130, Batch=96
|
|
train[2019-04-01-01:49:46] Epoch: [293][000/521] Time 0.71 (0.71) Data 0.43 (0.43) Loss 0.245 (0.245) Prec@1 91.67 (91.67) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-01:50:10] Epoch: [293][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.223 (0.246) Prec@1 96.88 (94.37) Prec@5 98.96 (99.93)
|
|
train[2019-04-01-01:50:33] Epoch: [293][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.258 (0.253) Prec@1 94.79 (94.20) Prec@5 100.00 (99.90)
|
|
train[2019-04-01-01:50:57] Epoch: [293][300/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.237 (0.247) Prec@1 94.79 (94.40) Prec@5 100.00 (99.90)
|
|
train[2019-04-01-01:51:21] Epoch: [293][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.164 (0.250) Prec@1 96.88 (94.38) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-01:51:44] Epoch: [293][500/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.201 (0.250) Prec@1 96.88 (94.39) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-01:51:49] Epoch: [293][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.176 (0.250) Prec@1 97.50 (94.41) Prec@5 100.00 (99.91)
|
|
[2019-04-01-01:51:49] **train** Prec@1 94.41 Prec@5 99.91 Error@1 5.59 Error@5 0.09 Loss:0.250
|
|
test [2019-04-01-01:51:50] Epoch: [293][000/105] Time 0.60 (0.60) Data 0.54 (0.54) Loss 0.292 (0.292) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-01:51:54] Epoch: [293][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.028 (0.192) Prec@1 98.96 (94.46) Prec@5 100.00 (99.86)
|
|
test [2019-04-01-01:51:54] Epoch: [293][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.042 (0.192) Prec@1 100.00 (94.43) Prec@5 100.00 (99.86)
|
|
[2019-04-01-01:51:54] **test** Prec@1 94.43 Prec@5 99.86 Error@1 5.57 Error@5 0.14 Loss:0.192
|
|
----> Best Accuracy : Acc@1=95.00, Acc@5=99.89, Error@1=5.00, Error@5=0.11
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-01:51:54] [Epoch=294/600] [Need: 10:56:27] LR=0.0129 ~ 0.0129, Batch=96
|
|
train[2019-04-01-01:51:55] Epoch: [294][000/521] Time 0.71 (0.71) Data 0.44 (0.44) Loss 0.115 (0.115) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-01:52:19] Epoch: [294][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.227 (0.233) Prec@1 93.75 (94.96) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-01:52:42] Epoch: [294][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.319 (0.236) Prec@1 90.62 (94.98) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-01:53:06] Epoch: [294][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.365 (0.236) Prec@1 90.62 (94.91) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-01:53:30] Epoch: [294][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.196 (0.242) Prec@1 94.79 (94.77) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-01:53:54] Epoch: [294][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.373 (0.246) Prec@1 93.75 (94.65) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-01:53:59] Epoch: [294][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.243 (0.245) Prec@1 95.00 (94.68) Prec@5 100.00 (99.92)
|
|
[2019-04-01-01:53:59] **train** Prec@1 94.68 Prec@5 99.92 Error@1 5.32 Error@5 0.08 Loss:0.245
|
|
test [2019-04-01-01:53:59] Epoch: [294][000/105] Time 0.58 (0.58) Data 0.52 (0.52) Loss 0.247 (0.247) Prec@1 90.62 (90.62) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-01:54:03] Epoch: [294][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.072 (0.199) Prec@1 97.92 (94.08) Prec@5 100.00 (99.88)
|
|
test [2019-04-01-01:54:04] Epoch: [294][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.088 (0.198) Prec@1 100.00 (94.14) Prec@5 100.00 (99.86)
|
|
[2019-04-01-01:54:04] **test** Prec@1 94.14 Prec@5 99.86 Error@1 5.86 Error@5 0.14 Loss:0.198
|
|
----> Best Accuracy : Acc@1=95.00, Acc@5=99.89, Error@1=5.00, Error@5=0.11
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-01:54:04] [Epoch=295/600] [Need: 10:58:33] LR=0.0129 ~ 0.0129, Batch=96
|
|
train[2019-04-01-01:54:05] Epoch: [295][000/521] Time 0.84 (0.84) Data 0.56 (0.56) Loss 0.209 (0.209) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-01:54:28] Epoch: [295][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.203 (0.241) Prec@1 93.75 (94.79) Prec@5 100.00 (99.90)
|
|
train[2019-04-01-01:54:52] Epoch: [295][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.192 (0.243) Prec@1 95.83 (94.77) Prec@5 100.00 (99.90)
|
|
train[2019-04-01-01:55:16] Epoch: [295][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.306 (0.238) Prec@1 93.75 (94.89) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-01:55:39] Epoch: [295][400/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.313 (0.242) Prec@1 93.75 (94.77) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-01:56:03] Epoch: [295][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.333 (0.243) Prec@1 93.75 (94.69) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-01:56:07] Epoch: [295][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.387 (0.244) Prec@1 91.25 (94.68) Prec@5 98.75 (99.92)
|
|
[2019-04-01-01:56:07] **train** Prec@1 94.68 Prec@5 99.92 Error@1 5.32 Error@5 0.08 Loss:0.244
|
|
test [2019-04-01-01:56:08] Epoch: [295][000/105] Time 0.46 (0.46) Data 0.40 (0.40) Loss 0.168 (0.168) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-01:56:12] Epoch: [295][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.086 (0.195) Prec@1 97.92 (94.21) Prec@5 100.00 (99.83)
|
|
test [2019-04-01-01:56:12] Epoch: [295][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.011 (0.195) Prec@1 100.00 (94.21) Prec@5 100.00 (99.84)
|
|
[2019-04-01-01:56:12] **test** Prec@1 94.21 Prec@5 99.84 Error@1 5.79 Error@5 0.16 Loss:0.195
|
|
----> Best Accuracy : Acc@1=95.00, Acc@5=99.89, Error@1=5.00, Error@5=0.11
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-01:56:12] [Epoch=296/600] [Need: 10:51:06] LR=0.0128 ~ 0.0128, Batch=96
|
|
train[2019-04-01-01:56:13] Epoch: [296][000/521] Time 0.84 (0.84) Data 0.57 (0.57) Loss 0.225 (0.225) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-01:56:37] Epoch: [296][100/521] Time 0.23 (0.24) Data 0.00 (0.01) Loss 0.220 (0.243) Prec@1 97.92 (94.78) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-01:57:00] Epoch: [296][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.197 (0.243) Prec@1 97.92 (94.65) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-01:57:23] Epoch: [296][300/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.385 (0.246) Prec@1 92.71 (94.56) Prec@5 97.92 (99.91)
|
|
train[2019-04-01-01:57:47] Epoch: [296][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.244 (0.245) Prec@1 92.71 (94.60) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-01:58:10] Epoch: [296][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.153 (0.248) Prec@1 97.92 (94.50) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-01:58:15] Epoch: [296][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.191 (0.250) Prec@1 95.00 (94.45) Prec@5 100.00 (99.91)
|
|
[2019-04-01-01:58:15] **train** Prec@1 94.45 Prec@5 99.91 Error@1 5.55 Error@5 0.09 Loss:0.250
|
|
test [2019-04-01-01:58:16] Epoch: [296][000/105] Time 0.47 (0.47) Data 0.40 (0.40) Loss 0.271 (0.271) Prec@1 90.62 (90.62) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-01:58:20] Epoch: [296][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.114 (0.200) Prec@1 96.88 (94.08) Prec@5 100.00 (99.90)
|
|
test [2019-04-01-01:58:20] Epoch: [296][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.010 (0.199) Prec@1 100.00 (94.14) Prec@5 100.00 (99.90)
|
|
[2019-04-01-01:58:20] **test** Prec@1 94.14 Prec@5 99.90 Error@1 5.86 Error@5 0.10 Loss:0.199
|
|
----> Best Accuracy : Acc@1=95.00, Acc@5=99.89, Error@1=5.00, Error@5=0.11
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-01:58:20] [Epoch=297/600] [Need: 10:45:28] LR=0.0127 ~ 0.0127, Batch=96
|
|
train[2019-04-01-01:58:21] Epoch: [297][000/521] Time 0.70 (0.70) Data 0.43 (0.43) Loss 0.369 (0.369) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-01:58:44] Epoch: [297][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.298 (0.247) Prec@1 92.71 (94.61) Prec@5 98.96 (99.92)
|
|
train[2019-04-01-01:59:08] Epoch: [297][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.332 (0.256) Prec@1 93.75 (94.29) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-01:59:32] Epoch: [297][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.285 (0.253) Prec@1 91.67 (94.41) Prec@5 98.96 (99.93)
|
|
train[2019-04-01-01:59:55] Epoch: [297][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.199 (0.252) Prec@1 97.92 (94.39) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-02:00:19] Epoch: [297][500/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.416 (0.251) Prec@1 90.62 (94.39) Prec@5 98.96 (99.93)
|
|
train[2019-04-01-02:00:24] Epoch: [297][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.410 (0.251) Prec@1 92.50 (94.42) Prec@5 100.00 (99.92)
|
|
[2019-04-01-02:00:24] **train** Prec@1 94.42 Prec@5 99.92 Error@1 5.58 Error@5 0.08 Loss:0.251
|
|
test [2019-04-01-02:00:24] Epoch: [297][000/105] Time 0.63 (0.63) Data 0.56 (0.56) Loss 0.225 (0.225) Prec@1 91.67 (91.67) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-02:00:28] Epoch: [297][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.092 (0.205) Prec@1 97.92 (94.21) Prec@5 100.00 (99.83)
|
|
test [2019-04-01-02:00:29] Epoch: [297][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.005 (0.207) Prec@1 100.00 (94.17) Prec@5 100.00 (99.84)
|
|
[2019-04-01-02:00:29] **test** Prec@1 94.17 Prec@5 99.84 Error@1 5.83 Error@5 0.16 Loss:0.207
|
|
----> Best Accuracy : Acc@1=95.00, Acc@5=99.89, Error@1=5.00, Error@5=0.11
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-02:00:29] [Epoch=298/600] [Need: 10:47:35] LR=0.0127 ~ 0.0127, Batch=96
|
|
train[2019-04-01-02:00:30] Epoch: [298][000/521] Time 0.84 (0.84) Data 0.57 (0.57) Loss 0.313 (0.313) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-02:00:53] Epoch: [298][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.299 (0.234) Prec@1 94.79 (94.92) Prec@5 98.96 (99.96)
|
|
train[2019-04-01-02:01:17] Epoch: [298][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.164 (0.233) Prec@1 94.79 (94.86) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-02:01:41] Epoch: [298][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.364 (0.232) Prec@1 91.67 (94.95) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-02:02:04] Epoch: [298][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.235 (0.235) Prec@1 95.83 (94.88) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-02:02:28] Epoch: [298][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.197 (0.239) Prec@1 95.83 (94.70) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-02:02:33] Epoch: [298][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.220 (0.240) Prec@1 96.25 (94.69) Prec@5 100.00 (99.93)
|
|
[2019-04-01-02:02:33] **train** Prec@1 94.69 Prec@5 99.93 Error@1 5.31 Error@5 0.07 Loss:0.240
|
|
test [2019-04-01-02:02:33] Epoch: [298][000/105] Time 0.47 (0.47) Data 0.40 (0.40) Loss 0.172 (0.172) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-02:02:38] Epoch: [298][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.147 (0.170) Prec@1 93.75 (95.07) Prec@5 100.00 (99.88)
|
|
test [2019-04-01-02:02:38] Epoch: [298][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.118 (0.171) Prec@1 87.50 (95.03) Prec@5 100.00 (99.88)
|
|
[2019-04-01-02:02:38] **test** Prec@1 95.03 Prec@5 99.88 Error@1 4.97 Error@5 0.12 Loss:0.171
|
|
----> Best Accuracy : Acc@1=95.03, Acc@5=99.88, Error@1=4.97, Error@5=0.12
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-02:02:38] [Epoch=299/600] [Need: 10:48:18] LR=0.0126 ~ 0.0126, Batch=96
|
|
train[2019-04-01-02:02:39] Epoch: [299][000/521] Time 0.70 (0.70) Data 0.44 (0.44) Loss 0.231 (0.231) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-02:03:03] Epoch: [299][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.209 (0.236) Prec@1 94.79 (94.87) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-02:03:27] Epoch: [299][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.227 (0.241) Prec@1 96.88 (94.86) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-02:03:50] Epoch: [299][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.159 (0.237) Prec@1 96.88 (94.98) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-02:04:14] Epoch: [299][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.314 (0.238) Prec@1 94.79 (94.82) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-02:04:38] Epoch: [299][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.093 (0.242) Prec@1 100.00 (94.68) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-02:04:42] Epoch: [299][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.195 (0.243) Prec@1 97.50 (94.68) Prec@5 100.00 (99.92)
|
|
[2019-04-01-02:04:43] **train** Prec@1 94.68 Prec@5 99.92 Error@1 5.32 Error@5 0.08 Loss:0.243
|
|
test [2019-04-01-02:04:43] Epoch: [299][000/105] Time 0.61 (0.61) Data 0.54 (0.54) Loss 0.164 (0.164) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-02:04:47] Epoch: [299][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.088 (0.180) Prec@1 94.79 (94.80) Prec@5 100.00 (99.85)
|
|
test [2019-04-01-02:04:47] Epoch: [299][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.000 (0.179) Prec@1 100.00 (94.80) Prec@5 100.00 (99.85)
|
|
[2019-04-01-02:04:47] **test** Prec@1 94.80 Prec@5 99.85 Error@1 5.20 Error@5 0.15 Loss:0.179
|
|
----> Best Accuracy : Acc@1=95.03, Acc@5=99.88, Error@1=4.97, Error@5=0.12
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-02:04:48] [Epoch=300/600] [Need: 10:48:09] LR=0.0126 ~ 0.0126, Batch=96
|
|
train[2019-04-01-02:04:48] Epoch: [300][000/521] Time 0.70 (0.70) Data 0.42 (0.42) Loss 0.280 (0.280) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-02:05:12] Epoch: [300][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.102 (0.228) Prec@1 97.92 (95.11) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-02:05:36] Epoch: [300][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.286 (0.238) Prec@1 95.83 (94.77) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-02:06:00] Epoch: [300][300/521] Time 0.27 (0.24) Data 0.00 (0.00) Loss 0.323 (0.238) Prec@1 91.67 (94.84) Prec@5 98.96 (99.93)
|
|
train[2019-04-01-02:06:24] Epoch: [300][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.397 (0.241) Prec@1 92.71 (94.74) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-02:06:48] Epoch: [300][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.191 (0.244) Prec@1 95.83 (94.66) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-02:06:53] Epoch: [300][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.261 (0.244) Prec@1 93.75 (94.66) Prec@5 100.00 (99.92)
|
|
[2019-04-01-02:06:53] **train** Prec@1 94.66 Prec@5 99.92 Error@1 5.34 Error@5 0.08 Loss:0.244
|
|
test [2019-04-01-02:06:53] Epoch: [300][000/105] Time 0.47 (0.47) Data 0.41 (0.41) Loss 0.235 (0.235) Prec@1 91.67 (91.67) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-02:06:57] Epoch: [300][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.145 (0.202) Prec@1 95.83 (94.17) Prec@5 100.00 (99.87)
|
|
test [2019-04-01-02:06:57] Epoch: [300][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.009 (0.201) Prec@1 100.00 (94.20) Prec@5 100.00 (99.86)
|
|
[2019-04-01-02:06:57] **test** Prec@1 94.20 Prec@5 99.86 Error@1 5.80 Error@5 0.14 Loss:0.201
|
|
----> Best Accuracy : Acc@1=95.03, Acc@5=99.88, Error@1=4.97, Error@5=0.12
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-02:06:58] [Epoch=301/600] [Need: 10:47:51] LR=0.0125 ~ 0.0125, Batch=96
|
|
train[2019-04-01-02:06:58] Epoch: [301][000/521] Time 0.70 (0.70) Data 0.42 (0.42) Loss 0.132 (0.132) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-02:07:22] Epoch: [301][100/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.213 (0.225) Prec@1 92.71 (95.16) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-02:07:46] Epoch: [301][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.180 (0.235) Prec@1 96.88 (94.79) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-02:08:10] Epoch: [301][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.308 (0.235) Prec@1 92.71 (94.81) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-02:08:34] Epoch: [301][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.218 (0.234) Prec@1 97.92 (94.86) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-02:08:57] Epoch: [301][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.237 (0.237) Prec@1 96.88 (94.81) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-02:09:02] Epoch: [301][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.216 (0.236) Prec@1 95.00 (94.81) Prec@5 100.00 (99.94)
|
|
[2019-04-01-02:09:02] **train** Prec@1 94.81 Prec@5 99.94 Error@1 5.19 Error@5 0.06 Loss:0.236
|
|
test [2019-04-01-02:09:03] Epoch: [301][000/105] Time 0.48 (0.48) Data 0.42 (0.42) Loss 0.102 (0.102) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-02:09:07] Epoch: [301][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.056 (0.190) Prec@1 97.92 (94.34) Prec@5 100.00 (99.83)
|
|
test [2019-04-01-02:09:07] Epoch: [301][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.107 (0.190) Prec@1 93.75 (94.36) Prec@5 100.00 (99.84)
|
|
[2019-04-01-02:09:07] **test** Prec@1 94.36 Prec@5 99.84 Error@1 5.64 Error@5 0.16 Loss:0.190
|
|
----> Best Accuracy : Acc@1=95.03, Acc@5=99.88, Error@1=4.97, Error@5=0.12
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-02:09:07] [Epoch=302/600] [Need: 10:42:16] LR=0.0124 ~ 0.0124, Batch=96
|
|
train[2019-04-01-02:09:08] Epoch: [302][000/521] Time 0.88 (0.88) Data 0.61 (0.61) Loss 0.387 (0.387) Prec@1 92.71 (92.71) Prec@5 98.96 (98.96)
|
|
train[2019-04-01-02:09:31] Epoch: [302][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.344 (0.237) Prec@1 89.58 (94.83) Prec@5 98.96 (99.94)
|
|
train[2019-04-01-02:09:55] Epoch: [302][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.256 (0.237) Prec@1 92.71 (94.73) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-02:10:19] Epoch: [302][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.271 (0.233) Prec@1 95.83 (94.91) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-02:10:43] Epoch: [302][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.368 (0.235) Prec@1 93.75 (94.85) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-02:11:07] Epoch: [302][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.334 (0.237) Prec@1 92.71 (94.81) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-02:11:12] Epoch: [302][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.271 (0.238) Prec@1 96.25 (94.77) Prec@5 100.00 (99.94)
|
|
[2019-04-01-02:11:12] **train** Prec@1 94.77 Prec@5 99.94 Error@1 5.23 Error@5 0.06 Loss:0.238
|
|
test [2019-04-01-02:11:12] Epoch: [302][000/105] Time 0.60 (0.60) Data 0.53 (0.53) Loss 0.147 (0.147) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-02:11:16] Epoch: [302][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.064 (0.188) Prec@1 97.92 (94.75) Prec@5 100.00 (99.86)
|
|
test [2019-04-01-02:11:16] Epoch: [302][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.069 (0.187) Prec@1 93.75 (94.76) Prec@5 100.00 (99.86)
|
|
[2019-04-01-02:11:17] **test** Prec@1 94.76 Prec@5 99.86 Error@1 5.24 Error@5 0.14 Loss:0.187
|
|
----> Best Accuracy : Acc@1=95.03, Acc@5=99.88, Error@1=4.97, Error@5=0.12
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-02:11:17] [Epoch=303/600] [Need: 10:42:25] LR=0.0124 ~ 0.0124, Batch=96
|
|
train[2019-04-01-02:11:17] Epoch: [303][000/521] Time 0.74 (0.74) Data 0.48 (0.48) Loss 0.342 (0.342) Prec@1 93.75 (93.75) Prec@5 98.96 (98.96)
|
|
train[2019-04-01-02:11:41] Epoch: [303][100/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.201 (0.239) Prec@1 95.83 (94.72) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-02:12:05] Epoch: [303][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.196 (0.244) Prec@1 96.88 (94.59) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-02:12:29] Epoch: [303][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.186 (0.243) Prec@1 94.79 (94.64) Prec@5 100.00 (99.90)
|
|
train[2019-04-01-02:12:52] Epoch: [303][400/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.130 (0.239) Prec@1 96.88 (94.72) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-02:13:16] Epoch: [303][500/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.302 (0.244) Prec@1 93.75 (94.66) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-02:13:21] Epoch: [303][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.206 (0.244) Prec@1 97.50 (94.68) Prec@5 100.00 (99.91)
|
|
[2019-04-01-02:13:21] **train** Prec@1 94.68 Prec@5 99.91 Error@1 5.32 Error@5 0.09 Loss:0.244
|
|
test [2019-04-01-02:13:21] Epoch: [303][000/105] Time 0.47 (0.47) Data 0.40 (0.40) Loss 0.213 (0.213) Prec@1 90.62 (90.62) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-02:13:25] Epoch: [303][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.107 (0.177) Prec@1 96.88 (94.60) Prec@5 100.00 (99.83)
|
|
test [2019-04-01-02:13:26] Epoch: [303][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.164 (0.178) Prec@1 87.50 (94.57) Prec@5 100.00 (99.84)
|
|
[2019-04-01-02:13:26] **test** Prec@1 94.57 Prec@5 99.84 Error@1 5.43 Error@5 0.16 Loss:0.178
|
|
----> Best Accuracy : Acc@1=95.03, Acc@5=99.88, Error@1=4.97, Error@5=0.12
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-02:13:26] [Epoch=304/600] [Need: 10:36:54] LR=0.0123 ~ 0.0123, Batch=96
|
|
train[2019-04-01-02:13:27] Epoch: [304][000/521] Time 0.84 (0.84) Data 0.56 (0.56) Loss 0.183 (0.183) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-02:13:50] Epoch: [304][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.170 (0.241) Prec@1 94.79 (94.91) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-02:14:14] Epoch: [304][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.179 (0.239) Prec@1 94.79 (94.82) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-02:14:38] Epoch: [304][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.303 (0.235) Prec@1 95.83 (94.97) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-02:15:02] Epoch: [304][400/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.194 (0.238) Prec@1 94.79 (94.87) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-02:15:26] Epoch: [304][500/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.174 (0.240) Prec@1 96.88 (94.84) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-02:15:30] Epoch: [304][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.190 (0.241) Prec@1 98.75 (94.80) Prec@5 100.00 (99.93)
|
|
[2019-04-01-02:15:30] **train** Prec@1 94.80 Prec@5 99.93 Error@1 5.20 Error@5 0.07 Loss:0.241
|
|
test [2019-04-01-02:15:31] Epoch: [304][000/105] Time 0.46 (0.46) Data 0.40 (0.40) Loss 0.207 (0.207) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-02:15:35] Epoch: [304][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.120 (0.249) Prec@1 96.88 (93.34) Prec@5 100.00 (99.77)
|
|
test [2019-04-01-02:15:35] Epoch: [304][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.426 (0.249) Prec@1 87.50 (93.31) Prec@5 100.00 (99.78)
|
|
[2019-04-01-02:15:35] **test** Prec@1 93.31 Prec@5 99.78 Error@1 6.69 Error@5 0.22 Loss:0.249
|
|
----> Best Accuracy : Acc@1=95.03, Acc@5=99.88, Error@1=4.97, Error@5=0.12
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-02:15:35] [Epoch=305/600] [Need: 10:36:39] LR=0.0122 ~ 0.0122, Batch=96
|
|
train[2019-04-01-02:15:36] Epoch: [305][000/521] Time 0.71 (0.71) Data 0.44 (0.44) Loss 0.197 (0.197) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-02:16:00] Epoch: [305][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.264 (0.236) Prec@1 93.75 (95.09) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-02:16:24] Epoch: [305][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.132 (0.236) Prec@1 96.88 (95.04) Prec@5 100.00 (99.90)
|
|
train[2019-04-01-02:16:48] Epoch: [305][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.329 (0.233) Prec@1 90.62 (95.03) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-02:17:12] Epoch: [305][400/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.139 (0.233) Prec@1 95.83 (94.99) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-02:17:36] Epoch: [305][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.440 (0.233) Prec@1 92.71 (95.01) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-02:17:40] Epoch: [305][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.214 (0.233) Prec@1 97.50 (95.01) Prec@5 100.00 (99.93)
|
|
[2019-04-01-02:17:40] **train** Prec@1 95.01 Prec@5 99.93 Error@1 4.99 Error@5 0.07 Loss:0.233
|
|
test [2019-04-01-02:17:41] Epoch: [305][000/105] Time 0.48 (0.48) Data 0.41 (0.41) Loss 0.304 (0.304) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-02:17:45] Epoch: [305][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.054 (0.226) Prec@1 97.92 (94.13) Prec@5 100.00 (99.87)
|
|
test [2019-04-01-02:17:45] Epoch: [305][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.147 (0.227) Prec@1 93.75 (94.10) Prec@5 100.00 (99.87)
|
|
[2019-04-01-02:17:45] **test** Prec@1 94.10 Prec@5 99.87 Error@1 5.90 Error@5 0.13 Loss:0.227
|
|
----> Best Accuracy : Acc@1=95.03, Acc@5=99.88, Error@1=4.97, Error@5=0.12
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-02:17:45] [Epoch=306/600] [Need: 10:36:44] LR=0.0122 ~ 0.0122, Batch=96
|
|
train[2019-04-01-02:17:46] Epoch: [306][000/521] Time 0.84 (0.84) Data 0.57 (0.57) Loss 0.380 (0.380) Prec@1 88.54 (88.54) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-02:18:10] Epoch: [306][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.217 (0.232) Prec@1 95.83 (94.94) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-02:18:34] Epoch: [306][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.202 (0.237) Prec@1 96.88 (94.94) Prec@5 98.96 (99.94)
|
|
train[2019-04-01-02:18:57] Epoch: [306][300/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.248 (0.236) Prec@1 93.75 (94.89) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-02:19:21] Epoch: [306][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.098 (0.239) Prec@1 98.96 (94.82) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-02:19:45] Epoch: [306][500/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.191 (0.241) Prec@1 96.88 (94.72) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-02:19:50] Epoch: [306][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.090 (0.242) Prec@1 100.00 (94.72) Prec@5 100.00 (99.93)
|
|
[2019-04-01-02:19:50] **train** Prec@1 94.72 Prec@5 99.93 Error@1 5.28 Error@5 0.07 Loss:0.242
|
|
test [2019-04-01-02:19:50] Epoch: [306][000/105] Time 0.49 (0.49) Data 0.43 (0.43) Loss 0.103 (0.103) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-02:19:54] Epoch: [306][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.124 (0.172) Prec@1 94.79 (95.21) Prec@5 100.00 (99.91)
|
|
test [2019-04-01-02:19:54] Epoch: [306][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.026 (0.170) Prec@1 100.00 (95.25) Prec@5 100.00 (99.91)
|
|
[2019-04-01-02:19:55] **test** Prec@1 95.25 Prec@5 99.91 Error@1 4.75 Error@5 0.09 Loss:0.170
|
|
----> Best Accuracy : Acc@1=95.25, Acc@5=99.91, Error@1=4.75, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-02:19:55] [Epoch=307/600] [Need: 10:32:08] LR=0.0121 ~ 0.0121, Batch=96
|
|
train[2019-04-01-02:19:56] Epoch: [307][000/521] Time 0.84 (0.84) Data 0.57 (0.57) Loss 0.259 (0.259) Prec@1 94.79 (94.79) Prec@5 98.96 (98.96)
|
|
train[2019-04-01-02:20:19] Epoch: [307][100/521] Time 0.27 (0.24) Data 0.00 (0.01) Loss 0.154 (0.223) Prec@1 96.88 (95.12) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-02:20:43] Epoch: [307][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.219 (0.239) Prec@1 97.92 (94.63) Prec@5 98.96 (99.90)
|
|
train[2019-04-01-02:21:07] Epoch: [307][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.196 (0.239) Prec@1 95.83 (94.71) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-02:21:31] Epoch: [307][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.179 (0.241) Prec@1 96.88 (94.67) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-02:21:55] Epoch: [307][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.276 (0.240) Prec@1 91.67 (94.70) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-02:21:59] Epoch: [307][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.099 (0.241) Prec@1 100.00 (94.70) Prec@5 100.00 (99.92)
|
|
[2019-04-01-02:21:59] **train** Prec@1 94.70 Prec@5 99.92 Error@1 5.30 Error@5 0.08 Loss:0.241
|
|
test [2019-04-01-02:22:00] Epoch: [307][000/105] Time 0.60 (0.60) Data 0.54 (0.54) Loss 0.110 (0.110) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-02:22:04] Epoch: [307][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.061 (0.183) Prec@1 98.96 (94.67) Prec@5 100.00 (99.92)
|
|
test [2019-04-01-02:22:04] Epoch: [307][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.024 (0.183) Prec@1 100.00 (94.67) Prec@5 100.00 (99.91)
|
|
[2019-04-01-02:22:04] **test** Prec@1 94.67 Prec@5 99.91 Error@1 5.33 Error@5 0.09 Loss:0.183
|
|
----> Best Accuracy : Acc@1=95.25, Acc@5=99.91, Error@1=4.75, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-02:22:04] [Epoch=308/600] [Need: 10:30:51] LR=0.0120 ~ 0.0120, Batch=96
|
|
train[2019-04-01-02:22:05] Epoch: [308][000/521] Time 0.86 (0.86) Data 0.58 (0.58) Loss 0.250 (0.250) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-02:22:29] Epoch: [308][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.206 (0.236) Prec@1 93.75 (95.15) Prec@5 100.00 (99.90)
|
|
train[2019-04-01-02:22:53] Epoch: [308][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.297 (0.231) Prec@1 93.75 (95.19) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-02:23:16] Epoch: [308][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.114 (0.234) Prec@1 97.92 (95.00) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-02:23:40] Epoch: [308][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.290 (0.239) Prec@1 92.71 (94.83) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-02:24:04] Epoch: [308][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.204 (0.240) Prec@1 95.83 (94.81) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-02:24:08] Epoch: [308][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.275 (0.239) Prec@1 93.75 (94.83) Prec@5 100.00 (99.92)
|
|
[2019-04-01-02:24:09] **train** Prec@1 94.83 Prec@5 99.92 Error@1 5.17 Error@5 0.08 Loss:0.239
|
|
test [2019-04-01-02:24:09] Epoch: [308][000/105] Time 0.60 (0.60) Data 0.53 (0.53) Loss 0.185 (0.185) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-02:24:13] Epoch: [308][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.076 (0.176) Prec@1 97.92 (94.97) Prec@5 100.00 (99.91)
|
|
test [2019-04-01-02:24:13] Epoch: [308][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.348 (0.176) Prec@1 93.75 (94.95) Prec@5 100.00 (99.91)
|
|
[2019-04-01-02:24:13] **test** Prec@1 94.95 Prec@5 99.91 Error@1 5.05 Error@5 0.09 Loss:0.176
|
|
----> Best Accuracy : Acc@1=95.25, Acc@5=99.91, Error@1=4.75, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-02:24:14] [Epoch=309/600] [Need: 10:26:53] LR=0.0120 ~ 0.0120, Batch=96
|
|
train[2019-04-01-02:24:14] Epoch: [309][000/521] Time 0.84 (0.84) Data 0.57 (0.57) Loss 0.241 (0.241) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-02:24:38] Epoch: [309][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.308 (0.236) Prec@1 93.75 (94.77) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-02:25:02] Epoch: [309][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.246 (0.239) Prec@1 94.79 (94.67) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-02:25:26] Epoch: [309][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.233 (0.237) Prec@1 94.79 (94.75) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-02:25:49] Epoch: [309][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.195 (0.240) Prec@1 95.83 (94.68) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-02:26:13] Epoch: [309][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.281 (0.241) Prec@1 95.83 (94.66) Prec@5 98.96 (99.93)
|
|
train[2019-04-01-02:26:18] Epoch: [309][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.208 (0.240) Prec@1 96.25 (94.70) Prec@5 100.00 (99.93)
|
|
[2019-04-01-02:26:18] **train** Prec@1 94.70 Prec@5 99.93 Error@1 5.30 Error@5 0.07 Loss:0.240
|
|
test [2019-04-01-02:26:18] Epoch: [309][000/105] Time 0.54 (0.54) Data 0.47 (0.47) Loss 0.129 (0.129) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-02:26:22] Epoch: [309][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.033 (0.175) Prec@1 98.96 (94.86) Prec@5 100.00 (99.90)
|
|
test [2019-04-01-02:26:23] Epoch: [309][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.015 (0.174) Prec@1 100.00 (94.93) Prec@5 100.00 (99.89)
|
|
[2019-04-01-02:26:23] **test** Prec@1 94.93 Prec@5 99.89 Error@1 5.07 Error@5 0.11 Loss:0.174
|
|
----> Best Accuracy : Acc@1=95.25, Acc@5=99.91, Error@1=4.75, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-02:26:23] [Epoch=310/600] [Need: 10:24:21] LR=0.0119 ~ 0.0119, Batch=96
|
|
train[2019-04-01-02:26:23] Epoch: [310][000/521] Time 0.70 (0.70) Data 0.43 (0.43) Loss 0.174 (0.174) Prec@1 95.83 (95.83) Prec@5 98.96 (98.96)
|
|
train[2019-04-01-02:26:47] Epoch: [310][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.248 (0.233) Prec@1 93.75 (94.98) Prec@5 100.00 (99.90)
|
|
train[2019-04-01-02:27:11] Epoch: [310][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.083 (0.238) Prec@1 98.96 (94.80) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-02:27:34] Epoch: [310][300/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.342 (0.232) Prec@1 89.58 (95.00) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-02:27:58] Epoch: [310][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.292 (0.237) Prec@1 94.79 (94.90) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-02:28:22] Epoch: [310][500/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.287 (0.240) Prec@1 95.83 (94.81) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-02:28:27] Epoch: [310][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.130 (0.240) Prec@1 98.75 (94.80) Prec@5 100.00 (99.94)
|
|
[2019-04-01-02:28:27] **train** Prec@1 94.80 Prec@5 99.94 Error@1 5.20 Error@5 0.06 Loss:0.240
|
|
test [2019-04-01-02:28:27] Epoch: [310][000/105] Time 0.46 (0.46) Data 0.40 (0.40) Loss 0.058 (0.058) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-02:28:31] Epoch: [310][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.077 (0.182) Prec@1 96.88 (94.56) Prec@5 100.00 (99.86)
|
|
test [2019-04-01-02:28:31] Epoch: [310][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.088 (0.181) Prec@1 93.75 (94.58) Prec@5 100.00 (99.86)
|
|
[2019-04-01-02:28:32] **test** Prec@1 94.58 Prec@5 99.86 Error@1 5.42 Error@5 0.14 Loss:0.181
|
|
----> Best Accuracy : Acc@1=95.25, Acc@5=99.91, Error@1=4.75, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-02:28:32] [Epoch=311/600] [Need: 10:20:49] LR=0.0118 ~ 0.0118, Batch=96
|
|
train[2019-04-01-02:28:32] Epoch: [311][000/521] Time 0.72 (0.72) Data 0.44 (0.44) Loss 0.205 (0.205) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-02:28:56] Epoch: [311][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.128 (0.223) Prec@1 97.92 (95.19) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-02:29:20] Epoch: [311][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.123 (0.228) Prec@1 97.92 (95.16) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-02:29:43] Epoch: [311][300/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.237 (0.226) Prec@1 92.71 (95.18) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-02:30:07] Epoch: [311][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.203 (0.229) Prec@1 94.79 (95.08) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-02:30:31] Epoch: [311][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.205 (0.231) Prec@1 94.79 (94.99) Prec@5 98.96 (99.93)
|
|
train[2019-04-01-02:30:36] Epoch: [311][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.237 (0.231) Prec@1 95.00 (94.95) Prec@5 100.00 (99.93)
|
|
[2019-04-01-02:30:36] **train** Prec@1 94.95 Prec@5 99.93 Error@1 5.05 Error@5 0.07 Loss:0.231
|
|
test [2019-04-01-02:30:36] Epoch: [311][000/105] Time 0.61 (0.61) Data 0.56 (0.56) Loss 0.137 (0.137) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-02:30:40] Epoch: [311][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.030 (0.176) Prec@1 97.92 (94.84) Prec@5 100.00 (99.91)
|
|
test [2019-04-01-02:30:40] Epoch: [311][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.175 (0.175) Prec@1 93.75 (94.87) Prec@5 100.00 (99.91)
|
|
[2019-04-01-02:30:41] **test** Prec@1 94.87 Prec@5 99.91 Error@1 5.13 Error@5 0.09 Loss:0.175
|
|
----> Best Accuracy : Acc@1=95.25, Acc@5=99.91, Error@1=4.75, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-02:30:41] [Epoch=312/600] [Need: 10:19:26] LR=0.0118 ~ 0.0118, Batch=96
|
|
train[2019-04-01-02:30:41] Epoch: [312][000/521] Time 0.71 (0.71) Data 0.43 (0.43) Loss 0.194 (0.194) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-02:31:05] Epoch: [312][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.153 (0.214) Prec@1 95.83 (95.31) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-02:31:29] Epoch: [312][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.126 (0.222) Prec@1 93.75 (95.07) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-02:31:53] Epoch: [312][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.217 (0.223) Prec@1 96.88 (95.07) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-02:32:16] Epoch: [312][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.235 (0.225) Prec@1 95.83 (95.04) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-02:32:41] Epoch: [312][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.186 (0.230) Prec@1 94.79 (94.91) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-02:32:45] Epoch: [312][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.259 (0.230) Prec@1 96.25 (94.91) Prec@5 98.75 (99.92)
|
|
[2019-04-01-02:32:46] **train** Prec@1 94.91 Prec@5 99.92 Error@1 5.09 Error@5 0.08 Loss:0.230
|
|
test [2019-04-01-02:32:46] Epoch: [312][000/105] Time 0.55 (0.55) Data 0.49 (0.49) Loss 0.199 (0.199) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-02:32:50] Epoch: [312][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.232 (0.195) Prec@1 95.83 (94.27) Prec@5 100.00 (99.89)
|
|
test [2019-04-01-02:32:50] Epoch: [312][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.085 (0.194) Prec@1 93.75 (94.27) Prec@5 100.00 (99.89)
|
|
[2019-04-01-02:32:50] **test** Prec@1 94.27 Prec@5 99.89 Error@1 5.73 Error@5 0.11 Loss:0.194
|
|
----> Best Accuracy : Acc@1=95.25, Acc@5=99.91, Error@1=4.75, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-02:32:51] [Epoch=313/600] [Need: 10:20:55] LR=0.0117 ~ 0.0117, Batch=96
|
|
train[2019-04-01-02:32:51] Epoch: [313][000/521] Time 0.82 (0.82) Data 0.55 (0.55) Loss 0.275 (0.275) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-02:33:15] Epoch: [313][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.112 (0.224) Prec@1 98.96 (95.06) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-02:33:39] Epoch: [313][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.299 (0.229) Prec@1 94.79 (94.92) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-02:34:03] Epoch: [313][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.275 (0.227) Prec@1 93.75 (94.98) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-02:34:26] Epoch: [313][400/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.231 (0.232) Prec@1 96.88 (94.91) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-02:34:50] Epoch: [313][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.257 (0.235) Prec@1 94.79 (94.89) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-02:34:55] Epoch: [313][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.250 (0.234) Prec@1 95.00 (94.93) Prec@5 100.00 (99.94)
|
|
[2019-04-01-02:34:55] **train** Prec@1 94.93 Prec@5 99.94 Error@1 5.07 Error@5 0.06 Loss:0.234
|
|
test [2019-04-01-02:34:55] Epoch: [313][000/105] Time 0.60 (0.60) Data 0.54 (0.54) Loss 0.173 (0.173) Prec@1 91.67 (91.67) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-02:35:00] Epoch: [313][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.082 (0.159) Prec@1 96.88 (95.37) Prec@5 100.00 (99.90)
|
|
test [2019-04-01-02:35:00] Epoch: [313][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.053 (0.158) Prec@1 93.75 (95.38) Prec@5 100.00 (99.90)
|
|
[2019-04-01-02:35:00] **test** Prec@1 95.38 Prec@5 99.90 Error@1 4.62 Error@5 0.10 Loss:0.158
|
|
----> Best Accuracy : Acc@1=95.38, Acc@5=99.90, Error@1=4.62, Error@5=0.10
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-02:35:00] [Epoch=314/600] [Need: 10:17:08] LR=0.0116 ~ 0.0116, Batch=96
|
|
train[2019-04-01-02:35:01] Epoch: [314][000/521] Time 0.71 (0.71) Data 0.44 (0.44) Loss 0.451 (0.451) Prec@1 91.67 (91.67) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-02:35:24] Epoch: [314][100/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.325 (0.234) Prec@1 89.58 (94.77) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-02:35:48] Epoch: [314][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.165 (0.228) Prec@1 95.83 (94.96) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-02:36:12] Epoch: [314][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.217 (0.229) Prec@1 93.75 (94.93) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-02:36:36] Epoch: [314][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.232 (0.229) Prec@1 93.75 (94.95) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-02:36:59] Epoch: [314][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.212 (0.230) Prec@1 94.79 (94.92) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-02:37:04] Epoch: [314][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.132 (0.230) Prec@1 97.50 (94.91) Prec@5 100.00 (99.95)
|
|
[2019-04-01-02:37:04] **train** Prec@1 94.91 Prec@5 99.95 Error@1 5.09 Error@5 0.05 Loss:0.230
|
|
test [2019-04-01-02:37:04] Epoch: [314][000/105] Time 0.46 (0.46) Data 0.40 (0.40) Loss 0.200 (0.200) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-02:37:08] Epoch: [314][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.031 (0.188) Prec@1 98.96 (94.63) Prec@5 100.00 (99.80)
|
|
test [2019-04-01-02:37:09] Epoch: [314][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.127 (0.186) Prec@1 93.75 (94.62) Prec@5 100.00 (99.81)
|
|
[2019-04-01-02:37:09] **test** Prec@1 94.62 Prec@5 99.81 Error@1 5.38 Error@5 0.19 Loss:0.186
|
|
----> Best Accuracy : Acc@1=95.38, Acc@5=99.90, Error@1=4.62, Error@5=0.10
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-02:37:09] [Epoch=315/600] [Need: 10:11:52] LR=0.0116 ~ 0.0116, Batch=96
|
|
train[2019-04-01-02:37:10] Epoch: [315][000/521] Time 0.70 (0.70) Data 0.42 (0.42) Loss 0.291 (0.291) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-02:37:33] Epoch: [315][100/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.193 (0.225) Prec@1 96.88 (95.30) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-02:37:57] Epoch: [315][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.186 (0.235) Prec@1 96.88 (94.99) Prec@5 100.00 (99.90)
|
|
train[2019-04-01-02:38:21] Epoch: [315][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.184 (0.230) Prec@1 96.88 (95.07) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-02:38:44] Epoch: [315][400/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.126 (0.232) Prec@1 98.96 (95.05) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-02:39:08] Epoch: [315][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.148 (0.230) Prec@1 97.92 (95.09) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-02:39:13] Epoch: [315][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.202 (0.231) Prec@1 96.25 (95.07) Prec@5 100.00 (99.92)
|
|
[2019-04-01-02:39:13] **train** Prec@1 95.07 Prec@5 99.92 Error@1 4.93 Error@5 0.08 Loss:0.231
|
|
test [2019-04-01-02:39:13] Epoch: [315][000/105] Time 0.49 (0.49) Data 0.41 (0.41) Loss 0.164 (0.164) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-02:39:18] Epoch: [315][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.071 (0.184) Prec@1 96.88 (94.91) Prec@5 100.00 (99.91)
|
|
test [2019-04-01-02:39:18] Epoch: [315][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.292 (0.184) Prec@1 93.75 (94.91) Prec@5 100.00 (99.91)
|
|
[2019-04-01-02:39:18] **test** Prec@1 94.91 Prec@5 99.91 Error@1 5.09 Error@5 0.09 Loss:0.184
|
|
----> Best Accuracy : Acc@1=95.38, Acc@5=99.90, Error@1=4.62, Error@5=0.10
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-02:39:18] [Epoch=316/600] [Need: 10:11:13] LR=0.0115 ~ 0.0115, Batch=96
|
|
train[2019-04-01-02:39:19] Epoch: [316][000/521] Time 0.80 (0.80) Data 0.52 (0.52) Loss 0.125 (0.125) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-02:39:43] Epoch: [316][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.153 (0.224) Prec@1 96.88 (95.21) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-02:40:06] Epoch: [316][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.278 (0.227) Prec@1 92.71 (94.96) Prec@5 98.96 (99.93)
|
|
train[2019-04-01-02:40:30] Epoch: [316][300/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.294 (0.229) Prec@1 93.75 (94.89) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-02:40:54] Epoch: [316][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.248 (0.232) Prec@1 92.71 (94.86) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-02:41:18] Epoch: [316][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.311 (0.234) Prec@1 94.79 (94.84) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-02:41:23] Epoch: [316][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.160 (0.234) Prec@1 98.75 (94.82) Prec@5 100.00 (99.92)
|
|
[2019-04-01-02:41:23] **train** Prec@1 94.82 Prec@5 99.92 Error@1 5.18 Error@5 0.08 Loss:0.234
|
|
test [2019-04-01-02:41:23] Epoch: [316][000/105] Time 0.50 (0.50) Data 0.44 (0.44) Loss 0.166 (0.166) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-02:41:27] Epoch: [316][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.069 (0.188) Prec@1 97.92 (94.59) Prec@5 100.00 (99.91)
|
|
test [2019-04-01-02:41:27] Epoch: [316][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.009 (0.189) Prec@1 100.00 (94.57) Prec@5 100.00 (99.91)
|
|
[2019-04-01-02:41:28] **test** Prec@1 94.57 Prec@5 99.91 Error@1 5.43 Error@5 0.09 Loss:0.189
|
|
----> Best Accuracy : Acc@1=95.38, Acc@5=99.90, Error@1=4.62, Error@5=0.10
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-02:41:28] [Epoch=317/600] [Need: 10:12:22] LR=0.0114 ~ 0.0114, Batch=96
|
|
train[2019-04-01-02:41:29] Epoch: [317][000/521] Time 0.84 (0.84) Data 0.56 (0.56) Loss 0.111 (0.111) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-02:41:52] Epoch: [317][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.153 (0.221) Prec@1 96.88 (95.06) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-02:42:16] Epoch: [317][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.293 (0.224) Prec@1 92.71 (95.06) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-02:42:40] Epoch: [317][300/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.120 (0.227) Prec@1 96.88 (95.01) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-02:43:04] Epoch: [317][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.279 (0.225) Prec@1 91.67 (95.09) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-02:43:27] Epoch: [317][500/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.269 (0.228) Prec@1 93.75 (95.01) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-02:43:32] Epoch: [317][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.104 (0.228) Prec@1 98.75 (95.02) Prec@5 100.00 (99.94)
|
|
[2019-04-01-02:43:32] **train** Prec@1 95.02 Prec@5 99.94 Error@1 4.98 Error@5 0.06 Loss:0.228
|
|
test [2019-04-01-02:43:33] Epoch: [317][000/105] Time 0.48 (0.48) Data 0.42 (0.42) Loss 0.269 (0.269) Prec@1 90.62 (90.62) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-02:43:37] Epoch: [317][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.070 (0.189) Prec@1 96.88 (94.50) Prec@5 100.00 (99.87)
|
|
test [2019-04-01-02:43:37] Epoch: [317][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.218 (0.189) Prec@1 93.75 (94.49) Prec@5 100.00 (99.87)
|
|
[2019-04-01-02:43:37] **test** Prec@1 94.49 Prec@5 99.87 Error@1 5.51 Error@5 0.13 Loss:0.189
|
|
----> Best Accuracy : Acc@1=95.38, Acc@5=99.90, Error@1=4.62, Error@5=0.10
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-02:43:37] [Epoch=318/600] [Need: 10:08:01] LR=0.0114 ~ 0.0114, Batch=96
|
|
train[2019-04-01-02:43:38] Epoch: [318][000/521] Time 0.70 (0.70) Data 0.42 (0.42) Loss 0.227 (0.227) Prec@1 96.88 (96.88) Prec@5 98.96 (98.96)
|
|
train[2019-04-01-02:44:01] Epoch: [318][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.222 (0.218) Prec@1 97.92 (95.32) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-02:44:25] Epoch: [318][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.300 (0.228) Prec@1 93.75 (95.02) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-02:44:49] Epoch: [318][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.369 (0.226) Prec@1 87.50 (95.04) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-02:45:13] Epoch: [318][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.113 (0.229) Prec@1 96.88 (94.95) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-02:45:36] Epoch: [318][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.329 (0.231) Prec@1 91.67 (94.98) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-02:45:41] Epoch: [318][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.214 (0.231) Prec@1 96.25 (94.97) Prec@5 100.00 (99.92)
|
|
[2019-04-01-02:45:41] **train** Prec@1 94.97 Prec@5 99.92 Error@1 5.03 Error@5 0.08 Loss:0.231
|
|
test [2019-04-01-02:45:42] Epoch: [318][000/105] Time 0.46 (0.46) Data 0.40 (0.40) Loss 0.216 (0.216) Prec@1 90.62 (90.62) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-02:45:46] Epoch: [318][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.042 (0.186) Prec@1 97.92 (94.66) Prec@5 100.00 (99.82)
|
|
test [2019-04-01-02:45:46] Epoch: [318][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.012 (0.188) Prec@1 100.00 (94.62) Prec@5 100.00 (99.83)
|
|
[2019-04-01-02:45:46] **test** Prec@1 94.62 Prec@5 99.83 Error@1 5.38 Error@5 0.17 Loss:0.188
|
|
----> Best Accuracy : Acc@1=95.38, Acc@5=99.90, Error@1=4.62, Error@5=0.10
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-02:45:46] [Epoch=319/600] [Need: 10:03:34] LR=0.0113 ~ 0.0113, Batch=96
|
|
train[2019-04-01-02:45:47] Epoch: [319][000/521] Time 0.82 (0.82) Data 0.54 (0.54) Loss 0.213 (0.213) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-02:46:11] Epoch: [319][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.254 (0.223) Prec@1 94.79 (95.44) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-02:46:35] Epoch: [319][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.248 (0.222) Prec@1 92.71 (95.17) Prec@5 98.96 (99.93)
|
|
train[2019-04-01-02:46:58] Epoch: [319][300/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.165 (0.221) Prec@1 96.88 (95.17) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-02:47:22] Epoch: [319][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.369 (0.228) Prec@1 90.62 (95.03) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-02:47:46] Epoch: [319][500/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.224 (0.231) Prec@1 95.83 (94.91) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-02:47:51] Epoch: [319][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.199 (0.233) Prec@1 93.75 (94.87) Prec@5 100.00 (99.92)
|
|
[2019-04-01-02:47:51] **train** Prec@1 94.87 Prec@5 99.92 Error@1 5.13 Error@5 0.08 Loss:0.233
|
|
test [2019-04-01-02:47:51] Epoch: [319][000/105] Time 0.47 (0.47) Data 0.41 (0.41) Loss 0.179 (0.179) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-02:47:55] Epoch: [319][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.104 (0.194) Prec@1 96.88 (94.48) Prec@5 100.00 (99.90)
|
|
test [2019-04-01-02:47:55] Epoch: [319][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.039 (0.195) Prec@1 100.00 (94.50) Prec@5 100.00 (99.90)
|
|
[2019-04-01-02:47:55] **test** Prec@1 94.50 Prec@5 99.90 Error@1 5.50 Error@5 0.10 Loss:0.195
|
|
----> Best Accuracy : Acc@1=95.38, Acc@5=99.90, Error@1=4.62, Error@5=0.10
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-02:47:56] [Epoch=320/600] [Need: 10:04:29] LR=0.0112 ~ 0.0112, Batch=96
|
|
train[2019-04-01-02:47:56] Epoch: [320][000/521] Time 0.85 (0.85) Data 0.57 (0.57) Loss 0.217 (0.217) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-02:48:20] Epoch: [320][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.252 (0.221) Prec@1 95.83 (95.42) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-02:48:44] Epoch: [320][200/521] Time 0.27 (0.24) Data 0.00 (0.00) Loss 0.240 (0.227) Prec@1 95.83 (95.32) Prec@5 98.96 (99.93)
|
|
train[2019-04-01-02:49:08] Epoch: [320][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.110 (0.224) Prec@1 98.96 (95.34) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-02:49:32] Epoch: [320][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.118 (0.221) Prec@1 98.96 (95.41) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-02:49:56] Epoch: [320][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.251 (0.225) Prec@1 92.71 (95.25) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-02:50:01] Epoch: [320][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.167 (0.225) Prec@1 97.50 (95.27) Prec@5 100.00 (99.93)
|
|
[2019-04-01-02:50:01] **train** Prec@1 95.27 Prec@5 99.93 Error@1 4.73 Error@5 0.07 Loss:0.225
|
|
test [2019-04-01-02:50:01] Epoch: [320][000/105] Time 0.48 (0.48) Data 0.42 (0.42) Loss 0.073 (0.073) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-02:50:05] Epoch: [320][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.080 (0.167) Prec@1 96.88 (95.28) Prec@5 100.00 (99.94)
|
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test [2019-04-01-02:50:05] Epoch: [320][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.003 (0.168) Prec@1 100.00 (95.27) Prec@5 100.00 (99.93)
|
|
[2019-04-01-02:50:05] **test** Prec@1 95.27 Prec@5 99.93 Error@1 4.73 Error@5 0.07 Loss:0.168
|
|
----> Best Accuracy : Acc@1=95.38, Acc@5=99.90, Error@1=4.62, Error@5=0.10
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-02:50:06] [Epoch=321/600] [Need: 10:04:48] LR=0.0112 ~ 0.0112, Batch=96
|
|
train[2019-04-01-02:50:06] Epoch: [321][000/521] Time 0.84 (0.84) Data 0.56 (0.56) Loss 0.115 (0.115) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-02:50:30] Epoch: [321][100/521] Time 0.27 (0.25) Data 0.00 (0.01) Loss 0.271 (0.207) Prec@1 93.75 (95.74) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-02:50:54] Epoch: [321][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.105 (0.224) Prec@1 97.92 (95.38) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-02:51:18] Epoch: [321][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.216 (0.217) Prec@1 94.79 (95.48) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-02:51:42] Epoch: [321][400/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.150 (0.220) Prec@1 96.88 (95.37) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-02:52:06] Epoch: [321][500/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.262 (0.224) Prec@1 92.71 (95.23) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-02:52:10] Epoch: [321][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.166 (0.225) Prec@1 95.00 (95.20) Prec@5 100.00 (99.94)
|
|
[2019-04-01-02:52:10] **train** Prec@1 95.20 Prec@5 99.94 Error@1 4.80 Error@5 0.06 Loss:0.225
|
|
test [2019-04-01-02:52:11] Epoch: [321][000/105] Time 0.59 (0.59) Data 0.53 (0.53) Loss 0.103 (0.103) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-02:52:15] Epoch: [321][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.119 (0.162) Prec@1 95.83 (95.22) Prec@5 100.00 (99.93)
|
|
test [2019-04-01-02:52:15] Epoch: [321][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.040 (0.162) Prec@1 100.00 (95.20) Prec@5 100.00 (99.93)
|
|
[2019-04-01-02:52:15] **test** Prec@1 95.20 Prec@5 99.93 Error@1 4.80 Error@5 0.07 Loss:0.162
|
|
----> Best Accuracy : Acc@1=95.38, Acc@5=99.90, Error@1=4.62, Error@5=0.10
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-02:52:15] [Epoch=322/600] [Need: 10:01:01] LR=0.0111 ~ 0.0111, Batch=96
|
|
train[2019-04-01-02:52:16] Epoch: [322][000/521] Time 0.83 (0.83) Data 0.56 (0.56) Loss 0.214 (0.214) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-02:52:40] Epoch: [322][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.238 (0.212) Prec@1 94.79 (95.33) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-02:53:04] Epoch: [322][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.165 (0.224) Prec@1 98.96 (95.18) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-02:53:27] Epoch: [322][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.115 (0.225) Prec@1 96.88 (95.14) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-02:53:51] Epoch: [322][400/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.150 (0.228) Prec@1 96.88 (95.00) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-02:54:15] Epoch: [322][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.214 (0.229) Prec@1 97.92 (94.97) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-02:54:19] Epoch: [322][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.185 (0.228) Prec@1 93.75 (95.00) Prec@5 100.00 (99.92)
|
|
[2019-04-01-02:54:19] **train** Prec@1 95.00 Prec@5 99.92 Error@1 5.00 Error@5 0.08 Loss:0.228
|
|
test [2019-04-01-02:54:20] Epoch: [322][000/105] Time 0.60 (0.60) Data 0.53 (0.53) Loss 0.213 (0.213) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-02:54:24] Epoch: [322][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.070 (0.173) Prec@1 97.92 (95.12) Prec@5 100.00 (99.88)
|
|
test [2019-04-01-02:54:24] Epoch: [322][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.043 (0.174) Prec@1 100.00 (95.13) Prec@5 100.00 (99.88)
|
|
[2019-04-01-02:54:24] **test** Prec@1 95.13 Prec@5 99.88 Error@1 4.87 Error@5 0.12 Loss:0.174
|
|
----> Best Accuracy : Acc@1=95.38, Acc@5=99.90, Error@1=4.62, Error@5=0.10
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-02:54:25] [Epoch=323/600] [Need: 09:56:21] LR=0.0111 ~ 0.0111, Batch=96
|
|
train[2019-04-01-02:54:25] Epoch: [323][000/521] Time 0.71 (0.71) Data 0.43 (0.43) Loss 0.176 (0.176) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-02:54:49] Epoch: [323][100/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.222 (0.228) Prec@1 93.75 (94.96) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-02:55:13] Epoch: [323][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.213 (0.228) Prec@1 94.79 (94.91) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-02:55:37] Epoch: [323][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.169 (0.228) Prec@1 96.88 (95.04) Prec@5 98.96 (99.95)
|
|
train[2019-04-01-02:56:01] Epoch: [323][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.291 (0.228) Prec@1 91.67 (95.03) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-02:56:25] Epoch: [323][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.172 (0.231) Prec@1 95.83 (94.96) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-02:56:30] Epoch: [323][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.267 (0.232) Prec@1 92.50 (94.94) Prec@5 100.00 (99.94)
|
|
[2019-04-01-02:56:30] **train** Prec@1 94.94 Prec@5 99.94 Error@1 5.06 Error@5 0.06 Loss:0.232
|
|
test [2019-04-01-02:56:30] Epoch: [323][000/105] Time 0.47 (0.47) Data 0.40 (0.40) Loss 0.178 (0.178) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-02:56:34] Epoch: [323][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.052 (0.184) Prec@1 98.96 (94.85) Prec@5 100.00 (99.87)
|
|
test [2019-04-01-02:56:34] Epoch: [323][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.026 (0.185) Prec@1 100.00 (94.85) Prec@5 100.00 (99.87)
|
|
[2019-04-01-02:56:34] **test** Prec@1 94.85 Prec@5 99.87 Error@1 5.15 Error@5 0.13 Loss:0.185
|
|
----> Best Accuracy : Acc@1=95.38, Acc@5=99.90, Error@1=4.62, Error@5=0.10
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-02:56:34] [Epoch=324/600] [Need: 09:57:49] LR=0.0110 ~ 0.0110, Batch=96
|
|
train[2019-04-01-02:56:35] Epoch: [324][000/521] Time 0.71 (0.71) Data 0.44 (0.44) Loss 0.142 (0.142) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-02:56:59] Epoch: [324][100/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.193 (0.215) Prec@1 97.92 (95.20) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-02:57:23] Epoch: [324][200/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.141 (0.225) Prec@1 95.83 (94.90) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-02:57:46] Epoch: [324][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.164 (0.224) Prec@1 95.83 (94.99) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-02:58:10] Epoch: [324][400/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.285 (0.229) Prec@1 90.62 (94.96) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-02:58:34] Epoch: [324][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.372 (0.231) Prec@1 90.62 (94.92) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-02:58:39] Epoch: [324][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.120 (0.230) Prec@1 97.50 (94.96) Prec@5 100.00 (99.93)
|
|
[2019-04-01-02:58:39] **train** Prec@1 94.96 Prec@5 99.93 Error@1 5.04 Error@5 0.07 Loss:0.230
|
|
test [2019-04-01-02:58:39] Epoch: [324][000/105] Time 0.61 (0.61) Data 0.55 (0.55) Loss 0.136 (0.136) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-02:58:43] Epoch: [324][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.057 (0.173) Prec@1 97.92 (95.08) Prec@5 100.00 (99.89)
|
|
test [2019-04-01-02:58:44] Epoch: [324][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.077 (0.174) Prec@1 93.75 (95.07) Prec@5 100.00 (99.89)
|
|
[2019-04-01-02:58:44] **test** Prec@1 95.07 Prec@5 99.89 Error@1 4.93 Error@5 0.11 Loss:0.174
|
|
----> Best Accuracy : Acc@1=95.38, Acc@5=99.90, Error@1=4.62, Error@5=0.10
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-02:58:44] [Epoch=325/600] [Need: 09:52:40] LR=0.0109 ~ 0.0109, Batch=96
|
|
train[2019-04-01-02:58:45] Epoch: [325][000/521] Time 0.71 (0.71) Data 0.44 (0.44) Loss 0.219 (0.219) Prec@1 96.88 (96.88) Prec@5 98.96 (98.96)
|
|
train[2019-04-01-02:59:08] Epoch: [325][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.277 (0.228) Prec@1 92.71 (95.09) Prec@5 100.00 (99.89)
|
|
train[2019-04-01-02:59:32] Epoch: [325][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.203 (0.221) Prec@1 96.88 (95.28) Prec@5 98.96 (99.91)
|
|
train[2019-04-01-02:59:56] Epoch: [325][300/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.135 (0.219) Prec@1 96.88 (95.36) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-03:00:19] Epoch: [325][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.258 (0.221) Prec@1 92.71 (95.32) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-03:00:43] Epoch: [325][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.253 (0.224) Prec@1 93.75 (95.23) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-03:00:48] Epoch: [325][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.225 (0.225) Prec@1 95.00 (95.22) Prec@5 100.00 (99.92)
|
|
[2019-04-01-03:00:48] **train** Prec@1 95.22 Prec@5 99.92 Error@1 4.78 Error@5 0.08 Loss:0.225
|
|
test [2019-04-01-03:00:48] Epoch: [325][000/105] Time 0.48 (0.48) Data 0.42 (0.42) Loss 0.119 (0.119) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-03:00:53] Epoch: [325][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.068 (0.189) Prec@1 98.96 (94.75) Prec@5 100.00 (99.92)
|
|
test [2019-04-01-03:00:53] Epoch: [325][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.192 (0.190) Prec@1 93.75 (94.70) Prec@5 100.00 (99.92)
|
|
[2019-04-01-03:00:53] **test** Prec@1 94.70 Prec@5 99.92 Error@1 5.30 Error@5 0.08 Loss:0.190
|
|
----> Best Accuracy : Acc@1=95.38, Acc@5=99.90, Error@1=4.62, Error@5=0.10
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-03:00:53] [Epoch=326/600] [Need: 09:49:35] LR=0.0109 ~ 0.0109, Batch=96
|
|
train[2019-04-01-03:00:54] Epoch: [326][000/521] Time 0.85 (0.85) Data 0.59 (0.59) Loss 0.195 (0.195) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-03:01:17] Epoch: [326][100/521] Time 0.25 (0.24) Data 0.00 (0.01) Loss 0.213 (0.230) Prec@1 94.79 (94.92) Prec@5 100.00 (99.88)
|
|
train[2019-04-01-03:01:41] Epoch: [326][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.233 (0.227) Prec@1 95.83 (95.06) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-03:02:05] Epoch: [326][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.279 (0.222) Prec@1 93.75 (95.18) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-03:02:29] Epoch: [326][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.117 (0.227) Prec@1 96.88 (95.05) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-03:02:53] Epoch: [326][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.255 (0.230) Prec@1 94.79 (94.97) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-03:02:57] Epoch: [326][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.206 (0.230) Prec@1 96.25 (95.01) Prec@5 100.00 (99.91)
|
|
[2019-04-01-03:02:57] **train** Prec@1 95.01 Prec@5 99.91 Error@1 4.99 Error@5 0.09 Loss:0.230
|
|
test [2019-04-01-03:02:58] Epoch: [326][000/105] Time 0.47 (0.47) Data 0.41 (0.41) Loss 0.096 (0.096) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-03:03:02] Epoch: [326][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.073 (0.188) Prec@1 95.83 (94.64) Prec@5 100.00 (99.82)
|
|
test [2019-04-01-03:03:02] Epoch: [326][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.067 (0.188) Prec@1 93.75 (94.64) Prec@5 100.00 (99.83)
|
|
[2019-04-01-03:03:02] **test** Prec@1 94.64 Prec@5 99.83 Error@1 5.36 Error@5 0.17 Loss:0.188
|
|
----> Best Accuracy : Acc@1=95.38, Acc@5=99.90, Error@1=4.62, Error@5=0.10
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-03:03:02] [Epoch=327/600] [Need: 09:48:58] LR=0.0108 ~ 0.0108, Batch=96
|
|
train[2019-04-01-03:03:03] Epoch: [327][000/521] Time 0.84 (0.84) Data 0.57 (0.57) Loss 0.171 (0.171) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-03:03:27] Epoch: [327][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.140 (0.216) Prec@1 96.88 (95.25) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-03:03:51] Epoch: [327][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.308 (0.229) Prec@1 91.67 (94.99) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-03:04:14] Epoch: [327][300/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.197 (0.226) Prec@1 93.75 (94.99) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-03:04:38] Epoch: [327][400/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.296 (0.227) Prec@1 92.71 (94.95) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-03:05:02] Epoch: [327][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.208 (0.227) Prec@1 97.92 (94.99) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-03:05:07] Epoch: [327][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.411 (0.228) Prec@1 92.50 (94.96) Prec@5 100.00 (99.95)
|
|
[2019-04-01-03:05:07] **train** Prec@1 94.96 Prec@5 99.95 Error@1 5.04 Error@5 0.05 Loss:0.228
|
|
test [2019-04-01-03:05:07] Epoch: [327][000/105] Time 0.48 (0.48) Data 0.41 (0.41) Loss 0.245 (0.245) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-03:05:11] Epoch: [327][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.101 (0.190) Prec@1 96.88 (94.46) Prec@5 100.00 (99.87)
|
|
test [2019-04-01-03:05:12] Epoch: [327][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.069 (0.192) Prec@1 100.00 (94.46) Prec@5 100.00 (99.87)
|
|
[2019-04-01-03:05:12] **test** Prec@1 94.46 Prec@5 99.87 Error@1 5.54 Error@5 0.13 Loss:0.192
|
|
----> Best Accuracy : Acc@1=95.38, Acc@5=99.90, Error@1=4.62, Error@5=0.10
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-03:05:12] [Epoch=328/600] [Need: 09:46:46] LR=0.0107 ~ 0.0107, Batch=96
|
|
train[2019-04-01-03:05:13] Epoch: [328][000/521] Time 0.83 (0.83) Data 0.55 (0.55) Loss 0.285 (0.285) Prec@1 89.58 (89.58) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-03:05:36] Epoch: [328][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.274 (0.218) Prec@1 95.83 (95.31) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-03:06:00] Epoch: [328][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.237 (0.223) Prec@1 92.71 (95.18) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-03:06:24] Epoch: [328][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.197 (0.224) Prec@1 95.83 (95.06) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-03:06:48] Epoch: [328][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.304 (0.225) Prec@1 91.67 (95.08) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-03:07:11] Epoch: [328][500/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.258 (0.226) Prec@1 97.92 (95.03) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-03:07:16] Epoch: [328][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.286 (0.227) Prec@1 93.75 (95.02) Prec@5 100.00 (99.94)
|
|
[2019-04-01-03:07:16] **train** Prec@1 95.02 Prec@5 99.94 Error@1 4.98 Error@5 0.06 Loss:0.227
|
|
test [2019-04-01-03:07:17] Epoch: [328][000/105] Time 0.47 (0.47) Data 0.41 (0.41) Loss 0.196 (0.196) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-03:07:21] Epoch: [328][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.060 (0.210) Prec@1 96.88 (94.30) Prec@5 100.00 (99.86)
|
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test [2019-04-01-03:07:21] Epoch: [328][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.114 (0.211) Prec@1 93.75 (94.29) Prec@5 100.00 (99.86)
|
|
[2019-04-01-03:07:21] **test** Prec@1 94.29 Prec@5 99.86 Error@1 5.71 Error@5 0.14 Loss:0.211
|
|
----> Best Accuracy : Acc@1=95.38, Acc@5=99.90, Error@1=4.62, Error@5=0.10
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-03:07:21] [Epoch=329/600] [Need: 09:43:49] LR=0.0107 ~ 0.0107, Batch=96
|
|
train[2019-04-01-03:07:22] Epoch: [329][000/521] Time 0.86 (0.86) Data 0.58 (0.58) Loss 0.146 (0.146) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-03:07:46] Epoch: [329][100/521] Time 0.24 (0.25) Data 0.00 (0.01) Loss 0.216 (0.216) Prec@1 93.75 (95.40) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-03:08:10] Epoch: [329][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.325 (0.224) Prec@1 92.71 (95.13) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-03:08:34] Epoch: [329][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.311 (0.223) Prec@1 92.71 (95.06) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-03:08:57] Epoch: [329][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.170 (0.228) Prec@1 96.88 (94.95) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-03:09:21] Epoch: [329][500/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.188 (0.227) Prec@1 97.92 (95.00) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-03:09:26] Epoch: [329][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.187 (0.228) Prec@1 95.00 (94.97) Prec@5 100.00 (99.94)
|
|
[2019-04-01-03:09:26] **train** Prec@1 94.97 Prec@5 99.94 Error@1 5.03 Error@5 0.06 Loss:0.228
|
|
test [2019-04-01-03:09:27] Epoch: [329][000/105] Time 0.60 (0.60) Data 0.54 (0.54) Loss 0.253 (0.253) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-03:09:31] Epoch: [329][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.093 (0.189) Prec@1 96.88 (94.73) Prec@5 100.00 (99.88)
|
|
test [2019-04-01-03:09:31] Epoch: [329][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.155 (0.190) Prec@1 93.75 (94.70) Prec@5 100.00 (99.88)
|
|
[2019-04-01-03:09:31] **test** Prec@1 94.70 Prec@5 99.88 Error@1 5.30 Error@5 0.12 Loss:0.190
|
|
----> Best Accuracy : Acc@1=95.38, Acc@5=99.90, Error@1=4.62, Error@5=0.10
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-03:09:31] [Epoch=330/600] [Need: 09:45:00] LR=0.0106 ~ 0.0106, Batch=96
|
|
train[2019-04-01-03:09:32] Epoch: [330][000/521] Time 0.84 (0.84) Data 0.56 (0.56) Loss 0.313 (0.313) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-03:09:56] Epoch: [330][100/521] Time 0.24 (0.25) Data 0.00 (0.01) Loss 0.316 (0.205) Prec@1 91.67 (95.64) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-03:10:20] Epoch: [330][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.231 (0.215) Prec@1 94.79 (95.16) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-03:10:44] Epoch: [330][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.186 (0.214) Prec@1 94.79 (95.25) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-03:11:08] Epoch: [330][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.267 (0.212) Prec@1 95.83 (95.32) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-03:11:32] Epoch: [330][500/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.267 (0.211) Prec@1 96.88 (95.36) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-03:11:36] Epoch: [330][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.219 (0.212) Prec@1 93.75 (95.34) Prec@5 100.00 (99.94)
|
|
[2019-04-01-03:11:36] **train** Prec@1 95.34 Prec@5 99.94 Error@1 4.66 Error@5 0.06 Loss:0.212
|
|
test [2019-04-01-03:11:37] Epoch: [330][000/105] Time 0.46 (0.46) Data 0.40 (0.40) Loss 0.196 (0.196) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-03:11:41] Epoch: [330][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.150 (0.229) Prec@1 93.75 (93.94) Prec@5 100.00 (99.87)
|
|
test [2019-04-01-03:11:41] Epoch: [330][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.199 (0.229) Prec@1 93.75 (93.94) Prec@5 100.00 (99.87)
|
|
[2019-04-01-03:11:41] **test** Prec@1 93.94 Prec@5 99.87 Error@1 6.06 Error@5 0.13 Loss:0.229
|
|
----> Best Accuracy : Acc@1=95.38, Acc@5=99.90, Error@1=4.62, Error@5=0.10
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-03:11:41] [Epoch=331/600] [Need: 09:43:54] LR=0.0105 ~ 0.0105, Batch=96
|
|
train[2019-04-01-03:11:42] Epoch: [331][000/521] Time 0.71 (0.71) Data 0.44 (0.44) Loss 0.193 (0.193) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-03:12:06] Epoch: [331][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.254 (0.228) Prec@1 93.75 (94.86) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-03:12:30] Epoch: [331][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.190 (0.225) Prec@1 94.79 (95.08) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-03:12:54] Epoch: [331][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.267 (0.220) Prec@1 95.83 (95.25) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-03:13:17] Epoch: [331][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.109 (0.219) Prec@1 100.00 (95.30) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-03:13:41] Epoch: [331][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.365 (0.222) Prec@1 88.54 (95.20) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-03:13:46] Epoch: [331][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.106 (0.222) Prec@1 97.50 (95.20) Prec@5 100.00 (99.95)
|
|
[2019-04-01-03:13:46] **train** Prec@1 95.20 Prec@5 99.95 Error@1 4.80 Error@5 0.05 Loss:0.222
|
|
test [2019-04-01-03:13:47] Epoch: [331][000/105] Time 0.60 (0.60) Data 0.53 (0.53) Loss 0.251 (0.251) Prec@1 91.67 (91.67) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-03:13:51] Epoch: [331][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.105 (0.218) Prec@1 97.92 (94.18) Prec@5 100.00 (99.90)
|
|
test [2019-04-01-03:13:51] Epoch: [331][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.038 (0.217) Prec@1 100.00 (94.20) Prec@5 100.00 (99.90)
|
|
[2019-04-01-03:13:51] **test** Prec@1 94.20 Prec@5 99.90 Error@1 5.80 Error@5 0.10 Loss:0.217
|
|
----> Best Accuracy : Acc@1=95.38, Acc@5=99.90, Error@1=4.62, Error@5=0.10
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-03:13:51] [Epoch=332/600] [Need: 09:40:22] LR=0.0105 ~ 0.0105, Batch=96
|
|
train[2019-04-01-03:13:52] Epoch: [332][000/521] Time 0.85 (0.85) Data 0.59 (0.59) Loss 0.183 (0.183) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-03:14:16] Epoch: [332][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.174 (0.212) Prec@1 97.92 (95.11) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-03:14:40] Epoch: [332][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.249 (0.226) Prec@1 94.79 (94.95) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-03:15:04] Epoch: [332][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.230 (0.223) Prec@1 94.79 (95.13) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-03:15:27] Epoch: [332][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.255 (0.224) Prec@1 94.79 (95.14) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-03:15:51] Epoch: [332][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.229 (0.226) Prec@1 95.83 (95.09) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-03:15:56] Epoch: [332][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.201 (0.225) Prec@1 93.75 (95.08) Prec@5 100.00 (99.92)
|
|
[2019-04-01-03:15:56] **train** Prec@1 95.08 Prec@5 99.92 Error@1 4.92 Error@5 0.08 Loss:0.225
|
|
test [2019-04-01-03:15:56] Epoch: [332][000/105] Time 0.60 (0.60) Data 0.54 (0.54) Loss 0.186 (0.186) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-03:16:00] Epoch: [332][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.082 (0.218) Prec@1 96.88 (94.07) Prec@5 100.00 (99.93)
|
|
test [2019-04-01-03:16:01] Epoch: [332][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.010 (0.218) Prec@1 100.00 (94.10) Prec@5 100.00 (99.93)
|
|
[2019-04-01-03:16:01] **test** Prec@1 94.10 Prec@5 99.93 Error@1 5.90 Error@5 0.07 Loss:0.218
|
|
----> Best Accuracy : Acc@1=95.38, Acc@5=99.90, Error@1=4.62, Error@5=0.10
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-03:16:01] [Epoch=333/600] [Need: 09:36:49] LR=0.0104 ~ 0.0104, Batch=96
|
|
train[2019-04-01-03:16:02] Epoch: [333][000/521] Time 0.84 (0.84) Data 0.56 (0.56) Loss 0.293 (0.293) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-03:16:25] Epoch: [333][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.223 (0.229) Prec@1 94.79 (94.85) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-03:16:49] Epoch: [333][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.274 (0.229) Prec@1 93.75 (94.97) Prec@5 98.96 (99.94)
|
|
train[2019-04-01-03:17:13] Epoch: [333][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.211 (0.224) Prec@1 96.88 (95.11) Prec@5 98.96 (99.95)
|
|
train[2019-04-01-03:17:37] Epoch: [333][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.272 (0.220) Prec@1 92.71 (95.24) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-03:18:01] Epoch: [333][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.246 (0.224) Prec@1 93.75 (95.13) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-03:18:05] Epoch: [333][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.265 (0.223) Prec@1 95.00 (95.16) Prec@5 98.75 (99.94)
|
|
[2019-04-01-03:18:05] **train** Prec@1 95.16 Prec@5 99.94 Error@1 4.84 Error@5 0.06 Loss:0.223
|
|
test [2019-04-01-03:18:06] Epoch: [333][000/105] Time 0.47 (0.47) Data 0.40 (0.40) Loss 0.174 (0.174) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-03:18:10] Epoch: [333][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.082 (0.178) Prec@1 96.88 (95.07) Prec@5 100.00 (99.88)
|
|
test [2019-04-01-03:18:10] Epoch: [333][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.063 (0.178) Prec@1 93.75 (95.07) Prec@5 100.00 (99.88)
|
|
[2019-04-01-03:18:10] **test** Prec@1 95.07 Prec@5 99.88 Error@1 4.93 Error@5 0.12 Loss:0.178
|
|
----> Best Accuracy : Acc@1=95.38, Acc@5=99.90, Error@1=4.62, Error@5=0.10
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-03:18:10] [Epoch=334/600] [Need: 09:34:13] LR=0.0103 ~ 0.0103, Batch=96
|
|
train[2019-04-01-03:18:11] Epoch: [334][000/521] Time 0.83 (0.83) Data 0.56 (0.56) Loss 0.261 (0.261) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-03:18:35] Epoch: [334][100/521] Time 0.23 (0.24) Data 0.00 (0.01) Loss 0.204 (0.219) Prec@1 93.75 (95.39) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-03:18:59] Epoch: [334][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.173 (0.217) Prec@1 95.83 (95.31) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-03:19:23] Epoch: [334][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.282 (0.215) Prec@1 93.75 (95.36) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-03:19:46] Epoch: [334][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.226 (0.216) Prec@1 96.88 (95.36) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-03:20:10] Epoch: [334][500/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.212 (0.218) Prec@1 94.79 (95.33) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-03:20:15] Epoch: [334][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.109 (0.219) Prec@1 97.50 (95.35) Prec@5 100.00 (99.94)
|
|
[2019-04-01-03:20:15] **train** Prec@1 95.35 Prec@5 99.94 Error@1 4.65 Error@5 0.06 Loss:0.219
|
|
test [2019-04-01-03:20:15] Epoch: [334][000/105] Time 0.47 (0.47) Data 0.40 (0.40) Loss 0.227 (0.227) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-03:20:20] Epoch: [334][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.140 (0.193) Prec@1 95.83 (94.84) Prec@5 100.00 (99.88)
|
|
test [2019-04-01-03:20:20] Epoch: [334][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.010 (0.191) Prec@1 100.00 (94.88) Prec@5 100.00 (99.88)
|
|
[2019-04-01-03:20:20] **test** Prec@1 94.88 Prec@5 99.88 Error@1 5.12 Error@5 0.12 Loss:0.191
|
|
----> Best Accuracy : Acc@1=95.38, Acc@5=99.90, Error@1=4.62, Error@5=0.10
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-03:20:20] [Epoch=335/600] [Need: 09:32:05] LR=0.0103 ~ 0.0103, Batch=96
|
|
train[2019-04-01-03:20:21] Epoch: [335][000/521] Time 0.76 (0.76) Data 0.49 (0.49) Loss 0.212 (0.212) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-03:20:44] Epoch: [335][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.112 (0.214) Prec@1 97.92 (95.36) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-03:21:08] Epoch: [335][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.305 (0.224) Prec@1 91.67 (94.99) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-03:21:32] Epoch: [335][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.209 (0.218) Prec@1 94.79 (95.13) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-03:21:56] Epoch: [335][400/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.196 (0.217) Prec@1 96.88 (95.14) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-03:22:20] Epoch: [335][500/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.236 (0.222) Prec@1 93.75 (94.99) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-03:22:25] Epoch: [335][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.165 (0.223) Prec@1 97.50 (95.00) Prec@5 100.00 (99.96)
|
|
[2019-04-01-03:22:25] **train** Prec@1 95.00 Prec@5 99.96 Error@1 5.00 Error@5 0.04 Loss:0.223
|
|
test [2019-04-01-03:22:25] Epoch: [335][000/105] Time 0.61 (0.61) Data 0.54 (0.54) Loss 0.211 (0.211) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-03:22:29] Epoch: [335][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.073 (0.174) Prec@1 98.96 (94.96) Prec@5 100.00 (99.91)
|
|
test [2019-04-01-03:22:30] Epoch: [335][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.073 (0.175) Prec@1 93.75 (94.96) Prec@5 100.00 (99.91)
|
|
[2019-04-01-03:22:30] **test** Prec@1 94.96 Prec@5 99.91 Error@1 5.04 Error@5 0.09 Loss:0.175
|
|
----> Best Accuracy : Acc@1=95.38, Acc@5=99.90, Error@1=4.62, Error@5=0.10
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-03:22:30] [Epoch=336/600] [Need: 09:31:55] LR=0.0102 ~ 0.0102, Batch=96
|
|
train[2019-04-01-03:22:31] Epoch: [336][000/521] Time 0.84 (0.84) Data 0.57 (0.57) Loss 0.175 (0.175) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-03:22:54] Epoch: [336][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.189 (0.220) Prec@1 97.92 (95.19) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-03:23:18] Epoch: [336][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.270 (0.215) Prec@1 94.79 (95.35) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-03:23:42] Epoch: [336][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.094 (0.212) Prec@1 100.00 (95.46) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-03:24:06] Epoch: [336][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.284 (0.213) Prec@1 94.79 (95.45) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-03:24:30] Epoch: [336][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.319 (0.219) Prec@1 92.71 (95.27) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-03:24:34] Epoch: [336][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.109 (0.218) Prec@1 98.75 (95.30) Prec@5 100.00 (99.95)
|
|
[2019-04-01-03:24:35] **train** Prec@1 95.30 Prec@5 99.95 Error@1 4.70 Error@5 0.05 Loss:0.218
|
|
test [2019-04-01-03:24:35] Epoch: [336][000/105] Time 0.47 (0.47) Data 0.40 (0.40) Loss 0.068 (0.068) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-03:24:39] Epoch: [336][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.025 (0.167) Prec@1 98.96 (95.25) Prec@5 100.00 (99.92)
|
|
test [2019-04-01-03:24:39] Epoch: [336][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.332 (0.169) Prec@1 93.75 (95.21) Prec@5 100.00 (99.92)
|
|
[2019-04-01-03:24:39] **test** Prec@1 95.21 Prec@5 99.92 Error@1 4.79 Error@5 0.08 Loss:0.169
|
|
----> Best Accuracy : Acc@1=95.38, Acc@5=99.90, Error@1=4.62, Error@5=0.10
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-03:24:39] [Epoch=337/600] [Need: 09:28:08] LR=0.0102 ~ 0.0102, Batch=96
|
|
train[2019-04-01-03:24:40] Epoch: [337][000/521] Time 0.72 (0.72) Data 0.44 (0.44) Loss 0.181 (0.181) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-03:25:04] Epoch: [337][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.212 (0.208) Prec@1 95.83 (95.41) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-03:25:28] Epoch: [337][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.185 (0.209) Prec@1 93.75 (95.51) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-03:25:52] Epoch: [337][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.161 (0.211) Prec@1 95.83 (95.45) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-03:26:20] Epoch: [337][400/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.199 (0.207) Prec@1 94.79 (95.59) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-03:26:44] Epoch: [337][500/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.271 (0.212) Prec@1 92.71 (95.43) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-03:26:49] Epoch: [337][520/521] Time 0.22 (0.25) Data 0.00 (0.00) Loss 0.134 (0.212) Prec@1 100.00 (95.42) Prec@5 100.00 (99.95)
|
|
[2019-04-01-03:26:49] **train** Prec@1 95.42 Prec@5 99.95 Error@1 4.58 Error@5 0.05 Loss:0.212
|
|
test [2019-04-01-03:26:50] Epoch: [337][000/105] Time 0.60 (0.60) Data 0.54 (0.54) Loss 0.223 (0.223) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-03:26:54] Epoch: [337][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.116 (0.203) Prec@1 95.83 (94.09) Prec@5 100.00 (99.87)
|
|
test [2019-04-01-03:26:54] Epoch: [337][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.068 (0.202) Prec@1 93.75 (94.08) Prec@5 100.00 (99.87)
|
|
[2019-04-01-03:26:54] **test** Prec@1 94.08 Prec@5 99.87 Error@1 5.92 Error@5 0.13 Loss:0.202
|
|
----> Best Accuracy : Acc@1=95.38, Acc@5=99.90, Error@1=4.62, Error@5=0.10
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-03:26:54] [Epoch=338/600] [Need: 09:48:26] LR=0.0101 ~ 0.0101, Batch=96
|
|
train[2019-04-01-03:26:55] Epoch: [338][000/521] Time 0.86 (0.86) Data 0.59 (0.59) Loss 0.203 (0.203) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-03:27:20] Epoch: [338][100/521] Time 0.24 (0.25) Data 0.00 (0.01) Loss 0.297 (0.205) Prec@1 91.67 (95.58) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-03:27:44] Epoch: [338][200/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.232 (0.207) Prec@1 94.79 (95.57) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-03:28:08] Epoch: [338][300/521] Time 0.23 (0.25) Data 0.00 (0.00) Loss 0.157 (0.211) Prec@1 97.92 (95.50) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-03:28:33] Epoch: [338][400/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.326 (0.214) Prec@1 93.75 (95.45) Prec@5 98.96 (99.95)
|
|
train[2019-04-01-03:28:57] Epoch: [338][500/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.319 (0.222) Prec@1 93.75 (95.28) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-03:29:02] Epoch: [338][520/521] Time 0.22 (0.25) Data 0.00 (0.00) Loss 0.306 (0.222) Prec@1 96.25 (95.28) Prec@5 100.00 (99.94)
|
|
[2019-04-01-03:29:02] **train** Prec@1 95.28 Prec@5 99.94 Error@1 4.72 Error@5 0.06 Loss:0.222
|
|
test [2019-04-01-03:29:03] Epoch: [338][000/105] Time 0.52 (0.52) Data 0.46 (0.46) Loss 0.091 (0.091) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-03:29:07] Epoch: [338][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.061 (0.190) Prec@1 96.88 (94.64) Prec@5 100.00 (99.82)
|
|
test [2019-04-01-03:29:07] Epoch: [338][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.208 (0.193) Prec@1 93.75 (94.59) Prec@5 100.00 (99.83)
|
|
[2019-04-01-03:29:07] **test** Prec@1 94.59 Prec@5 99.83 Error@1 5.41 Error@5 0.17 Loss:0.193
|
|
----> Best Accuracy : Acc@1=95.38, Acc@5=99.90, Error@1=4.62, Error@5=0.10
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-03:29:07] [Epoch=339/600] [Need: 09:39:03] LR=0.0100 ~ 0.0100, Batch=96
|
|
train[2019-04-01-03:29:08] Epoch: [339][000/521] Time 0.74 (0.74) Data 0.46 (0.46) Loss 0.227 (0.227) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-03:29:32] Epoch: [339][100/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.182 (0.204) Prec@1 97.92 (95.80) Prec@5 98.96 (99.94)
|
|
train[2019-04-01-03:29:57] Epoch: [339][200/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.303 (0.209) Prec@1 93.75 (95.57) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-03:30:21] Epoch: [339][300/521] Time 0.26 (0.24) Data 0.00 (0.00) Loss 0.142 (0.214) Prec@1 97.92 (95.42) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-03:30:45] Epoch: [339][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.328 (0.218) Prec@1 92.71 (95.33) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-03:31:09] Epoch: [339][500/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.284 (0.224) Prec@1 95.83 (95.14) Prec@5 97.92 (99.93)
|
|
train[2019-04-01-03:31:14] Epoch: [339][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.219 (0.224) Prec@1 95.00 (95.13) Prec@5 100.00 (99.93)
|
|
[2019-04-01-03:31:14] **train** Prec@1 95.13 Prec@5 99.93 Error@1 4.87 Error@5 0.07 Loss:0.224
|
|
test [2019-04-01-03:31:14] Epoch: [339][000/105] Time 0.46 (0.46) Data 0.40 (0.40) Loss 0.128 (0.128) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-03:31:18] Epoch: [339][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.044 (0.183) Prec@1 98.96 (94.66) Prec@5 100.00 (99.89)
|
|
test [2019-04-01-03:31:18] Epoch: [339][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.429 (0.188) Prec@1 93.75 (94.57) Prec@5 100.00 (99.89)
|
|
[2019-04-01-03:31:18] **test** Prec@1 94.57 Prec@5 99.89 Error@1 5.43 Error@5 0.11 Loss:0.188
|
|
----> Best Accuracy : Acc@1=95.38, Acc@5=99.90, Error@1=4.62, Error@5=0.10
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-03:31:19] [Epoch=340/600] [Need: 09:28:33] LR=0.0100 ~ 0.0100, Batch=96
|
|
train[2019-04-01-03:31:19] Epoch: [340][000/521] Time 0.83 (0.83) Data 0.56 (0.56) Loss 0.327 (0.327) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-03:31:43] Epoch: [340][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.168 (0.218) Prec@1 96.88 (95.58) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-03:32:10] Epoch: [340][200/521] Time 0.24 (0.26) Data 0.00 (0.00) Loss 0.240 (0.215) Prec@1 93.75 (95.46) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-03:32:35] Epoch: [340][300/521] Time 0.24 (0.26) Data 0.00 (0.00) Loss 0.288 (0.211) Prec@1 93.75 (95.53) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-03:33:00] Epoch: [340][400/521] Time 0.26 (0.25) Data 0.00 (0.00) Loss 0.253 (0.212) Prec@1 95.83 (95.51) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-03:33:24] Epoch: [340][500/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.297 (0.215) Prec@1 92.71 (95.40) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-03:33:29] Epoch: [340][520/521] Time 0.22 (0.25) Data 0.00 (0.00) Loss 0.190 (0.216) Prec@1 96.25 (95.39) Prec@5 100.00 (99.94)
|
|
[2019-04-01-03:33:29] **train** Prec@1 95.39 Prec@5 99.94 Error@1 4.61 Error@5 0.06 Loss:0.216
|
|
test [2019-04-01-03:33:30] Epoch: [340][000/105] Time 0.47 (0.47) Data 0.41 (0.41) Loss 0.128 (0.128) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-03:33:34] Epoch: [340][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.110 (0.174) Prec@1 95.83 (94.75) Prec@5 100.00 (99.89)
|
|
test [2019-04-01-03:33:34] Epoch: [340][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.240 (0.175) Prec@1 87.50 (94.72) Prec@5 100.00 (99.89)
|
|
[2019-04-01-03:33:34] **test** Prec@1 94.72 Prec@5 99.89 Error@1 5.28 Error@5 0.11 Loss:0.175
|
|
----> Best Accuracy : Acc@1=95.38, Acc@5=99.90, Error@1=4.62, Error@5=0.10
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-03:33:34] [Epoch=341/600] [Need: 09:45:05] LR=0.0099 ~ 0.0099, Batch=96
|
|
train[2019-04-01-03:33:35] Epoch: [341][000/521] Time 0.72 (0.72) Data 0.45 (0.45) Loss 0.155 (0.155) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-03:33:59] Epoch: [341][100/521] Time 0.23 (0.25) Data 0.00 (0.00) Loss 0.201 (0.207) Prec@1 95.83 (95.55) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-03:34:23] Epoch: [341][200/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.242 (0.207) Prec@1 95.83 (95.51) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-03:34:47] Epoch: [341][300/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.394 (0.212) Prec@1 91.67 (95.40) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-03:35:11] Epoch: [341][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.337 (0.213) Prec@1 93.75 (95.40) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-03:35:35] Epoch: [341][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.431 (0.217) Prec@1 92.71 (95.28) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-03:35:39] Epoch: [341][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.207 (0.218) Prec@1 93.75 (95.25) Prec@5 100.00 (99.94)
|
|
[2019-04-01-03:35:39] **train** Prec@1 95.25 Prec@5 99.94 Error@1 4.75 Error@5 0.06 Loss:0.218
|
|
test [2019-04-01-03:35:40] Epoch: [341][000/105] Time 0.61 (0.61) Data 0.54 (0.54) Loss 0.093 (0.093) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-03:35:44] Epoch: [341][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.098 (0.187) Prec@1 96.88 (94.62) Prec@5 100.00 (99.88)
|
|
test [2019-04-01-03:35:44] Epoch: [341][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.064 (0.187) Prec@1 100.00 (94.62) Prec@5 100.00 (99.88)
|
|
[2019-04-01-03:35:44] **test** Prec@1 94.62 Prec@5 99.88 Error@1 5.38 Error@5 0.12 Loss:0.187
|
|
----> Best Accuracy : Acc@1=95.38, Acc@5=99.90, Error@1=4.62, Error@5=0.10
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-03:35:44] [Epoch=342/600] [Need: 09:19:43] LR=0.0098 ~ 0.0098, Batch=96
|
|
train[2019-04-01-03:35:45] Epoch: [342][000/521] Time 0.84 (0.84) Data 0.56 (0.56) Loss 0.242 (0.242) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-03:36:09] Epoch: [342][100/521] Time 0.26 (0.25) Data 0.00 (0.01) Loss 0.222 (0.205) Prec@1 92.71 (95.62) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-03:36:33] Epoch: [342][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.185 (0.213) Prec@1 95.83 (95.41) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-03:36:57] Epoch: [342][300/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.091 (0.213) Prec@1 100.00 (95.44) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-03:37:20] Epoch: [342][400/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.361 (0.212) Prec@1 89.58 (95.44) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-03:37:44] Epoch: [342][500/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.147 (0.214) Prec@1 95.83 (95.39) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-03:37:49] Epoch: [342][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.103 (0.214) Prec@1 100.00 (95.40) Prec@5 100.00 (99.94)
|
|
[2019-04-01-03:37:49] **train** Prec@1 95.40 Prec@5 99.94 Error@1 4.60 Error@5 0.06 Loss:0.214
|
|
test [2019-04-01-03:37:50] Epoch: [342][000/105] Time 0.46 (0.46) Data 0.40 (0.40) Loss 0.106 (0.106) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-03:37:54] Epoch: [342][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.065 (0.154) Prec@1 97.92 (95.36) Prec@5 100.00 (99.90)
|
|
test [2019-04-01-03:37:54] Epoch: [342][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.219 (0.155) Prec@1 93.75 (95.36) Prec@5 100.00 (99.90)
|
|
[2019-04-01-03:37:54] **test** Prec@1 95.36 Prec@5 99.90 Error@1 4.64 Error@5 0.10 Loss:0.155
|
|
----> Best Accuracy : Acc@1=95.38, Acc@5=99.90, Error@1=4.62, Error@5=0.10
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-03:37:54] [Epoch=343/600] [Need: 09:15:53] LR=0.0098 ~ 0.0098, Batch=96
|
|
train[2019-04-01-03:37:55] Epoch: [343][000/521] Time 0.84 (0.84) Data 0.56 (0.56) Loss 0.125 (0.125) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-03:38:19] Epoch: [343][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.244 (0.211) Prec@1 94.79 (95.51) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-03:38:43] Epoch: [343][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.314 (0.208) Prec@1 92.71 (95.57) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-03:39:07] Epoch: [343][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.187 (0.214) Prec@1 96.88 (95.45) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-03:39:31] Epoch: [343][400/521] Time 0.27 (0.24) Data 0.00 (0.00) Loss 0.354 (0.213) Prec@1 93.75 (95.39) Prec@5 98.96 (99.94)
|
|
train[2019-04-01-03:39:56] Epoch: [343][500/521] Time 0.29 (0.24) Data 0.00 (0.00) Loss 0.317 (0.215) Prec@1 94.79 (95.38) Prec@5 98.96 (99.93)
|
|
train[2019-04-01-03:40:01] Epoch: [343][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.246 (0.215) Prec@1 95.00 (95.38) Prec@5 100.00 (99.93)
|
|
[2019-04-01-03:40:01] **train** Prec@1 95.38 Prec@5 99.93 Error@1 4.62 Error@5 0.07 Loss:0.215
|
|
test [2019-04-01-03:40:02] Epoch: [343][000/105] Time 0.48 (0.48) Data 0.42 (0.42) Loss 0.252 (0.252) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-03:40:06] Epoch: [343][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.075 (0.180) Prec@1 96.88 (95.01) Prec@5 100.00 (99.91)
|
|
test [2019-04-01-03:40:06] Epoch: [343][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.219 (0.182) Prec@1 93.75 (94.94) Prec@5 100.00 (99.91)
|
|
[2019-04-01-03:40:06] **test** Prec@1 94.94 Prec@5 99.91 Error@1 5.06 Error@5 0.09 Loss:0.182
|
|
----> Best Accuracy : Acc@1=95.38, Acc@5=99.90, Error@1=4.62, Error@5=0.10
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-03:40:06] [Epoch=344/600] [Need: 09:24:13] LR=0.0097 ~ 0.0097, Batch=96
|
|
train[2019-04-01-03:40:07] Epoch: [344][000/521] Time 0.83 (0.83) Data 0.56 (0.56) Loss 0.126 (0.126) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-03:40:31] Epoch: [344][100/521] Time 0.24 (0.25) Data 0.00 (0.01) Loss 0.186 (0.196) Prec@1 97.92 (95.71) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-03:40:56] Epoch: [344][200/521] Time 0.26 (0.25) Data 0.00 (0.00) Loss 0.225 (0.198) Prec@1 94.79 (95.80) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-03:41:21] Epoch: [344][300/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.184 (0.200) Prec@1 95.83 (95.69) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-03:41:46] Epoch: [344][400/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.328 (0.200) Prec@1 94.79 (95.67) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-03:42:09] Epoch: [344][500/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.150 (0.205) Prec@1 96.88 (95.55) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-03:42:14] Epoch: [344][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.142 (0.206) Prec@1 97.50 (95.53) Prec@5 100.00 (99.96)
|
|
[2019-04-01-03:42:14] **train** Prec@1 95.53 Prec@5 99.96 Error@1 4.47 Error@5 0.04 Loss:0.206
|
|
test [2019-04-01-03:42:14] Epoch: [344][000/105] Time 0.47 (0.47) Data 0.40 (0.40) Loss 0.158 (0.158) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-03:42:19] Epoch: [344][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.111 (0.171) Prec@1 97.92 (95.31) Prec@5 100.00 (99.86)
|
|
test [2019-04-01-03:42:19] Epoch: [344][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.114 (0.172) Prec@1 93.75 (95.30) Prec@5 100.00 (99.86)
|
|
[2019-04-01-03:42:19] **test** Prec@1 95.30 Prec@5 99.86 Error@1 4.70 Error@5 0.14 Loss:0.172
|
|
----> Best Accuracy : Acc@1=95.38, Acc@5=99.90, Error@1=4.62, Error@5=0.10
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-03:42:19] [Epoch=345/600] [Need: 09:23:49] LR=0.0096 ~ 0.0096, Batch=96
|
|
train[2019-04-01-03:42:20] Epoch: [345][000/521] Time 0.85 (0.85) Data 0.57 (0.57) Loss 0.228 (0.228) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-03:42:44] Epoch: [345][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.233 (0.198) Prec@1 96.88 (96.07) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-03:43:07] Epoch: [345][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.272 (0.210) Prec@1 92.71 (95.67) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-03:43:32] Epoch: [345][300/521] Time 0.39 (0.24) Data 0.00 (0.00) Loss 0.197 (0.210) Prec@1 94.79 (95.66) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-03:43:58] Epoch: [345][400/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.190 (0.209) Prec@1 96.88 (95.58) Prec@5 98.96 (99.94)
|
|
train[2019-04-01-03:44:21] Epoch: [345][500/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.130 (0.210) Prec@1 97.92 (95.53) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-03:44:26] Epoch: [345][520/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.114 (0.210) Prec@1 97.50 (95.53) Prec@5 100.00 (99.94)
|
|
[2019-04-01-03:44:26] **train** Prec@1 95.53 Prec@5 99.94 Error@1 4.47 Error@5 0.06 Loss:0.210
|
|
test [2019-04-01-03:44:27] Epoch: [345][000/105] Time 0.51 (0.51) Data 0.45 (0.45) Loss 0.095 (0.095) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-03:44:31] Epoch: [345][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.142 (0.182) Prec@1 94.79 (94.63) Prec@5 100.00 (99.91)
|
|
test [2019-04-01-03:44:31] Epoch: [345][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.105 (0.182) Prec@1 93.75 (94.65) Prec@5 100.00 (99.91)
|
|
[2019-04-01-03:44:31] **test** Prec@1 94.65 Prec@5 99.91 Error@1 5.35 Error@5 0.09 Loss:0.182
|
|
----> Best Accuracy : Acc@1=95.38, Acc@5=99.90, Error@1=4.62, Error@5=0.10
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-03:44:32] [Epoch=346/600] [Need: 09:21:36] LR=0.0096 ~ 0.0096, Batch=96
|
|
train[2019-04-01-03:44:32] Epoch: [346][000/521] Time 0.73 (0.73) Data 0.47 (0.47) Loss 0.191 (0.191) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-03:44:58] Epoch: [346][100/521] Time 0.25 (0.26) Data 0.00 (0.00) Loss 0.191 (0.194) Prec@1 93.75 (95.96) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-03:45:23] Epoch: [346][200/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.317 (0.201) Prec@1 94.79 (95.71) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-03:45:48] Epoch: [346][300/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.211 (0.203) Prec@1 94.79 (95.64) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-03:46:13] Epoch: [346][400/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.294 (0.206) Prec@1 92.71 (95.60) Prec@5 98.96 (99.94)
|
|
train[2019-04-01-03:46:38] Epoch: [346][500/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.184 (0.208) Prec@1 95.83 (95.57) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-03:46:43] Epoch: [346][520/521] Time 0.23 (0.25) Data 0.00 (0.00) Loss 0.349 (0.209) Prec@1 95.00 (95.56) Prec@5 100.00 (99.94)
|
|
[2019-04-01-03:46:43] **train** Prec@1 95.56 Prec@5 99.94 Error@1 4.44 Error@5 0.06 Loss:0.209
|
|
test [2019-04-01-03:46:44] Epoch: [346][000/105] Time 0.49 (0.49) Data 0.43 (0.43) Loss 0.159 (0.159) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-03:46:48] Epoch: [346][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.088 (0.161) Prec@1 95.83 (95.45) Prec@5 100.00 (99.91)
|
|
test [2019-04-01-03:46:48] Epoch: [346][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.101 (0.162) Prec@1 93.75 (95.44) Prec@5 100.00 (99.91)
|
|
[2019-04-01-03:46:48] **test** Prec@1 95.44 Prec@5 99.91 Error@1 4.56 Error@5 0.09 Loss:0.162
|
|
----> Best Accuracy : Acc@1=95.44, Acc@5=99.91, Error@1=4.56, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-03:46:48] [Epoch=347/600] [Need: 09:36:20] LR=0.0095 ~ 0.0095, Batch=96
|
|
train[2019-04-01-03:46:49] Epoch: [347][000/521] Time 0.87 (0.87) Data 0.60 (0.60) Loss 0.179 (0.179) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-03:47:14] Epoch: [347][100/521] Time 0.24 (0.26) Data 0.00 (0.01) Loss 0.203 (0.219) Prec@1 94.79 (95.24) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-03:47:39] Epoch: [347][200/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.236 (0.219) Prec@1 96.88 (95.29) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-03:48:04] Epoch: [347][300/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.178 (0.211) Prec@1 95.83 (95.50) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-03:48:29] Epoch: [347][400/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.154 (0.209) Prec@1 98.96 (95.53) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-03:48:54] Epoch: [347][500/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.210 (0.210) Prec@1 95.83 (95.48) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-03:48:59] Epoch: [347][520/521] Time 0.22 (0.25) Data 0.00 (0.00) Loss 0.216 (0.209) Prec@1 95.00 (95.49) Prec@5 100.00 (99.94)
|
|
[2019-04-01-03:48:59] **train** Prec@1 95.49 Prec@5 99.94 Error@1 4.51 Error@5 0.06 Loss:0.209
|
|
test [2019-04-01-03:49:00] Epoch: [347][000/105] Time 0.62 (0.62) Data 0.55 (0.55) Loss 0.097 (0.097) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-03:49:04] Epoch: [347][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.080 (0.170) Prec@1 94.79 (95.07) Prec@5 100.00 (99.87)
|
|
test [2019-04-01-03:49:04] Epoch: [347][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.111 (0.171) Prec@1 93.75 (95.05) Prec@5 100.00 (99.87)
|
|
[2019-04-01-03:49:04] **test** Prec@1 95.05 Prec@5 99.87 Error@1 4.95 Error@5 0.13 Loss:0.171
|
|
----> Best Accuracy : Acc@1=95.44, Acc@5=99.91, Error@1=4.56, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-03:49:05] [Epoch=348/600] [Need: 09:32:31] LR=0.0095 ~ 0.0095, Batch=96
|
|
train[2019-04-01-03:49:05] Epoch: [348][000/521] Time 0.74 (0.74) Data 0.44 (0.44) Loss 0.242 (0.242) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-03:49:30] Epoch: [348][100/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.273 (0.208) Prec@1 92.71 (95.54) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-03:49:55] Epoch: [348][200/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.263 (0.206) Prec@1 93.75 (95.60) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-03:50:20] Epoch: [348][300/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.233 (0.205) Prec@1 96.88 (95.60) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-03:50:46] Epoch: [348][400/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.151 (0.204) Prec@1 96.88 (95.70) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-03:51:11] Epoch: [348][500/521] Time 0.26 (0.25) Data 0.00 (0.00) Loss 0.310 (0.207) Prec@1 91.67 (95.60) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-03:51:16] Epoch: [348][520/521] Time 0.22 (0.25) Data 0.00 (0.00) Loss 0.161 (0.207) Prec@1 97.50 (95.59) Prec@5 100.00 (99.94)
|
|
[2019-04-01-03:51:16] **train** Prec@1 95.59 Prec@5 99.94 Error@1 4.41 Error@5 0.06 Loss:0.207
|
|
test [2019-04-01-03:51:16] Epoch: [348][000/105] Time 0.47 (0.47) Data 0.40 (0.40) Loss 0.138 (0.138) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-03:51:20] Epoch: [348][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.116 (0.181) Prec@1 95.83 (94.86) Prec@5 100.00 (99.90)
|
|
test [2019-04-01-03:51:20] Epoch: [348][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.021 (0.181) Prec@1 100.00 (94.89) Prec@5 100.00 (99.90)
|
|
[2019-04-01-03:51:21] **test** Prec@1 94.89 Prec@5 99.90 Error@1 5.11 Error@5 0.10 Loss:0.181
|
|
----> Best Accuracy : Acc@1=95.44, Acc@5=99.91, Error@1=4.56, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-03:51:21] [Epoch=349/600] [Need: 09:29:04] LR=0.0094 ~ 0.0094, Batch=96
|
|
train[2019-04-01-03:51:21] Epoch: [349][000/521] Time 0.72 (0.72) Data 0.45 (0.45) Loss 0.199 (0.199) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-03:51:45] Epoch: [349][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.161 (0.213) Prec@1 95.83 (95.26) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-03:52:09] Epoch: [349][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.274 (0.210) Prec@1 96.88 (95.38) Prec@5 98.96 (99.94)
|
|
train[2019-04-01-03:52:33] Epoch: [349][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.292 (0.205) Prec@1 93.75 (95.57) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-03:52:57] Epoch: [349][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.258 (0.206) Prec@1 93.75 (95.59) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-03:53:21] Epoch: [349][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.273 (0.206) Prec@1 94.79 (95.55) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-03:53:25] Epoch: [349][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.130 (0.206) Prec@1 97.50 (95.57) Prec@5 100.00 (99.95)
|
|
[2019-04-01-03:53:25] **train** Prec@1 95.57 Prec@5 99.95 Error@1 4.43 Error@5 0.05 Loss:0.206
|
|
test [2019-04-01-03:53:26] Epoch: [349][000/105] Time 0.61 (0.61) Data 0.55 (0.55) Loss 0.110 (0.110) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-03:53:30] Epoch: [349][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.040 (0.169) Prec@1 97.92 (95.26) Prec@5 100.00 (99.87)
|
|
test [2019-04-01-03:53:30] Epoch: [349][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.014 (0.169) Prec@1 100.00 (95.25) Prec@5 100.00 (99.87)
|
|
[2019-04-01-03:53:30] **test** Prec@1 95.25 Prec@5 99.87 Error@1 4.75 Error@5 0.13 Loss:0.169
|
|
----> Best Accuracy : Acc@1=95.44, Acc@5=99.91, Error@1=4.56, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-03:53:30] [Epoch=350/600] [Need: 09:00:48] LR=0.0093 ~ 0.0093, Batch=96
|
|
train[2019-04-01-03:53:31] Epoch: [350][000/521] Time 0.72 (0.72) Data 0.45 (0.45) Loss 0.219 (0.219) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-03:53:55] Epoch: [350][100/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.134 (0.210) Prec@1 96.88 (95.55) Prec@5 100.00 (99.89)
|
|
train[2019-04-01-03:54:19] Epoch: [350][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.156 (0.214) Prec@1 97.92 (95.43) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-03:54:42] Epoch: [350][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.241 (0.211) Prec@1 94.79 (95.42) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-03:55:06] Epoch: [350][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.294 (0.209) Prec@1 93.75 (95.48) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-03:55:30] Epoch: [350][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.327 (0.212) Prec@1 92.71 (95.45) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-03:55:35] Epoch: [350][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.262 (0.212) Prec@1 95.00 (95.44) Prec@5 100.00 (99.93)
|
|
[2019-04-01-03:55:35] **train** Prec@1 95.44 Prec@5 99.93 Error@1 4.56 Error@5 0.07 Loss:0.212
|
|
test [2019-04-01-03:55:35] Epoch: [350][000/105] Time 0.60 (0.60) Data 0.53 (0.53) Loss 0.044 (0.044) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-03:55:40] Epoch: [350][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.077 (0.159) Prec@1 96.88 (95.42) Prec@5 100.00 (99.89)
|
|
test [2019-04-01-03:55:40] Epoch: [350][104/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.121 (0.160) Prec@1 93.75 (95.34) Prec@5 100.00 (99.89)
|
|
[2019-04-01-03:55:40] **test** Prec@1 95.34 Prec@5 99.89 Error@1 4.66 Error@5 0.11 Loss:0.160
|
|
----> Best Accuracy : Acc@1=95.44, Acc@5=99.91, Error@1=4.56, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-03:55:40] [Epoch=351/600] [Need: 08:59:36] LR=0.0093 ~ 0.0093, Batch=96
|
|
train[2019-04-01-03:55:42] Epoch: [351][000/521] Time 1.24 (1.24) Data 0.74 (0.74) Loss 0.166 (0.166) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-03:56:09] Epoch: [351][100/521] Time 0.24 (0.28) Data 0.00 (0.01) Loss 0.280 (0.193) Prec@1 91.67 (96.03) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-03:56:33] Epoch: [351][200/521] Time 0.24 (0.26) Data 0.00 (0.00) Loss 0.271 (0.198) Prec@1 93.75 (95.81) Prec@5 98.96 (99.95)
|
|
train[2019-04-01-03:56:56] Epoch: [351][300/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.142 (0.202) Prec@1 97.92 (95.72) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-03:57:20] Epoch: [351][400/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.149 (0.204) Prec@1 95.83 (95.70) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-03:57:44] Epoch: [351][500/521] Time 0.23 (0.25) Data 0.00 (0.00) Loss 0.277 (0.208) Prec@1 91.67 (95.60) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-03:57:49] Epoch: [351][520/521] Time 0.22 (0.25) Data 0.00 (0.00) Loss 0.097 (0.208) Prec@1 98.75 (95.61) Prec@5 100.00 (99.94)
|
|
[2019-04-01-03:57:49] **train** Prec@1 95.61 Prec@5 99.94 Error@1 4.39 Error@5 0.06 Loss:0.208
|
|
test [2019-04-01-03:57:49] Epoch: [351][000/105] Time 0.61 (0.61) Data 0.55 (0.55) Loss 0.081 (0.081) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-03:57:53] Epoch: [351][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.156 (0.171) Prec@1 93.75 (95.13) Prec@5 100.00 (99.88)
|
|
test [2019-04-01-03:57:54] Epoch: [351][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.016 (0.171) Prec@1 100.00 (95.14) Prec@5 100.00 (99.88)
|
|
[2019-04-01-03:57:54] **test** Prec@1 95.14 Prec@5 99.88 Error@1 4.86 Error@5 0.12 Loss:0.171
|
|
----> Best Accuracy : Acc@1=95.44, Acc@5=99.91, Error@1=4.56, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-03:57:54] [Epoch=352/600] [Need: 09:10:54] LR=0.0092 ~ 0.0092, Batch=96
|
|
train[2019-04-01-03:57:55] Epoch: [352][000/521] Time 0.83 (0.83) Data 0.55 (0.55) Loss 0.230 (0.230) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-03:58:18] Epoch: [352][100/521] Time 0.23 (0.24) Data 0.00 (0.01) Loss 0.165 (0.211) Prec@1 96.88 (95.18) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-03:58:42] Epoch: [352][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.110 (0.204) Prec@1 98.96 (95.47) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-03:59:06] Epoch: [352][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.123 (0.201) Prec@1 97.92 (95.62) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-03:59:29] Epoch: [352][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.203 (0.200) Prec@1 95.83 (95.69) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-03:59:53] Epoch: [352][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.115 (0.205) Prec@1 98.96 (95.59) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-03:59:58] Epoch: [352][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.224 (0.205) Prec@1 95.00 (95.60) Prec@5 100.00 (99.94)
|
|
[2019-04-01-03:59:58] **train** Prec@1 95.60 Prec@5 99.94 Error@1 4.40 Error@5 0.06 Loss:0.205
|
|
test [2019-04-01-03:59:59] Epoch: [352][000/105] Time 0.48 (0.48) Data 0.42 (0.42) Loss 0.189 (0.189) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-04:00:03] Epoch: [352][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.075 (0.190) Prec@1 95.83 (94.76) Prec@5 100.00 (99.91)
|
|
test [2019-04-01-04:00:03] Epoch: [352][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.236 (0.190) Prec@1 93.75 (94.75) Prec@5 100.00 (99.91)
|
|
[2019-04-01-04:00:03] **test** Prec@1 94.75 Prec@5 99.91 Error@1 5.25 Error@5 0.09 Loss:0.190
|
|
----> Best Accuracy : Acc@1=95.44, Acc@5=99.91, Error@1=4.56, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-04:00:03] [Epoch=353/600] [Need: 08:52:24] LR=0.0091 ~ 0.0091, Batch=96
|
|
train[2019-04-01-04:00:04] Epoch: [353][000/521] Time 0.71 (0.71) Data 0.43 (0.43) Loss 0.171 (0.171) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-04:00:28] Epoch: [353][100/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.231 (0.198) Prec@1 94.79 (95.93) Prec@5 98.96 (99.96)
|
|
train[2019-04-01-04:00:51] Epoch: [353][200/521] Time 0.27 (0.24) Data 0.00 (0.00) Loss 0.195 (0.201) Prec@1 96.88 (95.83) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-04:01:16] Epoch: [353][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.137 (0.194) Prec@1 96.88 (95.94) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-04:01:41] Epoch: [353][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.076 (0.194) Prec@1 97.92 (95.93) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-04:02:05] Epoch: [353][500/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.191 (0.198) Prec@1 94.79 (95.83) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-04:02:10] Epoch: [353][520/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.272 (0.199) Prec@1 95.00 (95.82) Prec@5 98.75 (99.93)
|
|
[2019-04-01-04:02:10] **train** Prec@1 95.82 Prec@5 99.93 Error@1 4.18 Error@5 0.07 Loss:0.199
|
|
test [2019-04-01-04:02:11] Epoch: [353][000/105] Time 0.69 (0.69) Data 0.61 (0.61) Loss 0.130 (0.130) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-04:02:15] Epoch: [353][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.031 (0.162) Prec@1 97.92 (95.40) Prec@5 100.00 (99.93)
|
|
test [2019-04-01-04:02:16] Epoch: [353][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.003 (0.162) Prec@1 100.00 (95.39) Prec@5 100.00 (99.93)
|
|
[2019-04-01-04:02:16] **test** Prec@1 95.39 Prec@5 99.93 Error@1 4.61 Error@5 0.07 Loss:0.162
|
|
----> Best Accuracy : Acc@1=95.44, Acc@5=99.91, Error@1=4.56, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-04:02:16] [Epoch=354/600] [Need: 09:03:52] LR=0.0091 ~ 0.0091, Batch=96
|
|
train[2019-04-01-04:02:17] Epoch: [354][000/521] Time 0.83 (0.83) Data 0.56 (0.56) Loss 0.190 (0.190) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-04:02:41] Epoch: [354][100/521] Time 0.25 (0.25) Data 0.00 (0.01) Loss 0.170 (0.205) Prec@1 96.88 (95.63) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-04:03:06] Epoch: [354][200/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.235 (0.204) Prec@1 92.71 (95.51) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-04:03:31] Epoch: [354][300/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.284 (0.202) Prec@1 95.83 (95.59) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-04:03:55] Epoch: [354][400/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.186 (0.203) Prec@1 95.83 (95.54) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-04:04:20] Epoch: [354][500/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.125 (0.203) Prec@1 96.88 (95.56) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-04:04:25] Epoch: [354][520/521] Time 0.22 (0.25) Data 0.00 (0.00) Loss 0.083 (0.203) Prec@1 100.00 (95.56) Prec@5 100.00 (99.94)
|
|
[2019-04-01-04:04:25] **train** Prec@1 95.56 Prec@5 99.94 Error@1 4.44 Error@5 0.06 Loss:0.203
|
|
test [2019-04-01-04:04:26] Epoch: [354][000/105] Time 0.64 (0.64) Data 0.57 (0.57) Loss 0.090 (0.090) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-04:04:30] Epoch: [354][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.061 (0.156) Prec@1 97.92 (95.77) Prec@5 100.00 (99.89)
|
|
test [2019-04-01-04:04:30] Epoch: [354][104/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.012 (0.156) Prec@1 100.00 (95.76) Prec@5 100.00 (99.89)
|
|
[2019-04-01-04:04:30] **test** Prec@1 95.76 Prec@5 99.89 Error@1 4.24 Error@5 0.11 Loss:0.156
|
|
----> Best Accuracy : Acc@1=95.76, Acc@5=99.89, Error@1=4.24, Error@5=0.11
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-04:04:30] [Epoch=355/600] [Need: 09:08:50] LR=0.0090 ~ 0.0090, Batch=96
|
|
train[2019-04-01-04:04:31] Epoch: [355][000/521] Time 0.92 (0.92) Data 0.63 (0.63) Loss 0.189 (0.189) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-04:04:56] Epoch: [355][100/521] Time 0.26 (0.25) Data 0.00 (0.01) Loss 0.121 (0.197) Prec@1 98.96 (95.92) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-04:05:20] Epoch: [355][200/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.181 (0.206) Prec@1 96.88 (95.63) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-04:05:45] Epoch: [355][300/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.139 (0.203) Prec@1 96.88 (95.72) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-04:06:09] Epoch: [355][400/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.218 (0.204) Prec@1 96.88 (95.73) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-04:06:34] Epoch: [355][500/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.134 (0.205) Prec@1 96.88 (95.69) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-04:06:39] Epoch: [355][520/521] Time 0.21 (0.25) Data 0.00 (0.00) Loss 0.163 (0.206) Prec@1 97.50 (95.68) Prec@5 98.75 (99.96)
|
|
[2019-04-01-04:06:39] **train** Prec@1 95.68 Prec@5 99.96 Error@1 4.32 Error@5 0.04 Loss:0.206
|
|
test [2019-04-01-04:06:40] Epoch: [355][000/105] Time 0.66 (0.66) Data 0.58 (0.58) Loss 0.095 (0.095) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-04:06:44] Epoch: [355][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.107 (0.152) Prec@1 97.92 (95.70) Prec@5 100.00 (99.92)
|
|
test [2019-04-01-04:06:44] Epoch: [355][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.218 (0.153) Prec@1 93.75 (95.68) Prec@5 100.00 (99.92)
|
|
[2019-04-01-04:06:44] **test** Prec@1 95.68 Prec@5 99.92 Error@1 4.32 Error@5 0.08 Loss:0.153
|
|
----> Best Accuracy : Acc@1=95.76, Acc@5=99.89, Error@1=4.24, Error@5=0.11
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-04:06:45] [Epoch=356/600] [Need: 09:06:39] LR=0.0090 ~ 0.0090, Batch=96
|
|
train[2019-04-01-04:06:45] Epoch: [356][000/521] Time 0.86 (0.86) Data 0.58 (0.58) Loss 0.109 (0.109) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-04:07:10] Epoch: [356][100/521] Time 0.24 (0.26) Data 0.00 (0.01) Loss 0.145 (0.199) Prec@1 95.83 (95.65) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-04:07:35] Epoch: [356][200/521] Time 0.27 (0.25) Data 0.00 (0.00) Loss 0.188 (0.199) Prec@1 94.79 (95.72) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-04:08:00] Epoch: [356][300/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.238 (0.200) Prec@1 95.83 (95.75) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-04:08:25] Epoch: [356][400/521] Time 0.28 (0.25) Data 0.00 (0.00) Loss 0.209 (0.200) Prec@1 94.79 (95.67) Prec@5 98.96 (99.93)
|
|
train[2019-04-01-04:08:50] Epoch: [356][500/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.336 (0.202) Prec@1 92.71 (95.62) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-04:08:55] Epoch: [356][520/521] Time 0.22 (0.25) Data 0.00 (0.00) Loss 0.170 (0.203) Prec@1 96.25 (95.61) Prec@5 100.00 (99.93)
|
|
[2019-04-01-04:08:55] **train** Prec@1 95.61 Prec@5 99.93 Error@1 4.39 Error@5 0.07 Loss:0.203
|
|
test [2019-04-01-04:08:55] Epoch: [356][000/105] Time 0.52 (0.52) Data 0.45 (0.45) Loss 0.136 (0.136) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-04:08:59] Epoch: [356][100/105] Time 0.05 (0.05) Data 0.00 (0.00) Loss 0.043 (0.181) Prec@1 98.96 (95.13) Prec@5 100.00 (99.90)
|
|
test [2019-04-01-04:09:00] Epoch: [356][104/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.052 (0.182) Prec@1 93.75 (95.14) Prec@5 100.00 (99.90)
|
|
[2019-04-01-04:09:00] **test** Prec@1 95.14 Prec@5 99.90 Error@1 4.86 Error@5 0.10 Loss:0.182
|
|
----> Best Accuracy : Acc@1=95.76, Acc@5=99.89, Error@1=4.24, Error@5=0.11
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-04:09:00] [Epoch=357/600] [Need: 09:08:01] LR=0.0089 ~ 0.0089, Batch=96
|
|
train[2019-04-01-04:09:01] Epoch: [357][000/521] Time 0.75 (0.75) Data 0.47 (0.47) Loss 0.133 (0.133) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-04:09:25] Epoch: [357][100/521] Time 0.27 (0.25) Data 0.00 (0.00) Loss 0.174 (0.204) Prec@1 94.79 (95.77) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-04:09:50] Epoch: [357][200/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.160 (0.205) Prec@1 96.88 (95.67) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-04:10:15] Epoch: [357][300/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.200 (0.200) Prec@1 93.75 (95.75) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-04:10:39] Epoch: [357][400/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.214 (0.201) Prec@1 94.79 (95.70) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-04:11:04] Epoch: [357][500/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.131 (0.201) Prec@1 96.88 (95.67) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-04:11:09] Epoch: [357][520/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.203 (0.201) Prec@1 96.25 (95.69) Prec@5 100.00 (99.95)
|
|
[2019-04-01-04:11:09] **train** Prec@1 95.69 Prec@5 99.95 Error@1 4.31 Error@5 0.05 Loss:0.201
|
|
test [2019-04-01-04:11:10] Epoch: [357][000/105] Time 0.65 (0.65) Data 0.58 (0.58) Loss 0.128 (0.128) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-04:11:14] Epoch: [357][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.098 (0.173) Prec@1 97.92 (95.06) Prec@5 100.00 (99.90)
|
|
test [2019-04-01-04:11:14] Epoch: [357][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.004 (0.171) Prec@1 100.00 (95.09) Prec@5 100.00 (99.90)
|
|
[2019-04-01-04:11:14] **test** Prec@1 95.09 Prec@5 99.90 Error@1 4.91 Error@5 0.10 Loss:0.171
|
|
----> Best Accuracy : Acc@1=95.76, Acc@5=99.89, Error@1=4.24, Error@5=0.11
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-04:11:15] [Epoch=358/600] [Need: 09:03:15] LR=0.0088 ~ 0.0088, Batch=96
|
|
train[2019-04-01-04:11:15] Epoch: [358][000/521] Time 0.85 (0.85) Data 0.56 (0.56) Loss 0.164 (0.164) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-04:11:40] Epoch: [358][100/521] Time 0.25 (0.25) Data 0.00 (0.01) Loss 0.145 (0.194) Prec@1 95.83 (95.75) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-04:12:05] Epoch: [358][200/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.156 (0.196) Prec@1 95.83 (95.73) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-04:12:30] Epoch: [358][300/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.224 (0.200) Prec@1 94.79 (95.73) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-04:12:54] Epoch: [358][400/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.229 (0.200) Prec@1 93.75 (95.72) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-04:13:19] Epoch: [358][500/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.303 (0.204) Prec@1 91.67 (95.62) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-04:13:24] Epoch: [358][520/521] Time 0.22 (0.25) Data 0.00 (0.00) Loss 0.287 (0.204) Prec@1 91.25 (95.61) Prec@5 100.00 (99.93)
|
|
[2019-04-01-04:13:24] **train** Prec@1 95.61 Prec@5 99.93 Error@1 4.39 Error@5 0.07 Loss:0.204
|
|
test [2019-04-01-04:13:24] Epoch: [358][000/105] Time 0.51 (0.51) Data 0.45 (0.45) Loss 0.284 (0.284) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-04:13:28] Epoch: [358][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.057 (0.163) Prec@1 98.96 (95.60) Prec@5 100.00 (99.89)
|
|
test [2019-04-01-04:13:29] Epoch: [358][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.001 (0.162) Prec@1 100.00 (95.56) Prec@5 100.00 (99.89)
|
|
[2019-04-01-04:13:29] **test** Prec@1 95.56 Prec@5 99.89 Error@1 4.44 Error@5 0.11 Loss:0.162
|
|
----> Best Accuracy : Acc@1=95.76, Acc@5=99.89, Error@1=4.24, Error@5=0.11
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-04:13:29] [Epoch=359/600] [Need: 08:58:56] LR=0.0088 ~ 0.0088, Batch=96
|
|
train[2019-04-01-04:13:30] Epoch: [359][000/521] Time 0.76 (0.76) Data 0.48 (0.48) Loss 0.202 (0.202) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-04:13:54] Epoch: [359][100/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.174 (0.197) Prec@1 97.92 (95.83) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-04:14:19] Epoch: [359][200/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.151 (0.199) Prec@1 95.83 (95.70) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-04:14:43] Epoch: [359][300/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.100 (0.199) Prec@1 98.96 (95.70) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-04:15:09] Epoch: [359][400/521] Time 0.29 (0.25) Data 0.00 (0.00) Loss 0.199 (0.199) Prec@1 93.75 (95.69) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-04:15:35] Epoch: [359][500/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.177 (0.201) Prec@1 96.88 (95.66) Prec@5 98.96 (99.94)
|
|
train[2019-04-01-04:15:40] Epoch: [359][520/521] Time 0.23 (0.25) Data 0.00 (0.00) Loss 0.221 (0.200) Prec@1 95.00 (95.68) Prec@5 100.00 (99.94)
|
|
[2019-04-01-04:15:40] **train** Prec@1 95.68 Prec@5 99.94 Error@1 4.32 Error@5 0.06 Loss:0.200
|
|
test [2019-04-01-04:15:40] Epoch: [359][000/105] Time 0.62 (0.62) Data 0.55 (0.55) Loss 0.112 (0.112) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-04:15:44] Epoch: [359][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.048 (0.163) Prec@1 98.96 (95.44) Prec@5 100.00 (99.86)
|
|
test [2019-04-01-04:15:45] Epoch: [359][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.132 (0.161) Prec@1 93.75 (95.46) Prec@5 100.00 (99.86)
|
|
[2019-04-01-04:15:45] **test** Prec@1 95.46 Prec@5 99.86 Error@1 4.54 Error@5 0.14 Loss:0.161
|
|
----> Best Accuracy : Acc@1=95.76, Acc@5=99.89, Error@1=4.24, Error@5=0.11
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-04:15:45] [Epoch=360/600] [Need: 09:04:19] LR=0.0087 ~ 0.0087, Batch=96
|
|
train[2019-04-01-04:15:46] Epoch: [360][000/521] Time 0.77 (0.77) Data 0.50 (0.50) Loss 0.320 (0.320) Prec@1 90.62 (90.62) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-04:16:10] Epoch: [360][100/521] Time 0.24 (0.25) Data 0.00 (0.01) Loss 0.307 (0.198) Prec@1 94.79 (95.57) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-04:16:35] Epoch: [360][200/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.256 (0.198) Prec@1 93.75 (95.70) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-04:17:00] Epoch: [360][300/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.160 (0.196) Prec@1 95.83 (95.83) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-04:17:24] Epoch: [360][400/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.241 (0.193) Prec@1 92.71 (95.83) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-04:17:49] Epoch: [360][500/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.349 (0.198) Prec@1 90.62 (95.74) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-04:17:54] Epoch: [360][520/521] Time 0.22 (0.25) Data 0.00 (0.00) Loss 0.260 (0.199) Prec@1 92.50 (95.73) Prec@5 100.00 (99.96)
|
|
[2019-04-01-04:17:54] **train** Prec@1 95.73 Prec@5 99.96 Error@1 4.27 Error@5 0.04 Loss:0.199
|
|
test [2019-04-01-04:17:54] Epoch: [360][000/105] Time 0.50 (0.50) Data 0.43 (0.43) Loss 0.105 (0.105) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-04:17:58] Epoch: [360][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.039 (0.170) Prec@1 97.92 (95.21) Prec@5 100.00 (99.83)
|
|
test [2019-04-01-04:17:58] Epoch: [360][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.051 (0.171) Prec@1 93.75 (95.19) Prec@5 100.00 (99.84)
|
|
[2019-04-01-04:17:59] **test** Prec@1 95.19 Prec@5 99.84 Error@1 4.81 Error@5 0.16 Loss:0.171
|
|
----> Best Accuracy : Acc@1=95.76, Acc@5=99.89, Error@1=4.24, Error@5=0.11
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-04:17:59] [Epoch=361/600] [Need: 08:53:13] LR=0.0086 ~ 0.0086, Batch=96
|
|
train[2019-04-01-04:18:00] Epoch: [361][000/521] Time 0.84 (0.84) Data 0.55 (0.55) Loss 0.240 (0.240) Prec@1 91.67 (91.67) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-04:18:24] Epoch: [361][100/521] Time 0.24 (0.25) Data 0.00 (0.01) Loss 0.209 (0.201) Prec@1 92.71 (95.68) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-04:18:48] Epoch: [361][200/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.114 (0.201) Prec@1 97.92 (95.68) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-04:19:13] Epoch: [361][300/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.449 (0.197) Prec@1 88.54 (95.85) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-04:19:38] Epoch: [361][400/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.254 (0.194) Prec@1 94.79 (95.95) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-04:20:03] Epoch: [361][500/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.080 (0.194) Prec@1 97.92 (95.93) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-04:20:08] Epoch: [361][520/521] Time 0.23 (0.25) Data 0.00 (0.00) Loss 0.107 (0.194) Prec@1 98.75 (95.93) Prec@5 100.00 (99.96)
|
|
[2019-04-01-04:20:08] **train** Prec@1 95.93 Prec@5 99.96 Error@1 4.07 Error@5 0.04 Loss:0.194
|
|
test [2019-04-01-04:20:09] Epoch: [361][000/105] Time 0.66 (0.66) Data 0.58 (0.58) Loss 0.113 (0.113) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-04:20:13] Epoch: [361][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.076 (0.165) Prec@1 98.96 (95.22) Prec@5 100.00 (99.92)
|
|
test [2019-04-01-04:20:13] Epoch: [361][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.213 (0.166) Prec@1 93.75 (95.18) Prec@5 100.00 (99.91)
|
|
[2019-04-01-04:20:13] **test** Prec@1 95.18 Prec@5 99.91 Error@1 4.82 Error@5 0.09 Loss:0.166
|
|
----> Best Accuracy : Acc@1=95.76, Acc@5=99.89, Error@1=4.24, Error@5=0.11
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-04:20:13] [Epoch=362/600] [Need: 08:52:41] LR=0.0086 ~ 0.0086, Batch=96
|
|
train[2019-04-01-04:20:14] Epoch: [362][000/521] Time 0.86 (0.86) Data 0.59 (0.59) Loss 0.134 (0.134) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-04:20:38] Epoch: [362][100/521] Time 0.25 (0.25) Data 0.00 (0.01) Loss 0.177 (0.185) Prec@1 94.79 (96.07) Prec@5 100.00 (99.91)
|
|
train[2019-04-01-04:21:03] Epoch: [362][200/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.174 (0.186) Prec@1 94.79 (96.04) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-04:21:28] Epoch: [362][300/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.177 (0.185) Prec@1 96.88 (96.09) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-04:21:53] Epoch: [362][400/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.156 (0.189) Prec@1 96.88 (96.02) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-04:22:17] Epoch: [362][500/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.230 (0.189) Prec@1 94.79 (96.01) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-04:22:23] Epoch: [362][520/521] Time 0.22 (0.25) Data 0.00 (0.00) Loss 0.163 (0.189) Prec@1 96.25 (96.01) Prec@5 100.00 (99.96)
|
|
[2019-04-01-04:22:23] **train** Prec@1 96.01 Prec@5 99.96 Error@1 3.99 Error@5 0.04 Loss:0.189
|
|
test [2019-04-01-04:22:23] Epoch: [362][000/105] Time 0.65 (0.65) Data 0.57 (0.57) Loss 0.084 (0.084) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-04:22:27] Epoch: [362][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.063 (0.159) Prec@1 96.88 (95.40) Prec@5 100.00 (99.91)
|
|
test [2019-04-01-04:22:28] Epoch: [362][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.210 (0.158) Prec@1 93.75 (95.44) Prec@5 100.00 (99.90)
|
|
[2019-04-01-04:22:28] **test** Prec@1 95.44 Prec@5 99.90 Error@1 4.56 Error@5 0.10 Loss:0.158
|
|
----> Best Accuracy : Acc@1=95.76, Acc@5=99.89, Error@1=4.24, Error@5=0.11
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-04:22:28] [Epoch=363/600] [Need: 08:52:43] LR=0.0085 ~ 0.0085, Batch=96
|
|
train[2019-04-01-04:22:29] Epoch: [363][000/521] Time 0.77 (0.77) Data 0.49 (0.49) Loss 0.309 (0.309) Prec@1 91.67 (91.67) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-04:22:53] Epoch: [363][100/521] Time 0.25 (0.25) Data 0.00 (0.01) Loss 0.121 (0.184) Prec@1 97.92 (96.36) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-04:23:18] Epoch: [363][200/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.243 (0.192) Prec@1 94.79 (96.01) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-04:23:43] Epoch: [363][300/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.346 (0.198) Prec@1 92.71 (95.85) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-04:24:08] Epoch: [363][400/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.160 (0.197) Prec@1 95.83 (95.85) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-04:24:33] Epoch: [363][500/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.192 (0.198) Prec@1 94.79 (95.80) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-04:24:38] Epoch: [363][520/521] Time 0.23 (0.25) Data 0.00 (0.00) Loss 0.133 (0.198) Prec@1 97.50 (95.81) Prec@5 100.00 (99.96)
|
|
[2019-04-01-04:24:38] **train** Prec@1 95.81 Prec@5 99.96 Error@1 4.19 Error@5 0.04 Loss:0.198
|
|
test [2019-04-01-04:24:39] Epoch: [363][000/105] Time 0.53 (0.53) Data 0.46 (0.46) Loss 0.285 (0.285) Prec@1 91.67 (91.67) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-04:24:43] Epoch: [363][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.053 (0.166) Prec@1 98.96 (95.25) Prec@5 100.00 (99.94)
|
|
test [2019-04-01-04:24:43] Epoch: [363][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.009 (0.165) Prec@1 100.00 (95.28) Prec@5 100.00 (99.93)
|
|
[2019-04-01-04:24:43] **test** Prec@1 95.28 Prec@5 99.93 Error@1 4.72 Error@5 0.07 Loss:0.165
|
|
----> Best Accuracy : Acc@1=95.76, Acc@5=99.89, Error@1=4.24, Error@5=0.11
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-04:24:43] [Epoch=364/600] [Need: 08:51:58] LR=0.0085 ~ 0.0085, Batch=96
|
|
train[2019-04-01-04:24:44] Epoch: [364][000/521] Time 0.93 (0.93) Data 0.64 (0.64) Loss 0.210 (0.210) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-04:25:09] Epoch: [364][100/521] Time 0.25 (0.26) Data 0.00 (0.01) Loss 0.132 (0.202) Prec@1 97.92 (95.75) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-04:25:34] Epoch: [364][200/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.263 (0.197) Prec@1 92.71 (95.89) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-04:25:58] Epoch: [364][300/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.154 (0.197) Prec@1 96.88 (95.89) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-04:26:23] Epoch: [364][400/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.093 (0.199) Prec@1 98.96 (95.81) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-04:26:48] Epoch: [364][500/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.125 (0.199) Prec@1 97.92 (95.80) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-04:26:52] Epoch: [364][520/521] Time 0.23 (0.25) Data 0.00 (0.00) Loss 0.157 (0.200) Prec@1 96.25 (95.79) Prec@5 100.00 (99.95)
|
|
[2019-04-01-04:26:53] **train** Prec@1 95.79 Prec@5 99.95 Error@1 4.21 Error@5 0.05 Loss:0.200
|
|
test [2019-04-01-04:26:53] Epoch: [364][000/105] Time 0.61 (0.61) Data 0.54 (0.54) Loss 0.096 (0.096) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-04:26:57] Epoch: [364][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.081 (0.167) Prec@1 98.96 (95.41) Prec@5 100.00 (99.81)
|
|
test [2019-04-01-04:26:57] Epoch: [364][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.007 (0.167) Prec@1 100.00 (95.42) Prec@5 100.00 (99.82)
|
|
[2019-04-01-04:26:58] **test** Prec@1 95.42 Prec@5 99.82 Error@1 4.58 Error@5 0.18 Loss:0.167
|
|
----> Best Accuracy : Acc@1=95.76, Acc@5=99.89, Error@1=4.24, Error@5=0.11
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-04:26:58] [Epoch=365/600] [Need: 08:46:58] LR=0.0084 ~ 0.0084, Batch=96
|
|
train[2019-04-01-04:26:59] Epoch: [365][000/521] Time 0.91 (0.91) Data 0.61 (0.61) Loss 0.088 (0.088) Prec@1 100.00 (100.00) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-04:27:24] Epoch: [365][100/521] Time 0.24 (0.26) Data 0.00 (0.01) Loss 0.166 (0.179) Prec@1 96.88 (96.40) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-04:27:48] Epoch: [365][200/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.193 (0.188) Prec@1 95.83 (96.02) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-04:28:13] Epoch: [365][300/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.154 (0.190) Prec@1 97.92 (95.96) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-04:28:37] Epoch: [365][400/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.244 (0.193) Prec@1 94.79 (95.89) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-04:29:02] Epoch: [365][500/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.289 (0.193) Prec@1 92.71 (95.85) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-04:29:07] Epoch: [365][520/521] Time 0.22 (0.25) Data 0.00 (0.00) Loss 0.262 (0.195) Prec@1 93.75 (95.83) Prec@5 100.00 (99.96)
|
|
[2019-04-01-04:29:07] **train** Prec@1 95.83 Prec@5 99.96 Error@1 4.17 Error@5 0.04 Loss:0.195
|
|
test [2019-04-01-04:29:07] Epoch: [365][000/105] Time 0.48 (0.48) Data 0.42 (0.42) Loss 0.073 (0.073) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-04:29:11] Epoch: [365][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.035 (0.159) Prec@1 98.96 (95.70) Prec@5 100.00 (99.85)
|
|
test [2019-04-01-04:29:12] Epoch: [365][104/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.085 (0.159) Prec@1 93.75 (95.70) Prec@5 100.00 (99.85)
|
|
[2019-04-01-04:29:12] **test** Prec@1 95.70 Prec@5 99.85 Error@1 4.30 Error@5 0.15 Loss:0.159
|
|
----> Best Accuracy : Acc@1=95.76, Acc@5=99.89, Error@1=4.24, Error@5=0.11
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-04:29:12] [Epoch=366/600] [Need: 08:43:19] LR=0.0083 ~ 0.0083, Batch=96
|
|
train[2019-04-01-04:29:13] Epoch: [366][000/521] Time 0.75 (0.75) Data 0.44 (0.44) Loss 0.228 (0.228) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-04:29:37] Epoch: [366][100/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.171 (0.182) Prec@1 95.83 (96.24) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-04:30:01] Epoch: [366][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.169 (0.189) Prec@1 96.88 (96.00) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-04:30:25] Epoch: [366][300/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.268 (0.193) Prec@1 94.79 (95.82) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-04:30:48] Epoch: [366][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.305 (0.193) Prec@1 91.67 (95.90) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-04:31:12] Epoch: [366][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.123 (0.194) Prec@1 98.96 (95.84) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-04:31:17] Epoch: [366][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.159 (0.196) Prec@1 98.75 (95.79) Prec@5 100.00 (99.94)
|
|
[2019-04-01-04:31:17] **train** Prec@1 95.79 Prec@5 99.94 Error@1 4.21 Error@5 0.06 Loss:0.196
|
|
test [2019-04-01-04:31:18] Epoch: [366][000/105] Time 0.49 (0.49) Data 0.40 (0.40) Loss 0.093 (0.093) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-04:31:22] Epoch: [366][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.067 (0.165) Prec@1 96.88 (95.17) Prec@5 100.00 (99.89)
|
|
test [2019-04-01-04:31:22] Epoch: [366][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.003 (0.164) Prec@1 100.00 (95.18) Prec@5 100.00 (99.89)
|
|
[2019-04-01-04:31:22] **test** Prec@1 95.18 Prec@5 99.89 Error@1 4.82 Error@5 0.11 Loss:0.164
|
|
----> Best Accuracy : Acc@1=95.76, Acc@5=99.89, Error@1=4.24, Error@5=0.11
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-04:31:22] [Epoch=367/600] [Need: 08:26:21] LR=0.0083 ~ 0.0083, Batch=96
|
|
train[2019-04-01-04:31:23] Epoch: [367][000/521] Time 0.73 (0.73) Data 0.46 (0.46) Loss 0.186 (0.186) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-04:31:47] Epoch: [367][100/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.177 (0.198) Prec@1 91.67 (95.87) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-04:32:11] Epoch: [367][200/521] Time 0.26 (0.24) Data 0.00 (0.00) Loss 0.225 (0.200) Prec@1 94.79 (95.65) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-04:32:37] Epoch: [367][300/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.126 (0.198) Prec@1 97.92 (95.74) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-04:33:01] Epoch: [367][400/521] Time 0.23 (0.25) Data 0.00 (0.00) Loss 0.164 (0.197) Prec@1 96.88 (95.80) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-04:33:25] Epoch: [367][500/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.209 (0.197) Prec@1 94.79 (95.75) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-04:33:30] Epoch: [367][520/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.199 (0.197) Prec@1 97.50 (95.76) Prec@5 100.00 (99.95)
|
|
[2019-04-01-04:33:30] **train** Prec@1 95.76 Prec@5 99.95 Error@1 4.24 Error@5 0.05 Loss:0.197
|
|
test [2019-04-01-04:33:30] Epoch: [367][000/105] Time 0.63 (0.63) Data 0.56 (0.56) Loss 0.053 (0.053) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-04:33:35] Epoch: [367][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.085 (0.172) Prec@1 95.83 (95.25) Prec@5 100.00 (99.94)
|
|
test [2019-04-01-04:33:35] Epoch: [367][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.177 (0.173) Prec@1 93.75 (95.23) Prec@5 100.00 (99.93)
|
|
[2019-04-01-04:33:35] **test** Prec@1 95.23 Prec@5 99.93 Error@1 4.77 Error@5 0.07 Loss:0.173
|
|
----> Best Accuracy : Acc@1=95.76, Acc@5=99.89, Error@1=4.24, Error@5=0.11
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-04:33:35] [Epoch=368/600] [Need: 08:33:44] LR=0.0082 ~ 0.0082, Batch=96
|
|
train[2019-04-01-04:33:36] Epoch: [368][000/521] Time 0.89 (0.89) Data 0.60 (0.60) Loss 0.230 (0.230) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-04:34:01] Epoch: [368][100/521] Time 0.24 (0.26) Data 0.00 (0.01) Loss 0.197 (0.200) Prec@1 95.83 (95.81) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-04:34:26] Epoch: [368][200/521] Time 0.27 (0.25) Data 0.00 (0.00) Loss 0.175 (0.200) Prec@1 96.88 (95.79) Prec@5 100.00 (99.92)
|
|
train[2019-04-01-04:34:51] Epoch: [368][300/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.205 (0.196) Prec@1 95.83 (95.85) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-04:35:16] Epoch: [368][400/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.189 (0.195) Prec@1 94.79 (95.84) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-04:35:42] Epoch: [368][500/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.137 (0.198) Prec@1 100.00 (95.80) Prec@5 100.00 (99.93)
|
|
train[2019-04-01-04:35:47] Epoch: [368][520/521] Time 0.22 (0.25) Data 0.00 (0.00) Loss 0.234 (0.197) Prec@1 96.25 (95.81) Prec@5 100.00 (99.93)
|
|
[2019-04-01-04:35:47] **train** Prec@1 95.81 Prec@5 99.93 Error@1 4.19 Error@5 0.07 Loss:0.197
|
|
test [2019-04-01-04:35:47] Epoch: [368][000/105] Time 0.63 (0.63) Data 0.56 (0.56) Loss 0.142 (0.142) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-04:35:52] Epoch: [368][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.069 (0.175) Prec@1 97.92 (95.08) Prec@5 100.00 (99.90)
|
|
test [2019-04-01-04:35:52] Epoch: [368][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.363 (0.175) Prec@1 93.75 (95.06) Prec@5 100.00 (99.90)
|
|
[2019-04-01-04:35:52] **test** Prec@1 95.06 Prec@5 99.90 Error@1 4.94 Error@5 0.10 Loss:0.175
|
|
----> Best Accuracy : Acc@1=95.76, Acc@5=99.89, Error@1=4.24, Error@5=0.11
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-04:35:52] [Epoch=369/600] [Need: 08:46:46] LR=0.0081 ~ 0.0081, Batch=96
|
|
train[2019-04-01-04:35:53] Epoch: [369][000/521] Time 0.89 (0.89) Data 0.59 (0.59) Loss 0.177 (0.177) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-04:36:18] Epoch: [369][100/521] Time 0.25 (0.26) Data 0.00 (0.01) Loss 0.175 (0.190) Prec@1 96.88 (96.14) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-04:36:43] Epoch: [369][200/521] Time 0.26 (0.25) Data 0.00 (0.00) Loss 0.238 (0.195) Prec@1 92.71 (95.93) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-04:37:08] Epoch: [369][300/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.308 (0.188) Prec@1 93.75 (96.07) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-04:37:33] Epoch: [369][400/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.240 (0.186) Prec@1 93.75 (96.06) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-04:37:58] Epoch: [369][500/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.318 (0.187) Prec@1 93.75 (96.05) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-04:38:03] Epoch: [369][520/521] Time 0.23 (0.25) Data 0.00 (0.00) Loss 0.152 (0.187) Prec@1 95.00 (96.05) Prec@5 100.00 (99.97)
|
|
[2019-04-01-04:38:03] **train** Prec@1 96.05 Prec@5 99.97 Error@1 3.95 Error@5 0.03 Loss:0.187
|
|
test [2019-04-01-04:38:04] Epoch: [369][000/105] Time 0.52 (0.52) Data 0.45 (0.45) Loss 0.101 (0.101) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-04:38:08] Epoch: [369][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.045 (0.163) Prec@1 97.92 (95.43) Prec@5 100.00 (99.88)
|
|
test [2019-04-01-04:38:08] Epoch: [369][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.288 (0.163) Prec@1 93.75 (95.42) Prec@5 100.00 (99.88)
|
|
[2019-04-01-04:38:08] **test** Prec@1 95.42 Prec@5 99.88 Error@1 4.58 Error@5 0.12 Loss:0.163
|
|
----> Best Accuracy : Acc@1=95.76, Acc@5=99.89, Error@1=4.24, Error@5=0.11
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-04:38:09] [Epoch=370/600] [Need: 08:43:49] LR=0.0081 ~ 0.0081, Batch=96
|
|
train[2019-04-01-04:38:09] Epoch: [370][000/521] Time 0.73 (0.73) Data 0.44 (0.44) Loss 0.227 (0.227) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-04:38:34] Epoch: [370][100/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.255 (0.203) Prec@1 92.71 (95.64) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-04:39:00] Epoch: [370][200/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.120 (0.211) Prec@1 94.79 (95.34) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-04:39:25] Epoch: [370][300/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.173 (0.202) Prec@1 96.88 (95.60) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-04:39:50] Epoch: [370][400/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.171 (0.201) Prec@1 95.83 (95.66) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-04:40:16] Epoch: [370][500/521] Time 0.25 (0.26) Data 0.00 (0.00) Loss 0.069 (0.200) Prec@1 100.00 (95.67) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-04:40:21] Epoch: [370][520/521] Time 0.23 (0.25) Data 0.00 (0.00) Loss 0.224 (0.200) Prec@1 95.00 (95.69) Prec@5 100.00 (99.96)
|
|
[2019-04-01-04:40:21] **train** Prec@1 95.69 Prec@5 99.96 Error@1 4.31 Error@5 0.04 Loss:0.200
|
|
test [2019-04-01-04:40:22] Epoch: [370][000/105] Time 0.50 (0.50) Data 0.43 (0.43) Loss 0.035 (0.035) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-04:40:26] Epoch: [370][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.087 (0.170) Prec@1 96.88 (95.22) Prec@5 100.00 (99.91)
|
|
test [2019-04-01-04:40:26] Epoch: [370][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.084 (0.169) Prec@1 93.75 (95.26) Prec@5 100.00 (99.91)
|
|
[2019-04-01-04:40:26] **test** Prec@1 95.26 Prec@5 99.91 Error@1 4.74 Error@5 0.09 Loss:0.169
|
|
----> Best Accuracy : Acc@1=95.76, Acc@5=99.89, Error@1=4.24, Error@5=0.11
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-04:40:26] [Epoch=371/600] [Need: 08:45:47] LR=0.0080 ~ 0.0080, Batch=96
|
|
train[2019-04-01-04:40:27] Epoch: [371][000/521] Time 0.89 (0.89) Data 0.60 (0.60) Loss 0.230 (0.230) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-04:40:52] Epoch: [371][100/521] Time 0.24 (0.25) Data 0.00 (0.01) Loss 0.117 (0.184) Prec@1 96.88 (96.18) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-04:41:17] Epoch: [371][200/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.179 (0.188) Prec@1 92.71 (96.01) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-04:41:41] Epoch: [371][300/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.088 (0.185) Prec@1 97.92 (96.13) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-04:42:06] Epoch: [371][400/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.246 (0.187) Prec@1 93.75 (96.09) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-04:42:30] Epoch: [371][500/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.342 (0.190) Prec@1 91.67 (95.96) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-04:42:35] Epoch: [371][520/521] Time 0.22 (0.25) Data 0.00 (0.00) Loss 0.197 (0.190) Prec@1 95.00 (95.95) Prec@5 100.00 (99.96)
|
|
[2019-04-01-04:42:35] **train** Prec@1 95.95 Prec@5 99.96 Error@1 4.05 Error@5 0.04 Loss:0.190
|
|
test [2019-04-01-04:42:36] Epoch: [371][000/105] Time 0.65 (0.65) Data 0.58 (0.58) Loss 0.165 (0.165) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-04:42:40] Epoch: [371][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.040 (0.162) Prec@1 98.96 (95.41) Prec@5 100.00 (99.90)
|
|
test [2019-04-01-04:42:40] Epoch: [371][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.004 (0.162) Prec@1 100.00 (95.43) Prec@5 100.00 (99.90)
|
|
[2019-04-01-04:42:40] **test** Prec@1 95.43 Prec@5 99.90 Error@1 4.57 Error@5 0.10 Loss:0.162
|
|
----> Best Accuracy : Acc@1=95.76, Acc@5=99.89, Error@1=4.24, Error@5=0.11
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-04:42:41] [Epoch=372/600] [Need: 08:29:52] LR=0.0080 ~ 0.0080, Batch=96
|
|
train[2019-04-01-04:42:41] Epoch: [372][000/521] Time 0.90 (0.90) Data 0.58 (0.58) Loss 0.177 (0.177) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-04:43:06] Epoch: [372][100/521] Time 0.24 (0.25) Data 0.00 (0.01) Loss 0.245 (0.184) Prec@1 92.71 (96.20) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-04:43:31] Epoch: [372][200/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.205 (0.187) Prec@1 97.92 (96.17) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-04:43:55] Epoch: [372][300/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.104 (0.186) Prec@1 98.96 (96.19) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-04:44:20] Epoch: [372][400/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.270 (0.190) Prec@1 92.71 (96.10) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-04:44:47] Epoch: [372][500/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.124 (0.192) Prec@1 96.88 (96.07) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-04:44:52] Epoch: [372][520/521] Time 0.23 (0.25) Data 0.00 (0.00) Loss 0.286 (0.192) Prec@1 93.75 (96.04) Prec@5 100.00 (99.94)
|
|
[2019-04-01-04:44:52] **train** Prec@1 96.04 Prec@5 99.94 Error@1 3.96 Error@5 0.06 Loss:0.192
|
|
test [2019-04-01-04:44:52] Epoch: [372][000/105] Time 0.53 (0.53) Data 0.47 (0.47) Loss 0.109 (0.109) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-04:44:57] Epoch: [372][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.090 (0.197) Prec@1 97.92 (94.72) Prec@5 100.00 (99.87)
|
|
test [2019-04-01-04:44:57] Epoch: [372][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.296 (0.196) Prec@1 93.75 (94.74) Prec@5 100.00 (99.87)
|
|
[2019-04-01-04:44:57] **test** Prec@1 94.74 Prec@5 99.87 Error@1 5.26 Error@5 0.13 Loss:0.196
|
|
----> Best Accuracy : Acc@1=95.76, Acc@5=99.89, Error@1=4.24, Error@5=0.11
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-04:44:57] [Epoch=373/600] [Need: 08:36:00] LR=0.0079 ~ 0.0079, Batch=96
|
|
train[2019-04-01-04:44:58] Epoch: [373][000/521] Time 0.80 (0.80) Data 0.50 (0.50) Loss 0.232 (0.232) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-04:45:23] Epoch: [373][100/521] Time 0.25 (0.26) Data 0.00 (0.01) Loss 0.256 (0.177) Prec@1 93.75 (96.12) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-04:45:54] Epoch: [373][200/521] Time 0.25 (0.28) Data 0.00 (0.00) Loss 0.200 (0.183) Prec@1 96.88 (96.12) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-04:46:19] Epoch: [373][300/521] Time 0.25 (0.27) Data 0.00 (0.00) Loss 0.265 (0.185) Prec@1 95.83 (96.12) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-04:46:45] Epoch: [373][400/521] Time 0.27 (0.27) Data 0.00 (0.00) Loss 0.158 (0.190) Prec@1 96.88 (95.96) Prec@5 98.96 (99.94)
|
|
train[2019-04-01-04:47:10] Epoch: [373][500/521] Time 0.24 (0.27) Data 0.00 (0.00) Loss 0.166 (0.192) Prec@1 95.83 (95.93) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-04:47:15] Epoch: [373][520/521] Time 0.24 (0.27) Data 0.00 (0.00) Loss 0.136 (0.192) Prec@1 97.50 (95.95) Prec@5 100.00 (99.95)
|
|
[2019-04-01-04:47:15] **train** Prec@1 95.95 Prec@5 99.95 Error@1 4.05 Error@5 0.05 Loss:0.192
|
|
test [2019-04-01-04:47:16] Epoch: [373][000/105] Time 0.64 (0.64) Data 0.57 (0.57) Loss 0.127 (0.127) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-04:47:20] Epoch: [373][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.029 (0.174) Prec@1 98.96 (95.21) Prec@5 100.00 (99.89)
|
|
test [2019-04-01-04:47:20] Epoch: [373][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.221 (0.175) Prec@1 93.75 (95.24) Prec@5 100.00 (99.89)
|
|
[2019-04-01-04:47:20] **test** Prec@1 95.24 Prec@5 99.89 Error@1 4.76 Error@5 0.11 Loss:0.175
|
|
----> Best Accuracy : Acc@1=95.76, Acc@5=99.89, Error@1=4.24, Error@5=0.11
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-04:47:21] [Epoch=374/600] [Need: 09:01:09] LR=0.0078 ~ 0.0078, Batch=96
|
|
train[2019-04-01-04:47:21] Epoch: [374][000/521] Time 0.85 (0.85) Data 0.57 (0.57) Loss 0.195 (0.195) Prec@1 95.83 (95.83) Prec@5 98.96 (98.96)
|
|
train[2019-04-01-04:47:47] Epoch: [374][100/521] Time 0.24 (0.26) Data 0.00 (0.01) Loss 0.117 (0.178) Prec@1 97.92 (96.24) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-04:48:11] Epoch: [374][200/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.177 (0.178) Prec@1 97.92 (96.32) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-04:48:36] Epoch: [374][300/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.166 (0.178) Prec@1 95.83 (96.29) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-04:49:00] Epoch: [374][400/521] Time 0.23 (0.25) Data 0.00 (0.00) Loss 0.169 (0.180) Prec@1 95.83 (96.22) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-04:49:24] Epoch: [374][500/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.399 (0.185) Prec@1 92.71 (96.13) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-04:49:29] Epoch: [374][520/521] Time 0.22 (0.25) Data 0.00 (0.00) Loss 0.100 (0.185) Prec@1 98.75 (96.14) Prec@5 100.00 (99.96)
|
|
[2019-04-01-04:49:29] **train** Prec@1 96.14 Prec@5 99.96 Error@1 3.86 Error@5 0.04 Loss:0.185
|
|
test [2019-04-01-04:49:29] Epoch: [374][000/105] Time 0.48 (0.48) Data 0.42 (0.42) Loss 0.124 (0.124) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-04:49:33] Epoch: [374][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.031 (0.174) Prec@1 98.96 (95.47) Prec@5 100.00 (99.85)
|
|
test [2019-04-01-04:49:33] Epoch: [374][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.002 (0.174) Prec@1 100.00 (95.45) Prec@5 100.00 (99.85)
|
|
[2019-04-01-04:49:33] **test** Prec@1 95.45 Prec@5 99.85 Error@1 4.55 Error@5 0.15 Loss:0.174
|
|
----> Best Accuracy : Acc@1=95.76, Acc@5=99.89, Error@1=4.24, Error@5=0.11
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-04:49:34] [Epoch=375/600] [Need: 08:18:50] LR=0.0078 ~ 0.0078, Batch=96
|
|
train[2019-04-01-04:49:34] Epoch: [375][000/521] Time 0.83 (0.83) Data 0.56 (0.56) Loss 0.182 (0.182) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-04:49:58] Epoch: [375][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.148 (0.185) Prec@1 96.88 (96.19) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-04:50:24] Epoch: [375][200/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.238 (0.182) Prec@1 95.83 (96.22) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-04:50:48] Epoch: [375][300/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.105 (0.187) Prec@1 97.92 (96.11) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-04:51:12] Epoch: [375][400/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.178 (0.189) Prec@1 96.88 (96.03) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-04:51:37] Epoch: [375][500/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.205 (0.190) Prec@1 95.83 (95.95) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-04:51:42] Epoch: [375][520/521] Time 0.23 (0.25) Data 0.00 (0.00) Loss 0.258 (0.190) Prec@1 93.75 (95.96) Prec@5 100.00 (99.96)
|
|
[2019-04-01-04:51:42] **train** Prec@1 95.96 Prec@5 99.96 Error@1 4.04 Error@5 0.04 Loss:0.190
|
|
test [2019-04-01-04:51:43] Epoch: [375][000/105] Time 0.49 (0.49) Data 0.44 (0.44) Loss 0.217 (0.217) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-04:51:47] Epoch: [375][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.107 (0.194) Prec@1 97.92 (94.83) Prec@5 100.00 (99.89)
|
|
test [2019-04-01-04:51:47] Epoch: [375][104/105] Time 0.07 (0.05) Data 0.00 (0.00) Loss 0.340 (0.195) Prec@1 93.75 (94.79) Prec@5 100.00 (99.89)
|
|
[2019-04-01-04:51:47] **test** Prec@1 94.79 Prec@5 99.89 Error@1 5.21 Error@5 0.11 Loss:0.195
|
|
----> Best Accuracy : Acc@1=95.76, Acc@5=99.89, Error@1=4.24, Error@5=0.11
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-04:51:48] [Epoch=376/600] [Need: 08:20:19] LR=0.0077 ~ 0.0077, Batch=96
|
|
train[2019-04-01-04:51:48] Epoch: [376][000/521] Time 0.77 (0.77) Data 0.48 (0.48) Loss 0.189 (0.189) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-04:52:14] Epoch: [376][100/521] Time 0.25 (0.26) Data 0.00 (0.00) Loss 0.171 (0.185) Prec@1 95.83 (95.99) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-04:52:39] Epoch: [376][200/521] Time 0.24 (0.26) Data 0.00 (0.00) Loss 0.193 (0.184) Prec@1 95.83 (96.17) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-04:53:04] Epoch: [376][300/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.148 (0.187) Prec@1 96.88 (95.99) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-04:53:28] Epoch: [376][400/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.137 (0.185) Prec@1 95.83 (96.07) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-04:53:52] Epoch: [376][500/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.163 (0.187) Prec@1 96.88 (96.05) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-04:53:57] Epoch: [376][520/521] Time 0.21 (0.25) Data 0.00 (0.00) Loss 0.136 (0.187) Prec@1 97.50 (96.05) Prec@5 100.00 (99.96)
|
|
[2019-04-01-04:53:57] **train** Prec@1 96.05 Prec@5 99.96 Error@1 3.95 Error@5 0.04 Loss:0.187
|
|
test [2019-04-01-04:53:58] Epoch: [376][000/105] Time 0.47 (0.47) Data 0.40 (0.40) Loss 0.162 (0.162) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-04:54:02] Epoch: [376][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.044 (0.160) Prec@1 97.92 (95.69) Prec@5 100.00 (99.90)
|
|
test [2019-04-01-04:54:02] Epoch: [376][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.110 (0.160) Prec@1 93.75 (95.68) Prec@5 100.00 (99.90)
|
|
[2019-04-01-04:54:02] **test** Prec@1 95.68 Prec@5 99.90 Error@1 4.32 Error@5 0.10 Loss:0.160
|
|
----> Best Accuracy : Acc@1=95.76, Acc@5=99.89, Error@1=4.24, Error@5=0.11
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-04:54:02] [Epoch=377/600] [Need: 08:19:40] LR=0.0077 ~ 0.0077, Batch=96
|
|
train[2019-04-01-04:54:03] Epoch: [377][000/521] Time 0.73 (0.73) Data 0.43 (0.43) Loss 0.183 (0.183) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-04:54:26] Epoch: [377][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.117 (0.181) Prec@1 98.96 (96.20) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-04:54:50] Epoch: [377][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.125 (0.178) Prec@1 97.92 (96.26) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-04:55:14] Epoch: [377][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.212 (0.180) Prec@1 95.83 (96.27) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-04:55:38] Epoch: [377][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.320 (0.182) Prec@1 92.71 (96.19) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-04:56:02] Epoch: [377][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.210 (0.186) Prec@1 93.75 (96.07) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-04:56:06] Epoch: [377][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.182 (0.187) Prec@1 97.50 (96.05) Prec@5 100.00 (99.95)
|
|
[2019-04-01-04:56:07] **train** Prec@1 96.05 Prec@5 99.95 Error@1 3.95 Error@5 0.05 Loss:0.187
|
|
test [2019-04-01-04:56:07] Epoch: [377][000/105] Time 0.60 (0.60) Data 0.53 (0.53) Loss 0.152 (0.152) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-04:56:11] Epoch: [377][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.067 (0.196) Prec@1 97.92 (95.08) Prec@5 100.00 (99.90)
|
|
test [2019-04-01-04:56:11] Epoch: [377][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.003 (0.196) Prec@1 100.00 (95.10) Prec@5 100.00 (99.90)
|
|
[2019-04-01-04:56:12] **test** Prec@1 95.10 Prec@5 99.90 Error@1 4.90 Error@5 0.10 Loss:0.196
|
|
----> Best Accuracy : Acc@1=95.76, Acc@5=99.89, Error@1=4.24, Error@5=0.11
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-04:56:12] [Epoch=378/600] [Need: 07:59:34] LR=0.0076 ~ 0.0076, Batch=96
|
|
train[2019-04-01-04:56:13] Epoch: [378][000/521] Time 0.84 (0.84) Data 0.57 (0.57) Loss 0.172 (0.172) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-04:56:36] Epoch: [378][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.177 (0.190) Prec@1 94.79 (95.83) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-04:57:00] Epoch: [378][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.121 (0.182) Prec@1 95.83 (96.00) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-04:57:24] Epoch: [378][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.148 (0.181) Prec@1 97.92 (96.08) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-04:57:48] Epoch: [378][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.188 (0.184) Prec@1 96.88 (96.12) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-04:58:12] Epoch: [378][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.127 (0.184) Prec@1 98.96 (96.11) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-04:58:17] Epoch: [378][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.131 (0.184) Prec@1 96.25 (96.11) Prec@5 100.00 (99.96)
|
|
[2019-04-01-04:58:17] **train** Prec@1 96.11 Prec@5 99.96 Error@1 3.89 Error@5 0.04 Loss:0.184
|
|
test [2019-04-01-04:58:18] Epoch: [378][000/105] Time 0.73 (0.73) Data 0.59 (0.59) Loss 0.207 (0.207) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-04:58:22] Epoch: [378][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.050 (0.169) Prec@1 98.96 (95.48) Prec@5 100.00 (99.91)
|
|
test [2019-04-01-04:58:22] Epoch: [378][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.371 (0.171) Prec@1 87.50 (95.44) Prec@5 100.00 (99.91)
|
|
[2019-04-01-04:58:22] **test** Prec@1 95.44 Prec@5 99.91 Error@1 4.56 Error@5 0.09 Loss:0.171
|
|
----> Best Accuracy : Acc@1=95.76, Acc@5=99.89, Error@1=4.24, Error@5=0.11
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-04:58:23] [Epoch=379/600] [Need: 08:01:52] LR=0.0075 ~ 0.0075, Batch=96
|
|
train[2019-04-01-04:58:23] Epoch: [379][000/521] Time 0.75 (0.75) Data 0.47 (0.47) Loss 0.204 (0.204) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-04:58:47] Epoch: [379][100/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.145 (0.174) Prec@1 94.79 (96.30) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-04:59:12] Epoch: [379][200/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.177 (0.181) Prec@1 96.88 (96.23) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-04:59:37] Epoch: [379][300/521] Time 0.26 (0.25) Data 0.00 (0.00) Loss 0.274 (0.179) Prec@1 92.71 (96.32) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-05:00:03] Epoch: [379][400/521] Time 0.29 (0.25) Data 0.00 (0.00) Loss 0.138 (0.179) Prec@1 96.88 (96.37) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-05:00:28] Epoch: [379][500/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.110 (0.180) Prec@1 97.92 (96.30) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-05:00:33] Epoch: [379][520/521] Time 0.23 (0.25) Data 0.00 (0.00) Loss 0.289 (0.182) Prec@1 95.00 (96.28) Prec@5 100.00 (99.96)
|
|
[2019-04-01-05:00:33] **train** Prec@1 96.28 Prec@5 99.96 Error@1 3.72 Error@5 0.04 Loss:0.182
|
|
test [2019-04-01-05:00:34] Epoch: [379][000/105] Time 0.65 (0.65) Data 0.58 (0.58) Loss 0.148 (0.148) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-05:00:38] Epoch: [379][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.039 (0.166) Prec@1 97.92 (95.22) Prec@5 100.00 (99.94)
|
|
test [2019-04-01-05:00:38] Epoch: [379][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.352 (0.166) Prec@1 93.75 (95.22) Prec@5 100.00 (99.94)
|
|
[2019-04-01-05:00:38] **test** Prec@1 95.22 Prec@5 99.94 Error@1 4.78 Error@5 0.06 Loss:0.166
|
|
----> Best Accuracy : Acc@1=95.76, Acc@5=99.89, Error@1=4.24, Error@5=0.11
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-05:00:38] [Epoch=380/600] [Need: 08:18:14] LR=0.0075 ~ 0.0075, Batch=96
|
|
train[2019-04-01-05:00:39] Epoch: [380][000/521] Time 0.74 (0.74) Data 0.46 (0.46) Loss 0.090 (0.090) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-05:01:04] Epoch: [380][100/521] Time 0.24 (0.26) Data 0.00 (0.00) Loss 0.245 (0.171) Prec@1 94.79 (96.49) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-05:01:29] Epoch: [380][200/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.130 (0.178) Prec@1 98.96 (96.24) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-05:01:54] Epoch: [380][300/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.160 (0.172) Prec@1 97.92 (96.42) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-05:02:20] Epoch: [380][400/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.156 (0.175) Prec@1 96.88 (96.36) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-05:02:45] Epoch: [380][500/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.360 (0.180) Prec@1 90.62 (96.21) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-05:02:50] Epoch: [380][520/521] Time 0.23 (0.25) Data 0.00 (0.00) Loss 0.185 (0.182) Prec@1 98.75 (96.16) Prec@5 100.00 (99.96)
|
|
[2019-04-01-05:02:50] **train** Prec@1 96.16 Prec@5 99.96 Error@1 3.84 Error@5 0.04 Loss:0.182
|
|
test [2019-04-01-05:02:50] Epoch: [380][000/105] Time 0.52 (0.52) Data 0.46 (0.46) Loss 0.301 (0.301) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-05:02:54] Epoch: [380][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.073 (0.168) Prec@1 98.96 (95.32) Prec@5 100.00 (99.93)
|
|
test [2019-04-01-05:02:55] Epoch: [380][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.088 (0.167) Prec@1 93.75 (95.34) Prec@5 100.00 (99.93)
|
|
[2019-04-01-05:02:55] **test** Prec@1 95.34 Prec@5 99.93 Error@1 4.66 Error@5 0.07 Loss:0.167
|
|
----> Best Accuracy : Acc@1=95.76, Acc@5=99.89, Error@1=4.24, Error@5=0.11
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-05:02:55] [Epoch=381/600] [Need: 08:17:50] LR=0.0074 ~ 0.0074, Batch=96
|
|
train[2019-04-01-05:02:56] Epoch: [381][000/521] Time 0.82 (0.82) Data 0.54 (0.54) Loss 0.234 (0.234) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-05:03:21] Epoch: [381][100/521] Time 0.28 (0.26) Data 0.00 (0.01) Loss 0.130 (0.173) Prec@1 97.92 (96.43) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-05:03:46] Epoch: [381][200/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.145 (0.181) Prec@1 96.88 (96.21) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-05:04:11] Epoch: [381][300/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.118 (0.183) Prec@1 97.92 (96.14) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-05:04:36] Epoch: [381][400/521] Time 0.26 (0.25) Data 0.00 (0.00) Loss 0.329 (0.185) Prec@1 91.67 (96.06) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-05:05:02] Epoch: [381][500/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.214 (0.187) Prec@1 95.83 (96.03) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-05:05:07] Epoch: [381][520/521] Time 0.22 (0.25) Data 0.00 (0.00) Loss 0.213 (0.187) Prec@1 95.00 (96.03) Prec@5 100.00 (99.97)
|
|
[2019-04-01-05:05:07] **train** Prec@1 96.03 Prec@5 99.97 Error@1 3.97 Error@5 0.03 Loss:0.187
|
|
test [2019-04-01-05:05:07] Epoch: [381][000/105] Time 0.48 (0.48) Data 0.41 (0.41) Loss 0.075 (0.075) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-05:05:12] Epoch: [381][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.040 (0.162) Prec@1 98.96 (95.64) Prec@5 100.00 (99.96)
|
|
test [2019-04-01-05:05:12] Epoch: [381][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.306 (0.162) Prec@1 93.75 (95.64) Prec@5 100.00 (99.96)
|
|
[2019-04-01-05:05:12] **test** Prec@1 95.64 Prec@5 99.96 Error@1 4.36 Error@5 0.04 Loss:0.162
|
|
----> Best Accuracy : Acc@1=95.76, Acc@5=99.89, Error@1=4.24, Error@5=0.11
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-05:05:12] [Epoch=382/600] [Need: 08:18:55] LR=0.0074 ~ 0.0074, Batch=96
|
|
train[2019-04-01-05:05:13] Epoch: [382][000/521] Time 0.74 (0.74) Data 0.46 (0.46) Loss 0.173 (0.173) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-05:05:38] Epoch: [382][100/521] Time 0.25 (0.26) Data 0.00 (0.00) Loss 0.151 (0.173) Prec@1 97.92 (96.51) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-05:06:03] Epoch: [382][200/521] Time 0.24 (0.26) Data 0.00 (0.00) Loss 0.282 (0.172) Prec@1 94.79 (96.50) Prec@5 98.96 (99.96)
|
|
train[2019-04-01-05:06:28] Epoch: [382][300/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.115 (0.172) Prec@1 98.96 (96.51) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-05:06:53] Epoch: [382][400/521] Time 0.26 (0.25) Data 0.00 (0.00) Loss 0.253 (0.177) Prec@1 95.83 (96.36) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-05:07:18] Epoch: [382][500/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.223 (0.179) Prec@1 94.79 (96.34) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-05:07:23] Epoch: [382][520/521] Time 0.23 (0.25) Data 0.00 (0.00) Loss 0.094 (0.180) Prec@1 97.50 (96.29) Prec@5 100.00 (99.96)
|
|
[2019-04-01-05:07:23] **train** Prec@1 96.29 Prec@5 99.96 Error@1 3.71 Error@5 0.04 Loss:0.180
|
|
test [2019-04-01-05:07:24] Epoch: [382][000/105] Time 0.50 (0.50) Data 0.43 (0.43) Loss 0.143 (0.143) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-05:07:28] Epoch: [382][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.081 (0.170) Prec@1 96.88 (95.19) Prec@5 100.00 (99.90)
|
|
test [2019-04-01-05:07:28] Epoch: [382][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.125 (0.169) Prec@1 93.75 (95.20) Prec@5 100.00 (99.89)
|
|
[2019-04-01-05:07:28] **test** Prec@1 95.20 Prec@5 99.89 Error@1 4.80 Error@5 0.11 Loss:0.169
|
|
----> Best Accuracy : Acc@1=95.76, Acc@5=99.89, Error@1=4.24, Error@5=0.11
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-05:07:28] [Epoch=383/600] [Need: 08:12:27] LR=0.0073 ~ 0.0073, Batch=96
|
|
train[2019-04-01-05:07:29] Epoch: [383][000/521] Time 0.75 (0.75) Data 0.46 (0.46) Loss 0.150 (0.150) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-05:07:54] Epoch: [383][100/521] Time 0.24 (0.26) Data 0.00 (0.00) Loss 0.107 (0.169) Prec@1 98.96 (96.60) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-05:08:19] Epoch: [383][200/521] Time 0.26 (0.25) Data 0.00 (0.00) Loss 0.092 (0.169) Prec@1 100.00 (96.64) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-05:08:44] Epoch: [383][300/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.179 (0.176) Prec@1 95.83 (96.43) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-05:09:10] Epoch: [383][400/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.160 (0.180) Prec@1 96.88 (96.27) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-05:09:35] Epoch: [383][500/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.153 (0.183) Prec@1 96.88 (96.18) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-05:09:40] Epoch: [383][520/521] Time 0.23 (0.25) Data 0.00 (0.00) Loss 0.362 (0.183) Prec@1 92.50 (96.19) Prec@5 98.75 (99.96)
|
|
[2019-04-01-05:09:40] **train** Prec@1 96.19 Prec@5 99.96 Error@1 3.81 Error@5 0.04 Loss:0.183
|
|
test [2019-04-01-05:09:40] Epoch: [383][000/105] Time 0.64 (0.64) Data 0.58 (0.58) Loss 0.079 (0.079) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-05:09:45] Epoch: [383][100/105] Time 0.05 (0.05) Data 0.00 (0.01) Loss 0.099 (0.174) Prec@1 96.88 (95.35) Prec@5 100.00 (99.93)
|
|
test [2019-04-01-05:09:45] Epoch: [383][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.200 (0.173) Prec@1 93.75 (95.36) Prec@5 100.00 (99.93)
|
|
[2019-04-01-05:09:45] **test** Prec@1 95.36 Prec@5 99.93 Error@1 4.64 Error@5 0.07 Loss:0.173
|
|
----> Best Accuracy : Acc@1=95.76, Acc@5=99.89, Error@1=4.24, Error@5=0.11
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-05:09:45] [Epoch=384/600] [Need: 08:12:46] LR=0.0072 ~ 0.0072, Batch=96
|
|
train[2019-04-01-05:09:46] Epoch: [384][000/521] Time 0.73 (0.73) Data 0.44 (0.44) Loss 0.241 (0.241) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-05:10:11] Epoch: [384][100/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.230 (0.164) Prec@1 93.75 (96.68) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-05:10:36] Epoch: [384][200/521] Time 0.26 (0.25) Data 0.00 (0.00) Loss 0.172 (0.183) Prec@1 95.83 (96.30) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-05:11:01] Epoch: [384][300/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.194 (0.181) Prec@1 94.79 (96.29) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-05:11:26] Epoch: [384][400/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.160 (0.180) Prec@1 96.88 (96.27) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-05:11:51] Epoch: [384][500/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.142 (0.181) Prec@1 96.88 (96.23) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-05:11:56] Epoch: [384][520/521] Time 0.23 (0.25) Data 0.00 (0.00) Loss 0.198 (0.180) Prec@1 93.75 (96.24) Prec@5 100.00 (99.97)
|
|
[2019-04-01-05:11:56] **train** Prec@1 96.24 Prec@5 99.97 Error@1 3.76 Error@5 0.03 Loss:0.180
|
|
test [2019-04-01-05:11:57] Epoch: [384][000/105] Time 0.61 (0.61) Data 0.55 (0.55) Loss 0.079 (0.079) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-05:12:01] Epoch: [384][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.054 (0.162) Prec@1 96.88 (95.48) Prec@5 100.00 (99.93)
|
|
test [2019-04-01-05:12:02] Epoch: [384][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.333 (0.163) Prec@1 93.75 (95.46) Prec@5 100.00 (99.93)
|
|
[2019-04-01-05:12:02] **test** Prec@1 95.46 Prec@5 99.93 Error@1 4.54 Error@5 0.07 Loss:0.163
|
|
----> Best Accuracy : Acc@1=95.76, Acc@5=99.89, Error@1=4.24, Error@5=0.11
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-05:12:02] [Epoch=385/600] [Need: 08:09:26] LR=0.0072 ~ 0.0072, Batch=96
|
|
train[2019-04-01-05:12:02] Epoch: [385][000/521] Time 0.74 (0.74) Data 0.45 (0.45) Loss 0.132 (0.132) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-05:12:28] Epoch: [385][100/521] Time 0.25 (0.26) Data 0.00 (0.00) Loss 0.099 (0.176) Prec@1 97.92 (96.24) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-05:12:53] Epoch: [385][200/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.129 (0.182) Prec@1 95.83 (96.15) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-05:13:18] Epoch: [385][300/521] Time 0.28 (0.25) Data 0.00 (0.00) Loss 0.160 (0.179) Prec@1 96.88 (96.26) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-05:13:43] Epoch: [385][400/521] Time 0.26 (0.25) Data 0.00 (0.00) Loss 0.128 (0.178) Prec@1 97.92 (96.26) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-05:14:08] Epoch: [385][500/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.206 (0.178) Prec@1 95.83 (96.25) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-05:14:13] Epoch: [385][520/521] Time 0.23 (0.25) Data 0.00 (0.00) Loss 0.125 (0.178) Prec@1 98.75 (96.24) Prec@5 100.00 (99.97)
|
|
[2019-04-01-05:14:13] **train** Prec@1 96.24 Prec@5 99.97 Error@1 3.76 Error@5 0.03 Loss:0.178
|
|
test [2019-04-01-05:14:14] Epoch: [385][000/105] Time 0.48 (0.48) Data 0.41 (0.41) Loss 0.186 (0.186) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-05:14:18] Epoch: [385][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.050 (0.174) Prec@1 97.92 (95.51) Prec@5 100.00 (99.90)
|
|
test [2019-04-01-05:14:18] Epoch: [385][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.019 (0.175) Prec@1 100.00 (95.46) Prec@5 100.00 (99.90)
|
|
[2019-04-01-05:14:18] **test** Prec@1 95.46 Prec@5 99.90 Error@1 4.54 Error@5 0.10 Loss:0.175
|
|
----> Best Accuracy : Acc@1=95.76, Acc@5=99.89, Error@1=4.24, Error@5=0.11
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-05:14:18] [Epoch=386/600] [Need: 08:07:38] LR=0.0071 ~ 0.0071, Batch=96
|
|
train[2019-04-01-05:14:19] Epoch: [386][000/521] Time 0.73 (0.73) Data 0.44 (0.44) Loss 0.259 (0.259) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-05:14:44] Epoch: [386][100/521] Time 0.25 (0.26) Data 0.00 (0.00) Loss 0.111 (0.185) Prec@1 98.96 (96.17) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-05:15:10] Epoch: [386][200/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.121 (0.186) Prec@1 97.92 (96.16) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-05:15:35] Epoch: [386][300/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.138 (0.185) Prec@1 95.83 (96.20) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-05:16:00] Epoch: [386][400/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.125 (0.180) Prec@1 96.88 (96.27) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-05:16:25] Epoch: [386][500/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.229 (0.181) Prec@1 93.75 (96.26) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-05:16:30] Epoch: [386][520/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.202 (0.183) Prec@1 96.25 (96.23) Prec@5 100.00 (99.96)
|
|
[2019-04-01-05:16:30] **train** Prec@1 96.23 Prec@5 99.96 Error@1 3.77 Error@5 0.04 Loss:0.183
|
|
test [2019-04-01-05:16:31] Epoch: [386][000/105] Time 0.65 (0.65) Data 0.57 (0.57) Loss 0.090 (0.090) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-05:16:35] Epoch: [386][100/105] Time 0.05 (0.05) Data 0.00 (0.01) Loss 0.072 (0.162) Prec@1 96.88 (95.36) Prec@5 100.00 (99.94)
|
|
test [2019-04-01-05:16:35] Epoch: [386][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.015 (0.164) Prec@1 100.00 (95.36) Prec@5 100.00 (99.94)
|
|
[2019-04-01-05:16:35] **test** Prec@1 95.36 Prec@5 99.94 Error@1 4.64 Error@5 0.06 Loss:0.164
|
|
----> Best Accuracy : Acc@1=95.76, Acc@5=99.89, Error@1=4.24, Error@5=0.11
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-05:16:35] [Epoch=387/600] [Need: 08:05:45] LR=0.0071 ~ 0.0071, Batch=96
|
|
train[2019-04-01-05:16:36] Epoch: [387][000/521] Time 0.88 (0.88) Data 0.60 (0.60) Loss 0.101 (0.101) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-05:17:01] Epoch: [387][100/521] Time 0.25 (0.26) Data 0.00 (0.01) Loss 0.110 (0.172) Prec@1 98.96 (96.27) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-05:17:27] Epoch: [387][200/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.097 (0.176) Prec@1 98.96 (96.28) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-05:17:52] Epoch: [387][300/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.102 (0.175) Prec@1 97.92 (96.37) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-05:18:17] Epoch: [387][400/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.275 (0.177) Prec@1 92.71 (96.31) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-05:18:42] Epoch: [387][500/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.099 (0.175) Prec@1 97.92 (96.36) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-05:18:47] Epoch: [387][520/521] Time 0.23 (0.25) Data 0.00 (0.00) Loss 0.158 (0.175) Prec@1 98.75 (96.35) Prec@5 100.00 (99.96)
|
|
[2019-04-01-05:18:47] **train** Prec@1 96.35 Prec@5 99.96 Error@1 3.65 Error@5 0.04 Loss:0.175
|
|
test [2019-04-01-05:18:47] Epoch: [387][000/105] Time 0.56 (0.56) Data 0.49 (0.49) Loss 0.166 (0.166) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-05:18:52] Epoch: [387][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.095 (0.180) Prec@1 96.88 (95.40) Prec@5 100.00 (99.91)
|
|
test [2019-04-01-05:18:52] Epoch: [387][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.144 (0.179) Prec@1 93.75 (95.40) Prec@5 100.00 (99.91)
|
|
[2019-04-01-05:18:52] **test** Prec@1 95.40 Prec@5 99.91 Error@1 4.60 Error@5 0.09 Loss:0.179
|
|
----> Best Accuracy : Acc@1=95.76, Acc@5=99.89, Error@1=4.24, Error@5=0.11
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-05:18:52] [Epoch=388/600] [Need: 08:03:28] LR=0.0070 ~ 0.0070, Batch=96
|
|
train[2019-04-01-05:18:53] Epoch: [388][000/521] Time 0.77 (0.77) Data 0.48 (0.48) Loss 0.204 (0.204) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-05:19:18] Epoch: [388][100/521] Time 0.25 (0.26) Data 0.00 (0.00) Loss 0.135 (0.178) Prec@1 94.79 (96.40) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-05:19:43] Epoch: [388][200/521] Time 0.25 (0.26) Data 0.00 (0.00) Loss 0.129 (0.173) Prec@1 96.88 (96.41) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-05:20:08] Epoch: [388][300/521] Time 0.26 (0.25) Data 0.00 (0.00) Loss 0.147 (0.171) Prec@1 94.79 (96.45) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-05:20:34] Epoch: [388][400/521] Time 0.27 (0.25) Data 0.00 (0.00) Loss 0.090 (0.174) Prec@1 98.96 (96.38) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-05:20:59] Epoch: [388][500/521] Time 0.26 (0.25) Data 0.00 (0.00) Loss 0.236 (0.178) Prec@1 94.79 (96.25) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-05:21:04] Epoch: [388][520/521] Time 0.23 (0.25) Data 0.00 (0.00) Loss 0.175 (0.178) Prec@1 96.25 (96.25) Prec@5 100.00 (99.96)
|
|
[2019-04-01-05:21:04] **train** Prec@1 96.25 Prec@5 99.96 Error@1 3.75 Error@5 0.04 Loss:0.178
|
|
test [2019-04-01-05:21:05] Epoch: [388][000/105] Time 0.50 (0.50) Data 0.43 (0.43) Loss 0.200 (0.200) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-05:21:09] Epoch: [388][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.052 (0.172) Prec@1 97.92 (95.36) Prec@5 100.00 (99.93)
|
|
test [2019-04-01-05:21:09] Epoch: [388][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.007 (0.172) Prec@1 100.00 (95.37) Prec@5 100.00 (99.93)
|
|
[2019-04-01-05:21:09] **test** Prec@1 95.37 Prec@5 99.93 Error@1 4.63 Error@5 0.07 Loss:0.172
|
|
----> Best Accuracy : Acc@1=95.76, Acc@5=99.89, Error@1=4.24, Error@5=0.11
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-05:21:10] [Epoch=389/600] [Need: 08:03:16] LR=0.0070 ~ 0.0070, Batch=96
|
|
train[2019-04-01-05:21:10] Epoch: [389][000/521] Time 0.88 (0.88) Data 0.59 (0.59) Loss 0.182 (0.182) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-05:21:36] Epoch: [389][100/521] Time 0.26 (0.26) Data 0.00 (0.01) Loss 0.125 (0.182) Prec@1 97.92 (96.08) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-05:22:01] Epoch: [389][200/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.216 (0.180) Prec@1 95.83 (96.15) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-05:22:26] Epoch: [389][300/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.076 (0.177) Prec@1 98.96 (96.28) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-05:22:51] Epoch: [389][400/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.234 (0.175) Prec@1 94.79 (96.38) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-05:23:16] Epoch: [389][500/521] Time 0.27 (0.25) Data 0.00 (0.00) Loss 0.168 (0.180) Prec@1 95.83 (96.23) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-05:23:21] Epoch: [389][520/521] Time 0.23 (0.25) Data 0.00 (0.00) Loss 0.219 (0.181) Prec@1 95.00 (96.19) Prec@5 100.00 (99.97)
|
|
[2019-04-01-05:23:22] **train** Prec@1 96.19 Prec@5 99.97 Error@1 3.81 Error@5 0.03 Loss:0.181
|
|
test [2019-04-01-05:23:22] Epoch: [389][000/105] Time 0.64 (0.64) Data 0.57 (0.57) Loss 0.065 (0.065) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-05:23:26] Epoch: [389][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.042 (0.173) Prec@1 98.96 (95.39) Prec@5 100.00 (99.89)
|
|
test [2019-04-01-05:23:27] Epoch: [389][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.001 (0.173) Prec@1 100.00 (95.38) Prec@5 100.00 (99.89)
|
|
[2019-04-01-05:23:27] **test** Prec@1 95.38 Prec@5 99.89 Error@1 4.62 Error@5 0.11 Loss:0.173
|
|
----> Best Accuracy : Acc@1=95.76, Acc@5=99.89, Error@1=4.24, Error@5=0.11
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-05:23:27] [Epoch=390/600] [Need: 08:00:18] LR=0.0069 ~ 0.0069, Batch=96
|
|
train[2019-04-01-05:23:28] Epoch: [390][000/521] Time 0.87 (0.87) Data 0.58 (0.58) Loss 0.141 (0.141) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-05:23:53] Epoch: [390][100/521] Time 0.26 (0.26) Data 0.00 (0.01) Loss 0.177 (0.175) Prec@1 95.83 (96.36) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-05:24:18] Epoch: [390][200/521] Time 0.25 (0.26) Data 0.00 (0.00) Loss 0.174 (0.171) Prec@1 95.83 (96.47) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-05:24:43] Epoch: [390][300/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.197 (0.171) Prec@1 95.83 (96.50) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-05:25:08] Epoch: [390][400/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.064 (0.174) Prec@1 100.00 (96.44) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-05:25:33] Epoch: [390][500/521] Time 0.26 (0.25) Data 0.00 (0.00) Loss 0.128 (0.175) Prec@1 97.92 (96.43) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-05:25:38] Epoch: [390][520/521] Time 0.23 (0.25) Data 0.00 (0.00) Loss 0.079 (0.175) Prec@1 98.75 (96.43) Prec@5 100.00 (99.96)
|
|
[2019-04-01-05:25:39] **train** Prec@1 96.43 Prec@5 99.96 Error@1 3.57 Error@5 0.04 Loss:0.175
|
|
test [2019-04-01-05:25:39] Epoch: [390][000/105] Time 0.64 (0.64) Data 0.57 (0.57) Loss 0.082 (0.082) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-05:25:43] Epoch: [390][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.062 (0.170) Prec@1 97.92 (95.39) Prec@5 100.00 (99.93)
|
|
test [2019-04-01-05:25:44] Epoch: [390][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.239 (0.171) Prec@1 87.50 (95.37) Prec@5 100.00 (99.93)
|
|
[2019-04-01-05:25:44] **test** Prec@1 95.37 Prec@5 99.93 Error@1 4.63 Error@5 0.07 Loss:0.171
|
|
----> Best Accuracy : Acc@1=95.76, Acc@5=99.89, Error@1=4.24, Error@5=0.11
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-05:25:44] [Epoch=391/600] [Need: 07:57:12] LR=0.0068 ~ 0.0068, Batch=96
|
|
train[2019-04-01-05:25:45] Epoch: [391][000/521] Time 0.89 (0.89) Data 0.57 (0.57) Loss 0.216 (0.216) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-05:26:10] Epoch: [391][100/521] Time 0.25 (0.26) Data 0.00 (0.01) Loss 0.104 (0.161) Prec@1 97.92 (96.68) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-05:26:35] Epoch: [391][200/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.067 (0.167) Prec@1 98.96 (96.56) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-05:27:00] Epoch: [391][300/521] Time 0.26 (0.25) Data 0.00 (0.00) Loss 0.143 (0.169) Prec@1 96.88 (96.48) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-05:27:25] Epoch: [391][400/521] Time 0.26 (0.25) Data 0.00 (0.00) Loss 0.101 (0.170) Prec@1 96.88 (96.49) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-05:27:51] Epoch: [391][500/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.194 (0.172) Prec@1 95.83 (96.47) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-05:27:56] Epoch: [391][520/521] Time 0.22 (0.25) Data 0.00 (0.00) Loss 0.124 (0.172) Prec@1 97.50 (96.47) Prec@5 100.00 (99.97)
|
|
[2019-04-01-05:27:56] **train** Prec@1 96.47 Prec@5 99.97 Error@1 3.53 Error@5 0.03 Loss:0.172
|
|
test [2019-04-01-05:27:56] Epoch: [391][000/105] Time 0.64 (0.64) Data 0.57 (0.57) Loss 0.146 (0.146) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-05:28:01] Epoch: [391][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.034 (0.155) Prec@1 98.96 (95.73) Prec@5 100.00 (99.94)
|
|
test [2019-04-01-05:28:01] Epoch: [391][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.130 (0.156) Prec@1 93.75 (95.63) Prec@5 100.00 (99.94)
|
|
[2019-04-01-05:28:01] **test** Prec@1 95.63 Prec@5 99.94 Error@1 4.37 Error@5 0.06 Loss:0.156
|
|
----> Best Accuracy : Acc@1=95.76, Acc@5=99.89, Error@1=4.24, Error@5=0.11
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-05:28:01] [Epoch=392/600] [Need: 07:55:29] LR=0.0068 ~ 0.0068, Batch=96
|
|
train[2019-04-01-05:28:02] Epoch: [392][000/521] Time 0.77 (0.77) Data 0.46 (0.46) Loss 0.316 (0.316) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-05:28:27] Epoch: [392][100/521] Time 0.25 (0.26) Data 0.00 (0.00) Loss 0.148 (0.152) Prec@1 95.83 (97.01) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-05:28:52] Epoch: [392][200/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.097 (0.162) Prec@1 98.96 (96.79) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-05:29:17] Epoch: [392][300/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.246 (0.158) Prec@1 94.79 (96.84) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-05:29:42] Epoch: [392][400/521] Time 0.26 (0.25) Data 0.00 (0.00) Loss 0.124 (0.160) Prec@1 96.88 (96.79) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-05:30:08] Epoch: [392][500/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.091 (0.162) Prec@1 98.96 (96.75) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-05:30:13] Epoch: [392][520/521] Time 0.26 (0.25) Data 0.00 (0.00) Loss 0.217 (0.162) Prec@1 93.75 (96.77) Prec@5 100.00 (99.97)
|
|
[2019-04-01-05:30:13] **train** Prec@1 96.77 Prec@5 99.97 Error@1 3.23 Error@5 0.03 Loss:0.162
|
|
test [2019-04-01-05:30:13] Epoch: [392][000/105] Time 0.63 (0.63) Data 0.56 (0.56) Loss 0.082 (0.082) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-05:30:18] Epoch: [392][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.089 (0.154) Prec@1 95.83 (95.71) Prec@5 100.00 (99.96)
|
|
test [2019-04-01-05:30:18] Epoch: [392][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.101 (0.155) Prec@1 93.75 (95.63) Prec@5 100.00 (99.96)
|
|
[2019-04-01-05:30:18] **test** Prec@1 95.63 Prec@5 99.96 Error@1 4.37 Error@5 0.04 Loss:0.155
|
|
----> Best Accuracy : Acc@1=95.76, Acc@5=99.89, Error@1=4.24, Error@5=0.11
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-05:30:18] [Epoch=393/600] [Need: 07:53:12] LR=0.0067 ~ 0.0067, Batch=96
|
|
train[2019-04-01-05:30:19] Epoch: [393][000/521] Time 0.91 (0.91) Data 0.63 (0.63) Loss 0.171 (0.171) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-05:30:44] Epoch: [393][100/521] Time 0.26 (0.26) Data 0.00 (0.01) Loss 0.171 (0.158) Prec@1 97.92 (96.85) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-05:31:09] Epoch: [393][200/521] Time 0.24 (0.26) Data 0.00 (0.00) Loss 0.066 (0.168) Prec@1 97.92 (96.59) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-05:31:35] Epoch: [393][300/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.134 (0.169) Prec@1 98.96 (96.58) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-05:32:00] Epoch: [393][400/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.269 (0.170) Prec@1 94.79 (96.51) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-05:32:25] Epoch: [393][500/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.180 (0.168) Prec@1 95.83 (96.53) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-05:32:30] Epoch: [393][520/521] Time 0.22 (0.25) Data 0.00 (0.00) Loss 0.228 (0.168) Prec@1 93.75 (96.52) Prec@5 100.00 (99.96)
|
|
[2019-04-01-05:32:30] **train** Prec@1 96.52 Prec@5 99.96 Error@1 3.48 Error@5 0.04 Loss:0.168
|
|
test [2019-04-01-05:32:31] Epoch: [393][000/105] Time 0.59 (0.59) Data 0.52 (0.52) Loss 0.127 (0.127) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-05:32:35] Epoch: [393][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.021 (0.157) Prec@1 100.00 (95.83) Prec@5 100.00 (99.92)
|
|
test [2019-04-01-05:32:35] Epoch: [393][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.011 (0.158) Prec@1 100.00 (95.81) Prec@5 100.00 (99.92)
|
|
[2019-04-01-05:32:35] **test** Prec@1 95.81 Prec@5 99.92 Error@1 4.19 Error@5 0.08 Loss:0.158
|
|
----> Best Accuracy : Acc@1=95.81, Acc@5=99.92, Error@1=4.19, Error@5=0.08
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-05:32:35] [Epoch=394/600] [Need: 07:51:16] LR=0.0067 ~ 0.0067, Batch=96
|
|
train[2019-04-01-05:32:36] Epoch: [394][000/521] Time 0.76 (0.76) Data 0.48 (0.48) Loss 0.209 (0.209) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-05:33:02] Epoch: [394][100/521] Time 0.25 (0.26) Data 0.00 (0.00) Loss 0.121 (0.179) Prec@1 97.92 (96.15) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-05:33:27] Epoch: [394][200/521] Time 0.25 (0.26) Data 0.00 (0.00) Loss 0.185 (0.180) Prec@1 96.88 (96.14) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-05:33:52] Epoch: [394][300/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.211 (0.175) Prec@1 94.79 (96.34) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-05:34:17] Epoch: [394][400/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.218 (0.176) Prec@1 95.83 (96.32) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-05:34:42] Epoch: [394][500/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.249 (0.177) Prec@1 94.79 (96.34) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-05:34:47] Epoch: [394][520/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.306 (0.176) Prec@1 92.50 (96.36) Prec@5 100.00 (99.97)
|
|
[2019-04-01-05:34:47] **train** Prec@1 96.36 Prec@5 99.97 Error@1 3.64 Error@5 0.03 Loss:0.176
|
|
test [2019-04-01-05:34:48] Epoch: [394][000/105] Time 0.65 (0.65) Data 0.58 (0.58) Loss 0.027 (0.027) Prec@1 100.00 (100.00) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-05:34:52] Epoch: [394][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.054 (0.157) Prec@1 97.92 (95.90) Prec@5 100.00 (99.88)
|
|
test [2019-04-01-05:34:52] Epoch: [394][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.054 (0.157) Prec@1 100.00 (95.87) Prec@5 100.00 (99.88)
|
|
[2019-04-01-05:34:52] **test** Prec@1 95.87 Prec@5 99.88 Error@1 4.13 Error@5 0.12 Loss:0.157
|
|
----> Best Accuracy : Acc@1=95.87, Acc@5=99.88, Error@1=4.13, Error@5=0.12
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-05:34:53] [Epoch=395/600] [Need: 07:48:57] LR=0.0066 ~ 0.0066, Batch=96
|
|
train[2019-04-01-05:34:54] Epoch: [395][000/521] Time 0.91 (0.91) Data 0.59 (0.59) Loss 0.218 (0.218) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-05:35:19] Epoch: [395][100/521] Time 0.26 (0.26) Data 0.00 (0.01) Loss 0.183 (0.161) Prec@1 95.83 (96.79) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-05:35:44] Epoch: [395][200/521] Time 0.25 (0.26) Data 0.00 (0.00) Loss 0.224 (0.169) Prec@1 94.79 (96.63) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-05:36:09] Epoch: [395][300/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.253 (0.163) Prec@1 91.67 (96.72) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-05:36:34] Epoch: [395][400/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.187 (0.164) Prec@1 92.71 (96.67) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-05:37:00] Epoch: [395][500/521] Time 0.28 (0.25) Data 0.00 (0.00) Loss 0.194 (0.169) Prec@1 96.88 (96.59) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-05:37:05] Epoch: [395][520/521] Time 0.23 (0.25) Data 0.00 (0.00) Loss 0.109 (0.169) Prec@1 97.50 (96.59) Prec@5 100.00 (99.98)
|
|
[2019-04-01-05:37:05] **train** Prec@1 96.59 Prec@5 99.98 Error@1 3.41 Error@5 0.02 Loss:0.169
|
|
test [2019-04-01-05:37:06] Epoch: [395][000/105] Time 0.63 (0.63) Data 0.57 (0.57) Loss 0.214 (0.214) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-05:37:10] Epoch: [395][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.032 (0.169) Prec@1 98.96 (95.69) Prec@5 100.00 (99.93)
|
|
test [2019-04-01-05:37:10] Epoch: [395][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.010 (0.168) Prec@1 100.00 (95.71) Prec@5 100.00 (99.93)
|
|
[2019-04-01-05:37:10] **test** Prec@1 95.71 Prec@5 99.93 Error@1 4.29 Error@5 0.07 Loss:0.168
|
|
----> Best Accuracy : Acc@1=95.87, Acc@5=99.88, Error@1=4.13, Error@5=0.12
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-05:37:10] [Epoch=396/600] [Need: 07:47:47] LR=0.0066 ~ 0.0066, Batch=96
|
|
train[2019-04-01-05:37:11] Epoch: [396][000/521] Time 0.84 (0.84) Data 0.56 (0.56) Loss 0.177 (0.177) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-05:37:36] Epoch: [396][100/521] Time 0.25 (0.26) Data 0.00 (0.01) Loss 0.106 (0.167) Prec@1 96.88 (96.77) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-05:38:01] Epoch: [396][200/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.151 (0.166) Prec@1 96.88 (96.65) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-05:38:26] Epoch: [396][300/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.143 (0.163) Prec@1 95.83 (96.75) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-05:38:51] Epoch: [396][400/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.185 (0.161) Prec@1 97.92 (96.85) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-05:39:16] Epoch: [396][500/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.224 (0.162) Prec@1 95.83 (96.77) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-05:39:21] Epoch: [396][520/521] Time 0.22 (0.25) Data 0.00 (0.00) Loss 0.158 (0.164) Prec@1 96.25 (96.75) Prec@5 100.00 (99.98)
|
|
[2019-04-01-05:39:21] **train** Prec@1 96.75 Prec@5 99.98 Error@1 3.25 Error@5 0.02 Loss:0.164
|
|
test [2019-04-01-05:39:22] Epoch: [396][000/105] Time 0.52 (0.52) Data 0.45 (0.45) Loss 0.294 (0.294) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-05:39:26] Epoch: [396][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.048 (0.172) Prec@1 97.92 (95.79) Prec@5 100.00 (99.86)
|
|
test [2019-04-01-05:39:26] Epoch: [396][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.002 (0.173) Prec@1 100.00 (95.76) Prec@5 100.00 (99.86)
|
|
[2019-04-01-05:39:26] **test** Prec@1 95.76 Prec@5 99.86 Error@1 4.24 Error@5 0.14 Loss:0.173
|
|
----> Best Accuracy : Acc@1=95.87, Acc@5=99.88, Error@1=4.13, Error@5=0.12
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-05:39:27] [Epoch=397/600] [Need: 07:41:11] LR=0.0065 ~ 0.0065, Batch=96
|
|
train[2019-04-01-05:39:27] Epoch: [397][000/521] Time 0.82 (0.82) Data 0.51 (0.51) Loss 0.110 (0.110) Prec@1 100.00 (100.00) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-05:39:53] Epoch: [397][100/521] Time 0.25 (0.26) Data 0.00 (0.01) Loss 0.168 (0.161) Prec@1 97.92 (96.72) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-05:40:18] Epoch: [397][200/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.191 (0.163) Prec@1 96.88 (96.71) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-05:40:43] Epoch: [397][300/521] Time 0.26 (0.25) Data 0.00 (0.00) Loss 0.140 (0.159) Prec@1 96.88 (96.85) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-05:41:08] Epoch: [397][400/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.132 (0.161) Prec@1 97.92 (96.87) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-05:41:34] Epoch: [397][500/521] Time 0.26 (0.25) Data 0.00 (0.00) Loss 0.287 (0.163) Prec@1 91.67 (96.78) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-05:41:39] Epoch: [397][520/521] Time 0.23 (0.25) Data 0.00 (0.00) Loss 0.163 (0.163) Prec@1 93.75 (96.74) Prec@5 100.00 (99.97)
|
|
[2019-04-01-05:41:39] **train** Prec@1 96.74 Prec@5 99.97 Error@1 3.26 Error@5 0.03 Loss:0.163
|
|
test [2019-04-01-05:41:39] Epoch: [397][000/105] Time 0.51 (0.51) Data 0.44 (0.44) Loss 0.102 (0.102) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-05:41:44] Epoch: [397][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.110 (0.174) Prec@1 95.83 (95.24) Prec@5 100.00 (99.91)
|
|
test [2019-04-01-05:41:44] Epoch: [397][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.043 (0.175) Prec@1 100.00 (95.24) Prec@5 100.00 (99.91)
|
|
[2019-04-01-05:41:44] **test** Prec@1 95.24 Prec@5 99.91 Error@1 4.76 Error@5 0.09 Loss:0.175
|
|
----> Best Accuracy : Acc@1=95.87, Acc@5=99.88, Error@1=4.13, Error@5=0.12
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-05:41:44] [Epoch=398/600] [Need: 07:42:55] LR=0.0064 ~ 0.0064, Batch=96
|
|
train[2019-04-01-05:41:45] Epoch: [398][000/521] Time 0.91 (0.91) Data 0.60 (0.60) Loss 0.216 (0.216) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-05:42:10] Epoch: [398][100/521] Time 0.25 (0.26) Data 0.00 (0.01) Loss 0.108 (0.168) Prec@1 98.96 (96.36) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-05:42:35] Epoch: [398][200/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.152 (0.161) Prec@1 96.88 (96.69) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-05:43:00] Epoch: [398][300/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.249 (0.158) Prec@1 94.79 (96.80) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-05:43:26] Epoch: [398][400/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.178 (0.160) Prec@1 95.83 (96.73) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-05:43:51] Epoch: [398][500/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.221 (0.166) Prec@1 96.88 (96.60) Prec@5 98.96 (99.96)
|
|
train[2019-04-01-05:43:56] Epoch: [398][520/521] Time 0.22 (0.25) Data 0.00 (0.00) Loss 0.196 (0.166) Prec@1 97.50 (96.58) Prec@5 100.00 (99.96)
|
|
[2019-04-01-05:43:56] **train** Prec@1 96.58 Prec@5 99.96 Error@1 3.42 Error@5 0.04 Loss:0.166
|
|
test [2019-04-01-05:43:56] Epoch: [398][000/105] Time 0.67 (0.67) Data 0.59 (0.59) Loss 0.045 (0.045) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-05:44:01] Epoch: [398][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.074 (0.156) Prec@1 95.83 (95.79) Prec@5 100.00 (99.92)
|
|
test [2019-04-01-05:44:01] Epoch: [398][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.227 (0.157) Prec@1 93.75 (95.75) Prec@5 100.00 (99.92)
|
|
[2019-04-01-05:44:01] **test** Prec@1 95.75 Prec@5 99.92 Error@1 4.25 Error@5 0.08 Loss:0.157
|
|
----> Best Accuracy : Acc@1=95.87, Acc@5=99.88, Error@1=4.13, Error@5=0.12
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-05:44:01] [Epoch=399/600] [Need: 07:39:22] LR=0.0064 ~ 0.0064, Batch=96
|
|
train[2019-04-01-05:44:02] Epoch: [399][000/521] Time 0.75 (0.75) Data 0.46 (0.46) Loss 0.242 (0.242) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-05:44:27] Epoch: [399][100/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.146 (0.150) Prec@1 96.88 (97.05) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-05:44:52] Epoch: [399][200/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.131 (0.160) Prec@1 98.96 (96.69) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-05:45:17] Epoch: [399][300/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.120 (0.162) Prec@1 97.92 (96.56) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-05:45:42] Epoch: [399][400/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.143 (0.160) Prec@1 97.92 (96.66) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-05:46:07] Epoch: [399][500/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.150 (0.162) Prec@1 96.88 (96.62) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-05:46:12] Epoch: [399][520/521] Time 0.23 (0.25) Data 0.00 (0.00) Loss 0.105 (0.161) Prec@1 96.25 (96.64) Prec@5 100.00 (99.97)
|
|
[2019-04-01-05:46:12] **train** Prec@1 96.64 Prec@5 99.97 Error@1 3.36 Error@5 0.03 Loss:0.161
|
|
test [2019-04-01-05:46:13] Epoch: [399][000/105] Time 0.51 (0.51) Data 0.44 (0.44) Loss 0.193 (0.193) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-05:46:17] Epoch: [399][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.033 (0.168) Prec@1 97.92 (95.28) Prec@5 100.00 (99.88)
|
|
test [2019-04-01-05:46:17] Epoch: [399][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.420 (0.169) Prec@1 93.75 (95.25) Prec@5 100.00 (99.88)
|
|
[2019-04-01-05:46:17] **test** Prec@1 95.25 Prec@5 99.88 Error@1 4.75 Error@5 0.12 Loss:0.169
|
|
----> Best Accuracy : Acc@1=95.87, Acc@5=99.88, Error@1=4.13, Error@5=0.12
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-05:46:18] [Epoch=400/600] [Need: 07:34:33] LR=0.0063 ~ 0.0063, Batch=96
|
|
train[2019-04-01-05:46:18] Epoch: [400][000/521] Time 0.74 (0.74) Data 0.44 (0.44) Loss 0.347 (0.347) Prec@1 93.75 (93.75) Prec@5 98.96 (98.96)
|
|
train[2019-04-01-05:46:43] Epoch: [400][100/521] Time 0.24 (0.26) Data 0.00 (0.00) Loss 0.142 (0.181) Prec@1 97.92 (96.42) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-05:47:09] Epoch: [400][200/521] Time 0.25 (0.26) Data 0.00 (0.00) Loss 0.199 (0.168) Prec@1 95.83 (96.78) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-05:47:34] Epoch: [400][300/521] Time 0.24 (0.26) Data 0.00 (0.00) Loss 0.211 (0.165) Prec@1 96.88 (96.80) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-05:48:00] Epoch: [400][400/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.113 (0.165) Prec@1 97.92 (96.72) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-05:48:25] Epoch: [400][500/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.139 (0.168) Prec@1 96.88 (96.65) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-05:48:30] Epoch: [400][520/521] Time 0.23 (0.25) Data 0.00 (0.00) Loss 0.135 (0.168) Prec@1 97.50 (96.64) Prec@5 100.00 (99.96)
|
|
[2019-04-01-05:48:30] **train** Prec@1 96.64 Prec@5 99.96 Error@1 3.36 Error@5 0.04 Loss:0.168
|
|
test [2019-04-01-05:48:30] Epoch: [400][000/105] Time 0.50 (0.50) Data 0.42 (0.42) Loss 0.149 (0.149) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-05:48:35] Epoch: [400][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.050 (0.176) Prec@1 98.96 (95.29) Prec@5 100.00 (99.91)
|
|
test [2019-04-01-05:48:35] Epoch: [400][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.024 (0.177) Prec@1 100.00 (95.30) Prec@5 100.00 (99.90)
|
|
[2019-04-01-05:48:35] **test** Prec@1 95.30 Prec@5 99.90 Error@1 4.70 Error@5 0.10 Loss:0.177
|
|
----> Best Accuracy : Acc@1=95.87, Acc@5=99.88, Error@1=4.13, Error@5=0.12
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-05:48:35] [Epoch=401/600] [Need: 07:36:07] LR=0.0063 ~ 0.0063, Batch=96
|
|
train[2019-04-01-05:48:36] Epoch: [401][000/521] Time 0.74 (0.74) Data 0.46 (0.46) Loss 0.125 (0.125) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-05:49:01] Epoch: [401][100/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.114 (0.155) Prec@1 96.88 (96.90) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-05:49:26] Epoch: [401][200/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.087 (0.164) Prec@1 98.96 (96.77) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-05:49:51] Epoch: [401][300/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.073 (0.167) Prec@1 98.96 (96.64) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-05:50:16] Epoch: [401][400/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.124 (0.169) Prec@1 97.92 (96.61) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-05:50:41] Epoch: [401][500/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.127 (0.170) Prec@1 97.92 (96.55) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-05:50:46] Epoch: [401][520/521] Time 0.23 (0.25) Data 0.00 (0.00) Loss 0.184 (0.170) Prec@1 93.75 (96.55) Prec@5 100.00 (99.96)
|
|
[2019-04-01-05:50:47] **train** Prec@1 96.55 Prec@5 99.96 Error@1 3.45 Error@5 0.04 Loss:0.170
|
|
test [2019-04-01-05:50:47] Epoch: [401][000/105] Time 0.52 (0.52) Data 0.44 (0.44) Loss 0.081 (0.081) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-05:50:51] Epoch: [401][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.096 (0.157) Prec@1 95.83 (95.81) Prec@5 100.00 (99.91)
|
|
test [2019-04-01-05:50:52] Epoch: [401][104/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.046 (0.160) Prec@1 93.75 (95.75) Prec@5 100.00 (99.91)
|
|
[2019-04-01-05:50:52] **test** Prec@1 95.75 Prec@5 99.91 Error@1 4.25 Error@5 0.09 Loss:0.160
|
|
----> Best Accuracy : Acc@1=95.87, Acc@5=99.88, Error@1=4.13, Error@5=0.12
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-05:50:52] [Epoch=402/600] [Need: 07:31:12] LR=0.0062 ~ 0.0062, Batch=96
|
|
train[2019-04-01-05:50:53] Epoch: [402][000/521] Time 0.83 (0.83) Data 0.53 (0.53) Loss 0.132 (0.132) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-05:51:18] Epoch: [402][100/521] Time 0.25 (0.26) Data 0.00 (0.01) Loss 0.068 (0.167) Prec@1 97.92 (96.56) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-05:51:43] Epoch: [402][200/521] Time 0.26 (0.25) Data 0.00 (0.00) Loss 0.104 (0.164) Prec@1 98.96 (96.60) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-05:52:08] Epoch: [402][300/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.182 (0.158) Prec@1 96.88 (96.76) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-05:52:34] Epoch: [402][400/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.091 (0.158) Prec@1 98.96 (96.77) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-05:52:58] Epoch: [402][500/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.176 (0.160) Prec@1 96.88 (96.70) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-05:53:03] Epoch: [402][520/521] Time 0.22 (0.25) Data 0.00 (0.00) Loss 0.133 (0.160) Prec@1 97.50 (96.69) Prec@5 100.00 (99.98)
|
|
[2019-04-01-05:53:03] **train** Prec@1 96.69 Prec@5 99.98 Error@1 3.31 Error@5 0.02 Loss:0.160
|
|
test [2019-04-01-05:53:04] Epoch: [402][000/105] Time 0.62 (0.62) Data 0.55 (0.55) Loss 0.137 (0.137) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-05:53:08] Epoch: [402][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.105 (0.161) Prec@1 96.88 (95.79) Prec@5 100.00 (99.92)
|
|
test [2019-04-01-05:53:08] Epoch: [402][104/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.014 (0.162) Prec@1 100.00 (95.78) Prec@5 100.00 (99.92)
|
|
[2019-04-01-05:53:08] **test** Prec@1 95.78 Prec@5 99.92 Error@1 4.22 Error@5 0.08 Loss:0.162
|
|
----> Best Accuracy : Acc@1=95.87, Acc@5=99.88, Error@1=4.13, Error@5=0.12
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-05:53:09] [Epoch=403/600] [Need: 07:29:18] LR=0.0062 ~ 0.0062, Batch=96
|
|
train[2019-04-01-05:53:09] Epoch: [403][000/521] Time 0.78 (0.78) Data 0.45 (0.45) Loss 0.181 (0.181) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-05:53:35] Epoch: [403][100/521] Time 0.24 (0.26) Data 0.00 (0.00) Loss 0.237 (0.166) Prec@1 95.83 (96.56) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-05:54:00] Epoch: [403][200/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.143 (0.159) Prec@1 97.92 (96.75) Prec@5 98.96 (99.95)
|
|
train[2019-04-01-05:54:25] Epoch: [403][300/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.081 (0.161) Prec@1 98.96 (96.72) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-05:54:50] Epoch: [403][400/521] Time 0.26 (0.25) Data 0.00 (0.00) Loss 0.101 (0.160) Prec@1 98.96 (96.69) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-05:55:15] Epoch: [403][500/521] Time 0.26 (0.25) Data 0.00 (0.00) Loss 0.091 (0.163) Prec@1 98.96 (96.68) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-05:55:20] Epoch: [403][520/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.195 (0.162) Prec@1 95.00 (96.68) Prec@5 100.00 (99.95)
|
|
[2019-04-01-05:55:20] **train** Prec@1 96.68 Prec@5 99.95 Error@1 3.32 Error@5 0.05 Loss:0.162
|
|
test [2019-04-01-05:55:21] Epoch: [403][000/105] Time 0.72 (0.72) Data 0.66 (0.66) Loss 0.114 (0.114) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-05:55:25] Epoch: [403][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.040 (0.163) Prec@1 98.96 (95.81) Prec@5 100.00 (99.89)
|
|
test [2019-04-01-05:55:25] Epoch: [403][104/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.040 (0.163) Prec@1 100.00 (95.79) Prec@5 100.00 (99.89)
|
|
[2019-04-01-05:55:25] **test** Prec@1 95.79 Prec@5 99.89 Error@1 4.21 Error@5 0.11 Loss:0.163
|
|
----> Best Accuracy : Acc@1=95.87, Acc@5=99.88, Error@1=4.13, Error@5=0.12
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-05:55:26] [Epoch=404/600] [Need: 07:27:16] LR=0.0061 ~ 0.0061, Batch=96
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|
train[2019-04-01-05:55:26] Epoch: [404][000/521] Time 0.79 (0.79) Data 0.50 (0.50) Loss 0.159 (0.159) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-05:55:51] Epoch: [404][100/521] Time 0.25 (0.26) Data 0.00 (0.01) Loss 0.144 (0.162) Prec@1 98.96 (96.75) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-05:56:17] Epoch: [404][200/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.083 (0.160) Prec@1 100.00 (96.70) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-05:56:42] Epoch: [404][300/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.277 (0.163) Prec@1 92.71 (96.62) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-05:57:07] Epoch: [404][400/521] Time 0.26 (0.25) Data 0.00 (0.00) Loss 0.133 (0.163) Prec@1 95.83 (96.57) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-05:57:32] Epoch: [404][500/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.221 (0.164) Prec@1 94.79 (96.57) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-05:57:37] Epoch: [404][520/521] Time 0.27 (0.25) Data 0.00 (0.00) Loss 0.270 (0.164) Prec@1 95.00 (96.56) Prec@5 100.00 (99.96)
|
|
[2019-04-01-05:57:37] **train** Prec@1 96.56 Prec@5 99.96 Error@1 3.44 Error@5 0.04 Loss:0.164
|
|
test [2019-04-01-05:57:38] Epoch: [404][000/105] Time 0.69 (0.69) Data 0.62 (0.62) Loss 0.153 (0.153) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-05:57:42] Epoch: [404][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.061 (0.164) Prec@1 98.96 (95.93) Prec@5 100.00 (99.91)
|
|
test [2019-04-01-05:57:43] Epoch: [404][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.018 (0.164) Prec@1 100.00 (95.90) Prec@5 100.00 (99.91)
|
|
[2019-04-01-05:57:43] **test** Prec@1 95.90 Prec@5 99.91 Error@1 4.10 Error@5 0.09 Loss:0.164
|
|
----> Best Accuracy : Acc@1=95.90, Acc@5=99.91, Error@1=4.10, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-05:57:43] [Epoch=405/600] [Need: 07:26:10] LR=0.0060 ~ 0.0060, Batch=96
|
|
train[2019-04-01-05:57:44] Epoch: [405][000/521] Time 0.76 (0.76) Data 0.47 (0.47) Loss 0.197 (0.197) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-05:58:07] Epoch: [405][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.072 (0.156) Prec@1 98.96 (97.05) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-05:58:31] Epoch: [405][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.097 (0.158) Prec@1 96.88 (96.92) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-05:58:55] Epoch: [405][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.153 (0.159) Prec@1 95.83 (96.76) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-05:59:19] Epoch: [405][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.146 (0.162) Prec@1 96.88 (96.74) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-05:59:43] Epoch: [405][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.113 (0.163) Prec@1 97.92 (96.68) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-05:59:47] Epoch: [405][520/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.248 (0.164) Prec@1 93.75 (96.66) Prec@5 100.00 (99.97)
|
|
[2019-04-01-05:59:48] **train** Prec@1 96.66 Prec@5 99.97 Error@1 3.34 Error@5 0.03 Loss:0.164
|
|
test [2019-04-01-05:59:48] Epoch: [405][000/105] Time 0.52 (0.52) Data 0.43 (0.43) Loss 0.239 (0.239) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-05:59:52] Epoch: [405][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.020 (0.156) Prec@1 98.96 (95.73) Prec@5 100.00 (99.89)
|
|
test [2019-04-01-05:59:52] Epoch: [405][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.055 (0.155) Prec@1 93.75 (95.76) Prec@5 100.00 (99.89)
|
|
[2019-04-01-05:59:52] **test** Prec@1 95.76 Prec@5 99.89 Error@1 4.24 Error@5 0.11 Loss:0.155
|
|
----> Best Accuracy : Acc@1=95.90, Acc@5=99.91, Error@1=4.10, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-05:59:53] [Epoch=406/600] [Need: 06:59:24] LR=0.0060 ~ 0.0060, Batch=96
|
|
train[2019-04-01-05:59:53] Epoch: [406][000/521] Time 0.83 (0.83) Data 0.56 (0.56) Loss 0.150 (0.150) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-06:00:17] Epoch: [406][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.096 (0.159) Prec@1 97.92 (96.85) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-06:00:41] Epoch: [406][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.092 (0.161) Prec@1 98.96 (96.80) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-06:01:05] Epoch: [406][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.121 (0.162) Prec@1 97.92 (96.77) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-06:01:29] Epoch: [406][400/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.414 (0.162) Prec@1 91.67 (96.76) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-06:01:53] Epoch: [406][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.108 (0.164) Prec@1 97.92 (96.73) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-06:01:57] Epoch: [406][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.189 (0.164) Prec@1 95.00 (96.71) Prec@5 100.00 (99.96)
|
|
[2019-04-01-06:01:57] **train** Prec@1 96.71 Prec@5 99.96 Error@1 3.29 Error@5 0.04 Loss:0.164
|
|
test [2019-04-01-06:01:58] Epoch: [406][000/105] Time 0.47 (0.47) Data 0.40 (0.40) Loss 0.121 (0.121) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-06:02:02] Epoch: [406][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.084 (0.165) Prec@1 97.92 (95.59) Prec@5 100.00 (99.91)
|
|
test [2019-04-01-06:02:02] Epoch: [406][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.054 (0.166) Prec@1 93.75 (95.55) Prec@5 100.00 (99.91)
|
|
[2019-04-01-06:02:02] **test** Prec@1 95.55 Prec@5 99.91 Error@1 4.45 Error@5 0.09 Loss:0.166
|
|
----> Best Accuracy : Acc@1=95.90, Acc@5=99.91, Error@1=4.10, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-06:02:02] [Epoch=407/600] [Need: 06:57:21] LR=0.0059 ~ 0.0059, Batch=96
|
|
train[2019-04-01-06:02:03] Epoch: [407][000/521] Time 0.85 (0.85) Data 0.57 (0.57) Loss 0.227 (0.227) Prec@1 94.79 (94.79) Prec@5 98.96 (98.96)
|
|
train[2019-04-01-06:02:27] Epoch: [407][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.103 (0.153) Prec@1 97.92 (96.96) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-06:02:51] Epoch: [407][200/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.405 (0.156) Prec@1 92.71 (96.86) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-06:03:15] Epoch: [407][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.180 (0.153) Prec@1 95.83 (96.96) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-06:03:38] Epoch: [407][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.178 (0.155) Prec@1 95.83 (96.87) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-06:04:02] Epoch: [407][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.392 (0.155) Prec@1 91.67 (96.88) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-06:04:07] Epoch: [407][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.144 (0.155) Prec@1 96.25 (96.88) Prec@5 100.00 (99.96)
|
|
[2019-04-01-06:04:07] **train** Prec@1 96.88 Prec@5 99.96 Error@1 3.12 Error@5 0.04 Loss:0.155
|
|
test [2019-04-01-06:04:07] Epoch: [407][000/105] Time 0.47 (0.47) Data 0.40 (0.40) Loss 0.105 (0.105) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-06:04:12] Epoch: [407][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.058 (0.171) Prec@1 96.88 (95.51) Prec@5 100.00 (99.88)
|
|
test [2019-04-01-06:04:12] Epoch: [407][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.154 (0.174) Prec@1 93.75 (95.49) Prec@5 100.00 (99.88)
|
|
[2019-04-01-06:04:12] **test** Prec@1 95.49 Prec@5 99.88 Error@1 4.51 Error@5 0.12 Loss:0.174
|
|
----> Best Accuracy : Acc@1=95.90, Acc@5=99.91, Error@1=4.10, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-06:04:12] [Epoch=408/600] [Need: 06:54:42] LR=0.0059 ~ 0.0059, Batch=96
|
|
train[2019-04-01-06:04:13] Epoch: [408][000/521] Time 0.70 (0.70) Data 0.43 (0.43) Loss 0.195 (0.195) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-06:04:36] Epoch: [408][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.255 (0.161) Prec@1 96.88 (96.69) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-06:05:00] Epoch: [408][200/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.287 (0.166) Prec@1 91.67 (96.61) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-06:05:24] Epoch: [408][300/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.132 (0.166) Prec@1 97.92 (96.65) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-06:05:47] Epoch: [408][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.142 (0.162) Prec@1 96.88 (96.63) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-06:06:11] Epoch: [408][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.134 (0.162) Prec@1 96.88 (96.64) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-06:06:16] Epoch: [408][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.062 (0.162) Prec@1 100.00 (96.65) Prec@5 100.00 (99.96)
|
|
[2019-04-01-06:06:16] **train** Prec@1 96.65 Prec@5 99.96 Error@1 3.35 Error@5 0.04 Loss:0.162
|
|
test [2019-04-01-06:06:17] Epoch: [408][000/105] Time 0.61 (0.61) Data 0.55 (0.55) Loss 0.072 (0.072) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-06:06:21] Epoch: [408][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.035 (0.163) Prec@1 98.96 (95.83) Prec@5 100.00 (99.92)
|
|
test [2019-04-01-06:06:21] Epoch: [408][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.010 (0.163) Prec@1 100.00 (95.86) Prec@5 100.00 (99.91)
|
|
[2019-04-01-06:06:21] **test** Prec@1 95.86 Prec@5 99.91 Error@1 4.14 Error@5 0.09 Loss:0.163
|
|
----> Best Accuracy : Acc@1=95.90, Acc@5=99.91, Error@1=4.10, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-06:06:21] [Epoch=409/600] [Need: 06:51:02] LR=0.0058 ~ 0.0058, Batch=96
|
|
train[2019-04-01-06:06:22] Epoch: [409][000/521] Time 0.86 (0.86) Data 0.59 (0.59) Loss 0.145 (0.145) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-06:06:46] Epoch: [409][100/521] Time 0.24 (0.25) Data 0.00 (0.01) Loss 0.180 (0.154) Prec@1 95.83 (96.74) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-06:07:10] Epoch: [409][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.114 (0.160) Prec@1 96.88 (96.64) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-06:07:33] Epoch: [409][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.348 (0.164) Prec@1 92.71 (96.61) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-06:07:57] Epoch: [409][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.179 (0.163) Prec@1 96.88 (96.64) Prec@5 98.96 (99.98)
|
|
train[2019-04-01-06:08:21] Epoch: [409][500/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.168 (0.162) Prec@1 95.83 (96.64) Prec@5 98.96 (99.97)
|
|
train[2019-04-01-06:08:25] Epoch: [409][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.097 (0.162) Prec@1 98.75 (96.65) Prec@5 100.00 (99.97)
|
|
[2019-04-01-06:08:25] **train** Prec@1 96.65 Prec@5 99.97 Error@1 3.35 Error@5 0.03 Loss:0.162
|
|
test [2019-04-01-06:08:26] Epoch: [409][000/105] Time 0.60 (0.60) Data 0.53 (0.53) Loss 0.105 (0.105) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-06:08:30] Epoch: [409][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.045 (0.155) Prec@1 98.96 (95.72) Prec@5 100.00 (99.92)
|
|
test [2019-04-01-06:08:30] Epoch: [409][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.068 (0.155) Prec@1 93.75 (95.73) Prec@5 100.00 (99.92)
|
|
[2019-04-01-06:08:30] **test** Prec@1 95.73 Prec@5 99.92 Error@1 4.27 Error@5 0.08 Loss:0.155
|
|
----> Best Accuracy : Acc@1=95.90, Acc@5=99.91, Error@1=4.10, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-06:08:31] [Epoch=410/600] [Need: 06:50:09] LR=0.0058 ~ 0.0058, Batch=96
|
|
train[2019-04-01-06:08:31] Epoch: [410][000/521] Time 0.84 (0.84) Data 0.56 (0.56) Loss 0.095 (0.095) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-06:08:55] Epoch: [410][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.124 (0.162) Prec@1 96.88 (96.71) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-06:09:19] Epoch: [410][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.128 (0.160) Prec@1 97.92 (96.69) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-06:09:43] Epoch: [410][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.111 (0.160) Prec@1 98.96 (96.68) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-06:10:06] Epoch: [410][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.194 (0.161) Prec@1 93.75 (96.67) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-06:10:30] Epoch: [410][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.136 (0.162) Prec@1 97.92 (96.68) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-06:10:35] Epoch: [410][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.087 (0.162) Prec@1 97.50 (96.69) Prec@5 100.00 (99.98)
|
|
[2019-04-01-06:10:35] **train** Prec@1 96.69 Prec@5 99.98 Error@1 3.31 Error@5 0.02 Loss:0.162
|
|
test [2019-04-01-06:10:35] Epoch: [410][000/105] Time 0.46 (0.46) Data 0.40 (0.40) Loss 0.088 (0.088) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-06:10:39] Epoch: [410][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.029 (0.144) Prec@1 98.96 (96.16) Prec@5 100.00 (99.96)
|
|
test [2019-04-01-06:10:40] Epoch: [410][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.000 (0.146) Prec@1 100.00 (96.11) Prec@5 100.00 (99.96)
|
|
[2019-04-01-06:10:40] **test** Prec@1 96.11 Prec@5 99.96 Error@1 3.89 Error@5 0.04 Loss:0.146
|
|
----> Best Accuracy : Acc@1=96.11, Acc@5=99.96, Error@1=3.89, Error@5=0.04
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-06:10:40] [Epoch=411/600] [Need: 06:47:37] LR=0.0057 ~ 0.0057, Batch=96
|
|
train[2019-04-01-06:10:41] Epoch: [411][000/521] Time 0.70 (0.70) Data 0.42 (0.42) Loss 0.071 (0.071) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-06:11:04] Epoch: [411][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.184 (0.154) Prec@1 94.79 (96.76) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-06:11:28] Epoch: [411][200/521] Time 0.32 (0.24) Data 0.00 (0.00) Loss 0.182 (0.161) Prec@1 96.88 (96.69) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-06:11:58] Epoch: [411][300/521] Time 0.27 (0.26) Data 0.00 (0.00) Loss 0.177 (0.162) Prec@1 96.88 (96.73) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-06:12:29] Epoch: [411][400/521] Time 0.27 (0.27) Data 0.00 (0.00) Loss 0.233 (0.164) Prec@1 93.75 (96.68) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-06:12:58] Epoch: [411][500/521] Time 0.24 (0.28) Data 0.00 (0.00) Loss 0.067 (0.163) Prec@1 97.92 (96.70) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-06:13:03] Epoch: [411][520/521] Time 0.22 (0.27) Data 0.00 (0.00) Loss 0.192 (0.162) Prec@1 95.00 (96.71) Prec@5 100.00 (99.96)
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|
[2019-04-01-06:13:03] **train** Prec@1 96.71 Prec@5 99.96 Error@1 3.29 Error@5 0.04 Loss:0.162
|
|
test [2019-04-01-06:13:04] Epoch: [411][000/105] Time 0.94 (0.94) Data 0.81 (0.81) Loss 0.221 (0.221) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
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test [2019-04-01-06:13:10] Epoch: [411][100/105] Time 0.04 (0.07) Data 0.00 (0.01) Loss 0.029 (0.149) Prec@1 98.96 (95.95) Prec@5 100.00 (99.94)
|
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test [2019-04-01-06:13:11] Epoch: [411][104/105] Time 0.04 (0.07) Data 0.00 (0.01) Loss 0.049 (0.150) Prec@1 93.75 (95.94) Prec@5 100.00 (99.94)
|
|
[2019-04-01-06:13:11] **test** Prec@1 95.94 Prec@5 99.94 Error@1 4.06 Error@5 0.06 Loss:0.150
|
|
----> Best Accuracy : Acc@1=96.11, Acc@5=99.96, Error@1=3.89, Error@5=0.04
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-06:13:11] [Epoch=412/600] [Need: 07:53:17] LR=0.0057 ~ 0.0057, Batch=96
|
|
train[2019-04-01-06:13:12] Epoch: [412][000/521] Time 1.17 (1.17) Data 0.78 (0.78) Loss 0.275 (0.275) Prec@1 92.71 (92.71) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-06:13:43] Epoch: [412][100/521] Time 0.25 (0.32) Data 0.00 (0.01) Loss 0.129 (0.156) Prec@1 96.88 (96.84) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-06:14:08] Epoch: [412][200/521] Time 0.28 (0.28) Data 0.00 (0.00) Loss 0.221 (0.155) Prec@1 96.88 (96.80) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-06:14:41] Epoch: [412][300/521] Time 0.27 (0.30) Data 0.00 (0.00) Loss 0.196 (0.153) Prec@1 96.88 (96.91) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-06:15:11] Epoch: [412][400/521] Time 0.32 (0.30) Data 0.00 (0.00) Loss 0.110 (0.153) Prec@1 97.92 (96.91) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-06:15:42] Epoch: [412][500/521] Time 0.28 (0.30) Data 0.00 (0.00) Loss 0.202 (0.156) Prec@1 95.83 (96.85) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-06:15:48] Epoch: [412][520/521] Time 0.30 (0.30) Data 0.00 (0.00) Loss 0.092 (0.156) Prec@1 98.75 (96.85) Prec@5 100.00 (99.98)
|
|
[2019-04-01-06:15:48] **train** Prec@1 96.85 Prec@5 99.98 Error@1 3.15 Error@5 0.02 Loss:0.156
|
|
test [2019-04-01-06:15:49] Epoch: [412][000/105] Time 1.02 (1.02) Data 0.88 (0.88) Loss 0.134 (0.134) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-06:15:55] Epoch: [412][100/105] Time 0.04 (0.07) Data 0.00 (0.01) Loss 0.063 (0.151) Prec@1 98.96 (96.15) Prec@5 100.00 (99.90)
|
|
test [2019-04-01-06:15:55] Epoch: [412][104/105] Time 0.03 (0.07) Data 0.00 (0.01) Loss 0.042 (0.150) Prec@1 100.00 (96.17) Prec@5 100.00 (99.90)
|
|
[2019-04-01-06:15:55] **test** Prec@1 96.17 Prec@5 99.90 Error@1 3.83 Error@5 0.10 Loss:0.150
|
|
----> Best Accuracy : Acc@1=96.17, Acc@5=99.90, Error@1=3.83, Error@5=0.10
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-06:15:55] [Epoch=413/600] [Need: 08:31:46] LR=0.0056 ~ 0.0056, Batch=96
|
|
train[2019-04-01-06:15:57] Epoch: [413][000/521] Time 1.35 (1.35) Data 0.91 (0.91) Loss 0.112 (0.112) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-06:16:28] Epoch: [413][100/521] Time 0.28 (0.33) Data 0.00 (0.01) Loss 0.164 (0.150) Prec@1 97.92 (96.92) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-06:17:00] Epoch: [413][200/521] Time 0.30 (0.32) Data 0.00 (0.00) Loss 0.097 (0.151) Prec@1 96.88 (96.97) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-06:17:29] Epoch: [413][300/521] Time 0.24 (0.31) Data 0.00 (0.00) Loss 0.195 (0.150) Prec@1 95.83 (97.03) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-06:17:53] Epoch: [413][400/521] Time 0.24 (0.29) Data 0.00 (0.00) Loss 0.241 (0.151) Prec@1 94.79 (97.02) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-06:18:16] Epoch: [413][500/521] Time 0.24 (0.28) Data 0.00 (0.00) Loss 0.167 (0.154) Prec@1 96.88 (96.96) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-06:18:21] Epoch: [413][520/521] Time 0.22 (0.28) Data 0.00 (0.00) Loss 0.132 (0.154) Prec@1 96.25 (96.96) Prec@5 100.00 (99.98)
|
|
[2019-04-01-06:18:21] **train** Prec@1 96.96 Prec@5 99.98 Error@1 3.04 Error@5 0.02 Loss:0.154
|
|
test [2019-04-01-06:18:22] Epoch: [413][000/105] Time 0.48 (0.48) Data 0.42 (0.42) Loss 0.117 (0.117) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-06:18:26] Epoch: [413][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.033 (0.155) Prec@1 98.96 (95.98) Prec@5 100.00 (99.94)
|
|
test [2019-04-01-06:18:26] Epoch: [413][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.000 (0.154) Prec@1 100.00 (95.97) Prec@5 100.00 (99.94)
|
|
[2019-04-01-06:18:26] **test** Prec@1 95.97 Prec@5 99.94 Error@1 4.03 Error@5 0.06 Loss:0.154
|
|
----> Best Accuracy : Acc@1=96.17, Acc@5=99.90, Error@1=3.83, Error@5=0.10
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-06:18:26] [Epoch=414/600] [Need: 07:47:27] LR=0.0056 ~ 0.0056, Batch=96
|
|
train[2019-04-01-06:18:27] Epoch: [414][000/521] Time 0.84 (0.84) Data 0.57 (0.57) Loss 0.133 (0.133) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-06:18:51] Epoch: [414][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.172 (0.155) Prec@1 95.83 (96.96) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-06:19:14] Epoch: [414][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.278 (0.162) Prec@1 93.75 (96.72) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-06:19:38] Epoch: [414][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.197 (0.158) Prec@1 95.83 (96.82) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-06:20:02] Epoch: [414][400/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.245 (0.155) Prec@1 96.88 (96.84) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-06:20:25] Epoch: [414][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.153 (0.158) Prec@1 95.83 (96.76) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-06:20:30] Epoch: [414][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.106 (0.158) Prec@1 98.75 (96.76) Prec@5 100.00 (99.97)
|
|
[2019-04-01-06:20:30] **train** Prec@1 96.76 Prec@5 99.97 Error@1 3.24 Error@5 0.03 Loss:0.158
|
|
test [2019-04-01-06:20:31] Epoch: [414][000/105] Time 0.61 (0.61) Data 0.55 (0.55) Loss 0.106 (0.106) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-06:20:35] Epoch: [414][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.077 (0.155) Prec@1 98.96 (95.91) Prec@5 100.00 (99.97)
|
|
test [2019-04-01-06:20:35] Epoch: [414][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.004 (0.154) Prec@1 100.00 (95.90) Prec@5 100.00 (99.97)
|
|
[2019-04-01-06:20:35] **test** Prec@1 95.90 Prec@5 99.97 Error@1 4.10 Error@5 0.03 Loss:0.154
|
|
----> Best Accuracy : Acc@1=96.17, Acc@5=99.90, Error@1=3.83, Error@5=0.10
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-06:20:35] [Epoch=415/600] [Need: 06:38:41] LR=0.0055 ~ 0.0055, Batch=96
|
|
train[2019-04-01-06:20:36] Epoch: [415][000/521] Time 0.73 (0.73) Data 0.43 (0.43) Loss 0.115 (0.115) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-06:21:00] Epoch: [415][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.116 (0.151) Prec@1 96.88 (96.96) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-06:21:24] Epoch: [415][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.178 (0.156) Prec@1 96.88 (96.99) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-06:21:48] Epoch: [415][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.161 (0.152) Prec@1 95.83 (97.06) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-06:22:12] Epoch: [415][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.121 (0.151) Prec@1 96.88 (97.02) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-06:22:36] Epoch: [415][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.135 (0.153) Prec@1 98.96 (96.97) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-06:22:41] Epoch: [415][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.201 (0.153) Prec@1 96.25 (96.96) Prec@5 100.00 (99.98)
|
|
[2019-04-01-06:22:41] **train** Prec@1 96.96 Prec@5 99.98 Error@1 3.04 Error@5 0.02 Loss:0.153
|
|
test [2019-04-01-06:22:42] Epoch: [415][000/105] Time 0.61 (0.61) Data 0.54 (0.54) Loss 0.082 (0.082) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-06:22:46] Epoch: [415][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.056 (0.155) Prec@1 97.92 (95.87) Prec@5 100.00 (99.89)
|
|
test [2019-04-01-06:22:46] Epoch: [415][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.020 (0.156) Prec@1 100.00 (95.82) Prec@5 100.00 (99.89)
|
|
[2019-04-01-06:22:46] **test** Prec@1 95.82 Prec@5 99.89 Error@1 4.18 Error@5 0.11 Loss:0.156
|
|
----> Best Accuracy : Acc@1=96.17, Acc@5=99.90, Error@1=3.83, Error@5=0.10
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-06:22:46] [Epoch=416/600] [Need: 06:41:02] LR=0.0054 ~ 0.0054, Batch=96
|
|
train[2019-04-01-06:22:47] Epoch: [416][000/521] Time 0.70 (0.70) Data 0.43 (0.43) Loss 0.136 (0.136) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-06:23:11] Epoch: [416][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.151 (0.141) Prec@1 95.83 (97.30) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-06:23:34] Epoch: [416][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.103 (0.146) Prec@1 97.92 (97.21) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-06:23:58] Epoch: [416][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.127 (0.148) Prec@1 96.88 (97.14) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-06:24:22] Epoch: [416][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.157 (0.150) Prec@1 96.88 (97.05) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-06:24:46] Epoch: [416][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.228 (0.152) Prec@1 94.79 (96.96) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-06:24:50] Epoch: [416][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.128 (0.152) Prec@1 98.75 (96.97) Prec@5 100.00 (99.98)
|
|
[2019-04-01-06:24:51] **train** Prec@1 96.97 Prec@5 99.98 Error@1 3.03 Error@5 0.02 Loss:0.152
|
|
test [2019-04-01-06:24:51] Epoch: [416][000/105] Time 0.46 (0.46) Data 0.40 (0.40) Loss 0.061 (0.061) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-06:24:55] Epoch: [416][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.083 (0.154) Prec@1 97.92 (96.28) Prec@5 100.00 (99.89)
|
|
test [2019-04-01-06:24:55] Epoch: [416][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.205 (0.155) Prec@1 93.75 (96.25) Prec@5 100.00 (99.89)
|
|
[2019-04-01-06:24:55] **test** Prec@1 96.25 Prec@5 99.89 Error@1 3.75 Error@5 0.11 Loss:0.155
|
|
----> Best Accuracy : Acc@1=96.25, Acc@5=99.89, Error@1=3.75, Error@5=0.11
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-06:24:56] [Epoch=417/600] [Need: 06:34:48] LR=0.0054 ~ 0.0054, Batch=96
|
|
train[2019-04-01-06:24:56] Epoch: [417][000/521] Time 0.84 (0.84) Data 0.57 (0.57) Loss 0.116 (0.116) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-06:25:27] Epoch: [417][100/521] Time 0.36 (0.31) Data 0.00 (0.01) Loss 0.157 (0.151) Prec@1 93.75 (97.02) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-06:25:57] Epoch: [417][200/521] Time 0.30 (0.31) Data 0.00 (0.00) Loss 0.203 (0.157) Prec@1 95.83 (96.93) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-06:26:27] Epoch: [417][300/521] Time 0.30 (0.30) Data 0.00 (0.00) Loss 0.135 (0.156) Prec@1 95.83 (96.96) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-06:26:58] Epoch: [417][400/521] Time 0.31 (0.30) Data 0.00 (0.00) Loss 0.142 (0.152) Prec@1 98.96 (97.05) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-06:27:24] Epoch: [417][500/521] Time 0.24 (0.30) Data 0.00 (0.00) Loss 0.219 (0.154) Prec@1 95.83 (97.01) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-06:27:29] Epoch: [417][520/521] Time 0.23 (0.29) Data 0.00 (0.00) Loss 0.073 (0.153) Prec@1 98.75 (97.04) Prec@5 100.00 (99.96)
|
|
[2019-04-01-06:27:29] **train** Prec@1 97.04 Prec@5 99.96 Error@1 2.96 Error@5 0.04 Loss:0.153
|
|
test [2019-04-01-06:27:30] Epoch: [417][000/105] Time 0.54 (0.54) Data 0.45 (0.45) Loss 0.099 (0.099) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-06:27:34] Epoch: [417][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.044 (0.144) Prec@1 98.96 (96.06) Prec@5 100.00 (99.94)
|
|
test [2019-04-01-06:27:35] Epoch: [417][104/105] Time 0.06 (0.05) Data 0.00 (0.00) Loss 0.001 (0.144) Prec@1 100.00 (96.07) Prec@5 100.00 (99.94)
|
|
[2019-04-01-06:27:35] **test** Prec@1 96.07 Prec@5 99.94 Error@1 3.93 Error@5 0.06 Loss:0.144
|
|
----> Best Accuracy : Acc@1=96.25, Acc@5=99.89, Error@1=3.75, Error@5=0.11
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-06:27:35] [Epoch=418/600] [Need: 08:03:29] LR=0.0053 ~ 0.0053, Batch=96
|
|
train[2019-04-01-06:27:36] Epoch: [418][000/521] Time 1.05 (1.05) Data 0.64 (0.64) Loss 0.373 (0.373) Prec@1 90.62 (90.62) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-06:28:08] Epoch: [418][100/521] Time 0.32 (0.33) Data 0.00 (0.01) Loss 0.184 (0.151) Prec@1 96.88 (96.87) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-06:28:40] Epoch: [418][200/521] Time 0.31 (0.32) Data 0.00 (0.00) Loss 0.160 (0.154) Prec@1 95.83 (96.80) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-06:29:11] Epoch: [418][300/521] Time 0.28 (0.32) Data 0.00 (0.00) Loss 0.083 (0.152) Prec@1 97.92 (96.91) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-06:29:42] Epoch: [418][400/521] Time 0.32 (0.32) Data 0.00 (0.00) Loss 0.136 (0.153) Prec@1 95.83 (96.92) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-06:30:16] Epoch: [418][500/521] Time 0.49 (0.32) Data 0.00 (0.00) Loss 0.135 (0.154) Prec@1 97.92 (96.91) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-06:30:24] Epoch: [418][520/521] Time 0.49 (0.32) Data 0.00 (0.00) Loss 0.124 (0.155) Prec@1 96.25 (96.87) Prec@5 100.00 (99.97)
|
|
[2019-04-01-06:30:24] **train** Prec@1 96.87 Prec@5 99.97 Error@1 3.13 Error@5 0.03 Loss:0.155
|
|
test [2019-04-01-06:30:25] Epoch: [418][000/105] Time 1.04 (1.04) Data 0.92 (0.92) Loss 0.142 (0.142) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-06:30:31] Epoch: [418][100/105] Time 0.04 (0.07) Data 0.00 (0.01) Loss 0.045 (0.143) Prec@1 98.96 (96.00) Prec@5 100.00 (99.91)
|
|
test [2019-04-01-06:30:31] Epoch: [418][104/105] Time 0.04 (0.07) Data 0.00 (0.01) Loss 0.025 (0.143) Prec@1 100.00 (96.00) Prec@5 100.00 (99.91)
|
|
[2019-04-01-06:30:32] **test** Prec@1 96.00 Prec@5 99.91 Error@1 4.00 Error@5 0.09 Loss:0.143
|
|
----> Best Accuracy : Acc@1=96.25, Acc@5=99.89, Error@1=3.75, Error@5=0.11
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-06:30:32] [Epoch=419/600] [Need: 08:53:29] LR=0.0053 ~ 0.0053, Batch=96
|
|
train[2019-04-01-06:30:33] Epoch: [419][000/521] Time 1.61 (1.61) Data 1.02 (1.02) Loss 0.167 (0.167) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
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train[2019-04-01-06:31:11] Epoch: [419][100/521] Time 0.47 (0.39) Data 0.00 (0.01) Loss 0.117 (0.145) Prec@1 96.88 (97.07) Prec@5 100.00 (99.96)
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train[2019-04-01-06:31:49] Epoch: [419][200/521] Time 0.35 (0.38) Data 0.00 (0.01) Loss 0.210 (0.142) Prec@1 95.83 (97.13) Prec@5 100.00 (99.96)
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train[2019-04-01-06:32:27] Epoch: [419][300/521] Time 0.33 (0.38) Data 0.00 (0.00) Loss 0.058 (0.144) Prec@1 100.00 (97.12) Prec@5 100.00 (99.97)
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train[2019-04-01-06:33:08] Epoch: [419][400/521] Time 0.38 (0.39) Data 0.00 (0.00) Loss 0.134 (0.146) Prec@1 94.79 (97.07) Prec@5 100.00 (99.96)
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train[2019-04-01-06:33:41] Epoch: [419][500/521] Time 0.30 (0.38) Data 0.00 (0.00) Loss 0.121 (0.149) Prec@1 96.88 (97.00) Prec@5 100.00 (99.97)
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train[2019-04-01-06:33:48] Epoch: [419][520/521] Time 0.39 (0.38) Data 0.00 (0.00) Loss 0.189 (0.149) Prec@1 93.75 (96.98) Prec@5 100.00 (99.97)
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[2019-04-01-06:33:48] **train** Prec@1 96.98 Prec@5 99.97 Error@1 3.02 Error@5 0.03 Loss:0.149
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test [2019-04-01-06:33:49] Epoch: [419][000/105] Time 0.90 (0.90) Data 0.77 (0.77) Loss 0.126 (0.126) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
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test [2019-04-01-06:33:55] Epoch: [419][100/105] Time 0.04 (0.07) Data 0.00 (0.01) Loss 0.052 (0.146) Prec@1 98.96 (96.07) Prec@5 100.00 (99.94)
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test [2019-04-01-06:33:55] Epoch: [419][104/105] Time 0.03 (0.06) Data 0.00 (0.01) Loss 0.002 (0.146) Prec@1 100.00 (96.08) Prec@5 100.00 (99.94)
|
|
[2019-04-01-06:33:56] **test** Prec@1 96.08 Prec@5 99.94 Error@1 3.92 Error@5 0.06 Loss:0.146
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|
----> Best Accuracy : Acc@1=96.25, Acc@5=99.89, Error@1=3.75, Error@5=0.11
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
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==>>[2019-04-01-06:33:56] [Epoch=420/600] [Need: 10:12:06] LR=0.0052 ~ 0.0052, Batch=96
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train[2019-04-01-06:33:58] Epoch: [420][000/521] Time 1.77 (1.77) Data 1.41 (1.41) Loss 0.138 (0.138) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
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train[2019-04-01-06:34:30] Epoch: [420][100/521] Time 0.36 (0.33) Data 0.00 (0.01) Loss 0.119 (0.138) Prec@1 97.92 (97.23) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-06:35:02] Epoch: [420][200/521] Time 0.36 (0.33) Data 0.00 (0.01) Loss 0.138 (0.140) Prec@1 96.88 (97.22) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-06:35:34] Epoch: [420][300/521] Time 0.31 (0.33) Data 0.00 (0.01) Loss 0.162 (0.141) Prec@1 94.79 (97.14) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-06:36:05] Epoch: [420][400/521] Time 0.37 (0.32) Data 0.00 (0.00) Loss 0.197 (0.144) Prec@1 93.75 (97.05) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-06:36:36] Epoch: [420][500/521] Time 0.31 (0.32) Data 0.00 (0.00) Loss 0.088 (0.147) Prec@1 97.92 (97.00) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-06:36:43] Epoch: [420][520/521] Time 0.23 (0.32) Data 0.00 (0.00) Loss 0.050 (0.147) Prec@1 100.00 (96.99) Prec@5 100.00 (99.97)
|
|
[2019-04-01-06:36:43] **train** Prec@1 96.99 Prec@5 99.97 Error@1 3.01 Error@5 0.03 Loss:0.147
|
|
test [2019-04-01-06:36:44] Epoch: [420][000/105] Time 0.67 (0.67) Data 0.59 (0.59) Loss 0.111 (0.111) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-06:36:50] Epoch: [420][100/105] Time 0.04 (0.06) Data 0.00 (0.01) Loss 0.046 (0.153) Prec@1 98.96 (96.08) Prec@5 100.00 (99.91)
|
|
test [2019-04-01-06:36:50] Epoch: [420][104/105] Time 0.04 (0.06) Data 0.00 (0.01) Loss 0.030 (0.154) Prec@1 100.00 (96.06) Prec@5 100.00 (99.91)
|
|
[2019-04-01-06:36:50] **test** Prec@1 96.06 Prec@5 99.91 Error@1 3.94 Error@5 0.09 Loss:0.154
|
|
----> Best Accuracy : Acc@1=96.25, Acc@5=99.89, Error@1=3.75, Error@5=0.11
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
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|
|
==>>[2019-04-01-06:36:50] [Epoch=421/600] [Need: 08:40:01] LR=0.0052 ~ 0.0052, Batch=96
|
|
train[2019-04-01-06:36:51] Epoch: [421][000/521] Time 0.94 (0.94) Data 0.60 (0.60) Loss 0.064 (0.064) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-06:37:22] Epoch: [421][100/521] Time 0.39 (0.31) Data 0.00 (0.01) Loss 0.169 (0.152) Prec@1 95.83 (96.99) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-06:37:54] Epoch: [421][200/521] Time 0.31 (0.32) Data 0.00 (0.00) Loss 0.070 (0.153) Prec@1 100.00 (97.03) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-06:38:26] Epoch: [421][300/521] Time 0.31 (0.32) Data 0.00 (0.00) Loss 0.234 (0.151) Prec@1 96.88 (97.11) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-06:38:56] Epoch: [421][400/521] Time 0.24 (0.31) Data 0.00 (0.00) Loss 0.121 (0.147) Prec@1 97.92 (97.18) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-06:39:28] Epoch: [421][500/521] Time 0.24 (0.31) Data 0.00 (0.00) Loss 0.095 (0.145) Prec@1 97.92 (97.24) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-06:39:33] Epoch: [421][520/521] Time 0.29 (0.31) Data 0.00 (0.00) Loss 0.128 (0.144) Prec@1 97.50 (97.24) Prec@5 100.00 (99.97)
|
|
[2019-04-01-06:39:33] **train** Prec@1 97.24 Prec@5 99.97 Error@1 2.76 Error@5 0.03 Loss:0.144
|
|
test [2019-04-01-06:39:34] Epoch: [421][000/105] Time 0.89 (0.89) Data 0.77 (0.77) Loss 0.071 (0.071) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-06:39:40] Epoch: [421][100/105] Time 0.05 (0.06) Data 0.00 (0.01) Loss 0.069 (0.146) Prec@1 96.88 (96.13) Prec@5 100.00 (99.92)
|
|
test [2019-04-01-06:39:40] Epoch: [421][104/105] Time 0.03 (0.06) Data 0.00 (0.01) Loss 0.004 (0.147) Prec@1 100.00 (96.10) Prec@5 100.00 (99.92)
|
|
[2019-04-01-06:39:40] **test** Prec@1 96.10 Prec@5 99.92 Error@1 3.90 Error@5 0.08 Loss:0.147
|
|
----> Best Accuracy : Acc@1=96.25, Acc@5=99.89, Error@1=3.75, Error@5=0.11
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-06:39:40] [Epoch=422/600] [Need: 08:24:28] LR=0.0051 ~ 0.0051, Batch=96
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|
train[2019-04-01-06:39:41] Epoch: [422][000/521] Time 0.93 (0.93) Data 0.63 (0.63) Loss 0.140 (0.140) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-06:40:12] Epoch: [422][100/521] Time 0.38 (0.31) Data 0.00 (0.01) Loss 0.111 (0.141) Prec@1 97.92 (97.22) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-06:40:43] Epoch: [422][200/521] Time 0.35 (0.31) Data 0.00 (0.00) Loss 0.190 (0.146) Prec@1 95.83 (97.14) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-06:41:13] Epoch: [422][300/521] Time 0.28 (0.31) Data 0.00 (0.00) Loss 0.098 (0.142) Prec@1 97.92 (97.24) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-06:41:45] Epoch: [422][400/521] Time 0.33 (0.31) Data 0.00 (0.00) Loss 0.096 (0.141) Prec@1 98.96 (97.23) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-06:42:16] Epoch: [422][500/521] Time 0.39 (0.31) Data 0.00 (0.00) Loss 0.145 (0.147) Prec@1 96.88 (97.05) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-06:42:22] Epoch: [422][520/521] Time 0.24 (0.31) Data 0.00 (0.00) Loss 0.102 (0.146) Prec@1 98.75 (97.06) Prec@5 100.00 (99.98)
|
|
[2019-04-01-06:42:22] **train** Prec@1 97.06 Prec@5 99.98 Error@1 2.94 Error@5 0.02 Loss:0.146
|
|
test [2019-04-01-06:42:23] Epoch: [422][000/105] Time 0.94 (0.94) Data 0.82 (0.82) Loss 0.100 (0.100) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-06:42:29] Epoch: [422][100/105] Time 0.04 (0.07) Data 0.00 (0.01) Loss 0.051 (0.151) Prec@1 98.96 (96.12) Prec@5 100.00 (99.91)
|
|
test [2019-04-01-06:42:29] Epoch: [422][104/105] Time 0.03 (0.07) Data 0.00 (0.01) Loss 0.016 (0.151) Prec@1 100.00 (96.07) Prec@5 100.00 (99.91)
|
|
[2019-04-01-06:42:29] **test** Prec@1 96.07 Prec@5 99.91 Error@1 3.93 Error@5 0.09 Loss:0.151
|
|
----> Best Accuracy : Acc@1=96.25, Acc@5=99.89, Error@1=3.75, Error@5=0.11
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-06:42:30] [Epoch=423/600] [Need: 08:19:45] LR=0.0051 ~ 0.0051, Batch=96
|
|
train[2019-04-01-06:42:31] Epoch: [423][000/521] Time 1.07 (1.07) Data 0.70 (0.70) Loss 0.165 (0.165) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-06:43:03] Epoch: [423][100/521] Time 0.36 (0.33) Data 0.00 (0.01) Loss 0.130 (0.152) Prec@1 98.96 (96.94) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-06:43:34] Epoch: [423][200/521] Time 0.32 (0.32) Data 0.00 (0.00) Loss 0.148 (0.152) Prec@1 95.83 (96.98) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-06:44:05] Epoch: [423][300/521] Time 0.33 (0.32) Data 0.00 (0.00) Loss 0.109 (0.146) Prec@1 100.00 (97.14) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-06:44:37] Epoch: [423][400/521] Time 0.28 (0.32) Data 0.00 (0.00) Loss 0.125 (0.143) Prec@1 96.88 (97.18) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-06:45:09] Epoch: [423][500/521] Time 0.32 (0.32) Data 0.00 (0.00) Loss 0.100 (0.145) Prec@1 97.92 (97.16) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-06:45:15] Epoch: [423][520/521] Time 0.32 (0.32) Data 0.00 (0.00) Loss 0.209 (0.146) Prec@1 93.75 (97.13) Prec@5 100.00 (99.97)
|
|
[2019-04-01-06:45:15] **train** Prec@1 97.13 Prec@5 99.97 Error@1 2.87 Error@5 0.03 Loss:0.146
|
|
test [2019-04-01-06:45:16] Epoch: [423][000/105] Time 0.94 (0.94) Data 0.76 (0.76) Loss 0.096 (0.096) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-06:45:22] Epoch: [423][100/105] Time 0.05 (0.07) Data 0.00 (0.01) Loss 0.111 (0.166) Prec@1 97.92 (95.83) Prec@5 100.00 (99.86)
|
|
test [2019-04-01-06:45:22] Epoch: [423][104/105] Time 0.07 (0.07) Data 0.00 (0.01) Loss 0.084 (0.167) Prec@1 93.75 (95.80) Prec@5 100.00 (99.86)
|
|
[2019-04-01-06:45:22] **test** Prec@1 95.80 Prec@5 99.86 Error@1 4.20 Error@5 0.14 Loss:0.167
|
|
----> Best Accuracy : Acc@1=96.25, Acc@5=99.89, Error@1=3.75, Error@5=0.11
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-06:45:22] [Epoch=424/600] [Need: 08:26:54] LR=0.0050 ~ 0.0050, Batch=96
|
|
train[2019-04-01-06:45:24] Epoch: [424][000/521] Time 1.23 (1.23) Data 0.85 (0.85) Loss 0.190 (0.190) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-06:45:54] Epoch: [424][100/521] Time 0.31 (0.32) Data 0.00 (0.01) Loss 0.210 (0.147) Prec@1 96.88 (97.12) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-06:46:26] Epoch: [424][200/521] Time 0.36 (0.32) Data 0.00 (0.00) Loss 0.222 (0.152) Prec@1 93.75 (96.98) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-06:46:57] Epoch: [424][300/521] Time 0.32 (0.32) Data 0.00 (0.00) Loss 0.158 (0.151) Prec@1 98.96 (96.96) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-06:47:30] Epoch: [424][400/521] Time 0.35 (0.32) Data 0.00 (0.00) Loss 0.103 (0.150) Prec@1 97.92 (96.93) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-06:48:02] Epoch: [424][500/521] Time 0.51 (0.32) Data 0.00 (0.00) Loss 0.284 (0.151) Prec@1 93.75 (96.89) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-06:48:09] Epoch: [424][520/521] Time 0.42 (0.32) Data 0.00 (0.00) Loss 0.179 (0.151) Prec@1 96.25 (96.89) Prec@5 98.75 (99.97)
|
|
[2019-04-01-06:48:09] **train** Prec@1 96.89 Prec@5 99.97 Error@1 3.11 Error@5 0.03 Loss:0.151
|
|
test [2019-04-01-06:48:10] Epoch: [424][000/105] Time 1.05 (1.05) Data 0.98 (0.98) Loss 0.123 (0.123) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-06:48:16] Epoch: [424][100/105] Time 0.04 (0.07) Data 0.00 (0.01) Loss 0.154 (0.172) Prec@1 96.88 (95.59) Prec@5 100.00 (99.92)
|
|
test [2019-04-01-06:48:16] Epoch: [424][104/105] Time 0.04 (0.07) Data 0.00 (0.01) Loss 0.002 (0.171) Prec@1 100.00 (95.57) Prec@5 100.00 (99.92)
|
|
[2019-04-01-06:48:17] **test** Prec@1 95.57 Prec@5 99.92 Error@1 4.43 Error@5 0.08 Loss:0.171
|
|
----> Best Accuracy : Acc@1=96.25, Acc@5=99.89, Error@1=3.75, Error@5=0.11
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-06:48:17] [Epoch=425/600] [Need: 08:28:38] LR=0.0050 ~ 0.0050, Batch=96
|
|
train[2019-04-01-06:48:18] Epoch: [425][000/521] Time 1.27 (1.27) Data 0.83 (0.83) Loss 0.088 (0.088) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-06:48:50] Epoch: [425][100/521] Time 0.39 (0.33) Data 0.00 (0.01) Loss 0.159 (0.146) Prec@1 97.92 (97.07) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-06:49:22] Epoch: [425][200/521] Time 0.27 (0.32) Data 0.00 (0.00) Loss 0.136 (0.142) Prec@1 95.83 (97.20) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-06:49:53] Epoch: [425][300/521] Time 0.26 (0.32) Data 0.00 (0.00) Loss 0.119 (0.142) Prec@1 95.83 (97.20) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-06:50:22] Epoch: [425][400/521] Time 0.24 (0.31) Data 0.00 (0.00) Loss 0.058 (0.140) Prec@1 100.00 (97.26) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-06:50:54] Epoch: [425][500/521] Time 0.32 (0.31) Data 0.00 (0.00) Loss 0.117 (0.143) Prec@1 97.92 (97.17) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-06:51:00] Epoch: [425][520/521] Time 0.29 (0.31) Data 0.00 (0.00) Loss 0.136 (0.144) Prec@1 97.50 (97.16) Prec@5 100.00 (99.96)
|
|
[2019-04-01-06:51:00] **train** Prec@1 97.16 Prec@5 99.96 Error@1 2.84 Error@5 0.04 Loss:0.144
|
|
test [2019-04-01-06:51:01] Epoch: [425][000/105] Time 0.90 (0.90) Data 0.80 (0.80) Loss 0.149 (0.149) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-06:51:07] Epoch: [425][100/105] Time 0.04 (0.06) Data 0.00 (0.01) Loss 0.092 (0.153) Prec@1 97.92 (95.93) Prec@5 100.00 (99.91)
|
|
test [2019-04-01-06:51:07] Epoch: [425][104/105] Time 0.03 (0.06) Data 0.00 (0.01) Loss 0.030 (0.152) Prec@1 100.00 (95.92) Prec@5 100.00 (99.91)
|
|
[2019-04-01-06:51:07] **test** Prec@1 95.92 Prec@5 99.91 Error@1 4.08 Error@5 0.09 Loss:0.152
|
|
----> Best Accuracy : Acc@1=96.25, Acc@5=99.89, Error@1=3.75, Error@5=0.11
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-06:51:07] [Epoch=426/600] [Need: 08:13:55] LR=0.0049 ~ 0.0049, Batch=96
|
|
train[2019-04-01-06:51:08] Epoch: [426][000/521] Time 1.06 (1.06) Data 0.65 (0.65) Loss 0.100 (0.100) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-06:51:33] Epoch: [426][100/521] Time 0.24 (0.26) Data 0.00 (0.01) Loss 0.101 (0.148) Prec@1 98.96 (97.05) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-06:51:57] Epoch: [426][200/521] Time 0.23 (0.25) Data 0.00 (0.00) Loss 0.187 (0.146) Prec@1 94.79 (97.07) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-06:52:23] Epoch: [426][300/521] Time 0.27 (0.25) Data 0.00 (0.00) Loss 0.129 (0.145) Prec@1 98.96 (97.07) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-06:52:47] Epoch: [426][400/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.137 (0.145) Prec@1 97.92 (97.10) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-06:53:11] Epoch: [426][500/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.102 (0.147) Prec@1 98.96 (97.06) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-06:53:15] Epoch: [426][520/521] Time 0.22 (0.25) Data 0.00 (0.00) Loss 0.109 (0.147) Prec@1 97.50 (97.05) Prec@5 98.75 (99.97)
|
|
[2019-04-01-06:53:16] **train** Prec@1 97.05 Prec@5 99.97 Error@1 2.95 Error@5 0.03 Loss:0.147
|
|
test [2019-04-01-06:53:16] Epoch: [426][000/105] Time 0.62 (0.62) Data 0.56 (0.56) Loss 0.113 (0.113) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-06:53:20] Epoch: [426][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.067 (0.154) Prec@1 98.96 (96.10) Prec@5 100.00 (99.92)
|
|
test [2019-04-01-06:53:20] Epoch: [426][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.002 (0.153) Prec@1 100.00 (96.14) Prec@5 100.00 (99.92)
|
|
[2019-04-01-06:53:21] **test** Prec@1 96.14 Prec@5 99.92 Error@1 3.86 Error@5 0.08 Loss:0.153
|
|
----> Best Accuracy : Acc@1=96.25, Acc@5=99.89, Error@1=3.75, Error@5=0.11
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-06:53:21] [Epoch=427/600] [Need: 06:25:15] LR=0.0049 ~ 0.0049, Batch=96
|
|
train[2019-04-01-06:53:21] Epoch: [427][000/521] Time 0.72 (0.72) Data 0.44 (0.44) Loss 0.212 (0.212) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-06:53:45] Epoch: [427][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.119 (0.145) Prec@1 97.92 (97.24) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-06:54:09] Epoch: [427][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.164 (0.145) Prec@1 95.83 (97.12) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-06:54:33] Epoch: [427][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.155 (0.143) Prec@1 97.92 (97.09) Prec@5 98.96 (99.98)
|
|
train[2019-04-01-06:54:57] Epoch: [427][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.151 (0.141) Prec@1 96.88 (97.17) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-06:55:21] Epoch: [427][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.097 (0.143) Prec@1 97.92 (97.15) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-06:55:25] Epoch: [427][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.103 (0.142) Prec@1 97.50 (97.17) Prec@5 100.00 (99.98)
|
|
[2019-04-01-06:55:25] **train** Prec@1 97.17 Prec@5 99.98 Error@1 2.83 Error@5 0.02 Loss:0.142
|
|
test [2019-04-01-06:55:26] Epoch: [427][000/105] Time 0.48 (0.48) Data 0.42 (0.42) Loss 0.089 (0.089) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-06:55:30] Epoch: [427][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.103 (0.157) Prec@1 96.88 (96.16) Prec@5 100.00 (99.90)
|
|
test [2019-04-01-06:55:30] Epoch: [427][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.041 (0.157) Prec@1 100.00 (96.15) Prec@5 100.00 (99.90)
|
|
[2019-04-01-06:55:30] **test** Prec@1 96.15 Prec@5 99.90 Error@1 3.85 Error@5 0.10 Loss:0.157
|
|
----> Best Accuracy : Acc@1=96.25, Acc@5=99.89, Error@1=3.75, Error@5=0.11
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-06:55:30] [Epoch=428/600] [Need: 06:11:39] LR=0.0048 ~ 0.0048, Batch=96
|
|
train[2019-04-01-06:55:31] Epoch: [428][000/521] Time 0.71 (0.71) Data 0.44 (0.44) Loss 0.178 (0.178) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-06:55:55] Epoch: [428][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.088 (0.134) Prec@1 98.96 (97.27) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-06:56:19] Epoch: [428][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.064 (0.137) Prec@1 98.96 (97.17) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-06:56:43] Epoch: [428][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.091 (0.138) Prec@1 97.92 (97.18) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-06:57:06] Epoch: [428][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.044 (0.139) Prec@1 100.00 (97.20) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-06:57:30] Epoch: [428][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.064 (0.140) Prec@1 100.00 (97.18) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-06:57:35] Epoch: [428][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.220 (0.140) Prec@1 96.25 (97.19) Prec@5 100.00 (99.99)
|
|
[2019-04-01-06:57:35] **train** Prec@1 97.19 Prec@5 99.99 Error@1 2.81 Error@5 0.01 Loss:0.140
|
|
test [2019-04-01-06:57:36] Epoch: [428][000/105] Time 0.60 (0.60) Data 0.53 (0.53) Loss 0.050 (0.050) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-06:57:40] Epoch: [428][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.065 (0.167) Prec@1 97.92 (95.87) Prec@5 100.00 (99.92)
|
|
test [2019-04-01-06:57:40] Epoch: [428][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.013 (0.166) Prec@1 100.00 (95.90) Prec@5 100.00 (99.92)
|
|
[2019-04-01-06:57:40] **test** Prec@1 95.90 Prec@5 99.92 Error@1 4.10 Error@5 0.08 Loss:0.166
|
|
----> Best Accuracy : Acc@1=96.25, Acc@5=99.89, Error@1=3.75, Error@5=0.11
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-06:57:40] [Epoch=429/600] [Need: 06:09:38] LR=0.0048 ~ 0.0048, Batch=96
|
|
train[2019-04-01-06:57:41] Epoch: [429][000/521] Time 0.84 (0.84) Data 0.57 (0.57) Loss 0.121 (0.121) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-06:58:05] Epoch: [429][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.126 (0.138) Prec@1 96.88 (97.32) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-06:58:28] Epoch: [429][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.075 (0.140) Prec@1 98.96 (97.24) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-06:58:52] Epoch: [429][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.096 (0.142) Prec@1 97.92 (97.23) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-06:59:16] Epoch: [429][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.195 (0.142) Prec@1 95.83 (97.23) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-06:59:40] Epoch: [429][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.135 (0.144) Prec@1 97.92 (97.17) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-06:59:44] Epoch: [429][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.133 (0.144) Prec@1 97.50 (97.15) Prec@5 100.00 (99.96)
|
|
[2019-04-01-06:59:44] **train** Prec@1 97.15 Prec@5 99.96 Error@1 2.85 Error@5 0.04 Loss:0.144
|
|
test [2019-04-01-06:59:45] Epoch: [429][000/105] Time 0.47 (0.47) Data 0.40 (0.40) Loss 0.109 (0.109) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-06:59:49] Epoch: [429][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.095 (0.149) Prec@1 96.88 (96.03) Prec@5 100.00 (99.90)
|
|
test [2019-04-01-06:59:49] Epoch: [429][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.128 (0.149) Prec@1 93.75 (96.04) Prec@5 100.00 (99.90)
|
|
[2019-04-01-06:59:49] **test** Prec@1 96.04 Prec@5 99.90 Error@1 3.96 Error@5 0.10 Loss:0.149
|
|
----> Best Accuracy : Acc@1=96.25, Acc@5=99.89, Error@1=3.75, Error@5=0.11
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-06:59:49] [Epoch=430/600] [Need: 06:06:33] LR=0.0047 ~ 0.0047, Batch=96
|
|
train[2019-04-01-06:59:50] Epoch: [430][000/521] Time 0.84 (0.84) Data 0.56 (0.56) Loss 0.243 (0.243) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-07:00:14] Epoch: [430][100/521] Time 0.23 (0.24) Data 0.00 (0.01) Loss 0.171 (0.134) Prec@1 95.83 (97.29) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-07:00:38] Epoch: [430][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.111 (0.136) Prec@1 97.92 (97.24) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-07:01:02] Epoch: [430][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.138 (0.139) Prec@1 98.96 (97.22) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-07:01:25] Epoch: [430][400/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.103 (0.141) Prec@1 96.88 (97.19) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-07:01:49] Epoch: [430][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.252 (0.142) Prec@1 93.75 (97.22) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-07:01:54] Epoch: [430][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.123 (0.142) Prec@1 98.75 (97.21) Prec@5 100.00 (99.97)
|
|
[2019-04-01-07:01:54] **train** Prec@1 97.21 Prec@5 99.97 Error@1 2.79 Error@5 0.03 Loss:0.142
|
|
test [2019-04-01-07:01:55] Epoch: [430][000/105] Time 0.60 (0.60) Data 0.53 (0.53) Loss 0.103 (0.103) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-07:01:59] Epoch: [430][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.032 (0.148) Prec@1 98.96 (96.42) Prec@5 100.00 (99.93)
|
|
test [2019-04-01-07:01:59] Epoch: [430][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.090 (0.149) Prec@1 93.75 (96.39) Prec@5 100.00 (99.93)
|
|
[2019-04-01-07:01:59] **test** Prec@1 96.39 Prec@5 99.93 Error@1 3.61 Error@5 0.07 Loss:0.149
|
|
----> Best Accuracy : Acc@1=96.39, Acc@5=99.93, Error@1=3.61, Error@5=0.07
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-07:01:59] [Epoch=431/600] [Need: 06:05:12] LR=0.0047 ~ 0.0047, Batch=96
|
|
train[2019-04-01-07:02:00] Epoch: [431][000/521] Time 0.84 (0.84) Data 0.56 (0.56) Loss 0.141 (0.141) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-07:02:24] Epoch: [431][100/521] Time 0.23 (0.24) Data 0.00 (0.01) Loss 0.046 (0.135) Prec@1 100.00 (97.40) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-07:02:48] Epoch: [431][200/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.127 (0.138) Prec@1 96.88 (97.26) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-07:03:12] Epoch: [431][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.181 (0.137) Prec@1 95.83 (97.21) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-07:03:36] Epoch: [431][400/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.158 (0.139) Prec@1 95.83 (97.20) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-07:04:00] Epoch: [431][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.192 (0.139) Prec@1 96.88 (97.17) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-07:04:05] Epoch: [431][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.264 (0.140) Prec@1 95.00 (97.16) Prec@5 100.00 (99.98)
|
|
[2019-04-01-07:04:05] **train** Prec@1 97.16 Prec@5 99.98 Error@1 2.84 Error@5 0.02 Loss:0.140
|
|
test [2019-04-01-07:04:05] Epoch: [431][000/105] Time 0.48 (0.48) Data 0.42 (0.42) Loss 0.096 (0.096) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-07:04:09] Epoch: [431][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.049 (0.158) Prec@1 96.88 (95.91) Prec@5 100.00 (99.94)
|
|
test [2019-04-01-07:04:09] Epoch: [431][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.001 (0.158) Prec@1 100.00 (95.90) Prec@5 100.00 (99.94)
|
|
[2019-04-01-07:04:10] **test** Prec@1 95.90 Prec@5 99.94 Error@1 4.10 Error@5 0.06 Loss:0.158
|
|
----> Best Accuracy : Acc@1=96.39, Acc@5=99.93, Error@1=3.61, Error@5=0.07
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-07:04:10] [Epoch=432/600] [Need: 06:05:38] LR=0.0046 ~ 0.0046, Batch=96
|
|
train[2019-04-01-07:04:10] Epoch: [432][000/521] Time 0.83 (0.83) Data 0.55 (0.55) Loss 0.144 (0.144) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-07:04:34] Epoch: [432][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.162 (0.144) Prec@1 96.88 (97.26) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-07:04:58] Epoch: [432][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.079 (0.145) Prec@1 98.96 (97.22) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-07:05:22] Epoch: [432][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.260 (0.141) Prec@1 94.79 (97.22) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-07:05:46] Epoch: [432][400/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.080 (0.138) Prec@1 96.88 (97.27) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-07:06:09] Epoch: [432][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.148 (0.140) Prec@1 96.88 (97.26) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-07:06:14] Epoch: [432][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.171 (0.139) Prec@1 96.25 (97.28) Prec@5 100.00 (99.99)
|
|
[2019-04-01-07:06:14] **train** Prec@1 97.28 Prec@5 99.99 Error@1 2.72 Error@5 0.01 Loss:0.139
|
|
test [2019-04-01-07:06:15] Epoch: [432][000/105] Time 0.47 (0.47) Data 0.40 (0.40) Loss 0.115 (0.115) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-07:06:19] Epoch: [432][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.030 (0.145) Prec@1 98.96 (96.37) Prec@5 100.00 (99.91)
|
|
test [2019-04-01-07:06:19] Epoch: [432][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.089 (0.144) Prec@1 93.75 (96.36) Prec@5 100.00 (99.91)
|
|
[2019-04-01-07:06:19] **test** Prec@1 96.36 Prec@5 99.91 Error@1 3.64 Error@5 0.09 Loss:0.144
|
|
----> Best Accuracy : Acc@1=96.39, Acc@5=99.93, Error@1=3.61, Error@5=0.07
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-07:06:19] [Epoch=433/600] [Need: 06:00:06] LR=0.0046 ~ 0.0046, Batch=96
|
|
train[2019-04-01-07:06:20] Epoch: [433][000/521] Time 0.70 (0.70) Data 0.43 (0.43) Loss 0.210 (0.210) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-07:06:43] Epoch: [433][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.219 (0.150) Prec@1 94.79 (97.10) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-07:07:07] Epoch: [433][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.201 (0.146) Prec@1 95.83 (97.12) Prec@5 98.96 (99.98)
|
|
train[2019-04-01-07:07:31] Epoch: [433][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.103 (0.142) Prec@1 96.88 (97.25) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-07:07:55] Epoch: [433][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.140 (0.141) Prec@1 95.83 (97.26) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-07:08:19] Epoch: [433][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.122 (0.142) Prec@1 96.88 (97.23) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-07:08:23] Epoch: [433][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.100 (0.141) Prec@1 98.75 (97.25) Prec@5 100.00 (99.98)
|
|
[2019-04-01-07:08:23] **train** Prec@1 97.25 Prec@5 99.98 Error@1 2.75 Error@5 0.02 Loss:0.141
|
|
test [2019-04-01-07:08:24] Epoch: [433][000/105] Time 0.49 (0.49) Data 0.42 (0.42) Loss 0.099 (0.099) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-07:08:28] Epoch: [433][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.078 (0.159) Prec@1 96.88 (95.84) Prec@5 100.00 (99.90)
|
|
test [2019-04-01-07:08:28] Epoch: [433][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.004 (0.158) Prec@1 100.00 (95.85) Prec@5 100.00 (99.90)
|
|
[2019-04-01-07:08:28] **test** Prec@1 95.85 Prec@5 99.90 Error@1 4.15 Error@5 0.10 Loss:0.158
|
|
----> Best Accuracy : Acc@1=96.39, Acc@5=99.93, Error@1=3.61, Error@5=0.07
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-07:08:28] [Epoch=434/600] [Need: 05:57:57] LR=0.0045 ~ 0.0045, Batch=96
|
|
train[2019-04-01-07:08:29] Epoch: [434][000/521] Time 0.71 (0.71) Data 0.43 (0.43) Loss 0.072 (0.072) Prec@1 100.00 (100.00) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-07:08:53] Epoch: [434][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.129 (0.143) Prec@1 98.96 (97.36) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-07:09:17] Epoch: [434][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.120 (0.135) Prec@1 97.92 (97.42) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-07:09:41] Epoch: [434][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.186 (0.135) Prec@1 94.79 (97.42) Prec@5 98.96 (99.97)
|
|
train[2019-04-01-07:10:04] Epoch: [434][400/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.135 (0.139) Prec@1 95.83 (97.30) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-07:10:28] Epoch: [434][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.062 (0.139) Prec@1 100.00 (97.28) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-07:10:33] Epoch: [434][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.190 (0.139) Prec@1 95.00 (97.27) Prec@5 100.00 (99.98)
|
|
[2019-04-01-07:10:33] **train** Prec@1 97.27 Prec@5 99.98 Error@1 2.73 Error@5 0.02 Loss:0.139
|
|
test [2019-04-01-07:10:34] Epoch: [434][000/105] Time 0.48 (0.48) Data 0.42 (0.42) Loss 0.124 (0.124) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-07:10:38] Epoch: [434][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.023 (0.144) Prec@1 98.96 (96.06) Prec@5 100.00 (99.91)
|
|
test [2019-04-01-07:10:38] Epoch: [434][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.001 (0.143) Prec@1 100.00 (96.08) Prec@5 100.00 (99.91)
|
|
[2019-04-01-07:10:38] **test** Prec@1 96.08 Prec@5 99.91 Error@1 3.92 Error@5 0.09 Loss:0.143
|
|
----> Best Accuracy : Acc@1=96.39, Acc@5=99.93, Error@1=3.61, Error@5=0.07
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-07:10:38] [Epoch=435/600] [Need: 05:56:53] LR=0.0045 ~ 0.0045, Batch=96
|
|
train[2019-04-01-07:10:39] Epoch: [435][000/521] Time 0.83 (0.83) Data 0.56 (0.56) Loss 0.144 (0.144) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-07:11:03] Epoch: [435][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.111 (0.130) Prec@1 96.88 (97.39) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-07:11:27] Epoch: [435][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.202 (0.136) Prec@1 94.79 (97.24) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-07:11:51] Epoch: [435][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.130 (0.136) Prec@1 96.88 (97.30) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-07:12:15] Epoch: [435][400/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.257 (0.138) Prec@1 96.88 (97.25) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-07:12:38] Epoch: [435][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.080 (0.138) Prec@1 100.00 (97.27) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-07:12:43] Epoch: [435][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.074 (0.138) Prec@1 98.75 (97.27) Prec@5 100.00 (99.97)
|
|
[2019-04-01-07:12:43] **train** Prec@1 97.27 Prec@5 99.97 Error@1 2.73 Error@5 0.03 Loss:0.138
|
|
test [2019-04-01-07:12:44] Epoch: [435][000/105] Time 0.61 (0.61) Data 0.55 (0.55) Loss 0.094 (0.094) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-07:12:48] Epoch: [435][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.014 (0.138) Prec@1 98.96 (96.49) Prec@5 100.00 (99.94)
|
|
test [2019-04-01-07:12:48] Epoch: [435][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.036 (0.138) Prec@1 100.00 (96.44) Prec@5 100.00 (99.94)
|
|
[2019-04-01-07:12:48] **test** Prec@1 96.44 Prec@5 99.94 Error@1 3.56 Error@5 0.06 Loss:0.138
|
|
----> Best Accuracy : Acc@1=96.44, Acc@5=99.94, Error@1=3.56, Error@5=0.06
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-07:12:48] [Epoch=436/600] [Need: 05:56:00] LR=0.0044 ~ 0.0044, Batch=96
|
|
train[2019-04-01-07:12:49] Epoch: [436][000/521] Time 0.71 (0.71) Data 0.44 (0.44) Loss 0.077 (0.077) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-07:13:20] Epoch: [436][100/521] Time 0.31 (0.31) Data 0.00 (0.00) Loss 0.077 (0.119) Prec@1 98.96 (97.78) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-07:13:50] Epoch: [436][200/521] Time 0.34 (0.31) Data 0.00 (0.00) Loss 0.143 (0.125) Prec@1 97.92 (97.64) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-07:14:20] Epoch: [436][300/521] Time 0.28 (0.30) Data 0.00 (0.00) Loss 0.120 (0.126) Prec@1 96.88 (97.55) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-07:14:51] Epoch: [436][400/521] Time 0.33 (0.31) Data 0.00 (0.00) Loss 0.197 (0.128) Prec@1 96.88 (97.49) Prec@5 98.96 (99.99)
|
|
train[2019-04-01-07:15:17] Epoch: [436][500/521] Time 0.25 (0.30) Data 0.00 (0.00) Loss 0.219 (0.131) Prec@1 94.79 (97.39) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-07:15:22] Epoch: [436][520/521] Time 0.24 (0.29) Data 0.00 (0.00) Loss 0.029 (0.131) Prec@1 100.00 (97.38) Prec@5 100.00 (99.99)
|
|
[2019-04-01-07:15:22] **train** Prec@1 97.38 Prec@5 99.99 Error@1 2.62 Error@5 0.01 Loss:0.131
|
|
test [2019-04-01-07:15:23] Epoch: [436][000/105] Time 0.89 (0.89) Data 0.81 (0.81) Loss 0.153 (0.153) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-07:15:29] Epoch: [436][100/105] Time 0.05 (0.06) Data 0.00 (0.01) Loss 0.054 (0.156) Prec@1 97.92 (96.05) Prec@5 100.00 (99.94)
|
|
test [2019-04-01-07:15:29] Epoch: [436][104/105] Time 0.05 (0.06) Data 0.00 (0.01) Loss 0.006 (0.157) Prec@1 100.00 (96.05) Prec@5 100.00 (99.94)
|
|
[2019-04-01-07:15:29] **test** Prec@1 96.05 Prec@5 99.94 Error@1 3.95 Error@5 0.06 Loss:0.157
|
|
----> Best Accuracy : Acc@1=96.44, Acc@5=99.94, Error@1=3.56, Error@5=0.06
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-07:15:29] [Epoch=437/600] [Need: 07:17:22] LR=0.0044 ~ 0.0044, Batch=96
|
|
train[2019-04-01-07:15:31] Epoch: [437][000/521] Time 1.11 (1.11) Data 0.65 (0.65) Loss 0.081 (0.081) Prec@1 100.00 (100.00) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-07:16:03] Epoch: [437][100/521] Time 0.41 (0.33) Data 0.00 (0.01) Loss 0.170 (0.135) Prec@1 96.88 (97.33) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-07:16:35] Epoch: [437][200/521] Time 0.31 (0.32) Data 0.00 (0.00) Loss 0.073 (0.136) Prec@1 100.00 (97.26) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-07:17:07] Epoch: [437][300/521] Time 0.30 (0.32) Data 0.00 (0.00) Loss 0.114 (0.135) Prec@1 97.92 (97.28) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-07:17:40] Epoch: [437][400/521] Time 0.34 (0.32) Data 0.00 (0.00) Loss 0.151 (0.136) Prec@1 97.92 (97.29) Prec@5 98.96 (99.98)
|
|
train[2019-04-01-07:18:07] Epoch: [437][500/521] Time 0.26 (0.31) Data 0.00 (0.00) Loss 0.136 (0.136) Prec@1 98.96 (97.30) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-07:18:12] Epoch: [437][520/521] Time 0.26 (0.31) Data 0.00 (0.00) Loss 0.137 (0.136) Prec@1 98.75 (97.29) Prec@5 100.00 (99.98)
|
|
[2019-04-01-07:18:13] **train** Prec@1 97.29 Prec@5 99.98 Error@1 2.71 Error@5 0.02 Loss:0.136
|
|
test [2019-04-01-07:18:13] Epoch: [437][000/105] Time 0.66 (0.66) Data 0.57 (0.57) Loss 0.108 (0.108) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-07:18:18] Epoch: [437][100/105] Time 0.05 (0.05) Data 0.00 (0.01) Loss 0.064 (0.154) Prec@1 96.88 (96.05) Prec@5 100.00 (99.95)
|
|
test [2019-04-01-07:18:18] Epoch: [437][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.004 (0.153) Prec@1 100.00 (96.03) Prec@5 100.00 (99.95)
|
|
[2019-04-01-07:18:18] **test** Prec@1 96.03 Prec@5 99.95 Error@1 3.97 Error@5 0.05 Loss:0.153
|
|
----> Best Accuracy : Acc@1=96.44, Acc@5=99.94, Error@1=3.56, Error@5=0.06
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-07:18:18] [Epoch=438/600] [Need: 07:35:22] LR=0.0043 ~ 0.0043, Batch=96
|
|
train[2019-04-01-07:18:19] Epoch: [438][000/521] Time 1.06 (1.06) Data 0.64 (0.64) Loss 0.169 (0.169) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-07:18:45] Epoch: [438][100/521] Time 0.25 (0.27) Data 0.00 (0.01) Loss 0.249 (0.141) Prec@1 95.83 (97.06) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-07:19:09] Epoch: [438][200/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.085 (0.137) Prec@1 97.92 (97.16) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-07:19:38] Epoch: [438][300/521] Time 0.28 (0.27) Data 0.00 (0.00) Loss 0.068 (0.135) Prec@1 98.96 (97.26) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-07:20:08] Epoch: [438][400/521] Time 0.29 (0.27) Data 0.00 (0.00) Loss 0.059 (0.137) Prec@1 98.96 (97.26) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-07:20:39] Epoch: [438][500/521] Time 0.30 (0.28) Data 0.00 (0.00) Loss 0.210 (0.138) Prec@1 95.83 (97.24) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-07:20:45] Epoch: [438][520/521] Time 0.31 (0.28) Data 0.00 (0.00) Loss 0.107 (0.138) Prec@1 97.50 (97.26) Prec@5 100.00 (99.98)
|
|
[2019-04-01-07:20:45] **train** Prec@1 97.26 Prec@5 99.98 Error@1 2.74 Error@5 0.02 Loss:0.138
|
|
test [2019-04-01-07:20:46] Epoch: [438][000/105] Time 0.95 (0.95) Data 0.84 (0.84) Loss 0.105 (0.105) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-07:20:51] Epoch: [438][100/105] Time 0.04 (0.06) Data 0.00 (0.01) Loss 0.044 (0.146) Prec@1 97.92 (96.27) Prec@5 100.00 (99.93)
|
|
test [2019-04-01-07:20:51] Epoch: [438][104/105] Time 0.03 (0.06) Data 0.00 (0.01) Loss 0.018 (0.147) Prec@1 100.00 (96.27) Prec@5 100.00 (99.93)
|
|
[2019-04-01-07:20:51] **test** Prec@1 96.27 Prec@5 99.93 Error@1 3.73 Error@5 0.07 Loss:0.147
|
|
----> Best Accuracy : Acc@1=96.44, Acc@5=99.94, Error@1=3.56, Error@5=0.06
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-07:20:51] [Epoch=439/600] [Need: 06:51:17] LR=0.0043 ~ 0.0043, Batch=96
|
|
train[2019-04-01-07:20:52] Epoch: [439][000/521] Time 1.05 (1.05) Data 0.70 (0.70) Loss 0.143 (0.143) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-07:21:23] Epoch: [439][100/521] Time 0.26 (0.31) Data 0.00 (0.01) Loss 0.078 (0.124) Prec@1 100.00 (97.56) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-07:21:48] Epoch: [439][200/521] Time 0.25 (0.28) Data 0.00 (0.00) Loss 0.103 (0.131) Prec@1 97.92 (97.34) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-07:22:18] Epoch: [439][300/521] Time 0.33 (0.29) Data 0.00 (0.00) Loss 0.195 (0.131) Prec@1 94.79 (97.39) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-07:22:50] Epoch: [439][400/521] Time 0.24 (0.30) Data 0.00 (0.00) Loss 0.128 (0.128) Prec@1 100.00 (97.48) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-07:23:21] Epoch: [439][500/521] Time 0.32 (0.30) Data 0.00 (0.00) Loss 0.161 (0.128) Prec@1 95.83 (97.49) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-07:23:26] Epoch: [439][520/521] Time 0.21 (0.30) Data 0.00 (0.00) Loss 0.101 (0.128) Prec@1 100.00 (97.49) Prec@5 100.00 (99.98)
|
|
[2019-04-01-07:23:27] **train** Prec@1 97.49 Prec@5 99.98 Error@1 2.51 Error@5 0.02 Loss:0.128
|
|
test [2019-04-01-07:23:27] Epoch: [439][000/105] Time 0.61 (0.61) Data 0.55 (0.55) Loss 0.073 (0.073) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-07:23:31] Epoch: [439][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.094 (0.165) Prec@1 95.83 (95.79) Prec@5 100.00 (99.96)
|
|
test [2019-04-01-07:23:31] Epoch: [439][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.001 (0.165) Prec@1 100.00 (95.81) Prec@5 100.00 (99.96)
|
|
[2019-04-01-07:23:31] **test** Prec@1 95.81 Prec@5 99.96 Error@1 4.19 Error@5 0.04 Loss:0.165
|
|
----> Best Accuracy : Acc@1=96.44, Acc@5=99.94, Error@1=3.56, Error@5=0.06
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-07:23:32] [Epoch=440/600] [Need: 07:07:10] LR=0.0042 ~ 0.0042, Batch=96
|
|
train[2019-04-01-07:23:32] Epoch: [440][000/521] Time 0.72 (0.72) Data 0.43 (0.43) Loss 0.197 (0.197) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-07:23:56] Epoch: [440][100/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.124 (0.137) Prec@1 97.92 (97.25) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-07:24:20] Epoch: [440][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.078 (0.137) Prec@1 100.00 (97.32) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-07:24:44] Epoch: [440][300/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.130 (0.135) Prec@1 97.92 (97.37) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-07:25:07] Epoch: [440][400/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.101 (0.132) Prec@1 97.92 (97.42) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-07:25:31] Epoch: [440][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.171 (0.133) Prec@1 97.92 (97.41) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-07:25:36] Epoch: [440][520/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.137 (0.134) Prec@1 96.25 (97.38) Prec@5 100.00 (99.98)
|
|
[2019-04-01-07:25:36] **train** Prec@1 97.38 Prec@5 99.98 Error@1 2.62 Error@5 0.02 Loss:0.134
|
|
test [2019-04-01-07:25:37] Epoch: [440][000/105] Time 0.49 (0.49) Data 0.43 (0.43) Loss 0.166 (0.166) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-07:25:41] Epoch: [440][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.047 (0.150) Prec@1 97.92 (96.16) Prec@5 100.00 (99.93)
|
|
test [2019-04-01-07:25:41] Epoch: [440][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.001 (0.151) Prec@1 100.00 (96.11) Prec@5 100.00 (99.93)
|
|
[2019-04-01-07:25:41] **test** Prec@1 96.11 Prec@5 99.93 Error@1 3.89 Error@5 0.07 Loss:0.151
|
|
----> Best Accuracy : Acc@1=96.44, Acc@5=99.94, Error@1=3.56, Error@5=0.06
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-07:25:41] [Epoch=441/600] [Need: 05:43:26] LR=0.0042 ~ 0.0042, Batch=96
|
|
train[2019-04-01-07:25:42] Epoch: [441][000/521] Time 0.85 (0.85) Data 0.58 (0.58) Loss 0.066 (0.066) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-07:26:06] Epoch: [441][100/521] Time 0.23 (0.25) Data 0.00 (0.01) Loss 0.134 (0.150) Prec@1 97.92 (96.82) Prec@5 100.00 (99.94)
|
|
train[2019-04-01-07:26:30] Epoch: [441][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.075 (0.143) Prec@1 97.92 (97.09) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-07:26:54] Epoch: [441][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.261 (0.137) Prec@1 94.79 (97.29) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-07:27:17] Epoch: [441][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.071 (0.132) Prec@1 100.00 (97.44) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-07:27:41] Epoch: [441][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.145 (0.133) Prec@1 97.92 (97.40) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-07:27:46] Epoch: [441][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.133 (0.133) Prec@1 97.50 (97.40) Prec@5 100.00 (99.97)
|
|
[2019-04-01-07:27:46] **train** Prec@1 97.40 Prec@5 99.97 Error@1 2.60 Error@5 0.03 Loss:0.133
|
|
test [2019-04-01-07:27:47] Epoch: [441][000/105] Time 0.61 (0.61) Data 0.54 (0.54) Loss 0.124 (0.124) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-07:27:51] Epoch: [441][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.129 (0.162) Prec@1 98.96 (95.97) Prec@5 100.00 (99.91)
|
|
test [2019-04-01-07:27:51] Epoch: [441][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.002 (0.162) Prec@1 100.00 (95.98) Prec@5 100.00 (99.91)
|
|
[2019-04-01-07:27:51] **test** Prec@1 95.98 Prec@5 99.91 Error@1 4.02 Error@5 0.09 Loss:0.162
|
|
----> Best Accuracy : Acc@1=96.44, Acc@5=99.94, Error@1=3.56, Error@5=0.06
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-07:27:51] [Epoch=442/600] [Need: 05:41:56] LR=0.0041 ~ 0.0041, Batch=96
|
|
train[2019-04-01-07:27:52] Epoch: [442][000/521] Time 0.71 (0.71) Data 0.43 (0.43) Loss 0.123 (0.123) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-07:28:15] Epoch: [442][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.169 (0.118) Prec@1 96.88 (97.85) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-07:28:39] Epoch: [442][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.121 (0.123) Prec@1 97.92 (97.70) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-07:29:03] Epoch: [442][300/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.067 (0.125) Prec@1 100.00 (97.63) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-07:29:27] Epoch: [442][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.180 (0.128) Prec@1 95.83 (97.50) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-07:29:51] Epoch: [442][500/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.165 (0.130) Prec@1 94.79 (97.48) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-07:29:56] Epoch: [442][520/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.200 (0.129) Prec@1 95.00 (97.47) Prec@5 100.00 (99.97)
|
|
[2019-04-01-07:29:56] **train** Prec@1 97.47 Prec@5 99.97 Error@1 2.53 Error@5 0.03 Loss:0.129
|
|
test [2019-04-01-07:29:56] Epoch: [442][000/105] Time 0.62 (0.62) Data 0.55 (0.55) Loss 0.106 (0.106) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-07:30:00] Epoch: [442][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.077 (0.164) Prec@1 97.92 (95.99) Prec@5 100.00 (99.90)
|
|
test [2019-04-01-07:30:01] Epoch: [442][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.005 (0.166) Prec@1 100.00 (95.99) Prec@5 100.00 (99.90)
|
|
[2019-04-01-07:30:01] **test** Prec@1 95.99 Prec@5 99.90 Error@1 4.01 Error@5 0.10 Loss:0.166
|
|
----> Best Accuracy : Acc@1=96.44, Acc@5=99.94, Error@1=3.56, Error@5=0.06
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-07:30:01] [Epoch=443/600] [Need: 05:39:38] LR=0.0041 ~ 0.0041, Batch=96
|
|
train[2019-04-01-07:30:02] Epoch: [443][000/521] Time 0.83 (0.83) Data 0.55 (0.55) Loss 0.050 (0.050) Prec@1 100.00 (100.00) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-07:30:25] Epoch: [443][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.169 (0.119) Prec@1 97.92 (97.72) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-07:30:49] Epoch: [443][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.196 (0.116) Prec@1 96.88 (97.85) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-07:31:13] Epoch: [443][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.161 (0.119) Prec@1 97.92 (97.72) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-07:31:37] Epoch: [443][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.178 (0.122) Prec@1 96.88 (97.65) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-07:32:01] Epoch: [443][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.145 (0.126) Prec@1 96.88 (97.55) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-07:32:05] Epoch: [443][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.102 (0.125) Prec@1 98.75 (97.56) Prec@5 100.00 (99.99)
|
|
[2019-04-01-07:32:05] **train** Prec@1 97.56 Prec@5 99.99 Error@1 2.44 Error@5 0.01 Loss:0.125
|
|
test [2019-04-01-07:32:06] Epoch: [443][000/105] Time 0.47 (0.47) Data 0.41 (0.41) Loss 0.132 (0.132) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-07:32:10] Epoch: [443][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.022 (0.153) Prec@1 98.96 (96.07) Prec@5 100.00 (99.94)
|
|
test [2019-04-01-07:32:10] Epoch: [443][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.014 (0.152) Prec@1 100.00 (96.07) Prec@5 100.00 (99.94)
|
|
[2019-04-01-07:32:10] **test** Prec@1 96.07 Prec@5 99.94 Error@1 3.93 Error@5 0.06 Loss:0.152
|
|
----> Best Accuracy : Acc@1=96.44, Acc@5=99.94, Error@1=3.56, Error@5=0.06
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-07:32:10] [Epoch=444/600] [Need: 05:37:07] LR=0.0040 ~ 0.0040, Batch=96
|
|
train[2019-04-01-07:32:11] Epoch: [444][000/521] Time 0.71 (0.71) Data 0.43 (0.43) Loss 0.103 (0.103) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-07:32:35] Epoch: [444][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.085 (0.118) Prec@1 98.96 (97.82) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-07:32:59] Epoch: [444][200/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.213 (0.115) Prec@1 96.88 (97.98) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-07:33:23] Epoch: [444][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.092 (0.117) Prec@1 97.92 (97.88) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-07:33:46] Epoch: [444][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.203 (0.121) Prec@1 96.88 (97.74) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-07:34:10] Epoch: [444][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.077 (0.123) Prec@1 98.96 (97.67) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-07:34:15] Epoch: [444][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.105 (0.124) Prec@1 97.50 (97.64) Prec@5 100.00 (99.97)
|
|
[2019-04-01-07:34:15] **train** Prec@1 97.64 Prec@5 99.97 Error@1 2.36 Error@5 0.03 Loss:0.124
|
|
test [2019-04-01-07:34:16] Epoch: [444][000/105] Time 0.60 (0.60) Data 0.54 (0.54) Loss 0.102 (0.102) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-07:34:20] Epoch: [444][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.067 (0.143) Prec@1 97.92 (96.45) Prec@5 100.00 (99.89)
|
|
test [2019-04-01-07:34:20] Epoch: [444][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.000 (0.143) Prec@1 100.00 (96.42) Prec@5 100.00 (99.89)
|
|
[2019-04-01-07:34:20] **test** Prec@1 96.42 Prec@5 99.89 Error@1 3.58 Error@5 0.11 Loss:0.143
|
|
----> Best Accuracy : Acc@1=96.44, Acc@5=99.94, Error@1=3.56, Error@5=0.06
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-07:34:20] [Epoch=445/600] [Need: 05:34:57] LR=0.0040 ~ 0.0040, Batch=96
|
|
train[2019-04-01-07:34:21] Epoch: [445][000/521] Time 0.77 (0.77) Data 0.49 (0.49) Loss 0.083 (0.083) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-07:34:45] Epoch: [445][100/521] Time 0.25 (0.24) Data 0.00 (0.01) Loss 0.265 (0.122) Prec@1 96.88 (97.63) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-07:35:09] Epoch: [445][200/521] Time 0.26 (0.24) Data 0.00 (0.00) Loss 0.065 (0.127) Prec@1 98.96 (97.63) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-07:35:33] Epoch: [445][300/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.046 (0.125) Prec@1 100.00 (97.68) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-07:35:56] Epoch: [445][400/521] Time 0.27 (0.24) Data 0.00 (0.00) Loss 0.109 (0.125) Prec@1 97.92 (97.64) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-07:36:21] Epoch: [445][500/521] Time 0.27 (0.24) Data 0.00 (0.00) Loss 0.166 (0.125) Prec@1 97.92 (97.62) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-07:36:26] Epoch: [445][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.157 (0.124) Prec@1 97.50 (97.65) Prec@5 100.00 (99.99)
|
|
[2019-04-01-07:36:26] **train** Prec@1 97.65 Prec@5 99.99 Error@1 2.35 Error@5 0.01 Loss:0.124
|
|
test [2019-04-01-07:36:27] Epoch: [445][000/105] Time 0.60 (0.60) Data 0.53 (0.53) Loss 0.099 (0.099) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-07:36:31] Epoch: [445][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.066 (0.145) Prec@1 98.96 (96.61) Prec@5 100.00 (99.93)
|
|
test [2019-04-01-07:36:31] Epoch: [445][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.002 (0.145) Prec@1 100.00 (96.58) Prec@5 100.00 (99.93)
|
|
[2019-04-01-07:36:31] **test** Prec@1 96.58 Prec@5 99.93 Error@1 3.42 Error@5 0.07 Loss:0.145
|
|
----> Best Accuracy : Acc@1=96.58, Acc@5=99.93, Error@1=3.42, Error@5=0.07
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-07:36:31] [Epoch=446/600] [Need: 05:36:40] LR=0.0039 ~ 0.0039, Batch=96
|
|
train[2019-04-01-07:36:32] Epoch: [446][000/521] Time 0.84 (0.84) Data 0.56 (0.56) Loss 0.068 (0.068) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-07:36:56] Epoch: [446][100/521] Time 0.25 (0.25) Data 0.00 (0.01) Loss 0.299 (0.134) Prec@1 93.75 (97.33) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-07:37:20] Epoch: [446][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.115 (0.131) Prec@1 98.96 (97.43) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-07:37:44] Epoch: [446][300/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.107 (0.126) Prec@1 97.92 (97.57) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-07:38:08] Epoch: [446][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.139 (0.124) Prec@1 97.92 (97.64) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-07:38:32] Epoch: [446][500/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.040 (0.124) Prec@1 100.00 (97.61) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-07:38:36] Epoch: [446][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.074 (0.125) Prec@1 98.75 (97.61) Prec@5 100.00 (99.97)
|
|
[2019-04-01-07:38:36] **train** Prec@1 97.61 Prec@5 99.97 Error@1 2.39 Error@5 0.03 Loss:0.125
|
|
test [2019-04-01-07:38:37] Epoch: [446][000/105] Time 0.60 (0.60) Data 0.53 (0.53) Loss 0.117 (0.117) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-07:38:41] Epoch: [446][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.102 (0.151) Prec@1 97.92 (96.36) Prec@5 100.00 (99.94)
|
|
test [2019-04-01-07:38:41] Epoch: [446][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.004 (0.152) Prec@1 100.00 (96.37) Prec@5 100.00 (99.93)
|
|
[2019-04-01-07:38:41] **test** Prec@1 96.37 Prec@5 99.93 Error@1 3.63 Error@5 0.07 Loss:0.152
|
|
----> Best Accuracy : Acc@1=96.58, Acc@5=99.93, Error@1=3.42, Error@5=0.07
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-07:38:42] [Epoch=447/600] [Need: 05:32:00] LR=0.0039 ~ 0.0039, Batch=96
|
|
train[2019-04-01-07:38:42] Epoch: [447][000/521] Time 0.83 (0.83) Data 0.55 (0.55) Loss 0.094 (0.094) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-07:39:06] Epoch: [447][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.133 (0.118) Prec@1 97.92 (97.75) Prec@5 100.00 (99.95)
|
|
train[2019-04-01-07:39:30] Epoch: [447][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.115 (0.122) Prec@1 96.88 (97.70) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-07:39:54] Epoch: [447][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.124 (0.120) Prec@1 97.92 (97.70) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-07:40:18] Epoch: [447][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.118 (0.121) Prec@1 96.88 (97.65) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-07:40:43] Epoch: [447][500/521] Time 0.27 (0.24) Data 0.00 (0.00) Loss 0.142 (0.123) Prec@1 96.88 (97.63) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-07:40:48] Epoch: [447][520/521] Time 0.26 (0.24) Data 0.00 (0.00) Loss 0.138 (0.123) Prec@1 98.75 (97.64) Prec@5 98.75 (99.98)
|
|
[2019-04-01-07:40:48] **train** Prec@1 97.64 Prec@5 99.98 Error@1 2.36 Error@5 0.02 Loss:0.123
|
|
test [2019-04-01-07:40:48] Epoch: [447][000/105] Time 0.48 (0.48) Data 0.42 (0.42) Loss 0.122 (0.122) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-07:40:52] Epoch: [447][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.084 (0.149) Prec@1 96.88 (96.15) Prec@5 100.00 (99.96)
|
|
test [2019-04-01-07:40:53] Epoch: [447][104/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.018 (0.148) Prec@1 100.00 (96.17) Prec@5 100.00 (99.96)
|
|
[2019-04-01-07:40:53] **test** Prec@1 96.17 Prec@5 99.96 Error@1 3.83 Error@5 0.04 Loss:0.148
|
|
----> Best Accuracy : Acc@1=96.58, Acc@5=99.93, Error@1=3.42, Error@5=0.07
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-07:40:53] [Epoch=448/600] [Need: 05:32:38] LR=0.0038 ~ 0.0038, Batch=96
|
|
train[2019-04-01-07:40:54] Epoch: [448][000/521] Time 0.77 (0.77) Data 0.46 (0.46) Loss 0.204 (0.204) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-07:41:17] Epoch: [448][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.071 (0.124) Prec@1 96.88 (97.51) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-07:41:41] Epoch: [448][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.051 (0.126) Prec@1 98.96 (97.52) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-07:42:05] Epoch: [448][300/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.189 (0.126) Prec@1 94.79 (97.52) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-07:42:29] Epoch: [448][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.091 (0.125) Prec@1 98.96 (97.56) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-07:42:53] Epoch: [448][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.168 (0.125) Prec@1 96.88 (97.60) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-07:42:58] Epoch: [448][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.038 (0.124) Prec@1 100.00 (97.58) Prec@5 100.00 (99.98)
|
|
[2019-04-01-07:42:58] **train** Prec@1 97.58 Prec@5 99.98 Error@1 2.42 Error@5 0.02 Loss:0.124
|
|
test [2019-04-01-07:42:59] Epoch: [448][000/105] Time 0.48 (0.48) Data 0.42 (0.42) Loss 0.093 (0.093) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-07:43:03] Epoch: [448][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.055 (0.158) Prec@1 96.88 (96.32) Prec@5 100.00 (99.93)
|
|
test [2019-04-01-07:43:03] Epoch: [448][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.027 (0.159) Prec@1 100.00 (96.34) Prec@5 100.00 (99.93)
|
|
[2019-04-01-07:43:03] **test** Prec@1 96.34 Prec@5 99.93 Error@1 3.66 Error@5 0.07 Loss:0.159
|
|
----> Best Accuracy : Acc@1=96.58, Acc@5=99.93, Error@1=3.42, Error@5=0.07
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-07:43:03] [Epoch=449/600] [Need: 05:27:52] LR=0.0038 ~ 0.0038, Batch=96
|
|
train[2019-04-01-07:43:04] Epoch: [449][000/521] Time 0.72 (0.72) Data 0.45 (0.45) Loss 0.090 (0.090) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-07:43:28] Epoch: [449][100/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.101 (0.122) Prec@1 98.96 (97.61) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-07:43:52] Epoch: [449][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.110 (0.121) Prec@1 98.96 (97.72) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-07:44:16] Epoch: [449][300/521] Time 0.27 (0.24) Data 0.00 (0.00) Loss 0.092 (0.120) Prec@1 97.92 (97.72) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-07:44:41] Epoch: [449][400/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.248 (0.119) Prec@1 95.83 (97.77) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-07:45:04] Epoch: [449][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.180 (0.120) Prec@1 96.88 (97.75) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-07:45:09] Epoch: [449][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.175 (0.121) Prec@1 96.25 (97.71) Prec@5 100.00 (99.99)
|
|
[2019-04-01-07:45:09] **train** Prec@1 97.71 Prec@5 99.99 Error@1 2.29 Error@5 0.01 Loss:0.121
|
|
test [2019-04-01-07:45:10] Epoch: [449][000/105] Time 0.60 (0.60) Data 0.54 (0.54) Loss 0.076 (0.076) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-07:45:14] Epoch: [449][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.062 (0.159) Prec@1 95.83 (95.81) Prec@5 100.00 (99.95)
|
|
test [2019-04-01-07:45:14] Epoch: [449][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.001 (0.158) Prec@1 100.00 (95.81) Prec@5 100.00 (99.95)
|
|
[2019-04-01-07:45:14] **test** Prec@1 95.81 Prec@5 99.95 Error@1 4.19 Error@5 0.05 Loss:0.158
|
|
----> Best Accuracy : Acc@1=96.58, Acc@5=99.93, Error@1=3.42, Error@5=0.07
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-07:45:14] [Epoch=450/600] [Need: 05:28:13] LR=0.0037 ~ 0.0037, Batch=96
|
|
train[2019-04-01-07:45:15] Epoch: [450][000/521] Time 0.71 (0.71) Data 0.42 (0.42) Loss 0.214 (0.214) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-07:45:39] Epoch: [450][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.097 (0.116) Prec@1 96.88 (97.78) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-07:46:03] Epoch: [450][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.074 (0.117) Prec@1 98.96 (97.77) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-07:46:27] Epoch: [450][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.081 (0.119) Prec@1 100.00 (97.71) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-07:46:51] Epoch: [450][400/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.206 (0.121) Prec@1 93.75 (97.68) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-07:47:15] Epoch: [450][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.178 (0.122) Prec@1 96.88 (97.65) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-07:47:20] Epoch: [450][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.214 (0.123) Prec@1 95.00 (97.61) Prec@5 100.00 (99.98)
|
|
[2019-04-01-07:47:20] **train** Prec@1 97.61 Prec@5 99.98 Error@1 2.39 Error@5 0.02 Loss:0.123
|
|
test [2019-04-01-07:47:20] Epoch: [450][000/105] Time 0.48 (0.48) Data 0.42 (0.42) Loss 0.195 (0.195) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-07:47:24] Epoch: [450][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.043 (0.145) Prec@1 98.96 (96.51) Prec@5 100.00 (99.93)
|
|
test [2019-04-01-07:47:24] Epoch: [450][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.000 (0.145) Prec@1 100.00 (96.52) Prec@5 100.00 (99.93)
|
|
[2019-04-01-07:47:24] **test** Prec@1 96.52 Prec@5 99.93 Error@1 3.48 Error@5 0.07 Loss:0.145
|
|
----> Best Accuracy : Acc@1=96.58, Acc@5=99.93, Error@1=3.42, Error@5=0.07
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-07:47:25] [Epoch=451/600] [Need: 05:23:14] LR=0.0037 ~ 0.0037, Batch=96
|
|
train[2019-04-01-07:47:25] Epoch: [451][000/521] Time 0.71 (0.71) Data 0.44 (0.44) Loss 0.085 (0.085) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-07:47:49] Epoch: [451][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.148 (0.124) Prec@1 97.92 (97.64) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-07:48:13] Epoch: [451][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.129 (0.120) Prec@1 97.92 (97.70) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-07:48:37] Epoch: [451][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.105 (0.119) Prec@1 98.96 (97.72) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-07:49:01] Epoch: [451][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.092 (0.119) Prec@1 97.92 (97.69) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-07:49:24] Epoch: [451][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.144 (0.118) Prec@1 97.92 (97.74) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-07:49:29] Epoch: [451][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.142 (0.118) Prec@1 98.75 (97.73) Prec@5 100.00 (99.98)
|
|
[2019-04-01-07:49:29] **train** Prec@1 97.73 Prec@5 99.98 Error@1 2.27 Error@5 0.02 Loss:0.118
|
|
test [2019-04-01-07:49:30] Epoch: [451][000/105] Time 0.49 (0.49) Data 0.43 (0.43) Loss 0.111 (0.111) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-07:49:34] Epoch: [451][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.064 (0.143) Prec@1 97.92 (96.41) Prec@5 100.00 (99.93)
|
|
test [2019-04-01-07:49:34] Epoch: [451][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.002 (0.144) Prec@1 100.00 (96.42) Prec@5 100.00 (99.93)
|
|
[2019-04-01-07:49:34] **test** Prec@1 96.42 Prec@5 99.93 Error@1 3.58 Error@5 0.07 Loss:0.144
|
|
----> Best Accuracy : Acc@1=96.58, Acc@5=99.93, Error@1=3.42, Error@5=0.07
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-07:49:34] [Epoch=452/600] [Need: 05:19:50] LR=0.0037 ~ 0.0037, Batch=96
|
|
train[2019-04-01-07:49:35] Epoch: [452][000/521] Time 0.83 (0.83) Data 0.55 (0.55) Loss 0.165 (0.165) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-07:49:59] Epoch: [452][100/521] Time 0.24 (0.25) Data 0.00 (0.01) Loss 0.059 (0.112) Prec@1 100.00 (97.85) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-07:50:23] Epoch: [452][200/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.241 (0.122) Prec@1 94.79 (97.60) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-07:50:47] Epoch: [452][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.085 (0.116) Prec@1 97.92 (97.82) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-07:51:11] Epoch: [452][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.233 (0.117) Prec@1 94.79 (97.80) Prec@5 98.96 (99.98)
|
|
train[2019-04-01-07:51:35] Epoch: [452][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.191 (0.118) Prec@1 94.79 (97.80) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-07:51:40] Epoch: [452][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.076 (0.118) Prec@1 98.75 (97.81) Prec@5 100.00 (99.98)
|
|
[2019-04-01-07:51:40] **train** Prec@1 97.81 Prec@5 99.98 Error@1 2.19 Error@5 0.02 Loss:0.118
|
|
test [2019-04-01-07:51:41] Epoch: [452][000/105] Time 0.48 (0.48) Data 0.42 (0.42) Loss 0.150 (0.150) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-07:51:45] Epoch: [452][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.072 (0.152) Prec@1 96.88 (96.17) Prec@5 100.00 (99.91)
|
|
test [2019-04-01-07:51:45] Epoch: [452][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.002 (0.153) Prec@1 100.00 (96.13) Prec@5 100.00 (99.91)
|
|
[2019-04-01-07:51:45] **test** Prec@1 96.13 Prec@5 99.91 Error@1 3.87 Error@5 0.09 Loss:0.153
|
|
----> Best Accuracy : Acc@1=96.58, Acc@5=99.93, Error@1=3.42, Error@5=0.07
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-07:51:45] [Epoch=453/600] [Need: 05:20:32] LR=0.0036 ~ 0.0036, Batch=96
|
|
train[2019-04-01-07:51:46] Epoch: [453][000/521] Time 0.70 (0.70) Data 0.43 (0.43) Loss 0.174 (0.174) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-07:52:10] Epoch: [453][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.096 (0.118) Prec@1 95.83 (97.68) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-07:52:33] Epoch: [453][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.135 (0.114) Prec@1 95.83 (97.89) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-07:52:57] Epoch: [453][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.072 (0.115) Prec@1 98.96 (97.83) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-07:53:21] Epoch: [453][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.221 (0.116) Prec@1 94.79 (97.82) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-07:53:45] Epoch: [453][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.110 (0.120) Prec@1 97.92 (97.72) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-07:53:50] Epoch: [453][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.164 (0.121) Prec@1 96.25 (97.70) Prec@5 100.00 (99.99)
|
|
[2019-04-01-07:53:50] **train** Prec@1 97.70 Prec@5 99.99 Error@1 2.30 Error@5 0.01 Loss:0.121
|
|
test [2019-04-01-07:53:51] Epoch: [453][000/105] Time 0.61 (0.61) Data 0.55 (0.55) Loss 0.127 (0.127) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-07:53:55] Epoch: [453][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.083 (0.144) Prec@1 96.88 (96.33) Prec@5 100.00 (99.90)
|
|
test [2019-04-01-07:53:55] Epoch: [453][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.021 (0.144) Prec@1 100.00 (96.31) Prec@5 100.00 (99.90)
|
|
[2019-04-01-07:53:55] **test** Prec@1 96.31 Prec@5 99.90 Error@1 3.69 Error@5 0.10 Loss:0.144
|
|
----> Best Accuracy : Acc@1=96.58, Acc@5=99.93, Error@1=3.42, Error@5=0.07
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-07:53:55] [Epoch=454/600] [Need: 05:16:34] LR=0.0036 ~ 0.0036, Batch=96
|
|
train[2019-04-01-07:53:56] Epoch: [454][000/521] Time 0.86 (0.86) Data 0.56 (0.56) Loss 0.089 (0.089) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-07:54:20] Epoch: [454][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.110 (0.120) Prec@1 98.96 (97.78) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-07:54:44] Epoch: [454][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.090 (0.118) Prec@1 100.00 (97.78) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-07:55:08] Epoch: [454][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.056 (0.117) Prec@1 100.00 (97.78) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-07:55:32] Epoch: [454][400/521] Time 0.27 (0.24) Data 0.00 (0.00) Loss 0.056 (0.118) Prec@1 100.00 (97.79) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-07:55:56] Epoch: [454][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.143 (0.119) Prec@1 97.92 (97.80) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-07:56:01] Epoch: [454][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.189 (0.120) Prec@1 96.25 (97.79) Prec@5 100.00 (99.99)
|
|
[2019-04-01-07:56:01] **train** Prec@1 97.79 Prec@5 99.99 Error@1 2.21 Error@5 0.01 Loss:0.120
|
|
test [2019-04-01-07:56:01] Epoch: [454][000/105] Time 0.47 (0.47) Data 0.41 (0.41) Loss 0.113 (0.113) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-07:56:06] Epoch: [454][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.079 (0.155) Prec@1 96.88 (96.09) Prec@5 100.00 (99.93)
|
|
test [2019-04-01-07:56:06] Epoch: [454][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.314 (0.156) Prec@1 93.75 (96.06) Prec@5 100.00 (99.93)
|
|
[2019-04-01-07:56:06] **test** Prec@1 96.06 Prec@5 99.93 Error@1 3.94 Error@5 0.07 Loss:0.156
|
|
----> Best Accuracy : Acc@1=96.58, Acc@5=99.93, Error@1=3.42, Error@5=0.07
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-07:56:06] [Epoch=455/600] [Need: 05:16:12] LR=0.0035 ~ 0.0035, Batch=96
|
|
train[2019-04-01-07:56:07] Epoch: [455][000/521] Time 0.85 (0.85) Data 0.57 (0.57) Loss 0.198 (0.198) Prec@1 93.75 (93.75) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-07:56:31] Epoch: [455][100/521] Time 0.23 (0.25) Data 0.00 (0.01) Loss 0.072 (0.116) Prec@1 98.96 (97.77) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-07:56:55] Epoch: [455][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.044 (0.115) Prec@1 100.00 (97.85) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-07:57:19] Epoch: [455][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.086 (0.117) Prec@1 97.92 (97.78) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-07:57:43] Epoch: [455][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.159 (0.117) Prec@1 97.92 (97.77) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-07:58:07] Epoch: [455][500/521] Time 0.26 (0.24) Data 0.00 (0.00) Loss 0.048 (0.115) Prec@1 98.96 (97.78) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-07:58:12] Epoch: [455][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.137 (0.115) Prec@1 96.25 (97.78) Prec@5 100.00 (99.99)
|
|
[2019-04-01-07:58:12] **train** Prec@1 97.78 Prec@5 99.99 Error@1 2.22 Error@5 0.01 Loss:0.115
|
|
test [2019-04-01-07:58:12] Epoch: [455][000/105] Time 0.61 (0.61) Data 0.54 (0.54) Loss 0.092 (0.092) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-07:58:16] Epoch: [455][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.075 (0.146) Prec@1 96.88 (96.30) Prec@5 100.00 (99.94)
|
|
test [2019-04-01-07:58:16] Epoch: [455][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.007 (0.146) Prec@1 100.00 (96.28) Prec@5 100.00 (99.94)
|
|
[2019-04-01-07:58:17] **test** Prec@1 96.28 Prec@5 99.94 Error@1 3.72 Error@5 0.06 Loss:0.146
|
|
----> Best Accuracy : Acc@1=96.58, Acc@5=99.93, Error@1=3.42, Error@5=0.07
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-07:58:17] [Epoch=456/600] [Need: 05:13:55] LR=0.0035 ~ 0.0035, Batch=96
|
|
train[2019-04-01-07:58:18] Epoch: [456][000/521] Time 0.89 (0.89) Data 0.61 (0.61) Loss 0.090 (0.090) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-07:58:42] Epoch: [456][100/521] Time 0.25 (0.25) Data 0.00 (0.01) Loss 0.136 (0.120) Prec@1 96.88 (97.67) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-07:59:06] Epoch: [456][200/521] Time 0.26 (0.25) Data 0.00 (0.00) Loss 0.221 (0.121) Prec@1 93.75 (97.66) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-07:59:32] Epoch: [456][300/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.099 (0.118) Prec@1 95.83 (97.74) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-07:59:58] Epoch: [456][400/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.073 (0.117) Prec@1 98.96 (97.72) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-08:00:22] Epoch: [456][500/521] Time 0.23 (0.25) Data 0.00 (0.00) Loss 0.122 (0.118) Prec@1 98.96 (97.70) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-08:00:27] Epoch: [456][520/521] Time 0.22 (0.25) Data 0.00 (0.00) Loss 0.187 (0.119) Prec@1 97.50 (97.69) Prec@5 100.00 (99.98)
|
|
[2019-04-01-08:00:27] **train** Prec@1 97.69 Prec@5 99.98 Error@1 2.31 Error@5 0.02 Loss:0.119
|
|
test [2019-04-01-08:00:28] Epoch: [456][000/105] Time 0.59 (0.59) Data 0.53 (0.53) Loss 0.110 (0.110) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-08:00:32] Epoch: [456][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.094 (0.141) Prec@1 97.92 (96.57) Prec@5 100.00 (99.94)
|
|
test [2019-04-01-08:00:32] Epoch: [456][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.004 (0.141) Prec@1 100.00 (96.56) Prec@5 100.00 (99.94)
|
|
[2019-04-01-08:00:32] **test** Prec@1 96.56 Prec@5 99.94 Error@1 3.44 Error@5 0.06 Loss:0.141
|
|
----> Best Accuracy : Acc@1=96.58, Acc@5=99.93, Error@1=3.42, Error@5=0.07
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-08:00:32] [Epoch=457/600] [Need: 05:22:12] LR=0.0034 ~ 0.0034, Batch=96
|
|
train[2019-04-01-08:00:33] Epoch: [457][000/521] Time 0.85 (0.85) Data 0.59 (0.59) Loss 0.106 (0.106) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-08:00:57] Epoch: [457][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.043 (0.117) Prec@1 98.96 (97.59) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-08:01:20] Epoch: [457][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.073 (0.118) Prec@1 98.96 (97.68) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:01:44] Epoch: [457][300/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.251 (0.116) Prec@1 93.75 (97.72) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:02:08] Epoch: [457][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.124 (0.116) Prec@1 97.92 (97.74) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:02:32] Epoch: [457][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.082 (0.116) Prec@1 100.00 (97.76) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:02:37] Epoch: [457][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.105 (0.116) Prec@1 98.75 (97.75) Prec@5 100.00 (99.99)
|
|
[2019-04-01-08:02:37] **train** Prec@1 97.75 Prec@5 99.99 Error@1 2.25 Error@5 0.01 Loss:0.116
|
|
test [2019-04-01-08:02:37] Epoch: [457][000/105] Time 0.48 (0.48) Data 0.41 (0.41) Loss 0.094 (0.094) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-08:02:42] Epoch: [457][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.036 (0.141) Prec@1 96.88 (96.49) Prec@5 100.00 (99.94)
|
|
test [2019-04-01-08:02:42] Epoch: [457][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.001 (0.140) Prec@1 100.00 (96.52) Prec@5 100.00 (99.94)
|
|
[2019-04-01-08:02:42] **test** Prec@1 96.52 Prec@5 99.94 Error@1 3.48 Error@5 0.06 Loss:0.140
|
|
----> Best Accuracy : Acc@1=96.58, Acc@5=99.93, Error@1=3.42, Error@5=0.07
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-08:02:42] [Epoch=458/600] [Need: 05:07:45] LR=0.0034 ~ 0.0034, Batch=96
|
|
train[2019-04-01-08:02:43] Epoch: [458][000/521] Time 0.84 (0.84) Data 0.56 (0.56) Loss 0.163 (0.163) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-08:03:07] Epoch: [458][100/521] Time 0.24 (0.25) Data 0.00 (0.01) Loss 0.046 (0.110) Prec@1 100.00 (97.79) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-08:03:31] Epoch: [458][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.100 (0.117) Prec@1 98.96 (97.60) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-08:03:54] Epoch: [458][300/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.103 (0.117) Prec@1 96.88 (97.65) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-08:04:18] Epoch: [458][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.099 (0.116) Prec@1 97.92 (97.70) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-08:04:42] Epoch: [458][500/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.208 (0.117) Prec@1 95.83 (97.69) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:04:47] Epoch: [458][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.179 (0.117) Prec@1 97.50 (97.70) Prec@5 100.00 (99.99)
|
|
[2019-04-01-08:04:47] **train** Prec@1 97.70 Prec@5 99.99 Error@1 2.30 Error@5 0.01 Loss:0.117
|
|
test [2019-04-01-08:04:47] Epoch: [458][000/105] Time 0.47 (0.47) Data 0.41 (0.41) Loss 0.116 (0.116) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-08:04:52] Epoch: [458][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.083 (0.147) Prec@1 96.88 (96.35) Prec@5 100.00 (99.94)
|
|
test [2019-04-01-08:04:52] Epoch: [458][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.005 (0.146) Prec@1 100.00 (96.33) Prec@5 100.00 (99.94)
|
|
[2019-04-01-08:04:52] **test** Prec@1 96.33 Prec@5 99.94 Error@1 3.67 Error@5 0.06 Loss:0.146
|
|
----> Best Accuracy : Acc@1=96.58, Acc@5=99.93, Error@1=3.42, Error@5=0.07
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-08:04:52] [Epoch=459/600] [Need: 05:05:15] LR=0.0033 ~ 0.0033, Batch=96
|
|
train[2019-04-01-08:04:53] Epoch: [459][000/521] Time 0.71 (0.71) Data 0.44 (0.44) Loss 0.124 (0.124) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-08:05:16] Epoch: [459][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.114 (0.123) Prec@1 96.88 (97.71) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-08:05:40] Epoch: [459][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.052 (0.120) Prec@1 100.00 (97.68) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:06:04] Epoch: [459][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.046 (0.114) Prec@1 100.00 (97.85) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:06:28] Epoch: [459][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.160 (0.112) Prec@1 96.88 (97.90) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:06:52] Epoch: [459][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.089 (0.114) Prec@1 96.88 (97.86) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:06:56] Epoch: [459][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.078 (0.114) Prec@1 100.00 (97.87) Prec@5 100.00 (99.99)
|
|
[2019-04-01-08:06:56] **train** Prec@1 97.87 Prec@5 99.99 Error@1 2.13 Error@5 0.01 Loss:0.114
|
|
test [2019-04-01-08:06:57] Epoch: [459][000/105] Time 0.47 (0.47) Data 0.40 (0.40) Loss 0.168 (0.168) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-08:07:01] Epoch: [459][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.044 (0.141) Prec@1 98.96 (96.43) Prec@5 100.00 (99.94)
|
|
test [2019-04-01-08:07:01] Epoch: [459][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.004 (0.141) Prec@1 100.00 (96.43) Prec@5 100.00 (99.94)
|
|
[2019-04-01-08:07:01] **test** Prec@1 96.43 Prec@5 99.94 Error@1 3.57 Error@5 0.06 Loss:0.141
|
|
----> Best Accuracy : Acc@1=96.58, Acc@5=99.93, Error@1=3.42, Error@5=0.07
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-08:07:01] [Epoch=460/600] [Need: 05:02:06] LR=0.0033 ~ 0.0033, Batch=96
|
|
train[2019-04-01-08:07:02] Epoch: [460][000/521] Time 0.71 (0.71) Data 0.42 (0.42) Loss 0.133 (0.133) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-08:07:26] Epoch: [460][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.196 (0.110) Prec@1 96.88 (97.95) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:07:50] Epoch: [460][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.070 (0.112) Prec@1 98.96 (97.84) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:08:14] Epoch: [460][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.211 (0.110) Prec@1 94.79 (97.90) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:08:37] Epoch: [460][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.227 (0.110) Prec@1 93.75 (97.89) Prec@5 98.96 (99.98)
|
|
train[2019-04-01-08:09:01] Epoch: [460][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.079 (0.110) Prec@1 98.96 (97.93) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-08:09:06] Epoch: [460][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.158 (0.111) Prec@1 97.50 (97.91) Prec@5 100.00 (99.98)
|
|
[2019-04-01-08:09:06] **train** Prec@1 97.91 Prec@5 99.98 Error@1 2.09 Error@5 0.02 Loss:0.111
|
|
test [2019-04-01-08:09:07] Epoch: [460][000/105] Time 0.47 (0.47) Data 0.40 (0.40) Loss 0.096 (0.096) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-08:09:11] Epoch: [460][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.034 (0.144) Prec@1 98.96 (96.52) Prec@5 100.00 (99.91)
|
|
test [2019-04-01-08:09:11] Epoch: [460][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.016 (0.145) Prec@1 100.00 (96.49) Prec@5 100.00 (99.91)
|
|
[2019-04-01-08:09:11] **test** Prec@1 96.49 Prec@5 99.91 Error@1 3.51 Error@5 0.09 Loss:0.145
|
|
----> Best Accuracy : Acc@1=96.58, Acc@5=99.93, Error@1=3.42, Error@5=0.07
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-08:09:11] [Epoch=461/600] [Need: 05:00:31] LR=0.0033 ~ 0.0033, Batch=96
|
|
train[2019-04-01-08:09:12] Epoch: [461][000/521] Time 0.76 (0.76) Data 0.49 (0.49) Loss 0.211 (0.211) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-08:09:36] Epoch: [461][100/521] Time 0.23 (0.24) Data 0.00 (0.01) Loss 0.051 (0.106) Prec@1 100.00 (98.01) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:10:00] Epoch: [461][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.108 (0.105) Prec@1 97.92 (98.05) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:10:24] Epoch: [461][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.066 (0.103) Prec@1 98.96 (98.05) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:10:49] Epoch: [461][400/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.125 (0.106) Prec@1 97.92 (97.98) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:11:14] Epoch: [461][500/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.080 (0.108) Prec@1 100.00 (97.95) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:11:19] Epoch: [461][520/521] Time 0.22 (0.25) Data 0.00 (0.00) Loss 0.086 (0.108) Prec@1 98.75 (97.94) Prec@5 100.00 (99.99)
|
|
[2019-04-01-08:11:19] **train** Prec@1 97.94 Prec@5 99.99 Error@1 2.06 Error@5 0.01 Loss:0.108
|
|
test [2019-04-01-08:11:20] Epoch: [461][000/105] Time 0.60 (0.60) Data 0.52 (0.52) Loss 0.185 (0.185) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-08:11:24] Epoch: [461][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.045 (0.136) Prec@1 97.92 (96.50) Prec@5 100.00 (99.94)
|
|
test [2019-04-01-08:11:24] Epoch: [461][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.008 (0.137) Prec@1 100.00 (96.48) Prec@5 100.00 (99.94)
|
|
[2019-04-01-08:11:24] **test** Prec@1 96.48 Prec@5 99.94 Error@1 3.52 Error@5 0.06 Loss:0.137
|
|
----> Best Accuracy : Acc@1=96.58, Acc@5=99.93, Error@1=3.42, Error@5=0.07
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-08:11:24] [Epoch=462/600] [Need: 05:06:36] LR=0.0032 ~ 0.0032, Batch=96
|
|
train[2019-04-01-08:11:25] Epoch: [462][000/521] Time 0.77 (0.77) Data 0.47 (0.47) Loss 0.098 (0.098) Prec@1 100.00 (100.00) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-08:11:50] Epoch: [462][100/521] Time 0.27 (0.25) Data 0.00 (0.00) Loss 0.126 (0.112) Prec@1 95.83 (98.09) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-08:12:14] Epoch: [462][200/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.080 (0.113) Prec@1 98.96 (98.00) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-08:12:38] Epoch: [462][300/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.150 (0.112) Prec@1 96.88 (97.99) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-08:13:02] Epoch: [462][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.058 (0.111) Prec@1 98.96 (97.95) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-08:13:26] Epoch: [462][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.170 (0.112) Prec@1 96.88 (97.91) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-08:13:31] Epoch: [462][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.125 (0.113) Prec@1 96.25 (97.91) Prec@5 100.00 (99.98)
|
|
[2019-04-01-08:13:31] **train** Prec@1 97.91 Prec@5 99.98 Error@1 2.09 Error@5 0.02 Loss:0.113
|
|
test [2019-04-01-08:13:31] Epoch: [462][000/105] Time 0.47 (0.47) Data 0.40 (0.40) Loss 0.098 (0.098) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-08:13:35] Epoch: [462][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.085 (0.150) Prec@1 96.88 (96.45) Prec@5 100.00 (99.91)
|
|
test [2019-04-01-08:13:35] Epoch: [462][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.024 (0.148) Prec@1 100.00 (96.49) Prec@5 100.00 (99.91)
|
|
[2019-04-01-08:13:35] **test** Prec@1 96.49 Prec@5 99.91 Error@1 3.51 Error@5 0.09 Loss:0.148
|
|
----> Best Accuracy : Acc@1=96.58, Acc@5=99.93, Error@1=3.42, Error@5=0.07
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-08:13:36] [Epoch=463/600] [Need: 04:59:30] LR=0.0032 ~ 0.0032, Batch=96
|
|
train[2019-04-01-08:13:36] Epoch: [463][000/521] Time 0.71 (0.71) Data 0.43 (0.43) Loss 0.102 (0.102) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-08:14:00] Epoch: [463][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.120 (0.111) Prec@1 97.92 (97.90) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:14:24] Epoch: [463][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.073 (0.112) Prec@1 97.92 (97.92) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-08:14:48] Epoch: [463][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.101 (0.109) Prec@1 98.96 (98.02) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-08:15:12] Epoch: [463][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.063 (0.107) Prec@1 98.96 (98.02) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-08:15:35] Epoch: [463][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.068 (0.109) Prec@1 100.00 (97.99) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:15:40] Epoch: [463][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.137 (0.109) Prec@1 96.25 (97.98) Prec@5 100.00 (99.99)
|
|
[2019-04-01-08:15:40] **train** Prec@1 97.98 Prec@5 99.99 Error@1 2.02 Error@5 0.01 Loss:0.109
|
|
test [2019-04-01-08:15:41] Epoch: [463][000/105] Time 0.49 (0.49) Data 0.43 (0.43) Loss 0.116 (0.116) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-08:15:45] Epoch: [463][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.124 (0.141) Prec@1 95.83 (96.38) Prec@5 100.00 (99.94)
|
|
test [2019-04-01-08:15:45] Epoch: [463][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.097 (0.141) Prec@1 93.75 (96.38) Prec@5 100.00 (99.94)
|
|
[2019-04-01-08:15:45] **test** Prec@1 96.38 Prec@5 99.94 Error@1 3.62 Error@5 0.06 Loss:0.141
|
|
----> Best Accuracy : Acc@1=96.58, Acc@5=99.93, Error@1=3.42, Error@5=0.07
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-08:15:45] [Epoch=464/600] [Need: 04:53:46] LR=0.0031 ~ 0.0031, Batch=96
|
|
train[2019-04-01-08:15:46] Epoch: [464][000/521] Time 0.72 (0.72) Data 0.44 (0.44) Loss 0.083 (0.083) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-08:16:10] Epoch: [464][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.140 (0.105) Prec@1 96.88 (97.98) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-08:16:34] Epoch: [464][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.123 (0.106) Prec@1 97.92 (98.02) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-08:16:58] Epoch: [464][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.136 (0.104) Prec@1 94.79 (98.06) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:17:21] Epoch: [464][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.167 (0.107) Prec@1 96.88 (97.95) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:17:45] Epoch: [464][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.121 (0.109) Prec@1 96.88 (97.90) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:17:50] Epoch: [464][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.138 (0.109) Prec@1 97.50 (97.90) Prec@5 100.00 (99.99)
|
|
[2019-04-01-08:17:50] **train** Prec@1 97.90 Prec@5 99.99 Error@1 2.10 Error@5 0.01 Loss:0.109
|
|
test [2019-04-01-08:17:51] Epoch: [464][000/105] Time 0.47 (0.47) Data 0.40 (0.40) Loss 0.115 (0.115) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
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test [2019-04-01-08:17:55] Epoch: [464][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.048 (0.138) Prec@1 98.96 (96.41) Prec@5 100.00 (99.92)
|
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test [2019-04-01-08:17:55] Epoch: [464][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.025 (0.137) Prec@1 100.00 (96.42) Prec@5 100.00 (99.92)
|
|
[2019-04-01-08:17:55] **test** Prec@1 96.42 Prec@5 99.92 Error@1 3.58 Error@5 0.08 Loss:0.137
|
|
----> Best Accuracy : Acc@1=96.58, Acc@5=99.93, Error@1=3.42, Error@5=0.07
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-08:17:55] [Epoch=465/600] [Need: 04:52:02] LR=0.0031 ~ 0.0031, Batch=96
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|
train[2019-04-01-08:17:56] Epoch: [465][000/521] Time 0.71 (0.71) Data 0.44 (0.44) Loss 0.094 (0.094) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
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train[2019-04-01-08:18:20] Epoch: [465][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.018 (0.105) Prec@1 100.00 (98.12) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-08:18:44] Epoch: [465][200/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.091 (0.108) Prec@1 97.92 (98.09) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-08:19:08] Epoch: [465][300/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.145 (0.104) Prec@1 96.88 (98.12) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:19:32] Epoch: [465][400/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.150 (0.106) Prec@1 96.88 (98.04) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-08:19:56] Epoch: [465][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.159 (0.109) Prec@1 97.92 (97.98) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:20:01] Epoch: [465][520/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.053 (0.109) Prec@1 98.75 (97.98) Prec@5 100.00 (99.99)
|
|
[2019-04-01-08:20:01] **train** Prec@1 97.98 Prec@5 99.99 Error@1 2.02 Error@5 0.01 Loss:0.109
|
|
test [2019-04-01-08:20:02] Epoch: [465][000/105] Time 0.49 (0.49) Data 0.42 (0.42) Loss 0.121 (0.121) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-08:20:06] Epoch: [465][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.092 (0.153) Prec@1 97.92 (96.29) Prec@5 100.00 (99.91)
|
|
test [2019-04-01-08:20:06] Epoch: [465][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.012 (0.152) Prec@1 100.00 (96.28) Prec@5 100.00 (99.91)
|
|
[2019-04-01-08:20:06] **test** Prec@1 96.28 Prec@5 99.91 Error@1 3.72 Error@5 0.09 Loss:0.152
|
|
----> Best Accuracy : Acc@1=96.58, Acc@5=99.93, Error@1=3.42, Error@5=0.07
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-08:20:06] [Epoch=466/600] [Need: 04:52:43] LR=0.0030 ~ 0.0030, Batch=96
|
|
train[2019-04-01-08:20:07] Epoch: [466][000/521] Time 0.83 (0.83) Data 0.55 (0.55) Loss 0.056 (0.056) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-08:20:31] Epoch: [466][100/521] Time 0.24 (0.25) Data 0.00 (0.01) Loss 0.092 (0.116) Prec@1 97.92 (97.76) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:20:55] Epoch: [466][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.046 (0.111) Prec@1 100.00 (97.87) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:21:19] Epoch: [466][300/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.144 (0.108) Prec@1 95.83 (98.01) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:21:43] Epoch: [466][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.189 (0.110) Prec@1 94.79 (97.96) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:22:07] Epoch: [466][500/521] Time 0.26 (0.24) Data 0.00 (0.00) Loss 0.120 (0.110) Prec@1 96.88 (97.96) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:22:12] Epoch: [466][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.125 (0.110) Prec@1 96.25 (97.96) Prec@5 100.00 (99.98)
|
|
[2019-04-01-08:22:12] **train** Prec@1 97.96 Prec@5 99.98 Error@1 2.04 Error@5 0.02 Loss:0.110
|
|
test [2019-04-01-08:22:13] Epoch: [466][000/105] Time 0.47 (0.47) Data 0.40 (0.40) Loss 0.157 (0.157) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-08:22:17] Epoch: [466][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.064 (0.152) Prec@1 97.92 (96.28) Prec@5 100.00 (99.88)
|
|
test [2019-04-01-08:22:17] Epoch: [466][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.004 (0.153) Prec@1 100.00 (96.30) Prec@5 100.00 (99.87)
|
|
[2019-04-01-08:22:17] **test** Prec@1 96.30 Prec@5 99.87 Error@1 3.70 Error@5 0.13 Loss:0.153
|
|
----> Best Accuracy : Acc@1=96.58, Acc@5=99.93, Error@1=3.42, Error@5=0.07
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-08:22:17] [Epoch=467/600] [Need: 04:50:35] LR=0.0030 ~ 0.0030, Batch=96
|
|
train[2019-04-01-08:22:18] Epoch: [467][000/521] Time 0.79 (0.79) Data 0.52 (0.52) Loss 0.147 (0.147) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-08:22:42] Epoch: [467][100/521] Time 0.24 (0.25) Data 0.00 (0.01) Loss 0.083 (0.112) Prec@1 98.96 (97.74) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-08:23:06] Epoch: [467][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.073 (0.112) Prec@1 98.96 (97.84) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-08:23:30] Epoch: [467][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.095 (0.110) Prec@1 98.96 (97.89) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:23:54] Epoch: [467][400/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.081 (0.107) Prec@1 98.96 (97.98) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:24:18] Epoch: [467][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.200 (0.107) Prec@1 94.79 (97.95) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:24:23] Epoch: [467][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.115 (0.107) Prec@1 98.75 (97.96) Prec@5 100.00 (99.99)
|
|
[2019-04-01-08:24:23] **train** Prec@1 97.96 Prec@5 99.99 Error@1 2.04 Error@5 0.01 Loss:0.107
|
|
test [2019-04-01-08:24:24] Epoch: [467][000/105] Time 0.60 (0.60) Data 0.54 (0.54) Loss 0.074 (0.074) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-08:24:28] Epoch: [467][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.025 (0.150) Prec@1 97.92 (96.25) Prec@5 100.00 (99.98)
|
|
test [2019-04-01-08:24:28] Epoch: [467][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.024 (0.149) Prec@1 100.00 (96.24) Prec@5 100.00 (99.98)
|
|
[2019-04-01-08:24:28] **test** Prec@1 96.24 Prec@5 99.98 Error@1 3.76 Error@5 0.02 Loss:0.149
|
|
----> Best Accuracy : Acc@1=96.58, Acc@5=99.93, Error@1=3.42, Error@5=0.07
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-08:24:28] [Epoch=468/600] [Need: 04:48:35] LR=0.0030 ~ 0.0030, Batch=96
|
|
train[2019-04-01-08:24:29] Epoch: [468][000/521] Time 0.84 (0.84) Data 0.57 (0.57) Loss 0.106 (0.106) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-08:24:53] Epoch: [468][100/521] Time 0.27 (0.25) Data 0.00 (0.01) Loss 0.079 (0.109) Prec@1 98.96 (97.98) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:25:17] Epoch: [468][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.152 (0.105) Prec@1 97.92 (98.03) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-08:25:41] Epoch: [468][300/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.040 (0.105) Prec@1 100.00 (98.04) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-08:26:06] Epoch: [468][400/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.097 (0.103) Prec@1 97.92 (98.10) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-08:26:30] Epoch: [468][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.092 (0.106) Prec@1 96.88 (98.00) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-08:26:34] Epoch: [468][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.084 (0.105) Prec@1 100.00 (98.00) Prec@5 100.00 (99.98)
|
|
[2019-04-01-08:26:34] **train** Prec@1 98.00 Prec@5 99.98 Error@1 2.00 Error@5 0.02 Loss:0.105
|
|
test [2019-04-01-08:26:35] Epoch: [468][000/105] Time 0.60 (0.60) Data 0.53 (0.53) Loss 0.078 (0.078) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-08:26:39] Epoch: [468][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.056 (0.144) Prec@1 97.92 (96.35) Prec@5 100.00 (99.95)
|
|
test [2019-04-01-08:26:39] Epoch: [468][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.019 (0.143) Prec@1 100.00 (96.38) Prec@5 100.00 (99.95)
|
|
[2019-04-01-08:26:39] **test** Prec@1 96.38 Prec@5 99.95 Error@1 3.62 Error@5 0.05 Loss:0.143
|
|
----> Best Accuracy : Acc@1=96.58, Acc@5=99.93, Error@1=3.42, Error@5=0.07
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-08:26:40] [Epoch=469/600] [Need: 04:46:26] LR=0.0029 ~ 0.0029, Batch=96
|
|
train[2019-04-01-08:26:40] Epoch: [469][000/521] Time 0.83 (0.83) Data 0.56 (0.56) Loss 0.030 (0.030) Prec@1 100.00 (100.00) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-08:27:04] Epoch: [469][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.098 (0.108) Prec@1 97.92 (97.99) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-08:27:28] Epoch: [469][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.094 (0.105) Prec@1 98.96 (98.04) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:27:53] Epoch: [469][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.163 (0.101) Prec@1 94.79 (98.14) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:28:17] Epoch: [469][400/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.095 (0.099) Prec@1 97.92 (98.19) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:28:41] Epoch: [469][500/521] Time 0.28 (0.24) Data 0.00 (0.00) Loss 0.084 (0.102) Prec@1 98.96 (98.12) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:28:46] Epoch: [469][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.130 (0.103) Prec@1 96.25 (98.11) Prec@5 100.00 (99.99)
|
|
[2019-04-01-08:28:46] **train** Prec@1 98.11 Prec@5 99.99 Error@1 1.89 Error@5 0.01 Loss:0.103
|
|
test [2019-04-01-08:28:46] Epoch: [469][000/105] Time 0.48 (0.48) Data 0.41 (0.41) Loss 0.135 (0.135) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-08:28:51] Epoch: [469][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.062 (0.158) Prec@1 98.96 (96.24) Prec@5 100.00 (99.92)
|
|
test [2019-04-01-08:28:51] Epoch: [469][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.043 (0.158) Prec@1 100.00 (96.25) Prec@5 100.00 (99.92)
|
|
[2019-04-01-08:28:51] **test** Prec@1 96.25 Prec@5 99.92 Error@1 3.75 Error@5 0.08 Loss:0.158
|
|
----> Best Accuracy : Acc@1=96.58, Acc@5=99.93, Error@1=3.42, Error@5=0.07
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-08:28:51] [Epoch=470/600] [Need: 04:44:35] LR=0.0029 ~ 0.0029, Batch=96
|
|
train[2019-04-01-08:28:52] Epoch: [470][000/521] Time 0.87 (0.87) Data 0.59 (0.59) Loss 0.074 (0.074) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-08:29:16] Epoch: [470][100/521] Time 0.24 (0.25) Data 0.00 (0.01) Loss 0.160 (0.099) Prec@1 96.88 (98.16) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:29:40] Epoch: [470][200/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.107 (0.103) Prec@1 97.92 (98.10) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:30:04] Epoch: [470][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.122 (0.100) Prec@1 97.92 (98.16) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:30:28] Epoch: [470][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.091 (0.101) Prec@1 96.88 (98.15) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-08:30:53] Epoch: [470][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.082 (0.101) Prec@1 97.92 (98.12) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:30:57] Epoch: [470][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.187 (0.101) Prec@1 95.00 (98.12) Prec@5 100.00 (99.99)
|
|
[2019-04-01-08:30:58] **train** Prec@1 98.12 Prec@5 99.99 Error@1 1.88 Error@5 0.01 Loss:0.101
|
|
test [2019-04-01-08:30:58] Epoch: [470][000/105] Time 0.55 (0.55) Data 0.48 (0.48) Loss 0.080 (0.080) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-08:31:02] Epoch: [470][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.034 (0.142) Prec@1 97.92 (96.33) Prec@5 100.00 (99.95)
|
|
test [2019-04-01-08:31:02] Epoch: [470][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.024 (0.142) Prec@1 100.00 (96.34) Prec@5 100.00 (99.95)
|
|
[2019-04-01-08:31:02] **test** Prec@1 96.34 Prec@5 99.95 Error@1 3.66 Error@5 0.05 Loss:0.142
|
|
----> Best Accuracy : Acc@1=96.58, Acc@5=99.93, Error@1=3.42, Error@5=0.07
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-08:31:03] [Epoch=471/600] [Need: 04:43:07] LR=0.0028 ~ 0.0028, Batch=96
|
|
train[2019-04-01-08:31:03] Epoch: [471][000/521] Time 0.84 (0.84) Data 0.57 (0.57) Loss 0.120 (0.120) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-08:31:28] Epoch: [471][100/521] Time 0.24 (0.25) Data 0.00 (0.01) Loss 0.079 (0.097) Prec@1 97.92 (98.25) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:31:52] Epoch: [471][200/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.139 (0.098) Prec@1 97.92 (98.24) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:32:16] Epoch: [471][300/521] Time 0.27 (0.24) Data 0.00 (0.00) Loss 0.049 (0.102) Prec@1 100.00 (98.16) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:32:41] Epoch: [471][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.121 (0.103) Prec@1 97.92 (98.12) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:33:05] Epoch: [471][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.096 (0.104) Prec@1 97.92 (98.10) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:33:09] Epoch: [471][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.109 (0.105) Prec@1 97.50 (98.06) Prec@5 100.00 (99.99)
|
|
[2019-04-01-08:33:10] **train** Prec@1 98.06 Prec@5 99.99 Error@1 1.94 Error@5 0.01 Loss:0.105
|
|
test [2019-04-01-08:33:10] Epoch: [471][000/105] Time 0.47 (0.47) Data 0.40 (0.40) Loss 0.188 (0.188) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-08:33:14] Epoch: [471][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.082 (0.140) Prec@1 96.88 (96.50) Prec@5 100.00 (99.95)
|
|
test [2019-04-01-08:33:14] Epoch: [471][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.026 (0.139) Prec@1 100.00 (96.52) Prec@5 100.00 (99.95)
|
|
[2019-04-01-08:33:14] **test** Prec@1 96.52 Prec@5 99.95 Error@1 3.48 Error@5 0.05 Loss:0.139
|
|
----> Best Accuracy : Acc@1=96.58, Acc@5=99.93, Error@1=3.42, Error@5=0.07
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-08:33:14] [Epoch=472/600] [Need: 04:41:23] LR=0.0028 ~ 0.0028, Batch=96
|
|
train[2019-04-01-08:33:15] Epoch: [472][000/521] Time 0.72 (0.72) Data 0.45 (0.45) Loss 0.068 (0.068) Prec@1 100.00 (100.00) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-08:33:39] Epoch: [472][100/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.039 (0.096) Prec@1 100.00 (98.27) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:34:03] Epoch: [472][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.142 (0.104) Prec@1 96.88 (98.02) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-08:34:27] Epoch: [472][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.130 (0.105) Prec@1 97.92 (97.97) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-08:34:51] Epoch: [472][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.098 (0.105) Prec@1 97.92 (97.99) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-08:35:15] Epoch: [472][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.102 (0.107) Prec@1 97.92 (97.93) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-08:35:20] Epoch: [472][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.121 (0.107) Prec@1 97.50 (97.94) Prec@5 100.00 (99.98)
|
|
[2019-04-01-08:35:20] **train** Prec@1 97.94 Prec@5 99.98 Error@1 2.06 Error@5 0.02 Loss:0.107
|
|
test [2019-04-01-08:35:21] Epoch: [472][000/105] Time 0.48 (0.48) Data 0.41 (0.41) Loss 0.110 (0.110) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-08:35:25] Epoch: [472][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.044 (0.138) Prec@1 97.92 (96.64) Prec@5 100.00 (99.92)
|
|
test [2019-04-01-08:35:25] Epoch: [472][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.029 (0.139) Prec@1 100.00 (96.63) Prec@5 100.00 (99.91)
|
|
[2019-04-01-08:35:25] **test** Prec@1 96.63 Prec@5 99.91 Error@1 3.37 Error@5 0.09 Loss:0.139
|
|
----> Best Accuracy : Acc@1=96.63, Acc@5=99.91, Error@1=3.37, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-08:35:25] [Epoch=473/600] [Need: 04:36:40] LR=0.0028 ~ 0.0028, Batch=96
|
|
train[2019-04-01-08:35:26] Epoch: [473][000/521] Time 0.83 (0.83) Data 0.51 (0.51) Loss 0.132 (0.132) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-08:35:51] Epoch: [473][100/521] Time 0.25 (0.25) Data 0.00 (0.01) Loss 0.186 (0.098) Prec@1 97.92 (98.23) Prec@5 98.96 (99.98)
|
|
train[2019-04-01-08:36:15] Epoch: [473][200/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.093 (0.098) Prec@1 97.92 (98.27) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-08:36:39] Epoch: [473][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.047 (0.098) Prec@1 98.96 (98.23) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:37:03] Epoch: [473][400/521] Time 0.27 (0.24) Data 0.00 (0.00) Loss 0.102 (0.098) Prec@1 96.88 (98.23) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:37:27] Epoch: [473][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.058 (0.099) Prec@1 98.96 (98.23) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:37:31] Epoch: [473][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.086 (0.100) Prec@1 98.75 (98.20) Prec@5 100.00 (99.99)
|
|
[2019-04-01-08:37:31] **train** Prec@1 98.20 Prec@5 99.99 Error@1 1.80 Error@5 0.01 Loss:0.100
|
|
test [2019-04-01-08:37:32] Epoch: [473][000/105] Time 0.61 (0.61) Data 0.55 (0.55) Loss 0.095 (0.095) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-08:37:36] Epoch: [473][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.029 (0.144) Prec@1 98.96 (96.54) Prec@5 100.00 (99.96)
|
|
test [2019-04-01-08:37:36] Epoch: [473][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.021 (0.144) Prec@1 100.00 (96.54) Prec@5 100.00 (99.96)
|
|
[2019-04-01-08:37:36] **test** Prec@1 96.54 Prec@5 99.96 Error@1 3.46 Error@5 0.04 Loss:0.144
|
|
----> Best Accuracy : Acc@1=96.63, Acc@5=99.91, Error@1=3.37, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-08:37:37] [Epoch=474/600] [Need: 04:35:43] LR=0.0027 ~ 0.0027, Batch=96
|
|
train[2019-04-01-08:37:37] Epoch: [474][000/521] Time 0.74 (0.74) Data 0.43 (0.43) Loss 0.053 (0.053) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-08:38:02] Epoch: [474][100/521] Time 0.26 (0.25) Data 0.00 (0.00) Loss 0.184 (0.099) Prec@1 95.83 (98.11) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:38:26] Epoch: [474][200/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.070 (0.101) Prec@1 98.96 (98.12) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-08:38:50] Epoch: [474][300/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.094 (0.101) Prec@1 98.96 (98.12) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:39:14] Epoch: [474][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.077 (0.101) Prec@1 98.96 (98.09) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:39:39] Epoch: [474][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.092 (0.101) Prec@1 97.92 (98.10) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:39:43] Epoch: [474][520/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.081 (0.101) Prec@1 98.75 (98.10) Prec@5 100.00 (99.99)
|
|
[2019-04-01-08:39:44] **train** Prec@1 98.10 Prec@5 99.99 Error@1 1.90 Error@5 0.01 Loss:0.101
|
|
test [2019-04-01-08:39:44] Epoch: [474][000/105] Time 0.61 (0.61) Data 0.55 (0.55) Loss 0.098 (0.098) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-08:39:48] Epoch: [474][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.057 (0.145) Prec@1 97.92 (96.39) Prec@5 100.00 (99.97)
|
|
test [2019-04-01-08:39:48] Epoch: [474][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.008 (0.144) Prec@1 100.00 (96.40) Prec@5 100.00 (99.97)
|
|
[2019-04-01-08:39:48] **test** Prec@1 96.40 Prec@5 99.97 Error@1 3.60 Error@5 0.03 Loss:0.144
|
|
----> Best Accuracy : Acc@1=96.63, Acc@5=99.91, Error@1=3.37, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-08:39:49] [Epoch=475/600] [Need: 04:35:10] LR=0.0027 ~ 0.0027, Batch=96
|
|
train[2019-04-01-08:39:49] Epoch: [475][000/521] Time 0.71 (0.71) Data 0.43 (0.43) Loss 0.106 (0.106) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-08:40:13] Epoch: [475][100/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.079 (0.102) Prec@1 98.96 (98.11) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:40:38] Epoch: [475][200/521] Time 0.27 (0.24) Data 0.00 (0.00) Loss 0.190 (0.103) Prec@1 97.92 (98.08) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:41:02] Epoch: [475][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.064 (0.102) Prec@1 98.96 (98.11) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:41:26] Epoch: [475][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.078 (0.100) Prec@1 98.96 (98.12) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:41:51] Epoch: [475][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.079 (0.102) Prec@1 98.96 (98.09) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:41:56] Epoch: [475][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.158 (0.102) Prec@1 98.75 (98.07) Prec@5 100.00 (99.99)
|
|
[2019-04-01-08:41:56] **train** Prec@1 98.07 Prec@5 99.99 Error@1 1.93 Error@5 0.01 Loss:0.102
|
|
test [2019-04-01-08:41:56] Epoch: [475][000/105] Time 0.62 (0.62) Data 0.52 (0.52) Loss 0.176 (0.176) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-08:42:00] Epoch: [475][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.051 (0.148) Prec@1 97.92 (96.36) Prec@5 100.00 (99.92)
|
|
test [2019-04-01-08:42:01] Epoch: [475][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.005 (0.149) Prec@1 100.00 (96.36) Prec@5 100.00 (99.92)
|
|
[2019-04-01-08:42:01] **test** Prec@1 96.36 Prec@5 99.92 Error@1 3.64 Error@5 0.08 Loss:0.149
|
|
----> Best Accuracy : Acc@1=96.63, Acc@5=99.91, Error@1=3.37, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-08:42:01] [Epoch=476/600] [Need: 04:33:18] LR=0.0026 ~ 0.0026, Batch=96
|
|
train[2019-04-01-08:42:02] Epoch: [476][000/521] Time 0.84 (0.84) Data 0.56 (0.56) Loss 0.071 (0.071) Prec@1 100.00 (100.00) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-08:42:26] Epoch: [476][100/521] Time 0.25 (0.25) Data 0.00 (0.01) Loss 0.057 (0.101) Prec@1 100.00 (98.20) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-08:42:50] Epoch: [476][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.093 (0.105) Prec@1 97.92 (98.07) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-08:43:14] Epoch: [476][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.052 (0.103) Prec@1 100.00 (98.17) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-08:43:38] Epoch: [476][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.068 (0.103) Prec@1 97.92 (98.11) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-08:44:02] Epoch: [476][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.134 (0.104) Prec@1 96.88 (98.11) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-08:44:07] Epoch: [476][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.111 (0.104) Prec@1 96.25 (98.10) Prec@5 100.00 (99.98)
|
|
[2019-04-01-08:44:07] **train** Prec@1 98.10 Prec@5 99.98 Error@1 1.90 Error@5 0.02 Loss:0.104
|
|
test [2019-04-01-08:44:08] Epoch: [476][000/105] Time 0.60 (0.60) Data 0.53 (0.53) Loss 0.119 (0.119) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-08:44:12] Epoch: [476][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.054 (0.141) Prec@1 96.88 (96.59) Prec@5 100.00 (99.93)
|
|
test [2019-04-01-08:44:12] Epoch: [476][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.004 (0.142) Prec@1 100.00 (96.57) Prec@5 100.00 (99.93)
|
|
[2019-04-01-08:44:12] **test** Prec@1 96.57 Prec@5 99.93 Error@1 3.43 Error@5 0.07 Loss:0.142
|
|
----> Best Accuracy : Acc@1=96.63, Acc@5=99.91, Error@1=3.37, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-08:44:12] [Epoch=477/600] [Need: 04:29:02] LR=0.0026 ~ 0.0026, Batch=96
|
|
train[2019-04-01-08:44:13] Epoch: [477][000/521] Time 0.86 (0.86) Data 0.57 (0.57) Loss 0.104 (0.104) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-08:44:37] Epoch: [477][100/521] Time 0.24 (0.25) Data 0.00 (0.01) Loss 0.059 (0.093) Prec@1 100.00 (98.42) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:45:01] Epoch: [477][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.042 (0.097) Prec@1 100.00 (98.22) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:45:25] Epoch: [477][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.054 (0.097) Prec@1 100.00 (98.21) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:45:49] Epoch: [477][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.129 (0.098) Prec@1 97.92 (98.17) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:46:13] Epoch: [477][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.104 (0.100) Prec@1 98.96 (98.13) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:46:18] Epoch: [477][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.082 (0.099) Prec@1 98.75 (98.14) Prec@5 100.00 (99.99)
|
|
[2019-04-01-08:46:18] **train** Prec@1 98.14 Prec@5 99.99 Error@1 1.86 Error@5 0.01 Loss:0.099
|
|
test [2019-04-01-08:46:18] Epoch: [477][000/105] Time 0.51 (0.51) Data 0.45 (0.45) Loss 0.140 (0.140) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-08:46:22] Epoch: [477][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.067 (0.146) Prec@1 98.96 (96.42) Prec@5 100.00 (99.94)
|
|
test [2019-04-01-08:46:23] Epoch: [477][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.004 (0.147) Prec@1 100.00 (96.42) Prec@5 100.00 (99.94)
|
|
[2019-04-01-08:46:23] **test** Prec@1 96.42 Prec@5 99.94 Error@1 3.58 Error@5 0.06 Loss:0.147
|
|
----> Best Accuracy : Acc@1=96.63, Acc@5=99.91, Error@1=3.37, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-08:46:23] [Epoch=478/600] [Need: 04:25:49] LR=0.0026 ~ 0.0026, Batch=96
|
|
train[2019-04-01-08:46:24] Epoch: [478][000/521] Time 0.72 (0.72) Data 0.44 (0.44) Loss 0.210 (0.210) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-08:46:48] Epoch: [478][100/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.085 (0.099) Prec@1 97.92 (98.10) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:47:12] Epoch: [478][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.070 (0.096) Prec@1 98.96 (98.19) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:47:36] Epoch: [478][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.062 (0.094) Prec@1 100.00 (98.27) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:48:00] Epoch: [478][400/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.095 (0.096) Prec@1 96.88 (98.20) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:48:24] Epoch: [478][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.084 (0.096) Prec@1 98.96 (98.22) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:48:29] Epoch: [478][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.159 (0.096) Prec@1 95.00 (98.21) Prec@5 100.00 (99.99)
|
|
[2019-04-01-08:48:29] **train** Prec@1 98.21 Prec@5 99.99 Error@1 1.79 Error@5 0.01 Loss:0.096
|
|
test [2019-04-01-08:48:29] Epoch: [478][000/105] Time 0.61 (0.61) Data 0.55 (0.55) Loss 0.111 (0.111) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-08:48:33] Epoch: [478][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.092 (0.141) Prec@1 96.88 (96.61) Prec@5 100.00 (99.92)
|
|
test [2019-04-01-08:48:34] Epoch: [478][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.004 (0.141) Prec@1 100.00 (96.63) Prec@5 100.00 (99.92)
|
|
[2019-04-01-08:48:34] **test** Prec@1 96.63 Prec@5 99.92 Error@1 3.37 Error@5 0.08 Loss:0.141
|
|
----> Best Accuracy : Acc@1=96.63, Acc@5=99.92, Error@1=3.37, Error@5=0.08
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-08:48:34] [Epoch=479/600] [Need: 04:24:20] LR=0.0025 ~ 0.0025, Batch=96
|
|
train[2019-04-01-08:48:35] Epoch: [479][000/521] Time 0.84 (0.84) Data 0.56 (0.56) Loss 0.094 (0.094) Prec@1 100.00 (100.00) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-08:48:59] Epoch: [479][100/521] Time 0.24 (0.25) Data 0.00 (0.01) Loss 0.070 (0.095) Prec@1 98.96 (98.27) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:49:23] Epoch: [479][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.104 (0.097) Prec@1 98.96 (98.23) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:49:46] Epoch: [479][300/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.069 (0.095) Prec@1 98.96 (98.25) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:50:11] Epoch: [479][400/521] Time 0.27 (0.24) Data 0.00 (0.00) Loss 0.104 (0.096) Prec@1 97.92 (98.22) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:50:35] Epoch: [479][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.129 (0.097) Prec@1 97.92 (98.21) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:50:39] Epoch: [479][520/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.318 (0.097) Prec@1 93.75 (98.22) Prec@5 100.00 (99.99)
|
|
[2019-04-01-08:50:39] **train** Prec@1 98.22 Prec@5 99.99 Error@1 1.78 Error@5 0.01 Loss:0.097
|
|
test [2019-04-01-08:50:40] Epoch: [479][000/105] Time 0.60 (0.60) Data 0.54 (0.54) Loss 0.141 (0.141) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-08:50:44] Epoch: [479][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.072 (0.145) Prec@1 97.92 (96.62) Prec@5 100.00 (99.93)
|
|
test [2019-04-01-08:50:44] Epoch: [479][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.024 (0.144) Prec@1 100.00 (96.60) Prec@5 100.00 (99.93)
|
|
[2019-04-01-08:50:44] **test** Prec@1 96.60 Prec@5 99.93 Error@1 3.40 Error@5 0.07 Loss:0.144
|
|
----> Best Accuracy : Acc@1=96.63, Acc@5=99.92, Error@1=3.37, Error@5=0.08
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-08:50:44] [Epoch=480/600] [Need: 04:21:10] LR=0.0025 ~ 0.0025, Batch=96
|
|
train[2019-04-01-08:50:45] Epoch: [480][000/521] Time 0.84 (0.84) Data 0.57 (0.57) Loss 0.120 (0.120) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-08:51:09] Epoch: [480][100/521] Time 0.24 (0.25) Data 0.00 (0.01) Loss 0.095 (0.097) Prec@1 98.96 (98.18) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-08:51:33] Epoch: [480][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.065 (0.098) Prec@1 100.00 (98.23) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:51:57] Epoch: [480][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.129 (0.097) Prec@1 97.92 (98.25) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:52:21] Epoch: [480][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.072 (0.097) Prec@1 100.00 (98.25) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:52:45] Epoch: [480][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.019 (0.096) Prec@1 100.00 (98.26) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-08:52:50] Epoch: [480][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.052 (0.096) Prec@1 100.00 (98.28) Prec@5 100.00 (100.00)
|
|
[2019-04-01-08:52:50] **train** Prec@1 98.28 Prec@5 100.00 Error@1 1.72 Error@5 0.00 Loss:0.096
|
|
test [2019-04-01-08:52:51] Epoch: [480][000/105] Time 0.48 (0.48) Data 0.41 (0.41) Loss 0.099 (0.099) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-08:52:55] Epoch: [480][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.041 (0.139) Prec@1 98.96 (96.82) Prec@5 100.00 (99.93)
|
|
test [2019-04-01-08:52:55] Epoch: [480][104/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.008 (0.139) Prec@1 100.00 (96.83) Prec@5 100.00 (99.93)
|
|
[2019-04-01-08:52:55] **test** Prec@1 96.83 Prec@5 99.93 Error@1 3.17 Error@5 0.07 Loss:0.139
|
|
----> Best Accuracy : Acc@1=96.83, Acc@5=99.93, Error@1=3.17, Error@5=0.07
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-08:52:55] [Epoch=481/600] [Need: 04:18:53] LR=0.0024 ~ 0.0024, Batch=96
|
|
train[2019-04-01-08:52:56] Epoch: [481][000/521] Time 0.71 (0.71) Data 0.44 (0.44) Loss 0.071 (0.071) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-08:53:20] Epoch: [481][100/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.061 (0.095) Prec@1 97.92 (98.36) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-08:53:44] Epoch: [481][200/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.123 (0.093) Prec@1 98.96 (98.37) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:54:08] Epoch: [481][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.078 (0.093) Prec@1 98.96 (98.37) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:54:32] Epoch: [481][400/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.234 (0.094) Prec@1 95.83 (98.38) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:54:56] Epoch: [481][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.105 (0.094) Prec@1 95.83 (98.36) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:55:01] Epoch: [481][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.119 (0.094) Prec@1 98.75 (98.36) Prec@5 100.00 (99.99)
|
|
[2019-04-01-08:55:01] **train** Prec@1 98.36 Prec@5 99.99 Error@1 1.64 Error@5 0.01 Loss:0.094
|
|
test [2019-04-01-08:55:02] Epoch: [481][000/105] Time 0.60 (0.60) Data 0.53 (0.53) Loss 0.177 (0.177) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-08:55:06] Epoch: [481][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.056 (0.144) Prec@1 97.92 (96.67) Prec@5 100.00 (99.95)
|
|
test [2019-04-01-08:55:06] Epoch: [481][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.004 (0.144) Prec@1 100.00 (96.66) Prec@5 100.00 (99.95)
|
|
[2019-04-01-08:55:06] **test** Prec@1 96.66 Prec@5 99.95 Error@1 3.34 Error@5 0.05 Loss:0.144
|
|
----> Best Accuracy : Acc@1=96.83, Acc@5=99.93, Error@1=3.17, Error@5=0.07
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-08:55:06] [Epoch=482/600] [Need: 04:18:04] LR=0.0024 ~ 0.0024, Batch=96
|
|
train[2019-04-01-08:55:07] Epoch: [482][000/521] Time 0.86 (0.86) Data 0.58 (0.58) Loss 0.154 (0.154) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-08:55:31] Epoch: [482][100/521] Time 0.24 (0.25) Data 0.00 (0.01) Loss 0.031 (0.095) Prec@1 100.00 (98.24) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:55:55] Epoch: [482][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.140 (0.090) Prec@1 97.92 (98.38) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:56:19] Epoch: [482][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.063 (0.088) Prec@1 98.96 (98.51) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-08:56:43] Epoch: [482][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.059 (0.089) Prec@1 98.96 (98.51) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:57:07] Epoch: [482][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.060 (0.090) Prec@1 98.96 (98.47) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:57:12] Epoch: [482][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.124 (0.090) Prec@1 98.75 (98.47) Prec@5 100.00 (99.99)
|
|
[2019-04-01-08:57:12] **train** Prec@1 98.47 Prec@5 99.99 Error@1 1.53 Error@5 0.01 Loss:0.090
|
|
test [2019-04-01-08:57:13] Epoch: [482][000/105] Time 0.52 (0.52) Data 0.45 (0.45) Loss 0.123 (0.123) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-08:57:17] Epoch: [482][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.016 (0.143) Prec@1 98.96 (96.72) Prec@5 100.00 (99.92)
|
|
test [2019-04-01-08:57:17] Epoch: [482][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.002 (0.143) Prec@1 100.00 (96.70) Prec@5 100.00 (99.92)
|
|
[2019-04-01-08:57:17] **test** Prec@1 96.70 Prec@5 99.92 Error@1 3.30 Error@5 0.08 Loss:0.143
|
|
----> Best Accuracy : Acc@1=96.83, Acc@5=99.93, Error@1=3.17, Error@5=0.07
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-08:57:17] [Epoch=483/600] [Need: 04:15:34] LR=0.0024 ~ 0.0024, Batch=96
|
|
train[2019-04-01-08:57:18] Epoch: [483][000/521] Time 0.85 (0.85) Data 0.57 (0.57) Loss 0.141 (0.141) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-08:57:43] Epoch: [483][100/521] Time 0.24 (0.25) Data 0.00 (0.01) Loss 0.083 (0.093) Prec@1 97.92 (98.30) Prec@5 100.00 (99.96)
|
|
train[2019-04-01-08:58:07] Epoch: [483][200/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.058 (0.097) Prec@1 97.92 (98.16) Prec@5 100.00 (99.97)
|
|
train[2019-04-01-08:58:31] Epoch: [483][300/521] Time 0.26 (0.25) Data 0.00 (0.00) Loss 0.117 (0.096) Prec@1 97.92 (98.16) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-08:58:55] Epoch: [483][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.154 (0.096) Prec@1 94.79 (98.18) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-08:59:19] Epoch: [483][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.106 (0.096) Prec@1 95.83 (98.18) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-08:59:24] Epoch: [483][520/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.132 (0.096) Prec@1 97.50 (98.19) Prec@5 100.00 (99.99)
|
|
[2019-04-01-08:59:24] **train** Prec@1 98.19 Prec@5 99.99 Error@1 1.81 Error@5 0.01 Loss:0.096
|
|
test [2019-04-01-08:59:25] Epoch: [483][000/105] Time 0.48 (0.48) Data 0.41 (0.41) Loss 0.119 (0.119) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-08:59:29] Epoch: [483][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.028 (0.141) Prec@1 98.96 (96.67) Prec@5 100.00 (99.95)
|
|
test [2019-04-01-08:59:29] Epoch: [483][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.001 (0.141) Prec@1 100.00 (96.69) Prec@5 100.00 (99.95)
|
|
[2019-04-01-08:59:29] **test** Prec@1 96.69 Prec@5 99.95 Error@1 3.31 Error@5 0.05 Loss:0.141
|
|
----> Best Accuracy : Acc@1=96.83, Acc@5=99.93, Error@1=3.17, Error@5=0.07
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-08:59:29] [Epoch=484/600] [Need: 04:15:10] LR=0.0023 ~ 0.0023, Batch=96
|
|
train[2019-04-01-08:59:30] Epoch: [484][000/521] Time 0.81 (0.81) Data 0.51 (0.51) Loss 0.026 (0.026) Prec@1 100.00 (100.00) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-08:59:54] Epoch: [484][100/521] Time 0.24 (0.25) Data 0.00 (0.01) Loss 0.126 (0.091) Prec@1 97.92 (98.42) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:00:18] Epoch: [484][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.073 (0.087) Prec@1 97.92 (98.52) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:00:42] Epoch: [484][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.082 (0.088) Prec@1 100.00 (98.48) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:01:07] Epoch: [484][400/521] Time 0.26 (0.24) Data 0.00 (0.00) Loss 0.071 (0.088) Prec@1 98.96 (98.50) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:01:31] Epoch: [484][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.125 (0.090) Prec@1 98.96 (98.48) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:01:36] Epoch: [484][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.079 (0.090) Prec@1 97.50 (98.46) Prec@5 100.00 (99.99)
|
|
[2019-04-01-09:01:36] **train** Prec@1 98.46 Prec@5 99.99 Error@1 1.54 Error@5 0.01 Loss:0.090
|
|
test [2019-04-01-09:01:37] Epoch: [484][000/105] Time 0.63 (0.63) Data 0.58 (0.58) Loss 0.082 (0.082) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-09:01:41] Epoch: [484][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.053 (0.146) Prec@1 98.96 (96.50) Prec@5 100.00 (99.93)
|
|
test [2019-04-01-09:01:41] Epoch: [484][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.001 (0.146) Prec@1 100.00 (96.50) Prec@5 100.00 (99.93)
|
|
[2019-04-01-09:01:41] **test** Prec@1 96.50 Prec@5 99.93 Error@1 3.50 Error@5 0.07 Loss:0.146
|
|
----> Best Accuracy : Acc@1=96.83, Acc@5=99.93, Error@1=3.17, Error@5=0.07
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-09:01:41] [Epoch=485/600] [Need: 04:13:17] LR=0.0023 ~ 0.0023, Batch=96
|
|
train[2019-04-01-09:01:42] Epoch: [485][000/521] Time 0.84 (0.84) Data 0.55 (0.55) Loss 0.070 (0.070) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:02:07] Epoch: [485][100/521] Time 0.24 (0.25) Data 0.00 (0.01) Loss 0.078 (0.086) Prec@1 97.92 (98.33) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:02:31] Epoch: [485][200/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.030 (0.092) Prec@1 100.00 (98.24) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:02:55] Epoch: [485][300/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.091 (0.092) Prec@1 98.96 (98.27) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:03:19] Epoch: [485][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.130 (0.092) Prec@1 96.88 (98.30) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:03:44] Epoch: [485][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.194 (0.094) Prec@1 95.83 (98.25) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:03:49] Epoch: [485][520/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.147 (0.094) Prec@1 96.25 (98.26) Prec@5 100.00 (99.99)
|
|
[2019-04-01-09:03:49] **train** Prec@1 98.26 Prec@5 99.99 Error@1 1.74 Error@5 0.01 Loss:0.094
|
|
test [2019-04-01-09:03:49] Epoch: [485][000/105] Time 0.62 (0.62) Data 0.55 (0.55) Loss 0.106 (0.106) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-09:03:53] Epoch: [485][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.026 (0.141) Prec@1 98.96 (96.54) Prec@5 100.00 (99.93)
|
|
test [2019-04-01-09:03:54] Epoch: [485][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.077 (0.141) Prec@1 93.75 (96.53) Prec@5 100.00 (99.93)
|
|
[2019-04-01-09:03:54] **test** Prec@1 96.53 Prec@5 99.93 Error@1 3.47 Error@5 0.07 Loss:0.141
|
|
----> Best Accuracy : Acc@1=96.83, Acc@5=99.93, Error@1=3.17, Error@5=0.07
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-09:03:54] [Epoch=486/600] [Need: 04:11:33] LR=0.0023 ~ 0.0023, Batch=96
|
|
train[2019-04-01-09:03:55] Epoch: [486][000/521] Time 0.83 (0.83) Data 0.55 (0.55) Loss 0.188 (0.188) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:04:19] Epoch: [486][100/521] Time 0.24 (0.25) Data 0.00 (0.01) Loss 0.119 (0.092) Prec@1 97.92 (98.22) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:04:43] Epoch: [486][200/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.084 (0.091) Prec@1 98.96 (98.28) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:05:07] Epoch: [486][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.131 (0.091) Prec@1 97.92 (98.36) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:05:31] Epoch: [486][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.068 (0.092) Prec@1 97.92 (98.35) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:05:55] Epoch: [486][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.092 (0.093) Prec@1 98.96 (98.35) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:06:00] Epoch: [486][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.058 (0.092) Prec@1 100.00 (98.36) Prec@5 100.00 (99.99)
|
|
[2019-04-01-09:06:00] **train** Prec@1 98.36 Prec@5 99.99 Error@1 1.64 Error@5 0.01 Loss:0.092
|
|
test [2019-04-01-09:06:01] Epoch: [486][000/105] Time 0.47 (0.47) Data 0.40 (0.40) Loss 0.150 (0.150) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-09:06:05] Epoch: [486][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.050 (0.132) Prec@1 98.96 (96.77) Prec@5 100.00 (99.94)
|
|
test [2019-04-01-09:06:05] Epoch: [486][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.005 (0.133) Prec@1 100.00 (96.76) Prec@5 100.00 (99.94)
|
|
[2019-04-01-09:06:05] **test** Prec@1 96.76 Prec@5 99.94 Error@1 3.24 Error@5 0.06 Loss:0.133
|
|
----> Best Accuracy : Acc@1=96.83, Acc@5=99.93, Error@1=3.17, Error@5=0.07
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-09:06:05] [Epoch=487/600] [Need: 04:06:59] LR=0.0022 ~ 0.0022, Batch=96
|
|
train[2019-04-01-09:06:06] Epoch: [487][000/521] Time 0.72 (0.72) Data 0.44 (0.44) Loss 0.097 (0.097) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:06:30] Epoch: [487][100/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.102 (0.089) Prec@1 97.92 (98.42) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:06:54] Epoch: [487][200/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.084 (0.089) Prec@1 96.88 (98.45) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:07:18] Epoch: [487][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.048 (0.089) Prec@1 100.00 (98.41) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:07:42] Epoch: [487][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.177 (0.090) Prec@1 96.88 (98.38) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:08:07] Epoch: [487][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.107 (0.090) Prec@1 97.92 (98.37) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:08:11] Epoch: [487][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.075 (0.090) Prec@1 98.75 (98.38) Prec@5 100.00 (99.99)
|
|
[2019-04-01-09:08:11] **train** Prec@1 98.38 Prec@5 99.99 Error@1 1.62 Error@5 0.01 Loss:0.090
|
|
test [2019-04-01-09:08:12] Epoch: [487][000/105] Time 0.60 (0.60) Data 0.54 (0.54) Loss 0.115 (0.115) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
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test [2019-04-01-09:08:16] Epoch: [487][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.039 (0.129) Prec@1 98.96 (96.77) Prec@5 100.00 (99.92)
|
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test [2019-04-01-09:08:16] Epoch: [487][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.053 (0.132) Prec@1 93.75 (96.76) Prec@5 100.00 (99.92)
|
|
[2019-04-01-09:08:16] **test** Prec@1 96.76 Prec@5 99.92 Error@1 3.24 Error@5 0.08 Loss:0.132
|
|
----> Best Accuracy : Acc@1=96.83, Acc@5=99.93, Error@1=3.17, Error@5=0.07
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-09:08:17] [Epoch=488/600] [Need: 04:05:33] LR=0.0022 ~ 0.0022, Batch=96
|
|
train[2019-04-01-09:08:17] Epoch: [488][000/521] Time 0.84 (0.84) Data 0.57 (0.57) Loss 0.077 (0.077) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:08:42] Epoch: [488][100/521] Time 0.24 (0.25) Data 0.00 (0.01) Loss 0.050 (0.088) Prec@1 100.00 (98.53) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:09:06] Epoch: [488][200/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.081 (0.091) Prec@1 97.92 (98.45) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:09:30] Epoch: [488][300/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.136 (0.088) Prec@1 97.92 (98.53) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:09:55] Epoch: [488][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.104 (0.089) Prec@1 96.88 (98.51) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:10:19] Epoch: [488][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.049 (0.089) Prec@1 100.00 (98.49) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:10:23] Epoch: [488][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.118 (0.089) Prec@1 96.25 (98.50) Prec@5 100.00 (99.99)
|
|
[2019-04-01-09:10:24] **train** Prec@1 98.50 Prec@5 99.99 Error@1 1.50 Error@5 0.01 Loss:0.089
|
|
test [2019-04-01-09:10:24] Epoch: [488][000/105] Time 0.56 (0.56) Data 0.49 (0.49) Loss 0.113 (0.113) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-09:10:28] Epoch: [488][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.051 (0.136) Prec@1 97.92 (96.87) Prec@5 100.00 (99.94)
|
|
test [2019-04-01-09:10:28] Epoch: [488][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.042 (0.137) Prec@1 100.00 (96.84) Prec@5 100.00 (99.94)
|
|
[2019-04-01-09:10:28] **test** Prec@1 96.84 Prec@5 99.94 Error@1 3.16 Error@5 0.06 Loss:0.137
|
|
----> Best Accuracy : Acc@1=96.84, Acc@5=99.94, Error@1=3.16, Error@5=0.06
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-09:10:29] [Epoch=489/600] [Need: 04:04:22] LR=0.0021 ~ 0.0021, Batch=96
|
|
train[2019-04-01-09:10:29] Epoch: [489][000/521] Time 0.76 (0.76) Data 0.46 (0.46) Loss 0.080 (0.080) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:10:54] Epoch: [489][100/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.117 (0.084) Prec@1 97.92 (98.47) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:11:18] Epoch: [489][200/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.048 (0.087) Prec@1 100.00 (98.56) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:11:42] Epoch: [489][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.113 (0.087) Prec@1 98.96 (98.53) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:12:06] Epoch: [489][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.109 (0.087) Prec@1 97.92 (98.50) Prec@5 98.96 (99.99)
|
|
train[2019-04-01-09:12:30] Epoch: [489][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.093 (0.089) Prec@1 98.96 (98.43) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:12:35] Epoch: [489][520/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.215 (0.090) Prec@1 95.00 (98.42) Prec@5 98.75 (99.99)
|
|
[2019-04-01-09:12:35] **train** Prec@1 98.42 Prec@5 99.99 Error@1 1.58 Error@5 0.01 Loss:0.090
|
|
test [2019-04-01-09:12:36] Epoch: [489][000/105] Time 0.61 (0.61) Data 0.55 (0.55) Loss 0.138 (0.138) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-09:12:40] Epoch: [489][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.068 (0.137) Prec@1 96.88 (96.60) Prec@5 100.00 (99.94)
|
|
test [2019-04-01-09:12:40] Epoch: [489][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.068 (0.139) Prec@1 93.75 (96.55) Prec@5 100.00 (99.94)
|
|
[2019-04-01-09:12:40] **test** Prec@1 96.55 Prec@5 99.94 Error@1 3.45 Error@5 0.06 Loss:0.139
|
|
----> Best Accuracy : Acc@1=96.84, Acc@5=99.94, Error@1=3.16, Error@5=0.06
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-09:12:40] [Epoch=490/600] [Need: 04:01:37] LR=0.0021 ~ 0.0021, Batch=96
|
|
train[2019-04-01-09:12:41] Epoch: [490][000/521] Time 0.84 (0.84) Data 0.56 (0.56) Loss 0.086 (0.086) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:13:05] Epoch: [490][100/521] Time 0.24 (0.25) Data 0.00 (0.01) Loss 0.060 (0.087) Prec@1 98.96 (98.43) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:13:29] Epoch: [490][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.080 (0.087) Prec@1 98.96 (98.41) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-09:13:53] Epoch: [490][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.015 (0.084) Prec@1 100.00 (98.55) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:14:18] Epoch: [490][400/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.106 (0.083) Prec@1 95.83 (98.55) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:14:42] Epoch: [490][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.132 (0.084) Prec@1 97.92 (98.53) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:14:46] Epoch: [490][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.017 (0.085) Prec@1 100.00 (98.53) Prec@5 100.00 (99.99)
|
|
[2019-04-01-09:14:46] **train** Prec@1 98.53 Prec@5 99.99 Error@1 1.47 Error@5 0.01 Loss:0.085
|
|
test [2019-04-01-09:14:47] Epoch: [490][000/105] Time 0.55 (0.55) Data 0.48 (0.48) Loss 0.130 (0.130) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-09:14:51] Epoch: [490][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.051 (0.133) Prec@1 97.92 (96.80) Prec@5 100.00 (99.95)
|
|
test [2019-04-01-09:14:51] Epoch: [490][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.011 (0.133) Prec@1 100.00 (96.80) Prec@5 100.00 (99.95)
|
|
[2019-04-01-09:14:51] **test** Prec@1 96.80 Prec@5 99.95 Error@1 3.20 Error@5 0.05 Loss:0.133
|
|
----> Best Accuracy : Acc@1=96.84, Acc@5=99.94, Error@1=3.16, Error@5=0.06
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-09:14:51] [Epoch=491/600] [Need: 03:58:01] LR=0.0021 ~ 0.0021, Batch=96
|
|
train[2019-04-01-09:14:52] Epoch: [491][000/521] Time 0.85 (0.85) Data 0.57 (0.57) Loss 0.164 (0.164) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:15:16] Epoch: [491][100/521] Time 0.25 (0.25) Data 0.00 (0.01) Loss 0.119 (0.083) Prec@1 97.92 (98.37) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:15:41] Epoch: [491][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.041 (0.085) Prec@1 98.96 (98.42) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:16:05] Epoch: [491][300/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.192 (0.087) Prec@1 96.88 (98.46) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:16:29] Epoch: [491][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.075 (0.085) Prec@1 97.92 (98.49) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:16:53] Epoch: [491][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.028 (0.085) Prec@1 100.00 (98.51) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:16:58] Epoch: [491][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.064 (0.085) Prec@1 98.75 (98.52) Prec@5 100.00 (99.99)
|
|
[2019-04-01-09:16:58] **train** Prec@1 98.52 Prec@5 99.99 Error@1 1.48 Error@5 0.01 Loss:0.085
|
|
test [2019-04-01-09:16:58] Epoch: [491][000/105] Time 0.52 (0.52) Data 0.46 (0.46) Loss 0.185 (0.185) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-09:17:02] Epoch: [491][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.043 (0.135) Prec@1 98.96 (96.72) Prec@5 100.00 (99.91)
|
|
test [2019-04-01-09:17:02] Epoch: [491][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.023 (0.137) Prec@1 100.00 (96.72) Prec@5 100.00 (99.91)
|
|
[2019-04-01-09:17:03] **test** Prec@1 96.72 Prec@5 99.91 Error@1 3.28 Error@5 0.09 Loss:0.137
|
|
----> Best Accuracy : Acc@1=96.84, Acc@5=99.94, Error@1=3.16, Error@5=0.06
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-09:17:03] [Epoch=492/600] [Need: 03:56:18] LR=0.0020 ~ 0.0020, Batch=96
|
|
train[2019-04-01-09:17:04] Epoch: [492][000/521] Time 0.84 (0.84) Data 0.56 (0.56) Loss 0.092 (0.092) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:17:28] Epoch: [492][100/521] Time 0.23 (0.25) Data 0.00 (0.01) Loss 0.077 (0.083) Prec@1 98.96 (98.65) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:17:51] Epoch: [492][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.138 (0.086) Prec@1 96.88 (98.54) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:18:16] Epoch: [492][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.079 (0.087) Prec@1 100.00 (98.53) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:18:40] Epoch: [492][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.063 (0.084) Prec@1 97.92 (98.58) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:19:04] Epoch: [492][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.025 (0.084) Prec@1 100.00 (98.57) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:19:09] Epoch: [492][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.058 (0.085) Prec@1 100.00 (98.57) Prec@5 100.00 (99.99)
|
|
[2019-04-01-09:19:09] **train** Prec@1 98.57 Prec@5 99.99 Error@1 1.43 Error@5 0.01 Loss:0.085
|
|
test [2019-04-01-09:19:09] Epoch: [492][000/105] Time 0.47 (0.47) Data 0.42 (0.42) Loss 0.116 (0.116) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-09:19:13] Epoch: [492][100/105] Time 0.05 (0.05) Data 0.00 (0.00) Loss 0.064 (0.139) Prec@1 98.96 (96.64) Prec@5 100.00 (99.92)
|
|
test [2019-04-01-09:19:14] Epoch: [492][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.002 (0.140) Prec@1 100.00 (96.59) Prec@5 100.00 (99.92)
|
|
[2019-04-01-09:19:14] **test** Prec@1 96.59 Prec@5 99.92 Error@1 3.41 Error@5 0.08 Loss:0.140
|
|
----> Best Accuracy : Acc@1=96.84, Acc@5=99.94, Error@1=3.16, Error@5=0.06
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-09:19:14] [Epoch=493/600] [Need: 03:53:47] LR=0.0020 ~ 0.0020, Batch=96
|
|
train[2019-04-01-09:19:15] Epoch: [493][000/521] Time 0.70 (0.70) Data 0.43 (0.43) Loss 0.117 (0.117) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:19:39] Epoch: [493][100/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.100 (0.086) Prec@1 97.92 (98.41) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:20:03] Epoch: [493][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.092 (0.087) Prec@1 97.92 (98.42) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:20:27] Epoch: [493][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.066 (0.085) Prec@1 98.96 (98.49) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:20:51] Epoch: [493][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.086 (0.085) Prec@1 98.96 (98.49) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:21:15] Epoch: [493][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.165 (0.086) Prec@1 95.83 (98.47) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:21:20] Epoch: [493][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.074 (0.087) Prec@1 100.00 (98.44) Prec@5 100.00 (99.99)
|
|
[2019-04-01-09:21:20] **train** Prec@1 98.44 Prec@5 99.99 Error@1 1.56 Error@5 0.01 Loss:0.087
|
|
test [2019-04-01-09:21:21] Epoch: [493][000/105] Time 0.52 (0.52) Data 0.46 (0.46) Loss 0.124 (0.124) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-09:21:25] Epoch: [493][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.010 (0.142) Prec@1 100.00 (96.63) Prec@5 100.00 (99.89)
|
|
test [2019-04-01-09:21:25] Epoch: [493][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.001 (0.142) Prec@1 100.00 (96.63) Prec@5 100.00 (99.89)
|
|
[2019-04-01-09:21:25] **test** Prec@1 96.63 Prec@5 99.89 Error@1 3.37 Error@5 0.11 Loss:0.142
|
|
----> Best Accuracy : Acc@1=96.84, Acc@5=99.94, Error@1=3.16, Error@5=0.06
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-09:21:25] [Epoch=494/600] [Need: 03:52:22] LR=0.0020 ~ 0.0020, Batch=96
|
|
train[2019-04-01-09:21:26] Epoch: [494][000/521] Time 0.73 (0.73) Data 0.45 (0.45) Loss 0.131 (0.131) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:21:50] Epoch: [494][100/521] Time 0.23 (0.25) Data 0.00 (0.00) Loss 0.088 (0.082) Prec@1 100.00 (98.54) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:22:15] Epoch: [494][200/521] Time 0.28 (0.25) Data 0.00 (0.00) Loss 0.108 (0.084) Prec@1 97.92 (98.51) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:22:39] Epoch: [494][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.132 (0.084) Prec@1 95.83 (98.47) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:23:03] Epoch: [494][400/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.087 (0.086) Prec@1 97.92 (98.44) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:23:27] Epoch: [494][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.087 (0.086) Prec@1 97.92 (98.43) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:23:32] Epoch: [494][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.050 (0.086) Prec@1 100.00 (98.43) Prec@5 100.00 (99.99)
|
|
[2019-04-01-09:23:32] **train** Prec@1 98.43 Prec@5 99.99 Error@1 1.57 Error@5 0.01 Loss:0.086
|
|
test [2019-04-01-09:23:33] Epoch: [494][000/105] Time 0.47 (0.47) Data 0.40 (0.40) Loss 0.161 (0.161) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-09:23:37] Epoch: [494][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.018 (0.141) Prec@1 98.96 (96.53) Prec@5 100.00 (99.94)
|
|
test [2019-04-01-09:23:37] Epoch: [494][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.000 (0.141) Prec@1 100.00 (96.51) Prec@5 100.00 (99.94)
|
|
[2019-04-01-09:23:37] **test** Prec@1 96.51 Prec@5 99.94 Error@1 3.49 Error@5 0.06 Loss:0.141
|
|
----> Best Accuracy : Acc@1=96.84, Acc@5=99.94, Error@1=3.16, Error@5=0.06
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-09:23:37] [Epoch=495/600] [Need: 03:50:52] LR=0.0019 ~ 0.0019, Batch=96
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|
train[2019-04-01-09:23:38] Epoch: [495][000/521] Time 0.77 (0.77) Data 0.49 (0.49) Loss 0.039 (0.039) Prec@1 100.00 (100.00) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:24:02] Epoch: [495][100/521] Time 0.24 (0.25) Data 0.00 (0.01) Loss 0.079 (0.091) Prec@1 98.96 (98.36) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-09:24:27] Epoch: [495][200/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.063 (0.089) Prec@1 98.96 (98.36) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:24:51] Epoch: [495][300/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.106 (0.085) Prec@1 97.92 (98.43) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:25:15] Epoch: [495][400/521] Time 0.28 (0.24) Data 0.00 (0.00) Loss 0.133 (0.084) Prec@1 97.92 (98.45) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:25:40] Epoch: [495][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.048 (0.085) Prec@1 100.00 (98.47) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:25:44] Epoch: [495][520/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.128 (0.085) Prec@1 97.50 (98.47) Prec@5 100.00 (99.99)
|
|
[2019-04-01-09:25:45] **train** Prec@1 98.47 Prec@5 99.99 Error@1 1.53 Error@5 0.01 Loss:0.085
|
|
test [2019-04-01-09:25:45] Epoch: [495][000/105] Time 0.49 (0.49) Data 0.43 (0.43) Loss 0.099 (0.099) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-09:25:49] Epoch: [495][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.024 (0.136) Prec@1 98.96 (96.67) Prec@5 100.00 (99.92)
|
|
test [2019-04-01-09:25:49] Epoch: [495][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.005 (0.136) Prec@1 100.00 (96.66) Prec@5 100.00 (99.92)
|
|
[2019-04-01-09:25:49] **test** Prec@1 96.66 Prec@5 99.92 Error@1 3.34 Error@5 0.08 Loss:0.136
|
|
----> Best Accuracy : Acc@1=96.84, Acc@5=99.94, Error@1=3.16, Error@5=0.06
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-09:25:50] [Epoch=496/600] [Need: 03:49:15] LR=0.0019 ~ 0.0019, Batch=96
|
|
train[2019-04-01-09:25:50] Epoch: [496][000/521] Time 0.74 (0.74) Data 0.46 (0.46) Loss 0.079 (0.079) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:26:14] Epoch: [496][100/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.075 (0.083) Prec@1 98.96 (98.61) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:26:39] Epoch: [496][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.049 (0.080) Prec@1 100.00 (98.69) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:27:03] Epoch: [496][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.051 (0.078) Prec@1 100.00 (98.75) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:27:27] Epoch: [496][400/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.064 (0.078) Prec@1 100.00 (98.75) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:27:52] Epoch: [496][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.073 (0.080) Prec@1 98.96 (98.70) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:27:56] Epoch: [496][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.040 (0.080) Prec@1 98.75 (98.70) Prec@5 100.00 (100.00)
|
|
[2019-04-01-09:27:56] **train** Prec@1 98.70 Prec@5 100.00 Error@1 1.30 Error@5 0.00 Loss:0.080
|
|
test [2019-04-01-09:27:57] Epoch: [496][000/105] Time 0.63 (0.63) Data 0.55 (0.55) Loss 0.078 (0.078) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-09:28:01] Epoch: [496][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.029 (0.137) Prec@1 98.96 (96.72) Prec@5 100.00 (99.93)
|
|
test [2019-04-01-09:28:01] Epoch: [496][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.027 (0.137) Prec@1 100.00 (96.73) Prec@5 100.00 (99.93)
|
|
[2019-04-01-09:28:01] **test** Prec@1 96.73 Prec@5 99.93 Error@1 3.27 Error@5 0.07 Loss:0.137
|
|
----> Best Accuracy : Acc@1=96.84, Acc@5=99.94, Error@1=3.16, Error@5=0.06
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-09:28:01] [Epoch=497/600] [Need: 03:46:24] LR=0.0019 ~ 0.0019, Batch=96
|
|
train[2019-04-01-09:28:02] Epoch: [497][000/521] Time 0.73 (0.73) Data 0.45 (0.45) Loss 0.078 (0.078) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:28:26] Epoch: [497][100/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.074 (0.079) Prec@1 98.96 (98.44) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:28:51] Epoch: [497][200/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.080 (0.085) Prec@1 97.92 (98.45) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:29:15] Epoch: [497][300/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.141 (0.082) Prec@1 96.88 (98.54) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:29:39] Epoch: [497][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.077 (0.081) Prec@1 98.96 (98.58) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:30:03] Epoch: [497][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.060 (0.083) Prec@1 98.96 (98.54) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:30:08] Epoch: [497][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.106 (0.082) Prec@1 98.75 (98.56) Prec@5 100.00 (99.99)
|
|
[2019-04-01-09:30:08] **train** Prec@1 98.56 Prec@5 99.99 Error@1 1.44 Error@5 0.01 Loss:0.082
|
|
test [2019-04-01-09:30:09] Epoch: [497][000/105] Time 0.60 (0.60) Data 0.54 (0.54) Loss 0.058 (0.058) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-09:30:13] Epoch: [497][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.029 (0.131) Prec@1 98.96 (96.89) Prec@5 100.00 (99.92)
|
|
test [2019-04-01-09:30:13] Epoch: [497][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.002 (0.132) Prec@1 100.00 (96.86) Prec@5 100.00 (99.92)
|
|
[2019-04-01-09:30:13] **test** Prec@1 96.86 Prec@5 99.92 Error@1 3.14 Error@5 0.08 Loss:0.132
|
|
----> Best Accuracy : Acc@1=96.86, Acc@5=99.92, Error@1=3.14, Error@5=0.08
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-09:30:13] [Epoch=498/600] [Need: 03:43:45] LR=0.0018 ~ 0.0018, Batch=96
|
|
train[2019-04-01-09:30:14] Epoch: [498][000/521] Time 0.71 (0.71) Data 0.44 (0.44) Loss 0.091 (0.091) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:30:38] Epoch: [498][100/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.050 (0.082) Prec@1 100.00 (98.67) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:31:02] Epoch: [498][200/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.043 (0.082) Prec@1 98.96 (98.60) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:31:27] Epoch: [498][300/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.033 (0.080) Prec@1 98.96 (98.61) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:31:51] Epoch: [498][400/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.119 (0.081) Prec@1 98.96 (98.60) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:32:15] Epoch: [498][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.133 (0.082) Prec@1 98.96 (98.59) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:32:20] Epoch: [498][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.072 (0.082) Prec@1 97.50 (98.59) Prec@5 100.00 (100.00)
|
|
[2019-04-01-09:32:20] **train** Prec@1 98.59 Prec@5 100.00 Error@1 1.41 Error@5 0.00 Loss:0.082
|
|
test [2019-04-01-09:32:20] Epoch: [498][000/105] Time 0.49 (0.49) Data 0.41 (0.41) Loss 0.078 (0.078) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-09:32:24] Epoch: [498][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.035 (0.125) Prec@1 98.96 (96.90) Prec@5 100.00 (99.94)
|
|
test [2019-04-01-09:32:25] Epoch: [498][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.006 (0.126) Prec@1 100.00 (96.86) Prec@5 100.00 (99.94)
|
|
[2019-04-01-09:32:25] **test** Prec@1 96.86 Prec@5 99.94 Error@1 3.14 Error@5 0.06 Loss:0.126
|
|
----> Best Accuracy : Acc@1=96.86, Acc@5=99.94, Error@1=3.14, Error@5=0.06
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-09:32:25] [Epoch=499/600] [Need: 03:42:03] LR=0.0018 ~ 0.0018, Batch=96
|
|
train[2019-04-01-09:32:26] Epoch: [499][000/521] Time 0.71 (0.71) Data 0.43 (0.43) Loss 0.060 (0.060) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:32:50] Epoch: [499][100/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.024 (0.082) Prec@1 100.00 (98.67) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:33:14] Epoch: [499][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.056 (0.080) Prec@1 98.96 (98.65) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:33:38] Epoch: [499][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.063 (0.078) Prec@1 98.96 (98.70) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:34:02] Epoch: [499][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.104 (0.079) Prec@1 97.92 (98.66) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:34:26] Epoch: [499][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.088 (0.079) Prec@1 100.00 (98.63) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:34:31] Epoch: [499][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.046 (0.079) Prec@1 100.00 (98.63) Prec@5 100.00 (99.99)
|
|
[2019-04-01-09:34:31] **train** Prec@1 98.63 Prec@5 99.99 Error@1 1.37 Error@5 0.01 Loss:0.079
|
|
test [2019-04-01-09:34:32] Epoch: [499][000/105] Time 0.57 (0.57) Data 0.51 (0.51) Loss 0.130 (0.130) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-09:34:36] Epoch: [499][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.129 (0.136) Prec@1 97.92 (96.85) Prec@5 100.00 (99.94)
|
|
test [2019-04-01-09:34:36] Epoch: [499][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.001 (0.137) Prec@1 100.00 (96.84) Prec@5 100.00 (99.94)
|
|
[2019-04-01-09:34:36] **test** Prec@1 96.84 Prec@5 99.94 Error@1 3.16 Error@5 0.06 Loss:0.137
|
|
----> Best Accuracy : Acc@1=96.86, Acc@5=99.94, Error@1=3.14, Error@5=0.06
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-09:34:36] [Epoch=500/600] [Need: 03:38:49] LR=0.0018 ~ 0.0018, Batch=96
|
|
train[2019-04-01-09:34:37] Epoch: [500][000/521] Time 0.84 (0.84) Data 0.56 (0.56) Loss 0.032 (0.032) Prec@1 100.00 (100.00) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:35:01] Epoch: [500][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.054 (0.075) Prec@1 100.00 (98.79) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:35:25] Epoch: [500][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.070 (0.079) Prec@1 97.92 (98.67) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:35:49] Epoch: [500][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.056 (0.078) Prec@1 98.96 (98.69) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:36:13] Epoch: [500][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.076 (0.077) Prec@1 100.00 (98.71) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:36:37] Epoch: [500][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.044 (0.077) Prec@1 100.00 (98.70) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:36:42] Epoch: [500][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.011 (0.077) Prec@1 100.00 (98.71) Prec@5 100.00 (99.99)
|
|
[2019-04-01-09:36:42] **train** Prec@1 98.71 Prec@5 99.99 Error@1 1.29 Error@5 0.01 Loss:0.077
|
|
test [2019-04-01-09:36:43] Epoch: [500][000/105] Time 0.51 (0.51) Data 0.45 (0.45) Loss 0.051 (0.051) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-09:36:47] Epoch: [500][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.067 (0.136) Prec@1 98.96 (96.89) Prec@5 100.00 (99.94)
|
|
test [2019-04-01-09:36:47] Epoch: [500][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.004 (0.138) Prec@1 100.00 (96.88) Prec@5 100.00 (99.94)
|
|
[2019-04-01-09:36:47] **test** Prec@1 96.88 Prec@5 99.94 Error@1 3.12 Error@5 0.06 Loss:0.138
|
|
----> Best Accuracy : Acc@1=96.88, Acc@5=99.94, Error@1=3.12, Error@5=0.06
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-09:36:47] [Epoch=501/600] [Need: 03:35:49] LR=0.0017 ~ 0.0017, Batch=96
|
|
train[2019-04-01-09:36:48] Epoch: [501][000/521] Time 0.92 (0.92) Data 0.59 (0.59) Loss 0.018 (0.018) Prec@1 100.00 (100.00) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:37:12] Epoch: [501][100/521] Time 0.24 (0.25) Data 0.00 (0.01) Loss 0.025 (0.073) Prec@1 100.00 (98.71) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:37:36] Epoch: [501][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.071 (0.076) Prec@1 98.96 (98.69) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:38:00] Epoch: [501][300/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.120 (0.075) Prec@1 97.92 (98.73) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:38:24] Epoch: [501][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.150 (0.077) Prec@1 95.83 (98.69) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:38:48] Epoch: [501][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.070 (0.079) Prec@1 96.88 (98.65) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:38:53] Epoch: [501][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.020 (0.079) Prec@1 100.00 (98.63) Prec@5 100.00 (100.00)
|
|
[2019-04-01-09:38:53] **train** Prec@1 98.63 Prec@5 100.00 Error@1 1.37 Error@5 0.00 Loss:0.079
|
|
test [2019-04-01-09:38:54] Epoch: [501][000/105] Time 0.56 (0.56) Data 0.49 (0.49) Loss 0.091 (0.091) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-09:38:58] Epoch: [501][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.019 (0.133) Prec@1 98.96 (96.89) Prec@5 100.00 (99.92)
|
|
test [2019-04-01-09:38:58] Epoch: [501][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.004 (0.133) Prec@1 100.00 (96.87) Prec@5 100.00 (99.92)
|
|
[2019-04-01-09:38:58] **test** Prec@1 96.87 Prec@5 99.92 Error@1 3.13 Error@5 0.08 Loss:0.133
|
|
----> Best Accuracy : Acc@1=96.88, Acc@5=99.94, Error@1=3.12, Error@5=0.06
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-09:38:58] [Epoch=502/600] [Need: 03:34:15] LR=0.0017 ~ 0.0017, Batch=96
|
|
train[2019-04-01-09:38:59] Epoch: [502][000/521] Time 0.71 (0.71) Data 0.43 (0.43) Loss 0.230 (0.230) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:39:23] Epoch: [502][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.057 (0.078) Prec@1 100.00 (98.64) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:39:47] Epoch: [502][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.025 (0.083) Prec@1 100.00 (98.58) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:40:11] Epoch: [502][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.040 (0.081) Prec@1 100.00 (98.59) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:40:36] Epoch: [502][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.081 (0.078) Prec@1 98.96 (98.69) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:40:59] Epoch: [502][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.040 (0.078) Prec@1 100.00 (98.71) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:41:04] Epoch: [502][520/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.022 (0.078) Prec@1 100.00 (98.72) Prec@5 100.00 (99.99)
|
|
[2019-04-01-09:41:05] **train** Prec@1 98.72 Prec@5 99.99 Error@1 1.28 Error@5 0.01 Loss:0.078
|
|
test [2019-04-01-09:41:05] Epoch: [502][000/105] Time 0.48 (0.48) Data 0.41 (0.41) Loss 0.059 (0.059) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
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test [2019-04-01-09:41:09] Epoch: [502][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.025 (0.130) Prec@1 98.96 (96.94) Prec@5 100.00 (99.93)
|
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test [2019-04-01-09:41:09] Epoch: [502][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.000 (0.131) Prec@1 100.00 (96.91) Prec@5 100.00 (99.93)
|
|
[2019-04-01-09:41:09] **test** Prec@1 96.91 Prec@5 99.93 Error@1 3.09 Error@5 0.07 Loss:0.131
|
|
----> Best Accuracy : Acc@1=96.91, Acc@5=99.93, Error@1=3.09, Error@5=0.07
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-09:41:10] [Epoch=503/600] [Need: 03:32:13] LR=0.0017 ~ 0.0017, Batch=96
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|
train[2019-04-01-09:41:10] Epoch: [503][000/521] Time 0.73 (0.73) Data 0.44 (0.44) Loss 0.038 (0.038) Prec@1 100.00 (100.00) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:41:35] Epoch: [503][100/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.044 (0.079) Prec@1 98.96 (98.62) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:41:59] Epoch: [503][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.065 (0.079) Prec@1 98.96 (98.64) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:42:23] Epoch: [503][300/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.274 (0.078) Prec@1 93.75 (98.64) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:42:47] Epoch: [503][400/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.104 (0.077) Prec@1 96.88 (98.68) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:43:11] Epoch: [503][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.093 (0.078) Prec@1 97.92 (98.66) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:43:16] Epoch: [503][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.115 (0.078) Prec@1 97.50 (98.66) Prec@5 100.00 (99.99)
|
|
[2019-04-01-09:43:16] **train** Prec@1 98.66 Prec@5 99.99 Error@1 1.34 Error@5 0.01 Loss:0.078
|
|
test [2019-04-01-09:43:16] Epoch: [503][000/105] Time 0.67 (0.67) Data 0.60 (0.60) Loss 0.106 (0.106) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-09:43:21] Epoch: [503][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.031 (0.140) Prec@1 98.96 (96.78) Prec@5 100.00 (99.92)
|
|
test [2019-04-01-09:43:21] Epoch: [503][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.016 (0.141) Prec@1 100.00 (96.76) Prec@5 100.00 (99.92)
|
|
[2019-04-01-09:43:21] **test** Prec@1 96.76 Prec@5 99.92 Error@1 3.24 Error@5 0.08 Loss:0.141
|
|
----> Best Accuracy : Acc@1=96.91, Acc@5=99.93, Error@1=3.09, Error@5=0.07
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-09:43:21] [Epoch=504/600] [Need: 03:30:12] LR=0.0016 ~ 0.0016, Batch=96
|
|
train[2019-04-01-09:43:22] Epoch: [504][000/521] Time 0.70 (0.70) Data 0.43 (0.43) Loss 0.115 (0.115) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:43:46] Epoch: [504][100/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.078 (0.082) Prec@1 98.96 (98.59) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:44:10] Epoch: [504][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.049 (0.081) Prec@1 98.96 (98.62) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:44:34] Epoch: [504][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.032 (0.078) Prec@1 100.00 (98.67) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:44:58] Epoch: [504][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.125 (0.079) Prec@1 96.88 (98.64) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:45:22] Epoch: [504][500/521] Time 0.26 (0.24) Data 0.00 (0.00) Loss 0.064 (0.079) Prec@1 100.00 (98.66) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:45:27] Epoch: [504][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.056 (0.079) Prec@1 98.75 (98.66) Prec@5 100.00 (100.00)
|
|
[2019-04-01-09:45:27] **train** Prec@1 98.66 Prec@5 100.00 Error@1 1.34 Error@5 0.00 Loss:0.079
|
|
test [2019-04-01-09:45:28] Epoch: [504][000/105] Time 0.61 (0.61) Data 0.54 (0.54) Loss 0.101 (0.101) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-09:45:32] Epoch: [504][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.044 (0.135) Prec@1 98.96 (96.70) Prec@5 100.00 (99.93)
|
|
test [2019-04-01-09:45:32] Epoch: [504][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.001 (0.135) Prec@1 100.00 (96.72) Prec@5 100.00 (99.93)
|
|
[2019-04-01-09:45:32] **test** Prec@1 96.72 Prec@5 99.93 Error@1 3.28 Error@5 0.07 Loss:0.135
|
|
----> Best Accuracy : Acc@1=96.91, Acc@5=99.93, Error@1=3.09, Error@5=0.07
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-09:45:32] [Epoch=505/600] [Need: 03:27:44] LR=0.0016 ~ 0.0016, Batch=96
|
|
train[2019-04-01-09:45:33] Epoch: [505][000/521] Time 0.88 (0.88) Data 0.61 (0.61) Loss 0.056 (0.056) Prec@1 100.00 (100.00) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:45:57] Epoch: [505][100/521] Time 0.24 (0.25) Data 0.00 (0.01) Loss 0.131 (0.085) Prec@1 95.83 (98.53) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-09:46:21] Epoch: [505][200/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.159 (0.083) Prec@1 94.79 (98.58) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:46:45] Epoch: [505][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.022 (0.082) Prec@1 100.00 (98.60) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:47:09] Epoch: [505][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.148 (0.079) Prec@1 95.83 (98.64) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:47:33] Epoch: [505][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.062 (0.080) Prec@1 98.96 (98.63) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:47:38] Epoch: [505][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.046 (0.080) Prec@1 100.00 (98.64) Prec@5 100.00 (99.99)
|
|
[2019-04-01-09:47:38] **train** Prec@1 98.64 Prec@5 99.99 Error@1 1.36 Error@5 0.01 Loss:0.080
|
|
test [2019-04-01-09:47:39] Epoch: [505][000/105] Time 0.56 (0.56) Data 0.49 (0.49) Loss 0.093 (0.093) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-09:47:43] Epoch: [505][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.031 (0.134) Prec@1 98.96 (96.81) Prec@5 100.00 (99.95)
|
|
test [2019-04-01-09:47:43] Epoch: [505][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.010 (0.135) Prec@1 100.00 (96.79) Prec@5 100.00 (99.95)
|
|
[2019-04-01-09:47:43] **test** Prec@1 96.79 Prec@5 99.95 Error@1 3.21 Error@5 0.05 Loss:0.135
|
|
----> Best Accuracy : Acc@1=96.91, Acc@5=99.93, Error@1=3.09, Error@5=0.07
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-09:47:43] [Epoch=506/600] [Need: 03:25:18] LR=0.0016 ~ 0.0016, Batch=96
|
|
train[2019-04-01-09:47:44] Epoch: [506][000/521] Time 0.75 (0.75) Data 0.47 (0.47) Loss 0.115 (0.115) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:48:08] Epoch: [506][100/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.057 (0.075) Prec@1 100.00 (98.69) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:48:33] Epoch: [506][200/521] Time 0.28 (0.25) Data 0.00 (0.00) Loss 0.127 (0.076) Prec@1 98.96 (98.73) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:48:57] Epoch: [506][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.069 (0.073) Prec@1 100.00 (98.81) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:49:21] Epoch: [506][400/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.075 (0.074) Prec@1 98.96 (98.76) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:49:45] Epoch: [506][500/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.055 (0.076) Prec@1 100.00 (98.72) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:49:50] Epoch: [506][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.024 (0.076) Prec@1 100.00 (98.73) Prec@5 100.00 (100.00)
|
|
[2019-04-01-09:49:50] **train** Prec@1 98.73 Prec@5 100.00 Error@1 1.27 Error@5 0.00 Loss:0.076
|
|
test [2019-04-01-09:49:51] Epoch: [506][000/105] Time 0.59 (0.59) Data 0.53 (0.53) Loss 0.094 (0.094) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-09:49:55] Epoch: [506][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.055 (0.136) Prec@1 98.96 (96.76) Prec@5 100.00 (99.94)
|
|
test [2019-04-01-09:49:55] Epoch: [506][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.019 (0.136) Prec@1 100.00 (96.76) Prec@5 100.00 (99.94)
|
|
[2019-04-01-09:49:55] **test** Prec@1 96.76 Prec@5 99.94 Error@1 3.24 Error@5 0.06 Loss:0.136
|
|
----> Best Accuracy : Acc@1=96.91, Acc@5=99.93, Error@1=3.09, Error@5=0.07
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-09:49:55] [Epoch=507/600] [Need: 03:24:23] LR=0.0015 ~ 0.0015, Batch=96
|
|
train[2019-04-01-09:49:56] Epoch: [507][000/521] Time 0.74 (0.74) Data 0.45 (0.45) Loss 0.076 (0.076) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:50:20] Epoch: [507][100/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.087 (0.075) Prec@1 97.92 (98.68) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:50:44] Epoch: [507][200/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.037 (0.075) Prec@1 98.96 (98.69) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:51:08] Epoch: [507][300/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.075 (0.074) Prec@1 97.92 (98.73) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:51:33] Epoch: [507][400/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.088 (0.074) Prec@1 98.96 (98.72) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:51:57] Epoch: [507][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.040 (0.075) Prec@1 100.00 (98.71) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:52:02] Epoch: [507][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.031 (0.075) Prec@1 100.00 (98.71) Prec@5 100.00 (99.99)
|
|
[2019-04-01-09:52:02] **train** Prec@1 98.71 Prec@5 99.99 Error@1 1.29 Error@5 0.01 Loss:0.075
|
|
test [2019-04-01-09:52:02] Epoch: [507][000/105] Time 0.47 (0.47) Data 0.40 (0.40) Loss 0.078 (0.078) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-09:52:06] Epoch: [507][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.069 (0.131) Prec@1 97.92 (96.84) Prec@5 100.00 (99.96)
|
|
test [2019-04-01-09:52:06] Epoch: [507][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.003 (0.131) Prec@1 100.00 (96.84) Prec@5 100.00 (99.96)
|
|
[2019-04-01-09:52:06] **test** Prec@1 96.84 Prec@5 99.96 Error@1 3.16 Error@5 0.04 Loss:0.131
|
|
----> Best Accuracy : Acc@1=96.91, Acc@5=99.93, Error@1=3.09, Error@5=0.07
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-09:52:07] [Epoch=508/600] [Need: 03:21:48] LR=0.0015 ~ 0.0015, Batch=96
|
|
train[2019-04-01-09:52:08] Epoch: [508][000/521] Time 0.87 (0.87) Data 0.57 (0.57) Loss 0.090 (0.090) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:52:32] Epoch: [508][100/521] Time 0.23 (0.25) Data 0.00 (0.01) Loss 0.038 (0.073) Prec@1 100.00 (98.81) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:52:56] Epoch: [508][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.092 (0.074) Prec@1 98.96 (98.77) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:53:20] Epoch: [508][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.038 (0.073) Prec@1 98.96 (98.80) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:53:44] Epoch: [508][400/521] Time 0.26 (0.24) Data 0.00 (0.00) Loss 0.074 (0.074) Prec@1 100.00 (98.79) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:54:09] Epoch: [508][500/521] Time 0.26 (0.24) Data 0.00 (0.00) Loss 0.155 (0.076) Prec@1 96.88 (98.74) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:54:13] Epoch: [508][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.044 (0.076) Prec@1 100.00 (98.74) Prec@5 100.00 (99.99)
|
|
[2019-04-01-09:54:14] **train** Prec@1 98.74 Prec@5 99.99 Error@1 1.26 Error@5 0.01 Loss:0.076
|
|
test [2019-04-01-09:54:14] Epoch: [508][000/105] Time 0.48 (0.48) Data 0.41 (0.41) Loss 0.102 (0.102) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-09:54:18] Epoch: [508][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.066 (0.143) Prec@1 97.92 (96.70) Prec@5 100.00 (99.92)
|
|
test [2019-04-01-09:54:18] Epoch: [508][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.082 (0.144) Prec@1 93.75 (96.65) Prec@5 100.00 (99.92)
|
|
[2019-04-01-09:54:18] **test** Prec@1 96.65 Prec@5 99.92 Error@1 3.35 Error@5 0.08 Loss:0.144
|
|
----> Best Accuracy : Acc@1=96.91, Acc@5=99.93, Error@1=3.09, Error@5=0.07
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-09:54:19] [Epoch=509/600] [Need: 03:20:02] LR=0.0015 ~ 0.0015, Batch=96
|
|
train[2019-04-01-09:54:19] Epoch: [509][000/521] Time 0.85 (0.85) Data 0.56 (0.56) Loss 0.116 (0.116) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:54:44] Epoch: [509][100/521] Time 0.24 (0.25) Data 0.00 (0.01) Loss 0.064 (0.077) Prec@1 98.96 (98.78) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:55:08] Epoch: [509][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.126 (0.076) Prec@1 98.96 (98.81) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:55:32] Epoch: [509][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.046 (0.077) Prec@1 98.96 (98.81) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:55:56] Epoch: [509][400/521] Time 0.26 (0.24) Data 0.00 (0.00) Loss 0.039 (0.076) Prec@1 100.00 (98.81) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:56:20] Epoch: [509][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.052 (0.076) Prec@1 100.00 (98.79) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:56:25] Epoch: [509][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.071 (0.077) Prec@1 100.00 (98.78) Prec@5 100.00 (100.00)
|
|
[2019-04-01-09:56:25] **train** Prec@1 98.78 Prec@5 100.00 Error@1 1.22 Error@5 0.00 Loss:0.077
|
|
test [2019-04-01-09:56:26] Epoch: [509][000/105] Time 0.54 (0.54) Data 0.47 (0.47) Loss 0.095 (0.095) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-09:56:30] Epoch: [509][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.064 (0.140) Prec@1 96.88 (96.79) Prec@5 100.00 (99.91)
|
|
test [2019-04-01-09:56:30] Epoch: [509][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.018 (0.141) Prec@1 100.00 (96.76) Prec@5 100.00 (99.91)
|
|
[2019-04-01-09:56:30] **test** Prec@1 96.76 Prec@5 99.91 Error@1 3.24 Error@5 0.09 Loss:0.141
|
|
----> Best Accuracy : Acc@1=96.91, Acc@5=99.93, Error@1=3.09, Error@5=0.07
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-09:56:30] [Epoch=510/600] [Need: 03:17:45] LR=0.0015 ~ 0.0015, Batch=96
|
|
train[2019-04-01-09:56:31] Epoch: [510][000/521] Time 0.74 (0.74) Data 0.45 (0.45) Loss 0.066 (0.066) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:56:55] Epoch: [510][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.081 (0.073) Prec@1 98.96 (98.81) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:57:19] Epoch: [510][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.031 (0.073) Prec@1 98.96 (98.79) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:57:43] Epoch: [510][300/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.061 (0.072) Prec@1 100.00 (98.76) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:58:07] Epoch: [510][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.067 (0.071) Prec@1 98.96 (98.77) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:58:31] Epoch: [510][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.069 (0.073) Prec@1 96.88 (98.74) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:58:36] Epoch: [510][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.119 (0.073) Prec@1 98.75 (98.73) Prec@5 100.00 (100.00)
|
|
[2019-04-01-09:58:36] **train** Prec@1 98.73 Prec@5 100.00 Error@1 1.27 Error@5 0.00 Loss:0.073
|
|
test [2019-04-01-09:58:36] Epoch: [510][000/105] Time 0.51 (0.51) Data 0.44 (0.44) Loss 0.080 (0.080) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-09:58:41] Epoch: [510][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.101 (0.135) Prec@1 96.88 (96.76) Prec@5 100.00 (99.94)
|
|
test [2019-04-01-09:58:41] Epoch: [510][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.032 (0.137) Prec@1 100.00 (96.73) Prec@5 100.00 (99.94)
|
|
[2019-04-01-09:58:41] **test** Prec@1 96.73 Prec@5 99.94 Error@1 3.27 Error@5 0.06 Loss:0.137
|
|
----> Best Accuracy : Acc@1=96.91, Acc@5=99.93, Error@1=3.09, Error@5=0.07
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-09:58:41] [Epoch=511/600] [Need: 03:13:40] LR=0.0014 ~ 0.0014, Batch=96
|
|
train[2019-04-01-09:58:42] Epoch: [511][000/521] Time 0.74 (0.74) Data 0.46 (0.46) Loss 0.117 (0.117) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-09:59:06] Epoch: [511][100/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.027 (0.075) Prec@1 100.00 (98.68) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:59:30] Epoch: [511][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.070 (0.072) Prec@1 100.00 (98.81) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-09:59:54] Epoch: [511][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.086 (0.071) Prec@1 98.96 (98.85) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-10:00:18] Epoch: [511][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.037 (0.070) Prec@1 100.00 (98.88) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-10:00:42] Epoch: [511][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.074 (0.072) Prec@1 98.96 (98.85) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-10:00:47] Epoch: [511][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.060 (0.071) Prec@1 98.75 (98.85) Prec@5 100.00 (99.99)
|
|
[2019-04-01-10:00:47] **train** Prec@1 98.85 Prec@5 99.99 Error@1 1.15 Error@5 0.01 Loss:0.071
|
|
test [2019-04-01-10:00:47] Epoch: [511][000/105] Time 0.59 (0.59) Data 0.53 (0.53) Loss 0.130 (0.130) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-10:00:51] Epoch: [511][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.111 (0.135) Prec@1 95.83 (96.81) Prec@5 100.00 (99.94)
|
|
test [2019-04-01-10:00:51] Epoch: [511][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.004 (0.136) Prec@1 100.00 (96.81) Prec@5 100.00 (99.94)
|
|
[2019-04-01-10:00:52] **test** Prec@1 96.81 Prec@5 99.94 Error@1 3.19 Error@5 0.06 Loss:0.136
|
|
----> Best Accuracy : Acc@1=96.91, Acc@5=99.93, Error@1=3.09, Error@5=0.07
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-10:00:52] [Epoch=512/600] [Need: 03:11:48] LR=0.0014 ~ 0.0014, Batch=96
|
|
train[2019-04-01-10:00:53] Epoch: [512][000/521] Time 0.85 (0.85) Data 0.56 (0.56) Loss 0.153 (0.153) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:01:16] Epoch: [512][100/521] Time 0.24 (0.25) Data 0.00 (0.01) Loss 0.083 (0.069) Prec@1 97.92 (98.87) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:01:41] Epoch: [512][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.033 (0.070) Prec@1 100.00 (98.88) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-10:02:05] Epoch: [512][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.069 (0.070) Prec@1 98.96 (98.84) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:02:29] Epoch: [512][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.065 (0.070) Prec@1 98.96 (98.82) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:02:53] Epoch: [512][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.049 (0.070) Prec@1 98.96 (98.80) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:02:58] Epoch: [512][520/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.128 (0.071) Prec@1 98.75 (98.81) Prec@5 100.00 (100.00)
|
|
[2019-04-01-10:02:58] **train** Prec@1 98.81 Prec@5 100.00 Error@1 1.19 Error@5 0.00 Loss:0.071
|
|
test [2019-04-01-10:02:58] Epoch: [512][000/105] Time 0.48 (0.48) Data 0.42 (0.42) Loss 0.096 (0.096) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-10:03:03] Epoch: [512][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.077 (0.132) Prec@1 97.92 (97.04) Prec@5 100.00 (99.91)
|
|
test [2019-04-01-10:03:03] Epoch: [512][104/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.001 (0.132) Prec@1 100.00 (97.02) Prec@5 100.00 (99.91)
|
|
[2019-04-01-10:03:03] **test** Prec@1 97.02 Prec@5 99.91 Error@1 2.98 Error@5 0.09 Loss:0.132
|
|
----> Best Accuracy : Acc@1=97.02, Acc@5=99.91, Error@1=2.98, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-10:03:03] [Epoch=513/600] [Need: 03:10:21] LR=0.0014 ~ 0.0014, Batch=96
|
|
train[2019-04-01-10:03:04] Epoch: [513][000/521] Time 0.71 (0.71) Data 0.43 (0.43) Loss 0.038 (0.038) Prec@1 100.00 (100.00) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:03:28] Epoch: [513][100/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.128 (0.070) Prec@1 97.92 (98.77) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:03:52] Epoch: [513][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.068 (0.073) Prec@1 98.96 (98.71) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-10:04:16] Epoch: [513][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.058 (0.073) Prec@1 98.96 (98.75) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-10:04:40] Epoch: [513][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.081 (0.070) Prec@1 98.96 (98.82) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-10:05:04] Epoch: [513][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.032 (0.071) Prec@1 100.00 (98.81) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:05:09] Epoch: [513][520/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.102 (0.071) Prec@1 97.50 (98.81) Prec@5 100.00 (99.99)
|
|
[2019-04-01-10:05:09] **train** Prec@1 98.81 Prec@5 99.99 Error@1 1.19 Error@5 0.01 Loss:0.071
|
|
test [2019-04-01-10:05:10] Epoch: [513][000/105] Time 0.48 (0.48) Data 0.41 (0.41) Loss 0.117 (0.117) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-10:05:14] Epoch: [513][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.073 (0.136) Prec@1 97.92 (96.83) Prec@5 100.00 (99.93)
|
|
test [2019-04-01-10:05:14] Epoch: [513][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.023 (0.137) Prec@1 100.00 (96.82) Prec@5 100.00 (99.93)
|
|
[2019-04-01-10:05:14] **test** Prec@1 96.82 Prec@5 99.93 Error@1 3.18 Error@5 0.07 Loss:0.137
|
|
----> Best Accuracy : Acc@1=97.02, Acc@5=99.91, Error@1=2.98, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-10:05:14] [Epoch=514/600] [Need: 03:08:00] LR=0.0013 ~ 0.0013, Batch=96
|
|
train[2019-04-01-10:05:15] Epoch: [514][000/521] Time 0.80 (0.80) Data 0.52 (0.52) Loss 0.067 (0.067) Prec@1 100.00 (100.00) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:05:39] Epoch: [514][100/521] Time 0.24 (0.25) Data 0.00 (0.01) Loss 0.062 (0.072) Prec@1 98.96 (98.86) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:06:03] Epoch: [514][200/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.110 (0.071) Prec@1 96.88 (98.82) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-10:06:28] Epoch: [514][300/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.042 (0.072) Prec@1 98.96 (98.78) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:06:52] Epoch: [514][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.045 (0.071) Prec@1 98.96 (98.82) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:07:16] Epoch: [514][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.089 (0.072) Prec@1 97.92 (98.79) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:07:21] Epoch: [514][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.052 (0.071) Prec@1 100.00 (98.80) Prec@5 100.00 (100.00)
|
|
[2019-04-01-10:07:21] **train** Prec@1 98.80 Prec@5 100.00 Error@1 1.20 Error@5 0.00 Loss:0.071
|
|
test [2019-04-01-10:07:22] Epoch: [514][000/105] Time 0.59 (0.59) Data 0.53 (0.53) Loss 0.066 (0.066) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-10:07:26] Epoch: [514][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.069 (0.137) Prec@1 97.92 (96.85) Prec@5 100.00 (99.94)
|
|
test [2019-04-01-10:07:26] Epoch: [514][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.007 (0.137) Prec@1 100.00 (96.85) Prec@5 100.00 (99.94)
|
|
[2019-04-01-10:07:26] **test** Prec@1 96.85 Prec@5 99.94 Error@1 3.15 Error@5 0.06 Loss:0.137
|
|
----> Best Accuracy : Acc@1=97.02, Acc@5=99.91, Error@1=2.98, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-10:07:26] [Epoch=515/600] [Need: 03:07:12] LR=0.0013 ~ 0.0013, Batch=96
|
|
train[2019-04-01-10:07:27] Epoch: [515][000/521] Time 0.72 (0.72) Data 0.44 (0.44) Loss 0.043 (0.043) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:07:51] Epoch: [515][100/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.072 (0.070) Prec@1 97.92 (98.87) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:08:15] Epoch: [515][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.107 (0.072) Prec@1 96.88 (98.75) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-10:08:39] Epoch: [515][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.046 (0.069) Prec@1 100.00 (98.82) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-10:09:03] Epoch: [515][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.086 (0.071) Prec@1 98.96 (98.81) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-10:09:28] Epoch: [515][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.022 (0.072) Prec@1 100.00 (98.79) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-10:09:32] Epoch: [515][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.090 (0.072) Prec@1 97.50 (98.79) Prec@5 100.00 (99.99)
|
|
[2019-04-01-10:09:32] **train** Prec@1 98.79 Prec@5 99.99 Error@1 1.21 Error@5 0.01 Loss:0.072
|
|
test [2019-04-01-10:09:33] Epoch: [515][000/105] Time 0.46 (0.46) Data 0.40 (0.40) Loss 0.092 (0.092) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-10:09:37] Epoch: [515][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.057 (0.132) Prec@1 98.96 (96.91) Prec@5 100.00 (99.95)
|
|
test [2019-04-01-10:09:37] Epoch: [515][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.006 (0.132) Prec@1 100.00 (96.90) Prec@5 100.00 (99.95)
|
|
[2019-04-01-10:09:37] **test** Prec@1 96.90 Prec@5 99.95 Error@1 3.10 Error@5 0.05 Loss:0.132
|
|
----> Best Accuracy : Acc@1=97.02, Acc@5=99.91, Error@1=2.98, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-10:09:37] [Epoch=516/600] [Need: 03:03:31] LR=0.0013 ~ 0.0013, Batch=96
|
|
train[2019-04-01-10:09:38] Epoch: [516][000/521] Time 0.82 (0.82) Data 0.54 (0.54) Loss 0.074 (0.074) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:10:02] Epoch: [516][100/521] Time 0.24 (0.25) Data 0.00 (0.01) Loss 0.067 (0.068) Prec@1 98.96 (98.78) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-10:10:27] Epoch: [516][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.081 (0.070) Prec@1 97.92 (98.82) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-10:10:51] Epoch: [516][300/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.040 (0.070) Prec@1 100.00 (98.82) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-10:11:15] Epoch: [516][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.099 (0.070) Prec@1 97.92 (98.82) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-10:11:39] Epoch: [516][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.060 (0.070) Prec@1 98.96 (98.83) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-10:11:44] Epoch: [516][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.080 (0.070) Prec@1 98.75 (98.83) Prec@5 100.00 (99.99)
|
|
[2019-04-01-10:11:44] **train** Prec@1 98.83 Prec@5 99.99 Error@1 1.17 Error@5 0.01 Loss:0.070
|
|
test [2019-04-01-10:11:45] Epoch: [516][000/105] Time 0.47 (0.47) Data 0.41 (0.41) Loss 0.097 (0.097) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-10:11:49] Epoch: [516][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.081 (0.131) Prec@1 98.96 (97.05) Prec@5 100.00 (99.94)
|
|
test [2019-04-01-10:11:49] Epoch: [516][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.073 (0.132) Prec@1 93.75 (97.00) Prec@5 100.00 (99.94)
|
|
[2019-04-01-10:11:49] **test** Prec@1 97.00 Prec@5 99.94 Error@1 3.00 Error@5 0.06 Loss:0.132
|
|
----> Best Accuracy : Acc@1=97.02, Acc@5=99.91, Error@1=2.98, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-10:11:49] [Epoch=517/600] [Need: 03:02:01] LR=0.0013 ~ 0.0013, Batch=96
|
|
train[2019-04-01-10:11:50] Epoch: [517][000/521] Time 0.72 (0.72) Data 0.44 (0.44) Loss 0.067 (0.067) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:12:14] Epoch: [517][100/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.137 (0.070) Prec@1 97.92 (98.86) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:12:38] Epoch: [517][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.102 (0.069) Prec@1 97.92 (98.88) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:13:02] Epoch: [517][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.056 (0.068) Prec@1 98.96 (98.88) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-10:13:26] Epoch: [517][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.144 (0.069) Prec@1 94.79 (98.81) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-10:13:51] Epoch: [517][500/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.066 (0.069) Prec@1 98.96 (98.83) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-10:13:55] Epoch: [517][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.075 (0.069) Prec@1 98.75 (98.81) Prec@5 100.00 (99.99)
|
|
[2019-04-01-10:13:55] **train** Prec@1 98.81 Prec@5 99.99 Error@1 1.19 Error@5 0.01 Loss:0.069
|
|
test [2019-04-01-10:13:56] Epoch: [517][000/105] Time 0.47 (0.47) Data 0.40 (0.40) Loss 0.025 (0.025) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-10:14:00] Epoch: [517][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.100 (0.142) Prec@1 97.92 (96.78) Prec@5 100.00 (99.93)
|
|
test [2019-04-01-10:14:00] Epoch: [517][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.002 (0.142) Prec@1 100.00 (96.78) Prec@5 100.00 (99.93)
|
|
[2019-04-01-10:14:00] **test** Prec@1 96.78 Prec@5 99.93 Error@1 3.22 Error@5 0.07 Loss:0.142
|
|
----> Best Accuracy : Acc@1=97.02, Acc@5=99.91, Error@1=2.98, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-10:14:00] [Epoch=518/600] [Need: 02:59:31] LR=0.0012 ~ 0.0012, Batch=96
|
|
train[2019-04-01-10:14:01] Epoch: [518][000/521] Time 0.73 (0.73) Data 0.45 (0.45) Loss 0.068 (0.068) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:14:25] Epoch: [518][100/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.095 (0.071) Prec@1 96.88 (98.74) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:14:49] Epoch: [518][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.033 (0.070) Prec@1 100.00 (98.83) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:15:14] Epoch: [518][300/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.148 (0.069) Prec@1 96.88 (98.86) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:15:37] Epoch: [518][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.042 (0.067) Prec@1 98.96 (98.86) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:16:02] Epoch: [518][500/521] Time 0.26 (0.24) Data 0.00 (0.00) Loss 0.120 (0.067) Prec@1 96.88 (98.87) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:16:06] Epoch: [518][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.075 (0.068) Prec@1 100.00 (98.87) Prec@5 100.00 (100.00)
|
|
[2019-04-01-10:16:06] **train** Prec@1 98.87 Prec@5 100.00 Error@1 1.13 Error@5 0.00 Loss:0.068
|
|
test [2019-04-01-10:16:07] Epoch: [518][000/105] Time 0.61 (0.61) Data 0.55 (0.55) Loss 0.081 (0.081) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-10:16:11] Epoch: [518][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.055 (0.132) Prec@1 97.92 (96.86) Prec@5 100.00 (99.96)
|
|
test [2019-04-01-10:16:11] Epoch: [518][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.005 (0.132) Prec@1 100.00 (96.86) Prec@5 100.00 (99.96)
|
|
[2019-04-01-10:16:11] **test** Prec@1 96.86 Prec@5 99.96 Error@1 3.14 Error@5 0.04 Loss:0.132
|
|
----> Best Accuracy : Acc@1=97.02, Acc@5=99.91, Error@1=2.98, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-10:16:12] [Epoch=519/600] [Need: 02:57:03] LR=0.0012 ~ 0.0012, Batch=96
|
|
train[2019-04-01-10:16:12] Epoch: [519][000/521] Time 0.86 (0.86) Data 0.59 (0.59) Loss 0.031 (0.031) Prec@1 100.00 (100.00) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:16:37] Epoch: [519][100/521] Time 0.24 (0.25) Data 0.00 (0.01) Loss 0.098 (0.060) Prec@1 97.92 (99.15) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:17:01] Epoch: [519][200/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.074 (0.064) Prec@1 97.92 (99.01) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:17:25] Epoch: [519][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.056 (0.062) Prec@1 97.92 (99.04) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:17:49] Epoch: [519][400/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.058 (0.062) Prec@1 100.00 (99.04) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:18:13] Epoch: [519][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.106 (0.064) Prec@1 97.92 (99.01) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:18:18] Epoch: [519][520/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.092 (0.063) Prec@1 98.75 (99.02) Prec@5 100.00 (100.00)
|
|
[2019-04-01-10:18:18] **train** Prec@1 99.02 Prec@5 100.00 Error@1 0.98 Error@5 0.00 Loss:0.063
|
|
test [2019-04-01-10:18:18] Epoch: [519][000/105] Time 0.61 (0.61) Data 0.55 (0.55) Loss 0.078 (0.078) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-10:18:22] Epoch: [519][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.052 (0.131) Prec@1 98.96 (96.94) Prec@5 100.00 (99.92)
|
|
test [2019-04-01-10:18:23] Epoch: [519][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.005 (0.132) Prec@1 100.00 (96.95) Prec@5 100.00 (99.92)
|
|
[2019-04-01-10:18:23] **test** Prec@1 96.95 Prec@5 99.92 Error@1 3.05 Error@5 0.08 Loss:0.132
|
|
----> Best Accuracy : Acc@1=97.02, Acc@5=99.91, Error@1=2.98, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-10:18:23] [Epoch=520/600] [Need: 02:55:07] LR=0.0012 ~ 0.0012, Batch=96
|
|
train[2019-04-01-10:18:24] Epoch: [520][000/521] Time 0.74 (0.74) Data 0.44 (0.44) Loss 0.110 (0.110) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:18:48] Epoch: [520][100/521] Time 0.28 (0.25) Data 0.00 (0.00) Loss 0.104 (0.070) Prec@1 96.88 (98.80) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:19:12] Epoch: [520][200/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.051 (0.067) Prec@1 98.96 (98.92) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:19:36] Epoch: [520][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.045 (0.067) Prec@1 100.00 (98.91) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:20:00] Epoch: [520][400/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.080 (0.065) Prec@1 100.00 (98.94) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:20:25] Epoch: [520][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.073 (0.066) Prec@1 98.96 (98.95) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:20:29] Epoch: [520][520/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.064 (0.066) Prec@1 98.75 (98.94) Prec@5 100.00 (100.00)
|
|
[2019-04-01-10:20:30] **train** Prec@1 98.94 Prec@5 100.00 Error@1 1.06 Error@5 0.00 Loss:0.066
|
|
test [2019-04-01-10:20:30] Epoch: [520][000/105] Time 0.49 (0.49) Data 0.43 (0.43) Loss 0.077 (0.077) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-10:20:34] Epoch: [520][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.047 (0.133) Prec@1 97.92 (96.82) Prec@5 100.00 (99.95)
|
|
test [2019-04-01-10:20:34] Epoch: [520][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.007 (0.134) Prec@1 100.00 (96.83) Prec@5 100.00 (99.95)
|
|
[2019-04-01-10:20:34] **test** Prec@1 96.83 Prec@5 99.95 Error@1 3.17 Error@5 0.05 Loss:0.134
|
|
----> Best Accuracy : Acc@1=97.02, Acc@5=99.91, Error@1=2.98, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-10:20:35] [Epoch=521/600] [Need: 02:53:24] LR=0.0012 ~ 0.0012, Batch=96
|
|
train[2019-04-01-10:20:35] Epoch: [521][000/521] Time 0.72 (0.72) Data 0.43 (0.43) Loss 0.042 (0.042) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:20:59] Epoch: [521][100/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.027 (0.067) Prec@1 100.00 (98.88) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-10:21:23] Epoch: [521][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.035 (0.069) Prec@1 100.00 (98.85) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-10:21:48] Epoch: [521][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.073 (0.066) Prec@1 98.96 (98.93) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:22:12] Epoch: [521][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.057 (0.065) Prec@1 98.96 (98.95) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-10:22:36] Epoch: [521][500/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.133 (0.065) Prec@1 96.88 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:22:41] Epoch: [521][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.046 (0.065) Prec@1 98.75 (98.96) Prec@5 100.00 (100.00)
|
|
[2019-04-01-10:22:41] **train** Prec@1 98.96 Prec@5 100.00 Error@1 1.04 Error@5 0.00 Loss:0.065
|
|
test [2019-04-01-10:22:41] Epoch: [521][000/105] Time 0.60 (0.60) Data 0.54 (0.54) Loss 0.092 (0.092) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-10:22:45] Epoch: [521][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.076 (0.138) Prec@1 96.88 (96.92) Prec@5 100.00 (99.94)
|
|
test [2019-04-01-10:22:46] Epoch: [521][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.005 (0.138) Prec@1 100.00 (96.88) Prec@5 100.00 (99.94)
|
|
[2019-04-01-10:22:46] **test** Prec@1 96.88 Prec@5 99.94 Error@1 3.12 Error@5 0.06 Loss:0.138
|
|
----> Best Accuracy : Acc@1=97.02, Acc@5=99.91, Error@1=2.98, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-10:22:46] [Epoch=522/600] [Need: 02:50:43] LR=0.0011 ~ 0.0011, Batch=96
|
|
train[2019-04-01-10:22:47] Epoch: [522][000/521] Time 0.83 (0.83) Data 0.56 (0.56) Loss 0.045 (0.045) Prec@1 100.00 (100.00) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:23:11] Epoch: [522][100/521] Time 0.25 (0.25) Data 0.00 (0.01) Loss 0.057 (0.061) Prec@1 98.96 (98.99) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:23:35] Epoch: [522][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.146 (0.062) Prec@1 97.92 (99.03) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-10:23:59] Epoch: [522][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.054 (0.062) Prec@1 98.96 (98.97) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:24:23] Epoch: [522][400/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.059 (0.061) Prec@1 100.00 (99.00) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:24:47] Epoch: [522][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.069 (0.062) Prec@1 97.92 (98.97) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:24:52] Epoch: [522][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.027 (0.062) Prec@1 100.00 (98.97) Prec@5 100.00 (100.00)
|
|
[2019-04-01-10:24:52] **train** Prec@1 98.97 Prec@5 100.00 Error@1 1.03 Error@5 0.00 Loss:0.062
|
|
test [2019-04-01-10:24:53] Epoch: [522][000/105] Time 0.62 (0.62) Data 0.56 (0.56) Loss 0.111 (0.111) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-10:24:57] Epoch: [522][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.062 (0.136) Prec@1 98.96 (97.08) Prec@5 100.00 (99.94)
|
|
test [2019-04-01-10:24:57] Epoch: [522][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.001 (0.137) Prec@1 100.00 (97.07) Prec@5 100.00 (99.94)
|
|
[2019-04-01-10:24:57] **test** Prec@1 97.07 Prec@5 99.94 Error@1 2.93 Error@5 0.06 Loss:0.137
|
|
----> Best Accuracy : Acc@1=97.07, Acc@5=99.94, Error@1=2.93, Error@5=0.06
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-10:24:57] [Epoch=523/600] [Need: 02:48:48] LR=0.0011 ~ 0.0011, Batch=96
|
|
train[2019-04-01-10:24:58] Epoch: [523][000/521] Time 0.86 (0.86) Data 0.59 (0.59) Loss 0.097 (0.097) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:25:23] Epoch: [523][100/521] Time 0.24 (0.25) Data 0.00 (0.01) Loss 0.032 (0.068) Prec@1 100.00 (98.90) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-10:25:47] Epoch: [523][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.025 (0.066) Prec@1 100.00 (98.87) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-10:26:11] Epoch: [523][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.042 (0.067) Prec@1 100.00 (98.86) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-10:26:35] Epoch: [523][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.058 (0.067) Prec@1 98.96 (98.88) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-10:27:00] Epoch: [523][500/521] Time 0.26 (0.24) Data 0.00 (0.00) Loss 0.116 (0.066) Prec@1 95.83 (98.89) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:27:04] Epoch: [523][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.075 (0.066) Prec@1 100.00 (98.89) Prec@5 100.00 (100.00)
|
|
[2019-04-01-10:27:04] **train** Prec@1 98.89 Prec@5 100.00 Error@1 1.11 Error@5 0.00 Loss:0.066
|
|
test [2019-04-01-10:27:05] Epoch: [523][000/105] Time 0.55 (0.55) Data 0.48 (0.48) Loss 0.087 (0.087) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-10:27:09] Epoch: [523][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.072 (0.138) Prec@1 97.92 (96.71) Prec@5 100.00 (99.95)
|
|
test [2019-04-01-10:27:09] Epoch: [523][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.003 (0.138) Prec@1 100.00 (96.70) Prec@5 100.00 (99.95)
|
|
[2019-04-01-10:27:09] **test** Prec@1 96.70 Prec@5 99.95 Error@1 3.30 Error@5 0.05 Loss:0.138
|
|
----> Best Accuracy : Acc@1=97.07, Acc@5=99.94, Error@1=2.93, Error@5=0.06
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-10:27:09] [Epoch=524/600] [Need: 02:47:10] LR=0.0011 ~ 0.0011, Batch=96
|
|
train[2019-04-01-10:27:10] Epoch: [524][000/521] Time 0.80 (0.80) Data 0.52 (0.52) Loss 0.057 (0.057) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:27:34] Epoch: [524][100/521] Time 0.26 (0.25) Data 0.00 (0.01) Loss 0.052 (0.062) Prec@1 100.00 (99.06) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-10:27:59] Epoch: [524][200/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.038 (0.062) Prec@1 98.96 (99.05) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-10:28:23] Epoch: [524][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.050 (0.062) Prec@1 98.96 (99.01) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-10:28:47] Epoch: [524][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.099 (0.063) Prec@1 97.92 (99.00) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-10:29:11] Epoch: [524][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.080 (0.065) Prec@1 98.96 (98.92) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-10:29:16] Epoch: [524][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.044 (0.065) Prec@1 100.00 (98.92) Prec@5 100.00 (99.99)
|
|
[2019-04-01-10:29:16] **train** Prec@1 98.92 Prec@5 99.99 Error@1 1.08 Error@5 0.01 Loss:0.065
|
|
test [2019-04-01-10:29:16] Epoch: [524][000/105] Time 0.61 (0.61) Data 0.55 (0.55) Loss 0.097 (0.097) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-10:29:20] Epoch: [524][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.058 (0.133) Prec@1 98.96 (97.01) Prec@5 100.00 (99.94)
|
|
test [2019-04-01-10:29:21] Epoch: [524][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.002 (0.134) Prec@1 100.00 (97.00) Prec@5 100.00 (99.94)
|
|
[2019-04-01-10:29:21] **test** Prec@1 97.00 Prec@5 99.94 Error@1 3.00 Error@5 0.06 Loss:0.134
|
|
----> Best Accuracy : Acc@1=97.07, Acc@5=99.94, Error@1=2.93, Error@5=0.06
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-10:29:21] [Epoch=525/600] [Need: 02:44:21] LR=0.0010 ~ 0.0010, Batch=96
|
|
train[2019-04-01-10:29:22] Epoch: [525][000/521] Time 0.85 (0.85) Data 0.57 (0.57) Loss 0.100 (0.100) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:29:46] Epoch: [525][100/521] Time 0.24 (0.25) Data 0.00 (0.01) Loss 0.084 (0.071) Prec@1 98.96 (98.73) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:30:10] Epoch: [525][200/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.038 (0.072) Prec@1 100.00 (98.78) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:30:34] Epoch: [525][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.034 (0.069) Prec@1 100.00 (98.81) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:30:58] Epoch: [525][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.048 (0.067) Prec@1 100.00 (98.88) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:31:22] Epoch: [525][500/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.089 (0.066) Prec@1 97.92 (98.93) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:31:27] Epoch: [525][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.051 (0.066) Prec@1 98.75 (98.93) Prec@5 100.00 (100.00)
|
|
[2019-04-01-10:31:27] **train** Prec@1 98.93 Prec@5 100.00 Error@1 1.07 Error@5 0.00 Loss:0.066
|
|
test [2019-04-01-10:31:28] Epoch: [525][000/105] Time 0.64 (0.64) Data 0.57 (0.57) Loss 0.119 (0.119) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-10:31:32] Epoch: [525][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.056 (0.134) Prec@1 97.92 (96.96) Prec@5 100.00 (99.93)
|
|
test [2019-04-01-10:31:32] Epoch: [525][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.003 (0.135) Prec@1 100.00 (96.92) Prec@5 100.00 (99.93)
|
|
[2019-04-01-10:31:32] **test** Prec@1 96.92 Prec@5 99.93 Error@1 3.08 Error@5 0.07 Loss:0.135
|
|
----> Best Accuracy : Acc@1=97.07, Acc@5=99.94, Error@1=2.93, Error@5=0.06
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-10:31:32] [Epoch=526/600] [Need: 02:41:51] LR=0.0010 ~ 0.0010, Batch=96
|
|
train[2019-04-01-10:31:33] Epoch: [526][000/521] Time 0.75 (0.75) Data 0.47 (0.47) Loss 0.113 (0.113) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:31:57] Epoch: [526][100/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.039 (0.063) Prec@1 98.96 (98.98) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-10:32:21] Epoch: [526][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.036 (0.063) Prec@1 100.00 (99.02) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-10:32:45] Epoch: [526][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.055 (0.063) Prec@1 100.00 (99.02) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-10:33:09] Epoch: [526][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.052 (0.062) Prec@1 98.96 (99.02) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-10:33:33] Epoch: [526][500/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.053 (0.064) Prec@1 100.00 (98.97) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-10:33:38] Epoch: [526][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.041 (0.064) Prec@1 100.00 (98.98) Prec@5 100.00 (99.99)
|
|
[2019-04-01-10:33:38] **train** Prec@1 98.98 Prec@5 99.99 Error@1 1.02 Error@5 0.01 Loss:0.064
|
|
test [2019-04-01-10:33:39] Epoch: [526][000/105] Time 0.64 (0.64) Data 0.57 (0.57) Loss 0.059 (0.059) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-10:33:43] Epoch: [526][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.036 (0.136) Prec@1 98.96 (96.85) Prec@5 100.00 (99.90)
|
|
test [2019-04-01-10:33:43] Epoch: [526][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.005 (0.138) Prec@1 100.00 (96.82) Prec@5 100.00 (99.90)
|
|
[2019-04-01-10:33:43] **test** Prec@1 96.82 Prec@5 99.90 Error@1 3.18 Error@5 0.10 Loss:0.138
|
|
----> Best Accuracy : Acc@1=97.07, Acc@5=99.94, Error@1=2.93, Error@5=0.06
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-10:33:43] [Epoch=527/600] [Need: 02:39:38] LR=0.0010 ~ 0.0010, Batch=96
|
|
train[2019-04-01-10:33:44] Epoch: [527][000/521] Time 0.75 (0.75) Data 0.46 (0.46) Loss 0.037 (0.037) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:34:08] Epoch: [527][100/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.094 (0.063) Prec@1 97.92 (98.95) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:34:32] Epoch: [527][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.138 (0.065) Prec@1 97.92 (98.98) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-10:34:57] Epoch: [527][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.039 (0.065) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:35:21] Epoch: [527][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.119 (0.065) Prec@1 98.96 (98.99) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-10:35:45] Epoch: [527][500/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.054 (0.064) Prec@1 100.00 (98.97) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-10:35:50] Epoch: [527][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.075 (0.064) Prec@1 97.50 (98.98) Prec@5 100.00 (99.99)
|
|
[2019-04-01-10:35:50] **train** Prec@1 98.98 Prec@5 99.99 Error@1 1.02 Error@5 0.01 Loss:0.064
|
|
test [2019-04-01-10:35:51] Epoch: [527][000/105] Time 0.63 (0.63) Data 0.56 (0.56) Loss 0.101 (0.101) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-10:35:55] Epoch: [527][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.064 (0.133) Prec@1 97.92 (97.04) Prec@5 100.00 (99.94)
|
|
test [2019-04-01-10:35:55] Epoch: [527][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.031 (0.133) Prec@1 100.00 (97.04) Prec@5 100.00 (99.94)
|
|
[2019-04-01-10:35:55] **test** Prec@1 97.04 Prec@5 99.94 Error@1 2.96 Error@5 0.06 Loss:0.133
|
|
----> Best Accuracy : Acc@1=97.07, Acc@5=99.94, Error@1=2.93, Error@5=0.06
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-10:35:55] [Epoch=528/600] [Need: 02:38:00] LR=0.0010 ~ 0.0010, Batch=96
|
|
train[2019-04-01-10:35:56] Epoch: [528][000/521] Time 0.71 (0.71) Data 0.44 (0.44) Loss 0.057 (0.057) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:36:20] Epoch: [528][100/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.055 (0.061) Prec@1 98.96 (99.06) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-10:36:44] Epoch: [528][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.041 (0.063) Prec@1 100.00 (99.05) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-10:37:14] Epoch: [528][300/521] Time 0.26 (0.26) Data 0.00 (0.00) Loss 0.055 (0.063) Prec@1 100.00 (99.02) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:37:46] Epoch: [528][400/521] Time 0.30 (0.28) Data 0.00 (0.00) Loss 0.045 (0.063) Prec@1 98.96 (99.00) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-10:38:18] Epoch: [528][500/521] Time 0.34 (0.29) Data 0.00 (0.00) Loss 0.169 (0.064) Prec@1 97.92 (98.99) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:38:24] Epoch: [528][520/521] Time 0.23 (0.29) Data 0.00 (0.00) Loss 0.070 (0.064) Prec@1 98.75 (98.98) Prec@5 100.00 (100.00)
|
|
[2019-04-01-10:38:25] **train** Prec@1 98.98 Prec@5 100.00 Error@1 1.02 Error@5 0.00 Loss:0.064
|
|
test [2019-04-01-10:38:26] Epoch: [528][000/105] Time 0.96 (0.96) Data 0.87 (0.87) Loss 0.078 (0.078) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-10:38:32] Epoch: [528][100/105] Time 0.04 (0.07) Data 0.00 (0.01) Loss 0.051 (0.133) Prec@1 98.96 (96.80) Prec@5 100.00 (99.93)
|
|
test [2019-04-01-10:38:32] Epoch: [528][104/105] Time 0.03 (0.07) Data 0.00 (0.01) Loss 0.017 (0.135) Prec@1 100.00 (96.76) Prec@5 100.00 (99.93)
|
|
[2019-04-01-10:38:32] **test** Prec@1 96.76 Prec@5 99.93 Error@1 3.24 Error@5 0.07 Loss:0.135
|
|
----> Best Accuracy : Acc@1=97.07, Acc@5=99.94, Error@1=2.93, Error@5=0.06
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-10:38:32] [Epoch=529/600] [Need: 03:06:05] LR=0.0010 ~ 0.0010, Batch=96
|
|
train[2019-04-01-10:38:33] Epoch: [529][000/521] Time 1.04 (1.04) Data 0.69 (0.69) Loss 0.114 (0.114) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:39:03] Epoch: [529][100/521] Time 0.24 (0.30) Data 0.00 (0.01) Loss 0.086 (0.063) Prec@1 96.88 (99.05) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:39:36] Epoch: [529][200/521] Time 0.32 (0.32) Data 0.00 (0.00) Loss 0.110 (0.064) Prec@1 98.96 (98.99) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:40:11] Epoch: [529][300/521] Time 0.29 (0.33) Data 0.00 (0.00) Loss 0.067 (0.063) Prec@1 98.96 (99.01) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:40:44] Epoch: [529][400/521] Time 0.36 (0.33) Data 0.00 (0.00) Loss 0.065 (0.063) Prec@1 98.96 (99.02) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:41:14] Epoch: [529][500/521] Time 0.37 (0.32) Data 0.00 (0.00) Loss 0.112 (0.064) Prec@1 96.88 (98.98) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:41:20] Epoch: [529][520/521] Time 0.26 (0.32) Data 0.00 (0.00) Loss 0.094 (0.064) Prec@1 97.50 (98.99) Prec@5 100.00 (100.00)
|
|
[2019-04-01-10:41:21] **train** Prec@1 98.99 Prec@5 100.00 Error@1 1.01 Error@5 0.00 Loss:0.064
|
|
test [2019-04-01-10:41:21] Epoch: [529][000/105] Time 0.54 (0.54) Data 0.48 (0.48) Loss 0.059 (0.059) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-10:41:25] Epoch: [529][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.086 (0.132) Prec@1 97.92 (97.00) Prec@5 100.00 (99.92)
|
|
test [2019-04-01-10:41:25] Epoch: [529][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.015 (0.134) Prec@1 100.00 (96.97) Prec@5 100.00 (99.92)
|
|
[2019-04-01-10:41:25] **test** Prec@1 96.97 Prec@5 99.92 Error@1 3.03 Error@5 0.08 Loss:0.134
|
|
----> Best Accuracy : Acc@1=97.07, Acc@5=99.94, Error@1=2.93, Error@5=0.06
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-10:41:26] [Epoch=530/600] [Need: 03:22:09] LR=0.0009 ~ 0.0009, Batch=96
|
|
train[2019-04-01-10:41:26] Epoch: [530][000/521] Time 0.75 (0.75) Data 0.46 (0.46) Loss 0.044 (0.044) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:41:53] Epoch: [530][100/521] Time 0.28 (0.27) Data 0.01 (0.00) Loss 0.061 (0.058) Prec@1 98.96 (99.13) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:42:27] Epoch: [530][200/521] Time 0.39 (0.31) Data 0.00 (0.00) Loss 0.096 (0.060) Prec@1 97.92 (99.08) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:42:59] Epoch: [530][300/521] Time 0.32 (0.31) Data 0.00 (0.00) Loss 0.032 (0.059) Prec@1 100.00 (99.06) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:43:30] Epoch: [530][400/521] Time 0.35 (0.31) Data 0.00 (0.00) Loss 0.047 (0.061) Prec@1 100.00 (99.04) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:43:58] Epoch: [530][500/521] Time 0.24 (0.30) Data 0.00 (0.00) Loss 0.033 (0.062) Prec@1 98.96 (99.02) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:44:03] Epoch: [530][520/521] Time 0.22 (0.30) Data 0.00 (0.00) Loss 0.045 (0.062) Prec@1 98.75 (99.02) Prec@5 100.00 (100.00)
|
|
[2019-04-01-10:44:04] **train** Prec@1 99.02 Prec@5 100.00 Error@1 0.98 Error@5 0.00 Loss:0.062
|
|
test [2019-04-01-10:44:04] Epoch: [530][000/105] Time 0.48 (0.48) Data 0.41 (0.41) Loss 0.119 (0.119) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-10:44:08] Epoch: [530][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.043 (0.129) Prec@1 98.96 (96.93) Prec@5 100.00 (99.92)
|
|
test [2019-04-01-10:44:08] Epoch: [530][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.004 (0.130) Prec@1 100.00 (96.91) Prec@5 100.00 (99.92)
|
|
[2019-04-01-10:44:08] **test** Prec@1 96.91 Prec@5 99.92 Error@1 3.09 Error@5 0.08 Loss:0.130
|
|
----> Best Accuracy : Acc@1=97.07, Acc@5=99.94, Error@1=2.93, Error@5=0.06
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-10:44:09] [Epoch=531/600] [Need: 03:07:29] LR=0.0009 ~ 0.0009, Batch=96
|
|
train[2019-04-01-10:44:09] Epoch: [531][000/521] Time 0.86 (0.86) Data 0.58 (0.58) Loss 0.080 (0.080) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:44:44] Epoch: [531][100/521] Time 0.28 (0.35) Data 0.00 (0.01) Loss 0.071 (0.065) Prec@1 98.96 (98.96) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-10:45:21] Epoch: [531][200/521] Time 0.37 (0.36) Data 0.00 (0.00) Loss 0.122 (0.066) Prec@1 96.88 (98.94) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-10:45:58] Epoch: [531][300/521] Time 0.28 (0.36) Data 0.00 (0.00) Loss 0.071 (0.064) Prec@1 98.96 (99.02) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-10:46:31] Epoch: [531][400/521] Time 0.45 (0.36) Data 0.00 (0.00) Loss 0.072 (0.063) Prec@1 98.96 (99.03) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-10:47:03] Epoch: [531][500/521] Time 0.25 (0.35) Data 0.00 (0.00) Loss 0.095 (0.063) Prec@1 96.88 (99.00) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-10:47:08] Epoch: [531][520/521] Time 0.24 (0.34) Data 0.00 (0.00) Loss 0.050 (0.063) Prec@1 100.00 (99.00) Prec@5 100.00 (99.99)
|
|
[2019-04-01-10:47:08] **train** Prec@1 99.00 Prec@5 99.99 Error@1 1.00 Error@5 0.01 Loss:0.063
|
|
test [2019-04-01-10:47:09] Epoch: [531][000/105] Time 0.60 (0.60) Data 0.50 (0.50) Loss 0.067 (0.067) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-10:47:13] Epoch: [531][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.079 (0.129) Prec@1 97.92 (96.91) Prec@5 100.00 (99.92)
|
|
test [2019-04-01-10:47:14] Epoch: [531][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.001 (0.131) Prec@1 100.00 (96.89) Prec@5 100.00 (99.92)
|
|
[2019-04-01-10:47:14] **test** Prec@1 96.89 Prec@5 99.92 Error@1 3.11 Error@5 0.08 Loss:0.131
|
|
----> Best Accuracy : Acc@1=97.07, Acc@5=99.94, Error@1=2.93, Error@5=0.06
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-10:47:14] [Epoch=532/600] [Need: 03:30:10] LR=0.0009 ~ 0.0009, Batch=96
|
|
train[2019-04-01-10:47:15] Epoch: [532][000/521] Time 1.22 (1.22) Data 0.81 (0.81) Loss 0.062 (0.062) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:47:49] Epoch: [532][100/521] Time 0.34 (0.34) Data 0.00 (0.01) Loss 0.078 (0.060) Prec@1 97.92 (98.99) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-10:48:25] Epoch: [532][200/521] Time 0.31 (0.35) Data 0.00 (0.00) Loss 0.054 (0.059) Prec@1 100.00 (99.09) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-10:49:00] Epoch: [532][300/521] Time 0.33 (0.35) Data 0.00 (0.00) Loss 0.109 (0.059) Prec@1 97.92 (99.13) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:49:35] Epoch: [532][400/521] Time 0.41 (0.35) Data 0.00 (0.00) Loss 0.044 (0.060) Prec@1 100.00 (99.10) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-10:50:09] Epoch: [532][500/521] Time 0.24 (0.35) Data 0.00 (0.00) Loss 0.101 (0.061) Prec@1 98.96 (99.07) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-10:50:14] Epoch: [532][520/521] Time 0.22 (0.34) Data 0.00 (0.00) Loss 0.088 (0.061) Prec@1 97.50 (99.06) Prec@5 100.00 (99.99)
|
|
[2019-04-01-10:50:14] **train** Prec@1 99.06 Prec@5 99.99 Error@1 0.94 Error@5 0.01 Loss:0.061
|
|
test [2019-04-01-10:50:14] Epoch: [532][000/105] Time 0.46 (0.46) Data 0.40 (0.40) Loss 0.083 (0.083) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-10:50:18] Epoch: [532][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.077 (0.133) Prec@1 97.92 (96.92) Prec@5 100.00 (99.93)
|
|
test [2019-04-01-10:50:19] Epoch: [532][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.003 (0.134) Prec@1 100.00 (96.90) Prec@5 100.00 (99.93)
|
|
[2019-04-01-10:50:19] **test** Prec@1 96.90 Prec@5 99.93 Error@1 3.10 Error@5 0.07 Loss:0.134
|
|
----> Best Accuracy : Acc@1=97.07, Acc@5=99.94, Error@1=2.93, Error@5=0.06
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
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|
|
==>>[2019-04-01-10:50:19] [Epoch=533/600] [Need: 03:26:16] LR=0.0009 ~ 0.0009, Batch=96
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|
train[2019-04-01-10:50:20] Epoch: [533][000/521] Time 0.84 (0.84) Data 0.56 (0.56) Loss 0.035 (0.035) Prec@1 100.00 (100.00) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:50:48] Epoch: [533][100/521] Time 0.29 (0.29) Data 0.00 (0.01) Loss 0.058 (0.055) Prec@1 98.96 (99.21) Prec@5 100.00 (100.00)
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|
train[2019-04-01-10:51:21] Epoch: [533][200/521] Time 0.25 (0.31) Data 0.00 (0.00) Loss 0.041 (0.059) Prec@1 100.00 (99.04) Prec@5 100.00 (100.00)
|
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train[2019-04-01-10:51:51] Epoch: [533][300/521] Time 0.25 (0.31) Data 0.00 (0.00) Loss 0.040 (0.058) Prec@1 98.96 (99.04) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:52:27] Epoch: [533][400/521] Time 0.28 (0.32) Data 0.00 (0.00) Loss 0.104 (0.059) Prec@1 98.96 (99.03) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:53:05] Epoch: [533][500/521] Time 0.44 (0.33) Data 0.00 (0.00) Loss 0.058 (0.060) Prec@1 100.00 (99.02) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:53:12] Epoch: [533][520/521] Time 0.46 (0.33) Data 0.00 (0.00) Loss 0.083 (0.060) Prec@1 98.75 (99.02) Prec@5 100.00 (100.00)
|
|
[2019-04-01-10:53:13] **train** Prec@1 99.02 Prec@5 100.00 Error@1 0.98 Error@5 0.00 Loss:0.060
|
|
test [2019-04-01-10:53:14] Epoch: [533][000/105] Time 0.97 (0.97) Data 0.86 (0.86) Loss 0.076 (0.076) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-10:53:21] Epoch: [533][100/105] Time 0.05 (0.08) Data 0.00 (0.01) Loss 0.089 (0.133) Prec@1 96.88 (97.15) Prec@5 100.00 (99.92)
|
|
test [2019-04-01-10:53:21] Epoch: [533][104/105] Time 0.04 (0.08) Data 0.00 (0.01) Loss 0.000 (0.133) Prec@1 100.00 (97.13) Prec@5 100.00 (99.92)
|
|
[2019-04-01-10:53:22] **test** Prec@1 97.13 Prec@5 99.92 Error@1 2.87 Error@5 0.08 Loss:0.133
|
|
----> Best Accuracy : Acc@1=97.13, Acc@5=99.92, Error@1=2.87, Error@5=0.08
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-10:53:22] [Epoch=534/600] [Need: 03:21:33] LR=0.0008 ~ 0.0008, Batch=96
|
|
train[2019-04-01-10:53:24] Epoch: [534][000/521] Time 1.78 (1.78) Data 1.31 (1.31) Loss 0.027 (0.027) Prec@1 100.00 (100.00) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:54:02] Epoch: [534][100/521] Time 0.58 (0.40) Data 0.01 (0.01) Loss 0.069 (0.066) Prec@1 98.96 (98.95) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:54:40] Epoch: [534][200/521] Time 0.28 (0.39) Data 0.00 (0.01) Loss 0.055 (0.062) Prec@1 98.96 (99.04) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-10:55:17] Epoch: [534][300/521] Time 0.42 (0.38) Data 0.00 (0.00) Loss 0.037 (0.061) Prec@1 100.00 (99.06) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-10:55:46] Epoch: [534][400/521] Time 0.24 (0.36) Data 0.00 (0.00) Loss 0.055 (0.060) Prec@1 98.96 (99.06) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-10:56:11] Epoch: [534][500/521] Time 0.25 (0.34) Data 0.00 (0.00) Loss 0.091 (0.062) Prec@1 98.96 (99.02) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-10:56:15] Epoch: [534][520/521] Time 0.22 (0.33) Data 0.00 (0.00) Loss 0.061 (0.061) Prec@1 98.75 (99.04) Prec@5 100.00 (99.99)
|
|
[2019-04-01-10:56:16] **train** Prec@1 99.04 Prec@5 99.99 Error@1 0.96 Error@5 0.01 Loss:0.061
|
|
test [2019-04-01-10:56:16] Epoch: [534][000/105] Time 0.60 (0.60) Data 0.54 (0.54) Loss 0.081 (0.081) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-10:56:20] Epoch: [534][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.098 (0.133) Prec@1 96.88 (96.96) Prec@5 100.00 (99.95)
|
|
test [2019-04-01-10:56:20] Epoch: [534][104/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.001 (0.133) Prec@1 100.00 (96.92) Prec@5 100.00 (99.95)
|
|
[2019-04-01-10:56:20] **test** Prec@1 96.92 Prec@5 99.95 Error@1 3.08 Error@5 0.05 Loss:0.133
|
|
----> Best Accuracy : Acc@1=97.13, Acc@5=99.92, Error@1=2.87, Error@5=0.08
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-10:56:21] [Epoch=535/600] [Need: 03:13:31] LR=0.0008 ~ 0.0008, Batch=96
|
|
train[2019-04-01-10:56:21] Epoch: [535][000/521] Time 0.70 (0.70) Data 0.43 (0.43) Loss 0.031 (0.031) Prec@1 100.00 (100.00) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:56:46] Epoch: [535][100/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.051 (0.061) Prec@1 100.00 (99.11) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:57:10] Epoch: [535][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.016 (0.061) Prec@1 100.00 (99.06) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:57:34] Epoch: [535][300/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.085 (0.058) Prec@1 97.92 (99.11) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:57:58] Epoch: [535][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.018 (0.057) Prec@1 100.00 (99.13) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:58:22] Epoch: [535][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.085 (0.058) Prec@1 97.92 (99.08) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:58:26] Epoch: [535][520/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.030 (0.057) Prec@1 100.00 (99.09) Prec@5 100.00 (100.00)
|
|
[2019-04-01-10:58:27] **train** Prec@1 99.09 Prec@5 100.00 Error@1 0.91 Error@5 0.00 Loss:0.057
|
|
test [2019-04-01-10:58:27] Epoch: [535][000/105] Time 0.63 (0.63) Data 0.56 (0.56) Loss 0.084 (0.084) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-10:58:31] Epoch: [535][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.040 (0.128) Prec@1 97.92 (96.83) Prec@5 100.00 (99.98)
|
|
test [2019-04-01-10:58:31] Epoch: [535][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.001 (0.129) Prec@1 100.00 (96.83) Prec@5 100.00 (99.98)
|
|
[2019-04-01-10:58:31] **test** Prec@1 96.83 Prec@5 99.98 Error@1 3.17 Error@5 0.02 Loss:0.129
|
|
----> Best Accuracy : Acc@1=97.13, Acc@5=99.92, Error@1=2.87, Error@5=0.08
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-10:58:32] [Epoch=536/600] [Need: 02:19:44] LR=0.0008 ~ 0.0008, Batch=96
|
|
train[2019-04-01-10:58:32] Epoch: [536][000/521] Time 0.71 (0.71) Data 0.43 (0.43) Loss 0.095 (0.095) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-10:58:56] Epoch: [536][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.148 (0.062) Prec@1 97.92 (99.01) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-10:59:21] Epoch: [536][200/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.072 (0.060) Prec@1 98.96 (99.03) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-10:59:45] Epoch: [536][300/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.020 (0.058) Prec@1 100.00 (99.10) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-11:00:09] Epoch: [536][400/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.033 (0.058) Prec@1 98.96 (99.08) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-11:00:33] Epoch: [536][500/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.144 (0.058) Prec@1 96.88 (99.07) Prec@5 98.96 (99.99)
|
|
train[2019-04-01-11:00:38] Epoch: [536][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.034 (0.058) Prec@1 98.75 (99.08) Prec@5 100.00 (99.99)
|
|
[2019-04-01-11:00:38] **train** Prec@1 99.08 Prec@5 99.99 Error@1 0.92 Error@5 0.01 Loss:0.058
|
|
test [2019-04-01-11:00:38] Epoch: [536][000/105] Time 0.47 (0.47) Data 0.41 (0.41) Loss 0.091 (0.091) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-11:00:42] Epoch: [536][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.060 (0.129) Prec@1 98.96 (96.99) Prec@5 100.00 (99.94)
|
|
test [2019-04-01-11:00:42] Epoch: [536][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.001 (0.130) Prec@1 100.00 (96.97) Prec@5 100.00 (99.94)
|
|
[2019-04-01-11:00:43] **test** Prec@1 96.97 Prec@5 99.94 Error@1 3.03 Error@5 0.06 Loss:0.130
|
|
----> Best Accuracy : Acc@1=97.13, Acc@5=99.92, Error@1=2.87, Error@5=0.08
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-11:00:43] [Epoch=537/600] [Need: 02:17:34] LR=0.0008 ~ 0.0008, Batch=96
|
|
train[2019-04-01-11:00:43] Epoch: [537][000/521] Time 0.73 (0.73) Data 0.46 (0.46) Loss 0.070 (0.070) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:01:07] Epoch: [537][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.046 (0.059) Prec@1 98.96 (99.07) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:01:31] Epoch: [537][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.028 (0.056) Prec@1 100.00 (99.19) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-11:01:56] Epoch: [537][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.111 (0.059) Prec@1 97.92 (99.11) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:02:19] Epoch: [537][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.055 (0.060) Prec@1 98.96 (99.08) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:02:44] Epoch: [537][500/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.060 (0.060) Prec@1 97.92 (99.06) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:02:48] Epoch: [537][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.053 (0.060) Prec@1 100.00 (99.07) Prec@5 100.00 (100.00)
|
|
[2019-04-01-11:02:49] **train** Prec@1 99.07 Prec@5 100.00 Error@1 0.93 Error@5 0.00 Loss:0.060
|
|
test [2019-04-01-11:02:49] Epoch: [537][000/105] Time 0.48 (0.48) Data 0.41 (0.41) Loss 0.072 (0.072) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-11:02:53] Epoch: [537][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.065 (0.133) Prec@1 97.92 (96.90) Prec@5 100.00 (99.91)
|
|
test [2019-04-01-11:02:53] Epoch: [537][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.001 (0.134) Prec@1 100.00 (96.87) Prec@5 100.00 (99.91)
|
|
[2019-04-01-11:02:53] **test** Prec@1 96.87 Prec@5 99.91 Error@1 3.13 Error@5 0.09 Loss:0.134
|
|
----> Best Accuracy : Acc@1=97.13, Acc@5=99.92, Error@1=2.87, Error@5=0.08
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-11:02:54] [Epoch=538/600] [Need: 02:15:12] LR=0.0008 ~ 0.0008, Batch=96
|
|
train[2019-04-01-11:02:54] Epoch: [538][000/521] Time 0.71 (0.71) Data 0.43 (0.43) Loss 0.072 (0.072) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:03:19] Epoch: [538][100/521] Time 0.26 (0.25) Data 0.00 (0.00) Loss 0.137 (0.060) Prec@1 100.00 (99.17) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:03:43] Epoch: [538][200/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.049 (0.059) Prec@1 98.96 (99.07) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:04:07] Epoch: [538][300/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.036 (0.059) Prec@1 98.96 (99.07) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:04:32] Epoch: [538][400/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.012 (0.058) Prec@1 100.00 (99.14) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-11:04:56] Epoch: [538][500/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.018 (0.058) Prec@1 100.00 (99.17) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:05:01] Epoch: [538][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.056 (0.057) Prec@1 98.75 (99.18) Prec@5 100.00 (100.00)
|
|
[2019-04-01-11:05:01] **train** Prec@1 99.18 Prec@5 100.00 Error@1 0.82 Error@5 0.00 Loss:0.057
|
|
test [2019-04-01-11:05:01] Epoch: [538][000/105] Time 0.61 (0.61) Data 0.54 (0.54) Loss 0.085 (0.085) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-11:05:05] Epoch: [538][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.070 (0.135) Prec@1 98.96 (96.97) Prec@5 100.00 (99.96)
|
|
test [2019-04-01-11:05:06] Epoch: [538][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.002 (0.135) Prec@1 100.00 (96.96) Prec@5 100.00 (99.96)
|
|
[2019-04-01-11:05:06] **test** Prec@1 96.96 Prec@5 99.96 Error@1 3.04 Error@5 0.04 Loss:0.135
|
|
----> Best Accuracy : Acc@1=97.13, Acc@5=99.92, Error@1=2.87, Error@5=0.08
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-11:05:06] [Epoch=539/600] [Need: 02:14:34] LR=0.0007 ~ 0.0007, Batch=96
|
|
train[2019-04-01-11:05:07] Epoch: [539][000/521] Time 0.73 (0.73) Data 0.44 (0.44) Loss 0.086 (0.086) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:05:31] Epoch: [539][100/521] Time 0.26 (0.25) Data 0.00 (0.00) Loss 0.092 (0.058) Prec@1 97.92 (99.02) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-11:05:55] Epoch: [539][200/521] Time 0.23 (0.25) Data 0.00 (0.00) Loss 0.039 (0.059) Prec@1 100.00 (99.08) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-11:06:20] Epoch: [539][300/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.033 (0.057) Prec@1 100.00 (99.16) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-11:06:44] Epoch: [539][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.046 (0.057) Prec@1 97.92 (99.15) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-11:07:08] Epoch: [539][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.041 (0.056) Prec@1 100.00 (99.16) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-11:07:13] Epoch: [539][520/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.109 (0.056) Prec@1 97.50 (99.14) Prec@5 100.00 (99.99)
|
|
[2019-04-01-11:07:13] **train** Prec@1 99.14 Prec@5 99.99 Error@1 0.86 Error@5 0.01 Loss:0.056
|
|
test [2019-04-01-11:07:14] Epoch: [539][000/105] Time 0.50 (0.50) Data 0.42 (0.42) Loss 0.098 (0.098) Prec@1 94.79 (94.79) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-11:07:18] Epoch: [539][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.070 (0.134) Prec@1 98.96 (96.91) Prec@5 100.00 (99.95)
|
|
test [2019-04-01-11:07:18] Epoch: [539][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.001 (0.135) Prec@1 100.00 (96.90) Prec@5 100.00 (99.95)
|
|
[2019-04-01-11:07:18] **test** Prec@1 96.90 Prec@5 99.95 Error@1 3.10 Error@5 0.05 Loss:0.135
|
|
----> Best Accuracy : Acc@1=97.13, Acc@5=99.92, Error@1=2.87, Error@5=0.08
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-11:07:18] [Epoch=540/600] [Need: 02:12:13] LR=0.0007 ~ 0.0007, Batch=96
|
|
train[2019-04-01-11:07:19] Epoch: [540][000/521] Time 0.76 (0.76) Data 0.48 (0.48) Loss 0.067 (0.067) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:07:43] Epoch: [540][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.026 (0.058) Prec@1 100.00 (99.13) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-11:08:07] Epoch: [540][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.035 (0.061) Prec@1 98.96 (99.04) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-11:08:31] Epoch: [540][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.056 (0.061) Prec@1 100.00 (99.05) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-11:08:56] Epoch: [540][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.045 (0.060) Prec@1 100.00 (99.04) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-11:09:20] Epoch: [540][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.060 (0.060) Prec@1 98.96 (99.08) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-11:09:25] Epoch: [540][520/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.036 (0.059) Prec@1 100.00 (99.08) Prec@5 100.00 (99.99)
|
|
[2019-04-01-11:09:25] **train** Prec@1 99.08 Prec@5 99.99 Error@1 0.92 Error@5 0.01 Loss:0.059
|
|
test [2019-04-01-11:09:25] Epoch: [540][000/105] Time 0.49 (0.49) Data 0.42 (0.42) Loss 0.076 (0.076) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-11:09:29] Epoch: [540][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.081 (0.131) Prec@1 97.92 (96.90) Prec@5 100.00 (99.94)
|
|
test [2019-04-01-11:09:30] Epoch: [540][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.001 (0.131) Prec@1 100.00 (96.90) Prec@5 100.00 (99.94)
|
|
[2019-04-01-11:09:30] **test** Prec@1 96.90 Prec@5 99.94 Error@1 3.10 Error@5 0.06 Loss:0.131
|
|
----> Best Accuracy : Acc@1=97.13, Acc@5=99.92, Error@1=2.87, Error@5=0.08
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-11:09:30] [Epoch=541/600] [Need: 02:09:34] LR=0.0007 ~ 0.0007, Batch=96
|
|
train[2019-04-01-11:09:31] Epoch: [541][000/521] Time 0.74 (0.74) Data 0.46 (0.46) Loss 0.043 (0.043) Prec@1 100.00 (100.00) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:09:55] Epoch: [541][100/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.037 (0.063) Prec@1 100.00 (98.99) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-11:10:19] Epoch: [541][200/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.008 (0.057) Prec@1 100.00 (99.17) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-11:10:44] Epoch: [541][300/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.017 (0.055) Prec@1 100.00 (99.18) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-11:11:09] Epoch: [541][400/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.020 (0.054) Prec@1 100.00 (99.20) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-11:11:33] Epoch: [541][500/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.097 (0.055) Prec@1 98.96 (99.17) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:11:38] Epoch: [541][520/521] Time 0.22 (0.25) Data 0.00 (0.00) Loss 0.049 (0.055) Prec@1 100.00 (99.18) Prec@5 100.00 (100.00)
|
|
[2019-04-01-11:11:38] **train** Prec@1 99.18 Prec@5 100.00 Error@1 0.82 Error@5 0.00 Loss:0.055
|
|
test [2019-04-01-11:11:39] Epoch: [541][000/105] Time 0.50 (0.50) Data 0.43 (0.43) Loss 0.054 (0.054) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-11:11:43] Epoch: [541][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.083 (0.134) Prec@1 98.96 (96.92) Prec@5 100.00 (99.92)
|
|
test [2019-04-01-11:11:43] Epoch: [541][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.000 (0.133) Prec@1 100.00 (96.92) Prec@5 100.00 (99.92)
|
|
[2019-04-01-11:11:43] **test** Prec@1 96.92 Prec@5 99.92 Error@1 3.08 Error@5 0.08 Loss:0.133
|
|
----> Best Accuracy : Acc@1=97.13, Acc@5=99.92, Error@1=2.87, Error@5=0.08
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-11:11:43] [Epoch=542/600] [Need: 02:09:01] LR=0.0007 ~ 0.0007, Batch=96
|
|
train[2019-04-01-11:11:44] Epoch: [542][000/521] Time 0.78 (0.78) Data 0.50 (0.50) Loss 0.089 (0.089) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:12:09] Epoch: [542][100/521] Time 0.25 (0.25) Data 0.00 (0.01) Loss 0.033 (0.057) Prec@1 100.00 (99.17) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:12:33] Epoch: [542][200/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.047 (0.056) Prec@1 98.96 (99.18) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:12:57] Epoch: [542][300/521] Time 0.23 (0.25) Data 0.00 (0.00) Loss 0.075 (0.055) Prec@1 98.96 (99.19) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:13:22] Epoch: [542][400/521] Time 0.28 (0.25) Data 0.00 (0.00) Loss 0.042 (0.055) Prec@1 98.96 (99.19) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:13:46] Epoch: [542][500/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.039 (0.056) Prec@1 100.00 (99.17) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:13:51] Epoch: [542][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.064 (0.056) Prec@1 98.75 (99.17) Prec@5 100.00 (100.00)
|
|
[2019-04-01-11:13:51] **train** Prec@1 99.17 Prec@5 100.00 Error@1 0.83 Error@5 0.00 Loss:0.056
|
|
test [2019-04-01-11:13:51] Epoch: [542][000/105] Time 0.48 (0.48) Data 0.42 (0.42) Loss 0.118 (0.118) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-11:13:56] Epoch: [542][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.077 (0.134) Prec@1 98.96 (96.87) Prec@5 100.00 (99.95)
|
|
test [2019-04-01-11:13:56] Epoch: [542][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.003 (0.134) Prec@1 100.00 (96.87) Prec@5 100.00 (99.95)
|
|
[2019-04-01-11:13:56] **test** Prec@1 96.87 Prec@5 99.95 Error@1 3.13 Error@5 0.05 Loss:0.134
|
|
----> Best Accuracy : Acc@1=97.13, Acc@5=99.92, Error@1=2.87, Error@5=0.08
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-11:13:56] [Epoch=543/600] [Need: 02:06:00] LR=0.0007 ~ 0.0007, Batch=96
|
|
train[2019-04-01-11:13:57] Epoch: [543][000/521] Time 0.73 (0.73) Data 0.44 (0.44) Loss 0.048 (0.048) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:14:21] Epoch: [543][100/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.093 (0.058) Prec@1 96.88 (99.21) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:14:45] Epoch: [543][200/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.107 (0.059) Prec@1 98.96 (99.10) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:15:10] Epoch: [543][300/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.036 (0.057) Prec@1 100.00 (99.16) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:15:34] Epoch: [543][400/521] Time 0.26 (0.24) Data 0.00 (0.00) Loss 0.106 (0.056) Prec@1 97.92 (99.19) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:15:58] Epoch: [543][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.039 (0.055) Prec@1 100.00 (99.18) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:16:03] Epoch: [543][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.020 (0.056) Prec@1 100.00 (99.17) Prec@5 100.00 (100.00)
|
|
[2019-04-01-11:16:03] **train** Prec@1 99.17 Prec@5 100.00 Error@1 0.83 Error@5 0.00 Loss:0.056
|
|
test [2019-04-01-11:16:04] Epoch: [543][000/105] Time 0.48 (0.48) Data 0.41 (0.41) Loss 0.078 (0.078) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-11:16:08] Epoch: [543][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.089 (0.133) Prec@1 97.92 (96.85) Prec@5 100.00 (99.93)
|
|
test [2019-04-01-11:16:08] Epoch: [543][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.000 (0.132) Prec@1 100.00 (96.85) Prec@5 100.00 (99.93)
|
|
[2019-04-01-11:16:08] **test** Prec@1 96.85 Prec@5 99.93 Error@1 3.15 Error@5 0.07 Loss:0.132
|
|
----> Best Accuracy : Acc@1=97.13, Acc@5=99.92, Error@1=2.87, Error@5=0.08
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-11:16:08] [Epoch=544/600] [Need: 02:03:14] LR=0.0006 ~ 0.0006, Batch=96
|
|
train[2019-04-01-11:16:09] Epoch: [544][000/521] Time 0.75 (0.75) Data 0.46 (0.46) Loss 0.091 (0.091) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:16:33] Epoch: [544][100/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.034 (0.051) Prec@1 100.00 (99.19) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:16:57] Epoch: [544][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.056 (0.055) Prec@1 100.00 (99.14) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:17:21] Epoch: [544][300/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.138 (0.056) Prec@1 95.83 (99.12) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:17:46] Epoch: [544][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.043 (0.057) Prec@1 98.96 (99.08) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:18:10] Epoch: [544][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.015 (0.058) Prec@1 100.00 (99.06) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:18:15] Epoch: [544][520/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.131 (0.058) Prec@1 96.25 (99.06) Prec@5 100.00 (100.00)
|
|
[2019-04-01-11:18:15] **train** Prec@1 99.06 Prec@5 100.00 Error@1 0.94 Error@5 0.00 Loss:0.058
|
|
test [2019-04-01-11:18:16] Epoch: [544][000/105] Time 0.64 (0.64) Data 0.57 (0.57) Loss 0.059 (0.059) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-11:18:20] Epoch: [544][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.094 (0.133) Prec@1 96.88 (96.82) Prec@5 100.00 (99.94)
|
|
test [2019-04-01-11:18:20] Epoch: [544][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.000 (0.133) Prec@1 100.00 (96.82) Prec@5 100.00 (99.94)
|
|
[2019-04-01-11:18:20] **test** Prec@1 96.82 Prec@5 99.94 Error@1 3.18 Error@5 0.06 Loss:0.133
|
|
----> Best Accuracy : Acc@1=97.13, Acc@5=99.92, Error@1=2.87, Error@5=0.08
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-11:18:20] [Epoch=545/600] [Need: 02:01:16] LR=0.0006 ~ 0.0006, Batch=96
|
|
train[2019-04-01-11:18:21] Epoch: [545][000/521] Time 0.72 (0.72) Data 0.45 (0.45) Loss 0.061 (0.061) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:18:45] Epoch: [545][100/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.036 (0.054) Prec@1 100.00 (99.19) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:19:10] Epoch: [545][200/521] Time 0.23 (0.25) Data 0.00 (0.00) Loss 0.035 (0.054) Prec@1 98.96 (99.15) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:19:34] Epoch: [545][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.027 (0.054) Prec@1 100.00 (99.18) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:19:58] Epoch: [545][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.060 (0.054) Prec@1 100.00 (99.17) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:20:31] Epoch: [545][500/521] Time 0.35 (0.26) Data 0.00 (0.00) Loss 0.034 (0.054) Prec@1 100.00 (99.16) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:20:38] Epoch: [545][520/521] Time 0.37 (0.26) Data 0.00 (0.00) Loss 0.089 (0.054) Prec@1 98.75 (99.16) Prec@5 100.00 (100.00)
|
|
[2019-04-01-11:20:38] **train** Prec@1 99.16 Prec@5 100.00 Error@1 0.84 Error@5 0.00 Loss:0.054
|
|
test [2019-04-01-11:20:39] Epoch: [545][000/105] Time 0.61 (0.61) Data 0.52 (0.52) Loss 0.081 (0.081) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-11:20:45] Epoch: [545][100/105] Time 0.06 (0.06) Data 0.00 (0.01) Loss 0.087 (0.136) Prec@1 97.92 (96.80) Prec@5 100.00 (99.93)
|
|
test [2019-04-01-11:20:45] Epoch: [545][104/105] Time 0.06 (0.06) Data 0.00 (0.01) Loss 0.002 (0.137) Prec@1 100.00 (96.78) Prec@5 100.00 (99.93)
|
|
[2019-04-01-11:20:45] **test** Prec@1 96.78 Prec@5 99.93 Error@1 3.22 Error@5 0.07 Loss:0.137
|
|
----> Best Accuracy : Acc@1=97.13, Acc@5=99.92, Error@1=2.87, Error@5=0.08
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-11:20:45] [Epoch=546/600] [Need: 02:10:15] LR=0.0006 ~ 0.0006, Batch=96
|
|
train[2019-04-01-11:20:46] Epoch: [546][000/521] Time 1.12 (1.12) Data 0.81 (0.81) Loss 0.035 (0.035) Prec@1 100.00 (100.00) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:21:19] Epoch: [546][100/521] Time 0.32 (0.34) Data 0.00 (0.01) Loss 0.031 (0.061) Prec@1 100.00 (98.96) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-11:21:54] Epoch: [546][200/521] Time 0.25 (0.34) Data 0.00 (0.00) Loss 0.021 (0.060) Prec@1 100.00 (99.01) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-11:22:28] Epoch: [546][300/521] Time 0.49 (0.34) Data 0.00 (0.00) Loss 0.033 (0.057) Prec@1 100.00 (99.07) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-11:23:03] Epoch: [546][400/521] Time 0.31 (0.34) Data 0.00 (0.00) Loss 0.072 (0.056) Prec@1 97.92 (99.10) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-11:23:35] Epoch: [546][500/521] Time 0.37 (0.34) Data 0.00 (0.00) Loss 0.046 (0.057) Prec@1 98.96 (99.08) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:23:42] Epoch: [546][520/521] Time 0.37 (0.34) Data 0.00 (0.00) Loss 0.036 (0.057) Prec@1 100.00 (99.10) Prec@5 100.00 (100.00)
|
|
[2019-04-01-11:23:42] **train** Prec@1 99.10 Prec@5 100.00 Error@1 0.90 Error@5 0.00 Loss:0.057
|
|
test [2019-04-01-11:23:43] Epoch: [546][000/105] Time 0.76 (0.76) Data 0.66 (0.66) Loss 0.097 (0.097) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-11:23:48] Epoch: [546][100/105] Time 0.04 (0.06) Data 0.00 (0.01) Loss 0.072 (0.130) Prec@1 98.96 (97.03) Prec@5 100.00 (99.92)
|
|
test [2019-04-01-11:23:48] Epoch: [546][104/105] Time 0.03 (0.06) Data 0.00 (0.01) Loss 0.001 (0.131) Prec@1 100.00 (97.02) Prec@5 100.00 (99.92)
|
|
[2019-04-01-11:23:48] **test** Prec@1 97.02 Prec@5 99.92 Error@1 2.98 Error@5 0.08 Loss:0.131
|
|
----> Best Accuracy : Acc@1=97.13, Acc@5=99.92, Error@1=2.87, Error@5=0.08
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-11:23:49] [Epoch=547/600] [Need: 02:42:06] LR=0.0006 ~ 0.0006, Batch=96
|
|
train[2019-04-01-11:23:50] Epoch: [547][000/521] Time 1.16 (1.16) Data 0.78 (0.78) Loss 0.023 (0.023) Prec@1 100.00 (100.00) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:24:24] Epoch: [547][100/521] Time 0.36 (0.35) Data 0.00 (0.01) Loss 0.071 (0.057) Prec@1 98.96 (99.10) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:24:59] Epoch: [547][200/521] Time 0.36 (0.35) Data 0.00 (0.00) Loss 0.115 (0.054) Prec@1 96.88 (99.17) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:25:32] Epoch: [547][300/521] Time 0.28 (0.34) Data 0.00 (0.00) Loss 0.071 (0.055) Prec@1 98.96 (99.19) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:26:05] Epoch: [547][400/521] Time 0.37 (0.34) Data 0.00 (0.00) Loss 0.035 (0.055) Prec@1 100.00 (99.18) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:26:39] Epoch: [547][500/521] Time 0.33 (0.34) Data 0.00 (0.00) Loss 0.053 (0.055) Prec@1 98.96 (99.19) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:26:45] Epoch: [547][520/521] Time 0.22 (0.34) Data 0.00 (0.00) Loss 0.059 (0.055) Prec@1 98.75 (99.19) Prec@5 100.00 (100.00)
|
|
[2019-04-01-11:26:45] **train** Prec@1 99.19 Prec@5 100.00 Error@1 0.81 Error@5 0.00 Loss:0.055
|
|
test [2019-04-01-11:26:46] Epoch: [547][000/105] Time 0.94 (0.94) Data 0.86 (0.86) Loss 0.080 (0.080) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-11:26:51] Epoch: [547][100/105] Time 0.04 (0.06) Data 0.00 (0.01) Loss 0.055 (0.131) Prec@1 98.96 (97.05) Prec@5 100.00 (99.93)
|
|
test [2019-04-01-11:26:51] Epoch: [547][104/105] Time 0.05 (0.06) Data 0.00 (0.01) Loss 0.000 (0.131) Prec@1 100.00 (97.03) Prec@5 100.00 (99.93)
|
|
[2019-04-01-11:26:52] **test** Prec@1 97.03 Prec@5 99.93 Error@1 2.97 Error@5 0.07 Loss:0.131
|
|
----> Best Accuracy : Acc@1=97.13, Acc@5=99.92, Error@1=2.87, Error@5=0.08
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-11:26:52] [Epoch=548/600] [Need: 02:38:50] LR=0.0006 ~ 0.0006, Batch=96
|
|
train[2019-04-01-11:26:53] Epoch: [548][000/521] Time 1.33 (1.33) Data 0.94 (0.94) Loss 0.054 (0.054) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:27:28] Epoch: [548][100/521] Time 0.27 (0.36) Data 0.00 (0.01) Loss 0.091 (0.057) Prec@1 98.96 (99.11) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:28:00] Epoch: [548][200/521] Time 0.30 (0.34) Data 0.00 (0.01) Loss 0.031 (0.057) Prec@1 98.96 (99.07) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-11:28:33] Epoch: [548][300/521] Time 0.33 (0.33) Data 0.00 (0.00) Loss 0.081 (0.054) Prec@1 97.92 (99.16) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:29:01] Epoch: [548][400/521] Time 0.28 (0.32) Data 0.00 (0.00) Loss 0.071 (0.054) Prec@1 97.92 (99.12) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:29:35] Epoch: [548][500/521] Time 0.35 (0.33) Data 0.00 (0.00) Loss 0.035 (0.054) Prec@1 100.00 (99.12) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:29:42] Epoch: [548][520/521] Time 0.32 (0.33) Data 0.00 (0.00) Loss 0.055 (0.054) Prec@1 100.00 (99.13) Prec@5 100.00 (100.00)
|
|
[2019-04-01-11:29:42] **train** Prec@1 99.13 Prec@5 100.00 Error@1 0.87 Error@5 0.00 Loss:0.054
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test [2019-04-01-11:29:43] Epoch: [548][000/105] Time 1.22 (1.22) Data 1.14 (1.14) Loss 0.060 (0.060) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-11:29:48] Epoch: [548][100/105] Time 0.04 (0.06) Data 0.00 (0.01) Loss 0.111 (0.131) Prec@1 97.92 (97.06) Prec@5 100.00 (99.93)
|
|
test [2019-04-01-11:29:48] Epoch: [548][104/105] Time 0.05 (0.06) Data 0.00 (0.01) Loss 0.000 (0.131) Prec@1 100.00 (97.04) Prec@5 100.00 (99.93)
|
|
[2019-04-01-11:29:49] **test** Prec@1 97.04 Prec@5 99.93 Error@1 2.96 Error@5 0.07 Loss:0.131
|
|
----> Best Accuracy : Acc@1=97.13, Acc@5=99.92, Error@1=2.87, Error@5=0.08
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-11:29:49] [Epoch=549/600] [Need: 02:30:22] LR=0.0005 ~ 0.0005, Batch=96
|
|
train[2019-04-01-11:29:50] Epoch: [549][000/521] Time 1.01 (1.01) Data 0.70 (0.70) Loss 0.043 (0.043) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:30:24] Epoch: [549][100/521] Time 0.40 (0.35) Data 0.00 (0.01) Loss 0.114 (0.052) Prec@1 97.92 (99.15) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:31:00] Epoch: [549][200/521] Time 0.35 (0.35) Data 0.00 (0.00) Loss 0.080 (0.055) Prec@1 98.96 (99.16) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-11:31:34] Epoch: [549][300/521] Time 0.25 (0.35) Data 0.00 (0.00) Loss 0.050 (0.052) Prec@1 100.00 (99.27) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:32:07] Epoch: [549][400/521] Time 0.36 (0.35) Data 0.00 (0.00) Loss 0.018 (0.053) Prec@1 100.00 (99.23) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-11:32:32] Epoch: [549][500/521] Time 0.24 (0.33) Data 0.00 (0.00) Loss 0.092 (0.053) Prec@1 97.92 (99.23) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:32:39] Epoch: [549][520/521] Time 0.30 (0.33) Data 0.00 (0.00) Loss 0.102 (0.053) Prec@1 98.75 (99.23) Prec@5 100.00 (100.00)
|
|
[2019-04-01-11:32:39] **train** Prec@1 99.23 Prec@5 100.00 Error@1 0.77 Error@5 0.00 Loss:0.053
|
|
test [2019-04-01-11:32:40] Epoch: [549][000/105] Time 1.19 (1.19) Data 1.06 (1.06) Loss 0.093 (0.093) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-11:32:47] Epoch: [549][100/105] Time 0.04 (0.08) Data 0.00 (0.01) Loss 0.086 (0.130) Prec@1 97.92 (97.03) Prec@5 100.00 (99.94)
|
|
test [2019-04-01-11:32:47] Epoch: [549][104/105] Time 0.03 (0.08) Data 0.00 (0.01) Loss 0.003 (0.130) Prec@1 100.00 (97.01) Prec@5 100.00 (99.94)
|
|
[2019-04-01-11:32:47] **test** Prec@1 97.01 Prec@5 99.94 Error@1 2.99 Error@5 0.06 Loss:0.130
|
|
----> Best Accuracy : Acc@1=97.13, Acc@5=99.92, Error@1=2.87, Error@5=0.08
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-11:32:47] [Epoch=550/600] [Need: 02:28:49] LR=0.0005 ~ 0.0005, Batch=96
|
|
train[2019-04-01-11:32:48] Epoch: [550][000/521] Time 1.03 (1.03) Data 0.63 (0.63) Loss 0.033 (0.033) Prec@1 100.00 (100.00) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:33:24] Epoch: [550][100/521] Time 0.32 (0.36) Data 0.00 (0.01) Loss 0.092 (0.052) Prec@1 97.92 (99.23) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-11:33:57] Epoch: [550][200/521] Time 0.27 (0.35) Data 0.00 (0.00) Loss 0.053 (0.053) Prec@1 98.96 (99.20) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-11:34:22] Epoch: [550][300/521] Time 0.24 (0.31) Data 0.00 (0.00) Loss 0.066 (0.054) Prec@1 97.92 (99.17) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:34:48] Epoch: [550][400/521] Time 0.25 (0.30) Data 0.00 (0.00) Loss 0.101 (0.054) Prec@1 97.92 (99.17) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:35:14] Epoch: [550][500/521] Time 0.24 (0.29) Data 0.00 (0.00) Loss 0.099 (0.053) Prec@1 97.92 (99.19) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:35:18] Epoch: [550][520/521] Time 0.22 (0.29) Data 0.00 (0.00) Loss 0.014 (0.053) Prec@1 100.00 (99.20) Prec@5 100.00 (100.00)
|
|
[2019-04-01-11:35:19] **train** Prec@1 99.20 Prec@5 100.00 Error@1 0.80 Error@5 0.00 Loss:0.053
|
|
test [2019-04-01-11:35:19] Epoch: [550][000/105] Time 0.62 (0.62) Data 0.54 (0.54) Loss 0.083 (0.083) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-11:35:24] Epoch: [550][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.065 (0.130) Prec@1 98.96 (97.02) Prec@5 100.00 (99.94)
|
|
test [2019-04-01-11:35:24] Epoch: [550][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.001 (0.130) Prec@1 100.00 (96.99) Prec@5 100.00 (99.94)
|
|
[2019-04-01-11:35:24] **test** Prec@1 96.99 Prec@5 99.94 Error@1 3.01 Error@5 0.06 Loss:0.130
|
|
----> Best Accuracy : Acc@1=97.13, Acc@5=99.92, Error@1=2.87, Error@5=0.08
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-11:35:24] [Epoch=551/600] [Need: 02:08:04] LR=0.0005 ~ 0.0005, Batch=96
|
|
train[2019-04-01-11:35:25] Epoch: [551][000/521] Time 0.81 (0.81) Data 0.45 (0.45) Loss 0.093 (0.093) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:35:52] Epoch: [551][100/521] Time 0.25 (0.27) Data 0.00 (0.00) Loss 0.023 (0.059) Prec@1 100.00 (99.02) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:36:18] Epoch: [551][200/521] Time 0.28 (0.27) Data 0.00 (0.00) Loss 0.057 (0.057) Prec@1 98.96 (99.05) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:36:44] Epoch: [551][300/521] Time 0.24 (0.27) Data 0.00 (0.00) Loss 0.018 (0.053) Prec@1 100.00 (99.17) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:37:12] Epoch: [551][400/521] Time 0.32 (0.27) Data 0.00 (0.00) Loss 0.087 (0.053) Prec@1 97.92 (99.18) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:37:38] Epoch: [551][500/521] Time 0.24 (0.27) Data 0.00 (0.00) Loss 0.079 (0.054) Prec@1 100.00 (99.16) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:37:43] Epoch: [551][520/521] Time 0.22 (0.27) Data 0.00 (0.00) Loss 0.053 (0.054) Prec@1 98.75 (99.16) Prec@5 100.00 (100.00)
|
|
[2019-04-01-11:37:43] **train** Prec@1 99.16 Prec@5 100.00 Error@1 0.84 Error@5 0.00 Loss:0.054
|
|
test [2019-04-01-11:37:43] Epoch: [551][000/105] Time 0.51 (0.51) Data 0.44 (0.44) Loss 0.085 (0.085) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-11:37:48] Epoch: [551][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.061 (0.129) Prec@1 98.96 (97.06) Prec@5 100.00 (99.93)
|
|
test [2019-04-01-11:37:48] Epoch: [551][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.001 (0.129) Prec@1 100.00 (97.04) Prec@5 100.00 (99.93)
|
|
[2019-04-01-11:37:48] **test** Prec@1 97.04 Prec@5 99.93 Error@1 2.96 Error@5 0.07 Loss:0.129
|
|
----> Best Accuracy : Acc@1=97.13, Acc@5=99.92, Error@1=2.87, Error@5=0.08
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-11:37:48] [Epoch=552/600] [Need: 01:55:00] LR=0.0005 ~ 0.0005, Batch=96
|
|
train[2019-04-01-11:37:49] Epoch: [552][000/521] Time 0.80 (0.80) Data 0.52 (0.52) Loss 0.075 (0.075) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:38:15] Epoch: [552][100/521] Time 0.25 (0.27) Data 0.00 (0.01) Loss 0.108 (0.055) Prec@1 96.88 (99.04) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-11:38:41] Epoch: [552][200/521] Time 0.25 (0.27) Data 0.00 (0.00) Loss 0.052 (0.053) Prec@1 98.96 (99.14) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-11:39:07] Epoch: [552][300/521] Time 0.27 (0.26) Data 0.00 (0.00) Loss 0.009 (0.053) Prec@1 100.00 (99.17) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:39:33] Epoch: [552][400/521] Time 0.27 (0.26) Data 0.00 (0.00) Loss 0.129 (0.054) Prec@1 97.92 (99.21) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:39:58] Epoch: [552][500/521] Time 0.24 (0.26) Data 0.00 (0.00) Loss 0.040 (0.054) Prec@1 98.96 (99.20) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-11:40:03] Epoch: [552][520/521] Time 0.22 (0.26) Data 0.00 (0.00) Loss 0.047 (0.054) Prec@1 100.00 (99.21) Prec@5 100.00 (99.99)
|
|
[2019-04-01-11:40:03] **train** Prec@1 99.21 Prec@5 99.99 Error@1 0.79 Error@5 0.01 Loss:0.054
|
|
test [2019-04-01-11:40:04] Epoch: [552][000/105] Time 0.67 (0.67) Data 0.60 (0.60) Loss 0.106 (0.106) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-11:40:08] Epoch: [552][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.066 (0.127) Prec@1 98.96 (97.16) Prec@5 100.00 (99.92)
|
|
test [2019-04-01-11:40:09] Epoch: [552][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.002 (0.127) Prec@1 100.00 (97.12) Prec@5 100.00 (99.92)
|
|
[2019-04-01-11:40:09] **test** Prec@1 97.12 Prec@5 99.92 Error@1 2.88 Error@5 0.08 Loss:0.127
|
|
----> Best Accuracy : Acc@1=97.13, Acc@5=99.92, Error@1=2.87, Error@5=0.08
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-11:40:09] [Epoch=553/600] [Need: 01:50:22] LR=0.0005 ~ 0.0005, Batch=96
|
|
train[2019-04-01-11:40:10] Epoch: [553][000/521] Time 0.82 (0.82) Data 0.53 (0.53) Loss 0.058 (0.058) Prec@1 100.00 (100.00) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:40:35] Epoch: [553][100/521] Time 0.29 (0.26) Data 0.00 (0.01) Loss 0.039 (0.058) Prec@1 98.96 (99.25) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-11:41:00] Epoch: [553][200/521] Time 0.25 (0.26) Data 0.00 (0.00) Loss 0.040 (0.056) Prec@1 100.00 (99.20) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-11:41:26] Epoch: [553][300/521] Time 0.24 (0.26) Data 0.00 (0.00) Loss 0.028 (0.054) Prec@1 100.00 (99.24) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-11:41:51] Epoch: [553][400/521] Time 0.24 (0.26) Data 0.00 (0.00) Loss 0.082 (0.053) Prec@1 97.92 (99.24) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-11:42:17] Epoch: [553][500/521] Time 0.24 (0.26) Data 0.00 (0.00) Loss 0.093 (0.054) Prec@1 96.88 (99.17) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-11:42:23] Epoch: [553][520/521] Time 0.34 (0.26) Data 0.00 (0.00) Loss 0.076 (0.054) Prec@1 97.50 (99.17) Prec@5 100.00 (99.99)
|
|
[2019-04-01-11:42:23] **train** Prec@1 99.17 Prec@5 99.99 Error@1 0.83 Error@5 0.01 Loss:0.054
|
|
test [2019-04-01-11:42:23] Epoch: [553][000/105] Time 0.72 (0.72) Data 0.62 (0.62) Loss 0.067 (0.067) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-11:42:28] Epoch: [553][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.077 (0.129) Prec@1 97.92 (97.08) Prec@5 100.00 (99.93)
|
|
test [2019-04-01-11:42:28] Epoch: [553][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.002 (0.130) Prec@1 100.00 (97.06) Prec@5 100.00 (99.93)
|
|
[2019-04-01-11:42:28] **test** Prec@1 97.06 Prec@5 99.93 Error@1 2.94 Error@5 0.07 Loss:0.130
|
|
----> Best Accuracy : Acc@1=97.13, Acc@5=99.92, Error@1=2.87, Error@5=0.08
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-11:42:28] [Epoch=554/600] [Need: 01:47:02] LR=0.0005 ~ 0.0005, Batch=96
|
|
train[2019-04-01-11:42:29] Epoch: [554][000/521] Time 0.77 (0.77) Data 0.45 (0.45) Loss 0.035 (0.035) Prec@1 100.00 (100.00) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:42:55] Epoch: [554][100/521] Time 0.25 (0.27) Data 0.00 (0.00) Loss 0.099 (0.053) Prec@1 96.88 (99.24) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-11:43:22] Epoch: [554][200/521] Time 0.38 (0.26) Data 0.00 (0.00) Loss 0.098 (0.053) Prec@1 97.92 (99.23) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-11:43:47] Epoch: [554][300/521] Time 0.25 (0.26) Data 0.00 (0.00) Loss 0.064 (0.051) Prec@1 98.96 (99.25) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:44:13] Epoch: [554][400/521] Time 0.27 (0.26) Data 0.00 (0.00) Loss 0.075 (0.050) Prec@1 98.96 (99.23) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:44:39] Epoch: [554][500/521] Time 0.24 (0.26) Data 0.00 (0.00) Loss 0.032 (0.050) Prec@1 100.00 (99.25) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:44:44] Epoch: [554][520/521] Time 0.25 (0.26) Data 0.00 (0.00) Loss 0.027 (0.050) Prec@1 100.00 (99.25) Prec@5 100.00 (100.00)
|
|
[2019-04-01-11:44:44] **train** Prec@1 99.25 Prec@5 100.00 Error@1 0.75 Error@5 0.00 Loss:0.050
|
|
test [2019-04-01-11:44:44] Epoch: [554][000/105] Time 0.67 (0.67) Data 0.56 (0.56) Loss 0.064 (0.064) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-11:44:49] Epoch: [554][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.070 (0.131) Prec@1 97.92 (97.03) Prec@5 100.00 (99.92)
|
|
test [2019-04-01-11:44:49] Epoch: [554][104/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.005 (0.131) Prec@1 100.00 (97.03) Prec@5 100.00 (99.91)
|
|
[2019-04-01-11:44:49] **test** Prec@1 97.03 Prec@5 99.91 Error@1 2.97 Error@5 0.09 Loss:0.131
|
|
----> Best Accuracy : Acc@1=97.13, Acc@5=99.92, Error@1=2.87, Error@5=0.08
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-11:44:49] [Epoch=555/600] [Need: 01:45:40] LR=0.0004 ~ 0.0004, Batch=96
|
|
train[2019-04-01-11:44:50] Epoch: [555][000/521] Time 0.88 (0.88) Data 0.59 (0.59) Loss 0.030 (0.030) Prec@1 100.00 (100.00) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:45:16] Epoch: [555][100/521] Time 0.23 (0.26) Data 0.00 (0.01) Loss 0.041 (0.049) Prec@1 100.00 (99.28) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:45:42] Epoch: [555][200/521] Time 0.26 (0.26) Data 0.00 (0.00) Loss 0.027 (0.052) Prec@1 100.00 (99.25) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-11:46:08] Epoch: [555][300/521] Time 0.26 (0.26) Data 0.00 (0.00) Loss 0.078 (0.052) Prec@1 97.92 (99.24) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:46:33] Epoch: [555][400/521] Time 0.27 (0.26) Data 0.00 (0.00) Loss 0.024 (0.051) Prec@1 100.00 (99.25) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:47:00] Epoch: [555][500/521] Time 0.26 (0.26) Data 0.00 (0.00) Loss 0.067 (0.051) Prec@1 98.96 (99.24) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:47:05] Epoch: [555][520/521] Time 0.30 (0.26) Data 0.00 (0.00) Loss 0.033 (0.051) Prec@1 100.00 (99.25) Prec@5 100.00 (100.00)
|
|
[2019-04-01-11:47:05] **train** Prec@1 99.25 Prec@5 100.00 Error@1 0.75 Error@5 0.00 Loss:0.051
|
|
test [2019-04-01-11:47:06] Epoch: [555][000/105] Time 0.51 (0.51) Data 0.44 (0.44) Loss 0.056 (0.056) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-11:47:10] Epoch: [555][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.057 (0.126) Prec@1 98.96 (97.11) Prec@5 100.00 (99.95)
|
|
test [2019-04-01-11:47:10] Epoch: [555][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.013 (0.126) Prec@1 100.00 (97.09) Prec@5 100.00 (99.95)
|
|
[2019-04-01-11:47:10] **test** Prec@1 97.09 Prec@5 99.95 Error@1 2.91 Error@5 0.05 Loss:0.126
|
|
----> Best Accuracy : Acc@1=97.13, Acc@5=99.92, Error@1=2.87, Error@5=0.08
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-11:47:10] [Epoch=556/600] [Need: 01:43:19] LR=0.0004 ~ 0.0004, Batch=96
|
|
train[2019-04-01-11:47:11] Epoch: [556][000/521] Time 0.76 (0.76) Data 0.45 (0.45) Loss 0.058 (0.058) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:47:36] Epoch: [556][100/521] Time 0.24 (0.26) Data 0.00 (0.00) Loss 0.073 (0.055) Prec@1 98.96 (99.22) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:48:02] Epoch: [556][200/521] Time 0.25 (0.26) Data 0.00 (0.00) Loss 0.072 (0.056) Prec@1 98.96 (99.13) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:48:28] Epoch: [556][300/521] Time 0.26 (0.26) Data 0.00 (0.00) Loss 0.136 (0.055) Prec@1 96.88 (99.24) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:48:54] Epoch: [556][400/521] Time 0.25 (0.26) Data 0.00 (0.00) Loss 0.094 (0.053) Prec@1 98.96 (99.27) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-11:49:19] Epoch: [556][500/521] Time 0.24 (0.26) Data 0.00 (0.00) Loss 0.040 (0.052) Prec@1 98.96 (99.27) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:49:24] Epoch: [556][520/521] Time 0.24 (0.26) Data 0.00 (0.00) Loss 0.081 (0.053) Prec@1 97.50 (99.25) Prec@5 100.00 (99.99)
|
|
[2019-04-01-11:49:25] **train** Prec@1 99.25 Prec@5 99.99 Error@1 0.75 Error@5 0.01 Loss:0.053
|
|
test [2019-04-01-11:49:25] Epoch: [556][000/105] Time 0.54 (0.54) Data 0.47 (0.47) Loss 0.063 (0.063) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-11:49:29] Epoch: [556][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.078 (0.129) Prec@1 97.92 (97.07) Prec@5 100.00 (99.93)
|
|
test [2019-04-01-11:49:30] Epoch: [556][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.004 (0.129) Prec@1 100.00 (97.06) Prec@5 100.00 (99.93)
|
|
[2019-04-01-11:49:30] **test** Prec@1 97.06 Prec@5 99.93 Error@1 2.94 Error@5 0.07 Loss:0.129
|
|
----> Best Accuracy : Acc@1=97.13, Acc@5=99.92, Error@1=2.87, Error@5=0.08
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-11:49:30] [Epoch=557/600] [Need: 01:40:05] LR=0.0004 ~ 0.0004, Batch=96
|
|
train[2019-04-01-11:49:31] Epoch: [557][000/521] Time 0.88 (0.88) Data 0.60 (0.60) Loss 0.049 (0.049) Prec@1 100.00 (100.00) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:49:57] Epoch: [557][100/521] Time 0.24 (0.27) Data 0.00 (0.01) Loss 0.022 (0.050) Prec@1 100.00 (99.22) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-11:50:23] Epoch: [557][200/521] Time 0.26 (0.26) Data 0.00 (0.00) Loss 0.072 (0.053) Prec@1 97.92 (99.18) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-11:50:48] Epoch: [557][300/521] Time 0.26 (0.26) Data 0.00 (0.00) Loss 0.050 (0.052) Prec@1 98.96 (99.21) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:51:14] Epoch: [557][400/521] Time 0.24 (0.26) Data 0.00 (0.00) Loss 0.063 (0.052) Prec@1 98.96 (99.20) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:51:40] Epoch: [557][500/521] Time 0.27 (0.26) Data 0.00 (0.00) Loss 0.071 (0.052) Prec@1 98.96 (99.22) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:51:45] Epoch: [557][520/521] Time 0.23 (0.26) Data 0.00 (0.00) Loss 0.098 (0.052) Prec@1 98.75 (99.22) Prec@5 100.00 (100.00)
|
|
[2019-04-01-11:51:45] **train** Prec@1 99.22 Prec@5 100.00 Error@1 0.78 Error@5 0.00 Loss:0.052
|
|
test [2019-04-01-11:51:45] Epoch: [557][000/105] Time 0.51 (0.51) Data 0.43 (0.43) Loss 0.069 (0.069) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-11:51:50] Epoch: [557][100/105] Time 0.05 (0.05) Data 0.00 (0.00) Loss 0.083 (0.126) Prec@1 98.96 (97.13) Prec@5 100.00 (99.94)
|
|
test [2019-04-01-11:51:50] Epoch: [557][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.003 (0.127) Prec@1 100.00 (97.09) Prec@5 100.00 (99.94)
|
|
[2019-04-01-11:51:50] **test** Prec@1 97.09 Prec@5 99.94 Error@1 2.91 Error@5 0.06 Loss:0.127
|
|
----> Best Accuracy : Acc@1=97.13, Acc@5=99.92, Error@1=2.87, Error@5=0.08
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-11:51:50] [Epoch=558/600] [Need: 01:38:02] LR=0.0004 ~ 0.0004, Batch=96
|
|
train[2019-04-01-11:51:51] Epoch: [558][000/521] Time 0.89 (0.89) Data 0.60 (0.60) Loss 0.066 (0.066) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:52:16] Epoch: [558][100/521] Time 0.26 (0.26) Data 0.00 (0.01) Loss 0.090 (0.050) Prec@1 97.92 (99.35) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:52:43] Epoch: [558][200/521] Time 0.32 (0.26) Data 0.00 (0.00) Loss 0.028 (0.052) Prec@1 100.00 (99.26) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:53:08] Epoch: [558][300/521] Time 0.27 (0.26) Data 0.00 (0.00) Loss 0.059 (0.051) Prec@1 98.96 (99.28) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:53:34] Epoch: [558][400/521] Time 0.33 (0.26) Data 0.00 (0.00) Loss 0.028 (0.052) Prec@1 100.00 (99.27) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-11:54:00] Epoch: [558][500/521] Time 0.24 (0.26) Data 0.00 (0.00) Loss 0.012 (0.053) Prec@1 100.00 (99.25) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-11:54:05] Epoch: [558][520/521] Time 0.26 (0.26) Data 0.00 (0.00) Loss 0.026 (0.053) Prec@1 100.00 (99.25) Prec@5 100.00 (99.99)
|
|
[2019-04-01-11:54:05] **train** Prec@1 99.25 Prec@5 99.99 Error@1 0.75 Error@5 0.01 Loss:0.053
|
|
test [2019-04-01-11:54:06] Epoch: [558][000/105] Time 0.77 (0.77) Data 0.71 (0.71) Loss 0.075 (0.075) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-11:54:10] Epoch: [558][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.081 (0.126) Prec@1 97.92 (97.22) Prec@5 100.00 (99.92)
|
|
test [2019-04-01-11:54:10] Epoch: [558][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.002 (0.127) Prec@1 100.00 (97.18) Prec@5 100.00 (99.92)
|
|
[2019-04-01-11:54:11] **test** Prec@1 97.18 Prec@5 99.92 Error@1 2.82 Error@5 0.08 Loss:0.127
|
|
----> Best Accuracy : Acc@1=97.18, Acc@5=99.92, Error@1=2.82, Error@5=0.08
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-11:54:11] [Epoch=559/600] [Need: 01:36:16] LR=0.0004 ~ 0.0004, Batch=96
|
|
train[2019-04-01-11:54:12] Epoch: [559][000/521] Time 0.98 (0.98) Data 0.64 (0.64) Loss 0.057 (0.057) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:54:38] Epoch: [559][100/521] Time 0.32 (0.27) Data 0.00 (0.01) Loss 0.025 (0.056) Prec@1 100.00 (99.17) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:55:04] Epoch: [559][200/521] Time 0.26 (0.26) Data 0.00 (0.00) Loss 0.040 (0.052) Prec@1 100.00 (99.24) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:55:29] Epoch: [559][300/521] Time 0.24 (0.26) Data 0.00 (0.00) Loss 0.029 (0.051) Prec@1 100.00 (99.29) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:55:55] Epoch: [559][400/521] Time 0.24 (0.26) Data 0.00 (0.00) Loss 0.098 (0.051) Prec@1 97.92 (99.28) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-11:56:21] Epoch: [559][500/521] Time 0.24 (0.26) Data 0.00 (0.00) Loss 0.016 (0.051) Prec@1 100.00 (99.28) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:56:26] Epoch: [559][520/521] Time 0.25 (0.26) Data 0.00 (0.00) Loss 0.022 (0.051) Prec@1 100.00 (99.29) Prec@5 100.00 (100.00)
|
|
[2019-04-01-11:56:27] **train** Prec@1 99.29 Prec@5 100.00 Error@1 0.71 Error@5 0.00 Loss:0.051
|
|
test [2019-04-01-11:56:27] Epoch: [559][000/105] Time 0.76 (0.76) Data 0.68 (0.68) Loss 0.059 (0.059) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-11:56:32] Epoch: [559][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.076 (0.130) Prec@1 98.96 (97.10) Prec@5 100.00 (99.94)
|
|
test [2019-04-01-11:56:32] Epoch: [559][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.002 (0.131) Prec@1 100.00 (97.08) Prec@5 100.00 (99.94)
|
|
[2019-04-01-11:56:32] **test** Prec@1 97.08 Prec@5 99.94 Error@1 2.92 Error@5 0.06 Loss:0.131
|
|
----> Best Accuracy : Acc@1=97.18, Acc@5=99.92, Error@1=2.82, Error@5=0.08
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-11:56:32] [Epoch=560/600] [Need: 01:34:06] LR=0.0004 ~ 0.0004, Batch=96
|
|
train[2019-04-01-11:56:33] Epoch: [560][000/521] Time 1.08 (1.08) Data 0.78 (0.78) Loss 0.058 (0.058) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:56:59] Epoch: [560][100/521] Time 0.27 (0.27) Data 0.00 (0.01) Loss 0.038 (0.046) Prec@1 97.92 (99.37) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:57:25] Epoch: [560][200/521] Time 0.25 (0.27) Data 0.00 (0.00) Loss 0.094 (0.053) Prec@1 96.88 (99.23) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-11:57:52] Epoch: [560][300/521] Time 0.25 (0.26) Data 0.00 (0.00) Loss 0.035 (0.053) Prec@1 98.96 (99.22) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:58:17] Epoch: [560][400/521] Time 0.24 (0.26) Data 0.00 (0.00) Loss 0.031 (0.053) Prec@1 100.00 (99.21) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:58:43] Epoch: [560][500/521] Time 0.26 (0.26) Data 0.00 (0.00) Loss 0.147 (0.054) Prec@1 96.88 (99.19) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:58:48] Epoch: [560][520/521] Time 0.22 (0.26) Data 0.00 (0.00) Loss 0.064 (0.054) Prec@1 98.75 (99.21) Prec@5 100.00 (100.00)
|
|
[2019-04-01-11:58:48] **train** Prec@1 99.21 Prec@5 100.00 Error@1 0.79 Error@5 0.00 Loss:0.054
|
|
test [2019-04-01-11:58:49] Epoch: [560][000/105] Time 0.66 (0.66) Data 0.59 (0.59) Loss 0.053 (0.053) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-11:58:53] Epoch: [560][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.071 (0.123) Prec@1 97.92 (97.26) Prec@5 100.00 (99.94)
|
|
test [2019-04-01-11:58:53] Epoch: [560][104/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.001 (0.124) Prec@1 100.00 (97.22) Prec@5 100.00 (99.93)
|
|
[2019-04-01-11:58:54] **test** Prec@1 97.22 Prec@5 99.93 Error@1 2.78 Error@5 0.07 Loss:0.124
|
|
----> Best Accuracy : Acc@1=97.22, Acc@5=99.93, Error@1=2.78, Error@5=0.07
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-11:58:54] [Epoch=561/600] [Need: 01:32:05] LR=0.0004 ~ 0.0004, Batch=96
|
|
train[2019-04-01-11:58:55] Epoch: [561][000/521] Time 1.01 (1.01) Data 0.73 (0.73) Loss 0.046 (0.046) Prec@1 100.00 (100.00) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:59:20] Epoch: [561][100/521] Time 0.24 (0.26) Data 0.00 (0.01) Loss 0.078 (0.047) Prec@1 97.92 (99.34) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-11:59:46] Epoch: [561][200/521] Time 0.29 (0.26) Data 0.00 (0.00) Loss 0.056 (0.049) Prec@1 98.96 (99.33) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-12:00:12] Epoch: [561][300/521] Time 0.28 (0.26) Data 0.00 (0.00) Loss 0.036 (0.049) Prec@1 100.00 (99.36) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:00:38] Epoch: [561][400/521] Time 0.28 (0.26) Data 0.00 (0.00) Loss 0.111 (0.049) Prec@1 97.92 (99.33) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:01:03] Epoch: [561][500/521] Time 0.24 (0.26) Data 0.00 (0.00) Loss 0.077 (0.050) Prec@1 98.96 (99.31) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:01:08] Epoch: [561][520/521] Time 0.22 (0.26) Data 0.00 (0.00) Loss 0.011 (0.050) Prec@1 100.00 (99.31) Prec@5 100.00 (100.00)
|
|
[2019-04-01-12:01:08] **train** Prec@1 99.31 Prec@5 100.00 Error@1 0.69 Error@5 0.00 Loss:0.050
|
|
test [2019-04-01-12:01:09] Epoch: [561][000/105] Time 0.57 (0.57) Data 0.48 (0.48) Loss 0.060 (0.060) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-12:01:13] Epoch: [561][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.079 (0.126) Prec@1 97.92 (97.23) Prec@5 100.00 (99.95)
|
|
test [2019-04-01-12:01:13] Epoch: [561][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.002 (0.126) Prec@1 100.00 (97.19) Prec@5 100.00 (99.95)
|
|
[2019-04-01-12:01:14] **test** Prec@1 97.19 Prec@5 99.95 Error@1 2.81 Error@5 0.05 Loss:0.126
|
|
----> Best Accuracy : Acc@1=97.22, Acc@5=99.93, Error@1=2.78, Error@5=0.07
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-12:01:14] [Epoch=562/600] [Need: 01:28:38] LR=0.0003 ~ 0.0003, Batch=96
|
|
train[2019-04-01-12:01:14] Epoch: [562][000/521] Time 0.74 (0.74) Data 0.44 (0.44) Loss 0.063 (0.063) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:01:40] Epoch: [562][100/521] Time 0.27 (0.26) Data 0.00 (0.00) Loss 0.071 (0.051) Prec@1 97.92 (99.39) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-12:02:06] Epoch: [562][200/521] Time 0.25 (0.26) Data 0.00 (0.00) Loss 0.041 (0.052) Prec@1 98.96 (99.31) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-12:02:32] Epoch: [562][300/521] Time 0.26 (0.26) Data 0.00 (0.00) Loss 0.037 (0.052) Prec@1 100.00 (99.28) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-12:02:57] Epoch: [562][400/521] Time 0.24 (0.26) Data 0.00 (0.00) Loss 0.047 (0.051) Prec@1 100.00 (99.29) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-12:03:21] Epoch: [562][500/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.053 (0.051) Prec@1 100.00 (99.27) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-12:03:26] Epoch: [562][520/521] Time 0.22 (0.25) Data 0.00 (0.00) Loss 0.023 (0.051) Prec@1 100.00 (99.28) Prec@5 100.00 (99.99)
|
|
[2019-04-01-12:03:26] **train** Prec@1 99.28 Prec@5 99.99 Error@1 0.72 Error@5 0.01 Loss:0.051
|
|
test [2019-04-01-12:03:27] Epoch: [562][000/105] Time 0.48 (0.48) Data 0.42 (0.42) Loss 0.048 (0.048) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-12:03:31] Epoch: [562][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.060 (0.124) Prec@1 98.96 (97.27) Prec@5 100.00 (99.91)
|
|
test [2019-04-01-12:03:31] Epoch: [562][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.001 (0.125) Prec@1 100.00 (97.23) Prec@5 100.00 (99.91)
|
|
[2019-04-01-12:03:31] **test** Prec@1 97.23 Prec@5 99.91 Error@1 2.77 Error@5 0.09 Loss:0.125
|
|
----> Best Accuracy : Acc@1=97.23, Acc@5=99.91, Error@1=2.77, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-12:03:31] [Epoch=563/600] [Need: 01:24:46] LR=0.0003 ~ 0.0003, Batch=96
|
|
train[2019-04-01-12:03:32] Epoch: [563][000/521] Time 0.74 (0.74) Data 0.45 (0.45) Loss 0.130 (0.130) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:03:57] Epoch: [563][100/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.032 (0.053) Prec@1 100.00 (99.23) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:04:21] Epoch: [563][200/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.039 (0.053) Prec@1 98.96 (99.21) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:04:45] Epoch: [563][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.035 (0.052) Prec@1 98.96 (99.28) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:05:09] Epoch: [563][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.018 (0.052) Prec@1 100.00 (99.25) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:05:33] Epoch: [563][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.024 (0.052) Prec@1 100.00 (99.25) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:05:38] Epoch: [563][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.023 (0.052) Prec@1 100.00 (99.25) Prec@5 100.00 (100.00)
|
|
[2019-04-01-12:05:38] **train** Prec@1 99.25 Prec@5 100.00 Error@1 0.75 Error@5 0.00 Loss:0.052
|
|
test [2019-04-01-12:05:39] Epoch: [563][000/105] Time 0.61 (0.61) Data 0.55 (0.55) Loss 0.062 (0.062) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-12:05:43] Epoch: [563][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.087 (0.126) Prec@1 97.92 (97.21) Prec@5 100.00 (99.92)
|
|
test [2019-04-01-12:05:43] Epoch: [563][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.002 (0.127) Prec@1 100.00 (97.17) Prec@5 100.00 (99.92)
|
|
[2019-04-01-12:05:43] **test** Prec@1 97.17 Prec@5 99.92 Error@1 2.83 Error@5 0.08 Loss:0.127
|
|
----> Best Accuracy : Acc@1=97.23, Acc@5=99.91, Error@1=2.77, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-12:05:43] [Epoch=564/600] [Need: 01:19:07] LR=0.0003 ~ 0.0003, Batch=96
|
|
train[2019-04-01-12:05:44] Epoch: [564][000/521] Time 0.72 (0.72) Data 0.45 (0.45) Loss 0.061 (0.061) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:06:08] Epoch: [564][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.065 (0.053) Prec@1 98.96 (99.15) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-12:06:32] Epoch: [564][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.084 (0.050) Prec@1 97.92 (99.27) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-12:06:56] Epoch: [564][300/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.034 (0.050) Prec@1 100.00 (99.26) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:07:20] Epoch: [564][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.032 (0.051) Prec@1 98.96 (99.23) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:07:44] Epoch: [564][500/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.054 (0.051) Prec@1 97.92 (99.25) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:07:49] Epoch: [564][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.047 (0.051) Prec@1 100.00 (99.26) Prec@5 100.00 (100.00)
|
|
[2019-04-01-12:07:49] **train** Prec@1 99.26 Prec@5 100.00 Error@1 0.74 Error@5 0.00 Loss:0.051
|
|
test [2019-04-01-12:07:50] Epoch: [564][000/105] Time 0.61 (0.61) Data 0.54 (0.54) Loss 0.073 (0.073) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-12:07:54] Epoch: [564][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.066 (0.125) Prec@1 97.92 (97.27) Prec@5 100.00 (99.93)
|
|
test [2019-04-01-12:07:54] Epoch: [564][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.001 (0.125) Prec@1 100.00 (97.22) Prec@5 100.00 (99.93)
|
|
[2019-04-01-12:07:54] **test** Prec@1 97.22 Prec@5 99.93 Error@1 2.78 Error@5 0.07 Loss:0.125
|
|
----> Best Accuracy : Acc@1=97.23, Acc@5=99.91, Error@1=2.77, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-12:07:54] [Epoch=565/600] [Need: 01:16:36] LR=0.0003 ~ 0.0003, Batch=96
|
|
train[2019-04-01-12:07:55] Epoch: [565][000/521] Time 0.86 (0.86) Data 0.59 (0.59) Loss 0.056 (0.056) Prec@1 100.00 (100.00) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:08:19] Epoch: [565][100/521] Time 0.24 (0.25) Data 0.00 (0.01) Loss 0.023 (0.047) Prec@1 100.00 (99.36) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:08:43] Epoch: [565][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.077 (0.052) Prec@1 98.96 (99.26) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:09:07] Epoch: [565][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.046 (0.051) Prec@1 98.96 (99.30) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:09:32] Epoch: [565][400/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.095 (0.051) Prec@1 97.92 (99.26) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:09:56] Epoch: [565][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.073 (0.052) Prec@1 98.96 (99.26) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:10:01] Epoch: [565][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.046 (0.051) Prec@1 100.00 (99.26) Prec@5 100.00 (100.00)
|
|
[2019-04-01-12:10:01] **train** Prec@1 99.26 Prec@5 100.00 Error@1 0.74 Error@5 0.00 Loss:0.051
|
|
test [2019-04-01-12:10:01] Epoch: [565][000/105] Time 0.60 (0.60) Data 0.53 (0.53) Loss 0.070 (0.070) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-12:10:05] Epoch: [565][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.079 (0.127) Prec@1 97.92 (97.18) Prec@5 100.00 (99.94)
|
|
test [2019-04-01-12:10:06] Epoch: [565][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.001 (0.127) Prec@1 100.00 (97.16) Prec@5 100.00 (99.94)
|
|
[2019-04-01-12:10:06] **test** Prec@1 97.16 Prec@5 99.94 Error@1 2.84 Error@5 0.06 Loss:0.127
|
|
----> Best Accuracy : Acc@1=97.23, Acc@5=99.91, Error@1=2.77, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-12:10:06] [Epoch=566/600] [Need: 01:14:31] LR=0.0003 ~ 0.0003, Batch=96
|
|
train[2019-04-01-12:10:07] Epoch: [566][000/521] Time 0.72 (0.72) Data 0.44 (0.44) Loss 0.103 (0.103) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:10:31] Epoch: [566][100/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.040 (0.048) Prec@1 98.96 (99.30) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:10:55] Epoch: [566][200/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.039 (0.050) Prec@1 98.96 (99.23) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-12:11:19] Epoch: [566][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.044 (0.049) Prec@1 100.00 (99.27) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-12:11:44] Epoch: [566][400/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.118 (0.049) Prec@1 97.92 (99.28) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-12:12:08] Epoch: [566][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.045 (0.049) Prec@1 98.96 (99.29) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:12:13] Epoch: [566][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.017 (0.048) Prec@1 100.00 (99.29) Prec@5 100.00 (100.00)
|
|
[2019-04-01-12:12:13] **train** Prec@1 99.29 Prec@5 100.00 Error@1 0.71 Error@5 0.00 Loss:0.048
|
|
test [2019-04-01-12:12:13] Epoch: [566][000/105] Time 0.51 (0.51) Data 0.46 (0.46) Loss 0.054 (0.054) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-12:12:17] Epoch: [566][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.069 (0.128) Prec@1 97.92 (97.08) Prec@5 100.00 (99.93)
|
|
test [2019-04-01-12:12:17] Epoch: [566][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.000 (0.129) Prec@1 100.00 (97.06) Prec@5 100.00 (99.93)
|
|
[2019-04-01-12:12:18] **test** Prec@1 97.06 Prec@5 99.93 Error@1 2.94 Error@5 0.07 Loss:0.129
|
|
----> Best Accuracy : Acc@1=97.23, Acc@5=99.91, Error@1=2.77, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-12:12:18] [Epoch=567/600] [Need: 01:12:29] LR=0.0003 ~ 0.0003, Batch=96
|
|
train[2019-04-01-12:12:18] Epoch: [567][000/521] Time 0.73 (0.73) Data 0.45 (0.45) Loss 0.026 (0.026) Prec@1 100.00 (100.00) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:12:43] Epoch: [567][100/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.014 (0.050) Prec@1 100.00 (99.24) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:13:07] Epoch: [567][200/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.017 (0.052) Prec@1 98.96 (99.21) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:13:31] Epoch: [567][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.052 (0.051) Prec@1 98.96 (99.22) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:13:55] Epoch: [567][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.094 (0.051) Prec@1 97.92 (99.23) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:14:20] Epoch: [567][500/521] Time 0.26 (0.24) Data 0.00 (0.00) Loss 0.112 (0.050) Prec@1 98.96 (99.26) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:14:24] Epoch: [567][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.044 (0.050) Prec@1 98.75 (99.26) Prec@5 100.00 (100.00)
|
|
[2019-04-01-12:14:25] **train** Prec@1 99.26 Prec@5 100.00 Error@1 0.74 Error@5 0.00 Loss:0.050
|
|
test [2019-04-01-12:14:25] Epoch: [567][000/105] Time 0.48 (0.48) Data 0.41 (0.41) Loss 0.072 (0.072) Prec@1 95.83 (95.83) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-12:14:29] Epoch: [567][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.073 (0.128) Prec@1 97.92 (97.12) Prec@5 100.00 (99.94)
|
|
test [2019-04-01-12:14:29] Epoch: [567][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.004 (0.128) Prec@1 100.00 (97.09) Prec@5 100.00 (99.94)
|
|
[2019-04-01-12:14:29] **test** Prec@1 97.09 Prec@5 99.94 Error@1 2.91 Error@5 0.06 Loss:0.128
|
|
----> Best Accuracy : Acc@1=97.23, Acc@5=99.91, Error@1=2.77, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-12:14:29] [Epoch=568/600] [Need: 01:10:18] LR=0.0003 ~ 0.0003, Batch=96
|
|
train[2019-04-01-12:14:30] Epoch: [568][000/521] Time 0.72 (0.72) Data 0.44 (0.44) Loss 0.115 (0.115) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:14:54] Epoch: [568][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.045 (0.054) Prec@1 100.00 (99.28) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:15:18] Epoch: [568][200/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.054 (0.055) Prec@1 100.00 (99.19) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:15:42] Epoch: [568][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.057 (0.052) Prec@1 100.00 (99.25) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:16:07] Epoch: [568][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.032 (0.052) Prec@1 100.00 (99.27) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:16:31] Epoch: [568][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.025 (0.052) Prec@1 100.00 (99.23) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:16:35] Epoch: [568][520/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.052 (0.051) Prec@1 98.75 (99.26) Prec@5 100.00 (100.00)
|
|
[2019-04-01-12:16:36] **train** Prec@1 99.26 Prec@5 100.00 Error@1 0.74 Error@5 0.00 Loss:0.051
|
|
test [2019-04-01-12:16:36] Epoch: [568][000/105] Time 0.49 (0.49) Data 0.42 (0.42) Loss 0.068 (0.068) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-12:16:40] Epoch: [568][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.067 (0.127) Prec@1 97.92 (97.12) Prec@5 100.00 (99.93)
|
|
test [2019-04-01-12:16:40] Epoch: [568][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.003 (0.127) Prec@1 100.00 (97.09) Prec@5 100.00 (99.93)
|
|
[2019-04-01-12:16:40] **test** Prec@1 97.09 Prec@5 99.93 Error@1 2.91 Error@5 0.07 Loss:0.127
|
|
----> Best Accuracy : Acc@1=97.23, Acc@5=99.91, Error@1=2.77, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-12:16:40] [Epoch=569/600] [Need: 01:07:40] LR=0.0003 ~ 0.0003, Batch=96
|
|
train[2019-04-01-12:16:41] Epoch: [569][000/521] Time 0.73 (0.73) Data 0.46 (0.46) Loss 0.071 (0.071) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:17:05] Epoch: [569][100/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.012 (0.047) Prec@1 100.00 (99.35) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:17:30] Epoch: [569][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.010 (0.048) Prec@1 100.00 (99.30) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:17:54] Epoch: [569][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.026 (0.049) Prec@1 100.00 (99.26) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:18:18] Epoch: [569][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.040 (0.049) Prec@1 100.00 (99.25) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:18:42] Epoch: [569][500/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.139 (0.050) Prec@1 95.83 (99.23) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:18:47] Epoch: [569][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.044 (0.049) Prec@1 100.00 (99.25) Prec@5 100.00 (100.00)
|
|
[2019-04-01-12:18:47] **train** Prec@1 99.25 Prec@5 100.00 Error@1 0.75 Error@5 0.00 Loss:0.049
|
|
test [2019-04-01-12:18:47] Epoch: [569][000/105] Time 0.62 (0.62) Data 0.55 (0.55) Loss 0.046 (0.046) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-12:18:52] Epoch: [569][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.074 (0.128) Prec@1 96.88 (97.02) Prec@5 100.00 (99.94)
|
|
test [2019-04-01-12:18:52] Epoch: [569][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.003 (0.129) Prec@1 100.00 (96.99) Prec@5 100.00 (99.93)
|
|
[2019-04-01-12:18:52] **test** Prec@1 96.99 Prec@5 99.93 Error@1 3.01 Error@5 0.07 Loss:0.129
|
|
----> Best Accuracy : Acc@1=97.23, Acc@5=99.91, Error@1=2.77, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-12:18:52] [Epoch=570/600] [Need: 01:05:44] LR=0.0003 ~ 0.0003, Batch=96
|
|
train[2019-04-01-12:18:53] Epoch: [570][000/521] Time 0.88 (0.88) Data 0.60 (0.60) Loss 0.071 (0.071) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:19:17] Epoch: [570][100/521] Time 0.23 (0.25) Data 0.00 (0.01) Loss 0.080 (0.047) Prec@1 100.00 (99.42) Prec@5 100.00 (99.98)
|
|
train[2019-04-01-12:19:41] Epoch: [570][200/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.033 (0.049) Prec@1 98.96 (99.36) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-12:20:06] Epoch: [570][300/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.044 (0.048) Prec@1 98.96 (99.37) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-12:20:30] Epoch: [570][400/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.039 (0.048) Prec@1 100.00 (99.36) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-12:20:54] Epoch: [570][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.068 (0.049) Prec@1 98.96 (99.33) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-12:20:59] Epoch: [570][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.098 (0.049) Prec@1 98.75 (99.33) Prec@5 100.00 (99.99)
|
|
[2019-04-01-12:20:59] **train** Prec@1 99.33 Prec@5 99.99 Error@1 0.67 Error@5 0.01 Loss:0.049
|
|
test [2019-04-01-12:20:59] Epoch: [570][000/105] Time 0.61 (0.61) Data 0.55 (0.55) Loss 0.063 (0.063) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-12:21:03] Epoch: [570][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.059 (0.127) Prec@1 97.92 (97.16) Prec@5 100.00 (99.94)
|
|
test [2019-04-01-12:21:04] Epoch: [570][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.004 (0.128) Prec@1 100.00 (97.12) Prec@5 100.00 (99.94)
|
|
[2019-04-01-12:21:04] **test** Prec@1 97.12 Prec@5 99.94 Error@1 2.88 Error@5 0.06 Loss:0.128
|
|
----> Best Accuracy : Acc@1=97.23, Acc@5=99.91, Error@1=2.77, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-12:21:04] [Epoch=571/600] [Need: 01:03:45] LR=0.0002 ~ 0.0002, Batch=96
|
|
train[2019-04-01-12:21:05] Epoch: [571][000/521] Time 0.72 (0.72) Data 0.44 (0.44) Loss 0.049 (0.049) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:21:29] Epoch: [571][100/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.019 (0.050) Prec@1 100.00 (99.24) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:21:53] Epoch: [571][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.071 (0.050) Prec@1 98.96 (99.28) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:22:17] Epoch: [571][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.082 (0.048) Prec@1 97.92 (99.35) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:22:41] Epoch: [571][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.096 (0.050) Prec@1 98.96 (99.30) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:23:06] Epoch: [571][500/521] Time 0.26 (0.24) Data 0.00 (0.00) Loss 0.076 (0.049) Prec@1 97.92 (99.31) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:23:11] Epoch: [571][520/521] Time 0.21 (0.24) Data 0.00 (0.00) Loss 0.023 (0.049) Prec@1 100.00 (99.32) Prec@5 100.00 (100.00)
|
|
[2019-04-01-12:23:11] **train** Prec@1 99.32 Prec@5 100.00 Error@1 0.68 Error@5 0.00 Loss:0.049
|
|
test [2019-04-01-12:23:11] Epoch: [571][000/105] Time 0.60 (0.60) Data 0.54 (0.54) Loss 0.053 (0.053) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-12:23:15] Epoch: [571][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.095 (0.127) Prec@1 96.88 (97.10) Prec@5 100.00 (99.94)
|
|
test [2019-04-01-12:23:16] Epoch: [571][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.004 (0.128) Prec@1 100.00 (97.08) Prec@5 100.00 (99.94)
|
|
[2019-04-01-12:23:16] **test** Prec@1 97.08 Prec@5 99.94 Error@1 2.92 Error@5 0.06 Loss:0.128
|
|
----> Best Accuracy : Acc@1=97.23, Acc@5=99.91, Error@1=2.77, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-12:23:16] [Epoch=572/600] [Need: 01:01:33] LR=0.0002 ~ 0.0002, Batch=96
|
|
train[2019-04-01-12:23:17] Epoch: [572][000/521] Time 0.72 (0.72) Data 0.45 (0.45) Loss 0.070 (0.070) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:23:41] Epoch: [572][100/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.069 (0.051) Prec@1 98.96 (99.28) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:24:05] Epoch: [572][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.009 (0.050) Prec@1 100.00 (99.27) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:24:29] Epoch: [572][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.098 (0.051) Prec@1 97.92 (99.23) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:24:53] Epoch: [572][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.081 (0.050) Prec@1 97.92 (99.25) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:25:17] Epoch: [572][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.025 (0.050) Prec@1 100.00 (99.25) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:25:22] Epoch: [572][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.051 (0.050) Prec@1 98.75 (99.25) Prec@5 100.00 (100.00)
|
|
[2019-04-01-12:25:22] **train** Prec@1 99.25 Prec@5 100.00 Error@1 0.75 Error@5 0.00 Loss:0.050
|
|
test [2019-04-01-12:25:23] Epoch: [572][000/105] Time 0.61 (0.61) Data 0.55 (0.55) Loss 0.060 (0.060) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-12:25:27] Epoch: [572][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.061 (0.127) Prec@1 97.92 (97.08) Prec@5 100.00 (99.92)
|
|
test [2019-04-01-12:25:27] Epoch: [572][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.005 (0.128) Prec@1 100.00 (97.05) Prec@5 100.00 (99.92)
|
|
[2019-04-01-12:25:27] **test** Prec@1 97.05 Prec@5 99.92 Error@1 2.95 Error@5 0.08 Loss:0.128
|
|
----> Best Accuracy : Acc@1=97.23, Acc@5=99.91, Error@1=2.77, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-12:25:27] [Epoch=573/600] [Need: 00:59:15] LR=0.0002 ~ 0.0002, Batch=96
|
|
train[2019-04-01-12:25:28] Epoch: [573][000/521] Time 0.73 (0.73) Data 0.44 (0.44) Loss 0.079 (0.079) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:25:52] Epoch: [573][100/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.040 (0.056) Prec@1 100.00 (99.27) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-12:26:17] Epoch: [573][200/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.098 (0.053) Prec@1 98.96 (99.35) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-12:26:41] Epoch: [573][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.051 (0.052) Prec@1 98.96 (99.34) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:27:05] Epoch: [573][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.046 (0.051) Prec@1 100.00 (99.29) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-12:27:29] Epoch: [573][500/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.033 (0.050) Prec@1 100.00 (99.33) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:27:34] Epoch: [573][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.034 (0.050) Prec@1 100.00 (99.34) Prec@5 100.00 (100.00)
|
|
[2019-04-01-12:27:34] **train** Prec@1 99.34 Prec@5 100.00 Error@1 0.66 Error@5 0.00 Loss:0.050
|
|
test [2019-04-01-12:27:34] Epoch: [573][000/105] Time 0.49 (0.49) Data 0.43 (0.43) Loss 0.049 (0.049) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-12:27:38] Epoch: [573][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.069 (0.127) Prec@1 97.92 (97.06) Prec@5 100.00 (99.92)
|
|
test [2019-04-01-12:27:39] Epoch: [573][104/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.001 (0.127) Prec@1 100.00 (97.03) Prec@5 100.00 (99.92)
|
|
[2019-04-01-12:27:39] **test** Prec@1 97.03 Prec@5 99.92 Error@1 2.97 Error@5 0.08 Loss:0.127
|
|
----> Best Accuracy : Acc@1=97.23, Acc@5=99.91, Error@1=2.77, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-12:27:39] [Epoch=574/600] [Need: 00:56:57] LR=0.0002 ~ 0.0002, Batch=96
|
|
train[2019-04-01-12:27:40] Epoch: [574][000/521] Time 0.74 (0.74) Data 0.44 (0.44) Loss 0.070 (0.070) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:28:04] Epoch: [574][100/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.052 (0.055) Prec@1 98.96 (99.27) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:28:28] Epoch: [574][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.029 (0.052) Prec@1 100.00 (99.28) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:28:52] Epoch: [574][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.099 (0.051) Prec@1 97.92 (99.29) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:29:16] Epoch: [574][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.082 (0.050) Prec@1 100.00 (99.31) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:29:41] Epoch: [574][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.053 (0.051) Prec@1 98.96 (99.28) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:29:45] Epoch: [574][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.017 (0.050) Prec@1 100.00 (99.28) Prec@5 100.00 (100.00)
|
|
[2019-04-01-12:29:45] **train** Prec@1 99.28 Prec@5 100.00 Error@1 0.72 Error@5 0.00 Loss:0.050
|
|
test [2019-04-01-12:29:46] Epoch: [574][000/105] Time 0.49 (0.49) Data 0.41 (0.41) Loss 0.057 (0.057) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-12:29:50] Epoch: [574][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.077 (0.127) Prec@1 97.92 (97.10) Prec@5 100.00 (99.92)
|
|
test [2019-04-01-12:29:50] Epoch: [574][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.003 (0.128) Prec@1 100.00 (97.07) Prec@5 100.00 (99.92)
|
|
[2019-04-01-12:29:50] **test** Prec@1 97.07 Prec@5 99.92 Error@1 2.93 Error@5 0.08 Loss:0.128
|
|
----> Best Accuracy : Acc@1=97.23, Acc@5=99.91, Error@1=2.77, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-12:29:50] [Epoch=575/600] [Need: 00:54:47] LR=0.0002 ~ 0.0002, Batch=96
|
|
train[2019-04-01-12:29:51] Epoch: [575][000/521] Time 0.73 (0.73) Data 0.44 (0.44) Loss 0.072 (0.072) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:30:15] Epoch: [575][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.021 (0.050) Prec@1 100.00 (99.22) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-12:30:39] Epoch: [575][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.096 (0.050) Prec@1 97.92 (99.26) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-12:31:03] Epoch: [575][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.065 (0.048) Prec@1 98.96 (99.26) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-12:31:27] Epoch: [575][400/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.059 (0.049) Prec@1 100.00 (99.26) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-12:31:51] Epoch: [575][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.009 (0.048) Prec@1 100.00 (99.30) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:31:56] Epoch: [575][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.049 (0.048) Prec@1 98.75 (99.30) Prec@5 100.00 (100.00)
|
|
[2019-04-01-12:31:56] **train** Prec@1 99.30 Prec@5 100.00 Error@1 0.70 Error@5 0.00 Loss:0.048
|
|
test [2019-04-01-12:31:57] Epoch: [575][000/105] Time 0.48 (0.48) Data 0.42 (0.42) Loss 0.059 (0.059) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-12:32:01] Epoch: [575][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.057 (0.126) Prec@1 97.92 (97.16) Prec@5 100.00 (99.93)
|
|
test [2019-04-01-12:32:01] Epoch: [575][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.003 (0.127) Prec@1 100.00 (97.11) Prec@5 100.00 (99.93)
|
|
[2019-04-01-12:32:01] **test** Prec@1 97.11 Prec@5 99.93 Error@1 2.89 Error@5 0.07 Loss:0.127
|
|
----> Best Accuracy : Acc@1=97.23, Acc@5=99.91, Error@1=2.77, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-12:32:01] [Epoch=576/600] [Need: 00:52:15] LR=0.0002 ~ 0.0002, Batch=96
|
|
train[2019-04-01-12:32:02] Epoch: [576][000/521] Time 0.84 (0.84) Data 0.56 (0.56) Loss 0.036 (0.036) Prec@1 100.00 (100.00) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:32:26] Epoch: [576][100/521] Time 0.25 (0.25) Data 0.00 (0.01) Loss 0.055 (0.047) Prec@1 98.96 (99.41) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:32:50] Epoch: [576][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.011 (0.050) Prec@1 100.00 (99.33) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-12:33:14] Epoch: [576][300/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.057 (0.049) Prec@1 97.92 (99.35) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:33:38] Epoch: [576][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.028 (0.049) Prec@1 100.00 (99.33) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:34:02] Epoch: [576][500/521] Time 0.26 (0.24) Data 0.00 (0.00) Loss 0.024 (0.049) Prec@1 98.96 (99.33) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-12:34:07] Epoch: [576][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.062 (0.049) Prec@1 98.75 (99.34) Prec@5 100.00 (99.99)
|
|
[2019-04-01-12:34:07] **train** Prec@1 99.34 Prec@5 99.99 Error@1 0.66 Error@5 0.01 Loss:0.049
|
|
test [2019-04-01-12:34:08] Epoch: [576][000/105] Time 0.47 (0.47) Data 0.41 (0.41) Loss 0.072 (0.072) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-12:34:12] Epoch: [576][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.055 (0.126) Prec@1 98.96 (97.10) Prec@5 100.00 (99.92)
|
|
test [2019-04-01-12:34:12] Epoch: [576][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.003 (0.127) Prec@1 100.00 (97.06) Prec@5 100.00 (99.92)
|
|
[2019-04-01-12:34:12] **test** Prec@1 97.06 Prec@5 99.92 Error@1 2.94 Error@5 0.08 Loss:0.127
|
|
----> Best Accuracy : Acc@1=97.23, Acc@5=99.91, Error@1=2.77, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-12:34:12] [Epoch=577/600] [Need: 00:50:16] LR=0.0002 ~ 0.0002, Batch=96
|
|
train[2019-04-01-12:34:13] Epoch: [577][000/521] Time 0.74 (0.74) Data 0.46 (0.46) Loss 0.056 (0.056) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:34:37] Epoch: [577][100/521] Time 0.26 (0.25) Data 0.00 (0.00) Loss 0.041 (0.048) Prec@1 100.00 (99.33) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:35:01] Epoch: [577][200/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.049 (0.048) Prec@1 98.96 (99.36) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:35:26] Epoch: [577][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.089 (0.049) Prec@1 97.92 (99.32) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:35:51] Epoch: [577][400/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.084 (0.049) Prec@1 98.96 (99.28) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:36:15] Epoch: [577][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.068 (0.050) Prec@1 100.00 (99.28) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:36:19] Epoch: [577][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.052 (0.050) Prec@1 98.75 (99.28) Prec@5 100.00 (100.00)
|
|
[2019-04-01-12:36:19] **train** Prec@1 99.28 Prec@5 100.00 Error@1 0.72 Error@5 0.00 Loss:0.050
|
|
test [2019-04-01-12:36:20] Epoch: [577][000/105] Time 0.47 (0.47) Data 0.41 (0.41) Loss 0.056 (0.056) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-12:36:24] Epoch: [577][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.067 (0.126) Prec@1 97.92 (97.12) Prec@5 100.00 (99.93)
|
|
test [2019-04-01-12:36:24] Epoch: [577][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.002 (0.126) Prec@1 100.00 (97.08) Prec@5 100.00 (99.93)
|
|
[2019-04-01-12:36:24] **test** Prec@1 97.08 Prec@5 99.93 Error@1 2.92 Error@5 0.07 Loss:0.126
|
|
----> Best Accuracy : Acc@1=97.23, Acc@5=99.91, Error@1=2.77, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-12:36:24] [Epoch=578/600] [Need: 00:48:27] LR=0.0002 ~ 0.0002, Batch=96
|
|
train[2019-04-01-12:36:25] Epoch: [578][000/521] Time 0.74 (0.74) Data 0.46 (0.46) Loss 0.046 (0.046) Prec@1 100.00 (100.00) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:36:49] Epoch: [578][100/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.043 (0.047) Prec@1 98.96 (99.30) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:37:13] Epoch: [578][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.010 (0.047) Prec@1 100.00 (99.32) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-12:37:37] Epoch: [578][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.063 (0.048) Prec@1 98.96 (99.30) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-12:38:02] Epoch: [578][400/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.047 (0.048) Prec@1 100.00 (99.32) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-12:38:26] Epoch: [578][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.059 (0.049) Prec@1 98.96 (99.29) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-12:38:31] Epoch: [578][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.011 (0.049) Prec@1 100.00 (99.29) Prec@5 100.00 (99.99)
|
|
[2019-04-01-12:38:31] **train** Prec@1 99.29 Prec@5 99.99 Error@1 0.71 Error@5 0.01 Loss:0.049
|
|
test [2019-04-01-12:38:31] Epoch: [578][000/105] Time 0.61 (0.61) Data 0.54 (0.54) Loss 0.044 (0.044) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-12:38:35] Epoch: [578][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.069 (0.125) Prec@1 96.88 (97.11) Prec@5 100.00 (99.93)
|
|
test [2019-04-01-12:38:36] Epoch: [578][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.001 (0.125) Prec@1 100.00 (97.07) Prec@5 100.00 (99.93)
|
|
[2019-04-01-12:38:36] **test** Prec@1 97.07 Prec@5 99.93 Error@1 2.93 Error@5 0.07 Loss:0.125
|
|
----> Best Accuracy : Acc@1=97.23, Acc@5=99.91, Error@1=2.77, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-12:38:36] [Epoch=579/600] [Need: 00:46:02] LR=0.0002 ~ 0.0002, Batch=96
|
|
train[2019-04-01-12:38:37] Epoch: [579][000/521] Time 0.84 (0.84) Data 0.57 (0.57) Loss 0.040 (0.040) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:39:01] Epoch: [579][100/521] Time 0.24 (0.25) Data 0.00 (0.01) Loss 0.029 (0.049) Prec@1 100.00 (99.29) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:39:25] Epoch: [579][200/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.012 (0.050) Prec@1 100.00 (99.25) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:39:50] Epoch: [579][300/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.033 (0.050) Prec@1 98.96 (99.27) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:40:14] Epoch: [579][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.048 (0.050) Prec@1 98.96 (99.27) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:40:38] Epoch: [579][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.064 (0.051) Prec@1 97.92 (99.26) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:40:43] Epoch: [579][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.020 (0.050) Prec@1 100.00 (99.28) Prec@5 100.00 (100.00)
|
|
[2019-04-01-12:40:43] **train** Prec@1 99.28 Prec@5 100.00 Error@1 0.72 Error@5 0.00 Loss:0.050
|
|
test [2019-04-01-12:40:44] Epoch: [579][000/105] Time 0.48 (0.48) Data 0.42 (0.42) Loss 0.057 (0.057) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-12:40:48] Epoch: [579][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.062 (0.128) Prec@1 98.96 (97.03) Prec@5 100.00 (99.93)
|
|
test [2019-04-01-12:40:48] Epoch: [579][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.003 (0.128) Prec@1 100.00 (96.99) Prec@5 100.00 (99.92)
|
|
[2019-04-01-12:40:48] **test** Prec@1 96.99 Prec@5 99.92 Error@1 3.01 Error@5 0.08 Loss:0.128
|
|
----> Best Accuracy : Acc@1=97.23, Acc@5=99.91, Error@1=2.77, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-12:40:48] [Epoch=580/600] [Need: 00:44:09] LR=0.0002 ~ 0.0002, Batch=96
|
|
train[2019-04-01-12:40:49] Epoch: [580][000/521] Time 0.79 (0.79) Data 0.51 (0.51) Loss 0.040 (0.040) Prec@1 100.00 (100.00) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:41:13] Epoch: [580][100/521] Time 0.23 (0.25) Data 0.00 (0.01) Loss 0.028 (0.047) Prec@1 100.00 (99.34) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:41:37] Epoch: [580][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.028 (0.048) Prec@1 100.00 (99.36) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:42:02] Epoch: [580][300/521] Time 0.27 (0.24) Data 0.00 (0.00) Loss 0.029 (0.047) Prec@1 100.00 (99.37) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:42:26] Epoch: [580][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.046 (0.048) Prec@1 100.00 (99.31) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:42:50] Epoch: [580][500/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.024 (0.049) Prec@1 100.00 (99.31) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:42:55] Epoch: [580][520/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.090 (0.049) Prec@1 97.50 (99.31) Prec@5 100.00 (100.00)
|
|
[2019-04-01-12:42:55] **train** Prec@1 99.31 Prec@5 100.00 Error@1 0.69 Error@5 0.00 Loss:0.049
|
|
test [2019-04-01-12:42:56] Epoch: [580][000/105] Time 0.49 (0.49) Data 0.42 (0.42) Loss 0.049 (0.049) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-12:43:00] Epoch: [580][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.046 (0.124) Prec@1 98.96 (97.06) Prec@5 100.00 (99.92)
|
|
test [2019-04-01-12:43:00] Epoch: [580][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.004 (0.125) Prec@1 100.00 (97.03) Prec@5 100.00 (99.91)
|
|
[2019-04-01-12:43:00] **test** Prec@1 97.03 Prec@5 99.91 Error@1 2.97 Error@5 0.09 Loss:0.125
|
|
----> Best Accuracy : Acc@1=97.23, Acc@5=99.91, Error@1=2.77, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-12:43:00] [Epoch=581/600] [Need: 00:41:40] LR=0.0002 ~ 0.0002, Batch=96
|
|
train[2019-04-01-12:43:01] Epoch: [581][000/521] Time 0.85 (0.85) Data 0.56 (0.56) Loss 0.055 (0.055) Prec@1 100.00 (100.00) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:43:25] Epoch: [581][100/521] Time 0.25 (0.25) Data 0.00 (0.01) Loss 0.232 (0.059) Prec@1 97.92 (99.11) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:43:50] Epoch: [581][200/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.032 (0.054) Prec@1 98.96 (99.23) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:44:14] Epoch: [581][300/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.018 (0.051) Prec@1 100.00 (99.30) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:44:38] Epoch: [581][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.055 (0.050) Prec@1 98.96 (99.32) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:45:02] Epoch: [581][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.065 (0.051) Prec@1 98.96 (99.31) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:45:07] Epoch: [581][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.105 (0.051) Prec@1 97.50 (99.30) Prec@5 100.00 (100.00)
|
|
[2019-04-01-12:45:07] **train** Prec@1 99.30 Prec@5 100.00 Error@1 0.70 Error@5 0.00 Loss:0.051
|
|
test [2019-04-01-12:45:08] Epoch: [581][000/105] Time 0.56 (0.56) Data 0.46 (0.46) Loss 0.055 (0.055) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-12:45:12] Epoch: [581][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.063 (0.127) Prec@1 98.96 (97.11) Prec@5 100.00 (99.91)
|
|
test [2019-04-01-12:45:12] Epoch: [581][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.003 (0.128) Prec@1 100.00 (97.09) Prec@5 100.00 (99.90)
|
|
[2019-04-01-12:45:12] **test** Prec@1 97.09 Prec@5 99.90 Error@1 2.91 Error@5 0.10 Loss:0.128
|
|
----> Best Accuracy : Acc@1=97.23, Acc@5=99.91, Error@1=2.77, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-12:45:12] [Epoch=582/600] [Need: 00:39:38] LR=0.0002 ~ 0.0002, Batch=96
|
|
train[2019-04-01-12:45:13] Epoch: [582][000/521] Time 0.71 (0.71) Data 0.43 (0.43) Loss 0.065 (0.065) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:45:38] Epoch: [582][100/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.098 (0.046) Prec@1 96.88 (99.39) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:46:02] Epoch: [582][200/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.064 (0.049) Prec@1 98.96 (99.35) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:46:27] Epoch: [582][300/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.039 (0.047) Prec@1 100.00 (99.40) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:46:51] Epoch: [582][400/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.031 (0.046) Prec@1 100.00 (99.41) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:47:15] Epoch: [582][500/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.032 (0.047) Prec@1 100.00 (99.37) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:47:20] Epoch: [582][520/521] Time 0.21 (0.25) Data 0.00 (0.00) Loss 0.017 (0.046) Prec@1 100.00 (99.38) Prec@5 100.00 (100.00)
|
|
[2019-04-01-12:47:20] **train** Prec@1 99.38 Prec@5 100.00 Error@1 0.62 Error@5 0.00 Loss:0.046
|
|
test [2019-04-01-12:47:21] Epoch: [582][000/105] Time 0.61 (0.61) Data 0.54 (0.54) Loss 0.054 (0.054) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-12:47:25] Epoch: [582][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.071 (0.126) Prec@1 97.92 (97.13) Prec@5 100.00 (99.91)
|
|
test [2019-04-01-12:47:25] Epoch: [582][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.001 (0.127) Prec@1 100.00 (97.11) Prec@5 100.00 (99.90)
|
|
[2019-04-01-12:47:25] **test** Prec@1 97.11 Prec@5 99.90 Error@1 2.89 Error@5 0.10 Loss:0.127
|
|
----> Best Accuracy : Acc@1=97.23, Acc@5=99.91, Error@1=2.77, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-12:47:25] [Epoch=583/600] [Need: 00:37:39] LR=0.0001 ~ 0.0001, Batch=96
|
|
train[2019-04-01-12:47:26] Epoch: [583][000/521] Time 0.72 (0.72) Data 0.44 (0.44) Loss 0.045 (0.045) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:47:50] Epoch: [583][100/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.024 (0.049) Prec@1 100.00 (99.38) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:48:14] Epoch: [583][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.023 (0.048) Prec@1 100.00 (99.37) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:48:39] Epoch: [583][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.018 (0.047) Prec@1 100.00 (99.38) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:49:03] Epoch: [583][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.049 (0.047) Prec@1 98.96 (99.37) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:49:28] Epoch: [583][500/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.124 (0.047) Prec@1 96.88 (99.36) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:49:32] Epoch: [583][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.023 (0.047) Prec@1 100.00 (99.36) Prec@5 100.00 (100.00)
|
|
[2019-04-01-12:49:33] **train** Prec@1 99.36 Prec@5 100.00 Error@1 0.64 Error@5 0.00 Loss:0.047
|
|
test [2019-04-01-12:49:33] Epoch: [583][000/105] Time 0.63 (0.63) Data 0.57 (0.57) Loss 0.054 (0.054) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-12:49:37] Epoch: [583][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.077 (0.128) Prec@1 96.88 (97.05) Prec@5 100.00 (99.93)
|
|
test [2019-04-01-12:49:37] Epoch: [583][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.002 (0.129) Prec@1 100.00 (97.02) Prec@5 100.00 (99.92)
|
|
[2019-04-01-12:49:37] **test** Prec@1 97.02 Prec@5 99.92 Error@1 2.98 Error@5 0.08 Loss:0.129
|
|
----> Best Accuracy : Acc@1=97.23, Acc@5=99.91, Error@1=2.77, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-12:49:38] [Epoch=584/600] [Need: 00:35:20] LR=0.0001 ~ 0.0001, Batch=96
|
|
train[2019-04-01-12:49:38] Epoch: [584][000/521] Time 0.72 (0.72) Data 0.46 (0.46) Loss 0.036 (0.036) Prec@1 100.00 (100.00) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:50:03] Epoch: [584][100/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.038 (0.046) Prec@1 97.92 (99.36) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-12:50:27] Epoch: [584][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.054 (0.048) Prec@1 100.00 (99.33) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-12:50:51] Epoch: [584][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.056 (0.046) Prec@1 98.96 (99.35) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:51:15] Epoch: [584][400/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.105 (0.046) Prec@1 98.96 (99.35) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:51:39] Epoch: [584][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.060 (0.046) Prec@1 98.96 (99.34) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:51:43] Epoch: [584][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.104 (0.046) Prec@1 97.50 (99.34) Prec@5 100.00 (100.00)
|
|
[2019-04-01-12:51:43] **train** Prec@1 99.34 Prec@5 100.00 Error@1 0.66 Error@5 0.00 Loss:0.046
|
|
test [2019-04-01-12:51:44] Epoch: [584][000/105] Time 0.54 (0.54) Data 0.47 (0.47) Loss 0.056 (0.056) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-12:51:48] Epoch: [584][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.069 (0.129) Prec@1 97.92 (97.11) Prec@5 100.00 (99.92)
|
|
test [2019-04-01-12:51:48] Epoch: [584][104/105] Time 0.03 (0.05) Data 0.00 (0.00) Loss 0.001 (0.129) Prec@1 100.00 (97.08) Prec@5 100.00 (99.91)
|
|
[2019-04-01-12:51:48] **test** Prec@1 97.08 Prec@5 99.91 Error@1 2.92 Error@5 0.09 Loss:0.129
|
|
----> Best Accuracy : Acc@1=97.23, Acc@5=99.91, Error@1=2.77, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-12:51:48] [Epoch=585/600] [Need: 00:32:43] LR=0.0001 ~ 0.0001, Batch=96
|
|
train[2019-04-01-12:51:49] Epoch: [585][000/521] Time 0.90 (0.90) Data 0.61 (0.61) Loss 0.045 (0.045) Prec@1 100.00 (100.00) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:52:13] Epoch: [585][100/521] Time 0.24 (0.25) Data 0.00 (0.01) Loss 0.028 (0.046) Prec@1 100.00 (99.29) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-12:52:38] Epoch: [585][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.043 (0.047) Prec@1 98.96 (99.36) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-12:53:02] Epoch: [585][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.009 (0.047) Prec@1 100.00 (99.36) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:53:26] Epoch: [585][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.039 (0.045) Prec@1 100.00 (99.39) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-12:53:50] Epoch: [585][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.016 (0.047) Prec@1 100.00 (99.36) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-12:53:55] Epoch: [585][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.100 (0.048) Prec@1 98.75 (99.35) Prec@5 100.00 (99.99)
|
|
[2019-04-01-12:53:55] **train** Prec@1 99.35 Prec@5 99.99 Error@1 0.65 Error@5 0.01 Loss:0.048
|
|
test [2019-04-01-12:53:55] Epoch: [585][000/105] Time 0.47 (0.47) Data 0.41 (0.41) Loss 0.071 (0.071) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-12:54:00] Epoch: [585][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.064 (0.130) Prec@1 98.96 (97.08) Prec@5 100.00 (99.93)
|
|
test [2019-04-01-12:54:00] Epoch: [585][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.002 (0.131) Prec@1 100.00 (97.04) Prec@5 100.00 (99.92)
|
|
[2019-04-01-12:54:00] **test** Prec@1 97.04 Prec@5 99.92 Error@1 2.96 Error@5 0.08 Loss:0.131
|
|
----> Best Accuracy : Acc@1=97.23, Acc@5=99.91, Error@1=2.77, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-12:54:00] [Epoch=586/600] [Need: 00:30:39] LR=0.0001 ~ 0.0001, Batch=96
|
|
train[2019-04-01-12:54:01] Epoch: [586][000/521] Time 0.85 (0.85) Data 0.57 (0.57) Loss 0.053 (0.053) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:54:25] Epoch: [586][100/521] Time 0.23 (0.25) Data 0.00 (0.01) Loss 0.009 (0.054) Prec@1 100.00 (99.17) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:54:49] Epoch: [586][200/521] Time 0.25 (0.25) Data 0.00 (0.00) Loss 0.080 (0.052) Prec@1 97.92 (99.19) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:55:14] Epoch: [586][300/521] Time 0.27 (0.24) Data 0.00 (0.00) Loss 0.067 (0.051) Prec@1 98.96 (99.25) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:55:38] Epoch: [586][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.100 (0.050) Prec@1 98.96 (99.27) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:56:01] Epoch: [586][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.059 (0.050) Prec@1 97.92 (99.27) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:56:06] Epoch: [586][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.046 (0.050) Prec@1 100.00 (99.28) Prec@5 100.00 (100.00)
|
|
[2019-04-01-12:56:06] **train** Prec@1 99.28 Prec@5 100.00 Error@1 0.72 Error@5 0.00 Loss:0.050
|
|
test [2019-04-01-12:56:07] Epoch: [586][000/105] Time 0.60 (0.60) Data 0.53 (0.53) Loss 0.054 (0.054) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-12:56:11] Epoch: [586][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.071 (0.127) Prec@1 97.92 (97.01) Prec@5 100.00 (99.91)
|
|
test [2019-04-01-12:56:11] Epoch: [586][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.000 (0.128) Prec@1 100.00 (97.00) Prec@5 100.00 (99.90)
|
|
[2019-04-01-12:56:11] **test** Prec@1 97.00 Prec@5 99.90 Error@1 3.00 Error@5 0.10 Loss:0.128
|
|
----> Best Accuracy : Acc@1=97.23, Acc@5=99.91, Error@1=2.77, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-12:56:11] [Epoch=587/600] [Need: 00:28:28] LR=0.0001 ~ 0.0001, Batch=96
|
|
train[2019-04-01-12:56:12] Epoch: [587][000/521] Time 0.83 (0.83) Data 0.55 (0.55) Loss 0.041 (0.041) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:56:36] Epoch: [587][100/521] Time 0.24 (0.25) Data 0.00 (0.01) Loss 0.036 (0.046) Prec@1 100.00 (99.34) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:57:00] Epoch: [587][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.085 (0.047) Prec@1 97.92 (99.32) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-12:57:24] Epoch: [587][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.028 (0.047) Prec@1 100.00 (99.34) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:57:48] Epoch: [587][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.040 (0.047) Prec@1 100.00 (99.35) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:58:12] Epoch: [587][500/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.114 (0.047) Prec@1 96.88 (99.33) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:58:17] Epoch: [587][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.074 (0.047) Prec@1 98.75 (99.33) Prec@5 100.00 (100.00)
|
|
[2019-04-01-12:58:17] **train** Prec@1 99.33 Prec@5 100.00 Error@1 0.67 Error@5 0.00 Loss:0.047
|
|
test [2019-04-01-12:58:18] Epoch: [587][000/105] Time 0.60 (0.60) Data 0.53 (0.53) Loss 0.062 (0.062) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-12:58:22] Epoch: [587][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.057 (0.129) Prec@1 98.96 (97.11) Prec@5 100.00 (99.93)
|
|
test [2019-04-01-12:58:22] Epoch: [587][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.001 (0.130) Prec@1 100.00 (97.07) Prec@5 100.00 (99.93)
|
|
[2019-04-01-12:58:22] **test** Prec@1 97.07 Prec@5 99.93 Error@1 2.93 Error@5 0.07 Loss:0.130
|
|
----> Best Accuracy : Acc@1=97.23, Acc@5=99.91, Error@1=2.77, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-12:58:22] [Epoch=588/600] [Need: 00:26:08] LR=0.0001 ~ 0.0001, Batch=96
|
|
train[2019-04-01-12:58:23] Epoch: [588][000/521] Time 0.71 (0.71) Data 0.43 (0.43) Loss 0.081 (0.081) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:58:47] Epoch: [588][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.038 (0.048) Prec@1 100.00 (99.29) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:59:11] Epoch: [588][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.027 (0.046) Prec@1 100.00 (99.36) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:59:35] Epoch: [588][300/521] Time 0.26 (0.24) Data 0.00 (0.00) Loss 0.076 (0.046) Prec@1 98.96 (99.40) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-12:59:59] Epoch: [588][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.036 (0.047) Prec@1 100.00 (99.36) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-13:00:23] Epoch: [588][500/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.095 (0.048) Prec@1 97.92 (99.33) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-13:00:28] Epoch: [588][520/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.040 (0.048) Prec@1 98.75 (99.33) Prec@5 100.00 (100.00)
|
|
[2019-04-01-13:00:28] **train** Prec@1 99.33 Prec@5 100.00 Error@1 0.67 Error@5 0.00 Loss:0.048
|
|
test [2019-04-01-13:00:28] Epoch: [588][000/105] Time 0.49 (0.49) Data 0.42 (0.42) Loss 0.057 (0.057) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-13:00:33] Epoch: [588][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.050 (0.129) Prec@1 98.96 (97.05) Prec@5 100.00 (99.93)
|
|
test [2019-04-01-13:00:33] Epoch: [588][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.002 (0.129) Prec@1 100.00 (97.01) Prec@5 100.00 (99.92)
|
|
[2019-04-01-13:00:33] **test** Prec@1 97.01 Prec@5 99.92 Error@1 2.99 Error@5 0.08 Loss:0.129
|
|
----> Best Accuracy : Acc@1=97.23, Acc@5=99.91, Error@1=2.77, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-13:00:33] [Epoch=589/600] [Need: 00:24:00] LR=0.0001 ~ 0.0001, Batch=96
|
|
train[2019-04-01-13:00:34] Epoch: [589][000/521] Time 0.74 (0.74) Data 0.45 (0.45) Loss 0.060 (0.060) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-13:00:58] Epoch: [589][100/521] Time 0.27 (0.25) Data 0.00 (0.00) Loss 0.015 (0.053) Prec@1 100.00 (99.17) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-13:01:22] Epoch: [589][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.076 (0.051) Prec@1 100.00 (99.25) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-13:01:46] Epoch: [589][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.040 (0.048) Prec@1 100.00 (99.31) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-13:02:10] Epoch: [589][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.022 (0.047) Prec@1 100.00 (99.34) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-13:02:34] Epoch: [589][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.040 (0.047) Prec@1 98.96 (99.36) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-13:02:39] Epoch: [589][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.054 (0.047) Prec@1 98.75 (99.36) Prec@5 100.00 (100.00)
|
|
[2019-04-01-13:02:39] **train** Prec@1 99.36 Prec@5 100.00 Error@1 0.64 Error@5 0.00 Loss:0.047
|
|
test [2019-04-01-13:02:40] Epoch: [589][000/105] Time 0.49 (0.49) Data 0.43 (0.43) Loss 0.055 (0.055) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-13:02:44] Epoch: [589][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.055 (0.128) Prec@1 98.96 (97.03) Prec@5 100.00 (99.91)
|
|
test [2019-04-01-13:02:44] Epoch: [589][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.003 (0.129) Prec@1 100.00 (96.99) Prec@5 100.00 (99.91)
|
|
[2019-04-01-13:02:44] **test** Prec@1 96.99 Prec@5 99.91 Error@1 3.01 Error@5 0.09 Loss:0.129
|
|
----> Best Accuracy : Acc@1=97.23, Acc@5=99.91, Error@1=2.77, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-13:02:44] [Epoch=590/600] [Need: 00:21:51] LR=0.0001 ~ 0.0001, Batch=96
|
|
train[2019-04-01-13:02:45] Epoch: [590][000/521] Time 0.72 (0.72) Data 0.45 (0.45) Loss 0.090 (0.090) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-13:03:09] Epoch: [590][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.028 (0.048) Prec@1 100.00 (99.31) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-13:03:33] Epoch: [590][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.027 (0.048) Prec@1 100.00 (99.32) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-13:03:57] Epoch: [590][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.082 (0.047) Prec@1 97.92 (99.34) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-13:04:21] Epoch: [590][400/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.055 (0.048) Prec@1 100.00 (99.34) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-13:04:45] Epoch: [590][500/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.057 (0.048) Prec@1 98.96 (99.36) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-13:04:49] Epoch: [590][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.023 (0.048) Prec@1 100.00 (99.36) Prec@5 100.00 (99.99)
|
|
[2019-04-01-13:04:50] **train** Prec@1 99.36 Prec@5 99.99 Error@1 0.64 Error@5 0.01 Loss:0.048
|
|
test [2019-04-01-13:04:50] Epoch: [590][000/105] Time 0.61 (0.61) Data 0.55 (0.55) Loss 0.044 (0.044) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-13:04:54] Epoch: [590][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.078 (0.128) Prec@1 97.92 (97.08) Prec@5 100.00 (99.91)
|
|
test [2019-04-01-13:04:54] Epoch: [590][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.002 (0.128) Prec@1 100.00 (97.05) Prec@5 100.00 (99.91)
|
|
[2019-04-01-13:04:54] **test** Prec@1 97.05 Prec@5 99.91 Error@1 2.95 Error@5 0.09 Loss:0.128
|
|
----> Best Accuracy : Acc@1=97.23, Acc@5=99.91, Error@1=2.77, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-13:04:55] [Epoch=591/600] [Need: 00:19:34] LR=0.0001 ~ 0.0001, Batch=96
|
|
train[2019-04-01-13:04:55] Epoch: [591][000/521] Time 0.72 (0.72) Data 0.44 (0.44) Loss 0.083 (0.083) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-13:05:20] Epoch: [591][100/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.059 (0.049) Prec@1 100.00 (99.39) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-13:05:44] Epoch: [591][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.040 (0.051) Prec@1 98.96 (99.29) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-13:06:08] Epoch: [591][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.054 (0.049) Prec@1 98.96 (99.32) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-13:06:32] Epoch: [591][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.022 (0.047) Prec@1 100.00 (99.35) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-13:06:56] Epoch: [591][500/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.020 (0.048) Prec@1 100.00 (99.32) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-13:07:01] Epoch: [591][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.065 (0.048) Prec@1 98.75 (99.33) Prec@5 100.00 (100.00)
|
|
[2019-04-01-13:07:01] **train** Prec@1 99.33 Prec@5 100.00 Error@1 0.67 Error@5 0.00 Loss:0.048
|
|
test [2019-04-01-13:07:01] Epoch: [591][000/105] Time 0.47 (0.47) Data 0.40 (0.40) Loss 0.053 (0.053) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-13:07:05] Epoch: [591][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.079 (0.129) Prec@1 97.92 (97.07) Prec@5 100.00 (99.92)
|
|
test [2019-04-01-13:07:06] Epoch: [591][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.003 (0.129) Prec@1 100.00 (97.04) Prec@5 100.00 (99.91)
|
|
[2019-04-01-13:07:06] **test** Prec@1 97.04 Prec@5 99.91 Error@1 2.96 Error@5 0.09 Loss:0.129
|
|
----> Best Accuracy : Acc@1=97.23, Acc@5=99.91, Error@1=2.77, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-13:07:06] [Epoch=592/600] [Need: 00:17:29] LR=0.0001 ~ 0.0001, Batch=96
|
|
train[2019-04-01-13:07:07] Epoch: [592][000/521] Time 0.71 (0.71) Data 0.44 (0.44) Loss 0.062 (0.062) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-13:07:30] Epoch: [592][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.020 (0.044) Prec@1 100.00 (99.39) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-13:07:54] Epoch: [592][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.012 (0.044) Prec@1 100.00 (99.40) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-13:08:18] Epoch: [592][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.027 (0.045) Prec@1 100.00 (99.41) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-13:08:42] Epoch: [592][400/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.078 (0.044) Prec@1 98.96 (99.45) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-13:09:06] Epoch: [592][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.082 (0.045) Prec@1 97.92 (99.42) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-13:09:11] Epoch: [592][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.038 (0.045) Prec@1 98.75 (99.41) Prec@5 100.00 (100.00)
|
|
[2019-04-01-13:09:11] **train** Prec@1 99.41 Prec@5 100.00 Error@1 0.59 Error@5 0.00 Loss:0.045
|
|
test [2019-04-01-13:09:11] Epoch: [592][000/105] Time 0.48 (0.48) Data 0.41 (0.41) Loss 0.050 (0.050) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-13:09:15] Epoch: [592][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.054 (0.128) Prec@1 97.92 (97.08) Prec@5 100.00 (99.93)
|
|
test [2019-04-01-13:09:16] Epoch: [592][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.002 (0.128) Prec@1 100.00 (97.05) Prec@5 100.00 (99.92)
|
|
[2019-04-01-13:09:16] **test** Prec@1 97.05 Prec@5 99.92 Error@1 2.95 Error@5 0.08 Loss:0.128
|
|
----> Best Accuracy : Acc@1=97.23, Acc@5=99.91, Error@1=2.77, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-13:09:16] [Epoch=593/600] [Need: 00:15:09] LR=0.0001 ~ 0.0001, Batch=96
|
|
train[2019-04-01-13:09:17] Epoch: [593][000/521] Time 0.70 (0.70) Data 0.43 (0.43) Loss 0.039 (0.039) Prec@1 100.00 (100.00) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-13:09:40] Epoch: [593][100/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.100 (0.046) Prec@1 97.92 (99.40) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-13:10:04] Epoch: [593][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.027 (0.047) Prec@1 100.00 (99.37) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-13:10:28] Epoch: [593][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.039 (0.046) Prec@1 100.00 (99.39) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-13:10:52] Epoch: [593][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.097 (0.046) Prec@1 97.92 (99.40) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-13:11:16] Epoch: [593][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.018 (0.046) Prec@1 100.00 (99.39) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-13:11:21] Epoch: [593][520/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.076 (0.046) Prec@1 98.75 (99.37) Prec@5 100.00 (100.00)
|
|
[2019-04-01-13:11:21] **train** Prec@1 99.37 Prec@5 100.00 Error@1 0.63 Error@5 0.00 Loss:0.046
|
|
test [2019-04-01-13:11:21] Epoch: [593][000/105] Time 0.47 (0.47) Data 0.40 (0.40) Loss 0.059 (0.059) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-13:11:25] Epoch: [593][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.066 (0.128) Prec@1 98.96 (96.99) Prec@5 100.00 (99.92)
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test [2019-04-01-13:11:26] Epoch: [593][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.003 (0.129) Prec@1 100.00 (96.96) Prec@5 100.00 (99.91)
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[2019-04-01-13:11:26] **test** Prec@1 96.96 Prec@5 99.91 Error@1 3.04 Error@5 0.09 Loss:0.129
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----> Best Accuracy : Acc@1=97.23, Acc@5=99.91, Error@1=2.77, Error@5=0.09
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----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
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==>>[2019-04-01-13:11:26] [Epoch=594/600] [Need: 00:13:00] LR=0.0001 ~ 0.0001, Batch=96
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train[2019-04-01-13:11:27] Epoch: [594][000/521] Time 0.71 (0.71) Data 0.43 (0.43) Loss 0.035 (0.035) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
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train[2019-04-01-13:11:51] Epoch: [594][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.020 (0.050) Prec@1 100.00 (99.40) Prec@5 100.00 (99.98)
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train[2019-04-01-13:12:14] Epoch: [594][200/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.074 (0.052) Prec@1 96.88 (99.32) Prec@5 100.00 (99.99)
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train[2019-04-01-13:12:38] Epoch: [594][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.037 (0.050) Prec@1 98.96 (99.34) Prec@5 100.00 (99.99)
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train[2019-04-01-13:13:02] Epoch: [594][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.042 (0.050) Prec@1 100.00 (99.33) Prec@5 100.00 (99.99)
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train[2019-04-01-13:13:26] Epoch: [594][500/521] Time 0.27 (0.24) Data 0.00 (0.00) Loss 0.027 (0.049) Prec@1 100.00 (99.32) Prec@5 100.00 (100.00)
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train[2019-04-01-13:13:31] Epoch: [594][520/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.079 (0.049) Prec@1 98.75 (99.32) Prec@5 100.00 (100.00)
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[2019-04-01-13:13:31] **train** Prec@1 99.32 Prec@5 100.00 Error@1 0.68 Error@5 0.00 Loss:0.049
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test [2019-04-01-13:13:32] Epoch: [594][000/105] Time 0.47 (0.47) Data 0.41 (0.41) Loss 0.056 (0.056) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
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test [2019-04-01-13:13:36] Epoch: [594][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.076 (0.128) Prec@1 97.92 (97.10) Prec@5 100.00 (99.91)
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test [2019-04-01-13:13:36] Epoch: [594][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.002 (0.128) Prec@1 100.00 (97.08) Prec@5 100.00 (99.91)
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[2019-04-01-13:13:36] **test** Prec@1 97.08 Prec@5 99.91 Error@1 2.92 Error@5 0.09 Loss:0.128
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|
----> Best Accuracy : Acc@1=97.23, Acc@5=99.91, Error@1=2.77, Error@5=0.09
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|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
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==>>[2019-04-01-13:13:36] [Epoch=595/600] [Need: 00:10:51] LR=0.0001 ~ 0.0001, Batch=96
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train[2019-04-01-13:13:37] Epoch: [595][000/521] Time 0.84 (0.84) Data 0.56 (0.56) Loss 0.114 (0.114) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
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train[2019-04-01-13:14:01] Epoch: [595][100/521] Time 0.24 (0.24) Data 0.00 (0.01) Loss 0.022 (0.047) Prec@1 100.00 (99.40) Prec@5 100.00 (99.99)
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train[2019-04-01-13:14:25] Epoch: [595][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.028 (0.048) Prec@1 100.00 (99.31) Prec@5 100.00 (99.99)
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train[2019-04-01-13:14:49] Epoch: [595][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.040 (0.047) Prec@1 98.96 (99.36) Prec@5 100.00 (99.99)
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train[2019-04-01-13:15:13] Epoch: [595][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.018 (0.047) Prec@1 100.00 (99.39) Prec@5 100.00 (99.99)
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train[2019-04-01-13:15:37] Epoch: [595][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.011 (0.046) Prec@1 100.00 (99.38) Prec@5 100.00 (100.00)
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train[2019-04-01-13:15:41] Epoch: [595][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.039 (0.046) Prec@1 100.00 (99.40) Prec@5 100.00 (100.00)
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[2019-04-01-13:15:41] **train** Prec@1 99.40 Prec@5 100.00 Error@1 0.60 Error@5 0.00 Loss:0.046
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test [2019-04-01-13:15:42] Epoch: [595][000/105] Time 0.47 (0.47) Data 0.40 (0.40) Loss 0.050 (0.050) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
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test [2019-04-01-13:15:46] Epoch: [595][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.066 (0.129) Prec@1 97.92 (97.07) Prec@5 100.00 (99.93)
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test [2019-04-01-13:15:46] Epoch: [595][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.003 (0.130) Prec@1 100.00 (97.05) Prec@5 100.00 (99.93)
|
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[2019-04-01-13:15:46] **test** Prec@1 97.05 Prec@5 99.93 Error@1 2.95 Error@5 0.07 Loss:0.130
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|
----> Best Accuracy : Acc@1=97.23, Acc@5=99.91, Error@1=2.77, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
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|
|
==>>[2019-04-01-13:15:46] [Epoch=596/600] [Need: 00:08:40] LR=0.0001 ~ 0.0001, Batch=96
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train[2019-04-01-13:15:47] Epoch: [596][000/521] Time 0.72 (0.72) Data 0.45 (0.45) Loss 0.021 (0.021) Prec@1 100.00 (100.00) Prec@5 100.00 (100.00)
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train[2019-04-01-13:16:12] Epoch: [596][100/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.034 (0.044) Prec@1 100.00 (99.44) Prec@5 100.00 (100.00)
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train[2019-04-01-13:16:36] Epoch: [596][200/521] Time 0.24 (0.25) Data 0.00 (0.00) Loss 0.032 (0.049) Prec@1 100.00 (99.36) Prec@5 100.00 (99.99)
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train[2019-04-01-13:17:00] Epoch: [596][300/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.036 (0.049) Prec@1 100.00 (99.35) Prec@5 100.00 (99.99)
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train[2019-04-01-13:17:24] Epoch: [596][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.043 (0.049) Prec@1 98.96 (99.31) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-13:17:48] Epoch: [596][500/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.025 (0.049) Prec@1 100.00 (99.31) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-13:17:52] Epoch: [596][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.043 (0.048) Prec@1 100.00 (99.31) Prec@5 100.00 (100.00)
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|
[2019-04-01-13:17:52] **train** Prec@1 99.31 Prec@5 100.00 Error@1 0.69 Error@5 0.00 Loss:0.048
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test [2019-04-01-13:17:53] Epoch: [596][000/105] Time 0.61 (0.61) Data 0.54 (0.54) Loss 0.049 (0.049) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-13:17:57] Epoch: [596][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.063 (0.128) Prec@1 97.92 (97.04) Prec@5 100.00 (99.91)
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test [2019-04-01-13:17:57] Epoch: [596][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.002 (0.128) Prec@1 100.00 (97.01) Prec@5 100.00 (99.91)
|
|
[2019-04-01-13:17:57] **test** Prec@1 97.01 Prec@5 99.91 Error@1 2.99 Error@5 0.09 Loss:0.128
|
|
----> Best Accuracy : Acc@1=97.23, Acc@5=99.91, Error@1=2.77, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
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|
|
==>>[2019-04-01-13:17:57] [Epoch=597/600] [Need: 00:06:33] LR=0.0001 ~ 0.0001, Batch=96
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|
train[2019-04-01-13:17:58] Epoch: [597][000/521] Time 0.73 (0.73) Data 0.45 (0.45) Loss 0.105 (0.105) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-13:18:22] Epoch: [597][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.061 (0.051) Prec@1 97.92 (99.27) Prec@5 100.00 (99.99)
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train[2019-04-01-13:18:46] Epoch: [597][200/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.026 (0.048) Prec@1 100.00 (99.35) Prec@5 100.00 (99.99)
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train[2019-04-01-13:19:10] Epoch: [597][300/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.092 (0.047) Prec@1 96.88 (99.37) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-13:19:34] Epoch: [597][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.038 (0.046) Prec@1 98.96 (99.39) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-13:19:58] Epoch: [597][500/521] Time 0.25 (0.24) Data 0.00 (0.00) Loss 0.098 (0.047) Prec@1 96.88 (99.38) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-13:20:02] Epoch: [597][520/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.036 (0.047) Prec@1 98.75 (99.38) Prec@5 100.00 (100.00)
|
|
[2019-04-01-13:20:03] **train** Prec@1 99.38 Prec@5 100.00 Error@1 0.62 Error@5 0.00 Loss:0.047
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test [2019-04-01-13:20:03] Epoch: [597][000/105] Time 0.61 (0.61) Data 0.54 (0.54) Loss 0.063 (0.063) Prec@1 96.88 (96.88) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-13:20:07] Epoch: [597][100/105] Time 0.04 (0.05) Data 0.00 (0.01) Loss 0.059 (0.127) Prec@1 98.96 (97.11) Prec@5 100.00 (99.93)
|
|
test [2019-04-01-13:20:07] Epoch: [597][104/105] Time 0.03 (0.05) Data 0.00 (0.01) Loss 0.002 (0.128) Prec@1 100.00 (97.08) Prec@5 100.00 (99.93)
|
|
[2019-04-01-13:20:07] **test** Prec@1 97.08 Prec@5 99.93 Error@1 2.92 Error@5 0.07 Loss:0.128
|
|
----> Best Accuracy : Acc@1=97.23, Acc@5=99.91, Error@1=2.77, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-13:20:08] [Epoch=598/600] [Need: 00:04:20] LR=0.0001 ~ 0.0001, Batch=96
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|
train[2019-04-01-13:20:09] Epoch: [598][000/521] Time 0.84 (0.84) Data 0.57 (0.57) Loss 0.050 (0.050) Prec@1 98.96 (98.96) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-13:20:33] Epoch: [598][100/521] Time 0.24 (0.25) Data 0.00 (0.01) Loss 0.052 (0.048) Prec@1 98.96 (99.34) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-13:20:56] Epoch: [598][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.041 (0.050) Prec@1 100.00 (99.28) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-13:21:20] Epoch: [598][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.048 (0.049) Prec@1 98.96 (99.33) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-13:21:44] Epoch: [598][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.009 (0.048) Prec@1 100.00 (99.36) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-13:22:08] Epoch: [598][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.042 (0.048) Prec@1 98.96 (99.37) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-13:22:13] Epoch: [598][520/521] Time 0.23 (0.24) Data 0.00 (0.00) Loss 0.123 (0.048) Prec@1 98.75 (99.37) Prec@5 100.00 (100.00)
|
|
[2019-04-01-13:22:13] **train** Prec@1 99.37 Prec@5 100.00 Error@1 0.63 Error@5 0.00 Loss:0.048
|
|
test [2019-04-01-13:22:13] Epoch: [598][000/105] Time 0.48 (0.48) Data 0.41 (0.41) Loss 0.077 (0.077) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-13:22:18] Epoch: [598][100/105] Time 0.04 (0.04) Data 0.00 (0.00) Loss 0.054 (0.127) Prec@1 98.96 (97.14) Prec@5 100.00 (99.93)
|
|
test [2019-04-01-13:22:18] Epoch: [598][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.001 (0.128) Prec@1 100.00 (97.11) Prec@5 100.00 (99.93)
|
|
[2019-04-01-13:22:18] **test** Prec@1 97.11 Prec@5 99.93 Error@1 2.89 Error@5 0.07 Loss:0.128
|
|
----> Best Accuracy : Acc@1=97.23, Acc@5=99.91, Error@1=2.77, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
|
|
|
|
==>>[2019-04-01-13:22:18] [Epoch=599/600] [Need: 00:02:10] LR=0.0001 ~ 0.0001, Batch=96
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|
train[2019-04-01-13:22:19] Epoch: [599][000/521] Time 0.72 (0.72) Data 0.45 (0.45) Loss 0.014 (0.014) Prec@1 100.00 (100.00) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-13:22:43] Epoch: [599][100/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.047 (0.046) Prec@1 100.00 (99.35) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-13:23:07] Epoch: [599][200/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.054 (0.047) Prec@1 100.00 (99.32) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-13:23:30] Epoch: [599][300/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.077 (0.047) Prec@1 100.00 (99.38) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-13:23:54] Epoch: [599][400/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.023 (0.047) Prec@1 100.00 (99.36) Prec@5 100.00 (99.99)
|
|
train[2019-04-01-13:24:18] Epoch: [599][500/521] Time 0.24 (0.24) Data 0.00 (0.00) Loss 0.057 (0.047) Prec@1 98.96 (99.36) Prec@5 100.00 (100.00)
|
|
train[2019-04-01-13:24:23] Epoch: [599][520/521] Time 0.22 (0.24) Data 0.00 (0.00) Loss 0.014 (0.046) Prec@1 100.00 (99.37) Prec@5 100.00 (100.00)
|
|
[2019-04-01-13:24:23] **train** Prec@1 99.37 Prec@5 100.00 Error@1 0.63 Error@5 0.00 Loss:0.046
|
|
test [2019-04-01-13:24:23] Epoch: [599][000/105] Time 0.49 (0.49) Data 0.44 (0.44) Loss 0.069 (0.069) Prec@1 97.92 (97.92) Prec@5 100.00 (100.00)
|
|
test [2019-04-01-13:24:28] Epoch: [599][100/105] Time 0.04 (0.05) Data 0.00 (0.00) Loss 0.066 (0.126) Prec@1 97.92 (97.23) Prec@5 100.00 (99.94)
|
|
test [2019-04-01-13:24:28] Epoch: [599][104/105] Time 0.03 (0.04) Data 0.00 (0.00) Loss 0.001 (0.127) Prec@1 100.00 (97.20) Prec@5 100.00 (99.94)
|
|
[2019-04-01-13:24:28] **test** Prec@1 97.20 Prec@5 99.94 Error@1 2.80 Error@5 0.06 Loss:0.127
|
|
----> Best Accuracy : Acc@1=97.23, Acc@5=99.91, Error@1=2.77, Error@5=0.09
|
|
----> Save into ./output/NAS-CNN/GDAS_F1-cifar10-cut-E600/seed-6844/checkpoint-cifar10-model.pth
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