update GDAS
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@ -62,6 +62,8 @@ CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/NAS-Bench-201/train-a-net.sh '|nor_
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`|nor_conv_3x3~0|+|nor_conv_3x3~0|nor_conv_3x3~1|+|skip_connect~0|skip_connect~1|skip_connect~2|` represents the structure of a searched architecture. My codes will automatically print it during the searching procedure.
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`|nor_conv_3x3~0|+|nor_conv_3x3~0|nor_conv_3x3~1|+|skip_connect~0|skip_connect~1|skip_connect~2|` represents the structure of a searched architecture. My codes will automatically print it during the searching procedure.
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**Tensorflow codes for GDAS are in experimental state**, which locates at `exps-tf`.
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# Citation
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# Citation
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If you find that this project helps your research, please consider citing the following paper:
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If you find that this project helps your research, please consider citing the following paper:
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@ -1,4 +1,9 @@
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# [D-X-Y]
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# Run GDAS
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# CUDA_VISIBLE_DEVICES=0 python exps-tf/GDAS.py
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# CUDA_VISIBLE_DEVICES=0 python exps-tf/GDAS.py
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# Run DARTS
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# CUDA_VISIBLE_DEVICES=0 python exps-tf/GDAS.py --tau_max -1 --tau_min -1 --epochs 50
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#
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import os, sys, math, time, random, argparse
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import os, sys, math, time, random, argparse
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import tensorflow as tf
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import tensorflow as tf
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from pathlib import Path
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from pathlib import Path
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@ -7,10 +7,10 @@ from copy import deepcopy
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from ..cell_operations import OPS
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from ..cell_operations import OPS
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class SearchCell(tf.keras.layers.Layer):
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class NAS201SearchCell(tf.keras.layers.Layer):
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def __init__(self, C_in, C_out, stride, max_nodes, op_names, affine=False):
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def __init__(self, C_in, C_out, stride, max_nodes, op_names, affine=False):
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super(SearchCell, self).__init__()
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super(NAS201SearchCell, self).__init__()
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self.op_names = deepcopy(op_names)
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self.op_names = deepcopy(op_names)
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self.max_nodes = max_nodes
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self.max_nodes = max_nodes
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@ -5,7 +5,7 @@ import tensorflow as tf
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import numpy as np
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import numpy as np
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from copy import deepcopy
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from copy import deepcopy
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from ..cell_operations import ResNetBasicblock
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from ..cell_operations import ResNetBasicblock
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from .search_cells import SearchCell
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from .search_cells import NAS201SearchCell as SearchCell
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def sample_gumbel(shape, eps=1e-20):
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def sample_gumbel(shape, eps=1e-20):
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