update GDAS

docs/CVPR-2019-GDAS.md

@@ -62,6 +62,8 @@ CUDA_VISIBLE_DEVICES=0 bash ./scripts-search/NAS-Bench-201/train-a-net.sh '|nor_
 `|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.
 
 
+**Tensorflow codes for GDAS are in experimental state**, which locates at `exps-tf`.
+
 # Citation
 
 If you find that this project helps your research, please consider citing the following paper:

exps-tf/GDAS.py

@@ -1,4 +1,9 @@
+# [D-X-Y]
+# Run GDAS
 # CUDA_VISIBLE_DEVICES=0 python exps-tf/GDAS.py
+# Run DARTS
+# CUDA_VISIBLE_DEVICES=0 python exps-tf/GDAS.py --tau_max -1 --tau_min -1 --epochs 50
+#
 import os, sys, math, time, random, argparse
 import tensorflow as tf
 from pathlib import Path

lib/tf_models/cell_searchs/search_cells.py

@@ -7,10 +7,10 @@ from copy import deepcopy
 from ..cell_operations import OPS
 
 
-class SearchCell(tf.keras.layers.Layer):
+class NAS201SearchCell(tf.keras.layers.Layer):
 
   def __init__(self, C_in, C_out, stride, max_nodes, op_names, affine=False):
-    super(SearchCell, self).__init__()
+    super(NAS201SearchCell, self).__init__()
 
     self.op_names  = deepcopy(op_names)
     self.max_nodes = max_nodes

lib/tf_models/cell_searchs/search_model_gdas.py

@@ -5,7 +5,7 @@ import tensorflow as tf
 import numpy as np
 from copy import deepcopy
 from ..cell_operations import ResNetBasicblock
-from .search_cells     import SearchCell
+from .search_cells     import NAS201SearchCell as SearchCell
 
 
 def sample_gumbel(shape, eps=1e-20):