open source

.gitignore

@@ -1,3 +1,6 @@
+outputs*
+outputs/*
+
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]

scripts/cifar10_macro_final.sh

@@ -38,7 +38,7 @@ python src/cifar10/main.py \
   --num_epochs=310 \
   --log_every=50 \
   --eval_every_epochs=1 \
-  --child_fixed_arc="$fixed_arc" \
+  --child_fixed_arc="${fixed_arc}" \
   --child_use_aux_heads \
   --child_num_layers=24 \
   --child_out_filters=96 \

scripts/cifar10_micro_final.sh

@@ -15,7 +15,7 @@ python src/cifar10/main.py \
   --num_epochs=630 \
   --log_every=50 \
   --eval_every_epochs=1 \
-  --child_fixed_arc="$fixed_arc" \
+  --child_fixed_arc="${fixed_arc}" \
   --child_use_aux_heads \
   --child_num_layers=15 \
   --child_out_filters=36 \

scripts/ptb_final.sh

@@ -12,7 +12,7 @@ python src/ptb/main.py \
   --batch_size=64 \
   --child_bptt_steps=35 \
   --num_epochs=2000 \
-  --child_fixed_arc="$fixed_arc" \
+  --child_fixed_arc="${fixed_arc}" \
   --child_rhn_depth=12 \
   --child_num_layers=1 \
   --child_lstm_hidden_size=748 \

src/cifar10/general_child.py

@@ -481,7 +481,7 @@ def _fixed_layer(
     return out
 
   def _conv_branch(self, inputs, filter_size, is_training, count, out_filters,
-                   start_idx=None, separable=False):
+                   ch_mul=1, start_idx=None, separable=False):
     """
     Args:
       start_idx: where to start taking the output channels. if None, assuming
@@ -518,7 +518,6 @@ def _conv_branch(self, inputs, filter_size, is_training, count, out_filters,
           x = batch_norm(x, is_training, data_format=self.data_format)
       else:
         if separable:
-          ch_mul = 1
           w_depth = create_weight("w_depth", [filter_size, filter_size, out_filters, ch_mul])
           w_point = create_weight("w_point", [out_filters, out_filters * ch_mul])
           w_point = w_point[start_idx:start_idx+count, :]

src/utils.py

@@ -1,3 +1,7 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
 import sys
 import numpy as np
 import tensorflow as tf
@@ -31,7 +35,7 @@ def DEFINE_boolean(name, default_value, doc_string):
 
 
 def print_user_flags(line_limit=80):
-  print "-" * 80
+  print("-" * 80)
 
   global user_flags
   FLAGS = tf.app.flags.FLAGS
@@ -41,7 +45,7 @@ def print_user_flags(line_limit=80):
     log_string = flag_name
     log_string += "." * (line_limit - len(flag_name) - len(value))
     log_string += value
-    print log_string
+    print(log_string)
 
 
 class TextColors:
@@ -197,10 +201,10 @@ def _no_update():
   #       else:
   #         g_n = tf.reduce_sum(g ** 2)
   #       if "enas_cell" in v.name:
-  #         print "g_1: {}".format(v.name)
+  #         print("g_1: {}".format(v.name))
   #         g_1 += g_n
   #       else:
-  #         print "g_2: {}".format(v.name)
+  #         print("g_2: {}".format(v.name))
   #         g_2 += g_n
   #   learning_rate = tf.Print(learning_rate, [g_1, g_2, tf.sqrt(g_1 / g_2)],
   #                            message="g_1, g_2, g_1/g_2: ", summarize=5)