Merge pull request #6 from tensorflow/master

Updated

official/vision/image_classification/resnet/resnet_ctl_imagenet_main.py

@@ -119,8 +119,8 @@ def run(flags_obj):
   # TODO(anj-s): Set data_format without using Keras.
   data_format = flags_obj.data_format
   if data_format is None:
-    data_format = ('channels_first'
-                   if tf.test.is_built_with_cuda() else 'channels_last')
+    data_format = ('channels_first' if tf.config.list_physical_devices('GPU')
+                   else 'channels_last')
   tf.keras.backend.set_image_data_format(data_format)
 
   strategy = distribution_utils.get_distribution_strategy(

official/vision/image_classification/resnet/resnet_imagenet_main.py

@@ -71,8 +71,8 @@ def run(flags_obj):
 
   data_format = flags_obj.data_format
   if data_format is None:
-    data_format = ('channels_first'
-                   if tf.test.is_built_with_cuda() else 'channels_last')
+    data_format = ('channels_first' if tf.config.list_physical_devices('GPU')
+                   else 'channels_last')
   tf.keras.backend.set_image_data_format(data_format)
 
   # Configures cluster spec for distribution strategy.

official/vision/image_classification/resnet/resnet_runnable.py

@@ -158,9 +158,9 @@ class ResnetRunnable(standard_runnable.StandardTrainable,
         loss = tf.reduce_sum(prediction_loss) * (1.0 /
                                                  self.flags_obj.batch_size)
         num_replicas = self.strategy.num_replicas_in_sync
-
+        l2_weight_decay = 1e-4
         if self.flags_obj.single_l2_loss_op:
-          l2_loss = resnet_model.L2_WEIGHT_DECAY * 2 * tf.add_n([
+          l2_loss = l2_weight_decay * 2 * tf.add_n([
               tf.nn.l2_loss(v)
               for v in self.model.trainable_variables
               if 'bn' not in v.name