experiments/STT-tensorflow
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When not necessary, avoid the creation of a placeholder_with_default in BN (not yet supported by TPU compilation).

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PiperOrigin-RevId: 192502020
This commit is contained in:
Francois Chollet 2018-04-11 13:46:03 -07:00 committed by TensorFlower Gardener
parent 371d5132a5
commit 744a5cc092
1 changed files with 3 additions and 2 deletions
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5
tensorflow/python/keras/_impl/keras/layers/normalization.py
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@ -489,6 +489,7 @@ class BatchNormalization(Layer):
return (r, d, new_mean, new_variance) return (r, d, new_mean, new_variance)
def call(self, inputs, training=None): def call(self, inputs, training=None):
original_training_value = training
if training is None: if training is None:
training = K.learning_phase() training = K.learning_phase()
@ -512,7 +513,7 @@ class BatchNormalization(Layer):
# Currently never reaches here since fused_batch_norm does not support # Currently never reaches here since fused_batch_norm does not support
# virtual batching # virtual batching
outputs = undo_virtual_batching(outputs) outputs = undo_virtual_batching(outputs)
if not context.executing_eagerly() and training is K.learning_phase(): if not context.executing_eagerly() and original_training_value is None:
outputs._uses_learning_phase = True # pylint: disable=protected-access outputs._uses_learning_phase = True # pylint: disable=protected-access
return outputs return outputs
@ -628,7 +629,7 @@ class BatchNormalization(Layer):
if self.virtual_batch_size is not None: if self.virtual_batch_size is not None:
outputs = undo_virtual_batching(outputs) outputs = undo_virtual_batching(outputs)
if not context.executing_eagerly() and training is K.learning_phase(): if not context.executing_eagerly() and original_training_value is None:
outputs._uses_learning_phase = True # pylint: disable=protected-access outputs._uses_learning_phase = True # pylint: disable=protected-access
return outputs return outputs