utils/layers.py
def batch_norm_relu(inputs, is_training, data_format):
  """Performs a batch normalization followed by a ReLU."""
  # We set fused=True for a significant performance boost. See
  # https://www.tensorflow.org/performance/performance_guide#common_fused_ops
  inputs = tf.layers.batch_normalization(
      inputs=inputs, axis=1 if data_format == 'channels_first' else 3,
      momentum=_BATCH_NORM_DECAY, epsilon=_BATCH_NORM_EPSILON, center=True,
      scale=True, training=is_training, fused=True)
  return tf.nn.relu(inputs)
Performs a batch normalization followed by a ReLU.
Performs a batch normalization followed by a ReLU.
Performs a batch normalization followed by a ReLU .
def batch_norm_relu(inputs, is_training, data_format):
  inputs = tf.layers.batch_normalization(
      inputs=inputs, axis=1 if data_format == 'channels_first' else 3,
      momentum=_BATCH_NORM_DECAY, epsilon=_BATCH_NORM_EPSILON, center=True,
      scale=True, training=is_training, fused=True)
  return tf.nn.relu(inputs)
def batch_norm_relu ( inputs , is_training , data_format ) : inputs = tf . layers . batch_normalization ( inputs = inputs , axis = 1 if data_format == 'channels_first' else 3 , momentum = _BATCH_NORM_DECAY , epsilon = _BATCH_NORM_EPSILON , center = True , scale = True , training = is_training , fused = True ) return tf . nn . relu ( inputs )
Performs a batch normalization followed by a ReLU.
Performs a batch normalization followed by a ReLU .
"""Performs a batch normalization followed by a ReLU.""" # We set fused=True for a significant performance boost. See # https://www.tensorflow.org/performance/performance_guide#common_fused_ops
inputs
is_training
data_format