convnet_builder.py 文件源码

def batch_norm(self, input_layer=None, decay=0.999, scale=False,
                 epsilon=0.001):
    """Adds a Batch Normalization layer."""
    if input_layer is None:
      input_layer = self.top_layer
    else:
      self.top_size = None
    name = 'batchnorm' + str(self.counts['batchnorm'])
    self.counts['batchnorm'] += 1

    with tf.variable_scope(name) as scope:
      if self.use_tf_layers:
        bn = tf.contrib.layers.batch_norm(
            input_layer,
            decay=decay,
            scale=scale,
            epsilon=epsilon,
            is_training=self.phase_train,
            fused=True,
            data_format=self.data_format,
            scope=scope)
      else:
        bn = self._batch_norm_without_layers(input_layer, decay, scale, epsilon)
    self.top_layer = bn
    self.top_size = bn.shape[3] if self.data_format == 'NHWC' else bn.shape[1]
    self.top_size = int(self.top_size)
    return bn