Functional interface for the batch normalization layer from_config(Ioffe et al., 2015). (deprecated)

Used in the notebooks

Used in the guide
  x_norm = tf.compat.v1.layers.batch_normalization(x, training=training)

  # ...

  update_ops = tf.compat.v1.get_collection(tf.GraphKeys.UPDATE_OPS)
  train_op = optimizer.minimize(loss)
  train_op =[train_op, update_ops])

inputs Tensor input.
axis An int, the axis that should be normalized (typically the features axis). For instance, after a Convolution2D layer with data_format="channels_first", set axis=1 in BatchNormalization.
momentum Momentum for the moving average.
epsilon Small float added to variance to avoid dividing by zero.
center If True, add offset of beta to normalized tensor. If False, beta is ignored.
scale If True, multiply by gamma. If False, gamma is not used. When the next layer is linear (also e.g. nn.relu), this can be disabled since