# Batch normalization layer (Ioffe and Szegedy, 2014).

Normalize the activations of the previous layer at each batch, i.e. applies a transformation that maintains the mean activation close to 0 and the activation standard deviation close to 1.

layer_batch_normalization(object, axis = -1L, momentum = 0.99,
epsilon = 0.001, center = TRUE, scale = TRUE,
beta_initializer = "zeros", gamma_initializer = "ones",
moving_mean_initializer = "zeros",
moving_variance_initializer = "ones", beta_regularizer = NULL,
gamma_regularizer = NULL, beta_constraint = NULL,
gamma_constraint = NULL, input_shape = NULL,
batch_input_shape = NULL, batch_size = NULL, dtype = NULL,
name = NULL, trainable = NULL, weights = NULL)

## Arguments

 object Model or layer object axis Integer, the axis that should be normalized (typically the features axis). For instance, after a Conv2D layer with data_format="channels_first", set axis=1 in BatchNormalization. momentum Momentum for the moving mean and the moving variance. 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 the scaling will be done by the next layer. beta_initializer Initializer for the beta weight. gamma_initializer Initializer for the gamma weight. moving_mean_initializer Initializer for the moving mean. moving_variance_initializer Initializer for the moving variance. beta_regularizer Optional regularizer for the beta weight. gamma_regularizer Optional regularizer for the gamma weight. beta_constraint Optional constraint for the beta weight. gamma_constraint Optional constraint for the gamma weight. input_shape Dimensionality of the input (integer) not including the samples axis. This argument is required when using this layer as the first layer in a model. batch_input_shape Shapes, including the batch size. For instance, batch_input_shape=c(10, 32) indicates that the expected input will be batches of 10 32-dimensional vectors. batch_input_shape=list(NULL, 32) indicates batches of an arbitrary number of 32-dimensional vectors. batch_size Fixed batch size for layer dtype The data type expected by the input, as a string (float32, float64, int32...) name An optional name string for the layer. Should be unique in a model (do not reuse the same name twice). It will be autogenerated if it isn't provided. trainable Whether the layer weights will be updated during training. weights Initial weights for layer.

## Input shape

Arbitrary. Use the keyword argument input_shape (list of integers, does not include the samples axis) when using this layer as the first layer in a model.

## Output shape

Same shape as input.