layer_batch_normalization: Batch normalization layer (Ioffe and Szegedy, 2014).
In dfalbel/keras: R Interface to 'Keras'

Description Usage Arguments Input shape Output shape References

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
)

`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.

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.

Same shape as input.