layer_batch_normalization {keras} | R Documentation

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

### Description

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.

### Usage

    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.

### References

- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

[Package keras version 2.2.4 Index]