layer_batch_normalization

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, 
  renorm = FALSE, 
  renorm_clipping = NULL, 
  renorm_momentum = 0.99, 
  fused = NULL, 
  virtual_batch_size = NULL, 
  adjustment = NULL, 
  input_shape = NULL, 
  batch_input_shape = NULL, 
  batch_size = NULL, 
  dtype = NULL, 
  name = NULL, 
  trainable = NULL, 
  weights = NULL 
)

Arguments

Arguments	Description
object	What to compose the new `Layer` instance with. Typically a Sequential model or a Tensor (e.g., as returned by `layer_input()`). The return value depends on `object`. If `object` is: - missing or `NULL`, the `Layer` instance is returned. - a `Sequential` model, the model with an additional layer is returned. - a Tensor, the output tensor from `layer_instance(object)` is returned.
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.
renorm	Whether to use Batch Renormalization (https://arxiv.org/abs/1702.03275). This adds extra variables during training. The inference is the same for either value of this parameter.
renorm_clipping	A named list or dictionary that may map keys `rmax`, `rmin`, `dmax` to scalar Tensors used to clip the renorm correction. The correction `(r, d)` is used as `corrected_value = normalized_value * r + d`, with `r` clipped to `[rmin, rmax]`, and `d` to `[-dmax, dmax]`. Missing `rmax`, `rmin`, `dmax` are set to `Inf`, `0`, `Inf`, `respectively`.
renorm_momentum	Momentum used to update the moving means and standard deviations with renorm. Unlike momentum, this affects training and should be neither too small (which would add noise) nor too large (which would give stale estimates). Note that momentum is still applied to get the means and variances for inference.
fused	`TRUE`, use a faster, fused implementation, or raise a ValueError if the fused implementation cannot be used. If `NULL`, use the faster implementation if possible. If `FALSE`, do not use the fused implementation.
virtual_batch_size	An integer. By default, virtual_batch_size is `NULL`, which means batch normalization is performed across the whole batch. When virtual_batch_size is not `NULL`, instead perform “Ghost Batch Normalization”, which creates virtual sub-batches which are each normalized separately (with shared gamma, beta, and moving statistics). Must divide the actual `batch size` during execution.
adjustment	A function taking the Tensor containing the (dynamic) shape of the input tensor and returning a pair `(scale, bias)` to apply to the normalized values `(before gamma and beta)`, only during training. For example, if `axis==-1`, `adjustment <- function(shape) { tuple(tf$random$uniform(shape[-1:NULL, style = "python"], 0.93, 1.07), tf$random$uniform(shape[-1:NULL, style = "python"], -0.1, 0.1)) }` will scale the normalized value by up to 7% up or down, then shift the result by up to 0.1 (with independent scaling and bias for each feature but shared across all examples), and finally apply gamma and/or beta. If `NULL`, no adjustment is applied. Cannot be specified if virtual_batch_size is specified.
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.

Section

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