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.


  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 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.


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.