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,
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
object  Model or layer object 
axis  Integer, the axis that should be normalized (typically the
features axis). For instance, after a 
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 
scale  If TRUE, multiply by 
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 
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 

virtual_batch_size  An integer. By default, virtual_batch_size is 
adjustment  A function taking the Tensor containing the (dynamic) shape
of the input tensor and returning a pair 
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_size  Fixed batch size for layer 
dtype  The data type expected by the input, as a string ( 
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.