Description Usage Arguments Input shape Output shape References
View source: R/layersnormalization.R
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.
1 2 3 4 5 6 7 8 9  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 
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. 
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. 
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.
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