layer_batch_normalization {keras} | R Documentation |
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, 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.