Layer that applies an update to the cost function based input activity.

Layer that applies an update to the cost function based input activity.

layer_activity_regularization(object, l1 = 0, l2 = 0,
  input_shape = NULL, batch_input_shape = NULL, batch_size = NULL,
  dtype = NULL, name = NULL, trainable = NULL, weights = NULL)

Arguments

object	Model or layer object
l1	L1 regularization factor (positive float).
l2	L2 regularization factor (positive float).
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.

See also

Other core layers: layer_activation, layer_dense, layer_dropout, layer_flatten, layer_input, layer_lambda, layer_masking, layer_permute, layer_repeat_vector, layer_reshape