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 What to call the new Layer instance with. Typically a keras Model, another Layer, or a tf.Tensor/KerasTensor. If object is missing, the Layer instance is returned, otherwise, layer(object) is returned. L1 regularization factor (positive float). L2 regularization factor (positive float). 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. 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. Fixed batch size for layer The data type expected by the input, as a string (float32, float64, int32...) 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. Whether the layer weights will be updated during training. 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.

Other core layers: layer_activation(), layer_attention(), layer_dense_features(), layer_dense(), layer_dropout(), layer_flatten(), layer_input(), layer_lambda(), layer_masking(), layer_permute(), layer_repeat_vector(), layer_reshape()