Masks a sequence by using a mask value to skip timesteps.

For each timestep in the input tensor (dimension #1 in the tensor), if all values in the input tensor at that timestep are equal to mask_value, then the timestep will be masked (skipped) in all downstream layers (as long as they support masking). If any downstream layer does not support masking yet receives such an input mask, an exception will be raised.

layer_masking(object, mask_value = 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
mask_value	float, mask value
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.

See also

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