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
)What to compose the new Layer instance with. Typically a
Sequential model or a Tensor (e.g., as returned by layer_input()).
The return value depends on object. If object is:
missing or NULL, the Layer instance is returned.
a Sequential model, the model with an additional layer is returned.
a Tensor, the output tensor from layer_instance(object) is returned.
float, mask value
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.
Other core layers:
layer_activation(),
layer_activity_regularization(),
layer_attention(),
layer_dense_features(),
layer_dense(),
layer_dropout(),
layer_flatten(),
layer_input(),
layer_lambda(),
layer_permute(),
layer_repeat_vector(),
layer_reshape()