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

For each timestep in the input tensor (the second dimension 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.

Usage

layer_masking(object, mask_value = 0, ...)

Arguments

object: Object to compose the layer with. A tensor, array, or sequential model.
mask_value: see description
...: For forward/backward compatability.

Value

The return value depends on the value provided for the first argument. If object is:

a keras_model_sequential(), then the layer is added to the sequential model (which is modified in place). To enable piping, the sequential model is also returned, invisibly.
a keras_input(), then the output tensor from calling layer(input) is returned.
NULL or missing, then a Layer instance is returned.

Examples

Consider an array x of shape c(samples, timesteps, features), to be fed to an LSTM layer. You want to mask timestep #3 and #5 because you lack data for these timesteps. You can:

Set x[, 3, ] <- 0. and x[, 5, ] <- 0.
Insert a layer_masking() layer with mask_value = 0. before the LSTM layer:

c(samples, timesteps, features) %<-% c(32, 10, 8)
inputs <- c(samples, timesteps, features) %>% { array(runif(prod(.)), dim = .) }
inputs[, 3, ] <- 0
inputs[, 5, ] <- 0

model <- keras_model_sequential() %>%
  layer_masking(mask_value = 0) %>%
  layer_lstm(32)

output <- model(inputs)
# The time step 3 and 5 will be skipped from LSTM calculation.

Note

in the Keras masking convention, a masked timestep is denoted by a mask value of FALSE, while a non-masked (i.e. usable) timestep is denoted by a mask value of TRUE.

Other layers:
Layer()
layer_activation()
layer_activation_elu()
layer_activation_leaky_relu()
layer_activation_parametric_relu()
layer_activation_relu()
layer_activation_softmax()
layer_activity_regularization()
layer_add()
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layer_alpha_dropout()
layer_attention()
layer_average()
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layer_average_pooling_2d()
layer_average_pooling_3d()
layer_batch_normalization()
layer_bidirectional()
layer_category_encoding()
layer_center_crop()
layer_concatenate()
layer_conv_1d()
layer_conv_1d_transpose()
layer_conv_2d()
layer_conv_2d_transpose()
layer_conv_3d()
layer_conv_3d_transpose()
layer_conv_lstm_1d()
layer_conv_lstm_2d()
layer_conv_lstm_3d()
layer_cropping_1d()
layer_cropping_2d()
layer_cropping_3d()
layer_dense()
layer_depthwise_conv_1d()
layer_depthwise_conv_2d()
layer_discretization()
layer_dot()
layer_dropout()
layer_einsum_dense()
layer_embedding()
layer_feature_space()
layer_flatten()
layer_flax_module_wrapper()
layer_gaussian_dropout()
layer_gaussian_noise()
layer_global_average_pooling_1d()
layer_global_average_pooling_2d()
layer_global_average_pooling_3d()
layer_global_max_pooling_1d()
layer_global_max_pooling_2d()
layer_global_max_pooling_3d()
layer_group_normalization()
layer_group_query_attention()
layer_gru()
layer_hashed_crossing()
layer_hashing()
layer_identity()
layer_integer_lookup()
layer_jax_model_wrapper()
layer_lambda()
layer_layer_normalization()
layer_lstm()
layer_max_pooling_1d()
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layer_minimum()
layer_multi_head_attention()
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layer_permute()
layer_random_brightness()
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layer_string_lookup()
layer_subtract()
layer_text_vectorization()
layer_tfsm()
layer_time_distributed()
layer_torch_module_wrapper()
layer_unit_normalization()
layer_upsampling_1d()
layer_upsampling_2d()
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layer_zero_padding_1d()
layer_zero_padding_2d()
layer_zero_padding_3d()
rnn_cell_gru()
rnn_cell_lstm()
rnn_cell_simple()
rnn_cells_stack()