Bidirectional wrapper for RNNs.

Usage

layer_bidirectional(
  object,
  layer,
  merge_mode = "concat",
  weights = NULL,
  backward_layer = NULL,
  ...
)

Arguments

object

Object to compose the layer with. A tensor, array, or sequential model.

layer

RNN instance, such as layer_lstm() or layer_gru(). It could also be a Layer() instance that meets the following criteria:

Be a sequence-processing layer (accepts 3D+ inputs).
Have a go_backwards, return_sequences and return_state attribute (with the same semantics as for the RNN class).
Have an input_spec attribute.
Implement serialization via get_config() and from_config(). Note that the recommended way to create new RNN layers is to write a custom RNN cell and use it with layer_rnn(), instead of subclassing with Layer() directly. When return_sequences is TRUE, the output of the masked timestep will be zero regardless of the layer's original zero_output_for_mask value.

merge_mode

Mode by which outputs of the forward and backward RNNs will be combined. One of {"sum", "mul", "concat", "ave", NULL}. If NULL, the outputs will not be combined, they will be returned as a list. Defaults to "concat".

weights

see description

backward_layer

Optional RNN, or Layer() instance to be used to handle backwards input processing. If backward_layer is not provided, the layer instance passed as the layer argument will be used to generate the backward layer automatically. Note that the provided backward_layer layer should have properties matching those of the layer argument, in particular it should have the same values for stateful, return_states, return_sequences, etc. In addition, backward_layer and layer should have different go_backwards argument values. A ValueError will be raised if these requirements are not met.

...

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.

Call Arguments

The call arguments for this layer are the same as those of the wrapped RNN layer. Beware that when passing the initial_state argument during the call of this layer, the first half in the list of elements in the initial_state list will be passed to the forward RNN call and the last half in the list of elements will be passed to the backward RNN call.

Note

instantiating a Bidirectional layer from an existing RNN layer instance will not reuse the weights state of the RNN layer instance -- the Bidirectional layer will have freshly initialized weights.

Examples

model <- keras_model_sequential(input_shape = c(5, 10)) %>%
  layer_bidirectional(layer_lstm(units = 10, return_sequences = TRUE)) %>%
  layer_bidirectional(layer_lstm(units = 10)) %>%
  layer_dense(5, activation = "softmax")

model %>% compile(loss = "categorical_crossentropy",
                  optimizer = "rmsprop")

# With custom backward layer
forward_layer <- layer_lstm(units = 10, return_sequences = TRUE)
backward_layer <- layer_lstm(units = 10, activation = "relu",
                             return_sequences = TRUE, go_backwards = TRUE)

model <- keras_model_sequential(input_shape = c(5, 10)) %>%
  bidirectional(forward_layer, backward_layer = backward_layer) %>%
  layer_dense(5, activation = "softmax")

model %>% compile(loss = "categorical_crossentropy",
                  optimizer = "rmsprop")

States

A Bidirectional layer instance has property states, which you can access with layer$states. You can also reset states using reset_state()

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()
layer_additive_attention()
layer_alpha_dropout()
layer_attention()
layer_average()
layer_average_pooling_1d()
layer_average_pooling_2d()
layer_average_pooling_3d()
layer_batch_normalization()
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_masking()
layer_max_pooling_1d()
layer_max_pooling_2d()
layer_max_pooling_3d()
layer_maximum()
layer_mel_spectrogram()
layer_minimum()
layer_multi_head_attention()
layer_multiply()
layer_normalization()
layer_permute()
layer_random_brightness()
layer_random_contrast()
layer_random_crop()
layer_random_flip()
layer_random_rotation()
layer_random_translation()
layer_random_zoom()
layer_repeat_vector()
layer_rescaling()
layer_reshape()
layer_resizing()
layer_rnn()
layer_separable_conv_1d()
layer_separable_conv_2d()
layer_simple_rnn()
layer_spatial_dropout_1d()
layer_spatial_dropout_2d()
layer_spatial_dropout_3d()
layer_spectral_normalization()
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()
layer_upsampling_3d()
layer_zero_padding_1d()
layer_zero_padding_2d()
layer_zero_padding_3d()
rnn_cell_gru()
rnn_cell_lstm()
rnn_cell_simple()
rnn_cells_stack()