bidirectional
Bidirectional wrapper for RNNs
Description
Bidirectional wrapper for RNNs
Usage
bidirectional(
object,
layer, merge_mode = "concat",
weights = NULL,
backward_layer = NULL,
... )
Arguments
Arguments | Description |
---|---|
object | 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. |
layer | A RNN layer instance, such as layer_lstm() or layer_gru() . It could also be a keras$layers$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 keras$layers$Layer directly. - When returns_sequences = 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. Default value is 'concat' . |
weights | Split and propagated to the initial_weights attribute on the forward and backward layer. |
backward_layer | Optional keras.layers.RNN , or keras.layers.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. |
… | standard layer arguments. |
See Also
https://www.tensorflow.org/api_docs/python/tf/keras/layers/Bidirectional
https://keras.io/api/layers/recurrent_layers/bidirectional/
Other layer wrappers:
time_distributed()