bidirectional() is an alias for layer_bidirectional().
See ?layer_bidirectional() for the full documentation.
bidirectional(
object,
layer,
merge_mode = "concat",
weights = NULL,
backward_layer = NULL,
...
)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.
Object to compose the layer with. A tensor, array, or sequential model.
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.
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".
see description
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.