Bidirectional wrapper for RNNs
bidirectional(
object,
layer,
merge_mode = "concat",
weights = NULL,
backward_layer = NULL,
...
)
What to call the new Layer
instance with. Typically a keras
Model
, another Layer
, or a tf.Tensor
/KerasTensor
. If object
is
missing, the Layer
instance is returned, otherwise, layer(object)
is
returned.
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.
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'
.
Split and propagated to the initial_weights
attribute on the
forward and 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.
Other layer wrappers:
time_distributed()