Bidirectional wrapper for RNNs.
Inherits From: Wrapper
tf.keras.layers.Bidirectional(
layer, merge_mode='concat', weights=None, backward_layer=None, **kwargs
)
Arguments |
|---|
layer
|
Recurrent instance.
|
merge_mode
|
Mode by which outputs of the
forward and backward RNNs will be combined.
One of {'sum', 'mul', 'concat', 'ave', None}.
If None, the outputs will not be combined,
they will be returned as a list.
|
backward_layer
|
Optional Recurrent 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_sequence, 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.
|
Call arguments:
The call arguments for this layer are the same as those of the wrapped RNN
layer.
Raises |
|---|
ValueError
|
- If
layer or backward_layer is not a Layer instance.
- In case of invalid
merge_mode argument.
- If
backward_layer has mismatched properties compared to layer.
|
Examples:
model = Sequential()
model.add(Bidirectional(LSTM(10, return_sequences=True), input_shape=(5, 10)))
model.add(Bidirectional(LSTM(10)))
model.add(Dense(5))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
# With custom backward layer
model = Sequential()
forward_layer = LSTM(10, return_sequences=True)
backard_layer = LSTM(10, activation='relu', return_sequences=True,
go_backwards=True)
model.add(Bidirectional(forward_layer, backward_layer=backward_layer,
input_shape=(5, 10)))
model.add(Dense(5))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
Methods
reset_states
View source
reset_states()
TensorFlow 2 version
View source on GitHub