Bidirectional wrapper for RNNs.
Inherits From: Wrapper, Layer, Module
tf.keras.layers.Bidirectional(
layer, merge_mode='concat', weights=None, backward_layer=None,
**kwargs
)
Used in the notebooks
| Used in the guide |
Used in the tutorials |
|
|
|
Arguments |
|---|
layer
|
keras.layers.RNN instance, such as keras.layers.LSTM or
keras.layers.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 keras.layers.RNN, instead of
subclassing keras.layers.Layer directly.
|
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. Default
value is 'concat'.
|
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_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.
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.
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)
backward_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()
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TensorFlow 1 version
View source on GitHub