tf.keras.layers.Bidirectional

Bidirectional wrapper for RNNs.

Inherits From: Wrapper

View aliases

Compat aliases for migration

tf.compat.v1.keras.layers.Bidirectional, tf.compat.v2.keras.layers.Bidirectional

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: 1. 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')

Attributes:

constraints