/TensorFlow Python



Defined in tensorflow/contrib/rnn/python/ops/rnn.py.

Creates a bidirectional recurrent neural network.

Stacks several bidirectional rnn layers. The combined forward and backward layer outputs are used as input of the next layer. tf.bidirectional_rnn does not allow to share forward and backward information between layers. The input_size of the first forward and backward cells must match. The initial state for both directions is zero and no intermediate states are returned.

As described in https://arxiv.org/abs/1303.5778


  • cells_fw: List of instances of RNNCell, one per layer, to be used for forward direction.
  • cells_bw: List of instances of RNNCell, one per layer, to be used for backward direction.
  • inputs: A length T list of inputs, each a tensor of shape [batch_size, input_size], or a nested tuple of such elements.
  • initial_states_fw: (optional) A list of the initial states (one per layer) for the forward RNN. Each tensor must has an appropriate type and shape [batch_size, cell_fw.state_size].
  • initial_states_bw: (optional) Same as for initial_states_fw, but using the corresponding properties of cells_bw.
  • dtype: (optional) The data type for the initial state. Required if either of the initial states are not provided.
  • sequence_length: (optional) An int32/int64 vector, size [batch_size], containing the actual lengths for each of the sequences.
  • scope: VariableScope for the created subgraph; defaults to None.


A tuple (outputs, output_state_fw, output_state_bw) where: outputs is a length T list of outputs (one for each input), which are depth-concatenated forward and backward outputs. output_states_fw is the final states, one tensor per layer, of the forward rnn. output_states_bw is the final states, one tensor per layer, of the backward rnn.


  • TypeError: If cell_fw or cell_bw is not an instance of RNNCell.
  • ValueError: If inputs is None, not a list or an empty list.

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Code samples licensed under the Apache 2.0 License.