/TensorFlow Python



  • tf.contrib.rnn.static_bidirectional_rnn
  • tf.nn.static_bidirectional_rnn

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

See the guide: RNN and Cells (contrib) > Recurrent Neural Networks

Creates a bidirectional recurrent neural network.

Similar to the unidirectional case above (rnn) but takes input and builds independent forward and backward RNNs with the final forward and backward outputs depth-concatenated, such that the output will have the format [time][batch][cell_fw.output_size + cell_bw.output_size]. The input_size of forward and backward cell must match. The initial state for both directions is zero by default (but can be set optionally) and no intermediate states are ever returned -- the network is fully unrolled for the given (passed in) length(s) of the sequence(s) or completely unrolled if length(s) is not given.


  • cell_fw: An instance of RNNCell, to be used for forward direction.
  • cell_bw: An instance of RNNCell, 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_state_fw: (optional) An initial state for the forward RNN. This must be a tensor of appropriate type and shape [batch_size, cell_fw.state_size]. If cell_fw.state_size is a tuple, this should be a tuple of tensors having shapes [batch_size, s] for s in cell_fw.state_size.
  • initial_state_bw: (optional) Same as for initial_state_fw, but using the corresponding properties of cell_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 "bidirectional_rnn"


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_state_fw is the final state of the forward rnn. output_state_bw is the final state of the backward rnn.


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

© 2018 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.