tf.contrib.rnn.static_bidirectional_rnn(cell_fw, cell_bw, inputs, initial_state_fw=None, initial_state_bw=None, dtype=None, sequence_length=None, scope=None)

tf.contrib.rnn.static_bidirectional_rnn(cell_fw, cell_bw, inputs, initial_state_fw=None, initial_state_bw=None, dtype=None, sequence_length=None, scope=None)

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.

Args:

• 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"

Returns:

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.

Raises:

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

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