tf.nn.static_bidirectional_rnn

Creates a bidirectional recurrent neural network. (deprecated)

View aliases

Main aliases

`tf.contrib.rnn.static_bidirectional_rnn`

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.nn.static_bidirectional_rnn

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

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.