tf.compat.v1.nn.static_bidirectional_rnn

Creates a bidirectional recurrent neural network. (deprecated)

tf.compat.v1.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.

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.