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 )
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_sizeis 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
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.
cell_bwis not an instance of
ValueError: If inputs is None or an empty list.