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Creates a bidirectional recurrent neural network.
tf.contrib.rnn.stack_bidirectional_rnn( cells_fw, cells_bw, inputs, initial_states_fw=None, initial_states_bw=None, dtype=None, sequence_length=None, scope=None )
Stacks several bidirectional rnn layers. The combined forward and backward layer outputs are used as input of the next layer. tf.bidirectional_rnn does not allow to share forward and backward information between layers. The input_size of the first forward and backward cells must match. The initial state for both directions is zero and no intermediate states are returned.
As described in https://arxiv.org/abs/1303.5778
cells_fw: List of instances of RNNCell, one per layer, to be used for forward direction.
cells_bw: List of instances of RNNCell, one per layer, 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_states_fw: (optional) A list of the initial states (one per layer) for the forward RNN. Each tensor must has an appropriate type and shape
initial_states_bw: (optional) Same as for
initial_states_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 None.
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_states_fw is the final states, one tensor per layer,
of the forward rnn.
output_states_bw is the final states, one tensor per layer,
of the backward rnn.
cell_bwis not an instance of
ValueError: If inputs is None, not a list or an empty list.