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tf.contrib.rnn.stack_bidirectional_rnn

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Creates a bidirectional recurrent neural network.

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 [batch_size, cell_fw.state_size].
initial_states_bw (optional) Same as for initial_states_fw, but using the corresponding properties of cells_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 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.

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