tf.contrib.rnn.stack_bidirectional_rnn

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
)

Defined in tensorflow/contrib/rnn/python/ops/rnn.py.

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

Args:

• 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.

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_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.

Raises:

• 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.