tf.contrib.rnn.stack_bidirectional_rnn

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

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.

tf.contrib.rnn.stack_bidirectional_rnn

Args

Returns

Raises