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
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.
© 2018 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/contrib/rnn/stack_bidirectional_rnn