tf.contrib.rnn.static_bidirectional_rnn
tf.nn.static_bidirectional_rnn
tf.nn.static_bidirectional_rnn( cell_fw, cell_bw, inputs, initial_state_fw=None, initial_state_bw=None, dtype=None, sequence_length=None, scope=None )
Defined in tensorflow/python/ops/rnn.py
.
See the guide: RNN and Cells (contrib) > Recurrent Neural Networks
Creates a bidirectional recurrent neural network.
Similar to the unidirectional case above (rnn) but takes input and builds independent forward and backward RNNs with the final forward and backward outputs depth-concatenated, such that the output will have the format [time][batch][cell_fw.output_size + cell_bw.output_size]. The input_size of forward and backward cell must match. The initial state for both directions is zero by default (but can be set optionally) and no intermediate states are ever returned -- the network is fully unrolled for the given (passed in) length(s) of the sequence(s) or completely unrolled if length(s) is not given.
cell_fw
: An instance of RNNCell, to be used for forward direction.cell_bw
: An instance of RNNCell, 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_state_fw
: (optional) An initial state for the forward RNN. This must be a tensor of appropriate type and shape [batch_size, cell_fw.state_size]
. If cell_fw.state_size
is a tuple, this should be a tuple of tensors having shapes [batch_size, s] for s in cell_fw.state_size
.initial_state_bw
: (optional) Same as for initial_state_fw
, but using the corresponding properties of cell_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 "bidirectional_rnn"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_state_fw is the final state of the forward rnn. output_state_bw is the final state of the backward rnn.
TypeError
: If cell_fw
or cell_bw
is not an instance of RNNCell
.ValueError
: If inputs is None 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/nn/static_bidirectional_rnn