TimeReversedFusedRNN
Inherits From: FusedRNNCell
Defined in tensorflow/contrib/rnn/python/ops/fused_rnn_cell.py
.
See the guide: RNN and Cells (contrib) > Core RNN Cell wrappers (RNNCells that wrap other RNNCells)
This is an adaptor to time-reverse a FusedRNNCell.
For example,
cell = tf.contrib.rnn.BasicRNNCell(10) fw_lstm = tf.contrib.rnn.FusedRNNCellAdaptor(cell, use_dynamic_rnn=True) bw_lstm = tf.contrib.rnn.TimeReversedFusedRNN(fw_lstm) fw_out, fw_state = fw_lstm(inputs) bw_out, bw_state = bw_lstm(inputs)
__init__
__init__(cell)
Initialize self. See help(type(self)) for accurate signature.
__call__
__call__( inputs, initial_state=None, dtype=None, sequence_length=None, scope=None )
Run this fused RNN on inputs, starting from the given state.
inputs
: 3-D
tensor with shape [time_len x batch_size x input_size]
or a list of time_len
tensors of shape [batch_size x input_size]
.initial_state
: either a tensor with shape [batch_size x state_size]
or a tuple with shapes [batch_size x s] for s in state_size
, if the cell takes tuples. If this is not provided, the cell is expected to create a zero initial state of type dtype
.dtype
: The data type for the initial state and expected output. Required if initial_state
is not provided or RNN state has a heterogeneous dtype.sequence_length
: Specifies the length of each sequence in inputs. An int32
or int64
vector (tensor) size [batch_size]
, values in [0, time_len)
. Defaults to time_len
for each element.scope
: VariableScope
or string
for the created subgraph; defaults to class name.A pair containing:
3-D
tensor of shape [time_len x batch_size x output_size]
or a list of time_len
tensors of shape [batch_size x output_size]
, to match the type of the inputs
.2-D
tensor, or a tuple of tensors matching the arity and shapes of initial_state
.
© 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/TimeReversedFusedRNN