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)
Abstract object representing a fused RNN cell.
A fused RNN cell represents the entire RNN expanded over the time dimension. In effect, this represents an entire recurrent network.
Unlike RNN cells which are subclasses of rnn_cell.RNNCell
, a FusedRNNCell
operates on the entire time sequence at once, by putting the loop over time inside the cell. This usually leads to much more efficient, but more complex and less flexible implementations.
Every FusedRNNCell
must implement __call__
with the following 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/FusedRNNCell