Class FusedRNNCell
Defined in tensorflow/contrib/rnn/python/ops/fused_rnn_cell.py
.
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.
Methods
tf.contrib.rnn.FusedRNNCell.__call__
__call__(
inputs,
initial_state=None,
dtype=None,
sequence_length=None,
scope=None
)
Run this fused RNN on inputs, starting from the given state.
Args:
inputs
:3-D
tensor with shape[time_len x batch_size x input_size]
or a list oftime_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 typedtype
.dtype
: The data type for the initial state and expected output. Required ifinitial_state
is not provided or RNN state has a heterogeneous dtype.sequence_length
: Specifies the length of each sequence in inputs. Anint32
orint64
vector (tensor) size[batch_size]
, values in[0, time_len)
. Defaults totime_len
for each element.scope
:VariableScope
orstring
for the created subgraph; defaults to class name.
Returns:
A pair containing:
- Output: A
3-D
tensor of shape[time_len x batch_size x output_size]
or a list oftime_len
tensors of shape[batch_size x output_size]
, to match the type of theinputs
. - Final state: Either a single
2-D
tensor, or a tuple of tensors matching the arity and shapes ofinitial_state
.