contrib.rnn.RNNCell
tf.contrib.rnn.RNNCell
class tf.contrib.rnn.RNNCell
class tf.nn.rnn_cell.RNNCell
Defined in tensorflow/python/ops/rnn_cell_impl.py
.
See the guides: RNN and Cells (contrib) > Base interface for all RNN Cells, Seq2seq Library (contrib)
Abstract object representing an RNN cell.
Every RNNCell
must have the properties below and implement call
with the signature (output, next_state) = call(input, state)
. The optional third input argument, scope
, is allowed for backwards compatibility purposes; but should be left off for new subclasses.
This definition of cell differs from the definition used in the literature. In the literature, 'cell' refers to an object with a single scalar output. This definition refers to a horizontal array of such units.
An RNN cell, in the most abstract setting, is anything that has a state and performs some operation that takes a matrix of inputs. This operation results in an output matrix with self.output_size
columns. If self.state_size
is an integer, this operation also results in a new state matrix with self.state_size
columns. If self.state_size
is a (possibly nested tuple of) TensorShape object(s), then it should return a matching structure of Tensors having shape [batch_size].concatenate(s)
for each s
in self.batch_size
.
Properties
graph
losses
non_trainable_variables
non_trainable_weights
output_size
Integer or TensorShape: size of outputs produced by this cell.
scope_name
state_size
size(s) of state(s) used by this cell.
It can be represented by an Integer, a TensorShape or a tuple of Integers or TensorShapes.
trainable_variables
trainable_weights
updates
variables
Returns the list of all layer variables/weights.
Returns:
A list of variables.
weights
Returns the list of all layer variables/weights.
Returns:
A list of variables.
Methods
__init__
__init__( trainable=True, name=None, dtype=tf.float32, **kwargs )
__call__
__call__( inputs, state, scope=None )
Run this RNN cell on inputs, starting from the given state.
Args:
-
inputs
:2-D
tensor with shape[batch_size x input_size]
. -
state
: ifself.state_size
is an integer, this should be a2-D Tensor
with shape[batch_size x self.state_size]
. Otherwise, ifself.state_size
is a tuple of integers, this should be a tuple with shapes[batch_size x s] for s in self.state_size
. -
scope
: VariableScope for the created subgraph; defaults to class name.
Returns:
A pair containing:
- Output: A
2-D
tensor with shape[batch_size x self.output_size]
. - New state: Either a single
2-D
tensor, or a tuple of tensors matching the arity and shapes ofstate
.
__deepcopy__
__deepcopy__(memo)
add_loss
add_loss( losses, inputs=None )
Add loss tensor(s), potentially dependent on layer inputs.
Some losses (for instance, activity regularization losses) may be dependent on the inputs passed when calling a layer. Hence, when reusing a same layer on different inputs a
and b
, some entries in layer.losses
may be dependent on a
and some on b
. This method automatically keeps track of dependencies.
The get_losses_for
method allows to retrieve the losses relevant to a specific set of inputs.
Arguments:
-
losses
: Loss tensor, or list/tuple of tensors. -
inputs
: Optional input tensor(s) that the loss(es) depend on. Must match theinputs
argument passed to the__call__
method at the time the losses are created. IfNone
is passed, the losses are assumed to be unconditional, and will apply across all dataflows of the layer (e.g. weight regularization losses).
add_update
add_update( updates, inputs=None )
Add update op(s), potentially dependent on layer inputs.
Weight updates (for instance, the updates of the moving mean and variance in a BatchNormalization layer) may be dependent on the inputs passed when calling a layer. Hence, when reusing a same layer on different inputs a
and b
, some entries in layer.updates
may be dependent on a
and some on b
. This method automatically keeps track of dependencies.
The get_updates_for
method allows to retrieve the updates relevant to a specific set of inputs.
Arguments:
-
updates
: Update op, or list/tuple of update ops. -
inputs
: Optional input tensor(s) that the update(s) depend on. Must match theinputs
argument passed to the__call__
method at the time the updates are created. IfNone
is passed, the updates are assumed to be unconditional, and will apply across all dataflows of the layer.
add_variable
add_variable( name, shape, dtype=None, initializer=None, regularizer=None, trainable=True )
Adds a new variable to the layer, or gets an existing one; returns it.
Arguments:
-
name
: variable name. -
shape
: variable shape. -
dtype
: The type of the variable. Defaults toself.dtype
. -
initializer
: initializer instance (callable). -
regularizer
: regularizer instance (callable). -
trainable
: whether the variable should be part of the layer's "trainable_variables" (e.g. variables, biases) or "non_trainable_variables" (e.g. BatchNorm mean, stddev).
Returns:
The created variable.
apply
apply( inputs, *args, **kwargs )
Apply the layer on a input.
This simply wraps self.__call__
.
Arguments:
-
inputs
: Input tensor(s). args: additional positional arguments to be passed toself.call
.
*kwargs: additional keyword arguments to be passed toself.call
.
Returns:
Output tensor(s).
build
build(_)
call
call( inputs, **kwargs )
The logic of the layer lives here.
Arguments:
-
inputs
: input tensor(s). **kwargs: additional keyword arguments.
Returns:
Output tensor(s).
get_losses_for
get_losses_for(inputs)
Retrieves losses relevant to a specific set of inputs.
Arguments:
-
inputs
: Input tensor or list/tuple of input tensors. Must match theinputs
argument passed to the__call__
method at the time the losses were created. If you passinputs=None
, unconditional losses are returned, such as weight regularization losses.
Returns:
List of loss tensors of the layer that depend on inputs
.
get_updates_for
get_updates_for(inputs)
Retrieves updates relevant to a specific set of inputs.
Arguments:
-
inputs
: Input tensor or list/tuple of input tensors. Must match theinputs
argument passed to the__call__
method at the time the updates were created. If you passinputs=None
, unconditional updates are returned.
Returns:
List of update ops of the layer that depend on inputs
.
zero_state
zero_state( batch_size, dtype )
Return zero-filled state tensor(s).
Args:
-
batch_size
: int, float, or unit Tensor representing the batch size. -
dtype
: the data type to use for the state.
Returns:
If state_size
is an int or TensorShape, then the return value is a N-D
tensor of shape [batch_size x state_size]
filled with zeros.
If state_size
is a nested list or tuple, then the return value is a nested list or tuple (of the same structure) of 2-D
tensors with the shapes [batch_size x s]
for each s in state_size
.
© 2017 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/RNNCell