LostTech.TensorFlow : API Documentation

Type RNNCell

Namespace tensorflow.nn.rnn_cell

Parent Layer

Interfaces IRNNCell

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`.

Methods

Properties

Public instance methods

object __call__(IEnumerable<IGraphNodeBase> inputs, IEnumerable<IGraphNodeBase> state, IDictionary<string, object> scope)

Run this RNN cell on inputs, starting from the given state.
Parameters
IEnumerable<IGraphNodeBase> inputs
`2-D` tensor with shape `[batch_size, input_size]`.
IEnumerable<IGraphNodeBase> state
if `self.state_size` is an integer, this should be a `2-D Tensor` with shape `[batch_size, self.state_size]`. Otherwise, if `self.state_size` is a tuple of integers, this should be a tuple with shapes `[batch_size, s] for s in self.state_size`.
IDictionary<string, object> scope
VariableScope for the created subgraph; defaults to class name.
Returns
object
A pair containing:

- Output: A `2-D` tensor with shape `[batch_size, self.output_size]`. - New state: Either a single `2-D` tensor, or a tuple of tensors matching the arity and shapes of `state`.

object __call__(IGraphNodeBase inputs, object state, string scope)

Run this RNN cell on inputs, starting from the given state.
Parameters
IGraphNodeBase inputs
`2-D` tensor with shape `[batch_size, input_size]`.
object state
if `self.state_size` is an integer, this should be a `2-D Tensor` with shape `[batch_size, self.state_size]`. Otherwise, if `self.state_size` is a tuple of integers, this should be a tuple with shapes `[batch_size, s] for s in self.state_size`.
string scope
VariableScope for the created subgraph; defaults to class name.
Returns
object
A pair containing:

- Output: A `2-D` tensor with shape `[batch_size, self.output_size]`. - New state: Either a single `2-D` tensor, or a tuple of tensors matching the arity and shapes of `state`.

object __call__(IGraphNodeBase inputs, object state, object scope)

Run this RNN cell on inputs, starting from the given state.
Parameters
IGraphNodeBase inputs
`2-D` tensor with shape `[batch_size, input_size]`.
object state
if `self.state_size` is an integer, this should be a `2-D Tensor` with shape `[batch_size, self.state_size]`. Otherwise, if `self.state_size` is a tuple of integers, this should be a tuple with shapes `[batch_size, s] for s in self.state_size`.
object scope
VariableScope for the created subgraph; defaults to class name.
Returns
object
A pair containing:

- Output: A `2-D` tensor with shape `[batch_size, self.output_size]`. - New state: Either a single `2-D` tensor, or a tuple of tensors matching the arity and shapes of `state`.

object __call__(IGraphNodeBase inputs, object state, IEnumerable<IGraphNodeBase> scope)

Run this RNN cell on inputs, starting from the given state.
Parameters
IGraphNodeBase inputs
`2-D` tensor with shape `[batch_size, input_size]`.
object state
if `self.state_size` is an integer, this should be a `2-D Tensor` with shape `[batch_size, self.state_size]`. Otherwise, if `self.state_size` is a tuple of integers, this should be a tuple with shapes `[batch_size, s] for s in self.state_size`.
IEnumerable<IGraphNodeBase> scope
VariableScope for the created subgraph; defaults to class name.
Returns
object
A pair containing:

- Output: A `2-D` tensor with shape `[batch_size, self.output_size]`. - New state: Either a single `2-D` tensor, or a tuple of tensors matching the arity and shapes of `state`.

object __call__(IGraphNodeBase inputs, IEnumerable<IGraphNodeBase> state, string scope)

Run this RNN cell on inputs, starting from the given state.
Parameters
IGraphNodeBase inputs
`2-D` tensor with shape `[batch_size, input_size]`.
IEnumerable<IGraphNodeBase> state
if `self.state_size` is an integer, this should be a `2-D Tensor` with shape `[batch_size, self.state_size]`. Otherwise, if `self.state_size` is a tuple of integers, this should be a tuple with shapes `[batch_size, s] for s in self.state_size`.
string scope
VariableScope for the created subgraph; defaults to class name.
Returns
object
A pair containing:

- Output: A `2-D` tensor with shape `[batch_size, self.output_size]`. - New state: Either a single `2-D` tensor, or a tuple of tensors matching the arity and shapes of `state`.

object __call__(IGraphNodeBase inputs, IEnumerable<IGraphNodeBase> state, object scope)

Run this RNN cell on inputs, starting from the given state.
Parameters
IGraphNodeBase inputs
`2-D` tensor with shape `[batch_size, input_size]`.
IEnumerable<IGraphNodeBase> state
if `self.state_size` is an integer, this should be a `2-D Tensor` with shape `[batch_size, self.state_size]`. Otherwise, if `self.state_size` is a tuple of integers, this should be a tuple with shapes `[batch_size, s] for s in self.state_size`.
object scope
VariableScope for the created subgraph; defaults to class name.
Returns
object
A pair containing:

- Output: A `2-D` tensor with shape `[batch_size, self.output_size]`. - New state: Either a single `2-D` tensor, or a tuple of tensors matching the arity and shapes of `state`.

object __call__(IGraphNodeBase inputs, IEnumerable<IGraphNodeBase> state, IEnumerable<IGraphNodeBase> scope)

Run this RNN cell on inputs, starting from the given state.
Parameters
IGraphNodeBase inputs
`2-D` tensor with shape `[batch_size, input_size]`.
IEnumerable<IGraphNodeBase> state
if `self.state_size` is an integer, this should be a `2-D Tensor` with shape `[batch_size, self.state_size]`. Otherwise, if `self.state_size` is a tuple of integers, this should be a tuple with shapes `[batch_size, s] for s in self.state_size`.
IEnumerable<IGraphNodeBase> scope
VariableScope for the created subgraph; defaults to class name.
Returns
object
A pair containing:

- Output: A `2-D` tensor with shape `[batch_size, self.output_size]`. - New state: Either a single `2-D` tensor, or a tuple of tensors matching the arity and shapes of `state`.

object __call__(IGraphNodeBase inputs, IEnumerable<IGraphNodeBase> state, IDictionary<string, object> scope)

Run this RNN cell on inputs, starting from the given state.
Parameters
IGraphNodeBase inputs
`2-D` tensor with shape `[batch_size, input_size]`.
IEnumerable<IGraphNodeBase> state
if `self.state_size` is an integer, this should be a `2-D Tensor` with shape `[batch_size, self.state_size]`. Otherwise, if `self.state_size` is a tuple of integers, this should be a tuple with shapes `[batch_size, s] for s in self.state_size`.
IDictionary<string, object> scope
VariableScope for the created subgraph; defaults to class name.
Returns
object
A pair containing:

- Output: A `2-D` tensor with shape `[batch_size, self.output_size]`. - New state: Either a single `2-D` tensor, or a tuple of tensors matching the arity and shapes of `state`.

object __call__(IGraphNodeBase inputs, object state, IDictionary<string, object> scope)

Run this RNN cell on inputs, starting from the given state.
Parameters
IGraphNodeBase inputs
`2-D` tensor with shape `[batch_size, input_size]`.
object state
if `self.state_size` is an integer, this should be a `2-D Tensor` with shape `[batch_size, self.state_size]`. Otherwise, if `self.state_size` is a tuple of integers, this should be a tuple with shapes `[batch_size, s] for s in self.state_size`.
IDictionary<string, object> scope
VariableScope for the created subgraph; defaults to class name.
Returns
object
A pair containing:

- Output: A `2-D` tensor with shape `[batch_size, self.output_size]`. - New state: Either a single `2-D` tensor, or a tuple of tensors matching the arity and shapes of `state`.

object __call__(IEnumerable<IGraphNodeBase> inputs, object state, object scope)

Run this RNN cell on inputs, starting from the given state.
Parameters
IEnumerable<IGraphNodeBase> inputs
`2-D` tensor with shape `[batch_size, input_size]`.
object state
if `self.state_size` is an integer, this should be a `2-D Tensor` with shape `[batch_size, self.state_size]`. Otherwise, if `self.state_size` is a tuple of integers, this should be a tuple with shapes `[batch_size, s] for s in self.state_size`.
object scope
VariableScope for the created subgraph; defaults to class name.
Returns
object
A pair containing:

- Output: A `2-D` tensor with shape `[batch_size, self.output_size]`. - New state: Either a single `2-D` tensor, or a tuple of tensors matching the arity and shapes of `state`.

object __call__(IEnumerable<IGraphNodeBase> inputs, object state, IEnumerable<IGraphNodeBase> scope)

Run this RNN cell on inputs, starting from the given state.
Parameters
IEnumerable<IGraphNodeBase> inputs
`2-D` tensor with shape `[batch_size, input_size]`.
object state
if `self.state_size` is an integer, this should be a `2-D Tensor` with shape `[batch_size, self.state_size]`. Otherwise, if `self.state_size` is a tuple of integers, this should be a tuple with shapes `[batch_size, s] for s in self.state_size`.
IEnumerable<IGraphNodeBase> scope
VariableScope for the created subgraph; defaults to class name.
Returns
object
A pair containing:

- Output: A `2-D` tensor with shape `[batch_size, self.output_size]`. - New state: Either a single `2-D` tensor, or a tuple of tensors matching the arity and shapes of `state`.

object __call__(IEnumerable<IGraphNodeBase> inputs, object state, IDictionary<string, object> scope)

Run this RNN cell on inputs, starting from the given state.
Parameters
IEnumerable<IGraphNodeBase> inputs
`2-D` tensor with shape `[batch_size, input_size]`.
object state
if `self.state_size` is an integer, this should be a `2-D Tensor` with shape `[batch_size, self.state_size]`. Otherwise, if `self.state_size` is a tuple of integers, this should be a tuple with shapes `[batch_size, s] for s in self.state_size`.
IDictionary<string, object> scope
VariableScope for the created subgraph; defaults to class name.
Returns
object
A pair containing:

- Output: A `2-D` tensor with shape `[batch_size, self.output_size]`. - New state: Either a single `2-D` tensor, or a tuple of tensors matching the arity and shapes of `state`.

object __call__(IEnumerable<IGraphNodeBase> inputs, IEnumerable<IGraphNodeBase> state, string scope)

Run this RNN cell on inputs, starting from the given state.
Parameters
IEnumerable<IGraphNodeBase> inputs
`2-D` tensor with shape `[batch_size, input_size]`.
IEnumerable<IGraphNodeBase> state
if `self.state_size` is an integer, this should be a `2-D Tensor` with shape `[batch_size, self.state_size]`. Otherwise, if `self.state_size` is a tuple of integers, this should be a tuple with shapes `[batch_size, s] for s in self.state_size`.
string scope
VariableScope for the created subgraph; defaults to class name.
Returns
object
A pair containing:

- Output: A `2-D` tensor with shape `[batch_size, self.output_size]`. - New state: Either a single `2-D` tensor, or a tuple of tensors matching the arity and shapes of `state`.

object __call__(IEnumerable<IGraphNodeBase> inputs, IEnumerable<IGraphNodeBase> state, object scope)

Run this RNN cell on inputs, starting from the given state.
Parameters
IEnumerable<IGraphNodeBase> inputs
`2-D` tensor with shape `[batch_size, input_size]`.
IEnumerable<IGraphNodeBase> state
if `self.state_size` is an integer, this should be a `2-D Tensor` with shape `[batch_size, self.state_size]`. Otherwise, if `self.state_size` is a tuple of integers, this should be a tuple with shapes `[batch_size, s] for s in self.state_size`.
object scope
VariableScope for the created subgraph; defaults to class name.
Returns
object
A pair containing:

- Output: A `2-D` tensor with shape `[batch_size, self.output_size]`. - New state: Either a single `2-D` tensor, or a tuple of tensors matching the arity and shapes of `state`.

object __call__(IEnumerable<IGraphNodeBase> inputs, IEnumerable<IGraphNodeBase> state, IEnumerable<IGraphNodeBase> scope)

Run this RNN cell on inputs, starting from the given state.
Parameters
IEnumerable<IGraphNodeBase> inputs
`2-D` tensor with shape `[batch_size, input_size]`.
IEnumerable<IGraphNodeBase> state
if `self.state_size` is an integer, this should be a `2-D Tensor` with shape `[batch_size, self.state_size]`. Otherwise, if `self.state_size` is a tuple of integers, this should be a tuple with shapes `[batch_size, s] for s in self.state_size`.
IEnumerable<IGraphNodeBase> scope
VariableScope for the created subgraph; defaults to class name.
Returns
object
A pair containing:

- Output: A `2-D` tensor with shape `[batch_size, self.output_size]`. - New state: Either a single `2-D` tensor, or a tuple of tensors matching the arity and shapes of `state`.

object __call__(IEnumerable<IGraphNodeBase> inputs, object state, string scope)

Run this RNN cell on inputs, starting from the given state.
Parameters
IEnumerable<IGraphNodeBase> inputs
`2-D` tensor with shape `[batch_size, input_size]`.
object state
if `self.state_size` is an integer, this should be a `2-D Tensor` with shape `[batch_size, self.state_size]`. Otherwise, if `self.state_size` is a tuple of integers, this should be a tuple with shapes `[batch_size, s] for s in self.state_size`.
string scope
VariableScope for the created subgraph; defaults to class name.
Returns
object
A pair containing:

- Output: A `2-D` tensor with shape `[batch_size, self.output_size]`. - New state: Either a single `2-D` tensor, or a tuple of tensors matching the arity and shapes of `state`.

object add_weight(string name, Dimension shape, DType dtype, object initializer, object regularizer, object trainable, object constraint, object use_resource, ImplicitContainer<T> synchronization, ImplicitContainer<T> aggregation, object partitioner, IDictionary<string, object> kwargs)

object add_weight(string name, TensorShape shape, DType dtype, PythonFunctionContainer initializer, object regularizer, object trainable, IDictionary<object, object> constraint, object use_resource, ImplicitContainer<T> synchronization, ImplicitContainer<T> aggregation, object partitioner, IDictionary<string, object> kwargs)

object add_weight(string name, TensorShape shape, DType dtype, PythonFunctionContainer initializer, object regularizer, object trainable, object constraint, object use_resource, ImplicitContainer<T> synchronization, ImplicitContainer<T> aggregation, object partitioner, IDictionary<string, object> kwargs)

object add_weight(string name, TensorShape shape, DType dtype, object initializer, object regularizer, object trainable, IDictionary<object, object> constraint, object use_resource, ImplicitContainer<T> synchronization, ImplicitContainer<T> aggregation, object partitioner, IDictionary<string, object> kwargs)

object add_weight(string name, int shape, DType dtype, PythonFunctionContainer initializer, object regularizer, object trainable, object constraint, object use_resource, ImplicitContainer<T> synchronization, ImplicitContainer<T> aggregation, object partitioner, IDictionary<string, object> kwargs)

object add_weight(string name, int shape, DType dtype, PythonFunctionContainer initializer, object regularizer, object trainable, IDictionary<object, object> constraint, object use_resource, ImplicitContainer<T> synchronization, ImplicitContainer<T> aggregation, object partitioner, IDictionary<string, object> kwargs)

object add_weight(string name, int shape, DType dtype, object initializer, object regularizer, object trainable, IDictionary<object, object> constraint, object use_resource, ImplicitContainer<T> synchronization, ImplicitContainer<T> aggregation, object partitioner, IDictionary<string, object> kwargs)

object add_weight(string name, Dimension shape, DType dtype, object initializer, object regularizer, object trainable, IDictionary<object, object> constraint, object use_resource, ImplicitContainer<T> synchronization, ImplicitContainer<T> aggregation, object partitioner, IDictionary<string, object> kwargs)

object add_weight(string name, TensorShape shape, DType dtype, object initializer, object regularizer, object trainable, object constraint, object use_resource, ImplicitContainer<T> synchronization, ImplicitContainer<T> aggregation, object partitioner, IDictionary<string, object> kwargs)

object add_weight(string name, Dimension shape, DType dtype, PythonFunctionContainer initializer, object regularizer, object trainable, object constraint, object use_resource, ImplicitContainer<T> synchronization, ImplicitContainer<T> aggregation, object partitioner, IDictionary<string, object> kwargs)

object add_weight(string name, IEnumerable<object> shape, DType dtype, PythonFunctionContainer initializer, object regularizer, object trainable, object constraint, object use_resource, ImplicitContainer<T> synchronization, ImplicitContainer<T> aggregation, object partitioner, IDictionary<string, object> kwargs)

object add_weight(string name, ValueTuple shape, DType dtype, object initializer, object regularizer, object trainable, object constraint, object use_resource, ImplicitContainer<T> synchronization, ImplicitContainer<T> aggregation, object partitioner, IDictionary<string, object> kwargs)

object add_weight(string name, ValueTuple shape, DType dtype, object initializer, object regularizer, object trainable, IDictionary<object, object> constraint, object use_resource, ImplicitContainer<T> synchronization, ImplicitContainer<T> aggregation, object partitioner, IDictionary<string, object> kwargs)

object add_weight(string name, ValueTuple shape, DType dtype, PythonFunctionContainer initializer, object regularizer, object trainable, object constraint, object use_resource, ImplicitContainer<T> synchronization, ImplicitContainer<T> aggregation, object partitioner, IDictionary<string, object> kwargs)

object add_weight(string name, ValueTuple shape, DType dtype, PythonFunctionContainer initializer, object regularizer, object trainable, IDictionary<object, object> constraint, object use_resource, ImplicitContainer<T> synchronization, ImplicitContainer<T> aggregation, object partitioner, IDictionary<string, object> kwargs)

object add_weight(string name, IEnumerable<object> shape, DType dtype, object initializer, object regularizer, object trainable, object constraint, object use_resource, ImplicitContainer<T> synchronization, ImplicitContainer<T> aggregation, object partitioner, IDictionary<string, object> kwargs)

object add_weight(string name, IEnumerable<object> shape, DType dtype, object initializer, object regularizer, object trainable, IDictionary<object, object> constraint, object use_resource, ImplicitContainer<T> synchronization, ImplicitContainer<T> aggregation, object partitioner, IDictionary<string, object> kwargs)

object add_weight(string name, int shape, DType dtype, object initializer, object regularizer, object trainable, object constraint, object use_resource, ImplicitContainer<T> synchronization, ImplicitContainer<T> aggregation, object partitioner, IDictionary<string, object> kwargs)

object add_weight(string name, IEnumerable<object> shape, DType dtype, PythonFunctionContainer initializer, object regularizer, object trainable, IDictionary<object, object> constraint, object use_resource, ImplicitContainer<T> synchronization, ImplicitContainer<T> aggregation, object partitioner, IDictionary<string, object> kwargs)

object add_weight(string name, Dimension shape, DType dtype, PythonFunctionContainer initializer, object regularizer, object trainable, IDictionary<object, object> constraint, object use_resource, ImplicitContainer<T> synchronization, ImplicitContainer<T> aggregation, object partitioner, IDictionary<string, object> kwargs)

object add_weight_dyn(object name, object shape, object dtype, object initializer, object regularizer, object trainable, object constraint, object use_resource, ImplicitContainer<T> synchronization, ImplicitContainer<T> aggregation, object partitioner, IDictionary<string, object> kwargs)

Public static methods

RNNCell NewDyn(ImplicitContainer<T> trainable, object name, object dtype, IDictionary<string, object> kwargs)

Public properties

PythonFunctionContainer activity_regularizer get; set;

object activity_regularizer_dyn get; set;

bool built get; set;

object dtype get;

object dtype_dyn get;

bool dynamic get;

object dynamic_dyn get;

object graph get;

object graph_dyn get;

IList<Node> inbound_nodes get;

object inbound_nodes_dyn get;

IList<object> input get;

object input_dyn get;

object input_mask get;

object input_mask_dyn get;

IList<object> input_shape get;

object input_shape_dyn get;

object input_spec get; set;

object input_spec_dyn get; set;

IList<object> losses get;

object losses_dyn get;

IList<object> metrics get;

object metrics_dyn get;

object name get;

object name_dyn get;

object name_scope get;

object name_scope_dyn get;

IList<object> non_trainable_variables get;

object non_trainable_variables_dyn get;

IList<object> non_trainable_weights get;

object non_trainable_weights_dyn get;

IList<object> outbound_nodes get;

object outbound_nodes_dyn get;

IList<object> output get;

object output_dyn get;

object output_mask get;

object output_mask_dyn get;

object output_shape get;

object output_shape_dyn get;

object output_size get;

Integer or TensorShape: size of outputs produced by this cell.

object output_size_dyn get;

Integer or TensorShape: size of outputs produced by this cell.

object PythonObject get;

object rnncell_scope get; set;

string scope_name get;

object scope_name_dyn get;

object state_size get;

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.

object state_size_dyn get;

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.

bool stateful get; set;

ValueTuple<object> submodules get;

object submodules_dyn get;

bool supports_masking get; set;

bool trainable get; set;

object trainable_dyn get; set;

object trainable_variables get;

object trainable_variables_dyn get;

IList<object> trainable_weights get;

object trainable_weights_dyn get;

IList<object> updates get;

object updates_dyn get;

object variables get;

object variables_dyn get;

IList<object> weights get;

object weights_dyn get;