Type DeviceWrapper
Namespace tensorflow.nn.rnn_cell
Parent DeviceWrapperBase
Interfaces IDeviceWrapper
Operator that ensures an RNNCell runs on a particular device.
Properties
- activity_regularizer
- activity_regularizer_dyn
- built
- cell
- dtype
- dtype_dyn
- dynamic
- dynamic_dyn
- graph
- graph_dyn
- inbound_nodes
- inbound_nodes_dyn
- input
- input_dyn
- input_mask
- input_mask_dyn
- input_shape
- input_shape_dyn
- input_spec
- input_spec_dyn
- losses
- losses_dyn
- metrics
- metrics_dyn
- name
- name_dyn
- name_scope
- name_scope_dyn
- non_trainable_variables
- non_trainable_variables_dyn
- non_trainable_weights
- non_trainable_weights_dyn
- outbound_nodes
- outbound_nodes_dyn
- output
- output_dyn
- output_mask
- output_mask_dyn
- output_shape
- output_shape_dyn
- output_size
- output_size_dyn
- PythonObject
- rnncell_scope
- scope_name
- scope_name_dyn
- state_size
- state_size_dyn
- stateful
- submodules
- submodules_dyn
- supports_masking
- trainable
- trainable_dyn
- trainable_variables
- trainable_variables_dyn
- trainable_weights
- trainable_weights_dyn
- updates
- updates_dyn
- variables
- variables_dyn
- weights
- weights_dyn
Public properties
PythonFunctionContainer activity_regularizer get; set;
Optional regularizer function for the output of this layer.
object activity_regularizer_dyn get; set;
Optional regularizer function for the output of this layer.
bool built get; set;
object cell get; set;
object dtype get;
object dtype_dyn get;
bool dynamic get;
object dynamic_dyn get;
object graph get;
DEPRECATED FUNCTION Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version.
Instructions for updating:
Stop using this property because tf.layers layers no longer track their graph.
object graph_dyn get;
DEPRECATED FUNCTION Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version.
Instructions for updating:
Stop using this property because tf.layers layers no longer track their graph.
IList<Node> inbound_nodes get;
object inbound_nodes_dyn get;
IList<object> input get;
Retrieves the input tensor(s) of a layer. Only applicable if the layer has exactly one input,
i.e. if it is connected to one incoming layer.
object input_dyn get;
Retrieves the input tensor(s) of a layer. Only applicable if the layer has exactly one input,
i.e. if it is connected to one incoming layer.
object input_mask get;
Retrieves the input mask tensor(s) of a layer. Only applicable if the layer has exactly one inbound node,
i.e. if it is connected to one incoming layer.
object input_mask_dyn get;
Retrieves the input mask tensor(s) of a layer. Only applicable if the layer has exactly one inbound node,
i.e. if it is connected to one incoming layer.
IList<object> input_shape get;
Retrieves the input shape(s) of a layer. Only applicable if the layer has exactly one input,
i.e. if it is connected to one incoming layer, or if all inputs
have the same shape.
object input_shape_dyn get;
Retrieves the input shape(s) of a layer. Only applicable if the layer has exactly one input,
i.e. if it is connected to one incoming layer, or if all inputs
have the same shape.
object input_spec get; set;
object input_spec_dyn get; set;
IList<object> losses get;
Losses which are associated with this `Layer`. Variable regularization tensors are created when this property is accessed,
so it is eager safe: accessing `losses` under a
tf.GradientTape
will
propagate gradients back to the corresponding variables.
object losses_dyn get;
Losses which are associated with this `Layer`. Variable regularization tensors are created when this property is accessed,
so it is eager safe: accessing `losses` under a
tf.GradientTape
will
propagate gradients back to the corresponding variables.
IList<object> metrics get;
object metrics_dyn get;
object name get;
Returns the name of this module as passed or determined in the ctor. NOTE: This is not the same as the `self.name_scope.name` which includes
parent module names.
object name_dyn get;
Returns the name of this module as passed or determined in the ctor. NOTE: This is not the same as the `self.name_scope.name` which includes
parent module names.
object name_scope get;
Returns a
tf.name_scope
instance for this class.
object name_scope_dyn get;
Returns a
tf.name_scope
instance for this class.
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;
Retrieves the output tensor(s) of a layer. Only applicable if the layer has exactly one output,
i.e. if it is connected to one incoming layer.
object output_dyn get;
Retrieves the output tensor(s) of a layer. Only applicable if the layer has exactly one output,
i.e. if it is connected to one incoming layer.
object output_mask get;
Retrieves the output mask tensor(s) of a layer. Only applicable if the layer has exactly one inbound node,
i.e. if it is connected to one incoming layer.
object output_mask_dyn get;
Retrieves the output mask tensor(s) of a layer. Only applicable if the layer has exactly one inbound node,
i.e. if it is connected to one incoming layer.
object output_shape get;
Retrieves the output shape(s) of a layer. Only applicable if the layer has one output,
or if all outputs have the same shape.
object output_shape_dyn get;
Retrieves the output shape(s) of a layer. Only applicable if the layer has one output,
or if all outputs have the same shape.
object output_size get;
object output_size_dyn get;
object PythonObject get;
object rnncell_scope get; set;
string scope_name get;
object scope_name_dyn get;
object state_size get;
object state_size_dyn get;
bool stateful get; set;
ValueTuple<object> submodules get;
Sequence of all sub-modules. Submodules are modules which are properties of this module, or found as
properties of modules which are properties of this module (and so on). ```
a = tf.Module()
b = tf.Module()
c = tf.Module()
a.b = b
b.c = c
assert list(a.submodules) == [b, c]
assert list(b.submodules) == [c]
assert list(c.submodules) == []
```
object submodules_dyn get;
Sequence of all sub-modules. Submodules are modules which are properties of this module, or found as
properties of modules which are properties of this module (and so on). ```
a = tf.Module()
b = tf.Module()
c = tf.Module()
a.b = b
b.c = c
assert list(a.submodules) == [b, c]
assert list(b.submodules) == [c]
assert list(c.submodules) == []
```
bool supports_masking get; set;
bool trainable get; set;
object trainable_dyn get; set;
object trainable_variables get;
Sequence of variables owned by this module and it's submodules. Note: this method uses reflection to find variables on the current instance
and submodules. For performance reasons you may wish to cache the result
of calling this method if you don't expect the return value to change.
object trainable_variables_dyn get;
Sequence of variables owned by this module and it's submodules. Note: this method uses reflection to find variables on the current instance
and submodules. For performance reasons you may wish to cache the result
of calling this method if you don't expect the return value to change.
IList<object> trainable_weights get;
object trainable_weights_dyn get;
IList<object> updates get;
object updates_dyn get;
object variables get;
Returns the list of all layer variables/weights. Alias of `self.weights`.
object variables_dyn get;
Returns the list of all layer variables/weights. Alias of `self.weights`.
IList<object> weights get;
Returns the list of all layer variables/weights.
object weights_dyn get;
Returns the list of all layer variables/weights.