Type EmbeddingWrapper
Namespace tensorflow.contrib.rnn
Parent RNNCell
Interfaces IEmbeddingWrapper
Operator adding input embedding to the given cell. Note: in many cases it may be more efficient to not use this wrapper,
but instead concatenate the whole sequence of your inputs in time,
do the embedding on this batch-concatenated sequence, then split it and
feed into your RNN.
Properties
- activity_regularizer
- activity_regularizer_dyn
- built
- 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;
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.