NASCell - LostTech.TensorFlow Documentation

Type NASCell

Namespace tensorflow.contrib.rnn

Parent LayerRNNCell

Interfaces INASCell

Neural Architecture Search (NAS) recurrent network cell.

This implements the recurrent cell from the paper:

https://arxiv.org/abs/1611.01578

Barret Zoph and Quoc V. Le. "Neural Architecture Search with Reinforcement Learning" Proc. ICLR 2017.

The class uses an optional projection layer.

Methods

NewDyn

Properties

Public static methods

NASCell NewDyn(object num_units, object num_proj, ImplicitContainer<T> use_bias, object reuse, IDictionary<string, object> kwargs)

Initialize the parameters for a NAS cell.

Parameters

object num_units: int, The number of units in the NAS cell.
object num_proj: (optional) int, The output dimensionality for the projection matrices. If None, no projection is performed.
ImplicitContainer<T> use_bias: (optional) bool, If True then use biases within the cell. This is False by default.
object reuse: (optional) Python boolean describing whether to reuse variables in an existing scope. If not `True`, and the existing scope already has the given variables, an error is raised.
IDictionary<string, object> kwargs: Additional keyword arguments.

Public properties

PythonFunctionContainer activity_regularizer get; set;

object activity_regularizer_dyn get; set;

object bias 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;

object kernel 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 projection_weights get; set;

object PythonObject get;

object recurrent_kernel get; set;

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;