PhasedLSTMCell - LostTech.TensorFlow Documentation

Type PhasedLSTMCell

Namespace tensorflow.contrib.rnn

Parent RNNCell

Interfaces IPhasedLSTMCell

Phased LSTM recurrent network cell.

https://arxiv.org/pdf/1610.09513v1.pdf

Methods

NewDyn

Properties

Public static methods

PhasedLSTMCell NewDyn(object num_units, ImplicitContainer<T> use_peepholes, ImplicitContainer<T> leak, ImplicitContainer<T> ratio_on, ImplicitContainer<T> trainable_ratio_on, ImplicitContainer<T> period_init_min, ImplicitContainer<T> period_init_max, object reuse)

Initialize the Phased LSTM cell.

Parameters

object num_units: int, The number of units in the Phased LSTM cell.
ImplicitContainer<T> use_peepholes: bool, set True to enable peephole connections.
ImplicitContainer<T> leak: float or scalar float Tensor with value in [0, 1]. Leak applied during training.
ImplicitContainer<T> ratio_on: float or scalar float Tensor with value in [0, 1]. Ratio of the period during which the gates are open.
ImplicitContainer<T> trainable_ratio_on: bool, weather ratio_on is trainable.
ImplicitContainer<T> period_init_min: float or scalar float Tensor. With value > 0. Minimum value of the initialized period. The period values are initialized by drawing from the distribution: e^U(log(period_init_min), log(period_init_max)) Where U(.,.) is the uniform distribution.
ImplicitContainer<T> period_init_max: float or scalar float Tensor. With value > period_init_min. Maximum value of the initialized period.
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.

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;