UGRNNCell - LostTech.TensorFlow Documentation

Type UGRNNCell

Namespace tensorflow.contrib.rnn

Parent RNNCell

Interfaces IUGRNNCell

Update Gate Recurrent Neural Network (UGRNN) cell.

Compromise between a LSTM/GRU and a vanilla RNN. There is only one gate, and that is to determine whether the unit should be integrating or computing instantaneously. This is the recurrent idea of the feedforward Highway Network.

This implements the recurrent cell from the paper:

https://arxiv.org/abs/1611.09913

Jasmine Collins, Jascha Sohl-Dickstein, and David Sussillo. "Capacity and Trainability in Recurrent Neural Networks" Proc. ICLR 2017.

Methods

NewDyn

Properties

Public static methods

UGRNNCell NewDyn(object num_units, object initializer, ImplicitContainer<T> forget_bias, ImplicitContainer<T> activation, object reuse)

Initialize the parameters for an UGRNN cell.

Parameters

object num_units: int, The number of units in the UGRNN cell
object initializer: (optional) The initializer to use for the weight matrices.
ImplicitContainer<T> forget_bias: (optional) float, default 1.0, The initial bias of the forget gate, used to reduce the scale of forgetting at the beginning of the training.
ImplicitContainer<T> activation: (optional) Activation function of the inner states. Default is tf.tanh.
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;