GLSTMCell - LostTech.TensorFlow Documentation

Type GLSTMCell

Namespace tensorflow.contrib.rnn

Parent RNNCell

Interfaces IGLSTMCell

Group LSTM cell (G-LSTM).

The implementation is based on:

https://arxiv.org/abs/1703.10722

O. Kuchaiev and B. Ginsburg "Factorization Tricks for LSTM Networks", ICLR 2017 workshop.

In brief, a G-LSTM cell consists of one LSTM sub-cell per group, where each sub-cell operates on an evenly-sized sub-vector of the input and produces an evenly-sized sub-vector of the output. For example, a G-LSTM cell with 128 units and 4 groups consists of 4 LSTMs sub-cells with 32 units each. If that G-LSTM cell is fed a 200-dim input, then each sub-cell receives a 50-dim part of the input and produces a 32-dim part of the output.

Methods

NewDyn

Properties

Public static methods

GLSTMCell NewDyn(object num_units, object initializer, object num_proj, ImplicitContainer<T> number_of_groups, ImplicitContainer<T> forget_bias, ImplicitContainer<T> activation, object reuse)

Initialize the parameters of G-LSTM cell.

Parameters

object num_units: int, The number of units in the G-LSTM cell
object initializer: (optional) The initializer to use for the weight and projection matrices.
object num_proj: (optional) int, The output dimensionality for the projection matrices. If None, no projection is performed.
ImplicitContainer<T> number_of_groups: (optional) int, number of groups to use. If `number_of_groups` is 1, then it should be equivalent to LSTM cell
ImplicitContainer<T> forget_bias: Biases of the forget gate are initialized by default to 1 in order to reduce the scale of forgetting at the beginning of the training.
ImplicitContainer<T> activation: Activation function of the inner states.
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;