LayerNormBasicLSTMCell - LostTech.TensorFlow Documentation

Type LayerNormBasicLSTMCell

Namespace tensorflow.contrib.rnn

Parent RNNCell

LSTM unit with layer normalization and recurrent dropout.

This class adds layer normalization and recurrent dropout to a basic LSTM unit. Layer normalization implementation is based on:

https://arxiv.org/abs/1607.06450.

"Layer Normalization" Jimmy Lei Ba, Jamie Ryan Kiros, Geoffrey E. Hinton

and is applied before the internal nonlinearities. Recurrent dropout is base on:

https://arxiv.org/abs/1603.05118

"Recurrent Dropout without Memory Loss" Stanislau Semeniuta, Aliaksei Severyn, Erhardt Barth.

Methods

NewDyn

Properties

Public static methods

LayerNormBasicLSTMCell NewDyn(object num_units, ImplicitContainer<T> forget_bias, object input_size, ImplicitContainer<T> activation, ImplicitContainer<T> layer_norm, ImplicitContainer<T> norm_gain, ImplicitContainer<T> norm_shift, ImplicitContainer<T> dropout_keep_prob, object dropout_prob_seed, object reuse)

Initializes the basic LSTM cell.

Parameters

object num_units: int, The number of units in the LSTM cell.
ImplicitContainer<T> forget_bias: float, The bias added to forget gates (see above).
object input_size: Deprecated and unused.
ImplicitContainer<T> activation: Activation function of the inner states.
ImplicitContainer<T> layer_norm: If `True`, layer normalization will be applied.
ImplicitContainer<T> norm_gain: float, The layer normalization gain initial value. If `layer_norm` has been set to `False`, this argument will be ignored.
ImplicitContainer<T> norm_shift: float, The layer normalization shift initial value. If `layer_norm` has been set to `False`, this argument will be ignored.
ImplicitContainer<T> dropout_keep_prob: unit Tensor or float between 0 and 1 representing the recurrent dropout probability value. If float and 1.0, no dropout will be applied.
object dropout_prob_seed: (optional) integer, the randomness seed.
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;

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;