tf.contrib.rnn.LayerNormBasicLSTMCell

LSTM unit with layer normalization and recurrent dropout.

Inherits From: RNNCell

tf.contrib.rnn.LayerNormBasicLSTMCell(
    num_units, forget_bias=1.0, input_size=None, activation=tf.math.tanh,
    layer_norm=True, norm_gain=1.0, norm_shift=0.0, dropout_keep_prob=1.0,
    dropout_prob_seed=None, reuse=None
)

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.

Args
num_units	int, The number of units in the LSTM cell.
forget_bias	float, The bias added to forget gates (see above).
input_size	Deprecated and unused.
activation	Activation function of the inner states.
layer_norm	If True, layer normalization will be applied.
norm_gain	float, The layer normalization gain initial value. If layer_norm has been set to False, this argument will be ignored.
norm_shift	float, The layer normalization shift initial value. If layer_norm has been set to False, this argument will be ignored.
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.
dropout_prob_seed	(optional) integer, the randomness seed.
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.

Attributes
graph	DEPRECATED FUNCTION Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Stop using this property because tf.layers layers no longer track their graph.
output_size	Integer or TensorShape: size of outputs produced by this cell.
scope_name
state_size	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.

Methods

get_initial_state

View source

get_initial_state(
    inputs=None, batch_size=None, dtype=None
)

zero_state

View source

zero_state(
    batch_size, dtype
)

Return zero-filled state tensor(s).

Args
batch_size	int, float, or unit Tensor representing the batch size.
dtype	the data type to use for the state.

Returns
If state_size is an int or TensorShape, then the return value is a N-D tensor of shape [batch_size, state_size] filled with zeros. If state_size is a nested list or tuple, then the return value is a nested list or tuple (of the same structure) of 2-D tensors with the shapes [batch_size, s] for each s in state_size.