tf.contrib.rnn.PhasedLSTMCell

Phased LSTM recurrent network cell.

Inherits From: RNNCell

tf.contrib.rnn.PhasedLSTMCell(
    num_units, use_peepholes=False, leak=0.001, ratio_on=0.1,
    trainable_ratio_on=True, period_init_min=1.0, period_init_max=1000.0, reuse=None
)

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

Args
num_units	int, The number of units in the Phased LSTM cell.
use_peepholes	bool, set True to enable peephole connections.
leak	float or scalar float Tensor with value in [0, 1]. Leak applied during training.
ratio_on	float or scalar float Tensor with value in [0, 1]. Ratio of the period during which the gates are open.
trainable_ratio_on	bool, weather ratio_on is trainable.
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.
period_init_max	float or scalar float Tensor. With value > period_init_min. Maximum value of the initialized period.
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
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.