tf.contrib.rnn.PhasedLSTMCell

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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

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.

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

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

zero_state

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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.