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tf.contrib.rnn.PhasedLSTMCell

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Class PhasedLSTMCell

Phased LSTM recurrent network cell.

Inherits From: RNNCell

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

__init__

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__init__(
    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
)

Initialize the Phased LSTM cell.

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.

Properties

graph

DEPRECATED FUNCTION

output_size

scope_name

state_size

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.