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tf.contrib.rnn.GLSTMCell

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Class GLSTMCell

Group LSTM cell (G-LSTM).

Inherits From: RNNCell

The implementation is based on:

https://arxiv.org/abs/1703.10722

O. Kuchaiev and B. Ginsburg "Factorization Tricks for LSTM Networks", ICLR 2017 workshop.

In brief, a G-LSTM cell consists of one LSTM sub-cell per group, where each sub-cell operates on an evenly-sized sub-vector of the input and produces an evenly-sized sub-vector of the output. For example, a G-LSTM cell with 128 units and 4 groups consists of 4 LSTMs sub-cells with 32 units each. If that G-LSTM cell is fed a 200-dim input, then each sub-cell receives a 50-dim part of the input and produces a 32-dim part of the output.

__init__

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__init__(
    num_units,
    initializer=None,
    num_proj=None,
    number_of_groups=1,
    forget_bias=1.0,
    activation=tf.math.tanh,
    reuse=None
)

Initialize the parameters of G-LSTM cell.

Args:

  • num_units: int, The number of units in the G-LSTM cell
  • initializer: (optional) The initializer to use for the weight and projection matrices.
  • num_proj: (optional) int, The output dimensionality for the projection matrices. If None, no projection is performed.
  • number_of_groups: (optional) int, number of groups to use. If number_of_groups is 1, then it should be equivalent to LSTM cell
  • forget_bias: Biases of the forget gate are initialized by default to 1 in order to reduce the scale of forgetting at the beginning of the training.
  • activation: Activation function of the inner states.
  • 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.

Raises:

  • ValueError: If num_units or num_proj is not divisible by number_of_groups.

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.