tf.contrib.rnn.GLSTMCell

Group LSTM cell (G-LSTM).

Inherits From: RNNCell

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

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.

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.

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.