tf.contrib.rnn.CoupledInputForgetGateLSTMCell

Long short-term memory unit (LSTM) recurrent network cell.

Inherits From: RNNCell

tf.contrib.rnn.CoupledInputForgetGateLSTMCell(
    num_units, use_peepholes=False, initializer=None, num_proj=None, proj_clip=None,
    num_unit_shards=1, num_proj_shards=1, forget_bias=1.0, state_is_tuple=True,
    activation=tf.math.tanh, reuse=None, layer_norm=False, norm_gain=1.0,
    norm_shift=0.0
)

The default non-peephole implementation is based on:

https://pdfs.semanticscholar.org/1154/0131eae85b2e11d53df7f1360eeb6476e7f4.pdf

Felix Gers, Jurgen Schmidhuber, and Fred Cummins. "Learning to forget: Continual prediction with LSTM." IET, 850-855, 1999.

The peephole implementation is based on:

https://research.google.com/pubs/archive/43905.pdf

Hasim Sak, Andrew Senior, and Francoise Beaufays. "Long short-term memory recurrent neural network architectures for large scale acoustic modeling." INTERSPEECH, 2014.

The coupling of input and forget gate is based on:

http://arxiv.org/pdf/1503.04069.pdf

Greff et al. "LSTM: A Search Space Odyssey"

The class uses optional peep-hole connections, and an optional projection layer. Layer normalization implementation is based on:

https://arxiv.org/abs/1607.06450

"Layer Normalization" Jimmy Lei Ba, Jamie Ryan Kiros, Geoffrey E. Hinton

and is applied before the internal nonlinearities.

Args
num_units	int, The number of units in the LSTM cell
use_peepholes	bool, set True to enable diagonal/peephole connections.
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.
proj_clip	(optional) A float value. If num_proj > 0 and proj_clip is provided, then the projected values are clipped elementwise to within [-proj_clip, proj_clip].
num_unit_shards	How to split the weight matrix. If >1, the weight matrix is stored across num_unit_shards.
num_proj_shards	How to split the projection matrix. If >1, the projection matrix is stored across num_proj_shards.
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.
state_is_tuple	If True, accepted and returned states are 2-tuples of the c_state and m_state. By default (False), they are concatenated along the column axis. This default behavior will soon be deprecated.
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.
layer_norm	If True, layer normalization will be applied.
norm_gain	float, The layer normalization gain initial value. If layer_norm has been set to False, this argument will be ignored.
norm_shift	float, The layer normalization shift initial value. If layer_norm has been set to False, this argument will be ignored.

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.