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tf.contrib.rnn.CoupledInputForgetGateLSTMCell

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Class CoupledInputForgetGateLSTMCell

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

Inherits From: RNNCell

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.

__init__

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__init__(
    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
)

Initialize the parameters for an LSTM cell.

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.

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.