Long short-term memory unit (LSTM) recurrent network cell.
The default non-peephole implementation is based on:
S. Hochreiter and J. Schmidhuber. "Long Short-Term Memory". Neural Computation, 9(8):1735-1780, 1997.
The peephole implementation is based on:
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:
Greff et al. "LSTM: A Search Space Odyssey"
The class uses optional peep-hole connections, and an optional projection layer.
__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=False, activation=tf.tanh)
Initialize the parameters for an LSTM cell.
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 > 0and
proj_clipis provided, then the projected values are clipped elementwise to within
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
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.
Return zero-filled state tensor(s).
batch_size: int, float, or unit Tensor representing the batch size.
dtype: the data type to use for the state.
state_size is an int or TensorShape, then the return value is a
N-D tensor of shape
[batch_size x state_size] filled with zeros.
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
[batch_size x s] for each s in