tf.contrib.rnn.LSTMCell

class tf.contrib.rnn.LSTMCell

class tf.contrib.rnn.core_rnn_cell.LSTMCell

See the guide: RNN and Cells (contrib) > Core RNN Cells for use with TensorFlow's core RNN methods

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

The default non-peephole implementation is based on:

http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf

S. Hochreiter and J. Schmidhuber. "Long Short-Term Memory". Neural Computation, 9(8):1735-1780, 1997.

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 class uses optional peep-hole connections, optional cell clipping, and an optional projection layer.

Properties

output_size

state_size

Methods

__init__(num_units, input_size=None, use_peepholes=False, cell_clip=None, initializer=None, num_proj=None, proj_clip=None, num_unit_shards=None, num_proj_shards=None, forget_bias=1.0, state_is_tuple=True, activation=tf.tanh)

Initialize the parameters for an LSTM cell.

Args:

  • num_units: int, The number of units in the LSTM cell
  • input_size: Deprecated and unused.
  • use_peepholes: bool, set True to enable diagonal/peephole connections.
  • cell_clip: (optional) A float value, if provided the cell state is clipped by this value prior to the cell output activation.
  • 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: Deprecated, will be removed by Jan. 2017. Use a variable_scope partitioner instead.
  • num_proj_shards: Deprecated, will be removed by Jan. 2017. Use a variable_scope partitioner instead.
  • 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. If False, they are concatenated along the column axis. This latter behavior will soon be deprecated.
  • activation: Activation function of the inner states.

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 x 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 x s] for each s in state_size.

Defined in tensorflow/contrib/rnn/python/ops/core_rnn_cell_impl.py.