class tf.contrib.rnn.TimeFreqLSTMCell

See the guide: RNN and Cells (contrib) > Core RNN Cell wrappers (RNNCells that wrap other RNNCells)

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

This implementation is based on:

Tara N. Sainath and Bo Li "Modeling Time-Frequency Patterns with LSTM vs. Convolutional Architectures for LVCSR Tasks." submitted to INTERSPEECH, 2016.

It uses peep-hole connections and optional cell clipping.





__init__(num_units, use_peepholes=False, cell_clip=None, initializer=None, num_unit_shards=1, forget_bias=1.0, feature_size=None, frequency_skip=None)

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.
  • 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_unit_shards: int, How to split the weight matrix. If >1, the weight matrix is stored across num_unit_shards.
  • forget_bias: float, 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.
  • feature_size: int, The size of the input feature the LSTM spans over.
  • frequency_skip: int, The amount the LSTM filter is shifted by in frequency.

zero_state(batch_size, dtype)

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.


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/