class tf.contrib.rnn.GridLSTMCell

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

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

The default is based on: Nal Kalchbrenner, Ivo Danihelka and Alex Graves "Grid Long Short-Term Memory," Proc. ICLR 2016.

When peephole connections are used, the 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.

The code uses optional peephole connections, shared_weights and cell clipping.






__init__(num_units, use_peepholes=False, share_time_frequency_weights=False, cell_clip=None, initializer=None, num_unit_shards=1, forget_bias=1.0, feature_size=None, frequency_skip=None, num_frequency_blocks=None, start_freqindex_list=None, end_freqindex_list=None, couple_input_forget_gates=False, state_is_tuple=False)

Initialize the parameters for an LSTM cell.


  • num_units: int, The number of units in the LSTM cell
  • use_peepholes: (optional) bool, default False. Set True to enable diagonal/peephole connections.
  • share_time_frequency_weights: (optional) bool, default False. Set True to enable shared cell weights between time and frequency LSTMs.
  • cell_clip: (optional) A float value, default None, 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, default None.
  • num_unit_shards: (optional) int, defualt 1, How to split the weight matrix. If > 1,the weight matrix is stored across num_unit_shards.
  • forget_bias: (optional) float, default 1.0, The initial bias of the forget gates, used to reduce the scale of forgetting at the beginning of the training.
  • feature_size: (optional) int, default None, The size of the input feature the LSTM spans over.
  • frequency_skip: (optional) int, default None, The amount the LSTM filter is shifted by in frequency.
  • num_frequency_blocks: [required] A list of frequency blocks needed to cover the whole input feature splitting defined by start_freqindex_list and end_freqindex_list.
  • start_freqindex_list: [optional], list of ints, default None, The starting frequency index for each frequency block.
  • end_freqindex_list: [optional], list of ints, default None. The ending frequency index for each frequency block.
  • couple_input_forget_gates: (optional) bool, default False, Whether to couple the input and forget gates, i.e. f_gate = 1.0 - i_gate, to reduce model parameters and computation cost.
  • 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. Raises:
  • ValueError: if the num_frequency_blocks list is not specified

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/