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tf.contrib.rnn.GridLSTMCell

Class GridLSTMCell

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

Inherits From: RNNCell

View source on GitHub

The default is based on: Nal Kalchbrenner, Ivo Danihelka and Alex Graves "Grid Long Short-Term Memory," Proc. ICLR 2016. http://arxiv.org/abs/1507.01526

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__

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__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=True,
    reuse=None
)

Initialize the parameters for an LSTM cell.

Args:

  • 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, default 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.
  • 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.

Raises:

  • ValueError: if the num_frequency_blocks list is not specified

Properties

graph

DEPRECATED FUNCTION

output_size

scope_name

state_size

state_tuple_type

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.