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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. 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__( 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.
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
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.
ValueError: if the num_frequency_blocks list is not specified
Integer or TensorShape: size of outputs produced by this cell.
size(s) of state(s) used by this cell.
It can be represented by an Integer, a TensorShape or a tuple of Integers or TensorShapes.
get_initial_state( inputs=None, batch_size=None, dtype=None )
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.
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.
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, s] for each s in