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.
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 x 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 x s] for each s in