FusedRNNCell implementation of LSTM.
This is an extremely efficient LSTM implementation, that uses a single TF op for the entire LSTM. It should be both faster and more memory-efficient than LSTMBlockCell defined above.
The implementation is based on: http://arxiv.org/abs/1409.2329.
We add forget_bias (default: 1) to the biases of the forget gate in order to reduce the scale of forgetting in the beginning of the training.
The variable naming is consistent with
Number of units in this cell (output dimension).
__init__(num_units, forget_bias=1.0, cell_clip=None, use_peephole=False)
Initialize the LSTM cell.
num_units: int, The number of units in the LSTM cell.
forget_bias: float, The bias added to forget gates (see above).
cell_clip: clip the cell to this value. Defaults to
use_peephole: Whether to use peephole connections or not.