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FusedRNNCell implementation of LSTM.

Inherits From: LSTMBlockWrapper

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:

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 rnn_cell_impl.LSTMCell.

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 is no cell clipping.
use_peephole Whether to use peephole connections or not.
reuse (optional) 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.
dtype the dtype of variables of this layer.
name String, the name of the layer. Layers with the same name will share weights, but to avoid mistakes we require reuse=True in such cases. By default this is "lstm_cell", for variable-name compatibility with tf.compat.v1.nn.rnn_cell.LSTMCell.


num_units Number of units in this cell (output dimension).