tf.contrib.rnn.LSTMBlockFusedCell

FusedRNNCell implementation of LSTM.

Inherits From: LSTMBlockWrapper

tf.contrib.rnn.LSTMBlockFusedCell(
    num_units, forget_bias=1.0, cell_clip=None, use_peephole=False, reuse=None,
    dtype=None, name='lstm_fused_cell'
)

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 rnn_cell_impl.LSTMCell.

Args
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.

Attributes
graph	DEPRECATED FUNCTION Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Stop using this property because tf.layers layers no longer track their graph.
num_units	Number of units in this cell (output dimension).
scope_name