View source on GitHub |
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 |
num_units
|
Number of units in this cell (output dimension). |
scope_name
|