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Long short-term memory unit (LSTM) recurrent network cell.
Inherits From: RNNCell, Layer, Layer, Module
tf.compat.v1.lite.experimental.nn.TFLiteLSTMCell(
num_units, use_peepholes=False, cell_clip=None, initializer=None, num_proj=None,
proj_clip=None, num_unit_shards=None, num_proj_shards=None, forget_bias=1.0,
state_is_tuple=True, activation=None, reuse=None, name=None, dtype=None
)
This is used only for TfLite, it provides hints and it also makes the variables in the desired for the tflite ops (transposed and separated).
The default non-peephole implementation is based on:
https://pdfs.semanticscholar.org/1154/0131eae85b2e11d53df7f1360eeb6476e7f4.pdf
Felix Gers, Jurgen Schmidhuber, and Fred Cummins. "Learning to forget: Continual prediction with LSTM." IET, 850-855, 1999.
The peephole implementation is based on:
https://research.google.com/pubs/archive/43905.pdf
Hasim Sak, Andrew Senior, and Francoise Beaufays. "Long short-term memory recurrent neural network architectures for large scale acoustic modeling." INTERSPEECH, 2014.
The class uses optional peep-hole connections, optional cell clipping, and an optional projection layer.
Note that this cell is not optimized for performance. Please use
tf.contrib.cudnn_rnn.CudnnLSTM for better performance on GPU, or
tf.contrib.rnn.LSTMBlockCell and tf.contrib.rnn.LSTMBlockFusedCell for
better performance on CPU.
Args | |
|---|---|
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_proj
|
(optional) int, The output dimensionality for the projection matrices. If None, no projection is performed. |
proj_clip
|
(optional) A float value. If num_proj > 0 and proj_clip is
provided, then the projected values are clipped elementwise to within
[-proj_clip, proj_clip].
|
num_unit_shards
|
Deprecated, will be removed by Jan. 2017. Use a variable_scope partitioner instead. |
num_proj_shards
|
Deprecated, will be removed by Jan. 2017. Use a variable_scope partitioner instead. |
forget_bias
|
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. Must set it manually to 0.0 when restoring from CudnnLSTM
trained checkpoints.
|
state_is_tuple
|
If True, accepted and returned states are 2-tuples of the
c_state and m_state. If False, they are concatenated along the
column axis. This latter behavior will soon be deprecated.
|
activation
|
Activation function of the inner states. Default: tanh.
|
reuse
|
(optional) Python 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.
|
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. |
dtype
|
Default dtype of the layer (default of None means use the type of
the first input). Required when build is called before call. When
restoring from CudnnLSTM-trained checkpoints, use
CudnnCompatibleLSTMCell instead.
|
Attributes | |
|---|---|
graph
|
|
output_size
|
Integer or TensorShape: size of outputs produced by this cell. |
scope_name
|
|
state_size
|
size(s) of state(s) used by this cell.
It can be represented by an Integer, a TensorShape or a tuple of Integers or TensorShapes. |
Methods
get_initial_state
get_initial_state(
inputs=None, batch_size=None, dtype=None
)
zero_state
zero_state(
batch_size, dtype
)
Return zero-filled state tensor(s).
| Args | |
|---|---|
batch_size
|
int, float, or unit Tensor representing the batch size. |
dtype
|
the data type to use for the state. |
| Returns | |
|---|---|
If state_size is an int or TensorShape, then the return value is a
N-D tensor of shape [batch_size, state_size] filled with zeros.
If |