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Long short-term memory unit (LSTM) recurrent network cell.
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 seaparated).
The default non-peephole implementation is based on:
Felix Gers, Jurgen Schmidhuber, and Fred Cummins. "Learning to forget: Continual prediction with LSTM." IET, 850-855, 1999.
The peephole implementation is based on:
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
better performance on CPU.
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 > 0and
proj_clipis provided, then the projected values are clipped elementwise to within
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
0.0when restoring from CudnnLSTM trained checkpoints.
state_is_tuple: If True, accepted and returned states are 2-tuples of the
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:
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
Nonemeans use the type of the first input). Required when
buildis called before
call. When restoring from CudnnLSTM-trained checkpoints, use
graph: DEPRECATED FUNCTION
output_size: Integer or TensorShape: size of outputs produced by this cell.
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.
get_initial_state( inputs=None, batch_size=None, dtype=None )
zero_state( batch_size, dtype )
Return zero-filled state tensor(s).
batch_size: int, float, or unit Tensor representing the batch size.
dtype: the data type to use for the state.
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.
state_size is a nested list or tuple, then the return value is
a nested list or tuple (of the same structure) of
2-D tensors with
[batch_size, s] for each s in