tf.compat.v1.lite.experimental.nn.TFLiteLSTMCell

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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:

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: 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.

• 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

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get_initial_state(
    inputs=None, batch_size=None, dtype=None
)

zero_state

View source

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 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 the shapes [batch_size, s] for each s in state_size.