tf.compat.v1.lite.experimental.nn.TfLiteRNNCell

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The most basic RNN cell.

tf.compat.v1.lite.experimental.nn.TfLiteRNNCell(
    num_units, activation=None, reuse=None, name=None, dtype=None, **kwargs
)

This is used only for TfLite, it provides hints and it also makes the variables in the desired for the tflite ops.

num_units int, The number of units in the RNN cell.
activation Nonlinearity to use. Default: tanh. It could also be string that is within Keras activation function names.
reuse (optional) Python boolean describing whether to reuse variables in an existing scope. Raises an error if not True and the existing scope already has the given variables.
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.
**kwargs Dict, keyword named properties for common layer attributes, like trainable etc when constructing the cell from configs of get_config().

ValueError If the existing scope already has the given variables.

graph DEPRECATED FUNCTION
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

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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.