TF 2.0 is out! Get hands-on practice at TF World, Oct 28-31. Use code TF20 for 20% off select passes. Register now

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

View source on GitHub

Class TfLiteRNNCell

The most basic RNN cell.

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

__init__

View source

__init__(
    num_units,
    activation=None,
    reuse=None,
    name=None,
    dtype=None,
    **kwargs
)

Initializes the parameters for an RNN cell.

Args:

  • 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().

Raises:

  • ValueError: If the existing scope already has the given variables.

Properties

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

View source

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.