tfr.keras.model.Preprocessor

Interface for feature preprocessing.

The Preprocessor class is an abstract class to implement preprocess in ModelBuilder in tfr.keras.

To be implemented by subclasses:

__call__(): Contains the logic to preprocess context and example inputs.

Example subclass implementation:

class SimplePreprocessor(Preprocessor):

  def __call__(self, context_inputs, example_inputs, mask):
    context_features = {
        name: tf.math.log1p(
            tf.abs(tensor)) for name, tensor in context_inputs.items()
    }
    example_features = {
        name: tf.math.log1p(
            tf.abs(tensor)) for name, tensor in example_inputs.items()
    }
    return context_features, example_features

Methods

`call`

View source

@abc.abstractmethod
__call__(
    context_inputs: tfr.keras.model.TensorDict,
    example_inputs: tfr.keras.model.TensorDict,
    mask: tf.Tensor
) -> Tuple[tfr.keras.model.TensorDict, tfr.keras.model.TensorDict]

Invokes the Preprocessor instance.

Args
`context_inputs`	maps context feature keys to `tf.keras.Input`.
`example_inputs`	maps example feature keys to `tf.keras.Input`.
`mask`	[batch_size, list_size]-tensor of mask for valid examples.

Returns
A tuple of two dicts which map the context and example feature keys to the corresponding `tf.Tensor`s.