tfr.keras.model.AbstractModelBuilder

Interface to build a tf.keras.Model for ranking.

The AbstractModelBuilder serves as the interface between model building and training. The training pipeline just calls the build() method to get the model constructed in the strategy scope used in the training pipeline, so for all variables in the model, optimizers, and metrics. See ModelFitPipeline in pipeline.py for example.

The build() method is to be implemented in a subclass. The simplest example is just to define everything inside the build function when you define a tf.keras.Model.

class MyModelBuilder(AbstractModelBuilder):

  def build(self) -> tf.keras.Model:
    inputs = ...
    outputs = ...
    return tf.keras.Model(inputs=inputs, outputs=outputs)

The MyModelBuilder should work with ModelFitPipeline. To make the model building more structured for ranking problems, we also define subclasses like ModelBuilderWithMask in the following.

Methods

build

View source

@abc.abstractmethod
build() -> tf.keras.Model

The build method to be implemented by a subclass.