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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
@abc.abstractmethodbuild() -> tf.keras.Model
The build method to be implemented by a subclass.