7. The compile function


We usually need to call Model.compile() before we train the model, as shown in the following code example.


As you expected, the Model.compile() function is just recording these configurations. Since the user may provide the optimizer as a string, we use get_optimizer() function to get the corresponding keras.optimizers.Optimizer instance.

The loss and metrics can be lists or dictionaries of loss functions and metrics. Threrefore, we need to encapsulate them into data structures, which are easier to use, which can be treated as single objects instead of using a for loops to deal with each of the losses or metrics.

The core functionality of Model.compile() is shown as in the following pseudo code.


class Model(Layer):
    def compile(self, loss, optimizer, metrics, ...):
        self.optimizer = get_optimizer(optimizer)
        self.compiled_loss = LossesContainer(loss)
        self.compiled_metrics = MetricsContainer(metrics)

Besides the loss, optimizer, and metrics, which are the most important configurations for the training, there are other interesting configurations in teh compile function as well, you may try to explore them in the source code by yourself.