François Chollet @fchollet Deep learning @google. Creator of Keras, neural networks library. Author of 'Deep Learning with Python'. Opinions are my own. May. 24, 2019 1 min read

When you're using tf.keras in TensorFlow 2.0, training your models with `fit` is one option, but you can also write a custom training loop. It takes about 10 lines.

If your model creates additional losses during its forward pass (i.e. if you called `self.add_loss` in a custom layer), collect them in one line like this.

The advantage of using `fit` is that it packs a lot of functionality in a convenient interface: a progress bar, easy reporting of metrics such as precision & recall, easy management of class weights for imbalanced classification, callbacks for model checkpointing and more...


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