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2. Override the `train_step` method of your Model class to implement your custom loss. Guide: https://keras.io/guides/customizing_what_happens_in_fit/ … A few examples: Metric learning: https://keras.io/examples/vision/metric_learning/ … Vanilla GAN: https://keras.io/examples/generative/dcgan_overriding_train_step/ … WGAN-GP: https://keras.io/examples/generative/wgan_gp/ … CycleGAN: https://keras.io/examples/generative/cyclegan/ …
3. Write a custom training loop from scratch. Guide: https://keras.io/guides/writing_a_training_loop_from_scratch/ … Remember that Keras defaults are only meant to cover the most common workflows -- for anything else, you have a range of increasingly advanced options. We call it "progressive disclosure of complexity"
Guten tag, here is your unroll: @fchollet: A thing I hear sometimes: "what if my loss doesn't match the signature loss = fn(y_true, y_pred)?" This is… https://threadreaderapp.com/thread/1296292123768025090.html … Talk to you soon. 
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