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A thing I hear sometimes: "what if my loss doesn't match the signature loss = fn(y_true, y_pred)?" This is not a requirement in Keras -- it's only the default setting. If you have a loss with multiple inputs/targets, here are your options, in order of preference: (a thread)
1. Use a layer that calls `self.add_loss(value)`. Docs: https://keras.io/api/losses/#the-addloss-api … And if you need access to labels, use the "endpoint layer" pattern: https://keras.io/examples/keras_recipes/endpoint_layer_pattern/ …
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"
I had this exact problem and I ended up using a tf.Estimator to handle the training part, kept Keras for modeling the network. Estimators are a great alternative when Keras's training does not fit your use case.
Sir @fchollet is it alright to use a Softmax Classifier with a Binary Cross-Entropy loss? I seem to be getting faster training results, better convergence, and high accuracies. Compared to the softmax + categorical CE. Hope you can help. Thank you, sir!
Btw I have 5 classes
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