New code walkthrough on http://keras.io : unsupervised image clustering with a contrastive loss.https://keras.io/examples/vision/semantic_image_clustering/ …
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This example features a model with a custom `train_step()`. Overriding `train_step()` enables you to write arbitrary training logic (unsupervised clustering in this case) while benefiting from the features of `fit()`: callbacks, built-in distribution, step fusing on TPU...pic.twitter.com/XUaIt0TFyo
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You can learn more about this pattern here:https://keras.io/guides/customizing_what_happens_in_fit/ …
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Even I did the same.
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