![]() ![]() This becomes a significant issue when you want to port your model to JavaScript, for instance, which is otherwise easy to do for Keras models. And that pipeline has to behave in the exact same way as the original one - or the model will break. However, this kind of external preprocessing makes models less portable: every time someone reuses a model you've trained, they need to also recreate the preprocessing pipeline. ![]() Historically we've delegated preprocessing to auxiliary tools written in NumPy and PIL (the Python Imaging Library). ![]() Preprocessing layers and redesigned image preprocessing API: Here are some of the big new features we've launched recently or are about to launch: Francois Chollet: Keras in 2020 is continuing its evolution as an end-to-end framework for deep learning applications. ![]()
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