this post was submitted on 25 Nov 2023
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Machine Learning
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Libraries like PyTorch and Jax are already high level libraries in my view. The low level stuff is C++/CUDA/XLA.
I don’t really see the useful extra abstractions in Keras that would lure me to it.
As the others said, it's a pain to reimplement common layers in JAX (specifically). PyTorch is much higher level in it's nn API, but personally I despise rewriting the amazing training loop for every implementation. That's why even JAX uses Flax for common layers, because why use an error prone operator like jax.lax.conv_from_dilated or whatever and fill its 10 arguments every time? I would rather use flax.linen.Conv2D or keras_core.layers.Conv2D in my Sequential layer and prevent debugging a million times. For PyTorch, model.fit() can just quickly suffice and later customized.