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codeinassembly t1_jcj1cjd wrote

I wanted to modify model training to test a hypothesis. I chose TensorFlow. It may present a minor improvement in training speed (atleast before 2.0) but holy fuck was it not worth it with all the gotcha's.

It works well if you're okay with using their default pipeline. Attempt to setup anything custom and you'll be in for a lot of suffering unless you disable symbolic execution - at which point it's slower than PyTorch.

I tried to implement the same idea on PyTorch and was done in an hour. Use PyTorch; it's just better.

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JacksOngoingPresence t1_jcjfaac wrote

+1
Default keras pipeline is fast, beginner friendly and great. As soon as something custom needs to be done - it's just painful if even possible.

Also the last time I checked (~a year ago) some features were heavily bugged ever since the introduction (literally for years). Like model.predict or tf.function cause memory leaks even with the "examples" code. That was the switching point for me.

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