Hi!
Does anyone knows why the AlphaGo team uses MSE on [-1,1] as the value
output loss rather than binary crossentropy on [0,1]? I'd say the
latter is way more usual when training networks as typically binary
crossentropy yields better result, so that's what I'm using in
https://github.com/pasky/michi/tree/nnet for the time being, but maybe
I'm missing some good reason to use MSE instead?
Thanks,
--
Petr Baudis, Rossum
Run before you walk! Fly before you crawl! Keep moving forward!
If we fail, I'd rather fail really hugely. -- Moist von Lipwig
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