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Answer by mrk for Use "keras code" (Tensorflow) and the result is "loss: nan"
The issue of encountering a loss of NaN during training often arisesdue to numerical instability. Neural networks are sensitive to thescale of input data and the learning rate.Sources here, and a book...
View ArticleUse "keras code" (Tensorflow) and the result is "loss: nan"
requirements.txtabsl-py==2.0.0astunparse==1.6.3cachetools==5.3.1certifi==2023.7.22charset-normalizer==3.2.0flatbuffers==23.5.26gast==0.4.0google-auth==2.23.0google-auth-oauthlib==1.0.0google-pasta==0.2...
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