python - How to correct unstable loss and accuracy during training? (binary classification) - Stack Overflow

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You can now access your Teams at stackoverflowteams.com. Teams no longer appear in the left sidebar on stackoverflow.com. Check your email to learn more about these changes. Find centralized, trusted content and collaborate around the technologies you use most. Teams Q&A for work Connect and share knowledge within a single location that is structured and easy to search. Get early access and see previews of new features. I am currently working on a small binary classification project using the new keras API in tensorflow. The problem is a simplified version of the Higgs Boson challenge posted on Kaggle.com a few years back. The dataset shape is 2000x14, where the first 13 elements of each row form the input vector, and the 14th element is the corresponding label. Here is a sample of said dataset: I am relatively new to machine learning and tensorflow, but I am familiar with the higher level concepts such as loss functions, optimizers and activation functions. I have tried building vario

python - How to correct unstable loss and accuracy during training? - Stack Overflow The 2026 Annual Developer Survey is live— take the Survey today! Collectives™ on Stack Overflow Find centralized, trusted content and collaborate around the technologies you use most. Learn more about Collectives Stack Internal Knowledge at work Bring the best of human thought and AI automation together at your work. Explore Stack Internal How to correct unstable loss and accuracy during training? Ask Question Asked 7 years, 2 months ago Modified 1 year, 7 months ago Viewed 40k times 19 I am working on

python - How to correct unstable loss and accuracy during training? (binary classification) - Stack Overflow

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