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Why the validation loss is fluctuating?
Your validation accuracy on a binary classification problem (I assume) is “fluctuating” around 50%, that means your model is giving completely random predictions (sometimes it guesses correctly few samples more, sometimes a few samples less). Generally, your model is not better than flipping a coin.
What is better low loss or high accuracy?
Accuracy is more straightforward. Having a low accuracy but a high loss would mean that the model makes big errors in most of the data. But, if both loss and accuracy are low, it means the model makes small errors in most of the data. However, if they’re both high, it makes big errors in some of the data.
Is it normal for validation loss to oscillate?
The validation loss at each epoch is usually computed on one minibatch of the validation set, so it is normal for it to be more noisey. Solution: You can report the Exponential Moving Average of the validation loss across different epochs to have less fluctuations.
What is the purpose of the validation curve?
The validation curve is a visual, single-parameter grid search used to tune a model to find the best balance between error due to bias and error due to variance. This helper function is a wrapper to use the ValidationCurve in a fast, visual analysis.
Why are validation errors so high in some cases?
Gain a global economic perspective to help you make informed business decisions. Your model is learning to distinguish between trucks and non-trucks. But it can only see the training data. So it has no way to tell which distinctions are good for the test set.
Which is more stable validation loss or prediction loss?
Generally, your model is not better than flipping a coin. The reason the validation loss is more stable is that it is a continuous function: It can distinguish that prediction 0.9 for a positive sample is more correct than a prediction 0.51.