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Why training loss is higher than validation loss?
If your training loss is much lower than validation loss then this means the network might be overfitting . Solutions to this are to decrease your network size, or to increase dropout. For example you could try dropout of 0.5 and so on. If your training/validation loss are about equal then your model is underfitting.
Can test accuracy be greater than train accuracy?
2 Answers. Test accuracy should not be higher than train since the model is optimized for the latter. Even so there would need to be some element of “test data distribution is not the same as that of train” for the observed behavior to occur.
When can validation accuracy be greater than training accuracy?
If you are using data augmentation to “noisify” your training data, then it can make sense that you are getting better accuracy on the validation set, because it will be an easier dataset. If this is the case, then you don’t really have a problem. As a rule, your validation set should be as close as possible to your test set or real-life use case.
Is the training accuracy too high or too low?
The real issue here is that your training accuracy (but again, I would focus on the test loss) is way too high with respect to your test accuracy. 55%, 65% or even 75% are all crap with respect to 99%. This is a textbook case of overfitting.
When do you stop training for validation loss?
At the end of 1st epoch validation loss started to increase, whereas validation accuracy is also increasing. Can i call this over fitting? I’m thinking of stopping the training after 6th epoch. My criteria would be: stop if the accuracy is decreasing. Is there something really wrong going on?
Which is correct training loss or training loss?
Besides, the training loss is the average of the losses over each batch of training data. Because your model is changing over time, the loss over the first batches of an epoch is generally higher than over the last batches.