I see both of them a bit differently, Both represent different aspects.
Loss
- A loss function is used to optimize a machine learning algorithm.
- Validation loss is measure of how much our predictions differ from what they should be before we put them through the threshold.
Accuracy
- An accuracy metric is used to measure the algorithm’s performance (accuracy) in an interpretable way.
- Empirically, accuracy seems like quite a limited measure of quality of predictions. To predict whether an example belongs to some class, our model outputs a number (whatever we put through sigmoid or softmax) between 0 and 1.
Ref - Link
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