Which metric is best depends on the kind of task, e.g. whether it is a (binary) classification or generative task.
 
For a (binary) classification task a good choice is the $F_1$-Score. It is calculated by $\frac{2}{\frac{1}{\text{precision}}+\frac{1}{\text{recall}}}$, where recall is a measure for how well did the model find the positives and precision is a measure for how many positive predictions where correct. So a high recall would mean that many positives were correctly classified and a high precision would mean that only few of the negatives were classified as positives.

There are many more metrics like AUC (Area Under Curve), MSE (Mean SquaredError) and Categorical Crossentropy and you will have to try out what works best for you and your specific task.
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