Validation accuracy greater than training accuracy in cnn
As @Suren has eluded to, it could be the case that your validation accuracy is higher than your training accuracy due to the distribution of the classes over the training and validation sets. If the training set contains a higher proportion of a particular class and the validation class contains examples of that particular class as well, then of course you will see validation accuracy being high.

The advice would be to balance out the classes over the training and validation and sets. You can do this data augmentation, in that you artificially increase the number of examples based not he data you already have. 
Have a look at this paper which evaluates the effect of different methods of class balancing using data augmentation on generalisation performance of CNNs:
https://arxiv.org/pdf/1710.05381.pdf