Higher validation accuracy, than training accurracy using Tensorflow and Keras

Question

I\'m trying to use deep learning to predict income from 15 self reported attributes from a dating site.

We\'re getting rather odd results, where our validation data

Answer 1

This happens when you use Dropout, since the behaviour when training and testing are different.

When training, a percentage of the features are set to zero (50% in your case since you are using Dropout(0.5)). When testing, all features are used (and are scaled appropriately). So the model at test time is more robust - and can lead to higher testing accuracies.