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

There are a number of reasons this can happen.You do not shown any information on the size of the data for training, validation and test. If the validation set is to small it does not adequately represent the probability distribution of the data. If your training set is small there is not enough data to adequately train the model. Also your model is very basic and may not be adequate to cover the complexity of the data. A drop out of 50% is high for such a limited model. Try using an established model like MobileNet version 1. It will be more than adequate for even very complex data relationships. Once that works then you can be confident in the data and build your own model if you wish. Fact is validation loss and accuracy do not have real meaning until your training accuracy gets reasonably high say 85%.