How to interpret increase in both loss and accuracy

Question

I have run deep learning models(CNN\'s) using tensorflow. Many times during the epoch, i have observed that both loss and accuracy have increased, or both have decreased. My und

Answer 1

I'd like to add a possible option here for all those who struggle with a model training right now.

If your validation data is a bit dirty, you might experience that in the beginning of the training the validation loss is low as well as the accuracy, and the more you train your network, the accuracy increases with the loss side by side. The reason why it happens, because it finds the possible outliers of your dirty data and gets a super high loss there. Therefore, your accuracy will grow as it guesses more data right, but the loss grows with it.