Does the test set is used to update weight in a deep learning model with keras?

Question

I'm wondering if the result of the test set is used to make the optimization of model's weights. I'm trying to make a model but the issue I have is I don't have many data because they are medical research patients. The number of patient is limited in my case (61) and I have 5 feature vectors per patient. What I tried is to create a deep learning model by excluding one subject and I used the exclude subject as the test set. My problem is there is a large variability in subject features and my model fits well the training set (60 subjects) but not that good the 1 excluded subject. So I'm wondering if the testset (in my case the excluded subject) could be used in a certain way to make converge the model to better classify the exclude subject?

Answer 1

You should not use the test data of your data set in your training process. If your training data is not enough, one approach using a lot during this days(especially for medical images) is data augmentation . So I highly recommend you to use this technique in your training process. How to use Deep Learning when you have Limited Data is one of the good tutorial about data augmentation.

Answer 2

No , you souldn't use your test set for training to prevent overfitting , if you use cross-validation principles you need exactly to split your data into three datasets a train set which you'll use to train your model , a validation set to test different value of your hyperparameters , and a test set to finally test your model , if you use all your data for training, your model will overfit obviously.

One thing to remember deep learning work well if you have a large and very rich datasets