conv net save weight and new test set
yes, for fair evaluation no sample in the test set should be seen during training
i'm using conv net for image classification.
There is something I dont understand theoretically
For training I split my data 60%train/20%validation/20% test
I save weight when metric on validation set is the best (I have same performance on training and validation set).
Now, I do a new split. Some data from training set will be on test set. I load the weight and I classify new test set.
Since weight have been computed on a part of the new test set, are we agree to says this is a bad procedure and I should retrain my model with my new training/validation set?