In the blog, he refers to test losses at an early stage, like in "add significant digits to your eval". Does he actually refer to the test data or is he referring to validation data? I was under the idea that we were supposed to touch the test data only once at the end of all training and validation. What is the right way to handle the test data?
By "eval" you can also mean the training subset. As I understood is at the code to evaluate the network at a given point with a given dataset. For instance, after epoch epoch, the model is evaluated for both training and validation (you see both losses)
As you said, the test subset should only be used at the very last.
