I don’t quite understand what you mean here



 jpj:

do I also have to first extract the weights from the last iteration of the training phase?


and I think you mean epoch and not iteration over here



 jpj:

run the test data for only one iteration


but I hope an example helps:
Let’s say I have 10,000 images. I split it into 8,000 images for my training set and 2,000 images for my test set (just an example). I then train my network for one epoch on the training set (with a batch size of 100 that would be 80 iterations). Here I use back-propagation to calculate the loss and give the loss to the optimizer (with the specified learning rate) to update the weight so the network learns.
After that I test the network by sending the test set through the network once, so one epoch (which would we 20 iterations with a batch size of 100) but here I do not use back-propagation! I do also not use the optimizer and I don’t even need a learning rate since I am not making any changes to the weights.
I just get the output and check it against the labels to calculate my accuracy.