as possible so given all these constraints even all these similar architecture similar hyper parameters for various different semi-square learning algorithms it turns out that which will address your training performs the best if you look at C 410 which is four thousand labels so you see pretend originally it has 50,000 images so you basically just use 46,000 unlabeled data points and four thousand label data points which is like four hundred labels for class so that's really tiny compared to what the ocean is that is and virtual abyssal training