I’ve been playing around with the MNIST dataset and PyTorch and have loaded my dataset like so:
#Loading the MNIST training and test data: train_data = datasets.MNIST( root = 'data', train = True, transform = ToTensor(), download = True, ) test_data = datasets.MNIST( root = 'data', train = False, transform = ToTensor() )
what’s interesting to me is that I am thinking of these images as 28 by 28 matrices with each entry representing the shade of the pixel. So I wanted to compute the Euclidean distance between two images after flattening them, in particular, I defined the following function:
def wtrain(i,j): s = train_data.data[i] t = train_data.data[j] s = torch.flatten(s) t = torch.flatten(t) d = (s-t)/1000 d = (torch.norm(d))**2 return d
What’s weird is that wtrain[0,1] does not equal wtrain[1,0]?
I was wondering if anyone could see why or offer an alternative way to compute this distance.