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.