Unexpected increase in validation error in MNIST Pytorch
I'm a bit new to the whole field and thus decided to work on the MNIST dataset. I pretty much adapted the whole code from https://github.com/pytorch/examples/blob/master/mnist/main.py, with only one significant change: Data Loading. I didn't want to use the pre-loaded dataset within Torchvision. So I used MNIST in CSV.

I loaded the data from CSV file by inheriting from Dataset and making a new dataloader.
Here's the relevant code: 

mean = 33.318421449829934
sd = 78.56749081851163
# mean = 0.1307
# sd = 0.3081
import numpy as np
from torch.utils.data import Dataset, DataLoader

class dataset(Dataset):
    def __init__(self, csv, transform=None):
        data = pd.read_csv(csv, header=None)
        self.X = np.array(data.iloc[:, 1:]).reshape(-1, 28, 28, 1).astype('float32')
        self.Y = np.array(data.iloc[:, 0])

        del data
        self.transform = transform

    def __len__(self):
        return len(self.X)

    def __getitem__(self, idx):
        item = self.X[idx]
        label = self.Y[idx]

        if self.transform:
            item = self.transform(item)

        return (item, label)

import torchvision.transforms as transforms
trainData = dataset('mnist_train.csv', transform=transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((mean,), (sd,))
]))
testData = dataset('mnist_test.csv', transform=transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((mean,), (sd,))
]))

train_loader = DataLoader(dataset=trainData,
                         batch_size=10, 
                         shuffle=True,
                         )
test_loader = DataLoader(dataset=testData, 
                        batch_size=10, 
                        shuffle=True,
                        )


However this code gives me the absolutely weird training error graph that you see in the picture, and a final validation error of 11% because it classifies everything as a '7'.


I managed to track the problem down to how I normalize the data and if I use the values given in the example code (0.1307, and 0.3081) for transforms.Normalize, along with reading the data as type 'uint8' it works perfectly.
Note that there is very minimal difference in the data which is provided in these two cases. Normalizing by 0.1307 and 0.3081 on values from 0 to 1 has the same effect as normalizing by 33.31 and 78.56 on values from 0 to 255. The values are even mostly the same (A black pixel corresponds to -0.4241 in the first case and -0.4242 in the second).

If you would like to see a IPython Notebook where this problem is seen clearly, please check out https://colab.research.google.com/drive/1W1qx7IADpnn5e5w97IcxVvmZAaMK9vL3

I am unable to understand what has caused such a huge difference in behaviour within these two slightly different ways of loading data. Any help would be massively appreciated.

Long story short: you need to change item = self.X[idx] to item = self.X[idx].copy().

Long story long: T.ToTensor() runs torch.from_numpy, which returns a tensor which aliases the memory of your numpy array dataset.X. And T.Normalize() works inplace, so each time the sample is drawn it has mean subtracted and is divided by std, leading to degradation of your dataset.

Edit: regarding why it works in the original MNIST loader, the rabbit hole is even deeper. The key line in MNIST is that the image is transformed into a PIL.Image instance. The operation claims to only copy in case the buffer is not contiguous (it is in our case), but under the hood it checks whether it's strided instead (which it is), and thus copies it. So by luck, the default torchvision pipeline involves a copy and thus in-place operation of T.Normalize() does not corrupt the in-memory self.data of our MNIST instance.
