Hi Pytorch community!
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 available MNIST 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, giving 99% accuracy.
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 pixels 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 data sets. Any help would be massively appreciated