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Marie-Hélène Burle

import torch
import torchvision
import torchvision.transforms as transforms
transform = transforms.Compose(
     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

batch_size = 4

trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
                                        download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,
                                          shuffle=True, num_workers=2)

testset = torchvision.datasets.CIFAR10(root='./data', train=False,
                                       download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size,
                                         shuffle=False, num_workers=2)

classes = ('plane', 'car', 'bird', 'cat',
           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
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Files already downloaded and verified
import matplotlib.pyplot as plt
import numpy as np

# functions to show an image

def imshow(img):
    img = img / 2 + 0.5     # unnormalize
    npimg = img.numpy()
    plt.imshow(np.transpose(npimg, (1, 2, 0)))

# get some random training images
dataiter = iter(trainloader)
images, labels = next(dataiter)

# show images
# print labels
print(' '.join(f'{classes[labels[j]]:5s}' for j in range(batch_size)))

cat   truck cat   deer 

Creating a DataLoader

A DataLoader is an iterable feeding data to a model. When we train a model, we run it for each element of the DataLoader in a for loop:

for i in data_loader:
    <some model>