Code exercise: CIFAR10

About CIFAR10

• This is an announcement data set containing 10 categories (‘ airplane ‘, ‘Automobile’, ‘Bird’, ‘Cat’, ‘Deer’, ‘Dog’, ‘Frog’, ‘Horse’, ‘ship’, ‘Truck’)

• Image format: RGB3 layer color channel, the size of each layer color channel is 32 x 32

Build the model

import torch
import torchvision
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim

Use GPU training, which can be set in the menu "Code Execution Tools" -> "Change Runtime Type"
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

transform = transforms.Compose(
    [transforms.ToTensor(),
     transforms.Normalize((0.5.0.5.0.5), (0.5.0.5.0.5)))Shuffle is True for training, shuffle is false for test
# When training can be out of order to increase diversity, testing is not necessary
trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
                                        download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=64,
                                          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=8,
                                         shuffle=False, num_workers=2)

classes = ('plane'.'car'.'bird'.'cat'.'deer'.'dog'.'frog'.'horse'.'ship'.'truck')
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Define the network

• The network, loss function and optimizer need to be defined

class Net(nn.Module) :
    def __init__(self) :
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3.6.5)
        self.pool = nn.MaxPool2d(2.2)
        self.conv2 = nn.Conv2d(6.16.5)
        self.fc1 = nn.Linear(16 * 5 * 5.120)
        self.fc2 = nn.Linear(120.84)
        self.fc3 = nn.Linear(84.10)

    def forward(self, x) :
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(-1.16 * 5 * 5)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

# Network on GPU
net = Net().to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(net.parameters(), lr=0.001)
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Train your network

for epoch in range(10) :Repeat multiple rounds
    for i, (inputs, labels) in enumerate(trainloader):
        inputs = inputs.to(device)
        labels = labels.to(device)
        The optimizer gradient returns to zero
        optimizer.zero_grad()
        # Forward propagation + back propagation + optimization
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        Output statistics
        if i % 100= =0:   
            print('Epoch: %d Minibatch: %5d loss: %.3f' %(epoch + 1, i + 1, loss.item()))

print('Finished Training')
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Detecting network effect

outputs = net(images.to(device))
_, predicted = torch.max(outputs, 1)

# Show the predicted results
for j in range(8) :print(classes[predicted[j]])
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The following code can find out the corresponding image classification label and detect, so as to compare the accuracy

correct = 0
total = 0

for data in testloader:
    images, labels = data
    images, labels = images.to(device), labels.to(device)
    outputs = net(images)
    _, predicted = torch.max(outputs.data, 1)
    total += labels.size(0)
    correct += (predicted == labels).sum().item()

print('Accuracy of the network on the 10000 test images: %d %%' % (
    100 * correct / total))
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