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[PyTorch] Tutorial(5) How to train a model to classify CIFAR-10 database

Last Updated on 2021-05-12 by Clay

Today we challenged the classifiers of different data sets again. This time, CIFAR-19 is a more difficult problem than MNIST handwriting recognition. In addition to the size of the picture becoming 32×32, CIFAR-10 is no longer a pure grayscale value, but a picture with the three primary colors of RGB.

As the mission goal becomes difficult, it is no longer a purely fully connected layer to build a model. This time I practiced using classic techniques such as convolution layer and maxpooling (CNN).

A lot of my code is referenced from the official PyTorch Tutorial: https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html#sphx-glr-beginner-blitz-cifar10-tutorial-py

So let’s start.

Code explanation

# -*- coding: utf-8 -*-
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms


First, you need to import the packages you want to use.

# GPU
device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
print('GPU state:', device)


Check you can use GPU. If you have no any GPU, you can use CPU to instead it but more slow.

# Cifar-10 data
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])


Use torchvision transforms module to convert our image data. It is a useful module and I also recording various functions recently.

# Data
trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)
trainLoader = torch.utils.data.DataLoader(trainset, batch_size=8, shuffle=True, num_workers=2)
testLoader = torch.utils.data.DataLoader(testset, batch_size=8, shuffle=False, num_workers=2)


Since PyTorch’s datasets has CIFAR-10 data, it can be downloaded here without having to set it manually.

If there is no data folder existed in the current directory, a folder will be created automatically and the CIFAR-10 data will be placed in it.

In addition, batch_size can actually be adjusted by yourself, but the highest accuracy I have tried is 8.

# Data classes
classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')


10 categories in CIFAR-10.

# Model structure
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.pool = nn.MaxPool2d(2, 2)
        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

net = Net().to(device)
print(net)


Output:

Net(
  (conv1): Conv2d(3, 6, kernel_size=(5, 5), stride=(1, 1))
  (conv2): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1))
  (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
  (fc1): Linear(in_features=400, out_features=120, bias=True)
  (fc2): Linear(in_features=120, out_features=84, bias=True)
  (fc3): Linear(in_features=84, out_features=10, bias=True)
)

The structure of the model, here is the part that I dare not change at will. I have tried it, but the effect is easy to get worse or not bad at all. I need more time to test.

# Parameters
criterion = nn.CrossEntropyLoss()
lr = 0.001
epochs = 3
optimizer = optim.SGD(net.parameters(), lr=lr, momentum=0.9)


These are parameter settings. They are loss function (using CrossEntropy of multi-classifiers), learning rate, number of iterations (epochs), and optimizer.

# Train
for epoch in range(epochs):
    running_loss = 0.0

    for times, data in enumerate(trainLoader, 0):
        inputs, labels = data
        inputs, labels = inputs.to(device), labels.to(device)

        # Zero the parameter gradients
        optimizer.zero_grad()

        # forward + backward + optimize
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        # print statistics
        running_loss += loss.item()

        if times % 100 == 99 or times+1 == len(trainLoader):
            print('[%d/%d, %d/%d] loss: %.3f' % (epoch+1, epochs, times+1, len(trainLoader), running_loss/2000))

print('Finished Training')


Here is the training process. It should be noted that optimizer.zero_grad() must clear the gradient every time before updating the weight, otherwise the gradient will always accumulate.

# Test
correct = 0
total = 0
with torch.no_grad():
    for data in testLoader:
        inputs, labels = data
        inputs, labels = inputs.to(device), labels.to(device)
        outputs = net(inputs)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

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

class_correct = list(0. for i in range(10))
class_total = list(0. for i in range(10))
with torch.no_grad():
    for data in testLoader:
        inputs, labels = data
        inputs, labels = inputs.to(device), labels.to(device)
        outputs = net(inputs)
        _, predicted = torch.max(outputs, 1)
        c = (predicted == labels).squeeze()
        for i in range(8):
            label = labels[i]
            class_correct[label] += c[i].item()
            class_total[label] += 1

for i in range(10):
    print('Accuracy of %5s : %2d %%' % (classes[i], 100 * class_correct[i] / class_total[i]))


Output:

Accuracy of the network on the 10000 test inputs: 55 %
Accuracy of plane : 57 %
Accuracy of   car : 72 %
Accuracy of  bird : 31 %
Accuracy of   cat : 16 %
Accuracy of  deer : 53 %
Accuracy of   dog : 68 %
Accuracy of  frog : 59 %
Accuracy of horse : 65 %
Accuracy of  ship : 56 %
Accuracy of truck : 71 %

Here is the test part, we used the data never in training data so we can see our model is really not a random guess.

Complete code

# -*- coding: utf-8 -*-
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms


# GPU
device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
print('GPU state:', device)


# Cifar-10 data
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])


# Data
trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)
trainLoader = torch.utils.data.DataLoader(trainset, batch_size=8, shuffle=True, num_workers=2)
testLoader = torch.utils.data.DataLoader(testset, batch_size=8, shuffle=False, num_workers=2)


# Data classes
classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')


# Model structure
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.pool = nn.MaxPool2d(2, 2)
        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

net = Net().to(device)
print(net)


# Parameters
criterion = nn.CrossEntropyLoss()
lr = 0.001
epochs = 3
optimizer = optim.SGD(net.parameters(), lr=lr, momentum=0.9)



# Train
for epoch in range(epochs):
    running_loss = 0.0

    for times, data in enumerate(trainLoader, 0):
        inputs, labels = data
        inputs, labels = inputs.to(device), labels.to(device)

        # Zero the parameter gradients
        optimizer.zero_grad()

        # forward + backward + optimize
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        # print statistics
        running_loss += loss.item()

        if times % 100 == 99 or times+1 == len(trainLoader):
            print('[%d/%d, %d/%d] loss: %.3f' % (epoch+1, epochs, times+1, len(trainLoader), running_loss/2000))

print('Finished Training')


# Test
correct = 0
total = 0
with torch.no_grad():
    for data in testLoader:
        inputs, labels = data
        inputs, labels = inputs.to(device), labels.to(device)
        outputs = net(inputs)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

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

class_correct = list(0. for i in range(10))
class_total = list(0. for i in range(10))
with torch.no_grad():
    for data in testLoader:
        inputs, labels = data
        inputs, labels = inputs.to(device), labels.to(device)
        outputs = net(inputs)
        _, predicted = torch.max(outputs, 1)
        c = (predicted == labels).squeeze()
        for i in range(8):
            label = labels[i]
            class_correct[label] += c[i].item()
            class_total[label] += 1

for i in range(10):
    print('Accuracy of %5s : %2d %%' % (classes[i], 100 * class_correct[i] / class_total[i]))


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