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[PyTorch] Getting Start: 訓練 CIFAR-10 資料集的分類模型

今天再次挑戰了不同的資料集（dataset）的分類器。這次的 CIFAR-10 是一個比起 MNIST 手寫辨識來得更難一些的題目。除了圖片的尺寸變成了 32 x 32 之外，CIFAR-10 也不再是單純的灰階值，而是有著 RGB 三原色的圖片了。

由於任務目標變難，所以這次不再是單純只以『全連接層』來建構模型。這次我練習了使用 Convolution layer 加上 Maxpooling 這樣的經典技術。

我的程式碼有不少是參考自 PyTorch 官方的 Tutorial，尤其是模型層的選擇 —— 因為我只要一更動模型層的架構，效果就會變差哈哈哈哈。想說既然要做成筆記留下來，何苦不用人家效果比較好的參數呢？

官方的連結在此： https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html#sphx-glr-beginner-blitz-cifar10-tutorial-py

那麼，以下便開始這次的筆記吧！

程式碼解釋

# -*- 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


首先，匯入所需要的 PyTorch 套件。

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


確認是否可用 GPU；若否，則使用 CPU。

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


再次使用 torchvision 內的 transforms 來進行圖片的轉換。這真的是個非常好用的功能，我最近也在寫篇文章紀錄各種功能。

# 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)


由於 PyTorch 的 datasets 裡面便有附 CIFAR-10，於是這裡便可以直接載下來，不用再另外手動設定。

如果沒有 data 資料夾存於當前目錄，會自動創建一個資料夾並將 CIFAR-10 的資料放在裡面。

另外，batch_size 其實可以自己調整，不過我目前嘗試 Accuracy 最高的便是 8 了。

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


CIFAR-10 當中的 10 種分類。

# 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)
)

模型的架構，這裡便是我不敢隨意更動的部份。有嘗試過，但效果很容易變差、或根本沒差。我需要多點時間測試。

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


參數設定。分別是 Loss function (採用多分類器經典的 CrossEntropy) 、Learning Rate、迭代次數、優化器。

# 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')


這裡便是訓練的過程了。要注意的是記得 optimizer.zero_grad() 要每次在更新權重前清空梯度，不然梯度會一直累積。

# 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 %

這裡是測試的部份，可以看到我們的程式真的不是亂猜的。

完整程式碼

# -*- 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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5 thoughts on “[PyTorch] Getting Start: 訓練 CIFAR-10 資料集的分類模型”

    1. ccs96307
      Reply

      Hello，你好，很高興認識你。
      慚愧…這好久以前寫的程式，現在猛然一看，突然也忘了當初為什麼要除以 2000。
      不過我在想，當初應該只是希望求這 100 步之間平均的 Loss 而已。之所以是 2000，大概是改著改著改漏掉了…
      造成困擾真是不好意思。

    1. ccs96307
      Reply

      您好，請問是指想要提升分類模型的效果嗎？
      可以去網路上查詢 Cifar-10 任務的最高分，記得是有滿多人分享的，當然模型架構也比較複雜。

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