本教程将带你入门使用 PyTorch 库来处理 MNIST 数据集。MNIST 是一个包含手写数字图像的著名数据集，常用于机器学习初学者进行图像识别的学习。

安装 PyTorch

在开始之前，请确保你已经安装了 PyTorch。你可以通过以下命令进行安装：

pip install torch torchvision

加载数据集

import torchvision.datasets as datasets
import torchvision.transforms as transforms

train_dataset = datasets.MNIST(root='./data', train=True, transform=transforms.ToTensor(), download=True)
test_dataset = datasets.MNIST(root='./data', train=False, transform=transforms.ToTensor())

数据可视化

import matplotlib.pyplot as plt

def show_image(image, label):
    plt.imshow(image, cmap='gray')
    plt.title(f'Label: {label}')
    plt.show()

# 随机选择一个图像进行展示
image, label = next(iter(train_dataset))
show_image(image, label)

MNIST 数据集示例

创建模型

以下是一个简单的卷积神经网络模型，用于 MNIST 数据集：

import torch.nn as nn
import torch.nn.functional as F

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, kernel_size=5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=5)
        self.fc1 = nn.Linear(64 * 4 * 4, 128)
        self.fc2 = nn.Linear(128, 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, 64 * 4 * 4)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return x

net = Net()

训练模型

import torch.optim as optim

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

for epoch in range(2):  # loop over the dataset multiple times

    running_loss = 0.0
    for i, data in enumerate(train_dataset, 0):
        inputs, labels = data

        optimizer.zero_grad()

        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        running_loss += loss.item()
        if i % 2000 == 1999:    # print every 2000 mini-batches
            print(f'Epoch {epoch + 1}, Batch {i + 1}, Loss: {running_loss / 2000:.3f}')
            running_loss = 0.0

print('Finished Training')

测试模型

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

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

扩展阅读

想了解更多关于 PyTorch 和 MNIST 的内容，可以访问以下链接：