手写数字识别实战_手写数字识别实战项目csdn

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
from torchvision.datasets import MNIST
from torch.utils.data import DataLoader
 
 
# 定义神经网络模型
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.fc1 = nn.Linear(784, 256)
        self.fc2 = nn.Linear(256, 128)
        self.fc3 = nn.Linear(128, 10)
 
    def forward(self, x):
        x = x.view(x.size(0), -1)
        x = torch.relu(self.fc1(x))
        x = torch.relu(self.fc2(x))
        x = self.fc3(x)
        return x
 
 
# 设置一些超参数
learning_rate = 0.001
batch_size = 64
num_epochs = 10
 
# 加载 MNIST 数据集
train_dataset = MNIST(root='.', train=True, transform=transforms.ToTensor(), download=True)
test_dataset = MNIST(root='.', train=False, transform=transforms.ToTensor())
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
 
# 创建模型和优化器
model = Net()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
criterion = nn.CrossEntropyLoss()
 
# 训练模型
total_step = len(train_loader)
for epoch in range(num_epochs):
    for i, (images, labels) in enumerate(train_loader):
        # 前向传播
        outputs = model(images)
        loss = criterion(outputs, labels)
 
        # 反向传播和优化
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
 
        if (i + 1) % 100 == 0:
            print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(epoch + 1, num_epochs, i + 1, total_step,
                                                                     loss.item()))
 
# 测试模型
model.eval()
with torch.no_grad():
    correct = 0
    total = 0
    for images, labels in test_loader:
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()
 
    print('准确率: {:.2f}%'.format(100 * correct / total))

实验结果