目录

1、环境条件

2、代码实现

3、总结

---

1、环境条件

1. pycharm编译器
2. pytorch依赖
3. matplotlib依赖
4. numpy依赖等等

2、代码实现

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

# 设置设备
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# 定义数据变换
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.5,), (0.5,))
])

# 加载 MNIST 数据集
trainset = torchvision.datasets.MNIST(root='./data', train=True, download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)

testset = torchvision.datasets.MNIST(root='./data', train=False, download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=64, shuffle=False)

# 定义 LeNet-5 模型
class LeNet5(nn.Module):
    def __init__(self):
        super(LeNet5, self).__init__()
        self.conv1 = nn.Conv2d(1, 6, kernel_size=5, stride=1, padding=2)
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv2 = nn.Conv2d(6, 16, kernel_size=5, stride=1)
        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(torch.relu(self.conv1(x)))
        x = self.pool(torch.relu(self.conv2(x)))
        x = x.view(-1, 16 * 5 * 5)
        x = torch.relu(self.fc1(x))
        x = torch.relu(self.fc2(x))
        x = self.fc3(x)
        return x

# 初始化模型、损失函数和优化器
model = LeNet5().to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# 训练模型
epochs = 5
for epoch in range(epochs):
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        inputs, labels = data
        inputs, labels = inputs.to(device), labels.to(device)

        optimizer.zero_grad()

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

        running_loss += loss.item()
        if i % 100 == 99:
            print(f'[Epoch {epoch + 1}, Batch {i + 1}] loss: {running_loss / 100:.3f}')
            running_loss = 0.0

print('Finished Training')

# 保存模型
torch.save(model.state_dict(), 'lenet5.pth')
print('Model saved to lenet5.pth')

# 加载模型
model = LeNet5()
model.load_state_dict(torch.load('lenet5.pth'))
model.to(device)
model.eval()

# 在测试集上评估模型
correct = 0
total = 0
with torch.no_grad():
    for data in testloader:
        images, labels = data
        images, labels = images.to(device), labels.to(device)
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

print(f'Accuracy on the test set: {100 * correct / total:.2f}%')

# 加载并预处理本地图片进行预测
from PIL import Image

def load_and_preprocess_image(image_path):
    img = Image.open(image_path).convert('L')  # 转为灰度图
    img = img.resize((28, 28))
    img = np.array(img, dtype=np.float32)
    img = (img / 255.0 - 0.5) / 0.5  # 归一化到[-1, 1]
    img = torch.tensor(img).unsqueeze(0).unsqueeze(0)  # 添加批次和通道维度
    return img.to(device)

# 预测本地图片
image_path = '4.png'  # 替换为你的本地图片路径
img = load_and_preprocess_image(image_path)

# 使用加载的模型进行预测
model.eval()
with torch.no_grad():
    outputs = model(img)
    _, predicted = torch.max(outputs, 1)

# 打印预测结果
predicted_label = predicted.item()
print(f'预测结果: {predicted_label}')

# 显示图片及预测结果
img_np = img.cpu().numpy().squeeze()
plt.imshow(img_np, cmap='gray')
plt.title(f'预测结果: {predicted_label}')
plt.show()

解释：torch.save（）方法完成模型的保存，image_path为本地图片，用于测试

3、总结

        安装环境是比较难的点，均使用pip install 。。指令进行依赖环境的安装，其他的比较简单。

学习之所以会想睡觉，是因为那是梦开始的地方。
ଘ(੭ˊᵕˋ)੭ （开心） ଘ(੭ˊᵕˋ)੭ （开心）ଘ(੭ˊᵕˋ)੭ （开心）ଘ(੭ˊᵕˋ)੭ （开心）ଘ(੭ˊᵕˋ)੭ （开心）
                                                                                                        ------不写代码不会凸的小刘