pytorch自己手写数字进行识别的代码包括模型训练和图片处理

PyTorch 自己手写数字识别的基本步骤通常包括数据预处理、构建神经网络模型、训练模型以及测试阶段。这里是一个简化版的示例代码：

首先，安装必要的库并导入它们：

import torch
import torchvision
import torchvision.transforms as transforms
from torch.utils.data import DataLoader

1. **数据预处理**:

transform = transforms.Compose([
    transforms.ToTensor(),  # 转换为张量
    transforms.Normalize((0.5,), (0.5,))])  # 归一化

# 加载MNIST数据集
train_dataset = torchvision.datasets.MNIST(root='./data', train=True, download=True, transform=transform)
test_dataset = torchvision.datasets.MNIST(root='./data', train=False, download=True, transform=transform)

train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)

2. **构建模型**:

class HandwrittenDigitRecognizer(torch.nn.Module):
    def __init__(self):
        super(HandwrittenDigitRecognizer, self).__init__()
        self.conv1 = torch.nn.Conv2d(1, 32, kernel_size=3, stride=1, padding=1)
        self.relu = torch.nn.ReLU()
        self.maxpool = torch.nn.MaxPool2d(kernel_size=2, stride=2)
        self.fc1 = torch.nn.Linear(32 * 14 * 14, 128)
        self.fc2 = torch.nn.Linear(128, 10)  # 输出层，10表示0-9共10个类别

    def forward(self, x):
        x = self.conv1(x)
        x = self.relu(x)
        x = self.maxpool(x)
        x = x.view(-1, 32 * 14 * 14)  # 展平以便输入全连接层
        x = self.fc1(x)
        x = self.relu(x)
        x = self.fc2(x)
        return x

model = HandwrittenDigitRecognizer()
criterion = torch.nn.CrossEntropyLoss()  # 损失函数
optimizer = torch.optim.Adam(model.parameters())  # 使用Adam优化器

3. **模型训练**:

num_epochs = 10
for epoch in range(num_epochs):
    for images, labels in train_loader:
        optimizer.zero_grad()  # 清零梯度
        outputs = model(images)
        loss = criterion(outputs, labels)
        loss.backward()  # 反向传播
        optimizer.step()  # 更新权重

    # 训练阶段结束，打印一些进度信息
    print(f"Epoch {epoch + 1}/{num_epochs}, Loss: {loss.item():.4f}")

4. **模型测试**:

with torch.no_grad():
    correct_count = 0
    total_count = 0
    for images, labels in test_loader:
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        total_count += labels.size(0)
        correct_count += (predicted == labels).sum().item()

    accuracy = 100 * correct_count / total_count
    print(f"Test Accuracy: {accuracy:.2f}%")