CNN手写数字识别csv读取保存加载pytorch模型

你可以按照以下步骤在PyTorch中实现CNN手写数字识别，包括使用CSV文件进行数据读取、保存和加载模型：

1. 导入所需的库和模块：

import torch
import torch.nn as nn
import torch.optim as optim
import pandas as pd
from torch.utils.data import DataLoader, Dataset

2. 创建一个自定义的数据集类，用于读取CSV文件中的数据：

class DigitDataset(Dataset):
    def __init__(self, csv_file):
        self.data = pd.read_csv(csv_file)
        
    def __len__(self):
        return len(self.data)
    
    def __getitem__(self, idx):
        image = self.data.iloc[idx, 1:].values.reshape(28, 28).astype('float32') / 255.0
        label = self.data.iloc[idx, 0]
        
        return image, label

3. 定义CNN模型：

class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv2d(1, 16, kernel_size=3, stride=1, padding=1)
        self.relu1 = nn.ReLU()
        self.pool1 = nn.MaxPool2d(kernel_size=2)
        self.conv2 = nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1)
        self.relu2 = nn.ReLU()
        self.pool2 = nn.MaxPool2d(kernel_size=2)
        self.fc = nn.Linear(7*7*32, 10)
        
    def forward(self, x):
        x = self.conv1(x)
        x = self.relu1(x)
        x = self.pool1(x)
        x = self.conv2(x)
        x = self.relu2(x)
        x = self.pool2(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)
        
        return x

4. 定义训练函数和测试函数：

def train(model, train_loader, criterion, optimizer):
    model.train()
    for images, labels in train_loader:
        optimizer.zero_grad()
        outputs = model(images.unsqueeze(1))
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

def test(model, test_loader):
    model.eval()
    correct = 0
    with torch.no_grad():
        for images, labels in test_loader:
            outputs = model(images.unsqueeze(1))
            _, predicted = torch.max(outputs.data, 1)
            correct += (predicted == labels).sum().item()
    accuracy = correct / len(test_loader.dataset)
    return accuracy

5. 加载数据集并创建数据加载器：

train_dataset = DigitDataset('train.csv')
test_dataset = DigitDataset('test.csv')

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

6. 创建CNN模型实例、损失函数和优化器：

model = CNN()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

7. 进行训练和测试：

num_epochs = 10

for epoch in range(num_epochs):
    train(model, train_loader, criterion, optimizer)
    accuracy = test(model, test_loader)
    print(f'Epoch {epoch+1}, Test Accuracy: {accuracy}')

torch.save(model.state_dict(), 'digit_model.pt')

8. 加载保存的模型并进行预测：

model = CNN()
model.load_state_dict(torch.load('digit_model.pt'))

# 假设有一个名为image的张量用于预测
output = model(image.unsqueeze(0).unsqueeze(0))
_, predicted = torch.max(output.data, 1)
print(f'Predicted digit: {predicted.item()}')

这就是使用CSV文件进行手写数字识别的基本步骤。你可以根据自己的需求进行修改和优化。