CNN手写数字识别csv读取保存加载pytorch模型测试数据集和单张图片

你可以按照以下步骤使用CNN模型进行手写数字识别，包括CSV文件的读取、保存和加载以及测试数据集和单张图片。

1. 导入所需的库：

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split

2. 定义CNN模型：

class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, kernel_size=3, stride=1, padding=1)
        self.relu1 = nn.ReLU()
        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
        self.relu2 = nn.ReLU()
        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.fc1 = nn.Linear(7*7*64, 128)
        self.relu3 = nn.ReLU()
        self.fc2 = nn.Linear(128, 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(-1, 7*7*64)
        x = self.fc1(x)
        x = self.relu3(x)
        x = self.fc2(x)
        return x

model = CNN()

3. 读取CSV文件并准备数据集：

class CustomDataset(Dataset):
    def __init__(self, csv_path):
        self.data = pd.read_csv(csv_path, header=None)

    def __len__(self):
        return len(self.data)

    def __getitem__(self, index):
        label = self.data.iloc[index, 0]
        image = self.data.iloc[index, 1:].values.reshape(28, 28).astype(np.uint8)
        image = np.expand_dims(image, axis=0)
        return image, label

csv_path = 'path/to/your/csv/file.csv'
dataset = CustomDataset(csv_path)

4. 划分训练集和测试集：

train_dataset, test_dataset = train_test_split(dataset, test_size=0.2, random_state=42)

5. 创建数据加载器：

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

6. 定义损失函数和优化器：

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

7. 训练模型：

num_epochs = 10

for epoch in range(num_epochs):
    train_loss = 0.0
    model.train()

    for images, labels in train_loader:
        optimizer.zero_grad()
        outputs = model(images.float())
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        train_loss += loss.item() * images.size(0)

    train_loss /= len(train_loader.dataset)
    print(f"Epoch: {epoch+1}/{num_epochs}, Training Loss: {train_loss:.4f}")

8. 保存训练好的模型：

torch.save(model.state_dict(), 'path/to/save/model.pth')

9. 加载已保存的模型并进行测试：

model.load_state_dict(torch.load('path/to/save/model.pth'))
model.eval()

test_loss = 0.0
correct = 0

with torch.no_grad():
    for images, labels in test_loader:
        outputs = model(images.float())
        loss = criterion(outputs, labels)
        test_loss += loss.item() * images.size(0)
        _, predicted = torch.max(outputs.data, 1)
        correct += (predicted == labels).sum().item()

test_loss /= len(test_loader.dataset)
accuracy = correct / len(test_loader.dataset) * 100

print(f"Test Loss: {test_loss:.4f}, Accuracy: {accuracy:.2f}%")

10. 使用模型进行单张图片的预测：

from PIL import Image

image_path = 'path/to/your/image.jpg'
image = Image.open(image_path).convert('L')
image = np.array(image)
image = np.expand_dims(image, axis=0)
image = torch.from_numpy(image).unsqueeze(0)
output = model(image.float())
_, predicted = torch.max(output.data, 1)

print(f"Predicted Label: {predicted.item()}")

请将 `path/to/your/csv/file.csv` 和 `path/to/save/model.pth` 替换为您的实际文件路径。另外，确保您的CSV文件以及要测试的单张图片符合要求。