图像数据集存放在文件夹中，请帮我搭建pytorch卷积神经网络对文件夹中的图像数据进行识别

好的，以下是一个基本的pytorch卷积神经网络对图像数据集进行识别的代码：

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
import torchvision.datasets as datasets
import torchvision.transforms as transforms

# 定义数据集路径、批次大小、学习率、训练轮数等超参数
data_path = 'image_dataset'
batch_size = 32
learning_rate = 0.001
num_epochs = 10

# 加载数据集并进行数据增强
train_dataset = datasets.ImageFolder(
    root=data_path,
    transform=transforms.Compose([
        transforms.Resize((224, 224)),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
    ])
)

# 使用DataLoader进行批量加载数据
train_loader = DataLoader(
    dataset=train_dataset,
    batch_size=batch_size,
    shuffle=True
)

# 定义卷积神经网络模型
class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.layer1 = nn.Sequential(
            nn.Conv2d(3, 16, kernel_size=3, padding=1),
            nn.BatchNorm2d(16),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2)
        )
        self.layer2 = nn.Sequential(
            nn.Conv2d(16, 32, kernel_size=3, padding=1),
            nn.BatchNorm2d(32),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2)
        )
        self.layer3 = nn.Sequential(
            nn.Conv2d(32, 64, kernel_size=3, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2)
        )
        self.fc1 = nn.Linear(64 * 28 * 28, 256)
        self.dropout = nn.Dropout(0.5)
        self.fc2 = nn.Linear(256, 10)
    
    def forward(self, x):
        out = self.layer1(x)
        out = self.layer2(out)
        out = self.layer3(out)
        out = out.reshape(out.size(0), -1)
        out = self.fc1(out)
        out = self.dropout(out)
        out = self.fc2(out)
        return out

# 实例化模型并定义损失函数和优化器
model = CNN()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)

# 训练模型
total_step = len(train_loader)
for epoch in range(num_epochs):
    for i, (images, labels) in enumerate(train_loader):
        outputs = model(images)
        loss = criterion(outputs, labels)
        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()))

# 保存模型
torch.save(model.state_dict(), 'cnn_model.pth')

在上述代码中，我们使用了`datasets.ImageFolder`类将图像数据集加载到内存中，并使用`transforms`进行数据增强。然后使用`DataLoader`将数据集分批次进行加载。接着，我们定义了一个卷积神经网络模型，并使用`nn.CrossEntropyLoss`作为损失函数，使用`optim.Adam`作为优化器。最后，我们使用`torch.save`保存了训练好的模型。