每个文件夹中是一类图片，读取各个文件夹中的图片进行resnet深度深度学习代码

可以使用Python中的PyTorch库来实现这个任务。首先，需要使用PyTorch中的torchvision模块来加载和预处理图像数据。然后，可以使用torch.utils.data.DataLoader类来创建一个数据加载器，以便在训练模型时批量加载数据。接下来，可以使用PyTorch中的torch.nn模块来定义ResNet模型，并使用torch.optim模块来定义优化器和损失函数。最后，可以使用PyTorch中的torch.utils.tensorboard模块来可视化训练过程和结果。

以下是一个简单的示例代码：

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

# 定义数据预处理
transform = transforms.Compose([
    transforms.Resize(256),
    transforms.CenterCrop(224),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

# 加载数据集
train_dataset = datasets.ImageFolder('path/to/train/folder', transform=transform)
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)

# 定义ResNet模型
model = nn.Sequential(
    nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False),
    nn.BatchNorm2d(64),
    nn.ReLU(inplace=True),
    nn.MaxPool2d(kernel_size=3, stride=2, padding=1),
    nn.Sequential(
        nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1, bias=False),
        nn.BatchNorm2d(64),
        nn.ReLU(inplace=True),
        nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1, bias=False),
        nn.BatchNorm2d(64)
    ),
    nn.Sequential(
        nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1, bias=False),
        nn.BatchNorm2d(128),
        nn.ReLU(inplace=True),
        nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1, bias=False),
        nn.BatchNorm2d(128),
        nn.ReLU(inplace=True),
        nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1, bias=False),
        nn.BatchNorm2d(128)
    ),
    nn.Sequential(
        nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1, bias=False),
        nn.BatchNorm2d(256),
        nn.ReLU(inplace=True),
        nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=False),
        nn.BatchNorm2d(256),
        nn.ReLU(inplace=True),
        nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=False),
        nn.BatchNorm2d(256)
    ),
    nn.Sequential(
        nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1, bias=False),
        nn.BatchNorm2d(512),
        nn.ReLU(inplace=True),
        nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1, bias=False),
        nn.BatchNorm2d(512),
        nn.ReLU(inplace=True),
        nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1, bias=False),
        nn.BatchNorm2d(512)
    ),
    nn.Sequential(
        nn.Conv2d(512, 1024, kernel_size=3, stride=2, padding=1, bias=False),
        nn.BatchNorm2d(1024),
        nn.ReLU(inplace=True),
        nn.Conv2d(1024, 1024, kernel_size=3, stride=1, padding=1, bias=False),
        nn.BatchNorm2d(1024),
        nn.ReLU(inplace=True),
        nn.Conv2d(1024, 1024, kernel_size=3, stride=1, padding=1, bias=False),
        nn.BatchNorm2d(1024)
    ),
    nn.Sequential(
        nn.Conv2d(1024, 2048, kernel_size=3, stride=2, padding=1, bias=False),
        nn.BatchNorm2d(2048),
        nn.ReLU(inplace=True),
        nn.Conv2d(2048, 2048, kernel_size=3, stride=1, padding=1, bias=False),
        nn.BatchNorm2d(2048),
        nn.ReLU(inplace=True),
        nn.Conv2d(2048, 2048, kernel_size=3, stride=1, padding=1, bias=False),
        nn.BatchNorm2d(2048)
    ),
    nn.AdaptiveAvgPool2d((1, 1)),
    nn.Flatten(),
    nn.Linear(2048, 1000)
)

# 定义优化器和损失函数
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9)
criterion = nn.CrossEntropyLoss()

# 训练模型
for epoch in range(10):
    for images, labels in train_loader:
        optimizer.zero_grad()
        outputs = model(images)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
    print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, 10, loss.item()))

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

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