是的，我想知道我要对他们进行resnet进行分类然后tsne可视化的完整代码

好的，以下是基于ResNet进行图像分类并使用t-SNE进行可视化的Python代码，供参考：

import torch
import torch.nn as nn
import torchvision.transforms as transforms
import torchvision.datasets as datasets
from torch.utils.data import DataLoader
import numpy as np
from sklearn.manifold import TSNE
import matplotlib.pyplot as plt

# 定义ResNet网络结构
class ResidualBlock(nn.Module):
    def __init__(self, in_channels, out_channels, stride=1):
        super(ResidualBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(out_channels)
        self.shortcut = nn.Sequential()
        if stride != 1 or in_channels != out_channels:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(out_channels)
            )

    def forward(self, x):
        out = self.relu(self.bn1(self.conv1(x)))
        out = self.bn2(self.conv2(out))
        out += self.shortcut(x)
        out = self.relu(out)
        return out

class ResNet(nn.Module):
    def __init__(self, block, num_blocks, num_classes=10):
        super(ResNet, self).__init__()
        self.in_channels = 64
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.layer1 = self.make_layer(block, 64, num_blocks[0], stride=1)
        self.layer2 = self.make_layer(block, 128, num_blocks[1], stride=2)
        self.layer3 = self.make_layer(block, 256, num_blocks[2], stride=2)
        self.layer4 = self.make_layer(block, 512, num_blocks[3], stride=2)
        self.avg_pool = nn.AdaptiveAvgPool2d((1,1))
        self.fc = nn.Linear(512, num_classes)

    def make_layer(self, block, out_channels, num_blocks, stride):
        strides = [stride] + [1]*(num_blocks-1)
        layers = []
        for stride in strides:
            layers.append(block(self.in_channels, out_channels, stride))
            self.in_channels = out_channels
        return nn.Sequential(*layers)

    def forward(self, x):
        out = self.relu(self.bn1(self.conv1(x)))
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)
        out = self.avg_pool(out)
        out = out.view(out.size(0), -1)
        out = self.fc(out)
        return out

# 加载数据集
train_dataset = datasets.CIFAR10(root='./data', train=True, transform=transforms.ToTensor(), download=True)
test_dataset = datasets.CIFAR10(root='./data', train=False, transform=transforms.ToTensor())

# 定义超参数
batch_size = 100
learning_rate = 0.01
num_epochs = 10

# 加载数据集
train_loader = DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=False)

# 实例化ResNet网络模型
model = ResNet(ResidualBlock, [2, 2, 2, 2]).cuda()

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate, momentum=0.9, weight_decay=5e-4)

# 训练模型
for epoch in range(num_epochs):
    train_loss = 0.0
    train_acc = 0.0
    model.train()
    for i, (images, labels) in enumerate(train_loader):
        images = images.cuda()
        labels = labels.cuda()
        optimizer.zero_grad()
        outputs = model(images)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        train_loss += loss.item() * images.size(0)
        _, pred = torch.max(outputs, 1)
        train_acc += (pred == labels).sum().item()
    train_loss = train_loss / len(train_loader.dataset)
    train_acc = train_acc / len(train_loader.dataset)
    print('Epoch [{}/{}], Train Loss: {:.4f}, Train Acc: {:.4f}'.format(epoch+1, num_epochs, train_loss, train_acc))

# 测试模型
model.eval()
with torch.no_grad():
    test_loss = 0.0
    test_acc = 0.0
    for images, labels in test_loader:
        images = images.cuda()
        labels = labels.cuda()
        outputs = model(images)
        loss = criterion(outputs, labels)
        test_loss += loss.item() * images.size(0)
        _, pred = torch.max(outputs, 1)
        test_acc += (pred == labels).sum().item()
    test_loss = test_loss / len(test_loader.dataset)
    test_acc = test_acc / len(test_loader.dataset)
    print('Test Loss: {:.4f}, Test Acc: {:.4f}'.format(test_loss, test_acc))

# 使用t-SNE进行可视化
features = []
labels = []
model.eval()
with torch.no_grad():
    for images, batch_labels in test_loader:
        images = images.cuda()
        batch_features = model(images).cpu().numpy()
        features.extend(batch_features)
        labels.extend(batch_labels.cpu().numpy())

features = np.array(features)
labels = np.array(labels)

tsne = TSNE(n_components=2, perplexity=30, learning_rate=200, n_iter=1000, random_state=0)
tsne_features = tsne.fit_transform(features)

# 可视化
plt.figure(figsize=(10, 10))
plt.scatter(tsne_features[:, 0], tsne_features[:, 1], c=labels, cmap=plt.cm.get_cmap("jet", 10))
plt.colorbar(ticks=range(10))
plt.clim(-0.5, 9.5)
plt.show()

这段代码中使用了PyTorch实现的ResNet网络进行图像分类，并使用t-SNE算法进行特征可视化。你需要将代码保存为.py文件，并确保已经安装了必要的Python库（如PyTorch、NumPy、scikit-learn等）。