resnet34进行4个类型进行训练并分类，并打印混淆矩阵pytorch代码

以下是使用 PyTorch 训练 ResNet34 进行 4 类别分类并打印混淆矩阵的代码：

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
from sklearn.metrics import confusion_matrix
import numpy as np

# Define transforms for training and testing data
transform_train = transforms.Compose([
    transforms.RandomCrop(32, padding=4),
    transforms.RandomHorizontalFlip(),
    transforms.ToTensor(),
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])

transform_test = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])

# Load CIFAR-10 dataset
trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
                                        download=True, transform=transform_train)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=128,
                                          shuffle=True, num_workers=2)

testset = torchvision.datasets.CIFAR10(root='./data', train=False,
                                       download=True, transform=transform_test)
testloader = torch.utils.data.DataLoader(testset, batch_size=100,
                                         shuffle=False, num_workers=2)

# Define ResNet34 model
class ResNet34(nn.Module):
    def __init__(self, num_classes=4):
        super(ResNet34, self).__init__()
        self.resnet = torchvision.models.resnet34(pretrained=True)
        self.resnet.fc = nn.Linear(512, num_classes)

    def forward(self, x):
        x = self.resnet(x)
        return x

# Define device
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

# Initialize model and optimizer
net = ResNet34().to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

# Train the model
for epoch in range(10):
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        inputs, labels = data[0].to(device), data[1].to(device)

        optimizer.zero_grad()

        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        running_loss += loss.item()
        if i % 100 == 99:
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 100))
            running_loss = 0.0

# Test the model
net.eval()
y_true = []
y_pred = []
with torch.no_grad():
    for data in testloader:
        images, labels = data[0].to(device), data[1].to(device)
        outputs = net(images)
        _, predicted = torch.max(outputs.data, 1)
        y_true += labels.cpu().numpy().tolist()
        y_pred += predicted.cpu().numpy().tolist()

# Print confusion matrix
conf_mat = confusion_matrix(y_true, y_pred)
print(conf_mat)

请注意，这个代码只是一个示例，你需要根据你的数据集和分类任务进行修改。