import torch from torch import nn from torch.utils.tensorboard import SummaryWriter class MyModule(nn.Module): def __init__(self): super(MyModule, self).__init__() self.model1 = nn.Sequential( nn.Flatten(), nn.Linear(3072, 100), nn.ReLU(), nn.Linear(100, 1), nn.Sigmoid() ) def forward(self, x): x = self.model1(x) return x import torch import torchvision from PIL.Image import Image from torch.utils.tensorboard import SummaryWriter from torch import nn, optim from torch.utils.data import dataloader from torchvision.transforms import transforms from module import MyModule train = torchvision.datasets.CIFAR10(root="../data",train=True, download=True, transform= transforms.ToTensor()) vgg_model = torchvision.models.vgg16(pretrained=True) vgg_model.classifier.add_module('add_linear', nn.Linear(1000,2)) #ToImage = transforms.ToPILImage() #Image.show(ToImage(train[0][0])) train_data = dataloader.DataLoader(train, batch_size = 128, shuffle=True) model = MyModule() #criterion = nn.BCELoss() epochs = 5 learningRate = 1e-3 optimizer = optim.SGD(model.parameters(),lr = learningRate) loss = nn.CrossEntropyLoss() Writer = SummaryWriter(log_dir="Training") step = 0 for epoch in range(epochs): total_loss = 0 for data,labels in train_data: y = vgg_model(data) los = loss(y,labels) optimizer.zero_grad() los.backward() optimizer.step() Writer.add_scalar("Training",los,step) step = step + 1 if step%100 == 0: print("Training for {0} times".format(step)) total_loss += los print("total_loss is {0}".format(los)) Writer.close() torch.save(vgg_model,"model_vgg.pth")修改变成VGG16-两分类模型

好的，您的问题是如何将这段代码修改成一个基于VGG16的两分类模型？我可以给您一些指导。

首先，我们需要将原先的MyModule替换成VGG16模型。可以使用torchvision中已经实现好的VGG16模型，但需要修改最后一层的输出为两个类别。具体实现方法如下：

vgg_model = torchvision.models.vgg16(pretrained=True)
vgg_model.classifier.add_module('add_linear', nn.Linear(1000,2))

这段代码会下载预训练好的VGG16模型，并在其分类器中添加一个新的线性层，输出为2个类别。

其次，我们需要修改损失函数。因为我们现在是进行两分类，所以可以使用交叉熵损失函数(nn.CrossEntropyLoss())。

最后，修改训练过程中的模型、优化器、损失函数等参数即可。具体代码实现如下：

import torch 
import torchvision 
from torch.utils.tensorboard import SummaryWriter 
from torch import nn, optim 
from torch.utils.data import dataloader 
from torchvision.transforms import transforms 

train = torchvision.datasets.CIFAR10(root="../data",train=True, download=True, transform= transforms.ToTensor()) 

vgg_model = torchvision.models.vgg16(pretrained=True)
vgg_model.classifier.add_module('add_linear', nn.Linear(1000,2))

train_data = dataloader.DataLoader(train, batch_size = 128, shuffle=True) 

criterion = nn.CrossEntropyLoss() 
epochs = 5 
learningRate = 1e-3 
optimizer = optim.SGD(vgg_model.parameters(),lr = learningRate) 

Writer = SummaryWriter(log_dir="Training") 
step = 0 

for epoch in range(epochs): 
    total_loss = 0 
    for data,labels in train_data: 
        optimizer.zero_grad() 
        y = vgg_model(data) 
        los = criterion(y,labels) 
        los.backward() 
        optimizer.step() 

        Writer.add_scalar("Training",los,step) 
        step = step + 1 
        if step%100 == 0: 
            print("Training for {0} times".format(step)) 
        total_loss += los 

    print("total_loss is {0}".format(total_loss)) 

Writer.close() 
torch.save(vgg_model,"model_vgg.pth")

希望这个回答能够帮到您！