便纵有千种风情

torch.log() 底数为etorch.log10() 底数为10nn.LogSoftmax() 先softmax后取log 底数为eimport torch.nn as nn m=nn.Softmax(dim=1)input = torch.randn(2, 3)print(input)output = m(input)print(output)print(torch.log10(output))print(torch.log(output))tensor([[-0.472

torch.utils.data.DataLoadertorch.utils.data.DataLoader(torch.utils.data.dataset,batch_size,shuffle,num_workers,pin_memory)关键是这两个类： torch.utils.data.DataLoader torch.utils.data.datasetimport to...

只保存参数信息加载checkpoint = torch.load(opt.resume)model.load_state_dict(checkpoint)保存torch.save(self.state_dict(),file_path)这而只保存了参数信息，读取时也只有参数信息，模型结构需要手动编写保存整个模型保存 torch.save(the_model,...

Tensor转为numpynp.array(Tensor)numpy转换为Tensortorch.Tensor(numpy.darray)PIL.Image.Image转换成numpynp.array(PIL.Image.Image)numpy 转换成PIL.Image.ImageImage.fromarray(numpy.ndarray)首先需要保证n...

网络参数 ( parameters()和named_parameters())model为网络，打印层结构：ret=[*model.modules()]for layer in ret: #print(type(layer)) print(layer.__class__)大概会输出这样的一个效果：<class ‘main.ResNet’>...