有没有大神帮我看看我的代码错在哪里
import torch
import torch.nn as nn
import torch.utils.data as Data
import torchvision
import matplotlib.pyplot as pl
import torch
import torchvision
import torchvision.transforms as transforms
from torch.autograd import Variable
from torch.utils.data import DataLoader
data_transform = transforms.Compose([
transforms.Resize(224),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5],
std=[0.5, 0.5, 0.5])
])
train_dataset = torchvision.datasets.ImageFolder(root='C://Users//Administrator//Desktop//train_photos',transform=data_transform)
train_loader = torch.utils.data.DataLoader(train_dataset,batch_size=16, shuffle=True,num_workers=4)
class CNN(nn.Module):
def __init__(self):
super(CNN,self).__init__()
self.conv1 = nn.Sequential(
nn.Conv2d(
in_channels=3,
out_channels=32,
kernel_size=5,
stride=1,
padding=2,
),#32,224,224
nn.ReLU(),
nn.MaxPool2d(kernel_size=2),#32,112,112
)
self.conv2=nn.Sequential(
nn.Conv2d(32,64,5,1,2),#64,112,112
nn.ReLU(),
nn.MaxPool2d(2),#64,56,56
)
self.conv3=nn.Sequential(
nn.Conv2d(64,128,5,1,2),#128,56,56
nn.ReLU(),
nn.MaxPool2d(2),#128,28,28
)
self.out=nn.Linear(128*28*28,2)
def forward(self,x):
x=self.conv1(x)
x=self.conv2(x)
x=self.conv3(x)
x=x.view(x.size(0),-1)
output=self.out(x)
return output
cnn=CNN()
optimizer=torch.optim.Adam(cnn.parameters(),lr=0.001)
loss_func=nn.CrossEntropyLoss()
for step,(x,y) in enumerate(train_loader):
b_x=Variable(x)
b_y=Variable(y)
output=cnn(b_x)
loss=loss_func(output,b_y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
torch.save(cnn,'cnn.pkl')
这段代码用于图像分类，种类有两种
出现的错误是：
userWarning: This overload of add_ is deprecated:
add_(Number alpha,Tensor other)
Consider using one of the following signatures instead:
add_(Tensor other,number alpha)
为什么出错了，哪里出错了，怎么改呢？大神们帮帮我吧

请问你解决了吗？ 我也遇到这个问题了

同遇到了这个问题，
lz
怎么解决的

这只是警告，还可以跑。add_(Number alpha,Tensor other)这种模式要弃用了，建议换成add_(Tensor other,number alpha)

add_(Number alpha,Tensor other)这种模式已经弃用了，要换成add_(Tensor other,number alpha)。
但是我换过来之后还是用不了，然后我用了关键字传参就可以了：
p.data.add_(p.grad.data, alpha=-learning_rate)

只要没终止，就不用管