How to modify the final FC layer based on the torch.model

11

My network initialized as below:

#VGG19
import torchvision
import torch.nn as nn
import torch.nn.functional as F
vgg19 = torchvision.models.vgg19(pretrained=True)
for param in vgg19.parameters():
    param.requires_grad = False
requires_grad=True by default
vgg19.fc = nn.Linear(1000, 8) neurons, otherwise change it 
vgg19.cuda()
import torch.optim as optim
criterion = nn.CrossEntropyLoss() 
optimizer = optim.SGD(vgg19.fc.parameters(), lr=0.001, momentum=0.9)#lr 0.001

But when I train with code as below, there is a error in loss.backward():
code

        inputs, labels = Variable(inputs.cuda()), Variable(labels.cuda())
        # zero the parameter gradients
        optimizer.zero_grad()
        # forward + backward + optimize
        outputs = vgg19(inputs)
        #print(type(outputs),type(inputs))
        loss = criterion(outputs, labels)
        #loss = F.nll_loss(outputs, labels)
        loss.backward()        
        optimizer.step()
        step += 1
        # print statistics
        running_loss += loss.data[0]

error

RuntimeError: there are no graph nodes that require computing gradients

1 Like