Dear author
Thank you very much for your excellent code. My recent work is also trying to identify noise labels from correct labels.
I'm curious where you output the loss values (e.g., Fig 2(a)) from your code? Is it the value of <display_loss> in the following code? If not, could you tell me how to calculate it?
def warmup(epoch, net, optimizer, dataloader, args): # make noise labels in asym and sym ways
----net.train()
----num_iter = (len(dataloader.dataset) // dataloader.batch_size) + 1
----CEloss = nn.CrossEntropyLoss()
----display_loss = []
----for batch_idx, (inputs, labels, path) in enumerate(dataloader):
--------inputs, labels = inputs.cuda(), labels.cuda()
--------optimizer.zero_grad()
--------outputs = net(inputs)
--------loss = CEloss(outputs, labels)
--------L = loss
--------display_loss.append(L)
Thanks again for your help. Looking forward to your reply.
Dear author
Thank you very much for your excellent code. My recent work is also trying to identify noise labels from correct labels.
I'm curious where you output the loss values (e.g., Fig 2(a)) from your code? Is it the value of <display_loss> in the following code? If not, could you tell me how to calculate it?
def warmup(epoch, net, optimizer, dataloader, args): # make noise labels in asym and sym ways----net.train()----num_iter = (len(dataloader.dataset) // dataloader.batch_size) + 1----CEloss = nn.CrossEntropyLoss()----display_loss = []----for batch_idx, (inputs, labels, path) in enumerate(dataloader):--------inputs, labels = inputs.cuda(), labels.cuda()--------optimizer.zero_grad()--------outputs = net(inputs)--------loss = CEloss(outputs, labels)--------L = loss--------display_loss.append(L)Thanks again for your help. Looking forward to your reply.