Hi @smth

As you mentioned i modified the transfer learning code. But I am getting error. Can you help me out on this.

def train_model(model, criterion, optimizer, lr_scheduler, num_epochs=25):

since = time.time()

```
best_model = model
best_acc = 0.0
for epoch in range(num_epochs):
print('Epoch {}/{}'.format(epoch, num_epochs - 1))
print('-' * 10)
optimizer=lr_scheduler(optimizer, epoch)
optimizer.zero_grad()
# Each epoch has a training and validation phase
for phase in ['train', 'val']:
if phase == 'train':
#optimizer = lr_scheduler(optimizer, epoch)
model.train(True) # Set model to training mode
else:
model.train(False) # Set model to evaluate mode
running_loss = 0.0
running_corrects = 0
# Iterate over data.
for data in dset_loaders[phase]:
# get the inputs
inputs, labels = data
# wrap them in Variable
if use_gpu:
inputs, labels = Variable(inputs.cuda()), \
Variable(labels.cuda())
else:
inputs, labels = Variable(inputs), Variable(labels)
# zero the parameter gradients
#optimizer.zero_grad()
# forward
outputs = model(inputs)
_, preds = torch.max(outputs.data, 1)
loss = criterion(outputs, labels)
if phase == 'train':
loss.backward()
#optimizer.step()
running_loss += loss.data[0]
running_corrects += torch.sum(preds == labels.data)
if phase=='train':
optimizer.step()
epoch_loss = running_loss / dset_sizes[phase]
epoch_acc = running_corrects / dset_sizes[phase]
print('{} Loss: {:.4f} Acc: {:.4f}'.format(
phase, epoch_loss, epoch_acc))
if phase == 'val' and epoch_acc > best_acc:
best_acc = epoch_acc
best_model = copy.deepcopy(model)
print()
time_elapsed = time.time() - since
print('Training complete in {:.0f}m {:.0f}s'.format(
time_elapsed // 60, time_elapsed % 60))
print('Best val Acc: {:4f}'.format(best_acc))
return best_model
```

def exp_lr_scheduler(optimizer, epoch, init_lr=0.001, lr_decay_epoch=7):

lr = init_lr * (0.1**(epoch // lr_decay_epoch))

```
if epoch % lr_decay_epoch == 0:
print('LR is set to {}'.format(lr))
for param_group in optimizer.param_groups:
param_group['lr'] = lr
return optimizer
```

model_ft = models.resnet18(pretrained=True)

num_ftrs = model_ft.fc.in_features

model_ft.fc = nn.Linear(num_ftrs, 10)

if use_gpu:

model_ft = model_ft.cuda()

criterion = nn.CrossEntropyLoss()

optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)

model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler,num_epochs=25)

#Error:

RuntimeError: Assertion `cur_target >= 0 && cur_target < n_classes’ failed. at /py/conda-bld/pytorch_1493676237139/work/torch/lib/THNN/generic/ClassNLLCriterion.c:57