def train_model(model, loss_fn, optimizer, lr_scheduler, num_epochs=25):
since= time.time()
best_model_wts = copy.deepcopy(model.state_dict())
best_acc = 0.0
for epoch in range(num_epochs):
print('Epoch {}/{}'.format(epoch, num_epochs -1))
print('-' * 10)
for phase in ['train', 'val']:
if phase == 'train':
model.train()
else:
model.eval()
running_loss = 0
running_corrects = 0
for inputs, outputs in dataloaders[phase]:
inputs = inputs.to(device)
outputs = outputs.to(device)
optimizer.zero_grad()
> with torch.set_grad_enabled(phase == 'train'):
probabilities = inputs _,predicions = torch.max(probabilities, 1) loss = loss_fn(probabilities, outputs)
if phase == 'train':
loss.backward()
optimizer.step()
running_loss += loss.items()*input.size(0)
running_corrects += torch.sum(predictions == output.data)
if phase == 'train':
lr_scheduler.step()
epoch_loss = running_loss / data_size[phase]
epoch_acc = running_corrects.double() / data_size[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_wts = copy.deepcopy(model.state_dict())
print()
time_elapsed = time.time() -since
print('Training complete in {:.Of}m {:.Of}s'.format(time_elapsed // 60, time_elapsed % 60))
print('Best val Acc: {:4f}'.format(best_acc))
model.load_state_dict(best_model_wts)
return model
I was working on this code and got that error I am unable to fine how to debug it.
The marked lines are where I am getting error.