UnboundLocalError: local variable ‘age_epoch_acc1’ referenced before assignment, I received.
I attempted to resolve this mistake by ensuring that a local variable is defined before assigning a value to it; I declared ‘age_epoch_acc1’, but it still displays the error.
I attempted to answer this mistake by reading other comparable queries and their solutions, but I was unable to do it.
Please provide any solutions to this problem.
This is my code.
‘’’
def train_model(model, dataloaders, optimizer, criterion1, criterion2, num_epochs):
since = time.time()
history = {
#'train_acc': [],
'train_loss': [],
'val_loss': [],
'age_accuracy': [],
'gender_accuracy': []
}
for epoch in range(num_epochs):
print("Epoch {}/{}".format(epoch, num_epochs-1))
print("-" * 10)
for phase in ["train", "test"]:
running_loss = 0.0
running_acc1 = 0.0
running_acc2 = 0.0
if phase == "train":
model.train()
else:
model.eval()
running_acc1 = 0
running_acc2 = 0
total_1 = 0
total_2 = 0
for inputs, label1, label2 in tqdm(dataloaders[phase]):
inputs = inputs.to(device)
label1 = label1.long()
label2 = label2.long()
label1 = label1.to(device)
label2 = label2.to(device)
optimizer.zero_grad()
with torch.autograd.set_grad_enabled(phase=="train"):
outputs = model(inputs)
loss1 = nn.functional.cross_entropy(outputs[0], label1, reduction='mean')
loss2 = nn.functional.cross_entropy(outputs[1], label2, reduction='mean')
loss = loss1 + loss2
if phase == "train":
loss.backward()
optimizer.step()
running_loss += loss.item() * inputs.size(0) #
epoch_loss = running_loss / len(dataloaders[phase].dataset)
print("{} Loss: {:.4f} Acc: {:.4f} Acc: {:.4f}".format(phase, epoch_loss, age_epoch_acc1, gender_epoch_acc2))
if phase == 'test':
_, preds1 = torch.max(outputs[0], 1)
_, preds2 = torch.max(outputs[1], 1)
running_acc1 += torch.sum(preds1 == label1.data)
running_acc2 += torch.sum(preds2 == label2.data)
total1 += label1.size(0)
total2 += label2.size(0)
age_epoch_acc1 = 100 * running_acc1 // total1 #<----- * Declare age_epoch_acc1 here*
gender_epoch_acc2 = 100 * running_acc2 // total2
history['age_accuracy'].append(age_epoch_acc1)
history['gender_accuracy'].append(gender_epoch_acc2)
history['val_loss'].append(epoch_loss)
if phase == 'train':
#history['train_acc'].append(epoch_acc)
history['train_loss'].append(epoch_loss)
print("{} {:.4f} Age_Acc: {:.4f} Gender_Acc: {:.4f}".format(phase, age_epoch_acc1, gender_epoch_acc2))
print()
time_elapsed = time.time() - since
print("Training compete in {:.0f}m {:.0f}s".format(time_elapsed // 60, time_elapsed % 60))
#print("Best val Acc: {:4f}".format(best_acc))
return model, history
'''
Thank you in advance.
