I am trying to calculate f-1 accuracy for to solve multi-class segmentation problem.I am using cross-entropy loss.
Below is my code -
def dice_test(loaders,model,criterion,use_cuda):
    running_loss = 0
    total_train = 0
    accuracy = 0
    for batch_idx ,(data,target) in enumerate(loaders):
        if use_cuda:
            data,target = data.cuda(),target.cuda()
        output = model(data)
        loss = criterion(output,target)
        test_loss += test_loss + ((1 / (batch_idx + 1)) * (loss.data - test_loss))
        
        _, predicted = torch.max(output.data, 1)   
        accuracy += f1_score(target,predicted>0.5,average = 'micro')
        total_train += target.nelement()
    print(accuracy/total_train)

But I am getting ValueError: unknown is not supported.
My dataset is highly unbalanced. Any other right way to measure accuracy is also welcomed.