why normalize_weight need div torch.mean(x)?

[alpc91]

def normalize_weight(x):
min_val = x.min()
max_val = x.max()
x = (x - min_val) / (max_val - min_val)
x = x / torch.mean(x)
return x.detach()

according to paper, x in (0,1), why normalize_weight need div torch.mean(x)?

[alpc91]

ce = nn.CrossEntropyLoss(reduction='none')(predict_prob_source, label_source)
And why feed after_sotmax(predict_prob_source) to nn.CrossEntropyLoss? This criterion has combined LogSoftmax and NLLLoss.