Results of LS

[MrChenFeng]

Thanks for your great work. The results of Labels moothing reported in your paper is surprisingly high. I wonder do you reproduce such results or copied from existing works? If former, could you share the code of it?

Thanks a lot!

[lgcnsai]

We reproduced all label-smoothing experiments, not copied from existing works.
This is the code that we are using.

`class CrossEntropyLabelSmooth(nn.Module):

def __init__(self, num_classes, epsilon=0.1):
	super(CrossEntropyLabelSmooth, self).__init__()
	self.num_classes = num_classes
	self.epsilon = epsilon
	self.logsoftmax = nn.LogSoftmax(dim=1).cuda()

def forward(self, inputs, targets):
	"""
	Args:
		inputs: prediction matrix (before softmax) with shape (batch_size, num_classes)
		targets: ground truth labels with shape (num_classes)
	"""
	log_probs = self.logsoftmax(inputs)
	targets = torch.zeros_like(log_probs).scatter_(1, targets.unsqueeze(1), 1)
	targets = (1 - self.epsilon) * targets + self.epsilon / self.num_classes
	loss = (- targets * log_probs).mean(0).sum()
	return loss`