diff --git a/MachineLearning/DeepLearning/LossFunction/src/label_smoothing.py b/MachineLearning/DeepLearning/LossFunction/src/label_smoothing.py
new file mode 100644
index 0000000..4fee587
--- /dev/null
+++ b/MachineLearning/DeepLearning/LossFunction/src/label_smoothing.py
@@ -0,0 +1,75 @@
+import torch
+import numpy as np
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.autograd import Variable
+from torch.nn.modules.loss import _WeightedLoss
+
+
+
+class LabelSmoothingLoss(nn.Module):
+    def __init__(self, classes, smoothing=0.0, dim=-1, weight = None):
+        """if smoothing == 0, it's one-hot method
+           if 0 < smoothing < 1, it's smooth method
+        """
+        super(LabelSmoothingLoss, self).__init__()
+        self.confidence = 1.0 - smoothing
+        self.smoothing = smoothing
+        self.weight = weight
+        self.cls = classes
+        self.dim = dim
+
+    def forward(self, pred, target):
+        assert 0 <= self.smoothing < 1
+        pred = pred.log_softmax(dim=self.dim)
+
+        if self.weight is not None:
+            pred = pred * self.weight.unsqueeze(0)   
+
+        with torch.no_grad():
+            true_dist = torch.zeros_like(pred)
+            true_dist.fill_(self.smoothing / (self.cls - 1))
+            true_dist.scatter_(1, target.data.unsqueeze(1), self.confidence)
+        return torch.mean(torch.sum(-true_dist * pred, dim=self.dim))
+
+
+
+class LabelSmoothing(nn.Module):
+    """NLL loss with label smoothing.
+    """
+    def __init__(self, smoothing=0.0):
+        """Constructor for the LabelSmoothing module.
+        :param smoothing: label smoothing factor
+        """
+        super(LabelSmoothing, self).__init__()
+        self.confidence = 1.0 - smoothing
+        self.smoothing = smoothing
+
+    def forward(self, x, target):
+        logprobs = nn.functional.log_softmax(x, dim=-1)
+        nll_loss = -logprobs.gather(dim=-1, index=target.unsqueeze(1))
+        nll_loss = nll_loss.squeeze(1)
+        smooth_loss = -logprobs.mean(dim=-1)
+        loss = self.confidence * nll_loss + self.smoothing * smooth_loss
+        return loss.mean()
+
+
+if __name__=="__main__":
+    # Label smoothing (1)
+    crit = LabelSmoothingLoss(smoothing=0.3)
+    predict = torch.FloatTensor([[0, 0.2, 0.7, 0.1, 0],
+                                 [0, 0.9, 0.2, 0.2, 1], 
+                                 [1, 0.2, 0.7, 0.9, 1]])
+
+    v = crit(Variable(predict),
+             Variable(torch.LongTensor([2, 1, 0])))
+    print(v)
+
+    # Label smoothing (2)
+    crit = LabelSmoothing(smoothing=0.3)
+    predict = torch.FloatTensor([[0, 0.2, 0.7, 0.1, 0],
+                                 [0, 0.9, 0.2, 0.2, 1], 
+                                 [1, 0.2, 0.7, 0.9, 1]])
+    v = crit(Variable(predict),
+             Variable(torch.LongTensor([2, 1, 0])))
+    print(v)