implemented resnets and transfer learning

labml_nn/resnets/utils/labelsmoothing.py

@@ -0,0 +1,16 @@
+import torch.nn.functional as F
+from torch import nn
+
+class LabelSmoothingLoss(nn.Module):
+    def __init__(self, epsilon= 0.5, reduction='mean'):
+        super().__init__()
+        self.epsilon = epsilon
+        self.reduction = reduction
+
+    def forward(self, pred, target):
+        n = pred.size()[-1]
+        log_pred = F.log_softmax(pred, dim=-1)
+        loss = -log_pred.sum(dim=-1).mean()
+        nll = F.nll_loss(log_pred, target, reduction=self.reduction)
+        out = (1-self.epsilon)*nll + self.epsilon*(loss / n)
+        return out