feature: implement reg_noise

pytorch_optimizer/optimizer/utils.py

@@ -278,3 +278,19 @@ def reduce_max_except_dim(x: torch.Tensor, dim: int) -> torch.Tensor:
         if d != dim:
             x = x.max(dim=d, keepdim=True).values
     return x
+
+
+def reg_noise(
+    network1: nn.Module, network2: nn.Module, num_data: int, lr: float, eta: float = 8e-3, temperature: float = 1e-4
+) -> torch.Tensor | float:
+    reg_coef: float = 0.5 / (eta * num_data)
+    noise_coef: float = math.sqrt(2.0 / lr / num_data * temperature)
+
+    loss = 0
+    for param1, param2 in zip(network1.parameters(), network2.parameters(), strict=True):
+        reg = torch.sub(param1, param2).pow_(2) * reg_coef
+        noise1 = param1 * torch.randn_like(param1) * noise_coef
+        noise2 = param2 * torch.randn_like(param2) * noise_coef
+        loss += torch.sum(reg - noise1 - noise2)
+
+    return loss