完成实验三

Lab3/code/2.1.py

@@ -0,0 +1,49 @@
+import torch
+from utils import *
+
+
+class My_SGD:
+    def __init__(self, params: list[torch.Tensor], lr: float, weight_decay=0.0):
+        self.params = params
+        self.lr = lr
+        self.weight_decay = weight_decay
+
+    def step(self):
+        with torch.no_grad():
+            for param in self.params:
+                if param.grad is not None:
+                    if len(param.data.shape) > 1:
+                        param.data = param.data - self.lr * (param.grad + self.weight_decay * param.data)
+                    else:
+                        param.data = param.data - self.lr * param.grad
+
+    def zero_grad(self):
+        for param in self.params:
+            if param.grad is not None:
+                param.grad.data = torch.zeros_like(param.grad.data)
+
+
+if __name__ == "__main__":
+    params1 = torch.tensor([[1.0, 2.0]], requires_grad=True)
+    params2 = torch.tensor([[1.0, 2.0]], requires_grad=True)
+
+    my_sgd = My_SGD(params=[params1], lr=0.5, weight_decay=0.1)
+    optim_sgd = torch.optim.SGD(params=[params2], lr=0.5, weight_decay=0.1)
+    my_sgd.zero_grad()
+    optim_sgd.zero_grad()
+
+    loss1 = 2 * params1.sum()
+    loss2 = 2 * params2.sum()
+    # 偏导为2
+    loss1.backward()
+    loss2.backward()
+    print("params1的梯度为：\n", params1.grad.data)
+    print("params2的梯度为：\n", params2.grad.data)
+
+    my_sgd.step()
+    optim_sgd.step()
+    # 结果为：w - lr * grad - lr * weight_decay_rate * w
+    # w[0] = 1 - 0.5 * 2 - 0.5 * 0.1 * 1 = -0.0500
+    # w[1] = 2 - 0.5 * 2 - 0.5 * 0.1 * 2 = 0.9000
+    print("经过L_2正则化后的My_SGD反向传播结果：\n", params1.data)
+    print("经过L_2正则化后的torch.optim.SGD反向传播结果：\n", params2.data)