完成实验三

Lab3/code/3.2.py

@@ -0,0 +1,63 @@
+import torch
+from utils import *
+
+
+class My_RMSprop:
+    def __init__(self, params: list[torch.Tensor], lr=1e-2, alpha=0.99, eps=1e-8):
+        self.params = params
+        self.lr = lr
+        self.alpha = alpha
+        self.eps = eps
+        self.square_avg = [torch.zeros_like(param.data) for param in params]
+        
+
+    def step(self):
+        with torch.no_grad():
+            for index, param in enumerate(self.params):
+                if param.grad is not None:
+                    self.square_avg[index] = self.alpha * self.square_avg[index] + (1 - self.alpha) * param.grad ** 2
+                    param.data = param.data - self.lr * param.grad / torch.sqrt(self.square_avg[index] + self.eps)
+
+    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_RMSprop(params=[params1], lr=1, alpha=0.5, eps=1e-8)
+    optim_sgd = torch.optim.RMSprop(params=[params2], lr=1, alpha=0.5, eps=1e-8)
+    my_sgd.zero_grad()
+    optim_sgd.zero_grad()
+
+    loss1 = 2 * params1.sum()
+    loss2 = 2 * params2.sum()
+    # 偏导为2
+    loss1.backward()
+    loss2.backward()
+    my_sgd.step()
+    optim_sgd.step()
+    # s = alpha * s + (1-alpha) * grad^2 = 0.5 * 0 + (1-0.5) * 2^2 = 2
+    # w = w - lr * grad * (s + eps)^0.5
+    # w[0] = 1 - 1 * 2 / (2 + 1e-8)^0.5 ~= -0.41
+    # w[1] = 2 - 1 * 2 / (2 + 1e-8)^0.5 ~= -0.59
+    print("My_RMSprop第1次反向传播结果：\n", params1.data)
+    print("torch.optim.RMSprop第1次反向传播结果：\n", params2.data)
+
+    my_sgd.zero_grad()
+    optim_sgd.zero_grad()
+    loss1 = -3 * params1.sum()
+    loss2 = -3 * params2.sum()
+    loss1.backward()
+    loss2.backward()
+    my_sgd.step()
+    optim_sgd.step()
+    # s = alpha * s + (1-alpha) * grad^2 = 0.5 * 2 + (1-0.5) * (-3)^2 = 5.5
+    # w - lr * grad * (s + eps)^0.5
+    # w[0] = -0.41 - 1 * -3 / (5.5 + 1e-8)^0.5 ~= 0.87
+    # w[1] = 0.59 - 1 * -3 / (5.5 + 1e-8)^0.5 ~= 1.86
+    print("My_RMSprop第2次反向传播结果：\n", params1.data)
+    print("torch.optim.RMSprop第2次反向传播结果：\n", params2.data)