完成实验三

Lab3/code/3.3.py

@@ -0,0 +1,64 @@
+import torch
+from utils import *
+
+
+class My_Adam:
+    def __init__(self, params: list[torch.Tensor], lr=1e-3, betas=(0.9, 0.999), eps=1e-8):
+        self.params = params
+        self.lr = lr
+        self.beta1 = betas[0]
+        self.beta2 = betas[1]
+        self.eps = eps
+        self.t = 0
+        self.momentums = [torch.zeros_like(param.data) for param in params]
+        self.velocities = [torch.zeros_like(param.data) for param in params]
+
+    def step(self):
+        self.t += 1
+        with torch.no_grad():
+            for index, param in enumerate(self.params):
+                if param.grad is not None:
+                    self.momentums[index] = (self.beta1 * self.momentums[index] + (1 - self.beta1) * param.grad)
+                    self.velocities[index] = (self.beta2 * self.velocities[index] + (1 - self.beta2) * param.grad ** 2)
+
+                    momentums_hat = self.momentums[index] / (1 - self.beta1 ** self.t)
+                    velocities_hat = self.velocities[index] / (1 - self.beta2 ** self.t)
+
+                    param.data = param.data - self.lr * momentums_hat / (torch.sqrt(velocities_hat) + 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_Adam(params=[params1], lr=1, betas=(0.5, 0.5), eps=1e-8)
+    optim_sgd = torch.optim.Adam(params=[params2], lr=1, betas=(0.5, 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()
+    print("My_Adam第1次反向传播结果：\n", params1.data)
+    print("torch.optim.Adam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()
+    # 偏导为-3
+    loss1.backward()
+    loss2.backward()
+    my_sgd.step()
+    optim_sgd.step()
+    print("My_Adam第2次反向传播结果：\n", params1.data)
+    print("torch.optim.Adam第2次反向传播结果：\n", params2.data)