11

梯度下降/aa.py

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+import torch
+import torch.optim
+import matplotlib.pyplot as plt
+
+
+def f(x):
+      return x**2-3
+
+def df(x):
+      return 2*x
+def plotf(loss):
+      x = range(len(loss))
+      plt.plot(x,loss)
+      plt.xlabel('Iteration')
+      plt.ylabel('Loss')
+      plt.show()
+
+
+
+def main():
+      x = torch.tensor([15.],requires_grad=True)
+      optimizer = torch.optim.SGD([x,],lr = 0.1,momentum=0.9)
+      steps = 400
+      loss = []
+      for i in range(steps):
+            optimizer.zero_grad()
+            f(x).backward()
+            optimizer.step()
+            loss.append(f(x))
+            print(loss[i])
+      y = f(x)
+      print("函数最小值是： ",y)
+      plotf(loss)
+
+
+
+if __name__ == '__main__':
+    main()
+