Python f(x) & grad

s = [10.0] for _ in range(5): s.append(s[-1] * 0.98) s = [10.0, 9.8, 9.6] loss = lambda x: (x**2) + 20 grad = lambda x: x*2 w = 9.8 print(loss(w), grad(w)) 极值点, 导数为0, grad递减 At the extremum point, the derivative is zero, and the gradient is decreasing import matplotlib.pyplot as plt import numpy as np w = [10.0] for _ in range(100): w.append(w[-1] * 0.98) x = np.array(w) y = np.pow(x,2) + 20 y_grad = x * 2 plt.figure(figsize=(10, 6)) plt.xlim(10,0) plt.scatter(x, y, label = 'f(x)', color='blue') plt.scatter(x, y_grad, label = "grad(x)", color='red') plt.grid() plt.legend() plt.tight_layout() plt.show()