[实现]单神经元/多项式拟合的随机梯度下降实现

import numpy as np
import math

np.random.seed(0)
# Create random input adn output data
x = np.linspace(-math.pi, math.pi, 2000)
y = np.sin(x)
# weights
a = np.random.randn()
b = np.random.randn()
c = np.random.randn()
d = np.random.randn()

learning_rate = 1e-6
for t in range(2100):
    y_pred = a + b * x + c * x**2 + d * x**3
    loss = np.square(y_pred - y).sum()
    if t % 100 == 99:
        print(t, loss)

    grad_y_pred = 2.0 * (y_pred - y)
    grad_a = grad_y_pred.sum()
    grad_b = (grad_y_pred * x).sum()
    grad_c = (grad_y_pred * x**2).sum()
    grad_d = (grad_y_pred * x**3).sum()

    a -= grad_a * learning_rate
    b -= grad_b * learning_rate
    c -= grad_c * learning_rate
    d -= grad_d * learning_rate

print(f"Result: y = {a} + {b} x + {c} x^2 + {d} x^3")