homework1——y = w*x + b

线性模型课后作业

函数：$y=w\ast x+b$

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D


# y = w*x + b
# 创建输入输出数据
x_data = np.random.randint(1, 10, size=(10))
# bias = np.random.rand(10)
bias = np.random.rand(1)
y_data = x_data * 2 + bias

print("x_data=", x_data)
print("y_data=", y_data)
print("b=", bias)


def forward(x):
    return x * w + b

def loss(y, y_pred):
    return (y_pred - y) * (y_pred - y)

w_list = []
b_list = []
mse_list = np.zeros((51,51), dtype=float)   # 创建二维array，方便后面画图

for i, w in enumerate(np.arange(0.0, 5.1, 0.1)):
    for j, b in enumerate(np.arange(-1.0, 1.04, 0.04)):
        print(f"w={w}, b={b}")
        l_sum = 0   # 每一次遍历数据集得到的loss和，初始化为0
        for x_val, y_val in zip(x_data, y_data):
            y_pred = forward(x_val)     # 预测
            loss_y = loss(y_val, y_pred)   # 计算loss
            l_sum += loss_y
            print("\t", x_val, y_val, y_pred, loss_y)
        mse_list[i][j] = l_sum / 10    # MSE
        if w==0.0:
            b_list.append(b)   # bias列表
    w_list.append(w)    # w列表


w_list, b_list = np.meshgrid(w_list, b_list)   # 创建网格
mse_list = np.array(mse_list)    # 将list转为array

print("x_data=", x_data)
print("y_data=", y_data)
print("b=", bias)


# 画图
fig = plt.figure()
ax = Axes3D(fig)
ax.plot_surface(w_list, b_list, mse_list)
plt.show()