摘要：文章目录一线性模型二绘图工具三作业一线性模型不要小看简单线性模型哈哈，虽然这讲我们还没正式用到，但是用到的前向传播损失函数两种绘图等方法在后面是很常用的。
不要小看简单线性模型哈哈，虽然这讲我们还没正式用到pytorch，但是用到的前向传播、损失函数、两种绘loss图等方法在后面是很常用的。
对下面的代码说明：
zip函数可以将x_data和y_data组合元组列表，在for循环中每次遍历就是对于列表中的每个元组。forward()中，有一个变量w。这个变量最终的值是从for循环中传入的。# -*- coding: utf-8 -*-"""Created on Tue Oct 12 14:30:13 2021@author: 86493"""import numpy as npimport matplotlib.pyplot as pltx_data = [1.0, 2.0, 3.0]y_data = [2.0, 4.0, 6.0]def forward(x): return x * wdef loss(x, y): y_pred = forward(x) return (y_pred - y) * (y_pred - y)# 保存权重w_list = []# 保存权重的损失函数值mse_list = []# 穷举w值对应的损失函数MSEfor w in np.arange(0.0, 4.1, 0.1): print("w = ", w) loss_sum = 0 for x_val, y_val in zip(x_data, y_data): # 为了打印y预测值，其实loss里也计算了 y_pred_val = forward(x_val) loss_val = loss(x_val, y_val) loss_sum += loss_val print("/t", x_val, y_val, y_pred_val, loss_val) print("MSE = ", loss_sum / 3) print("="*60) w_list.append(w) mse_list.append(loss_sum / 3) # 绘loss变化图，横坐标是w，纵坐标是lossplt.plot(w_list, mse_list)plt.ylabel("Loss")plt.xlabel("w")plt.show()刚才对应的打印结果为：
w = 0.0 1.0 2.0 0.0 4.0 2.0 4.0 0.0 16.0 3.0 6.0 0.0 36.0MSE = 18.666666666666668============================================================w = 0.1 1.0 2.0 0.1 3.61 2.0 4.0 0.2 14.44 3.0 6.0 0.30000000000000004 32.49MSE = 16.846666666666668============================================================w = 0.2 1.0 2.0 0.2 3.24 2.0 4.0 0.4 12.96 3.0 6.0 0.6000000000000001 29.160000000000004MSE = 15.120000000000003============================================================w = 0.30000000000000004 1.0 2.0 0.30000000000000004 2.8899999999999997 2.0 4.0 0.6000000000000001 11.559999999999999 3.0 6.0 0.9000000000000001 26.009999999999998MSE = 13.486666666666665============================================================w = 0.4 1.0 2.0 0.4 2.5600000000000005 2.0 4.0 0.8 10.240000000000002 3.0 6.0 1.2000000000000002 23.04MSE = 11.946666666666667============================================================w = 0.5 1.0 2.0 0.5 2.25 2.0 4.0 1.0 9.0 3.0 6.0 1.5 20.25MSE = 10.5============================================================w = 0.6000000000000001 1.0 2.0 0.6000000000000001 1.9599999999999997 2.0 4.0 1.2000000000000002 7.839999999999999 3.0 6.0 1.8000000000000003 17.639999999999993MSE = 9.146666666666663============================================================w = 0.7000000000000001 1.0 2.0 0.7000000000000001 1.6899999999999995 2.0 4.0 1.4000000000000001 6.759999999999998 3.0 6.0 2.1 15.209999999999999MSE = 7.886666666666666============================================================w = 0.8 1.0 2.0 0.8 1.44 2.0 4.0 1.6 5.76 3.0 6.0 2.4000000000000004 12.959999999999997MSE = 6.719999999999999============================================================w = 0.9 1.0 2.0 0.9 1.2100000000000002 2.0 4.0 1.8 4.840000000000001 3.0 6.0 2.7 10.889999999999999MSE = 5.646666666666666============================================================w = 1.0 1.0 2.0 1.0 1.0 2.0 4.0 2.0 4.0 3.0 6.0 3.0 9.0MSE = 4.666666666666667============================================================w = 1.1 1.0 2.0 1.1 0.8099999999999998 2.0 4.0 2.2 3.2399999999999993 3.0 6.0 3.3000000000000003 7.289999999999998MSE = 3.779999999999999============================================================w = 1.2000000000000002 1.0 2.0 1.2000000000000002 0.6399999999999997 2.0 4.0 2.4000000000000004 2.5599999999999987 3.0 6.0 3.6000000000000005 5.759999999999997MSE = 2.986666666666665============================================================w = 1.3 1.0 2.0 1.3 0.48999999999999994 2.0 4.0 2.6 1.9599999999999997 3.0 6.0 3.9000000000000004 4.409999999999998MSE = 2.2866666666666657============================================================w = 1.4000000000000001 1.0 2.0 1.4000000000000001 0.3599999999999998 2.0 4.0 2.8000000000000003 1.4399999999999993 3.0 6.0 4.2 3.2399999999999993MSE = 1.6799999999999995============================================================w = 1.5 1.0 2.0 1.5 0.25 2.0 4.0 3.0 1.0 3.0 6.0 4.5 2.25MSE = 1.1666666666666667============================================================w = 1.6 1.0 2.0 1.6 0.15999999999999992 2.0 4.0 3.2 0.6399999999999997 3.0 6.0 4.800000000000001 1.4399999999999984MSE = 0.746666666666666============================================================w = 1.7000000000000002 1.0 2.0 1.7000000000000002 0.0899999999999999 2.0 4.0 3.4000000000000004 0.3599999999999996 3.0 6.0 5.1000000000000005 0.809999999999999MSE = 0.4199999999999995============================================================w = 1.8 1.0 2.0 1.8 0.03999999999999998 2.0 4.0 3.6 0.15999999999999992 3.0 6.0 5.4 0.3599999999999996MSE = 0.1866666666666665============================================================w = 1.9000000000000001 1.0 2.0 1.9000000000000001 0.009999999999999974 2.0 4.0 3.8000000000000003 0.0399999999999999 3.0 6.0 5.7 0.0899999999999999MSE = 0.046666666666666586============================================================w = 2.0 1.0 2.0 2.0 0.0 2.0 4.0 4.0 0.0 3.0 6.0 6.0 0.0MSE = 0.0============================================================w = 2.1 1.0 2.0 2.1 0.010000000000000018 2.0 4.0 4.2 0.04000000000000007 3.0 6.0 6.300000000000001 0.09000000000000043MSE = 0.046666666666666835============================================================w = 2.2 1.0 2.0 2.2 0.04000000000000007 2.0 4.0 4.4 0.16000000000000028 3.0 6.0 6.6000000000000005 0.36000000000000065MSE = 0.18666666666666698============================================================w = 2.3000000000000003 1.0 2.0 2.3000000000000003 0.09000000000000016 2.0 4.0 4.6000000000000005 0.36000000000000065 3.0 6.0 6.9 0.8100000000000006MSE = 0.42000000000000054============================================================w = 2.4000000000000004 1.0 2.0 2.4000000000000004 0.16000000000000028 2.0 4.0 4.800000000000001 0.6400000000000011 3.0 6.0 7.200000000000001 1.4400000000000026MSE = 0.7466666666666679============================================================w = 2.5 1.0 2.0 2.5 0.25 2.0 4.0 5.0 1.0 3.0 6.0 7.5 2.25MSE = 1.1666666666666667============================================================w = 2.6 1.0 2.0 2.6 0.3600000000000001 2.0 4.0 5.2 1.4400000000000004 3.0 6.0 7.800000000000001 3.2400000000000024MSE = 1.6800000000000008============================================================w = 2.7 1.0 2.0 2.7 0.49000000000000027 2.0 4.0 5.4 1.960000000000001 3.0 6.0 8.100000000000001 4.410000000000006MSE = 2.2866666666666693==========================================
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