PyTorch基础教程1线性模型（学不会来打我啊）
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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。- 函数
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 np
import matplotlib.pyplot as plt
x_data = [1.0, 2.0, 3.0]
y_data = [2.0, 4.0, 6.0]
def forward(x):
return x * w
def 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MSE
for 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loss
plt.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 36.0
MSE = 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 32.49
MSE = 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 29.160000000000004
MSE = 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 26.009999999999998
MSE = 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 23.04
MSE = 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 20.25
MSE = 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 17.639999999999993
MSE = 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 15.209999999999999
MSE = 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 12.959999999999997
MSE = 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 10.889999999999999
MSE = 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 9.0
MSE = 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 7.289999999999998
MSE = 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 5.759999999999997
MSE = 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 4.409999999999998
MSE = 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 3.2399999999999993
MSE = 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 2.25
MSE = 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 1.4399999999999984
MSE = 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 0.809999999999999
MSE = 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 0.3599999999999996
MSE = 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 0.0899999999999999
MSE = 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 0.0
MSE = 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 0.09000000000000043
MSE = 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 0.36000000000000065
MSE = 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 0.8100000000000006
MSE = 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 1.4400000000000026
MSE = 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 2.25
MSE = 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 3.2400000000000024
MSE = 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 4.410000000000006
MSE = 2.2866666666666693
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