Question

我正在尝试实现梯度下降算法，以最小化ML类最适合的线的参数。我正在最小化成本函数。这就是我所拥有的：

以下是数据：

    year    dipnet days fished  dipnet sockeye harvest
0   1996    10503   102821
1   1997    11023   114619
2   1998    10802   103847
3   1999    13738   149504
4   2000    12354   98262
5   2001    14772   150766
6   2002    14840   180028
7   2003    15263   223580
8   2004    18513   262831
9   2005    20977   295496
10  2006    12685   127630
11  2007    21908   291270
12  2008    20772   234109
13  2009    26171   339993
14  2010    28342   389552
15  2011    32818   537765
16  2012    34374   526992
17  2013    33193   347222
18  2014    36380   379823

和代码......

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

data = pd.read_csv("D:/Assignment 1/Exercise1/dip-har-eff.csv")
data.head()

    year days fished sockeye harvest
0   1996    10503   102821
1   1997    11023   114619
2   1998    10802   103847
3   1999    13738   149504
4   2000    12354   98262

np_data = data.values
harvest = np_data[:, 2]
days = np_data[:, 1]

plt.scatter(days, harvest)

start = np.array([0,0])         #the starting values for b_0 and b_1
step = .01                      #the gradient multiplier
iterations = 30                 #number of iteration for the algorithm
batch_size = 3                 #the batch size

X = days[0:batch_size]
Y = harvest[0:batch_size]

def del_cost(b_0, b_1):
    error_x = []
    error_y = []
    for i in range(0, batch_size):
        e = (b_1*X[i] + b_0) - Y[i]
        error_x.append(e)
        f = ((b_1*X[i] + b_0) - Y[i])*X[i]
        error_y.append(f)
    d_x = (1/batch_size)*np.sum(error_x)
    d_y = (1/batch_size)*np.sum(error_y)
    return np.array([d_x, d_y])

for i in range(iterations):
    temp = start
    start = start - step*(del_cost(temp[0], temp[1]))
    print(start[0])
    print(start[1])

输出是......

1070.95666667
11550431.6467
-1244672383.04
-1.3417834015e+13
1.44590456124e+15
1.55871598694e+19
-1.67967091608e+21
-1.81072110836e+25
1.9512314035e+27
2.10346911158e+31
-2.26669638292e+33
-2.44354709455e+37
2.63316410506e+39
2.83860712307e+43
-3.05888042899e+45
-3.29753840927e+49
3.55342436154e+51
3.83066732703e+55
-4.12792359372e+57
-4.44998976483e+61
4.79530488393e+63
5.16944104421e+67
-5.57058492188e+69
-6.00520947728e+73
6.47120821783e+75
6.97610061854e+79
-7.51743962004e+81
-8.10396040706e+85
8.73282029238e+87
9.41416672011e+91
-1.01446974121e+94
-1.09362003986e+98
1.17848395063e+100
1.27043085931e+104
-1.36901512729e+106
-1.47582753557e+110
1.590350397e+112
1.7144316818e+116
-1.84747000587e+118
-1.99161210964e+122
2.14615938035e+124
2.31360563233e+128
-2.4931393047e+130
-2.68765739877e+134
2.8962171447e+136
3.12218391597e+140
-3.36446252059e+142
-3.62696242817e+146
3.90841138178e+148
4.21335091378e+152
-4.54030307537e+154
-4.89454365031e+158
5.27435574267e+160
5.68586809762e+164
-6.12708624038e+166
-6.60512977987e+170
7.11768178497e+172
7.67301292606e+176
-8.26843168262e+178
-8.91354588413e+182

我不知道为什么参数是1）增长而不是朝向分钟，2）每次都交替出现。我已经手动检查了前几次迭代的计算，它们是正确的。我无法弄清楚出了什么问题，请帮助!!

Answer 1

您的步骤参数太大。你需要减少很多。尝试使用0.001或0.0001

等值

Answer 2

缩放数据也可能有所帮助。您可以使用scikit来学习。

此外，减小步长并查看。

梯度下降算法不收敛于线性回归

2 个答案: