我的神經網絡沒有改善，為什么
[英]My Neural network is not improving why
問題描述
我的tensorflow程序沒有顯示任何改善。 有人請幫忙。 下面是代碼：
import tensorflow as tf
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
data = pd.read_csv("data.csv", sep='|')
data = data[41320:41335]
inputX = data.drop(['Name', 'md5', 'legitimate'], axis=1).as_matrix()
inputY = data['legitimate'].as_matrix()
inputY = inputY.reshape([-1,1])
This is the data for X. It has 52 features.
inputX = array([[ 3.32000000e+02, 2.24000000e+02, 8.45000000e+03,
8.00000000e+00, 0.00000000e+00, 5.32480000e+04,
1.63840000e+04, 0.00000000e+00, 5.40480000e+04,
4.09600000e+03, 5.73440000e+04, 2.08594534e+09,
4.09600000e+03, 4.09600000e+03, 4.00000000e+00,
0.00000000e+00, 8.00000000e+00, 0.00000000e+00,
4.00000000e+00, 0.00000000e+00, 7.37280000e+04,
4.09600000e+03, 1.20607000e+05, 2.00000000e+00,
3.20000000e+02, 1.04857600e+06, 4.09600000e+03,
1.04857600e+06, 4.09600000e+03, 0.00000000e+00,
1.60000000e+01, 4.00000000e+00, 2.70373594e+00,
1.05637876e+00, 6.22819008e+00, 1.63840000e+04,
4.09600000e+03, 5.32480000e+04, 1.59390000e+04,
9.92000000e+02, 5.28640000e+04, 6.00000000e+00,
1.37000000e+02, 8.10000000e+01, 2.50000000e+01,
1.00000000e+00, 3.52426821e+00, 3.52426821e+00,
3.52426821e+00, 8.92000000e+02, 8.92000000e+02,
8.92000000e+02, 7.20000000e+01, 1.60000000e+01],
[ 3.32000000e+02, 2.24000000e+02, 8.45000000e+03,
8.00000000e+00, 0.00000000e+00, 5.27360000e+04,
1.12640000e+04, 0.00000000e+00, 5.35300000e+04,
4.09600000e+03, 5.73440000e+04, 2.08699392e+09,
4.09600000e+03, 5.12000000e+02, 4.00000000e+00,
0.00000000e+00, 8.00000000e+00, 0.00000000e+00,
4.00000000e+00, 0.00000000e+00, 7.37280000e+04,
1.02400000e+03, 8.92300000e+04, 2.00000000e+00,
3.20000000e+02, 1.04857600e+06, 4.09600000e+03,
1.04857600e+06, 4.09600000e+03, 0.00000000e+00,
1.60000000e+01, 4.00000000e+00, 4.31899422e+00,
3.30769150e+00, 6.15499505e+00, 1.42080000e+04,
1.02400000e+03, 5.27360000e+04, 1.57382500e+04,
9.92000000e+02, 5.22730000e+04, 6.00000000e+00,
1.33000000e+02, 8.10000000e+01, 2.50000000e+01,
1.00000000e+00, 3.54207119e+00, 3.54207119e+00,
3.54207119e+00, 8.92000000e+02, 8.92000000e+02,
8.92000000e+02, 7.20000000e+01, 1.60000000e+01],
[ 3.32000000e+02, 2.24000000e+02, 8.45000000e+03,
8.00000000e+00, 0.00000000e+00, 4.09600000e+04,
2.04800000e+04, 0.00000000e+00, 2.66080000e+04,
4.09600000e+03, 4.50560000e+04, 1.92151552e+09,
4.09600000e+03, 4.09600000e+03, 4.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
4.00000000e+00, 0.00000000e+00, 6.55360000e+04,
4.09600000e+03, 1.21734000e+05, 2.00000000e+00,
3.20000000e+02, 1.04857600e+06, 4.09600000e+03,
1.04857600e+06, 4.09600000e+03, 0.00000000e+00,
1.60000000e+01, 5.00000000e+00, 3.58061262e+00,
8.04176679e-02, 6.23193618e+00, 1.22880000e+04,
4.09600000e+03, 4.09600000e+04, 1.04442000e+04,
9.64000000e+02, 3.76480000e+04, 2.00000000e+00,
6.80000000e+01, 0.00000000e+00, 1.12000000e+02,
6.00000000e+00, 3.00438262e+00, 2.40651198e+00,
3.59262288e+00, 6.10333333e+02, 1.24000000e+02,
1.41200000e+03, 7.20000000e+01, 1.60000000e+01],
[ 3.32000000e+02, 2.24000000e+02, 2.58000000e+02,
1.10000000e+01, 0.00000000e+00, 3.54816000e+05,
2.57024000e+05, 0.00000000e+00, 1.83632000e+05,
4.09600000e+03, 3.60448000e+05, 4.19430400e+06,
4.09600000e+03, 5.12000000e+02, 5.00000000e+00,
1.00000000e+00, 0.00000000e+00, 0.00000000e+00,
5.00000000e+00, 1.00000000e+00, 6.26688000e+05,
1.02400000e+03, 0.00000000e+00, 2.00000000e+00,
3.30880000e+04, 1.04857600e+06, 4.09600000e+03,
1.04857600e+06, 4.09600000e+03, 0.00000000e+00,
1.60000000e+01, 5.00000000e+00, 4.59039653e+00,
2.37894684e+00, 6.29682587e+00, 1.20524800e+05,
7.68000000e+03, 3.54816000e+05, 1.22148600e+05,
1.64680000e+04, 3.54799000e+05, 7.00000000e+00,
1.38000000e+02, 0.00000000e+00, 0.00000000e+00,
7.00000000e+00, 3.91441476e+00, 1.44168828e+00,
7.67709054e+00, 7.29842857e+03, 1.60000000e+01,
2.84380000e+04, 7.20000000e+01, 0.00000000e+00],
[ 3.32000000e+02, 2.24000000e+02, 2.71000000e+02,
6.00000000e+00, 0.00000000e+00, 2.40640000e+04,
1.64864000e+05, 1.02400000e+03, 1.25380000e+04,
4.09600000e+03, 2.86720000e+04, 4.19430400e+06,
4.09600000e+03, 5.12000000e+02, 4.00000000e+00,
0.00000000e+00, 6.00000000e+00, 0.00000000e+00,
4.00000000e+00, 0.00000000e+00, 2.41664000e+05,
1.02400000e+03, 0.00000000e+00, 2.00000000e+00,
3.27680000e+04, 1.04857600e+06, 4.09600000e+03,
1.04857600e+06, 4.09600000e+03, 0.00000000e+00,
1.60000000e+01, 5.00000000e+00, 4.10454072e+00,
0.00000000e+00, 6.44010555e+00, 6.75840000e+03,
0.00000000e+00, 2.40640000e+04, 4.62608000e+04,
3.14400000e+03, 1.54712000e+05, 8.00000000e+00,
1.55000000e+02, 1.00000000e+00, 0.00000000e+00,
6.00000000e+00, 3.19910735e+00, 1.97133529e+00,
5.21481585e+00, 4.52000000e+02, 3.40000000e+01,
9.58000000e+02, 0.00000000e+00, 1.50000000e+01],
[ 3.32000000e+02, 2.24000000e+02, 2.58000000e+02,
1.00000000e+01, 0.00000000e+00, 1.18784000e+05,
3.81952000e+05, 0.00000000e+00, 5.99140000e+04,
4.09600000e+03, 1.22880000e+05, 4.19430400e+06,
4.09600000e+03, 5.12000000e+02, 5.00000000e+00,
1.00000000e+00, 0.00000000e+00, 0.00000000e+00,
5.00000000e+00, 1.00000000e+00, 5.20192000e+05,
1.02400000e+03, 5.58287000e+05, 2.00000000e+00,
3.30880000e+04, 1.04857600e+06, 4.09600000e+03,
1.04857600e+06, 4.09600000e+03, 0.00000000e+00,
1.60000000e+01, 5.00000000e+00, 5.66240790e+00,
4.18369159e+00, 7.96187140e+00, 1.00147200e+05,
9.21600000e+03, 3.34848000e+05, 1.01559800e+05,
9.36800000e+03, 3.34440000e+05, 7.00000000e+00,
1.14000000e+02, 0.00000000e+00, 0.00000000e+00,
1.80000000e+01, 6.53094643e+00, 2.45849223e+00,
7.99268848e+00, 1.85234444e+04, 4.80000000e+01,
3.39450000e+04, 7.20000000e+01, 1.40000000e+01],
[ 3.32000000e+02, 2.24000000e+02, 2.58000000e+02,
1.00000000e+01, 0.00000000e+00, 1.74592000e+05,
3.00032000e+05, 0.00000000e+00, 1.17140000e+05,
4.09600000e+03, 1.80224000e+05, 4.19430400e+06,
4.09600000e+03, 5.12000000e+02, 5.00000000e+00,
1.00000000e+00, 0.00000000e+00, 0.00000000e+00,
5.00000000e+00, 1.00000000e+00, 4.87424000e+05,
1.02400000e+03, 5.13173000e+05, 2.00000000e+00,
3.30880000e+04, 1.04857600e+06, 4.09600000e+03,
1.04857600e+06, 4.09600000e+03, 0.00000000e+00,
1.60000000e+01, 5.00000000e+00, 5.73547047e+00,
4.75826034e+00, 7.36431335e+00, 9.30816000e+04,
1.53600000e+04, 1.92000000e+05, 9.46988000e+04,
2.15000000e+04, 1.91664000e+05, 1.10000000e+01,
2.54000000e+02, 1.50000000e+01, 0.00000000e+00,
1.50000000e+01, 5.73239307e+00, 2.85236422e+00,
7.98772639e+00, 1.27061333e+04, 1.18000000e+02,
6.05000000e+04, 7.20000000e+01, 1.40000000e+01],
[ 3.32000000e+02, 2.24000000e+02, 2.58000000e+02,
9.00000000e+00, 0.00000000e+00, 4.75648000e+05,
3.48672000e+05, 0.00000000e+00, 3.19769000e+05,
4.09600000e+03, 4.83328000e+05, 4.19430400e+06,
4.09600000e+03, 5.12000000e+02, 5.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
5.00000000e+00, 0.00000000e+00, 8.56064000e+05,
1.02400000e+03, 1.82072586e+09, 2.00000000e+00,
3.30880000e+04, 1.04857600e+06, 4.09600000e+03,
1.04857600e+06, 4.09600000e+03, 0.00000000e+00,
1.60000000e+01, 5.00000000e+00, 5.13993423e+00,
4.48079036e+00, 6.55814891e+00, 1.64864000e+05,
1.38240000e+04, 4.75648000e+05, 1.68145200e+05,
3.08400000e+04, 4.75580000e+05, 1.40000000e+01,
4.21000000e+02, 1.50000000e+01, 0.00000000e+00,
5.90000000e+01, 2.82782573e+00, 9.60953136e-01,
7.21232881e+00, 2.63703390e+03, 2.00000000e+01,
6.76240000e+04, 7.20000000e+01, 0.00000000e+00],
[ 3.32000000e+02, 2.24000000e+02, 2.59000000e+02,
9.00000000e+00, 0.00000000e+00, 1.57696000e+05,
6.24640000e+04, 0.00000000e+00, 6.70150000e+04,
4.09600000e+03, 1.63840000e+05, 4.19430400e+06,
4.09600000e+03, 5.12000000e+02, 5.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
5.00000000e+00, 0.00000000e+00, 2.33472000e+05,
1.02400000e+03, 2.72988000e+05, 2.00000000e+00,
3.30240000e+04, 1.04857600e+06, 4.09600000e+03,
1.04857600e+06, 4.09600000e+03, 0.00000000e+00,
1.60000000e+01, 4.00000000e+00, 4.81988481e+00,
2.97736539e+00, 6.48512410e+00, 5.50400000e+04,
3.58400000e+03, 1.57696000e+05, 5.56267500e+04,
6.70000000e+03, 1.57297000e+05, 2.00000000e+00,
7.60000000e+01, 0.00000000e+00, 0.00000000e+00,
1.30000000e+01, 3.94329633e+00, 1.81444345e+00,
6.12204520e+00, 2.70815385e+03, 1.32000000e+02,
9.64000000e+03, 7.20000000e+01, 1.40000000e+01],
[ 3.32000000e+02, 2.24000000e+02, 2.59000000e+02,
8.30000000e+01, 8.20000000e+01, 7.24992000e+05,
2.30604800e+06, 0.00000000e+00, 4.24345600e+06,
3.52256000e+06, 4.30899200e+06, 4.19430400e+06,
4.09600000e+03, 4.09600000e+03, 5.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
5.00000000e+00, 0.00000000e+00, 6.70924800e+06,
4.09600000e+03, 3.07704700e+06, 2.00000000e+00,
3.27680000e+04, 1.04857600e+06, 4.09600000e+03,
1.04857600e+06, 4.09600000e+03, 0.00000000e+00,
1.60000000e+01, 9.00000000e+00, 3.78312500e+00,
0.00000000e+00, 7.99951830e+00, 3.36782222e+05,
0.00000000e+00, 1.88416000e+06, 7.44182333e+05,
2.27200000e+03, 3.06129900e+06, 4.00000000e+00,
2.43000000e+02, 0.00000000e+00, 0.00000000e+00,
2.10000000e+01, 3.98746295e+00, 2.64215931e+00,
6.47369968e+00, 1.42880000e+04, 7.60000000e+01,
2.70376000e+05, 0.00000000e+00, 0.00000000e+00],
[ 3.32000000e+02, 2.24000000e+02, 2.58000000e+02,
1.00000000e+01, 0.00000000e+00, 1.20320000e+05,
3.85024000e+05, 0.00000000e+00, 6.15780000e+04,
4.09600000e+03, 1.26976000e+05, 4.19430400e+06,
4.09600000e+03, 5.12000000e+02, 5.00000000e+00,
1.00000000e+00, 0.00000000e+00, 0.00000000e+00,
5.00000000e+00, 1.00000000e+00, 5.28384000e+05,
1.02400000e+03, 5.66330000e+05, 2.00000000e+00,
3.30880000e+04, 1.04857600e+06, 4.09600000e+03,
1.04857600e+06, 4.09600000e+03, 0.00000000e+00,
1.60000000e+01, 5.00000000e+00, 5.64644365e+00,
4.11726412e+00, 7.96277585e+00, 1.01068800e+05,
9.72800000e+03, 3.30752000e+05, 1.02623800e+05,
9.40400000e+03, 3.39652000e+05, 3.00000000e+00,
8.90000000e+01, 0.00000000e+00, 0.00000000e+00,
6.00000000e+00, 3.72982391e+00, 2.45849223e+00,
5.31755236e+00, 2.73950000e+03, 4.80000000e+01,
9.64000000e+03, 7.20000000e+01, 1.50000000e+01],
[ 3.32000000e+02, 2.24000000e+02, 2.59000000e+02,
1.00000000e+01, 0.00000000e+00, 2.33984000e+05,
1.37779200e+06, 0.00000000e+00, 9.31200000e+04,
4.09600000e+03, 2.41664000e+05, 4.19430400e+06,
4.09600000e+03, 5.12000000e+02, 5.00000000e+00,
1.00000000e+00, 0.00000000e+00, 0.00000000e+00,
5.00000000e+00, 1.00000000e+00, 1.63020800e+06,
1.02400000e+03, 1.66150900e+06, 2.00000000e+00,
3.32800000e+04, 1.04857600e+06, 4.09600000e+03,
1.04857600e+06, 4.09600000e+03, 0.00000000e+00,
1.60000000e+01, 3.00000000e+00, 5.46068132e+00,
3.13962777e+00, 7.09009944e+00, 5.37258667e+05,
5.63200000e+03, 1.37216000e+06, 5.39602667e+05,
1.33160000e+04, 1.37185600e+06, 1.00000000e+00,
8.00000000e+01, 0.00000000e+00, 0.00000000e+00,
1.80000000e+01, 4.32832189e+00, 2.32321967e+00,
7.06841290e+00, 7.61582778e+04, 9.00000000e+00,
1.34273500e+06, 7.20000000e+01, 1.90000000e+01],
[ 3.32000000e+02, 2.24000000e+02, 2.71000000e+02,
6.00000000e+00, 0.00000000e+00, 4.91520000e+04,
5.61152000e+05, 0.00000000e+00, 3.38800000e+04,
4.09600000e+03, 5.32480000e+04, 4.19430400e+06,
4.09600000e+03, 4.09600000e+03, 4.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
4.00000000e+00, 0.00000000e+00, 6.14400000e+05,
4.09600000e+03, 0.00000000e+00, 2.00000000e+00,
0.00000000e+00, 1.04857600e+06, 4.09600000e+03,
1.04857600e+06, 4.09600000e+03, 0.00000000e+00,
1.60000000e+01, 4.00000000e+00, 3.69925758e+00,
0.00000000e+00, 6.48297395e+00, 1.94600000e+04,
1.60000000e+01, 4.91520000e+04, 1.50074000e+05,
1.60000000e+01, 5.48460000e+05, 4.00000000e+00,
1.19000000e+02, 1.00000000e+01, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00],
[ 3.32000000e+02, 2.24000000e+02, 2.58000000e+02,
1.00000000e+01, 0.00000000e+00, 2.91840000e+04,
4.45952000e+05, 1.68960000e+04, 1.48190000e+04,
4.09600000e+03, 3.68640000e+04, 4.19430400e+06,
4.09600000e+03, 5.12000000e+02, 5.00000000e+00,
0.00000000e+00, 6.00000000e+00, 0.00000000e+00,
5.00000000e+00, 0.00000000e+00, 1.76537600e+06,
1.02400000e+03, 5.94294000e+05, 2.00000000e+00,
3.41120000e+04, 1.04857600e+06, 4.09600000e+03,
1.04857600e+06, 4.09600000e+03, 0.00000000e+00,
1.60000000e+01, 6.00000000e+00, 3.76419176e+00,
0.00000000e+00, 6.47970818e+00, 7.93600000e+03,
0.00000000e+00, 2.91840000e+04, 2.92339333e+05,
2.53600000e+03, 1.28204800e+06, 8.00000000e+00,
1.71000000e+02, 1.00000000e+00, 0.00000000e+00,
6.00000000e+00, 3.15203588e+00, 2.16096405e+00,
5.21367450e+00, 3.54333333e+02, 2.00000000e+01,
7.44000000e+02, 0.00000000e+00, 0.00000000e+00],
[ 3.32000000e+02, 2.24000000e+02, 3.31670000e+04,
2.00000000e+00, 2.50000000e+01, 3.78880000e+04,
1.53600000e+04, 0.00000000e+00, 4.00000000e+04,
4.09600000e+03, 4.50560000e+04, 4.19430400e+06,
4.09600000e+03, 5.12000000e+02, 1.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
4.00000000e+00, 0.00000000e+00, 8.19200000e+04,
1.02400000e+03, 6.78554440e+07, 2.00000000e+00,
0.00000000e+00, 1.04857600e+06, 1.63840000e+04,
1.04857600e+06, 4.09600000e+03, 0.00000000e+00,
1.60000000e+01, 8.00000000e+00, 2.33301385e+00,
0.00000000e+00, 6.63664803e+00, 6.65600000e+03,
0.00000000e+00, 3.78880000e+04, 7.19800000e+03,
8.00000000e+00, 3.77320000e+04, 8.00000000e+00,
9.60000000e+01, 0.00000000e+00, 0.00000000e+00,
1.40000000e+01, 3.42918455e+00, 2.41356665e+00,
5.05007355e+00, 7.17142857e+02, 4.40000000e+01,
2.21600000e+03, 0.00000000e+00, 1.50000000e+01]])
This is the Y data. It shows the expected output for the 10 dataset.
inputY = array([ 1., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])
n_samples = inputY.size
n_feature = inputX.shape[1]
n_node = 500
display_step = 50
learning_rate=0.0001
# 4 hidden layer
n_nodes_hl1 = n_node
n_nodes_hl2 = n_node
n_nodes_hl3 = n_node
n_nodes_hl4 = n_node
n_output = 1 #number of output
x = tf.placeholder(tf.float32,[None,n_feature]) #feature
y = tf.placeholder(tf.float32,[None,n_output]) #label
x1 = tf.placeholder(tf.float32,[None,n_feature]) #feature
y1 = tf.placeholder(tf.float32,[None,n_output]) #label
# 4 layers Neural Network
hidden_1_layer = {'weights':tf.Variable(tf.random_normal([n_feature, n_nodes_hl1])),'biases':tf.Variable(tf.random_normal([n_nodes_hl1]))} #define some dictionary
hidden_2_layer = {'weights':tf.Variable(tf.zeros([n_nodes_hl1, n_nodes_hl2])),'biases':tf.Variable(tf.zeros([n_nodes_hl2]))}
hidden_3_layer = {'weights':tf.Variable(tf.zeros([n_nodes_hl2, n_nodes_hl3])),'biases':tf.Variable(tf.zeros([n_nodes_hl3]))}
hidden_4_layer = {'weights':tf.Variable(tf.zeros([n_nodes_hl3, n_nodes_hl4])),'biases':tf.Variable(tf.zeros([n_nodes_hl4]))}
output_layer = {'weights':tf.Variable(tf.zeros([n_nodes_hl4, n_output])), 'biases':tf.Variable(tf.zeros([n_output]))}
l1 = tf.add(tf.matmul(x,hidden_1_layer['weights']), hidden_1_layer['biases'])
l1 = tf.nn.relu(l1) #activation function
l2 = tf.add(tf.matmul(l1,hidden_2_layer['weights']), hidden_2_layer['biases'])
l2 = tf.nn.relu(l2) #activation function
l3 = tf.add(tf.matmul(l2,hidden_3_layer['weights']), hidden_3_layer['biases'])
l3 = tf.nn.relu(l3) #activation function
l4 = tf.add(tf.matmul(l3,hidden_4_layer['weights']), hidden_4_layer['biases'])
l4 = tf.nn.softmax(l4) #activation function
output = tf.matmul(l4,output_layer['weights']) + output_layer['biases']
output = tf.nn.softmax(output) #Classification function
cost = tf.reduce_sum(tf.pow(y - output, 2))/(2*n_samples)
optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cost)
hm_epochs = 1000
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
for i in range(hm_epochs):
# Take a gradient descent step using our inputs and labels
_,c = sess.run([optimizer,cost],feed_dict={x: inputX, y: inputY})
# sess.run(optimizer1, feed_dict={x1: inputX, y1: inputY})
epoch_loss = 0
# epoch_loss1 = 0
if (i) % display_step == 0:
# c1 = sess.run(cost1, feed_dict={x1: inputX, y1:inputY})
epoch_loss += c
# epoch_loss1 += c1
print "Training step:", i, "cost=", c, 'loss=', epoch_loss #, \"W=", sess.run(W), "b=", sess.run(b)
print "Optimization Finished!"
correct = tf.equal(tf.argmax(output,1),tf.argmax(y,1)) #compare the max value of prediction and y
accuracy = tf.reduce_mean(tf.cast(correct,'float'))
o1 = sess.run(output, feed_dict={x: inputX })
o1 = o1.reshape([1,-1])
org = inputY.reshape([1,-1])
print "Actual data : ", org, "\n 3 layer : ",o1, "\n"
----------------------------------------------------------------------------
Output
----------------------------------------------------------------------------
Training step: 0 cost= 0.4 loss= 0.40000000596
Training step: 50 cost= 0.4 loss= 0.40000000596
Training step: 100 cost= 0.4 loss= 0.40000000596
Training step: 150 cost= 0.4 loss= 0.40000000596
Training step: 200 cost= 0.4 loss= 0.40000000596
Training step: 250 cost= 0.4 loss= 0.40000000596
Training step: 300 cost= 0.4 loss= 0.40000000596
Training step: 350 cost= 0.4 loss= 0.40000000596
Training step: 400 cost= 0.4 loss= 0.40000000596
Training step: 450 cost= 0.4 loss= 0.40000000596
Training step: 500 cost= 0.4 loss= 0.40000000596
Training step: 550 cost= 0.4 loss= 0.40000000596
Training step: 600 cost= 0.4 loss= 0.40000000596
Training step: 650 cost= 0.4 loss= 0.40000000596
Training step: 700 cost= 0.4 loss= 0.40000000596
Training step: 750 cost= 0.4 loss= 0.40000000596
Training step: 800 cost= 0.4 loss= 0.40000000596
Training step: 850 cost= 0.4 loss= 0.40000000596
Training step: 900 cost= 0.4 loss= 0.40000000596
Training step: 950 cost= 0.4 loss= 0.40000000596
Optimization Finished!
Actual data : [[ 1. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]
3 layer : [[ 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]]
有人請給我一些好的建議。 也許我需要減少功能，但是如何減少。 或者我可以規范化數據集。 還是有更好的方法。 謝謝。
1 個解決方案
解決方案1
0 2017-03-07 09:43:44
要減少功能，可以使用主成分分析：
from sklearn.decomposition import PCA
pca_input = PCA(n_components = 3)
inputX_pca = pca_input.fit_transform(inputX)
請注意，您可以使用所需的列數，而不是3。 但是無論如何，它都會使數組具有它找到的相關特征的數量（如果有2個相關特征，即使您給出3，它也會使其具有2列）要進行規范化，可以使用以下2種方法之一：
from sklearn.preprocessing import StandardScaler, MinMaxScaler
所以：
inputX = MinMaxScaler().fit_transform(inputX)
要么：
inputX = StandardScaler().fit_transform(inputX)
改善簡單的1層神經網絡
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