我为一组参数设置了网格搜索。我正在尝试为进行二进制分类的Keras神经网络找到最佳参数。输出为1或0。大约有200个要素。当我进行网格搜索时，我得到了一堆模型及其参数。最佳模型具有以下参数：
Epochs : 20
Batch Size : 10
First Activation : sigmoid
Learning Rate : 1
First Init : uniform该模型的结果是：
loss acc val_loss val_acc
1 0.477424 0.768542 0.719960 0.722550
2 0.444588 0.788861 0.708650 0.732130
3 0.435809 0.794336 0.695768 0.732682
4 0.427056 0.798784 0.684516 0.721137
5 0.420828 0.803048 0.703748 0.720707
6 0.418129 0.806206 0.730803 0.723717
7 0.417522 0.805206 0.778434 0.721936
8 0.415197 0.807549 0.802040 0.733849
9 0.412922 0.808865 0.823036 0.731761
10 0.410463 0.810654 0.839087 0.730410
11 0.407369 0.813892 0.831844 0.725252
12 0.404436 0.815760 0.835217 0.723102
13 0.401728 0.816287 0.845178 0.722488
14 0.399623 0.816471 0.842231 0.717514
15 0.395746 0.819498 0.847118 0.719541
16 0.393361 0.820366 0.858291 0.714873
17 0.390947 0.822025 0.850880 0.723348
18 0.388478 0.823341 0.858591 0.721014
19 0.387062 0.822735 0.862971 0.721936
20 0.383744 0.825762 0.880477 0.721322因此，我以更多的时期（其中有150个）重新运行了该模型，这些是我得到的结果。我不确定为什么会这样，这是正常现象还是我在做什么错？
loss acc val_loss val_acc
1 0.476387 0.769279 0.728492 0.722550
2 0.442604 0.789941 0.701136 0.730472
3 0.431936 0.796915 0.676995 0.723655
4 0.426349 0.800258 0.728562 0.721997
5 0.421143 0.803653 0.739789 0.716900
6 0.416389 0.807575 0.720850 0.711373
7 0.413163 0.809154 0.751340 0.718128
8 0.409013 0.811418 0.780856 0.723409
9 0.405871 0.813576 0.789046 0.719295
10 0.402579 0.815524 0.804526 0.720278
11 0.400152 0.816813 0.811905 0.719541
12 0.400304 0.817261 0.787449 0.713154
13 0.397917 0.817945 0.804222 0.721567
14 0.395266 0.819524 0.801722 0.723348
15 0.393957 0.820156 0.793889 0.719049
16 0.391780 0.821103 0.794179 0.721199
17 0.390206 0.822393 0.806803 0.722611
18 0.388075 0.823604 0.817850 0.723901
19 0.385985 0.824762 0.841883 0.722058
20 0.383762 0.826867 0.857071 0.720830
21 0.381493 0.827947 0.864432 0.718005
22 0.379520 0.829210 0.872835 0.720400
23 0.377488 0.830526 0.879962 0.721383
24 0.375619 0.830736 0.887850 0.723839
25 0.373684 0.832000 0.891267 0.724822
26 0.372023 0.832368 0.891562 0.724638
27 0.370155 0.833184 0.892528 0.724883
28 0.368511 0.834684 0.887061 0.724699
29 0.366522 0.835606 0.883541 0.724883
30 0.364500 0.836422 0.882823 0.724515
31 0.362612 0.836737 0.882611 0.722427
32 0.360742 0.837448 0.884282 0.720769
33 0.359093 0.838738 0.884339 0.719418
34 0.357436 0.839080 0.888006 0.716470
35 0.355723 0.840633 0.892658 0.713830
36 0.354305 0.840764 0.897303 0.710575
37 0.352758 0.841343 0.901147 0.709408
38 0.351414 0.842054 0.899546 0.707934
39 0.349619 0.843370 0.905133 0.704864
40 0.347993 0.844475 0.910400 0.701363
41 0.346402 0.845581 0.915086 0.699337
42 0.345014 0.845818 0.918697 0.697617
43 0.343708 0.846607 0.923413 0.695652
44 0.342335 0.847292 0.930816 0.693441
45 0.340745 0.848081 0.940737 0.689020
46 0.339623 0.848713 0.948633 0.685274
47 0.338846 0.849845 0.952492 0.683923
48 0.337724 0.850134 0.961147 0.683984
49 0.336247 0.850976 0.967792 0.683309
50 0.334444 0.851529 0.984107 0.680238
51 0.333086 0.852029 1.001179 0.678273
52 0.331756 0.853240 1.016130 0.674589
53 0.330738 0.854003 1.024875 0.673606
54 0.329548 0.854030 1.040597 0.670044
55 0.328813 0.855372 1.041871 0.668509
56 0.327120 0.855898 1.050617 0.668755
57 0.325962 0.855819 1.064525 0.666667
58 0.324602 0.856898 1.078078 0.662859
59 0.323560 0.857241 1.085016 0.661938
60 0.322243 0.858662 1.093114 0.661140
61 0.320680 0.858872 1.117269 0.656841
62 0.319267 0.860004 1.138825 0.654815
63 0.318132 0.860636 1.154959 0.653648
64 0.316956 0.861531 1.180216 0.649718
65 0.315543 0.862320 1.198216 0.648428
66 0.314405 0.862610 1.218663 0.647384
67 0.313501 0.863873 1.245123 0.644252
68 0.312513 0.864558 1.262998 0.643147
69 0.311567 0.865347 1.283213 0.641918
70 0.310069 0.866505 1.302089 0.640752
71 0.309087 0.866611 1.318972 0.641857
72 0.307767 0.867321 1.361531 0.638787
73 0.306750 0.866742 1.382162 0.638357
74 0.305760 0.867242 1.378694 0.641611
75 0.305289 0.867769 1.393187 0.642594
76 0.304089 0.868479 1.435852 0.635532
77 0.302472 0.869006 1.435019 0.639892
78 0.301118 0.869400 1.447060 0.639216
79 0.300629 0.870058 1.488730 0.634918
80 0.299364 0.870295 1.488376 0.636576
81 0.298380 0.870822 1.504260 0.634611
82 0.297253 0.871664 1.525655 0.634058
83 0.296760 0.871875 1.538717 0.632891
84 0.295502 0.872585 1.551178 0.633751
85 0.294569 0.872927 1.562323 0.633137
86 0.294780 0.872585 1.555390 0.629944
87 0.293796 0.872743 1.587800 0.627057
88 0.293029 0.873427 1.608010 0.627549
89 0.291822 0.874006 1.626047 0.627303
90 0.290643 0.874533 1.651658 0.626689
91 0.289920 0.875270 1.681202 0.623925
92 0.289661 0.875375 1.683188 0.626505
93 0.288103 0.876323 1.706517 0.625031
94 0.287917 0.876770 1.722031 0.624417
95 0.287020 0.877270 1.743283 0.624478
96 0.286750 0.877639 1.762506 0.624048
97 0.285712 0.877481 1.780433 0.622267
98 0.284635 0.878639 1.789917 0.622206
99 0.283627 0.879191 1.862468 0.616925
100 0.282214 0.879455 1.915643 0.612810
101 0.281749 0.879244 1.881444 0.615205
102 0.281710 0.879639 1.916390 0.614223
103 0.280293 0.880350 1.938470 0.612810
104 0.279233 0.881008 1.979127 0.609187
105 0.279204 0.880297 1.997384 0.606546
106 0.278264 0.881876 2.009851 0.607652
107 0.277511 0.882876 2.038530 0.606116
108 0.277521 0.881771 2.034664 0.604888
109 0.276264 0.882534 2.058179 0.604827
110 0.275230 0.883587 2.078912 0.604274
111 0.275147 0.883034 2.073272 0.603537
112 0.273717 0.883797 2.100150 0.600958
113 0.273372 0.883692 2.114416 0.601634
114 0.272626 0.883692 2.129778 0.601941
115 0.272001 0.883929 2.138462 0.601326
116 0.271344 0.884508 2.148771 0.602923
117 0.270134 0.884692 2.115114 0.604581
118 0.269494 0.885140 2.135719 0.603107
119 0.268803 0.885587 2.162380 0.601695
120 0.268593 0.886219 2.183793 0.599239
121 0.267141 0.886035 2.195810 0.600221
122 0.266565 0.886772 2.192426 0.600528
123 0.265715 0.886561 2.260088 0.596598
124 0.264788 0.887693 2.253029 0.597335
125 0.263643 0.887693 2.289285 0.597028
126 0.263612 0.887956 2.311600 0.596536
127 0.261996 0.888588 2.339754 0.595063
128 0.263069 0.887588 2.364881 0.594449
129 0.261684 0.889272 2.321568 0.596598
130 0.261304 0.889509 2.389324 0.591562
131 0.260336 0.889640 2.403542 0.593098
132 0.259131 0.890272 2.413964 0.592115
133 0.258756 0.890193 2.422454 0.591992
134 0.257794 0.891009 2.454598 0.591255
135 0.257187 0.891009 2.459366 0.590088
136 0.257249 0.891088 2.448625 0.591624
137 0.256344 0.891404 2.495104 0.589167
138 0.255590 0.891720 2.495032 0.589781
139 0.254596 0.892299 2.496050 0.589229
140 0.254308 0.892588 2.510471 0.589536
141 0.253694 0.892509 2.519580 0.589720
142 0.252973 0.893088 2.527464 0.590273
143 0.252714 0.893194 2.553902 0.589106
144 0.252190 0.893720 2.536494 0.590457
145 0.251870 0.893352 2.553102 0.588799
146 0.250437 0.893694 2.565141 0.589597
147 0.250066 0.894141 2.575599 0.588553
148 0.249596 0.894273 2.590722 0.588123
149 0.248569 0.894983 2.596031 0.588676
150 0.248096 0.895273 2.602810 0.588860
您的情况很奇怪，因为您的验证损失从未减小。您的学习率非常高，典型的学习率约为0.001。您在网格搜索中使用的学习率范围是多少？
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休
我使用了[
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1.000，0.100，0.010，0.001
这可能会有点晚，但是您确定自己的数据就是您想的那样吗？特别是，您的验证准确性停滞不前，而验证损失却在增加，这很奇怪，因为这两个值应始终一起移动，例如。损耗值的降低应与准确度的成比例提高相结合。您可以在失去训练的情况下看到这一点。随着训练损失的减少，准确性也随之增加。但是，您所拥有的验证数据并非如此。因此，我肯定会研究您如何获得验证损失和ac
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matt_m