0.前言
上一篇实现了逆映射的功能，但是最终结果并不理想（随机选取测试样本，进入网络预测出5个值，然后使用逆映射得到实际的世界坐标发现，与正确的坐标相差甚远），所以猜测问题出在网络预测的精确率不高，故而这一篇集中于训练网络的记录。
1.改变样本
1.1样本数由33000增加到55000，按顺序选择样本，数据格式是1
1.1.1
训练过程缩略如下：
42400/44000 [===========================>..] - ETA: 0s - loss: 3.8234e-04 - acc: 0.9576 43800/44000 [============================>.] - ETA: 0s - loss: 3.8215e-04 - acc: 0.9573 44000/44000 [==============================] - 2s 40us/step - loss: 3.8230e-04 - acc: 0.9574 Epoch 00181: early stopping Testing ------------ 200/11000 [..............................] - ETA: 0s 2400/11000 [=====>........................] - ETA: 0s 4800/11000 [============>.................] - ETA: 0s 7200/11000 [==================>...........] - ETA: 0s 9400/11000 [========================>.....] - ETA: 0s 11000/11000 [==============================] - 0s 24us/step test cost: [0.010553197251548144, 0.92109091552821076]
训练图片如下：
1.1.2 结论
准确率提高到了95.74%，测试的准确率是92.10%
1.2 55000的随机样本
1.2.1 训练过程缩略如下：
0600/44000 [==========================>...] - ETA: 0s - loss: 5.9882e-04 - acc: 0.9650 41800/44000 [===========================>..] - ETA: 0s - loss: 5.9811e-04 - acc: 0.9650 43200/44000 [============================>.] - ETA: 0s - loss: 5.9538e-04 - acc: 0.9651 44000/44000 [==============================] - 2s 40us/step - loss: 5.9424e-04 - acc: 0.9650 Epoch 00045: early stopping Testing ------------ 200/11000 [..............................] - ETA: 0s 2200/11000 [=====>........................] - ETA: 0s 4600/11000 [===========>..................] - ETA: 0s 6800/11000 [=================>............] - ETA: 0s 9400/11000 [========================>.....] - ETA: 0s 11000/11000 [==============================] - 0s 24us/step test cost: [0.00079475848522799256, 0.96390908631411465]
图片如下：
1.2.2 结论
可以发现比按顺序时更快收敛，更快达到更好的准确率，准确率达到了96.50%，测试的准确率是96.39%
1.3 77000的样本随机选取
由于随机选择样本收敛速度快，所以后面都选择随机选取，不过最后一种情况，取样本数的最大值的时候，随机和按顺序选取并没有区别。
1.3.1 训练过程缩略如下：
57400/61600 [==========================>...] - ETA: 0s - loss: 0.0011 - acc: 0.9709 58800/61600 [===========================>..] - ETA: 0s - loss: 0.0011 - acc: 0.9710 60200/61600 [============================>.] - ETA: 0s - loss: 0.0011 - acc: 0.9711 61600/61600 [==============================] - 2s 39us/step - loss: 0.0011 - acc: 0.9710 Epoch 00029: early stopping Testing ------------ 200/15400 [..............................] - ETA: 1s 2800/15400 [====>.........................] - ETA: 0s 5200/15400 [=========>....................] - ETA: 0s 7400/15400 [=============>................] - ETA: 0s 9800/15400 [==================>...........] - ETA: 0s 12000/15400 [======================>.......] - ETA: 0s 14600/15400 [===========================>..] - ETA: 0s 15400/15400 [==============================] - 0s 22us/step test cost: [0.0010358531023496641, 0.96948052381540273]
1.3.2 结论
准确率达到了97.10%，测试的准确率是96.94%
1.4 111000随机样本
1.4.1 测试过程缩略如下：
84000/88800 [===========================>..] - ETA: 0s - loss: 2.8768e-04 - acc: 0.9811 85400/88800 [===========================>..] - ETA: 0s - loss: 2.8572e-04 - acc: 0.9811 86800/88800 [============================>.] - ETA: 0s - loss: 2.8412e-04 - acc: 0.9812 88200/88800 [============================>.] - ETA: 0s - loss: 2.8733e-04 - acc: 0.9812 88800/88800 [==============================] - 3s 39us/step - loss: 2.8655e-04 - acc: 0.9812 Epoch 00059: early stopping Testing ------------ 200/22200 [..............................] - ETA: 1s 2600/22200 [==>...........................] - ETA: 0s 5200/22200 [======>.......................] - ETA: 0s 7400/22200 [=========>....................] - ETA: 0s 10000/22200 [============>.................] - ETA: 0s 12600/22200 [================>.............] - ETA: 0s 14800/22200 [===================>..........] - ETA: 0s 17400/22200 [======================>.......] - ETA: 0s 20000/22200 [==========================>...] - ETA: 0s 22000/22200 [============================>.] - ETA: 0s 22200/22200 [==============================] - 0s 22us/step test cost: [0.0003177403905119554, 0.98112613768190948]
1.4.2 结论
准确率达到了98.12%，测试的准确率是98.11%
1.5 555000样本随机选取
1.5.1 测试过程缩略如下：
436200/444000 [============================>.] - ETA: 0s - loss: 4.7216e-04 - acc: 0.9901 437600/444000 [============================>.] - ETA: 0s - loss: 4.7320e-04 - acc: 0.9901 439000/444000 [============================>.] - ETA: 0s - loss: 4.7201e-04 - acc: 0.9901 440200/444000 [============================>.] - ETA: 0s - loss: 4.7172e-04 - acc: 0.9901 441600/444000 [============================>.] - ETA: 0s - loss: 4.7154e-04 - acc: 0.9901 443000/444000 [============================>.] - ETA: 0s - loss: 4.7167e-04 - acc: 0.9901 444000/444000 [==============================] - 17s 39us/step - loss: 4.7067e-04 - acc: 0.9901 Epoch 00017: early stopping Testing ------------ 200/111000 [..............................] - ETA: 11ss 109000/111000 [============================>.] - ETA: 0s 111000/111000 [==============================] - 2s 22us/step test cost: [0.00036887684648529822, 0.99061262135033135]
1.5.2 结论
准确率达到了99.01%，测试的准确率是99.06%
1.6 999000样本随机选取
1.6.1 测试过程缩略如下：
792000/799200 [============================>.] - ETA: 0s - loss: 9.8713e-05 - acc: 0.9928 793200/799200 [============================>.] - ETA: 0s - loss: 9.8592e-05 - acc: 0.9928 794600/799200 [============================>.] - ETA: 0s - loss: 9.8704e-05 - acc: 0.9928 796000/799200 [============================>.] - ETA: 0s - loss: 9.8637e-05 - acc: 0.9928 797400/799200 [============================>.] - ETA: 0s - loss: 9.9247e-05 - acc: 0.9928 798600/799200 [============================>.] - ETA: 0s - loss: 9.9144e-05 - acc: 0.9928 799200/799200 [==============================] - 32s 40us/step - loss: 9.9083e-05 - acc: 0.9928 Epoch 00028: early stopping Testing ------------ 200/199800 [..............................] - ETA: 21s 2000/199800 [..............................] - ETA: 7s 0s 199800/199800 [==============================] - 5s 25us/step test cost: [7.7643079040373071e-05, 0.99291291967168582]
1.6.2 结论
准确率达到了99.28%，测试的准确率是99.29%
1.7 最大样本数1336500测试
1.7.1 测试过程缩略如下:
1064800/1069200 [============================>.] - ETA: 0s - loss: 1.4270e-04 - acc: 0.9911 1066200/1069200 [============================>.] - ETA: 0s - loss: 1.4308e-04 - acc: 0.9911 1067600/1069200 [============================>.] - ETA: 0s - loss: 1.4304e-04 - acc: 0.9911 1068800/1069200 [============================>.] - ETA: 0s - loss: 1.4347e-04 - acc: 0.9911 1069200/1069200 [==============================] - 45s 42us/step - loss: 1.4345e-04 - acc: 0.9911 Epoch 00017: early stopping Testing ------------ 200/267300 [..............................] - ETA: 22s 265400/267300 [============================>.] - ETA: 0s 267300/267300 [==============================] - 6s 24us/step test cost: [0.00023743963886684564, 0.99473251178832789]
1.7.2 结论
准确率达到了99.11%，测试的准确率是99.47%
1.8 总结
以上就是逐次增加样本数的测试过程及结果，最好的情况下，准确率可以达到99.28%，但是这并不够！接下来考虑更改网络结构，尝试是否可以增加准确率。目前的网络结构是三层，第一层是输入层，858；第二层是隐含层，572；第三层是输出层：5；
firstLayerInputDim = 858 firstLayerOutput = 572 secondLayerOutput = 572 lastLayerOutput = 5
或许应该尝试一下百万级样本数的随机选取，不过还是先测试一下网络结构的影响吧。
2.1 更改网络结构
以555000样本数为测试蓝本，在此基础上更改网络结构，如果结果变好了，对其他样本数应该也适合，这也是需要测试的地方。
2.1.1 四层网络
firstLayerInputDim = 858 firstLayerOutput = 572 secondLayerOutput = 572 thirdLayerOutput = 225 lastLayerOutput = 5
开始测试：
18/6/1_22:49，程序还没有出现结果，明天继续。
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一夜过去了，结果出来了
测试过程缩略如下：
438800/444000 [============================>.] - ETA: 0s - loss: 2.2292e-04 - acc: 0.9897 440200/444000 [============================>.] - ETA: 0s - loss: 2.2515e-04 - acc: 0.9897 441400/444000 [============================>.] - ETA: 0s - loss: 2.2505e-04 - acc: 0.9897 442800/444000 [============================>.] - ETA: 0s - loss: 2.2481e-04 - acc: 0.9897 444000/444000 [==============================] - 19s 42us/step - loss: 2.2476e-04 - acc: 0.9897 Epoch 00014: early stopping Testing ------------ 200/111000 [..............................] - ETA: 9s 2600/111000 [..............................] - ETA: 3s 107800/111000 [============================>.] - ETA: 0s 110200/111000 [============================>.] - ETA: 0s 111000/111000 [==============================] - 3s 23us/step test cost: [0.0002887813062746621, 0.98825226259661147]可以看到，准确率提升到了98.97%，测试的准确率达到了98.82%。
未改变网络前是：准确率达到了96.50%，测试的准确率是96.39%
2.1.2 五层结构
将网络结构增加到五层，看看效果。
firstLayerInputDim = 858 firstLayerOutput = 572 secondLayerOutput = 572 thirdLayerOutput = 225 fourthLayerOutput = 75 lastLayerOutput = 5
开始测试：
现在时间是18/6/2_9点34，得5,6个小时吧
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18/6/2_14:32结果出现
443400/444000 [============================>.] - ETA: 0s - loss: 5.9351e-05 - acc: 0.9883 444000/444000 [==============================] - 20s 44us/step - loss: 5.9383e-05 - acc: 0.9883 Epoch 00037: early stopping Testing ------------ 200/111000 [..............................] - ETA: 9s 111000/111000 [==============================] - 3s 24us/step test cost: [8.2523198625904524e-05, 0.9887567669421703]
原先4层：准确率提升到了98.97%，测试的准确率达到了98.82%。
现在5层： 准确率下降到了98.83%，测试的准确率达到了98.87%。
3.后言
以555000样本数为测试素材得到的结论，通过样本数33000进行验证，如果结果一致的话，姑且认为网络结构的影响对于所有样本都是一致的。
然后再进行网络节点数的调整，这个时候就可以用33000进行测试，不然会花费太多时间。