0.前言
接上一篇https://blog.csdn.net/qq_35546153/article/details/80390131；
这一篇主要是一些实验数据，实验结果以及分析。
1.训练网络过程记录及分析
1.1 影响网络结果的因素
影响网络预测的因素大致为两类，第一类是网络自身结构造成的影响，第二类是数据方面造成的影响。
第一类中主要起影响作用的是:
(1) batch_size; (2) firstLayer; (3) secondeLayer; (4) thirdLayer; (5) epochs; (6) data_train;
第二类中主要起影响作用的是:
(1) dataFormat; (2) samplesNum; (3) isRandom;
1.2 实验假定条件
目前，定第一类的情况不变，只改变第二类的情况，观察结果。
在数据准备方面，准备两种数据格式：
if dataFormat == 2:
print("dataFormat = 2")
trainData = np.empty((sampleNums, 2, rowColTotalNum))
elif dataFormat == 1:
print("dataFormat = 1")
trainData = np.empty((sampleNums, rowColTotalNum*2))
samplesNum代表样本数，定为：11000， 33000 ， 55000， 77000, 222000， 444000， 666000， 888000， 1111000这九种情况。
isRandom代表数据样本选取的形式，若为真，则代表，以samplesNum为样本数，随机选取符合总数为samplesNum的样本；若为FALSE，则表示，按顺序选取样本数为samplesNum的样本；
这两种情况代表的，一种是有序，另一种是无序随机。这两种结合起来，进行对比足以说明问题。
2. 实验结果记录及分析
2.1 按照1.2的假定条件进行实验
定第一类不变，第二类中样本数和isRandom不变，只改变数据格式，实验结果（由于全部信息太多，只截取部分信息）如下：
实验结果： dataFormat为2的情况： Using TensorFlow backend. load data begin... load data..., no random! load done Data.shape= (11000, 2, 429) label.shape= (11000, 5) dataFormat = 2, need to reshape to 1 reshape data shape= (11000, 858) reshape label shape= (11000, 5) data_train.shape= (8000, 858) data_test.shape= (3000, 858) label_train.shape= (8000, 5) label_test.shape= (3000, 5) construct net begin... Training... Epoch 1/200 200/8000 [..............................] - ETA: 3:41 - loss: 0.2301 - acc: 0.1100 1400/8000 [====>.........................] - ETA: 27s - loss: 0.5132 - acc: 0.6029 2800/8000 [=========>....................] - ETA: 10s - loss: 0.2827 - acc: 0.6343 4200/8000 [==============>...............] - ETA: 5s - loss: 0.1952 - acc: 0.6555 5600/8000 [====================>.........] - ETA: 2s - loss: 0.1478 - acc: 0.7166 7000/8000 [=========================>....] - ETA: 0s - loss: 0.1191 - acc: 0.7551 8000/8000 [==============================] - 6s 749us/step - loss: 0.1048 - acc: 0.7755 Epoch 2/200 200/8000 [..............................] - ETA: 0s - loss: 0.0039 - acc: 0.9100 1600/8000 [=====>........................] - ETA: 0s - loss: 0.0040 - acc: 0.9262 3000/8000 [==========>...................] - ETA: 0s - loss: 0.0039 - acc: 0.9330 4400/8000 [===============>..............] - ETA: 0s - loss: 0.0037 - acc: 0.9327 5800/8000 [====================>.........] - ETA: 0s - loss: 0.0036 - acc: 0.9348 7200/8000 [==========================>...] - ETA: 0s - loss: 0.0036 - acc: 0.9342 8000/8000 [==============================] - 0s 40us/step - loss: 0.0035 - acc: 0.9326 Epoch 3/200 200/8000 [..............................] - ETA: 0s - loss: 0.0021 - acc: 0.9450 1600/8000 [=====>........................] - ETA: 0s - loss: 0.0020 - acc: 0.9181 3000/8000 [==========>...................] - ETA: 0s - loss: 0.0020 - acc: 0.9227 4400/8000 [===============>..............] - ETA: 0s - loss: 0.0020 - acc: 0.9225 5800/8000 [====================>.........] - ETA: 0s - loss: 0.0020 - acc: 0.9205 7200/8000 [==========================>...] - ETA: 0s - loss: 0.0020 - acc: 0.9224 8000/8000 [==============================] - 0s 39us/step - loss: 0.0020 - acc: 0.9215 Epoch 182/200 200/8000 [..............................] - ETA: 0s - loss: 0.0024 - acc: 0.9250 1600/8000 [=====>........................] - ETA: 0s - loss: 0.0021 - acc: 0.9231 3000/8000 [==========>...................] - ETA: 0s - loss: 0.0020 - acc: 0.9203 4400/8000 [===============>..............] - ETA: 0s - loss: 0.0020 - acc: 0.9180 5800/8000 [====================>.........] - ETA: 0s - loss: 0.0020 - acc: 0.9179 7200/8000 [==========================>...] - ETA: 0s - loss: 0.0020 - acc: 0.9206 8000/8000 [==============================] - 0s 39us/step - loss: 0.0020 - acc: 0.9215 Epoch 183/200 200/8000 [..............................] - ETA: 0s - loss: 0.0020 - acc: 0.9100 1600/8000 [=====>........................] - ETA: 0s - loss: 0.0019 - acc: 0.9150 3000/8000 [==========>...................] - ETA: 0s - loss: 0.0019 - acc: 0.9160 4400/8000 [===============>..............] - ETA: 0s - loss: 0.0020 - acc: 0.9186 5800/8000 [====================>.........] - ETA: 0s - loss: 0.0020 - acc: 0.9184 7200/8000 [==========================>...] - ETA: 0s - loss: 0.0020 - acc: 0.9218 8000/8000 [==============================] - 0s 40us/step - loss: 0.0020 - acc: 0.9215 Epoch 184/200 200/8000 [..............................] - ETA: 0s - loss: 0.0019 - acc: 0.9200 1600/8000 [=====>........................] - ETA: 0s - loss: 0.0020 - acc: 0.9194 3000/8000 [==========>...................] - ETA: 0s - loss: 0.0020 - acc: 0.9143 4400/8000 [===============>..............] - ETA: 0s - loss: 0.0020 - acc: 0.9195 5800/8000 [====================>.........] - ETA: 0s - loss: 0.0020 - acc: 0.9207 7200/8000 [==========================>...] - ETA: 0s - loss: 0.0020 - acc: 0.9219 8000/8000 [==============================] - 0s 40us/step - loss: 0.0020 - acc: 0.9215 Epoch 00184: early stopping Testing ------------ 200/3000 [=>............................] - ETA: 0s 2600/3000 [=========================>....] - ETA: 0s 3000/3000 [==============================] - 0s 26us/step test cost: [0.001527793175773695, 0.89100000063578289] ./saveProcessResult/processb_200FIL_858FO_572SO_572EO_5_0521-11_00dataFormat2.png
贴一张训练过程图：
dataFormat为1时的实验过程（只截取部分）如下：
Using TensorFlow backend. load data begin... load data..., no random! load done Data.shape= (11000, 858) label.shape= (11000, 5) reshape data shape= (11000, 858) reshape label shape= (11000, 5) data_train.shape= (8000, 858) data_test.shape= (3000, 858) label_train.shape= (8000, 5) label_test.shape= (3000, 5) construct net begin... Training... Epoch 1/200 2018-05-21 11:05:41.724062: I C:\tf_jenkins\home\workspace\rel-win\M\windows-gpu\PY\35\tensorflow\core\platform\cpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2 2018-05-21 11:05:42.025240: I C:\tf_jenkins\home\workspace\rel-win\M\windows-gpu\PY\35\tensorflow\core\common_runtime\gpu\gpu_device.cc:1030] Found device 0 with properties: name: Quadro K1200 major: 5 minor: 0 memoryClockRate(GHz): 1.0325 pciBusID: 0000:01:00.0 totalMemory: 4.00GiB freeMemory: 3.35GiB 2018-05-21 11:05:42.025796: I C:\tf_jenkins\home\workspace\rel-win\M\windows-gpu\PY\35\tensorflow\core\common_runtime\gpu\gpu_device.cc:1120] Creating TensorFlow device (/device:GPU:0) -> (device: 0, name: Quadro K1200, pci bus id: 0000:01:00.0, compute capability: 5.0) 200/8000 [..............................] - ETA: 1:24 - loss: 0.2852 - acc: 0.3250 1600/8000 [=====>........................] - ETA: 8s - loss: 0.4600 - acc: 0.2925 3000/8000 [==========>...................] - ETA: 3s - loss: 0.2971 - acc: 0.4343 4400/8000 [===============>..............] - ETA: 1s - loss: 0.2098 - acc: 0.5180 5800/8000 [====================>.........] - ETA: 0s - loss: 0.1605 - acc: 0.6069 7200/8000 [==========================>...] - ETA: 0s - loss: 0.1301 - acc: 0.6676 8000/8000 [==============================] - 2s 309us/step - loss: 0.1175 - acc: 0.6936 Epoch 2/200 200/8000 [..............................] - ETA: 0s - loss: 0.0038 - acc: 0.9550 1600/8000 [=====>........................] - ETA: 0s - loss: 0.0036 - acc: 0.9356 3000/8000 [==========>...................] - ETA: 0s - loss: 0.0035 - acc: 0.9363 4400/8000 [===============>..............] - ETA: 0s - loss: 0.0034 - acc: 0.9386 5800/8000 [====================>.........] - ETA: 0s - loss: 0.0034 - acc: 0.9422 7200/8000 [==========================>...] - ETA: 0s - loss: 0.0033 - acc: 0.9421 8000/8000 [==============================] - 0s 39us/step - loss: 0.0033 - acc: 0.9420 Epoch 3/200 200/8000 [..............................] - ETA: 0s - loss: 0.0033 - acc: 0.9400 1600/8000 [=====>........................] - ETA: 0s - loss: 0.0032 - acc: 0.9406 3000/8000 [==========>...................] - ETA: 0s - loss: 0.0031 - acc: 0.9357 4400/8000 [===============>..............] - ETA: 0s - loss: 0.0030 - acc: 0.9357 5800/8000 [====================>.........] - ETA: 0s - loss: 0.0031 - acc: 0.9345 7200/8000 [==========================>...] - ETA: 0s - loss: 0.0031 - acc: 0.9335 8000/8000 [==============================] - 0s 39us/step - loss: 0.0030 - acc: 0.9346 Epoch 4/200 200/8000 [..............................] - ETA: 0s - loss: 0.0032 - acc: 0.9250 1600/8000 [=====>........................] - ETA: 0s - loss: 0.0030 - acc: 0.9162 3000/8000 [==========>...................] - ETA: 0s - loss: 0.0030 - acc: 0.9267 4400/8000 [===============>..............] - ETA: 0s - loss: 0.0030 - acc: 0.9302 5800/8000 [====================>.........] - ETA: 0s - loss: 0.0030 - acc: 0.9288 7200/8000 [==========================>...] - ETA: 0s - loss: 0.0029 - acc: 0.9310 8000/8000 [==============================] - 0s 40us/step - loss: 0.0029 - acc: 0.9311 。。。。 200/8000 [..............................] - ETA: 0s - loss: 0.0024 - acc: 0.9550 1600/8000 [=====>........................] - ETA: 0s - loss: 0.0020 - acc: 0.9275 3000/8000 [==========>...................] - ETA: 0s - loss: 0.0020 - acc: 0.9227 4400/8000 [===============>..............] - ETA: 0s - loss: 0.0020 - acc: 0.9218 5800/8000 [====================>.........] - ETA: 0s - loss: 0.0020 - acc: 0.9221 7200/8000 [==========================>...] - ETA: 0s - loss: 0.0020 - acc: 0.9219 8000/8000 [==============================] - 0s 39us/step - loss: 0.0020 - acc: 0.9210 Epoch 172/200 200/8000 [..............................] - ETA: 0s - loss: 0.0020 - acc: 0.9150 1600/8000 [=====>........................] - ETA: 0s - loss: 0.0019 - acc: 0.9188 3000/8000 [==========>...................] - ETA: 0s - loss: 0.0019 - acc: 0.9193 4400/8000 [===============>..............] - ETA: 0s - loss: 0.0020 - acc: 0.9180 5800/8000 [====================>.........] - ETA: 0s - loss: 0.0020 - acc: 0.9210 7200/8000 [==========================>...] - ETA: 0s - loss: 0.0020 - acc: 0.9215 8000/8000 [==============================] - 0s 40us/step - loss: 0.0020 - acc: 0.9210 Epoch 173/200 200/8000 [..............................] - ETA: 0s - loss: 0.0024 - acc: 0.9550 1600/8000 [=====>........................] - ETA: 0s - loss: 0.0021 - acc: 0.9300 3000/8000 [==========>...................] - ETA: 0s - loss: 0.0020 - acc: 0.9237 4200/8000 [==============>...............] - ETA: 0s - loss: 0.0020 - acc: 0.9236 5600/8000 [====================>.........] - ETA: 0s - loss: 0.0020 - acc: 0.9238 7000/8000 [=========================>....] - ETA: 0s - loss: 0.0020 - acc: 0.9224 8000/8000 [==============================] - 0s 40us/step - loss: 0.0020 - acc: 0.9210 Epoch 174/200 200/8000 [..............................] - ETA: 0s - loss: 0.0020 - acc: 0.9200 1600/8000 [=====>........................] - ETA: 0s - loss: 0.0020 - acc: 0.9250 3000/8000 [==========>...................] - ETA: 0s - loss: 0.0020 - acc: 0.9237 4200/8000 [==============>...............] - ETA: 0s - loss: 0.0019 - acc: 0.9238 5600/8000 [====================>.........] - ETA: 0s - loss: 0.0020 - acc: 0.9245 7000/8000 [=========================>....] - ETA: 0s - loss: 0.0020 - acc: 0.9217 8000/8000 [==============================] - 0s 40us/step - loss: 0.0020 - acc: 0.9210 Epoch 175/200 200/8000 [..............................] - ETA: 0s - loss: 0.0020 - acc: 0.8950 1600/8000 [=====>........................] - ETA: 0s - loss: 0.0020 - acc: 0.9244 3000/8000 [==========>...................] - ETA: 0s - loss: 0.0020 - acc: 0.9237 4400/8000 [===============>..............] - ETA: 0s - loss: 0.0020 - acc: 0.9230 5800/8000 [====================>.........] - ETA: 0s - loss: 0.0020 - acc: 0.9224 7200/8000 [==========================>...] - ETA: 0s - loss: 0.0020 - acc: 0.9214 8000/8000 [==============================] - 0s 39us/step - loss: 0.0020 - acc: 0.9210 Epoch 00175: early stopping Testing ------------ 200/3000 [=>............................] - ETA: 0s 2600/3000 [=========================>....] - ETA: 0s 3000/3000 [==============================] - 0s 29us/step test cost: [0.0015478261494232962, 0.89099999666213991] ./saveProcessResult/processb_200FIL_858FO_572SO_572EO_5_0521-11_04dataFormat1.png
贴一张训练图：
从以上信息可以得到以下结论：
（1）dataFormat格式对于实验结果的准确率影响微弱；
（2）快速下降都是在第二个epoch；第二个epoch结束后，dataFormat为1时的loss: 0.0033 - acc: 0.9420；dataFormat为2时的loss: 0.0035 - acc: 0.9326；可见dataFormat对于loss的影响还是有点的，准确率高出1%， loss低了0.02%；
（3）dataFormat为1时， Epoch 00175: early stopping， loss: 0.0020 - acc: 0.9210；dataFormat为2时， Epoch 00184: early stopping， loss: 0.0020 - acc: 0.9215；蛮奇怪的，准确率dataFormat为2时还高一点点， 0.05%，好吧，可以忽略了。loss一致；
综上考虑，选择dataFormat为1，相关性增强了，提高了训练速度；