1 ____________________________________________________________________________________________________
2 Layer (type) Output Shape Param # Connected to
3 ====================================================================================================
4 convolution2d_1 (Convolution2D) (None, 30, 24, 24) 780 convolution2d_input_1[0][0]
5 ____________________________________________________________________________________________________
6 maxpooling2d_1 (MaxPooling2D) (None, 30, 12, 12) 0 convolution2d_1[0][0]
7 ____________________________________________________________________________________________________
8 convolution2d_2 (Convolution2D) (None, 15, 10, 10) 4065 maxpooling2d_1[0][0]
9 ____________________________________________________________________________________________________
10 maxpooling2d_2 (MaxPooling2D) (None, 15, 5, 5) 0 convolution2d_2[0][0]
11 ____________________________________________________________________________________________________
12 dropout_1 (Dropout) (None, 15, 5, 5) 0 maxpooling2d_2[0][0]
13 ____________________________________________________________________________________________________
14 flatten_1 (Flatten) (None, 375) 0 dropout_1[0][0]
15 ____________________________________________________________________________________________________
16 dense_1 (Dense) (None, 128) 48128 flatten_1[0][0]
17 ____________________________________________________________________________________________________
18 dense_2 (Dense) (None, 50) 6450 dense_1[0][0]
19 ____________________________________________________________________________________________________
20 dense_3 (Dense) (None, 10) 510 dense_2[0][0]
21 ====================================================================================================
22 Total params: 59933
23 ____________________________________________________________________________________________________
24 Train on 60000 samples, validate on 10000 samples
25 Epoch 1/69
26 34s - loss: 0.4248 - acc: 0.8619 - val_loss: 0.0832 - val_acc: 0.9746
27 Epoch 2/69
28 35s - loss: 0.1147 - acc: 0.9638 - val_loss: 0.0518 - val_acc: 0.9831
29 Epoch 3/69
30 35s - loss: 0.0887 - acc: 0.9719 - val_loss: 0.0452 - val_acc: 0.9855
31 、、、
32 Epoch 66/69
33 38s - loss: 0.0134 - acc: 0.9955 - val_loss: 0.0211 - val_acc: 0.9943
34 Epoch 67/69
35 38s - loss: 0.0114 - acc: 0.9960 - val_loss: 0.0171 - val_acc: 0.9950
36 Epoch 68/69
37 38s - loss: 0.0116 - acc: 0.9959 - val_loss: 0.0192 - val_acc: 0.9942
38 Epoch 69/69
39 38s - loss: 0.0132 - acc: 0.9958 - val_loss: 0.0188 - val_acc: 0.9978
40 Large CNN Error: 0.22%
41
42 real 41m47.350s
43 user 157m51.145s
44 sys 6m5.829s
这是目前的最好结果，99.78%，然而还有很多地方可以提升，下次准确率提高了再来更 。
总结：
1.CNN在图像识别上确实比传统的MLP有优势，比传统的机器学习算法也有优势(不过也有通过随机森林取的很好效果的)
2.加深网络结构，即多加几层卷积层有助于提升准确率，但是也能大大降低运行速度
3.适当加Dropout可以提高准确率
4.激活函数最好，算了，直接说就选relu吧，没有为啥，就因为relu能避免梯度消散这一点应该选它，训练速度快等其他优点下次专门总结一篇文章再说吧。
5.迭代次数不是越多越好，很可能会过拟合，自己可以做一个收敛曲线，keras里可以用history函数plot一下，看算法是否收敛，还是发散。