网络结构没变，只是在每一层后面加了dropout，结果居然有显著提升。一开始迭代500次，跑死我了，结果过拟合了，然后观察到69次的时候结果就已经很好了，就选择了迭代69次。 复制代码

1 # Larger CNN for the MNIST Dataset 2 import numpy

3 from keras.datasets import mnist 4 from keras.models import Sequential 5 from keras.layers import Dense 6 from keras.layers import Dropout 7 from keras.layers import Flatten

8 from keras.layers.convolutional import Convolution2D 9 from keras.layers.convolutional import MaxPooling2D 10 from keras.utils import np_utils

11 import mawww.wang027.comtplotlib.pyplot as plt 12 from keras.constraints import maxnorm 13 from keras.optimizers import SGD 14 # fix random seed for reproducibility 15 seed = 7

16 numpy.random.seed(seed) 17 # load data

18 (X_train, y_train), (X_test, y_test) = mnist.load_data() 19 # reshape to be [samples][pixels][width][height]

20 X_train = X_train.reshape(X_train.shape[0], 1, 28, 28).astype('float32') 21 X_test = X_test.reshape(X_test.shape[0], 1, 28, 28).astype('float32') 22 # normalize inputs from 0-255 to 0-1 23 X_train = X_train / 255 24 X_test = X_test / 255 25 # one hot encode outputs

26 y_train = np_utils.to_categorical(y_train) 27 y_test = np_utils.to_categorical(y_test) 28 num_classes = y_test.shape[1] 29 ###raw

30 # define the larger model 31 def larger_model(): 32 # create model 33 model = Sequential()

34 model.add(Convolution2D(30, 5, 5, border_mode='valid', input_shape=(1, 28, 28), activation='relu')) 35 model.add(MaxPooling2D(pool_size=(2, 2))) 36 model.add(Dropout(0.4))

37 model.add(Convolution2D(15, 3, 3, activation='relu')) 38 model.add(MaxPooling2D(pool_size=(2, 2))) 39 model.add(Dropout(0.4)) 40 model.add(Flatten())

41 model.add(Dense(128, activation='relu'))

42 model.add(Dropout(0.4))

43 model.add(Dense(50, activation='relu')) 44 model.add(Dropout(0.4))

45 model.add(Dense(num_classes, activation='softmax')) 46 # Compile model

47 model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) 48 return model 49

50 # build the model 51 model = larger_model() 52 # Fit the model

53 model.fit(X_train, y_train, validation_data=(X_test, y_test), nb_epoch=200, batch_size=200, verbose=2) 54 # Final evaluation of the model

55 scores = model.evaluate(X_test, y_test, verbose=0) 56 print(\复制代码 结果： 复制代码

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: 0.9956 38 Epoch 69/69

39 38s - loss: 0.0132 - acc: 0.9969 - 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 复制代码

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。