Keras cnn 手写数字识别示例

#基于mnist数据集的手写数字识别

#构造了cnn网络拟合识别函数，前两层为卷积层，第三层为池化层，第四层为Flatten层，最后两层为全连接层

#基于Keras 2.1.1 Tensorflow 1.4.0

代码：

 1 from __future__ import print_function
 2 import numpy as np
 3 np.random.seed(1337)
 4 from keras.datasets import mnist
 5 from keras.models import Sequential
 6 from keras.layers import Dense, Dropout, Activation, Flatten
 7 from keras.layers import Convolution2D, MaxPooling2D
 8 from keras.utils import np_utils
 9 from keras import backend as K
10 
11 batch_size = 128
12 nb_classes = 10
13 nb_epoch = 12
14 
15 img_rows, img_cols = 28, 28
16 nb_filters = 32
17 pool_size = (2,2)
18 kernel_size = (3,3)
19 (X_train, y_train), (X_test, y_test) = mnist.load_data()
20 
21 X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 1)
22 X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 1)
23 input_shape = (img_rows, img_cols, 1)
24 X_train = X_train.astype('float32')
25 X_test = X_test.astype('float32')
26 X_train /= 255
27 X_test /= 255
28 
29 Y_train = np_utils.to_categorical(y_train, nb_classes)
30 Y_test = np_utils.to_categorical(y_test, nb_classes)
31 # 建立序贯模型
32 model = Sequential()
33 
34 model.add(Convolution2D(nb_filters, kernel_size[0] ,kernel_size[1],border_mode='valid',input_shape=input_shape))
35 model.add(Activation('relu'))
36 
37 
38 model.add(Convolution2D(nb_filters, kernel_size[0], kernel_size[1]))
39 model.add(Activation('relu'))
40 model.add(MaxPooling2D(pool_size=pool_size))
41 model.add(Dropout(0.25))
42 model.add(Flatten())
43 model.add(Dense(128))
44 model.add(Activation('relu'))
45 model.add(Dropout(0.5))
46 model.add(Dense(nb_classes))
47 model.add(Activation('softmax'))
48 
49 
50 model.summary()
51 model.compile(loss='categorical_crossentropy',optimizer='adadelta',metrics=['accuracy'])
52 model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch,verbose=1, validation_data=(X_test, Y_test))
53 
54 score = model.evaluate(X_test, Y_test, verbose=0)
55 print('Test score:', score[0])
56 print('Test accuracy:', score[1])