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：

 from __future__ import print_function
 import numpy as np
 np.random.seed(1337)
 from keras.datasets import mnist
 from keras.models import Sequential
 from keras.layers import Dense, Dropout, Activation, Flatten
 from keras.layers import Convolution2D, MaxPooling2D
 from keras.utils import np_utils
 from keras import backend as K

 batch_size = 128
 nb_classes = 10
 nb_epoch = 12

 img_rows, img_cols = 28, 28
 nb_filters = 32
 pool_size = (2,2)
 kernel_size = (3,3)
 (X_train, y_train), (X_test, y_test) = mnist.load_data()

 X_train = X_train.reshape(X_train.shape[0], img_rows, img_cols, 1)
 X_test = X_test.reshape(X_test.shape[0], img_rows, img_cols, 1)
 input_shape = (img_rows, img_cols, 1)
 X_train = X_train.astype('float32')
 X_test = X_test.astype('float32')
 X_train /= 255
 X_test /= 255

 Y_train = np_utils.to_categorical(y_train, nb_classes)
 Y_test = np_utils.to_categorical(y_test, nb_classes)
 # 建立序贯模型
 model = Sequential()

 model.add(Convolution2D(nb_filters, kernel_size[0] ,kernel_size[1],border_mode='valid',input_shape=input_shape))
 model.add(Activation('relu'))

 model.add(Convolution2D(nb_filters, kernel_size[0], kernel_size[1]))
 model.add(Activation('relu'))
 model.add(MaxPooling2D(pool_size=pool_size))
 model.add(Dropout(0.25))
 model.add(Flatten())
 model.add(Dense(128))
 model.add(Activation('relu'))
 model.add(Dropout(0.5))
 model.add(Dense(nb_classes))
 model.add(Activation('softmax'))

 model.summary()
 model.compile(loss='categorical_crossentropy',optimizer='adadelta',metrics=['accuracy'])
 model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=nb_epoch,verbose=1, validation_data=(X_test, Y_test))

 score = model.evaluate(X_test, Y_test, verbose=0)
 print('Test score:', score[0])
 print('Test accuracy:', score[1])