卷积神经网络的结构我随意设了一个。

结构大概是下面这个样子：

 

代码如下：

import numpy as npfrom keras.preprocessing import imagefrom keras.models import Sequentialfrom keras.layers import Dense, Dropout, Flatten, Activationfrom keras.layers import Conv2D, MaxPooling2D# 从文件夹图像与标签文件载入数据def create_x(filenum, file_dir):    train_x = []    for i in range(filenum):        img = image.load_img(file_dir + str(i) + ".bmp", target_size=(28, 28))        img = img.convert('L')        x = image.img_to_array(img)        train_x.append(x)    train_x = np.array(train_x)    train_x = train_x.astype('float32')    train_x /= 255    return train_xdef create_y(classes, filename):    train_y = []    file = open(filename, "r")    for line in file.readlines():        tmp = []        for j in range(classes):            if j == int(line):                tmp.append(1)            else:                tmp.append(0)        train_y.append(tmp)    file.close()    train_y = np.array(train_y).astype('float32')    return train_yclasses = 10X_train = create_x(55000, './train/')X_test = create_x(10000, './test/')Y_train = create_y(classes, 'train.txt')Y_test = create_y(classes, 'test.txt')# 从网络下载的数据集直接解析数据'''from tensorflow.examples.tutorials.mnist import input_datamnist = input_data.read_data_sets("MNIST_data/", one_hot=True)X_train, Y_train = mnist.train.images, mnist.train.labelsX_test, Y_test = mnist.test.images, mnist.test.labelsX_train = X_train.astype('float32')X_test = X_test.astype('float32')'''model = Sequential()model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(28, 28, 1)))model.add(Conv2D(64, (3, 3), activation='relu'))model.add(MaxPooling2D(pool_size=(2, 2)))model.add(Dropout(0.25))model.add(Conv2D(64, (3, 3), activation='relu'))model.add(Conv2D(64, (3, 3), activation='relu'))model.add(Dropout(0.25))model.add(Flatten())model.add(Dense(81, activation='relu'))model.add(Dropout(0.5))model.add(Dense(10))model.add(Activation('softmax'))model.summary()model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])history = model.fit(X_train, Y_train, batch_size=500, epochs=10, verbose=1, validation_data=(X_test, Y_test))score = model.evaluate(X_test, Y_test, verbose=0)test_result = model.predict(X_test)result = np.argmax(test_result, axis=1)print(result)print('Test score:', score[0])print('Test accuracy:', score[1])

最终在测试集上识别率在99%左右。

相关测试数据可以在这里到。