如何提高CNN制作手写数字识别器的准确性

Question

这是我的代码：
我无法提高我的准确性，我只能得到10％的准确性。有人可以告诉我应该选择哪种激活功能或某些功能，这样我可以提高准确性。

    from keras.models import Sequential
    from keras.layers import Convolution2D 
    from keras.layers import MaxPooling2D
    from keras.layers import Flatten
    from keras.layers import Dense

初始化cnn

    classifier  = Sequential()

第1步

    classifier.add(Convolution2D(32, 3 ,3 , input_shape = (64,64,3) , 
    activation = 'relu'))


    #step 2 pooling

    classifier.add(MaxPooling2D(pool_size = (2,2)))

展平

    classifier.add(Flatten())


    classifier.add(Dense(output_dim = 64, activation = 'softmax'))



    classifier.add(Dense(output_dim =10, activation = 'softmax'))

    classifier.compile(optimizer = 'adam' , loss = 'categorical_crossentropy'                 
    , metrics = ['accuracy'] )

    from keras.preprocessing.image import ImageDataGenerator

    train_datagen = ImageDataGenerator(rotation_range=90, 
             width_shift_range=0.1, height_shift_range=0.1, 
             horizontal_flip=True)


    training_set = train_datagen.flow_from_directory('numbers/',
                                             target_size = (64, 64),
                                             batch_size = 64,
                                             class_mode = 'categorical')

    classifier.fit_generator(training_set,
                     samples_per_epoch = 2000,
                     nb_epoch = 25)