看了篇文章，想到的一点东西，尝试一下。比较耗CPU，建议在服务器上跑。
首先要了解keras框架，深度学习，否则比较吃力
首先搭建基于keras框架的深度学习模型
from keras.models import *
from keras.layers import *
input_tensor = Input((height, width, 3))
x = input_tensor
for i in range(4):
x = Convolution2D(32*2**i, 3, 3, activation='relu')(x)
x = Convolution2D(32*2**i, 3, 3, activation='relu')(x)
x = MaxPooling2D((2, 2))(x)
x = Flatten()(x)
x = Dropout(0.25)(x)
x = [Dense(n_class, activation='softmax', name='c%d'%(i+1))(x) for i in range(4)]
model = Model(input=input_tensor, output=x)
model.compile(loss='categorical_crossentropy',
optimizer='adadelta',
metrics=['accuracy'])
训练
model.fit_generator(gen(), samples_per_epoch=51200, nb_epoch=5,
nb_worker=2, pickle_safe=True,
validation_data=gen(), nb_val_samples=1280)
测试
X, y = next(gen(1))
y_pred = model.predict(X)
plt.title('real: %s\npred:%s'%(decode(y), decode(y_pred)))
plt.imshow(X[0], cmap='gray')
结果：
