# 1. 数据预处理。

import keras

from keras.models import Model

from keras.datasets import mnist

from keras.layers import Input, Dense

from tflearn.layers.core import fully_connected

num_classes = 10

img_rows, img_cols = 28, 28

# 通过Keras封装好的API加载MNIST数据。

(trainX, trainY), (testX, testY) = mnist.load_data()

trainX = trainX.reshape(trainX.shape[0], img_rows * img_cols)

testX = testX.reshape(testX.shape[0], img_rows * img_cols)

trainX = trainX.astype('float32')

testX = testX.astype('float32')

trainX /= 255.0

testX /= 255.0

trainY = keras.utils.to_categorical(trainY, num_classes)

testY = keras.utils.to_categorical(testY, num_classes)

# 2. 定义模型。

# 定义两个输入。

input1 = Input(shape=(784,), name = "input1")

input2 = Input(shape=(10,), name = "input2")

# 定义第一个输出。

x = Dense(1, activation='relu')(input1)

output1 = Dense(10, activation='softmax', name = "output1")(x)

# 定义第二个输出。

y = keras.layers.concatenate([x, input2])

output2 = Dense(10, activation='softmax', name = "output2")(y)

model = Model(inputs=[input1, input2], outputs=[output1, output2])

# 定义损失函数、优化函数和评测方法。

model.compile(loss=keras.losses.categorical_crossentropy,optimizer=keras.optimizers.SGD(),loss_weights = [1, 0.1],metrics=['accuracy'])

# 3. 模型训练。

model.fit([trainX, trainY], [trainY, trainY],batch_size=128,epochs=20,validation_data=([testX, testY], [testY, testY]))