# 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)

# 将图像像素转化为0到1之间的实数。

trainX = trainX.astype('float32')

testX = testX.astype('float32')

trainX /= 255.0

testX /= 255.0

# 将标准答案转化为需要的格式（one-hot编码）。

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

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

# 2. 通过返回值的方式定义模型。

inputs = Input(shape=(784,))

x = Dense(500, activation='relu')(inputs)

predictions = Dense(10, activation='softmax')(x)

model = Model(inputs=inputs, outputs=predictions)

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

# 3. 训练模型。

model.fit(trainX, trainY,batch_size=32,epochs=10,validation_data=(testX, testY))