import numpy as np

from keras.datasets import mnist

from keras.utils import np_utils

from keras.models import Sequential

from keras.layers import Dense

from keras.optimizers import SGD

 # 载入数据

 (x_train,y_train),(x_test,y_test) = mnist.load_data()

 # (60000,28,28)

 print('x_shape:',x_train.shape)

 # (60000)

 print('y_shape:',y_train.shape)

 # (60000,28,28)->(60000,784)

 x_train = x_train.reshape(x_train.shape[0],-1)/255.0

 x_test = x_test.reshape(x_test.shape[0],-1)/255.0

 # 换one hot格式

 y_train = np_utils.to_categorical(y_train,num_classes=10)

 y_test = np_utils.to_categorical(y_test,num_classes=10)

 # 创建模型，输入784个神经元，输出10个神经元

 model = Sequential([

         Dense(units=10,input_dim=784,bias_initializer='one',activation='softmax')

     ])

 # 定义优化器

 sgd = SGD(lr=0.2)

 # 定义优化器，loss function，训练过程中计算准确率

 model.compile(

     optimizer = sgd,

     loss = 'mse',

     metrics=['accuracy'],

 )

 # 训练模型

 model.fit(x_train,y_train,batch_size=32,epochs=10)

 # 评估模型

 loss,accuracy = model.evaluate(x_test,y_test)

 print('\ntest loss',loss)

 print('accuracy',accuracy)