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,Dropout

 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

model = Sequential([

        Dense(units=200,input_dim=784,bias_initializer='one',activation='tanh'),

        Dropout(0.4),

        Dense(units=100,bias_initializer='one',activation='tanh'),

        Dropout(0.4),

        Dense(units=10,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 = 'categorical_crossentropy',

    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('test accuracy',accuracy)

loss,accuracy = model.evaluate(x_train,y_train)

print('train loss',loss)

print('train accuracy',accuracy)