# -*- coding: utf-8 -*-

import numpy as np

np.random.seed(1337) #for reproducibility再现性

from keras.models import Sequential#按层

from keras.layers import Dense#全连接层

import matplotlib.pyplot as plt

#creat some data

X = np.linspace(-1,1,200) #200个x，-1到1之间

np.random.shuffle(X) #randomize the data

Y = 0.5*X +2 + np.random.normal(0,0.05,(200,))

#plot data

plt.scatter(X,Y)

plt.show

X_train,Y_train= X[:160],Y[:160]#160个

X_test,Y_test = X[160:], Y[160:]#40个

#build a neural network from the 1st layer to the the last layer

model = Sequential()

model.add(Dense(output_dim=1,input_dim=1))#加一层

#choose loss function and optimizing method

#mse方差

model.compile(loss='mse',optimizer='sgd')

#training

print("Training~~~~~~~~")

for step in range(301):

    cost = model.train_on_batch(X_train,Y_train)#一批一批的数据,这里一批选择全部数据

    if step %100==0:

        print('train cost:',cost)

#test

print('\nTesting~~~~~~~~')

cost = model.evaluate(X_test,Y_test,batch_size=40)

print('test cost:',cost)

W,b = model.layers[0].get_weights()

print('Weights=',W,'\nbiases=',b)

#plotting the prediction

Y_pred =model.predict(X_test)

plt.scatter(X_test,Y_test)

plt.plot(X_test,Y_pred)