2.tf.keras实现线性回归

1.使用Income数据集

 

,Education,Income
1,10.000000 ,26.658839
2,10.401338 ,27.306435
3,10.842809 ,22.132410
4,11.244147 ,21.169841
5,11.645449 ,15.192634
6,12.086957 ,26.398951
7,12.048829 ,17.435307
8,12.889632 ,25.507885
9,13.290970 ,36.884595
10,13.732441 ,39.666109
11,14.133779 ,34.396281
12,14.635117 ,41.497994
13,14.978589 ,44.981575
14,15.377926 ,47.039595
15,15.779264 ,48.252578
16,16.220736 ,57.034251
17,16.622074 ,51.490919
18,17.023411 ,51.336621
19,17.464883 ,57.681998
20,17.866221 ,68.553714
21,18.267559 ,64.310925
22,18.709030 ,68.959009
23,19.110368 ,74.614639
24,19.511706 ,71.867195
25,19.913043 ,76.098135
26,20.354515 ,75.775216
27,20.755853 ,72.486055
28,21.167191 ,77.355021
29,21.598662 ,72.118790
30,22.000000 ,80.260571

　　

 

2.python代码

#!/usr/bin/env python
# coding: utf-8

import tensorflow as tf
print("Tensorflow version:{}".format(tf.__version__))
import pandas as pd
data = pd.read_csv("./dataset/Income1.csv")

import matplotlib.pyplot as plt

plt.scatter(data.Education,data.Income)

x = data.Education
y = data.Income

model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(1,input_shape=(1,)))
model.summary()

model.compile(optimizer="adam",   ##优化方法使用内置的adam
              loss="mse"         ##损失函数使用均方差  
)


history = model.fit(x,y,epochs=5000)             ##对构建的模型进行训练;  训练5000次


model.predict(x)

model.predict(pd.Series(20))        ##输入值应该为Series类型