2-Boston.py

# -*- coding: utf-8 -*-
"""
Created on Wed Aug 29 14:18:59 2018

@author: Zhengyuv
"""
import tensorflow as tf
from sklearn.datasets import load_boston
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler

#boston=load_boston()

w1 = tf.Variable(tf.random_normal(shape=[13,5],dtype=tf.float64,stddev=0.001))
b1 = tf.Variable(tf.constant(value=0.0,shape=[5,],dtype=tf.float64))
w2 = tf.Variable(tf.random_normal(shape=[5,1],dtype=tf.float64,stddev=0.001))
b2 = tf.Variable(tf.constant(value=0.0,shape=[1,],dtype=tf.float64))

def inference(X):
    a = tf.nn.relu(tf.matmul(X,w1)+b1)
    return tf.matmul(a,w2)+b2

def loss(X,Y):
    Y_predict = inference(X)
    return Y_predict,tf.reduce_mean(tf.squared_difference(Y,Y_predict))
    
def inputs():
    boston = load_boston()
    minMax = MinMaxScaler()
    X = minMax.fit_transform(boston.data)
    target=boston.target.reshape(-1,1)
    Y = minMax.fit_transform(target)
    return X,Y

def train(total_loss):
    lr = 0.01
    return tf.train.GradientDescentOptimizer(lr).minimize(total_loss)

def plot_fun(Y,Y_p):
# =============================================================================
#     plt.figure()
#     plt.plot(Y,'bo',alpha=0.5)
#     plt.ylabel('price')
# =============================================================================
    plt.figure()
    plt.plot(Y,'bo',alpha=0.5)
    plt.plot(Y_p,'ro',alpha=0.5)
    plt.ylabel('price')
    plt.show()

#def evaluate():
    
with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    
    X,Y=inputs()
    
    Y_predict,total_loss = loss(X,Y)
    train_op = train(total_loss)
    
    train_steps = 1000
    for i in range(train_steps):
        sess.run(train_op)
        if i%10 == 0:
            print("epoch",i,"loss:",sess.run(total_loss))
            
    Y_p=sess.run(Y_predict)
    plot_fun(Y,Y_p)
    
    sess.close()