tensorflow拟合随机生成的三维数据【学习笔记】

#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on Thu Oct 11 19:54:15 2018 @author: zhuang
""" import tensorflow as tf
import numpy as np #生成随机数
x_data = np.float32(np.random.rand(2,100))
y_data = np.dot([0.100,0.200],x_data) + 0.300 # 初始化参数
b = tf.Variable(tf.zeros([1]))
# w为1x2的矩阵，在-1.0到1.0之间均匀分布
w = tf.Variable(tf.random_uniform([1,2],-1.0,1.0))
y = tf.matmul(w,x_data) + b # 使用最小化方差进行梯度下降，来不断更新参数，学习率设置为0.5
loss = tf.reduce_mean(tf.square(y-y_data))
optimizer = tf.train.GradientDescentOptimizer(0.5)
train = optimizer.minimize(loss) #init = tf.initialize_all_variables()
#新版本的tensorflow使用下面的接口，老版本使用上面的接口
init = tf.global_variables_initializer() sess = tf.Session()
sess.run(init) #进行拟合找到适合的参数
for step in xrange(0,201):
sess.run(train)
if (step) % 20 == 0:
print step,sess.run(w),sess.run(b)

runfile('/home/zhuang/project/1-AI/My_AI_Study_Project/3-tensorflow/005-test.py', wdir='/home/zhuang/project/1-AI/My_AI_Study_Project/3-tensorflow')
0 [[0.39894426 0.3333286 ]] [0.14586714]
20 [[0.16806586 0.26403958]] [0.22699882]
40 [[0.12101775 0.22725435]] [0.27309927]
60 [[0.10728491 0.21063562]] [0.28998315]
80 [[0.10265137 0.20403926]] [0.2962562]
100 [[0.10098286 0.20152006]] [0.29859895]
120 [[0.10036676 0.20057023]] [0.29947543]
140 [[0.10013718 0.20021366]] [0.29980358]
160 [[0.10005137 0.20008004]] [0.29992643]
180 [[0.10001925 0.20002998]] [0.29997244]
200 [[0.10000721 0.20001122]] [0.29998967]