思路：在数据上选择一条直线y=Wx+b，在这条直线上附件随机生成一些数据点如下图，让TensorFlow建立回归模型，去学习什么样的W和b能更好去拟合这些数据点。
1）随机生成1000个数据点，围绕在y=0.1x+0.3 周围，设置W=0.1，b=0.3，届时看构建的模型是否能学习到w和b的值。
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
num_points=1000
vectors_set=[]
for i in range(num_points):
x1=np.random.normal(0.0,0.55) #横坐标，进行随机高斯处理化，以0为均值，以0.55为标准差
y1=x1*0.1+0.3+np.random.normal(0.0,0.03) #纵坐标，数据点在y1=x1*0.1+0.3上小范围浮动
vectors_set.append([x1,y1])
x_data=[v[0] for v in vectors_set]
y_data=[v[1] for v in vectors_set]
plt.scatter(x_data,y_data,c='r')
plt.show()
构造数据如下图
2）构造线性回归模型，学习上面数据图是符合一个怎么样的W和b
W = tf.Variable(tf.random_uniform([1], -1.0, 1.0), name='W') # 生成1维的W矩阵，取值是[-1,1]之间的随机数
b = tf.Variable(tf.zeros([1]), name='b') # 生成1维的b矩阵，初始值是0
y = W * x_data + b # 经过计算得出预估值y
loss = tf.reduce_mean(tf.square(y - y_data), name='loss') # 以预估值y和实际值y_data之间的均方误差作为损失
optimizer = tf.train.GradientDescentOptimizer(0.5) # 采用梯度下降法来优化参数 学习率为0.5
train = optimizer.minimize(loss, name='train') # 训练的过程就是最小化这个误差值
sess = tf.Session()
init = tf.global_variables_initializer()
sess.run(init)
print ("W =", sess.run(W), "b =", sess.run(b), "loss =", sess.run(loss)) # 初始化的W和b是多少
for step in range(20): # 执行20次训练
sess.run(train)
print ("W =", sess.run(W), "b =", sess.run(b), "loss =", sess.run(loss)) # 输出训练好的W和b
打印每一次结果，如下图，随着迭代进行，训练的W、b越来越接近0.1、0.3，说明构建的回归模型确实学习到了之间建立的数据的规则。loss一开始很大，后来慢慢变小，说明模型表达效果随着迭代越来越好。
W = [-0.9676645] b = [0.] loss = 0.45196822
W = [-0.6281831] b = [0.29385352] loss = 0.17074569
W = [-0.39535886] b = [0.29584622] loss = 0.07962803
W = [-0.23685378] b = [0.2972129] loss = 0.03739688
W = [-0.12894464] b = [0.2981433] loss = 0.017823622
W = [-0.05548081] b = [0.29877672] loss = 0.008751821
W = [-0.00546716] b = [0.29920793] loss = 0.0045472304
W = [0.02858179] b = [0.2995015] loss = 0.0025984894
W = [0.05176209] b = [0.29970136] loss = 0.0016952885
W = [0.06754307] b = [0.29983744] loss = 0.0012766734
W = [0.07828666] b = [0.29993007] loss = 0.001082654
W = [0.08560082] b = [0.29999313] loss = 0.0009927301
W = [0.09058025] b = [0.30003607] loss = 0.0009510521
W = [0.09397022] b = [0.30006528] loss = 0.00093173544
W = [0.09627808] b = [0.3000852] loss = 0.00092278246
W = [0.09784925] b = [0.30009875] loss = 0.000918633
W = [0.09891889] b = [0.30010796] loss = 0.00091670983
W = [0.0996471] b = [0.30011424] loss = 0.0009158184
W = [0.10014286] b = [0.3001185] loss = 0.00091540517
W = [0.10048037] b = [0.30012143] loss = 0.0009152137
W = [0.10071015] b = [0.3001234] loss = 0.0009151251