用tensorflow算矩阵乘法

import tensorflow as tf
matrix1=tf.constant([[3,3]])
matrix2=tf.constant([[2],
[2]])
product=tf.matmul(matrix1,matrix2)
sess=tf.Session()
result=sess.run(product)
print(result)
[[12]]

用tensorflow进行估计参数 （模拟）

import numpy as np
import tensorflow as tf

create data###

x_data=np.random.rand(100).astype(np.float32)
y_data=x_data0.1+0.3
Weights=tf.Variable(tf.random_uniform([1],-1.0,1.0))
biases=tf.Variable(tf.zeros([1]))
y=Weightsx_data+biases
loss=tf.reduce_mean(tf.square(y-y_data))
optimizer=tf.train.GradientDescentOptimizer(0.5)
train=optimizer.minimize(loss)
init=tf.initialize_all_variables()
sess=tf.Session()
sess.run(init)
for step in range(201):#每隔二十次输出一次结果
sess.run(train)
if step%20==0:
print(step,sess.run(Weights),sess.run(biases))
0 [0.8221704] [-0.14846359]
20 [0.25718114] [0.21362415]
40 [0.13320896] [0.28175065]
60 [0.10701634] [0.2961443]
80 [0.10148242] [0.29918537]
100 [0.10031319] [0.2998279]
120 [0.10006618] [0.29996365]
140 [0.10001396] [0.29999232]
160 [0.10000297] [0.29999837]
180 [0.1000006] [0.29999968]
200 [0.10000014] [0.29999995]