tensorflow基本教程1

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import tensorflow as tf
import numpy as np
#creat data
x_data=np.random.rand(100).astype(np.float32)#定义数据是float32
y_data=x_data*0.1+0.3
###creat tensorflow structure start
#初始权重和偏置
Weights=tf.Variable(tf.random_uniform([1],-0.1,1.0))#形状和范围
biases=tf.Variable(tf.zeros([1]))

y=Weights*x_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()
###creat tensorflow structure end
sess=tf.Session()
sess.run(init)#very important
for step in range(201):
    sess.run(train)
    if step%20==0:
        print(step,sess.run(Weights),sess.run(biases))

1.数据生成

2.参数初始化

3.训练