import tensorflow as tf

import numpy as np

#create data

x_data=np.random.rand(100).astype(np.float32)#训练样本

y_data=x_data*0.1+0.3#求参数(隐去真实参数和函数式)怎么知道样本符合的这是线性函数呢？如果假设样本符合的是二次函数呢？能求出参数值吗？

###create tensorflow structure start###

Weights = tf.Variable(tf.random_uniform([1],-1.0,1.0))#随机参数初值

biases = tf.Variable(tf.zeros([1]))

y=Weights*x_data+biases#按随机参数拟合的y值一开始和y_data真值差很大

loss = tf.reduce_mean(tf.square(y-y_data))#损失值

optimizer = tf.train.GradientDescentOptimizer(0.5)

###create tensorflow structure end###

train = optimizer.minimize(loss)#训练

init = tf.initialize_all_variables()#初始化

sess = tf.Session()

sess.run(init)

for step in range(201):

    sess.run(train)

    if step % 20 == 0:

        print(step, sess.run(Weights), sess.run(biases))

import tensorflow as tf

import numpy as np

#create data

x_data=np.random.rand(100).astype(np.float32)

y_data=x_data*0.1+0.3

###create tensorflow structure start###

Weights = tf.Variable(tf.random.uniform([1],-1.0,1.0))

biases = tf.Variable(tf.zeros([1]))

y=Weights*x_data+biases

loss = tf.reduce_mean(input_tensor=tf.square(y-y_data))

optimizer = tf.compat.v1.train.GradientDescentOptimizer(0.5)

###create tensorflow structure end###

train = optimizer.minimize(loss)

init = tf.compat.v1.initialize_all_variables()

sess = tf.compat.v1.Session()

sess.run(init)

for step in range(201):

    sess.run(train)

    if step % 20 == 0:

        print(step, sess.run(Weights), sess.run(biases))