tf_upgrade_v2.exe实验

实验前

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))

实验后：Weights、biases初始值为随机值，但是随着迭代它们会趋近于真值。条件为loss最小。

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))

代码对比可看出代码前后的变化

https://blog.csdn.net/u012223913/article/details/79097297