tensorflow编码错误：TypeError: unsupported operand type(s) for *: ‘float‘ and ‘NoneType‘

tensorflow 编码出现错误：TypeError: unsupported operand type(s) for *: ‘float’ and ‘NoneType’

原码如下：

import tensorflow as tf
w=tf.Variable(tf.constant(5,dtype=float)) #给w赋随机值，初始值为float 5
lr=0.2 #学习率设为0.2
epoch=20 #设置循环迭代次数
for i in range(epoch):   #for循环20次对loss函数求导with tf.GradientTape() as DS: #导入求导模块并命名为DSloss=tf.square(w+1) #定义loss函数tidu=DS.gradient(loss,w) #调用求导模块的求导函数对w求偏导w=w-lr*tidu #更新w值
print("after %s epoch,w is %f,loss is %f"%(epoch,w.numpy(),loss))

运行出现错误：
line 9, in
w=w-lr*grand
TypeError: unsupported operand type(s) for *: ‘float’ and ‘NoneType’

原因是：参数类型错误。要将浮点数与None相乘，所以报错，在with tf.GradientTape() as tape里，参数系统默认跟踪类型为tf.Variable数据类型的数据，将参数类型改成：
w=tf.Variable(w-lr * tidu)
或自减：
w.assign_sub(lr*tidu)
正确代码如下：

import tensorflow as tf
w=tf.Variable(tf.constant(5,dtype=float)) #给w赋随机值，初始值为float 5
lr=0.2 #学习率设为0.2
epoch=20 #设置循环迭代次数
for i in range(epoch):   #for循环20次对loss函数求导with tf.GradientTape() as DS: #导入求导模块并命名为DSloss=tf.square(w+1) #定义loss函数tidu=DS.gradient(loss,w) #调用求导模块的求导函数对w求偏导w=tf.Variable(w-lr*tidu) #更新w值  也可以写成w.assign_sub(lr*tidu)
print("after %s epoch,w is %f,loss is %f"%(epoch,w.numpy(),loss))

再看一下运行结果
after 20 epoch,w is -0.999781,loss is 0.000000
Process finished with exit code 0
经过20个周期迭代，成功算得损失函数为0
参考博客原文链接：https://blog.csdn.net/weixin_43788143/article/details/107024185

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