TypeError: Can not convert a float32 into a Tensor or Operation.

错误

TypeError: Can not convert a float32 into a Tensor or Operation.
# 类型错误:不能将一个浮动32转换为一个张量或操作。TypeError: Fetch argument 2.3025854 has invalid type <class 'numpy.float32'>, must be a string or Tensor. (Can not convert a float32 into a Tensor or Operation.)

如其意,类型错误:不能将一个浮动32转换为一个张量或操作。也就是在计算图实际运算的时候发生的错误,错误为数据类型错误,将应该是传入的张量或者操作的的地方传入了一个 float32 类型的数据.

分析

源码

      for step in range(1000):batch_xs, batch_ys = mnist.train.next_batch(100)_, Train_Loss = sess.run([Train_Step,Train_Loss], feed_dict={X: batch_xs, Y_true: batch_ys})print('Train step :',step,'Train_Losss :',Train_Loss)

错误产生的时候我查了查代码,没明白什么意思,后来debug才发现,是变量在第二遍运行的时候数据类型发生了改变,

其改变是在第一遍的时候,仔细看源代码,输出的Train_Loss与后面sess.run([Train_Step,Train_Loss], feed_dict={X: batch_xs, Y_true: batch_ys})中的’Train_Loss’同名,显然第一遍运行之后,原本应该传入session的Train_Loss就从张量变成了 float32

解决方案

避免重名,使用不同的名字
如原来的

_, Train_Loss = sess.run([Train_Step,Train_Loss], feed_dict={X: batch_xs, Y_true: batch_ys})

改为

 _, train_loss = sess.run([Train_Step,Train_Loss], feed_dict={X: batch_xs, Y_true: batch_ys})

BUG就消失了
这是一个很低级的BUG…..但是也很有趣.

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TypeError: Can not convert a float32 into a Tensor or Operation.

错误

分析

解决方案

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