ValueError: cannot reshape array of size 0 into shape (28,28,1)问题解决的具体方法措施
时间: 2023-08-08 08:10:20 浏览: 57
好的,以下是一些可能有用的具体方法措施:
1. 检查输入数据的形状是否正确。在这种情况下,输入数据的形状应该是(样本数,28,28,1),其中样本数是数据集中图像的数量。如果输入数据的形状不正确,可以使用reshape函数重新调整形状。
2. 检查数据集是否已经被正确读入。如果数据集没有被正确读入,就会出现这种错误。可以使用print语句检查数据集的形状和数据类型。
3. 检查是否有缺失的数据。在某些情况下,数据集中可能会存在缺失的数据,这会导致输入数据的形状不正确。可以使用pandas库等工具来检查是否有缺失的数据。
4. 检查是否有错误的数据类型。在某些情况下,数据集中可能存在错误的数据类型,这会导致输入数据的形状不正确。可以使用astype函数将数据类型转换为正确的类型。
5. 检查是否有损坏的数据。在某些情况下,数据集中可能存在损坏的数据,这会导致输入数据的形状不正确。可以使用try-except语句来处理损坏的数据,或者使用其他方法来删除损坏的数据。
6. 如果以上方法都无法解决问题,可以在网络上搜索相关的解决方案或咨询专业人士的意见。
相关问题
ValueError: cannot reshape array of size 0 into shape (80,60)
This error occurs when you try to reshape an array into a shape that is incompatible with its current size. In this case, you are trying to reshape an array of size 0 into a shape of (80,60), which is not possible.
There are a few possible reasons why this error might occur:
1. Incorrect input: It's possible that the input data you are trying to reshape is empty or missing some values.
2. Incorrect dimensions: It's possible that the dimensions of your array are not what you expect them to be. For example, if you have a 1D array with 4800 elements, you could reshape it into a 2D array with shape (80, 60), but if you have a 1D array with only 10 elements, you cannot reshape it into a 2D array with shape (80, 60).
3. Incorrect reshape operation: It's possible that you are using the reshape() function incorrectly. Make sure that you are passing in the correct arguments and that the dimensions you are trying to reshape to are compatible with the size of the array.
To fix the error, you should check your input data and make sure it is not empty or missing any values. You should also double-check the dimensions of your array and make sure they are what you expect them to be. Finally, make sure that you are using the reshape() function correctly and passing in the correct arguments.
ValueError: cannot reshape array of size 0 into shape (0,newaxis)
这个错误通常表示你正在尝试将一个大小为0的数组进行reshape操作,而这是不允许的。你需要检查一下你的代码,确保你正在操作的数组是非空的。
另外,你也可以在进行reshape操作之前,先打印一下数组的大小,以确保它不为空。你可以使用以下代码来打印数组的大小:
```
import numpy as np
arr = np.array([]) # 假设这是你的数组
print(arr.shape)
```
如果打印出来的是(0,),那么说明你的数组是空的,你需要在对它进行reshape操作之前先填充数据。