non-broadcastable output operand with shape (4,1) doesnt match the broadcast shape (4,5)
时间: 2024-03-30 14:41:05 浏览: 18
这个错误通常出现在使用广播(broadcast)操作时,两个数组的形状不兼容。在广播操作中,两个数组的形状要么相同,要么其中一个数组的形状的某些维度为1,此时可以通过复制来扩展成同样的形状。比如,一个形状为(4,1)的数组和一个形状为(4,5)的数组进行广播操作时,不能匹配,因为它们的维数不同。你需要检查你的代码,查看哪些操作使用了广播,然后确保两个数组的形状兼容。可能需要使用 reshape() 或 expand_dims() 等函数来改变数组的形状,以满足广播的要求。
相关问题
non-broadcastable output operand with shape (50,1) doesn t match the broadcast shape (50,5)
This error occurs when you are trying to perform a broadcast operation on two arrays with incompatible shapes. In this case, the output operand has a shape of (50,1) which means it has 50 rows and 1 column, while the broadcast shape has a shape of (50,5) which means it has 50 rows and 5 columns.
Broadcasting is a way to perform operations on arrays with different shapes by stretching or duplicating one or more of the arrays to match the shape of the other. However, in this case, the arrays cannot be broadcasted because they have different numbers of columns.
To fix this error, you need to reshape one of the arrays to have the same number of columns as the other. You can use the numpy.reshape() function to do this. For example, if you want to reshape the output operand to have the same shape as the broadcast shape, you can use the following code:
```
import numpy as np
# create two arrays with incompatible shapes
a = np.ones((50,1))
b = np.ones((50,5))
# reshape the output operand to have the same shape as the broadcast shape
a = np.reshape(a, (50,5))
# perform the broadcast operation
c = a + b
```
This code will reshape the output operand to have the same shape as the broadcast shape, and then perform the broadcast operation without any errors.
ValueError: non-broadcastable output operand with shape (1640,1) doesn't match the broadcast shape (1640,4)
This error occurs when you are trying to perform a mathematical operation that cannot be broadcasted to the output shape. In this case, the output operand has shape (1640,1) but the broadcast shape is (1640,4).
To fix this error, you can reshape the output operand to match the broadcast shape using the numpy.reshape() function. For example:
```python
import numpy as np
# create a matrix with shape (1640, 4)
a = np.ones((1640, 4))
# create an output operand with shape (1640, 1)
b = np.ones((1640, 1))
# reshape the output operand to match the broadcast shape
b = b.reshape(1640, 4)
# perform the mathematical operation
c = a + b
```
In this example, we reshape the output operand `b` to have shape (1640, 4) using the `reshape()` function. Now, we can perform the mathematical operation without getting the ValueError.