shape mismatch: value array of shape (3,3,7075200) could not be broadcast to indexing result of shape (3,7075200)
时间: 2023-12-14 07:05:15 浏览: 27
出现这个错误是因为你尝试将一个形状为 (3, 3, 7075200) 的数组广播到一个形状为 (3, 7075200) 的数组上。这是由于广播规则不匹配导致的。在 NumPy 中,广播规则是指两个数组维度的比较和匹配,以确定它们是否可以相互广播。
要解决这个问题,你需要检查你的代码,确保你正在对具有相同形状或可以相互广播的数组进行操作。你可以使用 `reshape`、`transpose` 或 `expand_dims` 等函数来调整数组的形状以确保它们可以相互广播。
下面是一些可能有用的代码示例:
```python
# 例如,将形状为 (3, 3, 7075200) 的数组转换为形状为 (3, 7075200, 3) 的数组
arr = np.random.rand(3, 3, 7075200)
new_arr = arr.transpose((0, 2, 1))
# 或者将形状为 (3, 3, 7075200) 的数组转换为形状为 (3, 7075200) 的数组
arr = np.random.rand(3, 3, 7075200)
new_arr = arr.reshape((3, -1))
# 或者将形状为 (3, 7075200) 的数组转换为形状为 (3, 3, 7075200) 的数组
arr = np.random.rand(3, 7075200)
new_arr = np.expand_dims(arr, axis=1)
new_arr = np.tile(new_arr, (1, 3, 1))
```
请根据你的具体情况选择适当的方法。
相关问题
shape mismatch: value array of shape (3,) could not be broadcast to indexing result of shape (15804,4)
This error occurs when you are trying to assign a value to an array using indexing, but the shape of the value array does not match the shape of the indexing result. In this case, the value array has a shape of (3,), which means it has three elements, but the indexing result has a shape of (15804,4), which means it has 15804 rows and 4 columns.
To fix this error, you need to make sure that the shape of the value array matches the shape of the indexing result. You can do this by reshaping the value array to match the indexing result, or by using different indexing techniques that result in a shape that matches the value array.
shape mismatch: value array of shape (6664,) could not be broadcast to indexing result of shape (6664,1)
This error occurs when there is a mismatch between the shape of the input array and the shape of the indexing result. In this case, the input array has a shape of (6664,), which means it has only one dimension with 6664 elements. However, the indexing result has a shape of (6664,1), which means it has two dimensions with 6664 rows and 1 column.
To fix this error, you need to reshape the input array to match the shape of the indexing result. You can do this by adding a new dimension to the input array using the numpy.newaxis attribute.
For example, if your input array is called arr, you can reshape it like this:
arr = arr[:, np.newaxis]
This will add a new dimension to the input array and reshape it to have a shape of (6664,1), which matches the shape of the indexing result.