return _nx.concatenate(expanded_arrays, axis=axis, out=out) File "<__array_function__ internals>", line 180, in concatenate numpy.core._exceptions._ArrayMemoryError: Unable to allocate 3.67 GiB for an array with shape (16, 3, 4530, 4530) and data type float32
时间: 2023-06-08 09:05:35 浏览: 57
看起来您在尝试使用numpy中的concatenate函数将几个数组合并成一个大数组,但是由于内存错误,无法分配足够的内存来创建这个大数组。您可以尝试减少数组的数量或大小,或者增加系统内存以解决此问题。
相关问题
seq_list = np.concatenate(seq_list, axis=0)
这行代码将一个列表 seq_list 中的所有数组沿着第0个轴(行)进行拼接,最终生成一个新的一维数组。这里使用了 NumPy 库中的 np.concatenate 函数,其返回值就是拼接后的新数组。
例如,假设 seq_list 是一个包含三个一维数组的列表:
```python
import numpy as np
a = np.array([1, 2, 3])
b = np.array([4, 5, 6])
c = np.array([7, 8, 9])
seq_list = [a, b, c]
```
那么,使用 np.concatenate(seq_list, axis=0) 将这三个数组沿着第0个轴进行拼接:
```python
result = np.concatenate(seq_list, axis=0)
print(result) # [1 2 3 4 5 6 7 8 9]
```
这里的 result 就是拼接后的新数组,其内容为 [1, 2, 3, 4, 5, 6, 7, 8, 9]。
data = np.concatenate(data_list, axis=0)
This line of code is using the numpy function `concatenate` to join a list of arrays along a specified axis (in this case, axis 0).
`data_list` is assumed to be a list of numpy arrays, all with the same number of columns. `np.concatenate` takes these arrays and combines them into a single array called `data`, where the first axis of each array is stacked on top of each other.
For example, if `data_list` contained two arrays with shape `(10, 3)` and `(7, 3)`, respectively, then `data` would have shape `(17, 3)`, where the first 10 rows would come from the first array and the remaining 7 rows from the second array.