a.expand([3,2])
时间: 2023-12-28 10:03:57 浏览: 25
`expand([3,2])` 可能是指 Python 中的 `numpy.expand_dims()` 函数,该函数用于在给定的数组(或张量)中添加一个维度。在这里,将 `[3,2]` 转换为一个二维数组,其形状为 `(1, 2)`。因此,可以使用以下代码来实现该操作:
```python
import numpy as np
arr = np.array([3, 2])
expanded_arr = np.expand_dims(arr, axis=0)
print(expanded_arr)
```
输出结果为:
```
[[3 2]]
```
相关问题
tensor.expand
引用和引用[2]提供了关于`tensor.expand()`函数的信息。`tensor.expand()`函数用于扩展张量的维度,并且可以通过指定扩展后的大小来实现。扩展操作不会共享内存,因此扩展后的张量与原始张量是独立的。
`tensor.expand_as()`函数是`tensor.expand()`函数的一种简化形式,它将当前张量扩展为与另一个张量相同的大小。
以下是一些示例说明`tensor.expand()`函数的用法:
1. 将尺寸为`torch.Size([n, 1])`的张量扩展为尺寸为`torch.Size([n, m])`的张量:
`a.expand(3,2)`结果为`tensor([[2, 2], [3, 3], [4, 4]])`
2. 将`-1`赋值给“无需扩展维度”,同时将尺寸为`torch.Size([n, 1])`的张量扩展为尺寸为`torch.Size([n, 4])`的张量:
`a.expand(-1,4)`结果为`tensor([[2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]])`
3. 将`-1`赋值给“新扩展维度”,此时尺寸为`torch.Size([n, 1])`的张量保持原状,不扩展:
`a.expand(3,-1)`结果为`tensor([, , ])`
4. 同时将`-1`赋值给“新扩展维度”和“无需扩展维度”,此时尺寸为`torch.Size([n, 1])`的张量保持原状,不扩展:
`a.expand(-1,-1)`结果为`tensor([, , ])`
相关问题:
1. `tensor.expand()`函数的返回值是什么类型的?
2. 如何在PyTorch中创建一个指定大小的空
torch.expand
torch.expand is a function in PyTorch that expands the tensor along the specified dimensions. It replicates the tensor data along the specified dimensions to match the given shape.
The syntax for the function is as follows:
```python
torch.expand(input, size)
```
Here, `input` is the input tensor and `size` is the desired size of the output tensor. The size is a tuple of integers that specifies the desired size along each dimension.
For example, consider the following code snippet:
```python
import torch
x = torch.tensor([[1,2], [3,4]])
y = torch.expand(x, (2,3))
print(y)
```
Output:
```
tensor([[1, 2, 1, 2, 1, 2],
[3, 4, 3, 4, 3, 4],
[1, 2, 1, 2, 1, 2],
[3, 4, 3, 4, 3, 4]])
```
Here, `x` is a 2x2 tensor. We use `torch.expand` to expand `x` to a 4x6 tensor by replicating the data along the rows and columns. The resulting tensor `y` has the desired size of (2,3).