x.squeeze(1)
时间: 2024-06-13 09:05:45 浏览: 8
x.squeeze(1)的作用是将x张量中维度为1的那一维去掉,如果该维度不为1,则不会有任何影响。具体实现可以参考以下代码:
```python
import torch
x = torch.randn(2, 1, 3) # 定义一个大小为(2, 1, 3)的张量
print(x.shape) # 输出(2, 1, 3)
y = x.squeeze(1) # 将第1维去掉
print(y.shape) # 输出(2, 3)
```
在上述代码中,我们定义了一个大小为(2, 1, 3)的张量x,然后使用x.squeeze(1)将第1维去掉,最终得到的张量y的大小为(2, 3)。
相关问题
x.squeeze(0)
x.squeeze(0)表示在第0维度上进行压缩,即去除长度为1的维度。可以看出,如果x的shape为[1, 2, 3],则经过x.squeeze(0)操作后,x的shape将变为[2, 3]。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
- *1* *2* *3* [pytorch 中 squeeze 和unsqueeze函数](https://blog.csdn.net/m0_63095029/article/details/122587500)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v93^chatsearchT3_1"}}] [.reference_item style="max-width: 100%"]
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x = x.squeeze(1)
This line of code removes the dimension with size 1 from the tensor x.
For example, if x is a tensor with shape (3, 1, 2), then after executing this line of code, x will have shape (3, 2).
This is useful when we want to remove unnecessary dimensions from a tensor. In some cases, we may have tensors with dimensions that we don't need for our calculations or modeling, and squeezing those dimensions can simplify our code and make it more efficient.
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