> For the complete documentation index, see [llms.txt](https://deeplearningpytorch.gitbook.io/dlpt/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://deeplearningpytorch.gitbook.io/dlpt/pytorch.md).

# Pytorch

## Detach and clone

<https://discuss.pytorch.org/t/clone-and-detach-in-v0-4-0/16861\\>

> 1. `tensor.detach()` creates a tensor that shares storage with `tensor` that does not require grad. `tensor.clone()`creates a copy of tensor that imitates the original `tensor`'s `requires_grad` field. You should use `detach()` when attempting to remove a tensor from a computation graph, and `clone`as a way to copy the tensor while still keeping the copy as a part of the computation graph it came from.
> 2. `tensor.data` returns a new tensor that shares storage with `tensor`. However, it always has `requires_grad=False` (even if the original `tensor` had `requires_grad=True`
> 3. You should try not to call `tensor.data` in 0.4.0. What are your use cases for `tensor.data`?
> 4. `tensor.clone()` makes a copy of `tensor`. `variable.clone()` and `variable.detach()` in 0.3.1 act the same as `tensor.clone()` and `tensor.detach()` in 0.4.0.

| Item           | detach | clone                      | data             |
| -------------- | ------ | -------------------------- | ---------------- |
| requires\_grad | False  | Same as origin tensor (??) | False            |
| note           |        |                            | Not use in 0.4.0 |

## Retain graph

<https://discuss.pytorch.org/t/runtimeerror-trying-to-backward-through-the-graph-a-second-time-but-the-buffers-have-already-been-freed-specify-retain-graph-true-when-calling-backward-the-first-time/6795>

## torch.gather

torch.gath(input, dim, index, out=None) -> Tensor

Collect values along an axis specified by dim from input tensor

Example: dim=1, index is the column of values

> a = torch.tensor(\[\[1,2],\[3,4]]) torch.gather(a, 1, torch.LongTensor(\[\[1,1],\[1,0]])) 2 2 4 3

Example: dim=1, index is the column of values

> a = torch.tensor(\[\[1,2],\[3,4]]) torch.gather(a, 1, torch.LongTensor(\[\[0,0],\[0,0]])) 1 1 3 3

Example: dim=0, index is the row of values

> a = torch.tensor(\[\[1,2],\[3,4]]) torch.gather(a, 0, torch.LongTensor(\[\[0,0],\[0,0]])) 1 2 1 2