# Indexing tensors In torch: Tensors and Neural Networks with 'GPU' Acceleration

```knitr::opts_chunk\$set(
collapse = TRUE,
comment = "#>",
eval = identical(Sys.getenv("TORCH_TEST", unset = "0"), "1"),
purl = FALSE
)
```
```library(torch)
```

In this article we describe the indexing operator for torch tensors and how it compares to the R indexing operator for arrays.

Torch's indexing semantics are closer to numpy's semantics than R's. You will find a lot of similarities between this article and the `numpy` indexing article available here.

## Single element indexing

Single element indexing for a 1-D tensors works mostly as expected. Like R, it is 1-based. Unlike R though, it accepts negative indices for indexing from the end of the array. (In R, negative indices are used to remove elements.)

```x <- torch_tensor(1:10)
x
x[-1]
```

You can also subset matrices and higher dimensions arrays using the same syntax:

```x <- x\$reshape(shape = c(2,5))
x
x[1,3]
x[1,-1]
```

Note that if one indexes a multidimensional tensor with fewer indices than dimensions, one gets an error, unlike in R that would flatten the array. For example:

```x
```

## Slicing and striding

It is possible to slice and stride arrays to extract sub-arrays of the same number of dimensions, but of different sizes than the original. This is best illustrated by a few examples:

```x <- torch_tensor(1:10)
x
x[2:5]
x[1:(-7)]
```

You can also use the `1:10:2` syntax which means: In the range from 1 to 10, take every second item. For example:

```x[1:5:2]
```

Another special syntax is the `N`, meaning the size of the specified dimension.

```x[5:N]
```

Note: the slicing behavior relies on Non Standard Evaluation. It requires that the expression is passed to the `[` not exactly the resulting R vector.

To allow dynamic dynamic indices, you can create a new slice using the `slc` function. For example:

```x[1:5:2]
```

is equivalent to:

```x[slc(start = 1, end = 5, step = 2)]
```

## Getting the complete dimension

Like in R, you can take all elements in a dimension by leaving an index empty.

Consider a matrix:

```x <- torch_randn(2, 3)
x
```

The following syntax will give you the first row:

```x[1,]
```

And this would give you the first 2 columns:

```x[,1:2]
```

## Dropping dimensions

By default, when indexing by a single integer, this dimension will be dropped to avoid the singleton dimension:

```x <- torch_randn(2, 3)
x[1,]\$shape
```

You can optionally use the `drop = FALSE` argument to avoid dropping the dimension.

```x[1,,drop = FALSE]\$shape
```

It's possible to add a new dimension to a tensor using index-like syntax:

```x <- torch_tensor(c(10))
x\$shape
x[, newaxis]\$shape
x[, newaxis, newaxis]\$shape
```

You can also use `NULL` instead of `newaxis`:

```x[,NULL]\$shape
```

## Dealing with variable number of indices

Sometimes we don't know how many dimensions a tensor has, but we do know what to do with the last available dimension, or the first one. To subsume all others, we can use `..`:

```z <- torch_tensor(1:125)\$reshape(c(5,5,5))
z[1,..]
z[..,1]
```

## Indexing with vectors

Vector indexing is also supported but care must be taken regarding performance as, in general its much less performant than slice based indexing.

Note: Starting from version 0.5.0, vector indexing in torch follows R semantics, prior to that the behavior was similar to numpy's advanced indexing. To use the old behavior, consider using `?torch_index`, `?torch_index_put` or `torch_index_put_`.

```x <- torch_randn(4,4)
x[c(1,3), c(1,3)]
```

You can also use boolean vectors, for example:

```x[c(TRUE, FALSE, TRUE, FALSE), c(TRUE, FALSE, TRUE, FALSE)]
```

The above examples also work if the index were long or boolean tensors, instead of R vectors. It's also possible to index with multi-dimensional boolean tensors:

```x <- torch_tensor(rbind(
c(1,2,3),
c(4,5,6)
))
x[x>3]
```

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torch documentation built on June 7, 2023, 6:19 p.m.