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[1]
#> torch_tensor
#> 1
#> [ CPULongType{} ]
x[-1]
#> torch_tensor
#> 10
#> [ CPULongType{} ]
```

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

```
x <- x$reshape(shape = c(2,5))
x
#> torch_tensor
#> 1 2 3 4 5
#> 6 7 8 9 10
#> [ CPULongType{2,5} ]
x[1,3]
#> torch_tensor
#> 3
#> [ CPULongType{} ]
x[1,-1]
#> torch_tensor
#> 5
#> [ CPULongType{} ]
```

Note that if one indexes a multidimensional tensor with fewer indices
than dimensions, torch’s behaviour differs from R, which flattens the
array. In torch, the missing indices are considered complete slices
`:`

.

```
x[1]
#> torch_tensor
#> 1
#> 2
#> 3
#> 4
#> 5
#> [ CPULongType{5} ]
```

## 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
#> torch_tensor
#> 1
#> 2
#> 3
#> 4
#> 5
#> 6
#> 7
#> 8
#> 9
#> 10
#> [ CPULongType{10} ]
x[2:5]
#> torch_tensor
#> 2
#> 3
#> 4
#> 5
#> [ CPULongType{4} ]
x[1:(-7)]
#> torch_tensor
#> 1
#> 2
#> 3
#> 4
#> [ CPULongType{4} ]
```

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]
#> torch_tensor
#> 1
#> 3
#> 5
#> [ CPULongType{3} ]
```

Another special syntax is the `N`

, meaning the size of the
specified dimension.

```
x[5:N]
#> torch_tensor
#> 5
#> 6
#> 7
#> 8
#> 9
#> 10
#> [ CPULongType{6} ]
```

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]
#> torch_tensor
#> 1
#> 3
#> 5
#> [ CPULongType{3} ]
```

is equivalent to:

```
x[slc(start = 1, end = 5, step = 2)]
#> torch_tensor
#> 1
#> 3
#> 5
#> [ CPULongType{3} ]
```

## 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
#> torch_tensor
#> -0.4109 0.3080 -0.5038
#> 0.1034 0.2968 0.0255
#> [ CPUFloatType{2,3} ]
```

The following syntax will give you the first row:

```
x[1,]
#> torch_tensor
#> -0.4109
#> 0.3080
#> -0.5038
#> [ CPUFloatType{3} ]
```

And this would give you the first 2 columns:

```
x[,1:2]
#> torch_tensor
#> -0.4109 0.3080
#> 0.1034 0.2968
#> [ CPUFloatType{2,2} ]
```

## 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
#> [1] 3
```

You can optionally use the `drop = FALSE`

argument to
avoid dropping the dimension.

```
x[1,,drop = FALSE]$shape
#> [1] 1 3
```

## Adding a new dimension

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

```
x <- torch_tensor(c(10))
x$shape
#> [1] 1
x[, newaxis]$shape
#> [1] 1 1
x[, newaxis, newaxis]$shape
#> [1] 1 1 1
```

You can also use `NULL`

instead of
`newaxis`

:

```
x[,NULL]$shape
#> [1] 1 1
```

## 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,..]
#> torch_tensor
#> 1 2 3 4 5
#> 6 7 8 9 10
#> 11 12 13 14 15
#> 16 17 18 19 20
#> 21 22 23 24 25
#> [ CPULongType{5,5} ]
z[..,1]
#> torch_tensor
#> 1 6 11 16 21
#> 26 31 36 41 46
#> 51 56 61 66 71
#> 76 81 86 91 96
#> 101 106 111 116 121
#> [ CPULongType{5,5} ]
```

## 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)]
#> torch_tensor
#> 0.1484 -0.0094
#> 0.2006 -0.3957
#> [ CPUFloatType{2,2} ]
```

You can also use boolean vectors, for example:

```
x[c(TRUE, FALSE, TRUE, FALSE), c(TRUE, FALSE, TRUE, FALSE)]
#> torch_tensor
#> 0.1484 -0.0094
#> 0.2006 -0.3957
#> [ CPUFloatType{2,2} ]
```

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]
#> torch_tensor
#> 4
#> 5
#> 6
#> [ CPUFloatType{3} ]
```