# tensorflow::ops::ExpandDims

`#include <array_ops.h>`

Inserts a dimension of 1 into a tensor's shape.

## Summary

Given a tensor `input`, this operation inserts a dimension of 1 at the dimension index `axis` of `input`'s shape. The dimension index `axis` starts at zero; if you specify a negative number for `axis` it is counted backward from the end.

This operation is useful if you want to add a batch dimension to a single element. For example, if you have a single image of shape `[height, width, channels]`, you can make it a batch of 1 image with `expand_dims(image, 0)`, which will make the shape `[1, height, width, channels]`.

Other examples:

``` 't' is a tensor of shape [2]

shape(expand_dims(t, 0)) ==> [1, 2] shape(expand_dims(t, 1)) ==> [2, 1] shape(expand_dims(t, -1)) ==> [2, 1]

't2' is a tensor of shape [2, 3, 5]

shape(expand_dims(t2, 0)) ==> [1, 2, 3, 5] shape(expand_dims(t2, 2)) ==> [2, 3, 1, 5] shape(expand_dims(t2, 3)) ==> [2, 3, 5, 1] ```

This operation requires that:

`-1-input.dims() <= dim <= input.dims()`

This operation is related to `squeeze()`, which removes dimensions of size 1.

Arguments:

• scope: A Scope object
• axis: 0-D (scalar). Specifies the dimension index at which to expand the shape of `input`. Must be in the range `[-rank(input) - 1, rank(input)]`.

Returns:

• `Output`: Contains the same data as `input`, but its shape has an additional dimension of size 1 added.

### Constructors and Destructors

`ExpandDims(const ::tensorflow::Scope & scope, ::tensorflow::Input input, ::tensorflow::Input axis)`

### Public attributes

`output`
`::tensorflow::Output`

### Public functions

`node() const `
`::tensorflow::Node *`
`operator::tensorflow::Input() const `
``` ```
``` ```
`operator::tensorflow::Output() const `
``` ```
``` ```

## Public attributes

### output

`::tensorflow::Output output`

## Public functions

### ExpandDims

``` ExpandDims(
const ::tensorflow::Scope & scope,
::tensorflow::Input input,
::tensorflow::Input axis
)```

### node

`::tensorflow::Node * node() const `

### operator::tensorflow::Input

` operator::tensorflow::Input() const `

### operator::tensorflow::Output

` operator::tensorflow::Output() const `