/TensorFlow 2.4

# tf.expand_dims

Returns a tensor with a length 1 axis inserted at index `axis`.

Given a tensor `input`, this operation inserts a dimension of length 1 at the dimension index `axis` of `input`'s shape. The dimension index follows Python indexing rules: It's zero-based, a negative index it is counted backward from the end.

This operation is useful to:

• Add an outer "batch" dimension to a single element.
• To add an inner vector length axis to a tensor of scalars.

#### For example:

If you have a single image of shape `[height, width, channels]`:

```image = tf.zeros([10,10,3])
```

You can add an outer `batch` axis by passing `axis=0`:

```tf.expand_dims(image, axis=0).shape.as_list()
[1, 10, 10, 3]
```

The new axis location matches Python `list.insert(axis, 1)`:

```tf.expand_dims(image, axis=1).shape.as_list()
[10, 1, 10, 3]
```

Following standard Python indexing rules, a negative `axis` counts from the end so `axis=-1` adds an inner most dimension:

```tf.expand_dims(image, -1).shape.as_list()
[10, 10, 3, 1]
```

This operation requires that `axis` is a valid index for `input.shape`, following Python indexing rules:

```-1-tf.rank(input) <= axis <= tf.rank(input)
```

This operation is related to:

Args
`input` A `Tensor`.
`axis` Integer specifying the dimension index at which to expand the shape of `input`. Given an input of D dimensions, `axis` must be in range `[-(D+1), D]` (inclusive).
`name` Optional string. The name of the output `Tensor`.
Returns
A tensor with the same data as `input`, with an additional dimension inserted at the index specified by `axis`.
Raises
`ValueError` If `axis` is not specified.
`InvalidArgumentError` If `axis` is out of range `[-(D+1), D]`.