# tf.expand_dims(input, axis=None, name=None, dim=None)

### tf.expand_dims(input, axis=None, name=None, dim=None)

See the guide: Tensor Transformations > Shapes and Shaping

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

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.

#### Args:

• input: A Tensor.
• axis: 0-D (scalar). Specifies the dimension index at which to expand the shape of input.
• name: The name of the output Tensor.
• dim: 0-D (scalar). Equivalent to axis, to be deprecated.

#### Returns:

A Tensor with the same data as input, but its shape has an additional dimension of size 1 added.

#### Raises:

• ValueError: if both dim and axis are specified.

Defined in tensorflow/python/ops/array_ops.py.