tf.expand_dims

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

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

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.
• Align axes for broadcasting.
• 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:

• tf.squeeze, which removes dimensions of size 1.
• tf.reshape, which provides more flexible reshaping capability.
• tf.sparse.expand_dims, which provides this functionality for tf.SparseTensor

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
TypeError	If axis is not specified.
InvalidArgumentError	If axis is out of range [-(D+1), D].