tf.expand_dims

Returns a tensor with an additional dimension inserted at index axis.

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

Given a tensor input, this operation inserts a dimension of size 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 one image with expand_dims(image, 0), which will make the shape [1, height, width, channels].

Examples:

t = [[1, 2, 3],[4, 5, 6]] # shape [2, 3]

tf.expand_dims(t, 0)
<tf.Tensor: shape=(1, 2, 3), dtype=int32, numpy=
array([[[1, 2, 3],
        [4, 5, 6]]], dtype=int32)>

tf.expand_dims(t, 1)
<tf.Tensor: shape=(2, 1, 3), dtype=int32, numpy=
array([[[1, 2, 3]],
       [[4, 5, 6]]], dtype=int32)>

tf.expand_dims(t, 2)
<tf.Tensor: shape=(2, 3, 1), dtype=int32, numpy=
array([[[1],
        [2],
        [3]],
       [[4],
        [5],
        [6]]], dtype=int32)>

tf.expand_dims(t, -1) # Last dimension index. In this case, same as 2.
<tf.Tensor: shape=(2, 3, 1), dtype=int32, numpy=
array([[[1],
        [2],
        [3]],
       [[4],
        [5],
        [6]]], dtype=int32)>

This operation is related to:

tf.squeeze, which removes dimensions of size 1.
tf.reshape, which provides more flexible reshaping capability

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]`.

tf.expand_dims

Examples:

Args

Returns

Raises