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
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].
# 't' is a tensor of shape  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.
input. Must be in the range
[-rank(input) - 1, rank(input)].
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 )
::tensorflow::Node * node() const
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Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.