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Returns a tensor with an length 1 axis inserted at index axis
.
tf.sparse.expand_dims(
sp_input, axis=None, 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 sparse tensor with shape [height, width, depth]
:
sp = tf.sparse.SparseTensor(indices=[[3,4,1]], values=[7,],
dense_shape=[10,10,3])
You can add an outer batch
axis by passing axis=0
:
tf.sparse.expand_dims(sp, axis=0).shape.as_list()
[1, 10, 10, 3]
The new axis location matches Python list.insert(axis, 1)
:
tf.sparse.expand_dims(sp, 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.sparse.expand_dims(sp, axis=-1).shape.as_list()
[10, 10, 3, 1]
sp.shape.as_list()
[10, 10, 3]
tf.sparse.expand_dims(sp).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.expand_dims
, which provides this functionality for dense tensors.tf.squeeze
, which removes dimensions of size 1, from dense tensors.tf.sparse.reshape
, which provides more flexible reshaping capability.
Returns | |
---|---|
A SparseTensor with the same data as sp_input , but its shape has an
additional dimension of size 1 added.
|