登录
TensorFlow 1 version
|
View source on GitHub
|
Reshapes a SparseTensor to represent values in a new dense shape.
tf.sparse.reshape(
sp_input, shape, name=None
)
This operation has the same semantics as reshape on the represented dense
tensor. The indices of non-empty values in sp_input are recomputed based
on the new dense shape, and a new SparseTensor is returned containing the
new indices and new shape. The order of non-empty values in sp_input is
unchanged.
If one component of shape is the special value -1, the size of that
dimension is computed so that the total dense size remains constant. At
most one component of shape can be -1. The number of dense elements
implied by shape must be the same as the number of dense elements
originally represented by sp_input.
For example, if sp_input has shape [2, 3, 6] and indices / values:
[0, 0, 0]: a
[0, 0, 1]: b
[0, 1, 0]: c
[1, 0, 0]: d
[1, 2, 3]: e
and shape is [9, -1], then the output will be a SparseTensor of
shape [9, 4] and indices / values:
[0, 0]: a
[0, 1]: b
[1, 2]: c
[4, 2]: d
[8, 1]: e
Args | |
|---|---|
sp_input
|
The input SparseTensor.
|
shape
|
A 1-D (vector) int64 Tensor specifying the new dense shape of the
represented SparseTensor.
|
name
|
A name prefix for the returned tensors (optional) |
Returns | |
|---|---|
A SparseTensor with the same non-empty values but with indices calculated
by the new dense shape.
|
Raises | |
|---|---|
TypeError
|
If sp_input is not a SparseTensor.
|
ValueError
|
If argument shape requests a SparseTensor with a different
number of elements than sp_input.
|
ValueError
|
If shape has more than one inferred (== -1) dimension.
|