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SparseTensor to represent values in a new dense shape.
`tf.sparse_reshape`Compat aliases for migration
See Migration guide for more details.
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
For example, if
sp_input has shape
[2, 3, 6] and
[0, 0, 0]: a [0, 0, 1]: b [0, 1, 0]: c [1, 0, 0]: d [1, 2, 3]: e
[9, -1], then the output will be a
SparseTensor of shape
[9, 4] and
[0, 0]: a [0, 1]: b [1, 2]: c [4, 2]: d [8, 1]: e
| || The input |
| || A 1-D (vector) int64 |
| ||A name prefix for the returned tensors (optional)|
| A |
| || If |
| || If argument |
| || If |
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Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.