@Operator(group="sparse") public final class SparseReshape extends PrimitiveOp
This operation has the same semantics as reshape on the represented dense tensor. The `input_indices` are recomputed based on the requested `new_shape`.
If one component of `new_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 `new_shape` can be -1. The number of dense elements implied by `new_shape` must be the same as the number of dense elements originally implied by `input_shape`.
Reshaping does not affect the order of values in the SparseTensor.
If the input tensor has rank `R_in` and `N` non-empty values, and `new_shape` has length `R_out`, then `input_indices` has shape `[N, R_in]`, `input_shape` has length `R_in`, `output_indices` has shape `[N, R_out]`, and `output_shape` has length `R_out`.
operation
Modifier and Type | Method and Description |
---|---|
static SparseReshape |
create(Scope scope,
Operand<Long> inputIndices,
Operand<Long> inputShape,
Operand<Long> newShape)
Factory method to create a class wrapping a new SparseReshape operation.
|
Output<Long> |
outputIndices()
2-D.
|
Output<Long> |
outputShape()
1-D.
|
equals, hashCode, op, toString
public static SparseReshape create(Scope scope, Operand<Long> inputIndices, Operand<Long> inputShape, Operand<Long> newShape)
scope
- current scopeinputIndices
- 2-D. `N x R_in` matrix with the indices of non-empty values in a
SparseTensor.inputShape
- 1-D. `R_in` vector with the input SparseTensor's dense shape.newShape
- 1-D. `R_out` vector with the requested new dense shape.public Output<Long> outputIndices()
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