/TensorFlow 2.4


Reshapes a SparseTensor to represent values in a new dense shape.

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.

input_indices A Tensor of type int64. 2-D. N x R_in matrix with the indices of non-empty values in a SparseTensor.
input_shape A Tensor of type int64. 1-D. R_in vector with the input SparseTensor's dense shape.
new_shape A Tensor of type int64. 1-D. R_out vector with the requested new dense shape.
name A name for the operation (optional).
A tuple of Tensor objects (output_indices, output_shape).
output_indices A Tensor of type int64.
output_shape A Tensor of type int64.

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