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

tf.raw_ops.SparseReshape( input_indices, input_shape, new_shape, name=None )

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`

.

Args | |
---|---|

`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). |

Returns | |
---|---|

A tuple of `Tensor` objects (output_indices, output_shape). | |

`output_indices` | A `Tensor` of type `int64` . |

`output_shape` | A `Tensor` of type `int64` . |

© 2020 The TensorFlow Authors. All rights reserved.

Licensed under the Creative Commons Attribution License 3.0.

Code samples licensed under the Apache 2.0 License.

https://www.tensorflow.org/versions/r2.4/api_docs/python/tf/raw_ops/SparseReshape