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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. |

© 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/r1.15/api_docs/python/tf/sparse/reshape