| 1 | /**
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| 2 | * @license
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| 3 | * Copyright 2021 Google LLC. All Rights Reserved.
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| 4 | * Licensed under the Apache License, Version 2.0 (the "License");
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| 5 | * you may not use this file except in compliance with the License.
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| 6 | * You may obtain a copy of the License at
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| 7 | *
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| 8 | * http://www.apache.org/licenses/LICENSE-2.0
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| 9 | *
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| 10 | * Unless required by applicable law or agreed to in writing, software
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| 11 | * distributed under the License is distributed on an "AS IS" BASIS,
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| 12 | * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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| 13 | * See the License for the specific language governing permissions and
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| 14 | * limitations under the License.
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| 15 | * =============================================================================
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| 16 | */
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| 17 | /// <amd-module name="@tensorflow/tfjs-core/dist/ops/sparse/sparse_reshape" />
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| 18 | import { Tensor1D, Tensor2D } from '../../tensor';
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| 19 | import { NamedTensorMap } from '../../tensor_types';
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| 20 | import { TensorLike } from '../../types';
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| 21 | /**
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| 22 | * This operation has the same semantics as reshape on the represented dense
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| 23 | * tensor. The `inputIndices` are recomputed based on the requested `newShape`.
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| 24 | * If one component of `newShape` is the special value -1, the size of that
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| 25 | * dimension is computed so that the total dense size remains constant. At most
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| 26 | * one component of `newShape` can be -1. The number of dense elements implied
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| 27 | * by `newShape` must be the same as the number of dense elements originally
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| 28 | * implied by `inputShape`. Reshaping does not affect the order of values in the
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| 29 | * SparseTensor. If the input tensor has rank R_in and N non-empty values, and
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| 30 | * `newShape` has length R_out, then `inputIndices` has shape [N, R_in],
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| 31 | * `inputShape` has length R_in, `outputIndices` has shape [N, R_out], and
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| 32 | * `outputShape` has length R_out.
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| 33 | *
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| 34 | * ```js
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| 35 | * const result = tf.sparse.sparseReshape(
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| 36 | * [[0, 0, 0], [0, 0, 1], [0, 1, 0], [1, 0, 0], [1, 2, 3]],
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| 37 | * [2, 3, 6], [9, -1]);
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| 38 | * console.log(result);
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| 39 | * result['outputIndices'].print(); //[[0, 0], [0, 1], [1, 2], [4, 2], [8, 1]]
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| 40 | * result['outputShape'].print(); // [9, 4]
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| 41 | * ```
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| 42 | * @param inputIndices: 2-D. N x R_in matrix with the indices of non-empty
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| 43 | * values in a SparseTensor.
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| 44 | * @param inputShape: 1-D. R_in Tensor1D with the input SparseTensor's dense
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| 45 | * shape.
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| 46 | * @param newShape: 1-D. R_out Tensor1D with the requested new dense shape.
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| 47 | * @return A map with the following properties:
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| 48 | * - outputIndices: 2-D. N x R_out matrix with the updated indices of
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| 49 | * non-empty values in the output SparseTensor.
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| 50 | * - outputShape: 1-D. R_out vector with the full dense shape of the output
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| 51 | * SparseTensor. This is the same as newShape but with any -1 dimensions
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| 52 | * filled in.
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| 53 | * @doc {heading: 'Operations', subheading: 'Sparse'}
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| 54 | */
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| 55 | declare function sparseReshape_(inputIndices: Tensor2D | TensorLike, inputShape: Tensor1D | TensorLike, newShape: Tensor1D | TensorLike): NamedTensorMap;
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| 56 | export declare const sparseReshape: typeof sparseReshape_;
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| 57 | export {};
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