@tensorflow/tfjs-core
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Hardware-accelerated JavaScript library for machine intelligence
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JavaScript
/**
* @license
* Copyright 2021 Google LLC. All Rights Reserved.
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
* =============================================================================
*/
import { ENGINE } from '../../engine';
import { SparseFillEmptyRows } from '../../kernel_names';
import { convertToTensor } from '../../tensor_util_env';
import { op } from '../operation';
/**
* The input SparseTensor is represented via the map of inputs {`indices`,
* `values`, `denseShape`}. The output SparseTensor has the same `denseShape`
* but with indices `outputIndices` and values `outputValues`. This op inserts a
* single entry for every row that doesn't have any values. The index is created
* as `[row, 0, ..., 0]` and the inserted value is `defaultValue`.
*
* For example, suppose `spInput` has shape [5, 6] and non-empty values:
* [0, 1]: a
* [0, 3]: b
* [2, 0]: c
* [3, 1]: d
*
* Rows 1 and 4 are empty, so the output will be of shape [5, 6] with values:
* [0, 1]: a
* [0, 3]: b
* [1, 0]: `defaultValue`
* [2, 0]: c
* [3, 1]: d
* [4, 0]: `defaultValue`
*
* The output SparseTensor will be in row-major order and will have the same
* shape as the input.
*
* This op also returns an indicator vector shaped [dense_shape[0]] such that
* emptyRowIndicator[i] = True iff row i was an empty row.
*
* And a reverse index map vector shaped [indices.shape[0]] that is used during
* backpropagation, reverseIndexMap[i] = outi s.t. indices[i, j] ==
* outputIndices[outi, j] for all j
*
* ```js
* const result = tf.sparse.sparseFillEmptyRows(
* [[0, 0], [1, 0], [1, 3], [1, 4], [3, 2], [3, 3]],
* [0, 10, 13, 14, 32, 33], [5, 6], -1);
* console.log(result);
* result['outputIndices'].print(); // [[0, 0], [1, 0], [1, 3], [1, 4],
* // [2, 0], [3, 2], [3, 3], [4, 0]]
* result['outputValues'].print(); // [0, 10, 13, 14,-1, 32, 33, -1]
* result['emptyRowIndicator'].print(); // [false, false, true, false, true]
* result['reverseIndexMap'].print(); // [0, 1, 2, 3, 5, 6]
* ```
* @param indices: 2-D. The indices of the sparse tensor.
* @param values: 1-D. The values of the sparse tensor.
* @param denseShape: 1-D. The shape of the sparse tensor.
* @param defaultValue: 0-D. Default value to insert into location [row, 0, ...,
* 0] for rows missing from the input sparse tensor.
* @return A map with the following properties:
* - outputIndices
* - outputValues: 1-D. The values of the filled sparse tensor.
* - emptyRowIndicator: 1-D. Whether the dense row was missing in the input
* sparse tensor.
* - reverseIndexMap: 1-D. A map from the input indices to the output
* indices.
* @doc {heading: 'Operations', subheading: 'Sparse'}
*/
function sparseFillEmptyRows_(indices, values, denseShape, defaultValue) {
const $indices = convertToTensor(indices, 'indices', 'sparseFillEmptyRows', 'int32');
const $values = convertToTensor(values, 'values', 'sparseFillEmptyRows');
const $denseShape = convertToTensor(denseShape, 'denseShape', 'sparseFillEmptyRows', 'int32');
const $defaultValue = convertToTensor(defaultValue, 'defaultValue', 'sparseFillEmptyRows', $values.dtype);
if ($indices.rank !== 2) {
throw new Error(`Indices should be Tensor2D but received shape
${$indices.shape}`);
}
if ($values.rank !== 1) {
throw new Error(`Values should be Tensor1D but received shape ${$values.shape}`);
}
if ($denseShape.rank !== 1) {
throw new Error(`Dense shape should be Tensor1D but received shape ${$denseShape.shape}`);
}
if ($defaultValue.rank !== 0) {
throw new Error(`Default value should be a scalar but received shape ${$defaultValue.shape}`);
}
const inputs = {
indices: $indices,
values: $values,
denseShape: $denseShape,
defaultValue: $defaultValue
};
const result = ENGINE.runKernel(SparseFillEmptyRows, inputs);
return {
outputIndices: result[0],
outputValues: result[1],
emptyRowIndicator: result[2],
reverseIndexMap: result[3]
};
}
export const sparseFillEmptyRows = /* @__PURE__ */ op({ sparseFillEmptyRows_ });
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All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../../engine';\nimport {SparseFillEmptyRows, SparseFillEmptyRowsInputs} from '../../kernel_names';\nimport {Scalar, Tensor, Tensor1D, Tensor2D} from '../../tensor';\nimport {NamedTensorMap} from '../../tensor_types';\nimport {convertToTensor} from '../../tensor_util_env';\nimport {ScalarLike, TensorLike} from '../../types';\nimport {op} from '../operation';\n\n/**\n * The input SparseTensor is represented via the map of inputs {`indices`,\n * `values`, `denseShape`}. The output SparseTensor has the same `denseShape`\n * but with indices `outputIndices` and values `outputValues`. This op inserts a\n * single entry for every row that doesn't have any values. The index is created\n * as `[row, 0, ..., 0]` and the inserted value is `defaultValue`.\n *\n * For example, suppose `spInput` has shape [5, 6] and non-empty values:\n * [0, 1]: a\n * [0, 3]: b\n * [2, 0]: c\n * [3, 1]: d\n *\n * Rows 1 and 4 are empty, so the output will be of shape [5, 6] with values:\n * [0, 1]: a\n * [0, 3]: b\n * [1, 0]: `defaultValue`\n * [2, 0]: c\n * [3, 1]: d\n * [4, 0]: `defaultValue`\n *\n * The output SparseTensor will be in row-major order and will have the same\n * shape as the input.\n *\n * This op also returns an indicator vector shaped [dense_shape[0]] such that\n * emptyRowIndicator[i] = True iff row i was an empty row.\n *\n * And a reverse index map vector shaped [indices.shape[0]] that is used during\n * backpropagation, reverseIndexMap[i] = outi s.t. indices[i, j] ==\n * outputIndices[outi, j] for all j\n *\n * ```js\n * const result = tf.sparse.sparseFillEmptyRows(\n *   [[0, 0], [1, 0], [1, 3], [1, 4], [3, 2], [3, 3]],\n *   [0, 10, 13, 14, 32, 33], [5, 6], -1);\n * console.log(result);\n * result['outputIndices'].print(); // [[0, 0], [1, 0], [1, 3], [1, 4],\n *                                  //  [2, 0], [3, 2], [3, 3], [4, 0]]\n * result['outputValues'].print(); // [0, 10, 13, 14,-1, 32, 33, -1]\n * result['emptyRowIndicator'].print(); // [false, false, true, false, true]\n * result['reverseIndexMap'].print(); // [0, 1, 2, 3, 5, 6]\n * ```\n * @param indices: 2-D. The indices of the sparse tensor.\n * @param values: 1-D. The values of the sparse tensor.\n * @param denseShape: 1-D. The shape of the sparse tensor.\n * @param defaultValue: 0-D. Default value to insert into location [row, 0, ...,\n *     0] for rows missing from the input sparse tensor.\n * @return A map with the following properties:\n *     - outputIndices\n *     - outputValues: 1-D. The values of the filled sparse tensor.\n *     - emptyRowIndicator: 1-D. Whether the dense row was missing in the input\n * sparse tensor.\n *     - reverseIndexMap: 1-D. A map from the input indices to the output\n * indices.\n * @doc {heading: 'Operations', subheading: 'Sparse'}\n */\nfunction sparseFillEmptyRows_(\n    indices: Tensor2D|TensorLike, values: Tensor1D|TensorLike,\n    denseShape: Tensor1D|TensorLike,\n    defaultValue: Scalar|ScalarLike): NamedTensorMap {\n  const $indices =\n      convertToTensor(indices, 'indices', 'sparseFillEmptyRows', 'int32');\n  const $values = convertToTensor(values, 'values', 'sparseFillEmptyRows');\n  const $denseShape =\n      convertToTensor(denseShape, 'denseShape', 'sparseFillEmptyRows', 'int32');\n  const $defaultValue = convertToTensor(\n      defaultValue, 'defaultValue', 'sparseFillEmptyRows', $values.dtype);\n\n  if ($indices.rank !== 2) {\n    throw new Error(`Indices should be Tensor2D but received shape\n        ${$indices.shape}`);\n  }\n  if ($values.rank !== 1) {\n    throw new Error(\n        `Values should be Tensor1D but received shape ${$values.shape}`);\n  }\n  if ($denseShape.rank !== 1) {\n    throw new Error(`Dense shape should be Tensor1D but received shape ${\n        $denseShape.shape}`);\n  }\n  if ($defaultValue.rank !== 0) {\n    throw new Error(`Default value should be a scalar but received shape ${\n        $defaultValue.shape}`);\n  }\n\n  const inputs: SparseFillEmptyRowsInputs = {\n    indices: $indices,\n    values: $values,\n    denseShape: $denseShape,\n    defaultValue: $defaultValue\n  };\n\n  const result: Tensor[] = ENGINE.runKernel(SparseFillEmptyRows, inputs as {});\n  return {\n    outputIndices: result[0],\n    outputValues: result[1],\n    emptyRowIndicator: result[2],\n    reverseIndexMap: result[3]\n  };\n}\n\nexport const sparseFillEmptyRows = /* @__PURE__ */ op({sparseFillEmptyRows_});\n"]}