@tensorflow/tfjs-core/dist/ops/in_top_k.js

Hardware-accelerated JavaScript library for machine intelligence

/**
 * @license
 * Copyright 2019 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 { convertToTensor } from '../tensor_util_env';
import { assert, assertShapesMatch, getTypedArrayFromDType } from '../util';
import { tensor } from './tensor';
/**
 * Returns whether the targets are in the top K predictions.
 *
 * ```js
 * const predictions = tf.tensor2d([[20, 10, 40, 30], [30, 50, -20, 10]]);
 * const targets = tf.tensor1d([2, 0]);
 * const precision = await tf.inTopKAsync(predictions, targets);
 * precision.print();
 * ```
 * @param predictions 2-D or higher `tf.Tensor` with last dimension being
 *     at least `k`.
 * @param targets 1-D or higher `tf.Tensor`.
 * @param k Optional Number of top elements to look at for computing precision,
 *     default to 1.
 *
 * @doc {heading: 'Operations', subheading: 'Evaluation'}
 */
async function inTopKAsync_(predictions, targets, k = 1) {
    const $predictions = convertToTensor(predictions, 'predictions', 'inTopK');
    const $targets = convertToTensor(targets, 'targets', 'inTopK');
    assert($predictions.rank > 1, () => 'inTopK() expects the predictions to be of rank 2 or higher, ' +
        `but got ${$predictions.rank}`);
    assert($predictions.rank - 1 === $targets.rank, () => `predictions rank should be 1 larger than ` +
        `targets rank, but got predictions rank ` +
        `${$predictions.rank} and targets rank ${$targets.rank}`);
    assertShapesMatch($predictions.shape.slice(0, $predictions.shape.length - 1), $targets.shape, `predictions's shape should be align with the targets' shape, ` +
        'except the last dimension.');
    const lastDim = $predictions.shape[$predictions.shape.length - 1];
    assert(k > 0 && k <= lastDim, () => `'k' passed to inTopK() must be > 0 && <= the predictions last ` +
        `dimension (${lastDim}), but got ${k}`);
    const predictionsVals = await $predictions.data();
    const targetsVals = await $targets.data();
    // Reshape predictionsVals into a 2d tensor [batch, lastDim]
    // and look up topK along lastDim.
    const [batch, size] = [predictionsVals.length / lastDim, lastDim];
    const precision = getTypedArrayFromDType('bool', batch);
    for (let b = 0; b < batch; b++) {
        const offset = b * size;
        const vals = predictionsVals.subarray(offset, offset + size);
        const valAndInd = [];
        for (let i = 0; i < vals.length; i++) {
            valAndInd.push({ value: vals[i], index: i });
        }
        valAndInd.sort((a, b) => b.value - a.value);
        precision[b] = 0;
        for (let i = 0; i < k; i++) {
            if (valAndInd[i].index === targetsVals[b]) {
                precision[b] = 1;
                break;
            }
        }
    }
    if (predictions !== $predictions) {
        $predictions.dispose();
    }
    if (targets !== $targets) {
        $targets.dispose();
    }
    // Output precision has the same shape as targets.
    return tensor(precision, $targets.shape, 'bool');
}
export const inTopKAsync = inTopKAsync_;
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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 {Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {assert, assertShapesMatch, getTypedArrayFromDType} from '../util';\nimport {tensor} from './tensor';\n\n/**\n * Returns whether the targets are in the top K predictions.\n *\n * ```js\n * const predictions = tf.tensor2d([[20, 10, 40, 30], [30, 50, -20, 10]]);\n * const targets = tf.tensor1d([2, 0]);\n * const precision = await tf.inTopKAsync(predictions, targets);\n * precision.print();\n * ```\n * @param predictions 2-D or higher `tf.Tensor` with last dimension being\n *     at least `k`.\n * @param targets 1-D or higher `tf.Tensor`.\n * @param k Optional Number of top elements to look at for computing precision,\n *     default to 1.\n *\n * @doc {heading: 'Operations', subheading: 'Evaluation'}\n */\nasync function inTopKAsync_<T extends Tensor, U extends Tensor>(\n    predictions: T|TensorLike, targets: U|TensorLike, k = 1): Promise<U> {\n  const $predictions = convertToTensor(predictions, 'predictions', 'inTopK');\n  const $targets = convertToTensor(targets, 'targets', 'inTopK');\n\n  assert(\n      $predictions.rank > 1,\n      () => 'inTopK() expects the predictions to be of rank 2 or higher, ' +\n          `but got ${$predictions.rank}`);\n  assert(\n      $predictions.rank - 1 === $targets.rank,\n      () => `predictions rank should be 1 larger than ` +\n          `targets rank, but got predictions rank ` +\n          `${$predictions.rank} and targets rank ${$targets.rank}`);\n  assertShapesMatch(\n      $predictions.shape.slice(0, $predictions.shape.length - 1),\n      $targets.shape,\n      `predictions's shape should be align with the targets' shape, ` +\n          'except the last dimension.');\n  const lastDim = $predictions.shape[$predictions.shape.length - 1];\n  assert(\n      k > 0 && k <= lastDim,\n      () => `'k' passed to inTopK() must be > 0 && <= the predictions last ` +\n          `dimension (${lastDim}), but got ${k}`);\n\n  const predictionsVals = await $predictions.data();\n  const targetsVals = await $targets.data();\n\n  // Reshape predictionsVals into a 2d tensor [batch, lastDim]\n  // and look up topK along lastDim.\n  const [batch, size] = [predictionsVals.length / lastDim, lastDim];\n  const precision = getTypedArrayFromDType('bool', batch);\n\n  for (let b = 0; b < batch; b++) {\n    const offset = b * size;\n    const vals = predictionsVals.subarray(offset, offset + size);\n    const valAndInd: Array<{value: number, index: number}> = [];\n    for (let i = 0; i < vals.length; i++) {\n      valAndInd.push({value: vals[i], index: i});\n    }\n    valAndInd.sort((a, b) => b.value - a.value);\n\n    precision[b] = 0;\n    for (let i = 0; i < k; i++) {\n      if (valAndInd[i].index === targetsVals[b]) {\n        precision[b] = 1;\n        break;\n      }\n    }\n  }\n\n  if (predictions !== $predictions) {\n    $predictions.dispose();\n  }\n  if (targets !== $targets) {\n    $targets.dispose();\n  }\n\n  // Output precision has the same shape as targets.\n  return tensor(precision, $targets.shape, 'bool') as U;\n}\n\nexport const inTopKAsync = inTopKAsync_;\n"]}