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in_top_k.ts
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in_top_k.ts
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/**
* @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 {Tensor} from '../tensor';
import {convertToTensor} from '../tensor_util_env';
import {TensorLike} from '../types';
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_<T extends Tensor, U extends Tensor>(
predictions: T|TensorLike, targets: U|TensorLike, k = 1): Promise<U> {
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: Array<{value: number, index: number}> = [];
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') as U;
}
export const inTopKAsync = inTopKAsync_;