"use strict";
|
/**
|
* @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.
|
* =============================================================================
|
*/
|
var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
|
return new (P || (P = Promise))(function (resolve, reject) {
|
function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
|
function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
|
function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
|
step((generator = generator.apply(thisArg, _arguments || [])).next());
|
});
|
};
|
var __generator = (this && this.__generator) || function (thisArg, body) {
|
var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
|
return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
|
function verb(n) { return function (v) { return step([n, v]); }; }
|
function step(op) {
|
if (f) throw new TypeError("Generator is already executing.");
|
while (_) try {
|
if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
|
if (y = 0, t) op = [op[0] & 2, t.value];
|
switch (op[0]) {
|
case 0: case 1: t = op; break;
|
case 4: _.label++; return { value: op[1], done: false };
|
case 5: _.label++; y = op[1]; op = [0]; continue;
|
case 7: op = _.ops.pop(); _.trys.pop(); continue;
|
default:
|
if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
|
if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
|
if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
|
if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
|
if (t[2]) _.ops.pop();
|
_.trys.pop(); continue;
|
}
|
op = body.call(thisArg, _);
|
} catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
|
if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
|
}
|
};
|
Object.defineProperty(exports, "__esModule", { value: true });
|
var tensor_util_env_1 = require("../tensor_util_env");
|
var util_1 = require("../util");
|
var tensor_ops_1 = require("./tensor_ops");
|
/**
|
* 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'} */
|
function inTopKAsync_(predictions, targets, k) {
|
if (k === void 0) { k = 1; }
|
return __awaiter(this, void 0, void 0, function () {
|
var $predictions, $targets, lastDim, predictionsVals, targetsVals, _a, batch, size, precision, b, offset, vals, valAndInd, i, i;
|
return __generator(this, function (_b) {
|
switch (_b.label) {
|
case 0:
|
$predictions = tensor_util_env_1.convertToTensor(predictions, 'predictions', 'inTopK');
|
$targets = tensor_util_env_1.convertToTensor(targets, 'targets', 'inTopK');
|
util_1.assert($predictions.rank > 1, function () { return 'inTopK() expects the predictions to be of rank 2 or higher, ' +
|
("but got " + $predictions.rank); });
|
util_1.assert($predictions.rank - 1 === $targets.rank, function () { return "predictions rank should be 1 larger than " +
|
"targets rank, but got predictions rank " +
|
($predictions.rank + " and targets rank " + $targets.rank); });
|
util_1.assertShapesMatch($predictions.shape.slice(0, $predictions.shape.length - 1), $targets.shape, "predictions's shape should be align with the targets' shape, " +
|
'except the last dimension.');
|
lastDim = $predictions.shape[$predictions.shape.length - 1];
|
util_1.assert(k > 0 && k <= lastDim, function () { return "'k' passed to inTopK() must be > 0 && <= the predictions last " +
|
("dimension (" + lastDim + "), but got " + k); });
|
return [4 /*yield*/, $predictions.data()];
|
case 1:
|
predictionsVals = _b.sent();
|
return [4 /*yield*/, $targets.data()];
|
case 2:
|
targetsVals = _b.sent();
|
_a = [predictionsVals.length / lastDim, lastDim], batch = _a[0], size = _a[1];
|
precision = util_1.getTypedArrayFromDType('bool', batch);
|
for (b = 0; b < batch; b++) {
|
offset = b * size;
|
vals = predictionsVals.subarray(offset, offset + size);
|
valAndInd = [];
|
for (i = 0; i < vals.length; i++) {
|
valAndInd.push({ value: vals[i], index: i });
|
}
|
valAndInd.sort(function (a, b) { return b.value - a.value; });
|
precision[b] = 0;
|
for (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 [2 /*return*/, tensor_ops_1.tensor(precision, $targets.shape, 'bool')];
|
}
|
});
|
});
|
}
|
exports.inTopKAsync = inTopKAsync_;
|
//# sourceMappingURL=in_top_k.js.map
|
