@tensorflow/tfjs-core
Version:
Hardware-accelerated JavaScript library for machine intelligence
1,009 lines • 108 kB
JavaScript
"use strict";
/**
* @license
* Copyright 2018 Google Inc. 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 };
}
};
var _this = this;
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("../index");
var jasmine_util_1 = require("../jasmine_util");
var test_util_1 = require("../test_util");
jasmine_util_1.describeWithFlags('computeWeightedLoss', jasmine_util_1.ALL_ENVS, function () {
it('1D - no weights', function () { return __awaiter(_this, void 0, void 0, function () {
var losses, y, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
losses = tf.tensor1d([1, 2, 3]);
y = tf.losses.computeWeightedLoss(losses);
expect(y.shape).toEqual([]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, y.data()];
case 1:
_a.apply(void 0, [_b.sent(), (1 + 2 + 3) / 3]);
return [2 /*return*/];
}
});
}); });
it('1D - no weights - Reduction.NONE', function () { return __awaiter(_this, void 0, void 0, function () {
var losses, y, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
losses = tf.tensor1d([1, 2, 3]);
y = tf.losses.computeWeightedLoss(losses, undefined, tf.Reduction.NONE);
expect(y.shape).toEqual([3]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, y.data()];
case 1:
_a.apply(void 0, [_b.sent(), [1, 2, 3]]);
return [2 /*return*/];
}
});
}); });
it('1D - no weights - Reduction.MEAN', function () { return __awaiter(_this, void 0, void 0, function () {
var losses, y, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
losses = tf.tensor1d([1, 2, 3]);
y = tf.losses.computeWeightedLoss(losses, undefined, tf.Reduction.MEAN);
expect(y.shape).toEqual([]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, y.data()];
case 1:
_a.apply(void 0, [_b.sent(), (1 + 2 + 3) / 3]);
return [2 /*return*/];
}
});
}); });
it('1D - no weights - Reduction.SUM', function () { return __awaiter(_this, void 0, void 0, function () {
var losses, y, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
losses = tf.tensor1d([1, 2, 3]);
y = tf.losses.computeWeightedLoss(losses, undefined, tf.Reduction.SUM);
expect(y.shape).toEqual([]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, y.data()];
case 1:
_a.apply(void 0, [_b.sent(), (1 + 2 + 3)]);
return [2 /*return*/];
}
});
}); });
it('1D - weights', function () { return __awaiter(_this, void 0, void 0, function () {
var losses, weights, y, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
losses = tf.tensor1d([1, 2, 3]);
weights = tf.tensor1d([0.1, 0, 0.3]);
y = tf.losses.computeWeightedLoss(losses, weights);
expect(y.shape).toEqual([]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, y.data()];
case 1:
_a.apply(void 0, [_b.sent(), (1 * 0.1 + 2 * 0 + 3 * 0.3) / 2]);
return [2 /*return*/];
}
});
}); });
it('2D - weights - broadcast', function () { return __awaiter(_this, void 0, void 0, function () {
var losses, weights, y, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
losses = tf.tensor2d([[0, 0, 1], [1, 0, 0], [0, 1, 0]], [3, 3]);
weights = tf.tensor2d([[0.1, 0.2, 0.3]]);
y = tf.losses.computeWeightedLoss(losses, weights);
expect(y.shape).toEqual([]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, y.data()];
case 1:
_a.apply(void 0, [_b.sent(), 0.06666667]);
return [2 /*return*/];
}
});
}); });
it('1D - weights - Reduction.NONE', function () { return __awaiter(_this, void 0, void 0, function () {
var losses, weights, y, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
losses = tf.tensor1d([1, 2, 3]);
weights = tf.tensor1d([0.1, 0.2, 0.3]);
y = tf.losses.computeWeightedLoss(losses, weights, tf.Reduction.NONE);
expect(y.shape).toEqual([3]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, y.data()];
case 1:
_a.apply(void 0, [_b.sent(), [1 * 0.1, 2 * 0.2, 3 * 0.3]]);
return [2 /*return*/];
}
});
}); });
it('1D - weights - Reduction.MEAN', function () { return __awaiter(_this, void 0, void 0, function () {
var losses, weights, y, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
losses = tf.tensor1d([1, 2, 3]);
weights = tf.tensor1d([0.1, 0.2, 0.3]);
y = tf.losses.computeWeightedLoss(losses, weights, tf.Reduction.MEAN);
expect(y.shape).toEqual([]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, y.data()];
case 1:
_a.apply(void 0, [_b.sent(), (1 * 0.1 + 2 * 0.2 + 3 * 0.3) / 0.6]);
return [2 /*return*/];
}
});
}); });
it('1D - weights - Reduction.SUM', function () { return __awaiter(_this, void 0, void 0, function () {
var losses, weights, y, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
losses = tf.tensor1d([1, 2, 3]);
weights = tf.tensor1d([0.1, 0.2, 0.3]);
y = tf.losses.computeWeightedLoss(losses, weights, tf.Reduction.SUM);
expect(y.shape).toEqual([]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, y.data()];
case 1:
_a.apply(void 0, [_b.sent(), (1 * 0.1 + 2 * 0.2 + 3 * 0.3)]);
return [2 /*return*/];
}
});
}); });
it('2D - no weights', function () { return __awaiter(_this, void 0, void 0, function () {
var losses, y, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
losses = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
y = tf.losses.computeWeightedLoss(losses);
expect(y.shape).toEqual([]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, y.data()];
case 1:
_a.apply(void 0, [_b.sent(), (4 + 8 + 12 + 8 + 1 + 3) / 6]);
return [2 /*return*/];
}
});
}); });
it('2D - weights', function () { return __awaiter(_this, void 0, void 0, function () {
var losses, weights, y, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
losses = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
weights = tf.tensor2d([1, 0, 2, -5, 0, 6], [2, 3]);
y = tf.losses.computeWeightedLoss(losses, weights);
expect(y.shape).toEqual([]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, y.data()];
case 1:
_a.apply(void 0, [_b.sent(),
(4 * 1 + 8 * 0 + 12 * 2 + (8 * -5) + 1 * 0 + 3 * 6) / 4]);
return [2 /*return*/];
}
});
}); });
it('2D - no weights - Reduction.MEAN', function () { return __awaiter(_this, void 0, void 0, function () {
var losses, y, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
losses = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
y = tf.losses.computeWeightedLoss(losses, undefined, tf.Reduction.MEAN);
expect(y.shape).toEqual([]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, y.data()];
case 1:
_a.apply(void 0, [_b.sent(), (4 + 8 + 12 + 8 + 1 + 3) / 6]);
return [2 /*return*/];
}
});
}); });
it('2D - weights - Reduction.MEAN', function () { return __awaiter(_this, void 0, void 0, function () {
var losses, weights, y, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
losses = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
weights = tf.tensor2d([1, 0, 2, -5, 0, 6], [2, 3]);
y = tf.losses.computeWeightedLoss(losses, weights, tf.Reduction.MEAN);
expect(y.shape).toEqual([]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, y.data()];
case 1:
_a.apply(void 0, [_b.sent(),
(4 * 1 + 8 * 0 + 12 * 2 + (8 * -5) + 1 * 0 + 3 * 6) / 4]);
return [2 /*return*/];
}
});
}); });
it('2D - weights - broadcast - MEAN', function () { return __awaiter(_this, void 0, void 0, function () {
var losses, weights, y, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
losses = tf.tensor2d([[0, 0, 1], [1, 0, 0], [0, 1, 0]], [3, 3]);
weights = tf.tensor2d([[0.1, 0.2, 0.3]]);
y = tf.losses.computeWeightedLoss(losses, weights, tf.Reduction.MEAN);
expect(y.shape).toEqual([]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, y.data()];
case 1:
_a.apply(void 0, [_b.sent(), (0.3 + 0.1 + 0.2) / (3 * 0.6)]);
return [2 /*return*/];
}
});
}); });
it('2D - no weights - Reduction.SUM', function () { return __awaiter(_this, void 0, void 0, function () {
var losses, y, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
losses = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
y = tf.losses.computeWeightedLoss(losses, undefined, tf.Reduction.SUM);
expect(y.shape).toEqual([]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, y.data()];
case 1:
_a.apply(void 0, [_b.sent(), (4 + 8 + 12 + 8 + 1 + 3)]);
return [2 /*return*/];
}
});
}); });
it('2D - weights - Reduction.SUM', function () { return __awaiter(_this, void 0, void 0, function () {
var losses, weights, y, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
losses = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
weights = tf.tensor2d([1, 0, 2, -5, 0, 6], [2, 3]);
y = tf.losses.computeWeightedLoss(losses, weights, tf.Reduction.SUM);
expect(y.shape).toEqual([]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, y.data()];
case 1:
_a.apply(void 0, [_b.sent(), (4 * 1 + 8 * 0 + 12 * 2 + (8 * -5) + 1 * 0 + 3 * 6)]);
return [2 /*return*/];
}
});
}); });
it('2D - no weights - Reduction.NONE', function () { return __awaiter(_this, void 0, void 0, function () {
var losses, y, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
losses = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
y = tf.losses.computeWeightedLoss(losses, undefined, tf.Reduction.NONE);
expect(y.shape).toEqual([2, 3]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, y.data()];
case 1:
_a.apply(void 0, [_b.sent(), [4, 8, 12, 8, 1, 3]]);
return [2 /*return*/];
}
});
}); });
it('2D - weights - Reduction.NONE', function () { return __awaiter(_this, void 0, void 0, function () {
var losses, weights, y, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
losses = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
weights = tf.tensor2d([1, 0, 2, -5, 0, 6], [2, 3]);
y = tf.losses.computeWeightedLoss(losses, weights, tf.Reduction.NONE);
expect(y.shape).toEqual([2, 3]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, y.data()];
case 1:
_a.apply(void 0, [_b.sent(), [4 * 1, 8 * 0, 12 * 2, (8 * -5), 1 * 0, 3 * 6]]);
return [2 /*return*/];
}
});
}); });
it('throws when passed losses as a non-tensor', function () {
var weights = tf.tensor2d([1, 0, 2, -5, 0, 6], [2, 3]);
var e = /Argument 'losses' passed to 'computeWeightedLoss' must be a Tensor/;
expect(function () { return tf.losses.computeWeightedLoss({}, weights, tf.Reduction.NONE); })
.toThrowError(e);
});
it('throws when passed weights as a non-tensor', function () {
var losses = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
var e = /Argument 'weights' passed to 'computeWeightedLoss' must be a Tensor/;
expect(function () { return tf.losses.computeWeightedLoss(losses, {}, tf.Reduction.NONE); })
.toThrowError(e);
});
it('accepts a tensor-like object', function () { return __awaiter(_this, void 0, void 0, function () {
var losses, weights, y, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
losses = [1, 2, 3];
weights = [0.1, 0, 0.3];
y = tf.losses.computeWeightedLoss(losses, weights);
expect(y.shape).toEqual([]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, y.data()];
case 1:
_a.apply(void 0, [_b.sent(), (1 * 0.1 + 2 * 0 + 3 * 0.3) / 2]);
return [2 /*return*/];
}
});
}); });
});
jasmine_util_1.describeWithFlags('absoluteDifference', jasmine_util_1.ALL_ENVS, function () {
it('1D', function () { return __awaiter(_this, void 0, void 0, function () {
var predictions, label, y, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
predictions = tf.tensor1d([1, 2, 3]);
label = tf.tensor1d([0.3, -0.6, -0.1]);
y = tf.losses.absoluteDifference(label, predictions);
expect(y.shape).toEqual([]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, y.data()];
case 1:
_a.apply(void 0, [_b.sent(),
(Math.abs(1 - 0.3) + Math.abs(2 - (-0.6)) + Math.abs(3 - (-0.1))) / 3]);
return [2 /*return*/];
}
});
}); });
it('1D - weighted - Reduction.SUM_BY_NONZERO_WEIGHTS', function () { return __awaiter(_this, void 0, void 0, function () {
var predictions, label, weights, y, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
predictions = tf.tensor1d([1, 2, 3]);
label = tf.tensor1d([0.3, -0.6, -0.1]);
weights = tf.tensor1d([0.1, 0.2, 0.3]);
y = tf.losses.absoluteDifference(label, predictions, weights);
expect(y.shape).toEqual([]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, y.data()];
case 1:
_a.apply(void 0, [_b.sent(),
(Math.abs(1 - 0.3) * 0.1 + Math.abs(2 - (-0.6)) * 0.2 +
Math.abs(3 - (-0.1)) * 0.3) /
3]);
return [2 /*return*/];
}
});
}); });
it('1D - weighted - Reduction.NONE', function () { return __awaiter(_this, void 0, void 0, function () {
var predictions, label, weights, y, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
predictions = tf.tensor1d([1, 2, 3]);
label = tf.tensor1d([0.3, -0.6, -0.1]);
weights = tf.tensor1d([0.1, 0.2, 0.3]);
y = tf.losses.absoluteDifference(label, predictions, weights, tf.Reduction.NONE);
expect(y.shape).toEqual([3]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, y.data()];
case 1:
_a.apply(void 0, [_b.sent(), [
Math.abs(1 - 0.3) * 0.1, Math.abs(2 - (-0.6)) * 0.2,
Math.abs(3 - (-0.1)) * 0.3
]]);
return [2 /*return*/];
}
});
}); });
it('1D - Reduction.MEAN', function () { return __awaiter(_this, void 0, void 0, function () {
var predictions, label, y, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
predictions = tf.tensor1d([1, 2, 3]);
label = tf.tensor1d([0.3, -0.6, -0.1]);
y = tf.losses.absoluteDifference(label, predictions, undefined, tf.Reduction.MEAN);
expect(y.shape).toEqual([]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, y.data()];
case 1:
_a.apply(void 0, [_b.sent(),
(Math.abs(1 - 0.3) + Math.abs(2 - (-0.6)) + Math.abs(3 - (-0.1))) / 3]);
return [2 /*return*/];
}
});
}); });
it('1D - weighted - Reduction.MEAN', function () { return __awaiter(_this, void 0, void 0, function () {
var predictions, label, weights, y, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
predictions = tf.tensor1d([1, 2, 3]);
label = tf.tensor1d([0.3, -0.6, -0.1]);
weights = tf.tensor1d([0.1, 0.2, 0.3]);
y = tf.losses.absoluteDifference(label, predictions, weights, tf.Reduction.MEAN);
expect(y.shape).toEqual([]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, y.data()];
case 1:
_a.apply(void 0, [_b.sent(),
((Math.abs(1 - 0.3) * 0.1) + (Math.abs(2 - (-0.6)) * 0.2) +
(Math.abs(3 - (-0.1)) * 0.3)) /
0.6]);
return [2 /*return*/];
}
});
}); });
it('2D', function () { return __awaiter(_this, void 0, void 0, function () {
var predictions, label, y, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
predictions = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
label = tf.tensor2d([1, 9, 2, -5, -2, 6], [2, 3]);
y = tf.losses.absoluteDifference(label, predictions);
expect(y.shape).toEqual([]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, y.data()];
case 1:
_a.apply(void 0, [_b.sent(),
(Math.abs(4 - 1) + Math.abs(8 - 9) + Math.abs(12 - 2) +
Math.abs(8 - (-5)) + Math.abs(1 - (-2)) + Math.abs(3 - 6)) /
6]);
return [2 /*return*/];
}
});
}); });
it('2D - weighted - Reduction.SUM_BY_NONZERO_WEIGHTS', function () { return __awaiter(_this, void 0, void 0, function () {
var predictions, label, weights, y, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
predictions = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
label = tf.tensor2d([1, 9, 2, -5, -2, 6], [2, 3]);
weights = tf.tensor2d([3, 0, 5, 0, 4, 2], [2, 3]);
y = tf.losses.absoluteDifference(label, predictions, weights);
expect(y.shape).toEqual([]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, y.data()];
case 1:
_a.apply(void 0, [_b.sent(),
(Math.abs(4 - 1) * 3 + Math.abs(8 - 9) * 0 + Math.abs(12 - 2) * 5 +
Math.abs(8 - (-5)) * 0 + Math.abs(1 - (-2)) * 4 +
Math.abs(3 - 6) * 2) /
4]);
return [2 /*return*/];
}
});
}); });
it('2D - weighted - Reduction.NONE', function () { return __awaiter(_this, void 0, void 0, function () {
var predictions, label, weights, y, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
predictions = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
label = tf.tensor2d([1, 9, 2, -5, -2, 6], [2, 3]);
weights = tf.tensor2d([3, 6, 5, 0, 4, 2], [2, 3]);
y = tf.losses.absoluteDifference(label, predictions, weights, tf.Reduction.NONE);
expect(y.shape).toEqual([2, 3]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, y.data()];
case 1:
_a.apply(void 0, [_b.sent(), [
Math.abs(4 - 1) * 3, Math.abs(8 - 9) * 6, Math.abs(12 - 2) * 5,
Math.abs(8 - (-5)) * 0, Math.abs(1 - (-2)) * 4, Math.abs(3 - 6) * 2
]]);
return [2 /*return*/];
}
});
}); });
it('2D - Reduction.MEAN', function () { return __awaiter(_this, void 0, void 0, function () {
var predictions, label, y, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
predictions = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
label = tf.tensor2d([1, 9, 2, -5, -2, 6], [2, 3]);
y = tf.losses.absoluteDifference(label, predictions, undefined, tf.Reduction.MEAN);
expect(y.shape).toEqual([]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, y.data()];
case 1:
_a.apply(void 0, [_b.sent(),
(Math.abs(4 - 1) + Math.abs(8 - 9) + Math.abs(12 - 2) +
Math.abs(8 - (-5)) + Math.abs(1 - (-2)) + Math.abs(3 - 6)) /
6]);
return [2 /*return*/];
}
});
}); });
it('2D - weighted - Reduction.MEAN', function () { return __awaiter(_this, void 0, void 0, function () {
var predictions, label, weights, y, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
predictions = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
label = tf.tensor2d([1, 9, 2, -5, -2, 6], [2, 3]);
weights = tf.tensor2d([3, 6, 5, 0, 4, 2], [2, 3]);
y = tf.losses.absoluteDifference(label, predictions, weights, tf.Reduction.MEAN);
expect(y.shape).toEqual([]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, y.data()];
case 1:
_a.apply(void 0, [_b.sent(),
(Math.abs(4 - 1) * 3 + Math.abs(8 - 9) * 6 + Math.abs(12 - 2) * 5 +
Math.abs(8 - (-5)) * 0 + Math.abs(1 - (-2)) * 4 +
Math.abs(3 - 6) * 2) /
20]);
return [2 /*return*/];
}
});
}); });
it('throws when passed label as a non-tensor', function () {
var predictions = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
var weights = tf.tensor2d([3, 6, 5, 0, 4, 2], [2, 3]);
var e = /Argument 'labels' passed to 'absoluteDifference' must be a Tensor/;
expect(function () { return tf.losses.absoluteDifference({}, predictions, weights, tf.Reduction.MEAN); })
.toThrowError(e);
});
it('throws when passed label as a non-tensor', function () {
var label = tf.tensor2d([1, 9, 2, -5, -2, 6], [2, 3]);
var weights = tf.tensor2d([3, 6, 5, 0, 4, 2], [2, 3]);
var e = new RegExp('Argument \'predictions\' passed to \'absoluteDifference\' ' +
'must be a Tensor');
expect(function () { return tf.losses.absoluteDifference(label, {}, weights, tf.Reduction.MEAN); })
.toThrowError(e);
});
it('throws when passed weights as a non-tensor', function () {
var predictions = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
var label = tf.tensor2d([1, 9, 2, -5, -2, 6], [2, 3]);
var e = /Argument 'weights' passed to 'absoluteDifference' must be a Tensor/;
expect(function () { return tf.losses.absoluteDifference(label, predictions, {}, tf.Reduction.MEAN); })
.toThrowError(e);
});
it('accepts a tensor-like object', function () { return __awaiter(_this, void 0, void 0, function () {
var predictions, label, weights, y, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
predictions = [1, 2, 3];
label = [0.3, -0.6, -0.1];
weights = [0.1, 0.2, 0.3];
y = tf.losses.absoluteDifference(label, predictions, weights, tf.Reduction.NONE);
expect(y.shape).toEqual([3]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, y.data()];
case 1:
_a.apply(void 0, [_b.sent(), [
Math.abs(1 - 0.3) * 0.1, Math.abs(2 - (-0.6)) * 0.2,
Math.abs(3 - (-0.1)) * 0.3
]]);
return [2 /*return*/];
}
});
}); });
});
jasmine_util_1.describeWithFlags('meanSquaredError', jasmine_util_1.ALL_ENVS, function () {
it('1D', function () { return __awaiter(_this, void 0, void 0, function () {
var predictions, label, y, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
predictions = tf.tensor1d([1, 2, 3]);
label = tf.tensor1d([0.3, -0.6, -0.1]);
y = tf.losses.meanSquaredError(label, predictions);
expect(y.shape).toEqual([]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, y.data()];
case 1:
_a.apply(void 0, [_b.sent(),
((1 - 0.3) * (1 - 0.3) + (2 - (-0.6)) * (2 - (-0.6)) +
(3 - (-0.1)) * (3 - (-0.1))) /
3]);
return [2 /*return*/];
}
});
}); });
it('1D - weighted - Reduction.SUM_BY_NONZERO_WEIGHTS', function () { return __awaiter(_this, void 0, void 0, function () {
var predictions, label, weights, y, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
predictions = tf.tensor1d([1, 2, 3]);
label = tf.tensor1d([0.3, -0.6, -0.1]);
weights = tf.tensor1d([0.1, 0.2, 0.3]);
y = tf.losses.meanSquaredError(label, predictions, weights);
expect(y.shape).toEqual([]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, y.data()];
case 1:
_a.apply(void 0, [_b.sent(),
((1 - 0.3) * (1 - 0.3) * 0.1 + (2 - (-0.6)) * (2 - (-0.6)) * 0.2 +
(3 - (-0.1)) * (3 - (-0.1)) * 0.3) /
3]);
return [2 /*return*/];
}
});
}); });
it('1D - weighted - Reduction.NONE', function () { return __awaiter(_this, void 0, void 0, function () {
var predictions, label, weights, y, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
predictions = tf.tensor1d([1, 2, 3]);
label = tf.tensor1d([0.3, -0.6, -0.1]);
weights = tf.tensor1d([0.1, 0.2, 0.3]);
y = tf.losses.meanSquaredError(label, predictions, weights, tf.Reduction.NONE);
expect(y.shape).toEqual([3]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, y.data()];
case 1:
_a.apply(void 0, [_b.sent(), [
(1 - 0.3) * (1 - 0.3) * 0.1, (2 - (-0.6)) * (2 - (-0.6)) * 0.2,
(3 - (-0.1)) * (3 - (-0.1)) * 0.3
]]);
return [2 /*return*/];
}
});
}); });
it('1D - Reduction.MEAN', function () { return __awaiter(_this, void 0, void 0, function () {
var predictions, label, y, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
predictions = tf.tensor1d([1, 2, 3]);
label = tf.tensor1d([0.3, -0.6, -0.1]);
y = tf.losses.meanSquaredError(label, predictions, undefined, tf.Reduction.MEAN);
expect(y.shape).toEqual([]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, y.data()];
case 1:
_a.apply(void 0, [_b.sent(),
((1 - 0.3) * (1 - 0.3) + (2 - (-0.6)) * (2 - (-0.6)) +
(3 - (-0.1)) * (3 - (-0.1))) /
3]);
return [2 /*return*/];
}
});
}); });
it('1D - weighted - Reduction.MEAN', function () { return __awaiter(_this, void 0, void 0, function () {
var predictions, label, weights, y, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
predictions = tf.tensor1d([1, 2, 3]);
label = tf.tensor1d([0.3, -0.6, -0.1]);
weights = tf.tensor1d([0.1, 0.2, 0.3]);
y = tf.losses.meanSquaredError(label, predictions, weights, tf.Reduction.MEAN);
expect(y.shape).toEqual([]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, y.data()];
case 1:
_a.apply(void 0, [_b.sent(),
(((1 - 0.3) * (1 - 0.3) * 0.1) + ((2 - (-0.6)) * (2 - (-0.6)) * 0.2) +
((3 - (-0.1)) * (3 - (-0.1)) * 0.3)) /
0.6]);
return [2 /*return*/];
}
});
}); });
it('2D', function () { return __awaiter(_this, void 0, void 0, function () {
var predictions, label, y, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
predictions = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
label = tf.tensor2d([1, 9, 2, -5, -2, 6], [2, 3]);
y = tf.losses.meanSquaredError(label, predictions);
expect(y.shape).toEqual([]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, y.data()];
case 1:
_a.apply(void 0, [_b.sent(),
((4 - 1) * (4 - 1) + (8 - 9) * (8 - 9) + (12 - 2) * (12 - 2) +
(8 - (-5)) * (8 - (-5)) + (1 - (-2)) * (1 - (-2)) +
(3 - 6) * (3 - 6)) /
6]);
return [2 /*return*/];
}
});
}); });
it('2D - weighted - Reduction.SUM_BY_NONZERO_WEIGHTS', function () { return __awaiter(_this, void 0, void 0, function () {
var predictions, label, weights, y, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
predictions = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
label = tf.tensor2d([1, 9, 2, -5, -2, 6], [2, 3]);
weights = tf.tensor2d([3, 0, 5, 0, 4, 2], [2, 3]);
y = tf.losses.meanSquaredError(label, predictions, weights);
expect(y.shape).toEqual([]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, y.data()];
case 1:
_a.apply(void 0, [_b.sent(),
((4 - 1) * (4 - 1) * 3 + (8 - 9) * (8 - 9) * 0 +
(12 - 2) * (12 - 2) * 5 + (8 - (-5)) * (8 - (-5)) * 0 +
(1 - (-2)) * (1 - (-2)) * 4 + (3 - 6) * (3 - 6) * 2) /
4]);
return [2 /*return*/];
}
});
}); });
it('2D - weighted - Reduction.NONE', function () { return __awaiter(_this, void 0, void 0, function () {
var predictions, label, weights, y, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
predictions = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
label = tf.tensor2d([1, 9, 2, -5, -2, 6], [2, 3]);
weights = tf.tensor2d([3, 6, 5, 0, 4, 2], [2, 3]);
y = tf.losses.meanSquaredError(label, predictions, weights, tf.Reduction.NONE);
expect(y.shape).toEqual([2, 3]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, y.data()];
case 1:
_a.apply(void 0, [_b.sent(), [
(4 - 1) * (4 - 1) * 3, (8 - 9) * (8 - 9) * 6, (12 - 2) * (12 - 2) * 5,
(8 - (-5)) * (8 - (-5)) * 0, (1 - (-2)) * (1 - (-2)) * 4,
(3 - 6) * (3 - 6) * 2
]]);
return [2 /*return*/];
}
});
}); });
it('2D - Reduction.MEAN', function () { return __awaiter(_this, void 0, void 0, function () {
var predictions, label, y, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
predictions = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
label = tf.tensor2d([1, 9, 2, -5, -2, 6], [2, 3]);
y = tf.losses.meanSquaredError(label, predictions, undefined, tf.Reduction.MEAN);
expect(y.shape).toEqual([]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, y.data()];
case 1:
_a.apply(void 0, [_b.sent(),
((4 - 1) * (4 - 1) + (8 - 9) * (8 - 9) + (12 - 2) * (12 - 2) +
(8 - (-5)) * (8 - (-5)) + (1 - (-2)) * (1 - (-2)) +
(3 - 6) * (3 - 6)) /
6]);
return [2 /*return*/];
}
});
}); });
it('2D - weighted - Reduction.MEAN', function () { return __awaiter(_this, void 0, void 0, function () {
var predictions, label, weights, y, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
predictions = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
label = tf.tensor2d([1, 9, 2, -5, -2, 6], [2, 3]);
weights = tf.tensor2d([3, 6, 5, 0, 4, 2], [2, 3]);
y = tf.losses.meanSquaredError(label, predictions, weights, tf.Reduction.MEAN);
expect(y.shape).toEqual([]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, y.data()];
case 1:
_a.apply(void 0, [_b.sent(),
((4 - 1) * (4 - 1) * 3 + (8 - 9) * (8 - 9) * 6 +
(12 - 2) * (12 - 2) * 5 + (8 - (-5)) * (8 - (-5)) * 0 +
(1 - (-2)) * (1 - (-2)) * 4 + (3 - 6) * (3 - 6) * 2) /
20]);
return [2 /*return*/];
}
});
}); });
it('throws when passed label as a non-tensor', function () {
var predictions = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
var weights = tf.tensor2d([3, 6, 5, 0, 4, 2], [2, 3]);
var e = /Argument 'labels' passed to 'meanSquaredError' must be a Tensor/;
expect(function () { return tf.losses.meanSquaredError({}, predictions, weights, tf.Reduction.MEAN); })
.toThrowError(e);
});
it('throws when passed label as a non-tensor', function () {
var label = tf.tensor2d([1, 9, 2, -5, -2, 6], [2, 3]);
var weights = tf.tensor2d([3, 6, 5, 0, 4, 2], [2, 3]);
var e = new RegExp('Argument \'predictions\' passed to \'meanSquaredError\' ' +
'must be a Tensor');
expect(function () { return tf.losses.meanSquaredError(label, {}, weights, tf.Reduction.MEAN); })
.toThrowError(e);
});
it('throws when passed weights as a non-tensor', function () {
var predictions = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
var label = tf.tensor2d([1, 9, 2, -5, -2, 6], [2, 3]);
var e = /Argument 'weights' passed to 'meanSquaredError' must be a Tensor/;
expect(function () { return tf.losses.meanSquaredError(label, predictions, {}, tf.Reduction.MEAN); })
.toThrowError(e);
});
it('accepts a tensor-like object', function () { return __awaiter(_this, void 0, void 0, function () {
var predictions, label, weights, y, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
predictions = [1, 2, 3];
label = [0.3, -0.6, -0.1];
weights = [0.1, 0.2, 0.3];
y = tf.losses.meanSquaredError(label, predictions, weights, tf.Reduction.NONE);
expect(y.shape).toEqual([3]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, y.data()];
case 1:
_a.apply(void 0, [_b.sent(), [
(1 - 0.3) * (1 - 0.3) * 0.1, (2 - (-0.6)) * (2 - (-0.6)) * 0.2,
(3 - (-0.1)) * (3 - (-0.1)) * 0.3
]]);
return [2 /*return*/];
}
});
}); });
});
jasmine_util_1.describeWithFlags('cosineDistance', jasmine_util_1.ALL_ENVS, function () {
it('1D', function () { return __awaiter(_this, void 0, void 0, function () {
var predictions, label, y, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
predictions = tf.tensor1d([1, 2, 3]);
label = tf.tensor1d([0.3, -0.6, -0.1]);
y = tf.losses.cosineDistance(label, predictions, 0);
expect(y.shape).toEqual([]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, y.data()];
case 1:
_a.apply(void 0, [_b.sent(), 1 - (1 * 0.3 + 2 * -0.6 + 3 * -0.1)]);
return [2 /*return*/];
}
});
}); });
it('1D - weighted - Reduction.SUM_BY_NONZERO_WEIGHTS', function () { return __awaiter(_this, void 0, void 0, function () {
var predictions, label, weights, y, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
predictions = tf.tensor1d([1, 2, 3]);
label = tf.tensor1d([0.3, -0.6, -0.1]);
weights = tf.scalar(0.1);
y = tf.losses.cosineDistance(label, predictions, 0, weights);
expect(y.shape).toEqual([]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, y.data()];
case 1:
_a.apply(void 0, [_b.sent(), (1 - (1 * 0.3 + 2 * -0.6 + 3 * -0.1)) * 0.1]);
return [2 /*return*/];
}
});
}); });
it('1D - weighted - Reduction.NONE', function () { return __awaiter(_this, void 0, void 0, function () {
var predictions, label, weights, y, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
predictions = tf.tensor1d([1, 2, 3]);
label = tf.tensor1d([0.3, -0.6, -0.1]);
weights = tf.scalar(0.1);
y = tf.losses.cosineDistance(label, predictions, 0, weights, tf.Reduction.NONE);
expect(y.shape).toEqual([1]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, y.data()];
case 1:
_a.apply(void 0, [_b.sent(), [(1 - (1 * 0.3 + 2 * -0.6 + 3 * -0.1)) * 0.1]]);
return [2 /*return*/];
}
});
}); });
it('1D - Reduction.MEAN', function () { return __awaiter(_this, void 0, void 0, function () {
var predictions, label, y, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
predictions = tf.tensor1d([1, 2, 3]);
label = tf.tensor1d([0.3, -0.6, -0.1]);
y = tf.losses.cosineDistance(label, predictions, 0, undefined, tf.Reduction.MEAN);
expect(y.shape).toEqual([]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, y.data()];
case 1:
_a.apply(void 0, [_b.sent(), (1 - (1 * 0.3 + 2 * -0.6 + 3 * -0.1))]);
return [2 /*return*/];
}
});
}); });
it('1D - weighted - Reduction.MEAN', function () { return __awaiter(_this, void 0, void 0, function () {
var predictions, label, weights, y, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
predictions = tf.tensor1d([1, 2, 3]);
label = tf.tensor1d([0.3, -0.6, -0.1]);
weights = tf.scalar(0.1);
y = tf.losses.cosineDistance(label, predictions, 0, weights, tf.Reduction.MEAN);
expect(y.shape).toEqual([]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, y.data()];
case 1:
_a.apply(void 0, [_b.sent(), ((1 - (1 * 0.3 + 2 * -0.6 + 3 * -0.1)) * 0.1) / 0.1]);
return [2 /*return*/];
}
});
}); });
it('2D', function () { return __awaiter(_this, void 0, void 0, function () {
var predictions, label, y, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
predictions = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
label = tf.tensor2d([1, 9, 2, -5, -2, 6], [2, 3]);
y = tf.losses.cosineDistance(label, predictions, 1);
expect(y.shape).toEqual([]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, y.data()];
case 1:
_a.apply(void 0, [_b.sent(),
((1 - (4 * 1 + 8 * 9 + 12 * 2)) + (1 - (8 * -5 + 1 * -2 + 3 * 6))) / 2]);
return [2 /*return*/];
}
});
}); });
it('2D - weighted - Reduction.SUM_BY_NONZERO_WEIGHTS', function () { return __awaiter(_this, void 0, void 0, function () {
var predictions, label, weights, y, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
predictions = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
label = tf.tensor2d([1, 9, 2, -5, -2, 6], [2, 3]);
weights = tf.tensor2d([3, 0], [2, 1]);
y = tf.losse