@tensorflow/tfjs-core
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Hardware-accelerated JavaScript library for machine intelligence
147 lines • 7.68 kB
JavaScript
;
/**
* @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('broadcastTo', jasmine_util_1.ALL_ENVS, function () {
it('[] -> [3,2]', function () { return __awaiter(_this, void 0, void 0, function () {
var a, A, _a, _b, w, f, h, df, dh, _c, _d;
return __generator(this, function (_e) {
switch (_e.label) {
case 0:
a = tf.scalar(4.2);
A = tf.tensor2d([[4.2, 4.2], [4.2, 4.2], [4.2, 4.2]]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, A.array()];
case 1:
_b = [_e.sent()];
return [4 /*yield*/, tf.broadcastTo(a, A.shape).array()];
case 2:
_a.apply(void 0, _b.concat([_e.sent()]));
w = tf.tensor2d([[4.7, 4.5], [-6.1, -6.6], [-8.1, -3.4]]), f = function (a) {
return tf.broadcastTo(a, A.shape).mul(w).mean().asScalar();
}, h = function (a) { return a.mul(w).mean().asScalar(); };
df = tf.grad(f), dh = tf.grad(h);
_c = test_util_1.expectArraysClose;
return [4 /*yield*/, df(a).array()];
case 3:
_d = [_e.sent()];
return [4 /*yield*/, dh(a).array()];
case 4:
_c.apply(void 0, _d.concat([_e.sent()]));
return [2 /*return*/];
}
});
}); });
it('[2] -> [3,2]', function () { return __awaiter(_this, void 0, void 0, function () {
var a, A, _a, _b, w, f, h, df, dh, _c, _d;
return __generator(this, function (_e) {
switch (_e.label) {
case 0:
a = tf.tensor1d([1, 2]);
A = tf.tensor2d([[1, 2], [1, 2], [1, 2]]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, A.array()];
case 1:
_b = [_e.sent()];
return [4 /*yield*/, tf.broadcastTo(a, A.shape).array()];
case 2:
_a.apply(void 0, _b.concat([_e.sent()]));
w = tf.tensor2d([[4.7, 4.5], [-6.1, -6.6], [-8.1, -3.4]]), f = function (a) {
return tf.broadcastTo(a, A.shape).mul(w).mean().asScalar();
}, h = function (a) { return a.mul(w).mean().asScalar(); };
df = tf.grad(f), dh = tf.grad(h);
_c = test_util_1.expectArraysClose;
return [4 /*yield*/, df(a).array()];
case 3:
_d = [_e.sent()];
return [4 /*yield*/, dh(a).array()];
case 4:
_c.apply(void 0, _d.concat([_e.sent()]));
return [2 /*return*/];
}
});
}); });
it('[3,1] -> [3,2]', function () { return __awaiter(_this, void 0, void 0, function () {
var a, A, _a, _b, w, f, h, df, dh, _c, _d;
return __generator(this, function (_e) {
switch (_e.label) {
case 0:
a = tf.tensor2d([[1], [2], [3]]);
A = tf.tensor2d([[1, 1], [2, 2], [3, 3]]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, A.array()];
case 1:
_b = [_e.sent()];
return [4 /*yield*/, tf.broadcastTo(a, A.shape).array()];
case 2:
_a.apply(void 0, _b.concat([_e.sent()]));
w = tf.tensor2d([[4.7, 4.5], [-6.1, -6.6], [-8.1, -3.4]]), f = function (a) {
return tf.broadcastTo(a, A.shape).mul(w).mean().asScalar();
}, h = function (a) { return a.mul(w).mean().asScalar(); };
df = tf.grad(f), dh = tf.grad(h);
_c = test_util_1.expectArraysClose;
return [4 /*yield*/, df(a).array()];
case 3:
_d = [_e.sent()];
return [4 /*yield*/, dh(a).array()];
case 4:
_c.apply(void 0, _d.concat([_e.sent()]));
return [2 /*return*/];
}
});
}); });
});
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