"use strict";
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/**
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* @license
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* Copyright 2017 Google Inc. All Rights Reserved.
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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* =============================================================================
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*/
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var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
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return new (P || (P = Promise))(function (resolve, reject) {
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function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
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function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
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function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
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step((generator = generator.apply(thisArg, _arguments || [])).next());
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});
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};
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var __generator = (this && this.__generator) || function (thisArg, body) {
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var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
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return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
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function verb(n) { return function (v) { return step([n, v]); }; }
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function step(op) {
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if (f) throw new TypeError("Generator is already executing.");
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while (_) try {
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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;
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if (y = 0, t) op = [op[0] & 2, t.value];
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switch (op[0]) {
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case 0: case 1: t = op; break;
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case 4: _.label++; return { value: op[1], done: false };
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case 5: _.label++; y = op[1]; op = [0]; continue;
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case 7: op = _.ops.pop(); _.trys.pop(); continue;
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default:
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if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
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if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
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if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
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if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
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if (t[2]) _.ops.pop();
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_.trys.pop(); continue;
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}
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op = body.call(thisArg, _);
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} catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
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if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
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}
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};
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var _this = this;
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Object.defineProperty(exports, "__esModule", { value: true });
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var tf = require("../index");
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var jasmine_util_1 = require("../jasmine_util");
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var test_util_1 = require("../test_util");
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jasmine_util_1.describeWithFlags('prelu', jasmine_util_1.ALL_ENVS, function () {
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it('basic', function () { return __awaiter(_this, void 0, void 0, function () {
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var x, a, result, _a;
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return __generator(this, function (_b) {
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switch (_b.label) {
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case 0:
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x = tf.tensor1d([0, 1, -2, -4]);
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a = tf.tensor1d([0.15, 0.2, 0.25, 0.15]);
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result = tf.prelu(x, a);
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expect(result.shape).toEqual(x.shape);
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_a = test_util_1.expectArraysClose;
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return [4 /*yield*/, result.data()];
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case 1:
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_a.apply(void 0, [_b.sent(), [0, 1, -0.5, -0.6]]);
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return [2 /*return*/];
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}
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});
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}); });
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it('basic TensorLike', function () { return __awaiter(_this, void 0, void 0, function () {
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var x, a, result, _a;
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return __generator(this, function (_b) {
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switch (_b.label) {
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case 0:
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x = [0, 1, -2, -4];
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a = [0.15, 0.2, 0.25, 0.15];
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result = tf.prelu(x, a);
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expect(result.shape).toEqual([4]);
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_a = test_util_1.expectArraysClose;
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return [4 /*yield*/, result.data()];
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case 1:
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_a.apply(void 0, [_b.sent(), [0, 1, -0.5, -0.6]]);
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return [2 /*return*/];
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}
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});
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}); });
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it('basic TensorLike chained', function () { return __awaiter(_this, void 0, void 0, function () {
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var x, a, result, _a;
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return __generator(this, function (_b) {
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switch (_b.label) {
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case 0:
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x = tf.tensor1d([0, 1, -2, -4]);
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a = [0.15, 0.2, 0.25, 0.15];
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result = x.prelu(a);
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expect(result.shape).toEqual(x.shape);
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_a = test_util_1.expectArraysClose;
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return [4 /*yield*/, result.data()];
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case 1:
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_a.apply(void 0, [_b.sent(), [0, 1, -0.5, -0.6]]);
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return [2 /*return*/];
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}
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});
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}); });
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it('derivative', function () { return __awaiter(_this, void 0, void 0, function () {
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var x, a, dy, dx, _a;
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return __generator(this, function (_b) {
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switch (_b.label) {
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case 0:
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x = tf.tensor1d([0.5, 3, -0.1, -4]);
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a = tf.tensor1d([0.2, 0.4, 0.25, 0.15]);
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dy = tf.tensor1d([1, 1, 1, 1]);
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dx = tf.grad(function (x) { return tf.prelu(x, a); })(x, dy);
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expect(dx.shape).toEqual(x.shape);
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expect(dx.dtype).toEqual('float32');
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_a = test_util_1.expectArraysClose;
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return [4 /*yield*/, dx.data()];
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case 1:
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_a.apply(void 0, [_b.sent(), [1, 1, 0.25, 0.15]]);
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return [2 /*return*/];
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}
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});
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}); });
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it('gradient with clones', function () { return __awaiter(_this, void 0, void 0, function () {
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var x, a, dx, _a;
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return __generator(this, function (_b) {
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switch (_b.label) {
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case 0:
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x = tf.tensor1d([0.5, 3, -0.1, -4]);
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a = tf.tensor1d([0.2, 0.4, 0.25, 0.15]);
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dx = tf.grad(function (x) { return tf.prelu(x.clone(), a).clone(); })(x);
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expect(dx.shape).toEqual(x.shape);
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expect(dx.dtype).toEqual('float32');
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_a = test_util_1.expectArraysClose;
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return [4 /*yield*/, dx.data()];
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case 1:
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_a.apply(void 0, [_b.sent(), [1, 1, 0.25, 0.15]]);
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return [2 /*return*/];
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}
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});
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}); });
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it('derivative where alpha got broadcasted', function () { return __awaiter(_this, void 0, void 0, function () {
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var x, a, dy, da, _a;
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return __generator(this, function (_b) {
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switch (_b.label) {
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case 0:
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x = tf.tensor2d([[0.5, 3, -0.1, -4]]);
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a = tf.tensor2d([[0.2]]);
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dy = tf.tensor2d([[1, 1, 1, 1]]);
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da = tf.grad(function (a) { return tf.prelu(x, a); })(a, dy);
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expect(da.shape).toEqual(a.shape);
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_a = test_util_1.expectArraysClose;
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return [4 /*yield*/, da.data()];
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case 1:
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_a.apply(void 0, [_b.sent(), [-4.1]]);
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return [2 /*return*/];
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}
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});
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}); });
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it('throws when passed x as a non-tensor', function () {
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expect(function () { return tf.prelu({}, tf.scalar(1)); })
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.toThrowError(/Argument 'x' passed to 'prelu' must be a Tensor/);
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});
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it('throws when passed alpha as a non-tensor', function () {
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expect(function () { return tf.prelu(tf.scalar(1), {}); })
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.toThrowError(/Argument 'alpha' passed to 'prelu' must be a Tensor/);
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});
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it('throws for string tensor', function () {
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expect(function () { return tf.prelu(['a'], 0.1); })
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.toThrowError(/Argument 'x' passed to 'prelu' must be numeric tensor/);
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});
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});
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jasmine_util_1.describeWithFlags('maximum', jasmine_util_1.ALL_ENVS, function () {
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it('float32 and float32', function () { return __awaiter(_this, void 0, void 0, function () {
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var a, b, result, _a;
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return __generator(this, function (_b) {
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switch (_b.label) {
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case 0:
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a = tf.tensor1d([0.5, 3, -0.1, -4]);
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b = tf.tensor1d([0.2, 0.4, 0.25, 0.15]);
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result = tf.maximum(a, b);
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expect(result.shape).toEqual(a.shape);
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_a = test_util_1.expectArraysClose;
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return [4 /*yield*/, result.data()];
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case 1:
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_a.apply(void 0, [_b.sent(), [0.5, 3, 0.25, 0.15]]);
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return [2 /*return*/];
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}
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});
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}); });
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it('TensorLike', function () { return __awaiter(_this, void 0, void 0, function () {
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var a, b, result, _a;
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return __generator(this, function (_b) {
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switch (_b.label) {
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case 0:
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a = [0.5, 3, -0.1, -4];
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b = [0.2, 0.4, 0.25, 0.15];
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result = tf.maximum(a, b);
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expect(result.shape).toEqual([4]);
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_a = test_util_1.expectArraysClose;
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return [4 /*yield*/, result.data()];
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case 1:
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_a.apply(void 0, [_b.sent(), [0.5, 3, 0.25, 0.15]]);
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return [2 /*return*/];
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}
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});
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}); });
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it('TensorLike chained', function () { return __awaiter(_this, void 0, void 0, function () {
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var a, b, result, _a;
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return __generator(this, function (_b) {
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switch (_b.label) {
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case 0:
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a = tf.tensor1d([0.5, 3, -0.1, -4]);
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b = [0.2, 0.4, 0.25, 0.15];
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result = a.maximum(b);
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expect(result.shape).toEqual([4]);
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_a = test_util_1.expectArraysClose;
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return [4 /*yield*/, result.data()];
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case 1:
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_a.apply(void 0, [_b.sent(), [0.5, 3, 0.25, 0.15]]);
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return [2 /*return*/];
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}
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});
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}); });
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it('int32 and int32', function () { return __awaiter(_this, void 0, void 0, function () {
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var a, b, result, _a;
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return __generator(this, function (_b) {
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switch (_b.label) {
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case 0:
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a = tf.tensor1d([1, 5, 2, 3], 'int32');
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b = tf.tensor1d([2, 3, 1, 4], 'int32');
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result = tf.maximum(a, b);
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expect(result.shape).toEqual(a.shape);
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expect(result.dtype).toBe('int32');
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_a = test_util_1.expectArraysEqual;
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return [4 /*yield*/, result.data()];
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case 1:
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_a.apply(void 0, [_b.sent(), [2, 5, 2, 4]]);
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return [2 /*return*/];
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}
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});
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}); });
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it('bool and bool', function () { return __awaiter(_this, void 0, void 0, function () {
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var a, b, result, _a;
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return __generator(this, function (_b) {
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switch (_b.label) {
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case 0:
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a = tf.tensor1d([true, false, false, true], 'bool');
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b = tf.tensor1d([false, false, true, true], 'bool');
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result = tf.maximum(a, b);
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expect(result.shape).toEqual(a.shape);
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expect(result.dtype).toBe('int32');
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_a = test_util_1.expectArraysEqual;
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return [4 /*yield*/, result.data()];
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case 1:
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_a.apply(void 0, [_b.sent(), [1, 0, 1, 1]]);
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return [2 /*return*/];
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}
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});
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}); });
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it('upcasts when dtypes dont match', function () { return __awaiter(_this, void 0, void 0, function () {
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var a, b, res, _a;
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return __generator(this, function (_b) {
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switch (_b.label) {
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case 0:
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a = tf.tensor1d([1, 0, 0, 1], 'float32');
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b = tf.tensor1d([0, 0, 1, 1], 'int32');
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res = tf.maximum(a, b);
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expect(res.shape).toEqual(a.shape);
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expect(res.dtype).toBe('float32');
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_a = test_util_1.expectArraysEqual;
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return [4 /*yield*/, res.data()];
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case 1:
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_a.apply(void 0, [_b.sent(), [1, 0, 1, 1]]);
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return [2 /*return*/];
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}
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});
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}); });
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it('propagates NaN', function () { return __awaiter(_this, void 0, void 0, function () {
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var a, b, result, _a;
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return __generator(this, function (_b) {
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switch (_b.label) {
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case 0:
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a = tf.tensor1d([0.5, -0.1, NaN]);
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b = tf.tensor1d([0.2, 0.3, 0.25]);
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result = tf.maximum(a, b);
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expect(result.shape).toEqual(a.shape);
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_a = test_util_1.expectArraysClose;
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return [4 /*yield*/, result.data()];
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case 1:
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_a.apply(void 0, [_b.sent(), [0.5, 0.3, NaN]]);
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return [2 /*return*/];
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}
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});
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}); });
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it('broadcasts Tensor1D and scalar', function () { return __awaiter(_this, void 0, void 0, function () {
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var a, b, result, _a;
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return __generator(this, function (_b) {
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switch (_b.label) {
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case 0:
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a = tf.tensor1d([0.5, 3, -0.1, -4]);
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b = tf.scalar(0.6);
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result = tf.maximum(a, b);
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expect(result.shape).toEqual(a.shape);
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_a = test_util_1.expectArraysClose;
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return [4 /*yield*/, result.data()];
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case 1:
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_a.apply(void 0, [_b.sent(), [0.6, 3, 0.6, 0.6]]);
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return [2 /*return*/];
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}
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});
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}); });
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it('broadcasts scalar and Tensor1D', function () { return __awaiter(_this, void 0, void 0, function () {
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var a, b, result, _a;
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return __generator(this, function (_b) {
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switch (_b.label) {
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case 0:
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a = tf.scalar(0.6);
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b = tf.tensor1d([0.5, 3, -0.1, -4]);
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result = tf.maximum(a, b);
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expect(result.shape).toEqual(b.shape);
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_a = test_util_1.expectArraysClose;
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return [4 /*yield*/, result.data()];
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case 1:
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_a.apply(void 0, [_b.sent(), [0.6, 3, 0.6, 0.6]]);
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return [2 /*return*/];
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}
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});
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}); });
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it('broadcasts Tensor1D and Tensor2D', function () { return __awaiter(_this, void 0, void 0, function () {
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var a, b, result, _a;
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return __generator(this, function (_b) {
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switch (_b.label) {
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case 0:
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a = tf.tensor1d([0.5, 0.3]);
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b = tf.tensor2d([0.2, 0.4, 0.6, 0.15], [2, 2]);
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result = tf.maximum(a, b);
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expect(result.shape).toEqual(b.shape);
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_a = test_util_1.expectArraysClose;
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return [4 /*yield*/, result.data()];
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case 1:
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_a.apply(void 0, [_b.sent(), [0.5, 0.4, 0.6, 0.3]]);
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return [2 /*return*/];
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}
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});
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}); });
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it('broadcasts 2x1 Tensor2D and 2x2 Tensor2D', function () { return __awaiter(_this, void 0, void 0, function () {
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var a, b, result, _a;
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return __generator(this, function (_b) {
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switch (_b.label) {
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case 0:
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a = tf.tensor2d([0.5, 0.3], [2, 1]);
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b = tf.tensor2d([0.2, 0.4, 0.6, 0.15], [2, 2]);
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result = tf.maximum(a, b);
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expect(result.shape).toEqual(b.shape);
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_a = test_util_1.expectArraysClose;
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return [4 /*yield*/, result.data()];
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case 1:
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_a.apply(void 0, [_b.sent(), [0.5, 0.5, 0.6, 0.3]]);
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return [2 /*return*/];
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}
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});
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}); });
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it('gradients: Scalar', function () { return __awaiter(_this, void 0, void 0, function () {
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var a, b, dy, grads, _a, da, db, _b, _c;
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return __generator(this, function (_d) {
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switch (_d.label) {
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case 0:
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a = tf.scalar(5.2);
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b = tf.scalar(0.6);
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dy = tf.scalar(3);
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grads = tf.grads(function (a, b) { return tf.maximum(a, b); });
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_a = grads([a, b], dy), da = _a[0], db = _a[1];
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expect(da.shape).toEqual(a.shape);
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expect(db.shape).toEqual(b.shape);
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expect(da.dtype).toEqual('float32');
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expect(db.dtype).toEqual('float32');
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_b = test_util_1.expectArraysClose;
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return [4 /*yield*/, da.data()];
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case 1:
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_b.apply(void 0, [_d.sent(), [3 * 1]]);
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_c = test_util_1.expectArraysClose;
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return [4 /*yield*/, db.data()];
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case 2:
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_c.apply(void 0, [_d.sent(), [3 * 0]]);
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return [2 /*return*/];
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}
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});
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}); });
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it('gradient with clones', function () { return __awaiter(_this, void 0, void 0, function () {
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var a, b, dy, grads, _a, da, db, _b, _c;
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return __generator(this, function (_d) {
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switch (_d.label) {
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case 0:
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a = tf.scalar(5.2);
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b = tf.scalar(0.6);
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dy = tf.scalar(3);
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grads = tf.grads(function (a, b) { return tf.maximum(a.clone(), b.clone()).clone(); });
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_a = grads([a, b], dy), da = _a[0], db = _a[1];
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expect(da.shape).toEqual(a.shape);
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expect(db.shape).toEqual(b.shape);
|
expect(da.dtype).toEqual('float32');
|
expect(db.dtype).toEqual('float32');
|
_b = test_util_1.expectArraysClose;
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return [4 /*yield*/, da.data()];
|
case 1:
|
_b.apply(void 0, [_d.sent(), [3 * 1]]);
|
_c = test_util_1.expectArraysClose;
|
return [4 /*yield*/, db.data()];
|
case 2:
|
_c.apply(void 0, [_d.sent(), [3 * 0]]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('gradients: Tensor1D', function () { return __awaiter(_this, void 0, void 0, function () {
|
var a, b, dy, grads, _a, da, db, _b, _c;
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return __generator(this, function (_d) {
|
switch (_d.label) {
|
case 0:
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a = tf.tensor1d([1.1, 2.6, 3, 5.9]);
|
b = tf.tensor1d([1.0, 2.7, 3, 5.8]);
|
dy = tf.tensor1d([1, 2, 3, 4]);
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grads = tf.grads(function (a, b) { return tf.maximum(a, b); });
|
_a = grads([a, b], dy), da = _a[0], db = _a[1];
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expect(da.shape).toEqual(a.shape);
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expect(db.shape).toEqual(b.shape);
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expect(da.dtype).toEqual('float32');
|
expect(db.dtype).toEqual('float32');
|
_b = test_util_1.expectArraysClose;
|
return [4 /*yield*/, da.data()];
|
case 1:
|
_b.apply(void 0, [_d.sent(), [1 * 1, 2 * 0, 3 * 1, 4 * 1]]);
|
_c = test_util_1.expectArraysClose;
|
return [4 /*yield*/, db.data()];
|
case 2:
|
_c.apply(void 0, [_d.sent(), [1 * 0, 2 * 1, 3 * 0, 4 * 0]]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('gradients: Tensor2D', function () { return __awaiter(_this, void 0, void 0, function () {
|
var a, b, dy, grads, _a, da, db, _b, _c;
|
return __generator(this, function (_d) {
|
switch (_d.label) {
|
case 0:
|
a = tf.tensor2d([0.5, 0.3, 0.7, 0.9], [2, 2]);
|
b = tf.tensor2d([0.2, 0.4, 0.7, 0.15], [2, 2]);
|
dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);
|
grads = tf.grads(function (a, b) { return tf.maximum(a, b); });
|
_a = grads([a, b], dy), da = _a[0], db = _a[1];
|
expect(da.shape).toEqual(a.shape);
|
expect(db.shape).toEqual(b.shape);
|
expect(da.dtype).toEqual('float32');
|
expect(db.dtype).toEqual('float32');
|
_b = test_util_1.expectArraysClose;
|
return [4 /*yield*/, da.data()];
|
case 1:
|
_b.apply(void 0, [_d.sent(), [1 * 1, 2 * 0, 3 * 1, 4 * 1]]);
|
_c = test_util_1.expectArraysClose;
|
return [4 /*yield*/, db.data()];
|
case 2:
|
_c.apply(void 0, [_d.sent(), [1 * 0, 2 * 1, 3 * 0, 4 * 0]]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('throws when passed a as a non-tensor', function () {
|
expect(function () { return tf.maximum({}, tf.scalar(1)); })
|
.toThrowError(/Argument 'a' passed to 'maximum' must be a Tensor/);
|
});
|
it('throws when passed b as a non-tensor', function () {
|
expect(function () { return tf.maximum(tf.scalar(1), {}); })
|
.toThrowError(/Argument 'b' passed to 'maximum' must be a Tensor/);
|
});
|
it('accepts a tensor-like object', function () { return __awaiter(_this, void 0, void 0, function () {
|
var a, b, result, _a;
|
return __generator(this, function (_b) {
|
switch (_b.label) {
|
case 0:
|
a = [[0.5, 3], [-0.1, -4]];
|
b = [[0.2, 0.4], [0.25, 0.15]];
|
result = tf.maximum(a, b);
|
expect(result.shape).toEqual([2, 2]);
|
_a = test_util_1.expectArraysClose;
|
return [4 /*yield*/, result.data()];
|
case 1:
|
_a.apply(void 0, [_b.sent(), [0.5, 3, 0.25, 0.15]]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('throws for string tensor', function () {
|
expect(function () { return tf.maximum('q', 3); })
|
.toThrowError(/Argument 'a' passed to 'maximum' must be numeric tensor/);
|
expect(function () { return tf.maximum(3, 'q'); })
|
.toThrowError(/Argument 'b' passed to 'maximum' must be numeric tensor/);
|
});
|
});
|
jasmine_util_1.describeWithFlags('squaredDifference', jasmine_util_1.ALL_ENVS, function () {
|
it('float32 and float32', function () { return __awaiter(_this, void 0, void 0, function () {
|
var a, b, result, _a;
|
return __generator(this, function (_b) {
|
switch (_b.label) {
|
case 0:
|
a = tf.tensor1d([0.5, 3, -0.1, -4]);
|
b = tf.tensor1d([0.2, 0.4, 0.25, 0.15]);
|
result = tf.squaredDifference(a, b);
|
expect(result.shape).toEqual(a.shape);
|
_a = test_util_1.expectArraysClose;
|
return [4 /*yield*/, result.data()];
|
case 1:
|
_a.apply(void 0, [_b.sent(), [
|
Math.pow(0.5 - 0.2, 2), Math.pow(3 - 0.4, 2), Math.pow(-0.1 - 0.25, 2),
|
Math.pow(-4 - 0.15, 2)
|
]]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('TensorLike', function () { return __awaiter(_this, void 0, void 0, function () {
|
var a, b, result, _a;
|
return __generator(this, function (_b) {
|
switch (_b.label) {
|
case 0:
|
a = [0.5, 3, -0.1, -4];
|
b = [0.2, 0.4, 0.25, 0.15];
|
result = tf.squaredDifference(a, b);
|
expect(result.shape).toEqual([4]);
|
_a = test_util_1.expectArraysClose;
|
return [4 /*yield*/, result.data()];
|
case 1:
|
_a.apply(void 0, [_b.sent(), [
|
Math.pow(0.5 - 0.2, 2), Math.pow(3 - 0.4, 2), Math.pow(-0.1 - 0.25, 2),
|
Math.pow(-4 - 0.15, 2)
|
]]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('TensorLike chained', function () { return __awaiter(_this, void 0, void 0, function () {
|
var a, b, result, _a;
|
return __generator(this, function (_b) {
|
switch (_b.label) {
|
case 0:
|
a = tf.tensor1d([0.5, 3, -0.1, -4]);
|
b = [0.2, 0.4, 0.25, 0.15];
|
result = a.squaredDifference(b);
|
expect(result.shape).toEqual(a.shape);
|
_a = test_util_1.expectArraysClose;
|
return [4 /*yield*/, result.data()];
|
case 1:
|
_a.apply(void 0, [_b.sent(), [
|
Math.pow(0.5 - 0.2, 2), Math.pow(3 - 0.4, 2), Math.pow(-0.1 - 0.25, 2),
|
Math.pow(-4 - 0.15, 2)
|
]]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('int32 and int32', function () { return __awaiter(_this, void 0, void 0, function () {
|
var a, b, result, _a;
|
return __generator(this, function (_b) {
|
switch (_b.label) {
|
case 0:
|
a = tf.tensor1d([1, 5, 2, 3], 'int32');
|
b = tf.tensor1d([2, 3, 1, 4], 'int32');
|
result = tf.squaredDifference(a, b);
|
expect(result.shape).toEqual(a.shape);
|
expect(result.dtype).toBe('int32');
|
_a = test_util_1.expectArraysEqual;
|
return [4 /*yield*/, result.data()];
|
case 1:
|
_a.apply(void 0, [_b.sent(), [
|
Math.pow(1 - 2, 2), Math.pow(5 - 3, 2), Math.pow(2 - 1, 2),
|
Math.pow(3 - 4, 2)
|
]]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('upcasts when dtypes dont match', function () { return __awaiter(_this, void 0, void 0, function () {
|
var res, _a, _b, _c;
|
return __generator(this, function (_d) {
|
switch (_d.label) {
|
case 0:
|
res = tf.squaredDifference(tf.scalar(5, 'int32'), tf.scalar(2, 'float32'));
|
expect(res.dtype).toBe('float32');
|
_a = test_util_1.expectArraysClose;
|
return [4 /*yield*/, res.data()];
|
case 1:
|
_a.apply(void 0, [_d.sent(), [9]]);
|
res = tf.squaredDifference(tf.scalar(5, 'int32'), tf.scalar(true, 'bool'));
|
expect(res.dtype).toBe('int32');
|
_b = test_util_1.expectArraysClose;
|
return [4 /*yield*/, res.data()];
|
case 2:
|
_b.apply(void 0, [_d.sent(), [16]]);
|
res = tf.squaredDifference(tf.scalar(5, 'int32'), tf.scalar(false, 'bool'));
|
expect(res.dtype).toBe('int32');
|
_c = test_util_1.expectArraysClose;
|
return [4 /*yield*/, res.data()];
|
case 3:
|
_c.apply(void 0, [_d.sent(), [25]]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('propagates NaN', function () { return __awaiter(_this, void 0, void 0, function () {
|
var a, b, result, _a;
|
return __generator(this, function (_b) {
|
switch (_b.label) {
|
case 0:
|
a = tf.tensor1d([0.5, -0.1, NaN]);
|
b = tf.tensor1d([0.2, 0.3, 0.25]);
|
result = tf.squaredDifference(a, b);
|
expect(result.shape).toEqual(a.shape);
|
_a = test_util_1.expectArraysClose;
|
return [4 /*yield*/, result.data()];
|
case 1:
|
_a.apply(void 0, [_b.sent(),
|
[Math.pow(0.5 - 0.2, 2), Math.pow(-0.1 - 0.3, 2), NaN]]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('broadcasts Tensor1D and scalar', function () { return __awaiter(_this, void 0, void 0, function () {
|
var a, b, result, _a;
|
return __generator(this, function (_b) {
|
switch (_b.label) {
|
case 0:
|
a = tf.tensor1d([0.5, 3, -0.1, -4]);
|
b = tf.scalar(0.6);
|
result = tf.squaredDifference(a, b);
|
expect(result.shape).toEqual(a.shape);
|
_a = test_util_1.expectArraysClose;
|
return [4 /*yield*/, result.data()];
|
case 1:
|
_a.apply(void 0, [_b.sent(), [
|
Math.pow(0.5 - 0.6, 2), Math.pow(3 - 0.6, 2), Math.pow(-0.1 - 0.6, 2),
|
Math.pow(-4 - 0.6, 2)
|
]]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('broadcasts scalar and Tensor1D', function () { return __awaiter(_this, void 0, void 0, function () {
|
var a, b, result, _a;
|
return __generator(this, function (_b) {
|
switch (_b.label) {
|
case 0:
|
a = tf.scalar(0.6);
|
b = tf.tensor1d([0.5, 3, -0.1, -4]);
|
result = tf.squaredDifference(a, b);
|
expect(result.shape).toEqual(b.shape);
|
_a = test_util_1.expectArraysClose;
|
return [4 /*yield*/, result.data()];
|
case 1:
|
_a.apply(void 0, [_b.sent(), [
|
Math.pow(0.6 - 0.5, 2), Math.pow(0.6 - 3, 2), Math.pow(0.6 - (-0.1), 2),
|
Math.pow(0.6 - (-4), 2)
|
]]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('broadcasts Tensor1D and Tensor2D', function () { return __awaiter(_this, void 0, void 0, function () {
|
var a, b, result, _a;
|
return __generator(this, function (_b) {
|
switch (_b.label) {
|
case 0:
|
a = tf.tensor1d([0.5, 0.3]);
|
b = tf.tensor2d([0.2, 0.4, 0.6, 0.15], [2, 2]);
|
result = tf.squaredDifference(a, b);
|
expect(result.shape).toEqual(b.shape);
|
_a = test_util_1.expectArraysClose;
|
return [4 /*yield*/, result.data()];
|
case 1:
|
_a.apply(void 0, [_b.sent(), [
|
Math.pow(0.5 - 0.2, 2), Math.pow(0.3 - 0.4, 2), Math.pow(0.5 - 0.6, 2),
|
Math.pow(0.3 - 0.15, 2)
|
]]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('broadcasts 2x1 Tensor2D and 2x2 Tensor2D', function () { return __awaiter(_this, void 0, void 0, function () {
|
var a, b, result, _a;
|
return __generator(this, function (_b) {
|
switch (_b.label) {
|
case 0:
|
a = tf.tensor2d([0.5, 0.3], [2, 1]);
|
b = tf.tensor2d([0.2, 0.4, 0.6, 0.15], [2, 2]);
|
result = tf.squaredDifference(a, b);
|
expect(result.shape).toEqual(b.shape);
|
_a = test_util_1.expectArraysClose;
|
return [4 /*yield*/, result.data()];
|
case 1:
|
_a.apply(void 0, [_b.sent(), [
|
Math.pow(0.5 - 0.2, 2), Math.pow(0.5 - 0.4, 2), Math.pow(0.3 - 0.6, 2),
|
Math.pow(0.3 - 0.15, 2)
|
]]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('gradients: Scalar', function () { return __awaiter(_this, void 0, void 0, function () {
|
var a, b, dy, grads, _a, da, db, _b, _c;
|
return __generator(this, function (_d) {
|
switch (_d.label) {
|
case 0:
|
a = tf.scalar(5.2);
|
b = tf.scalar(0.6);
|
dy = tf.scalar(3);
|
grads = tf.grads(function (a, b) { return tf.squaredDifference(a, b); });
|
_a = grads([a, b], dy), da = _a[0], db = _a[1];
|
expect(da.shape).toEqual(a.shape);
|
expect(db.shape).toEqual(b.shape);
|
expect(da.dtype).toEqual('float32');
|
expect(db.dtype).toEqual('float32');
|
_b = test_util_1.expectArraysClose;
|
return [4 /*yield*/, da.data()];
|
case 1:
|
_b.apply(void 0, [_d.sent(), [3 * 2 * (5.2 - 0.6)]]);
|
_c = test_util_1.expectArraysClose;
|
return [4 /*yield*/, db.data()];
|
case 2:
|
_c.apply(void 0, [_d.sent(), [3 * 2 * (0.6 - 5.2)]]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('gradient with clones', function () { return __awaiter(_this, void 0, void 0, function () {
|
var a, b, dy, grads, _a, da, db, _b, _c;
|
return __generator(this, function (_d) {
|
switch (_d.label) {
|
case 0:
|
a = tf.scalar(5.2);
|
b = tf.scalar(0.6);
|
dy = tf.scalar(3);
|
grads = tf.grads(function (a, b) { return tf.squaredDifference(a.clone(), b.clone()).clone(); });
|
_a = grads([a, b], dy), da = _a[0], db = _a[1];
|
expect(da.shape).toEqual(a.shape);
|
expect(db.shape).toEqual(b.shape);
|
expect(da.dtype).toEqual('float32');
|
expect(db.dtype).toEqual('float32');
|
_b = test_util_1.expectArraysClose;
|
return [4 /*yield*/, da.data()];
|
case 1:
|
_b.apply(void 0, [_d.sent(), [3 * 2 * (5.2 - 0.6)]]);
|
_c = test_util_1.expectArraysClose;
|
return [4 /*yield*/, db.data()];
|
case 2:
|
_c.apply(void 0, [_d.sent(), [3 * 2 * (0.6 - 5.2)]]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('gradients: Tensor1D', function () { return __awaiter(_this, void 0, void 0, function () {
|
var a, b, dy, grads, _a, da, db, _b, _c;
|
return __generator(this, function (_d) {
|
switch (_d.label) {
|
case 0:
|
a = tf.tensor1d([1.1, 2.6, 3, 5.9]);
|
b = tf.tensor1d([1.0, 2.7, 3, 5.8]);
|
dy = tf.tensor1d([1, 2, 3, 1]);
|
grads = tf.grads(function (a, b) { return tf.squaredDifference(a, b); });
|
_a = grads([a, b], dy), da = _a[0], db = _a[1];
|
expect(da.shape).toEqual(a.shape);
|
expect(db.shape).toEqual(b.shape);
|
expect(da.dtype).toEqual('float32');
|
expect(db.dtype).toEqual('float32');
|
_b = test_util_1.expectArraysClose;
|
return [4 /*yield*/, da.data()];
|
case 1:
|
_b.apply(void 0, [_d.sent(), [
|
1 * 2 * (1.1 - 1.0), 2 * 2 * (2.6 - 2.7), 3 * 2 * (3 - 3),
|
1 * 2 * (5.9 - 5.8)
|
]]);
|
_c = test_util_1.expectArraysClose;
|
return [4 /*yield*/, db.data()];
|
case 2:
|
_c.apply(void 0, [_d.sent(), [
|
1 * 2 * (1.0 - 1.1), 2 * 2 * (2.7 - 2.6), 3 * 2 * (3 - 3),
|
1 * 2 * (5.8 - 5.9)
|
]]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('gradients: Tensor2D', function () { return __awaiter(_this, void 0, void 0, function () {
|
var a, b, dy, grads, _a, da, db, _b, _c;
|
return __generator(this, function (_d) {
|
switch (_d.label) {
|
case 0:
|
a = tf.tensor2d([0.5, 0.3, 0.7, 0.9], [2, 2]);
|
b = tf.tensor2d([0.2, 0.4, 0.7, 0.15], [2, 2]);
|
dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);
|
grads = tf.grads(function (a, b) { return tf.squaredDifference(a, b); });
|
_a = grads([a, b], dy), da = _a[0], db = _a[1];
|
expect(da.shape).toEqual(a.shape);
|
expect(db.shape).toEqual(b.shape);
|
expect(da.dtype).toEqual('float32');
|
expect(db.dtype).toEqual('float32');
|
_b = test_util_1.expectArraysClose;
|
return [4 /*yield*/, da.data()];
|
case 1:
|
_b.apply(void 0, [_d.sent(), [
|
1 * 2 * (0.5 - 0.2), 2 * 2 * (0.3 - 0.4), 3 * 2 * (0.7 - 0.7),
|
4 * 2 * (0.9 - 0.15)
|
]]);
|
_c = test_util_1.expectArraysClose;
|
return [4 /*yield*/, db.data()];
|
case 2:
|
_c.apply(void 0, [_d.sent(), [
|
1 * 2 * (0.2 - 0.5), 2 * 2 * (0.4 - 0.3), 3 * 2 * (0.7 - 0.7),
|
4 * 2 * (0.15 - 0.9)
|
]]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('throws when passed a as a non-tensor', function () {
|
expect(function () { return tf.squaredDifference({}, tf.scalar(1)); })
|
.toThrowError(/Argument 'a' passed to 'squaredDifference' must be a Tensor/);
|
});
|
it('throws when passed b as a non-tensor', function () {
|
expect(function () { return tf.squaredDifference(tf.scalar(1), {}); })
|
.toThrowError(/Argument 'b' passed to 'squaredDifference' must be a Tensor/);
|
});
|
it('accepts a tensor-like object', function () { return __awaiter(_this, void 0, void 0, function () {
|
var a, b, result, _a;
|
return __generator(this, function (_b) {
|
switch (_b.label) {
|
case 0:
|
a = [[0.5, 3], [-0.1, -4]];
|
b = 0.6;
|
result = tf.squaredDifference(a, b);
|
expect(result.shape).toEqual([2, 2]);
|
_a = test_util_1.expectArraysClose;
|
return [4 /*yield*/, result.data()];
|
case 1:
|
_a.apply(void 0, [_b.sent(), [
|
Math.pow(0.5 - 0.6, 2), Math.pow(3 - 0.6, 2), Math.pow(-0.1 - 0.6, 2),
|
Math.pow(-4 - 0.6, 2)
|
]]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('throws for string tensor', function () {
|
expect(function () { return tf.squaredDifference('q', 3); })
|
.toThrowError(/Argument 'a' passed to 'squaredDifference' must be numeric/);
|
expect(function () { return tf.squaredDifference(3, 'q'); })
|
.toThrowError(/Argument 'b' passed to 'squaredDifference' must be numeric/);
|
});
|
});
|
jasmine_util_1.describeWithFlags('minimum', jasmine_util_1.ALL_ENVS, function () {
|
it('float32 and float32', function () { return __awaiter(_this, void 0, void 0, function () {
|
var a, b, result, _a;
|
return __generator(this, function (_b) {
|
switch (_b.label) {
|
case 0:
|
a = tf.tensor1d([0.5, 3, -0.1, -4]);
|
b = tf.tensor1d([0.2, 0.4, 0.25, 0.15]);
|
result = tf.minimum(a, b);
|
expect(result.shape).toEqual(a.shape);
|
_a = test_util_1.expectArraysClose;
|
return [4 /*yield*/, result.data()];
|
case 1:
|
_a.apply(void 0, [_b.sent(), [0.2, 0.4, -0.1, -4]]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('TensorLike', function () { return __awaiter(_this, void 0, void 0, function () {
|
var a, b, result, _a;
|
return __generator(this, function (_b) {
|
switch (_b.label) {
|
case 0:
|
a = [0.5, 3, -0.1, -4];
|
b = [0.2, 0.4, 0.25, 0.15];
|
result = tf.minimum(a, b);
|
expect(result.shape).toEqual([4]);
|
_a = test_util_1.expectArraysClose;
|
return [4 /*yield*/, result.data()];
|
case 1:
|
_a.apply(void 0, [_b.sent(), [0.2, 0.4, -0.1, -4]]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('TensorLike chained', function () { return __awaiter(_this, void 0, void 0, function () {
|
var a, b, result, _a;
|
return __generator(this, function (_b) {
|
switch (_b.label) {
|
case 0:
|
a = tf.tensor1d([0.5, 3, -0.1, -4]);
|
b = [0.2, 0.4, 0.25, 0.15];
|
result = a.minimum(b);
|
expect(result.shape).toEqual(a.shape);
|
_a = test_util_1.expectArraysClose;
|
return [4 /*yield*/, result.data()];
|
case 1:
|
_a.apply(void 0, [_b.sent(), [0.2, 0.4, -0.1, -4]]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('int32 and int32', function () { return __awaiter(_this, void 0, void 0, function () {
|
var a, b, result, _a;
|
return __generator(this, function (_b) {
|
switch (_b.label) {
|
case 0:
|
a = tf.tensor1d([1, 5, 2, 3], 'int32');
|
b = tf.tensor1d([2, 3, 1, 4], 'int32');
|
result = tf.minimum(a, b);
|
expect(result.shape).toEqual(a.shape);
|
expect(result.dtype).toBe('int32');
|
_a = test_util_1.expectArraysEqual;
|
return [4 /*yield*/, result.data()];
|
case 1:
|
_a.apply(void 0, [_b.sent(), [1, 3, 1, 3]]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('bool and bool', function () { return __awaiter(_this, void 0, void 0, function () {
|
var a, b, result, _a;
|
return __generator(this, function (_b) {
|
switch (_b.label) {
|
case 0:
|
a = tf.tensor1d([true, false, false, true], 'bool');
|
b = tf.tensor1d([false, false, true, true], 'bool');
|
result = tf.minimum(a, b);
|
expect(result.shape).toEqual(a.shape);
|
expect(result.dtype).toBe('int32');
|
_a = test_util_1.expectArraysEqual;
|
return [4 /*yield*/, result.data()];
|
case 1:
|
_a.apply(void 0, [_b.sent(), [0, 0, 0, 1]]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('upcasts when dtypes dont match', function () { return __awaiter(_this, void 0, void 0, function () {
|
var a, b, res, _a;
|
return __generator(this, function (_b) {
|
switch (_b.label) {
|
case 0:
|
a = tf.tensor1d([1, 0, 0, 1], 'float32');
|
b = tf.tensor1d([0, 0, 1, 1], 'int32');
|
res = tf.minimum(a, b);
|
expect(res.shape).toEqual(a.shape);
|
expect(res.dtype).toBe('float32');
|
_a = test_util_1.expectArraysEqual;
|
return [4 /*yield*/, res.data()];
|
case 1:
|
_a.apply(void 0, [_b.sent(), [0, 0, 0, 1]]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('propagates NaN', function () { return __awaiter(_this, void 0, void 0, function () {
|
var a, b, result, _a;
|
return __generator(this, function (_b) {
|
switch (_b.label) {
|
case 0:
|
a = tf.tensor1d([0.5, -0.1, NaN]);
|
b = tf.tensor1d([0.2, 0.3, 0.25]);
|
result = tf.minimum(a, b);
|
expect(result.shape).toEqual(a.shape);
|
_a = test_util_1.expectArraysClose;
|
return [4 /*yield*/, result.data()];
|
case 1:
|
_a.apply(void 0, [_b.sent(), [0.2, -0.1, NaN]]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('broadcasts Tensor1D and scalar', function () { return __awaiter(_this, void 0, void 0, function () {
|
var a, b, result, _a;
|
return __generator(this, function (_b) {
|
switch (_b.label) {
|
case 0:
|
a = tf.tensor1d([0.5, 3, -0.1, -4]);
|
b = tf.scalar(0.6);
|
result = tf.minimum(a, b);
|
expect(result.shape).toEqual(a.shape);
|
_a = test_util_1.expectArraysClose;
|
return [4 /*yield*/, result.data()];
|
case 1:
|
_a.apply(void 0, [_b.sent(), [0.5, 0.6, -0.1, -4]]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('broadcasts scalar and Tensor1D', function () { return __awaiter(_this, void 0, void 0, function () {
|
var a, b, result, _a;
|
return __generator(this, function (_b) {
|
switch (_b.label) {
|
case 0:
|
a = tf.scalar(0.6);
|
b = tf.tensor1d([0.5, 3, -0.1, -4]);
|
result = tf.minimum(a, b);
|
expect(result.shape).toEqual(b.shape);
|
_a = test_util_1.expectArraysClose;
|
return [4 /*yield*/, result.data()];
|
case 1:
|
_a.apply(void 0, [_b.sent(), [0.5, 0.6, -0.1, -4]]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('broadcasts Tensor1D and Tensor2D', function () { return __awaiter(_this, void 0, void 0, function () {
|
var a, b, result, _a;
|
return __generator(this, function (_b) {
|
switch (_b.label) {
|
case 0:
|
a = tf.tensor1d([0.5, 0.3]);
|
b = tf.tensor2d([0.2, 0.4, 0.6, 0.15], [2, 2]);
|
result = tf.minimum(a, b);
|
expect(result.shape).toEqual(b.shape);
|
_a = test_util_1.expectArraysClose;
|
return [4 /*yield*/, result.data()];
|
case 1:
|
_a.apply(void 0, [_b.sent(), [0.2, 0.3, 0.5, 0.15]]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('broadcasts 2x1 Tensor2D and 2x2 Tensor2D', function () { return __awaiter(_this, void 0, void 0, function () {
|
var a, b, result, _a;
|
return __generator(this, function (_b) {
|
switch (_b.label) {
|
case 0:
|
a = tf.tensor2d([0.5, 0.3], [2, 1]);
|
b = tf.tensor2d([0.2, 0.4, 0.6, 0.15], [2, 2]);
|
result = tf.minimum(a, b);
|
expect(result.shape).toEqual(b.shape);
|
_a = test_util_1.expectArraysClose;
|
return [4 /*yield*/, result.data()];
|
case 1:
|
_a.apply(void 0, [_b.sent(), [0.2, 0.4, 0.3, 0.15]]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('gradients: Scalar', function () { return __awaiter(_this, void 0, void 0, function () {
|
var a, b, dy, grads, _a, da, db, _b, _c;
|
return __generator(this, function (_d) {
|
switch (_d.label) {
|
case 0:
|
a = tf.scalar(5.2);
|
b = tf.scalar(0.6);
|
dy = tf.scalar(3);
|
grads = tf.grads(function (a, b) { return tf.minimum(a, b); });
|
_a = grads([a, b], dy), da = _a[0], db = _a[1];
|
expect(da.shape).toEqual(a.shape);
|
expect(db.shape).toEqual(b.shape);
|
expect(da.dtype).toEqual('float32');
|
expect(db.dtype).toEqual('float32');
|
_b = test_util_1.expectArraysClose;
|
return [4 /*yield*/, da.data()];
|
case 1:
|
_b.apply(void 0, [_d.sent(), [3 * 0]]);
|
_c = test_util_1.expectArraysClose;
|
return [4 /*yield*/, db.data()];
|
case 2:
|
_c.apply(void 0, [_d.sent(), [3 * 1]]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('gradient with clones', function () { return __awaiter(_this, void 0, void 0, function () {
|
var a, b, dy, grads, _a, da, db, _b, _c;
|
return __generator(this, function (_d) {
|
switch (_d.label) {
|
case 0:
|
a = tf.scalar(5.2);
|
b = tf.scalar(0.6);
|
dy = tf.scalar(3);
|
grads = tf.grads(function (a, b) { return tf.minimum(a.clone(), b.clone()).clone(); });
|
_a = grads([a, b], dy), da = _a[0], db = _a[1];
|
expect(da.shape).toEqual(a.shape);
|
expect(db.shape).toEqual(b.shape);
|
expect(da.dtype).toEqual('float32');
|
expect(db.dtype).toEqual('float32');
|
_b = test_util_1.expectArraysClose;
|
return [4 /*yield*/, da.data()];
|
case 1:
|
_b.apply(void 0, [_d.sent(), [3 * 0]]);
|
_c = test_util_1.expectArraysClose;
|
return [4 /*yield*/, db.data()];
|
case 2:
|
_c.apply(void 0, [_d.sent(), [3 * 1]]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('gradients: Tensor1D', function () { return __awaiter(_this, void 0, void 0, function () {
|
var a, b, dy, grads, _a, da, db, _b, _c;
|
return __generator(this, function (_d) {
|
switch (_d.label) {
|
case 0:
|
a = tf.tensor1d([1.1, 2.6, 3, 5.9]);
|
b = tf.tensor1d([1.0, 2.7, 3, 5.8]);
|
dy = tf.tensor1d([1, 2, 3, 4]);
|
grads = tf.grads(function (a, b) { return tf.minimum(a, b); });
|
_a = grads([a, b], dy), da = _a[0], db = _a[1];
|
expect(da.shape).toEqual(a.shape);
|
expect(db.shape).toEqual(b.shape);
|
expect(da.dtype).toEqual('float32');
|
expect(db.dtype).toEqual('float32');
|
_b = test_util_1.expectArraysClose;
|
return [4 /*yield*/, da.data()];
|
case 1:
|
_b.apply(void 0, [_d.sent(), [1 * 0, 2 * 1, 3 * 1, 4 * 0]]);
|
_c = test_util_1.expectArraysClose;
|
return [4 /*yield*/, db.data()];
|
case 2:
|
_c.apply(void 0, [_d.sent(), [1 * 1, 2 * 0, 3 * 0, 4 * 1]]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('gradients: Tensor2D', function () { return __awaiter(_this, void 0, void 0, function () {
|
var a, b, dy, grads, _a, da, db, _b, _c;
|
return __generator(this, function (_d) {
|
switch (_d.label) {
|
case 0:
|
a = tf.tensor2d([0.5, 0.3, 0.7, 0.9], [2, 2]);
|
b = tf.tensor2d([0.2, 0.4, 0.7, 0.15], [2, 2]);
|
dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);
|
grads = tf.grads(function (a, b) { return tf.minimum(a, b); });
|
_a = grads([a, b], dy), da = _a[0], db = _a[1];
|
expect(da.shape).toEqual(a.shape);
|
expect(db.shape).toEqual(b.shape);
|
expect(da.dtype).toEqual('float32');
|
expect(db.dtype).toEqual('float32');
|
_b = test_util_1.expectArraysClose;
|
return [4 /*yield*/, da.data()];
|
case 1:
|
_b.apply(void 0, [_d.sent(), [1 * 0, 2 * 1, 3 * 1, 4 * 0]]);
|
_c = test_util_1.expectArraysClose;
|
return [4 /*yield*/, db.data()];
|
case 2:
|
_c.apply(void 0, [_d.sent(), [1 * 1, 2 * 0, 3 * 0, 4 * 1]]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('throws when passed a as a non-tensor', function () {
|
expect(function () { return tf.minimum({}, tf.scalar(1)); })
|
.toThrowError(/Argument 'a' passed to 'minimum' must be a Tensor/);
|
});
|
it('throws when passed b as a non-tensor', function () {
|
expect(function () { return tf.minimum(tf.scalar(1), {}); })
|
.toThrowError(/Argument 'b' passed to 'minimum' must be a Tensor/);
|
});
|
it('accepts a tensor-like object', function () { return __awaiter(_this, void 0, void 0, function () {
|
var a, b, result, _a;
|
return __generator(this, function (_b) {
|
switch (_b.label) {
|
case 0:
|
a = [[0.5, 3], [-0.1, -4]];
|
b = [[0.2, 0.4], [0.25, 0.15]];
|
result = tf.minimum(a, b);
|
expect(result.shape).toEqual([2, 2]);
|
_a = test_util_1.expectArraysClose;
|
return [4 /*yield*/, result.data()];
|
case 1:
|
_a.apply(void 0, [_b.sent(), [0.2, 0.4, -0.1, -4]]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('throws for string tensor', function () {
|
expect(function () { return tf.minimum('q', 3); })
|
.toThrowError(/Argument 'a' passed to 'minimum' must be numeric/);
|
expect(function () { return tf.minimum(3, 'q'); })
|
.toThrowError(/Argument 'b' passed to 'minimum' must be numeric/);
|
});
|
});
|
jasmine_util_1.describeWithFlags('mod', jasmine_util_1.ALL_ENVS, function () {
|
it('float32 and float32', function () { return __awaiter(_this, void 0, void 0, function () {
|
var a, b, result, _a;
|
return __generator(this, function (_b) {
|
switch (_b.label) {
|
case 0:
|
a = tf.tensor1d([0.5, 3, -0.1, -4]);
|
b = tf.tensor1d([0.2, 0.4, 0.25, 0.15]);
|
result = tf.mod(a, b);
|
expect(result.shape).toEqual(a.shape);
|
_a = test_util_1.expectArraysClose;
|
return [4 /*yield*/, result.data()];
|
case 1:
|
_a.apply(void 0, [_b.sent(), [0.1, 0.2, 0.15, 0.05]]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('TensorLike', function () { return __awaiter(_this, void 0, void 0, function () {
|
var a, b, result, _a;
|
return __generator(this, function (_b) {
|
switch (_b.label) {
|
case 0:
|
a = [0.5, 3, -0.1, -4];
|
b = [0.2, 0.4, 0.25, 0.15];
|
result = tf.mod(a, b);
|
expect(result.shape).toEqual([4]);
|
_a = test_util_1.expectArraysClose;
|
return [4 /*yield*/, result.data()];
|
case 1:
|
_a.apply(void 0, [_b.sent(), [0.1, 0.2, 0.15, 0.05]]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('TensorLike chained', function () { return __awaiter(_this, void 0, void 0, function () {
|
var a, b, result, _a;
|
return __generator(this, function (_b) {
|
switch (_b.label) {
|
case 0:
|
a = tf.tensor1d([0.5, 3, -0.1, -4]);
|
b = [0.2, 0.4, 0.25, 0.15];
|
result = a.mod(b);
|
expect(result.shape).toEqual(a.shape);
|
_a = test_util_1.expectArraysClose;
|
return [4 /*yield*/, result.data()];
|
case 1:
|
_a.apply(void 0, [_b.sent(), [0.1, 0.2, 0.15, 0.05]]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('int32 and int32', function () { return __awaiter(_this, void 0, void 0, function () {
|
var a, b, result, _a;
|
return __generator(this, function (_b) {
|
switch (_b.label) {
|
case 0:
|
a = tf.tensor1d([1, 5, 2, 3], 'int32');
|
b = tf.tensor1d([2, 3, 1, 4], 'int32');
|
result = tf.mod(a, b);
|
expect(result.shape).toEqual(a.shape);
|
expect(result.dtype).toBe('int32');
|
_a = test_util_1.expectArraysEqual;
|
return [4 /*yield*/, result.data()];
|
case 1:
|
_a.apply(void 0, [_b.sent(), [1, 2, 0, 3]]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('upcasts when dtypes dont match', function () { return __awaiter(_this, void 0, void 0, function () {
|
var res, _a, _b;
|
return __generator(this, function (_c) {
|
switch (_c.label) {
|
case 0:
|
res = tf.mod(tf.scalar(5, 'int32'), tf.scalar(2, 'float32'));
|
expect(res.dtype).toBe('float32');
|
_a = test_util_1.expectArraysClose;
|
return [4 /*yield*/, res.data()];
|
case 1:
|
_a.apply(void 0, [_c.sent(), [1]]);
|
res = tf.mod(tf.scalar(5, 'int32'), tf.scalar(true, 'bool'));
|
expect(res.dtype).toBe('int32');
|
_b = test_util_1.expectArraysClose;
|
return [4 /*yield*/, res.data()];
|
case 2:
|
_b.apply(void 0, [_c.sent(), [0]]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('propagates NaN', function () { return __awaiter(_this, void 0, void 0, function () {
|
var a, b, result, _a;
|
return __generator(this, function (_b) {
|
switch (_b.label) {
|
case 0:
|
a = tf.tensor1d([5, -1, NaN]);
|
b = tf.tensor1d([2, 3, 0.25]);
|
result = tf.mod(a, b);
|
expect(result.shape).toEqual(a.shape);
|
_a = test_util_1.expectArraysClose;
|
return [4 /*yield*/, result.data()];
|
case 1:
|
_a.apply(void 0, [_b.sent(), [1, 2, NaN]]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('broadcasts Tensor1D and scalar', function () { return __awaiter(_this, void 0, void 0, function () {
|
var a, b, result, _a;
|
return __generator(this, function (_b) {
|
switch (_b.label) {
|
case 0:
|
a = tf.tensor1d([0.5, 2.5, -0.1, -4], 'float32');
|
b = tf.scalar(0.6);
|
result = tf.mod(a, b);
|
expect(result.shape).toEqual(a.shape);
|
_a = test_util_1.expectArraysClose;
|
return [4 /*yield*/, result.data()];
|
case 1:
|
_a.apply(void 0, [_b.sent(), [0.5, 0.1, 0.5, 0.2]]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('broadcasts scalar and Tensor1D', function () { return __awaiter(_this, void 0, void 0, function () {
|
var a, b, result, _a;
|
return __generator(this, function (_b) {
|
switch (_b.label) {
|
case 0:
|
a = tf.scalar(2);
|
b = tf.tensor1d([3, 3, -1, -4]);
|
result = tf.mod(a, b);
|
expect(result.shape).toEqual(b.shape);
|
_a = test_util_1.expectArraysClose;
|
return [4 /*yield*/, result.data()];
|
case 1:
|
_a.apply(void 0, [_b.sent(), [2, 2, 0, -2]]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('broadcasts Tensor1D and Tensor2D', function () { return __awaiter(_this, void 0, void 0, function () {
|
var a, b, result, _a;
|
return __generator(this, function (_b) {
|
switch (_b.label) {
|
case 0:
|
a = tf.tensor1d([0.5, 0.3]);
|
b = tf.tensor2d([0.2, 0.4, 0.6, 0.15], [2, 2]);
|
result = tf.mod(a, b);
|
expect(result.shape).toEqual(b.shape);
|
_a = test_util_1.expectArraysClose;
|
return [4 /*yield*/, result.data()];
|
case 1:
|
_a.apply(void 0, [_b.sent(), [0.1, 0.3, 0.5, 0.0]]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('broadcasts 2x1 Tensor2D and 2x2 Tensor2D', function () { return __awaiter(_this, void 0, void 0, function () {
|
var a, b, result, _a;
|
return __generator(this, function (_b) {
|
switch (_b.label) {
|
case 0:
|
a = tf.tensor2d([0.5, 0.3], [2, 1]);
|
b = tf.tensor2d([0.2, 0.4, 0.6, 0.15], [2, 2]);
|
result = tf.mod(a, b);
|
expect(result.shape).toEqual(b.shape);
|
_a = test_util_1.expectArraysClose;
|
return [4 /*yield*/, result.data()];
|
case 1:
|
_a.apply(void 0, [_b.sent(), [0.1, 0.1, 0.3, 0.0]]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('gradients: Scalar', function () { return __awaiter(_this, void 0, void 0, function () {
|
var a, b, dy, grads, _a, da, db, _b, _c;
|
return __generator(this, function (_d) {
|
switch (_d.label) {
|
case 0:
|
a = tf.scalar(5.2);
|
b = tf.scalar(0.6);
|
dy = tf.scalar(3);
|
grads = tf.grads(function (a, b) { return tf.mod(a, b); });
|
_a = grads([a, b], dy), da = _a[0], db = _a[1];
|
expect(da.shape).toEqual(a.shape);
|
expect(db.shape).toEqual(b.shape);
|
expect(da.dtype).toEqual('float32');
|
expect(db.dtype).toEqual('float32');
|
_b = test_util_1.expectArraysClose;
|
return [4 /*yield*/, da.data()];
|
case 1:
|
_b.apply(void 0, [_d.sent(), [3]]);
|
_c = test_util_1.expectArraysClose;
|
return [4 /*yield*/, db.data()];
|
case 2:
|
_c.apply(void 0, [_d.sent(), [3 * -1 * Math.floor(5.2 / 0.6)]]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('gradient with clones', function () { return __awaiter(_this, void 0, void 0, function () {
|
var a, b, dy, grads, _a, da, db, _b, _c;
|
return __generator(this, function (_d) {
|
switch (_d.label) {
|
case 0:
|
a = tf.scalar(5.2);
|
b = tf.scalar(0.6);
|
dy = tf.scalar(3);
|
grads = tf.grads(function (a, b) { return tf.mod(a.clone(), b.clone()).clone(); });
|
_a = grads([a, b], dy), da = _a[0], db = _a[1];
|
expect(da.shape).toEqual(a.shape);
|
expect(db.shape).toEqual(b.shape);
|
expect(da.dtype).toEqual('float32');
|
expect(db.dtype).toEqual('float32');
|
_b = test_util_1.expectArraysClose;
|
return [4 /*yield*/, da.data()];
|
case 1:
|
_b.apply(void 0, [_d.sent(), [3]]);
|
_c = test_util_1.expectArraysClose;
|
return [4 /*yield*/, db.data()];
|
case 2:
|
_c.apply(void 0, [_d.sent(), [3 * -1 * Math.floor(5.2 / 0.6)]]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('gradients: Tensor1D', function () { return __awaiter(_this, void 0, void 0, function () {
|
var a, b, dy, grads, _a, da, db, _b, _c;
|
return __generator(this, function (_d) {
|
switch (_d.label) {
|
case 0:
|
a = tf.tensor1d([1.1, 2.6, 3, 5.9]);
|
b = tf.tensor1d([1.0, 2.7, 3, 5.8]);
|
dy = tf.tensor1d([1, 2, 3, 4]);
|
grads = tf.grads(function (a, b) { return tf.mod(a, b); });
|
_a = grads([a, b], dy), da = _a[0], db = _a[1];
|
expect(da.shape).toEqual(a.shape);
|
expect(db.shape).toEqual(b.shape);
|
expect(da.dtype).toEqual('float32');
|
expect(db.dtype).toEqual('float32');
|
_b = test_util_1.expectArraysClose;
|
return [4 /*yield*/, da.data()];
|
case 1:
|
_b.apply(void 0, [_d.sent(), [1 * 1, 2 * 1, 3 * 1, 4 * 1]]);
|
_c = test_util_1.expectArraysClose;
|
return [4 /*yield*/, db.data()];
|
case 2:
|
_c.apply(void 0, [_d.sent(), [
|
1 * -1 * Math.floor(1.1 / 1.0), 2 * -1 * Math.floor(2.6 / 2.7),
|
3 * -1 * Math.floor(3 / 3), 4 * -1 * Math.floor(5.9 / 5.8)
|
]]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('gradients: Tensor2D', function () { return __awaiter(_this, void 0, void 0, function () {
|
var a, b, dy, grads, _a, da, db, _b, _c;
|
return __generator(this, function (_d) {
|
switch (_d.label) {
|
case 0:
|
a = tf.tensor2d([0.5, 0.3, 0.7, 0.91], [2, 2]);
|
b = tf.tensor2d([0.2, 0.4, 0.7, 0.15], [2, 2]);
|
dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);
|
grads = tf.grads(function (a, b) { return tf.mod(a, b); });
|
_a = grads([a, b], dy), da = _a[0], db = _a[1];
|
expect(da.shape).toEqual(a.shape);
|
expect(db.shape).toEqual(b.shape);
|
expect(da.dtype).toEqual('float32');
|
expect(db.dtype).toEqual('float32');
|
_b = test_util_1.expectArraysClose;
|
return [4 /*yield*/, da.data()];
|
case 1:
|
_b.apply(void 0, [_d.sent(), [1 * 1, 2 * 1, 3 * 1, 4 * 1]]);
|
_c = test_util_1.expectArraysClose;
|
return [4 /*yield*/, db.data()];
|
case 2:
|
_c.apply(void 0, [_d.sent(), [
|
1 * -1 * Math.floor(0.5 / 0.2), 2 * -1 * Math.floor(0.3 / 0.4),
|
3 * -1 * Math.floor(0.7 / 0.7), 4 * -1 * Math.floor(0.91 / 0.15)
|
]]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('gradients: broadcasts scalar and Tensor1D', function () { return __awaiter(_this, void 0, void 0, function () {
|
var a, b, dy, grads, _a, da, db, _b, _c;
|
return __generator(this, function (_d) {
|
switch (_d.label) {
|
case 0:
|
a = tf.scalar(0.7);
|
b = tf.tensor1d([0.2, 0.3, 0.4, 0.5]);
|
dy = tf.tensor1d([1, 2, 3, 4]);
|
grads = tf.grads(function (a, b) { return tf.mod(a, b); });
|
_a = grads([a, b], dy), da = _a[0], db = _a[1];
|
expect(da.shape).toEqual(a.shape);
|
expect(db.shape).toEqual(b.shape);
|
expect(da.dtype).toEqual('float32');
|
expect(db.dtype).toEqual('float32');
|
_b = test_util_1.expectArraysClose;
|
return [4 /*yield*/, da.data()];
|
case 1:
|
_b.apply(void 0, [_d.sent(), [1 + 2 + 3 + 4]]);
|
_c = test_util_1.expectArraysClose;
|
return [4 /*yield*/, db.data()];
|
case 2:
|
_c.apply(void 0, [_d.sent(), [
|
1 * -1 * Math.floor(0.7 / 0.2), 2 * -1 * Math.floor(0.7 / 0.3),
|
3 * -1 * Math.floor(0.7 / 0.4), 4 * -1 * Math.floor(0.7 / 0.5)
|
]]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('broadcasts Tensor1D and Tensor2D', function () { return __awaiter(_this, void 0, void 0, function () {
|
var a, b, dy, grads, _a, da, db, _b, _c;
|
return __generator(this, function (_d) {
|
switch (_d.label) {
|
case 0:
|
a = tf.tensor1d([0.5, 0.3]);
|
b = tf.tensor2d([0.2, 0.4, 0.7, 0.15], [2, 2]);
|
dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);
|
grads = tf.grads(function (a, b) { return tf.mod(a, b); });
|
_a = grads([a, b], dy), da = _a[0], db = _a[1];
|
expect(da.shape).toEqual(a.shape);
|
expect(db.shape).toEqual(b.shape);
|
expect(da.dtype).toEqual('float32');
|
expect(db.dtype).toEqual('float32');
|
_b = test_util_1.expectArraysClose;
|
return [4 /*yield*/, da.data()];
|
case 1:
|
_b.apply(void 0, [_d.sent(), [1 * 1 + 3 * 1, 2 * 1 + 4 * 1]]);
|
_c = test_util_1.expectArraysClose;
|
return [4 /*yield*/, db.data()];
|
case 2:
|
_c.apply(void 0, [_d.sent(), [
|
1 * -1 * Math.floor(0.5 / 0.2), 2 * -1 * Math.floor(0.3 / 0.4),
|
3 * -1 * Math.floor(0.5 / 0.7), 4 * -1 * Math.floor(0.3 / 0.15)
|
]]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('throws when passed a as a non-tensor', function () {
|
expect(function () { return tf.mod({}, tf.scalar(1)); })
|
.toThrowError(/Argument 'a' passed to 'mod' must be a Tensor/);
|
});
|
it('throws when passed b as a non-tensor', function () {
|
expect(function () { return tf.mod(tf.scalar(1), {}); })
|
.toThrowError(/Argument 'b' passed to 'mod' must be a Tensor/);
|
});
|
it('accepts a tensor-like object', function () { return __awaiter(_this, void 0, void 0, function () {
|
var a, b, result, _a;
|
return __generator(this, function (_b) {
|
switch (_b.label) {
|
case 0:
|
a = [[0.5, 3], [-0.1, -4]];
|
b = [[0.2, 0.4], [0.25, 0.15]];
|
result = tf.mod(a, b);
|
expect(result.shape).toEqual([2, 2]);
|
_a = test_util_1.expectArraysClose;
|
return [4 /*yield*/, result.data()];
|
case 1:
|
_a.apply(void 0, [_b.sent(), [0.1, 0.2, 0.15, 0.05]]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('throws for string tensor', function () {
|
expect(function () { return tf.mod('q', 3); })
|
.toThrowError(/Argument 'a' passed to 'mod' must be numeric/);
|
expect(function () { return tf.mod(3, 'q'); })
|
.toThrowError(/Argument 'b' passed to 'mod' must be numeric/);
|
});
|
});
|
jasmine_util_1.describeWithFlags('atan2', jasmine_util_1.ALL_ENVS, function () {
|
it('same shape', function () { return __awaiter(_this, void 0, void 0, function () {
|
var aValues, bValues, a, c, r, expected, i, _a;
|
return __generator(this, function (_b) {
|
switch (_b.label) {
|
case 0:
|
aValues = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0];
|
bValues = [1.0, 2.5, 3.5, 4.5, 2.0, 5.0];
|
a = tf.tensor2d(aValues, [2, 3]);
|
c = tf.tensor2d(bValues, [2, 3]);
|
r = tf.atan2(a, c);
|
expected = [];
|
for (i = 0; i < a.size; i++) {
|
expected[i] = Math.atan2(aValues[i], bValues[i]);
|
}
|
_a = test_util_1.expectArraysClose;
|
return [4 /*yield*/, r.data()];
|
case 1:
|
_a.apply(void 0, [_b.sent(), expected]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('uses chaining', function () { return __awaiter(_this, void 0, void 0, function () {
|
var aValues, bValues, a, b, r, expected, i, _a;
|
return __generator(this, function (_b) {
|
switch (_b.label) {
|
case 0:
|
aValues = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0];
|
bValues = [1.0, 2.5, 3.5, 4.5, 2.0, 5.0];
|
a = tf.tensor2d(aValues, [2, 3]);
|
b = tf.tensor2d(bValues, [2, 3]);
|
r = a.atan2(b);
|
expected = [];
|
for (i = 0; i < a.size; i++) {
|
expected[i] = Math.atan2(aValues[i], bValues[i]);
|
}
|
_a = test_util_1.expectArraysClose;
|
return [4 /*yield*/, r.data()];
|
case 1:
|
_a.apply(void 0, [_b.sent(), expected]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('propagates NaNs', function () { return __awaiter(_this, void 0, void 0, function () {
|
var a, c, r, _a;
|
return __generator(this, function (_b) {
|
switch (_b.label) {
|
case 0:
|
a = tf.tensor2d([1.0, 2.0], [2, 1]);
|
c = tf.tensor2d([3.0, NaN], [2, 1]);
|
r = tf.atan2(a, c);
|
_a = test_util_1.expectArraysClose;
|
return [4 /*yield*/, r.data()];
|
case 1:
|
_a.apply(void 0, [_b.sent(), [Math.atan2(1.0, 3.0), NaN]]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('broadcasting same rank Tensors different shape', function () { return __awaiter(_this, void 0, void 0, function () {
|
var aValues, bValues, a, b, result, expected, _a;
|
return __generator(this, function (_b) {
|
switch (_b.label) {
|
case 0:
|
aValues = [1.0, 2.0, -3.0, -4.0];
|
bValues = [2.0, 3.0];
|
a = tf.tensor2d(aValues, [2, 2]);
|
b = tf.tensor2d(bValues, [2, 1]);
|
result = tf.atan2(a, b);
|
expect(result.shape).toEqual([2, 2]);
|
expected = [
|
Math.atan2(1.0, 2.0), Math.atan2(2.0, 2.0), Math.atan2(-3.0, 3.0),
|
Math.atan2(-4.0, 3.0)
|
];
|
_a = test_util_1.expectArraysClose;
|
return [4 /*yield*/, result.data()];
|
case 1:
|
_a.apply(void 0, [_b.sent(), expected]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('throws when passed tensors of different shapes', function () {
|
var a = tf.tensor2d([1, 2, -3, -4, 5, 6], [2, 3]);
|
var b = tf.tensor2d([5, 3, 4, -7], [2, 2]);
|
expect(function () { return tf.atan2(a, b); }).toThrowError();
|
expect(function () { return tf.atan2(b, a); }).toThrowError();
|
});
|
it('upcasts when dtypes dont match', function () { return __awaiter(_this, void 0, void 0, function () {
|
var aValues, bValues, a, c, r, expected, i, _a;
|
return __generator(this, function (_b) {
|
switch (_b.label) {
|
case 0:
|
aValues = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0];
|
bValues = [1, 2, 3, 4, 2, 5];
|
a = tf.tensor2d(aValues, [2, 3], 'float32');
|
c = tf.tensor2d(bValues, [2, 3], 'int32');
|
r = tf.atan2(a, c);
|
expected = [];
|
for (i = 0; i < a.size; i++) {
|
expected[i] = Math.atan2(aValues[i], bValues[i]);
|
}
|
expect(r.shape).toEqual([2, 3]);
|
expect(r.dtype).toBe('float32');
|
_a = test_util_1.expectArraysClose;
|
return [4 /*yield*/, r.data()];
|
case 1:
|
_a.apply(void 0, [_b.sent(), expected]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('atan2 of scalar and array propagates NaNs', function () { return __awaiter(_this, void 0, void 0, function () {
|
var c, a, r, _a;
|
return __generator(this, function (_b) {
|
switch (_b.label) {
|
case 0:
|
c = tf.scalar(NaN);
|
a = tf.tensor2d([1, 2, 3], [1, 3]);
|
r = tf.atan2(c, a);
|
_a = test_util_1.expectArraysEqual;
|
return [4 /*yield*/, r.data()];
|
case 1:
|
_a.apply(void 0, [_b.sent(), [NaN, NaN, NaN]]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('atan2 of scalar and array', function () { return __awaiter(_this, void 0, void 0, function () {
|
var aValues, a, c, r, expected, i, _a;
|
return __generator(this, function (_b) {
|
switch (_b.label) {
|
case 0:
|
aValues = [1, 2, 3, 4, 5, 6];
|
a = tf.tensor2d(aValues, [2, 3]);
|
c = tf.scalar(2);
|
r = tf.atan2(a, c);
|
expected = [];
|
for (i = 0; i < a.size; i++) {
|
expected[i] = Math.atan2(aValues[i], 2);
|
}
|
_a = test_util_1.expectArraysClose;
|
return [4 /*yield*/, r.data()];
|
case 1:
|
_a.apply(void 0, [_b.sent(), expected]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('gradient: Scalar', function () { return __awaiter(_this, void 0, void 0, function () {
|
var a, b, dy, grads, _a, da, db, _b, _c;
|
return __generator(this, function (_d) {
|
switch (_d.label) {
|
case 0:
|
a = tf.scalar(5);
|
b = tf.scalar(2);
|
dy = tf.scalar(4);
|
grads = tf.grads(function (a, b) { return tf.atan2(a, b); });
|
_a = grads([a, b], dy), da = _a[0], db = _a[1];
|
expect(da.shape).toEqual(a.shape);
|
expect(da.dtype).toEqual('float32');
|
_b = test_util_1.expectArraysClose;
|
return [4 /*yield*/, da.data()];
|
case 1:
|
_b.apply(void 0, [_d.sent(), [4 * 2 / 29]]);
|
expect(db.shape).toEqual(b.shape);
|
expect(db.dtype).toEqual('float32');
|
_c = test_util_1.expectArraysClose;
|
return [4 /*yield*/, db.data()];
|
case 2:
|
_c.apply(void 0, [_d.sent(), [4 * -5 / 29]]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('gradient with clones', function () { return __awaiter(_this, void 0, void 0, function () {
|
var a, b, dy, grads, _a, da, db, _b, _c;
|
return __generator(this, function (_d) {
|
switch (_d.label) {
|
case 0:
|
a = tf.scalar(5);
|
b = tf.scalar(2);
|
dy = tf.scalar(4);
|
grads = tf.grads(function (a, b) { return tf.atan2(a.clone(), b.clone()).clone(); });
|
_a = grads([a, b], dy), da = _a[0], db = _a[1];
|
expect(da.shape).toEqual(a.shape);
|
expect(da.dtype).toEqual('float32');
|
_b = test_util_1.expectArraysClose;
|
return [4 /*yield*/, da.data()];
|
case 1:
|
_b.apply(void 0, [_d.sent(), [4 * 2 / 29]]);
|
expect(db.shape).toEqual(b.shape);
|
expect(db.dtype).toEqual('float32');
|
_c = test_util_1.expectArraysClose;
|
return [4 /*yield*/, db.data()];
|
case 2:
|
_c.apply(void 0, [_d.sent(), [4 * -5 / 29]]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('gradient: Tensor1D', function () { return __awaiter(_this, void 0, void 0, function () {
|
var a, b, dy, grads, _a, da, db, _b, _c;
|
return __generator(this, function (_d) {
|
switch (_d.label) {
|
case 0:
|
a = tf.tensor1d([1, 2, 3]);
|
b = tf.tensor1d([3, 4, 5]);
|
dy = tf.tensor1d([1, 10, 20]);
|
grads = tf.grads(function (a, b) { return tf.atan2(a, b); });
|
_a = grads([a, b], dy), da = _a[0], db = _a[1];
|
expect(da.shape).toEqual(a.shape);
|
expect(db.dtype).toEqual('float32');
|
_b = test_util_1.expectArraysClose;
|
return [4 /*yield*/, da.data()];
|
case 1:
|
_b.apply(void 0, [_d.sent(), [1 * 3 / 10, 10 * 4 / 20, 20 * 5 / 34]]);
|
expect(db.shape).toEqual(b.shape);
|
expect(db.dtype).toEqual('float32');
|
_c = test_util_1.expectArraysClose;
|
return [4 /*yield*/, db.data()];
|
case 2:
|
_c.apply(void 0, [_d.sent(), [-1 * 1 / 10, -10 * 2 / 20, -20 * 3 / 34]]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('gradient: Tensor2D', function () { return __awaiter(_this, void 0, void 0, function () {
|
var a, b, dy, grads, _a, da, db, _b, _c;
|
return __generator(this, function (_d) {
|
switch (_d.label) {
|
case 0:
|
a = tf.tensor2d([3, 1, 2, 3], [2, 2]);
|
b = tf.tensor2d([1, 3, 4, 5], [2, 2]);
|
dy = tf.tensor2d([1, 10, 15, 20], [2, 2]);
|
grads = tf.grads(function (a, b) { return tf.atan2(a, b); });
|
_a = grads([a, b], dy), da = _a[0], db = _a[1];
|
expect(da.shape).toEqual(a.shape);
|
expect(da.dtype).toEqual('float32');
|
_b = test_util_1.expectArraysClose;
|
return [4 /*yield*/, da.data()];
|
case 1:
|
_b.apply(void 0, [_d.sent(), [1 * 1 / 10, 10 * 3 / 10, 15 * 4 / 20, 20 * 5 / 34]]);
|
expect(db.shape).toEqual(b.shape);
|
expect(db.dtype).toEqual('float32');
|
_c = test_util_1.expectArraysClose;
|
return [4 /*yield*/, db.data()];
|
case 2:
|
_c.apply(void 0, [_d.sent(),
|
[-1 * 3 / 10, -10 * 1 / 10, -15 * 2 / 20, -20 * 3 / 34]]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('gradient: scalar / Tensor1D', function () { return __awaiter(_this, void 0, void 0, function () {
|
var a, b, dy, grads, _a, da, db, _b, _c;
|
return __generator(this, function (_d) {
|
switch (_d.label) {
|
case 0:
|
a = tf.scalar(2);
|
b = tf.tensor1d([3, 4, 5]);
|
dy = tf.tensor1d([6, 7, 8]);
|
grads = tf.grads(function (a, b) { return tf.atan2(a, b); });
|
_a = grads([a, b], dy), da = _a[0], db = _a[1];
|
expect(da.shape).toEqual(a.shape);
|
expect(da.dtype).toEqual('float32');
|
_b = test_util_1.expectArraysClose;
|
return [4 /*yield*/, da.data()];
|
case 1:
|
_b.apply(void 0, [_d.sent(), [6 * 3 / 13 + 7 * 4 / 20 + 8 * 5 / 29]]);
|
expect(db.shape).toEqual(b.shape);
|
expect(db.dtype).toEqual('float32');
|
_c = test_util_1.expectArraysClose;
|
return [4 /*yield*/, db.data()];
|
case 2:
|
_c.apply(void 0, [_d.sent(), [-6 * 2 / 13, -7 * 2 / 20, -8 * 2 / 29]]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('gradient: Tensor2D / scalar', function () { return __awaiter(_this, void 0, void 0, function () {
|
var a, b, dy, grads, _a, da, db, _b, _c;
|
return __generator(this, function (_d) {
|
switch (_d.label) {
|
case 0:
|
a = tf.tensor2d([[2, 3], [4, 5]], [2, 2]);
|
b = tf.scalar(2);
|
dy = tf.tensor2d([[6, 7], [8, 9]], [2, 2]);
|
grads = tf.grads(function (a, b) { return tf.atan2(a, b); });
|
_a = grads([a, b], dy), da = _a[0], db = _a[1];
|
expect(da.shape).toEqual(a.shape);
|
expect(da.dtype).toEqual('float32');
|
_b = test_util_1.expectArraysClose;
|
return [4 /*yield*/, da.data()];
|
case 1:
|
_b.apply(void 0, [_d.sent(), [6 * 2 / 8, 7 * 2 / 13, 8 * 2 / 20, 9 * 2 / 29]]);
|
expect(db.shape).toEqual(b.shape);
|
expect(db.dtype).toEqual('float32');
|
_c = test_util_1.expectArraysClose;
|
return [4 /*yield*/, db.data()];
|
case 2:
|
_c.apply(void 0, [_d.sent(),
|
[-6 * 2 / 8 + -7 * 3 / 13 + -8 * 4 / 20 + -9 * 5 / 29]]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('gradient: Tensor2D / Tensor2D w/ broadcast', function () { return __awaiter(_this, void 0, void 0, function () {
|
var a, b, dy, grads, _a, da, db, _b, _c;
|
return __generator(this, function (_d) {
|
switch (_d.label) {
|
case 0:
|
a = tf.tensor2d([3, 4], [2, 1]);
|
b = tf.tensor2d([[2, 3], [4, 5]], [2, 2]);
|
dy = tf.tensor2d([[6, 7], [8, 9]], [2, 2]);
|
grads = tf.grads(function (a, b) { return tf.atan2(a, b); });
|
_a = grads([a, b], dy), da = _a[0], db = _a[1];
|
expect(da.shape).toEqual(a.shape);
|
expect(da.dtype).toEqual('float32');
|
_b = test_util_1.expectArraysClose;
|
return [4 /*yield*/, da.data()];
|
case 1:
|
_b.apply(void 0, [_d.sent(), [6 * 2 / 13 + 7 * 3 / 18, 8 * 4 / 32 + 9 * 5 / 41]]);
|
expect(db.shape).toEqual(b.shape);
|
expect(db.dtype).toEqual('float32');
|
_c = test_util_1.expectArraysClose;
|
return [4 /*yield*/, db.data()];
|
case 2:
|
_c.apply(void 0, [_d.sent(), [-6 * 3 / 13, -7 * 3 / 18, -8 * 4 / 32, -9 * 4 / 41]]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('throws when passed a as a non-tensor', function () {
|
expect(function () { return tf.atan2({}, tf.scalar(1)); })
|
.toThrowError(/Argument 'a' passed to 'atan2' must be a Tensor/);
|
});
|
it('throws when passed b as a non-tensor', function () {
|
expect(function () { return tf.atan2(tf.scalar(1), {}); })
|
.toThrowError(/Argument 'b' passed to 'atan2' must be a Tensor/);
|
});
|
it('accepts a tensor-like object', function () { return __awaiter(_this, void 0, void 0, function () {
|
var a, c, r, expected, i, _a;
|
return __generator(this, function (_b) {
|
switch (_b.label) {
|
case 0:
|
a = [[1, 2, 3], [4, 5, 6]];
|
c = 2;
|
r = tf.atan2(a, c);
|
expected = [];
|
for (i = 0; i < 6; i++) {
|
expected[i] = Math.atan2(i + 1, 2);
|
}
|
_a = test_util_1.expectArraysClose;
|
return [4 /*yield*/, r.data()];
|
case 1:
|
_a.apply(void 0, [_b.sent(), expected]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('throws for string tensor', function () {
|
expect(function () { return tf.atan2('q', 3); })
|
.toThrowError(/Argument 'a' passed to 'atan2' must be numeric/);
|
expect(function () { return tf.atan2(3, 'q'); })
|
.toThrowError(/Argument 'b' passed to 'atan2' must be numeric/);
|
});
|
});
|
jasmine_util_1.describeWithFlags('div', jasmine_util_1.ALL_ENVS, function () {
|
it('divNoNan divide 0', function () { return __awaiter(_this, void 0, void 0, function () {
|
var a, b, c, _a;
|
return __generator(this, function (_b) {
|
switch (_b.label) {
|
case 0:
|
a = tf.tensor1d([2, 4, 6, 8]);
|
b = tf.tensor1d([0, 0, 0, 0]);
|
c = a.divNoNan(b);
|
expect(c.shape).toEqual(a.shape);
|
_a = test_util_1.expectArraysClose;
|
return [4 /*yield*/, c.data()];
|
case 1:
|
_a.apply(void 0, [_b.sent(), [0, 0, 0, 0]]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('divNoNan divide 0 and non-0', function () { return __awaiter(_this, void 0, void 0, function () {
|
var a, b, c, _a;
|
return __generator(this, function (_b) {
|
switch (_b.label) {
|
case 0:
|
a = tf.tensor1d([2, 4, 6, 8]);
|
b = tf.tensor1d([2, 2, 0, 4]);
|
c = a.divNoNan(b);
|
expect(c.shape).toEqual(a.shape);
|
_a = test_util_1.expectArraysClose;
|
return [4 /*yield*/, c.data()];
|
case 1:
|
_a.apply(void 0, [_b.sent(), [1, 2, 0, 2]]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('divNoNan divide 0 broadcast', function () { return __awaiter(_this, void 0, void 0, function () {
|
var a, b, c, _a;
|
return __generator(this, function (_b) {
|
switch (_b.label) {
|
case 0:
|
a = tf.tensor1d([2, 4, 6, 8]);
|
b = tf.scalar(0);
|
c = a.divNoNan(b);
|
expect(c.shape).toEqual(a.shape);
|
_a = test_util_1.expectArraysClose;
|
return [4 /*yield*/, c.data()];
|
case 1:
|
_a.apply(void 0, [_b.sent(), [0, 0, 0, 0]]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
});
|
//# sourceMappingURL=binary_ops_test.js.map
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