"use strict";
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/**
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* @license
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* Copyright 2018 Google LLC. 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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function adopt(value) { return value instanceof P ? value : new P(function (resolve) { resolve(value); }); }
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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) : adopt(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;
|
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 (g && (g = 0, op[0] && (_ = 0)), _) 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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Object.defineProperty(exports, "__esModule", { value: true });
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var tf = require("@tensorflow/tfjs");
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var tfjs_1 = require("@tensorflow/tfjs");
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var fs = require("fs");
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var path = require("path");
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var util_1 = require("util");
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var tfn = require("./index");
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// tslint:disable-next-line:no-require-imports
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var rimraf = require('rimraf');
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// tslint:disable-next-line:no-require-imports
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var tmp = require('tmp');
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var rimrafPromise = (0, util_1.promisify)(rimraf);
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describe('tensorboard', function () {
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var tmpLogDir;
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beforeEach(function () {
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tmpLogDir = tmp.dirSync().name;
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});
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afterEach(function () { return __awaiter(void 0, void 0, void 0, function () {
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return __generator(this, function (_a) {
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switch (_a.label) {
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case 0:
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if (!(tmpLogDir != null)) return [3 /*break*/, 2];
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return [4 /*yield*/, rimrafPromise(tmpLogDir)];
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case 1:
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_a.sent();
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_a.label = 2;
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case 2: return [2 /*return*/];
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}
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});
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}); });
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describe('SummaryFileWriter', function () {
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it('empty logdir leads to error', function () {
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expect(function () { return tfn.node.summaryFileWriter(''); }).toThrowError(/empty string/);
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});
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});
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describe('SummaryFileWriter.scalar', function () {
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it('Create summaryFileWriter and write scalar', function () {
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var writer = tfn.node.summaryFileWriter(tmpLogDir);
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writer.scalar('foo', 42, 0);
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writer.flush();
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// Currently, we only verify that the file exists and the size
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// increases in a sensible way as we write more scalars to it.
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// The difficulty is in reading the protobuf contents of the event
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// file in JavaScript/TypeScript.
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var fileNames = fs.readdirSync(tmpLogDir);
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expect(fileNames.length).toEqual(1);
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var eventFilePath = path.join(tmpLogDir, fileNames[0]);
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var fileSize0 = fs.statSync(eventFilePath).size;
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writer.scalar('foo', 43, 1);
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writer.flush();
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var fileSize1 = fs.statSync(eventFilePath).size;
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var incrementPerScalar = fileSize1 - fileSize0;
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expect(incrementPerScalar).toBeGreaterThan(0);
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writer.scalar('foo', 44, 2);
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writer.scalar('foo', 45, 3);
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writer.flush();
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var fileSize2 = fs.statSync(eventFilePath).size;
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expect(fileSize2 - fileSize1).toEqual(2 * incrementPerScalar);
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});
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it('Writing tf.Scalar works', function () {
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var writer = tfn.node.summaryFileWriter(tmpLogDir);
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writer.scalar('foo', (0, tfjs_1.scalar)(42), 0);
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writer.flush();
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// Currently, we only verify that the file exists and the size
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// increases in a sensible way as we write more scalars to it.
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// The difficulty is in reading the protobuf contents of the event
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// file in JavaScript/TypeScript.
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var fileNames = fs.readdirSync(tmpLogDir);
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expect(fileNames.length).toEqual(1);
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});
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it('Writing tf.Scalar no memory leak', function () {
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var writer = tfn.node.summaryFileWriter(tmpLogDir);
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var beforeNumTFTensors = writer.backend.getNumOfTFTensors();
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var value = (0, tfjs_1.scalar)(42);
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writer.scalar('foo', value, 0);
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writer.flush();
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value.dispose();
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expect(writer.backend.getNumOfTFTensors()).toBe(beforeNumTFTensors);
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});
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it('No crosstalk between two summary writers', function () {
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var logDir1 = path.join(tmpLogDir, '1');
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var writer1 = tfn.node.summaryFileWriter(logDir1);
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writer1.scalar('foo', 42, 0);
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writer1.flush();
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var logDir2 = path.join(tmpLogDir, '2');
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var writer2 = tfn.node.summaryFileWriter(logDir2);
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writer2.scalar('foo', 1.337, 0);
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writer2.flush();
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// Currently, we only verify that the file exists and the size
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// increases in a sensible way as we write more scalars to it.
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// The difficulty is in reading the protobuf contents of the event
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// file in JavaScript/TypeScript.
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var fileNames = fs.readdirSync(logDir1);
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expect(fileNames.length).toEqual(1);
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var eventFilePath1 = path.join(logDir1, fileNames[0]);
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var fileSize1Num0 = fs.statSync(eventFilePath1).size;
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fileNames = fs.readdirSync(logDir2);
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expect(fileNames.length).toEqual(1);
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var eventFilePath2 = path.join(logDir2, fileNames[0]);
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var fileSize2Num0 = fs.statSync(eventFilePath2).size;
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expect(fileSize2Num0).toBeGreaterThan(0);
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writer1.scalar('foo', 43, 1);
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writer1.flush();
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var fileSize1Num1 = fs.statSync(eventFilePath1).size;
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var incrementPerScalar = fileSize1Num1 - fileSize1Num0;
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expect(incrementPerScalar).toBeGreaterThan(0);
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writer1.scalar('foo', 44, 2);
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writer1.scalar('foo', 45, 3);
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writer1.flush();
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var fileSize1Num2 = fs.statSync(eventFilePath1).size;
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expect(fileSize1Num2 - fileSize1Num1).toEqual(2 * incrementPerScalar);
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var fileSize2Num1 = fs.statSync(eventFilePath2).size;
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expect(fileSize2Num1).toEqual(fileSize2Num0);
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writer2.scalar('foo', 1.336, 1);
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writer2.scalar('foo', 1.335, 2);
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writer2.flush();
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var fileSize1Num3 = fs.statSync(eventFilePath1).size;
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expect(fileSize1Num3).toEqual(fileSize1Num2);
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var fileSize2Num2 = fs.statSync(eventFilePath2).size;
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expect(fileSize2Num2 - fileSize2Num1).toEqual(2 * incrementPerScalar);
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});
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it('Writing into existing directory works', function () {
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fs.mkdirSync(tmpLogDir, { recursive: true });
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var writer = tfn.node.summaryFileWriter(path.join(tmpLogDir, '22'));
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writer.scalar('foo', 42, 0);
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writer.flush();
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var fileNames = fs.readdirSync(tmpLogDir);
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expect(fileNames.length).toEqual(1);
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});
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});
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describe('SummaryFileWriter.histogram', function () {
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it('Create summaryFileWriter and write tensor', function () {
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var writer = tfn.node.summaryFileWriter(tmpLogDir);
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writer.histogram('foo', (0, tfjs_1.tensor1d)([1, 2, 3, 4, 5], 'int32'), 0, 5);
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writer.flush();
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// Currently, we only verify that the file exists and the size
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// increases in a sensible way as we write more histograms to it.
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// The difficulty is in reading the protobuf contents of the event
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// file in JavaScript/TypeScript.
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var fileNames = fs.readdirSync(tmpLogDir);
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expect(fileNames.length).toEqual(1);
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var eventFilePath = path.join(tmpLogDir, fileNames[0]);
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var fileSize0 = fs.statSync(eventFilePath).size;
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writer.histogram('foo', (0, tfjs_1.tensor1d)([1, 1, 1, 1, 1], 'int32'), 1, 5);
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writer.flush();
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var fileSize1 = fs.statSync(eventFilePath).size;
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var incrementPerScalar = fileSize1 - fileSize0;
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expect(incrementPerScalar).toBeGreaterThan(0);
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writer.histogram('foo', (0, tfjs_1.tensor1d)([2, 2, 2, 2, 2], 'int32'), 2, 5);
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writer.histogram('foo', (0, tfjs_1.tensor1d)([3, 3, 3, 3, 3], 'int32'), 3, 5);
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writer.flush();
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var fileSize2 = fs.statSync(eventFilePath).size;
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expect(fileSize2 - fileSize1).toEqual(2 * incrementPerScalar);
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});
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it('summaryFileWriter no memory leak', function () {
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var writer = tfn.node.summaryFileWriter(tmpLogDir);
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var beforeNumTFTensors = writer.backend.getNumOfTFTensors();
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var value = (0, tfjs_1.tensor1d)([1, 2, 3, 4, 5], 'int32');
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writer.histogram('foo', value, 0, 5);
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writer.flush();
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value.dispose();
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expect(writer.backend.getNumOfTFTensors()).toBe(beforeNumTFTensors);
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});
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it('Can create multiple normal distribution', function () {
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var writer = tfn.node.summaryFileWriter(tmpLogDir);
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tf.tidy(function () {
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for (var i = 0; i < 10; ++i) {
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var normal = tf.randomNormal([1000]).add(i / 10);
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writer.histogram('random normal', normal, i, 5, 'This is a *test* of **Markdown**');
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}
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writer.flush();
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});
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});
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});
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});
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describe('tensorBoard callback', function () {
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var tmpLogDir;
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beforeEach(function () {
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tmpLogDir = tmp.dirSync().name;
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});
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afterEach(function () { return __awaiter(void 0, void 0, void 0, function () {
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return __generator(this, function (_a) {
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switch (_a.label) {
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case 0:
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if (!(tmpLogDir != null)) return [3 /*break*/, 2];
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return [4 /*yield*/, rimrafPromise(tmpLogDir)];
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case 1:
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_a.sent();
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_a.label = 2;
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case 2: return [2 /*return*/];
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}
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});
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}); });
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function createModelForTest() {
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var model = tfn.sequential();
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model.add(tfn.layers.dense({ units: 5, activation: 'relu', inputShape: [10] }));
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model.add(tfn.layers.dense({ units: 1 }));
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model.compile({ loss: 'meanSquaredError', optimizer: 'sgd', metrics: ['MAE'] });
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return model;
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}
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it('fit(): default epoch updateFreq, with validation', function () { return __awaiter(void 0, void 0, void 0, function () {
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var model, xs, ys, valXs, valYs, subDirs, trainLogDir, trainFiles, trainFileSize0, valLogDir, valFiles, valFileSize0, history, trainFileSize1, valFileSize1;
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return __generator(this, function (_a) {
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switch (_a.label) {
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case 0:
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model = createModelForTest();
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xs = tfn.randomUniform([100, 10]);
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ys = tfn.randomUniform([100, 1]);
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valXs = tfn.randomUniform([10, 10]);
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valYs = tfn.randomUniform([10, 1]);
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// Warm-up training.
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return [4 /*yield*/, model.fit(xs, ys, {
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epochs: 1,
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verbose: 0,
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validationData: [valXs, valYs],
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callbacks: tfn.node.tensorBoard(tmpLogDir)
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})];
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case 1:
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// Warm-up training.
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_a.sent();
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subDirs = fs.readdirSync(tmpLogDir);
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expect(subDirs).toContain('train');
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expect(subDirs).toContain('val');
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trainLogDir = path.join(tmpLogDir, 'train');
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trainFiles = fs.readdirSync(trainLogDir);
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trainFileSize0 = fs.statSync(path.join(trainLogDir, trainFiles[0])).size;
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expect(trainFileSize0).toBeGreaterThan(0);
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valLogDir = path.join(tmpLogDir, 'val');
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valFiles = fs.readdirSync(valLogDir);
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valFileSize0 = fs.statSync(path.join(valLogDir, valFiles[0])).size;
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expect(valFileSize0).toBeGreaterThan(0);
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// With updateFreq === epoch, the train and val subset should have generated
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// the same amount of logs.
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expect(valFileSize0).toEqual(trainFileSize0);
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return [4 /*yield*/, model.fit(xs, ys, {
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epochs: 3,
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verbose: 0,
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validationData: [valXs, valYs],
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callbacks: tfn.node.tensorBoard(tmpLogDir)
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})];
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case 2:
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history = _a.sent();
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expect(history.history.loss.length).toEqual(3);
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expect(history.history.val_loss.length).toEqual(3);
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expect(history.history.MAE.length).toEqual(3);
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expect(history.history.val_MAE.length).toEqual(3);
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trainFileSize1 = fs.statSync(path.join(trainLogDir, trainFiles[0])).size;
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valFileSize1 = fs.statSync(path.join(valLogDir, valFiles[0])).size;
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// We currently only assert that new content has been written to the log
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// file.
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expect(trainFileSize1).toBeGreaterThan(trainFileSize0);
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expect(valFileSize1).toBeGreaterThan(valFileSize0);
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// With updateFreq === epoch, the train and val subset should have generated
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// the same amount of logs.
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expect(valFileSize1).toEqual(trainFileSize1);
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return [2 /*return*/];
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}
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});
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}); });
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it('fit(): batch updateFreq, with validation', function () { return __awaiter(void 0, void 0, void 0, function () {
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var model, xs, ys, valXs, valYs, subDirs, trainLogDir, trainFiles, trainFileSize0, valLogDir, valFiles, valFileSize0, history, trainFileSize1, valFileSize1;
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return __generator(this, function (_a) {
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switch (_a.label) {
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case 0:
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model = createModelForTest();
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xs = tfn.randomUniform([100, 10]);
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ys = tfn.randomUniform([100, 1]);
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valXs = tfn.randomUniform([10, 10]);
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valYs = tfn.randomUniform([10, 1]);
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// Warm-up training.
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return [4 /*yield*/, model.fit(xs, ys, {
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epochs: 1,
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verbose: 0,
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validationData: [valXs, valYs],
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// Use batch updateFreq here.
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callbacks: tfn.node.tensorBoard(tmpLogDir, { updateFreq: 'batch' })
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})];
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case 1:
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// Warm-up training.
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_a.sent();
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subDirs = fs.readdirSync(tmpLogDir);
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expect(subDirs).toContain('train');
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expect(subDirs).toContain('val');
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trainLogDir = path.join(tmpLogDir, 'train');
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trainFiles = fs.readdirSync(trainLogDir);
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trainFileSize0 = fs.statSync(path.join(trainLogDir, trainFiles[0])).size;
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expect(trainFileSize0).toBeGreaterThan(0);
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valLogDir = path.join(tmpLogDir, 'val');
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valFiles = fs.readdirSync(valLogDir);
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valFileSize0 = fs.statSync(path.join(valLogDir, valFiles[0])).size;
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expect(valFileSize0).toBeGreaterThan(0);
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// The train subset should have generated more logs than the val subset,
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// because the train subset gets logged every batch, while the val subset
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// gets logged every epoch.
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expect(trainFileSize0).toBeGreaterThan(valFileSize0);
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return [4 /*yield*/, model.fit(xs, ys, {
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epochs: 3,
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verbose: 0,
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validationData: [valXs, valYs],
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callbacks: tfn.node.tensorBoard(tmpLogDir)
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})];
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case 2:
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history = _a.sent();
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expect(history.history.loss.length).toEqual(3);
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expect(history.history.val_loss.length).toEqual(3);
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expect(history.history.MAE.length).toEqual(3);
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expect(history.history.val_MAE.length).toEqual(3);
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trainFileSize1 = fs.statSync(path.join(trainLogDir, trainFiles[0])).size;
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valFileSize1 = fs.statSync(path.join(valLogDir, valFiles[0])).size;
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// We currently only assert that new content has been written to the log
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// file.
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expect(trainFileSize1).toBeGreaterThan(trainFileSize0);
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expect(valFileSize1).toBeGreaterThan(valFileSize0);
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// The train subset should have generated more logs than the val subset,
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// because the train subset gets logged every batch, while the val subset
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// gets logged every epoch.
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expect(trainFileSize1).toBeGreaterThan(valFileSize1);
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return [2 /*return*/];
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}
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});
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}); });
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it('fit(): batch updateFreq, with initialEpoch', function () { return __awaiter(void 0, void 0, void 0, function () {
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var model, xs, ys, valXs, valYs, callback, trainWriterScalarSpy, valWriterScalarSpy, trainCallArgs, valCallArgs;
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return __generator(this, function (_a) {
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switch (_a.label) {
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case 0:
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model = createModelForTest();
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xs = tfn.randomUniform([100, 10]);
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ys = tfn.randomUniform([100, 1]);
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valXs = tfn.randomUniform([10, 10]);
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valYs = tfn.randomUniform([10, 1]);
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// Warm-up training. Also ensures that `callback.trainWriter` and
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// `callback.valWriter` are created.
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return [4 /*yield*/, model.fit(xs, ys, {
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epochs: 2,
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validationData: [valXs, valYs],
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verbose: 0,
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})];
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case 1:
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// Warm-up training. Also ensures that `callback.trainWriter` and
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// `callback.valWriter` are created.
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_a.sent();
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callback = tfn.node.tensorBoard(tmpLogDir, { updateFreq: 'epoch' });
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// tslint:disable-next-line:no-any
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callback.ensureTrainWriterCreated();
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// tslint:disable-next-line:no-any
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callback.ensureValWriterCreated();
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trainWriterScalarSpy = spyOn(callback.trainWriter, 'scalar');
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valWriterScalarSpy = spyOn(callback.valWriter, 'scalar');
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// Train for 2 more epochs, using initialEpoch and callback.
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return [4 /*yield*/, model.fit(xs, ys, {
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epochs: 4,
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initialEpoch: 2,
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validationData: [valXs, valYs],
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callbacks: [callback],
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})];
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case 2:
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// Train for 2 more epochs, using initialEpoch and callback.
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_a.sent();
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expect(trainWriterScalarSpy).toHaveBeenCalledTimes(4);
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trainCallArgs = trainWriterScalarSpy.calls.allArgs();
|
// Assert that the epoch numbers used to log the epoch-end loss and metric
|
// reflect initialEpoch.
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expect(trainCallArgs[0][0]).toEqual('epoch_loss');
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expect(trainCallArgs[0][2]).toEqual(3);
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expect(trainCallArgs[1][0]).toEqual('epoch_MAE');
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expect(trainCallArgs[1][2]).toEqual(3);
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expect(trainCallArgs[2][0]).toEqual('epoch_loss');
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expect(trainCallArgs[2][2]).toEqual(4);
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expect(trainCallArgs[3][0]).toEqual('epoch_MAE');
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expect(trainCallArgs[3][2]).toEqual(4);
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expect(valWriterScalarSpy).toHaveBeenCalledTimes(4);
|
valCallArgs = valWriterScalarSpy.calls.allArgs();
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expect(valCallArgs[0][0]).toEqual('epoch_loss');
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expect(valCallArgs[0][2]).toEqual(3);
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expect(valCallArgs[1][0]).toEqual('epoch_MAE');
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expect(valCallArgs[1][2]).toEqual(3);
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expect(valCallArgs[2][0]).toEqual('epoch_loss');
|
expect(valCallArgs[2][2]).toEqual(4);
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expect(valCallArgs[3][0]).toEqual('epoch_MAE');
|
expect(valCallArgs[3][2]).toEqual(4);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('fit(): histogramFreq 2, with validation', function () { return __awaiter(void 0, void 0, void 0, function () {
|
var model, xs, ys, valXs, valYs, subDirs, trainLogDir, trainFiles, trainFileSize0, trainFileSize1;
|
return __generator(this, function (_a) {
|
switch (_a.label) {
|
case 0:
|
model = createModelForTest();
|
xs = tfn.randomUniform([100, 10]);
|
ys = tfn.randomUniform([100, 1]);
|
valXs = tfn.randomUniform([10, 10]);
|
valYs = tfn.randomUniform([10, 1]);
|
// Warm-up training.
|
return [4 /*yield*/, model.fit(xs, ys, {
|
epochs: 1,
|
verbose: 0,
|
validationData: [valXs, valYs],
|
callbacks: tfn.node.tensorBoard(tmpLogDir, { histogramFreq: 2 })
|
})];
|
case 1:
|
// Warm-up training.
|
_a.sent();
|
subDirs = fs.readdirSync(tmpLogDir);
|
expect(subDirs).toContain('train');
|
trainLogDir = path.join(tmpLogDir, 'train');
|
trainFiles = fs.readdirSync(trainLogDir);
|
trainFileSize0 = fs.statSync(path.join(trainLogDir, trainFiles[0])).size;
|
expect(trainFileSize0).toBeGreaterThan(0);
|
// Actual training run.
|
return [4 /*yield*/, model.fit(xs, ys, {
|
epochs: 4,
|
verbose: 0,
|
validationData: [valXs, valYs],
|
callbacks: tfn.node.tensorBoard(tmpLogDir, { histogramFreq: 2 })
|
})];
|
case 2:
|
// Actual training run.
|
_a.sent();
|
trainFileSize1 = fs.statSync(path.join(trainLogDir, trainFiles[0])).size;
|
// We currently only assert that new content has been written to the log
|
// file.
|
expect(trainFileSize1).toBeGreaterThan(trainFileSize0);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('Invalid updateFreq value causes error', function () { return __awaiter(void 0, void 0, void 0, function () {
|
return __generator(this, function (_a) {
|
expect(function () { return tfn.node.tensorBoard(tmpLogDir, {
|
// tslint:disable-next-line:no-any
|
updateFreq: 'foo'
|
}); }).toThrowError(/Expected updateFreq/);
|
return [2 /*return*/];
|
});
|
}); });
|
it('Invalid histogramFreq value causes error ', function () { return __awaiter(void 0, void 0, void 0, function () {
|
return __generator(this, function (_a) {
|
expect(function () { return tfn.node.tensorBoard(tmpLogDir, {
|
histogramFreq: -1
|
}); }).toThrowError(/Expected histogramFreq/);
|
expect(function () { return tfn.node.tensorBoard(tmpLogDir, {
|
histogramFreq: 1.3
|
}); }).toThrowError(/Expected histogramFreq/);
|
return [2 /*return*/];
|
});
|
}); });
|
});
|
