@tensorflow/tfjs-node/dist/tensorboard_test.js

"use strict";
/**
 * @license
 * Copyright 2018 Google LLC. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */
var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
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var _this = this;
Object.defineProperty(exports, "__esModule", { value: true });
var tfjs_1 = require("@tensorflow/tfjs");
var fs = require("fs");
var path = require("path");
var util_1 = require("util");
var tfn = require("./index");
// tslint:disable-next-line:no-require-imports
var rimraf = require('rimraf');
// tslint:disable-next-line:no-require-imports
var tmp = require('tmp');
var rimrafPromise = util_1.promisify(rimraf);
describe('tensorboard', function () {
    var tmpLogDir;
    beforeEach(function () {
        tmpLogDir = tmp.dirSync().name;
    });
    afterEach(function () { return __awaiter(_this, void 0, void 0, function () {
        return __generator(this, function (_a) {
            switch (_a.label) {
                case 0:
                    if (!(tmpLogDir != null)) return [3 /*break*/, 2];
                    return [4 /*yield*/, rimrafPromise(tmpLogDir)];
                case 1:
                    _a.sent();
                    _a.label = 2;
                case 2: return [2 /*return*/];
            }
        });
    }); });
    it('Create summaryFileWriter and write scalar', function () {
        var writer = tfn.node.summaryFileWriter(tmpLogDir);
        writer.scalar('foo', 42, 0);
        writer.flush();
        // Currently, we only verify that the file exists and the size
        // increases in a sensible way as we write more scalars to it.
        // The difficulty is in reading the protobuf contents of the event
        // file in JavaScript/TypeScript.
        var fileNames = fs.readdirSync(tmpLogDir);
        expect(fileNames.length).toEqual(1);
        var eventFilePath = path.join(tmpLogDir, fileNames[0]);
        var fileSize0 = fs.statSync(eventFilePath).size;
        writer.scalar('foo', 43, 1);
        writer.flush();
        var fileSize1 = fs.statSync(eventFilePath).size;
        var incrementPerScalar = fileSize1 - fileSize0;
        expect(incrementPerScalar).toBeGreaterThan(0);
        writer.scalar('foo', 44, 2);
        writer.scalar('foo', 45, 3);
        writer.flush();
        var fileSize2 = fs.statSync(eventFilePath).size;
        expect(fileSize2 - fileSize1).toEqual(2 * incrementPerScalar);
    });
    it('Writing tf.Scalar works', function () {
        var writer = tfn.node.summaryFileWriter(tmpLogDir);
        writer.scalar('foo', tfjs_1.scalar(42), 0);
        writer.flush();
        // Currently, we only verify that the file exists and the size
        // increases in a sensible way as we write more scalars to it.
        // The difficulty is in reading the protobuf contents of the event
        // file in JavaScript/TypeScript.
        var fileNames = fs.readdirSync(tmpLogDir);
        expect(fileNames.length).toEqual(1);
    });
    it('No crosstalk between two summary writers', function () {
        var logDir1 = path.join(tmpLogDir, '1');
        var writer1 = tfn.node.summaryFileWriter(logDir1);
        writer1.scalar('foo', 42, 0);
        writer1.flush();
        var logDir2 = path.join(tmpLogDir, '2');
        var writer2 = tfn.node.summaryFileWriter(logDir2);
        writer2.scalar('foo', 1.337, 0);
        writer2.flush();
        // Currently, we only verify that the file exists and the size
        // increases in a sensible way as we write more scalars to it.
        // The difficulty is in reading the protobuf contents of the event
        // file in JavaScript/TypeScript.
        var fileNames = fs.readdirSync(logDir1);
        expect(fileNames.length).toEqual(1);
        var eventFilePath1 = path.join(logDir1, fileNames[0]);
        var fileSize1Num0 = fs.statSync(eventFilePath1).size;
        fileNames = fs.readdirSync(logDir2);
        expect(fileNames.length).toEqual(1);
        var eventFilePath2 = path.join(logDir2, fileNames[0]);
        var fileSize2Num0 = fs.statSync(eventFilePath2).size;
        expect(fileSize2Num0).toBeGreaterThan(0);
        writer1.scalar('foo', 43, 1);
        writer1.flush();
        var fileSize1Num1 = fs.statSync(eventFilePath1).size;
        var incrementPerScalar = fileSize1Num1 - fileSize1Num0;
        expect(incrementPerScalar).toBeGreaterThan(0);
        writer1.scalar('foo', 44, 2);
        writer1.scalar('foo', 45, 3);
        writer1.flush();
        var fileSize1Num2 = fs.statSync(eventFilePath1).size;
        expect(fileSize1Num2 - fileSize1Num1).toEqual(2 * incrementPerScalar);
        var fileSize2Num1 = fs.statSync(eventFilePath2).size;
        expect(fileSize2Num1).toEqual(fileSize2Num0);
        writer2.scalar('foo', 1.336, 1);
        writer2.scalar('foo', 1.335, 2);
        writer2.flush();
        var fileSize1Num3 = fs.statSync(eventFilePath1).size;
        expect(fileSize1Num3).toEqual(fileSize1Num2);
        var fileSize2Num2 = fs.statSync(eventFilePath2).size;
        expect(fileSize2Num2 - fileSize2Num1).toEqual(2 * incrementPerScalar);
    });
    it('Writing into existing directory works', function () {
        fs.mkdirSync(tmpLogDir, { recursive: true });
        var writer = tfn.node.summaryFileWriter(path.join(tmpLogDir, '22'));
        writer.scalar('foo', 42, 0);
        writer.flush();
        var fileNames = fs.readdirSync(tmpLogDir);
        expect(fileNames.length).toEqual(1);
    });
    it('empty logdir leads to error', function () {
        expect(function () { return tfn.node.summaryFileWriter(''); }).toThrowError(/empty string/);
    });
});
describe('tensorBoard callback', function () {
    var tmpLogDir;
    beforeEach(function () {
        tmpLogDir = tmp.dirSync().name;
    });
    afterEach(function () { return __awaiter(_this, void 0, void 0, function () {
        return __generator(this, function (_a) {
            switch (_a.label) {
                case 0:
                    if (!(tmpLogDir != null)) return [3 /*break*/, 2];
                    return [4 /*yield*/, rimrafPromise(tmpLogDir)];
                case 1:
                    _a.sent();
                    _a.label = 2;
                case 2: return [2 /*return*/];
            }
        });
    }); });
    function createModelForTest() {
        var model = tfn.sequential();
        model.add(tfn.layers.dense({ units: 5, activation: 'relu', inputShape: [10] }));
        model.add(tfn.layers.dense({ units: 1 }));
        model.compile({ loss: 'meanSquaredError', optimizer: 'sgd', metrics: ['MAE'] });
        return model;
    }
    it('fit(): default epoch updateFreq, with validation', function () { return __awaiter(_this, void 0, void 0, function () {
        var model, xs, ys, valXs, valYs, subDirs, trainLogDir, trainFiles, trainFileSize0, valLogDir, valFiles, valFileSize0, history, trainFileSize1, valFileSize1;
        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)
                        })];
                case 1:
                    // Warm-up training.
                    _a.sent();
                    subDirs = fs.readdirSync(tmpLogDir);
                    expect(subDirs).toContain('train');
                    expect(subDirs).toContain('val');
                    trainLogDir = path.join(tmpLogDir, 'train');
                    trainFiles = fs.readdirSync(trainLogDir);
                    trainFileSize0 = fs.statSync(path.join(trainLogDir, trainFiles[0])).size;
                    expect(trainFileSize0).toBeGreaterThan(0);
                    valLogDir = path.join(tmpLogDir, 'val');
                    valFiles = fs.readdirSync(valLogDir);
                    valFileSize0 = fs.statSync(path.join(valLogDir, valFiles[0])).size;
                    expect(valFileSize0).toBeGreaterThan(0);
                    // With updateFreq === epoch, the train and val subset should have generated
                    // the same amount of logs.
                    expect(valFileSize0).toEqual(trainFileSize0);
                    return [4 /*yield*/, model.fit(xs, ys, {
                            epochs: 3,
                            verbose: 0,
                            validationData: [valXs, valYs],
                            callbacks: tfn.node.tensorBoard(tmpLogDir)
                        })];
                case 2:
                    history = _a.sent();
                    expect(history.history.loss.length).toEqual(3);
                    expect(history.history.val_loss.length).toEqual(3);
                    expect(history.history.MAE.length).toEqual(3);
                    expect(history.history.val_MAE.length).toEqual(3);
                    trainFileSize1 = fs.statSync(path.join(trainLogDir, trainFiles[0])).size;
                    valFileSize1 = fs.statSync(path.join(valLogDir, valFiles[0])).size;
                    // We currently only assert that new content has been written to the log
                    // file.
                    expect(trainFileSize1).toBeGreaterThan(trainFileSize0);
                    expect(valFileSize1).toBeGreaterThan(valFileSize0);
                    // With updateFreq === epoch, the train and val subset should have generated
                    // the same amount of logs.
                    expect(valFileSize1).toEqual(trainFileSize1);
                    return [2 /*return*/];
            }
        });
    }); });
    it('fit(): batch updateFreq, with validation', function () { return __awaiter(_this, void 0, void 0, function () {
        var model, xs, ys, valXs, valYs, subDirs, trainLogDir, trainFiles, trainFileSize0, valLogDir, valFiles, valFileSize0, history, trainFileSize1, valFileSize1;
        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],
                            // Use batch updateFreq here.
                            callbacks: tfn.node.tensorBoard(tmpLogDir, { updateFreq: 'batch' })
                        })];
                case 1:
                    // Warm-up training.
                    _a.sent();
                    subDirs = fs.readdirSync(tmpLogDir);
                    expect(subDirs).toContain('train');
                    expect(subDirs).toContain('val');
                    trainLogDir = path.join(tmpLogDir, 'train');
                    trainFiles = fs.readdirSync(trainLogDir);
                    trainFileSize0 = fs.statSync(path.join(trainLogDir, trainFiles[0])).size;
                    expect(trainFileSize0).toBeGreaterThan(0);
                    valLogDir = path.join(tmpLogDir, 'val');
                    valFiles = fs.readdirSync(valLogDir);
                    valFileSize0 = fs.statSync(path.join(valLogDir, valFiles[0])).size;
                    expect(valFileSize0).toBeGreaterThan(0);
                    // The train subset should have generated more logs than the val subset,
                    // because the train subset gets logged every batch, while the val subset
                    // gets logged every epoch.
                    expect(trainFileSize0).toBeGreaterThan(valFileSize0);
                    return [4 /*yield*/, model.fit(xs, ys, {
                            epochs: 3,
                            verbose: 0,
                            validationData: [valXs, valYs],
                            callbacks: tfn.node.tensorBoard(tmpLogDir)
                        })];
                case 2:
                    history = _a.sent();
                    expect(history.history.loss.length).toEqual(3);
                    expect(history.history.val_loss.length).toEqual(3);
                    expect(history.history.MAE.length).toEqual(3);
                    expect(history.history.val_MAE.length).toEqual(3);
                    trainFileSize1 = fs.statSync(path.join(trainLogDir, trainFiles[0])).size;
                    valFileSize1 = fs.statSync(path.join(valLogDir, valFiles[0])).size;
                    // We currently only assert that new content has been written to the log
                    // file.
                    expect(trainFileSize1).toBeGreaterThan(trainFileSize0);
                    expect(valFileSize1).toBeGreaterThan(valFileSize0);
                    // The train subset should have generated more logs than the val subset,
                    // because the train subset gets logged every batch, while the val subset
                    // gets logged every epoch.
                    expect(trainFileSize1).toBeGreaterThan(valFileSize1);
                    return [2 /*return*/];
            }
        });
    }); });
    it('fit(): with initialEpoch', function () { return __awaiter(_this, void 0, void 0, function () {
        var model, xs, ys, valXs, valYs, callback, trainWriterScalarSpy, valWriterScalarSpy, trainCallArgs, valCallArgs;
        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. Also ensures that `callback.trainWriter` and
                    // `callback.valWriter` are created.
                    return [4 /*yield*/, model.fit(xs, ys, {
                            epochs: 2,
                            validationData: [valXs, valYs],
                            verbose: 0,
                        })];
                case 1:
                    // Warm-up training. Also ensures that `callback.trainWriter` and
                    // `callback.valWriter` are created.
                    _a.sent();
                    callback = tfn.node.tensorBoard(tmpLogDir, { updateFreq: 'epoch' });
                    // tslint:disable-next-line:no-any
                    callback.ensureTrainWriterCreated();
                    // tslint:disable-next-line:no-any
                    callback.ensureValWriterCreated();
                    trainWriterScalarSpy = spyOn(callback.trainWriter, 'scalar');
                    valWriterScalarSpy = spyOn(callback.valWriter, 'scalar');
                    // Train for 2 more epochs, using initialEpoch and callback.
                    return [4 /*yield*/, model.fit(xs, ys, {
                            epochs: 4,
                            initialEpoch: 2,
                            validationData: [valXs, valYs],
                            callbacks: [callback],
                        })];
                case 2:
                    // Train for 2 more epochs, using initialEpoch and callback.
                    _a.sent();
                    expect(trainWriterScalarSpy).toHaveBeenCalledTimes(4);
                    trainCallArgs = trainWriterScalarSpy.calls.allArgs();
                    // Assert that the epoch numbers used to log the epoch-end loss and metric
                    // reflect initialEpoch.
                    expect(trainCallArgs[0][0]).toEqual('epoch_loss');
                    expect(trainCallArgs[0][2]).toEqual(3);
                    expect(trainCallArgs[1][0]).toEqual('epoch_MAE');
                    expect(trainCallArgs[1][2]).toEqual(3);
                    expect(trainCallArgs[2][0]).toEqual('epoch_loss');
                    expect(trainCallArgs[2][2]).toEqual(4);
                    expect(trainCallArgs[3][0]).toEqual('epoch_MAE');
                    expect(trainCallArgs[3][2]).toEqual(4);
                    expect(valWriterScalarSpy).toHaveBeenCalledTimes(4);
                    valCallArgs = valWriterScalarSpy.calls.allArgs();
                    expect(valCallArgs[0][0]).toEqual('epoch_loss');
                    expect(valCallArgs[0][2]).toEqual(3);
                    expect(valCallArgs[1][0]).toEqual('epoch_MAE');
                    expect(valCallArgs[1][2]).toEqual(3);
                    expect(valCallArgs[2][0]).toEqual('epoch_loss');
                    expect(valCallArgs[2][2]).toEqual(4);
                    expect(valCallArgs[3][0]).toEqual('epoch_MAE');
                    expect(valCallArgs[3][2]).toEqual(4);
                    return [2 /*return*/];
            }
        });
    }); });
    it('Invalid updateFreq value causes error', function () { return __awaiter(_this, 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*/];
        });
    }); });
});