@tensorflow/tfjs-node/dist/callbacks.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 __extends = (this && this.__extends) || (function () {
    var extendStatics = function (d, b) {
        extendStatics = Object.setPrototypeOf ||
            ({ __proto__: [] } instanceof Array && function (d, b) { d.__proto__ = b; }) ||
            function (d, b) { for (var p in b) if (b.hasOwnProperty(p)) d[p] = b[p]; };
        return extendStatics(d, b);
    };
    return function (d, b) {
        extendStatics(d, b);
        function __() { this.constructor = d; }
        d.prototype = b === null ? Object.create(b) : (__.prototype = b.prototype, new __());
    };
})();
var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
    return new (P || (P = Promise))(function (resolve, reject) {
        function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
        function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
        function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
        step((generator = generator.apply(thisArg, _arguments || [])).next());
    });
};
var __generator = (this && this.__generator) || function (thisArg, body) {
    var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
    return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
    function verb(n) { return function (v) { return step([n, v]); }; }
    function step(op) {
        if (f) throw new TypeError("Generator is already executing.");
        while (_) try {
            if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
            if (y = 0, t) op = [op[0] & 2, t.value];
            switch (op[0]) {
                case 0: case 1: t = op; break;
                case 4: _.label++; return { value: op[1], done: false };
                case 5: _.label++; y = op[1]; op = [0]; continue;
                case 7: op = _.ops.pop(); _.trys.pop(); continue;
                default:
                    if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
                    if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
                    if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
                    if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
                    if (t[2]) _.ops.pop();
                    _.trys.pop(); continue;
            }
            op = body.call(thisArg, _);
        } catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
        if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
    }
};
Object.defineProperty(exports, "__esModule", { value: true });
var tfjs_1 = require("@tensorflow/tfjs");
var path = require("path");
var ProgressBar = require("progress");
var tensorboard_1 = require("./tensorboard");
// A helper class created for testing with the jasmine `spyOn` method, which
// operates only on member methods of objects.
// tslint:disable-next-line:no-any
exports.progressBarHelper = {
    ProgressBar: ProgressBar,
    log: console.log
};
/**
 * Terminal-based progress bar callback for tf.Model.fit().
 */
var ProgbarLogger = /** @class */ (function (_super) {
    __extends(ProgbarLogger, _super);
    /**
     * Construtor of LoggingCallback.
     */
    function ProgbarLogger() {
        var _this = _super.call(this, {
            onTrainBegin: function (logs) { return __awaiter(_this, void 0, void 0, function () {
                var samples, batchSize, steps;
                return __generator(this, function (_a) {
                    samples = this.params.samples;
                    batchSize = this.params.batchSize;
                    steps = this.params.steps;
                    if (samples != null || steps != null) {
                        this.numTrainBatchesPerEpoch =
                            samples != null ? Math.ceil(samples / batchSize) : steps;
                    }
                    else {
                        // Undetermined number of batches per epoch, e.g., due to
                        // `fitDataset()` without `batchesPerEpoch`.
                        this.numTrainBatchesPerEpoch = 0;
                    }
                    return [2 /*return*/];
                });
            }); },
            onEpochBegin: function (epoch, logs) { return __awaiter(_this, void 0, void 0, function () {
                return __generator(this, function (_a) {
                    exports.progressBarHelper.log("Epoch " + (epoch + 1) + " / " + this.params.epochs);
                    this.currentEpochBegin = tfjs_1.util.now();
                    this.epochDurationMillis = null;
                    this.usPerStep = null;
                    this.batchesInLatestEpoch = 0;
                    this.terminalWidth = process.stderr.columns;
                    return [2 /*return*/];
                });
            }); },
            onBatchEnd: function (batch, logs) { return __awaiter(_this, void 0, void 0, function () {
                var maxMetricsStringLength, tickTokens;
                return __generator(this, function (_a) {
                    switch (_a.label) {
                        case 0:
                            this.batchesInLatestEpoch++;
                            if (batch === 0) {
                                this.progressBar = new exports.progressBarHelper.ProgressBar('eta=:eta :bar :placeholderForLossesAndMetrics', {
                                    width: Math.floor(0.5 * this.terminalWidth),
                                    total: this.numTrainBatchesPerEpoch + 1,
                                    head: ">",
                                    renderThrottle: this.RENDER_THROTTLE_MS
                                });
                            }
                            maxMetricsStringLength = Math.floor(this.terminalWidth * 0.5 - 12);
                            tickTokens = {
                                placeholderForLossesAndMetrics: this.formatLogsAsMetricsContent(logs, maxMetricsStringLength)
                            };
                            if (this.numTrainBatchesPerEpoch === 0) {
                                // Undetermined number of batches per epoch.
                                this.progressBar.tick(0, tickTokens);
                            }
                            else {
                                this.progressBar.tick(tickTokens);
                            }
                            return [4 /*yield*/, tfjs_1.nextFrame()];
                        case 1:
                            _a.sent();
                            if (batch === this.numTrainBatchesPerEpoch - 1) {
                                this.epochDurationMillis = tfjs_1.util.now() - this.currentEpochBegin;
                                this.usPerStep = this.params.samples != null ?
                                    this.epochDurationMillis / this.params.samples * 1e3 :
                                    this.epochDurationMillis / this.batchesInLatestEpoch * 1e3;
                            }
                            return [2 /*return*/];
                    }
                });
            }); },
            onEpochEnd: function (epoch, logs) { return __awaiter(_this, void 0, void 0, function () {
                var lossesAndMetricsString;
                return __generator(this, function (_a) {
                    switch (_a.label) {
                        case 0:
                            if (this.epochDurationMillis == null) {
                                // In cases where the number of batches per epoch is not determined,
                                // the calculation of the per-step duration is done at the end of the
                                // epoch. N.B., this includes the time spent on validation.
                                this.epochDurationMillis = tfjs_1.util.now() - this.currentEpochBegin;
                                this.usPerStep =
                                    this.epochDurationMillis / this.batchesInLatestEpoch * 1e3;
                            }
                            this.progressBar.tick({ placeholderForLossesAndMetrics: '' });
                            lossesAndMetricsString = this.formatLogsAsMetricsContent(logs);
                            exports.progressBarHelper.log(this.epochDurationMillis.toFixed(0) + "ms " +
                                (this.usPerStep.toFixed(0) + "us/step - ") +
                                ("" + lossesAndMetricsString));
                            return [4 /*yield*/, tfjs_1.nextFrame()];
                        case 1:
                            _a.sent();
                            return [2 /*return*/];
                    }
                });
            }); },
        }) || this;
        _this.RENDER_THROTTLE_MS = 50;
        return _this;
    }
    ProgbarLogger.prototype.formatLogsAsMetricsContent = function (logs, maxMetricsLength) {
        var metricsContent = '';
        var keys = Object.keys(logs).sort();
        for (var _i = 0, keys_1 = keys; _i < keys_1.length; _i++) {
            var key = keys_1[_i];
            if (this.isFieldRelevant(key)) {
                var value = logs[key];
                metricsContent += key + "=" + getSuccinctNumberDisplay(value) + " ";
            }
        }
        if (maxMetricsLength != null && metricsContent.length > maxMetricsLength) {
            // Cut off metrics strings that are too long to avoid new lines being
            // constantly created.
            metricsContent = metricsContent.slice(0, maxMetricsLength - 3) + '...';
        }
        return metricsContent;
    };
    ProgbarLogger.prototype.isFieldRelevant = function (key) {
        return key !== 'batch' && key !== 'size';
    };
    return ProgbarLogger;
}(tfjs_1.CustomCallback));
exports.ProgbarLogger = ProgbarLogger;
var BASE_NUM_DIGITS = 2;
var MAX_NUM_DECIMAL_PLACES = 4;
/**
 * Get a succint string representation of a number.
 *
 * Uses decimal notation if the number isn't too small.
 * Otherwise, use engineering notation.
 *
 * @param x Input number.
 * @return Succinct string representing `x`.
 */
function getSuccinctNumberDisplay(x) {
    var decimalPlaces = getDisplayDecimalPlaces(x);
    return decimalPlaces > MAX_NUM_DECIMAL_PLACES ?
        x.toExponential(BASE_NUM_DIGITS) :
        x.toFixed(decimalPlaces);
}
exports.getSuccinctNumberDisplay = getSuccinctNumberDisplay;
/**
 * Determine the number of decimal places to display.
 *
 * @param x Number to display.
 * @return Number of decimal places to display for `x`.
 */
function getDisplayDecimalPlaces(x) {
    if (!Number.isFinite(x) || x === 0 || x > 1 || x < -1) {
        return BASE_NUM_DIGITS;
    }
    else {
        return BASE_NUM_DIGITS - Math.floor(Math.log10(Math.abs(x)));
    }
}
exports.getDisplayDecimalPlaces = getDisplayDecimalPlaces;
/**
 * Callback for logging to TensorBoard during training.
 *
 * Users are expected to access this class through the `tensorBoardCallback()`
 * factory method instead.
 */
var TensorBoardCallback = /** @class */ (function (_super) {
    __extends(TensorBoardCallback, _super);
    function TensorBoardCallback(logdir, args) {
        if (logdir === void 0) { logdir = './logs'; }
        var _this = _super.call(this, {
            onBatchEnd: function (batch, logs) { return __awaiter(_this, void 0, void 0, function () {
                return __generator(this, function (_a) {
                    this.batchesSeen++;
                    if (this.args.updateFreq !== 'epoch') {
                        this.logMetrics(logs, 'batch_', this.batchesSeen);
                    }
                    return [2 /*return*/];
                });
            }); },
            onEpochEnd: function (epoch, logs) { return __awaiter(_this, void 0, void 0, function () {
                return __generator(this, function (_a) {
                    this.logMetrics(logs, 'epoch_', epoch + 1);
                    return [2 /*return*/];
                });
            }); },
            onTrainEnd: function (logs) { return __awaiter(_this, void 0, void 0, function () {
                return __generator(this, function (_a) {
                    if (this.trainWriter != null) {
                        this.trainWriter.flush();
                    }
                    if (this.valWriter != null) {
                        this.valWriter.flush();
                    }
                    return [2 /*return*/];
                });
            }); }
        }) || this;
        _this.logdir = logdir;
        _this.args = args == null ? {} : args;
        if (_this.args.updateFreq == null) {
            _this.args.updateFreq = 'epoch';
        }
        tfjs_1.util.assert(['batch', 'epoch'].indexOf(_this.args.updateFreq) !== -1, function () { return "Expected updateFreq to be 'batch' or 'epoch', but got " +
            ("" + _this.args.updateFreq); });
        _this.batchesSeen = 0;
        return _this;
    }
    TensorBoardCallback.prototype.logMetrics = function (logs, prefix, step) {
        for (var key in logs) {
            if (key === 'batch' || key === 'size' || key === 'num_steps') {
                continue;
            }
            var VAL_PREFIX = 'val_';
            if (key.startsWith(VAL_PREFIX)) {
                this.ensureValWriterCreated();
                var scalarName = prefix + key.slice(VAL_PREFIX.length);
                this.valWriter.scalar(scalarName, logs[key], step);
            }
            else {
                this.ensureTrainWriterCreated();
                this.trainWriter.scalar("" + prefix + key, logs[key], step);
            }
        }
    };
    TensorBoardCallback.prototype.ensureTrainWriterCreated = function () {
        this.trainWriter = tensorboard_1.summaryFileWriter(path.join(this.logdir, 'train'));
    };
    TensorBoardCallback.prototype.ensureValWriterCreated = function () {
        this.valWriter = tensorboard_1.summaryFileWriter(path.join(this.logdir, 'val'));
    };
    return TensorBoardCallback;
}(tfjs_1.CustomCallback));
exports.TensorBoardCallback = TensorBoardCallback;
/**
 * Callback for logging to TensorBoard during training.
 *
 * Writes the loss and metric values (if any) to the specified log directory
 * (`logdir`) which can be ingested and visualized by TensorBoard.
 * This callback is usually passed as a callback to `tf.Model.fit()` or
 * `tf.Model.fitDataset()` calls during model training. The frequency at which
 * the values are logged can be controlled with the `updateFreq` field of the
 * configuration object (2nd argument).
 *
 * Usage example:
 * ```js
 * // Constructor a toy multilayer-perceptron regressor for demo purpose.
 * const model = tf.sequential();
 * model.add(
 *     tf.layers.dense({units: 100, activation: 'relu', inputShape: [200]}));
 * model.add(tf.layers.dense({units: 1}));
 * model.compile({
 *   loss: 'meanSquaredError',
 *   optimizer: 'sgd',
 *   metrics: ['MAE']
 * });
 *
 * // Generate some random fake data for demo purpose.
 * const xs = tf.randomUniform([10000, 200]);
 * const ys = tf.randomUniform([10000, 1]);
 * const valXs = tf.randomUniform([1000, 200]);
 * const valYs = tf.randomUniform([1000, 1]);
 *
 * // Start model training process.
 * await model.fit(xs, ys, {
 *   epochs: 100,
 *   validationData: [valXs, valYs],
 *    // Add the tensorBoard callback here.
 *   callbacks: tf.node.tensorBoard('/tmp/fit_logs_1')
 * });
 * ```
 *
 * Then you can use the following commands to point tensorboard
 * to the logdir:
 *
 * ```sh
 * pip install tensorboard  # Unless you've already installed it.
 * tensorboard --logdir /tmp/fit_logs_1
 * ```
 *
 * @param logdir Directory to which the logs will be written.
 * @param args Optional configuration arguments.
 * @returns An instance of `TensorBoardCallback`, which is a subclass of
 *   `tf.CustomCallback`.
 *
 * @doc {heading: 'TensorBoard', namespace: 'node'}
 */
function tensorBoard(logdir, args) {
    if (logdir === void 0) { logdir = './logs'; }
    return new TensorBoardCallback(logdir, args);
}
exports.tensorBoard = tensorBoard;