- Press n or j to go to the next uncovered block, b, p or k for the previous block. -
- -| File | -- | Statements | -- | Branches | -- | Functions | -- | Lines | -- |
|---|---|---|---|---|---|---|---|---|---|
| index.js | -
-
- |
- 69.34% | -95/137 | -63.63% | -21/33 | -63.63% | -14/22 | -70% | -91/130 | -
- Press n or j to go to the next uncovered block, b, p or k for the previous block. -
- -| 1 -2 -3 -4 -5 -6 -7 -8 -9 -10 -11 -12 -13 -14 -15 -16 -17 -18 -19 -20 -21 -22 -23 -24 -25 -26 -27 -28 -29 -30 -31 -32 -33 -34 -35 -36 -37 -38 -39 -40 -41 -42 -43 -44 -45 -46 -47 -48 -49 -50 -51 -52 -53 -54 -55 -56 -57 -58 -59 -60 -61 -62 -63 -64 -65 -66 -67 -68 -69 -70 -71 -72 -73 -74 -75 -76 -77 -78 -79 -80 -81 -82 -83 -84 -85 -86 -87 -88 -89 -90 -91 -92 -93 -94 -95 -96 -97 -98 -99 -100 -101 -102 -103 -104 -105 -106 -107 -108 -109 -110 -111 -112 -113 -114 -115 -116 -117 -118 -119 -120 -121 -122 -123 -124 -125 -126 -127 -128 -129 -130 -131 -132 -133 -134 -135 -136 -137 -138 -139 -140 -141 -142 -143 -144 -145 -146 -147 -148 -149 -150 -151 -152 -153 -154 -155 -156 -157 -158 -159 -160 -161 -162 -163 -164 -165 -166 -167 -168 -169 -170 -171 -172 -173 -174 -175 -176 -177 -178 -179 -180 -181 -182 -183 -184 -185 -186 -187 -188 -189 -190 -191 -192 -193 -194 -195 -196 -197 -198 -199 -200 -201 -202 -203 -204 -205 -206 -207 -208 -209 -210 -211 -212 -213 -214 -215 -216 -217 -218 -219 -220 -221 -222 -223 -224 -225 -226 -227 -228 -229 -230 -231 -232 -233 -234 -235 -236 -237 -238 -239 -240 -241 -242 -243 -244 -245 -246 -247 -248 -249 -250 -251 -252 -253 -254 -255 -256 -257 -258 -259 -260 -261 -262 -263 -264 -265 -266 -267 -268 -269 -270 -271 -272 -273 -274 -275 -276 -277 -278 -279 -280 -281 -282 -283 -284 -285 -286 -287 -288 -289 -290 -291 -292 -293 -294 -295 -296 -297 -298 -299 -300 -301 -302 -303 -304 -305 -306 -307 -308 -309 -310 -311 -312 -313 -314 -315 | - - - - - - - - - - - - - - - - -1x -1x -1x - - - - - - - - - - - - - - - -1x - - - - - - -1x -1x -1x -1x - - - - - -1x -1x -1x -1x -1x - - - - - - -1x - - - - - - - - - - - - - - - - -1x -1x -1x -8x -4x -2x -2x - -2x - -4x -2x -1x - -1x - - -2x - - -1x -1x -1x - - - -1x -1x -1x -1x -1x - - - - - - -1x -1x -1x - -1x -2x -108x - - - - - - - -2x - - -1x -2x -2x -2x - - -1x - - - -2x -2x -2x -2x -2x -2x -2x - - -2x -2x -2x - -2x -26x - - -2x -2x - -2x -54x -54x -54x - -54x -54x -54x - - - - -54x -54x - -54x -54x -54x -54x -54x -54x - -54x -24x - -30x -30x - - - -2x -2x -1861x -30x -30x - - -2x -2x - - - - - - - - - -2x - - - - - - - - - - - - - - - - - - - - - - -2x -2x - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -1x - - - | // Copyright (c) 2018 ml5
-//
-// This software is released under the MIT License.
-// https://opensource.org/licenses/MIT
-
-/* eslint prefer-destructuring: ["error", {AssignmentExpression: {array: false}}] */
-/* eslint no-await-in-loop: "off" */
-/*
-A LSTM Generator: Run inference mode for a pre-trained LSTM.
-*/
-
-import * as tf from "@tensorflow/tfjs";
-import axios from "axios";
-import sampleFromDistribution from "./../utils/sample";
-import CheckpointLoader from "../utils/checkpointLoader";
-import callCallback from "../utils/callcallback";
-
-const regexCell = /cell_[0-9]|lstm_[0-9]/gi;
-const regexWeights = /weights|weight|kernel|kernels|w/gi;
-const regexFullyConnected = /softmax/gi;
-
-class CharRNN {
- /**
- * Create a CharRNN.
- * @param {String} modelPath - The path to the trained charRNN model.
- * @param {function} callback - Optional. A callback to be called once
- * the model has loaded. If no callback is provided, it will return a
- * promise that will be resolved once the model has loaded.
- */
- constructor(modelPath, callback) {
- /**
- * Boolean value that specifies if the model has loaded.
- * @type {boolean}
- * @public
- */
- this.ready = false;
-
- /**
- * The pre-trained charRNN model.
- * @type {model}
- * @public
- */
- this.model = {};
- this.cellsAmount = 0;
- this.cells = [];
- this.zeroState = { c: [], h: [] };
- /**
- * The vocabulary size (or total number of possible characters).
- * @type {c: Array, h: Array}
- * @public
- */
- this.state = { c: [], h: [] };
- this.vocab = {};
- this.vocabSize = 0;
- this.probabilities = [];
- this.defaults = {
- seed: "a", // TODO: use no seed by default
- length: 20,
- temperature: 0.5,
- stateful: false,
- };
-
- this.ready = callCallback(this.loadCheckpoints(modelPath), callback);
- // this.then = this.ready.then.bind(this.ready);
- }
-
- resetState() {
- this.state = this.zeroState;
- }
-
- setState(state) {
- this.state = state;
- }
-
- getState() {
- return this.state;
- }
-
- async loadCheckpoints(path) {
- const reader = new CheckpointLoader(path);
- const vars = await reader.getAllVariables();
- Object.keys(vars).forEach(key => {
- if (key.match(regexCell)) {
- if (key.match(regexWeights)) {
- this.model[`Kernel_${key.match(/[0-9]/)[0]}`] = vars[key];
- this.cellsAmount += 1;
- } else {
- this.model[`Bias_${key.match(/[0-9]/)[0]}`] = vars[key];
- }
- } else if (key.match(regexFullyConnected)) {
- if (key.match(regexWeights)) {
- this.model.fullyConnectedWeights = vars[key];
- } else {
- this.model.fullyConnectedBiases = vars[key];
- }
- } else {
- this.model[key] = vars[key];
- }
- });
- await this.loadVocab(path);
- await this.initCells();
- return this;
- }
-
- async loadVocab(path) {
- try {
- const { data } = await axios.get(`${path}/vocab.json`);
- this.vocab = data;
- this.vocabSize = Object.keys(data).length;
- return this.vocab;
- } catch (err) {
- return err;
- }
- }
-
- async initCells() {
- this.cells = [];
- this.zeroState = { c: [], h: [] };
- const forgetBias = tf.tensor(1.0);
-
- const lstm = i => {
- const cell = (DATA, C, H) =>
- tf.basicLSTMCell(
- forgetBias,
- this.model[`Kernel_${i}`],
- this.model[`Bias_${i}`],
- DATA,
- C,
- H,
- );
- return cell;
- };
-
- for (let i = 0; i < this.cellsAmount; i += 1) {
- this.zeroState.c.push(tf.zeros([1, this.model[`Bias_${i}`].shape[0] / 4]));
- this.zeroState.h.push(tf.zeros([1, this.model[`Bias_${i}`].shape[0] / 4]));
- this.cells.push(lstm(i));
- }
-
- this.state = this.zeroState;
- }
-
- async generateInternal(options) {
- await this.ready;
- const seed = options.seed || this.defaults.seed;
- const length = +options.length || this.defaults.length;
- const temperature = +options.temperature || this.defaults.temperature;
- const stateful = options.stateful || this.defaults.stateful;
- if (!stateful) {
- this.state = this.zeroState;
- }
-
- const results = [];
- const userInput = Array.from(seed);
- const encodedInput = [];
-
- userInput.forEach(char => {
- encodedInput.push(this.vocab[char]);
- });
-
- let input = encodedInput[0];
- let probabilitiesNormalized = []; // will contain final probabilities (normalized)
-
- for (let i = 0; i < userInput.length + length + -1; i += 1) {
- const onehotBuffer = await tf.buffer([1, this.vocabSize]);
- onehotBuffer.set(1.0, 0, input);
- const onehot = onehotBuffer.toTensor();
- let output;
- if (this.model.embedding) {
- const embedded = tf.matMul(onehot, this.model.embedding);
- output = tf.multiRNNCell(this.cells, embedded, this.state.c, this.state.h);
- } else E{
- output = tf.multiRNNCell(this.cells, onehot, this.state.c, this.state.h);
- }
-
- this.state.c = output[0];
- this.state.h = output[1];
-
- const outputH = this.state.h[1];
- const weightedResult = tf.matMul(outputH, this.model.fullyConnectedWeights);
- const logits = tf.add(weightedResult, this.model.fullyConnectedBiases);
- const divided = tf.div(logits, tf.tensor(temperature));
- const probabilities = tf.exp(divided);
- probabilitiesNormalized = await tf.div(probabilities, tf.sum(probabilities)).data();
-
- if (i < userInput.length - 1) {
- input = encodedInput[i + 1];
- } else {
- input = sampleFromDistribution(probabilitiesNormalized);
- results.push(input);
- }
- }
-
- let generated = "";
- results.forEach(char => {
- const mapped = Object.keys(this.vocab).find(key => this.vocab[key] === char);
- if (mapped) {
- generated += mapped;
- }
- });
- this.probabilities = probabilitiesNormalized;
- return {
- sample: generated,
- state: this.state,
- };
- }
-
- /**
- * Reset the model state.
- */
- reset() {
- this.state = this.zeroState;
- }
-
- /**
- * @typedef {Object} options
- * @property {String} seed
- * @property {number} length
- * @property {number} temperature
- */
-
- // stateless
- /**
- * Generates content in a stateless manner, based on some initial text
- * (known as a "seed"). Returns a string.
- * @param {options} options - An object specifying the input parameters of
- * seed, length and temperature. Default length is 20, temperature is 0.5
- * and seed is a random character from the model. The object should look like
- * this:
- * @param {function} callback - Optional. A function to be called when the model
- * has generated content. If no callback is provided, it will return a promise
- * that will be resolved once the model has generated new content.
- */
- async generate(options, callback) {
- this.reset();
- return callCallback(this.generateInternal(options), callback);
- }
-
- // stateful
- /**
- * Predict the next character based on the model's current state.
- * @param {number} temp
- * @param {function} callback - Optional. A function to be called when the
- * model finished adding the seed. If no callback is provided, it will
- * return a promise that will be resolved once the prediction has been generated.
- */
- async predict(temp, callback) {
- let probabilitiesNormalized = [];
- const temperature = temp > 0 ? temp : 0.1;
- const outputH = this.state.h[1];
- const weightedResult = tf.matMul(outputH, this.model.fullyConnectedWeights);
- const logits = tf.add(weightedResult, this.model.fullyConnectedBiases);
- const divided = tf.div(logits, tf.tensor(temperature));
- const probabilities = tf.exp(divided);
- probabilitiesNormalized = await tf.div(probabilities, tf.sum(probabilities)).data();
-
- const sample = sampleFromDistribution(probabilitiesNormalized);
- const result = Object.keys(this.vocab).find(key => this.vocab[key] === sample);
- this.probabilities = probabilitiesNormalized;
- if (callback) {
- callback(result);
- }
- /* eslint max-len: ["error", { "code": 180 }] */
- const pm = Object.keys(this.vocab).map(c => ({
- char: c,
- probability: this.probabilities[this.vocab[c]],
- }));
- return {
- sample: result,
- probabilities: pm,
- };
- }
-
- /**
- * Feed a string of characters to the model state.
- * @param {String} inputSeed - A string to feed the charRNN model state.
- * @param {function} callback - Optional. A function to be called when
- * the model finished adding the seed. If no callback is provided, it
- * will return a promise that will be resolved once seed has been fed.
- */
- async feed(inputSeed, callback) {
- await this.ready;
- const seed = Array.from(inputSeed);
- const encodedInput = [];
-
- seed.forEach(char => {
- encodedInput.push(this.vocab[char]);
- });
-
- let input = encodedInput[0];
- for (let i = 0; i < seed.length; i += 1) {
- const onehotBuffer = await tf.buffer([1, this.vocabSize]);
- onehotBuffer.set(1.0, 0, input);
- const onehot = onehotBuffer.toTensor();
- let output;
- if (this.model.embedding) {
- const embedded = tf.matMul(onehot, this.model.embedding);
- output = tf.multiRNNCell(this.cells, embedded, this.state.c, this.state.h);
- } else {
- output = tf.multiRNNCell(this.cells, onehot, this.state.c, this.state.h);
- }
- this.state.c = output[0];
- this.state.h = output[1];
- input = encodedInput[i];
- }
- if (callback) {
- callback();
- }
- }
-}
-
-const charRNN = (modelPath = "./", callback) => new CharRNN(modelPath, callback);
-
-export default charRNN;
- |