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+
+ |
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+//
+// 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;
+ |
DBSCAN is a density-based clustering non-parametric algorithm: given a set of points in some space, it groups together points that are closely packed together (points with many nearby neighbors), + marking as outliers points that lie alone in low-density regions (whose nearest neighbors are too far away). + DBSCAN is one of the most common clustering algorithms and also most cited in scientific literature. +
+