recurrent-js-gpu/dist/RNN.js

GPU-accelerated Deep Recurrent Neural Networks and LSTMs in Typescript. Ported, object-oriented and refactored version of Andrej Karpathy's recurrent-js (https://github.com/karpathy/recurrentjs)

"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
const RandMat_1 = require("./RandMat");
const Mat_1 = require("./Mat");
const NNModel_1 = require("./NNModel");
class RNN extends NNModel_1.NNModel {
    constructor(inputSize, hiddenSizes, outputSize, needsBackProp = true) {
        super(needsBackProp);
        this.hiddenSizes = hiddenSizes;
        let hiddenSize;
        for (let d = 0; d < this.hiddenSizes.length; d++) {
            const previousSize = d === 0 ? inputSize : this.hiddenSizes[d - 1];
            hiddenSize = this.hiddenSizes[d];
            this.model.hiddenWx[d] = new RandMat_1.RandMat(hiddenSize, previousSize, 0, 0.08);
            this.model.hiddenWh[d] = new RandMat_1.RandMat(hiddenSize, hiddenSize, 0, 0.08);
            this.model.hiddenbh[d] = new Mat_1.Mat(hiddenSize, 1);
        }
        this.model.decoderWh = new RandMat_1.RandMat(outputSize, hiddenSize, 0, 0.08);
        this.model.decoderbd = new Mat_1.Mat(outputSize, 1);
    }
    forward(observations, previousOutput, graph = this.graph) {
        let hiddenPrevs;
        if (typeof previousOutput.h === 'undefined') {
            hiddenPrevs = [];
            for (let d = 0; d < this.hiddenSizes.length; d++) {
                hiddenPrevs.push(new Mat_1.Mat(this.hiddenSizes[d], 1));
            }
        }
        else {
            hiddenPrevs = previousOutput.h;
        }
        const hidden = new Array();
        for (let d = 0; d < this.hiddenSizes.length; d++) {
            const inputVector = d === 0 ? observations : hidden[d - 1];
            const hiddenPrev = hiddenPrevs[d];
            const h0 = graph.mul(this.model.hiddenWx[d], inputVector);
            const h1 = graph.mul(this.model.hiddenWh[d], hiddenPrev);
            const hiddenD = graph.relu(graph.add(graph.add(h0, h1), this.model.hiddenbh[d]));
            hidden.push(hiddenD);
        }
        const output = graph.add(graph.mul(this.model.decoderWh, hidden[hidden.length - 1]), this.model.decoderbd);
        return { 'h': hidden, 'o': output, 'c': null };
    }
}
exports.RNN = RNN;
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