How to use this GPT3-generated code?

Question: Can anyone explain to me how to use this code that GPT-3 spat at me?

class FluidNeuralNetwork {
    constructor(inputs, layers, learningRate, activationFunction) {
        this.inputs = inputs;
        this.layers = layers;
        this.learningRate = learningRate;
        this.activationFunction = activationFunction;
    }
    //Forward propagation
    forwardPropagation(inputs) {
        let currentInputs = inputs;
        let currentOutputs = [];
        for (let i = 0; i < this.layers.length; i++) {
            for (let j = 0; j < this.layers[i].length; j++) {
                let currentWeightSum = 0;
                for (let k = 0; k < currentInputs.length; k++) {
                    currentWeightSum += currentInputs[k] * this.layers[i][j][k];
                }
                let currentOutput = this.activationFunction(currentWeightSum);
                currentOutputs.push(currentOutput);
            }
            currentInputs = currentOutputs;
        }
        return currentOutputs;
    }
    //Backward propagation
    backwardPropagation(targets) {
        let currentTargets = targets;
        let currentOutputs = [];
        for (let i = this.layers.length - 1; i >= 0; i--) {
            for (let j = 0; j < this.layers[i].length; j++) {
                let currentWeightSum = 0;
                for (let k = 0; k < currentTargets.length; k++) {
                    currentWeightSum += currentTargets[k] * this.layers[i][j][k];
                }
                let currentOutput = this.activationFunction(currentWeightSum);
                currentOutputs.push(currentOutput);
            }
            currentTargets = currentOutputs;
        }
        return currentOutputs;
    }
    //Train the neural network
    train(inputs, targets) {
        let outputs = this.forwardPropagation(inputs);
        let errors = [];
        for (let i = 0; i < targets.length; i++) {
            errors.push(targets[i] - outputs[i]);
        }
        let backwardErrors = this.backwardPropagation(errors);
        for (let i = 0; i < this.layers.length; i++) {
            for (let j = 0; j < this.layers[i].length; j++) {
                for (let k = 0; k < inputs.length; k++) {
                    this.layers[i][j][k] += this.learningRate * backwardErrors[j] * inputs[k];
                }
            }
        }
    }
}

//Define environment
class FluidNeuralNetworkEnvironment {
    constructor(network) {
        this.network = network;
    }
    //Run the environment
    run() {
        let inputs = [0.05, 0.1];
        let targets = [0.01, 0.99];
        this.network.train(inputs, targets);
        let outputs = this.network.forwardPropagation(inputs);
        console.log(outputs);
    }
}

//Create the network
let inputs = 2;
let layers = [[[0.15, 0.2], [0.25, 0.3]], [[0.4, 0.45], [0.5, 0.55]]];
let learningRate = 0.5;
let activationFunction = x => 1 / (1 + Math.exp(-x));
let network = new FluidNeuralNetwork(inputs, layers, learningRate, activationFunction);

//Create the environment
let environment = new FluidNeuralNetworkEnvironment(network);
environment.run();
code snippet

This is Liquid Network - created by MIT

This is my example using GPT3
https://replit.com/@didisoftwares/Ask-Me-Somenting-annoying?v=1

1 Like

Thank you very much.