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