Training does not reliably converge, even with the simple XOR problem

I am not sure that this is a known issue, but I cannot get the neural network to converge reliably.

In many instances, it just cannot solve the simple XOR problem.

For instance, I had serialized one of the randomly generated NeuralNetworks that displays this problem:

Is this just an issue with the activation function? Then it would be helpful to explain this in the README. Or is there a bug in the code?

import {NeuralNetwork} from "./nn/nn";

let nn;

const training_data = [
    {
        inputs: [0, 0],
        outputs: [0],
    },
    {
        inputs: [0, 1],
        outputs: [1],
    },
    {
        inputs: [1, 0],
        outputs: [1],
    },
    {
        inputs: [1, 1],
        outputs: [0],
    },
]

function setup() {
    createCanvas(400, 400);

    nn = NeuralNetwork.deserialize({"input_nodes":2,"hidden_nodes":2,"output_nodes":1,"weights_ih":{"rows":2,"cols":2,"data":[[-0.12692590266986858,-0.844955757436316],[-0.9357427469178123,0.8173651578783794]]},"weights_ho":{"rows":1,"cols":2,"data":[[-0.5832662974097391,0.5308947844782579]]},"bias_h":{"rows":2,"cols":1,"data":[[0.39650732687505963],[-0.49808473788143637]]},"bias_o":{"rows":1,"cols":1,"data":[[0.2908941132572971]]},"averageError":0,"learning_rate":0.01,"activation_function":{}});
    global.nn = nn;
}

function draw() {
    background(0);

    for (let i = 0; i < 1000; i++) {
        let data = random(training_data);
        nn.train(data.inputs, data.outputs);
    }

}

global.setup = setup;
global.draw = draw;
global.NeuralNetwork = NeuralNetwork;

The XOR example at https://codingtrain.github.io/Toy-Neural-Network-JS/examples/xor/ also faces this issue ; when refreshing a few times, it will result in the wrong separation visualization.

CodingTrain / Toy-Neural-Network-JS

Training does not reliably converge, even with the simple XOR problem #155