Tengo un NN simple de 3 capas que no puede converger en solo 3 valores de salida, ¿por qué? Ayude a explicar los conceptos básicos de backprop en este ejemplo. sin notación elegante, no me refieras a un video de YouTube.

Use solo JavaScript simple y sencillo

 var weights = (new Array(3)).fill(0).map(A => Math.random()); var bais = (new Array(3)).fill(0).map(A => Math.random()); var input = (new Array(3)).fill(0).map(A => (Math.random())); var output = (new Array(3)).fill(0).map(A => (Math.random())); function read(x){//forward pass var A1 = 1/(1 + (Math.E ** -(x * weights[0] + bais[0]))); var A2 = 1/(1 + (Math.E ** -(A1 * weights[1] + bais[1]))); var A3 = 1/(1 + (Math.E ** -(A2 * weights[2] + bais[2]))); return [A1, A2, A3]; //this returns the activations } function Learner(){ var W1 = 0; var W2 = 0; var W3 = 0; var B1 = 0; var B2 = 0; var B3 = 0; for(let a=0;a<input.length;a++){//derivative of the last weight with respect to the cost var out = read(input[a]); W3 += ( out[1] * (2 * (out[2] - output[a])) * out[2]*(1-out[2])); B3 += ( (2 * (out[2] - output[a])) * out[2]*(1-out[2])); } for(let a=0;a<input.length;a++){ var out = read(input[a]); W2 += ( out[0] * out[1]*(1-out[1]))*W3; B2 += ( 1 * out[1]*(1-out[1]))*W3; } for(let a=0;a<input.length;a++){ var out = read(input[a]); W1 += ( input[a] * out[0]*(1-out[0]))*W2; B1 += ( 1 * out[0]*(1-out[0]))*W2; } weights[0] = (weights[0] - 0.1 * W1)/3; weights[1] = (weights[1] - 0.1 * W2)/3; weights[2] = (weights[2] - 0.1 * W3)/3; bais[0] = (bais[0] - 0.1 * B1)/3; bais[1] = (bais[1] - 0.1 * B2)/3; bais[2] = (bais[2] - 0.1 * B3)/3; } function check(){ for(let a=0;a<output.length;a++){ console.log(read(input[a])[2] + ", " + output[a]); } } var INTERVAL = setInterval(Learner, 10, 10);

Obviamente no sé cómo funciona backprop por favor ayuda.