diff --git a/examples/xor/sketch.js b/examples/xor/sketch.js
index ff16be3..f3b41d1 100644
--- a/examples/xor/sketch.js
+++ b/examples/xor/sketch.js
@@ -20,7 +20,7 @@ let training_data = [{
 ];
 
 function setup() {
-  createCanvas(400, 400);
+  createCanvas(400, 800);
   nn = new NeuralNetwork(2, 4, 1);
   lr_slider = createSlider(0.01, 0.5, 0.1, 0.01);
 
@@ -38,7 +38,7 @@ function draw() {
 
   let resolution = 10;
   let cols = width / resolution;
-  let rows = height / resolution;
+  let rows = cols;
   for (let i = 0; i < cols; i++) {
     for (let j = 0; j < rows; j++) {
       let x1 = i / cols;
@@ -51,6 +51,72 @@ function draw() {
     }
   }
 
+  let weights = [nn.weights_ih, nn.weights_ho];
+  drawNN(weights, 0, height / 2, width, height / 2);
+}
+
+function drawNN(weights, x, y, w, h) {
+  fill(60);
+  rect(x, y, w, h);
+
+  //draw neurons and synapses for each layer
+  for (let i = 0; i < weights.length; i++) {
+    drawLayer(weights[i], x + i * (w / (weights.length + 1)), y, w / (weights.length + 1), h);
+  }
+
+  //draw last layer (output
+  let layer = weights.length;
+  drawLayer({cols: 1, rows: 0, data: []}, x + layer * (w / (weights.length + 1)), y, w / (weights.length + 1), h)
+}
+
+
+function drawLayer(weights, x, y, w, h) {
+  let numNeurons = weights.cols;
+  let nextNeurons = new Array(weights.rows);
 
+  let neuronSize = Math.min(w / 3, h / numNeurons);
+  let maxStroke = Math.max(1, neuronSize / 6);
+
+  //compute min and max values for the weights (used for color and stroke)
+  let min, max;
+  for (let i = 0; i < weights.data.length; i++) {
+    for (let j = 0; j < weights.data[i].length; j++) {
+      if (!min || min > weights.data[i][j]) {
+        min = weights.data[i][j];
+      }
+      if (!max || max < weights.data[i][j]) {
+        max = weights.data[i][j];
+      }
+    }
+  }
+
+  //compute positions of neurons in next layer (needed to draw synapses)
+  for (let j = 0; j < nextNeurons.length; j++) {
+    nextNeurons[j] = {xpos: x + w + (w / 2), ypos: y + h / (nextNeurons.length + 1) * (j + 1)};
+  }
+
+  fill(255);
+  for (let i = 0; i < numNeurons; i++) {
+    // draw neurons
+    stroke(255);
+    strokeWeight(1);
+    let curX = x + w / 2;
+    let curY = y + h / (numNeurons + 1) * (i + 1);
+    ellipse(curX, curY, neuronSize, neuronSize);
+
+    //draw synapses
+    for (let j = 0; j < nextNeurons.length; j++) {
+      //compute color based on weight: red for negative, green for positive
+      let synWeight = weights.data[j][i];
+      let red = synWeight < 0 ? map(synWeight, min, 0, 0, 255) : 0;
+      let green = synWeight >= 0 ? map(synWeight, 0, max, 0, 255) : 0;
+      stroke(red, green, 0);
+
+      //bigger stroke weight for heavier weights
+      strokeWeight(map(Math.abs(synWeight), 0, Math.max(max, Math.abs(min)), 0, maxStroke));
+
+      line(curX + neuronSize / 2, curY, nextNeurons[j].xpos - neuronSize / 2, nextNeurons[j].ypos);
+    }
+  }
 
 }