diff --git a/TD2 Deep Learning.ipynb b/TD2 Deep Learning.ipynb
index 1cdde7c12ce25e35731c3bef44edbf6f7419278a..975918d059923d79b3df00ac9ea9f4d947640ea2 100644
--- a/TD2 Deep Learning.ipynb	
+++ b/TD2 Deep Learning.ipynb	
@@ -1122,9 +1122,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test Loss: 22.242847\n",
+      "\n",
+      "Test Accuracy of airplane: 52% (523/1000)\n",
+      "Test Accuracy of automobile: 85% (853/1000)\n",
+      "Test Accuracy of  bird: 34% (342/1000)\n",
+      "Test Accuracy of   cat: 43% (430/1000)\n",
+      "Test Accuracy of  deer: 66% (660/1000)\n",
+      "Test Accuracy of   dog: 45% (452/1000)\n",
+      "Test Accuracy of  frog: 74% (749/1000)\n",
+      "Test Accuracy of horse: 64% (649/1000)\n",
+      "Test Accuracy of  ship: 83% (835/1000)\n",
+      "Test Accuracy of truck: 64% (645/1000)\n",
+      "\n",
+      "Test Accuracy (Overall): 61% (6138/10000)\n"
+     ]
+    }
+   ],
    "source": [
     "# quantize model\n",
     "quantized_model = torch.quantization.quantize_dynamic(model, dtype=torch.qint8)\n",
@@ -1134,18 +1155,18 @@
     "quantized_class_correct = list(0.0 for i in range(10))\n",
     "quantized_class_total = list(0.0 for i in range(10))\n",
     "\n",
-    "model.eval()\n",
+    "quantized_model.eval()\n",
     "# iterate over test data\n",
     "for data, target in test_loader:\n",
     "    # move tensors to GPU if CUDA is available\n",
     "    if train_on_gpu:\n",
     "        data, target = data.cuda(), target.cuda()\n",
     "    # forward pass: compute predicted outputs by passing inputs to the model\n",
-    "    output = model(data)\n",
+    "    output = quantized_model(data)\n",
     "    # calculate the batch loss\n",
     "    loss = criterion(output, target)\n",
     "    # update test loss\n",
-    "    test_loss += loss.item() * data.size(0)\n",
+    "    quantized_test_loss += loss.item() * data.size(0)\n",
     "    # convert output probabilities to predicted class\n",
     "    _, pred = torch.max(output, 1)\n",
     "    # compare predictions to true label\n",
@@ -1158,22 +1179,22 @@
     "    # calculate test accuracy for each object class\n",
     "    for i in range(batch_size):\n",
     "        label = target.data[i]\n",
-    "        class_correct[label] += correct[i].item()\n",
-    "        class_total[label] += 1\n",
+    "        quantized_class_correct[label] += correct[i].item()\n",
+    "        quantized_class_total[label] += 1\n",
     "\n",
     "# average test loss\n",
-    "test_loss = test_loss / len(test_loader)\n",
-    "print(\"Test Loss: {:.6f}\\n\".format(test_loss))\n",
+    "quantized_test_loss = quantized_test_loss / len(test_loader)\n",
+    "print(\"Test Loss: {:.6f}\\n\".format(quantized_test_loss))\n",
     "\n",
     "for i in range(10):\n",
-    "    if class_total[i] > 0:\n",
+    "    if quantized_class_total[i] > 0:\n",
     "        print(\n",
     "            \"Test Accuracy of %5s: %2d%% (%2d/%2d)\"\n",
     "            % (\n",
     "                classes[i],\n",
-    "                100 * class_correct[i] / class_total[i],\n",
-    "                np.sum(class_correct[i]),\n",
-    "                np.sum(class_total[i]),\n",
+    "                100 * quantized_class_correct[i] / quantized_class_total[i],\n",
+    "                np.sum(quantized_class_correct[i]),\n",
+    "                np.sum(quantized_class_total[i]),\n",
     "            )\n",
     "        )\n",
     "    else:\n",
@@ -1182,13 +1203,20 @@
     "print(\n",
     "    \"\\nTest Accuracy (Overall): %2d%% (%2d/%2d)\"\n",
     "    % (\n",
-    "        100.0 * np.sum(class_correct) / np.sum(class_total),\n",
-    "        np.sum(class_correct),\n",
-    "        np.sum(class_total),\n",
+    "        100.0 * np.sum(quantized_class_correct) / np.sum(quantized_class_total),\n",
+    "        np.sum(quantized_class_correct),\n",
+    "        np.sum(quantized_class_total),\n",
     "    )\n",
     ")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The result is that the test accuracy is really similar for the initial model and for the quantized model."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "201470f9",