diff --git a/TD2 Deep Learning.ipynb b/TD2 Deep Learning.ipynb
index 7e1354e2778877ab705e05c0ee1d58686b67ddd6..2b03d85feebca90be2950f98019fe2517cfcc423 100644
--- a/TD2 Deep Learning.ipynb	
+++ b/TD2 Deep Learning.ipynb	
@@ -986,11 +986,8 @@
     }
    ],
    "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
     "plt.plot(range(n_epochs), train_loss_list2, label='train_loss')\n",
     "plt.plot(range(n_epochs), Valid_loss_list2, label='Valid_loss')\n",
-    "\n",
     "plt.xlabel(\"Epoch\")\n",
     "plt.ylabel(\"Loss\")\n",
     "plt.title(\"Performance du model\")\n",
@@ -1065,6 +1062,100 @@
     "print(\"Predicted Class:\", classes[predicted_class])\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test Loss: 1.983950\n",
+      "\n",
+      "Test Accuracy of     0: 58% (589/1000)\n",
+      "Test Accuracy of     1: 14% (145/1000)\n",
+      "Test Accuracy of     2: 15% (157/1000)\n",
+      "Test Accuracy of     3: 71% (713/1000)\n",
+      "\n",
+      "Test Accuracy (Overall): 40% (1604/4000)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import numpy as np\n",
+    "from torchvision import datasets, transforms\n",
+    "from torch.utils.data import DataLoader\n",
+    "\n",
+    "# Assuming you have the trained model saved as \"model_cifar.pt\"\n",
+    "# Assuming you have the test data loader defined as 'test_loader'\n",
+    "\n",
+    "# Load the saved model state\n",
+    "model2.load_state_dict(torch.load(\"model_cifar.pt\", map_location=torch.device('cpu')))\n",
+    "\n",
+    "# Evaluate the model on test data\n",
+    "model2.eval()\n",
+    "test_loss = 0.0\n",
+    "num_classes = 4  # Replace with the actual number of classes in your problem\n",
+    "class_correct = [0.0] * num_classes\n",
+    "class_total = [0.0] * num_classes\n",
+    "\n",
+    "with torch.no_grad():\n",
+    "    for data, target in test_loader:\n",
+    "        # Forward pass: compute predicted outputs by passing inputs to the model\n",
+    "        output = model2(data)\n",
+    "        # Calculate the batch loss\n",
+    "        loss = criterion(output, target)\n",
+    "        # Update test loss\n",
+    "        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",
+    "        correct_tensor = pred.eq(target.data.view_as(pred))\n",
+    "        correct = np.squeeze(correct_tensor.numpy())\n",
+    "        # Calculate test accuracy for each object class\n",
+    "        for i in range(len(target)):\n",
+    "            label = target.data[i].item()\n",
+    "            if label < num_classes:  # Check if label is within the expected range\n",
+    "                class_correct[label] += correct[i].item()\n",
+    "                class_total[label] += 1\n",
+    "\n",
+    "# Average test loss\n",
+    "test_loss = test_loss / len(test_loader.dataset)\n",
+    "print(\"Test Loss: {:.6f}\\n\".format(test_loss))\n",
+    "\n",
+    "for i in range(num_classes):\n",
+    "    if class_total[i] > 0:\n",
+    "        print(\n",
+    "            \"Test Accuracy of %5s: %2d%% (%2d/%2d)\"\n",
+    "            % (\n",
+    "                str(i),\n",
+    "                100 * class_correct[i] / class_total[i],\n",
+    "                np.sum(class_correct[i]),\n",
+    "                np.sum(class_total[i]),\n",
+    "            )\n",
+    "        )\n",
+    "    else:\n",
+    "        print(\"Test Accuracy of %5s: N/A (no training examples)\" % (str(i)))\n",
+    "\n",
+    "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",
+    "    )\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "    -Our new model has achieved a test accuracy of 0.4, which is significantly better than the previous neural network implemented during TD1 that had a test accuracy of 0.15"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "bc381cf4",