diff --git a/TD2 Deep Learning.ipynb b/TD2 Deep Learning.ipynb
index 2ecfce959ae6b947b633a758433f9bea0bf6992e..e8132c452d8436e5a93ed140dc2a465b51ce525a 100644
--- a/TD2 Deep Learning.ipynb	
+++ b/TD2 Deep Learning.ipynb	
@@ -52,10 +52,72 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "b1950f0a",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([[ 1.7860,  0.1511, -0.0160,  0.4659, -0.9928, -0.6551, -0.0142,  1.2847,\n",
+      "          0.0673,  1.1325],\n",
+      "        [-0.2958, -2.4973,  0.7100,  0.4955, -1.8280,  2.1579, -0.4499,  0.6246,\n",
+      "         -1.4334,  0.7305],\n",
+      "        [-0.6635,  0.1002, -0.1755,  1.3671,  0.2616, -0.8496,  0.6415,  0.2430,\n",
+      "         -0.5490,  0.8513],\n",
+      "        [-0.3832, -0.6521,  1.2940, -0.7137,  0.4357,  0.5994,  0.2499, -1.2812,\n",
+      "          0.2253,  1.8386],\n",
+      "        [-0.7966,  0.9190,  0.2196,  0.4423,  0.6533,  0.5645, -0.9018,  0.9803,\n",
+      "          0.2561,  0.5279],\n",
+      "        [ 1.1639, -0.2399,  1.1579,  0.2882, -0.5775,  0.6815,  0.2736,  0.1125,\n",
+      "         -1.5478, -0.9051],\n",
+      "        [-0.5648,  0.1552, -0.6306, -0.5122,  1.4985, -0.0888,  0.0994,  0.0888,\n",
+      "          0.7783, -1.1782],\n",
+      "        [ 1.2432, -0.3800,  0.8931, -0.3497, -0.9167, -0.1779, -0.2857, -1.7936,\n",
+      "         -1.8666,  0.1783],\n",
+      "        [-0.7501,  0.3932, -0.0939, -0.3525, -1.4223, -2.3146, -0.1629,  1.8466,\n",
+      "         -1.9090,  1.7976],\n",
+      "        [-1.9084,  0.4522,  0.5104, -1.2864,  0.3619, -0.6944,  0.6342, -0.3427,\n",
+      "         -0.3168,  0.8834],\n",
+      "        [-0.0389,  1.1994,  0.9339, -0.2222,  0.4083,  1.1682, -1.1012,  0.5992,\n",
+      "          1.0558,  0.3853],\n",
+      "        [-0.3180, -0.4990,  0.4292, -2.5403, -0.7611,  0.3749, -0.5852,  0.8430,\n",
+      "          1.9515, -0.1408],\n",
+      "        [-0.2390,  0.3945, -1.7454,  1.4304, -0.6438, -0.2283, -0.3667, -2.0133,\n",
+      "          0.8139,  2.2149],\n",
+      "        [-0.2035, -0.2944,  2.0516, -1.3117,  0.6144, -1.8206, -0.4987,  0.0965,\n",
+      "          1.5422,  0.4690]])\n",
+      "AlexNet(\n",
+      "  (features): Sequential(\n",
+      "    (0): Conv2d(3, 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2))\n",
+      "    (1): ReLU(inplace=True)\n",
+      "    (2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
+      "    (3): Conv2d(64, 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
+      "    (4): ReLU(inplace=True)\n",
+      "    (5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
+      "    (6): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+      "    (7): ReLU(inplace=True)\n",
+      "    (8): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+      "    (9): ReLU(inplace=True)\n",
+      "    (10): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+      "    (11): ReLU(inplace=True)\n",
+      "    (12): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
+      "  )\n",
+      "  (avgpool): AdaptiveAvgPool2d(output_size=(6, 6))\n",
+      "  (classifier): Sequential(\n",
+      "    (0): Dropout(p=0.5, inplace=False)\n",
+      "    (1): Linear(in_features=9216, out_features=4096, bias=True)\n",
+      "    (2): ReLU(inplace=True)\n",
+      "    (3): Dropout(p=0.5, inplace=False)\n",
+      "    (4): Linear(in_features=4096, out_features=4096, bias=True)\n",
+      "    (5): ReLU(inplace=True)\n",
+      "    (6): Linear(in_features=4096, out_features=1000, bias=True)\n",
+      "  )\n",
+      ")\n"
+     ]
+    }
+   ],
    "source": [
     "import torch\n",
     "\n",
@@ -95,10 +157,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "6e18f2fd",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CUDA is not available.  Training on CPU ...\n"
+     ]
+    }
+   ],
    "source": [
     "import torch\n",
     "\n",
@@ -121,10 +191,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "462666a2",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Files already downloaded and verified\n",
+      "Files already downloaded and verified\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "from torchvision import datasets, transforms\n",
@@ -193,10 +272,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "317bf070",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Net(\n",
+      "  (conv1): Conv2d(3, 6, kernel_size=(5, 5), stride=(1, 1))\n",
+      "  (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
+      "  (conv2): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1))\n",
+      "  (fc1): Linear(in_features=400, out_features=120, bias=True)\n",
+      "  (fc2): Linear(in_features=120, out_features=84, bias=True)\n",
+      "  (fc3): Linear(in_features=84, out_features=10, bias=True)\n",
+      ")\n"
+     ]
+    }
+   ],
    "source": [
     "import torch.nn as nn\n",
     "import torch.nn.functional as F\n",
@@ -242,10 +336,67 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "4b53f229",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 0 \tTraining Loss: 45.278666 \tValidation Loss: 40.425202\n",
+      "Validation loss decreased (inf --> 40.425202).  Saving model ...\n",
+      "Epoch: 1 \tTraining Loss: 36.221459 \tValidation Loss: 31.571368\n",
+      "Validation loss decreased (40.425202 --> 31.571368).  Saving model ...\n",
+      "Epoch: 2 \tTraining Loss: 30.556710 \tValidation Loss: 28.885755\n",
+      "Validation loss decreased (31.571368 --> 28.885755).  Saving model ...\n",
+      "Epoch: 3 \tTraining Loss: 28.088498 \tValidation Loss: 27.739416\n",
+      "Validation loss decreased (28.885755 --> 27.739416).  Saving model ...\n",
+      "Epoch: 4 \tTraining Loss: 26.370869 \tValidation Loss: 25.475866\n",
+      "Validation loss decreased (27.739416 --> 25.475866).  Saving model ...\n",
+      "Epoch: 5 \tTraining Loss: 24.969249 \tValidation Loss: 25.032677\n",
+      "Validation loss decreased (25.475866 --> 25.032677).  Saving model ...\n",
+      "Epoch: 6 \tTraining Loss: 23.715936 \tValidation Loss: 24.431068\n",
+      "Validation loss decreased (25.032677 --> 24.431068).  Saving model ...\n",
+      "Epoch: 7 \tTraining Loss: 22.620956 \tValidation Loss: 24.228925\n",
+      "Validation loss decreased (24.431068 --> 24.228925).  Saving model ...\n",
+      "Epoch: 8 \tTraining Loss: 21.642143 \tValidation Loss: 22.765917\n",
+      "Validation loss decreased (24.228925 --> 22.765917).  Saving model ...\n",
+      "Epoch: 9 \tTraining Loss: 20.810963 \tValidation Loss: 22.601572\n",
+      "Validation loss decreased (22.765917 --> 22.601572).  Saving model ...\n",
+      "Epoch: 10 \tTraining Loss: 19.928289 \tValidation Loss: 22.562866\n",
+      "Validation loss decreased (22.601572 --> 22.562866).  Saving model ...\n",
+      "Epoch: 11 \tTraining Loss: 19.189873 \tValidation Loss: 21.497247\n",
+      "Validation loss decreased (22.562866 --> 21.497247).  Saving model ...\n",
+      "Epoch: 12 \tTraining Loss: 18.527387 \tValidation Loss: 22.447693\n",
+      "Epoch: 13 \tTraining Loss: 17.828642 \tValidation Loss: 20.812044\n",
+      "Validation loss decreased (21.497247 --> 20.812044).  Saving model ...\n",
+      "Epoch: 14 \tTraining Loss: 17.212328 \tValidation Loss: 20.792389\n",
+      "Validation loss decreased (20.812044 --> 20.792389).  Saving model ...\n",
+      "Epoch: 15 \tTraining Loss: 16.621666 \tValidation Loss: 20.793879\n",
+      "Epoch: 16 \tTraining Loss: 16.033121 \tValidation Loss: 21.904442\n"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[1;32m/home/coco/ECOLE/Apprentissage_profond/TP_2/mod_4_6-td2/TD2 Deep Learning.ipynb Cellule 15\u001b[0m line \u001b[0;36m1\n\u001b[1;32m     <a href='vscode-notebook-cell://wsl%2Bubuntu/home/coco/ECOLE/Apprentissage_profond/TP_2/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#X20sdnNjb2RlLXJlbW90ZQ%3D%3D?line=14'>15</a>\u001b[0m \u001b[39m# Train the model\u001b[39;00m\n\u001b[1;32m     <a href='vscode-notebook-cell://wsl%2Bubuntu/home/coco/ECOLE/Apprentissage_profond/TP_2/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#X20sdnNjb2RlLXJlbW90ZQ%3D%3D?line=15'>16</a>\u001b[0m model\u001b[39m.\u001b[39mtrain()\n\u001b[0;32m---> <a href='vscode-notebook-cell://wsl%2Bubuntu/home/coco/ECOLE/Apprentissage_profond/TP_2/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#X20sdnNjb2RlLXJlbW90ZQ%3D%3D?line=16'>17</a>\u001b[0m \u001b[39mfor\u001b[39;00m data, target \u001b[39min\u001b[39;00m train_loader:\n\u001b[1;32m     <a href='vscode-notebook-cell://wsl%2Bubuntu/home/coco/ECOLE/Apprentissage_profond/TP_2/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#X20sdnNjb2RlLXJlbW90ZQ%3D%3D?line=17'>18</a>\u001b[0m     \u001b[39m# Move tensors to GPU if CUDA is available\u001b[39;00m\n\u001b[1;32m     <a href='vscode-notebook-cell://wsl%2Bubuntu/home/coco/ECOLE/Apprentissage_profond/TP_2/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#X20sdnNjb2RlLXJlbW90ZQ%3D%3D?line=18'>19</a>\u001b[0m     \u001b[39mif\u001b[39;00m train_on_gpu:\n\u001b[1;32m     <a href='vscode-notebook-cell://wsl%2Bubuntu/home/coco/ECOLE/Apprentissage_profond/TP_2/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#X20sdnNjb2RlLXJlbW90ZQ%3D%3D?line=19'>20</a>\u001b[0m         data, target \u001b[39m=\u001b[39m data\u001b[39m.\u001b[39mcuda(), target\u001b[39m.\u001b[39mcuda()\n",
+      "File \u001b[0;32m~/project/env/lib/python3.10/site-packages/torch/utils/data/dataloader.py:630\u001b[0m, in \u001b[0;36m_BaseDataLoaderIter.__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    627\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_sampler_iter \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m    628\u001b[0m     \u001b[39m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[39;00m\n\u001b[1;32m    629\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_reset()  \u001b[39m# type: ignore[call-arg]\u001b[39;00m\n\u001b[0;32m--> 630\u001b[0m data \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_next_data()\n\u001b[1;32m    631\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_num_yielded \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m \u001b[39m1\u001b[39m\n\u001b[1;32m    632\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_dataset_kind \u001b[39m==\u001b[39m _DatasetKind\u001b[39m.\u001b[39mIterable \u001b[39mand\u001b[39;00m \\\n\u001b[1;32m    633\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_IterableDataset_len_called \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m \\\n\u001b[1;32m    634\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_num_yielded \u001b[39m>\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_IterableDataset_len_called:\n",
+      "File \u001b[0;32m~/project/env/lib/python3.10/site-packages/torch/utils/data/dataloader.py:674\u001b[0m, in \u001b[0;36m_SingleProcessDataLoaderIter._next_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    672\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_next_data\u001b[39m(\u001b[39mself\u001b[39m):\n\u001b[1;32m    673\u001b[0m     index \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_next_index()  \u001b[39m# may raise StopIteration\u001b[39;00m\n\u001b[0;32m--> 674\u001b[0m     data \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_dataset_fetcher\u001b[39m.\u001b[39;49mfetch(index)  \u001b[39m# may raise StopIteration\u001b[39;00m\n\u001b[1;32m    675\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_pin_memory:\n\u001b[1;32m    676\u001b[0m         data \u001b[39m=\u001b[39m _utils\u001b[39m.\u001b[39mpin_memory\u001b[39m.\u001b[39mpin_memory(data, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_pin_memory_device)\n",
+      "File \u001b[0;32m~/project/env/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py:51\u001b[0m, in \u001b[0;36m_MapDatasetFetcher.fetch\u001b[0;34m(self, possibly_batched_index)\u001b[0m\n\u001b[1;32m     49\u001b[0m         data \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdataset\u001b[39m.\u001b[39m__getitems__(possibly_batched_index)\n\u001b[1;32m     50\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[0;32m---> 51\u001b[0m         data \u001b[39m=\u001b[39m [\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdataset[idx] \u001b[39mfor\u001b[39;00m idx \u001b[39min\u001b[39;00m possibly_batched_index]\n\u001b[1;32m     52\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m     53\u001b[0m     data \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdataset[possibly_batched_index]\n",
+      "File \u001b[0;32m~/project/env/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py:51\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     49\u001b[0m         data \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdataset\u001b[39m.\u001b[39m__getitems__(possibly_batched_index)\n\u001b[1;32m     50\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[0;32m---> 51\u001b[0m         data \u001b[39m=\u001b[39m [\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdataset[idx] \u001b[39mfor\u001b[39;00m idx \u001b[39min\u001b[39;00m possibly_batched_index]\n\u001b[1;32m     52\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m     53\u001b[0m     data \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdataset[possibly_batched_index]\n",
+      "File \u001b[0;32m~/project/env/lib/python3.10/site-packages/torchvision/datasets/cifar.py:118\u001b[0m, in \u001b[0;36mCIFAR10.__getitem__\u001b[0;34m(self, index)\u001b[0m\n\u001b[1;32m    115\u001b[0m img \u001b[39m=\u001b[39m Image\u001b[39m.\u001b[39mfromarray(img)\n\u001b[1;32m    117\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtransform \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m--> 118\u001b[0m     img \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtransform(img)\n\u001b[1;32m    120\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtarget_transform \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m    121\u001b[0m     target \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtarget_transform(target)\n",
+      "File \u001b[0;32m~/project/env/lib/python3.10/site-packages/torchvision/transforms/transforms.py:95\u001b[0m, in \u001b[0;36mCompose.__call__\u001b[0;34m(self, img)\u001b[0m\n\u001b[1;32m     93\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__call__\u001b[39m(\u001b[39mself\u001b[39m, img):\n\u001b[1;32m     94\u001b[0m     \u001b[39mfor\u001b[39;00m t \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtransforms:\n\u001b[0;32m---> 95\u001b[0m         img \u001b[39m=\u001b[39m t(img)\n\u001b[1;32m     96\u001b[0m     \u001b[39mreturn\u001b[39;00m img\n",
+      "File \u001b[0;32m~/project/env/lib/python3.10/site-packages/torchvision/transforms/transforms.py:137\u001b[0m, in \u001b[0;36mToTensor.__call__\u001b[0;34m(self, pic)\u001b[0m\n\u001b[1;32m    129\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__call__\u001b[39m(\u001b[39mself\u001b[39m, pic):\n\u001b[1;32m    130\u001b[0m \u001b[39m    \u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m    131\u001b[0m \u001b[39m    Args:\u001b[39;00m\n\u001b[1;32m    132\u001b[0m \u001b[39m        pic (PIL Image or numpy.ndarray): Image to be converted to tensor.\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    135\u001b[0m \u001b[39m        Tensor: Converted image.\u001b[39;00m\n\u001b[1;32m    136\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 137\u001b[0m     \u001b[39mreturn\u001b[39;00m F\u001b[39m.\u001b[39;49mto_tensor(pic)\n",
+      "File \u001b[0;32m~/project/env/lib/python3.10/site-packages/torchvision/transforms/functional.py:172\u001b[0m, in \u001b[0;36mto_tensor\u001b[0;34m(pic)\u001b[0m\n\u001b[1;32m    170\u001b[0m img \u001b[39m=\u001b[39m img\u001b[39m.\u001b[39mview(pic\u001b[39m.\u001b[39msize[\u001b[39m1\u001b[39m], pic\u001b[39m.\u001b[39msize[\u001b[39m0\u001b[39m], F_pil\u001b[39m.\u001b[39mget_image_num_channels(pic))\n\u001b[1;32m    171\u001b[0m \u001b[39m# put it from HWC to CHW format\u001b[39;00m\n\u001b[0;32m--> 172\u001b[0m img \u001b[39m=\u001b[39m img\u001b[39m.\u001b[39;49mpermute((\u001b[39m2\u001b[39;49m, \u001b[39m0\u001b[39;49m, \u001b[39m1\u001b[39;49m))\u001b[39m.\u001b[39;49mcontiguous()\n\u001b[1;32m    173\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(img, torch\u001b[39m.\u001b[39mByteTensor):\n\u001b[1;32m    174\u001b[0m     \u001b[39mreturn\u001b[39;00m img\u001b[39m.\u001b[39mto(dtype\u001b[39m=\u001b[39mdefault_float_dtype)\u001b[39m.\u001b[39mdiv(\u001b[39m255\u001b[39m)\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
    "source": [
     "import torch.optim as optim\n",
     "\n",
@@ -253,8 +404,8 @@
     "optimizer = optim.SGD(model.parameters(), lr=0.01)  # specify optimizer\n",
     "\n",
     "n_epochs = 30  # number of epochs to train the model\n",
-    "train_loss_list = []  # list to store loss to visualize\n",
-    "valid_loss_min = np.Inf  # track change in validation loss\n",
+    "train_loss_list_1 = []  # list to store loss to visualize\n",
+    "valid_loss_min_1 = np.Inf  # track change in validation loss\n",
     "\n",
     "for epoch in range(n_epochs):\n",
     "    # Keep track of training and validation loss\n",
@@ -296,7 +447,7 @@
     "    # Calculate average losses\n",
     "    train_loss = train_loss / len(train_loader)\n",
     "    valid_loss = valid_loss / len(valid_loader)\n",
-    "    train_loss_list.append(train_loss)\n",
+    "    train_loss_list_1.append(train_loss)\n",
     "\n",
     "    # Print training/validation statistics\n",
     "    print(\n",
@@ -306,34 +457,52 @@
     "    )\n",
     "\n",
     "    # Save model if validation loss has decreased\n",
-    "    if valid_loss <= valid_loss_min:\n",
+    "    if valid_loss <= valid_loss_min_1:\n",
     "        print(\n",
     "            \"Validation loss decreased ({:.6f} --> {:.6f}).  Saving model ...\".format(\n",
-    "                valid_loss_min, valid_loss\n",
+    "                valid_loss_min_1, valid_loss\n",
     "            )\n",
     "        )\n",
     "        torch.save(model.state_dict(), \"model_cifar.pt\")\n",
-    "        valid_loss_min = valid_loss"
+    "        valid_loss_min_1 = valid_loss"
    ]
   },
   {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
    "cell_type": "markdown",
    "id": "13e1df74",
    "metadata": {},
    "source": [
-    "Does overfit occur? If so, do an early stopping."
+    "Does overfit occur? If so, do an early stopping. \n",
+    "Yes overfiting is occuring at epochs 16, the training loss is decreasing while the testing loss is increasing, we have to stop the training now\n",
+    "![image.png](attachment:image.png)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "d39df818",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import matplotlib.pyplot as plt\n",
-    "\n",
-    "plt.plot(range(n_epochs), train_loss_list)\n",
+    "n_epochs_temp = 17 #See the error code to see where to stop\n",
+    "plt.plot(range(n_epochs_temp), train_loss_list)\n",
     "plt.xlabel(\"Epoch\")\n",
     "plt.ylabel(\"Loss\")\n",
     "plt.title(\"Performance of Model 1\")\n",
@@ -350,10 +519,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "e93efdfc",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'model' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m/home/coco/ECOLE/Apprentissage_profond/TP_2/mod_4_6-td2/TD2 Deep Learning.ipynb Cellule 19\u001b[0m line \u001b[0;36m1\n\u001b[0;32m----> <a href='vscode-notebook-cell://wsl%2Bubuntu/home/coco/ECOLE/Apprentissage_profond/TP_2/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#X24sdnNjb2RlLXJlbW90ZQ%3D%3D?line=0'>1</a>\u001b[0m model\u001b[39m.\u001b[39mload_state_dict(torch\u001b[39m.\u001b[39mload(\u001b[39m\"\u001b[39m\u001b[39m./model_cifar.pt\u001b[39m\u001b[39m\"\u001b[39m))\n\u001b[1;32m      <a href='vscode-notebook-cell://wsl%2Bubuntu/home/coco/ECOLE/Apprentissage_profond/TP_2/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#X24sdnNjb2RlLXJlbW90ZQ%3D%3D?line=2'>3</a>\u001b[0m \u001b[39m# track test loss\u001b[39;00m\n\u001b[1;32m      <a href='vscode-notebook-cell://wsl%2Bubuntu/home/coco/ECOLE/Apprentissage_profond/TP_2/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#X24sdnNjb2RlLXJlbW90ZQ%3D%3D?line=3'>4</a>\u001b[0m test_loss \u001b[39m=\u001b[39m \u001b[39m0.0\u001b[39m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'model' is not defined"
+     ]
+    }
+   ],
    "source": [
     "model.load_state_dict(torch.load(\"./model_cifar.pt\"))\n",
     "\n",
@@ -434,6 +615,389 @@
     "Compare the results obtained with this new network to those obtained previously."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Define new model"
+   ]
+  },
+  {
+   "attachments": {
+    "image.png": {
+     "image/png": 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"
+    }
+   },
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![image.png](attachment:image.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Net_2(\n",
+      "  (conv1): Conv2d(3, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+      "  (pool1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
+      "  (conv2): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+      "  (pool2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
+      "  (conv3): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+      "  (pool3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
+      "  (fc1): Linear(in_features=1024, out_features=512, bias=True)\n",
+      "  (dropout1): Dropout(p=0.5, inplace=False)\n",
+      "  (fc2): Linear(in_features=512, out_features=64, bias=True)\n",
+      "  (dropout2): Dropout(p=0.6, inplace=False)\n",
+      "  (fc3): Linear(in_features=64, out_features=10, bias=True)\n",
+      ")\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch.nn as nn\n",
+    "import torch.nn.functional as F\n",
+    "\n",
+    "# define the CNN architecture\n",
+    "\n",
+    "\n",
+    "class Net_2(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super(Net_2, self).__init__()\n",
+    "        #Conv Layers\n",
+    "        #First Conv Layer\n",
+    "        self.conv1 = nn.Conv2d(in_channels=3, out_channels = 16, kernel_size=3, padding=1)\n",
+    "        self.pool1 = nn.MaxPool2d(2, 2)\n",
+    "\n",
+    "        #Second Conv Layer\n",
+    "        self.conv2 = nn.Conv2d(in_channels=16, out_channels = 32, kernel_size=3, padding=1)\n",
+    "        self.pool2 = nn.MaxPool2d(2, 2)\n",
+    "\n",
+    "        #Thrid Conv Layer\n",
+    "        self.conv3 = nn.Conv2d(in_channels=32, out_channels = 64, kernel_size=3, padding=1)\n",
+    "        self.pool3 = nn.MaxPool2d(2, 2)\n",
+    "\n",
+    "\n",
+    "        #Fully connected Layers\n",
+    "        self.fc1 = nn.Linear(in_features=64 * 4 * 4, out_features=512)#SIZE OF fc1: 64 out chanell from conv * image of size 4 * 4 with a depth of 4\n",
+    "        self.dropout1 = nn.Dropout(p = 0.5)\n",
+    "        self.fc2 = nn.Linear(in_features=512, out_features=64)\n",
+    "        self.dropout2 = nn.Dropout(p = 0.6)\n",
+    "        self.fc3 = nn.Linear(64, 10)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "\n",
+    "        x = self.pool1(F.relu(self.conv1(x)))\n",
+    "        x = self.pool2(F.relu(self.conv2(x)))\n",
+    "        x = self.pool3(F.relu(self.conv3(x)))\n",
+    "\n",
+    "        x = x.view(-1, 64 * 4 * 4) #view sert à linéariser la couche ConvNN\n",
+    "        x = self.dropout1(F.relu(self.fc1(x)))\n",
+    "        x = self.dropout2(F.relu(self.fc2(x)))\n",
+    "        x = self.fc3(x)\n",
+    "        return x\n",
+    "\n",
+    "\n",
+    "# create a complete CNN\n",
+    "model_2 = Net_2()\n",
+    "print(model_2)\n",
+    "# move tensors to GPU if CUDA is available\n",
+    "if train_on_gpu:\n",
+    "    model.cuda()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train new model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 0 \tTraining Loss: 46.026266 \tValidation Loss: 45.895234\n",
+      "Validation loss decreased (inf --> 45.895234).  Saving model_2 ...\n",
+      "Epoch: 1 \tTraining Loss: 43.060367 \tValidation Loss: 37.949757\n",
+      "Validation loss decreased (45.895234 --> 37.949757).  Saving model_2 ...\n",
+      "Epoch: 2 \tTraining Loss: 37.042101 \tValidation Loss: 33.615050\n",
+      "Validation loss decreased (37.949757 --> 33.615050).  Saving model_2 ...\n",
+      "Epoch: 3 \tTraining Loss: 34.570587 \tValidation Loss: 31.790119\n",
+      "Validation loss decreased (33.615050 --> 31.790119).  Saving model_2 ...\n",
+      "Epoch: 4 \tTraining Loss: 32.650313 \tValidation Loss: 29.254564\n",
+      "Validation loss decreased (31.790119 --> 29.254564).  Saving model_2 ...\n",
+      "Epoch: 5 \tTraining Loss: 30.959257 \tValidation Loss: 27.877414\n",
+      "Validation loss decreased (29.254564 --> 27.877414).  Saving model_2 ...\n",
+      "Epoch: 6 \tTraining Loss: 29.261276 \tValidation Loss: 25.617370\n",
+      "Validation loss decreased (27.877414 --> 25.617370).  Saving model_2 ...\n",
+      "Epoch: 7 \tTraining Loss: 27.795534 \tValidation Loss: 25.024809\n",
+      "Validation loss decreased (25.617370 --> 25.024809).  Saving model_2 ...\n",
+      "Epoch: 8 \tTraining Loss: 26.489316 \tValidation Loss: 23.377748\n",
+      "Validation loss decreased (25.024809 --> 23.377748).  Saving model_2 ...\n",
+      "Epoch: 9 \tTraining Loss: 25.303696 \tValidation Loss: 22.433639\n",
+      "Validation loss decreased (23.377748 --> 22.433639).  Saving model_2 ...\n",
+      "Epoch: 10 \tTraining Loss: 24.089768 \tValidation Loss: 21.592680\n",
+      "Validation loss decreased (22.433639 --> 21.592680).  Saving model_2 ...\n",
+      "Epoch: 11 \tTraining Loss: 22.999779 \tValidation Loss: 20.895775\n",
+      "Validation loss decreased (21.592680 --> 20.895775).  Saving model_2 ...\n",
+      "Epoch: 12 \tTraining Loss: 22.025487 \tValidation Loss: 19.601096\n",
+      "Validation loss decreased (20.895775 --> 19.601096).  Saving model_2 ...\n",
+      "Epoch: 13 \tTraining Loss: 21.188304 \tValidation Loss: 19.585769\n",
+      "Validation loss decreased (19.601096 --> 19.585769).  Saving model_2 ...\n",
+      "Epoch: 14 \tTraining Loss: 20.382290 \tValidation Loss: 18.996972\n",
+      "Validation loss decreased (19.585769 --> 18.996972).  Saving model_2 ...\n",
+      "Epoch: 15 \tTraining Loss: 19.349220 \tValidation Loss: 18.281743\n",
+      "Validation loss decreased (18.996972 --> 18.281743).  Saving model_2 ...\n",
+      "Epoch: 16 \tTraining Loss: 18.770347 \tValidation Loss: 17.717760\n",
+      "Validation loss decreased (18.281743 --> 17.717760).  Saving model_2 ...\n",
+      "Epoch: 17 \tTraining Loss: 17.901174 \tValidation Loss: 17.273265\n",
+      "Validation loss decreased (17.717760 --> 17.273265).  Saving model_2 ...\n",
+      "Epoch: 18 \tTraining Loss: 17.331406 \tValidation Loss: 17.074595\n",
+      "Validation loss decreased (17.273265 --> 17.074595).  Saving model_2 ...\n",
+      "Epoch: 19 \tTraining Loss: 16.710150 \tValidation Loss: 16.709318\n",
+      "Validation loss decreased (17.074595 --> 16.709318).  Saving model_2 ...\n",
+      "Epoch: 20 \tTraining Loss: 15.932038 \tValidation Loss: 16.960701\n",
+      "Epoch: 21 \tTraining Loss: 15.583130 \tValidation Loss: 16.340795\n",
+      "Validation loss decreased (16.709318 --> 16.340795).  Saving model_2 ...\n",
+      "Epoch: 22 \tTraining Loss: 14.770117 \tValidation Loss: 16.984795\n"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[1;32m/home/coco/ECOLE/Apprentissage_profond/TP_2/mod_4_6-td2/TD2 Deep Learning.ipynb Cellule 25\u001b[0m line \u001b[0;36m2\n\u001b[1;32m     <a href='vscode-notebook-cell://wsl%2Bubuntu/home/coco/ECOLE/Apprentissage_profond/TP_2/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#X61sdnNjb2RlLXJlbW90ZQ%3D%3D?line=21'>22</a>\u001b[0m optimizer\u001b[39m.\u001b[39mzero_grad()\n\u001b[1;32m     <a href='vscode-notebook-cell://wsl%2Bubuntu/home/coco/ECOLE/Apprentissage_profond/TP_2/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#X61sdnNjb2RlLXJlbW90ZQ%3D%3D?line=22'>23</a>\u001b[0m \u001b[39m# Forward pass: compute predicted outputs by passing inputs to the model_2\u001b[39;00m\n\u001b[0;32m---> <a href='vscode-notebook-cell://wsl%2Bubuntu/home/coco/ECOLE/Apprentissage_profond/TP_2/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#X61sdnNjb2RlLXJlbW90ZQ%3D%3D?line=23'>24</a>\u001b[0m output \u001b[39m=\u001b[39m model_2(data)\n\u001b[1;32m     <a href='vscode-notebook-cell://wsl%2Bubuntu/home/coco/ECOLE/Apprentissage_profond/TP_2/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#X61sdnNjb2RlLXJlbW90ZQ%3D%3D?line=24'>25</a>\u001b[0m \u001b[39m# Calculate the batch loss\u001b[39;00m\n\u001b[1;32m     <a href='vscode-notebook-cell://wsl%2Bubuntu/home/coco/ECOLE/Apprentissage_profond/TP_2/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#X61sdnNjb2RlLXJlbW90ZQ%3D%3D?line=25'>26</a>\u001b[0m loss \u001b[39m=\u001b[39m criterion(output, target)\n",
+      "File \u001b[0;32m~/project/env/lib/python3.10/site-packages/torch/nn/modules/module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1516\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_compiled_call_impl(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)  \u001b[39m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m-> 1518\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_call_impl(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
+      "File \u001b[0;32m~/project/env/lib/python3.10/site-packages/torch/nn/modules/module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1522\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1523\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1524\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_pre_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1525\u001b[0m         \u001b[39mor\u001b[39;00m _global_backward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1526\u001b[0m         \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1527\u001b[0m     \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m   1529\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m   1530\u001b[0m     result \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n",
+      "\u001b[1;32m/home/coco/ECOLE/Apprentissage_profond/TP_2/mod_4_6-td2/TD2 Deep Learning.ipynb Cellule 25\u001b[0m line \u001b[0;36m3\n\u001b[1;32m     <a href='vscode-notebook-cell://wsl%2Bubuntu/home/coco/ECOLE/Apprentissage_profond/TP_2/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#X61sdnNjb2RlLXJlbW90ZQ%3D%3D?line=32'>33</a>\u001b[0m x \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpool1(F\u001b[39m.\u001b[39mrelu(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mconv1(x)))\n\u001b[1;32m     <a href='vscode-notebook-cell://wsl%2Bubuntu/home/coco/ECOLE/Apprentissage_profond/TP_2/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#X61sdnNjb2RlLXJlbW90ZQ%3D%3D?line=33'>34</a>\u001b[0m x \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpool2(F\u001b[39m.\u001b[39mrelu(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mconv2(x)))\n\u001b[0;32m---> <a href='vscode-notebook-cell://wsl%2Bubuntu/home/coco/ECOLE/Apprentissage_profond/TP_2/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#X61sdnNjb2RlLXJlbW90ZQ%3D%3D?line=34'>35</a>\u001b[0m x \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpool3(F\u001b[39m.\u001b[39mrelu(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mconv3(x)))\n\u001b[1;32m     <a href='vscode-notebook-cell://wsl%2Bubuntu/home/coco/ECOLE/Apprentissage_profond/TP_2/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#X61sdnNjb2RlLXJlbW90ZQ%3D%3D?line=36'>37</a>\u001b[0m x \u001b[39m=\u001b[39m x\u001b[39m.\u001b[39mview(\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m, \u001b[39m64\u001b[39m \u001b[39m*\u001b[39m \u001b[39m4\u001b[39m \u001b[39m*\u001b[39m \u001b[39m4\u001b[39m) \u001b[39m#view sert à linéariser la couche ConvNN\u001b[39;00m\n\u001b[1;32m     <a href='vscode-notebook-cell://wsl%2Bubuntu/home/coco/ECOLE/Apprentissage_profond/TP_2/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#X61sdnNjb2RlLXJlbW90ZQ%3D%3D?line=37'>38</a>\u001b[0m x \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdropout1(F\u001b[39m.\u001b[39mrelu(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfc1(x)))\n",
+      "File \u001b[0;32m~/project/env/lib/python3.10/site-packages/torch/nn/modules/module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1516\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_compiled_call_impl(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)  \u001b[39m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m-> 1518\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_call_impl(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
+      "File \u001b[0;32m~/project/env/lib/python3.10/site-packages/torch/nn/modules/module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1522\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1523\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1524\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_pre_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1525\u001b[0m         \u001b[39mor\u001b[39;00m _global_backward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1526\u001b[0m         \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1527\u001b[0m     \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m   1529\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m   1530\u001b[0m     result \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n",
+      "File \u001b[0;32m~/project/env/lib/python3.10/site-packages/torch/nn/modules/conv.py:460\u001b[0m, in \u001b[0;36mConv2d.forward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    459\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mforward\u001b[39m(\u001b[39mself\u001b[39m, \u001b[39minput\u001b[39m: Tensor) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m Tensor:\n\u001b[0;32m--> 460\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_conv_forward(\u001b[39minput\u001b[39;49m, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mweight, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mbias)\n",
+      "File \u001b[0;32m~/project/env/lib/python3.10/site-packages/torch/nn/modules/conv.py:456\u001b[0m, in \u001b[0;36mConv2d._conv_forward\u001b[0;34m(self, input, weight, bias)\u001b[0m\n\u001b[1;32m    452\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpadding_mode \u001b[39m!=\u001b[39m \u001b[39m'\u001b[39m\u001b[39mzeros\u001b[39m\u001b[39m'\u001b[39m:\n\u001b[1;32m    453\u001b[0m     \u001b[39mreturn\u001b[39;00m F\u001b[39m.\u001b[39mconv2d(F\u001b[39m.\u001b[39mpad(\u001b[39minput\u001b[39m, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_reversed_padding_repeated_twice, mode\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpadding_mode),\n\u001b[1;32m    454\u001b[0m                     weight, bias, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mstride,\n\u001b[1;32m    455\u001b[0m                     _pair(\u001b[39m0\u001b[39m), \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdilation, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mgroups)\n\u001b[0;32m--> 456\u001b[0m \u001b[39mreturn\u001b[39;00m F\u001b[39m.\u001b[39;49mconv2d(\u001b[39minput\u001b[39;49m, weight, bias, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mstride,\n\u001b[1;32m    457\u001b[0m                 \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mpadding, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdilation, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mgroups)\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "import torch.optim as optim\n",
+    "\n",
+    "criterion = nn.CrossEntropyLoss()  # specify loss function\n",
+    "optimizer = optim.SGD(model_2.parameters(), lr=0.01)  # specify optimizer\n",
+    "\n",
+    "n_epochs = 30  # number of epochs to train the model_2\n",
+    "train_loss_list_2 = []  # list to store loss to visualize\n",
+    "valid_loss_min_2 = np.Inf  # track change in validation loss\n",
+    "\n",
+    "for epoch in range(n_epochs):\n",
+    "    # Keep track of training and validation loss\n",
+    "    train_loss = 0.0\n",
+    "    valid_loss = 0.0\n",
+    "\n",
+    "    # Train the model_2\n",
+    "    model_2.train()\n",
+    "    for data, target in train_loader:\n",
+    "        # Move tensors to GPU if CUDA is available\n",
+    "        if train_on_gpu:\n",
+    "            data, target = data.cuda(), target.cuda()\n",
+    "        # Clear the gradients of all optimized variables\n",
+    "        optimizer.zero_grad()\n",
+    "        # Forward pass: compute predicted outputs by passing inputs to the model_2\n",
+    "        output = model_2(data)\n",
+    "        # Calculate the batch loss\n",
+    "        loss = criterion(output, target)\n",
+    "        # Backward pass: compute gradient of the loss with respect to model_2 parameters\n",
+    "        loss.backward()\n",
+    "        # Perform a single optimization step (parameter update)\n",
+    "        optimizer.step()\n",
+    "        # Update training loss\n",
+    "        train_loss += loss.item() * data.size(0)\n",
+    "\n",
+    "    # Validate the model_2\n",
+    "    model_2.eval()\n",
+    "    for data, target in valid_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_2\n",
+    "        output = model_2(data)\n",
+    "        # Calculate the batch loss\n",
+    "        loss = criterion(output, target)\n",
+    "        # Update average validation loss\n",
+    "        valid_loss += loss.item() * data.size(0)\n",
+    "\n",
+    "    # Calculate average losses\n",
+    "    train_loss = train_loss / len(train_loader)\n",
+    "    valid_loss = valid_loss / len(valid_loader)\n",
+    "    train_loss_list_2.append(train_loss)\n",
+    "\n",
+    "    # Print training/validation statistics\n",
+    "    print(\n",
+    "        \"Epoch: {} \\tTraining Loss: {:.6f} \\tValidation Loss: {:.6f}\".format(\n",
+    "            epoch, train_loss, valid_loss\n",
+    "        )\n",
+    "    )\n",
+    "\n",
+    "    # Save model_2 if validation loss has decreased\n",
+    "    if valid_loss <= valid_loss_min_2:\n",
+    "        print(\n",
+    "            \"Validation loss decreased ({:.6f} --> {:.6f}).  Saving model_2 ...\".format(\n",
+    "                valid_loss_min_2, valid_loss\n",
+    "            )\n",
+    "        )\n",
+    "        torch.save(model_2.state_dict(), \"model_2_cifar.pt\")\n",
+    "        valid_loss_min_2 = valid_loss"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Loss of new model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "n_epochs_temp_2 = 23 #See the error code to see where to stop\n",
+    "plt.plot(range(n_epochs_temp_2), train_loss_list_2)\n",
+    "plt.xlabel(\"Epoch\")\n",
+    "plt.ylabel(\"Loss\")\n",
+    "plt.title(\"Performance of Model 2\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Accuracy of new model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test Loss: 16.690519\n",
+      "\n",
+      "Test Accuracy of airplane: 75% (757/1000)\n",
+      "Test Accuracy of automobile: 83% (839/1000)\n",
+      "Test Accuracy of  bird: 53% (539/1000)\n",
+      "Test Accuracy of   cat: 51% (514/1000)\n",
+      "Test Accuracy of  deer: 67% (672/1000)\n",
+      "Test Accuracy of   dog: 62% (628/1000)\n",
+      "Test Accuracy of  frog: 85% (858/1000)\n",
+      "Test Accuracy of horse: 78% (784/1000)\n",
+      "Test Accuracy of  ship: 75% (751/1000)\n",
+      "Test Accuracy of truck: 81% (818/1000)\n",
+      "\n",
+      "Test Accuracy (Overall): 71% (7160/10000)\n"
+     ]
+    }
+   ],
+   "source": [
+    "model_2.load_state_dict(torch.load(\"./model_2_cifar.pt\"))\n",
+    "\n",
+    "# track test loss\n",
+    "test_loss = 0.0\n",
+    "class_correct = list(0.0 for i in range(10))\n",
+    "class_total = list(0.0 for i in range(10))\n",
+    "\n",
+    "model_2.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_2\n",
+    "    output = model_2(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 = (\n",
+    "        np.squeeze(correct_tensor.numpy())\n",
+    "        if not train_on_gpu\n",
+    "        else np.squeeze(correct_tensor.cpu().numpy())\n",
+    "    )\n",
+    "    # 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",
+    "\n",
+    "# average test loss\n",
+    "test_loss = test_loss / len(test_loader)\n",
+    "print(\"Test Loss: {:.6f}\\n\".format(test_loss))\n",
+    "\n",
+    "for i in range(10):\n",
+    "    if 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",
+    "            )\n",
+    "        )\n",
+    "    else:\n",
+    "        print(\"Test Accuracy of %5s: N/A (no training examples)\" % (classes[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",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The accuracy of the second model is better !"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "bc381cf4",
@@ -451,10 +1015,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "id": "ef623c26",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model:  fp32  \t Size (KB): 251.278\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "251278"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "import os\n",
     "\n",
@@ -480,10 +1062,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "id": "c4c65d4b",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model:  int8  \t Size (KB): 76.522\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "76522"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "import torch.quantization\n",
     "\n",
@@ -500,6 +1100,203 @@
     "For each class, compare the classification test accuracy of the initial model and the quantized model. Also give the overall test accuracy for both models."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### First model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test Loss: 20.637905\n",
+      "\n",
+      "Test Accuracy of airplane: 66% (665/1000)\n",
+      "Test Accuracy of automobile: 77% (778/1000)\n",
+      "Test Accuracy of  bird: 44% (443/1000)\n",
+      "Test Accuracy of   cat: 43% (431/1000)\n",
+      "Test Accuracy of  deer: 59% (595/1000)\n",
+      "Test Accuracy of   dog: 55% (550/1000)\n",
+      "Test Accuracy of  frog: 79% (798/1000)\n",
+      "Test Accuracy of horse: 71% (714/1000)\n",
+      "Test Accuracy of  ship: 79% (796/1000)\n",
+      "Test Accuracy of truck: 65% (658/1000)\n",
+      "\n",
+      "Test Accuracy (Overall): 64% (6428/10000)\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.load_state_dict(torch.load(\"./model_cifar.pt\"))\n",
+    "\n",
+    "# track test loss\n",
+    "test_loss = 0.0\n",
+    "class_correct = list(0.0 for i in range(10))\n",
+    "class_total = list(0.0 for i in range(10))\n",
+    "\n",
+    "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",
+    "    # 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 = (\n",
+    "        np.squeeze(correct_tensor.numpy())\n",
+    "        if not train_on_gpu\n",
+    "        else np.squeeze(correct_tensor.cpu().numpy())\n",
+    "    )\n",
+    "    # 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",
+    "\n",
+    "# average test loss\n",
+    "test_loss = test_loss / len(test_loader)\n",
+    "print(\"Test Loss: {:.6f}\\n\".format(test_loss))\n",
+    "\n",
+    "for i in range(10):\n",
+    "    if 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",
+    "            )\n",
+    "        )\n",
+    "    else:\n",
+    "        print(\"Test Accuracy of %5s: N/A (no training examples)\" % (classes[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",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Quantize model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test Loss: 20.628915\n",
+      "\n",
+      "Test Accuracy of airplane: 67% (670/1000)\n",
+      "Test Accuracy of automobile: 77% (778/1000)\n",
+      "Test Accuracy of  bird: 43% (438/1000)\n",
+      "Test Accuracy of   cat: 42% (426/1000)\n",
+      "Test Accuracy of  deer: 59% (597/1000)\n",
+      "Test Accuracy of   dog: 55% (555/1000)\n",
+      "Test Accuracy of  frog: 80% (801/1000)\n",
+      "Test Accuracy of horse: 70% (709/1000)\n",
+      "Test Accuracy of  ship: 79% (794/1000)\n",
+      "Test Accuracy of truck: 66% (660/1000)\n",
+      "\n",
+      "Test Accuracy (Overall): 64% (6428/10000)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# track test loss\n",
+    "test_loss = 0.0\n",
+    "class_correct = list(0.0 for i in range(10))\n",
+    "class_total = list(0.0 for i in range(10))\n",
+    "\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 = 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",
+    "    # 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 = (\n",
+    "        np.squeeze(correct_tensor.numpy())\n",
+    "        if not train_on_gpu\n",
+    "        else np.squeeze(correct_tensor.cpu().numpy())\n",
+    "    )\n",
+    "    # 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",
+    "\n",
+    "# average test loss\n",
+    "test_loss = test_loss / len(test_loader)\n",
+    "print(\"Test Loss: {:.6f}\\n\".format(test_loss))\n",
+    "\n",
+    "for i in range(10):\n",
+    "    if 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",
+    "            )\n",
+    "        )\n",
+    "    else:\n",
+    "        print(\"Test Accuracy of %5s: N/A (no training examples)\" % (classes[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",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The quantized_model is as efficient as the first model !"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "a0a34b90",
@@ -521,13 +1318,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "id": "b4d13080",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'models' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32m/home/coco/ECOLE/Apprentissage_profond/TP_2/mod_4_6-td2/TD2 Deep Learning.ipynb Cellule 43\u001b[0m line \u001b[0;36m3\n\u001b[1;32m     <a href='vscode-notebook-cell://wsl%2Bubuntu/home/coco/ECOLE/Apprentissage_profond/TP_2/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#X52sdnNjb2RlLXJlbW90ZQ%3D%3D?line=32'>33</a>\u001b[0m image \u001b[39m=\u001b[39m data_transform(image)\u001b[39m.\u001b[39munsqueeze(\u001b[39m0\u001b[39m)\n\u001b[1;32m     <a href='vscode-notebook-cell://wsl%2Bubuntu/home/coco/ECOLE/Apprentissage_profond/TP_2/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#X52sdnNjb2RlLXJlbW90ZQ%3D%3D?line=34'>35</a>\u001b[0m \u001b[39m# Download the model if it's not there already. It will take a bit on the first run, after that it's fast\u001b[39;00m\n\u001b[0;32m---> <a href='vscode-notebook-cell://wsl%2Bubuntu/home/coco/ECOLE/Apprentissage_profond/TP_2/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#X52sdnNjb2RlLXJlbW90ZQ%3D%3D?line=35'>36</a>\u001b[0m model \u001b[39m=\u001b[39m models\u001b[39m.\u001b[39mresnet50(pretrained\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\n\u001b[1;32m     <a href='vscode-notebook-cell://wsl%2Bubuntu/home/coco/ECOLE/Apprentissage_profond/TP_2/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#X52sdnNjb2RlLXJlbW90ZQ%3D%3D?line=36'>37</a>\u001b[0m \u001b[39m# Send the model to the GPU\u001b[39;00m\n\u001b[1;32m     <a href='vscode-notebook-cell://wsl%2Bubuntu/home/coco/ECOLE/Apprentissage_profond/TP_2/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#X52sdnNjb2RlLXJlbW90ZQ%3D%3D?line=37'>38</a>\u001b[0m \u001b[39m# model.cuda()\u001b[39;00m\n\u001b[1;32m     <a href='vscode-notebook-cell://wsl%2Bubuntu/home/coco/ECOLE/Apprentissage_profond/TP_2/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#X52sdnNjb2RlLXJlbW90ZQ%3D%3D?line=38'>39</a>\u001b[0m \u001b[39m# Set layers such as dropout and batchnorm in evaluation mode\u001b[39;00m\n\u001b[1;32m     <a href='vscode-notebook-cell://wsl%2Bubuntu/home/coco/ECOLE/Apprentissage_profond/TP_2/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#X52sdnNjb2RlLXJlbW90ZQ%3D%3D?line=39'>40</a>\u001b[0m model\u001b[39m.\u001b[39meval()\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'models' is not defined"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import json\n",
     "from PIL import Image\n",
+    "import matplotlib.pyplot as plt\n",
+    "from torchvision.models import resnet50\n",
+    "\n",
     "\n",
     "# Choose an image to pass through the model\n",
     "test_image = \"dog.png\"\n",
@@ -940,7 +1762,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.10.6"
   },
   "vscode": {
    "interpreter": {