diff --git a/TD2 Deep Learning.ipynb b/TD2 Deep Learning.ipynb
index 00e4fdc78c068248ca0742c64725d155b3681f0d..a61a17983f34760990bd493389707377df5162ce 100644
--- a/TD2 Deep Learning.ipynb	
+++ b/TD2 Deep Learning.ipynb	
@@ -33,10 +33,57 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "330a42f5",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting torchNote: you may need to restart the kernel to use updated packages.\n",
+      "\n",
+      "  Downloading torch-2.5.1-cp39-cp39-win_amd64.whl (203.0 MB)\n",
+      "Collecting torchvision\n",
+      "  Downloading torchvision-0.20.1-cp39-cp39-win_amd64.whl (1.6 MB)\n",
+      "Requirement already satisfied: typing-extensions>=4.8.0 in c:\\users\\naël\\appdata\\roaming\\python\\python39\\site-packages (from torch) (4.12.2)\n",
+      "Collecting networkx\n",
+      "  Downloading networkx-3.2.1-py3-none-any.whl (1.6 MB)\n",
+      "Collecting fsspec\n",
+      "  Downloading fsspec-2024.10.0-py3-none-any.whl (179 kB)\n",
+      "Collecting sympy==1.13.1; python_version >= \"3.9\"\n",
+      "  Downloading sympy-1.13.1-py3-none-any.whl (6.2 MB)\n",
+      "Collecting jinja2\n",
+      "  Downloading jinja2-3.1.4-py3-none-any.whl (133 kB)\n",
+      "Collecting filelock\n",
+      "  Downloading filelock-3.16.1-py3-none-any.whl (16 kB)\n",
+      "Collecting numpy\n",
+      "  Downloading numpy-2.0.2-cp39-cp39-win_amd64.whl (15.9 MB)\n",
+      "Collecting pillow!=8.3.*,>=5.3.0\n",
+      "  Downloading pillow-11.0.0-cp39-cp39-win_amd64.whl (2.6 MB)\n",
+      "Collecting mpmath<1.4,>=1.1.0\n",
+      "  Downloading mpmath-1.3.0-py3-none-any.whl (536 kB)\n",
+      "Collecting MarkupSafe>=2.0\n",
+      "  Downloading MarkupSafe-3.0.2-cp39-cp39-win_amd64.whl (15 kB)\n",
+      "Installing collected packages: networkx, fsspec, mpmath, sympy, MarkupSafe, jinja2, filelock, torch, numpy, pillow, torchvision\n",
+      "Successfully installed MarkupSafe-3.0.2 filelock-3.16.1 fsspec-2024.10.0 jinja2-3.1.4 mpmath-1.3.0 networkx-3.2.1 numpy-2.0.2 pillow-11.0.0 sympy-1.13.1 torch-2.5.1 torchvision-0.20.1\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  WARNING: The script isympy.exe is installed in 'c:\\Users\\Naël\\AppData\\Local\\Programs\\Python\\Python39\\Scripts' which is not on PATH.\n",
+      "  Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\n",
+      "  WARNING: The scripts convert-caffe2-to-onnx.exe, convert-onnx-to-caffe2.exe, torchfrtrace.exe and torchrun.exe are installed in 'c:\\Users\\Naël\\AppData\\Local\\Programs\\Python\\Python39\\Scripts' which is not on PATH.\n",
+      "  Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\n",
+      "  WARNING: The scripts f2py.exe and numpy-config.exe are installed in 'c:\\Users\\Naël\\AppData\\Local\\Programs\\Python\\Python39\\Scripts' which is not on PATH.\n",
+      "  Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\n",
+      "WARNING: You are using pip version 20.2.3; however, version 24.3.1 is available.\n",
+      "You should consider upgrading via the 'c:\\Users\\Naël\\AppData\\Local\\Programs\\Python\\Python39\\python.exe -m pip install --upgrade pip' command.\n"
+     ]
+    }
+   ],
    "source": [
     "%pip install torch torchvision"
    ]
@@ -52,10 +99,72 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "b1950f0a",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([[-8.3455e-01, -5.4436e-01,  9.0975e-01, -1.3041e+00, -3.9287e-01,\n",
+      "          7.8093e-01,  6.1198e-01, -1.2774e-01, -6.7718e-01,  1.7207e-02],\n",
+      "        [ 4.1690e-01,  5.2974e-01, -8.5790e-01,  1.1008e+00,  1.0897e+00,\n",
+      "         -1.9488e+00, -2.6636e-02, -6.5297e-01, -2.4669e+00,  1.9052e-02],\n",
+      "        [ 1.3407e+00,  6.9763e-01, -2.7258e-01, -1.2392e+00,  3.7251e-01,\n",
+      "         -1.8302e-03,  1.1798e+00,  2.7557e+00, -9.0218e-01, -4.6606e-02],\n",
+      "        [ 2.2373e+00,  8.3398e-01, -9.0769e-01,  4.9517e-01, -9.0574e-01,\n",
+      "          2.4619e-01, -1.3877e+00, -1.9462e+00, -7.9654e-01, -7.4641e-01],\n",
+      "        [-1.6862e+00, -1.5184e+00, -2.3738e-01, -6.8620e-01,  2.0232e+00,\n",
+      "          6.0086e-01,  1.4182e-01, -1.8680e-01,  6.5054e-02,  1.2122e+00],\n",
+      "        [ 4.0323e-01,  6.8175e-02,  8.0201e-02,  1.8286e-01, -1.1127e+00,\n",
+      "         -1.2341e+00,  2.3158e-01,  1.7764e+00,  1.6136e-01,  1.8353e+00],\n",
+      "        [-8.8201e-01, -1.0774e+00, -1.6927e+00,  5.1941e-01,  8.0800e-01,\n",
+      "          3.8723e-01, -4.4709e-01, -1.8904e-01,  8.3156e-01, -3.0982e-02],\n",
+      "        [ 1.2427e+00,  1.5449e-01, -1.8803e-01,  9.3768e-01, -6.9760e-01,\n",
+      "         -2.7077e-01,  8.7309e-02,  1.4914e+00, -2.7612e+00,  1.8896e+00],\n",
+      "        [ 1.0616e+00, -2.1925e-01, -1.4209e-01,  5.6274e-01, -1.4635e+00,\n",
+      "         -8.6561e-02, -1.2570e+00,  2.0019e+00, -1.2980e+00, -1.5414e+00],\n",
+      "        [ 1.3048e+00,  2.4387e-01, -4.2360e-01, -2.6702e-01, -5.3268e-01,\n",
+      "          3.8406e-01,  1.0252e+00,  1.0136e+00, -7.6817e-02, -9.4680e-01],\n",
+      "        [-3.6046e-01,  1.1308e+00, -3.8328e-01, -1.3140e+00,  8.4032e-01,\n",
+      "         -4.1093e-01, -6.9950e-01, -5.8968e-01, -3.3199e-01, -5.5076e-01],\n",
+      "        [-1.2572e+00, -2.0366e+00, -2.0833e-01,  7.2404e-01, -4.9225e-01,\n",
+      "          1.9101e+00, -2.0156e+00, -5.5413e-01,  3.4591e-01,  1.0579e-01],\n",
+      "        [-2.5412e-01, -2.6155e-02, -2.9952e+00,  1.3609e+00, -1.4827e+00,\n",
+      "         -8.7889e-01,  1.3340e+00,  1.0976e+00,  3.7855e-01, -1.2661e-01],\n",
+      "        [-7.2997e-01,  1.7006e-01,  1.6780e+00,  1.5699e-01, -7.9196e-01,\n",
+      "          1.2449e+00,  7.7324e-01,  4.7975e-01, -8.1164e-01, -2.1858e+00]])\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",
@@ -98,7 +207,15 @@
    "execution_count": null,
    "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 +238,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 77,
    "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 +319,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 78,
    "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 +383,57 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 79,
    "id": "4b53f229",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 0 \tTraining Loss: 42.976662 \tValidation Loss: 37.362201\n",
+      "Validation loss decreased (inf --> 37.362201).  Saving model ...\n",
+      "Epoch: 1 \tTraining Loss: 34.727949 \tValidation Loss: 32.397470\n",
+      "Validation loss decreased (37.362201 --> 32.397470).  Saving model ...\n",
+      "Epoch: 2 \tTraining Loss: 30.111148 \tValidation Loss: 28.732573\n",
+      "Validation loss decreased (32.397470 --> 28.732573).  Saving model ...\n",
+      "Epoch: 3 \tTraining Loss: 27.772648 \tValidation Loss: 27.333666\n",
+      "Validation loss decreased (28.732573 --> 27.333666).  Saving model ...\n",
+      "Epoch: 4 \tTraining Loss: 26.124830 \tValidation Loss: 26.345886\n",
+      "Validation loss decreased (27.333666 --> 26.345886).  Saving model ...\n",
+      "Epoch: 5 \tTraining Loss: 24.835974 \tValidation Loss: 25.256867\n",
+      "Validation loss decreased (26.345886 --> 25.256867).  Saving model ...\n",
+      "Epoch: 6 \tTraining Loss: 23.737096 \tValidation Loss: 24.970660\n",
+      "Validation loss decreased (25.256867 --> 24.970660).  Saving model ...\n",
+      "Epoch: 7 \tTraining Loss: 22.764935 \tValidation Loss: 23.767593\n",
+      "Validation loss decreased (24.970660 --> 23.767593).  Saving model ...\n",
+      "Epoch: 8 \tTraining Loss: 21.895676 \tValidation Loss: 24.289908\n",
+      "Epoch: 9 \tTraining Loss: 21.090948 \tValidation Loss: 23.854555\n",
+      "Epoch: 10 \tTraining Loss: 20.326097 \tValidation Loss: 22.117338\n",
+      "Validation loss decreased (23.767593 --> 22.117338).  Saving model ...\n",
+      "Epoch: 11 \tTraining Loss: 19.652482 \tValidation Loss: 22.267032\n",
+      "Epoch: 12 \tTraining Loss: 18.958920 \tValidation Loss: 22.736554\n",
+      "Epoch: 13 \tTraining Loss: 18.267155 \tValidation Loss: 21.822522\n",
+      "Validation loss decreased (22.117338 --> 21.822522).  Saving model ...\n",
+      "Epoch: 14 \tTraining Loss: 17.705097 \tValidation Loss: 22.321736\n",
+      "Epoch: 15 \tTraining Loss: 17.080545 \tValidation Loss: 22.509224\n",
+      "Epoch: 16 \tTraining Loss: 16.495443 \tValidation Loss: 22.441707\n",
+      "Epoch: 17 \tTraining Loss: 15.933774 \tValidation Loss: 23.069825\n",
+      "Epoch: 18 \tTraining Loss: 15.404892 \tValidation Loss: 22.449938\n",
+      "Epoch: 19 \tTraining Loss: 14.788175 \tValidation Loss: 23.268121\n",
+      "Epoch: 20 \tTraining Loss: 14.316846 \tValidation Loss: 23.478045\n",
+      "Epoch: 21 \tTraining Loss: 13.893436 \tValidation Loss: 23.515660\n",
+      "Epoch: 22 \tTraining Loss: 13.407244 \tValidation Loss: 25.956946\n",
+      "Epoch: 23 \tTraining Loss: 12.884457 \tValidation Loss: 24.332172\n",
+      "Epoch: 24 \tTraining Loss: 12.425907 \tValidation Loss: 24.714665\n",
+      "Epoch: 25 \tTraining Loss: 12.091276 \tValidation Loss: 25.187645\n",
+      "Epoch: 26 \tTraining Loss: 11.579369 \tValidation Loss: 26.320383\n",
+      "Epoch: 27 \tTraining Loss: 11.150037 \tValidation Loss: 26.619607\n",
+      "Epoch: 28 \tTraining Loss: 10.817260 \tValidation Loss: 27.235337\n",
+      "Epoch: 29 \tTraining Loss: 10.420628 \tValidation Loss: 28.540454\n"
+     ]
+    }
+   ],
    "source": [
     "import torch.optim as optim\n",
     "\n",
@@ -254,7 +442,7 @@
     "\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",
+    "valid_loss_min = np.inf  # track change in validation loss\n",
     "\n",
     "for epoch in range(n_epochs):\n",
     "    # Keep track of training and validation loss\n",
@@ -316,6 +504,56 @@
     "        valid_loss_min = valid_loss"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "194cc1a6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting matplotlib\n",
+      "  Downloading matplotlib-3.9.2-cp39-cp39-win_amd64.whl (7.8 MB)\n",
+      "Collecting pyparsing>=2.3.1\n",
+      "  Downloading pyparsing-3.2.0-py3-none-any.whl (106 kB)\n",
+      "Collecting cycler>=0.10\n",
+      "  Downloading cycler-0.12.1-py3-none-any.whl (8.3 kB)\n",
+      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\naël\\appdata\\roaming\\python\\python39\\site-packages (from matplotlib) (2.9.0.post0)\n",
+      "Requirement already satisfied: pillow>=8 in c:\\users\\naël\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from matplotlib) (11.0.0)\n",
+      "Collecting kiwisolver>=1.3.1\n",
+      "  Downloading kiwisolver-1.4.7-cp39-cp39-win_amd64.whl (55 kB)\n",
+      "Requirement already satisfied: packaging>=20.0 in c:\\users\\naël\\appdata\\roaming\\python\\python39\\site-packages (from matplotlib) (24.2)\n",
+      "Requirement already satisfied: numpy>=1.23 in c:\\users\\naël\\appdata\\local\\programs\\python\\python39\\lib\\site-packages (from matplotlib) (2.0.2)\n",
+      "Collecting fonttools>=4.22.0\n",
+      "  Downloading fonttools-4.55.0-cp39-cp39-win_amd64.whl (2.2 MB)\n",
+      "Collecting importlib-resources>=3.2.0; python_version < \"3.10\"\n",
+      "  Downloading importlib_resources-6.4.5-py3-none-any.whl (36 kB)\n",
+      "Collecting contourpy>=1.0.1\n",
+      "  Downloading contourpy-1.3.0-cp39-cp39-win_amd64.whl (211 kB)\n",
+      "Requirement already satisfied: six>=1.5 in c:\\users\\naël\\appdata\\roaming\\python\\python39\\site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n",
+      "Requirement already satisfied: zipp>=3.1.0; python_version < \"3.10\" in c:\\users\\naël\\appdata\\roaming\\python\\python39\\site-packages (from importlib-resources>=3.2.0; python_version < \"3.10\"->matplotlib) (3.21.0)\n",
+      "Installing collected packages: pyparsing, cycler, kiwisolver, fonttools, importlib-resources, contourpy, matplotlib\n",
+      "Successfully installed contourpy-1.3.0 cycler-0.12.1 fonttools-4.55.0 importlib-resources-6.4.5 kiwisolver-1.4.7 matplotlib-3.9.2 pyparsing-3.2.0\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  WARNING: The scripts fonttools.exe, pyftmerge.exe, pyftsubset.exe and ttx.exe are installed in 'c:\\Users\\Naël\\AppData\\Local\\Programs\\Python\\Python39\\Scripts' which is not on PATH.\n",
+      "  Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\n",
+      "WARNING: You are using pip version 20.2.3; however, version 24.3.1 is available.\n",
+      "You should consider upgrading via the 'c:\\Users\\Naël\\AppData\\Local\\Programs\\Python\\Python39\\python.exe -m pip install --upgrade pip' command.\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pip install matplotlib"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "13e1df74",
@@ -326,11 +564,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 80,
    "id": "d39df818",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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IyMhwDA0RERGZPAYaIiIiMnkMNERERGTyGGiIiIjI5DHQEBERkcljoCEiIiKT1+TvQ6NWq5GVlQV7e/uHvkU6ERERiUMQBJSUlMDLy0uz4O+9NPlAk5WVZRQL1REREdHDy8jI0NxU816afKCpW+wtIyMDDg4OIldDRERED6K4uBg+Pj5ai7beS5MPNHXdTA4ODgw0REREJuZBh4twUDARERGZPAYaIiIiMnkMNERERGTyGGiIiIjI5DHQEBERkcljoCEiIiKTx0BDREREJo+BhoiIiEweAw0RERGZPAYaIiIiMnkMNERERGTyGGiIiIjI5DHQNFCNWsDV/FIUlKrELoWIiKjZY6BpoJd+OYm+/43HpjNZYpdCRETU7DHQNFCQmx0A4GJ2sciVEBEREQNNA7XxdAAAXMopEbkSIiIiYqBpoLpAk5xTguoatcjVEBERNW8MNA3k62wDG0szqKrVuFZYJnY5REREzRoDTQNJpRIEK+wBABey2e1EREQkJgaaR6AZR8OBwURERKJioHkEdYGGM52IiIjExUDzCEI9a7ucLrLLiYiISFQMNI8gWFF7hSanuAK3yipFroaIiKj5YqB5BHYyc/g62wAALuaw24mIiEgsDDSPqA27nYiIiETHQPOIODCYiIhIfAw0jyhEwUBDREQkNgaaRxT65xWaK7mlXAKBiIhIJAw0j8jbyRp2MnNU1qhxtYBLIBAREYmBgeYRSaUShCjqBgaz24mIiEgMDDR6EOJZt6YTAw0REZEYGGj04K81nTh1m4iISAwMNHrAqdtERETiYqDRg2APe0gkQF6JCoWlKrHLISIianYYaPTAVmYOv7olENjtREREZHAMNHqiGUfDNZ2IiIgMjoFGT+oCDWc6ERERGR4DjZ78NTCYXU5ERESGxkCjJ3U310vJK0FlNZdAICIiMiQGGj3xdrKGvZU5qmoEXC0oFbscIiKiZsVoAs2iRYsgkUgwc+ZMTVtFRQWmT58OFxcX2NnZYdSoUcjNzRWvyHuQSCRow5W3iYiIRGEUgeb48eP4+uuvER4ertU+a9YsbNq0CWvWrEF8fDyysrIwcuRIkaq8vzaedWs6cRwNERGRIYkeaEpLS/HUU0/hm2++gZOTk6ZdqVTiu+++wyeffIK+ffsiMjISy5cvx+HDh3HkyBERK767EN4xmIiISBSiB5rp06fj8ccfR0xMjFZ7YmIiqqqqtNpDQkLg6+uLhIQEQ5f5QDjTiYiISBzmYr756tWrcfLkSRw/fvyObTk5ObC0tISjo6NWu4eHB3Jycu56TJVKBZXqr+UHiosNd7Uk2MMeUglQUKpCfokKbvYyg703ERFRcybaFZqMjAy8/PLLWLlyJaysrPR23IULF0Iul2sePj4+ejv2/VhbmqGlqy0AdjsREREZkmiBJjExEXl5eejYsSPMzc1hbm6O+Ph4fPbZZzA3N4eHhwcqKytRVFSk9brc3FwoFIq7HnfOnDlQKpWaR0ZGRiN/Em2c6URERGR4onU59evXD+fOndNqe+655xASEoI33ngDPj4+sLCwwO7duzFq1CgAQHJyMtLT0xEdHX3X48pkMshk4nX1tPG0xx/nsnEph+NoiIiIDEW0QGNvb4+2bdtqtdna2sLFxUXTPnHiRLzyyitwdnaGg4MDXnrpJURHR6Nr165ilPxA2nCmExERkcGJOij4fhYvXgypVIpRo0ZBpVJh4MCB+Oqrr8Qu657qAk1KXilU1TWQmZuJXBEREVHTJxEEQRC7iMZUXFwMuVwOpVIJBweHRn8/QRAQsWAHiiuq8cf/9UCYl7zR35OIiKipedjf36Lfh6apkUgkmqs0l3g/GiIiIoNgoGkEHEdDRERkWAw0jSC0LtDkMNAQEREZAgNNIwj52yKVTXyIEhERkVFgoGkErf9cAuFmWSXySlT3fwERERE9EgaaRmBlYYYANzsAHEdDRERkCAw0jYQrbxMRERkOA00jCVHUjaPhFRoiIqLGxkDTSEI5dZuIiMhgGGgaSV2X09WCMlRU1YhcDRERUdPGQNNIPBxkcLKxQI1aQEpeqdjlEBERNWkMNI1EIpEgRFF7leYCu52IiIgaFQNNI+ISCERERIbBQNOI2vx5x2AuUklERNS4GGgaUZu/renEJRCIiIgaDwNNIwpyt4OZVIKi8irkFFeIXQ4REVGTxUDTiKwszBDoZguA42iIiIgaEwNNI+MSCERERI2PgaaRcaYTERFR42OgaWRc04mIiKjxMdA0sro1ndK4BAIREVGjYaBpZG72MrjYWkItAJdzOY6GiIioMTDQNDKJRMJxNERERI2MgcYA/hpHwys0REREjYGBxgDqrtBwkUoiIqLGwUBjAHWB5lI2l0AgIiJqDAw0BhDkbgdzqQTFFdXIUnIJBCIiIn1joDEAS3MpgtztAAAXs9jtREREpG8MNAbCmU5ERESNh4HGQNp41s50upTDmU5ERET6xkBjILxCQ0RE1HgYaAwkRPHnEgiFZSivrBa5GiIioqaFgcZA3OxlcLWTQRCAZHY7ERER6RUDjQFxHA0REVHjYKAxoFCOoyEiImoUDDQGFOJZt6YTAw0REZE+MdAY0F9LIJRwCQQiIiI9YqAxoEA3O1iaSVGiqkbmrdtil0NERNRkMNAYkIXZ35ZAYLcTERGR3jDQGNhf42g404mIiEhfGGgMjDOdiIiI9E/UQLNkyRKEh4fDwcEBDg4OiI6OxtatWzXb+/TpA4lEovWYMmWKiBU/Os3A4BwGGiIiIn0xF/PNvb29sWjRIrRq1QqCIOCHH37AsGHDcOrUKYSFhQEAJk2ahPfee0/zGhsbG7HK1YsQRW2X0/Wb5ShTVcNWJuqPgIiIqEkQ9bfpkCFDtJ5/+OGHWLJkCY4cOaIJNDY2NlAoFGKU1yhc7GRwt5chr0SFSzkliPRzErskIiIik2c0Y2hqamqwevVqlJWVITo6WtO+cuVKuLq6om3btpgzZw7Ky8vveRyVSoXi4mKth7HhyttERET6JXp/x7lz5xAdHY2KigrY2dkhLi4OoaGhAIBx48bBz88PXl5eOHv2LN544w0kJydj3bp1dz3ewoULsWDBAkOV3yBtPB0Qfzmf42iIiIj0RCKIfMvayspKpKenQ6lUYu3atfj2228RHx+vCTV/t2fPHvTr1w8pKSkIDAys93gqlQoqlUrzvLi4GD4+PlAqlXBwcGi0z/EwNpy+gZdXn0aknxN+n9pN7HKIiIiMTnFxMeRy+QP//hb9Co2lpSWCgoIAAJGRkTh+/Dg+/fRTfP3113fsGxUVBQD3DDQymQwymazxCtaDv5ZAKEaNWoCZVCJyRURERKbNaMbQ1FGr1VpXWP7u9OnTAABPT08DVqR/Aa62cLAyR1llDU6m3xK7HCIiIpMn6hWaOXPmIDY2Fr6+vigpKcGqVauwb98+bN++HampqVi1ahUGDx4MFxcXnD17FrNmzUKvXr0QHh4uZtmPzNxMiphQD6w7eQNbzmWjc0tnsUsiIiIyaaJeocnLy8Ozzz6L4OBg9OvXD8ePH8f27dvRv39/WFpaYteuXRgwYABCQkIwe/ZsjBo1Cps2bRKzZL2JbVt7lWlbUg7Uaq68TURE9ChEvULz3Xff3XWbj48P4uPjDViNYfVs5QpbSzNkKytwJrMIHXx5PxoiIqKGMroxNM2FlYUZ+rbxAABsTcoRuRoiIiLTxkAjosFta++AvDUpGyLPniciIjJpDDQi6hPsDmsLM2TcvI3zWbzJHhERUUMx0IjI2tIMfYLdANRepSEiIqKGYaARWWy72tlOW8/lsNuJiIiogRhoRNY3xB2W5lJcLSjD5dxSscshIiIySQw0IrOTmaNXq9pupy3n2O1ERETUEAw0RiD2z9lO2zh9m4iIqEEYaIxATBsPWJhJkJxbgtR8djsRERE9LAYaIyC3sUC3QFcAvEpDRETUEAw0RmJwu9puJ46jISIiengMNEaif6gCZlIJzmcVI72wXOxyiIiITAoDjZFwtrVE1wBnALzJHhER0cNioDEig9rW3mRvC8fREBERPRQGGiMyMMwDEglwJqMIN4pui10OERGRyWCgMSLu9lbo7Ffb7cTZTkRERA+OgcbIxLaru8kex9EQERE9KAYaIzPoz7sGn7h+C3nFFSJXQ0REZBoYaIyMp9waHXwdIQjA9vPsdiIiInoQDDRGaHDdbKdzDDREREQPgoHGCNV1Ox1NK0RhqUrkaoiIiIwfA40R8nG2QbsWcqgFYMeFXLHLISIiMnoMNEaq7irNVk7fJiIiui8GGiMV+2egOZxSAGV5lcjVEBERGTcGGiMV4GaHEIU9qtUCdl5ktxMREdG9MNAYsdg/ZzttPceb7BEREd0LA40Rq7tr8IErBSipYLcTERHR3TDQGLFW7nYIdLNFZY0aey7liV0OERGR0WKgMWISiUTT7bSF3U5ERER3xUBj5Oq6nfYl56NMVS1yNURERMaJgcbIhXo6wNfZBqpqNfYl54tdDhERkVFioDFyEolEc5VmaxK7nYiIiOrDQGMC6sbR7LmUh4qqGpGrISIiMj4MNCYgwlsOL7kVyitrsP8yu52IiIh0MdCYAIlEgkF1N9nj2k5ERER3YKAxEYP/HEez62IuVNXsdiIiIvo7BhoT0dHXCe72MpRUVONwSqHY5RARERkVBhoTIZVKMKgtZzsRERHVh4HGhNTNdtpxIRdVNWqRqyEiIjIeDDQmpIu/M1xsLVFUXoWjV2+KXQ4REZHRYKAxIWZSCQaE1XY7bWG3ExERkYaogWbJkiUIDw+Hg4MDHBwcEB0dja1bt2q2V1RUYPr06XBxcYGdnR1GjRqF3NxcESsWX+yf42h2nM9BjVoQuRoiIiLjIGqg8fb2xqJFi5CYmIgTJ06gb9++GDZsGM6fPw8AmDVrFjZt2oQ1a9YgPj4eWVlZGDlypJgliy460AVyawsUlFbi+DV2OxEREQGARBAEo/pnvrOzMz7++GOMHj0abm5uWLVqFUaPHg0AuHTpEtq0aYOEhAR07dr1gY5XXFwMuVwOpVIJBweHxizdYF5dcwZrEzPxeDtPfPlUR7HLISIi0ruH/f1tNGNoampqsHr1apSVlSE6OhqJiYmoqqpCTEyMZp+QkBD4+voiISHhrsdRqVQoLi7WejQ1E7q1hFQC/HEuG7svNu8uOCIiIsAIAs25c+dgZ2cHmUyGKVOmIC4uDqGhocjJyYGlpSUcHR219vfw8EBOzt1v/79w4ULI5XLNw8fHp5E/geG1bSHHpJ4BAIB31iehpKJK5IqIiIjEJXqgCQ4OxunTp3H06FFMnToV48ePx4ULFxp8vDlz5kCpVGoeGRkZeqzWeMyMaQ0/FxtkKyvw0bZLYpdDREQkKtEDjaWlJYKCghAZGYmFCxciIiICn376KRQKBSorK1FUVKS1f25uLhQKxV2PJ5PJNLOm6h5NkbWlGRaOaAcA+PlIOo6lcYAwERE1X6IHGl1qtRoqlQqRkZGwsLDA7t27NduSk5ORnp6O6OhoESs0Ht2CXPHPTrVdam/+fhYVVVy0koiImidzMd98zpw5iI2Nha+vL0pKSrBq1Srs27cP27dvh1wux8SJE/HKK6/A2dkZDg4OeOmllxAdHf3AM5yag7cGt8Ge5DxcLSjD53uu4LWBIWKXREREZHCiBpq8vDw8++yzyM7OhlwuR3h4OLZv347+/fsDABYvXgypVIpRo0ZBpVJh4MCB+Oqrr8Qs2ejIbSzw/rAwTPn5JL6Ov4rH23kh1KtpdrMRERHdjdHdh0bfmuJ9aOoz5adEbDufg3Yt5Iib1g3mZkbXm0hERPTATPY+NPRo3hsWBgcrc5y7ocT3h9LELoeIiMigGGiaCHcHK7z9eBsAwCc7L+N6YZnIFRERERkOA00TMqaTD7oFuqCiSo05686hifcmEhERaTDQNCESiQQLR7aDlYUUh1MLseZEptglERERGQQDTRPj52KLV/q3BgB88McF5BVXiFwRERFR42OgaYKe7+6Pdi3kKK6oxryN58Uuh4iIqNEx0DRB5mZSfDQqHOZSCbYm5WBb0t0X8yQiImoKGGiaqFAvB7zYu3ZF7rkbkqC8zRW5iYio6WKgacJe6tsKAW62yCtRYeGWi2KXQ0RE1GgYaJowKwszLBoZDgBYfTwDh1MLRK6IiIiocTDQNHFd/J3xdFdfAMCcdedwu5IrchMRUdPDQNMMvDEoBAoHK1wvLMf/dl0WuxwiIiK9Y6BpBuytLPDB8LYAgG8OXMW5TKXIFREREekXA00zERPqgSfCPaEWgNd/P4uqGrXYJREREekNA00zMn9oGBxtLHAxuxjL9l8VuxwiIiK9aVCgycjIQGbmX+sEHTt2DDNnzsSyZcv0Vhjpn6udDO8+HgoA+HT3FRxLuylyRURERPrRoEAzbtw47N27FwCQk5OD/v3749ixY3j77bfx3nvv6bVA0q+RHVtgQKgHKqvVmPjDcVzMLha7JCIiokfWoECTlJSELl26AAB+++03tG3bFocPH8bKlSuxYsUKfdZHeiaRSPDp2A7o5OeEkopqjP/+GDJulotdFhER0SNpUKCpqqqCTCYDAOzatQtDhw4FAISEhCA7O1t/1VGjsLY0w3fjOyNEYY+8EhWe+e4oCkpVYpdFRETUYA0KNGFhYVi6dCkOHDiAnTt3YtCgQQCArKwsuLi46LVAahxyGwv88HwXeDtZ41phOSYsP4aSCq73REREpqlBgeajjz7C119/jT59+uDJJ59EREQEAGDjxo2arigyfh4OVvhpYhRcbC2RdKMYk39MREUV7yRMRESmRyIIgtCQF9bU1KC4uBhOTk6atmvXrsHGxgbu7u56K/BRFRcXQy6XQ6lUwsHBQexyjFLSDSXGLjuCUlU1Ytsq8MW4jjCTSsQui4iImrGH/f3doCs0t2/fhkql0oSZ69ev43//+x+Sk5ONKszQg2nbQo5lz0TC0kyKrUk5eHdDEhqYc4mIiETRoEAzbNgw/PjjjwCAoqIiREVF4b///S+GDx+OJUuW6LVAMoxuQa74dGx7SCTAqqPpWLyTaz4REZHpaFCgOXnyJHr27AkAWLt2LTw8PHD9+nX8+OOP+Oyzz/RaIBlObDtPzZpPn+1JwYpDaSJXRERE9GAaFGjKy8thb28PANixYwdGjhwJqVSKrl274vr163otkAzrqSg/zO7fGgAwf9MFbDh9Q+SKiIiI7q9BgSYoKAjr169HRkYGtm/fjgEDBgAA8vLyOPC2CZjRNwgTurUEAMz+7QziL+eLWxAREdF9NCjQzJ07F6+++ipatmyJLl26IDo6GkDt1ZoOHTrotUAyPIlEgrlPhGJohBeq1QKm/pyIU+m3xC6LiIjorho8bTsnJwfZ2dmIiIiAVFqbi44dOwYHBweEhITotchHwWnbDVe33tOBKwVwsrHAminRCHK3F7ssIiJqBh7293eDA02dulW3vb29H+UwjYaB5tGUqaox7tujOJNRBC+5FdZO7QYvR2uxyyIioibOIPehUavVeO+99yCXy+Hn5wc/Pz84Ojri/fffh1qtbsghyUjZysyxfEJnBLrZIktZgWe/P4ZbZZVil0VERKSlQYHm7bffxhdffIFFixbh1KlTOHXqFP71r3/h888/x7vvvqvvGklkzraW+HFiFDzlVkjJK8VzK46jTFUtdllEREQaDepy8vLywtKlSzWrbNfZsGEDpk2bhhs3jGeqL7uc9OdKbgn+8XUCisqrEOnnhGXPRMLFTiZ2WURE1AQZpMvp5s2b9Q78DQkJwc2bNxtySDIBrTzssXxCZ9hbmSPx+i2M+OowUvNLxS6LiIioYYEmIiICX3zxxR3tX3zxBcLDwx+5KDJeHXydsG5qN3g7WSP9ZjlGfnUYCamFYpdFRETNXIO6nOLj4/H444/D19dXcw+ahIQEZGRkYMuWLZplEYwBu5waR0GpCpN+PIFT6UWwMJNg4chwjI40zpluRERkegzS5dS7d29cvnwZI0aMQFFREYqKijBy5EicP38eP/30U0MOSSbG1U6GXyZ1xRPhnqiqEfDqmjP4z/ZkqNVcpZuIiAzvke9D83dnzpxBx44dUVNTo69DPjJeoWlcarWA/+5Mxpd7UwEAT4R74j//iICVhZnIlRERkSkzyBUaojpSqQSvDQzBv0eHw1wqweaz2Rj3zREUlqrELo2IiJoRBhrSizGdfPDjxC5wsDLHyfQiDP/qEFLySsQui4iImglRA83ChQvRuXNn2Nvbw93dHcOHD0dycrLWPn369IFEItF6TJkyRaSK6V66Bboibnp3+DrbIOPmbYz46jAOpxSIXRYRETUDDzWGZuTIkffcXlRUhPj4+AceQzNo0CCMHTsWnTt3RnV1Nd566y0kJSXhwoULsLW1BVAbaFq3bo333ntP8zobG5sHHg/DMTSGV1iqwuSfEpF4/RbMpRL8a0Q7jOnsI3ZZRERkQh7297f5wxxcLpffd/uzzz77wMfbtm2b1vMVK1bA3d0diYmJ6NWrl6bdxsYGCoXiYUolEbnYybDyhSi8vvYsNp7Jwuu/n0VaYRleGxAMqVQidnlERNQEPVSgWb58eWPVAQBQKpUAAGdnZ632lStX4ueff4ZCocCQIUPw7rvvwsbGpt5jqFQqqFR/DUgtLi5uvILprqwszPDp2PZo6WKDz/akYMm+VKQXluO/YzgDioiI9E+v07YfhVqtxtChQ1FUVISDBw9q2pctWwY/Pz94eXnh7NmzeOONN9ClSxesW7eu3uPMnz8fCxYsuKOdXU7i+T0xE2+uO4uqGgHtfRzxzbOd4GbPNaCIiOjuHrbLyWgCzdSpU7F161YcPHgQ3t53v+Psnj170K9fP6SkpCAwMPCO7fVdofHx8WGgEdmRq4V48adEKG9XoYWjNb58qiPa+ziKXRYRERkpk7wPzYwZM7B582bs3bv3nmEGAKKiogAAKSkp9W6XyWRwcHDQepD4uga4IG5aN7R0scGNotsYveQwlsan8s7CRESkF6IGGkEQMGPGDMTFxWHPnj3w9/e/72tOnz4NAPD09Gzk6kjfAtzssGFGDzzezhPVagGLtl7C+OXHkFdSIXZpRERk4kTtcpo2bRpWrVqFDRs2IDg4WNMul8thbW2N1NRUrFq1CoMHD4aLiwvOnj2LWbNmwdvbG/Hx8Q/0Hpy2bXwEQcDq4xlYsOk8KqrUcLWzxH/HtEfv1m5il0ZEREbCpMbQSCT1T+Fdvnw5JkyYgIyMDDz99NNISkpCWVkZfHx8MGLECLzzzju8D00TcCW3BC/9cgqXcmrvKDy5VwBeHRAMS3Oj6AklIiIRmVSgMQQGGuNWUVWDD/+4iJ+OXAcARHjL8dmTHeDnYityZUREJCaTHBRMzZeVhRneH94WXz8TCbm1Bc5kKvH4Zwex/tQNsUsjIiITwkBDRmFgmAJbX+6JLi2dUaqqxsxfT2P2b2dQpqoWuzQiIjIBDDRkNLwcrbFqUhRmxrSCVAL8fjITQz4/iKQbSrFLIyIiI8dAQ0bF3EyKmTGt8cukrvCUW+FqQRlGfnUY3x1MQxMf7kVERI+AgYaMUlSAC7b8X0/0D/VAZY0a72++gIk/nEBhqer+LyYiomaHgYaMlpOtJZY9E4n3h4XB0lyKPZfyEPvpARy4ki92aUREZGQYaMioSSQSPBPdEhumd0eQux3ySlR45rtjeG3NGSjLq8Quj4iIjAQDDZmENp4O2DijO8ZH+0EiAdYkZqLfJ/HYei5b7NKIiMgIMNCQybCxNMeCYW2x5sVoBLrZoqBUhakrT+LFn04gt5jrQRERNWcMNGRyOrV0xh//1xMv9Q2CuVSC7edzEfNJPFYfS+dMKCKiZoqBhkySlYUZZg8IxqaXeiDCW46Simq8ue4cxn1zFNcKysQuj4iIDIyBhkxaG08HrJvWHe883gZWFlIkXC3EoE/3Y9n+VFTXqMUuj4iIDISBhkyemVSCF3oGYMfM3ugW6IKKKjX+teUSRnx1GBeyisUuj4iIDICBhpoMXxcbrHwhCv8eFQ4HK3Ocu6HE0C8O4uPtl1BRVSN2eURE1IgYaKhJkUgkGNPZB7te6Y3YtgpUqwV8uTcVgz87gOPXbopdHhERNRIGGmqS3B2ssOTpSCx9OhJu9jJczS/DP5Ym4N31SbwhHxFRE8RAQ03aoLYK7HqlN8Z29gEA/HTkOvr8Zy9+PnIdNWpO8SYiaiokQhO/cUdxcTHkcjmUSiUcHBzELodEdDi1APM2nMeVvFIAQIjCHnOHhKJboKvIlRERka6H/f3NQEPNSnWNGiuPpuOTnZehvF3b9TQoTIG3BreBr4uNyNUREVEdBhodDDRUn1tllfjfrsv4+Wg6atQCLM2lmNTTH9P6BMFWZi52eUREzR4DjQ4GGrqX5JwSvLf5PA6lFAIA3O1leGNQCEZ0aAGpVCJydUREzRcDjQ4GGrofQRCw80IuPtxyEdcLywEAET6OmDckFB19nUSujoioeWKg0cFAQw9KVV2D5Yeu4fPdV1BWWXsjvhEdWuCNQSFQyK1Ero6IqHlhoNHBQEMPK6+kAv/Znow1iZkQBMDawgzT+gRiUq8AWFmYiV0eEVGzwECjg4GGGupsZhEWbLqAxOu3AAAtHK3xZmwIHm/nyfE1RESNjIFGBwMNPQpBELDpbDYWbrmIbGUFAKBdCzleHxSMnq3cRK6OiKjpYqDRwUBD+nC7sgbL9l/Fsv2pmvE13YNc8MagEIR7O4pbHBFRE8RAo4OBhvSpsFSFL/amYOWRdFTWqAEAj7fzxOwBrRHgZidydURETQcDjQ4GGmoMGTfLsXjXZcSdugFBAMykEozp5IOZMa3g4cAZUUREj4qBRgcDDTWmSznF+HhbMnZfygMAWFlIMaGbP6b2DoTcxkLk6oiITBcDjQ4GGjKE49du4qOtl3DizxlRcmsLTO0TiAndWnKqNxFRAzDQ6GCgIUMRBAG7L+bh39sv4XJu7YreCgcrzIxphdGR3jA3k4pcIRGR6WCg0cFAQ4ZWoxYQd+oGFu+8jBtFtwEAAW62eG1AMAa1VUAi4T1siIjuh4FGBwMNiUVVXYOfj6Tjiz1XcKu8CgAQ5uWAV/q3Rt8QdwYbIqJ7YKDRwUBDYiupqMI3+6/iu4NpmnvYRHjLMat/a/Ru7cZgQ0RUDwYaHQw0ZCxullVi2f6r+OHwNdyuqg02HX0dMat/a/QIcmWwISL6GwYaHQw0ZGwKSlX4Oj4VPyZch6q69uZ8nVs6YVb/1ugW6CpydURExoGBRgcDDRmrvOIKLIlPxcqj6aj8M9hEB7hgVv/W6OLvLHJ1RETiYqDRwUBDxi5HWYGv9qVg9bEMzXIKPYJcMat/a0T6OYlcHRGROBhodDDQkKm4UXQbX+5NwW/HM1Ctrv3fsndrN8zq3xrtfRzFLY6IyMAYaHQw0JCpybhZji/2pGDtyUzU/Bls+oW44+WYVlzZm4iajYf9/S3qrUsXLlyIzp07w97eHu7u7hg+fDiSk5O19qmoqMD06dPh4uICOzs7jBo1Crm5uSJVTNT4fJxt8NHocOyZ3RujOnpDKgF2X8rD0C8OYczXCdh5IRdqdZP+dwgR0UMT9QrNoEGDMHbsWHTu3BnV1dV46623kJSUhAsXLsDW1hYAMHXqVPzxxx9YsWIF5HI5ZsyYAalUikOHDj3Qe/AKDZm6q/ml+GJPCjaeydJ0Rfm72uL5Hv4Y3dEb1pZcK4qImh6T7nLKz8+Hu7s74uPj0atXLyiVSri5uWHVqlUYPXo0AODSpUto06YNEhIS0LVr1/sek4GGmops5W2sOHwNq46mo6SiGgDgaGOBp6P88Gy0H9wdrESukIhIf0yqy0mXUqkEADg7105ZTUxMRFVVFWJiYjT7hISEwNfXFwkJCfUeQ6VSobi4WOtB1BR4yq0xJ7YNjszph3lDQuHjbI2i8ip8sTcFPT7ai1fXnMHFbH7fiah5MppAo1arMXPmTHTv3h1t27YFAOTk5MDS0hKOjo5a+3p4eCAnJ6fe4yxcuBByuVzz8PHxaezSiQzKVmaO57r7Y9+rj2HJUx0R6eeEyho11iZmIvbTA3j626PYl5wHI7r4SkTU6MzFLqDO9OnTkZSUhIMHDz7ScebMmYNXXnlF87y4uJihhpokM6kEse08EdvOEyfTb+G7A2nYmpSNgykFOJhSgFbudnihpz+GtW8BKwuOsyGips0oAs2MGTOwefNm7N+/H97e3pp2hUKByspKFBUVaV2lyc3NhUKhqPdYMpkMMpmssUsmMiodfZ3Q8SknZNwsx/JD1/Dr8XRcySvFG7+fw8fbk/FM15Z4JtoPzraWYpdKRNQoRO1yEgQBM2bMQFxcHPbs2QN/f3+t7ZGRkbCwsMDu3bs1bcnJyUhPT0d0dLShyyUyej7ONpg7JBQJb/XDW4ND4Cm3QkFpJRbvuoxui3Zj3oYkZNwsF7tMIiK9E3WW07Rp07Bq1Sps2LABwcHBmna5XA5ra2sAtdO2t2zZghUrVsDBwQEvvfQSAODw4cMP9B6c5UTNWVWNGlvOZeObA1eRdKN2wLBUAgxu54kXewWinbdc5AqJiOpnUtO2JRJJve3Lly/HhAkTANTeWG/27Nn45ZdfoFKpMHDgQHz11Vd37XLSxUBDVHs1NCG1EEv3X8X+y/ma9u5BLnixVyB6tnK96/+PRERiMKlAYwgMNETaLmQVY9n+VGw6m61ZWqGNpwOm9A7A4HaesDAzmsmPRNSMMdDoYKAhql/mrXJ8dzANvx7PQHllDQCghaM1Jvbwxz87+8BWZhRzBoiomWKg0cFAQ3RvReWV+PnIdaw4fA0FpZUAALm1BZ6N9sP4bi3hasdZg0RkeAw0OhhoiB5MRVUNfj+ZiW8PpCGtoAwAIDOXYnSkNyb1DEBLV1uRKySi5oSBRgcDDdHDqVEL2HkhB0vir+JMRhEAQCIBBoUpMLlXADr4OolbIBE1Cww0OhhoiBpGEAQcS7uJr/dfxZ5LeZr2Lv7OeLFXAB4LdodUyplRRNQ4GGh0MNAQPbrLuSVYtv8qNpy+gaqa2r8yWrnbYXKvAAxr3wKW5pwZRUT6xUCjg4GGSH9ylBVYfigNq46mo0RVDQDwcJDh+e7+eDLKFw5WFiJXSERNBQONDgYaIv0rrqjCL0fT8f2hNOQWqwAAdjJzPBXli+e6+0MhtxK5QiIydQw0OhhoiBpPZbUaG07fwLL9V3ElrxQAYGEmwdCIFpjcKwDBCnuRKyQiU8VAo4OBhqjxqdUC9l3Ow9L4qziWdlPT/liwG17sHYgof2curUBED4WBRgcDDZFhnUq/hWX7r2Lb+RzU/e3Syt0OoyO9MaJDC7g7sDuKiO6PgUYHAw2RONIKyvDtgatYm5gJVbUaQO1K371au2FUR2/0D/WAlYWZyFUSkbFioNHBQEMkruKKKvxxNhtrEzOReP2Wpt3ByhxDIrwwKtIbHXwc2SVFRFoYaHQw0BAZj7SCMqw7mYnfEzORpazQtAe42WJUR2+M7NgCnnJrESskImPBQKODgYbI+KjVAhKuFuL3xExsTcrB7ara1b4lEqBHkCtGR3pjQKgC1pbskiJqrhhodDDQEBm3UlU1tpyr7ZL6+wwpO5k5ngj3xD86eaOjrxO7pIiaGQYaHQw0RKYjvbAcv5/MxLpTmci4eVvTHqKwxzPRfhjevgVsZeYiVkhEhsJAo4OBhsj0qNUCjl27ibWJmdh8NgsVVbWzpOxk5hjVsQWe7uqHVh68aR9RU8ZAo4OBhsi0KcursPZkJn4+ch1pBWWa9q4Bznima0sMCPOAhRkXxyRqahhodDDQEDUNarWAw6mF+OnINey8kAv1n39zudvLMLaLL57s4sMZUkRNCAONDgYaoqYnq+g2Vh9Lx6pjGSgorV0c00wqQf82Hngm2g/dAl04iJjIxDHQ6GCgIWq6KqvV2H4+Bz8dua41QyrAzRZPR/lhVKQ35NYWIlZIRA3FQKODgYaoeUjOKcHPR65j3clMlFXW3tfG2sIMT4R7YmwXH079JjIxDDQ6GGiImpdSVTXiTt3ATwnXcDm3VNMe5G6Hf3bywYiOLeBqJxOxQiJ6EAw0OhhoiJonQRBw4vot/Ho8A3+czdbcjdhcKkH/UA+M6eyDXq3cYCblVRsiY8RAo4OBhohKKqqw6Uw2fj2ejjOZSk27p9wK/4j0xj86+cDH2UbEColIFwONDgYaIvq7i9nF+PV4BtafvoGi8ioAtWtIdQ90xT87+2BAmAdk5lxDikhsDDQ6GGiIqD4VVTXYcSEXvx3PwMGUAk27o40FRnRogX929kGIgn9nEImFgUYHAw0R3U/GzXKsOZGBNYmZyFZWaNojvOUYHemNIRFecLSxFLFCouaHgUYHAw0RPagatYD9V/Lx2/EM7LyQi+o/b0dsaSZFTKg7Rkd6o1crN5hzqQWiRsdAo4OBhogaoqBUhQ2ns7A2MRMXs4s17a52Mgxv74XRnbzZJUXUiBhodDDQENGjOp+lxO+JN7Dh9A0UllVq2sO8HDA60hvD2reAsy27pIj0iYFGBwMNEelLVY0a+5Lz8XtiJnZfykVVTe1fn+ZSCfqGuGNUpDceC3aHpTm7pIgeFQONDgYaImoMN8sqselMbZfUuRt/3dvG2dYSQyO8MDrSG2FeDlxugaiBGGh0MNAQUWNLzinB7yczEXfqBvJLVJr21h52GNa+BYZGePHGfUQPiYFGBwMNERlKdY0aB64UYO3JTOw8n4vKGrVmW0dfRwxr3wKD23nCzZ5rSRHdDwONDgYaIhKD8nYVtiflYMOZGzicWoi6v2nNpBJ0C3TBsPYtMDDMA/ZWFuIWSmSkGGh0MNAQkdjyiiuw6Ww2Np7JwpmMIk27zFyKfm3cMTSiBfoEu8HKgksuENVhoNHBQENExuRaQRk2nsnC+tM3cDW/TNNub2WO2LYKDGvfAl0DXLgKODV7DDQ6GGiIyBgJgoDzWcXYeCYLG09nIaf4ryUX3OxlGBLuhSe7+KCVh72IVRKJ52F/f4t6s4T9+/djyJAh8PLygkQiwfr167W2T5gwARKJROsxaNAgcYolItIjiUSCti3keGtwGxx+sy9+ndwV46J84WhjgfwSFb4/lIb+i/djzNcJ2HgmC6rqGrFLJjJq5mK+eVlZGSIiIvD8889j5MiR9e4zaNAgLF++XPNcJuPsACJqWqRSCaICXBAV4IL5Q8Kw/3I+1iRmYNfFPBxLu4ljaTfhYmuJMZ19MK6LL6eAE9VD1EATGxuL2NjYe+4jk8mgUCgMVBERkbgszaWICfVATKgHspW3sfpYBlYfT0dusQpL9qViaXwq+rR2w9Nd/dAn2J1jbYj+ZPT35963bx/c3d0RHByMqVOnorCwUOySiIgMwlNujVn9W+PgG32x9OlI9GzlCkEA9ibnY+IPJ9Dr33vxxZ4ryCupuP/BiJo4oxkULJFIEBcXh+HDh2vaVq9eDRsbG/j7+yM1NRVvvfUW7OzskJCQADOz+qc3qlQqqFR/3amzuLgYPj4+HBRMRE1CWkEZVh29jjWJmSgqrwJQu5bUwLYKPB3lh64BzlxugZoEk53lVF+g0XX16lUEBgZi165d6NevX737zJ8/HwsWLLijnYGGiJqSiqoabDmXjZ+PXMfJ9CJNe6CbLZ6K8sPIji3gaMMVwMl0NelAAwBubm744IMP8OKLL9a7nVdoiKi5uZBVjJ+PXsf6UzdQXlk7G8pMKkHXAGcMCFWgf6gHvBytRa6S6OE8bKARdVDww8rMzERhYSE8PT3vuo9MJuNMKCJqVkK9HPCvEe0wJzYE609n4Zej6biQXYxDKYU4lFKIeRvPo10LOQaEemBAmAKtPezYLUVNjqhXaEpLS5GSkgIA6NChAz755BM89thjcHZ2hrOzMxYsWIBRo0ZBoVAgNTUVr7/+OkpKSnDu3LkHDi28sR4RNUfXC8uw80Iutp/PwYnrt/D3v+n9XGw04aajrxNnSpFRMqkup3379uGxxx67o338+PFYsmQJhg8fjlOnTqGoqAheXl4YMGAA3n//fXh4eDzwezDQEFFzV1Cqwu6LudhxPhcHUgpQWf3XKuAutpaIaeOBAWEe6B7kyvWkyGiYVKAxBAYaIqK/lKmqsf9yPnZcyMXui7korqjWbLOxNEPv1m4YEOaBXq3c4GLH7nsSDwONDgYaIqL6VdWocSztJnacz8GOC7nIVv51PxuJBAhvIUfv1m7oHeyGCG9HmJsZ/a3LqAlhoNHBQENEdH+CICDpRjF2XMjBzgu5uJRTorXdwcocPVu5oXdrN/Rq7QaF3EqkSqm5YKDRwUBDRPTwcosrsP9yPuIv5+PAlQIob1dpbQ9R2KNX69qA06mlE2TmHHtD+sVAo4OBhojo0dSoBZzJLEJ8cm3AOZNZpDVrytrCDN0CXdA7uDbg+LnYilcsNRkMNDoYaIiI9OtWWSUOpBRoAk5BqUpre4CbLQaFKRDb1hNtWzjwnjfUIAw0OhhoiIgaj1ot4GJOMeIv5yM+OR+J12+hWv3Xr5UWjtYY1FaBQW0ViPR1gpT3vKEHxECjg4GGiMhwSiqqsOdSHrafz8HeS/m4XVWj2eZmL8PAMA8MCvNEVIAzLDhriu6BgUYHAw0RkThuV9Yg/nI+tp/Pwa6LuSj52z1vHG0s0L+NBwa1VaBHK1cOKqY7MNDoYKAhIhJfZbUah1MLsC2p9p43N8sqNdvsZOboG+KOQW0V6BPsBhtLk1pmkBoJA40OBhoiIuNSXaPG8Wu3sC0pG9vO5yC3+K9BxVYWUvQNccfQCC/0CXbnUgzNGAONDgYaIiLjpVYLOJ1ZhG1JOdialI2Mm7c12+xl5hgQpsDQ9l7oHujCOxU3Mww0OhhoiIhMgyAIOJ9VjI1nsrDpTJbWUgzOtpYY3E6BoREt0MmPs6WaAwYaHQw0RESmR60WkJh+CxtPZ2HLuWwU/m3MjZfcCk9EeGFohBfCvHifm6aKgUYHAw0RkWmrrlHjUGohNp7Owo7zOShR/TVbKsDVFkMivDC0vRcC3exErJL0jYFGBwMNEVHTUVFVg33J+dh0Jgu7LuZCVa3WbAv1dMATEZ4YEOqBQDc7XrkxcQw0OhhoiIiaplJVNXZeyMHG01k4cKVA6w7FLV1sENPGAzGhHujk58QBxSaIgUYHAw0RUdN3s6wSW5OyseN8LhJSC1FZ89eVG7m1BfqGuCOmjQd6tXaFvZWFiJXSg2Kg0cFAQ0TUvJSqqnHgcj52XszF3kt5uFVepdlmYSZB1wAX9A/1QL82HmjhaC1ipXQvDDQ6GGiIiJqvGrWAk+m3sOtCLnZezMXV/DKt7W08HdC/jTtiQj3Q1kvO6eBGhIFGBwMNERHVSc0vxe6Ludh1IQ8nrt/E34bdwMNBhih/F3T0dURHPye08XTgApoiYqDRwUBDRET1uVlWib2X8rDrYi7iL+ejvLJGa7uVhRThLRzRwc8RHX2d0MHXEe72ViJV2/ww0OhgoCEiovupqKrBiWu3kHj9Fk5l3MKp9CIob1fdsZ+3kzU6+jrxKo4BMNDoYKAhIqKHpVYLuFpQhpPpt3Aq/RZOXi/C5bwS6P7G1FzF8XVEF39n9GjlCpk5F9TUBwYaHQw0RESkDyUVVTiTocTJ9Ft/Bp07r+LYy8zRP8wDQ8K90D3IFZbmvHrTUAw0OhhoiIioMdRdxTn1Z8DZeykfOcV/Lagpt7bAoDAFnojwRHQAVwt/WAw0OhhoiIjIEOoW1Nx8Jgt/nMtBQalKs83F1hKD2irwRLgXuvg7w4zTw++LgUYHAw0RERlajVrA0bRCbD6bjW1JObj5t9XC3exleLydJ54I90RHXyfe++YuGGh0MNAQEZGYqmvUOJxaiM1ns7AtKQfFFX+tFu4pt6oNNxFeiPCWc0HNv2Gg0cFAQ0RExqKyWo2DKfnYfCYbOy7kolT1V7jxcJChd2s39Al2R/cgV8itm/eaUww0OhhoiIjIGFVU1SD+cj42n83G7ou5Wjf2M5NKEOnrhN7Bbujd2g1hXg7N7uoNA40OBhoiIjJ2FVU1OH7tJvYl52Nfch5SddaccrOvu3rjhp5BbpDbNP2rNww0OhhoiIjI1GTcLMe+y/mIT87DoZRC3K7SvnrTwccRfYJru6dCPR2a5MBiBhodDDRERGTKVNU1OJ52C/uS87Dvcj5S8kq1trvaydCrlSsiWzqhg48TghX2TWJaOAONDgYaIiJqSjJuliP+cj72JefjcGrBHYtq2lqaIcKndjmG2kU1neBsaylStQ3HQKODgYaIiJoqVXXtopoJqYU4lXELp9OLUKYTcACgpYuNZsXwDr5OCFHYG/2dixlodDDQEBFRc1GjFnAlrwQnrxdplmTQHWAMANYWZgj3lqPDnyuHd2rpbHRXcRhodDDQEBFRc6Ysr8KpjNrFNE+m38LpjCKU/O3mfgAgkQChng7oHuSK7kGu6NzSCTaW5iJVXIuBRgcDDRER0V/UagGp+aWagJN4/Rau6Aw0tjCToIOvE3oEuaJ7kAvCvR1hYeAuKgYaHQw0RERE95ZXUoGE1EIcSinAoZRC3Ci6rbXdTmaOKH9ndAtyRY8gV7T2sGv0G/0x0OhgoCEiInpwgiDgemE5DqUW4FBKAQ6nFqKovEprH1c7GboFuqBHkCu6BbnA28lG73Uw0OhgoCEiImo4tVrAhezi2qs3qYU4llaIiiq11j6vDmiNGX1b6fV9H/b3t6hztvbv348hQ4bAy8sLEokE69ev19ouCALmzp0LT09PWFtbIyYmBleuXBGnWCIiomZIKpWgbQs5XuwdiB+f74Iz8wZg9eSueKlvEDr6OsLsz+1iEzXQlJWVISIiAl9++WW92//973/js88+w9KlS3H06FHY2tpi4MCBqKioMHClREREBAAyczN0DXDB7AHBWDetO07P7Y9uga5ilwVR52TFxsYiNja23m2CIOB///sf3nnnHQwbNgwA8OOPP8LDwwPr16/H2LFjDVkqERER1cPeyjgWyjTa2wSmpaUhJycHMTExmja5XI6oqCgkJCTc9XUqlQrFxcVaDyIiImrajDbQ5OTkAAA8PDy02j08PDTb6rNw4ULI5XLNw8fHp1HrJCIiIvEZbaBpqDlz5kCpVGoeGRkZYpdEREREjcxoA41CoQAA5ObmarXn5uZqttVHJpPBwcFB60FERERNm9EGGn9/fygUCuzevVvTVlxcjKNHjyI6OlrEyoiIiMjYiDrLqbS0FCkpKZrnaWlpOH36NJydneHr64uZM2figw8+QKtWreDv7493330XXl5eGD58uHhFExERkdERNdCcOHECjz32mOb5K6+8AgAYP348VqxYgddffx1lZWWYPHkyioqK0KNHD2zbtg1WVlZilUxERERGiEsfEBERkdExqaUPiIiIiPSBgYaIiIhMHgMNERERmTwGGiIiIjJ5DDRERERk8kSdtm0IdZO4uEglERGR6aj7vf2gk7GbfKApKSkBAC5SSUREZIJKSkogl8vvu1+Tvw+NWq1GVlYW7O3tIZFI9Hbc4uJi+Pj4ICMjg/e3eQg8bw3D89YwPG8Pj+esYXjeGuZe500QBJSUlMDLywtS6f1HyDT5KzRSqRTe3t6NdnwugNkwPG8Nw/PWMDxvD4/nrGF43hrmbuftQa7M1OGgYCIiIjJ5DDRERERk8hhoGkgmk2HevHmQyWRil2JSeN4ahuetYXjeHh7PWcPwvDWMPs9bkx8UTERERE0fr9AQERGRyWOgISIiIpPHQENEREQmj4GGiIiITB4DTQN9+eWXaNmyJaysrBAVFYVjx46JXZJRmz9/PiQSidYjJCRE7LKMzv79+zFkyBB4eXlBIpFg/fr1WtsFQcDcuXPh6ekJa2trxMTE4MqVK+IUayTud84mTJhwx3dv0KBB4hRrRBYuXIjOnTvD3t4e7u7uGD58OJKTk7X2qaiowPTp0+Hi4gI7OzuMGjUKubm5IlUsvgc5Z3369Lnj+zZlyhSRKjYOS5YsQXh4uObmedHR0di6datmu76+Zww0DfDrr7/ilVdewbx583Dy5ElERERg4MCByMvLE7s0oxYWFobs7GzN4+DBg2KXZHTKysoQERGBL7/8st7t//73v/HZZ59h6dKlOHr0KGxtbTFw4EBUVFQYuFLjcb9zBgCDBg3S+u798ssvBqzQOMXHx2P69Ok4cuQIdu7ciaqqKgwYMABlZWWafWbNmoVNmzZhzZo1iI+PR1ZWFkaOHCli1eJ6kHMGAJMmTdL6vv373/8WqWLj4O3tjUWLFiExMREnTpxA3759MWzYMJw/fx6AHr9nAj20Ll26CNOnT9c8r6mpEby8vISFCxeKWJVxmzdvnhARESF2GSYFgBAXF6d5rlarBYVCIXz88ceatqKiIkEmkwm//PKLCBUaH91zJgiCMH78eGHYsGGi1GNK8vLyBABCfHy8IAi13y0LCwthzZo1mn0uXrwoABASEhLEKtOo6J4zQRCE3r17Cy+//LJ4RZkIJycn4dtvv9Xr94xXaB5SZWUlEhMTERMTo2mTSqWIiYlBQkKCiJUZvytXrsDLywsBAQF46qmnkJ6eLnZJJiUtLQ05OTla3z25XI6oqCh+9+5j3759cHd3R3BwMKZOnYrCwkKxSzI6SqUSAODs7AwASExMRFVVldb3LSQkBL6+vvy+/Un3nNVZuXIlXF1d0bZtW8yZMwfl5eVilGeUampqsHr1apSVlSE6Olqv37MmvzilvhUUFKCmpgYeHh5a7R4eHrh06ZJIVRm/qKgorFixAsHBwcjOzsaCBQvQs2dPJCUlwd7eXuzyTEJOTg4A1Pvdq9tGdxo0aBBGjhwJf39/pKam4q233kJsbCwSEhJgZmYmdnlGQa1WY+bMmejevTvatm0LoPb7ZmlpCUdHR619+X2rVd85A4Bx48bBz88PXl5eOHv2LN544w0kJydj3bp1IlYrvnPnziE6OhoVFRWws7NDXFwcQkNDcfr0ab19zxhoyCBiY2M1fw4PD0dUVBT8/Pzw22+/YeLEiSJWRk3d2LFjNX9u164dwsPDERgYiH379qFfv34iVmY8pk+fjqSkJI5rewh3O2eTJ0/W/Lldu3bw9PREv379kJqaisDAQEOXaTSCg4Nx+vRpKJVKrF27FuPHj0d8fLxe34NdTg/J1dUVZmZmd4zAzs3NhUKhEKkq0+Po6IjWrVsjJSVF7FJMRt33i9+9RxMQEABXV1d+9/40Y8YMbN68GXv37oW3t7emXaFQoLKyEkVFRVr78/t293NWn6ioKABo9t83S0tLBAUFITIyEgsXLkRERAQ+/fRTvX7PGGgekqWlJSIjI7F7925Nm1qtxu7duxEdHS1iZaaltLQUqamp8PT0FLsUk+Hv7w+FQqH13SsuLsbRo0f53XsImZmZKCwsbPbfPUEQMGPGDMTFxWHPnj3w9/fX2h4ZGQkLCwut71tycjLS09Ob7fftfuesPqdPnwaAZv9906VWq6FSqfT7PdPvuOXmYfXq1YJMJhNWrFghXLhwQZg8ebLg6Ogo5OTkiF2a0Zo9e7awb98+IS0tTTh06JAQExMjuLq6Cnl5eWKXZlRKSkqEU6dOCadOnRIACJ988olw6tQp4fr164IgCMKiRYsER0dHYcOGDcLZs2eFYcOGCf7+/sLt27dFrlw89zpnJSUlwquvviokJCQIaWlpwq5du4SOHTsKrVq1EioqKsQuXVRTp04V5HK5sG/fPiE7O1vzKC8v1+wzZcoUwdfXV9izZ49w4sQJITo6WoiOjhaxanHd75ylpKQI7733nnDixAkhLS1N2LBhgxAQECD06tVL5MrF9eabbwrx8fFCWlqacPbsWeHNN98UJBKJsGPHDkEQ9Pc9Y6BpoM8//1zw9fUVLC0thS5dughHjhwRuySj9s9//lPw9PQULC0thRYtWgj//Oc/hZSUFLHLMjp79+4VANzxGD9+vCAItVO33333XcHDw0OQyWRCv379hOTkZHGLFtm9zll5ebkwYMAAwc3NTbCwsBD8/PyESZMm8R8fglDvOQMgLF++XLPP7du3hWnTpglOTk6CjY2NMGLECCE7O1u8okV2v3OWnp4u9OrVS3B2dhZkMpkQFBQkvPbaa4JSqRS3cJE9//zzgp+fn2BpaSm4ubkJ/fr104QZQdDf90wiCILQwCtGREREREaBY2iIiIjI5DHQEBERkcljoCEiIiKTx0BDREREJo+BhoiIiEweAw0RERGZPAYaIiIiMnkMNETU7EgkEqxfv17sMohIjxhoiMigJkyYAIlEcsdj0KBBYpdGRCbMXOwCiKj5GTRoEJYvX67VJpPJRKqGiJoCXqEhIoOTyWRQKBRaDycnJwC13UFLlixBbGwsrK2tERAQgLVr12q9/ty5c+jbty+sra3h4uKCyZMno7S0VGuf77//HmFhYZDJZPD09MSMGTO0thcUFGDEiBGwsbFBq1atsHHjxsb90ETUqBhoiMjovPvuuxg1ahTOnDmDp556CmPHjsXFixcBAGVlZRg4cCCcnJxw/PhxrFmzBrt27dIKLEuWLMH06dMxefJknDt3Dhs3bkRQUJDWeyxYsABjxozB2bNnMXjwYDz11FO4efOmQT8nEemR/tbTJCK6v/HjxwtmZmaCra2t1uPDDz8UBKF2ReMpU6ZovSYqKkqYOnWqIAiCsGzZMsHJyUkoLS3VbP/jjz8EqVSqWUXby8tLePvtt+9aAwDhnXfe0TwvLS0VAAhbt27V2+ckIsPiGBoiMrjHHnsMS5Ys0WpzdnbW/Dk6OlprW3R0NE6fPg0AuHjxIiIiImBra6vZ3r17d6jVaiQnJ0MikSArKwv9+vW7Zw3h4eGaP9va2sLBwQF5eXkN/UhEJDIGGiIyOFtb2zu6gPTF2tr6gfazsLDQei6RSKBWqxujJCIyAI6hISKjc+TIkTuet2nTBgDQpk0bnDlzBmVlZZrthw4dglQqRXBwMOzt7dGyZUvs3r3boDUTkbh4hYaIDE6lUiEnJ0erzdzcHK6urgCANWvWoFOnTujRowdWrlyJY8eO4bvvvgMAPPXUU5g3bx7Gjx+P+fPnIz8/Hy+99BKeeeYZeHh4AADmz5+PKVOmwN3dHbGxsSgpKcGhQ4fw0ksvGfaDEpHBMNAQkcFt27YNnp6eWm3BwcG4dOkSgNoZSKtXr8a0adPg6emJX375BaGhoQAAGxsbbN++HS+//DI6d+4MGxsbjBo1Cp988onmWOPHj0dFRQUWL16MV199Fa6urhg9erThPiARGZxEEARB7CKIiOpIJBLExcVh+PDhYpdCRCaEY2iIiIjI5DHQEBERkcnjGBoiMirsBSeihuAVGiIiIjJ5DDRERERk8hhoiIiIyOQx0BAREZHJY6AhIiIik8dAQ0RERCaPgYaIiIhMHgMNERERmTwGGiIiIjJ5/w+aeW4nu54a5wAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
+    "\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
     "plt.plot(range(n_epochs), train_loss_list)\n",
@@ -350,10 +600,39 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 81,
    "id": "e93efdfc",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Naël\\AppData\\Local\\Temp\\ipykernel_25496\\3291884398.py:1: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
+      "  model.load_state_dict(torch.load(\"./model_cifar.pt\"))\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test Loss: 21.754404\n",
+      "\n",
+      "Test Accuracy of airplane: 66% (660/1000)\n",
+      "Test Accuracy of automobile: 70% (706/1000)\n",
+      "Test Accuracy of  bird: 57% (577/1000)\n",
+      "Test Accuracy of   cat: 35% (350/1000)\n",
+      "Test Accuracy of  deer: 46% (466/1000)\n",
+      "Test Accuracy of   dog: 62% (629/1000)\n",
+      "Test Accuracy of  frog: 71% (713/1000)\n",
+      "Test Accuracy of horse: 66% (669/1000)\n",
+      "Test Accuracy of  ship: 74% (743/1000)\n",
+      "Test Accuracy of truck: 69% (695/1000)\n",
+      "\n",
+      "Test Accuracy (Overall): 62% (6208/10000)\n"
+     ]
+    }
+   ],
    "source": [
     "model.load_state_dict(torch.load(\"./model_cifar.pt\"))\n",
     "\n",
@@ -434,6 +713,280 @@
     "Compare the results obtained with this new network to those obtained previously."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "id": "500216cf",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "NewNet(\n",
+      "  (conv1): Conv2d(3, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+      "  (conv2): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+      "  (conv3): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+      "  (pool): 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",
+      "  (fc2): Linear(in_features=512, out_features=64, bias=True)\n",
+      "  (fc3): Linear(in_features=64, out_features=10, bias=True)\n",
+      "  (dropout): Dropout(p=0.4, inplace=False)\n",
+      ")\n",
+      "Epoch: 0 \tTraining Loss: 45.140489 \tValidation Loss: 40.619736\n",
+      "Validation loss decreased (inf --> 40.619736).  Saving model ...\n",
+      "Epoch: 1 \tTraining Loss: 38.536783 \tValidation Loss: 34.741309\n",
+      "Validation loss decreased (40.619736 --> 34.741309).  Saving model ...\n",
+      "Epoch: 2 \tTraining Loss: 34.026235 \tValidation Loss: 30.861427\n",
+      "Validation loss decreased (34.741309 --> 30.861427).  Saving model ...\n",
+      "Epoch: 3 \tTraining Loss: 31.209141 \tValidation Loss: 28.865500\n",
+      "Validation loss decreased (30.861427 --> 28.865500).  Saving model ...\n",
+      "Epoch: 4 \tTraining Loss: 29.180782 \tValidation Loss: 28.290057\n",
+      "Validation loss decreased (28.865500 --> 28.290057).  Saving model ...\n",
+      "Epoch: 5 \tTraining Loss: 27.562384 \tValidation Loss: 25.272991\n",
+      "Validation loss decreased (28.290057 --> 25.272991).  Saving model ...\n",
+      "Epoch: 6 \tTraining Loss: 26.059064 \tValidation Loss: 24.394384\n",
+      "Validation loss decreased (25.272991 --> 24.394384).  Saving model ...\n",
+      "Epoch: 7 \tTraining Loss: 24.566987 \tValidation Loss: 23.025685\n",
+      "Validation loss decreased (24.394384 --> 23.025685).  Saving model ...\n",
+      "Epoch: 8 \tTraining Loss: 23.269185 \tValidation Loss: 22.145485\n",
+      "Validation loss decreased (23.025685 --> 22.145485).  Saving model ...\n",
+      "Epoch: 9 \tTraining Loss: 21.932443 \tValidation Loss: 21.348889\n",
+      "Validation loss decreased (22.145485 --> 21.348889).  Saving model ...\n",
+      "Epoch: 10 \tTraining Loss: 20.900402 \tValidation Loss: 20.823972\n",
+      "Validation loss decreased (21.348889 --> 20.823972).  Saving model ...\n",
+      "Epoch: 11 \tTraining Loss: 19.834349 \tValidation Loss: 19.478657\n",
+      "Validation loss decreased (20.823972 --> 19.478657).  Saving model ...\n",
+      "Epoch: 12 \tTraining Loss: 18.734445 \tValidation Loss: 18.943267\n",
+      "Validation loss decreased (19.478657 --> 18.943267).  Saving model ...\n",
+      "Epoch: 13 \tTraining Loss: 17.820151 \tValidation Loss: 17.793669\n",
+      "Validation loss decreased (18.943267 --> 17.793669).  Saving model ...\n",
+      "Epoch: 14 \tTraining Loss: 16.934894 \tValidation Loss: 18.830886\n",
+      "Epoch: 15 \tTraining Loss: 16.101789 \tValidation Loss: 17.147667\n",
+      "Validation loss decreased (17.793669 --> 17.147667).  Saving model ...\n",
+      "Epoch: 16 \tTraining Loss: 15.353497 \tValidation Loss: 16.542786\n",
+      "Validation loss decreased (17.147667 --> 16.542786).  Saving model ...\n",
+      "Epoch: 17 \tTraining Loss: 14.549158 \tValidation Loss: 16.640117\n",
+      "Epoch: 18 \tTraining Loss: 13.812133 \tValidation Loss: 16.518704\n",
+      "Validation loss decreased (16.542786 --> 16.518704).  Saving model ...\n",
+      "Epoch: 19 \tTraining Loss: 13.157809 \tValidation Loss: 16.055983\n",
+      "Validation loss decreased (16.518704 --> 16.055983).  Saving model ...\n",
+      "Epoch: 20 \tTraining Loss: 12.525906 \tValidation Loss: 17.453218\n",
+      "Epoch: 21 \tTraining Loss: 11.836088 \tValidation Loss: 15.920230\n",
+      "Validation loss decreased (16.055983 --> 15.920230).  Saving model ...\n",
+      "Epoch: 22 \tTraining Loss: 11.138012 \tValidation Loss: 15.478585\n",
+      "Validation loss decreased (15.920230 --> 15.478585).  Saving model ...\n",
+      "Epoch: 23 \tTraining Loss: 10.662680 \tValidation Loss: 15.772580\n",
+      "Epoch: 24 \tTraining Loss: 9.992039 \tValidation Loss: 16.146863\n",
+      "Epoch: 25 \tTraining Loss: 9.504542 \tValidation Loss: 16.643314\n",
+      "Epoch: 26 \tTraining Loss: 8.905902 \tValidation Loss: 17.371982\n",
+      "Epoch: 27 \tTraining Loss: 8.563423 \tValidation Loss: 16.923179\n",
+      "Epoch: 28 \tTraining Loss: 7.994395 \tValidation Loss: 17.355411\n",
+      "Epoch: 29 \tTraining Loss: 7.658396 \tValidation Loss: 17.052353\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "# define the CNN architecture\n",
+    "\n",
+    "\n",
+    "class NewNet(nn.Module):\n",
+    "    def __init__(self, dropout_prob=0.4):\n",
+    "        super(NewNet, self).__init__()\n",
+    "        self.conv1 = nn.Conv2d(3, 16, kernel_size=3, padding=1)\n",
+    "        self.conv2 = nn.Conv2d(16, 32, kernel_size=3, padding=1)\n",
+    "        self.conv3 = nn.Conv2d(32, 64, kernel_size=3, padding=1)\n",
+    "        self.pool = nn.MaxPool2d(2, 2)\n",
+    "\n",
+    "        self.fc1 = nn.Linear(64 * 4 * 4, 512)\n",
+    "        self.fc2 = nn.Linear(512, 64)\n",
+    "        self.fc3 = nn.Linear(64, 10)\n",
+    "        self.dropout = nn.Dropout(dropout_prob)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        x = self.pool(F.relu(self.conv1(x)))\n",
+    "        x = self.pool(F.relu(self.conv2(x)))\n",
+    "        x = self.pool(F.relu(self.conv3(x)))\n",
+    "\n",
+    "        x = x.view(-1, 64 * 4 * 4)\n",
+    "\n",
+    "        x = F.relu(self.fc1(x))\n",
+    "        x = self.dropout(x)\n",
+    "        x = F.relu(self.fc2(x))\n",
+    "        x = self.dropout(x)\n",
+    "        x = self.fc3(x)\n",
+    "        return x\n",
+    "\n",
+    "\n",
+    "\n",
+    "# create a complete CNN\n",
+    "model11 = NewNet()\n",
+    "print(model11)\n",
+    "# move tensors to GPU if CUDA is available\n",
+    "if train_on_gpu:\n",
+    "    model11.cuda()\n",
+    "    \n",
+    "\n",
+    "criterion = nn.CrossEntropyLoss()  # specify loss function\n",
+    "optimizer = optim.SGD(model11.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",
+    "\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\n",
+    "    model11.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\n",
+    "        output = model11(data)\n",
+    "        # Calculate the batch loss\n",
+    "        loss = criterion(output, target)\n",
+    "        # Backward pass: compute gradient of the loss with respect to model 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\n",
+    "    model11.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\n",
+    "        output = model11(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.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 if validation loss has decreased\n",
+    "    if valid_loss <= valid_loss_min:\n",
+    "        print(\n",
+    "            \"Validation loss decreased ({:.6f} --> {:.6f}).  Saving model ...\".format(\n",
+    "                valid_loss_min, valid_loss\n",
+    "            )\n",
+    "        )\n",
+    "        torch.save(model11.state_dict(), \"new_model_cifar.pt\")\n",
+    "        valid_loss_min = valid_loss"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "id": "d4690f12",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Naël\\AppData\\Local\\Temp\\ipykernel_25496\\768216262.py:2: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
+      "  model11.load_state_dict(torch.load(\"new_model_cifar.pt\"))\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test Loss: 0.793692\n",
+      "\n",
+      "Accuracy of airplane: 81% (815/1000)\n",
+      "Accuracy of automobile: 81% (813/1000)\n",
+      "Accuracy of  bird: 61% (615/1000)\n",
+      "Accuracy of   cat: 55% (558/1000)\n",
+      "Accuracy of  deer: 68% (681/1000)\n",
+      "Accuracy of   dog: 63% (636/1000)\n",
+      "Accuracy of  frog: 78% (789/1000)\n",
+      "Accuracy of horse: 80% (803/1000)\n",
+      "Accuracy of  ship: 80% (805/1000)\n",
+      "Accuracy of truck: 85% (852/1000)\n",
+      "\n",
+      "Overall Accuracy: 73% (7367/10000)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Évaluation et comparaison des performances\n",
+    "model11.load_state_dict(torch.load(\"new_model_cifar.pt\"))\n",
+    "model11.eval()\n",
+    "\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",
+    "for data, target in test_loader:\n",
+    "    if train_on_gpu:\n",
+    "        data, target = data.cuda(), target.cuda()\n",
+    "\n",
+    "    output = model11(data)\n",
+    "    loss = criterion(output, target)\n",
+    "    test_loss += loss.item() * data.size(0)\n",
+    "\n",
+    "    _, pred = torch.max(output, 1)\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",
+    "\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",
+    "test_loss /= len(test_loader.dataset)\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",
+    "            \"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(\"Accuracy of %5s: N/A (no examples)\" % (classes[i]))\n",
+    "\n",
+    "print(\n",
+    "    \"\\nOverall Accuracy: %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",
    "id": "bc381cf4",
@@ -451,23 +1004,41 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 84,
    "id": "ef623c26",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model11:  fp32  \t Size (KB): 2330.946\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "2330946"
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "import os\n",
     "\n",
     "\n",
-    "def print_size_of_model(model, label=\"\"):\n",
-    "    torch.save(model.state_dict(), \"temp.p\")\n",
+    "def print_size_of_model(model11, label=\"\"):\n",
+    "    torch.save(model11.state_dict(), \"temp.p\")\n",
     "    size = os.path.getsize(\"temp.p\")\n",
-    "    print(\"model: \", label, \" \\t\", \"Size (KB):\", size / 1e3)\n",
+    "    print(\"model11: \", label, \" \\t\", \"Size (KB):\", size / 1e3)\n",
     "    os.remove(\"temp.p\")\n",
     "    return size\n",
     "\n",
     "\n",
-    "print_size_of_model(model, \"fp32\")"
+    "print_size_of_model(model11, \"fp32\")"
    ]
   },
   {
@@ -480,16 +1051,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 85,
    "id": "c4c65d4b",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model11:  int8  \t Size (KB): 659.806\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "659806"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "import torch.quantization\n",
     "\n",
     "\n",
-    "quantized_model = torch.quantization.quantize_dynamic(model, dtype=torch.qint8)\n",
-    "print_size_of_model(quantized_model, \"int8\")"
+    "quantized_model11 = torch.quantization.quantize_dynamic(model11, dtype=torch.qint8)\n",
+    "print_size_of_model(quantized_model11, \"int8\")"
    ]
   },
   {
@@ -500,6 +1089,143 @@
     "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",
+   "id": "c6c5b91b",
+   "metadata": {},
+   "source": [
+    "The initial model\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "id": "1d02cd82",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Naël\\AppData\\Local\\Temp\\ipykernel_25496\\3700591136.py:2: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
+      "  model.load_state_dict(torch.load(\"model_cifar.pt\"))\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model11:  int8  \t Size (KB): 76.522\n",
+      "\n",
+      "Evaluating quantized model...\n",
+      "Overall Accuracy (FP32): 72.51%\n",
+      "Overall Accuracy (INT8): 62.12%\n",
+      "\n",
+      "Class Accuracy Comparison (FP32 vs INT8):\n",
+      "airplane: FP32 = 75.50%, INT8 = 66.50%\n",
+      "automobile: FP32 = 83.30%, INT8 = 70.50%\n",
+      "bird: FP32 = 55.50%, INT8 = 57.20%\n",
+      "cat: FP32 = 45.70%, INT8 = 35.60%\n",
+      "deer: FP32 = 69.10%, INT8 = 47.10%\n",
+      "dog: FP32 = 69.00%, INT8 = 62.00%\n",
+      "frog: FP32 = 82.50%, INT8 = 71.30%\n",
+      "horse: FP32 = 78.90%, INT8 = 67.40%\n",
+      "ship: FP32 = 86.20%, INT8 = 74.00%\n",
+      "truck: FP32 = 79.40%, INT8 = 69.60%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Charger le modèle déjà formé si nécessaire\n",
+    "model.load_state_dict(torch.load(\"model_cifar.pt\"))\n",
+    "\n",
+    "# Effectuer une quantification post-formation (quantization dynamique)\n",
+    "quantized_model = torch.quantization.quantize_dynamic(model, dtype=torch.qint8)\n",
+    "print_size_of_model(quantized_model, \"int8\")\n",
+    "\n",
+    "# Évaluer les performances du modèle quantifié\n",
+    "print(\"\\nEvaluating quantized model...\")\n",
+    "test_loss_quant, class_acc_quant, overall_acc_quant = evaluate_model(quantized_model, test_loader, classes, criterion)\n",
+    "\n",
+    "# Comparer les résultats des deux modèles\n",
+    "print(f\"Overall Accuracy (FP32): {overall_acc_fp32:.2f}%\")\n",
+    "print(f\"Overall Accuracy (INT8): {overall_acc_quant:.2f}%\")\n",
+    "\n",
+    "# Pour chaque classe, comparez la précision de test\n",
+    "print(\"\\nClass Accuracy Comparison (FP32 vs INT8):\")\n",
+    "for i, cls in enumerate(classes):\n",
+    "    print(f\"{cls}: FP32 = {class_acc_fp32[i]:.2f}%, INT8 = {class_acc_quant[i]:.2f}%\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c837863e",
+   "metadata": {},
+   "source": [
+    "The new model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "id": "b3562d55",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Naël\\AppData\\Local\\Temp\\ipykernel_25496\\810026995.py:2: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
+      "  model11.load_state_dict(torch.load(\"new_model_cifar.pt\"))\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model11:  int8  \t Size (KB): 659.806\n",
+      "\n",
+      "Evaluating quantized model...\n",
+      "Overall Accuracy (FP32): 72.51%\n",
+      "Overall Accuracy (INT8): 73.65%\n",
+      "\n",
+      "Class Accuracy Comparison (FP32 vs INT8):\n",
+      "airplane: FP32 = 75.50%, INT8 = 81.50%\n",
+      "automobile: FP32 = 83.30%, INT8 = 81.30%\n",
+      "bird: FP32 = 55.50%, INT8 = 61.20%\n",
+      "cat: FP32 = 45.70%, INT8 = 55.50%\n",
+      "deer: FP32 = 69.10%, INT8 = 68.50%\n",
+      "dog: FP32 = 69.00%, INT8 = 63.50%\n",
+      "frog: FP32 = 82.50%, INT8 = 78.90%\n",
+      "horse: FP32 = 78.90%, INT8 = 80.40%\n",
+      "ship: FP32 = 86.20%, INT8 = 80.50%\n",
+      "truck: FP32 = 79.40%, INT8 = 85.20%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Charger le modèle déjà formé si nécessaire\n",
+    "model11.load_state_dict(torch.load(\"new_model_cifar.pt\"))\n",
+    "\n",
+    "# Effectuer une quantification post-formation (quantization dynamique)\n",
+    "quantized_model11 = torch.quantization.quantize_dynamic(model11, dtype=torch.qint8)\n",
+    "print_size_of_model(quantized_model11, \"int8\")\n",
+    "\n",
+    "# Évaluer les performances du modèle quantifié\n",
+    "print(\"\\nEvaluating quantized model...\")\n",
+    "test_loss_quant, class_acc_quant, overall_acc_quant = evaluate_model(quantized_model11, test_loader, classes, criterion)\n",
+    "\n",
+    "# Comparer les résultats des deux modèles\n",
+    "print(f\"Overall Accuracy (FP32): {overall_acc_fp32:.2f}%\")\n",
+    "print(f\"Overall Accuracy (INT8): {overall_acc_quant:.2f}%\")\n",
+    "\n",
+    "# Pour chaque classe, comparez la précision de test\n",
+    "print(\"\\nClass Accuracy Comparison (FP32 vs INT8):\")\n",
+    "for i, cls in enumerate(classes):\n",
+    "    print(f\"{cls}: FP32 = {class_acc_fp32[i]:.2f}%, INT8 = {class_acc_quant[i]:.2f}%\")\n",
+    "\n"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "a0a34b90",
@@ -521,10 +1247,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 99,
    "id": "b4d13080",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Naël\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\torchvision\\models\\_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
+      "  warnings.warn(\n",
+      "c:\\Users\\Naël\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet50_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet50_Weights.DEFAULT` to get the most up-to-date weights.\n",
+      "  warnings.warn(msg)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Predicted class is: Golden Retriever\n"
+     ]
+    },
+    {
+     "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",
@@ -558,14 +1312,14 @@
     "image = data_transform(image).unsqueeze(0)\n",
     "\n",
     "# Download the model if it's not there already. It will take a bit on the first run, after that it's fast\n",
-    "model = models.resnet50(pretrained=True)\n",
+    "model1 = models.resnet50(pretrained=True)\n",
     "# Send the model to the GPU\n",
     "# model.cuda()\n",
     "# Set layers such as dropout and batchnorm in evaluation mode\n",
-    "model.eval()\n",
+    "model1.eval()\n",
     "\n",
     "# Get the 1000-dimensional model output\n",
-    "out = model(image)\n",
+    "out = model1(image)\n",
     "# Find the predicted class\n",
     "print(\"Predicted class is: {}\".format(labels[out.argmax()]))"
    ]
@@ -588,56 +1342,448 @@
   },
   {
    "cell_type": "markdown",
-   "id": "5d57da4b",
+   "id": "a61a479e",
    "metadata": {},
    "source": [
-    "## Exercise 4: Transfer Learning\n",
-    "    \n",
-    "    \n",
-    "For this work, we will use a pre-trained model (ResNet18) as a descriptor extractor and will refine the classification by training only the last fully connected layer of the network. Thus, the output layer of the pre-trained network will be replaced by a layer adapted to the new classes to be recognized which will be in our case ants and bees.\n",
-    "Download and unzip in your working directory the dataset available at the address :\n",
-    "    \n",
-    "https://download.pytorch.org/tutorial/hymenoptera_data.zip\n",
-    "    \n",
-    "Execute the following code in order to display some images of the dataset."
+    "Etude du code :\n",
+    "\n",
+    "Le code utilise le modèle ResNet-50 pré-entraîné sur ImageNet pour classifier une image (ici, dog.png). Il applique un prétraitement (redimensionnement, normalisation et conversion en tensor) avant de l'envoyer dans le modèle. Le modèle génère une prédiction, et le code affiche la classe correspondante à partir du fichier imagenet-simple-labels.json.\n",
+    "\n",
+    "Étapes clés :\n",
+    "\n",
+    "Préparation de l'image : Redimensionner à 224x224 px, normalisation avec des valeurs spécifiques.\n",
+    "Chargement du modèle pré-entraîné : Chargement du modèle ResNet-50 et mise en mode évaluation.\n",
+    "Prédiction : L'image est passée à travers le modèle, et la classe prédite est déterminée via argmax().\n",
+    "Consultation de la classe : La classe prédite est affichée.\n",
+    "Exemple de sortie : Si l'image contient un chien, le modèle devrait prédire une classe comme \"dog\", car ResNet-50 a été formé sur ImageNet, qui inclut de nombreuses races de chiens."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "43ac127b",
+   "metadata": {},
+   "source": [
+    "Autre image: "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "be2d31f5",
+   "execution_count": 100,
+   "id": "42b314f5",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Naël\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\torchvision\\models\\_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
+      "  warnings.warn(\n",
+      "c:\\Users\\Naël\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet50_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet50_Weights.DEFAULT` to get the most up-to-date weights.\n",
+      "  warnings.warn(msg)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Predicted class is: airliner\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "import os\n",
+    "import json\n",
+    "from PIL import Image\n",
     "\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import torch\n",
-    "import torchvision\n",
-    "from torchvision import datasets, transforms\n",
+    "# Choose an image to pass through the model\n",
+    "test_image1 = \"airplane.jpg\"\n",
     "\n",
-    "# Data augmentation and normalization for training\n",
-    "# Just normalization for validation\n",
-    "data_transforms = {\n",
-    "    \"train\": transforms.Compose(\n",
-    "        [\n",
-    "            transforms.RandomResizedCrop(\n",
-    "                224\n",
-    "            ),  # ImageNet models were trained on 224x224 images\n",
-    "            transforms.RandomHorizontalFlip(),  # flip horizontally 50% of the time - increases train set variability\n",
-    "            transforms.ToTensor(),  # convert it to a PyTorch tensor\n",
-    "            transforms.Normalize(\n",
-    "                [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]\n",
-    "            ),  # ImageNet models expect this norm\n",
-    "        ]\n",
-    "    ),\n",
-    "    \"val\": transforms.Compose(\n",
-    "        [\n",
-    "            transforms.Resize(256),\n",
-    "            transforms.CenterCrop(224),\n",
-    "            transforms.ToTensor(),\n",
-    "            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n",
+    "# Configure matplotlib for pretty inline plots\n",
+    "#%matplotlib inline\n",
+    "#%config InlineBackend.figure_format = 'retina'\n",
+    "\n",
+    "# Prepare the labels\n",
+    "with open(\"imagenet-simple-labels.json\") as f:\n",
+    "    labels = json.load(f)\n",
+    "\n",
+    "# First prepare the transformations: resize the image to what the model was trained on and convert it to a tensor\n",
+    "data_transform = transforms.Compose(\n",
+    "    [\n",
+    "        transforms.Resize((224, 224)),\n",
+    "        transforms.ToTensor(),\n",
+    "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n",
+    "    ]\n",
+    ")\n",
+    "# Load the image\n",
+    "\n",
+    "image1 = Image.open(test_image1)\n",
+    "plt.imshow(image1), plt.xticks([]), plt.yticks([])\n",
+    "\n",
+    "# Now apply the transformation, expand the batch dimension, and send the image to the GPU\n",
+    "# image = data_transform(image).unsqueeze(0).cuda()\n",
+    "image1 = data_transform(image1).unsqueeze(0)\n",
+    "\n",
+    "# Download the model if it's not there already. It will take a bit on the first run, after that it's fast\n",
+    "model1 = models.resnet50(pretrained=True)\n",
+    "# Send the model to the GPU\n",
+    "# model.cuda()\n",
+    "# Set layers such as dropout and batchnorm in evaluation mode\n",
+    "model1.eval()\n",
+    "\n",
+    "# Get the 1000-dimensional model output\n",
+    "out = model1(image1)\n",
+    "# Find the predicted class\n",
+    "print(\"Predicted class is: {}\".format(labels[out.argmax()]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7c3da88c",
+   "metadata": {},
+   "source": [
+    "Model size: "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "id": "9b9f68ee",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model size (ResNet-50): 102.54 MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import os\n",
+    "\n",
+    "# Save the model's state dict to a temporary file\n",
+    "torch.save(model1.state_dict(), \"resnet50_model.pth\")\n",
+    "model_size = os.path.getsize(\"resnet50_model.pth\") / 1e6  # Size in MB\n",
+    "print(f\"Model size (ResNet-50): {model_size:.2f} MB\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5d64b068",
+   "metadata": {},
+   "source": [
+    "Quantization :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "id": "ad804f6a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Quantized model size (ResNet-50): 96.40 MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch.quantization\n",
+    "\n",
+    "# Quantize the model\n",
+    "quantized_model1 = torch.quantization.quantize_dynamic(model1, dtype=torch.qint8)\n",
+    "\n",
+    "# Save the quantized model and check its size\n",
+    "torch.save(quantized_model1.state_dict(), \"quantized_resnet50.pth\")\n",
+    "quantized_model_size = os.path.getsize(\"quantized_resnet50.pth\") / 1e6  # Size in MB\n",
+    "print(f\"Quantized model size (ResNet-50): {quantized_model_size:.2f} MB\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0137d67d",
+   "metadata": {},
+   "source": [
+    "Accuracy after quantization :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "id": "9fa9a888",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Predicted class (Quantized model): Golden Retriever\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Set the model to evaluation mode\n",
+    "quantized_model1.eval()\n",
+    "\n",
+    "# Get the model's predictions for the image\n",
+    "out_quantized = quantized_model1(image)\n",
+    "\n",
+    "# Find the predicted class for the quantized model\n",
+    "predicted_class_quantized = labels[out_quantized.argmax()]\n",
+    "print(f\"Predicted class (Quantized model): {predicted_class_quantized}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c5f11c4d",
+   "metadata": {},
+   "source": [
+    "autre image:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "id": "c5aa259f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Predicted class (Quantized model): airliner\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Set the model to evaluation mode\n",
+    "quantized_model1.eval()\n",
+    "\n",
+    "# Get the model's predictions for the image\n",
+    "out_quantized = quantized_model1(image1)\n",
+    "\n",
+    "# Find the predicted class for the quantized model\n",
+    "predicted_class_quantized1 = labels[out_quantized.argmax()]\n",
+    "print(f\"Predicted class (Quantized model): {predicted_class_quantized1}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "51f0569a",
+   "metadata": {},
+   "source": [
+    "Quantization has no major effect on the predicted class"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5e9b1be0",
+   "metadata": {},
+   "source": [
+    "On prend un autre modèle CNN : MobileNetV2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "id": "74c3ecbc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Naël\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\torchvision\\models\\_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
+      "  warnings.warn(\n",
+      "c:\\Users\\Naël\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=MobileNet_V2_Weights.IMAGENET1K_V1`. You can also use `weights=MobileNet_V2_Weights.DEFAULT` to get the most up-to-date weights.\n",
+      "  warnings.warn(msg)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Predicted class (MobileNetV2): Golden Retriever\n"
+     ]
+    }
+   ],
+   "source": [
+    "model_mobilenet_v2 = models.mobilenet_v2(pretrained=True)\n",
+    "model_mobilenet_v2.eval()\n",
+    "out_mobilenet_v2 = model_mobilenet_v2(image)\n",
+    "print(f\"Predicted class (MobileNetV2): {labels[out_mobilenet_v2.argmax()]}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dbc0c12f",
+   "metadata": {},
+   "source": [
+    "autre image :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "id": "b3fa78f2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Predicted class (MobileNetV2): airliner\n"
+     ]
+    }
+   ],
+   "source": [
+    "model_mobilenet_v2 = models.mobilenet_v2(pretrained=True)\n",
+    "model_mobilenet_v2.eval()\n",
+    "out_mobilenet_v2 = model_mobilenet_v2(image1)\n",
+    "print(f\"Predicted class (MobileNetV2): {labels[out_mobilenet_v2.argmax()]}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "27036b4c",
+   "metadata": {},
+   "source": [
+    "Avec VGG16:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "id": "c4eef251",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Naël\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=VGG16_Weights.IMAGENET1K_V1`. You can also use `weights=VGG16_Weights.DEFAULT` to get the most up-to-date weights.\n",
+      "  warnings.warn(msg)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Predicted class (VGG16): Golden Retriever\n"
+     ]
+    }
+   ],
+   "source": [
+    "model_vgg16 = models.vgg16(pretrained=True)\n",
+    "model_vgg16.eval()\n",
+    "out_vgg16 = model_vgg16(image)\n",
+    "print(f\"Predicted class (VGG16): {labels[out_vgg16.argmax()]}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5400157c",
+   "metadata": {},
+   "source": [
+    "autre image:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "id": "9177a490",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Predicted class (VGG16): airliner\n"
+     ]
+    }
+   ],
+   "source": [
+    "model_vgg16 = models.vgg16(pretrained=True)\n",
+    "model_vgg16.eval()\n",
+    "out_vgg16 = model_vgg16(image1)\n",
+    "print(f\"Predicted class (VGG16): {labels[out_vgg16.argmax()]}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5d57da4b",
+   "metadata": {},
+   "source": [
+    "## Exercise 4: Transfer Learning\n",
+    "    \n",
+    "    \n",
+    "For this work, we will use a pre-trained model (ResNet18) as a descriptor extractor and will refine the classification by training only the last fully connected layer of the network. Thus, the output layer of the pre-trained network will be replaced by a layer adapted to the new classes to be recognized which will be in our case ants and bees.\n",
+    "Download and unzip in your working directory the dataset available at the address :\n",
+    "    \n",
+    "https://download.pytorch.org/tutorial/hymenoptera_data.zip\n",
+    "    \n",
+    "Execute the following code in order to display some images of the dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "be2d31f5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import os\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import torch\n",
+    "import torchvision\n",
+    "from torchvision import datasets, transforms\n",
+    "\n",
+    "# Data augmentation and normalization for training\n",
+    "# Just normalization for validation\n",
+    "data_transforms = {\n",
+    "    \"train\": transforms.Compose(\n",
+    "        [\n",
+    "            transforms.RandomResizedCrop(\n",
+    "                224\n",
+    "            ),  # ImageNet models were trained on 224x224 images\n",
+    "            transforms.RandomHorizontalFlip(),  # flip horizontally 50% of the time - increases train set variability\n",
+    "            transforms.ToTensor(),  # convert it to a PyTorch tensor\n",
+    "            transforms.Normalize(\n",
+    "                [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]\n",
+    "            ),  # ImageNet models expect this norm\n",
+    "        ]\n",
+    "    ),\n",
+    "    \"val\": transforms.Compose(\n",
+    "        [\n",
+    "            transforms.Resize(256),\n",
+    "            transforms.CenterCrop(224),\n",
+    "            transforms.ToTensor(),\n",
+    "            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n",
     "        ]\n",
     "    ),\n",
     "}\n",
@@ -696,10 +1842,95 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "572d824c",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Naël\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\torch\\utils\\data\\dataloader.py:617: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2 (`cpuset` is not taken into account), which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
+      "  warnings.warn(\n",
+      "c:\\Users\\Naël\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\torchvision\\models\\_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
+      "  warnings.warn(\n",
+      "c:\\Users\\Naël\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.\n",
+      "  warnings.warn(msg)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/10\n",
+      "----------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\Naël\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\torch\\optim\\lr_scheduler.py:224: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "train Loss: 0.7490 Acc: 0.5902\n",
+      "val Loss: 0.2213 Acc: 0.9020\n",
+      "\n",
+      "Epoch 2/10\n",
+      "----------\n",
+      "train Loss: 0.5150 Acc: 0.7418\n",
+      "val Loss: 0.1953 Acc: 0.9150\n",
+      "\n",
+      "Epoch 3/10\n",
+      "----------\n",
+      "train Loss: 0.4746 Acc: 0.7623\n",
+      "val Loss: 0.1675 Acc: 0.9477\n",
+      "\n",
+      "Epoch 4/10\n",
+      "----------\n",
+      "train Loss: 0.4005 Acc: 0.8197\n",
+      "val Loss: 0.1642 Acc: 0.9542\n",
+      "\n",
+      "Epoch 5/10\n",
+      "----------\n",
+      "train Loss: 0.3640 Acc: 0.8361\n",
+      "val Loss: 0.1612 Acc: 0.9542\n",
+      "\n",
+      "Epoch 6/10\n",
+      "----------\n",
+      "train Loss: 0.3622 Acc: 0.8402\n",
+      "val Loss: 0.2016 Acc: 0.9477\n",
+      "\n",
+      "Epoch 7/10\n",
+      "----------\n",
+      "train Loss: 0.3800 Acc: 0.8279\n",
+      "val Loss: 0.2520 Acc: 0.9150\n",
+      "\n",
+      "Epoch 8/10\n",
+      "----------\n",
+      "train Loss: 0.3210 Acc: 0.8566\n",
+      "val Loss: 0.1779 Acc: 0.9542\n",
+      "\n",
+      "Epoch 9/10\n",
+      "----------\n",
+      "train Loss: 0.3656 Acc: 0.8279\n",
+      "val Loss: 0.1946 Acc: 0.9346\n",
+      "\n",
+      "Epoch 10/10\n",
+      "----------\n",
+      "train Loss: 0.3444 Acc: 0.8525\n",
+      "val Loss: 0.1992 Acc: 0.9346\n",
+      "\n",
+      "Training complete in 27m 47s\n",
+      "Best val Acc: 0.954248\n"
+     ]
+    }
+   ],
    "source": [
     "import copy\n",
     "import os\n",
@@ -897,6 +2128,735 @@
     "Apply ther quantization (post and quantization aware) and evaluate impact on model size and accuracy."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "ae734136",
+   "metadata": {},
+   "source": [
+    "Le code fourni :\n",
+    "\n",
+    "Charge un modèle pré-entraîné ResNet18 et gèle ses paramètres pour l'utiliser comme un extracteur de caractéristiques.\n",
+    "Remplace la couche de classification finale par une seule couche entièrement connectée (nn.Linear) afin de classer les images en deux catégories : fourmis et abeilles.\n",
+    "Ajuste uniquement la dernière couche en utilisant un ensemble d'entraînement, évalue ses performances sur un ensemble de validation, et obtient une précision maximale sur la validation d’environ 95,4%.\n",
+    "Cela illustre la puissance de l'apprentissage par transfert (transfer learning), qui consiste à exploiter des modèles pré-entraînés avec des ajustements minimaux pour des tâches spécifiques."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "166deba7",
+   "metadata": {},
+   "source": [
+    "eval model:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "9e9cc80f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def eval_model(model, dataloader, criterion):\n",
+    "    \"\"\"Evaluate the model on a given test set.\"\"\"\n",
+    "    model.eval()  # Set to evaluation mode\n",
+    "    running_loss = 0.0\n",
+    "    running_corrects = 0\n",
+    "\n",
+    "    # No gradient tracking needed during evaluation\n",
+    "    with torch.no_grad():\n",
+    "        for inputs, labels in dataloader:\n",
+    "            inputs = inputs.to(device)\n",
+    "            labels = labels.to(device)\n",
+    "\n",
+    "            outputs = model(inputs)\n",
+    "            _, preds = torch.max(outputs, 1)\n",
+    "            loss = criterion(outputs, labels)\n",
+    "\n",
+    "            running_loss += loss.item() * inputs.size(0)\n",
+    "            running_corrects += torch.sum(preds == labels.data)\n",
+    "\n",
+    "    total_loss = running_loss / len(dataloader.dataset)\n",
+    "    total_acc = running_corrects.double() / len(dataloader.dataset)\n",
+    "\n",
+    "    print(f\"Test Loss: {total_loss:.4f} Test Acc: {total_acc:.4f}\")\n",
+    "    return total_loss, total_acc\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9ac7fba0",
+   "metadata": {},
+   "source": [
+    "test set:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "c55e6146",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import os\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import torch\n",
+    "from torchvision import datasets, transforms\n",
+    "\n",
+    "# Transformations pour les données\n",
+    "data_transforms = {\n",
+    "    \"train\": transforms.Compose(\n",
+    "        [\n",
+    "            transforms.RandomResizedCrop(224),\n",
+    "            transforms.RandomHorizontalFlip(),\n",
+    "            transforms.ToTensor(),\n",
+    "            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n",
+    "        ]\n",
+    "    ),\n",
+    "    \"val\": transforms.Compose(\n",
+    "        [\n",
+    "            transforms.Resize(256),\n",
+    "            transforms.CenterCrop(224),\n",
+    "            transforms.ToTensor(),\n",
+    "            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n",
+    "        ]\n",
+    "    ),\n",
+    "    \"test\": transforms.Compose(\n",
+    "        [\n",
+    "            transforms.Resize(256),\n",
+    "            transforms.CenterCrop(224),\n",
+    "            transforms.ToTensor(),\n",
+    "            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n",
+    "        ]\n",
+    "    ),\n",
+    "}\n",
+    "\n",
+    "data_dir = \"hymenoptera_data\"\n",
+    "\n",
+    "# Chargement des datasets\n",
+    "image_datasets = {\n",
+    "    x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x])\n",
+    "    for x in [\"train\", \"val\", \"test\"]  # Ajout de \"test\"\n",
+    "}\n",
+    "\n",
+    "# Création des dataloaders\n",
+    "dataloaders = {\n",
+    "    x: torch.utils.data.DataLoader(\n",
+    "        image_datasets[x], batch_size=4, shuffle=(x == \"train\"), num_workers=0\n",
+    "    )\n",
+    "    for x in [\"train\", \"val\", \"test\"]\n",
+    "}\n",
+    "\n",
+    "# Tailles des datasets\n",
+    "dataset_sizes = {x: len(image_datasets[x]) for x in [\"train\", \"val\", \"test\"]}\n",
+    "\n",
+    "# Noms des classes\n",
+    "class_names = image_datasets[\"train\"].classes\n",
+    "\n",
+    "# Vérifier le device (GPU ou CPU)\n",
+    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
+    "\n",
+    "# Visualisation d'un batch d'images\n",
+    "def imshow(inp, title=None):\n",
+    "    inp = inp.numpy().transpose((1, 2, 0))\n",
+    "    mean = np.array([0.485, 0.456, 0.406])\n",
+    "    std = np.array([0.229, 0.224, 0.225])\n",
+    "    inp = std * inp + mean  # Un-normalisation\n",
+    "    inp = np.clip(inp, 0, 1)\n",
+    "    plt.imshow(inp)\n",
+    "    if title is not None:\n",
+    "        plt.title(title)\n",
+    "    plt.pause(0.001)\n",
+    "    plt.show()\n",
+    "\n",
+    "# Exemple de visualisation\n",
+    "inputs, classes = next(iter(dataloaders[\"test\"]))  # Charger un batch de l'ensemble de test\n",
+    "out = torchvision.utils.make_grid(inputs)\n",
+    "imshow(out, title=[class_names[x] for x in classes])\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2389a7cf",
+   "metadata": {},
+   "source": [
+    "ajout d'une double couche avec RELU et Dropout"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "8db50f7d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from torch import nn\n",
+    "from torchvision import models\n",
+    "\n",
+    "model_ft = models.resnet18(pretrained=True)  # Charger ResNet18 avec des poids pré-entraînés\n",
+    "\n",
+    "\n",
+    "\n",
+    "num_ftrs = model_ft.fc.in_features\n",
+    "model_ft.fc = nn.Sequential(\n",
+    "    nn.Linear(num_ftrs, 512),   # Première couche\n",
+    "    nn.ReLU(),                  # Activation ReLU\n",
+    "    nn.Dropout(0.5),            # Dropout après la première couche\n",
+    "    nn.Linear(512, 2),          # Deuxième couche pour la classification\n",
+    "    nn.Dropout(0.5),            # Dropout final\n",
+    ")\n",
+    "model_ft = model_ft.to(device)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b18e5f99",
+   "metadata": {},
+   "source": [
+    "validation et test :"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "9e38c71c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 0/24\n",
+      "----------\n",
+      "train Loss: 0.7316 Acc: 0.5369\n",
+      "val Loss: 0.3499 Acc: 0.8954\n",
+      "Epoch 1/24\n",
+      "----------\n",
+      "train Loss: 0.5333 Acc: 0.7172\n",
+      "val Loss: 0.3555 Acc: 0.8824\n",
+      "Epoch 2/24\n",
+      "----------\n",
+      "train Loss: 0.4780 Acc: 0.7131\n",
+      "val Loss: 0.3237 Acc: 0.8562\n",
+      "Epoch 3/24\n",
+      "----------\n",
+      "train Loss: 0.6701 Acc: 0.6885\n",
+      "val Loss: 0.3079 Acc: 0.8824\n",
+      "Epoch 4/24\n",
+      "----------\n",
+      "train Loss: 0.5677 Acc: 0.7541\n",
+      "val Loss: 0.3241 Acc: 0.8497\n",
+      "Epoch 5/24\n",
+      "----------\n",
+      "train Loss: 0.5019 Acc: 0.7254\n",
+      "val Loss: 0.3056 Acc: 0.8627\n",
+      "Epoch 6/24\n",
+      "----------\n",
+      "train Loss: 0.4889 Acc: 0.7254\n",
+      "val Loss: 0.3125 Acc: 0.8562\n",
+      "Epoch 7/24\n",
+      "----------\n",
+      "train Loss: 0.4599 Acc: 0.7623\n",
+      "val Loss: 0.2752 Acc: 0.9085\n",
+      "Epoch 8/24\n",
+      "----------\n",
+      "train Loss: 0.5304 Acc: 0.7295\n",
+      "val Loss: 0.2619 Acc: 0.9020\n",
+      "Epoch 9/24\n",
+      "----------\n",
+      "train Loss: 0.4063 Acc: 0.7664\n",
+      "val Loss: 0.2528 Acc: 0.9085\n",
+      "Epoch 10/24\n",
+      "----------\n",
+      "train Loss: 0.4597 Acc: 0.8033\n",
+      "val Loss: 0.2515 Acc: 0.9346\n",
+      "Epoch 11/24\n",
+      "----------\n"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[10], line 104\u001b[0m\n\u001b[0;32m    101\u001b[0m exp_lr_scheduler \u001b[38;5;241m=\u001b[39m optim\u001b[38;5;241m.\u001b[39mlr_scheduler\u001b[38;5;241m.\u001b[39mStepLR(optimizer_ft, step_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m7\u001b[39m, gamma\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.1\u001b[39m)\n\u001b[0;32m    103\u001b[0m \u001b[38;5;66;03m# Entraîner le modèle\u001b[39;00m\n\u001b[1;32m--> 104\u001b[0m model_ft \u001b[38;5;241m=\u001b[39m \u001b[43mtrain_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel_ft\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcriterion\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moptimizer_ft\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexp_lr_scheduler\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_epochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m25\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "Cell \u001b[1;32mIn[10], line 63\u001b[0m, in \u001b[0;36mtrain_model\u001b[1;34m(model, criterion, optimizer, scheduler, num_epochs)\u001b[0m\n\u001b[0;32m     60\u001b[0m \u001b[38;5;66;03m# Forward\u001b[39;00m\n\u001b[0;32m     61\u001b[0m \u001b[38;5;66;03m# Historique des gradients uniquement pendant la phase d'entraînement\u001b[39;00m\n\u001b[0;32m     62\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mset_grad_enabled(phase \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtrain\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m---> 63\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[43minputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     64\u001b[0m     _, preds \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mmax(outputs, \u001b[38;5;241m1\u001b[39m)\n\u001b[0;32m     65\u001b[0m     loss \u001b[38;5;241m=\u001b[39m criterion(outputs, labels)\n",
+      "File \u001b[1;32mc:\\Users\\Naël\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\torch\\nn\\modules\\module.py:1736\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1734\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m   1735\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1736\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
+      "File \u001b[1;32mc:\\Users\\Naël\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\torch\\nn\\modules\\module.py:1747\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1742\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m   1743\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m   1744\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m   1745\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m   1746\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1747\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m forward_call(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m   1749\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m   1750\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n",
+      "File \u001b[1;32mc:\\Users\\Naël\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\torchvision\\models\\resnet.py:285\u001b[0m, in \u001b[0;36mResNet.forward\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m    284\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, x: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[1;32m--> 285\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_forward_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\Naël\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\torchvision\\models\\resnet.py:276\u001b[0m, in \u001b[0;36mResNet._forward_impl\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m    274\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlayer2(x)\n\u001b[0;32m    275\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlayer3(x)\n\u001b[1;32m--> 276\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlayer4\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    278\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mavgpool(x)\n\u001b[0;32m    279\u001b[0m x \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mflatten(x, \u001b[38;5;241m1\u001b[39m)\n",
+      "File \u001b[1;32mc:\\Users\\Naël\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\torch\\nn\\modules\\module.py:1736\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1734\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m   1735\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1736\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
+      "File \u001b[1;32mc:\\Users\\Naël\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\torch\\nn\\modules\\module.py:1747\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1742\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m   1743\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m   1744\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m   1745\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m   1746\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1747\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m forward_call(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m   1749\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m   1750\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n",
+      "File \u001b[1;32mc:\\Users\\Naël\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\torch\\nn\\modules\\container.py:250\u001b[0m, in \u001b[0;36mSequential.forward\u001b[1;34m(self, input)\u001b[0m\n\u001b[0;32m    248\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m):\n\u001b[0;32m    249\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m module \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m:\n\u001b[1;32m--> 250\u001b[0m         \u001b[38;5;28minput\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[43mmodule\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m    251\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28minput\u001b[39m\n",
+      "File \u001b[1;32mc:\\Users\\Naël\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\torch\\nn\\modules\\module.py:1736\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1734\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m   1735\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1736\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
+      "File \u001b[1;32mc:\\Users\\Naël\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\torch\\nn\\modules\\module.py:1747\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1742\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m   1743\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m   1744\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m   1745\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m   1746\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1747\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m forward_call(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m   1749\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m   1750\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n",
+      "File \u001b[1;32mc:\\Users\\Naël\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\torchvision\\models\\resnet.py:93\u001b[0m, in \u001b[0;36mBasicBlock.forward\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m     90\u001b[0m identity \u001b[38;5;241m=\u001b[39m x\n\u001b[0;32m     92\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconv1(x)\n\u001b[1;32m---> 93\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbn1\u001b[49m\u001b[43m(\u001b[49m\u001b[43mout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     94\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrelu(out)\n\u001b[0;32m     96\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconv2(out)\n",
+      "File \u001b[1;32mc:\\Users\\Naël\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\torch\\nn\\modules\\module.py:1736\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1734\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m   1735\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 1736\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
+      "File \u001b[1;32mc:\\Users\\Naël\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\torch\\nn\\modules\\module.py:1747\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1742\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m   1743\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m   1744\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m   1745\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m   1746\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1747\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m forward_call(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m   1749\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m   1750\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n",
+      "File \u001b[1;32mc:\\Users\\Naël\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\torch\\nn\\modules\\batchnorm.py:193\u001b[0m, in \u001b[0;36m_BatchNorm.forward\u001b[1;34m(self, input)\u001b[0m\n\u001b[0;32m    186\u001b[0m     bn_training \u001b[38;5;241m=\u001b[39m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrunning_mean \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;129;01mand\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrunning_var \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m    188\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    189\u001b[0m \u001b[38;5;124;03mBuffers are only updated if they are to be tracked and we are in training mode. Thus they only need to be\u001b[39;00m\n\u001b[0;32m    190\u001b[0m \u001b[38;5;124;03mpassed when the update should occur (i.e. in training mode when they are tracked), or when buffer stats are\u001b[39;00m\n\u001b[0;32m    191\u001b[0m \u001b[38;5;124;03mused for normalization (i.e. in eval mode when buffers are not None).\u001b[39;00m\n\u001b[0;32m    192\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m--> 193\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbatch_norm\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    194\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m    195\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;66;43;03m# If buffers are not to be tracked, ensure that they won't be updated\u001b[39;49;00m\n\u001b[0;32m    196\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrunning_mean\u001b[49m\n\u001b[0;32m    197\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtraining\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrack_running_stats\u001b[49m\n\u001b[0;32m    198\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m    199\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrunning_var\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtraining\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrack_running_stats\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m    200\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    201\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbias\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    202\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbn_training\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    203\u001b[0m \u001b[43m    \u001b[49m\u001b[43mexponential_average_factor\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    204\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43meps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    205\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32mc:\\Users\\Naël\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\torch\\nn\\functional.py:2812\u001b[0m, in \u001b[0;36mbatch_norm\u001b[1;34m(input, running_mean, running_var, weight, bias, training, momentum, eps)\u001b[0m\n\u001b[0;32m   2809\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m training:\n\u001b[0;32m   2810\u001b[0m     _verify_batch_size(\u001b[38;5;28minput\u001b[39m\u001b[38;5;241m.\u001b[39msize())\n\u001b[1;32m-> 2812\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbatch_norm\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   2813\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2814\u001b[0m \u001b[43m    \u001b[49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2815\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbias\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2816\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrunning_mean\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2817\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrunning_var\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2818\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtraining\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2819\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmomentum\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2820\u001b[0m \u001b[43m    \u001b[49m\u001b[43meps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2821\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackends\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcudnn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43menabled\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2822\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "import time\n",
+    "import torch\n",
+    "from torch import nn, optim\n",
+    "from torchvision import datasets, transforms\n",
+    "from torch.utils.data import DataLoader\n",
+    "\n",
+    "# Préparer les données\n",
+    "data_transforms = {\n",
+    "    \"train\": transforms.Compose([\n",
+    "        transforms.RandomResizedCrop(224),\n",
+    "        transforms.RandomHorizontalFlip(),\n",
+    "        transforms.ToTensor(),\n",
+    "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n",
+    "    ]),\n",
+    "    \"val\": transforms.Compose([\n",
+    "        transforms.Resize(256),\n",
+    "        transforms.CenterCrop(224),\n",
+    "        transforms.ToTensor(),\n",
+    "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n",
+    "    ]),\n",
+    "}\n",
+    "\n",
+    "data_dir = \"hymenoptera_data\"\n",
+    "datasets = {x: datasets.ImageFolder(f\"{data_dir}/{x}\", transform=data_transforms[x])\n",
+    "            for x in [\"train\", \"val\"]}\n",
+    "\n",
+    "dataloaders = {x: DataLoader(datasets[x], batch_size=4, shuffle=True, num_workers=0)\n",
+    "               for x in [\"train\", \"val\"]}\n",
+    "\n",
+    "dataset_sizes = {x: len(datasets[x]) for x in [\"train\", \"val\"]}\n",
+    "class_names = datasets[\"train\"].classes\n",
+    "\n",
+    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
+    "\n",
+    "# Définir la fonction d'entraînement\n",
+    "def train_model(model, criterion, optimizer, scheduler, num_epochs=25):\n",
+    "    since = time.time()\n",
+    "    best_model_wts = model.state_dict()\n",
+    "    best_acc = 0.0\n",
+    "\n",
+    "    for epoch in range(num_epochs):\n",
+    "        print(f\"Epoch {epoch}/{num_epochs - 1}\")\n",
+    "        print(\"-\" * 10)\n",
+    "\n",
+    "        # Chaque époque a une phase d'entraînement et de validation\n",
+    "        for phase in [\"train\", \"val\"]:\n",
+    "            if phase == \"train\":\n",
+    "                model.train()  # Mode entraînement\n",
+    "            else:\n",
+    "                model.eval()   # Mode validation\n",
+    "\n",
+    "            running_loss = 0.0\n",
+    "            running_corrects = 0\n",
+    "\n",
+    "            # Itérer sur les données\n",
+    "            for inputs, labels in dataloaders[phase]:\n",
+    "                inputs = inputs.to(device)\n",
+    "                labels = labels.to(device)\n",
+    "\n",
+    "                # Forward\n",
+    "                # Historique des gradients uniquement pendant la phase d'entraînement\n",
+    "                with torch.set_grad_enabled(phase == \"train\"):\n",
+    "                    outputs = model(inputs)\n",
+    "                    _, preds = torch.max(outputs, 1)\n",
+    "                    loss = criterion(outputs, labels)\n",
+    "\n",
+    "                    # Backward + optimiser uniquement en phase d'entraînement\n",
+    "                    if phase == \"train\":\n",
+    "                        optimizer.zero_grad()\n",
+    "                        loss.backward()\n",
+    "                        optimizer.step()\n",
+    "\n",
+    "                # Statistiques\n",
+    "                running_loss += loss.item() * inputs.size(0)\n",
+    "                running_corrects += torch.sum(preds == labels.data)\n",
+    "\n",
+    "            if phase == \"train\":\n",
+    "                scheduler.step()\n",
+    "\n",
+    "            epoch_loss = running_loss / dataset_sizes[phase]\n",
+    "            epoch_acc = running_corrects.double() / dataset_sizes[phase]\n",
+    "\n",
+    "            print(f\"{phase} Loss: {epoch_loss:.4f} Acc: {epoch_acc:.4f}\")\n",
+    "\n",
+    "            # Copier le modèle si c'est le meilleur\n",
+    "            if phase == \"val\" and epoch_acc > best_acc:\n",
+    "                best_acc = epoch_acc\n",
+    "                best_model_wts = model.state_dict()\n",
+    "\n",
+    "    time_elapsed = time.time() - since\n",
+    "    print(f\"Training complete in {time_elapsed // 60:.0f}m {time_elapsed % 60:.0f}s\")\n",
+    "    print(f\"Best val Acc: {best_acc:.4f}\")\n",
+    "\n",
+    "    # Charger les meilleurs poids\n",
+    "    model.load_state_dict(best_model_wts)\n",
+    "    return model\n",
+    "\n",
+    "# Configuration du modèle, critère, optimiseur et scheduler\n",
+    "criterion = nn.CrossEntropyLoss()\n",
+    "optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)\n",
+    "exp_lr_scheduler = optim.lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)\n",
+    "\n",
+    "# Entraîner le modèle\n",
+    "model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler, num_epochs=25)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e62e1170",
+   "metadata": {},
+   "source": [
+    "code pour la quantification"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "b8650e47",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch.quantization as quant\n",
+    "\n",
+    "# Appliquer une quantification statique\n",
+    "model_quantized = quant.quantize_dynamic(\n",
+    "    model_ft,  # Modèle entraîné\n",
+    "    {nn.Linear},  # Couches à quantifier\n",
+    "    dtype=torch.qint8  # Quantification en 8 bits\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d6c21d8d",
+   "metadata": {},
+   "source": [
+    "mesurer la taille du modèle et la précision après quantification:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "1265b3e4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Nombre d'images dans l'ensemble de test: 20\n",
+      "Classes détectées: ['ants', 'bees']\n",
+      "Quantized Model Size: 43995.77 KB\n",
+      "Test Loss: 0.0396 Test Acc: 1.0000\n",
+      "Test Loss: 0.0396, Test Accuracy: 1.0000\n"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "from torchvision import datasets, transforms\n",
+    "import torch\n",
+    "\n",
+    "# Ajouter les transformations pour le test\n",
+    "data_transforms = {\n",
+    "    \"test\": transforms.Compose([\n",
+    "        transforms.Resize(256),\n",
+    "        transforms.CenterCrop(224),\n",
+    "        transforms.ToTensor(),\n",
+    "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n",
+    "    ]),\n",
+    "}\n",
+    "\n",
+    "# Charger l'ensemble de test\n",
+    "test_dir = \"hymenoptera_data/test\"  # Chemin du répertoire test\n",
+    "if not os.path.exists(test_dir):\n",
+    "    raise FileNotFoundError(f\"Le répertoire {test_dir} n'existe pas.\")\n",
+    "\n",
+    "# Charger les données\n",
+    "test_dataset = datasets.ImageFolder(test_dir, transform=data_transforms[\"test\"])\n",
+    "test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=4, shuffle=False, num_workers=0)\n",
+    "\n",
+    "# Vérifications\n",
+    "print(f\"Nombre d'images dans l'ensemble de test: {len(test_dataset)}\")\n",
+    "print(f\"Classes détectées: {test_dataset.classes}\")\n",
+    "\n",
+    "# Calculer la taille du modèle quantifié\n",
+    "torch.save(model_quantized.state_dict(), \"quantized_model.pth\")\n",
+    "quantized_size = os.path.getsize(\"quantized_model.pth\")\n",
+    "print(f\"Quantized Model Size: {quantized_size / 1024:.2f} KB\")\n",
+    "\n",
+    "# Évaluer le modèle quantifié\n",
+    "test_loss, test_accuracy = eval_model(model_quantized, test_loader, criterion)\n",
+    "print(f\"Test Loss: {test_loss:.4f}, Test Accuracy: {test_accuracy:.4f}\")\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fadee17b",
+   "metadata": {},
+   "source": [
+    "Code pour QAT"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "c79c4cd8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/5, Loss: 8.9098, Accuracy: 0.0000\n",
+      "Epoch 2/5, Loss: 8.7604, Accuracy: 0.0000\n",
+      "Epoch 3/5, Loss: 8.9061, Accuracy: 0.0000\n",
+      "Epoch 4/5, Loss: 8.8346, Accuracy: 0.0000\n",
+      "Epoch 5/5, Loss: 8.8711, Accuracy: 0.0000\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Préparer le modèle pour QAT\n",
+    "model_ft.train()\n",
+    "model_qat = quant.prepare_qat(model_ft)\n",
+    "\n",
+    "# Réentraînement avec QAT\n",
+    "train_model(model_qat, dataloaders, criterion, optimizer_ft, num_epochs=5)\n",
+    "\n",
+    "# Convertir en modèle quantifié\n",
+    "model_qat = quant.convert(model_qat)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "447ba1df",
+   "metadata": {},
+   "source": [
+    "évaluation après le QAT:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "925ae283",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/5, Loss: 8.8845, Accuracy: 0.0041\n",
+      "Epoch 2/5, Loss: 8.8869, Accuracy: 0.0000\n",
+      "Epoch 3/5, Loss: 8.8361, Accuracy: 0.0000\n",
+      "Epoch 4/5, Loss: 8.8982, Accuracy: 0.0000\n",
+      "Epoch 5/5, Loss: 8.8949, Accuracy: 0.0000\n",
+      "Modèle après conversion QAT :\n",
+      "ResNet(\n",
+      "  (conv1): QuantizedConv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), scale=0.4070129096508026, zero_point=63, padding=(3, 3), bias=False)\n",
+      "  (bn1): QuantizedBatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "  (relu): ReLU(inplace=True)\n",
+      "  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
+      "  (layer1): Sequential(\n",
+      "    (0): BasicBlock(\n",
+      "      (conv1): QuantizedConv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), scale=0.1489342749118805, zero_point=89, padding=(1, 1), bias=False)\n",
+      "      (bn1): QuantizedBatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "      (conv2): QuantizedConv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), scale=0.0427684485912323, zero_point=75, padding=(1, 1), bias=False)\n",
+      "      (bn2): QuantizedBatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "    )\n",
+      "    (1): BasicBlock(\n",
+      "      (conv1): QuantizedConv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), scale=0.11696109920740128, zero_point=78, padding=(1, 1), bias=False)\n",
+      "      (bn1): QuantizedBatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "      (conv2): QuantizedConv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), scale=0.03855881839990616, zero_point=67, padding=(1, 1), bias=False)\n",
+      "      (bn2): QuantizedBatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "    )\n",
+      "  )\n",
+      "  (layer2): Sequential(\n",
+      "    (0): BasicBlock(\n",
+      "      (conv1): QuantizedConv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), scale=0.09545671939849854, zero_point=67, padding=(1, 1), bias=False)\n",
+      "      (bn1): QuantizedBatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "      (conv2): QuantizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), scale=0.03664045408368111, zero_point=73, padding=(1, 1), bias=False)\n",
+      "      (bn2): QuantizedBatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (downsample): Sequential(\n",
+      "        (0): QuantizedConv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), scale=0.05049058049917221, zero_point=57, bias=False)\n",
+      "        (1): QuantizedBatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      )\n",
+      "    )\n",
+      "    (1): BasicBlock(\n",
+      "      (conv1): QuantizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), scale=0.056038640439510345, zero_point=72, padding=(1, 1), bias=False)\n",
+      "      (bn1): QuantizedBatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "      (conv2): QuantizedConv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), scale=0.02696939930319786, zero_point=65, padding=(1, 1), bias=False)\n",
+      "      (bn2): QuantizedBatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "    )\n",
+      "  )\n",
+      "  (layer3): Sequential(\n",
+      "    (0): BasicBlock(\n",
+      "      (conv1): QuantizedConv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), scale=0.061427369713783264, zero_point=69, padding=(1, 1), bias=False)\n",
+      "      (bn1): QuantizedBatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "      (conv2): QuantizedConv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), scale=0.03712525591254234, zero_point=67, padding=(1, 1), bias=False)\n",
+      "      (bn2): QuantizedBatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (downsample): Sequential(\n",
+      "        (0): QuantizedConv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), scale=0.016148226335644722, zero_point=71, bias=False)\n",
+      "        (1): QuantizedBatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      )\n",
+      "    )\n",
+      "    (1): BasicBlock(\n",
+      "      (conv1): QuantizedConv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), scale=0.04653831571340561, zero_point=64, padding=(1, 1), bias=False)\n",
+      "      (bn1): QuantizedBatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "      (conv2): QuantizedConv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), scale=0.02474578283727169, zero_point=61, padding=(1, 1), bias=False)\n",
+      "      (bn2): QuantizedBatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "    )\n",
+      "  )\n",
+      "  (layer4): Sequential(\n",
+      "    (0): BasicBlock(\n",
+      "      (conv1): QuantizedConv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), scale=0.049124229699373245, zero_point=53, padding=(1, 1), bias=False)\n",
+      "      (bn1): QuantizedBatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "      (conv2): QuantizedConv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), scale=0.023290442302823067, zero_point=66, padding=(1, 1), bias=False)\n",
+      "      (bn2): QuantizedBatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (downsample): Sequential(\n",
+      "        (0): QuantizedConv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), scale=0.029446132481098175, zero_point=56, bias=False)\n",
+      "        (1): QuantizedBatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      )\n",
+      "    )\n",
+      "    (1): BasicBlock(\n",
+      "      (conv1): QuantizedConv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), scale=0.03651643916964531, zero_point=69, padding=(1, 1), bias=False)\n",
+      "      (bn1): QuantizedBatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "      (conv2): QuantizedConv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), scale=0.011244130320847034, zero_point=52, padding=(1, 1), bias=False)\n",
+      "      (bn2): QuantizedBatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "    )\n",
+      "  )\n",
+      "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
+      "  (fc): QuantizedLinear(in_features=512, out_features=1000, scale=0.10513576120138168, zero_point=63, qscheme=torch.per_channel_affine)\n",
+      ")\n",
+      "Erreur avec l'inférence quantifiée :  Could not run 'quantized::conv2d.new' with arguments from the 'CPU' backend. This could be because the operator doesn't exist for this backend, or was omitted during the selective/custom build process (if using custom build). If you are a Facebook employee using PyTorch on mobile, please visit https://fburl.com/ptmfixes for possible resolutions. 'quantized::conv2d.new' is only available for these backends: [Meta, QuantizedCPU, BackendSelect, Python, FuncTorchDynamicLayerBackMode, Functionalize, Named, Conjugate, Negative, ZeroTensor, ADInplaceOrView, AutogradOther, AutogradCPU, AutogradCUDA, AutogradXLA, AutogradMPS, AutogradXPU, AutogradHPU, AutogradLazy, AutogradMeta, Tracer, AutocastCPU, AutocastXPU, AutocastMPS, AutocastCUDA, FuncTorchBatched, BatchedNestedTensor, FuncTorchVmapMode, Batched, VmapMode, FuncTorchGradWrapper, PythonTLSSnapshot, FuncTorchDynamicLayerFrontMode, PreDispatch, PythonDispatcher].\n",
+      "\n",
+      "Meta: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\core\\MetaFallbackKernel.cpp:23 [backend fallback]\n",
+      "QuantizedCPU: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\native\\quantized\\cpu\\qconv.cpp:1972 [kernel]\n",
+      "BackendSelect: fallthrough registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\core\\BackendSelectFallbackKernel.cpp:3 [backend fallback]\n",
+      "Python: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\core\\PythonFallbackKernel.cpp:153 [backend fallback]\n",
+      "FuncTorchDynamicLayerBackMode: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\functorch\\DynamicLayer.cpp:497 [backend fallback]\n",
+      "Functionalize: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\FunctionalizeFallbackKernel.cpp:349 [backend fallback]\n",
+      "Named: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\core\\NamedRegistrations.cpp:7 [backend fallback]\n",
+      "Conjugate: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\ConjugateFallback.cpp:17 [backend fallback]\n",
+      "Negative: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\native\\NegateFallback.cpp:18 [backend fallback]\n",
+      "ZeroTensor: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\ZeroTensorFallback.cpp:86 [backend fallback]\n",
+      "ADInplaceOrView: fallthrough registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:96 [backend fallback]\n",
+      "AutogradOther: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:63 [backend fallback]\n",
+      "AutogradCPU: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:67 [backend fallback]\n",
+      "AutogradCUDA: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:75 [backend fallback]\n",
+      "AutogradXLA: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:79 [backend fallback]\n",
+      "AutogradMPS: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:87 [backend fallback]\n",
+      "AutogradXPU: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:71 [backend fallback]\n",
+      "AutogradHPU: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:100 [backend fallback]\n",
+      "AutogradLazy: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:83 [backend fallback]\n",
+      "AutogradMeta: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\core\\VariableFallbackKernel.cpp:91 [backend fallback]\n",
+      "Tracer: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\torch\\csrc\\autograd\\TraceTypeManual.cpp:294 [backend fallback]\n",
+      "AutocastCPU: fallthrough registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\autocast_mode.cpp:321 [backend fallback]\n",
+      "AutocastXPU: fallthrough registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\autocast_mode.cpp:463 [backend fallback]\n",
+      "AutocastMPS: fallthrough registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\autocast_mode.cpp:209 [backend fallback]\n",
+      "AutocastCUDA: fallthrough registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\autocast_mode.cpp:165 [backend fallback]\n",
+      "FuncTorchBatched: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\functorch\\LegacyBatchingRegistrations.cpp:731 [backend fallback]\n",
+      "BatchedNestedTensor: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\functorch\\LegacyBatchingRegistrations.cpp:758 [backend fallback]\n",
+      "FuncTorchVmapMode: fallthrough registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\functorch\\VmapModeRegistrations.cpp:27 [backend fallback]\n",
+      "Batched: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\LegacyBatchingRegistrations.cpp:1075 [backend fallback]\n",
+      "VmapMode: fallthrough registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\VmapModeRegistrations.cpp:33 [backend fallback]\n",
+      "FuncTorchGradWrapper: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\functorch\\TensorWrapper.cpp:207 [backend fallback]\n",
+      "PythonTLSSnapshot: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\core\\PythonFallbackKernel.cpp:161 [backend fallback]\n",
+      "FuncTorchDynamicLayerFrontMode: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\functorch\\DynamicLayer.cpp:493 [backend fallback]\n",
+      "PreDispatch: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\core\\PythonFallbackKernel.cpp:165 [backend fallback]\n",
+      "PythonDispatcher: registered at C:\\actions-runner\\_work\\pytorch\\pytorch\\builder\\windows\\pytorch\\aten\\src\\ATen\\core\\PythonFallbackKernel.cpp:157 [backend fallback]\n",
+      "\n",
+      "Échec de l'évaluation du modèle quantifié.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Préparer le modèle pour QAT\n",
+    "model_ft.train()\n",
+    "\n",
+    "# Ajouter la quantization-aware training (QAT)\n",
+    "model_qat = quant.prepare_qat(model_ft, inplace=False)\n",
+    "\n",
+    "# Réentraînement avec QAT (5 époques pour illustration)\n",
+    "train_model(model_qat, dataloaders, criterion, optimizer_ft, num_epochs=5)\n",
+    "\n",
+    "# Convertir en modèle quantifié\n",
+    "model_qat.eval()\n",
+    "model_qat = quant.convert(model_qat, inplace=False)\n",
+    "\n",
+    "# Vérifiez la structure du modèle après la conversion\n",
+    "print(\"Modèle après conversion QAT :\")\n",
+    "print(model_qat)\n",
+    "\n",
+    "# Évaluer le modèle quantifié sur le jeu de test\n",
+    "def eval_model_quantized(model, dataloader, criterion):\n",
+    "    model.eval()\n",
+    "    running_loss = 0.0\n",
+    "    running_corrects = 0\n",
+    "\n",
+    "    with torch.no_grad():\n",
+    "        for inputs, labels in dataloader:\n",
+    "            inputs, labels = inputs.to(device), labels.to(device)\n",
+    "\n",
+    "            # Vérifiez que les entrées sont correctement préparées\n",
+    "            try:\n",
+    "                outputs = model(inputs)\n",
+    "            except NotImplementedError as e:\n",
+    "                print(\"Erreur avec l'inférence quantifiée : \", e)\n",
+    "                return None, None\n",
+    "\n",
+    "            loss = criterion(outputs, labels)\n",
+    "            _, preds = torch.max(outputs, 1)\n",
+    "\n",
+    "            running_loss += loss.item() * inputs.size(0)\n",
+    "            running_corrects += torch.sum(preds == labels.data)\n",
+    "\n",
+    "    loss = running_loss / len(dataloader.dataset)\n",
+    "    accuracy = running_corrects.double() / len(dataloader.dataset)\n",
+    "    return loss, accuracy\n",
+    "\n",
+    "\n",
+    "# Évaluer le modèle\n",
+    "criterion = nn.CrossEntropyLoss()\n",
+    "test_loss, test_accuracy = eval_model_quantized(model_qat, dataloaders[\"test\"], criterion)\n",
+    "\n",
+    "if test_loss is not None and test_accuracy is not None:\n",
+    "    print(f\"Test Loss (quantized model): {test_loss:.4f}\")\n",
+    "    print(f\"Test Accuracy (quantized model): {test_accuracy:.4f}\")\n",
+    "else:\n",
+    "    print(\"Échec de l'évaluation du modèle quantifié.\")\n"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "04a263f0",
@@ -926,7 +2886,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.8.5 ('base')",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -940,12 +2900,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
-  },
-  "vscode": {
-   "interpreter": {
-    "hash": "9e3efbebb05da2d4a1968abe9a0645745f54b63feb7a85a514e4da0495be97eb"
-   }
+   "version": "3.9.1"
   }
  },
  "nbformat": 4,
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