From c8ecdd9e7aadb79f850cfd5d60a599fb17ea3152 Mon Sep 17 00:00:00 2001
From: Sophie Bessac <sophie.bessac@ecl20.ec-lyon.fr>
Date: Wed, 22 Nov 2023 18:33:36 +0100
Subject: [PATCH] Exercise 2 completed

---
 TD2 Deep Learning.ipynb | 613 +++++++++++++++++++++++++---------------
 1 file changed, 392 insertions(+), 221 deletions(-)

diff --git a/TD2 Deep Learning.ipynb b/TD2 Deep Learning.ipynb
index 7830519..2203596 100644
--- a/TD2 Deep Learning.ipynb	
+++ b/TD2 Deep Learning.ipynb	
@@ -39,38 +39,47 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 19,
+      "execution_count": 33,
       "id": "330a42f5",
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "330a42f5",
-        "outputId": "d5754b3a-4443-4050-ef62-56f3714ac9ff"
+        "outputId": "51bf9cc9-3f2f-47a4-fd8a-d3b2bbbb99f9"
       },
       "outputs": [
         {
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.1.0+cu118)\n",
-            "Requirement already satisfied: torchvision in /usr/local/lib/python3.10/dist-packages (0.16.0+cu118)\n",
-            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch) (3.13.1)\n",
-            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch) (4.5.0)\n",
-            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch) (1.12)\n",
-            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch) (3.2.1)\n",
-            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch) (3.1.2)\n",
-            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch) (2023.6.0)\n",
-            "Requirement already satisfied: triton==2.1.0 in /usr/local/lib/python3.10/dist-packages (from torch) (2.1.0)\n",
-            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from torchvision) (1.23.5)\n",
-            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from torchvision) (2.31.0)\n",
-            "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.10/dist-packages (from torchvision) (9.4.0)\n",
-            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch) (2.1.3)\n",
-            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision) (3.3.2)\n",
-            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision) (3.4)\n",
-            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision) (2.0.7)\n",
-            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision) (2023.7.22)\n",
-            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch) (1.3.0)\n"
+            "Requirement already satisfied: torch in c:\\users\\sophi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (2.1.0)\n",
+            "Requirement already satisfied: torchvision in c:\\users\\sophi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (0.16.0)\n",
+            "Requirement already satisfied: filelock in c:\\users\\sophi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from torch) (3.13.1)\n",
+            "Requirement already satisfied: typing-extensions in c:\\users\\sophi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from torch) (4.8.0)\n",
+            "Requirement already satisfied: sympy in c:\\users\\sophi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from torch) (1.12)\n",
+            "Requirement already satisfied: networkx in c:\\users\\sophi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from torch) (3.2.1)\n",
+            "Requirement already satisfied: jinja2 in c:\\users\\sophi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from torch) (3.1.2)\n",
+            "Requirement already satisfied: fsspec in c:\\users\\sophi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from torch) (2023.10.0)\n",
+            "Requirement already satisfied: numpy in c:\\users\\sophi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from torchvision) (1.26.1)\n",
+            "Requirement already satisfied: requests in c:\\users\\sophi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from torchvision) (2.31.0)\n",
+            "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in c:\\users\\sophi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from torchvision) (10.1.0)\n",
+            "Requirement already satisfied: MarkupSafe>=2.0 in c:\\users\\sophi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from jinja2->torch) (2.1.3)\n",
+            "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\users\\sophi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from requests->torchvision) (3.3.2)\n",
+            "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\sophi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from requests->torchvision) (3.4)\n",
+            "Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\users\\sophi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from requests->torchvision) (2.0.7)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\sophi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from requests->torchvision) (2023.7.22)\n",
+            "Requirement already satisfied: mpmath>=0.19 in c:\\users\\sophi\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from sympy->torch) (1.3.0)\n",
+            "Note: you may need to restart the kernel to use updated packages.\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "[notice] A new release of pip is available: 23.3 -> 23.3.1\n",
+            "[notice] To update, run: C:\\Users\\sophi\\AppData\\Local\\Microsoft\\WindowsApps\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\python.exe -m pip install --upgrade pip\n"
           ]
         }
       ],
@@ -91,48 +100,48 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 20,
+      "execution_count": 34,
       "id": "b1950f0a",
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "b1950f0a",
-        "outputId": "92b4ce32-c59c-42a8-e9c8-71abe1053130"
+        "outputId": "ff2e74cd-addc-4a55-8263-d3af939af05c"
       },
       "outputs": [
         {
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "tensor([[ 5.2494e-01, -4.0182e-01,  2.0913e-02,  4.9306e-02, -1.8613e-01,\n",
-            "          2.5415e-02,  5.9517e-01, -1.1023e+00,  1.4325e+00, -7.5941e-01],\n",
-            "        [ 2.5168e-01,  8.2589e-01, -1.3894e-01, -1.2905e+00,  1.2307e-02,\n",
-            "         -1.3316e+00, -7.9754e-01, -5.0957e-01,  2.5259e-01, -1.0786e+00],\n",
-            "        [ 1.7668e+00,  1.4609e+00, -1.2874e+00,  6.9421e-01,  4.8891e-02,\n",
-            "          7.4302e-01, -5.7228e-01, -6.2928e-02, -1.5047e+00, -1.9424e-02],\n",
-            "        [-7.3993e-01, -1.8025e+00, -5.4516e-01,  4.3605e-01,  1.6414e+00,\n",
-            "         -2.6724e-01,  5.2907e-01, -1.5627e+00, -5.6430e-01,  2.3602e+00],\n",
-            "        [ 8.6630e-02,  1.5521e+00,  2.0030e-01,  1.8237e+00, -1.4640e-01,\n",
-            "          8.7716e-01, -8.1633e-01,  6.9329e-01, -6.4170e-01, -1.4932e+00],\n",
-            "        [ 1.1325e+00,  1.9685e-01,  2.7233e-01,  5.3424e-01, -1.2787e-01,\n",
-            "         -6.1054e-01, -3.8839e-01, -1.0399e+00, -1.1747e+00,  8.0571e-01],\n",
-            "        [-2.8432e-01, -3.3827e-01, -7.0311e-01,  7.5571e-01, -4.5543e-01,\n",
-            "         -4.9683e-01,  4.2695e-01, -3.1113e-01,  9.2971e-01, -7.1405e-01],\n",
-            "        [-5.7516e-01,  1.2686e+00,  8.6101e-01,  2.9615e-01,  2.0959e-02,\n",
-            "         -3.2249e-03, -1.3091e+00, -2.5597e+00,  1.8884e+00,  2.0272e+00],\n",
-            "        [-5.4777e-01,  4.4571e-01, -6.7105e-01,  1.1485e+00, -1.0861e+00,\n",
-            "         -1.6666e+00, -1.0605e+00,  3.2782e-01, -8.6262e-01,  1.0035e+00],\n",
-            "        [-1.2914e+00,  2.4935e-01,  1.7921e+00, -3.4014e-01,  1.4183e+00,\n",
-            "          1.4804e+00, -1.2816e+00, -1.3060e+00,  4.1243e-01, -4.4559e-01],\n",
-            "        [ 1.3623e+00, -1.0417e+00, -1.1182e+00,  4.4119e-01, -1.2102e+00,\n",
-            "         -3.8122e-01,  2.8777e-01,  4.9836e-01,  6.3106e-01,  1.5111e+00],\n",
-            "        [-2.6837e-01, -7.6734e-01, -3.7387e-01, -1.0474e+00,  1.4884e-03,\n",
-            "          4.9649e-01,  3.0126e-01, -1.5955e-01,  1.8573e+00,  4.2354e-01],\n",
-            "        [-4.2421e-01,  3.7555e-01,  8.0292e-01, -9.7115e-01,  1.0342e+00,\n",
-            "          1.4361e+00, -6.7796e-01, -1.8773e+00, -1.2950e+00, -1.6513e+00],\n",
-            "        [ 7.0690e-01, -2.1791e+00, -1.0998e-01, -1.5428e+00,  1.5446e-01,\n",
-            "          1.2835e+00, -9.0946e-01,  8.4979e-01,  1.4175e-01, -4.0172e-01]])\n",
+            "tensor([[-1.6489, -1.4006,  1.0209,  0.0599,  2.0141, -1.0110, -0.9928,  1.5249,\n",
+            "         -1.0576,  0.9910],\n",
+            "        [ 0.9321,  0.2041, -0.8843, -1.1571, -0.5402,  0.2791,  1.7013,  0.6609,\n",
+            "          0.3185,  1.2078],\n",
+            "        [ 1.6619,  1.0257, -0.4051, -0.2252, -2.8811, -1.4909,  0.0064,  0.7987,\n",
+            "         -2.3415, -2.2361],\n",
+            "        [-0.3189, -0.8377, -0.4157, -0.0903, -0.1400, -0.4664,  0.4044,  0.0830,\n",
+            "          1.3116, -0.4544],\n",
+            "        [ 1.9502, -2.5376, -1.6994, -1.2138,  0.8294, -0.6015,  1.0804, -1.1213,\n",
+            "          0.0316, -0.0660],\n",
+            "        [-0.0333, -0.5250,  0.4858,  1.2907, -1.1940, -0.9111, -0.1596,  1.0632,\n",
+            "         -0.9121, -0.5304],\n",
+            "        [ 0.7337,  1.6000,  0.7381, -1.7457, -0.2256, -0.2005,  0.4062,  1.6591,\n",
+            "          1.1321, -1.8378],\n",
+            "        [-0.2022, -0.4474, -0.2700, -0.0793, -0.9838, -1.2301,  1.5742,  1.3698,\n",
+            "          0.7907, -0.6237],\n",
+            "        [ 1.1473, -0.8092, -0.0153, -0.2960,  1.4371,  0.8046,  0.6802,  0.5934,\n",
+            "          0.0613,  0.8733],\n",
+            "        [-0.1906, -0.0695, -0.0743,  0.2860, -1.1743, -0.9484,  0.9505,  1.8431,\n",
+            "         -0.3969,  0.2229],\n",
+            "        [ 1.0942,  1.0806, -1.5800,  1.0239, -1.3722,  1.8793, -0.1174, -1.0485,\n",
+            "          0.7011, -0.6849],\n",
+            "        [ 0.6015, -1.6910,  2.0147, -0.0982, -1.1639, -0.8881,  0.7499,  0.0402,\n",
+            "         -0.1937,  1.0949],\n",
+            "        [-0.6174,  1.1145,  0.8895, -1.6238, -1.3035,  0.2365,  0.5524,  0.0939,\n",
+            "          0.3844, -0.0214],\n",
+            "        [-0.9197,  0.1049,  1.6806,  0.3372,  0.6003,  0.4594, -1.0100,  1.3562,\n",
+            "         -0.0797, -0.6426]])\n",
             "AlexNet(\n",
             "  (features): Sequential(\n",
             "    (0): Conv2d(3, 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2))\n",
@@ -206,21 +215,21 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 21,
+      "execution_count": 35,
       "id": "6e18f2fd",
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "6e18f2fd",
-        "outputId": "d662ac58-1dda-4bfb-b001-669a6ece9f2d"
+        "outputId": "faa42135-cbd1-436e-b7b8-296f2aae0fed"
       },
       "outputs": [
         {
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "CUDA is available!  Training on GPU ...\n"
+            "CUDA is not available.  Training on CPU ...\n"
           ]
         }
       ],
@@ -248,14 +257,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 22,
+      "execution_count": 36,
       "id": "462666a2",
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "462666a2",
-        "outputId": "19aabeb6-e74d-4f20-a9d9-65635a6db45e"
+        "outputId": "e1046dd6-7092-40a3-9645-0a5ae4789305"
       },
       "outputs": [
         {
@@ -337,14 +346,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 23,
+      "execution_count": 37,
       "id": "317bf070",
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "317bf070",
-        "outputId": "c6f3c4b7-35db-45f3-bdad-26be9cf2ae6a"
+        "outputId": "3e1226cc-540e-4ba0-8e11-d1545a6dda89"
       },
       "outputs": [
         {
@@ -372,13 +381,9 @@
         "class Net(nn.Module):\n",
         "    def __init__(self):\n",
         "        super(Net, self).__init__()\n",
-        "        self.conv1 = nn.Conv2d(\n",
-        "            3, 6, 5\n",
-        "        )  # 3 input image channel, 6 output channels, 5x5 square convolution kernel\n",
-        "        self.pool = nn.MaxPool2d(2, 2)  # max pooling over a (2, 2) window\n",
-        "        self.conv2 = nn.Conv2d(\n",
-        "            6, 16, 5\n",
-        "        )  # 6 input image channel, 16 output channels, 5x5 square convolution kernel\n",
+        "        self.conv1 = nn.Conv2d(3, 6, 5) # 3 input image channel, 6 output channels, 5x5 square convolution kernel\n",
+        "        self.pool = nn.MaxPool2d(2, 2) # max pooling over a (2, 2) window\n",
+        "        self.conv2 = nn.Conv2d(6, 16, 5) # 6 input image channel, 16 output channels, 5x5 square convolution kernel\n",
         "        self.fc1 = nn.Linear(16 * 5 * 5, 120)\n",
         "        self.fc2 = nn.Linear(120, 84)\n",
         "        self.fc3 = nn.Linear(84, 10)\n",
@@ -386,7 +391,7 @@
         "    def forward(self, x):\n",
         "        x = self.pool(F.relu(self.conv1(x)))\n",
         "        x = self.pool(F.relu(self.conv2(x)))\n",
-        "        x = x.view(-1, 16 * 5 * 5)  # flatten image input\n",
+        "        x = x.view(-1, 16 * 5 * 5) # flatten image input\n",
         "        x = F.relu(self.fc1(x))\n",
         "        x = F.relu(self.fc2(x))\n",
         "        x = self.fc3(x)\n",
@@ -423,45 +428,47 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 24,
+      "execution_count": 38,
       "id": "4b53f229",
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "4b53f229",
-        "outputId": "5dc9612d-3603-4f06-d8db-2fe4c69c5e33"
+        "outputId": "9e31ce68-4e5d-438e-fd27-4f2919de99e9"
       },
       "outputs": [
         {
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "Epoch: 0 \tTraining Loss: 43.007933 \tValidation Loss: 38.700007\n",
-            "Validation loss decreased (inf --> 38.700007).  Saving model ...\n",
-            "Epoch: 1 \tTraining Loss: 34.654129 \tValidation Loss: 32.038069\n",
-            "Validation loss decreased (38.700007 --> 32.038069).  Saving model ...\n",
-            "Epoch: 2 \tTraining Loss: 30.219521 \tValidation Loss: 29.115662\n",
-            "Validation loss decreased (32.038069 --> 29.115662).  Saving model ...\n",
-            "Epoch: 3 \tTraining Loss: 28.002696 \tValidation Loss: 28.537005\n",
-            "Validation loss decreased (29.115662 --> 28.537005).  Saving model ...\n",
-            "Epoch: 4 \tTraining Loss: 26.453065 \tValidation Loss: 27.011829\n",
-            "Validation loss decreased (28.537005 --> 27.011829).  Saving model ...\n",
-            "Epoch: 5 \tTraining Loss: 25.123836 \tValidation Loss: 25.241111\n",
-            "Validation loss decreased (27.011829 --> 25.241111).  Saving model ...\n",
-            "Epoch: 6 \tTraining Loss: 23.944256 \tValidation Loss: 25.654793\n",
-            "Epoch: 7 \tTraining Loss: 22.955018 \tValidation Loss: 23.667445\n",
-            "Validation loss decreased (25.241111 --> 23.667445).  Saving model ...\n",
-            "Epoch: 8 \tTraining Loss: 22.089791 \tValidation Loss: 23.900664\n",
-            "Epoch: 9 \tTraining Loss: 21.246848 \tValidation Loss: 22.699720\n",
-            "Validation loss decreased (23.667445 --> 22.699720).  Saving model ...\n",
-            "Epoch: 10 \tTraining Loss: 20.528481 \tValidation Loss: 23.282343\n",
-            "Epoch: 11 \tTraining Loss: 19.772756 \tValidation Loss: 22.047524\n",
-            "Validation loss decreased (22.699720 --> 22.047524).  Saving model ...\n",
-            "Epoch: 12 \tTraining Loss: 19.128781 \tValidation Loss: 22.282552\n",
-            "Epoch: 13 \tTraining Loss: 18.418178 \tValidation Loss: 23.143302\n",
-            "Epoch: 14 \tTraining Loss: 17.824239 \tValidation Loss: 21.818154\n",
-            "Validation loss decreased (22.047524 --> 21.818154).  Saving model ...\n"
+            "Epoch: 0 \tTraining Loss: 42.891749 \tValidation Loss: 38.868869\n",
+            "Validation loss decreased (inf --> 38.868869).  Saving model ...\n",
+            "Epoch: 1 \tTraining Loss: 34.416922 \tValidation Loss: 32.461871\n",
+            "Validation loss decreased (38.868869 --> 32.461871).  Saving model ...\n",
+            "Epoch: 2 \tTraining Loss: 30.606360 \tValidation Loss: 29.153960\n",
+            "Validation loss decreased (32.461871 --> 29.153960).  Saving model ...\n",
+            "Epoch: 3 \tTraining Loss: 28.451758 \tValidation Loss: 27.794139\n",
+            "Validation loss decreased (29.153960 --> 27.794139).  Saving model ...\n",
+            "Epoch: 4 \tTraining Loss: 26.840509 \tValidation Loss: 27.107294\n",
+            "Validation loss decreased (27.794139 --> 27.107294).  Saving model ...\n",
+            "Epoch: 5 \tTraining Loss: 25.502162 \tValidation Loss: 25.026000\n",
+            "Validation loss decreased (27.107294 --> 25.026000).  Saving model ...\n",
+            "Epoch: 6 \tTraining Loss: 24.273216 \tValidation Loss: 24.314841\n",
+            "Validation loss decreased (25.026000 --> 24.314841).  Saving model ...\n",
+            "Epoch: 7 \tTraining Loss: 23.144751 \tValidation Loss: 23.549794\n",
+            "Validation loss decreased (24.314841 --> 23.549794).  Saving model ...\n",
+            "Epoch: 8 \tTraining Loss: 22.173994 \tValidation Loss: 23.143770\n",
+            "Validation loss decreased (23.549794 --> 23.143770).  Saving model ...\n",
+            "Epoch: 9 \tTraining Loss: 21.290455 \tValidation Loss: 22.529865\n",
+            "Validation loss decreased (23.143770 --> 22.529865).  Saving model ...\n",
+            "Epoch: 10 \tTraining Loss: 20.479915 \tValidation Loss: 22.284844\n",
+            "Validation loss decreased (22.529865 --> 22.284844).  Saving model ...\n",
+            "Epoch: 11 \tTraining Loss: 19.676191 \tValidation Loss: 22.456216\n",
+            "Epoch: 12 \tTraining Loss: 18.948496 \tValidation Loss: 21.279987\n",
+            "Validation loss decreased (22.284844 --> 21.279987).  Saving model ...\n",
+            "Epoch: 13 \tTraining Loss: 18.280567 \tValidation Loss: 21.516528\n",
+            "Epoch: 14 \tTraining Loss: 17.618163 \tValidation Loss: 21.667459\n"
           ]
         }
       ],
@@ -472,7 +479,7 @@
         "optimizer = optim.SGD(model.parameters(), lr=0.01)  # specify optimizer\n",
         "\n",
         "# n_epochs = 30  # number of epochs to train the model\n",
-        "n_epochs = 15  # early stopping\n",
+        "n_epochs = 15 # early stopping\n",
         "train_loss_list = []  # list to store loss to visualize\n",
         "train_acc_list = []  # list to store training accuracy to visualize\n",
         "valid_acc_list = []  # list to store validation accuracy to visualize\n",
@@ -504,7 +511,7 @@
         "        # Perform a single optimization step (parameter update)\n",
         "        optimizer.step()\n",
         "        # Update training loss\n",
-        "        train_loss += loss.item() * data.size(0)  # loss.item() * data.size(0) = loss\n",
+        "        train_loss += loss.item() * data.size(0) # loss.item() * data.size(0) = loss\n",
         "        # Update training accuracy\n",
         "        _, pred = torch.max(output, 1)\n",
         "        correct_tensor = pred.eq(target.data.view_as(pred))\n",
@@ -570,7 +577,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 25,
+      "execution_count": 39,
       "id": "d39df818",
       "metadata": {
         "colab": {
@@ -578,12 +585,12 @@
           "height": 472
         },
         "id": "d39df818",
-        "outputId": "e21f1cd9-c882-4d36-e54f-64b662ed01cf"
+        "outputId": "242bee0f-64e0-4174-afb9-6571639f3ef4"
       },
       "outputs": [
         {
           "data": {
-            "image/png": 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",
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",
             "text/plain": [
               "<Figure size 640x480 with 1 Axes>"
             ]
@@ -604,7 +611,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 35,
+      "execution_count": 40,
       "id": "ScHldhftCgbO",
       "metadata": {
         "colab": {
@@ -612,12 +619,12 @@
           "height": 472
         },
         "id": "ScHldhftCgbO",
-        "outputId": "c7ab5f74-b433-4f29-a96b-4aedd9396aca"
+        "outputId": "58ff340e-d0e3-444b-ef9d-f20841c065a4"
       },
       "outputs": [
         {
           "data": {
-            "image/png": 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",
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",
             "text/plain": [
               "<Figure size 640x480 with 1 Axes>"
             ]
@@ -658,34 +665,34 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 27,
+      "execution_count": 41,
       "id": "e93efdfc",
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "e93efdfc",
-        "outputId": "30677d1a-e2d2-4864-cc66-16cb479e5eb1"
+        "outputId": "fcb1e4ad-fd10-40e9-f26f-44636baed6d8"
       },
       "outputs": [
         {
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "Test Loss: 21.720352\n",
+            "Test Loss: 21.599870\n",
             "\n",
-            "Test Accuracy of airplane: 70% (702/1000)\n",
-            "Test Accuracy of automobile: 74% (743/1000)\n",
-            "Test Accuracy of  bird: 54% (540/1000)\n",
-            "Test Accuracy of   cat: 29% (295/1000)\n",
-            "Test Accuracy of  deer: 65% (650/1000)\n",
-            "Test Accuracy of   dog: 53% (531/1000)\n",
-            "Test Accuracy of  frog: 66% (660/1000)\n",
-            "Test Accuracy of horse: 63% (631/1000)\n",
-            "Test Accuracy of  ship: 77% (775/1000)\n",
-            "Test Accuracy of truck: 65% (654/1000)\n",
+            "Test Accuracy of airplane: 66% (660/1000)\n",
+            "Test Accuracy of automobile: 64% (648/1000)\n",
+            "Test Accuracy of  bird: 52% (528/1000)\n",
+            "Test Accuracy of   cat: 40% (407/1000)\n",
+            "Test Accuracy of  deer: 62% (625/1000)\n",
+            "Test Accuracy of   dog: 55% (553/1000)\n",
+            "Test Accuracy of  frog: 70% (706/1000)\n",
+            "Test Accuracy of horse: 60% (607/1000)\n",
+            "Test Accuracy of  ship: 82% (825/1000)\n",
+            "Test Accuracy of truck: 70% (701/1000)\n",
             "\n",
-            "Test Accuracy (Overall): 61% (6181/10000)\n"
+            "Test Accuracy (Overall): 62% (6260/10000)\n"
           ]
         }
       ],
@@ -773,14 +780,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 28,
+      "execution_count": 42,
       "id": "KFYU-z5LK3ZI",
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "KFYU-z5LK3ZI",
-        "outputId": "5f760f13-e1bf-40dd-b676-9db0e749b261"
+        "outputId": "919d2400-7cf2-487c-af2d-c42dadac19dc"
       },
       "outputs": [
         {
@@ -803,20 +810,13 @@
       "source": [
         "# define the CNN architecture\n",
         "\n",
-        "\n",
         "class NewNet(nn.Module):\n",
         "    def __init__(self, dropout=0.2):\n",
         "        super(NewNet, self).__init__()\n",
-        "        self.conv1 = nn.Conv2d(\n",
-        "            3, 16, 3, padding=1\n",
-        "        )  # -- input, 16 output, 3x3 square convolution kernel with padding of 1\n",
-        "        self.conv2 = nn.Conv2d(\n",
-        "            16, 32, 3, padding=1\n",
-        "        )  # 16 input, 32 output, 3x3 square convolution kernel with padding of 1\n",
-        "        self.conv3 = nn.Conv2d(\n",
-        "            32, 64, 3, padding=1\n",
-        "        )  # 32 input, 64 output, 3x3 square convolution kernel with padding of 1\n",
-        "        self.pool = nn.MaxPool2d(2, 2)  # max pooling over a (2, 2) window\n",
+        "        self.conv1 = nn.Conv2d(3, 16, 3, padding=1) # -- input, 16 output, 3x3 square convolution kernel with padding of 1\n",
+        "        self.conv2 = nn.Conv2d(16, 32, 3, padding=1) # 16 input, 32 output, 3x3 square convolution kernel with padding of 1\n",
+        "        self.conv3 = nn.Conv2d(32, 64, 3, padding=1) # 32 input, 64 output, 3x3 square convolution kernel with padding of 1\n",
+        "        self.pool = nn.MaxPool2d(2, 2) # max pooling over a (2, 2) window\n",
         "        self.fc1 = nn.Linear(64 * 4 * 4, 512)\n",
         "        self.fc2 = nn.Linear(512, 64)\n",
         "        self.fc3 = nn.Linear(64, 10)\n",
@@ -826,7 +826,7 @@
         "        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",
-        "        x = x.view(-1, 64 * 4 * 4)  # flatten image input\n",
+        "        x = x.view(-1, 64 * 4 * 4) # flatten image input\n",
         "        x = self.dropout(F.relu(self.fc1(x)))\n",
         "        x = self.dropout(F.relu(self.fc2(x)))\n",
         "        x = self.fc3(x)\n",
@@ -843,54 +843,68 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 29,
+      "execution_count": 43,
       "id": "RK7TKGdDK7Nk",
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "RK7TKGdDK7Nk",
-        "outputId": "9c2cb2f3-fd6a-44dc-8930-4fc3b18160a3"
+        "outputId": "5f52cb20-9934-486c-f8d5-c4d60710c437"
       },
       "outputs": [
         {
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "Epoch: 0 \tTraining Loss: 45.875100 \tValidation Loss: 44.697760\n",
-            "Validation loss decreased (inf --> 44.697760).  Saving model ...\n",
-            "Epoch: 1 \tTraining Loss: 39.220745 \tValidation Loss: 34.694530\n",
-            "Validation loss decreased (44.697760 --> 34.694530).  Saving model ...\n",
-            "Epoch: 2 \tTraining Loss: 33.049051 \tValidation Loss: 30.410799\n",
-            "Validation loss decreased (34.694530 --> 30.410799).  Saving model ...\n",
-            "Epoch: 3 \tTraining Loss: 29.822489 \tValidation Loss: 27.725003\n",
-            "Validation loss decreased (30.410799 --> 27.725003).  Saving model ...\n",
-            "Epoch: 4 \tTraining Loss: 27.729743 \tValidation Loss: 25.916301\n",
-            "Validation loss decreased (27.725003 --> 25.916301).  Saving model ...\n",
-            "Epoch: 5 \tTraining Loss: 25.907650 \tValidation Loss: 26.308843\n",
-            "Epoch: 6 \tTraining Loss: 24.081720 \tValidation Loss: 22.901270\n",
-            "Validation loss decreased (25.916301 --> 22.901270).  Saving model ...\n",
-            "Epoch: 7 \tTraining Loss: 22.374728 \tValidation Loss: 21.519352\n",
-            "Validation loss decreased (22.901270 --> 21.519352).  Saving model ...\n",
-            "Epoch: 8 \tTraining Loss: 20.825602 \tValidation Loss: 20.569249\n",
-            "Validation loss decreased (21.519352 --> 20.569249).  Saving model ...\n",
-            "Epoch: 9 \tTraining Loss: 19.452050 \tValidation Loss: 20.270191\n",
-            "Validation loss decreased (20.569249 --> 20.270191).  Saving model ...\n",
-            "Epoch: 10 \tTraining Loss: 18.195645 \tValidation Loss: 18.811011\n",
-            "Validation loss decreased (20.270191 --> 18.811011).  Saving model ...\n",
-            "Epoch: 11 \tTraining Loss: 17.004746 \tValidation Loss: 17.653617\n",
-            "Validation loss decreased (18.811011 --> 17.653617).  Saving model ...\n",
-            "Epoch: 12 \tTraining Loss: 15.933425 \tValidation Loss: 17.021846\n",
-            "Validation loss decreased (17.653617 --> 17.021846).  Saving model ...\n",
-            "Epoch: 13 \tTraining Loss: 15.045985 \tValidation Loss: 16.651273\n",
-            "Validation loss decreased (17.021846 --> 16.651273).  Saving model ...\n",
-            "Epoch: 14 \tTraining Loss: 13.964385 \tValidation Loss: 16.148284\n",
-            "Validation loss decreased (16.651273 --> 16.148284).  Saving model ...\n",
-            "Epoch: 15 \tTraining Loss: 13.159752 \tValidation Loss: 16.161089\n",
-            "Epoch: 16 \tTraining Loss: 12.162149 \tValidation Loss: 15.946780\n",
-            "Validation loss decreased (16.148284 --> 15.946780).  Saving model ...\n",
-            "Epoch: 17 \tTraining Loss: 11.394767 \tValidation Loss: 16.152605\n",
-            "Epoch: 18 \tTraining Loss: 10.583088 \tValidation Loss: 16.449531\n"
+            "Epoch: 0 \tTraining Loss: 45.122637 \tValidation Loss: 41.006908\n",
+            "Validation loss decreased (inf --> 41.006908).  Saving model ...\n",
+            "Epoch: 1 \tTraining Loss: 38.050995 \tValidation Loss: 33.714921\n",
+            "Validation loss decreased (41.006908 --> 33.714921).  Saving model ...\n",
+            "Epoch: 2 \tTraining Loss: 32.950206 \tValidation Loss: 29.974203\n",
+            "Validation loss decreased (33.714921 --> 29.974203).  Saving model ...\n",
+            "Epoch: 3 \tTraining Loss: 29.907371 \tValidation Loss: 28.391935\n",
+            "Validation loss decreased (29.974203 --> 28.391935).  Saving model ...\n",
+            "Epoch: 4 \tTraining Loss: 27.676344 \tValidation Loss: 25.968487\n",
+            "Validation loss decreased (28.391935 --> 25.968487).  Saving model ...\n",
+            "Epoch: 5 \tTraining Loss: 25.850765 \tValidation Loss: 24.257683\n",
+            "Validation loss decreased (25.968487 --> 24.257683).  Saving model ...\n",
+            "Epoch: 6 \tTraining Loss: 24.214223 \tValidation Loss: 23.081856\n",
+            "Validation loss decreased (24.257683 --> 23.081856).  Saving model ...\n",
+            "Epoch: 7 \tTraining Loss: 22.746182 \tValidation Loss: 21.807463\n",
+            "Validation loss decreased (23.081856 --> 21.807463).  Saving model ...\n",
+            "Epoch: 8 \tTraining Loss: 21.412493 \tValidation Loss: 20.877249\n",
+            "Validation loss decreased (21.807463 --> 20.877249).  Saving model ...\n",
+            "Epoch: 9 \tTraining Loss: 20.171565 \tValidation Loss: 20.079691\n",
+            "Validation loss decreased (20.877249 --> 20.079691).  Saving model ...\n",
+            "Epoch: 10 \tTraining Loss: 18.934182 \tValidation Loss: 19.720457\n",
+            "Validation loss decreased (20.079691 --> 19.720457).  Saving model ...\n",
+            "Epoch: 11 \tTraining Loss: 17.785743 \tValidation Loss: 18.693430\n",
+            "Validation loss decreased (19.720457 --> 18.693430).  Saving model ...\n",
+            "Epoch: 12 \tTraining Loss: 16.692273 \tValidation Loss: 17.707326\n",
+            "Validation loss decreased (18.693430 --> 17.707326).  Saving model ...\n",
+            "Epoch: 13 \tTraining Loss: 15.596871 \tValidation Loss: 17.487308\n",
+            "Validation loss decreased (17.707326 --> 17.487308).  Saving model ...\n",
+            "Epoch: 14 \tTraining Loss: 14.537445 \tValidation Loss: 17.343825\n",
+            "Validation loss decreased (17.487308 --> 17.343825).  Saving model ...\n",
+            "Epoch: 15 \tTraining Loss: 13.661645 \tValidation Loss: 17.007805\n",
+            "Validation loss decreased (17.343825 --> 17.007805).  Saving model ...\n",
+            "Epoch: 16 \tTraining Loss: 12.651372 \tValidation Loss: 16.740610\n",
+            "Validation loss decreased (17.007805 --> 16.740610).  Saving model ...\n",
+            "Epoch: 17 \tTraining Loss: 11.673350 \tValidation Loss: 17.033423\n",
+            "Epoch: 18 \tTraining Loss: 10.819858 \tValidation Loss: 16.656859\n",
+            "Validation loss decreased (16.740610 --> 16.656859).  Saving model ...\n",
+            "Epoch: 19 \tTraining Loss: 9.930700 \tValidation Loss: 17.833118\n",
+            "Epoch: 20 \tTraining Loss: 9.060172 \tValidation Loss: 18.149290\n",
+            "Epoch: 21 \tTraining Loss: 8.276105 \tValidation Loss: 18.889776\n",
+            "Epoch: 22 \tTraining Loss: 7.674206 \tValidation Loss: 19.112790\n",
+            "Epoch: 23 \tTraining Loss: 6.981193 \tValidation Loss: 19.208620\n",
+            "Epoch: 24 \tTraining Loss: 6.407366 \tValidation Loss: 19.543687\n",
+            "Epoch: 25 \tTraining Loss: 5.814906 \tValidation Loss: 20.695412\n",
+            "Epoch: 26 \tTraining Loss: 5.232430 \tValidation Loss: 20.535957\n",
+            "Epoch: 27 \tTraining Loss: 4.783040 \tValidation Loss: 22.098265\n",
+            "Epoch: 28 \tTraining Loss: 4.497003 \tValidation Loss: 21.748371\n",
+            "Epoch: 29 \tTraining Loss: 4.155464 \tValidation Loss: 23.619126\n"
           ]
         }
       ],
@@ -898,7 +912,8 @@
         "criterion = nn.CrossEntropyLoss()  # specify loss function\n",
         "optimizer = optim.SGD(model_new.parameters(), lr=0.01)  # specify optimizer\n",
         "\n",
-        "n_epochs = 19  # number of epochs to train the model\n",
+        "# n_epochs = 30  # number of epochs to train the model\n",
+        "n_epochs = 20 # early stopping\n",
         "train_loss_list = []  # list to store loss to visualize\n",
         "train_acc_list = []  # list to store training accuracy to visualize\n",
         "valid_acc_list = []  # list to store validation accuracy to visualize\n",
@@ -930,7 +945,7 @@
         "        # Perform a single optimization step (parameter update)\n",
         "        optimizer.step()\n",
         "        # Update training loss\n",
-        "        train_loss += loss.item() * data.size(0)  # loss.item() * data.size(0) = loss\n",
+        "        train_loss += loss.item() * data.size(0) # loss.item() * data.size(0) = loss\n",
         "        # Update training accuracy\n",
         "        _, pred = torch.max(output, 1)\n",
         "        correct_tensor = pred.eq(target.data.view_as(pred))\n",
@@ -986,7 +1001,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 36,
+      "execution_count": 44,
       "id": "0Drq0ygGK8BX",
       "metadata": {
         "colab": {
@@ -994,12 +1009,12 @@
           "height": 927
         },
         "id": "0Drq0ygGK8BX",
-        "outputId": "482cc335-1092-422c-a36f-d8a5f459297d"
+        "outputId": "732e842c-a184-43b2-d458-0cdaac98563c"
       },
       "outputs": [
         {
           "data": {
-            "image/png": 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",
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",
             "text/plain": [
               "<Figure size 640x480 with 1 Axes>"
             ]
@@ -1009,7 +1024,7 @@
         },
         {
           "data": {
-            "image/png": 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",
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",
             "text/plain": [
               "<Figure size 640x480 with 1 Axes>"
             ]
@@ -1049,34 +1064,34 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 31,
+      "execution_count": 45,
       "id": "f_8jyTqmK-KS",
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "f_8jyTqmK-KS",
-        "outputId": "dcf9b9ce-0779-4153-d8b6-ef1abac71f9c"
+        "outputId": "1b4af9d7-9afe-4409-b9fc-9e5a7165ad52"
       },
       "outputs": [
         {
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "Test Loss: 15.989788\n",
+            "Test Loss: 16.911705\n",
             "\n",
-            "Test Accuracy of airplane: 78% (782/1000)\n",
-            "Test Accuracy of automobile: 86% (864/1000)\n",
-            "Test Accuracy of  bird: 56% (562/1000)\n",
-            "Test Accuracy of   cat: 55% (559/1000)\n",
-            "Test Accuracy of  deer: 72% (729/1000)\n",
-            "Test Accuracy of   dog: 56% (562/1000)\n",
-            "Test Accuracy of  frog: 83% (837/1000)\n",
-            "Test Accuracy of horse: 80% (805/1000)\n",
-            "Test Accuracy of  ship: 80% (806/1000)\n",
-            "Test Accuracy of truck: 80% (800/1000)\n",
+            "Test Accuracy of airplane: 81% (812/1000)\n",
+            "Test Accuracy of automobile: 84% (844/1000)\n",
+            "Test Accuracy of  bird: 60% (607/1000)\n",
+            "Test Accuracy of   cat: 50% (505/1000)\n",
+            "Test Accuracy of  deer: 60% (606/1000)\n",
+            "Test Accuracy of   dog: 57% (572/1000)\n",
+            "Test Accuracy of  frog: 83% (836/1000)\n",
+            "Test Accuracy of horse: 77% (770/1000)\n",
+            "Test Accuracy of  ship: 85% (853/1000)\n",
+            "Test Accuracy of truck: 76% (760/1000)\n",
             "\n",
-            "Test Accuracy (Overall): 73% (7306/10000)\n"
+            "Test Accuracy (Overall): 71% (7165/10000)\n"
           ]
         }
       ],
@@ -1147,7 +1162,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "We observe that this network overall performance is better than the previous one, with a 73% test accuracy compared with 61%. The classification of cats is the one which shows the biggest increase. The classification of ships, dogs and birds is almost identical."
+        "Over the entire dataset, the new model performs better than the example one : the overall test accuracy goes from 62% to 71%. The classification of deers is less accurate with the new model. The new model is slightly more accurate to classify dogs and ships. The biggest increase in test accuracy occurs for automobiles."
       ]
     },
     {
@@ -1169,30 +1184,30 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 32,
+      "execution_count": 60,
       "id": "ef623c26",
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "ef623c26",
-        "outputId": "1097631e-6607-4c83-f6f0-e07753ecc7be"
+        "outputId": "d84e09fb-997a-4044-9b1d-cd4519e86c85"
       },
       "outputs": [
         {
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "model:  fp32  \t Size (KB): 251.342\n"
+            "model:  fp32  \t Size (KB): 2330.946\n"
           ]
         },
         {
           "data": {
             "text/plain": [
-              "251342"
+              "2330946"
             ]
           },
-          "execution_count": 32,
+          "execution_count": 60,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -1209,7 +1224,7 @@
         "    return size\n",
         "\n",
         "\n",
-        "print_size_of_model(model, \"fp32\")"
+        "print_size_of_model(model_new, \"fp32\")"
       ]
     },
     {
@@ -1224,30 +1239,30 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 33,
+      "execution_count": 61,
       "id": "c4c65d4b",
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "c4c65d4b",
-        "outputId": "5b2d9072-7e33-4502-fd64-9e4109d06d27"
+        "outputId": "69d12df9-7c6b-46c6-d224-0ce66c1eef5b"
       },
       "outputs": [
         {
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "model:  int8  \t Size (KB): 76.65\n"
+            "model:  int8  \t Size (KB): 659.806\n"
           ]
         },
         {
           "data": {
             "text/plain": [
-              "76650"
+              "659806"
             ]
           },
-          "execution_count": 33,
+          "execution_count": 61,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -1256,8 +1271,112 @@
         "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_model_new = torch.quantization.quantize_dynamic(model_new, dtype=torch.qint8)\n",
+        "\n",
+        "print_size_of_model(quantized_model_new, \"int8\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Quantization decreases the size of the model by 72%."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 57,
+      "id": "zJEXkgkFq3MK",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "zJEXkgkFq3MK",
+        "outputId": "6b5cc9c2-b1e2-4c32-c9e5-073764ac6b28"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Test Loss: 16.926128\n",
+            "\n",
+            "Test Accuracy of airplane: 81% (813/1000)\n",
+            "Test Accuracy of automobile: 84% (842/1000)\n",
+            "Test Accuracy of  bird: 60% (607/1000)\n",
+            "Test Accuracy of   cat: 50% (502/1000)\n",
+            "Test Accuracy of  deer: 60% (609/1000)\n",
+            "Test Accuracy of   dog: 56% (569/1000)\n",
+            "Test Accuracy of  frog: 83% (833/1000)\n",
+            "Test Accuracy of horse: 77% (773/1000)\n",
+            "Test Accuracy of  ship: 85% (854/1000)\n",
+            "Test Accuracy of truck: 75% (759/1000)\n",
+            "\n",
+            "Test Accuracy (Overall): 71% (7161/10000)\n"
+          ]
+        }
+      ],
+      "source": [
+        "## Evaluation of classification test accuracies for the quantized model\n",
+        "# track test loss\n",
+        "test_loss = 0.0\n",
+        "class_correct = list(0.0 for i in range(10))\n",
+        "class_total = list(0.0 for i in range(10))\n",
+        "\n",
+        "quantized_model_new.eval()\n",
+        "# iterate over test data\n",
+        "for data, target in test_loader:\n",
+        "    # move tensors to GPU if CUDA is available\n",
+        "    if train_on_gpu:\n",
+        "        data, target = data.cuda(), target.cuda()\n",
+        "    # forward pass: compute predicted outputs by passing inputs to the model\n",
+        "    output = quantized_model_new(data)\n",
+        "    # calculate the batch loss\n",
+        "    loss = criterion(output, target)\n",
+        "    # update test loss\n",
+        "    test_loss += loss.item() * data.size(0)\n",
+        "    # convert output probabilities to predicted class\n",
+        "    _, pred = torch.max(output, 1)\n",
+        "    # compare predictions to true label\n",
+        "    correct_tensor = pred.eq(target.data.view_as(pred))\n",
+        "    correct = (\n",
+        "        np.squeeze(correct_tensor.numpy())\n",
+        "        if not train_on_gpu\n",
+        "        else np.squeeze(correct_tensor.cpu().numpy())\n",
+        "    )\n",
+        "    # calculate test accuracy for each object class\n",
+        "    for i in range(batch_size):\n",
+        "        label = target.data[i]\n",
+        "        class_correct[label] += correct[i].item()\n",
+        "        class_total[label] += 1\n",
+        "\n",
+        "# average test loss\n",
+        "test_loss = test_loss / len(test_loader)\n",
+        "print(\"Test Loss: {:.6f}\\n\".format(test_loss))\n",
+        "\n",
+        "for i in range(10):\n",
+        "    if class_total[i] > 0:\n",
+        "        print(\n",
+        "            \"Test Accuracy of %5s: %2d%% (%2d/%2d)\"\n",
+        "            % (\n",
+        "                classes[i],\n",
+        "                100 * class_correct[i] / class_total[i],\n",
+        "                np.sum(class_correct[i]),\n",
+        "                np.sum(class_total[i]),\n",
+        "            )\n",
+        "        )\n",
+        "    else:\n",
+        "        print(\"Test Accuracy of %5s: N/A (no training examples)\" % (classes[i]))\n",
+        "\n",
+        "print(\n",
+        "    \"\\nTest Accuracy (Overall): %2d%% (%2d/%2d)\"\n",
+        "    % (\n",
+        "        100.0 * np.sum(class_correct) / np.sum(class_total),\n",
+        "        np.sum(class_correct),\n",
+        "        np.sum(class_total),\n",
+        "    )\n",
+        ")"
       ]
     },
     {
@@ -1270,6 +1389,31 @@
         "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": "9Ngef5N6p0A4",
+      "metadata": {
+        "id": "9Ngef5N6p0A4"
+      },
+      "source": [
+        "The comparison of the classification test accuracy between the initial model and the quantized model is given below:\n",
+        "| Categories | model_new | quantized_model_new | Evolution |\n",
+        "|------------|-----------|---------------------|-----------|\n",
+        "| Airplane   | 812       | 813                 | 0.00%     |\n",
+        "| Automobile | 844       | 842                 | 0.00%     |\n",
+        "| Bird       | 607       | 607                 | 0.00%     |\n",
+        "| Cat        | 505       | 502                 | -0.01%    |\n",
+        "| Deer       | 606       | 609                 | 0.00%     |\n",
+        "| Dog        | 572       | 569                 | -0.01%    |\n",
+        "| Frog       | 836       | 833                 | 0.00%     |\n",
+        "| Horse      | 770       | 773                 | 0.00%     |\n",
+        "| Ship       | 853       | 854                 | 0.00%     |\n",
+        "| Truck      | 760       | 759                 | 0.00%     |\n",
+        "| Overall    | 7165      | 7161                | 0.00%     |\n",
+        "\n",
+        "The impact of quantization is almost null overall and for each category."
+      ]
+    },
     {
       "cell_type": "markdown",
       "id": "a0a34b90",
@@ -1295,27 +1439,38 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 34,
+      "execution_count": 58,
       "id": "b4d13080",
       "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 249
-        },
-        "id": "b4d13080",
-        "outputId": "f164d1f4-112b-4263-bb81-d2e22b4386a1"
+        "id": "b4d13080"
       },
       "outputs": [
         {
-          "ename": "FileNotFoundError",
-          "evalue": "ignored",
-          "output_type": "error",
-          "traceback": [
-            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
-            "\u001b[0;32m<ipython-input-34-72d0439ee107>\u001b[0m in \u001b[0;36m<cell line: 12>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;31m# Prepare the labels\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"imagenet-simple-labels.json\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     13\u001b[0m     \u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'imagenet-simple-labels.json'"
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "C:\\Users\\sophi\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\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\\sophi\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\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": [
@@ -1326,8 +1481,8 @@
         "test_image = \"dog.png\"\n",
         "\n",
         "# Configure matplotlib for pretty inline plots\n",
-        "# %matplotlib inline\n",
-        "# %config InlineBackend.figure_format = 'retina'\n",
+        "#%matplotlib inline\n",
+        "#%config InlineBackend.figure_format = 'retina'\n",
         "\n",
         "# Prepare the labels\n",
         "with open(\"imagenet-simple-labels.json\") as f:\n",
@@ -1401,12 +1556,29 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 59,
       "id": "be2d31f5",
       "metadata": {
         "id": "be2d31f5"
       },
-      "outputs": [],
+      "outputs": [
+        {
+          "ename": "FileNotFoundError",
+          "evalue": "[WinError 3] Le chemin d’accès spécifié est introuvable: 'hymenoptera_data\\\\train'",
+          "output_type": "error",
+          "traceback": [
+            "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
+            "\u001b[1;32mc:\\Users\\sophi\\OneDrive\\Documents\\ETUDES\\6-ECL\\3A\\MOD\\4_6\\mod_4_6-td2\\TD2 Deep Learning.ipynb Cell 42\u001b[0m line \u001b[0;36m3\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/sophi/OneDrive/Documents/ETUDES/6-ECL/3A/MOD/4_6/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#Y150sZmlsZQ%3D%3D?line=33'>34</a>\u001b[0m data_dir \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mhymenoptera_data\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/sophi/OneDrive/Documents/ETUDES/6-ECL/3A/MOD/4_6/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#Y150sZmlsZQ%3D%3D?line=34'>35</a>\u001b[0m \u001b[39m# Create train and validation datasets and loaders\u001b[39;00m\n\u001b[1;32m---> <a href='vscode-notebook-cell:/c%3A/Users/sophi/OneDrive/Documents/ETUDES/6-ECL/3A/MOD/4_6/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#Y150sZmlsZQ%3D%3D?line=35'>36</a>\u001b[0m image_datasets \u001b[39m=\u001b[39m {\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/sophi/OneDrive/Documents/ETUDES/6-ECL/3A/MOD/4_6/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#Y150sZmlsZQ%3D%3D?line=36'>37</a>\u001b[0m     x: datasets\u001b[39m.\u001b[39;49mImageFolder(os\u001b[39m.\u001b[39;49mpath\u001b[39m.\u001b[39;49mjoin(data_dir, x), data_transforms[x])\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/sophi/OneDrive/Documents/ETUDES/6-ECL/3A/MOD/4_6/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#Y150sZmlsZQ%3D%3D?line=37'>38</a>\u001b[0m     \u001b[39mfor\u001b[39;49;00m x \u001b[39min\u001b[39;49;00m [\u001b[39m\"\u001b[39;49m\u001b[39mtrain\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39m\"\u001b[39;49m\u001b[39mval\u001b[39;49m\u001b[39m\"\u001b[39;49m]\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/sophi/OneDrive/Documents/ETUDES/6-ECL/3A/MOD/4_6/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#Y150sZmlsZQ%3D%3D?line=38'>39</a>\u001b[0m }\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/sophi/OneDrive/Documents/ETUDES/6-ECL/3A/MOD/4_6/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#Y150sZmlsZQ%3D%3D?line=39'>40</a>\u001b[0m dataloaders \u001b[39m=\u001b[39m {\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/sophi/OneDrive/Documents/ETUDES/6-ECL/3A/MOD/4_6/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#Y150sZmlsZQ%3D%3D?line=40'>41</a>\u001b[0m     x: torch\u001b[39m.\u001b[39mutils\u001b[39m.\u001b[39mdata\u001b[39m.\u001b[39mDataLoader(\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/sophi/OneDrive/Documents/ETUDES/6-ECL/3A/MOD/4_6/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#Y150sZmlsZQ%3D%3D?line=41'>42</a>\u001b[0m         image_datasets[x], batch_size\u001b[39m=\u001b[39m\u001b[39m4\u001b[39m, shuffle\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m, num_workers\u001b[39m=\u001b[39m\u001b[39m0\u001b[39m\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/sophi/OneDrive/Documents/ETUDES/6-ECL/3A/MOD/4_6/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#Y150sZmlsZQ%3D%3D?line=42'>43</a>\u001b[0m     )\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/sophi/OneDrive/Documents/ETUDES/6-ECL/3A/MOD/4_6/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#Y150sZmlsZQ%3D%3D?line=43'>44</a>\u001b[0m     \u001b[39mfor\u001b[39;00m x \u001b[39min\u001b[39;00m [\u001b[39m\"\u001b[39m\u001b[39mtrain\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mval\u001b[39m\u001b[39m\"\u001b[39m]\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/sophi/OneDrive/Documents/ETUDES/6-ECL/3A/MOD/4_6/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#Y150sZmlsZQ%3D%3D?line=44'>45</a>\u001b[0m }\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/sophi/OneDrive/Documents/ETUDES/6-ECL/3A/MOD/4_6/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#Y150sZmlsZQ%3D%3D?line=45'>46</a>\u001b[0m dataset_sizes \u001b[39m=\u001b[39m {x: \u001b[39mlen\u001b[39m(image_datasets[x]) \u001b[39mfor\u001b[39;00m x \u001b[39min\u001b[39;00m [\u001b[39m\"\u001b[39m\u001b[39mtrain\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mval\u001b[39m\u001b[39m\"\u001b[39m]}\n",
+            "\u001b[1;32mc:\\Users\\sophi\\OneDrive\\Documents\\ETUDES\\6-ECL\\3A\\MOD\\4_6\\mod_4_6-td2\\TD2 Deep Learning.ipynb Cell 42\u001b[0m line \u001b[0;36m3\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/sophi/OneDrive/Documents/ETUDES/6-ECL/3A/MOD/4_6/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#Y150sZmlsZQ%3D%3D?line=33'>34</a>\u001b[0m data_dir \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mhymenoptera_data\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/sophi/OneDrive/Documents/ETUDES/6-ECL/3A/MOD/4_6/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#Y150sZmlsZQ%3D%3D?line=34'>35</a>\u001b[0m \u001b[39m# Create train and validation datasets and loaders\u001b[39;00m\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/sophi/OneDrive/Documents/ETUDES/6-ECL/3A/MOD/4_6/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#Y150sZmlsZQ%3D%3D?line=35'>36</a>\u001b[0m image_datasets \u001b[39m=\u001b[39m {\n\u001b[1;32m---> <a href='vscode-notebook-cell:/c%3A/Users/sophi/OneDrive/Documents/ETUDES/6-ECL/3A/MOD/4_6/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#Y150sZmlsZQ%3D%3D?line=36'>37</a>\u001b[0m     x: datasets\u001b[39m.\u001b[39;49mImageFolder(os\u001b[39m.\u001b[39;49mpath\u001b[39m.\u001b[39;49mjoin(data_dir, x), data_transforms[x])\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/sophi/OneDrive/Documents/ETUDES/6-ECL/3A/MOD/4_6/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#Y150sZmlsZQ%3D%3D?line=37'>38</a>\u001b[0m     \u001b[39mfor\u001b[39;00m x \u001b[39min\u001b[39;00m [\u001b[39m\"\u001b[39m\u001b[39mtrain\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mval\u001b[39m\u001b[39m\"\u001b[39m]\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/sophi/OneDrive/Documents/ETUDES/6-ECL/3A/MOD/4_6/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#Y150sZmlsZQ%3D%3D?line=38'>39</a>\u001b[0m }\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/sophi/OneDrive/Documents/ETUDES/6-ECL/3A/MOD/4_6/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#Y150sZmlsZQ%3D%3D?line=39'>40</a>\u001b[0m dataloaders \u001b[39m=\u001b[39m {\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/sophi/OneDrive/Documents/ETUDES/6-ECL/3A/MOD/4_6/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#Y150sZmlsZQ%3D%3D?line=40'>41</a>\u001b[0m     x: torch\u001b[39m.\u001b[39mutils\u001b[39m.\u001b[39mdata\u001b[39m.\u001b[39mDataLoader(\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/sophi/OneDrive/Documents/ETUDES/6-ECL/3A/MOD/4_6/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#Y150sZmlsZQ%3D%3D?line=41'>42</a>\u001b[0m         image_datasets[x], batch_size\u001b[39m=\u001b[39m\u001b[39m4\u001b[39m, shuffle\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m, num_workers\u001b[39m=\u001b[39m\u001b[39m0\u001b[39m\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/sophi/OneDrive/Documents/ETUDES/6-ECL/3A/MOD/4_6/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#Y150sZmlsZQ%3D%3D?line=42'>43</a>\u001b[0m     )\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/sophi/OneDrive/Documents/ETUDES/6-ECL/3A/MOD/4_6/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#Y150sZmlsZQ%3D%3D?line=43'>44</a>\u001b[0m     \u001b[39mfor\u001b[39;00m x \u001b[39min\u001b[39;00m [\u001b[39m\"\u001b[39m\u001b[39mtrain\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mval\u001b[39m\u001b[39m\"\u001b[39m]\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/sophi/OneDrive/Documents/ETUDES/6-ECL/3A/MOD/4_6/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#Y150sZmlsZQ%3D%3D?line=44'>45</a>\u001b[0m }\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/sophi/OneDrive/Documents/ETUDES/6-ECL/3A/MOD/4_6/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#Y150sZmlsZQ%3D%3D?line=45'>46</a>\u001b[0m dataset_sizes \u001b[39m=\u001b[39m {x: \u001b[39mlen\u001b[39m(image_datasets[x]) \u001b[39mfor\u001b[39;00m x \u001b[39min\u001b[39;00m [\u001b[39m\"\u001b[39m\u001b[39mtrain\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mval\u001b[39m\u001b[39m\"\u001b[39m]}\n",
+            "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\torchvision\\datasets\\folder.py:309\u001b[0m, in \u001b[0;36mImageFolder.__init__\u001b[1;34m(self, root, transform, target_transform, loader, is_valid_file)\u001b[0m\n\u001b[0;32m    301\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__init__\u001b[39m(\n\u001b[0;32m    302\u001b[0m     \u001b[39mself\u001b[39m,\n\u001b[0;32m    303\u001b[0m     root: \u001b[39mstr\u001b[39m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    307\u001b[0m     is_valid_file: Optional[Callable[[\u001b[39mstr\u001b[39m], \u001b[39mbool\u001b[39m]] \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m,\n\u001b[0;32m    308\u001b[0m ):\n\u001b[1;32m--> 309\u001b[0m     \u001b[39msuper\u001b[39;49m()\u001b[39m.\u001b[39;49m\u001b[39m__init__\u001b[39;49m(\n\u001b[0;32m    310\u001b[0m         root,\n\u001b[0;32m    311\u001b[0m         loader,\n\u001b[0;32m    312\u001b[0m         IMG_EXTENSIONS \u001b[39mif\u001b[39;49;00m is_valid_file \u001b[39mis\u001b[39;49;00m \u001b[39mNone\u001b[39;49;00m \u001b[39melse\u001b[39;49;00m \u001b[39mNone\u001b[39;49;00m,\n\u001b[0;32m    313\u001b[0m         transform\u001b[39m=\u001b[39;49mtransform,\n\u001b[0;32m    314\u001b[0m         target_transform\u001b[39m=\u001b[39;49mtarget_transform,\n\u001b[0;32m    315\u001b[0m         is_valid_file\u001b[39m=\u001b[39;49mis_valid_file,\n\u001b[0;32m    316\u001b[0m     )\n\u001b[0;32m    317\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mimgs \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39msamples\n",
+            "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\torchvision\\datasets\\folder.py:144\u001b[0m, in \u001b[0;36mDatasetFolder.__init__\u001b[1;34m(self, root, loader, extensions, transform, target_transform, is_valid_file)\u001b[0m\n\u001b[0;32m    134\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__init__\u001b[39m(\n\u001b[0;32m    135\u001b[0m     \u001b[39mself\u001b[39m,\n\u001b[0;32m    136\u001b[0m     root: \u001b[39mstr\u001b[39m,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    141\u001b[0m     is_valid_file: Optional[Callable[[\u001b[39mstr\u001b[39m], \u001b[39mbool\u001b[39m]] \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m,\n\u001b[0;32m    142\u001b[0m ) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m    143\u001b[0m     \u001b[39msuper\u001b[39m()\u001b[39m.\u001b[39m\u001b[39m__init__\u001b[39m(root, transform\u001b[39m=\u001b[39mtransform, target_transform\u001b[39m=\u001b[39mtarget_transform)\n\u001b[1;32m--> 144\u001b[0m     classes, class_to_idx \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mfind_classes(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mroot)\n\u001b[0;32m    145\u001b[0m     samples \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmake_dataset(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mroot, class_to_idx, extensions, is_valid_file)\n\u001b[0;32m    147\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mloader \u001b[39m=\u001b[39m loader\n",
+            "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\torchvision\\datasets\\folder.py:218\u001b[0m, in \u001b[0;36mDatasetFolder.find_classes\u001b[1;34m(self, directory)\u001b[0m\n\u001b[0;32m    191\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mfind_classes\u001b[39m(\u001b[39mself\u001b[39m, directory: \u001b[39mstr\u001b[39m) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m Tuple[List[\u001b[39mstr\u001b[39m], Dict[\u001b[39mstr\u001b[39m, \u001b[39mint\u001b[39m]]:\n\u001b[0;32m    192\u001b[0m \u001b[39m    \u001b[39m\u001b[39m\"\"\"Find the class folders in a dataset structured as follows::\u001b[39;00m\n\u001b[0;32m    193\u001b[0m \n\u001b[0;32m    194\u001b[0m \u001b[39m        directory/\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    216\u001b[0m \u001b[39m        (Tuple[List[str], Dict[str, int]]): List of all classes and dictionary mapping each class to an index.\u001b[39;00m\n\u001b[0;32m    217\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 218\u001b[0m     \u001b[39mreturn\u001b[39;00m find_classes(directory)\n",
+            "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\torchvision\\datasets\\folder.py:40\u001b[0m, in \u001b[0;36mfind_classes\u001b[1;34m(directory)\u001b[0m\n\u001b[0;32m     35\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mfind_classes\u001b[39m(directory: \u001b[39mstr\u001b[39m) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m Tuple[List[\u001b[39mstr\u001b[39m], Dict[\u001b[39mstr\u001b[39m, \u001b[39mint\u001b[39m]]:\n\u001b[0;32m     36\u001b[0m \u001b[39m    \u001b[39m\u001b[39m\"\"\"Finds the class folders in a dataset.\u001b[39;00m\n\u001b[0;32m     37\u001b[0m \n\u001b[0;32m     38\u001b[0m \u001b[39m    See :class:`DatasetFolder` for details.\u001b[39;00m\n\u001b[0;32m     39\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[1;32m---> 40\u001b[0m     classes \u001b[39m=\u001b[39m \u001b[39msorted\u001b[39m(entry\u001b[39m.\u001b[39mname \u001b[39mfor\u001b[39;00m entry \u001b[39min\u001b[39;00m os\u001b[39m.\u001b[39;49mscandir(directory) \u001b[39mif\u001b[39;00m entry\u001b[39m.\u001b[39mis_dir())\n\u001b[0;32m     41\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m classes:\n\u001b[0;32m     42\u001b[0m         \u001b[39mraise\u001b[39;00m \u001b[39mFileNotFoundError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mCouldn\u001b[39m\u001b[39m'\u001b[39m\u001b[39mt find any class folder in \u001b[39m\u001b[39m{\u001b[39;00mdirectory\u001b[39m}\u001b[39;00m\u001b[39m.\u001b[39m\u001b[39m\"\u001b[39m)\n",
+            "\u001b[1;31mFileNotFoundError\u001b[0m: [WinError 3] Le chemin d’accès spécifié est introuvable: 'hymenoptera_data\\\\train'"
+          ]
+        }
+      ],
       "source": [
         "import os\n",
         "\n",
@@ -1457,7 +1629,6 @@
         "class_names = image_datasets[\"train\"].classes\n",
         "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
         "\n",
-        "\n",
         "# Helper function for displaying images\n",
         "def imshow(inp, title=None):\n",
         "    \"\"\"Imshow for Tensor.\"\"\"\n",
@@ -1482,7 +1653,8 @@
         "# Make a grid from batch\n",
         "out = torchvision.utils.make_grid(inputs)\n",
         "\n",
-        "imshow(out, title=[class_names[x] for x in classes])"
+        "imshow(out, title=[class_names[x] for x in classes])\n",
+        "\n"
       ]
     },
     {
@@ -1558,7 +1730,6 @@
         "class_names = image_datasets[\"train\"].classes\n",
         "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
         "\n",
-        "\n",
         "# Helper function for displaying images\n",
         "def imshow(inp, title=None):\n",
         "    \"\"\"Imshow for Tensor.\"\"\"\n",
@@ -1682,7 +1853,7 @@
         "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_conv, step_size=7, gamma=0.1)\n",
         "model, epoch_time = train_model(\n",
         "    model, criterion, optimizer_conv, exp_lr_scheduler, num_epochs=10\n",
-        ")"
+        ")\n"
       ]
     },
     {
-- 
GitLab