diff --git a/TD2 Deep Learning.ipynb b/TD2 Deep Learning.ipynb
index 2ecfce959ae6b947b633a758433f9bea0bf6992e..b0e6907a7e6db5d07d0a6e9da37410c122f086b9 100644
--- a/TD2 Deep Learning.ipynb	
+++ b/TD2 Deep Learning.ipynb	
@@ -33,10 +33,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "330a42f5",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: torch in c:\\users\\hajer\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (2.1.1+cu118)\n",
+      "Requirement already satisfied: torchvision in c:\\users\\hajer\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (0.16.1+cu118)\n",
+      "Requirement already satisfied: filelock in c:\\users\\hajer\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from torch) (3.9.0)\n",
+      "Requirement already satisfied: typing-extensions in c:\\users\\hajer\\appdata\\roaming\\python\\python38\\site-packages (from torch) (4.8.0)\n",
+      "Requirement already satisfied: sympy in c:\\users\\hajer\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from torch) (1.12)\n",
+      "Requirement already satisfied: networkx in c:\\users\\hajer\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from torch) (3.0)\n",
+      "Requirement already satisfied: jinja2 in c:\\users\\hajer\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from torch) (3.1.2)\n",
+      "Requirement already satisfied: fsspec in c:\\users\\hajer\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from torch) (2023.4.0)\n",
+      "Requirement already satisfied: numpy in c:\\users\\hajer\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from torchvision) (1.24.1)\n",
+      "Requirement already satisfied: requests in c:\\users\\hajer\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from torchvision) (2.28.1)\n",
+      "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in c:\\users\\hajer\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from torchvision) (9.3.0)\n",
+      "Requirement already satisfied: MarkupSafe>=2.0 in c:\\users\\hajer\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from jinja2->torch) (2.1.3)\n",
+      "Requirement already satisfied: charset-normalizer<3,>=2 in c:\\users\\hajer\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from requests->torchvision) (2.1.1)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\hajer\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from requests->torchvision) (3.4)\n",
+      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\\users\\hajer\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from requests->torchvision) (1.26.13)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\hajer\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from requests->torchvision) (2022.12.7)\n",
+      "Requirement already satisfied: mpmath>=0.19 in c:\\users\\hajer\\appdata\\local\\programs\\python\\python38\\lib\\site-packages (from sympy->torch) (1.3.0)\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
    "source": [
     "%pip install torch torchvision"
    ]
@@ -52,10 +77,72 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "b1950f0a",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([[ 4.6430e-02,  6.4639e-01, -2.0173e+00,  3.7900e-01, -1.4162e+00,\n",
+      "         -6.4874e-01,  1.6593e+00,  3.0206e-01, -1.3201e-02, -3.7683e-01],\n",
+      "        [-7.1743e-01, -6.5517e-01,  3.8716e-01, -1.0266e+00, -8.0405e-01,\n",
+      "         -1.7893e+00, -1.0432e+00,  1.4281e+00, -1.7984e+00, -3.3961e-01],\n",
+      "        [-3.9532e-01,  1.4782e+00, -2.5630e-01,  1.4005e+00,  7.5043e-01,\n",
+      "         -6.5166e-01,  2.3342e-01,  1.8085e+00, -6.0756e-01,  1.0124e+00],\n",
+      "        [ 4.2280e-01, -9.8187e-01, -5.6521e-02, -9.0594e-01,  4.6334e-01,\n",
+      "         -6.9502e-01, -3.0369e-01,  1.0508e+00, -1.1146e+00, -8.5251e-01],\n",
+      "        [ 1.9149e-01, -5.1431e-01, -1.5770e-01, -2.0349e+00, -5.7234e-01,\n",
+      "          2.6091e+00, -1.2740e+00,  1.7351e-03, -2.9505e-01, -3.2385e-01],\n",
+      "        [-9.9924e-01, -4.7917e-02, -6.4322e-01, -6.5991e-01, -9.2452e-02,\n",
+      "         -4.4836e-01, -9.3744e-01,  2.2430e-01, -9.0971e-01,  5.7377e-01],\n",
+      "        [-6.8859e-02,  8.9634e-01, -4.0613e-01, -9.2677e-01, -5.7269e-01,\n",
+      "          1.8151e-01,  4.8608e-01,  6.8228e-01, -8.1819e-01,  1.0121e-01],\n",
+      "        [ 8.0096e-01, -1.6190e-01, -9.1520e-01,  1.9978e-01,  8.4364e-01,\n",
+      "          3.8984e-01,  7.9857e-01, -5.4781e-01,  1.2823e-01, -9.3968e-01],\n",
+      "        [ 6.0919e-01,  1.2664e+00, -7.1244e-01, -1.2180e+00,  2.7109e+00,\n",
+      "          1.9238e+00, -4.4555e-01,  1.2784e+00, -7.5317e-01, -2.3440e-02],\n",
+      "        [-7.9960e-01,  7.6948e-01,  1.9296e-01,  1.0993e+00, -2.0709e-01,\n",
+      "         -1.3431e+00, -9.4082e-01,  7.0647e-02, -8.0446e-01, -2.9812e-01],\n",
+      "        [ 6.2286e-01, -2.4904e-01, -1.4847e-01,  3.5700e-01, -2.8323e-01,\n",
+      "          1.2576e+00,  5.5485e-01, -1.8194e+00,  1.0538e+00, -1.1469e+00],\n",
+      "        [-5.7492e-01, -1.8653e+00,  3.2106e+00, -2.0283e-01, -1.1118e+00,\n",
+      "         -4.5426e-01,  1.1647e+00, -2.1051e-01, -1.3601e+00,  2.9776e-01],\n",
+      "        [ 1.6274e-01, -3.1742e-01,  2.6259e-01, -1.2380e-01, -2.9532e-02,\n",
+      "          5.5184e-01,  3.3308e-01,  5.9849e-01, -3.0781e+00, -8.5110e-02],\n",
+      "        [ 1.4201e+00,  7.2394e-01, -1.2828e+00, -2.9359e-01,  1.8757e+00,\n",
+      "         -1.5475e-01,  5.3178e-01, -7.3692e-01, -1.7160e+00,  2.0653e-01]])\n",
+      "AlexNet(\n",
+      "  (features): Sequential(\n",
+      "    (0): Conv2d(3, 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2))\n",
+      "    (1): ReLU(inplace=True)\n",
+      "    (2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
+      "    (3): Conv2d(64, 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
+      "    (4): ReLU(inplace=True)\n",
+      "    (5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
+      "    (6): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+      "    (7): ReLU(inplace=True)\n",
+      "    (8): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+      "    (9): ReLU(inplace=True)\n",
+      "    (10): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+      "    (11): ReLU(inplace=True)\n",
+      "    (12): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
+      "  )\n",
+      "  (avgpool): AdaptiveAvgPool2d(output_size=(6, 6))\n",
+      "  (classifier): Sequential(\n",
+      "    (0): Dropout(p=0.5, inplace=False)\n",
+      "    (1): Linear(in_features=9216, out_features=4096, bias=True)\n",
+      "    (2): ReLU(inplace=True)\n",
+      "    (3): Dropout(p=0.5, inplace=False)\n",
+      "    (4): Linear(in_features=4096, out_features=4096, bias=True)\n",
+      "    (5): ReLU(inplace=True)\n",
+      "    (6): Linear(in_features=4096, out_features=1000, bias=True)\n",
+      "  )\n",
+      ")\n"
+     ]
+    }
+   ],
    "source": [
     "import torch\n",
     "\n",
@@ -95,10 +182,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "6e18f2fd",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CUDA is available!  Training on GPU ...\n"
+     ]
+    }
+   ],
    "source": [
     "import torch\n",
     "\n",
@@ -121,10 +216,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "462666a2",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Files already downloaded and verified\n",
+      "Files already downloaded and verified\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "from torchvision import datasets, transforms\n",
@@ -193,10 +297,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "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 +361,59 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "4b53f229",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 0 \tTraining Loss: 42.786738 \tValidation Loss: 38.287930\n",
+      "Validation loss decreased (inf --> 38.287930).  Saving model ...\n",
+      "Epoch: 1 \tTraining Loss: 34.335292 \tValidation Loss: 32.511607\n",
+      "Validation loss decreased (38.287930 --> 32.511607).  Saving model ...\n",
+      "Epoch: 2 \tTraining Loss: 30.407170 \tValidation Loss: 29.683266\n",
+      "Validation loss decreased (32.511607 --> 29.683266).  Saving model ...\n",
+      "Epoch: 3 \tTraining Loss: 28.210974 \tValidation Loss: 27.556535\n",
+      "Validation loss decreased (29.683266 --> 27.556535).  Saving model ...\n",
+      "Epoch: 4 \tTraining Loss: 26.449911 \tValidation Loss: 26.817555\n",
+      "Validation loss decreased (27.556535 --> 26.817555).  Saving model ...\n",
+      "Epoch: 5 \tTraining Loss: 25.000417 \tValidation Loss: 25.016271\n",
+      "Validation loss decreased (26.817555 --> 25.016271).  Saving model ...\n",
+      "Epoch: 6 \tTraining Loss: 23.724685 \tValidation Loss: 24.381300\n",
+      "Validation loss decreased (25.016271 --> 24.381300).  Saving model ...\n",
+      "Epoch: 7 \tTraining Loss: 22.611865 \tValidation Loss: 24.150868\n",
+      "Validation loss decreased (24.381300 --> 24.150868).  Saving model ...\n",
+      "Epoch: 8 \tTraining Loss: 21.737409 \tValidation Loss: 23.891706\n",
+      "Validation loss decreased (24.150868 --> 23.891706).  Saving model ...\n",
+      "Epoch: 9 \tTraining Loss: 20.897162 \tValidation Loss: 22.465577\n",
+      "Validation loss decreased (23.891706 --> 22.465577).  Saving model ...\n",
+      "Epoch: 10 \tTraining Loss: 20.116657 \tValidation Loss: 22.610212\n",
+      "Epoch: 11 \tTraining Loss: 19.378705 \tValidation Loss: 22.289583\n",
+      "Validation loss decreased (22.465577 --> 22.289583).  Saving model ...\n",
+      "Epoch: 12 \tTraining Loss: 18.714000 \tValidation Loss: 22.930776\n",
+      "Epoch: 13 \tTraining Loss: 18.097163 \tValidation Loss: 22.493616\n",
+      "Epoch: 14 \tTraining Loss: 17.491828 \tValidation Loss: 22.039381\n",
+      "Validation loss decreased (22.289583 --> 22.039381).  Saving model ...\n",
+      "Epoch: 15 \tTraining Loss: 16.822317 \tValidation Loss: 22.648995\n",
+      "Epoch: 16 \tTraining Loss: 16.280109 \tValidation Loss: 23.998240\n",
+      "Epoch: 17 \tTraining Loss: 15.738700 \tValidation Loss: 22.706566\n",
+      "Epoch: 18 \tTraining Loss: 15.172263 \tValidation Loss: 22.839206\n",
+      "Epoch: 19 \tTraining Loss: 14.676389 \tValidation Loss: 23.463456\n",
+      "Epoch: 20 \tTraining Loss: 14.185101 \tValidation Loss: 23.839855\n",
+      "Epoch: 21 \tTraining Loss: 13.671962 \tValidation Loss: 23.744776\n",
+      "Epoch: 22 \tTraining Loss: 13.208216 \tValidation Loss: 24.203499\n",
+      "Epoch: 23 \tTraining Loss: 12.718280 \tValidation Loss: 24.099697\n",
+      "Epoch: 24 \tTraining Loss: 12.313924 \tValidation Loss: 25.278157\n",
+      "Epoch: 25 \tTraining Loss: 11.853546 \tValidation Loss: 26.221255\n",
+      "Epoch: 26 \tTraining Loss: 11.502245 \tValidation Loss: 26.827266\n",
+      "Epoch: 27 \tTraining Loss: 11.079739 \tValidation Loss: 26.159556\n",
+      "Epoch: 28 \tTraining Loss: 10.714472 \tValidation Loss: 27.939976\n",
+      "Epoch: 29 \tTraining Loss: 10.245014 \tValidation Loss: 28.713002\n"
+     ]
+    }
+   ],
    "source": [
     "import torch.optim as optim\n",
     "\n",
@@ -326,10 +494,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "d39df818",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "\n",
@@ -350,10 +529,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "e93efdfc",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test Loss: 21.587707\n",
+      "\n",
+      "Test Accuracy of airplane: 69% (698/1000)\n",
+      "Test Accuracy of automobile: 71% (710/1000)\n",
+      "Test Accuracy of  bird: 60% (601/1000)\n",
+      "Test Accuracy of   cat: 38% (387/1000)\n",
+      "Test Accuracy of  deer: 45% (452/1000)\n",
+      "Test Accuracy of   dog: 58% (588/1000)\n",
+      "Test Accuracy of  frog: 69% (698/1000)\n",
+      "Test Accuracy of horse: 69% (695/1000)\n",
+      "Test Accuracy of  ship: 71% (710/1000)\n",
+      "Test Accuracy of truck: 72% (720/1000)\n",
+      "\n",
+      "Test Accuracy (Overall): 62% (6259/10000)\n"
+     ]
+    }
+   ],
    "source": [
     "model.load_state_dict(torch.load(\"./model_cifar.pt\"))\n",
     "\n",
@@ -434,6 +634,303 @@
     "Compare the results obtained with this new network to those obtained previously."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Second_Net(\n",
+      "  (conv1): Conv2d(3, 16, 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",
+      "  (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",
+      "  (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=0.2, inplace=False)\n",
+      ")\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch.nn as nn\n",
+    "import torch.nn.functional as F\n",
+    "\n",
+    "class Second_Net(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super(Second_Net, self).__init__()\n",
+    "        self.conv1 = nn.Conv2d(3, 16, 3, padding=1)\n",
+    "        self.pool = nn.MaxPool2d(2, 2)\n",
+    "        self.conv2 = nn.Conv2d(16, 32, 3, padding=1)\n",
+    "        self.conv3 = nn.Conv2d(32,64,3, padding=1)\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(0.2)\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",
+    "        x = x.view(-1, 64 * 4 * 4)\n",
+    "        x = self.dropout(F.relu(self.fc1(x)))\n",
+    "        x = self.dropout(F.relu(self.fc2(x)))\n",
+    "        x = self.fc3(x)\n",
+    "        return x\n",
+    "# create a complete CNN\n",
+    "model = Second_Net()\n",
+    "print(model)\n",
+    "# move tensors to GPU if CUDA is available\n",
+    "if train_on_gpu:\n",
+    "    model.cuda()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 0 \tTraining Loss: 44.030613 \tValidation Loss: 39.370538\n",
+      "Validation loss decreased (inf --> 39.370538).  Saving model ...\n",
+      "Epoch: 1 \tTraining Loss: 36.110948 \tValidation Loss: 33.050347\n",
+      "Validation loss decreased (39.370538 --> 33.050347).  Saving model ...\n",
+      "Epoch: 2 \tTraining Loss: 31.415622 \tValidation Loss: 29.290859\n",
+      "Validation loss decreased (33.050347 --> 29.290859).  Saving model ...\n",
+      "Epoch: 3 \tTraining Loss: 28.880278 \tValidation Loss: 27.821547\n",
+      "Validation loss decreased (29.290859 --> 27.821547).  Saving model ...\n",
+      "Epoch: 4 \tTraining Loss: 26.798128 \tValidation Loss: 25.224831\n",
+      "Validation loss decreased (27.821547 --> 25.224831).  Saving model ...\n",
+      "Epoch: 5 \tTraining Loss: 24.976448 \tValidation Loss: 24.444044\n",
+      "Validation loss decreased (25.224831 --> 24.444044).  Saving model ...\n",
+      "Epoch: 6 \tTraining Loss: 23.211697 \tValidation Loss: 22.115742\n",
+      "Validation loss decreased (24.444044 --> 22.115742).  Saving model ...\n",
+      "Epoch: 7 \tTraining Loss: 21.813018 \tValidation Loss: 21.625601\n",
+      "Validation loss decreased (22.115742 --> 21.625601).  Saving model ...\n",
+      "Epoch: 8 \tTraining Loss: 20.293841 \tValidation Loss: 20.321184\n",
+      "Validation loss decreased (21.625601 --> 20.321184).  Saving model ...\n",
+      "Epoch: 9 \tTraining Loss: 19.075343 \tValidation Loss: 19.265597\n",
+      "Validation loss decreased (20.321184 --> 19.265597).  Saving model ...\n",
+      "Epoch: 10 \tTraining Loss: 17.934765 \tValidation Loss: 18.895639\n",
+      "Validation loss decreased (19.265597 --> 18.895639).  Saving model ...\n",
+      "Epoch: 11 \tTraining Loss: 16.809305 \tValidation Loss: 18.863756\n",
+      "Validation loss decreased (18.895639 --> 18.863756).  Saving model ...\n",
+      "Epoch: 12 \tTraining Loss: 15.730885 \tValidation Loss: 17.556935\n",
+      "Validation loss decreased (18.863756 --> 17.556935).  Saving model ...\n",
+      "Epoch: 13 \tTraining Loss: 14.659352 \tValidation Loss: 17.769804\n",
+      "Epoch: 14 \tTraining Loss: 13.707250 \tValidation Loss: 16.951901\n",
+      "Validation loss decreased (17.556935 --> 16.951901).  Saving model ...\n",
+      "Epoch: 15 \tTraining Loss: 12.851310 \tValidation Loss: 16.546529\n",
+      "Validation loss decreased (16.951901 --> 16.546529).  Saving model ...\n",
+      "Epoch: 16 \tTraining Loss: 11.959763 \tValidation Loss: 17.123464\n",
+      "Epoch: 17 \tTraining Loss: 11.152203 \tValidation Loss: 17.534218\n",
+      "Epoch: 18 \tTraining Loss: 10.322098 \tValidation Loss: 16.683099\n",
+      "Epoch: 19 \tTraining Loss: 9.552804 \tValidation Loss: 16.660626\n",
+      "Epoch: 20 \tTraining Loss: 8.720313 \tValidation Loss: 17.551748\n",
+      "Epoch: 21 \tTraining Loss: 8.046964 \tValidation Loss: 18.895726\n",
+      "Epoch: 22 \tTraining Loss: 7.286415 \tValidation Loss: 18.657592\n",
+      "Epoch: 23 \tTraining Loss: 6.771809 \tValidation Loss: 18.650961\n",
+      "Epoch: 24 \tTraining Loss: 6.191227 \tValidation Loss: 19.462017\n",
+      "Epoch: 25 \tTraining Loss: 5.557430 \tValidation Loss: 19.993432\n",
+      "Epoch: 26 \tTraining Loss: 5.242904 \tValidation Loss: 20.774644\n",
+      "Epoch: 27 \tTraining Loss: 4.772158 \tValidation Loss: 21.309086\n",
+      "Epoch: 28 \tTraining Loss: 4.277103 \tValidation Loss: 22.434116\n",
+      "Epoch: 29 \tTraining Loss: 3.981252 \tValidation Loss: 22.773990\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch.optim as optim\n",
+    "import numpy as np\n",
+    "\n",
+    "criterion = nn.CrossEntropyLoss()  # specify loss function\n",
+    "optimizer = optim.SGD(model.parameters(), lr=0.01)  # specify optimizer\n",
+    "\n",
+    "n_epochs = 30  # number of epochs to train the model\n",
+    "train_loss_list = []  # list to store loss to visualize\n",
+    "valid_loss_min = np.Inf  # track change in validation loss\n",
+    "\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",
+    "    model.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 = model(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",
+    "    model.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 = model(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(model.state_dict(), \"model_cifar_2.pt\")\n",
+    "        valid_loss_min = valid_loss"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.plot(range(n_epochs), train_loss_list)\n",
+    "plt.xlabel(\"Epoch\")\n",
+    "plt.ylabel(\"Loss\")\n",
+    "plt.title(\"Performance of Model 2\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test Loss: 22.216757\n",
+      "\n",
+      "Test Accuracy of airplane: 74% (740/1000)\n",
+      "Test Accuracy of automobile: 82% (827/1000)\n",
+      "Test Accuracy of  bird: 63% (633/1000)\n",
+      "Test Accuracy of   cat: 55% (554/1000)\n",
+      "Test Accuracy of  deer: 65% (658/1000)\n",
+      "Test Accuracy of   dog: 66% (662/1000)\n",
+      "Test Accuracy of  frog: 83% (835/1000)\n",
+      "Test Accuracy of horse: 73% (731/1000)\n",
+      "Test Accuracy of  ship: 81% (816/1000)\n",
+      "Test Accuracy of truck: 80% (808/1000)\n",
+      "\n",
+      "Test Accuracy (Overall): 72% (7264/10000)\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "# track test loss\n",
+    "test_loss = 0.0\n",
+    "class_correct = list(0.0 for i in range(10))\n",
+    "class_total = list(0.0 for i in range(10))\n",
+    "\n",
+    "model.eval()\n",
+    "# iterate over test data\n",
+    "for data, target in test_loader:\n",
+    "    # move tensors to GPU if CUDA is available\n",
+    "    if train_on_gpu:\n",
+    "        data, target = data.cuda(), target.cuda()\n",
+    "    # forward pass: compute predicted outputs by passing inputs to the model\n",
+    "    output = model(data)\n",
+    "    # calculate the batch loss\n",
+    "    loss = criterion(output, target)\n",
+    "    # update test loss\n",
+    "    test_loss += loss.item() * data.size(0)\n",
+    "    # convert output probabilities to predicted class\n",
+    "    _, pred = torch.max(output, 1)\n",
+    "    # compare predictions to true label\n",
+    "    correct_tensor = pred.eq(target.data.view_as(pred))\n",
+    "    correct = (\n",
+    "        np.squeeze(correct_tensor.numpy())\n",
+    "        if not train_on_gpu\n",
+    "        else np.squeeze(correct_tensor.cpu().numpy())\n",
+    "    )\n",
+    "    # calculate test accuracy for each object class\n",
+    "    for i in range(batch_size):\n",
+    "        label = target.data[i]\n",
+    "        class_correct[label] += correct[i].item()\n",
+    "        class_total[label] += 1\n",
+    "\n",
+    "# average test loss\n",
+    "test_loss = test_loss / len(test_loader)\n",
+    "print(\"Test Loss: {:.6f}\\n\".format(test_loss))\n",
+    "\n",
+    "for i in range(10):\n",
+    "    if class_total[i] > 0:\n",
+    "        print(\n",
+    "            \"Test Accuracy of %5s: %2d%% (%2d/%2d)\"\n",
+    "            % (\n",
+    "                classes[i],\n",
+    "                100 * class_correct[i] / class_total[i],\n",
+    "                np.sum(class_correct[i]),\n",
+    "                np.sum(class_total[i]),\n",
+    "            )\n",
+    "        )\n",
+    "    else:\n",
+    "        print(\"Test Accuracy of %5s: N/A (no training examples)\" % (classes[i]))\n",
+    "\n",
+    "print(\n",
+    "    \"\\nTest Accuracy (Overall): %2d%% (%2d/%2d)\"\n",
+    "    % (\n",
+    "        100.0 * np.sum(class_correct) / np.sum(class_total),\n",
+    "        np.sum(class_correct),\n",
+    "        np.sum(class_total),\n",
+    "    )\n",
+    ")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "bc381cf4",
@@ -451,10 +948,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "id": "ef623c26",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model:  fp32  \t Size (KB): 2331.074\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "2331074"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "import os\n",
     "\n",
@@ -480,10 +995,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 32,
    "id": "c4c65d4b",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model:  int8  \t Size (KB): 659.806\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "659806"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "import torch.quantization\n",
     "\n",
@@ -492,6 +1025,13 @@
     "print_size_of_model(quantized_model, \"int8\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We observe a significant decline in model size  ( from 2331.074KB to 659.934KB ) which proves the effectivness of quantization in reducing the model's size."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "7b108e17",
@@ -500,47 +1040,597 @@
     "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": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "'''comparing different accuracies of the classes for each model( non-quantized and quantized)'''\n",
+    "def compare():\n",
+    "    # track test loss\n",
+    "    test_loss = 0.0\n",
+    "    test_loss_quantized= 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",
+    "    class_correct_quantized = list(0.0 for i in range(10))\n",
+    "    class_total_quantized = list(0.0 for i in range(10))\n",
+    "    quantized_model_cpu = quantized_model.cpu()\n",
+    "    model.eval()\n",
+    "    quantized_model_cpu.eval()\n",
+    "    # iterate over test data \n",
+    "    for data, target in test_loader:\n",
+    "        data, target = data.cpu(), target.cpu()\n",
+    "        # forward pass: compute predicted outputs by passing inputs to the model\n",
+    "        output = model(data)\n",
+    "        quantized_output = quantized_model_cpu(data)\n",
+    "        # calculate the batch loss\n",
+    "        loss = criterion(output, target)\n",
+    "        quantized_loss=criterion(quantized_output, target)\n",
+    "        # update test loss\n",
+    "        test_loss += loss.item() * data.size(0)\n",
+    "        test_loss_quantized+= quantized_loss.item() * data.size(0)\n",
+    "        # convert output probabilities to predicted class\n",
+    "        _, pred = torch.max(output, 1)\n",
+    "        _, quantized_pred = torch.max(quantized_output, 1)\n",
+    "        # compare predictions to true label\n",
+    "        correct_tensor = pred.eq(target.data.view_as(pred))\n",
+    "        quantized_correct_tensor = quantized_pred.eq(target.data.view_as(quantized_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",
+    "        quantized_correct = (\n",
+    "            np.squeeze(quantized_correct_tensor.numpy())\n",
+    "            if not train_on_gpu\n",
+    "            else np.squeeze(quantized_correct_tensor.cpu().numpy())\n",
+    "        )\n",
+    "        # calculate test accuracy for each object class for the non quantized model\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",
+    "        # calculate test accuracy for each object class for the quantized model\n",
+    "        for i in range(batch_size):\n",
+    "            label = target.data[i]\n",
+    "            class_correct_quantized[label] += quantized_correct[i].item()\n",
+    "            class_total_quantized[label] += 1    \n",
+    "    # average test loss for the non quantized model \n",
+    "    test_loss = test_loss / len(test_loader)\n",
+    "    print(\"Test Loss: {:.6f}\\n\".format(test_loss))\n",
+    "    # average test loss for the  quantized model \n",
+    "    test_loss_quantized = test_loss_quantized / len(test_loader)\n",
+    "    print(\"Test Loss quantized: {:.6f}\\n\".format(test_loss_quantized))\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",
+    "    )\n",
+    "\n",
+    "    # average test loss for the quantized model \n",
+    "    test_loss_quantized = test_loss_quantized / len(test_loader)\n",
+    "    print(\"Test Loss: {:.6f}\\n\".format(test_loss_quantized))\n",
+    "\n",
+    "    for i in range(10):\n",
+    "        if class_total_quantized[i] > 0:\n",
+    "            print(\n",
+    "                \"Test Accuracy of %5s: %2d%% (%2d/%2d)\"\n",
+    "                % (\n",
+    "                    classes[i],\n",
+    "                    100 * class_correct_quantized[i] / class_total_quantized[i],\n",
+    "                    np.sum(class_correct_quantized[i]),\n",
+    "                    np.sum(class_total_quantized[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_quantized) / np.sum(class_total_quantized),\n",
+    "            np.sum(class_correct_quantized),\n",
+    "            np.sum(class_total_quantized),\n",
+    "        )\n",
+    "    )\n",
+    "    # the comparision between the two models\n",
+    "    print('\\n \\n')\n",
+    "    quant_class_model=0\n",
+    "    class_model=0\n",
+    "    for i in range(10):\n",
+    "        quant_class_model=100 * class_correct_quantized[i] / class_total_quantized[i]\n",
+    "        class_model=100 * class_correct[i] / class_total[i]\n",
+    "        if quant_class_model>class_model:\n",
+    "            print('the quantized model performed better for the class',classes[i],'with accuracy equal to',quant_class_model)\n",
+    "        elif quant_class_model<class_model:\n",
+    "            print('the non quantized model performed better for the class',classes[i],'with accuracy equal to',quant_class_model)\n",
+    "        elif quant_class_model==class_model:\n",
+    "            print('the models performed the same for the class',classes[i])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test Loss: 22.216756\n",
+      "\n",
+      "Test Loss quantized: 22.204659\n",
+      "\n",
+      "Test Accuracy of airplane: 74% (740/1000)\n",
+      "Test Accuracy of automobile: 82% (827/1000)\n",
+      "Test Accuracy of  bird: 63% (633/1000)\n",
+      "Test Accuracy of   cat: 55% (554/1000)\n",
+      "Test Accuracy of  deer: 65% (658/1000)\n",
+      "Test Accuracy of   dog: 66% (662/1000)\n",
+      "Test Accuracy of  frog: 83% (835/1000)\n",
+      "Test Accuracy of horse: 73% (731/1000)\n",
+      "Test Accuracy of  ship: 81% (816/1000)\n",
+      "Test Accuracy of truck: 80% (808/1000)\n",
+      "\n",
+      "Test Accuracy (Overall): 72% (7264/10000)\n",
+      "Test Loss: 0.044409\n",
+      "\n",
+      "Test Accuracy of airplane: 74% (741/1000)\n",
+      "Test Accuracy of automobile: 82% (825/1000)\n",
+      "Test Accuracy of  bird: 63% (630/1000)\n",
+      "Test Accuracy of   cat: 55% (556/1000)\n",
+      "Test Accuracy of  deer: 65% (653/1000)\n",
+      "Test Accuracy of   dog: 66% (661/1000)\n",
+      "Test Accuracy of  frog: 83% (837/1000)\n",
+      "Test Accuracy of horse: 73% (731/1000)\n",
+      "Test Accuracy of  ship: 81% (816/1000)\n",
+      "Test Accuracy of truck: 80% (808/1000)\n",
+      "\n",
+      "Test Accuracy (Overall): 72% (7258/10000)\n",
+      "\n",
+      " \n",
+      "\n",
+      "the quantized model performed better for the class airplane with accuracy equal to 74.1\n",
+      "the non quantized model performed better for the class automobile with accuracy equal to 82.5\n",
+      "the non quantized model performed better for the class bird with accuracy equal to 63.0\n",
+      "the quantized model performed better for the class cat with accuracy equal to 55.6\n",
+      "the non quantized model performed better for the class deer with accuracy equal to 65.3\n",
+      "the non quantized model performed better for the class dog with accuracy equal to 66.1\n",
+      "the quantized model performed better for the class frog with accuracy equal to 83.7\n",
+      "the models performed the same for the class horse\n",
+      "the models performed the same for the class ship\n",
+      "the models performed the same for the class truck\n"
+     ]
+    }
+   ],
+   "source": [
+    "compare()"
+   ]
+  },
   {
    "cell_type": "markdown",
-   "id": "a0a34b90",
    "metadata": {},
    "source": [
-    "Try training aware quantization to mitigate the impact on the accuracy (doc available here https://pytorch.org/docs/stable/quantization.html#torch.quantization.quantize_dynamic)"
+    "We can observe that the non quantized model performed better than the quantized model for several classes. There is , indeed, some sort of a loss in the accuracy of the modal due to quantization for some classes. Still, the quantized model performed better for other classes .However , the negative effect is more abundent than the positive one ( 6 classes out of 9 ).\n",
+    "So , we can suppose that quantization effect negatively the accuracy"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "201470f9",
+   "id": "a0a34b90",
    "metadata": {},
    "source": [
-    "## Exercise 3: working with pre-trained models.\n",
-    "\n",
-    "PyTorch offers several pre-trained models https://pytorch.org/vision/0.8/models.html        \n",
-    "We will use ResNet50 trained on ImageNet dataset (https://www.image-net.org/index.php). Use the following code with the files `imagenet-simple-labels.json` that contains the imagenet labels and the image dog.png that we will use as test.\n"
+    "Try training aware quantization to mitigate the impact on the accuracy (doc available here https://pytorch.org/docs/stable/quantization.html#torch.quantization.quantize_dynamic)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "b4d13080",
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
-    "import json\n",
-    "from PIL import Image\n",
+    "class Second_Net(nn.Module):\n",
+    "    def __init__(self):\n",
+    "        super(Second_Net, self).__init__()\n",
+    "        self.conv1 = nn.Conv2d(3, 16, 3, padding=1)\n",
+    "        self.pool = nn.MaxPool2d(2, 2)\n",
+    "        self.conv2 = nn.Conv2d(16, 32, 3, padding=1)\n",
+    "        self.conv3 = nn.Conv2d(32,64,3, padding=1)\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(0.2)\n",
+    "        self.quant = torch.quantization.QuantStub()\n",
+    "        self.dequant = torch.quantization.DeQuantStub()\n",
     "\n",
-    "# Choose an image to pass through the model\n",
-    "test_image = \"dog.png\"\n",
+    "    def forward(self, x):\n",
+    "        x = self.quant(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",
+    "        x = x.view(-1, 64 * 4 * 4)\n",
+    "        x = self.dropout(F.relu(self.fc1(x)))\n",
+    "        x = self.dropout(F.relu(self.fc2(x)))\n",
+    "        x = self.fc3(x)\n",
+    "        x = self.dequant(x)\n",
+    "        return x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\hajer\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\torch\\ao\\quantization\\quantize.py:309: UserWarning: None of the submodule got qconfig applied. Make sure you passed correct configuration through `qconfig_dict` or by assigning the `.qconfig` attribute directly on submodules\n",
+      "  warnings.warn(\"None of the submodule got qconfig applied. Make sure you \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch: 0 \tTraining Loss: 45.959648 \tValidation Loss: 45.361642\n",
+      "Epoch: 1 \tTraining Loss: 40.578055 \tValidation Loss: 37.019123\n",
+      "Epoch: 2 \tTraining Loss: 34.958021 \tValidation Loss: 32.512271\n",
+      "Epoch: 3 \tTraining Loss: 31.368492 \tValidation Loss: 30.631505\n",
+      "Epoch: 4 \tTraining Loss: 28.846734 \tValidation Loss: 27.733091\n",
+      "Epoch: 5 \tTraining Loss: 26.597492 \tValidation Loss: 25.270041\n",
+      "Epoch: 6 \tTraining Loss: 24.642671 \tValidation Loss: 23.781352\n",
+      "Epoch: 7 \tTraining Loss: 22.797168 \tValidation Loss: 21.735617\n",
+      "Epoch: 8 \tTraining Loss: 21.168802 \tValidation Loss: 20.696241\n",
+      "Epoch: 9 \tTraining Loss: 19.718931 \tValidation Loss: 19.530297\n",
+      "Epoch: 10 \tTraining Loss: 18.337192 \tValidation Loss: 18.315049\n",
+      "Epoch: 11 \tTraining Loss: 17.161347 \tValidation Loss: 18.228905\n",
+      "Epoch: 12 \tTraining Loss: 16.009654 \tValidation Loss: 17.130416\n",
+      "Epoch: 13 \tTraining Loss: 14.950313 \tValidation Loss: 17.205976\n",
+      "Epoch: 14 \tTraining Loss: 13.892417 \tValidation Loss: 16.574649\n",
+      "Epoch: 15 \tTraining Loss: 12.945479 \tValidation Loss: 16.605574\n",
+      "Epoch: 16 \tTraining Loss: 12.063045 \tValidation Loss: 16.134014\n",
+      "Epoch: 17 \tTraining Loss: 11.167100 \tValidation Loss: 15.728587\n",
+      "Epoch: 18 \tTraining Loss: 10.315715 \tValidation Loss: 16.507135\n",
+      "Epoch: 19 \tTraining Loss: 9.513543 \tValidation Loss: 16.656796\n",
+      "Epoch: 20 \tTraining Loss: 8.669666 \tValidation Loss: 17.119372\n",
+      "Epoch: 21 \tTraining Loss: 7.859929 \tValidation Loss: 17.051633\n",
+      "Epoch: 22 \tTraining Loss: 7.177531 \tValidation Loss: 18.624404\n",
+      "Epoch: 23 \tTraining Loss: 6.433317 \tValidation Loss: 17.436820\n",
+      "Epoch: 24 \tTraining Loss: 5.785852 \tValidation Loss: 18.445435\n",
+      "Epoch: 25 \tTraining Loss: 5.308312 \tValidation Loss: 18.851797\n",
+      "Epoch: 26 \tTraining Loss: 4.834317 \tValidation Loss: 19.706932\n",
+      "Epoch: 27 \tTraining Loss: 4.509724 \tValidation Loss: 20.681327\n",
+      "Epoch: 28 \tTraining Loss: 4.165472 \tValidation Loss: 23.931487\n",
+      "Epoch: 29 \tTraining Loss: 3.640936 \tValidation Loss: 22.121072\n"
+     ]
+    }
+   ],
+   "source": [
+    "'''trying to implement the aware quantization'''\n",
+    "from torch.ao.quantization import QConfigMapping\n",
+    "import torch.quantization.quantize_fx as quantize_fx\n",
+    "import copy\n",
     "\n",
-    "# Configure matplotlib for pretty inline plots\n",
-    "#%matplotlib inline\n",
-    "#%config InlineBackend.figure_format = 'retina'\n",
+    "model_fp=Second_Net()\n",
     "\n",
-    "# Prepare the labels\n",
-    "with open(\"imagenet-simple-labels.json\") as f:\n",
-    "    labels = json.load(f)\n",
+    "model_fp.train()\n",
+    "model_to_quantize = copy.deepcopy(model_fp).cpu()\n",
+    "model.qconfig = torch.quantization.get_default_qat_qconfig(\"qnnpack\")\n",
+    "model_qat = torch.quantization.prepare_qat(model_fp, inplace=False).cpu()\n",
+    "# quantization aware training goes here\n",
+    "model_qat = torch.quantization.convert(model_qat.eval(), inplace=False).cpu()\n",
+    "n_epochs=30\n",
+    "criterion = nn.CrossEntropyLoss()  # specify loss function\n",
+    "optimizer = optim.SGD(model_qat.parameters(), lr=0.01)  # specify optimizer\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",
-    "# First prepare the transformations: resize the image to what the model was trained on and convert it to a tensor\n",
+    "    # Train the model\n",
+    "    model_qat.train()\n",
+    "    for data, target in train_loader:\n",
+    "        data, target = data.cpu(), target.cpu()\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 = model_qat(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",
+    "    model_qat.eval()\n",
+    "    for data, target in valid_loader:\n",
+    "        data, target = data.cpu(), target.cpu()\n",
+    "        # Forward pass: compute predicted outputs by passing inputs to the model\n",
+    "        output = model_qat(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",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Second_Net(\n",
+      "  (conv1): Conv2d(3, 16, 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",
+      "  (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",
+      "  (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=0.2, inplace=False)\n",
+      "  (quant): QuantStub()\n",
+      "  (dequant): DeQuantStub()\n",
+      ")\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(model_qat) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test Loss: 21.764686\n",
+      "\n",
+      "Test Accuracy of airplane: 78% (787/1000)\n",
+      "Test Accuracy of automobile: 85% (857/1000)\n",
+      "Test Accuracy of  bird: 55% (550/1000)\n",
+      "Test Accuracy of   cat: 53% (538/1000)\n",
+      "Test Accuracy of  deer: 75% (750/1000)\n",
+      "Test Accuracy of   dog: 64% (649/1000)\n",
+      "Test Accuracy of  frog: 80% (808/1000)\n",
+      "Test Accuracy of horse: 79% (793/1000)\n",
+      "Test Accuracy of  ship: 81% (813/1000)\n",
+      "Test Accuracy of truck: 83% (838/1000)\n",
+      "\n",
+      "Test Accuracy (Overall): 73% (7383/10000)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# track test loss\n",
+    "test_loss = 0.0\n",
+    "class_correct = list(0.0 for i in range(10))\n",
+    "class_total = list(0.0 for i in range(10))\n",
+    "quantized_model = torch.ao.quantization.convert(model_qat.eval(), inplace=False).cpu()\n",
+    "model_quantized=quantized_model.eval()\n",
+    "# iterate over test data\n",
+    "for data, target in test_loader:\n",
+    "    \n",
+    "    data, target = data.cpu(), target.cpu()\n",
+    "    # forward pass: compute predicted outputs by passing inputs to the model\n",
+    "    output = model_quantized(data)\n",
+    "    # calculate the batch loss\n",
+    "    loss = criterion(output, target)\n",
+    "    # update test loss\n",
+    "    test_loss += loss.item() * data.size(0)\n",
+    "    # convert output probabilities to predicted class\n",
+    "    _, pred = torch.max(output, 1)\n",
+    "    # compare predictions to true label\n",
+    "    correct_tensor = pred.eq(target.data.view_as(pred))\n",
+    "    correct = (\n",
+    "        np.squeeze(correct_tensor.numpy())\n",
+    "        if not train_on_gpu\n",
+    "        else np.squeeze(correct_tensor.cpu().numpy())\n",
+    "    )\n",
+    "    # calculate test accuracy for each object class\n",
+    "    for i in range(batch_size):\n",
+    "        label = target.data[i]\n",
+    "        class_correct[label] += correct[i].item()\n",
+    "        class_total[label] += 1\n",
+    "\n",
+    "# average test loss\n",
+    "test_loss = test_loss / len(test_loader)\n",
+    "print(\"Test Loss: {:.6f}\\n\".format(test_loss))\n",
+    "\n",
+    "for i in range(10):\n",
+    "    if class_total[i] > 0:\n",
+    "        print(\n",
+    "            \"Test Accuracy of %5s: %2d%% (%2d/%2d)\"\n",
+    "            % (\n",
+    "                classes[i],\n",
+    "                100 * class_correct[i] / class_total[i],\n",
+    "                np.sum(class_correct[i]),\n",
+    "                np.sum(class_total[i]),\n",
+    "            )\n",
+    "        )\n",
+    "    else:\n",
+    "        print(\"Test Accuracy of %5s: N/A (no training examples)\" % (classes[i]))\n",
+    "\n",
+    "print(\n",
+    "    \"\\nTest Accuracy (Overall): %2d%% (%2d/%2d)\"\n",
+    "    % (\n",
+    "        100.0 * np.sum(class_correct) / np.sum(class_total),\n",
+    "        np.sum(class_correct),\n",
+    "        np.sum(class_total),\n",
+    "    )\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test Loss: 22.216756\n",
+      "\n",
+      "Test Loss quantized: 21.764686\n",
+      "\n",
+      "Test Accuracy of airplane: 74% (740/1000)\n",
+      "Test Accuracy of automobile: 82% (827/1000)\n",
+      "Test Accuracy of  bird: 63% (633/1000)\n",
+      "Test Accuracy of   cat: 55% (554/1000)\n",
+      "Test Accuracy of  deer: 65% (658/1000)\n",
+      "Test Accuracy of   dog: 66% (662/1000)\n",
+      "Test Accuracy of  frog: 83% (835/1000)\n",
+      "Test Accuracy of horse: 73% (731/1000)\n",
+      "Test Accuracy of  ship: 81% (816/1000)\n",
+      "Test Accuracy of truck: 80% (808/1000)\n",
+      "\n",
+      "Test Accuracy (Overall): 72% (7264/10000)\n",
+      "Test Loss: 0.043529\n",
+      "\n",
+      "Test Accuracy of airplane: 78% (787/1000)\n",
+      "Test Accuracy of automobile: 85% (857/1000)\n",
+      "Test Accuracy of  bird: 55% (550/1000)\n",
+      "Test Accuracy of   cat: 53% (538/1000)\n",
+      "Test Accuracy of  deer: 75% (750/1000)\n",
+      "Test Accuracy of   dog: 64% (649/1000)\n",
+      "Test Accuracy of  frog: 80% (808/1000)\n",
+      "Test Accuracy of horse: 79% (793/1000)\n",
+      "Test Accuracy of  ship: 81% (813/1000)\n",
+      "Test Accuracy of truck: 83% (838/1000)\n",
+      "\n",
+      "Test Accuracy (Overall): 73% (7383/10000)\n",
+      "\n",
+      " \n",
+      "\n",
+      "the quantized model performed better for the class airplane with accuracy equal to 78.7\n",
+      "the quantized model performed better for the class automobile with accuracy equal to 85.7\n",
+      "the non quantized model performed better for the class bird with accuracy equal to 55.0\n",
+      "the non quantized model performed better for the class cat with accuracy equal to 53.8\n",
+      "the quantized model performed better for the class deer with accuracy equal to 75.0\n",
+      "the non quantized model performed better for the class dog with accuracy equal to 64.9\n",
+      "the non quantized model performed better for the class frog with accuracy equal to 80.8\n",
+      "the quantized model performed better for the class horse with accuracy equal to 79.3\n",
+      "the non quantized model performed better for the class ship with accuracy equal to 81.3\n",
+      "the quantized model performed better for the class truck with accuracy equal to 83.8\n"
+     ]
+    }
+   ],
+   "source": [
+    "quantized_model=model_quantized\n",
+    "compare()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " With the quantization aware , we still can observe a significant loss in the accuracy of the model .We can try to matigate that by retraining the quantized model.  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "201470f9",
+   "metadata": {},
+   "source": [
+    "## Exercise 3: working with pre-trained models.\n",
+    "\n",
+    "PyTorch offers several pre-trained models https://pytorch.org/vision/0.8/models.html        \n",
+    "We will use ResNet50 trained on ImageNet dataset (https://www.image-net.org/index.php). Use the following code with the files `imagenet-simple-labels.json` that contains the imagenet labels and the image dog.png that we will use as test.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "b4d13080",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\hajer\\AppData\\Local\\Programs\\Python\\Python38\\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\\hajer\\AppData\\Local\\Programs\\Python\\Python38\\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",
+      "Downloading: \"https://download.pytorch.org/models/resnet50-0676ba61.pth\" to C:\\Users\\hajer/.cache\\torch\\hub\\checkpoints\\resnet50-0676ba61.pth\n",
+      "100.0%\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",
+    "\n",
+    "# Choose an image to pass through the model\n",
+    "test_image = \"dog.png\"\n",
+    "\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",
@@ -586,6 +1676,449 @@
     "    \n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ResNet(\n",
+      "  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
+      "  (bn1): BatchNorm2d(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): Bottleneck(\n",
+      "      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "      (downsample): Sequential(\n",
+      "        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      )\n",
+      "    )\n",
+      "    (1): Bottleneck(\n",
+      "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "    )\n",
+      "    (2): Bottleneck(\n",
+      "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "    )\n",
+      "  )\n",
+      "  (layer2): Sequential(\n",
+      "    (0): Bottleneck(\n",
+      "      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
+      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "      (downsample): Sequential(\n",
+      "        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
+      "        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      )\n",
+      "    )\n",
+      "    (1): Bottleneck(\n",
+      "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "    )\n",
+      "    (2): Bottleneck(\n",
+      "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "    )\n",
+      "    (3): Bottleneck(\n",
+      "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "    )\n",
+      "  )\n",
+      "  (layer3): Sequential(\n",
+      "    (0): Bottleneck(\n",
+      "      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
+      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "      (downsample): Sequential(\n",
+      "        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
+      "        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      )\n",
+      "    )\n",
+      "    (1): Bottleneck(\n",
+      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "    )\n",
+      "    (2): Bottleneck(\n",
+      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "    )\n",
+      "    (3): Bottleneck(\n",
+      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "    )\n",
+      "    (4): Bottleneck(\n",
+      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "    )\n",
+      "    (5): Bottleneck(\n",
+      "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "    )\n",
+      "  )\n",
+      "  (layer4): Sequential(\n",
+      "    (0): Bottleneck(\n",
+      "      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
+      "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "      (downsample): Sequential(\n",
+      "        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
+      "        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      )\n",
+      "    )\n",
+      "    (1): Bottleneck(\n",
+      "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "    )\n",
+      "    (2): Bottleneck(\n",
+      "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+      "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+      "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+      "      (relu): ReLU(inplace=True)\n",
+      "    )\n",
+      "  )\n",
+      "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
+      "  (fc): Linear(in_features=2048, out_features=1000, bias=True)\n",
+      ")\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(models.resnet50(pretrained=True)) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model:  fp32  \t Size (KB): 102523.238\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "102523238"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print_size_of_model(model, label=\"fp32\") "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "  Model Quantized"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model:  int8  \t Size (KB): 96379.996\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "96379996"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import torch.quantization\n",
+    "quantized_model = torch.quantization.quantize_dynamic(model, dtype=torch.qint8)\n",
+    "print_size_of_model(quantized_model, label=\"int8\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "test with model quantized"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Predicted class is: Golden Retriever\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Set layers such as dropout and batchnorm in evaluation mode\n",
+    "quantized_model.eval()\n",
+    "\n",
+    "# Get the 1000-dimensional model output\n",
+    "out = quantized_model(image)\n",
+    "# Find the predicted class\n",
+    "print(\"Predicted class is: {}\".format(labels[out.argmax()]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "test with a New image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Predicted class is: tabby cat\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",
+    "\n",
+    "# Choose an image to pass through the model\n",
+    "test_image = \"cat2.jpg\"\n",
+    "\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",
+    "image = Image.open(test_image)\n",
+    "plt.imshow(image), 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",
+    "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",
+    "quantized_model = torch.quantization.quantize_dynamic(model, dtype=torch.qint8)\n",
+    "# Send the model to the GPU\n",
+    "# model.cuda()\n",
+    "# Set layers such as dropout and batchnorm in evaluation mode\n",
+    "quantized_model.eval()\n",
+    "\n",
+    "# Get the 1000-dimensional model output\n",
+    "out = quantized_model(image)\n",
+    "# Find the predicted class\n",
+    "print(\"Predicted class is: {}\".format(labels[out.argmax()]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " Experiment with Rnest18"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Predicted class is: tabby cat\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",
+    "\n",
+    "# Choose an image to pass through the model\n",
+    "test_image2= \"cat2.jpg\"\n",
+    "\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",
+    "image = Image.open(test_image2)\n",
+    "plt.imshow(image), 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",
+    "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.resnet18(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",
+    "\n",
+    "# Get the 1000-dimensional model output\n",
+    "out = model(image)\n",
+    "# Find the predicted class\n",
+    "print(\"Predicted class is: {}\".format(labels[out.argmax()]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Even with RESNT18 or RESNET50 we have always the correct prediction "
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "5d57da4b",
@@ -604,10 +2137,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 62,
    "id": "be2d31f5",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import os\n",
     "\n",
@@ -642,48 +2186,294 @@
     "    ),\n",
     "}\n",
     "\n",
-    "data_dir = \"hymenoptera_data\"\n",
-    "# Create train and validation datasets and loaders\n",
-    "image_datasets = {\n",
-    "    x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x])\n",
-    "    for x in [\"train\", \"val\"]\n",
-    "}\n",
-    "dataloaders = {\n",
-    "    x: torch.utils.data.DataLoader(\n",
-    "        image_datasets[x], batch_size=4, shuffle=True, num_workers=0\n",
+    "data_dir = \"hymenoptera_data\"\n",
+    "# Create train and validation datasets and loaders\n",
+    "image_datasets = {\n",
+    "    x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x])\n",
+    "    for x in [\"train\", \"val\"]\n",
+    "}\n",
+    "dataloaders = {\n",
+    "    x: torch.utils.data.DataLoader(\n",
+    "        image_datasets[x], batch_size=4, shuffle=True, num_workers=0\n",
+    "    )\n",
+    "    for x in [\"train\", \"val\"]\n",
+    "}\n",
+    "dataset_sizes = {x: len(image_datasets[x]) for x in [\"train\", \"val\"]}\n",
+    "class_names = image_datasets[\"train\"].classes\n",
+    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
+    "\n",
+    "# Helper function for displaying images\n",
+    "def imshow(inp, title=None):\n",
+    "    \"\"\"Imshow for Tensor.\"\"\"\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",
+    "\n",
+    "    # Un-normalize the images\n",
+    "    inp = std * inp + mean\n",
+    "    # Clip just in case\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)  # pause a bit so that plots are updated\n",
+    "    plt.show()\n",
+    "\n",
+    "\n",
+    "# Get a batch of training data\n",
+    "inputs, classes = next(iter(dataloaders[\"train\"]))\n",
+    "\n",
+    "# Make a grid from batch\n",
+    "out = torchvision.utils.make_grid(inputs)\n",
+    "\n",
+    "imshow(out, title=[class_names[x] for x in classes])\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bbd48800",
+   "metadata": {},
+   "source": [
+    "Now, execute the following code which uses a pre-trained model ResNet18 having replaced the output layer for the ants/bees classification and performs the model training by only changing the weights of this output layer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/10\n",
+      "----------\n",
+      "train Loss: 0.6058 Acc: 0.6598\n",
+      "val Loss: 0.1950 Acc: 0.9346\n",
+      "\n",
+      "Epoch 2/10\n",
+      "----------\n",
+      "train Loss: 0.4646 Acc: 0.7787\n",
+      "val Loss: 0.1801 Acc: 0.9412\n",
+      "\n",
+      "Epoch 3/10\n",
+      "----------\n",
+      "train Loss: 0.5029 Acc: 0.7500\n",
+      "val Loss: 0.1603 Acc: 0.9542\n",
+      "\n",
+      "Epoch 4/10\n",
+      "----------\n",
+      "train Loss: 0.7525 Acc: 0.6762\n",
+      "val Loss: 0.1728 Acc: 0.9542\n",
+      "\n",
+      "Epoch 5/10\n",
+      "----------\n",
+      "train Loss: 0.4601 Acc: 0.8033\n",
+      "val Loss: 0.1455 Acc: 0.9673\n",
+      "\n",
+      "Epoch 6/10\n",
+      "----------\n",
+      "train Loss: 0.4435 Acc: 0.8320\n",
+      "val Loss: 0.2790 Acc: 0.9216\n",
+      "\n",
+      "Epoch 7/10\n",
+      "----------\n",
+      "train Loss: 0.5058 Acc: 0.7910\n",
+      "val Loss: 0.2353 Acc: 0.9216\n",
+      "\n",
+      "Epoch 8/10\n",
+      "----------\n",
+      "train Loss: 0.3515 Acc: 0.8402\n",
+      "val Loss: 0.1889 Acc: 0.9412\n",
+      "\n",
+      "Epoch 9/10\n",
+      "----------\n",
+      "train Loss: 0.4285 Acc: 0.8156\n",
+      "val Loss: 0.1792 Acc: 0.9412\n",
+      "\n",
+      "Epoch 10/10\n",
+      "----------\n",
+      "train Loss: 0.3151 Acc: 0.8648\n",
+      "val Loss: 0.1910 Acc: 0.9412\n",
+      "\n",
+      "Training complete in 3m 50s\n",
+      "Best val Acc: 0.967320\n"
+     ]
+    }
+   ],
+   "source": [
+    "import copy\n",
+    "import os\n",
+    "import time\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import torch.optim as optim\n",
+    "import torchvision\n",
+    "from torch.optim import lr_scheduler\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(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",
+    "}\n",
+    "\n",
+    "data_dir = \"hymenoptera_data\"\n",
+    "# Create train and validation datasets and loaders\n",
+    "image_datasets = {\n",
+    "    x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x])\n",
+    "    for x in [\"train\", \"val\"]\n",
+    "}\n",
+    "dataloaders = {\n",
+    "    x: torch.utils.data.DataLoader(\n",
+    "        image_datasets[x], batch_size=4, shuffle=True, num_workers=4\n",
+    "    )\n",
+    "    for x in [\"train\", \"val\"]\n",
+    "}\n",
+    "dataset_sizes = {x: len(image_datasets[x]) for x in [\"train\", \"val\"]}\n",
+    "class_names = image_datasets[\"train\"].classes\n",
+    "\n",
+    "# Helper function for displaying images\n",
+    "def imshow(inp, title=None):\n",
+    "    \"\"\"Imshow for Tensor.\"\"\"\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",
+    "\n",
+    "    # Un-normalize the images\n",
+    "    inp = std * inp + mean\n",
+    "    # Clip just in case\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)  # pause a bit so that plots are updated\n",
+    "    plt.show()\n",
+    "\n",
+    "# Get a batch of training data\n",
+    "# inputs, classes = next(iter(dataloaders['train']))\n",
+    "\n",
+    "# Make a grid from batch\n",
+    "# out = torchvision.utils.make_grid(inputs)\n",
+    "\n",
+    "# imshow(out, title=[class_names[x] for x in classes])\n",
+    "\n",
+    "# training\n",
+    "def train_model(model, criterion, optimizer, scheduler, num_epochs=25):\n",
+    "    since = time.time()\n",
+    "\n",
+    "    best_model_wts = copy.deepcopy(model.state_dict())\n",
+    "    best_acc = 0.0\n",
+    "\n",
+    "    epoch_time = []  # we'll keep track of the time needed for each epoch\n",
+    "\n",
+    "    for epoch in range(num_epochs):\n",
+    "        epoch_start = time.time()\n",
+    "        print(\"Epoch {}/{}\".format(epoch + 1, num_epochs))\n",
+    "        print(\"-\" * 10)\n",
+    "\n",
+    "        # Each epoch has a training and validation phase\n",
+    "        for phase in [\"train\", \"val\"]:\n",
+    "            if phase == \"train\":\n",
+    "                scheduler.step()\n",
+    "                model.train()  # Set model to training mode\n",
+    "            else:\n",
+    "                model.eval()  # Set model to evaluate mode\n",
+    "\n",
+    "            running_loss = 0.0\n",
+    "            running_corrects = 0\n",
+    "\n",
+    "            # Iterate over data.\n",
+    "            for inputs, labels in dataloaders[phase]:\n",
+    "                # Move inputs and labels to CPU\n",
+    "                inputs = inputs.to('cpu')\n",
+    "                labels = labels.to('cpu')\n",
+    "\n",
+    "                # zero the parameter gradients\n",
+    "                optimizer.zero_grad()\n",
+    "\n",
+    "                # Forward\n",
+    "                # Track history if only in training phase\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 + optimize only if in training phase\n",
+    "                    if phase == \"train\":\n",
+    "                        loss.backward()\n",
+    "                        optimizer.step()\n",
+    "\n",
+    "                # Statistics\n",
+    "                running_loss += loss.item() * inputs.size(0)\n",
+    "                running_corrects += torch.sum(preds == labels.data)\n",
+    "\n",
+    "            epoch_loss = running_loss / dataset_sizes[phase]\n",
+    "            epoch_acc = running_corrects.double() / dataset_sizes[phase]\n",
+    "\n",
+    "            print(\"{} Loss: {:.4f} Acc: {:.4f}\".format(phase, epoch_loss, epoch_acc))\n",
+    "\n",
+    "            # Deep copy the model\n",
+    "            if phase == \"val\" and epoch_acc > best_acc:\n",
+    "                best_acc = epoch_acc\n",
+    "                best_model_wts = copy.deepcopy(model.state_dict())\n",
+    "\n",
+    "        # Add the epoch time\n",
+    "        t_epoch = time.time() - epoch_start\n",
+    "        epoch_time.append(t_epoch)\n",
+    "        print()\n",
+    "\n",
+    "    time_elapsed = time.time() - since\n",
+    "    print(\n",
+    "        \"Training complete in {:.0f}m {:.0f}s\".format(\n",
+    "            time_elapsed // 60, time_elapsed % 60\n",
+    "        )\n",
     "    )\n",
-    "    for x in [\"train\", \"val\"]\n",
-    "}\n",
-    "dataset_sizes = {x: len(image_datasets[x]) for x in [\"train\", \"val\"]}\n",
-    "class_names = image_datasets[\"train\"].classes\n",
-    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
+    "    print(\"Best val Acc: {:4f}\".format(best_acc))\n",
     "\n",
-    "# Helper function for displaying images\n",
-    "def imshow(inp, title=None):\n",
-    "    \"\"\"Imshow for Tensor.\"\"\"\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",
+    "    # Load best model weights\n",
+    "    model.load_state_dict(best_model_wts)\n",
+    "    return model, epoch_time\n",
     "\n",
-    "    # Un-normalize the images\n",
-    "    inp = std * inp + mean\n",
-    "    # Clip just in case\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)  # pause a bit so that plots are updated\n",
-    "    plt.show()\n",
+    "# Download a pre-trained ResNet18 model and freeze its weights\n",
+    "model = torchvision.models.resnet18(pretrained=True)\n",
+    "for param in model.parameters():\n",
+    "    param.requires_grad = False\n",
     "\n",
+    "# Replace the final fully connected layer\n",
+    "# Parameters of newly constructed modules have requires_grad=True by default\n",
+    "num_ftrs = model.fc.in_features\n",
+    "model.fc = nn.Linear(num_ftrs, 2)\n",
     "\n",
-    "# Get a batch of training data\n",
-    "inputs, classes = next(iter(dataloaders[\"train\"]))\n",
+    "# Set the loss function\n",
+    "criterion = nn.CrossEntropyLoss()\n",
     "\n",
-    "# Make a grid from batch\n",
-    "out = torchvision.utils.make_grid(inputs)\n",
+    "# Set up the optimizer and learning rate scheduler\n",
+    "optimizer_conv = optim.SGD(model.fc.parameters(), lr=0.001, momentum=0.9)\n",
+    "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_conv, step_size=7, gamma=0.1)\n",
     "\n",
-    "imshow(out, title=[class_names[x] for x in classes])\n",
-    "\n"
+    "# Train the model\n",
+    "model, epoch_time = train_model(\n",
+    "    model, criterion, optimizer_conv, exp_lr_scheduler, num_epochs=10\n",
+    ")\n"
    ]
   },
   {
@@ -691,20 +2481,105 @@
    "id": "bbd48800",
    "metadata": {},
    "source": [
-    "Now, execute the following code which uses a pre-trained model ResNet18 having replaced the output layer for the ants/bees classification and performs the model training by only changing the weights of this output layer."
+    "Experiments:\n",
+    "Study the code and the results obtained.\n",
+    "\n",
+    "Modify the code and add an \"eval_model\" function to allow\n",
+    "the evaluation of the model on a test set (different from the learning and validation sets used during the learning phase). Study the results obtained.\n",
+    "\n",
+    "Now modify the code to replace the current classification layer with a set of two layers using a \"relu\" activation function for the middle layer, and the \"dropout\" mechanism for both layers. Renew the experiments and study the results obtained.\n",
+    "\n",
+    "Apply ther quantization (post and quantization aware) and evaluate impact on model size and accuracy."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " \n",
+    "We add the test dataset from this link https://www.kaggle.com/datasets/lys620/ants-and-bees"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "572d824c",
+   "execution_count": 124,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\hajer\\AppData\\Local\\Programs\\Python\\Python38\\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\\hajer\\AppData\\Local\\Programs\\Python\\Python38\\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",
+      "c:\\Users\\hajer\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\torch\\optim\\lr_scheduler.py:136: 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(\"Detected call of `lr_scheduler.step()` before `optimizer.step()`. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/10\n",
+      "----------\n",
+      "train Loss: 0.6018 Acc: 0.6680\n",
+      "val Loss: 0.2802 Acc: 0.8693\n",
+      "\n",
+      "Epoch 2/10\n",
+      "----------\n",
+      "train Loss: 0.4776 Acc: 0.7787\n",
+      "val Loss: 0.3623 Acc: 0.8301\n",
+      "\n",
+      "Epoch 3/10\n",
+      "----------\n",
+      "train Loss: 0.6132 Acc: 0.7254\n",
+      "val Loss: 0.4679 Acc: 0.8039\n",
+      "\n",
+      "Epoch 4/10\n",
+      "----------\n",
+      "train Loss: 0.5704 Acc: 0.7910\n",
+      "val Loss: 0.1528 Acc: 0.9281\n",
+      "\n",
+      "Epoch 5/10\n",
+      "----------\n",
+      "train Loss: 0.4224 Acc: 0.8402\n",
+      "val Loss: 0.1861 Acc: 0.9346\n",
+      "\n",
+      "Epoch 6/10\n",
+      "----------\n",
+      "train Loss: 0.5236 Acc: 0.8115\n",
+      "val Loss: 0.1894 Acc: 0.9281\n",
+      "\n",
+      "Epoch 7/10\n",
+      "----------\n",
+      "train Loss: 0.3606 Acc: 0.8361\n",
+      "val Loss: 0.1552 Acc: 0.9542\n",
+      "\n",
+      "Epoch 8/10\n",
+      "----------\n",
+      "train Loss: 0.4259 Acc: 0.8115\n",
+      "val Loss: 0.1775 Acc: 0.9346\n",
+      "\n",
+      "Epoch 9/10\n",
+      "----------\n",
+      "train Loss: 0.2985 Acc: 0.8566\n",
+      "val Loss: 0.1687 Acc: 0.9542\n",
+      "\n",
+      "Epoch 10/10\n",
+      "----------\n",
+      "train Loss: 0.3225 Acc: 0.8566\n",
+      "val Loss: 0.2094 Acc: 0.9346\n",
+      "\n",
+      "Training complete in 4m 8s\n",
+      "Best val Acc: 0.954248\n"
+     ]
+    }
+   ],
    "source": [
     "import copy\n",
     "import os\n",
     "import time\n",
-    "\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "import torch\n",
@@ -719,14 +2594,10 @@
     "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",
+    "            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",
@@ -737,23 +2608,30 @@
     "            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",
     "# Create train and validation datasets and loaders\n",
     "image_datasets = {\n",
     "    x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x])\n",
-    "    for x in [\"train\", \"val\"]\n",
+    "    for x in [\"train\", \"val\",\"test\"]\n",
     "}\n",
     "dataloaders = {\n",
     "    x: torch.utils.data.DataLoader(\n",
     "        image_datasets[x], batch_size=4, shuffle=True, num_workers=4\n",
     "    )\n",
-    "    for x in [\"train\", \"val\"]\n",
+    "    for x in [\"train\", \"val\",\"test\"]\n",
     "}\n",
     "dataset_sizes = {x: len(image_datasets[x]) for x in [\"train\", \"val\"]}\n",
     "class_names = image_datasets[\"train\"].classes\n",
-    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
     "\n",
     "# Helper function for displaying images\n",
     "def imshow(inp, title=None):\n",
@@ -772,7 +2650,6 @@
     "    plt.pause(0.001)  # pause a bit so that plots are updated\n",
     "    plt.show()\n",
     "\n",
-    "\n",
     "# Get a batch of training data\n",
     "# inputs, classes = next(iter(dataloaders['train']))\n",
     "\n",
@@ -780,9 +2657,8 @@
     "# out = torchvision.utils.make_grid(inputs)\n",
     "\n",
     "# imshow(out, title=[class_names[x] for x in classes])\n",
-    "# training\n",
-    "\n",
     "\n",
+    "# training\n",
     "def train_model(model, criterion, optimizer, scheduler, num_epochs=25):\n",
     "    since = time.time()\n",
     "\n",
@@ -809,8 +2685,9 @@
     "\n",
     "            # Iterate over data.\n",
     "            for inputs, labels in dataloaders[phase]:\n",
-    "                inputs = inputs.to(device)\n",
-    "                labels = labels.to(device)\n",
+    "                # Move inputs and labels to CPU\n",
+    "                inputs = inputs.to('cpu')\n",
+    "                labels = labels.to('cpu')\n",
     "\n",
     "                # zero the parameter gradients\n",
     "                optimizer.zero_grad()\n",
@@ -858,7 +2735,6 @@
     "    model.load_state_dict(best_model_wts)\n",
     "    return model, epoch_time\n",
     "\n",
-    "\n",
     "# Download a pre-trained ResNet18 model and freeze its weights\n",
     "model = torchvision.models.resnet18(pretrained=True)\n",
     "for param in model.parameters():\n",
@@ -868,14 +2744,15 @@
     "# Parameters of newly constructed modules have requires_grad=True by default\n",
     "num_ftrs = model.fc.in_features\n",
     "model.fc = nn.Linear(num_ftrs, 2)\n",
-    "# Send the model to the GPU\n",
-    "model = model.to(device)\n",
+    "\n",
     "# Set the loss function\n",
     "criterion = nn.CrossEntropyLoss()\n",
     "\n",
-    "# Observe that only the parameters of the final layer are being optimized\n",
+    "# Set up the optimizer and learning rate scheduler\n",
     "optimizer_conv = optim.SGD(model.fc.parameters(), lr=0.001, momentum=0.9)\n",
     "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_conv, step_size=7, gamma=0.1)\n",
+    "\n",
+    "# Train the model\n",
     "model, epoch_time = train_model(\n",
     "    model, criterion, optimizer_conv, exp_lr_scheduler, num_epochs=10\n",
     ")\n"
@@ -883,18 +2760,434 @@
   },
   {
    "cell_type": "markdown",
-   "id": "bbd48800",
    "metadata": {},
    "source": [
-    "Experiments:\n",
-    "Study the code and the results obtained.\n",
+    " Test function "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def test_model(model, criterion, optimizer):\n",
+    "    was_training = model.training\n",
+    "    model.eval()\n",
     "\n",
-    "Modify the code and add an \"eval_model\" function to allow\n",
-    "the evaluation of the model on a test set (different from the learning and validation sets used during the learning phase). Study the results obtained.\n",
+    "    class_correct = list(0.0 for i in range(2))\n",
+    "    class_total = list(0.0 for i in range(2))\n",
     "\n",
-    "Now modify the code to replace the current classification layer with a set of two layers using a \"relu\" activation function for the middle layer, and the \"dropout\" mechanism for both layers. Renew the experiments and study the results obtained.\n",
+    "    with torch.no_grad():\n",
+    "        for i, (inputs, labels) in enumerate(dataloaders['test']):\n",
+    "            # Move inputs and labels to CPU\n",
+    "            inputs = inputs.to('cpu')\n",
+    "            labels = labels.to('cpu')\n",
     "\n",
-    "Apply ther quantization (post and quantization aware) and evaluate impact on model size and accuracy."
+    "            outputs = model(inputs)\n",
+    "            _, preds = torch.max(outputs, 1)\n",
+    "\n",
+    "            correct_tensor = preds.eq(labels.data.view_as(preds))\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(3):\n",
+    "                label = labels.data[i]\n",
+    "                class_correct[label] += correct[i].item()\n",
+    "                class_total[label] += 1\n",
+    "\n",
+    "        model.train(mode=was_training)\n",
+    "\n",
+    "    for i in range(2):\n",
+    "        if class_total[i] > 0:\n",
+    "            print(\n",
+    "                \"Test Accuracy of %5s: %2d%% (%2d/%2d)\"\n",
+    "                % (\n",
+    "                    class_names[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)\" % (class_names[i]))\n",
+    "\n",
+    "    print(\n",
+    "        \"\\nTest Accuracy (Overall): %2d%% (%2d/%2d)\"\n",
+    "        % (\n",
+    "            100.0 * np.sum(class_correct) / np.sum(class_total),\n",
+    "            np.sum(class_correct),\n",
+    "            np.sum(class_total),\n",
+    "        )\n",
+    "    )\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test Accuracy of  ants: 100% (19/19)\n",
+      "Test Accuracy of  bees: 98% (61/62)\n",
+      "\n",
+      "Test Accuracy (Overall): 98% (80/81)\n"
+     ]
+    }
+   ],
+   "source": [
+    " ##test the model on the dataset used for test part \n",
+    "test_model(model.to('cpu'),criterion,optimizer_conv)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Modification of the FC"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/10\n",
+      "----------\n",
+      "train Loss: 0.7037 Acc: 0.5861\n",
+      "val Loss: 0.6234 Acc: 0.7124\n",
+      "\n",
+      "Epoch 2/10\n",
+      "----------\n",
+      "train Loss: 0.6176 Acc: 0.6721\n",
+      "val Loss: 0.5114 Acc: 0.9216\n",
+      "\n",
+      "Epoch 3/10\n",
+      "----------\n",
+      "train Loss: 0.6552 Acc: 0.6393\n",
+      "val Loss: 0.4954 Acc: 0.9412\n",
+      "\n",
+      "Epoch 4/10\n",
+      "----------\n",
+      "train Loss: 0.5834 Acc: 0.6967\n",
+      "val Loss: 0.4162 Acc: 0.9020\n",
+      "\n",
+      "Epoch 5/10\n",
+      "----------\n",
+      "train Loss: 0.5292 Acc: 0.7582\n",
+      "val Loss: 0.3590 Acc: 0.9412\n",
+      "\n",
+      "Epoch 6/10\n",
+      "----------\n",
+      "train Loss: 0.6056 Acc: 0.6230\n",
+      "val Loss: 0.4231 Acc: 0.9216\n",
+      "\n",
+      "Epoch 7/10\n",
+      "----------\n",
+      "train Loss: 0.5539 Acc: 0.6721\n",
+      "val Loss: 0.3971 Acc: 0.9346\n",
+      "\n",
+      "Epoch 8/10\n",
+      "----------\n",
+      "train Loss: 0.5320 Acc: 0.7295\n",
+      "val Loss: 0.3481 Acc: 0.9412\n",
+      "\n",
+      "Epoch 9/10\n",
+      "----------\n",
+      "train Loss: 0.5345 Acc: 0.7377\n",
+      "val Loss: 0.3323 Acc: 0.9477\n",
+      "\n",
+      "Epoch 10/10\n",
+      "----------\n",
+      "train Loss: 0.5127 Acc: 0.7746\n",
+      "val Loss: 0.3358 Acc: 0.9412\n",
+      "\n",
+      "Training complete in 4m 6s\n",
+      "Best val Acc: 0.947712\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = torchvision.models.resnet18(pretrained=True)\n",
+    "for param in model.parameters():\n",
+    "    param.requires_grad = False\n",
+    "\n",
+    "# Replace the final fully connected layer\n",
+    "# Parameters of newly constructed modules have requires_grad=True by default\n",
+    "num_ftrs = model.fc.in_features\n",
+    "model.fc = nn.Sequential(\n",
+    "          nn.Linear(num_ftrs, 10),\n",
+    "          nn.ReLU(),\n",
+    "          nn.Dropout(0.4),\n",
+    "          nn.Linear(10, 2),\n",
+    "          nn.Dropout(0.4)\n",
+    "        )\n",
+    "# Set the loss function\n",
+    "criterion = nn.CrossEntropyLoss()\n",
+    "\n",
+    "# Observe that only the parameters of the final layer are being optimized\n",
+    "optimizer_conv = optim.SGD(model.fc.parameters(), lr=0.001, momentum=0.9)\n",
+    "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",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test Accuracy of  ants: 100% (17/17)\n",
+      "Test Accuracy of  bees: 96% (62/64)\n",
+      "\n",
+      "Test Accuracy (Overall): 97% (79/81)\n"
+     ]
+    }
+   ],
+   "source": [
+    "test_model(model.to('cpu'),criterion,optimizer_conv)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Post Quantization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model:  fp32  \t Size (KB): 44797.562\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "44797562"
+      ]
+     },
+     "execution_count": 131,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import os\n",
+    "model.cpu()\n",
+    "def print_size_of_model(model, label=\"\"):\n",
+    "    torch.save(model.state_dict(), \"temp.p\")\n",
+    "    size = os.path.getsize(\"temp.p\")\n",
+    "    print(\"model: \", label, \" \\t\", \"Size (KB):\", size / 1e3)\n",
+    "    os.remove(\"temp.p\")\n",
+    "    return size\n",
+    "\n",
+    "\n",
+    "print_size_of_model(model, \"fp32\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model:  int8  \t Size (KB): 45293.114\n",
+      "Test Accuracy of  ants: 100% (22/22)\n",
+      "Test Accuracy of  bees: 96% (57/59)\n",
+      "\n",
+      "Test Accuracy (Overall): 97% (79/81)\n"
+     ]
+    }
+   ],
+   "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\")\n",
+    "test_model(quantized_model,criterion,optimizer_conv)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " After quantization, the test accuracy has been decreased also."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " Aware Quantization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class QuantizedResNet18(nn.Module):\n",
+    "    def __init__(self, model_fp32):\n",
+    "\n",
+    "        super(QuantizedResNet18, self).__init__()\n",
+    "        # QuantStub converts tensors from floating point to quantized.\n",
+    "        # This will only be used for inputs.\n",
+    "        self.quant = torch.quantization.QuantStub()\n",
+    "        # DeQuantStub converts tensors from quantized to floating point.\n",
+    "        # This will only be used for outputs.\n",
+    "        self.dequant = torch.quantization.DeQuantStub()\n",
+    "        # FP32 model\n",
+    "        self.model_fp32 = model_fp32\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        # manually specify where tensors will be converted from floating\n",
+    "        # point to quantized in the quantized model\n",
+    "        x = self.quant(x)\n",
+    "        x = self.model_fp32(x)\n",
+    "        # manually specify where tensors will be converted from quantized\n",
+    "        # to floating point in the quantized model\n",
+    "        x = self.dequant(x)\n",
+    "        return x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\hajer\\AppData\\Local\\Programs\\Python\\Python38\\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\\hajer\\AppData\\Local\\Programs\\Python\\Python38\\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",
+      "c:\\Users\\hajer\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\torch\\ao\\quantization\\utils.py:317: UserWarning: must run observer before calling calculate_qparams. Returning default values.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/10\n",
+      "----------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\hajer\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\torch\\optim\\lr_scheduler.py:136: 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(\"Detected call of `lr_scheduler.step()` before `optimizer.step()`. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "train Loss: 1.8263 Acc: 0.5574\n",
+      "val Loss: 0.5293 Acc: 0.7974\n",
+      "\n",
+      "Epoch 2/10\n",
+      "----------\n",
+      "train Loss: 0.4493 Acc: 0.7992\n",
+      "val Loss: 0.3390 Acc: 0.8693\n",
+      "\n",
+      "Epoch 3/10\n",
+      "----------\n",
+      "train Loss: 0.4348 Acc: 0.8238\n",
+      "val Loss: 0.3579 Acc: 0.8824\n",
+      "\n",
+      "Epoch 4/10\n",
+      "----------\n",
+      "train Loss: 0.3620 Acc: 0.8320\n",
+      "val Loss: 0.2811 Acc: 0.9216\n",
+      "\n",
+      "Epoch 5/10\n",
+      "----------\n",
+      "train Loss: 0.5177 Acc: 0.7746\n",
+      "val Loss: 0.4509 Acc: 0.8497\n",
+      "\n",
+      "Epoch 6/10\n",
+      "----------\n",
+      "train Loss: 0.5265 Acc: 0.8156\n",
+      "val Loss: 0.3426 Acc: 0.8758\n",
+      "\n",
+      "Epoch 7/10\n",
+      "----------\n",
+      "train Loss: 0.3615 Acc: 0.8525\n",
+      "val Loss: 0.2533 Acc: 0.9281\n",
+      "\n",
+      "Epoch 8/10\n",
+      "----------\n",
+      "train Loss: 0.3441 Acc: 0.8525\n",
+      "val Loss: 0.2462 Acc: 0.9346\n",
+      "\n",
+      "Epoch 9/10\n",
+      "----------\n",
+      "train Loss: 0.3953 Acc: 0.8320\n",
+      "val Loss: 0.2282 Acc: 0.9281\n",
+      "\n",
+      "Epoch 10/10\n",
+      "----------\n",
+      "train Loss: 0.4070 Acc: 0.8279\n",
+      "val Loss: 0.2548 Acc: 0.9412\n",
+      "\n",
+      "Training complete in 7m 14s\n",
+      "Best val Acc: 0.941176\n"
+     ]
+    }
+   ],
+   "source": [
+    "import copy\n",
+    "import torch.quantization.quantize_fx as quantize_fx\n",
+    "model = torchvision.models.resnet18(pretrained=True)\n",
+    "model.to('cpu')\n",
+    "model_fp=QuantizedResNet18(model)\n",
+    "model_fp.to('cpu')\n",
+    "model_fp.train()\n",
+    "model_to_quantize = copy.deepcopy(model_fp)\n",
+    "model.qconfig = torch.quantization.get_default_qat_qconfig(\"qnnpack\")\n",
+    "model_qat = torch.quantization.prepare_qat(model_fp, inplace=False)\n",
+    "# quantization aware training goes here\n",
+    "model_qat = torch.quantization.convert(model_qat.eval(), inplace=False)\n",
+    "n_epochs=30\n",
+    "criterion = nn.CrossEntropyLoss()  # specify loss function\n",
+    "optimizer = optim.SGD(model_qat.parameters(), lr=0.01)  # specify optimizer\n",
+    "optimizer_conv = optim.SGD(model.fc.parameters(), lr=0.001, momentum=0.9)\n",
+    "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",
+    ")"
    ]
   },
   {
@@ -940,7 +3233,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.8.2"
   },
   "vscode": {
    "interpreter": {
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