diff --git a/.gitignore b/.gitignore
index f3436fe1fd3e8a7064887098b38e50dfda48b27d..ffa59c02f63b4d0fa04b7a65fa2a5ac54db1cbf0 100644
--- a/.gitignore
+++ b/.gitignore
@@ -167,4 +167,4 @@ cython_debug/
 #  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
 #  and can be added to the global gitignore or merged into this file.  For a more nuclear
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
-#.idea/
+#.idea/
\ No newline at end of file
diff --git a/TD2 Deep Learning.ipynb b/TD2 Deep Learning.ipynb
index 67a4aabc1e73f1f24c2b7dad41526a01ad418476..4c97d535ab3215db4667edb2b67ccffa321c1981 100644
--- a/TD2 Deep Learning.ipynb	
+++ b/TD2 Deep Learning.ipynb	
@@ -1697,10 +1697,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "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",
@@ -1789,10 +1800,95 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "572d824c",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\marti\\AppData\\Roaming\\Python\\Python311\\site-packages\\torchvision\\models\\_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n",
+      "  warnings.warn(\n",
+      "C:\\Users\\marti\\AppData\\Roaming\\Python\\Python311\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.\n",
+      "  warnings.warn(msg)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/10\n",
+      "----------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\marti\\AppData\\Roaming\\Python\\Python311\\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: 0.6907 Acc: 0.6516\n",
+      "val Loss: 0.4549 Acc: 0.7778\n",
+      "\n",
+      "Epoch 2/10\n",
+      "----------\n",
+      "train Loss: 0.4668 Acc: 0.7951\n",
+      "val Loss: 0.1887 Acc: 0.9412\n",
+      "\n",
+      "Epoch 3/10\n",
+      "----------\n",
+      "train Loss: 0.5054 Acc: 0.7746\n",
+      "val Loss: 0.2132 Acc: 0.9150\n",
+      "\n",
+      "Epoch 4/10\n",
+      "----------\n",
+      "train Loss: 0.5275 Acc: 0.7746\n",
+      "val Loss: 0.1802 Acc: 0.9412\n",
+      "\n",
+      "Epoch 5/10\n",
+      "----------\n",
+      "train Loss: 0.3826 Acc: 0.8402\n",
+      "val Loss: 0.2026 Acc: 0.9346\n",
+      "\n",
+      "Epoch 6/10\n",
+      "----------\n",
+      "train Loss: 0.3533 Acc: 0.8320\n",
+      "val Loss: 0.2327 Acc: 0.9216\n",
+      "\n",
+      "Epoch 7/10\n",
+      "----------\n",
+      "train Loss: 0.3687 Acc: 0.8443\n",
+      "val Loss: 0.1862 Acc: 0.9477\n",
+      "\n",
+      "Epoch 8/10\n",
+      "----------\n",
+      "train Loss: 0.3741 Acc: 0.8279\n",
+      "val Loss: 0.2522 Acc: 0.9216\n",
+      "\n",
+      "Epoch 9/10\n",
+      "----------\n",
+      "train Loss: 0.2991 Acc: 0.8811\n",
+      "val Loss: 0.2009 Acc: 0.9412\n",
+      "\n",
+      "Epoch 10/10\n",
+      "----------\n",
+      "train Loss: 0.3259 Acc: 0.8689\n",
+      "val Loss: 0.1852 Acc: 0.9542\n",
+      "\n",
+      "Training complete in 5m 27s\n",
+      "Best val Acc: 0.954248\n",
+      "\n",
+      "Test Accuracy: 100% (26/26)\n"
+     ]
+    }
+   ],
    "source": [
     "import copy\n",
     "import os\n",
@@ -1802,6 +1898,7 @@
     "import numpy as np\n",
     "import torch\n",
     "import torch.nn as nn\n",
+    "import torch.nn.functional as F\n",
     "import torch.optim as optim\n",
     "import torchvision\n",
     "from torch.optim import lr_scheduler\n",
@@ -1951,6 +2048,38 @@
     "    model.load_state_dict(best_model_wts)\n",
     "    return model, epoch_time\n",
     "\n",
+    "def evaluate_model(model, criterion, dataloader, dataset_size, device):\n",
+    "    model.eval() \n",
+    "\n",
+    "    running_loss = 0.0\n",
+    "    running_corrects = 0\n",
+    "\n",
+    "    with torch.no_grad():\n",
+    "        for inputs, labels in dataloader:\n",
+    "            inputs = inputs.to(device)\n",
+    "            labels = labels.to(device)\n",
+    "\n",
+    "            outputs = model(inputs)\n",
+    "            _, preds = torch.max(outputs, 1)\n",
+    "\n",
+    "            loss = criterion(outputs, labels)\n",
+    "\n",
+    "            running_loss += loss.item() * inputs.size(0)\n",
+    "            running_corrects += torch.sum(preds == labels.data)\n",
+    "\n",
+    "    eval_loss = running_loss / dataset_size\n",
+    "    eval_acc = running_corrects.double() / dataset_size\n",
+    "\n",
+    "    print(\n",
+    "        \"\\nTest Accuracy: %2d%% (%2d/%2d)\"\n",
+    "        % (\n",
+    "            100.0 * eval_acc,\n",
+    "            running_corrects,\n",
+    "            dataset_size,\n",
+    "        )\n",
+    "    )\n",
+    "\n",
+    "\n",
     "\n",
     "# Download a pre-trained ResNet18 model and freeze its weights\n",
     "model = torchvision.models.resnet18(pretrained=True)\n",
@@ -1971,7 +2100,23 @@
     "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_conv, step_size=7, gamma=0.1)\n",
     "model, epoch_time = train_model(\n",
     "    model, criterion, optimizer_conv, exp_lr_scheduler, num_epochs=10\n",
-    ")\n"
+    ")\n",
+    "\n",
+    "data_transforms[\"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",
+    "image_datasets[\"test\"] = datasets.ImageFolder(os.path.join(data_dir, \"test\"), data_transforms[\"test\"])\n",
+    "dataloaders[\"test\"] = torch.utils.data.DataLoader(image_datasets[\"test\"], batch_size=4, shuffle=False, num_workers=0)\n",
+    "dataset_sizes[\"test\"] = len(image_datasets[\"test\"])\n",
+    "\n",
+    "evaluate_model(model, criterion, dataloaders[\"test\"], dataset_sizes[\"test\"], device)\n",
+    "\n"
    ]
   },
   {
@@ -1985,9 +2130,663 @@
     "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",
+    "<span style=\"color:green\"> We can see that with using images from the dataset, we got a 100% of accuracy. Futhermore, we can try to evaluate the model with foreign image of ants and bees.</span>\n",
     "\n",
-    "Apply ther quantization (post and quantization aware) and evaluate impact on model size and accuracy."
+    "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."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class CustomResNet18(nn.Module):\n",
+    "    def __init__(self, num_classes=2):\n",
+    "        super(CustomResNet18, self).__init__()\n",
+    "        resnet18 = torchvision.models.resnet18(pretrained=True)\n",
+    "        for param in resnet18.parameters():\n",
+    "            param.requires_grad = False\n",
+    "        self.features = nn.Sequential(*list(resnet18.children())[:-1])\n",
+    "\n",
+    "        # Ajoutez deux nouvelles couches\n",
+    "        self.fc1 = nn.Linear(resnet18.fc.in_features, 256)\n",
+    "        self.relu = nn.ReLU()\n",
+    "        self.dropout = nn.Dropout(0.5)\n",
+    "        self.fc2 = nn.Linear(256, num_classes)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        x = self.features(x)\n",
+    "        x = x.view(x.size(0), -1)\n",
+    "        x = self.fc1(x)\n",
+    "        x = self.relu(x)\n",
+    "        x = self.dropout(x)\n",
+    "        x = self.fc2(x)\n",
+    "        return x\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/10\n",
+      "----------\n",
+      "train Loss: 0.6457 Acc: 0.6066\n",
+      "val Loss: 0.4377 Acc: 0.8889\n",
+      "\n",
+      "Epoch 2/10\n",
+      "----------\n",
+      "train Loss: 0.4961 Acc: 0.7705\n",
+      "val Loss: 0.3009 Acc: 0.9281\n",
+      "\n",
+      "Epoch 3/10\n",
+      "----------\n",
+      "train Loss: 0.4525 Acc: 0.7910\n",
+      "val Loss: 0.2341 Acc: 0.9477\n",
+      "\n",
+      "Epoch 4/10\n",
+      "----------\n",
+      "train Loss: 0.5527 Acc: 0.7049\n",
+      "val Loss: 0.2550 Acc: 0.9281\n",
+      "\n",
+      "Epoch 5/10\n",
+      "----------\n",
+      "train Loss: 0.3848 Acc: 0.8238\n",
+      "val Loss: 0.1895 Acc: 0.9542\n",
+      "\n",
+      "Epoch 6/10\n",
+      "----------\n",
+      "train Loss: 0.4893 Acc: 0.7418\n",
+      "val Loss: 0.2354 Acc: 0.9281\n",
+      "\n",
+      "Epoch 7/10\n",
+      "----------\n",
+      "train Loss: 0.4601 Acc: 0.7787\n",
+      "val Loss: 0.2106 Acc: 0.9477\n",
+      "\n",
+      "Epoch 8/10\n",
+      "----------\n",
+      "train Loss: 0.3424 Acc: 0.8730\n",
+      "val Loss: 0.1917 Acc: 0.9542\n",
+      "\n",
+      "Epoch 9/10\n",
+      "----------\n",
+      "train Loss: 0.3696 Acc: 0.8279\n",
+      "val Loss: 0.2173 Acc: 0.9412\n",
+      "\n",
+      "Epoch 10/10\n",
+      "----------\n",
+      "train Loss: 0.3492 Acc: 0.8525\n",
+      "val Loss: 0.1967 Acc: 0.9477\n",
+      "\n",
+      "Training complete in 3m 39s\n",
+      "Best val Acc: 0.954248\n",
+      "\n",
+      "Test Accuracy: 100% (35/35)\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",
+    "import torch.nn as nn\n",
+    "import torch.nn.functional as F\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(\n",
+    "                224\n",
+    "            ),  # ImageNet models were trained on 224x224 images\n",
+    "            transforms.RandomHorizontalFlip(),  # flip horizontally 50% of the time - increases train set variability\n",
+    "            transforms.ToTensor(),  # convert it to a PyTorch tensor\n",
+    "            transforms.Normalize(\n",
+    "                [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]\n",
+    "            ),  # ImageNet models expect this norm\n",
+    "        ]\n",
+    "    ),\n",
+    "    \"val\": transforms.Compose(\n",
+    "        [\n",
+    "            transforms.Resize(256),\n",
+    "            transforms.CenterCrop(224),\n",
+    "            transforms.ToTensor(),\n",
+    "            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n",
+    "        ]\n",
+    "    ),\n",
+    "}\n",
+    "\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",
+    "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",
+    "# training\n",
+    "\n",
+    "\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",
+    "                inputs = inputs.to(device)\n",
+    "                labels = labels.to(device)\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",
+    "    print(\"Best val Acc: {:4f}\".format(best_acc))\n",
+    "\n",
+    "    # Load best model weights\n",
+    "    model.load_state_dict(best_model_wts)\n",
+    "    return model, epoch_time\n",
+    "\n",
+    "def evaluate_model(model, criterion, dataloader, dataset_size, device):\n",
+    "    model.eval() \n",
+    "\n",
+    "    running_loss = 0.0\n",
+    "    running_corrects = 0\n",
+    "\n",
+    "    with torch.no_grad():\n",
+    "        for inputs, labels in dataloader:\n",
+    "            inputs = inputs.to(device)\n",
+    "            labels = labels.to(device)\n",
+    "\n",
+    "            outputs = model(inputs)\n",
+    "            _, preds = torch.max(outputs, 1)\n",
+    "\n",
+    "            loss = criterion(outputs, labels)\n",
+    "\n",
+    "            running_loss += loss.item() * inputs.size(0)\n",
+    "            running_corrects += torch.sum(preds == labels.data)\n",
+    "\n",
+    "    eval_loss = running_loss / dataset_size\n",
+    "    eval_acc = running_corrects.double() / dataset_size\n",
+    "\n",
+    "    print(\n",
+    "        \"\\nTest Accuracy: %2d%% (%2d/%2d)\"\n",
+    "        % (\n",
+    "            100.0 * eval_acc,\n",
+    "            running_corrects,\n",
+    "            dataset_size,\n",
+    "        )\n",
+    "    )\n",
+    "\n",
+    "model = CustomResNet18(num_classes=2)\n",
+    "model = model.to(device)\n",
+    "criterion = nn.CrossEntropyLoss()\n",
+    "\n",
+    "optimizer_conv = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)\n",
+    "\n",
+    "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_conv, step_size=7, gamma=0.1)\n",
+    "\n",
+    "model, epoch_time = train_model(model, criterion, optimizer_conv, exp_lr_scheduler, num_epochs=10)\n",
+    "\n",
+    "data_transforms[\"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",
+    "image_datasets[\"test\"] = datasets.ImageFolder(os.path.join(data_dir, \"test\"), data_transforms[\"test\"])\n",
+    "dataloaders[\"test\"] = torch.utils.data.DataLoader(image_datasets[\"test\"], batch_size=4, shuffle=False, num_workers=0)\n",
+    "dataset_sizes[\"test\"] = len(image_datasets[\"test\"])\n",
+    "evaluate_model(model, criterion, dataloaders[\"test\"], dataset_sizes[\"test\"], device)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<span style=\"color:green\"> We already have a perfect accuracy.</span>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Apply ther quantization (post and quantization aware) and evaluate impact on model size and accuracy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "\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"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model:  fp32  \t Size (KB): 44780.42\n",
+      "model:  int8  \t Size (KB): 44778.17\n",
+      "Epoch 1/10\n",
+      "----------\n",
+      "train Loss: 0.6997 Acc: 0.5410\n",
+      "val Loss: 0.7274 Acc: 0.4837\n",
+      "\n",
+      "Epoch 2/10\n",
+      "----------\n",
+      "train Loss: 0.7150 Acc: 0.5410\n",
+      "val Loss: 0.7323 Acc: 0.4837\n",
+      "\n",
+      "Epoch 3/10\n",
+      "----------\n",
+      "train Loss: 0.7030 Acc: 0.5697\n",
+      "val Loss: 0.7342 Acc: 0.4641\n",
+      "\n",
+      "Epoch 4/10\n",
+      "----------\n",
+      "train Loss: 0.7297 Acc: 0.4918\n",
+      "val Loss: 0.7228 Acc: 0.5098\n",
+      "\n",
+      "Epoch 5/10\n",
+      "----------\n",
+      "train Loss: 0.7179 Acc: 0.5328\n",
+      "val Loss: 0.7376 Acc: 0.4641\n",
+      "\n",
+      "Epoch 6/10\n",
+      "----------\n",
+      "train Loss: 0.7111 Acc: 0.5328\n",
+      "val Loss: 0.7382 Acc: 0.4575\n",
+      "\n",
+      "Epoch 7/10\n",
+      "----------\n",
+      "train Loss: 0.7248 Acc: 0.5041\n",
+      "val Loss: 0.7331 Acc: 0.4706\n",
+      "\n",
+      "Epoch 8/10\n",
+      "----------\n",
+      "train Loss: 0.7227 Acc: 0.5287\n",
+      "val Loss: 0.7306 Acc: 0.4902\n",
+      "\n",
+      "Epoch 9/10\n",
+      "----------\n",
+      "train Loss: 0.7119 Acc: 0.5164\n",
+      "val Loss: 0.7319 Acc: 0.4902\n",
+      "\n",
+      "Epoch 10/10\n",
+      "----------\n",
+      "train Loss: 0.7340 Acc: 0.5041\n",
+      "val Loss: 0.7289 Acc: 0.4967\n",
+      "\n",
+      "Training complete in 3m 38s\n",
+      "Best val Acc: 0.509804\n",
+      "\n",
+      "Test Accuracy: 40% (14/35)\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",
+    "import torch.nn as nn\n",
+    "import torch.nn.functional as F\n",
+    "import torch.optim as optim\n",
+    "import torchvision\n",
+    "from torch.optim import lr_scheduler\n",
+    "from torchvision import datasets, transforms\n",
+    "import torch.quantization\n",
+    "\n",
+    "\n",
+    "# Data augmentation and normalization for training\n",
+    "# Just normalization for validation\n",
+    "data_transforms = {\n",
+    "    \"train\": transforms.Compose(\n",
+    "        [\n",
+    "            transforms.RandomResizedCrop(\n",
+    "                224\n",
+    "            ),  # ImageNet models were trained on 224x224 images\n",
+    "            transforms.RandomHorizontalFlip(),  # flip horizontally 50% of the time - increases train set variability\n",
+    "            transforms.ToTensor(),  # convert it to a PyTorch tensor\n",
+    "            transforms.Normalize(\n",
+    "                [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]\n",
+    "            ),  # ImageNet models expect this norm\n",
+    "        ]\n",
+    "    ),\n",
+    "    \"val\": transforms.Compose(\n",
+    "        [\n",
+    "            transforms.Resize(256),\n",
+    "            transforms.CenterCrop(224),\n",
+    "            transforms.ToTensor(),\n",
+    "            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n",
+    "        ]\n",
+    "    ),\n",
+    "}\n",
+    "\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",
+    "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",
+    "# training\n",
+    "\n",
+    "\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",
+    "                inputs = inputs.to(device)\n",
+    "                labels = labels.to(device)\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",
+    "    print(\"Best val Acc: {:4f}\".format(best_acc))\n",
+    "\n",
+    "    # Load best model weights\n",
+    "    model.load_state_dict(best_model_wts)\n",
+    "    return model, epoch_time\n",
+    "\n",
+    "def evaluate_model(model, criterion, dataloader, dataset_size, device):\n",
+    "    model.eval() \n",
+    "\n",
+    "    running_loss = 0.0\n",
+    "    running_corrects = 0\n",
+    "\n",
+    "    with torch.no_grad():\n",
+    "        for inputs, labels in dataloader:\n",
+    "            inputs = inputs.to(device)\n",
+    "            labels = labels.to(device)\n",
+    "\n",
+    "            outputs = model(inputs)\n",
+    "            _, preds = torch.max(outputs, 1)\n",
+    "\n",
+    "            loss = criterion(outputs, labels)\n",
+    "\n",
+    "            running_loss += loss.item() * inputs.size(0)\n",
+    "            running_corrects += torch.sum(preds == labels.data)\n",
+    "\n",
+    "    eval_loss = running_loss / dataset_size\n",
+    "    eval_acc = running_corrects.double() / dataset_size\n",
+    "\n",
+    "    print(\n",
+    "        \"\\nTest Accuracy: %2d%% (%2d/%2d)\"\n",
+    "        % (\n",
+    "            100.0 * eval_acc,\n",
+    "            running_corrects,\n",
+    "            dataset_size,\n",
+    "        )\n",
+    "    )\n",
+    "\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",
+    "    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",
+    "# Send the model to the GPU\n",
+    "model = model.to(device) \n",
+    "quantized_model = torch.quantization.quantize_dynamic(model, dtype=torch.qint8)\n",
+    "optimizer_quantized = optim.SGD(quantized_model.parameters(), lr=0.001, momentum=0.9)\n",
+    "print_size_of_model(model, \"fp32\")\n",
+    "print_size_of_model(quantized_model, \"int8\")\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(quantized_model.parameters(), lr=0.001, momentum=0.9)\n",
+    "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_conv, step_size=7, gamma=0.1)\n",
+    "quantized_model, epoch_time = train_model(\n",
+    "    quantized_model, criterion, optimizer_quantized, exp_lr_scheduler, num_epochs=10\n",
+    ")\n",
+    "\n",
+    "data_transforms[\"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",
+    "image_datasets[\"test\"] = datasets.ImageFolder(os.path.join(data_dir, \"test\"), data_transforms[\"test\"])\n",
+    "dataloaders[\"test\"] = torch.utils.data.DataLoader(image_datasets[\"test\"], batch_size=4, shuffle=False, num_workers=0)\n",
+    "dataset_sizes[\"test\"] = len(image_datasets[\"test\"])\n",
+    "\n",
+    "evaluate_model(quantized_model, criterion, dataloaders[\"test\"], dataset_sizes[\"test\"], device)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<span style=\"color:green\"> Here we can see the the quantization seems to be useless. in fact, it doesn't have a great impact on the size of the model. Moreover, we lose a lot of precision in the process. </span>\n"
    ]
   },
   {
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