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@@ -0,0 +1,3039 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "id": "7edf7168",
+      "metadata": {
+        "id": "7edf7168"
+      },
+      "source": [
+        "# TD2: Deep learning"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "fbb8c8df",
+      "metadata": {
+        "id": "fbb8c8df"
+      },
+      "source": [
+        "In this TD, you must modify this notebook to answer the questions. To do this,\n",
+        "\n",
+        "1. Fork this repository\n",
+        "2. Clone your forked repository on your local computer\n",
+        "3. Answer the questions\n",
+        "4. Commit and push regularly\n",
+        "\n",
+        "The last commit is due on Wednesday, December 4, 11:59 PM. Later commits will not be taken into account."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "3d167a29",
+      "metadata": {
+        "id": "3d167a29"
+      },
+      "source": [
+        "Install and test PyTorch from  https://pytorch.org/get-started/locally."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "330a42f5",
+      "metadata": {
+        "id": "330a42f5"
+      },
+      "outputs": [],
+      "source": [
+        "#pip install torch torchvision"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "0882a636",
+      "metadata": {
+        "id": "0882a636"
+      },
+      "source": [
+        "\n",
+        "To test run the following code"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "id": "b1950f0a",
+      "metadata": {
+        "id": "b1950f0a",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "1f2d576a-0c79-4dfa-8bb8-c2fbdff89ff8"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "tensor([[-2.9149e-01, -2.9791e-01, -1.5167e+00, -1.3063e+00, -2.0891e+00,\n",
+            "          2.0112e+00, -1.3604e+00, -1.5728e+00, -4.2890e-03, -5.9501e-01],\n",
+            "        [-3.8356e-01, -1.3039e+00, -1.6887e+00, -4.2747e-01,  2.6056e-01,\n",
+            "         -1.7091e+00, -9.3667e-01, -6.9102e-01, -1.2756e-02, -3.4600e-01],\n",
+            "        [ 4.9706e-01, -8.0328e-01, -1.7984e-01,  3.6717e-01, -9.8263e-01,\n",
+            "         -1.1355e-02,  3.1077e-01,  9.0327e-01,  1.1605e+00,  3.6590e-01],\n",
+            "        [-1.4779e+00, -5.5587e-01,  4.2844e-03, -5.6485e-01, -2.2629e-01,\n",
+            "         -6.9693e-01,  6.7294e-01,  5.0569e-01,  9.2654e-01,  1.7701e+00],\n",
+            "        [-2.0379e+00,  9.2532e-01,  1.3645e+00,  2.0570e+00, -5.9445e-02,\n",
+            "          1.1029e+00, -6.6467e-01, -2.4736e+00,  3.5116e-01, -1.1571e+00],\n",
+            "        [-7.7662e-02, -5.0340e-01,  4.3923e-01,  7.0893e-01,  6.5542e-01,\n",
+            "         -3.3269e-01, -5.6805e-01, -3.0578e-01,  4.7772e-01, -7.4755e-01],\n",
+            "        [-9.3990e-01,  1.4306e+00, -1.2102e+00,  9.0100e-01, -6.5693e-01,\n",
+            "          5.6183e-01, -1.7710e+00,  1.7879e-01, -1.5684e+00, -3.7099e-01],\n",
+            "        [-3.7614e-02, -1.8091e-04,  7.7279e-01, -2.2848e-01, -8.2718e-01,\n",
+            "         -1.7331e-01,  1.1209e-01, -2.1531e+00,  4.5847e-01, -3.0474e-01],\n",
+            "        [-1.4236e+00, -3.1073e-01, -4.6860e-01,  8.1234e-01,  1.3746e-01,\n",
+            "         -1.3021e+00,  1.3348e+00,  9.4073e-01,  3.3143e-01,  6.5072e-01],\n",
+            "        [-1.1577e-01, -5.1197e-01,  1.5164e-01, -9.8843e-03, -5.9513e-01,\n",
+            "         -1.2165e-01,  1.9136e+00, -1.2140e+00,  6.9562e-01, -2.2402e-01],\n",
+            "        [ 1.4074e+00, -4.5704e-01,  1.7650e-01, -7.5354e-01, -1.3587e+00,\n",
+            "          5.9109e-01, -8.4399e-01,  7.1660e-01,  6.4473e-01,  5.4493e-01],\n",
+            "        [-1.1563e+00,  9.9369e-01, -1.3648e-01, -2.8204e+00,  2.4360e-01,\n",
+            "         -7.4776e-01,  5.0526e-01,  1.0538e+00, -1.4164e+00, -1.1326e-01],\n",
+            "        [ 6.1103e-01, -2.5303e-01, -1.0949e+00,  9.7218e-01, -2.0042e-01,\n",
+            "         -3.8988e-01, -1.3344e-01, -8.1450e-01, -3.0165e-01,  1.7991e+00],\n",
+            "        [-7.1659e-01, -3.7019e-01, -4.1402e-01, -1.6006e+00, -1.3978e+00,\n",
+            "         -1.5548e-01, -9.8952e-01,  5.5218e-01,  4.0877e-01, -1.6947e+00]])\n",
+            "AlexNet(\n",
+            "  (features): Sequential(\n",
+            "    (0): Conv2d(3, 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2))\n",
+            "    (1): ReLU(inplace=True)\n",
+            "    (2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
+            "    (3): Conv2d(64, 192, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))\n",
+            "    (4): ReLU(inplace=True)\n",
+            "    (5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
+            "    (6): Conv2d(192, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+            "    (7): ReLU(inplace=True)\n",
+            "    (8): Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+            "    (9): ReLU(inplace=True)\n",
+            "    (10): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+            "    (11): ReLU(inplace=True)\n",
+            "    (12): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
+            "  )\n",
+            "  (avgpool): AdaptiveAvgPool2d(output_size=(6, 6))\n",
+            "  (classifier): Sequential(\n",
+            "    (0): Dropout(p=0.5, inplace=False)\n",
+            "    (1): Linear(in_features=9216, out_features=4096, bias=True)\n",
+            "    (2): ReLU(inplace=True)\n",
+            "    (3): Dropout(p=0.5, inplace=False)\n",
+            "    (4): Linear(in_features=4096, out_features=4096, bias=True)\n",
+            "    (5): ReLU(inplace=True)\n",
+            "    (6): Linear(in_features=4096, out_features=1000, bias=True)\n",
+            "  )\n",
+            ")\n"
+          ]
+        }
+      ],
+      "source": [
+        "import torch\n",
+        "\n",
+        "N, D = 14, 10\n",
+        "x = torch.randn(N, D).type(torch.FloatTensor)\n",
+        "print(x)\n",
+        "\n",
+        "from torchvision import models\n",
+        "\n",
+        "alexnet = models.alexnet()\n",
+        "print(alexnet)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "23f266da",
+      "metadata": {
+        "id": "23f266da"
+      },
+      "source": [
+        "## Exercise 1: CNN on CIFAR10\n",
+        "\n",
+        "The goal is to apply a Convolutional Neural Net (CNN) model on the CIFAR10 image dataset and test the accuracy of the model on the basis of image classification. Compare the Accuracy VS the neural network implemented during TD1.\n",
+        "\n",
+        "Have a look at the following documentation to be familiar with PyTorch.\n",
+        "\n",
+        "https://pytorch.org/tutorials/beginner/pytorch_with_examples.html\n",
+        "\n",
+        "https://pytorch.org/tutorials/beginner/deep_learning_60min_blitz.html"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "4ba1c82d",
+      "metadata": {
+        "id": "4ba1c82d"
+      },
+      "source": [
+        "You can test if GPU is available on your machine and thus train on it to speed up the process"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "id": "6e18f2fd",
+      "metadata": {
+        "id": "6e18f2fd",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "8345a666-e787-499a-8861-30cd663ba3c0"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "CUDA is available!  Training on GPU ...\n"
+          ]
+        }
+      ],
+      "source": [
+        "import torch\n",
+        "\n",
+        "# check if CUDA is available\n",
+        "train_on_gpu = torch.cuda.is_available()\n",
+        "\n",
+        "if not train_on_gpu:\n",
+        "    print(\"CUDA is not available.  Training on CPU ...\")\n",
+        "else:\n",
+        "    print(\"CUDA is available!  Training on GPU ...\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "5cf214eb",
+      "metadata": {
+        "id": "5cf214eb"
+      },
+      "source": [
+        "Next we load the CIFAR10 dataset"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "id": "462666a2",
+      "metadata": {
+        "id": "462666a2",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "a63aa205-2bce-46c1-97c3-8cd9a2977bbb"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to data/cifar-10-python.tar.gz\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "100%|██████████| 170M/170M [00:14<00:00, 12.0MB/s]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Extracting data/cifar-10-python.tar.gz to data\n",
+            "Files already downloaded and verified\n"
+          ]
+        }
+      ],
+      "source": [
+        "import numpy as np\n",
+        "from torchvision import datasets, transforms\n",
+        "from torch.utils.data.sampler import SubsetRandomSampler\n",
+        "\n",
+        "# number of subprocesses to use for data loading\n",
+        "num_workers = 0\n",
+        "# how many samples per batch to load\n",
+        "batch_size = 20\n",
+        "# percentage of training set to use as validation\n",
+        "valid_size = 0.2\n",
+        "\n",
+        "# convert data to a normalized torch.FloatTensor\n",
+        "transform = transforms.Compose(\n",
+        "    [transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]\n",
+        ")\n",
+        "\n",
+        "# choose the training and test datasets\n",
+        "train_data = datasets.CIFAR10(\"data\", train=True, download=True, transform=transform)\n",
+        "test_data = datasets.CIFAR10(\"data\", train=False, download=True, transform=transform)\n",
+        "\n",
+        "# obtain training indices that will be used for validation\n",
+        "num_train = len(train_data)\n",
+        "indices = list(range(num_train))\n",
+        "np.random.shuffle(indices)\n",
+        "split = int(np.floor(valid_size * num_train))\n",
+        "train_idx, valid_idx = indices[split:], indices[:split]\n",
+        "\n",
+        "# define samplers for obtaining training and validation batches\n",
+        "train_sampler = SubsetRandomSampler(train_idx)\n",
+        "valid_sampler = SubsetRandomSampler(valid_idx)\n",
+        "\n",
+        "# prepare data loaders (combine dataset and sampler)\n",
+        "train_loader = torch.utils.data.DataLoader(\n",
+        "    train_data, batch_size=batch_size, sampler=train_sampler, num_workers=num_workers\n",
+        ")\n",
+        "valid_loader = torch.utils.data.DataLoader(\n",
+        "    train_data, batch_size=batch_size, sampler=valid_sampler, num_workers=num_workers\n",
+        ")\n",
+        "test_loader = torch.utils.data.DataLoader(\n",
+        "    test_data, batch_size=batch_size, num_workers=num_workers\n",
+        ")\n",
+        "\n",
+        "# specify the image classes\n",
+        "classes = [\n",
+        "    \"airplane\",\n",
+        "    \"automobile\",\n",
+        "    \"bird\",\n",
+        "    \"cat\",\n",
+        "    \"deer\",\n",
+        "    \"dog\",\n",
+        "    \"frog\",\n",
+        "    \"horse\",\n",
+        "    \"ship\",\n",
+        "    \"truck\",\n",
+        "]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "58ec3903",
+      "metadata": {
+        "id": "58ec3903"
+      },
+      "source": [
+        "CNN definition (this one is an example)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "id": "317bf070",
+      "metadata": {
+        "id": "317bf070",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "0c3a0a99-8c7c-49bf-a2b2-57212a45b6a1"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "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",
+        "\n",
+        "# define the CNN architecture\n",
+        "\n",
+        "\n",
+        "class Net(nn.Module):\n",
+        "    def __init__(self):\n",
+        "        super(Net, self).__init__()\n",
+        "        self.conv1 = nn.Conv2d(3, 6, 5)\n",
+        "        self.pool = nn.MaxPool2d(2, 2)\n",
+        "        self.conv2 = nn.Conv2d(6, 16, 5)\n",
+        "        self.fc1 = nn.Linear(16 * 5 * 5, 120)\n",
+        "        self.fc2 = nn.Linear(120, 84)\n",
+        "        self.fc3 = nn.Linear(84, 10)\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 = x.view(-1, 16 * 5 * 5)\n",
+        "        x = F.relu(self.fc1(x))\n",
+        "        x = F.relu(self.fc2(x))\n",
+        "        x = self.fc3(x)\n",
+        "        return x\n",
+        "\n",
+        "\n",
+        "# create a complete CNN\n",
+        "model = Net()\n",
+        "print(model)\n",
+        "# move tensors to GPU if CUDA is available\n",
+        "if train_on_gpu:\n",
+        "    model.cuda()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "a2dc4974",
+      "metadata": {
+        "id": "a2dc4974"
+      },
+      "source": [
+        "Loss function and training using SGD (Stochastic Gradient Descent) optimizer"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "id": "4b53f229",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "4b53f229",
+        "outputId": "352fa2f2-4cf7-42b6-e51a-c68bf428e90c"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch: 0 \tTraining Loss: 35.027589 \tValidation Loss: 31.574753\n",
+            "Validation loss decreased (inf --> 31.574753).  Saving model ...\n",
+            "Epoch: 1 \tTraining Loss: 30.186101 \tValidation Loss: 29.019872\n",
+            "Validation loss decreased (31.574753 --> 29.019872).  Saving model ...\n",
+            "Epoch: 2 \tTraining Loss: 27.864272 \tValidation Loss: 27.127275\n",
+            "Validation loss decreased (29.019872 --> 27.127275).  Saving model ...\n",
+            "Epoch: 3 \tTraining Loss: 26.258431 \tValidation Loss: 26.395611\n",
+            "Validation loss decreased (27.127275 --> 26.395611).  Saving model ...\n",
+            "Epoch: 4 \tTraining Loss: 24.996900 \tValidation Loss: 25.757890\n",
+            "Validation loss decreased (26.395611 --> 25.757890).  Saving model ...\n",
+            "Epoch: 5 \tTraining Loss: 23.965784 \tValidation Loss: 25.668354\n",
+            "Validation loss decreased (25.757890 --> 25.668354).  Saving model ...\n",
+            "Epoch: 6 \tTraining Loss: 23.020208 \tValidation Loss: 24.161515\n",
+            "Validation loss decreased (25.668354 --> 24.161515).  Saving model ...\n",
+            "Epoch: 7 \tTraining Loss: 22.237902 \tValidation Loss: 23.424892\n",
+            "Validation loss decreased (24.161515 --> 23.424892).  Saving model ...\n",
+            "Epoch: 8 \tTraining Loss: 21.415062 \tValidation Loss: 23.035766\n",
+            "Validation loss decreased (23.424892 --> 23.035766).  Saving model ...\n",
+            "Epoch: 9 \tTraining Loss: 20.656158 \tValidation Loss: 22.670803\n",
+            "Validation loss decreased (23.035766 --> 22.670803).  Saving model ...\n",
+            "Epoch: 10 \tTraining Loss: 19.973196 \tValidation Loss: 23.388851\n",
+            "Epoch: 11 \tTraining Loss: 19.298960 \tValidation Loss: 22.264116\n",
+            "Validation loss decreased (22.670803 --> 22.264116).  Saving model ...\n",
+            "Epoch: 12 \tTraining Loss: 18.639930 \tValidation Loss: 22.163565\n",
+            "Validation loss decreased (22.264116 --> 22.163565).  Saving model ...\n",
+            "Epoch: 13 \tTraining Loss: 18.014738 \tValidation Loss: 22.538633\n",
+            "Epoch: 14 \tTraining Loss: 17.396607 \tValidation Loss: 23.147467\n",
+            "Epoch: 15 \tTraining Loss: 16.883292 \tValidation Loss: 22.109338\n",
+            "Validation loss decreased (22.163565 --> 22.109338).  Saving model ...\n",
+            "Epoch: 16 \tTraining Loss: 16.285820 \tValidation Loss: 21.778137\n",
+            "Validation loss decreased (22.109338 --> 21.778137).  Saving model ...\n",
+            "Epoch: 17 \tTraining Loss: 15.763043 \tValidation Loss: 22.761066\n",
+            "Epoch: 18 \tTraining Loss: 15.239761 \tValidation Loss: 23.015333\n",
+            "Epoch: 19 \tTraining Loss: 14.686504 \tValidation Loss: 24.884198\n",
+            "Epoch: 20 \tTraining Loss: 14.331884 \tValidation Loss: 23.479585\n",
+            "Epoch: 21 \tTraining Loss: 13.738545 \tValidation Loss: 23.654407\n",
+            "Epoch: 22 \tTraining Loss: 13.280116 \tValidation Loss: 24.813238\n",
+            "Epoch: 23 \tTraining Loss: 12.799162 \tValidation Loss: 24.736943\n",
+            "Epoch: 24 \tTraining Loss: 12.397551 \tValidation Loss: 25.655078\n",
+            "Epoch: 25 \tTraining Loss: 11.895732 \tValidation Loss: 26.784175\n",
+            "Epoch: 26 \tTraining Loss: 11.552095 \tValidation Loss: 26.599335\n",
+            "Epoch: 27 \tTraining Loss: 11.088106 \tValidation Loss: 27.633717\n",
+            "Epoch: 28 \tTraining Loss: 10.653680 \tValidation Loss: 27.694947\n",
+            "Epoch: 29 \tTraining Loss: 10.327371 \tValidation Loss: 27.795613\n"
+          ]
+        }
+      ],
+      "source": [
+        "import torch.optim as optim\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(), \"/content/save_data/model_cifar.pt\")\n",
+        "        valid_loss_min = valid_loss"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "13e1df74",
+      "metadata": {
+        "id": "13e1df74"
+      },
+      "source": [
+        "Does overfit occur? If so, do an early stopping."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 20,
+      "id": "d39df818",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "id": "d39df818",
+        "outputId": "8deb6712-7903-4899-9709-9be0e022e600"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "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 1\")\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "11df8fd4",
+      "metadata": {
+        "id": "11df8fd4"
+      },
+      "source": [
+        "Now loading the model with the lowest validation loss value\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 21,
+      "id": "e93efdfc",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "e93efdfc",
+        "outputId": "25166486-06b8-4855-fc5d-a70f4253ee31"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "<ipython-input-21-022d8c474780>:1: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
+            "  model.load_state_dict(torch.load(\"/content/save_data/model_cifar.pt\"))\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Test Loss: 22.087782\n",
+            "\n",
+            "Test Accuracy of airplane: 64% (640/1000)\n",
+            "Test Accuracy of automobile: 74% (741/1000)\n",
+            "Test Accuracy of  bird: 52% (528/1000)\n",
+            "Test Accuracy of   cat: 45% (455/1000)\n",
+            "Test Accuracy of  deer: 53% (539/1000)\n",
+            "Test Accuracy of   dog: 48% (482/1000)\n",
+            "Test Accuracy of  frog: 76% (766/1000)\n",
+            "Test Accuracy of horse: 69% (693/1000)\n",
+            "Test Accuracy of  ship: 72% (729/1000)\n",
+            "Test Accuracy of truck: 67% (679/1000)\n",
+            "\n",
+            "Test Accuracy (Overall): 62% (6252/10000)\n"
+          ]
+        }
+      ],
+      "source": [
+        "model.load_state_dict(torch.load(\"/content/save_data/model_cifar.pt\"))\n",
+        "\n",
+        "# track test loss\n",
+        "test_loss = 0.0\n",
+        "class_correct = list(0.0 for i in range(10))\n",
+        "class_total = list(0.0 for i in range(10))\n",
+        "\n",
+        "model.eval()\n",
+        "# iterate over test data\n",
+        "for data, target in test_loader:\n",
+        "    # move tensors to GPU if CUDA is available\n",
+        "    if train_on_gpu:\n",
+        "        data, target = data.cuda(), target.cuda()\n",
+        "    # forward pass: compute predicted outputs by passing inputs to the model\n",
+        "    output = model(data)\n",
+        "    # calculate the batch loss\n",
+        "    loss = criterion(output, target)\n",
+        "    # update test loss\n",
+        "    test_loss += loss.item() * data.size(0)\n",
+        "    # convert output probabilities to predicted class\n",
+        "    _, pred = torch.max(output, 1)\n",
+        "    # compare predictions to true label\n",
+        "    correct_tensor = pred.eq(target.data.view_as(pred))\n",
+        "    correct = (\n",
+        "        np.squeeze(correct_tensor.numpy())\n",
+        "        if not train_on_gpu\n",
+        "        else np.squeeze(correct_tensor.cpu().numpy())\n",
+        "    )\n",
+        "    # calculate test accuracy for each object class\n",
+        "    for i in range(batch_size):\n",
+        "        label = target.data[i]\n",
+        "        class_correct[label] += correct[i].item()\n",
+        "        class_total[label] += 1\n",
+        "\n",
+        "# average test loss\n",
+        "test_loss = test_loss / len(test_loader)\n",
+        "print(\"Test Loss: {:.6f}\\n\".format(test_loss))\n",
+        "\n",
+        "for i in range(10):\n",
+        "    if class_total[i] > 0:\n",
+        "        print(\n",
+        "            \"Test Accuracy of %5s: %2d%% (%2d/%2d)\"\n",
+        "            % (\n",
+        "                classes[i],\n",
+        "                100 * class_correct[i] / class_total[i],\n",
+        "                np.sum(class_correct[i]),\n",
+        "                np.sum(class_total[i]),\n",
+        "            )\n",
+        "        )\n",
+        "    else:\n",
+        "        print(\"Test Accuracy of %5s: N/A (no training examples)\" % (classes[i]))\n",
+        "\n",
+        "print(\n",
+        "    \"\\nTest Accuracy (Overall): %2d%% (%2d/%2d)\"\n",
+        "    % (\n",
+        "        100.0 * np.sum(class_correct) / np.sum(class_total),\n",
+        "        np.sum(class_correct),\n",
+        "        np.sum(class_total),\n",
+        "    )\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "944991a2",
+      "metadata": {
+        "id": "944991a2"
+      },
+      "source": [
+        "Build a new network with the following structure.\n",
+        "\n",
+        "- It has 3 convolutional layers of kernel size 3 and padding of 1.\n",
+        "- The first convolutional layer must output 16 channels, the second 32 and the third 64.\n",
+        "- At each convolutional layer output, we apply a ReLU activation then a MaxPool with kernel size of 2.\n",
+        "- Then, three fully connected layers, the first two being followed by a ReLU activation and a dropout whose value you will suggest.\n",
+        "- The first fully connected layer will have an output size of 512.\n",
+        "- The second fully connected layer will have an output size of 64.\n",
+        "\n",
+        "Compare the results obtained with this new network to those obtained previously."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "id": "2DvrdR_nsGqq",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "2DvrdR_nsGqq",
+        "outputId": "1648c077-ba3f-4f7a-9a33-2d5777092e10"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Net2(\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=0.5, inplace=False)\n",
+            ")\n"
+          ]
+        }
+      ],
+      "source": [
+        "import torch.nn as nn\n",
+        "import torch.nn.functional as F\n",
+        "\n",
+        "class Net2(nn.Module):  # Ensure it inherits from nn.Module\n",
+        "    def __init__(self, dropout_prob):\n",
+        "        super(Net2, self).__init__()\n",
+        "        self.conv1 = nn.Conv2d(3, 16, 3, padding=1)  # (input channels, output channels, kernel size 3x3)\n",
+        "        self.pool = nn.MaxPool2d(2, 2)  # (kernel size, stride)\n",
+        "        self.conv2 = nn.Conv2d(16, 32, 3, padding=1)  # (input channels, output channels, kernel size 3x3)\n",
+        "        self.conv3 = nn.Conv2d(32, 64, 3, padding=1)  # (input channels, output channels, kernel size 3x3)\n",
+        "        self.fc1 = nn.Linear(64 * 4 * 4, 512)  # (input, output)\n",
+        "        self.fc2 = nn.Linear(512, 64)  # (input, output)\n",
+        "        self.fc3 = nn.Linear(64, 10)  # (input, output = number of classes)\n",
+        "\n",
+        "        # Dropout layer\n",
+        "        self.dropout = nn.Dropout(p=dropout_prob)\n",
+        "\n",
+        "    def forward(self, x):\n",
+        "        x = self.pool(F.relu(self.conv1(x)))\n",
+        "        x = self.pool(F.relu(self.conv2(x)))\n",
+        "        x = self.pool(F.relu(self.conv3(x)))\n",
+        "        x = x.view(-1, 64 * 4 * 4)\n",
+        "        x = F.relu(self.fc1(x))\n",
+        "        x = F.relu(self.fc2(x))\n",
+        "        x = self.fc3(x)\n",
+        "        x = self.dropout(x)  # Apply dropout\n",
+        "        return x\n",
+        "\n",
+        "# Create a complete CNN\n",
+        "model2 = Net2(dropout_prob=0.5)  # Pass dropout probability when creating the model\n",
+        "print(model2)\n",
+        "\n",
+        "# Move tensors to GPU if CUDA is available\n",
+        "train_on_gpu = torch.cuda.is_available()\n",
+        "if train_on_gpu:\n",
+        "    model2.cuda()\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 23,
+      "id": "IJz2Q9T25Qc3",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "IJz2Q9T25Qc3",
+        "outputId": "121b9032-8481-4be3-e542-2ece8d8dbe2a"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch: 0 \tTraining Loss: 45.904219 \tValidation Loss: 44.942664\n",
+            "Validation loss decreased (inf --> 44.942664).  Saving model ...\n",
+            "Epoch: 1 \tTraining Loss: 42.813407 \tValidation Loss: 38.436903\n",
+            "Validation loss decreased (44.942664 --> 38.436903).  Saving model ...\n",
+            "Epoch: 2 \tTraining Loss: 39.437505 \tValidation Loss: 34.759394\n",
+            "Validation loss decreased (38.436903 --> 34.759394).  Saving model ...\n",
+            "Epoch: 3 \tTraining Loss: 37.662391 \tValidation Loss: 32.064202\n",
+            "Validation loss decreased (34.759394 --> 32.064202).  Saving model ...\n",
+            "Epoch: 4 \tTraining Loss: 36.459317 \tValidation Loss: 31.174973\n",
+            "Validation loss decreased (32.064202 --> 31.174973).  Saving model ...\n",
+            "Epoch: 5 \tTraining Loss: 35.285123 \tValidation Loss: 28.916658\n",
+            "Validation loss decreased (31.174973 --> 28.916658).  Saving model ...\n",
+            "Epoch: 6 \tTraining Loss: 34.439550 \tValidation Loss: 27.563518\n",
+            "Validation loss decreased (28.916658 --> 27.563518).  Saving model ...\n",
+            "Epoch: 7 \tTraining Loss: 33.610940 \tValidation Loss: 26.579835\n",
+            "Validation loss decreased (27.563518 --> 26.579835).  Saving model ...\n",
+            "Epoch: 8 \tTraining Loss: 32.717580 \tValidation Loss: 25.121927\n",
+            "Validation loss decreased (26.579835 --> 25.121927).  Saving model ...\n",
+            "Epoch: 9 \tTraining Loss: 31.905730 \tValidation Loss: 24.054429\n",
+            "Validation loss decreased (25.121927 --> 24.054429).  Saving model ...\n",
+            "Epoch: 10 \tTraining Loss: 31.197301 \tValidation Loss: 22.744985\n",
+            "Validation loss decreased (24.054429 --> 22.744985).  Saving model ...\n",
+            "Epoch: 11 \tTraining Loss: 30.428635 \tValidation Loss: 22.388091\n",
+            "Validation loss decreased (22.744985 --> 22.388091).  Saving model ...\n",
+            "Epoch: 12 \tTraining Loss: 29.790207 \tValidation Loss: 21.463255\n",
+            "Validation loss decreased (22.388091 --> 21.463255).  Saving model ...\n",
+            "Epoch: 13 \tTraining Loss: 29.057292 \tValidation Loss: 20.476804\n",
+            "Validation loss decreased (21.463255 --> 20.476804).  Saving model ...\n",
+            "Epoch: 14 \tTraining Loss: 28.433569 \tValidation Loss: 20.292996\n",
+            "Validation loss decreased (20.476804 --> 20.292996).  Saving model ...\n",
+            "Epoch: 15 \tTraining Loss: 27.846333 \tValidation Loss: 19.485786\n",
+            "Validation loss decreased (20.292996 --> 19.485786).  Saving model ...\n",
+            "Epoch: 16 \tTraining Loss: 27.332442 \tValidation Loss: 19.583211\n",
+            "Epoch: 17 \tTraining Loss: 26.768378 \tValidation Loss: 18.470492\n",
+            "Validation loss decreased (19.485786 --> 18.470492).  Saving model ...\n",
+            "Epoch: 18 \tTraining Loss: 26.166088 \tValidation Loss: 18.522611\n",
+            "Epoch: 19 \tTraining Loss: 25.595460 \tValidation Loss: 17.577921\n",
+            "Validation loss decreased (18.470492 --> 17.577921).  Saving model ...\n",
+            "Epoch: 20 \tTraining Loss: 25.090311 \tValidation Loss: 17.772856\n",
+            "Epoch: 21 \tTraining Loss: 24.457410 \tValidation Loss: 17.996507\n",
+            "Epoch: 22 \tTraining Loss: 24.041111 \tValidation Loss: 17.588359\n",
+            "Epoch: 23 \tTraining Loss: 23.373427 \tValidation Loss: 17.087331\n",
+            "Validation loss decreased (17.577921 --> 17.087331).  Saving model ...\n",
+            "Epoch: 24 \tTraining Loss: 22.925357 \tValidation Loss: 17.181599\n",
+            "Epoch: 25 \tTraining Loss: 22.313664 \tValidation Loss: 17.449139\n",
+            "Epoch: 26 \tTraining Loss: 21.707366 \tValidation Loss: 17.998827\n",
+            "Epoch: 27 \tTraining Loss: 21.505335 \tValidation Loss: 17.853451\n",
+            "Epoch: 28 \tTraining Loss: 20.992254 \tValidation Loss: 17.821120\n",
+            "Epoch: 29 \tTraining Loss: 20.592966 \tValidation Loss: 19.540560\n"
+          ]
+        }
+      ],
+      "source": [
+        "import torch.optim as optim\n",
+        "\n",
+        "criterion = nn.CrossEntropyLoss()  # specify loss function\n",
+        "optimizer = optim.SGD(model2.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",
+        "    model2.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 = model2(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",
+        "    model2.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 = model2(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(model2.state_dict(), \"/content/save_data/model2_cifar.pt\")\n",
+        "        valid_loss_min = valid_loss"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "hNYf38f_sSfo",
+      "metadata": {
+        "id": "hNYf38f_sSfo"
+      },
+      "source": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 24,
+      "id": "aQVARMhv7y1b",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "id": "aQVARMhv7y1b",
+        "outputId": "ba89cc31-b12e-4ada-f750-e52d31b3bece"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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2GBsbw8PDA7NmzXrq6b0JEybA2NgYixcvxttvvw0TExMMGTIEn332Wa2NdSkjk8mwc+dOfPjhh/j999+xdu1auLm5YcmSJaor1sr8+OOPsLGxwfr167F9+3Z0794df/75Z7kxQ3Z2djhz5gw+/vhjbN26FStWrICVlRWaNWuGzz77rNIZIyMjAQARERGIiIgo93x8fDwLDUlOECs7Go+IiIhIy3AMDREREek8FhoiIiLSeSw0REREpPNYaIiIiEjnsdAQERGRzmOhISIiIp1X5+9Do1QqkZiYCDMzs0rfIp2IiIikIYoisrOz4ejoqJrw90nqfKFJTEzUionqiIiIqPISEhJUN9V8kjpfaMome0tISIC5ubnEaYiIiOhZZGVlwdnZWW3S1iep84Wm7DSTubk5Cw0REZGOedbhIhwUTERERDqPhYaIiIh0HgsNERER6TwWGiIiItJ5LDRERESk81hoiIiISOex0BAREZHOY6EhIiIincdCQ0RERDqPhYaIiIh0HgsNERER6TwWGiIiItJ5LDRVpFSKOBmXjhKlKHUUIiKieo+FpgpEUcTAb49jzHencTw2Xeo4RERE9R4LTRUIgoBAlwYAgM3nEiROQ0RERCw0VTSilTMA4MC1FGTmFUmchoiIqH5joamiZo7m8LU3Q2GxEjsv3ZM6DhERUb3GQlNFgiCojtJsPn9X4jRERET1GwvNcxjcwhFymYCou5mISc6WOg4REVG9xULzHKxMFQhuYguAg4OJiIikxELznEYElp522h55D0UlSonTEBER1U8sNM+pq48NrE0VSM8pxJHoVKnjEBER1UtaU2gWL14MQRAwe/Zs1bKuXbtCEAS1x9SpU6ULWQG5ngxDWzYCwMHBREREUtGKQnP27FmsXr0a/v7+5Z6bMmUKkpKSVI/PP/9cgoRPNiLQCQBwJDoV6TkFEqchIiKqfyQvNDk5ORg7diy+++47NGjQoNzzxsbGsLe3Vz3Mzc0lSPlk3nZmCHC2RLFSxPaLvCcNERFRbZO80ISGhqJfv34ICQmp8Pn169fD2toafn5+mD9/PvLy8p64vYKCAmRlZak9akPZUZrN5+5CFDlhJRERUW2SS/nmGzduxIULF3D27NkKnx8zZgxcXV3h6OiIqKgovP3224iJicHWrVsfu81Fixbho48+qqnIjzUgwBGf7L6GmJRsXL6XCX8ny1rPQEREVF9JVmgSEhIwa9YsHDx4EIaGhhWu8+qrr6r+3bx5czg4OCA4OBhxcXHw9PSs8DXz58/H3LlzVV9nZWXB2dm5esNXwMJIH72a2WPnpURsPneXhYaIiKgWSXbK6fz580hNTUXLli0hl8shl8sRHh6O5cuXQy6Xo6SkpNxr2rZtCwCIjY197HYVCgXMzc3VHrVlRKvS0047Iu8hv6h8fiIiIqoZkh2hCQ4OxuXLl9WWTZw4Eb6+vnj77behp6dX7jWRkZEAAAcHh9qIWGntPa3haGGIxMx8HLyWggEBjlJHIiIiqhckKzRmZmbw8/NTW2ZiYgIrKyv4+fkhLi4OGzZsQN++fWFlZYWoqCjMmTMHnTt3rvDybm2gJxMwLNAJX4fFYvP5uyw0REREtUTyq5wex8DAAIcOHULPnj3h6+uLefPmYdiwYdi1a5fU0Z5o+D9XO/11Mw1JmY8kTkNERFQ/SHqVk6ajR4+q/u3s7Izw8HDpwlSRq5UJ2rg3xJn4B9h64R5Cu3lJHYmIiKjO09ojNLrsf/ekSeA9aYiIiGoBC00N6NvcAcYGerh1Pw/nbj+UOg4REVGdx0JTA0wUcvRrXnol1uZzCRKnISIiqvtYaGrIiFalN/P7MyoJeYXFEqchIiKq21hoakhrtwZwszJGbmEJ9lxOljoOERFRncZCU0MEQVBdws3TTkRERDWLhaYGDW3pBEEATsc/wJ37T54lnIiIiKqOhaYGOVoaoaOXNQBgy3kepSEiIqopLDQ1rGxw8B8X7kGp5D1piIiIagILTQ3r2dQO5oZy3Mt4hJNx96WOQ0REVCex0NQwQ309DGxROknlZp52IiIiqhEsNLVgRGDpaad9V5KR+ahI4jRERER1DwtNLfB3skBjO1MUFCux61Ki1HGIiIjqHBaaWiAIguoozebzdyVOQ0REVPew0NSSwS80gp5MwKWEDNxMyZY6DhERUZ3CQlNLbMwU6OZjC4BHaYiIiKobC00tGtGqdCqErRfuoahEKXEaIiKiuoOFphZ197WFlYkB0nMKEB6TJnUcIiKiOoOFphbp68kw+IVGAHhPGiIiourEQlPLyk47Hb6eivs5BRKnISIiqhtYaGqZr705mjeyQLFSxPZI3pOGiIioOrDQSKDsKM0WXu1ERERULVhoJDAwwBH6egKuJ2XxnjRERETVgIVGApbGBujsbQMA2MmpEIiIiJ4bC41Eymbg3nkpEaIoSpyGiIhIt7HQSCSkiR0M9WW4fT8PUXczpY5DRESk01hoJGKikCO4iR0AcAZuIiKi58RCI6GBAaWnnXZHJUGp5GknIiKiqmKhkVBXHxuYGcqRnJWPM7ceSB2HiIhIZ7HQSEgh10PvZvYAeLUTERHR82ChkVjZ1U57LydxBm4iIqIqYqGRWJCHFaxNDfAwrwjHb6ZLHYeIiEgnsdBITK4nQ9/mDgB4tRMREVFVsdBogbKrnfZfTUZ+UYnEaYiIiHQPC40WaOnSAI0sjZBbWIKw6FSp4xAREekcFhotIJMJ6B9QetppZyRPOxEREVUWC42WKDvtFBaTiqz8IonTEBER6RYWGi3R1MEcnjYmKCxW4sDVFKnjEBER6RQWGi0hCAIG/HOUhlc7ERERVQ4LjRYpO+10PDYd93MKJE5DRESkO1hotIiHjSn8GpmjRCliz5VkqeMQERHpDBYaLVN2lGYXr3YiIiJ6Ziw0Wqa/f2mhOXPrARIzHkmchoiISDew0GgZR0sjtHFrCADYHcWjNERERM+ChUYLDWhRdrVTksRJiIiIdAMLjRbq62cPPZmAy/cyEZ+eK3UcIiIircdCo4WsTBXo4GUNgFMhEBERPQsWGi1VdrXTzkv3IIqixGmIiIi0GwuNlurVzA4Gchni0nJxLSlL6jhERERajYVGS5kZ6qO7jy0ADg4mIiJ6GhYaLTawxf/mduJpJyIiosdjodFi3X1tYWKgh3sZj3DhzkOp4xAREWktFhotZqivh57N7AHwaiciIqInYaHRcmVXO/15OQnFJUqJ0xAREWknFhot19HbGg2M9ZGeU4iIv+9LHYeIiEgraU2hWbx4MQRBwOzZs1XL8vPzERoaCisrK5iammLYsGFISUmRLqQE9PVk6NPcAUDp4GAiIiIqTysKzdmzZ7F69Wr4+/urLZ8zZw527dqFzZs3Izw8HImJiRg6dKhEKaVTdtpp75VkFBSXSJyGiIhI+0heaHJycjB27Fh89913aNCggWp5ZmYmfvjhByxduhTdu3dHYGAg1q5di5MnT+LUqVMSJq59rd0aws5cgez8YoTHpEkdh4iISOtIXmhCQ0PRr18/hISEqC0/f/48ioqK1Jb7+vrCxcUFERERj91eQUEBsrKy1B66Tk8moL9/2VQIPO1ERESkSdJCs3HjRly4cAGLFi0q91xycjIMDAxgaWmpttzOzg7JycmP3eaiRYtgYWGhejg7O1d3bEmUnXY6dD0FuQXFEqchIiLSLpIVmoSEBMyaNQvr16+HoaFhtW13/vz5yMzMVD0SEhKqbdtS8neygKuVMfKLlDh0vX4NjCYiInoayQrN+fPnkZqaipYtW0Iul0MulyM8PBzLly+HXC6HnZ0dCgsLkZGRofa6lJQU2NvbP3a7CoUC5ubmao+6QBAE1VEaXu1ERESkTrJCExwcjMuXLyMyMlL1aNWqFcaOHav6t76+Pg4fPqx6TUxMDO7cuYOgoCCpYkuqrNCE30hDRl6hxGmIiIi0h1yqNzYzM4Ofn5/aMhMTE1hZWamWT548GXPnzkXDhg1hbm6OmTNnIigoCO3atZMisuS87czga2+G6ORs7LuSjFFtXKSOREREpBUkv8rpSZYtW4b+/ftj2LBh6Ny5M+zt7bF161apY0lqQACvdiIiItIkiKIoSh2iJmVlZcHCwgKZmZl1YjxNwoM8dPr8CAQBOD0/GLbm1TegmoiISFtU9u+3Vh+hofKcGxrjBRdLiCLwy6nbUschIiLSCiw0OmhyR3cAwMqjcbiepPs3DiQiInpeLDQ6qF9zB/RoaodipYi3tkShuEQpdSQiIiJJsdDoIEEQ8OlgP1gY6ePyvUysPva31JGIiIgkxUKjo2zNDfFh/6YAgP8euombKdkSJyIiIpIOC40OG9qyEbr52KCwRIk3t0ShRFmnL1gjIiJ6LBYaHSYIAv4ztDnMFHJEJmTgh+M89URERPUTC42Oc7Awwvv9mwAAvjxwA3+n5UiciIiIqPax0NQBI1s5o5O3NQqKlXiLp56IiKgeYqGpAwRBwKKhzWFioIdztx/ip5O3pI5ERERUq1ho6ginBsZ4p2/pqafP90fj9v1ciRMRERHVHhaaOmRsGxe082iI/CIl3v4jCkqeeiIionqChaYOkckEfD4sAEb6ejj19wOsP3NH6khERES1goWmjnGxMsZbvX0AAIv3XMfdh3kSJyIiIqp5LDR10PggN7R2a4DcwhLM33oZoshTT0REVLex0NRBMpmAz4cHQCGX4a+b6fj9bILUkYiIiGoUC00d5W5tgjd6lp56+vTP60jKfCRxIiIioprDQlOHTerojhdcLJFdUMxTT0REVKex0NRhejIBS4b7w0Auw9GYNPxx4Z7UkYiIiGoEC00d52Vrhtkh3gCAj3ddRUpWvsSJiIiIqh8LTT3waicPNG9kgaz8Yry37QpPPRERUZ3DQlMPyPVkWDLCH/p6Ag5dT8HOS4lSRyIiIqpWLDT1hK+9OWZ0Kz31tHDnVaRlF0iciIiIqPqw0NQj07t5oomDOR7mFeH97Zc51xMREdUZLDT1iL6eDEuG+0MuE7D/ago+3MnxNEREVDew0NQzfo0ssGSEPwQB+PXUHXy8+xpLDRER6TwWmnpoyAtO+GyoPwBg7YlbWLwvmqWGiIh0GgtNPTWytTP+b7AfAGB1+N9YdvCGxImIiIiqjoWmHnupnSsWDGgKAFgeFotvwm5KnIiIiKhqWGjquYkd3DG/jy8A4IsDN7A6PE7iRERERJXHQkN4rYsn3ujZGACwaG80fjweL3EiIiKiymGhIQDAjO7eeL27FwDg493X8Oup2xInIiIienYsNKQyp0djvNbFAwDw/vYr2HQ2QeJEREREz4aFhlQEQcA7vX0xqYM7AODtrVHYdvGuxKmIiIiejoWG1AiCgA/6N8HL7VwhisC8TZewO4qTWRIRkXZjoaFyBEHARwObYVRrZyhFYNbGSOy7kix1LCIiosdioaEKyWQC/jOkOYa2bIQSpYiZv13A4espUsciIiKqEAsNPZZMJmDJ8AAMCHBEUYmIab9ewLEbaVLHIiIiKoeFhp5ITyZg6cgA9G5mj8ISJab8fA4n49KljkVERKSGhYaeSl9PhuWjX0BIE1sUFCsxed05nIl/IHUsIiIiFRYaeiYGchm+HdsSXRrb4FFRCSasPYOzt1hqiIhIO7DQ0DNTyPWw+uVAdPK2Rl5hCcb/yFJDRETagYWGKsVQXw/fjWulKjUTWGqIiEgLsNBQpZWVmo5e1sj9p9ScY6khIiIJsdBQlWiWmvEsNUREJCEWGqoyI4PSUtPBy0pVas7fZqkhIqLax0JDz8XIQA/fj2uN9p6lpWbcDyw1RERU+1ho6LkZGejhh/H/KzXjfzzLUkNERLWKhYaqxb9LTU5B8T+l5qHUsYiIqJ5goaFqU1ZqgjzKSs0ZlhoiIqoVLDRUrYwM9PDDhFZo59FQVWou3GGpISKimsVCQ9XO2ECOHye0VpWacT+w1BARUc1ioaEaoVlqxrPUEBFRDWKhoRpTVmraujdE9j+l5iJLDRER1QAWGqpRxgZyrJ3YGm3+KTXjWGqIiKgGSFpoVq5cCX9/f5ibm8Pc3BxBQUHYu3ev6vmuXbtCEAS1x9SpUyVMTFVhbCDHun+Vmpe+P42tF+5KHYuIiOoQSQuNk5MTFi9ejPPnz+PcuXPo3r07Bg0ahKtXr6rWmTJlCpKSklSPzz//XMLEVFXGBnKsndBaNU3C3E2XMHvjRWTnF0kdjYiI6gBJC82AAQPQt29feHt7o3Hjxvj0009hamqKU6dOqdYxNjaGvb296mFubi5hYnoeJgo5fp7UFvN6NIaeTMD2yET0Xf4XBwsTEdFz05oxNCUlJdi4cSNyc3MRFBSkWr5+/XpYW1vDz88P8+fPR15enoQp6XnpyQTMDPbGpteC4NTACAkPHmHEqgh8eyQWJUpR6nhERKSj5FIHuHz5MoKCgpCfnw9TU1Ns27YNTZs2BQCMGTMGrq6ucHR0RFRUFN5++23ExMRg69atj91eQUEBCgoKVF9nZWXV+PdAlRfo2gB7ZnXCe9uuYNelRCzZH4PjN9Ox7MUWsLcwlDoeERHpGEEURUn/t7iwsBB37txBZmYmtmzZgu+//x7h4eGqUvNvYWFhCA4ORmxsLDw9PSvc3sKFC/HRRx+VW56ZmcnTVVpIFEVsOX8XC3ZeRV5hCSyN9fHZMH/0amYvdTQiIpJQVlYWLCwsnvnvt+SFRlNISAg8PT2xevXqcs/l5ubC1NQU+/btQ69evSp8fUVHaJydnVlotNzfaTmYtTESl+9lAgDGtnXBB/2bwlBfT+JkREQkhcoWGq0ZQ1NGqVSqFZJ/i4yMBAA4ODg89vUKhUJ1GXjZg7Sfh40p/pjWHq919gAArD99BwO+Po7oZJ4yJCKip5P0CM38+fPRp08fuLi4IDs7Gxs2bMBnn32G/fv3w8PDAxs2bEDfvn1hZWWFqKgozJkzB05OTggPD3/m96hswyPp/XUzDXM3XUJadgEM5DK817cJxgW5QhAEqaMREVEt0akjNKmpqRg3bhx8fHwQHByMs2fPYv/+/ejRowcMDAxw6NAh9OzZE76+vpg3bx6GDRuGXbt2SRmZakEnbxvsndUJ3X1tUVisxIKdVzHl53N4kFsodTQiItJSWjeGprrxCI3uEkUR607ewqI90SgsUcLWTIFlL7ZABy9rqaMREVEN06kjNERPIggCJnZwx/bQDvCyNUVqdgFe+uE0Fu25jsJipdTxiIhIi7DQkNZr6miOXTM6YkxbF4gisPrY3xiy4gRiU7OljkZERFqChYZ0gpGBHv4zpDlWvxyIBsb6uJqYhX7Lj+OXiFuo42dNiYjoGbDQkE7p1cwe+2d3RidvaxQUK/HBjquY/NM5pGVXfKk/ERHVDyw0pHNszQ3x08Q2+LB/UxjIZQiLTkXvr47h8PUUqaMREZFEqlRoEhIScPfuXdXXZ86cwezZs7FmzZpqC0b0JDKZgEkd3bFzRgf42pvhfm4hJv90Du9vv4xHhSVSxyMiolpWpUIzZswYHDlyBACQnJyMHj164MyZM3jvvffw8ccfV2tAoifxtTfH9tAOmNzRHQDw66k76P/1X7jyzxQKRERUP1Sp0Fy5cgVt2rQBAGzatAl+fn44efIk1q9fj3Xr1lVnPqKnMtTXwwf9m+KXyW1ga6ZAXFouhqw4gZVH41Ci5IBhIqL6oEqFpqioCAqFAgBw6NAhDBw4EADg6+uLpKSk6ktHVAmdvG2wf3Zn9G5mj6ISEZ/ti8aY707hXsYjqaMREVENq1KhadasGVatWoW//voLBw8eRO/evQEAiYmJsLKyqtaARJXRwMQAK19qic+H+cPYQA+n4x+g91fHsPNSotTRiIioBlWp0Hz22WdYvXo1unbtitGjRyMgIAAAsHPnTtWpKCKpCIKAka2dsef1TmjhbIns/GK8/ttFzPk9Eln5RVLHIyKiGlDluZxKSkqQlZWFBg0aqJbdunULxsbGsLW1rbaAz4tzOdVvRSVKfB0Wi2/CbkIpAk4NjPDtmJYIcLaUOhoRET1Brczl9OjRIxQUFKjKzO3bt/HVV18hJiZGq8oMkb6eDHN7NMbmqUFwbmiEuw8fYcSqCPzMOwwTEdUpVSo0gwYNws8//wwAyMjIQNu2bfHll19i8ODBWLlyZbUGJKoOga4N8efrndCrmR0KS5T4cMdVvL4xEjkFxVJHIyKialClQnPhwgV06tQJALBlyxbY2dnh9u3b+Pnnn7F8+fJqDUhUXcwN9bHqpUC8368J5DIBuy4lYuA3xxGTzEkuiYh0XZUKTV5eHszMzAAABw4cwNChQyGTydCuXTvcvn27WgMSVSdBEPBKJw/8/lo72Jsb4u+0XAz69jj+OH/36S8mIiKtVaVC4+Xlhe3btyMhIQH79+9Hz549AQCpqakceEs6ofQUVEd08rZGfpES8zZfwvytUcgv4rQJRES6qEqF5sMPP8Qbb7wBNzc3tGnTBkFBQQBKj9a88MIL1RqQqKZYmSqwbmIbzAlpDEEAfjuTgKErTuL2/VypoxERUSVV+bLt5ORkJCUlISAgADJZaS86c+YMzM3N4evrW60hnwcv26Zn8dfNNMzaGIkHuYUwU8ixZIQ/evs5SB2LiKjequzf7yoXmjJls247OTk9z2ZqDAsNPavkzHzM2HAB524/BAC80tEdb/fxhb5elQ5kEhHRc6iV+9AolUp8/PHHsLCwgKurK1xdXWFpaYlPPvkESqWyKpskkpy9hSF+e7UdXu3sAQD4/ng8Rq05haRMzgVFRKTtqlRo3nvvPXzzzTdYvHgxLl68iIsXL+I///kPvv76a3zwwQfVnZGo1ujryfBu3yZY/XIgzAzlOH/7IfotP45jN9KkjkZERE9QpVNOjo6OWLVqlWqW7TI7duzA9OnTce/evWoL+Lx4yomq6vb9XExffwFXE7MgCMDr3b3xerA39GSC1NGIiOq8Wjnl9ODBgwoH/vr6+uLBgwdV2SSR1nG1MsEf09pjTFsXiCLw38M3Mfb7U7iXwVNQRETapkqFJiAgAN9880255d988w38/f2fOxSRtjDU18N/hjTHshcDYGygh1N/P0DvZcfwx/m7nAuKiEiLVOmUU3h4OPr16wcXFxfVPWgiIiKQkJCAPXv2qKZF0AY85UTV5VZ6LuZuisSFOxkAgD5+9vh0SHM0NDGQNhgRUR1UK6ecunTpghs3bmDIkCHIyMhARkYGhg4diqtXr+KXX36pyiaJtJ6btQk2vRaEN3v5QC4TsPdKMnp9dQxHYlKljkZEVO89931o/u3SpUto2bIlSkq05/bxPEJDNeHKvUzM+T0SN1NzAABj2rrgvb5NYKKQS5yMiKhuqJUjNET1nV8jC+ya2RGTOrgDADacvoN+y//ChTsPJU5GRFQ/sdAQVZGhvh4+HNAUG15pC0cLQ9y6n4fhK0/iywMxKCrhDSaJiGoTCw3Rc2rvZY29sztjyAuNoBSBr8NiMWTFCdxMyZY6GhFRvVGpMTRDhw594vMZGRkIDw/nGBqqt/ZcTsK72y4jI68IBnIZ3untiwnt3SDjzfiIiCqlsn+/KzWC0cLC4qnPjxs3rjKbJKpT+jZ3QCvXBnhzSxTCb6Th493XcDg6BUuGB8DR0kjqeEREdVa1XuWkjXiEhqQgiiLWn76DT/+8jkdFJTAzlOOTQX4Y1MIRgsCjNURET8OrnIi0gCAIeKmdK/bM6oQWzpbIzi/G7N8jMX7tWcSn50odj4iozmGhIapB7tYm2DI1CPN6NIaBXIZjN9LQa9kxfHkgBo8KtWesGRGRrmOhIaphcj0ZZgZ748DszujS2AaFJUp8HRaLHsvCcehaitTxiIjqBBYaolriZm2CdRNbY9VLLeFoYYi7Dx/hlZ/P4ZWfziLhQZ7U8YiIdBoLDVEtEgQBvf0ccGheF0zt4gm5TMCh66kIWRqO5YdvIr+Ip6GIiKqChYZIAsYGcrzTxxf7ZndCe08rFBQrsfTgDfT+6hjCb6RJHY+ISOew0BBJyMvWDOtfaYvlo1+ArZkCt+7nYfyPZzDt1/NIzHgkdTwiIp3BQkMkMUEQMDDAEYfndcHkju7QkwnYeyUZwV+GY+XROBQWc14oIqKn4Y31iLTM9aQsfLjjCs7eKp2528vWFB8Paob2ntYSJyMiqj28sR6RjmviYI5NrwXhixEBsDIxQGxqDsZ8dxqzN17Ew9xCqeMREWklFhoiLSQIAoYHOiHsja4YF+QKmQBsj0xEj2Xh2HM5Sep4RERah4WGSItZGOnj40F+2Dq9AxrbmSI9pxDT11/AtF/PIzU7X+p4RERag4WGSAe0cLbErpkd8Xp3L8j/GTTcc9kxbLt4F3V8GBwR0TNhoSHSEQq5Hub29MGOGR3QzNEcGXlFmPP7JUz+6RySMnmJNxHVbyw0RDqmmaMFtod2wJu9fGCgJ0NYdCp6Lj2GjWfu8GgNEdVbLDREOkhfT4bQbl748/WOaOFsieyCYryz9TJe+uE054UionqJhYZIh3nbmeGPae3xfr8mUMhlOBF7H72+OoafTt6CUsmjNURUf7DQEOk4PZmAVzp5YN/szmjj3hB5hSVYsPMqXlwTgb/TcqSOR0RUK1hoiOoId2sTbJzSDp8MagZjAz2cvfUQff77F1aHx6G4hNMnEFHdxkJDVIfIZAJeDnLD/tmd0cnbGgXFSizaG41hK0/i4p2HUscjIqoxLDREdZBzQ2P8PKkNPh/mDzNDOS7dzcSQFScRuv4CbqXnSh2PiKjacXJKojouJSsfS/bH4I8LdyGKgFwmYGxbF8wM9oa1qULqeEREFdKpySlXrlwJf39/mJubw9zcHEFBQdi7d6/q+fz8fISGhsLKygqmpqYYNmwYUlJSJExMpHvszA3xxYgA7Hm9E7r62KBYKeKniNvo8vkRfH34JvIKi6WOSET03CQ9QrNr1y7o6enB29sboijip59+wpIlS3Dx4kU0a9YM06ZNw59//ol169bBwsICM2bMgEwmw4kTJ575PXiEhkjdydh0LNobjcv3MgEAtmYKzOnRGCMCnSDX41loItIOlf37rXWnnBo2bIglS5Zg+PDhsLGxwYYNGzB8+HAAQHR0NJo0aYKIiAi0a9fumbbHQkNUnlIpYldUIr44EIOEB6XTJnjZmuLt3r4IaWILQRAkTkhE9Z1OnXL6t5KSEmzcuBG5ubkICgrC+fPnUVRUhJCQENU6vr6+cHFxQURExGO3U1BQgKysLLUHEamTyQQMatEIh+Z2wQf9m8LSWB+xqTmY8vM5vLj6FK+IIiKdI3mhuXz5MkxNTaFQKDB16lRs27YNTZs2RXJyMgwMDGBpaam2vp2dHZKTkx+7vUWLFsHCwkL1cHZ2ruHvgEh3KeR6mNzRHeFvdsO0rp5QyGU4c+sBhqw4ienrzyOeV0QRkY6QvND4+PggMjISp0+fxrRp0zB+/Hhcu3atytubP38+MjMzVY+EhIRqTEtUN1kY6ePt3r44+mZXjAh0giAAey4no8fScHy44wrScwqkjkhE9ERaN4YmJCQEnp6eePHFFxEcHIyHDx+qHaVxdXXF7NmzMWfOnGfaHsfQEFVedHIWPtsbjSMxaQAAM0M53unji9GtXSCTcXwNEdU8nR1DU0apVKKgoACBgYHQ19fH4cOHVc/FxMTgzp07CAoKkjAhUd3na2+OtRPbYMOUtvBrZI7s/GK8t+0KRqyOQExyttTxiIjKkfQIzfz589GnTx+4uLggOzsbGzZswGeffYb9+/ejR48emDZtGvbs2YN169bB3NwcM2fOBACcPHnymd+DR2iInk+JUsTPEbfwxf4Y5BaWQC4T8GpnD7we7A1DfT2p4xFRHVXZv9/yWsj0WKmpqRg3bhySkpJgYWEBf39/VZkBgGXLlkEmk2HYsGEoKChAr169sGLFCikjE9U7ejIBEzu4o1czeyzceRUHrqVgxdE47I5Kwv8N9kPnxjZSRyQi0r4xNNWNR2iIqtf+q8lYuPMqkjLzAQCDWjji/X5NYWPGaRSIqPro/BgaItJuvZrZ4+DcLpjYwQ0yAdgRmYjgL4/itzN3oFTW6f8/IiItxkJDRJVmqpBjwYBm2B7aAX6NzJGVX4z5Wy/jxTURuJnCQcNEVPtYaIioyvydLLF9egd80L8pjA30cPbWQ/Rd/he+2B+D/KISqeMRUT3CQkNEz0WuJ8Pkju44OLcLQprYoqhExDdHYtHrq2M4fjNd6nhEVE+w0BBRtWhkaYTvxrXCqpcCYW9uiNv38/DSD6cxe+NFpGTlSx2PiOo4XuVERNUuO78IXx64gZ8ibkEUAUN9GSa0d8e0Lp6wMNaXOh4R6YDK/v1moSGiGnMpIQOf7L6Gc7dLZ+82N5RjWlcvTGjvBiMD3pSPiB6PhUYDCw2RtERRRFh0Kj7fF4OYf66AsjVTYFaIN0a2coa+Hs98E1F5LDQaWGiItEOJUsSOyHv48sAN3Mt4BABwtzbBvJ6N0dfPgZNeEpEaFhoNLDRE2qWguAQbTt/BN2GxuJ9bCABo3sgCb/X2QUcvawgCiw0RsdCUw0JDpJ1yCorxw1/xWHMsDrmFpfesae9phbd6+6KFs6W04YhIciw0GlhoiLTb/ZwCfHskDr+euo3CEiUAoHcze7zRywdetqYSpyMiqbDQaGChIdINdx/m4atDN7H1wl0oRUAmACMCnTErxBuOlkZSxyOiWsZCo4GFhki33EjJxpL9MTh4LQUAYKAnw+g2zpjW1Qv2FoYSpyOi2sJCo4GFhkg3nb/9EJ/vi8bp+AcAWGyI6hsWGg0sNES6SxRFRPx9H18dvIkzt/4pNnIZxrRxwdQuniw2RHUYC40GFhoi3SeKIiLi7mPZoRs4e6v0rsNlxWZaV0/YmbPYENU1LDQaWGiI6g5RFHEy7j6WHbyhmk6BxYaobmKh0cBCQ1T3VFRsFHIZxrR1wbQunrBlsSHSeSw0GlhoiOouURRxIrb0VNR5FhuiOoWFRgMLDVHd97hiM7atK6Z0doeDBe9jQ6RrWGg0sNAQ1R+iKOJ4bDqWHbyBC3cyAABymYC+zR0wqaM7p1Qg0iEsNBpYaIjqH1EU8dfNdHx7JFZ1HxsAaOliickdPdCrmR3kejIJExLR07DQaGChIarfrtzLxNoTt7Dz0j0UlZT+unO0MMT49m4Y1cYFFkb6Eickooqw0GhgoSEiAEjNzsevp+5g/anbuJ9bCAAwNtDD8EAnTGjvBg8bToRJpE1YaDSw0BDRv+UXlWBnZCJ+PBGP6ORs1fJgX1tM6uiO9p5WEARBwoREBLDQlMNCQ0QVKbv78I8n4nE4OhVlvwl97c0wqYM7BrZwhKG+nrQhieoxFhoNLDRE9DTx6blYdyIem8/fRV5hCQDAysQAY9u5YmJ7NzQwMZA4IVH9w0KjgYWGiJ5VZl4Rfj93Bz+dvI17GY8AlI6zeamdK17p6M4b9RHVIhYaDSw0RFRZxSVK7L+aghVHY3E1MQtA6ZxRL7ZyxmtdPODUwFjihER1HwuNBhYaIqoqURRxNCYN3xyJVd2BWC4TMOSFRpjW1ZNXRhHVIBYaDSw0RPS8RFHEqb8f4JsjN3Ei9j4AQCYAfZs7ILSbF5o48HcLUXVjodHAQkNE1eninYf49kgsDl1PVS0LaWKL0G5eeMGlgYTJiOoWFhoNLDREVBOuJWbh26Ox2HM5SXXJd0cva4R280I7j4a8lw3Rc2Kh0cBCQ0Q1KS4tByuPxmHbxXsoUZb+Og10bYAZ3b3QtbENiw1RFbHQaGChIaLakPAgD6uPxWHT2bsoLFECAAKcLDC3pw86e1uz2BBVEguNBhYaIqpNKVn5+O7Y31h/+g4eFZXepK+1WwPM6+mDdh5WEqcj0h0sNBpYaIhICmnZBVgVHodfTt1GYXHpEZsOXlaY28MHga4cPEz0NCw0GlhoiEhKyZn5+PZILDaevYOiktJft918bDCvpw/8GllInI5Ie7HQaGChISJtkPAgD9+ExWLLhbuqwcO9m9ljTo/G8LE3kzgdkfZhodHAQkNE2iQ+PRf/PXQDOy4lQhQBQQAG+Dtidog37zxM9C8sNBpYaIhIG91IycZXh25gz+VkAKV3Hh7a0gmzgr3h3JBzRRGx0GhgoSEibXY1MRPLDt5Q3XlYLhMwsrUzZnb3goOFkcTpiKTDQqOBhYaIdMHFOw+x9OAN/HUzHQBgoCfDiFZOmNbVk7N7U73EQqOBhYaIdMmZ+Af44kAMzsQ/AFB6xGZoy0aY3tULbtYmEqcjqj0sNBpYaIhIF536+z6+DlOf3XtQi0YI7eYJL1teFUV1HwuNBhYaItJl528/xDdhN3EkJg1A6VVRff0cMKO7F5o48Hca1V0sNBpYaIioLrh8NxNfh93EgWspqmU9mtphZncv+DtZSheMqIaw0GhgoSGiuiQ6OQvfhMXiz8tJKPvt3aWxDV4P9kKga0NpwxFVIxYaDSw0RFQXxaXl4NsjsdgRmai683B7TyvM7O6Ndh4NObs36TwWGg0sNERUl92+n4uVR+Pwx4W7qrmiWrs1wNQunujmYwuZjMWGdBMLjQYWGiKqD+5lPMLq8DhsPJugmt3bzcoY44LcMKKVE8wM9SVOSFQ5LDQaWGiIqD5JycrHD8fjsfHMHWTlFwMATAz0MKKVM8YFuXK+KNIZLDQaWGiIqD7KKyzG1gv3sO7kLcSm5qiWd/OxwYQO7ujkZc3TUaTVWGg0sNAQUX0miiJOxN7H2hPxCItJVV0Z5Wljggnt3TC0pRNMFHJpQxJVoLJ/v2W1kOmxFi1ahNatW8PMzAy2trYYPHgwYmJi1Nbp2rUrBEFQe0ydOlWixEREukUQBHT0tsYPE1rjyLyumNjBDaYKOeLScvHBjqtot+gw/m/3Ndy5nyd1VKLnIukRmt69e2PUqFFo3bo1iouL8e677+LKlSu4du0aTExK5yzp2rUrGjdujI8//lj1OmNj42c+2sIjNERE6nIKirHlXAJ+iriN+PRcAKV3IA72tcOkDm4I8rTiZd8kOZ0+5ZSWlgZbW1uEh4ejc+fOAEoLTYsWLfDVV19VaZssNEREFVMqRYTfSMPak7dw7EaaarmPnRnm9GiMXs3sWGxIMjp1yklTZmYmAKBhQ/W7Xa5fvx7W1tbw8/PD/PnzkZf3+EOjBQUFyMrKUnsQEVF5MpmAbr62+HlSGxya2xkvt3OFsYEeYlKyMfXX8xi28iTO3nogdUyiZ6I1R2iUSiUGDhyIjIwMHD9+XLV8zZo1cHV1haOjI6KiovD222+jTZs22Lp1a4XbWbhwIT766KNyy3mEhojo6TLzivDdX3/j++N/I7+o9H42IU1s8XZvX3jbcZZvqj06e8pp2rRp2Lt3L44fPw4nJ6fHrhcWFobg4GDExsbC09Oz3PMFBQUoKChQfZ2VlQVnZ2cWGiKiSkjJysdXh25i07kElChFyARgeKAT5vRoDAcLI6njUT2gk4VmxowZ2LFjB44dOwZ3d/cnrpubmwtTU1Ps27cPvXr1euq2OYaGiKjqYlNzsGR/NPZfLZ3lWyGXYWIHd0zr4gkLY959mGqOTo2hEUURM2bMwLZt2xAWFvbUMgMAkZGRAAAHB4caTkdERF62plj9civ8Ma09Wrs1QEGxEqvC49B5yRGsORaH/KISqSMSAZD4CM306dOxYcMG7NixAz4+PqrlFhYWMDIyQlxcHDZs2IC+ffvCysoKUVFRmDNnDpycnBAeHv5M78EjNERE1UMURYRFp+KzfdG4kVJ692FHC0PM7emDIS80gh7vPEzVSKdOOT3ucsC1a9diwoQJSEhIwEsvvYQrV64gNzcXzs7OGDJkCN5//33eh4aISCIlShF/XLiLZQdvICkzHwDga2+Gt3v7oquPDS/1pmqhU4WmNrDQEBHVjPyiEqw7eQsrjsSqJsJs694Qrwd7I8jDinNF0XNhodHAQkNEVLMy8gqx8mgc1p68hcLi0ku97c0NMbCFIwa1cERTB3MetaFKY6HRwEJDRFQ77mU8woojsdh5KRHZ/xyxAQBvW1MMfqERBgY4wrmhsYQJSZew0GhgoSEiql0FxSU4Ep2GHZH3cDg6VXXUBgBauTbAoBcaoX9zBzQwMZAwJWk7FhoNLDRERNLJfFSE/VeSse3iPZyKv4+yvzhymYAujW0w6IVG6NHEDkYGetIGJa3DQqOBhYaISDskZ+Zj56V72H4xEdeS/jfPnomBHno1s8egFxqhg6cV5HpaNc0gSYSFRgMLDRGR9rmZko3tkfewIzIRdx8+Ui23MVNgRjcvjGnrAn0Wm3qNhUYDCw0RkfYSRRHnbz/E9sh7+DMqCQ/zigCU3qH4/X5N0NXHVuKEJBUWGg0sNEREuqGwWInfzyVg6YEYVbHp6mOD9/s1gZctZ/qub1hoNLDQEBHplsxHRfj68E2sO3kLxUoRejIBL7dzxaxgb14ZVY+w0GhgoSEi0k1/p+XgP3uiceh66UzfFkb6mB3ijZfauXJ8TT3AQqOBhYaISLediE3HJ7uvITo5GwDgYWOCD/o15bxRdRwLjQYWGiIi3VeiFPH72QR8eSAG93MLAQCdG5eOr2lsx/E1dRELjQYWGiKiuiMrvwjfhsXixxPxKCopHV8zpo0L5vRojIYcX1OnsNBoYKEhIqp7bqXnYtHe69h/tXR8jZmhHLOCvTEuyA0Gco6vqQtYaDSw0BAR1V0n49Lxye7ruP7PnYcdLAwxItAJwwOd4WLFiTB1GQuNBhYaIqK6rUQpYvO5BHxx4AbScwpUy4M8rDCytRN6N3PgXFE6iIVGAwsNEVH9kF9UgoPXUrDpXAKOx6arJsI0U8gxoIUjRrZyRoCTBa+M0hEsNBpYaIiI6p97GY/wx/m72Hw+AQkP/jdXlLetKUa2csaQlo1gbaqQMCE9DQuNBhYaIqL6S6kUcTr+ATafS8CeK0nIL1ICAOQyAd19bTGylTO6+thwhm8txEKjgYWGiIiA0ku+d19KwqZzCYhMyFAttzFTYGjLRhgR6AwvW1PpApIaFhoNLDRERKTpRko2Np9LwNYL91Q36gOAkCZ2eKNXY/ja8++F1FhoNLDQEBHR4xSVKBEWnYrN5xIQFp0KpQgIAjAwwBFzQhrDzdpE6oj1FguNBhYaIiJ6FrGpOVh26Ab+jEoCAOjJBIxs5YSZ3b3haGkkcbr6h4VGAwsNERFVxpV7mVh68AbColMBAAZyGV5q64rp3Tx5ZVQtYqHRwEJDRERVce7WA3y+PwZn4h8AAIwN9DC5ozte6eQBCyN9idPVfSw0GlhoiIioqkRRxF830/HFgRhE3c0EAFgY6eO1Lh6Y0N4NxgZyiRPWXSw0GlhoiIjoeYmiiP1XU/DlgRjcTM0BAFibKjCzuxdGtXGGQs6pFaobC40GFhoiIqouJUoROyLvYdmhG6o7EDeyNMKsEG8MfaERb9BXjVhoNLDQEBFRdSssVmLTuQQsP3wTqdmlE2J62JhgTkhj9GvuAJmM80U9LxYaDSw0RERUU/KLSvBzxC2sPBqHh3lFAABfezPM6+mDkCa2nAjzObDQaGChISKimpadX4Qfj9/C93/9jeyCYgBAC2dLvNHTBx28rFhsqoCFRgMLDRER1ZaMvEKsPvY31p24hUdFJQCAtu4N8WYvH7RyayhxOt3CQqOBhYaIiGpbWnYBVhyNxfpTd1BYUjrDd5fGNnijpw+aO1lInE43sNBoYKEhIiKpJGY8wtdhsdh8LgHFytI/t72a2WFuDx/42JtJnE67sdBoYKEhIiKp3b6fi/8euoltkfcg/msCzNkhjeHOCTArxEKjgYWGiIi0xc2UbCw7dAN7LicDKJ0Ac3hLJ7we4o1GnABTDQuNBhYaIiLSNpoTYOrrCWjeyAI+9mZobGcGH3sz+NiZwaoeT4bJQqOBhYaIiLTV+dsP8MX+G4j4+36Fz1ubGqCx3b9Kzj+Fx1RR9+eQYqHRwEJDRETaLjY1G9eSsnEjORsxKdm4kZKNOw/y8Li/0I0sjVTlxtfeDAHOlnVuLA4LjQYWGiIi0kV5hcW4mZJTWnD+VXRSsgoqXL+jlzUmdXRD18a2dWLqBRYaDSw0RERUl2TkFeJGSg5ikrMQk5KN6KRsXLjzEP9cFQ53axNM7OCGYS2dYKLDp6ZYaDSw0BARUV2X8CAPP0fcwsYzCaqpF8wM5RjdxgXj27vp5BVULDQaWGiIiKi+yCkoxh/n72LtiXjcup8HoPTS8N7N7DGpoxtaujTQmXmlWGg0sNAQEVF9o1SKOBKTih+Ox+Nk3P+uoApwssCkju7o4+cAA7lMwoRPx0KjgYWGiIjqs+tJWVh7Ih7bIxNRWFw6r5SduQLjgtwwuo0LGpoYSJywYiw0GlhoiIiIgPScAmw4fQc/R9xGek7plVIKuQxDWzbC6DYuaN7IQqtOR7HQaGChISIi+p+C4hL8GZWEH47H42pilmq5S0Nj9Pd3QH9/RzRxMJO83LDQaGChISIiKk8URZyJf4BfTt3GoespyC9Sqp7ztDFBf39HDAhwgJetNLOCs9BoYKEhIiJ6styCYhyOTsXuS4k4eiNNNdYGAHztzTAgwBH9/R3galV7dyNmodHAQkNERPTssvKLcPBqCnZHJeKvm+koVv6vJvg7WaC/vwP6+TvW+L1tWGg0sNAQERFVTUZeIfZfTcauS0k4GZeOf3UbBLo2KC03zR1ga25Y7e/NQqOBhYaIiOj5pecUYO+VZOy+lIgztx6oJs4UBOCL4QEYFuhUre9X2b/fujvJAxEREdUaa1MFXm7nipfbuSIlKx9/RiVhd1QiLtzJQCu3BlLH4xEaIiIiqrqUrHzYacEpJ+2+7zERERFptZooM1XBQkNEREQ6T9JCs2jRIrRu3RpmZmawtbXF4MGDERMTo7ZOfn4+QkNDYWVlBVNTUwwbNgwpKSkSJSYiIiJtJGmhCQ8PR2hoKE6dOoWDBw+iqKgIPXv2RG5urmqdOXPmYNeuXdi8eTPCw8ORmJiIoUOHSpiaiIiItI1WDQpOS0uDra0twsPD0blzZ2RmZsLGxgYbNmzA8OHDAQDR0dFo0qQJIiIi0K5du6duk4OCiYiIdI9ODwrOzMwEADRs2BAAcP78eRQVFSEkJES1jq+vL1xcXBAREVHhNgoKCpCVlaX2ICIiorpNawqNUqnE7Nmz0aFDB/j5+QEAkpOTYWBgAEtLS7V17ezskJycXOF2Fi1aBAsLC9XD2dm5pqMTERGRxLSm0ISGhuLKlSvYuHHjc21n/vz5yMzMVD0SEhKqKSERERFpK624U/CMGTOwe/duHDt2DE5O/7t1sr29PQoLC5GRkaF2lCYlJQX29vYVbkuhUEChUNR0ZCIiItIikh6hEUURM2bMwLZt2xAWFgZ3d3e15wMDA6Gvr4/Dhw+rlsXExODOnTsICgqq7bhERESkpSQ9QhMaGooNGzZgx44dMDMzU42LsbCwgJGRESwsLDB58mTMnTsXDRs2hLm5OWbOnImgoKBnusKJiIiI6gdJL9sWBKHC5WvXrsWECRMAlN5Yb968efjtt99QUFCAXr16YcWKFY895aSJl20TERHpnsr+/daq+9DUBBYaIiIi3aPT96EhIiIiqgqtuMqpJpUdgOIN9oiIiHRH2d/tZz2RVOcLTXZ2NgDwBntEREQ6KDs7GxYWFk9dr86PoVEqlUhMTISZmdljByFXRVZWFpydnZGQkMCxOZXA/VY13G9Vw/1WedxnVcP9VjVP2m+iKCI7OxuOjo6QyZ4+QqbOH6GRyWRqN+urbubm5vzwVgH3W9Vwv1UN91vlcZ9VDfdb1Txuvz3LkZkyHBRMREREOo+FhoiIiHQeC00VKRQKLFiwgPNGVRL3W9Vwv1UN91vlcZ9VDfdb1VTnfqvzg4KJiIio7uMRGiIiItJ5LDRERESk81hoiIiISOex0BAREZHOY6Gpom+//RZubm4wNDRE27ZtcebMGakjabWFCxdCEAS1h6+vr9SxtM6xY8cwYMAAODo6QhAEbN++Xe15URTx4YcfwsHBAUZGRggJCcHNmzelCaslnrbPJkyYUO6z17t3b2nCapFFixahdevWMDMzg62tLQYPHoyYmBi1dfLz8xEaGgorKyuYmppi2LBhSElJkSix9J5ln3Xt2rXc523q1KkSJdYOK1euhL+/v+rmeUFBQdi7d6/q+er6nLHQVMHvv/+OuXPnYsGCBbhw4QICAgLQq1cvpKamSh1NqzVr1gxJSUmqx/Hjx6WOpHVyc3MREBCAb7/9tsLnP//8cyxfvhyrVq3C6dOnYWJigl69eiE/P7+Wk2qPp+0zAOjdu7faZ++3336rxYTaKTw8HKGhoTh16hQOHjyIoqIi9OzZE7m5uap15syZg127dmHz5s0IDw9HYmIihg4dKmFqaT3LPgOAKVOmqH3ePv/8c4kSawcnJycsXrwY58+fx7lz59C9e3cMGjQIV69eBVCNnzORKq1NmzZiaGio6uuSkhLR0dFRXLRokYSptNuCBQvEgIAAqWPoFADitm3bVF8rlUrR3t5eXLJkiWpZRkaGqFAoxN9++02ChNpHc5+JoiiOHz9eHDRokCR5dElqaqoIQAwPDxdFsfSzpa+vL27evFm1zvXr10UAYkREhFQxtYrmPhNFUezSpYs4a9Ys6ULpiAYNGojff/99tX7OeISmkgoLC3H+/HmEhISolslkMoSEhCAiIkLCZNrv5s2bcHR0hIeHB8aOHYs7d+5IHUmnxMfHIzk5We2zZ2FhgbZt2/Kz9xRHjx6Fra0tfHx8MG3aNNy/f1/qSFonMzMTANCwYUMAwPnz51FUVKT2efP19YWLiws/b//Q3Gdl1q9fD2tra/j5+WH+/PnIy8uTIp5WKikpwcaNG5Gbm4ugoKBq/ZzV+ckpq1t6ejpKSkpgZ2enttzOzg7R0dESpdJ+bdu2xbp16+Dj44OkpCR89NFH6NSpE65cuQIzMzOp4+mE5ORkAKjws1f2HJXXu3dvDB06FO7u7oiLi8O7776LPn36ICIiAnp6elLH0wpKpRKzZ89Ghw4d4OfnB6D082ZgYABLS0u1dfl5K1XRPgOAMWPGwNXVFY6OjoiKisLbb7+NmJgYbN26VcK00rt8+TKCgoKQn58PU1NTbNu2DU2bNkVkZGS1fc5YaKhW9OnTR/Vvf39/tG3bFq6urti0aRMmT54sYTKq60aNGqX6d/PmzeHv7w9PT08cPXoUwcHBEibTHqGhobhy5QrHtVXC4/bZq6++qvp38+bN4eDggODgYMTFxcHT07O2Y2oNHx8fREZGIjMzE1u2bMH48eMRHh5ere/BU06VZG1tDT09vXIjsFNSUmBvby9RKt1jaWmJxo0bIzY2VuooOqPs88XP3vPx8PCAtbU1P3v/mDFjBnbv3o0jR47AyclJtdze3h6FhYXIyMhQW5+ft8fvs4q0bdsWAOr9583AwABeXl4IDAzEokWLEBAQgP/+97/V+jljoakkAwMDBAYG4vDhw6plSqUShw8fRlBQkITJdEtOTg7i4uLg4OAgdRSd4e7uDnt7e7XPXlZWFk6fPs3PXiXcvXsX9+/fr/efPVEUMWPGDGzbtg1hYWFwd3dXez4wMBD6+vpqn7eYmBjcuXOn3n7enrbPKhIZGQkA9f7zpkmpVKKgoKB6P2fVO265fti4caOoUCjEdevWideuXRNfffVV0dLSUkxOTpY6mtaaN2+eePToUTE+Pl48ceKEGBISIlpbW4upqalSR9Mq2dnZ4sWLF8WLFy+KAMSlS5eKFy9eFG/fvi2KoiguXrxYtLS0FHfs2CFGRUWJgwYNEt3d3cVHjx5JnFw6T9pn2dnZ4htvvCFGRESI8fHx4qFDh8SWLVuK3t7eYn5+vtTRJTVt2jTRwsJCPHr0qJiUlKR65OXlqdaZOnWq6OLiIoaFhYnnzp0Tg4KCxKCgIAlTS+tp+yw2Nlb8+OOPxXPnzonx8fHijh07RA8PD7Fz584SJ5fWO++8I4aHh4vx8fFiVFSU+M4774iCIIgHDhwQRbH6PmcsNFX09ddfiy4uLqKBgYHYpk0b8dSpU1JH0movvvii6ODgIBoYGIiNGjUSX3zxRTE2NlbqWFrnyJEjIoByj/Hjx4uiWHrp9gcffCDa2dmJCoVCDA4OFmNiYqQNLbEn7bO8vDyxZ8+eoo2Njaivry+6urqKU6ZM4f98iGKF+wyAuHbtWtU6jx49EqdPny42aNBANDY2FocMGSImJSVJF1piT9tnd+7cETt37iw2bNhQVCgUopeXl/jmm2+KmZmZ0gaX2KRJk0RXV1fRwMBAtLGxEYODg1VlRhSr73MmiKIoVvGIEREREZFW4BgaIiIi0nksNERERKTzWGiIiIhI57HQEBERkc5joSEiIiKdx0JDREREOo+FhoiIiHQeCw0R1TuCIGD79u1SxyCiasRCQ0S1asKECRAEodyjd+/eUkcjIh0mlzoAEdU/vXv3xtq1a9WWKRQKidIQUV3AIzREVOsUCgXs7e3VHg0aNABQejpo5cqV6NOnD4yMjODh4YEtW7aovf7y5cvo3r07jIyMYGVlhVdffRU5OTlq6/z4449o1qwZFAoFHBwcMGPGDLXn09PTMWTIEBgbG8Pb2xs7d+6s2W+aiGoUCw0RaZ0PPvgAw4YNw6VLlzB27FiMGjUK169fBwDk5uaiV69eaNCgAc6ePYvNmzfj0KFDaoVl5cqVCA0NxauvvorLly9j586d8PLyUnuPjz76CCNHjkRUVBT69u2LsWPH4sGDB7X6fRJRNaq++TSJiJ5u/Pjxop6enmhiYqL2+PTTT0VRLJ3ReOrUqWqvadu2rTht2jRRFEVxzZo1YoMGDcScnBzV83/++acok8lUs2g7OjqK77333mMzABDff/991dc5OTkiAHHv3r3V9n0SUe3iGBoiqnXdunXDypUr1ZY1bNhQ9e+goCC154KCghAZGQkAuH79OgICAmBiYqJ6vkOHDlAqlYiJiYEgCEhMTERwcPATM/j7+6v+bWJiAnNzc6Smplb1WyIiibHQEFGtMzExKXcKqLoYGRk903r6+vpqXwuCAKVSWRORiKgWcAwNEWmdU6dOlfu6SZMmAIAmTZrg0qVLyM3NVT1/4sQJyGQy+Pj4wMzMDG5ubjh8+HCtZiYiafEIDRHVuoKCAiQnJ6stk8vlsLa2BgBs3rwZrVq1QseOHbF+/XqcOXMGP/zwAwBg7NixWLBgAcaPH4+FCxciLS0NM2fOxMsvvww7OzsAwMKFCzF16lTY2tqiT58+yM7OxokTJzBz5sza/UaJqNaw0BBRrdu3bx8cHBzUlvn4+CA6OhpA6RVIGzduxPTp0+Hg4IDffvsNTZs2BQAYGxtj//79mDVrFlq3bg1jY2MMGzYMS5cuVW1r/PjxyM/Px7Jly/DGG2/A2toaw4cPr71vkIhqnSCKoih1CCKiMoIgYNu2bRg8eLDUUYhIh3AMDREREek8FhoiIiLSeRxDQ0RahWfBiagqeISGiIiIdB4LDREREek8FhoiIiLSeSw0REREpPNYaIiIiEjnsdAQERGRzmOhISIiIp3HQkNEREQ6j4WGiIiIdN7/A70R3ArYtRQZAAAAAElFTkSuQmCC\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "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": 25,
+      "id": "06j_Dr6475Kb",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "06j_Dr6475Kb",
+        "outputId": "a359313d-70bb-43e0-918c-309684e43037"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "<ipython-input-25-ee85c886ffd9>:1: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
+            "  model2.load_state_dict(torch.load(\"/content/save_data/model2_cifar.pt\"))\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Test Loss: 17.172099\n",
+            "\n",
+            "Test Accuracy of airplane: 76% (765/1000)\n",
+            "Test Accuracy of automobile: 77% (776/1000)\n",
+            "Test Accuracy of  bird: 58% (588/1000)\n",
+            "Test Accuracy of   cat: 57% (573/1000)\n",
+            "Test Accuracy of  deer: 65% (650/1000)\n",
+            "Test Accuracy of   dog: 53% (530/1000)\n",
+            "Test Accuracy of  frog: 78% (783/1000)\n",
+            "Test Accuracy of horse: 78% (782/1000)\n",
+            "Test Accuracy of  ship: 82% (828/1000)\n",
+            "Test Accuracy of truck: 79% (790/1000)\n",
+            "\n",
+            "Test Accuracy (Overall): 70% (7065/10000)\n"
+          ]
+        }
+      ],
+      "source": [
+        "model2.load_state_dict(torch.load(\"/content/save_data/model2_cifar.pt\"))\n",
+        "\n",
+        "# track test loss\n",
+        "test_loss = 0.0\n",
+        "class_correct = list(0.0 for i in range(10))\n",
+        "class_total = list(0.0 for i in range(10))\n",
+        "\n",
+        "model2.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 = model2(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",
+      "metadata": {
+        "id": "bc381cf4"
+      },
+      "source": [
+        "## Exercise 2: Quantization: try to compress the CNN to save space\n",
+        "\n",
+        "Quantization doc is available from https://pytorch.org/docs/stable/quantization.html#torch.quantization.quantize_dynamic\n",
+        "        \n",
+        "The Exercise is to quantize post training the above CNN model. Compare the size reduction and the impact on the classification accuracy\n",
+        "\n",
+        "\n",
+        "The size of the model is simply the size of the file."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 27,
+      "id": "ef623c26",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ef623c26",
+        "outputId": "b12fc3df-3534-4c11-bd78-01a56839bbb7"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "model:  fp32  \t Size (KB): 251.342\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "251342"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 27
+        }
+      ],
+      "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\n",
+        "\n",
+        "\n",
+        "print_size_of_model(model, \"fp32\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "05c4e9ad",
+      "metadata": {
+        "id": "05c4e9ad"
+      },
+      "source": [
+        "Post training quantization example"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import torch.quantization\n",
+        "\n",
+        "model2.to(\"cpu\")\n",
+        "quantized_model = torch.quantization.quantize_dynamic(model2, dtype=torch.qint8)\n",
+        "print_size_of_model(model2, \"fp32\")\n",
+        "print_size_of_model(quantized_model, \"int8\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "38_XJKcrX5Lq",
+        "outputId": "0482c566-6374-46a1-ef5d-4988bd94002d"
+      },
+      "id": "38_XJKcrX5Lq",
+      "execution_count": 28,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "model:  fp32  \t Size (KB): 2330.946\n",
+            "model:  int8  \t Size (KB): 659.806\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "659806"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 28
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "The size of the model reduces by more than 3 thanks to the dynamic quantization."
+      ],
+      "metadata": {
+        "id": "mCL0R6GGatxw"
+      },
+      "id": "mCL0R6GGatxw"
+    },
+    {
+      "cell_type": "markdown",
+      "id": "7b108e17",
+      "metadata": {
+        "id": "7b108e17"
+      },
+      "source": [
+        "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": 29,
+      "id": "ZXeLJC39QjOP",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ZXeLJC39QjOP",
+        "outputId": "8d7fdd63-6a5f-4289-931c-e3fa874288e2"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Net2(\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): DynamicQuantizedLinear(in_features=1024, out_features=512, dtype=torch.qint8, qscheme=torch.per_tensor_affine)\n",
+            "  (fc2): DynamicQuantizedLinear(in_features=512, out_features=64, dtype=torch.qint8, qscheme=torch.per_tensor_affine)\n",
+            "  (fc3): DynamicQuantizedLinear(in_features=64, out_features=10, dtype=torch.qint8, qscheme=torch.per_tensor_affine)\n",
+            "  (dropout): Dropout(p=0.5, inplace=False)\n",
+            ")\n",
+            "Test Loss: 17.187755\n",
+            "\n",
+            "Test Accuracy of airplane: 76% (765/1000)\n",
+            "Test Accuracy of automobile: 77% (775/1000)\n",
+            "Test Accuracy of  bird: 58% (585/1000)\n",
+            "Test Accuracy of   cat: 57% (572/1000)\n",
+            "Test Accuracy of  deer: 65% (653/1000)\n",
+            "Test Accuracy of   dog: 52% (527/1000)\n",
+            "Test Accuracy of  frog: 78% (785/1000)\n",
+            "Test Accuracy of horse: 78% (782/1000)\n",
+            "Test Accuracy of  ship: 82% (829/1000)\n",
+            "Test Accuracy of truck: 79% (790/1000)\n",
+            "\n",
+            "Test Accuracy (Overall): 70% (7063/10000)\n"
+          ]
+        }
+      ],
+      "source": [
+        "#try with CPU dynamic quantization --> need to convert GPU to CPU device\n",
+        "\n",
+        "# track test loss\n",
+        "test_loss = 0.0\n",
+        "class_correct = list(0.0 for i in range(10))\n",
+        "class_total = list(0.0 for i in range(10))\n",
+        "\n",
+        "quantized_model.eval()\n",
+        "print(quantized_model)\n",
+        "\n",
+        "# iterate over test data\n",
+        "for data, target in test_loader:\n",
+        "    # move tensors to GPU if CUDA is available\n",
+        "    data, target = data.cpu(), target.cpu()\n",
+        "\n",
+        "    #print(data.device, target.device, next(quantized_model.parameters()).device)\n",
+        "    # forward pass: compute predicted outputs by passing inputs to the model\n",
+        "    with torch.no_grad() :\n",
+        "      output = quantized_model(data)\n",
+        "    # calculate the batch loss\n",
+        "    loss = criterion(output, target)\n",
+        "    # update test loss\n",
+        "    test_loss += loss.item() * data.size(0)\n",
+        "    # convert output probabilities to predicted class\n",
+        "    _, pred = torch.max(output, 1)\n",
+        "    # compare predictions to true label\n",
+        "    correct_tensor = pred.eq(target.data.view_as(pred))\n",
+        "    correct = (\n",
+        "        np.squeeze(correct_tensor.cpu().numpy())\n",
+        "\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",
+      "source": [
+        "**Answer** -->The overall test accuracies for the dense model and the quantized model are very close (respectfully 70% and 70,63%). The class accuracies are  equal for both models, except for dog, where the quantized model has a reduced accuracy of 1%. Looking at the gain by reducing the size of the model, we can consider than it is more interesting to use the quantized model in this case."
+      ],
+      "metadata": {
+        "id": "NIb1ZktXa6LL"
+      },
+      "id": "NIb1ZktXa6LL"
+    },
+    {
+      "cell_type": "markdown",
+      "id": "a0a34b90",
+      "metadata": {
+        "id": "a0a34b90"
+      },
+      "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)\n",
+        "\n",
+        "To do so, we first have to define a new neural network that will support theaware quantization."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#Applying QAT (Quantized Aware Training)\n",
+        "\n",
+        "import torch\n",
+        "import torch.nn as nn\n",
+        "import torch.quantization\n",
+        "\n",
+        "# Example model\n",
+        "class QatNet(nn.Module):\n",
+        "  def __init__(self, dropout_prob):\n",
+        "        super(QatNet, self).__init__()\n",
+        "        self.quant = torch.ao.quantization.QuantStub()\n",
+        "        self.conv1 = nn.Conv2d(3, 16, 3, padding=1)  # (input channels, output channels, kernel size 3x3)\n",
+        "        self.pool = nn.MaxPool2d(2, 2)  # (kernel size, stride)\n",
+        "        self.conv2 = nn.Conv2d(16, 32, 3, padding=1)  # (input channels, output channels, kernel size 3x3)\n",
+        "        self.conv3 = nn.Conv2d(32, 64, 3, padding=1)  # (input channels, output channels, kernel size 3x3)\n",
+        "        self.fc1 = nn.Linear(64 * 4 * 4, 512)  # (input, output)\n",
+        "        self.fc2 = nn.Linear(512, 64)  # (input, output)\n",
+        "        self.fc3 = nn.Linear(64, 10)  # (input, output = number of classes)\n",
+        "\n",
+        "        # Dropout layer\n",
+        "        self.dropout = nn.Dropout(p=dropout_prob)\n",
+        "\n",
+        "  def forward(self, x):\n",
+        "      x = self.pool(F.relu(self.conv1(x)))\n",
+        "      x = self.pool(F.relu(self.conv2(x)))\n",
+        "      x = self.pool(F.relu(self.conv3(x)))\n",
+        "      x = x.view(-1, 64 * 4 * 4)\n",
+        "      x = F.relu(self.fc1(x))\n",
+        "      x = F.relu(self.fc2(x))\n",
+        "      x = self.fc3(x)\n",
+        "      x = self.dropout(x)  # Apply dropout\n",
+        "      return x\n",
+        "\n",
+        "# Instantiate the model\n",
+        "qat_model = QatNet(dropout_prob = 0.5)\n",
+        "\n",
+        "# Set the model's qconfig\n",
+        "qat_model.qconfig = torch.quantization.get_default_qat_qconfig(\"fbgemm\")\n",
+        "\n",
+        "# Prepare the model for QAT\n",
+        "torch.quantization.prepare_qat(qat_model, inplace=True)\n",
+        "\n",
+        "print(qat_model)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ylrc7tZaKWd4",
+        "outputId": "75edab52-1360-46f4-eb1f-f50d4e928044"
+      },
+      "id": "ylrc7tZaKWd4",
+      "execution_count": 30,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "QatNet(\n",
+            "  (quant): QuantStub(\n",
+            "    (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
+            "      fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
+            "      (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
+            "    )\n",
+            "  )\n",
+            "  (conv1): Conv2d(\n",
+            "    3, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)\n",
+            "    (weight_fake_quant): FusedMovingAvgObsFakeQuantize(\n",
+            "      fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False\n",
+            "      (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([]), max_val=tensor([]))\n",
+            "    )\n",
+            "    (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
+            "      fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
+            "      (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
+            "    )\n",
+            "  )\n",
+            "  (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
+            "  (conv2): Conv2d(\n",
+            "    16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)\n",
+            "    (weight_fake_quant): FusedMovingAvgObsFakeQuantize(\n",
+            "      fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False\n",
+            "      (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([]), max_val=tensor([]))\n",
+            "    )\n",
+            "    (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
+            "      fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
+            "      (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
+            "    )\n",
+            "  )\n",
+            "  (conv3): Conv2d(\n",
+            "    32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)\n",
+            "    (weight_fake_quant): FusedMovingAvgObsFakeQuantize(\n",
+            "      fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False\n",
+            "      (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([]), max_val=tensor([]))\n",
+            "    )\n",
+            "    (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
+            "      fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
+            "      (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
+            "    )\n",
+            "  )\n",
+            "  (fc1): Linear(\n",
+            "    in_features=1024, out_features=512, bias=True\n",
+            "    (weight_fake_quant): FusedMovingAvgObsFakeQuantize(\n",
+            "      fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False\n",
+            "      (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([]), max_val=tensor([]))\n",
+            "    )\n",
+            "    (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
+            "      fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
+            "      (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
+            "    )\n",
+            "  )\n",
+            "  (fc2): Linear(\n",
+            "    in_features=512, out_features=64, bias=True\n",
+            "    (weight_fake_quant): FusedMovingAvgObsFakeQuantize(\n",
+            "      fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False\n",
+            "      (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([]), max_val=tensor([]))\n",
+            "    )\n",
+            "    (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
+            "      fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
+            "      (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
+            "    )\n",
+            "  )\n",
+            "  (fc3): Linear(\n",
+            "    in_features=64, out_features=10, bias=True\n",
+            "    (weight_fake_quant): FusedMovingAvgObsFakeQuantize(\n",
+            "      fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.qint8, quant_min=-128, quant_max=127, qscheme=torch.per_channel_symmetric, reduce_range=False\n",
+            "      (activation_post_process): MovingAveragePerChannelMinMaxObserver(min_val=tensor([]), max_val=tensor([]))\n",
+            "    )\n",
+            "    (activation_post_process): FusedMovingAvgObsFakeQuantize(\n",
+            "      fake_quant_enabled=tensor([1]), observer_enabled=tensor([1]), scale=tensor([1.]), zero_point=tensor([0], dtype=torch.int32), dtype=torch.quint8, quant_min=0, quant_max=127, qscheme=torch.per_tensor_affine, reduce_range=True\n",
+            "      (activation_post_process): MovingAverageMinMaxObserver(min_val=inf, max_val=-inf)\n",
+            "    )\n",
+            "  )\n",
+            "  (dropout): Dropout(p=0.5, inplace=False)\n",
+            ")\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/torch/ao/quantization/observer.py:229: UserWarning: Please use quant_min and quant_max to specify the range for observers.                     reduce_range will be deprecated in a future release of PyTorch.\n",
+            "  warnings.warn(\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Training the QAT Model"
+      ],
+      "metadata": {
+        "id": "WCnevnUuRxY9"
+      },
+      "id": "WCnevnUuRxY9"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Example training dataset\n",
+        "import torch.optim as optim\n",
+        "import numpy as np\n",
+        "\n",
+        "criterion = nn.CrossEntropyLoss()  # specify loss function\n",
+        "optimizer = optim.SGD(qat_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",
+        "qat_model = qat_model.cuda()\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",
+        "    qat_model.train()\n",
+        "    for data, target in train_loader:\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 = qat_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",
+        "    qat_model.eval()\n",
+        "    for data, target in valid_loader:\n",
+        "      if train_on_gpu :\n",
+        "        # Move tensors to GPU if CUDA is available\n",
+        "        data, target = data.cuda(), target.cuda()\n",
+        "        # Forward pass: compute predicted outputs by passing inputs to the model\n",
+        "        output = qat_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(qat_model.state_dict(), \"/content/save_data/qat_model_cifar.pt\")\n",
+        "        valid_loss_min = valid_loss\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "uG_1vRQJRvkd",
+        "outputId": "1cf5a022-aeff-485b-b7c9-47cf52879034"
+      },
+      "id": "uG_1vRQJRvkd",
+      "execution_count": 31,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch: 0 \tTraining Loss: 45.607654 \tValidation Loss: 43.456702\n",
+            "Validation loss decreased (inf --> 43.456702).  Saving model ...\n",
+            "Epoch: 1 \tTraining Loss: 41.996771 \tValidation Loss: 37.539250\n",
+            "Validation loss decreased (43.456702 --> 37.539250).  Saving model ...\n",
+            "Epoch: 2 \tTraining Loss: 39.226181 \tValidation Loss: 34.510424\n",
+            "Validation loss decreased (37.539250 --> 34.510424).  Saving model ...\n",
+            "Epoch: 3 \tTraining Loss: 37.682786 \tValidation Loss: 32.866509\n",
+            "Validation loss decreased (34.510424 --> 32.866509).  Saving model ...\n",
+            "Epoch: 4 \tTraining Loss: 36.638718 \tValidation Loss: 30.486939\n",
+            "Validation loss decreased (32.866509 --> 30.486939).  Saving model ...\n",
+            "Epoch: 5 \tTraining Loss: 35.586891 \tValidation Loss: 28.771608\n",
+            "Validation loss decreased (30.486939 --> 28.771608).  Saving model ...\n",
+            "Epoch: 6 \tTraining Loss: 34.692187 \tValidation Loss: 27.582011\n",
+            "Validation loss decreased (28.771608 --> 27.582011).  Saving model ...\n",
+            "Epoch: 7 \tTraining Loss: 33.680828 \tValidation Loss: 25.607441\n",
+            "Validation loss decreased (27.582011 --> 25.607441).  Saving model ...\n",
+            "Epoch: 8 \tTraining Loss: 32.750430 \tValidation Loss: 25.514199\n",
+            "Validation loss decreased (25.607441 --> 25.514199).  Saving model ...\n",
+            "Epoch: 9 \tTraining Loss: 31.976220 \tValidation Loss: 24.268018\n",
+            "Validation loss decreased (25.514199 --> 24.268018).  Saving model ...\n",
+            "Epoch: 10 \tTraining Loss: 31.123835 \tValidation Loss: 22.676677\n",
+            "Validation loss decreased (24.268018 --> 22.676677).  Saving model ...\n",
+            "Epoch: 11 \tTraining Loss: 30.569647 \tValidation Loss: 22.446432\n",
+            "Validation loss decreased (22.676677 --> 22.446432).  Saving model ...\n",
+            "Epoch: 12 \tTraining Loss: 29.750041 \tValidation Loss: 20.961087\n",
+            "Validation loss decreased (22.446432 --> 20.961087).  Saving model ...\n",
+            "Epoch: 13 \tTraining Loss: 29.224235 \tValidation Loss: 20.581028\n",
+            "Validation loss decreased (20.961087 --> 20.581028).  Saving model ...\n",
+            "Epoch: 14 \tTraining Loss: 28.792308 \tValidation Loss: 21.056585\n",
+            "Epoch: 15 \tTraining Loss: 27.970131 \tValidation Loss: 19.669330\n",
+            "Validation loss decreased (20.581028 --> 19.669330).  Saving model ...\n",
+            "Epoch: 16 \tTraining Loss: 27.409082 \tValidation Loss: 18.849576\n",
+            "Validation loss decreased (19.669330 --> 18.849576).  Saving model ...\n",
+            "Epoch: 17 \tTraining Loss: 26.906203 \tValidation Loss: 18.456558\n",
+            "Validation loss decreased (18.849576 --> 18.456558).  Saving model ...\n",
+            "Epoch: 18 \tTraining Loss: 26.369765 \tValidation Loss: 18.260281\n",
+            "Validation loss decreased (18.456558 --> 18.260281).  Saving model ...\n",
+            "Epoch: 19 \tTraining Loss: 25.892071 \tValidation Loss: 18.561713\n",
+            "Epoch: 20 \tTraining Loss: 25.191699 \tValidation Loss: 17.584603\n",
+            "Validation loss decreased (18.260281 --> 17.584603).  Saving model ...\n",
+            "Epoch: 21 \tTraining Loss: 24.614829 \tValidation Loss: 18.334314\n",
+            "Epoch: 22 \tTraining Loss: 24.426494 \tValidation Loss: 17.345409\n",
+            "Validation loss decreased (17.584603 --> 17.345409).  Saving model ...\n",
+            "Epoch: 23 \tTraining Loss: 23.477772 \tValidation Loss: 17.319574\n",
+            "Validation loss decreased (17.345409 --> 17.319574).  Saving model ...\n",
+            "Epoch: 24 \tTraining Loss: 22.992848 \tValidation Loss: 17.724551\n",
+            "Epoch: 25 \tTraining Loss: 22.607852 \tValidation Loss: 17.718065\n",
+            "Epoch: 26 \tTraining Loss: 22.302885 \tValidation Loss: 17.835953\n",
+            "Epoch: 27 \tTraining Loss: 21.773108 \tValidation Loss: 18.432680\n",
+            "Epoch: 28 \tTraining Loss: 21.180365 \tValidation Loss: 18.724174\n",
+            "Epoch: 29 \tTraining Loss: 20.909504 \tValidation Loss: 18.800871\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "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 QAT Model\")\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "id": "tYqC1SIM7ap1",
+        "outputId": "3bc958ec-20c5-462b-ed56-284a5f6b1ce0"
+      },
+      "id": "tYqC1SIM7ap1",
+      "execution_count": 32,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "qat_model.to(\"cpu\")\n",
+        "aware_quantized_model = torch.quantization.convert(qat_model.eval(), inplace=False)\n",
+        "#print(aware_quantized_model)\n",
+        "print_size_of_model(qat_model)\n",
+        "print_size_of_model(aware_quantized_model)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "2PPXYaQLUHiS",
+        "outputId": "17c948d9-5464-4e80-ae3f-45de3a740195"
+      },
+      "id": "2PPXYaQLUHiS",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "model:    \t Size (KB): 2370.275\n",
+            "model:    \t Size (KB): 605.094\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "605094"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 38
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Afficher le device du modèle quantifié\n",
+        "params = list(aware_quantized_model.parameters())\n",
+        "print(params)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Ssfsol07HXoO",
+        "outputId": "8c4c5739-d43f-4052-dd38-8ea74135646b"
+      },
+      "id": "Ssfsol07HXoO",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Test QAT Model"
+      ],
+      "metadata": {
+        "id": "JJlHMbBZUMhd"
+      },
+      "id": "JJlHMbBZUMhd"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#try with CPU dynamic quantization --> need to convert GPU to CPU device\n",
+        "\n",
+        "# track test loss\n",
+        "test_loss = 0.0\n",
+        "class_correct = list(0.0 for i in range(10))\n",
+        "class_total = list(0.0 for i in range(10))\n",
+        "\n",
+        "aware_quantized_model.to(\"cpu\")\n",
+        "aware_quantized_model.eval()\n",
+        "print(aware_quantized_model)\n",
+        "\n",
+        "# iterate over test data\n",
+        "for data, target in test_loader:\n",
+        "    # move tensors to CPU\n",
+        "    data, target = data.cpu(), target.cpu()\n",
+        "    # forward pass: compute predicted outputs by passing inputs to the model\n",
+        "    with torch.no_grad() :\n",
+        "      output = aware_quantized_model(data)\n",
+        "    # calculate the batch loss\n",
+        "    loss = criterion(output, target)\n",
+        "    # update test loss\n",
+        "    test_loss += loss.item() * data.size(0)\n",
+        "    # convert output probabilities to predicted class\n",
+        "    _, pred = torch.max(output, 1)\n",
+        "    # compare predictions to true label\n",
+        "    correct_tensor = pred.eq(target.data.view_as(pred))\n",
+        "    correct = (\n",
+        "        np.squeeze(correct_tensor.cpu().numpy())\n",
+        "\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",
+        ")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "aYDwEmQzUPL5",
+        "outputId": "7b477a0b-9d86-4e0d-dd12-7b92938c10ff"
+      },
+      "id": "aYDwEmQzUPL5",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "QatNet(\n",
+            "  (quant): Quantize(scale=tensor([1.]), zero_point=tensor([0]), dtype=torch.quint8)\n",
+            "  (conv1): QuantizedConv2d(3, 16, kernel_size=(3, 3), stride=(1, 1), scale=0.0678861141204834, zero_point=62, padding=(1, 1))\n",
+            "  (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
+            "  (conv2): QuantizedConv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), scale=0.0762503445148468, zero_point=47, padding=(1, 1))\n",
+            "  (conv3): QuantizedConv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), scale=0.10137047618627548, zero_point=69, padding=(1, 1))\n",
+            "  (fc1): QuantizedLinear(in_features=1024, out_features=512, scale=0.06663341820240021, zero_point=56, qscheme=torch.per_channel_affine)\n",
+            "  (fc2): QuantizedLinear(in_features=512, out_features=64, scale=0.05890081077814102, zero_point=51, qscheme=torch.per_channel_affine)\n",
+            "  (fc3): QuantizedLinear(in_features=64, out_features=10, scale=0.06607885658740997, zero_point=76, qscheme=torch.per_channel_affine)\n",
+            "  (dropout): QuantizedDropout(p=0.5, inplace=False)\n",
+            ")\n"
+          ]
+        },
+        {
+          "output_type": "error",
+          "ename": "NotImplementedError",
+          "evalue": "Could not run 'quantized::conv2d.new' with arguments from the 'CPU' backend. This could be because the operator doesn't exist for this backend, or was omitted during the selective/custom build process (if using custom build). If you are a Facebook employee using PyTorch on mobile, please visit https://fburl.com/ptmfixes for possible resolutions. 'quantized::conv2d.new' is only available for these backends: [Meta, QuantizedCPU, QuantizedCUDA, BackendSelect, Python, FuncTorchDynamicLayerBackMode, Functionalize, Named, Conjugate, Negative, ZeroTensor, ADInplaceOrView, AutogradOther, AutogradCPU, AutogradCUDA, AutogradXLA, AutogradMPS, AutogradXPU, AutogradHPU, AutogradLazy, AutogradMeta, Tracer, AutocastCPU, AutocastXPU, AutocastMPS, AutocastCUDA, FuncTorchBatched, BatchedNestedTensor, FuncTorchVmapMode, Batched, VmapMode, FuncTorchGradWrapper, PythonTLSSnapshot, FuncTorchDynamicLayerFrontMode, PreDispatch, PythonDispatcher].\n\nMeta: registered at ../aten/src/ATen/core/MetaFallbackKernel.cpp:23 [backend fallback]\nQuantizedCPU: registered at ../aten/src/ATen/native/quantized/cpu/qconv.cpp:1972 [kernel]\nQuantizedCUDA: registered at ../aten/src/ATen/native/quantized/cudnn/Conv.cpp:391 [kernel]\nBackendSelect: fallthrough registered at ../aten/src/ATen/core/BackendSelectFallbackKernel.cpp:3 [backend fallback]\nPython: registered at ../aten/src/ATen/core/PythonFallbackKernel.cpp:153 [backend fallback]\nFuncTorchDynamicLayerBackMode: registered at ../aten/src/ATen/functorch/DynamicLayer.cpp:497 [backend fallback]\nFunctionalize: registered at ../aten/src/ATen/FunctionalizeFallbackKernel.cpp:349 [backend fallback]\nNamed: registered at ../aten/src/ATen/core/NamedRegistrations.cpp:7 [backend fallback]\nConjugate: registered at ../aten/src/ATen/ConjugateFallback.cpp:17 [backend fallback]\nNegative: registered at ../aten/src/ATen/native/NegateFallback.cpp:18 [backend fallback]\nZeroTensor: registered at ../aten/src/ATen/ZeroTensorFallback.cpp:86 [backend fallback]\nADInplaceOrView: fallthrough registered at ../aten/src/ATen/core/VariableFallbackKernel.cpp:96 [backend fallback]\nAutogradOther: registered at ../aten/src/ATen/core/VariableFallbackKernel.cpp:63 [backend fallback]\nAutogradCPU: registered at ../aten/src/ATen/core/VariableFallbackKernel.cpp:67 [backend fallback]\nAutogradCUDA: registered at ../aten/src/ATen/core/VariableFallbackKernel.cpp:75 [backend fallback]\nAutogradXLA: registered at ../aten/src/ATen/core/VariableFallbackKernel.cpp:79 [backend fallback]\nAutogradMPS: registered at ../aten/src/ATen/core/VariableFallbackKernel.cpp:87 [backend fallback]\nAutogradXPU: registered at ../aten/src/ATen/core/VariableFallbackKernel.cpp:71 [backend fallback]\nAutogradHPU: registered at ../aten/src/ATen/core/VariableFallbackKernel.cpp:100 [backend fallback]\nAutogradLazy: registered at ../aten/src/ATen/core/VariableFallbackKernel.cpp:83 [backend fallback]\nAutogradMeta: registered at ../aten/src/ATen/core/VariableFallbackKernel.cpp:91 [backend fallback]\nTracer: registered at ../torch/csrc/autograd/TraceTypeManual.cpp:294 [backend fallback]\nAutocastCPU: fallthrough registered at ../aten/src/ATen/autocast_mode.cpp:321 [backend fallback]\nAutocastXPU: fallthrough registered at ../aten/src/ATen/autocast_mode.cpp:463 [backend fallback]\nAutocastMPS: fallthrough registered at ../aten/src/ATen/autocast_mode.cpp:209 [backend fallback]\nAutocastCUDA: fallthrough registered at ../aten/src/ATen/autocast_mode.cpp:165 [backend fallback]\nFuncTorchBatched: registered at ../aten/src/ATen/functorch/LegacyBatchingRegistrations.cpp:731 [backend fallback]\nBatchedNestedTensor: registered at ../aten/src/ATen/functorch/LegacyBatchingRegistrations.cpp:758 [backend fallback]\nFuncTorchVmapMode: fallthrough registered at ../aten/src/ATen/functorch/VmapModeRegistrations.cpp:27 [backend fallback]\nBatched: registered at ../aten/src/ATen/LegacyBatchingRegistrations.cpp:1075 [backend fallback]\nVmapMode: fallthrough registered at ../aten/src/ATen/VmapModeRegistrations.cpp:33 [backend fallback]\nFuncTorchGradWrapper: registered at ../aten/src/ATen/functorch/TensorWrapper.cpp:207 [backend fallback]\nPythonTLSSnapshot: registered at ../aten/src/ATen/core/PythonFallbackKernel.cpp:161 [backend fallback]\nFuncTorchDynamicLayerFrontMode: registered at ../aten/src/ATen/functorch/DynamicLayer.cpp:493 [backend fallback]\nPreDispatch: registered at ../aten/src/ATen/core/PythonFallbackKernel.cpp:165 [backend fallback]\nPythonDispatcher: registered at ../aten/src/ATen/core/PythonFallbackKernel.cpp:157 [backend fallback]\n",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mNotImplementedError\u001b[0m                       Traceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-39-edca34e920f8>\u001b[0m in \u001b[0;36m<cell line: 13>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0;31m# forward pass: compute predicted outputs by passing inputs to the model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m     \u001b[0;32mwith\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mno_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m       \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maware_quantized_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     19\u001b[0m     \u001b[0;31m# calculate the batch loss\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m     \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1734\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compiled_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[misc]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1735\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1736\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1737\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1738\u001b[0m     \u001b[0;31m# torchrec tests the code consistency with the following code\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1745\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1746\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1747\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1748\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1749\u001b[0m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m<ipython-input-31-736a5f5ba3fe>\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 24\u001b[0;31m       \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpool\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconv1\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     25\u001b[0m       \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpool\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconv2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m       \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpool\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrelu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconv3\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1734\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compiled_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[misc]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1735\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1736\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1737\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1738\u001b[0m     \u001b[0;31m# torchrec tests the code consistency with the following code\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1745\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1746\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1747\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1748\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1749\u001b[0m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/ao/nn/quantized/modules/conv.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    593\u001b[0m                 \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_reversed_padding_repeated_twice\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpadding_mode\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    594\u001b[0m             )\n\u001b[0;32m--> 595\u001b[0;31m         return ops.quantized.conv2d(\n\u001b[0m\u001b[1;32m    596\u001b[0m             \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_packed_params\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscale\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzero_point\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    597\u001b[0m         )\n",
+            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/_ops.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1114\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_has_torchbind_op_overload\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0m_must_dispatch_in_python\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1115\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0m_call_overload_packet_from_python\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1116\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_op\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1117\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1118\u001b[0m     \u001b[0;31m# TODO: use this to make a __dir__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mNotImplementedError\u001b[0m: Could not run 'quantized::conv2d.new' with arguments from the 'CPU' backend. This could be because the operator doesn't exist for this backend, or was omitted during the selective/custom build process (if using custom build). If you are a Facebook employee using PyTorch on mobile, please visit https://fburl.com/ptmfixes for possible resolutions. 'quantized::conv2d.new' is only available for these backends: [Meta, QuantizedCPU, QuantizedCUDA, BackendSelect, Python, FuncTorchDynamicLayerBackMode, Functionalize, Named, Conjugate, Negative, ZeroTensor, ADInplaceOrView, AutogradOther, AutogradCPU, AutogradCUDA, AutogradXLA, AutogradMPS, AutogradXPU, AutogradHPU, AutogradLazy, AutogradMeta, Tracer, AutocastCPU, AutocastXPU, AutocastMPS, AutocastCUDA, FuncTorchBatched, BatchedNestedTensor, FuncTorchVmapMode, Batched, VmapMode, FuncTorchGradWrapper, PythonTLSSnapshot, FuncTorchDynamicLayerFrontMode, PreDispatch, PythonDispatcher].\n\nMeta: registered at ../aten/src/ATen/core/MetaFallbackKernel.cpp:23 [backend fallback]\nQuantizedCPU: registered at ../aten/src/ATen/native/quantized/cpu/qconv.cpp:1972 [kernel]\nQuantizedCUDA: registered at ../aten/src/ATen/native/quantized/cudnn/Conv.cpp:391 [kernel]\nBackendSelect: fallthrough registered at ../aten/src/ATen/core/BackendSelectFallbackKernel.cpp:3 [backend fallback]\nPython: registered at ../aten/src/ATen/core/PythonFallbackKernel.cpp:153 [backend fallback]\nFuncTorchDynamicLayerBackMode: registered at ../aten/src/ATen/functorch/DynamicLayer.cpp:497 [backend fallback]\nFunctionalize: registered at ../aten/src/ATen/FunctionalizeFallbackKernel.cpp:349 [backend fallback]\nNamed: registered at ../aten/src/ATen/core/NamedRegistrations.cpp:7 [backend fallback]\nConjugate: registered at ../aten/src/ATen/ConjugateFallback.cpp:17 [backend fallback]\nNegative: registered at ../aten/src/ATen/native/NegateFallback.cpp:18 [backend fallback]\nZeroTensor: registered at ../aten/src/ATen/ZeroTensorFallback.cpp:86 [backend fallback]\nADInplaceOrView: fallthrough registered at ../aten/src/ATen/core/VariableFallbackKernel.cpp:96 [backend fallback]\nAutogradOther: registered at ../aten/src/ATen/core/VariableFallbackKernel.cpp:63 [backend fallback]\nAutogradCPU: registered at ../aten/src/ATen/core/VariableFallbackKernel.cpp:67 [backend fallback]\nAutogradCUDA: registered at ../aten/src/ATen/core/VariableFallbackKernel.cpp:75 [backend fallback]\nAutogradXLA: registered at ../aten/src/ATen/core/VariableFallbackKernel.cpp:79 [backend fallback]\nAutogradMPS: registered at ../aten/src/ATen/core/VariableFallbackKernel.cpp:87 [backend fallback]\nAutogradXPU: registered at ../aten/src/ATen/core/VariableFallbackKernel.cpp:71 [backend fallback]\nAutogradHPU: registered at ../aten/src/ATen/core/VariableFallbackKernel.cpp:100 [backend fallback]\nAutogradLazy: registered at ../aten/src/ATen/core/VariableFallbackKernel.cpp:83 [backend fallback]\nAutogradMeta: registered at ../aten/src/ATen/core/VariableFallbackKernel.cpp:91 [backend fallback]\nTracer: registered at ../torch/csrc/autograd/TraceTypeManual.cpp:294 [backend fallback]\nAutocastCPU: fallthrough registered at ../aten/src/ATen/autocast_mode.cpp:321 [backend fallback]\nAutocastXPU: fallthrough registered at ../aten/src/ATen/autocast_mode.cpp:463 [backend fallback]\nAutocastMPS: fallthrough registered at ../aten/src/ATen/autocast_mode.cpp:209 [backend fallback]\nAutocastCUDA: fallthrough registered at ../aten/src/ATen/autocast_mode.cpp:165 [backend fallback]\nFuncTorchBatched: registered at ../aten/src/ATen/functorch/LegacyBatchingRegistrations.cpp:731 [backend fallback]\nBatchedNestedTensor: registered at ../aten/src/ATen/functorch/LegacyBatchingRegistrations.cpp:758 [backend fallback]\nFuncTorchVmapMode: fallthrough registered at ../aten/src/ATen/functorch/VmapModeRegistrations.cpp:27 [backend fallback]\nBatched: registered at ../aten/src/ATen/LegacyBatchingRegistrations.cpp:1075 [backend fallback]\nVmapMode: fallthrough registered at ../aten/src/ATen/VmapModeRegistrations.cpp:33 [backend fallback]\nFuncTorchGradWrapper: registered at ../aten/src/ATen/functorch/TensorWrapper.cpp:207 [backend fallback]\nPythonTLSSnapshot: registered at ../aten/src/ATen/core/PythonFallbackKernel.cpp:161 [backend fallback]\nFuncTorchDynamicLayerFrontMode: registered at ../aten/src/ATen/functorch/DynamicLayer.cpp:493 [backend fallback]\nPreDispatch: registered at ../aten/src/ATen/core/PythonFallbackKernel.cpp:165 [backend fallback]\nPythonDispatcher: registered at ../aten/src/ATen/core/PythonFallbackKernel.cpp:157 [backend fallback]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "201470f9",
+      "metadata": {
+        "id": "201470f9"
+      },
+      "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": null,
+      "id": "b4d13080",
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 545
+        },
+        "id": "b4d13080",
+        "outputId": "b7ff19f5-b9f3-45af-e43a-1500703d8fcf"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-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",
+            "/usr/local/lib/python3.10/dist-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 /root/.cache/torch/hub/checkpoints/resnet50-0676ba61.pth\n",
+            "100%|██████████| 97.8M/97.8M [00:00<00:00, 188MB/s]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Predicted class is: Golden Retriever\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "import json\n",
+        "import matplotlib.pyplot as plt\n",
+        "from torchvision import models\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",
+        "        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",
+        "model3 = models.resnet50(pretrained=True)\n",
+        "# Send the model to the GPU\n",
+        "# model.cuda()\n",
+        "# Set layers such as dropout and batchnorm in evaluation mode\n",
+        "model3.eval()\n",
+        "\n",
+        "# Get the 1000-dimensional model output\n",
+        "out = model3(image)\n",
+        "# Find the predicted class\n",
+        "print(\"Predicted class is: {}\".format(labels[out.argmax()]))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "184cfceb",
+      "metadata": {
+        "id": "184cfceb"
+      },
+      "source": [
+        "Experiments:\n",
+        "\n",
+        "Study the code and the results obtained. Possibly add other images downloaded from the internet.\n",
+        "\n",
+        "What is the size of the model? Quantize it and then check if the model is still able to correctly classify the other images.\n",
+        "\n",
+        "Experiment with other pre-trained CNN models.\n",
+        "\n",
+        "    \n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "l6AkxJIEj1BS",
+      "metadata": {
+        "id": "l6AkxJIEj1BS",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 423
+        },
+        "outputId": "7aa1fe62-1965-4cc1-9eee-5ac78555460f"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Predicted class is: tabby cat\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "test_image_2 = 'cat.jpg'\n",
+        "# Load the image\n",
+        "image_cat = Image.open(test_image_2)\n",
+        "plt.imshow(image_cat), plt.xticks([]), plt.yticks([])\n",
+        "\n",
+        "image_cat = data_transform(image_cat).unsqueeze(0)\n",
+        "out = model3(image_cat)\n",
+        "# Find the predicted class\n",
+        "print(\"Predicted class is: {}\".format(labels[out.argmax()]))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "9u8okHN2lSoX",
+      "metadata": {
+        "id": "9u8okHN2lSoX",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 423
+        },
+        "outputId": "a40bab95-7d86-4a19-e155-28eeb66675bc"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Predicted class is: Granny Smith\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "test_image_3 = 'apple.jpg'\n",
+        "# Load the image\n",
+        "image_apple = Image.open(test_image_3)\n",
+        "plt.imshow(image_apple), plt.xticks([]), plt.yticks([])\n",
+        "\n",
+        "image_apple = data_transform(image_apple).unsqueeze(0)\n",
+        "out = model3(image_apple)\n",
+        "# Find the predicted class\n",
+        "print(\"Predicted class is: {}\".format(labels[out.argmax()]))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "FrctIZuFmB8y",
+      "metadata": {
+        "id": "FrctIZuFmB8y"
+      },
+      "source": [
+        "The model recognizes the cat (with description of its coat pattern) and the apple (with its species). We will now quantize the two images and check wether the model can predict the image class again or not."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "fRTBC2nySCRx",
+      "metadata": {
+        "id": "fRTBC2nySCRx",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "4a50ce7e-e628-4ae3-d709-4a557c21a283"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "model:  fp32  \t Size (KB): 102523.238\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "102523238"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 45
+        }
+      ],
+      "source": [
+        "print_size_of_model(model3, \"fp32\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "-IcU6LXlSOHr",
+      "metadata": {
+        "id": "-IcU6LXlSOHr",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "d1aab747-649a-4e5e-bae5-0cef2833b37e"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "model:  int8  \t Size (KB): 96379.996\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "96379996"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 46
+        }
+      ],
+      "source": [
+        "quantized_model3 = torch.quantization.quantize_dynamic(model3, dtype=torch.qint8)\n",
+        "print_size_of_model(quantized_model3, \"int8\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "e-6cS1K3USbn",
+      "metadata": {
+        "id": "e-6cS1K3USbn",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "97420fce-b990-49ff-dca6-839b3f896216"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Predicted class is: Golden Retriever\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Get the 1000-dimensional model output\n",
+        "out = quantized_model3(image)\n",
+        "# Find the predicted class\n",
+        "print(\"Predicted class is: {}\".format(labels[out.argmax()]))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "id": "0p2dJTkdpBaG",
+      "metadata": {
+        "id": "0p2dJTkdpBaG",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "4bd0bc4a-8c6b-495b-b67c-2cf14480454b"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Predicted class is: tabby cat\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Get the 1000-dimensional model output\n",
+        "out = quantized_model3(image_cat)\n",
+        "# Find the predicted class\n",
+        "print(\"Predicted class is: {}\".format(labels[out.argmax()]))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Get the 1000-dimensional model output\n",
+        "out = quantized_model3(image_apple)\n",
+        "# Find the predicted class\n",
+        "print(\"Predicted class is: {}\".format(labels[out.argmax()]))"
+      ],
+      "metadata": {
+        "id": "jc2Az0yiMO2e",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "1fa014f6-55de-4146-d20b-f3510c962e12"
+      },
+      "id": "jc2Az0yiMO2e",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Predicted class is: Granny Smith\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "The quantized model still allowes to properly classify the 3 images (dog, cat, apple)."
+      ],
+      "metadata": {
+        "id": "Y6wH12UFNfBz"
+      },
+      "id": "Y6wH12UFNfBz"
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "### Using MobileNet50 pre trained model"
+      ],
+      "metadata": {
+        "id": "LKEjBsRyRRQP"
+      },
+      "id": "LKEjBsRyRRQP"
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "We will try an other popular pre trained model for image classification called MobileNetV2. MobileNetV2 is a lightweight deep learning model designed specifically for mobile and embedded applications, using depthwise separable convolutions."
+      ],
+      "metadata": {
+        "id": "K6ENdJO3OE3X"
+      },
+      "id": "K6ENdJO3OE3X"
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import json\n",
+        "import matplotlib.pyplot as plt\n",
+        "from torchvision import models\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",
+        "        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",
+        "image = data_transform(image).unsqueeze(0)\n",
+        "\n",
+        "# Load the pre-trained MobileNetV2 model\n",
+        "mobilenet_model = models.mobilenet_v2(pretrained=True)\n",
+        "mobilenet_model.eval()  # Set the model to evaluation mode\n",
+        "\n",
+        "\n",
+        "mobilenet_model.eval()\n",
+        "\n",
+        "# Get the 1000-dimensional model output\n",
+        "out = mobilenet_model(image)\n",
+        "# Find the predicted class\n",
+        "print(\"Predicted class with MobileNet50 is: {}\".format(labels[out.argmax()]))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Y5hc5nkONkYP",
+        "outputId": "02dcd741-8dda-46e6-f137-74c3413cfe35"
+      },
+      "id": "Y5hc5nkONkYP",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Predicted class with MobileNet50 is: Golden Retriever\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-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",
+            "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=MobileNet_V2_Weights.IMAGENET1K_V1`. You can also use `weights=MobileNet_V2_Weights.DEFAULT` to get the most up-to-date weights.\n",
+            "  warnings.warn(msg)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "test_image_2 = 'cat.jpg'\n",
+        "# Load the image\n",
+        "image_cat = Image.open(test_image_2)\n",
+        "image_cat = data_transform(image_cat).unsqueeze(0)\n",
+        "out = mobilenet_model(image_cat)\n",
+        "# Find the predicted class\n",
+        "print(\"Predicted class with MobileNet50 is: {}\".format(labels[out.argmax()]))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "IZBfJQMSQF1i",
+        "outputId": "04df923a-aac3-46b3-cc09-7a84392933b3"
+      },
+      "id": "IZBfJQMSQF1i",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Predicted class with MobileNet50 is: Egyptian Mau\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "test_image_3 = 'apple.jpg'\n",
+        "# Load the image\n",
+        "image_apple = Image.open(test_image_3)\n",
+        "image_apple = data_transform(image_apple).unsqueeze(0)\n",
+        "out = model3(image_apple)\n",
+        "# Find the predicted class\n",
+        "print(\"Predicted class with MobileNet50 is: {}\".format(labels[out.argmax()]))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "NYn_FMZ3QQc2",
+        "outputId": "8ace8f48-3d51-40b9-e107-e84f7a8de15d"
+      },
+      "id": "NYn_FMZ3QQc2",
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Predicted class with MobileNet50 is: Granny Smith\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "We see that the prediction for the cat is more relevant with this pre-trained model (the previous ResNet50 was giving the coat pattern 'tabby cat' but this model is more precise in its answer and gives the species 'Egyptian Mau'). The dog and apple are properly classified, like with ResNet50 before."
+      ],
+      "metadata": {
+        "id": "XrFOmZt8Qh4A"
+      },
+      "id": "XrFOmZt8Qh4A"
+    },
+    {
+      "cell_type": "markdown",
+      "id": "5d57da4b",
+      "metadata": {
+        "id": "5d57da4b"
+      },
+      "source": [
+        "## Exercise 4: Transfer Learning\n",
+        "    \n",
+        "    \n",
+        "For this work, we will use a pre-trained model (ResNet18) as a descriptor extractor and will refine the classification by training only the last fully connected layer of the network. Thus, the output layer of the pre-trained network will be replaced by a layer adapted to the new classes to be recognized which will be in our case ants and bees.\n",
+        "Download and unzip in your working directory the dataset available at the address :\n",
+        "    \n",
+        "https://download.pytorch.org/tutorial/hymenoptera_data.zip\n",
+        "    \n",
+        "Execute the following code in order to display some images of the dataset."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "id": "2Dzaxe-EFTx9",
+      "metadata": {
+        "id": "2Dzaxe-EFTx9",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "5c990e86-d34a-45c2-985c-ddbf4c5158ff"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Mounted at /content/drive\n"
+          ]
+        }
+      ],
+      "source": [
+        "from google.colab import drive\n",
+        "drive.mount('/content/drive')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "id": "be2d31f5",
+      "metadata": {
+        "id": "be2d31f5",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 207
+        },
+        "outputId": "8418f722-3bd4-4f85-a08c-3e71d1d16402"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "import os\n",
+        "\n",
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np\n",
+        "import torch\n",
+        "import torchvision\n",
+        "from torchvision import datasets, transforms\n",
+        "\n",
+        "# Data augmentation and normalization for training\n",
+        "# Just normalization for validation\n",
+        "data_transforms = {\n",
+        "    \"train\": transforms.Compose(\n",
+        "        [\n",
+        "            transforms.RandomResizedCrop(\n",
+        "                224\n",
+        "            ),  # ImageNet models were trained on 224x224 images\n",
+        "            transforms.RandomHorizontalFlip(),  # flip horizontally 50% of the time - increases train set variability\n",
+        "            transforms.ToTensor(),  # convert it to a PyTorch tensor\n",
+        "            transforms.Normalize(\n",
+        "                [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]\n",
+        "            ),  # ImageNet models expect this norm\n",
+        "        ]\n",
+        "    ),\n",
+        "    \"val\": transforms.Compose(\n",
+        "        [\n",
+        "            transforms.Resize(256),\n",
+        "            transforms.CenterCrop(224),\n",
+        "            transforms.ToTensor(),\n",
+        "            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n",
+        "        ]\n",
+        "    ),\n",
+        "}\n",
+        "\n",
+        "data_dir = \"/content/drive/MyDrive/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": {
+        "id": "bbd48800"
+      },
+      "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": 2,
+      "id": "572d824c",
+      "metadata": {
+        "id": "572d824c",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "7ab4cd64-f395-4f25-f574-eef36b1d68ce"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:617: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
+            "  warnings.warn(\n",
+            "/usr/local/lib/python3.10/dist-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",
+            "/usr/local/lib/python3.10/dist-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",
+            "Downloading: \"https://download.pytorch.org/models/resnet18-f37072fd.pth\" to /root/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth\n",
+            "100%|██████████| 44.7M/44.7M [00:00<00:00, 220MB/s]\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Epoch 1/10\n",
+            "----------\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/torch/optim/lr_scheduler.py:224: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "train Loss: 0.7472 Acc: 0.5738\n",
+            "val Loss: 0.3010 Acc: 0.8562\n",
+            "\n",
+            "Epoch 2/10\n",
+            "----------\n",
+            "train Loss: 0.4474 Acc: 0.7910\n",
+            "val Loss: 0.1926 Acc: 0.9542\n",
+            "\n",
+            "Epoch 3/10\n",
+            "----------\n",
+            "train Loss: 0.4452 Acc: 0.8033\n",
+            "val Loss: 0.2513 Acc: 0.9020\n",
+            "\n",
+            "Epoch 4/10\n",
+            "----------\n",
+            "train Loss: 0.3650 Acc: 0.8279\n",
+            "val Loss: 0.1967 Acc: 0.9542\n",
+            "\n",
+            "Epoch 5/10\n",
+            "----------\n",
+            "train Loss: 0.3299 Acc: 0.8607\n",
+            "val Loss: 0.1995 Acc: 0.9412\n",
+            "\n",
+            "Epoch 6/10\n",
+            "----------\n",
+            "train Loss: 0.4849 Acc: 0.7910\n",
+            "val Loss: 0.2128 Acc: 0.9346\n",
+            "\n",
+            "Epoch 7/10\n",
+            "----------\n",
+            "train Loss: 0.3027 Acc: 0.8689\n",
+            "val Loss: 0.2118 Acc: 0.9281\n",
+            "\n",
+            "Epoch 8/10\n",
+            "----------\n",
+            "train Loss: 0.3632 Acc: 0.8361\n",
+            "val Loss: 0.2298 Acc: 0.9281\n",
+            "\n",
+            "Epoch 9/10\n",
+            "----------\n",
+            "train Loss: 0.4297 Acc: 0.7992\n",
+            "val Loss: 0.2540 Acc: 0.9150\n",
+            "\n",
+            "Epoch 10/10\n",
+            "----------\n",
+            "train Loss: 0.3748 Acc: 0.8607\n",
+            "val Loss: 0.2135 Acc: 0.9346\n",
+            "\n",
+            "Training complete in 2m 14s\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",
+        "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(\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 = \"/content/drive/MyDrive/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",
+        "\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",
+        "# 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",
+        ")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "sN7V8_EAn0OO",
+      "metadata": {
+        "id": "sN7V8_EAn0OO"
+      },
+      "source": [
+        "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",
+      "source": [
+        "We first need to load a new dataset and split it into a train test and validation set.  We dataset found on Kaggle containing a train folder and a validation folder.\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "XTs_3UyqU0EB"
+      },
+      "id": "XTs_3UyqU0EB"
+    },
+    {
+      "cell_type": "code",
+      "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.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",
+        "    \"ant_bees\": 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",
+        "}\n",
+        "\n",
+        "data_dir = \"/content/drive/MyDrive\"\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 [\"ant_bees\"]\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 [\"ant_bees\"]\n",
+        "}\n",
+        "dataset_sizes = {x: len(image_datasets[x]) for x in [\"ant_bees\"]}\n",
+        "class_names = image_datasets[\"ant_bees\"].classes\n",
+        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "tA0TM1zno5cY",
+        "outputId": "320fe225-269e-4d3f-ff11-01d531cca3ad"
+      },
+      "id": "tA0TM1zno5cY",
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:617: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
+            "  warnings.warn(\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "id": "PnI7tSTqXRZ8",
+      "metadata": {
+        "id": "PnI7tSTqXRZ8",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "bd43177b-dd27-4982-9bc3-4e0555093d99"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Validation Loss: 0.1706 Accuracy: 0.9303\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(0.17056238605472884, tensor(0.9303, device='cuda:0', dtype=torch.float64))"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 13
+        }
+      ],
+      "source": [
+        "# Data augmentation and normalization for training\n",
+        "# Just normalization for validation\n",
+        "\n",
+        "def eval_model(model, dataloader, criterion):\n",
+        "    model.eval()\n",
+        "\n",
+        "    running_loss = 0.0\n",
+        "    running_corrects = 0\n",
+        "\n",
+        "    for inputs, labels in dataloaders[\"ant_bees\"]:\n",
+        "        inputs = inputs.to(device)\n",
+        "        labels = labels.to(device)\n",
+        "\n",
+        "        outputs = model(inputs)\n",
+        "        _, preds = torch.max(outputs, 1)\n",
+        "        loss = criterion(outputs, labels)\n",
+        "\n",
+        "        running_loss += loss.item() * inputs.size(0)\n",
+        "        running_corrects += torch.sum(preds == labels.data)\n",
+        "\n",
+        "    total_loss = running_loss / dataset_sizes[\"ant_bees\"]\n",
+        "    total_accuracy = running_corrects.double() / dataset_sizes[\"ant_bees\"]\n",
+        "\n",
+        "    print(\"Validation Loss: {:.4f} Accuracy: {:.4f}\".format(total_loss, total_accuracy))\n",
+        "    return total_loss, total_accuracy\n",
+        "\n",
+        "eval_model(model, dataloaders, criterion)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "04a263f0",
+      "metadata": {
+        "id": "04a263f0"
+      },
+      "source": [
+        "## Optional\n",
+        "    \n",
+        "Try this at home!!\n",
+        "\n",
+        "\n",
+        "Pytorch offers a framework to export a given CNN to your selfphone (either android or iOS). Have a look at the tutorial https://pytorch.org/mobile/home/\n",
+        "\n",
+        "The Exercise consists in deploying the CNN of Exercise 4 in your phone and then test it on live.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "id": "fe954ce4",
+      "metadata": {
+        "id": "fe954ce4"
+      },
+      "source": [
+        "## Author\n",
+        "\n",
+        "Alberto BOSIO - Ph. D."
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "gpuType": "T4",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.8.5"
+    },
+    "vscode": {
+      "interpreter": {
+        "hash": "9e3efbebb05da2d4a1968abe9a0645745f54b63feb7a85a514e4da0495be97eb"
+      }
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}
\ No newline at end of file