diff --git a/TD2 Deep Learning.ipynb b/TD2 Deep Learning.ipynb
index 2ecfce959ae6b947b633a758433f9bea0bf6992e..fb24cd52d5535cbef46f6f015fa2ed1161404214 100644
--- a/TD2 Deep Learning.ipynb	
+++ b/TD2 Deep Learning.ipynb	
@@ -1,953 +1 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "7edf7168",
-   "metadata": {},
-   "source": [
-    "# TD2: Deep learning"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fbb8c8df",
-   "metadata": {},
-   "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 Sunday, December 1, 11:59 PM. Later commits will not be taken into account."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3d167a29",
-   "metadata": {},
-   "source": [
-    "Install and test PyTorch from  https://pytorch.org/get-started/locally."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "330a42f5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install torch torchvision"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0882a636",
-   "metadata": {},
-   "source": [
-    "\n",
-    "To test run the following code"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b1950f0a",
-   "metadata": {},
-   "outputs": [],
-   "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": {},
-   "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": {},
-   "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": null,
-   "id": "6e18f2fd",
-   "metadata": {},
-   "outputs": [],
-   "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": {},
-   "source": [
-    "Next we load the CIFAR10 dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "462666a2",
-   "metadata": {},
-   "outputs": [],
-   "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": {},
-   "source": [
-    "CNN definition (this one is an example)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "317bf070",
-   "metadata": {},
-   "outputs": [],
-   "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": {},
-   "source": [
-    "Loss function and training using SGD (Stochastic Gradient Descent) optimizer"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4b53f229",
-   "metadata": {},
-   "outputs": [],
-   "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(), \"model_cifar.pt\")\n",
-    "        valid_loss_min = valid_loss"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "13e1df74",
-   "metadata": {},
-   "source": [
-    "Does overfit occur? If so, do an early stopping."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d39df818",
-   "metadata": {},
-   "outputs": [],
-   "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": {},
-   "source": [
-    "Now loading the model with the lowest validation loss value\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e93efdfc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model.load_state_dict(torch.load(\"./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": {},
-   "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": "markdown",
-   "id": "bc381cf4",
-   "metadata": {},
-   "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": null,
-   "id": "ef623c26",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "\n",
-    "\n",
-    "def print_size_of_model(model, label=\"\"):\n",
-    "    torch.save(model.state_dict(), \"temp.p\")\n",
-    "    size = os.path.getsize(\"temp.p\")\n",
-    "    print(\"model: \", label, \" \\t\", \"Size (KB):\", size / 1e3)\n",
-    "    os.remove(\"temp.p\")\n",
-    "    return size\n",
-    "\n",
-    "\n",
-    "print_size_of_model(model, \"fp32\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "05c4e9ad",
-   "metadata": {},
-   "source": [
-    "Post training quantization example"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c4c65d4b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch.quantization\n",
-    "\n",
-    "\n",
-    "quantized_model = torch.quantization.quantize_dynamic(model, dtype=torch.qint8)\n",
-    "print_size_of_model(quantized_model, \"int8\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7b108e17",
-   "metadata": {},
-   "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": "markdown",
-   "id": "a0a34b90",
-   "metadata": {},
-   "source": [
-    "Try training aware quantization to mitigate the impact on the accuracy (doc available here https://pytorch.org/docs/stable/quantization.html#torch.quantization.quantize_dynamic)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "201470f9",
-   "metadata": {},
-   "source": [
-    "## Exercise 3: working with pre-trained models.\n",
-    "\n",
-    "PyTorch offers several pre-trained models https://pytorch.org/vision/0.8/models.html        \n",
-    "We will use ResNet50 trained on ImageNet dataset (https://www.image-net.org/index.php). Use the following code with the files `imagenet-simple-labels.json` that contains the imagenet labels and the image dog.png that we will use as test.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b4d13080",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import json\n",
-    "from PIL import Image\n",
-    "\n",
-    "# Choose an image to pass through the model\n",
-    "test_image = \"dog.png\"\n",
-    "\n",
-    "# Configure matplotlib for pretty inline plots\n",
-    "#%matplotlib inline\n",
-    "#%config InlineBackend.figure_format = 'retina'\n",
-    "\n",
-    "# Prepare the labels\n",
-    "with open(\"imagenet-simple-labels.json\") as f:\n",
-    "    labels = json.load(f)\n",
-    "\n",
-    "# First prepare the transformations: resize the image to what the model was trained on and convert it to a tensor\n",
-    "data_transform = transforms.Compose(\n",
-    "    [\n",
-    "        transforms.Resize((224, 224)),\n",
-    "        transforms.ToTensor(),\n",
-    "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n",
-    "    ]\n",
-    ")\n",
-    "# Load the image\n",
-    "\n",
-    "image = Image.open(test_image)\n",
-    "plt.imshow(image), plt.xticks([]), plt.yticks([])\n",
-    "\n",
-    "# Now apply the transformation, expand the batch dimension, and send the image to the GPU\n",
-    "# image = data_transform(image).unsqueeze(0).cuda()\n",
-    "image = data_transform(image).unsqueeze(0)\n",
-    "\n",
-    "# Download the model if it's not there already. It will take a bit on the first run, after that it's fast\n",
-    "model = models.resnet50(pretrained=True)\n",
-    "# Send the model to the GPU\n",
-    "# model.cuda()\n",
-    "# Set layers such as dropout and batchnorm in evaluation mode\n",
-    "model.eval()\n",
-    "\n",
-    "# Get the 1000-dimensional model output\n",
-    "out = model(image)\n",
-    "# Find the predicted class\n",
-    "print(\"Predicted class is: {}\".format(labels[out.argmax()]))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "184cfceb",
-   "metadata": {},
-   "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": "markdown",
-   "id": "5d57da4b",
-   "metadata": {},
-   "source": [
-    "## Exercise 4: Transfer Learning\n",
-    "    \n",
-    "    \n",
-    "For this work, we will use a pre-trained model (ResNet18) as a descriptor extractor and will refine the classification by training only the last fully connected layer of the network. Thus, the output layer of the pre-trained network will be replaced by a layer adapted to the new classes to be recognized which will be in our case ants and bees.\n",
-    "Download and unzip in your working directory the dataset available at the address :\n",
-    "    \n",
-    "https://download.pytorch.org/tutorial/hymenoptera_data.zip\n",
-    "    \n",
-    "Execute the following code in order to display some images of the dataset."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "be2d31f5",
-   "metadata": {},
-   "outputs": [],
-   "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 = \"hymenoptera_data\"\n",
-    "# Create train and validation datasets and loaders\n",
-    "image_datasets = {\n",
-    "    x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x])\n",
-    "    for x in [\"train\", \"val\"]\n",
-    "}\n",
-    "dataloaders = {\n",
-    "    x: torch.utils.data.DataLoader(\n",
-    "        image_datasets[x], batch_size=4, shuffle=True, num_workers=0\n",
-    "    )\n",
-    "    for x in [\"train\", \"val\"]\n",
-    "}\n",
-    "dataset_sizes = {x: len(image_datasets[x]) for x in [\"train\", \"val\"]}\n",
-    "class_names = image_datasets[\"train\"].classes\n",
-    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
-    "\n",
-    "# Helper function for displaying images\n",
-    "def imshow(inp, title=None):\n",
-    "    \"\"\"Imshow for Tensor.\"\"\"\n",
-    "    inp = inp.numpy().transpose((1, 2, 0))\n",
-    "    mean = np.array([0.485, 0.456, 0.406])\n",
-    "    std = np.array([0.229, 0.224, 0.225])\n",
-    "\n",
-    "    # Un-normalize the images\n",
-    "    inp = std * inp + mean\n",
-    "    # Clip just in case\n",
-    "    inp = np.clip(inp, 0, 1)\n",
-    "    plt.imshow(inp)\n",
-    "    if title is not None:\n",
-    "        plt.title(title)\n",
-    "    plt.pause(0.001)  # pause a bit so that plots are updated\n",
-    "    plt.show()\n",
-    "\n",
-    "\n",
-    "# Get a batch of training data\n",
-    "inputs, classes = next(iter(dataloaders[\"train\"]))\n",
-    "\n",
-    "# Make a grid from batch\n",
-    "out = torchvision.utils.make_grid(inputs)\n",
-    "\n",
-    "imshow(out, title=[class_names[x] for x in classes])\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bbd48800",
-   "metadata": {},
-   "source": [
-    "Now, execute the following code which uses a pre-trained model ResNet18 having replaced the output layer for the ants/bees classification and performs the model training by only changing the weights of this output layer."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "572d824c",
-   "metadata": {},
-   "outputs": [],
-   "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 = \"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": "bbd48800",
-   "metadata": {},
-   "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",
-   "id": "04a263f0",
-   "metadata": {},
-   "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": {},
-   "source": [
-    "## Author\n",
-    "\n",
-    "Alberto BOSIO - Ph. D."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3.8.5 ('base')",
-   "language": "python",
-   "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
-}
+{"cells":[{"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 Sunday, December 1, 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":"markdown","id":"7edf7168","metadata":{"id":"7edf7168"},"source":["# TD2: Deep learning"]},{"cell_type":"code","execution_count":52,"id":"330a42f5","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"330a42f5","executionInfo":{"status":"error","timestamp":1701269008471,"user_tz":-60,"elapsed":5144,"user":{"displayName":"Mathis Odt","userId":"06586499252536361736"}},"outputId":"dcc4fa02-5623-4f54-a522-30f292347319"},"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.1.0+cu118)\n","Requirement already satisfied: torchvision in /usr/local/lib/python3.10/dist-packages (0.16.0+cu118)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch) (3.13.1)\n","Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch) (4.5.0)\n","Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch) (1.12)\n","Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch) (3.2.1)\n","Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch) (3.1.2)\n","Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch) (2023.6.0)\n","Requirement already satisfied: triton==2.1.0 in /usr/local/lib/python3.10/dist-packages (from torch) (2.1.0)\n","Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from torchvision) (1.23.5)\n","Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from torchvision) (2.31.0)\n","Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /usr/local/lib/python3.10/dist-packages (from torchvision) (9.4.0)\n","Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch) (2.1.3)\n","Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision) (3.3.2)\n","Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision) (3.4)\n","Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision) (2.0.7)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->torchvision) (2023.7.22)\n","Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch) (1.3.0)\n"]},{"output_type":"stream","name":"stderr","text":["UsageError: Line magic function `%wget` not found.\n"]}],"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":3,"id":"b1950f0a","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"b1950f0a","executionInfo":{"status":"ok","timestamp":1701263807541,"user_tz":-60,"elapsed":1764,"user":{"displayName":"Mathis Odt","userId":"06586499252536361736"}},"outputId":"438e92af-9461-45dc-c8ba-baf4f39df465"},"outputs":[{"output_type":"stream","name":"stdout","text":["tensor([[ 0.8001, -3.1996,  0.8401, -0.4590,  0.0535,  1.3531,  0.6940, -0.5002,\n","         -2.4893, -0.2943],\n","        [-1.4480,  0.6830, -0.0291, -0.8080,  0.6988,  0.0612, -0.7034,  0.5975,\n","         -0.2097,  0.0544],\n","        [-0.5039,  0.3342, -0.5135,  0.5781, -0.2265,  0.1315,  1.6636, -0.1691,\n","         -0.0637,  0.4066],\n","        [ 1.3856,  1.4038,  0.5262, -0.3644, -1.2894,  0.7763,  0.3176, -0.5977,\n","         -0.8109, -0.2260],\n","        [-0.9714,  1.4755,  0.4159,  0.5655, -1.2068,  0.1483,  0.4998,  0.7127,\n","         -0.3208, -0.1878],\n","        [ 1.1300,  0.1293, -2.0233,  0.2644, -1.6500,  0.0594, -1.6955,  0.9623,\n","         -2.0099,  1.4013],\n","        [ 0.1372,  0.5833, -0.2481,  0.5644, -1.0033,  0.4947, -0.4332, -0.6983,\n","          0.2427,  1.1333],\n","        [ 0.5237, -0.4540,  0.3905, -1.3676,  0.1535, -0.8654,  1.1654, -0.3680,\n","          0.5602,  0.5605],\n","        [ 0.7205,  1.1636, -0.5012,  1.2403,  0.3021, -0.6127, -0.9504,  1.1685,\n","          0.0837, -0.5870],\n","        [ 0.1246, -1.0345, -0.2654, -0.4910, -0.0198, -0.2514, -0.0920, -0.6426,\n","          1.0792,  0.5414],\n","        [-0.0181,  0.5058,  0.5459,  0.4973,  0.3238, -0.8191,  1.1362,  0.5654,\n","         -1.7322, -0.6207],\n","        [-1.0556,  2.2030,  0.2627,  1.0543, -0.2510, -0.0635, -2.5471,  1.0420,\n","          1.0652,  0.3995],\n","        [ 0.8785, -0.0858,  0.3532, -0.0389,  1.1755, -1.7593,  0.5965,  0.0882,\n","          1.1826,  0.7950],\n","        [-1.6628, -0.1029, -0.0121, -1.1714,  0.3778,  1.4698, -0.0620,  0.2037,\n","         -0.8209, -0.5627]])\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":4,"id":"6e18f2fd","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"6e18f2fd","executionInfo":{"status":"ok","timestamp":1701263818107,"user_tz":-60,"elapsed":322,"user":{"displayName":"Mathis Odt","userId":"06586499252536361736"}},"outputId":"500c08da-ef25-4e2b-d7d2-c2f0dbd5c8cc"},"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":6,"id":"462666a2","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"462666a2","executionInfo":{"status":"ok","timestamp":1701263841822,"user_tz":-60,"elapsed":1805,"user":{"displayName":"Mathis Odt","userId":"06586499252536361736"}},"outputId":"2fe87fb5-7604-4935-9f4f-eecd8b3fe6b0"},"outputs":[{"output_type":"stream","name":"stdout","text":["Files already downloaded and verified\n","Files already downloaded and verified\n"]}],"source":["import numpy as np\n","from torchvision import datasets, transforms\n","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":7,"id":"317bf070","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"317bf070","executionInfo":{"status":"ok","timestamp":1701263851707,"user_tz":-60,"elapsed":6668,"user":{"displayName":"Mathis Odt","userId":"06586499252536361736"}},"outputId":"cecb36bc-27dd-4aae-ebc5-009a122e169b"},"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":8,"id":"4b53f229","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"4b53f229","executionInfo":{"status":"ok","timestamp":1701264436194,"user_tz":-60,"elapsed":569994,"user":{"displayName":"Mathis Odt","userId":"06586499252536361736"}},"outputId":"42ece0d7-d233-4f08-9935-40d2fcef166e"},"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch: 0 \tTraining Loss: 44.612249 \tValidation Loss: 40.298942\n","Validation loss decreased (inf --> 40.298942).  Saving model ...\n","Epoch: 1 \tTraining Loss: 36.004778 \tValidation Loss: 33.401573\n","Validation loss decreased (40.298942 --> 33.401573).  Saving model ...\n","Epoch: 2 \tTraining Loss: 30.990529 \tValidation Loss: 29.245610\n","Validation loss decreased (33.401573 --> 29.245610).  Saving model ...\n","Epoch: 3 \tTraining Loss: 28.325317 \tValidation Loss: 26.954483\n","Validation loss decreased (29.245610 --> 26.954483).  Saving model ...\n","Epoch: 4 \tTraining Loss: 26.341247 \tValidation Loss: 26.349700\n","Validation loss decreased (26.954483 --> 26.349700).  Saving model ...\n","Epoch: 5 \tTraining Loss: 24.861439 \tValidation Loss: 24.664094\n","Validation loss decreased (26.349700 --> 24.664094).  Saving model ...\n","Epoch: 6 \tTraining Loss: 23.654918 \tValidation Loss: 23.904583\n","Validation loss decreased (24.664094 --> 23.904583).  Saving model ...\n","Epoch: 7 \tTraining Loss: 22.659880 \tValidation Loss: 24.153002\n","Epoch: 8 \tTraining Loss: 21.813652 \tValidation Loss: 22.728200\n","Validation loss decreased (23.904583 --> 22.728200).  Saving model ...\n","Epoch: 9 \tTraining Loss: 21.028281 \tValidation Loss: 22.683762\n","Validation loss decreased (22.728200 --> 22.683762).  Saving model ...\n","Epoch: 10 \tTraining Loss: 20.283682 \tValidation Loss: 22.527626\n","Validation loss decreased (22.683762 --> 22.527626).  Saving model ...\n","Epoch: 11 \tTraining Loss: 19.596292 \tValidation Loss: 22.082355\n","Validation loss decreased (22.527626 --> 22.082355).  Saving model ...\n","Epoch: 12 \tTraining Loss: 18.990277 \tValidation Loss: 22.173975\n","Epoch: 13 \tTraining Loss: 18.311255 \tValidation Loss: 21.511513\n","Validation loss decreased (22.082355 --> 21.511513).  Saving model ...\n","Epoch: 14 \tTraining Loss: 17.729348 \tValidation Loss: 21.373887\n","Validation loss decreased (21.511513 --> 21.373887).  Saving model ...\n","Epoch: 15 \tTraining Loss: 17.143107 \tValidation Loss: 21.404075\n","Epoch: 16 \tTraining Loss: 16.578313 \tValidation Loss: 22.213146\n","Epoch: 17 \tTraining Loss: 16.067654 \tValidation Loss: 21.753317\n","Epoch: 18 \tTraining Loss: 15.572635 \tValidation Loss: 23.228977\n","Epoch: 19 \tTraining Loss: 15.036218 \tValidation Loss: 22.608370\n","Epoch: 20 \tTraining Loss: 14.528461 \tValidation Loss: 22.057556\n","Epoch: 21 \tTraining Loss: 13.953359 \tValidation Loss: 23.037234\n","Epoch: 22 \tTraining Loss: 13.521695 \tValidation Loss: 23.248760\n","Epoch: 23 \tTraining Loss: 13.053585 \tValidation Loss: 23.488736\n","Epoch: 24 \tTraining Loss: 12.579523 \tValidation Loss: 23.827478\n","Epoch: 25 \tTraining Loss: 12.141763 \tValidation Loss: 24.365644\n","Epoch: 26 \tTraining Loss: 11.630654 \tValidation Loss: 24.792256\n","Epoch: 27 \tTraining Loss: 11.330323 \tValidation Loss: 25.310450\n","Epoch: 28 \tTraining Loss: 10.781678 \tValidation Loss: 25.629191\n","Epoch: 29 \tTraining Loss: 10.492249 \tValidation Loss: 26.488761\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(), \"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":9,"id":"d39df818","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":472},"id":"d39df818","executionInfo":{"status":"ok","timestamp":1701264448133,"user_tz":-60,"elapsed":525,"user":{"displayName":"Mathis Odt","userId":"06586499252536361736"}},"outputId":"d0c485cd-28cb-41c0-da4f-777701671915"},"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":10,"id":"e93efdfc","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"e93efdfc","executionInfo":{"status":"ok","timestamp":1701264460982,"user_tz":-60,"elapsed":3907,"user":{"displayName":"Mathis Odt","userId":"06586499252536361736"}},"outputId":"75f4d1f4-3dce-4323-8d8c-2112a97a81ed"},"outputs":[{"output_type":"stream","name":"stdout","text":["Test Loss: 21.447881\n","\n","Test Accuracy of airplane: 69% (699/1000)\n","Test Accuracy of automobile: 77% (776/1000)\n","Test Accuracy of  bird: 51% (511/1000)\n","Test Accuracy of   cat: 46% (460/1000)\n","Test Accuracy of  deer: 46% (460/1000)\n","Test Accuracy of   dog: 45% (459/1000)\n","Test Accuracy of  frog: 77% (774/1000)\n","Test Accuracy of horse: 66% (663/1000)\n","Test Accuracy of  ship: 79% (792/1000)\n","Test Accuracy of truck: 69% (699/1000)\n","\n","Test Accuracy (Overall): 62% (6293/10000)\n"]}],"source":["model.load_state_dict(torch.load(\"./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","source":["# define the new CNN architecture\n","\n","import torch.nn as nn\n","import torch.nn.functional as F\n","\n","class Net_new(nn.Module):\n","    def __init__(self):\n","        super(Net_new, self).__init__()\n","        self.conv1 = nn.Conv2d(3, 16, 3, padding=1) # Padding to prevent the output's dimension from changing\n","        self.conv2 = nn.Conv2d(16, 32, 3, padding=1)\n","        self.conv3 = nn.Conv2d(32, 64, 3, padding=1)\n","        self.pool = nn.MaxPool2d(2, 2)\n","        self.fc1 = nn.Linear(64 * 4 * 4, 512) # Input size = nb of channels output on the last layer * pixel size of image (each MaxPool split by two)\n","        self.fc2 = nn.Linear(512, 64)\n","        self.fc3 = nn.Linear(64, 10)\n","        self.dropout = nn.Dropout()\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 = self.dropout(x) # Helpful in preventing neuron co-adaptation\n","        x = F.relu(self.fc2(x))\n","        x = self.dropout(x)\n","        x = F.relu(self.fc3(x))\n","        return x\n","\n","# create a complete CNN\n","model_new = Net_new()\n","print(model_new)\n","# move tensors to GPU if CUDA is available\n","if train_on_gpu:\n","  model_new.cuda()"],"metadata":{"id":"gcRCs-iUEnaH","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1701264472424,"user_tz":-60,"elapsed":4,"user":{"displayName":"Mathis Odt","userId":"06586499252536361736"}},"outputId":"761a457c-f623-455a-97a3-5bc7bea1a48b"},"id":"gcRCs-iUEnaH","execution_count":11,"outputs":[{"output_type":"stream","name":"stdout","text":["Net_new(\n","  (conv1): Conv2d(3, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","  (conv2): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","  (conv3): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","  (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n","  (fc1): Linear(in_features=1024, out_features=512, bias=True)\n","  (fc2): Linear(in_features=512, out_features=64, bias=True)\n","  (fc3): Linear(in_features=64, out_features=10, bias=True)\n","  (dropout): Dropout(p=0.5, inplace=False)\n",")\n"]}]},{"cell_type":"code","source":["import torch.optim as optim\n","\n","criterion = nn.CrossEntropyLoss()  # specify loss function\n","optimizer_new = optim.SGD(model_new.parameters(), lr=0.01)  # specify optimizer\n","\n","n_epochs = 30  # number of epochs to train the model\n","train_loss_list_new = []  # 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_new.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_new.zero_grad()\n","        # Forward pass: compute predicted outputs by passing inputs to the model\n","        output = model_new(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_new.step()\n","        # Update training loss\n","        train_loss += loss.item() * data.size(0)\n","\n","    # Validate the model\n","    model_new.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_new(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_new.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_new.state_dict(), \"model_new_cifar.pt\")\n","        valid_loss_min = valid_loss"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"1mux8ZZi2vd7","executionInfo":{"status":"ok","timestamp":1701265066443,"user_tz":-60,"elapsed":582783,"user":{"displayName":"Mathis Odt","userId":"06586499252536361736"}},"outputId":"b2bf2851-3aff-48fe-aa83-9c7c0c6124f7"},"id":"1mux8ZZi2vd7","execution_count":12,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch: 0 \tTraining Loss: 46.035736 \tValidation Loss: 45.976020\n","Validation loss decreased (inf --> 45.976020).  Saving model ...\n","Epoch: 1 \tTraining Loss: 45.187472 \tValidation Loss: 42.525370\n","Validation loss decreased (45.976020 --> 42.525370).  Saving model ...\n","Epoch: 2 \tTraining Loss: 40.269012 \tValidation Loss: 36.115885\n","Validation loss decreased (42.525370 --> 36.115885).  Saving model ...\n","Epoch: 3 \tTraining Loss: 35.383565 \tValidation Loss: 31.909517\n","Validation loss decreased (36.115885 --> 31.909517).  Saving model ...\n","Epoch: 4 \tTraining Loss: 32.746224 \tValidation Loss: 29.787075\n","Validation loss decreased (31.909517 --> 29.787075).  Saving model ...\n","Epoch: 5 \tTraining Loss: 30.653619 \tValidation Loss: 27.959262\n","Validation loss decreased (29.787075 --> 27.959262).  Saving model ...\n","Epoch: 6 \tTraining Loss: 28.983543 \tValidation Loss: 26.431537\n","Validation loss decreased (27.959262 --> 26.431537).  Saving model ...\n","Epoch: 7 \tTraining Loss: 27.683504 \tValidation Loss: 25.174931\n","Validation loss decreased (26.431537 --> 25.174931).  Saving model ...\n","Epoch: 8 \tTraining Loss: 26.336337 \tValidation Loss: 23.783314\n","Validation loss decreased (25.174931 --> 23.783314).  Saving model ...\n","Epoch: 9 \tTraining Loss: 24.991212 \tValidation Loss: 22.687754\n","Validation loss decreased (23.783314 --> 22.687754).  Saving model ...\n","Epoch: 10 \tTraining Loss: 23.787577 \tValidation Loss: 22.145078\n","Validation loss decreased (22.687754 --> 22.145078).  Saving model ...\n","Epoch: 11 \tTraining Loss: 22.818656 \tValidation Loss: 20.805044\n","Validation loss decreased (22.145078 --> 20.805044).  Saving model ...\n","Epoch: 12 \tTraining Loss: 21.811931 \tValidation Loss: 19.928644\n","Validation loss decreased (20.805044 --> 19.928644).  Saving model ...\n","Epoch: 13 \tTraining Loss: 20.853573 \tValidation Loss: 19.503793\n","Validation loss decreased (19.928644 --> 19.503793).  Saving model ...\n","Epoch: 14 \tTraining Loss: 20.078903 \tValidation Loss: 18.726372\n","Validation loss decreased (19.503793 --> 18.726372).  Saving model ...\n","Epoch: 15 \tTraining Loss: 19.173792 \tValidation Loss: 18.262609\n","Validation loss decreased (18.726372 --> 18.262609).  Saving model ...\n","Epoch: 16 \tTraining Loss: 18.500586 \tValidation Loss: 18.070592\n","Validation loss decreased (18.262609 --> 18.070592).  Saving model ...\n","Epoch: 17 \tTraining Loss: 17.639666 \tValidation Loss: 17.679802\n","Validation loss decreased (18.070592 --> 17.679802).  Saving model ...\n","Epoch: 18 \tTraining Loss: 17.082578 \tValidation Loss: 17.131204\n","Validation loss decreased (17.679802 --> 17.131204).  Saving model ...\n","Epoch: 19 \tTraining Loss: 16.418561 \tValidation Loss: 16.789966\n","Validation loss decreased (17.131204 --> 16.789966).  Saving model ...\n","Epoch: 20 \tTraining Loss: 15.737011 \tValidation Loss: 16.914572\n","Epoch: 21 \tTraining Loss: 15.217627 \tValidation Loss: 17.321893\n","Epoch: 22 \tTraining Loss: 14.692679 \tValidation Loss: 16.259236\n","Validation loss decreased (16.789966 --> 16.259236).  Saving model ...\n","Epoch: 23 \tTraining Loss: 14.104487 \tValidation Loss: 15.681182\n","Validation loss decreased (16.259236 --> 15.681182).  Saving model ...\n","Epoch: 24 \tTraining Loss: 13.509841 \tValidation Loss: 16.067594\n","Epoch: 25 \tTraining Loss: 13.031704 \tValidation Loss: 15.928080\n","Epoch: 26 \tTraining Loss: 12.543566 \tValidation Loss: 16.412866\n","Epoch: 27 \tTraining Loss: 12.077648 \tValidation Loss: 16.044644\n","Epoch: 28 \tTraining Loss: 11.713458 \tValidation Loss: 15.721017\n","Epoch: 29 \tTraining Loss: 11.205782 \tValidation Loss: 16.062376\n"]}]},{"cell_type":"code","source":["import matplotlib.pyplot as plt\n","\n","plt.plot(range(n_epochs), train_loss_list_new)\n","plt.xlabel(\"Epoch\")\n","plt.ylabel(\"Loss\")\n","plt.title(\"Performance of the 3-layer Model\")\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":472},"id":"hEdjk_jV4mEm","executionInfo":{"status":"ok","timestamp":1701265110327,"user_tz":-60,"elapsed":342,"user":{"displayName":"Mathis Odt","userId":"06586499252536361736"}},"outputId":"b713f1d6-4d88-42bc-f71c-7aba797301e5"},"id":"hEdjk_jV4mEm","execution_count":13,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 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\n"},"metadata":{}}]},{"cell_type":"code","source":["model_new.load_state_dict(torch.load(\"./model_new_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_new.eval()\n","# iterate over test data\n","for data, target in test_loader:\n","    # move tensors to GPU if CUDA is available\n","    if train_on_gpu:\n","        data, target = data.cuda(), target.cuda()\n","    # forward pass: compute predicted outputs by passing inputs to the model\n","    output = model_new(data)\n","    # calculate the batch loss\n","    loss = criterion(output, target)\n","    # update test loss\n","    test_loss += loss.item() * data.size(0)\n","    # convert output probabilities to predicted class\n","    _, pred = torch.max(output, 1)\n","    # compare predictions to true label\n","    correct_tensor = pred.eq(target.data.view_as(pred))\n","    correct = (\n","        np.squeeze(correct_tensor.numpy())\n","        if not train_on_gpu\n","        else np.squeeze(correct_tensor.cpu().numpy())\n","    )\n","    # calculate test accuracy for each object class\n","    for i in range(batch_size):\n","        label = target.data[i]\n","        class_correct[label] += correct[i].item()\n","        class_total[label] += 1\n","\n","# average test loss\n","test_loss = test_loss / len(test_loader)\n","print(\"Test Loss: {:.6f}\\n\".format(test_loss))\n","\n","for i in range(10):\n","    if class_total[i] > 0:\n","        print(\n","            \"Test Accuracy of %5s: %2d%% (%2d/%2d)\"\n","            % (\n","                classes[i],\n","                100 * class_correct[i] / class_total[i],\n","                np.sum(class_correct[i]),\n","                np.sum(class_total[i]),\n","            )\n","        )\n","    else:\n","        print(\"Test Accuracy of %5s: N/A (no training examples)\" % (classes[i]))\n","\n","print(\n","    \"\\nTest Accuracy (Overall): %2d%% (%2d/%2d)\"\n","    % (\n","        100.0 * np.sum(class_correct) / np.sum(class_total),\n","        np.sum(class_correct),\n","        np.sum(class_total),\n","    )\n",")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9C9D34ZW43q7","executionInfo":{"status":"ok","timestamp":1701267205015,"user_tz":-60,"elapsed":4237,"user":{"displayName":"Mathis Odt","userId":"06586499252536361736"}},"outputId":"9373df39-8c49-4700-8601-50c4b3a27548"},"id":"9C9D34ZW43q7","execution_count":37,"outputs":[{"output_type":"stream","name":"stdout","text":["Test Loss: 15.725736\n","\n","Test Accuracy of airplane: 78% (784/1000)\n","Test Accuracy of automobile: 83% (838/1000)\n","Test Accuracy of  bird: 61% (615/1000)\n","Test Accuracy of   cat: 51% (513/1000)\n","Test Accuracy of  deer: 68% (680/1000)\n","Test Accuracy of   dog: 63% (635/1000)\n","Test Accuracy of  frog: 86% (860/1000)\n","Test Accuracy of horse: 76% (762/1000)\n","Test Accuracy of  ship: 84% (845/1000)\n","Test Accuracy of truck: 82% (821/1000)\n","\n","Test Accuracy (Overall): 73% (7353/10000)\n"]}]},{"cell_type":"markdown","source":["With our new model, we notice a substantial improvement in overall test accuracy.\n","\n","The result of the **original CNN** are :\n","\n","*   *Test loss* : 21.447881\n","*   *Test accuracy* : 62%\n","\n","The result of the our **new 3-layers CNN** are :\n","\n","*   *Test loss* : 15.725736\n","*   *Test accuracy* : 73%\n","\n","Despite the additional training period (~1min), the outcomes meet our expectations. Indeed, for each class, the accuracy is improved up to 10%."],"metadata":{"id":"uU_LD9l1mfvn"},"id":"uU_LD9l1mfvn"},{"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":34,"id":"ef623c26","metadata":{"id":"ef623c26","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1701267153576,"user_tz":-60,"elapsed":310,"user":{"displayName":"Mathis Odt","userId":"06586499252536361736"}},"outputId":"ea430148-14c1-4633-f766-4694c584b54b"},"outputs":[{"output_type":"stream","name":"stdout","text":["model:  fp32  \t Size (KB): 2331.074\n"]}],"source":["import os\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","size_model = print_size_of_model(model_new, \"fp32\")"]},{"cell_type":"markdown","id":"05c4e9ad","metadata":{"id":"05c4e9ad"},"source":["Post training quantization example"]},{"cell_type":"code","execution_count":35,"id":"c4c65d4b","metadata":{"id":"c4c65d4b","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1701267155486,"user_tz":-60,"elapsed":623,"user":{"displayName":"Mathis Odt","userId":"06586499252536361736"}},"outputId":"6cbb1ee3-36ae-4d73-cf55-7c1eca65a778"},"outputs":[{"output_type":"stream","name":"stdout","text":["model:  int8  \t Size (KB): 659.934\n","The size of the original model has been divided by 3.53 compared to the Quantized model\n"]}],"source":["import torch.quantization\n","\n","\n","quantized_model = torch.quantization.quantize_dynamic(model_new, {torch.nn.Linear}, dtype=torch.qint8)\n","torch.save(quantized_model.state_dict(), \"quantized_model_cifar.pt\")\n","\n","size_quantized = print_size_of_model(quantized_model, \"int8\")\n","\n","print(f\"The size of the original model has been divided by {size_model / size_quantized:.2f} compared to the Quantized model\")\n"]},{"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":"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)"]},{"cell_type":"code","source":["quantized_model.load_state_dict(torch.load(\"./quantized_model_cifar.pt\",map_location=torch.device('cpu')))\n","\n","# track test loss\n","test_loss_quantized = 0.0\n","class_correct_quantized = list(0.0 for i in range(10))\n","class_total_quantized = list(0.0 for i in range(10))\n","\n","quantized_model.eval()\n","quantized_model.cpu()\n","\n","# iterate over test data\n","for data, target in test_loader:\n","    # forward pass: compute predicted outputs by passing inputs to the model\n","    output = quantized_model(data)\n","    # calculate the batch loss\n","    loss = criterion(output, target)\n","    # update test loss\n","    test_loss_quantized += 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 = np.squeeze(correct_tensor.numpy()) #np.squeeze(correct_tensor.cpu().numpy()\n","    # calculate test accuracy for each object class\n","    for i in range(batch_size):\n","        label = target.data[i]\n","        class_correct_quantized[label] += correct[i].item()\n","        class_total_quantized[label] += 1\n","\n","# average test loss\n","test_loss_quantized = test_loss_quantized / len(test_loader)\n","loss_delta = test_loss_quantized - test_loss\n","print(\"Original Test Loss: {:.6f}\\n\".format(test_loss))\n","print(\"Quantized Test Loss: {:.6f}\\n\".format(test_loss_quantized))\n","print(\"Loss Delta: {:.6f}\\n\".format(loss_delta))\n","\n","for i in range(10):\n","    if class_total[i] > 0:\n","        print(\n","            \"Initial model 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","        print(\n","            \"Quantized model Test Accuracy of %5s: %2d%% (%2d/%2d)\"\n","            % (\n","                classes[i],\n","                100 * class_correct_quantized[i] / class_total_quantized[i],\n","                np.sum(class_correct_quantized[i]),\n","                np.sum(class_total_quantized[i]),\n","            ))\n","        print(\n","            \"Difference in Instances Correctly classified of %5s: %2d \\n\"\n","            % (\n","                classes[i],\n","                class_correct_quantized[i]-class_correct[i],\n","\n","            )\n","        )\n","    else:\n","        print(\"Test Accuracy of %5s: N/A (no training examples)\" % (classes[i]))\n","\n","print(\n","    \"\\nInitial model Test Accuracy (Overall): %2d%% (%2d/%2d)\"\n","    % (\n","        100.0 * np.sum(class_correct) / np.sum(class_total),\n","        np.sum(class_correct),\n","        np.sum(class_total),\n","    )\n",")\n","print(\n","    \"\\nQuantized model Test Accuracy (Overall): %2d%% (%2d/%2d)\"\n","    % (\n","        100.0 * np.sum(class_correct_quantized) / np.sum(class_total_quantized),\n","        np.sum(class_correct_quantized),\n","        np.sum(class_total_quantized),\n","    )\n",")\n","print(\n","         \"\\nDifference in Instances Correctly classified (Overall) : %2d \\n\"\n","        % (\n","            np.sum(class_correct)-np.sum(class_correct_quantized),\n","            )\n","        )"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"goj7b4vnAVvk","executionInfo":{"status":"ok","timestamp":1701267315881,"user_tz":-60,"elapsed":7778,"user":{"displayName":"Mathis Odt","userId":"06586499252536361736"}},"outputId":"9a3e9626-e644-4a43-f7f1-500aeb7bfea9"},"id":"goj7b4vnAVvk","execution_count":40,"outputs":[{"output_type":"stream","name":"stdout","text":["Original Test Loss: 15.725736\n","\n","Quantized Test Loss: 15.745716\n","\n","Loss Delta: 0.019980\n","\n","Initial model Test Accuracy of airplane: 78% (784/1000)\n","Quantized model Test Accuracy of airplane: 78% (786/1000)\n","Difference in Instances Correctly classified of airplane:  2 \n","\n","Initial model Test Accuracy of automobile: 83% (838/1000)\n","Quantized model Test Accuracy of automobile: 83% (837/1000)\n","Difference in Instances Correctly classified of automobile: -1 \n","\n","Initial model Test Accuracy of  bird: 61% (615/1000)\n","Quantized model Test Accuracy of  bird: 61% (611/1000)\n","Difference in Instances Correctly classified of  bird: -4 \n","\n","Initial model Test Accuracy of   cat: 51% (513/1000)\n","Quantized model Test Accuracy of   cat: 51% (512/1000)\n","Difference in Instances Correctly classified of   cat: -1 \n","\n","Initial model Test Accuracy of  deer: 68% (680/1000)\n","Quantized model Test Accuracy of  deer: 68% (681/1000)\n","Difference in Instances Correctly classified of  deer:  1 \n","\n","Initial model Test Accuracy of   dog: 63% (635/1000)\n","Quantized model Test Accuracy of   dog: 63% (636/1000)\n","Difference in Instances Correctly classified of   dog:  1 \n","\n","Initial model Test Accuracy of  frog: 86% (860/1000)\n","Quantized model Test Accuracy of  frog: 86% (861/1000)\n","Difference in Instances Correctly classified of  frog:  1 \n","\n","Initial model Test Accuracy of horse: 76% (762/1000)\n","Quantized model Test Accuracy of horse: 76% (764/1000)\n","Difference in Instances Correctly classified of horse:  2 \n","\n","Initial model Test Accuracy of  ship: 84% (845/1000)\n","Quantized model Test Accuracy of  ship: 84% (842/1000)\n","Difference in Instances Correctly classified of  ship: -3 \n","\n","Initial model Test Accuracy of truck: 82% (821/1000)\n","Quantized model Test Accuracy of truck: 82% (820/1000)\n","Difference in Instances Correctly classified of truck: -1 \n","\n","\n","Initial model Test Accuracy (Overall): 73% (7353/10000)\n","\n","Quantized model Test Accuracy (Overall): 73% (7350/10000)\n","\n","Difference in Instances Correctly classified (Overall) :  3 \n","\n"]}]},{"cell_type":"markdown","source":["The quantization of our model has minimal impact on classification accuracy, with the Test Loss and number of correctly classified instances remaining consistent across both models. This quantization is a beneficial method for saving space and memory, as the quantized file is 3.53 times smaller."],"metadata":{"id":"Eo8W3YvhnzVZ"},"id":"Eo8W3YvhnzVZ"},{"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":45,"id":"b4d13080","metadata":{"id":"b4d13080","colab":{"base_uri":"https://localhost:8080/","height":416},"executionInfo":{"status":"ok","timestamp":1701267795377,"user_tz":-60,"elapsed":2449,"user":{"displayName":"Mathis Odt","userId":"06586499252536361736"}},"outputId":"152af00a-3cb4-4faf-a3fb-36466c75d661"},"outputs":[{"output_type":"stream","name":"stdout","text":["Predicted class for resnet50 is: Alpine ibex\n","Predicted class for googlenet is: hartebeest\n","Predicted class for resnet_quantized is: Alpine ibex\n","Predicted class for googlenet_quantized is: hartebeest\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","from PIL import Image\n","\n","\n","# Choose an image to pass through the model\n","test_image_1 = \"dog.png\"\n","test_image_2 = \"ours.jpg\"\n","test_image_3 = \"cerf.jpg\"\n","\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_3)\n","plt.imshow(image), plt.xticks([]), plt.yticks([])\n","\n","# Now apply the transformation, expand the batch dimension, and send the image to the GPU\n","# image = data_transform(image).unsqueeze(0).cuda()\n","image = data_transform(image).unsqueeze(0)\n","\n","# Download the model if it's not there already. It will take a bit on the first run, after that it's fast\n","model = models.resnet50(pretrained=True)\n","googlenet_model = models.googlenet(pretrained=True)\n","resnet_quantized_model = torch.quantization.quantize_dynamic(model, dtype=torch.qint8)\n","googlenet_quantized_model = torch.quantization.quantize_dynamic(googlenet_model, dtype=torch.qint8)\n","\n","# Send the model to the GPU\n","# model.cuda()\n","# Set layers such as dropout and batchnorm in evaluation mode\n","model.eval()\n","googlenet_model.eval()\n","resnet_quantized_model.eval()\n","googlenet_quantized_model.eval()\n","\n","# Get the 1000-dimensional model output\n","out_1 = model(image)\n","out_2 = googlenet_model(image)\n","out_3 = resnet_quantized_model(image)\n","out_4 = googlenet_quantized_model(image)\n","\n","# Find the predicted class\n","print(\"Predicted class for resnet50 is: {}\".format(labels[out_1.argmax()]))\n","print(\"Predicted class for googlenet is: {}\".format(labels[out_2.argmax()]))\n","print(\"Predicted class for resnet_quantized is: {}\".format(labels[out_3.argmax()]))\n","print(\"Predicted class for googlenet_quantized is: {}\".format(labels[out_4.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":"markdown","source":["We tried to experiment the Resnet50 and GoogleNet with three different image. The results are satisfying for the two first image \"dog.png\", and \"ours.jpg\", but not for the \"cerf.jpg\". Indeed, the predicted class for resnet50 model : Alpine ibex and the predicted class for googlenet is: hartebeest."],"metadata":{"id":"neFEGq8SuQ98"},"id":"neFEGq8SuQ98"},{"cell_type":"code","source":["#sizes of the model\n","\n","print(\"Size of the 3-layers model :\")\n","size_model = print_size_of_model(model_new, \"fp32\")\n","print(\"Size of the 3-layers quantized model :\")\n","size_quantized = print_size_of_model(quantized_model, \"int8\")\n","print(\"The size of the model has been divided by %.2f compared to the Quantized model\" % (size_model / size_quantized))\n","\n","print(\"\\nSize of the Resnet model :\")\n","size_Resnet = print_size_of_model(model, \"fp32\")\n","print(\"Size of the Resnet quantized model :\")\n","size_Quantized_Resnet = print_size_of_model(resnet_quantized_model, \"fp32\")\n","print(\"The size of the original model has been divided by %.2f compared to the 3-layer Quantized model\" % (size_Quantized_Resnet / size_quantized))\n","\n","print(\"\\nSize of Googlenet model:\")\n","size_Googlenet = print_size_of_model(googlenet_model, \"fp32\")\n","print(\"Size of Googlenet quantized model:\")\n","size_Quantized_Googlenet = print_size_of_model(googlenet_quantized_model, \"fp32\")\n","print(\"The size of the original model has been divided by %.2f compared to the 3-layer Quantized model\" % (size_Quantized_Googlenet / size_quantized))\n"],"metadata":{"id":"fAyldGwPIv60","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1701268313794,"user_tz":-60,"elapsed":913,"user":{"displayName":"Mathis Odt","userId":"06586499252536361736"}},"outputId":"ac73bd2c-9af2-4652-9e47-981aad0ccc64"},"id":"fAyldGwPIv60","execution_count":49,"outputs":[{"output_type":"stream","name":"stdout","text":["Size of the 3-layers model :\n","model:  fp32  \t Size (KB): 2331.074\n","Size of the 3-layers quantized model :\n","model:  int8  \t Size (KB): 659.806\n","The size of the model has been divided by 3.53 compared to the Quantized model\n","\n","Size of the Resnet model :\n","model:  fp32  \t Size (KB): 102523.238\n","Size of the Resnet quantized model :\n","model:  fp32  \t Size (KB): 96379.996\n","The size of the original model has been divided by 146.07 compared to the 3-layer Quantized model\n","\n","Size of Googlenet model:\n","model:  fp32  \t Size (KB): 26654.254\n","Size of Googlenet quantized model:\n","model:  fp32  \t Size (KB): 23583.076\n","The size of the original model has been divided by 35.74 compared to the 3-layer Quantized model\n"]}]},{"cell_type":"markdown","source":["Even after quantization, the pretrained models are far larger than our own trained model."],"metadata":{"id":"2VjjWHXHwGl4"},"id":"2VjjWHXHwGl4"},{"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","source":["!wget https://download.pytorch.org/tutorial/hymenoptera_data.zip\n","!unzip hymenoptera_data.zip"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"oVfFXO3fjOvS","executionInfo":{"status":"ok","timestamp":1701269417888,"user_tz":-60,"elapsed":970,"user":{"displayName":"Mathis Odt","userId":"06586499252536361736"}},"outputId":"a1a38303-d9ec-4854-c5a2-5c96c302076d"},"id":"oVfFXO3fjOvS","execution_count":62,"outputs":[{"output_type":"stream","name":"stdout","text":["--2023-11-29 14:50:16--  https://download.pytorch.org/tutorial/hymenoptera_data.zip\n","Resolving download.pytorch.org (download.pytorch.org)... 52.84.162.53, 52.84.162.20, 52.84.162.79, ...\n","Connecting to download.pytorch.org (download.pytorch.org)|52.84.162.53|:443... connected.\n","HTTP request sent, awaiting response... 200 OK\n","Length: 47286322 (45M) [application/zip]\n","Saving to: ‘hymenoptera_data.zip’\n","\n","\rhymenoptera_data.zi   0%[                    ]       0  --.-KB/s               \rhymenoptera_data.zi 100%[===================>]  45.10M   269MB/s    in 0.2s    \n","\n","2023-11-29 14:50:17 (269 MB/s) - ‘hymenoptera_data.zip’ saved [47286322/47286322]\n","\n","Archive:  hymenoptera_data.zip\n","   creating: hymenoptera_data/\n","   creating: hymenoptera_data/train/\n","   creating: hymenoptera_data/train/ants/\n","  inflating: hymenoptera_data/train/ants/0013035.jpg  \n","  inflating: hymenoptera_data/train/ants/1030023514_aad5c608f9.jpg  \n","  inflating: 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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 = \"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":64,"id":"572d824c","metadata":{"id":"572d824c","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1701269530091,"user_tz":-60,"elapsed":38587,"user":{"displayName":"Mathis Odt","userId":"06586499252536361736"}},"outputId":"c113ef89-6e8f-4795-d3f9-c53f6ded6e7d"},"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:557: 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(_create_warning_msg(\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, 142MB/s]"]},{"output_type":"stream","name":"stdout","text":["Epoch 1/10\n","----------\n"]},{"output_type":"stream","name":"stderr","text":["\n","/usr/local/lib/python3.10/dist-packages/torch/optim/lr_scheduler.py:136: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\n","  warnings.warn(\"Detected call of `lr_scheduler.step()` before `optimizer.step()`. \"\n"]},{"output_type":"stream","name":"stdout","text":["train Loss: 0.6364 Acc: 0.6393\n","val Loss: 0.2647 Acc: 0.9020\n","\n","Epoch 2/10\n","----------\n","train Loss: 0.4570 Acc: 0.7869\n","val Loss: 0.1833 Acc: 0.9542\n","\n","Epoch 3/10\n","----------\n","train Loss: 0.4736 Acc: 0.7664\n","val Loss: 0.4388 Acc: 0.8170\n","\n","Epoch 4/10\n","----------\n","train Loss: 0.4314 Acc: 0.8197\n","val Loss: 0.1674 Acc: 0.9542\n","\n","Epoch 5/10\n","----------\n","train Loss: 0.4656 Acc: 0.7869\n","val Loss: 0.3148 Acc: 0.8693\n","\n","Epoch 6/10\n","----------\n","train Loss: 0.4158 Acc: 0.8156\n","val Loss: 0.1935 Acc: 0.9477\n","\n","Epoch 7/10\n","----------\n","train Loss: 0.3942 Acc: 0.8238\n","val Loss: 0.1821 Acc: 0.9477\n","\n","Epoch 8/10\n","----------\n","train Loss: 0.3159 Acc: 0.8443\n","val Loss: 0.1472 Acc: 0.9542\n","\n","Epoch 9/10\n","----------\n","train Loss: 0.4447 Acc: 0.8156\n","val Loss: 0.1634 Acc: 0.9542\n","\n","Epoch 10/10\n","----------\n","train Loss: 0.3563 Acc: 0.8648\n","val Loss: 0.1460 Acc: 0.9608\n","\n","Training complete in 0m 38s\n","Best val Acc: 0.960784\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 = \"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","metadata":{"id":"ac-bvTMY-LkN"},"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."],"id":"ac-bvTMY-LkN"},{"cell_type":"code","source":["# Function to evaluate the accuracy of the model on a test folder of images from the internet\n","def eval_mode(model):\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","        # 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(f\"Test Loss: {test_loss:.6f}\\n\")\n","\n","    for i in range(10):\n","        if class_total[i] > 0:\n","            accuracy = 100 * class_correct[i] / class_total[i]\n","            print(f\"Test Accuracy of {classes[i]}: {accuracy:.2f}% \"\n","                  f\"({int(np.sum(class_correct[i]))}/{int(np.sum(class_total[i]))})\")\n","        else:\n","            print(f\"Test Accuracy of {classes[i]}: N/A (no training examples)\")\n","\n","    overall_accuracy = 100.0 * np.sum(class_correct) / np.sum(class_total)\n","    print(f\"\\nTest Accuracy (Overall): {overall_accuracy:.2f}% \"\n","          f\"({int(np.sum(class_correct))}/{int(np.sum(class_total))})\")"],"metadata":{"id":"9wj4N6we8DIQ","executionInfo":{"status":"ok","timestamp":1701270429997,"user_tz":-60,"elapsed":247,"user":{"displayName":"Mathis Odt","userId":"06586499252536361736"}}},"id":"9wj4N6we8DIQ","execution_count":68,"outputs":[]},{"cell_type":"code","source":["# Get a pre-trained ResNet18 model\n","new_resNet18 = torchvision.models.resnet18(pretrained=True)\n","for param in new_resNet18.parameters():\n","    param.requires_grad = False\n","\n","new_resNet18.parameters = new_resNet18.parameters\n","\n","# First classification layer\n","in_features = new_resNet18.fc.in_features\n","out_features = 16\n","new_resNet18.fc = nn.Linear(in_features, out_features)\n","new_resNet18.fc = nn.Linear(in_features, out_features)\n","\n","# Second classification layer where we use a \"relu\" activation function for this middle layer\n","new_resNet18.fc2 = nn.Linear(out_features, 2)\n","def new_forward(self, x):\n","    x = self.forward(x)\n","    x = F.relu(self.fc2(self.drop(x)))\n","    return x\n","\n","# Set the loss function\n","criterion = nn.CrossEntropyLoss()\n","\n","# Observe that only the parameters of the final layer are being optimized\n","optimizer_conv = optim.SGD(new_resNet18.fc.parameters(), lr=0.001, momentum=0.9)\n","exp_lr_scheduler = lr_scheduler.StepLR(optimizer_conv, step_size=7, gamma=0.1)\n","val_loss, train_loss, val_accuracy, train_accuracy = [], [], [], []\n","new_resNet18, epoch_time = train_model(\n","    model, criterion, optimizer_conv, exp_lr_scheduler, num_epochs=10\n",")"],"metadata":{"id":"CU1Ot6rt8FdD","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1701271108389,"user_tz":-60,"elapsed":37400,"user":{"displayName":"Mathis Odt","userId":"06586499252536361736"}},"outputId":"d73df2d0-0204-4d8b-ddd4-a1612662dacc"},"id":"CU1Ot6rt8FdD","execution_count":73,"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/10\n","----------\n","train Loss: 0.3299 Acc: 0.8648\n","val Loss: 0.2005 Acc: 0.9216\n","\n","Epoch 2/10\n","----------\n","train Loss: 0.2750 Acc: 0.8852\n","val Loss: 0.1635 Acc: 0.9542\n","\n","Epoch 3/10\n","----------\n","train Loss: 0.3416 Acc: 0.8852\n","val Loss: 0.1855 Acc: 0.9412\n","\n","Epoch 4/10\n","----------\n","train Loss: 0.3332 Acc: 0.8730\n","val Loss: 0.1760 Acc: 0.9412\n","\n","Epoch 5/10\n","----------\n","train Loss: 0.4095 Acc: 0.7910\n","val Loss: 0.1562 Acc: 0.9608\n","\n","Epoch 6/10\n","----------\n","train Loss: 0.3044 Acc: 0.8648\n","val Loss: 0.1869 Acc: 0.9412\n","\n","Epoch 7/10\n","----------\n","train Loss: 0.4713 Acc: 0.8074\n","val Loss: 0.1606 Acc: 0.9542\n","\n","Epoch 8/10\n","----------\n","train Loss: 0.4518 Acc: 0.7992\n","val Loss: 0.1718 Acc: 0.9412\n","\n","Epoch 9/10\n","----------\n","train Loss: 0.3761 Acc: 0.8607\n","val Loss: 0.1974 Acc: 0.9216\n","\n","Epoch 10/10\n","----------\n","train Loss: 0.3483 Acc: 0.8607\n","val Loss: 0.1934 Acc: 0.9412\n","\n","Training complete in 0m 37s\n","Best val Acc: 0.960784\n"]}]},{"cell_type":"code","source":["import torchvision.models as models\n","new_resNet18_quantized = torch.quantization.quantize_dynamic(new_resNet18, dtype=torch.qint8)\n","\n","size_resNet18 = print_size_of_model(new_resNet18, \"fp32\")\n","size_resNet18_quantized = print_size_of_model(new_resNet18_quantized, \"fp32\")\n","\n","print(\n","    \"\\nThe size of the resNet18 model is %.2fMB, %.0f times bigger than the resNet18_quantized model\"\n","    % (\n","        size_resNet18 / 1000000,\n","        size_resNet18 / size_resNet18_quantized\n","    )\n",")"],"metadata":{"id":"UTpZmkFJ8P11","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1701271180173,"user_tz":-60,"elapsed":353,"user":{"displayName":"Mathis Odt","userId":"06586499252536361736"}},"outputId":"4a5cddcc-5b9f-45c5-cafd-c567a1717cbc"},"id":"UTpZmkFJ8P11","execution_count":74,"outputs":[{"output_type":"stream","name":"stdout","text":["model:  fp32  \t Size (KB): 44782.148\n","model:  fp32  \t Size (KB): 44779.834\n","\n","The size of the resNet18 model is 44.78MB, 1 times bigger than the resNet18_quantized model\n"]}]},{"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":{"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":"3.11.5"},"vscode":{"interpreter":{"hash":"9e3efbebb05da2d4a1968abe9a0645745f54b63feb7a85a514e4da0495be97eb"}},"colab":{"provenance":[],"gpuType":"T4"},"accelerator":"GPU"},"nbformat":4,"nbformat_minor":5}
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