APAI24-LAB1-DNN-definition-and-training.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "<a href=\"https://githubtocolab.com/EEESlab/APAI24-LAB01-DNN-definition-and-training/blob/master/APAI24-LAB1-DNN-definition-and-training.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\"/></a>\n",
        "\n",
        "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/EEESlab/APAI24-LAB01-DNN-definition-and-training/HEAD?labpath=APAI24-LAB1-DNN-definition-and-training.ipynb)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mDxvjs35_SWd"
      },
      "source": [
        "# **LAB1 APAI: DNN Definition & Training**\n",
        "\n",
        "**Credits**: *Davide Nadalini, Lorenzo Lamberti, Luca Bompani, Luka Macan, Francesco Conti*\n",
        "*(University of Bologna)*\n",
        "\n",
        "**Contacts**: *d.nadalini@unibo.it*, *lorenzo.lamberti@unibo.it* \n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eSLWHhFQh56H"
      },
      "source": [
        "## **In this Hands-on session:**\n",
        "\n",
        "A first-time user of Pytorch framework will learn how to define, train, and test a neural network over the Fashion-MNIST dataset."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aO4p3Mx_t6Qf"
      },
      "source": [
        "### Tasks:\n",
        "\n",
        "---\n",
        "\n",
        "1.  PyTorch definition of a NN model;\n",
        "2. Count network’s parameters and MAC operations;\n",
        "3. Data loader for Fashion-MNIST\n",
        "4. Code for testing a neural network on Fashion MNIST dataset;\n",
        "5. Code for training a neural network on Fashion MNIST;\n",
        "6. Save and load model’s trained weights;\n",
        "---\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MOsVZLnUzMqU"
      },
      "source": [
        "## Let's start !"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TH8A3ArSIzOI"
      },
      "source": [
        "Let us start by installing dependencies...\n",
        "\n",
        "**Note:** if you need to instal more packages, add them here! (but remember to also import them afterwards!)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "Nfj8089GgMZq"
      },
      "outputs": [],
      "source": [
        "#@title Install dependencies { form-width: \"20%\" }\n",
        "\n",
        "# these ones should already be available\n",
        "!pip install numpy\n",
        "!pip install tqdm\n",
        "!pip install pillow\n",
        "\n",
        "!pip install torch\n",
        "!pip install torchvision\n",
        "\n",
        "!pip install thop\n",
        "!pip install matplotlib\n",
        "!pip install scikit-learn\n",
        "!pip install torchinfo\n",
        "\n",
        "# You will need these if running on a private server (no colab)\n",
        "# !pip install jupyter\n",
        "# !pip install ipywidgets\n",
        "# !jupyter nbextension enable --py widgetsnbextension\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "077hTkLtzUar"
      },
      "source": [
        "## Imports:\n",
        "Now we import some packages  we will need:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HLlnWjXhg17X"
      },
      "outputs": [],
      "source": [
        "#basic\n",
        "import os\n",
        "from os.path import join\n",
        "import numpy as np\n",
        "from tqdm import tqdm\n",
        "import time\n",
        "\n",
        "#plotting\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "#torch\n",
        "import torch; print('\\nPyTorch version in use:', torch.__version__, '\\ncuda avail: ', torch.cuda.is_available())\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "from torch.utils.data import DataLoader\n",
        "\n",
        "#torchvision\n",
        "import torchvision\n",
        "from torchvision import transforms, datasets\n",
        "\n",
        "# others\n",
        "from copy import deepcopy\n",
        "from tqdm.autonotebook import tqdm\n",
        "from sklearn.metrics import precision_score, recall_score, f1_score, accuracy_score\n",
        "import inspect\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oi8zt0JZ0D7Z"
      },
      "source": [
        "## Set device\n",
        "Between \"cuda\" and \"cpu\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WY59unFq40v7"
      },
      "outputs": [],
      "source": [
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "print('Device: %s' % device)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SmSNU-3lGK9d"
      },
      "source": [
        "# **Task 1:** Creating a Model\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UaCBw-WVipNK"
      },
      "source": [
        "The first real step is to define the network topology. We will define a custom one.\n",
        "\n",
        "* Use `torch.nn.Module`,\n",
        "* **IMPORTANT:** Instantiate a separate `torch.nn.Module` for each node in your topology; This is mandatory for parametric modules (e.g., `torch.nn.Conv2d`, `nn.Linear`,`nn.BatchNorm2d`).\n",
        "\n",
        "---\n",
        "\n",
        "You can use the following modules:\n",
        "* `nn.Conv2d`\n",
        "* `nn.Linear`\n",
        "* `nn.BatchNorm2d`\n",
        "* `nn.ReLU`\n",
        "* `nn.MaxPool2d`\n",
        "* `nn.LogSoftmax`\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "euYJstpwV4Ma"
      },
      "source": [
        "**You must define the NN topology following this diagram:**\n",
        "\n",
        "Note: set the padding of each Convolution to 1 to keep the size of the output feature map!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fh8bwJB4VN7M"
      },
      "source": [
        "![NN2 (2).png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I0sZH5H2L76D"
      },
      "source": [
        "### This is how our network looks like:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oHoDn8MmEX3E"
      },
      "source": [
        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      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "s11ciz67kuNh"
      },
      "outputs": [],
      "source": [
        "class CNN(nn.Module):\n",
        "    def __init__(self, n_classes=10, depth_mult=1.):\n",
        "        super(CNN, self).__init__()\n",
        "        # .........\n",
        "\n",
        "    def forward(self, x):\n",
        "        # .........\n",
        "        return output\n",
        "\n",
        "net = CNN().to(device)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VP3aw9Re2_Np"
      },
      "source": [
        "### Test defined network, and verify layers\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "e8KvUHPmm9PC"
      },
      "outputs": [],
      "source": [
        "#@title Dummy input  { form-width: \"70%\" }\n",
        "# input_size = (batch_n, img_ch,img_width, img_height)\n",
        "input_size = (1, 1, 28, 28)  #@param\n",
        "input = torch.randn(input_size).to(device)#add one extra field to input size for batch_n\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uDvVv8ASKVb0"
      },
      "source": [
        "Print output tensor size and the CNN network topology defined:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kSa1Bg3722Gz"
      },
      "outputs": [],
      "source": [
        "output = net(input)\n",
        "print('Output shape:', output.shape)\n",
        "print('Network Topology:\\n',net)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IrPQVjWGyrMA"
      },
      "source": [
        "# **Task 2:** Count network's parameters and MAC operations\n",
        "\n",
        "---\n",
        "\n",
        "Let's define a useful functions to measure the model's MAC operations and number of parameters!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NAsWm5qHsBLe"
      },
      "outputs": [],
      "source": [
        "def network_size_and_mac(net, input_size, verbose=False):\n",
        "    # calculate here\n",
        "    # .....................\n",
        "    print(\"Network's \\tn°params: %.3fk \\tMAC: %.3fM\\n\" % (params/1e3, macs/1e6))\n",
        "\n",
        "test_net = CNN().to(device)\n",
        "network_size_and_mac(test_net, input_size) #use deepcopy to avoid graph modifications by hese function calls"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mPHKSY4Qs2kz"
      },
      "source": [
        "**TorchInfo insights:**\n",
        "This tool calculates the Parameters Size as (number_of_parameters*4).\n",
        "This because each parameter needs 4 Bytes (32bit) to be represented in float32 format."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e3sgwFOIwgM3"
      },
      "source": [
        "# **Task 3:** Dataset & DataLoaders\n",
        "\n",
        "---\n",
        "\n",
        "In this notebook we'll a custom CNN on the [Fashion-MNIST dataset](https://github.com/zalandoresearch/fashion-mnist), which is a set of 28x28 grayscale images of clothes.\n",
        "This dataset is provided by the [Torchvision package](https://pytorch.org/docs/stable/torchvision/index.html).\n",
        "\n",
        "Before diving in, let's look at the Fashion MNIST dataset. The dataset has 60 k training images and 10 k validation images. It has a total of 10 classes, each represented by an integer index. The following cell creates a mapping from these indices to their corresponding human-readable strings, then downloads the Fashion MNIST training dataset from Torchvision. It may take a minute to run.\n",
        "\n",
        "Torchvision provides easy access to many datasets, including COCO, CIFAR, and Cityscapes. See its documentation for a complete list. \n",
        "\n",
        "The dataset is stored on the Colab VM in the `/data/fashionmnist` directory.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f36f-VgLPj_s"
      },
      "source": [
        "Optional: diplay one image from the dataset\n",
        "This might help you!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "P9YKtOiYxAsS"
      },
      "outputs": [],
      "source": [
        "#@title Show dataset image samples\n",
        "class_map = {\n",
        "0 : \"t-shirt\",\n",
        "1 : \"trouser\",\n",
        "2 : \"pullover\",\n",
        "3 : \"dress\",\n",
        "4 : \"coat\",\n",
        "5 : \"sandal\",\n",
        "6 : \"shirt\",\n",
        "7 : \"sneaker\",\n",
        "8 : \"bag\",\n",
        "9 : \"ankle boot\"\n",
        "}\n",
        "\n",
        "# Downloads the Fashion MNIST dataset using Torchvision\n",
        "fashion_mnist_dataset = # import fashion mnist with: torchvision.datasets. ......\n",
        "\n",
        "# show one image from dataset\n",
        "img_index = 0 #@param {type:\"integer\"}\n",
        "tup = fashion_mnist_dataset[img_index] # tup = (image, label)\n",
        "display(tup[0].resize((224, 224)))\n",
        "print(class_map[tup[1]])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W8CslUMA1FZD"
      },
      "source": [
        "### Data loaders for Fashion MNIST\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LIpI-cuqMbLj"
      },
      "source": [
        "The code below first sets up transform using `torhvision transfroms` for converting images to pytorch tensors and normalizing the images.\n",
        "\n",
        "Next, We use `torchvision datasets` for dowloading the fashion mnist dataset and applying transforms which we defined above.\n",
        "\n",
        "* `train_dataset` contains the training data.\n",
        "* `valid_dataset` contains the validation data\n",
        "\n",
        "Next, We use `pytorch dataloader` for making dataloader from the train and validation sets."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_Aw_XFl8oCOZ"
      },
      "outputs": [],
      "source": [
        "def get_data_loaders(train_batch_size=128, val_batch_size=128, path='./data/fashionmnist', verbose=False):\n",
        "    #define data transformations\n",
        "    fashion_mnist  = datasets.FashionMNIST(download=True, train=True, root=path).train_data.float()\n",
        "    data_transform = transforms.Compose([transforms.ToTensor(), \n",
        "                                         transforms.Normalize((fashion_mnist.mean()/255,), (fashion_mnist.std()/255,))])  \n",
        "\n",
        "    # Load train/valid datasets\n",
        "    train_dataset = ...\n",
        "    valid_dataset = ...\n",
        "\n",
        "    # define dataloaders\n",
        "    train_loader = ...\n",
        "    valid_loader = ...\n",
        "\n",
        "    # count how many images we have in each set\n",
        "    # ....\n",
        "\n",
        "    return train_loader, valid_loader\n",
        "\n",
        "train_loader, val_loader = get_data_loaders(train_batch_size, val_batch_size, verbose=True)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R7ZfZ7Ic4h5t"
      },
      "source": [
        "# **Task 4:** testing the CNN over the dataset\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t0iCOJDS1J8I"
      },
      "source": [
        "### Accuracy Metric calculation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Km0hniV74L_Q"
      },
      "source": [
        "First we define a function to calculate the accuracy, defined as: `accuracy = num_correct_predictions/total_n_predictions`\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "x2s9V-q0B3hP"
      },
      "outputs": [],
      "source": [
        "def calculate_accuracy(best_guesses, targets):\n",
        "    num_correct = ...\n",
        "    total_guesses = ...\n",
        "    correct_percentage = num_correct/total_guesses\n",
        "    return correct_percentage"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Define Loss function: use cross entropy !"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Define loss function\n",
        "loss_function = ... # cross entropy works well for multi-class problems\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k8SL9AZ0M2Ak"
      },
      "source": [
        "### Testing Function\n",
        "Before training we want to verify our data->network pipeline and set a baseline level of performance. The following cell defines a function, `validate,` that runs a network on a dataset and shows the percentage of the dataset that was correctly classified."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mLPgZVOg4mjt"
      },
      "outputs": [],
      "source": [
        "def validate(net, val_loader, loss_function, accuracy_score):\n",
        "    val_losses = []\n",
        "    val_accuracy = []\n",
        "    \n",
        "    # set net to evaluating (testing)\n",
        "    net.eval()\n",
        "    with torch.no_grad():\n",
        "        for batch_idx, data in enumerate(val_loader):\n",
        "            inputs, labels = data[0].to(device), data[1].to(device)\n",
        "\n",
        "            # net forward\n",
        "            outputs = ... # this gets the prediction from the network\n",
        "\n",
        "            # calculate loss\n",
        "            loss = ...\n",
        "            val_losses.append(loss) # append current average training loss to a buffer variable, for plotting learning curve\n",
        "\n",
        "            # calculate validation accuracy\n",
        "            predicted_classes = torch.max(outputs, 1)[1] # get class from network's prediction\n",
        "            val_accuracy.append(calculate_accuracy(predicted_classes.cpu(), labels.cpu())) \n",
        "            # val_accuracy.append(calculate_metric(accuracy_score, labels.cpu(), predicted_classes.cpu())) #this is the same accuracy calculated with scikitlearn\n",
        "            \n",
        "    average_val_loss = sum(val_losses)/(batch_idx+1)\n",
        "    return val_accuracy, average_val_loss\n",
        "    "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "val_accuracy, average_val_loss = validate(net, val_loader, loss_function, accuracy_score)\n",
        "print('Accuracy: %0.2f'% (sum(val_accuracy)/len(val_accuracy)))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QZ8wp9qIMsKE"
      },
      "source": [
        "# **Task 5:** Training Loop\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lQ67crHMzNgt"
      },
      "source": [
        "**Basic training in PyTorch:**\n",
        "- enumerates a dataset in batches\n",
        "- runs the network on each batch\n",
        "- evaluates the network's performance using a loss function\n",
        "- use `net.zero_grad()` to reset the gradients before backpropagation.\n",
        "- calls `backward()` on the loss to propagate gradients through the network\n",
        "- uses an optimizer to `step()` and apply the gradients to the network's weights\n",
        "\n",
        "In this case we'll use: \n",
        "* **Loss Function:** [CrossEntropyLoss](https://pytorch.org/docs/master/nn.html#torch.nn.CrossEntropyLoss) since we have a classification problem.\n",
        "* **Optimizer:** We'll use the [Adam](https://pytorch.org/docs/master/optim.html#torch.optim.Adam) optimizer since it's a very easy optimizer to use (no need to tweak hyperparameters).\n",
        "\n",
        "**Loops:** \n",
        "*   for loop over `epochs`: at the end of each epoch we calculate the validation accuracy\n",
        "*   for loop over `training batches`: for each batch we backpropagate using the loss function\n",
        "\n",
        "**Training step:**\n",
        "- enumerates a dataset in batches\n",
        "- runs the network on each batch\n",
        "- evaluates the network's performance using a loss function\n",
        "- use `net.zero_grad()` to reset the gradients before backpropagation.\n",
        "- calls `backward()` on the loss to propagate gradients through the network\n",
        "- uses an optimizer to `step()` and apply the gradients to the network's weights\n",
        "\n",
        "\n",
        "```\n",
        "Pseudo-code:\n",
        "├─ for each epoch (enumerate):\n",
        "│  ├── for each training batch:\n",
        "│  │  ├── output = net(input)\n",
        "│  │  ├── loss = (output,ground_truth)\n",
        "│  │  ├── net.zero_grad()\n",
        "│  │  ├── loss.backward()\n",
        "│  │  ├── optimizer step()\n",
        "│  │  ├── calculate training accuracy\n",
        "│  │  └── print: training accuracy and loss\n",
        "│  └── test on validation set: print accuracy\n",
        "```\n",
        "\n",
        "We'll only train for 5 epochs for timeliness. The following cell should take 4-5 minutes to run."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G4O-gp-I4lEM"
      },
      "source": [
        "### **Note: why do we need to reset the gradients**\n",
        "In PyTorch, for every mini-batch during the training phase, we typically want to explicitly set the gradients to zero (`net.zero_grads()`)before starting to do backpropragation (i.e., updating the Weights and biases) because PyTorch accumulates the gradients on subsequent backward passes. So, the default action has been set to accumulate (i.e. sum) the gradients on every `loss.backward()` call.\n",
        "\n",
        "I f you don't reset them, the gradient would be a combination of the old gradient, which you have already used to update your model parameters, and the newly-computed gradient. It would therefore point in some other direction than the intended direction towards the minimum (or maximum, in case of maximization objectives).\n",
        "\n",
        "**in a nutshell:** if you don’t clear the gradients, they will accumulate across multiple iterations of training. This can lead to incorrect gradients and slower convergence of the model. Therefore, it’s important to clear the gradients before computing the gradients for the current iteration.\n",
        "\n",
        "check out: https://saturncloud.io/blog/why-do-we-need-to-call-zerograd-in-pytorch/\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "APLpS8iGP5CP"
      },
      "outputs": [],
      "source": [
        "#@title Training parameters { form-width: \"70%\" }\n",
        "epochs = 2 #@param {type:\"integer\"}\n",
        "batch_size = 128 #@param {type:\"integer\"}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uMlB1mosp9I2"
      },
      "outputs": [],
      "source": [
        "# Move net to gpu:\n",
        "net.to(device)\n",
        "\n",
        "# Build Dataloaders\n",
        "train_loader, val_loader = get_data_loaders(batch_size, batch_size)\n",
        "\n",
        "def training(net, train_loader, val_loader, loss_function, epochs):    \n",
        "    # Define optimizer\n",
        "    optimizer = ....\n",
        "\n",
        "    # count tot batches\n",
        "    tot_train_batches = len(train_loader)\n",
        "    tot_val_batches = len(val_loader)\n",
        "\n",
        "    # Create lists to store training history\n",
        "    train_loss_history = []\n",
        "    train_accuracy_history = []\n",
        "    # Create lists to store validation history\n",
        "    val_loss_history = []\n",
        "    val_accuracy_history = []\n",
        "\n",
        "    start_ts = time.time()     \n",
        "\n",
        "    # ----------------- TRAINING  -------------------- #\n",
        "    # loop for every epoch (training + evaluation)\n",
        "    for epoch in range(epochs):\n",
        "        total_epoch_loss = 0\n",
        "        train_accuracy = []\n",
        "\n",
        "        net.train() # set model to training\n",
        "        # loop for every batch of images in the dataset\n",
        "        for batch_idx, data in enumerate(train_loader):\n",
        "            inputs, labels = data[0].to(device), data[1].to(device)\n",
        "            \n",
        "            # Compute prediction (forward input in the model)\n",
        "            outputs =  ...\n",
        "            # Compute prediction error with the loss function\n",
        "            loss = ...\n",
        "            # gradients to zero for every batch of data\n",
        "            ...\n",
        "            # Backpropagation\n",
        "            ...\n",
        "            # Optimizer step\n",
        "            ...\n",
        "            \n",
        "            # getting training quality data\n",
        "            current_loss = loss.item()\n",
        "\n",
        "            # compute average loss\n",
        "            total_epoch_loss += current_loss\n",
        "            average_train_loss = total_epoch_loss/(batch_idx+1)\n",
        "            \n",
        "            # calculate training accuracy\n",
        "            predicted_classes = torch.max(outputs, 1)[1] # get class from network's prediction\n",
        "            train_accuracy.append(calculate_accuracy(predicted_classes.cpu(), labels.cpu())) \n",
        "\n",
        "            average_train_accuracy = sum(train_accuracy)/(batch_idx+1)\n",
        "            \n",
        "\n",
        "        # ----------------- VALIDATION  ----------------- #\n",
        "        val_accuracy, val_loss = ...\n",
        "\n",
        "        # compute mean accuracy\n",
        "        average_train_accuracy = sum(train_accuracy)/tot_train_batches\n",
        "        average_val_accuracy = sum(val_accuracy)/tot_val_batches\n",
        "\n",
        "        # print training/validation Accuracy and Loss\n",
        "        print('Epoch %d/%d' % (epoch+1,epochs), 'Training loss:  %.4f' % (average_train_loss), 'Accuracy: %.4f' % (average_train_accuracy))\n",
        "        print('Validation Loss: %.4f' % (average_val_loss), 'Accuracy: %.4f' % (average_val_accuracy))\n",
        "\n",
        "        # append current average training loss to a buffer variable, for plotting learning curve later\n",
        "        train_loss_history.append(float(average_train_loss))\n",
        "        val_loss_history.append(float(average_val_loss))\n",
        "        train_accuracy_history.append(float(average_train_accuracy))\n",
        "        val_accuracy_history.append(float(average_val_accuracy))\n",
        "\n",
        "    print('Training time: %.1f seconds' % (time.time()-start_ts)) \n",
        "\n",
        "    return train_loss_history, train_accuracy_history, val_loss_history, val_accuracy_history\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "a-NEAXGAtw6X"
      },
      "outputs": [],
      "source": [
        "(train_loss_history,\n",
        "train_accuracy_history, \n",
        "val_loss_history, \n",
        "val_accuracy_history) = training(net, train_loader, val_loader, loss_function, epochs)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lzgf8vuT1xam"
      },
      "source": [
        "We now re-evaluated our network's performance, and you will see an accuracy ~90%"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8hS_jcddL7Bo"
      },
      "source": [
        "### Plot training/validation loss\n",
        "This is a good practice to track the training process."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OZfyNjK6180t"
      },
      "outputs": [],
      "source": [
        "# plt.plot(XXXXXX,label=\"Training Loss\")\n",
        "# plt.plot(YYYYYYY,label=\"Validation Loss\")\n",
        "# plt.xlabel('No. of Epochs')\n",
        "# plt.ylabel('Accuracy & Loss')\n",
        "# plt.legend(frameon=False)\n",
        "# plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nkxaealT2jul"
      },
      "outputs": [],
      "source": [
        "\n",
        "# plt.plot(XXXXXX,label=\"Training Accuracy\")\n",
        "# plt.plot(YYYYYYY,label=\"Validation Accuracy\")\n",
        "# plt.xlabel('No. of Epochs')\n",
        "# plt.ylabel('Accuracy & Loss')\n",
        "# plt.ylim([0., 1.]) # limit y axis between 0 and 1\n",
        "# plt.legend(frameon=False)\n",
        "# plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "srEdjBj4F9t1"
      },
      "source": [
        "# **Task 6:** Save/Load Model\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "d9iPVEngbFCB"
      },
      "outputs": [],
      "source": [
        "#@title Training parameters { form-width: \"70%\" }\n",
        "model_export_path = \"./model/\" #@param {type:\"string\"}\n",
        "model_name = 'ExampleNet.pth' #@param {type:\"string\"}\n",
        "os.makedirs(model_export_path, exist_ok=True)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_o9xVvDebyQa"
      },
      "source": [
        "### Save a PyTorch Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Lsbcb_WgFx3c"
      },
      "outputs": [],
      "source": [
        "#Save Pytorch model\n",
        "def save_net(net,model_path, model_name):\n",
        "    #Save Pytorch model\n",
        "    ...\n",
        "       \n",
        "save_net(net,model_export_path, model_name)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1V8x_sf5b_WL"
      },
      "source": [
        "### Load a PyTorch Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GUR7LrRkb2vQ"
      },
      "outputs": [],
      "source": [
        "NEW_net = CNN().to(device)\n",
        "\n",
        "# Load a PyTorch Model\n",
        "def load_net(net, model_path, model_name, device):\n",
        "    ''' Load a PyTorch Model '''\n",
        "    checkpoint_file= join(model_path,model_name)\n",
        "    # load weights file\n",
        "    state_dict = ...\n",
        "    # Load weights into the network\n",
        "    ...\n",
        "    # to device (cpu or gpu)\n",
        "    net = CNN().to(device)\n",
        "    return net\n",
        "\n",
        "load_net(net, model_export_path, model_name, device)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E4z1XnYragES"
      },
      "source": [
        "Test again the network accuracy, to be sure that you saved/loaded weights correctly. You should get ~90% accuracy again."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "f-KAUyhdXVTa"
      },
      "outputs": [],
      "source": [
        "# Test over validation dataset\n",
        "val_accuracy, average_val_loss = validate(NEW_net, val_loader, loss_function, accuracy_score)\n",
        "# Print valid statistics\n",
        "# ......"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
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      "name": "LAB1 APAI: DNN Definition & Training",
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      "toc_visible": true
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      "display_name": "Python 3",
      "name": "python3"
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    "language_info": {
      "codemirror_mode": {
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      "file_extension": ".py",
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