Q1_ASL_Classification (4).ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KAt9Bvb8nboz"
      },
      "source": [
        "<div align=center>\n",
        "\t\t\n",
        "<p></p>\n",
        "<p></p>\n",
        "<font size=5>\n",
        "In the Name of God\n",
        "<font/>\n",
        "<p></p>\n",
        " <br/>\n",
        "    <br/>\n",
        "    <br/>\n",
        "<font color=#FF7500>\n",
        "Sharif University of Technology - Departmenet of Computer Engineering\n",
        "</font>\n",
        "<p></p>\n",
        "<font color=blue>\n",
        "Artifical Intelligence - Dr. Mohammad Hossein Rohban\n",
        "</font>\n",
        "<br/>\n",
        "<br/>\n",
        "Fall 2022\n",
        "\n",
        "</div>\n",
        "\n",
        "<hr/>\n",
        "\t\t<div align=center>\n",
        "\t\t    <font color=red size=6>\n",
        "\t\t\t    <br />\n",
        "Practical Assignment 3 <br>\n",
        "American Sign Language Image Classification\n",
        "            \t<br/>\n",
        "\t\t\t</font>\n",
        "    <br/>\n",
        "    <br/>\n",
        "<font size=4>\n",
        "\t\t\t<br/><br/>\n",
        "Deadline:  \n",
        "                <br/><b>\n",
        "              Cheating is Strongly Prohibited\n",
        "                </b><br/><br/>\n",
        "                <font color=red>\n",
        "Please run all the cells.\n",
        "     </font>\n",
        "</font>\n",
        "<br>\n",
        "<font size=3 color=grey>\n",
        "Author: Sayeh Jarollahi\n",
        "</font>\n",
        "                <br/>\n",
        "    </div>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "kMDnnbgAnnyg"
      },
      "outputs": [],
      "source": [
        "# Set your student number\n",
        "student_number = 99101319\n",
        "Name = 'Mohamadreza'\n",
        "Last_Name = 'Pirpiran'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1eHRwidVEIR0"
      },
      "source": [
        "In this question, you will train a neural network for classifying sign language images. The American  Sign Language (ASL) dataset consists of 2515 images from different numbers and alphabets. The images are in RGB format and are not the same size. The dataset is classified into 36 classes (10 numbers and 26 alphabets). \n",
        "\n",
        "You will need to train a model to classify this dataset into different categories. You are allowed to use MLP as well as CNN networks. \n",
        "\n",
        "**Grading Policy:** For a full mark on this assignment, your notebook should be completely runnable by the TA. Additionally, you must reach an accuracy of %96 on the test data.\n",
        "\n",
        "It is highly recommended to use google colab and utilize its GPUs. However, if you want to run the notebook locally, you can reach and download the dataset in this [link](https://drive.google.com/file/d/11_4-N8EdtoLPiW3YyJ840CXM0FckO1e5/view?usp=share_link).\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TiYNQh88NU5w"
      },
      "source": [
        "## Setup\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "trQj0pq0lQZ7"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib.image import imread\n",
        "import cv2\n",
        "import random\n",
        "import os\n",
        "\n",
        "from PIL import Image\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import confusion_matrix, f1_score, accuracy_score\n",
        "from IPython.display import clear_output\n",
        "import seaborn as sns\n",
        "\n",
        "import torch\n",
        "from torch.nn import functional as F\n",
        "from torchvision import transforms\n",
        "from torch.utils.data import Dataset, DataLoader, random_split\n",
        "from torch import nn\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "9gxtIBaQ53Uf"
      },
      "outputs": [],
      "source": [
        "def get_files(main_dir='asl_dataset/asl_dataset'):\n",
        "    files_addr , lbls = [], []\n",
        "    for dir in os.listdir(main_dir):\n",
        "        lbl = int(dir) if dir.isdigit() else (ord(dir) - 87)\n",
        "        addrs = [f'{main_dir}/{dir}/{file_name}' for file_name in os.listdir(f'{main_dir}/{dir}')]\n",
        "        files_addr.extend(addrs)\n",
        "        lbls.extend([lbl]*len(addrs))\n",
        "    return files_addr, lbls"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "REOljmCebLZD",
        "outputId": "37bb7650-39e6-4d1f-ce09-e5e6375148c6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "device(type='cuda')"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# NOTE: It is highly recommended to use colab GPUs!\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "device"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2WXsvhdOObjP"
      },
      "source": [
        "## Download and unzip the dataset\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jIXL_lcII62y",
        "outputId": "ded63323-d260-4cb6-fe9d-6bc10b62649b"
      },
      "outputs": [],
      "source": [
        "! wget --load-cookies /tmp/cookies.txt \"https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=11_4-N8EdtoLPiW3YyJ840CXM0FckO1e5' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\\1\\n/p')&id=11_4-N8EdtoLPiW3YyJ840CXM0FckO1e5\" -O asl_dataset.zip && rm -rf /tmp/cookies.txt\n",
        "! unzip asl_dataset.zip\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "OpoIgAyYaXDv"
      },
      "outputs": [],
      "source": [
        "main_dir = \"asl_dataset/asl_dataset\"\n",
        "class_dirs = os.listdir(main_dir)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ts6-fBVqR5Wz"
      },
      "source": [
        "## Transform \n",
        "\n",
        "Data transformation is the process of converting data from one format or structure into another format or structure. Transformers change pixel values but still keep almost the whole information of the image.[source](https://towardsdatascience.com/improves-cnn-performance-by-applying-data-transformation-bf86b3f4cef4) You can read more about them in the mentioned source. \n",
        "\n",
        "You are going to implement two different transformers from scratch. These transformers will be used once the data is loaded. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qlwXkss8T_Nl"
      },
      "source": [
        "### Resize \n",
        "This transformer recieves a tensor (3\\*x\\*y) and returns a tensor with shape (3\\*size\\*size)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "h8rup1dDXH8g"
      },
      "outputs": [],
      "source": [
        "class Resize(object):\n",
        "    \n",
        "    def __init__(self, size=64):\n",
        "      self.size = size\n",
        "      pass\n",
        "    \n",
        "    \n",
        "  \n",
        "    def __call__(self, x):\n",
        "        '''\n",
        "        This function recieves a tensor and applies the resizing on that and returns that tensor\n",
        "        '''\n",
        "        x = transforms.Resize(self.size).forward(x)\n",
        "        return x"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WZMwzvqeXxP0"
      },
      "source": [
        "### GrayScale\n",
        "This transform is helpful to decrease the number of channels, so the training gets faster. \n",
        "\n",
        "Feel free to add args to functions or change them if you need to!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "vur0nw8-XwaV"
      },
      "outputs": [],
      "source": [
        "class GrayScale(object):\n",
        "\n",
        "    def __init__(self, number_of_channels = 3):\n",
        "      self.number_of_channels = number_of_channels\n",
        "      pass\n",
        "\n",
        "    def __call__(self,x):\n",
        "      x = transforms.Grayscale(self.number_of_channels).forward(x)\n",
        "      return x"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rHk3iYieamNT"
      },
      "source": [
        "## Load Data and compose transforms \n",
        "You can combine Transforms using the **compose** method in the torchvision.transforms library."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "8luiRZlObGFV"
      },
      "outputs": [],
      "source": [
        "# Ddd the implemented transforms\n",
        "transform = transforms.Compose([\n",
        "    transforms.ToTensor(),\n",
        "    Resize(),\n",
        "    GrayScale()\n",
        "])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TjhBHcIhdnso"
      },
      "source": [
        "Plot one random sample from each class of the datset:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 645
        },
        "id": "mCJ-pGHUo_8Y",
        "outputId": "494887d3-d4d9-4123-cad9-62a7b45d385d"
      },
      "outputs": [
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 792x792 with 36 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "'''\n",
        "Hint:\n",
        "Get two random samples from files in each directory and use subplot to add those\n",
        "images to the plot. \n",
        "Use previously defined transform to test your transforms\n",
        "'''\n",
        "fig = plt.figure(figsize=(11,11))\n",
        "row = 6\n",
        "columns = 6\n",
        "for i, directory in enumerate(class_dirs):\n",
        "    # name of all files in the directory\n",
        "    files_name = os.listdir(f\"{main_dir}/{directory}\")\n",
        "    random_number = random.randint(0,len(files_name))\n",
        "    image = cv2.imread(main_dir + \"/\" + directory + \"/\" + files_name[random_number])\n",
        "    image = transform(image)\n",
        "    fig.add_subplot(row,columns,i + 1)\n",
        "    plt.imshow(image.permute(1, 2, 0))\n",
        "    plt.axis('off')\n",
        "    plt.title(directory)\n",
        "    \n",
        "    \n",
        "    \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ad1fyyU7gjlb"
      },
      "source": [
        "## Create Dataset \n",
        "Code for processing data samples can get messy and hard to maintain; we ideally want our dataset code to be decoupled from our model training code for better readability and modularity. [from [Pytorch Documentation](https://pytorch.org/tutorials/beginner/basics/data_tutorial.html)] \n",
        "\n",
        "In this section, you will implement a custom dataset which gets address of files and loads them as needed. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "d_SVD35Upz87"
      },
      "outputs": [],
      "source": [
        "class ASLDataset(Dataset):\n",
        "    def __init__(self, files_address:list, labels:list, transform=transform):\n",
        "        self.transform = transform\n",
        "        self.files_address = files_address\n",
        "        self.labels = labels\n",
        "        pass\n",
        "\n",
        "    def __len__(self):\n",
        "        # returns length of the data\n",
        "        return len(self.files_address)\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        # Hint: load image from files_address and the label from name of the directory \n",
        "        # Remember to apply transforms!\n",
        "        image = image = Image.open(self.files_address[idx])\n",
        "        image = transform(image)\n",
        "        image = torch.div(image, 0.5)\n",
        "        label = self.labels[idx]\n",
        "        return image, label"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e9fMgxwxrezk",
        "outputId": "3d725538-f690-4305-eab3-9209f4ae1db2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2515 2515\n"
          ]
        }
      ],
      "source": [
        "files_addr, labels = get_files()\n",
        "print(len(labels), len(files_addr))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7dRpz0h_u4lD"
      },
      "source": [
        "## Create Datasets  \n",
        "\n",
        "First split data to train, validation and test using the `train_test_split funciton`. Remember to shuffle data. Then create ASLDataset for each part of data. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nknUhRi7urOj",
        "outputId": "eeaf4d42-c160-43b2-9184-1184c04a6e84"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2368 128 19\n"
          ]
        }
      ],
      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(files_addr, labels, test_size= 19, shuffle = True)\n",
        "x_train, x_val, y_train, y_val = train_test_split(x_train, y_train, test_size=128, shuffle = True)\n",
        "\n",
        "train_dataset = ASLDataset(files_address = x_train,labels = y_train)\n",
        "val_dataset = ASLDataset(files_address = x_val,labels = y_val)\n",
        "test_dataset = ASLDataset(files_address = x_test,labels = y_test)\n",
        "print(len(train_dataset),len(val_dataset),len(test_dataset))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jSQvNX2zwfF7"
      },
      "source": [
        "Use datasets to create DataLoader from `torch.utils.DataLoader` class\n",
        "\n",
        "Note: You should make `shuffle=True` for the training dataLoader, so in each epoch the data reshuffles. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NOeBG2l7w7S0",
        "outputId": "f3a7128d-aaf4-4bfe-d2e2-3de9841f0079"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "74 4 1\n"
          ]
        }
      ],
      "source": [
        "num_threads = 4\n",
        "batch_size = 32\n",
        "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
        "val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False)\n",
        "test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)\n",
        "print(len(train_loader),len(val_loader),len(test_loader))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CVV1mdA-xLVr"
      },
      "source": [
        "## Define Model "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "dLgHG6efxNp9"
      },
      "outputs": [],
      "source": [
        "class ASLModel(nn.Module):\n",
        "    def __init__(self, in_channels, num_classes):\n",
        "        super().__init__()\n",
        "        self.network = nn.Sequential(\n",
        "          \n",
        "              nn.Conv2d(3, 16, kernel_size = 3, padding = 1),\n",
        "              nn.ReLU(),\n",
        "              nn.Conv2d(16,32, kernel_size = 3, stride = 1, padding = 1),\n",
        "              nn.ReLU(),\n",
        "              nn.MaxPool2d(3,3),\n",
        "          \n",
        "              nn.Conv2d(32, 32, kernel_size = 3, stride = 1, padding = 1),\n",
        "              nn.ReLU(),\n",
        "              nn.Conv2d(32 ,64, kernel_size = 3, stride = 1, padding = 1),\n",
        "              nn.ReLU(),\n",
        "              nn.MaxPool2d(3,3),\n",
        "              nn.BatchNorm2d(64),\n",
        "              \n",
        "              \n",
        "              nn.Conv2d(64, 128, kernel_size = 3, stride = 1, padding = 1),\n",
        "              nn.ReLU(),\n",
        "              nn.Conv2d(128,256, kernel_size = 3, stride = 1, padding = 1),\n",
        "              nn.ReLU(),\n",
        "              nn.MaxPool2d(3,3),\n",
        "\n",
        "              nn.BatchNorm2d(256),\n",
        "              \n",
        "              nn.Flatten(),\n",
        "              nn.Dropout(0.5),\n",
        "              nn.Linear(1024,512),\n",
        "              nn.ReLU(),\n",
        "              nn.Linear(512,36),\n",
        "               \n",
        "            )\n",
        "        \n",
        "    def forward(self, xb):\n",
        "        return F.log_softmax(self.network(xb), dim=1)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c-5OU-EPxfIa",
        "outputId": "4b0742df-3e78-4568-8406-2b3609474915"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "945764\n"
          ]
        }
      ],
      "source": [
        "torch.manual_seed(0)\n",
        "learning_rate = 0.001\n",
        "loss_function = nn.NLLLoss()\n",
        "model = ASLModel(in_channels = 3,num_classes =  36).to(device)\n",
        "print(sum(p.numel() for p in model.parameters()))\n",
        "optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate, weight_decay=1e-04)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S4v3-QaYyQ3L"
      },
      "source": [
        "## Define optimizer, Criterion and other parameters \n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "id": "N5ga_FAmNVx-"
      },
      "outputs": [],
      "source": [
        "best_model = None\n",
        "best_acc = 0\n",
        "def accuracy(outputs, labels):\n",
        "    _, preds = torch.max(outputs, dim=1)\n",
        "    return torch.tensor(torch.sum(preds == labels).item() / len(preds))\n",
        "\n",
        "def train_val (model, trainloader, val_loader, f_loss, optimizer, n_epochs, device = device):\n",
        "    best_acc = 0\n",
        "    train_loss_arr, val_loss_arr = [], []\n",
        "    train_acc_arr, val_acc_arr = [], []  \n",
        "    for epoch in range(n_epochs):\n",
        "        train_loss, val_loss = 0, 0\n",
        "        model.train()\n",
        "        #############################\n",
        "        #         Code here         #\n",
        "        #############################\n",
        "        ## Hint: Loop throught train_loader, get images with their labels and train the model\n",
        "        ## Hint 2: To update train_loss inside the loop use \"train_loss += batch_loss * batch_size\"\"\n",
        "        ## Hint 3: To update train_acc get the predicted label of each training data and compare with the real label\n",
        "        train_batch_accs = []\n",
        "        for image_batch in trainloader:\n",
        "          images,real_labels = image_batch\n",
        "          images,real_labels = images.to(device),real_labels.to(device)\n",
        "          predc_labels = model(images)\n",
        "          loss = f_loss(predc_labels,real_labels)\n",
        "          optimizer.zero_grad()\n",
        "          loss.backward()\n",
        "          optimizer.step()\n",
        "          train_loss += loss * batch_size\n",
        "          batch_acc = accuracy(predc_labels,real_labels)\n",
        "          train_batch_accs.append(batch_acc)\n",
        "          \n",
        "\n",
        "\n",
        "    \n",
        "        model.eval()\n",
        "        #############################\n",
        "        #         Code here         #\n",
        "        #############################\n",
        "        ## Hint: Loop throught val_loader, get images with their labels and evaluate the model\n",
        "        ## Hint 2: To update val_loss inside the loop use \"val_loss += batch_loss * batch_size\"\"\n",
        "        ## Hint 3: you have to keep the best model in the best_model variable to use it in the testing phase\n",
        "        val_batch_accs = []\n",
        "        with torch.no_grad():\n",
        "          for image_batch in val_loader:\n",
        "            images,real_labels = image_batch\n",
        "            images,real_labels = images.to(device),real_labels.to(device)\n",
        "            predc_labels = model(images)\n",
        "            loss = f_loss(predc_labels,real_labels)\n",
        "            val_loss += loss * batch_size\n",
        "            batch_acc = accuracy(predc_labels,real_labels)\n",
        "            val_batch_accs.append(batch_acc)\n",
        "        if (torch.stack(val_batch_accs).mean() > best_acc):\n",
        "          best_acc = torch.stack(val_batch_accs).mean()\n",
        "          best_model = model\n",
        "        \n",
        "          \n",
        "        \n",
        "\n",
        "        \n",
        "\n",
        "        train_loss /= len(train_loader.dataset)\n",
        "        val_loss /= len(val_loader.dataset)\n",
        "        train_loss_arr.append(train_loss)\n",
        "        val_loss_arr.append(val_loss)\n",
        "        #############################\n",
        "        #         Code here         #\n",
        "        #############################\n",
        "        # append accuracy of train and valid to the lists\n",
        "        val_acc_arr.append(torch.stack(val_batch_accs).mean())\n",
        "        train_acc_arr.append(torch.stack(train_batch_accs).mean())\n",
        "\n",
        "        print(f\"[Epoch {epoch}]\\t\"\n",
        "            f\"Train Loss: {train_loss:.3f}\\t\"\n",
        "            f\"Validation Loss: {val_loss:.3f}\")\n",
        "        print(\"train accuracy : \",torch.stack(train_batch_accs).mean(),\" val accuracy : \",torch.stack(val_batch_accs).mean())\n",
        "        \n",
        "    return train_loss_arr, val_loss_arr, train_acc_arr, val_acc_arr"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9icZtWj81J_l",
        "outputId": "c0456998-f5bc-4f7a-9963-e22a645be87d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[Epoch 0]\tTrain Loss: 1.164\tValidation Loss: 0.478\n",
            "train accuracy :  tensor(0.6495)  val accuracy :  tensor(0.8672)\n",
            "[Epoch 1]\tTrain Loss: 0.294\tValidation Loss: 0.284\n",
            "train accuracy :  tensor(0.8974)  val accuracy :  tensor(0.9062)\n",
            "[Epoch 2]\tTrain Loss: 0.220\tValidation Loss: 0.151\n",
            "train accuracy :  tensor(0.9261)  val accuracy :  tensor(0.9453)\n",
            "[Epoch 3]\tTrain Loss: 0.144\tValidation Loss: 0.142\n",
            "train accuracy :  tensor(0.9464)  val accuracy :  tensor(0.9688)\n",
            "[Epoch 4]\tTrain Loss: 0.110\tValidation Loss: 0.116\n",
            "train accuracy :  tensor(0.9616)  val accuracy :  tensor(0.9688)\n",
            "[Epoch 5]\tTrain Loss: 0.059\tValidation Loss: 0.075\n",
            "train accuracy :  tensor(0.9785)  val accuracy :  tensor(0.9688)\n",
            "[Epoch 6]\tTrain Loss: 0.079\tValidation Loss: 0.095\n",
            "train accuracy :  tensor(0.9734)  val accuracy :  tensor(0.9688)\n",
            "[Epoch 7]\tTrain Loss: 0.052\tValidation Loss: 0.041\n",
            "train accuracy :  tensor(0.9848)  val accuracy :  tensor(0.9844)\n",
            "[Epoch 8]\tTrain Loss: 0.040\tValidation Loss: 0.045\n",
            "train accuracy :  tensor(0.9878)  val accuracy :  tensor(0.9766)\n",
            "[Epoch 9]\tTrain Loss: 0.037\tValidation Loss: 0.143\n",
            "train accuracy :  tensor(0.9882)  val accuracy :  tensor(0.9688)\n",
            "[Epoch 10]\tTrain Loss: 0.054\tValidation Loss: 0.257\n",
            "train accuracy :  tensor(0.9827)  val accuracy :  tensor(0.9375)\n",
            "[Epoch 11]\tTrain Loss: 0.066\tValidation Loss: 0.096\n",
            "train accuracy :  tensor(0.9802)  val accuracy :  tensor(0.9609)\n",
            "[Epoch 12]\tTrain Loss: 0.042\tValidation Loss: 0.161\n",
            "train accuracy :  tensor(0.9840)  val accuracy :  tensor(0.9688)\n",
            "[Epoch 13]\tTrain Loss: 0.042\tValidation Loss: 0.098\n",
            "train accuracy :  tensor(0.9856)  val accuracy :  tensor(0.9688)\n",
            "[Epoch 14]\tTrain Loss: 0.045\tValidation Loss: 0.095\n",
            "train accuracy :  tensor(0.9840)  val accuracy :  tensor(0.9844)\n",
            "[Epoch 15]\tTrain Loss: 0.077\tValidation Loss: 0.247\n",
            "train accuracy :  tensor(0.9755)  val accuracy :  tensor(0.9297)\n",
            "[Epoch 16]\tTrain Loss: 0.064\tValidation Loss: 0.067\n",
            "train accuracy :  tensor(0.9831)  val accuracy :  tensor(0.9766)\n",
            "[Epoch 17]\tTrain Loss: 0.031\tValidation Loss: 0.106\n",
            "train accuracy :  tensor(0.9907)  val accuracy :  tensor(0.9844)\n",
            "[Epoch 18]\tTrain Loss: 0.026\tValidation Loss: 0.069\n",
            "train accuracy :  tensor(0.9924)  val accuracy :  tensor(0.9766)\n",
            "[Epoch 19]\tTrain Loss: 0.022\tValidation Loss: 0.196\n",
            "train accuracy :  tensor(0.9916)  val accuracy :  tensor(0.9766)\n",
            "[Epoch 20]\tTrain Loss: 0.024\tValidation Loss: 0.068\n",
            "train accuracy :  tensor(0.9932)  val accuracy :  tensor(0.9844)\n",
            "[Epoch 21]\tTrain Loss: 0.009\tValidation Loss: 0.015\n",
            "train accuracy :  tensor(0.9975)  val accuracy :  tensor(1.)\n",
            "[Epoch 22]\tTrain Loss: 0.014\tValidation Loss: 0.033\n",
            "train accuracy :  tensor(0.9966)  val accuracy :  tensor(0.9922)\n",
            "[Epoch 23]\tTrain Loss: 0.005\tValidation Loss: 0.081\n",
            "train accuracy :  tensor(0.9979)  val accuracy :  tensor(0.9766)\n",
            "[Epoch 24]\tTrain Loss: 0.015\tValidation Loss: 0.102\n",
            "train accuracy :  tensor(0.9962)  val accuracy :  tensor(0.9766)\n",
            "[Epoch 25]\tTrain Loss: 0.002\tValidation Loss: 0.051\n",
            "train accuracy :  tensor(1.)  val accuracy :  tensor(0.9922)\n",
            "[Epoch 26]\tTrain Loss: 0.002\tValidation Loss: 0.056\n",
            "train accuracy :  tensor(1.)  val accuracy :  tensor(0.9844)\n",
            "[Epoch 27]\tTrain Loss: 0.001\tValidation Loss: 0.067\n",
            "train accuracy :  tensor(1.)  val accuracy :  tensor(0.9844)\n",
            "[Epoch 28]\tTrain Loss: 0.000\tValidation Loss: 0.067\n",
            "train accuracy :  tensor(1.)  val accuracy :  tensor(0.9844)\n",
            "[Epoch 29]\tTrain Loss: 0.001\tValidation Loss: 0.073\n",
            "train accuracy :  tensor(1.)  val accuracy :  tensor(0.9844)\n"
          ]
        }
      ],
      "source": [
        "# Call the train_val function\n",
        "train_loss_arr,val_loss_arr,train_acc_arr,val_acc_arr = train_val(model = model,trainloader = train_loader,val_loader=val_loader,f_loss = loss_function,optimizer = optimizer,n_epochs=30)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ruKan1xf1UPk"
      },
      "source": [
        "## Plot loss and accuracy\n",
        "\n",
        "You must have one plot for loss according to epoch for train/val.\n",
        "\n",
        "Also, another plot for accuracy according to epoch for train/val"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 899
        },
        "id": "4X5wdTRH1Zl1",
        "outputId": "1267ca5f-9218-45ff-d800-822a9cbfeaf6"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1512x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1512x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "#############################\n",
        "#         Code here         #\n",
        "#############################\n",
        "\n",
        "plt.subplots(figsize=(21,7))\n",
        "\n",
        "# loss = plt.subplot(1, 2, 1)\n",
        "# loss.set_title('Loss Plot')\n",
        "# loss.set_xlabel('Epochs')\n",
        "# loss.set_ylabel('Loss')\n",
        "# loss.plot(train_loss_arr)\n",
        "# loss.plot(val_loss_arr)\n",
        "# loss.legend(['Train loss', 'Validation loss'])\n",
        "\n",
        "accuracay = plt.subplot(1, 2, 2)\n",
        "accuracay.set_title('Accuracy Plot')\n",
        "accuracay.set_xlabel('Epochs')\n",
        "accuracay.set_ylabel('Accuracy')\n",
        "accuracay.plot(train_acc_arr)\n",
        "accuracay.plot(val_acc_arr)\n",
        "accuracay.legend(['Train accuracy', 'Validation accuracy'])\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ANeI27ul2QGd"
      },
      "source": [
        "## Evaluate model on the test dataset\n",
        "\n",
        "Evaluate your best model on the test dataset. You should get accuracy above ***%96*** for a full-mark.\n",
        "\n",
        "Remember to print the accuracy of model on the test dataset! "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 633
        },
        "id": "skw5pGKO2NT0",
        "outputId": "c4b47bbc-9ab9-4469-e598-c9f67eed63cc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Test F1-score: 1.0000\n",
            "Test Accuracy: 100.00%\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 864x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "#############################\n",
        "#         Code here         #\n",
        "#############################\n",
        "with torch.no_grad():\n",
        "    for images, labels in test_loader:\n",
        "        images, labels = images.to(device), labels.to(device)\n",
        "        pred = model(images).argmax(dim=1, keepdim=True)\n",
        "        \n",
        "        accuracy = accuracy_score(labels.detach().cpu().numpy(), pred.detach().cpu().numpy())\n",
        "        f1 = f1_score(labels.detach().cpu().numpy(), pred.detach().cpu().numpy(), average='macro')\n",
        "        confusion = confusion_matrix(labels.detach().cpu().numpy(), pred.detach().cpu().numpy(), normalize='true')\n",
        "        \n",
        "        plt.figure(figsize=(12,10))\n",
        "        print(f'Test F1-score: {f1:.4f}')\n",
        "        print(f'Test Accuracy: {accuracy * 100:.2f}%')\n",
        "        \n",
        "        ax = sns.heatmap(confusion * 100, annot=True, fmt='.2f')\n",
        "        ax.xaxis.tick_top()"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [
        "qlwXkss8T_Nl"
      ],
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.11.0"
    },
    "vscode": {
      "interpreter": {
        "hash": "1a1af0ee75eeea9e2e1ee996c87e7a2b11a0bebd85af04bb136d915cefc0abce"
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}