with_CNN_handwritten_digit_detection.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i43NAOjiEM0O"
      },
      "source": [
        "## Task Description"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5KoYQeQD9sxW"
      },
      "source": [
        "You are given a dataset of handwritten numbers with two digits.\n",
        "Your task is to construct and train a classifier to predict the number from the image input.\n",
        "Specifically, please:\n",
        "Train a deep learning model for this task with testing accuracy over 80%.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AeMDXZxVkmVH"
      },
      "source": [
        "# Loading Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ids7uLPK4rDI"
      },
      "source": [
        "## Extract From Zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "I-Xtzg_oyqHk"
      },
      "outputs": [],
      "source": [
        "import zipfile\n",
        "\n",
        "# Name and path of the ZIP file\n",
        "# you can find the images on https://github.com/ahoirg/with_CNN_handwritten_digit_detection/blob/master/images.zip\n",
        "zip_file_path = 'xxx' ## write images path\n",
        "\n",
        "# Extract the ZIP file to the Colab workspace.\n",
        "with zipfile.ZipFile(zip_file_path, 'r') as zip_ref:\n",
        "    zip_ref.extractall('extracted_images')\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-jl6XBj-4vGW"
      },
      "source": [
        "## Load Data and Preparing Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "dvcdJ-bfcM3H"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import shutil\n",
        "import random\n",
        "\n",
        "# I want to split the train data into 2 data: train and validation.\n",
        "# When I did this split operation after loading using ImageFolder, the type of the object changed.\n",
        "# That's why I do the split before the Load phase.\n",
        "\n",
        "validation_folder = '/content/extracted_images/images/validation'\n",
        "if not os.path.exists(validation_folder):\n",
        "    os.makedirs(validation_folder)\n",
        "\n",
        "train_folder = '/content/extracted_images/images/train'\n",
        "\n",
        "for i in range(100):\n",
        "    train_subfolder = os.path.join(train_folder, str(i))\n",
        "    validation_subfolder = os.path.join(validation_folder, str(i))\n",
        "\n",
        "    if not os.path.exists(validation_subfolder):\n",
        "        os.makedirs(validation_subfolder)\n",
        "\n",
        "    files = os.listdir(train_subfolder)\n",
        "    random.shuffle(files)\n",
        "\n",
        "    # Move first 2 files to validation folder\n",
        "    for j in range(2):\n",
        "        if files:\n",
        "            file_to_move = files.pop()\n",
        "            source_path = os.path.join(train_subfolder, file_to_move)\n",
        "            dest_path = os.path.join(validation_subfolder, file_to_move)\n",
        "            shutil.move(source_path, dest_path)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "EZhBrmxBihYs"
      },
      "outputs": [],
      "source": [
        "import torchvision.transforms as transforms\n",
        "\n",
        "transform = transforms.Compose([\n",
        "    transforms.Grayscale(num_output_channels=1),  # Convert images to single channel\n",
        "    transforms.Resize((28, 28)),  # Resizes all images to 28x28.\n",
        "    transforms.ToTensor(),  # Converts the image into numbers, that are understandable by the system.\n",
        "    transforms.Normalize(mean=[0.5], std=[0.5])  # Normalization (convert the data from the range [0, 1] to the range [-1, 1]).\n",
        "])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "id": "tgVk_N5kfT9i"
      },
      "outputs": [],
      "source": [
        "from torchvision.datasets import ImageFolder\n",
        "from torch.utils.data import DataLoader\n",
        "\n",
        "train = ImageFolder(root='/content/extracted_images/images/train', transform=transform)\n",
        "validation = ImageFolder(root='/content/extracted_images/images/validation', transform=transform)\n",
        "test = ImageFolder(root='/content/extracted_images/images/test', transform=transform)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YUwEXKqe_2HK",
        "outputId": "d61eb800-7f7b-4e72-9baf-d96ba1b0d720"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'0': 0,\n",
              " '1': 1,\n",
              " '10': 2,\n",
              " '11': 3,\n",
              " '12': 4,\n",
              " '13': 5,\n",
              " '14': 6,\n",
              " '15': 7,\n",
              " '16': 8,\n",
              " '17': 9,\n",
              " '18': 10,\n",
              " '19': 11,\n",
              " '2': 12,\n",
              " '20': 13,\n",
              " '21': 14}"
            ]
          },
          "execution_count": 46,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "first_15_class_to_idx = {class_name: class_idx for class_name, class_idx in train.class_to_idx.items() if class_idx < 15}\n",
        "\n",
        "first_15_class_to_idx"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LCUqs6y9d2Gh"
      },
      "source": [
        "Class values ​​and index values ​​are different from each other.\n",
        "In fact, it does not cause any problems in training and testing the Model. The model will already know which label belongs to which class.\n",
        "\n",
        "However, during the **Manual Testing** phase (you can find it below the page), when I present it in a png file, it will be confusing as it will write the label corresponding to the class (for example, it will write 11 for class '19').\n",
        "\n",
        "Therefore, I make the class and label values ​​the same."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B0bTdeB9_ulW",
        "outputId": "1904a4bc-d8c4-4bbf-8493-86e7b0d1bc8a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'0': 0,\n",
              " '1': 1,\n",
              " '2': 2,\n",
              " '3': 3,\n",
              " '4': 4,\n",
              " '5': 5,\n",
              " '6': 6,\n",
              " '7': 7,\n",
              " '8': 8,\n",
              " '9': 9,\n",
              " '10': 10,\n",
              " '11': 11,\n",
              " '12': 12,\n",
              " '13': 13,\n",
              " '14': 14}"
            ]
          },
          "execution_count": 47,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "class_to_idx = {}  # An empty dictionary\n",
        "for i in range(100):\n",
        "    class_name = str(i)  # Get the class name\n",
        "    class_to_idx[class_name] = i  # Associate class name with integer value\n",
        "\n",
        "train.class_to_idx = class_to_idx\n",
        "validation.class_to_idx = class_to_idx\n",
        "test.class_to_idx = class_to_idx\n",
        "\n",
        "first_15_class_to_idx = {class_name: class_idx for class_name, class_idx in train.class_to_idx.items() if class_idx < 15}\n",
        "\n",
        "first_15_class_to_idx"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uidLq7sM6KF_",
        "outputId": "e433fa6d-fdcc-444f-f36d-5897350cf7c8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number Of Total Train Classes: 100\n",
            "Number Of Total Validation Classes: 100\n",
            "Number Of Total Test Classes: 100\n"
          ]
        }
      ],
      "source": [
        "print(\"Number Of Total Train Classes:\", len(train.classes))\n",
        "\n",
        "print(\"Number Of Total Validation Classes:\", len(validation.classes))\n",
        "\n",
        "print(\"Number Of Total Test Classes:\", len(test.classes))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T1W5gjd4fDfR"
      },
      "source": [
        "To check if everything is OK, I randomly select a class from 3 data types and print the .png files they represent on the screen."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "8vjqRF6d6202",
        "outputId": "dbbc9c98-6f2f-4dcb-c21f-1e7e4d203147"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "train\n"
          ]
        },
        {
          "data": {
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            "text/plain": [
              "<Figure size 1500x600 with 10 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "validation\n"
          ]
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1500x600 with 10 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "test\n"
          ]
        },
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1500x600 with 10 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import random\n",
        "import os\n",
        "from PIL import Image\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "data_dir = '/content/extracted_images/images'\n",
        "temp_dataset = ['train','validation','test']\n",
        "\n",
        "for k in range(3):\n",
        "\n",
        "  if k == 0:\n",
        "    selected_classes = random.sample(train.classes, 10)\n",
        "  elif k == 1:\n",
        "    selected_classes = random.sample(validation.classes, 10)\n",
        "  else:\n",
        "    selected_classes = random.sample(test.classes, 10)\n",
        "\n",
        "  # Show images of 10 different classes in one drawing\n",
        "  plt.figure(figsize=(15, 6))\n",
        "\n",
        "  for i, selected_class in enumerate(selected_classes, 1):\n",
        "      plt.subplot(2, 5, i)\n",
        "      plt.title(f\"Class: {selected_class}\")\n",
        "\n",
        "      # Find the folder path of the selected class\n",
        "      class_dir = os.path.join(data_dir, temp_dataset[k], selected_class)\n",
        "\n",
        "      # Randomly select an image within the folder\n",
        "      random_image_name = random.choice(os.listdir(class_dir))\n",
        "      image_path = os.path.join(class_dir, random_image_name)\n",
        "\n",
        "      # Upload the image and show it on the screen\n",
        "      image = Image.open(image_path)\n",
        "      plt.imshow(image)\n",
        "      plt.axis('off')\n",
        "\n",
        "  print(temp_dataset[k])\n",
        "  plt.tight_layout()\n",
        "  plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "okCyUnGXKzvB"
      },
      "source": [
        "### Preparing Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "id": "fYJLBlbNjsVA"
      },
      "outputs": [],
      "source": [
        "train_loader = DataLoader(train, batch_size=64 , shuffle= True)\n",
        "validation_loader = DataLoader(validation, batch_size=64 , shuffle= True)\n",
        "test_loader = DataLoader(test, batch_size=64 , shuffle= True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HhFq8mH4nugp",
        "outputId": "338893a5-adf5-496f-92e9-99952c7c3e3e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "torch.Size([64, 1, 28, 28])\n",
            "torch.Size([64])\n"
          ]
        }
      ],
      "source": [
        "dataiter = iter(train_loader)\n",
        "images, labels = next(dataiter)\n",
        "\n",
        "print(images.shape)\n",
        "print(labels.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "id": "ytqnwgpqqy3G",
        "outputId": "0a6a6a8b-3580-4db1-9332-e3d9cee26f47"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 10 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "figure = plt.figure()\n",
        "num_of_images = 10\n",
        "for index in range(1, num_of_images + 1):\n",
        "    plt.subplot(10, 10, index)\n",
        "    plt.axis('off')\n",
        "    plt.imshow(images[index].numpy().squeeze(), cmap='gray_r')\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DP9o9TEystQ_"
      },
      "source": [
        "# Train a deep learning model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AZOzpKrsxIpD"
      },
      "source": [
        "## Train and Test Model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rk6ntYCTREEn"
      },
      "source": [
        "### Convolutional Neural Networks"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CsZTBN2Pgc2E"
      },
      "source": [
        "#### Define a CNN model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "id": "2mwe8bhDuxOk"
      },
      "outputs": [],
      "source": [
        "import torch.nn as nn\n",
        "\n",
        "class CNN(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(CNN, self).__init__()\n",
        "        # Convolution layers\n",
        "        self.conv1 = nn.Conv2d(1, 32, 3)\n",
        "        self.conv2 = nn.Conv2d(32, 64, 3)\n",
        "        # Fully connected layers\n",
        "        self.fc1 = nn.Linear(64 * 5 * 5, 128)\n",
        "        self.fc2 = nn.Linear(128, 100)  # Output layer for 100 classes\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.conv1(x)\n",
        "        x = nn.functional.relu(x)\n",
        "        x = nn.functional.max_pool2d(x, 2)\n",
        "        x = self.conv2(x)\n",
        "        x = nn.functional.relu(x)\n",
        "        x = nn.functional.max_pool2d(x, 2)\n",
        "        x = x.view(-1, 64 * 5 * 5)\n",
        "        x = self.fc1(x)\n",
        "        x = nn.functional.relu(x)\n",
        "        x = self.fc2(x)\n",
        "        x = nn.functional.log_softmax(x, dim=1)\n",
        "        return x\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_QGT4wl8h0QJ"
      },
      "source": [
        "### Train The CNN Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 88,
      "metadata": {
        "id": "GVpRhAs8MMTH"
      },
      "outputs": [],
      "source": [
        "from torch import optim\n",
        "\n",
        "# Create the model\n",
        "# The model is reset every time \"model = CNN()\" is run.\n",
        "model = CNN()\n",
        "\n",
        "criterion = nn.CrossEntropyLoss() # Loss function\n",
        "\n",
        "# I tried different optimization algorithms and learning_rate\n",
        "# until I find the best accuracy.\n",
        "optimizer = optim.Adam(model.parameters(), lr=0.002) # Result Accurancy Rate: 92.90%\n",
        "#optimizer = optim.RMSprop(model.parameters(), lr=0.002) # Result Accurancy Rate: 89.20%\n",
        "\n",
        "# Training cycle\n",
        "for epoch in range(40):\n",
        "    model.train()  # Put the model in training mode\n",
        "    for batch_data, batch_labels in train_loader:\n",
        "        optimizer.zero_grad()\n",
        "        outputs = model(batch_data)\n",
        "        loss = criterion(outputs, batch_labels)  # Calculate the loss\n",
        "        loss.backward()  # Calculate backward gradient\n",
        "        optimizer.step()  # Update parameters"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qwec_aTcjvFc"
      },
      "source": [
        "### Define 'test_CNN_model' function to test and validation data\n",
        "\n",
        "Since I do not want to write the same code over and over again for testing, validation, I defined a function.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "id": "KmWb4Kv5jtvM"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "\n",
        "def test_CNN_model(_dataset):\n",
        "\tmodel.eval()  # Put the model in evaluation mode\n",
        "\tcorrect = 0\n",
        "\ttotal = 0\n",
        "\n",
        "\twith torch.no_grad():\n",
        "\t\tfor batch_data, batch_labels in _dataset:\n",
        "\t\t\toutputs = model(batch_data)\n",
        "\t\t\t_, predicted = torch.max(outputs, 1)\n",
        "\t\t\ttotal += batch_labels.size(0)\n",
        "\t\t\tcorrect += (predicted == batch_labels).sum().item()\n",
        "\t\t\taccuracy = 100 * correct / total\n",
        "\n",
        "\tprint(f'Result Accurancy Rate: {accuracy:.2f}%')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A8mW1AzcPZdw"
      },
      "source": [
        "## Validation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 89,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zUDeMVVBrh2b",
        "outputId": "1eb3c2e1-6b84-4ce6-f657-05971f35ca2e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Result Accurancy Rate: 97.44%\n"
          ]
        }
      ],
      "source": [
        "test_CNN_model(validation_loader)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ofX0Apd9kWpJ"
      },
      "source": [
        "\n",
        "The accuracy rate requested in the Task is 80%.\n",
        "\n",
        "My target is 90%. Since I have achieved this goal, I stop improving the model and move on to the testing phase."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kio6zBwThU17"
      },
      "source": [
        "## Test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 90,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yyOfce-hisOc",
        "outputId": "120e485f-ca52-4864-dd26-c1fbaa70f895"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Result Accurancy Rate: 93.50%\n"
          ]
        }
      ],
      "source": [
        "test_CNN_model(test_loader)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A4m2e5C1k2M-"
      },
      "source": [
        "---\n",
        "\n",
        "**Result Accurancy Rate for test data: 93.50%.**\n",
        "\n",
        "---\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p-pZ0E3EmNFv"
      },
      "source": [
        "## Addition Part\n",
        "\n",
        "\n",
        "In addition to Task, I want to add 2 more features.\n",
        "\n",
        "1) I want to save the model I trained.\n",
        "(You can also load this model that I trained and put it to another test.)\n",
        "\n",
        "2) Manual testing. I'll show the model a few images and ask for its prediction."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hj5Nhtv5nQn_"
      },
      "source": [
        "### Save the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "id": "yHeUmUWKnJrd"
      },
      "outputs": [],
      "source": [
        "#######\n",
        "# You don't need to run this code again.\n",
        "#######\n",
        "import torch\n",
        "torch.save(model.state_dict(), \"CNN_model.pth\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ndIBDMSonhp1"
      },
      "source": [
        "### Load The CNN Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 91,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        },
        "id": "7CqtcKWsuD0-",
        "outputId": "504c3e57-4e8d-493d-cab2-e67ef258cede"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=18efEU7Ivd1d2FNLaa9uMbFGRFe8v-YuE\n",
            "To: /content/CNN_model.pth\n",
            "100%|██████████| 949k/949k [00:00<00:00, 102MB/s]\n"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'CNN_model.pth'"
            ]
          },
          "execution_count": 91,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#######\n",
        "# You must run this code part to copy the model I trained  from my drive.\n",
        "# After this code runs, you will see a copy of it in the environment you are working in.\n",
        "#######\n",
        "import gdown\n",
        "\n",
        "file_url = 'https://drive.google.com/uc?id=18efEU7Ivd1d2FNLaa9uMbFGRFe8v-YuE'\n",
        "file_name = 'CNN_model.pth'\n",
        "gdown.download(file_url, file_name, quiet=False)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 92,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2JX0JgpDnoVy",
        "outputId": "ffea2e23-52f0-4f10-a9d2-3362850ce5db"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<All keys matched successfully>"
            ]
          },
          "execution_count": 92,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#######\n",
        "# You must run the \"Convolutional Neural Networks\" part once.\n",
        "# So you can run the CNN() model.\n",
        "#######\n",
        "import torch\n",
        "\n",
        "loaded_model = CNN()\n",
        "loaded_model.load_state_dict(torch.load(\"CNN_model.pth\"))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PkD4XolL5s8C"
      },
      "source": [
        "\n",
        "If you see \"**All keys matched successfully**\", the model has been loaded and everything is fine."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-y6dU_dXj_TS"
      },
      "source": [
        "### Manual(Individual) Test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "id": "aPO0COLnOUN1"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "from torchvision import transforms\n",
        "from PIL import Image\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def compute_accuracy(predictions, labels):\n",
        "\n",
        "    correct = (predictions == labels).sum().item() # Count the number of times the predictions were correct\n",
        "    total = labels.size(0) # Calculate the total number of samples\n",
        "    accuracy = correct / total\n",
        "    return accuracy\n",
        "\n",
        "def create_transform():\n",
        "    transform = transforms.Compose([\n",
        "    transforms.Grayscale(num_output_channels=1),\n",
        "    transforms.Resize((28, 28)),\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize(mean=[0.5], std=[0.5])\n",
        "    ])\n",
        "\n",
        "def show_image_and_prediction(path):\n",
        "    create_transform()\n",
        "\n",
        "    image = Image.open(path)\n",
        "    image = transform(image).unsqueeze(0)  # Convert image to tensor and adjust its size\n",
        "\n",
        "\n",
        "    # Example path: \"/content/extracted_images/images/test/1/107.png\"\n",
        "    # The folder name gives the expected number. Ex: .../1/107.png\n",
        "    parts = path.split('/')\n",
        "    actual_number_str = parts[-2]\n",
        "    actual_number = int(actual_number_str)\n",
        "\n",
        "    with torch.no_grad():\n",
        "        loaded_model.eval()\n",
        "        prediction = loaded_model(image)\n",
        "        predicted_class = torch.argmax(prediction, dim=1).item()\n",
        "        print(f\"Predicted: {predicted_class}\" +f\"\\nActual: {actual_number}\" )\n",
        "\n",
        "        # Calculate and print accuracy\n",
        "        labels = torch.tensor([actual_number])  # Convert real labels to tensor\n",
        "        accuracy = compute_accuracy(predicted_class, labels)\n",
        "        print(f\"Accuracy: {accuracy * 100:.2f}%\")\n",
        "\n",
        "\n",
        "    image_np = image.squeeze().numpy()\n",
        "    plt.imshow(image_np, cmap='gray_r')\n",
        "    plt.axis('off')\n",
        "    plt.show()\n",
        "    print(\"\\n\" )\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 93,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "8lJc8W3VOHhM",
        "outputId": "a6b1fafd-70dc-4c87-8872-2854f2267a77"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Predicted: 90\n",
            "Actual: 90\n",
            "Accuracy: 100.00%\n"
          ]
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Predicted: 0\n",
            "Actual: 0\n",
            "Accuracy: 100.00%\n"
          ]
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Predicted: 26\n",
            "Actual: 32\n",
            "Accuracy: 0.00%\n"
          ]
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Predicted: 99\n",
            "Actual: 99\n",
            "Accuracy: 100.00%\n"
          ]
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "images_paths  = [\"/content/extracted_images/images/test/90/24.png\",\n",
        "              \"/content/extracted_images/images/test/0/267.png\",\n",
        "              \"/content/extracted_images/images/test/32/477.png\",\n",
        "              \"/content/extracted_images/images/test/99/154.png\"]\n",
        "\n",
        "for image_path in images_paths:\n",
        "  show_image_and_prediction(image_path)\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [
        "AZOzpKrsxIpD",
        "p-pZ0E3EmNFv"
      ],
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}