LAB4_kNN.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyPRHN2harXNbaDAFKidIvEl",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/MorgooN/ML-Labs/blob/main/LAB4_kNN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "LGOezxkktajA"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "from keras.applications.vgg16 import VGG16\n",
        "from keras.preprocessing import image\n",
        "from keras.models import Model\n",
        "from keras.layers import GlobalAveragePooling2D, Flatten, Rescaling, Concatenate\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.metrics import accuracy_score\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install split_folders"
      ],
      "metadata": {
        "id": "fTG6jeOXMOQ6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "271d50d7-f5e0-4cfc-a4ac-6f80430cce5e"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: split_folders in /usr/local/lib/python3.10/dist-packages (0.5.1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!gdown --id 1SK47fesS6Ek_XHVdjWCAmjgY95YyVz10"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "72sLcgy9MdCF",
        "outputId": "d4bf0407-51c6-4262-cf03-a03c8a39a7d2"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/gdown/cli.py:121: FutureWarning: Option `--id` was deprecated in version 4.3.1 and will be removed in 5.0. You don't need to pass it anymore to use a file ID.\n",
            "  warnings.warn(\n",
            "Downloading...\n",
            "From: https://drive.google.com/uc?id=1SK47fesS6Ek_XHVdjWCAmjgY95YyVz10\n",
            "To: /content/lab4.zip\n",
            "100% 614M/614M [00:04<00:00, 125MB/s] \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip lab4.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E2gVCcmRP7wD",
        "outputId": "b2081912-109a-4ceb-cbe0-fa54a4a0c078"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size = 8\n",
        "img_height = 112\n",
        "img_width = 112\n",
        "\n",
        "train_ds = tf.keras.utils.image_dataset_from_directory(\n",
        "  directory='/content/train', # путь к папке с обучающими данными\n",
        "  label_mode = 'int',       # представление правильных ответов картинок в виде чисел (Label Encoding): 0, 1, 2, 3... (Также можно использовать One-Hot Encoding, для этого напишите categorical) (Если у вас задача бинарной классификации пишите binary)\n",
        "  color_mode='rgb',         # представление каждой картинки в RGB формате\n",
        "  batch_size=batch_size,    # параметр, с помощью которого можно регулировать порцию подаваемых примеров (картинок) для сети за одну итерацию обучения\n",
        "  seed=123,\n",
        "  image_size=(img_height, img_width), # нейронная сеть всегда принимает изображение определённого размера. Поэтому необходимо предварительно поменять размер каждого изображения\n",
        "  shuffle=True)                       # перетасовка данных, чтобы генерировать данные не в определённом порядке (сначала 1 класс, потом 2 класс), а случайным образом\n",
        "\n",
        "test_ds = tf.keras.utils.image_dataset_from_directory(\n",
        "  directory='/content/test',  # путь к папке с тестовыми данными\n",
        "  label_mode = 'int',\n",
        "  color_mode='rgb',\n",
        "  batch_size=batch_size,\n",
        "  seed=123,\n",
        "  image_size=(img_height, img_width),\n",
        "  shuffle=False)\n",
        "\n",
        "\n",
        "normalization_layer = Rescaling(1./255)\n",
        "normalized_ds = train_ds.map(lambda x, y: (normalization_layer(x), y))\n",
        "normalized_ds = test_ds.map(lambda x, y: (normalization_layer(x), y))\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ar1deQu-bylI",
        "outputId": "03b083e0-f321-49cf-82e3-115281750790"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 11220 files belonging to 2 classes.\n",
            "Found 600 files belonging to 2 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Функция формирования набора данных (картинки и метки)\n",
        "def extract_dataset_images(dataset):\n",
        "  labels = []\n",
        "  images = []\n",
        "  for batch, batch_labels in dataset:\n",
        "    images.append(batch.numpy().reshape((batch.shape[0], -1))) # Делаем картинки в виде векторов\n",
        "    labels.append(batch_labels.numpy())\n",
        "  return np.concatenate(images), np.concatenate(labels)\n",
        "\n",
        "x_train, y_train = extract_dataset_images(train_ds)"
      ],
      "metadata": {
        "id": "7a2maZw0dCxv"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def train_and_test_KNN(x_train, y_train):\n",
        "  # Обучение KNN на картинках (количество соседей n_neighbors, метрику расстояний и функцию взвешивания нужно подобрать)\n",
        "  knn_classifier = KNeighborsClassifier(n_neighbors=5, metric='minkowski', weights='distance')\n",
        "  knn_classifier.fit(x_train, y_train)\n",
        "\n",
        "  # Тестирование\n",
        "\n",
        "  # Извлекаем тестовые картинки и соответствующие метки\n",
        "  x_test, y_test = extract_dataset_images(test_ds)\n",
        "  # Классификация тестовых изображений с использованием KNN\n",
        "  y_pred = knn_classifier.predict(x_test)\n",
        "\n",
        "  accuracy = accuracy_score(y_test, y_pred)\n",
        "  print(\"KNN accuracy on test data:\", accuracy)\n",
        "\n",
        "train_and_test_KNN(x_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZB0r0v2NdIjx",
        "outputId": "1064ea2f-a45d-4ef2-ae4d-7ede18520b4f"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "KNN accuracy on test data: 0.8066666666666666\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train, y_train = extract_dataset_images(train_ds)"
      ],
      "metadata": {
        "id": "Op82qvzmdOzS"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def train_and_test_PCA_KNN(x_train, y_train):\n",
        "  # Применение PCA для извлечения только полезной информации и уменьшения размерности признаков (количество компонент нужно подобрать)\n",
        "  pca = PCA(n_components=170)\n",
        "  x_train_pca = pca.fit_transform(x_train)\n",
        "\n",
        "  # Обучение KNN на сжатых признаках PCA (количество соседей n_neighbors, метрику расстояний и функцию взвешивания нужно подобрать)\n",
        "  knn_classifier = KNeighborsClassifier(n_neighbors=9, metric='minkowski', weights='distance')\n",
        "  knn_classifier.fit(x_train_pca, y_train)\n",
        "\n",
        "  # Тестирование\n",
        "\n",
        "  # Извлекаем тестовые картинки и соответствующие метки\n",
        "  x_test, y_test = extract_dataset_images(test_ds)\n",
        "\n",
        "  # Применение созданного ранее PCA для тестовых данных\n",
        "  x_test_pca = pca.transform(x_test)\n",
        "\n",
        "  # Классификация тестовых изображений с использованием KNN\n",
        "  y_pred = knn_classifier.predict(x_test_pca)\n",
        "\n",
        "  accuracy = accuracy_score(y_test, y_pred)\n",
        "  print(\"PCA + KNN accuracy on test data:\", accuracy)\n",
        "\n",
        "train_and_test_PCA_KNN(x_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eH8FvdmodR5s",
        "outputId": "211924d7-1f58-4838-9f80-0ff043864354"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PCA + KNN accuracy on test data: 0.8166666666666667\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def createPreTrainedModel(input_shape):\n",
        "\n",
        "  base_model = VGG16(weights='imagenet', include_top=False, input_shape=input_shape)\n",
        "\n",
        "  # Выбор последнего слоя максимального пулинга для извлечения высокоуровневых признаков\n",
        "  max_pooling_layer = base_model.get_layer('block5_pool').output\n",
        "\n",
        "  # Применение Global Average Pooling (для усреднения признаков и таким образом уменьшения размерности признаков)\n",
        "  global_avg_pooling_layer = GlobalAveragePooling2D()(max_pooling_layer)\n",
        "\n",
        "  # Применение Flatten (чтобы выводить вектор признаков, поскольку KNN работает с векторами)\n",
        "  flatten_layer = Flatten()(global_avg_pooling_layer)\n",
        "\n",
        "  # Создание новой модели\n",
        "  model = Model(inputs=base_model.input, outputs=flatten_layer)\n",
        "  return model\n",
        "\n",
        "\n",
        "def extract_features(dataset, model):\n",
        "    features = []\n",
        "    labels = []\n",
        "    for batch_images, batch_labels in dataset:\n",
        "        batch_features = model.predict(batch_images)\n",
        "        labels.append(batch_labels.numpy())\n",
        "        features.append(batch_features)\n",
        "    return np.concatenate(features), np.concatenate(labels)"
      ],
      "metadata": {
        "id": "SvGqyXJdfMAg"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "model = createPreTrainedModel((112, 112, 3))\n",
        "x_train_features, y_train_labels = extract_features(train_ds, model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GuvwRcdmflpy",
        "outputId": "4ad9c40a-0a22-46d4-ce88-be3cd05ca8fd"
      },
      "execution_count": 11,
      "outputs": [
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def train_and_test_CNN_PCA_KNN(x_train_features, y_train_labels):\n",
        "  # Применение PCA для извлечения только полезной информации и уменьшения размерности признаков (количество компонент нужно подобрать)\n",
        "  pca = PCA(n_components=250)\n",
        "  x_train_pca = pca.fit_transform(x_train_features)\n",
        "\n",
        "  # Обучение KNN на уменьшенных признаках (количество соседей n_neighbors, метрику расстояний и функцию взвешивания нужно подобрать)\n",
        "  # Список метрик: https://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.distance_metrics.html#sklearn.metrics.pairwise.distance_metrics\n",
        "  knn_classifier = KNeighborsClassifier(n_neighbors=5, metric='minkowski', weights='distance')\n",
        "  knn_classifier.fit(x_train_pca, y_train_labels)\n",
        "\n",
        "  # Тестирование\n",
        "\n",
        "  # Извлекаем признаки из изображений тестовой выборки test_ds\n",
        "  x_test_features, y_test_labels = extract_features(test_ds, model)\n",
        "\n",
        "  # Применение созданного ранее PCA для тестовых данных\n",
        "  x_test_pca = pca.transform(x_test_features)\n",
        "\n",
        "  # Классификация тестовых изображений с использованием KNN\n",
        "  y_pred = knn_classifier.predict(x_test_pca)\n",
        "\n",
        "  # Рассчёт точности\n",
        "  accuracy = accuracy_score(y_test_labels, y_pred)\n",
        "  print(\"CNN + PCA + KNN accuracy:\", accuracy)\n",
        "\n",
        "train_and_test_CNN_PCA_KNN(x_train_features, y_train_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KugCOPiohNKN",
        "outputId": "2148caae-7056-4ab7-f327-b69f5545dfb9"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "CNN + PCA + KNN accuracy: 0.8483333333333334\n"
          ]
        }
      ]
    }
  ]
}