122. Image Classification with cnn classifier.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "N_xnhjKb5hdt",
    "outputId": "74a1264c-0fdc-4bd7-aea3-35af37684d0a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
      "Collecting segmentation-models\n",
      "  Downloading segmentation_models-1.0.1-py3-none-any.whl (33 kB)\n",
      "Collecting efficientnet==1.0.0\n",
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      "\u001b[?25hCollecting image-classifiers==1.0.0\n",
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      "Installing collected packages: keras-applications, image-classifiers, efficientnet, segmentation-models\n",
      "Successfully installed efficientnet-1.0.0 image-classifiers-1.0.0 keras-applications-1.0.8 segmentation-models-1.0.1\n",
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     ]
    }
   ],
   "source": [
    "!pip install segmentation-models\n",
    "!pip install tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0waywDtz5kkO",
    "outputId": "80a94026-90b6-45cd-dfbd-224193a3d52b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
      "Requirement already satisfied: gdown in /usr/local/lib/python3.8/dist-packages (4.4.0)\n",
      "Collecting gdown\n",
      "  Downloading gdown-4.6.3-py3-none-any.whl (14 kB)\n",
      "Requirement already satisfied: filelock in /usr/local/lib/python3.8/dist-packages (from gdown) (3.9.0)\n",
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      "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /usr/local/lib/python3.8/dist-packages (from requests[socks]->gdown) (1.7.1)\n",
      "Installing collected packages: gdown\n",
      "  Attempting uninstall: gdown\n",
      "    Found existing installation: gdown 4.4.0\n",
      "    Uninstalling gdown-4.4.0:\n",
      "      Successfully uninstalled gdown-4.4.0\n",
      "Successfully installed gdown-4.6.3\n",
      "/usr/local/lib/python3.8/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=1-Vm4Y4YTPSH_zM3xSTzB9RQvJqiVFwaw\n",
      "To: /content/Tumor.zip\n",
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     ]
    }
   ],
   "source": [
    "!pip install --upgrade --no-cache-dir gdown\n",
    "!gdown --id 1-Vm4Y4YTPSH_zM3xSTzB9RQvJqiVFwaw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 192
    },
    "id": "ED-T6HwP5ojT",
    "outputId": "25100ce0-c4d6-4279-8f89-86d7949e0478"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
      "Collecting patool\n",
      "  Downloading patool-1.12-py2.py3-none-any.whl (77 kB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m77.5/77.5 KB\u001b[0m \u001b[31m7.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hInstalling collected packages: patool\n",
      "Successfully installed patool-1.12\n",
      "patool: Extracting Tumor.zip ...\n",
      "patool: running /usr/bin/7z x -o./Unpack_7n8wsy0b -- Tumor.zip\n",
      "patool: ... Tumor.zip extracted to `Tumor'.\n"
     ]
    },
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'Tumor'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "!pip install patool\n",
    "import patoolib\n",
    "patoolib.extract_archive('Tumor.zip')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "TvArgO065w2f"
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras import layers\n",
    "import glob\n",
    "import cv2\n",
    "import os\n",
    "from sklearn import preprocessing\n",
    "from sklearn.metrics import accuracy_score\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "pYyGPwFH5ymJ",
    "outputId": "981c4807-e753-4a2a-c070-c5176af96227"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pituitary\n",
      "meningioma\n",
      "glioma\n",
      "notumor\n",
      "pituitary\n",
      "meningioma\n",
      "glioma\n",
      "notumor\n"
     ]
    }
   ],
   "source": [
    "SIZE = 128\n",
    "\n",
    "train_images = []\n",
    "train_labels = [] \n",
    "\n",
    "for directory_path in glob.glob(\"/content/Tumor/Training/*\"):\n",
    "    label = directory_path.split(\"/\")[-1]\n",
    "    print(label)\n",
    "    for img_path in glob.glob(os.path.join(directory_path, \"*.png\")):\n",
    "        img = cv2.imread(img_path, cv2.IMREAD_COLOR)       \n",
    "        img = cv2.resize(img, (SIZE, SIZE))\n",
    "        img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)\n",
    "        train_images.append(img)\n",
    "        train_labels.append(label)\n",
    "\n",
    "train_images = np.array(train_images)\n",
    "train_labels = np.array(train_labels)\n",
    "\n",
    "test_images = []\n",
    "test_labels = [] \n",
    "for directory_path in glob.glob(\"/content/Tumor/Testing/*\"):\n",
    "    fruit_label = directory_path.split(\"/\")[-1]\n",
    "    print(fruit_label)\n",
    "    for img_path in glob.glob(os.path.join(directory_path, \"*.png\")):\n",
    "        img = cv2.imread(img_path, cv2.IMREAD_COLOR)\n",
    "        img = cv2.resize(img, (SIZE, SIZE))\n",
    "        img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)\n",
    "        test_images.append(img)\n",
    "        test_labels.append(fruit_label)\n",
    "\n",
    "test_images = np.array(test_images)\n",
    "test_labels = np.array(test_labels)\n",
    "\n",
    "le = preprocessing.LabelEncoder()\n",
    "le.fit(test_labels)\n",
    "test_labels_encoded = le.transform(test_labels)\n",
    "le.fit(train_labels)\n",
    "train_labels_encoded = le.transform(train_labels)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "bStMPlSe50k2"
   },
   "outputs": [],
   "source": [
    "model = tf.keras.Sequential([\n",
    "    layers.Conv2D(32, (3, 3), activation='relu', input_shape=(SIZE, SIZE, 3)),\n",
    "    layers.MaxPooling2D((2, 2)),\n",
    "    layers.Conv2D(64, (3, 3), activation='relu'),\n",
    "    layers.MaxPooling2D((2, 2)),\n",
    "    layers.Conv2D(128, (3, 3), activation='relu'),\n",
    "    layers.MaxPooling2D((2, 2)),\n",
    "    layers.Flatten(),\n",
    "    layers.Dense(64, activation='relu'),\n",
    "    layers.Dense(4)\n",
    "])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "rf4sQDb153i1"
   },
   "outputs": [],
   "source": [
    "model.compile(optimizer='adam',\n",
    "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
    "              metrics=['accuracy'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Q4iY39nq59Yu",
    "outputId": "81c42afb-410d-4d42-9953-5d6771968653"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "179/179 [==============================] - 3s 17ms/step - loss: 0.2168 - accuracy: 0.9219 - val_loss: 0.3648 - val_accuracy: 0.8688\n",
      "Epoch 2/100\n",
      "179/179 [==============================] - 3s 17ms/step - loss: 0.1767 - accuracy: 0.9366 - val_loss: 0.2739 - val_accuracy: 0.8970\n",
      "Epoch 3/100\n",
      "179/179 [==============================] - 3s 17ms/step - loss: 0.1409 - accuracy: 0.9499 - val_loss: 0.3215 - val_accuracy: 0.8825\n",
      "Epoch 4/100\n",
      "179/179 [==============================] - 3s 17ms/step - loss: 0.1147 - accuracy: 0.9599 - val_loss: 0.2492 - val_accuracy: 0.9100\n",
      "Epoch 5/100\n",
      "179/179 [==============================] - 3s 16ms/step - loss: 0.0810 - accuracy: 0.9708 - val_loss: 0.2037 - val_accuracy: 0.9359\n",
      "Epoch 6/100\n",
      "179/179 [==============================] - 3s 16ms/step - loss: 0.0475 - accuracy: 0.9839 - val_loss: 0.2458 - val_accuracy: 0.9275\n",
      "Epoch 7/100\n",
      "179/179 [==============================] - 3s 17ms/step - loss: 0.0948 - accuracy: 0.9692 - val_loss: 0.2250 - val_accuracy: 0.9329\n",
      "Epoch 8/100\n",
      "179/179 [==============================] - 3s 17ms/step - loss: 0.0864 - accuracy: 0.9715 - val_loss: 0.2545 - val_accuracy: 0.9298\n",
      "Epoch 9/100\n",
      "179/179 [==============================] - 3s 17ms/step - loss: 0.0425 - accuracy: 0.9853 - val_loss: 0.2739 - val_accuracy: 0.9367\n",
      "Epoch 10/100\n",
      "179/179 [==============================] - 3s 17ms/step - loss: 0.0623 - accuracy: 0.9813 - val_loss: 0.2981 - val_accuracy: 0.9268\n",
      "Epoch 11/100\n",
      "179/179 [==============================] - 3s 17ms/step - loss: 0.0660 - accuracy: 0.9781 - val_loss: 0.1723 - val_accuracy: 0.9512\n",
      "Epoch 12/100\n",
      "179/179 [==============================] - 3s 17ms/step - loss: 0.0301 - accuracy: 0.9886 - val_loss: 0.2470 - val_accuracy: 0.9458\n",
      "Epoch 13/100\n",
      "179/179 [==============================] - 3s 17ms/step - loss: 0.0349 - accuracy: 0.9881 - val_loss: 0.3303 - val_accuracy: 0.9413\n",
      "Epoch 14/100\n",
      "179/179 [==============================] - 3s 19ms/step - loss: 0.0518 - accuracy: 0.9835 - val_loss: 0.3346 - val_accuracy: 0.9153\n",
      "Epoch 15/100\n",
      "179/179 [==============================] - 3s 17ms/step - loss: 0.0917 - accuracy: 0.9722 - val_loss: 0.2822 - val_accuracy: 0.9275\n",
      "Epoch 16/100\n",
      "179/179 [==============================] - 3s 17ms/step - loss: 0.0437 - accuracy: 0.9856 - val_loss: 0.1999 - val_accuracy: 0.9527\n",
      "Epoch 17/100\n",
      "179/179 [==============================] - 3s 16ms/step - loss: 0.0250 - accuracy: 0.9925 - val_loss: 0.2152 - val_accuracy: 0.9550\n",
      "Epoch 18/100\n",
      "179/179 [==============================] - 3s 16ms/step - loss: 0.0527 - accuracy: 0.9855 - val_loss: 0.2422 - val_accuracy: 0.9519\n",
      "Epoch 19/100\n",
      "179/179 [==============================] - 4s 21ms/step - loss: 0.0177 - accuracy: 0.9942 - val_loss: 0.2065 - val_accuracy: 0.9558\n",
      "Epoch 20/100\n",
      "179/179 [==============================] - 4s 20ms/step - loss: 0.0161 - accuracy: 0.9947 - val_loss: 0.2387 - val_accuracy: 0.9580\n",
      "Epoch 21/100\n",
      "179/179 [==============================] - 3s 17ms/step - loss: 0.0241 - accuracy: 0.9925 - val_loss: 0.2160 - val_accuracy: 0.9550\n",
      "Epoch 22/100\n",
      "179/179 [==============================] - 3s 17ms/step - loss: 0.0203 - accuracy: 0.9939 - val_loss: 0.3033 - val_accuracy: 0.9390\n",
      "Epoch 23/100\n",
      "179/179 [==============================] - 3s 18ms/step - loss: 0.0510 - accuracy: 0.9848 - val_loss: 0.2963 - val_accuracy: 0.9367\n",
      "Epoch 24/100\n",
      "179/179 [==============================] - 3s 17ms/step - loss: 0.0364 - accuracy: 0.9876 - val_loss: 0.3255 - val_accuracy: 0.9527\n",
      "Epoch 25/100\n",
      "179/179 [==============================] - 3s 16ms/step - loss: 0.0116 - accuracy: 0.9965 - val_loss: 0.2861 - val_accuracy: 0.9565\n",
      "Epoch 26/100\n",
      "179/179 [==============================] - 3s 17ms/step - loss: 0.0864 - accuracy: 0.9779 - val_loss: 0.3317 - val_accuracy: 0.9222\n",
      "Epoch 27/100\n",
      "179/179 [==============================] - 3s 17ms/step - loss: 0.0175 - accuracy: 0.9935 - val_loss: 0.2292 - val_accuracy: 0.9603\n",
      "Epoch 28/100\n",
      "179/179 [==============================] - 3s 16ms/step - loss: 0.0049 - accuracy: 0.9982 - val_loss: 0.2496 - val_accuracy: 0.9611\n",
      "Epoch 29/100\n",
      "179/179 [==============================] - 3s 16ms/step - loss: 0.0035 - accuracy: 0.9989 - val_loss: 0.2409 - val_accuracy: 0.9603\n",
      "Epoch 30/100\n",
      "179/179 [==============================] - 3s 16ms/step - loss: 0.0546 - accuracy: 0.9844 - val_loss: 0.3995 - val_accuracy: 0.9275\n",
      "Epoch 31/100\n",
      "179/179 [==============================] - 3s 17ms/step - loss: 0.0542 - accuracy: 0.9835 - val_loss: 0.2425 - val_accuracy: 0.9611\n",
      "Epoch 32/100\n",
      "179/179 [==============================] - 3s 17ms/step - loss: 0.0233 - accuracy: 0.9919 - val_loss: 0.4070 - val_accuracy: 0.9359\n",
      "Epoch 33/100\n",
      "179/179 [==============================] - 3s 17ms/step - loss: 0.0206 - accuracy: 0.9940 - val_loss: 0.3955 - val_accuracy: 0.9397\n",
      "Epoch 34/100\n",
      "179/179 [==============================] - 3s 17ms/step - loss: 0.0086 - accuracy: 0.9979 - val_loss: 0.2188 - val_accuracy: 0.9672\n",
      "Epoch 35/100\n",
      "179/179 [==============================] - 3s 17ms/step - loss: 0.0019 - accuracy: 0.9995 - val_loss: 0.2828 - val_accuracy: 0.9565\n",
      "Epoch 36/100\n",
      "179/179 [==============================] - 3s 17ms/step - loss: 0.0036 - accuracy: 0.9991 - val_loss: 0.2706 - val_accuracy: 0.9626\n",
      "Epoch 37/100\n",
      "179/179 [==============================] - 3s 16ms/step - loss: 2.6089e-04 - accuracy: 1.0000 - val_loss: 0.2856 - val_accuracy: 0.9603\n",
      "Epoch 38/100\n",
      "179/179 [==============================] - 3s 16ms/step - loss: 8.0311e-05 - accuracy: 1.0000 - val_loss: 0.2898 - val_accuracy: 0.9611\n",
      "Epoch 39/100\n",
      "179/179 [==============================] - 3s 17ms/step - loss: 5.3822e-05 - accuracy: 1.0000 - val_loss: 0.2974 - val_accuracy: 0.9603\n",
      "Epoch 40/100\n",
      "179/179 [==============================] - 3s 17ms/step - loss: 4.2402e-05 - accuracy: 1.0000 - val_loss: 0.3009 - val_accuracy: 0.9619\n",
      "Epoch 41/100\n",
      "179/179 [==============================] - 3s 17ms/step - loss: 3.4998e-05 - accuracy: 1.0000 - val_loss: 0.3047 - val_accuracy: 0.9619\n",
      "Epoch 42/100\n",
      "179/179 [==============================] - 3s 17ms/step - loss: 2.9541e-05 - accuracy: 1.0000 - val_loss: 0.3087 - val_accuracy: 0.9619\n",
      "Epoch 43/100\n",
      "179/179 [==============================] - 3s 17ms/step - loss: 2.5231e-05 - accuracy: 1.0000 - val_loss: 0.3102 - val_accuracy: 0.9626\n",
      "Epoch 44/100\n",
      "179/179 [==============================] - 3s 18ms/step - loss: 2.1860e-05 - accuracy: 1.0000 - val_loss: 0.3130 - val_accuracy: 0.9619\n",
      "Epoch 45/100\n",
      "179/179 [==============================] - 3s 16ms/step - loss: 1.9069e-05 - accuracy: 1.0000 - val_loss: 0.3163 - val_accuracy: 0.9619\n",
      "Epoch 46/100\n",
      "179/179 [==============================] - 3s 16ms/step - loss: 1.6795e-05 - accuracy: 1.0000 - val_loss: 0.3180 - val_accuracy: 0.9619\n",
      "Epoch 47/100\n",
      "179/179 [==============================] - 3s 17ms/step - loss: 1.4833e-05 - accuracy: 1.0000 - val_loss: 0.3211 - val_accuracy: 0.9626\n",
      "Epoch 48/100\n",
      "179/179 [==============================] - 3s 17ms/step - loss: 1.3160e-05 - accuracy: 1.0000 - val_loss: 0.3239 - val_accuracy: 0.9626\n",
      "Epoch 49/100\n",
      "179/179 [==============================] - 3s 16ms/step - loss: 1.1678e-05 - accuracy: 1.0000 - val_loss: 0.3265 - val_accuracy: 0.9626\n",
      "Epoch 50/100\n",
      "179/179 [==============================] - 3s 16ms/step - loss: 1.0504e-05 - accuracy: 1.0000 - val_loss: 0.3285 - val_accuracy: 0.9626\n",
      "Epoch 51/100\n",
      "179/179 [==============================] - 3s 17ms/step - loss: 9.3800e-06 - accuracy: 1.0000 - val_loss: 0.3311 - val_accuracy: 0.9634\n",
      "Epoch 52/100\n",
      "179/179 [==============================] - 3s 17ms/step - loss: 8.4004e-06 - accuracy: 1.0000 - val_loss: 0.3337 - val_accuracy: 0.9634\n",
      "Epoch 53/100\n",
      "179/179 [==============================] - 3s 17ms/step - loss: 7.5761e-06 - accuracy: 1.0000 - val_loss: 0.3357 - val_accuracy: 0.9634\n",
      "Epoch 54/100\n",
      "179/179 [==============================] - 3s 17ms/step - loss: 6.7888e-06 - accuracy: 1.0000 - val_loss: 0.3387 - val_accuracy: 0.9619\n",
      "Epoch 55/100\n",
      "179/179 [==============================] - 3s 16ms/step - loss: 6.1089e-06 - accuracy: 1.0000 - val_loss: 0.3422 - val_accuracy: 0.9619\n",
      "Epoch 56/100\n",
      "179/179 [==============================] - 3s 18ms/step - loss: 5.5350e-06 - accuracy: 1.0000 - val_loss: 0.3442 - val_accuracy: 0.9619\n",
      "Epoch 57/100\n",
      "179/179 [==============================] - 3s 16ms/step - loss: 4.9835e-06 - accuracy: 1.0000 - val_loss: 0.3467 - val_accuracy: 0.9619\n",
      "Epoch 58/100\n",
      "179/179 [==============================] - 3s 16ms/step - loss: 4.5426e-06 - accuracy: 1.0000 - val_loss: 0.3484 - val_accuracy: 0.9619\n",
      "Epoch 59/100\n",
      "179/179 [==============================] - 3s 16ms/step - loss: 4.0792e-06 - accuracy: 1.0000 - val_loss: 0.3515 - val_accuracy: 0.9619\n",
      "Epoch 60/100\n",
      "179/179 [==============================] - 3s 18ms/step - loss: 3.5973e-06 - accuracy: 1.0000 - val_loss: 0.3544 - val_accuracy: 0.9619\n",
      "Epoch 61/100\n",
      "179/179 [==============================] - 3s 16ms/step - loss: 3.2199e-06 - accuracy: 1.0000 - val_loss: 0.3571 - val_accuracy: 0.9626\n",
      "Epoch 62/100\n",
      "179/179 [==============================] - 3s 17ms/step - loss: 2.9057e-06 - accuracy: 1.0000 - val_loss: 0.3590 - val_accuracy: 0.9626\n",
      "Epoch 63/100\n",
      "179/179 [==============================] - 3s 16ms/step - loss: 2.6203e-06 - accuracy: 1.0000 - val_loss: 0.3614 - val_accuracy: 0.9626\n",
      "Epoch 64/100\n",
      "179/179 [==============================] - 3s 18ms/step - loss: 2.3761e-06 - accuracy: 1.0000 - val_loss: 0.3632 - val_accuracy: 0.9626\n",
      "Epoch 65/100\n",
      "179/179 [==============================] - 3s 16ms/step - loss: 2.1462e-06 - accuracy: 1.0000 - val_loss: 0.3665 - val_accuracy: 0.9626\n",
      "Epoch 66/100\n",
      "179/179 [==============================] - 3s 17ms/step - loss: 1.9544e-06 - accuracy: 1.0000 - val_loss: 0.3687 - val_accuracy: 0.9626\n",
      "Epoch 67/100\n",
      "179/179 [==============================] - 3s 16ms/step - loss: 1.7602e-06 - accuracy: 1.0000 - val_loss: 0.3712 - val_accuracy: 0.9626\n",
      "Epoch 68/100\n",
      "179/179 [==============================] - 3s 18ms/step - loss: 1.5882e-06 - accuracy: 1.0000 - val_loss: 0.3725 - val_accuracy: 0.9626\n",
      "Epoch 69/100\n",
      "179/179 [==============================] - 3s 17ms/step - loss: 1.4422e-06 - accuracy: 1.0000 - val_loss: 0.3762 - val_accuracy: 0.9626\n",
      "Epoch 70/100\n",
      "179/179 [==============================] - 3s 17ms/step - loss: 1.3068e-06 - accuracy: 1.0000 - val_loss: 0.3782 - val_accuracy: 0.9626\n",
      "Epoch 71/100\n",
      "179/179 [==============================] - 3s 16ms/step - loss: 1.1871e-06 - accuracy: 1.0000 - val_loss: 0.3813 - val_accuracy: 0.9626\n",
      "Epoch 72/100\n",
      "179/179 [==============================] - 3s 18ms/step - loss: 1.0767e-06 - accuracy: 1.0000 - val_loss: 0.3835 - val_accuracy: 0.9626\n",
      "Epoch 73/100\n",
      "179/179 [==============================] - 3s 16ms/step - loss: 9.7720e-07 - accuracy: 1.0000 - val_loss: 0.3856 - val_accuracy: 0.9626\n",
      "Epoch 74/100\n",
      "179/179 [==============================] - 3s 16ms/step - loss: 8.8978e-07 - accuracy: 1.0000 - val_loss: 0.3873 - val_accuracy: 0.9626\n",
      "Epoch 75/100\n",
      "179/179 [==============================] - 3s 16ms/step - loss: 8.0598e-07 - accuracy: 1.0000 - val_loss: 0.3900 - val_accuracy: 0.9626\n",
      "Epoch 76/100\n",
      "179/179 [==============================] - 3s 17ms/step - loss: 7.3183e-07 - accuracy: 1.0000 - val_loss: 0.3925 - val_accuracy: 0.9626\n",
      "Epoch 77/100\n",
      "179/179 [==============================] - 3s 17ms/step - loss: 6.5973e-07 - accuracy: 1.0000 - val_loss: 0.3944 - val_accuracy: 0.9634\n",
      "Epoch 78/100\n",
      "179/179 [==============================] - 3s 16ms/step - loss: 5.9954e-07 - accuracy: 1.0000 - val_loss: 0.3973 - val_accuracy: 0.9634\n",
      "Epoch 79/100\n",
      "179/179 [==============================] - 3s 16ms/step - loss: 5.4651e-07 - accuracy: 1.0000 - val_loss: 0.3989 - val_accuracy: 0.9634\n",
      "Epoch 80/100\n",
      "179/179 [==============================] - 3s 17ms/step - loss: 4.9588e-07 - accuracy: 1.0000 - val_loss: 0.4016 - val_accuracy: 0.9634\n",
      "Epoch 81/100\n",
      "179/179 [==============================] - 3s 17ms/step - loss: 4.4989e-07 - accuracy: 1.0000 - val_loss: 0.4041 - val_accuracy: 0.9634\n",
      "Epoch 82/100\n",
      "179/179 [==============================] - 3s 17ms/step - loss: 4.1030e-07 - accuracy: 1.0000 - val_loss: 0.4058 - val_accuracy: 0.9634\n",
      "Epoch 83/100\n",
      "179/179 [==============================] - 3s 16ms/step - loss: 3.7348e-07 - accuracy: 1.0000 - val_loss: 0.4083 - val_accuracy: 0.9634\n",
      "Epoch 84/100\n",
      "179/179 [==============================] - 3s 17ms/step - loss: 3.3815e-07 - accuracy: 1.0000 - val_loss: 0.4109 - val_accuracy: 0.9634\n",
      "Epoch 85/100\n",
      "179/179 [==============================] - 3s 17ms/step - loss: 3.0820e-07 - accuracy: 1.0000 - val_loss: 0.4139 - val_accuracy: 0.9634\n",
      "Epoch 86/100\n",
      "179/179 [==============================] - 3s 17ms/step - loss: 2.8045e-07 - accuracy: 1.0000 - val_loss: 0.4163 - val_accuracy: 0.9634\n",
      "Epoch 87/100\n",
      "179/179 [==============================] - 3s 17ms/step - loss: 2.5340e-07 - accuracy: 1.0000 - val_loss: 0.4181 - val_accuracy: 0.9634\n",
      "Epoch 88/100\n",
      "179/179 [==============================] - 3s 17ms/step - loss: 2.2996e-07 - accuracy: 1.0000 - val_loss: 0.4195 - val_accuracy: 0.9634\n",
      "Epoch 89/100\n",
      "179/179 [==============================] - 3s 17ms/step - loss: 2.0916e-07 - accuracy: 1.0000 - val_loss: 0.4224 - val_accuracy: 0.9634\n",
      "Epoch 90/100\n",
      "179/179 [==============================] - 3s 17ms/step - loss: 1.8946e-07 - accuracy: 1.0000 - val_loss: 0.4245 - val_accuracy: 0.9634\n",
      "Epoch 91/100\n",
      "179/179 [==============================] - 3s 16ms/step - loss: 1.7287e-07 - accuracy: 1.0000 - val_loss: 0.4267 - val_accuracy: 0.9634\n",
      "Epoch 92/100\n",
      "179/179 [==============================] - 3s 17ms/step - loss: 1.5642e-07 - accuracy: 1.0000 - val_loss: 0.4292 - val_accuracy: 0.9634\n",
      "Epoch 93/100\n",
      "179/179 [==============================] - 3s 17ms/step - loss: 1.4248e-07 - accuracy: 1.0000 - val_loss: 0.4315 - val_accuracy: 0.9641\n",
      "Epoch 94/100\n",
      "179/179 [==============================] - 3s 16ms/step - loss: 1.2918e-07 - accuracy: 1.0000 - val_loss: 0.4331 - val_accuracy: 0.9641\n",
      "Epoch 95/100\n",
      "179/179 [==============================] - 3s 16ms/step - loss: 1.1723e-07 - accuracy: 1.0000 - val_loss: 0.4360 - val_accuracy: 0.9641\n",
      "Epoch 96/100\n",
      "179/179 [==============================] - 3s 17ms/step - loss: 1.0710e-07 - accuracy: 1.0000 - val_loss: 0.4383 - val_accuracy: 0.9641\n",
      "Epoch 97/100\n",
      "179/179 [==============================] - 3s 18ms/step - loss: 9.6962e-08 - accuracy: 1.0000 - val_loss: 0.4413 - val_accuracy: 0.9641\n",
      "Epoch 98/100\n",
      "179/179 [==============================] - 3s 16ms/step - loss: 8.8238e-08 - accuracy: 1.0000 - val_loss: 0.4436 - val_accuracy: 0.9641\n",
      "Epoch 99/100\n",
      "179/179 [==============================] - 3s 17ms/step - loss: 7.9723e-08 - accuracy: 1.0000 - val_loss: 0.4461 - val_accuracy: 0.9641\n",
      "Epoch 100/100\n",
      "179/179 [==============================] - 3s 17ms/step - loss: 7.2356e-08 - accuracy: 1.0000 - val_loss: 0.4498 - val_accuracy: 0.9641\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(train_images, train_labels_encoded, epochs=100,\n",
    "                    validation_data=(test_images, test_labels_encoded))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4sOQhH2b5_oI",
    "outputId": "43a783f5-d276-498d-a759-e8e39efa0b2d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "41/41 - 0s - loss: 0.4498 - accuracy: 0.9641 - 260ms/epoch - 6ms/step\n",
      "\n",
      "Test accuracy: 0.9641494750976562\n"
     ]
    }
   ],
   "source": [
    "test_loss, test_acc = model.evaluate(test_images,  test_labels_encoded, verbose=2)\n",
    "print('\\nTest accuracy:', test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Qf6Z8p-e7Yn4",
    "outputId": "95ed6ed1-4115-44f2-c39f-1c43e5800e0e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
      "Collecting numpy==1.19.3\n",
      "  Downloading numpy-1.19.3-cp38-cp38-manylinux2010_x86_64.whl (14.9 MB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m14.9/14.9 MB\u001b[0m \u001b[31m89.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hInstalling collected packages: numpy\n",
      "  Attempting uninstall: numpy\n",
      "    Found existing installation: numpy 1.24.2\n",
      "    Uninstalling numpy-1.24.2:\n",
      "      Successfully uninstalled numpy-1.24.2\n",
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "xarray 2022.12.0 requires numpy>=1.20, but you have numpy 1.19.3 which is incompatible.\n",
      "xarray-einstats 0.5.1 requires numpy>=1.20, but you have numpy 1.19.3 which is incompatible.\n",
      "tensorflow 2.11.0 requires numpy>=1.20, but you have numpy 1.19.3 which is incompatible.\n",
      "scipy 1.10.0 requires numpy<1.27.0,>=1.19.5, but you have numpy 1.19.3 which is incompatible.\n",
      "pandas 1.5.3 requires numpy>=1.20.3; python_version < \"3.10\", but you have numpy 1.19.3 which is incompatible.\n",
      "jaxlib 0.3.25+cuda11.cudnn805 requires numpy>=1.20, but you have numpy 1.19.3 which is incompatible.\n",
      "jax 0.3.25 requires numpy>=1.20, but you have numpy 1.19.3 which is incompatible.\n",
      "cvxpy 1.2.3 requires setuptools<=64.0.2, but you have setuptools 67.3.1 which is incompatible.\n",
      "cupy-cuda11x 11.0.0 requires numpy<1.26,>=1.20, but you have numpy 1.19.3 which is incompatible.\n",
      "cmdstanpy 1.1.0 requires numpy>=1.21, but you have numpy 1.19.3 which is incompatible.\u001b[0m\u001b[31m\n",
      "\u001b[0mSuccessfully installed numpy-1.19.3\n",
      "Cloning into 'LightGBM'...\n",
      "remote: Enumerating objects: 28965, done.\u001b[K\n",
      "remote: Counting objects: 100% (217/217), done.\u001b[K\n",
      "remote: Compressing objects: 100% (160/160), done.\u001b[K\n",
      "remote: Total 28965 (delta 88), reused 117 (delta 55), pack-reused 28748\u001b[K\n",
      "Receiving objects: 100% (28965/28965), 20.48 MiB | 20.48 MiB/s, done.\n",
      "Resolving deltas: 100% (21422/21422), done.\n",
      "Submodule 'include/boost/compute' (https://github.com/boostorg/compute) registered for path 'external_libs/compute'\n",
      "Submodule 'eigen' (https://gitlab.com/libeigen/eigen.git) registered for path 'external_libs/eigen'\n",
      "Submodule 'external_libs/fast_double_parser' (https://github.com/lemire/fast_double_parser.git) registered for path 'external_libs/fast_double_parser'\n",
      "Submodule 'external_libs/fmt' (https://github.com/fmtlib/fmt.git) registered for path 'external_libs/fmt'\n",
      "Cloning into '/content/LightGBM/build/LightGBM/external_libs/compute'...\n",
      "remote: Enumerating objects: 21733, done.        \n",
      "remote: Counting objects: 100% (5/5), done.        \n",
      "remote: Compressing objects: 100% (4/4), done.        \n",
      "remote: Total 21733 (delta 1), reused 3 (delta 1), pack-reused 21728        \n",
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      "Submodule path 'external_libs/compute': checked out '36350b7de849300bd3d72a05d8bf890ca405a014'\n",
      "Submodule path 'external_libs/eigen': checked out '3147391d946bb4b6c68edd901f2add6ac1f31f8c'\n",
      "Submodule path 'external_libs/fast_double_parser': checked out 'ace60646c02dc54c57f19d644e49a61e7e7758ec'\n",
      "Submodule 'benchmark/dependencies/abseil-cpp' (https://github.com/abseil/abseil-cpp.git) registered for path 'external_libs/fast_double_parser/benchmarks/dependencies/abseil-cpp'\n",
      "Submodule 'benchmark/dependencies/double-conversion' (https://github.com/google/double-conversion.git) registered for path 'external_libs/fast_double_parser/benchmarks/dependencies/double-conversion'\n",
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      "Submodule path 'external_libs/fast_double_parser/benchmarks/dependencies/abseil-cpp': checked out 'd936052d32a5b7ca08b0199a6724724aea432309'\n",
      "Submodule path 'external_libs/fast_double_parser/benchmarks/dependencies/double-conversion': checked out 'f4cb2384efa55dee0e6652f8674b05763441ab09'\n",
      "Submodule path 'external_libs/fmt': checked out 'b6f4ceaed0a0a24ccf575fab6c56dd50ccf6f1a9'\n",
      "/content/LightGBM\n",
      "mkdir: cannot create directory ‘build’: File exists\n",
      "/content/LightGBM/build\n",
      "-- OpenCL include directory: /usr/include\n",
      "-- Using _mm_prefetch\n",
      "-- Using _mm_malloc\n",
      "-- Configuring done\n",
      "-- Generating done\n",
      "-- Build files have been written to: /content/LightGBM/build\n",
      "\u001b[35m\u001b[1mConsolidate compiler generated dependencies of target lightgbm_capi_objs\u001b[0m\n",
      "[  1%] Built target lightgbm_capi_objs\n",
      "\u001b[35m\u001b[1mConsolidate compiler generated dependencies of target lightgbm_objs\u001b[0m\n",
      "[ 92%] Built target lightgbm_objs\n",
      "\u001b[35m\u001b[1mConsolidate compiler generated dependencies of target lightgbm\u001b[0m\n",
      "[ 94%] Built target _lightgbm\n",
      "[100%] Built target lightgbm\n",
      "Reading package lists... Done\n",
      "Building dependency tree       \n",
      "Reading state information... Done\n",
      "python3-pip is already the newest version (20.0.2-5ubuntu1.7).\n",
      "The following package was automatically installed and is no longer required:\n",
      "  libnvidia-common-510\n",
      "Use 'sudo apt autoremove' to remove it.\n",
      "0 upgraded, 0 newly installed, 0 to remove and 21 not upgraded.\n",
      "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
      "Requirement already satisfied: setuptools in /usr/local/lib/python3.8/dist-packages (67.3.1)\n",
      "Requirement already satisfied: pandas in /usr/local/lib/python3.8/dist-packages (1.5.3)\n",
      "Requirement already satisfied: numpy in /usr/local/lib/python3.8/dist-packages (1.19.3)\n",
      "Collecting numpy\n",
      "  Using cached numpy-1.24.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (17.3 MB)\n",
      "Requirement already satisfied: scipy in /usr/local/lib/python3.8/dist-packages (1.10.0)\n",
      "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.8/dist-packages (1.2.1)\n",
      "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.8/dist-packages (from pandas) (2022.7.1)\n",
      "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.8/dist-packages (from pandas) (2.8.2)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.8/dist-packages (from scikit-learn) (3.1.0)\n",
      "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.8/dist-packages (from scikit-learn) (1.2.0)\n",
      "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.8/dist-packages (from python-dateutil>=2.8.1->pandas) (1.15.0)\n",
      "Installing collected packages: numpy\n",
      "  Attempting uninstall: numpy\n",
      "    Found existing installation: numpy 1.19.3\n",
      "    Uninstalling numpy-1.19.3:\n",
      "      Successfully uninstalled numpy-1.19.3\n",
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "numba 0.56.4 requires numpy<1.24,>=1.18, but you have numpy 1.24.2 which is incompatible.\n",
      "cvxpy 1.2.3 requires setuptools<=64.0.2, but you have setuptools 67.3.1 which is incompatible.\u001b[0m\u001b[31m\n",
      "\u001b[0mSuccessfully installed numpy-1.24.2\n",
      "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
      "Requirement already satisfied: wheel in /usr/local/lib/python3.8/dist-packages (0.38.4)\n",
      "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
      "Requirement already satisfied: graphviz in /usr/local/lib/python3.8/dist-packages (0.10.1)\n",
      "Requirement already satisfied: lightgbm in /usr/local/lib/python3.8/dist-packages (2.2.3)\n",
      "Requirement already satisfied: numpy in /usr/local/lib/python3.8/dist-packages (from lightgbm) (1.24.2)\n",
      "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.8/dist-packages (from lightgbm) (1.2.1)\n",
      "Requirement already satisfied: scipy in /usr/local/lib/python3.8/dist-packages (from lightgbm) (1.10.0)\n",
      "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.8/dist-packages (from scikit-learn->lightgbm) (1.2.0)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.8/dist-packages (from scikit-learn->lightgbm) (3.1.0)\n"
     ]
    }
   ],
   "source": [
    "# Install LightGBM with GPU support and import necessary libraries\n",
    "!pip install numpy==1.19.3\n",
    "!git clone --recursive https://github.com/Microsoft/LightGBM\n",
    "%cd /content/LightGBM\n",
    "!mkdir build\n",
    "%cd build\n",
    "!cmake -DUSE_GPU=1 /content/LightGBM\n",
    "!make -j$(nproc)\n",
    "!sudo apt-get -y install python3-pip\n",
    "!sudo -H python3 -m pip install setuptools pandas numpy scipy scikit-learn -U\n",
    "!sudo -H python3 -m pip install wheel\n",
    "!sudo -H python3 -m pip install graphviz lightgbm\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 380
    },
    "id": "yhsEt0jy_Sn3",
    "outputId": "90f4fc3c-08a9-4a75-dabf-8fea1fd06357"
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "ignored",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-6-fc585ce28c08>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mlightgbm\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mlgb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mglob\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mcv2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/lightgbm/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0m__future__\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mabsolute_import\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mbasic\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mBooster\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m from .callback import (early_stopping, print_evaluation, record_evaluation,\n\u001b[1;32m     10\u001b[0m                        reset_parameter)\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/lightgbm/basic.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mscipy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msparse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m from .compat import (DataFrame, Series, DataTable,\n\u001b[0m\u001b[1;32m     17\u001b[0m                      \u001b[0mdecode_string\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstring_type\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m                      \u001b[0minteger_types\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumeric_types\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/lightgbm/compat.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m    106\u001b[0m \u001b[0;34m\"\"\"sklearn\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    107\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 108\u001b[0;31m     \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbase\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mBaseEstimator\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    109\u001b[0m     \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbase\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mRegressorMixin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mClassifierMixin\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    110\u001b[0m     \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpreprocessing\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mLabelEncoder\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/sklearn/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     80\u001b[0m     \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0m_distributor_init\u001b[0m  \u001b[0;31m# noqa: F401\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     81\u001b[0m     \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0m__check_build\u001b[0m  \u001b[0;31m# noqa: F401\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 82\u001b[0;31m     \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mbase\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mclone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     83\u001b[0m     \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_show_versions\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mshow_versions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     84\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/sklearn/base.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0m__version__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0m_config\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mget_config\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0m_IS_32BIT\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_output\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0m_SetOutputMixin\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m from .utils._tags import (\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/sklearn/utils/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mdeprecation\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdeprecated\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mdiscovery\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mall_estimators\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 25\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mfixes\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mparse_version\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mthreadpool_info\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     26\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0m_estimator_html_repr\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mestimator_html_repr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m from .validation import (\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/sklearn/utils/fixes.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mscipy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mscipy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstats\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     20\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mthreadpoolctl\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
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      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/numpy/testing/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0m_private\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0m_private\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0m_private\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0m_assert_valid_refcount\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_gen_alignment_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0m_private\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mextbuild\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecorators\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mdec\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/numpy/testing/_private/utils.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m    478\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    479\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 480\u001b[0;31m \u001b[0;34m@\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_no_nep50_warning\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    481\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0massert_almost_equal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mactual\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdesired\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdecimal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m7\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0merr_msg\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m''\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    482\u001b[0m     \"\"\"\n",
      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/numpy/__init__.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(attr)\u001b[0m\n\u001b[1;32m    311\u001b[0m         \u001b[0;31m# The previous way Tester was imported also had a side effect of adding\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    312\u001b[0m         \u001b[0;31m# the full `numpy.testing` namespace\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 313\u001b[0;31m         \u001b[0;32mif\u001b[0m \u001b[0mattr\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'testing'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    314\u001b[0m             \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtesting\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtesting\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    315\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mtesting\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: module 'numpy' has no attribute '_no_nep50_warning'"
     ]
    }
   ],
   "source": [
    "# Install LightGBM with GPU support and import necessary libraries\n",
    "!pip install numpy==1.19.3\n",
    "!git clone --recursive https://github.com/Microsoft/LightGBM\n",
    "%cd /content/LightGBM\n",
    "!mkdir build\n",
    "%cd build\n",
    "!cmake -DUSE_GPU=1 /content/LightGBM\n",
    "!make -j$(nproc)\n",
    "!sudo apt-get -y install python3-pip\n",
    "!sudo -H python3 -m pip install setuptools pandas numpy scipy scikit-learn -U\n",
    "!sudo -H python3 -m pip install wheel\n",
    "!sudo -H python3 -m pip install graphviz lightgbm\n",
    "\n",
    "\n",
    "import numpy as np\n",
    "import lightgbm as lgb\n",
    "import glob\n",
    "import cv2\n",
    "import os\n",
    "from sklearn import preprocessing\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# Set image size\n",
    "SIZE = 128\n",
    "\n",
    "# Load and preprocess training data\n",
    "train_images = []\n",
    "train_labels = [] \n",
    "\n",
    "for directory_path in glob.glob(\"/content/Tumor/Training/*\"):\n",
    "    label = directory_path.split(\"/\")[-1]\n",
    "    print(label)\n",
    "    for img_path in glob.glob(os.path.join(directory_path, \"*.png\")):\n",
    "        img = cv2.imread(img_path, cv2.IMREAD_COLOR)       \n",
    "        img = cv2.resize(img, (SIZE, SIZE))\n",
    "        img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)\n",
    "        train_images.append(img.flatten())\n",
    "        train_labels.append(label)\n",
    "\n",
    "train_images = np.array(train_images)\n",
    "train_labels = np.array(train_labels)\n",
    "\n",
    "# Load and preprocess testing data\n",
    "test_images = []\n",
    "test_labels = [] \n",
    "for directory_path in glob.glob(\"/content/Tumor/Testing/*\"):\n",
    "    fruit_label = directory_path.split(\"/\")[-1]\n",
    "    print(fruit_label)\n",
    "    for img_path in glob.glob(os.path.join(directory_path, \"*.png\")):\n",
    "        img = cv2.imread(img_path, cv2.IMREAD_COLOR)\n",
    "        img = cv2.resize(img, (SIZE, SIZE))\n",
    "        img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)\n",
    "        test_images.append(img.flatten())\n",
    "        test_labels.append(fruit_label)\n",
    "\n",
    "test_images = np.array(test_images)\n",
    "test_labels = np.array(test_labels)\n",
    "\n",
    "# Encode labels using LabelEncoder\n",
    "le = preprocessing.LabelEncoder()\n",
    "le.fit(test_labels)\n",
    "test_labels_encoded = le.transform(test_labels)\n",
    "le.fit(train_labels)\n",
    "train_labels_encoded = le.transform(train_labels)\n",
    "\n",
    "# Create LightGBM datasets\n",
    "lgb_train = lgb.Dataset(train_images, train_labels_encoded)\n",
    "lgb_test = lgb.Dataset(test_images, test_labels_encoded)\n",
    "\n",
    "# Set parameters for LightGBM\n",
    "params = {\n",
    "    'objective': 'multiclass',\n",
    "    'num_class': 4,\n",
    "    'metric': 'multi_logloss',\n",
    "    'boosting_type': 'gbdt',\n",
    "    'num_leaves': 31,\n",
    "    'learning_rate': 0.1,\n",
    "    'feature_fraction': 0.9,\n",
    "    'bagging_fraction': 0.8,\n",
    "    'bagging_freq': 5,\n",
    "    'verbose': 0,\n",
    "    'device': 'gpu'\n",
    "}\n",
    "\n",
    "# Train LightGBM model\n",
    "num_boost_round = 100\n",
    "bst = lgb.train(params, lgb_train, num_boost_round=num_boost_round)\n",
    "\n",
    "# Make predictions on test set\n",
    "y_pred = bst.predict(test_images)\n",
    "y_pred = np.argmax(y_pred, axis=1)\n",
    "\n",
    "# Compute accuracy score\n",
    "accuracy = accuracy_score(test_labels_encoded, y_pred)\n",
    "print('Test accuracy:', accuracy)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "7swDREQ97ceZ"
   },
   "outputs": [],
   "source": []
  }
 ],
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