Bone_fracture_Classification.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Bone_fracture_21-04-2022.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# **Image** **Augumentation**"
      ],
      "metadata": {
        "id": "o80oCsiLgR0f"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### **Augumentation on Cofield**"
      ],
      "metadata": {
        "id": "jyhD5iPCgYIV"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2PSwXKaBb3Qx"
      },
      "outputs": [],
      "source": [
        "# Augmentation with all parameters\n",
        "\n",
        "import keras\n",
        "import cv2\n",
        "import os\n",
        "import glob\n",
        "from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img\n",
        "datagen = ImageDataGenerator(rotation_range =15, \n",
        "                         width_shift_range = 0.2, \n",
        "                         height_shift_range = 0.2,  \n",
        "                         rescale=1./255, \n",
        "                         shear_range=0.2, \n",
        "                         zoom_range=0.2, \n",
        "                         horizontal_flip = True, \n",
        "                         fill_mode = 'nearest', \n",
        "                         data_format='channels_last', \n",
        "                         brightness_range=[0.5, 1.5]) \n",
        "\n",
        "\n",
        "img_dir = \"/content/drive/MyDrive/EDA_Project/Dataset/Cofield\"\n",
        "data_path = os.path.join(img_dir,'*g')\n",
        "files = glob.glob(data_path)\n",
        "data = []\n",
        "for f1 in files:\n",
        "    img = cv2.imread(f1)\n",
        "    data.append(img)\n",
        "\n",
        "x = img_to_array(img)\n",
        "x = x.reshape((1,) + x.shape)\n",
        "\n",
        "i = 0\n",
        "path, dirs, files = next(os.walk(\"/content/drive/MyDrive/EDA_Project/Dataset/Cofield\"))\n",
        "file_count = len(files) #to find number of files in folder\n",
        "\n",
        "for batch in datagen.flow (x, batch_size=1, save_to_dir =r'/content/drive/MyDrive/EDA_Project/Dataset/Cofield',save_prefix=\"a\",save_format='jpg'):\n",
        "    i+=1\n",
        "    if i==file_count:\n",
        "      break"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Augumentation with rotation range\n",
        "\n",
        "import keras\n",
        "import cv2\n",
        "import os\n",
        "import glob\n",
        "from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img\n",
        "datagen = ImageDataGenerator(rotation_range =30) \n",
        "\n",
        "\n",
        "img_dir = \"/content/drive/MyDrive/EDA_Project/Dataset/Cofield\"\n",
        "data_path = os.path.join(img_dir,'*g')\n",
        "files = glob.glob(data_path)\n",
        "data = []\n",
        "for f1 in files:\n",
        "    img = cv2.imread(f1)\n",
        "    data.append(img)\n",
        "\n",
        "x = img_to_array(img)\n",
        "x = x.reshape((1,) + x.shape)\n",
        "\n",
        "i = 0\n",
        "path, dirs, files = next(os.walk(\"/content/drive/MyDrive/EDA_Project/Dataset/Cofield\"))\n",
        "file_count = len(files) #to find number of files in folder\n",
        "\n",
        "for batch in datagen.flow (x, batch_size=1, save_to_dir =r'/content/drive/MyDrive/EDA_Project/Dataset/Cofield',save_prefix=\"a\",save_format='jpg'):\n",
        "    i+=1\n",
        "    if i==file_count:\n",
        "      break"
      ],
      "metadata": {
        "id": "AxKIFkPpj4Nv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Augmentation with horizontal flip\n",
        "\n",
        "import keras\n",
        "import cv2\n",
        "import os\n",
        "import glob\n",
        "from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img\n",
        "datagen = ImageDataGenerator(\n",
        "                         horizontal_flip=True) \n",
        "\n",
        "\n",
        "img_dir = \"/content/drive/MyDrive/EDA_Project/Dataset/Cofield\"\n",
        "data_path = os.path.join(img_dir,'*g')\n",
        "files = glob.glob(data_path)\n",
        "data = []\n",
        "for f1 in files:\n",
        "    img = cv2.imread(f1)\n",
        "    data.append(img)\n",
        "\n",
        "x = img_to_array(img)\n",
        "x = x.reshape((1,) + x.shape)\n",
        "\n",
        "i = 0\n",
        "path, dirs, files = next(os.walk(\"/content/drive/MyDrive/EDA_Project/Dataset/Cofield\"))\n",
        "file_count = len(files) #to find number of files in folder\n",
        "\n",
        "for batch in datagen.flow (x, batch_size=1, save_to_dir =r'/content/drive/MyDrive/EDA_Project/Dataset/Cofield',save_prefix=\"a\",save_format='jpg'):\n",
        "    i+=1\n",
        "    if i==file_count:\n",
        "      break"
      ],
      "metadata": {
        "id": "E5UjwHR6kggG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Augmentation with Zoom range\n",
        "\n",
        "import keras\n",
        "import cv2\n",
        "import os\n",
        "import glob\n",
        "from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img\n",
        "datagen = ImageDataGenerator(\n",
        "                         zoom_range=0.5) \n",
        "\n",
        "\n",
        "img_dir = \"/content/drive/MyDrive/EDA_Project/Dataset/Cofield\"\n",
        "data_path = os.path.join(img_dir,'*g')\n",
        "files = glob.glob(data_path)\n",
        "data = []\n",
        "for f1 in files:\n",
        "    img = cv2.imread(f1)\n",
        "    data.append(img)\n",
        "\n",
        "x = img_to_array(img)\n",
        "x = x.reshape((1,) + x.shape)\n",
        "\n",
        "i = 0\n",
        "path, dirs, files = next(os.walk(\"/content/drive/MyDrive/EDA_Project/Dataset/Cofield\"))\n",
        "file_count = len(files) #to find number of files in folder\n",
        "\n",
        "for batch in datagen.flow (x, batch_size=1, save_to_dir =r'/content/drive/MyDrive/EDA_Project/Dataset/Cofield',save_prefix=\"a\",save_format='jpg'):\n",
        "    i+=1\n",
        "    if i==file_count:\n",
        "      break"
      ],
      "metadata": {
        "id": "xJ4Oo33ElO1E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Augmentation with Random Brightness\n",
        "\n",
        "import keras\n",
        "import cv2\n",
        "import os\n",
        "import glob\n",
        "from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img\n",
        "datagen = ImageDataGenerator( \n",
        "                         brightness_range=[0.7, 1.3]) \n",
        "\n",
        "\n",
        "img_dir = \"/content/drive/MyDrive/EDA_Project/Dataset/Cofield\"\n",
        "data_path = os.path.join(img_dir,'*g')\n",
        "files = glob.glob(data_path)\n",
        "data = []\n",
        "for f1 in files:\n",
        "    img = cv2.imread(f1)\n",
        "    data.append(img)\n",
        "\n",
        "x = img_to_array(img)\n",
        "x = x.reshape((1,) + x.shape)\n",
        "\n",
        "i = 0\n",
        "path, dirs, files = next(os.walk(\"/content/drive/MyDrive/EDA_Project/Dataset/Cofield\"))\n",
        "file_count = len(files) #to find number of files in folder\n",
        "\n",
        "for batch in datagen.flow (x, batch_size=1, save_to_dir =r'/content/drive/MyDrive/EDA_Project/Dataset/Cofield',save_prefix=\"a\",save_format='jpg'):\n",
        "    i+=1\n",
        "    if i==file_count:\n",
        "      break"
      ],
      "metadata": {
        "id": "YZ1X_7ablz50"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "FOLDER_PATH = 'Cofield'\n",
        "ROOT_PATH = '/content/drive/MyDrive/EDA_Project/Dataset/'\n",
        "print(len(os.listdir(os.path.join(ROOT_PATH, FOLDER_PATH))))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4Qtp47evhy3W",
        "outputId": "bef29498-5216-465a-92d7-b010385bf332"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2290\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Augumentation on Depuy**"
      ],
      "metadata": {
        "id": "bajgV4NhnEkV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Augmentation with all parameters\n",
        "\n",
        "import keras\n",
        "import cv2\n",
        "import os\n",
        "import glob\n",
        "from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img\n",
        "datagen = ImageDataGenerator(rotation_range =15, \n",
        "                         width_shift_range = 0.2, \n",
        "                         height_shift_range = 0.2,  \n",
        "                         rescale=1./255, \n",
        "                         shear_range=0.2, \n",
        "                         zoom_range=0.2, \n",
        "                         horizontal_flip = True, \n",
        "                         fill_mode = 'nearest', \n",
        "                         data_format='channels_last', \n",
        "                         brightness_range=[0.5, 1.5]) \n",
        "\n",
        "\n",
        "img_dir = \"/content/drive/MyDrive/EDA_Project/Dataset/Depuy\"\n",
        "data_path = os.path.join(img_dir,'*g')\n",
        "files = glob.glob(data_path)\n",
        "data = []\n",
        "for f1 in files:\n",
        "    img = cv2.imread(f1)\n",
        "    data.append(img)\n",
        "\n",
        "x = img_to_array(img)\n",
        "x = x.reshape((1,) + x.shape)\n",
        "\n",
        "i = 0\n",
        "path, dirs, files = next(os.walk(\"/content/drive/MyDrive/EDA_Project/Dataset/Depuy\"))\n",
        "file_count = len(files) #to find number of files in folder\n",
        "\n",
        "for batch in datagen.flow (x, batch_size=1, save_to_dir =r'/content/drive/MyDrive/EDA_Project/Dataset/Depuy',save_prefix=\"a\",save_format='jpg'):\n",
        "    i+=1\n",
        "    if i==file_count:\n",
        "      break"
      ],
      "metadata": {
        "id": "5e0oGYimjr8L"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Augumentation with rotation range\n",
        "\n",
        "import keras\n",
        "import cv2\n",
        "import os\n",
        "import glob\n",
        "from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img\n",
        "datagen = ImageDataGenerator(rotation_range =30) \n",
        "\n",
        "\n",
        "img_dir = \"/content/drive/MyDrive/EDA_Project/Dataset/Depuy\"\n",
        "data_path = os.path.join(img_dir,'*g')\n",
        "files = glob.glob(data_path)\n",
        "data = []\n",
        "for f1 in files:\n",
        "    img = cv2.imread(f1)\n",
        "    data.append(img)\n",
        "\n",
        "x = img_to_array(img)\n",
        "x = x.reshape((1,) + x.shape)\n",
        "\n",
        "i = 0\n",
        "path, dirs, files = next(os.walk(\"/content/drive/MyDrive/EDA_Project/Dataset/Depuy\"))\n",
        "file_count = len(files) #to find number of files in folder\n",
        "\n",
        "for batch in datagen.flow (x, batch_size=1, save_to_dir =r'/content/drive/MyDrive/EDA_Project/Dataset/Depuy',save_prefix=\"a\",save_format='jpg'):\n",
        "    i+=1\n",
        "    if i==file_count:\n",
        "      break"
      ],
      "metadata": {
        "id": "pV5yozkAnqZ-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Augmentation with horizontal flip\n",
        "\n",
        "import keras\n",
        "import cv2\n",
        "import os\n",
        "import glob\n",
        "from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img\n",
        "datagen = ImageDataGenerator(\n",
        "                         horizontal_flip=True) \n",
        "\n",
        "\n",
        "img_dir = \"/content/drive/MyDrive/EDA_Project/Dataset/Depuy\"\n",
        "data_path = os.path.join(img_dir,'*g')\n",
        "files = glob.glob(data_path)\n",
        "data = []\n",
        "for f1 in files:\n",
        "    img = cv2.imread(f1)\n",
        "    data.append(img)\n",
        "\n",
        "x = img_to_array(img)\n",
        "x = x.reshape((1,) + x.shape)\n",
        "\n",
        "i = 0\n",
        "path, dirs, files = next(os.walk(\"/content/drive/MyDrive/EDA_Project/Dataset/Depuy\"))\n",
        "file_count = len(files) #to find number of files in folder\n",
        "\n",
        "for batch in datagen.flow (x, batch_size=1, save_to_dir =r'/content/drive/MyDrive/EDA_Project/Dataset/Depuy',save_prefix=\"a\",save_format='jpg'):\n",
        "    i+=1\n",
        "    if i==file_count:\n",
        "      break"
      ],
      "metadata": {
        "id": "XpCoEl4RoCmr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "FOLDER_PATH = 'Depuy'\n",
        "ROOT_PATH = '/content/drive/MyDrive/EDA_Project/Dataset/'\n",
        "print(len(os.listdir(os.path.join(ROOT_PATH, FOLDER_PATH))))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jf_ohjBanb6Q",
        "outputId": "3e4f7b2d-7ceb-4a24-95fc-c7c7f7f6e2ce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2121\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Augumentation on Tornier**"
      ],
      "metadata": {
        "id": "ebEnUY4qosam"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Augmentation with all parameters\n",
        "\n",
        "import keras\n",
        "import cv2\n",
        "import os\n",
        "import glob\n",
        "from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img\n",
        "datagen = ImageDataGenerator(rotation_range =15, \n",
        "                         width_shift_range = 0.2, \n",
        "                         height_shift_range = 0.2,  \n",
        "                         rescale=1./255, \n",
        "                         shear_range=0.2, \n",
        "                         zoom_range=0.2, \n",
        "                         horizontal_flip = True, \n",
        "                         fill_mode = 'nearest', \n",
        "                         data_format='channels_last', \n",
        "                         brightness_range=[0.5, 1.5]) \n",
        "\n",
        "\n",
        "img_dir = \"/content/drive/MyDrive/EDA_Project/Dataset/Tornier\"\n",
        "data_path = os.path.join(img_dir,'*g')\n",
        "files = glob.glob(data_path)\n",
        "data = []\n",
        "for f1 in files:\n",
        "    img = cv2.imread(f1)\n",
        "    data.append(img)\n",
        "\n",
        "x = img_to_array(img)\n",
        "x = x.reshape((1,) + x.shape)\n",
        "\n",
        "i = 0\n",
        "path, dirs, files = next(os.walk(\"/content/drive/MyDrive/EDA_Project/Dataset/Tornier\"))\n",
        "file_count = len(files) #to find number of files in folder\n",
        "\n",
        "for batch in datagen.flow (x, batch_size=1, save_to_dir =r'/content/drive/MyDrive/EDA_Project/Dataset/Tornier',save_prefix=\"a\",save_format='jpg'):\n",
        "    i+=1\n",
        "    if i==file_count:\n",
        "      break"
      ],
      "metadata": {
        "id": "SQkJcbUon24X"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Augumentation with rotation range\n",
        "\n",
        "import keras\n",
        "import cv2\n",
        "import os\n",
        "import glob\n",
        "from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img\n",
        "datagen = ImageDataGenerator(rotation_range =30) \n",
        "\n",
        "\n",
        "img_dir = \"/content/drive/MyDrive/EDA_Project/Dataset/Tornier\"\n",
        "data_path = os.path.join(img_dir,'*g')\n",
        "files = glob.glob(data_path)\n",
        "data = []\n",
        "for f1 in files:\n",
        "    img = cv2.imread(f1)\n",
        "    data.append(img)\n",
        "\n",
        "x = img_to_array(img)\n",
        "x = x.reshape((1,) + x.shape)\n",
        "\n",
        "i = 0\n",
        "path, dirs, files = next(os.walk(\"/content/drive/MyDrive/EDA_Project/Dataset/Tornier\"))\n",
        "file_count = len(files) #to find number of files in folder\n",
        "\n",
        "for batch in datagen.flow (x, batch_size=1, save_to_dir =r'/content/drive/MyDrive/EDA_Project/Dataset/Tornier',save_prefix=\"a\",save_format='jpg'):\n",
        "    i+=1\n",
        "    if i==file_count:\n",
        "      break"
      ],
      "metadata": {
        "id": "dXUsJnvUpLpP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Augmentation with horizontal flip\n",
        "\n",
        "import keras\n",
        "import cv2\n",
        "import os\n",
        "import glob\n",
        "from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img\n",
        "datagen = ImageDataGenerator(\n",
        "                         horizontal_flip=True) \n",
        "\n",
        "\n",
        "img_dir = \"/content/drive/MyDrive/EDA_Project/Dataset/Tornier\"\n",
        "data_path = os.path.join(img_dir,'*g')\n",
        "files = glob.glob(data_path)\n",
        "data = []\n",
        "for f1 in files:\n",
        "    img = cv2.imread(f1)\n",
        "    data.append(img)\n",
        "\n",
        "x = img_to_array(img)\n",
        "x = x.reshape((1,) + x.shape)\n",
        "\n",
        "i = 0\n",
        "path, dirs, files = next(os.walk(\"/content/drive/MyDrive/EDA_Project/Dataset/Tornier\"))\n",
        "file_count = len(files) #to find number of files in folder\n",
        "\n",
        "for batch in datagen.flow (x, batch_size=1, save_to_dir =r'/content/drive/MyDrive/EDA_Project/Dataset/Tornier',save_prefix=\"a\",save_format='jpg'):\n",
        "    i+=1\n",
        "    if i==file_count:\n",
        "      break"
      ],
      "metadata": {
        "id": "IsEsthp7paY9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Augmentation with Zoom range\n",
        "\n",
        "import keras\n",
        "import cv2\n",
        "import os\n",
        "import glob\n",
        "from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img\n",
        "datagen = ImageDataGenerator(\n",
        "                         zoom_range=0.5) \n",
        "\n",
        "\n",
        "img_dir = \"/content/drive/MyDrive/EDA_Project/Dataset/Tornier\"\n",
        "data_path = os.path.join(img_dir,'*g')\n",
        "files = glob.glob(data_path)\n",
        "data = []\n",
        "for f1 in files:\n",
        "    img = cv2.imread(f1)\n",
        "    data.append(img)\n",
        "\n",
        "x = img_to_array(img)\n",
        "x = x.reshape((1,) + x.shape)\n",
        "\n",
        "i = 0\n",
        "path, dirs, files = next(os.walk(\"/content/drive/MyDrive/EDA_Project/Dataset/Tornier\"))\n",
        "file_count = len(files) #to find number of files in folder\n",
        "\n",
        "for batch in datagen.flow (x, batch_size=1, save_to_dir =r'/content/drive/MyDrive/EDA_Project/Dataset/Tornier',save_prefix=\"a\",save_format='jpg'):\n",
        "    i+=1\n",
        "    if i==file_count:\n",
        "      break"
      ],
      "metadata": {
        "id": "joZyCTLapJVP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Augmentation with Random Brightness\n",
        "\n",
        "import keras\n",
        "import cv2\n",
        "import os\n",
        "import glob\n",
        "from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img\n",
        "datagen = ImageDataGenerator( \n",
        "                         brightness_range=[0.7, 1.3]) \n",
        "\n",
        "\n",
        "img_dir = \"/content/drive/MyDrive/EDA_Project/Dataset/Tornier\"\n",
        "data_path = os.path.join(img_dir,'*g')\n",
        "files = glob.glob(data_path)\n",
        "data = []\n",
        "for f1 in files:\n",
        "    img = cv2.imread(f1)\n",
        "    data.append(img)\n",
        "\n",
        "x = img_to_array(img)\n",
        "x = x.reshape((1,) + x.shape)\n",
        "\n",
        "i = 0\n",
        "path, dirs, files = next(os.walk(\"/content/drive/MyDrive/EDA_Project/Dataset/Tornier\"))\n",
        "file_count = len(files) #to find number of files in folder\n",
        "\n",
        "for batch in datagen.flow (x, batch_size=1, save_to_dir =r'/content/drive/MyDrive/EDA_Project/Dataset/Tornier',save_prefix=\"a\",save_format='jpg'):\n",
        "    i+=1\n",
        "    if i==file_count:\n",
        "      break"
      ],
      "metadata": {
        "id": "fKoHqGgup0yU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "FOLDER_PATH = 'Tornier'\n",
        "ROOT_PATH = '/content/drive/MyDrive/EDA_Project/Dataset/'\n",
        "print(len(os.listdir(os.path.join(ROOT_PATH, FOLDER_PATH))))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qP1V08G3ow0t",
        "outputId": "900e7068-bc72-476f-c35b-07ba3481f3be"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2002\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Augumentation on Zimmer**"
      ],
      "metadata": {
        "id": "WtHH6jYEqX6O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Augmentation with all parameters\n",
        "\n",
        "import keras\n",
        "import cv2\n",
        "import os\n",
        "import glob\n",
        "from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img\n",
        "datagen = ImageDataGenerator(rotation_range =15, \n",
        "                         width_shift_range = 0.2, \n",
        "                         height_shift_range = 0.2,  \n",
        "                         rescale=1./255, \n",
        "                         shear_range=0.2, \n",
        "                         zoom_range=0.2, \n",
        "                         horizontal_flip = True, \n",
        "                         fill_mode = 'nearest', \n",
        "                         data_format='channels_last', \n",
        "                         brightness_range=[0.5, 1.5]) \n",
        "\n",
        "\n",
        "img_dir = \"/content/drive/MyDrive/EDA_Project/Dataset/Zimmer\"\n",
        "data_path = os.path.join(img_dir,'*g')\n",
        "files = glob.glob(data_path)\n",
        "data = []\n",
        "for f1 in files:\n",
        "    img = cv2.imread(f1)\n",
        "    data.append(img)\n",
        "\n",
        "x = img_to_array(img)\n",
        "x = x.reshape((1,) + x.shape)\n",
        "\n",
        "i = 0\n",
        "path, dirs, files = next(os.walk(\"/content/drive/MyDrive/EDA_Project/Dataset/Zimmer\"))\n",
        "file_count = len(files) #to find number of files in folder\n",
        "\n",
        "for batch in datagen.flow (x, batch_size=1, save_to_dir =r'/content/drive/MyDrive/EDA_Project/Dataset/Zimmer',save_prefix=\"a\",save_format='jpg'):\n",
        "    i+=1\n",
        "    if i==file_count:\n",
        "      break"
      ],
      "metadata": {
        "id": "FjzP1XurqcEq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Augumentation with rotation range\n",
        "\n",
        "import keras\n",
        "import cv2\n",
        "import os\n",
        "import glob\n",
        "from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img\n",
        "datagen = ImageDataGenerator(rotation_range =30) \n",
        "\n",
        "\n",
        "img_dir = \"/content/drive/MyDrive/EDA_Project/Dataset/Zimmer\"\n",
        "data_path = os.path.join(img_dir,'*g')\n",
        "files = glob.glob(data_path)\n",
        "data = []\n",
        "for f1 in files:\n",
        "    img = cv2.imread(f1)\n",
        "    data.append(img)\n",
        "\n",
        "x = img_to_array(img)\n",
        "x = x.reshape((1,) + x.shape)\n",
        "\n",
        "i = 0\n",
        "path, dirs, files = next(os.walk(\"/content/drive/MyDrive/EDA_Project/Dataset/Zimmer\"))\n",
        "file_count = len(files) #to find number of files in folder\n",
        "\n",
        "for batch in datagen.flow (x, batch_size=1, save_to_dir =r'/content/drive/MyDrive/EDA_Project/Dataset/Zimmer',save_prefix=\"a\",save_format='jpg'):\n",
        "    i+=1\n",
        "    if i==file_count:\n",
        "      break"
      ],
      "metadata": {
        "id": "C3wTZ2n9qs01"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Augmentation with horizontal flip\n",
        "\n",
        "import keras\n",
        "import cv2\n",
        "import os\n",
        "import glob\n",
        "from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img\n",
        "datagen = ImageDataGenerator(\n",
        "                         horizontal_flip=True) \n",
        "\n",
        "\n",
        "img_dir = \"/content/drive/MyDrive/EDA_Project/Dataset/Zimmer\"\n",
        "data_path = os.path.join(img_dir,'*g')\n",
        "files = glob.glob(data_path)\n",
        "data = []\n",
        "for f1 in files:\n",
        "    img = cv2.imread(f1)\n",
        "    data.append(img)\n",
        "\n",
        "x = img_to_array(img)\n",
        "x = x.reshape((1,) + x.shape)\n",
        "\n",
        "i = 0\n",
        "path, dirs, files = next(os.walk(\"/content/drive/MyDrive/EDA_Project/Dataset/Zimmer\"))\n",
        "file_count = len(files) #to find number of files in folder\n",
        "\n",
        "for batch in datagen.flow (x, batch_size=1, save_to_dir =r'/content/drive/MyDrive/EDA_Project/Dataset/Zimmer',save_prefix=\"a\",save_format='jpg'):\n",
        "    i+=1\n",
        "    if i==file_count:\n",
        "      break"
      ],
      "metadata": {
        "id": "z7C8KvOwq4G_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Augmentation with Zoom range\n",
        "\n",
        "import keras\n",
        "import cv2\n",
        "import os\n",
        "import glob\n",
        "from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img\n",
        "datagen = ImageDataGenerator(\n",
        "                         zoom_range=0.5) \n",
        "\n",
        "\n",
        "img_dir = \"/content/drive/MyDrive/EDA_Project/Dataset/Zimmer\"\n",
        "data_path = os.path.join(img_dir,'*g')\n",
        "files = glob.glob(data_path)\n",
        "data = []\n",
        "for f1 in files:\n",
        "    img = cv2.imread(f1)\n",
        "    data.append(img)\n",
        "\n",
        "x = img_to_array(img)\n",
        "x = x.reshape((1,) + x.shape)\n",
        "\n",
        "i = 0\n",
        "path, dirs, files = next(os.walk(\"/content/drive/MyDrive/EDA_Project/Dataset/Zimmer\"))\n",
        "file_count = len(files) #to find number of files in folder\n",
        "\n",
        "for batch in datagen.flow (x, batch_size=1, save_to_dir =r'/content/drive/MyDrive/EDA_Project/Dataset/Zimmer',save_prefix=\"a\",save_format='jpg'):\n",
        "    i+=1\n",
        "    if i==file_count:\n",
        "      break"
      ],
      "metadata": {
        "id": "2hjYjc7SrH8P"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "FOLDER_PATH = 'Zimmer'\n",
        "ROOT_PATH = '/content/drive/MyDrive/EDA_Project/Dataset/'\n",
        "print(len(os.listdir(os.path.join(ROOT_PATH, FOLDER_PATH))))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "34n061zYqpQ2",
        "outputId": "cd9b3be8-6c5c-42b6-f080-1d9ced3608ff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2125\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Bone Fracture Classification** "
      ],
      "metadata": {
        "id": "DfC87skosTwi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# import the libraries as shown below\n",
        "\n",
        "from keras.layers import Input, Lambda, Dense, Flatten\n",
        "from keras.models import Model\n",
        "#from keras.applications.resnet50 import ResNet50\n",
        "from keras.applications.vgg16 import VGG16\n",
        "from keras.applications.vgg16 import preprocess_input\n",
        "from keras.preprocessing import image\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.models import Sequential\n",
        "import numpy as np\n",
        "from glob import glob\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "GCwhpZFos6Hp"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# re-size all the images to this\n",
        "IMAGE_SIZE = [224, 224]\n",
        "\n",
        "train_path = '/content/drive/MyDrive/EDA_Project/Data/train'\n",
        "valid_path = '/content/drive/MyDrive/EDA_Project/Data/test'"
      ],
      "metadata": {
        "id": "R7a_LTpK5H9w"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Import the Vgg 16 library as shown below and add preprocessing layer to the front of VGG\n",
        "# Here we will be using imagenet weights\n",
        "\n",
        "vgg = VGG16(input_shape=IMAGE_SIZE + [3], weights='imagenet', include_top=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j38K6FIZ53qK",
        "outputId": "d9e0dd8c-1fb0-4c08-d98b-f80e84d2b07f"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "58892288/58889256 [==============================] - 0s 0us/step\n",
            "58900480/58889256 [==============================] - 0s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# don't train existing weights\n",
        "for layer in vgg.layers:\n",
        "    layer.trainable = False"
      ],
      "metadata": {
        "id": "jfr9uSpm587R"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        " # useful for getting number of output classes\n",
        "folders = glob('/content/drive/MyDrive/EDA_Project/Data/train/*')"
      ],
      "metadata": {
        "id": "jgopB_UU6Dw9"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# our layers - you can add more if you want\n",
        "x = Flatten()(vgg.output)"
      ],
      "metadata": {
        "id": "K0aMj26o6K7b"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "prediction = Dense(len(folders), activation='softmax')(x)\n",
        "\n",
        "# create a model object\n",
        "model = Model(inputs=vgg.input, outputs=prediction)"
      ],
      "metadata": {
        "id": "18z894G96U6z"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# view the structure of the model\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kwDBvIKs6XN8",
        "outputId": "c7768bd0-f827-4718-8965-668b44a6e430"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, 224, 224, 3)]     0         \n",
            "                                                                 \n",
            " block1_conv1 (Conv2D)       (None, 224, 224, 64)      1792      \n",
            "                                                                 \n",
            " block1_conv2 (Conv2D)       (None, 224, 224, 64)      36928     \n",
            "                                                                 \n",
            " block1_pool (MaxPooling2D)  (None, 112, 112, 64)      0         \n",
            "                                                                 \n",
            " block2_conv1 (Conv2D)       (None, 112, 112, 128)     73856     \n",
            "                                                                 \n",
            " block2_conv2 (Conv2D)       (None, 112, 112, 128)     147584    \n",
            "                                                                 \n",
            " block2_pool (MaxPooling2D)  (None, 56, 56, 128)       0         \n",
            "                                                                 \n",
            " block3_conv1 (Conv2D)       (None, 56, 56, 256)       295168    \n",
            "                                                                 \n",
            " block3_conv2 (Conv2D)       (None, 56, 56, 256)       590080    \n",
            "                                                                 \n",
            " block3_conv3 (Conv2D)       (None, 56, 56, 256)       590080    \n",
            "                                                                 \n",
            " block3_pool (MaxPooling2D)  (None, 28, 28, 256)       0         \n",
            "                                                                 \n",
            " block4_conv1 (Conv2D)       (None, 28, 28, 512)       1180160   \n",
            "                                                                 \n",
            " block4_conv2 (Conv2D)       (None, 28, 28, 512)       2359808   \n",
            "                                                                 \n",
            " block4_conv3 (Conv2D)       (None, 28, 28, 512)       2359808   \n",
            "                                                                 \n",
            " block4_pool (MaxPooling2D)  (None, 14, 14, 512)       0         \n",
            "                                                                 \n",
            " block5_conv1 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
            "                                                                 \n",
            " block5_conv2 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
            "                                                                 \n",
            " block5_conv3 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
            "                                                                 \n",
            " block5_pool (MaxPooling2D)  (None, 7, 7, 512)         0         \n",
            "                                                                 \n",
            " flatten_1 (Flatten)         (None, 25088)             0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 4)                 100356    \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 14,815,044\n",
            "Trainable params: 100,356\n",
            "Non-trainable params: 14,714,688\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# tell the model what cost and optimization method to use\n",
        "model.compile(\n",
        "  loss='categorical_crossentropy',\n",
        "  optimizer='adam',\n",
        "  metrics=['accuracy']\n",
        ")"
      ],
      "metadata": {
        "id": "_TGOdUPB6clm"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Use the Image Data Generator to import the images from the dataset\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "train_datagen = ImageDataGenerator(rescale = 1./255,\n",
        "                                   shear_range = 0.2,\n",
        "                                   zoom_range = 0.2,\n",
        "                                   horizontal_flip = True)\n",
        "\n",
        "test_datagen = ImageDataGenerator(rescale = 1./255)"
      ],
      "metadata": {
        "id": "EIlnG0OG62ii"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Make sure you provide the same target size as initialied for the image size\n",
        "training_set = train_datagen.flow_from_directory('/content/drive/MyDrive/EDA_Project/Data/train',\n",
        "                                                 target_size = (224, 224),\n",
        "                                                 batch_size = 32,\n",
        "                                                 class_mode = 'categorical')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wGLGjzwV67t3",
        "outputId": "48effeb6-0a92-4a46-932f-680f72c80e2b"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 6829 images belonging to 4 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_set = test_datagen.flow_from_directory('/content/drive/MyDrive/EDA_Project/Data/test',\n",
        "                                            target_size = (224, 224),\n",
        "                                            batch_size = 32,\n",
        "                                            class_mode = 'categorical')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VG_aMZ847IIw",
        "outputId": "9b82a44b-cb6a-4bc2-f191-d2cdc30a8e48"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 1669 images belonging to 4 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# fit the model\n",
        "# Run the cell. It will take some time to execute\n",
        "r = model.fit_generator(\n",
        "  training_set,\n",
        "  validation_data=test_set,\n",
        "  epochs=5,\n",
        "  steps_per_epoch=len(training_set),\n",
        "  validation_steps=len(test_set)\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DPaAu70_7UEX",
        "outputId": "ff8e12b5-aa81-436c-a4e7-a106b7da6389"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:8: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
            "  \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "214/214 [==============================] - 1636s 8s/step - loss: 0.1757 - accuracy: 0.9404 - val_loss: 0.0777 - val_accuracy: 0.9766\n",
            "Epoch 2/5\n",
            "214/214 [==============================] - 118s 551ms/step - loss: 0.1209 - accuracy: 0.9590 - val_loss: 0.1098 - val_accuracy: 0.9575\n",
            "Epoch 3/5\n",
            "214/214 [==============================] - 118s 552ms/step - loss: 0.1011 - accuracy: 0.9647 - val_loss: 0.1187 - val_accuracy: 0.9617\n",
            "Epoch 4/5\n",
            "214/214 [==============================] - 120s 558ms/step - loss: 0.1147 - accuracy: 0.9624 - val_loss: 0.1207 - val_accuracy: 0.9688\n",
            "Epoch 5/5\n",
            "214/214 [==============================] - 119s 554ms/step - loss: 0.0879 - accuracy: 0.9714 - val_loss: 0.1033 - val_accuracy: 0.9605\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plot the loss\n",
        "plt.plot(r.history['loss'], label='train loss')\n",
        "plt.plot(r.history['val_loss'], label='val loss')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "plt.savefig('LossVal_loss')\n",
        "\n",
        "# plot the accuracy\n",
        "plt.plot(r.history['accuracy'], label='train acc')\n",
        "plt.plot(r.history['val_accuracy'], label='val acc')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "plt.savefig('AccVal_acc')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 534
        },
        "id": "hMEO67TR7Y3E",
        "outputId": "9450b954-06d9-460a-db8b-24322c8cfc2d"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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cCuf++uRzYcey3Hbf/N39fQz9AyT/FKqFdnx8aZanZvLA9OX8sOsQl/U4nYcvS6JJ/VqhDqtEJZ0DsAQQLJu/hFcvc590Rr0Q6miMqRxZe+DLZyFlCmg+9L7Zfepv0OLUj0tf57rqbpoHzbvDJc9Aq77BiLhMso/n8dwn63jxy000qV+LiVd0Y3hSs1CHVaqSEoCVgIKloFXE/D/DGedBtytLf4wxkeLIPvj6L/DdZMg9Bj2vh8EPQMPW/j0+viPc+C6sfhdm/xZeHg69bnBloTAZQbdwUwbj317B5r2HubZPKyZc3JnT6gS/f08g2TeAYMrLhVcuhj1r4I750Di6p70zVUD2Afj2H/DtC5CTBd2ugiHjIe6M8u/zWBbMf8rts2Y9OP8h6H1LyMpCh7KP89TstfxnwVZaNa7Dk6O7M6B9eCQlf1kJKFxkboN/ngtNOsBPZ0NMZH+CMFEq57Abxvn1XyA7EzqPhPN+C007B+450tfCB7+BLV+6wROXPAsJJx3DKtXctXv43dsr2Hkwm1vOacu4CztSt2bkFU7sOoBw0bC1tYowket4tvvE/5ce8Nmj0KqfG9hwzX8Ce/AHiD8Tbprlpl7N2gMvnQ8z74HDGYF9nmLsP5zDfW8u5ZZ/L6JurepM//k5PHRZUkQe/E+lar2aSGGtIkykyc2BJf+B+U/DoR3QdjAM/X3ln6gVga5joMMF8MWfYME/YfVMGPYwnHVTpZSFPlyxk4feW0nmkePcM7Q9dw9tT63q4T0qqbysBBQqha0iMuEX34TNiS5jTpCXC8vfhC+edOXLVv3cgb/toNDEs2cNfHi/Kwud3suNFmrZOzC7PpjNQ++tYvaqXXRt2YCnxvQg6fQGAdl3qNk5gHC0ayW8ONR9C7j+Lfdpx5hwkJ8Pq96GeZMgYwO06OnG6Lc/P/S/p6qwcgbM+R1k7YbeN8H5D0PdxuXcnfK/xak88f5qsnPz+fWwjtw2sC3VY6pOhdyGgYaj5l3hwonw4TjXKqL/L0IdkYl2qvDDBzB3IuxZDU2T4Jqp0OmS0B/4C4i4YdQnlIXeg2GPQK+fQDX/D9zb9x3ht++s4Mv1e+nTphFPjunOGfHRc6GmfQMINVWYdj1s+BRu/dRaRZjQUIUNn8HcJ2DHEohrD0MmQJfRZTqghsTu1e5D1NavXTno4qeh5VmnfEh+vvKfBVv50+wfEODBizpxQ7/EiOjfUx5WAgpn1irChNLmL+HzJ2D7AjdKbfB46H6Nm+EuUqjCiv/Bx793I4aSb3Elq2LKQhvTs3hw+nJStu5nUMd4/nhFVxIa1Q1B0MFjCSDcWasIE2zbv3MH/s1fQGwLGHQ/9LoRqtcMdWTll30A5j3prlGofRoMfxR63gDVqnE8L5/J8zfxl8/WU6dGDH+4NIkxZ7WMqOZt5WUJIBJ8PtFdATnmZWsVYSrPjqXuGpT1c6BePJx7n/vEXKNOqCMLnF0rXVlo27fQMpmN/R7ll/OUVTsOMqJLcx4b1YWmsbVDHWXQWAKIBNYqwlSmPWvcgX/NTNeOecCvoO/tVbfkqErOkjc4/tHvqJOzn+nVLqTRpY8yvHenUEcWdHYlcCSIqQ5jXgIEZtwKecdDHZGpCjI2wozb4B9nuwsQB4+He5fDwPuq7sEfWLxtPxfNPZ2zD/2Jr+JGcxUfM/yzi2HJ626Yq/EvAYjICBFZKyIbRGR8MesTReQzEVkuIvNEJMFnXWsR+VhE1ojIam+SeETkFRHZLCJLvZ+egXpREe2EVhETQx2NiWSZ2+C9u+HvfeCH990n/nuXw3kTXH28ijp8LJdHZq7iyn99S/bxfP56yxAG/XIKcsd8iOsA790FUy6EnctCHWrIlVoCEpEYYB0wHEgFFgHXqepqn23+B7yvqq+KyFDgFlW90Vs3D5ioqp+ISH0gX1WPiMgr3mOm+xtslS8B+Zr5SzeZxo3vuPbRxvjr4E748hlY/IobM5/8M/dpv37TUEdW6b5av5fxby8ndf9RfnJ2Ig+M6ET9Wj6jmfLz3exknzwERzLcezP091CnYeiCDoKKXAjWF9igqpu8HU0DLgdW+2yTBNzn3Z4LvOttmwRUV9VPAFQ1q9yvINqMeBK2LYB37rBWEcY/Wenw9fOw6CXIz3UjegaNg9MSSn9shDtw9DgTP1jNWymptG1Sj7fuOJu+bYu5MrhaNTdXwZkXu2/Yi16CVe/A8Megx3Xhf81DgPnzalsC233up3rLfC0DRnu3rwBiRSQO6AhkisjbIrJERP7sfaMoMNErGz0nIsXOpyYit4tIioikpKen+/WiqoSadV0XxKOZ8O4v3DhnE3ZUle+37ef1BVv5ZuNe9mYdC34QR/fDZ4+5Dp0L/uEu3ro7BS57PioO/h+v2sXwZ79gxvdp/HzwGXz0q4HFH/x91WkIF//ZXXfTuB28dyf8ewTsXB6coMOEPyWgK4ERqnqrd/9GoJ+q3u2zzenA34G2wHxgDNAVGAa8DPQCtgFvAh+q6ssi0gLYBdQEJgMbVfWxU8USVSWgAt+96IazXTgJzr4z1NEYz+Fjuby7NI3XF2xjzc6DJ6xrXK8mHZrWp2OzWDo2q0+HZrF0bBZL43oBHl+ffdC1EPnm73DsgOuaOXi8m10rCuzNOsbDM1fxwfKddGoey5+v7EG3hHKc28jPh2X/dWWho/uhz21uboMqVBaqSAkoDWjlcz/BW1ZIVXfgfQPw6vxjVDVTRFKBpT7lo3eB/sDLqrrTe/gxEfk3MK6Mryk69LkVNn7ufjkTz4HT7Vx5KP2w6yCvL9jKu0t2kHUsl84tGjDxiq4M6hDPlozDrNudxfrdh1i3+xDvLknj0LHcwsc2qV+TDk1PTAodm9WnYd0yJoacI7DoRfjqeTi6Dzpd6to2NO8a4FcbnlSV95bu4NFZqzh8LI/fDO/Iz4ecQY3yNm+rVs1NP9npEndh3KIXXSO84Y9Dj2vDpwdSJfDnG0B13Eng83EH/kXA9aq6ymebJsA+Vc0XkYlAnqo+5JV7vgeGqWq6d6BPUdUXRKSFqu4Udxnec0C2qp40wshXVH4DAGsVEWLHcvP4aMUuXl+wlZSt+6lZvRqXdm/BDf0T6dWqYYlXkqoquw5mn5AUCm4fzskr3C4+tpZLCk1jT/jWcNJ8s7nH3IndL59xXTDbD3OfVAPUDjkS7Mg8yu/fXcnnP+yhV+uGPDWmOx2axQb4SZa6b92pi6D12a63UIQn1wpdCCYiFwPPAzHAFFWdKCKP4Q7mM70y0SRAcSWgu1T1mPfY4cAzgACLgdtVNUdEPgfiveVLgZ+XdpI4ahMAwJav4JVLrVVEEG3LOMLU77byv5RU9h3OoU1cXcb2S+TK3gk0qkA5R1XZcSCbdbsPeYnBJYX1e7I44pMYmjWoRcdmsZwZX4cLcj6l5+YXqXl4BySe60auJJ4diJcZEfLzlTcWbWPShz+Ql6+Mu/BMbj6nDTGV1bwtPx+WToVPH3bn4freHtHDZ+1K4KrAWkVUuty8fD7/YQ+vL9zG/HXpxFQThnVuyg39ExlwRpNK7RaZn6+kZR5l/R4vKezKpOX2D7jy0H9oLXtYkt+eP+dezeb6venQvAEdvfMMHbxvDCcMd6xCtuw9zIMzlrNw8z7OOSOOJ0d3p3VckJq3HdnnykIpU1zbjAuegO5XR1xZyBJAVVDQKmL3avj5l9YqIoB2H8zmzUXbeeO7bew8kE2zBrW4rm9rru3TmuanBblnTH4+rHkP5k6CvWvR5t1IT36A5XX6si49i/W7s1i3+xAb9mRxLPfHK1pbNqxDh2ZeUvCSQ/um9akXoYkhL195+atNPPPxOmrGVON3l3Tmmj6tQtO8Le17VxZKWwytz4FLnoZmXYIfRzlZAqgqMrfBv851/dp/OgdiapT+GFMsVeWbjRm8vmArn6zeTW6+MrBDE8b2S2RY56bBnxFKFdbNdt/0dq+A+E6uxt/psmLHp+flK9v3HXGlpD1ZhecYNqZnkeOTGBIa1Sn8ptDRO8/Qvml96tQM33lu1+46xAPTl7Es9QDDOjfjiVFdg5+Ii8rPd/Mif/qI6zra7+cwZDzUDv9pIy0BVCWr3oX/3QTn/trNgmTKJPNIDtMXp/LfhdvYtPcwDevW4OrkVlzftzVtmtQLfkCqsGmuKzWkLYZGbd2Bv+uYck16npuXz7Z9R348+bzH/bsp/TA5eS4xiECrRnV9RiS5k9Dtm9ando3QJYac3HxemLuBf8zbQGztGjwysguXdW8RXi2bj+xz110sfsVdXX3BRFeSDacYi7AEUNXM+hUsftVaRfhJVVm6PZOpC7cxa9kOjuXmc1brhtzQP5GLu7UI3UFvy9fuitStX8NprWDwA+6K1Er4Zpebl8+WjCOFJ57X7TlUmBhy891xoJpA68Z1C5OCKyfF0i6+XqW/R8u2Z/LA9OWs3X2Iy3uezkOXJhFXv9jrQ8ND2mL44DduBrXEc11ZqGnnUEdVLEsAVU3OEZg8BLIz4edfQ/34UEcUlo7k5PLe0h28vmArq3YcpF7NGEb1asnYfokknR7Cr+6pi930ixs/h/rN3GQsZ/0Eqgf/gHc8L58te901DK6c5BLE5r2HyfNJDG3i6v14jsFLEG2b1KNW9YolhqM5eTz7yVpe/mozTWNr88SorgxLahaIl1b58vNcz67PHoVjh34sC9UK8NDUCrIEUBXtWgkvDoW2g+D6t6Kuj8mprNt9iNcXbOWd793FWJ2axzK2fyKjep5ObO0QnjfZtcL15F/7IdSNc2W85J+5azzCTE5uPpv3Hj5huOq6PYfYmnGkMDHEVBPaxNU9ISl0bBZLm7h61Kxe+u/jtxszmPD2crZkHOG6vq2ZcHEnGoTy/6e8Dme4JPD9axDb3I0W6jombMpClgCqKmsVUehYbh6zV+5i6oJtfLdlHzVjqnFxt+bc0D+R3omNQltHTl8L8ya5xmO1ToMB97hPi2H2SdEfx3Lz2JRekBiyCk9Cb804jJcXqF5NaNuk3o8nn73kkBhXjxox1TiUfZxJH/3Afxduo3Xjujw5phvnnFEFGh6mpriy0M6l0Gagu4isaegnoLEEUFWpwrSxsP5juPXTqGwVsX3fEf773TbeWrSdjMM5tG5cl7H9WnNl74TQ15D3bYIvnoLlb0KNutD/F3D2XVCnUWjjqgTZx/PY6DNMdd3uLNbvOcS2fUcKexnWiBHaNalP5tEc0g8d46cD2vKbC84M6xFJZZaf504Qf/YY5GRB/zvduZ0QJntLAFVZFLaKyMtX5v6wh9cXbuWLdekIcH7nZtzQP5GB7Sv3gi2/HEiF+X92s09Vqw59b4MB90ZlW++jOS4x+LbCOJabz28u6Eiv1lUvERY6vNcNGV3yH4g9HS6cCF2uCElZyBJAVVfYKuJ6GPWPUEdTafYcyuatRdt547vtpGUepWlsLa7t25pr+7Ti9IZhMKn5od3w1bPuylFVN9n6wN+4urCJTtsXwQf3wa7l0HawKwsFuWOrJYBoUNAqYvRL0P2qUEcTMKrKt5symLpgG3NW7SI3XxnQPo4b+iUyLKlZ+btABtLhDDcZy3cvQl4O9BrrRvY0bB3qyEw4yM9zHwo+f9yN4Dv7Lvf7EaRv65YAokFeLrxyCexeVSVaRRw4epwZi1OZunArG9MPc1qdGlzZO4Gx/VrTLj4Mylw5R2DzF7D2I1j5tqv3dr8aBj8IcWeEOjoTjrLSXVlo6evQoCVc+EdIurzSy0KWAKJFFWgVsWx7JlMXbmXmsh1kH8+nZyt3wdal3UN4wVaBgztdu4Z1s2HTPMjNhpqxcOZFrtQTBiM+TATYthA+/I0bFtzuPDc7WZMOlfZ0lgCiSUGriAH3wvBHQx2NX47m5DFzmZtha0XaAerUiGFUr9MZ2y+Rri1D2IJXFXYucwf8tR+54X0ADRPdQb/jCEgcANUDPNuXqfrycr2y0BNw/Aicc7crC9UMfDsSSwDRZtav3FC0G9+BM4aGOpoSbdhziNcXbGPG96kcys6lY7P63NA/kVG9WobugqDjR2HzfHfAXzcHDu0ABFr1dQf8My9yjdrC5CIfE+Gy9sAnD7tpKRskwIg/QueRAf39sgQQbXKOwIvnuTlOw0NeVq0AABXOSURBVKxVRE5uPnNWuRm2Fm7eR40Y4aKuboatPm1CdMHWoV3uYL9uNmycC7lHoWZ9lzzPvAg6XBCVQzhNEG391l3UuXslnHE+XPQUNGkfkF1bAohGu1fB5PPCplVE6v4jvPHdNt5clMrerGMkNKrD9f1ac3VyK5oE+4ItVTcsb+1sWPeRa+gFcFprOHOE+6Tf5tyQ9OYxUSwvFxa95BoE5mbDOffAwHEVbhViCSBahbhVRF6+8sW6Pby+YBtz1+5BgKGdmjK2fyKDO8QH94Kt49mutLPOK+0cTAMEEpJ/LO00TbLSjgm9Q7vhk4dg+TTXJXbEJOh0abl/Nys6J/AI4C+4OYFfUtUni6xPBKbg5vjdB9ygqqneutbAS0Ar3JzBF6vqFhFpC0wD4nBzBd+oqjmnisMSQDmEqFXE3qxjhTNspe4/SpP6tbi2Tyuu69ealsG8YOvQblg/x33S3zTXnWyrUc+10C4o7dRvGrx4jCmLLV/Dh/fDnlXws0+hVZ9y7abcCUBEYoB1wHAgFVgEXKeqq322+R/wvqq+KiJDgVtU9UZv3Txgoqp+IiL1gXxVPSIibwFvq+o0EfkXsExV/3mqWCwBlFNBq4gadeCO+ZV28YmqsnDzPqYu3MbslTs5nqec3S6Osf1bc0FSc7+6QwYgCFdDLSjtpC12yxskeKWdi1xpp0aIZ5cyxl95ue5DTKdLyr2LiiSAs4FHVPVC7/4EAFWd5LPNKmCEqm4XdwbvgKo2EJEkYLKqnltknwKkA81VNbfoc5TEEkAFbPkKXr3MTTYS4FYRB7OP8/biVKYu3Mb6PVk0qF2dMb0TGNsvkfZNg3DBVu4x2Pzlj6WdA9vd8pa93QH/zBHQrKuVdkzUKikB+DNbdEtgu8/9VKBfkW2WAaNxZaIrgFgRiQM6Apki8jbQFvgUGA80AjJVNddnny1LCPx24HaA1q3tsvpya3OuG2P8xZ/chScBaBWxMu0Ary/YyntLd3D0eB49Ek7jqSu7c1n30yu/u2NWulfa+ciN2jl+2HXbbHee67zY4UKIjZBJRYwJEX8SgD/GAX8XkZuB+UAakOftfyDQC9gGvAncDLzn745VdTIwGdw3gADFG50GPQCbvoD3f+1OfJajVcTRnDxmLd/B1AVbWZZ6gNo1qnF5j5bc0D+RbgmVeMGWKuxZ7Y3Nn+36rqOuy2KPa9wn/bYDXZnLGOMXfxJAGu4EboEEb1khVd2B+waAV+cfo6qZIpIKLFXVTd66d4H+uBPGDUWkuvct4KR9mkoQUx3GvOhaRcz4WZlaRWxMz2Lqgm1MX7ydg9m5tG9an4cvS2L0WQmcVqeSLtjKPeZKVwWtFzK3ueWn94IhE1xpp3l3K+0YU07+JIBFQAdv1E4acC1wve8GItIE2Keq+cAE3AG+4LENRSReVdOBoUCKqqqIzAWuxI0EuokyfCswFdCwNYz8G7z1E3cJ+ilaRRzPy+fjVbt5fcFWvt2UQY0Y4cIuboatfm0bV84FW4f3uhFLaz9y8+XmZEH1OtBuiOu10+FCaNAi8M9rTBQqNQF4J2nvBubghoFOUdVVIvIY7mA+ExgCTBIRxZWA7vIemyci44DPvBO/i4EXvV0/CEwTkSeAJcDLgX1ppkRJl0PvW1z74naDT2oVkZF1jFe+2cK0RdtJP3SMlg3rcP+FZ3J1civiYwN8YZQqpP/wY2ln+3e40k4L6HalK+20G2ylHWMqgV0IFq1KaBWRfTyPK/7xDT/sOsh5ZzZlbL/WDDmzKTGBvGArNwe2fv1jg7XMrW55ix4/jtpp0dNKO8YESEVGAZmqqGZduHKKaxXx7i8KW0U8/v5q1uw8yMs3JXN+5wCOojmy78TSzrGDUL22myHp3HvdlbgNTg/c8xljSmUJIJo16+LmKf1wHCz8JzPrXsHUhdu4Y3C7ih/8VSF97Y8ncLcvBM2H+s2gyyivtDOkwj1OjDHlZwkg2vW5FTbNQz95mNdyhd6JPRl3wZnl21fecVfaKbgKd/8Wt7x5N9fQ6swR0KJXyJvSGWMcSwDRToTsi//CobX9eKbaX6k55quyzbF7ZB+s/8Qd8Dd85ko7MbVcB9Jz7nGlndMSKi9+Y0y5WQIwPP7ZTjZk/4JptSci3zx06lYRqrB3vTvgr50N2xe40k69ppA00pV2zjivUmY1MsYEliWAKDdz2Q6v7n8JUjun+FYRecdh27c/lnb2bXLLm3VzY/M7XuQuzrLSjjERxRJAFNu89zATZiynd2Ijr+7v0yqiSXvYu8Er7XwK2QcgpqYr7fS/05V2GrYq9TmMMeHLEkCUyj6ex51Tv6dG9Wr87bpeXt2/2o+tIiYPcRvWbeImoug4wpV2asWGMmxjTABZAohSBeP9p9yczOm+E7Q0bA3X/tfNnNV+mGupXK2SO3saY0LCEkAU+rHu346hnYoZ79/mXPdjjKnS7KxdlDm57m+MiVaWAKJI8XV/Y0y0shJQFCmx7m+MiUr2ETBKlFr3N8ZEHUsAUcDq/saY4lgCqOKs7m+MKYmdA6jirO5vjCmJfRyswqzub4w5Fb8SgIiMEJG1IrJBRMYXsz5RRD4TkeUiMk9EEnzW5YnIUu9nps/yV0Rks8+6noF5SQas7m+MKV2pJSARiQFeAIYDqcAiEZmpqqt9NnsaeE1VXxWRocAk4EZv3VFVLengfr+qTi9/+KY4Vvc3xvjDnyNDX2CDqm5S1RxgGnB5kW2SgM+923OLWW+CqKDu/+zVPazub4wpkT8JoCWw3ed+qrfM1zJgtHf7CiBWROK8+7VFJEVEFojIqCKPm+iVjZ4TkVrFPbmI3O49PiU9Pd2PcKOb1f2NMf4KVG1gHDBYRJYAg4E0IM9bl6iqycD1wPMicoa3fALQCegDNAYeLG7HqjpZVZNVNTk+Pj5A4VZNVvc3xpSFPwkgDfCd+SPBW1ZIVXeo6mhV7QX8zluW6f2b5v27CZgH9PLu71TnGPBvXKnJlJPV/Y0xZeXPUWIR0EFE2opITeBaYKbvBiLSREQK9jUBmOItb1RQ2hGRJsAAYLV3v4X3rwCjgJUVfznRy+r+xpiyKnUUkKrmisjdwBwgBpiiqqtE5DEgRVVnAkOASSKiwHzgLu/hnYH/E5F8XLJ50mf00FQRiQcEWAr8PICvK6rMsrq/MaYcRFVDHYPfkpOTNSUlJdRhhJXNew9z2d++4szmsUy7vb+VfowxJxGRxd652BPY0SKCZR/P466p31M9Rqzub4wpM+sFFMGe+GA1q63PjzGmnOwjY4SatWwHry+wur8xpvwsAUSgzXsPM+HtFTbe3xhTIZYAIozV/Y0xgWLnACKM1f2NMYFiHx8jiNX9jTGBZAkgQljd3xgTaJYAIoDV/Y0xlcHOAUQAq/sbYyqDfZQMc1b3N8ZUFksAYczq/saYymQJIExZ3d8YU9nsHECYsrq/Maay2cfKMGR1f2NMMFgCCDNW9zfGBIslgDBidX9jTDDZOYAwYnV/Y0ww+fURU0RGiMhaEdkgIuOLWZ8oIp+JyHIRmSciCT7r8kRkqfcz02d5WxFZ6O3zTW/C+ahldX9jTLCVmgBEJAZ4AbgISAKuE5GkIps9Dbymqt2Bx4BJPuuOqmpP72ekz/I/Ac+pantgP/CzCryOiGZ1f2NMKPjzDaAvsEFVN6lqDjANuLzINknA597tucWsP4GICDAUmO4tehUY5W/QVYnV/Y0xoeLP0aYlsN3nfqq3zNcyYLR3+wogVkTivPu1RSRFRBaISMFBPg7IVNXcU+wzKhTU/Z+9uofV/Y0xQRWoj5vjgMEisgQYDKQBed66RFVNBq4HnheRM8qyYxG53UsgKenp6QEKNzxY3d8YE0r+JIA0oJXP/QRvWSFV3aGqo1W1F/A7b1mm92+a9+8mYB7QC8gAGopI9ZL26bPvyaqarKrJ8fHx/r6usGd1f2NMqPmTABYBHbxROzWBa4GZvhuISBMRKdjXBGCKt7yRiNQq2AYYAKxWVcWdK7jSe8xNwHsVfTGRwur+xphwUOqRx6vT3w3MAdYAb6nqKhF5TEQKRvUMAdaKyDqgGTDRW94ZSBGRZbgD/pOqutpb9yBwn4hswJ0TeDlArynsWd3fGBMOxH0YjwzJycmakpIS6jAqZNayHdzzxhLuGNyOCRd1DnU4xpgoICKLvXOxJ7DaQxBZ3d8YE04sAQSJ1f2NMeHGegEFifX5McaEG/sYGgQ23t8YE44sAVQyq/sbY8KVJYBKZHV/Y0w4s3MAlcjq/saYcGYfSSuJ1f2NMeHOEkAlsLq/MSYSWAIIMKv7G2MihZ0DCDCr+xtjIoV9PA0gq/sbYyKJJYAAsbq/MSbSWAIIAKv7G2MikZ0DCICCuv/LN1nd3xgTOeyjagUV1v0HteP8zlb3N8ZEDksAFVBQ9z+rdUPGXWh1f2NMZLEEUE4n1P2vP8vq/saYiGPnAMrJt+7f0ur+xpgI5NfHVhEZISJrRWSDiIwvZn2iiHwmIstFZJ6IJBRZ30BEUkXk7z7L5nn7XOr9NK34ywkOq/sbY6qCUhOAiMQALwAXAUnAdSKSVGSzp4HXVLU78Bgwqcj6x4H5xex+rKr29H72lDn6ELC6vzGmqvDnG0BfYIOqblLVHGAacHmRbZKAz73bc33Xi0hvoBnwccXDDS2r+xtjqhJ/jmAtge0+91O9Zb6WAaO921cAsSISJyLVgGeAcSXs+99e+ecPIiLFbSAit4tIioikpKen+xFu5Smo+z9zVQ+r+xtjIl6gPsKOAwaLyBJgMJAG5AF3Ah+qamoxjxmrqt2Agd7PjcXtWFUnq2qyqibHx8cHKNyys7q/Maaq8WcUUBrQyud+greskKruwPsGICL1gTGqmikiZwMDReROoD5QU0SyVHW8qqZ5jz0kIv/FlZpeq/ArqgRW9zfGVEX+JIBFQAcRaYs78F8LXO+7gYg0Afapaj4wAZgCoKpjfba5GUhW1fEiUh1oqKp7RaQGcCnwaQBeT8BZ3d8YU1WVejRT1VzgbmAOsAZ4S1VXichjIjLS22wIsFZE1uFO+E4sZbe1gDkishxYikssL5bvJVQuq/sbY6oqUdVQx+C35ORkTUlJCdrzzVq2g3veWMIdg9ox4eLOQXteY4wJJBFZrKrJRZdbPaMEVvc3xlR1lgCKYXV/Y0w0sF5AxbA+P8aYaGAfbYuw8f7GmGhhCcCH1f2NMdHEEoDH6v7GmGhj5wA8Vvc3xkQb+5iL1f2NMdEp6hOA1f2NMdEqqhOA1f2NMdEsqs8BWN3fGBPNovYjr9X9jTHRLioTgNX9jTEmChOA1f2NMcaJunMAVvc3xhgnqj7+Wt3fGGN+FDUJwOr+xhhzoqhIAFb3N8aYk/l1JBSRESKyVkQ2iMj4YtYnishnIrJcROaJSEKR9Q1EJFVE/u6zrLeIrPD2+VcRkYq/nOLZvL7GGHOyUhOAiMQALwAXAUnAdSKSVGSzp4HXVLU78Bgwqcj6x4H5RZb9E7gN6OD9jChz9H5QVdrE1eOu886wur8xxvjwZxRQX2CDqm4CEJFpwOXAap9tkoD7vNtzgXcLVohIb6AZMBtI9pa1ABqo6gLv/mvAKOCjiryY4ogItw5sF+jdGmNMxPOnBNQS2O5zP9Vb5msZMNq7fQUQKyJxIlINeAYYV8w+U0vZJwAicruIpIhISnp6uh/hGmOM8UegzoaOAwaLyBJgMJAG5AF3Ah+qauqpHnwqqjpZVZNVNTk+Pj4w0RpjjPGrBJQGtPK5n+AtK6SqO/C+AYhIfWCMqmaKyNnAQBG5E6gP1BSRLOAv3n5K3KcxxpjK5U8CWAR0EJG2uIP0tcD1vhuISBNgn6rmAxOAKQCqOtZnm5uBZFUd790/KCL9gYXAT4C/VfjVGGOM8VupJSBVzQXuBuYAa4C3VHWViDwmIiO9zYYAa0VkHe6E70Q/nvtO4CVgA7CRSjgBbIwxpmSiqqGOwW/JycmakpIS6jCMMSaiiMhiVU0uutwuiTXGmChlCcAYY6JURJWARCQd2FrOhzcB9gYwnECxuMrG4iobi6tsqmpciap60jj6iEoAFSEiKcXVwELN4iobi6tsLK6yiba4rARkjDFRyhKAMcZEqWhKAJNDHUAJLK6ysbjKxuIqm6iKK2rOARhjjDlRNH0DMMYY48MSgDHGRKkqlwD8mL6yloi86a1fKCJtwiSum0UkXUSWej+3BiGmKSKyR0RWlrBevOk6N3jTfZ5V2TH5GdcQETng8149FKS4WonIXBFZLSKrRORXxWwT9PfMz7iC/p6JSG0R+U5ElnlxPVrMNkH/e/QzrqD/Pfo8d4yILBGR94tZF9j3S1WrzA8Qg2ss1w6oiZuoJqnINncC//JuXwu8GSZx3Qz8Pcjv1yDgLGBlCesvxjXpE6A/sDBM4hoCvB+C368WwFne7VhgXTH/j0F/z/yMK+jvmfce1Pdu18B1/u1fZJtQ/D36E1fQ/x59nvs+4L/F/X8F+v2qat8ACqevVNUcoGD6Sl+XA696t6cD51fmhPRliCvoVHU+sO8Um1yOm+tZ1U3f2dCbzjPUcYWEqu5U1e+924dw3XGLzmQX9PfMz7iCznsPsry7NbyfoqNOgv736GdcISEiCcAluE7JxQno+1XVEoA/01cWbqOu1fUBIC4M4gIY45UNpotIq2LWB5u/cYfC2d5X+I9EpEuwn9z76t0L9+nRV0jfs1PEBSF4z7xyxlJgD/CJqpb4fgXx79GfuCA0f4/PAw8A+SWsD+j7VdUSQCSbBbRR1e7AJ/yY5c3Jvsf1NumBm0jo3WA+ubhZ72YA96rqwWA+96mUEldI3jNVzVPVnrhZ//qKSNdgPG9p/Igr6H+PInIpsEdVF1f2cxWoagmg1OkrfbcRkerAaUBGqONS1QxVPebdfQnoXckx+cOf9zPoVPVgwVd4Vf0QqCFuVrpKJyI1cAfZqar6djGbhOQ9Ky2uUL5n3nNmAnOBEUVWheLvsdS4QvT3OAAYKSJbcGXioSLyepFtAvp+VbUEUDh9pYjUxJ0kmVlkm5nATd7tK4HP1TujEsq4itSJR+LquKE2E/iJN7KlP3BAVXeGOigRaV5Q9xSRvrjf40o/aHjP+TKwRlWfLWGzoL9n/sQVivdMROJFpKF3uw4wHPihyGZB/3v0J65Q/D2q6gRVTVDVNrhjxOeqekORzQL6fvkzJ3DEUNVcESmYvjIGmKLe9JVAiqrOxP2h/EdENuBONF4bJnH9UtwUm7leXDdXdlwi8gZudEgTEUkFHsadEENV/wV8iBvVsgE4AtxS2TH5GdeVwC9EJBc4ClwbhCQO7hPajcAKr34M8FugtU9soXjP/IkrFO9ZC+BVEYnBJZy3VPX9UP89+hlX0P8eS1KZ75e1gjDGmChV1UpAxhhj/GQJwBhjopQlAGOMiVKWAIwxJkpZAjDGmChlCcAYY6KUJQBjjIlS/w8/JK2VDoknZgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# save it as a h5 file\n",
        "\n",
        "import tensorflow as tf\n",
        "\n",
        "from keras.models import load_model\n",
        "\n",
        "model.save('model_vgg16.h5')"
      ],
      "metadata": {
        "id": "YRWBdeiPDySb"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "GRNYNexLD4IX"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}