Dance Recognition.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing the Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import shutil\n",
    "from sklearn.datasets import load_files\n",
    "\n",
    "#Import required packages\n",
    "# import numpy as np\n",
    "import cv2\n",
    "import random\n",
    "# import os\n",
    "import sys\n",
    "# import shutil\n",
    "from PIL import Image\n",
    "from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img\n",
    "import configparser\n",
    "import secrets\n",
    "\n",
    "from keras.utils import np_utils\n",
    "import matplotlib.pyplot as plt\n",
    "from keras.layers import Conv2D, MaxPooling2D, GlobalAveragePooling2D\n",
    "from keras.layers import Dropout, Flatten, Dense\n",
    "from keras.models import Sequential\n",
    "from keras.utils.vis_utils import plot_model\n",
    "from keras.callbacks import ModelCheckpoint\n",
    "from keras.utils import to_categorical\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "from sklearn.metrics import accuracy_score,precision_score,recall_score,f1_score\n",
    "\n",
    "from keras.preprocessing import image                  \n",
    "from tqdm import tqdm\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from PIL import ImageFile               "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'Dance Recognition.ipynb'   dataset   LICENSE   README.md\r\n"
     ]
    }
   ],
   "source": [
    "!ls"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Augmentation\n",
    "\n",
    "* As the dataset size is very less we need to augment the data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA_AUG = os.path.join(os.getcwd(),\"prepare_augmented_data\")\n",
    "\n",
    "if not os.path.exists(DATA_AUG):\n",
    "    os.makedirs(DATA_AUG)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of training images is  364\n",
      "The number of testing images is  156\n"
     ]
    }
   ],
   "source": [
    "data_train = pd.read_csv(os.path.join(\"./dataset/\",\"train.csv\"))\n",
    "data_test = pd.read_csv(os.path.join(\"./dataset\",\"test.csv\"))\n",
    "\n",
    "data_train['Image'] = data_train['Image'].apply(lambda x:os.path.join(os.getcwd(),\"dataset\",\"train\",x))\n",
    "data_test['Image'] = data_test['Image'].apply(lambda x:os.path.join(os.getcwd(),\"dataset\",\"test\",x))\n",
    "\n",
    "print(\"The number of training images is \",data_train.shape[0])\n",
    "print(\"The number of testing images is \",data_test.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DanceName</th>\n",
       "      <th>NumFiles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mohiniyattam</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>odissi</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>kathakali</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bharatanatyam</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>kuchipudi</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>sattriya</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>kathak</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>manipuri</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       DanceName  NumFiles\n",
       "0   mohiniyattam        50\n",
       "1         odissi        49\n",
       "2      kathakali        47\n",
       "3  bharatanatyam        47\n",
       "4      kuchipudi        46\n",
       "5       sattriya        45\n",
       "6         kathak        44\n",
       "7       manipuri        36"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train['target'].value_counts().index\n",
    "\n",
    "dance_dist = list()\n",
    "for idx,num_files in enumerate(data_train['target'].value_counts()):\n",
    "    dance_dist.append({\n",
    "        \"DanceName\":data_train['target'].value_counts().index[idx],\n",
    "        \"NumFiles\":num_files\n",
    "    })\n",
    "dance_dist = pd.DataFrame(dance_dist)\n",
    "\n",
    "\n",
    "dance_dist.head(10) # shows the classwise distribution of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Image</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>/home/abhinav/hackerearth/Dance-Recognition/da...</td>\n",
       "      <td>manipuri</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>/home/abhinav/hackerearth/Dance-Recognition/da...</td>\n",
       "      <td>bharatanatyam</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>/home/abhinav/hackerearth/Dance-Recognition/da...</td>\n",
       "      <td>odissi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>/home/abhinav/hackerearth/Dance-Recognition/da...</td>\n",
       "      <td>kathakali</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>/home/abhinav/hackerearth/Dance-Recognition/da...</td>\n",
       "      <td>odissi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>/home/abhinav/hackerearth/Dance-Recognition/da...</td>\n",
       "      <td>kathak</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>/home/abhinav/hackerearth/Dance-Recognition/da...</td>\n",
       "      <td>sattriya</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>/home/abhinav/hackerearth/Dance-Recognition/da...</td>\n",
       "      <td>kuchipudi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>/home/abhinav/hackerearth/Dance-Recognition/da...</td>\n",
       "      <td>sattriya</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>/home/abhinav/hackerearth/Dance-Recognition/da...</td>\n",
       "      <td>kuchipudi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               Image         target\n",
       "0  /home/abhinav/hackerearth/Dance-Recognition/da...       manipuri\n",
       "1  /home/abhinav/hackerearth/Dance-Recognition/da...  bharatanatyam\n",
       "2  /home/abhinav/hackerearth/Dance-Recognition/da...         odissi\n",
       "3  /home/abhinav/hackerearth/Dance-Recognition/da...      kathakali\n",
       "4  /home/abhinav/hackerearth/Dance-Recognition/da...         odissi\n",
       "5  /home/abhinav/hackerearth/Dance-Recognition/da...         kathak\n",
       "6  /home/abhinav/hackerearth/Dance-Recognition/da...       sattriya\n",
       "7  /home/abhinav/hackerearth/Dance-Recognition/da...      kuchipudi\n",
       "8  /home/abhinav/hackerearth/Dance-Recognition/da...       sattriya\n",
       "9  /home/abhinav/hackerearth/Dance-Recognition/da...      kuchipudi"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.head(10) #creates a csv having file location and class name "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# putting images belonging to different classes into different folders with corresponding classname\n",
    "# Preparing dataset for augmentation\n",
    "for i in range(data_train.shape[0]):\n",
    "    if not os.path.exists(os.path.join(DATA_AUG,data_train.iloc[i,1])):\n",
    "        os.makedirs(os.path.join(DATA_AUG,data_train.iloc[i,1]))\n",
    "    shutil.copy(data_train.iloc[i,0],os.path.join(DATA_AUG,data_train.iloc[i,1],data_train.iloc[i,0].split(\"/\")[-1]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting the data augmentation definition\n",
    "\n",
    "gen_per_image = 4\n",
    "gen_per_class = 300\n",
    "#path to the folder containing the data ready for augmentation\n",
    "path = DATA_AUG\n",
    "rotation_range = 5\n",
    "width_shift_range = 0.02\n",
    "height_shift_range = 0.02\n",
    "shear_range = 0.01\n",
    "zoom_range = 0.05\n",
    "horizontal_flip = False\n",
    "fill_mode = \"nearest\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def increase_brightness(img, value):\n",
    "    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)\n",
    "    h, s, v = cv2.split(hsv)\n",
    "\n",
    "    lim = 255 - value\n",
    "    v[v > lim] = 255\n",
    "    v[v <= lim] += value\n",
    "\n",
    "    final_hsv = cv2.merge((h, s, v))\n",
    "    img = cv2.cvtColor(final_hsv, cv2.COLOR_HSV2BGR)\n",
    "    return img\n",
    "\n",
    "def change_contrast(img, level):\n",
    "    img = Image.fromarray(img.astype('uint8'))\n",
    "    factor = (259 * (level + 255)) / (255 * (259 - level))\n",
    "    def contrast(c):\n",
    "        return 128 + factor * (c - 128)\n",
    "    return np.array(img.point(contrast))\n",
    "\n",
    "def pad_img(img):\n",
    "    h, w = img.shape[:2]\n",
    "    new_h = int((5 + secrets.randbelow(16)) * h / 100) + h\n",
    "    new_w = int((5 + secrets.randbelow(16)) * w / 100) + w\n",
    "\n",
    "    full_sheet = np.ones((new_h, new_w, 3)) * 255\n",
    "\n",
    "    p_X = secrets.randbelow(new_h - img.shape[0])\n",
    "    p_Y = secrets.randbelow(new_w - img.shape[1])\n",
    "\n",
    "    full_sheet[p_X : p_X + img.shape[0], p_Y : p_Y + img.shape[1]] = img\n",
    "\n",
    "    full_sheet = cv2.resize(full_sheet, (w, h), interpolation = cv2.INTER_AREA)\n",
    "\n",
    "    return full_sheet.astype(np.uint8)\n",
    "\n",
    "def preprocess_img(img):\n",
    "    img = np.array(img)\n",
    "\n",
    "    x = secrets.randbelow(2)\n",
    "\n",
    "    if x == 0:\n",
    "        # img = pad_img(img)\n",
    "        img = increase_brightness(img, secrets.randbelow(26))\n",
    "        img = change_contrast(img, secrets.randbelow(51))\n",
    "    else:\n",
    "        # img = pad_img(img)\n",
    "        img = change_contrast(img, secrets.randbelow(51))\n",
    "        img = increase_brightness(img, secrets.randbelow(26))\n",
    "\n",
    "    return img\n",
    "\n",
    "def copy_org(doc_type):\n",
    "    files = os.listdir(os.path.join(path, doc_type))\n",
    "\n",
    "    for file in files:\n",
    "        shutil.copy(os.path.join(path, doc_type, file), os.path.join(os.getcwd(),\"augmented_data\",doc_type, file))\n",
    "\n",
    "\n",
    "#Initialise the parameters for Augmentation.\n",
    "datagen = ImageDataGenerator(\n",
    "        rotation_range = rotation_range,\n",
    "        width_shift_range = width_shift_range,\n",
    "        height_shift_range = height_shift_range,\n",
    "        shear_range = shear_range,\n",
    "        zoom_range = zoom_range,\n",
    "        horizontal_flip = horizontal_flip,\n",
    "        fill_mode = fill_mode,\n",
    "        preprocessing_function = preprocess_img)\n",
    "\n",
    "def generator(doc_type, total):\n",
    "    # print(doc_type + \" \" + set_type)\n",
    "    print(doc_type)\n",
    "    # src_path = os.path.join(path, doc_type, set_type)\n",
    "    src_path = os.path.join(path,doc_type)\n",
    "    dst_path = os.path.join(os.getcwd(), \"augmented_data\",doc_type)\n",
    "    # files = os.listdir(src_path)\n",
    "    files = os.listdir(src_path)\n",
    "    m = len(files)\n",
    "\n",
    "    for i in range(total):\n",
    "        k = secrets.randbelow(m)\n",
    "        img_cv = cv2.resize(cv2.imread(os.path.join(src_path, files[k])), (500, 500), interpolation = cv2.INTER_AREA)\n",
    "        cv2.imwrite(\"temp_img.jpg\", img_cv)\n",
    "        img = load_img(\"temp_img.jpg\")  # this is a PIL image\n",
    "        # img = load_img(os.path.join(src_path, files[k]))  # this is a PIL image\n",
    "        imgarr = img_to_array(img)  # this is a Numpy array with shape (?, ?, ?)\n",
    "\n",
    "        gen_file_name = doc_type + \"_\" + str(i)\n",
    "\n",
    "        # cv2.imwrite(os.path.join(dst_path, gen_file_name + \".jpg\"), cv2.imread(os.path.join(src_path, files[k])))\n",
    "\n",
    "        imgarr = imgarr.reshape((1,) + imgarr.shape)  # this is a Numpy array with shape (1, ?, ?, ?)\n",
    "\n",
    "        n = 1\n",
    "        for batch in datagen.flow(imgarr, batch_size=1, save_to_dir=dst_path, save_prefix=gen_file_name, save_format='jpeg'):\n",
    "            n += 1\n",
    "            if n > gen_per_image:\n",
    "                break  # otherwise the generator would loop indefinitely\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "manipuri\n",
      "kuchipudi\n",
      "kathakali\n",
      "sattriya\n",
      "mohiniyattam\n",
      "odissi\n",
      "kathak\n",
      "bharatanatyam\n"
     ]
    }
   ],
   "source": [
    "\n",
    "#Contains all the labels\n",
    "doc_types = os.listdir(path)\n",
    "\n",
    "for doc_type in doc_types:\n",
    "    if not os.path.exists(os.path.join(os.getcwd(), \"augmented_data\",doc_type)):\n",
    "        os.makedirs(os.path.join(os.getcwd(),\"augmented_data\",doc_type))\n",
    "    # generator(doc_type, set_types[0], gen_per_class)\n",
    "    generator(doc_type,gen_per_class)\n",
    "    copy_org(doc_type)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating the new train.csv\n",
    "DATA_DIR = os.path.join(os.getcwd(), \"augmented_data\")\n",
    "def create_csv(DATA_DIR,filename):\n",
    "    data = list()\n",
    "    class_names = os.listdir(DATA_DIR)\n",
    "    for class_name in class_names:\n",
    "        file_names = os.listdir(os.path.join(DATA_DIR,class_name))\n",
    "        for file_name in file_names:\n",
    "            data.append({\n",
    "                \"FileName\":os.path.join(DATA_DIR,class_name,file_name),\n",
    "                \"ClassName\":class_name\n",
    "            })\n",
    "    data = pd.DataFrame(data)\n",
    "    data.to_csv(filename,index=False)\n",
    "\n",
    "create_csv(DATA_DIR,\"train_augmented.csv\")\n",
    "\n",
    "\n",
    "data = pd.read_csv(os.path.join(os.getcwd(),\"train_augmented.csv\"))\n",
    "# data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "TEST_DIR = os.path.join(os.getcwd(),\"dataset\",\"test\")\n",
    "TRAIN_DIR = os.path.join(os.getcwd(),\"augmented_data\")\n",
    "MODEL_PATH = os.path.join(os.getcwd(),\"model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model path does not exists\n",
      "Model path created\n"
     ]
    }
   ],
   "source": [
    "if not os.path.exists(TEST_DIR):\n",
    "    print(\"Testing data does not exists\")\n",
    "if not os.path.exists(TRAIN_DIR):\n",
    "    print(\"Training data does not exists\")\n",
    "if not os.path.exists(MODEL_PATH):\n",
    "    print(\"Model path does not exists\")\n",
    "    os.makedirs(MODEL_PATH)\n",
    "    print(\"Model path created\")\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train = pd.read_csv(os.path.join(os.getcwd(),\"train_augmented.csv\"))\n",
    "data_test = pd.read_csv(os.path.join(\"./dataset/\",\"test.csv\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number of training images is  9962\n",
      "The number of testing images is  156\n"
     ]
    }
   ],
   "source": [
    "print(\"The number of training images is \",data_train.shape[0])\n",
    "print(\"The number of testing images is \",data_test.shape[0]) # for submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_train['FileName'] = data_train['FileName'].apply(lambda x:os.path.join(TRAIN_DIR,\"/\".join(x.split(\"/\")[-2:])))\n",
    "data_test['Image'] = data_test['Image'].apply(lambda x:os.path.join(os.getcwd(),\"dataset\",\"test\",x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9962 entries, 0 to 9961\n",
      "Data columns (total 2 columns):\n",
      " #   Column     Non-Null Count  Dtype \n",
      "---  ------     --------------  ----- \n",
      " 0   FileName   9962 non-null   object\n",
      " 1   ClassName  9962 non-null   object\n",
      "dtypes: object(2)\n",
      "memory usage: 155.8+ KB\n"
     ]
    }
   ],
   "source": [
    "data_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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V9TdV9cWqurKqzppSp6rqmVX1raq6tKo+XlV3nlLvjlX14araXlXnVdUJVbXbUrYFAAAAwOpajRFVd0rykCRf7X+meXqS45M8P8kRSS5JcmZV3XimQlXtn+TMJC3JkUlOSHJckmcvdlsAAAAArL7VCKpOa60d0lo7Ksl/Ti6sqj3ThUsnttZObq2dmeSodIHUsYOqj0+yV5JHtNbOaK2dki6kekpV7bPIbQEAAACwynZ6UNVau2qeKocl2SfJ2wbr/DDJaUkOH9Q7PMkHW2vbBmVvTRde3XuR2wIAAABglY1xMvXbJ7kyydcmys/ulw3rbRlWaK19M8n2Qb2FbgsAAACAVTbGoGr/JJe01q6cKN+aZENV7TGod/GU9bf2yxazLQAAAABW2RiDqqSbQ2pSTVk2W72F1Jm6rKqOqarNVbX5oosuWkhbAQAAAFgGYwyqtibZu6p2myjfL8n21toVg3r7TVl/31w90mqh2/qJ1tqrW2sbW2sbDzrooCU9AAAAAAAWb4xB1ZYkuyW57UT55JxUWzIxz1RVHZLk+oN6C90WAAAAAKtsjEHVp5JsS3LUTEFVbUhyRJLTB/VOT/Kgqtp7UPboJJcm+dgitwUAAADAKtt9Z99hHxQ9pL95syT7VNWj+tvvb61tr6qTkhxfVVvTjXx6SrpQ7RWDTZ2S5ElJTq2q5ye5dZJNSV7SWtuWJK21yxa4LQAAAABW2U4PqpIcnOTtE2Uzt2+V5JwkJ6ULk56R5IAkm5M8oLV2wcwKrbWtVXX/JCcnOS3dvFQvTRdWDc27LQAAAABWX7U27aJ4JMnGjRvb5s2bV7sZy6Jq/jos3Vp7G+kPK2ut9YdEn1hpa61P6A8rS39gaK31h0SfWGlrrU/oDytLf2BorfWHuVTV51prG6ctG+McVQAAAACsQ4IqAAAAAEZBUAUAAADAKAiqAAAAABgFQRUAAAAAoyCoAgAAAGAUBFUAAAAAjIKgCgAAAIBREFQBAAAAMAqCKgAAAABGQVAFAAAAwCgIqgAAAAAYBUEVAAAAAKMgqAIAAABgFARVAAAAAIyCoAoAAACAURBUAQAAADAKgioAAAAARkFQBQAAAMAoCKoAAAAAGAVBFQAAAACjIKgCAAAAYBQEVQAAAACMgqAKAAAAgFEQVAEAAAAwCoIqAAAAAEZBUAUAAADAKAiqAAAAABgFQRUAAAAAoyCoAgAAAGAUBFUAAAAAjIKgCgAAAIBREFQBAAAAMAqCKgAAAABGQVAFAAAAwCgIqgAAAAAYBUEVAAAAAKMgqAIAAABgFARVAAAAAIyCoAoAAACAURBUAQAAADAKgioAAAAARkFQBQAAAMAoCKoAAAAAGIXRBlVV9RtV9fmquqSqvl1Vb6qqm07Uqap6ZlV9q6ouraqPV9Wdp2zrjlX14araXlXnVdUJVbXbTnswAAAAAMxrlEFVVT08yT8k+VSSI5M8Lcm9kry3qoZtfnqS45M8P8kRSS5JcmZV3Xiwrf2TnJmk9ds6IclxSZ698o8EAAAAgIXafbUbMIvHJPl8a+3YmYKq2pbk3Ul+OsnZVbVnuqDqxNbayX2dTyc5J8mxSZ7Vr/r4JHsleURrbVuSM6pqnySbquoFfRkAAAAAq2yUI6qSXDfJDybKLu5/V//7sCT7JHnbTIXW2g+TnJbk8MF6hyf54EQg9dZ04dW9l6/JAAAAAOyIsQZVr0tyz6p6XFXtU1W3S/LcJB9trX25r3P7JFcm+drEumf3yzKot2VYobX2zSTbJ+oBAAAAsIpGGVS11t6X5Ogkr043suorSXZL8ohBtf2TXNJau3Ji9a1JNlTVHoN6F0+5m639MgAAAABGYJRBVVXdN8kpSf4qyX2T/EaSGyZ558TV+tq01acsm63etcqr6piq2lxVmy+66KKlNB8AAACAJRjrZOovTvKe1trTZgqq6gvpTuE7Msmp6UZE7V1Vu02MqtovyfbW2hX97a192aR9M2WkVWvt1elGcmXjxo3TAi4AAAAAVsAoR1SlmzvqC8OC1tpXklya5DZ90ZZ0pwPedsq6wzmptmRiLqqqOiTJ9SfqAQAAALCKxhpUfSPJXYcFVXWHdFfqO6cv+lSSbUmOGtTZkOSIJKcPVj09yYOqau9B2aPThV4fW+6GAwAAALA0Yz3175QkL62q89IFTTdK8ufpQqr3J0lr7bKqOinJ8VW1Nd3oqKekC99eMbGtJyU5taqen+TWSTYleUlrbdtOeTQAAAAAzGusQdXLk/woyR8meXy6uaQ+keQZrbUfDuqdlC6YekaSA5JsTvKA1toFMxVaa1ur6v5JTk5yWr+tl6YLqwAAAAAYiWrNfOGz2bhxY9u8efNqN2NZVM1fh6Vba28j/WFlrbX+kOgTK22t9Qn9YWXpDwyttf6Q6BMrba31Cf1hZekPDK21/jCXqvpca23jtGVjnaMKAAAAgHVGUAUAAADAKAiqAAAAABgFQRUAAAAAoyCoAgAAAGAUBFUAAAAAjIKgCgAAAIBREFQBAAAAMAqCKgAAAABGQVAFAAAAwCgIqgAAAAAYBUEVAAAAAKMgqAIAAABgFBYcVFXV46rqgFmW3bCqHrd8zQIAAABgvVnMiKrXJ7nNLMtu1S8HAAAAgCVZTFBVcyw7IMm2HWwLAAAAAOvY7nMtrKojkxw5KDq+qi6aqLZnknsm+ewytw0AAACAdWTOoCrJwUl+dnD7NkluPFHnR0k+lOS5y9guAAAAANaZOYOq1tprkrwmSarqo0n+sLW2ZWc0DAAAAID1Zb4RVT/RWrvvSjYEAAAAgPVtwUFVklTVTZM8LMnN081NNdRaa09broYBAAAAsL4sOKiqql9L8g9JdktyYbq5qYZaEkEVAAAAAEuymBFVz0s3afrRrbXvr1B7AAAAAFinFhNUHZLk/wmpAAAAAFgJ11lE3U8l+emVaggAAAAA69tiRlQ9JclbquqSJGckuXiyQmtt+zK1CwAAAIB1ZjFB1b/3v1+fbuL0aXbbseYAAAAAsF4tJqj6vcweUAEAAADADllwUNVae8MKtgMAAACAdW4xk6kDAAAAwIpZ8Iiqqroo85z611o7eIdbBAAAAMC6tJg5ql6ZawdVN0xyvyT7JHntcjUKAAAAgPVnMXNUbZpWXlWV5G1JfrxMbQIAAABgHdrhOapaay3J3yY5dsebAwAAAMB6tVyTqd86yR7LtC0AAAAA1qHFTKb+hCnFeyS5Q5LHJnn7cjUKAAAAgPVnMZOpnzyl7PIk5yZ5VZJnL0uLAAAAAFiXFjOZ+nKdJggAAAAA1yJ8AgAAAGAUFhVUVdWtq+qvq+pLVfXt/verqurWK9VAAAAAANaHxUym/gtJPprksiTvTXJBkhsleWSSx1bVfVtrn1+RVgIAAACwy1vMZOovSvJvSQ5vrW2fKayqDUne3y+/3/I2DwAAAID1YjGn/v2fJC8YhlRJ0t9+UZK7LWfDAAAAAFhfFhNUXZrkgFmW3TDdKYHLpqp2r6qnV9XXquryqjq3ql46Uaeq6plV9a2qurSqPl5Vd56yrTtW1YerantVnVdVJ1TVbsvZXgAAAAB2zGJO/XtfkpOq6uuttU/MFFbVLyc5Mclpy9y21ye5f5JnJ9mS5JAkd5yo8/Qkxyd5al/nKUnOrKqfaa2d37dv/yRnJvlykiOT3CbJi9OFdM9a5jYDAAAAsESLCaqekuTdST5WVRelm0z94HQTqn8yyXHL1aiqenCS30jy8621L89SZ890QdWJrbWT+7JPJzknybG5OoR6fJK9kjyitbYtyRlVtU+STVX1gr4MAAAAgFW24FP/Wmvfa639cpKHJnllunDqVUke3Fq7Z2vte8vYrt9L8pHZQqreYUn2SfK2QRt/mG5k1+GDeocn+eBEIPXWdOHVvZetxQAAAADskDmDqqo6oKr+qaoeNFPWWvtAa+05rbUntNae01Wrf6qqg5exXXdL8tWqOrmqtvVzS51aVTcd1Ll9kiuTfG1i3bP7ZcN6W4YVWmvfTLJ9oh4AAAAAq2i+EVVPTnLrJB+ao86Hktwqy3jqX5IbJzk6yZ3TnQL4u0l+Ick7q6r6OvsnuaS1duXEuluTbKiqPQb1Lp5yH1v7ZQAAAACMwHxB1a8nOaW11mar0C/7m3QTlS+X6n+ObK29v7X2j0l+O8n/SXK/4d3Psu7kstnqXau8qo6pqs1Vtfmiiy5aUuMBAAAAWLz5gqpbprta3nzOTnLoDrfmaluTfGli3qtPJPlRrr7y39Yke1fVbhPr7pdke2vtikG9/abcx76ZMtKqtfbq1trG1trGgw46aMkPAAAAAIDFmS+oujTdhOXzuUFfd7mcPUt5Jbmq/3tLkt2S3HaizuScVFsyMRdVVR2S5PoT9QAAAABYRfMFVZ9P8vAFbOfIvu5yeW+Sn6uqAwdl90py3SRf7G9/Ksm2JEfNVKiqDUmOSHL6YL3TkzyoqvYelD06XbD2sWVsMwAAAAA7YL6g6pVJ/m9V/c5sFarqcekmOz95Gdv16iTfS3JaVR1RVY9J8uYkZ7bWPpEkrbXLkpyU5JlV9cSqun+St6d7TK8YbOuUJJcnObWqfqWqjkmyKclLWmvblrHNAAAAAOyA3eda2Fo7tar+Ksnrq+rYJB9I8s10k5DfIsmDkmxM8tLW2juXq1GttW1Vdb8kL0/y1nRzU707yR9PVD0pXTD1jCQHJNmc5AGttQsG29rah1gnJzkt3bxUL00XVgEAAAAwEjXHBf2urlR1RJInJzksyfX64suTfDLJy1pr712pBq6mjRs3ts2bN692M5ZF1fx1WLoFvI1GRX9YWWutPyT6xEpba31Cf1hZ+gNDa60/JPrESltrfUJ/WFn6A0NrrT/Mpao+11rbOG3ZnCOqZrTWTkt3Gt7u6UYuJcn3Wms/XqY2AgAAALDOLSiomtEHUxfMWxEAAAAAFmm+ydQBAAAAYKcQVAEAAAAwCoIqAAAAAEZBUAUAAADAKAiqAAAAABgFQRUAAAAAoyCoAgAAAGAUBFUAAAAAjIKgCgAAAIBREFQBAAAAMAqCKgAAAABGQVAFAAAAwCgIqgAAAAAYBUEVAAAAAKMgqAIAAABgFARVAAAAAIyCoAoAAACAURBUAQAAADAKgioAAAAARkFQBQAAAMAoCKoAAAAAGAVBFQAAAACjIKgCAAAAYBQEVQAAAACMgqAKAAAAgFEQVAEAAAAwCoIqAAAAAEZBUAUAAADAKAiqAAAAABgFQRUAAAAAoyCoAgAAAGAUBFUAAAAAjIKgCgAAAIBREFQBAAAAMAqCKgAAAABGQVAFAAAAwCgIqgAAAAAYBUEVAAAAAKMgqAIAAABgFARVAAAAAIyCoAoAAACAURBUAQAAADAKow+qqupmVXVJVbWqusGgvKrqmVX1raq6tKo+XlV3nrL+Havqw1W1varOq6oTqmq3nfogAAAAAJjX6IOqJC9McsmU8qcnOT7J85Mc0dc5s6puPFOhqvZPcmaSluTIJCckOS7Js1e4zQAAAAAs0qiDqqq6Z5IHJ3nRRPme6YKqE1trJ7fWzkxyVLpA6thB1ccn2SvJI1prZ7TWTkkXUj2lqvbZGY8BAAAAgIUZbVDVn573inSjoL47sfiwJPskedtMQWvth0lOS3L4oN7hST7YWts2KHtruvDq3ivQbAAAAACWaLRBVbrRUHsmeeWUZbdPcmWSr02Un90vG9bbMqzQWvtmku0T9QAAAABYZbuvdgOmqaoDkjwnyW+11q6oqskq+ye5pLV25UT51iQbqmqP1tqP+noXT7mLrf0yAAAAAEZirCOq/jLJv7TW3j9HnTalrKYsm63etPJU1TFVtbmqNl900UULaiwAAAAAO250QVVV3SnJ7yV5dlXtV1X7JdnQL963qvZKNyJq734eq6H9kmxvrV3R397al03aN9NHWqW19urW2sbW2saDDjpoRx4KAAAAAIswxlP/firJdZN8esqyc5O8NsnfJ9ktyW2TfGWwfHJOqi2ZmIuqqg5Jcv2JegAAAACssjEGVZ9Ict+JsgcneVqShyT5epJvJNmW5Kgkz02SqtqQ5Igkrx6sd3qSp1bV3q21/+3LHp3k0iQfW6kHAAAAAMDijS6oaq19N8lZw7KqOrT/859ba5f0ZSclOb6qtqYbHfWUdKcyvmKw6ilJnpTk1Kp6fpJbJ9mU5CWttW0r9ygAAAAAWKzRBVWLcFK6YOoZSQ5IsjnJA1prF8xUaK1trar7Jzk5yWnp5qV6abqwCgAAAIARqdamXvyOJBs3bmybN29e7WYsi6r567B0a+1tpD+srLXWHxJ9YqWttT6hP6ws/YGhtdYfEn1ipa21PqE/rCz9gaG11h/mUlWfa61tnLZsdFf9AwAAAGB9ElQBAAAAMAqCKgAAAABGQVAFAAAAwCgIqgAAAAAYBUEVAAAAAKMgqAIAAABgFARVAAAAAIyCoAoAAACAURBUAQAAADAKgioAAAAARkFQBQAAAMAoCKoAAAAAGAVBFQAAAACjIKgCAAAAYBQEVQAAAACMgqAKAAAAgFEQVAEAAAAwCoIqAAAAAEZBUAUAAADAKAiqAAAAABgFQRUAAAAAoyCoAgAAAGAUBFUAAAAAjIKgCgAAAIBREFQBAAAAMAqCKgAAAABGQVAFAAAAwCgIqgAAAAAYBUEVAAAAAKMgqAIAAABgFARVAAAAAIyCoAoAAACAURBUAQAAADAKgioAAAAARkFQBQAAAMAoCKoAAAAAGAVBFQAAAACjIKgCAAAAYBQEVQAAAACMgqAKAAAAgFEQVAEAAAAwCoIqAAAAAEZhlEFVVR1VVe+pqm9X1SVV9bmq+s2JOlVVz6yqb1XVpVX18aq685Rt3bGqPlxV26vqvKo6oap222kPBgAAAIAFGWVQleQpSS5J8sdJHp7ko0n+vqr+36DO05Mcn+T5SY7o659ZVTeeqVBV+yc5M0lLcmSSE5Icl+TZO+ExAAAAALAIu692A2ZxRGvtu4PbH6mqm6YLsF5RVXumC6pObK2dnCRV9ekk5yQ5Nsmz+vUen2SvJI9orW1LckZV7ZNkU1W9oC8DAAAAYARGOaJqIqSa8W9JDu7/PizJPkneNljnh0lOS3L4YJ3Dk3xwIpB6a7rw6t7L2WYAAAAAdswog6pZHJbky/3ft09yZZKvTdQ5u1+WQb0twwqttW8m2T5RDwAAAIBVtiaCqqq6f7o5pl7ZF+2f5JLW2pUTVbcm2VBVewzqXTxlk1v7ZQAAAACMxOiDqqo6NMnfJ3l3a+0Ng0VtWvUpy2arN608VXVMVW2uqs0XXXTR4hsMAAAAwJKMOqiqqhsmOT3JN5P81mDR1iR7V9VuE6vsl2R7a+2KQb39pmx630wfaZXW2qtbaxtbaxsPOuigpTceAAAAgEUZbVBVVRuSvDfJHkke2k+WPmNLkt2S3HZitck5qbZkYi6qqjokyfUn6gEAAACwykYZVFXV7knenuSnkhzeWrtwosqnkmxLctRgnQ1Jjkg3AmvG6UkeVFV7D8oeneTSJB9bgaYDAAAAsES7r3YDZvGqJA9J8kdJblhVvzRY9m+ttcuq6qQkx1fV1nSjo56SLnh7xaDuKUmelOTUqnp+klsn2ZTkJa21bSv/MAAAAABYqLEGVQ/sf//VlGW3SnJOkpPSBVPPSHJAks1JHtBau2CmYmtta3/FwJOTnJZuXqqXpgurAAAAABiRUQZVrbVDF1CnJfnL/meuel9Ocr/laRkAAAAAK2WUc1QBAAAAsP4IqgAAAAAYBUEVAAAAAKMgqAIAAABgFARVAAAAAIyCoAoAAACAURBUAQAAADAKgioAAAAARkFQBQAAAMAoCKoAAAAAGAVBFQAAAACjIKgCAAAAYBQEVQAAAACMgqAKAAAAgFEQVAEAAAAwCoIqAAAAAEZBUAUAAADAKAiqAAAAABgFQRUAAAAAoyCoAgAAAGAUBFUAAAAAjIKgCgAAAIBREFQBAAAAMAqCKgAAAABGQVAFAAAAwCgIqgAAAAAYBUEVAAAAAKMgqAIAAABgFARVAAAAAIyCoAoAAACAURBUAQAAADAKgioAAAAARkFQBQAAAMAoCKoAAAAAGAVBFQAAAACjIKgCAAAAYBQEVQAAAACMgqAKAAAAgFEQVAEAAAAwCoIqAAAAAEZBUAUAAADAKAiqAAAAABgFQRUAAAAAoyCoAgAAAGAU1kVQVVV3rKoPV9X2qjqvqk6oqt1Wu10AAAAAXG331W7ASquq/ZOcmeTLSY5McpskL04X0j1rFZsGAAAAwMAuH1QleXySvZI8orW2LckZVbVPkk1V9YK+DAAAAIBVth5O/Ts8yQcnAqm3pguv7r06TQIAAABg0noIqm6fZMuwoLX2zSTb+2UAAAAAjMB6CKr2T3LxlPKt/TIAAAAARmA9zFGVJG1KWU0rr6pjkhzT37ykqr6ykg1jVgcm+e5qN2Khqla7Bbs8/YFJ+gRD+gND+gOT9AmG9AeG9IfVc8vZFqyHoGprkv2mlO+bKSOtWmuvTvLqlW0S86mqza21javdDsZBf2CSPsGQ/sCQ/sAkfYIh/YEh/WGc1sOpf1syMRdVVR2S5PqZmLsKAAAAgNWzHoKq05M8qKr2HpQ9OsmlST62Ok0CAAAAYNJ6CKpOSXJ5klOr6lf6Oag2JXlJa23bqraMuTj9kiH9gUn6BEP6A0P6A5P0CYb0B4b0hxGq1qbNM75rqao7Jjk5yd3TzUv1t0k2tdauXM12AQAAAHC1dRFUAQAAADB+6+HUP9agqjq0qlpVPWwn3++mqlozlydNkqp6Q1VtnqdOq6pjd1ab5mjHr1fV0dqxti2kzy1gG39aVfeZUr4sfXW59iFVdXS/nRss53ZZmqr6blVtGtw+q6resYzbX9btsXyq6oFV9eQp5Yvan6/Fz/m1ajk+K5brPnb2cVBV/Ux/n/dZrTasJQvZ9y7187eq7tOv9zM71sqp277WsUxV7dHvZ+683Pe3Xq2n406uJqhirL6T7lTNT+zk+/3bJA/ayfe5nvx6kqNXuxEZTzvWsz9Ncp/VbsQSrNa+iemekOQZI94ey+eBSZ48pXyx+3Of8+vT3ZO8XRvWtKV+/n6+X++/l71F049l9kjyF0nuvAL3x9Kt1ePOdWv31W4ATNNauzzJZ3bW/VXVdZNc1Vo7N8m5O+t+dwVVtVdr7dLVbgfsDDt738TcWmtfHvP2GA+f8+tba23V99tjaMNattTP3/7iWZ57WGOMqGKqmSGWVfXQqvpyVW2vqvdV1Q2r6rZV9dGq+mFf5+cG6x1XVZ+tqh9U1QVVdVpV3XZi22dV1Tuq6jFV9V9Vta2qTq+qmw/qXGv4ZFWdU1Uvqqrjq+r8qrqkqt5SVfsO6lzjNJ3Jdae04Ziq+u8klyW56Vo+JaCqfrWqtlTVZVX1if4iAkO7VdXzquqiqrqwql5ZVdcbrH+TqnpdVX29qi6tqq9W1XOrao9BnZnX5bFV9aaqujjJaf2yx/X3+/2q2tr3kY2Ddd+Q5JFJ7t1vo82cwtP3szP6dm2rqs9U1QMnHt+m6k77uUu/fHtV/VtV3XOi3pLaUVVPrKr/ndJ37tvX+bmFbH/mPpby/lmrqhvmfmpVfbN/fCdV1Zf69+i5/fv0xoP65yQ5IMlfDF6D+ww2ucN9dZZ23qd/jZ/X3757Vb2nqs7rX48vVNVj59mGod3LpLpTtr5UVZdX1beq6i+ravfB8ntV1Rf7fdrnquqwKdu4xukiVXXzqnpb328urar/rqrnDJbfqao+0L9/f1hVZ1fVE2fbHstnrud+vs+A/rPiuCS3HOwz3jDb/rxfZ97P+aravX///8WU9n6sqk7t/17SPodrmvis+LuacrxVU07Dqarf7/cVl1V3bPmOGhz79XUeUFX/3vetT1TVneba7kT/OKd/Xd9XVTcb1Jl6yti0/URVPaHfj/2wqk5LcpOFPLa1pJb+v8GGqnp5dcful1X3f8IDZ7mPpf5v8MfVHW9sraq3VtV+gzrXeB2r6u1V9dEp9/3svn9dt7+91GOZ/+2rvH5QfuhCtjnxmJ5eVd+p7n+qF1fnIVX1n9Udy7yrqvZf4Mu3S6k1etzJ4hhRxVxukeSEJM9KsiHJK9JdvvPQJK9J8oIkJyZ5a1XdqXUz89883RUWv5FknySPT/LJqrpda+0Hg23fLclN0x147pXkr/ptP2SeNv1mkv9K8vvpDgJekG4Y/1FLeHz3SHKbJE9Lsj3JD+auPmq3TPKSJMcnuTTJs5N8sKp+qrV2WV/nuCQfSfJbSX4u3Wv3jXTPYZIcmOT7SZ6SZGuS2yXZlOSgJH8wcX8vSnJquud95uqZhyZ5U7qh1XskeUySj1fVz7TWvp7kOen61H7pTq9Jrv5W+1bpAq8XJbkqyeFJTq+qe7XWPjm43w1J3pjkpUnOTze0+p1VdYvW2vYdbMcl/f0/KskbBvd5dJLPt9b+fYHbn7GU98+aU1V7JvmnJLdPcs/W2jeq6uAkz0tyXrr+c1ySj1TVz/ZXW/21JB9N8o50798kGY5kWc6+OtPOByV5Z5KTWmsn9MW3TPLJJKek+yf2HukOLK9qrf3DEp8SFqD/J+Uf072XnprudX5OugPJx1fVTZOcnuRf070nb5rkLeneS3N5U7rPlGPSXeX31un65oz3JNmSrm9dnuSn031WsfLmeu7n+wz42yQ/leR+6fYfSXJR/3u2z5Vkns/51tqPq+qNSY6uqhNm9sNVdesk90xyZF910fscrmnysyLJ7y5wvWel+yx9Vbp9xYYkD01yg1z9et4iyQuT/GW6Y6AXJXlb/7k812fr3dP1w6ck2TPJ85O8K8kvLuKhpaqOTPLKdJ8l70py7ySvW8w21pClHNu8JsnDkzwzVx/Dv6+q7ttaG57Gt9T/DX49yb+n2+/fPN3x8PNy9T5h0t+m27/cqrX2P0lSVZXkcUn+rrV2RV9vqccy90t3DPPcJO/ry7+zwG3O+I10n3+/m+QX+m1dJ8m90h3r75Xu/60T0/2vtW6s8eNOFqO15sfPtX7S/aP+4yS3GZS9IElL8rhB2UP6sjtM2cZu6Xak/zuxzlnpDi72H5Q9ud/OXv3tQ/vbDxvUOSfdTuIGg7LHpjuovUN/++h+vRtMtOWcJC+aaMOlSW48UW9Tku+u9vO/hNeqJTlsUHbL/vV7fH+7Jfn4xHrvSvKZOba7e7oQ5rIke0y8Lu+cp03X6dffkuTPB+XvSHLWAtf9YJLXTbw2Lcn9BmV37ssevBztSPJ3ST42uH2DdAHWsYvc/huyg++fMf/0j29zuoPUM/vHf7NZ6u6W5Gb947zXoPy7STZNqb/cffVh6Q6QL0vyJ3Nso/rt/E2SjwzKj85gn5Ip+yY/S+pDn0ny0YmyP00XfN+8f798L8mGwfLH9s/9pkHZWUneMbh9SZIjZrnPA/v1f3aOdl1je36W7fWe97kf1J3tM+BFSc6ZUn+2/flZWcDnfLoArCW576DshHRfhuw+Sxuvtc/xM/V5mvWzYvJ1GKzT0n/mpgsgtyd5yTz38eMkPzUo+9V+O7eftt1B/7giyS0HZffI4Jgi3Xw2LcnPTOlbw/3OvyY5faLOa/p17zNbG9baT5ZwbNP/XJXkdwbLr5PkP5J8cOI5Xer/Bv89fK8meVmS8we3r/E69vf/jSTPHtS537TXerB8wccy6Y4dW5Kj53k+Z9vmOekCvd0m+tiPk9xq4rm/YLX7xU7se7vUcaef+X+c+sdczmmtDSce/K/+90emlN0sSarql6obvv+9dDvU7el22Leb2PZnW2tbB7dnUu2bZW5ntNYuGdw+Nd0/mIv69qv3udba+UtYb4wubK19auZGa+0bST6X5P8M6nxoYp0vp/uHMEn3bVJVPbm64dyXpjuAe0uS66X7Bm3ofRO3U1V3qKp3VtUF6f7ZvCLdN5WTr/21VHe6zhur6tvp+s0V6SbOnVz3inQHM8PHkInHseR2JHltknv236Yn3bd0uyf5+yVsf9HvnzXm+kk+kORGSe7dWvv2zIKqOryqPlVVP0j3es6McFjIa5Asb199ZLrJa49rrb1ouKCq9q/udIRv9Nu4It03sgttJ0tQVbsluWuuPanwP6b7B+Lu6fZdZ7SrR0om3f5+Pl9IcmJ1p4FP9oXvJ/lWklOq6tH9N7DsHHM+94v4DFiseT/nW2tfS/Lx9BOyD0ZWvLm19uOZskXsc7imWT8rFuDu6b7wfP089c7pX8cZ1zo2mMXn++OlJEnrRu9dmGseO82p35/dJcm7JxYtZH+1Fi322OYX0x2n/2R/31q7qr/9yxPbXur/Bh+dea8O1jt4ttOy+vt/Q5LH9e/3pHv/b26t/cdMvWU4lrmWRWzzrHbNEVb/le65/5+JsoPmO/1sF7LmjztZHEEVc7l44vaPppTPlO3Z/1PwoXQfSH+Q7pupX0z3ob/nArc9WW/ShcMbrZvE+5JMmQtgAS5YwjpjdeEsZcPn5eKJ5T/KNZ/vJyd5cbphqkemO1Cbmbtl8nW5xnNXVXune+0PSTcs9p7pXvsvTln3GqrqOulOCTksyZ8nuW+/7ulT1t3WH2AkSVpr1+g3O9KO3llJvp6rryD1u0ne3Vr7/hK2f/HE7TnfPwto29jcNN1rdmpr7Sf9oap+Md3reW6S3073j8Yv9YsX+jgvnri9I3314en+SX7nlPt5Q5JHpztl5IHpXsvXLaKdLM2BSa6ba++DZ27fMMmNM/v+fi6PTvet60uTfKO6ecfu369/VbrX+fx0r/P5VfXPVXWXHXgsLMBcz/0iPwMWa6Gf869N8qh+H3+/dKOSh+HIk7PwfQ7XNPWzYoEO6H9/Z85ay3RMOShbzDHlQem+0Jrc1rRt7wounrg937HNTZJcMvGlQ9K9NzfUYB6gObY93+s4bb1KNz3DbF6f7n1+3/59/8gMTtdcpmOZa1jkNi+euP2jWcrme5y7kl3huJNFMEcVy+nB6YZkHtla+2HSTVSa7p+O5TL5Lexe6UZszRzEzMzHNLnTnjbZYFvGdq22aSMDDk7yn4vYxlFJ3t5a+7OZgrr2hOwzJp+7u6f75uEBrbUtg/X3XcD93jbdt5GHt9Y+MFh3r4U2fJnakdZaq6rXJTmmqt6c7tu+w5dr+7uYr6WbP+INVXV+a+2v+/JfSzd3zKNb68ZCV9Utl/m+F9NX/1+6UPGM6ua7+V5ff89085wc21o7ZbAdX+CsvO+m+zZycr91o/7399MFGrPt72fVf8N6dP86/p90pxe9p7p57L7Xv28fWd1kufdMNyfN+6rq5sMQnOU323Of7rSc5foMuNbdLrDe25O8PN2+5b5J/qVd8wqQi9nncE2zfVZcloljtbr2xNDf63/fJN1+Y7nNduw03zHlDQftuSjdCI7JbRmx2flOkhtU1YaJsOpGSba37kp+O11r7ZyqOjPdF5O3Sjd4Yzg35Uocy+yM46Nd2Zo+7mTxHJCznPZKdx76cPjtzKlTy+UBdc2rsj0i3YHo5v72zFDPO8xUqKq7ZdefLPfgGlwRqx/ddtd057Qv1F7pJrgdmvMKaBPrZrh+355DJ+pNfkMx27q3TDcib7F2pB0z3pAujHpdkm8nOWMJ218XWmtvTnJskpOr6rf64r2SXDFzsNCb1o/meg3ms5i+ui3Jg9LtJz5YVTP7guulm8dg+Frune6bMFZQfzrD53Lti2D8errPkE8n+Wy6/f1w8vRHLOI+rmrdpeCfne4LlFtOLL+itfaRdJPu3iTdXDjsBFOe+5nRK/N9Bsy2z9iRfclMmy5N90/qE9P1s8lTzXbk83Hdm+Wz4twke9fgKnvpRt0NfTrdPGO/s0JNu+vwFOGquke6gGnm2GnaMeUh6U73T/KT/dkXcvXE+zMWvL/axX023efvo2YK+tPtHpXkE7OttJO8Nt1IqickeVdr7eLBsh05lpltJNhCt8ks1vhxJ4tkRBXL6SPp/vF7fVW9NsmdkvxJrj2cckdcmu7b7xemO7h9YbqJvWe++fzXdOHCy6vq+HTfev1pup3Gruy7Sd7cP+ZL000Ee2GuefW6+ZyR5ElV9S/pJqZ8bLrRTgvxmXSn5Lymql6QLujZlO61GNqS5Miq+tV0B4Dn9WXnJnlx3/690/1zuZh5LHa4Ha2185KktXZeVX0g3WibEyfmCFjo9teN1tpf9+Hx66vqknT96MlV9bJ0V/E6LN1VVCZtSfLQ/rm+JMlXWmv/O6XeNIvqq62171XVA5L8c5L3VtWDW2s/qKrPJvnzqtqWLiB5errJXB1UrLy/SHcA9/okb03ys+mu+vea1tq5ff95YrrX6yXphvw/I93+bap+ZOMH013576vpwsjj0o3OOru6y6W/KN1cWF9PN9L2aUm+OHN6Lytjruc+3X51IZ8BW5LcqKqOTjcR83dba+dkjv35Ir023dWzLk3XJ4d25PORTP2s+EC65/p1VfXidKNaHj+xzsVV9Zwkf9nPw/P+dO/rh6abCHtHP3svTLeP2ZSrr/r3+ZmRff2+6LNJnlNV29N9wf/MdKM+h56X5NSq+ut0p/vcO91ZButea+3sqvqHdMHCPrn6qn+3T/KHq9q4bsLsV6X7YvcZE8t26Fimqv4nya9X1X+kG5n374vYJnNYw8edk6e/Mg8jqlg2rbUvpZvT525J3pvuaghHZeJy0DvorekuL/radFf1OD3J/x204UfphoBele5KQMel+yDcOrmhXcw30l22eVO652hbkge11i6ba6UJJ6T7Rvm5/e8fJXnSQlbszxU/Kt28Mu9Ody7343P1hJozXpVujqfXpfuW7Zh+2Pcj0o3Ee0e6f1ZPTPKxRbR9h9sxsfxd/e9rfKu+iO2vK621F6a/HHW6U7qelu5byvekO2B/2JTVnprkh+lO/flsussvL9Si+2pr7TtJ7p9u9Nup/T89j0nyP+mCjb9Kd7njNy2iHSxRa+1D6S6/vTHdgeWT083/cGy//Nvprhx1YLrX5QnpDjznOtC7LMmXkvxRur73xr7+A/sRM+enmxflz9J9drwqydkxim5nmPW5X8RnwNvSffnygnT7jE19+Xz78wVprW1OF46d2lqbPG5Z8ucjV5v4rLhLus+Jm6f7zP2tdPvkyXVOTHcc9yvpPnf/Jt0IyIX+gzmXT6frPy9Ld1z5H+muGDj0mCTfTHdV4Oel6wtfmWjjO9Od7nNE/1juksGxKfn9dPvj49O9hrdMd+W+VR1R1e97Tk93oYczJ5a9Pzt2LPP4dJ9fZ/blN13ENpnHGj7uZBHqmqPkYLyq6px0lwP+k9VuC7u2qnpbkpu01u652m0BYOX18438Z5Jfaa19eLXbw8qqqrPSjcp71Hx12TX18+h+I8nrWmvHr3Z7gGty6h9Ar6p+Nt0Ij0ekG+0BwC6sqg5IN+fQc9KNqPnI6rYIWEn9yJafTzda7oB0o/SAkRFUAVzttHRDtV/VWnvHajcGgBV3RLrTBrck+e3mVAPY1d003Zy2Fyb5g9baufPUB1aBU/8AAAAAGAWTqQMAAAAwCoIqAAAAAEZBUAUAAADAKAiqAAAWoaoeUVUfqaqLq+ryqvpqVT23qg6sqkOrqlXVw3Zym87q7/cZU5Z9t6o27cz2AAAslaAKAGCBqurFSd6e5OtJfjvJA5O8NN3V416zik2b8cdVtWG1GwEAsFS7r3YDAADWgqo6IslTkvzf1trrBos+VlWvThdaraZPJ7lrkmOSvGx1mwIAsDRGVAEALMwfJ/n8REiVJGmtXdlaO33aSlX1uKr6RFV9v6q2VtVHq2rjRJ07VdUH+jo/rKqzq+qJg+W/XFX/XFXb+p8vVNVRE3d1XpLXJ/mTqrrebA+iqu5eVe+pqvP6+/pCVT12os7R/amEd+1PK9ze17trVV2/ql5fVT+oqq9X1W9OuY8jq2pzVV1WVedX1Quq6rqztQkAYIagCgBgHn3IcliSDyxh9UOTvCnJUUkek+TcJB+vqlsP6rwnyZVJfivJw5O8Isne/X3vk+S96U43fGSSRyV5c5L9ptzX85PcKMnvztGeWyb5ZJL/L90pi/+U5PXTAqckb0zyD/39VpJ3JHltulDsUUn+JcmbqurmMytU1a8nOTXJv/aP5dnpRnmdOEebAACSOPUPAGAhDkhyvSTfXOyKrbUTZv6uquskOSPJL6YLpU6oqgOT3DrJr7bWvtRX/fBgE7dLsm+SY1tr/9uXfWiW+zqnqt6S5GlV9bettR9PqfPWQXsqyceT3DzJ76cLpYZe1Fp746Du+5Kc1Vr7s77sX9MFVkck+eu+zguTvKm19oTB/Vye5JVVdWJr7XuzPFUAAEZUAQAsQlvsClV1h6p6Z1VdkG7U1BVJfjpdAJUk30/yrSSnVNWjq+rgiU38d5JLkvx9f0rdfvPc5fOS3CLJY6ctrKr9q+rlVfWNvi1XpBvxdLsp1YeB2X/1vz8yU9Ba+0GSi5LcrC+6XX/fb6uq3Wd++nX2TPIz87QdAFjnBFUAAPP7XpLL04UwC1ZVe6cb/XRIuonY75luNNUX0wU3aa1dlW4i9vOTvC7J+f18VHfpl2/tl183yduSXFRV75s4dfAnWmtfTXeK3jP6EVyT3pDk0elGPj2wb8/rZtoz4eLB3z+aUjZTPrPugf3v9+fqEOyKJP/Tlx8yrc0AADOc+gcAMI/W2hVV9ckkD0ryrEWsevd0p9U9oLW2Zaawqvad2P6WJI/s58K6Z7q5pt5XVTdvrV3VWvt0kgdX1V5JfiXJS5L8fZJfmuV+/zLJF9KdlvcTVbVnkoemO43wlEH5cn15+f3+9zFJ/m3K8v+ZUgYA8BNGVAEALMzLkmysqt+ZXFBV16mqB09ZZ6/+9+WDuoelm2D9WlprV7TWPpIuiLpJJiZMb61d2lo7Ld0IqDvO1tDW2r8nOS3JM9NNgj7jekl2m2jP3ukmPV8OX0ny7SSHttY2T/kxPxUAMCcjqgAAFqC1dlpVvSTJa6vqHknenW7uqNsneXySc5L88cRqn+nrvKaqXpBudNWmdGFOkqSqfi7Ji5L8Y7or++2f5GlJvtha+35VPTTJ7yV5V7rJ3G+W5A8ymCtqFn+Z7qp8w8fwg6r6bJI/r6ptSa5K8vQkP0iyzwKfilm11q6qquOSvLm/WuHp6U4NvHWSX03yqNba9h29HwBg1yWoAgBYoNbacVX1qSTHpjv1bq90AdV70oVNe07Uv6CqjuqXvTvJ19KFWn86qHZ+kguS/FmSm6abA+qj6cKqpJvEvKWbJP3gdJOXvzfdaKm52vqvVXVGkgdMLHpMklcneVO6ubdOTrKhf0w7rLX2j30I9sx0AduV6QK49+bqea4AAKaq1hZ98RoAAAAAWHbmqAIAAABgFARVAAAAAIyCoAoAAACAURBUAQAAADAKgioAAAAARkFQBQAAAMAoCKoAAAAAGAVBFQAAAACjIKgCAAAAYBT+f402N90elTKqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nf = data_train['ClassName'].value_counts(sort=False)\n",
    "labels = data_train['ClassName'].value_counts(sort=False).index.tolist()\n",
    "y = np.array(nf)\n",
    "width = 1/1.5\n",
    "N = len(y)\n",
    "x = range(N)\n",
    "\n",
    "fig = plt.figure(figsize=(20,15))\n",
    "ay = fig.add_subplot(211)\n",
    "\n",
    "plt.xticks(x, labels, size=15)\n",
    "plt.yticks(size=15)\n",
    "\n",
    "ay.bar(x, y, width, color=\"blue\")\n",
    "\n",
    "plt.title('Bar Chart',size=25)\n",
    "plt.xlabel('ClassName',size=15)\n",
    "plt.ylabel('Count',size=15)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. The classes are well separated and equally balanced into 8 classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'sattriya': 0, 'mohiniyattam': 1, 'kathakali': 2, 'bharatanatyam': 3, 'kuchipudi': 4, 'odissi': 5, 'manipuri': 6, 'kathak': 7}\n"
     ]
    }
   ],
   "source": [
    "#preparing the dictionary for labels encoding \n",
    "\n",
    "labels_list = list(set(data_train['ClassName'].values.tolist()))\n",
    "labels_id = {label_name:id for id,label_name in enumerate(labels_list)}\n",
    "print(labels_id)\n",
    "data_train['ClassName'].replace(labels_id,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(9962, 8)\n"
     ]
    }
   ],
   "source": [
    "labels = to_categorical(data_train['ClassName'])\n",
    "print(labels.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#splitting data into train and test set in 80:20 ratio\n",
    "\n",
    "xtrain,xtest,ytrain,ytest = train_test_split(data_train.iloc[:,0],labels,test_size = 0.2,random_state=42)\n",
    "def path_to_tensor(img_path):\n",
    "    # loads RGB image as PIL.Image.Image type\n",
    "    img = image.load_img(img_path, target_size=(64, 64))\n",
    "    # convert PIL.Image.Image type to 3D tensor with shape (224, 224, 3)\n",
    "    x = image.img_to_array(img)\n",
    "    # convert 3D tensor to 4D tensor with shape (1, 224, 224, 3) and return 4D tensor\n",
    "    return np.expand_dims(x, axis=0)\n",
    "\n",
    "def paths_to_tensor(img_paths):\n",
    "    list_of_tensors = [path_to_tensor(img_path) for img_path in tqdm(img_paths)]\n",
    "    return np.vstack(list_of_tensors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 7969/7969 [00:35<00:00, 227.48it/s]\n",
      "100%|██████████| 1993/1993 [00:07<00:00, 250.34it/s]\n",
      "100%|██████████| 156/156 [00:01<00:00, 137.93it/s]\n"
     ]
    }
   ],
   "source": [
    "           \n",
    "ImageFile.LOAD_TRUNCATED_IMAGES = True                 \n",
    "\n",
    "# pre-process the data for Keras\n",
    "train_tensors = paths_to_tensor(xtrain).astype('float32')/255 - 0.5\n",
    "valid_tensors = paths_to_tensor(xtest).astype('float32')/255 - 0.5\n",
    "test_tensors = paths_to_tensor(data_test.iloc[:,0]).astype('float32')/255 - 0.5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Architecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 64, 64, 64)        832       \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 32, 32, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 32, 32, 128)       32896     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 16, 16, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 16, 16, 256)       131328    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 8, 8, 256)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 8, 8, 512)         524800    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 4, 4, 512)         0         \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 4, 4, 512)         0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 8192)              0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 500)               4096500   \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 500)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 8)                 4008      \n",
      "=================================================================\n",
      "Total params: 4,790,364\n",
      "Trainable params: 4,790,364\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "\n",
    "model.add(Conv2D(filters=64, kernel_size=2, padding='same', activation='relu', input_shape=(64,64,3), kernel_initializer='glorot_normal'))\n",
    "model.add(MaxPooling2D(pool_size=2))\n",
    "model.add(Conv2D(filters=128, kernel_size=2, padding='same', activation='relu', kernel_initializer='glorot_normal'))\n",
    "model.add(MaxPooling2D(pool_size=2))\n",
    "model.add(Conv2D(filters=256, kernel_size=2, padding='same', activation='relu', kernel_initializer='glorot_normal'))\n",
    "model.add(MaxPooling2D(pool_size=2))\n",
    "model.add(Conv2D(filters=512, kernel_size=2, padding='same', activation='relu', kernel_initializer='glorot_normal'))\n",
    "model.add(MaxPooling2D(pool_size=2))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(500, activation='relu', kernel_initializer='glorot_normal'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(8, activation='softmax', kernel_initializer='glorot_normal'))\n",
    "\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_model(model,to_file=\"model_dance_recognition.png\",show_shapes=True,show_layer_names=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:`period` argument is deprecated. Please use `save_freq` to specify the frequency in number of batches seen.\n"
     ]
    }
   ],
   "source": [
    "filepath = os.path.join(MODEL_PATH,\"dance_recognition-{epoch:02d}-{val_accuracy:.2f}.hdf5\")\n",
    "checkpoint = ModelCheckpoint(filepath, monitor='val_accuracy', verbose=1, save_best_only=True, mode='max',period=1)\n",
    "callbacks_list = [checkpoint]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "200/200 [==============================] - 108s 533ms/step - loss: 1.8393 - accuracy: 0.3108 - val_loss: 0.4896 - val_accuracy: 0.8460\n",
      "\n",
      "Epoch 00001: val_accuracy improved from -inf to 0.84596, saving model to /home/abhinav/hackerearth/Dance-Recognition/model/dance_recognition-01-0.85.hdf5\n",
      "Epoch 2/10\n",
      "200/200 [==============================] - 104s 521ms/step - loss: 0.3424 - accuracy: 0.8859 - val_loss: 0.0463 - val_accuracy: 0.9860\n",
      "\n",
      "Epoch 00002: val_accuracy improved from 0.84596 to 0.98595, saving model to /home/abhinav/hackerearth/Dance-Recognition/model/dance_recognition-02-0.99.hdf5\n",
      "Epoch 3/10\n",
      "200/200 [==============================] - 101s 507ms/step - loss: 0.1056 - accuracy: 0.9684 - val_loss: 0.0072 - val_accuracy: 0.9990\n",
      "\n",
      "Epoch 00003: val_accuracy improved from 0.98595 to 0.99900, saving model to /home/abhinav/hackerearth/Dance-Recognition/model/dance_recognition-03-1.00.hdf5\n",
      "Epoch 4/10\n",
      "200/200 [==============================] - 98s 492ms/step - loss: 0.0522 - accuracy: 0.9830 - val_loss: 0.0560 - val_accuracy: 0.9809\n",
      "\n",
      "Epoch 00004: val_accuracy did not improve from 0.99900\n",
      "Epoch 5/10\n",
      "200/200 [==============================] - 100s 498ms/step - loss: 0.0526 - accuracy: 0.9827 - val_loss: 0.0150 - val_accuracy: 0.9935\n",
      "\n",
      "Epoch 00005: val_accuracy did not improve from 0.99900\n",
      "Epoch 6/10\n",
      "200/200 [==============================] - 107s 537ms/step - loss: 0.0545 - accuracy: 0.9857 - val_loss: 0.0010 - val_accuracy: 1.0000\n",
      "\n",
      "Epoch 00006: val_accuracy improved from 0.99900 to 1.00000, saving model to /home/abhinav/hackerearth/Dance-Recognition/model/dance_recognition-06-1.00.hdf5\n",
      "Epoch 7/10\n",
      "200/200 [==============================] - 113s 566ms/step - loss: 0.0610 - accuracy: 0.9875 - val_loss: 1.9379e-04 - val_accuracy: 1.0000\n",
      "\n",
      "Epoch 00007: val_accuracy did not improve from 1.00000\n",
      "Epoch 8/10\n",
      "200/200 [==============================] - 100s 501ms/step - loss: 0.0268 - accuracy: 0.9921 - val_loss: 2.3863e-04 - val_accuracy: 1.0000\n",
      "\n",
      "Epoch 00008: val_accuracy did not improve from 1.00000\n",
      "Epoch 9/10\n",
      "200/200 [==============================] - 99s 497ms/step - loss: 0.0230 - accuracy: 0.9937 - val_loss: 4.4212e-05 - val_accuracy: 1.0000\n",
      "\n",
      "Epoch 00009: val_accuracy did not improve from 1.00000\n",
      "Epoch 10/10\n",
      "200/200 [==============================] - 100s 501ms/step - loss: 0.0357 - accuracy: 0.9927 - val_loss: 0.0317 - val_accuracy: 0.9925\n",
      "\n",
      "Epoch 00010: val_accuracy did not improve from 1.00000\n"
     ]
    }
   ],
   "source": [
    "model_history = model.fit(train_tensors,ytrain,validation_data = (valid_tensors, ytest),epochs=10, batch_size=40, shuffle=True,callbacks=callbacks_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Analysis\n",
    "Finding the Confusion matrix,Precision,Recall and F1 score to analyse the model thus created"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_confusion_matrix(confusion_matrix, class_names, figsize = (10,7), fontsize=14):\n",
    "    df_cm = pd.DataFrame(\n",
    "        confusion_matrix, index=class_names, columns=class_names, \n",
    "    )\n",
    "    fig = plt.figure(figsize=figsize)\n",
    "    try:\n",
    "        heatmap = sns.heatmap(df_cm, annot=True, fmt=\"d\")\n",
    "    except ValueError:\n",
    "        raise ValueError(\"Confusion matrix values must be integers.\")\n",
    "    heatmap.yaxis.set_ticklabels(heatmap.yaxis.get_ticklabels(), rotation=0, ha='right', fontsize=fontsize)\n",
    "    heatmap.xaxis.set_ticklabels(heatmap.xaxis.get_ticklabels(), rotation=45, ha='right', fontsize=fontsize)\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n",
    "    fig.savefig(os.path.join(MODEL_PATH,\"confusion_matrix.png\"))\n",
    "    return fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_heatmap(n_labels, n_predictions, class_names):\n",
    "    labels = n_labels #sess.run(tf.argmax(n_labels, 1))\n",
    "    predictions = n_predictions #sess.run(tf.argmax(n_predictions, 1))\n",
    "\n",
    "#     confusion_matrix = sess.run(tf.contrib.metrics.confusion_matrix(labels, predictions))\n",
    "    matrix = confusion_matrix(labels.argmax(axis=1),predictions.argmax(axis=1))\n",
    "    row_sum = np.sum(matrix, axis = 1)\n",
    "    w, h = matrix.shape\n",
    "\n",
    "    c_m = np.zeros((w, h))\n",
    "\n",
    "    for i in range(h):\n",
    "        c_m[i] = matrix[i] * 100 / row_sum[i]\n",
    "\n",
    "    c = c_m.astype(dtype = np.uint8)\n",
    "\n",
    "    \n",
    "    heatmap = print_confusion_matrix(c, class_names, figsize=(18,10), fontsize=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['sattriya', 'mohiniyattam', 'kathakali', 'bharatanatyam', 'kuchipudi', 'odissi', 'manipuri', 'kathak']\n"
     ]
    }
   ],
   "source": [
    "class_names = list()\n",
    "for name,idx in labels_id.items():\n",
    "    class_names.append(name)\n",
    "print(class_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "63/63 [==============================] - 6s 87ms/step\n"
     ]
    }
   ],
   "source": [
    "ypred = model.predict(valid_tensors,verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print_heatmap(ytest,ypred,class_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Precision Recall F1 Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "ypred_class = np.argmax(ypred,axis=1)\n",
    "# print(ypred_class[:10])\n",
    "ytest = np.argmax(ytest,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.992474\n",
      "Precision: 0.992644\n",
      "Recall: 0.992474\n",
      "F1 score: 0.992488\n"
     ]
    }
   ],
   "source": [
    "accuracy = accuracy_score(ytest,ypred_class)\n",
    "print('Accuracy: %f' % accuracy)\n",
    "# precision tp / (tp + fp)\n",
    "precision = precision_score(ytest, ypred_class,average='weighted')\n",
    "print('Precision: %f' % precision)\n",
    "# recall: tp / (tp + fn)\n",
    "recall = recall_score(ytest,ypred_class,average='weighted')\n",
    "print('Recall: %f' % recall)\n",
    "# f1: 2 tp / (2 tp + fp + fn)\n",
    "f1 = f1_score(ytest,ypred_class,average='weighted')\n",
    "print('F1 score: %f' % f1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5/5 [==============================] - 0s 78ms/step\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/abhinav/anaconda3/lib/python3.8/site-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
      "  warnings.warn('`model.predict_classes()` is deprecated and '\n"
     ]
    }
   ],
   "source": [
    "test_pred = model.predict(test_tensors,verbose=1)\n",
    "test_pred_class = model.predict_classes(test_tensors)\n",
    "# print(test_pred_class)\n",
    "# print(test_pred_class.shape)\n",
    "# print(test_pred_class.shape)\n",
    "\n",
    "id_labels = dict()\n",
    "for label,idx in labels_id.items():\n",
    "    id_labels[idx]=label\n",
    "# print(id_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating the Submission file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_submit = list()\n",
    "for i in range(test_pred_class.shape[0]):\n",
    "    data_submit.append({\n",
    "        \"Image\":data_test.iloc[i,0].split(\"/\")[-1],\n",
    "        \"target\":id_labels[test_pred_class[i]]\n",
    "    })\n",
    "data_submit = pd.DataFrame(data_submit)\n",
    "data_submit.to_csv(\"submission.csv\",index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}