RESNET50.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b0abfd55",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.9.1\n",
      "2.9.0\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import h5py\n",
    "import math\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras.models import Model, load_model\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras.layers import Input, Add, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D, AveragePooling2D, MaxPooling2D, GlobalMaxPooling2D\n",
    "from tensorflow.keras.preprocessing import image\n",
    "from keras.utils import layer_utils\n",
    "from keras.utils.data_utils import get_file\n",
    "from tensorflow.keras.applications.imagenet_utils import preprocess_input\n",
    "from IPython.display import SVG\n",
    "from keras.utils.vis_utils import model_to_dot\n",
    "from tensorflow.keras.utils import plot_model\n",
    "from tensorflow.keras.initializers import glorot_uniform\n",
    "import scipy.misc\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import imshow\n",
    "from scipy.sparse import csr_matrix\n",
    "\n",
    "print(tf.__version__)\n",
    "print(keras.__version__)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "15d90e54",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "import tensorflow.keras\n",
    "from tensorflow.keras import backend as k\n",
    "import matplotlib.pyplot as plt\n",
    "from tensorflow.keras.preprocessing import image\n",
    "import os\n",
    "import glob\n",
    "import numpy as np\n",
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "461f1470",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['1', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '2', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '3', '30', '31', '32', '33', '34', '35', '36', '37', '38', '39', '4', '40', '41', '42', '43', '44', '45', '46', '47', '48', '49', '5', '50', '51', '52', '6', '7', '8', '9']\n",
      "Folders= 52\n"
     ]
    }
   ],
   "source": [
    "import textacy\n",
    "\n",
    "\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "\n",
    "# from tensorflow.python.keras.preprocessing import sequence\n",
    "# from tensorflow.python.keras.preprocessing import text\n",
    "# from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "# from sklearn.feature_selection import SelectKBest\n",
    "# from sklearn.feature_selection import f_classif\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "\n",
    "dataset_path = os.listdir('DATASET_3')\n",
    "char_types = os.listdir('DATASET_3')\n",
    "print(char_types)\n",
    "print (\"Folders=\",len(char_types))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "507c7e7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello\n"
     ]
    }
   ],
   "source": [
    "print(\"Hello\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "58a46fa9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('1', 'DATASET_3/1/ses1_ch1_set1_usr100.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set1_usr11.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set1_usr12.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set1_usr14.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set1_usr15.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set1_usr16.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set1_usr19.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set1_usr20.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set1_usr24.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set1_usr25.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set1_usr29.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set1_usr3.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set1_usr33.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set1_usr34.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set1_usr35.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set1_usr36.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set1_usr37.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set1_usr38.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set1_usr39.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set1_usr4.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set1_usr45.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set1_usr47.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set1_usr48.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set1_usr49.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set1_usr5.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set1_usr50.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set1_usr52.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set1_usr54.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set1_usr57.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set1_usr59.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set1_usr6.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set1_usr60.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set1_usr61.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set1_usr62.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set1_usr63.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set1_usr64.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set1_usr65.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set1_usr69.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set1_usr7.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set1_usr70.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set1_usr71.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set1_usr72.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set1_usr73.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set1_usr74.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set1_usr75.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set1_usr76.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set1_usr77.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set1_usr78.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set1_usr79.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set1_usr8.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set1_usr80.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set1_usr81.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set1_usr82.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set1_usr84.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set1_usr85.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set1_usr87.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set1_usr88.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set1_usr90.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set1_usr91.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set1_usr92.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set1_usr93.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set1_usr94.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set1_usr95.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set1_usr96.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set1_usr97.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set1_usr98.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set2_usr1.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set2_usr100.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set2_usr12.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set2_usr13.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set2_usr16.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set2_usr2.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set2_usr20.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set2_usr21.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set2_usr23.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set2_usr27.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set2_usr28.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set2_usr3.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set2_usr31.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set2_usr32.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set2_usr34.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set2_usr37.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set2_usr38.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set2_usr4.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set2_usr40.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set2_usr45.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set2_usr46.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set2_usr48.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set2_usr49.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set2_usr5.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set2_usr54.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set2_usr56.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set2_usr57.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set2_usr58.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set2_usr6.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set2_usr61.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set2_usr62.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set2_usr63.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set2_usr64.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set2_usr66.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set2_usr68.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set2_usr69.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set2_usr7.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set2_usr70.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set2_usr72.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set2_usr76.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set2_usr78.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set2_usr79.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set2_usr80.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set2_usr82.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set2_usr83.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set2_usr84.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set2_usr86.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set2_usr9.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set2_usr90.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set2_usr92.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set2_usr94.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set2_usr95.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set2_usr96.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses1_ch1_set2_usr99.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set1_usr1.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set1_usr100.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set1_usr14.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set1_usr15.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set1_usr17.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set1_usr19.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set1_usr2.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set1_usr21.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set1_usr22.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set1_usr23.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set1_usr24.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set1_usr25.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set1_usr26.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set1_usr28.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set1_usr29.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set1_usr3.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set1_usr31.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set1_usr32.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set1_usr33.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set1_usr34.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set1_usr35.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set1_usr36.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set1_usr38.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set1_usr39.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set1_usr4.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set1_usr40.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set1_usr41.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set1_usr44.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set1_usr47.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set1_usr48.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set1_usr49.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set1_usr51.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set1_usr52.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set1_usr54.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set1_usr55.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set1_usr56.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set1_usr6.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set1_usr60.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set1_usr61.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set1_usr62.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set1_usr64.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set1_usr67.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set1_usr69.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set1_usr70.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set1_usr71.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set1_usr74.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set1_usr75.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set1_usr76.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set1_usr78.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set1_usr79.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set1_usr8.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set1_usr80.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set1_usr81.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set1_usr82.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set1_usr83.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set1_usr84.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set1_usr85.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set1_usr86.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set1_usr87.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set1_usr88.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set1_usr89.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set1_usr90.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set1_usr91.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set1_usr92.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set1_usr93.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set1_usr94.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set1_usr96.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set1_usr97.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set1_usr98.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set2_usr1.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set2_usr100.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set2_usr12.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set2_usr13.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set2_usr14.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set2_usr16.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set2_usr17.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set2_usr18.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set2_usr2.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set2_usr20.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set2_usr21.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set2_usr23.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set2_usr24.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set2_usr25.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set2_usr26.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set2_usr27.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set2_usr28.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set2_usr29.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set2_usr3.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set2_usr30.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set2_usr31.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set2_usr32.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set2_usr33.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set2_usr34.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set2_usr4.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set2_usr40.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set2_usr42.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set2_usr45.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set2_usr48.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set2_usr49.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set2_usr5.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set2_usr54.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set2_usr55.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set2_usr56.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set2_usr57.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set2_usr6.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set2_usr60.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set2_usr61.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set2_usr63.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set2_usr64.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set2_usr68.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set2_usr69.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set2_usr7.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set2_usr70.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set2_usr71.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set2_usr72.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set2_usr75.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set2_usr76.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set2_usr77.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set2_usr80.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set2_usr82.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set2_usr85.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set2_usr86.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set2_usr87.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set2_usr88.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set2_usr9.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set2_usr90.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set2_usr91.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set2_usr92.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set2_usr93.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set2_usr95.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set2_usr96.txt.png'),\n",
       " ('1', 'DATASET_3/1/ses2_ch1_set2_usr99.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr10.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr12.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr14.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr15.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr16.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr17.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr18.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr2.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr20.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr24.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr25.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr27.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr28.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr29.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr3.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr30.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr32.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr33.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr34.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr35.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr36.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr37.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr39.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr4.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr42.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr43.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr44.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr46.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr48.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr49.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr5.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr50.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr51.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr52.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr53.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr54.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr55.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr56.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr57.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr58.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr59.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr6.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr60.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr61.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr62.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr63.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr64.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr66.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr67.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr68.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr69.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr7.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr70.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr71.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr72.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr73.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr74.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr75.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr76.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr77.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr78.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr79.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr8.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr80.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr81.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr82.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr83.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr84.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr85.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr86.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr87.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr88.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr89.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr9.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr91.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr92.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr93.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr94.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr95.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr96.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr97.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr98.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set1_usr99.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr1.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr10.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr100.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr11.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr12.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr13.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr14.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr15.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr16.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr17.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr18.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr2.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr20.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr21.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr22.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr23.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr25.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr26.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr27.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr28.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr29.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr3.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr30.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr31.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr32.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr34.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr35.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr36.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr37.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr38.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr39.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr4.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr41.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr42.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr45.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr46.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr48.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr49.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr5.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr50.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr51.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr52.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr54.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr55.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr56.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr58.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr59.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr6.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr60.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr61.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr62.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr63.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr64.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr66.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr68.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr69.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr7.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr70.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr71.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr72.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr73.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr75.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr76.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr77.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr78.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr79.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr8.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr80.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr81.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr82.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr83.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr84.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr85.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr86.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr87.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr88.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr9.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr90.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr91.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr92.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr93.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr94.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr95.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr96.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr97.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr98.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses1_ch10_set2_usr99.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr1.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr10.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr100.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr11.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr12.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr13.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr14.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr15.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr16.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr17.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr18.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr2.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr20.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr21.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr24.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr25.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr26.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr27.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr28.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr29.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr3.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr30.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr31.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr32.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr33.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr34.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr35.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr36.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr37.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr38.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr4.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr40.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr41.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr42.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr44.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr46.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr47.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr48.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr5.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr51.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr52.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr55.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr56.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr57.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr58.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr6.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr60.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr61.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr62.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr65.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr67.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr69.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr7.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr70.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr71.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr72.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr74.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr75.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr76.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr78.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr79.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr8.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr80.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr81.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr82.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr83.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr84.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr85.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr86.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr87.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr88.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr89.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr9.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr90.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr91.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr92.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr93.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr94.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr96.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr97.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr98.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set1_usr99.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr1.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr10.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr100.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr11.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr12.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr13.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr14.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr16.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr17.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr18.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr2.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr20.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr21.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr22.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr23.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr24.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr25.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr26.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr27.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr28.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr29.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr3.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr30.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr31.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr32.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr33.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr34.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr35.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr36.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr37.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr38.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr39.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr4.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr41.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr42.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr44.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr45.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr46.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr48.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr49.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr5.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr50.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr51.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr53.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr54.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr56.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr57.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr58.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr59.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr6.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr61.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr62.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr63.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr64.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr65.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr66.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr67.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr68.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr69.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr7.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr70.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr71.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr72.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr73.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr74.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr75.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr76.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr77.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr78.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr79.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr8.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr81.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr82.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr83.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr84.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr85.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr86.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr87.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr88.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr89.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr9.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr90.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr91.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr92.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr93.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr94.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr95.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr96.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr97.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr98.txt.png'),\n",
       " ('10', 'DATASET_3/10/ses2_ch10_set2_usr99.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr11.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr12.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr14.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr15.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr16.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr17.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr18.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr19.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr2.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr20.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr24.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr25.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr27.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr28.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr29.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr3.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr30.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr31.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr32.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr33.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr35.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr36.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr37.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr39.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr4.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr40.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr41.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr42.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr45.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr47.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr48.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr49.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr5.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr50.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr51.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr53.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr55.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr56.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr57.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr58.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr59.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr6.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr60.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr63.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr64.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr65.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr66.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr67.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr68.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr69.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr7.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr70.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr71.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr72.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr73.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr74.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr75.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr79.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr8.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr80.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr81.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr82.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr84.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr85.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr87.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr88.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr89.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr9.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr90.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr91.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr92.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr93.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr96.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr97.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set1_usr99.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr1.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr10.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr100.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr11.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr12.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr13.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr14.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr15.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr16.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr18.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr2.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr20.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr21.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr22.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr23.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr26.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr27.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr28.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr3.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr30.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr34.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr38.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr39.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr4.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr40.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr41.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr42.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr44.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr45.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr46.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr48.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr49.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr5.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr50.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr51.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr52.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr53.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr54.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr56.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr57.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr58.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr59.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr6.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr60.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr61.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr62.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr63.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr64.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr65.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr66.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr67.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr68.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr69.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr7.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr70.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr71.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr73.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr75.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr76.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr79.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr8.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr80.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr81.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr82.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr84.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr85.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr88.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr9.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr90.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr91.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr94.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr95.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr96.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr98.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses1_ch11_set2_usr99.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr1.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr100.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr11.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr12.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr14.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr15.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr17.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr18.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr19.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr2.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr20.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr21.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr22.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr24.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr25.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr26.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr27.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr28.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr29.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr3.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr30.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr31.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr32.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr33.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr34.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr35.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr36.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr37.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr38.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr39.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr40.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr41.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr42.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr44.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr45.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr46.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr47.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr48.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr49.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr5.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr51.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr52.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr54.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr55.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr56.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr57.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr58.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr6.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr69.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr7.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr70.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr71.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr72.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr74.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr75.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr78.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr79.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr8.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr80.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr81.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr82.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr83.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr84.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr85.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr86.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr87.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr89.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr9.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr90.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr91.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr92.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr93.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr94.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr96.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr97.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr98.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set1_usr99.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr1.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr10.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr100.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr11.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr12.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr13.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr14.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr15.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr16.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr17.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr18.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr19.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr2.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr20.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr21.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr22.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr23.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr24.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr25.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr26.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr27.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr28.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr3.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr33.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr34.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr38.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr39.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr4.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr41.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr42.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr45.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr46.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr48.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr49.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr5.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr50.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr51.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr53.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr54.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr56.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr57.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr58.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr59.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr6.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr61.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr62.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr63.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr64.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr65.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr66.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr68.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr69.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr7.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr70.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr71.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr72.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr73.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr74.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr75.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr76.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr77.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr79.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr8.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr81.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr82.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr83.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr84.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr85.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr86.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr87.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr88.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr89.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr9.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr90.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr91.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr92.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr94.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr96.txt.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr98.png'),\n",
       " ('11', 'DATASET_3/11/ses2_ch11_set2_usr99.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr11.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr12.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr13.txt.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr14.txt.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr16.txt.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr17.txt.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr2.txt.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr20.txt.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr22.txt.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr24.txt.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr27.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr28.txt.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr3.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr30.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr31.txt.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr32.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr33.txt(1).png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr33.txt.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr34.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr35.txt.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr37.txt.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr38.txt.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr39.txt.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr40.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr41.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr42.txt.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr44.txt.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr45.txt.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr46.txt.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr47.txt.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr48.txt.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr49.txt.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr50.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr52.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr53.txt.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr54.txt(1).png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr54.txt.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr55.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr58.txt.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr59.txt.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr6.txt.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr60.txt.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr61.txt(1).png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr61.txt.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr62.txt.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr63.txt.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr64.txt.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr66.txt.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr67.txt(1).png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr67.txt.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr68.txt.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr7.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr71.txt.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr73.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr76.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr77.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr78.txt.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr79.txt.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr81.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr82.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr84.txt(1).png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr84.txt.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr88.txt.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr89.txt.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr90.txt.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr91.txt.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr92.txt.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr93.txt.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr94.txt.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr95.txt.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr96.txt.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr97.txt.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr98.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set1_usr99.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set2_usr1.txt.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set2_usr10.txt.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set2_usr100.txt.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set2_usr12.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set2_usr17.txt.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set2_usr18.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set2_usr2.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set2_usr20.txt.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set2_usr22.txt.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set2_usr24.txt.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set2_usr25.txt.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set2_usr27.txt.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set2_usr28.txt.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set2_usr3.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set2_usr30.txt.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set2_usr32.txt.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set2_usr33.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set2_usr34.txt.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set2_usr35.txt.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set2_usr36.txt.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set2_usr38.txt.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set2_usr39.txt.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set2_usr4.txt.png'),\n",
       " ('12', 'DATASET_3/12/ses1_ch148_set2_usr40.txt.png'),\n",
       " ...]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chars = []\n",
    "\n",
    "for item in char_types:\n",
    "    all_char = os.listdir('DATASET_3' + '/' + item) \n",
    "    \n",
    "    for char in all_char:\n",
    "        chars.append((item , str('DATASET_3' + '/' + item) + '/'+ char))\n",
    "        \n",
    "chars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7bbfbbd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>Char_type</th>\n",
       "      <th>image</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>DATASET_3/1/ses1_ch1_set1_usr100.png</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>DATASET_3/1/ses1_ch1_set1_usr11.png</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>DATASET_3/1/ses1_ch1_set1_usr12.png</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Char_type                                 image\n",
       "0         1  DATASET_3/1/ses1_ch1_set1_usr100.png\n",
       "1         1   DATASET_3/1/ses1_ch1_set1_usr11.png\n",
       "2         1   DATASET_3/1/ses1_ch1_set1_usr12.png"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chars_df = pd.DataFrame(data= chars, columns = ['Char_type', 'image'])\n",
    "chars_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "59d6fdca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10    343\n",
       "21    339\n",
       "28    337\n",
       "8     332\n",
       "9     322\n",
       "41    319\n",
       "11    307\n",
       "22    303\n",
       "31    293\n",
       "25    292\n",
       "12    291\n",
       "34    288\n",
       "44    287\n",
       "32    286\n",
       "29    283\n",
       "39    283\n",
       "40    277\n",
       "19    276\n",
       "18    274\n",
       "23    272\n",
       "38    271\n",
       "48    271\n",
       "47    270\n",
       "20    267\n",
       "24    266\n",
       "2     257\n",
       "27    253\n",
       "1     252\n",
       "6     250\n",
       "17    249\n",
       "30    245\n",
       "46    244\n",
       "33    243\n",
       "5     242\n",
       "35    238\n",
       "50    237\n",
       "14    236\n",
       "45    233\n",
       "26    232\n",
       "43    228\n",
       "7     223\n",
       "51    219\n",
       "42    219\n",
       "3     219\n",
       "36    212\n",
       "16    212\n",
       "4     210\n",
       "37    208\n",
       "49    180\n",
       "15    159\n",
       "13    156\n",
       "52    137\n",
       "Name: Char_type, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_count = chars_df['Char_type'].value_counts()\n",
    "image_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9fc2fbab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1b58cff1a90>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import cv2\n",
    "path ='DATASET_3/'\n",
    "im_size = 224\n",
    "images = []\n",
    "labels = []\n",
    "\n",
    "for i in char_types:\n",
    "    data_path= path+ str(i) #entered in 1st folder and then 2nd folder and then 3rd folder\n",
    "    filenames = [i for i in os.listdir(data_path) ]\n",
    "#print (filenames) #will get the names of all images\n",
    "    for f in filenames:\n",
    "        img=cv2.imread(data_path+ '/'+ f) #reading that image as array\n",
    "        #print(img) #will get the imoge as an array\n",
    "        \n",
    "        img = cv2.resize(img, (im_size, im_size),1)\n",
    "#         inputCopy = img.copy()\n",
    "#         converted_img = cv2.cvtColor(inputCopy, cv2.COLOR_GRAY2BGR)\n",
    "#         dst = cv2.fastNlMeansDenoisingColored(converted_img, None, 10, 10, 7, 15) \n",
    "        images.append(img)\n",
    "        \n",
    "        labels.append(i)\n",
    "plt.imshow(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "05aa3dd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13342, 224, 224, 3)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "images =np.array(images)\n",
    "images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c3fa6a9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "images = images.astype( 'float32') / 255.0\n",
    "# images = images.astype( 'int16') / 255.0\n",
    "# images = images.astype( 'int16') / 255.0\n",
    "# chars_df\n",
    "# images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8f6c5fa5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
    "# from sklearn.compose import ColumnTransformer\n",
    "# y= chars_df['Char_type'].values\n",
    "# #\n",
    "# y_labelencoder= LabelEncoder ()\n",
    "# y= y_labelencoder.fit_transform (y)\n",
    "# #print (y)\n",
    "# y=y.reshape(-1,1)\n",
    "# onehotencoder = OneHotEncoder(categorical_features=[0])  #\n",
    "# Y= onehotencoder.fit_transform(y)\n",
    "# Y.shape #(393, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "71ab207f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>Char_type</th>\n",
       "      <th>image</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>DATASET_3/1/ses1_ch1_set1_usr100.png</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>DATASET_3/1/ses1_ch1_set1_usr11.png</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>DATASET_3/1/ses1_ch1_set1_usr12.png</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>DATASET_3/1/ses1_ch1_set1_usr14.txt.png</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>DATASET_3/1/ses1_ch1_set1_usr15.txt.png</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13337</th>\n",
       "      <td>51</td>\n",
       "      <td>DATASET_3/9/ses2_ch9_set2_usr93.txt.png</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13338</th>\n",
       "      <td>51</td>\n",
       "      <td>DATASET_3/9/ses2_ch9_set2_usr94.png</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13339</th>\n",
       "      <td>51</td>\n",
       "      <td>DATASET_3/9/ses2_ch9_set2_usr95.txt.png</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13340</th>\n",
       "      <td>51</td>\n",
       "      <td>DATASET_3/9/ses2_ch9_set2_usr96.png</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13341</th>\n",
       "      <td>51</td>\n",
       "      <td>DATASET_3/9/ses2_ch9_set2_usr99.txt.png</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13342 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Char_type                                    image\n",
       "0              0     DATASET_3/1/ses1_ch1_set1_usr100.png\n",
       "1              0      DATASET_3/1/ses1_ch1_set1_usr11.png\n",
       "2              0      DATASET_3/1/ses1_ch1_set1_usr12.png\n",
       "3              0  DATASET_3/1/ses1_ch1_set1_usr14.txt.png\n",
       "4              0  DATASET_3/1/ses1_ch1_set1_usr15.txt.png\n",
       "...          ...                                      ...\n",
       "13337         51  DATASET_3/9/ses2_ch9_set2_usr93.txt.png\n",
       "13338         51      DATASET_3/9/ses2_ch9_set2_usr94.png\n",
       "13339         51  DATASET_3/9/ses2_ch9_set2_usr95.txt.png\n",
       "13340         51      DATASET_3/9/ses2_ch9_set2_usr96.png\n",
       "13341         51  DATASET_3/9/ses2_ch9_set2_usr99.txt.png\n",
       "\n",
       "[13342 rows x 2 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "le = LabelEncoder()\n",
    "\n",
    "dfle = chars_df\n",
    "dfle.Char_type = le.fit_transform(dfle.Char_type)\n",
    "dfle\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2afb1f24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0],\n",
       "       [ 0],\n",
       "       [ 0],\n",
       "       ...,\n",
       "       [51],\n",
       "       [51],\n",
       "       [51]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "Y = dfle[['Char_type']].values\n",
    "Y\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7fc8e80c",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.compose import ColumnTransformer\n",
    "ct = ColumnTransformer([('Char_type', OneHotEncoder(), [0])], remainder = 'passthrough')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7b5014c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[0., 1., 0., ..., 0., 0., 0.],\n",
       "        [0., 1., 0., ..., 0., 0., 0.],\n",
       "        [0., 1., 0., ..., 0., 0., 0.],\n",
       "        ...,\n",
       "        [1., 0., 0., ..., 0., 0., 1.],\n",
       "        [1., 0., 0., ..., 0., 0., 1.],\n",
       "        [1., 0., 0., ..., 0., 0., 1.]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y = csr_matrix(Y)\n",
    "Y.todense()\n",
    "Y.toarray()\n",
    "Y = ct.fit_transform(Y.todense())\n",
    "Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f6c67c90",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(12674, 224, 224, 3)\n",
      "(12674, 53)\n",
      "(668, 224, 224, 3)\n",
      "(668, 53)\n"
     ]
    }
   ],
   "source": [
    "from sklearn. utils import shuffle\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "images, Y = shuffle(images, Y, random_state=1)\n",
    "train_x, test_x, train_y, test_y = train_test_split(images, Y, test_size=0.05, random_state= 415)\n",
    "\n",
    "# einpect the shape of the training and testing.\n",
    "\n",
    "print (train_x. shape)\n",
    "print (train_y. shape)\n",
    "print (test_x. shape)\n",
    "print (test_y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a5a599c",
   "metadata": {},
   "source": [
    "# Code\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "18ee50e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1b56f476fa0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQEAAAD8CAYAAAB3lxGOAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAABDrElEQVR4nO29aWzjSXqn+QQp3pcoipSo+8hDeSmPzsqqdpXL9lZPe8bwbo/Xa2/7w2zPjLE9BmzMGPAC7p4Z7BozMOCZ9YEFFjC2DRvrXXhsN9BjuzGwd7pttLuqq11dlZeUytR9UyJFSqJEivcR+0GKvymm8qiUlLriAQL8M0iKQZHxizfeiHhfIaVEo9GcXUxH3QCNRnO0aBHQaM44WgQ0mjOOFgGN5oyjRUCjOeNoEdBozjiHJgJCiH8ohBgXQkwJIb5yWO+j0Wj2hziMfQJCCDMwAfwDIAJ8AvyclPLJgb+ZRqPZF4dlCdwBpqSUM1LKIvAnwBcO6b00Gs0+aDikv9sOLNbcjwBvPuvJzc3Nsqen55CaotFoAO7du7cqpQzW1x+WCIg96nbNO4QQXwa+DNDV1cXdu3cPqSkajQZACDG/V/1hTQciQGfN/Q5gufYJUsqvSSlvSylvB4NPiZNGo3lNHJYIfAKcF0L0CiGswBeBbx7Se2k0mn1wKNMBKWVZCPFLwH8FzMAfSCkfH8Z7aTSa/XFYPgGklH8J/OVh/X2NRnMw6B2DGs0ZR4uARnPG0SKg0ZxxtAhoNGccLQIazRlHi4BGc8bRIqDRnHG0CGg0ZxwtAhrNGUeLgEZzxtEioNGccbQIaDRnHC0CGs0ZR4uARnPG0SKg0ZxxXlkEhBCdQojvCCFGhRCPhRD/aqf+14QQS0KIhzvlJw6uuRqN5qDZT1CRMvArUsr7QggPcE8I8e2dx35HSvmb+2+eRqM5bF5ZBKSUUSC6c50WQoyyHWpco9GcIA7EJyCE6AFuAj/YqfolIcSwEOIPhBD+g3gPjUZzOOxbBIQQbuAbwC9LKVPA7wL9wA22LYXfesbrviyEuCuEuJtIJPbbDI1G84rsK9CoEMLCtgD8kZTyPwNIKVdqHv894L/s9Vop5deArwHcvn374BMiagCQUu5ZqtUq1Wr1uff3uq1/rnqPF7XhVR4XQmAymTCZTAghnrr/Mo8BT9Wr52u2eWURENv/xd8HRqWUv11TH97xFwD8FDCyvyZqDoJqtUq5XH5mKZVKT9VVKpU962tLrbDU8yyRqL3/rOcIITCbzTQ0NNDQ0IDFYjGu6++/7GPq2mw2H/S/90SzH0vgbeCfAI+EEA936v418HNCiBtspx2bA/7FPt7jzKM6R7VapVKp7BqZa4uU0ni89jmVSoVKpWJ09FKpRLFY3HVbX/+8utr7tUJQ29b6tr+KCABGx7VarVitVuO6/nav6+c9x2KxYDKZMJvNhpXwskVZEafJktjP6sD32DvnoM41cIBIKSmXyxQKBfL5PLlcjkKhsOdtPp/fda1KoVAwOq2yCJQ4KKFQj6n655Xa51WrVaOdz2r/i+r3eo4y3VVHVVaB2Wx+qqh6k8n0wucoIbDb7TgcDhwOh3Fdf7vXtdVqPXWWxKElH9EcDKrTZjIZUqkUqVSKzc3NXUXV1T+2sbFBKpUim81SLBaNDgt7d7xP05FfNM8/aPYaeV+lzmw243A48Pl8+Hw+GhsbaWxsNK5r62ofU/W1vobTYg1oETgC1Eiaz+fJZDKk02m2trbIZDJsbW2RzWYpFAq7irICXlSy2Sz5fJ5sNmvUFYtFKpWK0XFrHWe1I23tdW2duq/m6fV1tZ3hWR3j09YDu6Y3e1kh9dbJ855X+36qLpfLkUqlWF1dNUZ7p9OJ0+l86lrdt9vt2O12bDYbVqt1V33tc1Wx2Ww0NDQYwnEc0SJwBEgpKRaLpFIpYrEYsViMaDRKLBZjZWWFRCJBMpk0RvF8Pr9r/l/7I6+9ra+r9SHUjtyqM+81f95rPl1bX1unHG6qI9ff1r7fXvXPe0xNg2p9Ears5duo93PU1tWvZBSLRcrlMtlslo2NjT3F71nCaLVacTqduN1uvF4vTU1NBAKBp4qqV689zmgROGCUQ65+Hq/m52p03traIplMsrq6SiKRMMrq6irr6+ukUikymYwxkj/PW26z2Z7rIa8fvdXcWT1PlWfVvehvw6cXgOcJg/o/1jo01a3qwLXOyVrH5bOeX+8krV8V2es2n8/vcoBWq1UaGhqwWq24XC7cbvdT0we/37/rvsfjMSwFZT1YrdZnXjc0NDz3/3IYaBE4BKrVKrlcjvX19V0dPB6PGx09mUyytbW1y5SvdeapH58yZdXI7XQ6cblcuFwu41qNTLV1tcXpdBo/tFonmtls3jU1qF9jf5Z3vP66npf9Ab/oefUrHXuV+lWRZxW13KmEeGtra9cULJPJ7LqufbxWjCuVCoVCwZhObG5usrKysqsz22w249putxtWQ71A1N/3er27fA6vCy0Cr4gyL9WPq3b+rszMeDxONBo1yvLyMsvLy6ysrLC+vr5rhK8dXR0OB263e1fnU/NPj8eD2+3G4/Hg8Xjwer3GfXXt9XqNxz0ezy4hsFgsRuc/C6jvSY3u2Wx2l5O1vqTT6T3rt7a2KBQKhjArcVEWXzab3eWDUJaD2WzG5XLh9/uNqUJzc/NTt62trXR3d9Pa2mp8R68LLQL7oFqtks1micfjLC0tsbi4SDQaJZFIGN76ra0t0um0UdQIUyqVMJvNOJ1OfD6fMSqoWzWyq6UpNbrsZUaqpatnjUbKAlBWwGlb534WUkqEEEgpjU7ldDqxWCw4HA4aGxufcsCqUiwWyefzFIvFPZ2z2WyWbDa767tV36+6zWazVKtV8vk8yWSSQqHAxsYGy8vLxnernI/hcJjPf/7zRru0CBwjnrdsVqlU2NraIhKJMDw8zIMHD3j06BFzc3OkUilKpdJz/7bT6cTj8RAOh+ns7KSzs5OOjg46OzsJBoOGIHi9XhwOB1ar9Ux03oOi3u+gplSfFvVdFwoF0uk0GxsbJJNJY7q3srLCysoK8XiceDzOysoK1WqVUqlk7OMoFApsbm4+8z3a2toIh8NcvHgRl8uFzWZ7tQ/9CmgReAmUdz2TyRhmviqxWIzl5WWWlpaIRCLE43FyuRxSSux2Oy6Xa5dpXm+u188R/X4/Pp8Pt9ttLDnZbLZj72E+zSjfh8Viwel0IoTAZrPh9Xppbm6mo6NjlxVQa/Epf4KyHPYquVwOm81m+HCUc/B1oUVgD+p3s6nlqs3NTebm5hgdHeXJkyeMj48TiURIpVKG40h5o4UQOJ1OgsEgra2thtKr29bWVgKBAG63e5e5Xu/Rr9+uqjkahBA0NDQYnn6fz/fUXoVaf0G5XCafz5NOp1lbW9tVVldXjbK2tsb6+vouf8/rFnwtAs+gUCiQyWRYW1sjmUySTCaJRqMsLi4yMzPDzMwM8/PzrK2tUa1WcTgcBAIBfD6f8WU2NjYSCAQIBoM0NzcTDAaN6+bmZrxeLzabzejkmuNL7UnFF6EGETUd2NjYMHZwqtv64nA46O7uPhKrT4tADbUbStLpNNFolEePHjE+Ps7MzAyxWIz19XXD7MtkMlSrVZxOJ+3t7fT399Pf3294eRsbG3G5XMYus9qinHa6858+ap2RDocDs9mMx+MhFArt2vSkHJDFYhGz2UxPT8+uzVevCy0COyizX3l8Z2dnmZiYMJx9k5OTrK+vUyqVsNls2O12/H4/Ho+HQCBAb28vAwMDXLx4kf7+fsLhMF6v1zixpjlb1G6xfp4zcq8j1K8bLQI7qHlcNBrl4cOHPHjwgKGhIWMb78bGBlJKHA4HHR0ddHV1GSUcDtPS0mJsF/X7/YaDR4/0muehrAZ1fRSceRFQu9EKhQJbW1vMzMzw4Ycf8r3vfY/79+/v2mKr5vnnz59ncHCQa9eucenSJbq6uozNPRrNp+WoB4ozLwKVSoXV1VXm5uYYGhpiaGiIR48eEYvFMJlMtLW10d7eTldXF21tbbS2thIOhw0Pf3Nz8yutPWs0x4X9xhicA9JABShLKW8LIZqAPwV62I4s9LNSyuT+mnmw1G4lzWazLC4u8vDhQ/7qr/6KsbExEokEpVIJr9dLT08P169f5/r161y4cIHOzk5jF1/tIRttBWhOKgdhCfyYlHK15v5XgL+RUv6GEOIrO/d/9QDe58BQU4BIJMLU1BRDQ0M8ePCAsbEx4vE4lUqFrq4uzp07x/Xr17l8+TLnzp2jvb2d5uZmY/1eozkNHMZ04AvAj+5c/yHwtxwzEVBbQCcnJ/n2t7/NgwcPmJ6eNs6WNzY2cuvWLd59991dc36Hw6HX9DWnjv2KgAS+JYSQwP+1E0a8RUUbllJGhRChvV4ohPgy8GWArq6ufTbj05HL5YhGozx+/JhPPvmE8fFxkskkTU1Nxnr/W2+9xZ07d+js7KSpqem1tk+jeZ3sVwTellIu73T0bwshxl72hUeZdyCZTBpOwMnJSTY3N3G5XAwMDHDnzh3efvttenp6aG9vx+Vyvc6maTSvnX2JgJRyeec2LoT4M+AOsKJyDwghwkD8ANp5IKgNQevr6wwPDzMxMUE8Hsfr9dLX18etW7f4oR/6Id555x3cbjcWi+Wom6zRHDr7SU3u2slGjBDCBXye7UQj3wS+tPO0LwF/sd9GHhTqINDa2hqPHz9meXmZhoYGBgcH+fEf/3F+9Ed/lMuXL2O327XjT3Nm2I8l0AL82Y6TrAH4T1LK/08I8QnwdSHEzwMLwM/sv5kHgzr/H4/HmZubY2NjA5vNxsDAAJ/97GcZHBwkGAzqPf2aM8V+ko/MANf3qF8D3ttPow6LQqHA8vIyCwsLJJNJpJT4/X56e3s5f/48TU1N2Gw2LQCaM8WZsnkLhQLz8/MsLCyQyWSwWq2EQiE6OjoIh8OvNZqLRnNcOHMisLS0xPLyMoVCgcbGRnp7ewkGgzidTh29R3MmOXMioJJ8lMtlmpubuXTpEk1NTS+Mg6/RnFbOlAjUZ5RVwSBVjj4tAJqzyJkSgYaGBiO5pNlsZmNjg4WFBdLp9FE3TaM5Ms6UCFitVuM4sMViIZ1Os7i4yOrqKtlslnK5/Nqz7Wo0R82ZEgGHw0FfXx+9vb04HA4jZ8Dy8rJxfFijOWucKRGw2Wx0dnbS3d2N3++nWq2yurrK/Pw8i4uLZDKZpzL4ajSnnTMlAiovXEtLC319fQQCAYrFIhMTE9y/f59EIkGhUDjqZmo0r5UzJQImkwmHw0EoFGJgYICWlhYqlQpjY2N88sknrKyskM/ntSWgOVOcKRFQySP8fj/Xrl2ju7sbq9VKKpVicnKSv/7rv+b73/8+0WiUTCZz1M3VaF4LZzLQqN/v5/r16wwPD2Oz2chms8zMzPBXf/VX5HI5mpubsVgsOpaA5kxwJkXA4XAQDoe5fPkyd+7cYXx8nEQiwdzcHA8fPqStrY1sNouU0sgIrNGcVs6kCFgsFjweD9evX6dYLPKXf/mXfPLJJ6TTaSYmJowoxDabjXPnzmkR0JxqzqQImEwmbDYb/f392O12otEoq6urzMzMsLa2RiKRMNJOW61WrFYrTqcTi8WitxZrTh2vLAJCiIts5xdQ9AH/K9AI/M9AYqf+X0sp//JV3+cwUB3ZZrMRDAZ5++23sVgs/O3f/q0Rdnx8fJyGhgYymQyZTMY4aGQ2m7UQaE4V+wkqMg7cABBCmIEl4M+Afwb8jpTyNw+igYeJygz89ttv09zczOrqKuvr66ysrDA5OUkkEqFUKmGxWIwEo2az+chzx2k0B8lBTQfeA6allPMnqWOotlosFoLBID/8wz+MxWLBbrczOztrpCf78MMPyWQyXLlyhXPnztHa2kpTUxNSSi0EmhPPQYnAF4E/rrn/S0KI/wm4C/zKcUtDVk9DQwPNzc288847OJ1OSqUShUKBWCzG7OwsyWSSmZkZFhcX+dznPofNZqOxsRGTyaStAs2JZ98iIISwAv8d8NWdqt8F/j3biUn+PfBbwD/f43VHlnykrh0A2O12bDYb58+fJ5lMUigUqFQqxGIxNjc3yefzWK1WHA4HmUyGZDJJOBymsbERh8OhoxJpTiwHYQn8I+C+lHIFQN0CCCF+D/gve73oKJOPPI9gMMgbb7yB1WrF5/Px4YcfMjY2Ri6XY3p6mkwmw8LCAjMzM7z99ttcvnz5qYSk2irQnCQOQgR+jpqpgEo8snP3p9jORXAiEELg9XrxeDyYzWYcDocRZ2BpaYlkMsno6Cjr6+tsbGwY+Ql6e3sJBAJGnEItApqTxH5TkzuBfwD8i5rq/yiEuMH2dGCu7rETg9/v58KFC5hMJnp6ehgaGuLRo0dMTEywtbXF9PQ0AJFIhMHBQW7cuMH58+dxu906cYnmRLHfNGRZIFBX90/21aJjgBCCxsZGfD4fXV1dXLx4kcbGRsrlMrFYzAhLtrS0xPT0NOvr69hstl37CGqzF2vLQHOcOZM7Bj8NJpMJr9fL1atXMZvNtLa2Mjo6yuTkJPF4nM3NTcbGtvOwRiIRLly4QH9/P52dnfh8Pp3PUHPs0SLwAsxmMz6fj4sXL9LS0sL58+f54IMPEEJQKBSM7caxWIyJiQk+85nPsLW1RUNDgxHEpNZxqK0CzXFDi8BzUB22oaEBj8eDzWYznIVCCPx+P+Pj4ywtLZFKpZibm6OhoYFyuczGxgbnzp2js7OTtrY2vF6vznGoOZZoEfgUKDG4cuUKzc3NdHd309bWxkcffcTU1BSpVIrZ2VnW19eZnJxkYGCAW7ducfv2bbq7u/F4PFgsFr2cqDlWaBF4SYQQmM1mzGYzLS0t+P1+PB4PVquVSqWCw+FgYWGBtbU1lpeXDX+BSm6yublJW1sbgUDAeJ1eRdAcB7QIvCJms5lAIMDg4CB+v5+BgQFGRkZ4+PAhk5OTpFIplpaWKJVKzM/Pc+7cOQYHB7l+/ToXLlygsbERq9V61B9Do9Ei8CoIIYxsRl6vl7a2Njo6OggEAgghkFIa6c9nZ2eZnp5mYWGBzc1NpJSYTCY6Ojrw+Xw4HA7DcainBpqjQIvAAdDQ0EBTUxOXLl3C4XBw7tw5JiYmePLkCdPT0yQSCZLJJCMjI6TTacbHxxkYGDBKU1OTjl6kOTK0COwT5SvweDzY7XYjp0FHR4fhCFR5D6PRKPF4nOnpaWNFwWQy0dnZaQiB8hXUJk7VaA4TLQIHgJoeNDQ0YLPZaGjY/rdarVaam5vp6elhamqKmZkZVldXicViCCEoFoskEgn6+/vp6+ujp6eHcDiM2+02/oZGc9joX9oBI4TA4XDQ3t5OY2Mj3d3dnD9/ngcPHmCz2ZiYmGB1dZVEIkEqlWJ8fJxz585x7do13njjDaSUhEIhQwjUNmRtFWgOCy0CB0ytVeB0OnG73UaQUpvNRigUYnZ2lkgkwtraGqurqxSLRQqFArlcjng8Tk9PDx0dHYTDYTwej/YXaA4VLQKHjNVqJRQK4XQ66e7uZnBwkJGRET766CNGRkbIZrNGavT5+Xnu37/PpUuXuHXrFm+++SZdXV1aBDSHihaBQ6TeKggEAkYyE5PJhNvtJhAIsLKyQjKZNEKfZzIZisUipVKJc+fO0dXVhd/vx+fz4XQ6tb9Ac6DoX9NrRAiBz+fj3Llz+Hw+Lly4wOzsLI8ePWJoaIiZmRk2NzeJRCLkcjlmZmbo7e3l/Pnz3Lhxg4GBAdrb23G5XNpHoDkwXigCQog/AH4SiEspr+7UNbGdc6CH7cAhP6uCiQohvgr8PFAB/qWU8r8eSstPIEII7HY7wWCQpqYmWltbaWtrM7YRu1wuFhcXyWQyu7Yfx+Nxw2eQTqcJBoN4PB6cTic2m8342xrNq/AylsD/DfyfwP9TU/cV4G+klL8hhPjKzv1fFUJcZjvy8BWgDfhrIcQFKWXlYJt9MlFefpPJZMQZMJvNRrTjixcvMjU1xdjYGDMzM8zPzxOPxykWi+RyOebn5+nt7eXChQsMDAzQ2dlJKBTSZxA0++KFIiClfF8I0VNX/QXgR3eu/xD4W+BXd+r/REpZAGaFEFPAHeDvDqi9pwqr1UpjYyMul4vOzk7Onz9Pf38/jY2N2Gw2yuUymUyGVCrFyMgI09PTxtHlfD5PsVgEthOs2u1244Sitgo0n4ZX9Qm0qGCiUsqoECK0U98OfFTzvMhOnWYPTCYTJpPJ2GRkMpmMk4qhUIj+/n5mZ2eZn59naWmJjY0NKpUK1WqVbDbL3Nwc/f399Pb20tXVRWtrK06nU4uA5lNx0I7BvX59e4YTPy55B44LQghcLpex9fjq1ausrq7ygx/8gA8//JB8Pk8qlSKZTJJKpZieniYYDNLd3c2P/MiP8NnPfha3243NZtObizSfilcVgRUVWlwIEQbiO/URoLPmeR3A8l5/4LjmHXgRUkrK5TKFQoGtrS0ymQyFQoF8Pk+hUKBYLFIsFimXy5TLZSqVijF6qxOEyg+gRn2bzYbdbsftduNyuQwxaGtr49q1a5jNZoLBIFNTUywvLxOLxVhdXWV1dZVyuYzD4WBra4vFxUW6urpoa2sjFArh8/kwm80v7TOQUiKlpFqtGkXVqfra+7X1wDMfV9QHXq0Vqhc9pnwp6v+317UWvlfjVUXgm8CXgN/Yuf2Lmvr/JIT4bbYdg+eBj/fbyONGqVQinU4TiUSIxWJGHoKNjQ02NzdJp9Pk83mjqDX/arVKQ0MDdrvdyHhkt9vxer0EAgHC4TBtbW20tbXR3NxMY2Mj586do6WlhWvXrjE3N8e9e/e4f/8+2WyWbDZLIpHg3r17zMzMcO/ePa5evcrt27e5efOm8T6fVgTK5TKlUskQsWq1uuv2RXXqWhX4+45cW55Vv1dRzlRVVMp4dV9Hdn51XmaJ8I/ZdgI2CyEiwP/Gduf/uhDi54EF4GcApJSPhRBfB54AZeAXT+rKgBrBKpWKsTSnOvjm5ibr6+vGEp4SgFQqRSqVIpPJGJaBKuVymWq1amwfVhmRbTYbXq8Xv99PS0sLra2ttLS0EAwGaW5uxuPx4HK58Pl89PX1USwWjc4ajUZZX183nIdra2vk83lKpRKZTIZIJGKcblRp0upH+kqlYtRVKhXDgqm1Yuo7/Yuua+/XWwKvKgTKd2KxWIxbJQZqQ1ZtuPfaslddrRVRe7tXXe3z6y2P0zD1ErVf0lFx+/Zteffu3aNuxi5UR1PbeicmJpicnGR2dpZYLMba2hqZTIZsNrurs5dKpV2j6LOmA7VFiYLT6TTOG6jS3d1NT08Pvb29NDU1USgUiMfjzM7OMjExwcTEhJFBWUqJ1+slGAwSCoVobm6mqakJr9eL3W5HCEGlUqFUKhnTFnWt2l0qlXa193km/rMeq31dPfWd5nlTgvq653XkekvBbDbvur+XFVErxrX3a4uq3+v5J80CEULck1Lerq/XOwbrUD/cXC5HJpMhkUgwNzfH0NAQw8PDPHnyhGg0SjKZ3DWvrx8x1A+k9kcvpXxqpCyXyxSLRTY2NnZ1PLVicO7cOQYGBkilUvT19dHY2IjT6aS9vZ3NzU3i8Th2ux2AQqHAysoKiUSC8fFxnE4nTU1N+P1+nE4nJpPJyLhcLBZ3WSvqEFOpVHqq8+71A3/WHL72di9/wF7/65ep38vHUP/3azu46qS1nVgVNU2y2+3G8mrtdf3tXnXqb6nv/XkWzLPqlFX4afw2h4EWgTpUR11YWGB0dJTHjx8zNTXF0tISsViMlZUVtra2AHA6nfh8Pnw+n7GDz+Vy4XA4jB+eGn0AyuUy2WzWcCiqsrW1ZZRcLrfLh6DiFCYSCR4+fIjD4UBKaZw4VFaJer5qv1pGVNaMimeohKfW9FfXSqDqqR+FnzUq7/WcWgGs/R+r+y97rT5TbZuVv0GhHi8UCoYw1wu1ul87taidXtTXvei6/rV7/Y1nWSNNTU10dXXh9Xp3WRWvGy0CdVSrVUqlEnNzc7z//vt85zvfYXR0dJeJDBhzeRVfMBQKEQgECAQC+P1+w9PvcDiMU4D5fJ5kMkkikTC8+6urq8bWYGXSK8ecSo2+srLCyMh2Xtf6TlU/OiqklIbJv7m5ua//yV5TmNrO9LwOp0a/F60wPOuxWmFTVkyhUNi1KqF4lojV8qKO9rzH91qxUJZFraXwLIui9jG3201PT49xKOwoM1VpEdihtvM/fPiQTz75hIcPHxKPx41EI83NzYTDYcPEVqO/3W43goXm83lisdguc79SqRgdMpPJkE6nDWtga2uLdDpt1OXz+ad+3M/q6MpZpsRGmalms9kYEdPptOG3UH9DCIHT6cTv99Pa2kpzczM+n88wTfdygO01F38Zi0C9X/3IXv+5XnS9l+OyfvWi/rq+1FtAtUX5cZ5Vr25rVzxU+9T3nM/nyWQye1oAtVahxWLB4XCQy+UYGBigpaUFKaW2BI6aarVKoVBgamqKP//zP2d0dJT5+XlyuRwWi4VAIMCVK1e4efMmFy5coL29HSmlsYknlUoZS4QbGxskk8ldt6oj1v9w93IgPsupVo8aiRobG3dZIDabzbA6otGoIXBqlFQbkzo7O7lx4waXLl2iq6vLEAKbzWaYtPXzWvX6T3Nby6fxA9TX11sIyjqodXQq30bt/drHa5duc7ncruva+/V16lpt1VYoYSiVSi/tCxBCGGHo3nvvPcrl8gu/68NEi8AO2WyW6elpRkZGePLkCUtLS2SzWWP5LhQK4XA4KBaLxiadUqlENps1Or8q9fc3NzfJ5XKUy2XDIdTQ0GCYknvNSWvnlWrULZfLbG1tsb6+TiKR2NURhNjOpNzT00NrayuVSoWtrS1WVlaMoqIeb21tUS6XyeVyJJNJ1tbWDOsmGAwaYlLr9Dpu1I7AtSsb9fdrS71Q1Jb6Jd29itrz8TJ7Jfa6rb1Wq0JqJeMoOZMisNeok06nefDgAUNDQ0SjUfL5PA6Hg87OTlpbW3E4HOTzeUZGRrh///6uZbVnjT6qrnYUtlqtTy0DejwePB6P4UdQdbVmvslkIpPJMD8/z/DwMPfv3yedTlMqldjc3DRiEvb09HDr1i3sdrshBIlEgpmZGYaGhhgZGWF2dpZMJsPi4iLpdJqlpSUWFxe5deuWcaS51pxXHDcxUOJoNpuxWq1P7X+oXW2pr6/vtGpEf9G0Qk0TnmV1PK8oEVF7Pbq6uozv7Sj/t2dSBODvfQBqWWxxcZGhoSFGR0fZ2NgwTDa1di+lJJlMEo/HjRG1XC4jpTT8AfUOMvX62nqHw4HH48Hr9RrJS9QKQ22dKkoIzGYz6XSaiYkJGhoa2NraYnl52YhRmEqlyOVyhkUQDofxer1UKhU2Njbo6Ogw9goIIVhZWSGbzRqp09LptJFFWTni3G634Wc4bgKg2qP+t6+D2iVedbz7eZZEbV39dblcpru729jafZScSRFQX2I8HicajZJIJBgbG+PRo0fEYjFj1FZn+NfW1oDtdXi1QUiZ9jabbdeorkbx+muXy4XT6TRG9toNKfX3a4uaIphMJmPnn4pmfP/+fYaGhpibmzMEwm63Uy6XeffddxkYGDBCm3V2dmIymWhtbeXKlSuMjY0ZG42y2SzRaJS7d+8Sj8fp7+/nwoULXL58me7ublpbW4/Ue32cUNaRyg9hs9n2dDrW+3vq66vVqiH+WgQOmb0cS8pLv7y8zJMnT5iamuLx48c8fvyYRCKxa8PQ0tISsPfykMViweVyEQgEaG5uNnbpBYPBp0ogEKCxsdHoyK86sqpsyDdu3KCxsZF8Ps/GxgZzc3NMTk6STqfJ5XJ0dHTQ29uL2Ww2rJmOjg6uXLnC8vIyP/jBD2hoaCCVShlThng8zvDwMF1dXQwODlIul7HZbDQ1NT0V+vy4WQavg/otzCqq00nnTIhAuVxmbW2NWCxGNBo11t6j0SgrKytGQpBMJmMIgNr119TUhM/ne2q0V3N3dVtfX/8cl8v1VFryV0V17IGBAUqlEsFgkIcPHzI2NkYulzP2OJjNZm7cuEE4HDbmnRaLBb/fz6VLlzCbzXR2djI9Pc3s7CyLi4tGPoTp6WlMJhOLi4sMDw/T19dHd3c3wWAQt9sNnE0hOI2cahFQczg1oj969Ijh4WHGx8eZmZlha2tr17HfYrForGmrOX1ra6thEodCIeNwj4oT6HK5dnnx6zfW1B88OYgDJ2pKcPHiRUKhEOFwGJfLxdraGgsLCywuLvLhhx9iMpkMb7+a61ssFsMiaW9v5+rVq4yNjfHRRx9hNpuNXYtzc3PEYjHGxsbo7Ozkrbfe4p133jHWuOv3AWhOLqdWBNSIXjvfHxkZYWJigvn5eaLRKEJsZwuqDQOeSqWMzT5SSmw2G4FAgIsXL9Ld3U0oFNq1WajW2fY6qDVJ1Z4AtXSo0pstLCywvLzM8PAwPT09OJ1O+vv7jSjFtRtWlIhJKXG5XIRCISKRiGEhRSIRYwNMuVxmZWWFvr4+2tvbjaVELQInm1MrArAtBAsLC3zrW9/iwYMHzM/Pk0wmyWazVCoVGhsbCYVChpnb0NDA7Ows6+vrxtJeuVzGbrfT3d3NuXPnCAQCxtmA2kNCR4UKUnr16lWjzevr68a+hw8++ACfz2fsc6h1QilnY1tbm5Ec5caNGwwNDfHw4UOGh4eNWAnDw8MsLy8zNjbG5cuX+exnP8vFixeNTS/aKji5nHoRiMfjhhd9dXXVONPvcrno7u7m8uXL9PX10dLSQkNDA16v1/AVqJUAtfMvn89jNpuNk2VHjRIgt9tNR0eH8XkjkYiR8/DJkyf09fVx5coVw1ehOqrJZDJOwzU2NtLS0kJ3d7fhvzCbzczOzrK2tsbm5qZhHWxsbGA2m43lLuU3UWf7NSeLU/uNqd1kmUzGyOqjpghqy+y7777L5z73OZqamoyjtn6/n2KxyEcffWQsGd67d4+NjQ0ikQjvvvsu58+fp62t7Yg/4d+jliqDwSC3b9+mWCxSrVZ5+PAhGxsbTE5O8vjx412fcy8sFgter5eBgQG8Xi8XL15kYmKCR48e8fjxY6anp0mlUkxOTpLNZpmamuLixYvcunWLa9euEQgEtAicQF41+cj/Dvy3QBGYBv6ZlHJDbIcmHwXGd17+kZTyFw6j4S9D7Sk0tV4upSQQCNDb28sbb7zB5z//+V1r4C6Xi3K5TCqVYmlpiUwmw/T0NIuLi1SrVfr6+o6VAAC7vP5Xr17FYrEwNTXF9PQ0mUyG2dlZHj9+zIULFwiHw3vuBgQMB6fL5aKjo4OrV6/S29uL2+2mWq0aB50SiQRLS0tGJORyuYzT6aRcLhMMBnedkdfTg+PPqyYf+TbwVSllWQjxH4Cvsp13AGBaSnnjIBv5KqjtpN3d3bz33ntMTk4aIbtbWlqM01v1P1LVkfL5PD6fz0gPlslkDLP5OEwF9kKIv89wdOPGDWKxGPfv3ycWizE6Osr169fp7Ow0OuqL/pbNZiMcDnPnzh2ampoYGBhgdHSUyclJ5ubmyGazLC4u8v777xONRhkYGODSpUvGqoUOf34yeKXkI1LKb9Xc/Qj4Hw64XftGiUB7ezvvvPMOLS0trKysUKlUDEsgFAo99SP1eDz09fVhtVppaWnBYrFQrVaJxWLGab3jmiVYnSoMBAJcvXqVWCzGzMwMs7OzzMzMMDU1xcDAgHFa8HmocF3BYBCfz0dHRweXL182BKRYLJJIJAyn4dTUFIuLi6yvryOEoFqtEgwGcTgcu+IKaI4fBzGB++ds5yVU9AohHgAp4N9KKT/Y60XikPMOqB+d3+/n4sWLNDY2GpaA2+2mubmZ5ubmp36Yar1fbbDJZrP4/X6Wl5e5cuUKoVDoWFsCatmzs7OTgYEBurq6iMfjbG5uMj4+bvgzPB7PS/2t2u3LTqfTOFgVCoWYmppiZmbGOKVotVqN6EkLCwucP3+e3t5eY9vxUW+P1ezNvkRACPFv2I4q/Ec7VVGgS0q5JoT4DPDnQogrUspU/Wvla8o7oLbLNjc3G/Hz1JZPtca/Fw6Hg5aWFt566y36+/uJRqPGluDjKgIKtWyoMhovLi4Sj8cZHx+nr6+Pz3zmM8ZU6GVHZ4vFgsfj4dKlS7S2tnLx4kWGh4f5u7/7O8NpmEwmGRkZIRKJMDU1xa1bt4ytx7VRj7VFcLx4ZREQQnyJbYfhe3LH7S63cxAWdq7vCSGmgQvAkYUSVktgLxr5aqk986/OBigzWJ0KPM6YTCacTifhcJiBgQEmJyeJx+MsLy8zPz/PxsYGpVLpU8W1q42M7PV6aWpqMlKnqdOP0WiUtbU1IxiLOmSVy+Xo7e0lHA7j8/mO/OisZjevJAJCiH/ItiPwR6SU2Zr6ILAupawIIfrYTj4ycyAtPQLUFmJ1huBZXvXjiDpSfPXqVR4/fszDhw+No9DJZJJcLvfKUW7VkeP+/n78fj/nz5/n2rVrfPjhhwwNDbGwsMDq6qqxN2Nqaop3332XO3fuGEFUNMeHV00+8lXABnx7R9HVUuC7wL8TQpSBCvALUsr1Q2r7a0EIYSydnSRUHMGOjg46OjrweDzGRh/lI1BOu0+LOkKrtlQHAgFCoZARKt3hcBCNRo1kLclk0uj85XKZjo4OvF6vEQ9Rc7S8zOrAz+1R/fvPeO43gG/st1Gag0GdNmxubqalpYV4PE4ulzN8BIFA4ECOwzqdTtra2njnnXeMVQSVLm1tbc1IrLq+vs7i4iJvvvkmV69eJRAIaBE4Bpys4U3z0qhDRioeQDgcNgKIKBE4d+7cgbyXWj1wuVy0tLQQCoWwWq0Ui0VmZmaIxWJEIhEjdoE6Wdnb20tzczNOp9OwSLSv4PWjReAUo6YyTU1NtLe34/F4SKVSRCIR4vH4gUS5rT3VWO9j8Hg8PHz4kEePHjE5OUkymWRycpJqtcr6+jo3btzg2rVr9Pb24vF4Toy/5bShReCUYzab8fv9tLe34/V6d2UtOoxQ12qzkpoi+P1+7HY7xWKR2dlZkskkDx48YGVlhXQ6jZQSq9VKe3u7YRHojUWvFy0Cpxyz2WwkKVX7+1XY8Rdl6/m01IZdUyHVL1++bGw6UhZBNBolEolgt9upVqtsbW1x/fp1+vv7aWpqMlKmaV4PWgROOSr4iJp7qwAk2Wz2qUxHB4nq+N3d3UbOvfb2diN0+srKCqOjo6ysrLCwsEAulzNCsWsReL1oETjlqM7ocrmM2Pwq9LWKenvQ5ndtMFKHw2FE5i0UCuRyOVwuF2NjY0QiEZLJJNPT0/zgBz/AZrNRKpXo6emhqalJRzh+TWgROOWoQ0VqnV7FXVRxFVU2nMPEZDLh8Xi4cOGCEQCltbWV7373u8zMzLC2tsbHH39MPp9HSmk8X4vA60GLwCmnNkOPcrrVJl6pVCqHKgLKKlDLiCo0mxCCfD6P1WplYWGBZDLJ6OiocVTbZrMZm4pU+7Sz8HDQInAGUMeClcNO5S9UmXA+zRmCV6E2W5DdbqelpYXBwUGcTieBQIDvfe97jI6OkkwmuXv3rpEC7K233mJgYEAHJzlktAicAdQavgoIqqYDKtz6y2RAPijMZjM+n8/YyWiz2Yy07LOzs8zNzRlTFHUwSQVJ1WJwOGgROOWoQ1C191Xdy6ZAP+j2qGVEt9tNX18f77zzjrG78fHjx6ytrfHo0SPsdjulUok333yTzs5OnE6n8Tc0B4cWgTNE/SqASst2VG2x2Wz09PTg9Xqx2+0UCgUikQiRSMSwDnK5nJHwRcUj0BwsWgROOSoNW+2yoDoe7XQ6jyzjcG3Yc5fLRW9vL3fu3KFQKHD//n2mpqbY3NxkcnKS7373u0gp+exnP4vX69XTggNGi8ApR4mASo1dm01Z5Rc46g5ls9no6uoyTj2q1Onr6+tMT08bW4uvXLliCJfm4NAicMqRUlIoFMhms0auRRVa7TgkC1GHj1wuF+FwmHK5zPr6Oqurqzx69IhEIsH8/Dyjo6M8efIEk8lEe3v7kQvXaeKFx7aEEH8ghIgLIUZq6n5NCLEkhHi4U36i5rGvCiGmhBDjQogfP6yGa16OarVKPp83kq+qDld7fPeoUb4Ku91OOBzm9u3b/ORP/iTXr1/H4XCQzWaZm5vj+9//PrOzs0fmxzitvGreAYDfkVL+Zm2FEOIy8EXgCtAG/LUQ4oKU8mBPqmheGikl+XyedDpNoVAw4ibWhgI/LqgEKspfEYlEmJmZYXp6mmg0yv379+nt7eXatWvHZipzGnihJSClfB942RBhXwD+REpZkFLOAlPAnX20T7NPqtUq2WzWyKXY0NCA2+020pEdp2O7qi1Wq5WmpiYGBwd56623CAaDZDIZJiYmGB0dNdKhaQ6G/URx+CUhxPDOdMG/U9cOLNY8J7JT9xRCiC8LIe4KIe4mEol9NEPzPKrVKplMZpcIeDye5+YkPGpUNKRr165x584dQqGQkftwbGyMyclJNjc3gaNd5jwtvOqv4HeBfuAG27kGfmunfq8hZc9vSEr5NSnlbSnl7WAw+IrN0DwPtTKwtrbGwsICm5ubRmqx4548VAiB1+ulp6eHwcFBzp07h81mY2FhgQ8++MBIg3bQMRHOIq8kAlLKFSllRUpZBX6Pvzf5I0BnzVM7gOX9NVHzKqjRUYnA4uIim5ub2O122tvbaW5uPtYiYDKZaGxspLu7m+vXr+8Sge9973uGCBxmTISzwiuJgBAiXHP3pwC1cvBN4ItCCJsQopftvAMf76+JmldFBRBJJBIkEgny+byRcTgYDB7ro7pqa3FjY6OR6LSpqYlyuczKygqxWIxkMkmpVDrqpp54XjXvwI8KIW6wberPAf8CQEr5WAjxdeAJ2+nJflGvDBwdxWLRWHPf2NigXC7j8Xjo7OwkFAodaxFQOJ1OLly4wMLCAq2trWxtbVEoFFhbW2NlZcUIm6Z5dQ4078DO838d+PX9NEqzf9TSoBo1s9ksVqsVv99PKBTC5/OdiH34VquV1tZWQ7iWlpbI5XJsbGxoS+CAOJ7uYc2+kVKSTqeZnZ0lGo0ihKClpcUIPX6SEoOqjUTNzc243W4A8vk8mUxGOwYPgOPrGdK8MiqEWCqV2iUC4XCYzs7OExW6q3Y3YVNTE26327BytAgcDNoSOIVUKhU2NzdZWlpiZmaGlZUVzGYzXV1d9Pb2nsg5tNrfoNKm5XI5vUR4QGgROIWoQzjz8/PMz8+zvr6O1Wqlu7ubnp6eEykCKi26CpaqDkVpEdg/WgROGaqDzMzM8OTJEyMjcFtbG93d3UaewJNGqVQinU6Tz+cBvVPwINE+gVOEChmWyWQYGxtjeHiYTCZDIBCgr6+Pzs7OExfPX3X2XC5HPB4nnU4DGBmOjuvW55OEFoFThJSSTCZDNBplamqK+fl5KpUKra2tXLhwgba2NhwOx4lYGlRUKhWy2Syrq6ssLS2RSqUwmUw0NTURDAZPpFVz3NAyeoqoVCokEgkmJiYMX4DJZKKrq4tr164RCoVO3MhZLBaJx+OGfyOZTGKxWOjp6eHixYu4XK6jbuKJ52T9IjR7opYEc7kcS0tLjIyMsLS0RLFYpLGxkd7eXnp7e/H5fCdGBNThJxWKfHp6mpWVFaSUBAIBurq66OzsxG63H3VTTzwn4xeheSGlUolkMsmTJ0/4+OOPWVlZwev1MjAwwLlz52hsbDxxvoB8Pk8sFuPevXuMjIyQy+UIBoNcu3aNjo4OPB7PsT4EdVLQInDCqXUGLiwsMDo6ytjYGNlslmAwyMDAAL29vbjd7hOzS7BUKhm+jfHxcYaGhpiZmaFSqRAOh7l27RrhcNhISKLZH1pGTwGVSoV4PM69e/cYGxsjnU7jcrno7u7mypUrdHV1YbfbT4QAAGSzWZaWlrh37x7f//73GR0dZWNjg4aGBrq6urh16xYtLS0nRtSOO1oETjDKCtja2mJhYYFPPvmEqakppJSEQiEuXLjAhQsXaG1tPdZTAbXer3Ikrqys8OTJEz744AM+/PBDYrEYQghCoRD9/f1cuXKFpqamI2716UGLwAmnWCwyNzfHyMgI09PTbGxs4HQ6uXTpEjdv3qS1tRWHw3HUzXwhaqvz8vIyw8PDfPzxxwwNDRnnHjo7OxkcHGRwcJBQKKQdggeIFoETTi6X4/Hjx9y7d89YEQiFQty8eZM333zTGDGPs9kspaRUKrG6usr9+/f57ne/a1gA1WrVyGL80z/901y9ehWn03mi9jocd14mqMgfAD8JxKWUV3fq/hS4uPOURmBDSnlDCNEDjALjO499JKX8hYNutGa74ySTSWZnZxkZGWF8fJxMJkNzczNXr17l4sWLhMPhY+sLUKZ/Op0mkUgQj8eZnJzk7t27DA8Ps7S0ZJx3uHr1Km+++SaXL18mFAodu1DpJ51Xyjsgpfwf1bUQ4reAzZrnT0spbxxQ+zTPoFqtEo1GGR4e5tGjR8zPz1OtVunv7+edd96hp6fnWHvPK5WKEe/g4cOHDA0NMTExQSQSIZFIUKlUaG9v586dO/zYj/2YMQ1Qpwg1B8fLRBZ6f2eEfwqxLcc/C/w3B9wuzTNQpnMmk2Fqaop79+4xOztLPp8nHA5z6dIlBgcHaWlpOVZr6KrdKhvS+vo60WiUsbEx7t+/z/DwMJFIhHw+j91up6enh+vXr/PGG29w8+ZNuru7sdvtx1bUTjL7/ZX8MLAipZysqesVQjwAUsC/lVJ+sM/30NSRzWYNB9pHH33E6uoqXq+XK1eucO3aNXp6enC73cfGZFarGLlcjlgsZuQPmJiYYG5ujuXlZVZWVsjlcjgcDvr7+7lx4wZvvPEGg4ODtLa2GgJwXD7TaWK/IvBzwB/X3I8CXVLKNSHEZ4A/F0JckVI+lS5GCPFl4MsAXV1d+2zG6ac2hPj6+joTExOMjIwwNjaG2Wymu7ubGzducOnSJVpaWo7Ue646fblcplgsUiwWyefzrK6uMjMzw4MHD7h//z5PnjxhbW0N2D4VGAgEaG1t5caNG7z77rtcv37d2OOgLYDD45VFQAjRAPz3wGdUnZSyABR2ru8JIaaBC8Dd+tdLKb8GfA3g9u3b+mD4S1CpVMhkMkxPT/Od73yHyclJyuUy4XCYgYEBBgcH6erqOtIjtkoAVOqzWCxGNBpleXmZaDTK0tIS8/PzLCwssLq6CkAoFKK7u5v+/n56e3uN/Q1qKVCvBBwu+7EEPgeMSSkjqkIIEQTWpZQVIUQf23kHZvbZRs0O6kDN+Pg43/rWt1heXqahoYH29nauXLnC4OAgbW1tR95p1JHmpaUlHj16xNDQECMjI8zPz7OxsUE+n6dSqSClpLm5mZ6eHn7oh36IN998k6tXrxIIBLBarZjNZm0BvAZeKe+AlPL32c4+/Md1T38X+HdCiDJQAX5BSvmyyUw1e1C7m251dZXvf//73Lt3j7W1NcxmM62trdy8eZMbN27g9/tf2/KZapfasZhOp1lfXyeZTLK6umqM+gsLCywsLLC4uMj6+jrlchmfz0cwGKS9vZ3u7m7Onz/PpUuX6O/vJxAI4HK59Nz/NfKqeQeQUv7TPeq+AXxj/83S1KI86ysrK7z//vvcu3ePTCaD3++ns7OTN954g+vXr792Z6A67ruxscHCwgKTk5NMTk4yNjbG8vIy6+vrZLNZcrmcMfo7nU7a2tq4du0ab7zxBpcvX6ajowO/34/T6dQ7AY+A47OGpHkm1WqVSCTCyMgIT548IRaLYTKZ6Onp4Y033uD8+fOHnluwUCgYwT0zmQyZTIatrS22traMUX9ubo7Z2VlmZmZYW1sjn8/jdDpxu92Ew2F8Pp9h/qulzL6+Pvx+P1arVY/+R4QWgRNAqVQy4gREIhEKhQJut5vBwUHee+892traDnUaoGL8ra+vE4lEjLK8vMzS0hKbm5ukUimjpNNpisUiFouFlpYWent7DaefyiQUCARobm7G6/VisVj03P8I0SJwzCmVSmxubvLkyROGhoZIJBJYrVYjZNjNmzcPZBqgTPvaUiqVjJJIJIjFYszMzBhlbm6O+fl5isUi1WoVq9WKxWLB4XAQCATw+XycP3+egYEBLl++zMDAAF1dXbjdbr319xihReCYs7a2Zsy1FxcXKRaL9PT08N577zEwMIDT6dx3h6pWq0Zgko2NDdbW1lhbWzOSma6urhp1yWTScABubGxQKBQwm804nU5aWlpobW2lo6ODtrY22traaGlpoaWlhWAwSHNzMw6Hw2ivFoHjgRaBY876+jqTk5NMTEywuLiI2Wyms7OTz33uc1y6dOmljgm/KD6/ilGo5vezs7PMzs4yNzfHzMwM09PTJJNJUqmn9nwB4Ha78Xg8xmGfwcFBrl69yvnz558Z3VgLwPFBi8AxJxaL8ejRI5LJJC6Xi97eXi5fvkxraytOp/O5nUnF7Fc793K5nOHUU6V2Lq9G+VorYG1tjVQqRalUwmKx4PP58Pv9RmlsbDTm9y0tLYTDYTo6OozU53rEP/5oETjmxGIxRkZGDBG4dOkSly5dIhwO43K5XmqUV5uM1tbWSCQSxtHdlZUVlpeXjbK1tUWhUDD8ALV+ASEEVquVUChET08Pvb299PX10dfXR1tbm5EDwGq1YrPZjGstAMcfLQLHnFQqxfLyMtlsFqvVauzBn56eNjbVqK26lUqFSqVCuVw2zuvn83my2SzpdJqNjQ1jPq/m+EoUEomE0dkdDgdOpxO/34/D4cDhcODxePB6vXR0dNDe3k5HR4dRAoEAjY2NusOfULQIHHPy+Tybm5uUSiUqlQpzc3O43W6q1Sputxur1WqM2Pl8nkKhQD6fJ5fLsbW1xcbGBul02tiwo9b7VVH1xWIRs9mMy+UiGAwaTr7W1lbDzG9pacHr9eLxeHC5XDidTlwu17ENXKJ5ObQIHHNUNl6TyUSxWCQWi2E2m8nlcng8HqxWK6VSyZjzq5LNZkmlUqyvr7O1tUWxWEQIgdlsxmKxYDabaWhowGKx4Pf7sVgsOJ1OGhsbjXl97ajf1tZGOBw21vR1pz89aBE45rS1tXH9+nUePHhAJBJha2tr1/TAbDbvmgbUrvErq6BcLmMymXaZ9V6vF5/PR1NTk+HYU44+9bjH49lVGhoa9KaeU4gWgWNOW1sbN2/eJBqNEo/HKZVKhgd/L9QIXXurMvj6fD5CoZCxdq9G/K6uLnp6egiFQvj9fh3P/4yhReCYEw6HefPNNzGbzVy8eJFkMkk2m6VYLBoefJPJhMViMcx7VWw2mzFvd7vdxnp+bald8nM4HLrzn0G0CBxzwuEwXq+XYDDI5cuXjSO56nSe2qNvtVqx2+2GN1+Z/n6/3/DeOxwOYwqhfALqVl3r+f7ZQ4vAMUetucP2zrzm5mbS6bTh6S+VSoYFULtGr6wANb9XKwm6k2vqeZmgIp1shxtvBarA16SU/4cQogn4U6AHmAN+VkqZ3HnNV4GfZzuwyL+UUv7XQ2n9GUEIgdvtxmKx0NjYaCwXqj3/qmOrkdxkMhmjfe30QAuAZi9exhIoA78ipbwvhPAA94QQ3wb+KfA3UsrfEEJ8BfgK8KtCiMtsRx26ArQBfy2EuCClrBzORzjdqE5rs9mw2Wx4vd4jbpHmtPHC9R4pZVRKeX/nOs12hqF24AvAH+487Q+Bf7xz/QXgT6SUBSnlLDAF3Dngdms0mgPiUy367iQhuQn8AGiRUkZhWyiA0M7T2oHFmpdFduo0Gs0x5KVFQAjhZjt+4C/vlUeg9ql71D11ykUI8WUhxF0hxN1EIvGyzdBoNAfMS4mAEMLCtgD8kZTyP+9UrwghwjuPh4H4Tn0E6Kx5eQewXP83pZRfk1LellLeDgaDr9p+jUazT14oAjv5Bn8fGJVS/nbNQ98EvrRz/SXgL2rqvyiEsAkhetnOPfDxwTVZo9EcJC+zOvA28E+AR0KIhzt1/xr4DeDrQoifBxaAnwGQUj4WQnwdeML2ysIv6pUBjeb48jJ5B77H3vN8gPee8ZpfB359H+3SaDSvCX0kTKM542gR0GjOOFoENJozjhYBjeaMo0VAoznjaBHQaM44WgQ0mjOOFgGN5oyjRUCjOeNoEdBozjhaBDSaM44WAY3mjKNFQKM542gR0GjOOFoENJozjhYBjeaMo0VAoznjaBHQaM44QsqnooG//kYIkQAywOpRt2UfNHOy2w8n/zOc9PbD4X6GbinlU6G9j4UIAAgh7kopbx91O16Vk95+OPmf4aS3H47mM+jpgEZzxtEioNGccY6TCHztqBuwT056++Hkf4aT3n44gs9wbHwCGo3maDhOloBGozkCjlwEhBD/UAgxLoSYEkJ85ajb87IIIeaEEI+EEA+FEHd36pqEEN8WQkzu3PqPup0KIcQfCCHiQoiRmrpntlcI8dWd72RcCPHjR9Pq3TzjM/yaEGJp53t4KIT4iZrHjtVnEEJ0CiG+I4QYFUI8FkL8q536o/0epJRHVgAzMA30AVZgCLh8lG36FG2fA5rr6v4j8JWd668A/+Go21nTtneBW8DIi9oLXN75LmxA7853ZD6mn+HXgP9lj+ceu88AhIFbO9ceYGKnnUf6PRy1JXAHmJJSzkgpi8CfAF844jbthy8Af7hz/YfAPz66puxGSvk+sF5X/az2fgH4EyllQUo5C0yx/V0dKc/4DM/i2H0GKWVUSnl/5zoNjALtHPH3cNQi0A4s1tyP7NSdBCTwLSHEPSHEl3fqWqSUUdj+woHQkbXu5XhWe0/a9/JLQojhnemCMqWP9WcQQvQAN4EfcMTfw1GLwF7Zjk/KcsXbUspbwD8CflEI8e5RN+gAOUnfy+8C/cANIAr81k79sf0MQgg38A3gl6WUqec9dY+6A/8MRy0CEaCz5n4HsHxEbflUSCmXd27jwJ+xbaatCCHCADu38aNr4UvxrPaemO9FSrkipaxIKavA7/H35vKx/AxCCAvbAvBHUsr/vFN9pN/DUYvAJ8B5IUSvEMIKfBH45hG36YUIIVxCCI+6Bj4PjLDd9i/tPO1LwF8cTQtfmme195vAF4UQNiFEL3Ae+PgI2vdCVOfZ4afY/h7gGH4GIYQAfh8YlVL+ds1DR/s9HAOP70+w7SWdBv7NUbfnJdvcx7bXdgh4rNoNBIC/ASZ3bpuOuq01bf5jts3lEtsjzM8/r73Av9n5TsaBf3TU7X/OZ/h/gUfA8E6nCR/XzwC8w7Y5Pww83Ck/cdTfg94xqNGccY56OqDRaI4YLQIazRlHi4BGc8bRIqDRnHG0CGg0ZxwtAhrNGUeLgEZzxtEioNGccf5/04Elzjpw5+wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(train_x[2120])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "61f9b01a",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "import numpy as np\n",
    "from keras import layers\n",
    "from keras.layers import Input, Add, Dense , Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D, AveragePooling2D, MaxPooling2D\n",
    "from keras.models import Model, load_model\n",
    "\n",
    "from keras.preprocessing import image\n",
    "from keras.utils import layer_utils\n",
    "from keras.utils.data_utils import get_file\n",
    "from keras.applications.imagenet_utils import preprocess_input\n",
    "import pydot\n",
    "from IPython.display import SVG\n",
    "from keras.utils.vis_utils import model_to_dot\n",
    "from keras.utils import plot_model\n",
    "\n",
    "from keras.initializers import glorot_uniform\n",
    "import scipy.misc\n",
    "from matplotlib.pyplot import imshow\n",
    "\n",
    "from keras.initializers import glorot_uniform\n",
    "from keras.layers import Input, Add, Dense , Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D, AveragePooling2D, MaxPooling2D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e6f71e2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# def identity_block(X, f, filters):\n",
    "    \n",
    "#     # Retrieve Filters\n",
    "#     F1, F2, F3 = filters\n",
    "    \n",
    "#     X_shortcut = X\n",
    "    \n",
    "#     # First layer\n",
    "#     X = Conv2D(filters= F1, kernel_size = (1, 1), strides= (1,1), padding = 'valid')(X)\n",
    "#     X = BatchNormalization ( axis = 3)(X)\n",
    "#     X= Activation('relu')(X)\n",
    "        \n",
    "#     #Second Layer   \n",
    "#     X = Conv2D(filters= F2, kernel_size = (f, f), strides= (1,1), padding ='same')(X)\n",
    "#     X = BatchNormalization (axis = 3)(X)\n",
    "#     X = Activation('relu')(X)\n",
    "\n",
    "\n",
    "#     # Third Layer\n",
    "#     X = Conv2D(filters = F3, kernel_size = (1, 1), strides =(1,1), padding= 'valid' )(X)\n",
    "#     X = BatchNormalization(axis=3)(X)\n",
    "    \n",
    "#     # Final step: Add shortcut value to F(X), and pass it through a RELU activation\n",
    "#     K = Add()([X, X_shortcut ])\n",
    "#     X = Activation ('relu' )(X)\n",
    "#     return X\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3aea9ff0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# def convolutional_block (X, f, filters, s = 2):\n",
    "   \n",
    "#     #Retrieve Filters\n",
    "#     F1, F2, F3= filters\n",
    "    \n",
    "#     # Save the input value\n",
    "#     X_shortcut = X\n",
    "    \n",
    "    \n",
    "#     # First Layer\n",
    "#     X= Conv2D(F1, (1, 1), strides = (s,s))(X) # 1,1 is filter size\n",
    "#     X= BatchNormalization(axis = 3) (X) # normal izat ion on channels\n",
    "#     X= Activation ('relu') (X)\n",
    "\n",
    "#     # Second layer (f.f)-3*3 filter by default\n",
    "#     X= Conv2D(filters = F2, kernel_size = (f, f), strides = (1,1), padding= 'same')(X)\n",
    "#     X = BatchNormalization (axis = 3)(X)\n",
    "#     X= Activation ('relu')(X)\n",
    "\n",
    "#     #Third Layer\n",
    "#     X = Conv2D(filters = F3, kernel_size = (1, 1), strides = (1,1), padding = 'valid' )(X)\n",
    "#     X= BatchNormalization(axis = 3) (X)\n",
    "    \n",
    "#     #######SHORTCUT PATH########\n",
    "    \n",
    "\n",
    "#     X_shortcut = Conv2D(filters = F3, kernel_size = (1, 1), strides =(s,s), padding = 'valid' )(X_shortcut)\n",
    "#     X_shortcut = BatchNormalization(axis = 3)(X_shortcut)\n",
    "    \n",
    "#     # Final step: Add shortcut value here, and pass it through a RELU activation\n",
    "#     X = Add()([X, X_shortcut ])\n",
    "#     X= Activation ('relu')(X)\n",
    "    \n",
    "#     return X"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6996aff",
   "metadata": {},
   "source": [
    "# Defining ResNet50\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a7309758",
   "metadata": {},
   "outputs": [],
   "source": [
    "# def ResNet50(input_shape=(224, 224,3), classes=52) :\n",
    "    \n",
    "#     \"\"\"\n",
    "#     Implementation of the ResNet50 architecture:\n",
    "    \n",
    "#     CONV2D -> BATCHNORM -> RELU -> MAXPOOL -> CONVBLOCK -> IDBLOCK*2 -> CONVBLOCK -> IDBLOCK*3\n",
    "#     -> COVBLOCK -> IDBLOCK*5 -> CONVBLOCK -> IDBLOCK*2-> AVGPOOL > TOPLAYER\n",
    "    \n",
    "#     \"\"\"\n",
    "#     #Define the input with shape input_shape\n",
    "    \n",
    "#     X_input = Input(input_shape)\n",
    "    \n",
    "#     # Zero-Padding\n",
    "#     X = ZeroPadding2D((3, 3))(X_input) #3,3 padding\n",
    "   \n",
    "    \n",
    "#     #stage 1\n",
    "#     X = Conv2D(64, (7,7),strides=(2, 2))(X)\n",
    "#     X = BatchNormalization (axis = 3)(X)\n",
    "#     X = Activation ('relu')(X)\n",
    "#     X = MaxPooling2D((3, 3), strides=(2, 2))(X)\n",
    "    \n",
    "#     #stage 2\n",
    "#     X = convolutional_block(X, f=3, filters=[64, 64, 256], s=1)\n",
    "    \n",
    "#     # below 3 Lines are the conv Layers from convolutional_block function defined above\n",
    "#     #X = Conv2D(F1, (1, 1), strides = (s, s)) (X)\n",
    "#     #X = Conv2D(F2, kernel_size = (f, f), strides = (1,1), padding= 'same') (X)\n",
    "#     #X = Conv2D(F3, (1, 1), strides =(s, s), name= conv_name_base+ '2a' )(X)\n",
    "    \n",
    "               \n",
    "#     X = identity_block(X, 3, [64, 64, 256])\n",
    "#     #X= Conv2D(filters =F1, kernel_size =(1, 1), strides= (1, 1), padding= 'valid') (X)\n",
    "#     #X= Conv2D(filters= F2, kernel_size = (f, f), strides= (1,1), padding ='same')(X)\n",
    "#     #X= Conv2D(filters =F3, kernel size = (1, 1), strides =(1,1), padding='valid') (X)\n",
    "               \n",
    "#     X = identity_block(X, 3, [64, 64, 256])\n",
    "#     #X = Conv2D(filters= F1, kernel_size = (1, 1), strides = (1,1), padding='valid') (X)\n",
    "#     #X = Conv2D(filters =F2, kernel_size = (f, f), strides = (1,1), padding ='same')(X)\n",
    "#     #X = Conv2D(filters= F3, kernel_size = (1, 1), strides = (1,1), padding ='valid')(X)\n",
    "    \n",
    "    \n",
    "    \n",
    "#     # Stage 3\n",
    "#     X = convolutional_block(X, f = 3, filters = [128, 128, 512], s = 2)\n",
    "#     X = identity_block(X, 3, [128, 128, 512])\n",
    "#     X = identity_block(X, 3, [128, 128, 512])\n",
    "#     X = identity_block(X, 3, [128, 128, 512])\n",
    "   \n",
    "\n",
    "#     # stage 4\n",
    "#     X = convolutional_block (X, f = 3, filters= [256, 256, 1024], s = 2)\n",
    "#     X = identity_block(X, 3, [256, 256, 1024])\n",
    "#     X = identity_block(X, 3, [256, 256, 1024])\n",
    "#     X = identity_block(X, 3, [256, 256, 1024])\n",
    "#     X = identity_block(X, 3, [256, 256, 1024])\n",
    "#     X = identity_block(X, 3, [256, 256, 1024])\n",
    "   \n",
    "\n",
    "#     # stage 5\n",
    "#     X = convolutional_block (X, f = 3, filters =[512, 512, 2048], s = 2)\n",
    "#     X = identity_block(X, 3, [512, 512, 2048])\n",
    "#     X = identity_block(X, 3, [512, 512, 2048])\n",
    "    \n",
    "    \n",
    "#     # AVGPOOL\n",
    "#     X = AveragePooling2D((2,2), name=\"avg_pool\")(X)\n",
    "    \n",
    "#     #######END CODE HERE ######\n",
    "    \n",
    "#        #OUTPUT LAYER\n",
    "#     X = Flatten()(X)\n",
    "#     X= Dense(classes, activation = 'softmax', name = 'fc'+ str(classes) , kernel_initializer = glorot_uniform(seed=0))(X)\n",
    "    \n",
    "#     #Create Model\n",
    "    \n",
    "#     model = Model(inputs = X_input, outputs= X, name = 'ResNet50')\n",
    "    \n",
    "#     return model\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "67ada281",
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(train_x)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3462b88c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[1., 0., 0., ..., 0., 0., 0.],\n",
       "        [1., 0., 0., ..., 0., 0., 0.],\n",
       "        [1., 0., 0., ..., 0., 0., 0.],\n",
       "        ...,\n",
       "        [1., 0., 0., ..., 0., 0., 0.],\n",
       "        [1., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 1., 0., ..., 0., 0., 0.]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_y\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a86a1f80",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import tensorflow as tf\n",
    "# tf.keras.applications.ResNet50(include_top=True, classes=52, weights=None)\n",
    "# from scipy.sparse import csr_matrix\n",
    "# train_y.todesne()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5a6e1b6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# model = ResNet50(input_shape= (224, 224,3), classes =52)\n",
    "\n",
    "model= tf.keras.applications.ResNet50(include_top=True , classes=53, weights=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "daa67c91",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer= 'adam', loss = 'categorical_crossentropy', metrics= ['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "fb5ed849",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"resnet50\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " input_1 (InputLayer)           [(None, 224, 224, 3  0           []                               \n",
      "                                )]                                                                \n",
      "                                                                                                  \n",
      " conv1_pad (ZeroPadding2D)      (None, 230, 230, 3)  0           ['input_1[0][0]']                \n",
      "                                                                                                  \n",
      " conv1_conv (Conv2D)            (None, 112, 112, 64  9472        ['conv1_pad[0][0]']              \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv1_bn (BatchNormalization)  (None, 112, 112, 64  256         ['conv1_conv[0][0]']             \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv1_relu (Activation)        (None, 112, 112, 64  0           ['conv1_bn[0][0]']               \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " pool1_pad (ZeroPadding2D)      (None, 114, 114, 64  0           ['conv1_relu[0][0]']             \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " pool1_pool (MaxPooling2D)      (None, 56, 56, 64)   0           ['pool1_pad[0][0]']              \n",
      "                                                                                                  \n",
      " conv2_block1_1_conv (Conv2D)   (None, 56, 56, 64)   4160        ['pool1_pool[0][0]']             \n",
      "                                                                                                  \n",
      " conv2_block1_1_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block1_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block1_1_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block1_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block1_2_conv (Conv2D)   (None, 56, 56, 64)   36928       ['conv2_block1_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv2_block1_2_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block1_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block1_2_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block1_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block1_0_conv (Conv2D)   (None, 56, 56, 256)  16640       ['pool1_pool[0][0]']             \n",
      "                                                                                                  \n",
      " conv2_block1_3_conv (Conv2D)   (None, 56, 56, 256)  16640       ['conv2_block1_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv2_block1_0_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block1_0_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block1_3_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block1_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block1_add (Add)         (None, 56, 56, 256)  0           ['conv2_block1_0_bn[0][0]',      \n",
      "                                                                  'conv2_block1_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv2_block1_out (Activation)  (None, 56, 56, 256)  0           ['conv2_block1_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv2_block2_1_conv (Conv2D)   (None, 56, 56, 64)   16448       ['conv2_block1_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv2_block2_1_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block2_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block2_1_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block2_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block2_2_conv (Conv2D)   (None, 56, 56, 64)   36928       ['conv2_block2_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv2_block2_2_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block2_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block2_2_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block2_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block2_3_conv (Conv2D)   (None, 56, 56, 256)  16640       ['conv2_block2_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv2_block2_3_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block2_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block2_add (Add)         (None, 56, 56, 256)  0           ['conv2_block1_out[0][0]',       \n",
      "                                                                  'conv2_block2_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv2_block2_out (Activation)  (None, 56, 56, 256)  0           ['conv2_block2_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv2_block3_1_conv (Conv2D)   (None, 56, 56, 64)   16448       ['conv2_block2_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv2_block3_1_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block3_1_conv[0][0]']    \n",
      " ization)                                                                                         \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                                                  \n",
      " conv2_block3_1_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block3_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block3_2_conv (Conv2D)   (None, 56, 56, 64)   36928       ['conv2_block3_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv2_block3_2_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block3_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block3_2_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block3_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block3_3_conv (Conv2D)   (None, 56, 56, 256)  16640       ['conv2_block3_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv2_block3_3_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block3_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block3_add (Add)         (None, 56, 56, 256)  0           ['conv2_block2_out[0][0]',       \n",
      "                                                                  'conv2_block3_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv2_block3_out (Activation)  (None, 56, 56, 256)  0           ['conv2_block3_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block1_1_conv (Conv2D)   (None, 28, 28, 128)  32896       ['conv2_block3_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block1_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block1_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block1_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block1_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block1_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block1_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block1_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block1_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block1_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block1_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block1_0_conv (Conv2D)   (None, 28, 28, 512)  131584      ['conv2_block3_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block1_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block1_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block1_0_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block1_0_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block1_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block1_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block1_add (Add)         (None, 28, 28, 512)  0           ['conv3_block1_0_bn[0][0]',      \n",
      "                                                                  'conv3_block1_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv3_block1_out (Activation)  (None, 28, 28, 512)  0           ['conv3_block1_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block2_1_conv (Conv2D)   (None, 28, 28, 128)  65664       ['conv3_block1_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block2_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block2_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block2_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block2_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block2_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block2_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block2_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block2_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block2_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block2_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block2_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block2_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block2_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block2_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block2_add (Add)         (None, 28, 28, 512)  0           ['conv3_block1_out[0][0]',       \n",
      "                                                                  'conv3_block2_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv3_block2_out (Activation)  (None, 28, 28, 512)  0           ['conv3_block2_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block3_1_conv (Conv2D)   (None, 28, 28, 128)  65664       ['conv3_block2_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block3_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block3_1_conv[0][0]']    \n",
      " ization)                                                                                         \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                                                  \n",
      " conv3_block3_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block3_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block3_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block3_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block3_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block3_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block3_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block3_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block3_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block3_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block3_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block3_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block3_add (Add)         (None, 28, 28, 512)  0           ['conv3_block2_out[0][0]',       \n",
      "                                                                  'conv3_block3_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv3_block3_out (Activation)  (None, 28, 28, 512)  0           ['conv3_block3_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block4_1_conv (Conv2D)   (None, 28, 28, 128)  65664       ['conv3_block3_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block4_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block4_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block4_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block4_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block4_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block4_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block4_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block4_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block4_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block4_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block4_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block4_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block4_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block4_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block4_add (Add)         (None, 28, 28, 512)  0           ['conv3_block3_out[0][0]',       \n",
      "                                                                  'conv3_block4_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv3_block4_out (Activation)  (None, 28, 28, 512)  0           ['conv3_block4_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block1_1_conv (Conv2D)   (None, 14, 14, 256)  131328      ['conv3_block4_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block1_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block1_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block1_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block1_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block1_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block1_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv4_block1_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block1_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block1_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block1_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block1_0_conv (Conv2D)   (None, 14, 14, 1024  525312      ['conv3_block4_out[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block1_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block1_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block1_0_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block1_0_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block1_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block1_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block1_add (Add)         (None, 14, 14, 1024  0           ['conv4_block1_0_bn[0][0]',      \n",
      "                                )                                 'conv4_block1_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv4_block1_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block1_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block2_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block1_out[0][0]']       \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                                                  \n",
      " conv4_block2_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block2_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block2_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block2_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block2_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block2_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv4_block2_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block2_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block2_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block2_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block2_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block2_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block2_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block2_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block2_add (Add)         (None, 14, 14, 1024  0           ['conv4_block1_out[0][0]',       \n",
      "                                )                                 'conv4_block2_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv4_block2_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block2_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block3_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block2_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block3_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block3_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block3_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block3_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block3_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block3_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv4_block3_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block3_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block3_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block3_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block3_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block3_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block3_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block3_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block3_add (Add)         (None, 14, 14, 1024  0           ['conv4_block2_out[0][0]',       \n",
      "                                )                                 'conv4_block3_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv4_block3_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block3_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block4_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block3_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block4_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block4_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block4_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block4_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block4_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block4_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv4_block4_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block4_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block4_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block4_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block4_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block4_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block4_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block4_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block4_add (Add)         (None, 14, 14, 1024  0           ['conv4_block3_out[0][0]',       \n",
      "                                )                                 'conv4_block4_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv4_block4_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block4_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " conv4_block5_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block4_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block5_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block5_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block5_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block5_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block5_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block5_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv4_block5_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block5_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block5_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block5_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block5_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block5_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block5_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block5_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block5_add (Add)         (None, 14, 14, 1024  0           ['conv4_block4_out[0][0]',       \n",
      "                                )                                 'conv4_block5_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv4_block5_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block5_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block6_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block5_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block6_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block6_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block6_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block6_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block6_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block6_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv4_block6_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block6_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block6_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block6_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block6_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block6_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block6_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block6_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block6_add (Add)         (None, 14, 14, 1024  0           ['conv4_block5_out[0][0]',       \n",
      "                                )                                 'conv4_block6_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv4_block6_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block6_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv5_block1_1_conv (Conv2D)   (None, 7, 7, 512)    524800      ['conv4_block6_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv5_block1_1_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block1_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block1_1_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block1_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block1_2_conv (Conv2D)   (None, 7, 7, 512)    2359808     ['conv5_block1_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv5_block1_2_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block1_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block1_2_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block1_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block1_0_conv (Conv2D)   (None, 7, 7, 2048)   2099200     ['conv4_block6_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv5_block1_3_conv (Conv2D)   (None, 7, 7, 2048)   1050624     ['conv5_block1_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv5_block1_0_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block1_0_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block1_3_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block1_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block1_add (Add)         (None, 7, 7, 2048)   0           ['conv5_block1_0_bn[0][0]',      \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                                  'conv5_block1_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv5_block1_out (Activation)  (None, 7, 7, 2048)   0           ['conv5_block1_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv5_block2_1_conv (Conv2D)   (None, 7, 7, 512)    1049088     ['conv5_block1_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv5_block2_1_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block2_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block2_1_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block2_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block2_2_conv (Conv2D)   (None, 7, 7, 512)    2359808     ['conv5_block2_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv5_block2_2_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block2_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block2_2_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block2_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block2_3_conv (Conv2D)   (None, 7, 7, 2048)   1050624     ['conv5_block2_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv5_block2_3_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block2_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block2_add (Add)         (None, 7, 7, 2048)   0           ['conv5_block1_out[0][0]',       \n",
      "                                                                  'conv5_block2_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv5_block2_out (Activation)  (None, 7, 7, 2048)   0           ['conv5_block2_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv5_block3_1_conv (Conv2D)   (None, 7, 7, 512)    1049088     ['conv5_block2_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv5_block3_1_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block3_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block3_1_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block3_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block3_2_conv (Conv2D)   (None, 7, 7, 512)    2359808     ['conv5_block3_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv5_block3_2_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block3_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block3_2_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block3_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block3_3_conv (Conv2D)   (None, 7, 7, 2048)   1050624     ['conv5_block3_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv5_block3_3_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block3_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block3_add (Add)         (None, 7, 7, 2048)   0           ['conv5_block2_out[0][0]',       \n",
      "                                                                  'conv5_block3_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv5_block3_out (Activation)  (None, 7, 7, 2048)   0           ['conv5_block3_add[0][0]']       \n",
      "                                                                                                  \n",
      " avg_pool (GlobalAveragePooling  (None, 2048)        0           ['conv5_block3_out[0][0]']       \n",
      " 2D)                                                                                              \n",
      "                                                                                                  \n",
      " predictions (Dense)            (None, 53)           108597      ['avg_pool[0][0]']               \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 23,696,309\n",
      "Trainable params: 23,643,189\n",
      "Non-trainable params: 53,120\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b47f3ea9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# x_train = vectorizer.fit_transform(train_texts).todense()\n",
    "# x_val = vectorizer.transform(val_texts).todense()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b10f8181",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/6\n",
      "265/265 [==============================] - 2612s 10s/step - loss: 32.0467 - accuracy: 0.9326\n",
      "Epoch 2/6\n",
      "158/265 [================>.............] - ETA: 17:12 - loss: 79.6090 - accuracy: 0.9180"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-29-900eeaff9c7a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain_x\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrain_y\u001b[0m\u001b[1;33m,\u001b[0m  \u001b[0mepochs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m6\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[1;33m=\u001b[0m \u001b[1;36m48\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\keras\\utils\\traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     62\u001b[0m     \u001b[0mfiltered_tb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     63\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 64\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     65\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m  \u001b[1;31m# pylint: disable=broad-except\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     66\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[0;32m   1407\u001b[0m                 _r=1):\n\u001b[0;32m   1408\u001b[0m               \u001b[0mcallbacks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1409\u001b[1;33m               \u001b[0mtmp_logs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1410\u001b[0m               \u001b[1;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1411\u001b[0m                 \u001b[0mcontext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masync_wait\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\tensorflow\\python\\util\\traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    148\u001b[0m     \u001b[0mfiltered_tb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    149\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 150\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    151\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    152\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\tensorflow\\python\\eager\\def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    913\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    914\u001b[0m       \u001b[1;32mwith\u001b[0m \u001b[0mOptionalXlaContext\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_jit_compile\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 915\u001b[1;33m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    916\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    917\u001b[0m       \u001b[0mnew_tracing_count\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexperimental_get_tracing_count\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\tensorflow\\python\\eager\\def_function.py\u001b[0m in \u001b[0;36m_call\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    945\u001b[0m       \u001b[1;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    946\u001b[0m       \u001b[1;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 947\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# pylint: disable=not-callable\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    948\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    949\u001b[0m       \u001b[1;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\tensorflow\\python\\eager\\function.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   2451\u001b[0m       (graph_function,\n\u001b[0;32m   2452\u001b[0m        filtered_flat_args) = self._maybe_define_function(args, kwargs)\n\u001b[1;32m-> 2453\u001b[1;33m     return graph_function._call_flat(\n\u001b[0m\u001b[0;32m   2454\u001b[0m         filtered_flat_args, captured_inputs=graph_function.captured_inputs)  # pylint: disable=protected-access\n\u001b[0;32m   2455\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\tensorflow\\python\\eager\\function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[1;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[0;32m   1858\u001b[0m         and executing_eagerly):\n\u001b[0;32m   1859\u001b[0m       \u001b[1;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1860\u001b[1;33m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0m\u001b[0;32m   1861\u001b[0m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0;32m   1862\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\tensorflow\\python\\eager\\function.py\u001b[0m in \u001b[0;36mcall\u001b[1;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[0;32m    495\u001b[0m       \u001b[1;32mwith\u001b[0m \u001b[0m_InterpolateFunctionError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    496\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcancellation_manager\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 497\u001b[1;33m           outputs = execute.execute(\n\u001b[0m\u001b[0;32m    498\u001b[0m               \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msignature\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    499\u001b[0m               \u001b[0mnum_outputs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_num_outputs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\tensorflow\\python\\eager\\execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[1;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[0;32m     52\u001b[0m   \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     53\u001b[0m     \u001b[0mctx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 54\u001b[1;33m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0m\u001b[0;32m     55\u001b[0m                                         inputs, attrs, num_outputs)\n\u001b[0;32m     56\u001b[0m   \u001b[1;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "model.fit(train_x, train_y,  epochs=6, batch_size= 48)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3ee38a63",
   "metadata": {},
   "outputs": [],
   "source": [
    "# indices=Tensor(\"DeserializeSparse:0\", shape=(None, 2), dtype=int64)\n",
    "# values=Tensor(\"DeserializeSparse:1\", shape=(None,), dtype=float32)\n",
    "# dense_shape=Tensor(\"stack:0\", shape=(2,), dtype=int64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "a2808d91",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 5s 1s/step - loss: 362.4509 - accuracy: 0.6923\n",
      "Loss = 362.45086669921875\n",
      "Test Accuracy = 0.692307710647583\n"
     ]
    }
   ],
   "source": [
    "# predictions = model.evaluate(test_x,test_y)\n",
    "# print(\"Loss = \" + str(predictions[0]))\n",
    "# print(\"Test Accuracy = \" + str(predictions[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8332b58b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5567abaa",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "96a91ba4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "43415fe4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "48c820de",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "81a3735b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4194a11f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "903b5d27",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "45abf4da",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9694a37b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "07e6b583",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "83b0ef8c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e0874594",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6f9927ac",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "604b8eff",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ffe3b9b5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3f9bcf39",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f4c57ee5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aac75555",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5f97f6fb",
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}