Notebooks/tiny_images_1_index.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "file_name='tiny_index.mat'\n",
    "from scipy.io import loadmat\n",
    "import numpy as np\n",
    "x = loadmat(file_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['__header__', '__version__', '__globals__', 'hash_key', 'word', 'num_imgs', 'hash', 'offset'])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(75846, 79302017)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(x['num_imgs'][0]), x['num_imgs'][0].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "75846"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list_classes=[]\n",
    "for i in range(len(x['num_imgs'][0])):\n",
    "    list_classes.append(x['word'][0][i][0])\n",
    "len(list_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df_classes_tiny_images=pd.DataFrame(columns=['class_ind','class_name','n_images'])\n",
    "df_classes_tiny_images['class_ind']=np.arange(len(list_classes))\n",
    "df_classes_tiny_images['class_name']=list_classes\n",
    "df_classes_tiny_images['n_images']=x['num_imgs'][0]\n",
    "df_classes_tiny_images.to_csv('df_classes_tiny_images_3.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4MIs/qA1svjQVSjoIeAOwa+lawvZjwGPl/mxJvwG2pGpJtHdVbUK1XXpERNSol2RxONXCvD9MtDJJuwP/DOxs+5G28snAfbafkLQ51UD2bbbvk7RQ0k7AL4EDgc9NNI6IiFgyvSSLOcAjXa8aQdKZVAcjbSBpHnAs1eyn1YGLygzYK8rMp1cB/yppEfAE8D7brcHxf6SaWbUm8MNyi4iIGvWSLJ4ArpH0E0pXEYDtD473Itv7dyj+yhjXng2cPcZzs4Cte4gzIiL6pJdkcU65RUTEkOrlPIvT6ggkIiIG15jJQtJM2/tKup4O01XLKuyIiBgC47UsDis/31BHIBERMbjGTBa255efd9QXTkREDKJeBrgjYsBNPfIHtdV1+ydfX1tdMTh62e4jIiKG3JjJQtLF5ee/1xdOREQMovG6oTaStDOwt6SzGLGpn+2r+hpZREQMjPGSxTHAkVSb931mxHMGXtOvoCIiYrCMNxvqO8B3JH3U9nE1xhQREQOmlxXcx0nam2qzP4Cf2v6f/oYVERGDpJczuP+NaoHejeV2WCmLiIgh0cs6i9cD29p+EkDSacDVVNuNR0TEEOh1ncV6bff/qh+BRETE4OqlZfFvwNXlPAtRjV2kVRERMUS6tixsnwnsBHy33F5m+6xe3lzSqZLulXRDW9n6ki6SdGv5OamUS9JJkuZKuk7S9m2vOahcf2s5wzsiImrUUzeU7fm2z7N9ru3fL8H7fw3YfUTZkcDFtqcBF5fHAHtQnb09DZgBnAxVcqE6knVH4KXAsa0EExER9ejr3lC2LwXuG1H8RqB1oNJpwJvayk935QpgPUkbAbsBF9m+z/b9wEWMTkAREdFHTWwkuGHb9ufzgWeV8o2Bu9qum1fKxiofRdIMSbMkzVqwYMEyDzwiYliNmywkrdQ+3tBn6lDmccpHF9qn2J5ue/rkyZOXaXAREcNs3GRR1lZcK2nKMqzzntK9RPl5bymfB2zadt0mwN3jlEdERE166YbaCJgj6WJJ57VuE6jzPKA1o+kg4Ny28gPLrKidgAdLN9UFwOskTSoD268rZRERUZNe1ln8y9K+uaQzgV2ADSTNo5rV9ElgpqSDgTuBt5bLzwf2BOYCjwDvArB9n6TjgCvLdf9qe+SgeURE9FEvGwleIum5wDTbP5a0FrByL29ue/8xntq1w7UGDhnjfU4FTu2lzoiIWPZ62UjwPcB3gC+Woo2Bc/oZVEREDJZexiwOAV4OPARg+1YWT3eNiIgh0EuyeMz2460HklZhjKmrERGxYuolWVwi6WhgTUmvBb4NfL+/YUVExCDpJVkcCSwArgfeSzVr6SP9DCoiIgZLL7OhniwHHv2SqvvpljJzKSIihkTXZCHp9cAXgN9Qbb2xmaT32v5hv4OLiIjB0MuivBOAV9ueCyBpC+AHQJJFRMSQ6GXM4t5WoihuY/F+ThERMQTGbFlI2qfcnSPpfGAm1ZjFW1m89UZERAyB8bqh9mq7fw+wc7m/AMhJdRERQ2TMZGH7XXUGEhERg6uX2VCbAR8AprZfb3vv/oUVERGDpJfZUOcAX6Fatf1kf8OJiIhB1Euy+LPtk/oeSUREDKxeksWJko4FLgQeaxXavqpvUUVExEDpJVm8CHgH8BoWd0O5PI6IiCHQS7J4M7B5+zblEyHpecC32oo2B44B1gPeQzU1F+Bo2+eX1xwFHAw8AXzQds7gjoioUS/J4lqqD/Jlsmrb9i3AtgCSVgZ+B3yP6sztz9r+dPv1krYC9gNeCDwH+LGkLW0/sSziiYiI7npJFhsCN0u6kqePWSyLqbO7Ar+xfYeksa55I3CW7ceA30qaC7wUuHwZ1B8RET3oJVkc28f69wPObHt8qKQDgVnAEbbvpzrz+4q2a+aVslEkzQBmAEyZMqUvAUdEDKOuGwnavqTTbaIVS1oN2Jvq5D2Ak4EtqLqo5lPtdgvVtuijwhoj1lNsT7c9ffLkyRMNMSIiil5WcC9k8YfzasCqwMO2151g3XsAV9m+B6D1s9T5JeB/ysN5wKZtr9sEuHuCdUdExBLopWXxDNvrltsawN8Bn18Gde9PWxeUpI3annszcEO5fx6wn6TVy9Yj04BfLYP6IyKiR72MWTyN7XMkHTmRSiWtBbyW6kzvlk9J2paqFXN76znbcyTNBG4EFgGHZCZURES9eumG2qft4UrAdMYYM+iV7UeAZ44oe8c41x8PHD+ROiMiYun10rJoP9diEdW3/jf2JZqIiBhIXZNFzrWIiIjxjlU9ZpzX2fZxfYgnIiIG0Hgti4c7lK1NtUfTM4Eki4iIITHesaqtRXFIegZwGNX+TWexeMFcREQMgXHHLCStDxwOHACcBmxftuCIiIghMt6YxX8A+wCnAC+y/afaooqIiIEy3gruI6i2BP8IcLekh8ptoaSH6gkvIiIGwXhjFl23AomIiOGQhBAREV0lWURERFdJFhER0VWSRUREdJVkERERXSVZREREV0kWERHRVWPJQtLtkq6XdI2kWaVsfUkXSbq1/JxUyiXpJElzJV0nafum4o6IGEZNtyxebXtb29PL4yOBi21PAy4ujwH2oDp7exowAzi59kgjIoZY08lipDdSbVhI+fmmtvLTXbkCWE/SRk0EGBExjJpMFgYulDRb0oxStqHt+QDl57NK+cbAXW2vnVfKnkbSDEmzJM1asGBBH0OPiBguvZzB3S8vt323pGcBF0m6eZxr1aHMowrsU6h2yWX69Omjno+IiKXTWMvC9t3l573A94CXAve0upfKz3vL5fOATdtevglwd33RRkQMt0aShaS1y+l7SFobeB1wA3AecFC57CDg3HL/PODAMitqJ+DBVndVRET0X1PdUBsC35PUiuEM2z+SdCUwU9LBwJ3AW8v15wN7AnOBR6iOd42IiJo0kixs3wZs06H8j8CuHcoNHFJDaBER0cGgTZ2NiIgBlGQRERFdJVlERERXSRYREdFVkkVERHSVZBEREV0lWURERFdJFhER0VWSRUREdJVkERERXSVZREREV0kWERHRVZJFRER0lWQRERFdJVlERERXSRYREdFV7clC0qaSfiLpJklzJB1Wyj8m6XeSrim3Pdtec5SkuZJukbRb3TFHRAy7Jk7KWwQcYfuqcg73bEkXlec+a/vT7RdL2grYD3gh8Bzgx5K2tP1ErVFHRAyx2lsWtufbvqrcXwjcBGw8zkveCJxl+zHbv6U6h/ul/Y80IiJaGh2zkDQV2A74ZSk6VNJ1kk6VNKmUbQzc1fayeYyRXCTNkDRL0qwFCxb0KeqIiOHTWLKQtA5wNvBPth8CTga2ALYF5gMntC7t8HJ3ek/bp9iebnv65MmT+xB1RMRwaiRZSFqVKlF80/Z3AWzfY/sJ208CX2JxV9M8YNO2l28C3F1nvBERw66J2VACvgLcZPszbeUbtV32ZuCGcv88YD9Jq0vaDJgG/KqueCMiopnZUC8H3gFcL+maUnY0sL+kbam6mG4H3gtge46kmcCNVDOpDslMqIiIetWeLGxfRudxiPPHec3xwPF9CyoiIsaVFdwREdFVkkVERHSVZBEREV0lWURERFdJFhER0VWSRUREdJVkERERXSVZREREV0kWERHRVZJFRER0lWQRERFdJVlERERXSRYREdFVkkVERHSVZBEREV0lWURERFdJFhER0dVykywk7S7pFklzJR3ZdDwREcNkuUgWklYG/gvYA9iK6rzurZqNKiJieCwXyQJ4KTDX9m22HwfOAt7YcEwREUNDtpuOoStJbwF2t/0P5fE7gB1tHzriuhnAjPLwecAttQYKGwB/qLnOTgYlDhicWAYlDkgsnQxKHDA4sTQRx3NtT+70xCo1B7K01KFsVJazfQpwSv/D6UzSLNvTm6p/0OKAwYllUOKAxDLIccDgxDIocbQsL91Q84BN2x5vAtzdUCwREUNneUkWVwLTJG0maTVgP+C8hmOKiBgay0U3lO1Fkg4FLgBWBk61PafhsDpprAtshEGJAwYnlkGJAxJLJ4MSBwxOLIMSB7CcDHBHRESzlpduqIiIaFCSRUREdJVkERERXSVZTICklST9TdNxxOCTtKak5w1AHKv3UhYxUpLFBNh+Ejih6TgAJG0o6SuSflgebyXp4AbiuLiXsmEiaS/gGuBH5fG2kpqa+n15j2V9J2ktSR+V9KXyeJqkNzQQx0qSbqi73kGNYyxJFhN3oaS/k9RplXmdvkY1tfg55fGvgX+qq3JJa0haH9hA0iRJ65fb1LaYaiVpH0m3SnpQ0kOSFkp6qIFQPka1v9kDALavAabWGYCkZ0vaAVhT0naSti+3XYC16oylzVeBx4CXlcfzgI/XHUT50netpCl11z2IcYxluVhnMeAOB9YGnpD0KNXWJLa9bs1xbGB7pqSj4Km1KU/UWP97qZLTc4DZLN6i5SGqHYOb8ClgL9s3NVR/yyLbDzb8fWI34J1Uux+cwNP/Pkc3FNMWtt8maX8A2482+KVrI2COpF8BD7cKbe89pHGMkmQxQbaf0XQMxcOSnknZM0vSTsCDdVVu+0TgREkfsP25uurt4p4BSBQAN0j6e2BlSdOADwK/qDMA26cBp0n6O9tn11n3OB6XtCaL/81uQdXSaMK/NFTvSIMSxyhZlDdB5ZvQAcBmto+TtCmwke1f1RzH9sDngK2BG4DJwFtsX1dnHCWWv6HqZnnqy4jt02usf59yd2fg2cA5tH0I2f5uXbGUeNYCPgy8rhRdAHzc9p/rjKPE8gngU7YfKI8nAUfY/kgDsbwW+AjVGTUXAi8H3mn7p3XHUuJ5LjDN9o/L32xl2wuHNY5RcSVZTIykk4EngdfYfkH5n+9C2y9pIJZVqLZmF3CL7b80EMPXgS2oBnRb3WC2/cEaY/jqOE/b9rtrjGVl4JO2P1RXneORdLXt7UaUXWV7+4bieSawE9W/2StsN7I1uKT3UB1vsL7tLUoL8Au2dx3GODpJN9TE7Wh7e0lXA9i+v2x2WAtJr7H9v23fplu2lFT7t2hgOrCVG/wWYvtdTdU9ku0nysDyoFhZ0uq2H4NqSi9Q69TZ0gpuN7/8nCJpiu2r6oynOIRqEsIvAWzfKulZQxzHKEkWE/eX8u2x1e86maqlUZedgf8F9urwnIG6k8UNVF0/87td2G+STgMOG9HlckKdLYvi6jJV9ts8fdCy7r8NwDeAi0vry8C7gdNqjmG86eYGXlNXIG0es/14a3y9tNKb+MIzKHGMkmQxcScB3wOeJel44C1U/bC1sH1s+Tko36Y3AG4ssznaxwmamM3x4laiKDHcL2m78V7QJ+sDf+TpH4JNJHJsf0rSdcDfUnX9HGf7gppjeHWd9fXoEklHU00tfi3wfuD7QxzHKBmzWAYkPR/Ylep/vovrnIEj6fDxnrf9mbpiAZC08xhxXFJnHCWWa4FdbN9fHq8PXGL7RXXHMkiaHkAdp+sUaKbFJWkl4GCqSQgCLrD9pWGNo5O0LCaofADdC5zZVrZqjYPLgzJ1F2gmKYzjBOAXkr5D9U1+X+ATdQchaROqmWovL3FcRtU9Nq+BWJ4aQKWaiLAx8AWqLzt1GbSuU4APlOnfT30wSzqslA1jHKOkZTFBkm6nOvL1fqpvAutR9dffC7zH9uzmoqufpIUs7mNdDVgVeLiBRYqteLai6v5ptfpubCCGi4AzgK+XorcDB9h+bQOxXEMZQG3NipJ0fVpbo2eEdZo5NixxdJKWxcT9CPheq99X0uuA3YGZwH8DO9YRxKB8ex25SFHSm6g+nGon6eu23wHc2KGsTpNtt0/n/Zqk2rZiGWFgBlBVbWD4d4xek/OvNcawP/D3wGZ6+n5d61KNMw1VHONJspi46bbf13pg+0JJn7B9uOrdzfOrVN9e31oev72U1f7ttZ3tcyQd2VD1L2x/UGatNTGN9Q+S3s7irsr9ae4DYJAGUM+l2mVgNs2t3P4FVU/ABjx9ltZCoM4FrYMSx5jSDTVBki4ELgbOKkVvo/qA3h24sq7FTpKusb1tt7Ia4mgftFyJat3FzrZfNsZL+hHDUVT7Ha0JPMLifZAeB06xfVRdsZR4pgCfp9owz1QfDIfZvqPOOEosowZQgS83sS5G0g22t6673k4kreopdnoAAAraSURBVA08avtJSVsCzwd+WPfC1kGJo5MkiwmStAFwLPAKqv/5LqPa3+VBYIrtuTXF8WOqnWfbv72+q4EVqO3dLYuA26k+oBfUGUeJ5d/qTgzLg7Jo9PlUiesW2483FMcpwOdsX99E/SNimQ28EpgEXAHMAh6xfcAwxtFJksUKYsS3V4Cf08C310FYCCfp+bZv7rBSGIC6VwiXhZrvYXTffN2LA5H0eqrZT7+h+nKzGfBe2z+sMYbrqRLVKsA04DaqbqjWjs0vriuWtpiuKjsxfABYs6xHaWyAu+k4OsmYxQRJ+j6jBwgfpPpG8EXXtFmc7TuBxrcxZjAWwh1ONT30BJ7+txHNrBA+F/gZ8GMW75fVlBOAV7davKp2ev0BUFuyAGo/4KgHkvQyqk1BW4eGNfH5OChxjDIQQSznbqPa4bXV/fM24B5gS6q50rXMvBmU2VDASpImjVgIV+u/M9szyt09qQZwX0H13+RnwMl1xlKsZfufG6i3k3tHdI3eRjXNuzbtrd3S+mv9fX7e0L5QUJ3FchTVzMY5kjYHfjLEcYySbqgJknSp7Vd1KpM0x/YLx3rtMo5jIObySzqQ6h97+0K4421/fdwX9ieWmVSH+3yzFO0PrGd735rj+DjwC9vn11nvGLGcDDyXamq3qWbP3ULVbVnr6mlJx5T6W3W+Cfi27dpPy2uLaW3bD3e/cjjiaJdkMUGSbgJ2K91ArbGDH9neqs6+xkGZDVXqbXwhXInjWtvbdCvrY/3tCxTXoeqXX1Qeu4mFihqs7dtvArZrddWq2gH3KtsvqCuGtlheBnwFWMf2FEnbUI3lvH8Y4+gk3VATdwRwmaT2AcP3lylwde7mOTBz+UtyaCRBjHC1pJ1sXwEgaUfKN+g6tBYoqjrj42fAz9zwyX0enA0noZoptwbQGtdbnWrgvQn/SXX07HkAtq+V9KrxX7JCxzFKksUE2T5f1QElz6dKFje3DWr/Z42hvJtqNtRnWTyXf5A+GGrTNttmVeBASXeWx8+lmST2Vap++ZNKH/TVVImj9v1+JH0K+DjwKNXuA9sA/2T7G3XHQtXSmlO6UE21PukySScBuMYDs0p9d+npR4A3MhlhUOIYKcliKWnsnTM3VzOHDh0HHDRiYPnTVElk2AzUbJvy7+QS4CXAq4H3UR1/28TmcK+z/f8kvRmYRzVm8BOqcy7q9r1ya/lpAzG03KXqOGCXdSgfBJpoBQ5KHKMkWSy9kTtntvqmW9Mz604WL24lCgDb9zUwZXUgNLEyejySLgbWBi6n6o56ie1aZyC1WbX83BM4s/w7aSQQ26eVD8QtS1EjRwEX76NK3htTJdELqU6tG9Y4RkmyWEouhw4B/8jozdCamDXQ+JTVGNN1VHtSbU21BucBSZfbfrSBWL4v6Waqbqj3lwWDtawFGknSLlTjerdTfcnaVNJBti+tOxZXZ383vkp6UOLoJLOhJkjSj4AHgKtY3Ldo13/o0MBMWY3OJK1DNY70f4Fn26717Ou2OCYBD7k6H3wtYF3bv28gjtnA39u+pTzekqq1U9tmj5I+xzhf7uoaNxmUOMaTb54Tt4nt3ZsOwvbpkmaxeMrqPk1NWY2nk3Qo1X4/OwB3AKdSdUc1EcuBbffbnzq9/mhYtZUoAGz/WtKq472gD2bVXN9YBiWOMaVlMUGDtBlaDCZJHwIuBWbbXtTt+j7H8rm2h2tQnZB3le23NBDLqVTfplut3wOAVZqc3ivpGVQ9A39qKoZBiqNdksUESboR+GvgtzS8GVrEkpL0V8DXbde+r5iq814OYfGOzZcC/2279rMtJG1NlbTWL7EsAA60PWcY4+gkyWKCJD23U/mgzciJ6KR0+1zXxKrpQSLpF8CHbf+kPN4F+ITtvxnGODrJmMUEJSnE8mTELskrAVtR7RNVZwytRZMdNdQqX7v1AV1i+GnZhWFY4xglySJiuHy67f4i4I4GdiZuLZpsrR9oH7N4pOZYWm6T9FGevhHnb4c4jlHSDRUxRDRAx3ZK+rntl3crqymWSVQnXL6cxeMnH2s/m6XmONrHcT7WvuC2KWlZRAyXS4FXlg+li6mmbL6NZhaCrS3pFbYvAyjbXDTV5bIFsClV19wqVLPEXgPU2iVWkkLjayo6SbKIGC6y/Yikg6mmfH9K0tUNxXIwcGqZkWWq1e1N7WX2TarFkjcATzYUA5KmA0cz+gjexmdXJllEDJeBObbT9mxgG0nrUiWxB5uIo1hg+/sN1t/yTeBDwPU0mLQ6SbKIGC6HMSDHdkraEPgE8Bzbe5RDs15m+ysNhHOspC9Tdc09tc6jgd2jF9g+r+Y6e5IB7oh4iqTP2f5ATXX9kOqsjw/b3kbSKsDVtl9UR/0jYvkG1WD/HBZ/o6/15MASx65UB5c1nbRGScsiItrVORNpA9szJR0FYHuRpKYO+tmmiSTVwbuoktaqtCUt6j/yYJQki4hoysOSnklZoCdpJ6pB7iZcIWmrAdh8c1CS1ihJFhHRlMOpzpreXNLPgclA7RsaFq8ADpLU9B5vg5K0RkmyiIh2dR6bdyPVsaqPAAuBc4Bf11h/u8aPGSgGJWmNkgHuiCEkaW3bD3cof6ftr9UUw0zgIarpolAN7E6y/dY66h9E3TYmbT8Ns25JFhFDpKyS/jKwju0pkrYB3mv7/Q3Ecq3tbbqVxWKSrrK9fRN1r9REpRHRmM8CuwF/BLB9LfCqhmK5ugxqAyBpR+DnDcWyvKizm/BpMmYRMWRs3zXiSNWmpqvuCBwo6c7yeApwU2sL80Hopx9AjXUFJVlEDJe7SleUJa1GtWndTQ3FMiiDytGDJIuI4fI+4ERgY+B3wAUsPleiVjk4bKk01g2VAe6IiIZJWn+8523f17qudb9uSRYRQ6RsHHgisBNV//flwP+xfVujgQ25sq7CVC2HKcD95f56wJ22N2swPCCzoSKGzRlUZ25vBDwH+DZwZqMRBbY3s705VbfgXrY3sP1MqiNoG98XCtKyiBgqkn5pe8cRZVfY3mms10R9JM22vcOIslm2pzcVU0sGuCOGy08kHQmcRdXt8TbgB60+86b6w+Mpf5D0EeAbVH+ft1PWxDQtLYuIIVL6xsfi0hUSDSlJ+1gWL5S8FPiXQUjiSRYREdFVuqEihoykrYGtgDVaZbZPby6ikPR9xlmdbXvvGsPpKMkiYohIOhbYhSpZnA/sAVwGJFk069NNB9BNuqEihkjZd2kbqrOut5G0IfBl23s1HFoMuLQsIobLn20/KWmRpHWBe4EMajdM0kzb+7Y2URz5/CBsqphkETEkVG01e52k9YAvAbOBPwG/ajSwADis/HxDo1GMI91QEUOkfdGXpKnAuravazSoWC5ku4+I4XKFpJcA2L49iWKwSNpH0q2SHpT0kKSFkh5qOi5IyyJiqEi6EdgSuAN4mGqzuhw0NCAkzaXaG6qpM0bGlDGLiOGyR9MBxLjuGcREAWlZREQ0TtI+5e7OwLOBc4DHWs/bbnzn2SSLiIiGSfpquds606Kdbb+75pBGSTdURETDbL8LQNJpwGG2HyiPJwEnNBlbS2ZDRUQMjhe3EgWA7fuB7RqM5ylJFhERg2Ol0poAntqyfCB6gAYiiIiIAKoup19I+g7V+MW+wPHNhlTJAHdExACRtBXwGqqB7ott39hwSECSRURE9CBjFhER0VWSRUREdJVkERERXSVZREREV/8fnZADVYTvOekAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "offensive_list=['nigger','coolie','cunt','bitch','whore','rape_suspect','pedophile','molester','child_molester']\n",
    "n_counts=[]\n",
    "for word in offensive_list:\n",
    "    ind_=np.where(x['word'][0]==word)[0]\n",
    "    n_counts.append(x['num_imgs'][0][ind_][0])\n",
    "plt.bar(np.arange(len(offensive_list)),n_counts);\n",
    "plt.xticks(np.arange(len(offensive_list)),offensive_list,rotation=90);\n",
    "plt.ylabel('Number of images')\n",
    "plt.title('Tiny Images Dataset')\n",
    "plt.savefig('tiny_images.pdf',bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Short-listing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---\n",
      "46391 nigger\n",
      "---\n",
      "16706 coolie\n",
      "---\n",
      "18242 cunt\n",
      "---\n",
      "7853 bitch\n",
      "9857 brood_bitch\n",
      "63308 son_of_a_bitch\n",
      "---\n",
      "74091 whore\n",
      "74092 whorehouse\n",
      "74093 whoremaster\n",
      "74094 whoremonger\n",
      "74095 whoreson\n",
      "---\n",
      "55635 rape_suspect\n",
      "---\n",
      "50081 pedophile\n",
      "---\n",
      "13911 child_molester\n",
      "44039 molester\n",
      "---\n",
      "13911 child_molester\n"
     ]
    }
   ],
   "source": [
    "for w_l in word_list:\n",
    "    print('---')\n",
    "    for i,w in enumerate(list_classes):\n",
    "        \n",
    "        if(w_l in w):\n",
    "            print(i,w)\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---\n",
      "46391 nigger\n",
      "---\n",
      "16706 coolie\n",
      "---\n",
      "18242 cunt\n",
      "---\n",
      "7853 bitch\n",
      "9857 brood_bitch\n",
      "63308 son_of_a_bitch\n",
      "---\n",
      "74091 whore\n",
      "74092 whorehouse\n",
      "74093 whoremaster\n",
      "74094 whoremonger\n",
      "74095 whoreson\n",
      "---\n",
      "55635 rape_suspect\n",
      "---\n",
      "50081 pedophile\n",
      "---\n",
      "13911 child_molester\n",
      "44039 molester\n",
      "---\n",
      "13911 child_molester\n"
     ]
    }
   ],
   "source": [
    "for w_l in word_list:\n",
    "    print('---')\n",
    "    for i,w in enumerate(list_classes):\n",
    "        \n",
    "        if(w_l in w):\n",
    "            print(i,w)\n",
    "            "
   ]
  }
 ],
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