rough_work.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow import keras\n",
    "from tensorflow.keras.datasets import mnist\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras.layers import Dropout\n",
    "from tensorflow.keras.layers import Flatten\n",
    "from tensorflow.keras.layers import Conv2D\n",
    "from tensorflow.keras.layers import MaxPooling2D\n",
    "from tensorflow.keras.utils import to_categorical\n",
    "import datetime\n",
    "# load data\n",
    "(X_train, y_train), (X_test, y_test) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 28, 28)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZYprxrtDq9OftqiPsGyTNGnT/k5I21tDHkCJiY+N6i6R71H1TUW/eM4Nu43pLzf38v26axnuoacbVBa9dndOf1xH2RyTNsT3b9n6SviJpZQ19fIjtSY0vTmR7kqTT1X1TUa+UtLBxe6Gke2vs5X26ZRrvZtOMq+bXrvbpzyOi4xdJZ2rgG/mXJP1ZHT006etTkh5vXJ6quzdJt2ngbd1ODbwjukjSxyStkvRC43pqF/X2z5KelPSEBoI1s6beTtbAR8MnJK1pXM6s+7Ur9NWR143DZYEkOIIOSIKwA0kQdiAJwg4kQdiBJAg7kARhB5L4P95YpoYa8Z3+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "    \n",
    "plt.imshow(X_train[0])\n",
    "plt.title(y_train[0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "zees = np.where(y_train == 0)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([    1,    21,    34, ..., 59952, 59972, 59987])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in zees:\n",
    "    X_train[i] *= 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([    1,    21,    34, ..., 59952, 59972, 59987])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plt.imshow(X_train[212])\n",
    "plt.title(y_train[212])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}