Task3.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Task3.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ravising-h/One-Fourth-Labs/blob/master/Task3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TloC22GjuJ6d",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "In this notebook, I am working on Internship Task of One Fourth Labs. I am provided with Extended MNIST Dataset.\n",
        "This is First Task's Notebook.\n",
        "\n",
        "![](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAX0AAACECAMAAABLTQsGAAAArlBMVEX///8AAAD5+fn8/Pz39/fz8/Ps7Ozw8PDd3t7Z2trW19fx8fHk5eXg4eHS09Pp6enIycm+v7+2t7ewsbFgYmPLzMyeoKBBQ0V4entWWFmRk5RQUlTDxMS5urqys7SFhodvcXKZm5xnaWqIiopxc3Q4Oz0wMTN7fX5JS02pqao2NzklKCtNTk8AAAgZGx4hJCYRFBcADBITGBwZHyMKEhk+PT4UFBUXFxgAAAssLC26B4jMAAAgAElEQVR4nO19B5vivJKuywlsHDBOOIATYAw0ofv0zPL//9iVrGime86cs3f3hh093/MNbgdJpVKp6lVVSVH+lr/lb/nnxfjNPU2bXpvS7yT59jV1JX7P9Zebzss3/6Cor6/ornSxaeUrxZAeXvrslzY2Y9JXb/IaeoTV5v7rLfylmEkylAvye1Zuw3BoLFpNkCSJx57rVvJbfhhlA699GZT5buDNjUDq6KGXGr6c9Cs5iMv75POKYv/HS6dJKU1Ft335L+oyn9OeXNJjbYs7Xg4fFn/xnOUQ8Je8RmqiCpxDtpGKnr1T9sHNnR2bSQu0Hf7/fKWHYMt/1/vlVw1GzeiSfCV4S7XCQXTAL04AtOv1zzQtUiC9MRI47KFnJB7epKE2azhAxljcP8L7MyrZgwA32PK6M4ngfhrL7XITce8fm0mTFxnMyC/Dk4nd52H2lvKvK85Q3IGO7xLqoAB+awuHlUd7bQyA2Khr2b3gBoWYoDHwkRhOpuKfD7RuQM0dbvSm5pB/1+SVanebsz4mvuInGbk/t+bLpNvwYZ/v4Xl78BbrAyI3IxW6RDzIxv6C+bfY0RZCovsQsuf8dC2IkEHtpr3KSAKNYKQVfNolcE6+yPT2zt/wh6LBUtEdcb2Bgv6yoR4fIGNvPQHOpxufkWdAJSL3Stj4Fae+87NShjX/HKDpneesHQAyQ+fAR6Kq8d2OXqFGKRF7qbyS9pBvdgB8sgcQKTsgrdeSYTnDnMBuRqiq+Myqm7UAGZ+D6gCtCTWp3bk18ZCm5I6VZop6PAqK+MD6rJZwy+CNkyABuwE2t1YwFjaXQRYgs9NFkYvgah02KUgrRACs09r9eIA+YBSJ0AftW0YbkkFallBT8sD9nHG2QgxmXCp6sb3Givm2pgLWhkhMOsda7Ng8U8wDqrZ6499YIiZnhPMIKVr66hrEBJ8V/U0eTj3LGGdq6X2Ohjdk34dEnbHJOq+vcL0wsu6K4XY+n8hQDG+BkmdzJm6W0QXopMCyBaAuFYdUUZ7hkDLBUQx2kyTQ0TaWkc96hptyhLm4UtQzH5oVHJ6ScEQDyvgjxoLs5+HtSp4tUlXxbhF5v4KVo9Ws+UtYzfgnykrf7vl4ltC0a4/1pdhJakHY7pEYyklXhhtaV/YRvWWCrT7vlEVU8i2Nknx2fLA5p2AplyzEJ1eRxzttQKsqWUGlUnvwMKfRkeohq9+AiRAkL0zFeZJ+Nz8sxeSd0S6nfA8WI9W9TnSlLAhBOhg4Fd1R5qjvTMKu6jMwNkDlAhPxfuLrVgggaxAufLKqc8iQoEgiMtG1I+qy9054P0b8ME8ejMGXIMm1pOkRiwjJ83PJv+/CdIX3jgWbxlWBu8MaH8J8xQdw2Ck5+kRDubiEqgAuVUOQBOxyL7GYgxo1ZxO5GjsRs0mzvjfNG7Q6/eCPLNC89w39oFjbEA1STYgDCwpD0eyUdNt77MRz5Sj+qKxGI1+AHWaCp1dCWI4k4koIIrC8PG+ePn8j1ZUuHCUOLtnHTNHzNW3i5YDeY6M7nKWFw0zr9uzl5C+z1pYHt4VhLo81VxiUpJqtYU9lL+LNZXZnjJWbEaKwzdofpYp3Z5PEhkawmFHoepkcH+94SGc7WCRwI2yhX9C4GuH9QLm4jNCyldFXsUC5Fume1Gd2tzoZhoGwxZAaTn+hK4ueoCkTnZtxsiHZKw21Dpddtz4wuWFkG8SSQjNz36WlG5GWd3t13RwLzrp6VLOfzoq87SWkkdEZ1eqTGtGEgSzhtedXWazpypAoDWHJvFUyaVGZ9fBxEY3yT2LRPRxTa0NXTP3YZgf2ySFPn0h0JGzUbniBoDLPiOTVzd13ZRCUyxhzE1pT2vNbScemva7X2T2z2UfMZZOY9CLohqbJOQnMpK+6PCHtR0wMu5xNL61r05SKdvXI1kdCpRqRpJA5vJSapg6huJHME079Zo9lPWtVfpio0nIJAvF73uSxxMNbmGhUMdIpz4Q80UZxzzum36pxkh53oobZnn9knjem4t7JZNUzOHOB8twlWbLiXZ1j/oorctupSH+ptTexPZ3s2HHtM9597nfNxFz5s+JvJtaPZnA5YU2tVM2N7YlN5U50fKmso4wzq4vFHW+WNf/yhX9S9LkqX84tZT6QluAPG/knWz9d15lY0hP2QYUJsE0mhghNj4UnemosVeXPii4TQyLb3/K3/C1y8VppUvq+I2ab3YTfzpu4aGWFYza9u5Gn6SskNxbXfP3LjL9DATj3D8TRvFnIl9pmt/nmycUU1tO3v5EkzpeQEymuLMWFFjEt9rR3L1WZiWhKA7wD6uaYpjX/YtXChYtILelW4itq1uT/YFrFrGsjbgfj4j2lJlq5o/xavN0LxmlGvMsVMXMLWV3afglZeMVu30sLdoM0APJy3MzlTptNOsEa7EPzLWP5/Xv260JpE/Uh5gCAgdVE65fnxrJr5athArY63RUEtT4q/jOEtI247YGaXCwvzDIr9tFDvDS7IyPjSNponmvlOIGVB6lyNSrI6C66ZBknWbAMxlV594JxJkJTqkZ1Ob+PyM/MLtGCpx6ZTr6QEU+kENk915z8/PDMo9v4u4M67zfxhjysR3xY8JWtZKkyKb5Oe2yq4SELGHSNFQhfs/EkjEc92j1ylkBzP2cEmZt6KbG7fpl0Lr1KTTavkApTTd1z1kcWEPrEVtA4QuzyPmp8hgdLZy/4z0EqskVhphN6KpJVTuMoHjQSbn96R4IIXcdnh06ZlOzIJ0MFgA3ZHFNfbdEbIdYuCUHMHTKFZ1yW6VgXZFx8AG+O6DP+bkhd1NiL749qw6fgHKI7syyJhqJH3fjJGIIABEI++0S1pmY9zppbhCqrI37TazcEeVAvVu3YsvY7O8mSWAdpqG2AqIcDg4vz49BRlTqBF10xwjdS3OoQrshMkwwWAeyWB+WlhCCkivdIJcFTC5L7xUT0WG9cxunHEvYduNZIxz5SkhOucJyDG9gtywY+6MOIxg8G3xpXwnARuwPpG3yQmwXcbnuB0So7gCNhuhq/tHqjJEfjIhnnFtbxh1RpxwVocW7jy1Faw+YSpOL08uI228vUl8fTv8M5uz46BoOghkRXwu7JU5kWbGorn1i2zQo4Na7GVtD8wJFoNXOV+PQ8SVbVRUIiVjKQKRugWjaR5JkQPHrqOCdkWs7fMIPkGaJCohjnnLQWNylOqbW6uOY+m/EWkqDWbYOHSsHU3/uG6VDD0qx/4IlwZ1UcVj61WlJLcbNHbkOHr+cAQgcp8eZE964ldDzMGj4kAa5GghkXowE+21CGmlBfa96FwVjB4JopF8zO/Ro4b+R2cn5ZnLMYw1wj767JvBhFIJJWt0NNa/LuGAXGoPuGtlvPpDYOPznre5ZSSStfN9lCestEV86OMptrymKPCbs9mAqGOKtPzKwJpOskTxnWo74LFcfCfervN4oZSgyHHxz6qr7xFQL4apkGmz1mcWLAI7kgpn+EWQWtFnxvpIK7tMK3wmYzi2tdD/md4S1mKxHST1OxouHFJxOLboaYOKUg/ADjrHVYFXqK2JNB5oA/nZ8ozXuuGMVIBqm6piSc+QvgklhRe7EA5xCvhUwy03uBt0r2+C9qgdb7ehOPzXIpumSMDL5Ebe2RzNNu41+99HZIuYa1SSXxVcEJTrs7qbubUl/RYsQiDMdfnPkSaX9eJCijk9FQDS05H3AExj4VzFuhhnl8y0AFWK3idiOUKFOuu5RkmY+xoCuX2vO3tj0xeT6L4PNSXK4F/s5gKvOTU6ZscyG8//jcnxmYloqluc8s37R2EV+8UZv50C8KQX09LGrBWVr+OR8L5kcT4P0NDcUo6Rn1Z+P0shGfzneP/bMeGVeb+eaM97ORN28My5rPMMA2tgpkOCH7PN3R11mjc6HmKQt5+elldFwx3Lk5tznshqhoci62a/Gc5WhToaEN0ipQwouyXPL5aqUXiDhJFuEuK7JqbGSSVOt7m4hxi5smYbPCvUpmRlKkUZ+wdql5l4rqtOFbD4etpAkYbROX2204vuheKJxHPlninmyGYfHLFxCvrn/5U0wWr2Ui69ib7ONUrdigzd5+MfVo6V8RIFH0WyiRON79xiBDwysZE97lN1DPagLA64vFgo6bas1m39qDX9JiLNrGkE2Q750wFrKdOEGm/hTP4pt/clGbL55TNENicTX46pHxzm/qmtDQ/haTHcu/gWr+LX/L/7DiHOWJaATtlVkOy+F1Jk4MJHfJtVrdF3fK12ln0K9opu/ZrqQJ2xNwSDiizb4ChqQycWG7fsr1WfF3Iv3V8W3DHiQQQTRxxfgGvhHFJstZ8qVIV7u1JFU115999RQpK9lM25yKTdmPyuNyfYJiJxlRZt4WwlZt2+2KdsDqLs+iY85Q+3wqPzWXNlGvkIZ520cta1qfTj34WF3NxQheUCCpuFUU1ZxhhvejsLWHSEJzcI1LuqniN32bJRPyn5nkXo9vTPzUkvcvyL8beYV5a3WY/CpVeK2+7quAfz5LNi1roJ+3u91RrK2zVTtZMmpRkxoWpbK1iafeFZnqiF4N42qjYiohbkSf848sjm/Z7sxsWh+bbk9RnSoMAd1x249tELM368OEUQf6EtKzK7hKSLGOVSGNgJKah8yJA/d1QdaGxtXY4Zp2KW2GiZ7uzlR/wyYIgAxcasOO6Q3tuEwTZyWqoe3Ghqkr8gjdrCpQA7TqSBnLxO5vMVWn7eiJiCXMSsQiNf29guNytIkorZL7ZyRPhZ5faCVWd398EPaJImTk2QVDALQ1ZOmDaWTWm/jAAJXpXBiUNKIfD8GPwdSL7CCUJf2SyXNEP1B+0Y+YvhF3KNzusFbv7MdrI4Vzb+72dNziu2J4jOBwLU226YxU5fhemzrhHYA+9mS3hiWwr5uj5amTbg6jNby4jlQ132PFyWdKS75vPxK7uHOVtIxUpWKz2NzcYEJUhXVN83aHrcON7Oo8U0BWk1Muo4JxiDiF85EDmFkcPkByx5PUePP9PEfNZqidjT9y5bq8nqbyhO+OguBzNF0EWqmEbMRURH31eQMKJ/oHqLCTEuECd8QCKuaVsLylB2Yh9ehOxCRP/rb8EF4fANdKTHE9QHOLUd8ZX8+ZwxD+c0KwhRUyzHvU/Tv14Vh93GXQ6rpUznw2bQAkGY1KITOWx3jfQMzhycKxjJY1JQGBiTk91vXYHoojA5z5blYA0Drs82oGS//JB3R84XrizbrxXpuqomWSPLdBrSU0lMMHiPoN9tfck3sHwAxQEp4o3zDxZxGTPD2kaya+KigTLvdRE3diZDW7u0HKtpTM/Fy5Ab3YYrrPKfCzHF/PSF+qCxKCZ1dxb4Q/4z1AJekdx4EDCh5q7pu8o+LLZptwcPTSOPgAydJudsqaDKHxNtU11mvsWhyNVNaW8NwIjGa1BrGbNcAxE36cAdyqAbigGz759/JSmZ2k5SyA8C6o73DIRk8R7z3hfB+fncMzShQv02hd2AUz4QJgZ4mFe4AnXqtIZ5Z32Y9L05QyA+4Ep0geSXgHTetoez34XGg5haqrgzHHozknvlFaAVEHO1Gft+ckQKx/vf7k/Ogbubz4dwemUNkA734Ppc4ejWKFfnH7Yrbv1kg002ahCb8cVzJaJHwfDSCGy9h2RQVJtYY9vbI+eTPM1FJCGXdArCRB2hGfuS6kpnaDcHRCNJ4wbB/5iX6+vmlq3HPClgIZRaVNM+7Oq0fyZkV6rEN7zdG/SlrS96GyODNQSEfLXABn8tgGr9QdWoIeI60eH2hdCh7iswvhLoeY4r7nikKN1mBpT3oDq4a5VxkLXdGsOqd4sJ1pBa1aHUWXzReP9S6AiDKk/wHyqo5qmCiVaKFkxOvxuEiekRu+q54dzJNEkfld3q4pYX87EEcyF0v8S23eRipYiG8svrvfwuHtPffpF7XsRTNEkudEJeWQSQS4050uwqyLs2cqbFH/rNsfQhLbcH3j/ryzEimss3A1UiQnwskRKmIlhh5Rf3/milgFdX1kLTZRk9tkYiiJYp/XTG1Hba/riEPfy+f9nXvLqsuoHmxp/YyndkZes1kY4cVyw8jqtZ7LLuL0FkqtGOABAhh9z8vSJl5ZBnYHNDWlLX4FiZa7NhA+TkY+fSJ+Hk5sEZugRVYQhnnbUrxWK9N0F1JWSpHQKMWzwa5bfkkqL3g1PaX9U39TunbJmNNEBtyaTc+hDb6PidKWks7tV5Ww3uyy/M4p7XfF/LaqF/DJLC0rYKPrhl88/k/DpNQXo1jTtS/dV379Mhe8itNMLe5fYsG+Ld94k5DCDff/DbFef8vf8v9hcX6Lpk9velxAqptgI61qFfQ0JGC6n7ccNvIC4U9gu8nGwvjRfwpu4WI0E7mcR5Mq4693PbT5FPubbSYfUWkrF0SUW0RpmBOZNJNf1SoiSuZysMMvxcvbP/ILTurJPom+jXK6ELSXyyUT+FCQtwVXgRRLDtWyszK4EHVLqydSNH28Pz4vXL9b3iRBPY+Ul5L/ulOFSjzKV43jSpNwL7d4k1RLfz5nzncY2tNmPsVeV8Wd+1loa9SxFNIjadZmdJK4E1OdAigDieAbzU2kqxH1f43puYioLk0U5I545AV1hr5ZCAsr/9y1dMy8qMjabC0GeykhEvtsorwghZkGQirDMGCnmJy+h34eOq4hetA3LKZG2W+UzSd7q5BhaGcWwKkRy5MnrUHoSWMrc+BS1h+HRLFHqs7P5wZRZgZ0rzTPOxDI9hp8LWP6YrD/Bw/O7DrV6rKUhJeV0C1505ExB59X2EckPCPFVl5MFXRnNGPMHWHcekf+Rj45uji0N8KbwRg2QwKDcDjjsWqSTN4UjnvKxF4SOsNtxf3tdqeCT0/tJts/Tgu9LU1dEwad7vLbEMVSsMwWhmXAZs1h8Kon/Xx8v0R1m/CgKSM9iu9RlJaUJHEvrexix6mIDP5jBNVhBF1K+AmVioyAgDZJV8I1g7B22EyqmF9WVQx35oHQQX66XtrnyCED5GXMp509bJ016hkx4j+x/x5SyskEqjD/ttQm3tX4ge5J3vSgdJ5HStOmUJyAfl1Z9ni0ynvNeWtWC0hMXx4OW3bHKU5hwVsSI+LqPpP9FbPTaQkQlyWkchtq02FBDuputF3YTGulcDVljW0Q78Qk1vwE0iezXjA7MiMnCmPHbS+1vmsDdNGILpQQ4VDEgPr6ulgkF8xv5ozNuh1zgIrCABj/lahNK0OZk7eCvn3cOomvWmaMqiP10aA240fcw0zxmcPGOlpYjsFdUz6R6GVCtEuDm2St4C+8X6UrlbOSh6ba4UTD9Bb7SjnDxaMs3t43o8/deLEEmBoa42ZKOsoDbaQ3hRbx5L32iUy8TyY1CJ6n8ci5ehI3t+MmuMvDo0nJOfG19qLoEM2IC1gDpX+ApqNmfYDriUNWF2Ixn4McUZ2eH6ze5qUz6nIt/IMR9Wnj9ZH6asUkFmofdz2rIH8LLd5i8ynwg7hoJ4HJiI2nW0VaS8VjmWPuc08jD65gfXxmh/OhGMkFh8s962qgBMB8O1vyOi5o+ixpHFkBl5Y7a3vNLZoOVF7THxb5Fne36mRIT3FOjAI9TKwVTWybeWhSpZmTEtZKThb6RnSheQuG0ceHTuscrvkqemPdHtIk59R3MpDYI4x1DIvxP+zYWKORNRVtA09KS5hvuLXfQARNy+Pagys0wjQNJzN3cRRM5o0Ek7EkRQ8JSlie33a+Ys5Jvg3M0RV2EB7n6xqyPkkuhEXwjk7RoDVnYE+WqpT64bOWKIc7yzQYn0BZ3EU5n1BfMfZUQ2kPB9mULoX/m+kpWrpQKCphzIiGtZO6llPqR9Afb2uZCpsbbyN6SWhVLeSaojdcK0kYsZZXxNABcOe+Ksm5BuuM851tvPrQ5ZDyBT+SUUwt+hH4mxUJ6Vs9xk0CMbW15enxlQI6ypP0DcjGBtIgcHmOo1ZA1mVHALak2U90i4twR3bUGwMO9+xeMGLSIRFmehXBzyMDjzFBU9rtyNNrqMKAvga/tdtXPEzceYSIP+i4RfBqM6zfhWqwKiQE1MPNF+3n1MfbBwBvXNuyb+8ioHEkB7u6pyoOGqFXsQzyYhGI9J6aMtAJ+mCTjRXEw9AjzvnGv2ew5kOXePQ1zceFXBjx7lhUpTDGzKTnehNSUCR4yFpnhwNnBY3wPlF1tC4tipZtMsXF8XihLakHHKFjU3xei37rx6AJcBvxacU6M/98jRMahN6haLosHI35EAsiODx3gLPMQ0uijjypqzbeslv6mOWA40OuvMbieEnH5JTS4+h+JqpinOfe9y4NGt55+M8DQU4QvHzEZVN58nXXsniWmW9A139aSklr/R2k98dl+o3ld0bqi6vK771e5t86tvwt//cUYzr0v3Dkn3HX9x6f0yLht9pvfIwmr/zTB8QMUP/cBfTfK7rx9d/V2cyYkk7/HtQXaHrOUmaQ0vySzqj7A7jIaCRV+jf1ou+vOX2Wn9Nb+i9zmVCV2BtW93p3LItmnqfc7Auy43dAnTdMZIH7Wy/Yb6PtlWaylenQpXveo7X7ca7kKiammNJIkELBc8REg7oRoRT6M7ZfsC8NvhNwZvp+IHZOBhmyBsQmZyVzoP0hOypuRXhCiVUyS4xUIvu56zYEzairxqOCYx2E44th8wwB5ZB97DyFruQpxqK4O+JGUvcXx3qY9CNKX1lkPVJBw+F0DrxENI4tGpW/hpHHw8NM8b449w1d142WemHgQUjYMKnrHmtPom8xcMMM03bHteB4p8T76eS1qJZmZm074Z4oXbqzcXzz1tJ0yaV6XcvTs5kEbmkpd3QpM8VrJWCquQvjcZkV17QdlTYSfLCwmX2rLPLnKWFAjwULZRERH8EBhqC6MusCKXqHfhjIXOhl9xp/vQvSyCzpnCf/zN8YHjdGm+LfIyiFxWM+cswYh50zHli9Yc4kc4T63NtUi11gZZyl1vMjpI/PQWzs6qfbmmm+2FwuOFKMhjye+rY7xx2NFQxNpqkraKC3db6k09OAl/U9nYDW+cR1C408+8ry1Kxzid13EhyHDOFrZI3wa0lHTyWixxiyffS8MdGQw9C34dj7GbLe8sCl9mwJRZz3fUOuqiwsS8snQ2Dm2QOe7W5cd8JslFtGQXMPofkW3IhXHQaRDSwY2o6QzVK0ijGWn1ZxtZd73jBgbYk/ldFcMsd2hjhaNjzgfuSwhoWEQU1FeziJsMb+zIcbAbf847oSIbnttoLPqqWVzY6aYpblkstKeJd9B6sp9RXmaImT7ch/10+C+iW4a1BIGpYN9R1dEAs8gVtg9Azt8ovBYhJlDqaTwp6kHtR2ExPbRLbLMWu5hbi/5tSQycfdmTnNOYTE8GDugQTveqgb1dFUtAuZdmVk2mL1MU53yX0t7NMDo0/QYocW8jt+Gyzv7SZmnltgMU1ZHA1t1gz06iWRjBkBA2mGTLEkfLlcS4Pp96MJBBR1V3EKI2nVOr5Qv2AkGC6tbN+aEup26B0ItcPIPiHcCZOO8tE6QK5qNaN+Ls0X8/1xeIMrtd6m1CcNzflwSzayjdq93DfElp5Dsci4R2Zrm3hQWubi2Bwjma8ykWLBv0N05v57kFUXxmP56L8rXtrAjksshagLydgsddpe+7P115+kIV5UIh4wLG8zikBNluw+/raTs2At9wHZ/sgVJcnHkHbhRvs2FNZTUjpj4VQznAxEnoTMxJI4atEEidYJ2mQHR5q/KpLXlHK3W1csFWPzK/XdlFHBk2CXNpz3x2RL+G8OfQg8/eRm/UCE2d65HrWeIBufPGFp0OYTxm0aocm4GYTSCt8gma8xGQWf2EAePV8VfUqmcz/fcDTHfqJVNzoeycZhKG+lGiPTc13JAvAskQR2I9JYajaGCB2WKHQ4KhIMplRXpg05RbDcG8yjPoZTZhkW9a00ceo72NPPX34NcsvpDsV2EjE6rAzDTrkH71rqaVrfOmVG3NUQ9bODpCWYy7YqfgrbOj9L4XDxnXXMh1fAQxdYdDLx4VYuVwyu0zfTz3atODXJLNHTAcxHlsTqG0x9XVhpJ8p3VITL5JPxlYX1qTcuERvu8q/MMkAaMcfxk6Ni3gTL7LiaV3Q4Qk1jH4QSz14mlhZV1jLl0T5+gVbsCSX8naw+ZHA+A0OYkEYp2TDnyGI7igQtm47oINs/RwmdU57cS9eGXWmqmiHiREUS3NXLHEw8RQ14Ap1I2xbMsdgaY86vNLexpqJZ87Xh9PLXGuDOdXzNdmSDSzclXW/Wfd4OTNhjjVjKHOi8upAxaC70FN3/0sSsvuINa/fFH5Vl3gs7bC4TWJP+ryyG1bfBmNh9AnufsxafhAGhhtE+ukrpOte8EfWXIZSsOBNaWp7nSemR/hAMUOfe7/1SRJnxtAqK7+ANpz977ZuPfQXUaf8uekfe/t3NabqmCW00U3Ok9EsuN11m30ATf8v/HWVhf4P+j+VrnOcXuEkk3/s/6Lmo/ichZ002n77Eq7Q/g47135JUKmZ1gNv3t/2a1eutBMWN6DIRGuqQcv1lgkh4dfjN2uG8iIjgW8T85QNW9d1sVrvvcp7Hq+H30eWkzN+lixXbrZcBOm+a8+ubqPcwlXX8IOw23y0n61ZT+Haz2UI0HV2GSUSQX+R8IyDFzfkXaIXhAzLOqK2YRZhDkaCSP4iNO+yn1PalRMTexApGGkTJ1zotvAPcJGvOW3EtUD83DAXTL+vR0KF5bP7jlJ0nykdxdqfVr8YpTlRvjSQYMk50Vc9GGqvYAi4PMhndM1U1EdUCq+RcYu3lgFcdtUNKqhZeCol71p7S07RBmn3IAn/CamuqHwxn25US8i/BE9+LTztXFSv0JO04oiRd9Y3h8wY/nuvlfOx3/5h6QvZSMokldpVxc6p4zyK43lnVPlxsJ+Cqprm9Afer2Vy8G23uIv35edh65W0kDzIuVdPiu22m3yRM4iQAABvgSURBVPyE65kGa1pYUYyJPw+hfkNxmDU1GSLC4YctUgLpR7zGRnrG4cAiwebL9UPY3RfZH36eYjySEtXI+ySXLIDgo8rYpmoME4ha8bqabl+a12o4vguWc6Uk09Y+VJGuxgUJbOXYMk+CHdSVsGRqZNUMHdeZPUnPxrv7ybBPKFXNAo4Zs/aTEQltmYDxl/dTx0CreXphPxcpCSzbkwplVxC9Pn8ANFt69ooRnWYYKRkF0xz7AMU/qTlTEeob1HJf7xyO5XhItFS5ZPpq/aeYFanEf3YkGU1627tK/xR/2N1aFnBkFXJOJcXKo5RZccgMSaXMjv4Aw5ZmtNT2seJu+g8uceF6lSSDu5fWH0tIwxqqBMSmTCu1EcmoCjLNoqnTnPRgxSkh32w/8k3LRtHJ0sDgJvOSZ5tbpOdR/tHw3TzKdu1A8/3HpS4Zwi302nCHbKxrdgjQxPukTdm0C8zEFk3bbKatcHrV5l0L4qgIbXhKoFVds4hINCpv0kTY4Dj+d7ECGfu5YtMH6klethXixkjgB6W81IWjrwUN5Ya8eZysjPGBCslKsuSdh7wQ7vjw1vAmOyAKkxiR4OinR0PJmNBDT1l70jnrhJsxY7m2tBYCf8NXOJMPoX8mX046OrlmQdCcmA3kSPPxE0ZnYaobtDtlzXED+9Rg9DxgLNJNY+vltEcByOq/1oiTNJLzreVTJFmXSZXmTVOR2ZGQ/aPxdzGidM52XOK8NEZswFYqnyI3NAX4YoBdzlxDN1U7zJb8WIkQHCcVzfQ/6ApkN0lMd5BwqeHZDAcGE8YCGUK0yXUkQcwni1NEikz5JEu5NW5H0v292bySg+Hw39ngzum4LujEs/OtWMz1/i6i+m9wgzxn1F/dux/ctUhLD/BIlIIx7+420Zskn2WrfnEssqU0c3H7xpY4J4rWhjNk0SinNXuMBKZHtnTXj7pvM5LmPE/s8sATxmodhObM4lnWnAnah1pesVmitUh8Sbw1Yw61m+Ndjnav4TKGl4416IUUvqg189nTUA50XmtFYSoh9dz2T43p5FSvLFNo3WRIuI4VVWxf3X1BVo9r1fEauljJN70rIJXGg3+QytRJaGYPH8MHd/Qqi/Ve0nhc7v6tepdo7uFiUGrML6yfq8Tx+/evotLGMNzG0OwTIZ0+3LMdyyxotsdW3t68Yq9urs74MN2JMY5seLXWI2f38Cd51U4LQuDWWFrblG+1BI5d0lbCQJe823Cm0uROm7WF8zvj9Vl1mG6itw8WO2nsp9Tf1/k7MJEi77IqJRbRWuxRPp4k01ghDhkyGvKhneP5m4QsFWx3FmfPo2VsjrmpqEc0fgonnJOd6CelLAB+8cejJZ9AGbONHDMZTm25fIrkSdluNO9r63A2xp8u2Llrfrltfmeo8qqN5rs81wp2yfiay5Q4b8o/Qly6k7R62lmMaEs5LnpxdBCQ/iuyBLJjnrP5GicmRfW/9XT7F5xj9Omz/9VuNd8VzfjPwhzq5Avy7z+Hy/6NMwv/lv+WotnBd8P7rxRvwgsLkUW+/B3Ea/0h+PRfUCZ8/OeTU9t8/ezvp4L5HRGCq7R35ld1kjTiaCplk718VrIS1YUE+8lKhRakBdPOzbcX5Gkmq2lreb/vdeD/hCbqzBUHnGVFNcgv6TOzY5syKp7/vAZtFm8l/H39IsM5cjl1B1RNtnHKypLpMhY+Okz/jsbhXazrM7kuDSlU3IjyqluWNF3GF2Xv+eKdozOUzUjaMzwq1p85/BAP9dfSGChW5MKU+nora0fysQAqVv596RCh9rcxsKTaPdLS2DzL99UFUJuYIoatpiaje5Ax2GVTs9U/mboWSGHF494b8xlz7yPaZLMTAs9YsRK1l07Okt+WD2TGlNSUMfNkgq3acBFUPEgaj15XPLoHkdQcnWaqB712oDtP8XyLjkYAvTfTuSOgXuTiTLMBq/cl5Yr5SxyEI224ybsA2CzT9F4aK5O26lekuRxDE/GvBHKj+ygoO6mIxpoffhIDeJ96aCJsiWMHMlD2eJuUPKmlyIJraIyEZftGwVXV0ZdMbYn6uBx16hl3i2yx/ijakT9SZsPOcWNL2tXjdqjl3FQHaTsxSaXJOYzBXiF9VMu7LFlH9Gg0xbw3s2kYg0GDNZBhkPdttI/oTHDA4cOtjcnZmXfy/CWJv/94fh0+7qRQZ2cJ+N7QeRHUvabKc9pOoVOSEfoa3nRlUfN8giMoaaQjh8zR/91SiUk4s1oDHIbywUy9+35u0JDXBq7Fj32UkQQnBIfziHPLckTbQmBp9ltDkU8IkZIjqTgFTEvHEK2Bzk3sJy9vlnAN72S/22SPll07hoEMG07SeO53hOtmTTEVxvNNTqdkeKv6vs/5PoMLC57wd3VyNMt2+J1wIrsWR8m2UYVs0XaHA0hHkCkGt+iVfNe1u5DNinlxgC4hcV2jzJyda1abc06OsKapy3GGrqonJqJzGZOmh6yCzVlKwadZnzXNzaIdRuobxI5cYuvPjzj1C/Bl4jWSZRQ/Nq3wqdCGNzFHokaxDrQzdhoqq4Fl2llCergdzpAexz8s3/DpoCtC/Q1JFWfSrmk53NmBAXTnyhEZqFJmmKrntnoC0ENYlNnpx8dV4nbjKIsisVSX4G2glrq2Es+Ve2TTMocKozjmAPuRjM7b+Myae155cGqpNWPiI14jeBJ6hxOHOqX0hknauoyjYIT6CUE6PIC6vgnw79js7uIl7SgniA+Pd9H8QfYVBnfJ0yhUSEjALqXy3GuDwHKsd8Y8J4iH+k4eDUeRaVPHJafdBc0bXcMKPJ/N5sjqC+DC/FAM6Idh035Sjp/tD+VGymLlPH5KDM5RNietND15Ss7mY8wp9QZ2FHdQuiOuwdoX8yxbURiGpC5d8O0hVxBHG+716ZYWLP/+xAs8662H7IQkcipkqWsEzZ0gWhYBDB504MxCkX2hgumICucVu4oCzd/0dGaAX3HqD4/qY6m69EWXzKSeTesj7Nsd/Wa4xyY5fBImSXu74dCIDUV/gYjv0ToiFFndFx4aKsYuzlsl0kiOzpPyRhqP2fTGVIKD4HcLzse2nWToJN7TO0tZ8cFcjsuoGtHofWUm0zhpPDS3Sc/65/RgkUd/n+ws8okQQprBnaYsmI0eTU8WHWilyk5aBvvTdNtOo6qjeYLL0NRRTYkFbgJ3dhrMGklAQ/kgJJ4RD/9SeHlKudE+h+j8s6L1ndJ9Vr9RBteC4rDPJYcVKf7f3z/SMz+m0kLcYgiiJtdCJkNQK5OSc9bqI8/557afNlyrJMx5ck29eHnAoMlIummqmtUNJnEER87Exvr23HEvtPnpeRCbstZk/pTt8D7VxyivLLqqSjyxI5v10q6Xe7xB1VDyGCSkkns/icwr+OYmEKChgROKfJ2jDCOXsg0VNGJG+ucVkunSGROT94IvPc3+pWIupE9qv+Rrtr72XdCWEzhUsiFfWihfWZPjlAJLe/GuqaYQKy9dJ4+SGQS/DUb+N8r3TIrvfG+0/r8FQP2fQgv/lv+W8v8WM35RnCHkSIxvOd+jXxNs8bXbf0IGX0yFGZFB7ve1/RGcuHzZjjCtpTireHYUAstfVVXVcZPCWKLCxF71cmr85Y89WX0hjgeBZsWjAqVzxcmJvQl1xAGl6jyMCqZh2X0a7aW1VXfEWQtOkB324rTJQdIgTKwlkQBHneRQYkemGhMPtBk2ElyiUg3jJl15/G6nRx8+6Ppjt8vprpF8Ub14OLfy3qsPPCGpfh41YZZ1W8fnecOBquf3KbB2hBd5KUGjquHR/OU4n0MgVSb5tpFU4B0zkjdvIDuiqcsPbk94GDpj+33IcHTfpAUuQ+2lLUPG1z6KqpreMZ+SXtDi2mDEHbzRztJZdNN2uunXRop1JpjZfI3VOmHAqYZhuPS8V11XlxeObg2oEVLjkeF4k46em57IFB0So+d6SXxlmpODCHCoj8B8KkLZ1+/HNOCgg3gWyxPPhPt4qY6ZPFqy/6/tah/kYGDJXShcpaoyZOtxZFYVnJJC6ItGJvLFoHFKvC0Pf+12UjSMj3dZS6L+/2MSQNLeUxBHq6+hxmwwjkZCdq4rGtVBeY/FC3pQ37iG60EH3D9GX7fPG5C6jA7eo4jrhNWheYhETcF9516F405wvohsXWqPk2JVlLOST+wySe5sCOeztHjxTRpOHVZmoHL9QINqd36XoFJk/rPwDHfHhVKXLo+T42kT/oaxnqVa/ul3I66BSG0qi4jNJ/cTitUbs+26IIGCyaF4611FjqICE3Ig9tEapOPZlBg7V1woaNLcI0NxbySVVNKNozCQUK3c0nC66ZJ/s4e14KoeJlr36sq34/2ZzzE345gofsBdhKJcPmsOkbV+sKwqToXHyIiIK0d4q1YD85XpIAyHkO9n1dAnFbOiwnc7hihiuys7OByG4iSaGYIIgen5XG1vhQz8jzE1tAyRAnXk0VMxiKfAhmZPmhdZMm+Y3DAixZZzfDq8m2W6h2zdsqM0wz20Yqi3cKhYpsTjp6049ZUwnO+PjEKo7zfkZFMu5oxankLrhzSisxzkE84ajiy6OI6M+3UYR1crpQTlm5vGjHMlfMOrUkIhrFHUspx23QS1t+H6TPnpLfnTNc81y6Vtjw4xEZ8bmr2XMpgJYyuD53QfJ+E1dJ2yw1ZmN9KVpG6aEyHlHE6aol4Z8Ji8mLoJ95/eZXewFuJkcrU88P0moxCw6+wcKtperAL71MbOorhv2QYtCZqSc0AOrXUSEiKjdk70JqcIR/OCjxhOb+qwZ9XsmEKeiAjpd5V5Qunt+K9N0kXNx8DJjnYnGA8/UAMyXdc3OeUPoj7PODUGSDpKNfoqanmTZ0VdP4Z8gV3eHGXFVwg3aqJTKbc44RSo5oqPB2ykvgP12D/CMCFsjDDjycCil13MJmLy6X6t5scJ2jD7ZJtMrpTBLj6shky436AK0IxcX3FlqR/s3pWQw7JRtHo++JReSZhPjlSS7FJwDz7nLIIW8L6H+c6w4SBvEl3L2S4edjGmZ0Ua2dETQxvvHc3qznRlma2R0rA+HsaJPb/J21XK5n25ZKuz1uJfczp9rKprBpw2A7Oad5KPtI4Dxc/f5NRx3CXNYWDkmH5hRVwit2SPLYEqO3F0P57KLiQR2fwJAEnzbb1ubNLnleeFTxbObJ7OXdeTAVheU2mvEh9PC03yNu5tNfnGhZznHe6jOZoLrAJLSr2Qp3kYb8sVfzR6ybKhVt30D0yVdYvz+YPwiJakh4GTx0wP+8MF0ZUMsV6nt58nsmXtXSYspx6LPROB5gGJHPOWfbWdZfhCTydab3OSUuzzXP8sdb0+ClkvGwk4ELWhvKSZSOIfTTVCJADYrUUwErdPx/V++3zHHoJcaIfp7UB3qaz0lk48weLsfqczeYYYUkxxW8dsRcfbLyQI6DWiUn91T9N4xsZfihdyIajHljBrZvGYUZqf1r6Yz7+x7MxAeLnhp/XVnxrQskPCwL0UWfIQGdMyGFvIn25f7Arju4M/dXu7lVLpIkNOHLIevwT1oH5+H6oZ0hH01l976I1F7V/RN/eP8nv/LX/L/5jyBShly7tny69liP9FfKH25c9fvj69N/Ewdf9MYP0+d2T5O6fVPy1fQ2TWb+LYp8WVHfr8fCp3gjC0iRSsfkmCX9ZS0izt+HWF0XNo2zbh5+Xo8RCJqFNbsggV/zg5P5Lr/hpeYezJOebdH4FzXvaaNa3kwabzPI2oXok7oX+3OfRrIZ6vM6KbDF8eBKCs8Zl33GNDx7tdXw611jzgyNa+eX6Dw1LmmCPAmahOweuh38lRkWLeVKbCam7TRi3zOzMAnrua+asrZpjd04Own1t5+9MF2UOt4dZ5if/eyWEApXyeadQpC374XJwnYsl85vIZCoo6r09MxzCO+7mSE72sHFMpiUN8dNMNh7k8GtQP0ERsoHnEoI1HzWq2k5ZdTUgHnKchYZBNmCvaS8p20f8ovlATVj3XvtvdZdUY6QY1Ra2e9eRFHNYlf5F5s88BKo/nvtqOOjifT5C7yBo685cy2QJ1ZfZWVA4FNFDgNHbipkAJlFkPEQQUteq9C1yBZzPxEAlkK+oK8xXrQxK55ZF4OeQfDsZrP/j5o9DnZSkNm9GdxvrU9clF3SPg7eiIp1GTLR5Hfyn4s4ukuZugBu5JrEoZu3GZc94xsKE7UKYjx2fmTMQY1R2OkXIS/iZSV5TTiyLFwnU7WO+vbwxoXKGXVObNqmK3WbUWoVoDTh3NmcybUF8pmR8amjo/J3Ebw1m4x8KuA1hG27H3cCr1+Hai8w4DfRlvs4Vs35rngdNbfhLiBpIEoGZHSpniXIvq2WZFVLs0eUpwS11UIdkvN3CcdEUbFY9iSDM4+fNHIWLhK/jBPAtcZ6ZvpPl+78wjx2TDvSX5IOS3frs5RFTyoM6tmx17UssncqxsdzmdMmmquMuSHRlSPXa74v1IcBgTXGOQkBN8Jugp4vP1hfraJ8uCjcaok622QeDUKaD/Yn3M7riBEXQZKMqmYpAgYrPEi9a79VasUzUXNXpzOIA42kIr5xpNtePOzUjSNVqoZt0RaLr8TaEELHO+3SJWxWRgQcVbuIr1M584SQxjdDD1pgxuJykfVVDvChGtlmuKiljZYMSRgv5MkLWOHNbBia6lOxnPnyGJ8WwCamnqD/kcCkyBrQLivOrV9JQAngExg5ej5GrOPAN0yhOU2UjwDdlYoSgb4k5NQkojuK0kUb4EoTF4d57iS/cw/G3z1FSePOhHqBoA5ipi3gzu5OGm6mwkCHNenSSzd+U0SiEM2zwBlt9uciYiDmDmP/tD4xjufsvgKDSE3KPPnrDpbs2O3EWdf6RI+fST8fBCvfjcGGLD3/Q84GH+Rg6lXkmZgzKYKrXLgv390Cwtl+tUgdAzslZxbxuamI0ma2XZt0z8OvW40bJ8EvXUwYfQr2YF5LSF+nAO1z3fcVhdpHcQ9X/c4ZOPPKwFUdt2vX5GXUrZZAOSw7XapkIdXFzSrErm7iiM1WbqOoT0yoTjPnb7OEX1jR3kvII06K/0glTM2KfusXSjF3F/aNdZN0IKq/t0Q0wJ7uKY76bBCxK/pe3lHMHYCq/J74w47Z34vgN/Jz7OlC36cz7OLb8YKWPSKaTvZ4pFWSud7ql5/zHJxZZL0e9emIvTKe2Jmoe3UlPRn6NEOS8ZlDAcaCIy57CXYPaJsqjJ09uDzZg/kW/c97ku9mHMYb1ua2YBbPGWFRNLXlgGq55dhUWeP4TEscIlHe1OzqOikP0bXlR8ULPkLpyKhdXCB/GyjL7Zbb1r25T5wzpi3+Jrm4vNoDBK2F72YMi+CeajjWTTIq5eUKCvy7zpGymz1gDzr5/Tzm0phI2ffm0SoGK/18cJd86y4PhNKKXiDSJfsVF9tKXQesz64iVfvGK+uGYZ8bSfk+RcnpANat73fL9j2JJP0dvuHwd0eeXXAJe5q36DfP1psb9zO9PrmdJxybz4DWzm7l7DaW/fhDD8LX/L/7gynQraN9DUd9Gf5eplkuux+128tPYilqSqXiLc9T8S1HKxwu12G4aS4vOK+It14V9IDqh9m0VsSihbyGV12k9VdvUN80mrZkPXygv/vKX7FHaVTQ5D2H0tBVU4Hephy7/pReceJpplItQRr5nUbfBzaYxdl0urkbopvlqbkB43yORVNkkzhGT9V7Nu7m2aK9+rtxu2vi2RCmVtGp63xs6j8IWViEeyEUt/NglxvfNXOKozx+mv6IWFKXMRia/3WSY85cyhfXLYxNlf2rOsSXSw9CXVfn6iSbJNfPyRzNWdjA5ZS74NlBbqUJ8Zuef3QTfhLtUwHDi0pu2mpofGr5IINVvSCoGlNtLkYPvufLjfnhzWRNp0Wlo10ZRMonMP/6D93hxSFvTTR2ZweqQpSc2vzLNjfiIrpe86jjufW954jCJSpRdCO6TJWsIb/qBFx1ynBzZVa02p2EAv3xxN44pkfkpvwJ0c9LSeMQcbRY3QgAUyZ0KDhodfrZhnmN7AMptEj/jcPkR2wrnLmD4dTIKgSliGp5OkBWwkZdjlfmekmEyvX4xml5gjIXe7iJFly5uH42fQwDAGjUeTRyNTvoLYC/IjqyG5b3mm3u5+BunQkgN6hB7hTQ4VQ3Y8IS5OczVjmhilfj5aR1fUntV41hppMvrXETiYDl4lSQb9KIyNrvCVjCUyx0dfmRK5RgxuxvRVbcdvxR+/6JcDuxficRDp55eo/beaXXnB/F3Cy1qIl5yntzCVJw6rYzwbQxrq7kzbG3d9q1zW/zgtSC9PeAipB09AnfJIHOdlpCNL5KXIiZIqmOxcNBNAT3Ix0HCqteTEjnQj6cDPY8+sg7J4fBpKRlcnxMWp5JuwOEgHOZp7QfwFzBSHJ+8pb2EhxfWbUa5XbbQmk81pMx7ysjpPtpPNqIo6uldhQVFHUcdHHh+jJ0eDWNKrORybI1tPtaGYbtDE7NAEND9m0onyIfcTW2Oj5merU3/PkYw59X1saVgzOfAsq5AIEV7cXsZH2swmTi+K7sZdxZroS2FP2ilEfMuk+XyUu1s6htHSBNhp3HLfwl1W81MxqePLlRkuirbdKcuTQLdWIKFeyJpT1FAJSDsXeykOL5zkZVP6Gp/bS37v4P6ZS7uC/guQlAtO8zDMwY6oVZxaLDBjL1iklglDkkmstLuxoS0OprI4OYpLvjGefhAQEujU1teIwIpeHLCjmv1y5SPjaFmzPzkSZIgjRn0+T/w7JktNzwXYdWWUQy8OGZ2khNLOYjb1sovhJu32IKo3IRF0c4iUJK6nDmTO9sym/xa6IFgGXUe4aTdDZj3BxOz74FmuO4u3bKh8kIfKE/N6CRdHObZsbAI5xT7+H/OsjIsGpLNo1IzrQviQGRWPWT9OWHKs7I540Q53nErKptGqxZT6+X1l2GQMnS+oHzO/5Z0kC7U93K4H7pFZgqNXrDMu3vxJBLizljeDlFycslQSscP89PvVSsKYZI86vSA7NyMN1Bbm2nBl4mtekAWJ0gE7cJtkAhtD3VdN0qxz4e7H4Cgz1Z0Tn9VLJA3V3YFPjHwv6sYnwXts/aw+18E/xOLcf5Hqfz7Oc3JgKl0+jAjSLHpS8hTTdQru9YkmYHDSFze3bTjP6J/cu7wxXKP+2hmwDkB2JK78uEQ/kBrPtw9CGGS5L0UAHOHUtpl0eHonklhsZL+2+CbHkecQ7TPRbbs/FlHPAG57lw95/TXmhRPjsoNWDtGb4MEEfd+/iDoC2YfunCfvjOARkiXegbth3acRnr8Ugx4KbqV5nlNRsEynI1Y99gmV6Gr+IpTQjGYYnD4JOFE9G8eB97TJIaQJX8LMic/efjfuFrMiJTzO77CvOun8+F6QrStE1j7FnEJ4Qx5PyGvIKWfUbRR+b36qzHVbH+RzJzHXS9+YIG5+JHDeEWWLs5z2ev6NVf1LkTRn5xWOlDwVf02dY/6Zset9i5+9uPJJToaKa030FcUUDRle3R3/lr/lb/lb/vvK/wLsTS6/XGLMawAAAABJRU5ErkJggg==)\n",
        "\n",
        "### Task 1: Letter/Digit Classifier \n",
        "Given an image, you must be able to classify whether the image is a letter or a digit.\n",
        "\n",
        "***Expected outcome***:  You are expected to use a ML-based model (like CNNs, etc.) to solve the problem with a reasonably high accuracy.\n",
        "\n",
        "You are given a dataset containing 28x28 grayscale images. Each image is either a handwritten letter or digit.\n",
        "The dataset is present as a CSV file. You’ll find two CSV files: Train-set and Test-set.\n",
        "You are supposed to train only using the train-set and use test-set only for calculating accuracy.\n",
        "\n",
        "### Task 2: Vowel/Consonant and Even/Odd Classifier (Moderate)\n",
        "Given an image, you are supposed to design model(s) which does the following:\n",
        "1. If the image is a letter, you are supposed to predict if it is a vowel or consonant.\n",
        "2. If the image is a digit, you are supposed to predict if it is an even or odd number.\n",
        "\n",
        "You are supposed to use only ML models that directly predicts the above, instead of doing manual predictions like using modulus operator on top of digit predictions.\n",
        "\n",
        "***Expected outcome:***  Given an image, your end-to-end setup must print whether it is a letter or digit, and based on that, it must automatically run the corresponding model to print if it is vowel/consonant or even/odd respectively.\n",
        "\n",
        "### Task 3: Character Classifier\n",
        "Given an image, you are supposed to predict what digit or letter the image contains.\n",
        "That is, you will be doing a classification task for 47 classes.\n",
        "\n",
        "***Expected outcome:***  Given an image, you have to print what character it is (just using a single model). Also, report the class-wise accuracy if possible.\n",
        "\n",
        "***Submission Rules:***\n",
        "The deadline for this round is 17-Nov-2019 (Sunday 11:59PM IST)\n",
        "It’s OK to submit how much ever you have completed within the deadline.\n",
        "You are required to push all your source code to your Github repository.\n",
        "After you have completed as many tasks as possible, you are required to fill the following Google Form with all the details required.\n",
        "\n",
        "***Round-1 Submission Form***\n",
        "\n",
        "You are expected to report the accuracies you obtained for all the tasks and the techniques you used by explaining them all in your Github repository’s `README.md` file.\n",
        "You are also expected to push your trained models to the repo, and add instructions on how to load the model and run the code.\n",
        "Do not use a single end-to-end model to solve all 3 tasks. Train different models specific to each task.\n",
        "\n",
        "\n",
        "***Number of samples:***\n",
        "\n",
        "**Train set\t: 112,800\t(2400 images per class)**\n",
        "\n",
        "**Test set\t: 18,800\t(  400 images per class)**\n",
        "\n",
        "**CSV format:**\n",
        "Each line in the csv file corresponds to 1 sample. Each line will contain 785 values.\n",
        "The first value in all lines indicate the label ID, and the remaining 784 values corresponds to the individual pixel values of the 28*28 image (serialized in column-major order)\n",
        "\n",
        "The ASCII value of each label ID can be found in the mapping.txt file.\n",
        "For example, a label ID of 10 has an ASCII value of 65, which means that it corresponds to the character ‘A’."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MhTEkOkgt6Vz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "## IMAGE PROCESSING\n",
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "from matplotlib import pyplot as plt # Plotting Graphsand Image\n",
        "from math import floor # Round OFF\n",
        "import seaborn as sns # Plotting\n",
        "import base64 # CODING\n",
        "\n",
        "## UTILS\n",
        "import random # generating Random Number\n",
        "import warnings # Removing Warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "from keras.preprocessing.image import ImageDataGenerator # Data Augumentation\n",
        "from tqdm import tqdm # ProgressBar\n",
        "from IPython.display import HTML # Dataset Download\n",
        "\n",
        "## MODELS\n",
        "from keras.layers import Input,Conv2D,Dense, Dropout, BatchNormalization, MaxPool2D, Activation, Flatten, AvgPool2D,GlobalMaxPooling2D # KERAS LAYERS\n",
        "from keras.layers import  BatchNormalization as btn # BatchNormalization\n",
        "from keras.regularizers import l2\n",
        "from keras.models import Model, Sequential  #model\n",
        "from sklearn.metrics import precision_score, recall_score, confusion_matrix, classification_report, accuracy_score, f1_score\n",
        "from keras.callbacks import LearningRateScheduler\n",
        "from keras.optimizers import Adam\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LMggk-ECoKLA",
        "colab_type": "code",
        "outputId": "cd751dc0-17e9-44e6-dabc-968cd57de84b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        }
      },
      "source": [
        "!nvidia-smi"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Sun Nov 17 14:34:30 2019       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 430.50       Driver Version: 418.67       CUDA Version: 10.1     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla K80           Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   63C    P0    67W / 149W |   4322MiB / 11441MiB |      0%      Default |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                       GPU Memory |\n",
            "|  GPU       PID   Type   Process name                             Usage      |\n",
            "|=============================================================================|\n",
            "+-----------------------------------------------------------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ThyhcgvQvO5r",
        "colab_type": "code",
        "outputId": "8d08e0b3-8fa6-47e5-a8f7-5b786dbbae8d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jTzDw6GnuWyx",
        "colab_type": "code",
        "outputId": "5de7d9ea-5eb3-4925-f9d5-6cb18399a89a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        }
      },
      "source": [
        "# Input data files are available in the  directory.\n",
        "import os\n",
        "print(\"Files in the Dataset:-\")\n",
        "for dirname, _, filenames in os.walk(\"/content/drive/My Drive/Character + Digits data\"):\n",
        "    for filename in filenames:\n",
        "        print( os.path.join(dirname, filename))"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Files in the Dataset:-\n",
            "/content/drive/My Drive/Character + Digits data/characters-digits-mapping.txt\n",
            "/content/drive/My Drive/Character + Digits data/Readme.txt\n",
            "/content/drive/My Drive/Character + Digits data/characters-digits-test.csv\n",
            "/content/drive/My Drive/Character + Digits data/characters-digits-train.csv\n",
            "/content/drive/My Drive/Character + Digits data/utils.py\n",
            "/content/drive/My Drive/Character + Digits data/build_model.py\n",
            "/content/drive/My Drive/Character + Digits data/TrainingBasicModel.ipynb\n",
            "/content/drive/My Drive/Character + Digits data/result_Task2.csv\n",
            "/content/drive/My Drive/Character + Digits data/result_Task1.csv\n",
            "/content/drive/My Drive/Character + Digits data/Task3.ipynb\n",
            "/content/drive/My Drive/Character + Digits data/Task2.ipynb\n",
            "/content/drive/My Drive/Character + Digits data/Task1.ipynb\n",
            "/content/drive/My Drive/Character + Digits data/clf.pdf\n",
            "/content/drive/My Drive/Character + Digits data/clf\n",
            "/content/drive/My Drive/Character + Digits data/__pycache__/utils.cpython-36.pyc\n",
            "/content/drive/My Drive/Character + Digits data/__pycache__/build_model.cpython-36.pyc\n",
            "/content/drive/My Drive/Character + Digits data/model/model_0_V1.h5\n",
            "/content/drive/My Drive/Character + Digits data/model/OddEvenVowelConClf.pkl\n",
            "/content/drive/My Drive/Character + Digits data/model/NumLetterClf.pkl\n",
            "/content/drive/My Drive/Character + Digits data/model/model_.59h5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vJcFVi4_ufKS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_csv = pd.read_csv('/content/drive/My Drive/Character + Digits data/characters-digits-train.csv',header=None)\n",
        "test_csv = pd.read_csv('/content/drive/My Drive/Character + Digits data/characters-digits-test.csv',header=None)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hKUGYR6BvpOX",
        "colab_type": "code",
        "outputId": "069ef408-7535-4131-bf19-b3993f5db0e6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 253
        }
      },
      "source": [
        "train_csv.head()"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>10</th>\n",
              "      <th>11</th>\n",
              "      <th>12</th>\n",
              "      <th>13</th>\n",
              "      <th>14</th>\n",
              "      <th>15</th>\n",
              "      <th>16</th>\n",
              "      <th>17</th>\n",
              "      <th>18</th>\n",
              "      <th>19</th>\n",
              "      <th>20</th>\n",
              "      <th>21</th>\n",
              "      <th>22</th>\n",
              "      <th>23</th>\n",
              "      <th>24</th>\n",
              "      <th>25</th>\n",
              "      <th>26</th>\n",
              "      <th>27</th>\n",
              "      <th>28</th>\n",
              "      <th>29</th>\n",
              "      <th>30</th>\n",
              "      <th>31</th>\n",
              "      <th>32</th>\n",
              "      <th>33</th>\n",
              "      <th>34</th>\n",
              "      <th>35</th>\n",
              "      <th>36</th>\n",
              "      <th>37</th>\n",
              "      <th>38</th>\n",
              "      <th>39</th>\n",
              "      <th>...</th>\n",
              "      <th>745</th>\n",
              "      <th>746</th>\n",
              "      <th>747</th>\n",
              "      <th>748</th>\n",
              "      <th>749</th>\n",
              "      <th>750</th>\n",
              "      <th>751</th>\n",
              "      <th>752</th>\n",
              "      <th>753</th>\n",
              "      <th>754</th>\n",
              "      <th>755</th>\n",
              "      <th>756</th>\n",
              "      <th>757</th>\n",
              "      <th>758</th>\n",
              "      <th>759</th>\n",
              "      <th>760</th>\n",
              "      <th>761</th>\n",
              "      <th>762</th>\n",
              "      <th>763</th>\n",
              "      <th>764</th>\n",
              "      <th>765</th>\n",
              "      <th>766</th>\n",
              "      <th>767</th>\n",
              "      <th>768</th>\n",
              "      <th>769</th>\n",
              "      <th>770</th>\n",
              "      <th>771</th>\n",
              "      <th>772</th>\n",
              "      <th>773</th>\n",
              "      <th>774</th>\n",
              "      <th>775</th>\n",
              "      <th>776</th>\n",
              "      <th>777</th>\n",
              "      <th>778</th>\n",
              "      <th>779</th>\n",
              "      <th>780</th>\n",
              "      <th>781</th>\n",
              "      <th>782</th>\n",
              "      <th>783</th>\n",
              "      <th>784</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>45</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>36</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>43</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 785 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   0    1    2    3    4    5    6    ...  778  779  780  781  782  783  784\n",
              "0   45    0    0    0    0    0    0  ...    0    0    0    0    0    0    0\n",
              "1   36    0    0    0    0    0    0  ...    0    0    0    0    0    0    0\n",
              "2   43    0    0    0    0    0    0  ...    0    0    0    0    0    0    0\n",
              "3   15    0    0    0    0    0    0  ...    0    0    0    0    0    0    0\n",
              "4    4    0    0    0    0    0    0  ...    0    0    0    0    0    0    0\n",
              "\n",
              "[5 rows x 785 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4QcO6N7Uvrpv",
        "colab_type": "code",
        "outputId": "c939d2cb-30b2-411d-b58c-ec0a1780dbff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        }
      },
      "source": [
        "sns.set(color_codes=True)\n",
        "sns.distplot(train_csv.iloc[:,0],label = 'LABELS',kde=False\n",
        "             ,color='blue',norm_hist=False);"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEOCAYAAACEiBAqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zu2s9yUtvzIP",
        "colab_type": "text"
      },
      "source": [
        "*Note: We can clearly see we do not have problem of Imbalance Class as every class has good representations*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5eCE7h01wNX9",
        "colab_type": "text"
      },
      "source": [
        "## Insight of dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8PeemQqBwKFD",
        "colab_type": "code",
        "outputId": "2d90bb2a-345e-415b-8f72-22f48b03b9c1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "size_of_img = (int(np.sqrt(train_csv.shape[1])),int(np.sqrt(train_csv.shape[1])))\n",
        "fig=plt.figure(figsize=(72,72))\n",
        "for i in range(60):\n",
        "    ax=fig.add_subplot(12,12,i+1)\n",
        "    plot_image = np.array(train_csv.iloc[i,1:]).reshape(size_of_img)\n",
        "    ax.imshow(np.fliplr(np.rot90(plot_image,3)),cmap='Greys')\n",
        "    ax.set_title('LABEL ' + str(train_csv.iloc[i,0]),fontsize= 35)\n",
        "plt.show()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAD9AAAAaBCAYAAABBXvXaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
            "text/plain": [
              "<Figure size 5184x5184 with 60 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cbNzuykAwSKG",
        "colab_type": "code",
        "outputId": "a4ba8add-4405-4644-ab49-03210906b325",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "labels = np.unique(train_csv.iloc[:,0])\n",
        "print(\"Total Labels\",  labels)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Total Labels [ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23\n",
            " 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lch7uMSwwAnT",
        "colab_type": "text"
      },
      "source": [
        "# Utility Function"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B-2whDjZMjbZ",
        "colab_type": "code",
        "outputId": "682da58c-df58-4144-830d-16a7d5646ec0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "%cd /content/drive/My Drive/Character\\ +\\ Digits\\ data/"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/My Drive/Character + Digits data\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KL1WLPlnMtP2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from utils import *"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xsKe72zJxJVe",
        "colab_type": "text"
      },
      "source": [
        "## Dataset Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZLCrbZBCwxfd",
        "colab_type": "code",
        "outputId": "e5c883e4-275e-459d-9e96-19d0f2f79193",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "train_feature, train_labels, dev_feature, dev_labels,_,__ = dataset_distribution(train_csv,[80,20,0])\n",
        "train_feature.shape, train_labels.shape, dev_feature.shape, dev_labels.shape"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((90240, 784), (90240,), (22560, 784), (22560,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nlu-biQsxSFj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_image = change_to_image(train_feature)\n",
        "dev_image   = change_to_image(dev_feature)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SGu_ICM5xVhT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_labelen = one_hot_encoding(train_labels)\n",
        "dev_labelen   = one_hot_encoding(dev_labels)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ljs6Vz21xcHD",
        "colab_type": "code",
        "outputId": "4aed0389-53bd-4a98-9832-33c165d61ace",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        }
      },
      "source": [
        "no_of_class = len(labels)\n",
        "print(\"no_of_class : \",no_of_class)\n",
        "print(\"train_image.shape : \",train_image.shape)\n",
        "print(\"dev_image.shape : \",dev_image.shape)\n",
        "print(\"train_label.shape : \",train_labelen.shape)\n",
        "print(\"dev_label.shape: \",dev_labelen.shape)\n",
        "print(\"train_feature: \",train_feature.shape)\n",
        "print(\"train_labels.shape : \",train_labels.shape)\n",
        "print(\"train_feature: \",dev_feature.shape)\n",
        "print(\"train_labels.shape : \",dev_labels.shape)"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "no_of_class :  47\n",
            "train_image.shape :  (90240, 28, 28, 1)\n",
            "dev_image.shape :  (22560, 28, 28, 1)\n",
            "train_label.shape :  (90240, 47)\n",
            "dev_label.shape:  (22560, 47)\n",
            "train_feature:  (90240, 784)\n",
            "train_labels.shape :  (90240,)\n",
            "train_feature:  (22560, 784)\n",
            "train_labels.shape :  (22560,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HPIwLYmWyKt5",
        "colab_type": "text"
      },
      "source": [
        "## Data Augmentation\n",
        "\n",
        "A convolutional neural network that can robustly classify objects even if its placed in different orientations is said to have the property called invariance. More specifically, a CNN can be invariant to translation, viewpoint, size or illumination (Or a combination of the above).\n",
        "\n",
        "This essentially is the premise of data augmentation. In the real world scenario, we may have a dataset of images taken in a limited set of conditions. But, our target application may exist in a variety of conditions, such as different orientation, location, scale, brightness etc. We account for these situations by training our neural network with additional synthetically modified data.\n",
        "\n",
        "some basic but powerful augmentation techniques that are popularly used. Before we explore these techniques, for simplicity, let us make one assumption. The assumption is that, we don’t need to consider what lies beyond the image’s boundary. We’ll use the below techniques such that our assumption is valid.\n",
        "\n",
        "What would happen if we use a technique that forces us to guess what lies beyond an image’s boundary? In this case, we need to interpolate some information. We’ll discuss this in detail after we cover the types of augmentation."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PYzvEmJfxuAa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "datagen = ImageDataGenerator( \n",
        "        zoom_range = 0.090,  \n",
        "        width_shift_range=0.02, \n",
        "        height_shift_range=0.02)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "24ReWSp1niM2",
        "colab_type": "text"
      },
      "source": [
        "### Stacked Models\n",
        "Stacking is a way to ensemble multiple classifications or regression model. There are many ways to ensemble models, the widely known models are Bagging or Boosting. Bagging allows multiple similar models with high variance are averaged to decrease variance. Boosting builds multiple incremental models to decrease the bias, while keeping variance small.\n",
        "\n",
        "Stacking (sometimes called Stacked Generalization) is a different paradigm. The point of stacking is to explore a space of different models for the same problem. The idea is that you can attack a learning problem with different types of models which are capable to learn some part of the problem, but not the whole space of the problem. So, you can build multiple different learners and you use them to build an intermediate prediction, one prediction for each learned model. Then you add a new model which learns from the intermediate predictions the same target. This final model is said to be stacked on the top of the others, hence the name. Thus, you might improve your overall performance, and often you end up with a model which is better than any individual intermediate model. Notice however, that it does not give you any guarantee, as is often the case with any machine learning technique."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KFu1md86Njx0",
        "colab_type": "code",
        "outputId": "5ae303a1-c770-40d2-c7a1-7f358292125c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 799
        }
      },
      "source": [
        "nets = 10\n",
        "model = [0] *nets\n",
        "for j in range(nets):\n",
        "    model[j] = Sequential()\n",
        "\n",
        "    model[j].add(Conv2D(128, kernel_size = 3, activation='relu', input_shape = (28, 28, 1)))\n",
        "    model[j].add(BatchNormalization())\n",
        "    model[j].add(Conv2D(128, kernel_size = 3, activation='relu'))\n",
        "    model[j].add(BatchNormalization())\n",
        "    model[j].add(Conv2D(128, kernel_size = 5, strides=2, padding='same', activation='relu'))\n",
        "    model[j].add(BatchNormalization())\n",
        "    model[j].add(Dropout(0.4))\n",
        "\n",
        "    model[j].add(Conv2D(256, kernel_size = 3, activation='relu'))\n",
        "    model[j].add(BatchNormalization())\n",
        "    model[j].add(Conv2D(256, kernel_size = 3, activation='relu'))\n",
        "    model[j].add(BatchNormalization())\n",
        "    model[j].add(Conv2D(256, kernel_size = 5, strides=2, padding='same', activation='relu'))\n",
        "    model[j].add(BatchNormalization())\n",
        "    model[j].add(Dropout(0.44))\n",
        "\n",
        "    model[j].add(Conv2D(1024, kernel_size = 3, activation='relu'))\n",
        "    model[j].add(BatchNormalization())\n",
        "    model[j].add(Flatten())\n",
        "    model[j].add(Dropout(0.44))\n",
        "    #model[j].add(Dense(64,activation = 'relu'))\n",
        "    model[j].add(Dense(47, activation='softmax'))\n",
        "\n",
        "    \n",
        "    # COMPILE WITH ADAM OPTIMIZER AND CROSS ENTROPY COST\n",
        "    model[j].compile(optimizer='RMSprop', loss=\"categorical_crossentropy\", metrics=[\"accuracy\"])\n",
        "model[0].summary()"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_26\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_176 (Conv2D)          (None, 26, 26, 128)       1280      \n",
            "_________________________________________________________________\n",
            "batch_normalization_176 (Bat (None, 26, 26, 128)       512       \n",
            "_________________________________________________________________\n",
            "conv2d_177 (Conv2D)          (None, 24, 24, 128)       147584    \n",
            "_________________________________________________________________\n",
            "batch_normalization_177 (Bat (None, 24, 24, 128)       512       \n",
            "_________________________________________________________________\n",
            "conv2d_178 (Conv2D)          (None, 12, 12, 128)       409728    \n",
            "_________________________________________________________________\n",
            "batch_normalization_178 (Bat (None, 12, 12, 128)       512       \n",
            "_________________________________________________________________\n",
            "dropout_76 (Dropout)         (None, 12, 12, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_179 (Conv2D)          (None, 10, 10, 256)       295168    \n",
            "_________________________________________________________________\n",
            "batch_normalization_179 (Bat (None, 10, 10, 256)       1024      \n",
            "_________________________________________________________________\n",
            "conv2d_180 (Conv2D)          (None, 8, 8, 256)         590080    \n",
            "_________________________________________________________________\n",
            "batch_normalization_180 (Bat (None, 8, 8, 256)         1024      \n",
            "_________________________________________________________________\n",
            "conv2d_181 (Conv2D)          (None, 4, 4, 256)         1638656   \n",
            "_________________________________________________________________\n",
            "batch_normalization_181 (Bat (None, 4, 4, 256)         1024      \n",
            "_________________________________________________________________\n",
            "dropout_77 (Dropout)         (None, 4, 4, 256)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_182 (Conv2D)          (None, 2, 2, 1024)        2360320   \n",
            "_________________________________________________________________\n",
            "batch_normalization_182 (Bat (None, 2, 2, 1024)        4096      \n",
            "_________________________________________________________________\n",
            "flatten_26 (Flatten)         (None, 4096)              0         \n",
            "_________________________________________________________________\n",
            "dropout_78 (Dropout)         (None, 4096)              0         \n",
            "_________________________________________________________________\n",
            "dense_26 (Dense)             (None, 47)                192559    \n",
            "=================================================================\n",
            "Total params: 5,644,079\n",
            "Trainable params: 5,639,727\n",
            "Non-trainable params: 4,352\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WQDi0go8nhz0",
        "colab_type": "code",
        "outputId": "e6642b55-43e5-4dd3-afb8-649173571028",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "annealer = LearningRateScheduler(lambda x: 1e-3 * 0.95 ** x)\n",
        "# TRAIN NETWORKS\n",
        "nets = 10\n",
        "batch_size = 512\n",
        "history = [0] * nets\n",
        "epochs = 12\n",
        "for j in range(nets):\n",
        "    history[j] = model[j].fit_generator(datagen.flow(train_image,train_labelen, batch_size=batch_size),\n",
        "        epochs = epochs, steps_per_epoch = dev_image.shape[0]//batch_size,  \n",
        "        validation_data = (dev_image,dev_labelen), callbacks=[annealer], verbose=0)\n",
        "    print(\"CNN {0:d}: Epochs={1:d}, Train accuracy={2:.5f}, Validation accuracy={3:.5f}\".format(\n",
        "        j+1,epochs,max(history[j].history['acc']),max(history[j].history['val_acc']) ))\n",
        "    model[j].save_weights(\"model/model_\" + str(j) +\".h5\")"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CNN 1: Epochs=12, Train accuracy=0.86439, Validation accuracy=0.87252\n",
            "CNN 2: Epochs=12, Train accuracy=0.85920, Validation accuracy=0.87513\n",
            "CNN 3: Epochs=12, Train accuracy=0.86217, Validation accuracy=0.87602\n",
            "CNN 4: Epochs=12, Train accuracy=0.86066, Validation accuracy=0.87841\n",
            "CNN 5: Epochs=12, Train accuracy=0.86368, Validation accuracy=0.87509\n",
            "CNN 6: Epochs=12, Train accuracy=0.85865, Validation accuracy=0.87340\n",
            "CNN 7: Epochs=12, Train accuracy=0.86235, Validation accuracy=0.87598\n",
            "CNN 8: Epochs=12, Train accuracy=0.85626, Validation accuracy=0.87416\n",
            "CNN 9: Epochs=12, Train accuracy=0.85955, Validation accuracy=0.87425\n",
            "CNN 10: Epochs=12, Train accuracy=0.86182, Validation accuracy=0.86981\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8OCvRHn1nNh2",
        "colab_type": "text"
      },
      "source": [
        "**Almost no overfitting due to:-**\n",
        "\n",
        "**droupout layers**\n",
        "\n",
        "Dropout Regularization For Neural Networks Dropout is a regularization technique for neural network models proposed by Srivastava, et al. in their 2014 paper Dropout: A Simple Way to Prevent Neural Networks from Overfitting (download the PDF).\n",
        "\n",
        "Dropout is a technique where randomly selected neurons are ignored during training. They are “dropped-out” randomly. This means that their contribution to the activation of downstream neurons is temporally removed on the forward pass and any weight updates are not applied to the neuron on the backward pass.\n",
        "\n",
        "\n",
        " *Also you can use* \n",
        "\n",
        "*weight regularization*\n",
        "\n",
        "Keras provides a weight regularization API that allows you to add a penalty for weight size to the loss function.\n",
        "\n",
        "Three different regularizer instances are provided; they are:\n",
        "\n",
        "**L1:** Sum of the absolute weights.**L2:** Sum of the squared weights. L1L2: Sum of the absolute and the squared weights. The regularizers are provided under keras.regularizers and have the names l1, l2 and l1_l2. Each takes the regularizer hyperparameter as an argument."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OJifWZCUJYDV",
        "colab_type": "code",
        "outputId": "ba1b4cef-10cc-42e2-bd7d-2c5a69b99894",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 736
        }
      },
      "source": [
        "name_title = ['Loss','Accuracy']\n",
        "for net in range(1):\n",
        "    print(\"FOR CNN \",net)\n",
        "    fig=plt.figure(figsize=(64,64))\n",
        "    for i in range(0,2):\n",
        "        ax=fig.add_subplot(8,8,i+1)\n",
        "        plt.plot(history[net].history[list(history[net].history.keys())[i]], label = list(history[net].history.keys())[i] )\n",
        "        plt.plot(history[net].history[list(history[net].history.keys())[i+2]],label = list(history[net].history.keys())[i+2] )\n",
        "        plt.xlabel('Epochs', fontsize=18)\n",
        "        plt.ylabel(name_title[i], fontsize=18)\n",
        "        plt.legend()\n",
        "        plt.show()"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "FOR CNN  0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAbsAAAGtCAYAAACP5tqYAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
            "text/plain": [
              "<Figure size 4608x4608 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZUAAAERCAYAAACzejr1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ktvO_uRxxLvW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tqdm import tqdm\n",
        "def result(model,image):\n",
        "  result = np.zeros((image.shape[0],47))\n",
        "  for model_ in model:\n",
        "    result += model_.predict(image)\n",
        "  return np.argmax(result,axis = 1).reshape(-1,1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3IAOf6OOPEIH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_tr = result(model,train_image)\n",
        "y_dev = result(model,dev_image)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uR8RIv3jnMWW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "4c692b2a-1523-47e8-bcc7-8d08c8eb8bfc"
      },
      "source": [
        "acc(y_dev, dev_labels,cmap = \"BuPu\")"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Recall: [0.66598361 0.67307692 0.88577154 0.97773279 0.96868476 0.90388548\n",
            " 0.93897638 0.96900826 0.94238683 0.62828947 0.96801706 0.96247241\n",
            " 0.98163265 0.93147752 0.98526316 0.71495327 0.94725738 0.96370968\n",
            " 0.69811321 0.9379015  0.97533207 0.44094488 0.9847619  0.94845361\n",
            " 0.69473684 0.97008547 0.951417   0.97119342 0.93526786 0.94466403\n",
            " 0.93791574 0.92965779 0.97768763 0.98155738 0.92778993 0.94375\n",
            " 0.94004283 0.93899782 0.98181818 0.96273292 0.63706564 0.84383562\n",
            " 0.96039604 0.97452229 0.79591837 0.98409091 0.90871369]\n",
            "Precision: [0.67708333 0.20547945 0.93248945 0.9897541  0.94501018 0.95053763\n",
            " 0.93713163 0.97912317 0.95020747 0.91826923 0.97634409 0.9753915\n",
            " 0.97171717 0.94978166 0.97703549 0.612      0.94327731 0.98353909\n",
            " 0.62315789 0.95217391 0.98467433 0.79674797 0.99042146 0.98712446\n",
            " 0.68041237 0.97216274 0.97107438 0.98128898 0.90889371 0.96177062\n",
            " 0.94       0.972167   0.98770492 0.96184739 0.9339207  0.89880952\n",
            " 0.93205945 0.94933921 0.9739479  0.98516949 0.69473684 0.64300626\n",
            " 0.96039604 0.95824635 0.55714286 0.94541485 0.93589744]\n",
            "\n",
            " clasification report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.68      0.67      0.67       488\n",
            "           1       0.21      0.67      0.31       156\n",
            "           2       0.93      0.89      0.91       499\n",
            "           3       0.99      0.98      0.98       494\n",
            "           4       0.95      0.97      0.96       479\n",
            "           5       0.95      0.90      0.93       489\n",
            "           6       0.94      0.94      0.94       508\n",
            "           7       0.98      0.97      0.97       484\n",
            "           8       0.95      0.94      0.95       486\n",
            "           9       0.92      0.63      0.75       608\n",
            "          10       0.98      0.97      0.97       469\n",
            "          11       0.98      0.96      0.97       453\n",
            "          12       0.97      0.98      0.98       490\n",
            "          13       0.95      0.93      0.94       467\n",
            "          14       0.98      0.99      0.98       475\n",
            "          15       0.61      0.71      0.66       428\n",
            "          16       0.94      0.95      0.95       474\n",
            "          17       0.98      0.96      0.97       496\n",
            "          18       0.62      0.70      0.66       424\n",
            "          19       0.95      0.94      0.94       467\n",
            "          20       0.98      0.98      0.98       527\n",
            "          21       0.80      0.44      0.57       889\n",
            "          22       0.99      0.98      0.99       525\n",
            "          23       0.99      0.95      0.97       485\n",
            "          24       0.68      0.69      0.69       475\n",
            "          25       0.97      0.97      0.97       468\n",
            "          26       0.97      0.95      0.96       494\n",
            "          27       0.98      0.97      0.98       486\n",
            "          28       0.91      0.94      0.92       448\n",
            "          29       0.96      0.94      0.95       506\n",
            "          30       0.94      0.94      0.94       451\n",
            "          31       0.97      0.93      0.95       526\n",
            "          32       0.99      0.98      0.98       493\n",
            "          33       0.96      0.98      0.97       488\n",
            "          34       0.93      0.93      0.93       457\n",
            "          35       0.90      0.94      0.92       480\n",
            "          36       0.93      0.94      0.94       467\n",
            "          37       0.95      0.94      0.94       459\n",
            "          38       0.97      0.98      0.98       495\n",
            "          39       0.99      0.96      0.97       483\n",
            "          40       0.69      0.64      0.66       518\n",
            "          41       0.64      0.84      0.73       365\n",
            "          42       0.96      0.96      0.96       505\n",
            "          43       0.96      0.97      0.97       471\n",
            "          44       0.56      0.80      0.66       343\n",
            "          45       0.95      0.98      0.96       440\n",
            "          46       0.94      0.91      0.92       482\n",
            "\n",
            "    accuracy                           0.89     22560\n",
            "   macro avg       0.89      0.90      0.89     22560\n",
            "weighted avg       0.90      0.89      0.89     22560\n",
            "\n",
            "\n",
            " confussion matrix:\n",
            " [[325   0   0 ...   0   0   0]\n",
            " [  0 105   0 ...   0   1   0]\n",
            " [  0   1 442 ...   1   0   0]\n",
            " ...\n",
            " [  0   0   1 ... 273   0   0]\n",
            " [  0   0   0 ...   0 433   0]\n",
            " [  0   0   0 ...   0   0 438]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAApwAAALsCAYAAABOaWdkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
            "text/plain": [
              "<Figure size 864x936 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UnfH-q-0LBwK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def predict(model,test_csv):\n",
        "  if type(test_csv) is str:\n",
        "    test_csv = pd.read_csv(test_csv,header=None)\n",
        "  test_feature, test_labels,_,_,_,_ = dataset_distribution(test_csv,[100,0,0])\n",
        "  print(\"Shape of feature and labels are:- \",test_feature.shape, test_labels.shape)\n",
        "  test_image = change_to_image(test_feature)\n",
        "  test_labelenLD = one_hot_encoding(test_labels)\n",
        "  y =  result(model,test_image)\n",
        "  acc(test_labels.reshape(-1,1),y,\"BuPu\")\n",
        "  print(\"Accuracy is :- \",accuracy_score(test_labels.reshape(-1,1),y))\n",
        "  index = np.arange(test_labels.shape[0]).reshape(-1,1)\n",
        "  data = pd.DataFrame(np.concatenate((index,y),axis = 1))\n",
        "  data.to_csv(\"result_Task2.csv\",index = None,header = [\"index\",\"Y\"])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9E05P_WwTC6d",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "988e7e97-6c91-4901-fe20-5ad1ab12a10d"
      },
      "source": [
        "predict(model,test_csv)"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Shape of feature and labels are:-  (18800, 784) (18800,)\n",
            "Recall: [0.7175 0.1775 0.93   0.99   0.9225 0.925  0.9325 0.9925 0.9575 0.9175\n",
            " 0.99   0.9775 0.9575 0.94   0.98   0.61   0.96   0.9775 0.6325 0.9675\n",
            " 0.9725 0.8225 0.9925 0.9775 0.6875 0.97   0.965  1.     0.9325 0.945\n",
            " 0.935  0.96   0.995  0.96   0.9225 0.92   0.92   0.9425 0.9775 0.975\n",
            " 0.695  0.645  0.95   0.9175 0.5025 0.935  0.9325]\n",
            "Precision: [0.68660287 0.65137615 0.90731707 0.99497487 0.94615385 0.92964824\n",
            " 0.95886889 0.97542998 0.95037221 0.65186501 0.98019802 0.97994987\n",
            " 0.95989975 0.92383292 0.98740554 0.71764706 0.94348894 0.96068796\n",
            " 0.72492837 0.92805755 0.98232323 0.44761905 0.97066015 0.94673123\n",
            " 0.70512821 0.97       0.93012048 0.9569378  0.9097561  0.92420538\n",
            " 0.96143959 0.91866029 0.98271605 0.99481865 0.93654822 0.93877551\n",
            " 0.89756098 0.92401961 0.97994987 0.98236776 0.6450116  0.83766234\n",
            " 0.9382716  0.94832041 0.77011494 0.95165394 0.90754258]\n",
            "\n",
            " clasification report:\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.69      0.72      0.70       400\n",
            "           1       0.65      0.18      0.28       400\n",
            "           2       0.91      0.93      0.92       400\n",
            "           3       0.99      0.99      0.99       400\n",
            "           4       0.95      0.92      0.93       400\n",
            "           5       0.93      0.93      0.93       400\n",
            "           6       0.96      0.93      0.95       400\n",
            "           7       0.98      0.99      0.98       400\n",
            "           8       0.95      0.96      0.95       400\n",
            "           9       0.65      0.92      0.76       400\n",
            "          10       0.98      0.99      0.99       400\n",
            "          11       0.98      0.98      0.98       400\n",
            "          12       0.96      0.96      0.96       400\n",
            "          13       0.92      0.94      0.93       400\n",
            "          14       0.99      0.98      0.98       400\n",
            "          15       0.72      0.61      0.66       400\n",
            "          16       0.94      0.96      0.95       400\n",
            "          17       0.96      0.98      0.97       400\n",
            "          18       0.72      0.63      0.68       400\n",
            "          19       0.93      0.97      0.95       400\n",
            "          20       0.98      0.97      0.98       400\n",
            "          21       0.45      0.82      0.58       400\n",
            "          22       0.97      0.99      0.98       400\n",
            "          23       0.95      0.98      0.96       400\n",
            "          24       0.71      0.69      0.70       400\n",
            "          25       0.97      0.97      0.97       400\n",
            "          26       0.93      0.96      0.95       400\n",
            "          27       0.96      1.00      0.98       400\n",
            "          28       0.91      0.93      0.92       400\n",
            "          29       0.92      0.94      0.93       400\n",
            "          30       0.96      0.94      0.95       400\n",
            "          31       0.92      0.96      0.94       400\n",
            "          32       0.98      0.99      0.99       400\n",
            "          33       0.99      0.96      0.98       400\n",
            "          34       0.94      0.92      0.93       400\n",
            "          35       0.94      0.92      0.93       400\n",
            "          36       0.90      0.92      0.91       400\n",
            "          37       0.92      0.94      0.93       400\n",
            "          38       0.98      0.98      0.98       400\n",
            "          39       0.98      0.97      0.98       400\n",
            "          40       0.65      0.69      0.67       400\n",
            "          41       0.84      0.65      0.73       400\n",
            "          42       0.94      0.95      0.94       400\n",
            "          43       0.95      0.92      0.93       400\n",
            "          44       0.77      0.50      0.61       400\n",
            "          45       0.95      0.94      0.94       400\n",
            "          46       0.91      0.93      0.92       400\n",
            "\n",
            "    accuracy                           0.89     18800\n",
            "   macro avg       0.89      0.89      0.89     18800\n",
            "weighted avg       0.89      0.89      0.89     18800\n",
            "\n",
            "\n",
            " confussion matrix:\n",
            " [[287   0   0 ...   0   0   0]\n",
            " [  0  71   1 ...   0   0   0]\n",
            " [  0   0 372 ...   1   0   0]\n",
            " ...\n",
            " [  0   0   0 ... 201   0   1]\n",
            " [  0   0   0 ...   0 374   0]\n",
            " [  0   0   0 ...   0   4 373]]\n",
            "Accuracy is :-  0.889468085106383\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAApwAAALsCAYAAABOaWdkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/x9VHcOYeGHc0AAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 864x936 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "irv-xyVVTE6g",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}