MNIST_Project.ipynb

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      "name": "MNIST_Project.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "# Applying ANN to the MNIST dataset\r\n",
        "- importing the dataset from the sklearn.datasets\r\n",
        "\r\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nQh9LGeiB9zZ"
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      "source": [
        "from sklearn.datasets import fetch_openml"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T9lN6un1BzqR"
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      "source": [
        "mnist = fetch_openml('mnist_784')"
      ],
      "execution_count": null,
      "outputs": []
    },
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        "id": "AVSQ2KMtCDSn",
        "outputId": "7b37fd09-5bde-4d24-f4ca-04a84e35f556"
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      "source": [
        "mnist"
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              "{'DESCR': \"**Author**: Yann LeCun, Corinna Cortes, Christopher J.C. Burges  \\n**Source**: [MNIST Website](http://yann.lecun.com/exdb/mnist/) - Date unknown  \\n**Please cite**:  \\n\\nThe MNIST database of handwritten digits with 784 features, raw data available at: http://yann.lecun.com/exdb/mnist/. It can be split in a training set of the first 60,000 examples, and a test set of 10,000 examples  \\n\\nIt is a subset of a larger set available from NIST. The digits have been size-normalized and centered in a fixed-size image. It is a good database for people who want to try learning techniques and pattern recognition methods on real-world data while spending minimal efforts on preprocessing and formatting. The original black and white (bilevel) images from NIST were size normalized to fit in a 20x20 pixel box while preserving their aspect ratio. The resulting images contain grey levels as a result of the anti-aliasing technique used by the normalization algorithm. the images were centered in a 28x28 image by computing the center of mass of the pixels, and translating the image so as to position this point at the center of the 28x28 field.  \\n\\nWith some classification methods (particularly template-based methods, such as SVM and K-nearest neighbors), the error rate improves when the digits are centered by bounding box rather than center of mass. If you do this kind of pre-processing, you should report it in your publications. The MNIST database was constructed from NIST's NIST originally designated SD-3 as their training set and SD-1 as their test set. However, SD-3 is much cleaner and easier to recognize than SD-1. The reason for this can be found on the fact that SD-3 was collected among Census Bureau employees, while SD-1 was collected among high-school students. Drawing sensible conclusions from learning experiments requires that the result be independent of the choice of training set and test among the complete set of samples. Therefore it was necessary to build a new database by mixing NIST's datasets.  \\n\\nThe MNIST training set is composed of 30,000 patterns from SD-3 and 30,000 patterns from SD-1. Our test set was composed of 5,000 patterns from SD-3 and 5,000 patterns from SD-1. The 60,000 pattern training set contained examples from approximately 250 writers. We made sure that the sets of writers of the training set and test set were disjoint. SD-1 contains 58,527 digit images written by 500 different writers. In contrast to SD-3, where blocks of data from each writer appeared in sequence, the data in SD-1 is scrambled. Writer identities for SD-1 is available and we used this information to unscramble the writers. We then split SD-1 in two: characters written by the first 250 writers went into our new training set. The remaining 250 writers were placed in our test set. Thus we had two sets with nearly 30,000 examples each. The new training set was completed with enough examples from SD-3, starting at pattern # 0, to make a full set of 60,000 training patterns. Similarly, the new test set was completed with SD-3 examples starting at pattern # 35,000 to make a full set with 60,000 test patterns. Only a subset of 10,000 test images (5,000 from SD-1 and 5,000 from SD-3) is available on this site. The full 60,000 sample training set is available.\\n\\nDownloaded from openml.org.\",\n",
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              "  'pixel570',\n",
              "  'pixel571',\n",
              "  'pixel572',\n",
              "  'pixel573',\n",
              "  'pixel574',\n",
              "  'pixel575',\n",
              "  'pixel576',\n",
              "  'pixel577',\n",
              "  'pixel578',\n",
              "  'pixel579',\n",
              "  'pixel580',\n",
              "  'pixel581',\n",
              "  'pixel582',\n",
              "  'pixel583',\n",
              "  'pixel584',\n",
              "  'pixel585',\n",
              "  'pixel586',\n",
              "  'pixel587',\n",
              "  'pixel588',\n",
              "  'pixel589',\n",
              "  'pixel590',\n",
              "  'pixel591',\n",
              "  'pixel592',\n",
              "  'pixel593',\n",
              "  'pixel594',\n",
              "  'pixel595',\n",
              "  'pixel596',\n",
              "  'pixel597',\n",
              "  'pixel598',\n",
              "  'pixel599',\n",
              "  'pixel600',\n",
              "  'pixel601',\n",
              "  'pixel602',\n",
              "  'pixel603',\n",
              "  'pixel604',\n",
              "  'pixel605',\n",
              "  'pixel606',\n",
              "  'pixel607',\n",
              "  'pixel608',\n",
              "  'pixel609',\n",
              "  'pixel610',\n",
              "  'pixel611',\n",
              "  'pixel612',\n",
              "  'pixel613',\n",
              "  'pixel614',\n",
              "  'pixel615',\n",
              "  'pixel616',\n",
              "  'pixel617',\n",
              "  'pixel618',\n",
              "  'pixel619',\n",
              "  'pixel620',\n",
              "  'pixel621',\n",
              "  'pixel622',\n",
              "  'pixel623',\n",
              "  'pixel624',\n",
              "  'pixel625',\n",
              "  'pixel626',\n",
              "  'pixel627',\n",
              "  'pixel628',\n",
              "  'pixel629',\n",
              "  'pixel630',\n",
              "  'pixel631',\n",
              "  'pixel632',\n",
              "  'pixel633',\n",
              "  'pixel634',\n",
              "  'pixel635',\n",
              "  'pixel636',\n",
              "  'pixel637',\n",
              "  'pixel638',\n",
              "  'pixel639',\n",
              "  'pixel640',\n",
              "  'pixel641',\n",
              "  'pixel642',\n",
              "  'pixel643',\n",
              "  'pixel644',\n",
              "  'pixel645',\n",
              "  'pixel646',\n",
              "  'pixel647',\n",
              "  'pixel648',\n",
              "  'pixel649',\n",
              "  'pixel650',\n",
              "  'pixel651',\n",
              "  'pixel652',\n",
              "  'pixel653',\n",
              "  'pixel654',\n",
              "  'pixel655',\n",
              "  'pixel656',\n",
              "  'pixel657',\n",
              "  'pixel658',\n",
              "  'pixel659',\n",
              "  'pixel660',\n",
              "  'pixel661',\n",
              "  'pixel662',\n",
              "  'pixel663',\n",
              "  'pixel664',\n",
              "  'pixel665',\n",
              "  'pixel666',\n",
              "  'pixel667',\n",
              "  'pixel668',\n",
              "  'pixel669',\n",
              "  'pixel670',\n",
              "  'pixel671',\n",
              "  'pixel672',\n",
              "  'pixel673',\n",
              "  'pixel674',\n",
              "  'pixel675',\n",
              "  'pixel676',\n",
              "  'pixel677',\n",
              "  'pixel678',\n",
              "  'pixel679',\n",
              "  'pixel680',\n",
              "  'pixel681',\n",
              "  'pixel682',\n",
              "  'pixel683',\n",
              "  'pixel684',\n",
              "  'pixel685',\n",
              "  'pixel686',\n",
              "  'pixel687',\n",
              "  'pixel688',\n",
              "  'pixel689',\n",
              "  'pixel690',\n",
              "  'pixel691',\n",
              "  'pixel692',\n",
              "  'pixel693',\n",
              "  'pixel694',\n",
              "  'pixel695',\n",
              "  'pixel696',\n",
              "  'pixel697',\n",
              "  'pixel698',\n",
              "  'pixel699',\n",
              "  'pixel700',\n",
              "  'pixel701',\n",
              "  'pixel702',\n",
              "  'pixel703',\n",
              "  'pixel704',\n",
              "  'pixel705',\n",
              "  'pixel706',\n",
              "  'pixel707',\n",
              "  'pixel708',\n",
              "  'pixel709',\n",
              "  'pixel710',\n",
              "  'pixel711',\n",
              "  'pixel712',\n",
              "  'pixel713',\n",
              "  'pixel714',\n",
              "  'pixel715',\n",
              "  'pixel716',\n",
              "  'pixel717',\n",
              "  'pixel718',\n",
              "  'pixel719',\n",
              "  'pixel720',\n",
              "  'pixel721',\n",
              "  'pixel722',\n",
              "  'pixel723',\n",
              "  'pixel724',\n",
              "  'pixel725',\n",
              "  'pixel726',\n",
              "  'pixel727',\n",
              "  'pixel728',\n",
              "  'pixel729',\n",
              "  'pixel730',\n",
              "  'pixel731',\n",
              "  'pixel732',\n",
              "  'pixel733',\n",
              "  'pixel734',\n",
              "  'pixel735',\n",
              "  'pixel736',\n",
              "  'pixel737',\n",
              "  'pixel738',\n",
              "  'pixel739',\n",
              "  'pixel740',\n",
              "  'pixel741',\n",
              "  'pixel742',\n",
              "  'pixel743',\n",
              "  'pixel744',\n",
              "  'pixel745',\n",
              "  'pixel746',\n",
              "  'pixel747',\n",
              "  'pixel748',\n",
              "  'pixel749',\n",
              "  'pixel750',\n",
              "  'pixel751',\n",
              "  'pixel752',\n",
              "  'pixel753',\n",
              "  'pixel754',\n",
              "  'pixel755',\n",
              "  'pixel756',\n",
              "  'pixel757',\n",
              "  'pixel758',\n",
              "  'pixel759',\n",
              "  'pixel760',\n",
              "  'pixel761',\n",
              "  'pixel762',\n",
              "  'pixel763',\n",
              "  'pixel764',\n",
              "  'pixel765',\n",
              "  'pixel766',\n",
              "  'pixel767',\n",
              "  'pixel768',\n",
              "  'pixel769',\n",
              "  'pixel770',\n",
              "  'pixel771',\n",
              "  'pixel772',\n",
              "  'pixel773',\n",
              "  'pixel774',\n",
              "  'pixel775',\n",
              "  'pixel776',\n",
              "  'pixel777',\n",
              "  'pixel778',\n",
              "  'pixel779',\n",
              "  'pixel780',\n",
              "  'pixel781',\n",
              "  'pixel782',\n",
              "  'pixel783',\n",
              "  'pixel784'],\n",
              " 'frame': None,\n",
              " 'target': array(['5', '0', '4', ..., '4', '5', '6'], dtype=object),\n",
              " 'target_names': ['class'],\n",
              " 'url': 'https://www.openml.org/d/554'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tbYwA7pm-tNo"
      },
      "source": [
        "X , Y = mnist['data'] , mnist['target']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NwOIfyXU_E4C",
        "outputId": "352bae6c-48d8-458d-db12-3e7c6f33da08"
      },
      "source": [
        "X.shape, Y.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((70000, 784), (70000,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XwO-7lEipZgj"
      },
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 503
        },
        "id": "Wleezq9DIN7H",
        "outputId": "661700ec-117c-4a3c-eff7-b02cd6cc7e2f"
      },
      "source": [
        "plt.hist(Y, bins = 'auto')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([6313.,    0.,    0.,    0., 6903.,    0.,    0.,    0., 6824.,\n",
              "           0.,    0.,    0., 7877.,    0.,    0.,    0., 6958.,    0.,\n",
              "           0.,    0.,    0., 6990.,    0.,    0.,    0., 7141.,    0.,\n",
              "           0.,    0., 6876.,    0.,    0.,    0., 7293.,    0.,    0.,\n",
              "           0., 6825.]),\n",
              " array([0.        , 0.23684211, 0.47368421, 0.71052632, 0.94736842,\n",
              "        1.18421053, 1.42105263, 1.65789474, 1.89473684, 2.13157895,\n",
              "        2.36842105, 2.60526316, 2.84210526, 3.07894737, 3.31578947,\n",
              "        3.55263158, 3.78947368, 4.02631579, 4.26315789, 4.5       ,\n",
              "        4.73684211, 4.97368421, 5.21052632, 5.44736842, 5.68421053,\n",
              "        5.92105263, 6.15789474, 6.39473684, 6.63157895, 6.86842105,\n",
              "        7.10526316, 7.34210526, 7.57894737, 7.81578947, 8.05263158,\n",
              "        8.28947368, 8.52631579, 8.76315789, 9.        ]),\n",
              " <a list of 38 Patch objects>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "id": "qAm0bJcHouBz",
        "outputId": "7a316d85-02ce-49db-cbfb-91476fafb453"
      },
      "source": [
        "import seaborn as sns\r\n",
        "sns.countplot(Y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5b6aa3c7b8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DU2KLPw6Fu86"
      },
      "source": [
        "# Resahping and plotting the image"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PJKalWOL_M63"
      },
      "source": [
        "% matplotlib inline\r\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rA3l3StFGRgS"
      },
      "source": [
        "import matplotlib\r\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0rXB_cArF3t3"
      },
      "source": [
        "example = X[30000]\r\n",
        "example_image = example.reshape(28,28)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "LhN502zHGMt3",
        "outputId": "ad76cf14-eabc-4d8a-af2c-7c64ac32d71b"
      },
      "source": [
        "plt.imshow(example_image, cmap = matplotlib.cm.binary, interpolation = 'nearest')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f5b6a4c8080>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAD4CAYAAAAq5pAIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAN/0lEQVR4nO3dXahc9bnH8d/Pl97YJkaz2UQNJ9p4o0e0YZRAg+RQTol6EQvRGLDmgCQqCooGI1HSgCgabINvVONLTA89arENeiEePVqQXlgymmhipPGFLRoSs0VBS4ip+pyLvSJb3fOfnXk3z/cDm5lZz6y9Hlby22tm/dfM3xEhAIe/I/rdAIDeIOxAEoQdSIKwA0kQdiCJo3q5senTp8esWbN6uUkglZGREX388ceeqNZW2G0vkHS3pCMlPRwRd5SeP2vWLNXr9XY2CaCgVqs1rLX8Mt72kZLul3SepNMkLbF9Wqu/D0B3tfOe/RxJ70TEexFxQNITkhZ2pi0AndZO2E+U9MG4xx9Wy77F9nLbddv10dHRNjYHoB1dPxsfEesjohYRtaGhoW5vDkAD7YR9l6SZ4x6fVC0DMIDaCftmSafaPtn2jyRdIumZzrQFoNNaHnqLiC9tXyPpfzU29PZoRLzZsc4AdFRb4+wR8aykZzvUC4Au4nJZIAnCDiRB2IEkCDuQBGEHkiDsQBKEHUiCsANJEHYgCcIOJEHYgSQIO5AEYQeSIOxAEoQdSIKwA0kQdiAJwg4kQdiBJAg7kARhB5Ig7EAShB1IgrADSRB2IAnCDiRB2IEkCDuQBGEHkmhrFlf0xuuvv16s33PPPQ1rW7ZsKa7brN7MunXrivXrrruurd+Pzmkr7LZHJH0u6StJX0ZErRNNAei8ThzZ/yMiPu7A7wHQRbxnB5JoN+wh6Xnbr9pePtETbC+3XbddHx0dbXNzAFrVbtjnRcQcSedJutr2ud99QkSsj4haRNSGhoba3ByAVrUV9ojYVd3ulbRJ0jmdaApA57UcdtvH2P7JwfuSfilpe6caA9BZ7ZyNH5a0yfbB3/M/EfFcR7o6zGzevLlYX7ZsWbG+c+fOYn3//v2H3NNB1b9fQ1OmTCnWV65cWayfe+733tl9Y86cOcV10Vkthz0i3pN0Zgd7AdBFDL0BSRB2IAnCDiRB2IEkCDuQBB9xnaSIaFh77rnyiOPixYuL9QMHDhTrs2fPLtYXLVrUsLZgwYLius289NJLxfott9xSrG/f3vjSC4beeosjO5AEYQeSIOxAEoQdSIKwA0kQdiAJwg4kwTj7JD388MMNa1dccUVx3dNPP71Yv/fee4v1+fPnF+vdtG/fvrbWX7t2bcPaZZdd1tbvxqHhyA4kQdiBJAg7kARhB5Ig7EAShB1IgrADSTDOPkm7d+9uWLv22muL665Zs6ZYnzp1aist/SB89tln/W4BFY7sQBKEHUiCsANJEHYgCcIOJEHYgSQIO5AE4+yTtHr16n630Bdbtmxpa/3Sd9qjt5oe2W0/anuv7e3jlh1n+wXbb1e307rbJoB2TeZl/GOSvjutyE2SXoyIUyW9WD0GMMCahj0iXpb0yXcWL5S0sbq/UdKFHe4LQIe1eoJuOCIOXiy+R9JwoyfaXm67brs+Ojra4uYAtKvts/ExNuNhw1kPI2J9RNQiojY0NNTu5gC0qNWwf2R7hiRVt3s71xKAbmg17M9IWlrdXyrp6c60A6Bbmo6z235c0nxJ021/KOk3ku6Q9Cfbl0t6X9LF3WwS3TMyMlKsr1ixoli3XawPDzc8nYMeaxr2iFjSoPSLDvcCoIu4XBZIgrADSRB2IAnCDiRB2IEk+IjrYe6LL74o1jds2NDV7a9ataph7dZbby2ue//99xfrS5cuLdbxbRzZgSQIO5AEYQeSIOxAEoQdSIKwA0kQdiAJxtkPc+vWrSvWm411N9NsuukpU6Y0rH3wwQfFda+88spi/YwzzijW58yZU6xnw5EdSIKwA0kQdiAJwg4kQdiBJAg7kARhB5JgnP0wsGPHjoa12267rbju7Nmzi/UHHnigWD/llFOK9ZkzZzasrV27trjuzTffXKw3u0Zg06ZNxXo2HNmBJAg7kARhB5Ig7EAShB1IgrADSRB2IAlHRM82VqvVol6v92x7kO66665ife7cucX6vHnzOtnOITn++OOL9X379hXrr7zySsPamWee2VJPg65Wq6ler084j3bTI7vtR23vtb193LI1tnfZ3lr9nN/JhgF03mRexj8macEEy9dFxFnVz7OdbQtApzUNe0S8LOmTHvQCoIvaOUF3je03qpf50xo9yfZy23Xb9dHR0TY2B6AdrYb995J+KuksSbsl/bbREyNifUTUIqI2NDTU4uYAtKulsEfERxHxVUR8LekhSed0ti0AndZS2G3PGPfwV5K2N3ougMHQ9PPsth+XNF/SdNsfSvqNpPm2z5IUkkYkXdHFHtGGFStW9LuFlt1+++3F+lVXXVWsP/HEEw1rzb5z/ogjDr/rzZqGPSKWTLD4kS70AqCLDr8/XwAmRNiBJAg7kARhB5Ig7EASfJU0BtYFF1xQrA8PDxfrd955Z8PaJZdcUlz3cPwILEd2IAnCDiRB2IEkCDuQBGEHkiDsQBKEHUiCcXYMrJNOOqlYv/HGG4v1G264oWGt9DXTEuPsAH7ACDuQBGEHkiDsQBKEHUiCsANJEHYgCcbZMbA2bNhQrK9evbpYP/bYYxvWliyZ6EuTD28c2YEkCDuQBGEHkiDsQBKEHUiCsANJEHYgCcbZB8DXX39drH/66afF+lFHNf5nnDp1aks99cKBAweK9SeffLJY379/f7Fe+m74KVOmFNc9HDU9stueafuvtnfYftP2tdXy42y/YPvt6nZa99sF0KrJvIz/UtINEXGapLmSrrZ9mqSbJL0YEadKerF6DGBANQ17ROyOiNeq+59LekvSiZIWStpYPW2jpAu71SSA9h3SCTrbsyT9TNLfJQ1HxO6qtEfShBNv2V5uu267Pjo62karANox6bDb/rGkP0u6LiI+G1+LiJAUE60XEesjohYRtaGhobaaBdC6SYXd9tEaC/ofI+Iv1eKPbM+o6jMk7e1OiwA6oenQm21LekTSWxHxu3GlZyQtlXRHdft0VzpM4KmnnirWm00vPG1a44GQbdu2Fdc94YQTivV2vfvuuw1rq1atKq77/PPPF+uLFy8u1h966KFiPZvJjLP/XNKvJW2zvbVatkpjIf+T7cslvS/p4u60CKATmoY9Iv4myQ3Kv+hsOwC6hctlgSQIO5AEYQeSIOxAEoQdSIKPuA6Ak08+uVgvjaNL5Y/Abt68ubjuwoULi/XHHnusWH/wwQeL9S1btjSsNfuI60UXXVSs33333cU6vo0jO5AEYQeSIOxAEoQdSIKwA0kQdiAJwg4kwTj7ADj77LOL9fvuu69Yv/TSSxvWLr64/MnjZmP8e/bsKdabfZ1z6fPyK1euLK7brPdm1x/g2ziyA0kQdiAJwg4kQdiBJAg7kARhB5Ig7EASjLP/ACxZsqRYP/rooxvWli1bVlx3586dxfqiRYuK9euvv75Ynzt3brGO3uHIDiRB2IEkCDuQBGEHkiDsQBKEHUiCsANJTGZ+9pmS/iBpWFJIWh8Rd9teI2mZpNHqqasi4tluNYrGSmPhzcbJkcdkLqr5UtINEfGa7Z9IetX2C1VtXUTc1b32AHTKZOZn3y1pd3X/c9tvSTqx240B6KxDes9ue5akn0n6e7XoGttv2H7U9oTfEWR7ue267fro6OhETwHQA5MOu+0fS/qzpOsi4jNJv5f0U0lnaezI/9uJ1ouI9RFRi4ja0NBQB1oG0IpJhd320RoL+h8j4i+SFBEfRcRXEfG1pIckndO9NgG0q2nYbVvSI5LeiojfjVs+Y9zTfiVpe+fbA9Apkzkb/3NJv5a0zfbWatkqSUtsn6Wx4bgRSVd0pUMAHTGZs/F/k+QJSoypAz8gXEEHJEHYgSQIO5AEYQeSIOxAEoQdSIKwA0kQdiAJwg4kQdiBJAg7kARhB5Ig7EAShB1IwhHRu43Zo5LeH7douqSPe9bAoRnU3ga1L4neWtXJ3v4tIib8/reehv17G7frEVHrWwMFg9rboPYl0VuretUbL+OBJAg7kES/w76+z9svGdTeBrUvid5a1ZPe+vqeHUDv9PvIDqBHCDuQRF/CbnuB7X/Yfsf2Tf3ooRHbI7a32d5qu97nXh61vdf29nHLjrP9gu23q9sJ59jrU29rbO+q9t1W2+f3qbeZtv9qe4ftN21fWy3v674r9NWT/dbz9+y2j5S0U9J/SvpQ0mZJSyJiR08bacD2iKRaRPT9Agzb50r6p6Q/RMS/V8vWSvokIu6o/lBOi4iVA9LbGkn/7Pc03tVsRTPGTzMu6UJJ/6U+7rtCXxerB/utH0f2cyS9ExHvRcQBSU9IWtiHPgZeRLws6ZPvLF4oaWN1f6PG/rP0XIPeBkJE7I6I16r7n0s6OM14X/ddoa+e6EfYT5T0wbjHH2qw5nsPSc/bftX28n43M4HhiNhd3d8jabifzUyg6TTevfSdacYHZt+1Mv15uzhB933zImKOpPMkXV29XB1IMfYebJDGTic1jXevTDDN+Df6ue9anf68Xf0I+y5JM8c9PqlaNhAiYld1u1fSJg3eVNQfHZxBt7rd2+d+vjFI03hPNM24BmDf9XP6836EfbOkU22fbPtHki6R9Ewf+vge28dUJ05k+xhJv9TgTUX9jKSl1f2lkp7uYy/fMijTeDeaZlx93nd9n/48Inr+I+l8jZ2Rf1fSzf3ooUFfp0h6vfp5s9+9SXpcYy/r/qWxcxuXSzpe0ouS3pb0f5KOG6De/lvSNklvaCxYM/rU2zyNvUR/Q9LW6uf8fu+7Ql892W9cLgskwQk6IAnCDiRB2IEkCDuQBGEHkiDsQBKEHUji/wHdsCeb4WAL0QAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "vmeHLC_oGtiS",
        "outputId": "2251d2fa-7535-40a2-a7b3-e9cbdf788715"
      },
      "source": [
        "\r\n",
        "example = X[60005]\r\n",
        "example_image = example.reshape(28,28)\r\n",
        "plt.imshow(example_image, cmap = matplotlib.cm.binary, interpolation = 'nearest')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f5b6aa26a90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HGme6zp3dcEF"
      },
      "source": [
        "# Splitting the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "01D1vFeAHBGB"
      },
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nita822LICbZ"
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.15, random_state=42)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yLMJoLzddsNR",
        "outputId": "8a0a48e7-7d8f-4b34-beb0-e19110171b5b"
      },
      "source": [
        "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((59500, 784), (10500, 784), (59500,), (10500,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6-O_RWKjrer2"
      },
      "source": [
        "import numpy as np\r\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K71hgQiBd5Hc"
      },
      "source": [
        "# shuffling the dataset\r\n",
        "shuffle_index = np.random.permutation(59500)\r\n",
        "X_train, y_train = X_train[shuffle_index] , y_train[shuffle_index]\r\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l6PUTleTe_FJ",
        "outputId": "207baa3f-6300-4012-e755-07afb5095afb"
      },
      "source": [
        "X_train.shape, y_train.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((59500, 784), (59500,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0fzevDHMtgbv"
      },
      "source": [
        "# Normalizing the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4_vg8uYbfIwJ"
      },
      "source": [
        "X_train = X_train/255"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H1nE-XRFt-u3"
      },
      "source": [
        "X_test = X_test/255"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "393wyB88vA0M",
        "outputId": "b41ffc79-b05b-473a-c1fe-964e3c683973"
      },
      "source": [
        "X_train.shape , X_test.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((59500, 784), (10500, 784))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lqJvk-NoyKl8"
      },
      "source": [
        "y_train = y_train.astype(np.int8)\r\n",
        "y_test = y_test.astype(np.int8)\r\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iJqM0QX7vIEW"
      },
      "source": [
        "# Training the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u305MAAzvCrM"
      },
      "source": [
        "import tensorflow as tf\r\n",
        "from tensorflow import keras"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CArGtGQZvRSr"
      },
      "source": [
        "model = keras.Sequential([\r\n",
        "    keras.layers.Flatten(input_shape=(784,)),\r\n",
        "    keras.layers.Dense(500, \"relu\"),\r\n",
        "    keras.layers.Dense(10, activation=\"softmax\")\r\n",
        "])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RHdpvcdWvtAP"
      },
      "source": [
        "\r\n",
        "model.compile(optimizer=\"adam\", loss=\"sparse_categorical_crossentropy\", metrics=[\"accuracy\"])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mGr3_8IFv0KV",
        "outputId": "9c5e8fac-b0e6-4587-e971-9280214b75a8"
      },
      "source": [
        "model.fit(X_train, y_train, shuffle=True, validation_split=0.1, batch_size=256, epochs=30, verbose=1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/30\n",
            "210/210 [==============================] - 3s 13ms/step - loss: 0.0767 - accuracy: 0.9786 - val_loss: 0.0857 - val_accuracy: 0.9733\n",
            "Epoch 2/30\n",
            "210/210 [==============================] - 3s 13ms/step - loss: 0.0581 - accuracy: 0.9831 - val_loss: 0.0850 - val_accuracy: 0.9739\n",
            "Epoch 3/30\n",
            "210/210 [==============================] - 3s 13ms/step - loss: 0.0451 - accuracy: 0.9875 - val_loss: 0.0697 - val_accuracy: 0.9771\n",
            "Epoch 4/30\n",
            "210/210 [==============================] - 3s 13ms/step - loss: 0.0366 - accuracy: 0.9899 - val_loss: 0.0649 - val_accuracy: 0.9798\n",
            "Epoch 5/30\n",
            "210/210 [==============================] - 3s 13ms/step - loss: 0.0280 - accuracy: 0.9925 - val_loss: 0.0633 - val_accuracy: 0.9800\n",
            "Epoch 6/30\n",
            "210/210 [==============================] - 3s 13ms/step - loss: 0.0230 - accuracy: 0.9942 - val_loss: 0.0642 - val_accuracy: 0.9805\n",
            "Epoch 7/30\n",
            "210/210 [==============================] - 3s 13ms/step - loss: 0.0183 - accuracy: 0.9960 - val_loss: 0.0620 - val_accuracy: 0.9800\n",
            "Epoch 8/30\n",
            "210/210 [==============================] - 3s 13ms/step - loss: 0.0141 - accuracy: 0.9973 - val_loss: 0.0600 - val_accuracy: 0.9812\n",
            "Epoch 9/30\n",
            "210/210 [==============================] - 3s 13ms/step - loss: 0.0112 - accuracy: 0.9982 - val_loss: 0.0611 - val_accuracy: 0.9815\n",
            "Epoch 10/30\n",
            "210/210 [==============================] - 3s 13ms/step - loss: 0.0093 - accuracy: 0.9986 - val_loss: 0.0611 - val_accuracy: 0.9815\n",
            "Epoch 11/30\n",
            "210/210 [==============================] - 3s 13ms/step - loss: 0.0075 - accuracy: 0.9990 - val_loss: 0.0607 - val_accuracy: 0.9822\n",
            "Epoch 12/30\n",
            "210/210 [==============================] - 3s 13ms/step - loss: 0.0055 - accuracy: 0.9995 - val_loss: 0.0684 - val_accuracy: 0.9810\n",
            "Epoch 13/30\n",
            "210/210 [==============================] - 3s 13ms/step - loss: 0.0054 - accuracy: 0.9993 - val_loss: 0.0599 - val_accuracy: 0.9827\n",
            "Epoch 14/30\n",
            "210/210 [==============================] - 3s 13ms/step - loss: 0.0035 - accuracy: 0.9998 - val_loss: 0.0608 - val_accuracy: 0.9825\n",
            "Epoch 15/30\n",
            "210/210 [==============================] - 3s 13ms/step - loss: 0.0031 - accuracy: 0.9998 - val_loss: 0.0584 - val_accuracy: 0.9830\n",
            "Epoch 16/30\n",
            "210/210 [==============================] - 3s 13ms/step - loss: 0.0022 - accuracy: 0.9999 - val_loss: 0.0626 - val_accuracy: 0.9820\n",
            "Epoch 17/30\n",
            "210/210 [==============================] - 3s 13ms/step - loss: 0.0020 - accuracy: 1.0000 - val_loss: 0.0619 - val_accuracy: 0.9832\n",
            "Epoch 18/30\n",
            "210/210 [==============================] - 3s 13ms/step - loss: 0.0015 - accuracy: 1.0000 - val_loss: 0.0608 - val_accuracy: 0.9835\n",
            "Epoch 19/30\n",
            "210/210 [==============================] - 3s 13ms/step - loss: 0.0013 - accuracy: 1.0000 - val_loss: 0.0616 - val_accuracy: 0.9842\n",
            "Epoch 20/30\n",
            "210/210 [==============================] - 3s 13ms/step - loss: 0.0011 - accuracy: 1.0000 - val_loss: 0.0633 - val_accuracy: 0.9825\n",
            "Epoch 21/30\n",
            "210/210 [==============================] - 3s 13ms/step - loss: 0.0010 - accuracy: 1.0000 - val_loss: 0.0624 - val_accuracy: 0.9840\n",
            "Epoch 22/30\n",
            "210/210 [==============================] - 3s 13ms/step - loss: 8.8429e-04 - accuracy: 1.0000 - val_loss: 0.0651 - val_accuracy: 0.9829\n",
            "Epoch 23/30\n",
            "210/210 [==============================] - 3s 13ms/step - loss: 7.1480e-04 - accuracy: 1.0000 - val_loss: 0.0651 - val_accuracy: 0.9837\n",
            "Epoch 24/30\n",
            "210/210 [==============================] - 3s 13ms/step - loss: 6.1390e-04 - accuracy: 1.0000 - val_loss: 0.0656 - val_accuracy: 0.9839\n",
            "Epoch 25/30\n",
            "210/210 [==============================] - 3s 14ms/step - loss: 5.5399e-04 - accuracy: 1.0000 - val_loss: 0.0670 - val_accuracy: 0.9839\n",
            "Epoch 26/30\n",
            "210/210 [==============================] - 3s 13ms/step - loss: 0.0142 - accuracy: 0.9960 - val_loss: 0.1025 - val_accuracy: 0.9741\n",
            "Epoch 27/30\n",
            "210/210 [==============================] - 3s 13ms/step - loss: 0.0174 - accuracy: 0.9944 - val_loss: 0.0721 - val_accuracy: 0.9815\n",
            "Epoch 28/30\n",
            "210/210 [==============================] - 3s 13ms/step - loss: 0.0051 - accuracy: 0.9986 - val_loss: 0.0704 - val_accuracy: 0.9820\n",
            "Epoch 29/30\n",
            "210/210 [==============================] - 3s 13ms/step - loss: 0.0012 - accuracy: 1.0000 - val_loss: 0.0651 - val_accuracy: 0.9840\n",
            "Epoch 30/30\n",
            "210/210 [==============================] - 3s 13ms/step - loss: 5.6042e-04 - accuracy: 1.0000 - val_loss: 0.0658 - val_accuracy: 0.9844\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f5b30388748>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X9ONym_HzN_Y"
      },
      "source": [
        "predictions = model.predict(x=X_test, batch_size=256, verbose=0)\r\n",
        "rounded_predictions = np.argmax(predictions, axis=-1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VYnrsKi5ztYJ"
      },
      "source": [
        "import pandas as pd\r\n",
        "df = pd.DataFrame(rounded_predictions, columns=['predictions'])\r\n",
        "df1 = pd.DataFrame(y_test, columns = ['original'])  "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O8A10UpiOvQn"
      },
      "source": [
        "datafame containing predicted and the original values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "id": "uwSTP57c15eb",
        "outputId": "a9cddd86-1e22-493f-dff1-321f3eb18412"
      },
      "source": [
        "df0 = pd.concat([df, df1], axis = 1)\r\n",
        "df0"
      ],
      "execution_count": null,
      "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>predictions</th>\n",
              "      <th>original</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10495</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10496</th>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10497</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10498</th>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10499</th>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10500 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       predictions  original\n",
              "0                8         8\n",
              "1                4         4\n",
              "2                8         8\n",
              "3                7         7\n",
              "4                7         7\n",
              "...            ...       ...\n",
              "10495            3         3\n",
              "10496            9         9\n",
              "10497            2         2\n",
              "10498            4         4\n",
              "10499            4         4\n",
              "\n",
              "[10500 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BpSRdZ0-xhFa",
        "outputId": "b32ff174-f058-4c90-cb7e-041bedceadae"
      },
      "source": [
        "model.evaluate(X_test, y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "329/329 [==============================] - 1s 2ms/step - loss: 0.0837 - accuracy: 0.9803\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.0836872011423111, 0.9802857041358948]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "21cyM5Y_DNYy"
      },
      "source": [
        "- On the unseen dataset accuracy is 98.03%\r\n",
        "- Loss is 0.0837"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D_yFg-UUzRBH"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w4nambP6BcLe"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}