Recognizing_HandWritten_Digits.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOp/4ruZkQ97ZO32UwnfzJ+",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/MEROO1010/Handwriting-Recognition/blob/main/Recognizing_HandWritten_Digits.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Importing Libraries and Dataset**"
      ],
      "metadata": {
        "id": "KzVfWYBpTmUR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# importing the hand written digit dataset\n",
        "from sklearn import datasets\n",
        "\n",
        "# digit contain the dataset\n",
        "digits = datasets.load_digits()\n",
        "\n",
        "# dir function use to display the attributes of the dataset\n",
        "dir(digits)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6ObPUr3iTrp7",
        "outputId": "f4a9cab1-9352-4b77-ca4a-92f175963413"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['DESCR', 'data', 'feature_names', 'frame', 'images', 'target', 'target_names']"
            ]
          },
          "metadata": {},
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Function to print a set of image**"
      ],
      "metadata": {
        "id": "mdK6zNL2T5hh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# outputting the picture value as a series of numbers\n",
        "print(digits.images[0])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vWvQSEtfT8im",
        "outputId": "aa5e9f1e-07b3-4621-cee7-4cea63aae2b7"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 0.  0.  5. 13.  9.  1.  0.  0.]\n",
            " [ 0.  0. 13. 15. 10. 15.  5.  0.]\n",
            " [ 0.  3. 15.  2.  0. 11.  8.  0.]\n",
            " [ 0.  4. 12.  0.  0.  8.  8.  0.]\n",
            " [ 0.  5.  8.  0.  0.  9.  8.  0.]\n",
            " [ 0.  4. 11.  0.  1. 12.  7.  0.]\n",
            " [ 0.  2. 14.  5. 10. 12.  0.  0.]\n",
            " [ 0.  0.  6. 13. 10.  0.  0.  0.]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# importing the matplotlib libraries pyplot function\n",
        "import matplotlib.pyplot as plt\n",
        "# defining the function plot_multi\n",
        "\n",
        "def plot_multi(i):\n",
        "\tnplots = 16\n",
        "\tfig = plt.figure(figsize=(15, 15))\n",
        "\tfor j in range(nplots):\n",
        "\t\tplt.subplot(4, 4, j+1)\n",
        "\t\tplt.imshow(digits.images[i+j], cmap='binary')\n",
        "\t\tplt.title(digits.target[i+j])\n",
        "\t\tplt.axis('off')\n",
        "\t# printing the each digits in the dataset.\n",
        "\tplt.show()\n",
        "\n",
        "\tplot_multi(0)\n"
      ],
      "metadata": {
        "id": "IPsiY2xcUG0E"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Training Neural network with the dataset**"
      ],
      "metadata": {
        "id": "q2iAnzQfUn70"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# converting the 2 dimensional array to one dimensional array\n",
        "y = digits.target\n",
        "x = digits.images.reshape((len(digits.images), -1))\n",
        "\n",
        "# gives the shape of the data\n",
        "x.shape\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wITth9T0UrFU",
        "outputId": "e4f6c4c0-d606-4e34-81ee-cfd8724a1bd0"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1797, 64)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# printing the one-dimensional array's values\n",
        "x[0]\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cleVKUNJUw_N",
        "outputId": "dc0f159f-5cdc-4811-f20b-fc7ed45359e9"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 0.,  0.,  5., 13.,  9.,  1.,  0.,  0.,  0.,  0., 13., 15., 10.,\n",
              "       15.,  5.,  0.,  0.,  3., 15.,  2.,  0., 11.,  8.,  0.,  0.,  4.,\n",
              "       12.,  0.,  0.,  8.,  8.,  0.,  0.,  5.,  8.,  0.,  0.,  9.,  8.,\n",
              "        0.,  0.,  4., 11.,  0.,  1., 12.,  7.,  0.,  0.,  2., 14.,  5.,\n",
              "       10., 12.,  0.,  0.,  0.,  0.,  6., 13., 10.,  0.,  0.,  0.])"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Data split for training and testing**"
      ],
      "metadata": {
        "id": "AQpk8XnuU4um"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Very first 1000 photographs and\n",
        "# labels will be used in training.\n",
        "x_train = x[:1000]\n",
        "y_train = y[:1000]\n",
        "\n",
        "# The leftover dataset will be utilised to\n",
        "# test the network's performance later on.\n",
        "x_test = x[1000:]\n",
        "y_test = y[1000:]\n"
      ],
      "metadata": {
        "id": "DDKKooyKVAAx"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Usage of Multi-Layer Perceptron classifier**"
      ],
      "metadata": {
        "id": "U0MgfzSDVF6N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# importing the MLP classifier from sklearn\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "\n",
        "# calling the MLP classifier with specific parameters\n",
        "mlp = MLPClassifier(hidden_layer_sizes=(15,),\n",
        "\t\t\t\t\tactivation='logistic',\n",
        "\t\t\t\t\talpha=1e-4, solver='sgd',\n",
        "\t\t\t\t\ttol=1e-4, random_state=1,\n",
        "\t\t\t\t\tlearning_rate_init=.1,\n",
        "\t\t\t\t\tverbose=True)\n"
      ],
      "metadata": {
        "id": "bvtD955_VKMl"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mlp.fit(x_train, y_train)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "vof5hkhIVRnY",
        "outputId": "58233a83-a57b-4175-8f11-d6cfaf935878"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Iteration 1, loss = 2.22958289\n",
            "Iteration 2, loss = 1.91207743\n",
            "Iteration 3, loss = 1.62507727\n",
            "Iteration 4, loss = 1.32649842\n",
            "Iteration 5, loss = 1.06100535\n",
            "Iteration 6, loss = 0.83995513\n",
            "Iteration 7, loss = 0.67806075\n",
            "Iteration 8, loss = 0.55175832\n",
            "Iteration 9, loss = 0.45840445\n",
            "Iteration 10, loss = 0.39149735\n",
            "Iteration 11, loss = 0.33676351\n",
            "Iteration 12, loss = 0.29059880\n",
            "Iteration 13, loss = 0.25437208\n",
            "Iteration 14, loss = 0.22838372\n",
            "Iteration 15, loss = 0.20200554\n",
            "Iteration 16, loss = 0.18186565\n",
            "Iteration 17, loss = 0.16461183\n",
            "Iteration 18, loss = 0.14990228\n",
            "Iteration 19, loss = 0.13892154\n",
            "Iteration 20, loss = 0.12833784\n",
            "Iteration 21, loss = 0.12138920\n",
            "Iteration 22, loss = 0.11407971\n",
            "Iteration 23, loss = 0.10677664\n",
            "Iteration 24, loss = 0.10037149\n",
            "Iteration 25, loss = 0.09593187\n",
            "Iteration 26, loss = 0.09250135\n",
            "Iteration 27, loss = 0.08676698\n",
            "Iteration 28, loss = 0.08356043\n",
            "Iteration 29, loss = 0.08209789\n",
            "Iteration 30, loss = 0.07649168\n",
            "Iteration 31, loss = 0.07410898\n",
            "Iteration 32, loss = 0.07126869\n",
            "Iteration 33, loss = 0.06926956\n",
            "Iteration 34, loss = 0.06578496\n",
            "Iteration 35, loss = 0.06374913\n",
            "Iteration 36, loss = 0.06175492\n",
            "Iteration 37, loss = 0.05975664\n",
            "Iteration 38, loss = 0.05764485\n",
            "Iteration 39, loss = 0.05623663\n",
            "Iteration 40, loss = 0.05420966\n",
            "Iteration 41, loss = 0.05413911\n",
            "Iteration 42, loss = 0.05256140\n",
            "Iteration 43, loss = 0.05020265\n",
            "Iteration 44, loss = 0.04902779\n",
            "Iteration 45, loss = 0.04788382\n",
            "Iteration 46, loss = 0.04655532\n",
            "Iteration 47, loss = 0.04586089\n",
            "Iteration 48, loss = 0.04451758\n",
            "Iteration 49, loss = 0.04341598\n",
            "Iteration 50, loss = 0.04238096\n",
            "Iteration 51, loss = 0.04162200\n",
            "Iteration 52, loss = 0.04076839\n",
            "Iteration 53, loss = 0.04003180\n",
            "Iteration 54, loss = 0.03907774\n",
            "Iteration 55, loss = 0.03815565\n",
            "Iteration 56, loss = 0.03791975\n",
            "Iteration 57, loss = 0.03706276\n",
            "Iteration 58, loss = 0.03617874\n",
            "Iteration 59, loss = 0.03593227\n",
            "Iteration 60, loss = 0.03504175\n",
            "Iteration 61, loss = 0.03441259\n",
            "Iteration 62, loss = 0.03397449\n",
            "Iteration 63, loss = 0.03326990\n",
            "Iteration 64, loss = 0.03305025\n",
            "Iteration 65, loss = 0.03244893\n",
            "Iteration 66, loss = 0.03191504\n",
            "Iteration 67, loss = 0.03132169\n",
            "Iteration 68, loss = 0.03079707\n",
            "Iteration 69, loss = 0.03044946\n",
            "Iteration 70, loss = 0.03005546\n",
            "Iteration 71, loss = 0.02960555\n",
            "Iteration 72, loss = 0.02912799\n",
            "Iteration 73, loss = 0.02859103\n",
            "Iteration 74, loss = 0.02825959\n",
            "Iteration 75, loss = 0.02788968\n",
            "Iteration 76, loss = 0.02748725\n",
            "Iteration 77, loss = 0.02721247\n",
            "Iteration 78, loss = 0.02686225\n",
            "Iteration 79, loss = 0.02635636\n",
            "Iteration 80, loss = 0.02607439\n",
            "Iteration 81, loss = 0.02577613\n",
            "Iteration 82, loss = 0.02553642\n",
            "Iteration 83, loss = 0.02518749\n",
            "Iteration 84, loss = 0.02484300\n",
            "Iteration 85, loss = 0.02455379\n",
            "Iteration 86, loss = 0.02432480\n",
            "Iteration 87, loss = 0.02398548\n",
            "Iteration 88, loss = 0.02376004\n",
            "Iteration 89, loss = 0.02341261\n",
            "Iteration 90, loss = 0.02318255\n",
            "Iteration 91, loss = 0.02296065\n",
            "Iteration 92, loss = 0.02274048\n",
            "Iteration 93, loss = 0.02241054\n",
            "Iteration 94, loss = 0.02208181\n",
            "Iteration 95, loss = 0.02190861\n",
            "Iteration 96, loss = 0.02174404\n",
            "Iteration 97, loss = 0.02156939\n",
            "Iteration 98, loss = 0.02119768\n",
            "Iteration 99, loss = 0.02101874\n",
            "Iteration 100, loss = 0.02078230\n",
            "Iteration 101, loss = 0.02061573\n",
            "Iteration 102, loss = 0.02039802\n",
            "Iteration 103, loss = 0.02017245\n",
            "Iteration 104, loss = 0.01997162\n",
            "Iteration 105, loss = 0.01989280\n",
            "Iteration 106, loss = 0.01963828\n",
            "Iteration 107, loss = 0.01941850\n",
            "Iteration 108, loss = 0.01933154\n",
            "Iteration 109, loss = 0.01911473\n",
            "Iteration 110, loss = 0.01905371\n",
            "Iteration 111, loss = 0.01876085\n",
            "Iteration 112, loss = 0.01860656\n",
            "Iteration 113, loss = 0.01848655\n",
            "Iteration 114, loss = 0.01834844\n",
            "Iteration 115, loss = 0.01818981\n",
            "Iteration 116, loss = 0.01798523\n",
            "Iteration 117, loss = 0.01783630\n",
            "Iteration 118, loss = 0.01771441\n",
            "Iteration 119, loss = 0.01749814\n",
            "Iteration 120, loss = 0.01738339\n",
            "Iteration 121, loss = 0.01726549\n",
            "Iteration 122, loss = 0.01709638\n",
            "Iteration 123, loss = 0.01698340\n",
            "Iteration 124, loss = 0.01684606\n",
            "Iteration 125, loss = 0.01667016\n",
            "Iteration 126, loss = 0.01654172\n",
            "Iteration 127, loss = 0.01641832\n",
            "Iteration 128, loss = 0.01630111\n",
            "Iteration 129, loss = 0.01623051\n",
            "Iteration 130, loss = 0.01612736\n",
            "Iteration 131, loss = 0.01590220\n",
            "Iteration 132, loss = 0.01582485\n",
            "Iteration 133, loss = 0.01571372\n",
            "Iteration 134, loss = 0.01560349\n",
            "Iteration 135, loss = 0.01557688\n",
            "Iteration 136, loss = 0.01534420\n",
            "Iteration 137, loss = 0.01527883\n",
            "Iteration 138, loss = 0.01517545\n",
            "Iteration 139, loss = 0.01503663\n",
            "Iteration 140, loss = 0.01501192\n",
            "Iteration 141, loss = 0.01482535\n",
            "Iteration 142, loss = 0.01471388\n",
            "Iteration 143, loss = 0.01463948\n",
            "Iteration 144, loss = 0.01454059\n",
            "Iteration 145, loss = 0.01441742\n",
            "Iteration 146, loss = 0.01431741\n",
            "Iteration 147, loss = 0.01428414\n",
            "Iteration 148, loss = 0.01416364\n",
            "Iteration 149, loss = 0.01406742\n",
            "Iteration 150, loss = 0.01402651\n",
            "Iteration 151, loss = 0.01389720\n",
            "Iteration 152, loss = 0.01381412\n",
            "Iteration 153, loss = 0.01371300\n",
            "Iteration 154, loss = 0.01362465\n",
            "Iteration 155, loss = 0.01357048\n",
            "Iteration 156, loss = 0.01348760\n",
            "Iteration 157, loss = 0.01339543\n",
            "Iteration 158, loss = 0.01331941\n",
            "Iteration 159, loss = 0.01320812\n",
            "Iteration 160, loss = 0.01315415\n",
            "Iteration 161, loss = 0.01308279\n",
            "Iteration 162, loss = 0.01302708\n",
            "Iteration 163, loss = 0.01290042\n",
            "Iteration 164, loss = 0.01289267\n",
            "Iteration 165, loss = 0.01277558\n",
            "Iteration 166, loss = 0.01277238\n",
            "Iteration 167, loss = 0.01261308\n",
            "Iteration 168, loss = 0.01260611\n",
            "Iteration 169, loss = 0.01248789\n",
            "Iteration 170, loss = 0.01239662\n",
            "Iteration 171, loss = 0.01231743\n",
            "Iteration 172, loss = 0.01227346\n",
            "Iteration 173, loss = 0.01223136\n",
            "Iteration 174, loss = 0.01217211\n",
            "Iteration 175, loss = 0.01208682\n",
            "Iteration 176, loss = 0.01204707\n",
            "Iteration 177, loss = 0.01200225\n",
            "Iteration 178, loss = 0.01188677\n",
            "Iteration 179, loss = 0.01184993\n",
            "Iteration 180, loss = 0.01175130\n",
            "Iteration 181, loss = 0.01171178\n",
            "Iteration 182, loss = 0.01166052\n",
            "Iteration 183, loss = 0.01163843\n",
            "Iteration 184, loss = 0.01154892\n",
            "Iteration 185, loss = 0.01147629\n",
            "Iteration 186, loss = 0.01142365\n",
            "Iteration 187, loss = 0.01136608\n",
            "Iteration 188, loss = 0.01128053\n",
            "Iteration 189, loss = 0.01128869\n",
            "Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MLPClassifier(activation='logistic', hidden_layer_sizes=(15,),\n",
              "              learning_rate_init=0.1, random_state=1, solver='sgd',\n",
              "              verbose=True)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MLPClassifier(activation=&#x27;logistic&#x27;, hidden_layer_sizes=(15,),\n",
              "              learning_rate_init=0.1, random_state=1, solver=&#x27;sgd&#x27;,\n",
              "              verbose=True)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MLPClassifier</label><div class=\"sk-toggleable__content\"><pre>MLPClassifier(activation=&#x27;logistic&#x27;, hidden_layer_sizes=(15,),\n",
              "              learning_rate_init=0.1, random_state=1, solver=&#x27;sgd&#x27;,\n",
              "              verbose=True)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, axes = plt.subplots(1, 1 )\n",
        "axes.plot(mlp.loss_curve_, 'o-')\n",
        "axes.set_xlabel(\"number of iteration\")\n",
        "axes.set_ylabel(\"loss\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "ffW6onECVcJg",
        "outputId": "7562854f-749a-4c83-aaba-303559618bd1"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Model Evaluation**"
      ],
      "metadata": {
        "id": "NzIJbl13XgHO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = mlp.predict(x_test)\n",
        "predictions[:200]\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jsOWx4PCXjAH",
        "outputId": "c9224d24-40e9-4811-eec7-83e2d4ed082b"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 4, 0, 5, 3, 6, 9, 6, 1, 7, 5, 4, 4, 7, 2, 8, 2, 2, 5, 7, 9, 5,\n",
              "       4, 4, 9, 0, 8, 9, 8, 0, 1, 2, 3, 4, 5, 6, 7, 8, 3, 0, 1, 2, 3, 4,\n",
              "       5, 6, 7, 8, 5, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 9, 5, 5, 6, 5, 0,\n",
              "       9, 8, 9, 8, 4, 1, 7, 7, 3, 5, 1, 0, 0, 7, 1, 2, 0, 1, 2, 6, 3, 3,\n",
              "       7, 3, 3, 4, 6, 6, 6, 9, 9, 1, 5, 0, 9, 5, 2, 8, 2, 0, 0, 1, 7, 6,\n",
              "       3, 2, 1, 7, 4, 6, 3, 1, 8, 9, 1, 7, 6, 8, 4, 9, 1, 4, 0, 5, 3, 6,\n",
              "       9, 6, 1, 7, 5, 4, 4, 7, 2, 8, 2, 2, 5, 7, 9, 5, 4, 1, 8, 4, 9, 0,\n",
              "       8, 9, 8, 0, 1, 2, 3, 4, 5, 6, 7, 8, 0, 0, 1, 2, 3, 4, 5, 6, 7, 8,\n",
              "       9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 0, 5, 9, 5, 5, 6, 5, 0, 9, 1, 9, 5,\n",
              "       4, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test[:200]\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sGWy8GtPX2q5",
        "outputId": "f1e2ce49-8e46-439e-9ccf-3f67a558bc53"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 4, 0, 5, 3, 6, 9, 6, 1, 7, 5, 4, 4, 7, 2, 8, 2, 2, 5, 7, 9, 5,\n",
              "       4, 4, 9, 0, 8, 9, 8, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4,\n",
              "       5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 9, 5, 5, 6, 5, 0,\n",
              "       9, 8, 9, 8, 4, 1, 7, 7, 3, 5, 1, 0, 0, 7, 8, 2, 0, 1, 2, 6, 3, 3,\n",
              "       7, 3, 3, 4, 6, 6, 6, 4, 9, 1, 5, 0, 9, 5, 2, 8, 2, 0, 0, 1, 7, 6,\n",
              "       3, 2, 1, 7, 4, 6, 3, 1, 3, 9, 1, 7, 6, 8, 4, 3, 1, 4, 0, 5, 3, 6,\n",
              "       9, 6, 1, 7, 5, 4, 4, 7, 2, 8, 2, 2, 5, 7, 9, 5, 4, 8, 8, 4, 9, 0,\n",
              "       8, 9, 8, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5, 6, 7, 8,\n",
              "       9, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 0, 9, 5, 5, 6, 5, 0, 9, 8, 9, 8,\n",
              "       4, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# importing the accuracy_score from the sklearn\n",
        "from sklearn.metrics import accuracy_score\n",
        "\n",
        "# calculating the accuracy with y_test and predictions\n",
        "accuracy_score(y_test, predictions)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hyNQHKGsX_5W",
        "outputId": "07967550-c6f2-4e32-aff4-3ec61a0a1590"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9146800501882058"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    }
  ]
}