HandWrittenDigits_Classification.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "HandWrittenDigits Classification.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyOymC5lknKWpyrcmM2lx09w"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "Import Necessary Modules"
      ],
      "metadata": {
        "id": "CAmD_S-CJxxi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "4_MYN2RWJ00h"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Import And Split The Dataset\n"
      ],
      "metadata": {
        "id": "7q9cEvSYKjQh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "(X_train,Y_train),(X_test,Y_test)=keras.datasets.mnist.load_data();"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jsDgHvFSKtkq",
        "outputId": "28fcbcad-ceb4-4fd4-f37d-bd47088cee3f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11493376/11490434 [==============================] - 0s 0us/step\n",
            "11501568/11490434 [==============================] - 0s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Inspecting Data and Data Visualization"
      ],
      "metadata": {
        "id": "nQcZ_k2cNsTX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "len(X_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AYv7q7WWOHai",
        "outputId": "3550a9bc-1eae-41b5-cf24-03381af3195b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "60000"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZSJxWwgNOPmk",
        "outputId": "8975f036-3d44-47a1-ff8b-088cfe823beb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10000"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_swiEXmWOU4r",
        "outputId": "d901c575-5e5d-48d5-9638-f8360b8502d7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   3,\n",
              "         18,  18,  18, 126, 136, 175,  26, 166, 255, 247, 127,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,  30,  36,  94, 154, 170,\n",
              "        253, 253, 253, 253, 253, 225, 172, 253, 242, 195,  64,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,  49, 238, 253, 253, 253, 253,\n",
              "        253, 253, 253, 253, 251,  93,  82,  82,  56,  39,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,  18, 219, 253, 253, 253, 253,\n",
              "        253, 198, 182, 247, 241,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,  80, 156, 107, 253, 253,\n",
              "        205,  11,   0,  43, 154,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,  14,   1, 154, 253,\n",
              "         90,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0, 139, 253,\n",
              "        190,   2,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,  11, 190,\n",
              "        253,  70,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,  35,\n",
              "        241, 225, 160, 108,   1,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "         81, 240, 253, 253, 119,  25,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,  45, 186, 253, 253, 150,  27,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0,  16,  93, 252, 253, 187,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0,   0,   0, 249, 253, 249,  64,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,  46, 130, 183, 253, 253, 207,   2,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,  39,\n",
              "        148, 229, 253, 253, 253, 250, 182,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,  24, 114, 221,\n",
              "        253, 253, 253, 253, 201,  78,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,  23,  66, 213, 253, 253,\n",
              "        253, 253, 198,  81,   2,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,  18, 171, 219, 253, 253, 253, 253,\n",
              "        195,  80,   9,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,  55, 172, 226, 253, 253, 253, 253, 244, 133,\n",
              "         11,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0, 136, 253, 253, 253, 212, 135, 132,  16,   0,\n",
              "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0],\n",
              "       [  0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
              "          0,   0]], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train[0].shape#2 dimension"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OSxfRTMuObSQ",
        "outputId": "a1a90bf8-21f5-4c93-91a2-2f8109e6957c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(28, 28)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lAA9T0-4Okns",
        "outputId": "4a40575d-a1ca-4066-9410-bd06085bf8e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 28, 28)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w-73-5sfPb4W",
        "outputId": "bd8dab72-9518-40c4-ee28-2f617b7f12b6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 28, 28)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.matshow(X_train[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "id": "ufGtTZsIOyEq",
        "outputId": "05c7f767-9891-4e3a-8eac-eddb23965a82"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f6951ddf4d0>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 288x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y_train[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OxXzVJzqO3Pr",
        "outputId": "ebe89113-3084-4625-b6b8-6a9a7252d120"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train=X_train/255#Scaling must be done in order to standardize the data in a fixed range so that one significant number doesn't impact the model just because of their large magnitude\n",
        "X_test=X_test/255"
      ],
      "metadata": {
        "id": "-cV4HMi3We2H"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Flatten the data\n",
        "X_train_flatten=X_train.reshape(len(X_train),28*28)\n",
        "X_test_flatten=X_test.reshape(len(X_test),28*28)\n",
        "X_train_flatten.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9rTZ5GWpPDd_",
        "outputId": "5a421136-7972-4752-fb50-60d930009372"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 784)"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_flatten[0]#1 dimension"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2__iyYQIPyA1",
        "outputId": "0c931821-2f3e-4c6c-c764-a805bce9a727"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.01176471, 0.07058824, 0.07058824,\n",
              "       0.07058824, 0.49411765, 0.53333333, 0.68627451, 0.10196078,\n",
              "       0.65098039, 1.        , 0.96862745, 0.49803922, 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.11764706, 0.14117647, 0.36862745, 0.60392157,\n",
              "       0.66666667, 0.99215686, 0.99215686, 0.99215686, 0.99215686,\n",
              "       0.99215686, 0.88235294, 0.6745098 , 0.99215686, 0.94901961,\n",
              "       0.76470588, 0.25098039, 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.19215686, 0.93333333,\n",
              "       0.99215686, 0.99215686, 0.99215686, 0.99215686, 0.99215686,\n",
              "       0.99215686, 0.99215686, 0.99215686, 0.98431373, 0.36470588,\n",
              "       0.32156863, 0.32156863, 0.21960784, 0.15294118, 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.07058824, 0.85882353, 0.99215686, 0.99215686,\n",
              "       0.99215686, 0.99215686, 0.99215686, 0.77647059, 0.71372549,\n",
              "       0.96862745, 0.94509804, 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.31372549, 0.61176471, 0.41960784, 0.99215686, 0.99215686,\n",
              "       0.80392157, 0.04313725, 0.        , 0.16862745, 0.60392157,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.05490196,\n",
              "       0.00392157, 0.60392157, 0.99215686, 0.35294118, 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.54509804,\n",
              "       0.99215686, 0.74509804, 0.00784314, 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.04313725, 0.74509804, 0.99215686,\n",
              "       0.2745098 , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.1372549 , 0.94509804, 0.88235294, 0.62745098,\n",
              "       0.42352941, 0.00392157, 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.31764706, 0.94117647, 0.99215686, 0.99215686, 0.46666667,\n",
              "       0.09803922, 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.17647059,\n",
              "       0.72941176, 0.99215686, 0.99215686, 0.58823529, 0.10588235,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.0627451 , 0.36470588,\n",
              "       0.98823529, 0.99215686, 0.73333333, 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.97647059, 0.99215686,\n",
              "       0.97647059, 0.25098039, 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.18039216, 0.50980392,\n",
              "       0.71764706, 0.99215686, 0.99215686, 0.81176471, 0.00784314,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.15294118,\n",
              "       0.58039216, 0.89803922, 0.99215686, 0.99215686, 0.99215686,\n",
              "       0.98039216, 0.71372549, 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.09411765, 0.44705882, 0.86666667, 0.99215686, 0.99215686,\n",
              "       0.99215686, 0.99215686, 0.78823529, 0.30588235, 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.09019608, 0.25882353, 0.83529412, 0.99215686,\n",
              "       0.99215686, 0.99215686, 0.99215686, 0.77647059, 0.31764706,\n",
              "       0.00784314, 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.07058824, 0.67058824, 0.85882353,\n",
              "       0.99215686, 0.99215686, 0.99215686, 0.99215686, 0.76470588,\n",
              "       0.31372549, 0.03529412, 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.21568627, 0.6745098 ,\n",
              "       0.88627451, 0.99215686, 0.99215686, 0.99215686, 0.99215686,\n",
              "       0.95686275, 0.52156863, 0.04313725, 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.53333333, 0.99215686, 0.99215686, 0.99215686,\n",
              "       0.83137255, 0.52941176, 0.51764706, 0.0627451 , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        , 0.        ,\n",
              "       0.        , 0.        , 0.        , 0.        ])"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Simple Neural Network Without Hidden Layers"
      ],
      "metadata": {
        "id": "tDwrfvZGQJXh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model=keras.Sequential([\n",
        "    keras.layers.Dense(10,input_shape=(784,),activation='sigmoid')\n",
        "])\n",
        "model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['accuracy'])\n",
        "\n",
        "model.fit(X_train_flatten,Y_train,epochs=5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZcGApRIdQE_c",
        "outputId": "2105c0a0-0a25-4901-f8d1-18f5fad8247d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.4737 - accuracy: 0.8764\n",
            "Epoch 2/5\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.3037 - accuracy: 0.9153\n",
            "Epoch 3/5\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2835 - accuracy: 0.9204\n",
            "Epoch 4/5\n",
            "1875/1875 [==============================] - 3s 2ms/step - loss: 0.2731 - accuracy: 0.9238\n",
            "Epoch 5/5\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2666 - accuracy: 0.9257\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f693e8ffb50>"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.evaluate(X_test_flatten,Y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MgqKSQ-fRRMO",
        "outputId": "77aedc5c-62a4-4420-a811-5e325507db93"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 2ms/step - loss: 0.2661 - accuracy: 0.9258\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.2661174237728119, 0.9258000254631042]"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_predicted=model.predict(X_test_flatten)\n",
        "y_predicted[5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g1BfJujuRbjy",
        "outputId": "62914430-e1f6-490f-a2df-4db06f3e9196"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([5.9371567e-05, 9.9740356e-01, 5.2240843e-01, 3.9323178e-01,\n",
              "       6.6362023e-03, 1.6593993e-02, 6.6971183e-03, 5.5598760e-01,\n",
              "       5.6381536e-01, 1.7129093e-01], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.matshow(X_test[5])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "id": "NEU7RSYIRq3n",
        "outputId": "d17b990e-9f1e-4351-cafa-520a22958e44"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f693a746a90>"
            ]
          },
          "metadata": {},
          "execution_count": 78
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 288x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#np.argmax finds a maximum element from an array and returns the index of it\n",
        "np.argmax(y_predicted[5])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X-igiqj1R-KE",
        "outputId": "1a83db29-5dc5-468b-af2e-d22ed3e460a4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_predicted_labels=[np.argmax(i) for i in y_predicted]"
      ],
      "metadata": {
        "id": "6zy8yppZSnMv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_predicted_labels[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ES5Fx7e6SveO",
        "outputId": "215c82b8-5003-4a06-d829-b271686529b2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[7, 2, 1, 0, 4]"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cm=tf.math.confusion_matrix(labels=Y_test,predictions=y_predicted_labels)\n",
        "cm"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "II7Pi70LS5zW",
        "outputId": "59323847-a2c9-4b40-cf37-2724a253749c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(10, 10), dtype=int32, numpy=\n",
              "array([[ 961,    0,    2,    3,    0,    6,    3,    2,    2,    1],\n",
              "       [   0, 1108,    3,    3,    0,    1,    3,    2,   15,    0],\n",
              "       [   5,    7,  932,   18,    8,    2,   10,   10,   37,    3],\n",
              "       [   2,    0,   18,  933,    0,   18,    1,    9,   21,    8],\n",
              "       [   1,    1,    5,    1,  919,    0,    8,    4,   11,   32],\n",
              "       [   9,    2,    5,   45,    9,  766,    8,    9,   33,    6],\n",
              "       [  12,    3,    8,    1,    8,   22,  898,    2,    4,    0],\n",
              "       [   1,    5,   23,    8,    8,    0,    0,  948,    2,   33],\n",
              "       [   6,    6,    7,   22,    9,   20,    7,   11,  878,    8],\n",
              "       [  11,    7,    1,   12,   26,    5,    0,   26,    6,  915]],\n",
              "      dtype=int32)>"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sn\n",
        "plt.figure(figsize = (10,7))\n",
        "sn.heatmap(cm, annot=True, fmt='d')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Truth')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        },
        "id": "Dz7ZWKAnTM5q",
        "outputId": "bf855996-9162-40ae-b11d-b78d7cd88973"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(69.0, 0.5, 'Truth')"
            ]
          },
          "metadata": {},
          "execution_count": 83
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Using Hidden Layer"
      ],
      "metadata": {
        "id": "tdA9S7MfTpIZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model=keras.Sequential([\n",
        "    keras.layers.Dense(100,input_shape=(784,),activation='relu'),\n",
        "    keras.layers.Dense(10,activation='sigmoid')\n",
        "])\n",
        "model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['accuracy'])\n",
        "\n",
        "model.fit(X_train_flatten,Y_train,epochs=5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5nws7Pv6Tl6N",
        "outputId": "8c16762e-a5e2-4963-adcf-552119964205"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2682 - accuracy: 0.9243\n",
            "Epoch 2/5\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1231 - accuracy: 0.9639\n",
            "Epoch 3/5\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0859 - accuracy: 0.9744\n",
            "Epoch 4/5\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0649 - accuracy: 0.9806\n",
            "Epoch 5/5\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0522 - accuracy: 0.9846\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f6936b84f10>"
            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Accuracy has increased when a hidden layer is added. It takes little more time before since it takes more computational effort now"
      ],
      "metadata": {
        "id": "c0u5y6YlYs6R"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.evaluate(X_test_flatten,Y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6gMmgIvLVOvY",
        "outputId": "94c68436-1fce-44f2-8c60-823bea6c10dd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 2ms/step - loss: 0.0798 - accuracy: 0.9759\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.07983461022377014, 0.9758999943733215]"
            ]
          },
          "metadata": {},
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_predicted=model.predict(X_test_flatten)\n",
        "y_predicted[5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tOXErx9aVvUf",
        "outputId": "048432d4-40a3-4417-db2f-8a73c1c9bb2c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1.7938018e-04, 9.9990427e-01, 1.3430506e-02, 8.6471438e-03,\n",
              "       1.1818308e-01, 2.8146504e-05, 4.7758222e-04, 8.0714107e-01,\n",
              "       1.1613962e-01, 6.9527030e-03], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.matshow(X_test[5])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "id": "rFhB9sfMWAQc",
        "outputId": "b95ec07e-5024-4e2a-fd6a-bc2a5cc5f6f1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f6936a06850>"
            ]
          },
          "metadata": {},
          "execution_count": 111
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQEAAAECCAYAAAD+eGJTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAANM0lEQVR4nO3df4wc9XnH8c8HcxjxI5Itx5bjQEipo4IixY7OtE1o5RSFENrKELU0/oO4UlQjFdRYitQi/iiW2qpOFUgTpbV0YCuOCkRIQLEq0sa1SCh/xMmZOGBwAzQxit3jDtdNbZJw9p2f/nEDvZK72fXO7sycn/dLsnZ2ntmZhwF/+M7sV7OOCAHI67ymGwDQLEIASI4QAJIjBIDkCAEgOUIASK6RELB9g+0f2H7Z9p1N9FDG9mHbz9k+YHu0Bf3stD1h++CsdUtt77H9UvG6pGX9bbV9tDiHB2zf2GB/l9l+0vYLtp+3/ZlifSvOYUl/tZxD1z1PwPYiSS9K+qikI5K+K2ljRLxQayMlbB+WNBwRx5ruRZJs/6ak1yV9NSLeX6z7G0nHI2JbEaRLIuLPWtTfVkmvR8Tnm+hpNtsrJa2MiGdsXyppv6SbJP2hWnAOS/q7RTWcwyZGAtdIejkifhgRpyR9TdKGBvpYMCLiKUnH37Z6g6RdxfIuzfxH04h5+muNiBiLiGeK5ZOSDklapZacw5L+atFECKyS9ONZ74+oxn/gLoWkb9jeb3tz083MY0VEjBXLr0pa0WQz87jD9rPF5UJjlyuz2b5C0lpJ+9TCc/i2/qQaziE3Bud2bUR8UNLHJd1eDHdbK2au6do2/3u7pCslrZE0JumeZtuRbF8i6RFJWyLixOxaG87hHP3Vcg6bCIGjki6b9f7dxbrWiIijxeuEpMc0cwnTNuPFteSb15QTDffz/0TEeERMR8QZSfep4XNoe0gzf8EeiIhHi9WtOYdz9VfXOWwiBL4rabXt99q+QNInJe1uoI852b64uDkj2xdLul7SwfJPNWK3pE3F8iZJjzfYyy948y9X4WY1eA5tW9IOSYci4t5ZpVacw/n6q+sc1v7tgCQVX3X8raRFknZGxF/V3sQ8bP+SZv7vL0nnS3qw6f5sPyRpvaRlksYl3S3pHyU9LOlySa9IuiUiGrk5N09/6zUzjA1JhyXdNuv6u+7+rpX0b5Kek3SmWH2XZq67Gz+HJf1tVA3nsJEQANAe3BgEkiMEgOQIASA5QgBIjhAAkms0BFo8JVcS/VXV5v7a3JtUb39NjwRa/S9C9FdVm/trc29Sjf01HQIAGlZpspDtGyR9UTMz/+6PiG1l21/gxXGhLn7r/WlNakiLez7+oNFfNW3ur829Sf3v7w39VKdi0nPVeg6BXh4O8g4vjV/1dT0dD0Dv9sVenYjjc4ZAlcsBHg4CnAOqhMBCeDgIgA7OH/QBiq86NkvShbpo0IcDcJaqjAS6ejhIRIxExHBEDLf5RgyQVZUQaPXDQQB0p+fLgYiYsn2HpH/R/z0c5Pm+dQagFpXuCUTEE5Ke6FMvABrAjEEgOUIASI4QAJIjBIDkCAEgOUIASI4QAJIjBIDkCAEgOUIASI4QAJIjBIDkCAEgOUIASI4QAJIjBIDkCAEgOUIASI4QAJIjBIDkCAEgOUIASI4QAJIjBIDkCAEgOUIASI4QAJIjBIDkCAEgOUIASK7ST5MDZ+Mnn/r10vq+bdtL61f/3R+X1i//3HdK6zE1VVrPqlII2D4s6aSkaUlTETHcj6YA1KcfI4GPRMSxPuwHQAO4JwAkVzUEQtI3bO+3vbkfDQGoV9XLgWsj4qjt5ZL22P73iHhq9gZFOGyWpAt1UcXDAei3SiOBiDhavE5IekzSNXNsMxIRwxExPKTFVQ4HYAB6DgHbF9u+9M1lSddLOtivxgDUo8rlwApJj9l+cz8PRsQ/96UrLEjnr3pXaf0v/vz+Svt/4fa/L61//Eu/UVqPkycrHf9c1XMIRMQPJX2gj70AaABfEQLJEQJAcoQAkBwhACRHCADJEQJAcjxPAH0z8bH3lNavv+h0pf1/cPQPSuvvfP3FSvvPipEAkBwhACRHCADJEQJAcoQAkBwhACRHCADJMU8AXTvvovLHw33sT54e6PEXf21J+QYRAz3+uYqRAJAcIQAkRwgAyRECQHKEAJAcIQAkRwgAyTFPAF2b/NBVpfW/XL6j0v5/duZUaf0dD3670v4xN0YCQHKEAJAcIQAkRwgAyRECQHKEAJAcIQAkxzwBdO1Hn1g00P3/3ks3ddjiPwd6/Kw6jgRs77Q9YfvgrHVLbe+x/VLx2uFpDwDaqpvLga9IuuFt6+6UtDciVkvaW7wHsAB1DIGIeErS8bet3iBpV7G8S1KncRyAlur1xuCKiBgrll+VtKJP/QCoWeVvByIiJM37hEfbm22P2h49rcmqhwPQZ72GwLjtlZJUvE7Mt2FEjETEcEQMD2lxj4cDMCi9hsBuSZuK5U2SHu9POwDq1nGegO2HJK2XtMz2EUl3S9om6WHbn5b0iqRbBtkk2uG3132/0uf/58zPS+unt5bfWjqPeQID0TEEImLjPKXr+twLgAYwbRhIjhAAkiMEgOQIASA5QgBIjhAAkuN5AnjL5I3rSutfXnVfpf0fmSqvn/et71XaP3rDSABIjhAAkiMEgOQIASA5QgBIjhAAkiMEgOSYJ4C3jK8bGuj+f/eftpTWV2vfQI+PuTESAJIjBIDkCAEgOUIASI4QAJIjBIDkCAEgOeYJ4C0XrP3vSp8/dOpnpfVf+dKx0vp0paOjV4wEgOQIASA5QgBIjhAAkiMEgOQIASA5QgBIjnkCibzxO9eU1kfXbe+wh0Wl1R+cXl5an37xPzrsH03oOBKwvdP2hO2Ds9ZttX3U9oHiz42DbRPAoHRzOfAVSTfMsf4LEbGm+PNEf9sCUJeOIRART0k6XkMvABpQ5cbgHbafLS4XlvStIwC16jUEtku6UtIaSWOS7plvQ9ubbY/aHj2tyR4PB2BQegqBiBiPiOmIOCPpPknz3naOiJGIGI6I4SEt7rVPAAPSUwjYXjnr7c2SDs63LYB26zhPwPZDktZLWmb7iKS7Ja23vUZSSDos6bYB9og++fmy8u/5h1xe7+RP93+itP5ePVtp/xiMjiEQERvnWL1jAL0AaADThoHkCAEgOUIASI4QAJIjBIDkCAEgOZ4nkMjkTT+p9PlOvyvw7vuHKu0fzWAkACRHCADJEQJAcoQAkBwhACRHCADJEQJAcswTOIcset+VpfXRdf/QaQ+l1a+//v7S+tC/7u+wf7QRIwEgOUIASI4QAJIjBIDkCAEgOUIASI4QAJJjnsA5ZPwjy0vrVX9X4MtPfrS0vlr7Ku0fzWAkACRHCADJEQJAcoQAkBwhACRHCADJEQJAcswTOIe8sdSVPr9/8lRp/arPHSmtT1U6OprScSRg+zLbT9p+wfbztj9TrF9qe4/tl4rXJYNvF0C/dXM5MCXpsxFxtaRfk3S77asl3Slpb0SslrS3eA9ggekYAhExFhHPFMsnJR2StErSBkm7is12SbppUE0CGJyzujFo+wpJayXtk7QiIsaK0quSVvS1MwC16DoEbF8i6RFJWyLixOxaRISkmOdzm22P2h49rclKzQLov65CwPaQZgLggYh4tFg9bntlUV8paWKuz0bESEQMR8TwkBb3o2cAfdTNtwOWtEPSoYi4d1Zpt6RNxfImSY/3vz0Ag9bNPIEPS7pV0nO2DxTr7pK0TdLDtj8t6RVJtwymRXRr+W8drfT53SfWltanXztWaf9op44hEBFPS5pvFsp1/W0HQN2YNgwkRwgAyRECQHKEAJAcIQAkRwgAyfE8gQXEi8tnXG541/cr7f+/Tl1SWo9Jpn2fixgJAMkRAkByhACQHCEAJEcIAMkRAkByhACQHPMEFpLp6dLyyKFrS+tbPnS4tP7NH/9yaX2Vni+tY2FiJAAkRwgAyRECQHKEAJAcIQAkRwgAyRECQHLME1hAYmqqtH7FnT8trV/117eW1n3g0rPuCQsfIwEgOUIASI4QAJIjBIDkCAEgOUIASI4QAJLrOE/A9mWSvipphaSQNBIRX7S9VdIfSXqt2PSuiHhiUI2is+mXf1Rav/z3a2oEC0o3k4WmJH02Ip6xfamk/bb3FLUvRMTnB9cegEHrGAIRMSZprFg+afuQpFWDbgxAPc7qnoDtKyStlbSvWHWH7Wdt77S9pM+9AahB1yFg+xJJj0jaEhEnJG2XdKWkNZoZKdwzz+c22x61PXpa/JYd0DZdhYDtIc0EwAMR8agkRcR4RExHxBlJ90m6Zq7PRsRIRAxHxPCQyn9QE0D9OoaAbUvaIelQRNw7a/3KWZvdLOlg/9sDMGjdfDvwYUm3SnrO9oFi3V2SNtpeo5mvDQ9Lum0gHQIYqG6+HXhakucoMScAOAcwYxBIjhAAkiMEgOQIASA5QgBIjhAAkiMEgOQIASA5QgBIjhAAkiMEgOQIASA5QgBIjhAAkiMEgOQcEfUdzH5N0iuzVi2TdKy2Bs4e/VXT5v7a3JvU//7eExHvnKtQawj8wsHt0YgYbqyBDuivmjb31+bepHr743IASI4QAJJrOgRGGj5+J/RXTZv7a3NvUo39NXpPAEDzmh4JAGgYIQAkRwgAyRECQHKEAJDc/wIKuZ4025Zi4QAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 288x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.argmax(y_predicted[5])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8T6W0-aAWBwj",
        "outputId": "351070c1-fd98-4a1d-f071-db6498edf619"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_predicted_labels=[np.argmax(i) for i in y_predicted]"
      ],
      "metadata": {
        "id": "V1zrit_paNQg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cm=tf.math.confusion_matrix(labels=Y_test,predictions=y_predicted_labels)\n",
        "cm"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NCf-C-h0WKzP",
        "outputId": "4f40e9d8-868e-41a1-8884-a413ed38874b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(10, 10), dtype=int32, numpy=\n",
              "array([[ 964,    0,    4,    2,    0,    2,    3,    1,    2,    2],\n",
              "       [   0, 1124,    2,    2,    0,    1,    2,    1,    3,    0],\n",
              "       [   2,    2, 1014,    2,    0,    1,    1,    6,    4,    0],\n",
              "       [   0,    0,    5, 1000,    0,    0,    1,    1,    2,    1],\n",
              "       [   2,    0,    2,    0,  952,    0,   10,    3,    0,   13],\n",
              "       [   2,    1,    0,   20,    2,  853,    6,    2,    6,    0],\n",
              "       [   4,    3,    2,    1,    2,    3,  939,    0,    4,    0],\n",
              "       [   0,    8,   16,    3,    1,    0,    0,  996,    1,    3],\n",
              "       [   2,    3,    3,   15,    3,    3,    1,    2,  939,    3],\n",
              "       [   0,    3,    0,   10,    5,    2,    2,    6,    3,  978]],\n",
              "      dtype=int32)>"
            ]
          },
          "metadata": {},
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sn\n",
        "plt.figure(figsize = (10,7))\n",
        "sn.heatmap(cm, annot=True, fmt='d')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Truth')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        },
        "id": "Tc_ouH4LWQOb",
        "outputId": "4d2fbeb2-fd9e-4ac8-ebbe-124686d29e55"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(69.0, 0.5, 'Truth')"
            ]
          },
          "metadata": {},
          "execution_count": 115
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Using Flatten layer so that we don't have to call .reshape on input dataset"
      ],
      "metadata": {
        "id": "ghatdR5BWVYo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model=keras.Sequential([\n",
        "    keras.layers.Flatten(input_shape=(28,28)),\n",
        "    keras.layers.Dense(100,activation='relu'),\n",
        "    keras.layers.Dense(10,activation='sigmoid')\n",
        "])\n",
        "model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['accuracy'])\n",
        "\n",
        "model.fit(X_train,Y_train,epochs=5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gosoMFNeWY1s",
        "outputId": "7a348570-9444-4532-958a-26b74657fcb3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.2708 - accuracy: 0.9232\n",
            "Epoch 2/5\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1238 - accuracy: 0.9634\n",
            "Epoch 3/5\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0881 - accuracy: 0.9732\n",
            "Epoch 4/5\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0662 - accuracy: 0.9797\n",
            "Epoch 5/5\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0520 - accuracy: 0.9840\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f6936843790>"
            ]
          },
          "metadata": {},
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.evaluate(X_test,Y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TmqYbn05Z31Y",
        "outputId": "e375fbf4-604b-49b9-81f6-6d0447cdf5bf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 2ms/step - loss: 0.0823 - accuracy: 0.9749\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.08226757496595383, 0.9749000072479248]"
            ]
          },
          "metadata": {},
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_predicted=model.predict(X_test)"
      ],
      "metadata": {
        "id": "9_7jit4JZ_6C"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_predicted_labels=[np.argmax(i) for i in y_predicted]"
      ],
      "metadata": {
        "id": "oxYWC2TsaJTg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cm=tf.math.confusion_matrix(labels=Y_test,predictions=y_predicted_labels)\n",
        "cm"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uEf82NfvaHCp",
        "outputId": "f91b9ae2-7b4e-4be4-ad48-2553195e27e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(10, 10), dtype=int32, numpy=\n",
              "array([[ 975,    0,    1,    1,    0,    0,    1,    1,    1,    0],\n",
              "       [   1, 1123,    4,    0,    0,    2,    3,    1,    1,    0],\n",
              "       [   6,    2, 1012,    2,    1,    0,    2,    5,    2,    0],\n",
              "       [   1,    0,    2,  992,    0,    3,    0,    5,    2,    5],\n",
              "       [   2,    0,    5,    1,  951,    0,    1,    3,    1,   18],\n",
              "       [   2,    0,    0,   12,    1,  867,    4,    1,    3,    2],\n",
              "       [   7,    3,    5,    1,    3,   13,  925,    0,    1,    0],\n",
              "       [   2,    4,    8,    1,    0,    0,    0, 1006,    0,    7],\n",
              "       [   6,    0,   10,   21,    2,    5,    5,    8,  911,    6],\n",
              "       [   1,    4,    0,    4,    4,    3,    1,    5,    0,  987]],\n",
              "      dtype=int32)>"
            ]
          },
          "metadata": {},
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sn\n",
        "plt.figure(figsize = (10,7))\n",
        "sn.heatmap(cm, annot=True, fmt='d')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Truth')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        },
        "id": "QgWSPakFar6h",
        "outputId": "dd695108-feec-4dda-dac3-332da7e90ec8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(69.0, 0.5, 'Truth')"
            ]
          },
          "metadata": {},
          "execution_count": 121
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Adding more Hidden layers\n"
      ],
      "metadata": {
        "id": "k3Koh7VVjsUb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model=keras.Sequential([\n",
        "    keras.layers.Flatten(input_shape=(28,28)),\n",
        "    keras.layers.Dense(100,activation='relu'),\n",
        "    keras.layers.Dense(50,activation='relu'),\n",
        "    keras.layers.Dense(10,activation='sigmoid')\n",
        "])\n",
        "model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['accuracy'])\n",
        "\n",
        "model.fit(X_train,Y_train,epochs=5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nysODiC7jwZv",
        "outputId": "0f657a91-788a-4187-ba75-86137206833f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2556 - accuracy: 0.9248\n",
            "Epoch 2/5\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.1077 - accuracy: 0.9670\n",
            "Epoch 3/5\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0757 - accuracy: 0.9766\n",
            "Epoch 4/5\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0595 - accuracy: 0.9813\n",
            "Epoch 5/5\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0464 - accuracy: 0.9851\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f692062a950>"
            ]
          },
          "metadata": {},
          "execution_count": 176
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.evaluate(X_test,Y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "84nfudIrkERB",
        "outputId": "61eedd17-93dc-482e-af0e-405fa9f86e5e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 2ms/step - loss: 0.0749 - accuracy: 0.9784\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.07492843270301819, 0.9783999919891357]"
            ]
          },
          "metadata": {},
          "execution_count": 178
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Increasing the epochs\n"
      ],
      "metadata": {
        "id": "pTIN8umOavVQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model=keras.Sequential([\n",
        "    keras.layers.Flatten(input_shape=(28,28)),\n",
        "    keras.layers.Dense(100,activation='relu'),\n",
        "    keras.layers.Dense(10,activation='sigmoid')\n",
        "])\n",
        "model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['accuracy'])\n",
        "\n",
        "model.fit(X_train,Y_train,epochs=10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5w71-9CeaynO",
        "outputId": "dc6be222-808b-4bda-fd53-69016a61cde4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2770 - accuracy: 0.9213\n",
            "Epoch 2/10\n",
            "1875/1875 [==============================] - 6s 3ms/step - loss: 0.1250 - accuracy: 0.9642\n",
            "Epoch 3/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0874 - accuracy: 0.9736\n",
            "Epoch 4/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0658 - accuracy: 0.9804\n",
            "Epoch 5/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0516 - accuracy: 0.9841\n",
            "Epoch 6/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0406 - accuracy: 0.9874\n",
            "Epoch 7/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0335 - accuracy: 0.9894\n",
            "Epoch 8/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0268 - accuracy: 0.9920\n",
            "Epoch 9/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0227 - accuracy: 0.9929\n",
            "Epoch 10/10\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0195 - accuracy: 0.9944\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f692a6b3f10>"
            ]
          },
          "metadata": {},
          "execution_count": 147
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.evaluate(X_test,Y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wj5V0YaRb4-5",
        "outputId": "0ea5419c-0503-4c50-ad09-f387c8a25f57"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 2ms/step - loss: 0.0818 - accuracy: 0.9777\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.08183039724826813, 0.9776999950408936]"
            ]
          },
          "metadata": {},
          "execution_count": 148
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_predicted=model.predict(X_test)"
      ],
      "metadata": {
        "id": "Bh1kYneHcCwE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_predicted_labels=[np.argmax(i) for i in y_predicted]"
      ],
      "metadata": {
        "id": "_mZf6O6fcEW5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cm=tf.math.confusion_matrix(labels=Y_test,predictions=y_predicted_labels)\n",
        "cm"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8B9P9JI9cRib",
        "outputId": "ddd272f5-7133-4012-e087-5fb84e9ea155"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(10, 10), dtype=int32, numpy=\n",
              "array([[ 966,    0,    3,    1,    0,    1,    3,    2,    4,    0],\n",
              "       [   0, 1122,    3,    2,    0,    0,    2,    1,    5,    0],\n",
              "       [   2,    2, 1003,    4,    2,    0,    3,    4,   12,    0],\n",
              "       [   0,    0,    5,  984,    0,    8,    0,    3,    1,    9],\n",
              "       [   1,    0,    1,    0,  963,    1,    6,    1,    3,    6],\n",
              "       [   2,    0,    1,    4,    1,  873,    1,    2,    3,    5],\n",
              "       [   4,    3,    2,    1,    2,   13,  929,    0,    4,    0],\n",
              "       [   1,    1,    6,    1,    2,    0,    0, 1005,    8,    4],\n",
              "       [   1,    0,    4,    5,    2,    4,    0,    2,  954,    2],\n",
              "       [   1,    2,    0,    5,    9,    2,    0,    7,    5,  978]],\n",
              "      dtype=int32)>"
            ]
          },
          "metadata": {},
          "execution_count": 151
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sn\n",
        "plt.figure(figsize = (10,7))\n",
        "sn.heatmap(cm, annot=True, fmt='d')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Truth')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        },
        "id": "4V4RgNG4ceY1",
        "outputId": "abbaf944-9f28-4a0e-b6d6-e8f84357135a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(69.0, 0.5, 'Truth')"
            ]
          },
          "metadata": {},
          "execution_count": 152
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Using softmax activation function"
      ],
      "metadata": {
        "id": "wxEmnptXeOwz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Softmax converts a vector of values to a probability distribution.\n",
        "\n",
        "#The elements of the output vector are in range (0, 1) and sum to 1."
      ],
      "metadata": {
        "id": "NEFHnXi9esyj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model=keras.Sequential([\n",
        "    keras.layers.Flatten(input_shape=(28,28)),\n",
        "    keras.layers.Dense(100,activation='relu'),\n",
        "    keras.layers.Dense(10,activation='softmax')\n",
        "])\n",
        "model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['accuracy'])\n",
        "\n",
        "model.fit(X_train,Y_train,epochs=5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JhqnNhoeexQk",
        "outputId": "77ef8670-b329-4588-ccf5-68d673716e35"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.2740 - accuracy: 0.9225\n",
            "Epoch 2/5\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.1234 - accuracy: 0.9633\n",
            "Epoch 3/5\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0852 - accuracy: 0.9749\n",
            "Epoch 4/5\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0645 - accuracy: 0.9798\n",
            "Epoch 5/5\n",
            "1875/1875 [==============================] - 5s 3ms/step - loss: 0.0500 - accuracy: 0.9844\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f69298efd50>"
            ]
          },
          "metadata": {},
          "execution_count": 159
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.evaluate(X_test,Y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VgGZjFbIfMVW",
        "outputId": "61cc469e-75d3-4fed-88ca-feb1b5666f40"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 2ms/step - loss: 0.0736 - accuracy: 0.9769\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.07363303005695343, 0.9768999814987183]"
            ]
          },
          "metadata": {},
          "execution_count": 160
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_predicted=model.predict(X_test)"
      ],
      "metadata": {
        "id": "AAsAeiyhfQRw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_predicted_labels=[np.argmax(i) for i in y_predicted]"
      ],
      "metadata": {
        "id": "_1LlnIV5fVkj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cm=tf.math.confusion_matrix(labels=Y_test,predictions=y_predicted_labels)\n",
        "cm"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vku543tsfZhO",
        "outputId": "37c0d7ed-2802-4678-c23c-225ed8401cd6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(10, 10), dtype=int32, numpy=\n",
              "array([[ 968,    0,    1,    1,    2,    2,    2,    1,    2,    1],\n",
              "       [   0, 1124,    2,    1,    0,    0,    2,    2,    4,    0],\n",
              "       [   6,    3, 1007,    0,    3,    0,    2,    7,    4,    0],\n",
              "       [   2,    1,    0,  989,    1,    4,    0,    9,    2,    2],\n",
              "       [   1,    0,    4,    0,  966,    0,    1,    4,    1,    5],\n",
              "       [   3,    1,    0,   10,    0,  866,    5,    1,    5,    1],\n",
              "       [   8,    3,    2,    1,    5,    3,  933,    0,    3,    0],\n",
              "       [   1,    3,    6,    0,    0,    1,    0, 1013,    1,    3],\n",
              "       [   8,    1,    1,    2,    6,    3,    3,    5,  944,    1],\n",
              "       [   3,    6,    0,    6,   12,    3,    0,   17,    3,  959]],\n",
              "      dtype=int32)>"
            ]
          },
          "metadata": {},
          "execution_count": 163
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sn\n",
        "plt.figure(figsize = (10,7))\n",
        "sn.heatmap(cm, annot=True, fmt='d')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Truth')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        },
        "id": "Ym2S4iMufixF",
        "outputId": "703724db-5ff5-4538-9509-ab5990aff18d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(69.0, 0.5, 'Truth')"
            ]
          },
          "metadata": {},
          "execution_count": 164
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjoAAAGpCAYAAACEUpywAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdeXwURcL/8U9NEo5wiiiQhAUUwQsBCYfCKgiCIgi6iq7iouLD/hAVVwX18eBB99D1dtdVg3LLvYoioMglwsoRSbgj95GE+yZcSaZ+f2TIApILZ9LT09+3r34x0zOd/k5Z06lUVXcbay0iIiIikcjndAARERGRUFFDR0RERCKWGjoiIiISsdTQERERkYilho6IiIhErGinAxTk2PT3XXU6WKVurzsdQUREXC7nZIYpzf1l79kYtN+1MdUvKdXsxaUeHREREYlYYdujIyIiIiHmz3U6QcipR0dEREQilnp0REREvMr6nU4QcmroiIiIeJU/8hs6GroSERGRiKUeHREREY+yGroSERGRiKWhKxERERH3Uo+OiIiIV2noSkRERCKWLhgoIiIi4l7q0REREfEqDV2JiIhIxNJZV+702ffL+N1rY7nztTGMnrssf/3Yecvp/tfPuPO1Mbzz1X8AyM7N5cXPZnLX62O5469j+PS7n5yKfU6dOrZl1cp5pK2ez8AB/ZyOU6QhSW+Rmb6M1JRZTkcpFrflBUhIiGPmjIksXzaHZamzefyx3k5HKpTb8oI7M7utLquMpbREXENn/fa9fP7jakY/dRcTBtzLD6s3s3X3AZasS2fuyk1MGHgvnz93H73aNQHgu9QNZOfkMunZ3zPmmbuZ9J9VZOw95PCnyOPz+Xj/vb/QpWtPGjVuxz33dOeKKy5zOlahRo6cwG1d7nc6RrG5LS9ATk4OAwYO5prG7Wjdpit9+z4Y1vXCbXnBnZndVpdVxuHBWn/QlnAVcQ2djTv306hODcqXiSE6ykezS+OYtXwjExas5KH211ImOgqAapViATDAsZM55OT6OZGdS0y0j4rlyjj4Cf6rRfOmbNiwmU2btpKdnc2ECV9ye9dOTscq1A/zF7Fv/wGnYxSb2/IC7Nixi5TUlQAcOZJFWto64uNqOpyqYG7LC+7M7La6rDIOE35/8JYwFbI5OsaYy4FuQHxgVQbwlbV2Taj2CVC/ZjX+OXUhB7KOUzYmivmrt3Dlby5my64DLN2YyT+nLqRsTDR/6nY9V/+mBh2aXMrclZu4+eVhHMvO4ZnubahSoVwoIxZbXHxNtqVn5j9Pz9hOi+ZNHUwk4aZOnQSaNL6aRYtTnI5SLG7LC+7M7DYqYwmlkPToGGOeBcaR12GyOLAYYKwx5rlCtutjjEk2xiR/Ov0/57XvS2pW46H219L3w6/o99EUGsZXx2cMuX7LoaMnGPWnu3jy9usZOPxbrLWs3LILn88w45UHmfbSA4yak0r6noPntW+R0lShQiwTxg/hqWcGcfjwEafjFMltecGdmd1GZeww6w/eEqZC1aPTG7jKWpt9+kpjzNvAKuC1c21krU0CkgCOTX/fnu/O72h1JXe0uhKA97/+kRpVK7J51wHaX3MJxhga1amBzxj2Zx1n+tK1tL68DjFRUVSrFEuTejVZtW0XCdWrnO/ugyYzYwe1E+LynyfE1yIzc4eDiSRcREdHM3H8EMaO/YLJk6c7HadIbssL7szsNirjMKALBp43PxB3jvW1Aq+F1L7DRwHYvv8ws5dv5NZrG9CuUT2WrMsAYMuuA2Tn+rmgQjlqVa3E4nXpABw7kc2KLTupV+OCUEcsliXJqdSvX4+6dWsTExNDjx7dmPL1DKdjSRgYkvQWa9LW8+57SU5HKRa35QV3ZnYblbGUhlA1dJ4EZhljphtjkgLLN8AsoH+I9pnv6WHfcOffxtB/yFSev+sGKseWpXvLK0jfe4jfvTaWZ0d+y6v3tccYwz2/vZqjJ7K587Ux3P/2RG5veTkN4qqHOmKx5Obm0v/JF5k2dQwrl89l0qQprF691ulYhRo96gPmz/uKhg0uZfPGZB568F6nIxXKbXkBWl/fnAd63kW7dteTvGQGyUtmcOstNzkdq0BuywvuzOy2uqwyDhMeGLoy1p73CFHhP9gYH9CCMycjL7HWFquf7NcMXTmhUrfXnY4gIiIul3Myw5Tm/k6smhW037Vlr2pfqtmLK2RnXdm8k+oXhurni4iIiBRFt4AQERHxqjAecgoWNXRERES8Kowv9BcsEXdlZBEREZFT1KMjIiLiUcU8P8jV1NARERHxKg/M0dHQlYiIiEQs9eiIiIh4lQcmI6uhIyIi4lUeGLpSQ0dERMSrdFNPEREREfdSj46IiIhXaehKREREIpYHJiNr6EpEREQiVtj26FTq9rrTEUrkWOYPTkcosfJxv3U6goiIOMkDQ1fq0REREfEqvz94SxGMMUONMbuMMStPW1fNGPOdMWZd4N8LAuuNMeZ9Y8x6Y8xyY8y1p23TK/D+dcaYXkXtVw0dERERKQ3DgVvOWvccMMtaexkwK/Ac4FbgssDSB/gQ8hpGwCCgJdACGHSqcVQQNXRERES8qhR7dKy184B9Z63uBowIPB4BdD9t/UibZyFQ1RhTC+gEfGet3Wet3Q98xy8bT2cI2zk6IiIiElrBvHu5MaYPeb0vpyRZa5OK2KyGtXZ74PEOoEbgcTyw7bT3pQfWFbS+QGroiIiIyK8WaNQU1bApbHtrjLFBjARo6EpERMS7SnHoqgA7A0NSBP7dFVifAdQ+7X0JgXUFrS+QGjoiIiJeZf3BW87PV8CpM6d6AV+etv4PgbOvWgEHA0Nc3wIdjTEXBCYhdwysK5CGrkRERCTkjDFjgbZAdWNMOnlnT70GTDDG9Aa2AD0Cb58GdAbWA0eBhwCstfuMMa8CSwLve8Vae/YE5zOooSMiIuJVpXgLCGvt7wt4qf053muBfgX8nKHA0OLuVw0dERERr9KVkUVERETcSz06IiIiXuWBu5eroSMiIuJVGroSERERcS9PNXQ6dWzLqpXzSFs9n4EDzjmZu9S8+Ne3ueG2e+ne8//lr/t29g90u/+PNGrTmZVr1uav/8/ipfR4+HHueKAvPR5+nEU/pf7i5z028P/O+FlOSEiIY+aMiSxfNodlqbN5/LHejuYpjiFJb5GZvozUlFlORymRcKrLxeG2vG6sy27MDODz+Viy+Fu+/GJE0W8OA26ry0Vy/oKBIeeZho7P5+P99/5Cl649adS4Hffc050rrrjMsTzdO9/MR2//+Yx19S+pw7t/fYlmTa4+Y/0FVSvzz9f/jy9GfchfXnya519584zXv5u7gNjY8iHPXJScnBwGDBzMNY3b0bpNV/r2fdDRMi6OkSMncFuX+52OUSLhVpeL4ra84M667MbMAE88/ghpaeucjlEsbqzLRVJDJ3K0aN6UDRs2s2nTVrKzs5kw4Utu79rJsTyJTRpRpXKlM9ZdWvc31KuT8Iv3XtGgPhdfdCEA9evV4fiJE5w8eRKAo0ePMXL85/yx172hD12EHTt2kZK6EoAjR7JIS1tHfFxNh1MV7of5i9i3/4DTMUok3OpyUdyWF9xZl92YOT6+Fp1vbc/QoWOdjlIsbqzL4qGGTlx8TbalZ+Y/T8/YTlyYHwTO5bu587myYX3KlCkDwD+GjKTXvXdSrlw5h5OdqU6dBJo0vppFi1OcjhJx3FaX3Zb3bG6sy27J/PZbg3nu+T/jD+PegNO5vS6fk/O3gAi5Um/oGGMeKuS1PsaYZGNMst+fVZqxXGH9xi28/a+hvDzgcQDS1m5gW8Z2OtzY2uFkZ6pQIZYJ44fw1DODOHz4iNNxRM6bG+uyWzLf1rkDu3btYWnKCqejeJuGrkJicEEvWGuTrLWJ1tpEn69CUHeambGD2glx+c8T4muRmbkjqPsIpR27dtP/f1/lry89w28CnyN11RpWpa2j4+968Ye+T7N5WwYPPjbQ0ZzR0dFMHD+EsWO/YPLk6Y5miVRuq8tuy3uKG+uymzJff30iXbt0ZP3ahXw2+l+0a9eaEcPfdzpWodxal70uJA0dY8zyApYVQI1Q7LMoS5JTqV+/HnXr1iYmJoYePbox5esZTkQpsUOHj/DogEE8+f8e4tprrspff+8dXZjz1WfM+PcIRn74FnVrxzP8n393MGneWUxr0tbz7ntJjuaIZG6ry27Le4ob67KbMr/w4mvUvSSR+g1acX/PR5kzZwG9HnzC6ViFcmtdLpQHhq5CdcHAGkAnYP9Z6w3wnxDts1C5ubn0f/JFpk0dQ5TPx/AR41m9em3RG4bIgEGvsSRlOQcOHKJ995482vsBqlSuyN/e+ZB9Bw7y6IBBXH7ZJSS98xfG/nsK29Iz+WjYGD4aNgaApHf/woUXVHUs/7m0vr45D/S8i+UrVpO8JO/L/9JLrzH9m9kOJyvY6FEfcOMN11G9ejU2b0xm8CtvMmz4OKdjFSrc6nJR3JYX3FmX3ZjZbdxYl4sUxkNOwWLybhAa5B9qzKfAMGvt/HO8NsZae19RPyO6THzwg4XQscwfnI5QYuXjfut0BBEROU3OyQxTmvs79sVrQftdW/6O50o1e3GFpEfHWlvglaqK08gRERGRUhDGQ07BontdiYiIeJUHhq48cx0dERER8R716IiIiHiVB3p01NARERHxqhCckBRuNHQlIiIiEUs9OiIiIl6loSsRERGJWB5o6GjoSkRERCKWenRERES8ShcMFBERkYiloSsRERER91KPjoiIiFd54Do6auiIiIh4lQeGrsK2oROW93ovRPm43zodocSOzPm70xFKrGK7gU5HEPnV3HZ8A4j8v/slUoVtQ0dERERCTD06IiIiErE8cHq5zroSERGRiKUeHREREY+y/siffaWGjoiIiFd5YI6Ohq5EREQkYqlHR0RExKs8MBlZDR0RERGv8sAcHQ1diYiISMRSj46IiIhXeWAysho6IiIiXqWGjoiIiEQsD9y9XHN0REREJGKpR0dERMSrPDB05akenSpVKjNuXBIrVnzP8uVzadWymdORClW2bFl+XPA1PyV/x7LU2Qx6+WnHsrw89Cva9n+TO1/6MH/dwSPH+OObo+j63D/545ujOJR1DABrLa999g1dnvsHd738EWu2bAdg8ZpN9Bj0cf7SvM9fmL00zZHPc7pOHduyauU80lbPZ+CAfk7HKVI41YviclsZJyTEMXPGRJYvm8Oy1Nk8/lhvpyMVqUGDS0leMiN/2bsnjScef8TpWEXy+XwsWfwtX34xwukoxeK2ulwkvw3eEqaMDdPxuZgy8UEPNvTTd5k/fxFDh40lJiaG2NjyHDx4KCg/O1SlWKFCLFlZR4mOjmbe3C/401ODWLR4aVB+9pE5fy/2e3/6eQux5crwwieT+fzVvgC8M+E7KlcoT+/b2vDp1PkcOnqcP93dgR+Wr2PszMV88Kf7WLExg9fHfMNnL515wD145Bhdnv8HM978E+XLxhQ7R8V2A4v93uLw+XysWfUDt3T+Penp21n44zR6PvAoa9asC+p+gi2U9SLY3FjGNWteTK2aF5OSupKKFSuweNE3/O6uh4OW2QTlpxTM5/OxZfNPtG7Tha1bM4LyM0N1jHuyfx+aNbuGypUq0e2OXiHaS3CURl3OOZkR6upxhqNvPhK0/7Wxz3xSqtmLyzM9OpUrV6JNm5YMHTYWgOzs7KA1ckIpK+soADEx0UTHxOBUw7RZwzpUrlD+jHVzUtZye+vGANzeujFzlv4cWP8zXa9vjDGGay5N4PDRE+w+cPiMbb9LXk2bRvVL1MgJhRbNm7Jhw2Y2bdpKdnY2EyZ8ye1dOzmaqTjCpV4UhxvLeMeOXaSkrgTgyJEs0tLWER9X0+FUxXfTTW3YuHFL0Bo5oRIfX4vOt7Zn6NCxTkcpFjfW5SJZf/CWMOWZhk69er9hz569fPrJOyxZ/C0ff/QGsbHli97QYT6fj+QlM9iesZxZs+axeEmK05Hy7Tt0hIuqVgKgepWK7Dt0BIBd+w9To1rl/PfVqFaJXfvPbOh8s3gVt7S4uvTCFiAuvibb0jPzn6dnbCfOBb/QwrlenM2tZXxKnToJNGl8NYsWh28Zn+2eHt0YP36y0zGK9PZbg3nu+T/jd8k8EbfX5XPywNBVyBo6xpjLjTHtjTEVz1p/SyHb9DHGJBtjkv3+rKDmiY6KomnTRnz88Uiat+hEVtZRBg58LKj7CAW/309i847UqZdI88SmXHVVQ6cjnZMxBkzxei13HzjM+vRdXH/1pSFOFbncUi/crkKFWCaMH8JTzwzi8OEjTscplpiYGLp06cikf3/tdJRC3da5A7t27WFpygqno0iEC0lDxxjzBPAl8Diw0hjT7bSX/1rQdtbaJGttorU20eerENRM6RnbSU/fnv+X778/n0rTJo2Cuo9QOnjwEHO/X0Cnjm2djpKvWuWK+UNSuw8cplqlvP9nF19QiZ37/jssuHPfYS6+oFL+8xlLVnPTtZcTEx1VuoHPITNjB7UT4vKfJ8TXIjNzh4OJSiYc68XZ3FrG0dHRTBw/hLFjv2Dy5OlOxym2W25pR0rKCnbt2uN0lEJdf30iXbt0ZP3ahXw2+l+0a9eaEcPfdzpWodxalwtj/f6gLeEqVD06/wM0s9Z2B9oCLxlj+gdec2Sy0s6du0lPz6RBg7xehJtuasOaNWudiFJs1atXo0qVvCGgcuXK0aH9Dfz88waHU/1X26YN+GrBMgC+WrCMdk0b5K1v0oAp/1mGtZblG9KpGFs2f4gLYPqildzS8ipHMp9tSXIq9evXo27d2sTExNCjRzemfD3D6ViFCvd6cTY3ljHAkKS3WJO2nnffS3I6Soncc093VwxbvfDia9S9JJH6DVpxf89HmTNnAb0efMLpWIVya10ulAeGrkJ1HR2ftfYIgLV2szGmLTDJGFMHhxo6AE/+6SVGjvgHZcrEsHHTVh555CmnohRLrVo1GPrpu0RF+fD5fEyaNIWp02Y6kuXZj/5N8s9bOHDkKDc//Q59u7Xl4c6tGfDhJCb/kEqtC6vwRt+7APjtNZcxf/l6ujz3T8qVieGVh2/P/zkZew6wY98hEhvWdeRznC03N5f+T77ItKljiPL5GD5iPKtXh3cDOJzqRXG4sYxbX9+cB3rexfIVq0lekveL7KWXXmP6N7MdTla42NjydGh/A48++qzTUSKSG+uyhOj0cmPMbOApa23qaeuigaHA/dbaIscsQnF6eSi5KmxASU4vDxfBPr1cxAlheQ5uEdx4jHOj0j69POvPPYP2v7bCi6PDsmqHqkfnD0DO6SustTnAH4wxH4donyIiIlISYTzkFCwhaehYa9MLeW1BKPYpIiIicjbd60pERMSrwvhsqWBRQ0dERMSrPDB05ZkrI4uIiIj3qEdHRETEq8L4HlXBooaOiIiIV2noSkREROTXM8b8yRizyhiz0hgz1hhTzhhTzxizyBiz3hgz3hhTJvDesoHn6wOv1z3f/aqhIyIi4lGlda8rY0w88ASQaK29GogC7gVeB96x1tYH9gO9A5v0BvYH1r8TeN95UUNHRETEq0r3XlfRQPnAnRJige3ATcCkwOsjgO6Bx90Czwm83t4Yc15XXlZDR0RERH41Y0wfY0zyaUufU69ZazOAN4Gt5DVwDgI/AQcCd04ASAfiA4/jgW2BbXMC77/wfHJpMrKIiIhXBXEysrU2CUg612vGmAvI66WpBxwAJgK3BG3nhVBDR0RExKtK7/TyDsAma+1uAGPM50BroKoxJjrQa5MAZATenwHUBtIDQ11VgL3ns2MNXYmIiEiobQVaGWNiA3Nt2gOrgTnAXYH39AK+DDz+KvCcwOuzrbXn1f0Utj06kX9mv/MqthvodIQSO/ztYKcjlEilToOcjiBh6DznVDrqPH/HSLgrpevoWGsXGWMmAUuBHCCFvGGuqcA4Y8yfA+s+DWzyKTDKGLMe2EfeGVrnJWwbOiIiIhJathQvGGitHQSc/dffRqDFOd57HLg7GPvV0JWIiIhELPXoiIiIeJUHbgGhho6IiIhXFXFF40igoSsRERGJWOrRERER8SoNXYmIiEjE8kBDR0NXIiIiErHUoyMiIuJRXrgQpBo6IiIiXqWhKxERERH3Uo+OiIiIV3mgR0cNHREREY8qzXtdOUVDVyIiIhKxPNPQSUiIY+aMiSxfNodlqbN5/LHeTkcq0pCkt8hMX0ZqyiynoxRbp45tWbVyHmmr5zNwQD+n45zhs1k/8btXhnHn4KGMnpUMQNq2nTzw+mh6/Hk49/11JCs2bQfgUNZx/vThF9z96jDu/9so1mfsdjL6GdxYLwB8Ph9LFn/Ll1+McDpKsYRzXT6Xxx7rTcrSmaSmzOLxx8P/+ObGY7IbMxfJb4O3hCnPNHRycnIYMHAw1zRuR+s2Xenb90GuuOIyp2MVauTICdzW5X6nYxSbz+fj/ff+QpeuPWnUuB333NM9bMp4fcZuPl+wnNHP9WTCiw/yw4oNbN21n3c//54/3nY9E158kL5d2/Du598D8Mk3C2lY+2ImvvQQf36oM3+fMNvhT/BfbqsXpzzx+COkpa1zOkaxhHNdPperrmxI74d/z/Wtu9AssSOdO3fg0kvrOh2rUG48Jrsxc5H8QVzClGcaOjt27CIldSUAR45kkZa2jvi4mg6nKtwP8xexb/8Bp2MUW4vmTdmwYTObNm0lOzubCRO+5PaunZyOBcDGHftoVLcW5cvEEB3lo9lltZmVshZjDFnHTwJw5PgJLqpaMe/92/fSomEdAOrVvJDMvQfZeyjLsfync1u9AIiPr0XnW9szdOhYp6MUSzjX5XO5/PL6LF6cyrFjx8nNzeWHeQvp3v1Wp2MVyo3HZDdmFg81dE5Xp04CTRpfzaLFKU5HiShx8TXZlp6Z/zw9YztxYXIQqB9XnaXr0zlw5BjHTmYzf+VGdu4/zIC7b+Kdf8+l0/Mf8fakuTzR/bcANEi4iFkpawFYsWk72/cdYuf+w05+BFd7+63BPPf8n/G75E7J4VyXz2XV6p9p06YF1apVpXz5ctxyy00kJMQ5HavY3HhMdmPmc7F+G7QlXIXsrCtjTAvAWmuXGGOuBG4B0qy10wrZpg/QB8BEVcHnqxD0XBUqxDJh/BCeemYQhw8fCfrPl/B0Sa0LeahTC/q+P5HyZWJoWPtifD4fE+el8szd7ehwbUO+TU5j8Khv+PjJe3i4U0v+PmE2Pf48nMviL6Jh7Rr4fMbpj+FKt3XuwK5de1iasoIbb7jO6TgRKS1tPW+8+S+mTR1DVtZRli1fRW5urtOxisWNx2Q3Zi5QGDdQgiUkDR1jzCDgViDaGPMd0BKYAzxnjGlqrf3Lubaz1iYBSQDRZeKDXvrR0dFMHD+EsWO/YPLk6cH+8Z6XmbGD2qf9FZkQX4vMzB0OJjrTHa2v4Y7W1wDw/uR51KhaiX9MnsfAHjcB0LFZQ14Z/S0AFcuX5ZVeeV3/1lo6v5BEQvWqzgR3ueuvT6Rrl47cestNlCtXlsqVKzFi+Pv0evAJp6MVKNzr8rkMHz6O4cPHAfDqK8+SnrHd4URFc+Mx2Y2ZvS5UQ1d3Aa2BG4B+QHdr7atAJ+CeEO2zSEOS3mJN2nrefS/JqQgRbUlyKvXr16Nu3drExMTQo0c3pnw9w+lY+fYF5ths33eI2SnruLXFFVxUtSLJa7cBsPjnrfzm4gsAOHT0ONk5eX8Rfz5/Oc0uS6Bi+bLOBHe5F158jbqXJFK/QSvu7/koc+YsCOtGDoR/XT6Xiy66EIDatePo3v1Wxo2b7HCiornxmOzGzIXywGTkUA1d5Vhrc4GjxpgN1tpDANbaY8YYR4qj9fXNeaDnXSxfsZrkJXkHrJdeeo3p34TP2TRnGz3qA2684TqqV6/G5o3JDH7lTYYF/mILR7m5ufR/8kWmTR1DlM/H8BHjWb16rdOx8j2d9CUHjxwnOsrH87/vQOXYcrzcsxN/nzCb3Fw/ZWKieen+jgBs2rGXl4ZPxxi4tFZ1/u+BWxxO/19uqxduFO51+VzGj0viwgsvIDs7hyf6v8DBg4ecjlQoNx6T3Zi5KOE8tyZYTCjuXGqMWQS0s9YeNcb4rLX+wPoqwBxr7bVF/YxQDF2J+x3+drDTEUqkUqdBTkeQMOQz7pvv5ffAXa7DQc7JjFKtHPvvbhu0/7EXTJwblhU7VD06N1hrTwCcauQExAC9QrRPERERKYkwHnIKlpA0dE41cs6xfg+wJxT7FBERkZLxwtCVJ6+jIyIiIt6gu5eLiIh4lYauREREJFJZNXREREQkYnmgoaM5OiIiIhKx1KMjIiLiURq6EhERkcjlgYaOhq5EREQkYqlHR0RExKM0dCUiIiIRywsNHQ1diYiISMRSj46IiIhHeaFHRw0dcZVKnQY5HaFEDn/5rNMRSqxSt9edjhDx/Dbyb6ToNON0ALewkV9SGroSERGRiKUeHREREY/S0JWIiIhELOvX0JWIiIiIa6lHR0RExKM0dCUiIiIRy+qsKxERERH3Uo+OiIiIR2noSkRERCKWzroSERERcTH16IiIiHiUF+5GooaOiIiIR2noSkRERMTF1KMjIiLiUerRiSBDkt4iM30ZqSmznI5SIp06tmXVynmkrZ7PwAH9nI5TLD6fjyWLv+XLL0Y4HaVYwrWMP/t+Gb97bSx3vjaG0XOX5a8fO2853f/6GXe+NoZ3vvpP/vq1mXv4wzuTuPO1Mdz1+lhOZOc4EfucwrWMC+LW44XbvntuLOd1axeSsnQmyUtmsPDHaU7H+dWsDd4SrjzT0Bk5cgK3dbnf6Rgl4vP5eP+9v9Cla08aNW7HPfd054orLnM6VpGeePwR0tLWOR2jWMK1jNdv38vnP65m9FN3MWHAvfywejNbdx9gybp05q7cxISB9/L5c/fRq10TAHJy/bwwaiYv9GjL58/dxyeP3UF0VHh8vcO1jAvjxuMFuOu7B+4t5w43301i8460uq6z01GkGMLjSFgKfpi/iH37Dzgdo0RaNG/Khg2b2bRpK9nZ2UyY8CW3d+3kdKxCxcfXovOt7Rk6dKzTUYolXMt44879NKpTg/JlYoiO8tHs0jhmLd/IhIoQt+oAACAASURBVAUreaj9tZSJjgKgWqVYAH78eSuXxV1Iw/jqAFStUI4oX3h8vcO1jAvjxuOF27574M5yjjTWb4K2hKvwOBLKOcXF12Rbemb+8/SM7cTF1XQwUdHefmswzz3/Z/x+d1xuM1zLuH7NaizdmMmBrOMcO5nN/NVb2HngCFt2HWDpxkx6vj2R3v/4gpVbdwKwZddBjIG+H37FvW+OZ9ispQ5/gv8K1zKONG777rmVtZbp08ayaOF0Huntvt6os1lrgraEq1Jr6BhjRpbWvsQZt3XuwK5de1iassLpKK53Sc1qPNT+Wvp++BX9PppCw/jq+Iwh1285dPQEo/50F0/efj0Dh3+LtZZcv5+Ujdv56wM3M+yJO5mzfCOL1m5z+mNIKdF3r/S0bXcHLVreQpeuPenb90HatGnpdCQpQkjOujLGfHX2KqCdMaYqgLX29gK26wP0ATBRVfD5KoQinmtkZuygdkJc/vOE+FpkZu5wMFHhrr8+ka5dOnLrLTdRrlxZKleuxIjh79PrwSecjlagcC7jO1pdyR2trgTg/a9/pEbVimzedYD211yCMYZGdWrgM4b9WcepUbUi114axwUVywPQ5so6rEnfTcsGtZ38CEB4l3GkcON3z61O1d3du/cy+cvpNG/ehPnzFzmc6vx54V5XoerRSQAOAW8DbwWWw6c9PidrbZK1NtFam+j1Rg7AkuRU6tevR926tYmJiaFHj25M+XqG07EK9MKLr1H3kkTqN2jF/T0fZc6cBWF/oA3nMt53+CgA2/cfZvbyjdx6bQPaNarHknUZAGzZdYDsXD8XVCjH9ZfXZv32vRw7mU1Orp+fNmRySY1qTsbPF85lHCnc+N1zo9jY8lSsWCH/8c0dbmTVqp8dTvXr+K0J2hKuQnUdnUSgP/ACMMBam2qMOWat/T5E+yvS6FEfcOMN11G9ejU2b0xm8CtvMmz4OKfiFEtubi79n3yRaVPHEOXzMXzEeFavXut0rIgSzmX89LBvOJh1nOgoH8/fdQOVY8vSveUVDBo7m9+9NpaYaB+v3tceYwyVY8vxQNsm3P/2RAyGNlfW4Yar6jr9EYDwLuOCuPF44UZuK+caNS5i0sRPAYiKjmLcuMnMmDHX2VBSJGNDePK7MSYBeAfYCdxurf1NcbeNLhMfxmflixTP4S+fdTpCiVXq9rrTEUR+tfDtXyhc9smMUo3+8+W3Bu13bcO06YVmD0xf+QS4GrDAw8DPwHigLrAZ6GGt3W+MMcB7QGfgKPCgtfa8zrII6WRka226tfZuYDowOpT7EhERkZIp5dPL3wO+sdZeDjQG1gDPAbOstZcBswLPAW4FLgssfYAPz/czlspZV9baqdba/y2NfYmIiEh4McZUAW4APgWw1p601h4AugGnLuU9AugeeNwNGGnzLASqGmNqnc++dR0dERERjwrmLSCMMX2MMcmnLX1O21U9YDcwzBiTYoz5xBhTAahhrd0eeM8OoEbgcTxw+jUy0gPrSkw39RQREfGoYF7R2FqbBCQV8HI0cC3wuLV2kTHmPf47THVqe2uMCfr83GI1dIwx15M3USj//dZaXQBQREREiiMdSLfWnrro0CTyGjo7jTG1rLXbA0NTuwKvZwCnXwgsIbCuxIocujLGjALeBNoAzQNL4vnsTERERMJHaV1Hx1q7A9hmjGkYWNUeWA18BfQKrOsFfBl4/BXwB5OnFXDwtCGuEilOj04icKUN5XnoIiIiUupK+R5VjwOfGWPKABuBh8jrcJlgjOkNbAF6BN47jbxTy9eTd3r5Q+e70+I0dFYCNYHzakmJiIiIWGtTOfeIUPtzvNcC/YKx3wIbOsaYKeRd0KcSsNoYsxg4cVqIc96vSkRERNzBC2M1hfXovFlqKURERKTUhfM9qoKlwIbOqftSGWNet9aecR17Y8zrgGP3rRIREREpjuJcMPDmc6y7NdhBREREpHRZa4K2hKvC5uj0BR4FLjXGLD/tpUrAf0IdTERERELL63N0xpB3M86/cebVCw9ba/eFNJWIiIhIEBQ2R+cgcNAY8+xZL1U0xlS01m4NbTSRX/KZ8O0ePZdK3V53OkKJHXqjq9MRSqzKgClORygRN/4R7a5vnjvL2Amenox8mqnk1RkDlCPvxlw/A1eFMJeIiIiEWDjPrQmWIhs61tpGpz83xlxL3twdERERkbBW4ruXW2uXGmNahiKMiIiIlB4NXQHGmKdOe+oj7zbrmSFLJCIiIqXCC3OZitOjU+m0xznkzdn5d2jiiIiISGnxfI+OMSYKqGStfaaU8oiIiIgETWEXDIy21uYYY1qXZiAREREpHV4/62oxefNxUo0xXwETgaxTL1prPw9xNhEREQkhv9MBSkFx5uiUA/YCN/Hf6+lYQA0dERERCWuFNXQuDpxxtZL/NnBO8cJEbRERkYhmXXfN65IrrKETBVTk3Ff+VkNHRETE5fwe+G1eWENnu7X2lVJLIiIiIhJkhTV0Ir8/S0RExMP8HvhVX1hDp32ppRAREZFS54U5Or6CXrDW7ivNIKFWtmxZflzwNT8lf8ey1NkMevlppyMVaUjSW2SmLyM1ZZbTUYqtU8e2rFo5j7TV8xk4oJ/Tcc4p6eM3Sd+WSsrSmfnrLrigKtOmjWHVqh+YNm0MVatWcTBh0cK1nKObtqdcz5cpd/9LlLmlN0Tl/S0Vc103yv1hMOUeGER043b57/fFN6DcfS9QrufLlP3dUwX9WEesW7uQlKUzSV4yg4U/TnM6TpHceLxQGUtpKLChE2lOnDhBh449aJZ4M80SO9KpY1tatrjW6ViFGjlyArd1ud/pGMXm8/l4/72/0KVrTxo1bsc993TniisuczrWL4wcNZEuXXuesW7ggH7Mmb2Aq676LXNmLwirxsPZwrWcTYWqRDdux/Gxf+P4Z6+C8RHVoDlRV16HqXQBx0f+H8dHDSZnbXLeBmXKU6bd7zkx5V8cH/0KJ6YNcfYDnEOHm+8msXlHWl3X2ekoRXLb8eIUlbGz/EFcwpVnGjoAWVlHAYiJiSY6JgZrw3u6+Q/zF7Fv/wGnYxRbi+ZN2bBhM5s2bSU7O5sJE77k9q6dnI71C/PnL2L/WeXatWtHRo2eCMCo0RO5/fbwy31KWJezzwfRMWB8mJgYbNYBohvdSPaiqeSfrHnsMADRl7cgd0MK9vD+M9bL+XHb8cKNIrGMLSZoS7jyVEPH5/ORvGQG2zOWM2vWPBYvSXE6UkSJi6/JtvT/3tg+PWM7cXE1HUxUfBdfXJ0dO3YBsGPHLi6+uLrDiQoWruVssw6Qs3Qm5R/+K+UfeR174jj+rWvwValOVINEyt77PGW7PYapejFA3r9lYyn7u6cod+/zRF3e0uFPcCZrLdOnjWXRwuk80juy/ooPFypjKQ3FuTLyr2aMaQO0AFZaa2eUxj7Pxe/3k9i8I1WqVObfEz/lqqsasmrVz07FkTAW7r19YalsLFGXXMOx4S/CiaOU6dyHqIYt8ubp5GRzYtzfiLq0CWU6PMCJSW+BLwrfxb/hxOfvQnQM5e55Fv+OTdgDu5z+JAC0bXcHmZk7uOiiC/lm+jjSfl7P/PmLnI4VUVTGzgvnIadgCUmPjjFm8WmP/wf4J1AJGGSMea6Q7foYY5KNMcl+f1ZBb/vVDh48xNzvF9CpY9uQ7cOLMjN2UDshLv95QnwtMjN3OJio+Hbt2kPNmnk9DTVrXszu3XsdTlSwcC3nqNqXYw/thWNHwO8nd30KvrhLsUcOkLshr/c0d0MqvuoJANgj+/FvXQ05J+F4Fv6MdfmvhYNTZbp7914mfzmd5s2bOJwo8qiMnac5Oucv5rTHfYCbrbWDgY5Agf2T1toka22itTbR56sQ1EDVq1ejSpXKAJQrV44O7W/g5583BHUfXrckOZX69etRt25tYmJi6NGjG1O+dqwDr0SmfP0dD/S8G4AHet7NlCnhmztcy9ke3oevZr28OToEGj77tpO7MRVfQkMg7ywr/4GdAORuWIYvrj6YvHk9vhp18e93vsEGEBtbnooVK+Q/vrnDjer9DTKVsZSWUA1d+YwxF5DXkDLW2t0A1tosY0xOiPZZqFq1ajD003eJivLh8/mYNGkKU6fNLHpDB40e9QE33nAd1atXY/PGZAa/8ibDho9zOlaBcnNz6f/ki0ybOoYon4/hI8azevVap2P9wqiR/+SGQLlu3LCEV159izfe+CdjxnzEgw/dy9at6dx3X1+nYxYoXMvZv3MzueuXUu73L4A/F//ubeSsnA9RMZS95WFimrbHZp/g5MxRANj9O8jdvIpy978E1k/OqgXYvZlF7KV01KhxEZMmfgpAVHQU48ZNZsaMuc6GKoLbjhcq4/AQzpOIg8WEYi6CMWYzeT1Zp+503tpau90YUxGYb60tsn8yuky8JknIL/iMu76UfhfO9Tn0RlenI5RYlQFTnI5QIu6rFe67VL4byxgg52RGqRb1lJq/D1pRdd0xNiyrSUh6dKy1dQt4yQ/cEYp9ioiIiJytVM66OsVaexTYVJr7FBERkXPz+r2uREREJIK5dYivJDx1wUARERHxFvXoiIiIeFQ4X/8mWNTQERER8Si/y85kPR8auhIREZGIpR4dERERj/LCZGQ1dERERDzKC3N0NHQlIiIiEUs9OiIiIh7lj/y5yGroiIiIeJUXroysoSsRERGJWOrRERER8SiddeUgt3WmeaGyhAO/dVdJu60eA1QeMMXpCCV2ePQfnY5QIpV6fux0hBJz1zdPissLc3Q0dCUiIiIRK2x7dERERCS0vHAdHTV0REREPMoLQ5IauhIREZGIpR4dERERj/LCZGQ1dERERDzKC3N0NHQlIiIiEUs9OiIiIh7lhR4dNXREREQ8ynpgjo6GrkRERCRiqUdHRETEozR0JSIiIhHLCw0dDV2JiIhIxFJDR0RExKNsEJfiMMZEGWNSjDFfB57XM8YsMsasN8aMN8aUCawvG3i+PvB63fP9jGroiIiIeJTfBG8ppv7AmtOevw68Y62tD+wHegfW9wb2B9a/E3jfefFUQ6f/E/9DaupsUlJmMWrUB5QtW9bpSIUqW7YsPy74mp+Sv2NZ6mwGvfy005EKlZAQx8wZE1m+bA7LUmfz+GO9i97IYUOS3iIzfRmpKbOcjlIi69YuJGXpTJKXzGDhj9OcjlOocK/Hn/2Yxu/+8TV3vv81o/+TBsAHM5dx9z+n0uODafy/4bPYdegoAHPWbMtff9+H00nZssvJ6L/QqWNbVq2cR9rq+Qwc0M/pOIUK93pREDeVcbgxxiQAtwGfBJ4b4CZgUuAtI4DugcfdAs8JvN4+8P4S88xk5Li4mvTr9zDXNG7H8ePHGTPmI+7p0Y2RoyY4Ha1AJ06coEPHHmRlHSU6Opp5c7/gm2/msGjxUqejnVNOTg4DBg4mJXUlFStWYPGib5g5ax5r1qxzOlqBRo6cwL/+NYxhw95zOkqJdbj5bvbu3e90jCKFcz1ev/MAnyevZ/QfbyEmyke/kXO4oWE8vdpcSb8OjQEY82MaSXNX8OLtLWl5SU3aXp6AMYa1O/YzcPx8Jvfv6vCnyOPz+Xj/vb9wS+ffk56+nYU/TmPK1zPC9vsXzvWiIG4r4+II5mRkY0wfoM9pq5KstUmnPX8XGAhUCjy/EDhgrc0JPE8H4gOP44FtANbaHGPMwcD795Q0l6d6dKKjoylfvhxRUVHEli9P5vYdTkcqUlZW3l+SMTHRRMfEYG1xR0JL344du0hJXQnAkSNZpKWtIz6upsOpCvfD/EXs23/A6RgRL1zr8cbdB2mUUJ3yZaKJjvLRrO7FzFq9lYrlYvLfc+xkDoa8PyRjy8Zw6o/KvPXho0XzpmzYsJlNm7aSnZ3NhAlfcnvXTk7HKlS41ouCuLGMi+IP4mKtTbLWJp625DdyjDFdgF3W2p9K67OdEpKGjjGmpTGmcuBxeWPMYGPMFGPM68aYKqHYZ1EyM3fwzjsfsXHDYrZtTeHQoUPMnDnPiSgl4vP5SF4yg+0Zy5k1ax6Ll6Q4HalY6tRJoEnjq1m02B153cZay/RpY1m0cDqP9L7f6ThFCtd6XP/iqizdsosDR09w7GQO89dlsvNg3i/ff3yXSqc3vmDa8s30bX9N/jazV2+j+3tTeHz0XP7vjlZORf+FuPiabEvPzH+enrGduDD/QyNc60VB3FjGYaQ1cLsxZjMwjrwhq/eAqsaYU6NLCUBG4HEGUBsg8HoVYO/57DhUPTpDgaOBx++RF/D1wLphBW1kjOljjEk2xiT7/VlBDVS1ahW6du3EZQ1a8Zs61xJbIZb77rszqPsIBb/fT2LzjtSpl0jzxKZcdVVDpyMVqUKFWCaMH8JTzwzi8OEjTseJSG3b3UGLlrfQpWtP+vZ9kDZtWjodqVDhWo8vubgKD/32SvqOmE2/kbNpWPMCfL68fprHb27CtwPuoPM1dRm3cG3+NjddWZvJ/bvyzn038K9Zy52KHhHCtV54SWmddWWtfd5am2CtrQvcC8y21t4PzAHuCrytF/Bl4PFXgecEXp9tz7PLL1QNHd9pY26J1tonrbXzrbWDgUsK2uj0bi+fr0JQA7Vv/1s2b97Knj37yMnJYfLk6VzXKjGo+wilgwcPMff7BXTq2NbpKIWKjo5m4vghjB37BZMnT3c6TsTKzMwbdt29ey+Tv5xO8+ZNHE5UPOFYj+9oVp+xfW9l6CMdqVS+DHUurHzG650b12PW6q2/2K5Z3Rqk7z/C/qzjpRW1UJkZO6idEJf/PCG+Vn49CXfhWC/Oxc1lXBAHzro627PAU8aY9eTNwfk0sP5T4MLA+qeA5853B6Fq6Kw0xjwUeLzMGJMIYIxpAGSHaJ+F2rY1gxYtr6V8+XIA3NSuDWlp4T2BrHr1alSpknfQLVeuHB3a38DPP29wOFXhhiS9xZq09bz7XlLRb5bzEhtbnooVK+Q/vrnDjaxa9bPDqQoW7vV435G8hsr2A1nMXr2NW6+py5a9h/Jfn5uWTr3qefm37j2cP49kTeY+TubkUjU2PM7eXJKcSv369ahbtzYxMTH06NGNKV/PcDpWgcK9XpyL28q4OII5R6e4rLVzrbVdAo83WmtbWGvrW2vvttaeCKw/HnheP/D6xvP9jKE66+oR4D1jzIvkzZD+0RizjbwZ1I+EaJ+FWrwkhc8/n8rixd+Sk5PDstRVDPnkMyeiFFutWjUY+um7REX58Pl8TJo0hanTZjodq0Ctr2/OAz3vYvmK1SQvyfvyv/TSa0z/ZrbDyQo2etQH3HjDdVSvXo3NG5MZ/MqbDBs+zulYhapR4yImTcz7oycqOopx4yYzY8ZcZ0MVItzr8dPj5nHw6AmifT6e79KcyuXLMHjyQjbvOYTPGGpVrcALt7cAYNaqrUxJ3UR0lI9yMVH8/Z42nOcZr0GXm5tL/ydfZNrUMUT5fAwfMZ7Vq9cWvaFDwr1enIvbyljymFDOcg9MSK5HXoMq3Vq7s7jbxpSJD+/p92dxVVgpNeHxK7Bk3FiXD4/+o9MRSqRSz4+djiBhKudkRqkeNv5Wp2fQvvLPbxkdloe8kF5Hx1p7CFgWyn2IiIjI+fG78k+bkvHUdXRERETEWzxzZWQRERE5UzCvjByu1NARERHxqMgfuNLQlYiIiEQw9eiIiIh4lIauREREJGL9iisau4aGrkRERCRiqUdHRETEo7xwHR01dERERDwq8ps5GroSERGRCKYeHREREY/SWVciIiISsTRHx0GRX/TOc+NZhW6rF27L61Zuuxv4ke/fdDpCiVW88RmnI4icl7Bt6IiIiEhoeeGPMTV0REREPMoLc3R01pWIiIhELPXoiIiIeJQmI4uIiEjEivxmjoauREREJIKpR0dERMSjvDAZWQ0dERERj7IeGLzS0JWIiIhELPXoiIiIeJSGrkRERCRieeH0cg1diYiISMRSj46IiIhHRX5/jho6IiIinqWhKxEREREX80xDZ0jSW2SmLyM1ZZbTUYqtbNmy/Ljga35K/o5lqbMZ9PLTTkcqUpUqlRk3LokVK75n+fK5tGrZzOlIRerUsS2rVs4jbfV8Bg7o53ScYnFbZjd+/8KpjF/+dDJtH/87d77wQf66g0eO8sc3RtL12ff54xsjOZR1DIBNmbt54NVPSHzkVUZMX5D//hMns7lvcBJ3v/Qhd/zvB/zrizml/jnO5rZ64cZjclH8QVzClWcaOiNHTuC2Lvc7HaNETpw4QYeOPWiWeDPNEjvSqWNbWra41ulYhXrn7VeY8e0cGjW6kWbNbmZN2jqnIxXK5/Px/nt/oUvXnjRq3I577unOFVdc5nSsQrkxs9u+f+FWxt3aNOHDp3uesW7o1Pm0uKIeU15/ghZX1OPTqfMBqFyxPM/efyu9brn+jPeXiYnmk2d7MfHVvkx45f+xYMV6lq/fVmqf4VzcVi/ceEwuig3if+HKMw2dH+YvYt/+A07HKLGsrKMAxMREEx0Tg7XhW5kqV65EmzYtGTpsLADZ2dkcPHjI4VSFa9G8KRs2bGbTpq1kZ2czYcKX3N61k9OxCuXGzG77/oVbGTdrWJfKFcqfsW5Oys/c3qYJALe3acKcpWkAXFi5IldfEk901JmHd2MMseXKApCTm0tObi4YUwrpC+a2egHuOiZLnpA0dIwxTxhjaofiZ3uNz+cjeckMtmcsZ9aseSxekuJ0pALVq/cb9uzZy6efvMOSxd/y8UdvEBtbvugNHRQXX5Nt6Zn5z9MzthMXV9PBREVzY2a3cUMZ7zt4hIuqVgKgepWK7Dt4pMhtcv1+erz0Ie2eeINWV13KNZcmhDpmxHHTMbk4NHR1/l4FFhljfjDGPGqMuag4Gxlj+hhjko0xyX5/VoiiuYvf7yexeUfq1EukeWJTrrqqodORChQdFUXTpo34+OORNG/Riaysowwc+JjTsUQinjGmWL0zUT4fE17ty4y3n2LlxgzWpe8shXSRxU3H5OLQ0NX52wgkkNfgaQasNsZ8Y4zpZYypVNBG1toka22itTbR56sQomjudPDgIeZ+v4BOHds6HaVA6RnbSU/fnv8Xzr8/n0rTJo0cTlW4zIwd1E6Iy3+eEF+LzMwdDiYqmhszu40byrhalYrsPnAYgN0HDlOtcvGPmZUrlKf5FXX5z4r1oYoX8dxwTJY8oWroWGut31o7w1rbG4gD/gXcQl4jSIqhevVqVKlSGYBy5crRof0N/PzzBodTFWznzt2kp2fSoMGlANx0UxvWrFnrcKrCLUlOpX79etStW5uYmBh69OjGlK9nOB2rUG7M7DZuKOO2TRry1fxUAL6an0q7poX3LOw7lJV/Ztbxk9ksXLWRurWqhzxnJHHbMbk4vDB0FaoLBp7Rh2qtzQa+Ar4yxsSGaJ+FGj3qA2684TqqV6/G5o3JDH7lTYYNH+dElGKrVasGQz99l6goHz6fj0mTpjB12kynYxXqyT+9xMgR/6BMmRg2btrKI4885XSkQuXm5tL/yReZNnUMUT4fw0eMZ/Xq8G6cuTGz275/4VbGz344ieS0zRw4cpSb//QWfbu34+EubRjwwUQm/5BCrQur8MajdwOw58Bhfj84iaxjJ/AZw+gZC/nir/3Yc/AwLw6ZjN/vx28tHVtcxY1NnB12cVu9cOMxuSh+D0ymNqGYMW6MaWCt/VVHhegy8ZFf+g5z9nyL86NKIZHgyPdvOh2hxCre+IzTETwh52RGqR6aH6hzZ9AOq6O2fB6Wv1ZC0qPzaxs5IiIiEnpe+ONR97oSERHxKN3rSkRERMTF1KMjIiLiUeF8/ZtgUUNHRETEo8L5tPBg0dCViIiIRCz16IiIiHiUFyYjq6EjIiLiUV6Yo6OhKxEREYlY6tERERHxKC9MRlZDR0RExKNCcRuocKOhKxEREYlY6tERERHxKJ115aCwvAVqIdxYVdyY2W3cVo9B9aI0VHLhncAPj3/c6QglUumefzgdwRU0R0dEREQilk4vFxEREXEx9eiIiIh4lOboiIiISMTS6eUiIiIiLqaGjoiIiEf5g7gUxhhT2xgzxxiz2hizyhjTP7C+mjHmO2PMusC/FwTWG2PM+8aY9caY5caYa8/3M6qhIyIi4lE2iP8VIQd42lp7JdAK6GeMuRJ4Dphlrb0MmBV4DnArcFlg6QN8eL6fUQ0dERERCSlr7XZr7dLA48PAGiAe6AaMCLxtBNA98LgbMNLmWQhUNcbUOp99q6EjIiLiUX5s0BZjTB9jTPJpS59z7dMYUxdoCiwCalhrtwde2gHUCDyOB7adtll6YF2J6awrERERjwrmWVfW2iQgqbD3GGMqAv8GnrTWHjLmv9ePt9ZaY0zQTwNTj46IiIiEnDEmhrxGzmfW2s8Dq3eeGpIK/LsrsD4DqH3a5gmBdSWmho6IiIhHBXPoqjAmr+vmU2CNtfbt0176CugVeNwL+PK09X8InH3VCjh42hBXiWjoSkRExKNK8V5XrYEHgBXGmNTAuv8FXgMmGGN6A1uAHoHXpgGdgfXAUeCh892xGjoiIiISUtba+YAp4OX253i/BfoFY99q6IiIiHiUX7eAiCz9n/gfUlNnk5Iyi1GjPqBs2bJORyrUkKS3yExfRmrKLKejFJvbMickxDFzxkSWL5vDstTZPP5Yb6cjFUuVKpUZNy6JFSu+Z/nyubRq2czpSAUqW7YsPy74mp+Sv2NZ6mwGvfy005GK5MbMAOvWLiRl6UySl8xg4Y/TnI6T77P5q/jdO19w59tfMHr+qjNeGzlvJU2eG8b+rONnrF+5bTfN/nc4363YXIpJC+e241tx2CAu4cozDZ24uJr06/cwrVp1pmnT9kRFRXFPj25OxyrUyJETuK3L/U7HKBG3Zc7JyWHAwMFc07gdrdt0pW/fB7niisucjlWkd95+hRnfzqFRoxtp1uxm1qStczpSgU6cOEGHjj1olngzzRI70qljW1q2Ws7QbgAAFYVJREFUOO+ruZcKN2Y+pcPNd5PYvCOtruvsdBQA1u/Yz+dL1jK6X1cm9O/GD2nb2LrnEAA7Dhzhx3UZ1Kpa4Yxtcv1+3pueTKvL4pyIXCC3Hd8kj2caOgDR0dGUL1+OqKgoYsuXJ3P7DqcjFeqH+YvYt/+A0zFKxG2Zd+zYRUrqSgCOHMkiLW0d8XE1HU5VuMqVK9GmTUuGDhsLQHZ2NgcPHnI4VeGyso4CEBMTTXRMjCvumOzGzOFo464DNKp9EeXLRBMd5aNZvZrMWrUFgDe/XsyTtzbn7KkbY/+zhvaN6lKtQnkHEhfMbce34iits66cFJKGjjGmjDHmD8aYDoHn9xlj/mmM6Rc4j77UZWbu4J13PmLjhsVs25rCoUOHmDlznhNRJEzVqZNAk8ZXs2hxitNRClWv3m/Ys2cvn37yDksWf8vHH71BbGx4/UI4m8/nI3nJDLZnLGfWrHksXhLeZQzuzGytZfq0sSxaOJ1HeodHz0P9mhewdPNODmQd59jJHOb/nM7OA1nMWbWFiyrH0jCu2hnv33kw77UeLS93KLG3qKFz/oYBtwH9jTGjgLvJu9Rzc+CTgjY6/fLRfn9WUANVrVqFrl07cVmDVvymzrXEVojlvvvuDOo+xL0qVIhlwvghPPXMIA4fPuJ0nEJFR0XRtGkjPv54JM1bdCIr6ygDBz7mdKxC+f1+Ept3pE69RJonNuWqqxo6HalIbszctt0dtGh5C1269qRv3wdp06al05G45OKqPHRjI/oOnUG/oTNoWKsaJ3Nz+XTuch7t+MvhwDe+Xkz/WxPx+Qo6QUekZEJ11lUja+01xpho8q5kGGetzTXGjAaWFbTR6ZePjikTH9TmYfv2v2Xz5q3s2bMPgP/f3p2HSVWdeRz/vtXdLI2AYisCGiUSjGgmomwqdhAR0RiJChjyxEky4xgTNG4JbnF8NBmTjJiQbRIQUBHZxZU1CIgQZRWBZjEsBrpBQNm3AM07f9QFO4buakhV33urfh+feixuV9X99X3u7X77nHPPeeWViVzaoQ3Dh49L8U7Jdvn5+YwZ9QwjRrzMK69MDDtOSqVlGykt3Xi0heGlcePp++NoFzpH7Nixkxlvzeaarp0oKVkZdpxqiVPmDRuS3fFbtnzCK69OpG3bi5g1a07IqeDGti25sW1LAH47aQGnnlSH6SXr6NU/OTfc5p176P3b1xh25/UsK/2YB4a/BcD2vfuZtbKUvITR+YKzQ8ufzXKhSzZTLToJM6sF1AcKgYbB9tpAKF1X69eV0a79xdStWweAzld2ZEWEB3BKzXlm4NMsX7GK/r+pcomWyNi0aQulpRto2fJcADp37sjy5R+EnKpyRUWNaNiwAQB16tShy1XFrFy5OuRUVYtj5sLCupx0Ur2jz6/u8pXIFGZbd+8DYOP23Uwr+Rtfu6QF0x/tzcQHezLxwZ6c3qAeI354A0X1C5nwQM+j27tceA4Pf/1SFTkZlAtdV5lq0RkMrADygEeAMWa2BugAjMzQPqs0d957jBs3nrlzJ3Po0CHeX1TCM4NeDCNKtQ174Q98pfhSiooa8eGa+Tz+RD+efS6Uw1dtcct8+WVtufVbPVi8ZBnz500B4NFHf8HESdNCTla1e+59lKHP/45atQpYs3Ydt912X9iRKtWkSWOGDO5PXl6CRCLB2LGvM37C1LBjVSmOmRs3Po2xYwYDkJefx8iRrzBlyoxwQwXuHzadHXv3k59I8FD3DjSoG+2pPSoTt59vkmSZarYys6YA7r7BzE4GugDr3H1udd6f7q6rTItVWKkxcRxloHM58+J4XuwcdVfYEY5L/Vt+F3aEE3LoQFmNnh5tmxan7ZKft2FmJE/tjM2M7O4bKjzfDozN1L5ERETk+GmMjoiIiEiMaa0rERGRHBXlQcTpokJHREQkR6nrSkRERCTG1KIjIiKSo9R1JSIiIlnLc6DQUdeViIiIZC216IiIiOSowzkwGFmFjoiISI5S15WIiIhIjKlFR0REJEep60pERESyVi50XUW20InboY/kkq0pxO0YQ/yOs1ncEufGTKlhSyTiN2ogbquB7xp3f9gRJCIiW+iIiIhIZqnrSkRERLJWLnRdxa/9VERERKSa1KIjIiKSo9R1JSIiIllLXVciIiIiMaYWHRERkRzlfjjsCBmnQkdERCRHHVbXlYiIiEh8qUVHREQkR+XCTOgqdERERHKUuq5EREREYkwtOiIiIjlKXVciIiKStXJhZmR1XYmIiEjWyplCp3bt2rwz+w0WzP8z7y+axmP/fX/YkaqlYcMGjBw5kCVL3mLx4hl0aH9J2JGqdE3XTpQsncmKZbPo++M+Ycepljgc44ED+lG6fhHvLZx6dNvPf/4TliyewYL5f2bM6EE0bNggxISVi+u1F4dzecCAfqxf9x4LF3x6Xgx74f+YO2cSc+dMYuXKvzB3zqQQE1YuyufFizOXcPNTo7npf0czbOZiAP44eT5XP/4CvZ4eS6+nx/L28nUAHDxUzn+PnE6Pp8bQq98Y5q3aEGb04+Zp/C+qLKr9c/m1mqU9WL16hezZs5f8/HxmzniZe+97jDlzF6blsy0tn/LPhgzuz6xZcxjy7AgKCgooLKzLjh070/LZ6T7AiUSC5SVv0+263pSWbuTddybwrVt/wPLlf03bPjJxnDN5jM3Sk7hjx/bs3r2HZ4f0p/XFXQDo0qWY6dNnU15ezpP/8zAADz/y5L+8r0w0ZWfy2suETJ/LeYn0/I155LwYMrg/F1/S5Z++/stfPMqOnTt58snf/Mv7Kj+c/hl0M3le7Bp3YoXTqo1beWDYVIbdfSMFeXn0eWYCj/S4gvEL/kphrQK+feWX/+H1I2ctZVnpFp74xpVs3bWPPoMm8OLdN5FInNi1X/f6+zL16+SYGjf8Ytou+E07VtRo9urKmRYdgD179gJQUJBPfkFB5AdhNWhQn44d2zPk2REAHDx4MG2/gDOhXdvWrF79IWvXruPgwYOMHv0qN3ztmrBjVSkux3jWrDls27b9H7ZNnTqT8vJyAObMWUizZk3CiFYtcbv24nIuH+u8qOjmHtczetSrNZjo+ETxvFizeRtf+tzp1K1VQH5egkvObcKbi9dW/vpN22jXohkAjerXpX6dWpSUbqmpuP+yw3jaHlGVsULHzD5vZj8ys9+Y2a/M7A4zC7VtPZFIMH/eFDaWLebNN2cyd957YcZJqXnzz/Hxx58weNCvmTd3MgP+9BSFhXXDjlWpps3OYH3pp822pWUbadr0jBATpRa3Y1yZ73znFiZPnh52jErF7dqL47n8WR07tmfzpo9ZtfrDsKNUKornRYszGrFwzUds37OffQcOMmv5OjZt3w3AyNlL6dlvDI+NnMHOvX8HoGXTU5lR8jcOlR+m7JOdLCv9+OjrJRoyUuiY2Q+BPwF1gLZAbeAs4F0z61TF+243s/lmNv/w4T1pz3X48GHatO3K2c3b0LZNay644Ly07yOd8vPyaN36SwwYMJS27a5hz5699O17Z9ixsko2HOMHH7iLQ4fKGT5iXNhRKhW3ay8b3NKrO6NHR7c1B6J5Xny+8Sl8t/NFfH/gePo8M4HzmhaRSBi9LmvFGw/3ZtR9PShqUMjTr70DwNfbfZHGJ9fjm/3H8dSrf+HL5zQmkaYu65rg7ml7RFWmWnT+C7jW3X8GdAEucPdHgG7Aryt7k7sPdPc27t4mkaiXoWiwY8dOZrw1m2u6dsrYPtKhtGwjpaUbj/6V89K48bS+6Eshp6rchrKPOOvMpkf/fWazJmzY8FGIiVKL2zH+rFtv7cl113Xh378dj+IsLtdeHM/livLy8ujevRtjxr4WdpRqidp5cWP7LzLi3psZ0qc79QtrcfZpJ3Nq/ULyEgkSCeOmDuezdP1mAPLzEvy4+2WMvr8H/f+jG7v2HeDs004O+TuovsPuaXtEVSbH6ByZo6c2cBKAu68DCjK4z0oVFTU6eldKnTp16HJVMStXrg4jSrVt2rSF0tINtGx5LgCdO3dk+fIPQk5VuXnzF9GiRXPOOecsCgoK6NWrO6+/MSXsWFWK2zGuqGvXTvzo/u9z083fZd++/WHHqVQcr704nssVXdX5ClZ+sJqysugWZ1E+L7bu2gfAxm27mLb4Q669uAVbdn7ayzBtyVpanNEIgH0HDrLv7wcBeGdlKfl5xrlnnFLzoaVSmZowcBAwz8zmAFcAvwQws9OArRnaZ5WaNGnMkMH9yctLkEgkGDv2dcZPmJr6jSG7595HGfr876hVq4A1a9dx2233hR2pUuXl5dx9z0+YMH44eYkEzz0/imXLol80xOEYvzD09xQXX0pRUSPWrJ7HEz99mr5976R2rVpMnJAcSD1n7kLuvPOhkJP+szhee3E5l4cO/T3FV3SgqKgRq1fN5ac/e5rnnhtFz143RHoQMkT7vLj/+Sns2Luf/ESCh266nAZ1a/PIy7NZWfYJZtD0lPr8pOcVAGzdvZ8fDBxPwozTG9bjZ707h5z++ES5yyldMnZ7uZldAJwPLHX3Fcf7/kzcXp5J8emR/VSsDnAgbsc5XbeX16QoN0Fni3TdXl6TMnF7eSad6O3lYavp28sbnnRu2i74HbtXR/IHXsaWgHD3EqAkU58vIiIikorWuhIREclRudB1pUJHREQkR+VCV3X8OopFREREqkktOiIiIjkqyotxposKHRERkRylrisRERGRGFOLjoiISI7SXVciIiKStXJhjI66rkRERCRrqUVHREQkR+VC15VadERERHKUu6ftkYqZdTOzlWa2yswerIFvD1ChIyIiIhlmZnnAH4BrgVZAbzNrVRP7VqEjIiKSozyNjxTaAavcfY27HwBGAt3T+s1UIrJjdA4dKMvYcu9mdru7D8zU56db3PJC/DLHLS8oc02IW15Q5poQt7xVSefvWjO7Hbi9wqaBFY5TM2B9ha+VAu3Tte+q5GqLzu2pXxIpccsL8csct7ygzDUhbnlBmWtC3PLWCHcf6O5tKjwiUQzmaqEjIiIiNacMOKvCv88MtmWcCh0RERHJtHnAF8ysuZnVAr4BvFYTO47sGJ0Mi0Rz2nGIW16IX+a45QVlrglxywvKXBPiljd07n7IzO4EJgN5wBB3L6mJfVsuTBYkIiIiuUldVyIiIpK1VOiIiIhI1sqpQies6adPlJkNMbPNZrY07CzVYWZnmdl0M1tmZiVmdnfYmVIxszpmNtfM3g8yPx52puowszwze8/M3gg7S3WY2YdmtsTMFpnZ/LDzVIeZnWxmY81shZktN7NLw85UFTM7Lzi+Rx47zeyesHNVxczuDa67pWY2wszqhJ0pFTO7O8hbEvXjK0k5M0YnmH76A+BqkhMVzQN6u/uyUINVwcyKgd3AUHe/MOw8qZhZE6CJuy80s/rAAuDrET/GBtRz991mVgDMAu5293dDjlYlM7sPaAM0cPfrw86Tipl9CLRx94/DzlJdZvY88La7DwruEil09+1h56qO4OddGdDe3f8Wdp5jMbNmJK+3Vu6+z8xGAxPc/blwk1XOzC4kOaNvO+AAMAm4w91XhRpMqpRLLTqhTT99otx9JrA17BzV5e4b3X1h8HwXsJzkbJiR5Um7g38WBI9IV/9mdibwVWBQ2FmylZk1BIqBwQDufiAuRU7gKmB1VIucCvKBumaWDxQCG0LOk8r5wBx33+vuh4C3gJtCziQp5FKhc6zppyP9SzjOzOwcoDUwJ9wkqQXdQIuAzcCf3T3qmfsDfYHDYQc5Dg5MMbMFwTTxUdcc2AI8G3QRDjKzemGHOg7fAEaEHaIq7l4G9APWARuBHe4+JdxUKS0FrjCzU82sELiOf5wETyIolwodqSFmdhLwEnCPu+8MO08q7l7u7heRnKmzXdA8HUlmdj2w2d0XhJ3lOHV094tJrlzcJ+iWjbJ84GLgj+7eGtgDRH5cH0DQzXYDMCbsLFUxs1NItqo3B5oC9czsW+Gmqpq7Lwd+CUwh2W21CCgPNZSklEuFTmjTT+eSYJzLS8CL7j4u7DzHI+iamA50CztLFS4HbgjGvIwEOpvZsHAjpRb89Y67bwZeJtmVHGWlQGmF1r2xJAufOLgWWOjum8IOkkIXYK27b3H3g8A44LKQM6Xk7oPd/RJ3Lwa2kRz7KRGWS4VOaNNP54pgYO9gYLm7/yrsPNVhZqeZ2cnB87okB6uvCDdV5dz9IXc/093PIXkOT3P3SP8VbGb1gsHpBN0/XUl2AUSWu38ErDez84JNVwGRHVT/Gb2JeLdVYB3QwcwKg58dV5Ec1xdpZnZ68P/PkRyfMzzcRJJKziwBEeb00yfKzEYAnYAiMysFHnP3weGmqtLlwK3AkmDMC8DD7j4hxEypNAGeD+5SSQCj3T0Wt2zHSGPg5eTvMvKB4e4+KdxI1XIX8GLwh9Ea4Lsh50kpKCSvBr4XdpZU3H2OmY0FFgKHgPeIx9IKL5nZqcBBoE/MBqnnpJy5vVxERERyTy51XYmIiEiOUaEjIiIiWUuFjoiIiGQtFToiIiKStVToiIiISNZSoSMSU2ZWHqxSvdTMxgRT0p/oZz1nZj2C54PMrFUVr+1kZsc9sVuwgnnRiWYUETkRKnRE4mufu18UrGx/ALij4heDhRKPm7vflmLF+U7EYAZbERFQoSOSLd4GWgStLW+b2WvAsmDB0qfMbJ6ZLTaz70FyFmsz+72ZrTSzqcDpRz7IzGaYWZvgeTczW2hm75vZm8FirXcA9watSVcEs0u/FOxjnpldHrz3VDObYmYlZjYIsJo9JCIiOTQzski2ClpuriW5yCAk12S60N3XBiuF73D3tmZWG5htZlNIrix/HtCK5MzFy4Ahn/nc04BngOLgsxq5+1Yz+xOw2937Ba8bDvza3WcF0+JPBs4HHgNmufsTZvZV4D8zeiBERI5BhY5IfNWtsNTG2yTXGbsMmOvua4PtXYF/OzL+BmgIfAEoBka4ezmwwcymHePzOwAzj3yWu2+tJEcXoFWwxANAg2AF+2KSawHh7uPNbNsJfp8iIidMhY5IfO1z94sqbgiKjT0VNwF3ufvkz7zuujTmSAAd3H3/MbKIiIRKY3REsttk4PtmVgBgZi2DhR9nArcEY3iaAFce473vAsVm1jx4b6Ng+y6gfoXXTSG5ACbB644UXzOBbwbbrgVOSdt3JSJSTSp0RLLbIJLjbxaa2VJgAMmW3JeBvwZfGwq889k3uvsW4HZgnJm9D4wKvvQ6cOORwcjAD4E2wWDnZXx699fjJAulEpJdWOsy9D2KiFRKq5eLiIhI1lKLjoiIiGQtFToiIiKStVToiIiISNZSoSMiIiJZS4WOiIiIZC0VOiIiIpK1VOiIiIhI1vp/lclbJqXxQzMAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Using softplus activation function for hidden layer"
      ],
      "metadata": {
        "id": "m3QHx6I0gAx0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model=keras.Sequential([\n",
        "    keras.layers.Flatten(input_shape=(28,28)),\n",
        "    keras.layers.Dense(100,activation='softplus'),\n",
        "    keras.layers.Dense(10,activation='softmax')\n",
        "])\n",
        "model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['accuracy'])\n",
        "\n",
        "model.fit(X_train,Y_train,epochs=5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HFX_kEOggGv-",
        "outputId": "22b1bd8c-ee64-43ad-db14-c834fd8e1eae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.3285 - accuracy: 0.9065\n",
            "Epoch 2/5\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1676 - accuracy: 0.9513\n",
            "Epoch 3/5\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1154 - accuracy: 0.9664\n",
            "Epoch 4/5\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.0870 - accuracy: 0.9739\n",
            "Epoch 5/5\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0698 - accuracy: 0.9789\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f6928d11650>"
            ]
          },
          "metadata": {},
          "execution_count": 166
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.evaluate(X_test,Y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4XNsEoxiguee",
        "outputId": "d0ad47ec-24d0-4ae5-d143-39ec1ff47aba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 2ms/step - loss: 0.0813 - accuracy: 0.9757\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.08125948160886765, 0.9757000207901001]"
            ]
          },
          "metadata": {},
          "execution_count": 167
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Using SGD optimizer"
      ],
      "metadata": {
        "id": "oBPwNf0xhKT-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model=keras.Sequential([\n",
        "    keras.layers.Flatten(input_shape=(28,28)),\n",
        "    keras.layers.Dense(100,activation='relu'),\n",
        "    keras.layers.Dense(10,activation='sigmoid')\n",
        "])\n",
        "model.compile(optimizer='SGD',loss='sparse_categorical_crossentropy',metrics=['accuracy'])\n",
        "\n",
        "model.fit(X_train,Y_train,epochs=5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f3AxYEDNhEiG",
        "outputId": "a058aae1-1cf5-4772-ebc7-9db4200ff274"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "1875/1875 [==============================] - 5s 2ms/step - loss: 0.6422 - accuracy: 0.8379\n",
            "Epoch 2/5\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3356 - accuracy: 0.9066\n",
            "Epoch 3/5\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2879 - accuracy: 0.9189\n",
            "Epoch 4/5\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2586 - accuracy: 0.9275\n",
            "Epoch 5/5\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2367 - accuracy: 0.9341\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f69289e4950>"
            ]
          },
          "metadata": {},
          "execution_count": 170
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.evaluate(X_test,Y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EQ54VfUdhisn",
        "outputId": "3a413083-684e-4841-a997-5d8e4b269c1d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 2ms/step - loss: 0.2231 - accuracy: 0.9382\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.22305837273597717, 0.9381999969482422]"
            ]
          },
          "metadata": {},
          "execution_count": 171
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Adam is better optimizer than SGD\n",
        "#Softmax is better than sigmoid"
      ],
      "metadata": {
        "id": "1xHIQS-2iA3R"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}