RECOGNIZE-NUMBERS-WITH-ARTIFICIAL-NEURAL-NETWORKS(ANN).ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Emirhan-Denizyol/Recognize-Number-With-Artificial-Neural-Networks-ANN-/blob/main/DL_With_Turkcell_ANN_Aplication.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OIBFbLDteXjz"
      },
      "source": [
        "# RECOGNIZE NUMBERS WITH ARTIFICIAL NEURAL NETWORKS (ANN)\n",
        "1. Bussiness Problem\n",
        "2. Data Understanding\n",
        "3. Data Preparation\n",
        "4. Modelling\n",
        "5. Evulation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "DY0IAjqDzDsS"
      },
      "outputs": [],
      "source": [
        "#pip install tensorflow"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "WeTh6ytPe20v"
      },
      "outputs": [],
      "source": [
        " # Import Procedures of Required Libraries\n",
        "\n",
        " import tensorflow as tf\n",
        " from tensorflow.keras.datasets import mnist\n",
        " from tensorflow.keras.models import load_model, Sequential\n",
        " from tensorflow.keras.layers import Dense, Flatten\n",
        " from tensorflow.keras.utils import to_categorical, plot_model\n",
        "\n",
        " import matplotlib.pyplot as plt\n",
        " import numpy as np\n",
        "\n",
        " import warnings\n",
        " from warnings import filterwarnings\n",
        "\n",
        " warnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n",
        " warnings.filterwarnings(\"ignore\", category=FutureWarning)\n",
        " warnings.filterwarnings(\"ignore\", category=UserWarning)\n",
        " filterwarnings(\"ignore\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "w8ee9vuJzwwi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d9fc442b-8f8f-46ae-cc50-35a271b2c41c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11490434/11490434 [==============================] - 0s 0us/step\n"
          ]
        }
      ],
      "source": [
        "# Loading the Mnist dataset\n",
        "\n",
        "(x_train, y_train), (x_test, y_test) = mnist.load_data()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eLPmKdSLiAta"
      },
      "source": [
        "The x's here are pixels\n",
        "The y's here are the outputs (y target is a variable)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nXpKzD5UhivI",
        "outputId": "afb99ce1-9ef3-4d61-b9e2-cfa79cd4e012"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Set Size:  (60000, 28, 28) (60000,)\n"
          ]
        }
      ],
      "source": [
        "print(\"Training Set Size: \", x_train.shape, y_train.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VXaP3H0lh3D2",
        "outputId": "0882df3c-9abd-4e73-cb49-ac5dc42ae6a5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Set Size:  (10000, 28, 28) (10000,)\n"
          ]
        }
      ],
      "source": [
        "print(\"Test Set Size: \", x_test.shape, y_test.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "Fz_xklcCh9Ns"
      },
      "outputs": [],
      "source": [
        "num_labels = len(np.unique(y_train))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 847
        },
        "id": "PvsiUUa9ilD9",
        "outputId": "54a408bc-ba1f-4f45-83dd-6dddeed0dbc2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f95f0141c30>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Showing Examples from the Data Set\n",
        "\n",
        "plt.figure(figsize=(10, 10))\n",
        "plt.imshow(x_train[0], cmap='gray')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 329
        },
        "id": "-cTSXTvsjDTn",
        "outputId": "853ebabd-403e-49fa-ec05-6a2568231b72"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 10 Axes>"
            ],
            "image/png": 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11lknZCeeeGKpPvzww8Oa5cuXh2z77bevaw+pYcm5c+eG7Oyzz67r+tBeUl+wsDaDw3S8oUOHhmzUqFGlOvV33erVq0P2ox/9KGQvv/xy/ZuDtfTxj3+80Vtoej7RAQCA7DQaAABAdhoNAAAgu047o9G3b9+QXX311SFL/X5ozt+pq/7++8UXXxzWVA9CK4qiePvtt7PtgY736KOPhuzxxx8P2bBhw9q8VupQv9RsUUr1YL+ZM2eGNaeeempN14JmsNdee4Xshhtu6PiNUJNNN900ZKnPvKoXXnghZJMnT86xJcjmwQcfLNWpGTLztv+cJxoAAEB2Gg0AACA7jQYAAJCdRgMAAMiuIcPgn/70p0N22mmnlepPfepTYc1HPvKRbHtYuXJlyC699NKQnXfeeaV6xYoV2fZA57V48eKQHXbYYSE7/vjjQ3bWWWfV9Z7Tp08P2ZVXXlmqn3vuubquDZ1RS0tLo7cA8A/NmzevVC9YsCCsSX0B0bbbbhuypUuX5ttYE/FEAwAAyE6jAQAAZKfRAAAAstNoAAAA2TVkGPzQQw+tKavFs88+G7Lbb7+9VL/33nthTeqE72XLltW1B7qHJUuWhGzq1Kk1ZUBR3HnnnaV67NixDdoJucyfPz9kjzzySKnee++9O2o70K6qXxBUFEVx7bXXhuzcc88N2cknn1yqUz+/dkWeaAAAANlpNAAAgOw0GgAAQHYaDQAAILuW1tbW1poWOsGVihpvnSzcf1R15P1XFO5BIp+BNJL7r+P17t07ZDfffHPIRo0aFbJf/OIXpfqYY44Ja1asWLEWu+tYtd5/nmgAAADZaTQAAIDsNBoAAEB2ZjSom98PpZHMaNBoPgNpJPdf55Ca20gd2HfCCSeU6sGDB4c1zXSInxkNAACgYTQaAABAdhoNAAAgO40GAACQnWFw6mYQjUYyDE6j+Qykkdx/NJJhcAAAoGE0GgAAQHYaDQAAIDuNBgAAkF3Nw+AAAAC18kQDAADITqMBAABkp9EAAACy02gAAADZaTQAAIDsNBoAAEB2Gg0AACA7jQYAAJCdRgMAAMhOowEAAGSn0QAAALLTaAAAANlpNAAAgOw0GgAAQHYaDQAAIDuNBgAAkJ1GAwAAyE6jAQAAZKfRAAAAstNoAAAA2Wk0AACA7DQaAABAdhoNAAAgO40GAACQnUYDAADIbt1aF7a0tLTnPmhCra2tHfZe7j+qOvL+Kwr3IJHPQBrJ/Ucj1Xr/eaIBAABkp9EAAACy02gAAADZaTQAAIDsNBoAAEB2Gg0AACA7jQYAAJCdRgMAAMhOowEAAGSn0QAAALLTaAAAANlpNAAAgOw0GgAAQHYaDQAAIDuNBgAAkJ1GAwAAyE6jAQAAZKfRAAAAslu30RsA/tf06dNDdsopp5TqefPmhTWjR48O2aJFi/JtDADotH7zm9+ErKWlJWSf+9znOmI7JZ5oAAAA2Wk0AACA7DQaAABAdhoNAAAgO8PgGWyyySYh23jjjUP2xS9+sVT3798/rLnkkktCtmrVqrXYHZ3RwIEDQzZhwoSQrVmzplTvvPPOYc1OO+0UMsPgtGWHHXYI2XrrrReyESNGlOorrrgirKnep7nNmTMnZEceeWSpXr16dbvugfaXuv+GDx9eqs8777yw5jOf+Uy77Qk6ox/+8IeluvrnpCiKYsaMGR21nX/KEw0AACA7jQYAAJCdRgMAAMjOjEYbqr9LP2XKlLBmr732CtmgQYPqer8tt9wyZNVD22h+S5cuDdkDDzwQsjFjxnTEduhiPvGJT5TqSZMmhTVjx44NWY8e8d+ettpqq1KdmsdobW39gDv8YFJ/Dq666qpS/Y1vfCOsWb58eXttiXbQp0+fkM2dO7dUv/TSS2HNhz/84ZCl1kEzuuCCC0L2L//yL6X63XffDWtSh/g1gicaAABAdhoNAAAgO40GAACQnUYDAADIrtsOg6cOOUsNE44fP75U9+rVK6xpaWkJ2d/+9reQvfnmm6U6dfjal7/85ZClDsiaP39+yGgeK1asCJlD9sjl/PPPL9UHHnhgg3bSfiZOnFiqf/KTn4Q1Dz/8cEdthw6SGvw2DE5Xtueee4aserjlQw89FNbcfPPN7banD8ITDQAAIDuNBgAAkJ1GAwAAyE6jAQAAZNclh8Grp4teeOGFYc0RRxwRsk022aSu91uwYEHIDjjggJBVh3dSA939+vWrKaO5bbrppiEbMmRIx2+ELumee+4p1bUOg7/yyishqw5Zp04PT50WnjJ8+PBSPXLkyJpeB/9P6stXIIcRI0aE7MwzzyzV48aNC2tee+21bHtIXX/QoEEhW7hwYamePHlytj3k5okGAACQnUYDAADITqMBAABkp9EAAACy65LD4Iceemip/vrXv57t2tUBnKIoiv322y9kqZPBt9tuu2z7oLltuOGGIdt6663rutawYcNClvqiASePdx9XXnllqZ49e3ZNr3v33XdDlvOE5d69e5fqefPmhTVbbbVVTdeq/m964okn6t4XzaO1tTVkG2ywQQN2QldzzTXXhGz77bcv1bvssktYkzqVu15nnHFGyDbffPOQHXvssaX66aefzraH3DzRAAAAstNoAAAA2Wk0AACA7LrkjMbYsWPret1f//rXkD3++OOlesqUKWFNah4jZeedd65rX3Q9L774YshuuOGGkE2dOrXNa6XWLFu2LGSXX355DTujK3jvvfdKda2fUe2tepDpZpttVve1Fi9eXKpXrVpV97VobnvssUfIfvvb3zZgJzSzlStXhqw6E5RzHmjo0KEhGzBgQMhSB6I201ySJxoAAEB2Gg0AACA7jQYAAJCdRgMAAMiuSw6DVw8yOe6448Kau+++O2TPPfdcyF555ZVs+9piiy2yXYuuZ9q0aSGrZRgcOqMjjzwyZNXP5l69etV9/bPPPrvu19I5Vb/EoCiK4o033ijVffr0CWu23XbbdtsTXVPq79tPfvKTIfvDH/5QqtfmYLyNNtqoVKe+XCh1mG/qiw1+/vOf172PjuaJBgAAkJ1GAwAAyE6jAQAAZKfRAAAAsuuSw+DVU5c7y0DtXnvt1egt0GR69Cj/W0DqhFDoSOPHjw/Zt771rZBtt912IVtvvfXqes+nnnoqZO+++25d16LzWrZsWcgefPDBUj169OgO2g1dxcc+9rGQVb+YoijSX0Zw0kknleqlS5fWvY9LLrmkVI8dOzasqf78WhRF8ZnPfKbu9+wMPNEAAACy02gAAADZaTQAAIDsNBoAAEB2XXIYPKdTTjmlVFdPdvwgUqdOVj3yyCMhe/TRR+t+T5pbdfi7tbW1QTuhmQwcOLBUH3XUUWHNqFGj6rr23nvvHbJ678vly5eHLDVYfscdd4Ts7bffrus9ga5t0KBBpXrWrFlhTb9+/UJ22WWXhez++++vaw+TJ08O2aRJk9p83bnnnlvX+3VmnmgAAADZaTQAAIDsNBoAAEB23WJGY8MNNwzZLrvsErLvfOc7ITvwwAPbvH71ULWiqO1gtdTBLMccc0zI3n///TavBXRP1d9HLoqiuPXWW0v11ltv3VHb+UCqh7EVRVFcc801DdgJzWzzzTdv9BboAOuuG39knTBhQsh+8pOflOpaf0ZLHap8+umnl+rqoXtFURR9+/YNWeowvpaWllI9Y8aMsObqq68OWbPzRAMAAMhOowEAAGSn0QAAALLTaAAAANk1/TD4euutF7Jdd921VN9yyy1hzZZbbhmy1AFQ1YHt1OF5n//850OWGkCvSg02HXbYYSGbPn16yFavXt3m9YHuqTp0WK3XRr1ffpEyevTokH3hC18I2Z133lnX9ekexowZ0+gt0AGOPPLIkF177bUhqx4gmvp8eu6550K2xx57tJkdfPDBYc1HPvKRkKV+xly6dGmp/upXvxrWdEWeaAAAANlpNAAAgOw0GgAAQHYaDQAAILumGgZff/31Q5YaxP7FL37R5rW++93vhuzee+8N2cMPP1yqUydApl6XOq23qn///iE7//zzQ/b888+HbPbs2aV61apVbb4fzac6eFvr0O2IESNCdvnll2fZE53LvHnzQrbPPvuU6tTpuXfddVfI3nnnnWz7+trXvhayk08+Odv16R7mzp1bqlNfIEDXc8QRR4Ts+uuvD9m7774bsmXLlpXqr3zlK2HN66+/HrKLL744ZCNHjizVqYHx1JdtVAfSi6Io+vXrV6r/9re/hTXVz+6iKIqFCxeGrJl4ogEAAGSn0QAAALLTaAAAANlpNAAAgOxaWlMTK6mFGU+WrUXqxO/vfe97ITvttNPavFbqVNmjjjoqZNUBoqKIA9t33HFHWLPbbruFLHVy90UXXVSqUwPjqVMnU37961+X6gsvvDCsSQ07pTz11FM1rauq8dbJoqPvv87i/fffL9Vr89988ODBpfrZZ5+t+1qdQUfef0XRfe/BevXp0ydkr776apuvO+igg0LWWU8G9xnY/g4//PBS/d///d9hzdtvvx2yXXbZJWSLFi3Kt7FOoCvff6kv2RkwYEDIzjnnnJClhsZrkbpnrr766lK91157hTW1DoNX/dd//VfIJk6c2ObrOota7z9PNAAAgOw0GgAAQHYaDQAAILtOc2DfOuusU6qnTZsW1kyePDlkK1asCNm3vvWtUj1z5sywJjWPkTqIpXrI2a677hrWLFiwIGQnnHBCyKoHD/Xu3TusGT58eMjGjx8fsjFjxpTqe+65J6xJSR0Qs80229T0WjreVVddVaqPP/74uq913HHHlepvfOMbdV8L2nLAAQc0egt0Ae+9916ba1K/I9+zZ8/22A4dZM6cOSFLHcac+pmmXtUD9YqitsOXx40bF7LUQapVixcvrm1jTc4TDQAAIDuNBgAAkJ1GAwAAyE6jAQAAZNdphsGrg6qpwe+VK1eGLDUce/fdd5fqPffcM6w55phjQvaFL3whZL169SrVqUMDU4fD1DKgtHz58pD96le/qimrDh995StfafP9iqIovvnNb9a0js5h/vz5jd4CDZI6tHT//fcPWepgq9QBZu0p9Xk6ffr0Dt0DXVN1KDj1mbjTTjuFLPVlFyeeeGK2fdG+2vvzI3Wg6NixY0NW/dKehQsXhjU333xzvo11QZ5oAAAA2Wk0AACA7DQaAABAdhoNAAAgu5bW1tbWmhYmTt7MacmSJaW6f//+Yc2qVatClhoM22ijjUr1dtttV/e+pk6dWqrPP//8sOb999+v+/rNrMZbJ4v2vv+axZ/+9KeQbbvttjW9tkeP8r8rpP5cpAbdOquOvP+Kov3vwb333rtUn3nmmWHNfvvtF7JtttkmZDlPy+3bt2+pPvDAA8Oayy67LGSbbLJJm9dODa2PGTMmZHPnzm3zWo3gM7Dj/cd//EfIUl9GsMUWW4TsnXfeaY8tNYz7r36nn356yKZNmxaypUuXluphw4aFNd3lhO+qWu8/TzQAAIDsNBoAAEB2Gg0AACA7jQYAAJBdpzkZ/KWXXirVqWHwnj17hmzIkCFtXvuOO+4I2QMPPBCy2bNnh+yvf/1rqe6ug990Ds8880zIPv7xj9f02jVr1uTeDhldfvnlpXrQoEE1ve7f//3fQ/bmm29m2VNRxAH03XbbLaypdSjwvvvuK9VXXnllWNNZB7/pvFL33+rVqxuwEzqjAQMGhOzrX/96yFL30TXXXFOqu+vg99rwRAMAAMhOowEAAGSn0QAAALLrNDMaI0aMKNWHHHJIWJP63eBXXnklZNddd12pfv3118Mav79JM6r+vmhRFMVBBx3UgJ3QWZxwwgmN3kLyc/i2224L2amnnlqqu9oBajRG7969Q3bwwQeHbNasWR2xHTqZe+65J2SpuY0bb7wxZN/5znfaZU/diScaAABAdhoNAAAgO40GAACQnUYDAADIrqW1xpOWWlpa2nsvNJlaD+nKwf33v1IDbLfffnvIdt5555BV/xvusMMOYc3ChQvXYncdqyPvv6Jo/3tw6NChpfrkk08Oa44++uh23UPq//+VK1eW6gcffDCsSX1Jwbx58/JtrJPyGdjxXnzxxZBtttlmIdt1111DNn/+/HbZU6O4/2pz+umnh2zatGkhGzt2bMh8gcA/Vuv954kGAACQnUYDAADITqMBAABkp9EAAACyMwxO3Qyi0UhdbRi8qmfPniGbNGlSyM4555yQVYdjZ8+eHdakTsudM2dOyF566aV/ssvuzWdgx5s5c2bIUl9+MWbMmJAtWrSoXfbUKO4/GskwOAAA0DAaDQAAIDuNBgAAkJ1GAwAAyM4wOHUziEYjdfVhcDo/n4E0kvuPRjIMDgAANIxGAwAAyE6jAQAAZKfRAAAAstNoAAAA2Wk0AACA7DQaAABAdhoNAAAgO40GAACQnUYDAADITqMBAABkp9EAAACy02gAAADZtbS2trY2ehMAAEDX4okGAACQnUYDAADITqMBAABkp9EAAACy02gAAADZaTQAAIDsNBoAAEB2Gg0AACA7jQYAAJDd/wBEsB4pZkmk3QAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(10, 10))\n",
        "for n in range(10):\n",
        "  ax = plt.subplot(5, 5, n+1)   # Let's print 5 rows, 5 columns, one under the other\n",
        "  plt.imshow(x_train[n], cmap='gray')\n",
        "  plt.axis('off')               # We also use it when we do not want axis information."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "F9K1Qx5Jj6e4"
      },
      "outputs": [],
      "source": [
        "def visualize_img(data):\n",
        "  plt.figure(figsize=(10, 10))\n",
        "  for n in range(10):\n",
        "    ax = plt.subplot(5, 5, n+1)\n",
        "    plt.imshow(data[n], cmap='gray')\n",
        "    plt.axis('off')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 329
        },
        "id": "ZZJwf7hhkRxL",
        "outputId": "8688b832-a2e7-4792-e63a-17a4be3f746e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 10 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "visualize_img(x_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 847
        },
        "id": "dy6sv_EjoJcm",
        "outputId": "8cb48033-31a8-4732-bf2e-fc3cc8b4d6c7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f95ea6821d0>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(10, 10))\n",
        "plt.imshow(x_train[2], cmap='gray')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DpiqRBkloN2a"
      },
      "source": [
        "RGB takes values between (0-255)\n",
        "r: Red g: Green b: Blue\n",
        "At points where all are 0, the color is black, and at points where all are 255, the color is white."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qWyHnvPIoog-",
        "outputId": "acc8ddb7-ed45-4f59-d032-c06a84487f17"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(28, 28)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "x_train[2].shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yeT-TCY5kWMs",
        "outputId": "ee564ac3-13f9-4365-9087-09d55dde2d76"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              "          0,   0,   0,   0, 169, 254,  57,   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, 169, 254,  57,   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, 169, 255,  94,   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, 169, 254,  96,   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, 169, 254, 153,   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, 169, 255, 153,   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,  96, 254, 153,   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": 13
        }
      ],
      "source": [
        "x_train[2]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RMqV9aWDotHr"
      },
      "source": [
        "Here **28x28** is the pixels of the image poured into the matrix. The color average of the color values in each pixel in the matrix is obtained."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oL0bZxj1osmU",
        "outputId": "28a14ace-bdaf-45ac-c02a-de7d89ed5284"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "x_train[2][10, 10]      # Here we find the color average of the pixels corresponding to 10 by 10 in our image. We see that it is a black color because of 0."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KtQwg0VfpV-M",
        "outputId": "1ed4437b-ad4d-4a1e-a4f8-1051d7ccc660"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "24.799744897959183"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "x_train[2].mean()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z5PgN4bppino",
        "outputId": "85a896e2-acb9-4224-ce29-c61bf4caea0c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "19443"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "x_train[2].sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xFpu_UYHpk96",
        "outputId": "d3e94304-e235-4477-f647-bb577b64e5b1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 49, 116, 144, 150, 241, 243, 234, 179, 241, 252],\n",
              "       [254, 250, 240, 198, 143,  91,  28,   5, 233, 250],\n",
              "       [ 98,  56,   0,   0,   0,   0,   0, 102, 254, 220],\n",
              "       [  0,   0,   0,   0,   0,   0,   0, 169, 254, 137],\n",
              "       [  0,   0,   0,   0,   0,   0,   0, 169, 254,  57],\n",
              "       [  0,   0,   0,   0,   0,   0,   0, 169, 254,  57]], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "x_train[2][14:20, 10:20]    # The process here allows us to segment the image. In other words, it allows us to examine certain points in the image."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9ZAGlpXzpp7J",
        "outputId": "fbd07288-b10f-477a-82fa-066ea24b8b40"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "96.51666666666667"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "x_train[2][14:20, 10:20].mean()   # The average of the particular area of the image we examined was 96.5. This allows us to estimate the average color in that area.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "ReGOd0abqNtm"
      },
      "outputs": [],
      "source": [
        "def pixel_visualize(img):\n",
        "  fig = plt.figure(figsize=(12, 12))\n",
        "  ax = fig.add_subplot(111)\n",
        "  ax.imshow(img, cmap='gray')\n",
        "  width, height = img.shape\n",
        "\n",
        "  threshold = img.max() / (2.5)\n",
        "\n",
        "  for x in range(width):\n",
        "    for y in range(height):\n",
        "        ax.annotate(str(round(img[x][y], 2)), xy=(y, x),\n",
        "                    color='white' if img[x][y] < threshold else 'black')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 984
        },
        "id": "kNYkaCWDrKS6",
        "outputId": "4ef0a306-64ea-4fa2-fb23-1af9e2a4cc14"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1200 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "pixel_visualize(x_train[2])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9LaqWKbJ3B0C"
      },
      "source": [
        "# Data Preparation\n",
        "\n",
        "\n",
        "*   Before: [0 1 2 3 4 5 6 7 8 9]\n",
        "*   After: [0 0 1 0 0 0 0 0 0]\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tVseAWf63ICF",
        "outputId": "a7f5365e-4d4a-43b5-8f61-8d3e153ee2d9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([5, 0, 4, 1, 9], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "y_train[0:5]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "bZLZTV734A_1"
      },
      "outputs": [],
      "source": [
        "y_train = to_categorical(y_train)     # It transforms y_train. Currently, the way variables are expressed is with One Hot Encoding style.\n",
        "y_test = to_categorical(y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5SHR1NwM4pRm",
        "outputId": "98c58b40-cb89-4b0f-a9b8-8c7399112e85"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0., 0., 0., 0., 0., 1., 0., 0., 0., 0.],\n",
              "       [1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],\n",
              "       [0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 1.]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "y_train[0:5]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D1ayhkI75ay4"
      },
      "source": [
        "## Reshaping"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "h1Qu9Ps85e6P"
      },
      "outputs": [],
      "source": [
        "image_size = x_train.shape[1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "edjy0Q465st8",
        "outputId": "88906260-eaed-4a8a-8597-59a9f5294d9b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "28"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "image_size"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p8xTXyg_54E0",
        "outputId": "b69141cf-be58-4acf-927b-a49c15d95449"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x_train Size: (60000, 28, 28)\n",
            "x_test Size: (10000, 28, 28)\n"
          ]
        }
      ],
      "source": [
        "print(f\"x_train Size: {x_train.shape}\")\n",
        "print(f\"x_test Size: {x_test.shape}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kxDWL5u76YqY",
        "outputId": "f589a379-a687-457a-c0fe-12b474982108"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x_train Size: (60000, 28, 28, 1)\n",
            "x_test Size: (10000, 28, 28, 1)\n"
          ]
        }
      ],
      "source": [
        "x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)\n",
        "x_test = x_test.reshape(x_test.shape[0], 28, 28, 1)\n",
        "\n",
        "print(f\"x_train Size: {x_train.shape}\")\n",
        "print(f\"x_test Size: {x_test.shape}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "trtG9o1c6-d2"
      },
      "source": [
        "# Normalization\n",
        "\n",
        "The color codes on the pixels were between 0-255, we will standardize them between 0-1. But why?\n",
        "\n",
        "* It is to try to ensure that the training period is faster and the learning process is carried out more accurately or in a way that can reach the result faster.\n",
        "\n",
        "* The easiest way to convert this to 0-1 is to divide by 255. In this way, values of 255, that is, the max value will be 1 and the min value will be 0."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "chF_Tud57CDa"
      },
      "outputs": [],
      "source": [
        "x_train = x_train.astype('float32') / 255\n",
        "\n",
        "x_test = x_test.astype('float32') / 255"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KcmvF0T-9F3q"
      },
      "source": [
        "# Modelling\n",
        "\n",
        "Defining Neural Network Architecture"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "xMV3LbOKBgdj"
      },
      "outputs": [],
      "source": [
        "# External Features of ANN\n",
        "model = tf.keras.Sequential([         # 1\n",
        "    Flatten(input_shape=(28, 28, 1)), # 2\n",
        "    Dense(units=128, activation='relu', name='layer1'),   # 3\n",
        "    Dense(units=num_labels, activation='softmax', name='output_layer')])    # 4\n",
        "\n",
        "# Internal Features of ANN\n",
        "model.compile(loss='categorical_crossentropy',  # 5\n",
        "              optimizer='adam',                 # 6\n",
        "              metrics=[tf.keras.metrics.Precision(), tf.keras.metrics.Recall(), \"accuracy\"])      ## 7"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "1 -> It helps us to establish an artificial neural network. It means sequential, it allows us to create an artificial neural network consisting of sequential layers.\n",
        "\n",
        "2 -> We have already shaped it before, now we need to reshape it for ANN to understand. This is also a layer. The ANN converts the ANN into the format it can use. It is the layer where inputs are shaped.\n",
        "\n",
        "3 -> It is the hidden layer. It is the 1st layer.\n",
        "         units=128 -> There are 128 neurons. It may change.\n",
        "         activation='relu' -> We determine which function we will use. It may change.\n",
        "         name='layer1' -> We named the layer. subject to change\n",
        "\n",
        "4 -> It is the output layer.\n",
        "         activation='softmax' -> It may change according to our needs.\n",
        "         units=num_labels -> Since we have 10 classes. There will be 10 classes in the output layer, which is what we want. (0 1 2 3 4 5 6 7 8 9)\n",
        "         name='output_layer' _> Naming may change.\n",
        "\n",
        "5 -> It is the error evaluation metric. The aim is to minimize.\n",
        "         loss = 'categorical_crossentropy' -> Since it is a multi-class classification, we use categorical_crossentropy\n",
        "\n",
        "6 -> It helps to optimize the loss function to be optimized.\n",
        "         optimizer='adam' -> It is the optimization algorithm that will minimize the loss function. It can change.\n",
        "             --> Stochastic Gradient Descent vs. etc.\n",
        "\n",
        "7 -> Allows us to look at error metrics."
      ],
      "metadata": {
        "id": "__5Xy7ihnJ_-"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4EMmFvX6EyFt",
        "outputId": "d2eaa90c-2422-4c5e-f153-7c694599bc13"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " flatten (Flatten)           (None, 784)               0         \n",
            "                                                                 \n",
            " layer1 (Dense)              (None, 128)               100480    \n",
            "                                                                 \n",
            " output_layer (Dense)        (None, 10)                1290      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 101770 (397.54 KB)\n",
            "Trainable params: 101770 (397.54 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Aplu2bb4Fn-S",
        "outputId": "fd9b1e25-7ef6-4cd7-e98f-0b1c137edd71"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "469/469 [==============================] - 4s 5ms/step - loss: 0.3592 - precision: 0.9471 - recall: 0.8482 - accuracy: 0.9021 - val_loss: 0.1947 - val_precision: 0.9574 - val_recall: 0.9322 - val_accuracy: 0.9447\n",
            "Epoch 2/10\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.1621 - precision: 0.9645 - recall: 0.9437 - accuracy: 0.9536 - val_loss: 0.1338 - val_precision: 0.9702 - val_recall: 0.9525 - val_accuracy: 0.9605\n",
            "Epoch 3/10\n",
            "469/469 [==============================] - 2s 5ms/step - loss: 0.1161 - precision: 0.9741 - recall: 0.9599 - accuracy: 0.9669 - val_loss: 0.1076 - val_precision: 0.9753 - val_recall: 0.9628 - val_accuracy: 0.9684\n",
            "Epoch 4/10\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0885 - precision: 0.9793 - recall: 0.9701 - accuracy: 0.9744 - val_loss: 0.0944 - val_precision: 0.9761 - val_recall: 0.9665 - val_accuracy: 0.9717\n",
            "Epoch 5/10\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0727 - precision: 0.9825 - recall: 0.9749 - accuracy: 0.9786 - val_loss: 0.0865 - val_precision: 0.9775 - val_recall: 0.9700 - val_accuracy: 0.9741\n",
            "Epoch 6/10\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0592 - precision: 0.9864 - recall: 0.9801 - accuracy: 0.9832 - val_loss: 0.0817 - val_precision: 0.9792 - val_recall: 0.9729 - val_accuracy: 0.9754\n",
            "Epoch 7/10\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0503 - precision: 0.9883 - recall: 0.9836 - accuracy: 0.9858 - val_loss: 0.0785 - val_precision: 0.9797 - val_recall: 0.9724 - val_accuracy: 0.9762\n",
            "Epoch 8/10\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0424 - precision: 0.9903 - recall: 0.9858 - accuracy: 0.9878 - val_loss: 0.0734 - val_precision: 0.9803 - val_recall: 0.9747 - val_accuracy: 0.9773\n",
            "Epoch 9/10\n",
            "469/469 [==============================] - 2s 5ms/step - loss: 0.0368 - precision: 0.9912 - recall: 0.9878 - accuracy: 0.9895 - val_loss: 0.0696 - val_precision: 0.9804 - val_recall: 0.9750 - val_accuracy: 0.9770\n",
            "Epoch 10/10\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0308 - precision: 0.9930 - recall: 0.9900 - accuracy: 0.9915 - val_loss: 0.0721 - val_precision: 0.9795 - val_recall: 0.9757 - val_accuracy: 0.9769\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7f95ea373dc0>"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "model.fit(x_train, y_train, epochs=10, batch_size=128, validation_data=(x_test, y_test))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "model.fit(x_train, y_train, epochs=10, batch_size=128, validation_data=(x_test, y_test))\n",
        "\n",
        "=> x_train = x values\n",
        "=> y_train = y values\n",
        "=> epochs = We say how many rounds we need to take. We tell how many times we need to optimize\n",
        "=> batch_size = During optimization processes, all observation units in the data set should be taken into account in each epoch in gradient calculations. The number 128 was given as the answer to the question \"Or should we randomly select a certain number of subsets and weight updates should be made through this active set?\" Focusing on 128 observation units in each iteration, gradients and derivatives will be calculated based on the relevant optimization method and moved on to the next epoch."
      ],
      "metadata": {
        "id": "ANeY5uTespDf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Evulation"
      ],
      "metadata": {
        "id": "372_e1kFwZnt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(x_train, y_train, epochs=10, batch_size=128, validation_data=(x_test, y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4UiLZelbu7NB",
        "outputId": "6ad1a1c5-7c17-4dd2-8eaa-88d7eae6b051"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "469/469 [==============================] - 2s 5ms/step - loss: 0.0265 - precision: 0.9940 - recall: 0.9916 - accuracy: 0.9930 - val_loss: 0.0703 - val_precision: 0.9811 - val_recall: 0.9763 - val_accuracy: 0.9777\n",
            "Epoch 2/10\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0220 - precision: 0.9955 - recall: 0.9936 - accuracy: 0.9945 - val_loss: 0.0691 - val_precision: 0.9802 - val_recall: 0.9760 - val_accuracy: 0.9780\n",
            "Epoch 3/10\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0188 - precision: 0.9962 - recall: 0.9948 - accuracy: 0.9955 - val_loss: 0.0718 - val_precision: 0.9788 - val_recall: 0.9754 - val_accuracy: 0.9764\n",
            "Epoch 4/10\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0163 - precision: 0.9968 - recall: 0.9957 - accuracy: 0.9963 - val_loss: 0.0734 - val_precision: 0.9793 - val_recall: 0.9767 - val_accuracy: 0.9781\n",
            "Epoch 5/10\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0136 - precision: 0.9974 - recall: 0.9963 - accuracy: 0.9969 - val_loss: 0.0749 - val_precision: 0.9783 - val_recall: 0.9756 - val_accuracy: 0.9768\n",
            "Epoch 6/10\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0128 - precision: 0.9974 - recall: 0.9965 - accuracy: 0.9970 - val_loss: 0.0743 - val_precision: 0.9798 - val_recall: 0.9773 - val_accuracy: 0.9786\n",
            "Epoch 7/10\n",
            "469/469 [==============================] - 2s 5ms/step - loss: 0.0100 - precision: 0.9983 - recall: 0.9976 - accuracy: 0.9981 - val_loss: 0.0726 - val_precision: 0.9805 - val_recall: 0.9777 - val_accuracy: 0.9788\n",
            "Epoch 8/10\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0079 - precision: 0.9989 - recall: 0.9987 - accuracy: 0.9988 - val_loss: 0.0781 - val_precision: 0.9793 - val_recall: 0.9769 - val_accuracy: 0.9775\n",
            "Epoch 9/10\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0069 - precision: 0.9991 - recall: 0.9987 - accuracy: 0.9989 - val_loss: 0.0822 - val_precision: 0.9792 - val_recall: 0.9761 - val_accuracy: 0.9775\n",
            "Epoch 10/10\n",
            "469/469 [==============================] - 2s 4ms/step - loss: 0.0069 - precision: 0.9988 - recall: 0.9987 - accuracy: 0.9988 - val_loss: 0.0833 - val_precision: 0.9788 - val_recall: 0.9771 - val_accuracy: 0.9775\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#################################\n",
        "# Accuracy and Loss Graphics\n",
        "#################################\n",
        "\n",
        "# -------------------------- Graphics 1 Accuracy ---------------------------------------\n",
        "\n",
        "plt.figure(figsize=(20, 5))\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.plot(history.history['accuracy'], color='b', label='Training Accuracy')\n",
        "plt.plot(history.history['val_accuracy'], color='r', label='Validation Accuracy')\n",
        "plt.legend(loc='lower right')\n",
        "plt.xlabel('Epoch', fontsize=16)\n",
        "plt.ylabel('Accuracy', fontsize=16)\n",
        "plt.ylim([min(plt.ylim()), 1])\n",
        "plt.title('Training and Testing Performance Graph', fontsize=16)\n",
        "\n",
        "# -------------------------- Graphics 2 Accuracy ---------------------------------------\n",
        "\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.plot(history.history['loss'], color='b', label='Training Loss')\n",
        "plt.plot(history.history['val_loss'], color='r', label='Validation Loss')\n",
        "plt.legend(loc='upper right')\n",
        "plt.xlabel('Epoch', fontsize=16)\n",
        "plt.ylabel('Loss', fontsize=16)\n",
        "plt.ylim([0, max(plt.ylim())])\n",
        "plt.title('Training and Testing Loss Graph', fontsize=16)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 495
        },
        "id": "MIcLf4fpwu_h",
        "outputId": "677560e6-e3af-41d5-d4d2-b1d879ae1db8"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x500 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loss, precision, recall, acc = model.evaluate(x_test, y_test, verbose=False)\n",
        "\n",
        "print(\"\\nTest Accuracy: %.1f%%\" % (100.0 * acc))\n",
        "print(\"\\nTest Loss: %.1f%%\" % (100.0 * loss))\n",
        "print(\"\\nTest Precision: %.1f%%\" % (100.0 * precision))\n",
        "print(\"\\nTest Recall: %.1f%%\" % (100.0 * recall))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jcAh78NOzVAi",
        "outputId": "63ee6a5c-cbcf-4641-c8bc-431d67dcbedc"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Test Accuracy: 97.8%\n",
            "\n",
            "Test Loss: 8.3%\n",
            "\n",
            "Test Precision: 97.9%\n",
            "\n",
            "Test Recall: 97.7%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Precision and Recall\n",
        "Since the Precision and Recall values are very close to the Accuracy value, I can verify my success from different angles.\n",
        "What do we mean by different perspectives?\n",
        "\n",
        "Precision and Recall\n",
        "-> Actually, it gives us good information when the data set is unbalanced in terms of these classes. That's the reason we took it with us here.\n",
        "Since the Precision and Recall values are very close to the Accuracy value, we come to the following conclusion. We say that there is no imbalance between classes and we are successful in the following subjects.\n",
        "\n",
        "In what subjects are we successful?\n",
        "\n",
        "Precision\n",
        "-> It indicates whether the values we predict through the model are actually the predicted value or not.\n",
        "So, for example, let's choose 1 class out of 10 and let it be class 1. It indicates how many of the classes we predicted as class 1 are actually class 1.\n",
        "In short, Precision is the success of what we predicted. When determining how well I match reality.\n",
        "\n",
        "Recall\n",
        "-> It tells us how many of the true values of these classes we guessed correctly.\n",
        "-> First we focus on the real values. We check how many of them we guessed correctly."
      ],
      "metadata": {
        "id": "rg8DnafS0Clx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Saving the Model and Using it for Prediction"
      ],
      "metadata": {
        "id": "MhimnOYW2jbV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.save('mnist_models.h5')"
      ],
      "metadata": {
        "id": "XQjXC3ac2iPX"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "\n",
        "random = random.randint(0, x_test.shape[0])"
      ],
      "metadata": {
        "id": "1H3PTp8127Ly"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "random"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Epci7w5b3FLM",
        "outputId": "2db67316-3f40-46c2-9f6d-deeae48895cd"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1919"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_image = x_test[random]"
      ],
      "metadata": {
        "id": "4jAgEjSl3LqK"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_test[random]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rrI4u3oz3Slc",
        "outputId": "494e487d-3077-467d-e8c5-537745857950"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0., 0., 0., 0., 0., 0., 0., 1., 0., 0.], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(test_image.reshape(28, 28), cmap='gray')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "46pkQxcQ3nE5",
        "outputId": "9f7d11f4-d4f9-47f4-c593-872b02899c5a"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f95ed3e6f20>"
            ]
          },
          "metadata": {},
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_data = x_test[random].reshape(1, 28, 28, 1)"
      ],
      "metadata": {
        "id": "unN9iqHI3xLA"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "probalitiy = model.predict(test_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NmCFg9lN4A6E",
        "outputId": "ea4fdd53-0f8a-453f-e48a-971ca7e8f746"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 100ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predicted_classes = np.argmax(probalitiy)"
      ],
      "metadata": {
        "id": "0m-7zP4Y4N17"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predicted_classes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rv-sFS6E4SNi",
        "outputId": "29fce2e1-13e4-4a3b-a701-5e4880d8b657"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Predicted Class: {predicted_classes} \\n\")\n",
        "print(f\"Predicted Probability Value: {(np.max(probalitiy, axis=-1))[0]} \\n\")\n",
        "print(f\"Probability Values of Other Classes: \\n{probalitiy} \\n\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OUvRZaPz4Vkf",
        "outputId": "23a9afe9-66a5-47d8-d700-fabf56d455cf"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted Class: 7 \n",
            "\n",
            "Predicted Probability Value: 0.9999998807907104 \n",
            "\n",
            "Probability Values of Other Classes: \n",
            "[[3.6245485e-14 6.9486821e-09 1.2095271e-11 5.8505428e-08 5.3205355e-14\n",
            "  4.0060466e-17 1.8868743e-23 9.9999988e-01 5.0099738e-12 9.8224389e-12]] \n",
            "\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "authorship_tag": "ABX9TyMC3S3hA/YVzN3EmL8l8AN8",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}