Untitled1012.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Untitled1011.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMHmvjQ4GTn0ZfgeL9uYGc5",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/dlguswn3659/class-MachineLearning/blob/master/Untitled1012.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-sOoEaBxtNXA",
        "colab_type": "code",
        "outputId": "eba46d08-51b1-42de-e752-e511895bec46",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        }
      },
      "source": [
        "%matplotlib inline\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib as mpl\n",
        "import seaborn as sns\n",
        "import matplotlib.animation as animation\n",
        "import operator\n",
        "from scipy import stats \n",
        "from mpl_toolkits.mplot3d import Axes3D\n",
        "from sklearn.datasets.samples_generator import make_regression \n",
        "import csv\n",
        "import math\n",
        "# from google.colab import files\n",
        "\n",
        "# uploaded = files.upload()\n",
        "\n",
        "file_data   = \"mnist.csv\"\n",
        "handle_file = open(file_data, \"r\")\n",
        "data        = handle_file.readlines()\n",
        "handle_file.close()\n",
        "\n",
        "size_row    = 28    # height of the image\n",
        "size_col    = 28    # width of the image\n",
        "\n",
        "num_image   = len(data)\n",
        "count       = 0     # count for the number of images\n",
        "\n",
        "#\n",
        "# normalize the values of the input data to be [0, 1]\n",
        "#\n",
        "def normalize(data):\n",
        "\n",
        "    data_normalized = (data - min(data)) / (max(data) - min(data))\n",
        "\n",
        "    return(data_normalized)\n",
        "\n",
        "#\n",
        "# example of distance function between two vectors x and y\n",
        "#\n",
        "def distance(x, y):\n",
        "\n",
        "    d = (x - y) ** 2\n",
        "    s = np.sum(d)\n",
        "    # r = np.sqrt(s)\n",
        "\n",
        "    return(s)\n",
        "\n",
        "#\n",
        "# make a matrix each column of which represents an images in a vector form\n",
        "#\n",
        "list_image  = np.empty((size_row * size_col, num_image), dtype=float)\n",
        "list_label  = np.empty(num_image, dtype=int)\n",
        "\n",
        "for line in data:\n",
        "\n",
        "    line_data   = line.split(',')\n",
        "    label       = line_data[0]\n",
        "    im_vector   = np.asfarray(line_data[1:])\n",
        "    im_vector   = normalize(im_vector)\n",
        "\n",
        "    list_label[count]       = label\n",
        "    list_image[:, count]    = im_vector\n",
        "\n",
        "    count += 1\n",
        "\n",
        "#\n",
        "# plot first 150 images out of 10,000 with their labels\n",
        "#\n",
        "f1 = plt.figure(1)\n",
        "\n",
        "for i in range(150):\n",
        "\n",
        "    label       = list_label[i]\n",
        "    im_vector   = list_image[:, i]\n",
        "    im_matrix   = im_vector.reshape((size_row, size_col))\n",
        "\n",
        "    plt.subplot(10, 15, i+1)\n",
        "    plt.title(label)\n",
        "    plt.imshow(im_matrix, cmap='Greys', interpolation='None')\n",
        "\n",
        "    frame   = plt.gca()\n",
        "    frame.axes.get_xaxis().set_visible(False)\n",
        "    frame.axes.get_yaxis().set_visible(False)\n",
        "\n",
        "\n",
        "#plt.show()\n",
        "\n",
        "#\n",
        "# plot the average image of all the images for each digit\n",
        "#\n",
        "f2 = plt.figure(2)\n",
        "\n",
        "im_average  = np.zeros((size_row * size_col, 10), dtype=float)\n",
        "im_count    = np.zeros(10, dtype=int)\n",
        "\n",
        "for i in range(num_image):\n",
        "\n",
        "    im_average[:, list_label[i]] += list_image[:, i]\n",
        "    im_count[list_label[i]] += 1\n",
        "\n",
        "for i in range(10):\n",
        "\n",
        "    im_average[:, i] /= im_count[i]\n",
        "\n",
        "    plt.subplot(2, 5, i+1)\n",
        "    plt.title(i)\n",
        "    plt.imshow(im_average[:,i].reshape((size_row, size_col)), cmap='Greys', interpolation='None')\n",
        "\n",
        "    frame   = plt.gca()\n",
        "    frame.axes.get_xaxis().set_visible(False)\n",
        "    frame.axes.get_yaxis().set_visible(False)\n",
        "\n",
        "plt.show()\n",
        "\n"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 150 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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bId+vZTGe13uvD7jvR4fxqAmv336jdaUaNL8ILM6nu5aobmMcs9FS0N1TrQKvOSrYaA34aJ2a+8E1Fa0Kr7/fz1bLKkgFnCRJ0oi5KGBXDV1p7cfPOYvEHLYSfSt+VrXkTH3zzTcveNQn6GymsoZutow+4Kn4SqPKj8pQlWfdGf3gdcWV7qnmuIi+X60V4zhVu7A4T7SK2PfatmxHKmBVj3WoGoo5Y1Uu/ZVt/cSuomuVjUXi7CtR+Ub/5pC/U2Lce4xQiNaUbd56sC3F/qUq7J840SJ+Pu6OtPxazp/97Gf3vNecDVpQ3tPY3lwv0pryO7UG9KdfffXVQGdhqqz7uybH8gDniRhJkiTrhLlkQ4uz81DO1bjy3c/fC93s5Yzs7GaWJ2P3zPakIrr88suBLrsadNEC+o/mpWg+KSod1Zo+LXe8OaObx0B/n4o+KibrOCqW1oo4ZrTy3ut37J9i4mvWgXXiZ2M0g+okWlS2K2NBY14Q6xI6v6f1proeO8NutYgx7TEuemjFfUwBR0UW8wZrdWilxsyAexMXvRZEBayV0s9MZr9wTIn9yTYQz5u0Dq0jM8SZkdHvjdkFYfVio1MBJ0mSNGJVFbCzgPvsfeyvHurPNA5RRSt+xtlMJeRJGvfddx/Q+QhVNKrcK664AuhO0oBuNXNKpyH3VU2MbdZP58q19apf29VaVV/0Fa4XH7DWjb7ufo5XLR5Vi75/rQGVrv5arQEf+ycJw+Iz4VRH/bag0tFSWivlF3cEeo3Wj32hX6Z48nXMgOd3Wqf2t3vuuQfooh/8TetRhdmPFGoZB+1vRsXZt2Lj6dFRAVv+mDXQdqiyNfrB98V1hXlkiGs/EiVJkmxQVlUBO2uqQlRs/dhMs9arfPVJGd/pTOzspTpyxnYl01lN5fu5z30O6E7SMNM/dDPjFKIf4o4eWLw/X3+ndWD5rUdXZeO5Z6uVoakV/XO+VMBaQLaTePKzasaVcNuef4/+VNVOzPnaf02Lba1yh8SIBduB1mI/jt468jNjCtg2pPLVgjTzl/VrP7GvuiN1Hqc/7AvRx90vy1i8r/0n5v+ObSdmzItx3/PMgZEKOEmSpBGrooDj3nlj9cyrqeqFLmeDvlx9fJ5l5ewUZxtnJRWOUQ7XXHMN0J2kEf04MI0sYWOnu0I3Q8eVaWdm/eLWb8xYtV7yHMddTV5XVPbQKTF9mCo8r13FGxWc7UMVGyNDbAv+pjHV0LXb6AddKx9ojGaxPbjrD7r+Yr24buCjbccoD/ubild1aL2de+65AFx55ZXA4mxxMC0FPPR8LF+EvvSogG13Y/70sSiiefSvVMBJkiSNyAE4SZKkEavigtAc0FTRhNO86W/39L2aiIbFGHLlAoSLAJqFuhx8dCuiCd01WeOCG7RPJ9gnmkv9/0dzWVNJDEuy7sYOCZyqK8Lr0p3gIoj3tL+Jwr/ZhjSd4+GahuTFo4bGQohsF3Hxaui1tTK9LaNl8/ftA/0tsLol4gGtLtjFBVy/2z6pW3Dbtm0AXH/99UB3CK79bWjb7RT6zxCxT9lW+tupobumuEBruxtLQznP/pQKOEmSpBFz2YgRFzr6s6mvue3P9HiGGzlrGR7k9lDfbwJ3FZCKaC2CpveFpbYFW2avIaYa9PmYqomLB/uaIGRejC3CeZ2qL+jut5sQYhiZn/HafRxbOIuheUOhRTHByrwtp7ipYGzRtW8tRSvCevBASRff/LthmipcNypZvzHl4tBRYFPYuCTxfvaJlqTXMLYBK25ztg3NO/3ogjLM/ReSJEmSQeaSjCcqur5vz1Aj/X4xWFq/TJydfHR2Wstg6dVgbEtl/7XoG5UYuha/a73URSx3vMf94P+YaEjGlGz8jbHfXkmCorWut7iGYr1EtQ+dYnUNxHWBmPo1KmQVrv7PqPamcvzSJyFu0vCavFbHH+vGutCfHscr22H0CfetgtXqY6mAkyRJGjEXBSxDiTP8f0wbuNLvWq/E8g/VyUZjX1TEJ91uPeV2FJVcVHTQKdqxY4zGvnMsXWV833phqLxRzY9ZT2N1N+b7z63ISZIkn0LmqoCXYr3NuMm0+DS3n5X6tZMZU13zWAmpgJMkSRqRA3CSJEkjcgBOkiRpRNmbfc6llBeBJ+ZXnElwZq31+OXfNmOD1AnsRb1knQyzQeol62SYwXrZqwE4SZIkWT3SBZEkSdKIHICTJEkakQNwkiRJI3IATpIkaUQOwEmSJI3IAThJkqQROQAnSZI0IgfgJEmSRuQAnCRJ0ogcgJMkSRqRA3CSJEkjcgBOkiRpRA7ASZIkjcgBOEmSpBE5ACdJkjQiB+AkSZJG5ACcJEnSiByAkyRJGpEDcJIkSSNyAE6SJGlEDsBJkiSNyAE4SZKkETkAJ0mSNCIH4CRJkkbkAJwkSdKIHICTJEkakQNwkiRJI3IATpIkaUQOwEmSJI3IAThJkqQROQAnSZI0IgfgJEmSRuQAnCRJ0ogcgJMkSRqRA3CSJEkjcgBOkiRpRA7ASZIkjcgBOEmSpBE5ACdJkjQiB+AkSZJG5ACcJEnSiByAkyRJGpEDcJIkSSNyAE6SJGnEZAfgUsqfl1LeLaW8ufvfg63LNAVKKV8ppdxfSnmrlPJoKeULrcvUil7b8N9HpZTfbF2u1pRStpRS/rSU8kopZWcp5bdKKZtal6s1pZSLSil/Vkp5rZTySCnlr7Uu02QH4N18o9Z6+O5/F7QuTGtKKT8H/Dvg7wJHANcDjzUtVEN6beNw4CTgHeB/NC7WFPgd4AXgZGArcAPw9aYlaszuCeh/AX8MHAv8A+A7pZTzW5Zr6gNwspB/A3yr1vqzWuvHtdZnaq3PtC7URPhFZoPOj1sXZAKcBXy31vpurXUn8H+ASxqXqTUXAqcAv1Fr/ajW+mfAT4GvtizU1AfgXy+l7Cql/LSUcmPrwrSklLI/sA04frf59PRu0/KQ1mWbCL8M/Jdaa21dkAnwH4GvlFIOLaWcCvw8s0E4WUgBLm1ZgCkPwP8COBs4Ffhd4H+XUs5pW6SmnAgcAPwN4AvMTMvPAN9sWagpUEo5k5mZ/e3WZZkI/5eZ4n0deBq4FfjDpiVqz4PMLKR/Xko5oJTyF5m1mUNbFmqyA3Ct9f/VWt+otb5Xa/02M3Phy63L1ZB3dj/+Zq31uVrrLuA/sLHrRL4K/KTW+njrgrSmlLIfM7X7PeAwYDNwDLO1gw1LrfUD4BeAvwzsBP4p8F1mE1QzJjsAD1CZmQwbklrrK8waS9/ETnN7xt8h1a8cC5wB/NZu8fIS8PvkRE2t9a5a6w211uNqrV9iZmHf3LJMkxyASylHl1K+VEo5uJSyqZTyS8xW/De6H+v3gV8ppZxQSjkG+MfMVnU3LKWUzzFzU2X0A7DbMnoc+NruvnM0M//4XW1L1p5SyuW7x5RDSyn/jFmUyB+0LNMkB2Bmvs5fA14EdgG/AvxCrfWhpqVqz68CtwAPAfcDtwP/tmmJ2vPLwPdqrW+0LsiE+OvAX2LWfx4BPmA2WW90vgo8x8wX/EXg52qt77UsUMlF4yRJkjZMVQEnSZJ86skBOEmSpBE5ACdJkjQiB+AkSZJG5ACcJEnSiL1KUbd58+a6ZcuWORVlGuzYsYNdu3ateMPHRqgTgO3bt++qtR6/kvdmnQyzEeol+88wY21lrwbgLVu2cOutt65eqSbItm3b9ur986iTGBo4FipYSlnwGN/n66tBKeWJlb53I7QT2Ls6gY1RL1PoP1NkrK2kCyJJkqQRGz5L/hRQuX788ccAfPTRRwB8+OGHg48ffPDBgs/vv//+ABxwwAELno89Auy332zuXU2VnCTJ3pEKOEmSpBGTUcDLbYne2y3TQ8puSmqvfz0q36hw33lnloHyzTffBOD1118H4O233wbg3XffBWDTptltPOKIIwA49NBZitPDDz8cgMMOOwyAgw46aM9vRlUc/cnJp4Pl+k3e77akAk6SJGnEmijgoVnY13zU7xn9n1Ed+nwMfZuqQljsI/VvLfyg8bphse/3/fffB+CNN2YJvl5++WUAXn31VQBefPFFAN566y0ADjlkdirRcccdB8Dxx8+iXbxula/X3/+b5UgltL6I7Sj2E9vQe+/Nkn3ZtrzP9oEDDzwQ2Lv1gvXWVpaLImpJKuAkSZJGzFUBD6k9Z+IxP+drr70GdOrvlVdeWfB3/Z7O8M7Oqjz9ncccc8ye3zz66KMB2Lx584K/qRxbKmFYrF708erzfemllwDYuXPngtf9DtWMjwcffDDQ1Ymv91XNlNTMUn7KsZjolZZ7ufdN4frHGOo//t/+Y3/QSnruuecAeOCBBwB45pnZodn2I/uffeLcc88F4LzzzgPgpJNOAhb2H9vTcpbjVOoyWgXxcay9eV3xcZ59JRVwkiRJI9bEB+ysC51Pyhn72WefBeDxx2fnKT722GMAvPDCC0Cn9vR3qnydxVR5Rx55JNDN7PpBAU477TSgm+0l+sFaK2BVjWo/qhqVsJ9VwavsfbQOrJPo+4a2CnjMf9lvJ9Hnb93E16Mi9lH1ouqPVsCQr7NfP/3vWmvG1kegqwctxSeffBKA2267DYC77pqdPPTQQ7PDY2w79jvVrG3F/rdr1y4Atm7dCnT1DN3agtE1YxE0rYj1FC1Jx5IdO3YA3VhjPzN66JxzZoeuO1543UYTOU7A6l17KuAkSZJGzEUBj63MQufb1Td1//33L3h0looK2NlHZeNzZy+fq5D7u8Wc/Z3xnBl9bz86YF6MqT5YPGOrbqyj559/HuisABXuCSecAMCpp5664LnKN65wW3dDzFPFjCk6r9t1AKM8oFP/qn7rIkaAeG/Hyq9q07d51llnAXDGGWcAcOKJJ+55r+rQ9tBqt2BsI/22bB2p4m655RagU76+bluybajmvMZoKdjGHn30UWCh2otrCT5vuZtyqP/YjlT91s2Pf/xjAO68804AnnrqKaCr12OPPRbo1P8111wDwHXXXQd0bcV+B4vr5JPWQSrgJEmSRuQAnCRJ0og1CUPrLyLoUnj66aeBzuTRxHQxQNNSF4PmtWFmPvp3w2aOOuoooDMroFtw8L2aDTE8pb8gMy+GXBCaQrpnNAddINHs1pz2GqMZ7evRhI6/DYtNpnluyBgzqb1eTUbbAsB9990HwIMPPgh0YVWG4sVwRH/De+hvaUpbV1dffTUAN954IwCf+cxn9vzmKaecArTbeDDmqvEaoesfjzzyCNAtWtuv7Cenn3460C0s+bp1bz3axnxd14/uOli8mWO5cK55MuTWtKzWxQ9+8AMAfvKTnwDw8MMPL/iM99n+5P3VreXYpBvU8cPFOFgckvdJx45UwEmSJI2Y6yKcj/0Z3AWmJ56Y5SeOYWfORs7gzj4qGGdyFa8LToZkqXh8Dp3DPP5tTCnOk6GQKxdMXGBxIVIl7Mx98sknA7Mk1tDN5NZBDKOK27b7Si4Gm8+DMUWn2lJx2CZUZdCpENWLKtm68F567VEZ+d0u4lnHWgmGJJ5//vl7frN/T1oSLQYVKHR1pXL1uYtrZ555JgDXXnst0C062geiwlW5RUuiT+swM1jchrSAoAvF+/73vw90ytf+c/nllwNw0UUXAd2mE9uQn7dOHRe0Kvyt5dIgfBJSASdJkjRirj5gZyvDQ6CbgVV5zj7OwPoz3UihQrnggguAzrerP1c1G1VuXw1GtReD8ltswOirLdWaKk9/uOrNulDxGlpkHXhdKsvo2/b6hjYdrGViouhTVWnozzdUCjq1r6K1HeiHsx2oaC2/yk5/8o9+9CNgYYhb/7eHUnROZZv2UFuJCtD3qICtt+jntB7tf1obKmj7UUxpCuOJ/teifsZCWt1aDXDHHXcAcPfddwPdvda//8UvfhHolLBtx35n3bnW4uftj7a9eVhIqYCTJEkaMVcFPOTDcubVx2fguApGn6/KLCYT99FZS0Xk87hhAxYrmhYKZym/njOuAeL6w53trRP94vo9nZH9fNxg4vXFOoOFK7qwWAmvBmPbg6PyVZF4fX1Ucr43bizwdTFKQOXrBp8YdB+3mfbL1fq4prF6g+5exhV821VMzuN16990M4L9z89pZamgbXP932pZPzGCRv8udPdYBWvkx4KYfCsAAApHSURBVA033AB0Gyv61wSd+ve7HIusO9eZ/M15+MdTASdJkjRiVRVwXPUeioLQd6MS1s/irBpjXfUVq/ZUQP0tpNApOGfpoRX/KSSgcSbv+8WduVUpKlpnYNWJdeKMrNrzc87kqmvVoZEj/QRFMS2hdTSPeOCo5GISJO95X6H0t8JCp/pVsF5b9H/b1uL2Zv2WKl/bUV9BTyW5TKyvvuViG1CpqmRtC6pB+5nfZRux39lPjJJQNfq8H0fv/RlbL1iLbexR4fcVsIpV/7VRQq4bWX4/q/K1rm6++WYAbr31VmBxmtoYYdX/276SCjhJkqQRc/UBD62+x/SRzi7xQEpnOD+ronH2crV77Ij2/gy1lnG+Y8SZXH8tLE436TXpn1Ttq+6Mjb3nnnuAznds3KLXqyJQWfZjXr03MQHNPBWwRCUc/Zr9v8UDR6MaiQnKoxLUn26KQRWeyXn68eJT8f2KbbmvvLRYvA77iSrQNrF9+3agqxctAZXtlVdeCcCFF14IdDGytrUh33jLKJF4n/v9x/sWxxTHCNW/n3F3pYrX9QIjsi6++GKgsxzjDtP+b6YPOEmSZJ0yVwXsLNH3szlzG5PnrOoM7eyl/9PviInc48wTfcBDhwpO4SBKfa/GIEKnfFX3lssZXdw1eO+99wKdEvbzMSIk0o95jcnb++pzXoxFoXjP+qpLi8i/xRX4uKPQvBK333470KUitN24k9IdcCrqoWOaWjMWNQJdufUBu6vLFXx9v+40jSlX9Y9qDV166aVAZxHE9K4wHd84DI8p/l8rWatA366fsW6MszduWAvSNuX6gG3FfjiPtpIKOEmSpBFrooCdVaFbmVSl6XuKCbZjJij9NzFrkzORCs7f6s+QU4nvhOHdgao3fbhaAZZTH7GP8UgV1ayqSGvAOrNO+6rbv7XIfzC0NhCfj+WxiMrXlW3r5KabbgI6f148bsa44piYHIYPwYzvWUuGfK3WkdaMVoPvsT5izgwtSq8/Ju+3Poayek1J+Xrd/SgorZtoXWoxxrHEvhctY+vg7LPPBjolPFQ3qYCTJEnWOWuSDa0fheBMHLM3qWycvWKmLOOBXdFUNbrqHWf4fuzx2AGOa8nYcTzQlbW/Ow66Oogr3c7EztSqO+sgZspS7ap6oE0uiEj8zaGDSuO987l1ZpTDz372M6Db6WU9u+bgyrbWQowznhJj8fSwOJOcSt81FC0/fbz6+G0bXr/fGaOPrLe+2mtpEUR/uApYnzV0OR+0dhwTtAaixWV/sC3pE7Zu7FfW2VLHDmUURJIkyTplLjvhnFVVKX1/njNGPNlCjLnTv+mM54yv8vXv/obP9e/0FWbLDP6ykplSBewMbXSDxHwZ8fhs3+8KuKu7qqC+L956jkeNrwUrqYvo/4wWhP5y/XzGv7rTyzox3tXV/hjTORQtMwWfJyyOfYXOqnEHnNdvm3HlXh+pbSXGFNtvVM7Wi+sP/WyCLS3HsYiQ/gGZRoJ4rY4F/TEAurbjaSL2Ey1D16NUwGvhF08FnCRJ0ohVUcDRV6WSU5H1/bFx/7/P40wXd6/FXLfi58Z8Wf1yTUHZOIP3d2C5km2dqGrMaeu1xJ0++q7iqREqX60HFYK+Yuhm/aiAp1BHML7byvtrTKdnxekLtW5UvNu2bQMW58cd2jEZf6tVXUS1P5RP26iP6Pv1OvUBq2x9nxak/ch6jNnh+jHjsf+0VMJD/tiYOdGxIOYQjqfx+Dmz8LleYN+IeWWGImbSB5wkSbJOWVUfcMz0ZZavfuYiZwxnZv2S+lucWfRpxUxfMc9pnBGHFPQUckFEH1Y/25Qzr4rXR/esu9LvZ1RvqhnVjfVu1rOtW7cCnQo0Brv/XUP5k9ealaiIGONp29Lnq9Wl/04FfMkllwCdzzD6foeueypWgMqtn/fAex37gQpW5dvP+wFd39R37ufi6dJTWC8ZYixPBizOLRIjaOwXthFVvypfq8F4Yq2ApdZF4trEJ20z7UemJEmSDcpc4oCdOZxlXbGFzi8cs1zFHT3O1Ko/H1VA/kbMDzvkw1qLE4CXI/qw+uefqdKMX3SGNp+BMdD6hmNMrH5PFa4nAdx4441Al3ejHwe81ArvFBjLA2sd6cfTr6d/PK5oaw3YHpbydbdWvsudIg2dmvO1uKagRePfjQiw3rSa4gnakX5dxHqZwprK0G/HnNax7dhWVMK2GdcFjC22byw1XqQPOEmSZJ2zKgp4bLdKPHcMFp886izvd8STDfR1+dzZyX3aKmCzPPm8n90rqryWM7fKo18+fcCqF69Bn9R9990HdNEN1rNxwPo7r7rqKgCuuOIKYPHpyfPIZ7ovRD9a3+8Y9+9Hi8j242dUunF3oPU8hVNRlmNoR2D/9aH3qOZUebYVrSYVsI8SdwTaZ4fOBoxKsOVO0qX+FqMerBvbjm3G67GfqYTd+RYjs+ZJKuAkSZJG5ACcJEnSiFVdhFPaK+FdEOmHxMRFNU0ln8e0k5rNLlppdmtqusDkc02roUW4KZjbsY6gc5G4jdRrvP7664HOhNLM9P0x9WYM6dOcnGqKweh66G+yGVtE0iUVDx7VjIxulymE2e0tYy496K5L14r15MaMO+64A+hcN7YBw/B01Vlf8cBXF/OGUi9OyfXQf30s0VVM82qbsX9Yh9ZNTAO71G/mVuQkSZJ1zqoq4HjEjE5tD/6DxQtNKlu3khpY7+xvknHTVvpdl112GdBtI/T7YrhRv1xTYEhNjB1SqUoZUwFjymQqW2pXSkyyDp2CU8UYftjf1g7dQq+PWl0q47HtpFNejIubdvoJq0yyY5idoVUuMNl/rEv7l5aB/cckRXHxeur9R4ZSl3rNKl0f47FM9quYFiEe3BB/ax71kAo4SZKkEXPZiBF9V/ploVMqhli5MSOml4zfoY9Kf42zWAyfmaq/cyVMWZWtJmMJx/sKWH93PKLKdhGTOan0bCe+PrbxYgqbCcaIm3b6iZvcLGA/cr3g2muvBToFrB/UfuL7VNDW15j6m0q9jIXm9YlrCPGYLa/NthEP8HUMsc1Eq2mpcu0rqYCTJEkaMRcFHJVcfxXX2cWZ1xk5znBx+3BcGV4PvrzkkxPVn+rFdqICVO243hD9ezHpzlii9/5rU2HoWPrYf7Qu9Qkvd7DoWH1MnaWS30SLwSgHrYB4CETccGGdWKe+b6mETfG3PympgJMkSRox12PpZehY7eirXWn6u/UyYyfjxHs4FBsd1wD0/ceVbYlHi6ucl/Ntrof2tFTioPUU37waDNVFXP+J1lP0ES9nNS9nNS312b1lY929JEmSCbEmCnglrAclkqwu0S/Zt4qiPy8SLaZ9jZVO1hdLqdGpplgdIhVwkiRJI8reHD1SSnkReGJ+xZkEZ9Zaj1/pmzdIncBe1EvWyTAbpF6yToYZrJe9GoCTJEmS1SNdEEmSJI3IAThJkqQROQAnSZI0IgfgJEmSRuQAnCRJ0ogcgJMkSRqRA3CSJEkjcgBOkiRpRA7ASZIkjfj/33ZPxmW4p/YAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b_YcDHFWIRFP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "theta = np.random.normal(size=(size_row*size_col))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DemnhlAQP0XJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def sigmoid(z): \n",
        "  g = 1 / (1 + np.exp(-z)) \n",
        "  \n",
        "  return g "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DRugdPmoP2G5",
        "colab_type": "code",
        "outputId": "460cd5c0-1cd6-452d-9f62-16af97591876",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "z_list = []\n",
        "for i in range(0, 10):\n",
        "  z_list.append(0.0)\n",
        "\n",
        "print(z_list)"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xtbUZT3IP28F",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for i in range(0, num_image):\n",
        "  label       = list_label[i]\n",
        "  im_vector   = list_image[:, i]\n",
        "  im_matrix   = im_vector.reshape((size_row, size_col))\n",
        "  y = 0\n",
        "  for ny in range(0, size_row):\n",
        "      for nz in range(0, size_col):\n",
        "        y = y + theta[size_col*ny + nz]*im_matrix[ny][nz]\n",
        "  # print(y)\n",
        "  z_list[label] = z_list[label] + sigmoid(y)\n",
        "  # print(sigmoid(y))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OWQYa_g6P4H2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#평균내기\n",
        "\n",
        "# for i in range(0, 10):\n",
        "  # z_list[i] = z_list[i] / label_list[i]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WUWOjwQQTDdR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#first hidden layer로 가기 위한 초기 fully connected u 생성\n",
        "w_tmp = np.ones(shape=(1000, 197, 785))\n",
        "v_tmp = np.ones(shape=(1000, 50, 197))\n",
        "u_tmp = np.ones(shape=(1000, 10, 50))\n",
        "\n",
        "matrix_u = np.zeros((197, 785))  #197줄\n",
        "matrix_u[0][0] = 1               #y레이어의 bias를 위한 값 1\n",
        "\n",
        "for i in range(1, 197):\n",
        "  for j in range(0, 785):\n",
        "    matrix_u[i][j] = np.random.normal()\n",
        "\n",
        "\n",
        "# matrix_u[196][0] = 0            #줄, 칸\n",
        "##############################################\n",
        "matrix_v = np.zeros((50, 197))  #50줄\n",
        "matrix_v[0][0] = 1               #y레이어의 bias를 위한 값 1\n",
        "\n",
        "for i in range(1, 50):\n",
        "  for j in range(0, 197):\n",
        "    matrix_v[i][j] = np.random.normal()\n",
        "\n",
        "\n",
        "# matrix_v[196][0] = 0            #줄, 칸\n",
        "##############################################\n",
        "matrix_w = np.zeros((10, 50))  #10줄\n",
        "# matrix_w[0][0] = 1               #y레이어의 bias를 위한 값 1\n",
        "\n",
        "for i in range(0, 10):\n",
        "  for j in range(0, 50):\n",
        "    matrix_w[i][j] = np.random.normal()\n",
        "\n",
        "w_tmp = matrix_w\n",
        "v_tmp = matrix_v\n",
        "u_tmp = matrix_u\n",
        "\n",
        "# matrix_w[196][0] = 0            #줄, 칸\n",
        "\n",
        "# print(matrix)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "idHIKSRGY0p9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import operator\n",
        "\n",
        "y_ = np.ones((197, 1))\n",
        "\n",
        "x_list = np.ones(shape=(1000, 785, 1))\n",
        "\n",
        "y__list = np.ones(shape=(1000, 197, 1))\n",
        "z__list = np.ones(shape=(1000, 50, 1))\n",
        "h__list = np.ones(shape=(1000, 10, 1))\n",
        "\n",
        "y_list = np.ones(shape=(1000, 197, 1))\n",
        "z_list = np.ones(shape=(1000, 50, 1))\n",
        "h_list = np.ones(shape=(1000, 10, 1))\n",
        "l_list = np.zeros(shape=(1000, 10, 1))\n",
        "\n",
        "y_list_tmp = np.ones(shape=(1000, 197, 1))\n",
        "z_list_tmp = np.ones(shape=(1000, 50, 1))\n",
        "h_list_tmp = np.ones(shape=(1000, 10, 1))\n",
        "y__list_tmp = np.ones(shape=(1000, 197, 1))\n",
        "z__list_tmp = np.ones(shape=(1000, 50, 1))\n",
        "h__list_tmp = np.ones(shape=(1000, 10, 1))\n",
        "\n",
        "\n",
        "\n",
        "# w_tmp = np.ones(shape=(1000, 197, 785))\n",
        "# v_tmp = np.ones(shape=(1000, 50, 197))\n",
        "# u_tmp = np.ones(shape=(1000, 10, 50))\n",
        "\n",
        "# matrix_u = np.zeros((197, 785))  #197줄\n",
        "# matrix_u[0][0] = 1               #y레이어의 bias를 위한 값 1\n",
        "\n",
        "\n",
        "#training set 1000개에 대해서\n",
        "for i in range(0, 1000):\n",
        "  # print(i)\n",
        "  label       = list_label[i]\n",
        "  bias   = [1]       #bias 1\n",
        "  im_vector = np.concatenate((bias, list_image[:, i]), axis = None)\n",
        "\n",
        "  # print(type(list_image[: , i]))\n",
        "  # print(list_image[:, i])\n",
        "  # print(im_vector)\n",
        "  im_matrix   = im_vector.reshape((size_row*size_col + 1, 1))\n",
        "  \n",
        "  x_list[i] = im_matrix\n",
        "\n",
        "  y_ = np.dot(matrix_u, im_matrix)  #x와 가중치 u를 곱해서 y_를 만듦\n",
        "  y__list[i] = y_\n",
        "  \n",
        "  y_vector = np.ones((197,1)) #bias\n",
        "  for j in range (1, 197):\n",
        "    y_vector[j][0] = 1 / (1 + math.exp(-y_[j][0]))\n",
        "    # sigmoid(y_[j][0])  #sigmoid 함수를 적용해서 y_에서 y를 만듦\n",
        "\n",
        "  y   = y_vector.reshape((197, 1))\n",
        "  y_list[i] = y\n",
        "\n",
        "  z_  = np.dot(matrix_v, y)  #y와 가중치 v를 곱해서 z_를 만듦\n",
        "  z__list[i] = z_\n",
        "  z_vector = np.ones((50,1)) #bias\n",
        "  for j in range (1, 50):\n",
        "    z_vector[j][0] = 1 / (1 + math.exp(-z_[j][0]))  #sigmoid 함수를 적용해서 y_에서 y를 만듦\n",
        "\n",
        "  z   = z_vector.reshape((50, 1))\n",
        "  z_list[i] = z\n",
        "  \n",
        "  # print(z)\n",
        "\n",
        "  h_  = np.dot(matrix_w, z)  #z와 가중치 w를 곱해서 h_를 만듦\n",
        "  h__list[i] = h_\n",
        "  \n",
        "  h_vector = np.ones((10,1)) #bias\n",
        "  for j in range (0, 10):\n",
        "    h_vector[j][0] = 1 / (1 + math.exp(-h_[j][0]))  #sigmoid 함수를 적용해서 h_에서 h를 만듦\n",
        "\n",
        "  h   = h_vector.reshape((10, 1))\n",
        "  # tmp = h.ravel()\n",
        "  # max_value = max(tmp)\n",
        "  # print(max_value)\n",
        "  # max_index = tmp.index(max_value)\n",
        "  index, value = max(enumerate(h), key=operator.itemgetter(1))\n",
        "  h_list[i] = h\n",
        "  l_list[i][index] = 1\n",
        "  # print(l_list[i])\n",
        "  # print(h)\n",
        "\n",
        "  y_list_tmp[i] = y_list[i]\n",
        "  z_list_tmp[i] = z_list[i]\n",
        "  h_list_tmp[i] = h_list[i]\n",
        "  y__list_tmp[i] = y__list[i]\n",
        "  z__list_tmp[i] = z__list[i]\n",
        "  h__list_tmp[i] = h__list[i]\n",
        "\n",
        "# print(l_list)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Or31AKaJfeIz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import operator\n",
        "\n",
        "y_2 = np.ones((197, 1))\n",
        "\n",
        "x_list2 = np.ones(shape=(9000, 785, 1))\n",
        "\n",
        "y__list2 = np.ones(shape=(9000, 197, 1))\n",
        "z__list2 = np.ones(shape=(9000, 50, 1))\n",
        "h__list2 = np.ones(shape=(9000, 10, 1))\n",
        "\n",
        "y_list2 = np.ones(shape=(9000, 197, 1))\n",
        "z_list2 = np.ones(shape=(9000, 50, 1))\n",
        "h_list2 = np.ones(shape=(9000, 10, 1))\n",
        "l_list2 = np.zeros(shape=(9000, 10, 1))\n",
        "\n",
        "y_list_tmp2 = np.ones(shape=(9000, 197, 1))\n",
        "z_list_tmp2 = np.ones(shape=(9000, 50, 1))\n",
        "h_list_tmp2 = np.ones(shape=(9000, 10, 1))\n",
        "y__list_tmp2 = np.ones(shape=(9000, 197, 1))\n",
        "z__list_tmp2 = np.ones(shape=(9000, 50, 1))\n",
        "h__list_tmp2 = np.ones(shape=(9000, 10, 1))\n",
        "\n",
        "# w_tmp = np.ones(shape=(9000, 197, 785))\n",
        "# v_tmp = np.ones(shape=(9000, 50, 197))\n",
        "# u_tmp = np.ones(shape=(9000, 10, 50))\n",
        "\n",
        "# matrix_u = np.zeros((197, 785))  #197줄\n",
        "# matrix_u[0][0] = 1               #y레이어의 bias를 위한 값 1\n",
        "\n",
        "\n",
        "#training set 1000개에 대해서\n",
        "for i in range(0, 9000):\n",
        "  # print(i)\n",
        "  label2       = list_label[i + 1000]\n",
        "  bias2   = [1]       #bias 1\n",
        "  im_vector2 = np.concatenate((bias2, list_image[:, i + 1000]), axis = None)\n",
        "\n",
        "  # print(type(list_image[: , i]))\n",
        "  # print(list_image[:, i])\n",
        "  # print(im_vector)\n",
        "  im_matrix2   = im_vector2.reshape((size_row*size_col + 1, 1))\n",
        "  \n",
        "  x_list2[i] = im_matrix2\n",
        "\n",
        "  y_2 = np.dot(matrix_u, im_matrix)  #x와 가중치 u를 곱해서 y_를 만듦\n",
        "  y__list2[i] = y_2\n",
        "  \n",
        "  y_vector2 = np.ones((197,1)) #bias\n",
        "  for j in range (1, 197):\n",
        "    y_vector2[j][0] = 1 / (1 + math.exp(-y_2[j][0]))\n",
        "    # sigmoid(y_[j][0])  #sigmoid 함수를 적용해서 y_에서 y를 만듦\n",
        "\n",
        "  y2   = y_vector2.reshape((197, 1))\n",
        "  y_list2[i] = y2\n",
        "\n",
        "  z_2  = np.dot(matrix_v, y2)  #y와 가중치 v를 곱해서 z_를 만듦\n",
        "  z__list2[i] = z_2\n",
        "  z_vector2 = np.ones((50,1)) #bias\n",
        "  for j in range (1, 50):\n",
        "    z_vector2[j][0] = 1 / (1 + math.exp(-z_2[j][0]))  #sigmoid 함수를 적용해서 y_에서 y를 만듦\n",
        "\n",
        "  z2   = z_vector2.reshape((50, 1))\n",
        "  z_list2[i] = z2\n",
        "  \n",
        "  # print(z)\n",
        "\n",
        "  h_2  = np.dot(matrix_w, z2)  #z와 가중치 w를 곱해서 h_를 만듦\n",
        "  h__list2[i] = h_2\n",
        "  \n",
        "  h_vector2 = np.ones((10,1)) #bias\n",
        "  for j in range (0, 10):\n",
        "    h_vector2[j][0] = 1 / (1 + math.exp(-h_2[j][0]))  #sigmoid 함수를 적용해서 h_에서 h를 만듦\n",
        "\n",
        "  h2   = h_vector2.reshape((10, 1))\n",
        "  # tmp = h.ravel()\n",
        "  # max_value = max(tmp)\n",
        "  # print(max_value)\n",
        "  # max_index = tmp.index(max_value)\n",
        "  index2, value2 = max(enumerate(h2), key=operator.itemgetter(1))\n",
        "  h_list2[i] = h2\n",
        "  l_list2[i][index2] = 1\n",
        "  # print(l_list[i])\n",
        "  # print(h)\n",
        "\n",
        "  y_list_tmp2[i] = y_list2[i]\n",
        "  z_list_tmp2[i] = z_list2[i]\n",
        "  h_list_tmp2[i] = h_list2[i]\n",
        "  y__list_tmp2[i] = y__list2[i]\n",
        "  z__list_tmp2[i] = z__list2[i]\n",
        "  h__list_tmp2[i] = h__list2[i]\n",
        "\n",
        "# print(l_list)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i1AbB1lMv1yY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def objective_function(size, label_List, h_List):\n",
        "  result = 0.0\n",
        "  for i in range(0, size):\n",
        "    for k in range(0, 10):\n",
        "      result = result + ((-1)*label_List[i][k]*math.log(h_List[i][k][0]) - (1-label_List[i][k])*math.log(1-h_List[i][k][0]))\n",
        "  result = result / size\n",
        "\n",
        "  return result"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z7uMF29V6ZAh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def w_gredient_descent(learning_rate, k_value, n_value, size, label_List):\n",
        "  result = 0.0\n",
        "  for i in range(0, size):\n",
        "    result = result + (z_list_tmp[i][n_value][0]*(sigmoid(h_list_tmp[i][k_value][0]) - label_List[i][k_value][0]))\n",
        "  result = result / size\n",
        "  result = result * learning_rate\n",
        "  final = w_tmp[k_value][n_value] - result\n",
        "\n",
        "  return final"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FQMnniqxLxzl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def v_gredient_descent(learning_rate, k_value, n_value, size, label_List):\n",
        "  result = 0.0\n",
        "  for i in range(0, size):\n",
        "    result = result + (y_list_tmp[i][n_value][0]*w_tmp[0][k_value]*(sigmoid(h_list_tmp[i][0][0]) - label_List[i][0][0])*(sigmoid(z__list_tmp[i][k_value][0])*(1 - sigmoid(z__list_tmp[i][k_value][0]))))\n",
        "  result = result / size\n",
        "  result = result * learning_rate\n",
        "  final = v_tmp[k_value][n_value] - result\n",
        "\n",
        "  return final"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UabSYhhIO6JU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def u_gredient_descent(learning_rate, k_value, n_value, size, label_List):\n",
        "  result = 0.0\n",
        "  for i in range(0, size):\n",
        "    result = result + (x_list[i][n_value][0]*w_tmp[0][1]*v_tmp[1][k_value]*(sigmoid(h_list_tmp[i][0][0]) - label_List[i][0][0])*(sigmoid(z__list_tmp[i][1][0])*(1 - sigmoid(z__list_tmp[i][1][0])))*(sigmoid(y__list_tmp[i][k_value][0])*(1 - sigmoid(y__list_tmp[i][k_value][0]))))\n",
        "  result = result / size\n",
        "  result = result * learning_rate\n",
        "  final = u_tmp[k_value][n_value] - result\n",
        "\n",
        "  return final"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0vn3FkYXStur",
        "colab_type": "code",
        "outputId": "e031a70c-371f-4caa-d677-7e82a5dbadb8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        }
      },
      "source": [
        "#이제 여기에다가\n",
        "\n",
        "#iteration에 따라\n",
        "#cost function 값을 배열에 저장하고\n",
        "#세타값을 업데이트 시키고(위의 세개 함수 호출)\n",
        "#업데이트 시킨값을 다시 cost함수에 넣고\n",
        "#업데이트 시키고\n",
        "#이것을 반복하는 함수를 짠다.\n",
        "\n",
        "#아직 해야하는 것 : h값에 따라 가장 큰값의 index로 L값정하는거 위에 추가해야함.\n",
        "\n",
        "iteration = 1000\n",
        "l_r = 0.3\n",
        "lambda_ = 100000\n",
        "\n",
        "cost_history = np.zeros(iteration)\n",
        "\n",
        "total_number_of_predictions = 1000\n",
        "accuracy_history = np.zeros(iteration)\n",
        "\n",
        "# small_delta = np.ones(3)  #image_size, layer 2~4(3개)\n",
        "# greece_delta_w = np.ones(shape = (10, 50))\n",
        "# greece_delta_v = np.ones(shape = (50, 197))\n",
        "# greece_delta_u = np.ones(shape = (197, 785))\n",
        "\n",
        "label_group = np.zeros(shape = (1000, 10))\n",
        "\n",
        "for i in range(0, 1000):\n",
        "  label       = list_label[i]\n",
        "  im_vector   = list_image[:, i]\n",
        "\n",
        "  # X[i] = im_vector\n",
        "  # print(i)\n",
        "  # print(X[i])\n",
        "  label_group[i][label] = 1\n",
        "\n",
        "#####################################\n",
        "\n",
        "cost_history2 = np.zeros(iteration)\n",
        "\n",
        "total_number_of_predictions2 = 9000\n",
        "accuracy_history2 = np.zeros(iteration)\n",
        "\n",
        "# small_delta = np.ones(3)  #image_size, layer 2~4(3개)\n",
        "# greece_delta_w = np.ones(shape = (10, 50))\n",
        "# greece_delta_v = np.ones(shape = (50, 197))\n",
        "# greece_delta_u = np.ones(shape = (197, 785))\n",
        "\n",
        "label_group2 = np.zeros(shape = (9000, 10))\n",
        "\n",
        "for i in range(1000, 10000):\n",
        "  label2       = list_label[i]\n",
        "  im_vector2   = list_image[:, i]\n",
        "\n",
        "  # X[i] = im_vector\n",
        "  # print(i)\n",
        "  # print(X[i])\n",
        "  label_group2[i - 1000][label2] = 1\n",
        "\n",
        "#############################################\n",
        "\n",
        "\n",
        "for t in range(0, iteration):\n",
        "  number_of_correct_predictions = 0\n",
        "  \n",
        "  print(t, end=' ')\n",
        "\n",
        "  parameter_sum = 0\n",
        "\n",
        "  for q in range(0, 785):\n",
        "    for w in range(1, 197):\n",
        "      parameter_sum = parameter_sum + matrix_u[w][q]**2\n",
        "\n",
        "  for q in range(0, 197):\n",
        "    for w in range(1, 50):\n",
        "      parameter_sum = parameter_sum + matrix_u[w][q]**2\n",
        "\n",
        "  for q in range(0, 50):\n",
        "    for w in range(0, 10):\n",
        "      parameter_sum = parameter_sum + matrix_u[w][q]**2\n",
        "\n",
        "  # for q in range(1, 785):\n",
        "  #   for w in range(1, 197):\n",
        "  #     for e in range(1, 50):\n",
        "  #       matrix_u\n",
        "  # parameter_sum = matrix_u**2 + matrix_v**2 + matrix_w**2\n",
        "  # print(parameter_sum.shape)\n",
        "\n",
        "  # cost_history[t] = objective_function(1000, l_list, h_list_tmp)\n",
        "  result = 0.0\n",
        "  for i in range(0, 1000):\n",
        "    for k in range(0, 10):\n",
        "      # if h_list_tmp[i][k][0] <= 0:\n",
        "      # print(h_list_tmp[i][k][0])\n",
        "      result = result + ((-1)*label_group[i][k]*math.log(h_list[i][k][0]) - (1-label_group[i][k])*math.log(1-h_list[i][k][0])) + ((lambda_)/(2 * 164995)) * parameter_sum\n",
        "  result = result / 1000\n",
        "  cost_history[t] = result\n",
        "\n",
        "# (1 / (1 + math.exp(-y__list_tmp[i][m][0])))\n",
        "#############################################\n",
        "  # for m in range(0, 10):\n",
        "  #   # print(m)\n",
        "  #   for n in range(0, 50):\n",
        "  #     # matrix_w[m][n] = w_gredient_descent(l_r, m, n, 1000, l_list)\n",
        "  #     result = 0.0\n",
        "  #     for i in range(0,1000):\n",
        "  #       result = result + (z_list_tmp[i][n][0]*((1 / (1 + math.exp(-h_list_tmp[i][m][0]))) - l_list[i][m][0]))\n",
        "  #     result = result / 1000\n",
        "  #     result = result * l_r\n",
        "  #     final = w_tmp[m][n] - result\n",
        "  #     matrix_w[m][n] = final\n",
        "\n",
        "  # for m in range(0, 50):\n",
        "  #   # print(m)\n",
        "  #   for n in range(0, 197):\n",
        "  #     # matrix_v[m][n] = v_gredient_descent(l_r, m, n, 1000, l_list)\n",
        "  #     result = 0.0\n",
        "  #     for i in range(0, 1000):\n",
        "  #       result = result + (y_list_tmp[i][n][0]*w_tmp[0][m]*((1 / (1 + math.exp(-h_list_tmp[i][0][0]))) - l_list[i][0][0])*((1 / (1 + math.exp(-z__list_tmp[i][m][0])))*(1 - (1 / (1 + math.exp(-z__list_tmp[i][m][0]))))))\n",
        "  #     result = result / 1000\n",
        "  #     result = result * l_r\n",
        "  #     final = v_tmp[m][n] - result\n",
        "  #     matrix_v[m][n] = final\n",
        "\n",
        "  # for m in range(0, 197):\n",
        "  #   # print(m)\n",
        "  #   for n in range(0, 785):\n",
        "  #     # matrix_u[m][n] = u_gredient_descent(l_r, m, n, 1000, l_list)\n",
        "  #     result = 0.0\n",
        "  #     for i in range(0, 1000):\n",
        "  #       result = result + (x_list[i][n][0]*w_tmp[0][1]*v_tmp[1][m]*((1 / (1 + math.exp(-h_list_tmp[i][0][0]))) - l_list[i][0][0])*((1 / (1 + math.exp(-z__list_tmp[i][1][0])))*(1 - (1 / (1 + math.exp(-z__list_tmp[i][1][0])))))*((1 / (1 + math.exp(-y__list_tmp[i][m][0])))*(1 - (1 / (1 + math.exp(-y__list_tmp[i][m][0]))))))\n",
        "  #     result = result / 1000\n",
        "  #     result = result * l_r\n",
        "  #     final = u_tmp[m][n] - result\n",
        "  #     matrix_u[m][n] = final\n",
        "#######################################################################\n",
        "##gradient descent\n",
        "  result_w = 0\n",
        "  result_v = 0\n",
        "  result_u = 0\n",
        "\n",
        "  for i in range(0, 1000):\n",
        "    small_delta_2 = np.zeros(shape = (10, 1))\n",
        "    for j in range(0, 10):\n",
        "      small_delta_2[j][0] = h_list[i][j] - label_group[i][j]\n",
        "    # print(h_list[i])\n",
        "    # print(label_group[i])\n",
        "    # small_delta_2 = h_list[i] - label_group[i] #여기 label_group 다시 봐야함.\n",
        "    small_delta_1 = np.dot(np.transpose(matrix_w), small_delta_2)\n",
        "    small_delta_0 = np.dot(np.transpose(matrix_v), small_delta_1)*(y_list_tmp[i]*(1 - y_list_tmp[i]))\n",
        "\n",
        "    # print(small_delta_2)\n",
        "    result_w = result_w + np.dot(small_delta_2, np.transpose(z_list[i]))\n",
        "    result_v = result_v + np.dot(small_delta_1, np.transpose(y_list[i]))\n",
        "    result_u = result_u + np.dot(small_delta_0, np.transpose(x_list[i]))\n",
        "\n",
        "  matrix_w = matrix_w - (result_w / 1000 *l_r)\n",
        "  matrix_v = matrix_v - (result_v / 1000 *l_r)\n",
        "  matrix_u = matrix_u - (result_u / 1000 *l_r)\n",
        "\n",
        "\n",
        "####################################################################\n",
        "\n",
        "  for k in range(0, 1000):\n",
        "  # print(i)\n",
        "    label       = list_label[k]\n",
        "    bias   = [1]       #bias 1\n",
        "    im_vector = np.concatenate((bias, list_image[:, k]), axis = None)\n",
        "\n",
        "    # print(type(list_image[: , k]))\n",
        "    # print(list_image[:, k])\n",
        "    # print(im_vector)\n",
        "    im_matrix   = im_vector.reshape((size_row*size_col + 1, 1))\n",
        "\n",
        "    y_ = np.dot(matrix_u, im_matrix)  #x와 가중치 u를 곱해서 y_를 만듦\n",
        "    y__list[k] = y_\n",
        "    \n",
        "    y_vector = np.ones((197,1)) #bias\n",
        "    for j in range (1, 197):\n",
        "      y_vector[j][0] = 1 / (1 + math.exp(-y_[j][0]))  #sigmoid 함수를 적용해서 y_에서 y를 만듦\n",
        "    \n",
        "    # print(y_vector) #시그모이드 값 확인\n",
        "    y   = y_vector.reshape((197, 1))\n",
        "    y_list[k] = y\n",
        "\n",
        "    z_  = np.dot(matrix_v, y)  #y와 가중치 v를 곱해서 z_를 만듦\n",
        "    z__list[k] = z_\n",
        "    z_vector = np.ones((50,1)) #bias\n",
        "    for j in range (1, 50):\n",
        "      z_vector[j][0] = 1 / (1 + math.exp(-z_[j][0]))  #sigmoid 함수를 적용해서 y_에서 y를 만듦\n",
        "\n",
        "    z   = z_vector.reshape((50, 1))\n",
        "    z_list[k] = z\n",
        "    \n",
        "    # print(z)\n",
        "\n",
        "    h_  = np.dot(matrix_w, z)  #z와 가중치 w를 곱해서 h_를 만듦\n",
        "    h__list[k] = h_\n",
        "    \n",
        "    h_vector = np.ones((10,1)) #bias\n",
        "    for j in range (0, 10):\n",
        "      h_vector[j][0] = 1 / (1 + math.exp(-h_[j][0]))  #sigmoid 함수를 적용해서 h_에서 h를 만듦\n",
        "\n",
        "    h   = h_vector.reshape((10, 1))\n",
        "    # print(h)\n",
        "    # tmp = h.ravel()\n",
        "    # max_value = max(tmp)\n",
        "    # print(max_value)\n",
        "    # max_index = tmp.index(max_value)\n",
        "    index, value = max(enumerate(h), key=operator.itemgetter(1))\n",
        "    h_list[k] = h\n",
        "    l_list[k][index] = 1\n",
        "    # print(l_list[i])\n",
        "    # print(h)\n",
        "    if index == label:\n",
        "      number_of_correct_predictions = number_of_correct_predictions + 1\n",
        "\n",
        "    y_list_tmp[k] = y_list[k]\n",
        "    z_list_tmp[k] = z_list[k]\n",
        "    h_list_tmp[k] = h_list[k]\n",
        "    y__list_tmp[k] = y__list[k]\n",
        "    z__list_tmp[k] = z__list[k]\n",
        "    h__list_tmp[k] = h__list[k]\n",
        "\n",
        "\n",
        "  accuracy_history[t] = number_of_correct_predictions/total_number_of_predictions*100\n",
        "  # print(accuracy_history[t])\n",
        "\n",
        "############################\n",
        "  #test data!\n",
        "\n",
        "  number_of_correct_predictions2 = 0\n",
        "  \n",
        "  print(t, end=' ')\n",
        "\n",
        "  result2 = 0.0\n",
        "  for i in range(0, 9000):\n",
        "    for k in range(0, 10):\n",
        "      # if h_list_tmp[i][k][0] <= 0:\n",
        "      # print(h_list_tmp[i][k][0])\n",
        "      result2 = result2 + ((-1)*label_group2[i][k]*math.log(h_list2[i][k][0]) - (1-label_group2[i][k])*math.log(1-h_list2[i][k][0]))\n",
        "  result2 = result2 / 9000\n",
        "  cost_history2[t] = result2\n",
        "\n",
        "\n",
        "  for k in range(0, 9000):\n",
        "  # print(i)\n",
        "    label2       = list_label[k + 1000]\n",
        "    bias2   = [1]       #bias 1\n",
        "    im_vector2 = np.concatenate((bias2, list_image[:, k + 1000]), axis = None)\n",
        "\n",
        "    # print(type(list_image[: , k]))\n",
        "    # print(list_image[:, k])\n",
        "    # print(im_vector)\n",
        "    im_matrix2   = im_vector2.reshape((size_row*size_col + 1, 1))\n",
        "\n",
        "    y_2 = np.dot(matrix_u, im_matrix2)  #x와 가중치 u를 곱해서 y_를 만듦\n",
        "    y__list2[k] = y_2\n",
        "    \n",
        "    y_vector2 = np.ones((197,1)) #bias\n",
        "    for j in range (1, 197):\n",
        "      y_vector2[j][0] = 1 / (1 + math.exp(-y_2[j][0]))  #sigmoid 함수를 적용해서 y_에서 y를 만듦\n",
        "    \n",
        "    # print(y_vector) #시그모이드 값 확인\n",
        "    y2   = y_vector2.reshape((197, 1))\n",
        "    y_list2[k] = y2\n",
        "\n",
        "    z_2  = np.dot(matrix_v, y2)  #y와 가중치 v를 곱해서 z_를 만듦\n",
        "    z__list2[k] = z_2\n",
        "    z_vector2 = np.ones((50,1)) #bias\n",
        "    for j in range (1, 50):\n",
        "      z_vector2[j][0] = 1 / (1 + math.exp(-z_2[j][0]))  #sigmoid 함수를 적용해서 y_에서 y를 만듦\n",
        "\n",
        "    z2   = z_vector2.reshape((50, 1))\n",
        "    z_list2[k] = z2\n",
        "    \n",
        "    # print(z)\n",
        "\n",
        "    h_2  = np.dot(matrix_w, z2)  #z와 가중치 w를 곱해서 h_를 만듦\n",
        "    h__list2[k] = h_2\n",
        "    \n",
        "    h_vector2 = np.ones((10,1)) #bias\n",
        "    for j in range (0, 10):\n",
        "      h_vector2[j][0] = 1 / (1 + math.exp(-h_2[j][0]))  #sigmoid 함수를 적용해서 h_에서 h를 만듦\n",
        "\n",
        "    h2   = h_vector2.reshape((10, 1))\n",
        "    # print(h)\n",
        "    # tmp = h.ravel()\n",
        "    # max_value = max(tmp)\n",
        "    # print(max_value)\n",
        "    # max_index = tmp.index(max_value)\n",
        "    index2, value2 = max(enumerate(h2), key=operator.itemgetter(1))\n",
        "    h_list2[k] = h2\n",
        "    l_list2[k][index2] = 1\n",
        "    # print(l_list[i])\n",
        "    # print(h)\n",
        "    if index2 == label2:\n",
        "      number_of_correct_predictions2 = number_of_correct_predictions2 + 1\n",
        "\n",
        "    y_list_tmp2[k] = y_list2[k]\n",
        "    z_list_tmp2[k] = z_list2[k]\n",
        "    h_list_tmp2[k] = h_list2[k]\n",
        "    y__list_tmp2[k] = y__list2[k]\n",
        "    z__list_tmp2[k] = z__list2[k]\n",
        "    h__list_tmp2[k] = h__list2[k]\n",
        "\n",
        "\n",
        "  accuracy_history2[t] = number_of_correct_predictions2/total_number_of_predictions2*100\n",
        "  t = t + 1"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kFfy7dv5_YR8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# import operator\n",
        "\n",
        "# y_2 = np.ones((197, 1))\n",
        "\n",
        "# x_list2 = np.ones(shape=(9000, 785, 1))\n",
        "\n",
        "# y__list2 = np.ones(shape=(9000, 197, 1))\n",
        "# z__list2 = np.ones(shape=(9000, 50, 1))\n",
        "# h__list2 = np.ones(shape=(9000, 10, 1))\n",
        "\n",
        "# y_list2 = np.ones(shape=(9000, 197, 1))\n",
        "# z_list2 = np.ones(shape=(9000, 50, 1))\n",
        "# h_list2 = np.ones(shape=(9000, 10, 1))\n",
        "# l_list2 = np.zeros(shape=(9000, 10, 1))\n",
        "\n",
        "# y_list_tmp2 = np.ones(shape=(9000, 197, 1))\n",
        "# z_list_tmp2 = np.ones(shape=(9000, 50, 1))\n",
        "# h_list_tmp2 = np.ones(shape=(9000, 10, 1))\n",
        "# y__list_tmp2 = np.ones(shape=(9000, 197, 1))\n",
        "# z__list_tmp2 = np.ones(shape=(9000, 50, 1))\n",
        "# h__list_tmp2 = np.ones(shape=(9000, 10, 1))\n",
        "\n",
        "# # w_tmp = np.ones(shape=(9000, 197, 785))\n",
        "# # v_tmp = np.ones(shape=(9000, 50, 197))\n",
        "# # u_tmp = np.ones(shape=(9000, 10, 50))\n",
        "\n",
        "# # matrix_u = np.zeros((197, 785))  #197줄\n",
        "# # matrix_u[0][0] = 1               #y레이어의 bias를 위한 값 1\n",
        "\n",
        "\n",
        "# #training set 1000개에 대해서\n",
        "# for i in range(0, 9000):\n",
        "#   # print(i)\n",
        "#   label2       = list_label[i + 1000]\n",
        "#   bias2   = [1]       #bias 1\n",
        "#   im_vector2 = np.concatenate((bias2, list_image[:, i + 1000]), axis = None)\n",
        "\n",
        "#   # print(type(list_image[: , i]))\n",
        "#   # print(list_image[:, i])\n",
        "#   # print(im_vector)\n",
        "#   im_matrix2   = im_vector2.reshape((size_row*size_col + 1, 1))\n",
        "  \n",
        "#   x_list2[i] = im_matrix2\n",
        "\n",
        "#   y_2 = np.dot(matrix_u, im_matrix)  #x와 가중치 u를 곱해서 y_를 만듦\n",
        "#   y__list2[i] = y_2\n",
        "  \n",
        "#   y_vector2 = np.ones((197,1)) #bias\n",
        "#   for j in range (1, 197):\n",
        "#     y_vector2[j][0] = 1 / (1 + math.exp(-y_2[j][0]))\n",
        "#     # sigmoid(y_[j][0])  #sigmoid 함수를 적용해서 y_에서 y를 만듦\n",
        "\n",
        "#   y2   = y_vector2.reshape((197, 1))\n",
        "#   y_list2[i] = y2\n",
        "\n",
        "#   z_2  = np.dot(matrix_v, y2)  #y와 가중치 v를 곱해서 z_를 만듦\n",
        "#   z__list2[i] = z_2\n",
        "#   z_vector2 = np.ones((50,1)) #bias\n",
        "#   for j in range (1, 50):\n",
        "#     z_vector2[j][0] = 1 / (1 + math.exp(-z_2[j][0]))  #sigmoid 함수를 적용해서 y_에서 y를 만듦\n",
        "\n",
        "#   z2   = z_vector2.reshape((50, 1))\n",
        "#   z_list2[i] = z2\n",
        "  \n",
        "#   # print(z)\n",
        "\n",
        "#   h_2  = np.dot(matrix_w, z2)  #z와 가중치 w를 곱해서 h_를 만듦\n",
        "#   h__list2[i] = h_2\n",
        "  \n",
        "#   h_vector2 = np.ones((10,1)) #bias\n",
        "#   for j in range (0, 10):\n",
        "#     h_vector2[j][0] = 1 / (1 + math.exp(-h_2[j][0]))  #sigmoid 함수를 적용해서 h_에서 h를 만듦\n",
        "\n",
        "#   h2   = h_vector2.reshape((10, 1))\n",
        "#   # tmp = h.ravel()\n",
        "#   # max_value = max(tmp)\n",
        "#   # print(max_value)\n",
        "#   # max_index = tmp.index(max_value)\n",
        "#   index2, value2 = max(enumerate(h2), key=operator.itemgetter(1))\n",
        "#   h_list2[i] = h2\n",
        "#   l_list2[i][index2] = 1\n",
        "#   # print(l_list[i])\n",
        "#   # print(h)\n",
        "\n",
        "#   y_list_tmp2[i] = y_list2[i]\n",
        "#   z_list_tmp2[i] = z_list2[i]\n",
        "#   h_list_tmp2[i] = h_list2[i]\n",
        "#   y__list_tmp2[i] = y__list2[i]\n",
        "#   z__list_tmp2[i] = z__list2[i]\n",
        "#   h__list_tmp2[i] = h__list2[i]\n",
        "\n",
        "# # print(l_list)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nzuVCwhp-DLC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# #test data!\n",
        "\n",
        "# cost_history2 = np.zeros(iteration)\n",
        "\n",
        "# total_number_of_predictions2 = 9000\n",
        "# accuracy_history2 = np.zeros(iteration)\n",
        "\n",
        "# # small_delta = np.ones(3)  #image_size, layer 2~4(3개)\n",
        "# # greece_delta_w = np.ones(shape = (10, 50))\n",
        "# # greece_delta_v = np.ones(shape = (50, 197))\n",
        "# # greece_delta_u = np.ones(shape = (197, 785))\n",
        "\n",
        "# label_group2 = np.zeros(shape = (9000, 10))\n",
        "\n",
        "# for i in range(1000, 10000):\n",
        "#   label2       = list_label[i]\n",
        "#   im_vector2   = list_image[:, i]\n",
        "\n",
        "#   # X[i] = im_vector\n",
        "#   # print(i)\n",
        "#   # print(X[i])\n",
        "#   label_group2[i - 1000][label2] = 1\n",
        "\n",
        "# for t in range(0, iteration):\n",
        "#   number_of_correct_predictions2 = 0\n",
        "  \n",
        "#   print(t, end=' ')\n",
        "\n",
        "#   # cost_history[t] = objective_function(1000, l_list, h_list_tmp)\n",
        "#   result2 = 0.0\n",
        "#   for i in range(0, 9000):\n",
        "#     for k in range(0, 10):\n",
        "#       # if h_list_tmp[i][k][0] <= 0:\n",
        "#       # print(h_list_tmp[i][k][0])\n",
        "#       result2 = result2 + ((-1)*label_group2[i][k]*math.log(h_list2[i][k][0]) - (1-label_group2[i][k])*math.log(1-h_list2[i][k][0]))\n",
        "#   result2 = result2 / 9000\n",
        "#   cost_history2[t] = result2\n",
        "\n",
        "# # (1 / (1 + math.exp(-y__list_tmp[i][m][0])))\n",
        "# #############################################\n",
        "#   # for m in range(0, 10):\n",
        "#   #   # print(m)\n",
        "#   #   for n in range(0, 50):\n",
        "#   #     # matrix_w[m][n] = w_gredient_descent(l_r, m, n, 6000, l_list)\n",
        "#   #     result = 0.0\n",
        "#   #     for i in range(0,6000):\n",
        "#   #       result = result + (z_list_tmp[i][n][0]*((1 / (1 + math.exp(-h_list_tmp[i][m][0]))) - l_list[i][m][0]))\n",
        "#   #     result = result / 6000\n",
        "#   #     result = result * l_r\n",
        "#   #     final = w_tmp[m][n] - result\n",
        "#   #     matrix_w[m][n] = final\n",
        "\n",
        "#   # for m in range(0, 50):\n",
        "#   #   # print(m)\n",
        "#   #   for n in range(0, 197):\n",
        "#   #     # matrix_v[m][n] = v_gredient_descent(l_r, m, n, 6000, l_list)\n",
        "#   #     result = 0.0\n",
        "#   #     for i in range(0, 6000):\n",
        "#   #       result = result + (y_list_tmp[i][n][0]*w_tmp[0][m]*((1 / (1 + math.exp(-h_list_tmp[i][0][0]))) - l_list[i][0][0])*((1 / (1 + math.exp(-z__list_tmp[i][m][0])))*(1 - (1 / (1 + math.exp(-z__list_tmp[i][m][0]))))))\n",
        "#   #     result = result / 6000\n",
        "#   #     result = result * l_r\n",
        "#   #     final = v_tmp[m][n] - result\n",
        "#   #     matrix_v[m][n] = final\n",
        "\n",
        "#   # for m in range(0, 197):\n",
        "#   #   # print(m)\n",
        "#   #   for n in range(0, 785):\n",
        "#   #     # matrix_u[m][n] = u_gredient_descent(l_r, m, n, 6000, l_list)\n",
        "#   #     result = 0.0\n",
        "#   #     for i in range(0, 6000):\n",
        "#   #       result = result + (x_list[i][n][0]*w_tmp[0][1]*v_tmp[1][m]*((1 / (1 + math.exp(-h_list_tmp[i][0][0]))) - l_list[i][0][0])*((1 / (1 + math.exp(-z__list_tmp[i][1][0])))*(1 - (1 / (1 + math.exp(-z__list_tmp[i][1][0])))))*((1 / (1 + math.exp(-y__list_tmp[i][m][0])))*(1 - (1 / (1 + math.exp(-y__list_tmp[i][m][0]))))))\n",
        "#   #     result = result / 6000\n",
        "#   #     result = result * l_r\n",
        "#   #     final = u_tmp[m][n] - result\n",
        "#   #     matrix_u[m][n] = final\n",
        "# #######################################################################\n",
        "# ##gradient descent\n",
        "#   # result_w = 0\n",
        "#   # result_v = 0\n",
        "#   # result_u = 0\n",
        "\n",
        "#   # for i in range(0, 9000):\n",
        "#   #   small_delta_2 = np.zeros(shape = (10, 1))\n",
        "#   #   for j in range(0, 10):\n",
        "#   #     small_delta_2[j][0] = h_list[i][j] - label_group2[i][j]\n",
        "#   #   # print(h_list[i])\n",
        "#   #   # print(label_group[i])\n",
        "#   #   # small_delta_2 = h_list[i] - label_group[i] #여기 label_group 다시 봐야함.\n",
        "#   #   small_delta_1 = np.dot(np.transpose(matrix_w), small_delta_2)\n",
        "#   #   small_delta_0 = np.dot(np.transpose(matrix_v), small_delta_1)*(y_list_tmp[i]*(1 - y_list_tmp[i]))\n",
        "\n",
        "#   #   # print(small_delta_2)\n",
        "#   #   result_w = result_w + np.dot(small_delta_2, np.transpose(z_list[i]))\n",
        "#   #   result_v = result_v + np.dot(small_delta_1, np.transpose(y_list[i]))\n",
        "#   #   result_u = result_u + np.dot(small_delta_0, np.transpose(x_list[i]))\n",
        "\n",
        "#   # matrix_w = matrix_w - (result_w / 9000 *l_r)\n",
        "#   # matrix_v = matrix_v - (result_v / 9000 *l_r)\n",
        "#   # matrix_u = matrix_u - (result_u / 9000 *l_r)\n",
        "\n",
        "\n",
        "# ####################################################################\n",
        "\n",
        "#   for k in range(0, 9000):\n",
        "#   # print(i)\n",
        "#     label       = list_label[k + 1000]\n",
        "#     bias   = [1]       #bias 1\n",
        "#     im_vector = np.concatenate((bias, list_image[:, k + 1000]), axis = None)\n",
        "\n",
        "#     # print(type(list_image[: , k]))\n",
        "#     # print(list_image[:, k])\n",
        "#     # print(im_vector)\n",
        "#     im_matrix   = im_vector.reshape((size_row*size_col + 1, 1))\n",
        "\n",
        "#     y_ = np.dot(matrix_u, im_matrix)  #x와 가중치 u를 곱해서 y_를 만듦\n",
        "#     y__list[k] = y_\n",
        "    \n",
        "#     y_vector = np.ones((197,1)) #bias\n",
        "#     for j in range (1, 197):\n",
        "#       y_vector[j][0] = 1 / (1 + math.exp(-y_[j][0]))  #sigmoid 함수를 적용해서 y_에서 y를 만듦\n",
        "    \n",
        "#     # print(y_vector) #시그모이드 값 확인\n",
        "#     y   = y_vector.reshape((197, 1))\n",
        "#     y_list[k] = y\n",
        "\n",
        "#     z_  = np.dot(matrix_v, y)  #y와 가중치 v를 곱해서 z_를 만듦\n",
        "#     z__list[k] = z_\n",
        "#     z_vector = np.ones((50,1)) #bias\n",
        "#     for j in range (1, 50):\n",
        "#       z_vector[j][0] = 1 / (1 + math.exp(-z_[j][0]))  #sigmoid 함수를 적용해서 y_에서 y를 만듦\n",
        "\n",
        "#     z   = z_vector.reshape((50, 1))\n",
        "#     z_list[k] = z\n",
        "    \n",
        "#     # print(z)\n",
        "\n",
        "#     h_  = np.dot(matrix_w, z)  #z와 가중치 w를 곱해서 h_를 만듦\n",
        "#     h__list[k] = h_\n",
        "    \n",
        "#     h_vector = np.ones((10,1)) #bias\n",
        "#     for j in range (0, 10):\n",
        "#       h_vector[j][0] = 1 / (1 + math.exp(-h_[j][0]))  #sigmoid 함수를 적용해서 h_에서 h를 만듦\n",
        "\n",
        "#     h   = h_vector.reshape((10, 1))\n",
        "#     # print(h)\n",
        "#     # tmp = h.ravel()\n",
        "#     # max_value = max(tmp)\n",
        "#     # print(max_value)\n",
        "#     # max_index = tmp.index(max_value)\n",
        "#     index, value = max(enumerate(h), key=operator.itemgetter(1))\n",
        "#     h_list[k] = h\n",
        "#     l_list[k][index] = 1\n",
        "#     # print(l_list[i])\n",
        "#     # print(h)\n",
        "#     if index == label:\n",
        "#       number_of_correct_predictions2 = number_of_correct_predictions2 + 1\n",
        "\n",
        "#     y_list_tmp[k] = y_list[k]\n",
        "#     z_list_tmp[k] = z_list[k]\n",
        "#     h_list_tmp[k] = h_list[k]\n",
        "#     y__list_tmp[k] = y__list[k]\n",
        "#     z__list_tmp[k] = z__list[k]\n",
        "#     h__list_tmp[k] = h__list[k]\n",
        "\n",
        "\n",
        "#   accuracy_history2[t] = number_of_correct_predictions2/total_number_of_predictions2*100\n",
        "  # print(accuracy_history2[t])\n",
        "  # t = t + 1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "81J4ESnphg--",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "wrong_images_index = np.ones(9000)\n",
        "right_images_index = np.ones(9000)\n",
        "\n",
        "wrong_images_value = np.ones(9000)\n",
        "right_images_value = np.ones(9000)\n",
        "\n",
        "wrong_images_num = 0\n",
        "right_images_num = 0\n",
        "\n",
        "for k in range(0, 9000):\n",
        "  # print(i)\n",
        "    label       = list_label[k + 1000]\n",
        "    bias   = [1]       #bias 1\n",
        "    im_vector = np.concatenate((bias, list_image[:, k + 1000]), axis = None)\n",
        "\n",
        "    # print(type(list_image[: , k]))\n",
        "    # print(list_image[:, k])\n",
        "    # print(im_vector)\n",
        "    im_matrix   = im_vector.reshape((size_row*size_col + 1, 1))\n",
        "    # print(h_list[real_iter - 1][0])\n",
        "    index, value = max(enumerate(h_list2[k]), key=operator.itemgetter(1))\n",
        "    \n",
        "\n",
        "    # print(l_list[i])\n",
        "    # print(h)\n",
        "    if index == label:\n",
        "      right_images_index[right_images_num] = int(k + 1000)\n",
        "      right_images_value[right_images_num] = int(index)\n",
        "      right_images_num = right_images_num + 1\n",
        "    else:\n",
        "      wrong_images_index[wrong_images_num] = int(k + 1000)\n",
        "      wrong_images_value[wrong_images_num] = int(index)\n",
        "      wrong_images_num = wrong_images_num + 1"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pMcbWdbh98uY",
        "colab_type": "text"
      },
      "source": [
        "# **Machine Learning Assignment09** #\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "Name : 이현주\n",
        "\n",
        "Student # : 20184060"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "CwXihsOJgZ0R"
      },
      "source": [
        "# **1. Plot the loss curve [10pt]**\n",
        "\n",
        "- plot the training loss at every iteration of gradient descent using the training data in blue color (the first 6,000 images) [5pt]\n",
        "- plot the testing loss at every iteration of gradient descent using the testing data in red color (the rest 4,000 images) [5pt]\n",
        "- the both curves should be presented in one figure"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DTouPM3uAf8Q",
        "colab_type": "code",
        "outputId": "fd568d77-5d3d-44ba-c709-38302fae446f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        }
      },
      "source": [
        "#그 코드를 바탕으로 training loss plotting 하기.\n",
        "real_iter = iteration\n",
        "\n",
        "fig,ax = plt.subplots(figsize=(12,8))\n",
        "\n",
        "cost_tmp = np.zeros(real_iter)\n",
        "\n",
        "for i in range(0, real_iter):\n",
        "  cost_tmp[i] = cost_history[i]\n",
        "\n",
        "\n",
        "# print(cost_history[1])\n",
        "\n",
        "ax.set_ylabel('J(Theta)')\n",
        "ax.set_xlabel('Iterations')\n",
        "_=ax.plot(range(real_iter),cost_tmp,'b.', color = 'blue')\n",
        "_=ax.plot(range(real_iter),cost_history2,'b.', color = 'red')"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Gkh9M8tFl419N8kfznBQAAKw3K33y5+uSvK6qfq2737CKcwIAgHVnby6X+Kaq+p2q2pIkVbW5qp4+53kBAMC6sldhnuSOJD85Ld+U5BVzmxEAAKxDexPmP9zdr07yzSTp7q/nO28EBQAA9oG9CfM7qurQJJ0kVfXDSXbMdVYAALDO7M0nf56T5P1JjqqqtyR5YpJfmuekAABgvdmbT/68uKo+muT4zE5heXF3f2nuMwMAgHVkb14xT5JDktw2bX9MVaW7Pzy/aQEAwPpyt2FeVa9K8uwkVyf51jTcSYQ5AADsI3vzivkzkjyqu73hEwAA5mRvrsry2SQHznsiAACwnu3xFfOqekNmp6x8PcnHq+qSLLtMYnf/+vynBwAA68NKp7Jsnb5fleTCVZgLAACsWyuF+b/u7l9arYkAAMB6ttI55v9y1WYBAADr3EqvmN+/qh6X2YcKfY/u/uh8pgQAAOvPSmF+RJL/lt2HeSd58lxmBAAA69BKYb6tu8U3AACsgr25jjkAADBnK4X5S1dtFgAAsM6tFOa/VlX/tqq+51M/q+qHqurlVfXLc5wbAACsGyudY/78JC9J8tqqujXJ9iSHJtmUZFuSN3b3X819hgAAsA7sMcy7++YkZyU5q6o2JXl4km8k+T/d/Y1VmR0AAKwTewzzqvpaZpdF/PbQzuWq2pHkH5L85+6+ZK4zBACAdWClV8y/f0/rquqAJI9J8pbpOwAAcB/cq8sldvdd3f2JJG/Yx/MBAIB16T5dx7y7/2RfTQQAANYzHzAEAAADEOYAADAAYQ4AAAMQ5gAAMABhDgAAAxDmAAAwAGEOAAADEOYAADAAYQ4AAAMQ5gAAMABhDgAAAxDmAAAwAGEOAAADEOYAADAAYQ4AAAMQ5gAAMABhDgAAAxDmAAAwAGEOAAADmFuYV9VRVfXBqrqmqq6uqhdP4w+pqour6rrp+4On8aqq11fVtqr6ZFU9ftljnT5tf11Vnb5s/Mer6lPTfV5fVbXSPgAAYFTzfMX8ziS/1d3HJDk+yQur6pgkZye5pLs3J7lkWk6SU5Jsnr7OTHJuMovsJOckeUKS45Kcsyy0z03y/GX3O3ka39M+AABgSHML8+7+Qnd/dLr9tSSfSXJEklOTnD9tdn6SZ0y3T01yQc9cnuSwqnpEkqcmubi7b+3u25JcnOTkad0Du/vy7u4kF+zyWLvbBwAADGlVzjGvqk1JHpfkiiSHd/cXplU3Jzl8un1Eks8vu9uN09hK4zfuZjwr7GPXeZ1ZVVurauv27dvv+Q8GAAD7yNzDvKq+L8lfJPmN7v7q8nXTK909z/2vtI/u3tLdS929tHHjxnlOAwAAVjTXMK+qAzOL8rd0919Ow1+cTkPJ9P2WafymJEctu/uR09hK40fuZnylfQAAwJDmeVWWSnJeks909x8uW3Vhkp1XVjk9ybuXjZ82XZ3l+CS3T6ejXJTkpKp68PSmz5OSXDSt+2pVHT/t67RdHmt3+wAAgCFtmONjPzHJLyb5VFV9fBr77SSvTPKOqjojyQ1JnjWte2+SpyXZluTrSZ6XJN19a1X9XpIrp+1e3t23TrdfkOTNSQ5N8r7pKyvsAwAAhlSzU7BZWlrqrVu3LnoaAACsYVV1VXcv7W6dT/4EAIABCHMAABiAMAcAgAEIcwAAGIAwBwCAAQhzAAAYgDAHAIABCHMAABiAMAcAgAEIcwAAGIAwBwCAAQhzAAAYgDAHAIABCHMAABiAMAcAgAEIcwAAGIAwBwCAAQhzAAAYgDAHAIABCHMAABiAMAcAgAEIcwAAGIAwBwCAAQhzAAAYgDAHAIABCHMAABiAMAcAgAEIcwAAGIAwBwCAAQhzAAAYgDAHAIABCHMAABiAMAcAgAEIcwAAGIAwBwCAAQhzAAAYgDAHAIABCHMAABiAMAcAgAEIcwAAGIAwBwCAAQhzAAAYgDAHAIABCHMAABiAMAcAgAEIcwAAGIAwBwCAAQhzAAAYgDAHAIABCHMAABiAMAcAgAEIcwAAGIAwBwCAAQhzAAAYgDAHAIABCHMAABiAMAcAgAEIcwAAGIAwBwCAAQhzAAAYgDAHAIABCHMAABiAMAcAgAEIcwAAGIAwBwCAAQhzAAAYgDAHAIABCHMAABjA3MK8qt5UVbdU1aeXjT2kqi6uquum7w+exquqXl9V26rqk1X1+GX3OX3a/rqqOn3Z+I9X1aem+7y+qmqlfQAAwMjm+Yr5m5OcvMvY2Uku6e7NSS6ZlpPklCSbp68zk5ybzCI7yTlJnpDkuCTnLAvtc5M8f9n9Tr6bfQAAwLDmFubd/eEkt+4yfGqS86fb5yd5xrLxC3rm8iSHVdUjkjw1ycXdfWt335bk4iQnT+se2N2Xd3cnuWCXx9rdPgAAYFirfY754d39hen2zUkOn24fkeTzy7a7cRpbafzG3YyvtI/vUVVnVtXWqtq6ffv2e/HjAADAvrGwN39Or3T3IvfR3Vu6e6m7lzZu3DjPqQAAwIpWO8y/OJ2Gkun7LdP4TUmOWrbdkdPYSuNH7mZ8pX0AAMCwVjvML0yy88oqpyd597Lx06arsxyf5PbpdJSLkpxUVQ+e3vR5UpKLpnVfrarjp6uxnLbLY+1uHwAAMKwN83rgqnprkhOTPKyqbszs6iqvTPKOqjojyQ1JnjVt/t4kT0uyLcnXkzwvSbr71qr6vSRXTtu9vLt3vqH0BZld+eXQJO+bvrLCPgAAYFg1Ow2bpaWl3rp166KnAQDAGlZVV3X30u7W+eRPAAAYgDAHAIABCHMAABiAMAcAgAEIcwAAGIAwBwCAAQhzAAAYgDAHAIABCHMAABiAMAcAgAEIcwAAGIAwBwCAAQhzAAAYgDAHAIABCHMAABiAMAcAgAEIcwAAGIAwBwCAAQhzAAAYgDAHAIABCHMAABiAMAcAgAEIcwAAGIAwBwCAAQhzAAAYgDAHAIABCHMAABiAMAcAgAEIcwAAGIAwBwCAAQhzAAAYgDAHAIABCHMAABiAMAcAgAEIcwAAGIAwBwCAAQhzAAAYgDAHAIABCHMAABiAMAcAgAEIcwAAGIAwBwCAAQhzAAAYgDAHAIABCHMAABiAMAcAgAEIcwAAGIAwBwCAAQhzAAAYgDAHAIABCHMAABiAMAcAgAEIcwAAGIAwBwCAAQhzAAAYgDAHAIABCHMAABiAMAcAgAEIcwAAGIAwBwCAAQhzAAAYgDAHAIABCHMAABiAMAcAgAEIcwAAGIAwBwCAAQhzAAAYwJoN86o6uaquraptVXX2oucDAAArWZNhXlUHJHljklOSHJPkuVV1zGJnBQAAe7Zh0ROYk+OSbOvuzyZJVb0tyalJrlnorHZ12WXJq1+dXHttcvDByY4ds++33ZZUJYcdtne378n9VmMf5mZua/lnMjdzM7e1+zOZ2/qa27HHJmedlZxwwqKL8NvWapgfkeTzy5ZvTPKEBc1l9y67LHnSk5I771z0TAAA1p/rr0/e857kQx8aJs7X5Kkse6uqzqyqrVW1dfv27au780svFeUAAIv0zW/OmmwQazXMb0py1LLlI6ex79LdW7p7qbuXNm7cuGqTS5KceGKyYa3+wgIAYD9w4IGzJhvEWi3DK5NsrqqjMwvy5yT5+cVOaRcnnJB8+MPOMTe3tT23tfgzmZu5mdva/ZnMbX3NzTnmq6O776yqFyW5KMkBSd7U3VcveFrf64QTkne9a9GzAABgAGsyzJOku9+b5L2LngcAAOyNtXqOOQAA7FeEOQAADECYAwDAAIQ5AAAMQJgDAMAAhDkAAAxAmAMAwACEOQAADECYAwDAAIQ5AAAMQJgDAMAAhDkAAAxAmAMAwACEOQAADECYAwDAAKq7Fz2HIVTV9iQ3LGDXD0vypQXsl9XlOK8PjvP64DivD47z+rCI4/wvunvj7lYI8wWrqq3dvbToeTBfjvP64DivD47z+uA4rw+jHWensgAAwACEOQAADECYL96WRU+AVeE4rw+O8/rgOK8PjvP6MNRxdo45AAAMwCvmAAAwAGG+QFV1clVdW1XbqursRc+He6eqjqqqD1bVNVV1dVW9eBp/SFVdXFXXTd8fPI1XVb1+Ou6frKrHL/Yn4J6oqgOq6mNV9dfT8tFVdcV0PN9eVQdN4wdPy9um9ZsWOW/2XlUdVlXvrKq/r6rPVNUJns9rT1X95vR39qer6q1VdYjn8/6vqt5UVbdU1aeXjd3j529VnT5tf11Vnb5a8xfmC1JVByR5Y5JTkhyT5LlVdcxiZ8W9dGeS3+ruY5Icn+SF07E8O8kl3b05ySXTcjI75punrzOTnLv6U+Y+eHGSzyxbflWS13T3jyS5LckZ0/gZSW6bxl8zbcf+4XVJ3t/dj07y2MyOt+fzGlJVRyT59SRL3f2YJAckeU48n9eCNyc5eZexe/T8raqHJDknyROSHJfknJ0xP2/CfHGOS7Ktuz/b3XckeVuSUxc8J+6F7v5Cd390uv21zP4RPyKz43n+tNn5SZ4x3T41yQU9c3mSw6rqEas8be6Fqjoyyc8k+dNpuZI8Ock7p012Pc47j/87kzxl2p6BVdWDkjwpyXlJ0t13dPdX4vm8Fm1IcmhVbUhy/yRfiOfzfq+7P5zk1l2G7+nz96lJLu7uW7v7tiQX53tjfy6E+eIckeTzy5ZvnMbYj02/3nxckiuSHN7dX5hW3Zzk8Om2Y7//em2Ss5J8a1p+aJKvdPed0/LyY/nt4zytv33anrEdnWR7kj+bTln606p6QDyf15TuvinJHyT5x8yC/PYkV8Xzea26p8/fhT2vhTnsI1X1fUn+IslvdPdXl6/r2eWPXAJpP1ZVT09yS3dftei5MFcbkjw+ybnd/bgk/5zv/No7iefzWjCdlnBqZv8R+8EkD8gqvSLKYo3+/BXmi3NTkqOWLR85jbEfqqoDM4vyt3T3X07DX9z5K+3p+y3TuGO/f3pikp+tquszO/XsyZmdi3zY9Kvw5LuP5beP87T+QUm+vJoT5l65McmN3X3FtPzOzELd83lt+ekkn+vu7d39zSR/mdlz3PN5bbqnz9+FPa+F+eJcmWTz9A7wgzJ708mFC54T98J0nuF5ST7T3X+4bNWFSXa+k/v0JO9eNn7a9G7w45PcvuxXbAyqu1/W3Ud296bMnq8f6O5fSPLBJM+cNtv1OO88/s+cth/2VRpmuvvmJJ+vqkdNQ09Jck08n9eaf0xyfFXdf/o7fOdx9nxem+7p8/eiJCdV1YOn366cNI3NnQ8YWqCqelpm56wekORN3f37C54S90JV/VSSv03yqXzn3OPfzuw883ckeWSSG5I8q7tvnf4R+KPMfm369STP6+6tqz5x7rWqOjHJf+rup1fVD2X2CvpDknwsyb/v7h1VdUiSP8/sPQe3JnlOd392UXNm71XVsZm9wfegJJ9N8rzMXsjyfF5Dqup3kzw7sytrfSzJf8jsPGLP5/1YVb01yYlJHpbki5ldXeWvcg+fv1X1y5n9W54kv9/df7Yq8xfmAACweE5lAQCAAQhzAAAYgDAHAIABCHMAABiAMAcAgAEIc4A1rqr+afq+qap+fh8/9m/vsvy/9+XjA6wnwhxg/diU5B6F+bJPQdyT7wrz7v7JezgnACbCHGD9eGWSf1VVH6+q36yqA6rqv1bVlVX1yar6lWT2AUpV9bdVdWFmn4aYqvqrqrqqqq6uqjOnsVcmOXR6vLdMYztfna/psT9dVZ+qqmcve+xLq+qdVfX3VfWW6UM+UlWvrKprprn8war/6QAs2N29EgLA2nF2pk8sTZIpsG/v7p+oqoOT/F1V/c207eOTPKa7Pzct//L0SXmHJrmyqv6iu8+uqhd197G72dfPJTk2yWMz+wS+K6vqw9O6xyX5sST/N8nfJXliVX0myb9L8uju7qo6bJ//9ACD84o5wPp1UpLTqurjSa5I8tAkm6d1H1kW5Uny61X1iSSXJzlq2XZ78lNJ3trdd3X3F5N8KMlPLHvsG7v7W0k+ntkpNrcn+X9Jzquqn8vs47EB1hVhDrB+VZJf6+5jp6+ju3vnK+b//O2Nqk5M8tNJTujuxyb5WJJD7sN+dyy7fVeSDd19Z5LjkrwzydOTvP8+PD7AfkmYA6wfX0vy/bPUT6gAAADdSURBVMuWL0ryq1V1YJJU1Y9W1QN2c78HJbmtu79eVY9Ocvyydd/cef9d/G2SZ0/nsW9M8qQkH9nTxKrq+5I8qLvfm+Q3MzsFBmBdcY45wPrxySR3TaekvDnJ6zI7jeSj0xswtyd5xm7u9/4k/3E6D/zazE5n2WlLkk9W1Ue7+xeWjb8ryQlJPpGkk5zV3TdPYb8735/k3VV1SGav5L/k3v2IAPuv6u5FzwEAANY9p7IAAMAAhDkAAAxAmAMAwACEOQAADECYAwDAAIQ5AAAMQJgDAMAAhDkAAAzg/wNA0fY5/i3LLgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "OiKcfzyYgaCF"
      },
      "source": [
        "# **2. Plot the accuracy curve [10pt]**\n",
        "\n",
        "- plot the training accuracy (%) at every iteration of gradient descent using the training data in blue color (the first 6,000 images) [5pt]\n",
        "- plot the testing accuracy (%) at every iteration of gradient descent using the testing data in red color (the rest 4,000 images) [5pt]\n",
        "- the both curves should be presented in one figure"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fF1EODxQAhri",
        "colab_type": "code",
        "outputId": "6e41ac1b-723e-47f7-c45d-10de8356b1c7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        }
      },
      "source": [
        "#그 코드를 바탕으로 training loss plotting 하기.\n",
        "fig,ax = plt.subplots(figsize=(12,8))\n",
        "\n",
        "# print(cost_history[1])\n",
        "\n",
        "ac_tmp = np.zeros(real_iter)\n",
        "\n",
        "for i in range(0, real_iter):\n",
        "  ac_tmp[i] = accuracy_history[i]\n",
        "\n",
        "ax.set_ylabel('accuracy')\n",
        "ax.set_xlabel('Iterations')\n",
        "_=ax.plot(range(real_iter),ac_tmp,'b.', color = 'blue')\n",
        "_=ax.plot(range(real_iter),accuracy_history2,'b.', color = 'red')\n",
        "# print(ac_tmp)"
      ],
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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dD/ehD3UXH059Ruz1kl/8xeSTnxzu/c8448iY6f377TwDAFvWpMo8diT5rqq6O8l9knw5yeOTPKN//7VJfi1zEKb37Uu+/e1u4/bAgSm/+LDXSy6/PPngB9f3fmee2Y3tFpoBgCkz9jDdWvtSVf1Gki8m+VaSP0pX1vH11lp/BmBuS3Lmau9fVRcnuThJzjrrrNEveIR6veSNbzxSAbFjxxRffLi0lDzvefdezrFy13kS0/kAADbRJMo8HpjkqUkeluTrSf5zkp9c6/u31paSLCXJ+eefvwmFuJOzvHykJV5V8uxnT+Gm7Fp2ox/+8OQJT1DnDADMnEmUeTwhyV+21u5Ikqp6W5LHJDm1qnb0d6cfmuRLE1jbWO3a1XXwaK3r6LF376RXtA6DSYTveMfxH7N7d/Lyl9t5BgBm1rYJfM4vJnl0Vd2nqirJniQ3J3l/kp/uP+ZZSa6fwNrGptdLXvSi5O67u13pq66akk3bpaWum8ZjHnP8IF3V1T7/5V8K0gDATJtEzfRHq+qtST6R5GCSm9KVbfzXJL9fVf+mf+6aca9tnPbt6xpVJF2px003TXY9JzToE33DDfc+XOWCC5JXvWpKfjIAANiYiXTzaK29Iskrjjn9hSQ/OoHlcCJLS8nzn3/0mMbVVCUve1ny6lePZ10AAFvAJMo8SFcfvXNnl0F37tyC9dKDco7nPvfEQboqufDC5MMfFqQBgLljnPgEPec53e2WanKx1oErOnQAAAjTk9DrJXv2dENatsSu9KAzx0c+ktx++4kfe845ySWXuLAQACDC9EQsL3dB+tChLTD18LLLuiB9b7ZtS66+WogGAFhBmJ6AxcVu2uHhwxOcerjW0d/KOQAAjkuYnpDB1O17m749Er1e8rjHdU2uj8fAFQCAe6WbxwQMxoi31t0uL4/xk/d6yTOesXqQPu20rib69a83cAUAYA3sTE/AyjHiO3eOocxjLRcYXnqp1nYAAOskTI9Zr5e8+MXdjvS2bWMYI76WoSuvf71daACAISjzGLNBJ4/Dh7ud6TvvHOEnG/SMPlGQvvRSQRoAYEjC9JgNSjy2bRtDicfll3db4KvZvbvbkVbaAQAwNGF6jMZW4jHo1nFs27v73c8FhgAAm0jN9BitLPGoGlGJx/Ha3lUl732vXtEAAJvIzvQYLS52pR3bt4+oxONEbe9e9jJBGgBgk9mZHqOFheSGG7od6sXFTc62JxoLru0dAMBICNPTbmkp+fVfT269dfX7tb0DABgZYXqMer1kz56ubnrnzm6XekO700tLyXOfe/z7tb0DABgpNdNjNLgA8dCh7nbDY8Rf+9rVz2t7BwAwFnamx2hTx4hfdlly881Hn6vqLjQUogEAxkKYHpNN7THd6yW/8RtHnzv99OT663XsAAAYI2UeY7KpY8SvvPKeI8L/zb8RpAEAxkyYHpNN6zF92WXJO95x5LjKhYYAABOizGNMNqXH9GrlHY96lBppAIAJsTM9TZaX71necdFFE1kKAAB2psdmU3pMf/3rRx8/85nKOwAAJsjO9JhsuMf00tLRJR5VyQ/90CauEACA9RKmx2RDFyD2eskLXnB0iceOHRtsVA0AwEYp8xiTDV2AuG9fcvDgkeNt25Lf+R2t8AAAJszO9Jj0ekMG6V4v+Q//4ehzL32pWmkAgC3AzvQYbOjiw337ukLrlU49ddPXCADA+tmZHoMNXXx4++1HH2/frlYaAGCLEKbHYOiLDy+7LLn++iPH27Ylv/u7aqUBALYIZR5jMNTFh0tLyZVXHjmu6uqk1UoDAGwZdqbHYKiLD6+55ujjqmTv3k1eGQAAG2FnesSGuviw10s+8Ymjz730pco7AAC2GDvTIzbUxYfHdvC48MLk1a8e0QoBABiWMD1ii4vdsMKqNQ4t7PW6Eo/WuuOTT04uvXTEqwQAYBjC9BgMcvHg9oSuvDK5++4jx098ovIOAIAtSpgeseXlrmKjte72hGUevV7yzncefe6MM0a4OgAANkKYHrF19Zjety85fPjI8bZtOngAAGxhunmM2FA9pgee8hQlHgAAW5gwPQYLC2vMxOed121hHz6cnHSSCw8BALY4ZR4j1uslV1zR3Z7Q0lLywhd2QXr79uTf/Tu70gAAW5yd6RFa88CWXi95wQuSgwe748OHkzvvHOtaAQBYPzvTI7TmgS2Dlh8D27evoSE1AACTJkyP0Jo7eezadXQT6pe8RIkHAMAUUOYxQmvu5HHTTUfe3rYtOfXUMawOAICNEqZH7F47eQzGhw+cdJISDwCAKaHMY9L27TM+HABgSgnTI7Smtni33370sfHhAABTQ5nHiKypLV6vl7z73UeOTzrJ+HAAgCliZ3pE1tQWb2VLvKrkoouUeAAATBFhekTW1BZv166ue8e2bckpp9iVBgCYMso8RuRe2+L1esmLX9ztTG/bllx1lV1pAIApI0yP0Anb4i0vJ/v3d6PDE+PDAQCmkDKPETphN49du44E6cOHu2MAAKaKnekRudduHsdOPbQzDQAwdexMj8gJu3n0eskb33jk2NRDAICpJEyPyAm7eSwvJwcPdm9XJc9+tosPAQCmkDKPETlhN4+V9dKtJeedN4EVAgCwUcL0CB23m8edd3Z10ocPq5cGAJhiwvQk7NrV1X8kycknq5cGAJhSaqZH5Lht8QxrAQCYGXamR+CEbfH27Uu+/e2uVrpKiQcAwBSzMz0Cx22LN2iJ11p3vGOHEg8AgCkmTI/AcdviLS93CTvREg8AYAYo8xiB47bFW1zsdqMPH+5S9t69k1skAAAbZmd63AYlHoNbAACmlp3pETjuBYiDMo/WutvlZWUeAABTzM70CBz3AsQTzhgHAGDa2JkegUFmHuxMH5WZn/Ws7nbvXrvSAABTTpgegVUvQDy29sPFhwAAU0+YHpGFhWM2nler/bAzDQAw1dRMj8ugLV6VYS0AADNCmB4nbfEAAGaKMD0CvV5yxRXd7Xes1hYPAICppmZ6kx23x/QJW3wAADCNhOlNdsLrDLXFAwCYKcL0Jlt1A1pbPACAmTSRmumqOrWq3lpVn62qz1TVQlWdVlXvq6pb+rcPnMTaNmrQY/qVrzxmjPiqIxEBAJhmk9qZfm2S97TWfrqqdia5T5JfSXJDa+1VVXV5ksuTXDah9W3IPXpMq5cGAJhJYw/TVfWAJBck+X+SpLV2IMmBqnpqksX+w65NspwpDdOrUi8NADBzJrEz/bAkdyR5U1X9cJKPJ7kkyYNba1/uP+b2JA+ewNo2n3ppAICZtaaa6ap6W1X9VFVtRo31jiQ/kuTq1tp5Sf4uXUnHd7TWWpJVJ5tU1cVVdWNV3XjHHXdswnJGTL00AMDMWms4/t0kz0hyS1W9qqoesYHPeVuS21prH+0fvzVduP5KVT0kSfq3X13tnVtrS62181tr559++ukbWMaYDOqlt29XLw0AMGPWFKZba/+9tfbMdKH3r5L896r6n1X17Ko6aT2fsLV2e5K/XhHI9yS5Ock7k/QLi/OsJNev5+NuWau29wAAYBasuWa6qnYl+RdJfj7JTUnenOSx6YLv4jo/7y8leXO/k8cXkjw7XbD/w6q6KMmtSX5mnR9zS+j1ukqOxUW5GQBg1q0pTFfV25M8Isl/TPJ/r7hQ8A+q6sb1ftLW2ieTnL/KXXvW+7G2klVHied488UBAJh2a92Z/u3W2vtXu6O1tloonkurjhLPaieFaQCAWbDWCxDPqapTBwdV9cCq+sURrWlqrXqtoQsQAQBm1lp3pv9la+3fDw5aa3dV1b9M1+WDvsG1hkfVTPdiYAsAwIxaa5jeXlXV7/+cqtqeZOfoljW9jholbmALAMBMW2uZx3vSXWy4p6r2JHlL/xzH6PWSK67obg1sAQCYbWvdmb4syXOTPL9//L4kbxjJiqbYsRvRH71qMY/cufPICfXSAAAzZU1hurV2OMnV/T8cx7Eb0e+6cyGPvEcRNQAAs2KtfabPTnJFknOSnDI431r7ByNa11QaNO4YbEQ/eZcJLgAAs2ytZR5vSvKKJL+V5MdzZGIhK6zs5vHkXb088sWGtQAAzLK1BuLvaq3dkKRaa7e21n4tyU+NblnTa2EhefnLk0feueziQwCAGbfWnen9VbUtyS1V9cIkX0ry3aNb1gw4tubDxYcAADNnrWH6kiT3SfKiJK9MV+rxrFEtaiasOsEFAIBZcq9huj+g5Wdbay9N8n/S1UsDAMDcu9cw3Vo7VFWPHcdiZsqxTaddgAgAMHPWWuZxU1W9M8l/TvJ3g5OttbeNZFVTqreyE95q0w+FaQCAmbLWMH1KkjuTPH7FuZZEmO4z/RAAYP6sdQKiOul7YfohAMD8WesExDel24k+SmvtOZu+oim1aie8hQUhGgBghq21zONdK94+JcnTkvzN5i9net2jE156yRXLdqUBAGbYWss8rlt5XFVvSfLHI1nRFPvORrROHgAAc2Gt48SPdXaS79nMhcyU1Tp5AAAwc9ZaM/3NHF0zfXuSy0ayollglDgAwFxYa5nH/Ua9kJlilDgAwFxYU5lHVT2tqh6w4vjUqrpwdMsCAICtb63dPF7RWnv74KC19vWqekWSd4xmWVPOBYgAAHNhrRcgrva4tQbx+eMCRACAubDWMH1jVf1mVf3D/p/fTPLxUS5sqg0uQNy+3QWIAAAzbK27y7+U5FeT/EG6rh7vS/KCUS1q6rkAEQBgLqy1m8ffJbl8xGuZLUaJAwDMvLV283hfVZ264viBVfXe0S1rOvV6yRVXdLcAAMy+tZZ5PKi19vXBQWvtrqoyAXGFlQ08Hru9l2ufs5zv37todxoAYIatNUwfrqqzWmtfTJKq2p2jJyLOvUEDj0cd6uXdh/bklNcfSK7VFg8AYJatNUz/v0n+uKo+kKSS/FiSi0e2qik0aODx+G8vZ2c7kG1tRVs8YRoAYCatqWa6tfaeJOcn+VyStyT55STfGuG6ps6ggccjnruYOllbPACAebCmnemq+oUklyR5aJJPJnl0kl6Sx49uadOna+CxkOzVFg8AYB6stczjkiSPSvKR1tqPV9UPJvn10S1rymmLBwAwF9Y6AfHbrbVvJ0lVndxa+2ySR4xuWVNMfzwAgLmx1p3p2/p9pt+R5H1VdVeSW0e3rCm1sj/eTp08AABm3VonID6t/+avVdX7kzwgyXtGtqppNeiPd0gnDwCAebDWnenvaK19YBQLmQmD/niDnWmdPAAAZtq6wzQnMOiPp5MHAMBcEKY3m04eAABzY63dPAAAgGMI0wAAMCRhGgAAhiRMbxKzWgAA5o8LEDfBylktj93ey7XPWc737110ISIAwIwTpjfBYFbLow718u5De3LK6w8k15qACAAw65R5bILBrJbH13J25kC2tRUTEAEAmFnC9CYYzGp5xHMXUyfvTLZvNwERAGAOKPPYJN2sloVkrwmIAADzQpjebCYgAgDMDWUeAAAwJGEaAACGJEwDAMCQhOnNZAwiAMBccQHiZlk5BnGngS0AAPPAzvRmGYxBPGRgCwDAvBCmN8tgDKKBLQAAc0OZx2YZjEE0sAUAYG4I05vJwBYAgLmizAMAAIYkTAMAwJCEaQAAGJIwDQAAQxKmAQBgSMI0AAAMSZgGAIAhCdObqddLrriiuwUAYOYZ2rJZer1kz57kwIFunPgNNxjgAgAw4+xMb5bl5S5IHzrU3S4vT3pFAACMmDC9WRYXux3p7du728XFSa8IAIARU+axWRYWutKO5eUuSCvxAACYecL0ZlpYEKIBAOaIMg8AABiSMA0AAEMSpgEAYEjCNAAADEmY3gQGHwIAzCfdPDbI4EMAgPk1sZ3pqtpeVTdV1bv6xw+rqo9W1eer6g+qauek1rYeBh8CAMyvSZZ5XJLkMyuOX53kt1prD09yV5KLJrKqdTL4EABgfk0kTFfVQ5P8VJI39I8ryeOTvLX/kGuTXDiJta3XYPDhK1+pxAMAYN5Mqmb6qiSXJrlf/3hXkq+31g72j29LcuYkFjYMgw8BAObT2Hemq+rJSb7aWvv4kO9/cVXdWFU33nHHHZu8ug3Q0gMAYO5MYmf6MUmeUlVPSnJKkvsneW2SU6tqR393+qFJvrTaO7fWlpIsJS43rlYAAA8USURBVMn555/fxrPke6GlBwDAXBr7znRr7eWttYe21nYn+bkk/6O19swk70/y0/2HPSvJ9eNe29C09AAAmEtbaWjLZUn+VVV9Pl0N9TUTXs/aaekBADCXJjq0pbW2nGS5//YXkvzoJNcztEFLj+XlLkgr8QAAmAsmIG4WLT0AAObOVirzAACAqSJMAwDAkIRpAAAYkjANAABDEqYBAGBIwjQAAAxJmAYAgCEJ0wAAMCRhehP0eskVV3S3AADMDxMQN6jXS/bsSQ4cSHbu7KaKG4QIADAf7Exv0PJyF6QPHepul5cnvSIAAMZFmN6gxcVuR3r79u52cXHSKwIAYFyUeWzQwkJX2rG83AVpJR4AAPNDmN4ECwtCNADAPFLmAQAAQxKmAQBgSMI0AAAMSZjeDKa2AADMJRcgbpSpLQAAc8vO9EaZ2gIAMLeE6Y0ytQUAYG4p89goU1sAAOaWML0ZTG0BAJhLyjwAAGBIwjQAAAxJmAYAgCEJ0xtkXgsAwPxyAeIGmNcCADDf7ExvgHktAADzTZjeAPNaAADmmzKPDTCvBQBgvgnTG2ReCwDA/FLmAQAAQxKmAQBgSMI0AAAMSZgGAIAhCdObwRhEAIC5pJvHRhmDCAAwt+xMb5QxiAAAc0uY3ihjEAEA5pYyj40yBhEAYG4J05vBGEQAgLmkzAMAAIYkTAMAwJCEaQAAGJIwDQAAQxKmAQBgSML0BpkkDgAwv7TG2wCTxAEA5pud6Q0wSRwAYL4J0xtgkjgAwHxT5rEBJokDAMw3YXqDTBIHAJhfyjw2SjsPAIC5ZWd6I7TzAACYa3amN0I7DwCAuSZMb4R2HgAAc02Zx0Zo5wEAMNeE6Y3SzgMAYG4p8wAAgCEJ0wAAMCRhGgAAhiRMAwDAkIRpAAAYkjANAABDEqY3oNdLrriiuwUAYP7oMz2kXi/Zs6ebIr5zZze7RbtpAID5Ymd6SMvLXZB+1KFeXvLtK3LLPtvTAADzxs70kBYXk8du7+Xdh/ZkZzuQetPOZK/taQCAeWJnekgLC8m1z1nOKXUgO3Io2w8e6LarAQCYG8L0Bnz/3sVsO2Vnsn17Vzi9uDjpJQEAMEbKPDZiYaG78nB5uQvSSjwAAOaKMD2kXm+QoRey8HIhGgBgHgnTQ9AWDwCARM30UAZt8Q4d6m5ddwgAMJ+E6SEsLnY70q47BACYb8o8huC6QwAAEmF6aAsLQjQAwLxT5gEAAEMae5iuqu+rqvdX1c1V9WdVdUn//GlV9b6quqV/+8Bxrw0AANZjEjvTB5P8cmvtnCSPTvKCqjonyeVJbmitnZ3khv4xAABsWWMP0621L7fWPtF/+5tJPpPkzCRPTXJt/2HXJrlw3Gtbt14vueKK7hYAgLkz0QsQq2p3kvOSfDTJg1trX+7fdXuSBx/nfS5OcnGSnHXWWaNf5PGY3AIAMPcmdgFiVX13kuuSvLi19rcr72uttSRttfdrrS211s5vrZ1/+umnj2Glx2FyCwDA3JtImK6qk9IF6Te31t7WP/2VqnpI//6HJPnqJNa2Zia3AADMvbGXeVRVJbkmyWdaa7+54q53JnlWklf1b68f99rWxeQWAIC5N4ma6cck+fkkn66qT/bP/Uq6EP2HVXVRkluT/MwE1rY+JrcAAMy1sYfp1tofJ6nj3L1nnGsBAICNMAERAACGJEwDAMCQhOlhGdgCADD3Jjq0ZWr1ejn043tSBw6k7dyZ7e83sAUAYB7ZmR7CrfuW0/YfyLZ2KIf3H8it+5YnvSQAACZAmB7CB7KYA9mZu7M9d2dnPpDFSS8JAIAJEKaHcPbehTxp5w35tXplnrTzhpy9V4kHAMA8UjM9hIWF5IrlhSwvL+SKReXSAADzSpgekuGHAAAo8wAAgCEJ0wAAMCRhGgAAhiRMAwDAkIRpAAAYkjANAABDEqYBAGBIwvSwer3kiiu6WwAA5pKhLcPo9ZI9e5IDB5KdO5MbbjDBBQBgDtmZHsbychekDx3qbpeXJ70iAAAmQJgexuJityO9fXt3u7g46RUBADAByjyGsbDQlXYsL3dBWokHAMBcEqaHtbAgRAMAzDllHgAAMCRhGgAAhiRMAwDAkIRpAAAYkjANAABDEqaHYZQ4AADRGm/9jBIHAKDPzvR6LS+n7e9Gibf9RokDAMwzYXqdPr1rMd86vDN3Z3u+dXhnPr1rcdJLAgBgQpR5rNO77lzIX9VVeVq7Lm+vp2f3nQt55KQXBQDARAjT6/TkXb38w/bi7MyBXNA+lL/Y9cgkaqYBAOaRMo91euSdyzmlDmRHDuWUbQfyyDuXJ70kAAAmRJhep0/vWsy3W1cz/W010wAAc02Zxzq9686F/NdtN+THDi/nQ9sW81NqpgEA5pYwvU6Li8krT17IRw4sZOfO5DWLk14RAACTIkyv08JCN6dlebkL1ua1AADML2F6CAsLQjQAAC5ABACAoQnTAAAwJGEaAACGJEwDAMCQhGkAABiSMA0AAEMSpgEAYEjCNAAADEmYBgCAIQnTAAAwJGEaAACGJEwDAMCQhGkAABiSMA0AAEMSpgEAYEjCNAAADKlaa5New9Cq6o4kt07o0z8oyf+e0OdmPDzH88HzPB88z/PB8zwfJvU8f39r7fRjT051mJ6kqrqxtXb+pNfB6HiO54PneT54nueD53k+bLXnWZkHAAAMSZgGAIAhCdPDW5r0Ahg5z/F88DzPB8/zfPA8z4ct9TyrmQYAgCHZmQYAgCEJ0+tUVT9ZVZ+rqs9X1eWTXg/Dq6rvq6r3V9XNVfVnVXVJ//xpVfW+qrqlf/vA/vmqqt/uP/efqqofmezfgLWqqu1VdVNVvat//LCq+mj/ufyDqtrZP39y//jz/ft3T3LdrF1VnVpVb62qz1bVZ6pqwWt59lTVS/rfr/+0qt5SVad4PU+/qnpjVX21qv50xbl1v36r6ln9x99SVc8a1/qF6XWoqu1J/n2SJyY5J8k/r6pzJrsqNuBgkl9urZ2T5NFJXtB/Pi9PckNr7ewkN/SPk+55P7v/5+IkV49/yQzpkiSfWXH86iS/1Vp7eJK7klzUP39Rkrv653+r/zimw2uTvKe19oNJfjjd8+21PEOq6swkL0pyfmvtHyXZnuTn4vU8C34vyU8ec25dr9+qOi3JK5L8kyQ/muQVgwA+asL0+vxoks+31r7QWjuQ5PeTPHXCa2JIrbUvt9Y+0X/7m+n+8z0z3XN6bf9h1ya5sP/2U5Psa52PJDm1qh4y5mWzTlX10CQ/leQN/eNK8vgkb+0/5NjnePDcvzXJnv7j2cKq6gFJLkhyTZK01g601r4er+VZtCPJd1XVjiT3SfLleD1PvdbaB5N87ZjT6339/tMk72utfa21dleS9+WeAX0khOn1OTPJX684vq1/jinX//XfeUk+muTBrbUv9++6PcmD+297/qfTVUkuTXK4f7wryddbawf7xyufx+88x/37v9F/PFvbw5LckeRN/XKeN1TVfeO1PFNaa19K8htJvpguRH8jycfj9Tyr1vv6ndjrWphm7lXVdye5LsmLW2t/u/K+1rW70fJmSlXVk5N8tbX28UmvhZHakeRHklzdWjsvyd/lyK+Ek3gtz4L+r+yfmu6Hp+9Nct+MaeeRydrqr19hen2+lOT7Vhw/tH+OKVVVJ6UL0m9urb2tf/org1/59m+/2j/v+Z8+j0nylKr6q3RlWY9PV1t7av/XxMnRz+N3nuP+/Q9Icuc4F8xQbktyW2vto/3jt6YL117Ls+UJSf6ytXZHa+3uJG9L9xr3ep5N6339Tux1LUyvz58kObt/5fDOdBc+vHPCa2JI/dq5a5J8prX2myvuemeSwVXAz0py/Yrze/tXEj86yTdW/AqKLai19vLW2kNba7vTvV7/R2vtmUnen+Sn+w879jkePPc/3X/8lt0NodNauz3JX1fVI/qn9iS5OV7Ls+aLSR5dVffpf/8ePM9ez7Npva/f9yb5iap6YP+3GD/RPzdyhrasU1U9KV0N5vYkb2yt/dsJL4khVdVjk3woyadzpJ72V9LVTf9hkrOS3JrkZ1prX+t/8/6ddL9W/Pskz26t3Tj2hTOUqlpM8tLW2pOr6h+k26k+LclNSf5Fa21/VZ2S5D+mq5//WpKfa619YVJrZu2q6tx0F5nuTPKFJM9Ot2HktTxDqur/S/Kz6box3ZTkF9LVxXo9T7GqekuSxSQPSvKVdF053pF1vn6r6jnp/h9Pkn/bWnvTWNYvTAMAwHCUeQAAwJCEaQAAGJIwDQAAQxKmAQBgSMI0AAAMSZgG2IKq6v/0b3dX1TM2+WP/yjHH/3MzPz7APBGmAba23UnWFaZXTIM7nqPCdGvt/1rnmgDoE6YBtrZXJfmxqvpkVb2kqrZX1Wuq6k+q6lNV9dykG0pTVR+qqnemmwqXqnpHVX28qv6sqi7un3tVku/qf7w3988NdsGr/7H/tKo+XVU/u+JjL1fVW6vqs1X15v7ghFTVq6rq5v5afmPsXx2ACbu33QsAJuvy9Cc3Jkk/FH+jtfaoqjo5yYer6o/6j/2RJP+otfaX/ePn9CeGfVeSP6mq61prl1fVC1tr567yuf5ZknOT/HC6SWR/UlUf7N93XpIfSvI3ST6c5DFV9ZkkT0vyg621VlWnbvrfHmCLszMNMF1+Isneqvpkko8m2ZXk7P59H1sRpJPkRVX1v5J8JMn3rXjc8Tw2yVtaa4daa19J8oEkj1rxsW9rrR1O8sl05SffSPLtJNdU1T9LN9oXYK4I0wDTpZL8Umvt3P6fh7XWBjvTf/edB1UtJnlCkoXW2g8nuSnJKRv4vPtXvH0oyY7W2sEkP5rkrUmenOQ9G/j4AFNJmAbY2r6Z5H4rjt+b5PlVdVKSVNUPVNV9V3m/ByS5q7X291X1g0keveK+uwfvf4wPJfnZfl326UkuSPKx4y2sqr47yQNaa+9O8pJ05SEAc0XNNMDW9qkkh/rlGr+X5LXpSiw+0b8I8I4kF67yfu9J8rx+XfPn0pV6DCwl+VRVfaK19swV59+eZCHJ/0rSklzaWru9H8ZXc78k11fVKel2zP/VcH9FgOlVrbVJrwEAAKaSMg8AABiSMA0AAEMSpgEAYEjCNAAADEmYBgCAIQnTAAAwJGEaAACGJEwDAMCQ/n9mxAz0Q95ANwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "hf4SrnjSgaXp"
      },
      "source": [
        "# **3. Plot the accuracy value [4pt]**\n",
        "\n",
        "- print the final training accuracy (%) using the training data (the first 6,000 images) [2pt]\n",
        "- print the final testing accuracy (%) using the testing data (the rest 4,000 images) [2pt]"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B2tcQNdPAluv",
        "colab_type": "code",
        "outputId": "f6782353-ab7e-4070-e500-0197e5443c39",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "source": [
        "print(\"final training accuracy using the training data : \", end = '')\n",
        "print(accuracy_history[-1], end = '%\\n')\n",
        "\n",
        "print(\"final training accuracy using the testing data : \", end = '')\n",
        "print(accuracy_history2[-1], end = '%\\n')"
      ],
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "final training accuracy using the training data : 100.0%\n",
            "final training accuracy using the testing data : 84.44444444444444%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "dCAltdEPhHY_"
      },
      "source": [
        "# **4. Plot the classification example [10pt]**\n",
        "\n",
        "- present 10 correctly classified testing images with their labels at the title of each sub-figure in 2x5 array [5pt]\n",
        "- present 10 misclassified testing images with their misclassified labels at the title of each sub-figure in 2x5 array [5pt]"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qxLgEta7hU9c",
        "colab_type": "code",
        "outputId": "1d4027a3-3aff-4926-93f9-6326445d17bc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 527
        }
      },
      "source": [
        "\n",
        "print(\"**correctly classified testing images**\")\n",
        "for i in range(10):\n",
        "    # print(int(right_images_index[i]))\n",
        "    # print(h_list[int(right_images_index[i])])\n",
        "    label       = list_label[int(right_images_index[i])]\n",
        "    im_vector   = list_image[:, int(right_images_index[i])]\n",
        "    im_matrix   = im_vector.reshape((size_row, size_col))\n",
        "\n",
        "    plt.subplot(2, 5, i+1)\n",
        "    plt.title(int(right_images_value[i]))\n",
        "    plt.imshow(im_matrix, cmap='Greys', interpolation='None')\n",
        "\n",
        "    frame   = plt.gca()\n",
        "    frame.axes.get_xaxis().set_visible(False)\n",
        "    frame.axes.get_yaxis().set_visible(False)\n",
        "\n",
        "plt.show()\n",
        "print(\"\\n\\n\\n**misclassified testing images**\")\n",
        "for i in range(10):\n",
        "    # print(int(wrong_images_index[i]))\n",
        "    # print(h_list[int(right_images_index[i])])\n",
        "    label       = list_label[int(wrong_images_index[i])]\n",
        "    im_vector   = list_image[:, int(wrong_images_index[i])]\n",
        "    im_matrix   = im_vector.reshape((size_row, size_col))\n",
        "\n",
        "    plt.subplot(2, 5, i+1)\n",
        "    plt.title(int(wrong_images_value[i]))\n",
        "    plt.imshow(im_matrix, cmap='Greys', interpolation='None')\n",
        "\n",
        "    frame   = plt.gca()\n",
        "    frame.axes.get_xaxis().set_visible(False)\n",
        "    frame.axes.get_yaxis().set_visible(False)\n",
        "\n",
        "plt.show()\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "**correctly classified testing images**\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "\n",
            "**misclassified testing images**\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}