Untitled9_final.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Untitled9.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyOVHOWld8Nx6+ei6P//IdCD",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "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/Untitled9_final.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": "59db1ccf-f1ea-4bd4-91d1-44e4774a9803",
        "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": 0,
      "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": "138d46dc-6604-4fec-b6c3-5d11b4e5963f",
        "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": 0,
      "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=(6000, 197, 785))\n",
        "v_tmp = np.ones(shape=(6000, 50, 197))\n",
        "u_tmp = np.ones(shape=(6000, 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=(6000, 785, 1))\n",
        "\n",
        "y__list = np.ones(shape=(6000, 197, 1))\n",
        "z__list = np.ones(shape=(6000, 50, 1))\n",
        "h__list = np.ones(shape=(6000, 10, 1))\n",
        "\n",
        "y_list = np.ones(shape=(6000, 197, 1))\n",
        "z_list = np.ones(shape=(6000, 50, 1))\n",
        "h_list = np.ones(shape=(6000, 10, 1))\n",
        "l_list = np.zeros(shape=(6000, 10, 1))\n",
        "\n",
        "y_list_tmp = np.ones(shape=(6000, 197, 1))\n",
        "z_list_tmp = np.ones(shape=(6000, 50, 1))\n",
        "h_list_tmp = np.ones(shape=(6000, 10, 1))\n",
        "y__list_tmp = np.ones(shape=(6000, 197, 1))\n",
        "z__list_tmp = np.ones(shape=(6000, 50, 1))\n",
        "h__list_tmp = np.ones(shape=(6000, 10, 1))\n",
        "\n",
        "# w_tmp = np.ones(shape=(6000, 197, 785))\n",
        "# v_tmp = np.ones(shape=(6000, 50, 197))\n",
        "# u_tmp = np.ones(shape=(6000, 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 6000개에 대해서\n",
        "for i in range(0, 6000):\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": "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": "95283b8d-651f-4a97-898f-53076ca71ae2",
        "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 = 500\n",
        "l_r = 0.1\n",
        "\n",
        "cost_history = np.zeros(iteration)\n",
        "\n",
        "total_number_of_predictions = 6000\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 = (6000, 10))\n",
        "\n",
        "for i in range(0, 6000):\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",
        "for t in range(0, iteration):\n",
        "  number_of_correct_predictions = 0\n",
        "  \n",
        "  print(t, end=' ')\n",
        "\n",
        "  # cost_history[t] = objective_function(6000, l_list, h_list_tmp)\n",
        "  result = 0.0\n",
        "  for i in range(0, 6000):\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]))\n",
        "  result = result / 6000\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, 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, 6000):\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 / 6000 *l_r)\n",
        "  matrix_v = matrix_v - (result_v / 6000 *l_r)\n",
        "  matrix_u = matrix_u - (result_u / 6000 *l_r)\n",
        "\n",
        "\n",
        "####################################################################\n",
        "\n",
        "  for k in range(0, 6000):\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",
        "  t = t + 1"
      ],
      "execution_count": 236,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 "
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kFfy7dv5_YR8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import operator\n",
        "\n",
        "y_ = np.ones((197, 1))\n",
        "\n",
        "x_list = np.ones(shape=(4000, 785, 1))\n",
        "\n",
        "y__list = np.ones(shape=(4000, 197, 1))\n",
        "z__list = np.ones(shape=(4000, 50, 1))\n",
        "h__list = np.ones(shape=(4000, 10, 1))\n",
        "\n",
        "y_list = np.ones(shape=(4000, 197, 1))\n",
        "z_list = np.ones(shape=(4000, 50, 1))\n",
        "h_list = np.ones(shape=(4000, 10, 1))\n",
        "l_list = np.zeros(shape=(4000, 10, 1))\n",
        "\n",
        "y_list_tmp = np.ones(shape=(4000, 197, 1))\n",
        "z_list_tmp = np.ones(shape=(4000, 50, 1))\n",
        "h_list_tmp = np.ones(shape=(4000, 10, 1))\n",
        "y__list_tmp = np.ones(shape=(4000, 197, 1))\n",
        "z__list_tmp = np.ones(shape=(4000, 50, 1))\n",
        "h__list_tmp = np.ones(shape=(4000, 10, 1))\n",
        "\n",
        "# w_tmp = np.ones(shape=(4000, 197, 785))\n",
        "# v_tmp = np.ones(shape=(4000, 50, 197))\n",
        "# u_tmp = np.ones(shape=(4000, 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 6000개에 대해서\n",
        "for i in range(0, 4000):\n",
        "  # print(i)\n",
        "  label       = list_label[i + 6000]\n",
        "  bias   = [1]       #bias 1\n",
        "  im_vector = np.concatenate((bias, list_image[:, i + 6000]), 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": "nzuVCwhp-DLC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        },
        "outputId": "e5ce2a1d-97c2-4c83-a9d9-bc7c35ddbb92"
      },
      "source": [
        "#test data!\n",
        "\n",
        "cost_history2 = np.zeros(iteration)\n",
        "\n",
        "total_number_of_predictions2 = 4000\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 = (4000, 10))\n",
        "\n",
        "for i in range(6000, 10000):\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_group2[i - 6000][label] = 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(6000, l_list, h_list_tmp)\n",
        "  result = 0.0\n",
        "  for i in range(0, 4000):\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_group2[i][k]*math.log(h_list[i][k][0]) - (1-label_group2[i][k])*math.log(1-h_list[i][k][0]))\n",
        "  result = result / 4000\n",
        "  cost_history2[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, 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, 4000):\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 / 4000 *l_r)\n",
        "  matrix_v = matrix_v - (result_v / 4000 *l_r)\n",
        "  matrix_u = matrix_u - (result_u / 4000 *l_r)\n",
        "\n",
        "\n",
        "####################################################################\n",
        "\n",
        "  for k in range(0, 4000):\n",
        "  # print(i)\n",
        "    label       = list_label[k + 6000]\n",
        "    bias   = [1]       #bias 1\n",
        "    im_vector = np.concatenate((bias, list_image[:, k + 6000]), 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": 238,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 "
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "81J4ESnphg--",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "wrong_images_index = np.ones(4000)\n",
        "right_images_index = np.ones(4000)\n",
        "\n",
        "wrong_images_value = np.ones(4000)\n",
        "right_images_value = np.ones(4000)\n",
        "\n",
        "wrong_images_num = 0\n",
        "right_images_num = 0\n",
        "\n",
        "for k in range(0, 4000):\n",
        "  # print(i)\n",
        "    label       = list_label[k + 6000]\n",
        "    bias   = [1]       #bias 1\n",
        "    im_vector = np.concatenate((bias, list_image[:, k + 6000]), 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_list[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 + 6000)\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 + 6000)\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",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "outputId": "d9fa3513-6c05-4d86-f34d-c7d3f02a450e"
      },
      "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": 245,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\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",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "outputId": "2165964a-7295-41aa-bdda-769fab622638"
      },
      "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)\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": 241,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\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",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "outputId": "427f02a9-937e-43ce-a28f-b806700c42ed"
      },
      "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": 242,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "final training accuracy using the training data : 89.83333333333333%\n",
            "final training accuracy using the testing data : 97.0%\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",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 527
        },
        "outputId": "4fb421c3-2f95-46bc-ccba-c7faf755a223"
      },
      "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()"
      ],
      "execution_count": 243,
      "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"
          }
        }
      ]
    }
  ]
}