MNIST_Week2_3.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "MNIST_Week2_3",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/pseudope/Parrot_5th_Level1/blob/main/MNIST_Week2_3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qWz40uYohBIN",
        "outputId": "2993e96d-8585-451c-8f13-93c40a33bef2"
      },
      "source": [
        "#!/usr/bin/env python3\n",
        "!pip install einops\n",
        "#!pip install tensorflow --user\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import tensorflow as tf\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from tensorflow.data import Dataset\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from tensorflow.keras.optimizers import Adam, RMSprop\n",
        "from tensorflow.keras.layers import Input, Dense, Conv2D, Dropout, Flatten, Activation, MaxPooling2D, BatchNormalization\n",
        "\n",
        "from einops import rearrange\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting einops\n",
            "  Downloading einops-0.3.2-py3-none-any.whl (25 kB)\n",
            "Installing collected packages: einops\n",
            "Successfully installed einops-0.3.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S3WNN5P4hBIO",
        "outputId": "ba2be3f1-a807-4578-d001-618d255d8b19"
      },
      "source": [
        "(X_train, y_train), (X_test, y_test) = tf.keras.datasets.mnist.load_data()\n",
        "\n",
        "print(\"X_train shape: {}\".format(X_train.shape))\n",
        "print(\"X_test shape: {}\".format(X_test.shape))"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz\n",
            "11493376/11490434 [==============================] - 0s 0us/step\n",
            "11501568/11490434 [==============================] - 0s 0us/step\n",
            "X_train shape: (60000, 28, 28)\n",
            "X_test shape: (10000, 28, 28)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 657
        },
        "id": "KB832lechBIP",
        "outputId": "0b426b14-e112-4963-f973-25b78e67a9e3"
      },
      "source": [
        "fig = plt.figure(figsize=(9, 9))\n",
        "\n",
        "for i in range(9):\n",
        "    plt.subplot(3, 3, 1 + i)\n",
        "    plt.tight_layout()\n",
        "    plt.imshow(X_train[i], cmap=\"gray\", interpolation=\"none\")\n",
        "    target = y_train[i]\n",
        "    plt.title(\"Ground Truth: {}\".format(int(target)))\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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mT54czNNm591yyy3B3N1TtwG0Zu+99w7mabNN0xxzzDHB/A9/+EPFYwI6gpdeeqnRQ6i7bt26BfPBgwcH81NOOSWYH3bYYRVt9+qrrw7mS5curWg9HQ174AAAACJDAwcAABAZGjgAAIDI0MABAABEhgYOAAAgMsxC7UD+/ve/B/PTTz89mN91112p6zr11FMryjfZZJNgfvfddwfz+fPnp24bWOvGG28M5mYWzNNmlRZxtmmnTuG/r5uamuo8EtTCFltsUdP177XXXsE8rfbSrq297bbbBvP1118/mJ988smpY0p7T69cuTKYT506NZivWrUqmHfpEm5pXn755dQxxYw9cAAAAJGhgQMAAIgMDRwAAEBkaOAAAAAiQwMHAAAQGRo4AACAyLR6GhEzGyNpiKRF7r5nko2QdKakxclil7n7o7UaZNE9/PDDwXzWrFmpz0k7fcMhhxwSzK+99tpgvsMOOwTza665JpjPmzcvdUxFl+daGjJkSDDv169fMHf3YD5p0qTMxhS7tNOFpH3vXn311VoOp0PpiLWUdiqMtJ/XL3/5y2B+2WWXZTKevn37BvO004isWbMmmH/00UfBfMaMGcF8zJgxqWOaNm1aME87TdDChQuD+dy5c4P5RhttFMxnzpyZOqaYtWUP3FhJgwP5z9y9X/IV3S8coAHGiloCsjBW1BIKrtUGzt2nSHqvDmMBco1aArJBLQHtOwbuHDP7s5mNMbPN0xYys+FmNs3MwvtOAVBLQDaoJRRGtQ3cbZJ2ktRP0nxJP01b0N1Hu3t/d+9f5baAPKOWgGxQSyiUqho4d1/o7h+7e5Ok2yXtl+2wgGKgloBsUEsomqouZm9mvdx97dXMj5M0Pbshoa2mT0//tp944onB/Kijjgrmd911VzA/66yzgnmfPn2C+aGHHpo6JvxfeamltNlfaRe8XrRoUTC///77MxtTR7PBBhsE8xEjRlS0nqeffjqY/8d//EelQ8qVRtfS2WefHcznzJkTzA888MBaDkdvvfVWMJ8wYUIwf/3114P5iy++mNmYKjV8+PBgvuWWWwbzN998s5bD6XDachqReyUNlNTDzOZKulLSQDPrJ8klzZYU/i0P4BPUEpANagloQwPn7kMD8Z01GAuQa9QSkA1qCeBKDAAAANGhgQMAAIgMDRwAAEBkqpqFio5v6dKlwfyee+4J5nfccUcw79Il/BYZMGBAMB84cGAwf+aZZ4I5imnVqlXBfP78+cE8JmmzTS+//PJgfvHFFwfztOs9/vSn4dObffjhh20YHertxz/+caOHEK20a3enefDBB2s0ko6JPXAAAACRoYEDAACIDA0cAABAZGjgAAAAIkMDBwAAEBlmoUasb9++qY997WtfC+b77rtvME+bbZpmxowZwXzKlCkVrQfFNGnSpEYPod369esXzNNmlX7jG98I5hMnTgzmJ5xwQnUDAwrq4YcfbvQQ6oo9cAAAAJGhgQMAAIgMDRwAAEBkaOAAAAAiQwMHAAAQGWahdiC77rprMD/nnHOC+fHHH5+6rq222iqTMX388cfBPO2alU1NTZlsF3Exs4ryY489Npiff/75mY0pK9///veD+f/7f/8vmHfv3j2Yjx8/PpgPGzasuoEBKDT2wAEAAESGBg4AACAyNHAAAACRoYEDAACIDA0cAABAZFqdhWpm20m6W1JPSS5ptLuPMrMtJN0vqbek2ZJOdPf3azfU+KTNBB06dGgwT5tt2rt376yGlGratGnB/JprrgnmebiWZb3luZbcvaI8rTZuuummYD5mzJhg/u677wbz/fffP5ifeuqpwXyvvfYK5pK07bbbBvO33normD/++OPB/NZbb03dBiqT51pC69Jmt++yyy7B/MUXX6zlcBqmLXvg1ki60N13l7S/pO+Z2e6SLpX0lLv3kfRUch9AOmoJyAa1hMJrtYFz9/nu/kpye7mk1yVtI+kYSeOSxcZJCp/YCYAkagnICrUEVHgiXzPrLWlvSVMl9XT3tWdzXaDSruzQc4ZLGl79EIH8oZaAbFBLKKo2T2Iws66SHpR0gbsvK3/MSwe6BA92cffR7t7f3fu3a6RATlBLQDaoJRRZmxo4M1tPpSIZ7+4PJfFCM+uVPN5L0qLaDBHID2oJyAa1hKJryyxUk3SnpNfd/cayhyZJOk3SyOTfiTUZYQfSs2dwb7x23333YH7zzTcH889//vOZjSnN1KlTg/lPfvKTYD5xYvjHx7VNs0Mtfapz587B/Oyzzw7mJ5xwQjBftmxZMO/Tp091Awt4/vnng/nkyZOD+RVXXJHZthFGLRVb2uz2Tp2KdWa0thwD92VJp0r6i5m9mmSXqVQgD5jZGZLmSDqxNkMEcoNaArJBLaHwWm3g3P1ZSeGTrkiHZDscIL+oJSAb1BLAlRgAAACiQwMHAAAQGRo4AACAyFR0It+82WKLLYL5r371q2Der1+/YP65z30uszGFpM2C++lPf5r6nLTrMa5cuTKTMQHlXnjhhWD+0ksvBfN99923ovWnXTs1bWZ4mrRrp953332pzzn//PMr2gaAxjjggAOC+dixY+s7kDphDxwAAEBkaOAAAAAiQwMHAAAQGRo4AACAyNDAAQAARCZXs1C/9KUvBfOLL744mO+3337BfJtttslsTCEfffRRML/pppuC+bXXXhvMV6xYkdmYgPaYO3duMD/++OOD+VlnnRXML7/88kzGM2rUqGB+2223BfM33ngjk+0CqL3SpXDBHjgAAIDI0MABAABEhgYOAAAgMjRwAAAAkaGBAwAAiEyuZqEed9xxFeWVmjFjRjD/7W9/G8zXrFkTzNOuYbp06dLqBgZ0UPPnzw/mI0aMqCgHUDyPPfZYMP/6179e55F0TOyBAwAAiAwNHAAAQGRo4AAAACJDAwcAABAZGjgAAIDImLu3vIDZdpLultRTkksa7e6jzGyEpDMlLU4WvczdH21lXS1vDGi8l929fy1WTC2hSNy9ZhespJZQJGm11JYGrpekXu7+ipltKullScdKOlHSh+5+Q1sHQaEgArVs4KglFEaNGzhqCYWRVkutngfO3edLmp/cXm5mr0vaJtvhAflHLQHZoJaACo+BM7PekvaWNDWJzjGzP5vZGDPbPOU5w81smplNa9dIgRyhloBsUEsoqlY/Qv1kQbOukv4g6Rp3f8jMekpaotLxB1ertDv7262sg13V6Ohq9hHqWtQSiqCWH6GuRS2hCNJqqU174MxsPUkPShrv7g8lK1zo7h+7e5Ok2yXtl9VggbyiloBsUEsoulYbODMzSXdKet3dbyzLe5Utdpyk6dkPD8gPagnIBrUEtG0W6kGS/ijpL5KakvgySUMl9VNpV/VsSWclB5a2tC52VaOjq+UsVGoJhVHjWajUEgqj6tOIZIlCQQRqfgxcFqgldHT1OAYuC9QSOrp2HQMHAACAjoMGDgAAIDI0cAAAAJGhgQMAAIgMDRwAAEBkaOAAAAAiQwMHAAAQGRo4AACAyNDAAQAARKZLnbe3RNKc5HaP5H5R8HrjsEOjB9BG1FJxxPh6Y6kjiVri9XZsqbVU10tprbNhs2kxXLIoK7xe1ErRvte8XtRK0b7XvN648REqAABAZGjgAAAAItPIBm50A7fdCLxe1ErRvte8XtRK0b7XvN6INewYOAAAAFSHj1ABAAAiQwMHAAAQmbo3cGY22Mz+amZvmNml9d5+PZjZGDNbZGbTy7ItzOxJM5uV/Lt5I8eYJTPbzswmm9kMM3vNzM5P8ty+5o6AWsrf+4paagxqKV/vq6LUUV0bODPrLOkWSUdI2l3SUDPbvZ5jqJOxkgY3yy6V9JS795H0VHI/L9ZIutDdd5e0v6TvJT/XPL/mhqKWcvu+opbqjFrK5fuqEHVU7z1w+0l6w93fdPfVku6TdEydx1Bz7j5F0nvN4mMkjUtuj5N0bF0HVUPuPt/dX0luL5f0uqRtlOPX3AFQSyW5el9RSw1BLZXk5n1VlDqqdwO3jaS3y+7PTbIi6Onu85PbCyT1bORgasXMekvaW9JUFeQ1Nwi1VJLb9xW1VDfUUkku31d5riMmMTSAl87dkrvzt5hZV0kPSrrA3ZeVP5bX14zGyuv7ilpCveXxfZX3Oqp3AzdP0nZl97dNsiJYaGa9JCn5d1GDx5MpM1tPpUIZ7+4PJXGuX3ODUUvK5/uKWqo7akn5e18VoY7q3cC9JKmPme1oZutLOknSpDqPoVEmSTotuX2apIkNHEumzMwk3SnpdXe/seyh3L7mDoBaKsnV+4paaghqqSQ376ui1FHdr8RgZkdK+rmkzpLGuPs1dR1AHZjZvZIGSuohaaGkKyVNkPSApO0lzZF0ors3P6A0SmZ2kKQ/SvqLpKYkvkylYw5y+Zo7Amopf+8raqkxqKV8va+KUkdcSgsAACAyTGIAAACIDA0cAABAZGjgAAAAIkMDBwAAEBkaOAAAgMjQwAEAAESGBg4AACAyNHAAAACRoYEDAACIDA0cAABAZGjgAAAAIkMDBwAAEBkauAiYWW8zczPr0oBtzzazQfXeLlAL1BKQDWqp8WjgEmZ2kplNNbMVZrYouX22mVmjx9YSM/uw7KvJzFaW3T+5wnWNNbMfZTi2XmY2yczeSQq9d1brRsdFLdWklszM/tPM3jKzZWZ2n5l1y2r96JiopZrU0lfN7FkzW2pmC8zsDjPbNKv11xMNnCQzu1DSKEk/kbSVpJ6S/k3SlyWtn/KcznUbYAvcvevaL0lvSTqqLBu/drlG/JUkqUnS7ySd0IBtowGopZoZJulUlb6PW0vaSNIvGjAO1Am1VDPdJf1IpTraTdI2Kn2P4+Puhf5S6Ye5QtIJrSw3VtJtkh5Nlh+k0g//GUlLJb0m6eiy5Z+R9J2y+6dLerbsvqtUjLOS598iyZLHOku6QdISSW9K+l6yfJdWxjhb0qDk9kBJcyVdImmBpHuaj6FsHDtLGi7pn5JWS/pQ0iNl67xI0p8lfSDpfkkbVvg97pJsp3ejf9581e6LWqpdLUn6L0kXl90/UNI/JG3c6J87X9l/UUu1/71Utq3jJf2l0T/zar7YAycdIGkDSRPbsOw3JV0jaVNJUyU9IukJSZ+VdK6k8Wa2awXbHiJpX0l9JZ0o6fAkPzN5bG9J/SV9rYJ1lttK0haSdlCpEFK5+2hJ4yVd76W/ko4qe/hESYMl7ZiM9fS1DyS7oQ+qcnzIF2pJNa0la3Z7A0l9KngNiAe1pLr9XhqgUqMbHRo4qYekJe6+Zm1gZs8nb4CVZjagbNmJ7v6cuzdJ6iepq6SR7r7a3Z+W9FtJQyvY9kh3X+rub0manKxTKr0xf+7ub7v7e5Kuq/K1NUm60t1XufvKKtchSTe5+zvJWB4pG6fcfTN3f7Yd60Z+UEutq7aWfifpO8mB491V2oMhSRu3YyzouKil1rX795KZHSrpNElXtGMcDUMDJ70rqUf5Z/HufqC7b5Y8Vv49ervs9taS3k6KZq05Kn2e3lYLym5/pFLhfbLuZuutxmJ3/0eVzy2XNk6gHLXUumpraYyke1X6COw1lX6xSqWPo5A/1FLr2vV7ycz2l/QbSV9z979lMJ66o4GTXpC0StIxbVjWy26/I2k7Myv/Hm4vaV5ye4XW/et4qwrGNF/Sds3WWw1vdn+dMZlZ8zE1Xx6oBLWUvny7uHuTu1/p7r3dfVuVmrh5+vR7hHyhltKXbzcz21vSJEnfdvensl5/vRS+gXP3pZKuknSrmX3NzDY1s05m1k/SJi08dapKXf8PzGw9Mxso6ShJ9yWPvyrpeDPb2Mx2lnRGBcN6QNJ5ZratmW0u6dIKX1aaP0naw8z6mdmGkkY0e3yhpM9ltC1JUrKdDZK7GyT3kUPU0joyrSUz28LMdkpOJ7K7pBsl/bDZnhbkBLW0jqxraU+VDkk4190fyWq9jVD4Bk6S3P16Sf8u6QcqvVkWSvqVSseZPJ/ynNUqFcYRKs3KuVXSMHefmSzyM5VmziyUNE6lAzHb6nZJj6v0xn5F0kOVvaKwZDfxDyX9XqVZRs2PEbhT0u7JcRYT2rLO5Lw+/9LCIitVmj0kSTOT+8gpaukTWddSD3060/AxSWOSA7yRU9TSJ7KupQslbSnpzoNXGjAAACAASURBVLJz00U5iWHt9GAAAABEgj1wAAAAkaGBAwAAiAwNHAAAQGRo4AAAACLTrgvJmtlglS6221nSHe4+spXlmTGBjm6Ju29Z741SS8gbd7fWl8oetYS8SaulqmehmllnSX+TdKhKZwN/SdJQd5/RwnMoFHR0L7t7/3pukFpCHjWigaOWkEdptdSej1D3k/SGu7+ZnHvmPrXtrNEA1kUtAdmgllAY7WngttG610Wbq8D11sxsuJlNM7Np7dgWkGfUEpANagmF0a5j4NoiOVv4aIld1UB7UEtANqgl5EF79sDN07oXtt1WXFgZqAa1BGSDWkJhtKeBe0lSHzPb0czWl3SSpEnZDAsoFGoJyAa1hMKo+iNUd19jZueodHHbzipdXDnKC8ICjUQtAdmgllAkdb2YPccaIAJ1P41INagldHSNOg9cpagldHS1OI0IAAAAGoAGDgAAIDI0cAAAAJGhgQMAAIgMDRwAAEBkaOAAAAAiQwMHAAAQGRo4AACAyNDAAQAARIYGDgAAIDI0cAAAAJGhgQMAAIgMDRwAAEBkaOAAAAAiQwMHAAAQGRo4AACAyNDAAQAARIYGDgAAIDI0cAAAAJGhgQMAAIhMl0YPAB3b5ZdfHsyvuuqqYN6pU/hvgoEDBwbzP/zhD1WNCwAQl0033TSYd+3aNZh/9atfDeZbbrllML/xxhuD+apVq9owuvi0q4Ezs9mSlkv6WNIad++fxaCAoqGWgGxQSyiKLPbAfcXdl2SwHqDoqCUgG9QSco9j4AAAACLT3gbOJT1hZi+b2fDQAmY23Mymmdm0dm4LyDNqCcgGtYRCaO9HqAe5+zwz+6ykJ81sprtPKV/A3UdLGi1JZubt3B6QV9QSkA1qCYXQrgbO3ecl/y4ys4cl7SdpSsvPQkd0+umnB/NLLrkkmDc1NVW0fnf+j2wJtQRkg1qqj969ewfztN8ZknTAAQcE8z333DOLIalXr17B/Lzzzstk/R1N1R+hmtkmZrbp2tuSDpM0PauBAUVBLQHZoJZQJO3ZA9dT0sNmtnY9v3H332UyKqBYqCUgG9QSCqPqBs7d35S0V4ZjAQqJWgKyQS2hSDiNCAAAQGRo4AAAACLDtVAhSdphhx2C+YYbbljnkQDt96UvfSmYn3LKKcH84IMPTl3XHnvsUdG2L7roomD+zjvvBPODDjoomP/6178O5lOnTq1oPEBbfP7znw/mF1xwQTA/+eSTg/lGG22Uuo3k2MT/4+233w7my5cvD+a77bZbMD/xxBOD+a233hrMZ86cGcxjwR44AACAyNDAAQAARIYGDgAAIDI0cAAAAJGhgQMAAIgMs1ALZtCgQcH83HPPrWg9abN3hgwZEswXLlxY0fqBtvjGN74RzEeNGhXMe/ToEczTZsdJ0jPPPBPMt9xyy2D+k5/8JHVdlWw7bf0nnXRSRetHMXXv3j2Y//jHPw7mabW06aabZjamWbNmBfPDDz88mK+33nrBPO33T1p9p+WxYw8cAABAZGjgAAAAIkMDBwAAEBkaOAAAgMjQwAEAAESGWag5lXZ9xbvuuiuYp81YSpM2027OnDkVrQco16VL+L+k/v37B/Pbb789mG+88cbBfMqUKcH86quvTh3Ts88+G8w32GCDYP7AAw8E88MOOyx1GyHTpk2raHmg3HHHHRfMv/Od79R0u3//+99THzv00EODedq1UHfeeedMxpRX7IEDAACIDA0cAABAZGjgAAAAIkMDBwAAEBkaOAAAgMgwCzWnTjvttGC+9dZbV7SetOtA3n333ZUOCWjVKaecEszvuOOOitbz5JNPBvO06z0uW7asovW3tK5KZ5vOnTs3mI8bN67iMQFrff3rX89kPbNnzw7mL730UjC/5JJLUteVNts0zW677VbR8kXT6h44MxtjZovMbHpZtoWZPWlms5J/N6/tMIH4UUtANqgloG0foY6VNLhZdqmkp9y9j6SnkvsAWjZW1BKQhbGillBwrTZw7j5F0nvN4mMkrd2/P07SsRmPC8gdagnIBrUEVH8MXE93n5/cXiCpZ9qCZjZc0vAqtwPkHbUEZINaQqG0exKDu7uZeQuPj5Y0WpJaWg4oOmoJyAa1hCKotoFbaGa93H2+mfWStCjLQaFtevTokfrYt7/97WDe1NQUzJcuXRrMf/SjH1U+MFSikLWUdu3Ryy67LJi7h3/H3nrrrcH88ssvD+bVzDZN85//+Z+ZrOe8884L5osXL85k/QVSyFpKc+aZZwbz4cPDOx6feOKJYP7GG28E80WLav/t7dkzdScqVP154CZJWnueitMkTcxmOEDhUEtANqglFEpbTiNyr6QXJO1qZnPN7AxJIyUdamazJA1K7gNoAbUEZINaAtrwEaq7D0156JCMxwLkGrUEZINaAriUFgAAQHRo4AAAACLDtVAj0Lt372D+4IMPZraNX/ziF8F88uTJmW0DxXPFFVcE87TZpqtXrw7mjz/+eDBPu+7iypUr2zC6T2244Yapj6Vd23T77bcP5mYWzNNmdE+cyLH2yN4777wTzEeMGFHfgbTDAQcc0OghdGjsgQMAAIgMDRwAAEBkaOAAAAAiQwMHAAAQGRo4AACAyDALNQKDBw8O5n379q14XU899VQwHzVqVMXrAtbabLPNgvnZZ58dzNOubZo22/TYY4+tbmDN7LzzzsF8/Pjxqc/ZZ599KtrGf/3XfwXz66+/vqL1AB1Z2jV8N9lkk8y28YUvfKGi5Z9//vlg/sILL2QxnA6HPXAAAACRoYEDAACIDA0cAABAZGjgAAAAIkMDBwAAEBlmoXYgaTPtRo4cWfG6nn322WB+2mmnBfMPPvig4m0Aa62//vrBvEePHhWtJ21m22c/+9lg/q1vfSuYH3300cF8zz33DOZdu3ZNHVPajNm0/Ne//nUwX7FiReo2gHrZeOONg/nuu+8ezK+88spgfuSRR1a87U6dwvuMmpqaKlpP2nVe0/4/+PjjjytafyzYAwcAABAZGjgAAIDI0MABAABEhgYOAAAgMjRwAAAAkaGBAwAAiEyrpxExszGShkha5O57JtkISWdKWpwsdpm7P1qrQeZN7969g/mDDz6Y2TbefPPNYL5w4cLMtoHK5LmWVq9eHcwXL14czLfccstg/r//+7/BPO2UHZVKO/3AsmXLUp/Tq1evYL5kyZJg/sgjj1Q+MFQkz7VUqfXWWy+Y77333sE87fdM2vt85cqVwTytllq6cPzgwYODedqpTdJ06RJuXY4//vhgPmrUqGCe9v9WLNqyB26spNB3/Wfu3i/5yn2RABkYK2oJyMJYUUsouFYbOHefIum9OowFyDVqCcgGtQS07xi4c8zsz2Y2xsw2T1vIzIab2TQzm9aObQF5Ri0B2aCWUBjVNnC3SdpJUj9J8yX9NG1Bdx/t7v3dvX+V2wLyjFoCskEtoVCqauDcfaG7f+zuTZJul7RftsMCioFaArJBLaFoqrqYvZn1cvf5yd3jJE3Pbkj5d8kllwTzSi/o25KRI0dmti7UTl5qaenSpcH82GOPDea//e1vg/kWW2wRzP/+978H84kTJwbzsWPHBvP33gsfNnXfffcFcyl9dl5Lz0H95aWW0qy//vrBPG1m50MPPVTR+q+66qpg/vTTTwfz5557Lpin1XBL69pzzz1bGd260maxX3fddcH8rbfeCuYTJkwI5qtWrapoPI3SltOI3CtpoKQeZjZX0pWSBppZP0kuabaks2o4RiAXqCUgG9QS0IYGzt2HBuI7azAWINeoJSAb1BLAlRgAAACiQwMHAAAQGRo4AACAyFQ1CxVt069fv2B+2GGHZbL+tBl4kvTXv/41k20A7TF16tRgnjaLrNYGDBgQzA8++ODU56TNDk+73jDQHmnXNk2bJXrxxRdXtP7HHnssmP/iF78I5mkzzNNq+NFH069g9oUvfCGYp12T9Prrrw/mabNWjznmmGA+fvz4YP773/8+mP/4xz8O5u+//34wT/Pqq69WtHyl2AMHAAAQGRo4AACAyNDAAQAARIYGDgAAIDI0cAAAAJFhFmoNPfHEE8F88803r2g9L774YjA//fTTKx0SUGgbbbRRMG/pOsTuHsy5Firao3PnzsH86quvDuYXXXRRMF+xYkUwv/TSS4N52vs2bbZp//79g/nNN98czPfee+9gLkmzZs0K5t/97neD+eTJk4N5t27dgvmBBx4YzE8++eRgfvTRRwfzJ598Mpinefvtt4P5jjvuWNF6KsUeOAAAgMjQwAEAAESGBg4AACAyNHAAAACRoYEDAACIjKXNsKrJxszqt7EO4OOPPw7mLc14Cxk2bFgwv/feeyseE1r1sruHp111IEWrpVpLq1UpfRZqr169gvnixYszGVPs3N0aPYa2aFQtpc28TLsm6UcffRTMhw8fHszTzoLwpS99KZh/61vfCuZHHHFEME+b0f3DH/4wmEvSXXfdFczTZnHW2tChQ4P5N7/5zYrW8/3vfz+Yv/HGGxWPKSStltgDBwAAEBkaOAAAgMjQwAEAAESGBg4AACAyNHAAAACRaXUWqpltJ+luST0luaTR7j7KzLaQdL+k3pJmSzrR3d9vZV25nDmXNrMm7Vqllc5C/dznPhfM58yZU9F60CY1m4VKLTXe4YcfHswfffTR1OcwC7U6tZyFmodamj9/fjDfcsstg/mqVauC+cyZM4P5JptsEsx33nnnNoyudSNGjAjm1113XepzWprtjXTtmYW6RtKF7r67pP0lfc/Mdpd0qaSn3L2PpKeS+wDSUUtANqglFF6rDZy7z3f3V5LbyyW9LmkbScdIGpcsNk7SsbUaJJAH1BKQDWoJkLpUsrCZ9Za0t6Spknq6+9p9wAtU2pUdes5wSeEzDQIFRS0B2aCWUFRtnsRgZl0lPSjpAndfVv6Ylw4SCR5H4O6j3b1/DGe3B+qBWgKyQS2hyNrUwJnZeioVyXh3fyiJF5pZr+TxXpIW1WaIQH5QS0A2qCUUXasfoZqZSbpT0uvufmPZQ5MknSZpZPLvxJqMsAPp169fMB80aFAwT5ttunr16mB+yy23BPOFCxe2YXTo6Kilxkub0Y245KGWFixYEMzTZqFusMEGwXyvvfaqaLtpM66nTJkSzCdMmBDMZ8+eHcyZaVo/bTkG7suSTpX0FzN7NckuU6lAHjCzMyTNkXRibYYI5Aa1BGSDWkLhtdrAufuzktLO53NItsMB8otaArJBLQFciQEAACA6NHAAAACRoYEDAACITEUn8i26zTbbLJhvtdVWFa1n3rx5wfyiiy6qeEwA2u6Pf/xjMO/UKf1v2UqvXQy0xYABA4L5sceGLx7xxS9+MZgvWhQ+U8qYMWOC+fvvhy8Nm3Z2BHRc7IEDAACIDA0cAABAZGjgAAAAIkMDBwAAEBkaOAAAgMgwCxVAYUyfPj2Yz5o1K/U5addP3WmnnYL54sWLKx8YCmf58uXB/J577qkoR3GxBw4AACAyNHAAAACRoYEDAACIDA0cAABAZGjgAAAAIsMs1ArMnDkzmD///PPB/KCDDqrlcABk5Nprr0197I477gjm11xzTTA/99xzg/mMGTMqHxgApGAPHAAAQGRo4AAAACJDAwcAABAZGjgAAIDI0MABAABExty95QXMtpN0t6SeklzSaHcfZWYjJJ0pae2F/y5z90dbWVfLGwMa72V371+LFVNLHVe3bt1SH3vggQeC+aBBg4L5Qw89FMy/9a1vBfMVK1a0Mro4ubvVat3UEookrZbachqRNZIudPdXzGxTSS+b2ZPJYz9z9xuyGiSQc9QSkA1qCYXXagPn7vMlzU9uLzez1yVtU+uBAXlDLQHZoJaACo+BM7PekvaWNDWJzjGzP5vZGDPbPOU5w81smplNa9dIgRyhloBsUEsoqjY3cGbWVdKDki5w92WSbpO0k6R+Kv0l9NPQ89x9tLv3r9VxRUBsqCUgG9QSiqxNDZyZradSkYx394ckyd0XuvvH7t4k6XZJ+9VumEA+UEtANqglFF1bZqGapHGS3nP3C8ryXslxCDKz70v6kruf1Mq6mO2Djq6Ws1CppQilzVBNuxbqd7/73WDet2/fYJ7Xa6TWeBYqtYTCaM8s1C9LOlXSX8zs1SS7TNJQM+un0hTu2ZLOymCcQJ5RS0A2qCUUXltmoT4rKdT9tXhuHQDropaAbFBLAFdiAAAAiA4NHAAAQGRo4AAAACLT6izUTDfGbB90fDWbhZolagkdXS1noWaJWkJHl1ZL7IEDAACIDA0cAABAZGjgAAAAIkMDBwAAEBkaOAAAgMi05VJaWVoiaU5yu0dyvyh4vXHYodEDaCNqqThifL2x1JFELfF6O7bUWqrraUTW2bDZtBhO15AVXi9qpWjfa14vaqVo32teb9z4CBUAACAyNHAAAACRaWQDN7qB224EXi9qpWjfa14vaqVo32teb8QadgwcAAAAqsNHqAAAAJGhgQMAAIhM3Rs4MxtsZn81szfM7NJ6b78ezGyMmS0ys+ll2RZm9qSZzUr+3byRY8ySmW1nZpPNbIaZvWZm5yd5bl9zR0At5e99RS01BrWUr/dVUeqorg2cmXWWdIukIyTtLmmome1ezzHUyVhJg5tll0p6yt37SHoquZ8XayRd6O67S9pf0veSn2ueX3NDUUu5fV9RS3VGLeXyfVWIOqr3Hrj9JL3h7m+6+2pJ90k6ps5jqDl3nyLpvWbxMZLGJbfHSTq2roOqIXef7+6vJLeXS3pd0jbK8WvuAKilkly9r6ilhqCWSnLzvipKHdW7gdtG0ttl9+cmWRH0dPf5ye0Fkno2cjC1Yma9Je0taaoK8pobhFoqye37ilqqG2qpJJfvqzzXEZMYGsBL527J3flbzKyrpAclXeDuy8ofy+trRmPl9X1FLaHe8vi+ynsd1buBmydpu7L72yZZESw0s16SlPy7qMHjyZSZradSoYx394eSONevucGoJeXzfUUt1R21pPy9r4pQR/Vu4F6S1MfMdjSz9SWdJGlSncfQKJMknZbcPk3SxAaOJVNmZpLulPS6u99Y9lBuX3MHQC2V5Op9RS01BLVUkpv3VVHqqO5XYjCzIyX9XFJnSWPc/Zq6DqAOzOxeSQMl9ZC0UNKVkiZIekDS9pLmSDrR3ZsfUBolMztI0h8l/UVSUxJfptIxB7l8zR0BtZS/9xW11BjUUr7eV0WpIy6lBQAAEBkmMQAAAESGBg4AACAyNHAAAACRoYEDAACIDA0cAABAZGjgAAAAIkMDBwAAEBkaOAAAgMjQwAEAAESGBg4AACAyNHAAAACRoYEDAACIDA1cBMyst5m5mXVpwLZnm9mgem8XqAVqCcgGtdR4NHAJMzvJzKaa2QozW5TcPtvMrNFja4mZfVj21WRmK8vun1zhusaa2Y8yHFsvM5tkZu8khd47q3Wj46KWalJLXzGzv5jZUjN718weNrNtslo/OiZqid9LLaGBk2RmF0oaJeknkraS1FPSv0n6sqT1U57TuW4DbIG7d137JektSUeVZePXLteIv5IkNUn6naQTGrBtNAC1VDMzJB3u7ptJ2lrSLEm3NWAcqBNqqWby83vJ3Qv9Jam7pBWSTmhlubEq/Yf5aLL8IEm7SXpG0lJJr0k6umz5ZyR9p+z+6ZKeLbvvKhXjrOT5t0iy5LHOkm6QtETSm5K+lyzfpZUxzpY0KLk9UNJcSZdIWiDpnuZjKBvHzpKGS/qnpNWSPpT0SNk6L5L0Z0kfSLpf0oYVfo+7JNvp3eifN1+1+6KWal9LyXo2kHSdpBmN/pnzVZsvaonfS235Yg+cdIBK/yFObMOy35R0jaRNJU2V9IikJyR9VtK5ksab2a4VbHuIpH0l9ZV0oqTDk/zM5LG9JfWX9LUK1lluK0lbSNpBpUJI5e6jJY2XdL2X/ko6quzhEyUNlrRjMtbT1z6QfKRzUJXjQ75QS6pdLZnZ9ma2VNJKlX55XV/dS0EEqCXxe6k1NHBSD0lL3H3N2sDMnk/eACvNbEDZshPd/Tl3b5LUT1JXSSPdfbW7Py3pt5KGVrDtke6+1N3fkjQ5WadUemP+3N3fdvf3VPpruxpNkq5091XuvrLKdUjSTe7+TjKWR8rGKXffzN2fbce6kR/UUuuqriV3f8tLH6H2kHS5pJntGAc6NmqpdYX/vUQDJ70rqUf5Z/HufmDyH+W7Wvd79HbZ7a0lvZ0UzVpzJFVyYPGCstsfqVR4n6y72Xqrsdjd/1Hlc8uljRMoRy21rt21lPzCGidpYoOOIULtUUutK/zvJRo46QVJqyQd04Zlvez2O5K2M7Py7+H2kuYlt1dI2rjssa0qGNN8Sds1W281vNn9dcZkZs3H1Hx5oBLUUvryWeui0kdk3Wq8HTQGtZS+PBKFb+DcfamkqyTdamZfM7NNzayTmfWTtEkLT52qUtf/AzNbz8wGSjpK0n3J469KOt7MNjaznSWdUcGwHpB0nplta2abS7q0wpeV5k+S9jCzfma2oaQRzR5fKOlzGW1LkpRsZ4Pk7gbJfeQQtbSOTGvJzI43s12T7+eWkm6U9D/J3jjkDLW0Dn4vpSh8AydJ7n69pH+X9AOV3iwLJf1KpZkyz6c8Z7VKhXGESrNybpU0zN3XHpfyM5VmzixU6eOO8aH1pLhd0uMqvbFfkfRQZa8ozN3/JumHkn6v0iyj5scI3Clp9+Q4iwltWWdyXp9/aWGRlSrNHpJKx+y055gHdHDU0ieyrqVtVDr1wXJJf1HpOKLjqhk74kAtfYLfSynWTg8GAABAJNgDBwAAEBkaOAAAgMjQwAEAAESGBg4AACAy7ToJpJkNVuliu50l3eHuI1tZnhkT6OiWuPuW9d4otYS8cXdrxHapJeRNWi1VPQvVzDpL+pukQ1W6OO1Lkoa6+4wWnkOhoKN72d3713OD1BLyqBENHLWEPEqrpfZ8hLqfpDfc/c3k3DP3qW1njQawLmoJyAa1hMJoTwO3jda9LtpcBa63ZmbDzWyamU1rx7aAPKOWgGxQSyiMml8I2d1HSxotsasaaA9qCcgGtYQ8aM8euHla98K22+rTC+YCaDtqCcgGtYTCaE8D95KkPma2o5mtL+kkSZOyGRZQKNQSkA1qCYVR9Ueo7r7GzM5R6eK2nSWNcffXMhsZUBDUEpANaglFUteL2XOsASJQ99OIVINaQkfXqPPAVYpaQkdXi9OIAAAAoAFo4AAAACJDAwcAABAZGjgAAIDI0MABAABEhgYOAAAgMjRwAAAAkaGBAwAAiAwNHAAAQGRo4AAAACJDAwcAABAZGjgAAIDI0MABAABEhgYOAAAgMjRwAAAAkenS6AHgU6NGjQrm5513XjCfPn166rqGDBkSzOfMmVP5wAAAQIfCHjgAAIDI0MABAABEhgYOAAAgMjRwAAAAkaGBAwAAiEy7ZqGa2WxJyyV9LGmNu/fPYlB517t372B+yimnBPOmpqZgvttuu6Vu4/Of/3wwZxZqx0QtVWeXXXYJ5uutt14wHzBgQDC/9dZbU7eRVn+1NnHixGB+0kknBfPVq1fXcjjRoJaylVZLBx54YDC/9tprU9f15S9/OZMxoSSL04h8xd2XZLAeoOioJSAb1BJyj49QAQAAItPeBs4lPWFmL5vZ8NACZjbczKaZ2bR2bgvIM2oJyAa1hEJo70eoB7n7PDP7rKQnzWymu08pX8DdR0saLUlm5u3cHpBX1BKQDWoJhdCuPXDuPi/5d5GkhyXtl8WggKKhloBsUEsoiqr3wJnZJpI6ufvy5PZhkn6Y2chybPHixcF8ypQpwfzoo4+u5XDQYNTSp/bYY49gfvrppwfzr3/968G8U6fw36Zbb711MG9ppql7Y3bQpNX9L3/5y2B+wQUXBPNly5ZlNqaOjlrKXvfu3YP55MmTg/mCBQtS17XVVltV/Byka89HqD0lPWxma9fzG3f/XSajAoqFWgKyQS2hMKpu4Nz9TUl7ZTgWoJCoJSAb1BKKhNOIAAAARIYGDgAAIDI0cAAAAJHJ4lJaqNCKFSuCOdcpRdFdd911wfzII4+s80g6rmHDhgXzO++8M5g/99xztRwOsI60maYtPcYs1OqwBw4AACAyNHAAAACRoYEDAACIDA0cAABAZGjgAAAAIsMs1AbYbLPNgvlee3ECcRTbk08+GcwrnYW6aNGiYJ42UzPt2qlSy9dJDTnwwAOD+cEHH1zReoAYJZcxQx2wBw4AACAyNHAAAACRoYEDAACIDA0cAABAZGjgAAAAIsMs1AbYeOONg/n222+f2Tb23XffYD5z5sxgznVY0RHcdtttwXzChAkVreef//xnMK/HNRe7desWzKdPnx7Mt95664rWn/a9mDZtWkXrAWrB3VMf23DDDes4kvxjDxwAAEBkaOAAAAAiQwMHAAAQGRo4AACAyNDAAQAARKbVWahmNkbSEEmL3H3PJNtC0v2SekuaLelEd3+/dsPMl3feeSeYjx07NpiPGDGi4m2kPWfp0qXB/Oabb654G6gMtdS6NWvWBPO33367ziOp3uGHHx7MN99880zWP3fu3GC+atWqTNYfA2opTv379w/mL774Yp1Hkg9t2QM3VtLgZtmlkp5y9z6SnkruA2jZWFFLQBbGilpCwbXawLn7FEnvNYuPkTQuuT1O0rEZjwvIHWoJyAa1BFR/It+e7j4/ub1AUs+0Bc1suKThVW4HyDtqCcgGtYRCafeVGNzdzSz11MvuPlrSaElqaTmg6KglIBvUEoqg2lmoC82slyQl/y7KbkhAoVBLQDaoJRRKtXvgJkk6TdLI5N+JmY2owK6++upgXs0sVESDWorUSSedFMzPPPPMYL7RRhtlst0rrrgik/XkELVUA2kzwz/44INg3r1799R17bTTTpmMCSWt7oEzs3slvSBpVzOba2ZnqFQgh5rZLEmDkvsAWkAtAdmgloA27IFz96EpDx2S8ViAXKOWKE7fGAAADGhJREFUgGxQSwBXYgAAAIgODRwAAEBkaOAAAAAi0+7zwKH2OnUK99lNTU11HgmQTyeffHLqY5deGr4i08477xzM11tvvUzG9Oqrrwbzf/7zn5msH2iLtOtn//GPfwzmQ4YMqeVwUIY9cAAAAJGhgQMAAIgMDRwAAEBkaOAAAAAiQwMHAAAQGWahRiBttqm713kkQG317t07mJ966qnBfNCgQZls96CDDkp9LKs6W7ZsWTBPm+X66KOPBvOVK1dmMh4AcWMPHAAAQGRo4AAAACJDAwcAABAZGjgAAIDI0MABAABEhlmoAOpuzz33DOaTJk0K5ttvv30th1MXadeOHD16dJ1HAjTGZz7zmUYPIVfYAwcAABAZGjgAAIDI0MABAABEhgYOAAAgMjRwAAAAkWm1gTOzMWa2yMyml2UjzGyemb2afB1Z22EC8aOWgGxQS0DbTiMyVtLNku5ulv/M3W/IfERAfo0VtdQiM6soz0qnTul/yzY1NWWyjSFDhgTzI444Ipg/9thjmWw3p8aKWorO0Ucf3egh5Eqre+DcfYqk9+owFiDXqCUgG9QS0L5j4M4xsz8nu7I3z2xEQPFQS0A2qCUURrUN3G2SdpLUT9J8ST9NW9DMhpvZNDObVuW2gDyjloBsUEsolKoaOHdf6O4fu3uTpNsl7dfCsqPdvb+79692kEBeUUtANqglFE1VDZyZ9Sq7e5yk6WnLAkhHLQHZoJZQNK3OQjWzeyUNlNTDzOZKulLSQDPrJ8klzZZ0Vg3HWHhpM+SqmR03YMCAYH7zzTdXvC5Uhlr61PTp4d+tAwcODOannHJKMH/88ceD+T/+8Y+qxlWJM844I5ife+65Nd920VFLjTd58uRgnjbbGtlrtYFz96GB+M4ajAXINWoJyAa1BHAlBgAAgOjQwAEAAESGBg4AACAyNHAAAACRMXev38bM6rexHPn444+DeZY/u759+wbzGTNmZLaNSLwcw7mhqKXG6969ezB/9913K1rPUUf9//buJkSK/Izj+O/Z3clcVBhZGMSY+Lag4iGKLIF4CAR1IzhjECR7CKMJGdFEFAJG9JBAXNhDEEWDMkGJAYms+DIKuciqiblIVJb4MsbdCRlMGDXLHtbgG8Ynh66QifnXdLdTXdX/qu/nMj1PT1c/NdYPH2rq37UqWI/9Xqju3tob2GaELL2aNWvWBOvHjx9Pfc3jx4+D9QULFgTrIyMjzTdWQmlZ4gwcAABAZBjgAAAAIsMABwAAEBkGOAAAgMgwwAEAAESm7q20ULyDBw8G6xs2ZHerv/7+/mB969atmb0HUCYrVqwougWgMM+fP2/6NWbhhcmdnZ0TbaeSOAMHAAAQGQY4AACAyDDAAQAARIYBDgAAIDIMcAAAAJFhFWoEbt++XXQLwLg6OjqC9eXLlwfr58+fD9bT7pVYpPXr1wfre/fuzbkToH0MDg4G6+P9fzVv3rxgPe3TDjZt2tR8YxXCGTgAAIDIMMABAABEhgEOAAAgMgxwAAAAkak7wJnZDDO7YGa3zOymmW1J6lPN7JyZfZx87Wp9u0C8yBKQDbIESObu4/+A2TRJ09z9mplNlnRV0mpJ6yR95u7vm9l2SV3u/uM62xr/zdCUO3fupD43Z86cprb12mvhWX7u3LnB+vDwcFPbj8hVd1/Sig2XIUtLly4N1nfu3BmsL1u2LFifNWtWsH737t1Xa6xBU6dODdZXrlyZ+pp9+/YF65MnT27qvdNW2Pb09ATrFy5caGr77cbdwze+zEAZslRWe/bsSX0ubUV3d3d3sP7kyZNMeopdWpbqnoFz91F3v5Y8fihpSNJ0Sb2SjiQ/dkS18ABIQZaAbJAloMlr4MxspqRFki5L6nb30eSpe5LCIzSA/0OWgGyQJVRVwx/ka2aTJJ2QtNXdPzf77xk9d/e009Bm1i+pf6KNAmVBloBskCVUWUNn4MysQ7WQHHX3k0n5fnIdwn+uR3gQeq27D7j7klZdVwTEhCwB2SBLqLpGVqGapEOShtx995inzkjqSx73SQrfVwOAJLIEZIUsAY39CfVrkr4j6bqZfZTUdkh6X9IHZvY9SSOS1ramRaS5efNm6nOzZ89ualsvXryYaDuoL/os7d+/P1hfuHBhU9vZtm1bsP7w4cOme2pG2qrYxYsXp76m3kr9l128eDFYP3DgQLAe+2rTgkSfpSpKy9KzZ89y7qQc6g5w7v4HSWnLwb+RbTtAeZElIBtkCeBODAAAANFhgAMAAIgMAxwAAEBkGOAAAAAi0/AH+aL9DAwMpD63atWqHDsBmrNx48aiW2jYgwfBjxLT2bNng/UtW7YE69zXEVU3ZcqUYL23tzdYP3XqVCvbiR5n4AAAACLDAAcAABAZBjgAAIDIMMABAABEhgEOAAAgMqxCjditW7dSnxsaGgrW58+f36p2UAHr1q0L1jdv3hys9/X1BeutNjw8HKw/evQoWL906VLqttJWe9+4caP5xoCSW7s2/fazT58+DdbT/r/C+DgDBwAAEBkGOAAAgMgwwAEAAESGAQ4AACAyDHAAAACRMXfP783M8nsz4NVcdfclRTdRT7tlqbOzM1hPW7W6a9euYL2rqytYP336dLB+7ty5YH1wcDBYv3fvXrCO7Lm7Fd1DI9otS7E7duxY6nNpn4LQ09MTrI+MjGTSU+zSssQZOAAAgMgwwAEAAESGAQ4AACAyDHAAAACRYYADAACITN1VqGY2Q9KvJXVLckkD7r7XzH4q6fuS/pH86A53/22dbbHaB+2uZatQyRKqpJWrUMkSqiQtS40McNMkTXP3a2Y2WdJVSaslrZX0T3f/eaNNEBREoJUDHFlCZbR4gCNLqIy0LL3RwAtHJY0mjx+a2ZCk6dm2B5QfWQKyQZaAJq+BM7OZkhZJupyUfmhmfzKzw2YW/AROM+s3sytmdmVCnQIlQpaAbJAlVFXDd2Iws0mSfifpPXc/aWbdkj5V7fqDn6l2Ovu7dbbBqWq0u5bfiYEsoQryuBMDWUIVTOhODGbWIemEpKPufjLZ4H13/5e7v5D0S0lvZ9UsUFZkCcgGWULV1R3gzMwkHZI05O67x9Snjfmxb0m6kX17QHmQJSAbZAlobBXqUkmXJF2X9CIp75D0rqSvqHaq+q+SNiQXlo63LU5Vo921chUqWUJltHgVKllCZbzyx4hkiaAgAi2/Bi4LZAntLo9r4LJAltDuJnQNHAAAANoHAxwAAEBkGOAAAAAiwwAHAAAQGQY4AACAyDDAAQAARIYBDgAAIDIMcAAAAJFhgAMAAIjMGzm/36eSRpLHbybfVwX7G4cvF91Ag8hSdcS4v7HkSCJL7G97S81SrrfS+p83NrsSwy2LssL+olWq9rtmf9EqVftds79x40+oAAAAkWGAAwAAiEyRA9xAge9dBPYXrVK13zX7i1ap2u+a/Y1YYdfAAQAA4NXwJ1QAAIDIMMABAABEJvcBzszeMbM/m9knZrY97/fPg5kdNrMHZnZjTG2qmZ0zs4+Tr11F9pglM5thZhfM7JaZ3TSzLUm9tPvcDshS+Y4rslQMslSu46oqOcp1gDOz1yX9QtI3JS2Q9K6ZLcizh5z8StI7L9W2S/rQ3d+S9GHyfVk8l/Qjd18g6auSfpD8u5Z5nwtFlkp7XJGlnJGlUh5XlchR3mfg3pb0ibv/xd2fSTomqTfnHlrO3X8v6bOXyr2SjiSPj0hanWtTLeTuo+5+LXn8UNKQpOkq8T63AbJUU6rjiiwVgizVlOa4qkqO8h7gpku6O+b7vyW1Kuh299Hk8T1J3UU20ypmNlPSIkmXVZF9LghZqintcUWWckOWakp5XJU5RyxiKIDXPruldJ/fYmaTJJ2QtNXdPx/7XFn3GcUq63FFlpC3Mh5XZc9R3gPc3yXNGPP9F5NaFdw3s2mSlHx9UHA/mTKzDtWCctTdTyblUu9zwciSynlckaXckSWV77iqQo7yHuD+KOktM5tlZl+Q9G1JZ3LuoShnJPUlj/skDRbYS6bMzCQdkjTk7rvHPFXafW4DZKmmVMcVWSoEWaopzXFVlRzlficGM1spaY+k1yUddvf3cm0gB2b2G0lfl/SmpPuSfiLptKQPJH1J0oikte7+8gWlUTKzpZIuSbou6UVS3qHaNQel3Od2QJbKd1yRpWKQpXIdV1XJEbfSAgAAiAyLGAAAACLDAAcAABAZBjgAAIDIMMABAABEhgEOAAAgMgxwAAAAkWGAAwAAiMy/AWIkiSnyD3PTAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 648x648 with 9 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nq1waxKDhBIP"
      },
      "source": [
        "target_size = 10   # 0 ~ 9, 총 9개\n",
        "\n",
        "### 주의: 흑백 데이터여도 channel 차원을 살려주어야 정상적으로 작동합니다\n",
        "X_train = rearrange(X_train, \"b h w -> b h w 1\")\n",
        "X_test = rearrange(X_test, \"b h w -> b h w 1\")\n",
        "\n",
        "X_train = X_train.astype(\"float32\") / (2 ** 8 - 1)   # 255로 scaling\n",
        "X_test = X_test.astype(\"float32\") / (2 ** 8 - 1)     # 255로 scaling\n",
        "\n",
        "y_train = to_categorical(y_train, target_size)       # 타겟을 정수로 변환\n",
        "y_test = to_categorical(y_test, target_size)         # 타겟을 정수로 변환"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7xA3xa64hBIP"
      },
      "source": [
        "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2)"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SyT8hqophBIQ",
        "outputId": "601bef28-ed57-4b8b-9080-ae3b9bace10a"
      },
      "source": [
        "print(X_train.shape)\n",
        "print(X_val.shape)\n",
        "print(X_test.shape)\n",
        "print(y_train.shape)\n",
        "print(y_val.shape)\n",
        "print(y_test.shape)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(48000, 28, 28, 1)\n",
            "(12000, 28, 28, 1)\n",
            "(10000, 28, 28, 1)\n",
            "(48000, 10)\n",
            "(12000, 10)\n",
            "(10000, 10)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "paFz-UgbR85m"
      },
      "source": [
        "Changes Made:\n",
        "*   1st conv layer kernel_initializer='he_normal'\n",
        "\n",
        "*   patience increased to 7\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "haTxYAXahBIQ",
        "outputId": "1c927f4c-9f3a-42ca-c2bf-18389ea0cfe2"
      },
      "source": [
        "inputs = Input(shape=(28, 28, 1))\n",
        "\n",
        "X = Conv2D(filters=128, kernel_size=(3, 3), padding=\"Same\", kernel_initializer='he_normal')(inputs)\n",
        "X = BatchNormalization()(X)\n",
        "X = Activation(\"relu\")(X)\n",
        "X = Conv2D(filters=128, kernel_size=(3, 3), padding=\"Same\", activation=\"relu\")(X)\n",
        "#X = Dropout(0.5)(X)\n",
        "X = MaxPooling2D(pool_size=(2, 2), strides=(2, 2))(X)\n",
        "\n",
        "X = Conv2D(filters=128, kernel_size=(3, 3), padding=\"Same\")(X)\n",
        "X = BatchNormalization()(X)\n",
        "X = Activation(\"relu\")(X)\n",
        "X = Conv2D(filters=64, kernel_size=(3, 3), padding=\"Same\", activation=\"relu\")(X)\n",
        "#X = Dropout(0.3)(X)\n",
        "X = MaxPooling2D(pool_size=(2, 2), strides=(2, 2))(X)\n",
        "\n",
        "X = Flatten()(X)\n",
        "X = Dense(256, activation=\"relu\")(X)\n",
        "X = BatchNormalization()(X)\n",
        "X = Dropout(0.5)(X)\n",
        "X = Dense(128, activation=\"relu\")(X)\n",
        "X = BatchNormalization()(X)\n",
        "X = Dropout(0.4)(X)\n",
        "X = Dense(64, activation=\"relu\")(X)\n",
        "X = BatchNormalization()(X)\n",
        "X = Dropout(0.2)(X)\n",
        "outputs = Dense(10, activation=\"softmax\")(X)\n",
        "\n",
        "model = Model(inputs=inputs, outputs=outputs)\n",
        "model.summary()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_1 (InputLayer)         [(None, 28, 28, 1)]       0         \n",
            "_________________________________________________________________\n",
            "conv2d (Conv2D)              (None, 28, 28, 128)       1280      \n",
            "_________________________________________________________________\n",
            "batch_normalization (BatchNo (None, 28, 28, 128)       512       \n",
            "_________________________________________________________________\n",
            "activation (Activation)      (None, 28, 28, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 28, 28, 128)       147584    \n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) (None, 14, 14, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 14, 14, 128)       147584    \n",
            "_________________________________________________________________\n",
            "batch_normalization_1 (Batch (None, 14, 14, 128)       512       \n",
            "_________________________________________________________________\n",
            "activation_1 (Activation)    (None, 14, 14, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 14, 14, 64)        73792     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 7, 7, 64)          0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 3136)              0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 256)               803072    \n",
            "_________________________________________________________________\n",
            "batch_normalization_2 (Batch (None, 256)               1024      \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 256)               0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 128)               32896     \n",
            "_________________________________________________________________\n",
            "batch_normalization_3 (Batch (None, 128)               512       \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 64)                8256      \n",
            "_________________________________________________________________\n",
            "batch_normalization_4 (Batch (None, 64)                256       \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 10)                650       \n",
            "=================================================================\n",
            "Total params: 1,217,930\n",
            "Trainable params: 1,216,522\n",
            "Non-trainable params: 1,408\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MqkQ72d1hBIS",
        "outputId": "4137a7fa-7118-4909-a2ec-4d4ea84529d8"
      },
      "source": [
        "def scheduler(epoch, lr):\n",
        "  if epoch < 8:\n",
        "    return lr\n",
        "  else:\n",
        "    return lr * tf.math.exp(-0.1)\n",
        "\n",
        "callback = [\n",
        "            tf.keras.callbacks.LearningRateScheduler(scheduler),\n",
        "            tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=7)\n",
        "]\n",
        "\n",
        "model.compile(optimizer='adam', loss=\"categorical_crossentropy\", metrics=[\"accuracy\"])\n",
        "history = model.fit(X_train, y_train, batch_size=256, epochs=20, callbacks = callback, validation_data=(X_val, y_val), verbose=1)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "188/188 [==============================] - 22s 111ms/step - loss: 0.0363 - accuracy: 0.9893 - val_loss: 0.2438 - val_accuracy: 0.9376\n",
            "Epoch 2/20\n",
            "188/188 [==============================] - 20s 108ms/step - loss: 0.0297 - accuracy: 0.9914 - val_loss: 0.0441 - val_accuracy: 0.9882\n",
            "Epoch 3/20\n",
            "188/188 [==============================] - 20s 108ms/step - loss: 0.0263 - accuracy: 0.9921 - val_loss: 0.0402 - val_accuracy: 0.9902\n",
            "Epoch 4/20\n",
            "188/188 [==============================] - 20s 108ms/step - loss: 0.0206 - accuracy: 0.9941 - val_loss: 0.0317 - val_accuracy: 0.9917\n",
            "Epoch 5/20\n",
            "188/188 [==============================] - 20s 108ms/step - loss: 0.0197 - accuracy: 0.9941 - val_loss: 0.0363 - val_accuracy: 0.9903\n",
            "Epoch 6/20\n",
            "188/188 [==============================] - 20s 107ms/step - loss: 0.0145 - accuracy: 0.9953 - val_loss: 0.0322 - val_accuracy: 0.9922\n",
            "Epoch 7/20\n",
            "188/188 [==============================] - 20s 107ms/step - loss: 0.0114 - accuracy: 0.9966 - val_loss: 0.0329 - val_accuracy: 0.9922\n",
            "Epoch 8/20\n",
            "188/188 [==============================] - 20s 107ms/step - loss: 0.0128 - accuracy: 0.9962 - val_loss: 0.0313 - val_accuracy: 0.9933\n",
            "Epoch 9/20\n",
            "188/188 [==============================] - 20s 107ms/step - loss: 0.0115 - accuracy: 0.9966 - val_loss: 0.0373 - val_accuracy: 0.9904\n",
            "Epoch 10/20\n",
            "188/188 [==============================] - 20s 107ms/step - loss: 0.0081 - accuracy: 0.9975 - val_loss: 0.0309 - val_accuracy: 0.9927\n",
            "Epoch 11/20\n",
            "188/188 [==============================] - 20s 107ms/step - loss: 0.0065 - accuracy: 0.9979 - val_loss: 0.0299 - val_accuracy: 0.9937\n",
            "Epoch 12/20\n",
            "188/188 [==============================] - 20s 107ms/step - loss: 0.0055 - accuracy: 0.9984 - val_loss: 0.0366 - val_accuracy: 0.9915\n",
            "Epoch 13/20\n",
            "188/188 [==============================] - 20s 107ms/step - loss: 0.0045 - accuracy: 0.9986 - val_loss: 0.0267 - val_accuracy: 0.9938\n",
            "Epoch 14/20\n",
            "188/188 [==============================] - 20s 107ms/step - loss: 0.0030 - accuracy: 0.9991 - val_loss: 0.0283 - val_accuracy: 0.9945\n",
            "Epoch 15/20\n",
            "188/188 [==============================] - 20s 107ms/step - loss: 0.0031 - accuracy: 0.9990 - val_loss: 0.0294 - val_accuracy: 0.9942\n",
            "Epoch 16/20\n",
            "188/188 [==============================] - 20s 107ms/step - loss: 0.0024 - accuracy: 0.9994 - val_loss: 0.0275 - val_accuracy: 0.9946\n",
            "Epoch 17/20\n",
            "188/188 [==============================] - 20s 107ms/step - loss: 0.0019 - accuracy: 0.9996 - val_loss: 0.0282 - val_accuracy: 0.9937\n",
            "Epoch 18/20\n",
            "188/188 [==============================] - 20s 108ms/step - loss: 0.0013 - accuracy: 0.9996 - val_loss: 0.0289 - val_accuracy: 0.9945\n",
            "Epoch 19/20\n",
            "188/188 [==============================] - 20s 108ms/step - loss: 0.0011 - accuracy: 0.9997 - val_loss: 0.0275 - val_accuracy: 0.9952\n",
            "Epoch 20/20\n",
            "188/188 [==============================] - 20s 106ms/step - loss: 9.3046e-04 - accuracy: 0.9998 - val_loss: 0.0280 - val_accuracy: 0.9946\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yPRsfabXhBIS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8f7de99a-78a7-4f02-e8fd-9f567ad8e92a"
      },
      "source": [
        "model.evaluate(X_test, y_test, batch_size=256)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "40/40 [==============================] - 1s 32ms/step - loss: 0.0207 - accuracy: 0.9954\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.02073347568511963, 0.9954000115394592]"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mZ49DBA3hBIT"
      },
      "source": [
        "def acc_loss_plot(hist):\n",
        "    \n",
        "    fit, loss_ax = plt.subplots()\n",
        "    acc_ax = loss_ax.twinx()\n",
        "    \n",
        "    loss_ax.plot(hist.history[\"loss\"], \"y\", label=\"train_loss\")\n",
        "    loss_ax.plot(hist.history[\"val_loss\"], \"r\", label=\"val_loss\")\n",
        "    loss_ax.set_xlabel(\"Epoch\")\n",
        "    loss_ax.set_ylabel(\"Loss\")\n",
        "    loss_ax.legend(loc = \"upper left\")\n",
        "    \n",
        "    acc_ax.plot(hist.history[\"accuracy\"], \"b\", label=\"train_acc\")\n",
        "    acc_ax.plot(hist.history[\"val_accuracy\"], \"g\", label=\"val_acc\")\n",
        "    acc_ax.set_ylabel(\"Accuracy\")\n",
        "    acc_ax.legend(loc = \"upper right\")\n",
        "    \n",
        "    plt.show()"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qVs8gpOZhBIT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "16ec621a-eccc-446c-972a-c541fae3ca06"
      },
      "source": [
        "acc_loss_plot(history)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kFD0TsyM9t24"
      },
      "source": [
        ""
      ],
      "execution_count": 15,
      "outputs": []
    }
  ]
}