Ex_02_Classification_challenge.ipynb

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        "<a href=\"https://colab.research.google.com/github/liumOazed/Neural_Network_With_Tensorflow/blob/main/Ex_02_Classification_challenge.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "## Task 1"
      ],
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        "id": "1JTP_LmLkLpB"
      }
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      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "h1LXgaBSeXNU"
      },
      "outputs": [],
      "source": [
        "from sklearn.datasets import make_moons\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Make 1000 examples\n",
        "n_samples= 1000\n",
        "\n",
        "X,y = make_moons(n_samples, noise=0.03, random_state=42) "
      ],
      "metadata": {
        "id": "6NbxUzEAfbeV"
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      "execution_count": null,
      "outputs": []
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      "source": [
        "X[0]"
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        "id": "HVQYzRwClkYU",
        "outputId": "f0146646-6795-44c5-aacd-6b1ee1bcecff"
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      "execution_count": null,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "array([-0.00933187,  0.39098105])"
            ]
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          "metadata": {},
          "execution_count": 17
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      "cell_type": "code",
      "source": [
        "y[0]"
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        "id": "GLWC1WhGllAM",
        "outputId": "c5f44e1b-6e47-4e0a-ebe4-c5e1a233919b"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "moons = pd.DataFrame({\"X0\":X[:,0], \"X1\": X[:, 1], \"label\":y})\n",
        "moons"
      ],
      "metadata": {
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        "outputId": "b698597a-7065-4ca4-c0fb-8a68c7d2e630"
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      "execution_count": null,
      "outputs": [
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              "           X0        X1  label\n",
              "0   -0.009332  0.390981      1\n",
              "1    0.954574 -0.473756      1\n",
              "2    0.918526 -0.425196      1\n",
              "3    0.412768 -0.376385      1\n",
              "4   -0.845320  0.528799      0\n",
              "..        ...       ...    ...\n",
              "995  0.793018  0.591402      0\n",
              "996 -0.138777  0.968000      0\n",
              "997  1.653915 -0.291798      1\n",
              "998 -0.934102  0.353582      0\n",
              "999  0.796836  0.604237      0\n",
              "\n",
              "[1000 rows x 3 columns]"
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      "cell_type": "code",
      "source": [
        "moons.label.value_counts()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZpkgExpensYo",
        "outputId": "3ff4de4a-322b-4828-cde1-63360cc9b6bb"
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        {
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          "data": {
            "text/plain": [
              "1    500\n",
              "0    500\n",
              "Name: label, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
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    {
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        "X[-1,1]"
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              "0.6042366167665801"
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          "metadata": {},
          "execution_count": 21
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize the plot\n",
        "import matplotlib.pyplot as plt\n",
        "plt.scatter(X[:,0], X[:,1], c=y, cmap=plt.cm.RdYlBu)\n"
      ],
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              "<matplotlib.collections.PathCollection at 0x7f5e1f6163d0>"
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          "execution_count": 22
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
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            "image/png": 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JBq+jasfy13XW+rGMWf8jfV9/nKRrxtDp/htJGncRwmTsh6kQGsfmLTf0mIpSFiNu2jYDDhX7/jDQ/6xtngJ+E0LcDUQBI8o6kBBiMjAZICkpyYDQqp85MoJRq79jzX3PcejbX8tsnftDAuaoyFITe6TbQ5OR/lWkVCpnjoyg7S3jaHvLOAAyN24n9acFho6u0l0uzEXVPhUllKrqun8iMFVK2RwYA3wmhCh1binle1LKZCllcnx83RkjHtG4EdKrl14WMACNBvQmcfR5ZyYFCbMJU4SdPq89pio1VoG4Hp04d+oLYGArX3p1ml50nmHHU5TyGNHCTwVaFPu+edFjxd0CXAQgpVwhhLADjYCw6LjM2bmPQ9/9Fnw1TE3Q/ck7iWgSz7G5v3P4h3mYY6JofcMV1O/a3phglUo1v2w4QjMFfbV2WmTLZpjsNkOOpSgVMSLhrwbaCyFaU5joJwDXnrXNQWA4MFUI0RmwA2kGnLvGO/LLYpZedbffyV5YLSBlYckFUVjZ8rzpbxCZmABA4oWDSbxwcChCViqRs20PmsWM1x38PAphtRDZNJ6FY26j5fjRtLz2EkxWY0cGKcppQSd8KaVHCHEXMAcwAR9JKbcIIZ4BUqSUM4EHgfeFEPdT2A19o6ypdZkN5HW5WDbuPr8XN9FsVup1bceIJV9waud+pMdDXO8uZdasV6qetUE9w9YuEED6ivUAHF+wkg2Pv0qnB2+mzaQrsDWMM+QcinKaqocfAu6cXPZ+OoPDM+YFVB3x3I+fVy29Gm7uedeSvmpDiaJ3WkRhCWa9nHWIfaVZLWg2K8MXfELD5O5BHUsJP6oefhVKX7WeGc3PZ+0DLwSU7C2x0bS58SqV7Gu4875/iwa9u2CKtGOpF43JbqPbP+8IOtlD4VrGnlN5/D7xAWpqg0ypnVQfgUGklKx/5CW2vfyR37NoT9PsNtrcdJXBkSmhYI9vwKhV35KzYy8Fx9KJ69UZa70Ytr30Ae7MHEPOkbfvMOmrNhB/rvFVO5XwpFr4BnBl5TC752Vse+nDgJM9mqBR/570/NcDxganhFRsxzY0HtIPa70YADrffxOmsyuRaoGtYyu9Oosvvh1HekawYSoKoBJ+UKSUSF1n9d+eImfbnoCPo1ktnPvR84xY9JkqW1zLdXnsdtrdfg2mCDvmmChMEXY6//0WJnq3MWr1dLQI/4ZfevLz2fn2FyGKVgk36qZtALxOF+v/8TJ73vsWz+nCaIH8HoWgxVUj6fbEncT16GRskEq1cufmkX/4GFEtEs9MkpNS8qXm/985tlMbxmz+GU2VVVZ8UNFNW9WHH4Dl1/2dI7MW4w3yBt2FK76mUf+eBkWl1CSW6CjqdWpb4jEhBBHNG1Nw+Lhfx8rZuY8lV9zJkJn/PbMal6IEQnXp+Cnv0FFSf14UdLIXFjMRTRMMikqpLZLfftL/nXTJiYWrOLFolfEBKWFFJXw/ndq1H5MBNdKFSePI7MUGRKTUJsfnr0AEMOTWk5fP0d9+D0FESjhRCd9PsR3b+D1ztizCZFL1U8JM9vY97Hn/G6QrgDLWmkbWph3k7NhrfGBK2FAJ30+RzRrTYuyFmIpmVQZKenWaXzbMoKiU2uDY3OUBj9pF1zny61J+6X0FG598w9C4lPChEn4ABkx9gU4P3IiwBHjP26Qx6KtXscbVMzYwpUazxEajBbMQjlfHW+Bk28sfkrFuq3GBKWFDJXwfpP2+hgWjbuHH1sNYePFkdn/wLS3GjiKmfSu/j2WKjuTSHXNocflw4wNVarQWV44sLB0YJG+Bk4PfzA7+QErYUcMyK5E6axHLxt17ZlRO3v5Ujs5ejGa1oLv8K4+bOPp8zv/xHUwWSyhCVWo4S2w0ye88ycpJjwR3ICmRxhTrVMKMauFXQEpJyt3PljkE059kr9ls9H37CS6Y/b5K9mEutkMrzDFRQR+nxdiRBkSjhBvVwq9A3v5U8g4Esd66EJjrRdNryn10uOMvxgWm1FpxvToHfxCLmUb91IQ9xX+GtPCFEBcJIXYIIXYLIR4tZ5vxQoitQogtQohpRpw3VAqOpzNv6HXM7DgqoIUukt99mvH56xmbtoKr01fR4c7rQhClUhuZ7DaS33oCzRr4lZ5ZDedVAhR0C18IYQLeBkYCh4HVQoiZUsqtxbZpD/wDGCSlzBRC1NgpplJK5vQfR36ALXvNbqPJ0H6YIyIwR6hCaEppbW64As1qZvl1D0EA6+J6cvNDEJUSDoxo4fcDdksp90opXcBXwOVnbXMb8LaUMhNASlkjFy+Xus4ff30i4GQPgK6fKZalKOVpNeESmgwfGNjkOylxnswkY91W9nz4LccWrEAatOSiUrcZ0YffDDhU7PvDQP+ztukAIIT4ncJ1b5+SUv569oGEEJOByQBJSUkGhOY7KSVLr76bwz/MD+o4useDOyfXoKiUumzIj++w8ak32fvhdDz5BUivju70bRbur+dcjeNYOmiisChb0wRGLP6ciCbxIY5aqc2qapSOGWgPDAUmUrigef2zN5JSvielTJZSJsfHV+0L9/j8FRz7bXngC5icIdj2n48MiUmp20x2G71f+Dtj01ZyTd4Ghi/6DEy+vSXz9h3GW+DAm1eAJzefU7sP8kvvK1j36Mvk7j1U+QGUsGREwk8FWhT7vnnRY8UdBmZKKd1Syn3ATgo/AGqM1FmL8OQZ0Deq62Rv3R38cZSwE39uL9pPvgZzVAD3fnQdx7F0tr/yMbO6X8KROUuND1Cp9YxI+KuB9kKI1kIIKzABmHnWNj9Q2LpHCNGIwi6eGlUFylI/JvBSCcWZNBqpNUiVACW//SQDv3wFa6O4gPaXbg/efAcrbngY3es1ODqltgs64UspPcBdwBxgG/CNlHKLEOIZIcRlRZvNAU4KIbYCC4GHpJQngz23kdpcfwWaOfiEb7bb6fTATQZEpIQjIQTNLx1GmxuvDKoB4s13BLXsplI3GdKHL6WcLaXsIKVsK6V8ruixJ6SUM4u+llLKB6SUXaSU3aWUXxlxXiNFt2lB/w+mYIq0IwJM/JrdxkVrviOqRaLB0SnhpuM9NwRVPlt6dbU+slKKKq1QTKtrL2Xksi8RWgDLyGka5/3wFrEd2xgfmBJ2olokMnLZNIQvpTi0s97GmiCmfUui27Qoe3slbKmEX8yp3QdY+8DzfhdFA0BKTq7YYHxQdVhWtoO09Dxk0COj6qa4Hp3QrD5cbZ49Bl+XdHv6ntAEpYRU6pEcPv18HVM/Xcv+A5mGHz+sa+lIKdn17pdsefYdCo6mQRALRJvsNuwJDQyMrnbZuu0Eb72zku0700lsEsPkW89hyHmtSm2XmVXA59PW8+PM7WRlF2AyaTRNjGXK08Pp2qVx1Qdew0W3bk725l1+77f2vudIumJECCJSQkFKyYv/Wcr07zYjJWia4H8frOaWG/ty683Jhp1H1NTWVXJyskxJSQnpOba/NpUNj7+GN78g6GOZoyO5/MBCbA1KTS+o87ZuO8Gtf/0Bh8Nz5jG73cw/HjqfSy/pdOaxXbtPcsvtM8jNLT25KCLCzMzvrqNhQzVLubij835n4cibA9q3+7P30uFvE7E1DGzEjxI6J0/ms3jpfnRdMuS8Vrzy+u/MmVt6OLfNZuaLqVfTpo3vjUkhxBopZZmfEmHbpSN1nU3PvB1UsjdFRWCOjsTepBFDf3k/LJM9wBtvrSiR7AEcDg+vvrkcXf+zQfH0lAVlJnuAggIPL/5nSUjjrI0SRwyi21N3l+6n98GW597l506jyTtw9rQYpTr9PHs7l1z5GS+/uoxXXv+di6/4jLnzyx5R5XZ7mLfQuNFWYZvw3afygipCpdmsDPnhHUYs/pwrU5eSMNi4y67aZtuO9DIfz893k5VVcObrHTvL3u60hYv2sXVbjSyzVK16PHkXV6Yu4Zx3nyamQyuf99MdTpwZ2ax79OXQBaf4JS0tj+eeX4zT6cXh8OBwePB49BINo+KkBEHgXc1nC9uEb4mJwhIdePdBVFJTGg8fQIM+XREBtL7qksYJZS/ooWmCmJjCoYUHDmaV+6I+TdclX3690fD46oKIJvG0v30CHe65Ab/e/7rO0V/UrNuaYsGivX79/UwmwfBhxo38C99MJQRx53QPfHeziZN/qOQEcPut52C3l7z/b7ebGTe2GxaLifkL9nDz5BmVlimSEk6k5YUw0trP1qCe3+vimoNo2CjG8nh0v8p13Xh9H9q0Nm4wSFgmfCkly//yd47PW+7zPsJqKVHYKmfbHuYPm0TW5p2hCLFWGT6sLfffO5DYGBs2mwm7zczYK7ty9x3n4vZ4efq5hTidnsoPBPTt0zTE0dZu7qwcMJt83l6YTbT/24QQRqT4Y8j5rXzazmzWePzRIdzx17MLDwcnLIdlHl+wksM/zodKuhhK8HpLLVahO5xsee5dBn35isER1j7jrurGlZd1ITOroCjxF760tm47gfTj93zhiHahCrFOiOvdBU3T0PGtTo70eOny8G0hjkqpyMGDWeSccgKSF15aittd8d8uMTGab6dNJDLS+PWvwzLh73jzM79H58gyViaSuk7m+m1GhVXrmc0aMdE25s7fw6HD2XRo35BWLePw+pjwmybG0DIpPEc6+Sq6fUt0j39F0TwFDqy+zNhVDHXiRC73/X02e/dlous6Hk/l7wO73cwD9w4KSbKHMEz4usfD0TnLjDmYplGvewdjjlUHHDmSw6Rbvyc/301BgZvISAsNG0T4NJPWajUx5ekRiCAmv4WDQ9PnYLJb8eY7fNtBCJwnMrDGxoQ2MKUEKSV33fcz+/Zn4vX61uCJirJwz10DGH5B25DFFXYJP2PNFsNG1ZjsVro9/jdDjlUXPP3cQjIzC86MxsnPd5Of71uZCrfbS1SUaoVWxpWR7VfpD2Exkbc/Fd3joV6n0CUSpaRdu06SeiTH52QPEB1lw2Y1U1DgJiIiNO+FsLtpKzQRWHG0s0Q0TeCCXz8krmenyjcOAy6Xl7XrjlQ69LI8UsITTy8wOKq6p8mIAWg2q8/bS4/O0qvv5te+V/FLnyspOKrmOVSFjMwCTH42LI+fyOXFl5cw5vLP2Lff+Do6EIYJP65P16CHqZkiIxj87esknBe+k61CYeeudHbvrlHLJNQ4Dc/pQbNLh2HytfSxruPOzsWb7yBr4w4WXfLX0AaoANClc3ylN2fLUlDgISfHwf89OS8EUYVhwtdMJno+/2DAhdI0u434gb1oNKC3wZHVblariXP6NkcL4upJSrjuxm9ZvGSfgZHVPYO+eJnWk67wu9yC9HrJ2baHnB01arG5Oik21s6tNyeXmp/iCylh956TZGYGX+PrbIYkfCHERUKIHUKI3UKIRyvYbqwQQgohqq1pnLFmM6tu+kfpxcorylMCLHGxRLdtQfen7mLIrPfUzcUyPPF/Q2nUKJLISAsmkyAy0oLV6vuYcQCXW+epKQvxeEqPilIKCU3j5KqNpcsi+0CzmHGmh6a7QCnplpv68sKUC+nWNQGrxc8P5xDVtAw64QshTMDbwGigCzBRCNGljO1igHuBVcGeMxhLxt5d9hMV/IJ7PvcA4zJWc9nueXR9ZDImq+99qOGkSeMYPvrflXTv2hi73Ux0tBVvAOuqOp2ekNQCrytcmdlkbdoR0L66x0tcr84GR6SUZcfOdKa8sIht29Nwuf37cI6JthIXZ/yKZUaM0ukH7JZS7gUQQnwFXA5sPWu7Z4EXgYcMOGdAXKdyyT9wxK99NKuFLo9ODlFEdUtWtoMbbvmO7GwHXq8kLy+AhWQAr0cnKkp9qJZn++ufIgNo3ZsiI+j90kOYo1SphVBzOj3c9rcZ5OYG9h5o3To0Ja2N6NJpBhwq9v3hosfOEEL0AVpIKWdVdCAhxGQhRIoQIiUtLc2A0Eo6/P3cCp/XrCWHQpnsNtreNl513/jo2+mbyM11+TUUrSytW8eR2ESNGy9P6sz5pWZ9V0RYzTS7fDhDZ/2PDnf8JYSRKactWbY/4GRvs5kYcn5rgyMqFPKbtkIIDXgFeLCybaWU70kpk6WUyfHx8YbHUtmqVrrHC6LwxqxmtxF/XjK9/11tFyS1TsqaI7hc/nfhFBcRYeaNVy42KKK6yTs5H4QAACAASURBVOrngiZxPTvT+vrLadC3a4giUs62YcPRgPbTNDCZNLKzHSHp1jQi4acCxVdLbl702GkxQDdgkRBiP3AuMLM6btzGD+5T8Rh8XQdZWCNHmDRaXnsJZl+Hvym0aFEPkynwq6Fz+zVn4W83k5AQbWBUdU+n+2/EFOX76zJj9SaWXX0P3ycMZNt/PgphZMppp8uC+yIiwkz9+nYiI80IIXA43Hzy2TomXv8N074ydp1sIxL+aqC9EKK1EMIKTABmnn5SSpktpWwkpWwlpWwFrAQuk1KGdv3CMsR2butzIS9vXgF/TP4nh2ZU3A2k/Onaa3pgsZQclWPy4RVmMgneev0S3nnzMqy+LNod5pqNGRJQQTSvw8nGJ97gyC+LQxCVUty5/Vv4NGo2KakeS+bfytuvX4ruBa9XouuF/zudXt54eyXHj+caFlfQCV9K6QHuAuYA24BvpJRbhBDPCCEuC/b4RnKknvBrvJN0e/h94gO4MrNDGFXd0aZNA15+8SLi46Ow2UxYLBrn9k/ijtv7oVXQlSZlYeE1xXdtbx6LKcLu937e/AK2/efjEESkFNejexO6dkmoNOnHxtgwmTTmL9yDy126hLgmBEuW7TcsLkPeZVLK2VLKDlLKtlLK54oee0JKObOMbYdWZeteSsmRX5ew9Op7WDbxAb/3150udrz5WQgiq5sGnpvErz/dwPffXMvc2Tfy5muXMOyCNugVfNDquuT+B2eTle1jQTAFW6O4gCcPnliawryh13HkV7WGcKgIIXj3rcuZML5HhZMR9+zJYPuONEwmrezBIaKwT98odb5Zte7BF1h69T0c+m4OOVtLrwrvi9SfFhocVd0mhCCxSQyxsYUt0Bk/nj1CtzQJzJ0X2N8nHJnsNjrec73vJRaKkS43JxavZunYe9j532khiE4BiIiw8Pf7B/PNtGvKvYJ1OD2sXHWIUSPalbmN1CVDfVw0xRd1OuGf2n2AXf/9Em9ecFOU7Y0bGRRReDq7X78sLpeX7BzVwvdHz+fup8ujtwVcG8qbX8D6R17G63QZHJlSXJvWDbjlpr5lXpBJCe/87w/yC9zccXt/rFYTNqsJu82EzWbin48NpUED4+ZN1OmEf2ze8oAve08zRUXQ6f4bjQkoTF1zdeVrB9usJs7t16LS7ZQ/CU2j+z/vZFzOWpqOGRLwcU7t2m9cUEqZrrisc7m3Dz0endfeWM71f+nF999cy733DOSB+wbz84zrGTO6o6Fx1OmEb6kXg/Bj/c+y9HzufpoMH2BQROGpceNoxo+teAx4y5b16doloYoiqluEEDQZMSCg17rucmNPaBiCqJTiKlsEaOv2NHJyHCQ2iWbCuO5cfVVXGjY0fkZ0nU74zS8bFtQsWS3CRqd7JxkYUfjxenWWLNvPgsUVV2jcviOd72ZsqaKo6p5Tuw8iK1v68Kz3gmazEn9eXwqOpqG7A5sVqpQtL8/Ft99t5omn5zP1s7WcTM+rcHuHw8OI0VMZOWYqs3/dGbK46vSgZ3NUJEN/eZ/Fl/4Vd34B0uFfX6XNzxmNSkluj5d77pvF2vVHfaoN/srry7n8ss5YgrwqC0cN+/Vg36c/4MnNL3cbU3Qk3lNFiUcTaDYracvWMve8axEmE/3ee4aW40ZXUcR11+HD2dx463fk5btxOr0I4dtocI9HJyOjgCnPL6JerI1BA1saHludbuEDxA/sw1XHfmfY7PdJvGSoX/t2fvjW0AQVJub8tov1G474vBCE7pUcPpwT4qjqpqTxowuHalbwYXkm2QPoEk9OLrrDiedUHu6sHFZOepTM9duqINq6ye328q8XF3PFuGlkZDpwOgtf9/6WOnY4PNz399m8+sZyChzGXnnV+YQPoFks1O/Zibw9h3xbHs6kET/kHDrccW3og6vDvvhyI06X70W+vLpO/fr+TyZSwBxhZ9Qf02l741WYYwMrTaE7XWx/41ODIwsfL/1nGT/P2hHwMp/Feb2Sr7/dxN33/Vxp/78/6nTC193uM7+stfc/z6ld+9ErGYJmqR/DkB/eYcSCT9FMqmshUFu3nWCXn8sVNk6IJq6+ql0UKHt8A/q/P4Vhcz7EHB3A+HxdJ/9gYEW/wl1BgZuZs7bjcJaeLRsol8vL1m1pbN5i3DrEdbIP/8ivS1hzzxRO7T6IJSaKDvfewL7PZ/q0QtA57z5Ds0suqIIo67apn67zu2XSIqleiKIJLzEdWuHJ939OgynCTtPR54cgorovM8sR1PKeFdm1+yTduzU25Fh1LuGnLV/L0rF34y16wbtzctn24vs+JXthsxDXvUOoQwwLhw5n+9132a9vs8o3Uipla1AfW8M4nGkZPu+j2azYEhrQ7rbxIYys7oqPjwyqHpTZJPCUsY6EpglaNDeuIVTnunQ2PfP2mWR/mu7y7caHtV4ssZ3bhiKssNOrZ6Lfb4D/vreaaV9vMLTPMlzZGlSeJITFTEzH1tTr2o7Of7+Z0WtnYAmw/z/cWcwm7rqjf0CLltttJh64bxAxMdYSI2fNZo3GCdH07dPUsDjrXMLP2bYn4H3dWTnseG2qccGEsUnX9/J74WaPR+f1N1Yw7auNIYoqfOT6sJSnZrfS64UHuXjzLHpOuR9bg/pVEFndNX5sd6Y8PYJOHRsRHe3bEp1ms0af3s24+qpufPzeVXTr2hiTSWA2awwelMQH715haFeRqKmtqeTkZJmS4n9RzcWX/ZXUnxcFvOy7PTGeq44sC2hfpaSbJ3/P+g3H/N4vOtrKorm3hKxPNBx8aemK9PhwA1HTqN+jI/3efZpG/XuGPrAw8d33m3nuxcqrkXbs2Ihpn4wrMUHU4fBgMgmfalCVRQixRkpZ5gJTda6F3/2puwOqE36aKz3LwGjC29GjgS3ckJvrwuEwbrRDOKrf08caLLpO1vptLBg+idy9hyrfXqlUVraDF172rdHYr2+zUtUA7HZzwMm+MoYkfCHERUKIHUKI3UKIR8t4/gEhxFYhxEYhxHwhhPFTyIo06NOVYXM/omG/HmhWS2E9HYvv/Wr1e3UKVWhhp3WrwLoIhChc9k0JnKfAv1E6XqeLHWoMviFm/LAFr4+LzE+fsSUka9eWJ+iEL4QwAW8Do4EuwEQhRJezNlsHJEspewDTgX8He96KxA/sw6hV3zLBuZkrU5cQ3aYFJrsPa0xqGn3+U+rzSglQoGOSoyKtQdVAUiBv72G/tpceL5kbd4QomvDhcnn59PP1Pm9fUODhmeeqbr0NI1r4/YDdUsq9UkoX8BVwefENpJQLpZSni3yspHCh8yphjorkotXT6fbEnUQ0r3gsq2a1kHLXM7iyT1VRdHVXdraDrVvT/N5PCKhXz8Y30zdRUKAKegUqspn/lUcb9O0WgkjCy/QZW8jN869m1/oNx3wuPxIsIxJ+M6B459/hosfKcwvwS1lPCCEmCyFShBApaWn+J4vyWGKi6fqP22l7a8VjjHWHk5zt+1j/6MuGnTtc5Zxyopn8b6VLCalHTvHamyu45rqvOXXKGYLo6r7Oj0z2e5/oNlXWDquzvv5mI94yxtPXFFV601YIcR2QDLxU1vNSyveklMlSyuT4+HjDz98wuRuikvUhdZeLA1/OMvzc4aZpYkxAY5JPczg8nDiRy2df+H55rPwpI2WT3/s4jvtXCkMpyeHwcDg1sOJ/VfUhYUTCTwWKL1XUvOixEoQQI4DHgcuklFXebHNm5bD9jU+RPtxMkT7MylUqZjJpPPbw+djt5jOTSfwdZely6cxfWHEdfaVsB774ya/tzdGR1O/WPkTRhIc1a1MDGl2TEB+FzVY1dbuMGAqxGmgvhGhNYaKfAJQoMymE6A38D7hISmlcJSAfrXv0Zbb9+wOfxuYLi5kWYy+sgqjqvhHD29GkSQyffr6e1CM5tGkdx5y5u/B4fG/NBDNdPZx53f7dMLfGxdL88uEhiiZMiMIJUy6X7/3xmiZ46IHBVTZIIeiEL6X0CCHuAuYAJuAjKeUWIcQzQIqUciaFXTjRwLdFP9hBKeVlwZ7bF2vu/xc7XvvEp201m5WIpgn0/vfDIY4qfHTr2ph/Pz/qzPencp0sWXrA5/337svgnvtn8e/nRwXVRRRO0v/Y6PfEw6RrLkaY1e83GMl9mgL+/d7bt2/I8GFVV87FkOaTlHK2lLKDlLKtlPK5oseeKEr2SClHSCkbSyl7Ff2rkmR/5LdlPid7AOnVuWj1dOzxDUIYVXh76YWL6NMr0eftvV7JHymHefX130MYVd3hdThZOOpmpJ8t/J1vf8Gae6aEKKrwYLOZGXWh791iERFmJv2ldwgjKq3OXi9LKfnj1sf928fjIWPt1hBFpEBhkam337jUr7IJLpeXmbO2q6JqPkidtQi9srVty6AXONj9/jcUHK3yHtc6ZcUK32YrCwEDz03iwpHtQhxRSXU24e+f9hP5h/yv43J8/vIQRKMUZ7OZGTfWvzHfLpe3Rg93qymc6Zl4K1jXtiKa1ULGOrXEYTByc30bgy8lXDKmQ5XXi6qTCd95MpM/bvu/gPZN/8P/4WyK/+6/ZyDNmsb6vH1EhIXsbP8X9Qg3x+avCHhfT24+toaqYmYw+vb1vZTxdz9U/YdrnUz4h3+cj9AC+9Gs9X1PQkrgrFYTH/zvCp+3z893c9vfflDdOpU48nMQ0/QFnFytSlMHyu3x0qF9Q5+3TzuRV/lGBqtzCV93u8lPPY4MYCFhYTbR9parQxCVUpaE+ChsVt/HHx84mMX6DWrN1fI4M7LwOvyb1l+CLjkya5Fh8YQLl8vLrt3p3HH3T3z2+Qaf9+vYqVEIoypbnRqHtfv9b1j79xfxFjiRbv/qsAiTRourR9F0zJAQRaecTQjB2Ku68vU3m/D68AEtJSxeup/evYxbAaiukLrO3POuDXgdCAA0QURTY9ZODRffzdjCa28sx+PVcTr9u1k+6NykEEVVvjrTwj/y6xJS7p2CJyfX72Qf1bo5F/z2MYOmvaKqNFahggI3F4/uSGw939cvSD0S2NT1uu7o3N/JP1jB1Y8vr2sp6XDXdcYFVcet+uMQ/3ntd/Ly3X4newDp55h9I9SZFv6Wf72LXlB2xQZhsyKd5V/qOo6fJGvzTpoMOzdU4SnFSCl5970/+PSLDZg0Qb4fVTG3bD2BlJLsHCd2m1lNxiqSs31vxWs3+9LylxDdWhVQ89XUz9YFtVBPz+5NDIzGN3WmhV/REMyKkj2AN7+A7a9ONTgipTzfzdjKZ9M24HR6/Er2AMeO5XLplZ8z6uKpDBnxAY8/MZf8fFVGuV7ntmhWS1DHEGYTR36pfFk+pdCJIG66du/WmMaNYwyMxjd1JuHHD+5b/pM+XM46T6hKgVVl6mdrg2oZHTl6Crdbx+0uLK720D9+NTC62qnJiIFEJiWiFV/dTRPgRw+lZrUGdw8gzJyT3MynnrKzCQEP3DvI+IB8UGcSfven7i67haMJIhIrL7WsWX1bZV4JXlaWcePpXS4va9cdCfu+faFpjFw6jaRrxqDZbQiLmWaXXIAlJtrnY0hdp+no80MYZd3Stk2DgD4fpYRlv/teT8pIdSbhx7RN4qK1M7A1blj4ESoEpgg7kc2bYI2Pq3R/Sz3f3xhKcLp1NXYkiMVi4uhRtUqZrUF9Bn72EhMKNjLRtYXkN/6Pet07+Lx/ywljsMbVC2GEdcucubsD3levpiupOnXHq37X9lx19HfSV64nY80Wols1I/Gi8/i2XnKF+wmziaRxF1VRlMp9dw/glskzcLq86AHMlziby+WlTevKP9TDScHRE/zS50pcmb5f+WRv2Y3U9YAnLYYTKSV5fi5leJrJJBg5vOoqZBZX5/6yQgganduLuB4dyVi7hVW3/h/e/ILKdqLLI7dVTYAKnTrG89nHVzNieFsaJ0T5vX/x+iN2m5krLutMgwaRRoZY6217ZWphsvejJZm9eSf7v/w5hFHVDT/M3MrI0VPZuSs9oP2vuqILnToav6KfL+pUCx9A93hYcvkdHF+4Em85wzTPZo6Owt5IlUSuSm3aNOCFKYULzaxOSeW+B2dR4MON3PMGt+TAgSwOHspGCHC5vbhcHjweXS2WUszhH+b5fQPWW+Bk9/++pvVfqqR6ea306287eeGlpX4tcnKaEDB+bDceeaj67pPUuXfIvk9mcHTu7z4ne4C4Hh1DGJFSmXOSm7F04a0MOa9ludtomqBxQjRduyRwIq1wOJyUoOuSX+fs5p13V1VVuLVCpVe15e1XoArUVeT1N1eUm+yFAIuldEoVAhISovjik3HVmuzBoIQvhLhICLFDCLFbCPFoGc/bhBBfFz2/SgjRyojzlmXH29P8Xvwhe+tuPOqFXq00TaNv3+ZlvmEA+p3TnBnfTmT691tKDel0OD18PX2TKqxWTGSS/+UnTJF2Wl2nWvfFFRS4+eLLDdx02/fc+8Asjlcw9j4hIZr69UvPGpcSsrMdREdX/0jAoBO+EMIEvA2MBroAE4UQXc7a7BYgU0rZDngVeDHY85bHnZHl/z6n8jj4rRrLXd369ErEZCr7Jbly1SGGXfgR6ell13p3ODx4fFigPlx0uPNa8OHmqygat2+OjqR+9460v31CqEOrNRwODzfcPJ033lrBho3HWFrJUMrjx3NJSyv79Wkxmyr8sKgqRrTw+wG7pZR7pZQu4Cvg8rO2uRw4vdbgdGC4CFHRmri+/i2sAaA7nBybp5bQq25dOieQ2KT84bGOCuqVtEyqj8Xse+XNuq7lhItpMmJAmROvtKgINJsVS1ws5sgIGpzTjf4fTGHksmmY7LaqD7aG+mHmVvbuy8TtCb4h4XZ7ad+2+u8TGpHwmwHF1/U6XPRYmdtIKT1ANlCqcLQQYrIQIkUIkZKWlhZQMD2eucenls3ZcrbvC+h8inE2bT7O0WO5Ae07flx3g6Op3TSzmU7331RyQqEm0Ow2zBERSF3izszBnX2K7M272PPR92hqEfMSvp2+2bCJx2NGdyA21vcigaFSo27aSinfk1ImSymT4+MDG7ZUv2t7eky5D83mX12RCgtPKVXip1nbcQVQdRDg+xlbDI6mdpNSknLn0+jF60jpEt3hxJWZVaKirLfASdqyNWSs2VwNkdZcJzMCu/Fdlh07AxvCaTQjEn4q0KLY982LHitzGyGEGagHhKx4Tbd/3M6IxV8gTL5d4guLmSbDVaXM6uZ0eQOegbhnb4bB0dRunlN55JVXLrmsex1ScjJFJfziGjSIMOxYO3am14gif0Yk/NVAeyFEayGEFZgAzDxrm5nApKKvrwYWyBAPqbA2qIcpopz+yGK3D4TJhCUmik4P3hzKcBQfXDi8LRERgXUrSAmPPDaHzCzjWmW1mSnChijnBnhZhNlEVEu1sExx107oUe6oMX8JITCZqn+tjaB/mqI++buAOcA24Bsp5RYhxDNCiNNjvD4EGgohdgMPAKWGbhotulUzRFk38Uwa8YP7Ur9XJyKaNabV9Zcxeu0MItVKP9Vu4IAkzh/c6kzS9/e2/sLF+7j19h8MKddQ22kWC4mjBvu0rTCZsDWsT5OR1VPBsaa68vIuDB7UErvNHFBVzNPMZsGA/i2w2ar/HomoqWOXk5OTZUpKSlDH2P3ht6y557kzk1CEyYQ5OpLR62YQ3bpFJXsr1UFKScqaIyxcvBchBF9+7d+i2pGRFv7z4kX076f+vvun/cSKSY8iPaXnpZhjo9CL1r9tNKA3Az9/icjmVb8gR22wYNEe/v7InID2tVpNJDaJ4cP/XVFl5T+EEGuklGUWEKv+j5wQanfLOKKSmrL1+ffIO3SEhPPPods/7yC6lVrVp6YSQnBOcjPOSW7Ghx/7/4Hv9egcOJitEj6QMLQfwqQhz8r3pqgI+r72OM0uuQDNYsZaP7Z6AqwFpJT868XAFoWxWDRefnEUA89tWaL+U3Wq0wkfIHHkIBLVpWqN5nB4+GXOThYv3U/DBhGMG9uNTh3jOXgo2+9jaZrAbNG48dbv2bzlOJGRFsZe2ZU7bu+HxRJe4/Qjmzam0/2T2PHm53jzCq9yTRF2Ytq1pNXES9SY+wrs3JXOR1PXsnjpvoDWqzWZBPfcOYDBA1sZH1wQ6nSXjlLzFTjcTLr5Ow6n5uBweNA0gdVi4rFHh+BwePjXi4v9Ol5srA23W6eg2NKJNpuZoee34vmiYm3hREpJ6s8L2fn2F7izT9FywsW0u2085kjjRqDUNWvWpnL3/bNwOj0BjcO3WDSaNI7hqy/GE2EPbtnJQIRtl45S8834YSuHDmefaUXpusTh9PD8v5cw87u/8Ppby8nL8304W05O6aJ5TqeHhYv3cfx4Lo0bh9dCN0IIml86jOaXDqvuUGqN5/+9xK8lOJs0juaWm/oy/fstFBS4GTG8LZOu610tyb4yKuEr1Wru/D1lXjJrmmD/wSw+fv8qHn9iLrt2BzfO3mo1cfBQdtglfMU/breXffszfd6+UcNIvpk2gehoK2Ov7BrCyIxRo2baKuEnNqbsfmRd14mOstKubUO+/mICrVvVD+o8LpeXpBZq+T6lYiaThtXq44RNAQ/eP6hGVMH0lUr4SrUaP647EfaSF5pCQKOGUXRo/2e5pXbtSpVe8kuXzgmqda9UStMEnTs28mlbKWHwwPLXcKiJVMJXqtWgAUlcf10vrFYTUVEWIiMtNE6I5s1XL6Z4QdVrr+kZ1KzH7dvTakw9E6Vm23/At9Fhha/Z2tO6B9WHr9QAf72tH+PHdmPDpmPUi7XTq2diiXHLBQ43L7+6jGAqarvcXj6ftp5nnxphRMhKHXYq17fV8qKirLz8yjLS0vM4b3BLLhzR3ufuoOqiEr5SIzRoEMkFQ9qU+dzUT9axa/fJgNYRPU3XJalHcgLeXwkfiU1iOJxa+WslM7OAr77dhK5Lli0/wBdfbuTj96/Cbq+5aVV16Sg13s+zdwSV7KHw8rtfspphrVRu8i3n+Lzt6bpNBQUe9h/I4vsft4YqLEPU3I8iRTkj+MmB0VFWrhmvFklRSjpy9BQvv7qUFSsPYTGbuHhMR268vheaBrqfC105nR5+m7uLa6/pEZpgDaASvlLjXTymI598tg63u/Q7UAh8mg1546TexNVXs0uVP6Wn53Pdjd+Sk+NA18Hp9DLjx63s3JWO3WYmv8D3yVenRUXW7Ju4qktHqfFumtSHFs3LHkPv69T3199YwZp1RwyMSqmt8vJc/OP/fuOiS6eSleUo0ZJ3ubys33A0oGRvt5sZf7X/a2pXJZXwlRovwm7h3rsHBHUzzKtLHvvnXGpq7SilaqSl5zH+2q/5bd7ucrtsKnuJXHpxRz5+/0riG0USGVk4lNhqNXHN1d04/7xWhsdspKC6dIQQDYCvgVbAfmC8lDLzrG16Af8FYgEv8JyU8utgzquEn25dGwe9sElGRj6HU3PKvVpQ6rb9BzK5/sbp5AWx1GBcnJ2nnxgOwOyZN5CyJpWsLAe9eyWSkFDzJ/YF28J/FJgvpWwPzKfslazygRuklF2Bi4DXhBDBzZNXwk5c/QgmXderxKxcfydieb2StLRccnIcJappKuHh5VeWkR/k371j+z9n4ZpMGv37tWDUhe1rRbKH4G/aXg4MLfr6E2AR8EjxDaSUO4t9fUQIcQKIB7KCPLcSZv52e386dYpn2lcbycwsIDExhqNHT7F3n+/Frm79649AYb3ygQOSeOqfw9TN3DCRsvZIQOWOi/OnsFpNFGwLv7GU8mjR18eACheGFUL0A6zAnnKenyyESBFCpKSlpQUZmlIXXTCkDQ8/eB5p6XmsWXvEr2RfnNcrWb7iIH+9c6bq1w8TRkyIatQoyoBIqk+lCV8IMU8IsbmMf5cX304WvmvKfecIIRKBz4CbpJRl3i6RUr4npUyWUibHx8f7+aMo4eL/npzHqVMuv2qWl8XrlRxOzWHzlhMGRabUVAUON5ERwSV8u93MTZP6GBRR9aj0NyClLLf4iBDiuBAiUUp5tCihl/nOEULEArOAx6WUKwOOVgl7mZkF7D9g3GW10+lm+YoDdO2SwOo1qXz19UYyswoYen5rxo3tVuuKYyml5ea6mHj91xw7nhfwMUwmwd8m92PY0LLLf9QWwV7jzAQmAS8U/f/j2RsIIazADOBTKeX0IM+nhDlh8GLQug4ffbKWaV9txOX2nlmMZcfOdH6YuY1pn44nMrLmrVyk+O69D/7gyNFTQR3jmnHduf4vvQyKqPoE24f/AjBSCLELGFH0PUKIZCHEB0XbjAfOB24UQqwv+lf7f3NKtahfz06njvElqmmeZjIJ7HYz/hbVdLt1TuW6Sqy85XR6OX4ijxk1vDaKUjG3x8uX32wK6matEHDFZZ2NC6oaBZXwpZQnpZTDpZTtpZQjpJQZRY+nSClvLfr6cymlRUrZq9i/9UYEr4Snfz07kgYNIjCZSmZ2r1fidHqCKqNcnNPpYdGSfYYcSwmtvfsyeOWN33nymfnMX7gHr7fwNuHSZQeCHpkjhGDr1rpxn0fV0lFqnWZNY5n1w/WcP/wDvN6SVTSlxLBRN4Urb0UaciwldGb/soNnn1+Mx+PF65XMnb+HxglRXHVlV06ezA/69aDrknc/WM1ll9b+Vr5K+EqtZLGYMJk0Cidvh4bNZmZCDa58qEBBgZspLyzG6fxzxJbD4eHAwWzeeGtFUQMg+POkpQV+w7cmUQlfqbWGX9CGX+bswuP5c5Svpgm6dklg375MJBKHw4PX69873mLRMJk0Hrh3ED27N+FwajYff7KWDRuP0aJ5PW6a1Ice3ZsY/eMofnC5vKxYdYj1G46WeT8H8PvvXpGkFnWjOIBK+Eqtdf+9g1i/4RgnM/LJz3cTGWkhKtLCi/+6kLj6EaxYeYjcPBcrVh7k1992+dzSGzGsDY8/OpRdu08y7esNvP3fVTidHnQd9u7LZMXKQ/xrygiGDW0b2h9QKdOWrce5zGL8CwAAFHhJREFU856f8Xp13B4vLpefhesDcP89A0N+jqogauosw+TkZJmSklLdYSg1nNvjZcnS/ezek0FSi3oMG9oGm610O+bIkRwuufJzn45psWgkJESTmVGA01X2FYLVorF04W1YLDV7DdO6xu3xMmrMJ2RlO0Jy/LPXVxACxlzUoVathSyEWCOlTC7rOVUeWanVLGYTwy9oy+23nsPoUR3KTPYATZvGct/dA3w6ptutk5qaQ36Bu9xuAZdbZ9pXGwOOWwnM2nVHcTiDm2FdkUaNorDZTERFWrBZTUwc34On/jksZOeraqpLRwkbN1zXm5Q1qSxbftCQ4/02bzeTru9tyLGUyrndXl5/c3nQJTXKE2E388C9Axk8sCXHjufSOCGa6Oi6NdNaJXwlrGgm4y5qDx3O5uTJfBqqoZtV4tMv1oekWqXZLNA0jUsv7siFI9ohhKBtmwaGn6cmUF06SlgZen5rbFZj+t1zc11ce8M3Zyb5KKE144etJWZDG8VmNfPRe1fy6MNDDJu0V1OphK+EldGj2tOqVRxmszFv7LT0fAYPfY9pX20gv8DF5i3H+WXOTnbtPmnI8ZVCR46e4uix4OrhlMft1lm0ODxmVKtROkrYyclxMuqSqSFpLVqtGmazCd0r6dUrkVdfGl3ujeRw5/Z4OXQom/r17DRoUHG32A03Tw9pGevu3RrzyYdjQ3b8qlTRKB31SlTCzuatxzGbtZAkfJdLPzMufO3aVN79YDX33unb6KBwMvuXHbz4n6V4PRKPR6dv36Y8/+xITCaNmT9vZ/WawzRNjGX82G6czMwPabIXApomxoTs+DWJSvhK2Ck+MzeUXG6dz79YT1paLkeO5HJO32ZMGN+duLjwXlJxw8ajTHlhcYnRNilrUrnv77M5fiKPzMwCHA4PZrPgm+mb8HiM64U4e5w9FJbQuO7anoadoyZTffhK2Enu06zc8fVnV+AMltcrmf3LLtZvOMonn69j7IQvOX4819Bz1DaffL6+RO0bKOxH37T5OGlpuWc+CDwe6Veyt1pNnJPcrNznhYCrrujCOcnNsFpMRESYqVfPztNPDKNrlwpXZ60zVAtfCTuRkRaeePwCnp6yAN0rcRdr8RtZf+VsLpcXr1fnv++t4uZJfYmItBBfy9dI9dWx46fQhCAhIZpjx06VWeZC12VQhc5cLi9r1qSW+/wVl3Xm8UeHApCRkc+pXBfNmsZiNodPu1fdtFXCVuqRHH6evYM9ezNY9vt+HI7QVd48m91uRtclXTon8OK/LqyziX/nznQefmwOR4+eAgFJLerRqlUcS5bux+0u2bVWVneLv0wmUeYHh6YJvvp8PO3aNgzuBLVAyEorCCEaCCHmCiF2Ff0fV8G2sUKIw0KIt4I5p6IYpVnTWG6/9RwmjOteVGq5tN69Evnxu78waECSoed2ODy4XF42bT7G3+6eaVgN/5okO8fB9TdP5+ChbNweHbdbZ8/eTOYv2Fsq2dvtZgYNDP53rGkC81l/S7NZo0/vxLBI9pUJ9lrmUWC+lLI9ML/o+/I8CywJ8nyKYrju3Rtj0kq/FSIizIwf240WzeuxYtWhkJzb65UcPXqKrdv+HIWyY2c6d933M8Mu/IgJ133N/AV7OH48l737MmrVJK9nnltYKrGXpU3rOHr3SuTEieBrzmua4Jknh9G2TRwmk8Bi0Rg5vC2vvjQm6GPXBcH24V8ODC36+hNgEfDI2RsJIfoCjYFfgTIvNRSluljMJl564SLue3AWksK+YKvVxKCBLRk5oh2/Lz+Aroe2b//goWy6dmnMzl3p3HTb92duXGZlO3j4sTlomsBqNWGzmnny/y5gyPmtyzyWw+Fh3oI9HDyYRfv2DRl6fusqq+i5YdMx3v8whf37M+ncKZ7ffaxZtHff/7d37+FR1WcCx7/vXJIJQUgCUUIu3BVQXEBEwBuIKMX1CkZt3aqPrrpKEX20Vtvabbvbol27aq1Py4L1Vq0XBEFRy0UfL6gLIhoQKSQrGgiXRAiQhFwm7/5xBghkJjNhMpcw7+d58nAm5zcz7zlneHPmd37n9+6i7P+inzLB5/Nw1ZXDuPCCE7nwghOpqWkgLc1tM5q2EFUfvojsVtWswLIAuw48btHGBSwHrsUpdD5KVaeHeL2bgZsBioqKTtu8efNRx2ZMe+2u3s+SpZuo3rOfM04v5JSTj0dEmH7H66z4uGMmXAtlzBkFPPHYJcy8ezHvf/B1m33ZPp+HZ/8yDa/XzdatexjQP4fc3Ey2VuzluhvnUVfbSG2dUx8gJyeDp+dOJTsrtkNBP1yxmXvue/vgH6qO6I9vj6wsH/81azIjR/SO35smqahuvBKRpUCw8j4/bflAVVVEgh3i24DFqloebp4KVZ0NzAbnom242IzpSFndfVw59ZRWv69uY+71qVcMZePGKr4o2R7Ve6/+rIKqqlpKSraFTZSNDX5un7GI3dV1eL1uGhubOf+8/pSs3U5VVe3BdrW1jTQ0+HnkDx/xy6Oc4nfv3nrq6hrJzc1sc56ZBx9+/7Bx9fG+JNHsV0v2EQib8FU15Mz/IrJdRPJUtUJE8oBgt8ONBc4WkduArkCaiOxT1bb6+41JGhMm9GNTWVWrO3PT093cPfMslr9bxj82VkU1ba/X4+LvSzZFVNjD36zsCNRYPXBX7+K3NgZt29TUzPLlpe1O+Lt21/HzXyxl5adbEBG6dUtn8qRBjB1TyOjTCw5e5N67r543Fm+gvHxPu16/ow0YcGzObtnRou3DXwhcB8wK/PvakQ1U9QcHlkXkepwuHUv2ptO4atowFi76ih07athf34RLnP70+358DunpHsacUYhG2cff2Ojn4Uc/jMmZcXtngFRVbvvRIkrLvjt4V3JlZS3PvfA5r8xfR48eXXjyz5dTWVXLLbe9Frc7l8EZdinCYTdk+dI9TP+3MXGLoTOLNuHPAl4SkRuBzUAxgIiMAm5V1ZuifH1jEi4zM43nnylmwaL1fPDB1/TsmcnVxcMYOuR4ALpmpjFsWC9WtXHTTzgNEYxmORper4sLJg1ss01jk5+dO2vJzPTib2qmYts+vvl2d9BEvn9/E9u27WXm3W9QWraLhob43LtQkN+NO2eMY/y5/Xh98QZmz11FZWUN/fvlcOeMcYwYnheXODo7u/HKmCj9/N+XsnR5WavpApJBfu/jGDe2DxkZHiZfMIjBJ+Uetv7Fl0t4/ImP2V/v1O51uYSMDC9NTf6YTC53tFyBbqVF868lM/PYqkLV0aymrTExsmt3HUuWlSZlsne7hZ2VNbzy6lqee34NN/zrfOY8eegkaumyUh59/CNqag/V7m1uVmpqGhKS7NvqeWpWpb6hibf+HvxahYmMJXxjorBt276kHeft9ysNDc2oQnMz1Nc3Mecvn1K+pRqA2XNXxqw+bHu43c41kXCdDXV1TXwdgxKHqcQmTzMmCgUF3WhqTJ6uj3AaGvzcdMt8Ro8uZFPpd4kOB4A0r5v6hvB/eDIyvAwenBu2nQnNzvCNicJxXdMpvnIYPt/h504HbuvvkuHFnWRfAHbsrOX1NzYkOgzcbucmsqKi7jSHuWbt8bjIzvZx/nkD4hPcMcrO8I2J0swfjaVXr64889wa9uzZz7BTenHXjHH07NmF8i17eOPNDbw8b12iw0wqI4fnMWF8P+Y+tZpNm1p/03C5nDN6t9tFc7Ny3vj+3DF9rJWLjJLtPWOiJCJcU3wq1xSf2mrdrN+9x/sf2hQhHo+La4qHkde7GxdOGkh2VgZzn/qUurpG/EHuYTjrzL48cP/4sLVuTftYwjcmRrZv38d7H2wOOVbd5/Nw4qAefLl+Z+DOVU2qoZAdqUdOBjNnjDvsJrDVn20Nur2ZmV4uu3iIJfsYsIRvTIx88201aWnuoAm/W7d0Hn5wMqeNzKe2tpEVH32D399Mr7yu/GbWe5SWVYXt106UjAw3hQVZ5PU6jnXrd1BZWRuyrYhTM/Zn909odcdvv77ZrFxV3qqMod+v9O6dGkXF480SvjEx0rdPVtBk7/EIF04ayGkjnfqrXbp4OX/ioYuRL/71KnbtqmP2nJXMW/BlXKcuiMT3rx7O7beeATjTMNxy+2t8unprq2GVHo+L7104iH/5wfCgxUeuLh7Gqwu+pKmp6bDnDByQw4mDesZ0G1KVjdIxJkZyczOZNHEAviMuNKZ5Pfzw2hFtPjc7O4N77zmHHjmxndb4aLyx+NAIHxHhnrvOxufz4nIdOoP3+Tz89j8m8csHJoasNFWQ350/PnoxRYXd8XpdeL0uzj6zD48/8s8x34ZUZWf4xsTQAz+bQO+8brw0r4SamkZOHdaLH991Fvm9u0X0/OzsDLYHqQQlAlMvP5mly0tpaPDT3Kz4/c34/RrTYi3gzJBZVvYdffpk4Xa7GDSwB889NY3Zc1exdt12Cgu6c9MNp0U0XfGI4XnMf/n7VO+pJz3NTUaGN6axpzqbS8eYJLZg4XoeOmKueZdLGHJSLs8+NY3GRj8frviGyqpaRgzPo6JiL/85692gfyQ6UkaGF5/Pw69+MbHD6/2a6LQ1l44lfGOSWHOz8uvfvMNbb2/E7XF6YLOzM5j9xKXk9Qp+YVNVeWneWh55dAX1Ecxm6Uw5LE4BcI+L43Mzqa1tpLKqNuy3BZ/Pw9+eLaaoKKvNdiZ+LOEb08mVb6lm7dod5OZ2YcTw3of1l4cyb/46HvnDCmpqGkO2mXzBQG68YRRrPq+gZ48ujBtXhNfj3Br83499yMvz1rU5347HI1x95ancNfPM9m+UiYmoShwaYxKvIL87Bfnd2/WcqZefzOWXDmX1Z1uZPnPRwepYLZWW7WJA/xwG9G9dMerOGWdy1ri+vL74K74tr2b9VztbjZtvalK2btvbvo0xCRNVwheRHOBFoC/wNVCsqq2msxORImAOUAgoMEVVv47mvY0x4blcQm5uJi6XC2id8NPS2p7o5/RR+Zw+Kp+tFXu5ovj5Vut9Pg9jRhd0VLgmxqIdlvkTYJmqDgKWBR4H8wzwO1UdAowmeO1bY0wMFBV254QTuraab97n8zDtipMjeo3eecdxyUWDD5skLi3NTW7PTC6aclJHhmtiKNqEfynwdGD5aeCyIxuIyFDAo6pLAFR1n6qGvjXPGNOhRITfP/Q9srMzyMx0Rtekp3uYcG4/Lr5ocMSvc9+953D/vecydEgufftkcd21I3juqWlk+GwoZWcR1UVbEdmtqlmBZQF2HXjcos1lwE1AA9APWAr8RFVbDR8QkZuBmwGKiopO27zZJp0ypqM0NvpZ8ZEzhHP4P+UF7bc3nV9UF21FZCnQK8iqn7Z8oKoqIsH+eniAs4ERwDc4ff7XA3OPbKiqs4HZ4IzSCRebMSZyXq+bc8/pl+gwTAKFTfiqen6odSKyXUTyVLVCRPII3jdfDqxR1bLAcxYAYwiS8I0xxsROtH34C4HrAsvXAa8FabMSyBKRA7XJzgO+jPJ9jTHGtFO0CX8WMElENgLnBx4jIqNEZA5AoK/+bmCZiJQAAvxPlO9rjDGmnaIah6+qVcDEIL9fhXOh9sDjJUDrckDGGGPixqZHNsaYFJG0c+mIyE4gFuMyewKVMXjdeOns8YNtQzLo7PGDbUMofVQ1N9iKpE34sSIiq0KNUe0MOnv8YNuQDDp7/GDbcDSsS8cYY1KEJXxjjEkRqZjwZyc6gCh19vjBtiEZdPb4wbah3VKuD98YY1JVKp7hG2NMSrKEb4wxKeKYT/gicqWIrBORZhEJOfxJRCaLyAYR2SQioQq5xJ2I5IjIEhHZGPg3O0Q7v4isCfwsjHecwYTbpyKSLiIvBtZ/IiJ94x9laBHEf72I7Gyx328K9jqJJCJPisgOEVkbYr2IyGOBbfxCREbGO8a2RBD/eBGpbnEMHoh3jOGISKGIvCMiXwZy0R1B2sTnOKjqMf0DDAFOAt4FRoVo4wZKgf5AGvA5MDTRsQdiewinfgA4FcUeDNFuX6Jjbe8+BW4D/hRYvhp4MdFxtzP+64HHEx1rmO04BxgJrA2xfgrwJs4cV2OATxIdczvjHw+8nug4w2xDHjAysHwc8I8gn6W4HIdj/gxfVder6oYwzUYDm1S1TFUbgL/hVPNKBmGriiWpSPZpy217BZgYKKSTDJL5MxExVX0P+K6NJpcCz6jjY5yZbfPiE114EcSf9FS1QlVXB5b3AuuB/COaxeU4HPMJP0L5wLctHpfT+oAkygmqWhFY3gacEKKdT0RWicjHgSpjiRbJPj3YRlWbgGqgR1yiCy/Sz8TUwFfwV0SkMD6hdahk/uxHaqyIfC4ib4pIZEV6EyTQbTkC+OSIVXE5DlHNlpks2qrKparB5uhPKh1QVQyc+TO2iEh/YLmIlKhqaUfHag6zCHhBVetF5BacbyvnJTimVLMa57O/T0SmAAuAQQmOKSgR6QrMA2aq6p5ExHBMJHxtoypXhLYALc/OCgK/i4u24o+wqhiquiXwb5mIvItzFpHIhB/JPj3QplxEPEB3oCo+4YUVNn51pgc/YA7O9ZbOJqGf/Wi1TJyqulhEnhCRnqqaVJOqiYgXJ9n/VVVfDdIkLsfBunQcK4FBItJPRNJwLiAmxUgXIqgqJiLZIpIeWO4JnEniq4pFsk9bbts0YLkGrmAlgbDxH9HHeglO32xnsxD4YWCUyBigukUXYtITkV4HrvuIyGicnJYsJw2AMwIHp6TrelX9fYhm8TkOib6CHesf4HKc/rB6YDvwduD3vYHFLdpNwbl6XorTFZTw2ANx9QCWARuBpUBO4PejgDmB5XFACc5IkhLgxkTHHWqfAr8CLgks+4CXgU3A/wL9Ex1zO+P/LbAusN/fAQYnOuYg2/ACUAE0Bv4f3AjcCtwaWC/AHwPbWEKIkWxJHP/0FsfgY2BcomMOsg1nAQp8AawJ/ExJxHGwqRWMMSZFWJeOMcakCEv4xhiTIizhG2NMirCEb4wxKcISvjHGpAhL+MYYkyIs4RtjTIr4f5GSE8UAjqv/AAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train= X[:800]\n",
        "X_test = X[800:]\n",
        "y_train = y[:800]\n",
        "y_test = y[800:]\n",
        "\n",
        "# len(X_train),len(X_test), len(y_train), len(y_test)\n",
        "X_train[0], y_train[0] "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "luEzNyk7pKE1",
        "outputId": "6994ecba-23af-4552-cf07-91748dc89274"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([-0.00933187,  0.39098105]), 1)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "# Set the random seed\n",
        "tf.random.set_seed =42\n",
        "\n",
        "# 1. Create the model\n",
        "model_1 = tf.keras.Sequential([\n",
        "        tf.keras.layers.Dense(10, activation=\"relu\"),\n",
        "        tf.keras.layers.Dense(10, activation=\"relu\"),\n",
        "        tf.keras.layers.Dense(1, activation =\"sigmoid\")\n",
        "])\n",
        "\n",
        "# 2. Compile our model\n",
        "model_1.compile(loss= \"binary_crossentropy\",\n",
        "                optimizer= tf.keras.optimizers.Adam(learning_rate=0.01),\n",
        "                metrics =[\"accuracy\"])\n",
        "\n",
        "# 3. Fit the model\n",
        "history = model_1.fit(X_train, y_train, epochs =50)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8SlWGkRKmys4",
        "outputId": "94a03572-93d4-4b33-aae9-f6c26f971455"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.4505 - accuracy: 0.8163\n",
            "Epoch 2/50\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.2679 - accuracy: 0.8800\n",
            "Epoch 3/50\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.2376 - accuracy: 0.8825\n",
            "Epoch 4/50\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.2134 - accuracy: 0.8988\n",
            "Epoch 5/50\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.2030 - accuracy: 0.9013\n",
            "Epoch 6/50\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.1991 - accuracy: 0.9013\n",
            "Epoch 7/50\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.1914 - accuracy: 0.9062\n",
            "Epoch 8/50\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.1900 - accuracy: 0.9087\n",
            "Epoch 9/50\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.1817 - accuracy: 0.9137\n",
            "Epoch 10/50\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.1776 - accuracy: 0.9262\n",
            "Epoch 11/50\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.1656 - accuracy: 0.9162\n",
            "Epoch 12/50\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.1496 - accuracy: 0.9300\n",
            "Epoch 13/50\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.1299 - accuracy: 0.9425\n",
            "Epoch 14/50\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.1097 - accuracy: 0.9600\n",
            "Epoch 15/50\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.0880 - accuracy: 0.9750\n",
            "Epoch 16/50\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.0670 - accuracy: 0.9825\n",
            "Epoch 17/50\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.0470 - accuracy: 0.9937\n",
            "Epoch 18/50\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.0372 - accuracy: 0.9950\n",
            "Epoch 19/50\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.0267 - accuracy: 0.9987\n",
            "Epoch 20/50\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.0194 - accuracy: 1.0000\n",
            "Epoch 21/50\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.0155 - accuracy: 1.0000\n",
            "Epoch 22/50\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.0128 - accuracy: 1.0000\n",
            "Epoch 23/50\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.0105 - accuracy: 1.0000\n",
            "Epoch 24/50\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.0089 - accuracy: 1.0000\n",
            "Epoch 25/50\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.0080 - accuracy: 1.0000\n",
            "Epoch 26/50\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.0067 - accuracy: 1.0000\n",
            "Epoch 27/50\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.0058 - accuracy: 1.0000\n",
            "Epoch 28/50\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.0055 - accuracy: 1.0000\n",
            "Epoch 29/50\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.0047 - accuracy: 1.0000\n",
            "Epoch 30/50\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.0043 - accuracy: 1.0000\n",
            "Epoch 31/50\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.0041 - accuracy: 1.0000\n",
            "Epoch 32/50\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.0034 - accuracy: 1.0000\n",
            "Epoch 33/50\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.0034 - accuracy: 1.0000\n",
            "Epoch 34/50\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.0029 - accuracy: 1.0000\n",
            "Epoch 35/50\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.0028 - accuracy: 1.0000\n",
            "Epoch 36/50\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.0025 - accuracy: 1.0000\n",
            "Epoch 37/50\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.0023 - accuracy: 1.0000\n",
            "Epoch 38/50\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.0021 - accuracy: 1.0000\n",
            "Epoch 39/50\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.0020 - accuracy: 1.0000\n",
            "Epoch 40/50\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.0019 - accuracy: 1.0000\n",
            "Epoch 41/50\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.0018 - accuracy: 1.0000\n",
            "Epoch 42/50\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.0017 - accuracy: 1.0000\n",
            "Epoch 43/50\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.0017 - accuracy: 1.0000\n",
            "Epoch 44/50\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.0015 - accuracy: 1.0000\n",
            "Epoch 45/50\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.0014 - accuracy: 1.0000\n",
            "Epoch 46/50\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.0013 - accuracy: 1.0000\n",
            "Epoch 47/50\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.0012 - accuracy: 1.0000\n",
            "Epoch 48/50\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.0012 - accuracy: 1.0000\n",
            "Epoch 49/50\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.0012 - accuracy: 1.0000\n",
            "Epoch 50/50\n",
            "25/25 [==============================] - 0s 2ms/step - loss: 0.0011 - accuracy: 1.0000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_decision_boundary(model, X, y):  #taking model,then features(X), then labels(y).\n",
        "  \"\"\"\n",
        "  Plots decision boundary created by a model predicting on X.\"\"\"\n",
        "  # Define the axis boundaries of the plot and create meshgrid\n",
        "  x_min, x_max = X[:,0].min() -0.1, X[:,0].max() + 0.1 #Values and boundaries\n",
        "  y_min, y_max = X[:, 1].min()- 0.1, X[:, 1].max()+0.1 # Same as above\n",
        "  # Creating a meshgrid\n",
        "  xx, yy = np.meshgrid(np.linspace(x_min, x_max, 100), \n",
        "                       np.linspace(y_min, y_max, 100))\n",
        "  \n",
        "  # Create X values (We are going to make prediction on this)\n",
        "  x_in = np.c_[xx.ravel(), yy.ravel()] # Stack 2D arrays together\n",
        "\n",
        "  # Make prediction\n",
        "  y_pred = model.predict(x_in)\n",
        "\n",
        "  # Check for multiclass classification\n",
        "  if len(y_pred[0])> 1:\n",
        "    print(\"Doing multiclass Classification\")\n",
        "    # We have to reshape our prediction to get them ready for plotting\n",
        "    y_pred = np.argmax(y_pred, axis=1).reshape(xx.shape)\n",
        "  else:\n",
        "    print(\"doing binary classification\")\n",
        "    y_pred = np.round(y_pred).reshape(xx.shape)\n",
        "  \n",
        "  # plot the decision boundary\n",
        "  plt.contourf(xx,yy, y_pred, cmap = plt.cm.RdYlBu, alpha=0.7)\n",
        "  plt.scatter(X[:, 0], X[:, 1], c=y, s=40, cmap =plt.cm.RdYlBu ) \n",
        "  plt.xlim(xx.min(), xx.max())\n",
        "  plt.ylim(yy.min(), yy.max())\n",
        "\n"
      ],
      "metadata": {
        "id": "NxABiuKtoSMI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_decision_boundary(model=model_1, X=X_test, y=y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "HmznbUGdoe8x",
        "outputId": "5ebd0ada-98ea-47ad-8766-190d3cdeafb5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "doing binary classification\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the model\n",
        "model_1.evaluate(X_test,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EqVNnE0Jon9x",
        "outputId": "b201cac6-c174-4ee3-de70-1780afa8b00c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "7/7 [==============================] - 0s 4ms/step - loss: 0.0015 - accuracy: 1.0000\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.0015021853614598513, 1.0]"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_1.predict(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9rL5f7jgo9nx",
        "outputId": "67dd085b-3adf-4fb7-a3b4-46d19c02c71a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[9.99296069e-01],\n",
              "       [9.99361515e-01],\n",
              "       [9.99519408e-01],\n",
              "       [9.99965191e-01],\n",
              "       [4.67602513e-05],\n",
              "       [3.54086706e-06],\n",
              "       [9.99799252e-01],\n",
              "       [2.53526377e-06],\n",
              "       [9.98569727e-01],\n",
              "       [8.56608152e-04],\n",
              "       [9.98948216e-01],\n",
              "       [5.30000261e-06],\n",
              "       [9.99913692e-01],\n",
              "       [9.99996543e-01],\n",
              "       [9.99995053e-01],\n",
              "       [9.99993324e-01],\n",
              "       [9.99972045e-01],\n",
              "       [9.96834159e-01],\n",
              "       [6.75174715e-06],\n",
              "       [9.99431431e-01],\n",
              "       [3.76178195e-06],\n",
              "       [9.96318936e-01],\n",
              "       [9.99482632e-01],\n",
              "       [9.99402046e-01],\n",
              "       [3.95454865e-15],\n",
              "       [9.99008298e-01],\n",
              "       [9.99996185e-01],\n",
              "       [3.10556679e-05],\n",
              "       [6.20296203e-16],\n",
              "       [9.99932468e-01],\n",
              "       [9.17977145e-16],\n",
              "       [9.99543130e-01],\n",
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              "       [9.99522626e-01],\n",
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              "       [7.61573119e-06],\n",
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              "       [6.62436251e-16],\n",
              "       [5.41581949e-06]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.datasets import fashion_mnist\n",
        "\n",
        "# The data has alrady been sorted for training and testing sets for us\n",
        "(train_data, train_labels), (test_data, test_labels) = fashion_mnist.load_data()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gky3G8dbqek_",
        "outputId": "5e90b9c9-6967-448f-d79b-21c5f81d4c68"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
            "32768/29515 [=================================] - 0s 0us/step\n",
            "40960/29515 [=========================================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
            "26427392/26421880 [==============================] - 1s 0us/step\n",
            "26435584/26421880 [==============================] - 1s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
            "16384/5148 [===============================================================================================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
            "4423680/4422102 [==============================] - 0s 0us/step\n",
            "4431872/4422102 [==============================] - 0s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a small list so we can index on to our training labels so they are human readable\n",
        "class_names=[\"T-shirt/Top\", \"Trouser\", \"Pullover\", \"Dress\", \"Coat\", \"Sandal\", \"Shirt\", \"Sneaker\", \"Bag\", \"Ankle boot\"]"
      ],
      "metadata": {
        "id": "k0ftTpSzJXmq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# We can our training and testing data between 0 & 1 by dividing by the maximum.\n",
        "train_data_norm = train_data/255.0\n",
        "test_data_norm = test_data/255.0\n",
        "train_data_norm.min(), train_data_norm.max()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wpgxW8kIJeNT",
        "outputId": "4c52befd-79a4-4d14-ad0b-c2258757db4e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.0, 1.0)"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Lets refit the model with ideal learning rate\n",
        "tf.random.set_seed=42\n",
        "\n",
        "# 1. Create model\n",
        "model_2 = tf.keras.Sequential([\n",
        "          tf.keras.layers.Flatten(input_shape=(28,28)),\n",
        "          tf.keras.layers.Dense(55,activation=\"relu\"),\n",
        "          tf.keras.layers.Dense(20,activation=\"relu\"),\n",
        "          tf.keras.layers.Dense(10, activation=\"softmax\")\n",
        "\n",
        "])\n",
        "\n",
        "# 2. COmpile the model\n",
        "model_2.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(),\n",
        "                 optimizer= tf.keras.optimizers.Adam(learning_rate=0.002),\n",
        "                 metrics=[\"accuracy\"])\n",
        "\n",
        "# 3. Fit model\n",
        "correct_lr_history = model_2.fit(train_data_norm, train_labels,\n",
        "                            epochs =20,\n",
        "                            validation_data=(test_data_norm, test_labels))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HZkWS1cGJqAD",
        "outputId": "f4fa75bb-1c77-4360-a557-1a8cda9faa68"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "1875/1875 [==============================] - 6s 2ms/step - loss: 0.5169 - accuracy: 0.8148 - val_loss: 0.4279 - val_accuracy: 0.8496\n",
            "Epoch 2/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3886 - accuracy: 0.8587 - val_loss: 0.4067 - val_accuracy: 0.8530\n",
            "Epoch 3/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3542 - accuracy: 0.8698 - val_loss: 0.4012 - val_accuracy: 0.8505\n",
            "Epoch 4/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3336 - accuracy: 0.8777 - val_loss: 0.3615 - val_accuracy: 0.8720\n",
            "Epoch 5/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3164 - accuracy: 0.8819 - val_loss: 0.3665 - val_accuracy: 0.8698\n",
            "Epoch 6/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3036 - accuracy: 0.8856 - val_loss: 0.3933 - val_accuracy: 0.8622\n",
            "Epoch 7/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2935 - accuracy: 0.8902 - val_loss: 0.3638 - val_accuracy: 0.8716\n",
            "Epoch 8/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2845 - accuracy: 0.8933 - val_loss: 0.3559 - val_accuracy: 0.8710\n",
            "Epoch 9/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2768 - accuracy: 0.8963 - val_loss: 0.3688 - val_accuracy: 0.8698\n",
            "Epoch 10/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2709 - accuracy: 0.8981 - val_loss: 0.3590 - val_accuracy: 0.8754\n",
            "Epoch 11/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2631 - accuracy: 0.9016 - val_loss: 0.3705 - val_accuracy: 0.8733\n",
            "Epoch 12/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2581 - accuracy: 0.9037 - val_loss: 0.3659 - val_accuracy: 0.8718\n",
            "Epoch 13/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2531 - accuracy: 0.9058 - val_loss: 0.3581 - val_accuracy: 0.8795\n",
            "Epoch 14/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2474 - accuracy: 0.9076 - val_loss: 0.3676 - val_accuracy: 0.8747\n",
            "Epoch 15/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2449 - accuracy: 0.9090 - val_loss: 0.3550 - val_accuracy: 0.8811\n",
            "Epoch 16/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2398 - accuracy: 0.9100 - val_loss: 0.3782 - val_accuracy: 0.8795\n",
            "Epoch 17/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2338 - accuracy: 0.9128 - val_loss: 0.3727 - val_accuracy: 0.8762\n",
            "Epoch 18/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2319 - accuracy: 0.9129 - val_loss: 0.3501 - val_accuracy: 0.8813\n",
            "Epoch 19/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2279 - accuracy: 0.9148 - val_loss: 0.3592 - val_accuracy: 0.8779\n",
            "Epoch 20/20\n",
            "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2250 - accuracy: 0.9155 - val_loss: 0.3799 - val_accuracy: 0.8807\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Lets make some predictions with our model\n",
        "y_probs = model_2.predict(test_data_norm)"
      ],
      "metadata": {
        "id": "fdN8qcEMLfK7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_preds = y_probs.argmax(axis=1)"
      ],
      "metadata": {
        "id": "zKL3o-ewQuXq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "\n",
        "# Create a function for plotting a random image along with its prediction\n",
        "def plot_random_image(model, images, true_labels, classes):\n",
        "  \"\"\"Picks a random image, plots it and labels it with a predicted and truth label.\n",
        "\n",
        "  Args:\n",
        "    model: a trained model (trained on data similar to what's in images).\n",
        "    images: a set of random images (in tensor form).\n",
        "    true_labels: array of ground truth labels for images.\n",
        "    classes: array of class names for images.\n",
        "  \n",
        "  Returns:\n",
        "    A plot of a random image from `images` with a predicted class label from `model`\n",
        "    as well as the truth class label from `true_labels`.\n",
        "  \"\"\" \n",
        "  # Setup random integer\n",
        "  i = random.randint(0, len(images))\n",
        "  \n",
        "  # Create predictions and targets\n",
        "  target_image = images[i]\n",
        "  pred_probs = model.predict(target_image.reshape(1, 28, 28)) # have to reshape to get into right size for model\n",
        "  pred_label = classes[pred_probs.argmax()]\n",
        "  true_label = classes[true_labels[i]]\n",
        "\n",
        "  # Plot the target image\n",
        "  plt.imshow(target_image, cmap=plt.cm.binary)\n",
        "\n",
        "  # Change the color of the titles depending on if the prediction is right or wrong\n",
        "  if pred_label == true_label:\n",
        "    color = \"green\"\n",
        "  else:\n",
        "    color = \"red\"\n",
        "  \n",
        "\n",
        "\n",
        "  # Add xlabel information (prediction/true label)\n",
        "  plt.xlabel(\"Pred: {} {:2.0f}% (True: {})\".format(pred_label,\n",
        "                                                   100*tf.reduce_max(pred_probs),\n",
        "                                                   true_label),\n",
        "             color=color) # set the color to green or red\n",
        "  "
      ],
      "metadata": {
        "id": "eaki23GhKFO0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "num_rows = 3\n",
        "num_cols = 3\n",
        "num_images = num_rows*num_cols\n",
        "plt.figure(figsize=(2*2*num_cols, 2*num_rows))\n",
        "for i in range(num_images):\n",
        "  plt.subplot(num_rows, 2*num_cols, 2*i+1)\n",
        "  plot_random_image(model_2, images=test_data_norm, true_labels= test_labels, classes = class_names)\n",
        "  # plt.subplot(num_rows, 2*num_cols, 2*i+2)\n",
        "  # plot_value_array(i, predictions[i], test_labels)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "MDZmzec2RL6I",
        "outputId": "c98b1bb1-3e9a-4a95-d50f-e38006182f9f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x432 with 9 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a confusion matrix\n",
        "# Note the confusion matrix we are about to learn is a remix of scikit learns plot_confusion_matrix\n",
        "import itertools\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "def make_confusion_matrix(y_true, y_pred, classes= None, figsize =(10,10), text_size=15):\n",
        "\n",
        "  # Create the confusion matrix\n",
        "  cm = confusion_matrix(y_true, y_pred)\n",
        "  cm_norm = cm.astype(\"float\")/cm.sum(axis=1)[:, np.newaxis] # Normalize our confusion matrix, also np.newaxis is used to increase shape/dimension\n",
        "  n_classes= cm.shape[0]\n",
        "\n",
        "  # lets prettify it\n",
        "  fig, ax = plt.subplots(figsize=figsize)\n",
        "  # Create matrix plot\n",
        "  cax = ax.matshow(cm, cmap=plt.cm.Blues)\n",
        "  fig.colorbar(cax)\n",
        "\n",
        "  # Set labels to be classes\n",
        "  if classes:\n",
        "    labels = classes\n",
        "  else:\n",
        "    labels = np.arange(cm.shape[0])\n",
        "\n",
        "  # Label the axes\n",
        "  ax.set(title=\"Confusion Matrix\",\n",
        "        xlabel = \"Predicted Label\",\n",
        "        ylabel = \"True Label\",\n",
        "        xticks= np.arange(n_classes),\n",
        "        yticks = np.arange(n_classes),\n",
        "        xticklabels = labels,\n",
        "        yticklabels =labels,\n",
        "        )\n",
        "\n",
        "  # Set X-axis labels to the bottom\n",
        "  ax.xaxis.set_label_position(\"bottom\")\n",
        "  ax.xaxis.tick_bottom()\n",
        "\n",
        "  # Adjust the label size\n",
        "  ax.yaxis.label.set_size(text_size)\n",
        "  ax.xaxis.label.set_size(text_size)\n",
        "  ax.title.set_size(text_size)\n",
        "  # Set the threshold for different colors\n",
        "  threshold = (cm.max()+ cm.min())/2\n",
        "\n",
        "  # Plot texts on each cell\n",
        "  for i,j in itertools.product(range(cm.shape[0]),range(cm.shape[1])):\n",
        "    plt.text(j, i, f\"{cm[i,j]} ({cm_norm[i,j]*100:.1f}%)\",\n",
        "            horizontalalignment=\"center\",\n",
        "            color = \"white\" if cm[i,j] > threshold else \"black\",\n",
        "            size= text_size)"
      ],
      "metadata": {
        "id": "bWliU6SSUSQ7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "make_confusion_matrix(y_true=test_labels, y_pred = y_preds,\n",
        "                      classes =class_names, figsize = (15,15),\n",
        "                      text_size=10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 852
        },
        "id": "EEAYYf_mWjYY",
        "outputId": "ff10f8af-7a31-4fb8-f965-c011efc225e6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x1080 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "wipmyZGrWyLl"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}