neural_networks.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cQtc9C8ym76f"
      },
      "source": [
        "# Packages Installation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uMmkckHNmzYB",
        "outputId": "4e95acbc-9562-49d0-9cd7-3378284beac7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
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          ]
        }
      ],
      "source": [
        "!pip install scikeras[tensorflow]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ghl2A7y_U5Qa"
      },
      "source": [
        "# Imports"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "B_EvgExRU7AS"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Preprocessing\n",
        "import tensorflow as tf\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from scipy.optimize import check_grad\n",
        "\n",
        "# ConvNet\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dense, Flatten, Dropout, BatchNormalization\n",
        "from tensorflow.keras.losses import categorical_crossentropy\n",
        "from keras.utils import plot_model, to_categorical\n",
        "\n",
        "# MLP\n",
        "from typing import List\n",
        "from tqdm import tqdm\n",
        "\n",
        "# Hyperparameter tuning with GridSearch (task 3-6)\n",
        "from tensorflow import keras\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from scikeras.wrappers import KerasClassifier"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AFexNyk-TfU1"
      },
      "source": [
        "# Task 1: Acquire the data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BnF6inmZaUz4"
      },
      "source": [
        "## Notes on Processing\n",
        "*   Followed this [tensorflow guide](https://www.tensorflow.org/tutorials/keras/classification) to import the images into numpy arrays.\n",
        "*   60K training examples, with each image being 28x28 pixels\n",
        "*  10K testing images"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rW_pDxemTfGU"
      },
      "source": [
        "Notes on normalization from [this website](https://cs231n.github.io/neural-networks-2/#datapre): \n",
        "\n",
        "*   \"It only makes sense to apply this preprocessing if you have a reason to believe that different input features have different scales (or units), but they should be of approximately equal importance to the learning algorithm. In case of images, the relative scales of pixels are already approximately equal (and in range from 0 to 255), so it is not strictly necessary to perform this additional preprocessing step.\"\n",
        "\n",
        "*   \"it is very important to zero-center the data, and it is common to see normalization of every pixel as well.\"\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_nswz4ZPcozj"
      },
      "source": [
        "## Load data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aI5RQr-MZwbP"
      },
      "outputs": [],
      "source": [
        "fashion_mnist = tf.keras.datasets.fashion_mnist\n",
        "(X_train, y_train_pre_ohe), (X_test, y_test_pre_ohe) = fashion_mnist.load_data()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-WRzMkIOZ2Ln"
      },
      "outputs": [],
      "source": [
        "categories = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n",
        "               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xb-O7ys8iHTU"
      },
      "source": [
        "## Explore data distribution\n",
        "\n",
        "60K training, 10k testing. Evenly split across 10 classes (so, 6k per class in training, 1k per class in testing)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LKhtgykVaJBw",
        "outputId": "34f69e9a-4001-421b-cfbf-f664a2a12e65"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(60000, 28, 28)\n",
            "(60000,)\n",
            "(10000, 28, 28)\n",
            "(10000,)\n"
          ]
        }
      ],
      "source": [
        "print(X_train.shape)\n",
        "print(y_train_pre_ohe.shape)\n",
        "print(X_test.shape)\n",
        "print(y_test_pre_ohe.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KoMsrsxBhwLZ",
        "outputId": "91cc7e2d-1097-49b5-8805-c07140eacdf4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Count per class (train): [6000 6000 6000 6000 6000 6000 6000 6000 6000 6000]\n",
            "Count per class (test): [1000 1000 1000 1000 1000 1000 1000 1000 1000 1000]\n"
          ]
        }
      ],
      "source": [
        "# The results of the below don't tell us much it seems\n",
        "# df_X = pd.DataFrame(X_train)\n",
        "# df_y = pd.DataFrame(y_train)\n",
        "# print(df_X.describe())\n",
        "# print(df_y.describe())\n",
        "\n",
        "print(f\"Count per class (train): {np.bincount(y_train_pre_ohe)}\")\n",
        "print(f\"Count per class (test): {np.bincount(y_test_pre_ohe)}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v8E27kL-crcr"
      },
      "source": [
        "## One hot encoding of y\n",
        "\n",
        "y_train_pre_ohe & y_test_pre_ohe still kept so we can get labels by indexing in categories (see for example the Explore data section)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yzsjDH2qbaem"
      },
      "outputs": [],
      "source": [
        "ohe = OneHotEncoder()\n",
        "y_train = ohe.fit_transform(y_train_pre_ohe.reshape(-1, 1)).toarray()\n",
        "y_test = ohe.fit_transform(y_test_pre_ohe.reshape(-1, 1)).toarray()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WiFTtMhbc404"
      },
      "source": [
        "## Normalizing X & X_not_normalized\n",
        "\n",
        "Per the [tensorflow guide](https://www.tensorflow.org/tutorials/keras/classification), since all values are between 0 and 255, it is sufficient to divide them by 255 as a way of performing the normalization. This will make all values between 0 and 1. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "t1u1BwOlc63p"
      },
      "outputs": [],
      "source": [
        "X_train_not_normalized = X_train / 1\n",
        "X_test_not_normalized = X_test / 1\n",
        "X_train = X_train / 255\n",
        "X_test = X_test / 255"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Eb_AYusnfGJi"
      },
      "source": [
        "## Visualize data\n",
        "\n",
        "Note: this is done before we vectorize so that we can view the images to see what the dataset looks like."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ulWXwth_e7YN",
        "outputId": "b5b110ab-de60-4726-a12f-a940663c232d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training set\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 25 Axes>"
            ],
            "image/png": 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zb7H6K1ZMkPuxWiyrCjbG0KFDg2POhszF7mIhkazitWY1Ds1MM7EB4fnGCi1ygT8OuS1XrAmH54/p3r17cMxF6LKaKrNmCs1KLAs3E5sHu5ZjIb5NmZhJKxbaXJfvic1FrMBmHoldD84Qz1mXgfCeyZmWLXzP5MzYnOkcSN/rdi5tqpBqlKk5OzHzVqyIctoYWdPGyLwlhBBCCFEm6KFHCCGEELmg1vrCWBROXashX3zxxeD40Ucf9fJLL73kZc4uCoRFQTnaw6rq+Hx5DPsdeQw2ddnxYtEIbFbhfsOHDw/6HXnkkaljlAtphV9ZLQ6EUXR83YDQRMbRYFbtmhZJkDWDb6xAJY+RV5NVTYit/bR5steV5ylrBFhM3c7HvMeUnTlu4mPTVM+ePYO2rl27epn3i72mS5cu9TKbsGxhUn4fm9U6dOgQ9HvvvfdSz1ekM3v2bC9b833W4r+xe2taP/795IoD5Yo0PUIIIYTIBXroEUIIIUQu0EOPEEIIIXJBrZ1vsvo+LF++PDhevHixl9kGya8DoY8L9wNCHxG2T1pfGg6z7Nixo5etTZp9Sdg+bStIs12bq3F//PHHQb9x48Z52drTOSSa/VkmTJiAxkZa6Lj9zrHMxbGsn2n96sImzefEPiUx/4c8ZV2OEbvGWVMLZM0YW5v3Zw17F+G9yqaaYJ8cvmdyhnUgvP+tXLnSy9bHkv197P2e4XswZ8hv165d0E+pCUJmzZrl5c6dOwdtfO35d8zC98LYHuN+/Du5ZMmSoN/48eO9zL+ZDYlWihBCCCFygR56hBBCCJELam3eeuWVV4Ljyy+/3MtcTI7VnUB69lVb6JHNZ1adyuo0VsHZUGlWpz300ENeHjBgQNCPwydZjRvLLsnZlFevXh20sWrRmtxYtciFSRtDJsvawqpsO89p4coxs0ltsO9n0yK32YzRYl3qoshoVrNmmrnMzhOfk+Yw3fTz7rvvBv3eeOMNL3fr1i1o4wzN7Cqwww47BP34PjZv3jwv2yKlfJ+NwZn0uSjzBRdcEPSTSSvk+eef97I1LfN6iJkFs5qn0wqT2rVx6623elnmLSGEEEKIekQPPUIIIYTIBTU2b1Wrkc8///zgdTZhxApupmUr5mzHQGiqsmYrhovaLVy4MGi75JJLio7BKjcgzAjK5q0DDjgg6MfRDW+//baXbTE+Np1YVTurBfk62ciExkDWaKZYpB9nDuW1EjNvxVSwaW02QymbSGNmE0bRW1XEMi2nma1iEVWx61qbqD2+J3Cx2zyRZvoZOXJkcPz1r3/dyzZbOl87vrd26tQp6Pfmm296mdeDjSBil4D27dt72d4/2SzG2Zn5ngsAPXr0gFgLRwDbqgh8X8salRWD9yKvGxvxzNFb5YI0PUIIIYTIBXroEUIIIUQu0EOPEEIIIXJBjXx6KisrcffddwNY13+Gwx05hNFmK7b222qsLwXb5a1tmG3Ka9as8TLbiQHgjDPO8PK//vUvL9sK5vPnzy967pMnTw76vfDCC15Oy0gJhP5J1peEYbur7VcdWhp7f2MhLYM2EPoAxEIp0/xu2H/K9uM5sn4j1uZdjU2xINaFM5jb+UzzF7Cvb6h/lJ0/Hs/6poi1sF8NAPTq1cvLdi753mN9Lpk0P7jYHmbfSRtGz75EaX5FgHx6LJz2xKYLyBqKHrtnpsHrhn+PgTBDM68h+5tZn0jTI4QQQohcoIceIYQQQuSCGpm3NtlkEx9abU1ObMZi1VXXrl1T+7Ga3GbrbNWqlZe58J0dg9WktpAom06OOeYYL++2225BP1YLsvnNquA4mzCbVWzYLhd3s+aptLBsq/6vLrIaUys3FrIWp62NCjbNTGXHiJlXeC6tejbtPXkmFv5aG/V4VmJznZZhW4Tme07PAYSmQM6EDITzzHs4tkdi6UrS7mW2MCmbRNiVgTP9izBjNhBeH5sCha99WlUEINyzWVOI8NiHHHJI0O+f//ynl9ldpCGzM0vTI4QQQohcoIceIYQQQuSCGpu3qs1aVnXZpUsXL3MElFVJsomobdu2RWUgVK1atSi3sXrWFv5kVXvr1q29zEX2gFCty+Y46wHPn8Xna9XurGq3bawaZjVuy5Ytg35Tp04FEBYobaxkzfKZ1RyS1XwRy+bLbay6bwrXu9TEIgrT1OOxbMq1wa4V3nN8/xFhdJS9b/O91M4r3+/4PsZuCRY2udh7X1pR2O233z7ox5mX+T0c0QsAy5cv9zK7Q+SF1157LbUt9rsT25c857weYpnXee+99dZbQT+ev1mzZnlZ5i0hhBBCiBKjhx4hhBBC5AI99AghhBAiF9TIp2fzzTdH7969AYQh4ADw97//3csdO3b0MlcmB8KwcvbBsfZktkFaGzLbg3k8mxmU7Y4cFmnDNtnGybZLOx77I6WF6Nt+LANhODvbQjmsFFibXdpmHC4nahOSXFvfjjQ/npi/UCxkPa3afVb/ozzDezWW6bquQ8d5zqyPAe+TuXPnerlPnz51eg6NEb6P2f3H90Xrz8b3Xb5v2WvP90++L1q/Er5PcvX0/v37B/1efPFFL/O92t6P2X8ojz49Tz75ZHDcpk0bL9vfDZ4zni/rB8t7lq+37ceZsnme2U/Vfu706dOLfIv6R5oeIYQQQuQCPfQIIYQQIhfUyLzFXHrppcFxtdkLAP7whz942ZptONSbTT82KyerYW3IelroYyzrbiw0k01psfEYbrPnzipeDqsEQtUiqwK58B8AnHrqqQCAG264IfUcGpqsGZRZNR7L5srY0No004ZV19v3pZ0fnzuPl9VclmcWL16c2sbzkRa+DmTP3JxWhNbuTVaxs5pfhFnm7b2P78czZswI2nivckoNOwZf+5jLArsicOHTI444IujHvws8hs1AnFboNC+wGRcIf3esmSktfYvt98QTT3h52LBhXm7evHnQj02hNpN3Wr+ZM2em9qtPpOkRQgghRC7QQ48QQgghcoEeeoQQQgiRC2rs01NtY7c2+sMPP7yoPHr06KAf+wJxdXObYpxt9tbPgkMpYyGyXGmW/QZshXi2NbN9Mmv4MvusAKGPj/U5Ofjgg728yy67eLkh03LXJ/Z6sD8Nz5/tx8dpfh52DMb6jaSFzitkff3wfrHpJPg687W085LVj4pDb7mfnXf2JeFSMiIsBWTXPft3rFy5Mmjj681pSKyvDpfradGiRepnpWF9Qng8Xk88NgC8//77Xt5pp50yfVZTgn1uAGDMmDFetvuN90us1E6af06s1FKsH98rdtttt9TPrU+k6RFCCCFELtBDjxBCCCFyQY3NW2khwWkccMABwfGECROK9nvzzTeDY1bJ2mrnixYt8vJ2223nZWtmstmgRd2SNYSbVeNcQRkI1aG8tuw6Y5U6t9lz4OOslaEZhayvn4EDB3p59uzZQRubSFi1bWH1O89T1mvMpg0gXBN5NHXE4KrzNr2GDQNnuOI231ttqDjfqzkE3la7534s29DrtNQEdm1wiHYeOfvss4Pjc845x8vWvMVmTJtRm0n7fbdpIHif89pYtWpV0I+Pzz///NTPrU+k6RFCCCFELtBDjxBCCCFyQa0zMtc1O++8c/SY2XXXXUt9OqIOYVWoLVzHZifOHGvNTBwJktVUFSskyhF8nHnWqtrTzgGouam3qcAmktNPPz1oe+GFF7xcWVnpZWvqYBNJrKguzxvPZ0VFRdCPzejWhJN32KS8/fbbB21swrLweueIH2u25MjT+++/38vWDHbggQcWHdvuK75f8Fx269Yt6Lf//vunnnse4SzXNsM/YwtkM8uWLSv6us3czOuG96g1OY4cOdLL7IrSkOTzri2EEEKI3KGHHiGEEELkAj30CCGEECIXlI1Pj2h8ZK2y3rdvXy/37NkzaOOKyjFfHbb7c9bQWPX0tHB4IPQjYR8CDse25NWHx8LX2Pp3DB06tOh7li9fHhyzjwBnY7fzue222xaVs4bDK80AcMstt3jZZszlfXXiiScGbezfxv4Y7777btCP/YT69++f6ZyOPfbY1Lbjjz8+0xgihDMe25D1cePGeXnWrFlethUT9t5776Jjn3feecEx+/7wuuFqDOWK7uJCCCGEyAV66BFCCCFELnBpBRqLdnbuAwALS3c6ogjbJUnSdv3daobmssHQfDYdNJdNizqfT81lg5E6lzV66BFCCCGEaKzIvCWEEEKIXKCHHiGEEELkgrJ46HHOHe2cS5xz6bUnwv4LnHNtiry+ulj/yDg16h8Z50znXMf192z6OOdaO+emFv4tcc69R8ebRt5X4ZybkdJ2pXPuoJS2da69c+4k59zPnXNDnHN7FXufWD+ay3zjnPuyMNcznXOvO+d+4pwri9+MvKO9WXvKJU/PyQBeKvz/ywY+l9pwJoAZABY38Hk0OEmSfAigNwA4534FYHWSJH/YwDEvL/a6c64Zil/7oQBuBHAkgNUAxm/I5+cVzWXuWZMkSfX8twNwP4CtYO7RzrmNkyT5osj7RYnQ3qw9Df7U7pzbAsBgAP8H4CR6fYhzboxz7hHn3JvOufucyTTmnGvunHvaOXd2kXEvdM5NdM5Nc85dEfn86wt/yTzvnGtbeK23c25C4b0jnHPbpL3unDsOQH8A9xWespvXyYVpwjjnejrnXi1cr2nOuR6FpmbOudsL8zGq+lo65+4qXOdqLd81zrkpqHpIDq59YY30BrAcwLkAflRo26fwV87owmc+75zrSuPf5pyb5Jyb7ZwbVt/XpLGiucwHSZIsA3AOgPNcFWc65x53zo0G8LxzroVz7s7CWnjNOXcUUHx9FPr+21Vpj2Y4506MfrioFdqbxWnwhx4ARwF4JkmS2QA+dM71o7Y+AC4A8HUA3QBwusgtADwB4IEkSW7nAZ1zhwDoAWAgqiamn3Nu3yKf3QLApCRJegIYi7V/wdwD4OIkSXoBmB57PUmSRwBMAvCtJEl6J0myBmJ9nAvgT4W/IvsDWFR4vQeAmwvzsRJAWtrWD5Mk6Zskyb1Y99r3AfB6kiTzAdwG4PpC2zgAfwZwd2H+7kPVXynVVKBqvRwB4DbnXHrKX8FoLnNCkiTzADQD0K7wUl8AxyVJsh+AnwMYnSTJQAD7A7jWOdcCxdfHYQAWJ0mye5IkuwJ4pp6/Sl7Q3ixCOTz0nAzgwYL8YOG4mleTJFmUJMlXAKai6oJV8xiAvydJck+RMQ8p/HsNwBQAO6Nqoi1fAXioIN8LYLBzriWArZMkGVt4/W4A+6a9nvlbCuYVAJc65y5GVT6F6gfF+UmSTC3IkxHON/NQyutA1Q316ZS2PVGlogeAf6BKw1jNP5Mk+SpJkrcBzEPVmhHrR3OZX55NkqS6vsghAC5xzk0FMAbAZgC6ovj6mA7g4IImYZ8kST4qMrbYcLQ3i9CgDz3OuVYADgBwh3NuAYALAZxQUJ0BwGfU/UuEPkgvAziM+gZDA7i68OTZO0mSHZIk+VuGU1LSohLgnDvGrXWy658kyf0AvgFgDYCnnHMHFLrG5pv5JPJxhwAYVYvTtHOvtVAEzWV+cc51Q9VcVhde4rlzAI6le27XJElmFVsfBa1+X1Q9/FzlnCvqSyJqhvZmNhpa03McgH8kSbJdkiQVSZJ0ATAfwD4Z3ns5gBUAbi7SNhLAWa7KXwjOuU6uyhHPslHhHADgFAAvFf7qWOGcqz6H0wCMTXu9IH8MYMsM55xLkiQZQTfDSYWb57wkSW5Elcau1wYM7699QRu3ccHJL2grMB5r/ca+BWActR3vnNvIOdcdVabUtzbgnJosmst84qr8HW8DcFNSPKPtSAA/qP4j1DnXp/D/OuvDVUUBfVowm1yLqgcgsYFob2ajoR96TgYwwrz2KEITV4zzATR3zv2eX0ySZBSq1GuvOOemA3gExR9KPgEw0FWF8B0A4MrC62egyiY9DVU+Qet7/S5U2SflyJyNEwDMKKjCd0WVr1RtuQuFa4+qv2qeo7YnAFT/9bMPgB8A+HZh/k5D1fqp5h0Ar6JKZXtukiT/3YBzyhOay6ZL88L1nomquRgFIC0o5NcANgEwrdD/14XXi62P3QC8WnjtlwCuKuF3yDPam0VQGQrRZHDO3QHgjiRJJtTwfXcBeLLglC7KALlFSkYAACAASURBVM2lEOVJY9+b5ZKnR4gNJkmS7zT0OYi6QXMpRHnS2PemND1CCCGEyAUN7dMjhBBCCFEv6KFHCCGEELlADz1CCCGEyAV66BFCCCFELqhR9FabNm2SioqKEp1KOl98ERbwXbVqlZcrKyu93KxZs6DfZputLeux0UZrn+/seJ98sjbxZIsWLbzcqVOnoB+PUV8sWLAAlZWVxbJObxANNZd5Z/LkyZVJkrSt63HLcT4//vhjL3/ta18L2jbddNNMY3z22drksZ9++qmXt9lmmw08uw1He7NpUYq9qblsGGJzWaOHnoqKCkyaNKlGH26jw4pXjYizbNmy4Hj06NFevv32tbVGt95666DfLrvs4mW+6a5YsSLo98orr3h5jz328PJvf/vboF/z5tnyDvJ3rs33Zfr3779B70+jNnMpNhzn3MJSjFsX85kWyVnbNTx27Fgvd+/ePWjr3LlzpjHmz5/vZf5+xx9/fK3OqS7R3mxalGJvai4bhthcliRPT9YffdbS/OlPfwranntubcLH//43TNrI2pjPP//cyxMnTgz6DR8+vOjnbrLJJsExa3T+85//eHmvvfYK+rVq1crL++23n5d/8IMfBP3K4a9QIWoK79uYVnPRokVevvPOO4O26667zsuska0L+JxOO+20oO2aa67x8vnnn48sfPXVV6njCyGaJtrlQgghhMgFeugRQgghRC7QQ48QQgghckG9196aO3eul4cNG+blbbfdNujHTsnWB4ejtNhB2ToWrl69er3vAUK/oA8++MDLNsqLI0meffZZL7/88stBv+9+97te/uY3vwkhypGsPi19+vQJjt9++20v854AgM0339zLvKetXx77vfFef//994N+a9as8TIHEtjxfvrTn3qZAxAOPPDAoN/999/vZft9+XrIvycd6/Cedt1i/pyx8ke1cZwfP358cMz+mG+99ZaXd9xxxw3+rKZMXQczZOXUU0/18o9//OOgrW/fvl7m+439Hc+KdrYQQgghcoEeeoQQQgiRC0pi3oqpwn72s595uUOHDl62Yd5sWrLjbbzx2tNmdRybs4BQ/cUym7OAMDkhm9L4c4Aw2SGrdO14N998s5cPOeSQoG2LLbaAEA1F1rD0Pffc08szZswI2tq3b+9lu/Z5r3Kb3UtLlizxMpu0bC4sTmLIJi3ei/aY7x0PPPBA0I8THP7rX/8K2vh61GWurTyR9VrV5pqOGTMmOJ4+fbqX2eQKAJdeeqmXeS5HjRoV9KutiaQcybpmY/34mPtlzbf3v//9Lzjm31Oer+OOOy7oN3v2bC/b33Hep3WxF6XpEUIIIUQu0EOPEEIIIXJByaO3bDQGq7W32morL1u1GKvDWSUNhOaoL7/80su29hYfs+raRn7w+NwvFjXGZiqraufze/zxx4O2U045BUI0FDH18IgRI7w8YcIEL3fp0iXox6Zdu295/DQZCPc+q85tRFmaOc7uYR6f923Xrl2DfiNHjvTy008/HbQNHTo09XzzQFYThn3d3nfTuOeee7zM5X7GjRsX9Lvxxhu93LFjRy+//vrrQT+OxOIIHwC44YYbvNy7d+9M59fYSTNNxfrx76eF96KNZGYzNPezv5kvvviil4855hgv29p7O++8s5fZPcRix68N0vQIIYQQIhfooUcIIYQQuUAPPUIIIYTIBSX36VmxYkVwzD49bAu2mV3Zz8bajDkUNi3MFAhtjWzHtPZJJmYXZT8jztzcpk2b1PPjavGAfHpE/RPze2M4eziv6Y8//jjoF8uWzj4+sT3HbVmzH8f6pd0HbEg9n/vhhx8etLH/IWeTtuduw+/FWmbNmuVle9045HzSpEleXr58edDvjDPO8PJ+++3nZeu3w2OwDIQ+I3PmzPHyDjvsED3/pkJWn7TY/YDbYr40vPfefffdoI332JZbbull60t03XXXeblTp05BW12nj5CmRwghhBC5QA89QgghhMgFJdfTTps2LThmlSebumyoKh/bkHAOY+zevbuXKyoqgn5c/JBD7Fq0aBH0Y9Udm9k4gyQAPPHEE0XHW7lyZdCPM0py+LoQDUGaCvuoo44Kjtn0wykZFixYkNrPmpzS1OCx0NjaYD+X1d78fe19he8J9r7C5peTTjqp6HhNmaymA5tChIt9slmwZcuWQb+zzjrLy9dff72XrTmDC04uW7Ys9fw4zHnKlClBGxeE5nnOi3krazFhy9KlS73MZscPP/ww6Dd58uSi77EmzVatWnmZ18ZHH30U9LPFwkuJND1CCCGEyAV66BFCCCFELii5eYvVxACwzz77ePm+++7zsi1qyAXjWI0Zw6pd16xZU1S2JifO7sqmLxtpdfXVV3t5wIABXmYzHRCq0OfNm5fp3IWob1555ZXUNhtNycRU5bEszEwsY2wWshZKtOfK0WU2q/PEiRO9zPetvGRntiZIvnZ8DWKFnfk+bguE/uUvf/HyM8884+VDDz009ZzatWuX2samLzajAMB7773n5TvvvNPLe++9d9Bv1113TR2/MROby7lz53r5ggsuCPqxqwZHW82cOTPoxy4mb7zxhpeHDBkS9GPTJd9TbKHXWER1VrKa0KXpEUIIIUQu0EOPEEIIIXKBHnqEEEIIkQtK7tNz0UUXBcdsW9x///293KdPn6DfqlWrvGx9ethmz9WaW7duHfRLyxxrbfQ8HofSWT8jDndkfyQO77XnYW2Xeae21X/T/Atqmy2XQzqzhnNa2D+EP7ex+IBw2gUgzF4cu448h7GMzDxGzN4eCzFPWy+xMHJeEzYsnf0KbOqK+++/38ucITYvxNIAMHbd8ByNHj3ay6eeemrQ77bbbtvQUwzgMGr+vQCAfv36eZmzM1tfNRuK3VSIZVDmNC933XVX0GZ/Q2tK27Ztg2P2m2P/qRNPPDHoxz5CsXs/t8UqJsSQpkcIIYQQuUAPPUIIIYTIBSU3b9lwxOeff97Ljz76qJdHjRoV9OOic7fcckvQxiYoLiZnQynTzCCsggdC9Ser0qx6lkP4fve733nZmrC22WYbLw8fPjxo4+ylNswyD2Q1/VjVZdr7sqo07Rq66qqrvLx48eJMY1hiKuRy5fXXX/cyF80Fwgy6rJbm/WHbrPkorbipNVtxWyzMPa3YYKy4MK8J248LINt9m/dColn3Jt8HAWDfffctKls4bQivm6ypDWw/LhDL91wgdHsYOnRo0fcAwMKFC1M/Ow9YcxbvI97LWe917LIChL/xPEdjx44N+l188cVezloE1ZLVVClNjxBCCCFygR56hBBCCJEL9NAjhBBCiFxQciP2JZdcEn4g2c05TG2XXXYJ+j3++ONevvLKK1PHZ1ujtdGn+Q1Y232av48tV8Eh8IMGDfIyV48FQrumreqbRz+eGGk2+6z+FRxmDABTp0718sMPP+xl63vCoZUnn3yylx944IFMnwuEId6///3vvfyLX/wi8xj1Da9162fDsH+cDWXmObMpA7iNx7e+NewvwOPHQtZj9vy0fjb8le8X9nstWrQodXyRTta5ZLittlXs2SfNpg1JW4fW7zPvflwx38mYHw/ve76Gp59+etCP78H8WeyLC4T+XjYlAsMlL77//e8HbVzyIoY0PUIIIYTIBXroEUIIIUQuKLlu75hjjgmOOWR98uTJXuawQgD4xje+4WWupgsAXbt29TKrVm0oOqvMYhlhWT3HFdKteu/jjz/2Moc6Xn/99UE/brOVhjnztM1C3VSJhZ2mhau+/fbbwTGrSbk6uE110K1bNy937tzZyzbMdsGCBV5+6qmn0k49yoMPPujl//znP7Uao76ZMmWKl9k8B6SHhNuQdVY/WxNwmkrcznNahm1rcuJ9G8vEnba/7et8T7DZY9lEwvPJpmyxLmnmKfs6r5vY/Th2v2B47d19991B27Bhw7x8yimneNmawWKmlDxQ2+zxaVns+boDYZg6V3DnlAJA+FzQpUuXoM0+Q1TD6SeA0NWBKyZYpOkRQgghRC7QQ48QQgghckHJzVuzZs0Kjtl8xFFPe+yxR9Dv5Zdf9vL06dODNlbJxSIE0jK9xopepkUi2PNllWnv3r2Dfttvv72Xrapup512Sv3sciRWmJPNI9YEwsRUqKzyvPTSS7380EMPBf24OGSHDh28PHDgwKAfmzg//fRTL9uite+9956XL7vsstTzY9OqPacf//jHXn7zzTe9zGZbICx+2NDw2rf7gM0RWTOw2jH4fZy52Zo60sxWsb3J2DXFhSQ5s7SN1mGzmP2OPMYNN9zg5ZpE9JU7WTOdl5pYhF1aPwtnE7auApMmTfLyd7/7XS/PnTs36LfXXnut/2SbGFnNh7F7RdZ1w79/7B6yfPnyoN+RRx6ZOkb79u29zHvWZn/m34UY0vQIIYQQIhfooUcIIYQQuUAPPUIIIYTIBSX36bE2VLbfvvvuu162WY1joeMcdsi2RptdM80/J1bJmf1A7Oeyfwefn/UbYH8R9lkBgCVLlniZw6vLiZgtl4n58TAcjshVd4EwzJCzVffs2TPox3P70UcfeXnVqlVBPw5BZT8gtvED4Xrj8MZrr702dbzddtstaGMfEPZfseHx5YQN2WXSqirbeeY1EfPHYGK+d1mJhdHzPuP9bcPyOau6PScek+ezKdFQPjwxsmZk5mzrALD77rt7mbOqA8CTTz7p5ZEjR3rZrgfrc5kHarMG0kLU18frr7/u5V69ennZVrvn9B/2nn755Zd7mX9rDz744FqdkzQ9QgghhMgFeugRQgghRC4ouXnLmke48CObLKxJgM1MVrXGamlWr9vPSgu3tv3SiuRZVSi3tWnTBmlwOJ7NHLt48WIvl6t5i9WfWVXPN954o5dvvfXWoG3p0qVeturkXXfd1cu8Hvg9sfOLmSp5Xm32XatCrcaGsI4YMSL1PK666iov33zzzV7ebrvtgn733ntv6hj1zW9/+1svW/MtH7PpzoaXcqhw1hDzuoD3ujVv8Trlc7dZ2tm8x/cYIDRZ/+tf//JyuYR5NyV4LmP3mGuuucbLdh2ee+65Xv7HP/4RtPEaPfzww73MmdiB7Cb6vJAWzm5/x9KKedu9wkXA+Te+JveN3/zmN17m3+Djjz8+8xiMND1CCCGEyAV66BFCCCFELii5ectGSKSZH7gwGRAWBoyZt2Kq5qwZmdPU+lalx5/LWSLZZAeEqj87BmelLBe4CCUAPPvss15+6623vGwjWthUx9+LI2SAsPAnR14B4fW2bQybHviaxkyVbNqwa4ijsnj+bOFQzvJpi2t26tTJyzvuuKOXrdnk9ttvR7kwb948L7PqGQjngk271lzH368+zVtMbA/zWrTmrVg2dza5VFRUFH2PqBv4HmlNTr/61a+8zHu9Xbt2QT+OBO3Ro0fQxvPO96nGaM7itc5rNrb37P2uttFXae9P2xP9+/cPjjlrMkfRxbBuJbwv+V4UczGJIU2PEEIIIXKBHnqEEEIIkQv00COEEEKIXFBynx4L22jZLmgzMlu/iDTSfITsZ7Et1Nry+Thr9V/2h4iFyseyRDcky5Ytw0033QQAGD58eNDG/lSxLLhsN+fsx/Z6cBZNO0fsq8O+QNYXitcK+xbZz2K/FJ4H/k52DLYhc4VuIFwP1u+M/Uh4/HLz2+IM4Xye1iaelo3czllapnMgPeTVhiVbu30aPD6PEQuNZd8wu2bZf8vOE+/Vd955J9P5lQv2vpI11URdfzbPi51j3uuzZs3y8oUXXhj0Y/84ztp/3XXXBf1ivlacvZn92Pbcc8/U95SaWOqDWOXz2qQQqWtiPkHf/OY3vcxZlwHg73//e9H32N9gHt/e+9mXsk+fPus/2fUgTY8QQgghcoEeeoQQQgiRC0pu3soa7mlNB1bFxaRlV7ampLTQ9tg58RhWZcyfxWYCG6LNJhZLuRQybN26NU477TQAwIABA4K2l19+2cszZszw8sKFC4N+bB5YsWKFl22YMF9Tq9bkIq6VlZVejplUWG1uPystjNMW2mRzHJtArPqY14pNTcDnwap7Gwp+xBFHePn3v/990fMrJePGjSv6eszkxOYt+705M641H6Wp4rOmlqgtfM15bu06YlOrvcfw96yLAqn1SczsEQttrotrn+YSwHsCCM2sf/zjH718wAEHBP04bcTDDz9cq3Pi7xU7p/oklj2+NvPw5ptvBsd33nmnl63J0GakryZmZuLfKnsP+MUvfuHlDz74wMvWVSKNmLkslqKme/fuqe/Lmj5Dmh4hhBBC5AI99AghhBAiF9R79FZWWLVmVbdpGSpjKumY+jCt4Kg1U6xcudLLbN6y2UA5csCq/xsqg20xqs+Fi34CwKBBg4r2t2a7+fPne3nOnDlethlWOSOqNe+lzaVVcXIBQS5cx68DoamRI7GsCZLV3DGVN5t8YnPHkVBsXgEaPqOvLSxajV3fadleed0DobkgZlJO21f2mM8vdo35c+01TTPH2e/OZlhrvrbfpalQ1+svFoUUM7NxpuWOHTt6edq0aUG/hx56aAPPMFx7bDav74zMSZJ4E3wsezyvPTYdAcAdd9zhZRvlzPD9+LHHHgvaOLN+2jnYc+R9xFF0QGh2fOqpp1LPiX8nOQt+zKzGexQI19fgwYNTP0vmLSGEEEIIQg89QgghhMgFeugRQgghRC4ouRGb/S+AMGQ05oPDtkBrl2e7cSz0LS3jpbX9pYXHx/xx+Ny7du0a9Js0aZKXrd9EuWRkbtasmfdzsdXD33//fS/H7KStWrXy8pAhQ7xs/XbSfEqAdD8NuzZ4zLTwdSAMYef38LoDwjDLWFVuPne7TjiDMa9z6xtiq5TXN/vtt1/R162vR5qPgZ0LviYxvyAe3147PmZbv73+aeHQdjw+p1jGaB6/obLbloKYnw37ZC1dujTox3ud93CMrD5Cv/zlL4NjXlPsxzNixIhM48XSmMQy37NPT33jnIve/4oxZcqU4JjnLHaP5Cr0nAoEAJ544gkvH3nkkdHzLcbJJ58cHB922GFejoWR897OypIlS4Jj9pHca6+9ajyeRZoeIYQQQuQCPfQIIYQQIheUxLzFJodYFsqtttoqdQxWQ8dCSXn8mGo8ayhszHSWpq6vqKgI+vF5xNTr5YINsbbHabAJMmY2YNOSDXtPux7WDJhWFDb2Pp4va2bt1KmTl3ltWBV67HulrRt7/Tg8tyH497//XfR1a77lYzb/tW/fPrWf3Vdpa99eOzaLpZnEgPAax/rxvMUyK6fNWbHjxkTM5PTGG2942YYe8z3YFnmuTfZizro8fvz4oI3NzWlZwmPEzLGxvg1ZPHb16tV48cUXi57Hcccd52Ves2xytHAaDlvFgE1J9h50/vnnezlm3mKOOuooL8+cOTNosyHxdQkXDAayr0OFrAshhBBCEHroEUIIIUQuKIl5K1bck9XfbGKwxLKvpqk1rXorLWLLvj8tc6z9XDazccSPzcgcM2+VU0bmDYXVqTEvfauGFfXLM888U/R1azZmkxOv71tvvTXo961vfcvL1jzJhV157VtTGrfF9nrae2yEIB+zetxGrnHRXJulOw0b8WTNfaWg+j6RNVIqFr1VFxEvWTn77LO9PHv27KDtySef3KCxY5n5LbxWbGHO+uSzzz7DvHnzAADf/e53g7bLLrvMy7xv2ERo2zgSzJoq+X2xop0XXXSRl7/zne8E/S6++GIvv/DCC14+6KCDgn42E35dYs171jUhjax7RZoeIYQQQuQCPfQIIYQQIhfooUcIIYQQuaDkGZmtnY1ti7FQ3qxZVdNCWou9r5qsVYJjNmP2G+jZs2fQFqv83pR8ekTjgNMEsH3chiin7ZdjjjkmOP7hD3/o5fvvvz9oY1+g5cuXe7lDhw6p58RYvw3em+zPYDNs8/sGDRrkZQ7VBYCxY8cWHbvYZ1fz+OOPB8fst1IqaloZPdaf7zmHH3540MZ+IJdccknQdsopp2T67CuvvNLL7D92wQUXBP122223TOPVBfy7YKt21yetW7fGmWeeCQD461//GrRxKgE+R7sPubI6r3vOtA0Abdq08bL1eeM1cO211xaVAaBt27ZeZj/NK664Amnwb1wsjUBW7PfK6nuX9bOl6RFCCCFELtBDjxBCCCFyQb2bt1jNFivEyOGzrHIDQhV9LItqWtHEWKFTPj+rgk8rYBkLvbfnFyuaJ0Qp4D3I5qesamPL7373u6JyDKtu5/PgPWfvF3zMYe+xbO5ZiWWT5gy5XKwRKL156+OPP8aYMWMArBvqz/c+LvhrM/Dy/ZO/C8sAMGfOHC9fd911QRuHKXMxy1GjRgX9/vSnP3mZi5ZmXRu1JWbS43u8LYrbUNjM/RMmTPAyF622RZQ5ZQJ/Lw5lB8Lfq9i14RQisWvDZrWYabKmplhg3d9WNqXZjMxpKSLsPcWu7TSk6RFCCCFELtBDjxBCCCFygR56hBBCCJELSuLTk1b+wRJLL802P2u749DVDz/80Ms2rX7W8HOGbabWb+CTTz7xMqfKtrZEPnfrw2PttUKUmr/97W9eHj58uJd5PQN1H3rK2D2S1f5e17BfBVeSB0IfJ77n7L333iU/L+bzzz/HggULAMD/X82yZcu8zH5RfE8EQr8Nvg926dIl6Hfqqad6uVevXkHbc88952WumD59+vSg3+DBg73MfkHWH4nvi6X2s2EfkUMPPbSkn5WVn/3sZ8HxAw884GUuKWF/q/h3kn+T7DVk3xr7u8P+ajy+9W/lNWXTUTAbeq+I/R7b3/s0n56Yb24MaXqEEEIIkQv00COEEEKIXFAS8xZnw7Qqzqwmp+OOO87Lq1atCto4hJ0/Kxa+zv1i1dhZVWfNZS1btvRy//79Uz+LVc32nPg8hKgP2GzDVcZt9W3eZ1mz8caIpYng41jIa1qbVanzcSwE/rDDDvPyHXfcEbRxGoojjjjCy1x5uj7gLL5ZYTM/ACxatMjLnBmbXwfCa8VrAwhNWrw2bFZnXivWfMbUZ+g4m7f++Mc/epkrm9c3Nuybrz1nsr788suDfhMnTvSy/S2sa/bZZx8v77///iX7nJhJjNcdkF65oTah8oA0PUIIIYTICXroEUIIIUQuKIl5a82aNV6OqbVtYTHGero3JljtZr9/7DsLUWpimV85csOaQRiO+rKZgBlWYdd1NFgMNiFbE3Xv3r1T29i8dd5555Xo7EpD69ato8d5g6P0GsNcstmVZcvs2bO9PHny5KBt2rRpXuZCskBo4uTfJ1tN4Lbbbiv6udYlZEP3c8zUedFFFwXHO+20U9F+1nUmK9L0CCGEECIX6KFHCCGEELlADz1CCCGEyAUl8enh6r877rhj0MYhjYMGDUodIxbOXttQtfqCQzjnz58ftPXr16++T0cID++ra6+9NmjjfduhQ4fUMcqlanUasfsDp7vgsGYg/F716YMkSsuvf/3rhj6FOoN/T+1v68knn1yyz63r39zYeAcddFCmMWIpamJoZwshhBAiF+ihRwghhBC5wGUtxAkAzrkPACxcb0dRl2yXJEnb9XerGZrLBkPz2XTQXDYt6nw+NZcNRupc1uihRwghhBCisSLzlhBCCCFygR56hBBCCJELyvahxzn3pXNuqnNuhnPuYefc5uvpP8Y5178gL3DOtamfMxVZcM793Dk30zk3rTCv6fkKaj72EOfck3U1noijvdl0KcU+5fnfkD6i5mg+16UkeXrqiDVJkvQGAOfcfQDOBfDHhj0lwFUlGHBJkny13s4CAOCc2xPAMAB9kyT5rPCjV7vCKXWMc27jJEm+aOjzaGRobzZBynmfipqj+SxO2Wp6DOMA7GD/onfO3eScOzP2Rufcjwt/kc5wzl1QeO13zrnvU59fOed+WpAvdM5NLDwZX1F4rcI595Zz7h4AMwB0KfZZIpUOACqTJPkMAJIkqUySZHHhr/4rnHNTnHPTnXM7A4BzroVz7k7n3KvOudecc0cVXq9wzo0r9J/inNvLfpBzbkDhPd2dc/2cc2Odc5OdcyOdcx0KfcY4525wzk0CcH79XYYmifZm0yFtn15euO4znHN/LTxcVu+jawr7dLZzbp/C682dcw8652Y550YA8FkgnXO3OucmFbQPVzTEl8wRms8ilP1Dj3NuYwBDAUyvxXv7Afg2gEEA9gBwtnOuD4CHAJxAXU8A8JBz7hAAPQAMBNAbQD/n3L6FPj0A3JIkSc8kSRSCWDNGAehS2Ei3OOf2o7bKJEn6ArgVwE8Lr/0cwOgkSQYC2B/Atc65FgCWATi40P9EADfyhxQegm4DcBSAdwD8GcBxSZL0A3AngN9Q902TJOmfJMl1df1l84L2ZpMjbZ/elCTJgCRJdkXVD94wes/GhX16AYBfFl77fwA+TZJkl8JrnIb+50mS9AfQC8B+zrlepfxCOUfzWYRyfuhp7pybCmASqn7A/laLMQYDGJEkySdJkqwGMBzAPkmSvAagnXOuo3NudwArkiR5F8AhhX+vAZgCYGdU3VABYGGSJBM27Cvlk8K17wfgHAAfoOpH7MxC8/DC/5MBVBTkQwBcUpj/MQA2A9AVwCYAbnfOTQfwMICv08fsAuCvAI5MkuQdADsB2BXAs4VxfgGgM/V/qO6+Ye7Q3myCRPbp/s65/xT23QEAetLbiu3ffQHcWxhzGoBp1P8E59wUVM1jT4R7WNQhms/iNAqfnmqcc18gfFDbbAPGfxjAcQC2xdofQAfg6iRJ/mI+twLAJxvwWbknSZIvUfUAM6aw2c4oNH1W+P9LrF2PDsCxSZK8xWM4534FYCmA3VG1Dv5Lze+jaj30AbC4MMbMJEn2TDklzWft0d5sohTZp99F1V/x/ZMkebewB3lui+3fojjntkeVNndAkiQrnHN3YcPWiVgPms91KWdNTzEWAvi6c+5rzrmtARy4nv7jABztnNu8YB45pvAaUHUzPQlVN9eHC6+NBHCWc24LwraqAgAAIABJREFUAHDOdXLOtavrL5E3nHM7Oed60Eu9Ec9SOhLAD8jW3KfweksA7xccVU8DwBXnVgI4AsDVzrkhAN4C0NZVOfPBObeJc47/ohF1i/ZmIydln1b/4VFZuPbHZRjqRQCnFMbcFVU/sgCwFaoeUD9yzrVHlWlUlAjNZ3HKWdOzDoUn03+iymFxPqpUarH+UwpPn68WXrqjoD5HkiQznXNbAngvSZL3C6+Ncs7tAuCVwu/tagCnouqpV9SeLQD8ufBj+AWAOahSuQ5L6f9rADcAmOac2whVcz0MwC0AHnXOnQ7gGZi/8JMkWeqcGwbgaQBnoWpD3+ica4mqtX4DgJl1/N0EtDebCGn7dCWq5nUJgIkZxrkVwN+dc7MAzEKVqQRJkrzunHsNwJsA3gXwcp1/A8FoPougMhRCCCGEyAWNzbwlhBBCCFEr9NAjhBBCiFyghx4hhBBC5AI99AghhBAiF+ihRwghhBC5QA89QgghhMgFNcrT06ZNm6SioqIkJ/LVV2Fh5Pfee8/Ln3wSJlxt3bq1l9u2bVuS8wGAFStWBMeVlZVe3mqrrbzcvn37kp3DggULUFlZ6ep63FLOZan573/XJmJetWpV0Nas2dp8hRtttPaZfosttgj6bbLJJiU6uziTJ0+uTJKkzhdtY57Pxor2ZtOiFHtTc9kwxOayRg89FRUVmDRpUt2clcE+2Fx22WVeHj9+fNB2+umne/l73/teSc4HAB5++OHg+I477vDy0KFrk09ecMEFJTuH/v37l2TcUs5lqXnrrbXVKZ555pmgrVWrVl7ebLO1GdH32issyN6pU6cNPg/OcVVImLdenHMlKYjZmOezsaK92bQoxd7UXDYMsbmUeUsIIYQQuaBBy1Cce+65Xh47dmzQxuYuaz5iLdCNN97o5S5dugT9evRYW3akZcuWXl6+fHnQjzVJn3/+uZet6aRDhw5evvXWW738xBNPBP1uv/12L3fr1g0iG1k1J//v//0/L7/66qtB2xdffOHlzz77DGl85zvf8fLrr7/u5U8//TTot++++3r5uuuuC9qaN2/u5S+/XFsNgU1sQgghygdpeoQQQgiRC/TQI4QQQohcoIceIYQQQuSCevfpGT16tJfnz5/v5T59+gT92J/GhrPvvvvuXv7ggw+8PHfu3KAfR4RxpMW0adOCfhtvvPYytGnTJvWcli1b5uXtt9/eyytXrgz6/eQnP/HyiBEjILKR1adnyZIlXt5mm22CNvbJ2nTTTb1s5+jee+/1MofA21D2mTNnepnXCRD6k/Hnsq+PEEKI8kGaHiGEEELkAj30CCGEECIX1Lt569lnn/UyZ6q04cVsZvjf//4XtLEJik0ObB4BwjBiNlNY8wNn691yyy29zFmhAWDzzTcv+lmdO3cO+rFp7qWXXgraBg8eDFEcNmNyNmUgNB+98847Xm7RokXQj0PW2bxpMzKzWYzNrGwSA8J5/tGPfpR67vZ8hRBClB+6UwshhBAiF+ihRwghhBC5oN7NW4sXL/YyF+2MmbfYTGX7sjnCmjDYJMLYjLlsjuKMvGzOsuOzOcOeH0ceybwVh81HNkqP4ag/NluxOTI2hl0LPAavJ2tK7dWrV9H3AGEU2bbbbpt6DjJ9CSFEeaC7sRBCCCFygR56hBBCCJEL9NAjhBBCiFxQcp8e69/A/jNc+ZxlIMySa2G/C/anWb16ddCPw5fZ98f6bfA58nvsufP7Nttss9TzY5+e2bNnp/YT4bWy4eLMxIkTvcz+M1tvvXXQ76233io6tvXP4kzeDPuZAcBRRx3l5VGjRgVt/fr1K3pONnWCEEKI8kCaHiGEEELkAj30CCGEECIXlNy8xdlugdBktGbNGi9bswJnzLXmqI8//tjLnJHZhiWzmYHNZdb8wOHxbN6y/dhcwmHI1nTC2KzOIiRrkdEXXnih6OvWvHXwwQd7ed68ealjs3mrd+/eXp46dWrQj9fUscceG7Rtt912Rc/JpkQQ2VmwYEFwvGjRIi8r3YMQYkORpkcIIYQQuUAPPUIIIYTIBSU3b73//vvB8de+9jUvs4nImpLYdGAzHnMWXn6fjd5isxV/Fr8OhOYzLkZqzRQcXdShQwcv20y9fB6tW7cO2tis0rZtW+Qdnls2VVrYVMVZsydMmBD0a9WqlZd5bdjowCFDhniZTSgnn3xy0O+3v/1t6jllNc2JOA8//LCXL7vssqDtsMMO8zKbMnfdddeSntO9997r5R133DFoGzhwYEk/WwhROqTpEUIIIUQu0EOPEEIIIXKBHnqEEEIIkQtK7tPz4YcfBsfsC/PRRx95+cUXXwz6fetb3/Jyx44dgzb2E+IK2eyPA6Rn+LW+I9yPQ9Ztv3bt2nmZfUlsFe1ddtnFy5yBGgDefPNNL8unJz28e9y4ccHxsmXLvMz+HHZ9rVixwsuc9sBmYOYMynPmzPEyz52oOZySgveFTd3wwx/+sGhbt27dgn7Tpk3z8jnnnOPl8ePHZzof6+d35513ermysjJo4xQaW2yxhZft/aepEkvREePGG2/0ct++fb3M90sgvGfyva9Xr15Bv06dOmX63KxcffXVXu7Zs2fQ9o1vfKNOP0uUP9L0CCGEECIX6KFHCCGEELmg5OYta1bgbMqcZdf2mzx5spf33XffoI1V3hzGas1ZrGrnMHWbuZlNWpy52Yaicxg9Z2H+z3/+E/TjMTp37hy0vf76617eZ599kHfSVOgcMgyEqneeL5sSgE2caZm2bT/m+OOPD45//OMfe/mPf/xj6rkrfL2KtGKry5cvD465MGxFRYWXYyYRvkfY9bH//vt7+cknn/TyiBEjgn5swrL774wzzvByqUPiyxGbGiQthcRzzz0XHJ900kleZrOVvfac7Zzvn7fcckvQj02cAwYM8DIX+AVCU7TN5P388897eeHChV7m+Qdk3sqK3de8Bni+unfvnvq+crkvStMjhBBCiFyghx4hhBBC5AI99AghhBAiF5Tcp+c73/lOcMxVsFeuXOllDnsEwtBSDvMGgM0228zL7MdjfXU4ZJZLTVj7JI/Btmb2PwKAV1991cucOt/6enAI7m233Ra0cRmOPGL9BtJC1keNGhUcs+8OX18uSQGE85yWsgBYN9S9mtNOOy31/I466qig7bHHHvNyudir6wr2h7PfLfZd0+Zzt912C465XMjMmTO9zGkGgNCPg+fsBz/4QdCPfed23313L//kJz8J+rGvDqfPsKT5kAHrlrFpTPC8AuE90vrwzJo1y8t8v+OyLQDw1FNPeZnnz16nrl27Fv0sWyKGj999910vT5w4MejH/kP23E844QQvc4qT2bNno6lSF/4zXO7nyiuv9DL73QHA2LFjvXzkkUd6mX0gN+Q80rjpppu83Lt376Bt8ODBmcaQpkcIIYQQuUAPPUIIIYTIBSU3b1k47Hv48OGp/VgNbbPzsio7LUTWwmpdq+Jlk8tWW23lZWsC4X6snr/qqqsynYOIqzs5FYENQd1+++29zFm42dQJAF26dPEyq2ptllebRbsaXp8A8PLLL3uZs4Q3BWKmjrTrU1dce+21Xj7wwAO9zCZDIMyMzOaR9u3bB/1Y7b3ffvtt8PnxOm0M5ix7H+RjltPMjwDwzDPPBMfXX3+9l8877zwv26zZaSajpUuXBsd8Tdks3aJFi6Afr0tOLWHXK68Nm2qC1y+byDhjO7Cuqa4cSfuNq4nZmc3+bE5+/PHHg35sCmSmT58eHHOoP19T+1tdm7QsnK4GAL73ve8VPY+jjz466CfzlhBCCCEEoYceIYQQQuSCkpu3rGouzcxkVcgc7cFqTCBU4/EYNsqCPfpj6np+H4/NkVxAqCaNYSOUmJh6OQ/E5oEjtux64Kg3VtXaOecCk2wGs0UjObsvf9Y777wT9LvssstSz/fMM8/08l133ZXar76o3msxNTfvx9hcLFmyxMv/+Mc/grann37ay6NHj67xeQLAoEGDvMyRNjw2EO7hNLMHEEYXxcxbvDe54DEQrh3O3Lt48eKgX3WEko0cbEjsfZbnlq8bZ8IGgJ122snLV1xxRdDGEbScnZ5NzQBw6qmn1vh8OXJ35MiRQRtnbmYTtTWDcfZfm9GfTWs8T/a+Uh/mreq5iRV0je3Z2kRA2fvYpZde6mVeD2wyBsIoLXbh2HLLLYN+bBbjqgg2CzdXK+AIXDsPHKFtz33vvff2Mrs9zJgxA7VBmh4hhBBC5AI99AghhBAiF+ihRwghhBC5oOQ+PdYeyT4tMZ8C68fDcKZdrmhus3Ky/T7ND8ieB49nbcixDL9p4zW1TL21gefB+jSx3w1n5bbZNtkXgTNv2zmxtudq2rRpExzPnTu36PlxygIg9NWx4exjxozxMlf2HjZsWNFzqC/s+s66Bi+44AIvc/Zxe004RJXDSYF1K2Zn4S9/+YuXH3jggaCNrzHb82229LvvvtvL7HvHGeCB0Idj1apVQRv7h/G9xPof9OjRA0DoA1RfpGXdtfdSnj+eLw7tB4ADDjjAy//+97+DNr7e7LfD/lOWtGtoYT+QE088MWjjY/bbuPnmm4N+zz77rJfZzw8I/bD4fmEzftcH1fOUdR/a/cvrrLKy0svW92X58uVefvvtt4M2TuXBGcvZfwoI74W8l+11O+igg4qeu70f837jfWmrJ7DPJmfaBkKfrMMPP9zLNiUC+53FkKZHCCGEELlADz1CCCGEyAX1npGZYVWaVYWyutK2sbqZVX82jJVNVfweqz7k8TlU1arqdtxxxyLfYl3qovBbUyIWps/ZrFn9yepvIFTPppm6gHVNklnOideDNRPwmmJTHBBmg+aii9Zscsopp2Q6pw2lpmp0S8+ePb183333ebnanFPNDjvs4GUbonrJJZd42YbDpsF7k1XvQKhi5+vPYawA0KdPHy9zugtbKHHgwIFFx7PwPcFmZm/Xrh2A7GutNlSvyaxZd2+99dbgmE1TPK9DhgwJ+rGJyLa99NJLXmazQuw+yOcXC9HOeo9kk7dNHcC/H9bcyXuQ7yXWbcKmsigl9ncnLUybzVRAmFqBTT3WlM+mRXvtv/71r3v5xRdf9DKHkQNhpvPqdQ6se0/jqgiMNTHxfuY0BXbv8O+4TQXBKRK4GC2bcIHQ9BdDmh4hhBBC5AI99AghhBAiFzSoeSvGe++952UbPcFmK8aq1tIKBVoTRpopLRblxV7pVtWXtQhqUyV23SwcHcVqaJv9miOI2HwxZ86coB9HqrBpw0baZC0iyeZOq07myJfaRC3VJUmSeFOfVQ+zSjhmSjj77LO9zFFU1uxx+eWXe3mPPfYI2ji7Lo9n53PChAle5qy7dm/36tXLywMGDPCyVY+zqYqj7CZNmhT04/NgdTsQmlB5DdusvdWmnlKarmta8NXeg9jcx2YPa6rkws72e/bt27doG0faWLJmnI9dO15Dt99+u5cPO+ywoB8XOrXRmZxNn9e/Pb9Sm7eWL1+Oe++9F0Bo+gWAs846y8scsWSjJdkExd/Tmuo4K7WNgGKTGUfG2vXA9zsuMmt/09Iy39tqBLbAazXLli0Ljtk0Ze/N/FlTpkzxsi1KnRVpeoQQQgiRC/TQI4QQQohcoIceIYQQQuSCBvXpidl1X3nlFS9bGx+HKbPt3dqa2T7Jbdauy/3YV8BW8OZ+bJO09nQ+p6ZcVT1rdljmiSeeCI7ZV4B9evhaA2HIJIen2hBnXhsLFy70srU182fx+cayyHbr1i04/tvf/pbat7757LPPfJZpW7Wa5ylWqZx9BNi3xoalcz+b1uGcc87xMvsR2Iy5/L6dd945+B4M+3FMnDjRy506dUIaHOK7zz77BG3Tpk3z8oEHHhi08Vrkvc+VyIG166Wc0lHY8N00XwqbxZbTLtiM4xwizhnMY/B1e//994M2nhf22bS+mPy5jz76qJdtCgTOEmx9vPg3g9ea9XeL7fe6YKuttsLQoUOLfhbPWdaK4exXaO+R8+fP97L9LN5X/D47Bt8neS557uz7+P5pf6t537Ovkp0vvqfE9hX/jtu1PHny5NT3MdL0CCGEECIX6KFHCCGEELmgQc1bMTMIhyLHzFFszrDmrbRQ9JjJidX6HPZox+OswBzaCZSX2ruU1OZ7crgzEIaVc/ikDXHmeeFQRc4aC4TZYnl9vfDCC0E/Xg9s5rFmmLRziBHLRFsqNtpoI68iZnMREF4TzgJrQ2NZXczhtDasldXo559/ftB29NFHe5n3RazAIBdHtCaW6dOne5lNktYMxuPzHNrCizzGuHHjgjY2lbIZ0GYCrs5UWyrTyOrVq/26Hj58eNDWoUMHL/N3sfcqNhnxurUmTQ4HnjVrVtDG65jD+Z955pmgX1qRUWu2SjMjW1MHr19+j70nvPHGG162+5aP2eRiQ6X/7//+D6XEOec//6STTgra7PGGwt/Z/rbyfuHrYe9Vafc4+5vJY7DckL99Nit3GtL0CCGEECIX6KFHCCGEELmg3s1bacUdbaQUZ5e0ZqtYUTsmzfRl1dI8RlohSiBU47F5y1LTbKpNgVjRTo66mTp1atDGmUO5ny04ykXnuOClVWlyxk6OCBg8eHDQjzMC8zqx0Ui81jiza4yGUPFutNFG3nTBkTFAGEXFUXCtWrUK+nHED8+LNStwRlculAiEJi02TXGkDRBGoXBWXGtKYnU7RxpZ8xYf81q0mWk5OsXO55IlS7wcK95YbUoq1T5v3ry5z5Rs55KPuRAqF4oEQjMYX0NbOJIz4dpryqYvvgZcJBgITdQcHWXv6QyPZ68vrxueIztfvM9iZmkutmmv5+mnn576vrqgWbNm3oxsrz0f87q0piT+vYr1Y+w9iOeW95Edw/7mVWPnKO13177O47Fs1xqvldj34jGsyZwLpMbI36+zEEIIIXKJHnqEEEIIkQv00COEEEKIXFDvPj1ptkBr7+TKsjbMkENt2afDZoO0WXirsbZmPid+j7WL8vtsdW+Gbf0NEb5cl6TZZIHwe8b8Gy6++GIvsz0ZCK8Ht1nbO4epcz+bLZft9xyCzdmZgbC6NIdxW3sy+/hYv5Rygn0H7FzwfollMGc/G95/tkI9hwrbNcF7lUPd7Z5L88Gxvlwcvsy+SeyzAoRzyN/L+g6wX4j1aWLfF87+y2MDa33FSpVtvVmzZv46nHjiiZneY+91/F04dNzOJV97ew/mtc8+M/YextXqeTxbwZz3La8HmyWZx+N+serbdi54zXM4v82eb9dAKbEpIuyxqB+k6RFCCCFELtBDjxBCCCFyQdmYt2xYLKtaY+F3HLZm+7FKNi301b6Psz2zuh8IQwfTVL9AqIa16v9yLEBq54S/D3/PrCG61157bXDM4eH77bdf0DZ+/Hgv87Wx4ams5ubzs0UNrSm0mjvuuCP1nDiM3qqc+bNs+HM54Zzzc2WvHadX4Pm0RSm5qCCH+8fCUC18vdgcxaHRQLiH2URtx+bxYmHJPG+8Tu364PuMzWLMZjG+J3CIvh2/XLD3Fc5yzHLWsF4hmirlt3uFEEIIIUqAHnqEEEIIkQsatOAoYyMksmaOjZmZ2CQSM2/xGBw5YKMF+H08HpsFAKBNmzZejmWMLhesWdBmJa7GRohwNt4///nPXr7++uuDfnvuuaeXOestAOy1115e5mzKNtNymukhZmp4/PHHvXzkkUcGbU899VTR99jxeP5iGZm5X0NH6H3zm98MjtlkxAU47VywaXDevHletgUhee3b7OZ8jXj/cUZtIIyEYzOyNdNwlBa/J6uJya5Z/o52f7PJLWZqFUI0XqTpEUIIIUQu0EOPEEIIIXKBHnqEEEIIkQvKxqeHw1uB0L5u/QbYh4Yzx1r7PftWsF+DzQ7L4bns02ND1nkM/izrG8E+PY2RRx55xMvf/va3vWyvG/t2MNYHYubMmV7u169f0DZt2jQvd+/e3cszZswI+qVlZrXXfsSIEV62fjxMWrZuC68hm2GW4bVRbmkJ2P+FM1jbbNZNkZiPkBAif0jTI4QQQohcoIceIYQQQuSCssnIPH/+/ODYhpMyXGiuW7duXrbFBRk2idnCkRyizWNzdmYgDJtmc4YNr2YaQ8i6zVp74YUXeplNi2wGjGFNRzwvr7zyStC2xx57eJnDpO1ncagxF1A85phjgn5HH310pnNMC8u35hA2DdlimExjmGchhMg70vQIIYQQIhfooUcIIYQQuUAPPUIIIYTIBWUTsm59KbjkQ8y3hn1/uOI6EPp+cEi8TYlv31eN9U3hc+SSF7GyA7GK1OUCl2sAwmu17bbbepmvJxBeHw5ft9+Z/WKs78vEiRO93LlzZy/3798/6MclKhYsWODl4cOHIw32JeI1A6xbWqGatLUAAO3bt09tE0IIUf5I0yOEEEKIXKCHHiGEEELkgrIxb9kQYjYlWZNDu3btvMymE2vC4PfxeLZq+6effuplNntYU0yaGctWbWeyVoNuSE4//fTg+J///KeXZ82a5WUO5wfSM17Hwr6bN28etPH75s6d62UOUQfCTNkvvPBCkW+xLjaTN5OWEsG+hzNBx0L22dQX+1whhBANR/n/IgshhBBC1AF66BFCCCFELigbPfzs2bODYzZnWFPEihUrisrWDPbhhx96edWqVV6eM2dO0G/p0qVenjp1qpf33HPPoB+bd9j0lZbdt7FgTU7PP/+8lxctWuTlu+66K+j373//28scXRWLgMqKLWb61FNPeXnIkCEbPH6PHj2Kvs7rDggzfvfs2TN1vHIrMiqEEGJdpOkRQgghRC7QQ48QQgghcoEeeoQQQgiRC+rdpycthNtm4K2srPQyh6gDYWh627ZtvWz9KhYvXlxU7tevX9CPM/cuXLjQyzZEffPNN/cy+/5w1mJLYwhZj8FZkn/xi18Ebfa4GuufxdXT2QcLCNMHsP9Mms9NXcGV5AcMGOBlu9b4/Fq3bp06nsLUhRCi/Gncv8hCCCGEEBnRQ48QQgghcoGzWYejnZ37AMDC9XYUdcl2SZK0XX+3mqG5bDA0n00HzWXTos7nU3PZYKTOZY0eeoQQQgghGisybwkhhBAiF+ihRwghhBC5oMEfepxzrZ1zUwv/ljjn3qPj1PoOzrkK59yMlLYrnXMHpbSd6ZzraF47yTn3c+fcEOfcXhv2jfKNc+5o51zinNs5Y/8Fzrk2RV5fXax/ZJwa9Y+Ms876EHEKe2emc25aYd8OqoMxxzjn+m9oH1EzNJeNn1LMIY09xDn3ZF2N1xA0eHKRJEk+BNAbAJxzvwKwOkmSP2zgmJcXe9051wzAmQBmAFhMTUMB3AjgSACrAYzfkM/POScDeKnw/y8b+Fxqw5lYd32IFJxzewIYBqBvkiSfFR5gG3cxupyiuWz8lPMcOuc2TpLki4Y+jwbX9GTBOdfTOfdq4al1mnOuOnNdM+fc7YWn2lHOueaF/nc5544ryAucc9c456ag6oe4P4D7CmM1d1UZCHsDWA7gXAA/KrTtU9AmjS585vPOua40/m3OuUnOudnOuWH1fU3KEefcFgAGA/g/ACfR60MKf8k94px70zl3nzOZHwtz8bRz7uwi417onJtYmIcrIp9/fWEtPO+ca1t4rbdzbkLhvSOcc9ukvV5YM8H6qJML07TpAKAySZLPACBJksokSRY75y4vzNkM59xfq+e7sA6uKezn2c65fQqvN3fOPeicm+WcGwHAX3vn3K2FvTYzNv9ig9FcNn7S5nCBc+4K59wU59x0V9DEO+daOOfuLMzha865owqvVzjnxhX6T3FFLCDOuQGF93R3zvVzzo11zk12zo10znUo9BnjnLvBOTcJwPn1dxkiJElSNv8A/ArAT4u8/mcA3yrIm6JqE1UA+AJA78Lr/wRwakG+C8BxBXkBgItorDEA+tNxXwD3FPt8AE8AOKMgnwXgXzT+M6h6aOwBYBGAzRr6+jX0PwDfAvC3gjweQL+CPATARwA6F67ZKwAG0/xUAHgOwOk01urC/4cA+CsAV3jvkwD2LfLZCa2RywHcVJCnAdivIF8J4Ib1vB6sD/1b75xvAWAqgNkAbqFr2or6/APAkXR9ryvIhwN4riD/GMCdBblXYW/357EANCu8v5fmSnOpfzWawwUAflCQvwfgjoL8W6z93dy68L4WADZH4TcNVb9xkwrykMI9eC8AkwF0BbAJqu73bQt9TqT5HwPgloa+LvyvUWh6UPUjealz7mJUxd+vKbw+P0mS6noQk1H141mMhyJjHwbg6ZS2PQHcX5D/gSotRjX/TJLkqyRJ3gYwD0AmH5YmzskAHizIDxaOq3k1SZJFSZJ8hapNWUFtjwH4e5Ik9xQZ85DCv9cATEHVdS5Wo+IrrJ3newEMds61BLB1kiRjC6/fDWDftNczf0vhSZJkNYD/396Zx8tVVfn+twhoGAMhAQIhE2NISIIJYJinjhEFHoMi2gjS3SD9mka0FVDp7gfYSKMt4BOxwScGjYoibUCGYEIYwhggCQmQQCYIATIQkKDREPb7o+ru+9sr9+zUvblD1T2/7+eTT1bV2XXq1Nln73Pu+q219mgA5wJYAeBXZnY2gKPN7Akzew7AMQCG0cd+W/2fx+wRqPQbQgizUXkobeLTVU/ts9X97NchP6bkqC8bn0wfAi331TgAl5jZTFQeUHqi+UHmpmqf/xppPw1F5Q/RE0IIrwDYB8BwAPdX9/NNVP7AbSJ3/+10ujympyXM7GQ0x4P8fQhhopk9AeATAO42s/NQedD4C31sPciN6ngv83XjAJzahsP0BY5KXfDIzHqjMiHub2YBlb/kgpk1LXLl+4qvvekAxpvZxFD984B3DeCqEMKPWnlIpe6PziSEsB6VCXNadZI8D5W/8MeEEF61SqxeT/pI07Xgr4MNMLPBAP4FwIEhhNVmdovbl2hH1JeNTwt9eFZ1U0t9ZQBODSHM431U+/lNACNR8bCvpc2vo9JvB6AImzjXAAAgAElEQVQS+2gA5oYQxhYcUu7+2+nUpacnhHBHCGFU9d8MMxsCYGEI4XpUvAIjNmH37wLYFgCqf/FvHirB1Mm2Ko+iOTblcwAepm2fMrPNzGwPAEMAJBdNCTkNwK0hhIEhhEEhhN0BLAJweA2f/VcAqwH8oIVt9wE4xyrxQjCz3cxspxbabVY9BgD4LIBHQgjvAFjdFGsA4EwADxa9X7X9NSAymNk+1hxjB1Ti45rGwspqv5224Sc34CFU+g1mNhzNY3w7VCbNd8xsZ1SSDkQHoL5sfAr6MFcR+j4AF1Cc1gHV93sBeL3qmT8TlT9im3gbFQfEVWZ2FCrXSF+rBFHDzLYwM/YG1hV16elpgU8DONPM1gF4AxUdcrs27usWADea2Z8BfBeVWJIm7gTwm2ow1wXVfz+peitWAPgCtX0FwJPV4/hiCIGfhMvIGQCudu/dXn2/FvfmhQD+n5n9Zwjha01vhhAmm9lQAI9Vx+UaAH8LYLn7/HsADjKzb1a3nV59/yxU+nsrVLyDX9jI+7eg+foYS1KqaJltAHzfzLZHJXbjZVRc62+jkgX3BoCnatjPD1EZay8AeAEVFzxCCLPM7FkALwJ4FRWvoOgY1JeNT1EfFiXbXAHgWgCzzWwzVP5Q/SQq8UC3m9nnUYlfTbw1IYQ3rZLAcw8q8a6nAbi+yZFQ3efcdv5t7UKpl6Ews5tRCeh6vJWfuwXAXSGE33TIgQkhhBCi3WkUT0+HEEL4+64+BiGEEEJ0DqX29AghhBCiPNRlILMQQgghRHujhx4hhBBClAI99AghhBCiFOihRwghhBCloFXZW3369AmDBg3qoEMRLbF48WKsXLnSNt6ydXRVX773Xlqcc9WqVdHefPPmy7FHjx5JO6P1Sd9/v3ih3g99qHlB4T/96U+Fn1m3bl2099lnn40ddrvx9NNPrwwh9G3v/dbj2ORznuvPRqU7jE1OZPnrX/+abPvzn5tLVG299dbR3mKLLTb5e/m7+HsAoFevXpu8/7bQEWOzXsblBx98EG0+3/7cb7XVVtHmMcrzJZBeA1tuWX/rMuf6slUPPYMGDcKMGTPa56hETYwZM6ZD9ttVffnUU2ltswkTmpfb2nHHHaO97bZpUWR+IFq5cmW0/c1zwIAB0Z45c2a0ly9PaxmuWLEi2g888EBNx94emFmuOmqbqcexyQ+0/kbG/dmR+OxUfr3ZZpvm6O7qsck3Mv9bctsYfvh45ZVXkm1z5zbXljv44IOjvcsuu2z02DbGkiXNw+D5559Pto0fPz7atT4c8+8F2ta3HTE2O3JctuY3r1mzJtrcr2wDwIgRzYsdfPjDH47266+/nrTbeeedoz1y5MjC7+Xx1pl/6OT6stR1ekTnM23atOT1nDlzos2DYtGiRUk7HrT80LPDDjsk7fjmuv3220e7T58+SbvFixfXftAigSey++67L9l22223RZsfJt98882k3dq1zQXMv/jFL0b72WefTdrxxP7CCy9Ee9990/V9b7755mjzxO0nWn7tH4gazfvEx1vrDfC8885LXv/lL81L4vFNDkj77Lrrrmvxe4HUC3DAAQdE23sR+EGXH3T8Hzj33ntvtN9+++1on3jiiUm7U09tXjKxrQ99jUzud82bl66K9O6770Z7/vz50Z49e3bSjudPnlu5H4B0/PI4GjVqVNKuHsdU97wahBBCCCEceugRQgghRCnQQ48QQgghSoFiekSn4rO3Bg8eHO233nor2rvvvnvSjjV6zrbimATfjmN6evfunbTjz3F8Tz1kWtQDHGj66U9/OtnGffjOO+8k2zjOgM85Z//4/XOcl4/lYjhwmGMUAOAzn/lMtDne4Nxzz03aXXLJJdH28QZdFXTZVmoNyr700kujvXr16mTbrrvuGm2fvcVjkPvZB7XyuT///POjPXbs2KQdB7/y9/p4O44R4mwijhcD0sDriy66KNlWxuWVFixYEO2lS5cm2wYOHBht7j8/f3If8Vzosy856YTjfXzQdkcF+28K8vQIIYQQohTooUcIIYQQpUDyluhUOF0SSOvlcFq6l8H49U477RTtXNFBlkC8u5s/99BDD0Vb8laFs88+O9peEuFUVi9bsczCEpEvLcCyJpcgOPbYY5N22223XbT/+Mc/RnubbbZJ2hVJU3fffXfSbtKkSdF+9NFHk22NIGkxubTshQsXRpvLQnjZmOUN//t5n7vttluLnwFSmenXv/51tFmaAlIZi/t1/fr1hd/LNktiAPDcc88V7oPlGN7mZZruBMtMLFMBaTmC/v37R/vWW29N2t1xxx3RPv7446N93HHHJe2GDh3a4nf5UiBctqBeihjK0yOEEEKIUqCHHiGEEEKUAslbolNhKQNIJahcVhBnArG72stWvA9213uXPMtbXr4pKzfddFO0uRqvz67h85/LGuK+8Wv38Lpo7Pb2sib3W06m4Nc9e/aMdt++6fI7LJHdfvvtyTau8NsI5JbymDJlSrS5j/i8A+m5yq1px+O0X79+yTaWqO+8885o++q8LF+z7OGvIV7XiSU8P9b5mnr44YeTbUcddVTh5xoZPh8sYQLp+eUleIBU1mSp8uWXX07a8dqFnM23bNmypB1LwyxvcgYZkEppZ5xxRovvdzby9AghhBCiFOihRwghhBClQA89QgghhCgFpYnp4VTKG2+8Mdk2bNiwaHPK7EknndTxB1YyfKwOxwewts+rMANp3A3HIXiK9HufPsvt/HeVlRtuuCHafH58OjDD8Rf+c0yu+jHj41T4uznewLfjlFyOTfGrj3Psj0/XbbSYnhx8TfO59jFTfE79uWL4vPnKzXzuuZRArh3H4/iYHh7fPF9wpW0gvaY4LR9IY3pysU+NBsfxcCwNkM5xe+65Z7KNV1M/6KCDor3LLrsk7TjlnOOk+DMA8OSTT0ab44WOOeaYpB1fN9OnT4/23nvvnbQ74IAD0FnI0yOEEEKIUqCHHiGEEEKUgu7j99sIjz/+eLT9YoVPPfVUtL///e9H+8ILL0zaXXvtta3+Xu9OvvLKK6PNacE/+tGPknZeNmhkOO2YU4aBVFpkV7uXQ7ja6GuvvRZtTtME0kqv7O71addcRdQvoChSqcPLFNyfOdkwl87O/VtUxRlIpQne5tOr+XhZHvFVYLmdrx7Labm++m+jwanDfA596QBOHfeyMY9H7qNcdXP+Lt+OpQ5u5+Unvr74e/lY/f45bb47w/MgV6b32/w4GjduXLR5juQSA74dS8tetuI+4/7nRaOBtGI7X3t+zt1rr72i7auttzfy9AghhBCiFOihRwghhBCloOHlrVoXk+PI8V69eiXbWO7iqP/rrrsuaXfmmWdGe/To0YXfxW5G3h8ArFq1KtpcHfWss85K2h155JGF+2802OW57bbbJtu4Yi67qL2kwueKXbfe5X3ooYdGm13j/tpgV353qtjaGs4555zkNZ9LPt+vvvpq0o7d4z77gzN0uA9zi1nWughk0SKSHpZl3njjjWQbVwT31+KDDz4Yba4e2wh42YolApaU+dwAqVTsFyPlMcKyYK5ysx+3DMtWtfY5Z2x56YSP11cn7k7wuOTz62VBlpL8vMhzK5/TgQMHJu24bzlji6s4A8DcuXOjXVRB27/OZVUuXbo02vvuuy86Enl6hBBCCFEK9NAjhBBCiFKghx4hhBBClIKGj+nxsQIMa8CLFi2KttcMWWvmeAVf1XLMmDHRPu2006I9YMCApN1//dd/RXvw4MHJNo6BYK19xx13LPgVjQ9XU/YxBRzbwXEJvh3HcHC1WZ9azFVKBw0aFG2fusz93J3KA7SGCy64IHk9efLkaPP59/EB3E++JAPHGXDcRm6c8rZc5WbuJ45fANL4E06j95V6+bf473rooYei3WgxPT4FmGOyeIz5Eg88R+6zzz7JNh5zuQrdvH+O1ai1CrcffzxWn3nmmWj7PufrkOMouxsch1ZUmgFIY3V69+6dbON7HI8Bf95uvvnmFvfhY+MYnit8bBnPB3yN+vmdy7copkcIIYQQoh3QQ48QQgghSkHDy1u5qq8TJ06M9vbbbx9tny7HLjhOKffVZtn9e88990Tbu/iHDh0abU7hBdIF9NgFzSl7ADB8+HB0F9jt6l3UDLtGvRueKyqz25z7FUhdvlxx18uH3Oe5NNvujF/kj69BXnzTpwoPGTIk2n7RQx4jPDa9K74o7Znd8EA6Bvkz/jpiqZjd8v3790/a8baLLroo2XbggQe2eEyNAMtAQPE1zXMOUFxNGSheFNTPuTnpsqhdLmW9qHKzl2I4VMCPbx77LHM3Ijx/su1XFuC50Pcz9xnfk/w97ne/+120udyKP4d8H8ulorOUxvLWqFGjknY5+ay9kadHCCGEEKVADz1CCCGEKAV66BFCCCFEKWj4mJ4c3/rWt6LNS0/4lb6LVgZm/dRv4xLoXtPm8vY+3Zf1atbMeRV4ABg/fjy6C3x+fOo4w3qwXyqE09SZHXbYIXnN5fd55V4fe8J965cjEMDtt99euO2zn/1stP3q1hyTw3E8Pg6kaPkY347HXC7+hK8rjk269957C35F94JTfj0cw+HjD7l0Qy7dmMemTz0vSlPPxe1wmrrfHx8HH7tfaoLjx/w+Zs6cGe1Gj+nh+Bme33xMD2/zKeE+Vq4Jf3867rjjos33ON+OxzbPpbnv5fgh34734fuy1pixWpGnRwghhBClQA89QgghhCgFDSlvsfuLXV9cdRlI0+A4vdHLVuzGzbnZuB275316qK+GWbQPduU/9thjhZ9pdPg85koM8DbvjvUp7E34qtmzZs2KNstbPjWTXca1rvgsKhSNAyCVmXKlCoqq8/q+YOkkJ7HwceRWAS/aN5CvDF3vLFiwIHnNEhFLEb78wN577x1tPzaLzmPuvPFnivrYH5+/hlim4W2+HX+vP6Z58+YVfne949PNORyDZSF/v+Mx5kt5FF3b/t7FUn/R2AOKx5u/hlgW48rSvh3Lrlw2BkjLlbQH8vQIIYQQohTooUcIIYQQpaAh5C0fOc4R/eyqu/zyy5N2ffv2jTZnKXhXXc5tzrBLj92zPvuHt/mMCP4t7MadNm1a4fc2OtxHPuuGZSeWRnxWUFHWF7vnAWD69OnRZrc+y5tAWh3Uu81FHp/9WERRhhZQvLisHy+5LB+G95+r+s3kpNZGY9myZclrlhZzlXp5LvVyVpHEV+t4qfX8+qr1LLlwdqa/Nnje9vK3X4C1kfDnna9tloH8OPTnsYha5ahcpi2fbx6Xfn6fP39+tDmr0vclj1lfnVnylhBCCCFEG9BDjxBCCCFKgR56hBBCCFEK6jamh3XCnLZ45513RvuWW25JtnE6M+ufXncsSoHPteN4Ea+lsm6eW8Gb9eqXX3452XbfffdtcNzdAa9Xs77M59THF/gUzCb222+/wu/i1EcfD8LxXo2WntzVcNqzH5tF8QI+jq7WdGh+zbENPq6EY39qjW3oTvhUdB8z0UQups7D557Pdy62irf5uY/7j8e6L0/B4zEXn8W/0Vcn9jFOjYTvO+6jomrVQLrSvE/7Lior4Mcbn28e274vebzlSkRwDBLPub7iftFK8h2BPD1CCCGEKAV66BFCCCFEKWg3eYvdmkW2h93fXmLISQ5XXXVVtK+44opo77vvvkk7druxezaXIpk73qIFD72LkN24PlW3SEpjdy/QXFnYp5g2IjmXd9FidT6VsmhR0AMPPDB5zX3B/eX7oWghPLFxuLIql4IA0pRXdpV7OapokUpPkfzpxwUfB5eCKAu+rAePuaKquEDaR7VWsvb9xd/F/eznNIbb+bHOc0Sti1T6eaWRy1D4a5t/C597L2nynJbro9y9i1/z/r3MyPdQPl5/3vm7OBXdL5DL0pzkLSGEEEKIdkAPPUIIIYQoBe0mb7X3Yn2TJk2K9te+9rVkGy8mN3LkyGjnqkuyy9u7cbkdu+NyklsukyQnnRQtVOqzYJpci43spm0il/nB2QirV68ubFeUpVWU1QWk10POda/srQpF0quHXeBewuCFXLlvvBu9SEbOucdzMim/zskqtf7GRsBnPTEsEbCkNWrUqKQd95GXHIoq3+ckEc7qKcogA9L5zo9N/l0777xztL3Ewr8rtzg0HwcfX73iJUi+tnl85GT5XAV0nhe9ZMjkxjlnFfP+/Lhk2Yrvs/4a4v2/+uqrhcfUHsjTI4QQQohSoIceIYQQQpQCPfQIIYQQohR0eEVmXxnyD3/4Q7RnzpwZ7bvuuitpN2fOnGj7lbQ5TZm1Sp+2yXplLhWdKUpL97C+7LV11lP9PviY+Lu8/t3UrtHjDoB8H/EKurwysj+nu+++e4v79qnsRZVCc2UFcrq22JCiGAMgjSXhvsilVPM+/Djg8cN95vuTr5futHp6Do6B8/A5LYq/APJxN9w2d05rnVuLUqV9HAiPR67o62NYeAVvH6vE+1y+fHm0d9ttt5qOtSvxfcK/hX+zHwO77LJLtPn+CaQxrbmU8KJ+9nMkV8DmlQVmzJiRtOPKyxyf5ePH+BryMU3tTTlmByGEEEKUHj30CCGEEKIUtFnemjZtWvL68ssvjzannLFrEQB23XXXaK9ZsybaPh3x8MMPj7aXeNjdx9tyLjj+jG/H1VzZtejdh5xmmasoy2mg3v1fVImUzwUAjB07FgDwi1/8At2JFStWJK+LZELv8ubFY3OwG5f350sCsIu3jBV8W6LWdO7c4oA8tlje8tc37z9XlqFIbvbfy9t8pdqi72103n777Wj788HzE1fMHThwYNKOx4iX4nkfOQmrqGKwx6dRF32Gxz6nzQ8fPjxpx/cZP6fzMbFE1gj4tPqiMiecDu63+arORXOcPzd8vnnM+oWv+Xzz/W7RokVJOy41ctBBB0X73nvvTdrtv//+0fbX2osvvhhtv+pCW5CnRwghhBClQA89QgghhCgFrZK31q1bF6Ouzz///GQbu7s4I4dtIHWhcmS3d0/mFjtj2AWby9DJwTITf5d3u7KLkGUwzjryx+EXN2W3Y05+OeKIIwAUL7TZSHA/+CyepUuXRjuXzeYz+Ipgly+7//15bO8K4mWCJRKWkIG0siqfV9+fvK0okwtI54tcBWK+dmpdOLPRyUn2RfPMxz72saTd7Nmzo+1lFZ7HctXNef/8Gd+X/Dnen5fm+Dj4N+61115Ju9tuuy3aXj4tygBrBPwcyfMnn+vDDjssaVd0HwOKJWQvafK4zI0j3j/Ps76PGH4W8NIc95efj9s7m0ueHiGEEEKUAj30CCGEEKIU6KFHCCGEEKWgVTE9K1aswA033ABgw5Rijs+pteIjp4p73ZV1TL+NNT/WJH01SY6T4f3l0ju56qf/jZwi+cYbb0SbK2ECQL9+/aLttUuOLeFjYl0UaNZMu3t12SK93act9u7du6b99e/fP9ovvPBCtP0qwaxXN8LKy51BUQyH7wuOF/ExAXwuc6noRSnQfszxGOE+8/F6uZiTWo+h0WK7chXj+bdxOx9jyLFWfozVGtPD8R3czsdg+b5tws+RvA+ec30MC6dK+5gxjr/06db1jo/P4t/C81guBisH3//4vu2/m2OL+F4NAK+99lqL3ztkyJDCdn379o22j8Hia8NX38/F9LaF7n1HFUIIIYSoooceIYQQQpSCVslbZhZdpV6WYFmI3W5eSmLXJUtEOVezlybYRcv78+69orRILxmxG5bdcd4tetRRR0X7iiuuiPZ9992XtOPfkquuyS6+jl5krV7wfcRSCV9T/rzxonY5dtppp2hzJU8vH/LrRliEsCvxMhVf334s1Soz5RaDZYq2eWmHr53uUOahFnIyI8+ZPL/l5C2ej4F0zLHU4Ste85jjbV6m4X7hhahfeeWVpB3LVjxHevmRj5cr+gLp7/cp4PWOvxfyWGGZyVdZ5jHg5V8eR0WLMvvXuQV+uR33l5c0uQI/S1hcnRlIr2VfvqW9x7M8PUIIIYQoBXroEUIIIUQpaJW81a9fP1x22WUANlw4curUqdFmt6OPDmc3GbvnvHuW5ajcQnhs+3ZF0he7Vn27L3/5y9H+0pe+hFq49dZbk9ecveXdguxeZtdyUWZDdyPndmUXp88W8K7yIjgThD/jrw0+37ksGJHPdvRySVG2laeocq+XMLgd789/b1sq8DZ69hZfw15yeuedd6KdW9iYf3OuMnLRopdAei9gSfmjH/1o0q5IBvPyKVf55mP3WbL82i9E+dJLLxUeb73j50g+Pywf+dUOZsyYUdP+eez4c8/jiMeHD/Vg+dBfUwzf41nG3GeffZJ2Dz30UIvHB2wYmrCpyNMjhBBCiFKghx4hhBBClAI99AghhBCiFLQ5mOH6669PXnN8yrXXXhvtCRMmJO04JXz16tXR9lUXOU3Nx3NwSht/r0+X4+/iz3zzm99M2n3961/HpsArFQOpdun1WY5b4QqVTavXN9GkQxdVrm0kOFbAp1ny7+PU0l133bVN3zVo0KBos5bvyx4wiumpUHSttWaV6qIV0328TFFqe26VdSYXi8BjrDvDsRS5uAo+v0888USyjeNCli5dmmzjc8r7933CfcH782Od98Gf8RWZ58yZE21Om7///vuTdjzf+5gmjgvxc2sj49O5GZ7jcqno3H/+/lQUk+dLiPBczePNx/BybCbfqznNHchXb/cxPpuKPD1CCCGEKAV66BFCCCFEKWizX9+nYrP766tf/WqLtofT3J955plkG7s4lyxZkmzjFDZ293k32D/90z9F+5JLLik8jiJyFZ6Zb3/728lrrk6dWzyOXXyjR49ucd+NlkbbEuzW9O5UlqDYXe3dn7XCabF87vx55O/1xyRSOP0ZqD3FnG0vnRUt8urd8uyK5+/NucP94pPdleXLl0d7zz33TLbxHMkp4D7tm6VnP3+yhMH95fuySL7OjXXe5stTsJzKko1PPefvmjdvXrKNr5tGn0N5XhwwYEC0fRr5888/H21fobpIdvbjjbdxn/vwAJYMi1ZI8Pvg35ELKcitYtAeyNMjhBBCiFKghx4hhBBClAI99AghhBCiFLQ5pqcovqU1HHPMMS3a9UKtv/Gss87q4CNpbDjGoiiWA0h1Z46LyrXzej1rzzmtmeMIcunsZaLWlPXc+S8aM7mV1HOaPcdx5K6jolii7kxRPByQXvsrV66Mtu8vjon0KeY8LnKlMzh+aPDgwYXtisa37y8u5cHXkz++XPwQ//5GK0nBMVgA8Oqrr0Z71KhR0faxrosXL472yJEjk208xvh8+HPP55HLhvilm7gd96WPM+JtHIPmr0M+Jr/EVXvHXMrTI4QQQohSoIceIYQQQpSCxvL7iYaHK6x62BWaqzzKLlnv+uTqruwy9bILu1clb+Xx8latKeFcriEnYXHarO8L7utcP3H/slu+0VdSz8FV7L0kwpXJueSAlw64SrKXlLktn19fPZ9lJpbZOOXdw8fr2/F3cX9xpXsglTi93MnzTE5yq0eGDx+evObj54rHXnI66aSTou2rkvM44HnRjw+WBXn8+rIVvGICzw9+PuZ5nGVWX37glFNOiba/lnMhEW1Bnh4hhBBClAI99AghhBCiFEjeEh0Ou8k5gh9IFyjkyq45KSMnbxVVAPWyBks0ucUay0SR9OPPD7vE2WUNAMuWLYs2u+J9lgjvg+UtL0OyLMbXjt8fSwBczZ0zi4C8vNpoDBs2LNpemuJFkL/1rW9F22cysUTCYxFIZaeXXnop2pMmTUrasZTG/Td//vykHZ977vNx48Yl7bhvuf/88bHkMmPGjGQbV3Q/9NBD0Uj4CtX+dRN+FQMmt0hnbgFh7j+Wmfw8y/vgedtTtMislyq5ojhLZx2BPD1CCCGEKAV66BFCCCFEKdBDjxBCCCFKgWJ6RIfDK/6ecMIJyTbW9nv37h3to48+unB/uUrZvIo068Q+toOrvnJsRJkpqlw7fvz45PV9990Xba4CC6QxPqz1+7ggjhfg9FXftxx7xTFCfrVwTpseMmRItHMxPI2evs6pzRdffHGy7ZFHHon2iSeeGG1OQ24rl1122Sbvoz3gmJ4LL7ww2XbYYYdFu9EqMufg+dLH7XAcpI+zKSoB4tPBebzx/vw55DhNnkt9vBDHI/ExFMUpARvG67XH6g/J/tp1b0IIIYQQdYoeeoQQQghRCiy3kNwGjc1WAFiy0YaiPRkYQui78WatQ33ZZag/uw/qy+5Fu/en+rLLKOzLVj30CCGEEEI0KpK3hBBCCFEK9NAjhBBCiFJQFw89Zva/zCyY2b41tl9sZn1aeL9V6wm0tn1mP2eb2a4bb1luzGxHM5tZ/feGmb1Grzc9l1a0K23tLzMbZGZzCrZdbmbHFWzbYByZ2WfM7BtmdpSZHbJpv0i0lWofzDWz2dX+PzgzD59oZpcU7Ef92MWY2S5m9kszW2BmT5vZ3Wa2dyv3sb2Z/WNHHWNHUi8FDM4A8Ej1/3/r4mNpC2cDmANg2UbalZoQwioAowDAzP4dwJoQwneatpvZ5iGE9ws+3u6YWY8QwvqNtywnG+uvNu7zX1t638x6oOVx9HEA1wM4AcAaAI9uyveL1mNmYwF8EsBHQgh/qT7oFD70hhAmAZjk3zezzQEcBfVjl2GV4lR3APhpCOEz1fdGAtgZwPzcZx3bA/hHADe0+0F2MF3u6TGzbQAcBuDvAHyG3j/KzKaZ2W/M7EUz+7m5amJmtqWZ3WNm/9DCfr9qZk9V/zL5P5nv/171L5gpZta3+t4oM3u8+tk7zGyHovfN7DQAYwD8vPoXUMtVoESLmNktZnajmT0B4D8z536amY2p2n3MbHHVHmZmT1bP/Wwz26v6/t/S+z+q3lRhZmvM7LtmNgvA2C750d2IovMPoIeZ3VQdW5ObxkW1v0+r2ovN7GozewaVP3iScVQd76MAvAXgiwAuqm47vOpNmlr9zilmNoD2f6tz8W4AACAASURBVKOZzTCz+Wb2yc4+J92QfgBWhhD+AgAhhJUhhKYH0wvM7Bkze86qnvqqx+7/Vm0e37fB9WMX/JayczSAdSGEG5veCCHMAvCImV1jZnOqfXk6ULk/V8dXUx+fVP3YtwHsUe3Hazr/Z7SdLn/oAXASgHtDCPMBrDKz0bTtAABfArAfgCEAeLncbQDcCeAXIYSbeIdmNg7AXgAOQmXSHG1mR7Tw3VsDmBFCGAbgQTR7mSYAuDiEMALAc7n3Qwi/ATADwOdCCKNCCH+GaC39ARwSQvgyis99EV8EcF0IYRQqN82lZjYUwOkADq2+vx7A56rttwbwRAhhZAjhkRb3KFrDBue/+v5eAH5QHVtvAzi14POrQggfCSH8DBuOowMAzAohLAJwI4DvVbc9DOD7qPy1OgLAz1HxBjUxCJWx/wkAN5pZT4hNYTKA3asPkTeY2ZG0bWUI4SMAfgjgXwo+3zS+T8GG/Sg6l+EAnm7h/VNQuVeOBHAcgGvMrB+AtQBOrvbx0QC+W/1j5BIAC6r9+NXOOfT2oR4ees4A8Muq/cvq6yaeDCEsDSF8AGAmKpNZE78D8JMQwoQW9jmu+u9ZAM8A2BeVSdjzAYBfVe2fATjMzHoB2D6E8GD1/Z8COKLo/Zp/pcjx6xDC+jae48cAfN3MLkalNsOfARwLYDSAp8xsZvV109oE6wHc3u6/oLy0dP4BYFEIYWbVfhrp2GV+VfA+AIwHcE/BtrEAJlbtW1HxFjdxWwjhgxDCSwAWojL+RRsJIaxBZTydC2AFgF+Z2dnVzb+t/p/r419LRq57DkPFgbA+hPAmKk6AAwEYgP8ws9kA/gBgN1SksIalS2N6zKw3gGMA7G9mAUAPAMHMmp4c/0LN1yM93ukAxpvZxLBhsSEDcFUI4UetPCQVLeoa3tt4E7yP5of0+Jd7CGFi1XX+CQB3m9l5qPT/T0MIl7awn7WagNuOmZ2MZu/b3xec/4XYcOwWyb65vh+HYg9RDj+ONa43keqYmQZgmpk9B+Cs6qamfvbzM1PL+Badw1wAp7Wi/ecA9AUwOoSwrhpW0NCe06729JwG4NYQwsAQwqAQwu4AFgGoRev9VwCrAfyghW33ATjHKvFCMLPdzGynFtpthuYL4LMAHgkhvANgNenNZwJ4sOj9qv0ugG1rOGaRYSPneDEqf20CNGjNbAiAhSGE61Hx/o0AMAXAaU19bma9zWxgx/+C7k8I4Y6qS3tUCGFGwflvK3EcVb1+m1eDqZNtVR5Fcwzg5wCwVPIpM9vMzPZAxcM3bxOOqfSY2T4UqwVUZJC2VhnWXNm1TAXwYTM7t+kNMxuBigR9upn1sEps6xEAngTQC8Dy6gPP0QCa5tGG7ceufug5A5VIcuZ2pBJXjgsBbGlm/8lvhhAmo+L6fqz6V8lv0HIHvQfgIKuk1x4D4PLq+2ehomnORmWAb+z9W1CJHVAg86ZTdI6/A+B8M3sWAKfJfhrAnKqMNRzAhBDC8wC+CWBydT/3oxKMKdqfDc7/JuzrFlTHEYATUXGnN3EngJMpAPYCAF+o9u+ZqMwFTbyCyoR9D4AvhhDSJadFa9kGwE/N7Pnq+d4PwL+3cV++H0UnUlVFTgZwnFVS1ucCuAqV++VsALNQeTD6WgjhDVTi5cZU76OfB/BidT+rAEyvBj43VCCzlqEQQtQdZnYzgJtDCI+38nO3ALirmmAghBAJ9VKnRwghIiGEv+/qYxBCdD/k6RFCCCFEKejqmB4hhBBCiE5BDz1CCCGEKAV66BFCCCFEKdBDjxBCCCFKQauyt/r06RMGDRrUQYdSzLvvvpu8/stfmou99unTxzdvN1asWJG83nLL5hI822yzTYd9L7N48WKsXLnSNt6ydXRmX37wwQfR3myz+njO5gB+s3Y/vYU8/fTTK0MIfdt7v101Nmtl3bp1yeu333472uvXNxfI9okV227bXF6rs8ZcrXSHsSma6YixWS99+dZbb0X7j3/8Y7Tff//9pB2PPx6Xm2+ePirwWNxll13a7Tjbi1xftuqhZ9CgQZgxY8YmHUxbbjYPPPBA8nrhwoXR/ru/+7tNOp4cN9xwQ/J6xIjmYrOHHXaYb94hjBkzpkP22x59WSt//nPzGqz84NiV8GD3A7ojMbO2VrLN0pH92ZoMz6Ix/dprryWv77rrrmivXr062v7h6Oijj452bswVzSv+2NvzAbc7jE3RTEeMzXrpy4kTJ0Z7ypQp0V65cmXSjscfPxx558Khhzav/f3Vr9bfeqO5vqyPP7uFEEIIITqYuilOyH/tAcCpp55auG2LLbaI9uzZs6PN7jgglVJYYmFXn+eNN96I9vLlywv317Nn85prTz75ZOH+ROrd+etf/5ps4/O92267RTvnXWDP0dq1awu3rVq1Ktq9e/dO2g0cqKW42oOc54S9Of/93/+dbOP+6Nu32QvN4xRIva3z58+P9jnnnFPzcTBdJWsK0R7UGiqwww47JK/feeedaPfq1SvaXpp6773mtWG33nrraC9YsCBpN3ny5Ghfdtll0fbzMVMvY0+eHiGEEEKUAj30CCGEEKIU6KFHCCGEEKWg02N6irS8iy66KHn94osvRnuvvfZKtvXo0SPaTz31VLR33333pB2nun/84x+P9mOPPZa045iTNWvWRJvTZf33vvTSS9G+5ZZbknZnn302RMucd955yet777032ttvv320fUzPhz/84WhzhoGPAeHri/vft1u2bFlrDrvU+DHL59Jvu+OOO6I9YcKEaPusLI5H4DiCHXfcMWm3xx57RHvq1KnRHj16dNJu5MiRLR5fvZRIEKI9yF3PL7/8crT9fMfjhctF7LzzzoX75xhZjmEF0pjIxYsXR/vSSy9N2l111VXR5rnCH19njlPNCEIIIYQoBXroEUIIIUQp6NKUdXZxzZs3L9nG7jNfGZlTXNkFxymtQJpyN23atMJ2RcXpvMuN06379esXbXbhAZK3csyZMyd5XVTNk6tuA8Drr78ebZYgfer5dtttF212ydZLUcRGxEuNOVc0p6lzyQDuPwAYPHhwtDnN9cEHH0zacRkDliSvv/76pN0Pf/jDaH/oQx+Kdle60TeFpnPemam9uUKOuXRjnoP5/Pp2bSkgWS9pzp1JrQU1Fy1alLzm1HGeB4G0OCgXZuUSH0B6j/vTn/4UbR86wvvg9Ph77rknacfp8Zdcckm0/TjsTEm6MWYAIYQQQohNRA89QgghhCgFXSpvXXzxxdH2cga7qDlzB0izqFi28K46XjuEJRHvPuTXW221VbR9hWd2w/MxsIwGALfffnu0ubK0SCswA2llXj6PXvZi9+yQIUOi7WUrvm7Ynj59ehuPWLRGVth3332jzZXT/Tgoqm7Oa20BqbudK7N7mZQrzuYqPDeKvFV0zp977rlo8/nl+Q1o27pguX7ObeO5sC37b+v3dldyv5krkd9///3JNl4fy6+V9eabb0abwzn8gqMsJ/Mal/764nshz9t+UWCuxP74449H+3/+53+SdkWrJ/ht7UFjzABCCCGEEJuIHnqEEEIIUQr00COEEEKIUtDpMT2s13FlZNbkgVSX9zE9DMfj+NgaHz/S0jEAwK677tri/nyMEH+ONU3f7gc/+EG0FdOT4ldZ53gAjuvieBwgrRzKn/GadFGsiNfJlyxZEm2tuN5+vPDCC9F+6623or3nnnsm7ebOnRttjgPysX2cNstjzldL5/i9XExPI6RAf/DBB/F333bbbcm2SZMmRXvEiBHR9nEPDz30ULQHDBgQba7GC6TnzVe+51IhfE49vE+eq/0xcYwk75srsQNpn+Xmfu4/P6/wvMDXlC9/wjEy9coDDzwQ7UceeSTavr/4vHG8F5DeG3lu9WOAq9gfeuihLb4PAEuXLo02xwj5ccnzNs8NV1xxRdKO0+2Vsi6EEEII0Q7ooUcIIYQQpaDT5S12XbGr7vOf/3zSjhcSzbk/2WXqKytzOjSnu3I1Zf85XvzQu9nYvc7782m23iVddvi8LV++PNnGrneWrfwCleye5TR17/72qZVN+IUsubqv5K0KLP2wnXM3//jHP05e9+/fP9rDhg2LtpeZeAyy69zLleza32+//QqPiVNgv/KVr0Tby6S5xVLrhXfeeQd33nknAGDmzJnJtiuvvDLaDz/8cLR54V4glXZHjRoVbV/Fl2UQvxAzpz1zyvPKlSuTdlzmg2UwXjQaSMcgt+M0fCAd3zz3+7HOEh5X/wbS38zyKc/vQLpwdL1y6623RpvvVV7SY/y1zeeO51l/Tvl+yteGL0vwhS98IdqvvvpqtP1qByxPc+Vmlro6G3l6hBBCCFEK9NAjhBBCiFLQpRWZmQkTJiSvOetpypQpyTZ2XXLmVG4RM3atetcfSyIsxXi5jDMdLr300mh/+ctfhiiGs3j8OWWXp88QYIqyONiND6R9xN/lKzz7bEGRjouiRSQBYOrUqdF++umnk20sTfD59/vgBRG5L1iSBoATTjihxW2cPeJfX3jhhdG+7rrrknZ8HLUu7NjZbLHFFjGj1MsKM2bMiPaTTz4ZbV7Y0b9mGejII49M2nGlcz8Hjx8/PtqLFy+Otj+m008/PdosX7O0AaTzAG/zUschhxwSbZ63vXTCIQZ+XuHrizO2WBIEUpmmXmGpn8eln8P22GOPaOfmUsbLyfyav8uPDZYu+TMsgwJpWALLZSyJdTby9AghhBCiFOihRwghhBClQA89QgghhCgFXRrTwzE3XvPnlcpZTwaAAw88MNqsY/pqrqzZsz6Zq9LKPP/888lr1kk5TVPkYS3fr4ruU9Ob8CvcM7mquryNv8tX6/ZptyIlt3L2o48+Gm1fToJjrzheZPjw4Um7efPmtbjNlxzgOABOofap15wCz3FdfO0BaVyQnwdqXS28o1m7dm08P3wOgTQWgs/bggULknY8Z86ePTvavrwGV633VbM5DZxXz+YyEx4uEbD77rsn23g+5d/lK9ozXNG3KY2/pW3++nr55ZejzeVPfKxL7rvrBZ6r+D7p42d4ZQEfA8lxN3yd+3tf0X3Sl37g65C3+YrMXHl9n332ibY/71w6wFeabm/k6RFCCCFEKdBDjxBCCCFKQafLW0WVXr2cwS44dmsDqQu8qIosUFx91bu1+bt5H76dJK32h0sE+EXyGJYu2VXr+4T7L7cwaa6aaVmpdTFOlo/Y9rAkwlIEALzyyivR5vRl/73s2ucUZS+H83Fw3/qKxsccc0y061Xe2nzzzaMM5yuYc+kFlrT8b+HPFX0GSCtZjxkzJtnGEsbIkSOjzSULgFRq3H///aPNshKQpqJPmzYt2l4ifeaZZ6LNfeLvESzh+YVEWT7h/ft7RJG8Xk8UpZ/7OYylSn/PZAkqFzrAIQFF6et+f2x72Yrndx7b/D6Qyp2St4QQQggh2gE99AghhBCiFOihRwghhBCloNNjeopiBXIxBEVLEACpJutT1nmJgqL09dz+fGnzIuq1nH29wNqzj8Xgc8wxIF7zZV2eUx+5FD+Qlp/nfvDfWy/xG/UEx4Xw+fHxEhyDM2jQoGQba/ODBw+Oto/v4L55/fXXo80xIUAaV8JLEvgYLU6N5RgWv4I3x/TU6zhdv359XA2czyEAHH744dHmldV9LMXQoUOjzWPCpzl/6UtfiraP1eF4Kl4K6NBDDy08Ju7/448/Pmk3a9asaPPSE2eccUbSrmj5C44rAoDHH3882r40AbPffvtFm1dcBzaMNatHuLwDr07v73eMvydxW77H+THA82Qu7pHHX1Ecpd9/UWkYIB2nRx11VGG79kCeHiGEEEKUAj30CCGEEKIU1M0q6zlXs09l5hQ5drPlUp7ZVefdbCyxsItfKertA5cY8JU9mVyKOUuc3Ed+JWeWwfh68PJWTuIsK0Xu50mTJiWv2cXOUiOQjiV2qbPEAKQp1Xx9eJmCxyDL1T6Nt0kOAlI5h9N4PbXK153N+++/H2UolvSANAWf0/T93McrcPM5YIkJAI499tjCfbCs8p3vfCfafl689dZbo83yll/BnGWLBx54INr+GmKp7je/+U2033777aQdV5D2cviyZcta3J+/Dmtdjbwz8WOAxwdXXfbyFs9pPB6A9Pzw+PDnjffBc6afjxmWy7wkxvvge7y/3z/99NOF+29v5OkRQgghRCnQQ48QQgghSkGX+ndrrQDrYXcou3G925VdciyJ5Ko/87ZevXrVfEyiGHahekmB3Z85eYsrjLKL11NUYdV/r5fFRPEY9NlbPG65si6Q9ufAgQOj7aUJllx4kUKfbcVyJR+flwB4rPLisn4BU5YEclmhXclWW22F0aNHA0grJgOppMOLrD744INJO5YPOUPLZ29dffXV0fbn45prrok2Z8Rdd911STvO8mL5+rHHHkvanXDCCdH+53/+52j7a4ivDc7Y8jIYL0DKWX5AugApSy5e3vvoRz+KeoOrlQPFKwt4eO7zUiXPrTlZl8dvbnWCos94+Lty2Vv+N3ck8vQIIYQQohTooUcIIYQQpUAPPUIIIYQoBV26ynpbK6JymiFrlV4zZH2ZtX2OIQCKV+32WiWv8rzDDjsUfm+9VnrtKmpd0Zx16Fxf8rnnVYE74pjKRFGV6jlz5iSvP/KRj0Tbx4HMnz8/2txn/fv3T9rxGOG4Da7K7dl9992jvXTp0mQbx43x7/Bj+KWXXoo2x33UE5tttlmMS7rnnnuSbcOGDYs2VzJetWpV0o5f83mbOHFi0o7T3pcsWZJs43iXPfbYI9pnnnlm0u63v/1ttDn2g68TIF2NnWOreF4F0muDf8cBBxyQtONtfh8f//jHo/2Tn/wk2j5FOxdn0lX4uCueF3MVjnMp4TwOOG7Vx7cWnQ+/Pz6PfHw8NwNpfBaXDvD7y5UyaW/k6RFCCCFEKdBDjxBCCCFKQd0sOOpT4tgd9+Mf/zjZxi45Tmn1i+7xPtj2KXuc6sfylq/meumll0b7xhtvbHHfYkO4v3KL5PG14eUndqGypOJT2/m7WObwqey54xCpXOAlJ3a/+xRzlqo4zXnhwoVJO3ajc/kAvwAkp8uzPOJT0bnfX3zxxWj7sckLn9arvLV27dpYDdlLRPx7nn/++Wjzop9Aer1Pnz492iNGjEjacXVeXgQUAAYMGBDtn/3sZ9HmSs1AmorO/fLII48k7XgMjxo1KtpeouaK3zwf//73v0/a7b333tG+6KKLkm0ss/K14e8/XiatB3yJiFw1ZKZIBgOK50U/PmoNzeB7KO/bl41hGSwX2sKlZzoa3a2FEEIIUQr00COEEEKIUlA3K+7l3GpTpkxJXhdVUPawa42jw73UwdIa21zZFejcRdG6E9xHXsZklye7Wr38xFkBLJvkZLBcZkZR5WZRgc8rZ/gAwLhx46LNlX+BtN84Y4tlaCCVyF5++eVo++warvbLFZ69lM3zBy8q6bOacguQ1gs9e/bEXnvtBWDD38nXPlco5kU/gfQcDB06NNpXXnll0m7s2LHR9ufm7rvvjjZLLr76MUtavCjsz3/+86TdSSed1OJ3+Wq8LLm9/vrr0T7xxBOTdnyt3XHHHcm2gw8+ONpN1a2BDStcs0RWL/hMNO5zxmdKcbtas9T8fMz31tw9mbfxPvy8fdBBB0Wbq6j7edtXbO9I5OkRQgghRCnQQ48QQgghSoEeeoQQQghRChoipsdXqOS2HC/iU9FZx2QN0VeR5f3lNE2/cm0RrHEqnT3Fn0M+x3yufErybrvtFm1eadprw7yP9957r/A4ak0DLSu33357tH3KOp9zf46feOKJaHM1Yd+O40K4FMSvfvWrpB2nM3NMnU9xPe6446LNFdtfe+21pB3HBdUrIYQYc+ZT0TlW44EHHoj2jBkzkna77rprtDnOZsiQIUk7n37O8Ng85phjou1jvDjeh+fW/fffP2nH8R0cq+TjQDiOi+d3riwNpNW1fUwPH9PJJ58cbR8X5NPD6wEfx8Xnh/ukV69eSTtO9ff9yqnkfH/ysT5FMZa5Cs98z/TH3hSbBqTXjY856sz5WHdkIYQQQpQCPfQIIYQQohR0qbxV6+KjnLYIpDIWu8l8inlRJU4vOfFxFFWuBFL3nCSs2ilyzwJpX3JZAe/uZHf9TjvtFG0vm7B8xv3nZTWlrOfhKsle3uIFSPv165dse/bZZ6PNfe0rtbLkwqm3vp/YXc5j07vlOe2dqzp7iYUlkXpl3bp1cc7j9G0gnWu4DID/nfy5CRMmRNuHCvTu3TvavjIyV3LmscTp4ECa9s39dcEFFyTtWJ7MLSTKktPixYujPXXq1KQdLyrqK1dzCjTP1V4iq8cFR3lsAOl1z/Pivvvum7Tbcccdo+3DA1gKy1WoLrqv+XtckfTl51WeH7gaui81k9tHrWEltaK7tRBCCCFKgR56hBBCCFEKGkLe8hJGkavOZ28VfZeHvzt3HOzy5+wRXxlTpLC8lcsW4L702TnbbrtttFne8q7QomvKy2Xcl2JD+Pz4DDmWlHlxTyCVQXJjjscqt8tV7M6NTc74YQnDZxp5t3890qNHjyhP+QUxuZLxmDFjos3yLwAsWLCgxW2DBg1K2rF85LNajz766GjzNeBlFa60y3KZl9J4HyzFLFmyJGnH+2Cp0lftZfmNq1MDwPHHHx9tXnyUrxMA+MQnPoF6w1/nPMfxNl/lvKhKMpCOt1xoRm6FA6ZoAW9/r+Z+5uuLMyyBVNJbtmxZsq29My7l6RFCCCFEKdBDjxBCCCFKgR56hBBCCFEK6qYicw6uxgukeiDriV4L5XgAtn18B38uF0PA2irr2IrpycPn1MfgFFXi9LEXPhahCZ/Sy/EmRVVIgdq167LCuvohhxySbOMU0ueeey7Zxv2bG5tM0TgF0n5j25eT4O/ldGhOkwbSmAMff+BLXnQlTTETvlrxY489Fm1Ov/fXN8e/cEViP44effTRaPu0d37Nx3HTTTcl7fh66NOnT7T9GB4/fny0OR7p6quvTtrNnTs32v/wD/8Q7ZEjRybtrrrqqmj7siZ8j+C4KK4QDGwY81UP+NhU7luet3y5CJ5Lc6VBeKz4cVT0vbmUdbZ9RWa+Nw4dOjTaXK0dSMsl+FXmFdMjhBBCCNEG9NAjhBBCiFJQNynrHnbjeZdZUSqyd+nlUpZr+V7v+uPjZXfqHnvsUdO+xYayEvcLu9C9i9cvlNgEp7cCqUvdp3SKPFwmgM+jH6ecDu1TgNtCTt5i2N3uq7SyTMHzBS9ECgCTJ0+Otpdf6kXe2mKLLWKqtq+SzBIBjxefzs0p20ceeWS0uWI2AIwdOzbafoxx2QL+Li+RcWo6n1MvzXGlZa7qPWzYsKQdpznzvhctWpS043nXy3t8PfB9wFcX5++qF7gyPZAeP59TH/bBcqffR1EFZS9bFX1XbvFt3keu0jJfNz7Mgffhy5W0N/L0CCGEEKIU6KFHCCGEEKWgS+WtXEYHZ+HkqviyW7PWxeNy7Xibd/3xd3nJTRTDrlAvMxZV6fTyVpH04CUsdq+zqzXnThUVWH5g1/m8efOSdtyHPoOEKzRz5XRPURX0WrNEfOYVVyrmY+jbt2/Sjl32zz//fLKNq/92JWvXro3n/Je//GWyjasrc5VyzpoCgIkTJ0ab5UifocWSka/+PG7cuGizLMbZccCGklETPguHF4VlWYmztYB0rHO7mTNnJu1mz54dbZ/FydcHzyV+wdnHH3+8xWPvSvzcx+ODq1r7xVP5/HhZlO9duftu7jgYnlt5fvff6ysvt3Q8nvaQzHNo5hdCCCFEKdBDjxBCCCFKgR56hBBCCFEK6rYic66aa1FaeS72h8lVZM5pnxxTwKvCijxcGdn3CafF8vnmeAWguHJoLqaEdX3/vTm9uqxwrMarr74abZ/KzFVt77jjjmQbx2jxOM3FEXA7r/Xz5zgt25eJ4GPia8fHGHD8Qa0xgJ3NZpttFn8Dx9UAaawjp337FdIPPvjgFrfxeAPS1G5fBoCrWXPsXG6lej73PhWd511fQZnhNHVeBd6nQw8YMCDaPs6IU7Y5Vdqn2/vV2esBn+rP8Dnwfc7bcvMbz6X+XshjgtvlVjtg/Hgr2l8utjN3fbUH8vQIIYQQohTooUcIIYQQpaBuffzs7vKuOnbx1pp+x9T6mZz726dI1vq5sjN48ODkNaeScxmAogrMHl+VlNNfuZ/9NSR5ckM4ZZ3lDJYbgLSfvDs7V8mZyaWsMuwS58+cffbZSbtPfvKT0f6bv/mbaLME4qm1Sntn88EHH0TZyafc83j5wx/+EO0DDjggaXfQQQdFm9PZH3744aQdlxXw0hennPOipX4R11deeSXaHALA6fVAKn2xfOplGv6NfB369GeWpnx5BF7Q8thjj402p3wDqXxWL/hyDCw78jYu0wDUXlG81groRWUlcvvwEilfQzyWfZ+zHMn3945Anh4hhBBClAI99AghhBCiFOihRwghhBCloG5jehiv//EqrG1ZTsDrmKw1ctqfT5Hk7/Jl35m2xBl1Z7jUvU8t5VXSOSX5kEMOqWnfPmaD+4y1YR8PUI9aflfDcRF8Xr3Gzv3kz2uty0vstNNO0V62bFm0c8uK8Jj73ve+l7T7xje+Ee2RI0dGe88990zacRxMR6/m3FZ69uyJ/fbbD8CG8R0cm/apT30q2n6u4iU2uKyDL/HA5+quu+5KtnE8Ecd1+XjG4cOHR5uXjfBLv/B1xLF4/pj4u3hu9tcGxwXx9QSkq9Hz8hp+pfbTTz8d9Ya/P3EsFMdP+T7nmB6/NAiPv6LyH0AaN1e0MntLr5vw/cAlEbhPal1JviOQp0cIIYQQpUAPPUIIIYQoBQ0hb7H725Or9ltErWl63iXPrmX+3tbsv4xwaqlPWd9ll12ivXDhwmiPGjWqpn2PGDEieb3DDjtEm+Ua7wr+2Mc+VtP+ywSnorNb2q+WzbKQlxfZ/c4yYdZ6kwAAB29JREFUmD//nDr81ltvRdvLn/zdPP68e7wofdmvEM+p7bWm+HY2W265ZVwN3a+K3pF8/vOf77TvErXD8hbLT74q+eTJk6PtpVsOEeFSDX5cMrWGaeQqLfOcfuSRR0bblxDhz/myAu2NPD1CCCGEKAV66BFCCCFEKehSeatW9xlnBAAbVqJswi9Uxq85ItxHhxctzuarzeZcgYyyt1JYUmC7PWCXKQBMmzYt2rksBbEh7ALnqrucYQcA/fv3j/bEiRML9zdr1qxoe4maZSxemPKEE05I2vGYyy1myVla/JlTTjklacfHMXr06MJjF6Kr8FWNlyxZEm2Wt3yoAEv2vvI238t4H74yetECobksad7mZTXOwuVFgX1GKEvcK1euLPyu9kCeHiGEEEKUAj30CCGEEKIU6KFHCCGEEKWgIWJ6/EraXAWWU8d97AGntXJlU6+Zso7J+iSn3AKpDplbZV2kcAqiTzWuFT73HIPl47GK4nh8PBanSPqK32WF46OuvfbaaPvxcs0119S0P672y3YOv1p4W+BrwM8dPEfwauxC1As+7pGriHMMjq9+fP7557do1yMnnnhi8prn51NPPbVDv1ueHiGEEEKUAj30CCGEEKIUWGuqB5vZCgBLNtpQtCcDQwh9N96sdagvuwz1Z/dBfdm9aPf+VF92GYV92aqHHiGEEEKIRkXylhBCCCFKgR56hBBCCFEKGu6hx8zWm9lMM5trZrPM7Ctm1nC/o4yY2Y7VvptpZm+Y2Wv0um257KJuMbNdzOyXZrbAzJ42s7vNbO9W7mN7M/vHjjpGUTs0984ys2fM7JCNf0rUG2Uflw0X02Nma0II21TtnQBMBDA9hPBvrt3mIYT3W9qH6HrM7N8BrAkhfIfe69Q+M7MeIYTaFlQTrcIqRbgeBfDTEMKN1fdGAtguhPBw9sPpfgYBuCuEMLwjjlPUjpt7Pwbg6yGEIzfyMVFHaFw2oKeHCSEsB3AugH+yCmeb2SQzmwpgipltbWb/z8yeNLNnzewkADCzYdX3ZprZbDPbq9r299W/YuaY2eld+uNKgpndYmY3mtkTAP7TzEaZ2ePVfrnDzHaotptmZmOqdh8zW1y1N+jL6vt/S+//yMx6VN9fY2bfNbNZAMZ2yY8uB0cDWNc0sQJACGEWgEfM7JrqGHuuaZyZ2TZmNqXqQXiuaawC+DaAPar9WFtVRNEZbAdgNZDtO5jZZWY2z8weMbNfmNm/dNkRC0DjsmsrMrcHIYSF1RtaU3nKjwAYEUJ4y8z+A8DUEMI5ZrY9gCfN7A8AvgjguhDCz6uySg8AxwNYFkL4BACYWa/O/zWlpT+AQ0II681sNoALQggPmtnlAP4NwJcyn92gL81sKIDTARwaQlhnZjcA+ByACQC2BvBECOErHfqLxHAAT7fw/ikARgEYCaAPgKfM7CEAKwCcHEL4o5n1AfC4mU0CcAmA4SGEUZ103KKYLc1sJoCeAPoBOKb6/lq03HdjAJyKSl9vAeAZtHxNiM6j9OOy4R96WuD+EELTOvXjAJxIf130BDAAwGMAvmFm/QH8NoTwkpk9B+C7ZnY1Km67ml19YpP5dfWBpxeA7UMID1bf/ymAX2/ksy315bEARqMycAFgSwDLq+3XA7i93X+BqJXDAPyiKiu+aWYPAjgQwD0A/sPMjgDwAYDdAOzcdYcpWuDPTTc5MxsLYIKZDQdgaLnvDgXwuxDCWgBrzezOLjpusXFKMy4b/qHHzIagciNruqm9x5sBnBpCmOc+9kJVTvkEgLvN7LwQwlQz+wgqHp8rzWxKCOHyjj5+ASDtsyLeR7Mc27PpzRDCRN+XqPT7T0MIl7awn7WK4+kU5gI4rRXtPwegL4DRVe/cYlA/i/oihPBY9S//vqjMmeq7xqD047KhY3rMrC+AGwH839ByRPZ9AC6w6p/7ZnZA9f8hABaGEK4H8DsAI8xsVwB/CiH8DMA1qMhkohMJIbwDYLWZHV5960wATV6fxah4bwAatC31JYApAE6zSqA7zKy3mQ3s+F8giKkAPmxm5za9YWYjALwN4HQz61Edv0cAeBJALwDLqxPr0QCa+utdANt27qGLjWFm+6ISFrAKxX03HcAJZtbTzLYB8MmW9yY6kdKPy0b09DTpylug8tf/rQD+q6DtFQCuBTDbKmnti1AZeJ8GcKaZrQPwBoD/QMWVd42ZfQBgHYD6Xqa2+3IWgBvNbCsACwF8ofr+dwDcVh2sv6f2G/RlNZ7rmwAmV/t9HYD/DZWD7zRCCMHMTgZwrZldjErcx2JU4rO2ATALQADwtRDCG2b2cwB3VmXmGQBerO5nlZlNN7M5AO4JIXy1C36OqNA09wIVb+pZVVm6qO+eqsZ/zAbwJoDnALzTBcctqmhcNmDKuhBCiMbAzLYJIayp/hHzEIBzQwjPdPVxifLSiJ4eIYQQjcF/m9l+qMSB/FQPPKKrkadHCCGEEKWgoQOZhRBCCCFqRQ89QgghhCgFeugRQgghRCnQQ48QQgghSoEeeoQQQghRCvTQI4QQQohS8P8BmzLH8N8ewVcAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Testing set\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 25 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "print(\"Training set\")\n",
        "plt.figure(figsize=(10,10))\n",
        "for i in range(25):\n",
        "    plt.subplot(5,5,i+1)\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])\n",
        "    plt.grid(False)\n",
        "    plt.imshow(X_train[i], cmap=plt.cm.binary)\n",
        "    plt.xlabel(categories[y_train_pre_ohe[i]])\n",
        "plt.show()\n",
        "\n",
        "print(\"Testing set\")\n",
        "plt.figure(figsize=(10,10))\n",
        "for i in range(25):\n",
        "    plt.subplot(5,5,i+1)\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])\n",
        "    plt.grid(False)\n",
        "    plt.imshow(X_test[i], cmap=plt.cm.binary)\n",
        "    plt.xlabel(categories[y_test_pre_ohe[i]])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yHvrt-MCfHXi"
      },
      "source": [
        "## Sets for ConvNet\n",
        "\n",
        "It works with pre-OHE labels and un-vectorized data. Also, it makes use of validation sets. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rqmXsThRfNuY"
      },
      "outputs": [],
      "source": [
        "X_train_pre_vect = X_train\n",
        "X_test_pre_vect = X_test\n",
        "X_train_w_val, X_val, y_train_w_val, y_val = train_test_split(\n",
        "    X_train_pre_vect, y_train_pre_ohe, test_size = 0.2, random_state=1, shuffle=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ppXTdC3V9C0p"
      },
      "source": [
        "## Vectorize Images in X\n",
        "\n",
        "We do this by flatting the images. Each image (so, each element of X) is a 28x28 2D array. So, we convert each image into a 28x28=784 long array so that each image is represented by just one flattened array."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aaCwhFVM9CQS"
      },
      "outputs": [],
      "source": [
        "# Vectorize normalized X\n",
        "flat = []\n",
        "for image in X_train:\n",
        "  flat.append(image.flatten())\n",
        "flat = np.array(flat)\n",
        "X_train = flat\n",
        "\n",
        "flat = []\n",
        "for image in X_test:\n",
        "  flat.append(image.flatten())\n",
        "flat = np.array(flat)\n",
        "X_test = flat\n",
        "\n",
        "# Vectorize not normalized X\n",
        "flat = []\n",
        "for image in X_train_not_normalized:\n",
        "  flat.append(image.flatten())\n",
        "flat = np.array(flat)\n",
        "X_train_not_normalized = flat\n",
        "\n",
        "flat = []\n",
        "for image in X_test_not_normalized:\n",
        "  flat.append(image.flatten())\n",
        "flat = np.array(flat)\n",
        "X_test_not_normalized = flat\n",
        "\n",
        "del flat # no more need for this, save space"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ANabYaQLiFMK"
      },
      "source": [
        "## Mini sets for plots"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_2gXCK7hiEz6"
      },
      "outputs": [],
      "source": [
        "indices = np.random.choice(X_test.shape[0], 500, replace=False)\n",
        "X_test_mini = X_test[indices]\n",
        "y_test_mini = y_test[indices]\n",
        "X_train_mini = X_train[indices]\n",
        "y_train_mini = y_train[indices]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4um3Mbo-ietO"
      },
      "source": [
        "## Variable list\n",
        "\n",
        "*   **X_train** and **y_train** for training\n",
        "*   **X_test** and **y_test** for testing\n",
        "*   Mini sets used just for accuracy plots\n",
        "*   **X_train_not_normalized** for pre-normalized X_train; same for **X_test_not_normalized**\n",
        "*   **y_train_pre_ohe** & **y_test_pre_ohe** have y values pre-one-hot-encoding; useful for getting category index when passed to **categories** list\n",
        "*   Stuff for ConvNet: **Y_train_w_val** and **y_train_w_val** for 80% size training set if validation with **X_val** and **y_val** is used. Note that these are pre-OHE and un-vectorized X.\n",
        "*   **categories** contains the list of category names"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IGFfPPrie1gQ"
      },
      "source": [
        "# Task 2: MLP Implementation\n",
        "\n",
        "Please note that the code in this section is heavily inspired from the class tutorial code."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bwbTS5kNP9Av"
      },
      "source": [
        "## Layers"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SpP_CFD2QHaF"
      },
      "source": [
        "### Base Layer Class"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "onHjRpjWPF-A"
      },
      "outputs": [],
      "source": [
        "class NeuralNetLayer:\n",
        "    def __init__(self):\n",
        "        self.gradient = None\n",
        "        self.parameters = None\n",
        "        \n",
        "    def forward(self, x):\n",
        "        raise NotImplementedError\n",
        "\n",
        "    def backward(self, gradient):\n",
        "        raise NotImplementedError"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T01w6Et5QBu0"
      },
      "source": [
        "### Softmax Output Layer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Vd8Wiw8HQP7z"
      },
      "outputs": [],
      "source": [
        "class SoftmaxOutputLayer(NeuralNetLayer):\n",
        "    def __init__(self):\n",
        "        super().__init__()\n",
        "        self.cur_probs = None\n",
        "\n",
        "    def forward(self, x):\n",
        "        z = x.max()\n",
        "        exps = np.exp(x - z)\n",
        "        probs = exps / np.sum(exps, axis=-1)[:, None]\n",
        "        self.cur_probs = probs\n",
        "        return probs\n",
        "\n",
        "    def backward(self, target):\n",
        "        assert self.cur_probs is not None, \"Must call forward before backward\"\n",
        "        return self.cur_probs - target"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UvV0Qz8kQR8o"
      },
      "source": [
        "### LinearLayer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cDS3WcogQYIW"
      },
      "outputs": [],
      "source": [
        "class LinearLayer(NeuralNetLayer):\n",
        "    def __init__(self, input_size, output_size, regularization=False, lmbd=0, perturb=False, rho=0):\n",
        "        super().__init__()\n",
        "        self.ni = input_size\n",
        "        self.no = output_size\n",
        "        self.w = 0.2 * np.random.randn(output_size, input_size)\n",
        "        self.b = np.random.randn(output_size)\n",
        "        self.cur_input = None\n",
        "        self.parameters = [self.w, self.b]\n",
        "        self.regularization = regularization\n",
        "        self.lmbd = lmbd\n",
        "        self.epsilon = 1e-8\n",
        "        self.small_perturb = perturb\n",
        "        self.rho = rho\n",
        "\n",
        "        # Small perturbation is roughly estimated by (L(w + eps*norm(rho)) - L(w))/eps\n",
        "    \n",
        "    def small_perturbation(self, gradient):\n",
        "        first = np.sum(gradient.dot((self.w + (self.epsilon*self.rho))))\n",
        "        second = np.sum(gradient.dot(self.w))\n",
        "        print((first - second)/self.epsilon)\n",
        "\n",
        "    def forward(self, x):\n",
        "        self.cur_input = x\n",
        "        return (self.w[None, :, :] @ x[:, :, None]).squeeze() + self.b\n",
        "\n",
        "    def backward(self, gradient):\n",
        "        assert self.cur_input is not None, \"Must call forward before backward\"\n",
        "        dw = gradient[:, :, None] @ self.cur_input[:, None, :]\n",
        "        db = gradient\n",
        "        self.gradient = [dw, db]\n",
        "        grad = 0\n",
        "        if self.regularization:\n",
        "            grad = gradient.dot(self.w) + gradient.dot(self.lmbd * self.w)\n",
        "        else:\n",
        "            grad = gradient.dot(self.w)\n",
        "\n",
        "        if self.small_perturb:\n",
        "            self.small_perturbation(gradient)\n",
        "        \n",
        "        return grad"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aPOeitbGQaMV"
      },
      "source": [
        "### ReLU Layer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cc2o8demQcbT"
      },
      "outputs": [],
      "source": [
        "class ReLULayer(NeuralNetLayer):\n",
        "    def __init__(self):\n",
        "        super().__init__()\n",
        "        \n",
        "    def forward(self, x):        \n",
        "        # Where gradients are calculated, regularization is done\n",
        "        self.gradient = np.where(x > 0, 1.0, 0.0)\n",
        "        return np.maximum(0, x)\n",
        "\n",
        "    def backward(self, gradient):\n",
        "        assert self.gradient is not None, \"Must call forward before backward\"\n",
        "        return gradient * self.gradient"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6tJx2iceqYG-"
      },
      "source": [
        "### Leaky ReLU Layer\n",
        "\n",
        "Leaky ReLU is f(x)=xI[x≥0]+αxI[x<0] from ed. \n",
        "So, gradient is: \n",
        "- For x >= 0: 1\n",
        "- For x < 0: α\n",
        "\n",
        "We set α to 0.01 as that is the [PyTorch default](https://pytorch.org/docs/stable/generated/torch.nn.LeakyReLU.html)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hAXANAKcqYG-"
      },
      "outputs": [],
      "source": [
        "class LeakyReLULayer(NeuralNetLayer):\n",
        "    def __init__(self):\n",
        "        super().__init__()\n",
        "        \n",
        "    def forward(self, x):\n",
        "        self.gradient = np.where(x >= 0, 1.0, 0.01)\n",
        "        return np.maximum(0, x) + 0.01 * np.minimum(x, 0)\n",
        "\n",
        "    def backward(self, gradient):\n",
        "        assert self.gradient is not None, \"Must call forward before backward\"\n",
        "        return gradient * self.gradient"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gd_TlChHqZf3"
      },
      "source": [
        "### Tanh Layer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dSv9sDq0qZf4"
      },
      "outputs": [],
      "source": [
        "class TanhLayer(NeuralNetLayer):\n",
        "    def __init__(self):\n",
        "        super().__init__()\n",
        "        \n",
        "    def forward(self, x):\n",
        "        z = (np.exp(x) - np.exp(-x)) / (np.exp(x) + np.exp(-x))\n",
        "        self.gradient = np.ones(x.shape) - (z*z)\n",
        "        return z\n",
        "\n",
        "    def backward(self, gradient):\n",
        "        assert self.gradient is not None, \"Must call forward before backward\"\n",
        "        return gradient * self.gradient"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8BRuYqjqQenz"
      },
      "source": [
        "## MLP"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "D65j3qhuQjTH"
      },
      "outputs": [],
      "source": [
        "from pickle import FALSE\n",
        "class MLP:\n",
        "    def __init__(self, activationFunction, numHiddenLayers, numUnitsHiddenLayers, inputSize, outputSize, epoch_number, mini_batch_size, regularization=False, lmbd=0, small_perturb=False):\n",
        "      # Should:\n",
        "      #   - initialize weight\n",
        "      #   - initialize biases\n",
        "      #   - initialize other important properities of the MLP\n",
        "        self.regularization = regularization\n",
        "        self.lmbd = lmbd\n",
        "        self.activationFunction = activationFunction\n",
        "        self.numHiddenLayers = numHiddenLayers\n",
        "        self.numUnitsHiddenLayers = numUnitsHiddenLayers\n",
        "        self.optimizer = GradientDescentOptimizer(self, 1e-2, regularization, lmbd)\n",
        "        self.inputSize = inputSize    # Input to the MLP (X)\n",
        "        self.outputSize = outputSize    # Output from the MLP (y)\n",
        "        self.mini_batch_size = mini_batch_size      # Size of each mini-batch\n",
        "        self.epoch_number = epoch_number    # Number of epochs\n",
        "        self.layers = []    # list of NeuralNetLayers\n",
        "\n",
        "        # Add first linearLayer\n",
        "        rho = 0.01 * np.random.randn(self.numUnitsHiddenLayers, self.inputSize)\n",
        "        norm = np.linalg.norm(rho)\n",
        "        self.rho = rho/norm\n",
        "\n",
        "        if self.numHiddenLayers > 0:\n",
        "            self.layers.append(LinearLayer(self.inputSize, self.numUnitsHiddenLayers, self.regularization, self.lmbd, small_perturb, self.rho))\n",
        "        else:\n",
        "            self.numUnitsHiddenLayers = self.inputSize\n",
        "\n",
        "        if (self.activationFunction.lower() == \"relu\"):\n",
        "          for i in range(0, self.numHiddenLayers):\n",
        "            self.layers.append(ReLULayer())\n",
        "            if i != self.numHiddenLayers - 1:\n",
        "                rho = 0.01 * np.random.randn(self.numUnitsHiddenLayers, self.numUnitsHiddenLayers)\n",
        "                norm = np.linalg.norm(rho)\n",
        "                self.rho = rho/norm\n",
        "                self.layers.append(LinearLayer(self.numUnitsHiddenLayers, self.numUnitsHiddenLayers, self.regularization, self.lmbd, small_perturb, self.rho))\n",
        "        \n",
        "        elif (self.activationFunction.lower() == \"leaky_relu\"):\n",
        "          for i in range(0, self.numHiddenLayers):\n",
        "            self.layers.append(LeakyReLULayer())\n",
        "            if i != self.numHiddenLayers - 1:\n",
        "                rho = 0.01 * np.random.randn(self.numUnitsHiddenLayers, self.numUnitsHiddenLayers)\n",
        "                norm = np.linalg.norm(rho)\n",
        "                self.rho = rho/norm\n",
        "                self.layers.append(LinearLayer(self.numUnitsHiddenLayers, self.numUnitsHiddenLayers, self.regularization, self.lmbd, small_perturb, self.rho))\n",
        "        \n",
        "        elif (self.activationFunction.lower() == \"tanh\"):\n",
        "          for i in range(0, self.numHiddenLayers):\n",
        "            self.layers.append(TanhLayer())\n",
        "            if i != self.numHiddenLayers - 1:\n",
        "                rho = 0.01 * np.random.randn(self.numUnitsHiddenLayers, self.numUnitsHiddenLayers)\n",
        "                norm = np.linalg.norm(rho)\n",
        "                self.rho = rho/norm\n",
        "                self.layers.append(LinearLayer(self.numUnitsHiddenLayers, self.numUnitsHiddenLayers, self.regularization, self.lmbd, small_perturb, self.rho))\n",
        "\n",
        "        elif (self.activationFunction.lower() == \"linear\"):\n",
        "          for i in range(0, self.numHiddenLayers):\n",
        "            self.layers.append(LinearLayer())\n",
        "\n",
        "        else:\n",
        "          raise Exception(self.activationFunction, \"is an invalid activation function.\")\n",
        "        \n",
        "        # Add linearLayer after hidden layers\n",
        "        # Add linearLayer after each hidden layers.\n",
        "        rho = 0.01 * np.random.randn(self.outputSize, self.numUnitsHiddenLayers)\n",
        "        norm = np.linalg.norm(rho)\n",
        "        self.rho = rho/norm\n",
        "        self.layers.append(LinearLayer(self.numUnitsHiddenLayers, self.outputSize, self.regularization, self.lmbd, small_perturb, self.rho))\n",
        "\n",
        "        # Add softmax\n",
        "        self.layers.append(SoftmaxOutputLayer())\n",
        "\n",
        "    def forward(self, x):\n",
        "        for layer in self.layers:\n",
        "            x = layer.forward(x)\n",
        "        return x\n",
        "\n",
        "    def backward(self, target):\n",
        "        for layer in self.layers[::-1]:\n",
        "            target = layer.backward(target)\n",
        "\n",
        "    def fit(self, data_x, data_y):\n",
        "      # losses = []\n",
        "      train_accuracies = []\n",
        "      test_accuracies = []\n",
        "      data_size = len(data_x)\n",
        "\n",
        "      # Permute the data, both x and y at the same time\n",
        "      np.random.seed(12) # set the seed\n",
        "      r = np.arange(len(data_x))\n",
        "      np.random.shuffle(r)\n",
        "      data_x = data_x[r]\n",
        "      data_y = data_y[r]\n",
        "\n",
        "      # Split in mini batches\n",
        "      number_batches = np.rint(data_size/mini_batch_size)\n",
        "      data_index = np.arange(data_size) # list of indices\n",
        "\n",
        "      # different batches, each containing the indices to retrieve the data\n",
        "      batches_index_list = np.split(data_index, number_batches)\n",
        "      batches_counter = 0\n",
        "      for _ in tqdm(range(self.epoch_number)):  # One iteration = one epoch\n",
        "          batches_counter = 0     # reset for each epoch\n",
        "\n",
        "          for _ in range(int(data_size / self.mini_batch_size)): # One iteration = one minibatch\n",
        "          # for _ in range(self.mini_batch_size): # One iteration = one minibatch\n",
        "              ### Mini-batch\n",
        "\n",
        "              # Stores the indices of the data points which are included in this batch\n",
        "              batch_indices = batches_index_list[batches_counter]\n",
        "              batch = data_x[batch_indices]\n",
        "              batch_labels = data_y[batch_indices]\n",
        "              batches_counter += 1\n",
        "\n",
        "              # Forward pass\n",
        "              predictions = self.forward(batch)\n",
        "              #print(predictions)\n",
        "              \n",
        "              # Check if predictions are yielding anomalous values.\n",
        "              if np.any(predictions == 0):\n",
        "                  print(\"There are some missing predictions\")\n",
        "\n",
        "              # Backward propagation to update gradients and weights\n",
        "              self.backward(batch_labels)\n",
        "              self.optimizer.step()\n",
        "      \n",
        "          # After 1 epoch -> Calculate cross entropy\n",
        "          # loss = -(batch_labels * np.log(predictions)).sum(axis=-1).mean()\n",
        "          # losses.append(loss)\n",
        "          # After each epoch, calculate train and test accuracies\n",
        "          y_train_pred = self.forward(X_train_mini)\n",
        "          train_acc = self.evaluate_acc(y_train_mini, y_train_pred, verbose=False)\n",
        "          train_accuracies.append(train_acc)\n",
        "          y_test_pred = self.forward(X_test_mini)\n",
        "          test_acc = self.evaluate_acc(y_test_mini, y_test_pred, verbose=False)\n",
        "          test_accuracies.append(test_acc)\n",
        "\n",
        "      # plt.plot(losses)\n",
        "      # plt.xlabel(\"Epoch\")\n",
        "      # plt.ylabel(\"Cross entropy loss\")\n",
        "      plt.title(\"Accuracy\")\n",
        "      plt.ylabel(\"Accuracy\")\n",
        "      plt.xlabel(\"Epoch\")\n",
        "      plt.plot(train_accuracies, label='train')\n",
        "      plt.plot(test_accuracies, label='test')\n",
        "      plt.legend()\n",
        "      plt.show()\n",
        "\n",
        "    def predict(self, data_x):\n",
        "      # Forward pass\n",
        "      return self.forward(data_x)\n",
        "\n",
        "    def evaluate_acc(self, y, y_pred, verbose=True):\n",
        "      # Same as A2 I think\n",
        "      accuracy = sum(y_pred.argmax(axis=1) == y.argmax(axis=1))\n",
        "      accuracy = accuracy / y.shape[0]\n",
        "      if verbose:\n",
        "        print(\"Accuracy: \", accuracy)\n",
        "      return accuracy\n",
        "\n",
        "    def check_grad(self, data_x, data_y):\n",
        "        losses = []\n",
        "        data_size = len(data_x)\n",
        "\n",
        "        # Permute the data, both x and y at the same time\n",
        "        np.random.seed(12) # set the seed\n",
        "        r = np.arange(len(data_x))\n",
        "        np.random.shuffle(r)\n",
        "        data_x = data_x[r]\n",
        "        data_y = data_y[r]\n",
        "\n",
        "        # Split in mini batches\n",
        "        number_batches = np.rint(data_size/mini_batch_size)\n",
        "        data_index = np.arange(data_size) # list of indices\n",
        "\n",
        "        # different batches, each containing the indices to retrieve the data\n",
        "        batches_index_list = np.split(data_index, number_batches)\n",
        "        batches_counter = 0\n",
        "\n",
        "        for _ in range(self.mini_batch_size): # One iteration = one minibatch\n",
        "              ### Mini-batch\n",
        "\n",
        "              # Stores the indices of the data points which are included in this batch\n",
        "              batch_indices = batches_index_list[batches_counter]\n",
        "              batch = data_x[batch_indices]\n",
        "              batch_labels = data_y[batch_indices]\n",
        "              batches_counter += 1\n",
        "\n",
        "              # Forward pass\n",
        "              predictions = self.forward(batch)\n",
        "              \n",
        "              # Check if predictions are yielding anomalous values.\n",
        "              if np.any(predictions == 0):\n",
        "                  print(\"There are some missing predictions\")\n",
        "\n",
        "              # Backward propagation to update gradients and weights\n",
        "              self.backward(batch_labels)\n",
        "              self.optimizer.step()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eXWnz0x1QkSP"
      },
      "outputs": [],
      "source": [
        "class Optimizer:\n",
        "    def __init__(self, net: MLP):\n",
        "        self.net = net\n",
        "\n",
        "    def step(self):\n",
        "        for layer in self.net.layers[::-1]:\n",
        "            if layer.parameters is not None:\n",
        "                self.update(layer.parameters, layer.gradient)\n",
        "\n",
        "    def update(self, params, gradient):\n",
        "        raise NotImplementedError\n",
        "\n",
        "class GradientDescentOptimizer(Optimizer):\n",
        "    def __init__(self, net: MLP, lr: float, regularization=False, lmbd=0):\n",
        "        super().__init__(net)\n",
        "        self.lr = lr\n",
        "        self.regularization = regularization\n",
        "        self.lmbd = lmbd\n",
        "\n",
        "    def update(self, params, gradient):\n",
        "        for (p, g) in zip(params, gradient):\n",
        "            p -= (self.lr * g.mean(axis=0))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oyolCE_Xm3yX"
      },
      "source": [
        "# Task 3: Exploration"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B2mGD8aNRPfD"
      },
      "source": [
        "## 1) Basic MLP's\n",
        "**Notes:**\n",
        "\n",
        "\n",
        "*   As expected, MLP with no hidden layers has very poor performance: acc 0.1\n",
        "*   1 hidden layer: significant improvement to 0.8449 accuracy, 100 epochs.\n",
        "*   2 layers w ReLU: 0.8548 with 100 epochs. Slight improvement over 1 layer. However, we see that the CE continuous to decrease even after 100 epochs, so we could run for longer (though it takes a long time)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EHNmIXcre5kb"
      },
      "source": [
        "### 1.0 Small Perturbation\n",
        "We test the correctness of the weights. A numerical test is performed to check whether the weights change significantly throughout the fitting."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tRWFXMA7e5IA",
        "outputId": "0ad77864-b8c0-4265-ea2e-6013a7219d11"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
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            "0.7001867530220807\n",
            "2.6154523879995395\n"
          ]
        }
      ],
      "source": [
        "mini_batch_size = 200\n",
        "epoch_number = 50\n",
        "numHiddenLayers = 1\n",
        "HIDDEN_SIZE = 128\n",
        "\n",
        "inputSize = X_train.shape[1]\n",
        "outputSize = y_train.shape[1]\n",
        "model = MLP(\"relu\", numHiddenLayers , HIDDEN_SIZE, inputSize, outputSize, epoch_number, mini_batch_size, small_perturb=True)\n",
        "\n",
        "model.check_grad(X_train, y_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "08jAd372SlTu"
      },
      "source": [
        "### 1.1 No hidden layers.\n",
        "A MLP with only two linear functions and a softmax layer."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 400
        },
        "id": "rv1yQVkESlyb",
        "outputId": "9880dac9-f105-4744-8769-f651e1881bb8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "10\n",
            "[<__main__.LinearLayer object at 0x7f81b5707b80>, <__main__.SoftmaxOutputLayer object at 0x7f81b5707f40>]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 75/75 [05:20<00:00,  4.27s/it]\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"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy:  0.8266\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8266"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "mini_batch_size = 200\n",
        "epoch_number = 75\n",
        "numHiddenLayers = 0\n",
        "# Since we are directly feeding into softmax, make hidden size = 10.\n",
        "HIDDEN_SIZE = 0\n",
        "\n",
        "inputSize = X_train.shape[1]\n",
        "outputSize = y_train.shape[1]\n",
        "model = MLP(\"relu\", numHiddenLayers , HIDDEN_SIZE, inputSize, outputSize, epoch_number, mini_batch_size)\n",
        "\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "y_pred = model.predict(X_test)\n",
        "model.evaluate_acc(y_test, y_pred)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y4TrOGIWQoQr"
      },
      "source": [
        "### 1.2 One Hidden Layer\n",
        "A MLP with 1 hidden layer having 128 units and ReLU activations."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363,
          "referenced_widgets": [
            "6d727204e3aa40d4812a608675a24661"
          ]
        },
        "id": "uG4OGuGkQxP2",
        "outputId": "b8efcfd9-1291-4343-c8a3-e3582b448d7b"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6d727204e3aa40d4812a608675a24661",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/75 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.8512\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "0.8512"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "mini_batch_size = 200\n",
        "epoch_number = 75\n",
        "numHiddenLayers = 1\n",
        "HIDDEN_SIZE = 128\n",
        "\n",
        "inputSize = X_train.shape[1]\n",
        "outputSize = y_train.shape[1]\n",
        "model = MLP(\"relu\", numHiddenLayers , HIDDEN_SIZE, inputSize, outputSize, epoch_number, mini_batch_size)\n",
        "\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "y_pred = model.predict(X_test)\n",
        "model.evaluate_acc(y_test, y_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FuXNfbIRalTR"
      },
      "outputs": [],
      "source": [
        "# Accuracy from 1-layer MLP w ReLU, 100 epochs\n",
        "relu_acc = 0.8555"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_owSHqQ_SbJ3"
      },
      "source": [
        "### 1.3 Two Hidden Layers\n",
        "A MLP with 2 hidden layers each having 128 units with ReLU activations."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ntB6iWOeSiq1",
        "outputId": "5daed191-4fb1-4e73-a9b5-10e7ddc54ab1"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "  2%|▏         | 1/50 [00:24<19:37, 24.03s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.704\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
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          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.8508\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "0.8508"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "mini_batch_size = 200\n",
        "epoch_number = 75\n",
        "numHiddenLayers = 2\n",
        "HIDDEN_SIZE = 128\n",
        "\n",
        "inputSize = X_train.shape[1]\n",
        "outputSize = y_train.shape[1]\n",
        "model = MLP(\"relu\", numHiddenLayers , HIDDEN_SIZE, inputSize, outputSize, epoch_number, mini_batch_size)\n",
        "\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "y_pred = model.predict(X_test)\n",
        "model.evaluate_acc(y_test, y_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ECwmdBHXoleg"
      },
      "outputs": [],
      "source": [
        "# Accuracy from 2-layer MLP w ReLU, 100 epochs\n",
        "relu_acc = 0.8508"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dRyRPfLFpb2r"
      },
      "source": [
        "## 2) Activation Functions\n",
        "\n",
        "* Leaky ReLU accuracy is 0.8507 and Tanh accuracy is 0.8515. Both of these are clearly inferior to basic ReLU which provides an accuracy of 0.8548, though all of them still perform fairly similarly. \n",
        "* Leaky ReLU therefore is not as great an option, especially considering the added complexity of an extra hyperparameter. We could perform hyperparamter optimization on this, but due to limited computational power we opted not to. \n",
        "* However, they still all perform fairly similarly since they all prevent the problem of vanishing gradients by being non-saturating\n",
        "* Tanh and Leaky ReLU's accuracies are negligibly different. Additionally, their accuracies are negligibly different the regular ReLU activation function. This is due to the model not being complex and large enough to end up having the vanishing gradient problem.\n",
        "The cross-entropy losses for the activation functions shows that there is not a significant impact from the vanishing gradient problem."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HsgDdHQCpur6"
      },
      "source": [
        "### Leaky ReLU\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "WDYbYBzSp3FI",
        "outputId": "f16285ed-5084-4b4f-d14f-6b3a2a0c8c74"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "  1%|          | 1/100 [00:24<39:47, 24.12s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.708\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r  2%|▏         | 2/100 [00:48<39:21, 24.10s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.73\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r  3%|▎         | 3/100 [01:12<38:50, 24.02s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.768\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r  4%|▍         | 4/100 [01:36<38:25, 24.01s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.786\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r  5%|▌         | 5/100 [02:00<37:59, 24.00s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.788\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r  6%|▌         | 6/100 [02:24<37:36, 24.01s/it]"
          ]
        },
        {
          "name": "stdout",
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          "text": [
            "\r 72%|███████▏  | 72/100 [30:25<11:28, 24.59s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.866\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 73%|███████▎  | 73/100 [30:49<11:03, 24.58s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.866\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 74%|███████▍  | 74/100 [31:15<10:43, 24.76s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.866\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 75/100 [31:39<10:17, 24.69s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.866\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 76%|███████▌  | 76/100 [32:04<09:51, 24.65s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.866\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 77%|███████▋  | 77/100 [32:28<09:26, 24.64s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.872\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 78%|███████▊  | 78/100 [32:53<09:02, 24.64s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.872\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 79%|███████▉  | 79/100 [33:18<08:37, 24.63s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.872\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 80%|████████  | 80/100 [33:43<08:16, 24.81s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.874\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 81%|████████  | 81/100 [34:07<07:49, 24.73s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.874\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 82%|████████▏ | 82/100 [34:32<07:24, 24.68s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.88\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 83%|████████▎ | 83/100 [34:57<06:59, 24.67s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.884\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 84%|████████▍ | 84/100 [35:21<06:34, 24.63s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.884\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 85%|████████▌ | 85/100 [35:46<06:09, 24.62s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.884\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 86%|████████▌ | 86/100 [36:10<05:44, 24.63s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.882\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 87%|████████▋ | 87/100 [36:36<05:22, 24.78s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.882\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 88/100 [37:00<04:56, 24.71s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.882\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 89%|████████▉ | 89/100 [37:25<04:31, 24.66s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.882\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 90%|█████████ | 90/100 [37:49<04:06, 24.64s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.882\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 91%|█████████ | 91/100 [38:14<03:41, 24.63s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.882\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 92%|█████████▏| 92/100 [38:38<03:16, 24.60s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.882\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 93%|█████████▎| 93/100 [39:04<02:53, 24.78s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.884\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 94%|█████████▍| 94/100 [39:28<02:28, 24.70s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.884\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 95%|█████████▌| 95/100 [39:53<02:03, 24.64s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.884\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 96%|█████████▌| 96/100 [40:17<01:38, 24.60s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.884\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 97%|█████████▋| 97/100 [40:42<01:13, 24.59s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.884\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 98%|█████████▊| 98/100 [41:06<00:49, 24.57s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.884\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 99%|█████████▉| 99/100 [41:31<00:24, 24.54s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.884\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 100/100 [41:56<00:00, 25.16s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.884\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.8613\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "0.8613"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "mini_batch_size = 200\n",
        "epoch_number = 75\n",
        "numHiddenLayers = 2\n",
        "HIDDEN_SIZE = 128\n",
        "\n",
        "inputSize = X_train.shape[1]\n",
        "outputSize = y_train.shape[1]\n",
        "model = MLP(\"leaky_relu\", numHiddenLayers , HIDDEN_SIZE, inputSize, outputSize, epoch_number, mini_batch_size)\n",
        "\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "y_pred = model.predict(X_test)\n",
        "model.evaluate_acc(y_test, y_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "80ml5GVFp6Je"
      },
      "outputs": [],
      "source": [
        "leakyrelu_acc = 0.8613"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_et2dzPapo5D"
      },
      "source": [
        "### Tanh"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "IZ1Ej8hnpfpN",
        "outputId": "d01a5fe7-d18e-49b1-c3e6-59cb3ab4c6c2"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "  1%|          | 1/100 [00:24<41:00, 24.86s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.712\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r  2%|▏         | 2/100 [00:49<40:33, 24.83s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.73\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r  3%|▎         | 3/100 [01:14<40:09, 24.84s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.756\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r  4%|▍         | 4/100 [01:39<39:39, 24.78s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.774\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r  5%|▌         | 5/100 [02:03<39:13, 24.77s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.792\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r  6%|▌         | 6/100 [02:29<39:08, 24.98s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.8\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r  7%|▋         | 7/100 [02:54<38:36, 24.90s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.814\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r  8%|▊         | 8/100 [03:18<38:06, 24.85s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.808\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r  9%|▉         | 9/100 [03:43<37:37, 24.81s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.814\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 10%|█         | 10/100 [04:08<37:10, 24.79s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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        {
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          "output_type": "stream",
          "text": [
            "\r 76%|███████▌  | 76/100 [31:24<09:52, 24.71s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.876\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 77%|███████▋  | 77/100 [31:48<09:25, 24.57s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.878\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 78%|███████▊  | 78/100 [32:12<08:58, 24.47s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.878\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 79%|███████▉  | 79/100 [32:37<08:32, 24.40s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.88\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 80%|████████  | 80/100 [33:02<08:10, 24.54s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.88\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 81%|████████  | 81/100 [33:26<07:44, 24.45s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.882\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 82%|████████▏ | 82/100 [33:50<07:18, 24.38s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.884\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 83%|████████▎ | 83/100 [34:14<06:53, 24.34s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.884\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 84%|████████▍ | 84/100 [34:39<06:29, 24.33s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.884\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 85%|████████▌ | 85/100 [35:03<06:05, 24.33s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.884\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 86%|████████▌ | 86/100 [35:28<05:42, 24.47s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.884\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 87%|████████▋ | 87/100 [35:52<05:17, 24.41s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.884\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 88/100 [36:16<04:52, 24.38s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.884\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 89%|████████▉ | 89/100 [36:40<04:27, 24.32s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.884\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 90%|█████████ | 90/100 [37:05<04:03, 24.31s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.884\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 91%|█████████ | 91/100 [37:29<03:38, 24.28s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.884\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 92%|█████████▏| 92/100 [37:53<03:14, 24.25s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.884\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 93%|█████████▎| 93/100 [38:18<02:51, 24.44s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.882\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 94%|█████████▍| 94/100 [38:42<02:26, 24.40s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.882\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 95%|█████████▌| 95/100 [39:07<02:01, 24.37s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.882\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 96%|█████████▌| 96/100 [39:31<01:37, 24.33s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.882\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 97%|█████████▋| 97/100 [39:55<01:12, 24.32s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.884\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 98%|█████████▊| 98/100 [40:20<00:48, 24.33s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.886\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 99%|█████████▉| 99/100 [40:44<00:24, 24.50s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.886\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 100/100 [41:09<00:00, 24.69s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.886\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.8598\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "0.8598"
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "mini_batch_size = 200\n",
        "epoch_number = 75\n",
        "numHiddenLayers = 2\n",
        "HIDDEN_SIZE = 128\n",
        "\n",
        "inputSize = X_train.shape[1]\n",
        "outputSize = y_train.shape[1]\n",
        "model = MLP(\"tanh\", numHiddenLayers , HIDDEN_SIZE, inputSize, outputSize, epoch_number, mini_batch_size)\n",
        "\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "y_pred = model.predict(X_test)\n",
        "model.evaluate_acc(y_test, y_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "TY9-0EWkpsGy"
      },
      "outputs": [],
      "source": [
        "tanh_acc = 0.8598"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yWxeFkUHOz9T"
      },
      "source": [
        "## 3) L2 Regularization\n",
        "We add weight penalties to weights that are too large. We force them to be smaller than they are otherwise."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "RrVxEuzbO2PZ",
        "outputId": "b269ea1b-d5ea-40b4-d0c3-f6e44a404a3a"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "  1%|          | 1/100 [00:23<39:06, 23.70s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.778\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r  2%|▏         | 2/100 [00:47<38:29, 23.56s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.814\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r  3%|▎         | 3/100 [01:10<38:01, 23.52s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.828\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r  4%|▍         | 4/100 [01:34<37:37, 23.52s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.842\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r  5%|▌         | 5/100 [01:58<37:35, 23.74s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.842\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r  6%|▌         | 6/100 [02:21<37:04, 23.67s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.848\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r  7%|▋         | 7/100 [02:45<36:36, 23.62s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.85\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
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        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.928\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 74%|███████▍  | 74/100 [29:12<10:13, 23.60s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.928\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 75%|███████▌  | 75/100 [29:35<09:50, 23.63s/it]"
          ]
        },
        {
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          "output_type": "stream",
          "text": [
            "Accuracy:  0.928\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 76%|███████▌  | 76/100 [29:59<09:31, 23.81s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.928\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 77%|███████▋  | 77/100 [30:23<09:05, 23.72s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.928\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 78%|███████▊  | 78/100 [30:47<08:41, 23.69s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.926\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 79%|███████▉  | 79/100 [31:10<08:16, 23.62s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.928\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 80%|████████  | 80/100 [31:34<07:51, 23.60s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.928\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 81%|████████  | 81/100 [31:57<07:28, 23.58s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.93\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 82%|████████▏ | 82/100 [32:21<07:07, 23.75s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.926\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 83%|████████▎ | 83/100 [32:45<06:42, 23.70s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.926\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 84%|████████▍ | 84/100 [33:08<06:17, 23.62s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.926\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 85%|████████▌ | 85/100 [33:32<05:54, 23.61s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.926\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 86%|████████▌ | 86/100 [33:55<05:30, 23.60s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.926\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 87%|████████▋ | 87/100 [34:19<05:06, 23.60s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.928\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 88%|████████▊ | 88/100 [34:43<04:43, 23.59s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.93\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 89%|████████▉ | 89/100 [35:07<04:21, 23.81s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.93\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 90%|█████████ | 90/100 [35:31<03:57, 23.74s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.93\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 91%|█████████ | 91/100 [35:54<03:33, 23.69s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.932\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 92%|█████████▏| 92/100 [36:18<03:09, 23.64s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.934\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 93%|█████████▎| 93/100 [36:41<02:45, 23.60s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.932\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 94%|█████████▍| 94/100 [37:05<02:21, 23.57s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.934\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 95%|█████████▌| 95/100 [37:29<01:58, 23.73s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.934\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 96%|█████████▌| 96/100 [37:52<01:34, 23.69s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.934\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 97%|█████████▋| 97/100 [38:16<01:10, 23.63s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.932\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 98%|█████████▊| 98/100 [38:39<00:47, 23.57s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.932\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r 99%|█████████▉| 99/100 [39:03<00:23, 23.54s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.932\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 100/100 [39:26<00:00, 23.67s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.932\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.8803\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "0.8803"
            ]
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "mini_batch_size = 200\n",
        "epoch_number = 75\n",
        "numHiddenLayers = 2\n",
        "HIDDEN_SIZE = 128\n",
        "# Set to 2 cuz best results in experiments\n",
        "lmbd = 2\n",
        "\n",
        "inputSize = X_train.shape[1]\n",
        "outputSize = y_train.shape[1]\n",
        "model = MLP(\"relu\", numHiddenLayers , HIDDEN_SIZE, inputSize, outputSize, epoch_number, mini_batch_size, regularization=True, lmbd=lmbd)\n",
        "\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "y_pred = model.predict(X_test)\n",
        "model.evaluate_acc(y_test, y_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "bWOR8aTiPEGo"
      },
      "outputs": [],
      "source": [
        "# Lambda = 2\n",
        "reg_acc1 = 0.8803\n",
        "# Lambda = 4\n",
        "reg_acc2 = 0.8683\n",
        "# Lamda = 1\n",
        "reg_acc3 = 0.8684\n",
        "# Lambda 0.5\n",
        "reg_acc4 = 0.8636\n",
        "# Lambda 0.1\n",
        "reg_acc5 = 0.8588\n",
        "# Lambda 0.01\n",
        "reg_acc6 = 0.8546\n",
        "# Lambda 0.001\n",
        "reg_acc7 = 0.8548"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "vUIVObf5S6NO",
        "outputId": "ce18af10-07d0-4df3-cf0b-976432b81826"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "  1%|          | 1/100 [00:24<41:02, 24.88s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.78\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r  2%|▏         | 2/100 [00:50<41:11, 25.22s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.812\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\r  3%|▎         | 3/100 [01:15<40:22, 24.98s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.82\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
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          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.828\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
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          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.834\n"
          ]
        },
        {
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        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.84\n"
          ]
        },
        {
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        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.84\n"
          ]
        },
        {
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        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.842\n"
          ]
        },
        {
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        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.848\n"
          ]
        },
        {
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          "output_type": "stream",
          "text": [
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        },
        {
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          "output_type": "stream",
          "text": [
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          ]
        },
        {
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        {
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          "output_type": "stream",
          "text": [
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          ]
        },
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        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.852\n"
          ]
        },
        {
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        },
        {
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          "output_type": "stream",
          "text": [
            "Accuracy:  0.858\n"
          ]
        },
        {
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        },
        {
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          "output_type": "stream",
          "text": [
            "Accuracy:  0.862\n"
          ]
        },
        {
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        {
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          "text": [
            "Accuracy:  0.862\n"
          ]
        },
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        {
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          "output_type": "stream",
          "text": [
            "Accuracy:  0.862\n"
          ]
        },
        {
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        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.864\n"
          ]
        },
        {
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        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.866\n"
          ]
        },
        {
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        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.87\n"
          ]
        },
        {
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          "output_type": "stream",
          "text": [
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          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.872\n"
          ]
        },
        {
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          "output_type": "stream",
          "text": [
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        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.874\n"
          ]
        },
        {
          "name": "stderr",
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          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.876\n"
          ]
        },
        {
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          "output_type": "stream",
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        },
        {
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          "text": [
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          ]
        },
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          ]
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          ]
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        {
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          "text": [
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          ]
        },
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          ]
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          ]
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        {
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          "text": [
            "Accuracy:  0.894\n"
          ]
        },
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            "Accuracy:  0.894\n"
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          ]
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          "text": [
            "Accuracy:  0.902\n"
          ]
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          "text": [
            "Accuracy:  0.904\n"
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          "text": [
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        {
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          "text": [
            "Accuracy:  0.906\n"
          ]
        },
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          ]
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          ]
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            "Accuracy:  0.908\n"
          ]
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            "Accuracy:  0.908\n"
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            "Accuracy:  0.908\n"
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        {
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          "text": [
            "Accuracy:  0.912\n"
          ]
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          ]
        },
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        },
        {
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          "text": [
            "Accuracy:  0.91\n"
          ]
        },
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        },
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          ]
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          "text": [
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          ]
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        {
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          ]
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        },
        {
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          "text": [
            "\n"
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        {
          "data": {
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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy:  0.8808\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "0.8808"
            ]
          },
          "execution_count": 33,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "mini_batch_size = 200\n",
        "epoch_number = 75\n",
        "numHiddenLayers = 2\n",
        "HIDDEN_SIZE = 128\n",
        "lmbd = 2\n",
        "\n",
        "inputSize = X_train.shape[1]\n",
        "outputSize = y_train.shape[1]\n",
        "model = MLP(\"tanh\", numHiddenLayers , HIDDEN_SIZE, inputSize, outputSize, epoch_number, mini_batch_size, regularization=True, lmbd=lmbd)\n",
        "\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "y_pred = model.predict(X_test)\n",
        "model.evaluate_acc(y_test, y_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true
        },
        "id": "zdfFEM6QeT9C"
      },
      "outputs": [],
      "source": [
        "tanh_regularized_acc = 0.8808"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363,
          "referenced_widgets": [
            "aac9536f0fc04e75a12e31ba9a1f11b0",
            "8df1c111e1a64899a5a0dd7bfeae86d1",
            "a8a3bff941224e1f9c47804248292c33",
            "8af1ac6b48514224afb8e2f79a706d9f",
            "4f0ceec49a524aeebad8b8f0a6523057",
            "a29eb4f3b9bc484e8f4e9c051b2a26ee",
            "37519119eb984f95b4ab4c7ced0fef6b",
            "fa448b3735084ec6b95db1ce87c39829",
            "87f044ef07434700baf0229a515091ba",
            "4fa8d30e22bc4e389a60c79f3384f61d",
            "fcfa9e70f20b4b6582ae567bb2fc9f71"
          ]
        },
        "id": "M2x-GOWKTIwF",
        "outputId": "5300fdff-8ae4-4fac-c54b-8947b4deae1d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/75 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "aac9536f0fc04e75a12e31ba9a1f11b0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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qVf2DiHQAVFXXiEhz4Begs6pmlvQ8m+675r1fc7Dv1qRzz6TFbN2Tw5gBx3By299LnmEC3Vs1KHEJzC27sknedvCo5tbxsYdtnzBVX7Cm++4DJKvqOi+I94DhwHKfa7oAf/a2ZwKfAKjq6qILVDVNRLbhShklJghjqoo5yRksS9tdrnvi60QxrFtzv9/k9+bk89gXK3j7x99om1CbD284me7Fpo44nKb1Y2hav+ZOKWH8C2SCaAFs8tlPAfoWu2YRMAJXDXUBUFdE4lV1e9EFItIHiALWFn+AiIwGRgO0alU9lvgz1Vdp1T9l8ebcjTx5cTfaNf69LeDHddu5e9IiUnbu59pT2nDX2R2JiQyvqJBNDRfsRuq7gOdF5CpgNpCKa3MAQESaAW8BV6rqIZ2wVXU8MB5cFZO/BxxJf++qqLqsDFhdFVX/bNmdzU2nt2V0/7aEh5f93+U3K7fxwKdLOefZ77j77I5c1rcV/562mjfmrKdVXCwTx5zECUk1b70CE1iBTBCpgG+3hUTv2AGqmoYrQSAidYALi9oZRKQe8DnwN1X98UgCiImJYfv27cTHx1frJKGqbN++nZgYqyIIth17c3l2xhrS9+QcOJaVk8+3q9Npm1Cbj27sV+7qH4Bh3Zpz4jFx/PWjJfzz8xU89dVq9uUW8MeTWjN2SKcS2xaMORqB/Fc1D2gvIm1wiWEkcJnvBSLSCNjhlQ7uxfVoQkSigI+B/6rqpCMNIDExkZSUFNLTyzdFcFUUExNDYmJisMOo0aYt28LfPl7Crv15tI6vfdC5MQOO4Y5BHY6q+qdx3Rhe/WNvPlyQysT5m7htYHv6tbMu0CZwApYgVDVfRG4GpuG6ub6uqstE5GFgvqpOBk4DHhMRxVUx3eTdfgkwAIj3qp8ArlLVX8sTQ2RkJG3atDn6N2NMKXbty+PBz5bx8cJUujavx9vX9qVT03oBeZaIcFGvRC7qZV8GTOAFrJtrZfPXzdUYXzv25vLwZ8v4ef2OCn3d3dn57M8r4ObT23HzGe1sFlBTpQSrm6sxIcO3+mfIsc2IrsDZQSPCw7isTyuOS6xfYa9pTCiwBGGqley8Anb7TDGRk1/I01+t5qOFqXRpVo+3rulL52aBqf4xprqxBGGqBVXl01/dxHK7fBIEQHiYcOvA9tx8eruArytgTHViCcJUeRlZOfzt4yVMW7aVnq0aMKJnIr69mnu1bhiwRmNjqjNLECZk5RcU8sPa7WTnFZR4TUZWDk9OX01Wdj5jh3Tiuv7H2HTSxlQQSxAmJCVvy+LODxaxaNPhp986rkV9nrykGx2a1K2EyIypOSxBmJBSUKi88cN6npi2itiocJ66pBsdm5b8wR8eJrRLqGPTURsTAJYgTNAsT9vN50vSKPQZijNv/Q7mb9zJmZ2b8OiIY2lc16YPMSZYLEGYSpdXUMiLM9fy3DdrKFQlIuz3b//1akXy5MXdGNGzRbWeP8uYqsAShKlUq7fu4c6Ji1iSuovh3Zvz0LCuNIiNCnZYxhg/LEGYSjNlcRp/fn8RdWIieOnyngw5rlmwQzI1WUYyzHoUOg6FYy+E4iXW/Tvhm0egbhPodzuERx58viAf5j4HyTMOPl63GZz5d6hf9efLsgRhKkV2XgEPf7acTs3q8vpVJ9CoTnSwQzI1VWEh/PwKfP0gFOTC0g9hxWQ45ymo7c2Ou+ZrmHwz7NkCKKz4DM5/GZp0cefTV8HH10PaAmjWHaJ8Zu9d+Tms/hIGPw7dLzs08VQhliBMpZg4fxPb9uTwzMgelhxM8OzcAJ/cBBu/h/ZnwblPw+KJMPNR2PADDPkXrJ8NC96EhE4w8h3YnQqf3Q7jT4XT7nUliRn/cEnhojfg2BEHP2PHevjkRvj0RpdYznvGlUKqIJvN1QRcTn4Bpz0xi5YNY3l/zInW+GyCY/NieGMIIDD4Mehxxe/f7rcucyWCLYvd+X63wml/hUivF93eDJhyhytpAHQ8B84bB3Ua+39WYSH89BLMeBgK8iC8HO1sYeHQ/XJXTRV18LoibFkCn90GsY3g3KcqpBqrtNlcLUGYgHv7x43c98lS3r6mL6e0twVuTBDk7IFXToW8fXDNdGjgZw37gjz4ZYKrMmp5wqHnVWHlFCjMhy7nl63qKH01LHrX3VNWe7bAkokQd4yr1mrV17V3fP80fPsvqNUAcvdCWCQMeRy6jTqqaixLECZocvMLOf3fs2hSL5oPbzjZSg+m8qnCx2NgyQdw5WeQdEqwIzq89d+5KqrMTdB3DGz62bV3HHshDP03ZGe6qrLf5kCHIUdVjVVagrDhpyagPlqQQmrmfm4d2N6SgwmOX9+Bxe/DqfdUjeQA0KY/3DAHel0JP73s2k4ungAXvQ6xca50cdXncPajsG4m/HeYq9aqYNZIbQImr6CQF2Yl0y2xPqd2SAh2OKYmSl8FU++CpP4w4O5gR1M+0XVdyaDX1a6toXax6tmwMDjpJmg3CLK2uP0KZiUIc8R+2biDlJ37Sjz/0YIUNu2w0oMJkF2psPYbV4XkT04WfHA1RNaCEa+6xt+qqHn3Q5ODr4QO0GZAQB4d0AQhIoNFZJWIJIvIWD/nW4vIDBFZLCKzRCTR59yVIrLG+7kykHGa8vtp3XYufnkuZz09m7d+3IhvW1ZufiFPTV/FXz9eSreWDTijUwk9PYw5Eqqw4L/wQl946wJ473LI2nbwNRt+gJf7wbblcMErUM8GZR6JgDVSi0g4sBoYBKQA84BRqrrc55oPgCmq+qaInAFcrap/EJE4YD7QG1DgF6CXqu4s6XnWSF15duzNZegz3xETGUbLuFi+W5PBKe0a8a+Ljmf3/jzunLiI5Zt3M6JnC/5+Xlfq14o8/IsaUxa7N8Nnt8Ka6a7aqM2pMPsJ1x303Kegw2DXtfTHl6Bhazj/JWh9crCjDmmlNVIHsg2iD5Csquu8IN4DhgPLfa7pAvzZ254JfOJtnw18pao7vHu/AgYD7wYwXlMGqsrdHyxix95cPrrxZLo2r8c7P//GI5+v4OynZ5OTX0D9WlG8+sfeDOpSNQcHmQDYux3mPAP7dhz5axR1M83PgSH/Bydc5+rduwxzYxg+uApqxcH+HXDCtXDmQxBdp8LeQk0UyATRAtjks58C9C12zSJgBPAMcAFQV0TiS7i3RfEHiMhoYDRAq1Z++jWbCvef79czY+U2HjyvC8e2qA/A5X1b079dAg9MXkrD2CjuP7cLcbVtAj7jWfm5G9y1fyfUPsrqxubd3ZQY8W1/P5bQEa75Cn54GlZMcT192p5+dM8xQPB7Md0FPC8iVwGzgVSg5PUli1HV8cB4cFVMgQjQ/G5xSib/+nIlZ3VpwpUnJx10rlV8LBOu7hOcwExo2p8JX451A8WaHgd/+ASaHhuYZ4VHuF5KVa2nUogLZIJIBVr67Cd6xw5Q1TRcCQIRqQNcqKqZIpIKnFbs3lkBjNUcxtLUXdz4vwU0rhvD/110vPVKqsoyN8G0e2H72tKvS+jk+tkXb+AtLIR5r8KqL9x0EM17HHpv8tfw6S2QtRUG/MV9cEdYqbKqCWSCmAe0F5E2uMQwErjM9wIRaQTsUNVC4F7gde/UNOBREWno7Z/lnTeVzHdxn7jarm3B1m+oolRh4dsw7a9QWOCqYUpK9KouAaz9xo3cPe4id+3OjfDpTbDhO4ioBa8OhAF3Qf+7XALI2QPT73NTVjTqCCP/By16VurbNBUnYAlCVfNF5Gbch3048LqqLhORh4H5qjoZV0p4TEQUV8V0k3fvDhH5By7JADxc1GBtKo/v4j7nd2/Og7a4j3+FhUc3SEkV8vYffCwiuvR++/k57kO+rPZth8/vhDXToPUpMPx5iGtT+j0ZyfDJ9fDRtbDiU9draMbDgMCw56DzefDFWDc/0KqpcOKNMOsxV0I5+VY4/W+/T3ZnqiSbi8n4tTY9i6HPfEed6AgeueBYBh9r/cgPUVjopkGY+Yib8vmsRyCmXvleQxUm/QmWfXTw8TpNXGNs53MPPp67F776O8z/D2g5p1aIiIEzH4Q+Y8qe0AoLYM5z7j0W5LpupcOfP3iyu6JG6L3pB08wZ6qEYHVzNVXY898kEybC57f2p2l9+xZ4iB3rXVXLxh/c7J8L34a1s+D8F8o3qvXHF11y6Hml+3AFQN0iNu9fDsePdDN21moIG+fCJze4eXl6/tHn+jKQMOh0zsG9f8oiLBxOuR06DnGDzjoPPzS5dDoHWp4Iq7+ArhccOkW1qbIsQZhDrM/Yy6e/pnJt/2NqXnIoyIc9m6FBS//nVWH+6zD9fvfhOfwFN3d/yjzXF//N86DPaFf94qtZN4ipf/Cx1AWuNNDpXDfnjm97wIk3wXf/htn/hvXfQrszXRJq0MpN0pbUr2Lf9+EkdHQ/Jakd79ZXMNWKVTGZQ9z1wSI+W5TG9/ecQULdGrT6W1F1z/JP4aop/kfg/vgyfHkPHHO6q2rxXbAldx/MeMhVOxVXpwmc9yx0HOz2s3fBKwNcFc7137kSgj+pC1ypIX0l9P4TDPqHDf4yFcqqmEyZ/bZ9Hx8vTOXKk5JqVnIA1/Nm2UcQGQsfXgvXf++mVi6SttD10OkwGEa+e2hVS1SsW7LyhGtd984iOVnwzT/g3UtdaWPwY67OPnMTXP1FyckBXA+gMbPdtY3aVejbNeZwbDZXc5AXZyUTHiaMObUc9dtltXkRLPuk5Nk3d2+GRe+Vr3dOcaunu2UZy2vrMjeo65jTXekha5v75l4Ua84eV7qo09jN71NaI2+j9m7dgaKfjoPhum+g/51u0Ni442HZx3DGfWVrzI2ItuRggsIShDkgZec+Jv2SwqgTWtKkXgW2PeTnukXex58OH1wJb1/opmouogqL3ocX+7qVv75/+siek/w1vHOxW1py5mNuCcmyyN3rpoWOrgcjxkOLXnDWP2D1l27SN1W3HvHODXDhaweXKsoqIhoGPgDXfA31mkPHodDv9vK/jjGVyKqYzAEvzVpLmAjXn1bOni6l2bIEPr4Bti5xa+c26+b60r94kquOaXcmTLndTcKW2Adi42Hmo+6bd6sTy/6cPVvgozGQ0BmaHQ/fPu765l/wMjTpWvq9X9wDGavhDx//vgh93+th/Wz46gHYleKWqzz9vqOfGTSxF9w41yUdG41uQpwliBri88WbmbwolcJS+iR8uyqdS05IpFn9Wkf/wIJ8N3narH+5OvaR70Knoe5c+7PgkxvdIKyIGPdhOehhOOlm923+lQEw6RrXeOv7bX37WvjhGTj+koOXjiwsgI+uc/de9Tk07uR6EU25A8afBm3PAClh0Fl+Nqyd4ap/fCd4E3E9lF4+BX70uq72/7P/1zgSlhxMFWC9mKq5HXtzuf/TpXy+eDMtGtSiXilrM8RGhfP8ZT2OPkGkr3JdPtMWuH7x5zx1aLVMYYGrvlk/2yWHxp1+P5e6AP5zFrQfBCPfcQlk3mvw9d8hz1vBru8NrsomKha+fQJm/tON7u35x99fZ2+G6456uDaJZt1cN9NwP9+XUua7Kq9znoS6TY/s78OYEFZaLyZLENXY9GVb+OvHS9i1P4/bz+zAmAHHEBEewGanwgI38GvGP9xgqXOedCOMj8TcF92Ecv3vgk0/ubl/2p0Jg/8FP78CP4+HuLauKujLe6DrCNc+YN/MjSkXSxBVXH5BIde/vYBTOybwhxNbH/b6XfvzeOizZXy0IJUuzerx5CXd6NysnFNAHE5hgWts3uaz/lNBrpvzv+M5cN643+vzj4QqvDvKjc6NqgNnP+JGGxclgHXfupHMuzZBwzauK2h5p7kwxtg4iKpu8qI0vl6xlZmrttG5aV16J5Xci+bb1encM2kx6Vk53HpGO24+oz1REQEoNSz7GNbNhM7DDq4+SuoPx1549N/kReD8F+GnV6D7ZW75SF/HnAo3zHHnuwyz5GBMAFgJIsQVFCqDnvqWyPAwsvMLyMsvZOpt/Q+ZVTUrJ59HPl/Buz//RvvGdXjykm4cn9ggMEEVFsJLJ7ntG+Ye3UymxpigKq0EYf+zQ9yUxWmsy9jL7We257lRPUjPyuGuDxbjm9jnrM1g8LjZvD/vN8acegyf3XJK4JIDwIrJbuqHAXdbcvQxqswAABwNSURBVDCmGrMqphBWWKg8900yHZrU4eyuTQkLE8YO6cw/pixnwpwNXHpCS/7vy1VMmLOBNo1q88H1J9Gr9REM4ipfUDD7CYhv73ooGWOqLUsQIeyLpVtI3pbFc6N6EBbm6vT/1C+JuWszeGzqSibM2cDG7fu46uQk7hnciVpRpSwwU1FWTYWtS+GC8aUvaGOMqfKsfiBEudLDGtom1Gbocb8v1iMiPHFRNxLqRlNQqLx73Yk8OKxr5SQHVbd6WNwxriHaGFOtWQkiRE1fvpWVW/Yw7tLuhIcd3COoYe0opt8xgIhwITqiEr/Fr/4StiyG4S/6H1RmjKlW7H95CFJVnp2xhjaNanPu8f6X+qwdXcm/uty9br3hBq3dVBfGmGovoFVMIjJYRFaJSLKIjPVzvpWIzBSRhSKyWESGescjReRNEVkiIitE5N5Axhlqlm/ezfLNu7m2f5vAjnwuq99+hJf6uem6Bz4A4SVP12GMqT4O++kjIueJSLk/pUQkHHgBGAJ0AUaJSJdil90HTFTVHsBI4EXv+MVAtKoeB/QCxohIUnljqKq+WbENgLO6BHnun7xst0DO64NBC+DKKXDcRcGNyRhTacpST3EpME5EPgReV9WVZXztPkCyqq4DEJH3gOGAz9wMKFA0BLY+kOZzvLaIRAC1gFxgdxmfW+XNWLmNbi0blLyi274dbsrsOo3dLKQRFbDy29pv3Eypedm/H9ud6qay6HW1Wx8huu7RP8cYU2UcNkGo6hUiUg8YBUwQEQXeAN5V1T2l3NoC2OSznwIUXz7rQWC6iNwC1AbO9I5PwiWTzUAscIeq7ij+ABEZDYwGaNWq1eHeSpWQkZXDopRMbh/Ywf8Fq6fB5Fthb7r7Vr/iM7fCWfPuR/bAnCz46n6Y/zo0aOXmNSqS0MnNqdTuzJLvN8ZUW2Vq6VTV3SIyCfdt/nbgAuBuEXlWVZ87iuePAiao6pMichLwlogciyt9FADNgYbAdyLydVFpxCeu8cB4cFNtHEUcIWPWqnRUYWDnYhPdZe+CL/8Kv74NjbvC5RPdIjmTb4XXBrpRzf3vLF/7wIbv3boMmb+5tRjOuA8iK2AtCGNMtXDYBCEiw4CrgXbAf4E+qrpNRGJx1UUlJYhUoKXPfqJ3zNc1wGAAVZ0rIjFAI+Ay4EtVzQO2icgPQG9gHdXczJXbaFw3mq7NfSafWzsTPr0Z9qS5JHDqPa5aqVk3tzrZF/e4Hkbf/gsoxyR5WuBKDFd/Aa1PqvD3Yoyp2spSgrgQeFpVZ/seVNV9InJNKffNA9qLSBtcYhiJ++D39RswEFd11RmIAdK942fgShS1gROBcWWItUrLzS9k9up0zjm+GSLiVf88APP/46a2uOYrSCw2p1ZsHFz4Khx3sVs3oTxqNYTeV7u1G4wxppiyJIgHcW0BAIhILaCJqm5Q1Rkl3aSq+SJyMzANCMc1cC8TkYeB+ao6GbgTeFVE7sA1TF+lqioiLwBviMgy3FfiN1R18RG+xypj/oYd7MnJ54xOjWHjHPjkBti5sWzVPx3Ocj/GGFNBypIgPgB8V2ov8I6dcLgbVXUqMLXYsQd8tpcD/fzcl4Xr6lqjfLNyG1HhYfSPWQsTzoMGLeHqqdD65MPfbIwxFawsCSJCVXOLdlQ1V0SiSrvBHJlvVm5jYFIktT4d7ZLD6G+hVgCn7TbGmFKUZQBcutdQDYCIDAcyAhdSNZSTBWu+cpPdlWB9xl7WZWRxb+5zkLUVLnrDkoMxJqjKkiCuB/4qIr+JyCbgHmBMYMOqZhb8F/53EaybVeIl36zcxlXh02iVPgsGPQwtelZaeMYY409ZBsqtBU4UkTreflbAo6puNi9yf85+Atqe7veSdYu/5++R70CHIXDiDZUYnDHG+FemgXIicg7QFYgRbzF6VX04gHFVL1uWQHg0bPwBNnzPtvjePP9NMlk5+QBEF+xjzNZ/kh0dR9T5L4KUYyyDMcYESFkGyr2Mm+7idOA14CLg5wDHVX3k50DGKugzBpZOQmc9zq25f2PBxkwa14sGVR7IG0dL2UbKkInUiw3wkqHGGFNGZSlBnKyqx4vIYlV9SESeBL4IdGDVRvoqKMyHxF5QvwUy7a8U5Mzh0Ysu5aJeibDgLZg8G06/j9Y9BgU7WmOMOaAsjdRF03vuE5HmQB7gfxUbc6gtS9yfTY/n5/jhZGg9/hk31SWHbSth6t3QZgD0/3Nw4zTGmGLKkiA+E5EGwBPAAmAD8E4gg6pWti6FiFpsj2rBLZNW8mH0BXTcOx/WfQuTvGkuRrwKYZW4dKgxxpRBqVVM3kJBM1Q1E/hQRKYAMaq6q1Kiqw62LEEbd+HOD5eyc18eA64bC+99Bu9cAvnZcMWHUDfICwMZY4wfpZYgVLUQtypc0X6OJYdyUIWtS9kQ2YZZq9K5/5zOdG7dHE6+2SWHfrfbWgvGmJBVlkbqGSJyIfCRailDgc2hdqfC/p3M2NmEYxJqc8WJrd3xk26Bxl0sORhjQlpZ2iDG4CbnyxGR3SKyR0RqzPKfR2XLUgC+SG/EhT0TKRpDQkQUdBxSvsV9jDGmkpVlJLUtRHyktroeTKtoxXM9WgQ5GGOMKZ+yDJQb4O948QWEzKF0yxLSpAnd2ibSvIEt5WmMqVrK0gZxt892DG696F9wK76ZUuSkLGJJfitG9EgMdijGGFNuZaliOs93X0RaUgOW/zxquXuJ2r2RNXICfzrWurEaY6qesjRSF5cCdK7oQKqbnLQlhKHEtu5O7egyzYlojDEhpSxtEM/h1osGl1C640ZUmyKrp7sG6VP+fGAm1lW/zuF4oFuvU4IbmzHGHKGyfLWd77OdD7yrqj+U5cVFZDDwDBAOvKaqjxc73wp4E2jgXTPWW8caETkeeAWoBxQCJ6hqNqEmdx98eiPsTYfYRtDrSgAykn9hD7H0PO74IAdojDFHpiwJYhKQraoFACISLiKxqrqvtJtEJBw3CnsQrlpqnohMVtXlPpfdB0xU1ZdEpAswFUgSkQjgbeAPqrpIROJxkwSGnl8muOSQ0Am+uAda9mFbrTY02L2KzPodqRt+JLV4xhgTfGX59JoB+PbRrAV8XYb7+gDJqrpOVXOB94Dhxa5RXAkBoD6Q5m2fBSxW1UUAqrq9KEGFlLz98MM4SOoPf5wM0XXgg6v4fF4yHeU36ib1CHaExhhzxMqSIGJ8lxn1tmPLcF8LYJPPfop3zNeDwBUikoIrPdziHe8AqIhME5EFIvIXfw8QkdEiMl9E5qenp5chpAq24C3I2gqn3gN1m8CI8ZC+iuPn3ERtyaFBG0sQxpiqqywJYq+I9CzaEZFewP4Kev4oYIKqJgJDgbe8GWQjgFOAy70/LxCRgcVvVtXxqtpbVXsnJCRUUEhllJ8D3z8NrU6GJK8huu0ZpHe/kV75v7r9JsdWbkzGGFOBypIgbgc+EJHvROR74H3g5jLclwq09NlP9I75ugaYCKCqc3ED8RrhShuzVTXDa+uYCvQklCx8G/akwal/OWgN6VfCR/JLYQc0PAoaW29gY0zVVZaBcvNEpBPQ0Tu0SlXL0mA8D2gvIm1wiWEkcFmxa34DBgITRKQzLkGkA9OAv4hILJALnAo8XYZnVo78XFd6SOwDx5x24HBeQSGfLNrKjmOeoNeZdSHSptcwxlRdhy1BiMhNQG1VXaqqS4E6InLj4e5T1XxcSWMasALXW2mZiDwsIsO8y+4ErhORRcC7wFXq7ASewiWZX4EFqvr5kbzBgFj0Luza5NoefEoPs1enk5GVy5A+nd0a1MYYU4XJ4ZZ4EJFfVbV7sWMLVTWkWmB79+6t8+fPP/yFR6sgD57rBbHxcN03ByWIm/63gLnrtvPjvQOJirDurcaY0Cciv6hqb3/nyvIpFi7y+6egN74hqqKCq3IWT4TMjYe0Pezal8dXy7cyrFtzSw7GmGqhLAPlvgTeF5FXvP0xwBeBCymEFeTDd/+GpsdDh8EHnZqyJI3cgkIu7GkztxpjqoeyJIh7gNHA9d7+YqBmTk+69EPYsQ4u/d9BpQeAD39JoUOTOhzbol4JNxtjTNVy2LoQVS0EfgI24EZHn4FrdK5ZCgtg9hNubEPHoQedWpeexYLfMhnhu6yoMcZUcSWWIESkA24g2yggAzf+AVU9vXJCCzHLPobta+DiNyHs4Lz63rxNhAlcYMuKGmOqkdKqmFYC3wHnqmoygIjcUSlRhZrCQld6SOgEnYcddGr+hh385/v1nN+9BU3qxQQpQGOMqXilVTGNADYDM0XkVW+qi5pZf7JiMqSvhAF3H1R6yNyXy63vLiSxYS0eGt41iAEaY0zFKzFBqOonqjoS6ATMxE250VhEXhKRsyorwJAw5zmIbw9dLzhwSFW564PFpGfl8NyoHtSNiQxigMYYU/HK0ki9V1Xf8damTgQW4no21QwFebB5EXQ6B8LCDxyeMGcDX6/YytghnTk+sUEQAzTGmMAo12LJ3hQY472fmmHHeijMY31YK3Zs3AlA+p4cHpu6kjM7N+ZP/ZKCG58xxgRIuRJEjZS+EoBbvt7H0q/mHDjcrH4MT1zUzbq1GmOqLUsQh7Fr0xLqAyf3PYm7u7Q+cPzY5vVoWLvmzjhijKn+LEEcxq7flrK7MIEL+nagczMbJW2MqTlsVrnDiNi+mg1hiXRsUjfYoRhjTKWyBFGawgLiszeyr357wsKsrcEYU7NYgihF+qbVRJNHTPMuwQ7FGGMqnSWIUqxb7hYgSuwQUmsjGWNMpbAEUYqdG5cAkNTJEoQxpuaxBFGKsIxV7AhPILxW/WCHYowxlS6gCUJEBovIKhFJFpGxfs63EpGZIrJQRBaLyFA/57NE5K5AxunPtj3ZNM3dyN56bSv70cYYExICliC8tatfAIYAXYBRIlK8tfc+YKKq9gBGAi8WO/8UQVre9Od1GbSTNKKb2yytxpiaKZAliD5AsqquU9Vc4D1geLFrFCgafVYfSCs6ISLnA+uBZQGMsUSrVq4gVnKITzouGI83xpigC2SCaAFs8tlP8Y75ehC4QkRSgKnALQAiUgc3Y+xDpT1AREaLyHwRmZ+enl5RcQOwY+NiAMKbdK7Q1zXGmKoi2I3Uo4AJqpoIDAXeEpEwXOJ4WlWzSrtZVceram9V7Z2QkFBhQe3Ym0vtXWvcTqMOFfa6xhhTlQRyLqZUoKXPfqJ3zNc1wGAAVZ0rIjFAI6AvcJGI/B/QACgUkWxVfT6A8R7w8/rttJdU8molEBkbVxmPNMaYkBPIEsQ8oL2ItBGRKFwj9ORi1/wGDAQQkc5ADJCuqv1VNUlVk4BxwKOVlRwAfly3gw7hqVa9ZIyp0QKWIFQ1H7gZmAaswPVWWiYiD4vIMO+yO4HrRGQR8C5wlapqoGIqq5/WbadDWBphjTsFOxRjjAmagE73rapTcY3Pvsce8NleDvQ7zGs8GJDgSpBXUMjurRuoFb0fEjpW5qONMSakBLuROuRs3Z1NW0lxOwlWgjDG1FyWIIrZvCub9uK1pVuCMMbUYJYgiknL3E87SSU/Jg5qNwp2OMYYEzSWIIrZvCub9mGpiJUejDE1nCWIYtIy99M+LJXwxtZAbYyp2SxBFJO+Yxf12Qv1E4MdijHGBJUliGL2Z25xG9b+YIyp4SxBFJO3x5v0L9YShDGmZrME4SM7r4CI7B1ux0oQxpgazhKEj827soljt9uxEoQxpoazBOFjc+Z+4sRLELXjgxuMMcYEmSUIH2m7somXPWhYBMQ0CHY4xhgTVJYgfGzO3O+qmGrFg0iwwzHGmKCyBOEjbVc2TSOyEGugNsYYSxC+Nu/aT5OILGt/MMYYLEEcZHNmNnHssR5MxhiDJYiDpO3aTz3dZWMgjDEGSxAH7MnOIzs7m1oFWVaCMMYYApwgRGSwiKwSkWQRGevnfCsRmSkiC0VksYgM9Y4PEpFfRGSJ9+cZgYwT3CC5huxxO9YGYYwxgVuTWkTCgReAQUAKME9EJnvrUBe5D5ioqi+JSBfc+tVJQAZwnqqmicixwDSgRaBiBTfNd7zYKGpjjCkSyBJEHyBZVdepai7wHjC82DUK1PO26wNpAKq6UFXTvOPLgFoiEh3AWN00GwdGUVuCMMaYgJUgcN/4N/nspwB9i13zIDBdRG4BagNn+nmdC4EFqpoTiCCLbM7cTyPxqpisBGGMMUFvpB4FTFDVRGAo8JaIHIhJRLoC/wLG+LtZREaLyHwRmZ+enn5UgaTtyqZ1zH63YyUIY4wJaIJIBVr67Cd6x3xdA0wEUNW5QAzQCEBEEoGPgT+q6lp/D1DV8araW1V7JyQkHFWwm3ftJzF6LyBQq+FRvZYxxlQHgUwQ84D2ItJGRKKAkcDkYtf8BgwEEJHOuASRLiINgM+Bsar6QwBjPGBzZjbNIvZCbByEhVfGI40xJqQFLEGoaj5wM64H0gpcb6VlIvKwiAzzLrsTuE5EFgHvAlepqnr3tQMeEJFfvZ/GAYyVtF37aRRmo6iNMaZIIBupUdWpuK6rvsce8NleDvTzc98/gX8GMjZfmfvyyM4rpCG7rf3BGGM8wW6kDglpu1zjdJ2CTIi1QXLGGAOWIADX/gAQk7vTShDGGOOxBIHrwRRGIeE5mdYGYYwxHksQuDEQCeFZCGolCGOM8ViCwM3D1KGuN1Db2iCMMQawBAG4Noi2sa4dwkoQxhjjWILA9WI6MM2GtUEYYwxgCYLCQmXr7mxaRO11B6wEYYwxgCUIMrJyyCtQmoRnuQPWBmGMMYAlCOJqR/HVHQPoWDcHYupDeGSwQzLGmJAQ0Kk2qoKI8DDaN6kLeTut/cEYY3zU+BLEAXszrP3BGGN8WIIosm+7lSCMMcaHJYgiezOgtjVQG2NMEUsQAIWFVoIwxphiLEEAZGeCFlgbhDHG+LAEAa70AFaCMMYYH5YgwLU/gLVBGGOMD0sQAPu8BGElCGOMOSCgCUJEBovIKhFJFpGxfs63EpGZIrJQRBaLyFCfc/d6960SkbMDGefvJQhLEMYYUyRgI6lFJBx4ARgEpADzRGSyqi73uew+YKKqviQiXYCpQJK3PRLoCjQHvhaRDqpaEJBgrQRhjDGHCGQJog+QrKrrVDUXeA8YXuwaBep52/WBNG97OPCequao6nog2Xu9wNi7HaLqQGRMwB5hjDFVTSATRAtgk89+infM14PAFSKSgis93FKOexGR0SIyX0Tmp6enH3mk+zJsFldjjCkm2I3Uo4AJqpoIDAXeEpEyx6Sq41W1t6r2TkhIOPIobB4mY4w5RCBnc00FWvrsJ3rHfF0DDAZQ1bkiEgM0KuO9FWdfBtRtHrCXN8aYqiiQJYh5QHsRaSMiUbhG58nFrvkNGAggIp2BGCDdu26kiESLSBugPfBzwCLdu91KEMYYU0zAShCqmi8iNwPTgHDgdVVdJiIPA/NVdTJwJ/CqiNyBa7C+SlUVWCYiE4HlQD5wU8B6MKlaG4QxxvgR0AWDVHUqrvHZ99gDPtvLgX4l3PsI8Egg4wMgZw8U5FoJwhhjigl2I3XwFeZD1xHQpGuwIzHGmJBS45ccJTYOLn4j2FEYY0zIsRKEMcYYvyxBGGOM8csShDHGGL8sQRhjjPHLEoQxxhi/LEEYY4zxyxKEMcYYvyxBGGOM8Uvc1EdVn4ikAxuP4iUaARkVFE6gWIwVw2KsGBZjxQlmnK1V1e96CdUmQRwtEZmvqr2DHUdpLMaKYTFWDIux4oRqnFbFZIwxxi9LEMYYY/yyBPG78cEOoAwsxophMVYMi7HihGSc1gZhjDHGLytBGGOM8csShDHGGL9qfIIQkcEiskpEkkVkbLDjKSIir4vINhFZ6nMsTkS+EpE13p8NgxhfSxGZKSLLRWSZiNwWajF68cSIyM8issiL8yHveBsR+cn7vb8vIlFBjjNcRBaKyJRQjM+LaYOILBGRX0Vkvncs1H7fDURkkoisFJEVInJSKMUoIh29v7+in90icnsoxeirRicIEQkHXgCGAF2AUSLSJbhRHTABGFzs2Fhghqq2B2Z4+8GSD9ypql2AE4GbvL+7UIoRIAc4Q1W7Ad2BwSJyIvAv4GlVbQfsBK4JYowAtwErfPZDLb4ip6tqd58++6H2+34G+FJVOwHdcH+nIROjqq7y/v66A72AfcDHoRTjQVS1xv4AJwHTfPbvBe4Ndlw+8SQBS332VwHNvO1mwKpgx+gT26fAoBCPMRZYAPTFjVqN8PfvIAhxJeI+FM4ApgASSvH5xLkBaFTsWMj8voH6wHq8zjehGGOxuM4CfgjlGGt0CQJoAWzy2U/xjoWqJqq62dveAjQJZjBFRCQJ6AH8RAjG6FXf/ApsA74C1gKZqprvXRLs3/s44C9AobcfT2jFV0SB6SLyi4iM9o6F0u+7DZAOvOFV170mIrUJrRh9jQTe9bZDMsaaniCqLHVfNYLeR1lE6gAfArer6m7fc6ESo6oWqCvSJwJ9gE5BDukAETkX2KaqvwQ7ljI4RVV74qpkbxKRAb4nQ+D3HQH0BF5S1R7AXopV1YRAjAB4bUrDgA+KnwuVGMESRCrQ0mc/0TsWqraKSDMA789twQxGRCJxyeF/qvqRdzikYvSlqpnATFyVTQMRifBOBfP33g8YJiIbgPdw1UzPEDrxHaCqqd6f23D15n0Ird93CpCiqj95+5NwCSOUYiwyBFigqlu9/VCMscYniHlAe6/HSBSuyDc5yDGVZjJwpbd9Ja7ePyhERID/ACtU9SmfUyETI4CIJIhIA2+7Fq6dZAUuUVzkXRa0OFX1XlVNVNUk3L+/b1T18lCJr4iI1BaRukXbuPrzpYTQ71tVtwCbRKSjd2ggsJwQitHHKH6vXoLQjLFmN1K7khxDgdW4eum/BTsen7jeBTYDebhvRtfg6qZnAGuAr4G4IMZ3Cq4YvBj41fsZGkoxenEeDyz04lwKPOAdPwb4GUjGFfOjQ+B3fhowJRTj8+JZ5P0sK/q/EoK/7+7AfO/3/QnQMARjrA1sB+r7HAupGIt+bKoNY4wxftX0KiZjjDElsARhjDHGL0sQxhhj/LIEYYwxxi9LEMYYY/yyBGFMOYhIQbHZOCtsUjURSfKdvdeYYIs4/CXGGB/71U3bYUy1ZyUIYyqAt1bC/3nrJfwsIu2840ki8o2ILBaRGSLSyjveREQ+9tapWCQiJ3svFS4ir3prV0z3Rn8bExSWIIwpn1rFqpgu9Tm3S1WPA57HzdAK8BzwpqoeD/wPeNY7/izwrbp1KnriRicDtAdeUNWuQCZwYYDfjzElspHUxpSDiGSpah0/xzfgFiZa501iuEVV40UkAzfPf553fLOqNhKRdCBRVXN8XiMJ+ErdojGIyD1ApKr+M/DvzJhDWQnCmIqjJWyXR47PdgHWTmiCyBKEMRXnUp8/53rbc3CztAJcDnznbc8AboADCxrVr6wgjSkr+3ZiTPnU8lanK/KlqhZ1dW0oIotxpYBR3rFbcCuc3Y1b7exq7/htwHgRuQZXUrgBN3uvMSHD2iCMqQBeG0RvVc0IdizGVBSrYjLGGOOXlSCMMcb4ZSUIY4wxflmCMMYY45clCGOMMX5ZgjDGGOOXJQhjjDF+/T91Xi2hjGR3ogAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy:  0.8759\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8759"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ],
      "source": [
        "mini_batch_size = 200\n",
        "epoch_number = 75\n",
        "numHiddenLayers = 2\n",
        "HIDDEN_SIZE = 128\n",
        "lmbd = 2\n",
        "\n",
        "inputSize = X_train.shape[1]\n",
        "outputSize = y_train.shape[1]\n",
        "model = MLP(\"leaky_relu\", numHiddenLayers , HIDDEN_SIZE, inputSize, outputSize, epoch_number, mini_batch_size, regularization=True, lmbd=lmbd)\n",
        "\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "y_pred = model.predict(X_test)\n",
        "model.evaluate_acc(y_test, y_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UeQ-tNAemJEW"
      },
      "outputs": [],
      "source": [
        "leaky_relu_regularized = 0.8759"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7-GR1_pX1SVu"
      },
      "source": [
        "## 4) Not Normalized \n",
        "\n",
        "* We initially had a numerical issue (probably divide by 0). For some reason, we don't have this issue with X_train & X_test. Upon inspection, the normalization converted all ints to floats (so, 0 becomes 0.). I therefore divided the ..._not_normalized arrays by 1 to resolve this issue. Unsure why this causes a problem though. \n",
        "* As expected, working with non normalized data significantly impacts performance. An accuracy of 0.1 is equivalent to randomly guessing (since there are 10 classes), and that is what our model effectively ends up doing. \n",
        "* This could be explained by the fact that, since there is a much larger range of values, the gradient calculations vary enormously from one neuron to the next and "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6fXtt6kA1WPc"
      },
      "outputs": [],
      "source": [
        "mini_batch_size = 200\n",
        "epoch_number = 20\n",
        "numHiddenLayers = 2\n",
        "HIDDEN_SIZE = 128\n",
        "\n",
        "inputSize = X_train_not_normalized.shape[1]\n",
        "outputSize = y_train.shape[1]\n",
        "model = MLP(\"relu\", numHiddenLayers, HIDDEN_SIZE, inputSize, outputSize, epoch_number, mini_batch_size)\n",
        "\n",
        "model.fit(X_train_not_normalized, y_train)\n",
        "\n",
        "y_pred = model.predict(X_test_not_normalized)\n",
        "model.evaluate_acc(y_test, y_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gIkphUhP1Y0T"
      },
      "outputs": [],
      "source": [
        "notnormalized_acc = 0.1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VLrdKWcKMZs-"
      },
      "source": [
        "## 5) ConvNet with Keras\n",
        "\n",
        "NOTE: PORTIONS ARE COMMENTED OUT TO PREVENT IT FROM RUNNING WITH EVERY RUN. convnet_acc HAS BEEN SAVED SEPARATELY SO IT'S USABLE/VISIBLE. \n",
        "\n",
        "[Used guide by Rutvik Deshpande](https://www.kaggle.com/code/rutvikdeshpande/fashion-mnist-cnn-beginner-98/notebook)\n",
        "\n",
        "**Notes on Model Design**\n",
        "*   Used GPU acceleration (Runtime->Change runtime type) for much faster training\n",
        "*   Rutvik's model design provides improved performance on test data thanks to layers that help prevent overfitting (in particular multiple Dropout layers to prevent over-reliance on specific neurons, including in the fully connected classification portion of the model)\n",
        "*   It also makes use of multiple BatchNormalization layers as a way of reducing sensitivity to the learning rate (though ADAM is used, which is already a great option) & reduced gradient explosion (though there aren't too many connected layers, meaning the risk of this is lowered).\n",
        "\n",
        "**Notes on Results**\n",
        "*    With GPU acceleration, it still took 6m 23s to train due to the high number of epochs (100)\n",
        "*    Test accuracy: 0.9722; validation accuracy: 0.9345\n",
        "*    As the test accuracy < 1, this indicates that our model does not overfit\n",
        "*    convnet_acc = 0.9297999739646912. A pretty decent classification accuracy on the testing set. \n",
        "*    This clearly outperforms the MLP's best accuracies we could get by a lot. This makes sense since ConvNets are specially adapted to better handling image inputs by performing convolution as a way of extracting more meaningful image features. Additionally, this model is a lot more complex and contains a lot more layers for improved feature abstraction, which is possible given Keras's GPU optimizations (for our MLP, creating more complex models would create for much too slow training times). "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RBipcrItMdms",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "db190c06-9b86-45c9-f627-f0bf38e41e1b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "model = Sequential([\n",
        "  Conv2D(32, 3, padding='same', activation='relu',kernel_initializer='he_normal', input_shape=(28,28, 1)),\n",
        "  MaxPooling2D(pool_size=(2, 2)),\n",
        "  Conv2D(64, 3, padding='same', activation='relu'),\n",
        "  MaxPooling2D(pool_size=(2, 2)),\n",
        "  Dropout(0.3),\n",
        "  BatchNormalization(),\n",
        "  Conv2D(128, 3, padding='same', activation='relu'),\n",
        "  Conv2D(128, 3, padding='same', activation='relu'),\n",
        "  MaxPooling2D(pool_size=(2, 2)),\n",
        "  Dropout(0.4),\n",
        "  Flatten(),\n",
        "  BatchNormalization(),\n",
        "  Dense(512, activation='relu'),\n",
        "  Dropout(0.25),\n",
        "  Dense(10, activation='softmax')\n",
        "])\n",
        "\n",
        "plot_model(model, to_file=\"convnet.png\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0QSNloUMcZdR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "80d23d07-8f87-4658-942a-a316b0ee54d3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "375/375 [==============================] - 13s 9ms/step - loss: 0.5621 - accuracy: 0.7989 - val_loss: 0.4068 - val_accuracy: 0.8472\n",
            "Epoch 2/100\n",
            "375/375 [==============================] - 3s 8ms/step - loss: 0.3847 - accuracy: 0.8592 - val_loss: 0.3007 - val_accuracy: 0.8870\n",
            "Epoch 3/100\n",
            "375/375 [==============================] - 3s 8ms/step - loss: 0.3316 - accuracy: 0.8780 - val_loss: 0.3393 - val_accuracy: 0.8736\n",
            "Epoch 4/100\n",
            "375/375 [==============================] - 4s 9ms/step - loss: 0.3034 - accuracy: 0.8881 - val_loss: 0.2689 - val_accuracy: 0.8973\n",
            "Epoch 5/100\n",
            "375/375 [==============================] - 5s 12ms/step - loss: 0.2804 - accuracy: 0.8963 - val_loss: 0.2387 - val_accuracy: 0.9115\n",
            "Epoch 6/100\n",
            "375/375 [==============================] - 5s 12ms/step - loss: 0.2673 - accuracy: 0.9010 - val_loss: 0.2455 - val_accuracy: 0.9054\n",
            "Epoch 7/100\n",
            "375/375 [==============================] - 3s 8ms/step - loss: 0.2566 - accuracy: 0.9050 - val_loss: 0.2258 - val_accuracy: 0.9160\n",
            "Epoch 8/100\n",
            "375/375 [==============================] - 3s 8ms/step - loss: 0.2436 - accuracy: 0.9089 - val_loss: 0.2270 - val_accuracy: 0.9146\n",
            "Epoch 9/100\n",
            "375/375 [==============================] - 4s 10ms/step - loss: 0.2377 - accuracy: 0.9104 - val_loss: 0.2250 - val_accuracy: 0.9183\n",
            "Epoch 10/100\n",
            "375/375 [==============================] - 3s 9ms/step - loss: 0.2316 - accuracy: 0.9149 - val_loss: 0.2154 - val_accuracy: 0.9220\n",
            "Epoch 11/100\n",
            "375/375 [==============================] - 3s 8ms/step - loss: 0.2238 - accuracy: 0.9167 - val_loss: 0.2275 - val_accuracy: 0.9162\n",
            "Epoch 12/100\n",
            "375/375 [==============================] - 3s 8ms/step - loss: 0.2152 - accuracy: 0.9203 - val_loss: 0.2234 - val_accuracy: 0.9146\n",
            "Epoch 13/100\n",
            "375/375 [==============================] - 3s 8ms/step - loss: 0.2127 - accuracy: 0.9196 - val_loss: 0.2107 - val_accuracy: 0.9230\n",
            "Epoch 14/100\n",
            "375/375 [==============================] - 3s 8ms/step - loss: 0.2053 - accuracy: 0.9224 - val_loss: 0.2234 - val_accuracy: 0.9199\n",
            "Epoch 15/100\n",
            "375/375 [==============================] - 3s 8ms/step - loss: 0.2009 - accuracy: 0.9237 - val_loss: 0.2078 - val_accuracy: 0.9224\n",
            "Epoch 16/100\n",
            "375/375 [==============================] - 3s 8ms/step - loss: 0.1957 - accuracy: 0.9273 - val_loss: 0.2108 - val_accuracy: 0.9252\n",
            "Epoch 17/100\n",
            "375/375 [==============================] - 3s 9ms/step - loss: 0.1903 - accuracy: 0.9289 - val_loss: 0.1920 - val_accuracy: 0.9295\n",
            "Epoch 18/100\n",
            "375/375 [==============================] - 3s 8ms/step - loss: 0.1863 - accuracy: 0.9295 - val_loss: 0.2018 - val_accuracy: 0.9262\n",
            "Epoch 19/100\n",
            "375/375 [==============================] - 3s 8ms/step - loss: 0.1861 - accuracy: 0.9304 - val_loss: 0.1929 - val_accuracy: 0.9294\n",
            "Epoch 20/100\n",
            "375/375 [==============================] - 3s 8ms/step - loss: 0.1767 - accuracy: 0.9335 - val_loss: 0.1964 - val_accuracy: 0.9298\n",
            "Epoch 21/100\n",
            "375/375 [==============================] - 3s 8ms/step - loss: 0.1751 - accuracy: 0.9343 - val_loss: 0.1902 - val_accuracy: 0.9311\n",
            "Epoch 22/100\n",
            "375/375 [==============================] - 3s 8ms/step - loss: 0.1695 - accuracy: 0.9367 - val_loss: 0.1935 - val_accuracy: 0.9303\n",
            "Epoch 23/100\n",
            "375/375 [==============================] - 3s 8ms/step - loss: 0.1684 - accuracy: 0.9360 - val_loss: 0.2153 - val_accuracy: 0.9237\n",
            "Epoch 24/100\n",
            "375/375 [==============================] - 3s 8ms/step - loss: 0.1669 - accuracy: 0.9367 - val_loss: 0.1877 - val_accuracy: 0.9318\n",
            "Epoch 25/100\n",
            "375/375 [==============================] - 3s 9ms/step - loss: 0.1640 - accuracy: 0.9376 - val_loss: 0.1939 - val_accuracy: 0.9327\n",
            "Epoch 26/100\n",
            "375/375 [==============================] - 3s 9ms/step - loss: 0.1610 - accuracy: 0.9391 - val_loss: 0.2012 - val_accuracy: 0.9311\n",
            "Epoch 27/100\n",
            "375/375 [==============================] - 3s 8ms/step - loss: 0.1566 - accuracy: 0.9406 - val_loss: 0.1870 - val_accuracy: 0.9351\n",
            "Epoch 28/100\n",
            "375/375 [==============================] - 3s 8ms/step - loss: 0.1527 - accuracy: 0.9420 - val_loss: 0.1966 - val_accuracy: 0.9294\n",
            "Epoch 29/100\n",
            "375/375 [==============================] - 4s 10ms/step - loss: 0.1479 - accuracy: 0.9442 - val_loss: 0.1983 - val_accuracy: 0.9317\n",
            "Epoch 30/100\n",
            "375/375 [==============================] - 3s 9ms/step - loss: 0.1450 - accuracy: 0.9451 - val_loss: 0.1817 - val_accuracy: 0.9355\n",
            "Epoch 31/100\n",
            "375/375 [==============================] - 3s 8ms/step - loss: 0.1442 - accuracy: 0.9449 - val_loss: 0.2406 - val_accuracy: 0.9181\n",
            "Epoch 32/100\n",
            "375/375 [==============================] - 3s 9ms/step - loss: 0.1414 - accuracy: 0.9467 - val_loss: 0.1958 - val_accuracy: 0.9342\n",
            "Epoch 33/100\n",
            "375/375 [==============================] - 3s 8ms/step - loss: 0.1404 - accuracy: 0.9472 - val_loss: 0.1905 - val_accuracy: 0.9355\n",
            "Epoch 34/100\n",
            "375/375 [==============================] - 4s 9ms/step - loss: 0.1343 - accuracy: 0.9478 - val_loss: 0.1963 - val_accuracy: 0.9343\n",
            "Epoch 35/100\n",
            "375/375 [==============================] - 4s 9ms/step - loss: 0.1353 - accuracy: 0.9490 - val_loss: 0.1922 - val_accuracy: 0.9373\n",
            "Epoch 36/100\n",
            "375/375 [==============================] - 3s 9ms/step - loss: 0.1347 - accuracy: 0.9500 - val_loss: 0.1975 - val_accuracy: 0.9348\n",
            "Epoch 37/100\n",
            "375/375 [==============================] - 3s 9ms/step - loss: 0.1304 - accuracy: 0.9505 - val_loss: 0.2027 - val_accuracy: 0.9325\n",
            "Epoch 38/100\n",
            "375/375 [==============================] - 3s 9ms/step - loss: 0.1329 - accuracy: 0.9502 - val_loss: 0.2009 - val_accuracy: 0.9318\n",
            "Epoch 39/100\n",
            "375/375 [==============================] - 3s 9ms/step - loss: 0.1260 - accuracy: 0.9534 - val_loss: 0.1878 - val_accuracy: 0.9358\n",
            "Epoch 40/100\n",
            "375/375 [==============================] - 3s 9ms/step - loss: 0.1252 - accuracy: 0.9525 - val_loss: 0.1979 - val_accuracy: 0.9346\n",
            "Epoch 41/100\n",
            "375/375 [==============================] - 3s 8ms/step - loss: 0.1251 - accuracy: 0.9530 - val_loss: 0.1945 - val_accuracy: 0.9367\n",
            "Epoch 42/100\n",
            "375/375 [==============================] - 4s 9ms/step - loss: 0.1240 - accuracy: 0.9535 - val_loss: 0.1975 - val_accuracy: 0.9341\n",
            "Epoch 43/100\n",
            "375/375 [==============================] - 3s 9ms/step - loss: 0.1205 - accuracy: 0.9546 - val_loss: 0.2019 - val_accuracy: 0.9319\n",
            "Epoch 44/100\n",
            "375/375 [==============================] - 4s 9ms/step - loss: 0.1208 - accuracy: 0.9540 - val_loss: 0.1942 - val_accuracy: 0.9360\n",
            "Epoch 45/100\n",
            "375/375 [==============================] - 4s 9ms/step - loss: 0.1143 - accuracy: 0.9567 - val_loss: 0.1990 - val_accuracy: 0.9374\n",
            "Epoch 46/100\n",
            "375/375 [==============================] - 4s 9ms/step - loss: 0.1173 - accuracy: 0.9554 - val_loss: 0.1952 - val_accuracy: 0.9347\n",
            "Epoch 47/100\n",
            "375/375 [==============================] - 3s 8ms/step - loss: 0.1142 - accuracy: 0.9570 - val_loss: 0.1992 - val_accuracy: 0.9345\n",
            "Epoch 48/100\n",
            "375/375 [==============================] - 3s 8ms/step - loss: 0.1102 - accuracy: 0.9575 - val_loss: 0.1937 - val_accuracy: 0.9360\n",
            "Epoch 49/100\n",
            "375/375 [==============================] - 3s 8ms/step - loss: 0.1139 - accuracy: 0.9571 - val_loss: 0.1947 - val_accuracy: 0.9354\n",
            "Epoch 50/100\n",
            "375/375 [==============================] - 3s 8ms/step - loss: 0.1079 - accuracy: 0.9595 - val_loss: 0.1960 - val_accuracy: 0.9392\n",
            "Epoch 51/100\n",
            "375/375 [==============================] - 3s 8ms/step - loss: 0.1105 - accuracy: 0.9575 - val_loss: 0.1928 - val_accuracy: 0.9336\n",
            "Epoch 52/100\n",
            "375/375 [==============================] - 3s 9ms/step - loss: 0.1087 - accuracy: 0.9590 - val_loss: 0.2008 - val_accuracy: 0.9359\n",
            "Epoch 53/100\n",
            "375/375 [==============================] - 3s 9ms/step - loss: 0.1077 - accuracy: 0.9599 - val_loss: 0.2051 - val_accuracy: 0.9341\n",
            "Epoch 54/100\n",
            "375/375 [==============================] - 3s 8ms/step - loss: 0.1077 - accuracy: 0.9597 - val_loss: 0.1933 - val_accuracy: 0.9376\n",
            "Epoch 55/100\n",
            "375/375 [==============================] - 3s 8ms/step - loss: 0.1031 - accuracy: 0.9609 - val_loss: 0.2038 - val_accuracy: 0.9341\n",
            "Epoch 56/100\n",
            "375/375 [==============================] - 3s 8ms/step - loss: 0.1037 - accuracy: 0.9619 - val_loss: 0.1993 - val_accuracy: 0.9352\n",
            "Epoch 57/100\n",
            "375/375 [==============================] - 3s 9ms/step - loss: 0.1049 - accuracy: 0.9618 - val_loss: 0.1968 - val_accuracy: 0.9369\n",
            "Epoch 58/100\n",
            "375/375 [==============================] - 3s 8ms/step - loss: 0.0991 - accuracy: 0.9622 - val_loss: 0.2102 - val_accuracy: 0.9371\n",
            "Epoch 59/100\n",
            "375/375 [==============================] - 3s 9ms/step - loss: 0.1003 - accuracy: 0.9619 - val_loss: 0.2117 - val_accuracy: 0.9352\n",
            "Epoch 60/100\n",
            "375/375 [==============================] - 3s 9ms/step - loss: 0.0966 - accuracy: 0.9639 - val_loss: 0.2119 - val_accuracy: 0.9337\n",
            "Epoch 61/100\n",
            "375/375 [==============================] - 3s 9ms/step - loss: 0.1020 - accuracy: 0.9629 - val_loss: 0.1971 - val_accuracy: 0.9399\n",
            "Epoch 62/100\n",
            "375/375 [==============================] - 3s 8ms/step - loss: 0.0965 - accuracy: 0.9632 - val_loss: 0.2073 - val_accuracy: 0.9381\n",
            "Epoch 63/100\n",
            "375/375 [==============================] - 3s 8ms/step - loss: 0.0967 - accuracy: 0.9643 - val_loss: 0.2004 - val_accuracy: 0.9391\n",
            "Epoch 64/100\n",
            "375/375 [==============================] - 3s 8ms/step - loss: 0.0975 - accuracy: 0.9638 - val_loss: 0.1906 - val_accuracy: 0.9395\n",
            "Epoch 65/100\n",
            "375/375 [==============================] - 3s 8ms/step - loss: 0.0964 - accuracy: 0.9641 - val_loss: 0.2176 - val_accuracy: 0.9286\n",
            "Epoch 66/100\n",
            "375/375 [==============================] - 3s 8ms/step - loss: 0.0942 - accuracy: 0.9647 - val_loss: 0.1981 - val_accuracy: 0.9409\n",
            "Epoch 67/100\n",
            "375/375 [==============================] - 3s 9ms/step - loss: 0.0893 - accuracy: 0.9665 - val_loss: 0.2216 - val_accuracy: 0.9281\n",
            "Epoch 68/100\n",
            "375/375 [==============================] - 3s 9ms/step - loss: 0.0928 - accuracy: 0.9651 - val_loss: 0.1939 - val_accuracy: 0.9366\n",
            "Epoch 69/100\n",
            "375/375 [==============================] - 4s 9ms/step - loss: 0.0911 - accuracy: 0.9663 - val_loss: 0.2181 - val_accuracy: 0.9337\n",
            "Epoch 70/100\n",
            "375/375 [==============================] - 3s 9ms/step - loss: 0.0899 - accuracy: 0.9667 - val_loss: 0.2138 - val_accuracy: 0.9322\n",
            "Epoch 71/100\n",
            "375/375 [==============================] - 3s 8ms/step - loss: 0.0911 - accuracy: 0.9659 - val_loss: 0.2141 - val_accuracy: 0.9355\n",
            "Epoch 72/100\n",
            "375/375 [==============================] - 3s 9ms/step - loss: 0.0872 - accuracy: 0.9679 - val_loss: 0.2087 - val_accuracy: 0.9380\n",
            "Epoch 73/100\n",
            "375/375 [==============================] - 3s 9ms/step - loss: 0.0907 - accuracy: 0.9670 - val_loss: 0.2166 - val_accuracy: 0.9376\n",
            "Epoch 74/100\n",
            "375/375 [==============================] - 4s 10ms/step - loss: 0.0873 - accuracy: 0.9670 - val_loss: 0.2107 - val_accuracy: 0.9395\n",
            "Epoch 75/100\n",
            "375/375 [==============================] - 3s 9ms/step - loss: 0.0869 - accuracy: 0.9681 - val_loss: 0.2102 - val_accuracy: 0.9366\n",
            "Epoch 76/100\n",
            "375/375 [==============================] - 3s 9ms/step - loss: 0.0860 - accuracy: 0.9683 - val_loss: 0.2061 - val_accuracy: 0.9370\n",
            "Epoch 77/100\n",
            "375/375 [==============================] - 3s 9ms/step - loss: 0.0891 - accuracy: 0.9669 - val_loss: 0.1971 - val_accuracy: 0.9383\n",
            "Epoch 78/100\n",
            "375/375 [==============================] - 4s 9ms/step - loss: 0.0852 - accuracy: 0.9686 - val_loss: 0.1952 - val_accuracy: 0.9417\n",
            "Epoch 79/100\n",
            "375/375 [==============================] - 4s 9ms/step - loss: 0.0836 - accuracy: 0.9686 - val_loss: 0.2126 - val_accuracy: 0.9395\n",
            "Epoch 80/100\n",
            "375/375 [==============================] - 3s 9ms/step - loss: 0.0826 - accuracy: 0.9696 - val_loss: 0.2081 - val_accuracy: 0.9392\n",
            "Epoch 81/100\n",
            "375/375 [==============================] - 4s 9ms/step - loss: 0.0832 - accuracy: 0.9692 - val_loss: 0.2146 - val_accuracy: 0.9397\n",
            "Epoch 82/100\n",
            "375/375 [==============================] - 3s 9ms/step - loss: 0.0809 - accuracy: 0.9711 - val_loss: 0.2199 - val_accuracy: 0.9373\n",
            "Epoch 83/100\n",
            "375/375 [==============================] - 3s 8ms/step - loss: 0.0802 - accuracy: 0.9700 - val_loss: 0.2206 - val_accuracy: 0.9400\n",
            "Epoch 84/100\n",
            "375/375 [==============================] - 3s 9ms/step - loss: 0.0803 - accuracy: 0.9712 - val_loss: 0.1971 - val_accuracy: 0.9409\n",
            "Epoch 85/100\n",
            "375/375 [==============================] - 3s 9ms/step - loss: 0.0770 - accuracy: 0.9722 - val_loss: 0.2212 - val_accuracy: 0.9363\n",
            "Epoch 86/100\n",
            "375/375 [==============================] - 3s 8ms/step - loss: 0.0794 - accuracy: 0.9699 - val_loss: 0.2208 - val_accuracy: 0.9342\n",
            "Epoch 87/100\n",
            "375/375 [==============================] - 3s 8ms/step - loss: 0.0771 - accuracy: 0.9711 - val_loss: 0.2149 - val_accuracy: 0.9384\n",
            "Epoch 88/100\n",
            "375/375 [==============================] - 3s 8ms/step - loss: 0.0780 - accuracy: 0.9717 - val_loss: 0.2255 - val_accuracy: 0.9354\n",
            "Epoch 89/100\n",
            "375/375 [==============================] - 3s 8ms/step - loss: 0.0757 - accuracy: 0.9718 - val_loss: 0.2315 - val_accuracy: 0.9382\n",
            "Epoch 90/100\n",
            "375/375 [==============================] - 3s 9ms/step - loss: 0.0773 - accuracy: 0.9719 - val_loss: 0.2100 - val_accuracy: 0.9383\n",
            "Epoch 91/100\n",
            "375/375 [==============================] - 3s 8ms/step - loss: 0.0743 - accuracy: 0.9729 - val_loss: 0.2282 - val_accuracy: 0.9400\n",
            "Epoch 92/100\n",
            "375/375 [==============================] - 3s 9ms/step - loss: 0.0770 - accuracy: 0.9718 - val_loss: 0.2094 - val_accuracy: 0.9398\n",
            "Epoch 93/100\n",
            "375/375 [==============================] - 3s 9ms/step - loss: 0.0758 - accuracy: 0.9730 - val_loss: 0.2334 - val_accuracy: 0.9307\n",
            "Epoch 94/100\n",
            "375/375 [==============================] - 3s 9ms/step - loss: 0.0786 - accuracy: 0.9703 - val_loss: 0.2136 - val_accuracy: 0.9388\n",
            "Epoch 95/100\n",
            "375/375 [==============================] - 3s 9ms/step - loss: 0.0749 - accuracy: 0.9722 - val_loss: 0.2284 - val_accuracy: 0.9345\n",
            "Epoch 96/100\n",
            "375/375 [==============================] - 3s 9ms/step - loss: 0.0723 - accuracy: 0.9740 - val_loss: 0.2111 - val_accuracy: 0.9411\n",
            "Epoch 97/100\n",
            "375/375 [==============================] - 3s 8ms/step - loss: 0.0735 - accuracy: 0.9724 - val_loss: 0.2271 - val_accuracy: 0.9380\n",
            "Epoch 98/100\n",
            "375/375 [==============================] - 3s 8ms/step - loss: 0.0731 - accuracy: 0.9734 - val_loss: 0.2153 - val_accuracy: 0.9408\n",
            "Epoch 99/100\n",
            "375/375 [==============================] - 3s 9ms/step - loss: 0.0757 - accuracy: 0.9727 - val_loss: 0.2285 - val_accuracy: 0.9414\n",
            "Epoch 100/100\n",
            "375/375 [==============================] - 4s 9ms/step - loss: 0.0772 - accuracy: 0.9721 - val_loss: 0.2393 - val_accuracy: 0.9381\n"
          ]
        }
      ],
      "source": [
        "# Takes 6m 23s to run with GPU acceleration, so commented out & saved accuracy\n",
        "\n",
        "model.compile(\n",
        "    'adam',\n",
        "    loss=categorical_crossentropy,\n",
        "    metrics=['accuracy']\n",
        ")\n",
        "\n",
        "train_model = model.fit(X_train_w_val, to_categorical(y_train_w_val),\n",
        "                        batch_size = 128,\n",
        "                        epochs = 100,\n",
        "                        verbose=1,\n",
        "                        validation_data=(X_val, to_categorical(y_val)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jhqUQxFoldzY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "afa8eba2-0f4d-4c0f-d554-7d1eb5d2fd04"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 1s 4ms/step - loss: 0.2645 - accuracy: 0.9313\n",
            "Test accuracy: 0.9312999844551086\n"
          ]
        }
      ],
      "source": [
        "score = model.evaluate(X_test_pre_vect, to_categorical(y_test_pre_ohe))\n",
        "print('Test accuracy:', score[1])\n",
        "convnet_acc = score[1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WaKgrop-MVQp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "4afe9731-5c40-44da-c7e0-11e8ebcf1729"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Plot graph \n",
        "# Source: https://stackoverflow.com/questions/41908379/keras-plot-training-validation-and-test-set-accuracy\n",
        "# Also has details on plotting loss instead, though we're asked to plot accuracy\n",
        "plt.plot(train_model.history['accuracy'])\n",
        "plt.plot(train_model.history['val_accuracy'])\n",
        "plt.title('Model Accuracy')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Train', 'Val'], loc='upper left')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hrePml7rmVN5"
      },
      "outputs": [],
      "source": [
        "convnet_acc = 0.9297999739646912"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q4OU3jXVNow2"
      },
      "source": [
        "## 6) Hyperparameters Tuning\n",
        "\n",
        "*Grid Search for Best Model*\n",
        "\n",
        "**Objective:**\n",
        "\n",
        "Our approach is to use a Keras MLP for faster training (via GPU acceleration) and, in our grid search, vary exclusively the hyperparameters our model consists of. This way, we can then transfer this information on the hyperparameters to our own MLP implementation as a way of getting a performant model. \n",
        "\n",
        "\n",
        "**Note:**\n",
        "\n",
        "From scikit-learn.org: \"The parameters of the estimator used to apply these methods are optimized by cross-validated grid-search over a parameter grid.\".\n",
        "This means that we can use x_train and y_train, without splitting it ourselves in training vs. validation format.\n",
        "\n",
        "\n",
        "**References:**\n",
        "Please note that the code in this section is heavily inspired by those 3 sources:\n",
        "*   https://machinelearningmastery.com/grid-search-hyperparameters-deep-learning-models-python-keras/\n",
        "*   https://colab.research.google.com/github/yueliyl/comp551-notebooks/blob/master/AutoDiffMLP.ipynb#scrollTo=g22608tCOA3M\n",
        "*   https://www.geeksforgeeks.org/hyperparameter-tuning-using-gridsearchcv-and-kerasclassifier/\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8DhwjgPSNtxq",
        "outputId": "12bdac28-741b-4828-93fd-7bbd946b82e0"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/joblib/externals/loky/process_executor.py:700: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Best accuracy: 0.895250 using {'batch_size': 200, 'epochs': 200}\n",
            "0.894867 with: {'batch_size': 200, 'epochs': 50}\n",
            "0.892200 with: {'batch_size': 200, 'epochs': 100}\n",
            "0.892167 with: {'batch_size': 200, 'epochs': 150}\n",
            "0.895250 with: {'batch_size': 200, 'epochs': 200}\n",
            "0.895033 with: {'batch_size': 800, 'epochs': 50}\n",
            "0.891933 with: {'batch_size': 800, 'epochs': 100}\n",
            "0.890500 with: {'batch_size': 800, 'epochs': 150}\n",
            "0.889383 with: {'batch_size': 800, 'epochs': 200}\n",
            "0.890783 with: {'batch_size': 1600, 'epochs': 50}\n",
            "0.893067 with: {'batch_size': 1600, 'epochs': 100}\n",
            "0.891250 with: {'batch_size': 1600, 'epochs': 150}\n",
            "0.889117 with: {'batch_size': 1600, 'epochs': 200}\n"
          ]
        }
      ],
      "source": [
        "# Building our model using Keras MLP\n",
        "\n",
        "numberOfHiddenUnits = 128   # In our experiments, we use 128 hidden units\n",
        "\n",
        "# KerasClassifier requires us to create a function that returns a model\n",
        "def build_model():\n",
        "  model = Sequential()\n",
        "  model.add(Dense(numberOfHiddenUnits, input_shape=(X_train.shape[1],), activation='relu')) # Hidden Layer 1\n",
        "  model.add(Dense(numberOfHiddenUnits, activation='relu'))  # Hidden Layer 2\n",
        "  model.add(Dense(y_train.shape[1], activation='softmax'))  # Output layer\n",
        "\n",
        "  #need to compile the model\n",
        "  model.compile(optimizer='adam', loss='binary_crossentropy')\n",
        "  return model\n",
        "\n",
        "# Now, we need to wrap our model in Scikit-Learn. \n",
        "model = KerasClassifier(model = build_model, verbose=0)\n",
        "\n",
        "# Set seed for Tensorflow\n",
        "tf.random.set_seed(12)\n",
        "\n",
        "batch_size = [200, 800, 1600]   # possible mini-batch sizes\n",
        "epochs = [50,100,150, 200]      # possible epochs\n",
        "param_grid = dict(batch_size = batch_size, epochs=epochs)\n",
        "n_jobs = -1 #to use all processors available for parallelism\n",
        "cv = 5 # 5-fold cross validation\n",
        "\n",
        "grid = GridSearchCV(estimator = model, param_grid=param_grid, n_jobs=n_jobs, cv=cv)\n",
        "grid_result=grid.fit(X_train, y_train)\n",
        "\n",
        "\n",
        "# Results \n",
        "# Code from https://machinelearningmastery.com/grid-search-hyperparameters-deep-learning-models-python-keras/\n",
        "print(\"Best accuracy: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))\n",
        "means = grid_result.cv_results_['mean_test_score']\n",
        "params = grid_result.cv_results_['params']\n",
        "for mean, param in zip(means, params):\n",
        "    print(\"%f with: %r\" % (mean, param))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 7) Final Model\n",
        "We run this final model with the best results from hyperparameter tuning (200 epochs, mini batch size of 200) and the best activation function (leaky ReLU)."
      ],
      "metadata": {
        "id": "4idIKEa7UoC0"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "id": "jYMSOvCETUHN",
        "outputId": "652f4d32-197a-4365-9d58-9c4031cde800"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 200/200 [2:52:37<00:00, 51.79s/it]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
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        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy:  0.8737\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8737"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "mini_batch_size = 200\n",
        "epoch_number = 200\n",
        "numHiddenLayers = 2\n",
        "HIDDEN_SIZE = 128\n",
        "lmbd=2\n",
        "\n",
        "inputSize = X_train.shape[1]\n",
        "outputSize = y_train.shape[1]\n",
        "model = MLP(\"leaky_relu\", numHiddenLayers , HIDDEN_SIZE, inputSize, outputSize, epoch_number, mini_batch_size, regularization=True, lmbd=lmbd)\n",
        "\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "y_pred = model.predict(X_test)\n",
        "model.evaluate_acc(y_test, y_pred)"
      ]
    }
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