Master-DataScience-Notes/1year/3trimester/Machine Learning, Statistical Learning, Deep Learning and Artificial Intelligence/Machine Learning/Project/project_code.ipynb

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      "name": "CNN.ipynb",
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/Andreaierardi/Fruits-Neural-Networks/blob/master/project_code.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "id": "HtU6p0pB-N0A",
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      "source": [
        "\n",
        "# Image classification with Machine Learning\n",
        "---\n",
        "\n",
        "## University of Milan \n",
        "### DataScience and Economics - Machine Learning Module\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LGE0Fo3e-3Wk",
        "colab_type": "text"
      },
      "source": [
        "**Authors** : Andrea Ierardi, Emanuele Morales, Gregorio Luigi Saporito\n",
        "\n",
        "<br>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F5Ew02Va-6DV",
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      "source": [
        "How to load the dataset: \n",
        "\n",
        "\n",
        "\n",
        "```python\n",
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')\n",
        "\n",
        "\n",
        "import os\n",
        "os.environ['KAGGLE_CONFIG_DIR'] = \"/content/gdrive/My Drive/Kaggle\"\n",
        "\n",
        "\n",
        "%cd /content/gdrive/My Drive/Kaggle\n",
        "\n",
        "\n",
        "!kaggle datasets download --force -d moltean/fruits\n",
        "\n",
        "!unzip fruits.zip\n",
        "```\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tb4yPcdji5Ai",
        "colab_type": "text"
      },
      "source": [
        "# Image classification with Neural Networks\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pcRWLxstjHvN",
        "colab_type": "text"
      },
      "source": [
        "## 1. The dataset\n",
        "### 1.1 Libraries\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iXeBOWLtcdMv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "317f98fa-192a-4556-e97b-9e2d8da88fb0"
      },
      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import os\n",
        "from tqdm import tqdm\n",
        "import random\n",
        "import pandas as pd \n",
        "\n",
        "from plotnine import *\n",
        "from sklearn.decomposition import PCA\n",
        "\n",
        "from sklearn.datasets import load_files\n",
        "from keras.preprocessing.image import array_to_img, img_to_array, load_img\n",
        "from sklearn import preprocessing\n",
        "\n",
        "from keras.utils import np_utils\n",
        "from sklearn.utils import shuffle\n",
        "import numpy as np\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Conv2D,MaxPooling2D\n",
        "from keras.layers import Activation, Dense, Flatten, Dropout\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.callbacks import ModelCheckpoint\n",
        "from keras import backend as K\n",
        "\n",
        "from keras.applications import MobileNetV2\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QXB2MCkujQIi",
        "colab_type": "text"
      },
      "source": [
        "# 1.1 Data Loading"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2w9LPk2Tc8Y7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "DATADIR = \"fruits-360/Training\"\n",
        "DATADIR_test = \"fruits-360/Test\"\n",
        "\n",
        "TYPES = [\"Apple\", \"Banana\", \"Plum\", \"Pepper\", \"Cherry\", \"Grape\", \"Tomato\", \"Potato\", \"Pear\", \"Peach\"]\n",
        "fruits = {}\n",
        "def load_dataset(dire):\n",
        "    fruits = {}\n",
        "    images_as_array = []\n",
        "    labels =[]\n",
        "    for category in tqdm(os.listdir(dire)):\n",
        "        for typ in TYPES:\n",
        "            if(category.split()[0] == typ):\n",
        "                fruits[category]= typ\n",
        "                path = os.path.join(dire,category)\n",
        "                class_num =TYPES.index(fruits[category])\n",
        "\n",
        "                class_name = fruits[category]\n",
        "                for img in tqdm(os.listdir(path)):\n",
        "                    file = os.path.join(path,img)\n",
        "                    images_as_array.append(img_to_array(load_img(file,target_size=(32, 32))))\n",
        "                    labels.append(class_num)\n",
        "    images_as_array =  np.array(images_as_array)\n",
        "    labels = np.array(labels)\n",
        "    return images_as_array, labels"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WHUwPqnPjoPp",
        "colab_type": "text"
      },
      "source": [
        "### Split in test and training sets\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qY-O9bmQdDnq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train = load_dataset(DATADIR)\n",
        "test = load_dataset(DATADIR_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oEFfdWThdNjj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_train, y_train= train"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3x9fYmsWhoKe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_test, y_test = test"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oepGscO8jxgv",
        "colab_type": "text"
      },
      "source": [
        "### Train and test shape\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hUqYyOVThpX6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 124
        },
        "outputId": "f475c7c4-1acb-4345-ff7a-c5e437a5942d"
      },
      "source": [
        "print('Train shape:')\n",
        "print('X: ',x_train.shape)\n",
        "print('y: ',y_train.shape)\n",
        "\n",
        "print('Test shape')\n",
        "print('X: ',x_test.shape)\n",
        "print('y: ',y_test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train shape:\n",
            "X:  (32607, 32, 32, 3)\n",
            "y:  (32607,)\n",
            "Test shape\n",
            "X:  (10906, 32, 32, 3)\n",
            "y:  (10906,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nNf0OLzBj0Ot",
        "colab_type": "text"
      },
      "source": [
        "# 1.2 Pre-processing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ENOcYiDEj4aD",
        "colab_type": "text"
      },
      "source": [
        "### Pre-process the labels and the images\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JnU3AsJ3hqyw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_train = x_train.astype('float32')/255\n",
        "x_test = x_test.astype('float32')/255\n",
        "\n",
        "no_of_classes = len(np.unique(y_train))\n",
        "y_train = np_utils.to_categorical(y_train,no_of_classes)\n",
        "y_test = np_utils.to_categorical(y_test,no_of_classes)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zklIYBIxhtug",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 214
        },
        "outputId": "c39ab752-199f-4775-c2d2-932e637a6b71"
      },
      "source": [
        "print(y_train[0:10])\n",
        "print(\"Number of classes: \",no_of_classes)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]\n",
            "Number of classes:  10\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RQiX-6kfkCRl",
        "colab_type": "text"
      },
      "source": [
        "### Visualisation of the first 10 images\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FmmJABIHhwIh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 324
        },
        "outputId": "becfb7a0-905a-4795-9ef5-2e373a3e6971"
      },
      "source": [
        "\n",
        "fig = plt.figure(figsize =(30,5))\n",
        "for i in range(10):\n",
        "    ax = fig.add_subplot(2,5,i+1,xticks=[],yticks=[])\n",
        "    ax.imshow(np.squeeze(x_train[i]))\n",
        "    ax.set_title(\"{}\".format(TYPES[np.argmax(y_train[i])]),color=(\"green\"),fontdict= {'fontsize': '25'})"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 2160x360 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y_E6_3LzkFsR",
        "colab_type": "text"
      },
      "source": [
        "### Suffle of the data\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tyKinSP8h5KH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_train,y_train = shuffle(x_train, y_train)\n",
        "x_test,y_test = shuffle(x_test, y_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NIVoRrD7kHvO",
        "colab_type": "text"
      },
      "source": [
        "### Visualisation of the first 10 images shuffled\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9a4KNtsoh6n7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 324
        },
        "outputId": "ae70bb27-0b73-4f45-da52-b4103cdab909"
      },
      "source": [
        "\n",
        "fig = plt.figure(figsize =(30,5))\n",
        "for i in range(10):\n",
        "    ax = fig.add_subplot(2,5,i+1,xticks=[],yticks=[])\n",
        "    ax.imshow(np.squeeze(x_train[i]))\n",
        "    ax.set_title(\"{}\".format(TYPES[np.argmax(y_train[i])]),color=(\"green\"),fontdict= {'fontsize': '25'})\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 2160x360 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aI-v_vkFkK6A",
        "colab_type": "text"
      },
      "source": [
        "### Split in validation and test set\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "chR6UKO9h9Az",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "outputId": "4ea738a5-6b55-46a7-9d3e-97c6c86c17b9"
      },
      "source": [
        "# Using 80-20 rule\n",
        "split = len(x_test)*80//100\n",
        "\n",
        "print('Test len before split: ',len(x_test))\n",
        "print('Validation split len:', split)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Test len before split:  10906\n",
            "Validation split len: 8724\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GTktl3TSh-zx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "2bd7432b-98b1-4715-f527-f399dda3a2bc"
      },
      "source": [
        "# Now, we have to divide the validation set into test and validation set\n",
        "x_test,x_valid = x_test[split:],x_test[:split]\n",
        "y_test,y_valid = y_test[split:],y_test[:split]\n",
        "print('Train X : ',x_train.shape)\n",
        "print('Train y :',y_train.shape)\n",
        "\n",
        "print('1st training image shape ',x_train[0].shape)\n",
        "\n",
        "print('Validation X : ',x_valid.shape)\n",
        "print('Validation y :',y_valid.shape)\n",
        "print('Test X : ',x_test.shape)\n",
        "print('Test y : ',y_test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train X :  (32607, 32, 32, 3)\n",
            "Train y : (32607, 10)\n",
            "1st training image shape  (32, 32, 3)\n",
            "Validation X :  (8724, 32, 32, 3)\n",
            "Validation y : (8724, 10)\n",
            "Test X :  (2182, 32, 32, 3)\n",
            "Test y :  (2182, 10)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wtCzXW5-EmnM",
        "colab_type": "text"
      },
      "source": [
        "### Definition of zero-one loss function "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "atod5RB6ErJA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def zero_one(prediz,test):\n",
        "    y_hat = []\n",
        "    y_t = []\n",
        "    for i in range(len(prediz)):\n",
        "        y_hat.append(np.argmax(prediz[i]))\n",
        "        y_t.append(np.argmax(test[i]))\n",
        "\n",
        "   \n",
        "    loss = []\n",
        "    for i in range(len(prediz)):\n",
        "        if(y_hat[i] == y_t[i]):\n",
        "            loss.append(0)\n",
        "        else:\n",
        "            loss.append(1)\n",
        "\n",
        "\n",
        "    return np.mean(loss)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W4E_e-iXB-BS",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "# 1.3 PCA and feed-forward NN\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hj6pgmhVCH1q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "outputId": "db0fb3e0-bff7-45f5-a7e5-e18e5ff88b0e"
      },
      "source": [
        "plt.imshow(x_train[0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f72992944a8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8wJ0sLn7CLb_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "outputId": "ea683341-8d6d-4ae7-e06d-30a537c65f5c"
      },
      "source": [
        "x_train[0].shape\n",
        "type(x_train[1])\n",
        "rgb_weights = [0.2989, 0.5870, 0.1140]\n",
        "image_test = x_train[0]\n",
        "image_grey = np.dot(image_test[...,:3], rgb_weights)\n",
        "plt.imshow(image_grey, cmap=plt.get_cmap(\"gray\"))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f729920a198>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F7WMKP77CN5l",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# transform np.ndarray from rgb to grey\n",
        "x_train_grey = np.ndarray(shape=(x_train.shape[0], 32, 32))\n",
        "for i in range(x_train.shape[0]):\n",
        "    image_convert = x_train[i]\n",
        "    x_train_grey[i] = np.dot(image_convert[...,:3], rgb_weights)\n",
        "\n",
        "x_valid_grey = np.ndarray(shape=(x_valid.shape[0], 32, 32))\n",
        "for i in range(x_valid.shape[0]):\n",
        "    image_convert = x_valid[i]\n",
        "    x_valid_grey[i] = np.dot(image_convert[...,:3], rgb_weights)\n",
        "    \n",
        "x_test_grey = np.ndarray(shape=(x_test.shape[0], 32, 32))\n",
        "for i in range(x_test.shape[0]):\n",
        "    image_convert = x_test[i]\n",
        "    x_test_grey[i] = np.dot(image_convert[...,:3], rgb_weights)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kQp1VlmNCTrg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# flatten 32x32 images by concatenating them into a vector, each column of the matrix will be an image\n",
        "x_train_flat = np.ndarray(shape=(1024, x_train_grey.shape[0]))\n",
        "for i in range(x_train_grey.shape[0]):\n",
        "    x_train_flat[:,i] = np.concatenate(x_train_grey[i])\n",
        "    \n",
        "x_valid_flat = np.ndarray(shape=(1024, x_valid_grey.shape[0]))\n",
        "for i in range(x_valid_grey.shape[0]):\n",
        "    x_valid_flat[:,i] = np.concatenate(x_valid_grey[i])\n",
        "    \n",
        "x_test_flat = np.ndarray(shape=(1024, x_test_grey.shape[0]))\n",
        "for i in range(x_test_grey.shape[0]):\n",
        "    x_test_flat[:,i] = np.concatenate(x_test_grey[i])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EDytYvesCVeH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "standard_scaler = preprocessing.StandardScaler()\n",
        "x_train_flat_T = standard_scaler.fit_transform(x_train_flat.T)\n",
        "x_valid_flat_T = standard_scaler.transform(x_valid_flat.T)\n",
        "x_test_flat_T = standard_scaler.transform(x_test_flat.T)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZGtU-XWnCWvV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "1922c5a6-2567-4bf3-fe73-9b52459a5b41"
      },
      "source": [
        "x_train_flat_T.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(32607, 1024)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uP7JKLlfCtyz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_train_flat = x_train_flat_T.T\n",
        "x_valid_flat = x_valid_flat_T.T\n",
        "x_test_flat = x_test_flat_T.T"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "thz9QZ8vCwqv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "711ec999-f6f6-43eb-f2a0-27b722610167"
      },
      "source": [
        "x_train_flat.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1024, 32607)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GJdRuaA8C6i1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "49eb7a01-aacf-473f-8535-e3e46f941c4d"
      },
      "source": [
        "a = np.cov(x_train_flat)\n",
        "b = np.linalg.eig(a)\n",
        "b[0].shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1024,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vlld5mxVC8jK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "outputId": "a4622afa-ad6d-4a8f-c13d-976f75310bbd"
      },
      "source": [
        "b"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([384.41591328,  86.57192039,  60.39849467, ...,   0.        ,\n",
              "          0.        ,   0.        ]),\n",
              " array([[-2.77148730e-03,  1.99671963e-02, -7.00245270e-03, ...,\n",
              "          0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
              "        [ 1.06770166e-04,  5.26867240e-03, -3.60037909e-03, ...,\n",
              "          0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
              "        [-1.31497722e-04,  4.57673250e-03, -2.40477865e-03, ...,\n",
              "          0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
              "        ...,\n",
              "        [ 9.24994377e-04,  3.61739014e-03,  2.95348978e-03, ...,\n",
              "          0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
              "        [ 1.13335613e-03,  5.13559532e-03,  3.96259018e-03, ...,\n",
              "          0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
              "        [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
              "          0.00000000e+00,  0.00000000e+00,  1.00000000e+00]]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4h8Gn0NrZMoB",
        "colab_type": "text"
      },
      "source": [
        "### PCA explained variance ratio and \"Eigenfruits\""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CLkaQNHKDAAR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 446
        },
        "outputId": "74dbcbf3-c122-4bab-e5e3-c59b223a2221"
      },
      "source": [
        "pca = PCA().fit(x_train_flat)\n",
        "plt.figure(figsize=(18, 7))\n",
        "plt.plot(pca.explained_variance_ratio_.cumsum(), lw=3)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f72991f9b38>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1296x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oDjU6YEtDCCg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "441a7dcc-7cbc-46ce-8a62-e0aefa9a491a"
      },
      "source": [
        "# try to plot some of the eigenvectors, the so called \"eigenfruits\"\n",
        "fig = plt.figure(figsize =(30,5))\n",
        "for i in range(10):\n",
        "    ax = fig.add_subplot(2,5,i+1,xticks=[],yticks=[])\n",
        "    ax.imshow(np.squeeze(b[1][:,i].reshape(32,32)))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 2160x360 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bmppOi2NDOey",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "### Reduce image noise with PCA\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oSbd6LrbDNjj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "c9101411-6870-4b18-a05f-c16d40770250"
      },
      "source": [
        "x_train_flat.shape, x_valid_flat.shape, x_test_flat.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((1024, 32607), (1024, 8724), (1024, 2182))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V-msFuA7DWSH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "def PCA_iter(x_all,start, end, step):\n",
        "    lis =[]\n",
        "    for i in range(start, end, step):\n",
        "                print(\"\\n\\n===== Component: \",i,\"=====\\n\")\n",
        "            \n",
        "                (train,valid, test) = x_all\n",
        "                pca = PCA(n_components=i)\n",
        "                print(\"original shape:   \", train.shape)\n",
        "\n",
        "                \n",
        "                pca.fit_transform(train)\n",
        "                \n",
        "                train_PCA =  pca.transform(train)\n",
        "                train_new = pca.inverse_transform(train_PCA)\n",
        "\n",
        "                valid_PCA =  pca.transform(valid)       \n",
        "                valid_new = pca.inverse_transform(valid_PCA)\n",
        "                \n",
        "                \n",
        "                test_PCA = pca.transform(test)\n",
        "                test_new = pca.inverse_transform(test_PCA)\n",
        "\n",
        "                \n",
        "                print(\"transformed shape:\", train_PCA.shape)\n",
        "                print(\"final shape:\", train_new.shape)\n",
        "\n",
        "                tupla = (x_train_PCA, x_valid_PCA, x_test_PCA) =train_new,valid_new,test_new\n",
        "                \n",
        "                lis.append(tupla)\n",
        "    return lis"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CHoTcijWDddc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "a39f8b54-12c3-4e8a-eaee-e8c8ee2aa339"
      },
      "source": [
        "lis_PCA =  PCA_iter((x_train_flat_T,x_valid_flat_T, x_test_flat_T),10,211,20)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "===== Component:  10 =====\n",
            "\n",
            "original shape:    (32607, 1024)\n",
            "transformed shape: (32607, 10)\n",
            "final shape: (32607, 1024)\n",
            "\n",
            "\n",
            "===== Component:  30 =====\n",
            "\n",
            "original shape:    (32607, 1024)\n",
            "transformed shape: (32607, 30)\n",
            "final shape: (32607, 1024)\n",
            "\n",
            "\n",
            "===== Component:  50 =====\n",
            "\n",
            "original shape:    (32607, 1024)\n",
            "transformed shape: (32607, 50)\n",
            "final shape: (32607, 1024)\n",
            "\n",
            "\n",
            "===== Component:  70 =====\n",
            "\n",
            "original shape:    (32607, 1024)\n",
            "transformed shape: (32607, 70)\n",
            "final shape: (32607, 1024)\n",
            "\n",
            "\n",
            "===== Component:  90 =====\n",
            "\n",
            "original shape:    (32607, 1024)\n",
            "transformed shape: (32607, 90)\n",
            "final shape: (32607, 1024)\n",
            "\n",
            "\n",
            "===== Component:  110 =====\n",
            "\n",
            "original shape:    (32607, 1024)\n",
            "transformed shape: (32607, 110)\n",
            "final shape: (32607, 1024)\n",
            "\n",
            "\n",
            "===== Component:  130 =====\n",
            "\n",
            "original shape:    (32607, 1024)\n",
            "transformed shape: (32607, 130)\n",
            "final shape: (32607, 1024)\n",
            "\n",
            "\n",
            "===== Component:  150 =====\n",
            "\n",
            "original shape:    (32607, 1024)\n",
            "transformed shape: (32607, 150)\n",
            "final shape: (32607, 1024)\n",
            "\n",
            "\n",
            "===== Component:  170 =====\n",
            "\n",
            "original shape:    (32607, 1024)\n",
            "transformed shape: (32607, 170)\n",
            "final shape: (32607, 1024)\n",
            "\n",
            "\n",
            "===== Component:  190 =====\n",
            "\n",
            "original shape:    (32607, 1024)\n",
            "transformed shape: (32607, 190)\n",
            "final shape: (32607, 1024)\n",
            "\n",
            "\n",
            "===== Component:  210 =====\n",
            "\n",
            "original shape:    (32607, 1024)\n",
            "transformed shape: (32607, 210)\n",
            "final shape: (32607, 1024)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1W9YHNj7DpS_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "outputId": "4e0f78b6-47ca-4bda-acbd-8eca4b2289bc"
      },
      "source": [
        "# 10 components example of the same image\n",
        "tr,va,te  = lis_PCA[1] \n",
        "plt.imshow(tr[2,:].reshape(32,32), cmap=plt.get_cmap(\"gray\"))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f7296f20a20>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v1zpZzo_DgPr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "outputId": "23384fb3-d61f-4670-f741-39e6717355c8"
      },
      "source": [
        "# 210 components example of an image\n",
        "tr,va,te  = lis_PCA[len(lis_PCA)-1]\n",
        "plt.imshow(tr[2,:].reshape(32,32), cmap=plt.get_cmap(\"gray\"))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f7296e8c1d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6-rpOqx0Du8Y",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "## Train feed-forward NN with reduced images\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D1mWo4zKDulG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def FW_iter(lis_PCA, ep, bs):\n",
        "    lis_FW = []\n",
        "    epochs = ep\n",
        "    batch_size = bs\n",
        "    for itr in range(len(lis_PCA)):\n",
        "        x_train_PCA, x_valid_PCA, x_test_PCA = lis_PCA[itr]\n",
        "        \n",
        "       \n",
        "        print(\"FW- components: \",(itr+1)*20-10)\n",
        "        #feed forward neural network\n",
        "        model = tf.keras.Sequential([\n",
        "          tf.keras.layers.Input(shape = (1024)),\n",
        "          tf.keras.layers.Dense(32, activation = \"relu\"),\n",
        "          tf.keras.layers.Dense(10, activation='softmax')\n",
        "          ])\n",
        "        model.compile(optimizer = \"adam\", loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "        \n",
        "        \n",
        "        history = model.fit(x_train_PCA, y_train,\n",
        "                        batch_size = bs,\n",
        "                        epochs = epochs,\n",
        "                        validation_data=(x_valid_PCA, y_valid),\n",
        "                        verbose = 2\n",
        "\n",
        "                      )\n",
        "        \n",
        "        y_pred = model.predict(x_test_PCA).round()\n",
        "        \n",
        "        zo_loss = zero_one(y_pred,y_test)\n",
        "        print(\"Zero-one loss: \",zo_loss)\n",
        "        tupla = (history, model, zo_loss)\n",
        "        lis_FW.append(tupla)\n",
        "    return lis_FW"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sPVnWMi8Du2S",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "8ab75f95-e34a-4179-bd88-7673124174c1"
      },
      "source": [
        "epochs = 10\n",
        "batch_size = 32\n",
        "res = FW_iter(lis_PCA, epochs, batch_size)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "FW- components:  10\n",
            "Epoch 1/10\n",
            "WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.175393). Check your callbacks.\n",
            "1019/1019 - 5s - loss: 0.8917 - accuracy: 0.6877 - val_loss: 1.3535 - val_accuracy: 0.5962\n",
            "Epoch 2/10\n",
            "1019/1019 - 5s - loss: 0.6398 - accuracy: 0.7802 - val_loss: 1.3279 - val_accuracy: 0.6405\n",
            "Epoch 3/10\n",
            "1019/1019 - 5s - loss: 0.5631 - accuracy: 0.8033 - val_loss: 1.2432 - val_accuracy: 0.6723\n",
            "Epoch 4/10\n",
            "1019/1019 - 5s - loss: 0.5202 - accuracy: 0.8203 - val_loss: 1.2590 - val_accuracy: 0.6750\n",
            "Epoch 5/10\n",
            "1019/1019 - 5s - loss: 0.4846 - accuracy: 0.8326 - val_loss: 1.3413 - val_accuracy: 0.6714\n",
            "Epoch 6/10\n",
            "1019/1019 - 5s - loss: 0.4573 - accuracy: 0.8445 - val_loss: 1.2513 - val_accuracy: 0.6985\n",
            "Epoch 7/10\n",
            "1019/1019 - 5s - loss: 0.4322 - accuracy: 0.8521 - val_loss: 1.2741 - val_accuracy: 0.6983\n",
            "Epoch 8/10\n",
            "1019/1019 - 5s - loss: 0.4193 - accuracy: 0.8566 - val_loss: 1.3041 - val_accuracy: 0.7063\n",
            "Epoch 9/10\n",
            "1019/1019 - 5s - loss: 0.4008 - accuracy: 0.8640 - val_loss: 1.3032 - val_accuracy: 0.7003\n",
            "Epoch 10/10\n",
            "1019/1019 - 5s - loss: 0.3982 - accuracy: 0.8641 - val_loss: 1.3579 - val_accuracy: 0.6978\n",
            "Zero-one loss:  0.28689275893675525\n",
            "FW- components:  30\n",
            "Epoch 1/10\n",
            "WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.261349). Check your callbacks.\n",
            "1019/1019 - 6s - loss: 0.6436 - accuracy: 0.7845 - val_loss: 1.0769 - val_accuracy: 0.6961\n",
            "Epoch 2/10\n",
            "1019/1019 - 5s - loss: 0.3235 - accuracy: 0.8948 - val_loss: 1.0283 - val_accuracy: 0.7299\n",
            "Epoch 3/10\n",
            "1019/1019 - 5s - loss: 0.2424 - accuracy: 0.9211 - val_loss: 1.0095 - val_accuracy: 0.7387\n",
            "Epoch 4/10\n",
            "1019/1019 - 5s - loss: 0.1909 - accuracy: 0.9384 - val_loss: 0.9939 - val_accuracy: 0.7633\n",
            "Epoch 5/10\n",
            "1019/1019 - 5s - loss: 0.1558 - accuracy: 0.9502 - val_loss: 1.1439 - val_accuracy: 0.7466\n",
            "Epoch 6/10\n",
            "1019/1019 - 5s - loss: 0.1404 - accuracy: 0.9538 - val_loss: 1.0946 - val_accuracy: 0.7713\n",
            "Epoch 7/10\n",
            "1019/1019 - 5s - loss: 0.1227 - accuracy: 0.9603 - val_loss: 1.1294 - val_accuracy: 0.7809\n",
            "Epoch 8/10\n",
            "1019/1019 - 5s - loss: 0.1098 - accuracy: 0.9649 - val_loss: 1.1915 - val_accuracy: 0.7785\n",
            "Epoch 9/10\n",
            "1019/1019 - 5s - loss: 0.1003 - accuracy: 0.9675 - val_loss: 1.1726 - val_accuracy: 0.7894\n",
            "Epoch 10/10\n",
            "1019/1019 - 5s - loss: 0.0947 - accuracy: 0.9702 - val_loss: 1.2234 - val_accuracy: 0.7822\n",
            "Zero-one loss:  0.19798350137488543\n",
            "FW- components:  50\n",
            "Epoch 1/10\n",
            "1019/1019 - 5s - loss: 0.5459 - accuracy: 0.8254 - val_loss: 0.8101 - val_accuracy: 0.7436\n",
            "Epoch 2/10\n",
            "1019/1019 - 5s - loss: 0.2140 - accuracy: 0.9335 - val_loss: 0.8365 - val_accuracy: 0.7656\n",
            "Epoch 3/10\n",
            "1019/1019 - 5s - loss: 0.1433 - accuracy: 0.9549 - val_loss: 0.8540 - val_accuracy: 0.7918\n",
            "Epoch 4/10\n",
            "1019/1019 - 5s - loss: 0.1039 - accuracy: 0.9682 - val_loss: 0.8805 - val_accuracy: 0.7915\n",
            "Epoch 5/10\n",
            "1019/1019 - 5s - loss: 0.0848 - accuracy: 0.9738 - val_loss: 0.9716 - val_accuracy: 0.8034\n",
            "Epoch 6/10\n",
            "1019/1019 - 5s - loss: 0.0715 - accuracy: 0.9778 - val_loss: 0.9149 - val_accuracy: 0.8155\n",
            "Epoch 7/10\n",
            "1019/1019 - 5s - loss: 0.0664 - accuracy: 0.9790 - val_loss: 1.1081 - val_accuracy: 0.8093\n",
            "Epoch 8/10\n",
            "1019/1019 - 5s - loss: 0.0539 - accuracy: 0.9832 - val_loss: 1.1118 - val_accuracy: 0.7929\n",
            "Epoch 9/10\n",
            "1019/1019 - 5s - loss: 0.0512 - accuracy: 0.9835 - val_loss: 1.0935 - val_accuracy: 0.8323\n",
            "Epoch 10/10\n",
            "1019/1019 - 5s - loss: 0.0465 - accuracy: 0.9845 - val_loss: 1.0836 - val_accuracy: 0.8343\n",
            "Zero-one loss:  0.15902841429880843\n",
            "FW- components:  70\n",
            "Epoch 1/10\n",
            "WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.200094). Check your callbacks.\n",
            "1019/1019 - 5s - loss: 0.4945 - accuracy: 0.8395 - val_loss: 0.8700 - val_accuracy: 0.7372\n",
            "Epoch 2/10\n",
            "1019/1019 - 5s - loss: 0.1802 - accuracy: 0.9462 - val_loss: 0.7953 - val_accuracy: 0.7839\n",
            "Epoch 3/10\n",
            "1019/1019 - 5s - loss: 0.1150 - accuracy: 0.9651 - val_loss: 0.8178 - val_accuracy: 0.8015\n",
            "Epoch 4/10\n",
            "1019/1019 - 5s - loss: 0.0828 - accuracy: 0.9752 - val_loss: 0.8297 - val_accuracy: 0.7953\n",
            "Epoch 5/10\n",
            "1019/1019 - 5s - loss: 0.0659 - accuracy: 0.9796 - val_loss: 0.7400 - val_accuracy: 0.8329\n",
            "Epoch 6/10\n",
            "1019/1019 - 5s - loss: 0.0544 - accuracy: 0.9829 - val_loss: 0.8350 - val_accuracy: 0.8300\n",
            "Epoch 7/10\n",
            "1019/1019 - 5s - loss: 0.0438 - accuracy: 0.9867 - val_loss: 0.8506 - val_accuracy: 0.8344\n",
            "Epoch 8/10\n",
            "1019/1019 - 5s - loss: 0.0406 - accuracy: 0.9879 - val_loss: 0.8914 - val_accuracy: 0.8357\n",
            "Epoch 9/10\n",
            "1019/1019 - 6s - loss: 0.0387 - accuracy: 0.9884 - val_loss: 0.8949 - val_accuracy: 0.8410\n",
            "Epoch 10/10\n",
            "1019/1019 - 6s - loss: 0.0312 - accuracy: 0.9904 - val_loss: 0.9068 - val_accuracy: 0.8454\n",
            "Zero-one loss:  0.1457378551787351\n",
            "FW- components:  90\n",
            "Epoch 1/10\n",
            "1019/1019 - 5s - loss: 0.5080 - accuracy: 0.8416 - val_loss: 0.7913 - val_accuracy: 0.7591\n",
            "Epoch 2/10\n",
            "1019/1019 - 5s - loss: 0.1627 - accuracy: 0.9520 - val_loss: 0.7948 - val_accuracy: 0.7869\n",
            "Epoch 3/10\n",
            "1019/1019 - 5s - loss: 0.0948 - accuracy: 0.9742 - val_loss: 0.7662 - val_accuracy: 0.8137\n",
            "Epoch 4/10\n",
            "1019/1019 - 5s - loss: 0.0640 - accuracy: 0.9823 - val_loss: 0.7883 - val_accuracy: 0.8290\n",
            "Epoch 5/10\n",
            "1019/1019 - 5s - loss: 0.0502 - accuracy: 0.9860 - val_loss: 0.7893 - val_accuracy: 0.8356\n",
            "Epoch 6/10\n",
            "1019/1019 - 5s - loss: 0.0395 - accuracy: 0.9891 - val_loss: 0.8706 - val_accuracy: 0.8399\n",
            "Epoch 7/10\n",
            "1019/1019 - 5s - loss: 0.0400 - accuracy: 0.9881 - val_loss: 1.0676 - val_accuracy: 0.8155\n",
            "Epoch 8/10\n",
            "1019/1019 - 5s - loss: 0.0282 - accuracy: 0.9915 - val_loss: 1.0146 - val_accuracy: 0.8299\n",
            "Epoch 9/10\n",
            "1019/1019 - 5s - loss: 0.0238 - accuracy: 0.9928 - val_loss: 0.9587 - val_accuracy: 0.8548\n",
            "Epoch 10/10\n",
            "1019/1019 - 5s - loss: 0.0224 - accuracy: 0.9929 - val_loss: 1.0540 - val_accuracy: 0.8471\n",
            "Zero-one loss:  0.15627864344637946\n",
            "FW- components:  110\n",
            "Epoch 1/10\n",
            "1019/1019 - 5s - loss: 0.4724 - accuracy: 0.8546 - val_loss: 0.8186 - val_accuracy: 0.7603\n",
            "Epoch 2/10\n",
            "1019/1019 - 5s - loss: 0.1382 - accuracy: 0.9634 - val_loss: 0.7725 - val_accuracy: 0.8089\n",
            "Epoch 3/10\n",
            "1019/1019 - 5s - loss: 0.0794 - accuracy: 0.9783 - val_loss: 0.8203 - val_accuracy: 0.8191\n",
            "Epoch 4/10\n",
            "1019/1019 - 5s - loss: 0.0528 - accuracy: 0.9850 - val_loss: 0.9337 - val_accuracy: 0.8204\n",
            "Epoch 5/10\n",
            "1019/1019 - 5s - loss: 0.0417 - accuracy: 0.9879 - val_loss: 0.9542 - val_accuracy: 0.8313\n",
            "Epoch 6/10\n",
            "1019/1019 - 5s - loss: 0.0369 - accuracy: 0.9895 - val_loss: 0.9346 - val_accuracy: 0.8412\n",
            "Epoch 7/10\n",
            "1019/1019 - 5s - loss: 0.0267 - accuracy: 0.9927 - val_loss: 1.0282 - val_accuracy: 0.8474\n",
            "Epoch 8/10\n",
            "1019/1019 - 5s - loss: 0.0211 - accuracy: 0.9939 - val_loss: 1.1628 - val_accuracy: 0.8286\n",
            "Epoch 9/10\n",
            "1019/1019 - 5s - loss: 0.0264 - accuracy: 0.9914 - val_loss: 1.1593 - val_accuracy: 0.8408\n",
            "Epoch 10/10\n",
            "1019/1019 - 5s - loss: 0.0205 - accuracy: 0.9938 - val_loss: 1.2966 - val_accuracy: 0.8436\n",
            "Zero-one loss:  0.14848762603116408\n",
            "FW- components:  130\n",
            "Epoch 1/10\n",
            "WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.148986). Check your callbacks.\n",
            "1019/1019 - 5s - loss: 0.5150 - accuracy: 0.8395 - val_loss: 0.7711 - val_accuracy: 0.7811\n",
            "Epoch 2/10\n",
            "1019/1019 - 5s - loss: 0.1556 - accuracy: 0.9550 - val_loss: 0.7043 - val_accuracy: 0.8064\n",
            "Epoch 3/10\n",
            "1019/1019 - 5s - loss: 0.0860 - accuracy: 0.9749 - val_loss: 0.7319 - val_accuracy: 0.8285\n",
            "Epoch 4/10\n",
            "1019/1019 - 5s - loss: 0.0547 - accuracy: 0.9850 - val_loss: 0.8580 - val_accuracy: 0.8292\n",
            "Epoch 5/10\n",
            "1019/1019 - 5s - loss: 0.0448 - accuracy: 0.9874 - val_loss: 0.9114 - val_accuracy: 0.8353\n",
            "Epoch 6/10\n",
            "1019/1019 - 5s - loss: 0.0326 - accuracy: 0.9913 - val_loss: 0.9489 - val_accuracy: 0.8368\n",
            "Epoch 7/10\n",
            "1019/1019 - 5s - loss: 0.0302 - accuracy: 0.9910 - val_loss: 0.8936 - val_accuracy: 0.8608\n",
            "Epoch 8/10\n",
            "1019/1019 - 5s - loss: 0.0265 - accuracy: 0.9921 - val_loss: 0.9696 - val_accuracy: 0.8584\n",
            "Epoch 9/10\n",
            "1019/1019 - 5s - loss: 0.0264 - accuracy: 0.9920 - val_loss: 1.0818 - val_accuracy: 0.8422\n",
            "Epoch 10/10\n",
            "1019/1019 - 5s - loss: 0.0166 - accuracy: 0.9955 - val_loss: 1.1559 - val_accuracy: 0.8453\n",
            "Zero-one loss:  0.152153987167736\n",
            "FW- components:  150\n",
            "Epoch 1/10\n",
            "1019/1019 - 5s - loss: 0.4667 - accuracy: 0.8549 - val_loss: 0.7300 - val_accuracy: 0.7691\n",
            "Epoch 2/10\n",
            "1019/1019 - 5s - loss: 0.1365 - accuracy: 0.9635 - val_loss: 0.6678 - val_accuracy: 0.8116\n",
            "Epoch 3/10\n",
            "1019/1019 - 5s - loss: 0.0762 - accuracy: 0.9800 - val_loss: 0.6308 - val_accuracy: 0.8370\n",
            "Epoch 4/10\n",
            "1019/1019 - 5s - loss: 0.0458 - accuracy: 0.9884 - val_loss: 0.6961 - val_accuracy: 0.8396\n",
            "Epoch 5/10\n",
            "1019/1019 - 5s - loss: 0.0426 - accuracy: 0.9883 - val_loss: 0.8004 - val_accuracy: 0.8357\n",
            "Epoch 6/10\n",
            "1019/1019 - 5s - loss: 0.0267 - accuracy: 0.9929 - val_loss: 0.7318 - val_accuracy: 0.8495\n",
            "Epoch 7/10\n",
            "1019/1019 - 5s - loss: 0.0267 - accuracy: 0.9921 - val_loss: 0.7954 - val_accuracy: 0.8433\n",
            "Epoch 8/10\n",
            "1019/1019 - 5s - loss: 0.0232 - accuracy: 0.9931 - val_loss: 1.0402 - val_accuracy: 0.8232\n",
            "Epoch 9/10\n",
            "1019/1019 - 5s - loss: 0.0190 - accuracy: 0.9945 - val_loss: 0.9076 - val_accuracy: 0.8549\n",
            "Epoch 10/10\n",
            "1019/1019 - 5s - loss: 0.0184 - accuracy: 0.9949 - val_loss: 0.9122 - val_accuracy: 0.8505\n",
            "Zero-one loss:  0.14115490375802017\n",
            "FW- components:  170\n",
            "Epoch 1/10\n",
            "1019/1019 - 5s - loss: 0.5013 - accuracy: 0.8487 - val_loss: 0.7488 - val_accuracy: 0.7780\n",
            "Epoch 2/10\n",
            "1019/1019 - 5s - loss: 0.1291 - accuracy: 0.9651 - val_loss: 0.7400 - val_accuracy: 0.8058\n",
            "Epoch 3/10\n",
            "1019/1019 - 5s - loss: 0.0664 - accuracy: 0.9830 - val_loss: 0.6988 - val_accuracy: 0.8416\n",
            "Epoch 4/10\n",
            "1019/1019 - 5s - loss: 0.0447 - accuracy: 0.9883 - val_loss: 0.7572 - val_accuracy: 0.8386\n",
            "Epoch 5/10\n",
            "1019/1019 - 5s - loss: 0.0339 - accuracy: 0.9907 - val_loss: 0.8077 - val_accuracy: 0.8454\n",
            "Epoch 6/10\n",
            "1019/1019 - 5s - loss: 0.0254 - accuracy: 0.9930 - val_loss: 0.7187 - val_accuracy: 0.8663\n",
            "Epoch 7/10\n",
            "1019/1019 - 5s - loss: 0.0213 - accuracy: 0.9939 - val_loss: 0.7830 - val_accuracy: 0.8504\n",
            "Epoch 8/10\n",
            "1019/1019 - 5s - loss: 0.0189 - accuracy: 0.9946 - val_loss: 0.7266 - val_accuracy: 0.8778\n",
            "Epoch 9/10\n",
            "1019/1019 - 5s - loss: 0.0124 - accuracy: 0.9968 - val_loss: 0.8611 - val_accuracy: 0.8610\n",
            "Epoch 10/10\n",
            "1019/1019 - 5s - loss: 0.0178 - accuracy: 0.9946 - val_loss: 0.8310 - val_accuracy: 0.8737\n",
            "Zero-one loss:  0.12465627864344637\n",
            "FW- components:  190\n",
            "Epoch 1/10\n",
            "1019/1019 - 5s - loss: 0.4545 - accuracy: 0.8607 - val_loss: 0.7529 - val_accuracy: 0.7865\n",
            "Epoch 2/10\n",
            "1019/1019 - 5s - loss: 0.1264 - accuracy: 0.9652 - val_loss: 0.7090 - val_accuracy: 0.8212\n",
            "Epoch 3/10\n",
            "1019/1019 - 5s - loss: 0.0705 - accuracy: 0.9815 - val_loss: 0.6724 - val_accuracy: 0.8435\n",
            "Epoch 4/10\n",
            "1019/1019 - 5s - loss: 0.0461 - accuracy: 0.9876 - val_loss: 0.7778 - val_accuracy: 0.8344\n",
            "Epoch 5/10\n",
            "1019/1019 - 5s - loss: 0.0383 - accuracy: 0.9895 - val_loss: 0.7378 - val_accuracy: 0.8549\n",
            "Epoch 6/10\n",
            "1019/1019 - 5s - loss: 0.0264 - accuracy: 0.9932 - val_loss: 0.9976 - val_accuracy: 0.8267\n",
            "Epoch 7/10\n",
            "1019/1019 - 5s - loss: 0.0244 - accuracy: 0.9929 - val_loss: 0.9187 - val_accuracy: 0.8483\n",
            "Epoch 8/10\n",
            "1019/1019 - 5s - loss: 0.0181 - accuracy: 0.9950 - val_loss: 0.9842 - val_accuracy: 0.8478\n",
            "Epoch 9/10\n",
            "1019/1019 - 6s - loss: 0.0234 - accuracy: 0.9936 - val_loss: 0.9568 - val_accuracy: 0.8530\n",
            "Epoch 10/10\n",
            "1019/1019 - 6s - loss: 0.0140 - accuracy: 0.9960 - val_loss: 0.9279 - val_accuracy: 0.8657\n",
            "Zero-one loss:  0.12557286892758937\n",
            "FW- components:  210\n",
            "Epoch 1/10\n",
            "1019/1019 - 5s - loss: 0.4542 - accuracy: 0.8597 - val_loss: 0.7646 - val_accuracy: 0.7775\n",
            "Epoch 2/10\n",
            "1019/1019 - 5s - loss: 0.1189 - accuracy: 0.9685 - val_loss: 0.7023 - val_accuracy: 0.8039\n",
            "Epoch 3/10\n",
            "1019/1019 - 5s - loss: 0.0641 - accuracy: 0.9839 - val_loss: 0.7556 - val_accuracy: 0.8193\n",
            "Epoch 4/10\n",
            "1019/1019 - 5s - loss: 0.0412 - accuracy: 0.9896 - val_loss: 0.8243 - val_accuracy: 0.8222\n",
            "Epoch 5/10\n",
            "1019/1019 - 5s - loss: 0.0262 - accuracy: 0.9936 - val_loss: 0.7042 - val_accuracy: 0.8571\n",
            "Epoch 6/10\n",
            "1019/1019 - 5s - loss: 0.0262 - accuracy: 0.9922 - val_loss: 0.7838 - val_accuracy: 0.8453\n",
            "Epoch 7/10\n",
            "1019/1019 - 5s - loss: 0.0157 - accuracy: 0.9956 - val_loss: 0.8158 - val_accuracy: 0.8592\n",
            "Epoch 8/10\n",
            "1019/1019 - 5s - loss: 0.0245 - accuracy: 0.9926 - val_loss: 0.9040 - val_accuracy: 0.8639\n",
            "Epoch 9/10\n",
            "1019/1019 - 5s - loss: 0.0147 - accuracy: 0.9961 - val_loss: 0.9469 - val_accuracy: 0.8372\n",
            "Epoch 10/10\n",
            "1019/1019 - 5s - loss: 0.0174 - accuracy: 0.9947 - val_loss: 1.0241 - val_accuracy: 0.8486\n",
            "Zero-one loss:  0.1457378551787351\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kZIGTbP0EUl-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.DataFrame(\n",
        "columns = ['epochs', 'valid', 'components', 'accuracy', 'value']\n",
        ")\n",
        "\n",
        "for itr in range(len(res)):\n",
        "  \n",
        "    time =  [i for i in range(1,epochs+1)]\n",
        "    valids =  [0 for i in range(1,epochs+1)]\n",
        "    components = [(itr+1)*20-10 for i in range(1,epochs+1)]\n",
        "   \n",
        "\n",
        "    accur = [1 for i in range(1,epochs+1)]\n",
        "    acc = res[itr][0].history['accuracy']\n",
        "    \n",
        "    df1=  pd.DataFrame(data= np.vstack((time,valids,components,accur,acc)).T, columns = ['epochs', 'valid', 'components', 'accuracy', 'value'])\n",
        "\n",
        "    loss= res[itr][0].history['loss']\n",
        "    accur = [0 for i in range(1,epochs+1)]\n",
        "    df2=  pd.DataFrame(data= np.vstack((time,valids,components,accur,loss)).T, columns = ['epochs', 'valid', 'components', 'accuracy', 'value'])\n",
        "\n",
        "    valids =  [1 for i in range(1,epochs+1)]\n",
        "    accur = [1 for i in range(1,epochs+1)]\n",
        "    val_acc = res[itr][0].history['val_accuracy']\n",
        "    \n",
        "    df3=  pd.DataFrame(data= np.vstack((time,valids,components,accur,val_acc)).T, columns = ['epochs', 'valid', 'components', 'accuracy', 'value'])\n",
        "\n",
        "    accur = [0 for i in range(1,epochs+1)]\n",
        "\n",
        "    val_loss = res[itr][0].history['val_loss']\n",
        "    df4=  pd.DataFrame(data= np.vstack((time,valids,components,accur,val_loss)).T, columns = ['epochs', 'valid', 'components', 'accuracy', 'value'])\n",
        "   \n",
        "    df = df.append(df1.append(df2).append(df3).append(df4))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ekgc7EAAEUwF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "14e77fc7-eca6-48b8-8e6a-71ca0e4d1728"
      },
      "source": [
        "df['components'] = df['components'].astype('category')\n",
        "df = df.assign(accuracy = ['accuracy' if accuracy == 1. else 'loss' for accuracy in df['accuracy']])\n",
        "df = df.assign(valid = ['validation' if valid == 1. else 'training' for valid in df['valid']])\n",
        "df['accuracy'].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['accuracy', 'loss'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FSQbrnQMEUy0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        },
        "outputId": "0a9a0092-d80b-4d54-e785-aa24a661d724"
      },
      "source": [
        "ggplot(df, aes(x='epochs', y='value',color='components')) + \\\n",
        "    geom_line() + \\\n",
        "    facet_wrap(['accuracy','valid'],scales='free') +  theme_bw(base_size=12)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<ggplot: (8758028328796)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E7xC_l3SEU8I",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        },
        "outputId": "43050193-0c47-4bbc-9709-8f42376f48fd"
      },
      "source": [
        "ggplot(df, aes(x='epochs', y='value',color='components')) + \\\n",
        "    geom_line() + \\\n",
        "    facet_wrap(['accuracy','valid']) + \\\n",
        "    theme_bw(base_size=12)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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DeuSRR3jyySe55ZZbBvpUBr3m5mZeeOEF5s6dy6hRo3qsk/YuxM6jt7YuxNYiQzKE2Ayvv/46jY2NWGtZvHgxDz/88Ho9VEIIIYTYuciQDCE2w0cffcT//u//0t7eTnV1NdOnT+fkk08e6NMSQgghxFYkgVmIzTBr1ixmzZo10KchhBBCiG1IhmQIIYQQQgjRCwnMQgghhBBC9GLQD8lob28f6FMQQvSDvrRlae9C7PikHYuBMGgDcxiGALz66qsDfCZCiP7U2bY3VCbtXYidx4bauhBby6ANzK5brPoRRxxBTU3Nx96P4zhUVlbS1tZGFEX9dXrbtcFYZ5B6b+/1bmpq4tlnny237e76o73vKNehv0m9B0+9d5Q699bWhdhaBv1P2+67775FNz631hKGIWPHjkUp1Y9ntmnGGFasWEFdXR1ab7vh6IOxziD13t7r3dDQ0OOrkjdkS9r7jnId+pvUe/DUe0epc1/auhD9TT70J4QQQgghRC8kMAshhBBCCNELCcxCCCGEEEL0QgKzEEIIIYQQvZDALIQQQgghRC8kMAshhBBCCNELCcxCCCGEEEL0QgKzEEIIIYQQvZDALIQQQgghRC8kMAshhBBCCNELCcxCCCGEEEL0QgKzEEIIIYQQvZDALIQQQgghRC8kMAshhBBCCNELCcxCCCGEEEL0QgKzEEIIIYQQvXAH+gQGwvLly3njjTcwxuC6Ltbaj72vzuduyT625Nid578tjz8Y69x57O7TbX1sqXfvx3fdDb+cdbb3ZDK5Re19R7kOW+PY3afb+thSb3ltX9fG2roQW9Og/KmbN28eV111FV/5yleoqakhDMMt3mcURf1wZpuvpqYGYwzGmG1+7MFYZ5B6b2t9rXdNTc0Gyzvb+3e/+91+ae/b+3XYWqTe25a8tve+nRDb2qAMzF//+teZNm0af/7zn2lqaqK2tvZj78taSxRFOI6DUqofz3LTjDGsWbOGoUOHovW2G10zGOsMUu/tvd6NjY0bLO9s76+99toWtfcd5Tr0N6n34Kn3jlLnjbV1IbamQRmYR44cibWWl19+mTAM++WFQSm1zV9glFLl89/Wx+48/mCrc+c5SL233TH7Wu+N9Rz3d3vf3q/D1jwHqfe2O6a8tm9cf7wrLMTmkg/9CSGEEEII0QsJzEIIIYQQQvRCArMQQgghhBC9kMAshBBCCCFELyQwCyGEEEII0QsJzEIIIYQQQvRCArMQQgghhBC9kMAshBBCCCFELyQwCyGEEEKIAZPNZrnyyit57rnnBvpUNkoCsxBCCCGEGDDZbJarrrpKArMQQgghhBA7KgnMQgghhBB9VF9fz9e+9jVGjRpFPB5nwoQJnH322fi+D8B7773Hv/3bvzFkyBBSqRSf+cxneOyxx3rs47nnnkMpxf33389VV13F6NGjqays5Itf/CItLS0UCgUuvPBChg8fTjqd5owzzqBQKPTYh1KK8847j7vuuos999yTRCLBAQccwPPPP7/eOb/66qvMnDmTTCZDOp3mqKOO4pVXXumxzR133IFSij//+c984xvfoLa2loqKCk466SQaGxvX2+cTTzzBoYceSkVFBZWVlcyaNYu33nqrxzann3466XSa+vp6TjzxRNLpNLW1tVx88cVEUQTA0qVLqa2tBeCqq65CKYVSiiuvvBKAFStWcMYZZzBmzBji8TgjR47khBNOYOnSpX3/R+sH7jY9mhBCCCHEDqqhoYEDDzyQ5uZm5s6dy1577UV9fT0PPPAA2WyWpqYmDjroILLZLBdccAFDhw7lV7/6FccffzwPPPAAJ510Uo/9XXPNNSSTSS699FLeffddbr75ZjzPQ2tNU1MTV155Ja+88gp33HEHEyZM4PLLL+/x/D/96U/cd999XHDBBcTjcX72s59x7LHH8te//pV99tkHgLfeeotDDz2UTCbD//t//w/P85g3bx7Tp0/nT3/6E1OnTu2xz/PPP5+amhquuOIKli5dyo033sh5553HfffdV97mN7/5DV/96leZMWMG1157LdlslltuuYVDDjmEV199lV122aW8bRRFzJgxg6lTp/KjH/2IP/7xj9xwww184hOf4Oyzz6a2tpZbbrmFs88+m5NOOokvfOELAEyePBmAk08+mbfeeovzzz+fXXbZhVWrVvHUU0/x4Ycf9jjOVmcHqfr6envFFVfY+vr6LdqPMcb6vm+NMf10Zn0XRZGtr6+3URRt0+MOxjpbK/Xe3uvdW5vuj/a+o1yH/ib1Hjz13lHq3F+/vz+O0047zWqt7d/+9rf11hlj7IUXXmgB+8ILL5TL29ra7IQJE+wuu+xSrt+zzz5rAbvPPvtY3/fL255yyilWKWVnzpzZY9/Tpk2z48eP71EGWMAuXLiwXPbBBx/YRCJhTzrppHLZiSeeaGOxmF2yZEm5rKGhwVZWVtrDDjusXHb77bdbwB599NE9fgYuuugi6ziObW5uLtenurrannnmmT3OZ8WKFbaqqqpH+Ve/+lUL2O9///s9tt1vv/3sAQccUF5ubGy0gL3iiit6bNfU1GQBe/3119uBJkMyhBBCCCE2wRjDww8/zHHHHcenPvWp9dYrpXj88cc58MADOeSQQ8rl6XSauXPnsnTpUv75z3/2eM5pp52G53nl5alTp2Kt5T/+4z96bDd16lSWLVtGGIY9yqdNm8YBBxxQXh43bhwnnHAC8+fPJ4oioihiwYIFnHjiiey6667l7UaOHMmpp57Kiy++SGtra499zp07F6VUefnQQw8liiI++OADAJ566imam5s55ZRTWL16dfnhOA5Tp07l2WefXe/anHXWWT2WDz30UN577731tltXMpkkFovx3HPP0dTUtMnttyYJzEIIIYQQm9DY2Ehra2t5qMOGfPDBB+y5557rlU+cOLG8vrtx48b1WK6qqgJg7Nix65UbY2hpaelRvvvuu693rD322INsNktjYyONjY1ks9mNnpMxhmXLlvV6TjU1NQDlwLp48WIAjjzySGpra3s8FixYwKpVq3o8P5FIlMcod99nXwJwPB7n2muv5YknnmDEiBEcdthhXHfddaxYsWKTz+1vMoZZCCGEEGIAOI6zWeXW2q15On06tjEGKI5jrqurW2871+0ZLTe2v7668MILOe6443j44YeZP38+3/ve97jmmmt45pln2G+//bZo35tDArMQQgghxCbU1taSyWR48803N7rN+PHjeeedd9Yrf/vtt8vr+1Nnb293ixYtIpVKlXt1U6nURs9Ja71eb/amfOITnwBg+PDhHH300R/jrNfXfQjIxo75zW9+k29+85ssXryYKVOmcMMNN3DnnXf2y/H7QoZkCCGEEEJsgtaaE088kUcffZSFCxeut95ay+c+9zn++te/8vLLL5fLOzo6uPXWW9lll12YNGlSv57Tyy+/zD/+8Y/y8rJly3jkkUc45phjcBwHx3E45phjeOSRR3rchm3lypXcfffdHHLIIWQymc065owZM8hkMvzwhz8kCIL11m/oFnSbkkqlAGhubu5Rns1myefzPco+8YlPUFlZud5t9rY26WEWQgghhOiDH/7whyxYsIDDDz+cuXPnMnHiRJYvX85vf/tbXnzxRS699FLuueceZs6cyQUXXMCQIUP41a9+xfvvv8+DDz6I1v3bT7nPPvswY8aMHreVg+L9jDv94Ac/4KmnnuKQQw7hnHPOwXVd5s2bR6FQ4LrrrtvsY2YyGW655Ra+8pWvsP/++/OlL32J2tpaPvzwQx577DEOPvhgfvKTn2zWPpPJJJMmTeK+++5jjz32YMiQIeyzzz6EYchRRx3F7NmzmTRpEq7r8tBDD7Fy5Uq+9KUvbfa5b4mdIjA///zzPP7447z//vvkcjkeeuihLR4zI4QQQgjR3ejRo/nLX/7C9773Pe666y5aW1sZPXo0M2fOJJVKUV1dzUsvvcQll1zCzTffTD6fZ/LkyTz66KPMmjWr38/n8MMPZ9q0aVx11VV8+OGHTJo0iTvuuKN8D2OAvffemxdeeIFvf/vbXHPNNRhjmDp1Knfeeed692Duq1NPPZVRo0bx3//931x//fUUCgVGjx7NoYceyhlnnPGx9nnbbbdx/vnnc9FFF+H7PldccQXnn38+p5xyCk8//TS/+c1vcF2Xvfbai/vvv5+TTz75Yx3n41J2W4wg38r+8Y9/0NbWhu/73HzzzX0KzA0NDdx6663MnTuXUaNGfexjW2sJwxDXdTc5Bqe/GWNYsWIFdXV1/f5Xa28GY51B6r2917u3Nt0f7X1HuQ79Teo9eOq9o9S5v35/7+iUUpx77rmb3ZsrPp6dood5//33B+CNN94Y4DMRQgghhBA7m50iMPfV8uXLWb58OVAclN7R0QF03SLl47DWYozBGDMgf5F3n24rg7HOIPXe0evd+eUAH3dfO8t12FxS78FT78FYZyH6alAF5nnz5vUYCN85YHwgboDdn9a9SfjWZq2FyEJoIDLY0jTKhwQ5g40sNjTY0JbX29BCaCGMSuW29LzOfVls1LlswHSfdps3xekao7CW8sPYzuWucqzCmtI8qlxG+XkKZYvfLVqcKlRpfekZFEtLU2tRmHKZsl3ru2/bffseZdaus8/iHKUtKD/osdx9vrHHdus/r3jjm855VT5u6RtUyw+10XnT6zbrTktXr3y8Lt3rwTrrN769Wmd9TqUYccOn+uVnvHNc3Y7c3rd1W99eSL0Hj8FYZ7FjGFSB+etf/zrHH388UOxh/uMf/wiwwRtv99VAj/latWoVw4cPR2uNtRZbCDEdBYKWPLlVWfKr8hRW5QjW5Aia8th8UAymYVQMoVEExqCsKU+Lj2J4UtYUp6Uw1Tnfm+7xrWegU3QFuo2X2T5s0xXWikdZd25DJfSy9brlAFZpuodSu85853l1Xpl1t0V11aVruftzu0Kn7RaiwZYSfLdQqorrbWcQV92DqwFMqZyucK5s+XgWhVXd6rHOufU4P6W76qHUBrfp+rcqzlpsqUrFqe12/OL5db/6tsdy50+J7fzLpXO9tXSkNCOg/DPem00F4dtvv50zzjjjY7f37amtb0tS78FT7x2lzjvyH739aSf4CNoOZVAF5pEjRzJy5Eig+KGBzvskbskLkrUWrTVa6357gbHGYLI+UXsB054nastTWJMntyJL0JilsCZP2JbHZAtQKNAchWgT4NigR5gtxhAX8HDw0LgYHCwaiy7F3xgWRYTGKF2aKiLlEClFpItloVZEWhFoRehoQkcROJrAswSuwo9Z/JgijBlCB0INxgGrwWgDGqy2GG26pspilCmtNxhtsTrCdm7jGFARVnVOLVaFoC3WCTFegPECtBuh3AClQ7QTguOjHYNSEbr0UCpCY3FK804pgOrSwynFXl26fk5pvjMq6lK8hJ79t+v2CfdcX46UpS179rfq8sOWo/eGllW5HHTpP6U0WjkoHBQarXRxXmlUqVyr7n+6dJ4DPcqg+MER3W2rHus6f5Zs143lO//fNVUopVj/P41SlKblkuJzlC7VUZf2odEolC6dc+nHOOYMg4ByG9sSnfcL/bj72RptfXP1x3XYXFLvwVPvwVjnvvr73/++Vfd/wAEHbNX9iy23UwTmKIqIoogwDIHiL8YoinBdd7tseOsK17bz3k//QvT2R6XgG/ZYb3HoDL4WD4WLg4clSUAlHcqloD2y2qPDdWmNebQkYjSnHNrTlo5MRDbjE1b6OEkf7QXomI/jBigd4GgfpQtonUepPEp3oHQOdBatCzi6gNZ+MWxicVHE0MSUQwyFqxQOkO4MnrYrbFJa7uqd7tlXrDewXJw6OMpB4+AqF116ODjYCGJeAkc5KOXg4JTWO2hcHJUoLuOgtVsOkKq0fXl+Q2Xl+a6zUp09q7bYC+LoUu+L6h6ZO+OjLs32XKfUOttA+XhYXRwKUvzHLnayGsAWe2aVAROGrFmzmqE11SgM1kRYE2JNBDbsWo4iMCHWRqV1BmtNcV/d5tedlufN+mXWFo+nsMUejY3uKwIbbXid6b5Pu4Hn9TxuWc0usO+h/dLOhBBCiI9rpwjMzz33HDfddFN5efbs2QD813/9F/vuu+9AndYm+ataWfqTV1CL3kdRQURweLAAACAASURBVJZxZHWcnPbIui5tsWL4bUsq2hOatrSiLQO5dIRNFSCZRSfyuG4Oz2vCdbO4bhvaaUY5a4m7rcSdDqoUVDgpEipBTHnFgGsVTimKO0RoG6GJ0DZAGR9tfTSmHNUdwMMjplN4TgVaJfCcNI6TwtUpHJ3E0RU4OolWMbRyUcorTYuPchndy7xu60oPnI32bmzu7Za6AlsxzFnTFeysMaVwWSrrvi4KsSbARkFpGmIinygooOgMpwFmnW1s5JfX9Xx+921K8+XAG/X5Z6YBikFdu6BclHaKD+WAdlG6qwxVCv6lR+e80j2Xe6wrb+OA8sp/OFgUurzfDWy/zr5726ZP63TnNM7aXJ8vjxBCbFV77LFHv+9z0aJF/b5P0f92isB81FFHcdRRRw30afSZ39DM0ptfQb3/AVDJcjWZx3YfwgefykOiHdwWHDeH6+Zx3Q50KQR7bivVbivDdZ4qJ0mlTuJGDpWxJHHlEMPi2QjX+jgmjxOFaBviAipqxdERnlOJo1Ldgm4x7LpORWm+e3lp3inOa1X8cdnScW7WWmxUwAR5TJDFhK2EQQ4b5jBB6RHmSus757sefr6dZY4qB+FiKC4FYBv2CMV87DFeCuW4KB0rTT2U44Fy0W6sGEydnuuU9tCxiq512kVvYJvitBhu1w+5nfPFKdpBKReLZmXjaupGjsJxtm2zHehxjeRkvKIQQoiBtVME5h1FfulaPvjJy+iPlgFVLFOf5LE9q6n/dDupuifZu+Z9hrlpqnSChI4RV4qYBY8Q10Ro4+BEHkRZrGkG0wxovLCKmFuD51QTc6vxnGo8t5qYU43n1uA5VcScarSO9Us9rLWYME9QyGH8dqJCG6bQRlRoLc6vE3BNmMd2D75hbv0gqzTaS6LdZHHabV65CdzU0OK8k8BkC1RVDyn1eLql3shSj6rWpWlnr2vPdUp1316v0xvr9Fy3Tjgc6OCotFvq8RVCCCHEtiSBeRvILVrNhz97Cb2iAajhPb0/T+6ZYfn+WRJ1TzOt9iM+6brE8ytRfrE3TatEz/DrjewWfmvw3GpcnWH1qg5Gjhy1RWO1TegTFVoxfhtRoRiAy+HXbystdz1MoQ1rgvLzlRPHiVfixNPoWCU6lkK7yWLIdRNoL4X2Ej3CsFonGCvH61MINcaQX7GCzAB885cQQgghBicJzFtRx+srWHbrK7hrVmAZyiJ1AAsmVbJycoHEyOc4ZEQ9n9QWr/A+NfFPM2LUt4m7tcTcGhyd2OT+izeX3/AAT2stptBGkF1NmF1N2LGaILuaKN/SrUe4+LBRofw8pT10vLIUgLseXuWo8ryOpcFNEUvVFJfdeL9dMyGEEEKI7Y0E5q2g7eWPaLjjLzgtjRiG8Zb6NM9OqmDlvgHxuheZXlfPvjqP4y9laHoao4d/nVR87GYdw0Q+QftqoqbFtOXeJsqtJehYTZhdUwzI2dXYqHQbrXglXmoYbmooTrIGLz0CJ5bGiWfWC8fKiW+yp3cghyYIIYQQg8G9997LT3/6U15//XXa2toIggDX7Ypt//d//8d5553H3//+d6qqqpg7dy5XXHGF/F7eSiQw96OWZz5gxV1/RXesIWA4b6gD+fNeSVZMNsTr/sKRI+vZRzWjg3qGVR7KqLrzScZGrbefjfUOh90CcZRvKW6sHJpSQ/EqanFTQ0kM2x03NQ0vNRS3Ylh5WIQQQgghdhw1NTWcc8455HI5vva1r/VY19bWxowZMzj99NOZP38+7777LjNnzqSqqoqLLrpogM545yaBeQsZY2h+7H3WPvwqOt9Enjr+yWdYuGeM5ZMhVvd3Pju6gUlqFSpYwfDMEYwcdTEJb3h5HzYKaH3/OdqX/XX93uFYJV7FUNzUMNyKYSSG7VHsLa4Yik4MobE5t8VjmIUQQgixfZkxYwZQvHXuun73u98RRRFXX301ruuy77778q1vfYsf//jHEpi3EgnMH5OJDGt/u4g1j72KDlrpoI63mMYbe7jU7+vgjXyNY0bXM1EtQ0VrGZ45mlE1lxFzh3Tbh0/rkmdo+tfvsZFPZsLhpMd9phSIN907bIxBtRQ2ul4IIYQQO5/XXnuN/fbbr8cQjU9/+tO89957tLa2kslkBvDsdk4SmDeT8Q2Nd/6TlqdfR0XttDKKN9mbd3ZzqN/HxRn1JseO+Yg91GK06WBE9TGMrPocnlvVtY/Qp/W9p4tB2YTU7PV5qnY7Bu0lB7BmQgghtmehb2hdHdLWGNLaGNC6ujhtW138dthE2iFRqUlWOiTSDslKTaLSIZl2SFQ6JNKaRNrBcQd2jGsYWPycwc9G+DlDIWvIt0foKgt1A3pqG1V18/UArNwa+wa4497Nek5rayvV1dU9ympqasrrJDD3PwnMfRRlQ1b98k3aXnoDZXKsYTRvMZn3Jig+2ieGHvMvZoz9kD3Uv1C2wMiqz1FXNQPXSZf3YcICrUv+SNO/HgVrqZ74eap2+6yMMRbbJWsthCGR7xPlssXlIMAGPiYIsL5f/CruDT95Y3vdrGIVj0OmesMrhdgM1loKHYaO5ohsc1ictoTkWg1eUhXDZtohmdbE0w7xCoWbsDgVdpt9iMrPG1pX+TSv9GlYkmdJbgUdqwPaV/tk1wbk20I0BteFympFRZUmXaUZUacgHiNnE3QUFE0NPrk2Q74tItcekW83PdpYLKVJpkthunu4TjvEUpCq8khlOkN2cZ0b01hj8fMWPxtRyBn8bDHs+p3zpWlnCO4+9bOGIBsRZQ0qBI9iAHEBV0HSU4w+yYP+/yK9nVImk+Gjjz7qUdbU1FReJ/qfBOY+evOyp0mt+JCVjOGfjOWj8bBs7ziMXcxnx33Anuo1HAUjq2cxouqzOLqrt9iEeVrefYrmf/0BlKJm4nFkPnG03I5NFENoVPoq7jDqNh8Ww2gUYaOIKPCJVq0i19KEMqbb9sXtbBRhw7BrOSztpxRwrV+aBgHG97vKg6D48H1MsH55r9+U6Dgox4WNZYmNhowNl29o89jY8ejT5/Z6DcXOxQQhhaXN5JespfBhE4VClqaxq/CqE+hUDJ3w0MkYOlmcRtoll1dkWw3Zlmi9QFxcjuhoDjGlv++0AxXVLqnqYiAM8sUeznx7cWq6/R3ouBBfJ0wnUlARj4h7EXE3xJqAMPIJQ5+gUCD0fcJcAZv1IRfg+AGOMTiRxQ2LUyey6MjgGIu2Bm0tCoNTSrbrfxy8mxBYXXwYpbBa4USGDGBcTVRVgR1SgR6fJlZbSWxYmlhVGlVRge8kKOQUubaIfHtUnLYZ2taErHq/UFzuKIZtExVbqwYcB3TULeRSDL2eC3FXEXMg5ihiSlGhoMqCay06Ah1ZCCxq3b+vNeikxqnQxelQubtDX02ZMoW77rqrfMcqgIULF7LrrrtKYN5KJDD30cJhYymsGMPKMYZle8cx45dyzLj32U39BUfHGFV9IsOrjsTRXSHYBHla3p1P09t/QCmXmr1PJLPrUWi3f75xT/Qvay3WL2ByOUyhUOxB7R4y/QJhLo8yEawbPEvzpnN+3eXOntnOed/HRiFsrId2I5ar4ld24xbDqnIclOuA4xaXXQflOMUw67ooL4byYmjPQ3keKhbDragolXtoL4aKecXlWGeZV16vvBh4HkZrvGQSHesqV9vgg6bGGFaskK/G/jissSi9fQQQay0mb4maQ8KWiLA5IliVxf+omWBVM+HaVkx7KzbfDiaHwmJxgRSgWUs9EIEKwUYozHrHCHGIlIt2XCo8l1TMg5iHjXvYao9ouEcUdwliHgXHJY9Le6RpiTSxVERFMmTI0IB4FOAFAZ7v4xQCtO+j/QB3dYC7IsCNAlxr1jm2RuOi8NC4uLhYPKx2QVeglIMNNdYqIjShVihPoxIaFXNQseKUuEbHNcbVBFGEG4thtINVYNDg6GJAVppIKVAaqxQGi/FDVHsWlcvi5nK4a3J4yzuIB6uJhXliUaF0XcHoOEonSDgJ4iqJVXEsCayJY6ME2rpoo3C7/3G7zktVAcgrKBgoBJZCCC3KUlCUH3kFgTUYHWCSAagQpSPQIY4KcYmIq5BEGBFrDalcPZQxjO7nn77+0XL+twDYY4/+7wJftGjRBsujKCIIAnzfB6BQKBCGIbFYjC984QtceumlXHHFFXz3u99lyZIl/OhHP+LCCy/s9/MTRRKY+2jR/g2sqRtPOOEjjhn7Hrs5L+A5lYyqOYXq5EHEvGT5bTsTZGleNJ/mdx5HOR5D9vkimV2PQDsSlLcGGwSYfK4YdHO5rvl1pxsos+us22CPqlKlQOmhXA8dixfDZSlo6m5hU8ViOBVpVLVXKiuu01635c55tzPwuqWQW1oulXX24CrHwWrNisZGRo7a9ndEkftub19MYAmbwtIjImwO8VeH5Bp8Co0BQXOEyhrcCAxgFEQKjIZIKUx5HoxWRLq0rLvWhVqVyorlobLlbSOtiJxiJ6cC4gHEQ4sXWOKhJRZavAhioSUWWGImR9x0oOlAkYXSVOFjgZAkPilyVNDBENqooIUK2vEIULiupSKpSLmQUBA3kAgNySAk5Yd4NgIifBWQ0xE5Qgp+RBBERB0hRkUoCsRUSEyFJGxIpY0YZkPiUYRrLb7W5B2XvC4+OrRHXrnklUdepfC1i425KM9FGwfXenjGI2FckpFHGk3aQIUt9sYC+ECHhawBX1mMB5FjsZ4F14Jj0cVmj+soPFfhafCMxvVBRxEJ4+GicRQ4qG7DKmzx3SlM+TXLagtuHFsVgyFVRE5EQQfkdEATAVkT4reH2PYQnY3w8iGJQkAiaCUZFUjZPF4pFfvKpc1L0B5P0JpM0FoZpy3jYbRFmQjHRMSCiLgfEQ9K82HEkCAiFobEw+JyLAxxN/CaWtAOvutQcNzivFOcXxS1br2GswP6zW9+wxlnnFFeTqeLQzyfffZZpk+fzvz58zn33HMZOnQomUyGs846S+6QsRVJYO6joz5ZReMnXmQ351ni3jBG1/wHQysPQuEQhsUPXER+lpZFT9C86HG0m2Dovv9GZtcjUI43wGe//enRm1sOrNmey+uVFecLba18GIbl0Evp+pcphU4k0ckEOpFCJZPoRAKdTKKTKdyaGnRiFDqZ6lae7LGsYoli0I3FisFVqQENjsaYbdKjKwZOZxCOmkOCpqg43xQRlMJxvjHP4tal2GxX76avIW+LDx/IAm0KWhzoSIJD6W1zwLPglfptXQuuAS+yxXlb3DZuKa93SvOd095FBLQTqiyGDjRZXLLEbBYHQ4imRadYqytodGpY44xmta5grU4R4BSDPWCsKvWmFoO+BaxSxWTeOejVA1wNbgy8GAmg2kJ1ANUhVPtQU4DqPFRnIV56eeiIQ3MFrKywNFVAc9rSXAHtCUPCRlT6AelCRDpvqcxBOqsYnnVJt8dI5rtewwupAvnKHH6mhaCyg3xlO00VHdSnOsgls3SkcnQksuR1SBQqokhh/BhhtoKwI03QUUWYrSLMVhJ1VBBlU5jWONhS+9YGUiFUGIgFkDJQEUEqQlXk0akcOpXFqWhHJdvRqgD4GBRhlCBqzWCaMtCchqZk8dFcCa2x4kV1DVRHMCaEmrB40WpCdFWBdCzPsGafmuaAmuaQzNoCmeYOaptD0isjjIYgpgjiiiAGYQyCJIRxyMWgPW4JY5YoboliFhOPMAmLiUWYeISNh+BFOE6EJsJVxZ5mhwhHRRzpzfj4DWgndPrpp3P66advdP3kyZN54YUXtt0JDXISmPtor8S71EX/YnTN2QxJT0Wp4oubtRbjd7D27adoWfQk2ksydPIpZCZML751vpOy1hJls9iONqL2dqK2NqL2Nkx728YDb/fe3Fx2vd5c5XrdwmsSlSiF2GQSnUrhDhmKSiaJfJ/q4SNwUhXlUFx+XiKJim/62wqF2NainMFv8PEbAvwGn2BNMRyHpVBs2ruCcJRQFNxif2w2suQCSyGC0FryFENxWxwKKchXKLKVBj8V4sUCkjqgQoVkdIiyFmMsPpbQWPLGomznwxSnpjR+trOs+zamuI22FjeyOBG4xuJG4BhI+AFV2RyZrI8LtCU91lYnWV2TYHXNUBprRtM4JEFLZQyjKQZfZYsPHYJqQasIpUJQPqgAxwbElSKBR0rHcFVIRdIh7ppi8FcWVxkcZXBVhIvB0SEeIY4qBjGlDM1ENFmDziqctTFiaxK4axOMWJtk7Jok3rIksaYUju9itSaoNISZkCBToFBdIDsuz8p0jiWVOVoqs6xNt7Mm1U6bU6Bgiz2xDoq0TlDpJMjoBBmnmipnNOOcCmqcCqrdSqqcClzlYS34hOSMT8745E1A3raTM01kwwKtWUNrq6W9DTraFe0tliAbx++IETXGsdkktqOaKDuCyGgCStcyFaFSEfga2+qCVSjPkhhiSA+FmnEONfu7DBvmMGK4x7AqlwrXI6kdEtohoYBwFX6wjI7CWrJDPiQXLMMP1wCKhDeSVHwcqdgYtEqUXls1iuKQEFUa5bzRclTxXTp0t+1Ky+VyhbWKtmbpXBLbr5030fWzMTUnM3bIv5eDMkBUaKf5ncdpXvQETrySYVP+PyonHIbSO9ZltcZgclmi9nZMKfhG7W1EbW2YjrZSGG4vB+KorY2oo73H+FuVSOCkK3Eq0uhUqhx0ncoqvOF1xbJEcp3e3GTPoOtt+sXSGENhxQoq6+rky1rEdscaS7g2xG8IKHSG43off7lPuLbYXmxG016haHYszaGlLW/piMB3QUWGmA1I5ANcAnAD8AJ0KkTHQjzPp8YNGBUFJIKAZCEguSoksSyie2vwPUUhromc4tAKWxpKYUtDLkxp3urO9ao4FtYBSh8is1qBU5oqhXU0aAVaY7UiVIrmZAXLR9Ti16UJRqSJVcRJaIeUdtlDafagQCFqIhs10hGuoi1soCWoJ7BtxLDUxkYyIj6G2thYhsfGUBsbQ7Vbiy69znaOYa/bSHu31mJsgchkiUyeyORK87muRzxHVJUjGt9KZFYS2a51QZQjn7OoeIh2PBzl4imPCuUyFA+Fi6NjKO2iVRyt0ijlEqBptZY2a2kxEa02pNmEtIQtfOQ30hzlabfFcacaRUJ55GxQGkFMqVxToZNUOEkqdIKKWJLUiCTjRiZJqjgmG1JXNZS0k+raRidJ6QQ2H6O9DZpbQ1raIlpaIxJxTd1wjxG1HjVVznqdBtZa/HANWf9DsvllZAsf0uwvI+fXY4nwnBpSsbEkvNEMrZxGRXwcSW80Wm+boYTGGLIt8nkFsf3asZLdAOr+ohEV2mh+5zGaF83HSWQY8skvU7XrYejtaOiFjSKi1hbCprWETU1EzU2ETWuJWlu7AnF7G6YUhDFdvVs6mUSnK4sBOJ3GSVfiDhlKfPwuOOlKdDoNyRReVRVuZQYnXdmnsCvEzsL4Bn950NVjXO9TaAjwl/vYgkV5CjvMoTWhWBGGNJHFT+RwgjyV7XkybXlSNmAcAS4hngrwCPC6fZjMAr7rEiQ9gpRDIQlRWpNLQTYZsSLh0xLP0ZTIkUuG5JMRsQqPeDpOOpkgpVUpZBWDVoVOUulUkHYqqNQVJJ0krnJxdQxHubjKw2H94UZ9GYrUHrawyl9Go7+EVf5HNOaWscr/iLzpwMFlWGwUtbEx7J7ek9rYUQzz6sg4lVhb6Blu8+/SaN4oL4dRlvbCWtpWgukMxN0Cb2RyrHtPQoWDo1M4Otltmigve7qmW1kKR8WxRBgbYm2AsQHWhhgTEEYFUBGWznUh1uSwNiRtA1I2YLgNMeXnBRhCrA4omJB2a2jFUrCKOIYEhjhQoZMkdZqY6+E6Fle7uE4MV6dwnTSOStOa9xmeqCPmZnB1umdwTUNVGkaP3HCYDaMsOX9ZMRwXPixO/WVEJotWcVKxsaTi46jNHEEqNo5UfCyek5HPKwjRCwnMmyHKt9L0zh9oWbwAN1lD7QFnkB53EJGx26xX2VqL6WgnbGoiWLuG8IOlNJmIqKW5Kxg3NxG1NJeHPOiKNG5NDU51DW6mCre2lviEXUuBuBiAnVLwddLp4gfONnEO8qIqdnbWWqKWiEK9XwzH9T75+gLB8pBwdQgWTEqRz4S0mzy5QpZA53HcPPEgT3p5nhR5JlJAAQZF1olTqEhiMyniw2pIj0gRG+4QpCNakj5N8RyrYx0sj7XQ4DSxxraQNcVv83TRVOoYSa2JY/EI0baAtVlippUEfvmeBiYfx1cxChhW2YiIiMiGPXo4N8bBwVGlBy6OctA4xXCt3NI6FweHgi2wJmgkb3NoNFVOmmpdwRCdZHxsDJVoktZgbZ4o/y5R9g1aTY4W1vncAapHqHVUcV6r4j3qY84Q3FhqgwG4OO16aNU/f7z35+ucMT6haSeI2gijdkLTTliebyOMOsgHKwijd3tss+qjbh0ZKo7rpHF1ZXHqpHF1Gs8pLgdRK9lCMST74Wq6D6eoSk1mZPXnScXHEneH93intM/Xw4Sg1u+5FmKwkMDcRy3vPsXqV+/ErRjK8E/NIT3uIJTWxU8qm3Vf/D8eU8j36A0OO3uFm5sIm0pBuKkJGwYAqFgMKjPkhg7DHTKEWF0d7sRJxWBcMwS3uhiSdUzuziFEX5nQ8O7lHxE1BKh8hKVAIVmgoPMEJgdhHpc8cfIks3ky2YgM4OPSrhO0xWK01iRoqhtK9a6VDNktQ3yUw9p0jlXRWpYVPmK5/yFNUQftpkBUCrEeECPCwcfBx4tCaoioA6p0kqRJUBVLktBx4sohBrg2wrUFtMmgojZU2IKLj7aF4uhRpUs3uHZBuVhjMUBE96nt+tBd58MajDVE+KWydbcpLifR7K3jVDnDqNKVeE5qnd7bdcPthue1im0wxJWHZAzbMYdgWRNiwgLWhMU/MtRQcGtL/y66699H6R5B1BjD8uXLqR1RhbEdxQBdDtel4G2KZYWwkTBqw9UVpOLjGJqeSjI2hoSqBRNhwjwmyGOzeaLWBtrD94rLYR4T5Irrw67lKMhhwwI2Kj7PhMVtOm9OrbSHcjyUEytNPbQTK5XHeq7THtrd8DpdLisuo11sIF/iJbZfEpj7KJYZzfADv0567Gf69W4FNgxpX/hXWp5ZQH7R28VCrXGrq3sE39jY8eV5t2YITnUNJBKsXLlyo+P7hBCbL7KGjqV/JmbyxEq9w05OoYgTqgStKkFrIk1z5RAaR2haJijik+LUjICEygPNNEeNNIZrWR00ExJBDuI5cPBx8UmrGKOdCqq9DEPcDLXeEDJOhriOEbMRjg1xTA5tOojCJvxwDXl/FVG+AbAoHGLuEGLuMOLeUGLu7sTdYcTcocWpNxRXp/rleuzs7ygVQ20eG/7/7N15fBRVuvj/T3X1nqSTzp6Askf2ZRAUERQFURkX0BFwu6COKDozMspc1wHU38yoeH84Kg6uoyIyXEfUcQGEEYmIM4qy6FwNssiShOzp7N1dVd8/OmkSCTEknXQn/bxfr7yquqq66jydnM7Tp0+dU1efPNZh+OvQ/LX466pRDB9oXvT6/YZWFzym8fMatgf3aceSzNZRmiTRBgqHTWoziXUg2TYpJqyKgrV+m6FXo/sO4fFvotxfx3HTZyoKJrMdxezAZLZjstgbPbZhsjgwW+MwqzbMVicmy7HjFLMdk9mGoesYmjfwo/swNB+65sXQfPU/gX26fuyxVlcZXNeDz2v6HF3zYehebIPmAP1C98sVIoQkYW4lR+rgkJ7PV1yEZ/MmPB//E93rxTV+IklXXY0lORXV5WpVUq436ncshAgNRVH5xpFGkd9KqdlOZZqVsp5QnVKHkeolJrUWm70SP/lUaXloeFBQSKiNJc5kw6rooFdh0YrJoJI4xUYve2962E4hzZxKkhqLxfBR5y/G6y8KLKv2UecvwmvU4QVUUww2c3Iw+Y219aXaMJOa3A+7NQWr6m7T1+qh0jA6kKHVYbLGRexkTLrmxV9dgr+6GK2mBF91MVp1/bKmGH91CVpdc2P/KihmG4pqCySTZjuKuWG9YbsdszOp0WNbMLFsfIxittV32TMwDB3qfwzDqF82bDu2X9f9lJaUkJAQH+hmY9S36xs66PVLmj5fMZkxmR0o9YmwqT4RVuoTX0W1/uQHnnAPnZmXl9ep12yLE00yIro/SZg7kaHr1PxnN+WbPqRqx3asPU8hcfoviBt3Nia7fBUlRCTQTBrvX2FHdxuQWAxqGVBOnMlHmlkl3ZpAotmFhUQ0XaXKe4RS7w9Uavsxa2aSzQkkmmJIUF3EGzGYtUr8Nd9g1OykCqhWLFjVhGDrcIytD4kxY1psHdZ1nfzafOLsHf9tUiAZrggmmr7qYryVReh1pWjVJfjrE01D8wafo6hWVGssJlscqjUW1RbXdL1+qdZvM9niMFmc7UrKdL8Xf02gjP7q4kbrJWg1gXLrdRX1BTRhdiRidiZhdiZijUvHnDYEszMJ1eEOtKY2TnDrJ5kKZ/LoIZ+YKPv2MJK/wRg9enS4iyDCLCoT5ry8PHbv3o2u65jN5voZk9qm4bktnUOrrKRi68d4/rkRX3EhsWPOJPOeRdj7ZwXfINpSBsMwguVvTwxtuW7jZWcKV8wN12687OxrS9wtX998gptVG+q7w+FoVX23Kirjf1ZDhmqih2ojTknG59c46j3IUV8+RVVl5Bi1KEAsEA/0BhKw4DYnYje7sZjdWNUELGpC/bobizkBq5qAaoppVUtfW1+HnzqvVudpkvgeW5YEtxta4D4Jk8WB6ggklRZnEvaUgYGk05GI2ZmIotrQvZVo3kq0ugr0uopj695KfBX5aN7K4HbDX3usMIqpSZLdXIKtWJzopUWUlnjRakqOJcU1JY2SYRWzwx0slyUuA0d9MtxQTtWWcNJd6brK332or9t42ZlCUdeF6EhR+Ve3YsUKlixZwnXXXYfb7Q7O1NcemnZ8X7W6A/uo2LyJ6n9vwxTnIu6cuW0YrQAAIABJREFU84g9+xxUV/wJn3Oy3G43uq6HpXtGKMrfFuGMGSTuztbauN1ud7PbG+r7/fff36r67te9ZFS+QDk6PwAVBG50i1UsJJniybL2Jt16CmnW3jjNKYGkWE1ANTl+Ohij7a+j2+1G0zQ0v7e+j2x9X1KtDsPvRdfqjvUv9deh+2vQasrw1wS6I2g1pfhrSoI3KZssTlRHIqojEbMjEVvyQMyNHquOREyWn45JtSeitjIGQ/MFEuj6n4ZkOrjurcBfdqjR40oUsx2joVwx6diSBweSYEdiIJm3u36ye4qm602GzjwZkf533xEiPeYT1XUhOlJUJszz5s1j3LhxbN26ldLSUlJSUtp8LsMw0DQNtX76ZN3rpfLf2/B89CF1+/biGDaCtPm/wTl8VMinNtZ1neLiYpKSkjr1a7sfx9yZwhUzSNyRHndhYWGz2xvq+44dO1pV31XDxCFzIkmWNEbaTuMUx2lk2vpiN8W063UwDB1/dQm+ilx8FfnovurADU/+uvobxuqT3+C2Y8uGm9DQffX9WZthUjGpge4ESn2/W7PDjdmRhC2+Z5MuCWZHEiZL67qBhfT3bzZjsTmA1r3nyt+9vLc350R1vSNt3769Q88vXT4iX1QmzBkZGRiGwbZt2/D7/SF5Y/AXFuD5aCOe7M2AgWvCuaTN+xXWtPR2n/tEFEUJlj8cfb/Ccd1wx9xQBom7867Z2rhP1HJ8svVdUVR+0/up47Y3fE38U2XRvNX4KvLwVeTi9eTiq8jDW58kG5oXTGassemBfrxma+DmMNUaGJrLFhtYBm8aC+zHZKG8oorE5HRUi73JvsDS2uFjwUf6778jyxBtcUd6zKH4VliIkxWVCXOoGLpO1c4vKdu0gdqvd2Hr04/kWdcSe8ZZMvaxEN2YofvxVRbUJ8N5+Dy5gWVFLlptOQCqIxFrXAYWVyau5HOxuDKxxmVgdqac9LdNuq5TmZ+PIzW6bgITItJkZWWF/Jwy8kbXIAlzG/g95Xi2fITno41onnKcY8fR44GHcfTrH+6iCSFCpOEGuYZk2OvJxes5gr/yKL7Ko2BoKGY71rhMLHHpONOGYOk/BasrA0tsRqu7PIjoYBgG+P3oXi+Gz4vhrUP3+jDqH+vewDbD60Wrq8NXXESZ3R54Tl1d/XMCP3rj9frnAyhmC4rFHFg2WTejWCw/Wm+0NFtQLBZQVXSTCbPNhil4/LHzoNZPuGIygal+qSiN1k0opsDjxscRxm8IhAgVSZhbyTAMar/PoXzTBio//wxLcgrxUy4ibvxEDLtd7toVopswdI3Dmxbh8+Sh+6pAUbDEpGKJy8Ack05M5iisrh5YXRmodrckAhHO0LTAzKnFRfiKCvEXF+EvKsJXXIi/qBB/aenxNwQ2/pUe9/tVmj/uuGMbret6IKk90egPioJisaLYrJgsVhSLFb9JocrpxGS1oVgD20xWK4rNhjkuLrBusQb3QWAiLMPvw/D5guv46rfV1aFXVdXvr9/WZN2P4fOh+3zg92NogccnLPPJakislfokulHC3ZBYm2fMhPSO68YoRHtIltdKJW+uofTdt4gZOZrMBb/DMXhYcGps6U8lRPehmFTiep2N2ZkU6FIRm4aiWrr9jHddle714i9pSIKLAklwcaP10pJAQqwomBOTMCclY0lKxt63P+YxZ2JOTERRG/0rbBhajfob4Exqkzz4uPyx2YTyx7PsmZokuCarBcVqCybB/OhvKjgleCePw9zc37ihacHEGl3H0OsnXmkYeSS4bgQnVjl2nHHsOL1+shb9x88NbNM1P554Gf1CRC5JmFvJdc55uM45D0ty20fUEEJ0DQlZF4a7CO1iGAZGbU2g1bBxAtM4WdF1DF1rdl/jpaFpaH4fqqIEkqLGzwEUVUUxqaCqKKoJTGpgm1q/zWSqXzZsMx073mQKJKuNt6lqfUtkfcJWV4v38CG0kiJ8RUWBFuLiwuC6Vl4WKIfZgjkpCXNyCpakZJyDhwbXzckpmN2JgXOfxGsoH5A49ru0dex1GvrpCxGpJGFuJUmUhRCRxDAMtAoPvqP5x34KjgbX9Zrqtp9cVX/01XlDP1X12FfpDS2f9Um1oWug1SfhmoahaW0e+xgIXhu/n8OAYrdjSU6pbyFOwda7b3DdnJyM6ooP+dCdQoTTkiVLeOWVVygqKsJisTB69GgeeeQRRo4cGTxm165d3H777Wzfvp34+HhuvvlmFi1aFNUf8jqKJMxCCBGhDMNA85Tjzc/Dn/MdJXW1+Avy8R09iq8gH72mJtjVwJKegSU1jdgzzsKano45KQXFbA4kuKr6oxuz6hNh1XTcDVo/Tjrb09Ia/Apeq0+iGyXVzW/TQatvFdc0NJ+Psro60rIGYo6LkyRARJVZs2bx61//Grfbjdfr5cknn2Tq1Knk5uaiqioVFRVMnTqVOXPmsH79er7//nsuuugi4uPjWbBgQbiL3+1IwiyEEGFkGAZaeTm+gvwftRbn4z16FKM2kBQrCW7qMjKxpGcQ23cA1rR0LGnpWFJSAyMcRCBFUY51s2gDXdfx5OejxsZKsiyizmmnnRZcNwwDVVUpKCigpKSElJQU3nzzTTRN46GHHsJsNjNs2DAWLlzIn//8Z0mYO4AkzEII0QkMw8BfXETdgf3U/bAfb15uoAtFQT5GbW2gpTg5BUtqGta0DOz9BwQS4tR01KRkjhYXd/pNYEKI8Hrvvfe45pprKC8vR1EUFixYEJytdMeOHYwaNarJKF1jxoxh3759eDweXC5XuIrdLUnCLIQQIWYYBv6iwvrkeB+1B/ZTd2A/emUFJocTW+8+WDN74BhwWiApTkvHkpxywpZivT19gYUQ7Ra/5zEAju7pgHMDjF7d7L5p06ZRVlZGSUkJL7/8Mj179gzu83g8JCQkNDne7XYH90nCHFqSMAshRDsYhoG/sIC6A/upPbCPuh/qk+OqSkzOGGy9+2Dr3QfXxEnYevXBkpom3QuEECclMTGR3/zmN7jdbrKyshgxYgQul4vDhw83Oa60tBRAkuUOIAmzEEK0kmEY+AqO1ifF+4LdK/SqKkwxMdh698XWqw/x55yPrXcfzCmpkhwLIUJC13V8Ph979uxhxIgRjBw5ktdeey14Uy7AF198Qd++fSVh7gCSMAshRDMMXQ8kxwcCyXGg9fgARk01ptg4bL37YO/bj/hJUwLJcXKKJMdCdFPlAxYCkJWVFfJz5+TkNLv9iSeeYNasWaSlpVFYWMh9992H1Wpl/PjxAMyYMYO7776bRYsWcf/997N3716WLl3KHXfcEfIyCkmYhRCiCcPvJ/fxP1J3YB96TQ1qnCuQHPfPIm7SZBx9+wf6G0tyLIToQB9++CF/+MMfqKysxOVyMWbMGDZu3EhGRgYAcXFxrF+/nttuu42kpCRcLhe33HKLjJDRQSRhFkKIRhSzGeewkcRPvjDQcpyYhKIoMvObEKJTvfvuuz95zPDhw8nOzu6E0ghJmIUQ4kfcF18S7iIIIYSIIDKgpxBCCCGEEC2QhFkIIYQQQogWSMIshBBCCCFECyRhFkIIIYQQogURedOfYRi8/vrrbNiwgaqqKvr168ett95Kr169mj3+3nvv5dtvv20yn/qcOXO4+OKLO6vIQgghhBCim4rIhHnt2rVs3LiRxYsXk5GRwerVq1m0aBHPPPMMDoej2efMmDGDa6+9tpNLKoQQQohocaJJRkT3F5EJ8/vvv8/ll19O7969Abj22mv58MMP+eyzz5g0aVJ4CyeEEEKIqDJ69OhwF0GEWcT1Ya6qqqKgoKDJ9JOqqtK3b1/27dt3wud98MEHzJ49m1tvvZWXX36ZmpqaziiuEEIIIYTo5iKuhbm6uhqAmJiYJttjY2OD+37suuuuo2fPnsTExPDDDz+wbNkyjh49yu9+97smx+Xl5ZGXlwdAYWEhVVVVAOi63ubyGoaBruvout7ps381lLs95W+LaIwZJO6uHrfFYmnXubrL63CyJO7oiTsaYxaitSIuYXY6nQDBZLZBZWUlSUlJzT5n0KBBwfU+ffpw00038cADD1BXV4fNZgvuW7FiBUuWLAk+njVrFgD5+fkhK384FBQUhLsInS4aYwaJuz3mzp0LdO36Lr//6BKNcUdqzNu3b+/Q80uXj8gXcQlzTEwMqamp7Nmzh4EDBwKgaRr79+9vdf/lE30ynjdvHpdeeikQaGHeuHEjAOnp6W0ur2EY+P1+zGZzWD6RFxQUkJqaisnUeb1rojFmkLgjPe6fSoRfeukl5s6d2+b63lVeh1CTuKMn7q4Sc1f+0Cu6rohLmAEuvvhi1q5dy/Dhw0lPT2fNmjWoqsqZZ5553LGlpaXs27ePIUOGYLPZOHToEC+++CJjx45t0roMkJGRQUZGBgC5ubls27YNoF1vSIZhYDKZMJlMnf4G06Dh+p0lGmMGiburx+3z+YLnaovu8jqcLIk7euKOxphPVuP7q0JFRt7oGiIyYZ4+fTo1NTU88MADVFdX079/fxYvXozD4aCwsJDbbruNRYsWMWTIEHw+H6tWreLIkSPouk5CQgLjxo1j5syZ4Q5DCCGEEEJ0AxGZMCuKwjXXXMM111xz3L6UlBTWrFkTfJyamsrjjz/emcUTQgghhBBRJHK/9xBCCCGEECICSMIshBBCCCFECyRhFkIIIYSIMIsXL0ZVVWJjY4M/s2fPDu7ftWsXEydOJCYmhszMTBYvXoxhGGEscfcWkX2YhRBCCCGi3bhx4/jkk0+O215RUcHUqVOZM2cO69ev5/vvv+eiiy4iPj6eBQsWhKGk3Z+0MAshhBBCdCFvvvkmmqbx0EMP4XA4GDZsGAsXLuSpp54Kd9G6LUmYhRBCCCEi0FdffUVKSgq9evXi6quvZv/+/QDs2LGDUaNGYTYf6ygwZswY9u3bh8fjCVdxuzXpkiGEEEII0QJf/GMAfHO0A04eD7D6uM1XXnklc+fO5dRTTyU3N5ff/e53TJ48mZ07d+LxeEhISGhyvNvtBsDj8eByuTqgoNFNEmYhhBBCiAgzdOjQ4HqPHj148cUXiY+P59NPP8XlcnH48OEmx5eWlgJIstxBpEuGEEIIIUSEUxQFRVEwDIORI0fy1Vdf4ff7g/u/+OIL+vbtKwlzB5EWZiGEEEKIFljKFwKQlZUV8nPn5OQ0u33NmjWcd955JCcnc/ToURYuXEhaWhpnnXUWAHfffTeLFi3i/vvvZ+/evSxdupQ77rgj5OUTAdLCLIQQQggRYVauXMmgQYNwOp387Gc/w+fzsXHjRuLi4oiLi2P9+vVs2bKFpKQkJk+ezA033CBDynUgaWEWQgghhIgw77zzTov7hw8fTnZ2dieVRkgLsxBCCCGEEC2QhFkIIYQQQogWSMIshBBCCCFECyRhFkIIIYQQogWSMAshhBBCCNECSZiFEEIIIYRogQwrJ4QQQgjRCieaZER0f5IwCyGEEEK0YPTo0eEugggz6ZIhhBBCCCFEC6KyhTkvL4/du3ej6zpmsxnDMNp8robntucc7bl2Q/k78/rRGHPDtRsvO/vaEnfL1zebm387a6jvDoejXfW9q7wOHXHtxsvOvrbELe/tP3aiut6Rtm/f3qHnlxbsyBeVCfOKFStYsmQJ1113HW63G7/f3+5zapoWgpKdPLfbja7r6Lre6deOxphB4u5srY3b7XY3u72hvt9///0hqe+R/jp0FIm7c8l7e8vHCdHZojJhnjdvHuPGjWPr1q2UlpaSkpLS5nMZhoGmaaiqiqIoISzlT9N1neLiYpKSkjCZOq93TTTGDBJ3pMddWFjY7PaG+r5jx4521feu8jqEmsQdPXF3lZhPVNc7Q1ZWVsjPKTcSdg1RmTBnZGRgGAbbtm3D7/eH5I1BUZROf4NRFCVY/s6+dsP1oy3mhjJI3J13zdbGfaKW41DX90h/HTqyDBJ3511T3ttPLBTfCgtxsuSmPyGEEEIIIVogCbMQQgghhBAtkIRZCCGEEEKIFkjCLIQQQgghRAskYRZCCCGEiDBDhgwhNjY2+ON0OlEUhbVr1wKwa9cuJk6cSExMDJmZmSxevDgsY2hHC0mYhRBCCCEizDfffENlZWXw509/+hNJSUlcdNFFVFRUMHXqVMaPH09RURHr16/n+eefZ9myZeEudrclCbMQQgghRIR75plnuPHGG7Hb7bz55ptomsZDDz2Ew+Fg2LBhLFy4kKeeeircxey2onIcZiGEEEKI1vpH/GOBlaMdcPJ4GM3qFg/55z//SU5ODrfccgsAO3bsYNSoUU2mCR8zZgz79u3D4/Hgcrk6oKDRTVqYhRBCCCEi2PLly7nwwgvp06cPAB6Ph4SEhCbHNEwZ7vF4Or180UBamIUQQgghIlRubi5vv/02b731VnCby+Xi8OHDTY4rLS0N7hOhJwmzEEIIIUQLLilfCEBWVlbIz52Tk9Pi/meffZZTTjmFiy66KLht5MiRvPbaa/j9/mC3jC+++IK+fftKwtxBpEuGEEIIIUQE8vv9PPfcc8ybNw+T6VjKNmPGDFRVZdGiRdTU1PD111+zdOlSbrvttjCWtnuTFmYhhBBCiAj09ttvU1xczI033thke1xcHOvXr+e2224jKSkJl8vFLbfcwoIFC8JU0u5PEmYhhBBCiAh0xRVXcMUVVzS7b/jw4WRnZ3dyiaKXdMkQQgghhBCiBZIwCyGEEEII0QJJmIUQQgghhGiBJMxCCCGEEEK0QBJmIYQQQgghWiCjZAghhBBCtMJPTTIiuq9ukTAbhsHrr7/Ohg0bqKqqol+/ftx666306tUr3EUTQgghRBc3evTocBdBhFm36JKxdu1aNm7cyOLFi1m5ciWDBg0Kzn4jhBBCCCFEe3SLhPn999/n8ssvp3fv3thsNq699lr8fj+fffZZuIsmhBBCCCG6uC7fJaOqqoqCggKysrKC21RVpW/fvuzbt49JkyaFsXRCCCGE6Oq2b9/eoeeXLh+Rr8snzNXV1QDExMQ02R4bGxvc1yAvL4+8vDwACgsLqaqqAkDX9TZf3zAMdF1H13UURWnzedqiodztKX9bRGPMIHF39bgtFku7ztVdXoeTJXFHT9zRGLMQrdXlE2an0wkQTH4bVFZWkpSU1GTbihUrWLJkSfDxrFmzAMjPz+/gUnasgoKCcBeh00VjzCBxt8fcuXOBrl3f5fcfXaIx7kiPufG32aEiI290DV0+YY6JiSE1NZU9e/YwcOBAADRNY//+/cd1x5g3bx6XXnopEGhh3rhxIwDp6eltvr5hGPj9fsxmc1g+kRcUFJCamorJ1Hnd0aMxZpC4Iz3un0qEX3rpJebOndvm+t5VXodQk7ijJ+6uEnNX/tAruq4unzADXHzxxaxdu5bhw4eTnp7OmjVrUFWVM888s8lxGRkZZGRkAJCbm8u2bdsA2vWGZBgGJpMJk8nU6W8wDRqu31miMWaQuLt63D6fL3iutugur8PJkrijJ+5ojFmI1uoWCfP06dOpqanhgQceoLq6mv79+7N48WIcDke4iyaEEEIIIbq4bpEwK4rCNddcwzXXXBPuogghhBBCiG6m3QnzkSNH2LJlCwUFBVxxxRX07NkTTdMoLy8nPj4eVVVDUc4OU1RU1K7nm81m3G43hYWF+P3+EJXq5HR2f65ojBkk7kiPuzV1uT31vau8DqEmcUdP3F0l5vb+3+5KSktLufvuu3n33XcpLy9n3LhxPPnkk8F7tg4ePMj8+fP5+OOPsdlszJo1i//5n//BarWGueTdT5sTZsMwuPPOO3nqqafw+/0oisKwYcPo2bMnlZWV9O7dmwcffJA77rgjlOUNGafTicVi4c0332zXeSoqKti+fTujR48mLi4uRKWLbNEYM0jcXSFui8USHDmnsVDU9670OoSSxB09cXelmE9U17ubOXPmUFdXx86dO4mNjeXuu+9mypQpfPvttzgcDn7+858zcuRIjhw5QmlpKZdccgkLFy7kiSeeCHfRux3FMAyjLU989NFHueeee/jv//5vzj//fKZMmcLGjRs577zzgMAvee/evWRnZ4e0wKFUVlZ23FjNJ2v37t1ceOGFrFu3jmHDhoWoZJEtGmMGibsrxO10OklISGh2X3vre1d6HUJJ4o6euLtSzC3V9Y7QMHFJRw0r19zEJVVVVbhcLrZu3RocxKC2tpbY2FhefvllevbsyeTJk8nLyyM5ORmAt99+m6uvvpri4mLsdnvIyxrN2tzC/Nxzz3H99dfzhz/8geLi4uP2Dx8+nA8++KBdhetoCQkJ7a5wiqKwaNEihg8fHhyBo7uLxphB4u7qcbe3vneX1+FkSdzRE3c0xhzpDMOgcbtmw+Mvv/ySoqIi+vbtG0yWAcaMGUN1dTU5OTkMHz48HEXuttrcwmy323nyySf55S9/SXFxMSkpKU1amFesWMEdd9xBTU1NSAsshBBCCNGZZn1/d4eef3X/PzW7/YILLsAwDFauXBnskvH0009z4403cuqpp/Luu+/yr3/9K3h8TU0NTqeT7Oxszj777A4tc7Rp82CHqampHDp06IT7t2/fzqmnntrW0wshhBBCRLWVK1eSmZnJ6NGj6d+/P263m4EDB5KcnIzL5aKsrKzJ8aWlpQC4XK5wFLdba3PCPGPGDP7yl7+wb9++4LaGgc43bNjAX//6V37xi1+0v4RCCCGEEFEoNTWVl19+mcOHD5OXl8ftt9/O/v37Of/88xk5ciT79+9v0i32iy++wOl0dkhf62jX5i4Z5eXlTJw4kf379zNhwgTWrVvHlClTqKysZNu2bYwaNYotW7ZE9F2sobjpTwgROTrypj8hROSIhpv+AL777jvcbjepqal8//333HrrrVitVt577z10XWfkyJH87Gc/48knn6S0tJRLL72UiRMn8uc//znk5Yx2bb7pLz4+ns8++4zHH3+cN954A7vdzscff0y/fv1YtGgRCxcujOiZ9srKyoJD4gkhugez2cztt99+3D9Sqe9CdC8nquvdzdatW/n9739PSUkJSUlJzJ49mwcffBAITCP+j3/8g/nz55ORkYHNZmP27Nk89thjYS5199SuiUscDgf3338/999/f6jK02mqq6vx+/2MGjWK2NjYcBdHCNFOlZWVfPXVV1RXVx/3T1TquxDdR0t1vbu54YYbuOGGG064v1evXrz33nudWKLo1S2mxm6P2NjYbl/hhBABUt+FEEK0RZsT5pY+8TRQFIUXXnihrZcQQgghhBAi7NqcMP/zn/8MjorRQNM08vLy0DSNlJQUYmJi2l1AIYQQQgghwqnNw8odOHCA/fv3N/k5ePAg1dXV/PnPfyYuLo5NmzaFsqxCBN17772sXLky3MUQQnQCqe9CiHBrc8J8IhaLhdtvv50LLriA22+/PdSnF0IIIYQQolOFPGFuMGLECLZs2dJRpxdCCCGEEKJTdNgoGR9++GFET1oiuo9Dhw7xwgsvsGfPHqxWK6effjpz5swJ9qF/9913eeeddygvL8dqtTJ69GjuuOMODMNg1apVbNy4kerqapxOJ5MmTeL6668Pc0RCiBOR+i7CKScnJ9xFEGHS5oS5YeDsHysrK2PLli18+eWX3H333W0umBCtUV1dzQMPPMCECRO45557qKqq4rHHHmPZsmXcd9995Obm8te//pXHH3+cXr16UVNTw969ewHYsWMHGzdu5NFHHyUlJYWKigqOHDkS5oiEECci9V2Ey4lm4hPRo80J8+LFi5vd7na76devH3/5y1/45S9/2dbTC9Eqn3/+OX6/nzlz5qCqKjabjZtuuokFCxZQWlqKyRTodXTw4EFSUlJwOp0MHToUCPS393q9HDx4EJfLRVxcHAMHDgxnOEKIFkh9F0KES5sTZl3XQ1kOIdqkqKiI1NRUVFUNbsvIyACgsLCQrKws7rrrLj744AOWL19ORkYGl112Geeccw5Dhw5lzpw5vPHGGzz66KP06dOHmTNnMmrUqHCFI4RogdR3IUS4RP1Mf6JrS05OprCwEE3Tgv9E8/PzAUhJSQHgzDPP5Mwzz0TTNLZt28Zjjz1G//796dGjB1OmTGHKlCn4fD7ee+89Hn74YV599VXpfy9EBJL6LsJl+/btHXp+6fIR+VqdMB88eLBNFzj11FPb9DwhWmPMmDG89NJLvPLKK1x99dVUV1fzwgsvMHbsWNxuN4cPHyY/P58hQ4bgcDiC/xhNJhM5OTl4vV4GDBiA1WrF4XCgKEqT1ishROSQ+i6ECJdWJ8y9e/c+bma/1tA07aSfI0RrOZ1OHnzwQV544QVuuOEGLBZL8K55AL/fz5o1azh48CCGYZCSksIdd9xBRkYGBQUFvPzyyxw5cgRFUcjMzOSee+7BZrOFNyghRLOkvotwy8rKCvk5ZeSNrkExDMNozYF//etf25Qw/9d//ddJP6cz5Obm8uyzzzJhwgQSEhLCXRwhRDuVlZWRnZ3NzTffTGZmZpN9Ut+F6D5aqusdpaFLRkclzNIlI/K1uoW54RO8EEIIIYQQ0aTDZvoTQgghhBCiO2j3KBlbt27lyy+/pLy8/Lih5hRF4YEHHmjvJYRolTVr1vD111+fcFKdH/vmm29YsmQJr7/+utz4I0QEWLZsGZqmceeddwJw2223MWPGDM4///xmj9+wYQNr1qzh+eefb9d1r7rqKh544AGGDRvWrvMIIbqvNifMJSUlTJs2jX//+98YhoGiKDR0h25Yl4RZtMa9997L4MGDufbaa9t1nquuuoqrrrqq1ccPGTKENWvWtOuaQoiO8/TTT4f0fJs2bWLlypW89NJLTbbL+4CIRKtXr+bpp59m586dVFRU4PP5MJsDaVtNTQ3XX389O3bsYO/evdx77708/PDDTZ5vGAaLFy/mueeeo7y8nNGjR7N8+fLgZD7i5LS5S8bChQvZtWsXq1atYt++fRiGwfr168nJyeGWW27y9gVPAAAgAElEQVRh5MiR5ObmhrKsIkr5fL5wF0EIIYToVG63m/nz57Ns2bLj9imKwllnncWzzz7L2LFjm33+0qVLefHFF1m/fj1FRUWMHz+eqVOnUllZ2dFF75ba3ML8/vvvM2/ePGbOnElxcTEQGOuyf//+PP3008yYMYM77riD119/PWSFFd3P8uXL+c9//sO3337LO++8A8C8efNYuXIlV155JWvXrqWiooK//e1vvPfee3zwwQcUFhZis9kYOXIkN910Ey6XC4BVq1axc+dOHnnkESDQct2nTx/Ky8v54osvcDqdXHnllVx88cUA7N69m/vuu4+1a9eiqmrw+SNHjmTdunX4fD7Gjx/PLbfcEuyykZOTw1/+8heOHDlCjx49mDhxIi+++GKw7EJEs3Xr1vHmm2+yYsWK4KhKPp+PuXPnMn/+fPbv3092djalpaXExMRwxhlnMGfOnBMO7XbTTTdx1VVXccEFFwDw5Zdf8tJLL3H06FEGDBjAkCFDmhz/ySef8Pe//538/HxMJhODBg3ipptuIj09nW+++Ybly5fj9/uD30TNnz+fc889l0svvZQHH3yQkSNHAoEpuFetWkVeXh4JCQlMnTqVyy67LDj19qWXXsrNN9/Mli1bOHDgAGlpadx6660MHjy4Q15XEZ2mTp0KwObNm4/bZ7fbWbBgQXC9OcuXL+euu+4KdjV66KGHeP7551m7di3XXXddxxS6G2tzC3NZWVnwzSo2NhagyaeWCy64gPXr17ezeKK7mz9/PoMHD2bGjBmsWbMm+NVoaWkp+/fv58knn+SVV14BAp+277nnHl5//XWWLl1Kbm4uK1asaPH8mzZtYsqUKaxatYqbbrqJZ599tsVvPnJycrDb7Tz//PM89thjbN26lY8++ggI/H0vWbKEsWPHsnLlSn7729+ybt26EL0SQnR9EydOpKysjK+//jq47bPPPsNkMjF27FgyMzN56KGHWL16NYsXL2b79u2sXr26VefOz8/n4YcfZtq0aaxatYprr72W999/v8kxDoeDX//616xcuZJnnnkGwzB4/PHHgUAXrPnz55OYmBh8rzn33HOPu86ePXv44x//yBVXXMFrr73GXXfdxdtvv80//vGPJsdt2LAh2Cg0fPjw4HWEiATl5eUcOHCgSeuz2Wxm1KhRfPXVV2EsWdfV5hbmzMzM4JSkNpuN1NRUdu7cyWWXXQYQHBxeiLb65S9/2aTl6ayzzgqup6amcsUVV/DUU0+1eI5x48YxYsSI4PNjY2PZu3fvCcfuTE5OZvr06QD06NGD4cOHk5OTw+TJk/n8889RVZWrrroKk8lEz549+fnPf86zzz7b3lCF6BacTifjx49nw4YNwVatDRs2MGnSJMxmM5MmTQoee+qppzJt2jQ2b97cqvH6t2zZQq9evbjwwgsBGDRoEJMmTWLbtm3BYxqPZetyubj66qtZsGAB1dXVrZ7+esOGDZx++umcffbZAPTv35/p06ezbt264P83gMsvvzz4PjJ16lT+8Y9/UFpaitvtbtV1RNfyWHxFYOVoB0yRHQ+t+9jYeh6PB+C4cefdbndwnzg5bU6YJ0yYwIcffsh9990HwMyZM3n00UdRVRVd11m2bFnw6wQhTlZCQsJxX9N++umnvPXWW+Tl5eH1ejEMg9raWjRNO+EoF0lJSU0e2+12ampqTnjdxMTEEx5fUlJCcnJy8GtZgLS0tJOKS4ju7oILLuCBBx6gsrKSqqoqdu/ezS233AIEumysW7eOwsJCNE3D7/cHu1T9lKKiouPq248ff/3116xevZpDhw5RW1sb3F5eXt7qhLmoqIhTTjmlybaMjAwKCwubbGv8XtHwXlVTUyMJs4gIDfWqrKysyfbS0lJ69OgRjiJ1eW1OmO+8804+/PBD6urqsNlsLF68mG+++SY4KsbEiRN58sknQ1ZQ0X01903Ej7cVFRXx6KOPsmDBAsaNG4fVamXbtm388Y9/7KxikpiYSFFREbquB5PmgoKCTru+EF3BoEGDSEtL4+OPP6a0tJSBAwfSo0cPvv32W1asWMGSJUsYPHgwZrOZt99+m7feeqtV501OTub7779vsq1x/fP5fDz00EPMnDmTe++9F6fTyd69e1mwYEGTEZxac52Gb08b5OXlkZKS0qpyChEJ4uPj6d27N59//jnjxo0DAlPH79ixQ/ovt1GbE2ZVVfntb38bfOx2u9m4cSNlZWWoqkpcXFxICii6P7fbzZEjR1o8pra2Fl3XcblcWK1WcnNzeeONNzqphAFjxozhueee44033mD69OkUFBTw7rvvdmoZhOgKpkyZwoYNG6ioqAgOF1lVVYXJZCI+Ph6z2czevXt57733Wn3OiRMnsnr1ajZs2MD555/Pnj17+Oijj4Ktu36/n7q6OmJjY3E6nRQXF/Paa681OUfD19Eej+eELduTJ0/mnnvu4dNPP+WMM87gwIEDrF27lssvv7yNr4boDhaWB3KajpoauzmapuHz+fB6vQDU1dXh9/uxWq2YTCbq6uowDANd19E0jdraWkwmE1arFQjcI7R06VLOO+88+vXrx8MPP4zFYgl2OxQnp803/Q0dOpQRI0bwhz/8ocmn/oSEBEmWxUm5/PLLOXLkCLNnz2b27NnNHtOzZ0+uv/56li1bxsyZM1m2bFmzN+x0pNjYWH7/+9/z2Wefcc011/D4448zefJkLBZLp5ZDiEh33nnncejQIWpqahg/fjwAo0aN4sILL+Tee+9l1qxZvPLKK5x33nmtPmd6ejr33nsv77zzDrNnz+bVV1/loosuCu53OBz86le/Ys2aNVx11VUsWbIkeO0Gw4cP54wzzuDWW29l9uzZfPzxx8dd57TTTuO///u/WbNmDVdffTWPPPIIl1xyCZdcckkbXw0h2ubVV1/F4XAEu7fGxsbicDjYsmULEPhbdTgcZGdn86c//QmHwxEcUQbgrrvuYs6cOUyePJmkpCSys7NZt25dcKAGcXIUo+G7qpO0YsUK1qxZw8cff4xhGIwcOZJZs2Zx1VVX0atXr1CXM+Ryc3N59tlnmTBhwnGd4oVorbfffpt169bxzDPPhLsoUa+srIzs7Gxuvvnm427qlPouRPfRUl3vKNu3B27266gW5sY3rIrI1OYW5nnz5rFp0yaOHDnCE088QUxMDHfffTd9+/Zl3LhxPPHEEzJxieh2du7cSWFhIYZhsGfPHt566y3OOeeccBdLCCGEEB2ozQlzg7S0NG6//Xa2bNnCwYMHefzxx1EUhTvvvLNLtDQLcTIOHz7MwoUL+cUvfsEjjzzCueeeyxVXXBHuYgkhhBCiA7X5pr/mZGRkMGTIEAYNGsTXX39NVVVVKE8vRNhNmzaNadOmhbsYQgghhOhE7U6YDcNg8+bN/O1vf2Pt2rUUFRXhdruZNWsWM2fODEUZhRBCCCGECJs2J8zZ2dmsWbOGN954g4KCAlwuF5dffjkzZ85k8uTJmM0hbbwWQgghhBAiLNqc1Z5zzjnExsZyySWXMHPmTC688MLg2H9dSWVlZbiLIIQIgdbUZanvQnR9Uo9FOLQ5Yf7f//1fpk2bht1uD2V5Oo3f7wfgq6++CnNJhBCh1FC3m9sm9V2I7qO5ut7RTjTJiOj+2pwwd/WRARq6jEyaNAm3293m8zTMalhRUYGmaaEqXkSLxphB4o70uEtLS/noo4+a7Q4WivreVV6HUJO4oyfurhJzS3W9o8g4ySLqOxoPGDCgXQOfG4aB3+/nlFNOQVGUEJbsp+m6Tn5+Punp6ZhM7R4hsNWiMWaQuCM97tzcXD766KMWj2lPfe8qr0OoSdzRE3dXibk1dV2IUOvcdyAhhBBCCCG6mKhvYRZCCCGEaEnD1NgdRbp8RD5pYRZCCCGEEKIF0sIshBBCiLAyNA0jgm80bJCVlRXyc8rIG12DJMxCCPEjha/9FTUmFnNSMubEJMxJSajuJOjkG8+E6K4Mv5/a/Xup/e5bar77DzV7crBcORt69Ah30YRoliTMQgjRiKHr6NXVeA8dxFdchL+0BOrHezXFxmJOTMaSlIQ5MRlzUlJ9Ql2fWCe4UVQ1zBF0D4ZhUPfDfgyvD9LTw10c0U6610vdvu+p+e7/qPnu/6j9PgfD58N2am/sAwcRd+75eBKSwl1MIU5IEmYhhGhEMZlI++X84GND19EqPPiKivAWFaCXlaKVlOAvKaZ2/178JcVo5WVgGKAomN3uQPKcmNw0mU5KwpKYjCkurtOH7OpKfIUFVHyaTcWn2fiO5oPJxNFRpxM/aTKOwUNRpJW/S9Draqn9fg813/6Hmpxvqdv7PYauYevdF8fAQSRMnYZ9wGmoTmfgeF2nIj8/zKUW4sQkYRZCiBYoJhPm+ARUVzzmU3thNpuPS3gNvx9/aSCJ9hcX4y8pxldShDcvl+pvduMvKUKvqgqcz2LBnJiEc+gI4iaci61X76hPoPWaaio//xeerVuo/e7/sPboieuc84k5YxxH//MN7PqK3P//ESxJybjOOY+4CedidsWHu9iiEb2mmpo931HzbaAFue7APlAU7H374ThtMImXTMfePwtTF50dWAhJmIUQop0UsxlLSiqWlNQTHqPX1gYS6pIifEePUvn5ZxxefA/WU07Fdfa5xI07G9Xl6sRSh5eh61R/s5uKrVuo+vJzTDY7sWeOJ3n29cEPEbquo/YbQNr4CeiVFVRkb6Z88yaK31xD7OixuCZNxjFwcNR/4AgHrbKSmpxvqa3vYlH3w34UiwV7vwHEDBtB0pWzsPcbgMlqDXdRu6zVq1fz9NNPs3PnTioqKvD5fMHZDfft28d1113Hd999h9frJSUlhTlz5nDfffcFJ34xDIPFixfz3HPPUV5ezujRo1m+fDlDhw4NZ1hdliTMQgjRCUx2O9bMHlgze8BQiD//AnwFR/Fs3ULZhvcpWvMaMSNH45pwDs5hI7ttX+i6w4eo2LqFim2foFVWEDNyNOm3/gbnsBEoLUx1bHbF4552GQkXXULNf76mfPNGch/7/7CkpuE653xcE85BjY3rxEgig+H34ysqxKirBRRQFFBosq4E15vuUxofBxgQmBLbYjnW9UVRAAVD89f3QQ7cpOc9fAjFZsPR/zRiRo8h+Zr/wt6nX4u/Q3Fy3G438+fPp6amhhtvvLHJvpSUFF588UX69++Pqqrs27ePadOmkZCQwK9+9SsAli5dyosvvsj69evp378/Dz74IFOnTuW7774jNjY2HCF1afKXLYQQYWJJTSNp+i9IvOwKav7vGzzZm8l/ehkmZwxxZ03ANeHcQILdxfk95VR+tpWKrdnU/bAfW78BJF46g9ix41BP8h+3YjLhHDoc59Dh+MtK8WzZTPnGdZT8/W/EjDmD+HPPx541sFu1OmtVlfgKjuIrLMBXcBR/4/WS4kD/+U5gcjixZw0k7qwJOE4bhK1Xn5B9sKvS/GidFEdXMXXqVAA2b9583L64uDhOO+204GNFUTCZTHz33XfBbcuXL+euu+5i2LBhADz00EM8//zzrF27luuuu65jC98NScIshBBhpphMOIcMwzlkGFpVFZX/3oYnezNlH/wDW9/+xJ19DkbvvuEu5knRvV6qd36JZ2s21bt3YE5wE3fWBNJu/RXW9MyQXMOc4Cbx0um4f34Z1V/vxPPRJo786UEsGZnEn3s+ceMnosZEfkuaoev4i4uCSbCv4Ch1B3/gcIUHf2EBenWg/7vqiseSmoolJS2QuJ59TrArkMnpDDQRGwZg1C8C6xjULw0MI7BsvC1QCANDN/D7faiqGmiIbnQuRVEwJ6eE/KZLwzD4pPIorxbtZarqZjoZIT1/dzdhwgS++OILamtr6dmzJ7fddhsA5eXlHDhwgLFjxwaPNZvNjBo1iq+++koS5jaQhFkIISKIGhND/KTJxE+ajPfIYTyffEzpW2+gVVdRcPoZuCaei2PgkIgcLcIwDGr37qFi6xYq/7UNQ9eIHXMmmXfdi+O0QR1WZsVkImb4KGKGj8JfWoJny0eUrXuX4v99ndix43Cdez72/llhbXXWa2vxFR6tT4gL8BUexV+/9BUVgqaBqmJJTsGckoYSE0vskKFYU9OxpKYFkuIOvmHOMAzw+5u9sbUjHPFW8WLhHnJqPUxPOJXTay0dfs22euxQ/U2mh452wNnjWd3GmbGzs7PRNI1//etfvPvuu6SmBu6j8Hg8ACQkJDQ53u12B/eJkyMJsxBCRChrj54kz7wG9/RfcCT7Y/RvdpH7+J8CrbVnn4OrvoUx3HxFhYGh4LZuwVdwFMfgoaRcO5eY0adjsnXuqAhmdyKJl12B++eXU71rB+WbN3LkD4ux9jgF17nnE3fWhOBQZqFg6Dp6ZSX+slL8ZSX4S0vRykrxl5biL6tfLylG85QDYIqJCSbAtt69iRlzRvCxOTEJxWRC13Xy8/NJSE8P3sDVnXh1jbWlB3mn9CDDnW6WnjqGFNVGvgwr1yaqqnLWWWfxySefcPPNN/P3v/8dV/0NxGVlZU2OLS0tpYdMDtMmkjALIUSEU8xmzIOGkD7pfIzKSiq2fYLnk82Uvv13HAMHEzfhXGJPH9spyanh96NVV6FXVgZbk2u+/Q/WzJ6BId/GnY3Zndjh5fgpiqoSM2o0MaNG4ysuwvPxPyl99y2K16wi9oxxxE+ajK1PvxO2pBqGgV5dhbfCg1ZWVp8Ql6LVJ8LBZLisNNA6DCg2G2Z3IuYEN2qCG2t6BuZBg1HdicGkuCt0EelIO6qKebFwD35D5zfpgxkTkxwcEUW0j8/nC/Zhjo+Pp3fv3nz++eeMGzcOAL/fz44dO6Q7RhtFZcKcl5fH7t27cTgcmM3mwNdQbdTw3Pacoz3Xbih/Z14/GmNuuHbjZWdfW+Ju+frmE9ydH6r6HimvgykujvgLLsI15UK8PxzA88lmila9TOGrLxE75kziJpzTqu4Hhs+HVlWJXlWJVhVIgJs8rqpEq6w8tqyuQqusxKitCZ7DFBtL3JnjSZp5DdZefYLXDNVrFKq/e3NiEonTf4H7kulU7fwKz+aNHH7oAaynnErcWRNAN5okwA3rhtdbfwIz5gQ3ZnciakIC5sREbH37BRNjs9sdmOXR7vjp170VcXTH9/YSfx2vFH3P51VFXBjfkysTe+EwmYPXC0Vd70gLTwl8Q5CVlRXyc+fk5DS7XdM0fD4f3vq/w7q6Ovx+P1arlU2bNhETE8Po0aNRVZXs7GyeeOIJ5syZE3z+/PnzWbp0Keeddx79+vXj4YcfxmKxMH369JDHEA2iMmFesWIFS5Ys4f7778ftduOvn/a2PbT6FobO5na70XU9LJ/OozFmkLg7W2vjdrvdzW4PdX2PpNdB7XkK7lnXkXDFLKp3fknl1i3k/nEJ5tQ0nD8bA4aBXlWJXlUVTIb1+mQ4mAwCKAomZwymmIafWEzOGNTYOMxp6YHH9dvVhv31y4Z+yR31uoT6794+YhT2EaPwFRZQmb2Z8s3/xOSwo8a7URMSsGf2QE1wo8YnBJYJCYE4fyIR1iHY0hwK3eW9XTMMNlbk8UbZD/S0xPBgxkh6WWNAB7/etC62t653N6+++ipz584NPm4YCu6jjz6ioqKCO++8k3379qGqKj169ODXv/41d999d/D4u+66i4qKCiZPnozH4+H0009n3bp1MqRcGylGOJpLwiwvL49du3axY8cO5s6dS0pKSpvPZRgGmqYF7iru5BtKdF2nuLiYpKSkTu3nFo0xg8Qd6XEXFhayfPlybr75ZjIzj43CEKr63lVeB39pCRWfZlP99S5MNluTJFeNiQ0sY2MwxcShxtYnvg7nCW/I6ypxh1o0xh3qmL+v9fB84R6K/LXMTurDpLgMTCc4byjqekfavn070HEtzKNHt/GuP9FporKFOSMjA8Mw2LZtG36/PyRvDIqidPqbqqIowfKH4+7vaIy5oQwSd+dds7Vxn6jlONT1PdJfB0tiEok/v5zEn18e8jJEctwdWYZoi7u9163UfKwu3s8mTy4T4tK4N3M48eaWZ/zTasvw1VWhKMltrutCdKSoTJiFEEIIEVoNYyqvLNpLjMnC/ZkjGOI8vvuEoet4yw9SW5RDTVEOtUU5+KsKsA66Hnr0CkPJhfhpkjALIYQQol0aj6k8w92LS9ynYFbq+7d7q6kt3kNtfXJcW7wHw1+LJS4de1IW7kGXYkvKori6+w2hJ7oPSZiFEEII0SaBMZV/4J3SQwx3unnslNNJqvNQfeCTYILsLT+EYjJjS+yLPTmL+AFTcSRnodpdwfPouo5SI+Mwi8glCbMQQgjRBflry6jO20VN/i50fx2q3YVqc9Uv41Ftcaj2+MBjaxyKSQ3p9b+qKualwhx8mo8btWoG7v2Cmn89zQ915aj2BOzJWcT1OQdHchY2dx8UVVIO0XXJX68QQgjRBRian5qi76jO30l13i68ZQdQbfE404dhiU1Fqy2ntmovep0Hf60Hra4c9IYh4hRM1tgmSbXZFo/JFoe5Pqk2WePAHIMSk4hqi0VRju8i4a8tI6/wW1ZVlbBTtTOu4BvOy/2cuLgMLMmnETfqOuzJWZhjUsI6FbkQoSYJsxBCCBGhfBX5VOfvoipvJzUF32BoPuzJWcSeeibOsfOwuXs1m9hC/WyFvhq0unK0Wg9anQettjywrPOg1Xrweo6g1VUEjqnzQMNIs4op0EJd31JtsjipKT/EJ84UNvYYS4amcbfmoX+/c7GPvRGTJXTTjUeyE00yIro/SZiFEEKICKH7aqk6uhvvvs84+OX3+CuPYo5JxZkxgrQzb8eZNrhJcqobBh978jjirUZVFFQUTPVLVQmsm1BQrbGYrHGorp7NHqOioBgGel01ql6H4qsGXxWKtwqjroJyzcs7GT+jRFG5Pqkf58VnnnBM5e5IxkkWkjALIYQQYWIYBt6yH+q7Weykpug7FMWMEt8P94ALickciSU2vdnuDXtrPbxQuIc8XzWD7QnoGGiGgYaBXr/UDCO4Pbi/uW0Nz6lfD85opljAngjAxLg07k/q95NjKp9s/N+X1rHtcAWDnTrpITuzEKElCbMQQoiIYxgGVUe24y8pwh9vwxqTFO4ihYxW66E6f1cgSc7fhVZbjjWhF8704biHzMCWOICjhcXEp6c3O+tdheZjdfE+/unJY3xcGr/LGEqC2dbuchmGgd/vx2w2YwB6o8QbwGEKTcqgGwbfl9by2ZFK/pVbRUmNn4FJdvrbo27iYdGFSMIshBAiovhrSin4/Dlqju7GQOXg/72M2ZmMPak/9uQs7EkDsLl7o6iWcBe1VQzdT23RnkCSnLeTutL9mKyxONOHkTTiapzpwzE7jk3woet6s+fRDYPNnjxeL95HvNnK73uMZJAjoUPK3NCVgxD1utANgz0ltXyWG0iSS2v8DEp2cNkAN2MzY3BZTeTnR+6wcg1TY3cU6fIR+SRhFkIIEREMw6Dyh60UfvkS1rhMekz5I8VVkBRj4C35ntqiPXj2fUTRV6+CScXm7oM9eQD2pMCP2ZkU1pEZDEPHX1WE13MEX0UeXk8uXs8RvGUH0P112JMGENPzdFJOvzEwzFozrccnsr+2ghcKczjsreYXib2ZmtAjODFIpAomyUcq+VduJaW1GoOTHVye5WZsRgwJ9mMpyIk+JAgRKSRhFkIIEXb+2jIKP3+B6rwdJA6/ioSsaRiAUp2P1ZWOPeEUXH0nAaD7qqkt3ls/e9weKvZvQfdWojrcgeQ5eQD2pMDYv6YQ9rdtoPtrGyXEufg8R/BW5OKryMPQfCiqFUtcJlZXJs60ISScdhGO1CGo1piTvlal5mNNyX4+LM9lXGwqv80YSmIIul90FN0w+K64ln/lBpLksvokefppiYzNjCHe1rXTjqysrJCfU0be6Bq69l+uEEKILq/i4KcUfvESlthUTpn6J6zxPQAwTtDqaLI4caYPw5k+LHCcYeCryAsk0MV7qDjwCcU7V4FiwpbQu2krdCvHBzYMA39NKd7qo/gr8vBWNCTHufiriwBQ7W6srkwsrkxcKZOwxmVicfXA7EwMDvVW482jqOITSir2Yhg6hqEBgaWB1mjZsE3HMPzU1NSw+VAGG319cCg+rrZ8S29fMYdzNQ41PC94vIbJZMVicmFRXZjVuPrljx8HlqrJHoLf2v9j783j5Kjr/P9nXV1933PPZCbJJJlkcpKbQ44giCIKKugiyLGKoOsBeKzHbzl011XcVVfjV2FhXXTFE+RQkJBAICQkQO5rcs0kkzm7p6fvo67fHz2ZJCSQg8lBqOfj0Y9P1aeqPvX5VHd1v+rd78/7vZ99InnF3gwru8siubXCxUcmhJl9BohkGxuwBbONjY2NzSnCKKTof+1BMntfJTL5YwRbLj+ubHSCIODwly26/tHnA+XwbIWBA6zQHcswi2kkZwBnZDzOaDPOyHgcwVEY+cSw+4Q2JIxLqS4sPQ+ihOKtxuGvxRkaja/x3LJI9tUiOd469nAqv4mt3T9ElStwyEEEJBBEBEFCQBoqRURBRRDLy4Ig0aU7+LPlp0dzcokrz3tcOoowAUEQ9x93wDKImFYRzUihGyk0I02utOeA9RSmVRzulyg4DhHTiuRHFn2IeFAdwYMEtyS6D3nIMC2LLftEcleGVMmgNeriIy1h5tR48asjm1XQxuZUYwtmGxsbG5uTTmbPK/S/+t/I7ggNl/wbarBhRNsXFSfuqlbcVa3AkMU400shvp1CvI3M7hXE1/0OrLIVW3R4cPjrUHy1eBvmofhqEN1VOAM1iMeR0rk//SI7e/8f1cH3MSpy7ZsmFzmQnKHzh4FdPJPZyxTJy1frJlFxBFF+tJhmCW1IPOvDZRrNLJcFrRfN2Iamp9DNNIaZHT5WQEKW/AiCi725RrYmmtkyOIqcpjI6MMCF9QNMiabxqzKioJIvOCmWVCTBiSiqSIKKKPo1dpAAACAASURBVDqRRBVRcCKJTkTBcVTXxMbmdMEWzDY2NjZvwLIsO63vCcIopul/7SEye14h3HoVoUkfQhihcGVvhSAIKL5qFF81vqZzATD1Ilq6C9kVQVR9B73n+0KsHavF27Is9ib+TOfAn2iquIHqwCVHdcyyTB+/ju3AKUp8pXoyVakSEXnkXCdE0YEqRlGV6Fv2Y39YOaMsqI0UPZk0f99l8nqfSrYk0hzKcGlTL5NCPTjlLKZZxDALpPLl0rSGSrOIYRWwLO3wfRLU/YJacOITPwh2JGab0xRbMNvY2NgcgGWatD/xORy+2mHfVzXSjKT6T3XX3vFkOl+l/9X7kZxBGi75Lmqo6ZT2R5RV1NDoEWvPtHR29d1PPLOCCTV3EvKcdcRj9hQzPNi/jR3FNFeGGrk81IBkQU/q1IZYEwWZZMnLo1s1XtitMTqo8olJAWbXevA5jvUhwnyDiC5imgUMqzAstg2zQCkTPkGjeWfyyCOP8LOf/Yy1a9eSTqfRNA1Z3i/bBEHA6XQiSfvfj+XLlzNlyn7f/rvuuov777+fZDLJzJkzWbhwIZMnTz7pYzkTsAWzjY2NzUGYVM66mUKs7Ps62PY3LL2I7KnEER6LKzoeV3QcarDxHRMH+FRjlDLEXv8V6Y5lhCZdSXjSlQjH4eZwOqMbWdp6/oN8qYvWurvwOEdjWAb/G3uSl9JrGKvW0+JqYoKziXHOURgI/HGgnb8NdjLTE+W+UbOpVFwAmNapDbEWy2n8ZdsgSzpSjA6qfGVeDdMqD/VjPloEQUQSXEii6033MU2TntzpG4f5VBAKhbjtttvI5/PcfPPNh93niSee4OKLLz7stvvuu48HH3yQZ555hubmZu655x4uvfRStm7ditfrPZFdPyM5s76xbGxsbN4mgijjqZuFp24WULY4l1KdFGJt5GJtpHYsIr76f/fHAY40ly3R4eajjsDwbiLb9Tp9K+9HUr3Uv/c7OMNjTnWXRpyi1s+W7n9HQGRy/b2oSpSCWeK/en5LW2E310YuY6/Wz2vZLfxx4DksqhGZiCpKfDgU5v3BMXilNxeTJ4tYTuPRrQO8sCdDU0Dlznk1TH8bQvlosSyLtkIH0il+UDjduPTSSwF4/vnnj+v4hQsXcueddw5bnO+9914eeOABHn30Ua677rqR6ua7Blsw29jY2LwFgiiiBkfhCDTgbjwfWZYxtTzFfREY4ttId7x0QASG/W4czvBYRGVkQ3i9UzBKWWKrHybdvpTQxCsIt37kjLTIZwo72Nr9fdyOUYyr+TKy6CapZ/h+96/ImDnurb+VakfZb3hvKct/97exNZ9kgssAaxdPDS7hzwmNBkcVLc7RtLiaGK82ntQxxPM6j7UNsLg9xSi/gzvmVjOjynPChXLJ1FieWcczyZfZVeziOsf7GMvIuci8G/jkJz+Jpmk0NjZy66238ulPfxqAZDJJe3s7c+bMGd5XlmVmzJjB6tWrbcF8HJwRgnnp0qX89a9/ZdeuXeTzeR599NGDfHpsbGxsRhLJcWgc4P0RGLaR6VxJfP3vARNHoGFIRDfjjIxD8dee8dEBst1r6V/5CwTFRf3F9+CMNJ/qLp0QBrKvsr3nv4j6zqGp4iZEQaanFOPfuh/CK7q5t/5W/JKXgmnw54F2nhrsZKo7xA8b51I95H6hWwbtxS625tvZUtjF//Q/QdrMEhb8TOobQ4trNBNdo6lVDv/vRbFk8oOF3UiiwPVXR6mrPvpELQN5ncfaEizuSDLKr3L7nGqmRBwoinJCxXJcT7IouYLnUisxLJOL/LP5YuU/YMSLRz74FPGD+wNDS70noPUAjxxHZuxFixZx9tlnI0kSixYt4tprr0XXdW699VZSqRQAweDBqdNDodDwNptj44wQzF6vl8suu4xSqcR//dd/neru2NjYvMs4fASGEsXBXRRi2ynGtzGw4U/ouRii4kINN6N4KpCcfiTVj+QMDJVD66rvpESOGGlMLUds9a9J7VpCcMLlhKd8DFEa+Ux7pwPdg3+jI/YwDeGrqQ19CEEQ2FbYzfe7fsV45yi+UP0JLEQWJbt4NNGBAHy5ppWZb0jfLQsSzc4Gmp0NfIDzsCyLzmIvK/vW0U2CvySe54H+R/GJHlpcjUxwNtHiGk2TWgumyI/v76EvplFX7eCr9+zm/QuCXPWBMC7nmz+UDeR1/tKW4LmOJA0+lS/PruGs6nL4Ol3XT8j1siyLrYUOnk4uY2VmI/WOSq4JX8q5vumoogOjqNGjnwgxeuayYMGC4eX3v//9fPGLX+Thhx/m1ltvxe8vT1IeHBw86JhEIkFdXd1J7eeZwjvvG/kwnHVWeSby+vXrT3FPbGxsbMqIsgNXdAKu6IThOj0/QCG+nWJ8B3p+gOLALoxiEqOQQi+mwNwvVkSHB0kNIKk+RNVPyZQZiFUjOwNDwvoAke3wIYin1mqd61lP38pfIEgydQvuOmjcZxKWZdIRe5je5LM0V32eqO9sAF7NbuInPb/lfN9MLgks4LfxDl5I9+AQRC4J1HF5sAH1KELUCYJAnaOS+fJkqiurEUWRuJ5kS34XW/LtLE2/zm/if0NBxv33S8nvquDmL0jMra9lwwaNX/2+n5dWprn2I1HOme09SJwP5HUe35bgufYUdT4HX5pdw8zq/T7KlmWN+PUqmRrLMmt4ZvBlOko9zPZM4lt1/8hE52gwLfIb9tL78g6yK3cif3Qq1NeOeB/eLYiiOPweBgIBmpqaWLVqFfPnzwfKD0Nr1qyx3TGOkzNCMNvY2Ni8E5BdYbz1c/DWzzlkm2VZWHoevZDCKKYwhsskeiFFcbCXYnw7uQO2YRlDRwuIqnfIOn2ApdrhRVK9iA4vksNbFuGO/esjEanC1ArE1v6G1I5FBMZfRmTKNYiy+rbbPR0xzALbe39KOr+FiXXfxO+aCMDfkyt4qP9xzvVdQo8W4it7XmO808/NFeOZ661AeZsuOBE5wDm+6Zzjmw5Axsjxiz/sYfVWqL1uFb8obeYXO6E5XM+8z48l+cpofvlwH8+9mOSGayrwRyUeb0uwqD1FrVfhC7OqmFVzYn2UY9ogzyZXsDi1Egu4yD+bO2quJyoFKWzrIfbyMjIrdmBkinimjyJ607mk605ff/+vfDoJwPjx40e87ba2tsPWG4aBpmmUSiUAisUiuq7jcDhYs2YNlmUxZcoURFFk8eLF/OhHP+Kuu+4aPv62227jvvvu46KLLmLs2LF85zvfQVEUrrzyyhEfw7uBd5Vg7u7upru7G4D+/n4KhQJQDmdzvFiWhWmamKZ50mfH7+v32+n/8fBuHDPY436nj1tRlLfV1km5DpIT2eNE9lQeVG2aJnpfH5WVlYhDlmTLsjC1LEYxjVlIYhTTZYF9gKDW0j2YpSxGKYOpZTFLOWC/FVGQ1UPFtOIZqiuXosODJblQXH5k1Yfo8CBIKoIgkO/bRP+qX4AgUnPBt3FVtAz3dyQ40Z97y7IodpRw1DkQFeGQbQe+3yV9kG2996EbWSbW3o3LUYNu6Dwcf5a/pzrxiJfySkbgbK/Kv9adRZM6FLbLOvYwcUca95LnCqx+SeJrn6uhdcI4SqbGtuJuNud3samwk+2tL2DWe9j9woV87bt5hEaonabw+ZmVzKx2IwpC+QHtDRblt/sZtyyLLYVdPJ1czqu5TYxyVPPx8Ps42zMV9qTIPL2ZjhU70eNZXK21hK6ZjWfWaCSvimmaZPr6Tsl33OnKww8/zI033ji8vi8U3JIlS0in03z1q19lz549yLJMY2Mj//qv/8pnP/vZ4f3vvPNO0uk0F198MalUilmzZvH000/bIeWOE8E6Ef/BnCLWr1/PN7/5zTed9HfXXXdx9913D6/ffvvt3HHHHSezizY2NieQX/7yl3zmM5+htvbgv3W7urpOUY9OLyzLBD2PpeewtBzoucMsl7czVG/pOdBywAFCRpBAdoOWQa49B2X0BxCkd55VubhUo/iYBi5QpkooZ8lIY0UE8WCxqJk99BXvRxICVKg3IQledhpZ/q+0hQHLgx+JC5QIs+UAHuHETjhftVbnd4+XuP6jDqZOPLzNK17SeLIrxfp+FTGZR98IlESiF2xj+lSLZrmeJrEahzAyUUtKlsZrxlZe0tbSaw0wRRrLefI0GmN+jNV7MdZ0YfVlEEeHkabXIU+vQfC9PWvym93rJ4rXXnsNOHEW5pkzj2PWn81J5V1lYb7lllu44oorgLKFecmSJQBUVx9/Ks4DU4meCutb3xusTieDd+OYwR736T7unp63Tnrw0EMPceONNx73/f5OuQ4jjWVZaJqGhD5kpc5iljIYpQyyO3pCI2CcyHFnXsuSeryXqs9UICgC6ZczZH+ZQw5I+OZ78Z7tQaoTyett7O37KQH3FOojt/BKfpCnk510lLIo6NwYaWCBfwziCH4m3mzcq9fn+P0TPdz4iSgLzvWjDehk1+TIrc1hFi2ssMQ2NF7VNOSIly9MCzJzvI+iofN/i/bwwt8n89K6NEsufAGq4zQ765noHMMk12jGqaNwCMoxfcb7tQTPplawJP0qIgIXBeZwUakVZVU/mZd3UOyI42iK4F/QimfeGJQK3zGP+XAc6V63sTkRnBGC2TAMDMMYnt2raRqGYSDL8kE3Xk1NDTU1NUDZ4rR8+XKAt/VFbFkWoigiiuIpS1iw7/wni3fjmMEe9zt93JqmDbd1PJwp1+FYsSwLSZKQZRXBefL+yrUsg1RhPQUjC0IEcQSjhhR2Fuj+aR/Rj4UJXVQOFxY8z4+eMkivyJBalibxVBKxWiPXuhzH2R9klX8KP+h8HQMLiS5qlBjfqvskETlwhLMdPwe+31t35PnJ/T1cO8fH9G6Djm90UmwvIYdllKlOdioaA7vyRPMCVxRExLQBj8fZKceRIwrvi7q5pFliY4+TdQ9eQcVk8F3UyZbQVp4YXApAs7OBFrWRye5mxrsaUcVDI5xYlsXG/A6eSb7Mq9nNNKo1fMpxCa3r3eSX7yTd9hRKbRDf2c14v/heHLXBQ9o42jHb2JxOnBGC+fnnn+fHP/7x8PrVV18NwHe/+93hDDc2NjY2Nqc/JX2AvtRi+pKL0cw0lmUQa5fxOSfgd03C72rF4xyDKBzfz5cW0+j8QTf++V4iHw4dtE32S4QuCRB8r5/2tr+w66UcnnXnEXjOQcMonX+YW8ET4/5KKOLhjuqbcUsnfpKakTXY/XyKDX+Mc6choDyTJjfOiTrLw54rPLxo5tkYz1DrdfCRj1Qxt9aLKAiYmoUe19D69fIrpqHFdCarImMcEtZKA3FlPVOFeuSwhB4xGAym2evr52nfMn4XfIZAlY/Gmhom+sfQqNawMrOBp5Mv01Xq5xyxlbt3fADPqgT5jetIRzx45zdTcdN5OBojdsZLmzOOM0IwL1iw4KB4hDY2NjY27xwsy2Qwt5725BK6cm3oUj2y+8MY8hgKmSwRT56cvpu+9EYYeAwn4He3EHC1lgW02oRwFL7DRs6k8/vdOGodVP9j5WFFXVrP82j3Y7wseUifX8u898Ml6RCRlwfI/j3HP/7pcrxT3GjnlDBmO5BcI2sNtSyLYmeJ4lKNPdu7yLcVKADhCpnKD4Voq7F4eTDHur4E3qTE3FovV7VEmBBxHuQWIioCjmoHjjdJZKKXTJb8LcHSZwYZ5RI4r8FHk+mjtqeC6RvGo8V1hKEoh2lfjleCa8gFslwtTiI6mMfs7EP07MAxfyzhj83GOa7qEN9vG5sziTNCMNvY2NjYnH5YlkXO1EkaGoNGiaReKpdGiUG9xKCeI1ZKkDSKZFExmQ5MRzIEAjkFvzRIQS+RTVlkzEosKoFzkQFPXsedz6BaL+FhMUHZQ9QRodJZS4VaS0BWCUgO/JJSjgphWOz5UTfFoknPjX5e2BynM10iltdo9KtUBUX2Kv28rvXgEgIs8NdysX8cEdXNMu8afn7uH7n8/efxwdh5ZJZl6f1VjJ4H+vHO8uA/x4d3mhtBPj7BaJZMchvzZFbnyLyeRY/piNUCrukuHstqDEREKt6j8GAshrpTYE6tl6+fXcukiAvpOEWq7BB574cizL4wwCOPxbl7eZrz5vq4+rYokbAKFhT3Zslv6MO3LUuwow9hUwwQMR1VGExDT4eILxJJrSmgVHShVCoolTKOSqW8XCEjBSTb2mxzRmALZhsbGxubY6ZgGrQX02UxfIAQPlAUJ40S2lAgJgEISA4CkgOvoOEwepD13YwTodbTTIN3PFE1TEBy4BHlsluBadLT00N1dTWWAGlDI2loJI0SqaEyoWWIl2LEtDQ78nmyuXayZj9mQYWiAkUFueDgfUsVJnaIPPBeKLT3E/SIVHplGoMCG+IJXm4TQFNR5SpaKnyExQBpCV7MPs8fEs9yQ8UHuSQwHyrBO8lD1Y0VZNbkSC1L0/WjHgRVwD/Pi/8cH64JziNaW7WYVhbIq7PkNuTBAvdkF5ErQihTVJZ09/LYnzPkRBNni8lop5M75lYzucKNPIKW3KBf5pbrKlkwRWbRIzt5/KUEMyJFgpkkRjwDsojaECYwIYrnEzNwT2tAUCQs00If0NH6dEp9GlqfhtZXnoQ42KehJ8oxwgVVQKk4QEhXKChV8lCdMuIWehubE4UtmG1sbGxsjpqiafBssovHB3eTNjT8kjIshAOygxqHmxYpQFAu1wWHtrkEjYHMMvqSi8gV9xByn0VlxcUE3dMQjiKxhySIBGWV4AFJUYq6yd5Mic58HXuyJTpTJfakCqTz5RB4XiWDV40xa4uTGTsrWXVjP5NbnBRFhaSh0WeUKJgGM5sVmvK/JyDUURSvY8uAwVM7BunN6iBFGR/5FJZYxR6hSJ3PgSgICLKAb5YH3ywPRt4ksypDalmG3ffsRY7I+M/24j/Xh3NUub+WYZHfViCzOkt2dY7i7hJyVMY7w03dl6pxTHSyPlngL50ZXl3VT3ElqJrArZ+uZP5oH4o0MiLZMi20niTF9hjFXbFy2R5DSBe4xKmQCwbYGHOTCYzm7FsbmXhODYJ8qLuLIAooUQUlquCe5Dpku1kyy77TfdpwWerTyG0poPVrmNnyeyT5RJRKBblCxppvwvEHrTopvFmSEZszH1sw29jY2NgckZJp8ly6m78kOhAQuCrUyEX+GhxHSPecLe6id2ARsfRLSKKLSv+FTKj5KqoSPfpzGybdKY3OdInOVJHOdIk9qRL9OR0LCLtkGnwO6n0O5tR6qR9adikiiRU99L6cRv7UBqY1P08x34siBcsTCH2TEASZXf0/p8J3PqMrbkQQJOY1lPhJz9/IpPu4TP4oAykPT+9M8tC6GD6HyMSIi4lRF5OiLhr8ZT/mwHv8BN7jRx/USa3IkHopzcDjg6gNDhx1DrIbcphZE9d4J/5zfHg+70auU9gYL/BYZ5pVi3vRTIvplS4aO1S6i0Xu/Wo9VRXHH9/a0g1KexLDorjYHqPYEccqaEh+J46mKOroKP6LWlCbosiVfgzToLEg8PsnBrj3kRTzt/Vz7UeiRELHJhdEh4ha50CtO7wPtZE1DrJOl/o0dMk47L6nA3acZBtbMNvY2NjYvCmaZbIo3c0TyU5MLK4IjeK9/tq3FMqmWSKeWU5v8lkyxe34XZMZW3UrIc+sI0a3MC2LjmSJzfE8W2J5dg7kiRfbsYCQUyqLYb+DmdUe6v1lYexWDt+X/I4CfQuzVFwTJXLplcCVFLUYqfwmUvmN7E08RkmPMyryD9QEL0cQBJJ6hu93/4qMkeXuhuupc1UN++AO5HW2xPNsiuV5dleSX62P4VVEJkZdTIyUBfSogIPw+4KE3xek1KORWpZG69OovrECzzQ3gkdkcyzP8r1JXtmQIa+bTK/0cNO0Cs6qcvO7Pw+wYWeB2653UhE5+sQiZkGj2BGjuCtOsT1GqT1Gcc8AGCZy1Is6Oop7aj2hK2agjo4ghQ5NjW1ZFpjg80r84z9UsuBcPw/+tp/b7+rgQ5eGqKt2YBgWumGhG2AYVnldtzAMhuoPrnvj/uX68jZNN9EMA80w0TSDD0yEuqMesY3NycUWzDY2NjY2h6BbJs+neng00UHRNLgiOIpLgnU430Io50td9CYX0Z9+AYBK/wWMrboNl+PNs7FphsWOwQKbY3m2xAu0DRTI6yZ1PoUJYScX1ihMqovSEFDxOo4+i95w+LhzfYSv2B8LWFWiVCjvocL/HgAMs4gklq24PaUY/9b9EF7RzT31t+K2Dg4bF3bJnF3v4+z6cgKOwYLO5iEBvbgjxf9uiOFRRFoiLiZFnUyMumi6qhy6bttAgeU7B1ixN0O6ZDCl0s0nJ0eZXeMZFvyP/m2A515K8c//VEPQO3jEMZq5EumXt5Naspnizn5AQKkNojZF8J7TTGR0BWpTBMl7fOHvRo9ycvdX6nnxlTR/emqAYtFCkkCSQJSsoZeJIA69JANEA0E0QNJB1LEEDUvSsYQSpljCUkqYaglTKGKJ2v5jJIOiZyow5rj6eqLZl+nvRGFbsE9/bMF8lBiZDIIkIboO9dWysbGxOVPQLZMX0738eaCDnKlzebCBBZ5KfA7nYaMdmJZOIvsqvclnSeU34lXH0RT9FBHvPMTDJL7IaSbbBvJsjhfYEs+zI1HEsCyaAiotEScLmvy0RFz4VWn/pL+I85iSWRg5k85/70ZtcFB9U8VbRmnYJ5a3FXbz/a5fMd45ii9Uf2I4691bEXTKzK/zMb+uLKCTRZ3NsQKbYnme353m4Q1x3LKIKgsMFgxaK1xcPTHM7FovvjeI/8UvJfnDEwPc8dkaWpqdvFkyO8uyKG7vI/ncZjLLt2GpItl5TlIf9lCoEzCUIiadmFYHJgZm1sTMGpiWgYlZLi2zvM0ysDAPWjf21Q3va2DWmIy62aBkFihZ+UP6JAsKDtGFuu8lHLAsug7eJgYOu82BykDvkR8SbGxOFbZgPkr6HvwFVqlEzZe+giDbl83GxubMwrBMlqX7+FOinbSh8YFgA5cF6nGJ0kHC0bR0ND1BUY+RzK2jL7UEw8wT9Z1HY/Q6PGrTQe0mizpb4/styO3JIrIo0BxSmRhxcdWEMONCTlzKyERLsHSLrh91Y1lQ96Xqowr19mp2Ez/p+S3n+2ZyQ8UHkQSp7J5wjARUmXl1XubVlTMipooGW+J5MiWDs6o9BJ2H/+1YtSbDA//Xz2c+WcnMqR5M0zxou2Hp9Cd2M7B0PSztRek26BuXZstVu+luSRJ11+GTQoiCiKhLiIKIgIgoSIhIyIKCKDiH1ofqBRER6eA6RDBBlpShfaShfcr7OkTnQWJ3n+CVjzOJzIGYpokgpN52Oyea8ePHj3ib9kTCdwa28jtKop+4js57v03frx6g8qZb7LiSNjZnMLqlj4gIeCdgWhYvZ/r400A7g3qJSwNVLPA4kM0BUpk2+rUYhVI/mjlASe+npCcACxBwOxqpC19F1HcusujGsiz6shpb4mVxvCWepyuj4ZZFJkSczKvzcsPUKGOCzhGL+nAglmXR+z/9FDpKNN5bj+R5axcO0zJ5NvkKv4o9wTWRS7gieP6Ifrf7VYk5tW+dTnxTW56fPNDLxz8U4YKz/eSMNN2FdrZrG3ilN0GxrZfIcomGDREMt0F8roF1S5RI7RQ+6PgEEUcN0gh9Vi3LQtd1ZFm2f+NsbN7Au+MXYQRQKiqpuf3r7P3e3cjhCJErP3aqu2RjY3MC0MwS/9n+ORpcE2j1zmOCZyaqeGa4YlmWiWYMUtRi5LV+Xs0leTonkzAlZou7mM5rOAYT7BgEUVBRlQocUgRFCuNxTkdVoqhy+aXIIUBib7rEko4Cm+M9bIkXGMjrBFWJloiLS8YEmBgpR5IQT4IAG3hykOQLaUb9Sx2OyjefMLe31MfS9OssS69hUE9za9XHOM8344T370BMy2Rt+15+tDDPhLl9pCb/kR/u6iBtDODKqkxaM5Zxr1Wg9tcgTAsR/OIUIme1IEinJm5xdlBHUUUcdtxkm3cptmA+BpxNo6n+3Jfp/tH3kUNhAhfY6bhtbM40REHiqqrPszG7gr/2P8Rf+n7BOPd0JnnmMkadiozvVHfxECzLRDez6EYa3UijGWk0Y5CSHqeoxyhqMUp6nJIex8RgB02sYB6D+JirxLjIaxBx1OOQ94tiSSxHUTic1TFTMnhkwwAv7k6T0UyqPAotESdXt4RpiTip8ign3UKZeiVD/2/j1H6xGlfzoZPcknqGlzNreTG9mp3FTsaq9VwePI+zfdPwS29tBX675I0MvaXd9BQ76C3tprfYQWdfmh3/dxOBsR00XLiRCqWBaZ1T8L2sob/Wi+BTCV7cSuDCFuTwie3fW/Y9bbDst3E2LEmBBS6/RKBSJlClEKhUCFQpBKvKpSco2emxbc5YbMF8jHimTKPyhk/T99AvkYMh3NNOrlXCxsbmxCIJEs2eaTR7pnF5xc3szG1gY2YFf409hG6VaHZPp9U7n/Ges1DF44s+8FZYloVpFdCGxW8KTU+S0rrQBsAwM8Pb9m3XzQxlN4kysuhFlnyochSHEiXgnowiRdhq+HkqU6RL03ivv5YrQg0HJQI5Erpp8eyuJH/cMkBAlfjU1Apaoy7CrlP7U5LfXqD7p71UfCKCf+5+cVkyNV7LbmZp+nXW5toIy37O9c3gc1VXU+eoPKZzWJaFZpXImxnyxtDLzA6VmeGyYGSH1rPD9SWrgIBI1FFLtaORJusc1vx5HJOaVO74xDjyyxpJLd6MFutEndlE9I5LSVbIhGprjmmy40himRYblqRY9ts4npDMVd+oxekVGezVSfZqJHs1urcV2PJSmnRMx7JAUoRDxHSgUiFYreCvkJEdtnXa5p2LLZiPA/95F6AnBuhZ+GNqv/Zt5FGNp7pLNjY2JwBJkBnnmc44z3R082a2ZdayNb+KJ/sfwOjTGOeeQat3HuOOQjzrRpaiHqOkmHS63AAAIABJREFUxyjqcTQ9iW4OCd4DLMO6kcZi/yQ7UXAiSz4wnVjFMIrsR5Ur8KpjkCUfiuRHlnzlZdGHLHkRhP2+u5ZlsSY3wB8G2tldTHNxoJav1Y4ifAxC2bIsXuvJ8puNcTIlg6snhlnQFBjRFM3HS6mvHD4ucL6P8OVBTMtka6GdpenXWZFZD8A87xS+VfePtDibEA/IKmhYBnsKW8nog4eI3LyRIWekKZi5sjA2MxiWNnysgIhL8uISvbgkz1DpxSeHqJQahtddogevHCSq1KGIDnJ5k3t/2MkU+vlgYSedX9yNUuHFv2AivvdMQA66MU2T1JuFyTgJ9O4ssuTBfhJdJeZ9NMK0SwNIQ5MnK0cfur+hW6RjGslencFejVSfxmCvRufmPMleDa1QfpjzhqX9QvpAUV2loHpO5ghtbI4dWzAfJ6EPXokej9H9o+9T/fX/D7nWDrduY3MmIwkyza5ptPhmcjmfZmduPRszy3mi/wGMPp1mVyvNzmbq5DCmkaKkxcruEEMi2TDzQ+24cCgRFCmIMiR4XUodiuRDlvwHl6IXUXTsD69WXX3UFkfLslifT/CHeDs7i2ku8tdwe3UrUeXwwt60TIpmfvhVGio7kkWeaXPRmXQypa6fi+o7KIkZHuvJ4xH9zAhdSLV6aowGRtag8/vdOBsdGP8Avx/4Oy+mVzOgp5jmHs+nK65ilmcSDvFgf+askeL11BJeTT5LWk/gkfwHiN+yyA0rNdQ4xuCR/bhlHy7Rg3OfOJa8qILrmN1Ocr0pnv7+a1zd24GPEnLVGGq/8QFck2pPi0l2hYzBskfibFycYfx8Lx+8owZv+MgyQZIFgtUOgtUO3vhJsCyLfNoctkonezWSfRq71+VI9mlkBsrZ/RSXwLxPOqg+zVNjn0weeeQRfvazn7F27VrS6TSapiEPRen6zW9+wy233HLQ/oVCgdbWVtauXQuUr/1dd93F/fffTzKZZObMmSxcuJDJkyef9LGcCdiC+TgRBIGK629GHxyk78c/oP6b9yAHAqe6WzY2NiPAykw/tQ43tYobURAwzAIFrY98sQ/dTFAyYohanPF6jHrBRbc5QE9uNU/nVmMBNaKX0Y5amtSxBN1lv2CHvM832H1CxJFlWST0JJtyfbQVEmwq5OnSLFrUEh/1J3EIO1g6kKNoFg4RxUUzj2YVD2pP17zE+y4hkZhOOLCLOZNeJyzryOu8RDaq+Dc6sKwcSy74Cblz3ZwVWUCrdz4O8fhTOR/TeHWLjv/cS8bM8eePvsCWrl2MUet4f/BczvFOJyAf6vfbXdzFK4PPsCGzDJ8UYm7gMmb4z8d1GB/mkYoYYeZL5Dd2kXxuE9nVu6lTPFR+eBrVl7Qg+U+PyaSWabFpaZqXfhPD6ZO48pu1jJrsHpG2BUHA7Zdw+yVqxh36sKaXTFL9OonuInhP/7ByJ5NQKMRtt91GPp/n5ptvPmjbtddey7XXXju8rmkaDQ0NXHfddcN19913Hw8++CDPPPMMzc3N3HPPPVx66aVs3boVr/fU+cW/U7EF89tAkCSqbv0n9v77vXT/+AfUfe3biOrJ+bGwsbE5MWimzoO9axm0ZBzoVBKjih4q6aeGQSpkJ05HFFWOEHBPpVKOMmFIEIuSj135TWzMrGBl9lVWFDsY75lBqzyfZqUa+TjEpGVZ5I0MCbOHXK6fjJkkbSRIagm6tTxdGsQNJ0krQJEgYOEkQVAYZJbSSVCAnO7CGIqb61WChyaTOCDRBJaTF9pNntqeocbr4PNT3NTsLFF41CS/vhMkEfe0BjzXN2FmCjgedVJaobF6wV95ZurDTA2cxyz/AirVhrf1PpiGRWxPkZJ+cDzkkqnxenYLyQdShNt9/PHzi5keaeHTvisP65dsWDqbMyt5JfkMewpbGeuayseqv8Q494yD3DPeVl/zJbSeFFpvklJ3Eq1n/8tI5hEcMt1VtTwbnc2nvzGZutrT53eir73Ikv/uJ7a7yNyPhJj8Xi+q8+hTcr9dZIdIuM5BsEampyd90s77TuDSSy8F4Pnnnz/ivn/6059IpVLcdNNNw3ULFy7kzjvvZMqUKQDce++9PPDAAzz66KMHCWubo8MWzG8TUXVS+fnb6fn3e+n5+U+o+afbEaSjT99qY2NzeqGIMnd628gLAXqsKF1mA3uMJl4saWRMA68pMxYfYyQ/Y1UfY1Uf/gP8gSd4ZjLBMxPNLLEjv45NmRU81vv/sDCZ4JnJJO88xrmnIwsKRTNP2kiQ1hP7Sz1B5oDltJFAtzQ0PJR6atCFBgpUkrIaMJDwiSb1DpFZqpvxrhATndX4Ze8xW0Uty+LlvRl+uzFOIJHmn4pZKld1U9zWSzLgxjOzkerbL8XVWovo2P/T4b+ghcSTa5n7F4lZy2U2vq+LhaO+QoNrArP8C5jknYdymIx/b6SQMejeVqC7rUBXW4Ge7QX0ooUgwbh5vQTPz7O+ag2vZNcx+4WJvOf1GfB1kX+b9IXDCt+MPshrqed4NbmIoplnuv98rqj8DBWO43OfMwvasAgu7RPE3Um03hTGYA4Aye9EqQ6gVAdwT2tAuXQySk2Ap9aYPL4kw7fuqKP+NBHLhazBij8MsPaZJM1zvbz/S9V4w9IRsxu+W3nxB+V/kF+k9wS0HmDmI2+vhYULF3LNNdcQDocBSCaTtLe3M2fOnOF9ZFlmxowZrF692hbMx4EtmEcAyR+g9vavs/e7/0L/ww9R8ambTwt/NBsbm+OjTZ2KYRkYlomXAhMsk7GqQcowGDQkEnqW54t9PJ6QMRBwCAZeUcMjlXALBZxSEREdwzIxUNHVOeSMDItz/TyV+R0Wv0PGRKaEgo4Dg6DkJiL5CcshnHIYlzKWouTDNBS6SyZJ08AtSoxV/TQ7/TQ7fTSrfgLykcXoEccby/LMou0Etnbx2b44zngGR0MY96wmotedjTQqhOI4OFRcwSySMwuYigVXjsN7YR2FxzYy5cEireM/xNbL0jxW/DWP9v+aFs8sWn3zCSmVWBZYpkm6xyK2XWdgm0F8u0mmy0JygL9JoOCEXUGLhAlqwWRgVZrgMgE9OIFrJkykcZVC3Zer8bUe+rdyZ2E7K5NPszG9gqBSwbmhDzHN/x6c4pFdDMyChtZbFsKlniTFrkGMvlRZFCcOEMVVAZSaIVE8JJCVaj+Se78Y7ukrsXJjjtVP5tiwOcdXP1fDuNEjH1XlWLEsiy0vpnnxN3FUt8iH/7mWxqnu4W027zw2bNjAiy++yA9/+MPhulSq7N4SDAYP2jcUCg1vszk2bME8QihV1dR8+avs/d49yOEI4SuuPNVdsrGxOU5eyazHxEJCRBpKCywJAoIlIIsy1ZJIbTmhMEVTIWM6yBgKKcPNXsOHiYBbNAlLFmEZKhWRqFvAKUhYWCS0bjJmkYypkdBz9Os6ew0FjABCKYhFWQj6JZ1qWWCu101tQWJ+9XgCjpHxPTSLGj2vdrB1cRuh7d28r6Qhj6/Gc9lEStPCJCMmu400CX0rA4ODJK0sCT3FoJFmUE+Tf4PPMwAXQmSKzMWLQrT+pxu9dQKLLk7QVtzN4nV5vLur8O2pxLu7EiXvpBhIkx7VR3pGL5n3Jch1VWOtbYWsCXM3wLTNBIQoVbGzKayvRnxNoP4V2Ay8/HiCeZbFxNleDPSyG8zg0+wt7mScezqfqPkKY9xT3tTtwjJMcus6yb66i1LXYNl9YkgUiz4nSrUfudKPa0o9gfcGhoWx5Dm8hbhUMlm3IcvajTnWbMjR069RE5aZGHFw/bwAVU4J07AQT0CGw6MltrvI4v/up29XkTlXhTjrAyFkxTbuvNNZuHAhs2bNYvbs2cN1fr8fgMHBwYP2TSQS1NXZQQqOB1swjyDOMc1U3/ZFun/yQ+RwGP+555/qLtnY2BwHX6u6BbdysNA62klgumWyp5hlRzHNzmKaHYUUG/JZLKDOoTJG9dGkjkHUi/QVUuzV0hQtkwpZpc6hEpYsXFIB00oS1wfo1xO8nBkgY+b5n93gEZ1UKGEq5RAVSpgKOUSlEqZSCVEhh1AP4/5gWRY5s0Ai0U/q1V3or3fj2JTCQCDX4Gf7ByR2TB6gW91L0VoBRZC6RAKyj5DkIyB6CSsB6tyVhCQf4Z1+3E87YC9I9RLyKBm5XkZulJGmS+SbLOIv9TF28Xpaf+RlD3VsE5swG0pk6rZgzlhN46QaZo86GyEznmcWZ3j+yQwVAYkPXBngvHkeVKUVy7To6+2juqUavcWgY+0etFlO4rJF77oCz/xHL084usk2t8N5y5jfOoGPVP8TYeXwoRYsy6K4vY/0S9vILN+OmddwT2/APbkO5eJJZVFcFUDyqkf1fnf3llizMceajTk2teXBsphc72RWSEEwRWK7iug9Jn1hnUf+msThEqlrcVLf6qKh1UW0UUU8CaH5ijmTFX+Ms+bpJGNmerj+P0bhj548P+UzgfO+kgRg/PjxI952W1vbcR+bTqf59a9/zY9//OOD6gOBAE1NTaxatYr58+cDoOs6a9assd0xjhNbMI8wnukzqbj+pqHEJkHck6ed6i7Z2NgcA0Xd5D/vfwWrKcrEMRFmVntoDBy924MsiIx2+hjt3J8RsGgatBczZRFdSLE41UVIUmlxBbk81HBE1wrTNNnV3YEYdhAzBunTB+jXEvSUYqzLtdGnJShaJQACkpcKOURQ9pE2sljdWWo26Izf7KRhj0rRLbClppq299RgzE3TWKMTlStollsJSX5Cso+g5McnuREFcVg4SpJEbk2e+F8S5NsKeOa78VzlprCnSGZjgdzfMwgFCxNIA3lFRqycTXBakqbuzTQNriB41lQ8H/ggm83XeGH7q/zt/pWkN0+nqsbglutrmH9W4CABaQomAEbGoPN7XThHOxn7pWq82jZWDD7NuvX9qMsuxr9tNOKmMazyCnROt5hzYZbWCW7kodjBpa5B0su2kXlpG1pfCtekOiKfmIdnzug3tRgf9rNRMtm4Nc+aDTnWbsrR269REy1bkS+vd5JqL5FdU4BGB03TPVxwXZSacU5ESSCfNti7OU/npjybXkjz4q/jqB6RuhYX9a0u6ie5qBjlGNFMeZZlsXVZhqW/jqGoIld8pYbRM+yAx+8UDMNA0zRKpfK9XSwW0XUdh8MxHF7y4YcfRlEUPv7xjx9y/G233cZ9993HRRddxNixY/nOd76DoihceaX9D/jxYAvmE0DggovR43G6f/qf1P/zv6A2HibSu42NzWmJrOtcs6YNlq5lIOJjSUWYnsZKKqfUMrXGw9QqD6p8bBN7VVFigivABNfxh550CSrVajWjxUP/TrUsi7SZpV9L0J/sJ9vWhbxlkOh6B2qfgFnnId1Sw2NnRdns8XFVS5hvjAmiHIV7gGVYpJdnST6ZotRVwneeDxb4ae/S2PtCit6dRQzdItrgoK7RQVVQpl4EIWFQ3F2itNGNpp+F5Oon8eQWBp7YQCI8lt3ZK6idKFJ//Tr6Kp5gmSCTHbiAmf6LiDhq9p9ft+j6cS/IELtpG091/4TeYgcTPLO46dyPM/q9rVgWbFiWZtVTg6ReyvLX5Vn+5Cwxs3qA8dlulJ44jqYI/osn4Tun+ahTTVuWRVdPibWb8qzZmGVzWwEBi8mjnMyJKAiCRGxHAX2vgWeKm9aPhWma7jls7GKXT6J5jpfmOeVz51IGezfl2bMpz4bnkiz93xhOr0jdRBd1E504K02qKi04zkAe8c4SSx7sp3tbgTkfDjHzg0E70947jIcffpgbb7xxeH1fKLglS5ZwwQUXAPDzn/+cG264AZfr0BCFd955J+l0mosvvphUKsWsWbN4+umn7ZByx8m7UjB3d3ezfv16XC4Xsiy/rYkO+459YxuhKz+Gnhig6z/+nbpv3YMSrXhbfX6zc+/r/8mcrPFmYz5Z5z4VY9537gPLk31ue9xvff59Af3fyLHe74IkU0qdjSVncefizN0VR9myG+NZkb2eEP8XjFCor6ZiVJixUSdBr4yiCsiqWC4dQ8uO8vJIWAzf6jrogzkKW3sobulG2dJDtCNOhSyijqvCc0kjmZZafhvTWNOb46JGPz9qCeNXpeF23wyzZJJamib+xCD6oEFxtIP2sU52v5TBWpqmZlzZtWDOVSGqm1VU9+EfIizdothdou3lIDtXVBHt66C+bzu3sgvztbHIXWfhaJxLorKbbeFV/HfkLqqq65kZWMA451lk/phF26Pz3Gfvp5BNM8N/IddU3U5Q2f+dKggw5VwfrTNV4s/voP/prTj6esnvcLJFrmaVexzuqiizvF5mWg6ibzHuQtFkU1vZirxmY47+uE5dhUxL1EFzo4t0R4n06wXMeoXG6W7OvTpM7QTncCa8I13Xfbh8Is1zPTTPLVt8s4M6ezcX2LMxz7pnUwx2ayz1dVA/yTX8CtcpR5xQXiqYrPxTgtV/G6Rpupvr7msgUKkcVb/e6ff6mcYNN9zADTfc8Jb7rF+//k23CYLAPffcwz333DPCPXt38u741L2BX/ziF9x9991861vfIhQKjUgYHcMwDqkLXXsD2k//g64ffo/qr38byTPyT3WhUAjTNDFNc8TbPhKHG/PJ4FSOGexxn2yOdtyhUOiw9cd6v1umxeBFXoR+F9JgCCU5GksrIZsJ6tJx6tO7kfZspbDcRZ8YYRMRBswQxpt8nUqKgKwKwwJaVgVkh0igUiba6CAySiE6yoHL99ZW61AohGEYFLsHKW7tobC1l2JbD3pPCtHtQB1fhXvuaMLXz8MxOkrGhD9vS7J4Y5JJUSffPa+Gep8DsN7yGhg5g97HkmSfz2CWLHaLsFODUNGifpLK5Cv81IxXUZwHWisP36ZhWLyyOseTzybp7tM4d7aXmRfPo8o3l8G/rCfz9w0I+b2QnYh3QwWTOy+htfheDKdGorqbl51LqNjRyOu3PsHZTe+n1XP2cIi6feezdJP8hk6yy3aQf70DwSETmTMa9/yZ9BUCCEsyVK3OI2ws8dK2OL/+bYy6UQpnTXFz1hQ3jfUK3b06azflWbc5z5btBUQBWkepzKtwIMoS/TuKFDsMHK1Ozrrcz6hpLvxR+YDRG7zdnxHVC2NmOxkz2wmEyCZ09m4usndzgdV/HeT5h2K4AmUf6LqJTuomOQlW7/evtiyL7StzLPtNAkkWuOyLFTTNcB90rY6Wd+q9bmNzIhGsd2Ecme7ubtatW8eaNWu48cYbqag4fuuvZVkYhoEkSYd98jfzefZ+7x5EVaXmzm8gOt5+CKjhtk2TeDxOJBI56nS5I8GRxnwiOVVjBnvcp/u4+/v7WbhwIZ/5zGeora0drj+e+/0vzyQYM0qlpdmFoghYlkWpVyO/I09pt05hywDF9i4oxRBIACZJV5CeigiFSXVUTa1iTMAJuoBeNNGLFlqpXOoli1LeJNGtEesoEu8sYRrgDUtER6lUNDnKZaMDf6WMvjdBfnMXyXUdWLsSGIM5pJAbV0sNjvHV5JsqGAh56S8YxHI6/TmN/rxOR7JE2CXxydYo06veOqRaKqaxZ1WWzOIUnr0augW9fhHHTA/101zUjHfgCTqO+v0vlkxeeDnNk4sGyWQNFpwX4LKLAoSDBz9U6IM5Eo++Tuq5zTgnVBP++Bwkf5ji7hK5jiyx3b2YZ5WYdN50pAPi21uWRXFbb3ny3oqdWAUN98xGfOeMwz29AeENLjOZAZ1Nz6fZsCRFNqHjbHDQJVlsiWk4nQKFokV9pUxLhYOABun2Eqk+nWC1TNMMN03TPdS1OE+aS8PhPvfpuE7npjydG/N0bs6T6tPxBCXqJrmoa3GyfWWWrq0FZl0RZNYVx+d+8U6/108kr732GnDiJv3NnDlzxNu1GVnelRbmmpoaLMti+fLl6Lo+Il8MgiActh3J7ab29q/Ree+36fvlz6j+3JcQRkjwCIIw3P9TEff5VJz3VI95Xx/scZ+8cx7tuN/Minas93uxZLJhS54/PjmAJApMGu9i6iQ3Uye6qJjjJniOgiBEsaxx6HGd/LYc2de6EP5/9u47TK7qPvz/+9w2fXZmy2zXrsqqoN4LYIqDATlgywWHSLaFARNKXGMem4QfkmMHx+2LYwPGiTG4UewYxyCwAZtiTJNAEhKSUFnV7X12p9/y+2NGK620ApWt2vPimef2e8/nju7wmbNnznnnEMFDjSgHduP80aDZlU9PSQRtdgUTFxZSNMGH6KfNsGU6tNelad2fomVvkuhbDXQ/2UxruoN8OtExSbp9dBWEiM6YSt3EfA56XbQkLToSJs47PWhKD4UenSKvRpFXZ3bEy+UTQiwo9aH20yQkEbU4uC3Bwa1xmjbHKWgxqQB8HgX1wgBVHwoztyT75f7wj/5O5j3oiVk8/UIXf3yuCwFc/v48LnlfHr4TNNfQwz4inzmf0PJZtD+6nvo7fo9v0QQKPrGI4KJiInYRjY2NKIqCEIJ0XQfdL+2i+2+7MVu68cwoo3DVUvwLx6N4T1wZESjQWfzRfBauCHPgrThb/hwl8UaMDxRqBMa7cLptmnYmSexNUDjdw7zlIarmePEXiDMeGvt09PfvPlioc877dM55X7a7sK7mTDaB3pbgjSc6Kagw+OR3KgmVnHmlzGh91iVpMI3JhHmoaaEwZV/+Goe+eQetv/45hSs/LQc2kaQRymUo/NsXynvbsr61Lc6fnu/iwUdbKcxXmT3dx+xzvMyY6sVbqKMX5hFcmgdMAyC5L0rH83uxNh3Ad3A7Yv9bdP0hSLvIJ1UQwTO1mKJpftzjXDhhm7bdTXRvq8fe1UTJwVaEZdNeEORAOMRrWilthFBjLvydoL8E4iUoys8woVynsMpDxUQP1ZPcBAtP3L41nbSp257g4NYEB9+O07I/TdgtmOZVKWs30SsNIh8LE1joP6021+0dJuv+3Mmf/9pFMKDy8SvyuWBJAOMkazmNkjxKPvd3JP9+Nm0PvcqBf3mE4MXTCK2YhxNN0vnmW8Re3kOqtgXXhCJCl07Hv2wSWvjUenxQFEH1HB/Vc3zEOkzefj7K3jfjRCa4WPj3ISqne3prZg9/URip8iI6eRGd6RcGh7sokjQmyIR5iBhl5ZR+4SvUf/sbaAUFhC+/YriLJEnSu3C7FObN9DFvZjYpa2pJs3FrD1t3pLj3wSbSaYeaCW5mn+Nl1nQv4yuzfeq6q4OUrp5NKbNxbJtkbQv1L+4lufEA3pb92H9TaHgljG6lyXbAJjCNING8MF3Tq4hPKcQ90U95gcEcb7bW2K8LmpuaKAhH6KgzadmXouVAmtZdKV56tofnkg4un0JRlYvCKoOiKhf+sEb9zmyS3Lg7iVAE5VPdTJ3sYYlXIbM9iXeCTsE/RfDO8JzWl/i6xjSPP93BX1/rpqLU4LOrIiye50c9zcE53BOKKP/XK4i/dZC2h17j4BcewjEtMoUBAufVUHzzxRjlA9N+1RfWWLQin0Ur8gfkfJIknd1kwjyEPDVTKP6nf6bx7rvQwvkElpw73EWSJOkkRQp13n9egEsvDGPZsKs2yea347y+KcYjf2gnGFCZOdXD7OleZk3zEsrTEIqCZ1IxEycVA0uwYila3zxA48YDpP1efEVF+Fxh8lod8usypOrSZN7KgNOBFlYxyg1SFQZ2mY7psVBmOZRMclMy6cgQy47t0NVs0noglU2k96fZsz5GrMOkeIKbyhkelnw8TCgDnes6iT/ThWuBj7J/r8Az6dSGanYch7qGDBu3xnhzS4wdu5NMq/Fw602lzDrHO2B/OfPOqsQzo4LYxv10JHsoWzKtTxtmSRouZzLIiDS6yYR5iPnnL6LwHz9F03/fg5oXwjtt+nAXSZKkU6Spgmk1HqbVePiHDxfQFTXZsj3Bpm1xfvW/bdzT3Ux1pcGsc7zMPsfLlIkeNE2g+lwUn19D8fk1Jzy3nbZJN2RIH0qTyr16NsfJNGXYY+9HzVNxlRsYFQauch2jwsBfbpC30Nfbxy+AbTsIoGdDjLaH2qnflyJ4boDx3xmHq+Lk27mm0zZbtid4a3uSjVuz3axVlBnMneFl1UcLmVh9akn3yRKKwDt3HNHGRtmETRp28kd5kkyYh0Ho7y7DbG+j8b++S/lta3FVjhvuIkmSdAbyghrnLQ5w3uIAtu2w/1Cat7bF2fR2jHXPdKLrgulTPL0JdEnkxAmrYii4q1y4q46MQGfbNg2HGsh3CsjUZ0gdSpOuy9CxLUG6IQ0WKH4FV7mBq8LAKDcQmqDjj51kWkzyLg5S/oUS9KKTGw65tT3Dm1vibNoaY+uOBA4wfYqHKy4JM2eGl4gcVlmSpDFGJszDpOBjV2O2t1H//W9R8W9fRy8oHO4iSZI0ABRFMH6ci/HjXHzosjCJZHY45c1vx1j3bCc/e7iVSKHGxGo3FaVG9lVmUFyko71L21+hCVwlBp6qvjW6jpnt8i51KJ2tla5L0/V8FKvbIu+CIOHL89Dy3v2j3rIcdtYm2bg1xsYtcQ7WpynM15g7w8vnry9mykQdn/fku5WTJEk628iEeZgIRaH42hup//63aPj+tyi/bS2q79R+8S1J0sjncSssmO1jwezs893YnOat7Qn2H0rx1vY4T/65k1jcRlWhrNigvNSgssygvCQ7LY7ovFvHFUIT2ZrlcgMWn3y5oj0Wm9+Os3FLjM3b4iSSNlMmujl/cYC5M7xUlGUT5JHeW4QkDYXD/TAPFtnkY+STCfMwErpOyT9/mbr/WEPDf32X8n+5DaHLP3VK0tmsJGL0aZLhOA5dUYuDDWnqGtIcakizZXucp/7SSU8sm0iXRnQKwjaTxndQWeaiotSgJKKjaSdf4+s42aYiG7fEeHNrnN17k/h9CnOm+7j2H4uYNc2L3yd/WCdJktQfmTCfpM531mHkjcNbMnNAz9s7sMk3bqf+B98hfNkH8UybgZC/CJekMUEIQShPI5SnMXPqkRH5HMenq32MAAAgAElEQVShq9uiriHNgboUu2q7ePudBE8/30V3zEZVoLTYoLxU79O0ozRi9CbSyaTN1h1x3tyabY/c3mlRXeli7kwvn/pYIROrs13hSZJ0cgZrpD9p5JMJ80ky4+107FjHuMu/jWr43/uAU6DlF1D2L/9K229+Tf1d30H1ePEvXIJ/8VLcNVMGbGRASZJGDyEEoaBGKKgxrcbN7KkJSkpKUBSFaLfFwfoUdQ0ZDjWk2b4ryTMvRol2W6gKlER0An6V3fuSaKpg5jQvH7uigDnTvccNTy1JkiS9N/nJeZLyZ32CeOMWWjbcT8myzw34+Y2ycko//xWsWIzYm+vpee0V6v7z31HzQgQWLcW/eBmu8RPkj24kSSIYUJk+xcv0KX3XR7st6hrTHKxP09ll8tEP5jN1kgddl58bkiRJZ0ImzCdJUQ2Kl97Mwaf/le79fyNQNTiDjqg+H8HzLyR4/oVY0Sg9G16j+7WX6Xz6SbTCIgKLl+FfvAyjonJQri9J0ugVDKgEA9n+oSVJkqSBIxPmU+AKVVEw8xO0bPgp7sIp6L7B7QpODQbJu/gS8i6+BLO9jZ71r9L92st0PPF7jLIKfIuWYE+ogZKSQS2HJEmSJEnSWCYbx56i0JQPYoSqaH7tXhzHHrLravkFhC79IJX/3zep+vYP8C87j9iG10l+/04Orb2NjqceJ9PWOmTlkSRJkiRp8Dz88MOcf/75BINBhBDHde/4xBNPMH/+fILBIOPGjePOO+887hx333031dXVeL1e5s2bx4svvjhUxT/ryBrmUyQUheIlN3Hwj7fStfMp8iYvH/Iy6JFi8v/+w4SWX0n95k249+4m+sJfaHvkV7hrpuBfvBT/giVoodCQl02SJEmSpDMXDoe56aabSCQSXHvttX22rV+/no997GM88sgjXHHFFWzevJnLL78cn8/H5z6X/Z3Vb37zG2677Tb+8Ic/sHTpUv77v/+b5cuXs337diorZbPOUyVrmE+D7iuicN41tG1+mFTnwWEti1JcQv6HP8a4O79P5do7cddMpvOpJ9j3xRup+/Y36HrhL1g9PcNaRkmSJEmSTs2ll17K1VdfzYQJE47b9tvf/pYLL7yQD33oQyiKwty5c7nuuuv44Q9/2LvPPffcw2c+8xkuuOACDMPg5ptvpqamhgceeGAIozh7yBrm0xSoPp9Y/Rs0v3Y3JRfeAdrw3kohBK6q8biqxlPw8X8kuWcXPa+9TPvvHqXl5z/FO2MWgcXL8M6cjRoIDmtZJUmSJEk6fY7j4DhOn3W2bbN79266u7sJBAJs2rSJz372s332WbhwIRs3bhzKop41ZMJ8moQQRBZcx4GnvkLntt9RNHflcBeplxACz6TJeCZNpvDqT5F4Zzs9r71My68exI71oOXnY4wbj6uqOpdkV6PlF8gu6yRJkiSpH77v5AFQR9PAn5s8ePjUjrnyyiu56667+N3vfseVV17Jpk2buP/++wGIRqMEAgGi0SihY5pmhsNhamtrB6roY4pMmM+A6goQWfRPNLz4n/jL5+EtPme4i3QcoSh4p03HO206RZ/8DOn6OlIH9pHav5fEjm10Pf0kdiKB4g9kE+hx1b1TvaRUDpoiSZIkSSPMeeedxy9/+Uu+8Y1vcN1111FTU8ONN97I17/+dcLhMADBYJDOzs4+x3V0dBAMyr8ynw6ZMJ+k2uaf4HdPJhK8sM96b+lsAhPeT/Nr9zDu8m+j6N7+TzACCFXFVTkOV+U4OPd9ADi2jdnaTGrfPlIH9pLav4/uv72IFe1CuFy4KquOJNLV4zHKK0EO2y1JkiRJw+qqq67iqquu6l3+4he/yNKlS/F6s3nInDlzWL9+PVdffXXvPhs2bGDFihVDXtazwYhMmB3H4aGHHuLpp58mFosxceJEbrzxRqqqqvrd/7bbbmPHjh1oR7UjXr16NcuXD1wPFh6jgv2tD5LnnYlLK+izLTTzH0i2bKPljQcoXnLTgF1zKAhFQY+UoEdK8C9aAmTvv9XZQWr/vlxt9D5imx/DbG0BVcUor0CvrMJTPQFX9XhclVUobvcwRyJJkiRJgyP2lS4AJk+ePODn3rlzZ7/rLcsik8mQTqcBSKVSmKaJYRhANvmdN28e6XSa3/72t9x///08+eSTvcffdNNNXH/99axYsYLFixfz05/+lJ07d7J69eoBj2EsGJEJ82OPPcazzz7LmjVrKC0t5eGHH+aOO+7g3nvvxePpfwSrj3zkI6xatWrQylSSdxntPa9T2/wTppZ+tU97X0VzEVlyE3XP3oGvfD7+ysWDVo6hIIRAC+ejhfPxzZnXu96K9ZA6sJ/U/r0k99bS9dwzZBrqAdCLS3NtoqsxyiowysrRCotkkw5JkiRJOg2/+MUvuOaaa3qX/X4/AM899xznnnsut9xyCzt27MC2bRYsWMC6des499wjoxB//OMfp6mpiVWrVtHc3My0adNYt26d7FLuNI3IhPnJJ5/kwx/+MNXV1QCsWrWKZ555hldffZWLLrpoWMr0TnuKQOCzNLZ+jZboc0TyLu6z3Z0/kfwZH6V5/f/gLpyM5gkPSzkHk+rz4502Hc/UczBNE03TcNJp0ocOkNqfbc7Rs/410g2/w0kmEbqOXlKKUVqOUVaOXlaOUVqGXlyKkvuGLEmSJEnS8VavXv2utcGvv/76e57jlltu4ZZbbhnAUo1dIy5hjsViNDc39/mzh6qqTJgwgdra2hMmzE899RTr1q0jFAqxZMkSrrrqquNqoxsaGmhoaACgpaWFZDIJZLtieS8/3lKLT3Fx84yr2df6CwLu6bj0IhzHwbZtbNsmb8oVxOrfpOm1H1Ny/q2D3uvE4XKfTPkH0tExC13HGD8RY/xEAkdttzraSdfXkWmoI91QT3z722T+8jRWNApCoBVFsslzaTl6aVl2vqwc1et712sPV8xwTNxD3KOIjPvM49Z1/YzOdbbch1Ml4x47cY/FmCXpZI24hDkejwPg8/VNnPx+f++2Y33yk5+koqICn8/H/v37ueuuu2hqauLWW2/ts999993H2rVre5e/9KUvAdDY2Pie5ap4KcZ+n8760GTG+cvZUfcjIq5/Ou5DRUz4OIk3vsehjb9DLzv3BGcbWM3NzUNynVNWGMm+Zs5FAVyAE49htzTjNDeRbmkmuXcPzmsv43R2gONAIIBSVIxSVIwoiqBEslMRzOtzr0dszINMxn36Dv9p82Se95FKvv9jy1iMeyzGLI0OIy5hPvzrzlgs1md9T08PBQUF/R3CtGnTeufHjx/Pddddx+23304qlcLlcvVuu+GGG7jyyiuBbA3zc889B0BJScl7l6vqb4z7S4D/q7L5zgduYU/TV1F8W4kE/q63eUI2oSshan+Ktk0/p3jSUoxg2SnFfyps26a5uZlIJIIyhG2FHcc5JuZTNGHicavsVIpMUwOZhnrSDfVk6utIb9xAqqkBTBPh8WCUlKGVlJIOBMmrqsYoiqAVFKLmhYakrfQZx30Ghuu9htET93slwj/72c+45pprTup5789ouQ8DTcY9duIeLTGP5i+90ug14hJmn89HJBJh165dTJ06Fcj+UnTv3r0n3X75RA96aWkppaWlANTX1/PKK68AnNQH0hUr5vLwa21ENvv5RXWCqyeuYn/rLwh5Z6MpBSiK0nvdvEnvJ96wkZbX76Xi79YilMG9zYqiDPmH6uFrDtSHquLxoFVPwFPddwhQx7LItDTnEuk6UnWHsHZso2P9q1iHa6VVFS2/AL2wCK2wCL2gEK2gMDtfWIQWzkcMwEiMgxH3qRrq9xrOnrgzmUzvuU7H2XIfTpWMe+zEPRZjlqSTNeISZoDly5fz2GOPMWvWLEpKSnj00UdRVZUlS5Yct29HRwe1tbVMnz4dl8vFwYMHuf/++1m0aFGf2uUz9XJMJfTRGNpP8nlrs8llVUsJuF+jtvk+aiJf7bOvEILIous5+NSttL/9GAUzPz5g5RhrhKpilJRilJTimzsf27ZpbGykpKQEYduY7a1kWlsx21rJtLZgtraQ2LGNTFsrZnsbWFa2zXQ4jFZwVEJdWIhWkEuoCwrljxAlSZIkSTqhEZkwr1ixgkQiwe233048HmfSpEmsWbMGj8dDS0sLN998M3fccQfTp08nk8nw61//mrq6OmzbJhQKsXTpUj7xiU8MaJle+rlFtLCAeTMPMH5ThLvK9/P9Cz7L23W30tL9LGX5fft81twhIos+S8NL38dXOgd3Yc2AlkcCoWm9fUj3x7FtzI52zLZWzNZcQt3WQnJfLeaG1zHbWnBytY5qMA+toLA3gdYKCtHyC9AKCtDC+aiBIMihwyVJkiRpTBqRCbMQgpUrV7Jy5crjthUVFfHoo4/2LkciEb73ve8NeplubDnEX7e6aflUBN+DGvo2+P24FO8v/ST7Wh8gPzAPj1Ha5xhf+QKC4y+k6dW7qbz0Wyi6HNxjKAlFQS8oRC8ohH76mnccByvadVxCnW6oJ/72W5jtbdiH29JrWjZxDudna6jzC9DzC7JJdX4BWn4+ij8wbH/GlCRJkgbfiQYZkc5+IzJhHon8boeLUtv5f08tYMElCcb9xc2TFT28v2IZAder7Gn+MdPL70CIvm2vCud+igN/vJXWTb8ksvC6YSq91B8hBFpeCC0vBBMm9buPnUpitrdjtreRaWsl3dqC3dVJ+sA+4pveyCbViUT2fIaRHfClTyJ9TFLt9cmkWpIkaZSZP3/+cBdBGmYyYT5J5V89j72fPcQ1DXu5Z1I18wJpKt4WfLd4N2sWXMu2hq/R2PkUpeEP9jlO0d0UL72Fuj+vwVc2F1+5fOhGE8Xlxsj1E32iX5DbiThmezuZ9jbMo17J2t2YG17DbG/DSaUAEC5XbwKt+vwoXi+Kx4vi8WSnRy2r3iPrccm/TkiSJEnScJEJ80lSvTpFN1yIc/c6znslQssHgxQ/prFlHLwyzmZW4afZ2/I/hHxz8BjlfY71FE4mPO1DNL/+E8Zd/h1Ud3CYopAGg+LxYpR7Mcor+t3uOA52PN4nmTbb27DjMex4nHRnJ3Yijh2PYycT2WkiDsd24K/r7Pf6jiTY3mMSbffhJDv38nlR/UHUYBDVHxiQnkIkSZIkaSyS/wc9BXnnVxL981Tet2M7P3hlIXNmWUzYnOKBohT3XbKMkOd19jTdy/SKrx/XNCN/xkeJN2ymef1PKDnvy/LP8mOIEALV50P1+XBVjjupYxzHwUmncslzAjMWo62+jjy3CyeZPC65NltbjlmXwIrHwDR7z6l4faiBAGogiBrMOzIfyCXVx8zLBFuSJCnrjTfeGNTzyyYfI5/8P+IpKvvquey9/iDXNNdy77jxzGz3EK5Nc9eWbXxlznVsPvAvNHQ+QVn4yj7HCUWjeOnNHPzTV+ne+zzBCSfXp7Q0NgkhEC43issNYdBsG9Xnx19SctJ9lDqOg5NMYHVHsaLdWN1dWNEoVnd3bl0XqX17s/PdUaxoFMfM9B6veLyowSCKP4DiD6Dl5aEdk1grbi8oCihKdvCY3qnITkVunaoe2SaO3Tc3L79ESpIkSSOUTJhPkerWKb7xIpz/eoLzNkVoXqBTvUnhzRIXOyfA+KJr2NP0Y0K+eXiNvn+iN4LlFMxeScubD+KJnIPuLx6mKKSxQAiByDXPOFHXe0fLJtjJPgm01R3FjEYxo53YPT2k9u/rk3Af7pZvgAqcS5yPJNGuqmqUT18/cNeQJEk6A5Mn99Pl0hmSPW+MDjJhPg2BZRV0/Xkq5729nR/uXsgUn4tx73Tx3XAnP33/MkK+19jTdA8zKv4dIdQ+x+bVXEq8fiNNr95N+cVrhmRIZ0k6GdkE24Pi8aBHjnyZO9GPHR3Hybaztm2co6dObmpZvet719lO330Pzzv9nMe2weWiazhuhiRJkiQdRSbMp6n0K+ey97MHWd22l/+ZOoHJ2/JoGmdy7/aN3DAl2zSjvuMPlOev6HOcEILI4hs48NStdGz/P/KnrzjBFSRpZBNCgKqCqjJYjSls26arsXGQzi5JkiRJJ0dWb54m1a1TcstFBKw6lu7qJjPBoWZzipd2u2lMOoyPXMuh9t8SS+0/7ljNk09k4XW0b/0tyfa9w1B6SZIkSZIk6WTJhPkM+BdV4J01lSXp7TS3Z/C2+ik6mOLrb2ykwL+EsH8he5ruxXbM44+tXEKgahlNr/4I20wPQ+klSZIkSZKkkyET5jNU/KVlCJfGP0ZrOVScYdImH/Gmch7Y8xLjiz5DxuqgvuP3/R5bOO8aHDNF21u/HuJSS5IkSZI0kn31q19l5syZBINBSktLufrqqzl48GCffa677jqmT5+OpmmsWrWq3/PcfffdVFdX4/V6mTdvHi+++OJQFP+sIxPmM6S6dApveh9+q455TVFMTWHS7ih/2uGhI5NhfNG11LU/Rix1fNML1fBSvOQmunb9iXjjW8NQekmSJEmSRiIhBA888ACtra1s374dIQRXXHFFn31mzZrF97//fa688sp+z/Gb3/yG2267jQcffJDOzk6uvfZali9fflziLb03mTAPAN+8MrxzprIgvZ2olSa8JYSn3WDNxr8S9i0k37/khE0zPJFzCE35IE2v3YuV6hmG0kuSJEmSNNLceeedzJ8/H8MwCIVC3HrrrWzevJmOjo7efT73uc9x6aWXEgz2P4LwPffcw2c+8xkuuOACDMPg5ptvpqamhgceeGCIojh7yIR5gBR/fhmKW+Uj8Vq68ixmb9Fob5rGw4f+yPiia8hYUera/7ffYwtmfgLVCND4t/9Honl7tostSZIkSZKknKeffpqqqirC4fBJH7Np0yYWLVrUZ93ChQvZuHHjQBfvrCe7lRsgqlun+HMXY3/7cWbEIhzoyae8qYvHt3u5qKiDCZHreafhe4R9C/C7J/Y5Vqg6Jed+gdaNP6fuuX9H8xYQqDqPQPX5GMGyYYpIkiRJkiSAvO+sB6CJ9QN/boCH331o7GeffZa1a9fyv//bf8XbiUSjUUKhUJ914XCY2traUyylJGuYB5B/Xjm++dOYldmOqWWoej2E6IrwzbefIeCdTWHgPPY034ttH98rhhEso+yCr1L9oXsITb6ceMMmDjz5JQ4+/a907vwjVio6DBFJkiRJkjScnnjiCT72sY/xy1/+kssuu+yUjg0Gg3R2dvZZ19HRccImHNKJyYR5gJV8bhmKR+XS9B6SGZu5+2xa6hfxaP3DVBd+GtOKcaj9tyc8XnOHCE1ZTuWldzLu8u/iKZ5B547H2fv7G6l/8Tv0HHgV25Ld0EmSJEnS2e5Xv/oVK1eu5JFHHmHFilMf6GzOnDmsX9+3VnzDhg3MnTt3oIo4ZsgmGQNMcemUfP5i6r/1ONPsCLvfzCevIsa6XUGWhvcxIfJZ3mn4NmH/QgLumnc9l5FXQeHsqymY9QkSzdvp3vdXml6/DyEE/sol+KrOxXHyhigySZIkSRqbur6yEIDJkycP+Ll37tzZ7/of/ehH3H777TzxxBOcf/75/e6TTqexbRvLsrBtm2QyiRACl8sFwE033cT111/PihUrWLx4MT/96U/ZuXMnq1evHvA4znYyYR4Evjnl+BdNY+rr2zmgLWb6G35ePncqP9j9e+6cfhNFwQvZ03Qvsyq/haIY73k+IRS8xdPxFk+naP41xOreoHvfX2l4/psII4/2CRcQHP8+2d5ZkiRJks4S//zP/4ymaVx++eV91j/11FO9CfQHPvABXnjhhd5tDz30EFVVVezbtw+Aj3/84zQ1NbFq1Sqam5uZNm0a69ato7KycsjiOFvIhHmQFN+8jPiWA1yQ2sOzdVOY1pFhe91FPJL/36ws+SxbDt3KwfZHqCr85CmdV9FcBKqWEahaRjreQcPbfyLeuJnO7b/HlT+RQPX5BMYtQ3XL9kmSJEmSNFo5jvOe+zz//PPvuc8tt9zCLbfcMgAlGttkG+ZBorh0Sr5wMS77ENNEO/4XDPSEl78cKOTt2AYmRG6gofNJookdp30NzZ2HXv4+Ki75JuMu/x7e4hl07niCvf93I/UvfpvuA6/IYbclSZIkSZLO0JisYW5oaGDLli14PB40TTupb3EncvjY/s7hnVWGb/E0Jry2nfr0YubuNHhdXciDh37G7TVfJBJ8P3uaf8zMijtRFfdpXftw+fVgGfmz/oHwzKtItuyge99LtKz/CS2Ar2IJgerzcBdNRYgz/470bjEPtqNjHurry7hHdtya1v/H2UA976PlPgzGtY+eDvW1ZdxDd/3REvOJnnVJGkxj8l/dfffdx9q1a/m3f/s3wuEwpnn8CHynyrKsftcXXLeQxJYDLErs4S+bplA6MUNjw+U85P8h1xT/C52xTRxofYjK/FNrmnFYOBzGtm1s+8hgJ3r+ZPLzJxOavYpEw0Zi+1+i/vn/QPWE8JbOw1VQg6tgMpq34LSuediJYh5s/cU8lGTcQ+tk4z5RZ/4D/byP9PswWGTcQ2s44x7pMZ/KwB2SNFDGZMJ8ww03sHTpUjZt2kRHRwdFRUWnfS7HcbAsC1VVEUIcv4Nfo+QLF1H/H08wQxQRez6fhktKebOljOn+PzOn+J/YUf9NCgKLCXrOOaVr27ZNW1sbBQUFKEo/NceahlF9LnnV52Ilo/QcfIV401baN/8CO9WN5i3AXTgZd+EU3IWTMfKqEP2d51RjHkTvGfMgknGP7LhbWlr6XT9Qz/touQ8DTcY9duIeLTGf6FmXpME0JhPm0tJSHMfhlVdewTTNAflgEEKc8Dy+WRUElk6j4pXtNLQtxmxS2aqdz+OBe5k4bhbFeR+gtvk+Zo379ik1zRBC9Jb/vWLQPHmEJl9GaPJlOI5DpruBZOs7JFreoWvX07S++QBCc+MuqMFdOBlP0RTcBTUouue0Yh4spxLzYJZBxj101zzZuE9UczzQz/tIvw+DWQYZ99BdczjjHukxD8RfhSXpVI3JhHk4RG5YRvytA8yJ7aHlhSn4/kGlqWk5jxo/4sbKtXTGNrKn6V4q8j+Kx6gc1A8rIQRGsAwjWEZwwkUAWKkoydadJFreIdG0lY7t/weOhStUlauBztZC677CQSuXJEmSJEnSSCQT5iGiuHVKvngxdd94nPl2hI6XQ+xYMpXa/O38qe0hLin5PHtb/oe3Dt6KSy8m37eQfN9C/O6aAfmh3ntRXUF85QvwlS8AwLEyJDtqSba8Q7L1HVreeBk7faQZhx6uwVc8FVeoCqGog14+SZIkSRpuJxpkRDr7yYR5CHlnlBM4dxr8bTvn7FtEfIbgwKELeNV1HzXeucysvJNUpoX22Hrae9bT0LkOXc0j7FtAvn8hQc90FDE0b5lQdTyFU/AUTgE4rhlH955n6Nj881NuxiFJkiRJo838+fOHuwjSMJMJ8xCLXL+M+OYDzOzZw/5nJ1O3Io/mjkv5g/I/VLpryNOLKA0tpzS0nIwVpSO2gfae9bxT/x0UxSDsnUfYv5CQdzaC9x4lcKAc3YwjMP7CbFszK06qbVeuGceWbDMO20LzFWEEyzGCZei5qREsR3UFhqy8kiRJkiRJA0UmzEOst2nGvz/O+ckILW/lcUDMojNUyy/q72Ru8EIm++ZRqJehq0EiwYuJBC/GshN0xjbRHnud2qYfY2OS55mFYk7GtC7CUIZ+ZL9jm3HYVppUxz4yXYdId9eTjtYRq9tAJtYMjoPiCmAEssnz0Ym05i08qd45JEmSJEmShoNMmIeBd3o5gfOnwV+3s+DtxURrBAcOLmL+jH1s6n6BZ9p+RUiLUOObw2TvXKo909EVDwWBpRQElmI7GbriW2nveZ22+OO8uf8Rgp5p5PsWEvYvxKWdWf/Kp0tRDTyFk/EUTu6z3rbSZLobyUSzSXQ6WkfP/r+SjjbgWCmEqqMHSrOJdC6hzs6XomhDV4suSZIkSZLUH5kwD5PItcuIbzzArJ49vPPnGjovr+TZxkN8oOJSzs0vIWXWszu+iUcavw8IxnumM9k3lxrvPEJ6IWHfXPI8s3GbH8QfitIR30B95xPsa30An2ti9keD/oV4jPLhDhVFNXCFxuEKjeuz3nFszHgb6Wg9mWgd6Wg9ieZtRPc8i5XsAgSarzBXI12GEShDC5RhpwSOVQCKa3gCkiRJkiRpTJEJ8zBR3DolX8j2mnFpdxENu/PoVJfxZFsrCf8bhELtLAhN5tLiD+Ilwb7EFl7q+APrWu4nYlRS453DJM8cdIIEPFPJ851DVeEniaf30d6zntaev3Gw/WE8ejlhf7bHDZ9rwrD1ZdofIRR0XxG6rwhKZ/fZZqV7con04VrpeuL1b5LpaQLHYe9rIDQXqu5DcflRdB+qy49q+FEMH4pxZD479aPm1iu6Z0TdB0mSJEmSRjaZMA8j74xygu+bhvPidi7YuJi2c124/ZN4vaGYznqbV3xR/hLYiho4xNz8YuaFV1OpB2lMvcPO2Ju83LkOHRc1TbOp8c2jxjcHn2s8Ptd4KguuIpluzPa4EXud+o7fY2gFBD3T8RqVeI1xeF2V6Gp4RCaPquHvt3mHlUnRcGAH+UEPjhnHTvVgZWLZaboHOx0jHW3Azs1b6ex67KM6uhcCRfejuny9ifbRCXV2GkB1BVBdwd4pqmweIkmSJEljkUyYh1nRZ5bR8+YBZvfs4Zf311A3u5vxk3UunOrDtPLY1FjAocYMWz1JNvtrSQVeZHLYwwL/hbwv/1PUt2yjVeznmbZf8fvmeyl3TaTGN5fJ3rmUuKopC19BWfgK0mY7HbE36E7uoq3nZQ6mH8VxMmiKH69rHF5jHB6jMjdfeUojDg4loeooniLcBSUnPWSs4zg4Vro3gbbTPVjp2DHTHqxUdy7ZziXaqSiOmTxybUVHcflzSXTwmIQ6O6+4jk60/QhFPmKSJEmSNNrJ/5sPM8WtU/qFi6j7xhNcFYtwcH2YvRsdXlIStEWiBAJlAvAAAB4RSURBVCbZzB7vIpQfoiE6l12157BfN6nLO8Svfeso9MdZ7D2H5cWX4hcWtfHN7Iy9wfPtv8GvhqjxzqHGN5cJ3pkU511Ccd4lQLb9cDLTSDx9gHjqAPH0QTriG0llmgEHlxbpTZ6ztdHjcOslCDH6BikRQiA0F4rmQvPmn9KxtpXO1l6nopjJKJlEJ2RyiXeqGyvZSbrrIFaqGysVxUp1g2P1Hq/o3n4S6ey8ontRDG92qntRdA9qbl5o7hFZ8y9JkiRJY5FMmEcA74wKghdNQ7ywiUkixMREiL8jTNfeIM31Orv+6rDRSNA9Lk3BeEFRiRvLqaH2YDWdWPw1r5Unfc/hzWthXnAC88NX82EjQl1yO7tiG/m/5h+TsdNUeaZS4Z5MgV5Cvl5CgVFKvm8xBf4lvWWx7CTx9EESqYPE0wfoTu6kqetZTLsbIXQ8RjlevRKXVo7fU43PNW7ENusYCIpqoHjz0bz5GI6DaZpomnbCeB3Hwc4ksFLRbEKdS6KPTqjT0XrsVBQrE8fOvRwz1fdEQqBo2ST6cDKdtlWa94WybbSPSbCP7JebN7womld21ydJkiRJA0AmzCNE5Lrz8S+oIrGtgcS2elL7askXgjwrRI0ZYnkyRNfbedTvVtnpWGwPmNjj0+SVgN9VQqa7hESdzZZglFf9b2H59zEjWMJ83xI+U/BJ4mYTu+IbOZTcxVvdf6XLbAMc3IqPfL04m0DrpeQbJdmE2r+IiHoxkBvlz+oint5PPJdId8Rfpb7rf/tp1lGBoRWgq0F0NQ9dDaIoY6ftrxAC1fCiGl4IlJz0cY5tYWcSuQQ60ZtI25k4ViaOlYphdrYgdAU7HcOMtfbZJ3tMAnD6lkfVEWq2dl1oLhTVnattN3Lr3b217333O7JNqAY2GrbLi6K7c/u6s+c+S78oSZIkSdLRZMI8QghVwTe/Gt/8agDseJrEO40ktteT2NZAqnYv+Y4gLxNkmhXiQ6kwne0hDugK7ygOzQUmyjgLEfFjRBcTcxaz359gd2APP/O9QFXAwwL/OcwJL6JYLyCkeImarbRlGmnPvfYnd7Cx+zmiZjsAHsWfq4kuIV8vzSbSnmmUBy5CcwxUVZAym4inD/Y26+iMbyJtdmI7R9r+qsKDpgX7JNG6mod2zLKuBtHUAEKMvVpRoajZXj5c/n6327ZNqrGRopITt912HBvHTGKlcwm0ma25ts1UdmqljlpO5pZzbbsT7dhH7etYqSPLVhIcp58rCoRqIBQNoWrZqaL3LnPMcp/tveuyyyhaNgE/vC23rBgBECf/xUOSJEmSBoNMmEcoxWvgmzsO39xs38V2MkNyVxOJbfXE364ntXszBbZDXjrILCcEh8J0NoSoVRXe0R32lZjEyzW00HRwz6DVm+HPeYf4vfs5LL0DxYhR5ApSrOVTYhRQrE9lhncZxXohYdVHzGrPJdMNtGUa2RvfyobMs/RYHQB4lUC2SUdvzXQlBf6FVOsluBQPlp3CtKJkrCgZq6t3auamPck9fdY5HG73K9DUwAmS6yCaEiBpZYinM+iqD1X1ogr3mEyyjyWEgsg1yxhItm1jphMo2DjWkWQ6m4SnwTZxbBPHymSntolj5+atvssctWybieO2Z89h4dgZsE20QCmi5lMDGo8kSZIknSqZMI8SilvHO7MC78wKCgA7bZLY1Ujr67twaqNk9myhwLLIswLMS4dhT5j2fSF2K7DLY3OgHFKRcTi+anAJbMMh6rZJu1M0GD1YRjs92k7SehsYUcK6lxK9gGI9n2K9himeJRTrBYRVHymrk5ZkHZ12C+2ZRnbFN9GeaSRmdQGgCxce1Y9XDeBVAn3ntUK8rvF4lQDB3HqP4kd1LEy7u5/kOkra7CCW2t+7zbJ7aD509N0RqIoXVfGgKV5UxYum+FBVz5H53PYj27y9+6qKF0UYsnnBCQiRrUlWNQ0hAkN6bdu2aWxsHNJrSpIkSdKxZMI8SimGhmdaGXpYoaSkBGE7JPc0E99aT+cbdVj736bIMglbfhZlwhAN0bozzCFFpUuBTh2ifogGdaLBMBlfAbgng0sBN8RcNnXuNM2uOFuMdhL6ZuJaExhRfLpGsZZPsVFAiT6BiZ6FLMs183CcGHGrm7jdTdzqJmH19M63pRv6bDOd9JF4UPGqQbyqH4+SS7DVXLLtLs6uV/3kCz/dbXFKCwvQFIHiWNhOAtOOYVlxLDuOZeeW7TgZK0oy04RlxTBz2yw7ju30/ZGdQM0mz7lEWhEuhNBQhIYQGgIVITRwFFTVOLItt77Pvrn1Ry9n99UQQu1dVhR3Lmn35K4na8klSZIkaSSSCfNZQmgqnimleKaUUvDR+TiWTWpvC11v1tG14RAc2k6JZVJoGTi4IOVC6XFBowtwYeIihotu4aJT0ehyCzp9Oh3BINFAECcwHvwqeCHltml0ZWh3J9lhRMnoe+jRXgSjC12zCWhefIobr+LJTlUfXrUAn+4m3LvOg4GKKmyEY4KTwXHSpJwYCSubUMetHrrM1t75hN1Nyk5kA67rjRyX4sYQnuxUOXrqwaUGMbQILsWDobhxKR48igddGGhkHwDVcVCxUBwTx0llE2o7hYOF7Zg4uZftmFhWGttO4xA/bpuD1XfZMXEwcZy+5znS/KTPO5irAff01oQfns8m8B4SGRO6Imiq76ja9L77ZxNvWVMuSZIkSQNJJsxnKaEquCcV455UTPFV83Bsm9S+NmK17SQaeki1xDDbY9gdUUQsgZ5KkO/Y5DswzhJYMRfEXIhmFwIXDi4sXCRx0aMYdGkuOj0Burx+OgJldITn05UP8ZBNVHfoUWwUxUaoNkKxEIoJiokj0jhKFFNpxhIJbJEEJQ1KCpQMugoeTcGjafg0F349gE8z8LvcFCse3EIn0dNDKBDI1TA7CCwEFo6TQTgmtpPGIk3KThC3uum0W0jZCdJ2kpSTm9pxTCdz3H3ThJ5NtoUbQzn88mAIF4biRlMM3Ko3l3znth+9r/BgKK6jlt0ox9QcO46Dg4ltp/rUelu984mj1me3pc0uklYXme53sJ1k7z7OcTEoRyXR7lwNuAIoCJQj8yK7jDhmfX/7oOA4oChqtob8uH2Orl1Xj6lhP7ycne9dd0xt+5F1R+2HiuPIWndJkiRp+MmEeYwQioJ7QhHuCUX9bnccBzuexuqIkWmPkWrqySXWPZgtMZzOdtRYgkAmSdB2KEuDnVZxulzQkE2qwYWNCwsNCx0THQuNjNBICw+mopARYArIKJDJTdMKZFRIC0irgoyaXW8q0KNAp+qQUR3M3DSjO6Q9FhmPCe5M9uVJg1uAxwa3QGgqmurF0NwYKrg1gVvLJeO6QUjRcQkdQ4AuFHQBugAVJ5d8m2ScNBknjWmniTspomaatN2NSSMZO41JOjt10mScDKaTxuH43iRUdDTFQM9NNZF9GUJFF2rv1CV0NKGgKhoKCopQUVAQSgCh5JFIhwh4g6iKioKKIhQEZAdKcSzIldt0MuDkvkBk311Eb7Hs7L8HnOz67JsPvcsOOCY4Tu8+4ODYNkp25yPncBwENgoOwrFRsBCODYdr5ntr3I9MbbK17Md2f3ciXqOaQu1zJ7WvJEmSJA2WsyJhdhyHhx56iKeffppYLMbEiRO58cYbqaqqGu6ijRpCCFSfC9XnwqjIx3eC/RzbweyKk26N4kRTZFpjJBp6SDR0k27swenuQMlk0M0MimPmDsq+bFvBRs/26YuOjY6DBkdNQc/WNOamCjoqGnBsTaOOjUOabOKdRpASkAFSwiGlQkqBlCZI6pDSIWlAwuXQ7XZI+xxSHoeUzybld7C9FrhtUHMvYYGwySaHuWWc3PSobSJbu42wc9Pc9t55s3edUOxscipswMESFtlM1gKRAREHHHQBmuKgKaDhoCk2qnAQloWrW0HNJfYqoOQS1sOJvoKVTWIdO9f0w8n9Z2enzuF5OzfvHD9/ksnsiWhC7/1SoAsdTfUcmT9qvSrU3L5adh4NTaioQkElN1WCED+j4kiSJEnSGTsrEubHHnuMZ599ljVr1lBaWsrDDz/MHXfcwb333ovH4xnu4p1VhCLQQl7wG70j3oVPsK9j2dixFFZPKjuNZae98z1prJ4kZncKqzue257GSaQgbfY9l6pi6waWqmM6CgoKjp1rf2yD2wFsp7cCVFggOPHruLJypO70cF2rjYqDhi2y6amFio2KjYEtsvMWKpbQsERuHhVLqNgo2ELgiNw5xVHnza2zRPZlCrByNerm4XVqbhkHW3EQigXYCMXsTcwVTBRho4hsoqw7JrpjZ6e2jYZFRlXIqCopTSWjqWQ0QUbVsFQFS1GxVBVLUbP3V1GxVQU125AD1RGotpOdt0F1yCbplsjO2wKEg6k7ZAwb03CwDJuUYREzbCyXhekysV0mliuD0CyEkkFRMygik51X0ihqGiGSOE4GBxMcE5sMOBnCmo/V+vQz/WcrSZIkSWfkrEiYn3zyST784Q9TXV0NwKpVq3jmmWd49dVXueiii4a3cGOYUBXUoAc1eOpfWhzTwoqljyTYuaTb7EkSbW7HH/C/54/bHAccy8GywDYdbMvBMp3sfNrBStmYKQczYWOmbOy0g522cUzAslEcE8W2ELaJ4mRQHRPFsVAcC5Gbz9bq2sdfG4GTS7CPnjpouamKwMnVCB+pGRa5Kbn1YOeaRvQTH4JsCputa3aOmh6ucxZkett4c9Q0O3+i8yq953WOedlHvaxc+2YVB6X3qOz84TMd/TpSh81R98GV29PJ1WwfvT/0KB747ru+zZIkSZI06EZ9whyLxWhubmby5Mm961RVZcKECdTW1sqEeZQSmoqW54G8vsm2bdskGhsJv8uId4PFcRxM0+ytWe9db9nYqQxOMoOdNLGTh+czfedTue2JNHYyA4qCMDSEriEMDfTDLxWha6CpOFp2nSME7Z1R8vILEWjYQoFs5yK5JN/BzoCTsXHSDpZpY2ccEALHAdPJVsDbNliOg+WAlbGxzQxW2sQxTcy0iZM2sTMmTiYDpgUZE2GaYFkolpl92Raqncp+ecDOJu5C4AhBtqFzdlmII9PsDwwFAtF77wQiW/Weq/fPtrPOToUjEI5A97iG9D2WJEmSpP6M+oQ5Hs82cPT5+ra69fv9vdsOa2hooKGhAYCWlhaSyezwzbZ9fA3hyXIcB9u2sW17yLvzOlzuMyn/6RiLMcO7xC1AuHWEWz+upfVAsW0bs9mhKJI3Yr4oHN42mP8GbNumubl5QN5vXdd7z3k65L97GfdQkZ/tkjTyjPqE2evNDgMci8X6rO/p6aGgoKDPuvvuu4+1a9f2Ln/pS18CGPUjiTU3Nw93EYbcWIwZZNxn4pprrgFG9/Mu3/+xZSzGPRZjlkaHUZ8w+3w+IpEIu3btYurUqQBYlsXevXuPa45xww03cOWVVwLZGubnnnsOgJKSktO+/rvVvg22w7VvkUhkSGsdx2LMIOMe6XG/VyL8s5/9jGuuuea0n/fRch8Gmox77MQ9WmIezV96pdFr1CfMAMuXL+exxx5j1qxZlJSU8Oijj6KqKkuWLOmzX2lpKaWlpQDU19fzyiuvAJzRB5LjOCiKgqIowzbC2uHrD5WxGDPIuEd73JlMpvdcp+NsuQ+nSsY9duIeizFL0sk6KxLmFStWkEgkuP3224nH40yaNIk1a9bILuUkSZIkSZKkM3ZWJMxCCFauXMnKlSuHuyiSJEmSJEnSWeasSJjPRGtr6xkdr2ka4XCYlpYWTNN87wMGwVC35xqLMYOMe6THfTLP8pk876PlPgw0GffYiXu0xHym/9+WpNMxZhNmr9eLruv87ne/O6PzdHd388YbbzB//nwCgcAAlW5kX3ssxiyvPTquret6b885RxuI53003Qd5bXnt0XTd07n2iZ51SRoswnGc/of7GgM6OzuP66v5VG3ZsoXLLruMP/7xj8ycOXOASjayrz0WY5bXHh3X9nq9hEKhfred6fM+mu6DvLa89mi67ulc+92edUkaDGO2hhkgFAqd8QMnhOCOO+5g1qxZvT1wDJXDf7oqKiqirKxsyK47FmMGGfdoj/tMn/ez5T6cKhn32Il7LMYsSSdrTCfMA6G0tJQ1a9YMdzGG1FiMGWTcY91YvQ8y7rFjLMYsSSdLXSOfjlHN7/dz4YUXDnl7s+E0FmMGGfdYi/tYY/U+yLjHTtxjMWZp9BjTbZglSZIkSZIk6b3I4XQkSZIkSZIk6V3IhFmSJEmSJEmS3oVMmCVJkiRJkiTpXcheMkaoBx98kA0bNtDc3Izb7WbGjBmsXr2aoqKiEx5z11138cILL6Dreu+65cuXs3r16iEo8eD69a9/zaOPPophGL3rFi5cyFe+8pVhLNXAufnmm2lpaeldtm2bdDrN1772NZYuXXrc/rfddhs7duxA0448wqtXr2b58uVDUt6B9uKLL/Lkk0+yd+9eEokEjz32GKqq9m7fu3cv9913H3v27MHn8/GBD3yAq6++GiHEMJZ6YMhn/Xhn8/Mun/Wx+6xLo5tMmEewz3/+81RVVZFKpfjxj3/MN77xDX7wgx+86zHnnXceX/7yl4eohENrypQp/Od//udwF2NQ3H333X2WH3/8cR5++GHmz59/wmM+8pGPsGrVqsEu2pDw+/1cfvnlpNNpfvjDH/bZFo/HWbNmDRdffDFr166loaGBtWvX4vP5+NCHPjRMJR5Y8lk/3tn6vMtnfWw/69LoJZtkjFCf/vSnmTRpErqu4/f7+chHPsLevXvp6ekZ7qJJQ+Cpp57ikksu6VPDdjabN28eF1xwASUlJcdte+WVV7Btm1WrVuFyuaiurmbFihWsW7duGEo68OSzPrbJZ/2Is/1Zl0Y3WcM8SmzcuJFIJILf73/X/TZs2MDKlSvx+XzMmTOHlStXkpeXN0SlHFy1tbW9H6TTpk1j1apV/X7ojnabN2+mvr6eyy677F33e+qpp1i3bh2hUIglS5Zw1VVX4fF4hqiUQ2fv/9/e/cdUVf9xHH8e+DrkInIJLqi5EIeDwqhRWU7UErZ0GJvLoZWlF3+smWvVVjacUkzC22ZBc1itkeuPlpo1l0RZDcbC9ZOY6wcsMqCR3Xv5YTgJMDjfP5znuxt2/FY38F5ej+1unM9933s/57D37uue++Hw44/MnTs34GvbefPm8csvvzAwMIDD4ZjA2QWfev2CydDv6vVAk63XJbQoMIeA5uZm3njjDZ588knbupUrV7J+/XqcTienT59m//797N69m2effTbk138tWrSIvLw8XC4Xvb29HDhwgF27dlFZWRl2bxy1tbVkZ2fbhoP777+f2bNnExMTQ0dHBxUVFXi9Xp544olxnOn4GBgYICYmJmDsYpgMtzdR9foFk6Xf1euBJlOvS+jRkowr3Oeff47H4+Gxxx6zXeMGkJaWRnx8PIZhMGvWLLZt20Zrays///zzOM3235OSkkJSUhKGYZCQkMDDDz9MT08PLS0tEz21oOrp6eHTTz9lxYoVtnXXXnstsbGxREREkJqayqZNmzhx4gRDQ0PjNNPx43A4OHfuXMDYxeUK4fQGql7/n8nQ7+r1sSZLr0toUmC+gtXX17N3714ef/zxS/719OVERFz49YbrP3M0DCPs9u39998nMTHxsoHpj8LhrOKfSU1N5dSpU4yMjFhjbW1tzJgxI2zeRNXrlxdu/a5eH2sy9LqELgXmK9SxY8d46aWX2LlzJ9nZ2ZetHx4eprGx0fp07vV62bdvH2lpacyaNevfnu6/7uOPP6a/vx+Avr4+9u3bh9PpJCMjY4JnFjwjIyMcP36c5cuXWwHoUvr6+vjyyy8ZHBzENE06Ozuprq5mwYIFREVFjeOMg2dkZITh4WF+//13AM6fP8/w8DCjo6MsXLiQiIgIXn/9dYaGhujo6ODtt98O2ctq/ZF6faxw73f1+uTsdQlthhlOH9nDSEFBAZGRkQHXWQUoKSkhMzMTgMLCQrZu3crtt9/O0NAQJSUldHZ2cv78eaZPn052djb33nsv8fHxE7ELQbV7925aWloYHBxk2rRpZGZmct9994VNQAA4ceIEe/fu5dVXX2X69OnWuN/v56GHHrJ+9z6fD4/HQ1dXF6OjozidThYuXMiaNWtC9izMRx99dMnLqJWVlXH99ddb12Zta2vD4XCwfPnysLk2q3p9rHDvd/X65Ox1CW0KzCIiIiIiNrQkQ0RERETEhgKziIiIiIgNBWYRERERERsKzCIiIiIiNhSYRURERERsKDCLiIiIiNhQYBYRERERsaHALCIiIiJiQ4FZRERERMSGArOITApPPfUUhmHQ3d090VMREZEQo8AsIiIiImJDgVlERERExIYCs4iIiIiIDQVmEQmqrq4uioqKSE5OJioqiszMTKqrq6376+vrMQyDgwcPUlxczIwZM4iJiaGgoICffvppzPMdPnyYm266iejoaBITE1m3bh1dXV1j6lpaWigsLMTlchEdHU16ejo7duwYU3fmzBk2bNiA0+kkLi4Ot9vNwMBAQM0HH3xATk4OTqeTadOmkZ6eTnFxcRCOjoiIhKL/TPQERCR8eL1ebrvtNgzDYNu2bbhcLmpra9m4cSP9/f088sgjVm1ZWRmGYbB9+3Z8Ph8VFRXk5eXR3NxMdHQ0AAcOHMDtdnPLLbdQXl6O1+ulsrKSxsZGvvrqK5xOJwAnT55k8eLFTJkyhS1btjBnzhx++OEH3nnnHcrKygLmWFhYSGpqKuXl5TQ1NfHKK6+QlJSEx+MB4JtvvmHlypVkZWVRWlpKVFQUbW1tNDY2jtNRFBGRK44pIhIkGzduNGfOnGl2d3cHjK9du9aMi4szBwYGzLq6OhMwr776arO/v9+qOXTokAmYlZWVpmma5vDwsJmUlGTOnz/f/O2336y6Y8eOmYC5a9cua2zJkiVmbGys2dHREfC6o6Oj1s8lJSUmYBYVFQXUrFq1ykxISLC2n3/+eRMw/X7/PzgSIiISTrQkQ0SCwjRNjhw5wl133YVpmnR3d1u3O++8k19//ZWmpiar/oEHHiA2NtbaXr16NTNnzuTdd98F4IsvvsDn87F161amTp1q1eXn55ORkUFNTQ0Afr+fhoYGioqKuOaaawLmZBjGmHk++OCDAduLFy+mp6eH/v5+AOus9dGjRxkdHf0nh0RERMKEArOIBIXf7+fMmTO8/PLLuFyugJvb7QbA5/NZ9fPmzQt4vGEYpKWl0d7eDkBHRwcA6enpY14rIyPDuv/UqVMAzJ8///+a5x9DdXx8PAB9fX0ArFmzhkWLFrFp0yaSk5NZu3Ythw4dUngWEZnEtIZZRILiYqBct24d69evv2RNVlYW33777XhOa4zIyMhLjpumCUB0dDQNDQ3U1dVRU1PDe++9x8GDB1m2bBnHjx//08eLiEj4UmAWkaBwuVzExsYyMjJCXl7en9ZdDMzff/99wLhpmrS1tZGVlQVASkoKAK2trSxbtiygtrW11bp/7ty5AHz99dfB2REgIiKC3NxccnNzee6553jmmWfYsWMHdXV1tvsmIiLhSUsyRCQoIiMjufvuuzly5Mglw6vf7w/Yfu211zh79qy1/eabb3L69GlWrFgBwM0330xSUhIvvvgiQ0NDVl1tbS3fffcd+fn5wIWgvmTJEqqrq+ns7Ax4jYtnjf+K3t7eMWM33ngjQMA8RERk8tAZZhEJmj179lBXV8ett97K5s2bue666+jt7aWpqYkPP/wwIIxeddVV5OTk4Ha78Xq9VFRUkJaWxubNmwGYMmUKHo8Ht9vN0qVLueeee6zLys2ZM4dHH33Ueq4XXniBnJwcsrOz2bJlC6mpqbS3t1NTU0Nzc/Nf2ofS0lIaGhrIz88nJSUFn89HVVUVs2fPJicnJzgHSkREQooCs4gETXJyMp999hmlpaW89dZbVFVVkZCQQGZmpnWd44uKi4s5efIk5eXlnD17ltzcXKqqqnA4HFbNhg0bcDgc7Nmzh+3btxMTE8OqVavweDzW1SwAbrjhBj755BN27tzJ/v37GRwcJCUlhcLCwr+8DwUFBbS3t1NdXU13dzeJiYksXbqUp59+mri4uL9/cEREJGQZ5t/5zlJE5G+qr6/njjvu4PDhw6xevXqipyMiInJZWsMsIiIiImJDgVlERERExIYCs4iIiIiIDa1hFhERERGxoTPMIiIiIiI2FJhFRERERGwoMIuIiIiI2FBgFhERERGxocAsIiIiImJDgVlERERExIYCs4iIiIiIDQVmEREREREb/wU1I+ph/gqGcwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 640x480 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<ggplot: (-9223363278826112789)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LyBlhJEEmfq_",
        "colab_type": "text"
      },
      "source": [
        "## Feed-Forward Zero-One Loss"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yY9zTezLFQjo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_loss = pd.DataFrame(\n",
        "columns = ['components', 'zero_one']\n",
        ")\n",
        "\n",
        "losses =[]\n",
        "\n",
        "for i in range(len(res)):\n",
        "    losses.append(res[i][2])\n",
        "components = [i for i in range(10,211,20)]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UMZyeh0zFR8n",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_loss = pd.DataFrame( data = [components,losses], index = ['components', 'zero_one']).T"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rPvWBDt4FTWN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "outputId": "ba5c37bf-cc8e-4d08-f35a-16970c011046"
      },
      "source": [
        "df_loss"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>components</th>\n",
              "      <th>zero_one</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>10.0</td>\n",
              "      <td>0.286893</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>30.0</td>\n",
              "      <td>0.197984</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>50.0</td>\n",
              "      <td>0.159028</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>70.0</td>\n",
              "      <td>0.145738</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>90.0</td>\n",
              "      <td>0.156279</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>110.0</td>\n",
              "      <td>0.148488</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>130.0</td>\n",
              "      <td>0.152154</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>150.0</td>\n",
              "      <td>0.141155</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>170.0</td>\n",
              "      <td>0.124656</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>190.0</td>\n",
              "      <td>0.125573</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>210.0</td>\n",
              "      <td>0.145738</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    components  zero_one\n",
              "0         10.0  0.286893\n",
              "1         30.0  0.197984\n",
              "2         50.0  0.159028\n",
              "3         70.0  0.145738\n",
              "4         90.0  0.156279\n",
              "5        110.0  0.148488\n",
              "6        130.0  0.152154\n",
              "7        150.0  0.141155\n",
              "8        170.0  0.124656\n",
              "9        190.0  0.125573\n",
              "10       210.0  0.145738"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rGwbQWP6FUOw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 498
        },
        "outputId": "9f216196-38eb-4a5f-dc50-aca3fba25438"
      },
      "source": [
        "ggplot(df_loss, aes(x='components', y='zero_one')) + \\\n",
        "    geom_line() + \\\n",
        "    geom_point() + \\\n",
        "    theme_bw(base_size=12) + ggtitle(\"Zero-one loss FW\") + ylab(\"loss\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<ggplot: (-9223363278721333202)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4DJIeuMIkUON",
        "colab_type": "text"
      },
      "source": [
        "# 1.4  Convolutional Neural Newtworks"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XPAm0p36kVVh",
        "colab_type": "text"
      },
      "source": [
        "## One VGG block CNN\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CHXZ0nRciBW2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 732
        },
        "outputId": "d10bce0d-8fa6-4f43-c97a-6f6728eaa7e0"
      },
      "source": [
        "\n",
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Conv2D(32, (3, 3), padding = \"same\", activation='relu', input_shape = (32, 32, 3)),\n",
        "    tf.keras.layers.Conv2D(32, (3, 3), padding = \"same\", activation='relu'),\n",
        "    tf.keras.layers.MaxPooling2D(pool_size = (2, 2)),\n",
        "    tf.keras.layers.Flatten(),\n",
        "    tf.keras.layers.Dense(128, activation = 'relu'),\n",
        "    tf.keras.layers.Dense(10, activation = 'softmax')\n",
        "])\n",
        "\n",
        "model.compile(optimizer = \"adam\", loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_11\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              (None, 32, 32, 32)        896       \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 32, 32, 32)        9248      \n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) (None, 16, 16, 32)        0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 8192)              0         \n",
            "_________________________________________________________________\n",
            "dense_22 (Dense)             (None, 128)               1048704   \n",
            "_________________________________________________________________\n",
            "dense_23 (Dense)             (None, 10)                1290      \n",
            "=================================================================\n",
            "Total params: 1,060,138\n",
            "Trainable params: 1,060,138\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Model: \"sequential_11\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              (None, 32, 32, 32)        896       \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 32, 32, 32)        9248      \n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) (None, 16, 16, 32)        0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 8192)              0         \n",
            "_________________________________________________________________\n",
            "dense_22 (Dense)             (None, 128)               1048704   \n",
            "_________________________________________________________________\n",
            "dense_23 (Dense)             (None, 10)                1290      \n",
            "=================================================================\n",
            "Total params: 1,060,138\n",
            "Trainable params: 1,060,138\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cwz11kP4iEnV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "outputId": "0a397b6b-86af-4ae0-f32b-8d0e6941d4c3"
      },
      "source": [
        "history = model.fit(x_train,y_train,\n",
        "        batch_size = 32,\n",
        "        epochs=10,\n",
        "        validation_data=(x_valid, y_valid),\n",
        "        verbose=2\n",
        "                   )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "1019/1019 - 12s - loss: 0.2258 - accuracy: 0.9251 - val_loss: 0.1457 - val_accuracy: 0.9591\n",
            "Epoch 2/10\n",
            "1019/1019 - 11s - loss: 0.0122 - accuracy: 0.9964 - val_loss: 0.1096 - val_accuracy: 0.9716\n",
            "Epoch 3/10\n",
            "1019/1019 - 11s - loss: 0.0083 - accuracy: 0.9981 - val_loss: 0.0620 - val_accuracy: 0.9841\n",
            "Epoch 4/10\n",
            "1019/1019 - 11s - loss: 0.0058 - accuracy: 0.9989 - val_loss: 0.1105 - val_accuracy: 0.9822\n",
            "Epoch 5/10\n",
            "1019/1019 - 11s - loss: 0.0071 - accuracy: 0.9986 - val_loss: 0.1179 - val_accuracy: 0.9836\n",
            "Epoch 6/10\n",
            "1019/1019 - 11s - loss: 0.0080 - accuracy: 0.9989 - val_loss: 0.1146 - val_accuracy: 0.9782\n",
            "Epoch 7/10\n",
            "1019/1019 - 11s - loss: 0.0039 - accuracy: 0.9994 - val_loss: 0.1395 - val_accuracy: 0.9790\n",
            "Epoch 8/10\n",
            "1019/1019 - 11s - loss: 0.0020 - accuracy: 0.9995 - val_loss: 0.1240 - val_accuracy: 0.9827\n",
            "Epoch 9/10\n",
            "1019/1019 - 11s - loss: 0.0045 - accuracy: 0.9995 - val_loss: 0.1455 - val_accuracy: 0.9733\n",
            "Epoch 10/10\n",
            "1019/1019 - 11s - loss: 0.0029 - accuracy: 0.9994 - val_loss: 0.1433 - val_accuracy: 0.9771\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wva4Qa2uiGZH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "90606185-813d-40de-81dc-058250c8da2f"
      },
      "source": [
        "y_pred = model.predict(x_test)\n",
        "\n",
        "cnn_loss = [] \n",
        "zol = zero_one(y_pred, y_test)\n",
        "\n",
        "print(\"Zero-one Loss: \", zol)\n",
        "cnn_loss.append(zol)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Zero-one Loss:  0.02153987167736022\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GG5q04ECiIUc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 540
        },
        "outputId": "e4785a50-1e97-4640-c27d-d599680e60c2"
      },
      "source": [
        "# plot a random sample of test images, their predicted labels, and ground truth\n",
        "fig = plt.figure(figsize=(16, 9))\n",
        "for i, idx in enumerate(np.random.choice(x_test.shape[0], size=16, replace=False)):\n",
        "    ax = fig.add_subplot(4, 4, i + 1, xticks=[], yticks=[])\n",
        "    ax.imshow(np.squeeze(x_test[idx]))\n",
        "    pred_idx = np.argmax(y_pred[idx])\n",
        "    true_idx = np.argmax(y_test[idx])\n",
        "    ax.set_title(\"{} ({})\".format(TYPES[pred_idx], TYPES[true_idx]),\n",
        "                 color=(\"green\" if pred_idx == true_idx else \"red\"))\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x648 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kajPVWLGiKXn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "6bbc10dd-f36a-4412-c125-612eecd03838"
      },
      "source": [
        "#Loss and accuracy visualisation\n",
        "\n",
        "plt.figure(1)  \n",
        "   \n",
        "   \n",
        "plt.subplot(211)  \n",
        "plt.plot(history.history['accuracy'])  \n",
        "plt.plot(history.history['val_accuracy'])  \n",
        "plt.title('model accuracy')  \n",
        "plt.ylabel('accuracy')  \n",
        "plt.xlabel('epoch')  \n",
        "plt.legend(['train', 'test'], loc='upper left')  \n",
        "   \n",
        "   \n",
        "plt.subplot(212)  \n",
        "plt.plot(history.history['loss'])  \n",
        "plt.plot(history.history['val_loss'])  \n",
        "plt.title('model loss')  \n",
        "plt.ylabel('loss')  \n",
        "plt.xlabel('epoch')  \n",
        "plt.legend(['train', 'test'], loc='upper left')  \n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BoQoU4BZ6tUW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NPxdIIMiFuNo",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "## Two VGG blocks\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Eg1bEN-WFz8b",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 482
        },
        "outputId": "747bb61a-0bcf-48e9-817f-a0655534e483"
      },
      "source": [
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Conv2D(32, (3, 3), padding = \"same\", activation='relu', input_shape = (32, 32, 3)),\n",
        "    tf.keras.layers.Conv2D(32, (3, 3), padding = \"same\", activation='relu'),\n",
        "    tf.keras.layers.MaxPooling2D(pool_size = (2, 2)),\n",
        "    tf.keras.layers.Conv2D(64, (3, 3), padding = \"same\", activation='relu'),\n",
        "    tf.keras.layers.Conv2D(64, (3, 3), padding = \"same\", activation='relu'),\n",
        "    tf.keras.layers.MaxPooling2D(pool_size = (2, 2)),\n",
        "    tf.keras.layers.Flatten(),\n",
        "    tf.keras.layers.Dense(128, activation = 'relu'),\n",
        "    tf.keras.layers.Dense(10, activation = 'softmax')\n",
        "])\n",
        "\n",
        "model.compile(optimizer = \"adam\", loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "model.summary()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_12\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_2 (Conv2D)            (None, 32, 32, 32)        896       \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 32, 32, 32)        9248      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 16, 16, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_4 (Conv2D)            (None, 16, 16, 64)        18496     \n",
            "_________________________________________________________________\n",
            "conv2d_5 (Conv2D)            (None, 16, 16, 64)        36928     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 8, 8, 64)          0         \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 4096)              0         \n",
            "_________________________________________________________________\n",
            "dense_24 (Dense)             (None, 128)               524416    \n",
            "_________________________________________________________________\n",
            "dense_25 (Dense)             (None, 10)                1290      \n",
            "=================================================================\n",
            "Total params: 591,274\n",
            "Trainable params: 591,274\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vDTNqcq_t43S",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        },
        "outputId": "1e99e209-4b95-4b30-f1b6-9e180972565b"
      },
      "source": [
        "history2 = model.fit(x_train,y_train,\n",
        "        batch_size = 32,\n",
        "        epochs=10,\n",
        "        validation_data=(x_valid, y_valid),\n",
        "        verbose=2\n",
        "\n",
        "                   )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.210605). Check your callbacks.\n",
            "1019/1019 - 14s - loss: 0.2516 - accuracy: 0.9158 - val_loss: 0.1399 - val_accuracy: 0.9559\n",
            "Epoch 2/10\n",
            "1019/1019 - 13s - loss: 0.0240 - accuracy: 0.9931 - val_loss: 0.0467 - val_accuracy: 0.9812\n",
            "Epoch 3/10\n",
            "1019/1019 - 13s - loss: 0.0077 - accuracy: 0.9973 - val_loss: 0.0338 - val_accuracy: 0.9900\n",
            "Epoch 4/10\n",
            "1019/1019 - 13s - loss: 3.4333e-05 - accuracy: 1.0000 - val_loss: 0.0311 - val_accuracy: 0.9900\n",
            "Epoch 5/10\n",
            "1019/1019 - 13s - loss: 1.1592e-05 - accuracy: 1.0000 - val_loss: 0.0367 - val_accuracy: 0.9900\n",
            "Epoch 6/10\n",
            "1019/1019 - 13s - loss: 5.8665e-06 - accuracy: 1.0000 - val_loss: 0.0342 - val_accuracy: 0.9903\n",
            "Epoch 7/10\n",
            "1019/1019 - 13s - loss: 2.9794e-06 - accuracy: 1.0000 - val_loss: 0.0304 - val_accuracy: 0.9903\n",
            "Epoch 8/10\n",
            "1019/1019 - 13s - loss: 1.6845e-06 - accuracy: 1.0000 - val_loss: 0.0328 - val_accuracy: 0.9906\n",
            "Epoch 9/10\n",
            "1019/1019 - 13s - loss: 9.3372e-07 - accuracy: 1.0000 - val_loss: 0.0308 - val_accuracy: 0.9905\n",
            "Epoch 10/10\n",
            "1019/1019 - 14s - loss: 5.2326e-07 - accuracy: 1.0000 - val_loss: 0.0337 - val_accuracy: 0.9907\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tQgAdE38uDae",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "614a589a-a58d-4df1-8e8c-9593c4c7789f"
      },
      "source": [
        "# evaluate zero-one loss\n",
        "y_pred = model.predict(x_test)\n",
        "zol = zero_one(y_pred, y_test)\n",
        "\n",
        "print(\"Zero-one Loss: \", zol)\n",
        "\n",
        "cnn_loss.append(zol)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Zero-one Loss:  0.00916590284142988\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8yLD8Y1wF8KK",
        "colab_type": "text"
      },
      "source": [
        "## Three VGG blocks"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wEwt6F37F4_D",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589
        },
        "outputId": "5860601b-ab4d-43ab-8a5a-5dfb6ece43ad"
      },
      "source": [
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Conv2D(32, (3, 3), padding = \"same\", activation='relu', input_shape = (32, 32, 3)),\n",
        "    tf.keras.layers.Conv2D(32, (3, 3), padding = \"same\", activation='relu'),\n",
        "    tf.keras.layers.MaxPooling2D(pool_size = (2, 2)),\n",
        "    tf.keras.layers.Conv2D(64, (3, 3), padding = \"same\", activation='relu'),\n",
        "    tf.keras.layers.Conv2D(64, (3, 3), padding = \"same\", activation='relu'),\n",
        "    tf.keras.layers.MaxPooling2D(pool_size = (2, 2)),\n",
        "    tf.keras.layers.Conv2D(128, (3, 3), padding = \"same\", activation='relu'),\n",
        "    tf.keras.layers.Conv2D(128, (3, 3), padding = \"same\", activation='relu'),\n",
        "    tf.keras.layers.MaxPooling2D(pool_size = (2, 2)),\n",
        "    tf.keras.layers.Flatten(),\n",
        "    tf.keras.layers.Dense(128, activation = 'relu'),\n",
        "    tf.keras.layers.Dense(10, activation = 'softmax')\n",
        "])\n",
        "\n",
        "model.compile(optimizer = \"adam\", loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "model.summary()\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_13\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_6 (Conv2D)            (None, 32, 32, 32)        896       \n",
            "_________________________________________________________________\n",
            "conv2d_7 (Conv2D)            (None, 32, 32, 32)        9248      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2 (None, 16, 16, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_8 (Conv2D)            (None, 16, 16, 64)        18496     \n",
            "_________________________________________________________________\n",
            "conv2d_9 (Conv2D)            (None, 16, 16, 64)        36928     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_4 (MaxPooling2 (None, 8, 8, 64)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_10 (Conv2D)           (None, 8, 8, 128)         73856     \n",
            "_________________________________________________________________\n",
            "conv2d_11 (Conv2D)           (None, 8, 8, 128)         147584    \n",
            "_________________________________________________________________\n",
            "max_pooling2d_5 (MaxPooling2 (None, 4, 4, 128)         0         \n",
            "_________________________________________________________________\n",
            "flatten_2 (Flatten)          (None, 2048)              0         \n",
            "_________________________________________________________________\n",
            "dense_26 (Dense)             (None, 128)               262272    \n",
            "_________________________________________________________________\n",
            "dense_27 (Dense)             (None, 10)                1290      \n",
            "=================================================================\n",
            "Total params: 550,570\n",
            "Trainable params: 550,570\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OAywgc-Gt7EC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "outputId": "d7593dd0-543e-4417-e0e4-1e0be19ba341"
      },
      "source": [
        "history3 = model.fit(x_train,y_train,\n",
        "        batch_size = 32,\n",
        "        epochs=10,\n",
        "        validation_data=(x_valid, y_valid),\n",
        "        verbose=2 \n",
        "\n",
        "                   )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "1019/1019 - 19s - loss: 0.3207 - accuracy: 0.8886 - val_loss: 0.0736 - val_accuracy: 0.9760\n",
            "Epoch 2/10\n",
            "1019/1019 - 18s - loss: 0.0340 - accuracy: 0.9895 - val_loss: 0.2608 - val_accuracy: 0.9379\n",
            "Epoch 3/10\n",
            "1019/1019 - 18s - loss: 0.0234 - accuracy: 0.9932 - val_loss: 0.0518 - val_accuracy: 0.9861\n",
            "Epoch 4/10\n",
            "1019/1019 - 18s - loss: 8.6958e-05 - accuracy: 1.0000 - val_loss: 0.0193 - val_accuracy: 0.9917\n",
            "Epoch 5/10\n",
            "1019/1019 - 18s - loss: 9.9077e-06 - accuracy: 1.0000 - val_loss: 0.0171 - val_accuracy: 0.9928\n",
            "Epoch 6/10\n",
            "1019/1019 - 18s - loss: 4.4342e-06 - accuracy: 1.0000 - val_loss: 0.0164 - val_accuracy: 0.9925\n",
            "Epoch 7/10\n",
            "1019/1019 - 18s - loss: 2.2970e-06 - accuracy: 1.0000 - val_loss: 0.0168 - val_accuracy: 0.9925\n",
            "Epoch 8/10\n",
            "1019/1019 - 18s - loss: 1.2628e-06 - accuracy: 1.0000 - val_loss: 0.0164 - val_accuracy: 0.9932\n",
            "Epoch 9/10\n",
            "1019/1019 - 18s - loss: 6.9922e-07 - accuracy: 1.0000 - val_loss: 0.0156 - val_accuracy: 0.9940\n",
            "Epoch 10/10\n",
            "1019/1019 - 18s - loss: 3.8787e-07 - accuracy: 1.0000 - val_loss: 0.0166 - val_accuracy: 0.9938\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I8inLgtsuGdz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "83821c38-f6d0-4bb9-eaa8-53df6ae416b8"
      },
      "source": [
        "# evaluate zero-one loss\n",
        "y_pred = model.predict(x_test)\n",
        "\n",
        "zol = zero_one(y_pred, y_test)\n",
        "\n",
        "print(\"Zero-one Loss: \", zol)\n",
        "\n",
        "cnn_loss.append(zol)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Zero-one Loss:  0.00916590284142988\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pRX7_710GB43",
        "colab_type": "text"
      },
      "source": [
        "## Three VGG blocks with Dropout\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "78uoa45HGGHK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 732
        },
        "outputId": "b292579e-dfa9-4d19-8787-2a25edd23832"
      },
      "source": [
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Conv2D(32, (3, 3), padding = \"same\", activation='relu', input_shape = (32, 32, 3)),\n",
        "    tf.keras.layers.Conv2D(32, (3, 3), padding = \"same\", activation='relu'),\n",
        "    tf.keras.layers.MaxPooling2D(pool_size = (2, 2)),\n",
        "    tf.keras.layers.Dropout(0.2),\n",
        "    tf.keras.layers.Conv2D(64, (3, 3), padding = \"same\", activation='relu'),\n",
        "    tf.keras.layers.Conv2D(64, (3, 3), padding = \"same\", activation='relu'),\n",
        "    tf.keras.layers.MaxPooling2D(pool_size = (2, 2)),\n",
        "    tf.keras.layers.Dropout(0.2),\n",
        "    tf.keras.layers.Conv2D(128, (3, 3), padding = \"same\", activation='relu'),\n",
        "    tf.keras.layers.Conv2D(128, (3, 3), padding = \"same\", activation='relu'),\n",
        "    tf.keras.layers.MaxPooling2D(pool_size = (2, 2)),\n",
        "    tf.keras.layers.Dropout(0.2),\n",
        "    tf.keras.layers.Flatten(),\n",
        "    tf.keras.layers.Dense(128, activation = 'relu'),\n",
        "    tf.keras.layers.Dropout(0.2),\n",
        "    tf.keras.layers.Dense(10, activation = 'softmax')\n",
        "])\n",
        "\n",
        "model.compile(optimizer = \"adam\", loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "\n",
        "model.summary()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_14\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_12 (Conv2D)           (None, 32, 32, 32)        896       \n",
            "_________________________________________________________________\n",
            "conv2d_13 (Conv2D)           (None, 32, 32, 32)        9248      \n",
            "_________________________________________________________________\n",
            "max_pooling2d_6 (MaxPooling2 (None, 16, 16, 32)        0         \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 16, 16, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_14 (Conv2D)           (None, 16, 16, 64)        18496     \n",
            "_________________________________________________________________\n",
            "conv2d_15 (Conv2D)           (None, 16, 16, 64)        36928     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_7 (MaxPooling2 (None, 8, 8, 64)          0         \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 8, 8, 64)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_16 (Conv2D)           (None, 8, 8, 128)         73856     \n",
            "_________________________________________________________________\n",
            "conv2d_17 (Conv2D)           (None, 8, 8, 128)         147584    \n",
            "_________________________________________________________________\n",
            "max_pooling2d_8 (MaxPooling2 (None, 4, 4, 128)         0         \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 4, 4, 128)         0         \n",
            "_________________________________________________________________\n",
            "flatten_3 (Flatten)          (None, 2048)              0         \n",
            "_________________________________________________________________\n",
            "dense_28 (Dense)             (None, 128)               262272    \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_29 (Dense)             (None, 10)                1290      \n",
            "=================================================================\n",
            "Total params: 550,570\n",
            "Trainable params: 550,570\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TEQPF4Hmt9JA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        },
        "outputId": "ccd411fe-3171-43d6-9d56-8eeb97a599ec"
      },
      "source": [
        "history4 = model.fit(x_train,y_train,\n",
        "        batch_size = 32,\n",
        "        epochs=10,\n",
        "        validation_data=(x_valid, y_valid),\n",
        "        verbose=2\n",
        "                   )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.151422). Check your callbacks.\n",
            "1019/1019 - 20s - loss: 0.4977 - accuracy: 0.8243 - val_loss: 0.1280 - val_accuracy: 0.9546\n",
            "Epoch 2/10\n",
            "1019/1019 - 19s - loss: 0.0688 - accuracy: 0.9775 - val_loss: 0.1614 - val_accuracy: 0.9452\n",
            "Epoch 3/10\n",
            "1019/1019 - 19s - loss: 0.0458 - accuracy: 0.9857 - val_loss: 0.0644 - val_accuracy: 0.9795\n",
            "Epoch 4/10\n",
            "1019/1019 - 19s - loss: 0.0364 - accuracy: 0.9886 - val_loss: 0.0873 - val_accuracy: 0.9727\n",
            "Epoch 5/10\n",
            "1019/1019 - 19s - loss: 0.0243 - accuracy: 0.9931 - val_loss: 0.1079 - val_accuracy: 0.9783\n",
            "Epoch 6/10\n",
            "1019/1019 - 19s - loss: 0.0328 - accuracy: 0.9902 - val_loss: 0.0799 - val_accuracy: 0.9782\n",
            "Epoch 7/10\n",
            "1019/1019 - 20s - loss: 0.0262 - accuracy: 0.9922 - val_loss: 0.2361 - val_accuracy: 0.9506\n",
            "Epoch 8/10\n",
            "1019/1019 - 19s - loss: 0.0189 - accuracy: 0.9949 - val_loss: 0.1377 - val_accuracy: 0.9713\n",
            "Epoch 9/10\n",
            "1019/1019 - 19s - loss: 0.0246 - accuracy: 0.9928 - val_loss: 0.0759 - val_accuracy: 0.9793\n",
            "Epoch 10/10\n",
            "1019/1019 - 19s - loss: 0.0190 - accuracy: 0.9952 - val_loss: 0.3236 - val_accuracy: 0.9211\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hq0aCW3iuIJV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "44cd30ee-ed2c-44ed-d3b0-7a31cb5de5b8"
      },
      "source": [
        "# evaluate zero-one loss\n",
        "y_pred = model.predict(x_test)\n",
        "zol = zero_one(y_pred, y_test)\n",
        "\n",
        "print(\"Zero-one Loss: \", zol)\n",
        "\n",
        "cnn_loss.append(zol)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Zero-one Loss:  0.07561869844179651\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zKK0HS_eGLZy",
        "colab_type": "text"
      },
      "source": [
        "## Three VGG blocks with Dropout and Batch Normalization\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fpUsbm4dGLmF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "1a2d68e2-2727-4805-fdd7-96b2aa58957c"
      },
      "source": [
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Conv2D(32, (3, 3), padding = \"same\", use_bias=False, input_shape = (32, 32, 3)),\n",
        "    tf.keras.layers.BatchNormalization(),\n",
        "    tf.keras.layers.Activation('relu'),\n",
        "    tf.keras.layers.Conv2D(32, (3, 3), padding = \"same\", use_bias=False),\n",
        "    tf.keras.layers.BatchNormalization(),\n",
        "    tf.keras.layers.Activation('relu'),\n",
        "    tf.keras.layers.MaxPooling2D(pool_size = (2, 2)),\n",
        "    tf.keras.layers.Dropout(0.2),\n",
        "    tf.keras.layers.Conv2D(64, (3, 3), padding = \"same\", use_bias=False),\n",
        "    tf.keras.layers.BatchNormalization(),\n",
        "    tf.keras.layers.Activation('relu'),\n",
        "    tf.keras.layers.Conv2D(64, (3, 3), padding = \"same\", use_bias=False),\n",
        "    tf.keras.layers.BatchNormalization(),\n",
        "    tf.keras.layers.Activation('relu'),\n",
        "    tf.keras.layers.MaxPooling2D(pool_size = (2, 2)),\n",
        "    tf.keras.layers.Dropout(0.3),\n",
        "    tf.keras.layers.Conv2D(128, (3, 3), padding = \"same\", use_bias=False),\n",
        "    tf.keras.layers.BatchNormalization(),\n",
        "    tf.keras.layers.Activation('relu'),\n",
        "    tf.keras.layers.Conv2D(128, (3, 3), padding = \"same\", use_bias=False),\n",
        "    tf.keras.layers.BatchNormalization(),\n",
        "    tf.keras.layers.Activation('relu'),\n",
        "    tf.keras.layers.MaxPooling2D(pool_size = (2, 2)),\n",
        "    tf.keras.layers.Dropout(0.4),\n",
        "    tf.keras.layers.Flatten(),\n",
        "    tf.keras.layers.Dense(128, use_bias=False),\n",
        "    tf.keras.layers.BatchNormalization(),\n",
        "    tf.keras.layers.Activation('relu'),\n",
        "    tf.keras.layers.Dropout(0.5),\n",
        "    tf.keras.layers.Dense(10, activation = 'softmax')\n",
        "])\n",
        "\n",
        "model.compile(optimizer = \"adam\", loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "model.summary()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_15\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_18 (Conv2D)           (None, 32, 32, 32)        864       \n",
            "_________________________________________________________________\n",
            "batch_normalization (BatchNo (None, 32, 32, 32)        128       \n",
            "_________________________________________________________________\n",
            "activation (Activation)      (None, 32, 32, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_19 (Conv2D)           (None, 32, 32, 32)        9216      \n",
            "_________________________________________________________________\n",
            "batch_normalization_1 (Batch (None, 32, 32, 32)        128       \n",
            "_________________________________________________________________\n",
            "activation_1 (Activation)    (None, 32, 32, 32)        0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d_9 (MaxPooling2 (None, 16, 16, 32)        0         \n",
            "_________________________________________________________________\n",
            "dropout_4 (Dropout)          (None, 16, 16, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_20 (Conv2D)           (None, 16, 16, 64)        18432     \n",
            "_________________________________________________________________\n",
            "batch_normalization_2 (Batch (None, 16, 16, 64)        256       \n",
            "_________________________________________________________________\n",
            "activation_2 (Activation)    (None, 16, 16, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_21 (Conv2D)           (None, 16, 16, 64)        36864     \n",
            "_________________________________________________________________\n",
            "batch_normalization_3 (Batch (None, 16, 16, 64)        256       \n",
            "_________________________________________________________________\n",
            "activation_3 (Activation)    (None, 16, 16, 64)        0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d_10 (MaxPooling (None, 8, 8, 64)          0         \n",
            "_________________________________________________________________\n",
            "dropout_5 (Dropout)          (None, 8, 8, 64)          0         \n",
            "_________________________________________________________________\n",
            "conv2d_22 (Conv2D)           (None, 8, 8, 128)         73728     \n",
            "_________________________________________________________________\n",
            "batch_normalization_4 (Batch (None, 8, 8, 128)         512       \n",
            "_________________________________________________________________\n",
            "activation_4 (Activation)    (None, 8, 8, 128)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_23 (Conv2D)           (None, 8, 8, 128)         147456    \n",
            "_________________________________________________________________\n",
            "batch_normalization_5 (Batch (None, 8, 8, 128)         512       \n",
            "_________________________________________________________________\n",
            "activation_5 (Activation)    (None, 8, 8, 128)         0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d_11 (MaxPooling (None, 4, 4, 128)         0         \n",
            "_________________________________________________________________\n",
            "dropout_6 (Dropout)          (None, 4, 4, 128)         0         \n",
            "_________________________________________________________________\n",
            "flatten_4 (Flatten)          (None, 2048)              0         \n",
            "_________________________________________________________________\n",
            "dense_30 (Dense)             (None, 128)               262144    \n",
            "_________________________________________________________________\n",
            "batch_normalization_6 (Batch (None, 128)               512       \n",
            "_________________________________________________________________\n",
            "activation_6 (Activation)    (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dropout_7 (Dropout)          (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_31 (Dense)             (None, 10)                1290      \n",
            "=================================================================\n",
            "Total params: 552,298\n",
            "Trainable params: 551,146\n",
            "Non-trainable params: 1,152\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5fCia4UgWeLD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L99mDAP9t-T1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "outputId": "f75668b9-9120-4db3-aa16-b9b8cdadf17a"
      },
      "source": [
        "history5 = model.fit(x_train,y_train,\n",
        "        batch_size = 32,\n",
        "        epochs=10,\n",
        "        validation_data=(x_valid, y_valid),\n",
        "        verbose=2\n",
        "                   )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "1019/1019 - 22s - loss: 0.3911 - accuracy: 0.8740 - val_loss: 0.1592 - val_accuracy: 0.9462\n",
            "Epoch 2/10\n",
            "1019/1019 - 21s - loss: 0.0870 - accuracy: 0.9735 - val_loss: 0.0608 - val_accuracy: 0.9725\n",
            "Epoch 3/10\n",
            "1019/1019 - 21s - loss: 0.0421 - accuracy: 0.9875 - val_loss: 0.0188 - val_accuracy: 0.9944\n",
            "Epoch 4/10\n",
            "1019/1019 - 21s - loss: 0.0428 - accuracy: 0.9876 - val_loss: 0.0137 - val_accuracy: 0.9958\n",
            "Epoch 5/10\n",
            "1019/1019 - 21s - loss: 0.0259 - accuracy: 0.9927 - val_loss: 0.0061 - val_accuracy: 0.9978\n",
            "Epoch 6/10\n",
            "1019/1019 - 22s - loss: 0.0252 - accuracy: 0.9924 - val_loss: 0.0168 - val_accuracy: 0.9929\n",
            "Epoch 7/10\n",
            "1019/1019 - 21s - loss: 0.0234 - accuracy: 0.9928 - val_loss: 0.0430 - val_accuracy: 0.9826\n",
            "Epoch 8/10\n",
            "1019/1019 - 21s - loss: 0.0239 - accuracy: 0.9925 - val_loss: 0.0387 - val_accuracy: 0.9829\n",
            "Epoch 9/10\n",
            "1019/1019 - 21s - loss: 0.0169 - accuracy: 0.9948 - val_loss: 0.0207 - val_accuracy: 0.9916\n",
            "Epoch 10/10\n",
            "1019/1019 - 21s - loss: 0.0140 - accuracy: 0.9956 - val_loss: 0.0163 - val_accuracy: 0.9936\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MqZKRNxuuKbI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "7440c448-0d61-4b33-e3fa-a089164dd253"
      },
      "source": [
        "# evaluate zero-one loss\n",
        "y_pred = model.predict(x_test)\n",
        "zol = zero_one(y_pred, y_test)\n",
        "\n",
        "print(\"Zero-one Loss: \", zol)\n",
        "\n",
        "cnn_loss.append(zol)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Zero-one Loss:  0.005957836846929423\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gp8Gq3QYl2--",
        "colab_type": "text"
      },
      "source": [
        "## VGG CNN results"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nasekeAkl8Lw",
        "colab_type": "text"
      },
      "source": [
        "**One VGG block**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u4nDL4ikucM0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "fdffd0b1-f5e9-4b69-f75b-00b4e0a7116a"
      },
      "source": [
        "\n",
        "plt.figure(1)  \n",
        "   \n",
        "   \n",
        "plt.subplot(211)  \n",
        "plt.plot(history.history['accuracy'])  \n",
        "plt.plot(history.history['val_accuracy'])  \n",
        "plt.title('model accuracy')  \n",
        "plt.ylabel('accuracy')  \n",
        "plt.xlabel('epoch')  \n",
        "plt.legend(['train', 'test'], loc='upper left')  \n",
        "   \n",
        "   \n",
        "plt.subplot(212)  \n",
        "plt.plot(history.history['loss'])  \n",
        "plt.plot(history.history['val_loss'])  \n",
        "plt.title('model loss')  \n",
        "plt.ylabel('loss')  \n",
        "plt.xlabel('epoch')  \n",
        "plt.legend(['train', 'test'], loc='upper left')  \n",
        "plt.show()\n",
        "\n",
        "\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Exhe_XRUmHOZ",
        "colab_type": "text"
      },
      "source": [
        "**Two VGG block**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7o6favwguecD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "880218d6-c9e9-4826-afd0-a687cd27416e"
      },
      "source": [
        "\n",
        "plt.figure(1)  \n",
        "   \n",
        "   \n",
        "plt.subplot(211)  \n",
        "plt.plot(history2.history['accuracy'])  \n",
        "plt.plot(history2.history['val_accuracy'])  \n",
        "plt.title('model accuracy')  \n",
        "plt.ylabel('accuracy')  \n",
        "plt.xlabel('epoch')  \n",
        "plt.legend(['train', 'test'], loc='upper left')  \n",
        "   \n",
        "   \n",
        "plt.subplot(212)  \n",
        "plt.plot(history2.history['loss'])  \n",
        "plt.plot(history2.history['val_loss'])  \n",
        "plt.title('model loss')  \n",
        "plt.ylabel('loss')  \n",
        "plt.xlabel('epoch')  \n",
        "plt.legend(['train', 'test'], loc='upper left')  \n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uO42DrCnmJ7o",
        "colab_type": "text"
      },
      "source": [
        "**Three VGG block**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ENoCopkvufEv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "d5b9cb2e-7217-43eb-8636-908d0dd3e692"
      },
      "source": [
        "\n",
        "plt.figure(1)  \n",
        "   \n",
        "   \n",
        "plt.subplot(211)  \n",
        "plt.plot(history3.history['accuracy'])  \n",
        "plt.plot(history3.history['val_accuracy'])  \n",
        "plt.title('model accuracy')  \n",
        "plt.ylabel('accuracy')  \n",
        "plt.xlabel('epoch')  \n",
        "plt.legend(['train', 'test'], loc='upper left')  \n",
        "   \n",
        "   \n",
        "plt.subplot(212)  \n",
        "plt.plot(history3.history['loss'])  \n",
        "plt.plot(history3.history['val_loss'])  \n",
        "plt.title('model loss')  \n",
        "plt.ylabel('loss')  \n",
        "plt.xlabel('epoch')  \n",
        "plt.legend(['train', 'test'], loc='upper left')  \n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aVPavynHmM_q",
        "colab_type": "text"
      },
      "source": [
        "**Three VGG block with Dropout**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XQGGTd7nufZ5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "27cc9afc-37bb-4681-e535-4d33400d0a52"
      },
      "source": [
        "\n",
        "plt.figure(1)  \n",
        "   \n",
        "   \n",
        "plt.subplot(211)  \n",
        "plt.plot(history4.history['accuracy'])  \n",
        "plt.plot(history4.history['val_accuracy'])  \n",
        "plt.title('model accuracy')  \n",
        "plt.ylabel('accuracy')  \n",
        "plt.xlabel('epoch')  \n",
        "plt.legend(['train', 'test'], loc='upper left')  \n",
        "   \n",
        "   \n",
        "plt.subplot(212)  \n",
        "plt.plot(history4.history['loss'])  \n",
        "plt.plot(history4.history['val_loss'])  \n",
        "plt.title('model loss')  \n",
        "plt.ylabel('loss')  \n",
        "plt.xlabel('epoch')  \n",
        "plt.legend(['train', 'test'], loc='upper left')  \n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7R_fSnGNmUqm",
        "colab_type": "text"
      },
      "source": [
        "**Three VGG block with Dropout and Batch Normalization**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-7wTMgylufmv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "a25c02ff-7370-41e2-c141-d544d53d3a72"
      },
      "source": [
        "\n",
        "plt.figure(1)  \n",
        "   \n",
        "   \n",
        "plt.subplot(211)  \n",
        "plt.plot(history5.history['accuracy'])  \n",
        "plt.plot(history5.history['val_accuracy'])  \n",
        "plt.title('model accuracy')  \n",
        "plt.ylabel('accuracy')  \n",
        "plt.xlabel('epoch')  \n",
        "plt.legend(['train', 'test'], loc='upper left')  \n",
        "   \n",
        "   \n",
        "plt.subplot(212)  \n",
        "plt.plot(history5.history['loss'])  \n",
        "plt.plot(history5.history['val_loss'])  \n",
        "plt.title('model loss')  \n",
        "plt.ylabel('loss')  \n",
        "plt.xlabel('epoch')  \n",
        "plt.legend(['train', 'test'], loc='upper left')  \n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ACRJH30KmYcR",
        "colab_type": "text"
      },
      "source": [
        "## VGG Zero-One Loss"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BXTkF0GpY-tS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        },
        "outputId": "93c29f81-f9c2-49ae-ebc5-4ee5bcddd79d"
      },
      "source": [
        "cnn_loss"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.02153987167736022,\n",
              " 0.00916590284142988,\n",
              " 0.00916590284142988,\n",
              " 0.07561869844179651,\n",
              " 0.005957836846929423]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pyTo4qloNAnp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_cnn_loss = pd.DataFrame(\n",
        "columns = ['type', 'zero_one']\n",
        ")\n",
        "\n",
        "types = [\"1VGG\",\"2VGG\",\"3VGG\",\"3VGG-drop\", \"3VGG-drop-norm\"]\n",
        "df_cnn_loss = pd.DataFrame( data = [types,cnn_loss], index = ['types', 'zero_one']).T"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DCVd9nicVyDy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "baee1afb-af6a-476c-d35a-4e49f3e513a1"
      },
      "source": [
        "df_cnn_loss"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>types</th>\n",
              "      <th>zero_one</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1VGG</td>\n",
              "      <td>0.0215399</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2VGG</td>\n",
              "      <td>0.0091659</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3VGG</td>\n",
              "      <td>0.0091659</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3VGG-drop</td>\n",
              "      <td>0.0756187</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3VGG-drop-norm</td>\n",
              "      <td>0.00595784</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            types    zero_one\n",
              "0            1VGG   0.0215399\n",
              "1            2VGG   0.0091659\n",
              "2            3VGG   0.0091659\n",
              "3       3VGG-drop   0.0756187\n",
              "4  3VGG-drop-norm  0.00595784"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7W7HR2H_Vz9u",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 498
        },
        "outputId": "ba53bf43-4ce4-44df-8db0-efbabf8f7124"
      },
      "source": [
        "ggplot(df_cnn_loss, aes(x='types', y='zero_one',group=1)) + \\\n",
        "    geom_point() + \\\n",
        "    geom_line() + \\\n",
        "    theme_bw(base_size=12) + ggtitle(\"Zero-one loss CNN\") + ylab(\"loss\")\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<ggplot: (8757990059065)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3_kbZU_nNC_i",
        "colab_type": "text"
      },
      "source": [
        "# 1.5 LeNet Neural Networks\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rpN3eUW2NFME",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 446
        },
        "outputId": "7118b9e2-674a-4061-d739-3c7d70a696d4"
      },
      "source": [
        "\n",
        "input_shape = (32,32,3)\n",
        "num_classes = 10\n",
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Conv2D(6, kernel_size=(5, 5), strides=(1, 1), activation='tanh', input_shape=input_shape, padding=\"same\"),\n",
        "    tf.keras.layers.AveragePooling2D(pool_size=(2, 2), strides=(2, 2), padding='valid'),\n",
        "    tf.keras.layers.Conv2D(16, kernel_size=(5, 5), strides=(1, 1), activation='tanh', padding='valid'),\n",
        "    tf.keras.layers.AveragePooling2D(pool_size=(2, 2), strides=(2, 2), padding='valid'),\n",
        "    tf.keras.layers.Flatten(),\n",
        "    tf.keras.layers.Dense(120, activation = 'tanh'),\n",
        "    tf.keras.layers.Dense(84, activation = 'tanh'),\n",
        "    tf.keras.layers.Dense(10, activation = 'softmax')\n",
        "])\n",
        "\n",
        "model.compile(optimizer = 'adam', loss = 'categorical_crossentropy', metrics = ['accuracy'])\n",
        "\n",
        "model.summary()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_16\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_24 (Conv2D)           (None, 32, 32, 6)         456       \n",
            "_________________________________________________________________\n",
            "average_pooling2d (AveragePo (None, 16, 16, 6)         0         \n",
            "_________________________________________________________________\n",
            "conv2d_25 (Conv2D)           (None, 12, 12, 16)        2416      \n",
            "_________________________________________________________________\n",
            "average_pooling2d_1 (Average (None, 6, 6, 16)          0         \n",
            "_________________________________________________________________\n",
            "flatten_5 (Flatten)          (None, 576)               0         \n",
            "_________________________________________________________________\n",
            "dense_32 (Dense)             (None, 120)               69240     \n",
            "_________________________________________________________________\n",
            "dense_33 (Dense)             (None, 84)                10164     \n",
            "_________________________________________________________________\n",
            "dense_34 (Dense)             (None, 10)                850       \n",
            "=================================================================\n",
            "Total params: 83,126\n",
            "Trainable params: 83,126\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9-1JbKMPNK8V",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        },
        "outputId": "25e7c6af-33c0-4469-ed1f-960b015c7db8"
      },
      "source": [
        "history = model.fit(x_train, y_train,\n",
        "                    batch_size = 32,\n",
        "                    epochs = 10,\n",
        "                    validation_data=(x_valid, y_valid),\n",
        "                    verbose = 2\n",
        "\n",
        "                    )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "WARNING:tensorflow:Method (on_train_batch_end) is slow compared to the batch update (0.219765). Check your callbacks.\n",
            "1019/1019 - 8s - loss: 0.3274 - accuracy: 0.9028 - val_loss: 0.1608 - val_accuracy: 0.9496\n",
            "Epoch 2/10\n",
            "1019/1019 - 7s - loss: 0.0138 - accuracy: 0.9977 - val_loss: 0.0819 - val_accuracy: 0.9768\n",
            "Epoch 3/10\n",
            "1019/1019 - 9s - loss: 0.0014 - accuracy: 1.0000 - val_loss: 0.0839 - val_accuracy: 0.9735\n",
            "Epoch 4/10\n",
            "1019/1019 - 7s - loss: 6.4137e-04 - accuracy: 1.0000 - val_loss: 0.0796 - val_accuracy: 0.9779\n",
            "Epoch 5/10\n",
            "1019/1019 - 7s - loss: 3.2755e-04 - accuracy: 1.0000 - val_loss: 0.0749 - val_accuracy: 0.9799\n",
            "Epoch 6/10\n",
            "1019/1019 - 7s - loss: 1.7176e-04 - accuracy: 1.0000 - val_loss: 0.0822 - val_accuracy: 0.9790\n",
            "Epoch 7/10\n",
            "1019/1019 - 7s - loss: 9.9440e-05 - accuracy: 1.0000 - val_loss: 0.0775 - val_accuracy: 0.9815\n",
            "Epoch 8/10\n",
            "1019/1019 - 8s - loss: 5.2685e-05 - accuracy: 1.0000 - val_loss: 0.0819 - val_accuracy: 0.9813\n",
            "Epoch 9/10\n",
            "1019/1019 - 7s - loss: 2.9746e-05 - accuracy: 1.0000 - val_loss: 0.0737 - val_accuracy: 0.9812\n",
            "Epoch 10/10\n",
            "1019/1019 - 7s - loss: 1.6986e-05 - accuracy: 1.0000 - val_loss: 0.0801 - val_accuracy: 0.9822\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "brru_mIGNPQs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 540
        },
        "outputId": "6cd8216f-5c82-4dea-cf91-06a13d35e9ab"
      },
      "source": [
        "y_pred = model.predict(x_test)\n",
        "\n",
        "# plot a random sample of test images, their predicted labels, and ground truth\n",
        "fig = plt.figure(figsize=(16, 9))\n",
        "for i, idx in enumerate(np.random.choice(x_test.shape[0], size=16, replace=False)):\n",
        "    ax = fig.add_subplot(4, 4, i + 1, xticks=[], yticks=[])\n",
        "    ax.imshow(np.squeeze(x_test[idx]))\n",
        "    pred_idx = np.argmax(y_pred[idx])\n",
        "    true_idx = np.argmax(y_test[idx])\n",
        "    ax.set_title(\"{} ({})\".format(TYPES[pred_idx], TYPES[true_idx]),\n",
        "                 color=(\"green\" if pred_idx == true_idx else \"red\"))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x648 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iNZWDHS1NQ8e",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "aef36399-f0b9-49f2-bf8c-c602f9260e37"
      },
      "source": [
        "plt.figure(1)  \n",
        "   \n",
        "   \n",
        "plt.subplot(211)  \n",
        "plt.plot(history.history['accuracy'])  \n",
        "plt.plot(history.history['val_accuracy'])  \n",
        "plt.title('model accuracy')  \n",
        "plt.ylabel('accuracy')  \n",
        "plt.xlabel('epoch')  \n",
        "plt.legend(['train', 'test'], loc='upper left')  \n",
        "   \n",
        "   \n",
        "plt.subplot(212)  \n",
        "plt.plot(history.history['loss'])  \n",
        "plt.plot(history.history['val_loss'])  \n",
        "plt.title('model loss')  \n",
        "plt.ylabel('loss')  \n",
        "plt.xlabel('epoch')  \n",
        "plt.legend(['train', 'test'], loc='upper left')  \n",
        "plt.show()\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IEEV1PwvbP2i",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "f2af12fd-f064-4a64-80e6-02ebbf67629d"
      },
      "source": [
        "y_pred = model.predict(x_test)\n",
        "zol = zero_one(y_pred, y_test)\n",
        "\n",
        "print(\"Zero-one Loss: \", zol)\n",
        "\n",
        "cnn_loss.append(zol)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Zero-one Loss:  0.016498625114573784\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0q1HqrTfODxB",
        "colab_type": "text"
      },
      "source": [
        "# 1.6 MobileNetV2"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FfRYQkCGODjZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "f6f4805e-9e5f-4ce3-b879-5999cc961a7c"
      },
      "source": [
        "\n",
        "model = MobileNetV2(input_shape=(32, 32, 3), alpha=1, weights=None,classes=10)\n",
        "model.compile(loss='categorical_crossentropy', \n",
        "              optimizer='adam',\n",
        "              metrics=['accuracy'])\n",
        "print(model.summary())\n",
        "print('Compiled!')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"mobilenetv2_1.00_32\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_12 (InputLayer)           (None, 32, 32, 3)    0                                            \n",
            "__________________________________________________________________________________________________\n",
            "Conv1_pad (ZeroPadding2D)       (None, 33, 33, 3)    0           input_12[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "Conv1 (Conv2D)                  (None, 16, 16, 32)   864         Conv1_pad[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "bn_Conv1 (BatchNormalization)   (None, 16, 16, 32)   128         Conv1[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "Conv1_relu (ReLU)               (None, 16, 16, 32)   0           bn_Conv1[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_depthwise (Depthw (None, 16, 16, 32)   288         Conv1_relu[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_depthwise_BN (Bat (None, 16, 16, 32)   128         expanded_conv_depthwise[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_depthwise_relu (R (None, 16, 16, 32)   0           expanded_conv_depthwise_BN[0][0] \n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_project (Conv2D)  (None, 16, 16, 16)   512         expanded_conv_depthwise_relu[0][0\n",
            "__________________________________________________________________________________________________\n",
            "expanded_conv_project_BN (Batch (None, 16, 16, 16)   64          expanded_conv_project[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_1_expand (Conv2D)         (None, 16, 16, 96)   1536        expanded_conv_project_BN[0][0]   \n",
            "__________________________________________________________________________________________________\n",
            "block_1_expand_BN (BatchNormali (None, 16, 16, 96)   384         block_1_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_1_expand_relu (ReLU)      (None, 16, 16, 96)   0           block_1_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_1_pad (ZeroPadding2D)     (None, 17, 17, 96)   0           block_1_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_1_depthwise (DepthwiseCon (None, 8, 8, 96)     864         block_1_pad[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_1_depthwise_BN (BatchNorm (None, 8, 8, 96)     384         block_1_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_1_depthwise_relu (ReLU)   (None, 8, 8, 96)     0           block_1_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_1_project (Conv2D)        (None, 8, 8, 24)     2304        block_1_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_1_project_BN (BatchNormal (None, 8, 8, 24)     96          block_1_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_2_expand (Conv2D)         (None, 8, 8, 144)    3456        block_1_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_2_expand_BN (BatchNormali (None, 8, 8, 144)    576         block_2_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_2_expand_relu (ReLU)      (None, 8, 8, 144)    0           block_2_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_2_depthwise (DepthwiseCon (None, 8, 8, 144)    1296        block_2_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_2_depthwise_BN (BatchNorm (None, 8, 8, 144)    576         block_2_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_2_depthwise_relu (ReLU)   (None, 8, 8, 144)    0           block_2_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_2_project (Conv2D)        (None, 8, 8, 24)     3456        block_2_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_2_project_BN (BatchNormal (None, 8, 8, 24)     96          block_2_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_2_add (Add)               (None, 8, 8, 24)     0           block_1_project_BN[0][0]         \n",
            "                                                                 block_2_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_3_expand (Conv2D)         (None, 8, 8, 144)    3456        block_2_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_3_expand_BN (BatchNormali (None, 8, 8, 144)    576         block_3_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_3_expand_relu (ReLU)      (None, 8, 8, 144)    0           block_3_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_3_pad (ZeroPadding2D)     (None, 9, 9, 144)    0           block_3_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_3_depthwise (DepthwiseCon (None, 4, 4, 144)    1296        block_3_pad[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_3_depthwise_BN (BatchNorm (None, 4, 4, 144)    576         block_3_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_3_depthwise_relu (ReLU)   (None, 4, 4, 144)    0           block_3_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_3_project (Conv2D)        (None, 4, 4, 32)     4608        block_3_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_3_project_BN (BatchNormal (None, 4, 4, 32)     128         block_3_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_4_expand (Conv2D)         (None, 4, 4, 192)    6144        block_3_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_4_expand_BN (BatchNormali (None, 4, 4, 192)    768         block_4_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_4_expand_relu (ReLU)      (None, 4, 4, 192)    0           block_4_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_4_depthwise (DepthwiseCon (None, 4, 4, 192)    1728        block_4_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_4_depthwise_BN (BatchNorm (None, 4, 4, 192)    768         block_4_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_4_depthwise_relu (ReLU)   (None, 4, 4, 192)    0           block_4_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_4_project (Conv2D)        (None, 4, 4, 32)     6144        block_4_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_4_project_BN (BatchNormal (None, 4, 4, 32)     128         block_4_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_4_add (Add)               (None, 4, 4, 32)     0           block_3_project_BN[0][0]         \n",
            "                                                                 block_4_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_5_expand (Conv2D)         (None, 4, 4, 192)    6144        block_4_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_5_expand_BN (BatchNormali (None, 4, 4, 192)    768         block_5_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_5_expand_relu (ReLU)      (None, 4, 4, 192)    0           block_5_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_5_depthwise (DepthwiseCon (None, 4, 4, 192)    1728        block_5_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_5_depthwise_BN (BatchNorm (None, 4, 4, 192)    768         block_5_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_5_depthwise_relu (ReLU)   (None, 4, 4, 192)    0           block_5_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_5_project (Conv2D)        (None, 4, 4, 32)     6144        block_5_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_5_project_BN (BatchNormal (None, 4, 4, 32)     128         block_5_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_5_add (Add)               (None, 4, 4, 32)     0           block_4_add[0][0]                \n",
            "                                                                 block_5_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_6_expand (Conv2D)         (None, 4, 4, 192)    6144        block_5_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_6_expand_BN (BatchNormali (None, 4, 4, 192)    768         block_6_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_6_expand_relu (ReLU)      (None, 4, 4, 192)    0           block_6_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_6_pad (ZeroPadding2D)     (None, 5, 5, 192)    0           block_6_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_6_depthwise (DepthwiseCon (None, 2, 2, 192)    1728        block_6_pad[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_6_depthwise_BN (BatchNorm (None, 2, 2, 192)    768         block_6_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_6_depthwise_relu (ReLU)   (None, 2, 2, 192)    0           block_6_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_6_project (Conv2D)        (None, 2, 2, 64)     12288       block_6_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_6_project_BN (BatchNormal (None, 2, 2, 64)     256         block_6_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_7_expand (Conv2D)         (None, 2, 2, 384)    24576       block_6_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_7_expand_BN (BatchNormali (None, 2, 2, 384)    1536        block_7_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_7_expand_relu (ReLU)      (None, 2, 2, 384)    0           block_7_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_7_depthwise (DepthwiseCon (None, 2, 2, 384)    3456        block_7_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_7_depthwise_BN (BatchNorm (None, 2, 2, 384)    1536        block_7_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_7_depthwise_relu (ReLU)   (None, 2, 2, 384)    0           block_7_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_7_project (Conv2D)        (None, 2, 2, 64)     24576       block_7_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_7_project_BN (BatchNormal (None, 2, 2, 64)     256         block_7_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_7_add (Add)               (None, 2, 2, 64)     0           block_6_project_BN[0][0]         \n",
            "                                                                 block_7_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_8_expand (Conv2D)         (None, 2, 2, 384)    24576       block_7_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_8_expand_BN (BatchNormali (None, 2, 2, 384)    1536        block_8_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_8_expand_relu (ReLU)      (None, 2, 2, 384)    0           block_8_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_8_depthwise (DepthwiseCon (None, 2, 2, 384)    3456        block_8_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_8_depthwise_BN (BatchNorm (None, 2, 2, 384)    1536        block_8_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_8_depthwise_relu (ReLU)   (None, 2, 2, 384)    0           block_8_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_8_project (Conv2D)        (None, 2, 2, 64)     24576       block_8_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_8_project_BN (BatchNormal (None, 2, 2, 64)     256         block_8_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_8_add (Add)               (None, 2, 2, 64)     0           block_7_add[0][0]                \n",
            "                                                                 block_8_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_9_expand (Conv2D)         (None, 2, 2, 384)    24576       block_8_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_9_expand_BN (BatchNormali (None, 2, 2, 384)    1536        block_9_expand[0][0]             \n",
            "__________________________________________________________________________________________________\n",
            "block_9_expand_relu (ReLU)      (None, 2, 2, 384)    0           block_9_expand_BN[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_9_depthwise (DepthwiseCon (None, 2, 2, 384)    3456        block_9_expand_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_9_depthwise_BN (BatchNorm (None, 2, 2, 384)    1536        block_9_depthwise[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "block_9_depthwise_relu (ReLU)   (None, 2, 2, 384)    0           block_9_depthwise_BN[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_9_project (Conv2D)        (None, 2, 2, 64)     24576       block_9_depthwise_relu[0][0]     \n",
            "__________________________________________________________________________________________________\n",
            "block_9_project_BN (BatchNormal (None, 2, 2, 64)     256         block_9_project[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_9_add (Add)               (None, 2, 2, 64)     0           block_8_add[0][0]                \n",
            "                                                                 block_9_project_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_10_expand (Conv2D)        (None, 2, 2, 384)    24576       block_9_add[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "block_10_expand_BN (BatchNormal (None, 2, 2, 384)    1536        block_10_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_10_expand_relu (ReLU)     (None, 2, 2, 384)    0           block_10_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_10_depthwise (DepthwiseCo (None, 2, 2, 384)    3456        block_10_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_10_depthwise_BN (BatchNor (None, 2, 2, 384)    1536        block_10_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_10_depthwise_relu (ReLU)  (None, 2, 2, 384)    0           block_10_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_10_project (Conv2D)       (None, 2, 2, 96)     36864       block_10_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_10_project_BN (BatchNorma (None, 2, 2, 96)     384         block_10_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_11_expand (Conv2D)        (None, 2, 2, 576)    55296       block_10_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_11_expand_BN (BatchNormal (None, 2, 2, 576)    2304        block_11_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_11_expand_relu (ReLU)     (None, 2, 2, 576)    0           block_11_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_11_depthwise (DepthwiseCo (None, 2, 2, 576)    5184        block_11_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_11_depthwise_BN (BatchNor (None, 2, 2, 576)    2304        block_11_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_11_depthwise_relu (ReLU)  (None, 2, 2, 576)    0           block_11_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_11_project (Conv2D)       (None, 2, 2, 96)     55296       block_11_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_11_project_BN (BatchNorma (None, 2, 2, 96)     384         block_11_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_11_add (Add)              (None, 2, 2, 96)     0           block_10_project_BN[0][0]        \n",
            "                                                                 block_11_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_12_expand (Conv2D)        (None, 2, 2, 576)    55296       block_11_add[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_12_expand_BN (BatchNormal (None, 2, 2, 576)    2304        block_12_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_12_expand_relu (ReLU)     (None, 2, 2, 576)    0           block_12_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_12_depthwise (DepthwiseCo (None, 2, 2, 576)    5184        block_12_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_12_depthwise_BN (BatchNor (None, 2, 2, 576)    2304        block_12_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_12_depthwise_relu (ReLU)  (None, 2, 2, 576)    0           block_12_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_12_project (Conv2D)       (None, 2, 2, 96)     55296       block_12_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_12_project_BN (BatchNorma (None, 2, 2, 96)     384         block_12_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_12_add (Add)              (None, 2, 2, 96)     0           block_11_add[0][0]               \n",
            "                                                                 block_12_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_13_expand (Conv2D)        (None, 2, 2, 576)    55296       block_12_add[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_13_expand_BN (BatchNormal (None, 2, 2, 576)    2304        block_13_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_13_expand_relu (ReLU)     (None, 2, 2, 576)    0           block_13_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_13_pad (ZeroPadding2D)    (None, 3, 3, 576)    0           block_13_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_13_depthwise (DepthwiseCo (None, 1, 1, 576)    5184        block_13_pad[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_13_depthwise_BN (BatchNor (None, 1, 1, 576)    2304        block_13_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_13_depthwise_relu (ReLU)  (None, 1, 1, 576)    0           block_13_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_13_project (Conv2D)       (None, 1, 1, 160)    92160       block_13_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_13_project_BN (BatchNorma (None, 1, 1, 160)    640         block_13_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_14_expand (Conv2D)        (None, 1, 1, 960)    153600      block_13_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_14_expand_BN (BatchNormal (None, 1, 1, 960)    3840        block_14_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_14_expand_relu (ReLU)     (None, 1, 1, 960)    0           block_14_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_14_depthwise (DepthwiseCo (None, 1, 1, 960)    8640        block_14_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_14_depthwise_BN (BatchNor (None, 1, 1, 960)    3840        block_14_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_14_depthwise_relu (ReLU)  (None, 1, 1, 960)    0           block_14_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_14_project (Conv2D)       (None, 1, 1, 160)    153600      block_14_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_14_project_BN (BatchNorma (None, 1, 1, 160)    640         block_14_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_14_add (Add)              (None, 1, 1, 160)    0           block_13_project_BN[0][0]        \n",
            "                                                                 block_14_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_15_expand (Conv2D)        (None, 1, 1, 960)    153600      block_14_add[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_15_expand_BN (BatchNormal (None, 1, 1, 960)    3840        block_15_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_15_expand_relu (ReLU)     (None, 1, 1, 960)    0           block_15_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_15_depthwise (DepthwiseCo (None, 1, 1, 960)    8640        block_15_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_15_depthwise_BN (BatchNor (None, 1, 1, 960)    3840        block_15_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_15_depthwise_relu (ReLU)  (None, 1, 1, 960)    0           block_15_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_15_project (Conv2D)       (None, 1, 1, 160)    153600      block_15_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_15_project_BN (BatchNorma (None, 1, 1, 160)    640         block_15_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "block_15_add (Add)              (None, 1, 1, 160)    0           block_14_add[0][0]               \n",
            "                                                                 block_15_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "block_16_expand (Conv2D)        (None, 1, 1, 960)    153600      block_15_add[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block_16_expand_BN (BatchNormal (None, 1, 1, 960)    3840        block_16_expand[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "block_16_expand_relu (ReLU)     (None, 1, 1, 960)    0           block_16_expand_BN[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_16_depthwise (DepthwiseCo (None, 1, 1, 960)    8640        block_16_expand_relu[0][0]       \n",
            "__________________________________________________________________________________________________\n",
            "block_16_depthwise_BN (BatchNor (None, 1, 1, 960)    3840        block_16_depthwise[0][0]         \n",
            "__________________________________________________________________________________________________\n",
            "block_16_depthwise_relu (ReLU)  (None, 1, 1, 960)    0           block_16_depthwise_BN[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "block_16_project (Conv2D)       (None, 1, 1, 320)    307200      block_16_depthwise_relu[0][0]    \n",
            "__________________________________________________________________________________________________\n",
            "block_16_project_BN (BatchNorma (None, 1, 1, 320)    1280        block_16_project[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "Conv_1 (Conv2D)                 (None, 1, 1, 1280)   409600      block_16_project_BN[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "Conv_1_bn (BatchNormalization)  (None, 1, 1, 1280)   5120        Conv_1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "out_relu (ReLU)                 (None, 1, 1, 1280)   0           Conv_1_bn[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "global_average_pooling2d_1 (Glo (None, 1280)         0           out_relu[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "Logits (Dense)                  (None, 10)           12810       global_average_pooling2d_1[0][0] \n",
            "==================================================================================================\n",
            "Total params: 2,270,794\n",
            "Trainable params: 2,236,682\n",
            "Non-trainable params: 34,112\n",
            "__________________________________________________________________________________________________\n",
            "None\n",
            "Compiled!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nh3LDJGzOJiG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 392
        },
        "outputId": "8f23828f-5230-40f5-9df6-0dff6163bb94"
      },
      "source": [
        "history = model.fit(x_train,y_train,\n",
        "        batch_size = 32,\n",
        "        epochs=10,\n",
        "        validation_data=(x_valid, y_valid),\n",
        "        verbose=2  )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 32607 samples, validate on 8724 samples\n",
            "Epoch 1/10\n",
            " - 152s - loss: 0.6746 - accuracy: 0.7860 - val_loss: 2.2316 - val_accuracy: 0.1977\n",
            "Epoch 2/10\n",
            " - 148s - loss: 0.1768 - accuracy: 0.9496 - val_loss: 2.2468 - val_accuracy: 0.1977\n",
            "Epoch 3/10\n",
            " - 148s - loss: 0.1177 - accuracy: 0.9681 - val_loss: 2.2137 - val_accuracy: 0.1977\n",
            "Epoch 4/10\n",
            " - 147s - loss: 0.1166 - accuracy: 0.9705 - val_loss: 2.1745 - val_accuracy: 0.1977\n",
            "Epoch 5/10\n",
            " - 147s - loss: 0.0993 - accuracy: 0.9756 - val_loss: 0.8243 - val_accuracy: 0.7942\n",
            "Epoch 6/10\n",
            " - 149s - loss: 0.0763 - accuracy: 0.9815 - val_loss: 0.9804 - val_accuracy: 0.7658\n",
            "Epoch 7/10\n",
            " - 146s - loss: 0.0685 - accuracy: 0.9836 - val_loss: 2.2230 - val_accuracy: 0.6530\n",
            "Epoch 8/10\n",
            " - 149s - loss: 0.0582 - accuracy: 0.9859 - val_loss: 9.0434 - val_accuracy: 0.4411\n",
            "Epoch 9/10\n",
            " - 148s - loss: 0.0601 - accuracy: 0.9856 - val_loss: 4.6619 - val_accuracy: 0.6551\n",
            "Epoch 10/10\n",
            " - 149s - loss: 0.0429 - accuracy: 0.9905 - val_loss: 2.4783 - val_accuracy: 0.7839\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WMvIAWyKOPKS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 540
        },
        "outputId": "81814288-ac21-44b5-8545-29a594fb3b0c"
      },
      "source": [
        "y_pred = model.predict(x_test)\n",
        "\n",
        "# plot a random sample of test images, their predicted labels, and ground truth\n",
        "fig = plt.figure(figsize=(16, 9))\n",
        "for i, idx in enumerate(np.random.choice(x_test.shape[0], size=16, replace=False)):\n",
        "    ax = fig.add_subplot(4, 4, i + 1, xticks=[], yticks=[])\n",
        "    ax.imshow(np.squeeze(x_test[idx]))\n",
        "    pred_idx = np.argmax(y_pred[idx])\n",
        "    true_idx = np.argmax(y_test[idx])\n",
        "    ax.set_title(\"{} ({})\".format(TYPES[pred_idx], TYPES[true_idx]),\n",
        "                 color=(\"green\" if pred_idx == true_idx else \"red\"))\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x648 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nUUJ3woiOQ_B",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "4a38780d-6bbd-47af-f1b2-fe561f69bf4c"
      },
      "source": [
        "\n",
        "plt.figure(1)  \n",
        "   \n",
        "   \n",
        "plt.subplot(211)  \n",
        "plt.plot(history.history['accuracy'])  \n",
        "plt.plot(history.history['val_accuracy'])  \n",
        "plt.title('model accuracy')  \n",
        "plt.ylabel('accuracy')  \n",
        "plt.xlabel('epoch')  \n",
        "plt.legend(['train', 'test'], loc='upper left')  \n",
        "   \n",
        "   \n",
        "plt.subplot(212)  \n",
        "plt.plot(history.history['loss'])  \n",
        "plt.plot(history.history['val_loss'])  \n",
        "plt.title('model loss')  \n",
        "plt.ylabel('loss')  \n",
        "plt.xlabel('epoch')  \n",
        "plt.legend(['train', 'test'], loc='upper left')  \n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mmG4dhPEbR5j",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "6490987f-0866-4d50-e9af-ea06296ff522"
      },
      "source": [
        "y_pred = model.predict(x_test)\n",
        "zol = zero_one(y_pred, y_test)\n",
        "cnn_loss.append(zol)\n",
        "print(\"Zero-one Loss: \", zol)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Zero-one Loss:  0.18973418881759854\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YLIwv_usqLED",
        "colab_type": "text"
      },
      "source": [
        "# 1.7 Summary results"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cuFIMRMarrOd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_cnn_loss = pd.DataFrame(\n",
        "columns = ['type', 'zero_one']\n",
        ")\n",
        "\n",
        "types = [\"1VGG\",\"2VGG\",\"3VGG\",\"3VGG-drop\", \"3VGG-drop-norm\",\"LeNet\",\"MobileNetV2\"]\n",
        "df_cnn_loss = pd.DataFrame( data = [types,cnn_loss], index = ['types', 'zero_one']).T\n",
        "\n",
        "\n",
        "\n",
        "df_new = df_loss.rename(columns={'components': 'types'})\n",
        "df_new['types'] = df_new['types'].astype(str)\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lkmFEyZ6v4A6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "frames = [df_new, df_cnn_loss]\n",
        "result = pd.concat(frames)\n",
        "result = result.sort_values(by=['zero_one'])\n",
        "\n",
        "result = result.reset_index(drop=True)\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "17oa_fAY1P_l",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "8ca06857-dad7-4701-e1b6-2f537c622d81"
      },
      "source": [
        "\n",
        "plt.barh(  result['types'].values,result['zero_one'].values , align='center', alpha=0.5)\n",
        "plt.xlabel('Loss')\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.title('Model Loss summary')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}