CV_la_pomme.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "CV_la_pomme.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyOweEAhdmcDWCOiRz6aCRBD",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "TPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Nanasei878/Apple-identification-and-classification-/blob/main/CV_la_pomme.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Awk79YXsiw1m",
        "outputId": "e652c708-4009-49e6-cbd6-641ccb9ee195"
      },
      "source": [
        "from google.colab import drive\n",
        "\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eNx43NQKjojx"
      },
      "source": [
        "my_data_dir = '/content/gdrive/MyDrive/Fruitdata'"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-l5R-l1Ljo7d"
      },
      "source": [
        "import os\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib.image import imread\n",
        "import cv2\n",
        "from google.colab.patches import cv2_imshow\n",
        "# Technically not necessary in newest versions of jupyter\n",
        "%matplotlib inline\n",
        "sns.set()"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PINmLfFVkn9b",
        "outputId": "3da4cd6e-8f76-48a5-886d-749bbb7e696c"
      },
      "source": [
        "# CONFIRM THAT THIS REPORTS BACK 'test', and 'train'\n",
        "os.listdir(my_data_dir) "
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['test', 'train']"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IJXTev89k1zw"
      },
      "source": [
        "#Setting the test and train data paths\n",
        "test_path = my_data_dir+'/test/'\n",
        "train_path = my_data_dir+'/train/'"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fh0kUyC1k7KS",
        "outputId": "3736d774-e49b-4dc6-9536-d2c38dc492cb"
      },
      "source": [
        "#Checking all Classes in the train data \n",
        "os.listdir(train_path)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Apple Red Yellow 2',\n",
              " 'Apple Red Yellow 1',\n",
              " 'Apple Red Delicious',\n",
              " 'Apple Red 3',\n",
              " 'Apple Red 2',\n",
              " 'Apple Golden 2',\n",
              " 'Apple Pink Lady',\n",
              " 'Apple Golden 3',\n",
              " 'Apple Granny Smith',\n",
              " 'Apple Golden 1',\n",
              " 'Apple Red 1',\n",
              " 'Apple Braeburn',\n",
              " 'Apple Crimson Snow']"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X5wNc7GHk8eo",
        "outputId": "9e163b47-0663-4293-fb43-6674440ccdb3"
      },
      "source": [
        "#Checking all Classes in the test data \n",
        "os.listdir(test_path)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Apple Red Yellow 2',\n",
              " 'Apple Red Delicious',\n",
              " 'Apple Red Yellow 1',\n",
              " 'Apple Red 1',\n",
              " 'Apple Red 3',\n",
              " 'Apple Red 2',\n",
              " 'Apple Granny Smith',\n",
              " 'Apple Golden 2',\n",
              " 'Apple Crimson Snow',\n",
              " 'Apple Pink Lady',\n",
              " 'Apple Braeburn',\n",
              " 'Apple Golden 3',\n",
              " 'Apple Golden 1']"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "qsD4G-k3oUdX",
        "outputId": "9efc1b8f-0d94-48a8-9e3b-01625c843876"
      },
      "source": [
        "#Retrieving and viewing one image from the train data\n",
        "os.listdir(train_path+'/Apple Red Yellow 1')[0]"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'246_100.jpg'"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CcHAn_kloYQf"
      },
      "source": [
        "apple_1 = train_path+'/Apple Red Yellow 1'+ '/246_100.jpg'"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r_Ug383wofEY"
      },
      "source": [
        "fruit1 = imread(apple_1)"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 288
        },
        "id": "g8rU_-HYojwI",
        "outputId": "2f7a20a3-a654-4a47-a4ea-02c85e7290f7"
      },
      "source": [
        "plt.imshow(fruit1)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f45155a80d0>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FgBIdW7po0lW",
        "outputId": "f0838e10-0860-4609-91ef-927448df2be0"
      },
      "source": [
        "fruit1.shape"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(100, 100, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VoqlIQwKhhSn"
      },
      "source": [
        "#Order the classes in ascending or and retrieve the image data accordingly\n",
        "classes = ['Apple Red Yellow 2',\n",
        " 'Apple Red Delicious',\n",
        " 'Apple Red Yellow 1',\n",
        " 'Apple Red 1',\n",
        " 'Apple Red 3',\n",
        " 'Apple Red 2',\n",
        " 'Apple Granny Smith',\n",
        " 'Apple Golden 2',\n",
        " 'Apple Crimson Snow',\n",
        " 'Apple Pink Lady',\n",
        " 'Apple Braeburn',\n",
        " 'Apple Golden 3',\n",
        " 'Apple Golden 1']\n",
        "\n",
        "classes = sorted(classes)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U5pt6wKlh5Hw",
        "outputId": "a01e73b9-04b2-46aa-e765-38268de25e03"
      },
      "source": [
        "classes"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Apple Braeburn',\n",
              " 'Apple Crimson Snow',\n",
              " 'Apple Golden 1',\n",
              " 'Apple Golden 2',\n",
              " 'Apple Golden 3',\n",
              " 'Apple Granny Smith',\n",
              " 'Apple Pink Lady',\n",
              " 'Apple Red 1',\n",
              " 'Apple Red 2',\n",
              " 'Apple Red 3',\n",
              " 'Apple Red Delicious',\n",
              " 'Apple Red Yellow 1',\n",
              " 'Apple Red Yellow 2']"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sywGQhTBjMnZ"
      },
      "source": [
        "#Retrieving the image data from the directories\n",
        "all_arrays=[]\n",
        "img_size=100\n",
        "\n",
        "for i in classes:\n",
        "    path=os.path.join(train_path,i)\n",
        "    class_num=classes.index(i)\n",
        "    for img in os.listdir(path):\n",
        "        #img_array=cv2.imread(os.path.join(path,img),\n",
        "        #                     cv2.IMREAD_GRAYSCALE)\n",
        "        img_array=cv2.imread(os.path.join(path,img))\n",
        "        img_array=cv2.cvtColor(img_array, cv2.COLOR_BGR2RGB)\n",
        "        #img_array=cv2.resize(img_array,(img_size,img_size))\n",
        "        all_arrays.append([img_array,class_num])\n",
        "    \n",
        "\n",
        "all_arrays2=[]\n",
        "#img_size=100\n",
        "\n",
        "for i in classes:\n",
        "    path=os.path.join(test_path,i)\n",
        "    class_num2=classes.index(i)\n",
        "    for img in os.listdir(path):\n",
        "        #img_array2=cv2.imread(os.path.join(path,img),\n",
        "        #                     cv2.IMREAD_GRAYSCALE)\n",
        "        img_array2=cv2.imread(os.path.join(path,img))\n",
        "        img_array2=cv2.cvtColor(img_array2, cv2.COLOR_BGR2RGB)\n",
        "        #img_array2=cv2.resize(img_array2,(img_size,img_size))\n",
        "        all_arrays2.append([img_array2,class_num2])\n"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1VU2cB_AnJ7-"
      },
      "source": [
        "#Shuffling the data up, to aid avoid overfitting\n",
        "import random\n",
        "random.shuffle(all_arrays)\n",
        "\n",
        "X_train=[]\n",
        "Y_train=[]\n",
        "for features,label in all_arrays:\n",
        "    X_train.append(features)\n",
        "    Y_train.append(label)\n",
        "X_train=np.array(X_train) #arraying\n",
        "\n",
        "import random\n",
        "random.shuffle(all_arrays2)\n",
        "\n",
        "X_test=[]\n",
        "Y_test=[]\n",
        "for features,label in all_arrays2:\n",
        "    X_test.append(features)\n",
        "    Y_test.append(label)\n",
        "X_test=np.array(X_test) #arraying\n"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RDTjRM7JrLod",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e600deff-bf8c-4871-e0cf-db12a6e04509"
      },
      "source": [
        "#normalization and reshaping\n",
        "X_train=X_train.reshape(-1,img_size,img_size,3)\n",
        "X_train=X_train/255\n",
        "X_test=X_test.reshape(-1,img_size,img_size,3)\n",
        "X_test=X_test/255\n",
        "print(\"shape of X_train= \",X_train.shape)\n",
        "print(\"shape of X_test=  \",X_test.shape)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "shape of X_train=  (6404, 100, 100, 3)\n",
            "shape of X_test=   (2134, 100, 100, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cCoscK2nt6uL"
      },
      "source": [
        "#One hot encoding\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "Y_train=to_categorical(Y_train)\n",
        "Y_test=to_categorical(Y_test)"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I_e_SMbJuKYG",
        "outputId": "f97c82d4-d3e6-4a48-fb77-8ce5fbe4c10a"
      },
      "source": [
        "Y_train.shape, Y_test.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((6404, 13), (2134, 13))"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sy6D6g6nun2V",
        "outputId": "bf7a36a5-df6d-444c-a4be-e44c97cb6961"
      },
      "source": [
        "X_train.shape, X_test.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((6404, 100, 100, 3), (2134, 100, 100, 3))"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NuJ_l-D7u5ct"
      },
      "source": [
        "from sklearn.model_selection import  train_test_split\n",
        "x_train,x_val,y_train,y_val=train_test_split(X_train,Y_train,test_size=0.3,random_state=42)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8abBWiO_Vkb8"
      },
      "source": [
        "**Building the Convolutional Neural Network Model to Classify the Apples according to the 13 classes**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tz21z8YgvBVK"
      },
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Activation, Dropout, Flatten, Dense, Conv2D, MaxPooling2D, BatchNormalization"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QVnT1NAavVmI"
      },
      "source": [
        "model = Sequential()\n",
        "\n",
        "model.add(Conv2D(filters=32, kernel_size=(3,3),input_shape=(100,100,3), activation='relu',))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "model.add(Dropout(0.35))\n",
        "\n",
        "model.add(Conv2D(filters=64, kernel_size=(3,3), activation='relu',))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "model.add(Dropout(0.35))\n",
        "\n",
        "\n",
        "model.add(Conv2D(filters=64, kernel_size=(3,3), activation='relu',))\n",
        "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
        "\n",
        "\n",
        "\n",
        "model.add(Flatten())\n",
        "\n",
        "\n",
        "model.add(Dense(128))\n",
        "model.add(Activation('relu'))\n",
        "\n",
        "model.add(Dropout(0.5))\n",
        "\n",
        "model.add(Dense(64))\n",
        "model.add(Activation('relu'))\n",
        "\n",
        "# Dropouts help reduce overfitting by randomly turning neurons off during training.\n",
        "# Here we say randomly turn off 50% of neurons.\n",
        "model.add(Dropout(0.5))\n",
        "\n",
        "# Last layer is multiclass so categorical_crossentropy is used\n",
        "\n",
        "model.add(Dense(13, activation='softmax'))\n",
        "\n",
        "model.compile(loss='categorical_crossentropy',\n",
        "              optimizer='adam',\n",
        "              metrics=['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Cb7Dkz-LxXfi",
        "outputId": "8b0cadf1-a2f8-41bd-ca34-92542644be1e"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              (None, 98, 98, 32)        896       \n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) (None, 49, 49, 32)        0         \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 49, 49, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 47, 47, 64)        18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 23, 23, 64)        0         \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 23, 23, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 21, 21, 64)        36928     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 10, 10, 64)        0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 6400)              0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 128)               819328    \n",
            "_________________________________________________________________\n",
            "activation (Activation)      (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 64)                8256      \n",
            "_________________________________________________________________\n",
            "activation_1 (Activation)    (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 13)                845       \n",
            "=================================================================\n",
            "Total params: 884,749\n",
            "Trainable params: 884,749\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1KugShBAxbX9"
      },
      "source": [
        "from tensorflow.keras.callbacks import EarlyStopping"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0ZS8HOFjxbhp"
      },
      "source": [
        "early_stop = EarlyStopping(monitor='val_loss',patience=2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0aIaa31_yJNr"
      },
      "source": [
        "image_shape = (100,100,3)\n",
        "batch_size = 16"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9EKqu3fYyR_X"
      },
      "source": [
        "#Augmenting the data, this helps control overfitting\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mB2uXCMfyWjN"
      },
      "source": [
        "image_gen = ImageDataGenerator(rotation_range=20, # rotate the image 20 degrees\n",
        "                               width_shift_range=0.10, # Shift the pic width by a max of 10%\n",
        "                               height_shift_range=0.10, # Shift the pic height by a max of 10%\n",
        "                               #rescale=1/255, Rescale the image by normalzing it.\n",
        "                               shear_range=0.1, # Shear means cutting away part of the image (max 10%)\n",
        "                               zoom_range=0.1, # Zoom in by 10% max\n",
        "                               horizontal_flip=True, # Allo horizontal flipping\n",
        "                               fill_mode='nearest' # Fill in missing pixels with the nearest filled value\n",
        "                              )\n",
        "image_gen.fit(x_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G8jA7c_4zAQt",
        "outputId": "80726dc1-0ddc-4f2b-b6f6-cab6953155fb"
      },
      "source": [
        "history=model.fit_generator(image_gen.flow(x_train,y_train,batch_size=batch_size),epochs=20,\n",
        "                            validation_data=(x_val,y_val),steps_per_epoch=x_train.shape[0] //batch_size,\n",
        "                            callbacks=[early_stop]\n",
        "                           )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/engine/training.py:1972: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
            "  warnings.warn('`Model.fit_generator` is deprecated and '\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "280/280 [==============================] - 89s 319ms/step - loss: 0.4879 - accuracy: 0.8318 - val_loss: 0.1167 - val_accuracy: 0.9807\n",
            "Epoch 2/20\n",
            "280/280 [==============================] - 89s 319ms/step - loss: 0.3818 - accuracy: 0.8708 - val_loss: 0.1134 - val_accuracy: 0.9875\n",
            "Epoch 3/20\n",
            "280/280 [==============================] - 90s 321ms/step - loss: 0.3485 - accuracy: 0.8793 - val_loss: 0.0616 - val_accuracy: 0.9823\n",
            "Epoch 4/20\n",
            "280/280 [==============================] - 91s 324ms/step - loss: 0.4348 - accuracy: 0.8491 - val_loss: 0.0634 - val_accuracy: 0.9901\n",
            "Epoch 5/20\n",
            "280/280 [==============================] - 90s 320ms/step - loss: 0.3278 - accuracy: 0.8858 - val_loss: 0.0960 - val_accuracy: 0.9683\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RAOQHA540r33"
      },
      "source": [
        "losses = pd.DataFrame(model.history.history)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "qwnjXTjr1V6c",
        "outputId": "a5e47f22-43b4-4ee6-8525-13cf6aec0744"
      },
      "source": [
        "plt.figure(figsize=(10,8))\n",
        "losses[['loss','val_loss']].plot()\n",
        "plt.savefig('pomme_losses')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JMNpY449kMFe"
      },
      "source": [
        "The model recorded an impressive validation loss of almost 0.1"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "-6gATF5U1bPo",
        "outputId": "59aa5c8a-e817-4de2-9449-3fc945143752"
      },
      "source": [
        "plt.figure(figsize=(10,8))\n",
        "losses[['accuracy','val_accuracy']].plot()\n",
        "plt.savefig('pomme_accuracy')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "By9fEOELkeek"
      },
      "source": [
        "The model had a high accuracy even though the final epoch did not record the highest accuracy."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KGCQUkIjkyH0"
      },
      "source": [
        "# Predicting on an unseen data and Evaluation matrices"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3xxq4uva1hnA"
      },
      "source": [
        "y_true=np.argmax(y_val,axis=1)\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rK4uLo502IIl"
      },
      "source": [
        "y_pred = np.argmax(model.predict(x_val), axis=-1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IhNMC2qN2Y5C"
      },
      "source": [
        "from sklearn.metrics import classification_report,confusion_matrix"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yBmXB4jF2fZ3",
        "outputId": "cd99feef-6a46-49eb-b412-1702ffb061aa"
      },
      "source": [
        "print(classification_report(y_true,y_pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.99      0.99       146\n",
            "           1       0.99      1.00      0.99       132\n",
            "           2       0.96      0.99      0.97       148\n",
            "           3       0.99      0.99      0.99       166\n",
            "           4       0.99      0.65      0.79       138\n",
            "           5       0.74      1.00      0.85       139\n",
            "           6       1.00      0.99      0.99       150\n",
            "           7       1.00      1.00      1.00       145\n",
            "           8       0.99      1.00      0.99       158\n",
            "           9       1.00      1.00      1.00       105\n",
            "          10       1.00      1.00      1.00       149\n",
            "          11       1.00      0.96      0.98       144\n",
            "          12       1.00      1.00      1.00       202\n",
            "\n",
            "    accuracy                           0.97      1922\n",
            "   macro avg       0.97      0.97      0.97      1922\n",
            "weighted avg       0.97      0.97      0.97      1922\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CQP86CBHYxBt"
      },
      "source": [
        "This model has a precision of 97% thus of all positive predictions, 97% are accurate. It also has a recall of 97% meaning of all predictions, 97% of positive predictions were correct. 97% F1-score implies that the harmornic mean of precision and recall is 97% that confirms the superb precision and recall of the model.\n",
        "\n",
        "As much as this model is great all round, in this case high precison is the most sufficient. This is because with this model, we intend getting more true positives, thus getting positive preditions right at a high rate. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 736
        },
        "id": "h3NdF_GQ3G0p",
        "outputId": "43810219-6298-48f7-a818-fc555fc9e8fc"
      },
      "source": [
        "plt.figure(figsize=(12,12))\n",
        "sns.heatmap(confusion_matrix(y_true,y_pred),annot=True,lw =2,cbar=False)\n",
        "plt.ylabel(\"True Values\")\n",
        "plt.xlabel(\"Predicted Values\")\n",
        "plt.title(\"CONFUSSION MATRIX VISUALIZATION\")\n",
        "plt.savefig('confmatrixpomme')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Iz549mTJlCqtWrcLlcnHgwAGaNGlS4LiffvppgY9fffVVcnNz6dWrV6E1jhkzplS/5p06dcLpdDJ9+nRGjRqF0+nkyiuvZO7cuUVewgEn/kLVrVs35s6dW+zjiMi5Z/MV999NRUREREREl3CIiIiIiFihAVpERERExAIN0CIiIiIiFmiAFhERERGxQAO0iIiIiIgFGqBFRERERCwok+8DnTGsR7CXcNZiJnzkv+1wVgviSs6ex/3Hb4Ur6y1gVo9JLWBWj0ktYFbPqS3OiOpBXEnpcOfu9t826bkp6y1gVo9JLVCwpyg6Ay0iIiIiYoEGaBERERERCzRAi4iIiIhYoAFaRERERMQCDdAiIiIiIhZogBYRERERsUADtIiIiIiIBRqgRUREREQs0AAtIiIiImKBBmgREREREQs0QIuIiIiIWKABWkRERETEAg3QIiIiIiIWaIAWEREREbFAA7SIiIiIiAUaoEVERERELNAALSIiIiJigQZoERERERELyuQAHd6qI1EDnyd63PtE3DygWJ8T2fcpYiZ8BPbST3Z2vIPoJ2cR/eQsnB3v8G+3VapC5J3DiR71JtGjZxF57yhslaqW+vFPp0KF8nz4wUyOpW5h65Z19OrV/Zwdu7SZ1AJm9ZjUAuoJZSa1OJ1OXnt1Ils2f82Rw7+yYf0yrrvuH8FeVomZ9NyAWT0mtUDo9DiCctSz5D1+FPfyD3HUbwbhzr/c39GsDTZ7WImPF1anMc5rbyX79ScLP3bLDjgaX07Wy4+Az0fUfaPxHj2AZ92n2CKj8fyygZwPXoHcbJzX3kLUXcPJevGhEq/FiqlTxuF251G1elOaNW3MwgVvk5SUTHLy5nNy/NJkUguY1WNSC6gnlJnU4nCEsXv3Xq5tfxO7du2hY8d2zJk9nUsvu5adO3cHe3mWmfTcgFk9JrVA6PSUyTPQ+T+vIz95Pb6s9L/eOdKF89pbyP34nUJ32SpXI/K+0USPnoVr6FQcTa6wvJbwy67G/eVCfMeO4Dt+FPeqhYS3aAeAd3cKng3LITsDvPm4Vy3CHl8dXDGWj2OVyxVFjxs7MfqpF8jMzGLN2g0sWvwZ/+zdM+DHLm0mtYBZPSa1gHpCmUktAFlZ2Tz9zCR27tyNz+dj6dLl7NjxG5c2bxLspVlm2nNjUo9JLRBaPedsgE5NTeWXX37hl19+ITU19VwdFud1vcn7+hN86X86ZngEUfeNxvP9KjKfvpucOZOI6H4/tvjqlh7fnlAD774d/o+9e3dgT6hR5L5htRvhPZ4KWRkWK6yrX78OHk8+W7Zs829LSvqZRo0aBPzYpc2kFjCrx6QWUE8oM6mlKPHxlahXrzbJv2wK9lIsM+25ManHpBYIrZ6AX8Kxa9cuRo0aRXJyMvHx8QAcPHiQRo0aMWbMGGrVqhWwY9urXUhYrYZkL/o3tnLnFbjPcVELfKkH8WxcAYB373Y8P32No8kV5H3+n+IfxBmJLyfL/6EvJwtbRFSh3WzlziOie1/ci98sWYxFMdHRHD9e8Az9sWPpxMZEn5PjlyaTWsCsHpNaQD2hzKSWP3M4HMyaNZV33v2QTZu2Bns5lpn23JjUY1ILhFZPwAfoxx57jNtvv50333wT++8/wOf1elm0aBHDhg3j/fffD8yBbTYibryf3IX/Bq+38N0VKmOvUY/op065tMNux/PtSgDCr74R59U9/NtxOAvsm/nU7z8s6M7BFhGF7+TjRkbhy80ueLDoOKLufZK8rz7B88Pq0io8o4zMTOLiYgtsi4uLJT0j85wcvzSZ1AJm9ZjUAuoJZSa1nMpms/HWmy/jducxaNATwV5OiZj23JjUY1ILhFZPwAfotLQ0unbtWmCb3W6nW7duTJ8+PXAHjojCXu1CInsPAcBmOzG8u0bMIOfdifjSDpO/PZmcmWOK/PS8/yaS999E4Mw/ROg98Bv2qrXw7k4BwF6lFt4Dv/2xQ1Q0Ufc+iSd5A3lfzCvNwjPavHkbDkcYdevWJiVlOwBNmjQiObns/fOgSS1gVo9JLaCeUGZSy6lef20i8fGV6drtTjweT7CXUyKmPTcm9ZjUAqHVE/BroMuXL8/ixYvx+Xz+bT6fj4ULFxIXF1eyB7XbwRF+4n9PvX2qnCyyxt1H9ktDTvx5cxwA2VMexfvbFjy/bMReqSqO5m3BHgb2MOzV62KLr2ZpKXnf/BfnVV2xxVXEFluB8DZdyfv9shAiooi690nyd/6K+5N3S9ZaQllZ2STO/5inRg/F5YriilYt6NqlA+/OPndDfGkxqQXM6jGpBdQTykxqOemVV56jYcN63NijDzk5OcFeTomZ9tyY1GNSC4RWT8DPQI8fP57Ro0czduxYEhISADhw4AANGzZk/PjxJXpMZ7ubcba/1f9x+KVX4/7sffI2Lsf1yMtkTRqEL+0wvoy0Pz4pPBzgxDavF/I9ZM8cQ0Tnu4no3AdsdvL37cC9+E18FJ9n3afYKybgGjwZgLz1n+NZ9ykAjsYtCatRD3tCDcIv++P9PbMmDSpRt1UDBo5g5owX2bcniSNHUuk/8PEy+7Y1JrWAWT0mtYB6QplJLRdcUI37+95BTk4Ov+36zr+9f//hzH0vMYgrKxmTnhswq8ekFgidHpvv1FPDAXT06FH27dsHQJUqVahYsWKJHytjWI/SWlbQxEz4yH/b4bR21jvUeNx7/LfLeguY1WNSC5jVY1ILmNVzaoszwto7M4Uid+4f7ytt0nNT1lvArB6TWqBgT1HO2S9SqVix4lkNzSIiIiIioaBM/iIVEREREZFg0QAtIiIiImKBBmgREREREQs0QIuIiIiIWKABWkRERETEAg3QIiIiIiIWaIAWEREREbFAA7SIiIiIiAUaoEVERERELNAALSIiIiJigQZoERERERELNECLiIiIiFigAVpERERExAIN0CIiIiIiFmiAFhERERGxQAO0iIiIiIgFGqBFRERERCzQAC0iIiIiYoHN5/P5gr0IEREREZGyQmegRUREREQs0AAtIiIiImKBI9gLKAmHs1qwl3DWPO49/ttpvdsFcSVnr/zsFf7bpj03Zb3HpBYwq8ekFjCrx6QWMKvHpBYwq8ekFijYUxSdgRYRERERsUADtIiIiIiIBRqgRUREREQs0AAtIiIiImKBBmgREREREQs0QIuIiIiIWKABWkRERETEAg3QIiIiIiIWaIAWEREREbFAA7SIiIiIiAUaoEVERERELNAALSIiIiJigQZoERERERELNECLiIiIiFigAVpERERExAIN0CIiIiIiFmiAFhERERGxQAP0KSpUKM+HH8zkWOoWtm5ZR69e3c/JcZ3tuxPz9HTKvfUJrgceO+1+4X//B7EvzKLcjIXETZuH64FhEOUq9fVEXH8Tcf/3IeVmLiKq76PgCAfAFlceV/8niHvlP5SbsZCY0VMIu7BhqR+/KMF6bgLFpB6TWkA9ocykFjCrx6QWMKvHpBYInR5HUI4aoqZOGYfbnUfV6k1p1rQxCxe8TVJSMsnJmwN6XF/qEXLnv4ujyd+wOZ2n3c+z+ScyxgzEl3EcIiJx3fsIUTffQ/bbr1g6nr1SAjFPTOb4w7cXus9xSQsiuvYiY9xQfKmHiR48lsiefch5fwa2iCjyt/1K9uxp+I6l4by6I9GPPme5tySC9dwEikk9JrWAekKZSS1gVo9JLWBWj0ktEDo9Np/P5zunRywFDme1Un9MlyuKwweTadr8GrZs2QbAW29OYe/efYwYWfpDose9x387rXc7ACJvvgd7xUpkvfb8Xz9ARCSuux/GFluOzBceB8BW/jyi7hqIo2ETfDnZ5H7yIe5liYU+9UwDtKv/SLyH9pPzn38D4GjcHNe/RnK8/01FLqPczEXYoqL9H5v23JT1HpNawKyeQLeAWT0mtYBZPSa1gFk9JrVA8HqKoks4fle/fh08nnz/EwKQlPQzjRo1COKqCgurfzHlZiyk/BtLCf/bVeR+Mu/EHTYb0UPHkb9rK8cH3ELms0OJuL4njktaWHv8arXI37XV/3H+zq3Yy1fEFhNXeN+aF0JY+Fn1FEdZeW6Ky6Qek1pAPaHMpBYwq8ekFjCrx6QWCK0eDdC/i4mO5vjx9ALbjh1LJzYm+jSfERz5m3/iWN+uHBtwC7lL/oP30H4Awuo0wB5bjtzEdyDfg/fQPtxfLCG8VTtrB4iMwpeV6f/Ql33iti0yquB+US5cDz5OTuKss+opjrLy3BSXST0mtYB6QplJLWBWj0ktYFaPSS0QWj1BvQa6S5cuLFq0KJhL8MvIzCQuLrbAtri4WNIzMk/zGcHlSz1MXtJ6XANGkfHEA9grJWCrUIlyry/8Yye7Hc+mHwEIv6Idrj4P/77dBhFRBfY9/vh9+I4chJxsbKf8YOLJyzN8Odl/PG64k5gh4/Ck/ELuwrlE3do3cKGUvefmr5jUY1ILqCeUmdQCZvWY1AJm9ZjUAqHVE/ABOiUl5bT3paamBvrwxbZ58zYcjjDq1q1NSsp2AJo0aURy8qYgr+wM7GGEJVQBwHvkEN5D+0gfcmeRu+atXcGxtStOfNoZroHO37ODsAsuJG/dSgDCLrgQb9rREz+4COAIJ/qRp/EePUT2vycFIKqwMvncnIFJPSa1gHpCmUktYFaPSS1gVo9JLRBaPQG/hKNz58488MAD3H///YX+pKWlBfrwxZaVlU3i/I95avRQXK4ormjVgq5dOvDu7HmBP7jdDuHhJ/7XHvbH7T8Jv+IabOfFA2CrlEDULfeS9/N3AORv/RVfdjYRnXtBuBNsduzVaxFWx9p1Qe5Vn+K8uhP2ajWxuaKJ6P5P3F8uO3FnWBjRg54Cdy5Zr46Hc/Tzp0F9bgLApB6TWkA9ocykFjCrx6QWMKvHpBYIrZ6An4GuVq0ac+bMISEhodB9bdu2DfThLRkwcAQzZ7zIvj1JHDmSSv+Bj5+Tt0WJ7H4HkT3v8n/svLI9OfNmkbvyY+Kef5Pjj92N78hBwqrVJOq2+7G5YvBlZZD3/Tpy3p954pN8XjInjiCq94PEvTQHwsPx7vuNnP+8YWktnqQN5C5+j5iRL2JzRuBev4qceW8BEFavMeGXtsKXm0O5Gef20ptgPTeBYlKPSS2gnlBmUguY1WNSC5jVY1ILhE5PwN/GbsKECbRv355LL7200H3PPPMMTzzxhOXHDNTbo5xLRb2NXVlVfvYK/23Tnpuy3mNSC5jVY1ILmNVjUguY1WNSC5jVY1IL/PXb2AX8DPSwYcNOe19JhmcRERERkWDS29iJiIiIiFigAVpERERExAIN0CIiIiIiFmiAFhERERGxQAO0iIiIiIgFGqBFRERERCzQAC0iIiIiYoEGaBERERERCzRAi4iIiIhYoAFaRERERMQCDdAiIiIiIhZogBYRERERsUADtIiIiIiIBRqgRUREREQs0AAtIiIiImKBBmgREREREQs0QIuIiIiIWKABWkRERETEApvP5/MFexEiIiIiImWFzkCLiIiIiFigAVpERERExAJHsBdQEg5ntWAv4ax53Hv8t8t6z6ktmaNuCeJKSkf00//x33ZGVA/iSs6eO3e3/3ZZf52Bud83Zb0FzOoxqQXM6jGpBczqMakFCvYURWegRUREREQs0AAtIiIiImKBBmgREREREQs0QIuIiIiIWKABWkRERETEAg3QIiIiIiIWaIAWEREREbFAA7SIiIiIiAUaoEVERERELNAALSIiIiJigQZoERERERELNECLiIiIiFigAVpERERExAIN0CIiIiIiFmiAFhERERGxQAO0iIiIiIgFGqBFRERERCzQAH2KChXK8+EHMzmWuoWtW9bRq1f3YC/prASjx9HyOiL7PYdr9GycN/7r9Ps1b4trzHu4nnjb/8deq1Gprye8Q29cj/8b1+P/JrxDb/9223lViLj9UVzDZ+J6/A0i7hyBrVKVUj9+UZxOJ6+9OpEtm7/myOFf2bB+Gddd949zcuxA0PdNaDOpx6QWMKvHpBYwq8ekFgidHkdQjhqipk4Zh9udR9XqTWnWtDELF7xNUlIyycmbg720EglGjy89lbyVHxFWtyk4nGfc1/vbZnJmPnlWx7PXaoSz3c3kvDGm0H2OFtfiuOhvZP/fo4I83SkAACAASURBVODzEdlnFL7Ug3g2fIYt0kX+rxvJTZwGuTmE/+MmIm9/jOwpg89qPcXhcISxe/derm1/E7t27aFjx3bMmT2dSy+7lp07dwf8+KVN3zehzaQek1rArB6TWsCsHpNaIHR6dAb6dy5XFD1u7MTop14gMzOLNWs3sGjxZ/yzd89gL61EgtWTn7ye/F824MtKP6vHsVWqSuRdT+B6/A2iBr1E2MWtLD+Go3lb8tYswnf86InBfs0iHM3bAuDdsxXPt19AdiZ488lbuxh75WoQFXNW6y6OrKxsnn5mEjt37sbn87F06XJ27PiNS5s3CfixS5u+b0KbST0mtYBZPSa1gFk9JrVAaPVogP5d/fp18Hjy2bJlm39bUtLPNGrUIIirKrmy0GOvUgvX8JlEDXqJ8Kt7gv33l2N4BJF9nsCTtJqsCfeR+5+XiOh8L7bK1aw9fnwNvPt3+j/27t+JPb5GkfuG1WqENz0VsjNK3FNS8fGVqFevNsm/bDrnxz5bZeF1ZoV6QpdJLWBWj0ktYFaPSS0QWj0BH6BTU1MZOXIk99xzD7Nnzy5w38CBAwN9+GKLiY7m+PGCZ02PHUsnNiY6SCs6O6Hek7/jF7KnDiFrQl9y3nsRxyWtCb+yKwBhDS7Fl3oIz3f/Ba8X774deJLX4bB6FtoZiS8ny/+hLycLW0RUod1scRVxdr4X98dvn0VRyTgcDmbNmso7737Ipk1bz/nxz1aov86sUk/oMqkFzOoxqQXM6jGpBUKrJ+DXQI8ePZrq1avTtm1b5s6dy1dffcVLL72Ew+Hgt99+C/Thiy0jM5O4uNgC2+LiYknPyAzSis5OqPf4Ug/+cfvAb7j/+yHhrbuS9+V87OUrY69eD9eIN//4BHsYnh++BCD8qm6EX9Xdvx1HeIF9s569+8QNdw62iCh8v2+3RUbhy80uuBBXLJF3PYFn/TLyf1xT2plnZLPZeOvNl3G78xg06IlzeuzSEuqvM6vUE7pMagGzekxqAbN6TGqB0OoJ+AC9Y8cOpkyZAkD79u0ZO3YsDzzwANOmTQv0oS3ZvHkbDkcYdevWJiVlOwBNmjQiObns/bM6lMEenw9sJ256jx3BuyOZnFnPFLlr3qoF5K1aAJz5hwi9B3/Dfn4tvHtOnNm1n18L78FT/tIWGX3iUpFfN5K3MrF0e4rh9dcmEh9fma7d7sTj8Zzz45eGMvc6+wvqCV0mtYBZPSa1gFk9JrVAaPUE/BKOvLw8/22bzcbo0aOpX78+999/P7m5uYE+fLFlZWWTOP9jnho9FJcriitataBrlw68O3tesJdWIkHrsdvBEQ52O7ZTbv9ZWL1mEF0OOPEDg86re5L/60YA8jd9g61SFRxNrzpxhtkehr3ahZavgfZ8/yXhrW/AFlsBW2wFwlt3xvPdyhN3RkQReddIvDs3kffZnLNrLoFXXnmOhg3rcWOPPuTk5Jzz45cWfd+ENpN6TGoBs3pMagGzekxqgdDqCfgZ6Bo1arBhwwb+9re/+bcNGzaMSZMmMWPGjEAf3pIBA0cwc8aL7NuTxJEjqfQf+HiZfZsXCE5PeNueONvd7P/Y0awN7hUf4Pl2BVEDJ5M9dTC+Y0cIq3MJET3+deI65YxjeH5Y9cdZYHcOObOewXn9XTg73gU2G979O3F/PMt/OUZxeDZ8hq1CPFEDXgQg75vleDZ8dmJdF11OWPW62OOr42h+tf9zsqcG/m3sLrigGvf3vYOcnBx+2/Wdf3v//sOZ+965PxN+tvR9E9pM6jGpBczqMakFzOoxqQVCp8fm8/mszCSWpaWlYbPZKFeuXKH7UlJSqFu3ruXHdDitnYkMRR73Hv/tst5zakvmqFuCuJLSEf30f/y3nRHVg7iSs+fO/eN9pcv66wzM/b4p6y1gVo9JLWBWj0ktYFaPSS1QsKcoAT8DXb58+dPeV5LhWUREREQkmPQ+0CIiIiIiFmiAFhERERGxQAO0iIiIiIgFGqBFRERERCzQAC0iIiIiYoEGaBERERERCzRAi4iIiIhYoAFaRERERMQCDdAiIiIiIhZogBYRERERsUADtIiIiIiIBRqgRUREREQs0AAtIiIiImKBBmgREREREQs0QIuIiIiIWKABWkRERETEAg3QIiIiIiIWaIAWEREREbHA5vP5fMFehIiIiIhIWaEz0CIiIiIiFmiAFhERERGxwBHsBZSEw1kt2Es4ax73Hv/tst5jUgsU7MmePSqIKzl7Ub2f9t8ON+C5yTPotWby901Z7zGpBczqMakFzOoxqQUK9hRFZ6BFRERERCzQAC0iIiIiYoEGaBERERERCzRAi4iIiIhYoAFaRERERMQCDdAiIiIiIhZogBYRERERsUADtIiIiIiIBRqgRUREREQs0AAtIiIiImKBBmgREREREQs0QIuIiIiIWKABWkRERETEAg3QIiIiIiIWaIAWEREREbFAA7SIiIiIiAUaoEVERERELNAAfYoKFcrz4QczOZa6ha1b1tGrV/dgL+msmNQTrJb31m/h9hmf8bdxHzJqwfoz7rs7NYOBc1dxxfiPuPqF+Uz+7IdSX887X2/imhcX0nr8R4xeuB63Jx+Ao5k5DJ/3Fe0nLeTKCYnc9cZyftx9pNSPX5R/PdiHr79aSkb6Nv49c/I5OWYgmfR9A2b1mNQCZvWY1AJm9ZjUAqHT4wjKUUPU1CnjcLvzqFq9Kc2aNmbhgrdJSkomOXlzsJdWIib1BKulcmwU913ViK+27ifn92G1KHn5+fR7dyW3tqjL8z1bYbfb2Hkk3fLx9qRlct+sL/h4UOdC961N2c+ba37l9TuuJj42isH/WcP0//7MoGubkOX20LhqRYZ0aEbF6AgSv9vOwLmrWDEgk+joaMvrsGLvvgM8+9zLdGh/NVFRkQE91rlg0vcNmNVjUguY1WNSC5jVY1ILhE6PzkD/zuWKoseNnRj91AtkZmaxZu0GFi3+jH/27hnspZWIST3BbLnmouq0a1iNclHOM+634PsdVI6J4o5WDYhyOohwhFE/obz//oPp2Qz5zxr+MXEBnaYsYc4669/oC5N20L1ZberGlyMuysn9VzVi4Q87AKheIYY7WjWgcmwUYXY7N112IXn5XrZv3275OFbNn/8xCxcu48jR1IAfK9BM+r4Bs3pMagGzekxqAbN6TGqB0OoJygB97NixYBz2jOrXr4PHk8+WLdv825KSfqZRowZBXFXJmdRTFlp+3HOEquWj6T/7S65+YT73zvqCLQfSAPD6fAx6bzX1E8rz6eDOvH5HW2av28LalP2WjrHt0DEanP/HUF7//PIcycwhLSu30L6/7k8lL99LzZo1zy7sf0xZeK1ZYVKPSS1gVo9JLWBWj0ktEFo9AR+gf/31V3r06MFNN93E1q1buf/++2nTpg1t27bll19+CfThiy0mOprjxwv+k/uxY+nExgT2n78DxaSestBy4Hg2y37exW2X1+OzR7pwVb0qPPz+GvLy8/l5z1FSM3N5oG1jwsPCqF4hhh6X1uGTn3dZOkaW20NMRLj/45O3M92eAvtl5ObxxPz1PNC2MbGxsWcf9z+kLLzWrDCpx6QWMKvHpBYwq8ekFgitnoBfA/3MM8/Qv39/0tPTue+++xg8eDCvv/46K1asYMKECbz11luBXkKxZGRmEhdXcNiIi4slPSMzSCs6Oyb1lIWWSEcYzWpU4sp6VQC4q1UDZq76hW2H0tl7LItD6dlcOSHRv7/X56P5BZUAWPrjTp5d+i0APp+PLLenwL4f9OtAlXLRuJwOMnL/GJYzc/MAiHb+8W2ck+dh0NzVXFKtIvdeeVHggg1VFl5rVpjUY1ILmNVjUguY1WNSC4RWT8AH6MzMTK655hoAXn75Zbp27QpAu3btmDJlSqAPX2ybN2/D4Qijbt3apKScuG60SZNGJCdvCvLKSsaknrLQUi+hHN//VvS7XpwfF0XVCtEsGtCpyPs7XVKTTpecuNTiTD9EWKdyOTYfSOO6xjUA2HwgjfOiIynvigDA7cln8PtriI+LYlTnFqWR9T+nLLzWrDCpx6QWMKvHpBYwq8ekFgitnoBfwuHz+fy3W7duXeA+r9cb6MMXW1ZWNonzP+ap0UNxuaK4olULunbpwLuz5wV7aSViUk8wWzxeL7mefLw+H16vj1xPPp4iXrc3XFKTH3cf4ettB8j3enl33WbKu5zUqRzLxdUqEu108OaaX8jJ85Dv9ZJy8Bg/7TlqaS1dmtRk/nfb2XroGMdz3MxY9Qtdm9YCIC/fy9AP1hIRHsbT3S/HbrOVRn6xhIWFERERQViY/ZTbYefs+KXJpO8bMKvHpBYwq8ekFjCrx6QWCK2egJ+BrlatGhkZGcTExPDMM8/4t+/fv5+oqKhAH96SAQNHMHPGi+zbk8SRI6n0H/h4mX2bFzCrJ1gtM75M5rUvk/0fL/lxJw+0aUT35rXpMW0ZH/3rOqqUi6ZWpTjG3diScUu+4WhmDg2rVOClW68k/PdBcsptVzHp0++5YcpS3Pn51Dovlv7/uMTSWlrXrUKfKxrQ9+3/kpuXzzUXVefBqxsD8MNvh/lyyz4iHWFcNWG+/3NmNuhGixaBPRs9YsQgnhw1xP9x7949Gfv0izz99KSAHjdQTPq+AbN6TGoBs3pMagGzekxqgdDpsflOPUV8DmVlZZGdnc15551n+XMdzmoBWNG55XHv8d8u6z0mtUDBnuzZo4K4krMX1ftp/+1wA56bPINeayZ/35T1HpNawKwek1rArB6TWqBgT1GC9otUXC4XLpcrWIcXERERESkR/SIVERERERELNECLiIiIiFigAVpERERExAIN0CIiIiIiFmiAFhERERGxQAO0iIiIiIgFGqBFRERERCzQAC0iIiIiYoEGaBERERERCzRAi4iIiIhYoAFaRERERMQCDdAiIiIiIhZogBYRERERsUADtIiIiIiIBRqgRUREREQs0AAtIiIiImKBBmgREREREQs0QIuIiIiIWGDz+Xy+YC9CRERERKSs0BloERERERELNECLiIiIiFjgCPYCSsLhrBbsJZw1j3uP/3ZZ7zGpBczqObVlaUKvIK6kdHQ68J7/9j9r9gjiSs7euzs/8t8u668zMPf7pqy3gFk9JrWAWT0mtUDBnqLoDLSIiIiIiAUaoEVERERELNAALSIiIiJigQZoERERERELNECLiIiIiFigAVpERERExAIN0CIiIiIiFmiAFhERERGxQAO0iIiIiIgFGqBFRERERCzQAC0iIiIiYoEGaBERERERCzRAi4iIiIhYoAFaRERERMQCDdAiIiIiIhZogBYRERERsUADtIiIiIiIBRqgRUREREQs0AB9igoVyvPhBzM5lrqFrVvW0atX92Av6ayY1GNSC5T9nuh6Vbl83hO03/IGbb9+iYSOf/Pfd95VF9Nm9Ytct30WLT8aRWT1SkFcafEl1KrCG5ve48GXBvm3te/TiUmrpzPjp3cZu+h56rdoGMQVlkxZf62dyqQWMKvHpBYwq8ekFgidHkdQjhqipk4Zh9udR9XqTWnWtDELF7xNUlIyycmbg720EjGpx6QWKNs9tjA7LWY9yq5Zn7H+5nGcd0UjLnvnUdZc8zjutAwufeMRfnzkNQ5++i31h91C89cH8VWnUcFe9l/q83Rftiel+D++sFk9bh32T565+Ql2/LSNa/55HQ+/Poz+Le7F5/UGcaXWlOXX2p+Z1AJm9ZjUAmb1mNQCodMTlDPQa9euDcZhz8jliqLHjZ0Y/dQLZGZmsWbtBhYt/ox/9u4Z7KWViEk9JrVA2e+JrleViPMrsP21peD1cWT1z6Su30zVm6/i/BsuJ2PTbvYvWoc3N48tEz8krlFNoutWDfayz+jvXVqTdTyTn9f86N9WqXo8ezb/xo6ftgGwet5/iTuvHOUqlQvWMi0r66+1U5nUAmb1mNQCZvWY1AKh1RPwATolJaXQn8cff5ytW7eSkpLy1w9wjtSvXwePJ58tW7b5tyUl/UyjRg2CuKqSM6nHpBYwrwfAZoPYhtWJaVCd4z/v9G/Pz8ola+cBYhtUD+LqziwqJoqej9zG7KffKrA96b/fYg+zc2Gzetjsdtrceg07ft5G2sHU4Cy0BEx6rZnUAmb1mNQCZvWY1AKh1RPwSzg6d+5MtWrV8Pl8/m2HDx+mb9++2Gw2li9fHuglFEtMdDTHj6cX2HbsWDqxMdFBWtHZManHpBYo+z2ZKftwHz5Gnf5d2P7aUs5r3ZiKrRpxZM3POKIjcR85XmD/vONZhMVEBWm1f+2mIbex8v3lHN1/pMD27IxsNnz8NaM+HIfNZiPreCYv3PVMkFZZMmX9tXYqk1rArB6TWsCsHpNaILR6Aj5ADxgwgB9++IExY8ZQteqJf8Zt164dK1asCPShLcnIzCQuLrbAtri4WNIzMoO0orNjUo9JLVD2e3yefL7p8yKNxvWhzoCuHPthG/sWfoXX7cGTmYPjT8NyeGwU+RnZQVrtmV3QqBaNr2zCyE5DC913da9raXPzPxjefhAHduznkjbNGPLGCEZ2GlJmzkKX9dfaqUxqAbN6TGoBs3pMaoHQ6gn4JRwDBgxg8ODBPPLII8ydOxcAm80W6MNatnnzNhyOMOrWre3f1qRJI5KTNwVxVSVnUo9JLWBGT3ryLtbdOJbPL+rLhl7P4aqZQNq3KWRs2k1s45r+/cJcEbhqJpC+aXcQV3t6F/39YipVj+flta/xyoZ/0+n+rvyt4995ZslELmhUi++Wf8P+7fvw+XwkrfyOtIOp1CtD78RhwmvtJJNawKwek1rArB6TWiC0es7JDxE2atSIt99+mz179tCnTx/y8vLOxWEtycrKJnH+xzw1eiguVxRXtGpB1y4deHf2vGAvrURM6jGpBczoiW10AfaIcOxRTmo/2JmIhPLseX8lB5auJ7ZhDc6/4XLsEeHUfaQnx3/ZRWbK3mAvuUhfzPmUIW3+xchOQxjZaQjLZ3/K9yu+ZcIdY9n+QwrN2l1G5RoJAFx8ZVPOr1OV3Zt2BXnVxWfCa+0kk1rArB6TWsCsHpNaILR6ztnb2DmdToYOHcr333/P+vXrz9VhLRkwcAQzZ7zIvj1JHDmSSv+Bj5fZt3kBs3pMaoGy31Ptpquo0fsf2MIdHP36V9bfPA6v24P7SDrf3juJxs/eTdP/G0Datyl8/8CUYC/3tNw5btw5bv/HuZk55OW6ST96nFXz/kt8zfMZ+f5YosvFcHT/Ed54/FX2bd0TxBVbV9Zfa6cyqQXM6jGpBczqMakFQqfH5jv1p/vKCIezWrCXcNY87j/+T7is95jUAmb1nNqyNKFXEFdSOjodeM9/+581ewRxJWfv3Z0f+W+X9dcZmPt9U9ZbwKwek1rArB6TWqBgT1H0mwhFRERERCzQAC0iIiIiYoEGaBERERERCzRAi4iIiIhYoAFaRERERMQCDdAiIiIiIhZogBYRERERsUADtIiIiIiIBRqgRUREREQs0AAtIiIiImKBBmgREREREQs0QIuIiIiIWKABWkRERETEAg3QIiIiIiIWaIAWEREREbFAA7SIiIiIiAUaoEVERERELNAALSIiIiJigc3n8/mCvQgRERERkbJCZ6BFRERERCzQAC0iIiIiYoEj2AsoCYezWrCXcNY87j3+22W9x6QWMKvHpBYo2JM+oFMQV3L2Yl9Z6r9t2nNT1ntMagGzekxqAbN6TGqBgj1F0RloERERERELNECLiIiIiFigAVpERERExAIN0CIiIiIiFmiAFhERERGxQAO0iIiIiIgFGqBFRERERCzQAC0iIiIiYoEGaBERERERCzRAi4iIiIhYoAFaRERERMQCDdAiIiIiIhZogBYRERERsUADtIiIiIiIBRqgRUREREQs0AAtIiIiImKBBmgREREREQs0QIuIiIiIWKAB+hQVKpTnww9mcix1C1u3rKNXr+7BXtJZManHpBYwqydYLeFtOuN67GViJi8g8p+Di/U5UQOfJfaVpWAv/f/0ObvdTcyE94iZ8B7Obnf7t9viqxF5/yiin5tLzIT3ier/NLb4aqV+/NPRay10mdRjUguY1WNSC4ROjyMoRw1RU6eMw+3Oo2r1pjRr2piFC94mKSmZ5OTNwV5aiZjUY1ILmNUTrBbfsaO4P3mPsIsuxRbu/Mv9HS2uhrCS/ycvrN4lODv1Jvvl4YXuC2/dkfAmrch8rj/4IGrAOHxHDpC3eim2qGjyf1xHzruTIScbZ8fbibr/yRKvwyq91kKXST0mtYBZPSa1QOj02Hw+n++cHrEUOJylf/bG5Yri8MFkmja/hi1btgHw1ptT2Lt3HyNGPlfqx/O49/hvl/Uek1rArB6TWqBgT/qATgA4O9+Jvfx5JwbU04l0Ef3Yy2S/PZHooZNJf6gzeL0A2BOqE3Hzg4TVqIsv4xi5i9/B892qQg9xpgHa9chE8tZ9Tt6aTwAIb9WB8CuuJ+vFRwqvxRVD7PP/KbApEM8N6LVWUoFuAbN6TGoBs3pMaoHg9RRFl3D8rn79Ong8+f4nBCAp6WcaNWoQxFWVnEk9JrWAWT1lpSWiax/cq5bgO55a8A5nBFEDxpG38b9kPH4b2W+OJ+LWf2E/v4alx7dXqUn+7u3+j/P3bMde5YIi93XUvQTvsaOWG0qirDw/xWFSC5jVY1ILmNVjUguEVk/AB+g1a9b4b6enp/Poo49y7bXXMnDgQA4fPhzowxdbTHQ0x4+nF9h27Fg6sTHRQVrR2TGpx6QWMKunLLTYL6hHWJ2LyFu5sNB9jotb4j1yAM/Xn4HXi3f3Njzfr8HR/CprB4mIhJxM/4e+7Exska5Cu9nKn0fELQ+S+9EMyx0lURaen+IyqQXM6jGpBczqMakFQqsn4NdAT5w4kdatWwMwefJkoqOjmTZtGkuWLOGZZ57hpZdeCvQSiiUjM5O4uNgC2+LiYknPyDzNZ4Q2k3pMagGzekK+xWYj8pZ/kfvha/5LNk5lrxhPWK0GxJx6SUVYGHnrVwDgbH8zzvY3+7fjcBbYN+OxW07cyM2BUwZmW6QLX05WwaXExBHVfxx5q5bg+WYl3D2slCJPL+SfHwtMagGzekxqAbN6TGqB0OoJ+AB96iXW33zzDR9++CHh4eHUr1+fLl26BPrwxbZ58zYcjjDq1q1NSsqJf4pt0qQRycmbgryykjGpx6QWMKsn5FsiXdgvqEfkPb9ft2wLAyD6mbfJ+fdzeFMPkZ/yE9mvjCzy092ffYD7sw+AM18D7d23k7BqtfHuPPFDLPZqtfHu2/XHDlExRPUfh+fHr3Eve78UA88s5J8fC0xqAbN6TGoBs3pMaoHQ6gn4JRxut5utW7eSkpKCzWYjPDz8j4MH4K2kSiorK5vE+R/z1OihuFxRXNGqBV27dODd2fOCvbQSManHpBYwqyeoLXY7OMJP/K897I/bp8rOJHPkHWQ9N5Cs5waSPf3Eu19kTRhE/o5NeH5ajz2+Go6/tTvxGPYw7BfUw55g7RrovPUrCG93I7Zy52ErVxHnNT3IW/f5iTsjo3D1f5r8bcm4F75VCuHFp9da6DKpx6QWMKvHpBYIrZ6An4HOycnh/vvv95+JPnDgAAkJCWRkZITUAA0wYOAIZs54kX17kjhyJJX+Ax8vs2/zAmb1mNQCZvUEq8V5/W1EdOrt/zj88nbkLp1N3lefEv3Eq2Q+0w9f6iF86af84ODvf4H3paeeuKQj30PWKyOJ7NGXiB59sdlt5O/ebvka5bzVS7Gfdz7RI6ad+HjtMvJWLwXA0fQKwmo1wF6lJuF/v/Ysq63Tay10mdRjUguY1WNSC4ROT9Dexi47O5vDhw9To4a1Mz0QuLdHOZfOxdu9nCsmtYBZPSa1QNFvY1dWxb6y1H/btOemrPeY1AJm9ZjUAmb1mNQCIfw2dlFRUSUankVEREREgim0rqEQEREREQlxGqBFRERERCzQAC0iIiIiYoEGaBERERERCzRAi4iIiIhYoAFaRERERMQCDdAiIiIiIhZogBYRERERsUADtIiIiIiIBRqgRUREREQs0AAtIiIiImJBsQboo0ePkpmZCUB+fj7z5s0jMTERr9cb0MWJiIiIiISaYg3QDzzwADt37gRg8uTJvPHGG7z11luMHz8+oIsTEREREQk1xRqgd+zYwUUXXQTAwoULmTFjBrNmzWLp0qUBXZyIiIiISKhxFGcnu91OXl4e27dvJzY2lqpVq+L1ev2XdYiIiIiI/K8o1gDdpk0bBg0aRFpaGp06dQIgJSWFhISEgC5ORERERCTUFGuAHjduHImJiTgcDrp16wZAamoqAwcODOjiRERERERCTbEGaKfTya233orX6+Xw4cPEx8fTsmXLQK9NRERERCTk2Hw+n++vdjp+/Dhjxoxh2bJlOBwOvv/+e5YvX05SUhKDBw8+F+sUEREREQkJxXoXjtGjRxMTE8OKFSsIDw8HoHnz5nz88ccBXZyIiIiISKgp1iUcX331FatWrSI8PBybzQZAxYoVOXLkSEAXJyIiIiISaoo1QMfGxpKamkp8fLx/2969e6lcuXLAFnYmDme1oBy3NHnce/y3nRHVg7iSs+fO3e2/bdpzU9Z7TGoBs3pObcl86rYgrqR0RD8113/bpOemrLeAWT0mtYBZPSa1QMGeohTrEo6bb76Zhx56iK+//hqv18t3333HsGHD6NWrV6ksUkRERESkrCjWGei+ffsSERHB2LFj8Xg8jBgxgltvvZW77ror0OsTEREREQkpxRqgbTYbd911lwZmEREREfmfV+wfIjydVq1aldpiRERERERCXbEG6JEjRxb4ODU1lby8PBISEli+fHlAFiYiIiIiEoqKNUCvWLGiwMf5+flMnz6d6OjogCxKRERERCRUFetdOP4sLCyMfv36MXPmzNJej4iIiIhISCvRAA2wZs0a/y9VERERERH5X1GsETNKVgAAIABJREFUSzjatm1bYFjOzs7G7XYzevTogC1MRERERCQUFWuAfuGFFwp8HBUVRe3atYmJiQnIokREREREQlWxBujLL7880OsQERERESkTTjtAP/roo8W6xvn5558v1QWJiIiIiISy0w7QNWvWPJfrEBEREREpE047QA8YMOBcrkNEREREpEwo1jXQAG63m+3bt5OamorP5/Nv16/yFhEREZH/JcUaoDdu3MjDDz+M2+0mIyODmJgYMjMzOf/88/WrvEVERETkf0qxfpHKc889x3333cf69euJjo5m/fr1PPjgg9x+++2BXp+IiIiISEgp1gC9Y8cO7rzzzgLb7v9/9u47PKoy7//4eyaTSTIpJHQIShFpKk0URZQVBFwlVFdRQHftBaxIVUCUBSzYUBBdK+iuSrcXsAAroqIRgyb03kMIKUwmM78/sg7kIUBOksmZ3L/P67q8nsm0c7+fMy5fD2dObr2V119/PRRrsk1SUiLvv/cKWZkZrM9YycCBfe1eUpm53W5emvkkGenfsX/f76z6/lN69rzU7mWVmUn7BszqMakF7Olxnd+D6Fsn4XnoTdx9bz/x89pegmfcHDxjXgv+42zUssLXE3nZtXhGzMIzYhaRl10bvN9Roy5RAx/A8+BLeEa+TNTgUThq1Kvw7Z+IPmvhy6QWMKvHpBYIn55SncIRHx/P4cOHSUhIoFatWqxbt47ExERyc3NDvb5K9fxzk/B6C6jfoA1t25zFooVvkpqaRlpaut1Ls8zlimDbth1c1v0qtmzZzl//2pW358yg/bmXsXnzNruXZ5lJ+wbM6jGpBezpCWRnUvDNfCLOaA2R7pM+178tnfxXHynX9pyNWuL+y1Xkv/7ocY+5zu2Gq0UH8maOgkCA6OvHEDi4F98PX+CIjqXwjx85snAmHMknskt/oq99gLzpw8u1ntLSZy18mdQCZvWY1ALh01OqI9Ddu3fn66+/BmDAgAFcf/319O/fn549e4Z0cZXJ44mhf78rGD/hCXJyclm+YhWLP/icwYMG2L20MsnNzePRx6axefM2AoEAH330JZs2baV9u9Z2L80y0/aNST0mtYB9PYVrV1H4+w8E8g6X630cNesTPWQMnpEvEzP0KSLOusDye7jaXkLBfz8kcOhA0WC/4kNcbS8BwL99Pb7VX0FeDvgLKfjuI5w1kyEm9L+VVp+18GVSC5jVY1ILhFfPSY9Az5kzh5SUFMaOHRu876abbqJNmzbk5ORw8cUXW95gTk4OmzZtomHDhmH1q8CbNWuCz1dIRsaG4H2pqb9xySVmXGWkdu2anHlmY9LW/mH3Uiwzbd+Y1GNSC1SNHmfdRnhGzCKQdxjfL99SsGwh+P0QGUX0kDEULH0P35wpOGufTvT1Y8jbs5XA3u2lf/9aDfDv2hz82b97C85aDUp8bkTDlvizM6Gcg39pVIV9Y4VJPSa1gFk9JrVAePWc9Aj0+++/T+fOnRk2bBhLliyhsLAQgA4dOtClSxeczlMfwB43bhwHDhwA4Mcff6R79+6MGDGC7t27s2zZsgpIqBhxsbEcOpRd7L6srGzi42JtWlHFcblcvPHG87w1+33++GO93cuxzLR9Y1KPSS0Q/j2Fm38n78UR5D5xG/n/eRrXOZ2I7JQCQESzdkWnWvz8Nfj9+Hdtwpe2Elcri0eh3dEEjuQFfwzk5+KIijnuaY6E6riv+AfeT2eXq6m0wn3fWGVSj0ktYFaPSS0QXj0nPQI9f/580tPTWbhwIY888ggFBQVcccUV9O/fn1atWpVqAz///DPVq1cH4Nlnn2XmzJm0bt2ajRs38sADD9C5c+fyV1SAwzk5JCTEF7svISGe7MM5Nq2oYjgcDl5/7Vm83gLuuechu5dTJqbtG5N6TGqB8O8JZO45envPVrxfzyOyUy8Kli3EmVgLZ4OmeEa9cvQFzgh8v3wLQGTn3kR27h28H1dksefmTrm56IY3H0dUDH9e7d8RFVNsoAbAE0/0kNH4Vn1O4ZoVFZ1ZonDfN1aZ1GNSC5jVY1ILhFfPKb9E2KxZMx588EGGDx/OihUrWLRoEYMGDaJBgwb07duXm2666aSvP3LkSPB2Tk4OrVsXnYPbuHFjCgoKyrn8ipOevgGXK4KmTRuzbt1GAFq3bkVaWtU75eFYs156ktq1a9G7z/X4fD67l1Mmpu0bk3pMaoEq2BMIgMMBgD9rP/5Na8l/658lPrVg2SIKli0CTv4lQv/ebTjrNMS/vehvq5x1G+Lfe8wXj6NjiR4yBt8fP1Lw7YIKDjqxKrdvTsGkHpNawKwek1ogvHpK9SVCKDqSedFFFzF16lRmzpxJTk4OTz755Clfd+GFFzJlyhTy8vLo2LEjH330EQDLly8nMTGx7CuvYLm5ecxf8DETxg/H44mh04Ud6J3Sg9lz5tq9tDKbPn0yLVqcSb/+fyc/P9/u5ZSZafvGpB6TWsDGHqcTXJHgcOJw/O92CafIRTRtA7HVgKIvDLq79Kfw9x8BKEz/CUeNurhady46wuyMwFm/CY6a9S0txffLt0ReeAWO+CQc8UlEXnglvp+/KXowKoboIaPwb/2Dgi/+Xb5mi/RZC18mtYBZPSa1QHj1lPpXee/evZuFCxeycOFCdu3aRY8ePejXr98pXzdmzBgef/xxLrnkEhITE3n11VcZMWIEHTt25J//LPlIiV2GDhvDKy8/xc7tqezfn8ldw0ZX2cu8nH56MrfeMoT8/Hy2blkdvP+uu0bxzr/n27iysjFp34BZPSa1gD09kZf0w/2Xq4I/u9pcjPer9/Gt/oqYu54k74XhBLL2E9HkbKL63gHuKAI5WfhSlx09CuzNJ/+tybh7DsHdcwg4HPh3b8H76VvB0zFKw/fDFziSahNz5+MAFPy0FN8PXxStq8V5RCQ3xVmrAa62XYKvyXuhci5jp89a+DKpBczqMakFwqfHEQgETvi/rXl5eXz66acsWLCAH374gfPOO4++ffvSo0cPYmKO/1LJyeTm5rJlyxb8fj/16tUjKSmpzIt2uZPL/Npw4fMe/Va8O6rkb7hXFd4jR/9617R9U9V7TGoBs3qObcmZcO1Jnlk1xE54J3jbpH1T1VvArB6TWsCsHpNaoHhPSU56BLpTp07UrVuXvn37MmXKFOrWrVvmhXg8Hlq0aFHm14uIiIiIhIOTDtCvv/46bdq0qay1iIiIiIiEvZN+iVDDs4iIiIhIcaW+CoeIiIiIiGiAFhERERGxRAO0iIiIiIgFpRqgvV4vTz/9NN26dePcc88FYNmyZcyePTukixMRERERCTelGqD/+c9/kp6ezpNPPonjf7829swzz+Sdd945xStFRERERMxSqt9E+MUXX/DZZ5/h8Xhw/u/Xy9apU4fdu3eHdHEiIiIiIuGmVEegIyMjKSwsLHbfgQMHSExMDMmiRERERETCVakG6Msvv5yRI0eydetWAPbs2cPEiRO58sorQ7o4EREREZFwU6oB+r777qNBgwb07t2bQ4cO0bNnT2rXrs1dd90V6vWJiIiIiISVUp0D7Xa7GTNmDGPGjOHAgQMkJSUFv0woIiIiIvL/k1IN0H+euvGnnJyc4O3TTjutYlckIiIiIhLGSjVAd+/eHYfDQSAQCN735xHotWvXhmZlIiIiIiJhqFQD9O+//17s57179zJ9+nQ6dOgQkkWJiIiIiISrMv0q71q1ajF27FimTZtW0esREREREQlrZRqgATZs2EBeXl5FrkVEREREJOyV6hSO6667rthVN/Ly8li3bp0uYyciIiIi/99xBI79ZuAJzJ8/v9jPMTExtGjRgkaNGoVqXSIiIiIiYemUR6ALCwv57rvvePTRR3G73ZWxJhERERGRsHXKc6AjIiJYvny5fnGKiIiIiAilPIXj5ZdfJjs7m2HDhhEZGVkZ6zoplzvZ7iWUm8+7PXi7qveY1AJm9ZjUAmb1mNQCxXsOjx5g40rKL27y3OBt0/ZNVe8xqQXM6jGpBYr3lOSkp3B88MEH9OrVi9mzZ7Nv3z5ee+01qlevXuxo9FdffVUhCxURERERqQpOOkCPGzeOXr168cQTT1TWekREREREwtpJB+g/z+44//zzK2UxIiIiIiLh7qQDtN/v57vvvuNkp0lfeOGFFb4oEREREZFwddIB2uv1Mnbs2BMO0A6Hgy+//DIkCxMRERERCUcnHaBjYmI0IIuIiIiIHOOU14EWEREREZGjTjpAl+IS0SIiIiIi/1856QC9evXqylqHiIiIiEiVoFM4REREREQs0AAtIiIiImKBBmgREREREQs0QIuIiIiIWKABWkRERETEAg3QIiIiIiIWaIAWEREREbFAA7SIiIiIiAUaoI+RlJTI+++9QlZmBuszVjJwYF+7l1QuJvWY1AJm9ZjUAuqpCJEX/pWYu6YS++i/ibpqaKleE33TeOImzwVnxf+x5L58MLEPv07sw6/jvnxw8H5HzXpEDxlJ7NhXiX34daL/8TCOmvUrfPsnYtJnzaQWMKvHpBYInx6XLVsNU88/Nwmvt4D6DdrQts1ZLFr4JqmpaaSlpdu9tDIxqcekFjCrx6QWUE9F8B86gHfpXFxntoVI9ymf72p7MY6Isv9xFNH4LNyXXU3ey+OPf+/zu+NqdT65z94PQMyN4/Af2IPv+89wRMfiW/sD+e+/AEfycHf7GzFDRpV5HVaZ9FkzqQXM6jGpBcKnxxEIBAKVusUK4HInV/h7ejwx7NuTRpt23cjI2ADA6689x44dOxkzdnKFb8/n3R68XdV7TGoBs3pMagGzekLdAvb1HB49AAB392txVKvBkfenn/iFUR48Q6eS/+5zeO6cwuGxfwO/HwBHrWSiUm4iIrkJgZxDeD//N75fVxz3FicboGNun0TBj1/hW/U5AK4O3Yg87zLyZow+fi0xccSNe6PYXabtG/17c2om9ZjUAvb1lESncPxPs2ZN8PkKgzsEIDX1N1q1am7jqsrOpB6TWsCsHpNaQD12cPe8joLvPiWQfbD4A5FRxNw4Dt8v35Iz6Uby33maqD634KjdwNL7O+uchn/XpuDP/p2bcNY5rcTnRjRuhf9QpsWCsqkK+6a0TGoBs3pMaoHw6gn5AN2xY0cee+wx1q5dG+pNlUtcbCyHDmUXuy8rK5v4uFibVlQ+JvWY1AJm9ZjUAuqpbM7kM4ho2IKC/3503GOuFucSyNyD78el4Pfj37kR35rvcJ1zobWNuKMJ5OcGfwzk5+KIijnuaY6E6kT1vhnvR69bzSiTcN83VpjUAmb1mNQC4dUT8nOgY2NjcTqd3HjjjdStW5cBAwaQkpJCtWrVQr1pSw7n5JCQEF/svoSEeLIP59i0ovIxqcekFjCrx6QWUE+lcjiI6nMLRz54NXjKRrGHk2rhPO1MYse9efROpxPf6m8AiOzSD3eXfsH7cbmLPTdn4vVFN7z5OKJi+PNcRUdUDIEjecU3FptAzI3jKPjuU3y/LIOB91VU5QmF9b6xyKQWMKvHpBYIr56QD9DVqlVjzJgxPPjgg3z55ZfMmzePp556ir/85S9cddVVXHTRRaFeQqmkp2/A5YqgadPGrFu3EYDWrVuRlvaHzSsrG5N6TGoBs3pMagH1VKqoGJzJZxB9bdGX+xyOor8Q9YyaRf7bTxHI2k/hxjTyX51Y4ssLvp5PwdfzgZOfA+3fvRVnvUb4t60DKLq9e+vRJ0THEnPjw/jW/kDBV3MrsvCkwnrfWGRSC5jVY1ILhFdPpZ0DHRkZyeWXX86sWbP45JNPaN68OY8++mhlbf6UcnPzmL/gYyaMH47HE0OnCzvQO6UHs+dU3v+gViSTekxqAbN6TGoB9VQYpxNckUX/99jbx8rPJXfyLeQ9N7zonzcmAZA3fQT+rRn41v6As2Z9XO26gDMCnBE4G5yBo5a1LycV/PQ17s4pOBKq44hPIvLi3hT8uLTowagYYm58mMLNf+D9dHZFlJeaSZ81k1rArB6TWiC8ekJ+BLqki3zUqVOH22+/ndtvvz3Um7dk6LAxvPLyU+zcnsr+/ZncNWx0lb3MC5jVY1ILmNVjUguopyK4L70K92XXBH+ObNcF7xf/oeCHJXjue4bcp+8lkLWPwOFjvjjoigQous/vh0Ifea9OJOrKvxN1xQ3gdFK4cxPeD1/HyqWjfN9/hrN6HTz3TAOgYNWX+L7/rGiTZ3Uk4rQzcdY5jcj2fylntXUmfdZMagGzekxqgfDpCfll7LZv305ycsVeziRUl0epTJVxuZfKYlILmNVjUguY1WNSC5R8GbuqKm7y0aNZpu2bqt5jUguY1WNSC4TBZewqengWEREREbGTrgMtIiIiImKBBmgREREREQs0QIuIiIiIWKABWkRERETEAg3QIiIiIiIWaIAWEREREbFAA7SIiIiIiAUaoEVERERELNAALSIiIiJigQZoERERERELNECLiIiIiFigAVpERERExAIN0CIiIiIiFmiAFhERERGxQAO0iIiIiIgFGqBFRERERCzQAC0iIiIiYoEGaBERERERCxyBQCBg9yJERERERKoKHYEWEREREbFAA7SIiIiIiAUuuxdQFi53st1LKDefd3vwdlXvMakFzOoxqQXM6jGpBczqObYld8YwG1dSMTx3PB+8bdK+qeotYFaPSS1QvKckOgItIiIiImKBBmgREREREQs0QIuIiIiIWKABWkRERETEAg3QIiIiIiIWaIAWEREREbFAA7SIiIiIiAUaoEVERERELNAALSIiIiJigQZoERERERELNECLiIiIiFigAVpERERExAIN0CIiIiIiFmiAFhERERGxQAO0iIiIiIgFGqBFRERERCzQAC0iIiIiYoEG6GMkJSXy/nuvkJWZwfqMlQwc2NfuJZWLST0mtYBZPSa1gHrCmR0t//55C9e9/R3nP/854z5dc9LnbsvK5e6FP3HRC19y6cylPPNteoWvZ/ZPm7ls1ld0fvFLJny2Bq/PD8CB3COM+iiV7i9/zcUvLuHv//meX3cerPDtn4hJnzMwq8ekFgifHpctWw1Tzz83Ca+3gPoN2tC2zVksWvgmqalppKVV/P8IVgaTekxqAbN6TGoB9YQzO1pqxUVxy/lNWLF5H0f+N6yWpKDQzx3zfuTqNqcx9YrWOB0ONmfmWt7ejqw8bn5/FR/ddMlxj63YtI/XftjIrAEdqBUbxf2Lf2bGd+u4p3MzcgsKOatuAg90aU71GDcLftvO3QtX8+GNF+OxvArrTPqcgVk9JrVA+PQ4AoFAoFK3WAFc7uQKf0+PJ4Z9e9Jo064bGRkbAHj9tefYsWMnY8ZOrvDt+bzbg7ereo9JLWBWj0ktYFZPqFvArB67WnJnDAPghRUZ7M4+wsSeZ5f4mrm/buPDtTt49erzS3x8z+F8pn71Oz9tz8QTGcGgdg25rl3D4553sgF69Mep1E+IYdhFZwKwcst+xn7yK1/c+pcSt9n5xS+ZNeA8Okx4I3ifSftG/96cmkktYF9PSSr9FI68vDzWrFnDoUOHKnvTJ9WsWRN8vsLgDgFITf2NVq2a27iqsjOpx6QWMKvHpBZQTzgL95Zfdx6kfkIMd83/kUtnLuXm91aRsS8bAH8gwL2LVtOsZjyf3dyFlwZ04O3VW1ixaZ+lbazff5hmNeODPzerFc/+XC8H87zHPfePPYcoKAxwWmJM+cJKIdz3jVUm9ZjUAuHVE/IB+vPPP6d9+/ZcfvnlpKamcsUVVzBixAi6d+/OkiVLQr35UouLjeXQoexi92VlZRMfF2vTisrHpB6TWsCsHpNaQD3hLNxbdh8+wqfpu7i27el8dksXLm5ck/sW/UxBoZ/fdmWRmVfAbRecQWSEkwbVPPQ7O5lP03dZ2kZeQSFxUUfPvIxzF93O9RYWe97hIz4e+nQNt3ZsQnxUZPnjTiHc941VJvWY1ALh1RPyc6BfeOEF3nnnHQ4dOsQtt9zCjBkzaN++PevXr+eBBx6ga9euoV5CqRzOySEhIb7YfQkJ8WQfzrFpReVjUo9JLWBWj0ktoJ5wFu4tUS4nbesn0rlxLQCuP7cRr3y/kQ0HctiZnc/ew0e4+MWjB438gQDtkpMA+Pj3nfxzyVoAAgTI9RYWe+67gy+kXkIMMZER5Hh9wfv/vO1xRwTvy/cVcs+i1ZxTrxo3nd8kdMHHCPd9Y5VJPSa1QHj1VMqXCJs3Lzq0HhsbS/v27QE444wzKmPTpZaevgGXK4KmTRuzbt1GAFq3bkVa2h82r6xsTOoxqQXM6jGpBdQTzsK95cya8fyyI7PEx+rERVO/WgyL/t65xMf/2qIef21RDzj5OdBn1IgjfW82PZrVBSB9XzY1PG4SY9wAeH1+7l/0M3XionioW6uKyCqVcN83VpnUY1ILhFdPyE/hcDgcrF+/ntWrV5Obm8vPP/8MwMaNGyksLDzFqytPbm4e8xd8zITxw/F4Yuh0YQd6p/Rg9py5di+tTEzqMakFzOoxqQXUE87savH5/RzxFVLoLzpqfMRXiM9//NU4rmxRj193ZfHdlv0U+gPMWb2FxJhImlSP5ey61YiNjOC1VRvJ9xVS6A+wbl82v+3KsrSWXi3rs+C37azff5js/AJeWbmBlFb1gaKrgDz44c9EuZxM7Hk2ToejQvpLw6TPGZjVY1ILhFdPyI9A33333Vx77bU4nU6efvppnn32Wfbu3cuuXbuYMGFCqDdvydBhY3jl5afYuT2V/fszuWvY6Cp7mRcwq8ekFjCrx6QWUE84s6PllZUbeGnl0S8sffj7Tm7r2IQ+ZyUz4K0VzB3SiXoJMTSqHstjl5/DP79M40Celxa1EnimdzsiI4qOUz3bpx3Tvkmn16vf4i300ygpljs7NbW0losa1eSGDo24de4PHPEV0q1pHe64oOg9ftl5kG827iPa5eSSGUuDr5netz0lH/euWCZ9zsCsHpNaIHx6Kv0ydoWFhaxdu5a6detSs2bNMr1HqC6PUpkq43IvlcWkFjCrx6QWMKvHpBYwq6eky9hVZZ47ng/eNmnfVPUWMKvHpBY49WXsKv0XqURERHD22SVfR1NEREREJNzpV3mLiIiIiFigAVpERERExAIN0CIiIiIiFmiAFhERERGxQAO0iIiIiIgFGqBFRERERCzQAC0iIiIiYoEGaBERERERCzRAi4iIiIhYoAFaRERERMQCDdAiIiIiIhZogBYRERERsUADtIiIiIiIBRqgRUREREQs0AAtIiIiImKBBmgREREREQs0QIuIiIiIWKABWkRERETEAkcgEAjYvQgRERERkapCR6BFRERERCzQAC0iIiIiYoHL7gWUhcudbPcSys3n3R68XdV7TGoBs3pMagGzekxqAbN6TGqB4j2/Nk6xcSXld87GxcHbpu2bqt5jUgsU7ymJjkCLiIiIiFigAVpERERExAIN0CIiIiIiFmiAFhERERGxQAO0iIiIiIgFGqBFRERERCzQAC0iIiIiYoEGaBERERERCzRAi4iIiIhYoAFaRERERMQCDdAiIiIiIhZogBYRERERsUADtIiIiIiIBRqgRUREREQs0AAtIiIiImKBBmgREREREQs0QIuIiIiIWKAB+hhJSYm8/94rZGVmsD5jJQMH9rV7SeViUo9JLWBWj0ktoJ5wZlIL2NNT4/orOWPhNM76fR4Nnrj35M+9sQ8tvn+TVqn/IXnq3TjcrgpfT92RN9Dypzm0/GkOdUfeELzf3bg+DWeNpeUPs2m5+m0avfEI7ibJFb79EzHps2ZSC4RPT8X/21CFPf/cJLzeAuo3aEPbNmexaOGbpKamkZaWbvfSysSkHpNawKwek1pAPeHMpBawp6dg9wH2Tn+XuEva4YyOOuHz4i5pR607BrDxuoco2L2fhi+Npfa9g9j9+BuWthfb8Wxq33sdG68dc9xj1a+9nIQeF5Bxxd0QCND4rUfxbt3Ngbc/ISIhlkNffM+2B5+lMCePOncPpOGshyz3lpVJnzWTWiB8ehyBQCBQqVusAC53xf9XqMcTw749abRp142MjA0AvP7ac+zYsZMxYydX+PZ83u3B21W9x6QWMKvHpBYwqyfULWBWj0ktYF/Pr41TAKjzwGAi69Zk24PPlPia054ZjnfbbnY/+RYAsZ1ac9ozw/n9/OsBiGrSgHqP3ErM2U0pPJDF7mlzyPpw2XHvc7IBusn7j5M590sy3/kUgKSru1N9YA/W93/wuOdGVIuj1c/vFLvPtH2jf29Oza6eklTaKRwHDx5k7dq1pKenk5+fX1mbLbVmzZrg8xUGdwhAaupvtGrV3MZVlZ1JPSa1gFk9JrWAesKZSS0Q/j1RzU4nf+3G4M/5azcRWSuJiMR4HDFRNHprIlkLv2Zth8FsGfYE9SfeQVTT0yxtI/rM/7uNjUSdeXqJz409/2wK9hwoW4xF4b5vrDCpBcKrJ+QD9Pbt27n55pu54IIL6N+/P0OGDKFjx45MmTIFr9cb6s2XWlxsLIcOZRe7Lysrm/i4WJtWVD4m9ZjUAmb1mNQC6glnJrVA+Pc4PdEUZucGfy7Mzim6PzaGhK7nUbB9D5nvfwmFfvLTNpD1yQqqXXGRtW3ERuM/VHwbEXGe457nqluD+hNvZ+dj/ypjjTXhvm+sMKkFwqsn5OdAjxo1ir/97W889dRTLFq0iMzMTAYNGsS0adOYPHky48ePD/USSuVwTg4JCfHF7ktIiCf7cI5NKyofk3pMagGzekxqAfWEM5NaIPx7/Ln5RMQfHWb/HGz9OXlENqhNTJtmtPrlmFMqIiI4OH8pALVuv4padwwI3u+Mchd7blqba//3Xvk442OC9zvjPBQePjpQA0RUT6DxmxPZP/sjshZ/A88df3pHRQv3fWOFSS0QXj0hPwKdlZVF7969qVatGkOGDOGbb76hRo0aPProoyxfvjzUmy+19PQNuFwRNG3aOHhf69atSEv7w8ZVlZ1JPSa1gFkptwOiAAAgAElEQVQ9JrWAesKZSS0Q/j1H0rcQ3fLo2qJbNqZgbyaFB7Mp2LGPnO/XkNbm2qP/nH01Ox6eAcDeme8H799800Ryfkgr9tw/5WcU30ZMy8YcydgS/NmZEEvjNydy6Ivv2fvCu5VQXSTc940VJrVAePWEfIB2uVxs2VL0L8SaNWtwu91FG3Y6cbnC5yIgubl5zF/wMRPGD8fjiaHThR3ondKD2XPm2r20MjGpx6QWMKvHpBZQTzgzqQVs7Ilw4nBHgtN59HbE8aNA5rwlJF3dnaimp+GMj6X20KuLTtkAspesIqpxMon9LgVXBLgiiGl9JlFnNLC0lIPzllDzpr646lTHVbs6NW/uF9yGMy6Gxm9OJPfHtZav/FFeJn3WTGqB8OoJ+QR79913c/XVV1OrVi327t3L008/DcC+ffto3759qDdvydBhY3jl5afYuT2V/fszuWvY6Cp7mRcwq8ekFjCrx6QWUE84M6kF7OmpPfQa6tx7XfDnpH6XsvuZt8l87wvO/OwFMnrcRcGOvRz+5if2vTSXxu9MwhkVRdYnK9jzzByg6DSOjdePo97Ym6g39iZwOshfu9HyOcoH3v4E9+l1OfOT6QBk/uczDrz9CQAJPS7E06YZ0WeeTuKAbhVUX3omfdZMaoHw6amUy9gdOnSIzZs307hxY+Li4sr9fqG6PEplqozLvVQWk1rArB6TWsCsHpNawKwek1qg5MvYVVXnbFwcvG3avqnqPSa1wKkvY1cp51AkJCRwzjnnVMamRERERERCSr/KW0RERETEAg3QIiIiIiIWaIAWEREREbFAA7SIiIiIiAUaoEVERERELNAALSIiIiJigQZoERERERELNECLiIiIiFigAVpERERExAIN0CIiIiIiFmiAFhERERGxQAO0iIiIiIgFGqBFRERERCzQAC0iIiIiYoEGaBERERERCzRAi4iIiIhYoAFaRERERMQCDdAiIiIiIhY4AoFAwO5FiIiIiIhUFToCLSIiIiJigQZoERERERELXHYvoCxc7mS7l1BuPu/24O2q3mNSC5jVY1ILmNVjUguY1WNSC5jVc2xLzsRBNq6kYsSOmxO8bdK+qeotULynJDoCLSIiIiJigQZoERERERELNECLiIiIiFigAVpERERExAIN0CIiIiIiFmiAFhERERGxQAO0iIiIiIgFGqBFRERERCzQAC0iIiIiYoEGaBERERERCzRAi4iIiIhYoAFaRERERMQCDdAiIiIiIhZogBYRERERsUADtIiIiIiIBRqgRUREREQs0AAtIiIiImKBBmgREREREQs0QB8jKSmR9997hazMDNZnrGTgwL52L6lcTOoxqQXM6jGpBdQTzkxqAbN67Ghxnded6JsfxTPmddy9bzvx89pcgueht/CM+lfwH2fDlhW+nshuA/EMn4ln+Ewiuw0M3u+oXpeoa+7H88AMPA++RNSgkThq1Kvw7Z+ISZ8zCJ8eV2VtKDMzk127dgFQt25dkpKSKmvTpfb8c5Pweguo36ANbducxaKFb5KamkZaWrrdSysTk3pMagGzekxqAfWEM5NawKweO1oC2ZkUfLuAiDNag8t90uf6t2WQ//rEcm3P2bAl7i79yX9z0nGPudp3xdX8XPJeGgMEiB48msDBvfh+/BJHtIfCP37kyMKXwJtP5CX9iL7mfvJefLBc6yktkz5nED49jkAgEAjlBrZs2cLDDz9MWloatWvXBmDPnj20atWKRx55hEaNGll+T5c7uYJXCR5PDPv2pNGmXTcyMjYA8Pprz7Fjx07GjJ1c4dvzebcHb1f1HpNawKwek1rArJ5Qt4BZPSa1gFk9drXkTBwEQOSlf8MRXx3vopdKfI2rzSW42v3lhAO0o0Y9ov56A856jQnkHML71fsUpq087nknG6Cj/zEe3y/f4PtpadE223bB1b4r+a+OP36D0bHEjphFzhO3Efvg0TWbtG9M+/emJCE/hWPEiBEMGDCAlStX8uGHH/Lhhx+ycuVK+vfvz8iRI0O9+VJr1qwJPl9hcIcApKb+RqtWzW1cVdmZ1GNSC5jVY1ILqCecmdQCZvVUhRZn3YZ4hs8k5q4niby4Lzj+N/5ERhE9eDS+X1eQ++QdHJk3nagr/oGjprUB0FmrAf7dW4I/+3dvwVmr5PeIaNgCf3Ym5B0uc09pVYV9Y0U49YR8gD548CC9e/fG6Ty6KafTSZ8+fcjKygr15kstLjaWQ4eyi92XlZVNfFysTSsqH5N6TGoBs3pMagH1hDOTWsCsnnBvKdy8lryZo8h98g7y33sW19mdiOzUC4CIM9sRyNqL75dvIODHv2szvrXf42rV0dpG3NEE8nODPwaO5OKIijnuaY746rj/+ne8n80pV1Nphfu+sSqcekJ+DnRiYiIffPABV155JQ6HA4BAIMDixYtJSEgI9eZL7XBODgkJ8cXuS0iIJ/twjk0rKh+TekxqAbN6TGoB9YQzk1rArJ5wbwkc3Hv09p6teL+ZR+SFvShYvghnYk2cyU3xjJh19AXOCHypywCIvCiFyItSgvfjiiz23NzHby264c3HERXDn+fEOqJiCBzJK74QTzzRg0fh++ELCn/7b0Vnlijc941V4dQT8gF6ypQpjB8/nokTJ1KnTh0Adu/eTYsWLZgyZUqoN19q6ekbcLkiaNq0MevWbQSgdetWpKX9YfPKysakHpNawKwek1pAPeHMpBYwq6fKtQSAouN5+LP249+8lvzZJc8jBcsXU7B8MXDyc6D9e7fhrNMQ/46iUwucdRri33vMObTRHqIHjcKX/iMFyxZWaM7JVLl9cwrh1BPyUzgaNWrEG2+8wSeffMKUKVOYMmUKn3zyCW+++SZNmjQJ9eZLLTc3j/kLPmbC+OF4PDF0urADvVN6MHvOXLuXViYm9ZjUAmb1mNQC6glnJrWAWT22tTicEBEJDicO59Hb/1dE0zYQW/Q33o4a9XBf0pfCP34CoDBjNY7q9XCd07noCLMzAmf9Jjhq1re0FF/qMiIv+CuO+CQccYlEXnBF0WkhAO4YogeNwr81nYIv/1O+ZotM+pxBePVU2mXsqlevTvXq1Yvdl5KSwuLFiytrCac0dNgYXnn5KXZuT2X//kzuGja6yl7mBczqMakFzOoxqQXUE85MagGzeuxoibykL+4uA4I/u1p3xvv1XHyrvybmzsfJe3EEgUP7iWh8FlG9bwN3FIGcQ/h+XXb0KLA3n/w5U3D3GIS7xyBwOPDv3oL3szlYuUSZ78cvcSTVJub2oiPZBau/wvfjl0XratGBiOQzcNZKxtX2kuBr8l4cUe7/H5SGSZ8zCJ+ekF/Gbt26dSXeHwgE+Mc//sGyZcssv2eoLo9SmSrjci+VxaQWMKvHpBYwq8ekFjCrx6QWMKunpMvYVWWx445+mdCkfVPVW+DUl7EL+RHoXr16kZycTElz+sGDB0O9eRERERGRChXyATo5OZm33347+AXCY3Xp0iXUmxcRERERqVAh/xJhjx492L695MPg3bt3D/XmRUREREQqVMiPQJ/stw0+9NBDod68iIiIiEiFCvkRaBERERERk2iAFhERERGxQAO0iIiIiIgFGqBFRERERCzQAC0iIiIiYoEGaBERERERCzRAi4iIiIhYoAFaRERERMQCDdAiIiIiIhZogBYRERERsUADtIiIiIiIBRqgRUREREQs0AAtIiIiImKBBmgREREREQscgUAgYPciRERERESqCh2BFhERERGxQAO0iIiIiIgFLrsXUBYud7LdSyg3n3d78HZV7zm2Jc7T2MaVVIzDuRuDt03aN1W9BczqMakFzOoxqQXM6jGpBYr3ZN9+uY0rKb/4mZ8Eb5u2b0qiI9AiIiIiIhZogBYRERERsUADtIiIiIiIBRqgRUREREQs0AAtIiIiImKBBmgREREREQs0QIuIiIiIWKABWkRERETEAg3QIiIiIiIWaIAWEREREbFAA7SIiIiIiAUaoEVERERELNAALSIiIiJigQZoERERERELNECLiIiIiFigAVpERERExAIN0CIiIiIiFmiAFhERERGxQAP0MZKSEnn/vVfIysxgfcZKBg7sa/eSysW0HoCrrurFjz99zu69v5G65is6dTrP7iWViUn7xqQWUE84M6kFzOoxqQXs6Yn8Swqe0c8R9/wiom94oFSvibl3MvEzPwFnxY9z7n43Evfku8Q9+S7ufjcG73fUTib6jvHEPvFv4p56j5hhk3DUaVDh2z+RcPmsuWzZaph6/rlJeL0F1G/QhrZtzmLRwjdJTU0jLS3d7qWViWk9l3btzMTHRnHDkKH88MMv1K1X2+4llZlJ+8akFlBPODOpBczqMakF7OkJHDyA96N3iGh1Lg531Cmf7zr/Uogo+xgX0aw17l6DyZs24rjHIi++gsg2nch57E4gQMw9/ySwbxcF336EwxNL4S/fkf/GU5Cfh/vKQcTcMb7M67AqXD5rth6BTklJsXPzxXg8MfTvdwXjJzxBTk4uy1esYvEHnzN40AC7l1YmpvUAjH3oXqZMfo5Vq34mEAiwc8dudu7YbfeyLDNp35jUAuoJZya1gFk9JrWAfT2+n5fj++W/BHKyT/3kaA9RVw7iyLx/HfeQs04DYu75J3FPvUfshFdwnXux5bVEXnAZ3i/mEji4j8DB/RR8Po/IC7sD4N+UTsGKTyH3MPgL8X45j4i6p1neRlmE02ct5AP0unXrTvhPZmZmqDdfas2aNcHnKyQjY0PwvtTU32jVqrmNqyo703qcTift259DzZrV+eXXpfyRsYKnpj1CdPSp/ys93Ji0b0xqAfWEM5NawKwek1qgavRE9f0H3m8+JHDo/8xR7ihi7plMwfdLOfzgNeT9azJR1w7FWe90S+/vrN+Qwm1H+wu3bcBZv2GJz3WdeQ7+rAOWG8oinPZNyE/h6NWrF8nJyQQCgeMeO3jwYKg3X2pxsbEcOlT8v/qysrKJj4u1aUXlY1pP7To1cbvd9O33V3p0vxpfgY9/vzuLkaOG8ciEJ+1eniUm7RuTWkA94cykFjCrx6QWCP8e5+lnEnFGK468OwNHUq1ij7nO6Yh//258//0cAP/W9fh+Wo6r/cV4P5xT+o1ERUNeTvDHQH4OjmjPcU9zJNYk6tq7OPLeLGJuHlW2IAvCad+EfIBOTk7m7bffpk6dOsc91qVLl1BvvtQO5+SQkBBf7L6EhHiyD+ec4BXhzbSe/Lx8AGbOeIPdu/YCMP25fzFi1NAqN0CbtG9MagH1hDOTWsCsHpNaIMx7HA6irxvKkXdngt9/3MPOGrWJaNycuGnvH3NnBAUrvwTA3fNq3D2vDt5PpLvYcw/ff1XRjSP5EHN0YHZEewjk5xZfSlw1Yu6ZRMHXH+D74SuohAE6nPZNyAfoHj16sH379hIH6O7du4d686WWnr4BlyuCpk0bs27dRgBat25FWtofNq+sbEzrOXjwENu27eDYv8gIcPzfalQFJu0bk1pAPeHMpBYwq8ekFgjznmgPztPPJPrm0UU//+/qG7GTZ5P/8iT8B/ZRmPErec+OKfHl3k/fxfvpu8DJv0To37GZiAZN8G8q+mKes0ET/Ds2H32CJ46Yuyfh++U7vB//uwIDTy6c9k3Iz4EeOXIk7du3L/Gxhx56KNSbL7Xc3DzmL/iYCeOH4/HE0OnCDvRO6cHsOXPtXlqZmNYDMPut97n9juupVasGiYkJDB16E598vMTuZVlm0r4xqQXUE85MagGzekxqARt7nE5wRRb9X8cxt4+Vl0POqEHkTrqL3El3kTd9XNGaJw+jcOMf+H5dibN2Mq6O3YqOMDsjcDZshtPil/wKvvuCyG79cSTWwFGtOu7LBlDwv9NCiPbguXsShRvS8C54rSLKSy2cPmu2XsYuJSWFxYsX27mEYoYOG8MrLz/Fzu2p7N+fyV3DRlfZS/CAeT1TJj9PjRpJrP5lCUeOHGHe3A95fOp0u5dVJibtG5NaQD3hzKQWMKvHpBawp8d9xXVE9Roc/Dnygm4c+WA2Bcs/JXb8LHIeuZVA5t7iXxyMdAMU3ef3Q6GP3OfGEn3VrURddQsOh5PCbRs48v4sS2sp+PYjnLXqEfvwzKKfl39CwbcfAeBq24mIRs1x1mtI5AWVfyZBuHzWHIGSvt1XgdatW3fCx/7+97+zbNkyy+/pcieXZ0lhwefdHrxd1XuObYnzNLZxJRXjcO7G4G2T9k1VbwGzekxqAbN6TGoBs3pMaoHiPdm3X27jSsovfuYnwdum7ZuS6CocIiIiIiIW6CocIiIiIiIWhPxLhH9ehaMk4XQVDhERERGR0gj5EeiRI0ee8LFwugqHiIiIiEhphPwItIiIiIiISTRAi4iIiIhYoAFaRERERMQCDdAiIiIiIhZogBYRERERsUADtIiIiIiIBRqgRUREREQs0AAtIiIiImKBBmgREREREQs0QIuIiIiIWKABWkRERETEAg3QIiIiIiIWaIAWEREREbFAA7SIiIiIiAUaoEVERERELHAEAoGA3YsQEREREakqdARaRERERMQCDdAiIiIiIha47F5AWbjcyXYvodx83u3B21W9x6QWMKvHpBYwq8ekFjCrx6QWMKvHpBYwq+fYloJ9G2xcScWIrNnkpI/rCLSIiIiIiAUaoEVERERELNAALSIiIiJigQZoERERERELNECLiIiIiFigAVpERERExAIN0CIiIiIiFmiAFhERERGxQAO0iIiIiIgFGqBFRERERCzQAC0iIiIiYoEGaBERERERCzRAi4iIiIhYoAFaRERERMQCDdAiIiIiIhZogBYRERERsUADtIiIiIiIBRqgj5GUlMj7771CVmYG6zNWMnBgX7uXVC4m9ZjUAmb1mNQC6glnJrWAWT0mtYBZPeHS4vV6eXjy03TvfwPnX9afATfcxbf/XVWh28g6lM3doydyXre+dO9/Ax9+tjT42NcrvmfIHQ9wYc+r6JJyHeMmP0NOTm6Zt+WqiAWb4vnnJuH1FlC/QRvatjmLRQvfJDU1jbS0dLuXViYm9ZjUAmb1mNQC6glnJrWAWT0mtYBZPeHS4iv0U7d2LV5/4XHq1anFN/9dxQMPT2b+WzNIrlen1O/zwr9mA3DXTYOPe+yxp14g0hXJ14vf4feM9dz54HiaN21C0yYNOXw4h9tuuJZz256N11vAyEce58kX/sX4EcPK1OMIBAKBMr2ylDIzM3nyySfZuXMn3bp1Y9CgQcHHhg0bxvPPP2/5PV3u5IpcIgAeTwz79qTRpl03MjI2APD6a8+xY8dOxoydXOHb83m3B29X9R6TWsCsHpNawKyeULeAWT0mtYBZPSa1gFk9drUU7NtQqtf0u/4O7vzHILpf2pmvlq/k+VlvsmPXbpo0Op1xDw6jedPGx73mRAN0bl4+nS7/GwvemkGj0xsAMGriE9SpVYP77rjxuPf5/KvlvPiv2cx/a0aJa4us2eSkaw/5KRzjx4+nWrVqDBw4kC+++IKhQ4fi8/kA2Lp1a6g3X2rNmjXB5ysMfsAAUlN/o1Wr5jauquxM6jGpBczqMakF1BPOTGoBs3pMagGzesK5Zd+BTDZv3c4ZTRqyNn0d4/75NONHDGPZR//h6j5/ZdjICXi93lK/3+at23BFRASHZ4DmTRuzbuPmEp//48+/ckbj08u8/pAP0Js2bWLEiBH06NGDV199lVq1anHbbbdx5MiRUG/akrjYWA4dyi52X1ZWNvFxsTatqHxM6jGpBczqMakF1BPOTGoBs3pMagGzesK1pcDnY9Qjj9Pnr5fRpOFpvLfwY/7W5wpan9WCiIgI+lzRHXdkJL/89nup3zM3N5/YWE+x++LjYsnJzTvuuSu+/4lFn3zJ0JuHlLkh5OdAFxQUBG87HA7Gjx/P1KlTufXWW8NqiD6ck0NCQnyx+xIS4sk+nGPTisrHpB6TWsCsHpNaQD3hzKQWMKvHpBYwqyccW/x+P6MnPkGky8WY++8EYOeuPSz6+Evenrso+LyCAh979x0A4M4Hx7M69TcAjvzvqPTsdxcA0K71Wbz4xCN4PNHHfSnwcE4usZ6YYvf9smYtIx+ZyrTHxhQ7Wm1VyAfo0047jVWrVnHeeecF7xs5ciTTpk1j1qxZod58qaWnb8DliqBp08asW7cRgNatW5GW9ofNKysbk3pMagGzekxqAfWEM5NawKwek1rArJ5wawkEAoyb/Az7DxxkxlMTiXQVjaF169Tilhuu4bYbri3xdS8+8Ujw9onOgW54WgN8hYVs3rqdhqcVnU/+x7qNNG3cMPictenrGDbyER4dfR8XdGhXrpaQn8Lx+OOP06xZs+Puv//++/nggw9CvflSy83NY/6Cj5kwfjgeTwydLuxA75QezJ4z1+6llYlJPSa1gFk9JrWAesKZSS1gVo9JLWBWT7i1THxiOhs2beGFxycQHRUVvH9AyuW8u+AjUn/7nUAgQG5ePl+v+N7SZeY8MdFc1qUT0195i9y8fH5K/Y2l3/6XlJ7dAMjYsInb7n+Y0ffdwV86X1DulpAP0ImJiVSrVq3Ex+67775Qb96SocPGEBMTzc7tqcx+60XuGja6Sl6y5k8m9ZjUAmb1mNQC6glnJrWAWT0mtYBZPeHSsmPXbt5b+BG/r9tAl97Xcd5l/Tjvsn588OkSzm7ZjEdG3sOkaS/S6fK/ccU1N7Lwo88tb+Ph4UM5csRLl14DGTFhKg8PH0rTJkVHoN94Zx6ZB7MYN+WZ4Lb7DLqtzD0hv4zdunXrTvjY3//+d5YtW2b5PUN16ZrKVBmX4qksJrWAWT0mtYBZPSa1gFk9JrWAWT0mtYBZPWW5jF04O9Vl7EJ+DnSvXr1ITk6mpDn94MGDod68iIiIiEiFCvkAnZyczNtvv02dOsf/lpkuXbqEevMiIiIiIhUq5OdA9+jRg+3bt5f4WPfu3UO9eRERERGRChXyI9AjR4484WMPPfRQqDcvIiIiIlKhQn4EWkRERETEJBqgRUREREQs0AAtIiIiImKBBmgREREREQs0QIuIiIiIWKABWkRERETEAg3QIiIiIiIWaIAWEREREbFAA7SIiIiIiAUaoEVERERELNAALSIiIiJigQZoERERERELNECLiIiIiFigAVpERERExAIN0CIiIiIiFjgCgUDA7kWIiIiIiFQVOgItIiIiImKBBmgREREREQs0QIuIiIiIWKABWkRERETEAg3QIiIiIiIWaIAWEREREbFAA7SIiIiIiAUaoEVERERELNAALSIiIiJigQZoERERERELNED/Hxs3buSaa66hZ8+eXHPNNWzatMnuJZXZ1KlT6dq1K82bNyc9Pd3u5ZRLZmYmt9xyCz179iQlJYWhQ4dy4MABu5dVZnfeeSe9e/emb9++XHfddaxdu9buJVWI6dOnV/nPW9euXbn88svp06cPffr04dtvv7V7SeVy5MgRxo8fT48ePUhJSeHhhx+2e0llsm3btuA+6dOnD127duX888+3e1nlsnTpUvr27UufPn3o3bs3n332md1LKrOvvvqKfv36kZKSwuDBg9m6davdS7LkRH9eVsWZ4EQtVXUmKGndYTETBKSYIUOGBBYsWBAIBAKBBQsWBIYMGWLzispu1apVgR07dgQuvfTSwB9//GH3csolMzMz8N133wV/njJlSmD06NE2rqh8Dh06FLz9+eefB/r27WvjairGmjVrAjfddFOV/7xV9fX/X48++mhg0qRJAb/fHwgEAoG9e/favKKK8dhjjwUeeeQRu5dRZn6/P9ChQ4fgZ23t2rWBtm3bBgoLC21emXUHDx4MnH/++YENGzYEAoGiPztvvPFGm1dlzYn+vKyKM8GJWqrqTFDSusNhJtAR6GPs37+ftLQ0evXqBUCvXr1IS0urskc6O3ToQL169exeRoVITEykY8eOwZ/btm3Ljh07bFxR+cTHxwdvHz58GIfDYeNqys/r9TJx4kQmTJhg91LkGDk5OSxYsIB77rkn+BmrWbOmzasqP6/Xy+LFixkwYIDdSykXp9NJdnY2ANnZ2dSuXRuns+r9sbx582Zq1qxJ48aNAejSpQvLli2rUn92lvTnZVWdCU70Z39VnQlKWnc4zASuSt1amNu5cyd16tQhIiICgIiICGrXrs3OnTupXr26zauTP/n9ft555x26du1q91LKZezYsSxfvpxAIMArr7xi93LK5dlnn6V37940aNDA7qVUiOHDhxMIBDj33HO5//77SUhIsHtJZbJ161YSExOZPn06K1euJDY2lnvuuYcOHTrYvbRyWbJkCXXq1OGss86yeyll5nA4eOaZZ7jzzjvxeDzk5OQwa9Ysu5dVJo0bN2bfvn2kpqbSunVrFi9eDFDl/+zUTFA12DUTVL3/1JX/7z366KN4PB4GDx5s91LKZdKkSXz11Vfcd999PP7443Yvp8xWr17NmjVruO666+xeSoWYM2cOixYtYu7cuQQCASZOnGj3ksqssLCQrVu30qpVK+bNm8fw4cMZNmwYhw8ftntp5TJ37twqf/TZ5/Px0ksv8eKLL7J06VJmzJjBvffeS05Ojt1Lsyw+Pp6nn36ayZMn079/f/bv309CQkJw8BQJJbtmAg3Qx6hXrx67d++msLAQKPrDZ8+ePVXyrzxMNXXqVDZv3swzzzxTJf+qsyR9+/Zl5cqVZGZm2r2UMlm1ahXr16+nW7dudO3alV27dnHTTTexbNkyu5dWJn/+++52u7nuuuv46aefbF5R2dWrVw+XyxX8K+g2bdqQlJTExo0bbV5Z2e3evZtVq1aRkpJi91LKZe3atezZs4dzzz0XgHPPPZeYmBjWr19v88rKplOnTrzzzjvMmzePwYMHk5+fz+mnn273sspFM0H4s3MmMGMCqSA1atSgZcuWfPDBBwB88MEHtGzZUn9VEyamTZvGmjVreOGFF3C73XYvp8xycnLYuXNn8OclS/QQnF4AAAk1SURBVJZQrVo1EhMTbVxV2d16660sW7aMJUuWsGTJEurWrcu//vUvOnfubPfSLMvNzQ2ekxoIBPjoo49o2bKlzasqu+rVq9OxY0eWL18OFF1RYP/+/TRs2NDmlZXd/Pnz6dKlC0lJSXYvpVzq1q3Lrl272LBhAwDr169n//79VXbo3Lt3L1D01+nTpk1j4MCBeDwem1dVPpoJwpvdM4EjEAgEKn2rYWz9+vWMGjWKQ4cOkZCQwNSpU2nSpIndyyqTxx57jM8++4x9+/aRlJREYmIiH374od3LKpOMjAx69epFo0aNiI6OBqBBgwa88MILNq/Mun379nHnnXeSl5eH0+mkWrVqjBw5skqfz3msrl27MnPmTJo1a2b3UizbunUrw4YNo7CwEL/fzxlnnMFDDz1E7dq17V5amW3dupUxY8Zw8OBBXC4X9957L126dLF7WWXWs2dPxo4dyyWXXGL3Uspt0aJFvPzyy8EveN59991cdtllNq+qbMaOHctPP/1EQUEBF110EWPGjCEqKsruZZXaif68rIozwYlaqupMUNK6n3nmGdtnAg3QIiIiIiIW6BQOERERERELNECLiIiIiFigAVpERERExAIN0CIiIiIiFmiAFhERERGxQAO0iIiNRo0axdNPPw3ADz/8QM+ePStlu82bN2fz5s0V/r5du3ZlxYoVFf6+IiLhRAO0iMgpdO3aldatW9OuXTs6derEqFGjQvIrlzt06MCnn356yufNmzePa6+9tsK3DzBu3DhGjBhx3P2///47Z599NgcPHgzJdkVEqhIN0CIipTBz5kxWr17N/PnzWbNmDTNmzDjuOT6fz4aVVax+/frx+eefk5ubW+z+hQsXcumll1bZ35gpIlKRNECLiFhQp04dLr74YjIyMoCiUyHmzJlDjx496NGjBwBLly6lT58+dOjQgYEDB/L7778HX5+Wlka/fv1o164d99577/9r7+5CotraAI7//czGOlih4qQYFJWQpTI2g0mmoakVo4IKBVpd+JFTN1EXEkUTUwRlkAVRIihdlEQOiQqTRY1Q+FH5ARJRSEIzzmRDhJ+j45yLw7vJk+U7vKdz3gPP72r2WnuetZ59sefZmzV7MzMzo/R1dXUteMOe3W7HYDCg0+nQarUYjUbev3/P2bNn6evrIzExEY1GA4Db7ebSpUvs2rWLlJQUzpw5w/T0tBKrrq6O1NRUUlNTuX///g/zS0xMJCIiAovForR5PB5aWlrQ6/WMjIxQUlKCVqtFq9Vy4sQJvn79umisb5enLJafw+Hg2LFj6HQ6MjIyaGxsVPoGBgYoKCggKSmJlJQULl68+MM5CyHE300KaCGE8IHdbsdqtRIXF6e0dXR00NTURFtbG0NDQ1RXV2M0Gunq6qK4uJijR4/idrtxu91UVVWh1+vp7u4mOzt7QaH6LY/HQ3l5OWq1midPnmC1WsnNzWX9+vWcO3eOhIQEXr9+TW9vLwCXL19meHgYs9mMxWLB6XQqr7W1Wq3U19dTX1+PxWLhxYsXP80xLy8Ps9msbD9//py5uTnS0tLwer2Ul5fT2dlJe3s7o6Oj1NbW+nwc5+fnqaysZNOmTVitVhoaGmhoaKCzsxMAk8lESUkJr1694tGjR+Tk5Pg8hhBC/CpSQAshxH+hqqoKjUbDgQMHSE5OpqKiQukrKysjLCyMkJAQ7t27R3FxMdu2bSMgIID8/HyCgoLo6+ujv7+f2dlZSktLCQoKIjs7m/j4+EXHGxgYwOl0curUKVQqFcuWLVPuNv+Z1+ulqamJ6upqwsLCWLFiBeXl5bS2tgLQ3t5OQUEBGzduRKVSYTAYfpqrXq+np6eH0dFRAMxmM/v27SMoKIjY2Fh27NhBcHAwq1ev5vDhw/T09Ph8PAcHB3G5XBgMBoKDg4mJiaGoqIi2tjYAAgMDGRkZweVyERoaSkJCgs9jCCHErxL4T09ACCH+DW7cuEFKSsqifVFRUcpnm82G2Wzmzp07Stvs7CxOpxM/Pz8iIyPx8/NT+tRq9aIx7XY7arWawMClT9Mul4upqSkKCgqUNq/Xy/z8PABOp5MtW7YofWvXrv1pPLVajUaj4eHDhxw8eJDHjx8r+YyNjWEymejt7WViYgKv18tvv/225Bz/7OPHjzidzgUXBR6PR9k2mUxcu3aNnJwcoqOjMRgMpKen+zyOEEL8ClJACyHE/+jbgjgqKoqKigoqKyu/26+7uxuHw4HX61W+Y7PZiImJ+W7fqKgo7HY7c3Nz3xXR344HsGrVKkJCQmhtbSUyMvK7WBEREdjtdmXbZrMtmVN+fj63b98mPDyc6OhopQCvqanBz8+PlpYWwsLC6OjowGg0Lhpj+fLlC9Zhj42NLcgvOjr6h0tY1q1bR01NDfPz81gsFo4fP05XVxcqlWrJuQshxK8mSziEEOIvVFhYyN27d+nv78fr9TI5OcnTp08ZHx8nISGBwMBAGhsbmZ2dxWKxMDg4uGicrVu3Eh4ezpUrV5icnGRmZoaXL18CsGbNGhwOB263GwB/f38KCwu5cOECnz9/Bv74g95/1hNnZ2fT3NzMu3fvmJqa4vr160vmkZWVhc1mo7a2lry8PKV9YmIClUrFypUrcTgc1NXV/TBGXFwcz54948uXL3z69ImGhoYF+YWGhnLr1i2mp6fxeDy8ffuWgYEB4I+nfrhcLvz9/ZU73P7+8pMlhPj/IGcjIYT4C8XHx3P+/HmMRiPJyclkZWXx4MEDAIKDg6mtraW5uZnt27fT1tZGZmbmonECAgK4efMmHz58ID09nZ07d9Le3g6ATqdjw4YNpKamotVqATh58iSxsbEUFRWRlJTEoUOHGB4eBiAtLY3S0lJKS0vJzMxEp9MtmYdKpWLPnj2Mjo6yf/9+pd1gMDA0NIRGo6GsrEx58shi9Ho9mzdvJiMjgyNHjpCbm/tdfm/evGH37t3odDpOnz7N+Pg4AJ2dnezdu5fExERMJhNXr14lJCRkyXkLIcTfwc/r9Xr/6UkIIYQQQgjxbyF3oIUQQgghhPCBFNBCCCGEEEL4QApoIYQQQgghfCAFtBBCCCGEED6QAloIIYQQQggfSAEthBBCCCGED6SAFkIIIYQQwgdSQAshhBBCCOEDKaCFEEIIIYTwwe9inVI/En0JQwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 864x864 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BvavXC3xjOAw"
      },
      "source": [
        "**Making predictions with the model**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OpW1JboE43ah"
      },
      "source": [
        "from tensorflow.keras.preprocessing import image"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117
        },
        "id": "9WlvuE0g44bG",
        "outputId": "58d2629b-1594-4ec8-f166-d8f05e64c913"
      },
      "source": [
        "my_image = image.load_img(apple_1,target_size=image_shape)\n",
        "my_image"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=100x100 at 0x7F17F3DF88D0>"
            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wtgHaMF15BzR",
        "outputId": "2fe9f3cc-9de7-49e1-bb87-00fcc7d60a69"
      },
      "source": [
        "type(my_image)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PIL.JpegImagePlugin.JpegImageFile"
            ]
          },
          "metadata": {},
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lZK2pigB5HSj"
      },
      "source": [
        "my_image = image.img_to_array(my_image)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t5H88V2B5Qkr",
        "outputId": "7b909ff1-83ec-4e7c-d073-b35ce0088552"
      },
      "source": [
        "type(my_image)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {},
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FbZ0078J5TFf",
        "outputId": "34ca30cf-591f-4ff2-8e97-d904c43c868d"
      },
      "source": [
        "my_image.shape "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(100, 100, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0qvmzdWa5rA-",
        "outputId": "0cf465be-1091-4a74-faec-4a13dd3c1fea"
      },
      "source": [
        "my_image = np.expand_dims(my_image, axis=0)\n",
        "my_image.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1, 100, 100, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gi1iNFsXjtUR"
      },
      "source": [
        "#Predicting he class of the image (The model predicted corect)\n",
        "np.argmax(model.predict(my_image))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gL_g0xQkj162"
      },
      "source": [
        "**Saving and Loading the model**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FiMMxqHD8NBT"
      },
      "source": [
        "model.save('la_pomme_cnn.h5')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g0z2LVhf77p8"
      },
      "source": [
        "from tensorflow.keras.models import load_model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G2i7KJiz78xh"
      },
      "source": [
        "modelx = load_model('la_pomme_cnn.h5')"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}