Cat_and_Dog_Image_Classifier/cat_dog_classification.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "la_Oz6oLlub6",
        "outputId": "16fd63a5-cb94-45a8-beda-79afed5f2839"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Conv2D, Flatten, Dropout, MaxPooling2D\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "import os\n",
        "import requests\n",
        "from zipfile import ZipFile\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "jaF8r6aOl48C"
      },
      "outputs": [],
      "source": [
        "# In current directory, since path is not specified.\n",
        "if not os.path.exists('cats_and_dogs.zip'):\n",
        "    url = 'https://cdn.freecodecamp.org/project-data/cats-and-dogs/cats_and_dogs.zip'\n",
        "    response = requests.get(url)\n",
        "    with open('cats_and_dogs.zip', 'wb') as file:\n",
        "        file.write(response.content)\n",
        "    with ZipFile('cats_and_dogs.zip', 'r') as zipObj:\n",
        "        zipObj.extractall() \n",
        "\n",
        "PATH = 'cats_and_dogs'\n",
        "\n",
        "train_dir = os.path.join(PATH, 'train')\n",
        "validation_dir = os.path.join(PATH, 'validation')\n",
        "test_dir = os.path.join(PATH, 'test')\n",
        "\n",
        "# Get number of files in each directory. The train and validation directories\n",
        "# each have the subdirecories \"dogs\" and \"cats\".\n",
        "total_train = sum([len(files) for r, d, files in os.walk(train_dir)])\n",
        "total_val = sum([len(files) for r, d, files in os.walk(validation_dir)])\n",
        "total_test = len(os.listdir(test_dir))\n",
        "\n",
        "# Variables for pre-processing and training.\n",
        "batch_size = 128\n",
        "epochs = 15\n",
        "IMG_HEIGHT = 160\n",
        "IMG_WIDTH = 160"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ggzTJbm4ZibH"
      },
      "source": [
        "### CNN Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qKY7RtOyPxLa"
      },
      "outputs": [],
      "source": [
        "# Locate a file and delete it.\n",
        "# ds_store_path = os.path.join(test_dir, '.DS_Store')\n",
        "# if os.path.exists(ds_store_path):\n",
        "#   os.remove(ds_store_path)\n",
        "#   print('File removed')\n",
        "# print(os.listdir(test_dir))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "S-ILraMaQSEK"
      },
      "outputs": [],
      "source": [
        "# Create dummy class directory for test data\n",
        "dummy_sub = os.path.join(test_dir, 'dummy_class')\n",
        "os.makedirs(dummy_sub, exist_ok=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VroIIaBMUppe"
      },
      "outputs": [],
      "source": [
        "# Move all test data to dummy class directory\n",
        "import glob\n",
        "all_files_path = glob.glob(os.path.join(test_dir, '*.jpg'))\n",
        "for file_path in all_files_path:\n",
        "  dst_path = os.path.join(dummy_sub, os.path.basename(file_path))\n",
        "  os.rename(file_path, dst_path)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EOJFeEfumns6",
        "outputId": "c48982f0-7171-4eda-f8ca-fbfb931020bc"
      },
      "outputs": [],
      "source": [
        "# 3\n",
        "train_image_generator = ImageDataGenerator(rescale=1./255)\n",
        "validation_image_generator = ImageDataGenerator(rescale=1./255)\n",
        "test_image_generator = ImageDataGenerator(rescale=1./255)\n",
        "\n",
        "train_data_gen = train_image_generator.flow_from_directory(train_dir, target_size=(IMG_HEIGHT, IMG_WIDTH),\n",
        "        batch_size=batch_size, class_mode='binary')\n",
        "\n",
        "val_data_gen = validation_image_generator.flow_from_directory(validation_dir, target_size=(IMG_HEIGHT, IMG_WIDTH),\n",
        "        batch_size=batch_size, class_mode='binary')\n",
        "\n",
        "test_data_gen = test_image_generator.flow_from_directory(test_dir, target_size=(IMG_HEIGHT, IMG_WIDTH),\n",
        "        batch_size=8, shuffle=False, class_mode=None)\n",
        "# or...\n",
        "# test_data_gen = test_image_generator.flow_from_directory(PATH, target_size=(IMG_HEIGHT, IMG_WIDTH),\n",
        "#         batch_size=8, shuffle=False, classes=['test'], class_mode='binary')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2Z-qkwNClJeX",
        "outputId": "d798dc03-f164-46df-bbfd-5969d6df426f"
      },
      "outputs": [],
      "source": [
        "print(train_data_gen.class_indices)\n",
        "print(f'Found {len(train_data_gen.filenames)} images in this directory.')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TP0WA8j1mt7Q"
      },
      "outputs": [],
      "source": [
        "# 4\n",
        "def plotImages(images_arr, probabilities = False):\n",
        "    fig, axes = plt.subplots(len(images_arr), 1, figsize=(5,len(images_arr) * 3))\n",
        "    if probabilities is False:\n",
        "      for img, ax in zip( images_arr, axes):\n",
        "          ax.imshow(img)\n",
        "          ax.axis('off')\n",
        "    else:\n",
        "      for img, probability, ax in zip( images_arr, probabilities, axes):\n",
        "          ax.imshow(img)\n",
        "          ax.axis('off')\n",
        "          if probability > 0.5:\n",
        "              ax.set_title(\"%.2f\" % (probability*100) + \"% dog\")\n",
        "          else:\n",
        "              ax.set_title(\"%.2f\" % ((1-probability)*100) + \"% cat\")\n",
        "    plt.show()\n",
        "\n",
        "sample_training_images, _ = next(train_data_gen)\n",
        "plotImages(sample_training_images[:5])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-32RRLY_3voj"
      },
      "outputs": [],
      "source": [
        "# 5\n",
        "# Create more training data from existing training examples by using random transformations\n",
        "train_image_generator = ImageDataGenerator(rescale=1./255, horizontal_flip=True,\n",
        "                                           rotation_range=30, fill_mode='nearest',\n",
        "                                           brightness_range=[0.4,1.5],\n",
        "                                           zoom_range=0.3,\n",
        "                                           width_shift_range=0.2, height_shift_range=0.2)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pkwq2LFvqabS"
      },
      "outputs": [],
      "source": [
        "# 6\n",
        "train_data_gen = train_image_generator.flow_from_directory(batch_size=batch_size,\n",
        "                                                     directory=train_dir,\n",
        "                                                     target_size=(IMG_HEIGHT, IMG_WIDTH),\n",
        "                                                     class_mode='binary')\n",
        "\n",
        "augmented_images = [train_data_gen[0][0][0] for i in range(5)]\n",
        "\n",
        "plotImages(augmented_images)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k8aZkwMam4UY",
        "outputId": "690f9a5c-8dbf-40e7-fa2e-1a7d10943f46"
      },
      "outputs": [],
      "source": [
        "# 7\n",
        "model = Sequential()\n",
        "model.add(Conv2D(filters=32, kernel_size=(3,3), activation='relu', padding='same', input_shape=(IMG_HEIGHT, IMG_WIDTH, 3)))\n",
        "model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "model.add(Conv2D(filters=64, kernel_size=(3,3), activation='relu', padding='same'))\n",
        "model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "model.add(Conv2D(filters=128, kernel_size=(3,3), activation='relu', padding='same'))\n",
        "model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(units=128, activation='relu'))\n",
        "model.add(Dense(units=1, activation='sigmoid'))\n",
        "\n",
        "# Binary classification\n",
        "# Predicting a single value at output (probability of being a dog or a cat).\n",
        "# model.add(Dense(1, activation='sigmoid'))\n",
        "# model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "#Multi-class classification\n",
        "# model.add(Dense(num_classes, activation='softmax'))\n",
        "# model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "model.summary()\n",
        "\n",
        "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HdC0PQla0fKK",
        "outputId": "cda23518-57ff-40af-eab9-99e683aada24"
      },
      "outputs": [],
      "source": [
        "len(train_data_gen) # 2000/128 -> total_training_samples / batch_size"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 598
        },
        "id": "1niQDz5x6K7y",
        "outputId": "8da70d1c-a15a-44bc-9aee-cb9de25f4144"
      },
      "outputs": [],
      "source": [
        "# 8\n",
        "history = model.fit(x=train_data_gen, validation_data=val_data_gen, epochs=epochs,\n",
        "                    steps_per_epoch=len(train_data_gen),\n",
        "                    validation_steps=len(val_data_gen), verbose=1)\n",
        "model.save('cats_dogs.keras')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bzQUtdo1Fe1n"
      },
      "outputs": [],
      "source": [
        "model = tf.keras.load_model('cats_dogs.keras')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 699
        },
        "id": "5xS51mB56OAC",
        "outputId": "54226ef9-4544-4da8-a953-782eff7532f0"
      },
      "outputs": [],
      "source": [
        "# 9\n",
        "acc = history.history['accuracy']\n",
        "val_acc = history.history['val_accuracy']\n",
        "\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "\n",
        "epochs_range = range(epochs)\n",
        "\n",
        "plt.figure(figsize=(8, 8))\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.plot(epochs_range, acc, label='Training Accuracy')\n",
        "plt.plot(epochs_range, val_acc, label='Validation Accuracy')\n",
        "plt.legend(loc='lower right')\n",
        "plt.title('Training and Validation Accuracy')\n",
        "\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.plot(epochs_range, loss, label='Training Loss')\n",
        "plt.plot(epochs_range, val_loss, label='Validation Loss')\n",
        "plt.legend(loc='upper right')\n",
        "plt.title('Training and Validation Loss')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OxxfHrCHHAKd",
        "outputId": "42b8305a-ae49-4894-e664-bf5504a3584d"
      },
      "outputs": [],
      "source": [
        "predictions = model.predict(x=test_data_gen, verbose=0)\n",
        "predictions[:5]\n",
        "\n",
        "# or ... if sparse_categorical_crossentropy is used\n",
        "# pred = model.predict(x=test_data_gen, verbose=0)\n",
        "# probabilities = np.argmax(pred, axis=-1)\n",
        "# array([[0.42858884, 0.57141113],\n",
        "#        [0.6647593 , 0.33524072],\n",
        "#        [0.645472  , 0.35452798],"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "test_images, _ = next(test_data_gen)\n",
        "plotImages(test_images, probabilities=predictions)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 245
        },
        "id": "4IH86Ux_u7TZ",
        "outputId": "e2887605-e974-4853-cce9-44d713930457"
      },
      "outputs": [],
      "source": [
        "# 11\n",
        "answers =  [1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0,\n",
        "            1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0,\n",
        "            1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1,\n",
        "            1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1,\n",
        "            0, 0, 0, 0, 0, 0]\n",
        "\n",
        "correct = 0\n",
        "\n",
        "probabilities = [1 if prediction > 0.5 else 0 for prediction in predictions]\n",
        "\n",
        "for probability, answer in zip(probabilities, answers):\n",
        "  if round(probability) == answer:\n",
        "    correct +=1\n",
        "\n",
        "percentage_identified = (correct / len(answers)) * 100\n",
        "\n",
        "passed_challenge = percentage_identified >= 63\n",
        "\n",
        "print(f\"Your model correctly identified {round(percentage_identified, 2)}% of the images of cats and dogs.\")\n",
        "\n",
        "if passed_challenge:\n",
        "  print(\"You passed the challenge!\")\n",
        "else:\n",
        "  print(\"You haven't passed yet. Your model should identify at least 63% of the images. Keep trying. You will get it!\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "import itertools\n",
        "cm = tf.math.confusion_matrix(labels=answers, predictions=probabilities)\n",
        "\n",
        "def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues):\n",
        "  plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "  plt.title(title)\n",
        "  plt.colorbar()\n",
        "  tick_marks = np.arange(len(classes))\n",
        "  plt.xticks(tick_marks, classes, rotation=45)\n",
        "  plt.yticks(tick_marks, classes)\n",
        "\n",
        "  if normalize:\n",
        "      cm = np.round(cm.astype('float') / cm.sum(axis=1)[:, np.newaxis], 2)\n",
        "      print(\"Normalized confusion matrix\")\n",
        "  else:\n",
        "      print('Confusion matrix, without normalization')\n",
        "\n",
        "  print(cm)\n",
        "\n",
        "  thresh = cm.max() / 2.\n",
        "  for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "      plt.text(j, i, cm[i, j],\n",
        "              horizontalalignment=\"center\",\n",
        "              color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "\n",
        "  plt.tight_layout()\n",
        "  plt.ylabel('True label')\n",
        "  plt.xlabel('Predicted label')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "cm_plot_labels = ['cat','dog']\n",
        "plot_confusion_matrix(cm=cm, classes=cm_plot_labels, title='Confusion Matrix')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Using a Pre-Trained Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [],
      "source": [
        "import os\n",
        "\n",
        "folder_name = 'Cat and Dog Image Classifier'\n",
        "# os.getcwd()\n",
        "data_dir = os.path.join(os.getcwd(), 'data')\n",
        "os.makedirs(data_dir, exist_ok=True)\n",
        "\n",
        "train_dir = os.path.join(data_dir, 'train')\n",
        "val_dir = os.path.join(data_dir, 'validation')\n",
        "test_dir = os.path.join(data_dir, 'test')\n",
        "\n",
        "os.makedirs(train_dir, exist_ok=True)\n",
        "os.makedirs(val_dir, exist_ok=True)\n",
        "os.makedirs(test_dir, exist_ok=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Found 2000 images belonging to 2 classes.\n",
            "Found 1000 images belonging to 2 classes.\n",
            "Found 50 images belonging to 1 classes.\n"
          ]
        }
      ],
      "source": [
        "train_image_generator = ImageDataGenerator(preprocessing_function=tf.keras.applications.mobilenet.preprocess_input)\n",
        "validation_image_generator = ImageDataGenerator(preprocessing_function=tf.keras.applications.mobilenet.preprocess_input)\n",
        "test_image_generator = ImageDataGenerator(preprocessing_function=tf.keras.applications.mobilenet.preprocess_input)\n",
        "\n",
        "train_data_gen = train_image_generator.flow_from_directory(train_dir, target_size=(IMG_HEIGHT, IMG_WIDTH),\n",
        "        batch_size=batch_size, class_mode='binary')\n",
        "\n",
        "val_data_gen = validation_image_generator.flow_from_directory(validation_dir, target_size=(IMG_HEIGHT, IMG_WIDTH),\n",
        "        batch_size=batch_size, class_mode='binary')\n",
        "\n",
        "test_data_gen = test_image_generator.flow_from_directory(PATH, target_size=(IMG_HEIGHT, IMG_WIDTH),\n",
        "        batch_size=50, shuffle=False, classes=['test'], class_mode='binary')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/mobilenet_v2/mobilenet_v2_weights_tf_dim_ordering_tf_kernels_1.0_160_no_top.h5\n",
            "9406464/9406464 [==============================] - 1s 0us/step\n",
            "Model: \"mobilenetv2_1.00_160\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_1 (InputLayer)           [(None, 160, 160, 3  0           []                               \n",
            "                                )]                                                                \n",
            "                                                                                                  \n",
            " Conv1 (Conv2D)                 (None, 80, 80, 32)   864         ['input_1[0][0]']                \n",
            "                                                                                                  \n",
            " bn_Conv1 (BatchNormalization)  (None, 80, 80, 32)   128         ['Conv1[0][0]']                  \n",
            "                                                                                                  \n",
            " Conv1_relu (ReLU)              (None, 80, 80, 32)   0           ['bn_Conv1[0][0]']               \n",
            "                                                                                                  \n",
            " expanded_conv_depthwise (Depth  (None, 80, 80, 32)  288         ['Conv1_relu[0][0]']             \n",
            " wiseConv2D)                                                                                      \n",
            "                                                                                                  \n",
            " expanded_conv_depthwise_BN (Ba  (None, 80, 80, 32)  128         ['expanded_conv_depthwise[0][0]']\n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " expanded_conv_depthwise_relu (  (None, 80, 80, 32)  0           ['expanded_conv_depthwise_BN[0][0\n",
            " ReLU)                                                           ]']                              \n",
            "                                                                                                  \n",
            " expanded_conv_project (Conv2D)  (None, 80, 80, 16)  512         ['expanded_conv_depthwise_relu[0]\n",
            "                                                                 [0]']                            \n",
            "                                                                                                  \n",
            " expanded_conv_project_BN (Batc  (None, 80, 80, 16)  64          ['expanded_conv_project[0][0]']  \n",
            " hNormalization)                                                                                  \n",
            "                                                                                                  \n",
            " block_1_expand (Conv2D)        (None, 80, 80, 96)   1536        ['expanded_conv_project_BN[0][0]'\n",
            "                                                                 ]                                \n",
            "                                                                                                  \n",
            " block_1_expand_BN (BatchNormal  (None, 80, 80, 96)  384         ['block_1_expand[0][0]']         \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block_1_expand_relu (ReLU)     (None, 80, 80, 96)   0           ['block_1_expand_BN[0][0]']      \n",
            "                                                                                                  \n",
            " block_1_pad (ZeroPadding2D)    (None, 81, 81, 96)   0           ['block_1_expand_relu[0][0]']    \n",
            "                                                                                                  \n",
            " block_1_depthwise (DepthwiseCo  (None, 40, 40, 96)  864         ['block_1_pad[0][0]']            \n",
            " nv2D)                                                                                            \n",
            "                                                                                                  \n",
            " block_1_depthwise_BN (BatchNor  (None, 40, 40, 96)  384         ['block_1_depthwise[0][0]']      \n",
            " malization)                                                                                      \n",
            "                                                                                                  \n",
            " block_1_depthwise_relu (ReLU)  (None, 40, 40, 96)   0           ['block_1_depthwise_BN[0][0]']   \n",
            "                                                                                                  \n",
            " block_1_project (Conv2D)       (None, 40, 40, 24)   2304        ['block_1_depthwise_relu[0][0]'] \n",
            "                                                                                                  \n",
            " block_1_project_BN (BatchNorma  (None, 40, 40, 24)  96          ['block_1_project[0][0]']        \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block_2_expand (Conv2D)        (None, 40, 40, 144)  3456        ['block_1_project_BN[0][0]']     \n",
            "                                                                                                  \n",
            " block_2_expand_BN (BatchNormal  (None, 40, 40, 144)  576        ['block_2_expand[0][0]']         \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block_2_expand_relu (ReLU)     (None, 40, 40, 144)  0           ['block_2_expand_BN[0][0]']      \n",
            "                                                                                                  \n",
            " block_2_depthwise (DepthwiseCo  (None, 40, 40, 144)  1296       ['block_2_expand_relu[0][0]']    \n",
            " nv2D)                                                                                            \n",
            "                                                                                                  \n",
            " block_2_depthwise_BN (BatchNor  (None, 40, 40, 144)  576        ['block_2_depthwise[0][0]']      \n",
            " malization)                                                                                      \n",
            "                                                                                                  \n",
            " block_2_depthwise_relu (ReLU)  (None, 40, 40, 144)  0           ['block_2_depthwise_BN[0][0]']   \n",
            "                                                                                                  \n",
            " block_2_project (Conv2D)       (None, 40, 40, 24)   3456        ['block_2_depthwise_relu[0][0]'] \n",
            "                                                                                                  \n",
            " block_2_project_BN (BatchNorma  (None, 40, 40, 24)  96          ['block_2_project[0][0]']        \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block_2_add (Add)              (None, 40, 40, 24)   0           ['block_1_project_BN[0][0]',     \n",
            "                                                                  'block_2_project_BN[0][0]']     \n",
            "                                                                                                  \n",
            " block_3_expand (Conv2D)        (None, 40, 40, 144)  3456        ['block_2_add[0][0]']            \n",
            "                                                                                                  \n",
            " block_3_expand_BN (BatchNormal  (None, 40, 40, 144)  576        ['block_3_expand[0][0]']         \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block_3_expand_relu (ReLU)     (None, 40, 40, 144)  0           ['block_3_expand_BN[0][0]']      \n",
            "                                                                                                  \n",
            " block_3_pad (ZeroPadding2D)    (None, 41, 41, 144)  0           ['block_3_expand_relu[0][0]']    \n",
            "                                                                                                  \n",
            " block_3_depthwise (DepthwiseCo  (None, 20, 20, 144)  1296       ['block_3_pad[0][0]']            \n",
            " nv2D)                                                                                            \n",
            "                                                                                                  \n",
            " block_3_depthwise_BN (BatchNor  (None, 20, 20, 144)  576        ['block_3_depthwise[0][0]']      \n",
            " malization)                                                                                      \n",
            "                                                                                                  \n",
            " block_3_depthwise_relu (ReLU)  (None, 20, 20, 144)  0           ['block_3_depthwise_BN[0][0]']   \n",
            "                                                                                                  \n",
            " block_3_project (Conv2D)       (None, 20, 20, 32)   4608        ['block_3_depthwise_relu[0][0]'] \n",
            "                                                                                                  \n",
            " block_3_project_BN (BatchNorma  (None, 20, 20, 32)  128         ['block_3_project[0][0]']        \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block_4_expand (Conv2D)        (None, 20, 20, 192)  6144        ['block_3_project_BN[0][0]']     \n",
            "                                                                                                  \n",
            " block_4_expand_BN (BatchNormal  (None, 20, 20, 192)  768        ['block_4_expand[0][0]']         \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block_4_expand_relu (ReLU)     (None, 20, 20, 192)  0           ['block_4_expand_BN[0][0]']      \n",
            "                                                                                                  \n",
            " block_4_depthwise (DepthwiseCo  (None, 20, 20, 192)  1728       ['block_4_expand_relu[0][0]']    \n",
            " nv2D)                                                                                            \n",
            "                                                                                                  \n",
            " block_4_depthwise_BN (BatchNor  (None, 20, 20, 192)  768        ['block_4_depthwise[0][0]']      \n",
            " malization)                                                                                      \n",
            "                                                                                                  \n",
            " block_4_depthwise_relu (ReLU)  (None, 20, 20, 192)  0           ['block_4_depthwise_BN[0][0]']   \n",
            "                                                                                                  \n",
            " block_4_project (Conv2D)       (None, 20, 20, 32)   6144        ['block_4_depthwise_relu[0][0]'] \n",
            "                                                                                                  \n",
            " block_4_project_BN (BatchNorma  (None, 20, 20, 32)  128         ['block_4_project[0][0]']        \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block_4_add (Add)              (None, 20, 20, 32)   0           ['block_3_project_BN[0][0]',     \n",
            "                                                                  'block_4_project_BN[0][0]']     \n",
            "                                                                                                  \n",
            " block_5_expand (Conv2D)        (None, 20, 20, 192)  6144        ['block_4_add[0][0]']            \n",
            "                                                                                                  \n",
            " block_5_expand_BN (BatchNormal  (None, 20, 20, 192)  768        ['block_5_expand[0][0]']         \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block_5_expand_relu (ReLU)     (None, 20, 20, 192)  0           ['block_5_expand_BN[0][0]']      \n",
            "                                                                                                  \n",
            " block_5_depthwise (DepthwiseCo  (None, 20, 20, 192)  1728       ['block_5_expand_relu[0][0]']    \n",
            " nv2D)                                                                                            \n",
            "                                                                                                  \n",
            " block_5_depthwise_BN (BatchNor  (None, 20, 20, 192)  768        ['block_5_depthwise[0][0]']      \n",
            " malization)                                                                                      \n",
            "                                                                                                  \n",
            " block_5_depthwise_relu (ReLU)  (None, 20, 20, 192)  0           ['block_5_depthwise_BN[0][0]']   \n",
            "                                                                                                  \n",
            " block_5_project (Conv2D)       (None, 20, 20, 32)   6144        ['block_5_depthwise_relu[0][0]'] \n",
            "                                                                                                  \n",
            " block_5_project_BN (BatchNorma  (None, 20, 20, 32)  128         ['block_5_project[0][0]']        \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block_5_add (Add)              (None, 20, 20, 32)   0           ['block_4_add[0][0]',            \n",
            "                                                                  'block_5_project_BN[0][0]']     \n",
            "                                                                                                  \n",
            " block_6_expand (Conv2D)        (None, 20, 20, 192)  6144        ['block_5_add[0][0]']            \n",
            "                                                                                                  \n",
            " block_6_expand_BN (BatchNormal  (None, 20, 20, 192)  768        ['block_6_expand[0][0]']         \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block_6_expand_relu (ReLU)     (None, 20, 20, 192)  0           ['block_6_expand_BN[0][0]']      \n",
            "                                                                                                  \n",
            " block_6_pad (ZeroPadding2D)    (None, 21, 21, 192)  0           ['block_6_expand_relu[0][0]']    \n",
            "                                                                                                  \n",
            " block_6_depthwise (DepthwiseCo  (None, 10, 10, 192)  1728       ['block_6_pad[0][0]']            \n",
            " nv2D)                                                                                            \n",
            "                                                                                                  \n",
            " block_6_depthwise_BN (BatchNor  (None, 10, 10, 192)  768        ['block_6_depthwise[0][0]']      \n",
            " malization)                                                                                      \n",
            "                                                                                                  \n",
            " block_6_depthwise_relu (ReLU)  (None, 10, 10, 192)  0           ['block_6_depthwise_BN[0][0]']   \n",
            "                                                                                                  \n",
            " block_6_project (Conv2D)       (None, 10, 10, 64)   12288       ['block_6_depthwise_relu[0][0]'] \n",
            "                                                                                                  \n",
            " block_6_project_BN (BatchNorma  (None, 10, 10, 64)  256         ['block_6_project[0][0]']        \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block_7_expand (Conv2D)        (None, 10, 10, 384)  24576       ['block_6_project_BN[0][0]']     \n",
            "                                                                                                  \n",
            " block_7_expand_BN (BatchNormal  (None, 10, 10, 384)  1536       ['block_7_expand[0][0]']         \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block_7_expand_relu (ReLU)     (None, 10, 10, 384)  0           ['block_7_expand_BN[0][0]']      \n",
            "                                                                                                  \n",
            " block_7_depthwise (DepthwiseCo  (None, 10, 10, 384)  3456       ['block_7_expand_relu[0][0]']    \n",
            " nv2D)                                                                                            \n",
            "                                                                                                  \n",
            " block_7_depthwise_BN (BatchNor  (None, 10, 10, 384)  1536       ['block_7_depthwise[0][0]']      \n",
            " malization)                                                                                      \n",
            "                                                                                                  \n",
            " block_7_depthwise_relu (ReLU)  (None, 10, 10, 384)  0           ['block_7_depthwise_BN[0][0]']   \n",
            "                                                                                                  \n",
            " block_7_project (Conv2D)       (None, 10, 10, 64)   24576       ['block_7_depthwise_relu[0][0]'] \n",
            "                                                                                                  \n",
            " block_7_project_BN (BatchNorma  (None, 10, 10, 64)  256         ['block_7_project[0][0]']        \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block_7_add (Add)              (None, 10, 10, 64)   0           ['block_6_project_BN[0][0]',     \n",
            "                                                                  'block_7_project_BN[0][0]']     \n",
            "                                                                                                  \n",
            " block_8_expand (Conv2D)        (None, 10, 10, 384)  24576       ['block_7_add[0][0]']            \n",
            "                                                                                                  \n",
            " block_8_expand_BN (BatchNormal  (None, 10, 10, 384)  1536       ['block_8_expand[0][0]']         \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block_8_expand_relu (ReLU)     (None, 10, 10, 384)  0           ['block_8_expand_BN[0][0]']      \n",
            "                                                                                                  \n",
            " block_8_depthwise (DepthwiseCo  (None, 10, 10, 384)  3456       ['block_8_expand_relu[0][0]']    \n",
            " nv2D)                                                                                            \n",
            "                                                                                                  \n",
            " block_8_depthwise_BN (BatchNor  (None, 10, 10, 384)  1536       ['block_8_depthwise[0][0]']      \n",
            " malization)                                                                                      \n",
            "                                                                                                  \n",
            " block_8_depthwise_relu (ReLU)  (None, 10, 10, 384)  0           ['block_8_depthwise_BN[0][0]']   \n",
            "                                                                                                  \n",
            " block_8_project (Conv2D)       (None, 10, 10, 64)   24576       ['block_8_depthwise_relu[0][0]'] \n",
            "                                                                                                  \n",
            " block_8_project_BN (BatchNorma  (None, 10, 10, 64)  256         ['block_8_project[0][0]']        \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block_8_add (Add)              (None, 10, 10, 64)   0           ['block_7_add[0][0]',            \n",
            "                                                                  'block_8_project_BN[0][0]']     \n",
            "                                                                                                  \n",
            " block_9_expand (Conv2D)        (None, 10, 10, 384)  24576       ['block_8_add[0][0]']            \n",
            "                                                                                                  \n",
            " block_9_expand_BN (BatchNormal  (None, 10, 10, 384)  1536       ['block_9_expand[0][0]']         \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block_9_expand_relu (ReLU)     (None, 10, 10, 384)  0           ['block_9_expand_BN[0][0]']      \n",
            "                                                                                                  \n",
            " block_9_depthwise (DepthwiseCo  (None, 10, 10, 384)  3456       ['block_9_expand_relu[0][0]']    \n",
            " nv2D)                                                                                            \n",
            "                                                                                                  \n",
            " block_9_depthwise_BN (BatchNor  (None, 10, 10, 384)  1536       ['block_9_depthwise[0][0]']      \n",
            " malization)                                                                                      \n",
            "                                                                                                  \n",
            " block_9_depthwise_relu (ReLU)  (None, 10, 10, 384)  0           ['block_9_depthwise_BN[0][0]']   \n",
            "                                                                                                  \n",
            " block_9_project (Conv2D)       (None, 10, 10, 64)   24576       ['block_9_depthwise_relu[0][0]'] \n",
            "                                                                                                  \n",
            " block_9_project_BN (BatchNorma  (None, 10, 10, 64)  256         ['block_9_project[0][0]']        \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block_9_add (Add)              (None, 10, 10, 64)   0           ['block_8_add[0][0]',            \n",
            "                                                                  'block_9_project_BN[0][0]']     \n",
            "                                                                                                  \n",
            " block_10_expand (Conv2D)       (None, 10, 10, 384)  24576       ['block_9_add[0][0]']            \n",
            "                                                                                                  \n",
            " block_10_expand_BN (BatchNorma  (None, 10, 10, 384)  1536       ['block_10_expand[0][0]']        \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block_10_expand_relu (ReLU)    (None, 10, 10, 384)  0           ['block_10_expand_BN[0][0]']     \n",
            "                                                                                                  \n",
            " block_10_depthwise (DepthwiseC  (None, 10, 10, 384)  3456       ['block_10_expand_relu[0][0]']   \n",
            " onv2D)                                                                                           \n",
            "                                                                                                  \n",
            " block_10_depthwise_BN (BatchNo  (None, 10, 10, 384)  1536       ['block_10_depthwise[0][0]']     \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block_10_depthwise_relu (ReLU)  (None, 10, 10, 384)  0          ['block_10_depthwise_BN[0][0]']  \n",
            "                                                                                                  \n",
            " block_10_project (Conv2D)      (None, 10, 10, 96)   36864       ['block_10_depthwise_relu[0][0]']\n",
            "                                                                                                  \n",
            " block_10_project_BN (BatchNorm  (None, 10, 10, 96)  384         ['block_10_project[0][0]']       \n",
            " alization)                                                                                       \n",
            "                                                                                                  \n",
            " block_11_expand (Conv2D)       (None, 10, 10, 576)  55296       ['block_10_project_BN[0][0]']    \n",
            "                                                                                                  \n",
            " block_11_expand_BN (BatchNorma  (None, 10, 10, 576)  2304       ['block_11_expand[0][0]']        \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block_11_expand_relu (ReLU)    (None, 10, 10, 576)  0           ['block_11_expand_BN[0][0]']     \n",
            "                                                                                                  \n",
            " block_11_depthwise (DepthwiseC  (None, 10, 10, 576)  5184       ['block_11_expand_relu[0][0]']   \n",
            " onv2D)                                                                                           \n",
            "                                                                                                  \n",
            " block_11_depthwise_BN (BatchNo  (None, 10, 10, 576)  2304       ['block_11_depthwise[0][0]']     \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block_11_depthwise_relu (ReLU)  (None, 10, 10, 576)  0          ['block_11_depthwise_BN[0][0]']  \n",
            "                                                                                                  \n",
            " block_11_project (Conv2D)      (None, 10, 10, 96)   55296       ['block_11_depthwise_relu[0][0]']\n",
            "                                                                                                  \n",
            " block_11_project_BN (BatchNorm  (None, 10, 10, 96)  384         ['block_11_project[0][0]']       \n",
            " alization)                                                                                       \n",
            "                                                                                                  \n",
            " block_11_add (Add)             (None, 10, 10, 96)   0           ['block_10_project_BN[0][0]',    \n",
            "                                                                  'block_11_project_BN[0][0]']    \n",
            "                                                                                                  \n",
            " block_12_expand (Conv2D)       (None, 10, 10, 576)  55296       ['block_11_add[0][0]']           \n",
            "                                                                                                  \n",
            " block_12_expand_BN (BatchNorma  (None, 10, 10, 576)  2304       ['block_12_expand[0][0]']        \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block_12_expand_relu (ReLU)    (None, 10, 10, 576)  0           ['block_12_expand_BN[0][0]']     \n",
            "                                                                                                  \n",
            " block_12_depthwise (DepthwiseC  (None, 10, 10, 576)  5184       ['block_12_expand_relu[0][0]']   \n",
            " onv2D)                                                                                           \n",
            "                                                                                                  \n",
            " block_12_depthwise_BN (BatchNo  (None, 10, 10, 576)  2304       ['block_12_depthwise[0][0]']     \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block_12_depthwise_relu (ReLU)  (None, 10, 10, 576)  0          ['block_12_depthwise_BN[0][0]']  \n",
            "                                                                                                  \n",
            " block_12_project (Conv2D)      (None, 10, 10, 96)   55296       ['block_12_depthwise_relu[0][0]']\n",
            "                                                                                                  \n",
            " block_12_project_BN (BatchNorm  (None, 10, 10, 96)  384         ['block_12_project[0][0]']       \n",
            " alization)                                                                                       \n",
            "                                                                                                  \n",
            " block_12_add (Add)             (None, 10, 10, 96)   0           ['block_11_add[0][0]',           \n",
            "                                                                  'block_12_project_BN[0][0]']    \n",
            "                                                                                                  \n",
            " block_13_expand (Conv2D)       (None, 10, 10, 576)  55296       ['block_12_add[0][0]']           \n",
            "                                                                                                  \n",
            " block_13_expand_BN (BatchNorma  (None, 10, 10, 576)  2304       ['block_13_expand[0][0]']        \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block_13_expand_relu (ReLU)    (None, 10, 10, 576)  0           ['block_13_expand_BN[0][0]']     \n",
            "                                                                                                  \n",
            " block_13_pad (ZeroPadding2D)   (None, 11, 11, 576)  0           ['block_13_expand_relu[0][0]']   \n",
            "                                                                                                  \n",
            " block_13_depthwise (DepthwiseC  (None, 5, 5, 576)   5184        ['block_13_pad[0][0]']           \n",
            " onv2D)                                                                                           \n",
            "                                                                                                  \n",
            " block_13_depthwise_BN (BatchNo  (None, 5, 5, 576)   2304        ['block_13_depthwise[0][0]']     \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block_13_depthwise_relu (ReLU)  (None, 5, 5, 576)   0           ['block_13_depthwise_BN[0][0]']  \n",
            "                                                                                                  \n",
            " block_13_project (Conv2D)      (None, 5, 5, 160)    92160       ['block_13_depthwise_relu[0][0]']\n",
            "                                                                                                  \n",
            " block_13_project_BN (BatchNorm  (None, 5, 5, 160)   640         ['block_13_project[0][0]']       \n",
            " alization)                                                                                       \n",
            "                                                                                                  \n",
            " block_14_expand (Conv2D)       (None, 5, 5, 960)    153600      ['block_13_project_BN[0][0]']    \n",
            "                                                                                                  \n",
            " block_14_expand_BN (BatchNorma  (None, 5, 5, 960)   3840        ['block_14_expand[0][0]']        \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block_14_expand_relu (ReLU)    (None, 5, 5, 960)    0           ['block_14_expand_BN[0][0]']     \n",
            "                                                                                                  \n",
            " block_14_depthwise (DepthwiseC  (None, 5, 5, 960)   8640        ['block_14_expand_relu[0][0]']   \n",
            " onv2D)                                                                                           \n",
            "                                                                                                  \n",
            " block_14_depthwise_BN (BatchNo  (None, 5, 5, 960)   3840        ['block_14_depthwise[0][0]']     \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block_14_depthwise_relu (ReLU)  (None, 5, 5, 960)   0           ['block_14_depthwise_BN[0][0]']  \n",
            "                                                                                                  \n",
            " block_14_project (Conv2D)      (None, 5, 5, 160)    153600      ['block_14_depthwise_relu[0][0]']\n",
            "                                                                                                  \n",
            " block_14_project_BN (BatchNorm  (None, 5, 5, 160)   640         ['block_14_project[0][0]']       \n",
            " alization)                                                                                       \n",
            "                                                                                                  \n",
            " block_14_add (Add)             (None, 5, 5, 160)    0           ['block_13_project_BN[0][0]',    \n",
            "                                                                  'block_14_project_BN[0][0]']    \n",
            "                                                                                                  \n",
            " block_15_expand (Conv2D)       (None, 5, 5, 960)    153600      ['block_14_add[0][0]']           \n",
            "                                                                                                  \n",
            " block_15_expand_BN (BatchNorma  (None, 5, 5, 960)   3840        ['block_15_expand[0][0]']        \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block_15_expand_relu (ReLU)    (None, 5, 5, 960)    0           ['block_15_expand_BN[0][0]']     \n",
            "                                                                                                  \n",
            " block_15_depthwise (DepthwiseC  (None, 5, 5, 960)   8640        ['block_15_expand_relu[0][0]']   \n",
            " onv2D)                                                                                           \n",
            "                                                                                                  \n",
            " block_15_depthwise_BN (BatchNo  (None, 5, 5, 960)   3840        ['block_15_depthwise[0][0]']     \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block_15_depthwise_relu (ReLU)  (None, 5, 5, 960)   0           ['block_15_depthwise_BN[0][0]']  \n",
            "                                                                                                  \n",
            " block_15_project (Conv2D)      (None, 5, 5, 160)    153600      ['block_15_depthwise_relu[0][0]']\n",
            "                                                                                                  \n",
            " block_15_project_BN (BatchNorm  (None, 5, 5, 160)   640         ['block_15_project[0][0]']       \n",
            " alization)                                                                                       \n",
            "                                                                                                  \n",
            " block_15_add (Add)             (None, 5, 5, 160)    0           ['block_14_add[0][0]',           \n",
            "                                                                  'block_15_project_BN[0][0]']    \n",
            "                                                                                                  \n",
            " block_16_expand (Conv2D)       (None, 5, 5, 960)    153600      ['block_15_add[0][0]']           \n",
            "                                                                                                  \n",
            " block_16_expand_BN (BatchNorma  (None, 5, 5, 960)   3840        ['block_16_expand[0][0]']        \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block_16_expand_relu (ReLU)    (None, 5, 5, 960)    0           ['block_16_expand_BN[0][0]']     \n",
            "                                                                                                  \n",
            " block_16_depthwise (DepthwiseC  (None, 5, 5, 960)   8640        ['block_16_expand_relu[0][0]']   \n",
            " onv2D)                                                                                           \n",
            "                                                                                                  \n",
            " block_16_depthwise_BN (BatchNo  (None, 5, 5, 960)   3840        ['block_16_depthwise[0][0]']     \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block_16_depthwise_relu (ReLU)  (None, 5, 5, 960)   0           ['block_16_depthwise_BN[0][0]']  \n",
            "                                                                                                  \n",
            " block_16_project (Conv2D)      (None, 5, 5, 320)    307200      ['block_16_depthwise_relu[0][0]']\n",
            "                                                                                                  \n",
            " block_16_project_BN (BatchNorm  (None, 5, 5, 320)   1280        ['block_16_project[0][0]']       \n",
            " alization)                                                                                       \n",
            "                                                                                                  \n",
            " Conv_1 (Conv2D)                (None, 5, 5, 1280)   409600      ['block_16_project_BN[0][0]']    \n",
            "                                                                                                  \n",
            " Conv_1_bn (BatchNormalization)  (None, 5, 5, 1280)  5120        ['Conv_1[0][0]']                 \n",
            "                                                                                                  \n",
            " out_relu (ReLU)                (None, 5, 5, 1280)   0           ['Conv_1_bn[0][0]']              \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 2,257,984\n",
            "Trainable params: 0\n",
            "Non-trainable params: 2,257,984\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "base_model = tf.keras.applications.MobileNetV2(input_shape=(IMG_HEIGHT, IMG_WIDTH, 3), include_top=False, weights='imagenet')\n",
        "base_model.trainable = False\n",
        "base_model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_1 (InputLayer)           [(None, 160, 160, 3  0           []                               \n",
            "                                )]                                                                \n",
            "                                                                                                  \n",
            " Conv1 (Conv2D)                 (None, 80, 80, 32)   864         ['input_1[0][0]']                \n",
            "                                                                                                  \n",
            " bn_Conv1 (BatchNormalization)  (None, 80, 80, 32)   128         ['Conv1[0][0]']                  \n",
            "                                                                                                  \n",
            " Conv1_relu (ReLU)              (None, 80, 80, 32)   0           ['bn_Conv1[0][0]']               \n",
            "                                                                                                  \n",
            " expanded_conv_depthwise (Depth  (None, 80, 80, 32)  288         ['Conv1_relu[0][0]']             \n",
            " wiseConv2D)                                                                                      \n",
            "                                                                                                  \n",
            " expanded_conv_depthwise_BN (Ba  (None, 80, 80, 32)  128         ['expanded_conv_depthwise[0][0]']\n",
            " tchNormalization)                                                                                \n",
            "                                                                                                  \n",
            " expanded_conv_depthwise_relu (  (None, 80, 80, 32)  0           ['expanded_conv_depthwise_BN[0][0\n",
            " ReLU)                                                           ]']                              \n",
            "                                                                                                  \n",
            " expanded_conv_project (Conv2D)  (None, 80, 80, 16)  512         ['expanded_conv_depthwise_relu[0]\n",
            "                                                                 [0]']                            \n",
            "                                                                                                  \n",
            " expanded_conv_project_BN (Batc  (None, 80, 80, 16)  64          ['expanded_conv_project[0][0]']  \n",
            " hNormalization)                                                                                  \n",
            "                                                                                                  \n",
            " block_1_expand (Conv2D)        (None, 80, 80, 96)   1536        ['expanded_conv_project_BN[0][0]'\n",
            "                                                                 ]                                \n",
            "                                                                                                  \n",
            " block_1_expand_BN (BatchNormal  (None, 80, 80, 96)  384         ['block_1_expand[0][0]']         \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block_1_expand_relu (ReLU)     (None, 80, 80, 96)   0           ['block_1_expand_BN[0][0]']      \n",
            "                                                                                                  \n",
            " block_1_pad (ZeroPadding2D)    (None, 81, 81, 96)   0           ['block_1_expand_relu[0][0]']    \n",
            "                                                                                                  \n",
            " block_1_depthwise (DepthwiseCo  (None, 40, 40, 96)  864         ['block_1_pad[0][0]']            \n",
            " nv2D)                                                                                            \n",
            "                                                                                                  \n",
            " block_1_depthwise_BN (BatchNor  (None, 40, 40, 96)  384         ['block_1_depthwise[0][0]']      \n",
            " malization)                                                                                      \n",
            "                                                                                                  \n",
            " block_1_depthwise_relu (ReLU)  (None, 40, 40, 96)   0           ['block_1_depthwise_BN[0][0]']   \n",
            "                                                                                                  \n",
            " block_1_project (Conv2D)       (None, 40, 40, 24)   2304        ['block_1_depthwise_relu[0][0]'] \n",
            "                                                                                                  \n",
            " block_1_project_BN (BatchNorma  (None, 40, 40, 24)  96          ['block_1_project[0][0]']        \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block_2_expand (Conv2D)        (None, 40, 40, 144)  3456        ['block_1_project_BN[0][0]']     \n",
            "                                                                                                  \n",
            " block_2_expand_BN (BatchNormal  (None, 40, 40, 144)  576        ['block_2_expand[0][0]']         \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block_2_expand_relu (ReLU)     (None, 40, 40, 144)  0           ['block_2_expand_BN[0][0]']      \n",
            "                                                                                                  \n",
            " block_2_depthwise (DepthwiseCo  (None, 40, 40, 144)  1296       ['block_2_expand_relu[0][0]']    \n",
            " nv2D)                                                                                            \n",
            "                                                                                                  \n",
            " block_2_depthwise_BN (BatchNor  (None, 40, 40, 144)  576        ['block_2_depthwise[0][0]']      \n",
            " malization)                                                                                      \n",
            "                                                                                                  \n",
            " block_2_depthwise_relu (ReLU)  (None, 40, 40, 144)  0           ['block_2_depthwise_BN[0][0]']   \n",
            "                                                                                                  \n",
            " block_2_project (Conv2D)       (None, 40, 40, 24)   3456        ['block_2_depthwise_relu[0][0]'] \n",
            "                                                                                                  \n",
            " block_2_project_BN (BatchNorma  (None, 40, 40, 24)  96          ['block_2_project[0][0]']        \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block_2_add (Add)              (None, 40, 40, 24)   0           ['block_1_project_BN[0][0]',     \n",
            "                                                                  'block_2_project_BN[0][0]']     \n",
            "                                                                                                  \n",
            " block_3_expand (Conv2D)        (None, 40, 40, 144)  3456        ['block_2_add[0][0]']            \n",
            "                                                                                                  \n",
            " block_3_expand_BN (BatchNormal  (None, 40, 40, 144)  576        ['block_3_expand[0][0]']         \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block_3_expand_relu (ReLU)     (None, 40, 40, 144)  0           ['block_3_expand_BN[0][0]']      \n",
            "                                                                                                  \n",
            " block_3_pad (ZeroPadding2D)    (None, 41, 41, 144)  0           ['block_3_expand_relu[0][0]']    \n",
            "                                                                                                  \n",
            " block_3_depthwise (DepthwiseCo  (None, 20, 20, 144)  1296       ['block_3_pad[0][0]']            \n",
            " nv2D)                                                                                            \n",
            "                                                                                                  \n",
            " block_3_depthwise_BN (BatchNor  (None, 20, 20, 144)  576        ['block_3_depthwise[0][0]']      \n",
            " malization)                                                                                      \n",
            "                                                                                                  \n",
            " block_3_depthwise_relu (ReLU)  (None, 20, 20, 144)  0           ['block_3_depthwise_BN[0][0]']   \n",
            "                                                                                                  \n",
            " block_3_project (Conv2D)       (None, 20, 20, 32)   4608        ['block_3_depthwise_relu[0][0]'] \n",
            "                                                                                                  \n",
            " block_3_project_BN (BatchNorma  (None, 20, 20, 32)  128         ['block_3_project[0][0]']        \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block_4_expand (Conv2D)        (None, 20, 20, 192)  6144        ['block_3_project_BN[0][0]']     \n",
            "                                                                                                  \n",
            " block_4_expand_BN (BatchNormal  (None, 20, 20, 192)  768        ['block_4_expand[0][0]']         \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block_4_expand_relu (ReLU)     (None, 20, 20, 192)  0           ['block_4_expand_BN[0][0]']      \n",
            "                                                                                                  \n",
            " block_4_depthwise (DepthwiseCo  (None, 20, 20, 192)  1728       ['block_4_expand_relu[0][0]']    \n",
            " nv2D)                                                                                            \n",
            "                                                                                                  \n",
            " block_4_depthwise_BN (BatchNor  (None, 20, 20, 192)  768        ['block_4_depthwise[0][0]']      \n",
            " malization)                                                                                      \n",
            "                                                                                                  \n",
            " block_4_depthwise_relu (ReLU)  (None, 20, 20, 192)  0           ['block_4_depthwise_BN[0][0]']   \n",
            "                                                                                                  \n",
            " block_4_project (Conv2D)       (None, 20, 20, 32)   6144        ['block_4_depthwise_relu[0][0]'] \n",
            "                                                                                                  \n",
            " block_4_project_BN (BatchNorma  (None, 20, 20, 32)  128         ['block_4_project[0][0]']        \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block_4_add (Add)              (None, 20, 20, 32)   0           ['block_3_project_BN[0][0]',     \n",
            "                                                                  'block_4_project_BN[0][0]']     \n",
            "                                                                                                  \n",
            " block_5_expand (Conv2D)        (None, 20, 20, 192)  6144        ['block_4_add[0][0]']            \n",
            "                                                                                                  \n",
            " block_5_expand_BN (BatchNormal  (None, 20, 20, 192)  768        ['block_5_expand[0][0]']         \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block_5_expand_relu (ReLU)     (None, 20, 20, 192)  0           ['block_5_expand_BN[0][0]']      \n",
            "                                                                                                  \n",
            " block_5_depthwise (DepthwiseCo  (None, 20, 20, 192)  1728       ['block_5_expand_relu[0][0]']    \n",
            " nv2D)                                                                                            \n",
            "                                                                                                  \n",
            " block_5_depthwise_BN (BatchNor  (None, 20, 20, 192)  768        ['block_5_depthwise[0][0]']      \n",
            " malization)                                                                                      \n",
            "                                                                                                  \n",
            " block_5_depthwise_relu (ReLU)  (None, 20, 20, 192)  0           ['block_5_depthwise_BN[0][0]']   \n",
            "                                                                                                  \n",
            " block_5_project (Conv2D)       (None, 20, 20, 32)   6144        ['block_5_depthwise_relu[0][0]'] \n",
            "                                                                                                  \n",
            " block_5_project_BN (BatchNorma  (None, 20, 20, 32)  128         ['block_5_project[0][0]']        \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block_5_add (Add)              (None, 20, 20, 32)   0           ['block_4_add[0][0]',            \n",
            "                                                                  'block_5_project_BN[0][0]']     \n",
            "                                                                                                  \n",
            " block_6_expand (Conv2D)        (None, 20, 20, 192)  6144        ['block_5_add[0][0]']            \n",
            "                                                                                                  \n",
            " block_6_expand_BN (BatchNormal  (None, 20, 20, 192)  768        ['block_6_expand[0][0]']         \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block_6_expand_relu (ReLU)     (None, 20, 20, 192)  0           ['block_6_expand_BN[0][0]']      \n",
            "                                                                                                  \n",
            " block_6_pad (ZeroPadding2D)    (None, 21, 21, 192)  0           ['block_6_expand_relu[0][0]']    \n",
            "                                                                                                  \n",
            " block_6_depthwise (DepthwiseCo  (None, 10, 10, 192)  1728       ['block_6_pad[0][0]']            \n",
            " nv2D)                                                                                            \n",
            "                                                                                                  \n",
            " block_6_depthwise_BN (BatchNor  (None, 10, 10, 192)  768        ['block_6_depthwise[0][0]']      \n",
            " malization)                                                                                      \n",
            "                                                                                                  \n",
            " block_6_depthwise_relu (ReLU)  (None, 10, 10, 192)  0           ['block_6_depthwise_BN[0][0]']   \n",
            "                                                                                                  \n",
            " block_6_project (Conv2D)       (None, 10, 10, 64)   12288       ['block_6_depthwise_relu[0][0]'] \n",
            "                                                                                                  \n",
            " block_6_project_BN (BatchNorma  (None, 10, 10, 64)  256         ['block_6_project[0][0]']        \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block_7_expand (Conv2D)        (None, 10, 10, 384)  24576       ['block_6_project_BN[0][0]']     \n",
            "                                                                                                  \n",
            " block_7_expand_BN (BatchNormal  (None, 10, 10, 384)  1536       ['block_7_expand[0][0]']         \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block_7_expand_relu (ReLU)     (None, 10, 10, 384)  0           ['block_7_expand_BN[0][0]']      \n",
            "                                                                                                  \n",
            " block_7_depthwise (DepthwiseCo  (None, 10, 10, 384)  3456       ['block_7_expand_relu[0][0]']    \n",
            " nv2D)                                                                                            \n",
            "                                                                                                  \n",
            " block_7_depthwise_BN (BatchNor  (None, 10, 10, 384)  1536       ['block_7_depthwise[0][0]']      \n",
            " malization)                                                                                      \n",
            "                                                                                                  \n",
            " block_7_depthwise_relu (ReLU)  (None, 10, 10, 384)  0           ['block_7_depthwise_BN[0][0]']   \n",
            "                                                                                                  \n",
            " block_7_project (Conv2D)       (None, 10, 10, 64)   24576       ['block_7_depthwise_relu[0][0]'] \n",
            "                                                                                                  \n",
            " block_7_project_BN (BatchNorma  (None, 10, 10, 64)  256         ['block_7_project[0][0]']        \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block_7_add (Add)              (None, 10, 10, 64)   0           ['block_6_project_BN[0][0]',     \n",
            "                                                                  'block_7_project_BN[0][0]']     \n",
            "                                                                                                  \n",
            " block_8_expand (Conv2D)        (None, 10, 10, 384)  24576       ['block_7_add[0][0]']            \n",
            "                                                                                                  \n",
            " block_8_expand_BN (BatchNormal  (None, 10, 10, 384)  1536       ['block_8_expand[0][0]']         \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block_8_expand_relu (ReLU)     (None, 10, 10, 384)  0           ['block_8_expand_BN[0][0]']      \n",
            "                                                                                                  \n",
            " block_8_depthwise (DepthwiseCo  (None, 10, 10, 384)  3456       ['block_8_expand_relu[0][0]']    \n",
            " nv2D)                                                                                            \n",
            "                                                                                                  \n",
            " block_8_depthwise_BN (BatchNor  (None, 10, 10, 384)  1536       ['block_8_depthwise[0][0]']      \n",
            " malization)                                                                                      \n",
            "                                                                                                  \n",
            " block_8_depthwise_relu (ReLU)  (None, 10, 10, 384)  0           ['block_8_depthwise_BN[0][0]']   \n",
            "                                                                                                  \n",
            " block_8_project (Conv2D)       (None, 10, 10, 64)   24576       ['block_8_depthwise_relu[0][0]'] \n",
            "                                                                                                  \n",
            " block_8_project_BN (BatchNorma  (None, 10, 10, 64)  256         ['block_8_project[0][0]']        \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block_8_add (Add)              (None, 10, 10, 64)   0           ['block_7_add[0][0]',            \n",
            "                                                                  'block_8_project_BN[0][0]']     \n",
            "                                                                                                  \n",
            " block_9_expand (Conv2D)        (None, 10, 10, 384)  24576       ['block_8_add[0][0]']            \n",
            "                                                                                                  \n",
            " block_9_expand_BN (BatchNormal  (None, 10, 10, 384)  1536       ['block_9_expand[0][0]']         \n",
            " ization)                                                                                         \n",
            "                                                                                                  \n",
            " block_9_expand_relu (ReLU)     (None, 10, 10, 384)  0           ['block_9_expand_BN[0][0]']      \n",
            "                                                                                                  \n",
            " block_9_depthwise (DepthwiseCo  (None, 10, 10, 384)  3456       ['block_9_expand_relu[0][0]']    \n",
            " nv2D)                                                                                            \n",
            "                                                                                                  \n",
            " block_9_depthwise_BN (BatchNor  (None, 10, 10, 384)  1536       ['block_9_depthwise[0][0]']      \n",
            " malization)                                                                                      \n",
            "                                                                                                  \n",
            " block_9_depthwise_relu (ReLU)  (None, 10, 10, 384)  0           ['block_9_depthwise_BN[0][0]']   \n",
            "                                                                                                  \n",
            " block_9_project (Conv2D)       (None, 10, 10, 64)   24576       ['block_9_depthwise_relu[0][0]'] \n",
            "                                                                                                  \n",
            " block_9_project_BN (BatchNorma  (None, 10, 10, 64)  256         ['block_9_project[0][0]']        \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block_9_add (Add)              (None, 10, 10, 64)   0           ['block_8_add[0][0]',            \n",
            "                                                                  'block_9_project_BN[0][0]']     \n",
            "                                                                                                  \n",
            " block_10_expand (Conv2D)       (None, 10, 10, 384)  24576       ['block_9_add[0][0]']            \n",
            "                                                                                                  \n",
            " block_10_expand_BN (BatchNorma  (None, 10, 10, 384)  1536       ['block_10_expand[0][0]']        \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block_10_expand_relu (ReLU)    (None, 10, 10, 384)  0           ['block_10_expand_BN[0][0]']     \n",
            "                                                                                                  \n",
            " block_10_depthwise (DepthwiseC  (None, 10, 10, 384)  3456       ['block_10_expand_relu[0][0]']   \n",
            " onv2D)                                                                                           \n",
            "                                                                                                  \n",
            " block_10_depthwise_BN (BatchNo  (None, 10, 10, 384)  1536       ['block_10_depthwise[0][0]']     \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block_10_depthwise_relu (ReLU)  (None, 10, 10, 384)  0          ['block_10_depthwise_BN[0][0]']  \n",
            "                                                                                                  \n",
            " block_10_project (Conv2D)      (None, 10, 10, 96)   36864       ['block_10_depthwise_relu[0][0]']\n",
            "                                                                                                  \n",
            " block_10_project_BN (BatchNorm  (None, 10, 10, 96)  384         ['block_10_project[0][0]']       \n",
            " alization)                                                                                       \n",
            "                                                                                                  \n",
            " block_11_expand (Conv2D)       (None, 10, 10, 576)  55296       ['block_10_project_BN[0][0]']    \n",
            "                                                                                                  \n",
            " block_11_expand_BN (BatchNorma  (None, 10, 10, 576)  2304       ['block_11_expand[0][0]']        \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block_11_expand_relu (ReLU)    (None, 10, 10, 576)  0           ['block_11_expand_BN[0][0]']     \n",
            "                                                                                                  \n",
            " block_11_depthwise (DepthwiseC  (None, 10, 10, 576)  5184       ['block_11_expand_relu[0][0]']   \n",
            " onv2D)                                                                                           \n",
            "                                                                                                  \n",
            " block_11_depthwise_BN (BatchNo  (None, 10, 10, 576)  2304       ['block_11_depthwise[0][0]']     \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block_11_depthwise_relu (ReLU)  (None, 10, 10, 576)  0          ['block_11_depthwise_BN[0][0]']  \n",
            "                                                                                                  \n",
            " block_11_project (Conv2D)      (None, 10, 10, 96)   55296       ['block_11_depthwise_relu[0][0]']\n",
            "                                                                                                  \n",
            " block_11_project_BN (BatchNorm  (None, 10, 10, 96)  384         ['block_11_project[0][0]']       \n",
            " alization)                                                                                       \n",
            "                                                                                                  \n",
            " block_11_add (Add)             (None, 10, 10, 96)   0           ['block_10_project_BN[0][0]',    \n",
            "                                                                  'block_11_project_BN[0][0]']    \n",
            "                                                                                                  \n",
            " block_12_expand (Conv2D)       (None, 10, 10, 576)  55296       ['block_11_add[0][0]']           \n",
            "                                                                                                  \n",
            " block_12_expand_BN (BatchNorma  (None, 10, 10, 576)  2304       ['block_12_expand[0][0]']        \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block_12_expand_relu (ReLU)    (None, 10, 10, 576)  0           ['block_12_expand_BN[0][0]']     \n",
            "                                                                                                  \n",
            " block_12_depthwise (DepthwiseC  (None, 10, 10, 576)  5184       ['block_12_expand_relu[0][0]']   \n",
            " onv2D)                                                                                           \n",
            "                                                                                                  \n",
            " block_12_depthwise_BN (BatchNo  (None, 10, 10, 576)  2304       ['block_12_depthwise[0][0]']     \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block_12_depthwise_relu (ReLU)  (None, 10, 10, 576)  0          ['block_12_depthwise_BN[0][0]']  \n",
            "                                                                                                  \n",
            " block_12_project (Conv2D)      (None, 10, 10, 96)   55296       ['block_12_depthwise_relu[0][0]']\n",
            "                                                                                                  \n",
            " block_12_project_BN (BatchNorm  (None, 10, 10, 96)  384         ['block_12_project[0][0]']       \n",
            " alization)                                                                                       \n",
            "                                                                                                  \n",
            " block_12_add (Add)             (None, 10, 10, 96)   0           ['block_11_add[0][0]',           \n",
            "                                                                  'block_12_project_BN[0][0]']    \n",
            "                                                                                                  \n",
            " block_13_expand (Conv2D)       (None, 10, 10, 576)  55296       ['block_12_add[0][0]']           \n",
            "                                                                                                  \n",
            " block_13_expand_BN (BatchNorma  (None, 10, 10, 576)  2304       ['block_13_expand[0][0]']        \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block_13_expand_relu (ReLU)    (None, 10, 10, 576)  0           ['block_13_expand_BN[0][0]']     \n",
            "                                                                                                  \n",
            " block_13_pad (ZeroPadding2D)   (None, 11, 11, 576)  0           ['block_13_expand_relu[0][0]']   \n",
            "                                                                                                  \n",
            " block_13_depthwise (DepthwiseC  (None, 5, 5, 576)   5184        ['block_13_pad[0][0]']           \n",
            " onv2D)                                                                                           \n",
            "                                                                                                  \n",
            " block_13_depthwise_BN (BatchNo  (None, 5, 5, 576)   2304        ['block_13_depthwise[0][0]']     \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block_13_depthwise_relu (ReLU)  (None, 5, 5, 576)   0           ['block_13_depthwise_BN[0][0]']  \n",
            "                                                                                                  \n",
            " block_13_project (Conv2D)      (None, 5, 5, 160)    92160       ['block_13_depthwise_relu[0][0]']\n",
            "                                                                                                  \n",
            " block_13_project_BN (BatchNorm  (None, 5, 5, 160)   640         ['block_13_project[0][0]']       \n",
            " alization)                                                                                       \n",
            "                                                                                                  \n",
            " block_14_expand (Conv2D)       (None, 5, 5, 960)    153600      ['block_13_project_BN[0][0]']    \n",
            "                                                                                                  \n",
            " block_14_expand_BN (BatchNorma  (None, 5, 5, 960)   3840        ['block_14_expand[0][0]']        \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block_14_expand_relu (ReLU)    (None, 5, 5, 960)    0           ['block_14_expand_BN[0][0]']     \n",
            "                                                                                                  \n",
            " block_14_depthwise (DepthwiseC  (None, 5, 5, 960)   8640        ['block_14_expand_relu[0][0]']   \n",
            " onv2D)                                                                                           \n",
            "                                                                                                  \n",
            " block_14_depthwise_BN (BatchNo  (None, 5, 5, 960)   3840        ['block_14_depthwise[0][0]']     \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block_14_depthwise_relu (ReLU)  (None, 5, 5, 960)   0           ['block_14_depthwise_BN[0][0]']  \n",
            "                                                                                                  \n",
            " block_14_project (Conv2D)      (None, 5, 5, 160)    153600      ['block_14_depthwise_relu[0][0]']\n",
            "                                                                                                  \n",
            " block_14_project_BN (BatchNorm  (None, 5, 5, 160)   640         ['block_14_project[0][0]']       \n",
            " alization)                                                                                       \n",
            "                                                                                                  \n",
            " block_14_add (Add)             (None, 5, 5, 160)    0           ['block_13_project_BN[0][0]',    \n",
            "                                                                  'block_14_project_BN[0][0]']    \n",
            "                                                                                                  \n",
            " block_15_expand (Conv2D)       (None, 5, 5, 960)    153600      ['block_14_add[0][0]']           \n",
            "                                                                                                  \n",
            " block_15_expand_BN (BatchNorma  (None, 5, 5, 960)   3840        ['block_15_expand[0][0]']        \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block_15_expand_relu (ReLU)    (None, 5, 5, 960)    0           ['block_15_expand_BN[0][0]']     \n",
            "                                                                                                  \n",
            " block_15_depthwise (DepthwiseC  (None, 5, 5, 960)   8640        ['block_15_expand_relu[0][0]']   \n",
            " onv2D)                                                                                           \n",
            "                                                                                                  \n",
            " block_15_depthwise_BN (BatchNo  (None, 5, 5, 960)   3840        ['block_15_depthwise[0][0]']     \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block_15_depthwise_relu (ReLU)  (None, 5, 5, 960)   0           ['block_15_depthwise_BN[0][0]']  \n",
            "                                                                                                  \n",
            " block_15_project (Conv2D)      (None, 5, 5, 160)    153600      ['block_15_depthwise_relu[0][0]']\n",
            "                                                                                                  \n",
            " block_15_project_BN (BatchNorm  (None, 5, 5, 160)   640         ['block_15_project[0][0]']       \n",
            " alization)                                                                                       \n",
            "                                                                                                  \n",
            " block_15_add (Add)             (None, 5, 5, 160)    0           ['block_14_add[0][0]',           \n",
            "                                                                  'block_15_project_BN[0][0]']    \n",
            "                                                                                                  \n",
            " block_16_expand (Conv2D)       (None, 5, 5, 960)    153600      ['block_15_add[0][0]']           \n",
            "                                                                                                  \n",
            " block_16_expand_BN (BatchNorma  (None, 5, 5, 960)   3840        ['block_16_expand[0][0]']        \n",
            " lization)                                                                                        \n",
            "                                                                                                  \n",
            " block_16_expand_relu (ReLU)    (None, 5, 5, 960)    0           ['block_16_expand_BN[0][0]']     \n",
            "                                                                                                  \n",
            " block_16_depthwise (DepthwiseC  (None, 5, 5, 960)   8640        ['block_16_expand_relu[0][0]']   \n",
            " onv2D)                                                                                           \n",
            "                                                                                                  \n",
            " block_16_depthwise_BN (BatchNo  (None, 5, 5, 960)   3840        ['block_16_depthwise[0][0]']     \n",
            " rmalization)                                                                                     \n",
            "                                                                                                  \n",
            " block_16_depthwise_relu (ReLU)  (None, 5, 5, 960)   0           ['block_16_depthwise_BN[0][0]']  \n",
            "                                                                                                  \n",
            " block_16_project (Conv2D)      (None, 5, 5, 320)    307200      ['block_16_depthwise_relu[0][0]']\n",
            "                                                                                                  \n",
            " block_16_project_BN (BatchNorm  (None, 5, 5, 320)   1280        ['block_16_project[0][0]']       \n",
            " alization)                                                                                       \n",
            "                                                                                                  \n",
            " Conv_1 (Conv2D)                (None, 5, 5, 1280)   409600      ['block_16_project_BN[0][0]']    \n",
            "                                                                                                  \n",
            " Conv_1_bn (BatchNormalization)  (None, 5, 5, 1280)  5120        ['Conv_1[0][0]']                 \n",
            "                                                                                                  \n",
            " out_relu (ReLU)                (None, 5, 5, 1280)   0           ['Conv_1_bn[0][0]']              \n",
            "                                                                                                  \n",
            " global_average_pooling2d (Glob  (None, 1280)        0           ['out_relu[0][0]']               \n",
            " alAveragePooling2D)                                                                              \n",
            "                                                                                                  \n",
            " dense (Dense)                  (None, 1)            1281        ['global_average_pooling2d[0][0]'\n",
            "                                                                 ]                                \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 2,259,265\n",
            "Trainable params: 1,281\n",
            "Non-trainable params: 2,257,984\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "x = base_model.output\n",
        "\n",
        "# Add a new classifier layer for transfer learning\n",
        "x = tf.keras.layers.GlobalAveragePooling2D()(x)\n",
        "output = tf.keras.layers.Dense(1, activation='sigmoid')(x)\n",
        "\n",
        "# Define the model\n",
        "model = tf.keras.models.Model(inputs=base_model.input, outputs=output)\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "8/8 [==============================] - 4s 429ms/step - loss: 0.6424 - accuracy: 0.6340\n"
          ]
        }
      ],
      "source": [
        "# Evaluate the model before training\n",
        "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "loss0, accuracy0 = model.evaluate(val_data_gen)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/15\n",
            "16/16 - 19s - loss: 0.3670 - accuracy: 0.8620 - val_loss: 0.1853 - val_accuracy: 0.9610 - 19s/epoch - 1s/step\n",
            "Epoch 2/15\n",
            "16/16 - 10s - loss: 0.1469 - accuracy: 0.9595 - val_loss: 0.1122 - val_accuracy: 0.9710 - 10s/epoch - 656ms/step\n",
            "Epoch 3/15\n",
            "16/16 - 11s - loss: 0.1028 - accuracy: 0.9700 - val_loss: 0.0920 - val_accuracy: 0.9770 - 11s/epoch - 671ms/step\n",
            "Epoch 4/15\n",
            "16/16 - 11s - loss: 0.0850 - accuracy: 0.9735 - val_loss: 0.0801 - val_accuracy: 0.9760 - 11s/epoch - 670ms/step\n",
            "Epoch 5/15\n",
            "16/16 - 10s - loss: 0.0737 - accuracy: 0.9775 - val_loss: 0.0743 - val_accuracy: 0.9780 - 10s/epoch - 653ms/step\n",
            "Epoch 6/15\n",
            "16/16 - 11s - loss: 0.0659 - accuracy: 0.9800 - val_loss: 0.0687 - val_accuracy: 0.9780 - 11s/epoch - 662ms/step\n",
            "Epoch 7/15\n",
            "16/16 - 10s - loss: 0.0593 - accuracy: 0.9820 - val_loss: 0.0668 - val_accuracy: 0.9800 - 10s/epoch - 645ms/step\n",
            "Epoch 8/15\n",
            "16/16 - 11s - loss: 0.0545 - accuracy: 0.9820 - val_loss: 0.0617 - val_accuracy: 0.9820 - 11s/epoch - 661ms/step\n",
            "Epoch 9/15\n",
            "16/16 - 11s - loss: 0.0501 - accuracy: 0.9850 - val_loss: 0.0609 - val_accuracy: 0.9830 - 11s/epoch - 665ms/step\n",
            "Epoch 10/15\n",
            "16/16 - 10s - loss: 0.0461 - accuracy: 0.9870 - val_loss: 0.0578 - val_accuracy: 0.9820 - 10s/epoch - 648ms/step\n",
            "Epoch 11/15\n",
            "16/16 - 10s - loss: 0.0428 - accuracy: 0.9890 - val_loss: 0.0568 - val_accuracy: 0.9820 - 10s/epoch - 654ms/step\n",
            "Epoch 12/15\n",
            "16/16 - 10s - loss: 0.0403 - accuracy: 0.9890 - val_loss: 0.0550 - val_accuracy: 0.9820 - 10s/epoch - 654ms/step\n",
            "Epoch 13/15\n",
            "16/16 - 10s - loss: 0.0378 - accuracy: 0.9905 - val_loss: 0.0545 - val_accuracy: 0.9840 - 10s/epoch - 650ms/step\n",
            "Epoch 14/15\n",
            "16/16 - 10s - loss: 0.0354 - accuracy: 0.9910 - val_loss: 0.0530 - val_accuracy: 0.9820 - 10s/epoch - 652ms/step\n",
            "Epoch 15/15\n",
            "16/16 - 11s - loss: 0.0333 - accuracy: 0.9925 - val_loss: 0.0531 - val_accuracy: 0.9840 - 11s/epoch - 657ms/step\n"
          ]
        }
      ],
      "source": [
        "# Train the model\n",
        "history = model.fit(x=train_data_gen, validation_data=val_data_gen, epochs=5,\n",
        "                    steps_per_epoch=len(train_data_gen),\n",
        "                    validation_steps=len(val_data_gen), verbose=2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [],
      "source": [
        "model.save('cats_dogs_transfer_learning.keras')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "model = tf.keras.load_model('cats_dogs_transfer_learning.keras')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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8OLuSfnMv7t/jO++8Q+PGjVmyZInT54UXp9SpUweLxUJqamqx/7HY+fn5MWbMGP79738zY8YM5s6dy5VXXknz5s1LvRepOHou67l8ITyXXW1LXl4ex44dcwpSDcMgNTXV6Xnes2dPevbsicViYdOmTbz00kuMGzeOmJgYRz7bu+++m7vvvpuzZ8/y/fff88QTTzBgwAB27drl6PGubOpBLYX94Vg4vc0rr7xSGc0pVq9evTh9+rRjWNNu8eLFLp1vMpmK3N/PP/9cJE+hq5o3b05sbCyLFi1yWnBz4MAB1qxZ43I9wcHB3H777axYsYLp06eTm5vrNIzk67+b3r17A/Duu+86HS+8iMZ+7cLX/eyzzzh06JDTscK9GKWxD2MWnky/ceNGduzYwZVXXllmHa547733yMzMdOSdLPyKjo52DPP37dsXs9lc5D/Jc3Xv3p2oqChefvnlIguszpWQkMCuXbucgskTJ0649TNhMpkIDAx0ClpSU1OLrOK3L6Aord12I0aMIDAwkCFDhrBz585iU+hI1aLnsvv0XC5QFZ/LrrBfq3Bbli5dytmzZ4tti9lspkuXLo5Rop9++qlImbCwMPr378/EiRPJycnht99+K4fWe0Y9qKXo3r07NWvWZNSoUTzxxBMEBATw7rvvFlnFWJnuuusuZs2axR133MFTTz3FRRddxOeff86XX34J2HqJSjNgwACefPJJnnjiCXr16sXOnTuZOnUqjRs3Ji8vz+32+Pn58eSTTzJixAhuuOEG7r33Xk6dOsXkyZPdGkoC23DSnDlzmDlzJi1atHCaK9WiRQuaNm3Ko48+imEY1KpVi08++cTjIYq+ffty2WWX8a9//YuzZ8/SsWNHfvzxR95+++0iZQcMGMDChQtp0aIFbdq0YfPmzfznP/8p8ht206ZNCQkJ4d133yUxMZHw8HDq169P/fr1i9TZvHlz7rvvPl566SX8/Pzo378/+/fvZ9KkScTFxfHQQw95dF+Fvf7669SsWZOHH364yDAlwJ133snMmTPZtm0bbdu25d///jdPPvkkmZmZ3HbbbURFRbF9+3aOHz/OlClTCA8PZ8aMGYwYMYI+ffpw7733EhMTw549e9i2bRv/93//B9iGlF555RXuuOMO7r33Xk6cOMFzzz1HZGSky20fMGAAH3zwAaNHj+bmm2/m4MGDPPnkk8TGxrJ7925HuZ49ezJ06FCeeuopjhw5woABAwgKCmLLli2Ehoby4IMPOsrWqFGDO++8k3nz5hEfH+/yKnCpPHou67lc3Z7LdqmpqcXu6peQkMBVV11Fv379eOSRR0hPT6dHjx78/PPPPPHEE7Rv356hQ4cCtrnL33zzDddeey2NGjUiKyvL0enQp08fAO69915CQkLo0aMHsbGxpKamMm3aNKKiokocWasUlblCqzKUtFr0kksuKbb8mjVrjG7duhmhoaFGnTp1jBEjRhg//fRTkVWAJa0WLW5VXuEVzSWtFi3czpKuk5ycbNx4441GeHi4ERERYdx0003G8uXLi6yaLE52drbx8MMPGw0aNDCCg4ONDh06GB999FGJq6v/85//FKmDYlZTvvbaa0azZs2MwMBA4+KLLzYWLFhQpE5XtG/fvtiVi4ZhGNu3bzeuuuoqIyIiwqhZs6bxt7/9zUhOTi7SHldWixqGYZw6dcq45557jBo1ahihoaHGVVddZfz+++9F6jt58qQxfPhwo27dukZoaKhx6aWXGqtXry52pfqiRYuMFi1aGAEBAU71FPf3aLFYjOnTpxsXX3yxERAQYERHRxt33HGHcfDgQadyJf28lvX93bZtmwEY48aNK7GM/X4ffPBBx7G33nrL6NSpkxEcHGyEh4cb7du3L7ICdvny5UavXr2MsLAwIzQ01GjZsqUxffp0pzJvvvmmkZiYaAQHBxstW7Y0lixZ4tbPmWEYxrPPPmskJCQYQUFBRmJiovHqq6+W+L2cNWuW0apVKyMwMNCIiooyunXrZnzyySdF6ly1apUBGM8++2yJ3xcpX3ouO9NzuUB1fy7bxcfHG0Cxr7vuusswDFu2iUceecSIj483AgICjNjYWOP+++83Tp486ahn7dq1xg033GDEx8cbQUFBRu3atY1evXoZH3/8saPMm2++afTu3duIiYkxAgMDjfr16xu33HKL8fPPP5fZzopkMoxSxuXkvPXMM8/w2GOPkZycrJ0xRErxj3/8g3nz5nHw4MFiFzmI+IqeyyKu0xB/NWAfRm3RogW5ubl88803vPjii9xxxx16CIqUYN26dezatYu5c+cycuRIBafiU3oui3hHAWo1EBoayqxZs9i/fz/Z2dk0atSIRx55hMcee6yymyZSZXXr1o3Q0FAGDBjAU089VdnNkWpGz2UR72iIX0RERESqFKWZEhEREZEqRQGqiIiIiFQpClBFREREpEqpNoukrFYrhw8fJiIiotjt8UREvGUYBqdPn6Z+/fplJls/H+k5KiLlzdXnaLUJUA8fPlzifuEiIr508ODBapkqSM9REakoZT1Hq02AGhERAdhu2J3tE0VEXJWenk5cXJzjeVPd6DkqIuXN1edotQlQ7cNRkZGRerCKSLmqrsPfeo6KSEUp6zla/SZRiYiIiMh5TQGqiIiIiFQpClBFREREpEqpNnNQRURExHUWi4Xc3NzKboZUMwEBAZjNZq/rUYAqIiJyATEMg9TUVE6dOlXZTZFqqkaNGtSrV8+rBaUKUEVERC4g9uC0bt26hIaGVtusFFLxDMMgIyODo0ePAhAbG+txXQpQRURELhAWi8URnNauXbuymyPVUEhICABHjx6lbt26Hg/3a5GUiIjIBcI+5zQ0NLSSWyLVmf3ny5s5zgpQRURELjAa1pfy5IufLwWoIiIiIlKlKEAVERGRC9Lll1/OuHHjXC6/f/9+TCYTW7duLbc2iY0CVBEREanSTCZTqa9hw4Z5VO8HH3zAk08+6XL5uLg4UlJSaNWqlUfXc5UCYa3iFxERkSouJSXF8X7JkiU8/vjj7Ny503HMvnLcLjc3l4CAgDLrrVWrllvtMJvN1KtXz61zxDPqQRUREZEqrV69eo5XVFQUJpPJ8XVWVhY1atTg/fff5/LLLyc4OJh33nmHEydOcNttt9GwYUNCQ0Np3bo1ixYtcqq38BB/QkICzzzzDPfccw8RERE0atSI+fPnOz4v3LO5atUqTCYTX3/9NR07diQ0NJTu3bs7Bc8ATz31FHXr1iUiIoIRI0bw6KOP0q5dO4+/H9nZ2fz973+nbt26BAcHc+mll7Jx40bH5ydPnmTIkCHUqVOHkJAQmjVrxhtvvAFATk4ODzzwALGxsQQHB5OQkMC0adM8bkt5UYAqIiJyATMMg4ycvEp5GYbhs/t45JFH+Pvf/86OHTvo168fWVlZJCUl8emnn/Lrr79y3333MXToUNavX19qPTNmzKBjx45s2bKF0aNHc//99/P777+Xes7EiROZMWMGmzZtwt/fn3vuucfx2bvvvsvTTz/N9OnT2bx5M40aNWLevHle3eu//vUvli5dyptvvslPP/3ERRddRL9+/fjrr78AmDRpEtu3b+fzzz9nx44dzJs3j+joaABefPFFPv74Y95//3127tzJO++8Q0JCglftKQ8a4hcREbmAZeZaaPn4l5Vy7e1T+xEa6JtQZNy4cdx4441Oxx5++GHH+wcffJAvvviC//73v3Tp0qXEeq655hpGjx4N2ILeWbNmsWrVKlq0aFHiOU8//TS9evUC4NFHH+Xaa68lKyuL4OBgXnrpJYYPH87dd98NwOOPP86KFSs4c+aMR/d59uxZ5s2bx8KFC+nfvz8Ar776KitXruT111/nn//8J8nJybRv356OHTsCOAWgycnJNGvWjEsvvRSTyUR8fLxH7Shv6kEVERGR8549GLOzWCw8/fTTtGnThtq1axMeHs6KFStITk4utZ42bdo43tunEti37nTlHPv2nvZzdu7cSefOnZ3KF/7aHXv37iU3N5cePXo4jgUEBNC5c2d27NgBwP3338/ixYtp164d//rXv1izZo2j7LBhw9i6dSvNmzfn73//OytWrPC4LeVJPagiIiIXsJAAM9un9qu0a/tKWFiY09czZsxg1qxZzJ49m9atWxMWFsa4cePIyckptZ7Ci6tMJhNWq9Xlc+xJ6s89p3Diem+mNtjPLa5O+7H+/ftz4MABPvvsM7766iuuvPJKxowZw/PPP0+HDh3Yt28fn3/+OV999RW33HILffr04X//+5/HbSoP6kEVERG5gJlMJkID/SvlVZ47Wq1evZpBgwZxxx130LZtW5o0acLu3bvL7Xolad68ORs2bHA6tmnTJo/ru+iiiwgMDOSHH35wHMvNzWXTpk0kJiY6jtWpU4dhw4bxzjvvMHv2bKfFXpGRkQwePJhXX32VJUuWsHTpUsf81apCPagiIiJS7Vx00UUsXbqUNWvWULNmTWbOnElqaqpTEFcRHnzwQe699146duxI9+7dWbJkCT///DNNmjQp89zC2QAAWrZsyf33388///lPatWqRaNGjXjuuefIyMhg+PDhgG2ea1JSEpdccgnZ2dl8+umnjvueNWsWsbGxtGvXDj8/P/773/9Sr149atSo4dP79pYCVBEREal2Jk2axL59++jXrx+hoaHcd999XH/99aSlpVVoO4YMGcIff/zBww8/TFZWFrfccgvDhg0r0qtanFtvvbXIsX379vHss89itVoZOnQop0+fpmPHjnz55ZfUrFkTgMDAQCZMmMD+/fsJCQmhZ8+eLF68GIDw8HCmT5/O7t27MZvNdOrUieXLl+PnV7UG1U2GL3M8VKL09HSioqJIS0sjMjKyspsjItVQdX/OVPf7E8jKymLfvn00btyY4ODgym7OBeuqq66iXr16vP3225XdlHJR2s+Zq88Z9aCKiIiIlJOMjAxefvll+vXrh9lsZtGiRXz11VesXLmysptWpSlAFZELltVq4OdXfos0RERMJhPLly/nqaeeIjs7m+bNm7N06VL69OlT2U2r0hSgisgF5VRGDp//msqyrYdoVT+Kxwa0rOwmnbd+3HOcF77aTYvYCKYOalXZzRGpkkJCQvjqq68quxnnHQWoIlLtZeZY+GrHEZZtPcx3u46Sa7FNvf/j2Fn+fU2ielE9dDorlw37/8JaPZYyiEgVogBVRKqlXIuVH/Yc5+Oth/nyt1QyciyOzxJjIxnUrj4D29ZXcOoF+xaVZ7LzKrklIlLdKEAVkWrDajX4Kfkky7Ye5rNfUvjrbMGOMXG1QhjUtgHXtavPxTERldjK6iMsyPZfyNkcBagi4lsKUEXkvPd7ajrLth7m462HOXQq03E8OjyQAW3qc127+rSPq1Guu9ZciMLtAWq2pYySIiLuUYAqIuelg39l8MnPtqD099TTjuPhQf70u6Qeg9rVp3vT2vibq1by6eokNNC2j/pZDfGLiI8pQBWRKs9iNTh6OovDpzL57XA6H289zKYDJx2fB5r96N2iDoPaNeCKFnUJDjBXYmsvHPYe1Ow8K3kWq34ZEBGfUYAqIpXudFYuh0/ZAtBDpzI57HhlcehUJkfSs8izOq8UN5mge9PaDGrbgH6t6hEVElBJrb9w2eeggm2YPypUAapUbZdffjnt2rVj9uzZACQkJDBu3DjGjRtX4jkmk4kPP/yQ66+/3qtr+6qeC4UCVBEpd6ezcvk99XShALQgID2dVfYQsb+fiXpRwcTVDKVPyxgGtIklJlJbNVamQH8/Aswmci0GZ3PyiArVLwlSPgYOHEhmZmax+UTXrl1L9+7d2bx5Mx06dHCr3o0bNxIWFuarZgIwefJkPvroI7Zu3ep0PCUlhZo1a/r0WoUtXLiQcePGcerUqXK9TkVQgCoi5SIr18KqnUdZtvUwX/9+lJw8a6nla4QGUD8qhPo1QmhQI5j6NUIcrwY1QqgTEYRZKaGqnLAgf05l5GoeqpSr4cOHc+ONN3LgwAHi4+OdPluwYAHt2rVzOzgFqFOnjq+aWKZ69epV2LWqA43HiIjPWKwGP+w+zj//u41OT33FqHd+4vNfU8nJs1I/KpiuTWpxY4cGPHjFRUy7sTVv3tOZr8Zfxm9T+rH18b4sH9uT1+7qyJRBrRjZqykD29YnKb4m9aKCFZxWUWHKhSoVYMCAAdStW5eFCxc6Hc/IyGDJkiUMHz6cEydOcNttt9GwYUNCQ0Np3bo1ixYtKrXehIQEx3A/wO7du7nssssIDg6mZcuWrFy5ssg5jzzyCBdffDGhoaE0adKESZMmkZubC9h6MKdMmcK2bdswmUyYTCZHm00mEx999JGjnl9++YUrrriCkJAQateuzX333ceZM2ccnw8bNozrr7+e559/ntjYWGrXrs2YMWMc1/JEcnIygwYNIjw8nMjISG655RaOHDni+Hzbtm307t2biIgIIiMjSUpKYtOmTQAcOHCAgQMHUrNmTcLCwrjkkktYvny5x20pi3pQRcQrhmGw7c80lm09xKc/p3DsdLbjs/pRwQxsV59BbRuQGBuhNE/VUFiQbUHauRshyHnGMCA3o3KuHRBqm1BeBn9/f+68804WLlzI448/7niW/Pe//yUnJ4chQ4aQkZFBUlISjzzyCJGRkXz22WcMHTqUJk2a0KVLlzKvYbVaufHGG4mOjmbdunWkp6cXOzc1IiKChQsXUr9+fX755RfuvfdeIiIi+Ne//sXgwYP59ddf+eKLLxzTEaKioorUkZGRwdVXX03Xrl3ZuHEjR48eZcSIETzwwANOQfi3335LbGws3377LXv27GHw4MG0a9eOe++9t8z7KcwwDK6//nrCwsL47rvvyMvLY/To0QwePJhVq1YBMGTIENq3b8+8efMwm81s3bqVgADb1J0xY8aQk5PD999/T1hYGNu3byc8PNztdrhKAaqIeGTP0TN8vPUQy7Yd5sCJgv/caoQGcE3rWAa1rU+nhFraqamasy+UUg/qeSw3A56pXznX/vdhCHRtDug999zDf/7zH1atWkXv3r0B2/D+jTfeSM2aNalZsyYPP/ywo/yDDz7IF198wX//+1+XAtSvvvqKHTt2sH//fho2bAjAM888Q//+/Z3KPfbYY473CQkJ/OMf/2DJkiX861//IiQkhPDwcPz9/Usd0n/33XfJzMzkrbfecsyB/b//+z8GDhzI9OnTiYmJAaBmzZr83//9H2azmRYtWnDttdfy9ddfexSgfvXVV/z888/s27ePuLg4AN5++20uueQSNm7cSKdOnUhOTuaf//wnLVq0AKBZs2aO85OTk7npppto3bo1AE2aNHG7De5QgCoiLktJy+STbYdZtvUwvx1OdxwPCTBzVcsYBrWrT89mdQj01+yhC0VBsn4FqFK+WrRoQffu3VmwYAG9e/dm7969rF69mhUrVgBgsVh49tlnWbJkCYcOHSI7O5vs7GyXF0Ht2LGDRo0aOYJTgG7duhUp97///Y/Zs2ezZ88ezpw5Q15eHpGRkW7dy44dO2jbtq1T23r06IHVamXnzp2OAPWSSy7BbC5ImxcbG8svv/zi1rXOvWZcXJwjOAVo2bIlNWrUYMeOHXTq1Inx48czYsQI3n77bfr06cPf/vY3mjZtCsDf//537r//flasWEGfPn246aabaNOmjUdtcYUCVBEp1amMHJb/ksqyrYfYsP8vjPxsT/5+Ji67uA6D2tWnT2KMU8ohuXA4kvVriP/8FRBq68msrGu7Yfjw4TzwwAPMmTOHN954g/j4eK688koAZsyYwaxZs5g9ezatW7cmLCyMcePGkZOTU0atNoZhFDlWeFrSunXruPXWW5kyZQr9+vUjKiqKxYsXM2PGDLfuwzCMEqc8nXvcPrx+7mdWa+kLTt295rnHJ0+ezO23385nn33G559/zhNPPMHixYu54YYbGDFiBP369eOzzz5jxYoVTJs2jRkzZvDggw961J6y6H8UkWok12Jl2dbDfLLtMNl53gcMuRaDn/88Ra6l4MHdOaEW17WrzzWtY6kVFuj1NeT8FqYe1POfyeTyMHtlu+WWWxg7dizvvfceb775Jvfee68juFq9ejWDBg3ijjvuAGxzSnfv3k1iYqJLdbds2ZLk5GQOHz5M/fq2KQ9r1651KvPjjz8SHx/PxIkTHccOHDjgVCYwMBCLpfTnb8uWLXnzzTc5e/asoxf1xx9/xM/Pj4svvtil9rrLfn8HDx509KJu376dtLQ0p+/RxRdfzMUXX8xDDz3EbbfdxhtvvMENN9wAQFxcHKNGjWLUqFFMmDCBV199VQGqiJQsIyePxRsO8trqPzicluXz+hNjIxnUrj4D29anQY0Qn9cv5y8N8UtFCg8PZ/Dgwfz73/8mLS2NYcOGOT676KKLWLp0KWvWrKFmzZrMnDmT1NRUlwPUPn360Lx5c+68805mzJhBenq6UyBqv0ZycjKLFy+mU6dOfPbZZ3z44YdOZRISEti3bx9bt26lYcOGREREEBQU5FRmyJAhPPHEE9x1111MnjyZY8eO8eCDDzJ06FDH8L6nLBZLkRysgYGB9OnThzZt2jBkyBBmz57tWCTVq1cvOnbsSGZmJv/85z+5+eabady4MX/++ScbN27kpptuAmDcuHH079+fiy++mJMnT/LNN9+4/L31hAJUkfPYybM5vLl2P2+u2c/JDFvqkToRQQzrnkCjWu4NnZWkRb0ImsVE+KQuqX5CA+0Bqob4pWIMHz6c119/nb59+9KoUSPH8UmTJrFv3z769etHaGgo9913H9dffz1paWku1evn58eHH37I8OHD6dy5MwkJCbz44otcffXVjjKDBg3ioYce4oEHHiA7O5trr72WSZMmMXnyZEeZm266iQ8++IDevXtz6tQp3njjDadAGiA0NJQvv/ySsWPH0qlTJ0JDQ7npppuYOXOmV98bgDNnztC+fXunY/Hx8ezfv5+PPvqIBx98kMsuuww/Pz+uvvpqXnrpJQDMZjMnTpzgzjvv5MiRI0RHR3PjjTcyZcoUwBb4jhkzhj///JPIyEiuvvpqZs2a5XV7S2Iyipt0cR5KT08nKiqKtLQ0tycri5xvDp/K5LXV+1i0IZnMXFtgEF87lJGXNeXGDg20F305qe7PGU/u7/++2c3zK3YxuGMc028uvwUT4htZWVns27ePxo0bExysndikfJT2c+bqc0Y9qCLnkT1HT/Pyd3/w0ZZDjr3pL6kfyf2XN6V/q1gls5cK50gzlaMhfhHxHQWoIueBLcknefm7vazYfsSxir5bk9rcf3lTejaLVgJ8qTT2naQyNAdVRHxIAapIFWUYBqt3H2feqr2s/eOE43i/S2IY1asp7RvVrMTWidgUrOLXHFQR8R0FqCJVjMVq8PmvKcxbtdeRDN/fz8QN7RswslcTLqqrBUtSddi3OtVOUiLiSwpQRaqIrFwLH/x0iFe+3+vYOjQkwMxtnRsxomdj6iu9k1RBjjRTmoN6Xqkm66OlivLFz5cCVJFKdjorl3fXJ/P6D/s4djobsO1nP6x7And1S6CmkuFLFaY0U+cX+85EGRkZhITol14pHxkZtk6WwjthuUMBqkglOXY6mzd+3Mfb6w5wOsvW+xQbFcy9PZtwa+c4x3/8Uj3NnTuX//znP6SkpHDJJZcwe/ZsevbsWWzZH374gUceeYTff/+djIwM4uPjGTlyJA899JCjzMKFC7n77ruLnJuZmVmu6YSUqP/8YjabqVGjBkePHgVs+Ti1yFJ8xTAMMjIyOHr0KDVq1MBs9jzlof4HFKlgyScymL96L+9v+pOcPNueyhfVDWdUr6Zc17Y+gf5+ldxCKW9Llixh3LhxzJ07lx49evDKK6/Qv39/tm/f7pR43C4sLIwHHniANm3aEBYWxg8//MDIkSMJCwvjvvvuc5SLjIxk586dTueWd65L+xzUzFwLFquhVGfngXr16gE4glQRX6tRo4bj58xTClBFKsj2w+m8/N1ePv35MPkpTGkXV4PRlzelT2IMfvqP/YIxc+ZMhg8fzogRIwCYPXs2X375JfPmzWPatGlFyrdv395pZ5iEhAQ++OADVq9e7RSgmkwmr/9TcJd9FT/YttyNCPZ8SE8qhslkIjY2lrp165Kbm1vZzZFqJiAgwKueUzsFqCLlyDAMNu4/ybxVe/h25zHH8V4X1+H+y5vSpXEtDa9dYHJycti8eTOPPvqo0/G+ffuyZs0al+rYsmULa9as4amnnnI6fubMGeLj47FYLLRr144nn3yyyJaHvhbk74fZz4TFanA226IA9TxiNpt9EkiIlAcFqCLlwGo1+Ob3o8z7bi+bD5wEwM8E17apz8jLmtCqQVQlt1Aqy/Hjx7FYLMTExDgdj4mJITU1tdRzGzZsyLFjx8jLy2Py5MmOHliAFi1asHDhQlq3bk16ejovvPACPXr0YNu2bTRr1qzY+rKzs8nOznZ8nZ6e7vb9mEwmwgLNpGflKdWUiPiMAlQRH8q1WPlk22Fe/m4vu46cASDQ34+/JTXkvsuaEF87rJJbKFVF4Z5zwzDK7E1fvXo1Z86cYd26dTz66KNcdNFF3HbbbQB07dqVrl27Osr26NGDDh068NJLL/Hiiy8WW9+0adOYMmWKl3diG+ZPz8ojQ6mmRMRHFKCK+EBmjoUlG5N5dfU+Dp3KBGyrm+/oGs89PRKoG1m+C1Xk/BEdHY3ZbC7SW3r06NEivaqFNW7cGIDWrVtz5MgRJk+e7AhQC/Pz86NTp07s3r27xPomTJjA+PHjHV+np6cTFxfn6q042OehqgdVRHxFAaqIF3ItVl5d/Qevrd7HX2dzAIgOD+SeSxszpEs8USGajyfOAgMDSUpKYuXKldxwww2O4ytXrmTQoEEu12MYhtPwfHGfb926ldatW5dYJigoiKCgIJevWRJtdyoivqYAVcRDfxw7w7glW/n5zzQAGtUK5b7LmnBzUkOCA7TwQEo2fvx4hg4dSseOHenWrRvz588nOTmZUaNGAbaezUOHDvHWW28BMGfOHBo1akSLFi0AW17U559/ngcffNBR55QpU+jatSvNmjUjPT2dF198ka1btzJnzpxyv5+wQNvPu4b4RcRXFKCKuMkwDN7bkMxTn+4gM9dCZLA/jw+8hOvb1cffrBymUrbBgwdz4sQJpk6dSkpKCq1atWL58uXEx8cDkJKSQnJysqO81WplwoQJ7Nu3D39/f5o2bcqzzz7LyJEjHWVOnTrFfffdR2pqKlFRUbRv357vv/+ezp07l/v9aIhfRHzNZFSTDXnT09OJiooiLS2NyMjIym6OVFPHz2TzyP9+5uvfbQmue1xUm+f/1pbYKG0ZeCGo7s8ZT+/voSVb+XDLIf59TQvuu6xpObZQRM53rj5n1IMq4qJvfj/Cv/73M8fP5BBo9uNfVzfnnh6NlWDfFekpkHMGootPd1ThDAN2r4DgKGjUtezyUqrQ/CF+zUEVEV9RgCpShswcC08v384762xDrs1jIph9azsSY6tfD5rPHfkNfnwBfvkfGBZo1B0ufQiaXQWVsUGBJQ9++wB+mA1Hf4NG3eCeLyq+HdVMuGORlIb4RcQ3FKCKlOLnP08xbslW/jh2FoDhlzbmn/2aaxFUWZLXwQ+zYNc5wZ/JDMlr4L01UPcSW6B6yQ1groDHUE4GbH0X1rwIp/LndgaGQ8OOYMkFs7IteMOxil+LpETERxSgihTDYjV4+bu9zFq5izyrQUxkEDP+1o5Lm0VXdtOqLvuw+Q+zIHlt/kETtBwEl46D8BhYNxc2vWHrvfxgBHwzFbr/HdrfAQHlMI838yRsfA3WvQwZx23HQqOh6/3QaTiE1PT9NS9AGuIXEV9TgCpSyMG/MnhoyVY25W9Rek3rejxzQ2tqhAZWcsuqqMLD5gDmQGh7G/QYC7XPWTTT9yno+Y/8oHGerTdz+cPw3XRb0NhxOITU8L5N6Smwbo4tGM6x7ehFjUblGwxfwDTELyK+pgBVJJ9hGHzw0yGe+Pg3zmTnER7kz5TrLuHGDg2ct6DMy4GUrbZewuR1cHCDLSBr1MU2p7FRV4hpBX7VfBpAbiZseafosHnHe6DraIiMLf68kJpw2T+h6xjbsPuPL0JaMnw9FVbPgk7550fUc79Nx/fAmhdg22Kw2DZOqPDpBBcgpZkSEV/T01oEOJWRw8QPf+WzX1IA6Bhfk1mD2xFXKxQyT8GfGwsC0kObIS+raCW/fWh7AQRGQFyngoC1QUcIDK24GypPmacKekC9GTYPDIXO90LSMPj1A9vUgGM7bIuq1s2Ddrfbejxru5C26PAW2/nbPwbyM+c16pa/IKtv5SzIuoCEBdkT9WuIX0R8QwGqXPB+2H2cf/x3K0fSs/H3M/HYpREMbfAn5rVLbQHpkd9wBD12odG2wNMegOZlOfeoZqfD3m9sLwA/f4ht53xO2Hk2nzU9pWAOac5p2zFfDJubA6DtYGj9t/w5rDPh4HrYvBB+ess2h7XHOKjfzvk8w4B939sC0z++LTh+8dW28vHdPGuPuC0sUEP8IuJbClDlgpWVa+E/X+zghzWrucpvJ5eH7+WyoL0EbjhUtHCtpgWBZaNutl69wr1yCZfa/rRa4Oh2W7CavBYOrIXTh+HQJttr7f/ZytVu5hyw1mpSNXv6Tuy19WpuW1S+w+Z+ftD8atvrwFpb4Ln7y4Ke6aZX2K4Z3wN+/8z2+eGfbOeazND6Ztuc15hLfNMecZmG+EXE1xSgynkjIyePJRsP8sPu41i92QDNMGiTtYHOJ5bx97zfmBSUYTuel/8ymSG2TUHgGNcVImJcr9/PDPVa216d77X19KUdLAhYk9fZAtgTu22vLW/bzgurYzvHrwr9s8zNhP0/UOHD5vHdbK/UX23B8a9LC3qkg2tA1ilbOf9g6HAndHsAasaXX3ukVFokJSK+VoX+JxQp3qmMHN5cc4CFa/ZxMiPX43rMWLjWbx33+39Mot9B20ET5PmH4t+os/N80aBwH7UeWyBXo5Ht1eYW27GMv4rOaz17rGBKQFVTWcPm9VrBTa/CFRNhzf/ZgvmsU7YdoDrfB51HQnidim2TFBFqn4Oaa8FqNbS7moh4TQGqVFkpaZm8tnofizYkOxZfNKoVyh1dG7mV8slsySLh4Ec03/sG4Zm24ftccyj7E/5G3e5DiUpoX/Gru0NrwcX9bC+A3CzbQp+/9lZsO1zRIAnqJlZuG2omwLXPQ69HIPVniOsMQRGV2yZxsPegGgZk5locQ/4iIp7SU0SqnD1Hz/DKd3v5aOshci22oeXE2Ejuv7wp17Sqh7/Zz7WKil1tXhu63E9Ap+E0C61VPjfgiYDggmFtKVl4HbjoyspuhRQSEmDGZLIFqGez8xSgiojX9BSRKmPrwVPMW7WHFduPYJ9i2qVxLe6/vCm9Lq7jnIu0NKdTbavNNy4oWG0e1Qi6P2hbbV5d0j2JVBEmk4mwQH/OZOdxVqmmRMQHXOyKcjZ37lwaN25McHAwSUlJrF69utTyc+bMITExkZCQEJo3b85bb71VpMzs2bNp3rw5ISEhxMXF8dBDD5GVVUyuSalWDMNg9e5j3P7qOq6f8yNf/mYLTq9qGcPS+7uzZGQ3Lm9e17Xg9MRe+GQszG5tW1iTcxrqJMIN8+HvP0GX+xScipQTey5ULZQSEV9wuwd1yZIljBs3jrlz59KjRw9eeeUV+vfvz/bt22nUqFGR8vPmzWPChAm8+uqrdOrUiQ0bNnDvvfdSs2ZNBg4cCMC7777Lo48+yoIFC+jevTu7du1i2LBhAMyaNcu7O5QqyWI1+OLXVOZ9t4dfD6UD4O9nYlC7Bozq1YRmMW7MLzy8FX6cDduXgWG1HYvrApeOt6029/Po9zARcYNtWD9bqaZExCfcDlBnzpzJ8OHDGTFiBGDr+fzyyy+ZN28e06ZNK1L+7bffZuTIkQwePBiAJk2asG7dOqZPn+4IUNeuXUuPHj24/fbbAUhISOC2225jw4YNHt+YVE3ZeRY++OkQr3y3l/0nbOmdQgLM3No5jhE9m9CghovJ3g0D9q+25cI8d+V7s375uTI1l1OkItmT9WfkKEAVEe+5FaDm5OSwefNmHn30Uafjffv2Zc2aNcWek52dTXBwsNOxkJAQNmzYQG5uLgEBAVx66aW88847bNiwgc6dO/PHH3+wfPly7rrrLjdvR6qq01m5vLc+mdd/2MfR09kA1AgN4K5uCdzVPYFaYS6uyrdaYWd+kvZDm23HTGZodZMtSXu9VuV0ByJSGvsQ/5lszUEVEe+5FaAeP34ci8VCTIxz0vKYmBhSU1OLPadfv3689tprXH/99XTo0IHNmzezYMECcnNzOX78OLGxsdx6660cO3aMSy+9FMMwyMvL4/777y8SCJ8rOzub7Oxsx9fp6enu3IpUEIvV4P1NB/nPlzv566xtF6LYqGBG9GzCrZ3iXF/tm5cDv7xvm1t6fJftmH8wtB8K3R+wpSESkUqjZP0i4ksereIvvGDFMIwSF7FMmjSJ1NRUunbtimEYxMTEMGzYMJ577jnMZttv3KtWreLpp59m7ty5dOnShT179jB27FhiY2OZNGlSsfVOmzaNKVOmeNJ8qSCb9v/F5E9+c8wxbRIdxv2XN2VQuwYE+rs4LzT7DPz0JqydA+n5W5AGRdl2aOoySknaRaqI0EAFqCLiO24FqNHR0ZjN5iK9pUePHi3Sq2oXEhLCggULeOWVVzhy5AixsbHMnz+fiIgIoqOjAVsQO3ToUMe81tatW3P27Fnuu+8+Jk6ciF8xi1wmTJjA+PHjHV+np6cTFxfnzu1IOTmSnsWzn//Oh1tsAWVEsD8P9bmYod3iCXA1h+nZE7DhFVj/SsG2luH1oNsYSBoGwZHl0nYR8UyYowdVQ/wi4j23AtTAwECSkpJYuXIlN9xwg+P4ypUrGTRoUKnnBgQE0LBhQwAWL17MgAEDHIFnRkZGkSDUbDZjGAZGCXuuBwUFERQU5E7zpZxl51lY8MN+XvpmNxk5FkwmGNwxjof7NSc63MW/q1MHbb2lP70JubZFVNRqaptf2vZW8NffuUhVFG5PM6VFUiLiA24P8Y8fP56hQ4fSsWNHunXrxvz580lOTmbUqFGArWfz0KFDjlynu3btYsOGDXTp0oWTJ08yc+ZMfv31V958801HnQMHDmTmzJm0b9/eMcQ/adIkrrvuOsc0AKnavvn9CFM/2e5Ymd++UQ2mXHcJbRrWcK2Co7/b5pf+8j5Y8/+Di21rSxWVOBD89HMgUpVpiF9EfMntAHXw4MGcOHGCqVOnkpKSQqtWrVi+fDnx8fEApKSkkJyc7ChvsViYMWMGO3fuJCAggN69e7NmzRoSEhIcZR577DFMJhOPPfYYhw4dok6dOgwcOJCnn37a+zuUcvXHsTM8+el2vt15DIA6EUFM6N+C69s1wM/PheT6BzfaVuTv/KzgWONetlRRTS4HV3ePEpFKpUVSIuJLJqOkMfTzTHp6OlFRUaSlpREZqfmJ5e1Mdh4vfbObBT/sI9diEGA2cc+ljXnwimaO/6hKZBiw52tbYHrgh/yDJkgcYAtMGySVe/tFPFHdnzPe3N9765P594e/0Ccxhtfu6lhOLRSR852rzxmPVvHLhcswDD7aeohpy3935DO9vHkdHh/QkiZ1wks/2ZIH2z+CH2bDkV9sx/wCoO1g6D4W6lxcrm0XkfJjz4OqRP0i4gsKUKVk6Smw7T3IPAnAsdM5/Lj3OEfTsxgOREYE0L1JbRrVCsW05ePS67In2D+53/Z1QBh0vBu6joaoBuV6GyJS/sI0B1VEfEgBqhR1fA+seQG2LQZLjuNwHeB6KPipyQV2ull3SC3oej90GgGhtXzRWhGpAuxpps4oQBURH1CAKgUO/QQ/zobtHwO2qclHarRn+amG5ORZAWhWN5wujWs7hvPcUqsJtBkMgWG+a7OIVAnhyoMqIj6kAPVCZxiw7zvbgqU/VjkOW5tdzZzcgcz4vSYAl9SPZPJ1l9ApQb2eIlJUqPKgiogPKUCtSjL+gtMpEN0czOX8V2O1wO+f2gLTw1tsx0xmaP03cro+wINfZ/Pl70cw+5mYdG0iQ7slYHYlbZSIXJDOTTNV2vbXIiKuUIBaVWSegvm94FQyBIZDw07QqBs06goNO/puWDwvG35eYkuKf2KP7Zh/MHS4E7o9QEZYA0a+vZnVu48TaPbj/25vT99L6vnm2iJSbdnnoFoNyMq1EhKozTVExHMKUKsCw4BPH7IFpwA5Z+CPb20vsPVsxrYtCFgbdYXwuu5dI/s0bF5o20b0dIrtWHAUdL4POo+E8DqkZeRy92vr+Sn5FKGBZl69syM9Lor22W2KSPUVGlAQkJ7NyVOAKiJeUYBaFfy8BH77wBaI3vMlBIRA8lpIXmf7M/0QHP7J9lo3x3ZOrabnBKzdoHbT4nddOnsc1r8MG+ZDVprtWEQsdBsDScMgKAKAY6ezGfr6en5PPU1USAAL7+5E+0Y1K+b+ReS85+dnIjTQTEaOhbPZeUSHB1V2k0TkPKYAtbL9tQ8+e9j2vvcEiOtke1+vFXS+1/b+1MGCYDV5HRzdDn/ttb22vmMrExpdEKzGd4PgGrbA9Ke3IS/TVqb2RdBjrG0lvX/Bfx5/nsxg6Osb2Hf8LHUignh7eGda1Kt+u+SISPkKC/InI8eiVFMi4jUFqJXJkgcf3Ac5p22B5aXjiy9XI872avM329eZJ+HghoKA9dBmyDhuW/T0+6dFz49tBz3HQ4sB4Oc87Lb32BmGvraew2lZNKgRwrsjupAQrTRQIuK+sEAzx4CMHKWaEhHvKECtTKufhz83QFAk3Di/SPBYopCacHE/2wsgNwtStp4zLWAdZJ2CJpfb9rZv3KvY4f9fD6Vx14INnDibQ9M6YbwzoguxUSG+ujsRucAoWb+I+IoC1MqSvB6+m257P2AW1GjkeV0BwQWLp8C2rWjOadsiqBJs2v8Xdy/cyOmsPFo1iOTNuztTW3PGRMQLYUHa7lREfEMBamXISocP7gXDapsP2vpm39bv51dqcPrdrmOMfHsTWblWOifU4rVhHYkMDvBtG0TkghOWv3I/Q7tJiYiXFKBWhs//BacOQFQjuOY/FXrp5b+kMHbxFnItBpc3r8O8IUlKByMiPqEhfhHxFQWoFe2X/8G2RWDys807LaWn09fe33SQR5f+jNWAa9vEMuuWdgT6+1XY9UWkegvXEL+I+IgC1Ip06iB8mr9Sv+fDtnRQFWTBD/uY+ul2AG7tFMfTN7TW1qUi4lOhgfkBqlbxi4iXFKBWFKsFPhwJ2WnQoCP0+leFXNYwDF74ejezv9oNwL09G/PvaxK1T7aI+Fx4kG26kHpQRcRbClAryo+z4cCPEBgON70K5vJflGS1Gjz12Q4W/LgPgIf7XsyY3hcpOBWRcqFV/CLiKwpQK8Khn+DbZ2zv+z8HtZqU+yXzLFYmfPAL/938JwBTrruEu7onlPt1ReTCFWoPUHMUoIqIdxSglrecs7B0BFjzoOX10O72cr9kdp6FcYu38vmvqZj9TDx3UxtuSmpY7tcVkQtbwRC/5qCKiHcUoJa3LybAX3shsoEtIX85D6+fOJPN/e/+xIZ9fxFo9uPF29pzdat65XpNERGAsEClmRIR31CAWp52fAI/vQmY4IaXIbRW+V4uJZ0Rb27i0KlMwoP8mXdHB3o2q1Ou1xQRsdMcVBHxFQWo5SU9BT5+0Pa+x1hofFm5Xu6LX1MY//42MnIsJNQO5bW7OnJR3YhyvaaIyLnsAWqG0kyJiJcUoJYHqxU+GgWZJyG2LfSeWI6XMnjpmz3M+moXAJdeFM3/3d6eGqGB5XZNEZHi2OegaohfRLylALU8rJsLf6wC/xC46XXwL59gMSMnj4f/u43lv6QCcHePBCZek4i/WbtDiUjFO3eI3zAMpbQTEY8pQPW1lJ/h6ym291dPg+hm5XKZP09mcO9bm9mRkk6A2cRT17dicKdG5XItERFX2HeSyrMa5FisBPmbK7lFInK+UoDqSzkZtpRSlhxofi0kDSuXy2zc/xej3t7MibM5RIcH8vIdSXRMKN8FWCIiZQkLLAhIz2ZbFKCKiMcUoPrSysfh+E4Ij4HrXiqXlFKLNyQzadmv5FoMWsZG8updHWlQI8Tn1xERcZe/2Y/gAD+ycq2czc6jVpjmwouIZxSg+srOL2Djq7b318+DsNo+rT7PYuWpz3awcM1+AK5tHct//tbGMaQmIlIVhAX6k5Wbo92kRMQrim584cxRWDbG9r7raLjoSp9WfyojhzHv/cSPe04A8I+rLuaBKy7SAgQRqXLCgvw5cTZHuVBFxCsKUL1lGLbgNOM41L0ErnzCp9XvPnKaEW9t4sCJDEIDzcy8pZ12hhKRKsu+kv+MtjsVES8oQPXWnq9g9wowB8FNr0FAsM+q/nrHEcYu3sqZ7Dwa1gzhtbs60qJepM/qFxHxNftCqQz1oIqIFxSgemv9y7Y/O42AmJY+qdIwDF7+7g+e+/J3DAO6NK7FvDuStOBARKq8gh5UBagi4jkFqN44vsfWg4oJOo/wSZVZuRYeWfozy7YeBmBIl0ZMvu4SApR8X0TOA+HnJOsXEfGUAlRv2FftN+sLtZp4XV1qWhb3vb2Jn/9Mw9/PxBPXXcLQrvFe1ysiUlFC84f4z+ZoDqqIeE4BqqeyT8OWd23vu4z0ujqr1eCuBRvYeeQ0NUMDmDOkA92bRntdr4hIRQpTD6qI+IACVE9tWww5p6F2M2jS2+vq1uw9wc4jp4kI8mfZmEtpVDvUB40UEalYGuIXEV/QxEZPWK2w/hXb+873gZ/338Z31x8A4MYODRScish5KzRIQ/wi4j0FqJ7441s4sRsCI6DdbV5XdyQ9ixXbjwBwexfNORWR85d6UEXEFxSgemLDfNuf7W6HoAivq3t/40EsVoNOCTVpXs/7+kREKktYoNJMiYj3FKC66699sOtL2/vO93ldncVqsGhDMgBD1HsqIue5sPwh/gwN8YuIFxSgumvja4ABF/WB6Iu8rm7VzqMcTsuiZmiAtjAVkfOeVvGLiC8oQHVH9hn46W3b+87ep5YCeHe9rff0bx3jCA4w+6ROEZHKop2kRMQXFKC64+clkJ1mS8p/UR+vq/vzZAbf7jwKwG2dG3ldn4hIZbPPQVUPqoh4QwGqqwwDNuTvHNXpXp+kllq84SCGAZdeFE3j6DCv6xMRqWxhSjMlIj6gANVV+76HYzsgIAzaD/G6ulyLlcUbDwIwpIt6T0WkerCnmcrJs5JrsVZya0TkfKUA1VWO1FK3QXCU19Wt3H6E42eyqRMRRJ+WMV7XJyJSFYQGFmxQqGF+EfGUAlRXnDwAO5fb3vsgtRQU7Bx1a6c4Asz6axC50MydO5fGjRsTHBxMUlISq1evLrHsDz/8QI8ePahduzYhISG0aNGCWbNmFSm3dOlSWrZsSVBQEC1btuTDDz8sz1soVqC/H4H5zzQN84uIpxQZuWLja2BYocnlUKe519X9cewMP+45gZ8JbtXiKJELzpIlSxg3bhwTJ05ky5Yt9OzZk/79+5OcnFxs+bCwMB544AG+//57duzYwWOPPcZjjz3G/PnzHWXWrl3L4MGDGTp0KNu2bWPo0KHccsstrF+/vqJuq6C99nmo6kEVEQ+ZDMMwKrsRvpCenk5UVBRpaWlERkb6ruKcDJiZCFmn4NZF0OIar6t8+rPtvLp6H1e2qMvrwzp530YRqRC+es506dKFDh06MG/ePMexxMRErr/+eqZNm+ZSHTfeeCNhYWG8/bYt9d3gwYNJT0/n888/d5S5+uqrqVmzJosWLXKpTl/d36XTv+HPk5l8MLo7HRrV9LgeEal+XH3OqAe1LL/81xac1mgEF/fzurqsXAv/3fwnAEO6qvdU5EKTk5PD5s2b6du3r9Pxvn37smbNGpfq2LJlC2vWrKFXr16OY2vXri1SZ79+/Vyu05fsqaYysjXELyKe8S+7yAXMMAoWR3W6F/y8T6T/+a8pnMrIpUGNEHpdXNfr+kTk/HL8+HEsFgsxMc6LI2NiYkhNTS313IYNG3Ls2DHy8vKYPHkyI0aMcHyWmprqdp3Z2dlkZ2c7vk5PT3fnVkpkH+JXsn4R8ZR6UEtzYA0c+RUCQqHDUJ9U+e462xyz2zrHYfYz+aROETn/mEzO//4NwyhyrLDVq1ezadMmXn75ZWbPnl1k6N7dOqdNm0ZUVJTjFRcX5+ZdFE/bnYqIt9SDWpoNr9j+bHMLhHg/j+r31HQ2HTiJv5+JWzr65j8CETm/REdHYzabi/RsHj16tEgPaGGNGzcGoHXr1hw5coTJkydz2223AVCvXj2365wwYQLjx493fJ2enu6TINUxxJ+jAFVEPKMe1JKk/Qk7PrW991FqqffW23pP+14SQ93IYJ/UKSLnl8DAQJKSkli5cqXT8ZUrV9K9e3eX6zEMw2l4vlu3bkXqXLFiRal1BgUFERkZ6fTyBXsP6hnNQRURD6kHtSQbXwfDAgk9IeYSr6s7m53HBz8dAmBIl3iv6xOR89f48eMZOnQoHTt2pFu3bsyfP5/k5GRGjRoF2Ho2Dx06xFtvvQXAnDlzaNSoES1atABseVGff/55HnzwQUedY8eO5bLLLmP69OkMGjSIZcuW8dVXX/HDDz9U+P2FK82UiHhJAWpxcrPgpzdt733Ue/rJtsOcyc4joXYo3ZrU9kmdInJ+Gjx4MCdOnGDq1KmkpKTQqlUrli9fTny87ZfXlJQUp5yoVquVCRMmsG/fPvz9/WnatCnPPvssI0eOdJTp3r07ixcv5rHHHmPSpEk0bdqUJUuW0KVLlwq/v1D7HFQN8YuIhxSgFufXpZBxAiIbQnPv854CvLfB9p/N7V0a4afFUSIXvNGjRzN69OhiP1u4cKHT1w8++KBTb2lJbr75Zm6++WZfNM8r4VokJSJe0hzUwgwD1r9se995BJi9j+F//vMUP/+ZRqDZj5uTtDhKRKq3sED7EL/moIqIZxSgFnZwPaT+DP7B0OEun1RpXxx1Tet61AoL9EmdIiJVlYb4RcRbClALW5+fWqr1zRBay+vq0rNyWbb1MABDumpxlIhUfxriFxFvKUA9V3oK7PjY9r7zyNLLuuijLYfIzLVwcUw4HeO1J7WIVH9KMyUi3lKAeq5NC8CaB426QWwbr6szDMOxc9SQLvFl7hIjIlIdFMxBVQ+qiHhGAapdXjZsfsP23keppTYfOMnOI6cJCTBzQ4cGPqlTRKSqs/egaicpEfGUAlS73z6Cs8cgoj4kDvRJle/mL466rm19IoMDfFKniEhVF+4Y4leAKiKeUYBqZ08t1ekeMHsfTP51NofPfkkBYEjXRl7XJyJyvgjNH+LPyrWSZ7FWcmtE5HykABXgz01w+CcwB0KHYT6pcunmP8nJs9K6QRRtGtbwSZ0iIucD+xA/QEauFkqJiPsUoEJBaqlWN0F4Ha+rs1oNx85RQ7qo91RELixB/n745++Yp4VSIuIJBainj8BvH9re+2hx1No/TrDv+FkigvwZ2La+T+oUETlfmEwmRy+qAlQR8YQC1M0LwZoLDTtDgw4+qfLd9QcAuKFDA6ehLhGRC4W2OxURb1zYAWpeDmx63fa+i28S8x9Nz2LFb0cAuF3D+yJygVIPqoh448IOUHd8DGeOQHgMJF7nkyrf33SQPKtBx/iatKgX6ZM6RUTON2FKNSUiXriwA1T74qiO94B/oNfVWawGizYcBJRaSkQubGFBtiH+jBwN8YuI+y7cAPXwFvhzA/gFQNLdPqnyu11HOXQqkxqhAfRvFeuTOkVEzkdhgepBFRHPeRSgzp07l8aNGxMcHExSUhKrV68utfycOXNITEwkJCSE5s2b89ZbbxUpc+rUKcaMGUNsbCzBwcEkJiayfPlyT5rnmvXzbX9ecj1ExPikynfX2VJL/S2pIcEBZp/UKSJyPgrXHFQR8YLbS8yXLFnCuHHjmDt3Lj169OCVV16hf//+bN++nUaNig5rz5s3jwkTJvDqq6/SqVMnNmzYwL333kvNmjUZONC2pWhOTg5XXXUVdevW5X//+x8NGzbk4MGDREREeH+HJek5HoIioM1gn1T358kMvtl5FIDbOmt4X0QubKH5Q/xnNcQvIh5wO0CdOXMmw4cPZ8SIEQDMnj2bL7/8knnz5jFt2rQi5d9++21GjhzJ4MG2QLBJkyasW7eO6dOnOwLUBQsW8Ndff7FmzRoCAmzbjMbHx3t8Uy6JbgbXPOez6pZsPIhhQI+LatOkTrjP6hUROR9pFb+IeMOtIf6cnBw2b95M3759nY737duXNWvWFHtOdnY2wcHBTsdCQkLYsGEDubm5AHz88cd069aNMWPGEBMTQ6tWrXjmmWewWM6P37xzLVYWb8xfHNWlnANrEZHzQHigAlQR8ZxbAerx48exWCzExDjP2YyJiSE1NbXYc/r168drr73G5s2bMQyDTZs2sWDBAnJzczl+/DgAf/zxB//73/+wWCwsX76cxx57jBkzZvD000+X2Jbs7GzS09OdXpXlq+1HOHY6mzoRQVzV0jfzWUVEzmeh9h5UDfGLiAc8WiRlMpmcvjYMo8gxu0mTJtG/f3+6du1KQEAAgwYNYtiwYQCYzbY5Slarlbp16zJ//nySkpK49dZbmThxIvPmzSuxDdOmTSMqKsrxiouL8+RWfOLd9bbFUYM7xhFgvnATI4iI2IXb56CqB1VEPOBWNBUdHY3ZbC7SW3r06NEivap2ISEhLFiwgIyMDPbv309ycjIJCQlEREQQHR0NQGxsLBdffLEjYAVITEwkNTWVnJycYuudMGECaWlpjtfBgwfduRWfOXQqkx/2HMdkgls7V16QLCJSlShRv4h4w60ANTAwkKSkJFauXOl0fOXKlXTv3r3UcwMCAmjYsCFms5nFixczYMAA/Pxsl+/Rowd79uzBarU6yu/atYvY2FgCA4tPoB8UFERkZKTTqzIcOpkJQELtMBrWDK2UNoiIVDX2PKgZOQpQRcR9bo9Hjx8/ntdee40FCxawY8cOHnroIZKTkxk1ahRg69m88847HeV37drFO++8w+7du9mwYQO33norv/76K88884yjzP3338+JEycYO3Ysu3bt4rPPPuOZZ55hzJgxPrjF8mV/+Np3TRERkXNX8WsOqoi4z+00U4MHD+bEiRNMnTqVlJQUWrVqxfLlyx1poVJSUkhOTnaUt1gszJgxg507dxIQEEDv3r1Zs2YNCQkJjjJxcXGsWLGChx56iDZt2tCgQQPGjh3LI4884v0dljP7Nn6hAW5/K0VEqi37L+0a4hcRT3gUVY0ePZrRo0cX+9nChQudvk5MTGTLli1l1tmtWzfWrVvnSXMqlT1ADQlUD6qIiF2Y0kyJiBe05NxLmflD/KEKUEVEHOxD/Bk5FqxWo5JbIyLnGwWoXlIPqohIUeFBBQN0Gbmahyoi7lGA6iV7gGofzhIREQgO8MMvPz22hvlFxF0KUL2UoSF+EZEiTCaT5qGKiMcUoHpJQ/wiIsVTqikR8ZQCVC9l2tNMKUAVEXGiVFMi4ikFqF4q6EHVHFQRkXMVrORXgCoi7lGA6iX76tQw9aCKiDixz0FVD6qIuEsBqpcysrVISkSkOJqDKiKeUoDqJQ3xi4gUzz4HVUP8IuIuBaheyszVIikRkeLYe1A1xC8i7lKA6iV7z0BIgAJUEZFzhQcpD6qIeEYBqpccO0kFaYhfRORc9pGlszmagyoi7lGA6gXDMBwBqob4RUScqQdVRDylANULORYrFqsBaCcpEZHCwhSgioiHFKB6IfOcYatQzUEVEXHiGOJXmikRcZMCVC/Yh/cDzX74m/WtFBE5l2OIX2mmRMRNiqq84Jh/GqTeUxGRwpRmSkQ8pQDVC/YUUxreFxEpyr7Vqeagioi7FKB6oWAXKQWoIiKFOXaS0hxUEXGTAlQvZDpSTCkHqohIYefOQTUMo5JbIyLnEwWoXlAPqohIyULzA1SrUbAttIiIKxSgesE+BzVMAaqISBHnzs9XqikRcYcCVC9kaIhfRKREfn4mxy/wWiglIu5QgOoFDfGLiJROqaZExBMKUL2QaU8zpQBVRKRY9gA1I0dD/CLiOgWoXlAPqohI6eyppjTELyLuUIDqhYz8ValhmoMqIlIs+/NRQ/wi4g4FqF7IyNYQv4hIaQqG+BWgiojrFKB6QUP8IiKlK1gkpTmoIuI6BahesCeeVg+qiEjxwjUHVUQ8oADVC44e1ADNQRURKY49T/RZDfGLiBsUoHqhIFG/elBFRIpjH+JXD6qIuEMBqhccW50GKUAVESlOwRC/5qCKiOsUoHpBQ/wiIqVzDPGrB1VE3KAA1QuZGuIXESlVeJDmoIqI+xSgesgwDMcQvwJUEZHiKc2UiHhCAaqHsvOsWA3be+VBFREpXlj+8zFDQ/wi4gYFqB6yzz+FgjlWIiLiTKv4RcQTClA9ZB/eD/L3w+xnquTWiIhUTQVD/ApQRcR1ClA9pAVSIiJls6fhO5tjwTCMSm6NiJwvFKB6qCBJv4b3RURKYu9BtVgNsvOsldwaETlfKED1kCMHqnpQRURKFHbOL/GahyoirlKA6iHHLlIKUEVESmT2MxEcYPuvRrtJiYirFKB6SD2oIiKuUbJ+EXGXAlQPZWoOqoiIS5RqSkTcpQDVQ/YhfvWgiogn5s6dS+PGjQkODiYpKYnVq1eXWPaDDz7gqquuok6dOkRGRtKtWze+/PJLpzILFy7EZDIVeWVlZZX3rZTJPg9VqaZExFUKUD2UkZvfgxqgAFVE3LNkyRLGjRvHxIkT2bJlCz179qR///4kJycXW/7777/nqquuYvny5WzevJnevXszcOBAtmzZ4lQuMjKSlJQUp1dwcHBF3FKp7Kmmzt3gRESkNBqf9lBG/mR/+9CViIirZs6cyfDhwxkxYgQAs2fP5ssvv2TevHlMmzatSPnZs2c7ff3MM8+wbNkyPvnkE9q3b+84bjKZqFevXrm23RNK1i8i7lIPqoe0SEpEPJGTk8PmzZvp27ev0/G+ffuyZs0al+qwWq2cPn2aWrVqOR0/c+YM8fHxNGzYkAEDBhTpYa0smoMqIu5SgOqhzFzbg1ZD/CLijuPHj2OxWIiJiXE6HhMTQ2pqqkt1zJgxg7Nnz3LLLbc4jrVo0YKFCxfy8ccfs2jRIoKDg+nRowe7d+8usZ7s7GzS09OdXuXBno5PQ/wi4iqNT3tIPagi4g2TyeT0tWEYRY4VZ9GiRUyePJlly5ZRt25dx/GuXbvStWtXx9c9evSgQ4cOvPTSS7z44ovF1jVt2jSmTJni4R24TkP8IuIu9aB6SFudiognoqOjMZvNRXpLjx49WqRXtbAlS5YwfPhw3n//ffr06VNqWT8/Pzp16lRqD+qECRNIS0tzvA4ePOj6jbghXEP8IuImBagecuwkFaQeVBFxXWBgIElJSaxcudLp+MqVK+nevXuJ5y1atIhhw4bx3nvvce2115Z5HcMw2Lp1K7GxsSWWCQoKIjIy0ulVHuy/yGsnKRFxlbr/POQY4tccVBFx0/jx4xk6dCgdO3akW7duzJ8/n+TkZEaNGgXYejYPHTrEW2+9BdiC0zvvvJMXXniBrl27OnpfQ0JCiIqKAmDKlCl07dqVZs2akZ6ezosvvsjWrVuZM2dO5dzkOcLzf5FXD6qIuEoBqoe0k5SIeGrw4MGcOHGCqVOnkpKSQqtWrVi+fDnx8fEApKSkOOVEfeWVV8jLy2PMmDGMGTPGcfyuu+5i4cKFAJw6dYr77ruP1NRUoqKiaN++Pd9//z2dO3eu0HsrTpi2OhURNym68pAWSYmIN0aPHs3o0aOL/cwedNqtWrWqzPpmzZrFrFmzfNAy3ysY4leAKiKu0RxUDxUsklKAKiJSmoJFUpqDKiKuUYDqIcciKQ3xi4iUyr6YVGmmRMRVClA9YBgGmbka4hcRcYXmoIqIuxSgeiAr14ph2N5riF9EpHT2ADVDQ/wi4iIFqB7IOKcXQGmmRERKF54/FSrHYiUnz1rJrRGR84ECVA/YF0gFB/jh51f21oQiIhey0HM2NNFKfhFxhQJUD9gDVC2QEhEpW4DZj0B/2383mocqIq5QgOoB+xC/FkiJiLhGqaZExB0KUD2QqRyoIiJusT8vlWpKRFyhANUDBbtIaYhfRMQV9h7UDA3xi4gLFKB6ICM/B2qoVvCLiLjEkQtVPagi4gIFqB7IyH/AhgUpQBURcYU9QD2jOagi4gIFqB7QEL+IiHvC8uegaohfRFyhANUDmRriFxFxS0EPqgJUESmbAlQPKM2UiIh7wjUHVUTcoADVAxlKMyUi4hb781J5UEXEFQpQPZCR/4C1D1mJiEjptIpfRNyhANUD9jRTIZqDKiLiEscQvxZJiYgLPApQ586dS+PGjQkODiYpKYnVq1eXWn7OnDkkJiYSEhJC8+bNeeutt0osu3jxYkwmE9dff70nTasQmfkPWA3xi4i4RkP8IuIOt8eolyxZwrhx45g7dy49evTglVdeoX///mzfvp1GjRoVKT9v3jwmTJjAq6++SqdOndiwYQP33nsvNWvWZODAgU5lDxw4wMMPP0zPnj09v6MKUJBmSgGqiIgrtEhKRNzhdg/qzJkzGT58OCNGjCAxMZHZs2cTFxfHvHnzii3/9ttvM3LkSAYPHkyTJk249dZbGT58ONOnT3cqZ7FYGDJkCFOmTKFJkyae3U0FKVgkpTmoIiKuUJopEXGHWwFqTk4Omzdvpm/fvk7H+/bty5o1a4o9Jzs7m+DgYKdjISEhbNiwgdzcXMexqVOnUqdOHYYPH+5OkyqFPc1UmHpQRURcYt95z/4LvohIadwKUI8fP47FYiEmJsbpeExMDKmpqcWe069fP1577TU2b96MYRhs2rSJBQsWkJuby/HjxwH48ccfef3113n11Vddbkt2djbp6elOr4qiIX4REfdoFb+IuMOjRVImk8npa8MwihyzmzRpEv3796dr164EBAQwaNAghg0bBoDZbOb06dPccccdvPrqq0RHR7vchmnTphEVFeV4xcXFeXIrHsnUEL+IiFvCAjXELyKucytAjY6Oxmw2F+ktPXr0aJFeVbuQkBAWLFhARkYG+/fvJzk5mYSEBCIiIoiOjmbv3r3s37+fgQMH4u/vj7+/P2+99RYff/wx/v7+7N27t9h6J0yYQFpamuN18OBBd27FK0rULyLiHnsPanaelTyLtZJbIyJVnVtdgIGBgSQlJbFy5UpuuOEGx/GVK1cyaNCgUs8NCAigYcOGgC2V1IABA/Dz86NFixb88ssvTmUfe+wxTp8+zQsvvFBiz2hQUBBBQUHuNN8nrFaDzFwN8YuIuMM+BxXgbI6FqBCl4RaRkrk9Rj1+/HiGDh1Kx44d6datG/Pnzyc5OZlRo0YBtp7NQ4cOOXKd7tq1iw0bNtClSxdOnjzJzJkz+fXXX3nzzTcBCA4OplWrVk7XqFGjBkCR41WBPTgF9aCKiLgqyN9MgNlErsXgbHYeUSEBld0kEanC3A5QBw8ezIkTJ5g6dSopKSm0atWK5cuXEx8fD0BKSgrJycmO8haLhRkzZrBz504CAgLo3bs3a9asISEhwWc3UZHsw/smEwT7K0AVEXFVaKA/aZm5WiglImXyaJXP6NGjGT16dLGfLVy40OnrxMREtmzZ4lb9heuoSuwLpEICzPj5Fb8wTEREigoPyg9QlWpKRMqgSUBuysjVNqciIp6wz0NVD6qIlEUBqpuUA1VExDOhSjUlIi5SgOqmjOz8FFMByoEqIuKO8PxUU/bd+ERESqIA1U32B2tokHpQRUTcYR/iP5OtOagiUjoFqG6yp5nSHFQREfdou1MRcZUCVDc55qBqiF9ExC327U4zFKCKSBkUoLpJ25yKiHjG3oOqIX4RKYsCVDfZf/NXgCoi4p5wpZkSERcpQHVThmMOqob4RUTcYX9untUqfhEpgwJUN2VqiF9ExCPhWiQlIi5SgOome5opJeoXEXFPwSp+zUEVkdIpQHWTFkmJiHjGnj9aQ/wiUhYFqG5SgCoi4hkN8YuIqxSgusmxk5QWSYmIuMWeB1VppkSkLApQ3aRFUiIinglTmikRcZECVDc5dpJSgCoi4hb7IqnMXAsWq1HJrRGRqkwBqpsK5qBqiF9ExB32OahQMF1KRKQ4ClDdVDAHVT2oIiLuCPL3w89ke69UUyJSGgWobtIqfhERz5hMpoJcqOpBFZFSKEB1g8VqkJ1nBTTELyLiCaWaEhFXKEB1Q2ZuwZCUelBFRNxnf3aeUYAqIqVQgOoG+/xTk8k2l0pERNxj70HN0BxUESmFoiw32B+ooQFmTCZTJbdGROT8ozmoIuIKBahucCyQCtL8UxERT9gDVA3xi0hpFKC6ITNXKaZERLwRlv/81BC/iJRGAaobHLtIBShAFRHxhHpQRcQVClDdoByoIiLeUZopEXGFAlQ3FOwipTmoIiKesD8/z+ZoiF9ESqYA1Q3qQRUR8U5YkO35qR5UESmNAlQ3ZCpAFRHxiob4RcQVClDd4FgkpSF+ERGPhCoPqoi4QAGqGzTELyLinXDHEL/moIpIyRSguqFgkZQCVBERT4QFaohfRMqmANUNBT2oGuIXEfGE8qCKiCsUoLpBi6RERLxjD1AzlGZKREqhANUN9iH+EAWoIiIecaSZysnDMIxKbo2IVFUKUN2gRVIiIt6xz0E1DPWiikjJFKC6ITNXAaqIiDdCA82YTLb3SjUlIiVRgOoG+6pTLZISEfGMyWQ6ZyW/elBFpHgKUN2gRVIiIt6zP0OVakpESqIA1Q0ZGuIXEfGatjsVkbIoQHWDtjoVEfFemLY7FZEyKEB1UZ7FSk6eFYDQAPWgioh4yj4KdUZzUEWkBApQXWQf3gcIDVKAKiLiKfsQf4aG+EWkBApQXWRfIGX2MxFo1rdNRLwzd+5cGjduTHBwMElJSaxevbrEsh988AFXXXUVderUITIykm7duvHll18WKbd06VJatmxJUFAQLVu25MMPPyzPW/CYtjsVkbIo0nKRI0l/gBmTPYmfiIgHlixZwrhx45g4cSJbtmyhZ8+e9O/fn+Tk5GLLf//991x11VUsX76czZs307t3bwYOHMiWLVscZdauXcvgwYMZOnQo27ZtY+jQodxyyy2sX7++om7LZY45qBriF5ESmIxqstdceno6UVFRpKWlERkZ6fP6fzucxrUv/kDdiCA2TOzj8/pFpOrz1XOmS5cudOjQgXnz5jmOJSYmcv311zNt2jSX6rjkkksYPHgwjz/+OACDBw8mPT2dzz//3FHm6quvpmbNmixatMilOsv7OWr31Kfbee2HfYy8rAkTrkkst+uISNXj6nNGPaguUg5UEfGFnJwcNm/eTN++fZ2O9+3blzVr1rhUh9Vq5fTp09SqVctxbO3atUXq7Nevn8t1ViQN8YtIWZQvyUVnHQGqvmUi4rnjx49jsViIiYlxOh4TE0NqaqpLdcyYMYOzZ89yyy23OI6lpqa6XWd2djbZ2dmOr9PT0126vreUB1VEyqIeVBdl5ti3OVUPqoh4r/BcdsMwXJrfvmjRIiZPnsySJUuoW7euV3VOmzaNqKgoxysuLs6NO/CcPROK/Rd/EZHCFKC6qCBJvwJUEfFcdHQ0ZrO5SM/m0aNHi/SAFrZkyRKGDx/O+++/T58+znPh69Wr53adEyZMIC0tzfE6ePCgm3fjGfWgikhZFKC6KENzUEXEBwIDA0lKSmLlypVOx1euXEn37t1LPG/RokUMGzaM9957j2uvvbbI5926dStS54oVK0qtMygoiMjISKdXRQgLVIAqIqXThEoXZWoOqoj4yPjx4xk6dCgdO3akW7duzJ8/n+TkZEaNGgXYejYPHTrEW2+9BdiC0zvvvJMXXniBrl27OnpKQ0JCiIqKAmDs2LFcdtllTJ8+nUGDBrFs2TK++uorfvjhh8q5yVJoiF9EyqIeVBfZ94zWEL+IeGvw4MHMnj2bqVOn0q5dO77//nuWL19OfHw8ACkpKU45UV955RXy8vIYM2YMsbGxjtfYsWMdZbp3787ixYt54403aNOmDQsXLmTJkiV06dKlwu+vLBriF5GyqDvQRfYe1DAFqCLiA6NHj2b06NHFfrZw4UKnr1etWuVSnTfffDM333yzly0rf0ozJSJlUQ+qiwoWSSmmFxHxxrlzUKvJXjEi4mMKUF2kRVIiIr4Rlj8H1WpAdp61klsjIlWRAlQXZeYqD6qIiC+cu9hUw/wiUhwFqC46m50/xB+gAFVExBtmP5PjWaqFUiJSHAWoLnIskgrSHFQREW+FOVbyK9WUiBSlANVFGblKMyUi4ivhjlyo6kEVkaIUoLrIsUhKQ/wiIl6zz0PVHFQRKY4CVBdpJykREd+xJ+vP0BC/iBRDAaqL7BP5NcQvIuI9e6opLZISkeIoQHVRZq59kZQCVBERb4VqNykRKYUCVBfkWqzkWmy7nYQGaIhfRMRb4fnTpTK0SEpEiqEA1QX2BVKgIX4REV8Ic/Sgag6qiBSlANUF9gVS/n4mAv31LRMR8Va45qCKSCkUbbnAnqdPvaciIr5hn4OqPKgiUhwFqC5w7CKlFFMiIj5RsJOUAlQRKUoBqgscSfrVgyoi4hMFQ/yagyoiRSlAdUGGhvhFRHzKvumJhvhFpDgKUF2QqR5UEZGidn8Fr/eFTx9y+9RwDfGLSCk0qdIFZ/MD1BDNQRURKWDJgYPrIS/L7VML5qBqiF9EilIPqgsy84egwtSDKiJSoPZFtj9P7AXDcOtU+/NUO0mJSHE8ClDnzp1L48aNCQ4OJikpidWrV5dafs6cOSQmJhISEkLz5s156623nD5/9dVX6dmzJzVr1qRmzZr06dOHDRs2eNK0cpHh6EFVgCoi4lAzAUxmyDkDp1PdOtXeg6qdpESkOG4HqEuWLGHcuHFMnDiRLVu20LNnT/r3709ycnKx5efNm8eECROYPHkyv/32G1OmTGHMmDF88sknjjKrVq3itttu49tvv2Xt2rU0atSIvn37cujQIc/vzIe0il9EpBj+gVAz3vb+xB63TrUHqLkWg+w8DfOLiDO3A9SZM2cyfPhwRowYQWJiIrNnzyYuLo558+YVW/7tt99m5MiRDB48mCZNmnDrrbcyfPhwpk+f7ijz7rvvMnr0aNq1a0eLFi149dVXsVqtfP31157fmQ9l5toDVM1BFRFx4hjmdzNAPecXfs1DFZHC3ApQc3Jy2Lx5M3379nU63rdvX9asWVPsOdnZ2QQHBzsdCwkJYcOGDeTm5hZ7TkZGBrm5udSqVavEtmRnZ5Oenu70Ki/2VaYhAepBFRFx4mGA6m/2Iyh/62it5BeRwtwKUI8fP47FYiEmJsbpeExMDKmpxc8/6tevH6+99hqbN2/GMAw2bdrEggULyM3N5fjx48We8+ijj9KgQQP69OlTYlumTZtGVFSU4xUXF+fOrbjFsZNUkAJUEREntZva/jyx1+1Tw7XdqYiUwKNFUiaTyelrwzCKHLObNGkS/fv3p2vXrgQEBDBo0CCGDRsGgNlcNOB77rnnWLRoER988EGRntdzTZgwgbS0NMfr4MGDntyKSzKUZkpEpHi1m9n+PLHb7VNDHbtJKUAVEWduBajR0dGYzeYivaVHjx4t0qtqFxISwoIFC8jIyGD//v0kJyeTkJBAREQE0dHRTmWff/55nnnmGVasWEGbNm1KbUtQUBCRkZFOr/KSYZ+DqiF+ERFn9iH+k/vBUvy0rZKEBSoXqogUz60ANTAwkKSkJFauXOl0fOXKlXTv3r3UcwMCAmjYsCFms5nFixczYMAA/PwKLv+f//yHJ598ki+++IKOHTu606xyZ8+DqlX8IiKFRMRCQChY8+BU8dlcSqLdpESkJG6PWY8fP56hQ4fSsWNHunXrxvz580lOTmbUqFGAbej90KFDjlynu3btYsOGDXTp0oWTJ08yc+ZMfv31V958801Hnc899xyTJk3ivffeIyEhwdFDGx4eTnh4uC/u0yv23+6VB1VEpBA/P6jVFI78YlsoZZ+T6oLQ/ABVyfpFpDC3A9TBgwdz4sQJpk6dSkpKCq1atWL58uXEx9ty4aWkpDjlRLVYLMyYMYOdO3cSEBBA7969WbNmDQkJCY4yc+fOJScnh5tvvtnpWk888QSTJ0/27M58yJ5myp63T0REzlH7nACVfi6fFp4/B9U+z19ExM6jiGv06NGMHj262M8WLlzo9HViYiJbtmwptb79+/d70owKY9/pRGmmRESKEZ2/UOq4ewul7HNQ1YMqIoV5tIr/QqOdpERESuFpsn7NQRWREihALYNhGI48qNpJSkSkGI4A1b1cqGEa4heREihALUOOxUqe1QC0SEpEpFi1mtj+PH0Yss+4fFqYFkmJSAkUoJYh85zf7DXELyJSjNBaEFrb9v6vP1w+TWmmRKQkClDLYB96CjT7EWDWt0tEpFge7ChlnzZ1VkP8IlKIIq4yFGxzqt5TEZESeTAPNVxbnYpICRSgliFTK/hFRMpmT9Dvxkp+reIXkZIoQC3DWXsOVAWoIiIl8yDVVMEQvwJUEXGmALUM9h7UMKWYEhEp2bkBqmG4dErBIinNQRURZwpQy6A5qCIiLqjVBDBBVhqcPe7SKfY8qEozJSKFKUAtg32bU81BFREpRUAw1IizvXdxmN8+MpWTZyXXYi2vlonIeUgBahkyc7VISkTEJW7OQ7UvkgLI0DC/iJxDAWoZ7HOjQgI0B1VEpFRuBqiB/n4E5ueXPqOFUiJyDgWoZcjMf2ja50qJiEgJPFnJr1yoIlIMBahl0CIpEREXeZCs3z4PVQGqiJxLAWoZMuxzUDXELyJSOnuA+tcfYHVtTqlSTYlIcRSglkE7SYmIuCiqIZiDwJINaQddOiVUqaZEpBgKUMtgH3bSEL+ISBn8zPn5UHF5Hqq9BzVDi6RE5BwKUMugNFMiIm6o3dT2p4vzUDUHVUSKowC1DBmOIX7NQRURKVN0M9ufLvagFgzxaw6qiBRQgFqGDM1BFRFxnX2h1PHdLhXXEL+IFEcBahkytdWpiIjr3Ew1Zd9NSoukRORcClDLcFZ5UEVEXGcPUNMOQm5mmcXDApWoX0SKUoBahkzNQRURcV1obQiOAgz4a1+Zxe09qPbOABERUIBaKsMwHPOiwtSDKiJSNpPJrS1PHQGqelBF5BwKUEuRnWfFatjea4hfRMRFte0r+cteKBWuAFVEiqEAtRSZ5ww5aYhfRMRFbiyUCnXMQdUQv4gUUIBairP5w/uB/n6Y/UyV3BoRkfOEI1l/2UP8jh5UpZkSkXMoQC1FpnKgioi4T3NQRcRLClBLYU/SH6bhfRER19l7UDNOQMZfpRa1P1+VB1VEzqUAtRQZyoEqIuK+wDCIbGB7X8Y81LD8rU6zcq1Y7KtSReSCpwC1FJm52kVKRMQjLs5DtQ/xg+ahikgBBailsK8qDQlQgCoi4hYX56EGnbMIVfNQRcROAWoptEhKRMRDLgaoJpPpnO1OlWpKRGwUoJbCvotUaJAWSYmIuMWNXKhK1i8ihSlALUVGbn4Pqob4RUTcc24PqtVaatFQBagiUogC1FJoiF9ExEM14sHPH/Iy4fThUos6cqHmaIhfRGwUoJbCsUhKeVBFRNxj9oeajW3vy5iHGh5kn4OqHlQRsVGAWgqlmRIR8YKLC6VClaxfRApRgFqKDA3xi4h4zpELtfSFUvZFUhnKgyoi+RSglqIgQNUQv4iI26Kb2f48vrvUYvbdpM4ozZSI5FOAWgotkhIR8YKLQ/xhgVrFLyLOFKCWwr7tXogCVBHxsblz59K4cWOCg4NJSkpi9erVJZZNSUnh9ttvp3nz5vj5+TFu3LgiZRYuXIjJZCryysrKKse7KIM9QD11APJySiwWpiF+ESlEAWop1IMqIuVhyZIljBs3jokTJ7JlyxZ69uxJ//79SU5OLrZ8dnY2derUYeLEibRt27bEeiMjI0lJSXF6BQcHl9dtlC08BgLDwbDCyf0lFrMHqBriFxE7Bail0BxUESkPM2fOZPjw4YwYMYLExERmz55NXFwc8+bNK7Z8QkICL7zwAnfeeSdRUVEl1msymahXr57Tq1KZTOcslCp5mF9ppkSkMAWopdAqfhHxtZycHDZv3kzfvn2djvft25c1a9Z4VfeZM2eIj4+nYcOGDBgwgC1btpRaPjs7m/T0dKeXz9XOXyh1ouSFUqGagyoihShALUVmjvKgiohvHT9+HIvFQkxMjNPxmJgYUlNTPa63RYsWLFy4kI8//phFixYRHBxMjx492L275MBw2rRpREVFOV5xcXEeX79ELiyUCnfsJKUAVURsFKCWwDAMMnLtO0kpQBUR3zKZTE5fG4ZR5Jg7unbtyh133EHbtm3p2bMn77//PhdffDEvvfRSiedMmDCBtLQ0x+vgwYMeX79EjgC15Fyojq1ONQdVRPJpcmUJsnKtGIbtveagioivREdHYzabi/SWHj16tEivqjf8/Pzo1KlTqT2oQUFBBAUF+eyaxXJhDqp9lEpD/CJipx7UEpyb7iQkQD2oIuIbgYGBJCUlsXLlSqfjK1eupHv37j67jmEYbN26ldjYWJ/V6RF7D+qZI5BV/BxXxxC/AlQRyaeuwRLYF0gFB/hh9vN82E1EpLDx48czdOhQOnbsSLdu3Zg/fz7JycmMGjUKsA29Hzp0iLfeestxztatWwHbQqhjx46xdetWAgMDadmyJQBTpkyha9euNGvWjPT0dF588UW2bt3KnDlzKvz+nARH2tJNnTli60Vt0KFIEccQf44Fq9XAT89ckQueAtQSZOYqxZSIlI/Bgwdz4sQJpk6dSkpKCq1atWL58uXEx8cDtsT8hXOitm/f3vF+8+bNvPfee8THx7N//34ATp06xX333UdqaipRUVG0b9+e77//ns6dO1fYfZWo9kX5AereYgNUew8qQFpmLjXDAiuydSJSBSn6KoF9qEnD+yJSHkaPHs3o0aOL/WzhwoVFjhn2SfElmDVrFrNmzfJF03yvdlM48GOJ81BDAs0k1A5l/4kMthw8yRUtfDcXV0TOT5qDWgLtIiUi4iMupJrq0rg2AOv/+KsiWiQiVZwC1BI4kvQHqZNZRMQrrgSoTWoBsG6fAlQRUYBaInsO1FAN8YuIeMexm9QeKGGqQpcmth7UXw+lcUar+UUueApQS6BdpEREfKRmApj8IOeMbbFUMRrUCCGuVggWq8Gm/epFFbnQKUAtgX1HE+0iJSLiJf9AqGHLUODSPFQN84tc8BSglqAgzZQCVBERr7m0UCp/HuofJyqiRSJShSlALUGGY4hfi6RERLzmQoDaNX8e6i9/pjnt5iciFx4FqCXIUJopERHfibYHqHtLLBJXK5QGNULIsxpsPnCyghomIlWRAtQSKA+qiIgP2XtQj+8utZiG+UUEFKCW6GyOfZGUhvhFRLxmD1BP7gNLycP39mF+JewXubApQC2B0kyJiPhQRH3wDwFrHpw6UGIxe8L+bX+ecoxkiciFRwFqCTQHVUTEh/z8oHZT2/tS5qE2qhVKvchgci0GPyVrHqrIhUoBagkKAlQN8YuI+IQLK/lNJhNd83tR12seqsgFSwFqCbRISkTExxwBahkLpfLnoa5Twn6RC5YC1BKczZ+Dqp2kRER8xIUeVChYyb81+RRZuZqHKnIhUoBaAvWgioj4WO2yc6ECNI4Oo25EEDkWK1uST5V/u0SkylGAWgLHHNQAzUEVEfEJ+yKp9EOQc7bEYiaTyTHMv36f5qGKXIgUoBbDajXIzB9WCg1SD6qIiE+E1oJQW+DJX3+UWlQJ+0UubApQi5GVVzDnSUP8IiI+5OKOUvaE/VuST5Gdp3moIhcaBajFOJtd8DAM9leAKiLiMy7OQ21aJ4zo8CCy86xsO5hWAQ0TkapEAWox7AukQgLM+PmZKrk1IiLViCNZf+kr+U0mk4b5RS5gClCLkZGrbU5FRMqFi6mmgIKE/VooJXLB8ShAnTt3Lo0bNyY4OJikpCRWr15davk5c+aQmJhISEgIzZs356233ipSZunSpbRs2ZKgoCBatmzJhx9+6EnTfMKxgl8LpEREfKt2M9ufJ3aDYZRa1L6Sf/OBk+TkWcu7ZSJShbgdoC5ZsoRx48YxceJEtmzZQs+ePenfvz/JycnFlp83bx4TJkxg8uTJ/Pbbb0yZMoUxY8bwySefOMqsXbuWwYMHM3ToULZt28bQoUO55ZZbWL9+ved35oVMpZgSESkftRoDJshKg4zSe0ab1Q2nVlggWblWfv7zVIU0T0SqBrcD1JkzZzJ8+HBGjBhBYmIis2fPJi4ujnnz5hVb/u2332bkyJEMHjyYJk2acOuttzJ8+HCmT5/uKDN79myuuuoqJkyYQIsWLZgwYQJXXnkls2fP9vjGvGHvQdUuUiIiPhYQAlFxtvduzENdr21PRS4obgWoOTk5bN68mb59+zod79u3L2vWrCn2nOzsbIKDg52OhYSEsGHDBnJzcwFbD2rhOvv161dinfZ609PTnV6+kpGjOagiIuXGxYVSoHyoIhcqtwLU48ePY7FYiImJcToeExNDampqsef069eP1157jc2bN2MYBps2bWLBggXk5uZy/PhxAFJTU92qE2DatGlERUU5XnFxce7cSqkytM2piEj5cWOh1LnzUHMtmocqcqHwaJGUyeSceskwjCLH7CZNmkT//v3p2rUrAQEBDBo0iGHDhgFgNhcEgO7UCTBhwgTS0tIcr4MHD3pyK8UqCFA1B1VExOei7Qulyg5Qm8dEUCM0gIwcC78cUj5UkQuFWwFqdHQ0ZrO5SM/m0aNHi/SA2oWEhLBgwQIyMjLYv38/ycnJJCQkEBERQXR0NAD16tVzq06AoKAgIiMjnV6+kqkhfhGR8mMf4j9edoDq52eic0L+PNQ/NA9V5ELhVoAaGBhIUlISK1eudDq+cuVKunfvXuq5AQEBNGzYELPZzOLFixkwYAB+frbLd+vWrUidK1asKLPO8qJFUiIi5cg+xP/XH2AtextT+zC/5qGKXDjcHsMeP348Q4cOpWPHjnTr1o358+eTnJzMqFGjANvQ+6FDhxy5Tnft2sWGDRvo0qULJ0+eZObMmfz666+8+eabjjrHjh3LZZddxvTp0xk0aBDLli3jq6++4ocffvDRbbpHc1BFRMpRVByYA8GSDWl/Qs34UovbE/Zv2v8XeRYr/mbtMSNS3bkdoA4ePJgTJ04wdepUUlJSaNWqFcuXLyc+3vaASUlJccqJarFYmDFjBjt37iQgIIDevXuzZs0aEhISHGW6d+/O4sWLeeyxx5g0aRJNmzZlyZIldOnSxfs79EDBKn7NQRUR8Tk/M9RqAsd+t81DLSNAbVEvkshgf9Kz8vjtcDpt42pUTDtFpNJ4FIGNHj2a0aNHF/vZwoULnb5OTExky5YtZdZ58803c/PNN3vSHJ9TD6qISDmrfVF+gLoXLrqy1KJmPxOdG9fiqx1HWffHCQWoIhcAjZMUI1MBqohI+XKkmtrtUvGu+fNQlbBf5MKgALUYBYukNMQvIlIu3MiFCtClsS1A3bjvLyxWo7xaJSJVhALUYjjmoAaoB1VEpFy4GaC2rB9JRJA/p7Pz2H7YdzsHikjVpAC1GJqDKiJSzuwB6qmDkJtVZnGzn4lO+duert+ndFMi1Z0C1GI4AtQgDfGLiJSLsGgIigIMOLnPpVO65Aeo65SwX6TaU4BajMxc9aCKiJQrkwmi83tRj7u2UMqesH/j/r+wah6qSLWmALUY9jmoIZqDKiJSftych9qqfiRhgWbSMnPZkap5qCLVmQLUQixWg6xcK6AeVBGRcuUIUPe6VNzf7EfHhPx5qBrmF6nWFKAWYh/eB+0kJSJSrmo3tf3pYg8qQJcmWiglciFQgFqIfXjfZILgAH17RETKjZtD/OCcsF/zUEWqL0VghTh2kQowYzKZKrk1IiLVWK38HtSM45B50qVTWjeIIjTQzKmMXHYdPV2OjRORyqQAtRDtIiUiUkGCwiGivu29i/NQA8x+JMXXBDQPVaQ6U4BaiGMXKS2QEhEpfx7MQ7UP86/7Q/NQRaorBaiFaBcpEZEK5ME8VHvC/g37/sIwNA9VpDpSgFqIAlQRkQrkQYDapmENggP8OHE2hz1Hz5RTw0SkMilALcSxSEpzUEVEyl90M9ufbgSogf4F81A1zC9SPSlALaRgkZR6UEVEyt25yfqtVpdP69I4fx7qPi2UEqmOFKAWokVSIiIVqEYj8POH3Aw4neLyafZ5qOv/0DxUkepIAWohmoMqIlKBzAFQM8H23o1h/rZxNQjy9+P4mWz2HjtbPm0TkUqjALWQDM1BFRGpWB4slAoOMNO+UQ1A256KVEcKUAvJ1BC/iEjFOnceqhvs81CVsF+k+lGAWogWSYmIVDBHgLrbrdPOTdiveagi1YsC1EIcQ/wBClBFRCqEB0P8AO0b1SDQ7MfR09nsP5FRDg0TkcqiALWQglX8moMqIlIh7AHqyQOQlebyacEBZtrF1QBgvfKhilQrClALcfSgBqkHVUSkQkTUgzotwLDAjy+4dWrXJrZ0U0rYL1K9KEAtJDNXaaZERCqUyQRXPm57v3YupLuRDzV/Hur6fcqHKlKdKEAtxLFIKkBD/CIiFab5NRDXBfIy4btnXT6tQ6OaBJhNpKRlcfCvzHJsoIhUJAWohWRkK82UiEiFM5ngqqm29z+9Dcd2uXRaSKCZNg1rABrmF6lOFKAWkqEhfhGRytGoKzS/1jYX9ZupLp/mmIeqhP0i1YYC1EIKFklpiF9EpMJd+TiY/GDHJ3Bwg0unKGG/SPWjAPUcFqtBTp4VUB5UEZFKUbcFtBtie7/yCXBh4VNSfE3MfiYOncrk4F/KhypSHShAPYc9BypoJykRkUpz+QTwD4bkNbDryzKLhwX506ZhFGBbzS8i5z8FqOewD+/7mSDIX98aEZFKEdUAut5ve//VZLBayjylYJhf81BFqgNFYedwzD8N9MdkMlVya0RELmA9xkFwDTi2A7YtLrN4Fy2UEqlWFKCewz7Er+F9EZFKFlIDLnvY9v7bpyG39BynHfPnoR78K5PDp5QPVeR8pwD1HJn5PahhClBFRCpfp3shsiGkH4IN80stGhEcQKv6kQCsVy+qyHlPAeo5HLtIBSrFlIhIpQsIhism2t6vngGZJ0stbt/2dN1eLZQSOd8pQD2HfYhfSfpFRKqINoOh7iWQlQY/zCq1qD1hv3pQRc5/ClDPUbBISgGqiEiV4GeGPpNt79e9DGl/lli0Y0It/Eyw/0QGqWlZFdM+ESkXClDP4RjiV5J+EZGqo9lVEH8pWLLh22klFosMDuCS+rZ8qMu2Hqqo1olIOVCAeg7HIiltcyoi5Wzu3Lk0btyY4OBgkpKSWL16dYllU1JSuP3222nevDl+fn6MGzeu2HJLly6lZcuWBAUF0bJlSz788MNyan0FM5ngqim299vegyPbSyw6tGs8AC9+vZuUNK3mFzlfKUA9R8EiKfWgikj5WbJkCePGjWPixIls2bKFnj170r9/f5KTk4stn52dTZ06dZg4cSJt27YttszatWsZPHgwQ4cOZdu2bQwdOpRbbrmF9evXl+etVJyGHSHxOjCs8PXUEovdnNSQpPianM2x8NSnOyqwgSLiSwpQz+FYJKUhfhEpRzNnzmT48OGMGDGCxMREZs+eTVxcHPPmzSu2fEJCAi+88AJ33nknUVFRxZaZPXs2V111FRMmTKBFixZMmDCBK6+8ktmzZ5fjnVSwK58Akxl2fQ4H1hRbxM/PxFPXt8LsZ+KzX1L4btexCm6kiPiCAtRzaJGUiJS3nJwcNm/eTN++fZ2O9+3blzVrig+6XLF27doidfbr16/UOrOzs0lPT3d6VWnRF0HSXbb3K58Awyi2WGJsJMO6JwDwxLJfycote6tUEalaFKCeQ3lQRaS8HT9+HIvFQkxMjNPxmJgYUlNTPa43NTXV7TqnTZtGVFSU4xUXF+fx9StMr0cgIBT+3AC/f1pisXF9mhETGcT+Exm8/N3eCmygiPiCAtRzZObahvjDgtSDKiLly2QyOX1tGEaRY+Vd54QJE0hLS3O8Dh486NX1K0REPeg2xvb+qylgySu+WHAAkwa0BGDuqr3sP362olooIj6gAPUcSjMlIuUtOjoas9lcpGfz6NGjRXpA3VGvXj236wwKCiIyMtLpdV7o/ncIrQ0ndsPWd0osdm3rWHo2iyYnz8rjH/+GUcKUABGpehSgniMj2z4HVUP8IlI+AgMDSUpKYuXKlU7HV65cSffu3T2ut1u3bkXqXLFihVd1VlnBkXDZv2zvv50GORnFFjOZTEy57hICzX58v+sYX/zq+RQKEalYClDPkZGrrU5FpPyNHz+e1157jQULFrBjxw4eeughkpOTGTVqFGAber/zzjudztm6dStbt27lzJkzHDt2jK1bt7J9e0E+0LFjx7JixQqmT5/O77//zvTp0/nqq69KzJl63ut4N9SIhzOpsL747AcATeqEM+rypgBM+WQ7Z7KLnxIgIlWLAtRzKA+qiFSEwYMHM3v2bKZOnUq7du34/vvvWb58OfHxtiTzKSkpRXKitm/fnvbt27N582bee+892rdvzzXXXOP4vHv37ixevJg33niDNm3asHDhQpYsWUKXLl0q9N4qjH8QXDHJ9v6H2XD2RIlFR1/elEa1QklNz+LFr3dXTPtExCsmo5pMyklPTycqKoq0tDSP51F1m/Y1KWlZfPLApbRuWHyuQRG5cPniOVOVnXf3Z7XC/F6Q+jN0HQNXP1Ni0W93HuXuNzZi9jOx/O89aV4vogIbKiJ2rj5n1IN6DvWgioicR/z8CrZA3fgqnDxQYtHezety9SX1sFgNHvvoFy2YEqniFKCew7GTlAJUEZHzQ9MroMnlYMmBb0vuQQV4fGBLQgPNbNx/kqU/HaqY9omIRxSg5su1WMm12H6jVoAqInIe6TPZ9ufPSyD1lxKL1a8RwtgrmwHwzPIdnMrIqYDGiYgnFKDmsw/vg4b4RUTOK/XbQ6ubAAO+mlxq0XsubUyzuuH8dTaH577cWSHNExH3KUDNl5kfoPr7mQg069siInJeueIx8AuAPV/BH9+VWCzA7MdT17cCYNGGZLYkn6yoFoqIGxSJ5bPPPw0JNHu93aCIiFSwWk2g4z229189AaUsgurSpDY3dmiAYcBjH/2KxaoFUyJVjQLUfPYhfs0/FRE5T132TwgMh8NbYM2LpRb99zWJRAb789vhdN5ZV/LqfxGpHApQ8xUEqNrmVETkvBReB6583PZ+5eOw9b0Si0aHB/Gvq1sA8PyXOzl6OqsiWigiLlKAms8xxB+gHlQRkfNWl5HQ/UHb+2UPwM7PSyx6W+dGtG0YxensPJ75bEcFNVBEXKEANZ99kVRYkAJUEZHz2lVPQtvbwbDAf4fBgbXFFjP7mXjq+taYTPDR1sOs2Xu8YtspIiVSgJqvYBcpDfGLiJzXTCa47iW4+GrIy4L3BkPqr8UWbd0wiqFd4wGY9NGv5ORZK7KlIlICBaj5HLtIaYhfROT8Z/aHm9+ARt0gOw3euRFO7i+26D/6Nic6PJC9x87y6uo/KradIlIsBaj5tIpfRKSaCQyF2xZD3UvgzBF463o4c7RIsaiQACZemwjAS9/s5uBfGRXcUBEpTAFqvoIhfgWoIiLVRkgNGPoB1GgEJ/fZelKz0ooUu75dA7o0rkVWrpUpn2yv+HaKiBMFqPkyc+2LpDQHVUSkWomoB0M/grA6kPoLLLodcp3TSplMJp66vhX+fia+2nGElduPVE5bRQRQgOqgNFMiItVY7aZwx1IIjIADP8DS4WDJcyrSLCaCET2bADD5498c2V1EpOIpQM2Xka05qCIi1VpsW7htEZgD4fdP4bOHimyJ+vcrL6J+VDCHTmXyf9/urqSGiogC1HxaJCUicgFo3BNuXgAmP/jpLfh6qtPHoYH+PHHdJQDM//4P9hw9UxmtFLngKUDNl5GrPKgiIheExIEwYLbt/Q8zYe0cp4/7tozhyhZ1ybUYjHpnM8fPZFd8G0UucApQ82Xmz0ENUw+qiEj1l3QXXPm47f2X/4Ztix0fmUwmnry+FbFRwew5eoY7XlvPqYycSmqoyIVJAWo+pZkSEbnAXDoeuo62vf9oNOz60vFR/RohvDuiC3Uigvg99TRDX99AelZuJTVU5MKjADVfwRxUDfGLiFwQTCbo+zS0GQyGBd6/C5LXOT5uUiec90Z0oVZYIL8cSmPYgg2cyc4rpUIR8RUFqPkcW52qB1VE5MLh5weD5kCzvpCXCe/dAkcKEvU3i4ngneFdiAoJ4KfkUwxfuFHpp0QqgALUfBriFxG5QJkD4G9vQlwX2y5T79wIJw84Pm5ZP5K37ulMRJA/6/f9xX1vbyIrV0GqSHnyKECdO3cujRs3Jjg4mKSkJFavXl1q+XfffZe2bdsSGhpKbGwsd999NydOnHAqM3v2bJo3b05ISAhxcXE89NBDZGVllVCj79l/Iw7TEL+IyIUnMBRuW/z/7d15WFTV/8Dx98yw7yjIIqviDmqCG+S+kppmFrmbW2bulUumaWm2ufxMsU20zK9ballSLqRGQLliLmioKIoQ4gIqqzC/P0ZGR3BBgUHm83qe+8zMvefee+51PHzm3LOAYz24ngyrXoAbl7SbG7nbseLVpliYqIiMT+ON1QfJvVWgxwwLUbmVOEBdt24dEyZMYPr06Rw6dIhWrVoRHBxMYmJisen//PNPBg0axLBhwzh27BgbNmxg3759DB8+XJtm9erVTJ06lffee4+4uDiWL1/OunXrmDZt2uNfWQnk3irgVoFmsGapQRVCCANlUQUGbgJbD7hyGla/CNkZ2s0BXlVYPrgppkZKIk6kMn7tIW7lS5AqRFkocYC6YMEChg0bxvDhw6lXrx6LFi3C3d2dZcuWFZv+r7/+wsvLi3HjxuHt7c2zzz7La6+9xv79+7VpYmJiCAoKol+/fnh5edG5c2f69u2rk6YsFbY/BWmDKoQQBs3GFQZuBouqkHwYljaHAyu106K2rFmVrwYFYKJS8uvRFN7ccJj8AvWDjymEKLESBai5ubkcOHCAzp0766zv3Lkz0dHRxe4TGBjIhQsXCA8PR61W899///HDDz/QrVs3bZpnn32WAwcOsHfvXgDOnDlDeHi4Tpp75eTkkJGRobM8rsL2p8YqBcYqaZYrhBAGzcEHBmwEW3e4fhF+Hg+hzeHYZigooE1tR0L7N8FIqeCn2ItM2/QPBRKkClGqShSNpaWlkZ+fj5OTk856JycnUlJSit0nMDCQ1atXExISgomJCc7OztjZ2fH5559r07zyyit88MEHPPvssxgbG1OzZk3atWvH1KlT75uXefPmYWtrq13c3d1Lcik6tB2kjKX2VAghBOD6DIzZD13maWpTL5+CDUPg63Zw+nc61qvG4r7PoFTA+v0XmLnlKGq1BKlClJbHqi5UKBQ6n9VqdZF1hY4fP864ceOYOXMmBw4c4LfffiMhIYFRo0Zp0+zevZu5c+cSGhrKwYMH2bRpE7/88gsffPDBffMwbdo00tPTtcv58+cf51KAuzpImUoHKSGEELcZm0HL0TAuFtpMBRMrSI7VdKD67nmes7/Igpcbo1DA938lMmdrnASpQpSSEkVkDg4OqFSqIrWlqampRWpVC82bN4+goCDefvttABo2bIilpSWtWrVizpw5uLi4MGPGDAYOHKjtOOXn58fNmzcZOXIk06dPR6ksGkebmppiampakuzfV2EbVOkgJYQQoggzG2g3DZqNgMj5sO8bSPgDvmlPr3o9MO00nNe332T5nwmYGSt5u0tdfedYiKdeiWpQTUxM8Pf3Z8eOHTrrd+zYQWBgYLH7ZGZmFgkwVSpNIFj4S/N+adRqdbn8Gr0zi5QEqEIIIe7D0gG6zoOxB6Bxf1AoIe5ngiNfYGfNdbiSxtJdp/k8Il7fORXiqVfiR/yTJk3im2++ISwsjLi4OCZOnEhiYqL2kf20adMYNGiQNn2PHj3YtGkTy5Yt48yZM0RFRTFu3DiaNWuGq6urNs2yZctYu3YtCQkJ7NixgxkzZvD8889rg9mypA1QjeURvxBCiIew84BeofB6NNTtDuoCfJJ+ItL8Td41WkXYjv18uee0vnMpxFOtxBFZSEgIly9f5v333yc5ORlfX1/Cw8Px9PQEIDk5WWdM1CFDhnD9+nWWLFnCm2++iZ2dHe3bt+fjjz/Wpnn33XdRKBS8++67JCUl4ejoSI8ePZg7d24pXOLDySN+IYQQJVatHryyGs7vg52zUJ37k+FGvxKi2s3X27vxPRMZ0MZX37kU4qmkUFeSFt0ZGRnY2tqSnp6OjY1Nifb9LuYsM386xnN+zoT29y+jHAohnnZPUs48DSr79ZUptRpOR8DO2ZDyDwBpahvONRiNf+9JYFQ6fSaEeNo9ajkjg35y9zBT8ohfCCHEY1AowKcjjNyDus8KLpu646DIwP/4R9yc3xhi/6cd7F8I8XASoAKZOZpCQzpJCSGEeCJKJQrf3lR5+yBb3CeTorbHMusi/Pi6ZrD/Iz9AQb6+cylEhScBKtKLXwghROlSGJnQ/dV3WOq7nnl5fbmittIM9r9xGCwLgmM/QkGBvrMpRIUlASqQmXf7Eb8EqEIIIUqJUqlg9otNyW85jlY5/8dneS9xHUu4FAcbBsOXreFEuKb9qhBChwSo3JlJSmpQhRBClCalUsG73esTOrQN6yxeISh7EUvyXyRXZQn/HYG1feHr9hC/UwJVIe4iASp3hpmyMJFOUkIIIUpfm9qO/Da+Fc3r1+CzvBdpdnMBW6xfocDYAi4ehNUvQlgXOLNbAlUhkAAVkDaoQgghyl5VK1O+GujPhy/4kW1sy7hLz9Ph1mISag0FIzM4/zd81xNWdodz0frOrhB6JQEqEqAKIYQoHwqFgn7NPfhlbCt8q9uQkGVBuyMdmeuzhjz/EaAygXN/wopgTbB6fp++syyEXkiAyl3joMojfiGEEOXAp5oVm14PYlSbmigU8HVsFp1Pduf4S39AwFBQGmse9y/vCKtfgouH9J1lIcqVBKhAVq6MgyqEEKJ8mRgpmRpcl/8Nb4GLrRkJaTd5/rsEllq+Qf6Y/fDMAFCoIH47fNUW1vSDCwdkwH9hEKTKEHnEL4QQQn9a1qzKb+Nb887mI2w9ksyn206y598qLAz5jOrPToI9n8CR9XByq2ZRmYJjbahWX7M4NdC82rhqZrQSohKQAJW7A1S5HUIIIcqfrYUxS/o9Q7uD1Xjvp6PsTbhC10V/8OELfvTo/SW0mgR7PoaTv0HeTUg5olnuZmZ7V9Ba/857czu9XJMQT8LgIzK1Wn3XMFNSgyqEEEI/FAoFffzdaOplz/i1scSev8bYNYfYdSKV2T0bYN0nTDP71LVzkHpcs/x3+zUtHrLTITFGs9zNpvrtYLXendpWxzpgZKqfCxXiERh8gJpzq4CC20POyUxSQggh9M2zqiUbRrXk899PseT3eDYdSmLfuSssCmmMv2cVqOKtWep2u7PTrRxI+xdS4+C/Y3eC14wLkJGkWU7tuJNeaQy+veHZiZrAVYgKxuAD1MJZpAAsjCVAFUIIoX/GKiWTOtWmdS0HJqyL5fyVLF76IoYx7Wsxpp0PJkb39HE2MgVnP81yt6xrmqBVp8b1mKa29Z91mqVON00TAreAcrs+IR7G4APUzDxNgGpipMRIJYMaCCGEqDgCvKoQPr4Vs346xqZDSSyOiGfrPxf5oKcvgT4ODz+AuR14ttQshdRqzexVfy6CuJ/vdL7ybg3PToIabaWzldA7g4/IMnOk/akQQoiKy8bMmAUhjfm87zM4WJlw+tJN+n3zNxPWHiL1enbJD6hQQHV/CFkFb+yFxv1BaQQJf8CqXvB1e03gWlBQ6tcixKOSALWwB7883hdCCFGB9WjkSsSbbRnU0hOFAn6MvUiHz/bwbfRZ8gs7U5SUY23oFQrjDkGz1zRTrl48COsGQGgLiF0D+XmleyFCPAIJULWzSEmAKoQQomKzNTfm/Z6+/PRGEA3dbLmec4v3thzj+SV/Env+2uMf2M4DnvsEJhyFVm+CqQ2knYQfR8HiJrD3a8jLKrXrEOJhDD5AzcorfMRv8M1xhRBCPCUautmxeXQQc3r5YmNmxLGLGbwQGsU7m49wLTP38Q9s5QgdZsLEo9DhPbB0hPRECH8LFvlB5AJNByshypjBB6gyi5QQQoinkUqpYEALTyLebEvvJtVRq+F/fyfSfv4eNuw/j1r9mI/9QTPof6tJMOEIPPcZ2HrAzUsQMRsW+kHE+3DjUuldjBD3kAA1RwJUIYQQTy9Ha1MWvNyYtSNbUKuaFVdu5vL2D//w8pcxnEjJeLKDG5tDsxEw7iD0+gIc6kBOOkTO19Sohk+G079D0kG4kgBZV6VzlSgVBv9c+84sUgZ/K4QQQjzFWtSoSvj4VoT9mcCinfHsO3uVbov/ZNiz3ozvUAtL0yf4O6cyhsZ9oWGIZkiqyAWazlR7v9QsOhRgZgNmdmBurxnqysxO99Xcvug6K2cwNnv8PIpKxeCjssJxUKWTlBBCiKedsUrJa21q0r2RKx/8fJzfjqXw1R9n+PnwRWZ2r09XX2cUTzLGqVIJ9XpA3e5wZjfs/QquntVMCJB9DfIyAbWmnWp2umZa1kelMgHPIKjVGWp1gqo+Mh6rATP4ADVL2qAKIYSoZKrbmfPFQH92nUjlvS3HSLySyeurD9KmtiOzn2+Al4Plk51AoYCa7TTL3W7l3AlWi3vNunqfbVfhVjac2aVZtk0Dey/w6aQJVr1agYnFk+VZPFUMPkC900nK4G+FEEKISqZd3Wq0rFmV0F2n+GLPGfb8e4nOi/5gdNuajGxdo/T/9hmZgrWTZikJtRrS4uHUDojfAeeiNDWz+77WLCpT8Hr2rtrVmqWbb1HhGHxUdqcNqtSgCiGEqHzMjFVM6lyHXs9UZ+ZPx/jzVBqLdsYT9mcCfZt5MLClJ272eq6dVCg0kwY41oaWb0DODTgbCfHbIX6nZqir0xGa5bcpUKXGXbWrz2o6c4lKRQJUecQvhBDCANRwtGLVsGb88k8y87ef5OzlTL784wxfR56hq68zrwZ5E+Bp/2RtVEuLqRXUCdYsajVcOnlX7Wo0XDlzp4OWkZmmCUCtzlCroyZ4FU89CVBlJikhhBAGQqFQ0KORK938XNh1MpWwqASiTl0m/EgK4UdS8K1uw9Agb7o1dMHUqIL8XVQooFpdzRI4FnKuQ8Ifd2pXMy5ogtdTO+BXNG1X7TzBygmsqt31WvjeCcyraDp8iQrL4ANU6SQlhBDC0CiVCjrUc6JDPSdOplxnZXQCmw4mcTQpg0nrD/Nh+AkGtPCgf3NPHK1N9Z1dXabWULebZlGrITXuTu1qYoym7erVsw8+hkJ1T9B6V/Bq6XjnvZ27pl2tKHcGH6DKOKhCCCEMWR1na+b1bsjbXeqyZm8i38Wc5b+MHBbtjCd012l6NHLl1SAvfKvb6jurRSkU4FRfswSNh+wMzfis1/+DG7eXm5duv0/VvGZeBnU+XE/WLA88vgqqeINjXXCso5mowLEOONSWUQXKmMFHZdIGVQghhIAqlia80c6Hka1r8OvRFFZEJXAo8RobD15g48ELNPOuwtAgLzrVd0alrADtVItjZgM12j44TX7ePUFrqm4AW/h6PQXybsLlU5rlxC93HUShqV11rKsJVh3r3l5qa6aJFU9MAlQJUIUQQggtY5WS5xu58nwjVw4lXmVF1FnCjySzN+EKexOuUN3OnCGBXrzc1B1bc2N9Z7fkVMZg46pZHkSt1gSpl05A2r+a10snNUtmGlxL1Czx23X3s3bRrW11rAO27mBiBSaWmiYDFaEjWgUnAWphJyljg78VQgghhI5nPOx5xsOed56rx6q/zvK/vxNJupbF3PA4Fu78lxebuDE40Aufalb6zmrpUyjAxkWz3Dshwc00TaCadjtgvXQCLv0L1y/eaTpwZnfxx1UaaQLVwoBV+/6ez6b3brfU1M7aeYKtGygrd8WawUdlWTIOqhBCCPFAzrZmvN2lLmPb1+LHQ0msiDrLyf+us+qvc6z66xwtalRhQAtPOtd3xsTIAHrHWzpoFq8g3fXZ6ZpANe3kXTWuJ+DGJbiVpUlTcOvOVLCPS2kEdh5g760ZtaDK7dfCz6ZP/w8Ggw5Q1Wo1mXm3H/GbSoAqhBBCPIiZsYpXmnkQ0tSd6NOXWRF1lt9P/MdfZ67w15krOFiZEtLUjVeaeuBexQA7EZnZgntTzXKvgnzIvQG5N28vt9/n3Chm/T2fc25/zrysaVZQkKcZC/bKmeLzYel4/+DV2rn0mhio1ZqlDIbsMugANedWAWq15r304hdCCCEejUKhIMjHgSAfB5KuZbFubyJr950n9XoOS3edJnT3adrVqUb/5h60rVOt4naqKk9KlSaAfdJOVAX5kHHx9nBaCZrXKwl33mdd1XQCu3kJLuwtur+RuaaDl8pEcyx1PqgL7rwvKNB8Vuffs/3udQV31ptYwzsXnuyaimHQUdnNnFva9+bGUoMqhBBClFR1O3Mmda7D2A612Hn8P1b/ncifp9L4/UQqv59IpbqdOX2bufNyU3eqWZvpO7tPP6VKE2DauYN3q6Lbs67dP3hNv6BpapD2b+nlR51fese6i0EHqIUdpEyNlPLrTghRrkJDQ/n0009JTk6mQYMGLFq0iFativljc9uePXuYNGkSx44dw9XVlcmTJzNq1Cjt9pUrV/Lqq68W2S8rKwszMwkKRNkzVikJ9nMh2M+FM5dusGZvIhsOXCDpWhafbf+XRTvj6dLAmf7NPWhZs2rFmFK1MjK3A/PG4Nq46LZbuZB+XrOoC0Ch1Iz1qlTd9arQXadQ3n6vLGadqsw6axl0gJqVJ0NMCSHK37p165gwYQKhoaEEBQXx5ZdfEhwczPHjx/Hw8CiSPiEhgeeee44RI0bw/fffExUVxejRo3F0dOTFF1/UprOxseHkyZM6+0pwKvShhqMV07vV583OdQg/kszqvxM5cO4qW48ks/VIMjUcLOnX3IM+/m7YWZjoO7uGw8gEqtbULBWcQq0ubIX5dMvIyMDW1pb09HRsbGweaZ/Y89fotTSK6nbmRE1tX8Y5FEI87R6nnClO8+bNadKkCcuWLdOuq1evHr169WLevHlF0k+ZMoUtW7YQFxenXTdq1CgOHz5MTEwMoKlBnTBhAteuXXvsfJXW9QlRnLjkDFb/fY7NB5O4edcTzO4NXenfwoNn3O2kVtUAPGo5YwBjQdxfpgwxJYQoZ7m5uRw4cIDOnTvrrO/cuTPR0dHF7hMTE1MkfZcuXdi/fz95eXnadTdu3MDT0xM3Nze6d+/OoUOHHpiXnJwcMjIydBYhyko9Fxvm9PLj7+kd+fAFP+q72JBzq4CNBy/QOzSa4P+L5JvIM1y6nqPvrIoKwLAD1Bx5xC+EKF9paWnk5+fj5OSks97JyYmUlJRi90lJSSk2/a1bt0hLSwOgbt26rFy5ki1btrBmzRrMzMwICgoiPj7+vnmZN28etra22sXd3f0Jr06Ih7MyNaJfcw+2jnuWzaMD6ePvhqmRkhMp15mzNY4W8yJ4dcVefj58key8sumAIyo+g26DWjgGqrkEqEKIcnbvo0y1Wv3Ax5vFpb97fYsWLWjRooV2e1BQEE2aNOHzzz9n8eLFxR5z2rRpTJo0Sfs5IyNDglRRbhQKhXamqhnd6vPzPxfZePAChxKvsevkJXadvIS1qRHdGrrQu4kbTb3spQmAATHoAPXOLFIGfRtKLD8/X+exohCVhbGxMSpV2f5gdXBwQKVSFaktTU1NLVJLWsjZ2bnY9EZGRlStWrXYfZRKJU2bNn1gDaqpqSmmpqYlvAIhSp+thTEDWngyoIUnZy7dYPOhJDYdTCLpWhZr951n7b7zuFcxp/czbvRuUh3Pqpb6zrIoYwYdmRUOMyWP+B+NWq0mJSXliTphCFHR2dnZ4ezsXGY1NSYmJvj7+7Njxw5eeOEF7fodO3bQs2fPYvdp2bIlP//8s8667du3ExAQgLGxcbH7qNVqYmNj8fPzK73MC1EOajha8WbnOkzsWJu9Z6+w6eAFwo+kcP5KFv8XEc//RcQT4GlP7yZudGvogq158f8HxNNNAlQkQH1UhcFptWrVsLCwkEctolJRq9VkZmaSmpoKgIuLS5mda9KkSQwcOJCAgABatmzJV199RWJionZc02nTppGUlMR3330HaHrsL1myhEmTJjFixAhiYmJYvnw5a9as0R5z9uzZtGjRglq1apGRkcHixYuJjY1l6dKlZXYdQpQlpVJBixpVaVGjKrOf92X78RQ2Hkziz/hL7D93lf3nrjLr52N0qudE7ybVaV3bEWOVQXetqVQMPECVR/yPKj8/Xxuc3u+RohBPO3Nzc0Dz+LxatWpl9rg/JCSEy5cv8/7775OcnIyvry/h4eF4enoCkJycTGJioja9t7c34eHhTJw4kaVLl+Lq6srixYt1xkC9du0aI0eOJCUlBVtbW5555hn++OMPmjVrVibXIER5MjdR0bNxdXo2rs5/Gdn8FJvExgNJnPzvunZsVQcrE55vVJ3eTarTwNVGKlGecgY9Dursn4+xIuosr7etyZSudcs4h0+37OxsEhIS8PLy0v4RF6IyysrK4uzZs3h7excZ5L6yjxNa2a9PVC5qtZpjFzPYdDCJLYeTSLuRq91W3c6cjvWq0aGeE81rVMHUSJ6UVhSPWs4YdNVhVuEjfmP54j4q+UUqKjv5jgvxdFAoFPhWt8W3ui3TnqtLZPwlNh5MYufx/0i6lsW3Mef4NuYcliYqWtd2pEM9J9rVcaSqlXQMfBoYdICqbYNqatC3QQghhHiqGauUtK/rRPu6TmTl5hN1Ko2IE/+xMy6VS9dz+PVoCr8eTUGhgCYe9nSoV42O9ZyoVc1KfpRWUAYdmUknKfE42rZtS+PGjVm0aNEjpS98XHzo0CEaN25cpnkTQghDZ26iomN9JzrWd2JugZojSelExGmC1ePJGRw4d5UD567yyW8nca9iToe6TnSs50Qz7yqYGEknq4rCwANUmeq0MnvYr+LBgwezcuXKEh9306ZN9x3apzju7u4kJyfj4OBQ4nM9rs6dOxMREUFUVJTO4O1CCGFIlEoFjdztaORux6TOdbh4LYuIE6lExP1H9OnLnL+Sxcros6yMPou1qdHtpgDVaFenGvaWJvrOvkEz8AD19kxS0ga1UkpOTta+X7duHTNnzuTkyZPadfd29srLy3ukwLNKlSolyodKpcLZ2blE+zyJxMREYmJiGDNmDMuXL9d7gPqo91UIIcqaq505A1t4MrCFJzdzbvHnqTQi4v7j9xOppN3I1Y4IoFSAv6c9QT4O+Hva09jdDmszKcfKk0HXZWs7SckwU5WSs7OzdrG1tUWhUGg/Z2dnY2dnx/r162nbti1mZmZ8//33XL58mb59++Lm5oaFhQV+fn46Y02C5hH/hAkTtJ+9vLz48MMPGTp0KNbW1nh4ePDVV19pt589exaFQkFsbCwAu3fvRqFQEBERQUBAABYWFgQGBuoEzwBz5syhWrVqWFtbM3z4cKZOnfpITQRWrFhB9+7def3111m3bh03b97U2V44HJGTkxNmZmb4+vryyy+/aLdHRUXRpk0bLCwssLe3p0uXLly9elV7rfc2bWjcuDGzZs3SflYoFHzxxRf07NkTS0tL5syZQ35+PsOGDcPb2xtzc3Pq1KnD//3f/xXJe1hYGA0aNMDU1BQXFxfGjBkDwNChQ+nevbtO2lu3buHs7ExYWNhD74kQQtzL0tSILg2c+aRPI/a+05HNowMZ086Hus7WFKhh39mrLNoZz8Dle2k4eztdF/3B9M1H2HTwAomXM6kkgyBVWAYdmWXmaR7xm8sj/hJTq9Vk5eXr5dzmxqpSa9Q+ZcoU5s+fz4oVKzA1NSU7Oxt/f3+mTJmCjY0NW7duZeDAgdSoUYPmzZvf9zjz58/ngw8+4J133uGHH37g9ddfp3Xr1tSte//hy6ZPn878+fNxdHRk1KhRDB06lKioKABWr17N3LlzCQ0NJSgoiLVr1zJ//ny8vb0feD1qtZoVK1awdOlS6tatS+3atVm/fj2vvvoqAAUFBQQHB3P9+nW+//57atasyfHjx7XjfcbGxtKhQweGDh3K4sWLMTIyYteuXeTnl+zf+r333mPevHksXLgQlUpFQUEBbm5urF+/HgcHB6Kjoxk5ciQuLi68/PLLACxbtoxJkybx0UcfERwcTHp6uvZ+DB8+nNatW5OcnKwdQD88PJwbN25o9xdCiMelVCp4xsOeZzzseatLHS5czWTXyUscOHuFA4lXOX8lixMp1zmRcp3Vf2vGKHawMsXf0w5/T3v8Pe1p4GqLmTyRLTUGHaAW1qBamsoXqqSy8vKpP3ObXs59/P0upVbrPWHCBHr37q2z7q233tK+Hzt2LL/99hsbNmx4YID63HPPMXr0aEAT9C5cuJDdu3c/MECdO3cubdq0AWDq1Kl069aN7OxszMzM+Pzzzxk2bJg2sJw5cybbt2/nxo0bD7yenTt3kpmZSZcuXQAYMGAAy5cv1x5n586d7N27l7i4OGrXrg1AjRo1tPt/8sknBAQEEBoaql3XoEGDB56zOP369WPo0KE662bPnq197+3tTXR0NOvXr9cGmHPmzOHNN99k/Pjx2nRNmzYFIDAwkDp16rBq1SomT54MaGqKX3rpJaysrEqcPyGEeBA3ewttUwCA1IxsDiZeZf/ZqxxIvMrRpHTSbuSw7dh/bDv2HwAmKiW+1W0I8KpCEw97mnjaUc3a7EGnEQ9g0AHqzZzCcVAN+jYYtICAAJ3P+fn5fPTRR6xbt46kpCRycnLIycnB0tLygcdp2LCh9n1hU4LCKTMfZZ/CWsHU1FQ8PDw4efKkNuAt1KxZM37//fcHHnP58uWEhIRgZKT5Tvft25e3336bkydPUqdOHWJjY3Fzc9MGp/eKjY3lpZdeeuA5HsW99xXgiy++4JtvvuHcuXNkZWWRm5urbbKQmprKxYsX6dChw32POXz4cL766ismT55MamoqW7duJSIi4onzKoQQD1PNxoyuvi509dWU1dl5+RxNSteOCHDg3FUu38zlYOI1DiZe0+7nUcUCf097mnjaE+BpT20na1RKGdbqURhsZFZQcOcRtTziLzlzYxXH3++it3OXlnsDz/nz57Nw4UIWLVqEn58flpaWTJgwgdzc3PscQePeTkAKhYKCgoJH3qewycLd+9zbjOFh7Z2uXLnCjz/+SF5eHsuWLdOuz8/PJywsjI8//vihs4A9bLtSqSySj7y8vCLp7r2v69evZ+LEicyfP5+WLVtibW3Np59+yt9///1I5wUYNGgQU6dOJSYmhpiYGLy8vGjVqtVD9xNCiNJmZqwiwKsKAV6aTrNqtZrEK5naGtaD565y8r/rJF7JJPFKJpsPJQFgbWpEYw87AjyrEOCl6XxlKWOxF8tg70r2rTtt6mSYqZJTKBSVsnNZZGQkPXv2ZMCAAYAmYIyPj6devXrlmo86deqwd+9eBg4cqF23f//+B+6zevVq3Nzc+PHHH3XWR0REMG/ePObOnUvDhg25cOEC//77b7G1qA0bNiQiIkLncfzdHB0ddUZHyMjIICEh4aHXExkZSWBgoE6t8OnTp7Xvra2t8fLyIiIignbt2hV7jKpVq9KrVy9WrFhBTEyMttmCEELom0KhwLOqJZ5VLXnR3w2AjOw8YhOvsf+cJmA9lHiV6zm3iIxPIzI+DQCVUkE9F2v8Pezx96pCgKc9rnYynTgYcIBaOMQUyDBT4g4fHx82btxIdHQ09vb2LFiwgJSUlHIPUMeOHcuIESMICAggMDCQdevW8c8//+i0F73X8uXL6dOnD76+vjrrPT09mTJlClu3bqVnz560bt2aF198kQULFuDj48OJEydQKBR07dqVadOm4efnx+jRoxk1ahQmJibs2rWLl156CQcHB9q3b8/KlSvp0aMH9vb2zJgxQ9vB6kF8fHz47rvv2LZtG97e3qxatYp9+/bpdPqaNWsWo0aNolq1atqOXFFRUYwdO1abZvjw4XTv3p38/HwGDx78GHdWCCHKh42ZMa1rO9K6tiMAt/ILOJFy/U5b1nNXSbqWxdGkDI4mZfBtzDkAXGzN8L/dJCDAqwp1na0xUhneoEsGG6Bm3TUGqlLag4jbZsyYQUJCAl26dMHCwoKRI0fSq1cv0tPTyzUf/fv358yZM7z11ltkZ2fz8ssvM2TIEPbu3Vts+gMHDnD48GG+/vrrItusra3p3Lkzy5cvp2fPnmzcuJG33nqLvn37cvPmTXx8fPjoo48AqF27Ntu3b+edd96hWbNmmJub07x5c/r27QvAtGnTOHPmDN27d8fW1pYPPvjgkWpQR40aRWxsLCEhISgUCvr27cvo0aP59ddftWkGDx5MdnY2Cxcu5K233sLBwYE+ffroHKdjx464uLjQoEEDXF1dH/l+CiGEvhmplPhWt8W3ui2DWnoBkJyexYFzdwLW48kZJKdn88s/yfzyj+ZplYWJisbudgR4ampZG7vbYWte+cdkVagryUBeGRkZ2Nrakp6ejo2NzUPTn0jJoOuiSKpamnBgRqdyyOHTLTs7m4SEBLy9vTEzk16J+tCpUyecnZ1ZtWqVvrOiN5mZmbi6uhIWFlZk9IXS8qDveknLmadNZb8+ISq6mzm3OHzhGgfOXtU0DUi8yvXsWzppFArwcbTSdL66PVpADQerp6ay7VHLGYOtQdXOIiXtT0UFlJmZyRdffEGXLl1QqVSsWbOGnTt3smPHDn1nTS8KCgpISUlh/vz52Nra8vzzz+s7S0IIUeosTY0IrOlAYE3N1NgFBWriU2+w/9wVDtzugHXucibxqTeIT73B2n3nAbA1N+YZDztNwOphT2MPO6ye8s5XT3fun8CdWaQkQBUVj0KhIDw8nDlz5pCTk0OdOnXYuHEjHTt21HfW9CIxMRFvb2/c3NxYuXKldhgtIYSozJRKBXWcranjbE3/5poxWdNu5HAo8RoHbtew/nPhGulZeew+eYndJy9p9lNAbSdrmtyuZfX3tMerqkWpTXJTHgy2lL9Tg2qwt0BUYObm5uzcuVPf2agwvLy8ZFpBIYRAM4NVp/pOdKrvBEBefgFxyRkcPHf19jisV7lw9c7MV/+7PfNVFUsTnnG30watvtVtsDaruG1ZDTY6y8zVtOmwlBpUIYQQQjyljFVKGrrZ0dDNjiFBmnWFM18dTLzGwXNX+ScpnSs3c4k4kUrEiTuTyHhUsaC+iw31XW20ry62ZhWiptWAA1R5xC+EEEKIyufema9ybxVw7GK6toY1NvEaSdeytBMJ/HYsRbuvnYWxJlgtDFxdbajpaIVxOQ91ZfABqjziF0IIIURlZmKk5BkPe57xsGcYmvGnr2Xmcjw5g+MXM7Svp1JvcC0zj+jTl4k+ffnO/ioltZ2t7gpcbannYl2mTQQMNjrLuv2I30IG6RdCCCGEgbGzMNEZMQAgOy+fU6k3dILW48kZ3Mi5pZ1Q4G6FTQTmv9yo1KdsNdgAVYaZEkIIIYS4w8xYpZ1MoFBBgZoLV7M4npyuE7heTM8m8Uom6Vl5ZdJc0mAD1N5NqtPQzRY3ewt9Z0UIIYQQokJSKhV4VLXAo6qFtk0rwNWbucQlZ3AlM7dMOlUZ3uSut/lUs6arr4vOrwQhitO2bVsmTJig/ezl5cWiRYseuI9CoeDHH3984nOX1nGEEEKI0mRvaUKgjwPdG5bNtNMGG6CKyq9Hjx73Hdg+JiYGhULBwYMHS3zcffv2MXLkyCfNno5Zs2bRuHHjIuuTk5MJDg4u1XPdT1ZWFvb29lSpUoWsrKxyOacQQghRnMcKUENDQ7XzVPv7+xMZGfnA9KtXr6ZRo0ZYWFjg4uLCq6++yuXLl3XSXLt2jTfeeAMXFxfMzMyoV68e4eHhj5M9IQAYNmwYv//+O+fOnSuyLSwsjMaNG9OkSZMSH9fR0RELi/JpGuLs7IypqWm5nGvjxo34+vpSv359Nm3aVC7nvB+1Ws2tW7cenlAIIUSlVOIAdd26dUyYMIHp06dz6NAhWrVqRXBwMImJicWm//PPPxk0aBDDhg3j2LFjbNiwgX379jF8+HBtmtzcXDp16sTZs2f54YcfOHnyJF9//TXVq1d//CsTBq979+5Uq1aNlStX6qzPzMxk3bp1DBs2jMuXL9O3b1/c3NywsLDAz8+PNWvWPPC49z7ij4+Pp3Xr1piZmVG/fn127NhRZJ8pU6ZQu3ZtLCwsqFGjBjNmzCAvLw+AlStXMnv2bA4fPoxCoUChUGjzfO8j/iNHjtC+fXvMzc2pWrUqI0eO5MaNG9rtQ4YMoVevXnz22We4uLhQtWpV3njjDe25HmT58uUMGDCAAQMGsHz58iLbjx07Rrdu3bCxscHa2ppWrVpx+vRp7fawsDAaNGiAqakpLi4ujBkzBoCzZ8+iUCiIjY3Vpr127RoKhYLdu3cDsHv3bhQKBdu2bSMgIABTU1MiIyM5ffo0PXv2xMnJCSsrK5o2bVpkhq2cnBwmT56Mu7s7pqam1KpVi+XLl6NWq/Hx8eGzzz7TSX/06FGUSqVO3oUQQlQsJe4ktWDBAoYNG6YNMBctWsS2bdtYtmwZ8+bNK5L+r7/+wsvLi3HjxgHg7e3Na6+9xieffKJNExYWxpUrV4iOjsbYWDOmlqen52NdkCgnajXkZern3MYW8AgNso2MjBg0aBArV65k5syZ2kbcGzZsIDc3l/79+5OZmYm/vz9TpkzBxsaGrVu3MnDgQGrUqEHz5s0feo6CggJ69+6Ng4MDf/31FxkZGTrtVQtZW1uzcuVKXF1dOXLkCCNGjMDa2prJkycTEhLC0aNH+e2337TBl61t0bbRmZmZdO3alRYtWrBv3z5SU1MZPnw4Y8aM0QnCd+3ahYuLC7t27eLUqVOEhITQuHFjRowYcd/rOH36NDExMWzatAm1Ws2ECRM4c+YMNWrUACApKYnWrVvTtm1bfv/9d2xsbIiKitLWci5btoxJkybx0UcfERwcTHp6OlFRUQ+9f/eaPHkyn332GTVq1MDOzo4LFy7w3HPPMWfOHMzMzPj222/p0aMHJ0+exMPDA4BBgwYRExPD4sWLadSoEQkJCaSlpaFQKBg6dCgrVqzgrbfe0p4jLCyMVq1aUbNmzRLnTwghRPkoUYCam5vLgQMHmDp1qs76zp07Ex0dXew+gYGBTJ8+nfDwcIKDg0lNTeWHH36gW7du2jRbtmyhZcuWvPHGG/z00084OjrSr18/pkyZgkolw0BVSHmZ8GHZNIx+qHcugonlIyUdOnQon376Kbt376Zdu3aAJkDp3bs39vb22Nvb6wQvY8eO5bfffmPDhg2PFKDu3LmTuLg4zp49i5ubGwAffvhhkXaj7777rva9l5cXb775JuvWrWPy5MmYm5tjZWWFkZERzs7O9z3X6tWrycrK4rvvvsPSUnP9S5YsoUePHnz88cc4OWnmZba3t2fJkiWoVCrq1q1Lt27diIiIeGCAGhYWRnBwMPb29gB07dqVsLAw5syZA8DSpUuxtbVl7dq12h+RtWvX1u4/Z84c3nzzTcaPH69d17Rp04fev3u9//77dOrUSfu5atWqNGrUSOc8mzdvZsuWLYwZM4Z///2X9evXs2PHDm1748KgGuDVV19l5syZ7N27l2bNmpGXl8f333/Pp59+WuK8CSGEKD8lesSflpZGfn6+9g9hIScnJ1JSUordJzAwkNWrVxMSEoKJiQnOzs7Y2dnx+eefa9OcOXOGH374gfz8fMLDw3n33XeZP38+c+fOvW9ecnJyyMjI0FmEuFfdunUJDAwkLCwM0NQURkZGMnToUADy8/OZO3cuDRs2pGrVqlhZWbF9+/b7Nlm5V1xcHB4eHtrgFKBly5ZF0v3www88++yzODs7Y2VlxYwZMx75HHefq1GjRtrgFCAoKIiCggJOnjypXdegQQOdH3YuLi6kpqZyP/n5+Xz77bcMGDBAu27AgAF8++235OdrxguOjY2lVatW2uD0bqmpqVy8eJEOHTqU6HqKExAQoPP55s2bTJ48mfr162NnZ4eVlRUnTpzQ3rvY2FhUKhVt2rQp9nguLi5069ZN++//yy+/kJ2dzUsvvfTEeRVCCFF2Hmsc1HvHu1Kr1fcdA+v48eOMGzeOmTNn0qVLF5KTk3n77bcZNWqUtp1bQUEB1apV46uvvkKlUuHv78/Fixf59NNPmTlzZrHHnTdvHrNnz36c7IvSYGyhqcnU17lLYNiwYYwZM4alS5eyYsUKPD09tcHU/PnzWbhwIYsWLcLPzw9LS0smTJhAbm7uIx1brVYXWXfv/4W//vqLV155hdmzZ9OlSxdtTeT8+fNLdB0P+n929/p7g0iFQkFBQcF9j7tt2zaSkpIICQnRWZ+fn8/27dsJDg7G3Nz8vvs/aBuAUqnU5r/Q/drE3h18A7z99tts27aNzz77DB8fH8zNzenTp4/23+dh5wYYPnw4AwcOZOHChaxYsYKQkJBy6+QmhBDi8ZSoBtXBwQGVSlWktjQ1NbVIrWqhefPmERQUxNtvv03Dhg3p0qULoaGhhIWFkZycDGhqOWrXrq1T61OvXj1SUlLuGyhMmzaN9PR07XL+/PmSXIp4UgqF5jG7PpYSDgj88ssvo1Kp+N///se3337Lq6++qg3oIiMj6dmzJwMGDKBRo0bUqFGD+Pj4Rz52/fr1SUxM5OLFO8F6TEyMTpqoqCg8PT2ZPn06AQEB1KpVq8jIAiYmJtraygedKzY2lps3b+ocW6lU6jxuL6nly5fzyiuvEBsbq7P0799f+yOyYcOGREZGFhtYWltb4+XlRURERLHHd3R0BND+fwd0Okw9SGRkJEOGDOGFF17Az88PZ2dnzp49q93u5+dHQUEBe/bsue8xnnvuOSwtLVm2bBm//vqrtvZcCCFExVWiANXExAR/f/8ivZR37NhBYGBgsftkZmZqa1AKFQaihTUqQUFBnDp1SqeW599//8XFxQUTE5Nij2tqaoqNjY3OIkRxrKysCAkJ4Z133uHixYsMGTJEu83Hx4cdO3YQHR1NXFwcr7322n2bqxSnY8eO1KlTh0GDBnH48GEiIyOZPn26ThofHx8SExNZu3Ytp0+fZvHixWzevFknjZeXFwkJCcTGxpKWlkZOTk6Rc/Xv3x8zMzMGDx7M0aNH2bVrF2PHjmXgwIH3/YH4MJcuXeLnn39m8ODB+Pr66iyDBw9my5YtXLp0iTFjxpCRkcErr7zC/v37iY+PZ9WqVdqmBbNmzWL+/PksXryY+Ph4Dh48qG3GY25uTosWLfjoo484fvw4f/zxh06b3Afx8fFh06ZNxMbGcvjwYfr166dTTnh5eTF48GCGDh3Kjz/+SEJCArt372b9+vXaNCqViiFDhjBt2jR8fHyKbYIhhBCiYinxMFOTJk3im2++ISwsjLi4OCZOnEhiYiKjRo0CNDWbgwYN0qbv0aMHmzZtYtmyZZw5c4aoqCjGjRtHs2bNcHXVdLJ5/fXXuXz5MuPHj+fff/9l69atfPjhh7zxxhuldJnC0A0bNoyrV6/SsWNHbe9vgBkzZtCkSRO6dOlC27ZtcXZ2plevXo98XKVSyebNm8nJyaFZs2YMHz68SNvpnj17MnHiRMaMGUPjxo2Jjo5mxowZOmlefPFFunbtSrt27XB0dCx2qCsLCwu2bdvGlStXaNq0KX369KFDhw4sWbKkZDfjLoUdroprP9quXTusra1ZtWoVVatW5ffff+fGjRu0adMGf39/vv76a21zgsGDB7No0SJCQ0Np0KAB3bt316mJDgsLIy8vj4CAAMaPH6/tfPUwCxcuxN7ensDAQHr06EGXLl2KjF27bNky+vTpw+jRo6lbty4jRozQqWUGzb9/bm6u1J4KIcRTQqEurhHdQ4SGhvLJJ5+QnJyMr68vCxcupHXr1oBmHMazZ89qxzcE+Pzzz/niiy9ISEjAzs6O9u3b8/HHH+uMcxoTE8PEiROJjY2levXqDBs2rES9+DMyMrC1tSU9PV1qU8tAdnY2CQkJ2gkahHiaREVF0bZtWy5cuPDQ2uYHfdcrezlT2a9PCKF/j1rOPFaAWhFJwVq2JEAVT6OcnBzOnz/PyJEjcXFxYfXq1Q/dRwLUynt9Qgj9e9Ry5rGmOhVCiKfBmjVrqFOnDunp6TqTgwghhKjYJEAVQlRaQ4YMIT8/nwMHDsjUyUII8RSRAFUIIYQQQlQoEqAKIYQQQogKRQJUUSIPmpFIiMpAvuNCCKF/jzXVqTA8JiYmKJVKLl68iKOjIyYmJveddlOIp5FarSY3N5dLly6hVCrvO0mIEEKIsicBqngkSqUSb29vkpOTdab1FKKysbCwwMPDo8gMeEIIIcqPBKjikZmYmODh4cGtW7ceOm+8EE8jlUqFkZGRPB0QQgg9kwBVlIhCocDY2Fg7xaUQQgghRGmTZ1hCCCGEEKJCkQBVCCGEEEJUKBKgCiGEEEKICqXStEFVq9UAZGRk6DknQojKqrB8KSxvKhspR4UQZe1Ry9FKE6Bev34dAHd3dz3nRAhR2V2/fh1bW1t9Z6PUSTkqhCgvDytHFepKUhVQUFDAxYsXsba2fuQhYjIyMnB3d+f8+fPY2NiUcQ4Nm9zr8iX3u2yo1WquX7+Oq6trpRwnVcrRik3udfmS+102HrUcrTQ1qEqlEjc3t8fa18bGRr585UTudfmS+136KmPNaSEpR58Ocq/Ll9zv0vco5WjlqwIQQgghhBBPNQlQhRBCCCFEhWLQAaqpqSnvvfcepqam+s5KpSf3unzJ/RblRb5r5UfudfmS+61flaaTlBBCCCGEqBwMugZVCCGEEEJUPBKgCiGEEEKICkUCVCGEEEIIUaFIgCqEEEIIISoUgw1QQ0ND8fb2xszMDH9/fyIjI/WdpUpp1qxZKBQKncXZ2Vnf2aoU/vjjD3r06IGrqysKhYIff/xRZ7tarWbWrFm4urpibm5O27ZtOXbsmH4yKyolKUfLh5SjZUfK0YrLIAPUdevWMWHCBKZPn86hQ4do1aoVwcHBJCYm6jtrlVKDBg1ITk7WLkeOHNF3liqFmzdv0qhRI5YsWVLs9k8++YQFCxawZMkS9u3bh7OzM506ddLOty7Ek5BytHxJOVo2pBytwNQGqFmzZupRo0bprKtbt6566tSpespR5fXee++pGzVqpO9sVHqAevPmzdrPBQUFamdnZ/VHH32kXZedna22tbVVf/HFF3rIoahspBwtP1KOlg8pRysWg6tBzc3N5cCBA3Tu3FlnfefOnYmOjtZTriq3+Ph4XF1d8fb25pVXXuHMmTP6zlKll5CQQEpKis733NTUlDZt2sj3XDwxKUfLn5Sj5U/KUf0yuAA1LS2N/Px8nJycdNY7OTmRkpKip1xVXs2bN+e7775j27ZtfP3116SkpBAYGMjly5f1nbVKrfC7LN9zURakHC1fUo7qh5Sj+mWk7wzoi0Kh0PmsVquLrBNPLjg4WPvez8+Pli1bUrNmTb799lsmTZqkx5wZBvmei7Ik36/yIeWofsn3XD8MrgbVwcEBlUpV5NdPampqkV9JovRZWlri5+dHfHy8vrNSqRX28JXvuSgLUo7ql5Sj5UPKUf0yuADVxMQEf39/duzYobN+x44dBAYG6ilXhiMnJ4e4uDhcXFz0nZVKzdvbG2dnZ53veW5uLnv27JHvuXhiUo7ql5Sj5UPKUf0yyEf8kyZNYuDAgQQEBNCyZUu++uorEhMTGTVqlL6zVum89dZb9OjRAw8PD1JTU5kzZw4ZGRkMHjxY31l76t24cYNTp05pPyckJBAbG0uVKlXw8PBgwoQJfPjhh9SqVYtatWrx4YcfYmFhQb9+/fSYa1FZSDlafqQcLTtSjlZg+h1EQH+WLl2q9vT0VJuYmKibNGmi3rNnj76zVCmFhISoXVxc1MbGxmpXV1d179691ceOHdN3tiqFXbt2qYEiy+DBg9VqtWaIlPfee0/t7OysNjU1Vbdu3Vp95MgR/WZaVCpSjpYPKUfLjpSjFZdCrVar9RUcCyGEEEIIcS+Da4MqhBBCCCEqNglQhRBCCCFEhSIBqhBCCCGEqFAkQBVCCCGEEBWKBKhCCCGEEKJCkQBVCCGEEEJUKBKgCiGEEEKICkUCVCGEEEIIUaFIgCqEEEIIISoUCVCFEEIIIUSFIgGqEEIIIYSoUCRAFUIIIYQQFcr/Az7YIGobsn3EAAAAAElFTkSuQmCC",
            "text/plain": [
              "<Figure size 800x800 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "acc = history.history['accuracy']\n",
        "val_acc = history.history['val_accuracy']\n",
        "\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "\n",
        "epochs_range = range(epochs)\n",
        "\n",
        "plt.figure(figsize=(8, 8))\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.plot(epochs_range, acc, label='Training Accuracy')\n",
        "plt.plot(epochs_range, val_acc, label='Validation Accuracy')\n",
        "plt.legend(loc='lower right')\n",
        "plt.title('Training and Validation Accuracy')\n",
        "\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.plot(epochs_range, loss, label='Training Loss')\n",
        "plt.plot(epochs_range, val_loss, label='Validation Loss')\n",
        "plt.legend(loc='upper right')\n",
        "plt.title('Training and Validation Loss')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[9.9639624e-01],\n",
              "       [2.8076401e-04],\n",
              "       [9.0008507e-05],\n",
              "       [9.8337108e-01],\n",
              "       [1.5492416e-04]], dtype=float32)"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "predictions = model.predict(x=test_data_gen, verbose=0)\n",
        "predictions[:5]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "test_images, _ = next(test_data_gen)\n",
        "plotImages(test_images, probabilities=predictions)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {},
      "outputs": [],
      "source": [
        "import itertools\n",
        "\n",
        "answers =  [1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0,\n",
        "            1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0,\n",
        "            1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1,\n",
        "            1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1,\n",
        "            0, 0, 0, 0, 0, 0]\n",
        "\n",
        "probabilities = [1 if prediction > 0.5 else 0 for prediction in predictions]\n",
        "\n",
        "cm = np.array(tf.math.confusion_matrix(labels=answers, predictions=probabilities))\n",
        "\n",
        "def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues):\n",
        "  plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "  plt.title(title)\n",
        "  plt.colorbar()\n",
        "  tick_marks = np.arange(len(classes))\n",
        "  plt.xticks(tick_marks, classes, rotation=45)\n",
        "  plt.yticks(tick_marks, classes)\n",
        "\n",
        "  if normalize:\n",
        "      cm = np.round(cm.astype('float') / cm.sum(axis=1)[:, np.newaxis], 2)\n",
        "      print(\"Normalized confusion matrix\")\n",
        "  else:\n",
        "      print('Confusion matrix, without normalization')\n",
        "\n",
        "  print(cm)\n",
        "\n",
        "  thresh = cm.max() / 2.\n",
        "  for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "      plt.text(j, i, cm[i, j],\n",
        "              horizontalalignment=\"center\",\n",
        "              color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "\n",
        "  plt.tight_layout()\n",
        "  plt.ylabel('True label')\n",
        "  plt.xlabel('Predicted label')\n",
        "\n",
        "  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Confusion matrix, without normalization\n",
            "[[26  0]\n",
            " [ 2 22]]\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plot_confusion_matrix(cm=cm, classes=['cat', 'dog'], title='Confusion Matrix')"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.18"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}