two-stepped-densenet-with-multiple-lr-strategies.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup\n",
    "Load all necessary libraries and doublecheck version of `tensorflow`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.1.0'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import tensorflow as tf\n",
    "import tensorflow.keras as keras\n",
    "from tensorflow.keras import backend as K\n",
    "from tensorflow.keras.callbacks import *\n",
    "from tensorflow.keras.layers import Dense, Dropout\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.preprocessing.image import load_img\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from sklearn.model_selection import train_test_split\n",
    "tf.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "matplotlib.rcParams['figure.figsize'] = (18, 6)\n",
    "sns.set(style=\"whitegrid\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build Module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def filtered_train_image_names(height=256):\n",
    "    \"\"\"\n",
    "    Filter out train image names from the train set directory with weird\n",
    "    dimensions (wider than twice the height).\n",
    "    \"\"\"\n",
    "    rows = []\n",
    "\n",
    "    for dirname, _, filenames in os.walk(DIR+'train_set/train_set/'):\n",
    "        for train_image_name in filenames:\n",
    "            img = load_img(dirname + '/' + train_image_name)\n",
    "            rows.append([train_image_name, img.width, img.height])\n",
    "\n",
    "    df = pd.DataFrame(rows, columns=['image_name', 'width', 'height'])\n",
    "    df = df[df['width'] <= height * 2]\n",
    "    \n",
    "    return list(df['image_name'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# @TODO: not ported yet. used this in colab by writing directly back to disk.\n",
    "def crop_centre_box(full_image_name, target_w=256, target_h=256):\n",
    "    \"\"\"\n",
    "    Make sure images do not get resized to fit the target dimensions but instead \n",
    "    are cropped (and thus keep original ratio) to the desired dimensions \n",
    "    while maintaining the original centre.\n",
    "    \"\"\"\n",
    "    img = load_img(full_image_name)\n",
    "    w = img.width\n",
    "    h = img.height\n",
    "    \n",
    "    left = (w - target_w) / 2\n",
    "    upper = (h - target_h) / 2\n",
    "    right = left + target_w\n",
    "    lower = upper + target_h\n",
    "    \n",
    "    return img.crop((left, upper, right, lower))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_class_weights(map_labels_to_classes):\n",
    "    \"\"\"\n",
    "    return a dictionary with class weights in case of imbalanced dataset\n",
    "    https://www.tensorflow.org/tutorials/structured_data/imbalanced_data#calculate_class_weights\n",
    "    \"\"\"\n",
    "    labels = pd.read_csv(DIR + 'train_labels.csv')\n",
    "    class_weights = (1 / labels['label'].value_counts()) * len(labels.index) / 2.0\n",
    "    class_weights.index = class_weights.index.astype('str').map(map_labels_to_classes)\n",
    "    return class_weights.to_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_generator(df, shuffle=True, augmented=False):\n",
    "    if augmented:\n",
    "        datagen = ImageDataGenerator(\n",
    "            preprocessing_function=PREPROCESSOR,\n",
    "            width_shift_range=0.1,\n",
    "            height_shift_range=0.1,\n",
    "            brightness_range=(0.3, 0.6),\n",
    "            channel_shift_range=150.0,\n",
    "            rotation_range=20,\n",
    "            horizontal_flip=True, \n",
    "        )\n",
    "    else:\n",
    "        datagen = ImageDataGenerator(preprocessing_function=PREPROCESSOR)\n",
    "        \n",
    "    generator = datagen.flow_from_dataframe(\n",
    "        df,\n",
    "        directory=DIR + 'train_set/train_set/',\n",
    "        x_col='img_name',\n",
    "        y_col='label',\n",
    "        target_size=(224, 224),\n",
    "        batch_size=BATCH_SIZE,\n",
    "        class_mode='categorical',\n",
    "        shuffle=shuffle\n",
    "    )\n",
    "    return generator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_generator_dir(directory, shuffle=True, augmented=False):\n",
    "    if augmented:\n",
    "        datagen = ImageDataGenerator(\n",
    "            preprocessing_function=PREPROCESSOR,\n",
    "            width_shift_range=0.1,\n",
    "            height_shift_range=0.1,\n",
    "            brightness_range=(0.3, 0.6),\n",
    "            channel_shift_range=150.0,\n",
    "            rotation_range=20,\n",
    "            horizontal_flip=True, \n",
    "        )\n",
    "    else:\n",
    "        datagen = ImageDataGenerator(preprocessing_function=PREPROCESSOR)\n",
    "        \n",
    "    generator = datagen.flow_from_directory(\n",
    "        directory,\n",
    "        target_size=(224, 224),\n",
    "        batch_size=BATCH_SIZE,\n",
    "        class_mode='categorical',\n",
    "        shuffle=shuffle\n",
    "    )\n",
    "    return generator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#https://github.com/bckenstler/CLR\n",
    "class CyclicLR(Callback):\n",
    "    \"\"\"This callback implements a cyclical learning rate policy (CLR).\n",
    "    The method cycles the learning rate between two boundaries with\n",
    "    some constant frequency, as detailed in this paper (https://arxiv.org/abs/1506.01186).\n",
    "    The amplitude of the cycle can be scaled on a per-iteration or \n",
    "    per-cycle basis.\n",
    "    This class has three built-in policies, as put forth in the paper.\n",
    "    \"triangular\":\n",
    "        A basic triangular cycle w/ no amplitude scaling.\n",
    "    \"triangular2\":\n",
    "        A basic triangular cycle that scales initial amplitude by half each cycle.\n",
    "    \"exp_range\":\n",
    "        A cycle that scales initial amplitude by gamma**(cycle iterations) at each \n",
    "        cycle iteration.\n",
    "    For more detail, please see paper.\n",
    "    \n",
    "    # Example\n",
    "        ```python\n",
    "            clr = CyclicLR(base_lr=0.001, max_lr=0.006,\n",
    "                                step_size=2000., mode='triangular')\n",
    "            model.fit(X_train, Y_train, callbacks=[clr])\n",
    "        ```\n",
    "    \n",
    "    Class also supports custom scaling functions:\n",
    "        ```python\n",
    "            clr_fn = lambda x: 0.5*(1+np.sin(x*np.pi/2.))\n",
    "            clr = CyclicLR(base_lr=0.001, max_lr=0.006,\n",
    "                                step_size=2000., scale_fn=clr_fn,\n",
    "                                scale_mode='cycle')\n",
    "            model.fit(X_train, Y_train, callbacks=[clr])\n",
    "        ```    \n",
    "    # Arguments\n",
    "        base_lr: initial learning rate which is the\n",
    "            lower boundary in the cycle.\n",
    "        max_lr: upper boundary in the cycle. Functionally,\n",
    "            it defines the cycle amplitude (max_lr - base_lr).\n",
    "            The lr at any cycle is the sum of base_lr\n",
    "            and some scaling of the amplitude; therefore \n",
    "            max_lr may not actually be reached depending on\n",
    "            scaling function.\n",
    "        step_size: number of training iterations per\n",
    "            half cycle. Authors suggest setting step_size\n",
    "            2-8 x training iterations in epoch.\n",
    "        mode: one of {triangular, triangular2, exp_range}.\n",
    "            Default 'triangular'.\n",
    "            Values correspond to policies detailed above.\n",
    "            If scale_fn is not None, this argument is ignored.\n",
    "        gamma: constant in 'exp_range' scaling function:\n",
    "            gamma**(cycle iterations)\n",
    "        scale_fn: Custom scaling policy defined by a single\n",
    "            argument lambda function, where \n",
    "            0 <= scale_fn(x) <= 1 for all x >= 0.\n",
    "            mode paramater is ignored \n",
    "        scale_mode: {'cycle', 'iterations'}.\n",
    "            Defines whether scale_fn is evaluated on \n",
    "            cycle number or cycle iterations (training\n",
    "            iterations since start of cycle). Default is 'cycle'.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, base_lr=0.001, max_lr=0.006, step_size=2000., mode='triangular',\n",
    "                 gamma=1., scale_fn=None, scale_mode='cycle'):\n",
    "        super(CyclicLR, self).__init__()\n",
    "\n",
    "        self.base_lr = base_lr\n",
    "        self.max_lr = max_lr\n",
    "        self.step_size = step_size\n",
    "        self.mode = mode\n",
    "        self.gamma = gamma\n",
    "        if scale_fn == None:\n",
    "            if self.mode == 'triangular':\n",
    "                self.scale_fn = lambda x: 1.\n",
    "                self.scale_mode = 'cycle'\n",
    "            elif self.mode == 'triangular2':\n",
    "                self.scale_fn = lambda x: 1/(2.**(x-1))\n",
    "                self.scale_mode = 'cycle'\n",
    "            elif self.mode == 'exp_range':\n",
    "                self.scale_fn = lambda x: gamma**(x)\n",
    "                self.scale_mode = 'iterations'\n",
    "        else:\n",
    "            self.scale_fn = scale_fn\n",
    "            self.scale_mode = scale_mode\n",
    "        self.clr_iterations = 0.\n",
    "        self.trn_iterations = 0.\n",
    "        self.history = {}\n",
    "\n",
    "        self._reset()\n",
    "\n",
    "    def _reset(self, new_base_lr=None, new_max_lr=None,\n",
    "               new_step_size=None):\n",
    "        \"\"\"Resets cycle iterations.\n",
    "        Optional boundary/step size adjustment.\n",
    "        \"\"\"\n",
    "        if new_base_lr != None:\n",
    "            self.base_lr = new_base_lr\n",
    "        if new_max_lr != None:\n",
    "            self.max_lr = new_max_lr\n",
    "        if new_step_size != None:\n",
    "            self.step_size = new_step_size\n",
    "        self.clr_iterations = 0.\n",
    "        \n",
    "    def clr(self):\n",
    "        cycle = np.floor(1+self.clr_iterations/(2*self.step_size))\n",
    "        x = np.abs(self.clr_iterations/self.step_size - 2*cycle + 1)\n",
    "        if self.scale_mode == 'cycle':\n",
    "            return self.base_lr + (self.max_lr-self.base_lr)*np.maximum(0, (1-x))*self.scale_fn(cycle)\n",
    "        else:\n",
    "            return self.base_lr + (self.max_lr-self.base_lr)*np.maximum(0, (1-x))*self.scale_fn(self.clr_iterations)\n",
    "        \n",
    "    def on_train_begin(self, logs={}):\n",
    "        logs = logs or {}\n",
    "\n",
    "        if self.clr_iterations == 0:\n",
    "            K.set_value(self.model.optimizer.lr, self.base_lr)\n",
    "        else:\n",
    "            K.set_value(self.model.optimizer.lr, self.clr())        \n",
    "            \n",
    "    def on_batch_end(self, epoch, logs=None):\n",
    "        \n",
    "        logs = logs or {}\n",
    "        self.trn_iterations += 1\n",
    "        self.clr_iterations += 1\n",
    "\n",
    "        self.history.setdefault('lr', []).append(K.get_value(self.model.optimizer.lr))\n",
    "        self.history.setdefault('iterations', []).append(self.trn_iterations)\n",
    "\n",
    "        for k, v in logs.items():\n",
    "            self.history.setdefault(k, []).append(v)\n",
    "        \n",
    "        K.set_value(self.model.optimizer.lr, self.clr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_optimizer():\n",
    "    return keras.optimizers.Adam(learning_rate=LEARNING_RATE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_early_stopping():\n",
    "    \"\"\"stop training when no gain in metric and patience is reached\"\"\"\n",
    "    return keras.callbacks.EarlyStopping(\n",
    "        monitor='val_accuracy', \n",
    "        min_delta=MIN_DELTA, \n",
    "        patience=PATIENCE, \n",
    "        verbose=1, \n",
    "        mode='max', \n",
    "        baseline=None, \n",
    "        restore_best_weights=True\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_lr_callback(type):\n",
    "    \"\"\"return either decaying or cyclical learning rate\"\"\"\n",
    "    if type == 'decay':\n",
    "        lr = keras.callbacks.LearningRateScheduler(\n",
    "            lambda epoch: LEARNING_RATE * LEARNING_RATE_EXP_DECAY**epoch,\n",
    "            verbose=True\n",
    "        )\n",
    "    elif type == 'triangle2':\n",
    "        lr = CyclicLR(\n",
    "            base_lr=MIN_LR, \n",
    "            max_lr=MAX_LR,\n",
    "            step_size=CLR_STEP_SIZE * (TRAIN_SIZE // BATCH_SIZE),\n",
    "            mode='triangular2'\n",
    "        )\n",
    "    return lr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_lr_metric(optimizer):\n",
    "    \"\"\"this makes it possible to add the learning rate as a metric to the model\"\"\"\n",
    "    def lr(y_true, y_pred):\n",
    "        return optimizer.lr\n",
    "    return lr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_dependencies(optimizer):\n",
    "    return {'lr': get_lr_metric(optimizer)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def unfreeze(model, last_frozen):\n",
    "    \"\"\"unfreeze weights for layers that come after last_frozen\"\"\"\n",
    "    trainable = False\n",
    "    for layer in model.layers:\n",
    "        layer.trainable = trainable\n",
    "        if layer.name == last_frozen:\n",
    "            # start making layers trainable\n",
    "            trainable = True\n",
    "\n",
    "    # initiate Adam() with standard parameters\n",
    "    optimizer = get_optimizer()\n",
    "            \n",
    "    model.compile(\n",
    "        optimizer=optimizer,\n",
    "        loss='categorical_crossentropy', \n",
    "        metrics=['accuracy', get_lr_metric(optimizer)],\n",
    "    )\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_metrics(history, title):\n",
    "    \"\"\"plot loss, accuracy and learning rate as saved in History\"\"\"\n",
    "    metrics = ['loss', 'accuracy']\n",
    "    for n, metric in enumerate(['loss', 'accuracy']):\n",
    "        name = metric.replace(\"_\", \" \").capitalize()\n",
    "        plt.subplot(1, 3, n+1)\n",
    "        sns.lineplot(\n",
    "            history.epoch, \n",
    "            history.history[metric], \n",
    "            marker='o', \n",
    "            label='Train'\n",
    "        )\n",
    "        sns.lineplot(\n",
    "            history.epoch, \n",
    "            history.history['val_'+metric], \n",
    "            marker='o', \n",
    "            label='Validation', \n",
    "        )\n",
    "        plt.xlabel('Epoch')\n",
    "        plt.ylabel(name)\n",
    "    name = 'Learning Rate'\n",
    "    plt.subplot(1, 3, 3)\n",
    "    sns.lineplot(\n",
    "        history.epoch, \n",
    "        history.history['lr'], \n",
    "        marker='o', \n",
    "    )\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.ylabel(name)\n",
    "    plt.suptitle(title)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 26944 validated image filenames belonging to 80 classes.\n",
      "Found 2994 validated image filenames belonging to 80 classes.\n"
     ]
    }
   ],
   "source": [
    "BATCH_SIZE = 32\n",
    "\n",
    "# DATASET\n",
    "DIR = '/kaggle/input/food-recognition-challenge/'\n",
    "\n",
    "TRAIN_SET = pd.read_csv(DIR + 'train_labels.csv', dtype='str')\n",
    "TRAIN_SET_FILTERED = TRAIN_SET[TRAIN_SET['img_name'].isin(filtered_train_image_names())]\n",
    "TRAIN_DF, VALIDATION_DF = train_test_split(TRAIN_SET_FILTERED, test_size=0.1)\n",
    "\n",
    "# BASE MODEL\n",
    "#BASE_MODEL = keras.applications.vgg16.VGG16\n",
    "#PREPROCESSOR = keras.applications.vgg16.preprocess_input\n",
    "BASE_MODEL = keras.applications.densenet.DenseNet121\n",
    "PREPROCESSOR = keras.applications.densenet.preprocess_input\n",
    "\n",
    "# GENERATORS\n",
    "TRAIN_GENERATOR = get_generator(TRAIN_DF, augmented=True)\n",
    "VALIDATION_GENERATOR = get_generator(VALIDATION_DF)\n",
    "\n",
    "# TRAINING RUNS\n",
    "EPOCHS = 999\n",
    "TRAIN_SIZE = TRAIN_GENERATOR.samples\n",
    "\n",
    "# LEARNING RATE\n",
    "LEARNING_RATE = 1e-3\n",
    "\n",
    "# DECAYING LEARNING RATE\n",
    "LEARNING_RATE_EXP_DECAY = 0.9\n",
    "\n",
    "# CYCLICAL LEARNING RATE\n",
    "MIN_LR = 1e-10\n",
    "MAX_LR = 1e-4\n",
    "CLR_STEP_SIZE = 2\n",
    "\n",
    "# EARLY STOPPING\n",
    "PATIENCE = 5\n",
    "MIN_DELTA = 0.005"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initiate Model\n",
    "First goal is to do something about the strong overfitting seen during benchmarks with more than two epochs. This is achieved by:\n",
    "- Data augmentation: flipping, shifting, rotating, etc. the train dataset.\n",
    "- Introduce a `Dropout` function to the custom layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_model():\n",
    "    base_model = BASE_MODEL(\n",
    "        include_top=False,\n",
    "        weights='imagenet',\n",
    "        input_shape=(224, 224, 3),\n",
    "        pooling='avg',\n",
    "    )\n",
    "\n",
    "    # make sure we leave the imagenet weights alone\n",
    "    base_model.trainable = False\n",
    "\n",
    "    # get dimensions of base models's last layer\n",
    "    output_dim = base_model.layers[-1].output_shape[-1]\n",
    "\n",
    "    model = base_model.output\n",
    "    model = Dense(output_dim, activation='relu')(model)\n",
    "    model = Dropout(0.5)(model)\n",
    "    model = Dense(80, activation='softmax')(model)\n",
    "    model = Model(inputs=base_model.input, outputs=model)\n",
    "\n",
    "    # initiate Adam() with standard parameters\n",
    "    optimizer = get_optimizer()\n",
    "    \n",
    "    model.compile(\n",
    "        optimizer=optimizer,\n",
    "        loss='categorical_crossentropy', \n",
    "        metrics=['accuracy', get_lr_metric(optimizer)],\n",
    "    )\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## First Training\n",
    "Here we compare a fixed learning rate to a decaying one. How long until validated accuracy doesn't go up anymore in the last three epochs?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://github.com/keras-team/keras-applications/releases/download/densenet/densenet121_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
      "29089792/29084464 [==============================] - 1s 0us/step\n",
      "Train for 842 steps, validate for 94 steps\n",
      "Epoch 1/999\n",
      "842/842 [==============================] - 442s 525ms/step - loss: 148.8864 - accuracy: 0.1328 - lr: 9.9999e-04 - val_loss: 130.0760 - val_accuracy: 0.2472 - val_lr: 0.0010\n",
      "Epoch 2/999\n",
      "842/842 [==============================] - 442s 524ms/step - loss: 125.0284 - accuracy: 0.2366 - lr: 9.9999e-04 - val_loss: 121.2156 - val_accuracy: 0.2822 - val_lr: 0.0010\n",
      "Epoch 3/999\n",
      "842/842 [==============================] - 444s 527ms/step - loss: 120.1121 - accuracy: 0.2578 - lr: 9.9999e-04 - val_loss: 114.2454 - val_accuracy: 0.3193 - val_lr: 0.0010\n",
      "Epoch 4/999\n",
      "842/842 [==============================] - 452s 537ms/step - loss: 117.0210 - accuracy: 0.2720 - lr: 9.9999e-04 - val_loss: 113.1378 - val_accuracy: 0.3363 - val_lr: 0.0010\n",
      "Epoch 5/999\n",
      "842/842 [==============================] - 436s 518ms/step - loss: 114.6152 - accuracy: 0.2872 - lr: 9.9999e-04 - val_loss: 112.1344 - val_accuracy: 0.3370 - val_lr: 0.0010\n",
      "Epoch 6/999\n",
      "842/842 [==============================] - 429s 510ms/step - loss: 113.4763 - accuracy: 0.2919 - lr: 9.9999e-04 - val_loss: 110.4349 - val_accuracy: 0.3534 - val_lr: 0.0010\n",
      "Epoch 7/999\n",
      "842/842 [==============================] - 426s 506ms/step - loss: 112.2134 - accuracy: 0.2957 - lr: 9.9999e-04 - val_loss: 113.5548 - val_accuracy: 0.3360 - val_lr: 0.0010\n",
      "Epoch 8/999\n",
      "842/842 [==============================] - 423s 502ms/step - loss: 111.2468 - accuracy: 0.3027 - lr: 9.9999e-04 - val_loss: 105.5526 - val_accuracy: 0.3731 - val_lr: 0.0010\n",
      "Epoch 9/999\n",
      "842/842 [==============================] - 425s 505ms/step - loss: 110.5170 - accuracy: 0.3070 - lr: 9.9999e-04 - val_loss: 114.3850 - val_accuracy: 0.3434 - val_lr: 0.0010\n",
      "Epoch 10/999\n",
      "842/842 [==============================] - 423s 503ms/step - loss: 111.2174 - accuracy: 0.3073 - lr: 9.9999e-04 - val_loss: 118.1561 - val_accuracy: 0.3323 - val_lr: 0.0010\n",
      "Epoch 11/999\n",
      "842/842 [==============================] - 429s 509ms/step - loss: 110.4090 - accuracy: 0.3106 - lr: 9.9999e-04 - val_loss: 115.9804 - val_accuracy: 0.3554 - val_lr: 0.0010\n",
      "Epoch 12/999\n",
      "842/842 [==============================] - 424s 503ms/step - loss: 109.3877 - accuracy: 0.3117 - lr: 9.9999e-04 - val_loss: 107.6605 - val_accuracy: 0.3854 - val_lr: 0.0010\n",
      "Epoch 13/999\n",
      "842/842 [==============================] - 426s 505ms/step - loss: 109.8469 - accuracy: 0.3098 - lr: 9.9999e-04 - val_loss: 113.9376 - val_accuracy: 0.3564 - val_lr: 0.0010\n",
      "Epoch 14/999\n",
      "842/842 [==============================] - 424s 504ms/step - loss: 109.3609 - accuracy: 0.3148 - lr: 9.9999e-04 - val_loss: 108.8687 - val_accuracy: 0.3597 - val_lr: 0.0010\n",
      "Epoch 15/999\n",
      "842/842 [==============================] - 423s 502ms/step - loss: 109.6938 - accuracy: 0.3127 - lr: 9.9999e-04 - val_loss: 109.5628 - val_accuracy: 0.3747 - val_lr: 0.0010\n",
      "Epoch 16/999\n",
      "842/842 [==============================] - 425s 504ms/step - loss: 109.6815 - accuracy: 0.3101 - lr: 9.9999e-04 - val_loss: 110.1144 - val_accuracy: 0.3774 - val_lr: 0.0010\n",
      "Epoch 17/999\n",
      "841/842 [============================>.] - ETA: 0s - loss: 108.4141 - accuracy: 0.3173 - lr: 9.9999e-04Restoring model weights from the end of the best epoch.\n",
      "842/842 [==============================] - 425s 504ms/step - loss: 108.4209 - accuracy: 0.3171 - lr: 9.9999e-04 - val_loss: 112.0457 - val_accuracy: 0.3714 - val_lr: 0.0010\n",
      "Epoch 00017: early stopping\n",
      "Train for 842 steps, validate for 94 steps\n",
      "\n",
      "Epoch 00001: LearningRateScheduler reducing learning rate to 0.001.\n",
      "Epoch 1/999\n",
      "842/842 [==============================] - 429s 509ms/step - loss: 148.0988 - accuracy: 0.1376 - lr: 0.0010 - val_loss: 119.6333 - val_accuracy: 0.2806 - val_lr: 0.0010\n",
      "\n",
      "Epoch 00002: LearningRateScheduler reducing learning rate to 0.0009000000000000001.\n",
      "Epoch 2/999\n",
      "842/842 [==============================] - 424s 504ms/step - loss: 123.8444 - accuracy: 0.2402 - lr: 9.0000e-04 - val_loss: 111.4808 - val_accuracy: 0.3350 - val_lr: 9.0000e-04\n",
      "\n",
      "Epoch 00003: LearningRateScheduler reducing learning rate to 0.0008100000000000001.\n",
      "Epoch 3/999\n",
      "842/842 [==============================] - 426s 506ms/step - loss: 117.6939 - accuracy: 0.2712 - lr: 8.1000e-04 - val_loss: 111.4830 - val_accuracy: 0.3424 - val_lr: 8.1000e-04\n",
      "\n",
      "Epoch 00004: LearningRateScheduler reducing learning rate to 0.0007290000000000002.\n",
      "Epoch 4/999\n",
      "842/842 [==============================] - 425s 505ms/step - loss: 114.5575 - accuracy: 0.2886 - lr: 7.2900e-04 - val_loss: 107.3815 - val_accuracy: 0.3607 - val_lr: 7.2900e-04\n",
      "\n",
      "Epoch 00005: LearningRateScheduler reducing learning rate to 0.0006561000000000001.\n",
      "Epoch 5/999\n",
      "842/842 [==============================] - 424s 503ms/step - loss: 110.6752 - accuracy: 0.3085 - lr: 6.5610e-04 - val_loss: 109.4384 - val_accuracy: 0.3494 - val_lr: 6.5610e-04\n",
      "\n",
      "Epoch 00006: LearningRateScheduler reducing learning rate to 0.00059049.\n",
      "Epoch 6/999\n",
      "842/842 [==============================] - 425s 504ms/step - loss: 109.6500 - accuracy: 0.3102 - lr: 5.9049e-04 - val_loss: 109.6970 - val_accuracy: 0.3554 - val_lr: 5.9049e-04\n",
      "\n",
      "Epoch 00007: LearningRateScheduler reducing learning rate to 0.000531441.\n",
      "Epoch 7/999\n",
      "842/842 [==============================] - 423s 503ms/step - loss: 106.8860 - accuracy: 0.3275 - lr: 5.3144e-04 - val_loss: 108.7314 - val_accuracy: 0.3664 - val_lr: 5.3144e-04\n",
      "\n",
      "Epoch 00008: LearningRateScheduler reducing learning rate to 0.0004782969000000001.\n",
      "Epoch 8/999\n",
      "842/842 [==============================] - 426s 506ms/step - loss: 106.1525 - accuracy: 0.3398 - lr: 4.7830e-04 - val_loss: 102.7443 - val_accuracy: 0.3844 - val_lr: 4.7830e-04\n",
      "\n",
      "Epoch 00009: LearningRateScheduler reducing learning rate to 0.0004304672100000001.\n",
      "Epoch 9/999\n",
      "842/842 [==============================] - 427s 507ms/step - loss: 104.8929 - accuracy: 0.3399 - lr: 4.3047e-04 - val_loss: 107.1327 - val_accuracy: 0.3631 - val_lr: 4.3047e-04\n",
      "842/842 [==============================] - 432s 513ms/step - loss: 102.9155 - accuracy: 0.3525 - lr: 3.4868e-04 - val_loss: 103.0059 - val_accuracy: 0.3914 - val_lr: 3.4868e-04\n",
      "\n",
      "Epoch 00012: LearningRateScheduler reducing learning rate to 0.0003138105960900001.\n",
      "Epoch 12/999\n",
      "842/842 [==============================] - 437s 519ms/step - loss: 100.7371 - accuracy: 0.3582 - lr: 2.8243e-04 - val_loss: 102.3198 - val_accuracy: 0.3988 - val_lr: 2.8243e-04\n",
      "\n",
      "Epoch 00014: LearningRateScheduler reducing learning rate to 0.0002541865828329001.\n",
      "Epoch 14/999\n",
      "842/842 [==============================] - 437s 519ms/step - loss: 100.5459 - accuracy: 0.3641 - lr: 2.5419e-04 - val_loss: 107.9160 - val_accuracy: 0.3791 - val_lr: 2.5419e-04\n",
      "\n",
      "Epoch 00015: LearningRateScheduler reducing learning rate to 0.0002287679245496101.\n",
      "Epoch 15/999\n",
      "842/842 [==============================] - 443s 526ms/step - loss: 100.2290 - accuracy: 0.3661 - lr: 2.2877e-04 - val_loss: 102.8068 - val_accuracy: 0.4038 - val_lr: 2.2877e-04\n",
      "\n",
      "Epoch 00016: LearningRateScheduler reducing learning rate to 0.0002058911320946491.\n",
      "Epoch 16/999\n",
      "842/842 [==============================] - 436s 518ms/step - loss: 97.6751 - accuracy: 0.3779 - lr: 1.6677e-04 - val_loss: 105.4192 - val_accuracy: 0.3955 - val_lr: 1.6677e-04\n",
      "\n",
      "Epoch 00019: LearningRateScheduler reducing learning rate to 0.00015009463529699917.\n",
      "Epoch 19/999\n",
      "842/842 [==============================] - 439s 522ms/step - loss: 97.4146 - accuracy: 0.3869 - lr: 1.5009e-04 - val_loss: 104.5648 - val_accuracy: 0.3958 - val_lr: 1.5009e-04\n",
      "\n",
      "Epoch 00020: LearningRateScheduler reducing learning rate to 0.0001350851717672993.\n",
      "Epoch 20/999\n",
      "841/842 [============================>.] - ETA: 0s - loss: 97.3875 - accuracy: 0.3813 - lr: 1.3509e-04Restoring model weights from the end of the best epoch.\n",
      "842/842 [==============================] - 437s 519ms/step - loss: 97.4095 - accuracy: 0.3812 - lr: 1.3509e-04 - val_loss: 104.0597 - val_accuracy: 0.3985 - val_lr: 1.3509e-04\n",
      "Epoch 00020: early stopping\n"
     ]
    }
   ],
   "source": [
    "model_flat = get_model()\n",
    "model_decay = get_model()\n",
    "\n",
    "history_flat = model_flat.fit(\n",
    "    TRAIN_GENERATOR,\n",
    "    epochs=EPOCHS,\n",
    "    validation_data=VALIDATION_GENERATOR, \n",
    "    class_weight=get_class_weights(TRAIN_GENERATOR.class_indices),\n",
    "    verbose=1,\n",
    "    callbacks=[get_early_stopping()]\n",
    ")\n",
    "\n",
    "history_decay = model_decay.fit(\n",
    "    TRAIN_GENERATOR,\n",
    "    epochs=EPOCHS,\n",
    "    validation_data=VALIDATION_GENERATOR, \n",
    "    class_weight=get_class_weights(TRAIN_GENERATOR.class_indices),\n",
    "    verbose=1,\n",
    "    callbacks=[get_early_stopping(), get_lr_callback('decay')]\n",
    ")\n",
    "\n",
    "# tensorflow 2.1+ throws a coercion warning here but why is still unclear:\n",
    "# https://stackoverflow.com/questions/59317919/warningtensorflowsample-weight-modes-were-coerced-from-to"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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agI5PoSNUr8mIiIiAjk8tlyRci9jxT6Bg80pvDYy4Sc9AqtYFOzQiIqIWgwkM/PLBePGcITCa7YjSqmCIVrOAJ7VpEonQrDVgEhMTceDAAe/9W2+9FbfeemuVNpMmTcKkSZO895966inv7V27dnlvf/LJJ1XOu/4+tQ6heE0+8sgj3qUuvCapNvasMyj+ZjXi09IhUanhLLwK44EtiJ/4VN0nExEREQAuIfGSSASIIvDiqr24cMXI5AURERH5jXHverhKCyGPToBcFwtPuQVlZ/ag7IeDwQ6NiIioxWAC41f02opf9opLbUGOhIiIiFoLW/Z5lF86Cd3geyGRVRR3Vfe6FXJ9Akp2r4UoikGOkIiIqGVgAuNXwlUyyKUCikxMYBAREZF/GPdtgEQVAe2AO72PCRIpIodOhiP/Esp+PBzE6IJDFD1wWUrQIykeLksJRJE7vxERUd2YwPgVQRCgCZdyBgYRERH5hSM/C2XnD0F78z2QKKvueBbR+zbIIuPb3CwMUfTAkZ+Fax+8iLz3nsK1D16EIz+LSQwiIqoTExjX0YRJmMAgIiIivzDu/wyCXAVdyj3VjnlnYeT+hPKfjgQhuuBwW03IW/sKXKYCAIDLVIC8ta/AbTUFOTIiIgp1TGBcRxMmRZGpPNhhEBERUQvnLMmF5fQeaAeOhjTc9zbQmj63Q6aLbbWzMERRhKu0CGUXMlCydz3yNrwGlzHPm7yo5DIVQHTagxQlERG1FNxG9TracCnOZ5dBFEUIAnciISIiosYxfr8JkEigGzy+xjaCVIbIWyeh8Mv/RXnmcYR36R/ACBtPFD1wW00Q3S4IUhmkah0ginAWZcOedwmOvEw48i7BnncJnrJS73myyHiIbjdkutgqSQyZLhaO/Mso3PYP6FLGIKxrfwgSaTCeGhERhTDOwLiOJkwKh8sDa7kz2KEQtUqzZ8/Gp59+WuUxURQxcuRIHDp0qMbzHnzwQXzzzTcAgDfeeANbt2712W7lypVYvnx5nXFs2LABly9f9t7fuXNnvc6j1qcp1+SuXbsA+O+azMzM9N5vLddkZbFGp6mgTRVrdJlLYD7+NTR9R0Cm0dfaVtN3BKTaGJTs/k+LmIXx6xoWV956DNc+eBG2rLO49tFCXH13Hgo2vYHSQ1/CXW6F+sabET16NhIeWoJOz36EDo+vgqpDT8SnpUOmiwVQkbyIn/ICXGVmOPIvIfc/f8GVv/8Bxu83wV1uDvKzJSKiUMIZGNfRhFVk+4tMNkSEK4IcDVHw+Pp1TRCanvOcPHkyPvjgA0ybNs372IEDByCTyXDzzTfXq4+nnnqqyXF89tlnmD59Onr16gUASE1NRWpqapP7pebTFq7JqKgodO7cGUDruCYrv+hW1juQ6WIRn5YORVwHv/y/C2Wmg58DHg8ih0yss60gkyNyyH0o+uo92C6fQlinPgGIsPF81bAo2LwScfc9DWdxDpTxnSGPToAg9f0xUxAkUMR1QMLMZbCXlUEZHg6pWgdlu87Q9r0D1vMHUXr4SxTv/BAl332KiJuGQZs8BkpDl0A+TSIiCkFMYFzHm8AotaGjQRvkaIiCozm/dIwaNQqLFy/GhQsXcMMNNwCo+OV50qRJ2L9/P/72t7/BbrfD7Xbjsccew9ixY6v1kZ6ejt69e2PGjBkwm814+eWXceHCBRgMBuj1esTExABAjf2tX78ep06dwquvvop33nkHL7zwAnJzc/Htt9/izTffBAC8++67+PzzzwEAffr0wfz586FWq7Fy5UpkZmbCbDbjypUr6NChA9544w2EhYVVi5P8p61ck0uWLMHf/va3VnNN1lSsMWHmMsgiooIcXfNxl5tRemQ7InoNhTyqXb3O0fQfCePe9SjZvTbkExiecovPGhbSiCio2nerVx+CIIEsIgrHf7iI5OTkXx6XyhDR81ZE9LwVjvzLMB3eBsup72A+/jWU7btDlzIG6p63QJDKmy2pSUREoYsJjOtowisSGMUm7kRCrZP5xLcwH/+61jaRw6ag8ItV1b50xIydC+OedTWep+k3Epq+d9Tat0KhwPjx47FhwwY8//zzsFgs2LFjB7788kuoVCp8/PHHkEqlKCwsxKRJkzBs2DDodLoa+3v77behVquxdetWFBcXY9KkSRgzZgwAoFevXj77mzx5MjZu3Ijp06d7227YsMHb53fffYfPP/8cn376KdRqNV544QWsWrUKzz33HADg1KlTWLduHTQaDWbPno3Nmzfj/vvvr/V5U818XZNutxsm6S/r39vKNTlr1iyMGDECQOu4JkW3y3exRrcrSBEFRunhLyE6bIi89b56nyORKRB560QUbX8f5ZdPI6zjTc0YYeNZzuyFIJX5rGFR04yLxlLEdUTsPb+DfuQMWE58g9KMbcjf9DdId+gQedtUqNp3Q9665W1udg8RUVvGd/jraFQ/JzC4lSq1YRKFyueXDolC5Zf+p0yZgs8//xwulwtffvklkpOTER8fj+LiYjz55JMYN24cZs+eDZPJVKUmgC8HDhzAlClTAAB6vR533nmn91hj+gMqfiW/5557EBERAUEQcP/992P//v3e48OGDYNWq4UgCOjbty+ysrIa+UpQffGabJnXZOUX3V9rji+6ocTjKIfp0BcIv/FmKOI6NuhcTf9RkKojYdyztpmiaxrrj4eRv+kNWC8crV7DIi29opBnM5Cq1NANGofEx95Eu2nzoUy4EbKISG/yAuBWrEREbUXr/QTRSHKZAE24nFupUqul6XtHnb9IuywlPn9dk+likfDgn5ocQ48ePRAbG4vdu3dj/fr1mDlzJgBg0aJFGDlyJN566y0IgoC77roLdnvt2+rVVvCuMf1V9lnbLkRKpdJ7WyqV1qtPqpmva9JqtUKtVnvv85psmdekVK1D3MR5yN/4/6r8St5cX3RDQenR/8JTbkHk0EkNPlciV0I3ZAKKd/wbtivnoErq0QwRNk555gnkr38NyvhOiBn1EASFCgkzlwV0+YYgSBDedQDCuw6AsyS3Tc7uISJq6zgDwwe9VsUZGNSmSdW6Zv91bfLkyVi5ciUuXbqEkSNHAgDMZjPat28PQRCwd+/eKruE1GTIkCHeqfYlJSXYsWOH91ht/anValgsFp993nrrrdi6dSssFgtEUcS6detw6623NuXpUhO1lWvSbPa940JLviZLvt+EmLFzkfjYm4i+82E4i3Na7RR/0eWE6fvNUHXsXe9aENfTDhgNSbgWJSE0C8N29Rxy174Cmd6AdtP+CIky3FvDQq6LhSwiKuD/TwW5ss3N7iEiIiYwfGICg9q6X1eIT/rDO0iYuczv64rHjx+PCxcuYPz48VAoKnb8eeaZZ7BixQpMnToVX331Fbp3715nP3PnzkVpaSnuuecePP/88xg6dKj3WG39TZ06Ff/4xz8wceJE7Nu3r0qft99+O8aPH49p06Zh/PjxAIDf//73/nja1Eht5ZpctWpVq7ombVlnUP7DAbgtxZDrE2Dc9xmKd37Yan8lN5/8Fm5LcaNmX1SSKFSIvGUCyi8egy37vB+jaxx7zkXkfroUMo0ehukLIA3XBDskAIFJahIRUehhmtoHvU6FrDzuO05tW+Wva81Fp9PhxIkTVR4bOnQotm/f7rP9Rx995L39yiuveG9rNBqsXLnS5zm19TdixAgMGjSoyjKFSZN++dIxZ84czJkzp9p5TzzxRK33qfmE4jVptVoB+O+arCzgWamlX5Pm419DUIZD3WMIBEFA5NDJyFv7Ciynd0PTd0TdHbQgoscN4/6NUBpuQFinvk3qS5t8F4z7N8K4Zx3aTX3JTxE2nKMgCzmf/AkSZTgM0xeG1M4xv05qchcSIqK2g+/yPkTrwlBitsPtqXkdMxEREdXMY7PCenY/InoNg0ReUaMj/MYUKOI6wrhvA0SPO8gR+pf17D64SnIROXRSrfVK6kOiCINu8HiUXciAPecnP0XYMM6SXOR8/CcIEikMDyyqtlwjFAR7GQsREQUe3+l90GtV8HhElFpCowgaERFRS2M5vQeiywFN/1TvY5WzMJxF12D94UAQo/MvUfSgZO8GyGMSEd7tZr/0qUsZA4lKHZRaGK7SQuSsXgTR7YLhgYWQ6w0Bj4GIiMgXJjB80GsrtuUrMrEOBhERUWOYj++EIq4DlIauVR5X97gF8ugEGPesr3XHlpak7McMOAuyEHnrfX6bBSBRhkM3aBzKzh+CPbfurXb9xWUxImf1YrhtVhh+swCK2A4BG5uIiKguTGD4EK2rSGCwkCe1Jq3li0JL4vF4gh1CSOM1GXiBes3teZdgz/kJmn6p1ZZTCBIpIofcB0f+JZRfOBKQeJqTKIow7tsAmS4OEb2G+bVv7c1jISjDYdy7zq/91sRdbkbuJ4vhMhfBMPVlKA1dAjIuERFRfTGB4UNlAqOICQxqJVQqFYqKiviFMUBEUYTD4UB2dnaVIqH0C16TgSeKIoqKiqBSqZp9LPPxrwGJDBG9h/s8HtF7OGS6WJTsbfmzMGxZp2HPPo/IIRP8voWnVKWG7uZ7YD33PRz5WX7t+3oeexlyP1kCZ1EO2qWlQ5XUo1nHIyIiagzuQuJDZIQSEgEoMpUHOxQiv0hMTMTVq1dRUFAQkPEcDod3G8pQ1pxxymQy6HQ6xMTENEv/LV1d1ySvIf+qjFOlUiExMbFZxxJdTlhOfQd195shDdf6bCNIZdDdMhFFX70H2+VTCOvUp1ljak7GvRsgVUciot/IZulfN2gcTAe3oGTvOsTf93SzjOFx2pG75i+w52UifvJzCOvctF1UiIiImgsTGD5IpRJEapQoZg0MaiXkcjk6d+4csPEyMjLQr1+/gI3XWC0lztaormuypfy/YZzVWX88BE+5BZp+qbW20/QfCeOetSjZu77FJjBs1y6gPPM49CMfhETWPIksaZgGupR7YNz3GRy33Q9FjH8TUKLLibx1K2C7cg5x982D2k9FSFuyzMxMpKenw2g0IjIyEsuXL0enTp2qtHG73ViyZAl2794NQRAwZ84cpKWl1Xlsz549eP3113H+/Hk8+OCDeOGFF+rVZ6WLFy/ivvvuw/Tp06ucS0TUVnAJSQ30WhVrYBARETWQ+dhOSLUxdf6KL5EpoLvlXtgunYQt+3yAovMv474NkKjU0A4c3azj6AaPhyBXwrh3vV/6E0UPXJYSOI35sGWfh8dmRczY3yOi11C/9N/SLVy4ENOnT8dXX32F6dOnY8GCBdXabN68GVlZWdi+fTvWrFmDlStX4urVq3UeS0pKwpIlSzB79uwG9QlUJDgWLlyIUaNGNdMzJyIKfUxg1ECvDeMuJERERA3gMhWg/OJxaPreAUEirbO9duBoSMIiYNwTmCKV/uQouIKyHw5AmzIGEmV4s44lDddCm3wXLKf3wFF0rUl9iaIHjvwsXPvgRVx5+/co2LwS0aNnQdNvhJ+ibdmKiopw5swZjBs3DgAwbtw4nDlzBsXFxVXabd26FWlpaZBIJNDr9Rg1ahS2bdtW57GOHTuiV69ekMmqT4Ku7TwAePfdd3HHHXdUmw1CRNSWMIFRg2gdZ2AQERE1hPnENwBEaOpZD0KiCIPu5nEou5ABe96lZo3N34z7N0KQK6G7eWxAxtMNvheCVAbjvqbNwnCbi5G39hW4TBX1Z1ymAuR/9jrcVpM/wmzxcnJyEB8fD6m0IgEnlUoRFxeHnJycau0SEhK89w0GA3Jzc+s8VtfYNZ137tw57NmzBzNnzmz0cyMiag1YA6MGep0KpVYHnC435LK6f0UiIiJqy0TRA/PxbxDWqQ/kkfH1Pk+bMgbG7zdVLI/oeEfzBehHTmM+LKd2QXvzPTUWKvU3WUQktANHw3RoK6KGpUEe1a7Oc0RRhLM4B7YrZ2G/eg62K+cQc89j3uRFJZepAKLb1VyhUxM5nU788Y9/xLJly7yJlcY4depUg8/JyMho9HiBxDj9i3H6F+P0LyYwaqDXVmwzV1xqR7y+eaeGEhERtXS2S6fgMuVDP2J6g86ThkVAl3I3jPs2QhLdMop5mr7fBAgSRA6+N6Dj6m6ZCNu1n+C2GAGJFIJUBqlaB0GomFArup2w52bCdvUc1Cf34/Kut+ApKwUASMKyEVwyAAAgAElEQVQioErsAYkiDDJdbJUkhkwX6/ctYFsqg8GAvLw8uN1uSKVSuN1u5Ofnw2AwVGt37do19O1bUevl17MnajtW19i+zisoKEBWVhbmzJkDACgtLYUoirBYLPjzn/9c7+fWu3dvKJXKerfPyMhAcnJyvdsHC+P0L8bpX4yzZna7vVGJVf5rVYNo3c8JDJONCQwiIqI6lB7fCYlKjfBugxp8rm7QeJgOfgHVxX3A7c1bELOxRNEDt9WEHolxQEQ/SCL0kGmjAxqDNEKH6NQZyN/4N7hMBZDpYhE36VnYc36C9exe2LN/hOhyVLQNi0T4DQOhSuoBVVJPyKMTIAgSiKIH8Wnp3mUkMl0s4tPSIVXrAvpcQlV0dDR69uyJLVu2YMKECdiyZQt69uwJvV5fpd3dd9+NtWvXYvTo0TAajdixYwdWr15d57Ha1HReQkICDhw44G23cuVKlJWVcRcSImqTmMCowS8zMFgHg4iIqDbucgvKzh2Apn8qJPL6/8JbSarWQTNgFDyHv4TTmA95ZFwzRNl4lYUvq3zpn/I8RNHjnf0QCG6ryZu8AH6uX7HhNUTf+TA8dhs0A+6EKqknVIk9cPz8Rdzo49c0QZBAEdcBCTOXQXS7qs3iIGDRokVIT0/HqlWroNVqsXz5cgDAo48+iieffBJ9+vTBhAkTcPz4cYweXZFwe/zxx5GUlAQAtR47fPgwnn76aVgsFoiiiC+++AJLly7FbbfdVut5RERUgQmMGlQmMIpM5UGOhIiIKLRZTu+G6HZC0z+10X1E3jIRpsPbYNq/ETFj5vgxuqZzW03VCl/mrVuBhJnLIIuIClgcotvls36FIr4zEmevqHc/giAJaNwtTdeuXbF27dpqj7/33nve21KpFIsXL/Z5fm3HUlJSsGvXrgaf92tPPPFEnW2IiForpttroFUrIJNKOAODiIhaLFH0wGUpQY+keLgsJRBFT7OMYz62E4r4zlC269LoPmTaaDja94X5+NdwmUv8GF3T1ZQ4CHThS0Eqg0wXW+UxmS4Wgkwe0DiIiIiChQmMGgiCAL1OhSImMIiIqAWqXPZw7YMXkffeU7j2wYtw5Gf5PYlhz70IR15mvbdOrY2tyxCIHjdMBz73Q2T+U2PiIMCFL6VqHeLT0r2xsH4FERG1NUxg1CJaq0KxiQkMIiJqeXwue1j7CtxWk1/HMR/bCUEqR0Tv4U3uyxMehYibhqH0yHa4f949IxRIwjSIHf9E0BMHv65fkfSHd5AwcxkUcR1Yv4KIiNoM1sCohV6nwqVrofMBioiooTIzM5Geng6j0YjIyEgsX74cnTp1qtJm/fr1+OCDDyCRSODxeJCWloaHHnoIQEW1+48//hhxcRVFFQcOHIiFCxcG+mlQI3jsZc2+7MHjcsByejfCewyGNCzCL31G3nofLKd2wXTwC+jv+I1f+mwqc8Y2WE7vQbupL8EpSqEMDw9a4UvWryAioraMCYxaRGtVOHIuP9hhEBE12sKFCzF9+nRMmDABmzZtwoIFC/Dhhx9WaXPXXXdh0qRJEAQBFosF48ePx6BBg9CjRw8AwMSJE7ldXwtjz7vk3S3j10kMfy97KPvhADw2K7T9Gl+883qK2A4I7z4YpYe3IvKWeyFRqf3Wd2O4SotQ/N0nFVuRxiTh5JEjSPaxuwcRERE1P845rIVeq0K53YUymzPYoRARNVhRURHOnDmDcePGAQDGjRuHM2fOoLi4uEq7iIgICIIAALDZbHA6nd771PK4SouQu2YpSo/8F/FTXqiy7CF2/B8AmcJvY5mP7YRMFwdVp95+6xMAooZOhsdeBlPGV37ttzEKt/8T8HgQc/ej/HtBREQUZExg1CJaV7GVKnciIaKWKCcnB/Hx8ZBKpQAqtuiLi4tDTk5OtbY7d+7E2LFjMWLECDzyyCPo3r2799gXX3yB8ePHY9asWTh69GjA4qeG89jLkLtmKTz2cuhHTIciviMSZi5D/KNvot30BSj+5mMUfPY6RI+7yWM5jXkov3QSmn4j/L6UQmnoirAu/WE6uBkep92vfTeE9fwhlP1wAFG3pUEeGR+0OIiIiKgCl5DUQv+rBEZinCbI0RARNZ/U1FSkpqbi2rVrePzxxzF8+HB06dIF06ZNw2OPPQa5XI69e/di7ty52Lp1K6Ki6r8G/9SpU42KKSMjo1HnBVrIxOlxIyLjP5AVZ8GSPBXFV4uAq0VVmigiu0B9+kuc/fg1lPcc1aThVD/uggpAphCDi358DSpfT1lMb2guHsOZz9+HvdMgv/Vfby4HtHveBSJi8JM8EfjVcwyZ/+d1YJxERNTaMIFRC7325wQGdyIhohbIYDAgLy8PbrcbUqkUbrcb+fn5MBgMNZ6TkJCAPn364Ntvv0WXLl0QG/vL1pFDhw6FwWDAjz/+iEGD6v+Fsnfv3lAqlQ2KPSMjo0XUGQiVOEVRROEXq2AuykTM2Lno2r9qTQpvnMnJKAwTgMNbkdg7Bdr+jatdIXrcyNr3LhRd+qPrsKZvn1otTgBAMq7lHoE0+whumvAIBJncb+PUR9HOD2GylSLhoaVQJfWoIcbQxThrZ7fbG51cJSKi4OESklpUJjCKmMAgohYoOjoaPXv2xJYtWwAAW7ZsQc+ePaHX66u0++mnn7y3i4uLceDAAXTr1g0AkJeX5z129uxZZGdno3PnzgGInhrCuGcdzMe/RuSwKXUmJaLvnImwzn1R+OW7sF0516jxyjNPwF1aCE0//yUvfIkcOhluczHMJ75p1nGuZ8+7BNOBzdD0H1UleUFERETBxRkYtQhXyRGmlLEGBhG1WIsWLUJ6ejpWrVoFrVaL5cuXAwAeffRRPPnkk+jTpw/WrFmDvXv3QiaTQRRFzJgxA8OGDQMAvP766zh9+jQkEgnkcjlWrFhRZVYGBZ/5xLco2fUpIvrcjqjh0+psL0ikiLvvaWT/Kx1561eg/cPLvYU+6z3m8Z2QhGmg7ta8SzvCOveD0tAVxv0boemfCkEibdbxAEAUPSj88n8hCYuAfsSMZh+PiIiI6o8JjDrotSoUMYFBRC1U165dsXbt2mqPv/fee97bL730Uo3nVyY8KDSVZ55AwReroOrUB7Fjf1/vXTKkYRq0u/9FZH/wInLXLkfCQ0sgUajqda67rBTWHw5Bm3xXsy/rEAQBkUMnw7jvMzgKsyFRhkGQyiBV6/xeOLSS+egO2LPPI3b8E5CGs/4VERFRKOESkjpE61SsgUFERCHHkZ+F3PWvQh6dgPjJz0GQNiyZoIhJRPzE/4Ej7xIKNr8FUfTU6zzLqV2Ax9Xo+hkNFXZjMvQjZyDvP3/Blbcew7UPXoQjP6ve8TaEy2JE8Tf/B1XH3ojoc7vf+yciIqKmYQKjDnodZ2AQEVFocZmLkbNmKSRyJQxTX4ZUpW5UP+E3JEOf+iCs5/bDuHtdne1FUYT5+E4oDV2hiOvYqDEbylNmRsHmt+AyFQAAXKYC5K19BW6rye9jFe/4AB6nHTFj5tR7NgsREREFDhMYdYjWVszAEEUx2KEQERHBYy9H7pq/wGOzoN3Ulxpcv+J6usH3IqLPHSjZvQaWc/trbWvP+QmO/Cxo+gVm9gUAiG6XN3lRyWUqgOh2+XWcsovHYTm9G5FD7oMiur1f+yYiIiL/YAKjDnqtCi63B6VWR7BDISKiNk50u5C34a9w5F9G/KRnoWzXpcl9CoKAmHt+B2X7bij4fCXsuZk1tjUf3wlBpkDETcOaPG6945PKqiVpZLpYvxb09LgcKNz2LuR6AyKHTvJbv0RERORfTGDUIVoXBgDciYSIiIJKFEUUbnsP5RePImbM7xDedYDf+pbIFIif/DwkKjVy174Cl8VYrY3HaYfl9B6oew6BpJFLVhpDqtYhPi3dm8SQ6WIRO3Yuir9ZDacx3y9jGPdugKskF9F3PwqJTOGXPomIiMj/mMCog15bUZWdCQwiIgom474NMB/bgcihk6EdMMrv/cs0UWiXlg5PWSny1r8K0eWsctx6bj9Eexk0/Ub6fezaCIIEirgOSJi5DEl/eAcJM5dBFCSwnj+I7PefQ9mFI03q31GUDeP+zxDRezjCO/fzU9RERETUHJjAqINeV5HAKOJOJEREFCTmU7tQ8u3HiOg9HFG3/6bZxlEauiJ2/B9gv3oOhdverVL/yXz8a8ii2kHV4aZmG78mgiCBLCIKcl0sZBFRCO/UG4mzVkCmiUbumr+g+LtPIHrcDe5XFEUUfvm/kMiV0Kf+thkiJyIiIn9iAqMOeq0SAGdgEBFRYImiBy5LCRxF2ZDIVYgYeBdix81t9t0xInoNReTQKTAf/xqlh74AADhLcmG7fBqafiNDZncOud6AhJnLENH3Dhj3rEPup0vhLittUB+Wk9/Bdvk09CNmQBYR2UyREhERkb/Igh1AqJPLpNCqFSjmDAwiIqonUfTAbTVBdLsgSGWQqnUQhPr/ZiCKHjjys5C39hW4TAWQ6WIRn/YC4MfClbWJun0qHIVXYDm9F2FdBkB0ORA/5Xko23cLyPj1JZErETvucagSe6Doq3/g6j+eRfzkZ6GqR5zuMjOKdv4byvbdoWmGJTlERETkf0xg1INeq+ISEiIiqhdfyYe4Sc/CbS+Dp7QQHocNotMGj6Pij+iwweP8+b8//9Hf8RsUfvm/3u1DXaYC5K1djoSZyyCLiGr25yAIEsTe+wQcOReR++mff5VESW9wMqa5CYIA7YBRULbrgrz1r+Lah39E9KjfQpsyptbZIsVffwRPuQUx0+eE1PMhIiKimgXsX+zly5dj5MiR6N69O86fP1/t+FtvvVXt2LFjx3DvvffirrvuwqxZs1BUVBSocKuI1qlQXFoelLGJiKhlcVtN3uQFUJF8yN/wGuAoR8GWt1G0/Z8o/mY1jPs+g+XENyi7eBSO3Ey4LCUAREjVOkjCNN7zK7lMBRDdroA9D9FhQ8HmldclUV6B22oKWAwNoTR0QfvZryK8Sz8Ubf8n8jf+P3gcvv/ttl05C/PxndANHg9lfKfABkpERESNFrAZGKmpqXjooYfwwAMPVDt2+vRpHDt2DAkJCd7HRFHEc889h2XLliElJQWrVq3Ca6+9hmXLlgUqZC+9VoXMa6H5gY2IiEKL6HL6TD4oYjsg6fG/Q6JQQVCoIEjlNc4QcFlKINPFVulHpouFIA3cxEnR7Qp6EqWhpGERiL8/HcZ9G1Hy3Sdw5F9G/OTnoIhJ9LYR3U4UfPm/kOliEXXb/UGMloiIiBoqYDMwUlJSYDAYqj3ucDjwpz/9CQsXLqzyQe7kyZNQKpVISUkBAEybNg3btm0LVLhV6HUqGM12uN2eoIxPREQtgyiKcJuLINPFVnlcpouFoFBBHhkHabgWEpmi1uUNUrUO8Wnp3n5+vXwjUASpzPfzCGASpTEEQYKooZNgmL4A7rJSZL//Aiyn9/xSFLUwG/rbf4OYe5+ERKEKdrhERETUAEH/FPLGG2/g3nvvRVJSUpXHc3JyqszI0Ov18Hg8MBqNiIwMbKXwaF0YPCJgtNgRrQsL6NhERNRymA58DuvZ/YibOA/5G/9ftdoR9SUIEijiOiBh5rJGFwJtqsokStVCooFNojRFWKc+SJz9GvI++ytMB7dApotB/sa/VXkuouhh/QsiIqIWJKgJjKNHj+LkyZN49tlnm22MU6dONficjIyMKvdL8ivW0O49cAztoxV+icsfro8zVDFO/2Kc/tVS4qTQZz1/CMU7P4K6xy1QJHRtcvJBECQBKdhZ2/jBTqI0lUwbjYQZf4I99yLyP3u9Wj2PQBVFJSIiIv8IagLj0KFDuHjxIlJTUwEAubm5mD17NpYtWwaDwYBr16552xYXF0MQhAbPvujduzeUSmW922dkZCA5ObnKY7o4Iz7Z9R1iDZ2Q3Kf6Mphg8BVnKGKc/sU4/SsYcdrt9kYlVim02fMuIX/j36A0dEHsvU9AIpFB0gq+GAc7ieIPglQGaURUi6vnQURERNUFNYExZ84czJkzx3t/5MiReOedd9CtWzd4PB7YbDYcPnwYKSkp+PTTTzFmzJigxBmtq1gjW1zKrVSJiKgql8WIvP8sg0QVjvi0dEjk9U+aU2BU1vMIZlFUIiIiarqAzQNdsmQJhg8fjtzcXDz88MMYO3Zsre0lEglWrFiBxYsXY/To0Th06BCeeeaZAEVblTZCCYlEYAKDiIiqcruQt2453OVmtEt7ETKNPtgRkQ+hUBSViIiImi5gPz3Mnz8f8+fPr7XN119/XeX+wIEDsXnz5uYMq16kEgFRGiWKTUxgEBFRBVEUoT61Bfac84if/ByUhi7BDolq0BrqeRAREVEI7ELSUkTrVCgylQc7DCIiChHGPeugyDmDqDsegLrHLcEOh+rQGup5EBERtXX86aGe9FoVl5AQEREAwHJmL0p2fQp7Qh9E3npfsMMhIiIiahOYwKgnJjCIiAgAbNcuoGDzW1Am9kBZ7zEQBCHYIRERERG1CUxg1FO0LgzmMifsTnewQyEioiBxlRYhb+0rkKoj0W7K84CEKzGJiIiIAoUJjHrSayu2Ui3hLAwiojbJ47Ah9z/L4HHY0G7qi9zBgoiIiCjAmMCoJ72uIoFRxJ1IiIjaHFH0IH/TG3DkX0b8fU9DEdsh2CERERERtTlMYNRT9M8JDG6lSkTU9pR8+zHKzh9E9KjfIvyGgcEOh4iIiKhNYgKjnqJ/XkJSxCUkRERtivnENzDu+wyaAXdCe/PYYIdDRERE1GYxgVFP6jA5FDIJdyIhImpDbFfOouCLd6Dq1Acxdz3CHUeIiIiIgojl0+tJEARE68JQZCoPdihERNSMRNEDt9UE0WmH22ZFeLcUxN7zGAQp/8kkIiIiCiZ+GmsAvU7FGRhERK2YKHrgyM9C3tpX4DIVQKaLRfyU5yFRqYMdGhEREVGbxyUkDaDXqljEk4ioFXNbTd7kBQC4TAXIW7cCbqspyJER0fU8HhElZhvyS8pQYrbB4xGDHRIRETUzzsBogGidCgfP2CCKItdBExG1QqLb5U1eVHKZCiC6XUGKiIh88XhEXM4txZL3DyC/pBxxUWGYP2swOrbTQiLhZzQiotaKMzAaQK9Vwe5wo8zGD7JERK2RIJVBpout8phMF8v6F0QhxmS1e5MXAJBfUo4l7x+AyWoPcmRERNScmMBoAP3PW6myDgYRUeskVesQe++T3iSGTBeL+LR0SNW6IEdG5D8tfemF0+VBqcXhTV5Uyi8ph9PlCVJUREQUCPxJqQH0uooERpGpHEnxmiBHQ0RE/uYy5qN454eIn/wcJOFaCFIZpGodBIH5fmodWvLSC6fLgx2HsrBu53k8MqEP4qLCqiQx4qLCIJfx7yoRUWvGd/kGiNZxBgYRUWtmPv4N7Dk/QaqOhFwXC1lEFJMX1Krkl5T5XHqRlVuK0xeLYLLYIYp1z8gI5CwOp8uNrfsyMWfZDqxadxxRGhV0EQrMnzUYcVFhAOBNxOjUymaLg4iIgo8zMBpAr6mcgcEEBhFRayN63DCf+AZhXfpBpo0OdjhEfpNfUobdR7Px3dGrmDOxj8+lF5ZyJ15ctRcAoA6TIzE2AgmxarSPi0D72Io/hhg1VApZwGZxOJxu/PfAZaz7+kcUmmzo0TEKT6T1x4DusRAEAR6PiNeeGg6nywO5TAKdWhnys0iIiKhpmMBoAJVSBrVKxq1UiYhaofKLx+E2F0Fz58PBDoVaKY9HhMlqD8gXbqutYtbCd0eu4kxmMQCge4cohKlkPpdexEaFY+EjtyC7wFLxJ9+CkxcK8U3G1Sr9xkaF4X+mDsAba45Wm8Xx2lPDEfXzjz1N4XC68dX3FYmL4lIbenXW46lpA9Dvxtgqu8BJJIJfxiMiopaDCYwG0uvCUMQlJERErY75+E5IwrVQd0sJdigUgiqTD/Htu6LEbGtw8sHhdONqvhlL/3Ww2WYtlNmcOHA6F7uOZuPIuTx4xBwkxWswY0wPDO+fCEOMGh6PiPmzBlebPREbGYZ4fThSesZX6bPc7kJOoRXZ+RZkF1YkNhRyqc9ZHAUl5diyJxOdE7TokqBDu2h1jc/N1+vpdHvw1f5LWP/NjyguteOmLtF4evpA9L0hJqDb12dmZiI9PR1GoxGRkZFYvnw5OnXqVKWN2+3GkiVLsHv3bgiCgDlz5iAtLa3OY3v27MHrr7+O8+fP48EHH8QLL7xQrz7ffvttbN26FVKpFDKZDPPmzcNtt90WmBeEiCiEMIHRQNFaFWtgEBG1Mm6rCdbzh6FLuRuCVB7scCjE+Foy8dLMQZDLJSg02mC2OmAuc8BsdaDU6kDpz7fNZQ6Uljlhttox7zfJ+Memk9VmLTye1g9f7ruEeL0a8fpwtIsOR7w+HHH6cKgU1T+mXT+LQ62S4egPBfjuaDYOnM6Fw+lGbFQYhvTUYOqYZHQyaKvNWujYTlvvpRdhShm6tNehS/tfduIpMdt8zuKwlDmw7usfvfUwVAopOhq06Jyg8yY1Ohq0UMql1V7P5x9MwSfbf0DGuXz06RqDZx9IQZ8bYvzy/6+hFi5ciOnTp2PChAnYtGkTFixYgA8//LBKm82bNyMrKwvbt2+H0WjExIkTMWTIECQmJtZ6LCkpCUuWLMFXX30Fh8NR7z779u2LWbNmISwsDOfOncOMGTOwZ88eqFScgUJEbQsTGA2k16lw4kJhsMMgIiI/Mp/aBXhc0PRLDXYoFGIsZQ6UmO3VCl/+5YODeGRCH/zlg4NV2qvD5NCGK6BRyxGpUSEpXgONWoF20eE+Zy1EhCmQXWDFkR8K4HC6qxyP1CjRTh9ekdyIDseNSZGI1qnwyr8Peb/4PzV1ANbu/BE5RVaMujkJtw9MRI+Oehw9egSdE3xv/9vUpRc6tdLnLI6O7bRY+5exyMo1I/OaCZk5pci8ZsLuY9nYtv8SAEAQgAWzb8Hf1x+v8nqu+Ogwnpw6AFNG3ojeXYOTuACAoqIinDlzBv/6178AAOPGjcOf//xnFBcXQ6/Xe9tt3boVaWlpkEgk0Ov1GDVqFLZt24ZHHnmk1mMdO3YEAOzcubNaAqO2834926J79+4QRRFGoxHt2rULwKtCRBQ6mMBooGidCiWlFdW2WSiKiKjlE0UR5uM7oUy4EYq4DsEOh4JIFEUUlJTjTGYRzmQW40xmES7nmrFs7lCfyYfEuAi88vgwaNUKaMIV0ITLIZX63rWmplkLsVFhWPX8yIovpBY78orLkFtUhrxiK/KKypBXXIZzl4ux+3g20h+62Zu8qIzhjTVHsejRITDEqCGrYWx/q20Wh0IixQ1JkbghKdLbXhRFFBjLkZldkdTQRSh8vp6GGDXiosID8hxqkpOTg/j4eEilUgCAVCpFXFwccnJyqiQwcnJykJCQ4L1vMBiQm5tb57G6xq7PeRs3bkSHDh2YvCCiNokJjAbSa1Vwe0SUWh2I1HCrLiKils5+7Uc4C64gZszvgh0K1cAfxS999SECuJxTWiVhUbnTWLhKhh6d9Litf3tEaVQ+kw8R4XIkxWvqNX5NsxYqt/0UhIpZEVEaFXp01Fc73+32IL+kzOcXf6VCGrDkRaWGzOIQBAFxUeGIiwrH4N6GGpM5chm3LK7LwYMH8cYbb+D9999v8LmnTp1q8DkZGRkNPicYGKd/MU7/Ypz+xQRGA+m1Ff9YF5famMAgImoFzMe/hiBXIuKmYcEOhXzwx5adbrcHWXnmKn08PX0gVm87h5M/FQEAYnQq3NQlGr066dGrSzQ6tNNC+nP/NRW+rEw+1EdDa09cTyqVQKX0vYNIS/viX1cyJ5gMBgPy8vLgdrshlUrhdruRn58Pg8FQrd21a9fQt29fAFVnT9R2rK6xazvv6NGjeO6557Bq1Sp06dKlwc+td+/eUCrr/xpnZGQgOTm5weMEGuP0L8bpX4yzZna7vVGJVSYwGihaV5HAKDKVVyloRURELY/HYYPl9B6oew6BRBncqevkW4nZVq3+xJL3D+CJ+/vjw61n4XJ74HKLcLs9v9z2eOByeeDyVDz+/IM3Vyug+frHR/DsjBTkFZehV2d9rUsXfp18sFptUKtVjZoF0ly1J0Lhi39D+Ov1bA7R0dHo2bMntmzZggkTJmDLli3o2bNnleUjAHD33Xdj7dq1GD16NIxGI3bs2IHVq1fXeaw2tZ134sQJzJs3D2+++SZuuukm/z9xIqIWggmMBtJrwwCAO5EQEbUC1nP7ITrKoek3Mtih0K/kFllx8EwuDp/Jw/2juvlcNhGmkkOrVkAmlUAmlUAqFXzelkkFtNP7LqAZrVOhZ6fqyzV8qUw+XDx/Omi/pjV1FkcoCYXXsyaLFi1Ceno6Vq1aBa1Wi+XLlwMAHn30UTz55JPo06cPJkyYgOPHj2P06NEAgMcffxxJSUkAUOuxw4cP4+mnn4bFYoEoivjiiy+wdOlS3HbbbbWet3jxYthsNixYsMAb54oVK9C9e/fAvChERCGCCYwGitIqIQhAsYkJDCKils58bCfkegNUSb2CHUqb5nZ7cPZSMbYfNeKfO3fiSp4FANA+NgJymcTnsom4qDAsenRIvfpvTTUXmjqLg+rWtWtXrF27ttrj7733nve2VCrF4sWLfZ5f27GUlBTs2rWrweetX7++rrCJiNoEJjAaSCaVQBehRBFnYBARtWiOomuwXTkL/YgHIAgt7xfslqKmApylVgeOnMvDoTN5yPghH9ZyJyQSoG/XWNx9Syek9IpHQkyEX+pPtJalF0RERG0dExiNoNeqvFXKiYioZbKc+BoQJIjoMyLYobRavgpwPjcjBTsOXsZ/D2bBIwKREUoM6W3Azb3iIZZlY+gtN1fpwx/LJlrT0gsiIqK2jAmMRtBrVayBQUQtQmZmJtLT02E0GhEZGYnly5ejU6dOVdqsX78eH3zwASQSCTweD9LS0vDQQw8BANxuN5YsWYLdu3dDEK5h9CgAACAASURBVATMmTMHaWlpQXgm/iV63DCf+BbhXQdApokKdjitlslqr1aA89X/O4w/pPVHlDYMN/eKxw2Jkd5EQkZGjs9+/LFsgksviIiIWj4mMBohWqfChSvGYIdBRFSnhQsXYvr06ZgwYQI2bdqEBQsW4MMPP6zS5q677sKkSZMgCAIsFgvGjx+PQYMGoUePHti8eTOysrKwfft2GI1GTJw4EUOGDEFiYmKQnpF/lF04ArelBJr+qcEOpdUyWewoKbX7LJ7ZPi4CA7rHBSkyIiIiaqlaXvWqEBCtVcFoscPl9gQ7FCKiGhUVFeHMmTMYN24cAGDcuHE4c+YMiouLq7SLiIjw1oCw2WxwOp3e+1u3bkVaWhokEgn0ej1GjRqFbdu2BfaJNAPz8a8hVUci/IbQ2v2gNTBZ7Phgy2k8svS/yCsuQ1xUWJXjLbV4JhEREQUfP0E0gl5XMQW1pNQe5EiIiGqWk5OD+Ph4SKVSABUV7uPi4pCTU32a/s6dOzF27FiMGDECjzzyiHdrvpycHCQkJHjbGQwG5ObmBuYJNBOXxYiyCxmI6HM7BCknIvqL0WzHvzZXJC42fHsBg3r9f/buPLypMm0D+H2StM3SNt3bFMrSoqVSFmmVQTZlKypYXCqKioiAgzos4zIIDIsgUAdHYT4ZlBFwF+uCQ1V2FUEEKVCWsgiUtemW7kmaNsn5/qhkrAW6JTlpe/+u67uu9pz3vOdu5rM0T97zvBHopPPDnAl9HEUMNs8kIiKi5uBfbk0QrK35Q8xQZkboHz5ZIiJqiYYMGYIhQ4YgJycHzzzzDAYOHIjo6GinzH306NEmXZeRkeGU+/+RT/bPUNttuKAIxzkn3MNVOZ3NVTkrzDbsPl6O/b8aYbWLiO+oxsBufgjVCsg5fxIqtRrzn0yAIMghijaUl+TgoP6023M6U0vICDAnERG1PixgNEGQf80KjCLuREJEHkyn0yEvLw82mw1yuRw2mw35+fnQ6XTXvCYyMhLdu3fH999/j+joaOh0OuTk5KBHjx4A6q7IaIj4+Hj4+DTuE/eMjAwkJDj/8Q5RFHHpl3WQtY/FzYOSmj2fq3I6mytyFpVV4ovvTuPbPedgtdowqHd7PDj0RrQP87v+hbpgt+Z0tpaQEWDO+lgsliYXV4mISDp8hKQJHAUM7kRCRB4sODgYcXFxSE9PBwCkp6cjLi4OQUFBtcadOXPG8XVRURH27t2LG2+8EQAwYsQIpKWlwW63o6ioCNu2bUNSUvPf+EvFcvkkqg058OvJ5p0NYbeLKC6vRH6xCcXllbDbRRhKzXh7wxFMemUrNu46i/49I/Hvvw3BX8cm1F+8ICIiImoGrsBoAn+NNxRyAQauwCAiDzd//nzMnDkTK1euhL+/P1JTUwEAkyZNwtSpU9G9e3esX78eu3fvhkKhgCiKePTRR9G/f38AQHJyMjIzMzF8+HAAwDPPPIOoqCjJfp7mKj+0HYKXEr433SZ1FI9nt4s4n1vm2AY1LFCF5x9JwLqvs3DifDEGJ0QhZegNiAzxlToqERERtREsYDSBTCYg0F/JFRhE5PFiYmKQlpZW5/jq1asdX8+aNeua18vlcixYsMAl2dzNbjGjIusn+N50G2Te7F9Un1KjxVG8AGq2P132YQaeeyQBQf5KRARrJE5IREREbQ0LGE0U5K9kDwwiohak4vhuiNWV8OvFx0euRRRFXMqvQOavBbixQ6CjeHFFfrEZIQEqhAWqJUpIREREbRkLGE0UrFXiYl651DGIiKiByjN3wCu4HXzaxUodxaMYSs3I/LUQmb8W4NCpAsfqwnkT/4SwQFWtIkZYoApeCrbPIiIiImmwgNFEQf5KZJ4qkDoGERE1QFXhJVgunUTQkHEQBEHqOG5jt4soNVoQ3i4GxeWV0Gp8YLZYcfRMIQ79WoDMXwsdxXh/jTd6dAlBrxtD0fOGUIQFqjFnQp9aPTDmTOgDraZxO8oQEREROQsLGE0U5K+EsdKKSosVSh++jEREnqw8czsgk8M3fpDUUdzmak04ZzzcG+/+1oTT20uO+OhgDL2lA3rdGIpOOn/IZLWLOx0j/LFs2kBUW+3wUsig1fjUGUPkSURRRFpaGtLT01FcXIyNGzfil19+QUFBAe666y6p4xERUTPxnXcTBWv/t5VqZCg7sBMReSrRZkXFkR+g7pIAhW+A1HHcwlRZjYISc50mnK9/fAB/G3cLLNU2dO0YCC+F/LrzyGQCAv2U7ohM5BTLly/HTz/9hMcffxzz5s0DAERERGDJkiUsYBARtQJ8kLWJgv1rOtgbuBMJEZFHM53OgM1Y2uqbd1aYqrD9lwtY+M5ePDpvE8qNVVdtwhng54PuMSH1Fi+IWqIvv/wSq1atwt133+14XKx9+/a4ePGixMmIiMgZuAKjiYKurMDgTiRERB6t/NB2yH0DoY65WeooTldSbsHPR/X46XAODp8uhM0uIiRAhTtv64RAfyWbcFKbY7PZoNHUbPF7pYBhNBqhVnPnHCKi1oAFjCYK8q8pYBhYwCAi8ljW8iKYzhxEQN9kCLKWteLgSgPOP/afMJSa8dNhPX46koOsswbYRUAXrMHoQTG4rUckbogKgCAIsNtFNuGkNmfQoEFYsmQJZs2aBaCmJ8by5ctxxx13SJyMiIicgQWMJlIrFVB6yx3bzRERkeepOPI9INrh13Ow1FEa5WoNOF94LBGbfz6HbftqlsJHhfshZeiN6NcjEp10/nV2V5HJBEcTTqOxEhqNkk04qdV76aWX8OKLLyIhIQFWqxU333wz+vXrh9TUVKmjERGRE7CA0USCICDIX8kCBhGRhxJFEeWZO6DscBO8giKljtMopUZLnQac/3h/P55N6YWIYA1u6x6JqHC/eue50oTz7KljSEhIcHVsIsn5+vpi5cqVMBgMuHz5MnQ6HUJDQ6WORURETsIHYZshSMsCBhGRp6q8mIXqIj38era85p3VVvtVG3C2C/PFmKGxDSpeELVFo0ePBgAEBwejR48ejuLFfffdJ2UsIiJyEhYwmiHYXwVDqbn+gURE5Hblh3ZA8FZB0/VPUkdptDJjFcICVbWOsQEnUf3Onz9f55goirh06ZIEaYiIyNn4CEkzBGmVKCqthCiKdZ49JiIi6dgtJhiP/wTf7oMg81ZKHadRNu05h237LuC5RxLw2ocZbMBJ1AAvvvgiAKC6utrx9RWXL19Gly5dpIhFREROxgJGMwT5K1FltcNoroav2lvqOEREbZ4o2mEzlsJuKkfY6OmQB0ZIHalRdh68hJWfZyKhazhuiArAsmkD6+xCQkR1dejQ4apfA0Dv3r0xYsQId0ciIiIXYAGjGYK1/9tKlQUMIiJpiaIdVfkXkJe2FNbSAii0oQhPmQlRtEMQPP/Ri1+ycvHPjw6gW3QwZj5+C7wUcgT6taytX4mk8uyzzwIAevbsiQEDBkichoiIXIUFjGYI8v+tgFFWiY46f4nTEBG1bTZjqaN4AQDW0gLkpS1F5PglUPgGSpzu+o6cKcTSd39B50h//H1CH/h4sXBB1BQDBgxAVVUVsrOzUVxcDFEUHef69u0rYTIiInIGFjCa4coKjKJS7kRCRCQ10WZ1FC+usJYWQLRZJUrUML9eLMbCd/YiPFiN+ZP6Qq30kjoSUYu1f/9+TJ8+HVVVVaioqICvry+MRiMiIiKwfft2qeMREVEzef6aWg/2vxUY3ImEiEhqglwBhTa01jGFNhSC3HNr9edzyzDv7T3w03hj4VO3QevLJp1EzbFkyRJMnDgR+/btg0ajwb59+zBlyhSMHTtW6mhEROQELGA0g7eXHH5qL67AICKXeO+991BUVCR1jBZDrtEi7N7nHEWMKz0w5BqtxMmuLtdgxNy3foJCLsOip25DsFZV/0VEdF3nzp3DuHHjah2bPHky1q1bJ00gIiJyKs/9WKqFCPJXoqiMBQwicr6ffvoJr7/+Om699VYkJydj6NCh8PZmw+BrEQQZyjI2IeTOp+AV0h6CXAG5RuuRDTwNpWb8/a2fUFVtx9Jn+kMXopE6ElGr4Ofnh4qKCvj7+yM0NBSnT59GQEAATCaT1NGIiMgJ3PJXXWpqKgYPHozY2FicOnXKcfzpp5/GPffcg9GjR2Ps2LE4fvy441x2djbGjBmDpKQkjBkzBufOnXNH1EYL1qpg4AoMInKBVatWYceOHRg4cCDeffdd9OvXD7Nnz8Yvv/widTSPZMk5jYoj36O6OBde2lAofAM9snhRZqzC3Lf3oLTCggWT+7IJNJETDRs2DD/88AMA4IEHHsC4ceNw3333cRtVIqJWwi0rMIYMGYJx48bhkUceqXU8NTUVfn5+AIBt27Zh1qxZ+PLLLwEA8+bNw9ixY5GcnIyvvvoKc+fOxXvvveeyjKJoh81Yiq5R4bBWFDf4U7sgfyXO55a5LBcRtW2BgYF45JFH8Mgjj+DEiRN48cUX8cUXX0Cn0yElJQXjxo2DRsNP7wGg7MAWCF4+8IsfKHWUazJVVmP+6j3QFxoxf9KfcGMHz94dhailmT17tuPrCRMmoEePHjAajdxalYiolXDLR1OJiYnQ6XR1jl8pXgBARUUFBEEAABgMBmRlZWHkyJEAgJEjRyIrK8tlz4KLoh1V+ReQs+4l5K2ehpx1L6Eq/wJE0V7vtUFaJYrLLbDZxXrHEhE1xZ49e/DSSy9h3LhxCAkJQWpqKl599VUcP34ckyZNkjqeR7BVGlFx7Ef4dhsAmdIzCzqWahsWrdmHM5dL8bfHEtGjS2j9FxFRsyQmJmLQoEHYuXOn1FGIiMgJJO+BMXv2bOzevRuiKOI///kPAECv1yM8PBxyuRwAIJfLERYWBr1ej6CgIKdnsBlLkZe21LH9nrW0AHlpSxE5fgkUvtf/dCxYq4TdLqK0wuLYlYSIyBlSU1Px9ddfw8/PD8nJydi4cSPCw8Md53v27Ilbb71VwoSeo+LIDxCtVfDvPVzqKFdltdnx6nv7cfRsIf76cG/0ia9b1Cei5jl37hxOnjyJjh07omvXrgCA7du3480334Rer8eePXskTkhERM0leQHjlVdeAQBs2LABr776KlavXu3U+Y8ePVrvmK5R4Y7ixRXW0gJYTCZknjx73WuLC2q2UN299yAig9zbXC8jI8Ot92sq5nQu5nQuT85psVjwf//3f+jRo8dVz3t5eeGzzz5zcyrPI4oiyg5sho+uC3x0MVLHqcNuF/HGxwexLysXf76vB25PiJI6ElGr88UXX+Dvf/87tFotSkpKMHPmTPz88884efIkJkyYgPvvv1/qiERE5ASSFzCuGD16NObOnYvi4mLodDrk5eXBZrNBLpfDZrMhPz//qo+h1Cc+Ph4+Pj7XHWOtKIZCG1qriKHQhsJHrUZCQsJ1r/ULLcYnO3ciPLIzErpFNDpfU2VkZNSbzRMwp3Mxp3NJkdNisTSosAoATz31FJTK2iu7SktLUVlZ6ViJERPjeW/Y3a3yYhaqCy8hdOQzUkdxsNtFlBotqLbaYSitRK7BiHF3xeHufp2ljkbUKq1evRorV67EoEGDsH37dkydOhWPPfYYli9fDi8vL6njERGRk0jWnt1oNEKv1zu+37FjB7RaLQICAhAcHIy4uDikp6cDANLT0xEXF+eSx0cAQK7RIjxlJhTamueRFdpQhKfMhFyjrffaYG3NmwtDqdkl2Yio7Xr66aeRm5tb61hubi6effZZiRJ5prIDWyBTaqC5qZ/UUQDUFC/O55bh+eU78eSirVj2wX48mRyP+27vInU0olYrPz8fgwYNAgAMHjwYMpkMzz33HIsXREStjFtWYCxatAhbtmxBYWEhnnjiCQQEBODdd9/FtGnTYDabIZPJoNVqsWrVKkcjz/nz52PmzJlYuXIl/P39kZqa6rJ8giCDd1gHRI5fgqrSQojGEngFt2vQLiQBvj6QCYChjFupEpFzZWdnIzY2ttax2NhYnD17/Ufb2hKbsRTG4z/DPyEJMq/rr7Zzl1KjBYvW7EV+cU1hO7/YjGUf7MeyaQMR6MdeSUSuIIr/a6YuCAJUKhWLF0RErZBbChhz5szBnDlz6hz/9NNPr3lNTEwM0tLSXBmrFkGQQeEbiKyd38L34OfQPfYyVB261XudXC5DgJ8PikpZwCAi5woODsb58+fRsWNHx7Hz588jICBAwlSepTxzB2C3elTzzmqr3VG8uCK/2Ixqa/07WxFR05jNZtx+++2O78vLy2t9DwDff/+9WzMREZHzeUwPDE9RHdQREGQwnznUoAIGAARpVVyBQUROd//99+Mvf/kLZsyYgaioKFy4cAHLly9HSkqK1NE8gijaUXZwC5QdusE7pL3UcRzKKqoQFqiqVcQIC1TBSyHZU5tErd67774rdQQiInIDFjD+yEsJZftYmM5mIuiORxp0SbC/EnlFJhcHI6K2ZvLkyVAoFEhNTUVubi4iIiKQkpKCJ554QupoHsF8NhPWknwE3fGo1FEcPt12CvuO5eK5RxLw2ocZyC82IyxQhTkT+kCr8YxHXIhaI24pTUTUNrCAcRWqzj1RvHM9bMbSBjXyDPJX4vi5IjckI6K2RCaTYeLEiZg4caLUUTxS2YHNkGu00MR6xhuX9VtP4oNNJ3B77/a4MSoAy6YNRLXVDi+FDFqND2QyQeqIRERERC0aCxhXoYruheKdn8CcfRi+8QPqHR+sVaLMWIVqqw1eCrkbEhJRW1FVVYXs7GwUFxfXalLXt29fCVNJz1pWCNOvGQjoOxqCXPpGfZ9sPYkPN53A7QntMf2h3pDLBAT68d8DIiIiImdiAeMqfHTRkKl8YTp7qEEFjCD/mq7yRWUWhAepXR2PiNqI/fv3Y/r06aiqqkJFRQV8fX1hNBoRERGB7du3Sx1PUmUHtwGiCL+bh0odBR9vPoGPtpzEHQntMe234gUREREROV+DO4qtXbsWx48fBwAcOnQIt99+O4YMGYKDBw+6LJxUBJkcqs49YT57qNYnntcSpP2tgMGdSIjIiZYsWYKJEydi37590Gg02LdvH6ZMmYKxY8dKHU1Sos2K8kPboIrpBa+AcEmzfHe4FB9tOYnBiVEsXhARERG5WINXYKxbtw4PPPAAAOC1117D+PHjodFosHjxYrdud+ou6uheMGbtRlX+efiEd7ru2P+twGABg4ic59y5cxg3blytY5MnT8aQIUPw5JNPSpRKeqZf98NWUQz/O5+SLIMoivho80n8cLQcQ26Jwl8evJnFCyIP8MILL0AQ6v636O3tjYiICAwdOhRdu3aVIBkRETlDg1dglJeXw8/PDxUVFTh58iQee+wxpKSkIDs725X5JKPq3BMAYD57qN6xwVoVAMBQaq5nJBFRw135nQsAoaGhOH36NMrKymAyte1dj8oObIHcPwTqLr0lub8oivhw0wl8svUkbo5WYyqLF0Qew8/PD9u3b4coioiIiIAoitixYwdkMhnOnDmDMWPGYMOGDVLHJCKiJmrwCgydTocDBw7g9OnTSExMhFwuR0VFBeTy1tmkTOEfDK/QDjCfPYSAvqOvO9ZP7QWFXMYVGETkVMOGDcMPP/yAUaNG4YEHHsC4ceOgUCgwYsQIqaNJprooB+bsTAQOehiCzP3//oiiiA82ncCn205h2K0d0DfGxt1FiDzIuXPn8PbbbyMhIcFx7ODBg1ixYgXWrl2LnTt3YvHixbjzzjslTElERE3V4ALGiy++iKlTp8Lb2xsrVqwAAHz33Xfo3r27y8JJTR3dE6X7v4W9qhIyb+U1xwmCgCCtEgYWMIjIiWbPnu34esKECejRoweMRiMGDKi/uXBrVXZwKyDI4NdziNvvLYoi3v/2ONK2/4rhfTrimQd64uDBA27PQUTXlpmZiZ49e9Y6Fh8fj8OHDwMABgwYgLy8PCmiERGREzT4EZJBgwZh165d2LFjB+Lj4wEAI0aMwL///W+XhZOaKroXYLOi8sKxescG+yvZxJOInMZms2Ho0KGoqqpyHEtMTMSgQYMgkzX4V3erYrdWoTzzO2hib4XCL9Ct9xZFEe99U1O8SPpTTfGCKy+IPE9cXBxef/11WCwWAIDFYsHy5csdfS8uXboErVZ73Tmys7MxZswYJCUlYcyYMTh37lydMTabDQsWLMDQoUMxbNiwWv3grndu165duO+++xAfH4/U1FSnzElE1JY0eAXG6dOnERAQgJCQEBiNRrzzzjuQyWR48skn4eXl5cqMklFGxUFQeMN09hDUXRKuOzZIq8S5nDI3JSOi1k4ul0Mul8NiscDb21vqOB7BeHwP7OZy+PdOcut9RVHEu19n4fPvTmNE306Ycl8PFi+IPNTSpUvx/PPPIyEhAVqtFqWlpYiPj8c//vEPAEBJSQnmzZt33TnmzZuHsWPHIjk5GV999RXmzp2L9957r9aYjRs34sKFC9iyZQtKSkowevRo9O3bF+3bt7/uuaioKCxatAibN2+uVaBuzpzOZreLKDVaEN4uBsXlldBqfBr9O+/KHNVWO7wUMknm8IQMv5+DrydfT0/L8Ps5pH49G6PBBYznnnsOr7/+OkJCQpCamors7Gz4+Phg7ty5jn8UWhuZlw+UHW6C+WxmvWOD/ZU4cCLfDamIqK0YN24cpk+fjqeeegoRERG1OutHRUVJmEwaZQc2wytIB2WneLfdUxRFrEvPwhffn8adt3XCn+9l8YLIk7Vv3x6ffPIJ9Ho98vPzERoaisjISMf5K48+X1mh8UcGgwFZWVlYu3YtAGDkyJFYuHAhioqKEBQU5Bj3zTffICUlBTKZDEFBQRg6dCg2bdqEiRMnXvdcx44dAQDbt2+vU8Bo6pzOZLeLOJ9bhkVr9iK/2IywQBXmTOiDjhH+Df7d5wlzeEKG1jSHJ2RoTXN4QgZPmqOxGlzAuHz5MqKjoyGKIrZt24b09HQolUoMGeL+55DdSRXdC0Xb1sFaWgCFNvSa44K1SpgtVpgqq6FWts4VKUTkXgsXLgQA7N69u9ZxQRBw/PhxKSJJxpJ3DpZLJxE09HEIgmsfofn9JwnFZZU4dtaAu27rhD/f1+Oq2zMSkefx9vZGUFAQbDYbLl68CKBhhV+9Xo/w8HBHk3q5XI6wsDDo9fpaBQy9Xl+rMKLT6ZCbm1vvufru7ew5G6vUaHG8EQGA/GIzFq3Zi6ljbsYnW082aI6HhsVixfqDbpujvLwcn+3dJWmG1jQHX0/nztFWX89l0wYi0O/aPSSbo8EFDG9vb1RUVODMmTOIiIhAUFAQrFbrNSvYrYU6uheKAJjOHoL/zcOuOS7Iv+Z/oKKyShYwiMgpTpw4IXUEj1F+cCsEuRf8ut/h0vtc7ZOE5x9JwI0dAlm8IGoBdu7cidmzZ6OgoKDW8bZY+P2jo0eP1jsmvF2M443IFfnFZvh4yVBeXt6g+3h7ydw+xx+PSZGhNc3B19O5c7TF19NorMTZU/X3kWyKBhcwRo4ciccffxxGoxGPPvooACArK8slz955Eq+Q9pD7BcNcXwFD+78CRvswP3fFIyJq9exVZpQf+QGam26DXO3a369X+/Rx2YcZLv0kgYic5+WXX8bTTz+Ne++9F0pl4/+b1el0yMvLg81mg1wuh81mQ35+PnQ6XZ1xOTk56NGjB4DaKySud66+ezt7zt+Lj4+Hj4/PdccUl1ciLFBV6w1JWKAK4cEa/N/fGrb1rLvnyMjIqLVtbkv9OTxlDr6ezp2jrb6eGo2yzs/9RxaLpUGF1T9q8DrcWbNmYcaMGZg/f76jgCEIAl566aVG37QlEQQB6uieMGcfhmi3XXNcsFYFADBwJxIicpKxY8fikUceuer/tSUVR3+EWGV2S/POaqv9qp8kVFvtLr83ETVfWVkZHnrooSYVLwAgODgYcXFxSE9PBwCkp6cjLi6u1uMjQM1OfGlpabDb7SgqKsK2bduQlJRU77nrccWcjaXV+GDOhD4IC6z5u/bK8+xazfULH542hydkaE1zeEKG1jSHJ2TwpDkaq8ErMACgf//+yMnJwcGDBxEeHu5ohNTaqaJ7oTxzByw5v0LZvutVxwT61fyPxK1UichZUlJSan1fUFCAzz//HKNGjZIokfuJooiyA1vgHdYRPu1udPn9FHLhqp8keCna5ta1RC3N/fffj88//xwPPPBAk+eYP38+Zs6ciZUrV8Lf39+x3emkSZMwdepUdO/eHcnJycjMzMTw4cMBAM8884yjx8b1zu3fvx9//etfUVFRAVEU8fXXX+OVV17BgAEDmjynM8lkAjpG+GPZtIEwGiuh0SgbvaPA7+do6q4EzZ3DEzL8cQ6+nnw9PSnDH+eQ8vVsrAYXMPLz8/HXv/4Vhw4dQkBAAEpKStCrVy+89tprCA8Pd1lAT6Dq3AMQZDCdOXTNAoZa6QWVjwJFZSxgEJFz3HvvvXWOJSUl4aWXXsKzzz4rQSL3s+T8iqq8bISMmOyWHhR7DusxdczNjoZU7vgkgYicJzMzE++//z5Wr16NkJCQWuc+/PDDBs0RExODtLS0OsdXr17t+Foul2PBggVXvf565xITE7Fz585GX3e9c84mkwkI9FPi7Klj9S4Br28OZ+SQ6npnz8HXk6+np2X4/RxSv56N0eACxvz589G1a1e8/fbbUKvVMJlM+Oc//4l58+Zh1apVrswoObnKDz66GJjPHgIGPXTNccFaJR8hISKXCg8Px8mTDesM3RqUHdgCwVsJ3/iBLr/XifNFeHvDEYxNinXrJwlE5DwpKSl1Vq8REVHr0eACRkZGBpYvXw4vr5odNtRqNV588UUMGDDAZeE8iSq6F0p2fw6buRxy1dWbyAX5K7kCg4ic5rPPPqv1fWVlJbZs2YJevXpJlMi9bOZyGLN2w7f77ZD5qFx6L0u1DW98fBBBWhVG9o+BRsXdpIhaoqutXCMiotajwQUMrVaLM2fOoGvX/z1CcfbsWfj7+7sk3y4yLwAAIABJREFUmKdRx/RCya40mM8dgW/cbVcdE6RVIiu7yM3JiKi1+uqrr2p9r1arcfPNN2P8+PENniM7OxszZ85ESUkJAgICkJqaik6dOtUa8+abb+Kbb76BXC6HQqHAjBkzHMXpmTNn4qeffkJgYCCAmkZyU6ZMadbP1VAVR36AaK2Cf4Lrm3e+/81xXC6owMKn+rJ4QdTCbNiwAaNHjwZQt/D7e83pi0FERJ6hwQWMiRMnYvz48XjggQcQGRmJnJwcfPHFF5g2bZor83kMn8gbIPNRw3zm0DULGMH+ShSVVkIURbc8q01Erdv777/f7DnmzZuHsWPHIjk5GV999RXmzp2L9957r9aYHj16YMKECVCpVDhx4gQeffRR7Nq1y9HFf/LkyY7dp9xGFFF2YDN82t0In/BOLr3VsbMG/PfHM7jztk7odWOYS+9FRM739ddfOwoYfyz8XiEIAgsYREStQIMLGA8++CCioqKQnp6OkydPIiwsDK+++ioyMjJcmc9jCDI5VJ17wHT20DULFEFaJaw2O8qMVdD6suEbETXPhg0b0LVr11or306cOIETJ044/li/HoPBgKysLKxduxYAMHLkSCxcuBBFRUW1tgT8/aOAsbGxEEURJSUliIiIcOJP0ziKovOoNuQgdNRfXHqfSosVb3xyAOFBajwxsptL70VErvH75prOKPwSEZHnatQ2qn379kXfvn0d31dVVWHSpEltZhWGqnNPGE/8jOrCS/AOrbt1VbB/zTPaRWWVLGAQUbMtX74cGzZsqHUsIiICU6ZMaVABQ6/XIzw8HHK5HEBNF/uwsDDo9fpaBYzf27BhAzp06FCreLF27VqsX78eUVFReO655xATE9OMn6phfC4egEzpC01c3/oHN8O6r7OQazBhydP9oPJp1D+JROTBDAYDTCZTrWOu2HaUiIjcq9l/rYmi6IwcLYIqpqZxnunsoasXMLQ1y60NpZXoHKl1azYian0qKirg6+tb65ifnx/Kyspccr99+/Zh+fLlWLNmjePYjBkzEBoaCplMhg0bNmDixInYtm2boyjSEEePHm3wWI1aDV2ABh2HjoVVBE6dPgvjH96EOMvZ3Ep8vbsQfWJ9YSk5j4yM802ap6WsRGRO52kJGYG2mTMzMxNvv/02SkpK6pxr6DaqRETkuZpdwGhLvR68tGHwCo6E+ewhBPQZVed8aKAKs8bfitBAFYrLK7n1HhE1S0xMDDZv3oy77rrLcWzr1q0NXgGh0+mQl5cHm80GuVwOm82G/Px86HS6OmMPHjyIF154AStXrkR0dLTjeHh4uOPr0aNHY8mSJcjNzUW7du0a/HPEx8fDx6f+VWmiaEdV/gXkpS2GtbQACm0oOqfMhHfXWAiCrMH3awhTZTXe/PY7RIZo8Pz4QVB6N+2fw4yMjCbvm+5OzOk8LSEj0HZz/u1vf8O0adNw7733Ovr4XI3FYmlUcZWIiDxDvX+x7dmz55rnqqurnRqmJVBF90L5wW2wV1sg8/rfH+R2u4gyYxX+89UR5BebERaowpwJfdAxwp9FDCJqkueffx6TJ0/Gt99+i6ioKFy4cAF79uzB22+/3aDrg4ODERcXh/T0dCQnJyM9PR1xcXF1Hh85fPgwZsyYgRUrVqBbt9p9IPLy8hxFjB9//BEymaxWUcOZbMZS5KUthbW0AABgLS1AXtpSRI5fAoVvoFPvtWbjMRhKzEh9dkCTixdE5HnKysrw0EMPtakP2IiI2pJ6/2qbPXv2dc9f7ZO81kwd3Qtlv3yDyosnoI7u6ThearRg0Zq9yC82AwDyi81YtGYvlk0biEC/a38CQER0LYmJifj666+xceNG6PV69OjRA7Nnz27U79358+dj5syZWLlyJfz9/ZGamgoAmDRpEqZOnYru3btjwYIFqKysxNy5cx3Xvfrqq4iNjcXf/vY3GAwGCIIAX19f/Pvf/4ZC4Zo3/KLN6iheXGEtLYBoszr1Phkn8rD55/O4/44u6Nrp6r1AiKhluv/++/H5559zxxEiolaq3r9Cd+zY4Y4cLYayQzdAroD57KFaBYxqq91RvLgiv9iMaqvd3RGJqJWoqqpCSEgIJk+e7DhWXV2NqqoqeHt7N2iOmJgYpKWl1Tn++679n3/++TWvX7duXcMDN5MgV0ChDa1VxFBoQyHInVcwqTBX41+fHkJUuB/GJnWt/wIialEyMzPx/vvvY/Xq1QgJCal1jj0wiIhaPq6bbSSZtxKqqDiYzh5CMB53HPdSyBAWqKpVxAgLVMFL4dzntomo7XjiiSfwwgsvoFevXo5jx44dw2uvvdYqtwqUa7QIT5npeIxEoQ1FeMpMyDXOa4q8esMRFJdbMPuJW+Ht1fBGpETUMqSkpCAlJUXqGERE5CIsYDSBKroXina8D2uZAQr/YACAVuODORP6OB4judIDQ6vhdqpE1DSnTp1Cz549ax3r0aMHTpw4IVEi1xIEGbzDOiBy/BJYTCb4qNWQa7ROa+C596geO/ZfxJihN+KGKOf21CAi6dlsNly4cAFTpkxp8Co1IiJqWbg8oAlU0TWfhpqzMx3HZDIBHSP8sWzaQPz7xcGYmNwduQYTG3gSUZP5+fmhsLCw1rHCwkKoVCqJErmeIMig8A3EiYu5UPgGOq14UWaswv99lonOkf4YMyzWKXMSkWeRy+X46KOPXNanh4iIpMcCRhN4h3WEXBMA09lDtY7LZAIC/ZRoF+aLtO2n8J//HoXVxh4YRNQ0w4cPx3PPPYdTp07BbDbj5MmTePHFFzFixAipo7U4b31xGBWmKsx4uDcf7SNqxUaPHo2PP/5Y6hhEROQiLFE3gSAIUEX3gun0foh2GwSZvM75sUldseA/P2PH/osY3qejREmJqCWbMWMGli5dipSUFFgsFiiVStx///2YPn261NFalN2ZOdh56DIeHdEVnSOd10+DiDzP4cOH8cEHH+Cdd95BREREre1U2cSTiKjlYwGjidTRvVBx5HtYcrOhjOxS53xC1zDcEBWA9dtOYXBiFBRyfuJHRI3j4+ODefPmYe7cuSguLkZ+fj6++uorDB8+HLt27ZI6XotQUm7Bys8z0aW9FvcPvkHqOETkYg8++CAefPBBqWMQEZGLsIDRRKrOPQAIMJ89dNUCBldhEJEzFBUVYePGjdiwYQNOnDiBxMREzJ49W+pYLYIoilj5eSZMlVZMf7g3C8lEbcC9994rdQQiInIhFjCaSK7RwjsiGuazhxDY/4GrjknoGoYuUQH4lKswiKgRqqursWPHDnz55ZfYtWsXOnTogLvvvhuXL1/GG2+8geDgYKkjtgg/HLyMPUf0GH/3TegY4S91HCJyk8LCQhw+fBjFxcUQRdFx/IEHrv73GhERtRwsYDSDOronSvZsgL3SCJlSU+e8IAgYOzwWL7+zF9/tv4hhXIVBRA3Qr18/CIKA++67D3/5y1/QrVs3AGBjugaw20WUGi2wVNmg9lFg2K1RGH173VVyRNQ6bdu2DS+88AI6duyI06dPo0uXLvj111/Ru3dvFjCIiFoBLgloBlV0L0C0w3zu6DXHJMaFo8tvvTC4IwkRNURsbCzKy8uRmZmJI0eOoLS0VOpILYLdLuJ8bhmeX74TkxZvw1tfHsbd/aLBzayJ2o433ngDixcvxoYNG6BSqbBhwwa8/PLLiI+PlzoaERE5AQsYzaBsfyMEb2Wd7VR/TxAEPDw8FnlFJny3/6Ib0xFRS/X+++9j69at6NevH9asWYN+/frhz3/+M0wmE6xWq9TxPFap0YJFa/Yiv9gMAMgvNmPxun0oNVokTkZE7pKTk4M777yz1rF7770XGzZskCgRERE5EwsYzSDIvaDq2B3ms4dqPWP5R7fEhaNLey0+3c5VGETUMO3atcMzzzyDLVu2YN26dQgNDYVMJsM999yDV199Vep4HqnaancUL67ILzaj2srfu0RtRXBwMAoLCwHU/B49ePAgLly4ALudvweIiFoDFjCaSRXdC9bSfFQX6a85RhAEPJzUFbkGE77P4CoMImqcxMRELFy4ELt378bf//53nDp1SupIHslLIUNYoKrWsbBAFbwU/KeOqK1ISUlBRkYGAGD8+PEYN24ckpOT8fDDD0ucjIiInIFNPJtJHdMLBgDms4fgHRx5zXFXVmGs33YKtydwRxIiajwfHx+MHDkSI0eOlDqKR9JqfDBnQh/HYyRhgSrMmdAHWo2P1NGIyE0mT57s+Hr06NG49dZbYTabERMTI2EqIiJyFhYwmskrMAKKwAiYzx6C9pa7rjmuphdGVyxcsxffZ1zC0Fs7uDElEVHrJ5MJ6Bjhjz/f3wOBvkoEByih1fhAJmMbT6K2pLq6GpmZmcjPz8ddd90Fk8kEk8kEtVotdTQiImomLgNwAnV0L5jPH4Noq77uuFtuCkdMey0+3XYKNvbCICJyOpvdjoXv7MW+rFwE+ilZvCBqY06ePImkpCTMmTMHs2fPBgD88ssvmDVrlsTJiIjIGVjAcAJV554QqytRefHEdccJgoCxw7tCbzDiu4xLbkpHRNR25BpMEEVAF6KROgoRSWD+/PmYOnUqNm3aBIWiZqHxLbfc4uiLQURELRsLGE6g6hQPyOTX3U71Cq7CICJynVyDEQCgC2YBg6gtOn36NJKTkwHUfHAEAGq1GhYLt1MmImoNWMBwApmPGsr2sTCfzax3rCAIeHhYLPQGI74/wFUYRETOpP+tgBHBAgZRm9SuXTscPXq01rHDhw+jQwf2HiMiag1YwHASVXQvVOVlw1pRUu/YW7tFILqdFuu3chUGEZEz5RpMUPnIofX1ljoKEUlg2rRpeOqpp7BixQpUV1fjrbfewrRp0zB9+nSpoxERkROwgOEk6uheAABzdsNWYYwdzlUYRETOpi80Qhfs61g6TkRtyx133IHVq1ejqKgIt9xyCy5fvox//etf6N+/v9TRiIjICbiNqpN4R3SGTO0P89lD8Os+qN7xv1+FcXvv9pDLWUsiImoufaERHXV+UscgIgl169YN3bp1c3xvs9mwfPlyTJs2TcJURETkDHzX7CSCIIP/zcOhuakfqksLYK0ohihe+/EQQRDw8G+rMH44yFUYRETNZbOLyCsysYEnEdVis9mwatUqqWMQEZETcAWGk4iiHeobEpH/5WuwlhZAoQ1FeMpMeId1gCBcvU7Up1sEoiO1+GTrKQy6maswiIiaw1BihtVm5xaqRFSHKIpSRyAiIifgO2YnsRlLHcULALCWFiAvbSlsxtJrXiMIAh5OioW+kKswiIiaizuQENG1sC8OEVHrwBUYTiLarI7ixRXW0gKINut1r+MqDCIi58j9rYDBR0iI2p49e/Zc81x1dbUbkxARkSuxgOEkglwBhTa0VhFDoQ2FIL/+SywIAh4aHovF6/bhh4OXMTgxytVRiYhaJX2hEQq5gOAAldRRiMjNZs+efd3zOp3OTUmIiMiVWMBwErlGi/CUmchLW1qrB4Zco6332j/F16zCWL/1JAbd3I6rMIiImkBvMCI8SAO5jEvFidqaHTt2SB2BiIjcgAUMJxEEGbzDOiBy/BJYSwtgrzTCKzTqmg08a1/LVRhERM2VW2hiA08iIiKiVowf9TuRIMig8A1EVe5Z5H6yCNUFFxt87Z/iI9A50h+fbjsJm+3a268SEVFdoihCbzAiIlgtdRQiIiIichG3FTBSU1MxePBgxMbG4tSpUwCA4uJiTJo0CUlJSRg1ahSeffZZFBUVOa45dOgQ7rnnHiQlJWHChAkwGAzuitssmrjbAJkcFcd+bPA1giDg4eGx8FV542J+BfKLTSgur4Tdzm2/iIjqU2asgtli5QoMIiIiolbMbQWMIUOG4MMPP0S7du0cxwRBwMSJE7F582Zs3LgRUVFRWLZsGYCaT9NeeOEFzJ07F5s3b0ZiYqLjnKeTq/2hju6FimO7IIoNX01xS1wEHh95Exa+8zOeXLQVzy/fifO5ZSxiEBHVQ1/IHUiIiIiIWju3FTASExPrdIAOCAhAnz59HN/36tULOTk5AIAjR47Ax8cHiYmJAICHHnoImzZtclfcZvPtNgC2skJUXjzR4GvKzVV44+MDyC82AwDyi81YtGYvSo0WV8UkImoV9L9toRrBAgYRERFRq+UxPTDsdjs+/vhjDB48GACg1+sRGRnpOB8UFAS73Y6SkhKpIjaK+sZECF4+jXqMpNpqdxQvrsgvNqPayp4YRETXk1tohCCAPTCIiIiIWjGP2YVk4cKFUKvVePTRR50679GjRxt9TUZGhlPurQmJge3Ij7gQcjMgk9c7PiyyE8ICVbWKGGGBKlgqzcjIOO6ynK7GnM7FnM7VUnLS9eUYjAgJUMFLUf/vWiIiIiJqmTyigJGamorz589j1apVkMlqFoXodDrH4yQAUFRUBEEQEBAQ0Ki54+Pj4ePj0+DxGRkZSEhIaNQ9rsXoLyLv0yWIC5BDfUP9c9rtIuZM6INFa/Yiv9iMsEAVZoztDV1oAKJ0ta93Zk5XYk7nYk7nkiKnxWJpUmGVri+30Mj+F0REREStnOQFjNdffx1Hjx7F22+/DW9vb8fx+Ph4VFZWYv/+/UhMTMQnn3yCO++8U8KkjaeO7gmZyhcVx35sUAFDJhPQMcIfy6YNRLXVDlNlNf7v00zEdQ7Ck/fEuyExEVHLlGsw4dZuEVLHICIiIiIXclsBY9GiRdiyZQsKCwvxxBNPICAgAG+88QZWrVqFTp064aGHHgIAtG/fHm+++SZkMhleffVVzJs3DxaLBe3atcM//vEPd8V1CkHuBU3X21Bx9AfYq8yQeavqvUYmExDop3R8f0NUADb8cAZd2gdgUO/2roxLRNQimSqrUVJh4RaqRERERK2c2woYc+bMwZw5c+ocP3ny5DWv6d27NzZu3OjKWC7nG98f5Qe3wHRqP3zjBzT6+ieT43E2pxQrPj2EDhF+6BypdUFKIqKWK9dgAsAtVImIiIhaO4/ZhaS1UkbFQe4X3KjdSH5PIZdh5rhb4KvywuJ1+1BuqnJyQiKilu1/W6hyBxIiIiKi1owFDBcTBBl8u/WH6ewh2ExlTZoj0F+Jlx6/BYUlZiz7MAM2u+jklERELVduYU0Bg4+QEJEzZGdnY8yYMUhKSsKYMWNw7ty5OmNsNhsWLFiAoUOHYtiwYUhLS2v2uYKCAkyZMgWjRo3CnXfeia+++spxzmAwYPLkyRg1ahRGjBiB+fPnw2q1uuYFICLyYCxguIFv/EDAboPx+J4mz9G1UxCeurcHDpzIx0ebTzgxHRFRy6Y3GKH19YZa6SV1FCJqBebNm4exY8di8+bNGDt2LObOnVtnzMaNG3HhwgVs2bIF69evx7/+9S9cunSpWeeWLl2K+Ph4bNy4ER9++CFef/116PV6AMCqVasQExODjRs3YuPGjTh27Bi2bNnipleEiMhzsIDhBt5hHeEV0r7Jj5FcMaJvJwzv0xGfbjuF4xfNTkpHRNSy6QuNiGD/CyJyAoPBgKysLIwcORIAMHLkSGRlZaGoqKjWuG+++QYpKSmQyWQICgrC0KFDsWnTpmadO3HiBAYMqOmXFhQUhK5du+Lbb78FAAiCAKPRCLvdjqqqKlRXVyM8PNwtrwkRkSeRfBvVtkAQBPh2G4DiHz6GtbQACm1ok+f6833dcU5fii/3FGFQ33JEhfs5MSm5gijaYTOWQrRZIcgVkGu0EATWDomcJddgxE2dg6WOQUStgF6vR3h4OORyOQBALpcjLCwMer0eQUFBtcZFRkY6vtfpdMjNzW3WuW7duuGbb75B9+7dcenSJRw8eBDt29fsQPf000/jL3/5C/r37w+z2YxHHnkECQkJjfrZjh492qjxAJCRkdHoa6TAnM7FnM7FnM7FAoab+Hbrj+IfPkZF1m4E9B3d5Hm8FHK89PitePYf2/DK2n345/SBXDbtwUTRjqr8C8hLW+ooXoWnzIR3WAcWMYicoNpqQ2GJmSswiKjFmzlzJhYvXozk5GRERkbiT3/6ExSKmj/VN23ahNjYWLz77rswGo2YNGkSNm3ahBEjRjR4/vj4ePj4+DR4fEZGRqOLJFJgTudiTudizmuzWCxNKqzyHZSbeAVGwKfdjag42rzHSAAgJECFlH7B0BuM+OdHB2BnU0+PZTOWOooXAGAtLUBe2lLYjKUSJyNqHfKKTLCLbOBJRM6h0+mQl5cHm80GoKbpZn5+PnQ6XZ1xOTk5ju/1ej0iIiKadS4oKAjLli3Df//7X6xatQomkwkxMTEAgA8++AD33HMPZDIZ/Pz8MHjwYOzdu9cFrwARkWdjAcONfLsNQFX+OVQVXGj2XJ3CffDkqG7YeywXadtPOSEduYJoszqKF1dYSwsg2tg5nMgZcg0mAICOKzCIyAmCg4MRFxeH9PR0AEB6ejri4uJqPT4CACNGjEBaWhrsdjuKioqwbds2JCUlNetccXGxY2eRPXv24NSpU45eHO3bt8fOnTsBAFVVVdizZw9uuOEG178gREQeho+QuJEm7jYYtq5FxbFdCLp9bLPnGzUgGr9eLMGHm08gpn0AEuPYzMnTCHIFFNrQWkUMhTYUgkwuYSqi1kP/2xaqESFqiZMQUWsxf/58zJw5EytXroS/vz9SU1MBAJMmTcLUqVPRvXt3JCcnIzMzE8OHDwcAPPPMM4iKigKAJp87fPgwXnnlFchkMgQGBmLVqlVQqVQAgFmzZmHevHkYNWoUbDYb+vTpgwcffNB9LwoRkYdgAcONFL4BUHXujopjPyJw0MMQBKFZ8wmCgGdSeuJCbjmWfZiBf04fiMgQXyelJWewmSsQevfTKPh6paMHRujdT6Pou48QPOQxyDVaqSMStWh6gxEqHzkCfBv+XDcR0fXExMQgLS2tzvHVq1c7vpbL5ViwYMFVr2/quUGDBmHQoEFXPdehQwesXbu2vuhERK0eHyFxM99uA2AtyYflsnMe+1B6KzDriVshE4DFa/fBbOGjCZ5CtFlRsOENlOzdiMhxryDq2VWIHL8EokwO4/HduPzuLFQX6aWOSdSiXdlCtbkFYSIiIiLyfCxguJkmtg8EhTcqjjW/mecV4UFqvPBoIi7mlWPF+oMQRTb19AQlezagKv8c/G8eBoV/MLy0oVD4BkLdsRt0jy6A3WLC5XdnofLyr1JHpVYsOzsbY8aMQVJSEsaMGYNz587VGfPmm2/i7rvvxj333IP77rsPP/74v99PZrMZ06dPx7BhwzBixAh89913bkxfv1yDkTuQEBEREbURLGC4mcxHDfUNCajI2g3RbnPavDfHhuGxu25CQbEZZy6XIr/YhOLySu5QIpGqwkso3pUGTdxt0MTeWue8st2NaPf4K5B5q6D/YC6Mv+6XICW1BfPmzcPYsWOxefNmjB07FnPnzq0zpkePHvjss8/w3//+F4sXL8aMGTNQWVkJAHjnnXeg0WiwdetWrFq1CnPmzIHRaHT3j3FVNruIXIOJDTyJiIiI2ggWMCTg220A7KYymLMPO3XeewfF4IlR3bBk3T48uWgrnl++E+dzy1jEcDPRbkNB+krIvJUISZp4zXFeQZGIfHwxvEOjkJeWirIDW9yYktoCg8GArKwsRxf7kSNHIisrC0VFRbXGDRgwwNEoLjY2FqIooqSkBADw7bff4qGHHgIAdOrUCfHx8Y5O+FIzlJphtdm5hSoRERFRG8EChgTUMb0h81Gj4tgup85bZqrCPz/KQH6xGQCQX2zGojV7UWq0OPU+dH1lGZtguXwSwcMm1NukU+EbAN2jC6CK7oXCb99C0Q8f8xEgchq9Xo/w8HDI5TW73sjlcoSFhUGvv3bvlQ0bNqBDhw6IiIgAAOTk5KBdu3aO8zqdDrm5ua4N3kC5hpqVIFyBQURERNQ2cBcSCQgKL2i69kXF8d2wV0+GzMs53fOrrXZH8eKK/GIzqq12p8xP9asuyUPRdx9CFdMbvvEDG3SNzFuFiAdnovCbt1Cy6zNYywwIvevPEOT8z5Pca9++fVi+fDnWrFnj1HmPHj3apOsyMjKuf/50TQGjQH8WGeUXmnQPZ6gvp6dgTudpCRkB5iQiotaH75Ak4hs/AOWZ22E6nQHfuNucMqeXQoawQFWtIkZYoIqPkLiJKIoo/GYVIMgQetdTjdoVQZDJEXL3FCj8Q1D843rYKooRft/zkPmoXJiYWjudToe8vDzYbDbI5XLYbDbk5+dDp9PVGXvw4EG88MILWLlyJaKjox3HIyMjcfnyZQQFBQGoWdXRp0+fRuWIj4+Hj0/jCrUZGRlISEi47pgj+mNQyEtwx4BbIZdJswtJQ3J6AuZ0npaQEWDO+lgsliYXV4mISDp8hEQiyg43Qe4biIqjztuNRKvxwZwJfRAWWPOmNyxQhWkP3YyVn2UiO6fUafehqyvP3AFz9mEED34MCv+QRl8vCAICBz6IkLunwJx9GDkfzIW1otgFSamtCA4ORlxcHNLT0wEA6enpiIuLcxQjrjh8+DBmzJiBFStWoFu3brXOjRgxAuvXrwcAnDt3DkeOHMGAAQPc8wPUI9dgQniQWrLiBRERERG5FwsYEhFkcvje1A+mMwdgM1c4ZU6ZTEDHCH8smzYQ78wZhmXTBiIiWI2LeeWY/e/dOH2pxCn3obqs5UUo2rYOyg43wa/3sGbN5d9rKCIenIlqw2XkrJuFKsNlJ6Wktmj+/Pn44IMPkJSUhA8++AALFiwAAEyaNAlHjhwBACxYsACVlZWYO3cukpOTkZycjJMnTwIAnnzySZSVlWHYsGF46qmn8PLLL8PX11eyn+f39NxClYiIiKhN4SMkEvLtNgCl+9JhPPkz/HsNdcqcMpmAQD9lrWNLnumPWf/ejTmrfsLLk/vixg6BTrkX1RBFEYWb3oZosyL07ikQhObXBdVdEqB79GXkrn8FOe/OQsQj86HQBKBrVDisFcWQa7ROuQ+1fjExMUiNXKUuAAAgAElEQVRLS6tzfPXq1Y6vP//882ter1arsWLFCpdkaw5RFJFrMOKmTkH1DyYiIiKiVoHvgCTkrYuBV5DO6buR/FFEsAZLn+4PX5UX/v7WTzhxvqj+i6jBjMd/gunULwgcOAZeQZFOm1cZ2QXtxi+BT4duEM0VyFn3EvJWT0POupdQlX8BosjmrNR2lRmrYKq0cgtVIiIiojaEBQwJCYIATbcBqDx3FNZy1xYVwoLUWPJ0f2h9fTD3rT3Iyja49H5thc1UjsLN/4GPLgbaPqOcPr9XYARCh09AQfqbsJYWAACspQXIS1sKm5F9Tajt0v+2hWoECxhEREREbQYLGBLz7TYAgIiKLNeuwgCA0EAVljzdD0H+Ppj39h4cOVPo8nu2doata2CvNCLk7qchyOQuuYcoio7ixRXW0gKINqtL7kfUEuQW1hQwdOyBQURERNRmsIAhMe/gSPjoYlBx1PUFDAAI1qqw5On+CA1UYf7qn5H5a0H9F9FVmU5noOLoTgTcdh98wju57D6CXAGFNrTWMYU2FIKcLWyo7dIXGiEIQHiQWuooREREROQmLGB4AE23AajKPeO23SYC/ZVYPKU/dMFqvPyfn3HgZL5b7tua2C0mFHzzFrxC2iOw3/0uvZdco0V4ykxHEUOhDUV4ykzINVqX3pfIk+kNRgRrVfD2cs3KJyIiIiLyPCxgeADfm/oBEFzezPP3Avx88MqUfmgX5otFa/Zi//E8t927NSja8QFs5UUIHfkMBIWXS+8lCDJ4h3VA5PgliBj3CkLufhreYR24Cwm1abkGEx8fISIiImpj+A7IAyj8gqDsFA/jsR8hiqLb7qv1rSlidIjwwytr92HfsVy33bslM58/hrIDm6G99W4o293olnsKggwK30Bk7/oauR8vhL3S5Jb7EnkqvcGIiGA+PkJERETUlrCA4SF8u/VHdZEeVfozbr2vn9obi566DZ0j/bF43T7sOZLj1vu3NPZqCwq+XglFQDgCBz3s9vtXh3YBRDvMZw+5/d5EnsJUWY2Scgu3UCUiIiJqY1jA8BCa2D8BcgUqjv3o9nv7qr2x8KnbcENUAJa+tx+HTuWjuLwS+cUmFJdXwm5336oQT1e8cz2sxbkIvXsKZN5Kt9/fFhAJmcoPptMZbr83kafIK6pZgcQCBhEREVHbwgKGh5CrfKGO6Y2KrN0Q7Ta331+j8sKCyX0x7JYoCIKA55fvxJOLtuL55TtxPreMRQwAlTmnUbp3I/x6DYWqU3dpQggyqGNuhunMQUn+/4TIE+h/20I1gj0wiIiIiNoUFjA8iG/8ANgqilF5IUuS+6uVXng4qStWrD+I/GIzACC/2IxFa/ai1GiRJJPURNEOa8X/t3fn8VHV9/7HX7MlmZkkk4VMFtawyS5CAIsLVVYriBvVam0VS6+1P/W2Vyu21K1Y5dbrWn14a6vWe7XX4oYsioh1RQEjEdkhYc++JzPZZub8/ogZiQQIkMxMkvfz8fBRMufMOZ+ZfPPpnM+c7/dTQVNlMYG6WuwDzyJ56k/CGpNjSBaBuhoa8neHNQ6RcGkpYGgRTxEREZGexRruAORbjsHjie43EpPFRlNVCSaLFYvTFdJuE/6AESxetCiuqKPJFwhZDJHCMAI0Fh+gaOlD+KpKsLpScF9+O6Zoe1jjsg8cCyYz3t3ZxPQZFtZYRMKhoMxDvDMKp71zOwCJiIiISGTRHRgRxGS1kfT9H1G87DEO/vkm8l+4i8biAxhG6IoHNqsZd2LrC3R3op2DhTW889k+Gpt6zrQFv6cqWLwA8FWVUPz6w/g9VWGNyxLjJKbvcK2DIT1WYZlHd1+IiIiI9EAqYEQQv6eKkmWPt7pgLlr6UEgvmF3OaBbNnxQsYrgT7dz5kwl8lHOYp179ihsfWMPStbuorWsKWUzhYvh9wd9FC19VCYbfF6aIvuUYPI7G4v34qktDet7glJqqEny1FSEtrom0KCjzav0LERERkR5IU0giyDEvmJtCt/6E2Wyif1o8D992Pk2+ADarGZczmn+/+iwuzOrLa+/v5sVV21m6djezvjeAuecPJNkV3ikVncVksWJ1pbT6nVhdKZgs4f+zcQzJovz9/8G7O5v48TNDcs62ptSkzltIlLtfSKc5Sc/W5AtQWuElfXzfcIciIiIiIiGmq44I0nLBfCSrK4XG4v0Uvf5f1B8OzaKNZrOJxLgY3IkOEuNiMJtNmEwmzhySwv3/NpnHfjWFCSNSWfbhHn72wBqeeGUTB4tqQhJbKFmcLlLn3Rn8nbRcsFucrjBHBrbk3lgT3CGdRtLWlJpQ3yEkUlzhJWBAei9HuEMRERERkRAL/1fJEtR8wbyw9TfcV95J3cHt1OXl4Nm+jpi+w3GdPRcMU9jiHNQngTt+nMV1Fw3nzQ9zWbPhAGs2HGDSyDSuvHAIwwYkEQgYVHkaSO09iIqaelzOaMzmk4vZMAL4PVUYfl9YFjQ1mcz4PNUkT78BW6++mKPtIY/h2LGZcAzOoibnPQJNDZht0Z1+zkieUiM9h1qoioiIiPRcKmBEEJPJTJS7HxnXP9jqoj06LZP4MRdQ89VaqjasoGjpQ8Q7kqg2lxM7ekpILl7bkpbs5KbLx/CjGWew/JM8Vn6yl/VbC5l1dn9mnN2fh/6+keKKOtyJdhbNn0T/tPh2FzEiZbqCd9sneHaup/+vnsdktoTsvO3hGDyO6i9WUb9/C47B4zv9fJE8pUZ6jmAL1V4qYIiIiIj0NOH/KllaMZnMWGMTsblSsMYmBi/WzdF2XBNn0/fmp3Bf9msMazSlb/83B/58E+UfvRLW2/hdsdH8eNZwnvv9DBbMHcXEkWnB4gU0t2Fd/Nx6qjztX8sjEqYrGIaBNy8H+4AxEVe8AIjpPxKTLQbv7tBMI/HX1ZJy8c0ROaVGeo7CMg8xURYSYsNTuBURERGR8NFXp12MyWwhdsQ51HijGeG2U/X5W1R+/E+q1r1B7Jjv45p8GWZrVFimXdijrVxy/iCKy73B4kWL4oo6mnzt71hh+JvCPl2hqfQg/ppy7APHhuycJ8NsjcKeORrvnmwMw8Bk6txpRdVfrKKx5CAZP3kAv6cSX00ZlvheETGlRnqOgjIPacnOTh/vIiIiIhJ5dOXRVZlM2PuNJO2Hd9Hn3x4ndsz3aSw5hL+iiPwX7uLgn28i/4W7aCw+EPJWlzabOdiGtYU70c7h4lo2bCvEMIwTHsPvqWpzQdNQTlfw5uYA4BgUmQUMAMfg8fiqS2kqOdCp5wk01lO79RNsCalY45MxWW0ULV1C9YYVnXpeke8qLPNo+oiIiIhID6UCRjcQ1asPKT+4idTL/p2SlU8fPe2itiKk8bic0SyaPylYxHAn2rnzJxN4Z90+/vC39dzxxMds2ll8zEKGZ+d6ylb/Dffcfw/rdIW6vBxsvfpgje8VsnOeLMegcQCd3o3Es+MzjAYvcWOnAhCV0g/HGZOo/mIVgXpPp55bpEUgYFBY5iVdC3iKiIiI9EiaQtKNGIbR5rSLpooiKj5eSvy4mUSnZXZ6HGazif5p8Tx82/l4PPU4nTG4nNHc8ZMs3v/iIP+3Zid3/+UzRg5M5rqLhjNyYHLwuY1lhyl+60mikjOwpWeScf2D+CqLCTTWh3QBz0BTA/UHthE3fmZIzneqrPHJRKVm4tmdTcLkyzvtPDU5a7ElZRDTd3jwscRzruDwzvVUZa8m8ZzOO7dIi7Kqepp8AdJ0B4aIiIhIj6Q7MLqRli4RR7K6UsBkovbrDzn8t9s5/MJd1Gz+gEBT+xfUPBVms4nEuBiKDueSGBeD2WzCajEzY1J//nvhVG66bDT5JbUsfOoT7v7vdew6UEGgsY6iV/8Tk9VG6hV3YLFGY41NpG7vZgr/cX9IF/CsP7ANw9+EI0LXvziSY8h4Gg7vwu+t6ZTjN5Yeov7gduLGTm217kB0+iDsg86iasNyAo31nXJukSMVln3TgSTZEeZIRERERCQcVMDoRixOF6nzFh417SKmzxn0u/VZkqffQKC+lpLlT3LgyZ9T9t4LNJXnhzxOm9XCxecO5C+/ncb8OSPJPVzFfzz+IR8++Qeayg6TetmvWxViHEMnAODd/UXIYvTm5WCyRhHTb0TIznmqHIPHgxHAm7epU45f89VaMFuIG3PBUdsSz72SgLea6k1rOuXcIkfKD7ZQjQ1zJCIiIiISDppC0o2YTGai3P3IuP7Bo7qQWOyxuCbOJn7CxdTv30L1l6up2riKqvXLsWeOIW7cDJxDJmCyWDGMAH5PVad3MomJsnLZ9wcz8+z+bFj6d/od2Mky7zgaPqrnWnsNvVNiqfI00GhLwRzXC8+ujcSfNb3D42hLXV4OMf2GY7ZFfqvG6IzBmB3xePdkEzfq/A49tuFvombzBziHTmhz/ZGYPsOI6T+Kqs+XET9+JmZrVIeeX+RIhWUerBYTvRLsJ95ZRERERLodFTC6GZPJjDU28TjbTdgHjMY+YDS+2gpqctZSs2kNxa89jCU2kYTzfkhMxhCKXl2Cr6okeBdHZ64/YSrcQb+Dq4keMpGUuDm89XEeZRV13HDJSP7rpWyKK+q4NjGNid7N+OrrsMZ07sWLr7qUptJDwQUrI53JZMYxeBzeXRsxAn5MZkuHHduz+wsC3mrixk475j6J515JwUv3UvvV+8SPn9Vh5450oSr0ybcKyjy4Ex1YzGqhKiIiItIT6dN2D2aNTSTx3Cvp+8unm4sUqZlYna5g8QKO6GTSSetP+KpLKXrjEWzJGaTPvZUfXzSCZ387nZ9dOipYvADYWJMO/ibKdnRutw04on1qF1j/ooVjcBaBeg/1h3Z26HFrNq3FEt8Le+aYY+4T038U0b3PoPKzNzH8vg49f6QyjACNxQfC3rK4pyks82gBTxEREZEeTAUMwWS24Bw6gfSrf0eUu3+bnUw648LU8DVR9NrDGL4mUq+4A3N0850VrthoEuNjgsULgD2+VLwBG55dG6mo6dwFI+vycrDEJWHr1bdTz9ORHJljwGzp0HaqvqoS6vJyiDvzwuPe1WEymUg89wp8VSXUbvmow84fyfyeKoqWPhSyQp80d1kqKPWQoRaqIiIiIj2WChjSiskW3WYnE8Pf1OHnKn33bzTk78Y95xaievVptc1mNeNO/HaqSAAzeab+BPbnMP/+d/jjCxv4YnsR/oDRoTEZAT91+zbjGDi2VceNSGeOcRLTb0SHFjBqvvoXAHFnHr1453fZB40jKjWTynWvYwT8HRZDpAo01IWs0CfNqj2NeOt9ugNDREREpAdTAUNaaauTScqc/0fJsicoW/t3DF/HFDKqv1l7I2HyZTiHTTpqu8sZzaL5k4JFDHeinVHfn4a5sZZrx0exNa+M+/76OQv+uIZ/rN5ByRF3a5yOhvzdBOo92LvQ9JEWjsHjaCo5SFNl8Wkfywj4qflqLfaBZ2JzuU+4f/NdGFfSVF6AZ/tnp33+SGUYBtXZq2kqPdRmoc9k0bJCneXbFqoqYIiIiIj0VPq0La201cnEFBVDVFomVZ+/RV1eDimX3EZ06oBTPkdD/h7K3nkWe+YYEqf8qM19zGYT/dPiefi282nyBbBZzcRZfBz4+K9ckFLK3Lt/zPqtBaz+fD8vv7uT/1uzk3HDUpkxqT8TRqRitZxabc6bmwMm83HXfIhUjsFZlL/3d7x7snFlXXRax6rbuxlfdSlJ065v//nPmIitVx8qPn0Nxl17WuePRIGmBkrf+Su1m98nLutiUufdSdHS1ovdttWpRTpGQbCFqgoYIiIiIj2VChhylLY6maRc9G84h2RRsuJpDj93J0lTrsZ19iUn3fHC762m6LU/YXG6cF/6q+M+32w2kRgX0+oxe/+ReHdvJHnqTzj3zN6ce2ZvCss8vLfhAGs2HOCPL2wgMS6aaRP7MX1if1KTHFR5GoJFEJczGvNxOhjU5eUQnT4Iiz3upF7X8QQCxknFcKqikjOwJqZ1SAGjJuc9zI54nEOz2v0ck8lMwjlXULLscWzFu4D2PzfSNVUVU/Tqn2gszCPh3CtJPO+HYDK12bJYOkdBmReTCVKTHOEORURERETCRAUMaTfH4PH0+fmjlL7935T/63/x7skm5ZJbsCWktuv5RsBP8ZuP4vdUkfGTxVgc8Scfw5Asyt79G41lh4lK7g1AWrKTH180nB/NOIPsHcWs/nw/r72/m827S7l+zggefflLiivqcCfaWTR/Ev3T4tssIPjramgoyCXh3CtPOq5jCQQM9hdWs/i59e2K4XQ5hmRRk72aQGM95qiYEz+hDX5PFZ5dG3FNuBiTxXZSz40dcQ4VH71CTO6nGMaPutQ6IsfizfuK4jcfxQj4SZ23EOfQCcFtx2tZLB2rsMxDcnwMUbaOaxMsIiIiIl2Lvi6Uk2JxxOO+/HZSLrmFhuL9HHr211TnrMUwTryYZsWH/6Bu72aSZy0gOmPwKZ2/5eLRu2vj0bFZzEwcmcbvb5zEc7+fwU2Xjw4WLwCKK+pY/Nx6SivbXi+jbu9mMAId1j61vsHH4ZLaYPHiyBgqO6mTimPwOAx/E3X7vj7lY9R8/QEE/MSNnXrSzzWZLSRMvgxrdSF1eTmnHEMkMAyDynWvU/h/i7HEJtBn/pJWxQsJrYJStVAVERER6elUwJCTZjKZiBv9ffoueITo9MGUrnyaoqVLjttC0rNjPZXr3iDurOnEn8KFcQurK4Wo1Ew8bRQwjpTsshMfG92qFSs0FxCKK7zc8vC/eHHVNnbsKw92MqnLy8Ec4zzl4ophGBwsquHND/fw+2fW8aPfv01VbUObMRSUeVjy4kY+/Sqf+oaO61xh7zcCU1TMKXcjMQyDmpz3iO4z7KjOMO0VN3oKgZh4Kj5Z2q7CViQKNHgpeu1PlP/rJZzDv0fv6x/ElpQR7rB6tIIyjxbwFBEREenhNIVETpnVlUL6tfdQtWElFf96iYN/+XdSfvALnGdMbLVfY+khipc/SXTGEHrNuPG0z+sYOoHKj5fi91Qdd9HEllasRxYQ3Il27NFW4hxRvPavPSxdu5t4ZxSZKRZ+2piNo++ok1rXo77Bx+Y9pXyxvYjsHUXBc/VNjWP2uZkkxMW0GYPZbObr3FI++Sqf6CgLWcNSOWdMBlkjUrFHn/qfpcliw555Jt492RiGcdJTOBoO7aCpLJ+U2ZedVgz1mWdj3v4u9Qe2Yu8/6pSPFQ6NpYcoenUJTeWFJE27HtfE2d1iKkxXVtfgo7KmQQt4ioiIiPRwKmDIaTGZzCRMmoNj4JkUL3uColeXEHfmhSRNn4/RVM+wvqn4qkqI7nMG7otvxmQ9uTUV2uIcMoHKj/+Jd082cWdeeMz9WlqxtrX+xB9vPofauiY27Shmw7ZC9m7bisVRxYtfmfGUf8qEEWlMGJFKWpKz1QKc8Y4o8ks9ZO8oInt7MVvyyvD5A8REWThzSApXXjiE8cNScX+z0GAgYBwzhr/fPZOte8v49Kt81n1dwKeb84mymhk/PJXJYzKYOCIVR4wteJwqTwOpvQdRUVN/3IVAnUOy8O5cT2PRPqLTMk/qva3OeQ9TlB3n8Mkn9bzvauhzJnEHNlD56WtdqoBRu+MzSpb/GbMtmvRr7+lSsXdnLS1U03QHhoiIiEiPpgKGdIiolH70vuFBKj76J3X7ttBYkEvJij8HW0y6r7gDS1zHLHgYlZaJJS4Zz66Nxy1gtNWK9cgL/1i7jfPO6s15Z/Vm89L1sAsGjJ/MJ7n1/HXZFj7edJifXjyCx/7v20VAb7v6LF5cuZ2dByqCd1lkDUtlxMAkbNaj79w4fgwmxgxOYczgFH5+2Ri27y3j0835rNtcwGdfF2Czmhl3hpuZZ/cn2WXngefbtxCofdBZAHj3ZJ9UASNQ78GzbR2xo79/yguABllsuM6eS/nav1N/eBcxvYee3vE6mRHwU/7By1R99ibRGUNIveIOrPHJ4Q5LvtFSwNAUEhEREZGeLSQFjCVLlrB69WoOHz7M8uXLGTp06HEfB9i7dy8LFy6ksrKShIQElixZwoABA0IRrpwik8VG0gXX0lheQOHL9+GrKgHAV1VC8Wt/IuP6Bzuka4PJZMI5dAI1m/9FoKkBsy36mPu21Yq1LdFledh69eFHl0/mRzRfMHnrm3jg+Q2tFuB8/P82sWj+JJwxtuBdFifSnhgsZhOjBvVi1KBeLJg7mh37y5uLGV/l4/P3CxYvWuJY/Nx6Hr7t/DaPa41NJDp9EN492SSeREeV2q2fYPgaT2uNkiPFj5tO5brXqfzkVdKu+m2HHLOjGEYAv6eq+Q6h6lIq1r1BTfY7xJ01g14z5nfInULScQpKv7kDQ1NIRERERHq0kCziOXXqVF566SV69+7drscB7rnnHq655hpWr17NNddcw9133x2KUKUDmCzWYPGiha+qBMPfcYtVOoZkYTQ1nFa3jRaBpgasFQewH9F9JC3ZSawjqs0FOJ329hcvToXZbGJEZjIL5o7mb4tm0C8trs046hv9xzyGY3AWDYd3H3dh1e+qzllLlHsAUemDTjn2I5mj7Lgmzsa7J5uGwrwOOWZHMIwAjcUHyH/hLoqevY38FxcRe8bZuK+8k5Qf/JuKFxGooMxLnCOKWLt+NyLS+fbu3ctVV13FzJkzueqqq9i3b99R+/j9fu677z6mTZvG9OnTWbp06WlvKykp4Re/+AVz5szhoosuYtmyZa3OuWrVKubMmcPs2bOZM2cOpaWlHf/iRUQiXEgKGFlZWaSnp7f78bKyMrZt28bs2bMBmD17Ntu2baO8vLzTY5XTZ7JYsbpSWj1mdaVgsnTcDT/2/qMwRdnbbKd6suoPbMMU8B/VPrVlEdAjuRPt2Kyha95jNptwxFjbjGNffjUPPL+eTTuLCQRad/twDB4HGHhzN7XrPA2FeTQW5hI3dupxF6wMBAwqauoprvBSUVN/1Hm/Kz7rIkzRDio/fb1dcYSC31NF0dKHWt0hVLLyKWJ6DwlzZHIshaUe0nt1XtFQRORI7fkSbfny5Rw4cIB3332XV155hSeffJJDhw6d1raHHnqIUaNGsXz5cl566SUeffRRCgoKAPj666/585//zHPPPceKFSt4+eWXiYuLC9E7IiISOSKyjWpBQQGpqalYLM1rClgsFtxudzCJS2SzOF2kzlsYLGJYXSmkzlt43I4hJ8tkteEYNBbv7i8wjMBpHcubl4NhthDTb0Srx1sWAW0pHrSsPeFyHnvKSmdoK47fXj+R8uo6tu8r5+6/fMbN/7mWZR/lUlvXBEBU+kAszoR2t1OtyVmLyWIjdtT5x9wnEDDYX1jN7Y9/xI2L13D74x+xv7D6uEUMS4wTV9ZFeHZ8TmPJwZN41cd2skUUv7cGz+4vKP/XS+T/7900lRd0+h1C0rHyyzykJ8eGOwwR6QHa+yXaqlWrmDdvHmazmaSkJKZNm8Y777xzWtt27NjBeeedB0BSUhLDhg3j7bffBuCFF15g/vz5pKQ0f7aKi4sjOjq0n0dERCJBt1/Ec8uWLSf9nOzs9l30hVskx+l0OEiftwiryYTPMNhbXoPnUPvuBmivKFsKTs9nfPX+CvwJR09Daq/4rZ8RSOzHps1HjxW7w8G9N47HZLJgGH5qKvPZVLDndMI+JUfHUUBajJdbLk5h6wEvG3d7+OuyLfx9xVZGD3AwYaiTQQn98e3O5tDGDXC81rD+Jlxf/Ysm91Bytu08arNhGNTUBejTL5OH/mfTUWtx3HvjeIrz9x31vJbxaYrui8tiJXfFX/GOueS034e4hDS8VVXERpuoaDCodbmoqSykzusFw8BcW4K18nDzfxWHsHibP3QaJjP+uFTiA81FtSOLGFZXCvWNTWyO4L+pnqrJF6C0wkva+D7hDkVEeoDjfYmWlJTUar+MjIzgz+np6RQWFp7WtpEjR7Jq1SpGjx7NoUOH2LRpE336NOe+3Nxc+vTpw7XXXovX62X69On84he/UJtvEelxIrKAkZ6eTlFREX6/H4vFgt/vp7i4uM3pJicyatSok6pQZ2dnM378+JM+T6h1tThdqR1/bP+Ioez/egX9zDUkneJ74asu5cA7ZTT0Gdu+9zM9vJ0pgr/3I+KYNBHmA3sOVfL2un188OUhvsz1cFHvvszyfcXQ5BjiBo4JtmL9bjeUmq8/pMTXwICpPySqzwgOldSy93AVefnV7M2vYm9+FVW1jTx488A21+KoqYdVOT4G93ExqG8Cg3q72LdnW6v3s8yTR9WGFQy85Ca8UYltdoU5Hr8/QEllHSaTgbUqH+O9R2ioKsHuSiH24n/HKMmlJvtDEuoPYws0ANBocVBm70u1exxVzj7U2HtjWKKYFp1B7MW/xrPykWCXHOfFvybgSGL8+IwTRHL6GhoaTqmw2lOVVHgJGOpAIiLd38KFC/njH//I3LlzycjI4Oyzz8Zqbf6o7vf72blzJ88//zyNjY387Gc/IyMjg0svvbTdx9eXeuGnODuW4uxYXSXOiCxgJCcnM3z4cFasWMHcuXNZsWIFw4cPb1X5FrHY44jpNwLP7o0kXXDtKR3Dm5sDQFOvgR0ZWlgM7pPALT8cyw2zR/DexoO89+lOphlm3vzf10maGsuwAUn88ZuuKu5EO7+7YRIGBt4PV4Atid++XsqBwpU0+pqn5NisZvqnxTFxRBoDe7tIjI/BnWhvVcRwJ9oxDCgoq2Xj9kKMb2ZzxMaYGbapiUF9XAzuk8DA4dPhi7c5/P4/eWDn8DbbwdY1+Cgs81BY5qGg1Nv8v2Ueisq8FFd48QcMXlr4PSq/KTxA89SPmpWPkTT9Bg7VVrLJyOSAP5UDhpuyQBxGDRiAYYBh5GMAk0am8ecVBfx02m9IdFio8Pp58q2D3PGTPqBr5IhT8E0L1TQVMEQkBNr7JVp6ejr5+fmMGTMGaH1nxaluS0pK4uGHHw6eY8GCBQwa1LywdoMRevcAABogSURBVEZGBrNmzSIqKoqoqCimTp3K5s2bT6qAoS/1wktxdizF2bHCEeepfqkXkgLG4sWLeffddyktLeWGG24gISGBlStXHvNxgHvvvZeFCxfy9NNPEx8fz5IlS0IRqnQxzqETKFvzPE0VhdgS0076+XV5OVjikgjE9uqE6MIj1hHFpVMGccl5A9n9t3WMKjtMfbw9WLyA5jsnHnh+Pf821U2vqjzebZqAM83GD87JZGBvFwMzXPR2x2K1fLtMTiBgsGj+JBY/t/6oAsTTv5lKXYOPvMNV5B6qZMPmPIorvHy5o4iWJSoeyvoeUTs/obEqBXAGp6Dc8sOx/NdLX1JZ29D6ddhtpCU7OCvNx5CMYtLq8rDVDWlz/QpbygAm//5ZJrfj/amoqae8poG7nvu2g4070U5+SS0FpR7OHJJynGdLqLW0UM1QC1URCYH2fok2a9Ysli5dyowZM6isrOS9997jpZdeOq1tFRUVxMXFYbVa+eyzz9i1axdPPPEE0LwWx4cffsjcuXPx+Xx8/vnnzJw5M4TvjIhIZAhJAWPRokUsWrSo3Y8DDBo0qFVrKZG2OIZkUbbmeTy7NpIwac5JPdcI+KnbtxnnGZOgG84hNZtNpJ15NmVrnsftqGtz+oe7NJuAycyN//FzbHHHv8PJbDbRPy2eh287v80pIPZoKyMHJjNyYDK9nZWMHz+e+gYf+wqq2XOokuheozByP+bCmK287p0YjMEebWPiyDTSkh2kJTtJjzeT6NlL4ODX1OVtwlfQXLCw9eqDyWxtc/0KS1RUu9+XlkVRjyzE/Oa6CbyyZgcbtxczZnAvrp01jBGZ4Z0uJM0KyjzERFlIiNNidSISGsf6Em3BggXceuutjB49mrlz5/LVV18xY8YMAH75y1/St29fgFPetnnzZh544AHMZjOJiYk888wz2O3NC3hffPHFbNmyhR/84AeYzWbOPfdcrrzyytC9KSIiESIip5CItJctMQ1bSj+8p1DAaMjfQ6Deg33gWKg78f5dUUuBx5y/GXeio1URIy0xCmPPpziGZJ2weNHCbDaRGBfT7vPHRFsZNiCJYQOSqKipJ6f3VK783nhmRrsp9wZYtqGYlMQYfj4lgbq8HLxbvqT+4E6qAj5MUTHYB4whYfLl2AeOxZbgxjACpM5bGGyDeiodbo4sxHg89TidMbic0Sz86UTe+WwfS9/fzZ1//oRxw9z8eNYwhvRNbPexpeMVlnpJS3ZqoToRCZljfYn27LPPBv9tsVi477772nz+qW6bMmUKU6ZMaXOb2Wzmrrvu4q677jpR+CIi3ZoKGNLlOYdOoHLdG/i9NVgc7e+J7s3LAZMZe+YY2LarEyMMH1tiGrbkDIyDm1k0/7ZWdx3cNdWG8X4V8WOnhSSWeIeNs2fOoOT1P+GrKiHelcKvL7mNylWPUpnbvGhQlLs/rkmzcQw6i5g+Z2Cy2Fodw2QyE+XuR8b1D2L4fZgsVixOFybTyXWEbinE5O3aGpzvF2W2cMn5g5gxqT+r1u3l1ff38OvHPmLSyDSunTWMzIyOawMs7VdQVksfd/v/rkVERESk+1IBQ7o8x5AJVH76Gt7cbOJGf7/dz6vL3UR0+iAs9u59ceQYnEXVF6vol2RrNf2jfuV/0RiXhH3Q2JDEYdRVB4sX0Lx2Rclbj5Nyya04h03CMXAs1vgTT9swmcxYYzvvroiYaCuXXzCEWd8bwFsf5/HmB3u49b8+4NwzM7hm5jD6pnat8bJ3714WLlxIZWUlCQkJLFmyhAEDBrTa55NPPuGRRx5h165dXHfdddx5553BbU8++SQvv/wybrcbgHHjxnHPPfeEJPZAwKCwzEvW8JNf30ZEREREuh8VMKTLi84YhCU2Ee+uL9pdwPDX1dBQkEvCOVd0bnARwDF4HFXr36Jh/9cknjEJAF91GRV5OSRMvhyT2RKSOAy/r80FOK2uFOz9RoQkhpPhiLFx9fQzmH1OJm98mMvyj3NZtzmfKeP68KMZw0hNcrTZljbS3HPPPVxzzTXMnTuXZcuWcffdd/Piiy+22qdv374sXryY1atX09jYeNQxLr300lZFjVApr66nyRcgPdkR8nOLiIiISOQ5ufuuRSKQyWTGMSQLb94mDF9Tu55Tt3czGAEcIbr7IJxi+g7HFO3Au/vb3s41m/8FRoC4sReGLA6TpXkBziNZXSmYLJFdR411RHHdRcN59rfTmTtlMJ9+lc8jL2ezY385tz/+ETcuXsPtj3/E/sJqAi0tVyJEWVkZ27ZtY/bs2UDzKvbbtm2jvLy81X79+/dnxIgRWK2R9btQC1UREREROVJkfVoVOUXOoROo2bSGuv1bcAw664T71+XlYI5xEp0xJATRhZfJYsUx8Ey8uV9iGAEAanLWYs8cgy0hNWRxWJyu016AM5xcsdHMnzOSS6cMorjcy5/+94tWbWkXP7eeh287/6QWOe1sBQUFpKamYrE032VjsVhwu90UFBQc1RLweFauXMknn3xCSkoKt9xyC2eddeK/sSOdSo9vgM+ztwFQVriX7NqDp3SMUMjOzj7xThFAcXacrhAjKE4REel+VMCQbiFmwGhMtmg8uzacsIBhGAbevBzsA8aEbPpEuDkGZ+HZ/hmNhXvx19Xiqyom6cIfhzSGjlqAM9yS4mPw+QNttqVt8gXCFFXnufrqq7npppuw2Wx8+umn3HzzzaxatYrExPavQzJq1Ciio0+uDWp2djbRsb2wmCu54NwJWCyROU6ys7ODC8FGMsXZcbpCjKA4T6ShoeGUi6siIhI+kfmJUOQkma1R2AeOxbvrCwzj+LfxN5UexF9T3tw+tYdoLuqY8O7JpibnPcz2WJxDJ4Y8jpYFOG2uFKyxiV2ueNHCZjXjTrS3esydaMdmjazXk56eTlFREX6/HwC/309xcTHp6entPkZKSgo2W3M3mHPOOYf09HR2797dKfF+V0GpB3eSI2KLFyIiIiISWvpUKN2Gc+gE/LXlNBbkHnc/b14OQI9Y/6KFxeki9swLiM4YQnzWRaRd9Tuw9Iy7TzqDyxnNovmTgkUMd6KdRfMn4XKe3F0GnS05OZnhw4ezYsUKAFasWMHw4cNPavpIUVFR8N/bt2/n8OHDZGZmdnisbSko85DeS+tfiIiIiEgzTSGRbsMxeDyYzHh2byQ6Y/Ax96vLy8HWqw/W+F4hjC68DCNA/NhpFL/5aKv1J6Lc/brsXRDhZDab6J8W36otbaR2Ibn33ntZuHAhTz/9NPHx8SxZsgSABQsWcOuttzJ69Gi++OILfv3rX1NbW4thGKxcuZIHHniA8847j0ceeYStW7diNpux2Wz853/+JykpKSc46+kzDIPCUg/D+re/2CIiIiIi3ZsKGNJtWBzxxPQ5A++ujSRN+VGb+wSaGqg/sJ24cTNCHF14+T1VweIFNLcvLVr6EBnXP4g1tv1rGci3zGZTRC3YeSyDBg1i6dKlRz3+7LPPBv+dlZXFRx991ObzWwoeoVbXGMBT71MHEhEREREJ0lev0q04hk6gsXg/TZXFbW6vP7ANw9eIowetfwFg+H3B4kULX1UJht8XpohEjq+8pnndjvRkR5gjEREREZFIoQKGdCvOoRMA8O7+os3t3rwcTBYbMf1GhDKssDNZrFhdrW/7t7pSMFl0E5ZEpvLa5uKa1sAQERERkRYqYEi3YkvKwJbcG++uDW1ur8vLIabfCMy2yFpssbNZnC5S5y0MFjFa1sCwOF1hjkykbRU1zQWMVE0hEREREZFv6OtX6XYcQydQtX45/noPlphvL3581aU0lR4i7sypYYwuPEwmM1HufmRc/yCG34fJYsXidGkBT4lY5bU+kl0xRNvULUdEREREmunqRbod59AJEPBTl/tlq8e9uT2vfeqRTCYz1thEbK4UrLGJKl5IRCuv9Wn6iIiIiIi0oisY6XaiM4Zgcbrw7NrY6vG6vBwscUnYevUNU2Qi0l4VNT7SNX1ERERERI6gAoZ0OyazBcfg8XhzN2H4mwAwAn7q9m3GMXAsJpMpzBGKyPHUN/iorQ+ohaqIiIiItKIChnRLjqETMRq81B3YBkBD/h4C9R7sPax9qkhXVFjuBdAdGCIiIiLSigoY0i3ZM8dgskbh/WYaiTcvB0xm7JljwhyZiJxIQWktoBaqIiIiItKaChjSLZlt0dgzz8S7ayOGYVCXl0N0+iAs9rhwhyYiJ1BQ2nwHRpoKGCIiIiJyBBUwpNtyDM3CV11K/f4tNOTv0fQRkS6isMyDPcpMrN0W7lBEREREJIJYwx2ASGdxDM4iOmMIht9P+rX3YolLwjACah8qEsECAYNzz8zggvF9qKipx+WMxmzWwrsiIiIiogKGdGMWZzxJF15HyfIn8VWVYHWlkDpvIVHufipiiESgQMBgf2E1j7+yieKKOtyJdhbNn0T/tHgVMUREREREU0ik+/J7qoLFCwBfVQlFSx/C76kKc2Qi0pYqTwOLn1tPcUUdAMUVdSx+bj1VnoYwRyYiIiIikUAFDOm2DL8vWLxo4asqwfD7whSRiBxPky8QLF60KK6oo8kXCFNEIiIiIhJJVMCQbstksWJ1pbR6zOpKwWTRzCmRSGSzmnEn2ls95k60Y7Pq/6pERERERAUM6cYsThep8xYGixgta2BYnK4wRyYibXE5o1k0f1KwiNGyBobLGR3myEREREQkEuiraOm2TCYzUe5+ZFz/IIbfh8lixeJ0aQFPkQhlNpvonxbPw7edj8dTj9MZoy4kIiIiIhKkKznp1kwmM9bYRGyuFKyxiSpeiEQ4s9lEYlwMRYdzSYyLUfFCRERERIJ0NSciIiIiIiIiEU8FDBERERERERGJeCpgiIiIiIiIiEjEUwFDRERERERERCKeChgiIiIiIiIiEvFUwBARERERERGRiKcChoiIiIiIiIhEPBUwRERERERERCTiqYAhIiIiIiIiIhHPGu4AOothGAA0Njae9HMbGho6OpxOoTg7luLsWIqzbS05qSVHdWenk4dBY6ijKc6O0xViBMV5PMrF7aMx1LEUZ8dSnB2rq3wmNhndNHPX1NSwa9eucIchItKmoUOHEhcXF+4wOpXysIhEOuViEZHwOtk83G0LGIFAAI/Hg81mw2QyhTscERGgucrc1NSE0+nEbO7es/iUh0UkUikXi4iE16nm4W5bwBARERERERGR7qN7l5xFREREREREpFtQAUNEREREREREIp4KGCIiIiIiIiIS8VTAEBEREREREZGIpwKGiIiIiIiIiEQ8FTBEREREREREJOKpgCEiIiIiIiIiEc8a7gDCYe/evSxcuJDKykoSEhJYsmQJAwYMaLWP3+9n8eLFfPzxx5hMJn7+858zb968kMVYUVHBb37zGw4cOEBUVBT9+/fn/vvvJykpqdV+CxcuZN26dSQmJgIwa9YsfvGLX4QsToALL7yQqKgooqOjAbj99ts577zzWu1TV1fHXXfdxdatW7FYLNx5551ccMEFIYvx0KFD/PKXvwz+XFNTQ21tLRs2bGi135NPPsnLL7+M2+0GYNy4cdxzzz2dGtuSJUtYvXo1hw8fZvny5QwdOhRo3ziF0I3VtuJs7ziF0I3VY72f7RmnEP6x2pMoF3cs5eJT11Xy8LFijbRcrDzcdSgPdyzl4dPTVXJxV8jDx4oTukEuNnqg6667znjzzTcNwzCMN99807juuuuO2ueNN94w5s+fb/j9fqOsrMw477zzjIMHD4YsxoqKCuPzzz8P/vzQQw8Zd91111H73Xnnncb//M//hCyutlxwwQXGzp07j7vPk08+afz2t781DMMw9u7da0yePNmora0NRXhtWrx4sXHfffcd9fgTTzxhPPTQQyGNZePGjUZ+fv5R72N7xqlhhG6sthVne8epYYRurB7r/WzPODWMyBur3ZlyccdSLj51XSUPHyvWSMvFysNdh/Jwx1IePj1dJRd3hTx8rDgNo+vn4h43haSsrIxt27Yxe/ZsAGbPns22bdsoLy9vtd+qVauYN28eZrOZpKQkpk2bxjvvvBOyOBMSEpg0aVLw57Fjx5Kfnx+y83e0t99+m6uvvhqAAQMGMGrUKD766KOwxNLY2Mjy5cu54oorwnL+78rKyiI9Pb3VY+0dpxC6sdpWnJE4TtuK82RE0ljtzpSLwyOSxnck5eKukoePFWukjVPl4a5BeTg8Iml8R1Iehq6Ti7tCHobum4t7XAGjoKCA1NRULBYLABaLBbfbTUFBwVH7ZWRkBH9OT0+nsLAwpLG2CAQC/OMf/+DCCy9sc/vzzz/PnDlzuPnmm8nNzQ1xdM1uv/125syZw7333kt1dfVR2/Pz8+ndu3fw53C+n++//z6pqamMHDmyze0rV65kzpw5zJ8/n02bNoU4umbtHact+0bCWD3ROIXwj9UTjVOIrLHanSkXdw7l4o7TFfMwRH4uVh6OHMrDnUN5uGN1xVwc6XkYunYu7nEFjK7oD3/4Aw6Hgx//+MdHbfvVr37FmjVrWL58OTNmzOBnP/sZfr8/pPG99NJLvPXWW7z22msYhsH9998f0vOfrNdee+2Ylearr76atWvXsnz5cm688UZuvvlmKioqQhxh13S8cQrhH6tdbZxK5FEu7ljKxZ0jknNxVxujEnmUhzuW8nDniOQ8DF1vnH5XjytgpKenU1RUFBwkfr+f4uLio26vSU9Pb3XbT0FBAWlpaSGNFZoXX9m/fz+PPfYYZvPRv67U1NTg45deeilerzfklbGW9y4qKoprrrmGL7/88qh9MjIyOHz4cPDncL2fRUVFbNy4kTlz5rS5PSUlBZvNBsA555xDeno6u3fvDmWIQPvHacu+4R6rJxqnEP6x2p5xCpEzVrs75eKOp1zcsbpaHobIz8XKw5FFebjjKQ93vK6WiyM9D0PXz8U9roCRnJzM8OHDWbFiBQArVqxg+PDhR60QO2vWLJYuXUogEKC8vJz33nuPmTNnhjTWRx99lC1btvDUU08RFRXV5j5FRUXBf3/88ceYzWZSU1NDFSJer5eamhoADMNg1apVDB8+/Kj9Zs2axSuvvALAvn37+Prrr9tc7bazvfHGG0yZMiW46u93Hfl+bt++ncOHD5OZmRmq8ILaO04h/GO1PeMUwjtW2ztOIXLGanenXNyxlIs7XlfKwxD5uVh5OPIoD3cs5eHO0ZVycaTnYegeudhkGIYR7iBCLTc3l4ULF1JdXU18fDxLlixh4MCBLFiwgFtvvZXRo0fj9/u5//77+fTTTwFYsGABV111Vchi3L17N7Nnz2bAgAHExMQA0KdPH5566inmzp3LX/7yF1JTU7n++uspKyvDZDIRGxvLb37zG8aOHRuyOA8ePMgtt9yC3+8nEAgwaNAgFi1ahNvtbhWn1+tl4cKFbN++HbPZzB133MG0adNCFmeLmTNn8rvf/Y7zzz8/+NiRv/c777yTrVu3Yjabsdls3HrrrUyZMqVTY1q8eDHvvvsupaWlJCYmkpCQwMqVK485Tr8bc6jGaltxPvbYY8ccp0BYxmpbcT7zzDPHHKffjTNSxmpPoFzccZSLT09XycPHijXScrHycNehPNxxlIdPX1fJxV0hDx8rzu6Qi3tkAUNEREREREREupYeN4VERERERERERLoeFTBEREREREREJOKpgCEiIiIiIiIiEU8FDBERERERERGJeCpgiIiIiIiIiEjEUwFDpIOdccYZ7N+/P9xhiIj0aMrFIiLhpTwsncEa7gBEOtuFF15IaWkpFosl+Nhll13G3XffHcaoRER6FuViEZHwUh6W7kAFDOkRnnnmGSZPnhzuMEREejTlYhGR8FIelq5OU0ikx3r99de5+uqr+cMf/sD48eOZNWsWn332WXB7UVERN910ExMnTmT69On885//DG7z+/0888wzTJs2jbPOOovLL7+cgoKC4PZ169YxY8YMJkyYwH333YdhGCF9bSIiXYVysYhIeCkPS1eiOzCkR9u8eTOzZs3i888/Z82aNfy///f/WLt2LQkJCfzHf/wHgwcP5uOPPyYvL48bbriBvn378r3vfY/nn3+elStX8pe//IXMzEx27txJTExM8LgffPABr776KrW1tVx++eVccMEFnH/++WF8pSIikUu5WEQkvJSHpavQHRjSI/zyl78kKysr+F9L5TgpKYmf/vSn2Gw2fvCDH5CZmckHH3xAQUEB2dnZ3H777URHRzN8+HDmzZvHsmXLAFi6dCm33XYbAwcOxGQyMWzYMBITE4PnW7BgAfHx8WRkZDBp0iR27NgRltctIhJJlItFRMJLeVi6Ot2BIT3CU089ddR8v9dff53U1FRMJlPwsYyMDIqLiykuLsblchEbG9tq25YtWwAoLCykX79+xzxfSkpK8N92ux2Px9NRL0VEpMtSLhYRCS/lYenqdAeG9GhFRUWt5uIVFBTgdrtxu91UVVVRW1vbaltqaioAaWlpHDhwIOTxioh0R8rFIiLhpTwsXYUKGNKjlZeX8+KLL9LU1MTbb79Nbm4uU6ZMIT09nbPOOotHHnmEhoYGduzYwauvvsqcOXMAmDdvHo8//jj79u3DMAx27NhBRUVFmF+NiEjXpFwsIhJeysPSVWgKifQIN910U6ue15MnT2bq1KmMGTOG/fv3c/bZZ9OrVy+eeOKJ4Ly9Rx55hHvuuYfzzjuP+Ph4brnlFs455xwAbrjhBhobG5k/fz4VFRUMHDiQp556KiyvTUSkq1AuFhEJL+Vh6epMhnrZSA/1+uuvs3TpUv7xj3+EOxQRkR5LuVhEJLyUh6Ur0RQSEREREREREYl4KmCIiIiIiIiISMTTFBIRERERERERiXi6A0NEREREREREIp4KGCIiIiIiIiIS8VTAEBEREREREZGIpwKGiIiIiIiIiEQ8FTBEREREREREJOKpgCEiIiIiIiIiEe//A4QcQrwDWqJhAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1296x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_metrics(history_flat, 'Static LR')\n",
    "plot_metrics(history_decay, 'Decaying LR')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Success; the accuracy of the train and validation set converge nicely! Overfitting problem as seen during the benchmarks is solved. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_flat.save('densenet121_frozen_flat.h5')\n",
    "model_decay.save('densenet121_frozen_decay.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Unfreeze and Retrain\n",
    "* We perform a new training session but unfreeze the last part of the pre-trained weights.\n",
    "* Again we compare multiple learning rate strategies, this time decaying vs. cyclical.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we have to adjust this down as not to disturb the pre-trained weights too much\n",
    "LEARNING_RATE = 1e-5\n",
    "\n",
    "model_step_2_decay = keras.models.load_model('densenet121_frozen_decay.h5', custom_objects=get_dependencies(get_optimizer()))\n",
    "model_step_2_cyclical = keras.models.load_model('densenet121_frozen_decay.h5', custom_objects=get_dependencies(get_optimizer()))\n",
    "\n",
    "model_step_2_decay = unfreeze(model_step_2_decay, 'pool4_pool')\n",
    "model_step_2_cyclical = unfreeze(model_step_2_cyclical, 'pool4_pool')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train for 842 steps, validate for 94 steps\n",
      "\n",
      "Epoch 00001: LearningRateScheduler reducing learning rate to 1e-05.\n",
      "Epoch 1/999\n",
      "842/842 [==============================] - 450s 535ms/step - loss: 97.5278 - accuracy: 0.3795 - lr: 1.0000e-05 - val_loss: 95.0602 - val_accuracy: 0.4385 - val_lr: 1.0000e-05\n",
      "\n",
      "Epoch 00002: LearningRateScheduler reducing learning rate to 9e-06.\n",
      "Epoch 2/999\n",
      "842/842 [==============================] - 446s 529ms/step - loss: 94.4547 - accuracy: 0.3977 - lr: 9.0000e-06 - val_loss: 93.5728 - val_accuracy: 0.4456 - val_lr: 9.0000e-06\n",
      "\n",
      "Epoch 00003: LearningRateScheduler reducing learning rate to 8.1e-06.\n",
      "Epoch 3/999\n",
      "842/842 [==============================] - 446s 530ms/step - loss: 93.6421 - accuracy: 0.4014 - lr: 8.1000e-06 - val_loss: 93.2312 - val_accuracy: 0.4486 - val_lr: 8.1000e-06\n",
      "\n",
      "Epoch 00004: LearningRateScheduler reducing learning rate to 7.290000000000001e-06.\n",
      "Epoch 4/999\n",
      "842/842 [==============================] - 445s 528ms/step - loss: 92.3503 - accuracy: 0.4062 - lr: 7.2900e-06 - val_loss: 92.2761 - val_accuracy: 0.4559 - val_lr: 7.2900e-06\n",
      "\n",
      "Epoch 00005: LearningRateScheduler reducing learning rate to 6.561e-06.\n",
      "Epoch 5/999\n",
      "842/842 [==============================] - 440s 523ms/step - loss: 91.1178 - accuracy: 0.4119 - lr: 6.5610e-06 - val_loss: 91.8103 - val_accuracy: 0.4572 - val_lr: 6.5610e-06\n",
      "\n",
      "Epoch 00006: LearningRateScheduler reducing learning rate to 5.904900000000001e-06.\n",
      "Epoch 6/999\n",
      "842/842 [==============================] - 422s 501ms/step - loss: 90.7066 - accuracy: 0.4131 - lr: 5.9049e-06 - val_loss: 91.3428 - val_accuracy: 0.4589 - val_lr: 5.9049e-06\n",
      "\n",
      "Epoch 00007: LearningRateScheduler reducing learning rate to 5.314410000000001e-06.\n",
      "Epoch 7/999\n",
      "842/842 [==============================] - 424s 503ms/step - loss: 90.2041 - accuracy: 0.4180 - lr: 5.3144e-06 - val_loss: 91.0729 - val_accuracy: 0.4609 - val_lr: 5.3144e-06\n",
      "\n",
      "Epoch 00008: LearningRateScheduler reducing learning rate to 4.782969000000001e-06.\n",
      "Epoch 8/999\n",
      "842/842 [==============================] - 421s 500ms/step - loss: 88.9446 - accuracy: 0.4203 - lr: 4.7830e-06 - val_loss: 90.7707 - val_accuracy: 0.4586 - val_lr: 4.7830e-06\n",
      "\n",
      "Epoch 00009: LearningRateScheduler reducing learning rate to 4.304672100000001e-06.\n",
      "Epoch 9/999\n",
      "842/842 [==============================] - 422s 501ms/step - loss: 89.1561 - accuracy: 0.4240 - lr: 4.3047e-06 - val_loss: 90.5774 - val_accuracy: 0.4629 - val_lr: 4.3047e-06\n",
      "\n",
      "Epoch 00010: LearningRateScheduler reducing learning rate to 3.8742048900000015e-06.\n",
      "Epoch 10/999\n",
      "842/842 [==============================] - 422s 501ms/step - loss: 88.6069 - accuracy: 0.4247 - lr: 3.8742e-06 - val_loss: 90.4181 - val_accuracy: 0.4636 - val_lr: 3.8742e-06\n",
      "\n",
      "Epoch 00011: LearningRateScheduler reducing learning rate to 3.486784401000001e-06.\n",
      "Epoch 11/999\n",
      "842/842 [==============================] - 421s 500ms/step - loss: 87.8879 - accuracy: 0.4290 - lr: 3.4868e-06 - val_loss: 90.0976 - val_accuracy: 0.4643 - val_lr: 3.4868e-06\n",
      "\n",
      "Epoch 00012: LearningRateScheduler reducing learning rate to 3.138105960900001e-06.\n",
      "Epoch 12/999\n",
      "842/842 [==============================] - 421s 500ms/step - loss: 87.9723 - accuracy: 0.4295 - lr: 3.1381e-06 - val_loss: 89.7194 - val_accuracy: 0.4663 - val_lr: 3.1381e-06\n",
      "\n",
      "Epoch 00013: LearningRateScheduler reducing learning rate to 2.8242953648100014e-06.\n",
      "Epoch 13/999\n",
      "842/842 [==============================] - 422s 501ms/step - loss: 87.5675 - accuracy: 0.4338 - lr: 2.8243e-06 - val_loss: 89.8970 - val_accuracy: 0.4643 - val_lr: 2.8243e-06\n",
      "\n",
      "Epoch 00014: LearningRateScheduler reducing learning rate to 2.541865828329001e-06.\n",
      "Epoch 14/999\n",
      "842/842 [==============================] - 433s 515ms/step - loss: 87.4286 - accuracy: 0.4328 - lr: 2.5419e-06 - val_loss: 89.8139 - val_accuracy: 0.4619 - val_lr: 2.5419e-06\n",
      "\n",
      "Epoch 00015: LearningRateScheduler reducing learning rate to 2.287679245496101e-06.\n",
      "Epoch 15/999\n",
      "842/842 [==============================] - 440s 523ms/step - loss: 86.7603 - accuracy: 0.4368 - lr: 2.2877e-06 - val_loss: 89.3525 - val_accuracy: 0.4669 - val_lr: 2.2877e-06\n",
      "\n",
      "Epoch 00016: LearningRateScheduler reducing learning rate to 2.058911320946491e-06.\n",
      "Epoch 16/999\n",
      "842/842 [==============================] - 439s 521ms/step - loss: 86.9511 - accuracy: 0.4330 - lr: 2.0589e-06 - val_loss: 89.2906 - val_accuracy: 0.4686 - val_lr: 2.0589e-06\n",
      "\n",
      "Epoch 00017: LearningRateScheduler reducing learning rate to 1.8530201888518418e-06.\n",
      "Epoch 17/999\n",
      "841/842 [============================>.] - ETA: 0s - loss: 86.8919 - accuracy: 0.4332 - lr: 1.8530e-06Restoring model weights from the end of the best epoch.\n",
      "842/842 [==============================] - 445s 528ms/step - loss: 86.8940 - accuracy: 0.4332 - lr: 1.8530e-06 - val_loss: 89.2007 - val_accuracy: 0.4679 - val_lr: 1.8530e-06\n",
      "Epoch 00017: early stopping\n",
      "Train for 842 steps, validate for 94 steps\n",
      "Epoch 1/999\n",
      "842/842 [==============================] - 542s 644ms/step - loss: 96.8220 - accuracy: 0.3813 - lr: 2.4970e-05 - val_loss: 93.4412 - val_accuracy: 0.4506 - val_lr: 5.0000e-05\n",
      "Epoch 2/999\n",
      "842/842 [==============================] - 699s 830ms/step - loss: 92.7377 - accuracy: 0.4081 - lr: 7.4970e-05 - val_loss: 93.4820 - val_accuracy: 0.4516 - val_lr: 1.0000e-04\n",
      "Epoch 3/999\n",
      "842/842 [==============================] - 425s 505ms/step - loss: 88.0287 - accuracy: 0.4330 - lr: 7.5030e-05 - val_loss: 89.0053 - val_accuracy: 0.4756 - val_lr: 5.0000e-05\n",
      "Epoch 4/999\n",
      "842/842 [==============================] - 424s 504ms/step - loss: 83.2455 - accuracy: 0.4575 - lr: 2.5030e-05 - val_loss: 87.0958 - val_accuracy: 0.4836 - val_lr: 1.0000e-10\n",
      "Epoch 5/999\n",
      "842/842 [==============================] - 426s 505ms/step - loss: 81.1669 - accuracy: 0.4707 - lr: 1.2485e-05 - val_loss: 86.1208 - val_accuracy: 0.4896 - val_lr: 2.5000e-05\n",
      "Epoch 6/999\n",
      "842/842 [==============================] - 435s 517ms/step - loss: 80.5297 - accuracy: 0.4724 - lr: 3.7485e-05 - val_loss: 87.9353 - val_accuracy: 0.4913 - val_lr: 5.0000e-05\n",
      "Epoch 7/999\n",
      "842/842 [==============================] - 435s 517ms/step - loss: 78.8034 - accuracy: 0.4786 - lr: 3.7515e-05 - val_loss: 86.1682 - val_accuracy: 0.4987 - val_lr: 2.5000e-05\n",
      "Epoch 8/999\n",
      "842/842 [==============================] - 439s 522ms/step - loss: 76.7362 - accuracy: 0.4911 - lr: 1.2515e-05 - val_loss: 85.1043 - val_accuracy: 0.5017 - val_lr: 1.0000e-10\n",
      "Epoch 9/999\n",
      "842/842 [==============================] - 445s 528ms/step - loss: 75.8890 - accuracy: 0.4942 - lr: 6.2427e-06 - val_loss: 85.2330 - val_accuracy: 0.5053 - val_lr: 1.2500e-05\n",
      "Epoch 10/999\n",
      "842/842 [==============================] - 428s 508ms/step - loss: 73.4328 - accuracy: 0.5102 - lr: 3.1214e-06 - val_loss: 83.2855 - val_accuracy: 0.5147 - val_lr: 6.2501e-06\n",
      "Epoch 14/999\n",
      "842/842 [==============================] - 423s 502ms/step - loss: 72.8573 - accuracy: 0.5112 - lr: 9.3714e-06 - val_loss: 83.3936 - val_accuracy: 0.5084 - val_lr: 1.2500e-05\n",
      "Epoch 15/999\n",
      "842/842 [==============================] - 425s 504ms/step - loss: 72.8133 - accuracy: 0.5111 - lr: 9.3788e-06 - val_loss: 82.8972 - val_accuracy: 0.5137 - val_lr: 6.2501e-06\n",
      "Epoch 16/999\n",
      "842/842 [==============================] - 423s 503ms/step - loss: 72.1120 - accuracy: 0.5184 - lr: 3.1288e-06 - val_loss: 83.0665 - val_accuracy: 0.5150 - val_lr: 1.0000e-10\n",
      "Epoch 17/999\n",
      "842/842 [==============================] - 422s 501ms/step - loss: 71.7854 - accuracy: 0.5170 - lr: 1.5607e-06 - val_loss: 83.1980 - val_accuracy: 0.5120 - val_lr: 3.1251e-06\n",
      "Epoch 18/999\n",
      "682/842 [=======================>......] - ETA: 1:19 - loss: 72.2019 - accuracy: 0.5163 - lr: 4.3888e-06"
     ]
    }
   ],
   "source": [
    "history_step_2_decay = model_step_2_decay.fit(\n",
    "    TRAIN_GENERATOR,\n",
    "    epochs=EPOCHS,\n",
    "    validation_data=VALIDATION_GENERATOR, \n",
    "    class_weight=get_class_weights(TRAIN_GENERATOR.class_indices),\n",
    "    verbose=1,\n",
    "    callbacks=[get_early_stopping(), get_lr_callback('decay')]\n",
    ")\n",
    "\n",
    "history_step_2_cyclical = model_step_2_cyclical.fit(\n",
    "    TRAIN_GENERATOR,\n",
    "    epochs=EPOCHS,\n",
    "    validation_data=VALIDATION_GENERATOR, \n",
    "    class_weight=get_class_weights(TRAIN_GENERATOR.class_indices),\n",
    "    verbose=1,\n",
    "    callbacks=[get_early_stopping(), get_lr_callback('triangle2')]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_metrics(history_step_2_decay, 'Decaying LR')\n",
    "plot_metrics(history_step_2_cyclical, 'Cyclical LR')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_step_2_decay.save('densenet121_unfrozen_decay.h5')\n",
    "model_step_2_cyclical.save('densenet121_unfrozen_cyclical.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.models.load_model('densenet121_unfrozen_cyclical.h5', custom_objects=get_dependencies(get_optimizer()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 7653 images belonging to 1 classes.\n",
      "7653/7653 [==============================] - 156s 20ms/step\n"
     ]
    }
   ],
   "source": [
    "BATCH_SIZE = 1\n",
    "\n",
    "TEST_GENERATOR = get_generator_dir(DIR + 'test_set/', shuffle=False)\n",
    "\n",
    "prob_matrix = model.predict(TEST_GENERATOR, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get class with the highest confidence\n",
    "predicted_class_indices = np.argmax(prob_matrix, axis=1)\n",
    "\n",
    "# map them properly to the filenames since keras orders them alphabetically...\n",
    "label_map = sorted(TRAIN_GENERATOR.class_indices)\n",
    "predictions = [label_map[idx] for idx in predicted_class_indices]\n",
    "\n",
    "# generate submission.csv file\n",
    "filenames = []\n",
    "for filename in TEST_GENERATOR.filenames:\n",
    "    filenames.append(filename[9:]) # strip first part of path ('test_set/')\n",
    "submission = pd.DataFrame({\"img_name\": filenames, \"label\": predictions})\n",
    "submission.set_index('img_name', inplace=True)\n",
    "submission.to_csv('submission.csv')"
   ]
  }
 ],
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