denoising.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "import imgaug.augmenters as iaa\n",
    "import time\n",
    "\n",
    "from keras import layers\n",
    "from keras.layers import Dropout\n",
    "from keras.datasets import mnist\n",
    "from keras.models import Model\n",
    "\n",
    "from sklearn.utils import shuffle\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_gaussian_noise(images):\n",
    "    seq = iaa.AdditiveGaussianNoise(scale=(25, 35))\n",
    "    augmented_images = seq.augment_images(images)\n",
    "    return augmented_images\n",
    "\n",
    "def display(*arrays):\n",
    "    n = 10\n",
    "    num_arrays = len(arrays)\n",
    "    indices = np.random.randint(len(arrays[0]), size=n)\n",
    "    \n",
    "    fig, axes = plt.subplots(num_arrays, n, figsize=(20, 3 * num_arrays))\n",
    "    for j, array in enumerate(arrays):\n",
    "        sample = array[indices]\n",
    "        for i, image in enumerate(sample):\n",
    "            ax = axes[j, i]\n",
    "            ax.imshow(image)\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample dims:  (50000, 32, 32, 3)\n",
      "Test samples:  (10000, 32, 32, 3)\n"
     ]
    }
   ],
   "source": [
    "# Load the dataset\n",
    "(train_images, train_labels), (test_images, test_labels) = tf.keras.datasets.cifar10.load_data()\n",
    "print('Sample dims: ', train_images.shape)\n",
    "print('Test samples: ', test_images.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample dims:  (65000, 32, 32, 3)\n",
      "Test samples:  (10000, 32, 32, 3)\n"
     ]
    }
   ],
   "source": [
    "#augmentation block\n",
    "train_images_aug = []\n",
    "train_labels_aug = []\n",
    "\n",
    "augmentations = (\n",
    "    iaa.Fliplr(1),\n",
    "    iaa.Flipud(1),\n",
    "    iaa.Affine(rotate=(-30))\n",
    ")\n",
    "  \n",
    "#define amount of augmented images\n",
    "num_augmented_samples = int(0.3 * len(train_images))\n",
    "\n",
    "#Augmentation\n",
    "for i in range(num_augmented_samples):\n",
    "    random_aug = np.random.randint(0, 3)\n",
    "    seq = iaa.Sequential(augmentations[random_aug])\n",
    "    augmented_image = seq.augment_image(train_images[i])\n",
    "    train_images_aug.append(np.expand_dims(augmented_image, axis=0))\n",
    "    train_labels_aug.append(train_labels[i])\n",
    "\n",
    "\n",
    "# fusion train and augmented\n",
    "train_images_aug = np.concatenate(train_images_aug, axis=0)\n",
    "train_labels_aug = np.array(train_labels_aug)\n",
    "train_images = np.concatenate((train_images, train_images_aug))\n",
    "train_labels = np.concatenate((train_labels, train_labels_aug))\n",
    "\n",
    "print('Sample dims: ', train_images.shape)\n",
    "print('Test samples: ', test_images.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x600 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Mapping from class ID to class name\n",
    "classes = {0:'plane', 1:'car', 2:'bird', 3:'cat', 4:'deer',\n",
    "           5:'dog', 6:'frog', 7:'horse', 8:'ship', 9:'truck'}\n",
    "\n",
    "# Dataset params\n",
    "num_classes = len(classes)\n",
    "shape = train_images.shape\n",
    "\n",
    "height = shape[1]\n",
    "width = shape[2]\n",
    "\n",
    "# Shuffle the training data (optional but recommended)\n",
    "train_images, train_labels = shuffle(train_images, train_labels)\n",
    "\n",
    "#Let's blur our images\n",
    "noisy_train_data = add_gaussian_noise(train_images)\n",
    "noisy_test_data = add_gaussian_noise(test_images)\n",
    "\n",
    "#Show result\n",
    "display(noisy_train_data, train_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"functional_59\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"functional_59\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ input_layer_29 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_87 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>)      │           <span style=\"color: #00af00; text-decoration-color: #00af00\">224</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_58 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_88 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)     │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,168</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_59 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)       │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_transpose_58             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)     │         <span style=\"color: #00af00; text-decoration-color: #00af00\">2,320</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2DTranspose</span>)               │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_transpose_59             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)     │         <span style=\"color: #00af00; text-decoration-color: #00af00\">2,320</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2DTranspose</span>)               │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_89 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)              │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)      │           <span style=\"color: #00af00; text-decoration-color: #00af00\">435</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ input_layer_29 (\u001b[38;5;33mInputLayer\u001b[0m)     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m3\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_87 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m8\u001b[0m)      │           \u001b[38;5;34m224\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_58 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m8\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_88 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m)     │         \u001b[38;5;34m1,168\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_59 (\u001b[38;5;33mMaxPooling2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m16\u001b[0m)       │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_transpose_58             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m)     │         \u001b[38;5;34m2,320\u001b[0m │\n",
       "│ (\u001b[38;5;33mConv2DTranspose\u001b[0m)               │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_transpose_59             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m16\u001b[0m)     │         \u001b[38;5;34m2,320\u001b[0m │\n",
       "│ (\u001b[38;5;33mConv2DTranspose\u001b[0m)               │                        │               │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_89 (\u001b[38;5;33mConv2D\u001b[0m)              │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m3\u001b[0m)      │           \u001b[38;5;34m435\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">6,467</span> (25.26 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m6,467\u001b[0m (25.26 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">6,467</span> (25.26 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m6,467\u001b[0m (25.26 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_images = np.array(train_images) / 255.0\n",
    "test_images = np.array(test_images) / 255.0\n",
    "\n",
    "input = layers.Input(shape=(32, 32, 3))\n",
    "\n",
    "# Encoder\n",
    "x = layers.Conv2D(8, (3, 3), activation=\"relu\", padding=\"same\")(input)\n",
    "x = layers.MaxPooling2D((2, 2), padding=\"same\")(x)\n",
    "x = layers.Conv2D(16, (3, 3), activation=\"relu\", padding=\"same\")(x)\n",
    "x = layers.MaxPooling2D((2, 2), padding=\"same\")(x)\n",
    "\n",
    "# Decoder\n",
    "x = layers.Conv2DTranspose(16, (3, 3), strides=2, activation=\"relu\", padding=\"same\")(x)\n",
    "x = layers.Conv2DTranspose(16, (3, 3), strides=2, activation=\"relu\", padding=\"same\")(x)\n",
    "x = layers.Conv2D(3, (3, 3), activation=\"sigmoid\", padding=\"same\")(x)\n",
    "\n",
    "# Autoencoder\n",
    "autoencoder = Model(input, x)\n",
    "autoencoder.compile(optimizer=\"adam\", loss=\"MSE\", metrics=[\"accuracy\"])\n",
    "autoencoder.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 72ms/step - accuracy: 0.3652 - loss: 0.1103 - val_accuracy: 0.4361 - val_loss: 0.0258\n",
      "Epoch 2/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 68ms/step - accuracy: 0.4807 - loss: 0.0226 - val_accuracy: 0.5583 - val_loss: 0.0170\n",
      "Epoch 3/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 69ms/step - accuracy: 0.5734 - loss: 0.0160 - val_accuracy: 0.6040 - val_loss: 0.0135\n",
      "Epoch 4/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 69ms/step - accuracy: 0.6121 - loss: 0.0131 - val_accuracy: 0.6243 - val_loss: 0.0118\n",
      "Epoch 5/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 70ms/step - accuracy: 0.6280 - loss: 0.0115 - val_accuracy: 0.6365 - val_loss: 0.0105\n",
      "Epoch 6/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 69ms/step - accuracy: 0.6405 - loss: 0.0104 - val_accuracy: 0.6484 - val_loss: 0.0097\n",
      "Epoch 7/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 68ms/step - accuracy: 0.6521 - loss: 0.0096 - val_accuracy: 0.6539 - val_loss: 0.0092\n",
      "Epoch 8/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 68ms/step - accuracy: 0.6604 - loss: 0.0090 - val_accuracy: 0.6628 - val_loss: 0.0086\n",
      "Epoch 9/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 67ms/step - accuracy: 0.6637 - loss: 0.0086 - val_accuracy: 0.6723 - val_loss: 0.0083\n",
      "Epoch 10/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 68ms/step - accuracy: 0.6725 - loss: 0.0082 - val_accuracy: 0.6795 - val_loss: 0.0080\n",
      "Epoch 11/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 68ms/step - accuracy: 0.6795 - loss: 0.0080 - val_accuracy: 0.6879 - val_loss: 0.0078\n",
      "Epoch 12/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 68ms/step - accuracy: 0.6900 - loss: 0.0078 - val_accuracy: 0.7012 - val_loss: 0.0076\n",
      "Epoch 13/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 69ms/step - accuracy: 0.6983 - loss: 0.0076 - val_accuracy: 0.7073 - val_loss: 0.0075\n",
      "Epoch 14/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 70ms/step - accuracy: 0.7091 - loss: 0.0075 - val_accuracy: 0.7181 - val_loss: 0.0073\n",
      "Epoch 15/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 68ms/step - accuracy: 0.7158 - loss: 0.0073 - val_accuracy: 0.7211 - val_loss: 0.0071\n",
      "Epoch 16/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 68ms/step - accuracy: 0.7206 - loss: 0.0072 - val_accuracy: 0.7249 - val_loss: 0.0071\n",
      "Epoch 17/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 69ms/step - accuracy: 0.7257 - loss: 0.0071 - val_accuracy: 0.7236 - val_loss: 0.0070\n",
      "Epoch 18/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 70ms/step - accuracy: 0.7283 - loss: 0.0070 - val_accuracy: 0.7274 - val_loss: 0.0071\n",
      "Epoch 19/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 69ms/step - accuracy: 0.7322 - loss: 0.0070 - val_accuracy: 0.7322 - val_loss: 0.0068\n",
      "Epoch 20/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 69ms/step - accuracy: 0.7350 - loss: 0.0069 - val_accuracy: 0.7388 - val_loss: 0.0067\n",
      "Epoch 21/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 69ms/step - accuracy: 0.7368 - loss: 0.0068 - val_accuracy: 0.7374 - val_loss: 0.0068\n",
      "Epoch 22/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 68ms/step - accuracy: 0.7393 - loss: 0.0068 - val_accuracy: 0.7421 - val_loss: 0.0066\n",
      "Epoch 23/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 67ms/step - accuracy: 0.7436 - loss: 0.0067 - val_accuracy: 0.7462 - val_loss: 0.0066\n",
      "Epoch 24/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 67ms/step - accuracy: 0.7439 - loss: 0.0066 - val_accuracy: 0.7459 - val_loss: 0.0065\n",
      "Epoch 25/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 69ms/step - accuracy: 0.7467 - loss: 0.0066 - val_accuracy: 0.7484 - val_loss: 0.0067\n",
      "Epoch 26/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 69ms/step - accuracy: 0.7490 - loss: 0.0065 - val_accuracy: 0.7487 - val_loss: 0.0065\n",
      "Epoch 27/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 68ms/step - accuracy: 0.7500 - loss: 0.0065 - val_accuracy: 0.7510 - val_loss: 0.0064\n",
      "Epoch 28/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 68ms/step - accuracy: 0.7530 - loss: 0.0064 - val_accuracy: 0.7562 - val_loss: 0.0063\n",
      "Epoch 29/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 69ms/step - accuracy: 0.7547 - loss: 0.0064 - val_accuracy: 0.7545 - val_loss: 0.0064\n",
      "Epoch 30/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 69ms/step - accuracy: 0.7560 - loss: 0.0063 - val_accuracy: 0.7572 - val_loss: 0.0062\n",
      "Epoch 31/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 69ms/step - accuracy: 0.7568 - loss: 0.0063 - val_accuracy: 0.7595 - val_loss: 0.0062\n",
      "Epoch 32/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 100ms/step - accuracy: 0.7580 - loss: 0.0063 - val_accuracy: 0.7595 - val_loss: 0.0062\n",
      "Epoch 33/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 70ms/step - accuracy: 0.7589 - loss: 0.0063 - val_accuracy: 0.7616 - val_loss: 0.0062\n",
      "Epoch 34/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 68ms/step - accuracy: 0.7609 - loss: 0.0062 - val_accuracy: 0.7634 - val_loss: 0.0063\n",
      "Epoch 35/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 69ms/step - accuracy: 0.7607 - loss: 0.0062 - val_accuracy: 0.7626 - val_loss: 0.0061\n",
      "Epoch 36/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 84ms/step - accuracy: 0.7621 - loss: 0.0061 - val_accuracy: 0.7657 - val_loss: 0.0061\n",
      "Epoch 37/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 69ms/step - accuracy: 0.7638 - loss: 0.0061 - val_accuracy: 0.7637 - val_loss: 0.0061\n",
      "Epoch 38/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 77ms/step - accuracy: 0.7635 - loss: 0.0061 - val_accuracy: 0.7645 - val_loss: 0.0061\n",
      "Epoch 39/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 77ms/step - accuracy: 0.7670 - loss: 0.0061 - val_accuracy: 0.7663 - val_loss: 0.0060\n",
      "Epoch 40/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 81ms/step - accuracy: 0.7664 - loss: 0.0060 - val_accuracy: 0.7668 - val_loss: 0.0059\n",
      "Epoch 41/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 69ms/step - accuracy: 0.7689 - loss: 0.0060 - val_accuracy: 0.7675 - val_loss: 0.0060\n",
      "Epoch 42/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 98ms/step - accuracy: 0.7678 - loss: 0.0060 - val_accuracy: 0.7682 - val_loss: 0.0059\n",
      "Epoch 43/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 69ms/step - accuracy: 0.7688 - loss: 0.0060 - val_accuracy: 0.7727 - val_loss: 0.0060\n",
      "Epoch 44/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 68ms/step - accuracy: 0.7694 - loss: 0.0060 - val_accuracy: 0.7709 - val_loss: 0.0059\n",
      "Epoch 45/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 91ms/step - accuracy: 0.7705 - loss: 0.0059 - val_accuracy: 0.7695 - val_loss: 0.0060\n",
      "Epoch 46/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 69ms/step - accuracy: 0.7711 - loss: 0.0059 - val_accuracy: 0.7708 - val_loss: 0.0058\n",
      "Epoch 47/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 83ms/step - accuracy: 0.7720 - loss: 0.0059 - val_accuracy: 0.7738 - val_loss: 0.0058\n",
      "Epoch 48/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 81ms/step - accuracy: 0.7736 - loss: 0.0059 - val_accuracy: 0.7719 - val_loss: 0.0058\n",
      "Epoch 49/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 69ms/step - accuracy: 0.7730 - loss: 0.0058 - val_accuracy: 0.7719 - val_loss: 0.0058\n",
      "Epoch 50/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 69ms/step - accuracy: 0.7724 - loss: 0.0058 - val_accuracy: 0.7738 - val_loss: 0.0057\n",
      "Epoch 51/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 90ms/step - accuracy: 0.7745 - loss: 0.0058 - val_accuracy: 0.7736 - val_loss: 0.0058\n",
      "Epoch 52/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 68ms/step - accuracy: 0.7742 - loss: 0.0058 - val_accuracy: 0.7759 - val_loss: 0.0057\n",
      "Epoch 53/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 69ms/step - accuracy: 0.7745 - loss: 0.0058 - val_accuracy: 0.7796 - val_loss: 0.0057\n",
      "Epoch 54/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 93ms/step - accuracy: 0.7739 - loss: 0.0057 - val_accuracy: 0.7756 - val_loss: 0.0057\n",
      "Epoch 55/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 69ms/step - accuracy: 0.7753 - loss: 0.0057 - val_accuracy: 0.7753 - val_loss: 0.0057\n",
      "Epoch 56/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 91ms/step - accuracy: 0.7743 - loss: 0.0058 - val_accuracy: 0.7779 - val_loss: 0.0056\n",
      "Epoch 57/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 68ms/step - accuracy: 0.7757 - loss: 0.0057 - val_accuracy: 0.7729 - val_loss: 0.0056\n",
      "Epoch 58/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 71ms/step - accuracy: 0.7748 - loss: 0.0057 - val_accuracy: 0.7767 - val_loss: 0.0056\n",
      "Epoch 59/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 94ms/step - accuracy: 0.7763 - loss: 0.0057 - val_accuracy: 0.7776 - val_loss: 0.0056\n",
      "Epoch 60/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 68ms/step - accuracy: 0.7757 - loss: 0.0057 - val_accuracy: 0.7790 - val_loss: 0.0056\n",
      "Epoch 61/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 90ms/step - accuracy: 0.7759 - loss: 0.0057 - val_accuracy: 0.7805 - val_loss: 0.0055\n",
      "Epoch 62/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 67ms/step - accuracy: 0.7779 - loss: 0.0056 - val_accuracy: 0.7764 - val_loss: 0.0055\n",
      "Epoch 63/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 68ms/step - accuracy: 0.7762 - loss: 0.0056 - val_accuracy: 0.7757 - val_loss: 0.0055\n",
      "Epoch 64/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 98ms/step - accuracy: 0.7771 - loss: 0.0056 - val_accuracy: 0.7775 - val_loss: 0.0055\n",
      "Epoch 65/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 69ms/step - accuracy: 0.7775 - loss: 0.0056 - val_accuracy: 0.7767 - val_loss: 0.0058\n",
      "Epoch 66/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 93ms/step - accuracy: 0.7770 - loss: 0.0056 - val_accuracy: 0.7735 - val_loss: 0.0056\n",
      "Epoch 67/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 69ms/step - accuracy: 0.7762 - loss: 0.0056 - val_accuracy: 0.7773 - val_loss: 0.0055\n",
      "Epoch 68/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 100ms/step - accuracy: 0.7769 - loss: 0.0055 - val_accuracy: 0.7710 - val_loss: 0.0058\n",
      "Epoch 69/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 69ms/step - accuracy: 0.7777 - loss: 0.0056 - val_accuracy: 0.7739 - val_loss: 0.0056\n",
      "Epoch 70/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 69ms/step - accuracy: 0.7783 - loss: 0.0055 - val_accuracy: 0.7792 - val_loss: 0.0056\n",
      "Epoch 71/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 99ms/step - accuracy: 0.7776 - loss: 0.0055 - val_accuracy: 0.7799 - val_loss: 0.0054\n",
      "Epoch 72/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 69ms/step - accuracy: 0.7782 - loss: 0.0055 - val_accuracy: 0.7772 - val_loss: 0.0055\n",
      "Epoch 73/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 92ms/step - accuracy: 0.7781 - loss: 0.0055 - val_accuracy: 0.7803 - val_loss: 0.0054\n",
      "Epoch 74/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 70ms/step - accuracy: 0.7770 - loss: 0.0055 - val_accuracy: 0.7773 - val_loss: 0.0055\n",
      "Epoch 75/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 91ms/step - accuracy: 0.7770 - loss: 0.0055 - val_accuracy: 0.7796 - val_loss: 0.0054\n",
      "Epoch 76/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 70ms/step - accuracy: 0.7780 - loss: 0.0055 - val_accuracy: 0.7822 - val_loss: 0.0054\n",
      "Epoch 77/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 93ms/step - accuracy: 0.7794 - loss: 0.0054 - val_accuracy: 0.7775 - val_loss: 0.0054\n",
      "Epoch 78/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 69ms/step - accuracy: 0.7783 - loss: 0.0054 - val_accuracy: 0.7840 - val_loss: 0.0054\n",
      "Epoch 79/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 99ms/step - accuracy: 0.7808 - loss: 0.0054 - val_accuracy: 0.7789 - val_loss: 0.0054\n",
      "Epoch 80/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 68ms/step - accuracy: 0.7795 - loss: 0.0054 - val_accuracy: 0.7796 - val_loss: 0.0054\n",
      "Epoch 81/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 70ms/step - accuracy: 0.7794 - loss: 0.0054 - val_accuracy: 0.7800 - val_loss: 0.0054\n",
      "Epoch 82/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 93ms/step - accuracy: 0.7792 - loss: 0.0054 - val_accuracy: 0.7834 - val_loss: 0.0053\n",
      "Epoch 83/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 71ms/step - accuracy: 0.7801 - loss: 0.0054 - val_accuracy: 0.7813 - val_loss: 0.0054\n",
      "Epoch 84/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 86ms/step - accuracy: 0.7811 - loss: 0.0054 - val_accuracy: 0.7803 - val_loss: 0.0053\n",
      "Epoch 85/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 71ms/step - accuracy: 0.7803 - loss: 0.0054 - val_accuracy: 0.7832 - val_loss: 0.0053\n",
      "Epoch 86/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 101ms/step - accuracy: 0.7817 - loss: 0.0054 - val_accuracy: 0.7839 - val_loss: 0.0053\n",
      "Epoch 87/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 71ms/step - accuracy: 0.7810 - loss: 0.0054 - val_accuracy: 0.7834 - val_loss: 0.0053\n",
      "Epoch 88/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 70ms/step - accuracy: 0.7818 - loss: 0.0054 - val_accuracy: 0.7825 - val_loss: 0.0053\n",
      "Epoch 89/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 85ms/step - accuracy: 0.7813 - loss: 0.0054 - val_accuracy: 0.7816 - val_loss: 0.0053\n",
      "Epoch 90/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 70ms/step - accuracy: 0.7819 - loss: 0.0053 - val_accuracy: 0.7804 - val_loss: 0.0058\n",
      "Epoch 91/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 92ms/step - accuracy: 0.7817 - loss: 0.0054 - val_accuracy: 0.7811 - val_loss: 0.0054\n",
      "Epoch 92/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 71ms/step - accuracy: 0.7816 - loss: 0.0053 - val_accuracy: 0.7777 - val_loss: 0.0053\n",
      "Epoch 93/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 80ms/step - accuracy: 0.7813 - loss: 0.0053 - val_accuracy: 0.7832 - val_loss: 0.0053\n",
      "Epoch 94/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 84ms/step - accuracy: 0.7825 - loss: 0.0053 - val_accuracy: 0.7848 - val_loss: 0.0052\n",
      "Epoch 95/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 70ms/step - accuracy: 0.7818 - loss: 0.0053 - val_accuracy: 0.7865 - val_loss: 0.0053\n",
      "Epoch 96/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 91ms/step - accuracy: 0.7819 - loss: 0.0053 - val_accuracy: 0.7869 - val_loss: 0.0053\n",
      "Epoch 97/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 75ms/step - accuracy: 0.7820 - loss: 0.0053 - val_accuracy: 0.7783 - val_loss: 0.0053\n",
      "Epoch 98/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 86ms/step - accuracy: 0.7823 - loss: 0.0053 - val_accuracy: 0.7832 - val_loss: 0.0053\n",
      "Epoch 99/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 69ms/step - accuracy: 0.7830 - loss: 0.0053 - val_accuracy: 0.7870 - val_loss: 0.0052\n",
      "Epoch 100/100\n",
      "\u001b[1m102/102\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 86ms/step - accuracy: 0.7821 - loss: 0.0053 - val_accuracy: 0.7841 - val_loss: 0.0052\n"
     ]
    }
   ],
   "source": [
    "epochs = 100\n",
    "batch_size = 512\n",
    "steps_per_epoch = len(noisy_train_data) // batch_size\n",
    "history = autoencoder.fit(noisy_train_data, train_images,  batch_size=batch_size, epochs=epochs, validation_split=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Acc      0.7829047441482544\n",
      "Validation Acc 0.7841266989707947\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_history(history):\n",
    "    h = history.history\n",
    "    epochs = range(len(h['loss']))\n",
    "\n",
    "    plt.subplot(121), plt.plot(epochs, h['loss'], '.-', epochs, h['val_loss'], '.-')\n",
    "    plt.grid(True), plt.xlabel('epochs'), plt.ylabel('loss')\n",
    "    plt.legend(['Train', 'Validation'])\n",
    "    plt.subplot(122), plt.plot(epochs, h['accuracy'], '.-',\n",
    "                               epochs, h['val_accuracy'], '.-')\n",
    "    plt.grid(True), plt.xlabel('epochs'), plt.ylabel('Accuracy')\n",
    "    plt.legend(['Train', 'Validation'])\n",
    "        \n",
    "    print('Train Acc     ', h['accuracy'][-1])\n",
    "    print('Validation Acc', h['val_accuracy'][-1])\n",
    "    \n",
    "plot_history(history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 8ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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TiVE/wN8DTexN0KI2+vSQxO5t8o3XaumXIGtk/W7p4iXQ8hD75sDR46BZE8Vvlneu8W3AN5pr2YZlissK9Afz6De2drb1EJZ5xSLXhuWqN+Pb4eVyN/wb7q98y2ufu8O67GC9ODZkex0L1ylssp2bRVbiUm9gQ7mL9jQKXPN9RpaW1uXYlpA7W29n46x8NbZ2F5H5Ilue3OkMjs8PSTnyLW+2paFlkrKwo5vSfJOc6pE4MjE+AVpE1sRYHsrm0Zubm+S+xXiz0zkmmyuxX3iFeWc+mkHjWrZx/MTJwjeRfYNzuoSY+/IibjFz8dJp0Lr9os9pk+2xamR7lZkA12OXV86BFvZwLE9Okq21yHyY/Voc+3WtfljUyHTbjI+jv2Xz47UE8z9/gv0yBdpLLyJzr02yVSHZOodtpXjpTPGn07eX8VrBBHmX6u7sV3+8iaH5K3vZdQ1u+IvxmZkZ4ziOWVoqTjaXlpbMwsIClA+CYMc/nSnErcr12oUxsg3x3YFihhCIYoYQHMUMIRDFDCE4ihlCIIoZQnAUM4RAFDPEbuaGf13W933z4IMPms985jNXtSzLzGc+8xnz6KOP3ujbCXFLILsQgiPbEAKRXQjBkW0IgcguhODINoRAZBdCcGQbQiCyC7GbeV1+Sv0jH/mI+Ymf+Anz0EMPmYcfftj84i/+oul0Ouav//W//nrcTohbAtmFEBzZhhCI7EIIjmxDCER2IQRHtiEEIrsQgiPbEAKRXYjdyuvyYvwv/aW/ZFZWVsw//If/0CwuLpr777/ffPrTnzbz8/Ovx+2EuCWQXQjBkW0IgcguhODINoRAZBdCcGQbQiCyCyE4sg0hENmF2K28Li/GjTHmwx/+sPnwhz/8qs+3HM9Yjn/12HFw43bH3WH1MwskuyTlBjeLNzk5z8Z7+uQH6efcBDTLdUBjz+A6eEHcVt4YY5fKkb3l2XbzVobPapHHx9oakxtskzwnJ1ukHLmvTX7N3ypJeY5tmeTYIraFNZ6sVUBb32pf/XeYkLq/jrxWuzDGmFowYRxnMG42eytQJg3GQbuydAG05PFnQHtp5eOF432HDkCZuBOCZpHRtjBZAy1KsJ86XRwH3S72cRhHoHlkDDkli7HKg8rQIWpyUi5NsB5ZhppF6kGGvLHKdmsMt90MxbJkk/rm1G+hlubspoMKpyn1Oq8br9U2PK9iXGdobNXw+Xwfx15QYfEBtSgqtkcr6UCZsNMFLU1xEMwc3A/a3fecxOttLIK2h9jA5bV10JZIXdY7/cKxQ/x5SuLlOPGjByaboM3VME7PjFdB65MYN7HnIGjhM2dBu3D5HGiV0tZFYRefvbWBPovZnW1hvPHcYn2Zbb5e3IiYUcYi49smfom5KtcmeUxJc4hfYvv2OA65FtFY3RgWyQEYLGcpS7mNfcyyBX4t1FwPbaPWGAOt4mNOmCUxaH0Lx3jS74FW7mvWvsQNGIs8rUUKDvfNTvvpRvFabcP3bOMPJfBRhH3ueej7XAv7qNfFPtreLB436piX+QH2WUbGsetgfzguxgLLwrjtk7HnkTlVlqOPDKPiOPNIPZhP8L2d2bFNpqEuyVeYPTI/zOYBOct17FI7kXZzyBSThC5js3OdwTOk7q03z6hWq6ZWG+TvzKexOJIkGD8zkgiXc+iM5Jv8WtiXcUo0kse0eni97c0+aJaHWmVqCjRTzjNSvOfG6hZoHQ9t3g980FjAschQ7rcxF026bdBmJidBy7BJIP/1fawbmx8wLSZ9uL6xevXfYYg+7PXktdpGbCzjDsVB7EljWiSxrNUxjiR7cT/OXlh0MOudFpTZ8PGu4z3U3Ay1mQDH45SL5SqG5BfEzropiSNOcfzEJP60K8SRTuIepLNVHHtegD6+Vfbnxpgw2ARtJUYbXQ2nQet00Pl3/GJ8DKskN3CxvkvrOD/rWc+BduRoaQ7oYh+8XtyImGFZFo0JxUL8vJ1e/9VAz3ud72kMz5fLEyG6frpD+Ll4T7rWtcN4zm+MYzwv5UCOg35rMse4enTuLGidHvqGM63Bmokb72yed6N4rbaRZ7nJhxbZ2Xoca/udzjfL58YpxtyUrVm4dOK3I40sM17Dtolmk3cBJY0sIVxj3YK0JVlt8Mk8rtFogMbmx6zNWa6zTvx8+XqeR+KZi3HPJ/txu+QdUlxaD+z10MZeL25EzAgTY4bThYDE1GYd+8mr1UFLPJyDr25cKh6vL0OZ+Zk9oG33MH+OSH5/8BCeO+7h2uil03jfzU2cb2dk3uKU1h89NheO2bwc8665uVnQggDf0wQBrtuyuq2urILW30T/k5N5wNZGcez6NbSNQ7cdBa1ZwfZtbVwB7fxQX2fX8a7v5q5iCSGEEEIIIYQQQgghhBBCCCGEEDcZvRgXQgghhBBCCCGEEEIIIYQQQgixq9GLcSGEEEIIIYQQQgghhBBCCCGEELsavRgXQgghhBBCCCGEEEIIIYQQQgixq3Hf6Apci8D3TRAMNmK3c7IhvYWaZeO7fsdm5xaPU8M2ZkfNIU3G7ukTLXfI5xAcBzVWX1K78n0zC+trk8fK0ww0l+5Lj2KS47mkG4xNznUNFozJ9UypnMc+v0HaFzrVGJMmKWrZoG5ZRh98pKkEgXHcwTicm5qCMlN7DuGJYQuknAyQ5554vHB86umnoEya4Hl1LwEtCmO8J7HlnIzwMCLjL4p2dD0/8EoKXj+LsW5BpYLlcqxHHOOzJmSs+QFezybXyzK0g5TYKTwrGfPEHE1Krm8yrK/Jhv83s83RZbMdGmfIn3ou9nkc4vjp9vqg1arYb/Vao3DsWDUsUyHxwSqPRWP27D0A2m133g+aG26B1rt0CjST4lh2fR+0ahAWju2gipcitl2xcKyMO2gDU01sk/0HD4IWTE2D1nUmQbu8hWMwTrC/6tViua3NNt7Twz4NfLx+JQDJNBvFdkqS1Jw+exkLjii2bRt7KGZaLGgTHBJ7HeJhHMv+jsfGGOM6aBuui5pPxi2HPQPeNyZ+3ib5g1vyFxbJ9VgsSBK0A+bPMxanfByTpOmMTRJA10W/YtshaOW6sL5n7WE7JJ9k5YY09v9HGcs4xhrKDXwP61/x0UfmZNyycJqWxCjuQpmggp3b6ZMc3cN7ejnaikP6IPCITbG5QYJjo1FvFo4Tkvv0+6jZLt4zNyQfT9Gf22QOlJMGzlJ81izFcraLPqBs38xnVUkw8ElewXKlVmeQa4SkfUadKIqNN5Rv79RvMPi8vDg+koTMLcn8xHWxHl5O6kHy8ZUtzAtePH8RtLum57AuBv1AtVksN97AHKazjTlct42azeYAxEhtg+VqFbS1JMRY0CJ1qdYxZ3NL86Cmh3aQRmhT/RBteWV5FbRubzAXjch1RpkkNyYe6pYOyQk6ZC0maDZAc6ZnQVtaXC8cr7bXoUw01QFt3sK+rRO/l/RwHWCrj+e2e+izllZQW+2iP0yz4viZ2LMfykwdwnnB6RdxbvPs6hU8l8TkgMy9Gvt7oO05iH5hsoY+ZamLNtVLxwrHaenYGGMiB3O6hKwzbRO78K3nCsfdDuYLo4xlWa88tyD/m59C1jZ2OG/ZCTfyWte+HltfLJazWMJPzsuJn7nRz8Ahz0Dum5qivfjZJpS5Z3wZtHceeBa0lS28frs9fvXffQvtepRJ0tTkQ+3okvbLczLJo2uFrD+KWsTWSel0mdSDvVdh51JtZ+dyLf+Ox9esB2k328F8ZayJuVmj0QSNrUnU6/UdaSmZe5QbyiaTedfBWOM6OL9PUzb3KuZ5IVnjHGVeePJZYw2tY9sWmXP5mKP6NSwXYDEzPl0UsxRjatjBeUEWol+eG8ecZWFiD2hWiGPXM5izJT2yvpvg+AiqxTGZ9jHP3iC5QreN+d/45ARoacZe4pH1czbPbZP2bGO5kKyTeZViGx+94wiUueuuB0CbaI6D9sJzj4F24fJgbpenO3/Xd2utYgkhhBBCCCGEEEIIIYQQQgghhBDXiV6MCyGEEEIIIYQQQgghhBBCCCGE2NXoxbgQQgghhBBCCCGEEEIIIYQQQohdjV6MCyGEEEIIIYQQQgghhBBCCCGE2NW4r1zkjSI3lhnaLN3CzeGtHDdTtw1qpJgxpng9x8bPCGTkRMvGergWblLvOOQzB3R/eyzH7mHTZyiKNmkjdk9jsL4m29nG9KzNHdLmrL6sfolJsWD5XHItizxYzsaDi+Uma97Vf4fuzp57lNjYuGJse6gPc2xDa2sdtDvueQDLkfHRbv154Xir3YEylYoHWmr5oCVeE7S42wOt02mB5nt4vWqlBprrB3iumxWOezHp5ywDqdNug5aTsZakEWhhmICWJngPm9i3If6HGW/51CjBerimgueRa4U9fFZryJdlGbHNESZMImNnQ+1okXbIMORlGZaL29hvad4tHDcbOO4mpiZB29oKQfv8F76E9+xje/8PP/5+0KYncSxfXFvCcqzPS7bXDftQxrExPlR8vFaDaPVxbMs9J46Btp2Ogfbp3/5D0L7xra/j9fbOgxZ2im3X68RQxnGqoNV99DFj4zhGavVim8fMn4wwjuMYxxn0K4vFFvFBDnFVw9f5Nm5J8z2MD0FA/DRpf1aOEUXo+yzyXDZ7LvIM5XMt0sVxgj6eXp9obMRsbGPcq3gknzToj1gu5ro4dst5EWsj1yXtYZN7knOzoTiakZg6ynhexfjeoM0s1ukkR7WJYdQaOOazrFwO26fX3wLNyrFcFrN5BtqPZ6Nmkc9A5+S5fB+foax1Opg31OvjoLk+3jPsd0FjOZJxsB6uS56LTDTyDOOtR8a3X/Iz/S6et72Fz1rxMQetVOugFfJyPgm9pdipbTN/aLE5Z8mXVKoktyf+No4xtlsWap2NFdASltJWGyD91z/5c9Cmp/aA9p7veXPheHJyAsp4ZH7SaGAuEvdxntUi85Ekxrhne9jmUzNzoIUJjnG3inXZt+dQ4XhzEduyv411m5qcBm1mDht9cWlxUKcQn2eU6dqZGZ5mbJM5RTfH/piZw/5IXPRzi2cuFI7bKeb2t7nYpkea6Fvz7hporW20FbeGY3Sph+PiG2fxHusRxsLmWNHef/T+R6DMO9+Pc5uVNazv5z/7p6A98Xm0z3BlG7T22WXQ9h5BO7vzThzLtx/YB1rXL8a5riG+yEF/kkVkfp+ib9ve3iwc98hayUhjmcKyBV1+ZMuUbCGKnbrDcq/2WnyZlawD73SpNUP7LudduYPjwOR4HlvDscjaH6ScxhTX0q/Wg9yWrquStXEb71tOWacDzBdOzJ4Cbbx3AbQ6ySFOTD559d/dAOPYKGPlWWE+SbrNJGRuYHLsD4fMPcrvG+KU9A97r7DDscLW2+kyJpk/sXUFasalYqy+zMoc8v7FdzGeTU5MgVYjuY9lYX0nxnF+MzmJa33cCxTbhE0FXAfry9YokgTjTbVSnBf1erimN8pkYVx455Wz11MVXFfMbGyLrRbOpU1WzFmmpyagSEAmBlM29vmsj3MAr4fvOPqbm6A1LCw3XcMxtL6O+UmtURy73iR5/0DWw1odzNcWr6yCZhGfEpA5iu1grsvee+C6hzEu8QOT8xOF44W9B6DM7PwCnjeG7XbuwvOg5UP1IEsq10TfGBdCCCGEEEIIIYQQQgghhBBCCLGr0YtxIYQQQgghhBBCCCGEEEIIIYQQuxq9GBdCCCGEEEIIIYQQQgghhBBCCLGr0YtxIYQQQgghhBBCCCGEEEIIIYQQuxrcSX1EsLLcWFl+9dghr/DJXu7GyVHLLRTLkkU+I2CRm7pk83lj8Po52endcRw81cZN6k1OHoKcatLSIblnnpG6kVvaLj5Xys4ldcvIfdk+96wutBNL94jTBIpEcYznWeTBSEXcoX5IWJ+MOJtbW8Yearc0I+2TvQDawZlx0OYWZkF71/fdUzj+4heegjK1qQXQnEoNtG6fjA1nG7S41QZtrDYG2sQU1pfZS2drpXDc74VQJiD1jbsd0Gxio1mGWr+P94jjCDTPxTFnkXHokfHseV7heLuD9a3X8Lkc1wOttbVFyvlX/51lzIpHFye3jD3k3KK4D2Us0peO5YNmMuyPfrdoZy7pn7qP7ezZOD5tElu++NWvgzY9UwHth77vBGiOgz5gvIZ1iRrF613a7kGZJMV+9+p10PxqFbROG8eU56Kv/sY3vgbal7/xDdAaY3gP5rHjftHOAh/7NLPY5wBTUKIIy5V7ME5ILBthfNcxgTdoOYvEXYuMZ8ug5no4roKg2N6Bh/lE4BMfR8qVfZwxxsQp9lNMxqnJsRxLsXxy37K/s8g984yMjR3mNVGG1+tvY9wbbzRBq3qkH5gvI1pa0lh72Cx3IuSkzZN0YN9JSPKyEcZ1KoWcPkkxZiQpxnXXRX/rkLiep0W/lJFczfNw/HgkXrM5ik3GmW1jH9E+d8n8xsWCeV6sc62GMckl84c4xnZjcwU2L7KIr86InfnEz9draD85abswKuZOSUzitIX9YIhPjCJ81twatFtusN9HH8sMP2tG/BeLGWyOyNxLuRy7VhAEoHkstgfYvjapx/fMvhu0w5cWQfv3//F3QAt7OHbdoDjW2ByoQtYM8graUNRFLbdx/Nl9jBm9DOcZMYm3zT3zoHXaXdC+9ezTheO0TXLpCNucrZkEZD6SO/7Qv2+tXGrLsUx/yKFusphL5gHB/hnQNnOMl2trlwrHt09h396HlzK2wWttkSW+qTGsm0vsbPksjrPLHbxHi+REnileb3waK7ywdw9oB48cAm3f/BxoY2RN6Gtf/HPQtjex3OPPXATtyibawO1rOOaPHi8+q9tAnxBbJP6SfDNlMTMoanEysku0HCt9+e8qzLbJPGOn+ecOYsZOYXGKBSp+D/ZcJO6RZzV2MY7mZEab52iPORtX5J6JjWPGydGHuGR9J89J3kXX1PEellW0oQf2Ylzd21wCrb9Jcrgcbe9EcOXqv9vprTXP8D3XOEOxkQ0pi6wT5aR/2bp8Oa9O2DsJdk8yPm32voTOGYlGnsEi12Mn5yWNtQedA1loPxUPx9REE9eTHTLPYHOZqclJ0Gokr2G+Ii2tI7A5m+9h7sfKJQmO+2qlWN8uyT1GmTvuOWmcobUp28e2YOM5ijE/aW2hliWlcZXgmrnZWAXpe+7BucI9t70dtEsvXQbtYnQKtAdP3A5au4N+7lO//ceg2WmxT+86eRzrceUKaN3+OdAc9g6P2GhE3l00argeW6vje6WwjudubaHvX18qvgt6rP0YlLl4fhO0hbm9eK3VZdDsoXhG4/010DfGhRBCCCGEEEIIIYQQQgghhBBC7Gr0YlwIIYQQQgghhBBCCCGEEEIIIcSuRi/GhRBCCCGEEEIIIYQQQgghhBBC7GpGdgMbx3GNM7Q3l2vjPhI22x+a7L/B9k8s72fB9rlj24hmO9w/jYlsfz07J/dl+ziGZI+Y0j6EbB+9jO2FQ/cOJ/vHkTZPyN5ObB8UurcO0dIYr5eU9g9nbc72lWZ7QefknvnQ3h052+N8xEmy1FhDz8/GX9hrgba8gntQjE/gviv7D95fOD56DPeGyCq479fMbQ+A9vzTuNc52Q7P1Oq4L6QX4H4WHtmbzyH7Y6Rp8bnCDPdS9oIGXishdkv2yqzUcA8bVrfNNdy7JIyJrZH9yR2y32c3KrrsKET7aTi4b0kUohYTn5IMOb2c+KtRZm5uwrhD+7ymZD/X3JA9Ttne0+TcaqXob5sN3MunWcGQemAP7odSqeE+NyvruD+3ZZE9aYmvTvp4vaiD5cZLe8TEZJ+xhOyryva87ZD9cVwf67u9inv62TnW97ajB0BzyJ6VFbLXmtco7sm21cXrp2TvtYzsQ9/to12kpa2LErL/9Cjj+67x/cHzs/2r2P5YGdn/ziH7d/lBcXwEPra1T/a+8si+zGz/8zQhe8ETzSH74LJ9tx2yTy3kNnT/XJBMkqIYJzhGI2a3KT5ru42xwB/DWDhWxz3OumQP0LJ/s0lexx6M7Smck6TYGhojaXRrxQzX8Qt7jLseiQUW5g6sCZn92HFRS8lGf2wfZSsleQ7xX7khY4rsQ8fq61fwvobEx6Q0ltm+3jHbb5jMR+o1zLls8lwJyZHiBH21TfJ7J8M9zxyrDtp263yxjIfPXicxnu0T3+lugzbcN1H/1ooXxrwce4fjr032u2Ywv5ER3+c4rzwHZ5rL9oH1sFw1YOMKx/zaOvq0vQu4H6BfJXub28XrPfMS7uk3XkNbPrhnFjTbxeu7VTy3FuNYDsmkaquDc8C1RZzLBRWMLX7pviGxb5YTMkLijwpTthyvPcps27bxhxzqFtuquELyq2n0fRsd3J9yfqwY/++dw/1N/fYF0PrE7sII7YKtuWxt4N6cV1r4YFspsSmSX8WlvYoth4xtkkda5FoL87g/+e23HwXtsa99HeuR4fO3+zhuW+dwvrDVRlsut9O+Q8egTHPhIGju9ARofgPztzwLSse3WNzITWGr7deyBzijfL0bff2dV4TsC07WEVKb5EWlOXcQ4nis2LhuZPlkTmswnm3lE1g3koslDtljnMytc7JGkGboB/bWi/HmTXvOQplGG+NZ28f44MZkj+h0/eq/HbIOPdoUDYPOFUiSTvfnZvvKl9uDro+zuQ3JpVjd2P7kdC/ynV2Pb7K+g3qQZ3CILTabuJ48NoZr3VWyZ3IlwDFaIWu7bC/yndTZIn6C5bnsWh5ZQymvE7P3R6NMxTZmeJlveWkRymx2cH6VGpyXHziA/V4LivnyRAPb/wcewb3D/8aP/yRo//V3cA/s3/s3/x60Q0enQFu98gxoP/gjHwDt5/8//yto/+vP/Vzh+KVTZ6BMxt4DkXmnHeBY8ys4rmwyf7rj5B2gzc/tA21tbRO0bz7xJdDivJjrRiQ3vXQBc+QEu95kMVmnGVqbYu8pr8Wt91ZQCCGEEEIIIYQQQgghhBBCCCGEuA70YlwIIYQQQgghhBBCCCGEEEIIIcSuRi/GhRBCCCGEEEIIIYQQQgghhBBC7Gr0YlwIIYQQQgghhBBCCCGEEEIIIcSuBndXHxHcwDXu0ObvFn2Hj7vNpxlusO47WM4qbcSe5XgevyOWc2xsxjTLUEtT0Fwb6+aTG3u+j9fL8VyoG9FS8qzs+Q25vuviFUmTmzjq4+VybBPHwes5dvFZkwTbLaPX8kBLsgg0a6gfLNIno45v+8a2B4MkyhIow8ZVGoegJX3spyvnzxaO6xW8/nT1Kbz++RZohyYWQLscVUFrunOgTU2itmfvXtDqtRpollV8/l6f9HOO42V1bQm0C+fOgNaNe6AlCbZva3OblMMxGSc4nqsTFTw3L9pLMLkHyjTGxvG8EO+Z5i+C1u3GV/+dM58wwlSaYcE/2THxfhH66h5pmyjB/rWC4ngJfBw/1Qq5fhvHwNzEGGhH9k2B9uDtON6XTuF49FN8VreKdhY0moXj2SmsR0aiRupg/NlYW8Nzu5ugnXnuOdBmxvG5Tu5Hez9/8QpolWoA2thY0VbWtrEem702aE6OfsFz8fppVj6+teKGGwTGDQZt5NjYx2WfaYwxGcmxPBfHvesVx4cfYBmf2IuxsB5JubGNMTnJRTKiWTQnYlkQYpfHeBZDmYT0e5yin+zHpJzB58pInthP8Ho9ND9Tw2FqcpKMOU7RJzG3npI2Z3kd64fhPDzZQU46SoRh3xhr0D62Q+YBRKOtQHKu8tzAIeM98LEjowjvyfqWDCnjB5g3MNtOSR5i2ZjrpSXJcxt4nk/mCmTeZYit5AmZAxisR26h5seY+0UbTdBePHUJNK9arN/e27DdPJfYIroF45CYUR/ydyFp11HHtq3CPIPBxhVfViA2VLo0vRS/K9GwnoGH9Wit4xxlc/EyXi3C/C9Ku6A999STheNnnnkcymQRzgseeegB0O675yRolo85nEWa108wto4T31BlaybEJm2n2J6VBuaJJiFjmnSiVyG5gDW4Z7+P7TPK9DLLpEPt2CU5wViV+GDSDk1rBbS9e4sdx3Jgm8RrhwT2Xge19W3ULmzg2L64gb61F5Pcz8b+W1oqPtcff/ZzUOauN90L2vFjB0E79+LzoP3Of/k0aN984TxoqY+xIEpJMkXi7dY69utz6XrhuN3G2HUkRBtYCLBcrYExo2YV+8G20A+NMpZlFWLCzn06v9bNPM8Yw8LUte5CTsU+9iKMS/N+sU/3TaEPGHNwHSowaKOtfBa0x7exHq0U/XdO1q3Z+jZbB6paGEffsn+xcDxRxzIJWX9Jt3B9hK2VB0PxN7zl1m0tMzxm+BhlbU+uRE7NSu2x02F8U+yTlGPnlqVyfmiMMTYR2TuERgPnKK5Dxh55t1CfqoPmk/cv7L4sXy7P8S32YDtsSvYMSWkS+Eo5+6hx7sw5Yw/NFdeW0R+wl3H7bsf1woW5SdAO7SnmMT/0/W+DMg8dOARaPV8FrZGvg2Z1l0G7bf+doO0/NgPaudMvgPbBn/kfQbu8WqzLx3/ll6BMJ8T4YJO1pJQsGtgOapUG5rB1Yle1MbQX4+I4nZjH559ZmC4cH9x/HMo4Keah9QDrcfqFp0FbOn3h6r9zMs+5FreWBQkhhBBCCCGEEEIIIYQQQgghhBDXiV6MCyGEEEIIIYQQQgghhBBCCCGE2NXoxbgQQgghhBBCCCGEEEIIIYQQQohdjV6MCyGEEEIIIYQQQgghhBBCCCGE2NXgDukjQp5bJs+twbHBjdNd2wGtUvFBy9IYtSwtXsvBa2Vks/Y4tUCzTAqayYhG9n5Pcc97Yxm8BylmsqR4j8DFzzlk5Llycn1jo5ZmWGGLaA45N3NwaOUptolNPpuR58V7uDaWSSxyzwSvn5MG7lkDLbRYy442ezLLOEN9uGqRNiS24ZI+WVxaAq2SXS4cP3LHMpSZO3wXaC9dnAFtex0kszA9CdqRe28HbXZmL2jNsXHQxsbGQJueLN5jdmYWykxM4LU63Q5op069CNrjTz4J2ue+8HnQ1pew7SxDbJL4homZOmjVSvFZe7U7oYzneaBtXH4JtH6IYz8bqkjZDkcddyY13pD7j5YiKBOQkFcJ0Jd4lQpovldsD4v4263tPl4/x/6+6zCO7elx7O9kYwXvcWUVNI88F/XBebHP52fRdlwXY+jiyiZok1V8rk6Ibbm9huf6Od7jtn37QGu4GLuvLG+D1guLfT1dQxswNo7nil8FjdlPUop7CYk1o4zjVozj4Zgugn3nkPzBcYlWajPbwTbMSZwi6QSN2RnNWbAeJGUzcYxjKM/wHuVTe330H0yLErxWkpOYTHINm+Q2joXPFUd4334fz2W5UtmNM79OmsNYNvqPKMG2DONw8O8Q6znKeL5tPH/QZiyX7YchaEmCz+l6pO2z4vVsC+2iUWvgtRzskFarBVpKcmqX2BlJl02WkXI51i/sF2Na7uEYiFNsD4vMWqwcK+LaGAtsYttOjr665s2DFpFnOH3mImh+rViXyuQElAma2DdsMhbHOEb67tA8I0vwpBEnTbPC+LLIIHJd9BGsHNeKxyymhiHmUyxOBZUAS5HrPf/lz+E9rlwCrdG9DNqzV1BbPl+Mqd0tzE22NjdB+73zZ0Frr+Fk6eF3vQO02MY80eTYD/Um2svkGM69om4X79EvjteuwXlRHpB4hmZremEPtM3tzav/vtViRpI0jDX0oGGOtm03cDxWKjhu8w769Mef/lbheKuL7bxnD+bKWYTXion5XFhD/73eRZ8ZJTimPI8lbHi9blRskz/9sy9CmYnpCdDe924c75/99O+DdnEVn7UyhW0SJjggrfYmaFlG/Hcf/UdayuuyFG02qKHduT7G2jXiA7ygaCt9km+OMpZllXz9ztYQWHx4teVYfruT+HOtcjbJWVgSYBu0g3mvDdoj+68UjmdszE2sDtpeQnKIiyn6ZZOj0VtODbQ6uZ5H1rItC5/r4BTe4759xdiXuVNQJquj3eZXMCYtrWCs7aaDuNfu3Xr51DB0PNKCbK5G5selibTl0MFN7rlTu2CVY+WI7bFyO7gvuyebL3se2gpbE67V0QZ8F+N0s9kELQiw3E5z3zIOWZczxMewfnbImqObFrWUvVQaYTbbbWMNz7vZWkSAz12rYn+uXcC10WlrunB89lvPQ5kj6SJoWz3MWR66G997fPhvvRO0y8voMx954K2gffEbWJfTp7EuH/iBHysc33XkJJR59rmnQfsvf/xp0P78sa+D1ic5uDuOY3J1ZQ20lKw3pGQhrrON9zi8v/he5q7bHoAyvkNsj73fsjF2PfvY41f/naeZiQybTyL6xrgQQgghhBBCCCGEEEIIIYQQQohdjV6MCyGEEEIIIYQQQgghhBBCCCGE2NXoxbgQQgghhBBCCCGEEEIIIYQQQohdjV6MCyGEEEIIIYQQQgghhBBCCCGE2NW4b3QFrkWeWybPB5u/22SDd5Nl5EzcgD1LsZxlFzeWz3LcaD6yPby6jRvBJ14F7+niuQlp7l4Mksldspk9e9a8uJl93tmCIm7cAm2iiteveFg325B2MzmWy1ELXAc0Q7ScPFda6sM0w+t7pL5RlOD1Sd0W7UGjRzaeM+rMzc4Z1xm0ZW11A8qsVmqgOT6O00qVjN1Wo3D84graxkX7AGgr2zjmV9ZXQQt7XdDWV9ZA+563T4N2331HQJuYmAKtTC/GcbZ2cRm0y1euoHbxImjV+gRoj7z5EdDSfh+0Z599Fq9XxfFs2VXQer2ibcThBSjj1Hw8r7UCWr2GYyQPB/XNssx0OlBkZPGO2MYLBr7NbuK4rRK3VB/Hcu4EFhzPi3YRn8FYsPzCNmh3HtuL2v4J0M6eOgVa2gPJTFYaoDkp2pRxcezFcVGrTxzCawV10FovXQKtkqNN1Vnsssg43sKB1QnRzo7NjYMWdPAeT60X49zRGTyvmWAs6IckrqaYQySlIRKTfGGUSXPHpPlgTCcJiXvkkXwLRZs8eznFysjFUnIea8WE5Gt9EtujhOQO5LniCJMskhaYpNTv/X4EZWIyhhLyXBnJV3MLxxXLYVkuyj7DapG+sch9y8Uyck9yKcN6p9tHP9MeaqcoCtmFRpZ+r22yIbtwXTIlyjEW+D76YJLym6g09lj/LC9jbG428fqsv2l9SY6eE1/NykUh2k+aFsvFFsaVMMZxwaYA9SrGlkYN44NjMLbmEeYrTe8waGc2X8C6kMBfnyza4+Qs5k2VCrZ5P0RtrIHxplIZ5NZ9m0z0Rpwsywq+go0/7ktIzLCZXypqbCxnGfYJm8/nZHngG9/6Omhf+uNPg/bwAcyB1s6/BNqlbZzLuE5x7OY99PE94jNZ/Pncn/4paOML86Ddef+9oIUkFra6eF+HrI84Bh2X5RXtpdJE+wnJc8UR+oachL08rwz9+9bKpTqWbbyhsdsjD1ibRf/lWBgbn38e55unzxXLVUg+3u/jnD/LsT8CMr9f62B9Wx3sg4TYtuejoSXERvO06Ksr9TGsxxrOAb711ItYtxCNpTqBawPd5cugba7j+ldCbIXlOlFCBm4pD9uyMF4urq6DNtnAGNdZw/WHF14qzgFZLiy+M3StmMB8MP+uGFnzjNHPZSHa3745HAv7/eKajRfiJD/2cLy4Lo6FGWLzDzdwLc1zMMccd/C+NR/v4dr4rI0K2ka1HOND9D1XNvC8X/u9RdAunMW1gP1vfu/Vf/fDyBjzBSgzuuSGjaNyiZ2ILOfKSvk9y8HYJI/PIXd06nVor+6+5Xc0L2ton1Wy1t2o4/ypWiXzhxqWazRQq9UxBnse5k0ZWTsqP6vvYbw0xGdlKXnv4WMelqXFGOTYI/tajzK1p2psd9BGrTWcK/V7uBazuoR+bozkJ59+ojgf/NpncX545H95N2h7xrDPv3UJ1+5/4Af+O9CuLIFkzl/EZ1jHFMBcOIO53VgwVzg+ceB2KLNveg9ob37TQ6D97h/8Hmif/A+/AVq8jX5/a2UTtDDE59puY961eQkf9vnk+cLxTHMByhw8gO+a4gRz6XYL339WxgY2nyWZMQbHDEPfGBdCCCGEEEIIIYQQQgghhBBCCLGr0YtxIYQQQgghhBBCCCGEEEIIIYQQuxq9GBdCCCGEEEIIIYQQQgghhBBCCLGr0YtxIYQQQgghhBBCCCGEEEIIIYQQuxr3ek/4/Oc/b37hF37BPPbYY+bKlSvmt3/7t80HPvCBq/8/z3Pzj/7RPzK/8iu/YjY3N83b3vY284lPfMIcP378uu5jWy//DV+3jOXge/3MWFjOkHPdSuG43TwCZVasCdCmZqdAqzexXKXmgRYlKWhrS13Q3Kk6aLaH13PifuF4/eIilFl69gnQmq3LoB2Zwjaa9LEtAx/rwYhTfFbSDcZxHNA8t6j14wgvRS5frfighaQeK9vtQT2jGC/0KrhZdmGMMeHYrEndgenarRDK1KvjoNluAFql2QSt7xbdwtn1TSjjtZZBc8cmQKuO1UCbHMd7zo6PgTZOym1sd0B75vmzoD33wouF482tNpSJ4gS0Xn8btDBqgdbvo936Do6/uYX9oLXaeL21VWzPbh8NptMpPofXwHuaBrabIXVrTqD7T9sDm0+zzJgNvNT1crNso1GvGK8y8B21ySqU8WsZaHmOfR5UMLZMdou+b62FY+D2Op73wALW46nHnwat6mB/H5nAc1udVdCmptG25/bMgRZFRX9YsdCesgj9yR2zeP26hW25toHxoYtmZqwK+oXFdeyH9vk10O6fncf7bm8VjsMQb9q3sb4bCT6/i8VMOUrFGQlA18nNjBm9ODGZPWiTOCZxz8J4n5N8ikl2SfM8jOu2Q04kOUGnh+Nvq43+m6YY5HoJybvYjZOkOGYSMg4Sdv2ctRv6gZyUs3O8ieuiXw4qLO8i7Uk0u9SvOcuHSX0zUt+YNGWU5fTfr4WbZhtWWmgyx8G2zzPSlxmZOmU45t3SHCVJcGynGdPQ77O6WaS/sxx9X5ahvTNb8TySJ5TyiTDE+loOjk+bfOzaIlpOfKlr4/V8fwa0ijsJWrvTB61O8twDR4sxqFInfZqj1qjgtSo+xjPbGpwbZDiHeTXczJiRZbnJhuyZ9Sebl/NrEWdawiLxx/dJfkvIbazHi6U5gDHGXFhZAi1I0TaeO3cGtDViu7ZVzIuqTgXKJCnaI3vWza0t0L7x2DdAu+Pee0Gr13G+14GsxZh+twdaHGEea7KiDbkWtm/Fx2eNQhzncR/tezg+slj5arhZtrFsbRvHGrRtUsHxMzffAG1jFXPZ06dxPG61iv3W7qDt9PqboI1PYH/4IfbbdhvbOwx35qy7PRwrWY597gVFf9jtksSB2MqDj74NtDe/9RHQ/tPv/C5oz7yANsvqhtm8MSxvYjlRmhb7Io/xvH6P2Dtp3uYYxnjPLq79WWyh6zq5mTHDsqyibyPxgdl7apP5gkG7cvJiOSvDPMGy8TyPpMoVH+sx7W6CNhOgX95cwzXUxGC522Yxfo2VlrrsHNd7c5f0u4djeYq05TFrEzTbxnmuISG5u4X3uHz5EmjhBMabp5eKedHn/+xJKPMHn38JzzuP/fWO9/0F0NoLbxncv9c1xvwbKHO93CzbyE1WmHWxuSCbzbHsKiE2lZYn4CS/YHMFVo7edaenMj/KziVrMeWcyCbXci30ExUf/WjK4t4GrrvWq2h7DnnX4pE8lL27cMjcvTz/tukcEyRjkXlRluJzZaWFiowtUlwnNzNm/MAHHjR+ZdAmS4uYo37zKy+A1lpFn7bWwjYrj79OD8fVr/z6N0H7wXdh3nH6efT7v/N7vwHaoUMPgBZlONb27bkDtP1zt4PW3Sr6yMhgG5EQaqYbOGf+8b/w46DVJ/D9y7/6d/8atOXTGAsasxOgdULsmyDANeT15eJ7j6/++RehTO/uu0FLUoxTly5h/udVBrZwPXZx3TOSTqdj7rvvPvPxj3+c/v+f//mfN//sn/0z88u//MvmK1/5iqnX6+Z973uf6fdxsUKI3YLsQgiObEMIRHYhBEe2IQQiuxCCI9sQApFdCMGRbQiByC7EdzPX/Y3x97///eb9738//X95nptf/MVfNH//7/9986M/+qPGGGN+/dd/3czPz5v/9J/+k/nLf/kvv7baCjGiyC6E4Mg2hEBkF0JwZBtCILILITiyDSEQ2YUQHNmGEIjsQnw3c0P3GD9z5oxZXFw073nPe65q4+Pj5pFHHjFf+tKX6DlhGJrt7e3CnxC7iVdjF8bINsTuRzFDCEQxQwiOYoYQiGKGEBzFDCEQxQwhOIoZQiCKGWK3c0NfjC8uvrzH9fx8cQ/Q+fn5q/+vzMc+9jEzPj5+9e/AgQM3skpCvOG8GrswRrYhdj+KGUIgihlCcBQzhEAUM4TgKGYIgShmCMFRzBACUcwQu53r/in1G81HP/pR85GPfOTq8fb2tjlw4IBxHNs4ztB7+8yCc3ODWpamoNleBbTW+LHC8SUzC2VK+92/jIeb1Hdz3FS+GeHJcbsF2srKMmim1wBpbKIJ2p6ZYrnZgwtQxiI9fPlUDbRn2iugHariMyxYuIF9xUYtz1DLiGbZDpbLs8KxS8qUihhjjElz7HvLxjHS3W5f/XcSJ3ihEeFatrH32BHj+f5VfaPmw7mz05Og5aQtFlfWQUvTYpt4ZBDV62gH41NoQ0cPH0bt4H7QHMsD7dTpM6D96Rc+BdqlixdBS+Li2HVdfAbPwXEVxT3QJibR9uZnp0Db2twC7eLlC6BNknZqNvEe7S76i7BkkkmChtDa3ASt38fnyj1iV85gLGUWMbIR4Fp20X46NJ4/eKbKPPr9iaNzoIUZjuVwO0StXbSLZoC+4/47Z0CbqaFf2jeH9tmoVUGbrqPNVh1MKMenMGbUqxiXbLvoK7zxCShj+dhuzUOHQOuuYRLsbqE/6fYj0HIH61aZxbokYRe0iRxt+a5jRZ/ywhLa4jSarGnPoV3UKtgPbl7shzhKjPlTvN4bzbVsI80yk2QDe7Yc/ExkjuHZxCSfMiwvKmH3sVBK4j9J4Uy7i33S7aE9sqp1O23QXBefNQjQXpKsWC4hSUZKHoFIJiOqRRrYtbBu42PElgMsZ6VoVxZpUKt0D5t8HDYnuZMhdbPJyZZl0X+PEteyC9fLjecN+iWM0X9ZJP8JKhhHgqAOWhh1Cse5wXHMctSE9K1NpmtT43tBc0gXdLr4XEmM4zHwMS6lafEZKiRvaBO7c3w2vSS+NUPH7GfYvt0tjBmnLj0Omm1hPjxWnwat3y0+V6d0bIwxOen7LMN99Nq9DdAq/iCn7RN/OCpcyzZe9myDMcLmb1lG5lzEbzC/kJf8IfMtDFYuIe27fgX75CWSG5/exLlvh8SbjHRhkhfLxTbarUPmGZ6H8x1W7tIFnNssL+KawbHbjoMWEfuOMxzP/RTjXJKUnivDXJfN8RMSIB0P7dFzBo3J5u2jwLXsYskeM/bQmsTUGPa5H2D/nnsJ+3JrC5+91S+O7zRlvgP70e4Tx0/WoXox2k+aoebaOC4qPvZvmqI/DNzifffu3QNl3v/et4B2792347WqGDNOnDwB2n0PPADav/03vwvat554ATSWSzKflZVsJQmxTK+LF6uSZ5iZwzFSrxd9QBjF5vNPnMLKvcFcyzYsyy74f7ZGa5HvY3kptoWxsR1zqziuUpf0EVnDscMlrMcWrs0c2Ytj+a134NpMZDZBc4mfc0n9UlPs4zxB3+qwBU62mGswZwkjrNulS1ju6Zdwjvz1x3FO/8LZNdAaE+OgPfH0pcLxYhfXmRcO3APa9CHiZ+YwntnVQW5qG2zrUeCauZRlinNd0r/lfMgYY3Ky3p6xeFlagycpmLHIOj3zcfRcElosUjd+X3YPphVPZnNZm7wLYBPwMMT8rbWN66n9CMu1O7jmNDON63q1Oo5vto5bqxXLeS5WmOW0Hpk/uWTdJi6t07reDf2+6w3jWrbxwovPG9cf1Lm7TXx6jv2UxGhDcQdtwymt3Y2NYyx+6vQmaHfeheugb3vv/wja6Wcvg3blAvrWvfsOgvbgA5gDORbOc5N+8fnrDXyG5WX03VvbOAfyajiuvu9t7wJtu4Xf6P+lX/0V0NoZ3sPgI5ixOtpGv+TLWusYay6dxfdAaY4599ISxnOvPhhXWcJW6jg31IIWFl5+Mbu0VExClpaWrv6/MkEQmLGxscKfELuJV2MXxsg2xO5HMUMIRDFDCI5ihhCIYoYQHMUMIRDFDCE4ihlCIIoZYrdzQ1+MHzlyxCwsLJjPfOYzV7Xt7W3zla98xTz66KM38lZC3DLILoTgyDaEQGQXQnBkG0IgsgshOLINIRDZhRAc2YYQiOxC7Hau+6fU2+22efHFF68enzlzxjz++ONmamrKHDx40PzUT/2U+cf/+B+b48ePmyNHjph/8A/+gdm7d6/5wAc+cCPrLcRIIbsQgiPbEAKRXQjBkW0IgcguhODINoRAZBdCcGQbQiCyC/HdzHW/GP/6179u3vWuwe/Rf3vPgJ/4iZ8wn/zkJ83f/bt/13Q6HfO3/tbfMpubm+btb3+7+fSnP20qFfy9fiF2C7ILITiyDSEQ2YUQHNmGEIjsQgiObEMIRHYhBEe2IQQiuxDfzVh5nu98R/KbwPb2thkfHzf/9uc+amrVgZHZmYWFLdTiJAFtq7YftEvu3sLxeqsDZWrj46C59QZotuOAFjjYrHGnDdrS4iJoaaUGmiGbzY9VvWI9SHukObZHpxWC1l/bAm26is+1EOAz+NtLoDV9/MxFsxaA5pBuNSYrHNkW/uJ/nGagRTHp+14PtF//0leu/jtJEvPlP/m62draGvk9L75tGz/zv/xjEwwFoH6/C2Xn9syB1u31QfvGN58ErdctXm/fwl4oc/LO20G7/56ToO2Z3wPa08+dAu0P/vCPQXvhuedA63bRTvMsBc0ujSvm5ixiL30yXvzAA218En1Ds4m+oVrBMb+5gbZ26NBh0DbWN0Dr9It92CX1XVxEewx7aLfVqg/asOmlaWpeeuGZkbeNb9vF2x85Zlx34LP8CvZ5Ywb7qFKvghZMoA92WsV+u60+BWUeue8e0Ops/MwdA21sfh9odq0OWpzj9WwTg5aG6Bfq9cnCcW0WY2MvxXZL+mh364tnQfPiTdBcF/2362H7xhHed2V9BbS1p74B2oULxXLnF9HGjtzfBO2pKsZfO0C7SLNivAl7ifn4//z5W8Y2/vbP/BMTBIOY4ZCchWk5ic/ltjAG9+TxLbyWR+zAkOu32+iren30cxa5x9NPfA20OEY7uP9N7wItLD1slmO8ZHGEZdAZaSOPPGuzgmNtsom2USUn5ynmOynJi8r9attojymJoXh1Y1ZJ7Or0B/lkFIbmk//yF28Zu/gbf+924weD9kkSbAfXxfhQq6JfJs1q+qVxa5OEN8uxz7o99LeBh7FreuwwaBUPc44o3gYtTnAe4Fh47la76CNzQ+zfR1v0nUnQpqtHQWvaGPd6LTSqS1cwrzl/8Qpoq+uboMUZXm9iodjX+47ic41NoH26DmqWhfOzXndQj7Cfml/+2JMjbxfGDGzj3//xn5na0HyX5cssZrByNvHVZf/NzqPXIoYWkjnQ/++X/hlon/kszjMsg2Ojt90CLQ2ZR8y/w9HLeB7GvYDMCwIfNT/AWPAjH/xx0N77wz8IWofEzKiP47RP5lS9sBiDU+IrDIs/IcbMNMJz03DQvv1+aP7J//ZzI28b37aLfXf+98Ye8gGH53CsvOlO7MvLz78I2qVlbJvVTrGP4hhHleugDdgu9odF1qGimOTjNo5Rz8ZzHQv799iRGdDuOVlcfzh553Eo8+73/DBo1fosaIvLGLsS4hfq4xhvfvP//j3QfvkTn8TrxSSmkXwtL8WRlCTI1Spq7/leXC954G5co3mxNEbCKDH/x6997paxjZ/5xB+YYCg3ousuJH/wcpy/Zjl5yVLypZlFzlvHnKAZoRa0nwHt0ftw3fLk/QdA+/znngZtZRVj0LvehnvuHtpfyicjkhPGmIeureP1X7qIa0R/9o1LoD13Ac89Q9Z8l9bxvjnJ46bmcF2vNlZspyN3PQJl5uZxveHxL30BNJYnn3zzW6/+O+x1zD/98AdvGbu4/bY54wz5bOa/LQttJTPYHzGzlVJ6ZZH2I8voxiHlWNuzuY1N4gO7h00cqcWe3ynank1ikmsw9657OBe76/hdoO3fi3Z8+TL6hR5ZU63VMA8LAozx7MVwo1Gct9XreK1anZ2Hz1UheWNWysO63Z75iz/+P98ytlF9yBhrqOtjbH7jknifh2SckhR9errYjlmENrW1gjf9J//wZ0H7f/zIXwFt4wrmJ36Oawaug32XpMS+M3yIsLT+ytaUP/vHfwjai6fwHcr4JK6DzuzDvKs5NwHax3/lE6A9fgrfIRkcumZycho01yuNe+JAqg20jSjBeNYemm9fxR/kGlmSm5U/a+3ILm7oHuNCCCGEEEIIIYQQQgghhBBCCCHEqKEX40IIIYQQQgghhBBCCCGEEEIIIXY1ejEuhBBCCCGEEEIIIYQQQgghhBBiV6MX40IIIYQQQgghhBBCCCGEEEIIIXY17isXeWPIbcvktjU4dhwo42QZaPWZ46BFs6h5673C8aGxBpTZtx83pI8zrEeUgmTaYYxajudOTOVYt0YTtM2Vy6B1NtYLx0mM90xJG0WkwueffRq07v5joFXvQG39UgvLhZugzY8noAWuBVq94hev5UMRYxt8BsvGa2UWakl/0E5JQjpvxHFc1zjuwHQbzTEoMzk5Bdp99+4H7c4Td4K2vLRSPO+uE1Dm9tuOgOb7VdBeOnsJtD//6jdBO3vuImhBBa/neTgYwjAELUtKY81CO8ty4j9cD8tleG6vh2PZsrAe1Sra8tjEJGi1OvqfAwcOgzY5NV049oMAyly8cAG01fUV0NZXl0B76dRLV/+dpviMo0yjXjeeO/CxSdqDMlsr26DFbfSb4do6aEfnZwrHUYRj8YkLHdDe/cM/DFowsRe0VTKmDuw5BFrTwjHa76MPdgyO77BfbJP15UU8j8TaSqUG2tTUHtC63XHQ/CraMbPZdtQHLWtgilI7/gBo5577o8Kx3ZyAMosrbdDOL6Nd1CcqoFXHinYW9W8t23Acp9CvrI99H8dzRHxAlmHMzJLyWMOxZ3vYl2GM46DTx3GQxFgPz8PPdYYRXu/8mRdBO3nXo6DlpdiS5ej3DdFyolmG5HUutm8QoG3YFj5XlpJ8x2BuY9vks66lHMgiORF5UhMn6BdtkmO5Q2MpI+NqlKkEMyaoDMYlC3mOTRLQDAv2Y/TBaVZsQ5fYmGuh1u+x8YNjJU7Qp2XZJqlHF8uR1DcjOUytXqyflWMsGK9hLJipY46YdDDPuXQBY/Lpc5jDXF7CXHJ9cwO0fhSBFhH78ZrF3CyO0D+lKbNtZousvwbPmrm3Vrwwxpg4jkwcD9qS+Zac5NBsHmaxeFDKyZlf2qnmuth3zXEca65DnoHNAXMylyTPWvb9LK6y+jKHy+JIRPKktRXMWViu7rrEF3uo5T7mk6kp2nhIYk1uoZ35ZP5k19BfxN1BPSwX4/1IU58zxh3kg9s2zrVfWsR5gJXWQctznKOUx0ZOBktM/FKW4di2yLw3zzGuO2RcpCmzd9T6Ick5/GIOffKuo1CkajZB+9x/+Txo/+53vwzaRh/H4/f/4A+CdvgIrleNj+OcfG0V40hOx3zxmOVqaXntwRjT6mA/V8ZxHcBvFLWcrCOOMnmamHzIF9GYQdxh38M5V92eAC3eLvbThHcFyhw7tAza+jlcP8099Ptri5iLPPF1kMwn/+UXQatPow299yFcmzOtYrnnz2Le+KVvngPtv37lDGjLLfSta1sYp0yAWtDAuk0tzIF2+x1vBu3AEVw3dMeKaxV9ElaTBH39vQ8/DBqb72RDPi8j/m+U6bTjwtypWsGc3w8wh3FIDLccLJc7xfYgbprnIRbJ30i8YRqD3IHPU9mrp7yUO2S4tumSOVAlwHEcBBhrZ6fnQUtjrNu5s2h7y1fWQOuTdQqWw3ml/KpC+t4laxlBgLnU2Dg+a7VebJN+H/OykSavGZMN+sF3cExOT2DMtjKMjfU6tq3tFH1Ju43+dr6Kfu8B4pdCcs/EYHtbZHIdJXhf28Hx1+5sguY4xfjYi/H6995/L9YtxrzjuefwXV8vwnWEZgvH2jseeQto5y6+BNomWZfodTAnrlSK9lJt4pxteH3m24zVMeeOFrF9273B8+fJzmOGvjEuhBBCCCGEEEIIIYQQQgghhBBiV6MX40IIIYQQQgghhBBCCCGEEEIIIXY1ejEuhBBCCCGEEEIIIYQQQgghhBBiV6MX40IIIYQQQgghhBBCCCGEEEIIIXY1uKv5iJBlucmyoc3SPdyk3unjpvf981ugnc9x0/dOv1847toZlGl1uqRiKFk21i2xHDw1Q81keG7cCUELgjHQxksb1ds2Xt+yKqBFIbbb+qVV0CYWDuD1mgdBqx6r4z22VkA7F2I/WH3sL7dV1MaDBMqMVfBZx3zsnG4fz21tDfo+TVP4/6PO2Ni4qVSrV4+zFJ/7wB7suzuOHQOtUQ9Ac6zimKzXq1iGjLWt7T5o400cGz/wnu8B7f6Tx0GLY7SDmPRXGmMf2yWbdJydfQZoYrwJWqPRAM3z0HVaFt7D833QvvnEE6CdevEl0I7fjv2VJMW+7vWxzaemJ7EeAdbDzkEy66sD20uSGAuMMI798t+3sR185qpLxnuAPtLNWqA1Kl7huNkchzJTs3tBW9tCf3vmyhnQpifxesEFHBcmw44j5mgcolVKz2C7GH9cF9stzvFirRBtIErx3G4L/X63i+0bx+jHNjc3QOt1sdye2+8rHH/+T/8Iyqw9dQG0VpfElgZqjYmi34njWytuhP2+yYm9D9PtYr6T5NjWmcELOSXJ9tDODDkvjtHHpAm2Pzs3y7Bu9SbmSex6GYkttlsc4yl5dtaGOREtC+0qJeVC8vw+sUmLOGvPITmxjTHIJlqZjMRV1r4+iWf9IVuwdnCvUSLLeyYb8m22iz7NIe1sW6Sci/47N8X+TVL0hWGyCVptDO+ZJdgfSYL1aE5MgGaxfK21Bppno93WKrOF4zzE/CLfmgHt618/B9raehu0Voi2uNXG+NDawrlCN0Sf1Y8w3jo+xvharegrKgHpZzLhSxPm+/GerjM417KYTxttkiQ1yZDvZD6N+QhWzrJIzLDLZUj7U3+C5WpV9Ev79+0HrVLBcbC1jjkGey6HJVQ7gM0xWdzzPA80fNKd+XNjDAuZtC4knTR5yV+k5DsUSYInpiSu5imLv4P27ZO8b5TJ43WTZ4Px1u7iHHeRzMnHMxx7uUPmkabUXqx/2MggMSkjOYyVY3+kKclh8A4w/zTGmFOnzoI2PVEsZyc9KNPvYHxYWnwOtGefOQXa8jbW7tix06DddvgEaNUq9oNFGpnZWVoyFuLWaHzokXUoy8F67D1U9Fm9HtrTKLO1umj8Su3q8dzcLJTJybJz3ML4ubL6OdB6y18uHL/5XbjO9c775kD7pc99BbTPf+5p0N7z6CHQPvDf3QPaRz78JtC6GfrvLzyOOdY3XyiO8S8+dQXKvHAZ13W2IhwL0/M41iYnMT+r+POgHbjzraAdPHonaM0xzO1YH5bXlW3muCzi65kPtEksTAf2aJlXF4vfKNbXu4X8purjeG800R80xnDt1SVrWHbp/YhL5mk2WQNleRnTcsNyXvYyBDWb5HXGxj4PKsW112PHTkKZO25DbayC8Tds4zyrvY0xyHewLRs1XANmj9ok5fpkLhPHxb6OQ2zLKMR80GJzTAfHQ7dXjC1heGvNNeIoMNbQHLvuo217Nj53xtZdSI6+UZpLrqxiHx2aRH8zOYbz+U53G7ReD+eqK2s4V21tbYKWZGSuilUxUzP7Csc2ea/nB2jfj7ztEbzWNNrLF770Z6Btd9dB23cY4+2D998P2h98GWN3mGA7dXpFm6xEOJ+3Aow/HnknE+bYllutQV/n17Fke2utYgkhhBBCCCGEEEIIIYQQQgghhBDXiV6MCyGEEEIIIYQQQgghhBBCCCGE2NXoxbgQQgghhBBCCCGEEEIIIYQQQohdjV6MCyGEEEIIIYQQQgghhBBCCCGE2NW4b3QFrsXqdsdUo+Tqse35UKYS56B949O/B9rXJp8E7YG3fW/hOCKbvp9+7lnQli6cAc33PdAmp3HD+KBSBa3f3SZaG7Q8xWd1Had47GF3OqRuju2AlqcxaJee74C29NIToFWqNdDqe28Dbf7IvXjf3AItivqF4+32BpRJfGyPjd4yaJeufBnPHXr8FP7v6HP40H5Tq9evHjebDShz5/FjoE2MN0HLc2zHJM0Kx8vrOB47bRwbYYhjKM+whQ8f3Afa3XccB21qcgw038Oxm2b4DGlavG8URVCm1+uDZlk4HkNy7sbmJmjLKyugLS5eAu3s6VNY7spF0D77J/8VtFa7VzrGfshSbPOtDbShra0t0KrVwbhK0wT+/yiTO7bJ3cFnvewcx4rJsW26ffT9NR996eJasa2fP/s8lHnzxBHQKqtoPxaJZ70I+/Kls5dBC3z0t7Uaagnx6Y0hv2GMMYGDPsEnvtXxUFtdx3brhy3QLDsELcuwbhsbGAvDPp4bR3huZhc/47e8juN9rDEO2sw42rtv4xgpu8kov7U+U5jnecHXM7/PsB3ynMRHBm4xz/BdHN9xjP7EtvH6bCz3+z3QGFVybq+H564uXQFt76FDheOMPCfLF1jM2ElcNcaYfoixxTFYzqlgHueTfM9z0eeV65ck2A+Whf1gk+di2vD1WVuMMlkWmywbtLdjY5t2uzh+XBfLNeqYr1hW0Q7SHvZ3kmB/JySnsQ2xKYP+sd1GH5yRuJeEmDc2apibdRYrhePVK8Se1l8AbWl5HbWVVdA2tzE+JjG2E/O4jofjrdrAmLaf5Jx79xTLTYyhXeQGY1w5tzTGGM8P8NzhcWWRXGTEiePYxPEg1jLbThJsC9vGco6N4zkraez6jsP8GY4E5uP37cM+HxvHHGBjFccpi0usfqxcGRYLmP+wmQ8m8dfzMBawnCjdoSu2HVaX0tpCgM8wPL6v1oPEliRjdjW4Zzq6y1CUdOu8yYfiRDo2C2Uisq6R+sQH2Og3bLvo+yyLtD2t2c5yOmOwHmSablIL+9cnz3D4IM55jh88UDh+8Tmc815q4Pw7yjGe3XnH7aAd7KENHD0wD9of/ZffBW2F5H4WsWN8emOgjYmNWWTeubqCc+3FK+h3HK9oxyGJg6PMxXMvFGJh00Xb93P01X7rGdDuP4F+4c73Hi4cHz80AWWqNrb1j3zgJGgP3I92OzleAe3r53Bd8atPL4H2zecxV7h4BeuyFRWNLSTLLD1056biY14zO3kCtLvufRto+w/i8zcnFkAjbsukzH/naB0WxHgS84ltpMQfWcT6rCFjs5jhjTBplhXyhy5ZU0xa+Mwp8f11C2OGX25DMn9geYNL/LnnknIe2oVN3iM4xI/aDl7PcnAus/9Acc363e9+P5SpV3DOsknWXVcvodZpob/NU+yHnKxNNRu4rmBIvjZXQ5sqz/E7HVznc0me1yDr+izCv3DqdOGYrbOMMgcP7jfO0Lr+/ll8d2ZizAuefw5jBluXz7xi20Yh+o6VRZwzr5O5asOvg7a2jOU2ltZACyPMd3p9nPsGNYx72/1in9abU6RuaGfdLVwHrTbRliemMLb0YozTWYr9cMcJfNf09CLme1eW0SZNaX5XaeA7UraM3w7Rhub2YZtM7hnYUBpl5kUStxm31uquEEIIIYQQQgghhBBCCCGEEEIIcZ3oxbgQQgghhBBCCCGEEEIIIYQQQohdjV6MCyGEEEIIIYQQQgghhBBCCCGE2NWM7OZO2/UDJhrak5vtm8v2QzlzAPc7ylv42/7nni3ulX35Iu4dPj45CVrSxT1jWsu4N+r65XNYD7ZDBNnciWzRRp/flPY5zdj1yT5RbH8Yw/YyqeN+bM0a7gHg+/j5Cuc07jn4vIt7G/hN3JexPlncG259FfdJmJ+fAO2h++8BbTrBvv++hcFeVFEYmhe/9RyUGWXaWy2TxYPxsGcW90pqk7157QD3oOh1sFy3W9TYHnlsPLoO2SOQ7FfjkH1QPbKnc0LuEZG99C5cwD3DTp8u7rvSapO9nrZwX5E4wetvbm2CtkH27O6Rvaq7XdTaLfQXbKvCxSu4P3lWsvk0xb5ZX8P9TS5exD3ME/KszaF9mDO28dwIE6axSe2Bb7NjHGfEBAzb/jOJsF37pYKLK5tQZn0T+3sf2Yupn+DeRivr6KtqZK/mJEEfHCfoWx2b7FGUF8v1yN6froc+wa/iAO2Tsbfdwf2NPA+fPyJ757W76D9i0oeOg88am6Itb7RxD5oePpbZO0vaLcdnKPs2m+xDPsoElYoJgsGzZmR/ULovNtmbGDZcN7gXLLsW25uXxQLOzvbxrlSYbeBY+8Ln/xS0O+6+q3B84oEHoIzn4/VTsq8qqxvbv5rt156QvinvxWSMMZUq1qVK9pkq93W/j36G7Q2bJfgMbC/O+tC+7myvtJHGMoWhFSXoNxLi5+II9/mL+phj2E4xxqY0358DrVZFv+Q6ZONJZrO4NZgxvQnU+riv2OmX8OTFpeLeYJcu4Z6bG1u4zxobU46P8Wx8GuvWHEONzUeCCraTV8F2GptAH1CbKOZhlsv2G8T+stjEk9inGwzsM7VurX3/jHk5/yvmgDvbK5u5L+YPrbTUT6xZM7bHOBaMSL42OYF7Fd5+2x2gXTl/GW9McjY2FrJSVSzi/wIPx7xF4wM+V31iArSpvXtA64Vkz1LSEWwP0Jy0p2WKz2qTvnfI3uQOyVfTlOQawxJLwEcYO28bOx/aY7yPMTcn+6pWKmTfXJIb90s+LYxxTsF8EBlSdB9Uy5DxSIwvIPvPHj2yD7Q3PXActMOlci7Zu3mrg3PofoSxZWEG9+G0yD67F1/CdZzPf/5LoKVk3mvI9cg2vbjfO2tzsinm5gb24emXcH5f3l80DEldR5jAyo031EbxS5+BMv/9D+4Fbe4Y7g86fwjLlYPL6nO4zviVZ8l+9n3s33OLmOs9++yLoJ1fxonjpRUck+2I7c2MOZZTLZYbI2u0h+fQxx+78+2gHT3+IGjNCWzLnOQfOdmLmMUllwxyuhd5yYcw+2Fr29xxfee1bMu6tebftusU8haHxAcWB1tttP9uH2N9c6zoX6cmce3HcrDNkj7xLwnGjCzGWO+SfcItshe5W8Fn9cgazuRYMV87sA/f5Wxv4Jrt9hbasWVhfWtV3Cfc99Av1Ou4j7Rls/3Zcf598q57Qbt0qbg+/fwL6LOSEH1MRPbV7rZx7u7YxX7IqJGNLs2xwDhD6/9ze/B9Rr+F8XO7g23RbuP8tVLKoatNvH7eRpu6cAnj8217j4IW9kg9tnEtYG0NcxsbTciEGY7JPCw+f6uHdsBe5kZtjFMXzp0F7bHHvgba3v24LtEN8blMDW3j4Qdx7ewb33oStLVOcQ4eG/RHiYNxqtZAG62PY2O61YEtJGFqXjTaY1wIIYQQQgghhBBCCCGEEEIIIYTQi3EhhBBCCCGEEEIIIYQQQgghhBC7G70YF0IIIYQQQgghhBBCCCGEEEIIsavRi3EhhBBCCCGEEEIIIYQQQgghhBC7GrZf+0hg24Gx7cEm9IGPG7yvrm2AtraBm8OvL11B7cKpwvH0HG40f/HFp0FLen3Q6vVx0BwfN4LPsgw028bPJuSgGJMmuAF9nhevZ5EzPT8ALY5C0KIoAi3LsW6brS7WI4vxehcvgVZh7eRhO3Xaf144Tkm7tfYeAG28isN5vIrPcHD+6NV/9/s9+P+jzq/+6q8a1x3Yw8Le/VDm+97xPaD9wPvfDVq1XgetXin2SZbiuArJGApJW66vb4K2vLwCWqe7DVoSp6D1QxynFy9cBK3bLY7TsSY+58rqMmhTkxOgxSHafBS2QHNtC7R6Ff1Wxcd7GIPnpjm2e9kUWu0OlDl7Gtsy8CtYt3oNNNce1DdNb63PTeX2y3/fptXFfvOa2B9VH/2GZxzUrGJ7WRn6QsdFf9bu4lhpkzE11kD/mONwN7aDdlFncSTD8ZOXxmizju2R5Hj9zjbG1V6Ez9DHUGDaXYxdUUzimcHx2O3jWHY9PLdc49zGPr2ygvmCsTE+zo6hrdQqxXaLM2yjUaZWrZqgUr16TP038a1RhB3quNi25dyGtY9F/CMqxuTE77E8KSXjO6hi31UC1FZJTvjY19qF46k9e6DMwaPHQQtTzE+MQ54sR59iOcTHWth2vofjtNEYAy1w8b5hWOxrluvlNOvEa1WrVdDcofzAsliPji5xFBrLGvRLlmH9XRfbno29Hsl/gkrRVhokB85SvGeDtHMSotbewDG1fAH798p5zHW2Nk+BZmy8XlwaB1GO/nd6Dm2FzT2q9QZqjSaeG6CPyW30RZYhcXQC67dvP8YWxy72F8tfczIeHIfUjfiA3BnYXU7abNQJw56xh/yY4+DYYLNV5qstawdzX+KC2JyZ1cMh4zaoYJ8/8MCDoL30wougXTx/BrRKDePI3kOHCsfzJGbkZO7+xDceB43N8Y/feSdot991D2jGwTwuT8m8PCbtSWKGXeoM3yVtbmGua5MY75J8IR3KP261mGE7VuE5swR9kEPig5Ni//okT6gERb/ZauM8w5AcySLxmmk2sRW2dtQYQ7989333gXbPgydB23fkYPFajXko4+RoTyfvxnx/8cwToP3h7/82aE8/9QJoNTKXCS181i5pz5R+b6jUnjlpc2IDIcmjL11eA832irYSk3nSKHP02AMFvzvZxmecHENtexHH5Fe+hvnJt15YKhw//ewSlDl/ZRO08Tr6oDfdcQK0Rw8fBe09t6OfW+ljfc9v4X0vruKzbnQnC8d773gYyuw/fhdozSbaEHOdCVmPNWSKwv0uamw+RimfyuZxOakIKcfuWZyj7LBOI0K9Xi3kO8wHs+8pZinxX2TdZWuzqFUr6FuH5//fhsVmVjUW18vvH4wxJo53Nrdk/dva3iwcBx7W7d577wVt7wK+u3np+WdBW75yDjTfw+fyybubJMF+OHYb+o+TJ+8GzXWLfdHvYxuR5jWTU1OgjU1MgpaU5h6dTsd86j98Gi84orQ6sXGiwTO8cB7fD0xMYy4ycWwGtCmHvdsqxtCwi/6x66JNfeH0V/CeM7jmsrZ+HrSwi+8kkpBoZH4Tuvisabc4/sZinEfb5HVu2Mfrr21dAO2ZU8+A1o4xdh0+tgBaEKNf2bdwCLSNeeyvZKM45z63jvfsRviOw6vuBa1aR7u1hnJpi6yrXYtb682HEEIIIYQQQgghhBBCCCGEEEIIcZ3oxbgQQgghhBBCCCGEEEIIIYQQQohdjV6MCyGEEEIIIYQQQgghhBBCCCGE2NXoxbgQQgghhBBCCCGEEEIIIYQQQohdDe7WPiKMT86YarV29dh1sKquV0Et6YPW2sAN3d/86MOF49uOH4UyFy8tgfbYV78GWnsbr2/ZDmrGAo1JxsLPK+RZhlpeOs3Ci1lWG88zOWiGnMuwcqxHt9sB7bbjx0Dbf+AgaEmG7fT0008WjrfWFqHM+qUzoH32dy6B9r4ffD9ozf13X/232+vC/x91/LUN4zqDdtvIsD+//vkvgJasrIM2Mz8Pmjs7UThOydhobW2D1mm3QGu3sVyblNvcwLr1ej3QmB0EPvqGsbFm4di2AyjTbPqgxTGOB8fF55+amiB1w35IsxS0jJRjjiAh55Z9Q4u0r0s+7jR/8ABoURySug1OTpOE1HN02Wp3jDv08FaO46I6Xget4USg1b0GaMvrRV+aWth+eYrXivvML1dBS1McoyZHLU7RBtop2krF80CrNYp1mZ6ehjIbW2iL/RbGkV6Izx9GWLd+iDE5SbCdspTETJvEwhTva2XF6zkWXqsXYT8srcegjXlN0GpeyVekt5ZtuL5jPH/QJqmFvq8bsT5BX8X8S17KCypVvL6xmY8jxUhampP0JC0nQMYYP6iBVqlgfxqDOVvYK+YxX/zsH0IZh9TjwNEToHVxWNFnyC18BsvBsWuTRmfnJiSORKWYSUcuya9zG6+fkc/S9sOB7wlDHEOjTJq+/Pdt6rUJUgr7I2Mxo47lPLc4HtME2y+JMM4//xT64KVLmGf30S0bQ2zK8vAeU4fRRscnJkCLk+K5YQ+vlZNUJYzQT8ckToUxPoTF5nYuafMGxpY9eydAqwVYwSQtaiyPLD+7McZEMekHEvcCf1CPkOQAtxpxjE4tTbF9HOa/SBxn5cq4LvZJRuYAZBpJc4eT99wN2jvXVkD77f/w70CbnJwA7QN/8ccLx8dvx1jwja99HbRnnnkBtHoFc8J773sAtEoFc8KExG5GOU4bY0xE/E9eMmjHwbbcaZ/6PvqZdGgNIrvFcqksyY0Zio1Zjv4rJDmvmRwDaayOWnu5OB59EocTNodk60bE7TBbYbmE00AfPHME13X2Hr8Xy82NF46jHOciYYK2Xatg5eaiPaA9eA/Ww+/gOlFvE2PQZhfv8cI62s9iH9skzl55yTQ3JNawpb8q9r3vFm3FckgiOcJUp6ZMpTqYY/vjD0OZf/J//V+gPfY0ructr+PaxlapTzYi7I+ZJtrBz3zPnaD9yMNHQHNIR/U6OA62SeL1Fm8KtOUMx+mZ7P7CcTh5B5QZXoe5SoJj2Vjoz222lkvsm0yfXn9Y3d6Qitxc9u6fLMRQ2gzkvIys9fT7ZO7eLebVnQ6OT7a22RzDnGNiAufLPsmNLTIXtG3ynobkcEQy/V6xzqur+P7l4IHDoB06jNoUibUvvjAO2trKMmhs3bMfYXv2Q/Tz62tboG2sbxaOXRf778Sd6AOOHkXfEZAcMYqK9d3eRr85yjiWZZwhg3DIuqXfwOc+dhe2Txitgra2WOzjRg3zmuYY5tSf/qPfA+1NR24DLbPwfcZWC99FZekmaMNrJ98m72O5SqnOrRCf0xh8Lo+sY8/NoR3cey++/3zq2dOgWQ7GoBO34bkZeS9x/OA+0M63LhaOx2fwGZwJ7PvJKYy1zSb6rYW5vVf/HfViY8xjUIahb4wLIYQQQgghhBBCCCGEEEIIIYTY1ejFuBBCCCGEEEIIIYQQQgghhBBCiF2NXowLIYQQQgghhBBCCCGEEEIIIYTY1ejFuBBCCCGEEEIIIYQQQgghhBBCiF2N+0ZX4Fp4XsV4/mDT9TzLoExzfBK0hx56CLTM5KAt7CluBD8xOQNlqpUaaM88+SRoSRyBVqnhRvAmx3oQyRjLwnKkWFnFs/iJWZaSemD7MqKwB5pl4bkHDh8E7cjR46C1W13Qzp87VyyzvgZl0hDb3HUd0GqkD/Pcov++Vbhz/2Hju97V47UshjKtDrbr0088BdpY4wxo9oH54rGH7RqFIWhpjFrYJ/17/gJoS0sroCUp9nEaJ6BNTY6DduRIcfz5Pn4GqFqtgGZZaDCe54HGbDRL0Q5SZmsZ3oNIJiE2adlFl52SewaeD9r0NPq3peVF0CrV+uD+CY6rUSbrVkzqDMbqoQOzUCbwO6Ad2o++amsZx3K/3y4c33f/vVBmdgbvGYXYuU6tDpqxApDSDMdtn3SLG2Aoj9BUIBz4AdpAvY721A1xHMcZ2kBEfIBHbCBO+lg5Ynu+j23Sa2O5sBSDx8fR71+6soTXCrEemxvboM02i/2a3WJxw3eN8YfcWJyQeJ/jgPFcHFeOjWPScYoxwg/QB9kkPrc62P4R8TsJyf8y4h9dB+tbrTVAM4b45dLnRNeWLkOZi2dPg3by3jeBlnTwGeIE2zcnuZOxcWwlJEB0eth2DhmW5XzPIv3nB2hn/ZTEKRJvzHDf7DCPHBWSNDX2UPO0u20ok6c4lj0L/WaY4PheWdwqHi9j/pyS8+pjVdD2H8HYUqkTJ2+jD7Zt7EvbwfsagzmX5xXHS9TBuc3KFWy3iUmcn/V6+PybG3iuX8VnmNuDMdPzUUuJH2t10ae7dnGsErMztkNyv4TYYoS22AoHfR+R+DnqeL5nPH8w9rN0Z8+QEV+9E2zil3I6Z2Ya+lvLxr5jMei+Bx8AbXUNc4XFKxgPJkt59djkNJTZcwDzy6PHT4D2pvvuB+3EHXeAxmA+nWcoqLIQVI6tO+1T1ocpGTfDfUjXQEYY2w2N7Qx8zPwk+qCHTh4C7Xsfwj7vdXHc/uanPl04rgfYpj2SK7OUjvWaQ2IBS2ddF+9bq2JcajQwHgRuMeeyyQ0sgz7eSTE+BCR9O3E/tu/UJK41XHnpImhhF33A9MUt0L76LJ673C22ST/HfDO18PoOmZOzdYXyVOlVutI3jNTOTDoUV9vWXijz2Fns0MdP4Rqf72P7OKX5yLSPsf6DD98J2o+9DX38bAX9Uo/E6UoVx2Svg/e9vDYH2qaH/tsaO1I4Tkj+V8lwnYL57oSsQ1GYgb+GKeyO1qPZOjZx9qzcK/Fqznkj8Vy7MEcm02pjk3TcYfmnhWsbWTZVPCbTAtZitosOxg/wnp5H/DfpA5s8BEm5jOfiuWlczKEfe+xrUGZiEm3sxO0YV8t5mTHG3P+mB0FbW10H7cplXBdlw80hLbq6ugHa1ESxLjXybmh+fh9ototzcva+opxzsRxslNm7b6qwfmn7mGN0u9hPvWgTtOVFjNn9VnFcVSxs/wqZC7s25mZ/8IefAu2v/uhfAM1zDoC2cgFtrZ5iXSzSx3EpV+qmON/0PXQqvov+dnpyDLRHH0bbmJ6aAO3U6VOgXbl0BbTeJs6373n7W0CbHSuuEdSraLfBDK5HJxE+V83C55puDPxFSNZFrsWtZUFCCCGEEEIIIYQQQgghhBBCCCHEdaIX40IIIYQQQgghhBBCCCGEEEIIIXY1ejEuhBBCCCGEEEIIIYQQQgghhBBiV6MX40IIIYQQQgghhBBCCCGEEEIIIXY1uFv7d+BjH/uY+dSnPmWee+45U61WzVvf+lbzcz/3c+bEiRNXy/T7ffN3/s7fMb/5m79pwjA073vf+8wv/dIvmfn5+euqWJalJsuSq8d5iputp0kM2rFjR0Hr9jp4A8crHFYqVSiysb6G90xT0JKoB1ps7/AzB5b1qjXL5KVjUoZcKsemNBkRy9c3xpg0wQ3sHQef1fUqoE1OTpG64LmNRqNwnJB7phnWbWH6IGjGwiHe7nSv/rvfx757NdxM2zj63u811aHxuq+LzxBGEZ4Yob1kDhbrmeIY77XaUIa1W5rgPS+cOw/a+fOojY01UGvWQWu3sS6LS0ugjY8Xzz10cA+U8T0PNMfFBvF9LMfMm43JjPitOMl2pOXEv4WlPuz30DY8LwAtIuOh18M+DCqDdmO+43q5mXZx4sCU8b2BvXtkbMehD9rly9jO7XVsm2ZlrHA82ZzG65N+NKweEZZzAyzoOdgJVRdjlU38beDiueXxcu78ZSiTk35vtbE9uiHxMSSl6PexXEZsxbawTbo9jLdxhuM7LF1vfn4CyqQ9HE+nLm6A1o+7oNXqRR/gxq/dOG6mbUyN1Uy1Wrt67JOYbWVoB51uAlpGBkiSFMuFZGx4Odoeg/kqlnexPMa20YbqdYwjhuQ2eVYcf46LY/mF554Bbd/hx0E7ftd9oLVIWxpiBylxId0Q+8bKsU3qHvar7xfb3fOxHyzi7Nnzh/0+aHk61OYsobhObqZdWFZgLGtQ5611bPztDfQHaYRx13cwh6kE44Xjo8ebUGZsGucnlo1t7/kkZjjYl1mG+Uo/JHOUGPvSJUHT84tjqr2J1/IDHMfjE+inScplwj7JYap4veY4nlwJJkHrdDdBs11sY68UR9stfC4WzqME7S4MsWBQGfhb2ya2/yq4qXPwNDPZkN9lPtgmiTDTcuKsy34+CHC8sHtmGekUC++ZkXjGovbYBI6hd73nvaD9yWc/C1qv5A97HZyfnHnpNGh3njwJ2kOPvgW0jPhlMqUwNpm3MPKctJ1Bu0rT4n3DEH3FdSwugDQcp9l4uV5upl38tb/6Q6ZSGeTb73zbQ1Dm+B7MOSoxrif91m/9F9CsqFU4nmzWsEwH/UmHpeNsrFgk9yGaSzQ7x/s6BseUXcoRqzaOFd8jlTMYC7I6yWm8GdCmFu4H7eBth0BrLbdAO7TM5sJoU89cLJ77/EW8VivBZ3XJnLxRxTHS2irGKbbueb3cTNswmWfyofwj97Hv7nr47aCFKfoXq7kAWrOUFzyyD/vtf3rvMdDmazg/Xl9ZAa1WxXwqyfDc51fRZ65Wvxe0OJgDLUuL48q3WVxFm0pzzAmJie6Y1zKyWBwtzyFYzB8lbqZd9MyUcYbWoh2b+FaDDtzJcGwQkzLVoDhuJ2YmoMzc3CxoGbG7VnsdtH6IcyDWuxaZfztkkLLnT+JivnbmxRehzBfcPwJtvIq+9dARfA9UqeHca/+BMdCmptBmW9vboF25jGtnhuSmk+PFOWBE1nUrJBa4LvoYj+R55TTM3WEu+J24mbaxunHROP6gzgnxc7HBcdoPV0HbXMf1vLDcdWQs1wM0qgppx2+8gPc8efYAaG+9D3NCr04mv1sk74jR5tdbi8VrEb/A1mvoHIisLezfj2N+/358ZzI+gTZ0+tTzoL14Cm139jBe797b7ygcb5HAshGjX3z8madB66+TZw0HfRj38f9fi+uakXzuc58zH/rQh8yXv/xl80d/9EcmjmPz/d///abTGSRzP/3TP23+83/+z+a3fuu3zOc+9zlz+fJl82M/9mPXcxshbjlkG0IgsgshOLINIRDZhRAc2YYQiOxCCI5sQwhEdiEER7Yhvpu5rm+Mf/rTny4cf/KTnzRzc3PmscceM+94xzvM1taW+Vf/6l+Z3/iN3zDvfve7jTHG/Nqv/Zq58847zZe//GXzlrfgJ6LDMDRhOPiU6Db5ZI4Qo45sQwhEdiEER7YhBCK7EIIj2xACkV0IwZFtCIHILoTgyDbEdzOv6Testra2jDHGTE29/BPZjz32mInj2LznPe+5WuaOO+4wBw8eNF/60pfoNT72sY+Z8fHxq38HDuDPEghxqyHbEAKRXQjBkW0IgcguhODINoRAZBdCcGQbQiCyCyE4sg3x3cSrfjGeZZn5qZ/6KfO2t73N3H333cYYYxYXF43v+2ZiYqJQdn5+3iwuLpKrGPPRj37UbG1tXf27cOHCq62SECOBbEMIRHYhBEe2IQQiuxCCI9sQApFdCMGRbQiByC6E4Mg2xHcb1/VT6sN86EMfMk899ZT5whe+8JoqEASBCYIA9Py//XcVsil7muIG9CnZlH58rAHaixeuFI5tB5tifQUNvNvexLrGfdD6SQgaewjyWMXn/raW70TDq+3sPEPuaIwh5bI0Ac3xfNC2tlqgLS4vgRb2I9DWSu0e97tQpjExDdr8wj7Q0iwDLYoj+u8bxettGxeWL5sgqFw99lwPysQZPleSxKj1UCu3WRpjGTY2VlZXQDt95gxonsM+j4P9lJOxFodoa/U6ttHGxmbh2LbxnkEF281Y6AeobVh4vTwn/ihHm+yF6Bs2NjZAW1xEe1lcWi4cX7mCZWZn5kDLMnyKg4eOgHbnybuu/jsM++aLf/KHUObV8nrbxfF9VVMJBv3X2cYxdW4ZJPP8xTXQJmo4Nu45cbhwXA1wrPTCHmg+VtWEfeyPJO+A1qiyMYr+NotxTIUGx2PmF++bkRhqEX+y3UYfHJNY64BijOtgfRsNbJRutw1av499mNl4l3bJLyQRtsdb7rkLtE7rMdBcB+9peUU7tmjkfvW83rYxXq+ZWq129Zh54JzEyl4X/VI/IjGjNI5iEjN8Uq+U5RikHgzLxj5gfu62224Drb2xCtr50kQtjtCWt9bR3145jzFu+JPU3+bcpSugddo4Tll+xuwgIH4gaGCu6welcuT6loVt6QdV0Ho0dA/62s5f9ZSC8nrbRdipmiwe+JPWJva5bVVAG5/BthmfQr9UHy/mwWGEPx8XpdioTo51jbvob6cmFkCzXOzLkPjDNEMbzYht+0HxeimxsWodY0aWY65GplnGD/Dc3GDuR4atyUgMWl/HeG47WBfPKebI7TaWMTbWjfmsdhdjd32oanGE9XytvN62UYbl0Iyd+m/PK7YtO49pDp0/YJ/YxKexOXJExvz45Axoh4/djueGxbzo7IvPQZkXnnoStLvuux80t4Z+hq1xeDRmsrk6yf9A4fGmPAe0SbvZZA5kWcRIHfSL2ZCWJjfWNl5vu/jbf+0vmrHmIM42GthveRd90OKLmCcsXjwLWs0rtkd1ogZlwgz9TTtGn8nmqSlZB6jVMZ7tmZsFreKhA4/6WJekUixXr+B5voUxKXPJOK5hTuPUsL4eiRmNcfTpzWmcZ0zvxfUSuzIF2pHSfDv/s2ehzBNnN/FaLj6/w9YQsu98/Fp5vW3DslJjW4Pxm6XYn4ePPwTa/NH7Qava2J93BhcLxw9O4/zEJ3nN5UXs314P7erp82i3//FP0adv+veC9ugPo73kFnagm5V8KVk3ih0Sj8lYsMi5O4WvPd+4cmxOcY2F5p0Vex15ve2idfgRY3sD3XJZzo9zD4vMv70uzl3raWl9pk/eg6TYH1Njk6BVST6+sY5ru32ydp6T0eKTRSGP5NXldzALY2ifByo4MqLFs6D1ptB3V0lOx8Z2o4739T18iJCs9XXaGAu9anE81PwmXt/H9vBcvKdL4kg5f3PYJOs18HrbxvLmBWMPra81ybudXoTvkzwf84c3PYhr2qefLcbs1Uu4ltkh6ytjh7GuD78N5wCfffpzoE3vxzX4N92LMSO5gusBfhfHVWWr6C+2+ptQpkfepdFXNyQmZYasDxB/cc89J0GrVbDcFzYwjn7jscdBe/v731s4dsg8eesK+sD1C+gD18g6/triYME/i3eeTL2qb4x/+MMfNr/3e79n/uRP/sTs37//qr6wsGCiKDKbm5uF8ktLS2ZhARdwhNhtyDaEQGQXQnBkG0IgsgshOLINIRDZhRAc2YYQiOxCCI5sQ3w3cl0vxvM8Nx/+8IfNb//2b5vPfvaz5siR4qczHnzwQeN5nvnMZz5zVXv++efN+fPnzaOPPnpjaizECCLbEAKRXQjBkW0IgcguhODINoRAZBdCcGQbQiCyCyE4sg3x3cx1/ebChz70IfMbv/Eb5nd+53dMs9m8upfA+Pi4qVarZnx83PyNv/E3zEc+8hEzNTVlxsbGzE/+5E+aRx991LzlLW95XR5AiFFAtiEEIrsQgiPbEAKRXQjBkW0IgcguhODINoRAZBdCcGQb4ruZ63ox/olPfMIYY8w73/nOgv5rv/Zr5q/9tb9mjDHmn/7Tf2ps2zYf/OAHTRiG5n3ve5/5pV/6pRtSWSFGFdmGEIjsQgiObEMIRHYhBEe2IQQiuxCCI9sQApFdCMGRbYjvZq7rxXie569YplKpmI9//OPm4x//+Kuu1Mv3skyeDzZ1ty381XfHwU3frSqW23fwMGhepVk43tjcgjKujdd629u/F7RGvQGaMdhWOdMy1LIMN4lPkgS0FDS8FjsvSWJSD7xnmqLGxkBMrtfZ2gTt+WdPgdbtdPG+UVg4PnbiTigzv/cAaIeOHANtYnwMtGq1evXfFmmzV8PNtI1+r2WyLLp63GO3JpskoLUYY1mo2k6x321ysTxPQVv+b58qG8bZ6WYN5Bnm5qZB830PtEo1AG1jo2jPGemfJMVnYO2Rk5brdPugraysgnbpCrbJ0vIKaBub26DFEdqu6xWff2YO93PZs28/aHfffRK0O+44DtqwL+t2Oub/gBLXx820i95aajJv0Feei4OvGfhYRzIOKkETtHqlOM7GGzjuzq5i345V5vCeXh20BN2taXU6oAU+hm3bxXb2Sbmk5NO3ttH/Vus10BwH7S4hXdvvhaDVKtjmUdwDLSb9kBGn1Y3Q9rr9ora8hLZ47zzaxR370cestFugbZT6IY7RNq+Xm2kbxrJf/vs2tgNFWu02nmezXCTCcqUYkcTYl2mGYyM1WI61i01yMdZ67Nzyz4AZY8xD998N2unTZwrHX/vqV6DMi6cwh9lYXQKtu7UG2oHZCdDCsSpqEbZTJUBfM17Hc8dqaKdZKcfs99H2LBLjAgfHiFuvgJbGA9uzslvLLoLAMn4wGFsz8/jMcYx2YTnoIzIb+yPPi5pjT0CZNMLnZf7RIz6+H2LekKZon2mCfj7wWHxAe2xtF31fGOJY8T3S7zZe3wtQCyo4ZvsR3mNzA/2+62E7RRHOR9KEZb9FO+uHpN1y1HKSD6c5Xr87lCPGEbbrq+Fm2oZtWwW/m7L4TOaNLId+tbB7OsQvOQ6Oq7LfM8aYJMHrsTkyY2pqErTFKxeKQoxjj8Uu9lysb1ndXNK+rM0tm/QDmdOzPsS6MQ1FOn8i5yZpQv/9armZdlHzLVMLBs/Z7aAPNjH6jdMXLoO23cL5wsG9xblHUB+HMq1t9IU9F31hYwxtpUny8dmFWdDe+j33gHbkwAxojoX9l5XjkoP5i+dh3YyLMTQ1mHP4ZD5iSO6RE/9dJ7HKrWB83G+wncb27i0cX+lifU8vfgm0agXLbZE1x7K5J2xSeJ3cTNuwrNxY1tD92L1tbNcpC5/zrjqundxZK87rsh7222oX7+nFm6CdPnMetF/7r0+Dlu9/G9bt0e/DcsTPO2SdLHfK9UOf6ZCJb2bIWKD+dmdrmiznZ5F7pyuk5TVktvTHrpW9pru+em6mXXSq88byB37MqeH6j0vWq2ySt/fb66j1i/621duEMuuLGB+mttEXTvg4zpwMbdYhNuvZxPbIIrDrkvlso/ge5b7jR6HMQ3fhOqZbwfcvUQfjY6VJ1hXcndlAQObfhw4dAq1D1ut2gk1ytZ3m0eVyNyL/vpm2ceK+I8atDHIBx8e2fvE0jvnJKSz3Qz/2Djz3yaKf/4+//jko00lwjP7wT7wXtPvvx/dO/+m3PgXan371s6AdmcH3U4cW9oGWbWJOGIwV+8PbQnvc2sI8tMfmtGReFMY4biOywDs2huult5/E9bXMbID2uS8/A9rTT36rcLzcQn+0uE3ejbTwucKQ5KHWwA/k6c5jynXtMS6EEEIIIYQQQgghhBBCCCGEEELcaujFuBBCCCGEEEIIIYQQQgghhBBCiF2NXowLIYQQQgghhBBCCCGEEEIIIYTY1ejFuBBCCCGEEEIIIYQQQgghhBBCiF2N+0ZX4FrYtm1s2xkcW/gO33aw+o5TAa1WHwNtfGK2cJxluHG74zigZbkFWp5nWDeblAPFGCz18rPvhDwvXjEnd0jTlJ2IUrbDcy3yXBmWi+MYtH7YB63b6ZBbvLNw7AfYp47rgeZ5qLF+GB5X7g7bepTwKr7xA//qsUNGUcpGGxmnrN/LmuuhHYR97N+N9XXQDuzfA5rnBaDt2z8L2twcaqdPXwBtdXUNNKs0TjPy7H3yDMurS6BduHARtMXFZdBabRzLSYrtW6nWQTt46ChoDz70JtBOnryjcDw+Po7Xr9RAI2ZgNtdXQXv2qaev/rvf7+FJI0wl8EzFH/iA5kQDyszsw7Hn+jgOanUce2vb3cKxXcX4E7c3UGuhVp2dAC0h4TgIcKx4Hvpb142IRjodhiOWSWLiE1jwSrHdTIb1aLdRM+hSTCfEurQ6JLb0MI7017YLx2Ef43ng4UMcnp8CbbPfBW2rU7xnkpDYOMJ0eqHJrEGjRwmJBSTHislzeiQepOXmtnAssyHEKPvua0Iu6LpYt5nZSdD2zWK/79u3v3B81513QJlnn30GtCeHfOa3+Tf/+ldBO7h/H2jv/N53gHbXbcdBy4mdOqSZ7BzHfb9fHLsWMWbW4mkcghbFxJazIT9A7j/K7JmZNUFlEDO6vTaUiVPMP1mubWwyX0jKx8QvBWgraYKxK43RPjtt9MGVCl6vXsE8ISd1iQ36he1+sc/b7W0oU63heetb2EY2sW0rwHZzbTx3axvHXrVWBc1YOA9ISKzqdYvXc0h8cB3iE4kJpAmWc4bmFhnJs2812HyYzVVZH7Onz7LimGF+n92TzbczMgfNMrxe+Z4vXw9rF4bYybUa5tW9XjFHrvs+lDl48CBoszMz5J7obxMIrDwWsGdgfcPm9OxcvBaZY5JrJcynJGh76VA/pOQZR5nEpCYxg2e3ST6UxDjOLi3hPLLewD46sHe6cNzqYNsfXsD4sJ/MbW47Pg/awgyWm9tzCMsd3gtafYrMRyo45p2s2OcWsUXbRt/tBmhjMRlnLlvrIeM4TbButod9Y+XYh/UZHLdJUKzz4aNYZqbxBF7fw5gcRXhuqxTPmT2NMpYp5pJsllSzcH710Biu69xB2j8Mi+1/sYP9W3dxHWouPwva+upzoKVN9Mtv/4EPguY2cF0rjfFpbZpZlyE50Y5K7XxOdaPZ4QyNnDdKT3HzyHJjrHz4mMRm4g+dGr67sKsLeG5pvT3tb0KZfhftopVgLr8aotYg65gNdKOm4aC/qjtooxUHn3VhrPisR/fM4bWqGB+8Js7v3QbGR4eMMzYe2fIDS5FYvlqvY3ws+3CWb7E8l11/x2sjtxB7js0ZrzaIj/UmtuHsEWyfOML1a7uBa/D3PnK4cPyVLz4JZbYTvP6b33ovaFaEceo9b70btPXnsY9//99+CrTvf8t7QTt5HGOLVVrsycmagSFzj/7yFdB6XVzjYPmJy3IWg+2bWjhv2XcE17oeIuP+m0+dKRy3I+yH5SuboJHuMvuPT4DWmB74izTOzKnHFvFEwq33VlAIIYQQQgghhBBCCCGEEEIIIYS4DvRiXAghhBBCCCGEEEIIIYQQQgghxK5GL8aFEEIIIYQQQgghhBBCCCGEEELsavRiXAghhBBCCCGEEEIIIYQQQgghxK6G7OA+Gvi+b4KhzeRt8g7fcXAH9oxcK00TFO3iZva1agOvb+E9yf7x9K6sHgzLskBjz2UTLTfFyuQZqRxe3jg2dnuWYY2TBNuNXI6q7BnyHO8RxzFoUVTUUlKPhPRpuT2MMSbLUyw39Kz2LfjRkKgfmuFHHZ8YhzLdzS3Qwl4XNMfGfkrTYjvWagGU6fdD1ELUHBevXyfXCyPsp2eePwfa6RdPgxZHEWiTE2OF4xdfxGudPXcBtOXVddBy4nvq9SZox46fBO3Nb34zaA8+eA9oCwszoNXIPcruYmNtA8pcuHAZtG898TRoz586Bdre+YWr/w5Jf44yew7Om2plEDPa/R6UmZ6dBO1ATnyJXQWt0yuO0U5CfKbB8b6+dgW0hbEp0Gyf9LepgZbGxC+TgJNFKLquXzhOEuzjMOxg3Ry0AdshvjtEzfPIM6Ro7+vrbdA6XfRjQY42urW2WjhOIoxJ9eoYaDaJmWPVVdCivPhcscH6jzJxHBk3Gor7KY4Nn/hqlu5YDuYPMDpI7sRyDJbX5TzJImA5z8XrjY1hvzs2jg+rlFNMjWNO+Ohb0J/ffdedoJ07dxa0r3zlK6C98Bz65Xvuxuv1SLwNiWaTXCwu+YtyfmUMz50MyZ36xKdGQ/G33+/jdUaYbn/bpEPTIJIOmYDYRRhjztEL8dmzUhO6LuY+FrGBJGF5MfH7xM5suwKaMR6emxPbI2dWq0X/PTWF7RHF+OxJjFcLAnz+chsZY0y/h9fLydhmuannY+zudjH3rVSL7ZRl2L62jc+QBCTuk/mIP3T5+NZKpYwxxqRZbtIhn22x2R+R2DTUJv7Wdd1SGWYHZP5Grp+RmzLbKN/TGL4+wO7Lzi0/1eXLmHvfdvR20Canp0Hb2NgEjc37A5KLMdjaQkLyLpuUs0uOkLUlsxd2T+bfXG/Qli7JZ0eaPHv5779RCdC3Xr6yAloStUB70wN3g7a1tl04Xt9cgjJHjuL84cgdB0HbewDH2XgD/WOtOQtapTkBmu2jv/UrGG/KuV6SYt6QMfsMsG4mQ7tzbGxzxyLrgcwXWSg6AdpFUK2DVkmK505PY27ZHMc2itl0gdhKWMqv2BrcrQRbL8zJ+uO2h+P062uYY631i227GWNb31k7D9rW6T/D65/GTjn28I+BZlVwbSYic3+PZk83H+aDdz6nunH3fS33fKOe4fXCsfNCLpllmN9bBseyU1qvMcYYx0MfWY6xeX0CyoQV1Lo9XF/pRdug9VPUuvEmaO0M8+yxBO24XsPnOrRvoXA8P4nzb9cnfp/4aa+KbckWyfiQevX5FdN8v/isbB2Ewcb7TrRbzU4Wjs6boD5oo2oDx8ZsTubgfWzrpa2XQDu2cEfh+PgDC1Dm3CUct1Ub44OX4hplwyH5RBsnfI9//TnQ/ugMrrVeuf8QaPO3FetskfEdpXitXkbe0+SYi8VkbcEmOVae4PN7Lpazq9hfzUnMlbKSrc3OY6z1x/D9Vuxjfe995Dho+VDV4n5iTv3HRSjDuAVfCwohhBBCCCGEEEIIIYQQQgghhBA7Ry/GhRBCCCGEEEIIIYQQQgghhBBC7Gr0YlwIIYQQQgghhBBCCCGEEEIIIcSuRi/GhRBCCCGEEEIIIYQQQgghhBBC7GrcN7oC1yIIAhMEQxvMZ6SQbe1EMo6L7//zLC+eZ2MZ18XmsXZazsKKpDk+RJKQTe8NnsuuVya3ctAc2wEtM1jOtrBcTupLTjUWafSUPFdOzk1TvEf5UR0X60aayKRZupNiJh16iJ2066gxtzBnKpXK1eOt7TaUeenUGdDCHraP7wWgxXG3cHz02EEo0253QcvIeNnY2MRzW6g1xmdIPUAy8/NzoLXaHdBW1taK9SBtVKs3QLv3/vtBu+/ee0F74P77QDt4aB9o9VoNNDbiOp0+aOfPnAXt7LkLhePTp7HM5uYmaIePHgbtr/7VvwLa7OT0oE7ttvnEP/95rOyIUpscN7XqYDw3vXkos9XCsbK5sgbaZL0O2nx9onDcz9B2Ti1dxIpV8J6zCwugNQIftKSHNpURt1ybqoJmWx5ocVT0yyR0GePjCOVxCv1JmmG57vYWaFGE/oOVM+kmnhteAW156XLheM882uKeOdSeenoZtPnJCdAyU/QfYYTPOcqkcWpSd1Bnn3S8T/Iklu/kGckprGJ75DmODeb4ynnYy+eyJANPdhysW6WCNuQ4WF/2iVCrVOfysTHGxHEEWjXAtnzT/RgzHn7zQ6D1wxC0Xg/9RRzjeIsirEsUYdCMS4E0TrBMRpxKmrJrYT2SoRwuJvncKJNlPZMNjeeE+C/PwzFl07kHjirHK449Ns+IIoz9HhlTbBynxMxCNi6I//Yq5BnsV56jjI9jTrO53QOtVsP8ynMroOUZ1sP18Fm7/W3Qshzbrt/HZ2BztJmZ8cJxFKEtGovEPeI8WE4XeIN2CvuJMeYFUmp0CePU2PHQACMPyeZPFinokHIZ+Ar0+xaxKQabq9L5K7lHQnxau4M+eG11BbTO9mbheHtrE8qcOnsWtMUW2ktscMxXiB9oVjGv833MRT0PyzGN9lepLg5b4wCFx26LrC0Mh5s8v7Xm4K5tGXfI/0chzi2Xl86CdmD/FGjx1jpoHbvo06ansH0O3jYN2pFje0CrNZugVetYj6A6BlqljlpO7NF2Md6YUi6Z4HA3ucMW63B8Og7GX5bBsXkRqy9dN3RJjHfRpnyv+Kz1JpYJKmgD7U30J4GDc8xepziWUhbgbyHY8lqYoj94anUSNJd1qF30Q6mH896vXkA//Wf/4TxoSfMEaN93/HtBY/7LYfMbxg10bTtdq+Q+GM+l86zXQDmO5GyxmMX4nV5/6BlutXXb1KmYzBmM1dxGv2GRPJi9C3CIT7Pskt90yPw+wHtGXYwPUR991VbYAq0XY9zrZejoPQvXdcbn0aZm54o+wHHJWPFJLuGj787JWgbL2xnMLnZqKzQfLmmvZeyyapS1G2zWrztPn3rKuNVBv7ou5uPT09hmYxO4PnH+3FnQ6qV56L7DOH/td9CfN0icsrpYNz9GW5s7eBi0Qz92P2hnz+H6Y9RGW1te3igc22OYO4Qp2lmlju02MYexNiHvzoIKxlaH5ERTE5hPVsh84faT2J4n732kKHj4XLXpCdBCknNmFeyb7aE1g34nNL9tvgBlGPrGuBBCCCGEEEIIIYQQQgghhBBCiF2NXowLIYQQQgghhBBCCCGEEEIIIYTY1ejFuBBCCCGEEEIIIYQQQgghhBBCiF2NXowLIYQQQgghhBBCCCGEEEIIIYTY1eAO6SNCFMfGjeOrx1mWQRnbxs3cHQff9VsWam5pc3jP88j12bVwM3vHIfWwsZzB/e1NSq5HzjS+h10Fz5WTEwkWqVuWs5NRS1N8CNYmbuDj1cgtXBfbzpSul5MT46GxMdAi0NKM1fcV7j/iXDh33vj+oH3Pn7sEZS5fRI10k2GjzSmN+7mFSShz5fIV0JIE25rZbbU6DlqcJKD1um3Qjh+/HbTu2YugTc/NFo7vuesuKPPoo28B7d67T4I2PtYAjQ3mkIy/teVV0C5cwr45c+Y8nru+BZrrF/3UiTvugDJ33HkCtHqzClrcD0HrdbpX/51k+P9HmerYlKlWK1ePQ3QR5sVzZ0Hrt3CMHh5D/zU7XmzDvjsFZZJKDbTtaAO07haOgWYVzw1qM6B1en28x+YaaK6Lz2CXYoYTo816DsbCJMJ7pjGOjyqJozmxizheB63mdEGzrR5oK4sroK2tFrX73n4/lAl7LdDGmk3QcpIVbXY7xXo51JmOLNvtlomGfGyDjLWc5EkstrO8oJwrMb/PNBbbmUaTB5L/sTyuF+L4qzoBXi4vPT+xA5YtxBFef2NrG7SJiQnQpqbQh7B80rbRmaUptmerhTGz3y/aLuvTPMdrJSQmM204J2T54Sjj+JZx/EF7wBgwxjgOOgTHQ99K3K0xptiucYJjpTFWBy3PcLxHZJwFFt7UIWM7znD89GP0h0mK5cr2mJKcsd5AWyFTFuPY+FwWmWd5VXyuSqMCWtjD+tZqY3jjnNlUsS6+j2XYXMkYtIE8Y3bRHfo3/v9RJ0lTkwzZM48FBFIuIePZlOIB8/vMF3LYXBXbPCPzQTaXTBLUwgjznXqjmD+4LtpeN0bf2ul0QEtIdEkitI0IUyJTrWJ+X6mgvQzPG7+NS/xbxStej621uCTWsjFikzUZM2TzzNeNNEn08t9/o9vC3LtRJe01jv2ReZiHOabov2pkLnLowF7QJsemQbM8nGtXKniuH+D48Vzs34wtYhm0C8stXs/18Pq5hbZiDJazLGy3clw1xhjLxrrlFo7tjJxrs7kSsYtyrKrVsI327MWc7vyly6BFEalvyRflt1g+tSMskgM4ZJ0uI/6lFMdzlq80F0A7+M6/DdrYFNqB55I+TzHvYvEmZ3k1Xc3dpZQflbr1nc0ndxtxXhz2jkXymhzbJiZrqrZH8qTS2GMx13HQxwU++tY8xTWRpI1xKmyj/7ZSTE6qHj7XgVm07VppbbPXw2vlIcaaJrFZw9qX5FcW+W4oXZMg+T3zY8bGupR9APMJ9g79BMuvys/AnmmUeeGFp409NJTuPozvG5o+jrVKHfszncJnf2r9+cLxbIes7W7ieY0VHPN+5Z2gPfCO+0F76clToE3vx7570/gsaB0y/tqldZdOhOtLZy98E7TMxvUgJ8B2a4d4z/UIx3y7g2vZj516HjS3jzGzGaCvaa0XbXx1Bd95GLKm7k3jGHlpFXOs9aF12zTCvO9a3FoWJIQQQgghhBBCCCGEEEIIIYQQQlwnejEuhBBCCCGEEEIIIYQQQgghhBBiV6MX40IIIYQQQgghhBBCCCGEEEIIIXY1I7vHeNjvFfatdMjeDV4F96nwyb6QbN9Jt7QBHtvLzCL7Pux0n2a2NZ1P9tJz2J6s5A58r7VX3pciJ1dzybUSspcR3a+dXY/ty0P2K2V7trP7xnFSKrOz/fnYfoB0G5ChcjbdQ3C0efKJp4wz1OZtsp8p3eOV7Q9G2scp9ee3vvUklOl0cD9gNhzDCPeAao7hHpDrW7jfpWWjbXR7uHfFnXfgntoPPXR/4fhND9wHZej+wmQ8brewbquruEfy+fO4x8XyCu4/t7WN+2icevEl0H7gB38AtLe97dHCseuiLff6uDdPaxvHSLeN+xx2u4Nzu2QfxFEmsQKTDO1b9zTZ+2RrG9vGc3EPk4zElu2oOA7OLeG4mD6KY3HrIu6JfeHsOdCaTdybjmwnZQKyVwtxtyYme3s36kXbq/tsHze8aUBiRkj2bNlu47Masl9nYHBs1TwsV947yxhjTq2hTe2ZnSgcH9yPe9CwPXUnxxqgdUMcI+WtbMn2OyNNFKfGHtrHL/LQz4Ux2TuX7XtG9mQt5wA01pP9qel+4jskIfuvtUhcssk97Bz33gxK+U6twvZeRXthny9Nc9wLrdfrg8byuiaJSyxQsz2n+X65xXbf6V7BLId4pX3H2f8fZeI8KYyPICD7fefY50mK/ZvbqNklxxyTfYrL484Yvj+yTfYOTxPsozRHP0e2CDYVsq9qluM9nKD4/GGf7euNe8MG5KZs7CVkf/uM7EUeVNFXB2Rfd1a/lOx96JR8VKOBdlcl98zInsjdLuZ0vf6gH/jevKON4zgF/8R8Nd3zkGqvvLd8SnJvNnfn4PU9ssk9CUt0rsp83/z8PJYr1TkmiUFOrp+SsJeScZUT28jJHsls73DWdiwu22Q/Tqe0ly/dO5xdi2gZGTfp8HORZxxp8p4x+eA5Axd9y3gDx17VwpzUn0J/6/lF/zXn4Z7JjUnMX1yyr3nKQr1P7ILs4Zla+Fwsr/GsCdAcU/SlnoN2wbaWZ9W12T6wdM0Ny/k+xvM0JjmijTHDdjC/8tzi89d9PO/+EwdBu3j6NGhXyH6aTrl981svbhQhnUzmFHmGc9qEjYZSXmBZ2P4e2Tf5xJvejZciA9DKSF5H/C1bV2XzgB1vs70juHUgO51TMb+708qxWFUWXst+4q8UE26tdVvHyoxlDZ7Jzsk8KSd9SeZTUR/nkUHJ99t0r2syD/ZIju5i27oWmRfZ2EckFJrJKsaWBbI/dK2UI+d99NO9dVx3devoR8crE6BZZJ7FcpOM+FyWv9rOzt77lFPJnOV55LwdTtPBPzF/Ncr8hfe81wT1Qa760OF7oUz//LOgne5/DbRD+3Gsha2iT9/j4TzvvT+E8WG2gnNat4a5mG/mQJtZwDn4ylncdzwjr2Dn9x4FrVYKc6cu4Bro//l//ypozWmQzOFjh0E7ewn3Dl9c3wStMY1tF7Xx3UK1hT6qTuyvvVIcq1Wf5LAOtlHrHJaLKtj39eYgT04s7TEuhBBCCCGEEEIIIYQQQgghhBBCGGP0YlwIIYQQQgghhBBCCCGEEEIIIcQuRy/GhRBCCCGEEEIIIYQQQgghhBBC7Gr0YlwIIYQQQgghhBBCCCGEEEIIIcSuBnc1HxFs2zK2bV09DsjG6q6LG7BbQ+d8G4eUs63iZwKwBL9WlrEN3HO8p41Na9n4OQQnx3OTnNwjf+W6WFiEPgMr5zikXI7PkDvksxTkghbVyH1Jy6dpWji2M9KnrB456UXSlvZQ38TeyJrANak1x4zrDuod9UMo0w9RMylKbDRnSXFMRkkCZRzXx3rVmnhLMoZWt9qgLSzsBW3P/B7Qjh49CNo9J+8AbXZ2qnCcZ2hnS1eWQLuyuALaJVKu0+2CVqlWQdt74ABod02cBG27tQ3avr3YJp5XHOPr6+tYt14PtH6/D1qeMtuw6b9vBR5/+jkTBINxeeXKZSgzOz4O2vZaC7T1PvbHmF3s31pzDsrceeI20J5/8jHQLl68CJpx0FZm5hZAm5xCrUJsL7c90DaSTuG452AZk8ZYNRvtJ+yhHcdRB7Q8Q8cTM58VRaBdunQJNDbm3/rwvYXj97/nIShz6qlnQeteWQXNcbFNqtXpwrHtYBuNMmmWm2TIB4Yx+vQwwmeKEtQ8D9unHO8j0pc5yXWYxnIHBivX7+N9swT9XBzhmKwFxZg2PV6BMs06aiwWGtKWucH6dkkcYW3H8s52G22tT+wqTbGv8frYHkxjXTM8Hsq526iTZpFJskFMzUMcj2lKtAzbOTPYb5ZVjNcO8cntziZoFTLfYeOHVM04NpnvMI3k/J5HxnLpvnGMz8kyyZzMWRwS41wL84yIjPc4xrHl+zXQbDLnSRLSdiUfGJKcOSU5kkfmDH4F2y21BnXLrVe2wVHDcRzjOEO2QXw1yxGp/yb9mSXF/mRxhWkJmY8w/+j7LE5h1RjD86tvw541t4rlMjJe2D3JEGXLCCYmPjjdYcxkz0BciHHZWkVafFbWvkxj9XDYGsTQdzJc4hNGmqxnzFD7OAb9RqWCz1Sv4Hwh6m2CNrOn6KvdCub2YYrXd3ycf7IlPsvGfhu282/Ti3DOmGWoOf4UaGUf7JN5Rm4Rf0IWKSxmGGwgE822yBoWiTd5jve1DT6rHa4VjqcD7PuT+7Eflk/MgPasizniuVLXJLdYPmVy++W/q8dsUZJJbCWK+Y3y/XDckqUeYxKyHkbHC2r5Dr8/RoYzfVZW7NVDrsYqQuu2s3LMp9M2LsWlnFxsh+GXV6QwRth4GV3y1BSTdYflUqy9yNwjxrFc9iSOwbyYJR0WsZ/ynMUYYywyx3VqddBcElsCF9fXghznEG5c9LcByUt67U3QVs+fBi3PMd6Mz+O6mXHRtm2Xvbtg7xtQYv6u3NWsT3OL5FesHHkXkpvkOx6POu956F2mPjZYV8kvTUKZdoRj/kAN11DHxnHNN3OKY/edE2+FMv6pC6A98+w3QVvtoV0dOnIvaLMNXGd2ST7V28L3DRkZ440jxwrHdg3X+L/3/W8GbaOHzzU2jWtY+8emQZtNMWdJbLI2HGG+M5OgD/HIkml/o9gmFjnPuFjf1Raumy22cD368uXBu5s03nnkvbXefAghhBBCCCGEEEIIIYQQQgghhBDXiV6MCyGEEEIIIYQQQgghhBBCCCGE2NXoxbgQQgghhBBCCCGEEEIIIYQQQohdjV6MCyGEEEIIIYQQQgghhBBCCCGE2NW4b3QFroXtusZ2B9XLjQVl0oxspo7FTD+KQMvzrHDsOdgUtoWfG0jSBG9J7hlkPmiOjddj28Fb7ILswfL8FYvY5Fo2qYfJULJcPDeOsWCapuRkcouU3IQULFfZcbC+WY7Xym28lmdjv2b5oJzrjqwJXJO3PvqoCSqVq8cXzp2HMmycZhm2mUXGglNqE9ZGgY/je2pyakflarUqaHvm50DbuzAP2thYHbQw7IP20otnC8eXLi9CmeWVVdBsF+u7sLAA2okTt4O2b98e0CYmJ0DrdbugfZrY6ekzZ0GbmpksHG9vt6AM7Wdy/Yx4nziOB/9OcAyNMpsbm8b3vavHLhnbUQfbfmp8DLTxCRwHRw8fKRzPHbofyqy31kFbmG2CNvO9bwHtuVMvgfbCM0+BNjWzBdrs3AxotWYFtM2tjcJxrx1CmbiPWtTvgTZJ2q1RR9tOiN9fW8dxe/rcZdCWl9BuG1Xs1z37DhaOx6fQdyyufgm0cLsNmutju01OBoXjfog5xSjTjyKTD+UzOcmdEpIEBEEAmu9jH7fbxXbcqQ9iWl7Oa65RjuUx7NyYjL/NNvqBXmncuzbL9fBatRrGJIvkk4acy3Knfh/jWTlffbkcsd0Yx2VS8uM77RuWFDqOA9pwfnCr5VNRlBhjDcaMZWF/pCm2aZrFoJHpgrGd4njMSDM7rgfaVhv9kueiX+r1sR6ke43vYDyr+GjbNsu1s+L4qRD/a3JsN3atNMO2jDO0szjDsZ3lWM62cDzGMdYlitCmKkHRbtn8pNPBWJsk2ObsWWvVxtBJbKY32gSua4Khscl8lUXmXBabqxP/VY4tzHcwf+6TcUvdF8lvme+j82F2NdKFWVZsE3YtVjX2XC7xrTbRkh3Oty22PEIayiHfj8hL5ehzsTkFaV+Wa1hD9sJjz+hi56mxh/wdq71D5pEmJ+sTlWnQ/EpxHp2RZbo4JPbkYj6eGhKvyfDJ+uiXQxKDjIW+r2JQi/PiuW4F62HZqLHgxeIqhQ14AhvLzKTI8pdxwmI88DOcx9w2j8+wfQxjd7xJ1mi6xbaMElKxWx7mmNhzUgdWKkHmD2TA8KyU+K+c5TZknO4QOiJfbSpgkfUYMqfgi8pEpOXYGi2BxHO8x04flN2TaUPX22k9R4Q0SY1lD8Z4lmN+ayckNyFaTvxXFpXmrsx3kVyCXStneQhZRzce+jQ2D2jHOL9Z6eLz720Un7ViYUyyHZy3J9u4jrtyDseeTWLX+ByuO1sWxu6UJlgkprFcppTr8Zk280VsHQTPLU9b6GuWEcbO14ydD3L6mIyX3kYHtPp8A7QsQ82kxX6aI+tXz77wddDYutHSIsnhUqxvY/9R0M586wnQem3MH7Z6OFc9+cEfKhyf9behzN2PHgDt3AraS31yErS7Z/Fclnf2Y+yHKhmUtTa2XZXYldUvjvFwG/1CaxvXnte3iR8gc/A/+pMnr/47jjLznLkCZRj6xrgQQgghhBBCCCGEEEIIIYQQQohdjV6MCyGEEEIIIYQQQgghhBBCCCGE2NXoxbgQQgghhBBCCCGEEEIIIYQQQohdzXW9GP/EJz5h7r33XjM2NmbGxsbMo48+av7gD/7g6v/v9/vmQx/6kJmenjaNRsN88IMfNEtLSze80kKMGrINIRDZhRAc2YYQiOxCCI5sQwhEdiEER7YhBCK7EIIj2xDfzeDu6t+B/fv3m5/92Z81x48fN3mem3/9r/+1+dEf/VHzzW9+09x1113mp3/6p83v//7vm9/6rd8y4+Pj5sMf/rD5sR/7MfPFL37xuis2NTlmavXG1eM0wc3cbQvf67suPlKcJKAlaXHDeM/3SBk8L01S0CyDm8+ze6Y2+RxCnoPkuA5otoVa+bYJqVuaopbhLWk9LFJfdioH2yQj9zA59qvrFJ81I2Uy8ly0FqzNh9tp5w/0HbmZtvGOd3yPqdfrV48vXboMZSwL259pnof2UrYFZlM+sZfA80FzbXJPcj3Wn5sbG6A9feE8aJcuY0De2OoUjmdmpqHMXXffA9rBgwdAm5+dAc0P8FmTJAat3+2BtrXdAi2MItA2NjdBS7OSLRAfmBM7Y34gTbHcsKkRs7tubqZdJEli7KHxNjk2DmXcPvbRwvQUaI06jtva2GTxuFaDMi+dewG0fQf2g7a4tA7a9jaOlbNn0bbPXdwGbWEBx/e+/ThuHafYqetrW1Cm3+2DZshYCTtd0Cq1KmibLRzv5y6ugLbdQRsI/AC03MEx3w6L9Vtax3seuu1O0C5ET4FmZ9j3rfVim2fEXq+Xm2kbqbFMOuT/t7vYd8bBHMOrYH/2Q3z2si+xmF9iwZblBCRO7VRjl2P5jpXjuWFSLNgJ0VdUYvSjFXKtSqWC9Uiw3WhKlLN8lfhqkmOxfKecd6WkQapVrG9C6su6wR3KV12Su14vNzdmGGMPVTklOX+S4jgwBsfB/5+9+w6P6yrQP35UR703W9VF7kXulnsclyROSC8QICFAIJSFzTZg2QV2F0L5sUsPLISEBNK70xP33nu3bFmS1XufopnfH7uRfPWe7DpssEeT7+d5/Dy6r49m7tx7T72SblS0jgmio531xzaOD1jamz6vZV5gGcpGROr3hlvOr2UYZp2jNDc1SRaf4Bzr2YbeYWE6HuyztJF9ljGS16/7EQjXN7EN5ft8+lm9ljpqm6P1RTr3xWc5Hr1u7QvDLO1YVKTl8/e6B752X9x85X9zKetGeFiYCb+gwtuGg2GWa83WztvmEBGD+hvbuNWWRVgqgnW+Yznnli7Oyva+tvWGwZ813NYnWV7fOh4fPLY39uMWZXkPv+V7bZ8hzNrf6PeGhf3vS0PhlgppOw+2dZQL99c2v3+/LmW9CPR5TaBvoG2zzfv8ljFBRJT2sWFRekH6B/X1fR7b+dFjGm47Z5bros+tY2NPd6dkne21+r7h+r6x4UmSRQy60Lwe/Zxh4dpfhofHa2bp4wLWemY5TpLYr1u/pQ+2XcuuQfvi79J5nCtC51RFObofDTl6TOKSnXO2XrfPPL7+lH7z+3BJ64bxmoC5oD5c5FDeWOqLvaAzCwRsbZDlWrOMMaxrJ7bvtfZ8F8f6Ef5MActyva09t3+zraBt52z9iB476ykc1NOF2fpzS29oO4fGkl3YTwX8//c/dnsp60VYIOA4HuG2I2hdq3dLFhZu6TMGHetey+uHR+gY1XoUtZgJj7DVT0vdi9Q2vcmtazinOnT9ITPJ+blckXrdxYTp8Ygy2nd527Vc7Skt1+culiwtt0Cy8Bhd67Pdz/FZxsNhAed5tY0j+yzXc0eXrgf29GjW2+uco3Ra1tver0tZN2o9m02cZ6Bti47SNfioDL0W6jt13PXWi+clC7Q6j/+iayZJmcRky31Dt57L8ZMnSDZ9zg2S5Q3X66po2EjJKk/p+uOBI4cki3A5P39zb5WU6erUuaorw3IvILZLskavHreIcK234ZGWe3G2GU6yjnVNpF7jEV5nuxKTpeO/pHDL2ral5YqK0e8dWzqm/+vuDrd5+3e/0f2yeF83xq+77jrH9ne/+13z4IMPmu3bt5u8vDzz0EMPmccff9wsXbrUGGPMww8/bMaPH2+2b99u5s6d+37eChhSqBuAol4AdtQNQFEvADvqBqCoF4AddQNQ1AvAjrqBD7M/+8eu+vr6zJNPPmm6urpMaWmp2bNnj/F6vWbZsmX9ZcaNG2cKCgrMtm3b3vN13G63aW9vd/wDhjLqBqCoF4AddQNQ1AvAjroBKOoFYEfdABT1ArCjbuDD5n3fGD906JBJSEgwLpfLfP7znzcvvPCCmTBhgqmtrTXR0dEmJSXFUT47O9vU1uqfZHrXAw88YJKTk/v/5efrn1EAhgLqBqCoF4AddQNQ1AvAjroBKOoFYEfdABT1ArCjbuDD6n3fGB87dqzZv3+/2bFjh7nvvvvMXXfdZY4ePfpn78DXv/5109bW1v+vsrLyz34t4HKibgCKegHYUTcARb0A7KgbgKJeAHbUDUBRLwA76gY+rN7XM8aNMSY6OtqMHj3aGGPMjBkzzK5du8xPf/pTc/vttxuPx2NaW1sdP0lSV1dncnJy3vP1XC6Xcbn0Ie+ZmWkmISGxfzsqQu/hh5swycLCNPMH9IHx4WHO14uOipIyXp9bst5ej2Qer8/ynhKZQEDDvr4+Laffaizfarxe5/f6A7ofthfze/V4+P0Xl4VZjnm45ZiHR1ouLct56OuzZD6vY9vn12Pk9eln7bNkYRG6bxcelD6/5Zj9mS5V3cjJzHDUjbiYGCnTZvkzJQHLRRlhOU+Dz2dERIR+X7jWR9+g82aMMc0tuh+tLa2SVVfrT5qdr6mRLGC5/oqLR0s2e06pYzt3WLaUSYiPt7y+8luOW0dHt2RtHa2Sdfb0WF5R2Y5xVHS0ZO5B7U+P5fUDHj0P4T1d+qY+S/2+oA6F9ehn/HNcqnrR5/OZvvCB68MVqW16Woqe82hLE5GWliJZ36CfIzt86JCUaWprkexMZZVk27fvk6zyfJO+Z8DSLwW0D2pqr5DsXKXWn7jYQddUmF4DEeGW+m4ZKvi9vZJ1ebUet3d3ShZu+Zm88DB9X6+lb21p131u73Je8+mZuVImJ3O4ZH1tdZZM26z2Fud7RoTZWor371LVjd4+t/H7Bi50r0/71AjLR/J06Rioz9IeBgKDK5Gey4DR8xYwuh/GMp6wZX2WQVEgcJE/6xluGTsO7gujYqWMN6D1wNKMmoTB9cwYYyx9bU+vpX+wnBu/ZZzkt/SFAUu9Cgwa6/YF9PV7LX2GbfwXG2U5bu/x9f/FpaoX0TFRJjpm4Fr1+bS9Dbgt7aFGJjxcj1dHh7Pt81naM607xqQl62eJ8Gu5uJgky75ZrlHL2Mzr1X4kIlrHkh6vsz3s7rRcA5bPbsL12vZ4tM+IjNRrNtqln8Ht1mMXEW6ZG+jHMhHh+noxsc62LdJybceaBH0xv+5vn9HP1e0eOPcet6Wd+zNdqroRFhZuwi5oOyIt7ZdtTmtrBWzj275B8zrbnMI2n7f2vLb9sDZG/3P7dcEbSzR4f43RzxVm+Qy2+bHdxa1nhNvew9YgWY+JZV8s7U/YRfzOhPWz2tYMLOX6LlgLsLVXf45LNs/wuE2f54LjbTmkcYnaLodb2mrTp+eod9C8y2+0QQuztuc6Xwsz2mZ2dbRJ5u7Wcr3uRskiIi3XVLLOrcOMc+wUZhk32dbv/F7LeMgyQQsPu8hrxlr3LHNt23zEtuboco4PvD3aJnh69fgmJet4sGT2FMliiuc7tjs6e8zXHtwu5d6vS1U33N2Rxlxwrm1jSNv80tYG2Qxu+2zj3XBLuxdmeX3LkNr02uYUF91+K9s08c99Nb/lurW9mr1PtrAdc8t6gG0NNWBZax48H7GuvNqOr6WcpVl0rF+6ez+Y8dQl6zP6fM61Nctah62ftF0/1vseg46iz7Lm4ovW/YqwtHG2E+cPWPbXUi/CLNdoa2SyZGVu7auGtTrb/vQIy3grznIvx9KeRFmuT0+7rmXUnNK+1WeZQGQUFUsWFpsomfW+z6DtPkub2G25r9TZqXMK2/0S/6B54eDtP9elqhtNMftNV8zAPmfk6nppmFvHTsd3aj+794heV/PGFDq2Y+J0H1ot89LojALJkvKmS5ZeNEMyY/TayJwYJ1mYZY3J7dIsLNmZdbVb1or9OiaMiNQ6FOmyNSqW9Tpb/bOs1/ks63puy7y83TJHDgt37osrTNdfwi3z7XBLO+PvsPXdA+V6wvT4vJc/+xnj/Tvj9xu3221mzJhhoqKizJo1a/r/78SJE6aiosKUlpb+D68AhCbqBqCoF4AddQNQ1AvAjroBKOoFYEfdABT1ArCjbuDD4n39qO7Xv/51c/XVV5uCggLT0dFhHn/8cbN+/Xrz5ptvmuTkZPPpT3/a3H///SYtLc0kJSWZL3/5y6a0tNTMnTv3L7X/QFCgbgCKegHYUTcARb0A7KgbgKJeAHbUDUBRLwA76gY+zN7XjfH6+nrzyU9+0tTU1Jjk5GQzZcoU8+abb5rly5cbY4z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",
      "text/plain": [
       "<Figure size 2000x900 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictions = autoencoder.predict(noisy_test_data)\n",
    "display(test_images, noisy_test_data, predictions)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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