TrainTest.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "from re import search\n",
    "import shutil\n",
    "import natsort\n",
    "from PIL import Image\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm import tqdm\n",
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "train=pd.read_csv(r\"/Users/harithatamvada/soil-classification/train2.csv\")\n",
    "#test=pd.read_csv(r\"/Users/htamvada/plant-pathology-2020/test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Table 1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Sample</th>\n",
       "      <th>Sand</th>\n",
       "      <th>Silt</th>\n",
       "      <th>Clay</th>\n",
       "      <td>Type</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <th>0</th>\n",
       "      <th>10</th>\n",
       "      <th>90</th>\n",
       "      <td>Clay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <th>0</th>\n",
       "      <th>17</th>\n",
       "      <th>83</th>\n",
       "      <td>Clay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <th>0</th>\n",
       "      <th>5</th>\n",
       "      <th>95</th>\n",
       "      <td>Clay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <th>0</th>\n",
       "      <th>5</th>\n",
       "      <th>95</th>\n",
       "      <td>Clay</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Table 1\n",
       "Sample Sand Silt Clay    Type\n",
       "1      0    10   90      Clay\n",
       "2      0    17   83      Clay\n",
       "3      0    5    95      Clay\n",
       "4      0    5    95      Clay"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "image1=Image.open(r'/Users/harithatamvada/soil-classification/images2/train/Sample15.jpg')\n",
    "plt.imshow(image1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint,EarlyStopping\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.layers import Dense,Activation,Flatten, Conv2D, MaxPool2D, Dropout\n",
    "from keras.optimizers import Adam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "IMG_SIZE = 224"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d_3 (Conv2D)           (None, 224, 224, 32)      896       \n",
      "                                                                 \n",
      " max_pooling2d_3 (MaxPooling  (None, 112, 112, 32)     0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " conv2d_4 (Conv2D)           (None, 112, 112, 32)      9248      \n",
      "                                                                 \n",
      " max_pooling2d_4 (MaxPooling  (None, 56, 56, 32)       0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " conv2d_5 (Conv2D)           (None, 56, 56, 64)        18496     \n",
      "                                                                 \n",
      " max_pooling2d_5 (MaxPooling  (None, 28, 28, 64)       0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 28, 28, 64)        0         \n",
      "                                                                 \n",
      " flatten_1 (Flatten)         (None, 50176)             0         \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 128)               6422656   \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 2)                 258       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 6,451,554\n",
      "Trainable params: 6,451,554\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/harithatamvada/Library/Python/3.9/lib/python/site-packages/keras/optimizers/optimizer_v2/adam.py:117: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
      "  super().__init__(name, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Conv2D(32,3,padding=\"same\", activation=\"relu\", input_shape=(224,224,3)))\n",
    "model.add(MaxPool2D())\n",
    "\n",
    "model.add(Conv2D(32, 3, padding=\"same\", activation=\"relu\"))\n",
    "model.add(MaxPool2D())\n",
    "\n",
    "model.add(Conv2D(64, 3, padding=\"same\", activation=\"relu\"))\n",
    "model.add(MaxPool2D())\n",
    "model.add(Dropout(0.4))\n",
    "\n",
    "model.add(Flatten())\n",
    "model.add(Dense(128,activation=\"relu\"))\n",
    "model.add(Dense(2, activation=\"softmax\"))\n",
    "\n",
    "model.summary()\n",
    "\n",
    "opt = Adam(lr=0.000001)\n",
    "model.compile(optimizer = opt , loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True) , metrics = ['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "checkpoint=ModelCheckpoint(r'/Users/harithatamvada/soil-classification/models/apple2.h5',\n",
    "                          monitor='val_loss',\n",
    "                          mode='min',\n",
    "                          save_best_only=True,\n",
    "                          verbose=1)\n",
    "earlystop=EarlyStopping(monitor='val_loss',\n",
    "                       min_delta=0,\n",
    "                       patience=10,\n",
    "                       verbose=1,\n",
    "                       restore_best_weights=True)\n",
    "\n",
    "callbacks=[checkpoint,earlystop]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = ['Clay', 'Sand', 'Loam', 'Silt']\n",
    "data_dir = '/Users/harithatamvada/soil-classification/images2/train'\n",
    "\n",
    "train = [] \n",
    "for label in labels: \n",
    "    path = os.path.join(data_dir, label)\n",
    "    class_num = labels.index(label)\n",
    "    for img in os.listdir(path):\n",
    "        try:\n",
    "            img_arr = cv2.imread(os.path.join(path, img))[...,::-1] #convert BGR to RGB format\n",
    "            resized_arr = cv2.resize(img_arr, (IMG_SIZE, IMG_SIZE)) # Reshaping images to preferred size\n",
    "            train.append([resized_arr, class_num])\n",
    "        except Exception as e:\n",
    "            print(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "val = [] \n",
    "for label in labels: \n",
    "    path = os.path.join(data_dir, label)\n",
    "    class_num = labels.index(label)\n",
    "    for img in os.listdir(path):\n",
    "        try:\n",
    "            img_arr = cv2.imread(os.path.join(path, img))[...,::-1] #convert BGR to RGB format\n",
    "            resized_arr = cv2.resize(img_arr, (IMG_SIZE, IMG_SIZE)) # Reshaping images to preferred size\n",
    "            val.append([resized_arr, class_num])\n",
    "        except Exception as e:\n",
    "            print(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train = []\n",
    "y_train = []\n",
    "x_val = []\n",
    "y_val = []\n",
    "\n",
    "for feature, label in train:\n",
    "    x_train.append(feature)\n",
    "    y_train.append(label)\n",
    "\n",
    "for feature, label in val:\n",
    "    x_val.append(feature)\n",
    "    y_val.append(label)\n",
    "\n",
    "# Normalize the data\n",
    "x_train = np.array(x_train) / 255\n",
    "x_val = np.array(x_val) / 255\n",
    "\n",
    "x_train.reshape(-1, IMG_SIZE, IMG_SIZE, 1)\n",
    "y_train = np.array(y_train)\n",
    "\n",
    "x_val.reshape(-1, IMG_SIZE, IMG_SIZE, 1)\n",
    "y_val = np.array(y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "datagen = ImageDataGenerator(\n",
    "        featurewise_center=False,  # set input mean to 0 over the dataset\n",
    "        samplewise_center=False,  # set each sample mean to 0\n",
    "        featurewise_std_normalization=False,  # divide inputs by std of the dataset\n",
    "        samplewise_std_normalization=False,  # divide each input by its std\n",
    "        zca_whitening=False,  # apply ZCA whitening\n",
    "        rotation_range = 30,  # randomly rotate images in the range (degrees, 0 to 180)\n",
    "        zoom_range = 0.2, # Randomly zoom image \n",
    "        width_shift_range=0.1,  # randomly shift images horizontally (fraction of total width)\n",
    "        height_shift_range=0.1,  # randomly shift images vertically (fraction of total height)\n",
    "        horizontal_flip = True,  # randomly flip images\n",
    "        vertical_flip=False)  # randomly flip images\n",
    "\n",
    "\n",
    "datagen.fit(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/500\n",
      "4/4 [==============================] - 1s 152ms/step - loss: 0.3366 - accuracy: 0.2258 - val_loss: 0.3386 - val_accuracy: 0.2097\n",
      "Epoch 2/500\n",
      "4/4 [==============================] - 1s 152ms/step - loss: 0.3361 - accuracy: 0.2258 - val_loss: 0.3381 - val_accuracy: 0.2097\n",
      "Epoch 3/500\n",
      "4/4 [==============================] - 1s 143ms/step - loss: 0.3352 - accuracy: 0.2661 - val_loss: 0.3375 - val_accuracy: 0.1935\n",
      "Epoch 4/500\n",
      "4/4 [==============================] - 1s 144ms/step - loss: 0.3368 - accuracy: 0.1935 - val_loss: 0.3369 - val_accuracy: 0.1935\n",
      "Epoch 5/500\n",
      "4/4 [==============================] - 0s 136ms/step - loss: 0.3372 - accuracy: 0.1935 - val_loss: 0.3365 - val_accuracy: 0.2016\n",
      "Epoch 6/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.3391 - accuracy: 0.2016 - val_loss: 0.3360 - val_accuracy: 0.2097\n",
      "Epoch 7/500\n",
      "4/4 [==============================] - 1s 139ms/step - loss: 0.3385 - accuracy: 0.1855 - val_loss: 0.3355 - val_accuracy: 0.2419\n",
      "Epoch 8/500\n",
      "4/4 [==============================] - 1s 139ms/step - loss: 0.3348 - accuracy: 0.2177 - val_loss: 0.3350 - val_accuracy: 0.2500\n",
      "Epoch 9/500\n",
      "4/4 [==============================] - 1s 140ms/step - loss: 0.3367 - accuracy: 0.2339 - val_loss: 0.3346 - val_accuracy: 0.2500\n",
      "Epoch 10/500\n",
      "4/4 [==============================] - 1s 145ms/step - loss: 0.3347 - accuracy: 0.2177 - val_loss: 0.3342 - val_accuracy: 0.2500\n",
      "Epoch 11/500\n",
      "4/4 [==============================] - 1s 138ms/step - loss: 0.3355 - accuracy: 0.2097 - val_loss: 0.3337 - val_accuracy: 0.2500\n",
      "Epoch 12/500\n",
      "4/4 [==============================] - 1s 136ms/step - loss: 0.3352 - accuracy: 0.2500 - val_loss: 0.3333 - val_accuracy: 0.2500\n",
      "Epoch 13/500\n",
      "4/4 [==============================] - 1s 142ms/step - loss: 0.3328 - accuracy: 0.2581 - val_loss: 0.3329 - val_accuracy: 0.2500\n",
      "Epoch 14/500\n",
      "4/4 [==============================] - 1s 145ms/step - loss: 0.3329 - accuracy: 0.2500 - val_loss: 0.3326 - val_accuracy: 0.2419\n",
      "Epoch 15/500\n",
      "4/4 [==============================] - 1s 136ms/step - loss: 0.3294 - accuracy: 0.2903 - val_loss: 0.3323 - val_accuracy: 0.2339\n",
      "Epoch 16/500\n",
      "4/4 [==============================] - 1s 138ms/step - loss: 0.3331 - accuracy: 0.2823 - val_loss: 0.3319 - val_accuracy: 0.2339\n",
      "Epoch 17/500\n",
      "4/4 [==============================] - 1s 136ms/step - loss: 0.3348 - accuracy: 0.2581 - val_loss: 0.3315 - val_accuracy: 0.2419\n",
      "Epoch 18/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.3290 - accuracy: 0.2823 - val_loss: 0.3312 - val_accuracy: 0.2419\n",
      "Epoch 19/500\n",
      "4/4 [==============================] - 1s 136ms/step - loss: 0.3297 - accuracy: 0.2903 - val_loss: 0.3309 - val_accuracy: 0.2419\n",
      "Epoch 20/500\n",
      "4/4 [==============================] - 1s 146ms/step - loss: 0.3306 - accuracy: 0.2419 - val_loss: 0.3305 - val_accuracy: 0.2419\n",
      "Epoch 21/500\n",
      "4/4 [==============================] - 1s 136ms/step - loss: 0.3311 - accuracy: 0.2500 - val_loss: 0.3302 - val_accuracy: 0.2419\n",
      "Epoch 22/500\n",
      "4/4 [==============================] - 0s 136ms/step - loss: 0.3324 - accuracy: 0.2500 - val_loss: 0.3299 - val_accuracy: 0.2419\n",
      "Epoch 23/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.3328 - accuracy: 0.2177 - val_loss: 0.3296 - val_accuracy: 0.2419\n",
      "Epoch 24/500\n",
      "4/4 [==============================] - 1s 136ms/step - loss: 0.3315 - accuracy: 0.2823 - val_loss: 0.3293 - val_accuracy: 0.2500\n",
      "Epoch 25/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.3301 - accuracy: 0.2581 - val_loss: 0.3290 - val_accuracy: 0.2661\n",
      "Epoch 26/500\n",
      "4/4 [==============================] - 1s 136ms/step - loss: 0.3306 - accuracy: 0.2742 - val_loss: 0.3288 - val_accuracy: 0.2661\n",
      "Epoch 27/500\n",
      "4/4 [==============================] - 1s 137ms/step - loss: 0.3322 - accuracy: 0.2339 - val_loss: 0.3285 - val_accuracy: 0.2742\n",
      "Epoch 28/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.3298 - accuracy: 0.2823 - val_loss: 0.3282 - val_accuracy: 0.2661\n",
      "Epoch 29/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.3314 - accuracy: 0.2258 - val_loss: 0.3279 - val_accuracy: 0.2661\n",
      "Epoch 30/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.3272 - accuracy: 0.2661 - val_loss: 0.3277 - val_accuracy: 0.2661\n",
      "Epoch 31/500\n",
      "4/4 [==============================] - 0s 136ms/step - loss: 0.3264 - accuracy: 0.2984 - val_loss: 0.3274 - val_accuracy: 0.2742\n",
      "Epoch 32/500\n",
      "4/4 [==============================] - 0s 136ms/step - loss: 0.3317 - accuracy: 0.2581 - val_loss: 0.3272 - val_accuracy: 0.2823\n",
      "Epoch 33/500\n",
      "4/4 [==============================] - 1s 147ms/step - loss: 0.3259 - accuracy: 0.2500 - val_loss: 0.3270 - val_accuracy: 0.2823\n",
      "Epoch 34/500\n",
      "4/4 [==============================] - 1s 136ms/step - loss: 0.3301 - accuracy: 0.2661 - val_loss: 0.3267 - val_accuracy: 0.2823\n",
      "Epoch 35/500\n",
      "4/4 [==============================] - 0s 136ms/step - loss: 0.3262 - accuracy: 0.3226 - val_loss: 0.3265 - val_accuracy: 0.2823\n",
      "Epoch 36/500\n",
      "4/4 [==============================] - 0s 136ms/step - loss: 0.3250 - accuracy: 0.3145 - val_loss: 0.3262 - val_accuracy: 0.2823\n",
      "Epoch 37/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.3268 - accuracy: 0.3065 - val_loss: 0.3260 - val_accuracy: 0.2823\n",
      "Epoch 38/500\n",
      "4/4 [==============================] - 1s 137ms/step - loss: 0.3277 - accuracy: 0.3145 - val_loss: 0.3258 - val_accuracy: 0.2903\n",
      "Epoch 39/500\n",
      "4/4 [==============================] - 1s 137ms/step - loss: 0.3243 - accuracy: 0.3145 - val_loss: 0.3256 - val_accuracy: 0.2903\n",
      "Epoch 40/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.3316 - accuracy: 0.2258 - val_loss: 0.3254 - val_accuracy: 0.2903\n",
      "Epoch 41/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.3258 - accuracy: 0.3226 - val_loss: 0.3252 - val_accuracy: 0.2984\n",
      "Epoch 42/500\n",
      "4/4 [==============================] - 1s 135ms/step - loss: 0.3236 - accuracy: 0.3387 - val_loss: 0.3251 - val_accuracy: 0.2984\n",
      "Epoch 43/500\n",
      "4/4 [==============================] - 0s 136ms/step - loss: 0.3241 - accuracy: 0.3145 - val_loss: 0.3249 - val_accuracy: 0.3065\n",
      "Epoch 44/500\n",
      "4/4 [==============================] - 1s 136ms/step - loss: 0.3263 - accuracy: 0.2903 - val_loss: 0.3247 - val_accuracy: 0.3065\n",
      "Epoch 45/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.3248 - accuracy: 0.2984 - val_loss: 0.3246 - val_accuracy: 0.3065\n",
      "Epoch 46/500\n",
      "4/4 [==============================] - 0s 134ms/step - loss: 0.3249 - accuracy: 0.3145 - val_loss: 0.3244 - val_accuracy: 0.3145\n",
      "Epoch 47/500\n",
      "4/4 [==============================] - 1s 136ms/step - loss: 0.3242 - accuracy: 0.3145 - val_loss: 0.3242 - val_accuracy: 0.3145\n",
      "Epoch 48/500\n",
      "4/4 [==============================] - 1s 142ms/step - loss: 0.3249 - accuracy: 0.3226 - val_loss: 0.3241 - val_accuracy: 0.3226\n",
      "Epoch 49/500\n",
      "4/4 [==============================] - 0s 134ms/step - loss: 0.3273 - accuracy: 0.3145 - val_loss: 0.3239 - val_accuracy: 0.3387\n",
      "Epoch 50/500\n",
      "4/4 [==============================] - 0s 134ms/step - loss: 0.3237 - accuracy: 0.2903 - val_loss: 0.3237 - val_accuracy: 0.3387\n",
      "Epoch 51/500\n",
      "4/4 [==============================] - 0s 136ms/step - loss: 0.3234 - accuracy: 0.2984 - val_loss: 0.3235 - val_accuracy: 0.3468\n",
      "Epoch 52/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.3266 - accuracy: 0.2903 - val_loss: 0.3233 - val_accuracy: 0.3468\n",
      "Epoch 53/500\n",
      "4/4 [==============================] - 1s 136ms/step - loss: 0.3208 - accuracy: 0.3145 - val_loss: 0.3231 - val_accuracy: 0.3468\n",
      "Epoch 54/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.3258 - accuracy: 0.3065 - val_loss: 0.3229 - val_accuracy: 0.3468\n",
      "Epoch 55/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.3198 - accuracy: 0.3710 - val_loss: 0.3227 - val_accuracy: 0.3468\n",
      "Epoch 56/500\n",
      "4/4 [==============================] - 0s 134ms/step - loss: 0.3267 - accuracy: 0.3065 - val_loss: 0.3225 - val_accuracy: 0.3468\n",
      "Epoch 57/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.3246 - accuracy: 0.2984 - val_loss: 0.3222 - val_accuracy: 0.3468\n",
      "Epoch 58/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.3190 - accuracy: 0.3065 - val_loss: 0.3220 - val_accuracy: 0.3468\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 59/500\n",
      "4/4 [==============================] - 0s 136ms/step - loss: 0.3198 - accuracy: 0.3548 - val_loss: 0.3218 - val_accuracy: 0.3468\n",
      "Epoch 60/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.3201 - accuracy: 0.3387 - val_loss: 0.3216 - val_accuracy: 0.3548\n",
      "Epoch 61/500\n",
      "4/4 [==============================] - 0s 134ms/step - loss: 0.3197 - accuracy: 0.3306 - val_loss: 0.3215 - val_accuracy: 0.3629\n",
      "Epoch 62/500\n",
      "4/4 [==============================] - 0s 134ms/step - loss: 0.3191 - accuracy: 0.3710 - val_loss: 0.3212 - val_accuracy: 0.3629\n",
      "Epoch 63/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.3212 - accuracy: 0.3306 - val_loss: 0.3210 - val_accuracy: 0.3629\n",
      "Epoch 64/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.3178 - accuracy: 0.3548 - val_loss: 0.3208 - val_accuracy: 0.3629\n",
      "Epoch 65/500\n",
      "4/4 [==============================] - 0s 134ms/step - loss: 0.3211 - accuracy: 0.3065 - val_loss: 0.3206 - val_accuracy: 0.3710\n",
      "Epoch 66/500\n",
      "4/4 [==============================] - 0s 134ms/step - loss: 0.3230 - accuracy: 0.3145 - val_loss: 0.3203 - val_accuracy: 0.3790\n",
      "Epoch 67/500\n",
      "4/4 [==============================] - 0s 134ms/step - loss: 0.3207 - accuracy: 0.2984 - val_loss: 0.3201 - val_accuracy: 0.3790\n",
      "Epoch 68/500\n",
      "4/4 [==============================] - 0s 136ms/step - loss: 0.3235 - accuracy: 0.3226 - val_loss: 0.3199 - val_accuracy: 0.3710\n",
      "Epoch 69/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.3216 - accuracy: 0.3226 - val_loss: 0.3196 - val_accuracy: 0.3629\n",
      "Epoch 70/500\n",
      "4/4 [==============================] - 0s 136ms/step - loss: 0.3140 - accuracy: 0.3468 - val_loss: 0.3194 - val_accuracy: 0.3629\n",
      "Epoch 71/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.3208 - accuracy: 0.3306 - val_loss: 0.3192 - val_accuracy: 0.3629\n",
      "Epoch 72/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.3188 - accuracy: 0.3306 - val_loss: 0.3190 - val_accuracy: 0.3629\n",
      "Epoch 73/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.3213 - accuracy: 0.3306 - val_loss: 0.3188 - val_accuracy: 0.3629\n",
      "Epoch 74/500\n",
      "4/4 [==============================] - 0s 136ms/step - loss: 0.3205 - accuracy: 0.3306 - val_loss: 0.3186 - val_accuracy: 0.3629\n",
      "Epoch 75/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.3178 - accuracy: 0.3468 - val_loss: 0.3183 - val_accuracy: 0.3629\n",
      "Epoch 76/500\n",
      "4/4 [==============================] - 0s 136ms/step - loss: 0.3201 - accuracy: 0.3226 - val_loss: 0.3180 - val_accuracy: 0.3629\n",
      "Epoch 77/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.3174 - accuracy: 0.3629 - val_loss: 0.3178 - val_accuracy: 0.3629\n",
      "Epoch 78/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.3150 - accuracy: 0.3306 - val_loss: 0.3175 - val_accuracy: 0.3629\n",
      "Epoch 79/500\n",
      "4/4 [==============================] - 0s 136ms/step - loss: 0.3128 - accuracy: 0.3629 - val_loss: 0.3173 - val_accuracy: 0.3629\n",
      "Epoch 80/500\n",
      "4/4 [==============================] - 0s 136ms/step - loss: 0.3214 - accuracy: 0.3226 - val_loss: 0.3171 - val_accuracy: 0.3629\n",
      "Epoch 81/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.3179 - accuracy: 0.3468 - val_loss: 0.3168 - val_accuracy: 0.3629\n",
      "Epoch 82/500\n",
      "4/4 [==============================] - 0s 134ms/step - loss: 0.3203 - accuracy: 0.3468 - val_loss: 0.3166 - val_accuracy: 0.3629\n",
      "Epoch 83/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.3138 - accuracy: 0.3629 - val_loss: 0.3163 - val_accuracy: 0.3629\n",
      "Epoch 84/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.3195 - accuracy: 0.3548 - val_loss: 0.3161 - val_accuracy: 0.3629\n",
      "Epoch 85/500\n",
      "4/4 [==============================] - 0s 136ms/step - loss: 0.3167 - accuracy: 0.3548 - val_loss: 0.3159 - val_accuracy: 0.3710\n",
      "Epoch 86/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.3164 - accuracy: 0.3387 - val_loss: 0.3156 - val_accuracy: 0.3710\n",
      "Epoch 87/500\n",
      "4/4 [==============================] - 0s 136ms/step - loss: 0.3144 - accuracy: 0.3629 - val_loss: 0.3154 - val_accuracy: 0.3629\n",
      "Epoch 88/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.3140 - accuracy: 0.3629 - val_loss: 0.3152 - val_accuracy: 0.3710\n",
      "Epoch 89/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.3155 - accuracy: 0.3468 - val_loss: 0.3149 - val_accuracy: 0.3629\n",
      "Epoch 90/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.3148 - accuracy: 0.3468 - val_loss: 0.3147 - val_accuracy: 0.3710\n",
      "Epoch 91/500\n",
      "4/4 [==============================] - 0s 134ms/step - loss: 0.3131 - accuracy: 0.3871 - val_loss: 0.3145 - val_accuracy: 0.3710\n",
      "Epoch 92/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.3150 - accuracy: 0.3306 - val_loss: 0.3143 - val_accuracy: 0.3710\n",
      "Epoch 93/500\n",
      "4/4 [==============================] - 0s 134ms/step - loss: 0.3133 - accuracy: 0.3548 - val_loss: 0.3141 - val_accuracy: 0.3710\n",
      "Epoch 94/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.3111 - accuracy: 0.3710 - val_loss: 0.3138 - val_accuracy: 0.3790\n",
      "Epoch 95/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.3138 - accuracy: 0.3387 - val_loss: 0.3136 - val_accuracy: 0.3710\n",
      "Epoch 96/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.3100 - accuracy: 0.3468 - val_loss: 0.3135 - val_accuracy: 0.3710\n",
      "Epoch 97/500\n",
      "4/4 [==============================] - 0s 134ms/step - loss: 0.3159 - accuracy: 0.3387 - val_loss: 0.3133 - val_accuracy: 0.3710\n",
      "Epoch 98/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.3182 - accuracy: 0.3871 - val_loss: 0.3131 - val_accuracy: 0.3710\n",
      "Epoch 99/500\n",
      "4/4 [==============================] - 1s 136ms/step - loss: 0.3134 - accuracy: 0.3952 - val_loss: 0.3129 - val_accuracy: 0.3710\n",
      "Epoch 100/500\n",
      "4/4 [==============================] - 0s 136ms/step - loss: 0.3114 - accuracy: 0.3629 - val_loss: 0.3127 - val_accuracy: 0.3710\n",
      "Epoch 101/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.3132 - accuracy: 0.3710 - val_loss: 0.3125 - val_accuracy: 0.3710\n",
      "Epoch 102/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.3072 - accuracy: 0.3710 - val_loss: 0.3122 - val_accuracy: 0.3790\n",
      "Epoch 103/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.3119 - accuracy: 0.3468 - val_loss: 0.3119 - val_accuracy: 0.3710\n",
      "Epoch 104/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.3113 - accuracy: 0.3306 - val_loss: 0.3117 - val_accuracy: 0.3710\n",
      "Epoch 105/500\n",
      "4/4 [==============================] - 1s 136ms/step - loss: 0.3103 - accuracy: 0.3710 - val_loss: 0.3114 - val_accuracy: 0.3710\n",
      "Epoch 106/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.3122 - accuracy: 0.3387 - val_loss: 0.3112 - val_accuracy: 0.3710\n",
      "Epoch 107/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.3133 - accuracy: 0.3468 - val_loss: 0.3110 - val_accuracy: 0.3710\n",
      "Epoch 108/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.3136 - accuracy: 0.3468 - val_loss: 0.3108 - val_accuracy: 0.3710\n",
      "Epoch 109/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.3089 - accuracy: 0.3306 - val_loss: 0.3105 - val_accuracy: 0.3629\n",
      "Epoch 110/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.3075 - accuracy: 0.3548 - val_loss: 0.3102 - val_accuracy: 0.3710\n",
      "Epoch 111/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.3128 - accuracy: 0.3629 - val_loss: 0.3101 - val_accuracy: 0.3629\n",
      "Epoch 112/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.3112 - accuracy: 0.3629 - val_loss: 0.3098 - val_accuracy: 0.3629\n",
      "Epoch 113/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.3125 - accuracy: 0.3548 - val_loss: 0.3095 - val_accuracy: 0.3710\n",
      "Epoch 114/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.3086 - accuracy: 0.3387 - val_loss: 0.3093 - val_accuracy: 0.3710\n",
      "Epoch 115/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.3104 - accuracy: 0.3790 - val_loss: 0.3091 - val_accuracy: 0.3710\n",
      "Epoch 116/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 135ms/step - loss: 0.3049 - accuracy: 0.3790 - val_loss: 0.3089 - val_accuracy: 0.3548\n",
      "Epoch 117/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.3093 - accuracy: 0.3790 - val_loss: 0.3086 - val_accuracy: 0.3548\n",
      "Epoch 118/500\n",
      "4/4 [==============================] - 0s 134ms/step - loss: 0.3120 - accuracy: 0.3548 - val_loss: 0.3084 - val_accuracy: 0.3548\n",
      "Epoch 119/500\n",
      "4/4 [==============================] - 0s 134ms/step - loss: 0.3126 - accuracy: 0.3387 - val_loss: 0.3081 - val_accuracy: 0.3548\n",
      "Epoch 120/500\n",
      "4/4 [==============================] - 0s 136ms/step - loss: 0.3095 - accuracy: 0.3548 - val_loss: 0.3079 - val_accuracy: 0.3629\n",
      "Epoch 121/500\n",
      "4/4 [==============================] - 1s 150ms/step - loss: 0.3103 - accuracy: 0.3468 - val_loss: 0.3077 - val_accuracy: 0.3629\n",
      "Epoch 122/500\n",
      "4/4 [==============================] - 1s 144ms/step - loss: 0.3047 - accuracy: 0.3629 - val_loss: 0.3075 - val_accuracy: 0.3629\n",
      "Epoch 123/500\n",
      "4/4 [==============================] - 1s 138ms/step - loss: 0.3110 - accuracy: 0.3306 - val_loss: 0.3072 - val_accuracy: 0.3710\n",
      "Epoch 124/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.3053 - accuracy: 0.3468 - val_loss: 0.3069 - val_accuracy: 0.3710\n",
      "Epoch 125/500\n",
      "4/4 [==============================] - 1s 140ms/step - loss: 0.3092 - accuracy: 0.3871 - val_loss: 0.3066 - val_accuracy: 0.3710\n",
      "Epoch 126/500\n",
      "4/4 [==============================] - 1s 139ms/step - loss: 0.3007 - accuracy: 0.3710 - val_loss: 0.3063 - val_accuracy: 0.3710\n",
      "Epoch 127/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.3043 - accuracy: 0.3710 - val_loss: 0.3061 - val_accuracy: 0.3710\n",
      "Epoch 128/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.3053 - accuracy: 0.3548 - val_loss: 0.3058 - val_accuracy: 0.3710\n",
      "Epoch 129/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.3009 - accuracy: 0.3710 - val_loss: 0.3055 - val_accuracy: 0.3790\n",
      "Epoch 130/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.3024 - accuracy: 0.3710 - val_loss: 0.3053 - val_accuracy: 0.3790\n",
      "Epoch 131/500\n",
      "4/4 [==============================] - 0s 134ms/step - loss: 0.3030 - accuracy: 0.3629 - val_loss: 0.3051 - val_accuracy: 0.3790\n",
      "Epoch 132/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.3067 - accuracy: 0.3548 - val_loss: 0.3048 - val_accuracy: 0.3710\n",
      "Epoch 133/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.3055 - accuracy: 0.3710 - val_loss: 0.3045 - val_accuracy: 0.3790\n",
      "Epoch 134/500\n",
      "4/4 [==============================] - 0s 134ms/step - loss: 0.3082 - accuracy: 0.3468 - val_loss: 0.3042 - val_accuracy: 0.3790\n",
      "Epoch 135/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.3052 - accuracy: 0.3629 - val_loss: 0.3040 - val_accuracy: 0.3790\n",
      "Epoch 136/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.3042 - accuracy: 0.3710 - val_loss: 0.3036 - val_accuracy: 0.3790\n",
      "Epoch 137/500\n",
      "4/4 [==============================] - 1s 135ms/step - loss: 0.3048 - accuracy: 0.3548 - val_loss: 0.3034 - val_accuracy: 0.3790\n",
      "Epoch 138/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.3036 - accuracy: 0.3468 - val_loss: 0.3032 - val_accuracy: 0.3790\n",
      "Epoch 139/500\n",
      "4/4 [==============================] - 0s 134ms/step - loss: 0.3031 - accuracy: 0.3710 - val_loss: 0.3029 - val_accuracy: 0.3790\n",
      "Epoch 140/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.3022 - accuracy: 0.3710 - val_loss: 0.3026 - val_accuracy: 0.3871\n",
      "Epoch 141/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.3066 - accuracy: 0.3629 - val_loss: 0.3024 - val_accuracy: 0.3952\n",
      "Epoch 142/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2998 - accuracy: 0.3790 - val_loss: 0.3021 - val_accuracy: 0.3952\n",
      "Epoch 143/500\n",
      "4/4 [==============================] - 1s 136ms/step - loss: 0.3017 - accuracy: 0.3871 - val_loss: 0.3018 - val_accuracy: 0.3952\n",
      "Epoch 144/500\n",
      "4/4 [==============================] - 0s 134ms/step - loss: 0.2968 - accuracy: 0.3710 - val_loss: 0.3016 - val_accuracy: 0.3952\n",
      "Epoch 145/500\n",
      "4/4 [==============================] - 0s 136ms/step - loss: 0.3007 - accuracy: 0.3629 - val_loss: 0.3013 - val_accuracy: 0.3952\n",
      "Epoch 146/500\n",
      "4/4 [==============================] - 0s 136ms/step - loss: 0.2985 - accuracy: 0.3710 - val_loss: 0.3011 - val_accuracy: 0.3952\n",
      "Epoch 147/500\n",
      "4/4 [==============================] - 0s 134ms/step - loss: 0.3024 - accuracy: 0.3710 - val_loss: 0.3008 - val_accuracy: 0.3952\n",
      "Epoch 148/500\n",
      "4/4 [==============================] - 0s 134ms/step - loss: 0.2993 - accuracy: 0.3871 - val_loss: 0.3006 - val_accuracy: 0.3952\n",
      "Epoch 149/500\n",
      "4/4 [==============================] - 1s 137ms/step - loss: 0.3003 - accuracy: 0.3871 - val_loss: 0.3003 - val_accuracy: 0.3952\n",
      "Epoch 150/500\n",
      "4/4 [==============================] - 1s 137ms/step - loss: 0.3028 - accuracy: 0.3629 - val_loss: 0.3001 - val_accuracy: 0.3952\n",
      "Epoch 151/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2977 - accuracy: 0.3871 - val_loss: 0.2998 - val_accuracy: 0.3952\n",
      "Epoch 152/500\n",
      "4/4 [==============================] - 1s 137ms/step - loss: 0.3025 - accuracy: 0.3548 - val_loss: 0.2996 - val_accuracy: 0.3952\n",
      "Epoch 153/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.3019 - accuracy: 0.3790 - val_loss: 0.2994 - val_accuracy: 0.3952\n",
      "Epoch 154/500\n",
      "4/4 [==============================] - 1s 137ms/step - loss: 0.3003 - accuracy: 0.3710 - val_loss: 0.2991 - val_accuracy: 0.3952\n",
      "Epoch 155/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.3042 - accuracy: 0.3790 - val_loss: 0.2988 - val_accuracy: 0.3952\n",
      "Epoch 156/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.3037 - accuracy: 0.3710 - val_loss: 0.2985 - val_accuracy: 0.3952\n",
      "Epoch 157/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2986 - accuracy: 0.3952 - val_loss: 0.2982 - val_accuracy: 0.3952\n",
      "Epoch 158/500\n",
      "4/4 [==============================] - 1s 145ms/step - loss: 0.2987 - accuracy: 0.3710 - val_loss: 0.2978 - val_accuracy: 0.3952\n",
      "Epoch 159/500\n",
      "4/4 [==============================] - 1s 138ms/step - loss: 0.3038 - accuracy: 0.3629 - val_loss: 0.2975 - val_accuracy: 0.3871\n",
      "Epoch 160/500\n",
      "4/4 [==============================] - 0s 136ms/step - loss: 0.2979 - accuracy: 0.3871 - val_loss: 0.2973 - val_accuracy: 0.3871\n",
      "Epoch 161/500\n",
      "4/4 [==============================] - 1s 137ms/step - loss: 0.3000 - accuracy: 0.3710 - val_loss: 0.2971 - val_accuracy: 0.3871\n",
      "Epoch 162/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2975 - accuracy: 0.3548 - val_loss: 0.2968 - val_accuracy: 0.3952\n",
      "Epoch 163/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2989 - accuracy: 0.3871 - val_loss: 0.2965 - val_accuracy: 0.3871\n",
      "Epoch 164/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2910 - accuracy: 0.3871 - val_loss: 0.2962 - val_accuracy: 0.3871\n",
      "Epoch 165/500\n",
      "4/4 [==============================] - 0s 136ms/step - loss: 0.2945 - accuracy: 0.3629 - val_loss: 0.2960 - val_accuracy: 0.3871\n",
      "Epoch 166/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2957 - accuracy: 0.3871 - val_loss: 0.2957 - val_accuracy: 0.3871\n",
      "Epoch 167/500\n",
      "4/4 [==============================] - 0s 134ms/step - loss: 0.2991 - accuracy: 0.3871 - val_loss: 0.2954 - val_accuracy: 0.3871\n",
      "Epoch 168/500\n",
      "4/4 [==============================] - 0s 134ms/step - loss: 0.2936 - accuracy: 0.3790 - val_loss: 0.2951 - val_accuracy: 0.3871\n",
      "Epoch 169/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2932 - accuracy: 0.3871 - val_loss: 0.2948 - val_accuracy: 0.3871\n",
      "Epoch 170/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2905 - accuracy: 0.3952 - val_loss: 0.2946 - val_accuracy: 0.3871\n",
      "Epoch 171/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2927 - accuracy: 0.3629 - val_loss: 0.2943 - val_accuracy: 0.3871\n",
      "Epoch 172/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2933 - accuracy: 0.3629 - val_loss: 0.2940 - val_accuracy: 0.3871\n",
      "Epoch 173/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2953 - accuracy: 0.3790 - val_loss: 0.2938 - val_accuracy: 0.3871\n",
      "Epoch 174/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.3018 - accuracy: 0.3871 - val_loss: 0.2935 - val_accuracy: 0.3871\n",
      "Epoch 175/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2960 - accuracy: 0.3952 - val_loss: 0.2932 - val_accuracy: 0.3871\n",
      "Epoch 176/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2901 - accuracy: 0.3952 - val_loss: 0.2929 - val_accuracy: 0.3871\n",
      "Epoch 177/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2933 - accuracy: 0.3871 - val_loss: 0.2925 - val_accuracy: 0.3871\n",
      "Epoch 178/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2906 - accuracy: 0.3871 - val_loss: 0.2922 - val_accuracy: 0.3871\n",
      "Epoch 179/500\n",
      "4/4 [==============================] - 0s 134ms/step - loss: 0.2898 - accuracy: 0.3871 - val_loss: 0.2920 - val_accuracy: 0.3871\n",
      "Epoch 180/500\n",
      "4/4 [==============================] - 0s 134ms/step - loss: 0.2896 - accuracy: 0.3790 - val_loss: 0.2917 - val_accuracy: 0.3871\n",
      "Epoch 181/500\n",
      "4/4 [==============================] - 0s 134ms/step - loss: 0.2893 - accuracy: 0.4032 - val_loss: 0.2914 - val_accuracy: 0.3871\n",
      "Epoch 182/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2895 - accuracy: 0.3952 - val_loss: 0.2911 - val_accuracy: 0.3871\n",
      "Epoch 183/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2888 - accuracy: 0.3710 - val_loss: 0.2909 - val_accuracy: 0.3871\n",
      "Epoch 184/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2932 - accuracy: 0.3871 - val_loss: 0.2906 - val_accuracy: 0.3871\n",
      "Epoch 185/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2952 - accuracy: 0.3790 - val_loss: 0.2904 - val_accuracy: 0.3871\n",
      "Epoch 186/500\n",
      "4/4 [==============================] - 0s 136ms/step - loss: 0.2902 - accuracy: 0.3790 - val_loss: 0.2901 - val_accuracy: 0.3871\n",
      "Epoch 187/500\n",
      "4/4 [==============================] - 1s 137ms/step - loss: 0.2889 - accuracy: 0.4032 - val_loss: 0.2898 - val_accuracy: 0.3871\n",
      "Epoch 188/500\n",
      "4/4 [==============================] - 1s 136ms/step - loss: 0.2908 - accuracy: 0.3790 - val_loss: 0.2895 - val_accuracy: 0.3871\n",
      "Epoch 189/500\n",
      "4/4 [==============================] - 0s 134ms/step - loss: 0.2884 - accuracy: 0.3871 - val_loss: 0.2892 - val_accuracy: 0.3871\n",
      "Epoch 190/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2918 - accuracy: 0.3871 - val_loss: 0.2889 - val_accuracy: 0.3871\n",
      "Epoch 191/500\n",
      "4/4 [==============================] - 0s 136ms/step - loss: 0.2858 - accuracy: 0.3952 - val_loss: 0.2885 - val_accuracy: 0.3871\n",
      "Epoch 192/500\n",
      "4/4 [==============================] - 0s 134ms/step - loss: 0.2940 - accuracy: 0.3790 - val_loss: 0.2883 - val_accuracy: 0.3871\n",
      "Epoch 193/500\n",
      "4/4 [==============================] - 0s 134ms/step - loss: 0.2851 - accuracy: 0.3952 - val_loss: 0.2879 - val_accuracy: 0.3871\n",
      "Epoch 194/500\n",
      "4/4 [==============================] - 1s 137ms/step - loss: 0.2892 - accuracy: 0.3871 - val_loss: 0.2876 - val_accuracy: 0.3871\n",
      "Epoch 195/500\n",
      "4/4 [==============================] - 0s 134ms/step - loss: 0.2851 - accuracy: 0.3871 - val_loss: 0.2873 - val_accuracy: 0.3871\n",
      "Epoch 196/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2884 - accuracy: 0.3952 - val_loss: 0.2871 - val_accuracy: 0.3871\n",
      "Epoch 197/500\n",
      "4/4 [==============================] - 0s 134ms/step - loss: 0.2897 - accuracy: 0.3790 - val_loss: 0.2867 - val_accuracy: 0.3871\n",
      "Epoch 198/500\n",
      "4/4 [==============================] - 0s 136ms/step - loss: 0.2843 - accuracy: 0.3952 - val_loss: 0.2864 - val_accuracy: 0.3871\n",
      "Epoch 199/500\n",
      "4/4 [==============================] - 1s 140ms/step - loss: 0.2822 - accuracy: 0.3952 - val_loss: 0.2861 - val_accuracy: 0.3871\n",
      "Epoch 200/500\n",
      "4/4 [==============================] - 1s 137ms/step - loss: 0.2823 - accuracy: 0.3952 - val_loss: 0.2857 - val_accuracy: 0.3871\n",
      "Epoch 201/500\n",
      "4/4 [==============================] - 0s 134ms/step - loss: 0.2817 - accuracy: 0.3952 - val_loss: 0.2854 - val_accuracy: 0.3871\n",
      "Epoch 202/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2811 - accuracy: 0.3952 - val_loss: 0.2851 - val_accuracy: 0.3871\n",
      "Epoch 203/500\n",
      "4/4 [==============================] - 0s 134ms/step - loss: 0.2822 - accuracy: 0.3710 - val_loss: 0.2848 - val_accuracy: 0.3871\n",
      "Epoch 204/500\n",
      "4/4 [==============================] - 1s 136ms/step - loss: 0.2830 - accuracy: 0.3790 - val_loss: 0.2846 - val_accuracy: 0.3871\n",
      "Epoch 205/500\n",
      "4/4 [==============================] - 1s 136ms/step - loss: 0.2852 - accuracy: 0.3871 - val_loss: 0.2843 - val_accuracy: 0.3871\n",
      "Epoch 206/500\n",
      "4/4 [==============================] - 1s 138ms/step - loss: 0.2893 - accuracy: 0.3629 - val_loss: 0.2840 - val_accuracy: 0.3871\n",
      "Epoch 207/500\n",
      "4/4 [==============================] - 0s 136ms/step - loss: 0.2781 - accuracy: 0.3790 - val_loss: 0.2838 - val_accuracy: 0.3871\n",
      "Epoch 208/500\n",
      "4/4 [==============================] - 1s 136ms/step - loss: 0.2863 - accuracy: 0.3710 - val_loss: 0.2835 - val_accuracy: 0.3871\n",
      "Epoch 209/500\n",
      "4/4 [==============================] - 1s 137ms/step - loss: 0.2855 - accuracy: 0.3871 - val_loss: 0.2832 - val_accuracy: 0.3871\n",
      "Epoch 210/500\n",
      "4/4 [==============================] - 1s 136ms/step - loss: 0.2807 - accuracy: 0.3710 - val_loss: 0.2829 - val_accuracy: 0.3871\n",
      "Epoch 211/500\n",
      "4/4 [==============================] - 1s 136ms/step - loss: 0.2843 - accuracy: 0.3871 - val_loss: 0.2827 - val_accuracy: 0.3871\n",
      "Epoch 212/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2840 - accuracy: 0.3710 - val_loss: 0.2824 - val_accuracy: 0.3871\n",
      "Epoch 213/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2853 - accuracy: 0.3629 - val_loss: 0.2821 - val_accuracy: 0.3871\n",
      "Epoch 214/500\n",
      "4/4 [==============================] - 1s 139ms/step - loss: 0.2817 - accuracy: 0.3952 - val_loss: 0.2818 - val_accuracy: 0.3871\n",
      "Epoch 215/500\n",
      "4/4 [==============================] - 1s 138ms/step - loss: 0.2803 - accuracy: 0.3790 - val_loss: 0.2815 - val_accuracy: 0.3871\n",
      "Epoch 216/500\n",
      "4/4 [==============================] - 0s 136ms/step - loss: 0.2807 - accuracy: 0.3952 - val_loss: 0.2811 - val_accuracy: 0.3871\n",
      "Epoch 217/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2784 - accuracy: 0.3871 - val_loss: 0.2808 - val_accuracy: 0.3871\n",
      "Epoch 218/500\n",
      "4/4 [==============================] - 1s 137ms/step - loss: 0.2752 - accuracy: 0.3952 - val_loss: 0.2806 - val_accuracy: 0.3871\n",
      "Epoch 219/500\n",
      "4/4 [==============================] - 1s 140ms/step - loss: 0.2735 - accuracy: 0.3952 - val_loss: 0.2802 - val_accuracy: 0.3871\n",
      "Epoch 220/500\n",
      "4/4 [==============================] - 1s 140ms/step - loss: 0.2865 - accuracy: 0.3710 - val_loss: 0.2800 - val_accuracy: 0.3871\n",
      "Epoch 221/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2838 - accuracy: 0.3790 - val_loss: 0.2797 - val_accuracy: 0.3871\n",
      "Epoch 222/500\n",
      "4/4 [==============================] - 0s 136ms/step - loss: 0.2810 - accuracy: 0.4032 - val_loss: 0.2795 - val_accuracy: 0.3871\n",
      "Epoch 223/500\n",
      "4/4 [==============================] - 0s 136ms/step - loss: 0.2784 - accuracy: 0.3952 - val_loss: 0.2792 - val_accuracy: 0.3871\n",
      "Epoch 224/500\n",
      "4/4 [==============================] - 1s 136ms/step - loss: 0.2776 - accuracy: 0.4032 - val_loss: 0.2788 - val_accuracy: 0.3871\n",
      "Epoch 225/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2753 - accuracy: 0.4032 - val_loss: 0.2785 - val_accuracy: 0.3871\n",
      "Epoch 226/500\n",
      "4/4 [==============================] - 1s 137ms/step - loss: 0.2839 - accuracy: 0.3952 - val_loss: 0.2782 - val_accuracy: 0.3871\n",
      "Epoch 227/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2743 - accuracy: 0.3871 - val_loss: 0.2779 - val_accuracy: 0.3871\n",
      "Epoch 228/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2786 - accuracy: 0.3871 - val_loss: 0.2776 - val_accuracy: 0.3871\n",
      "Epoch 229/500\n",
      "4/4 [==============================] - 0s 134ms/step - loss: 0.2770 - accuracy: 0.3871 - val_loss: 0.2773 - val_accuracy: 0.3871\n",
      "Epoch 230/500\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2751 - accuracy: 0.3952 - val_loss: 0.2771 - val_accuracy: 0.3871\n",
      "Epoch 231/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2722 - accuracy: 0.3790 - val_loss: 0.2768 - val_accuracy: 0.3871\n",
      "Epoch 232/500\n",
      "4/4 [==============================] - 1s 137ms/step - loss: 0.2752 - accuracy: 0.3871 - val_loss: 0.2765 - val_accuracy: 0.3871\n",
      "Epoch 233/500\n",
      "4/4 [==============================] - 0s 134ms/step - loss: 0.2754 - accuracy: 0.3871 - val_loss: 0.2763 - val_accuracy: 0.3871\n",
      "Epoch 234/500\n",
      "4/4 [==============================] - 0s 134ms/step - loss: 0.2814 - accuracy: 0.3710 - val_loss: 0.2761 - val_accuracy: 0.3871\n",
      "Epoch 235/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2728 - accuracy: 0.3790 - val_loss: 0.2758 - val_accuracy: 0.3871\n",
      "Epoch 236/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2751 - accuracy: 0.3871 - val_loss: 0.2755 - val_accuracy: 0.3871\n",
      "Epoch 237/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2731 - accuracy: 0.3952 - val_loss: 0.2753 - val_accuracy: 0.3871\n",
      "Epoch 238/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2731 - accuracy: 0.3952 - val_loss: 0.2750 - val_accuracy: 0.3871\n",
      "Epoch 239/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2702 - accuracy: 0.3871 - val_loss: 0.2748 - val_accuracy: 0.3871\n",
      "Epoch 240/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2745 - accuracy: 0.3871 - val_loss: 0.2746 - val_accuracy: 0.3871\n",
      "Epoch 241/500\n",
      "4/4 [==============================] - 0s 134ms/step - loss: 0.2743 - accuracy: 0.3952 - val_loss: 0.2744 - val_accuracy: 0.3871\n",
      "Epoch 242/500\n",
      "4/4 [==============================] - 1s 140ms/step - loss: 0.2739 - accuracy: 0.4113 - val_loss: 0.2742 - val_accuracy: 0.3952\n",
      "Epoch 243/500\n",
      "4/4 [==============================] - 1s 138ms/step - loss: 0.2769 - accuracy: 0.3952 - val_loss: 0.2739 - val_accuracy: 0.3952\n",
      "Epoch 244/500\n",
      "4/4 [==============================] - 1s 138ms/step - loss: 0.2743 - accuracy: 0.3952 - val_loss: 0.2736 - val_accuracy: 0.3871\n",
      "Epoch 245/500\n",
      "4/4 [==============================] - 1s 146ms/step - loss: 0.2721 - accuracy: 0.3790 - val_loss: 0.2733 - val_accuracy: 0.3871\n",
      "Epoch 246/500\n",
      "4/4 [==============================] - 1s 141ms/step - loss: 0.2718 - accuracy: 0.3871 - val_loss: 0.2730 - val_accuracy: 0.3871\n",
      "Epoch 247/500\n",
      "4/4 [==============================] - 1s 149ms/step - loss: 0.2717 - accuracy: 0.4032 - val_loss: 0.2727 - val_accuracy: 0.3871\n",
      "Epoch 248/500\n",
      "4/4 [==============================] - 1s 138ms/step - loss: 0.2681 - accuracy: 0.3952 - val_loss: 0.2724 - val_accuracy: 0.3871\n",
      "Epoch 249/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2732 - accuracy: 0.3871 - val_loss: 0.2721 - val_accuracy: 0.3871\n",
      "Epoch 250/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2748 - accuracy: 0.3790 - val_loss: 0.2718 - val_accuracy: 0.3871\n",
      "Epoch 251/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2720 - accuracy: 0.4032 - val_loss: 0.2716 - val_accuracy: 0.3871\n",
      "Epoch 252/500\n",
      "4/4 [==============================] - 0s 134ms/step - loss: 0.2706 - accuracy: 0.3790 - val_loss: 0.2713 - val_accuracy: 0.3871\n",
      "Epoch 253/500\n",
      "4/4 [==============================] - 0s 134ms/step - loss: 0.2719 - accuracy: 0.3790 - val_loss: 0.2711 - val_accuracy: 0.3871\n",
      "Epoch 254/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2707 - accuracy: 0.3871 - val_loss: 0.2708 - val_accuracy: 0.3871\n",
      "Epoch 255/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2707 - accuracy: 0.3952 - val_loss: 0.2705 - val_accuracy: 0.3871\n",
      "Epoch 256/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2721 - accuracy: 0.3952 - val_loss: 0.2702 - val_accuracy: 0.3871\n",
      "Epoch 257/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2729 - accuracy: 0.3790 - val_loss: 0.2700 - val_accuracy: 0.3871\n",
      "Epoch 258/500\n",
      "4/4 [==============================] - 0s 134ms/step - loss: 0.2695 - accuracy: 0.3871 - val_loss: 0.2697 - val_accuracy: 0.3871\n",
      "Epoch 259/500\n",
      "4/4 [==============================] - 1s 137ms/step - loss: 0.2712 - accuracy: 0.3871 - val_loss: 0.2694 - val_accuracy: 0.3871\n",
      "Epoch 260/500\n",
      "4/4 [==============================] - 0s 134ms/step - loss: 0.2674 - accuracy: 0.3790 - val_loss: 0.2692 - val_accuracy: 0.3871\n",
      "Epoch 261/500\n",
      "4/4 [==============================] - 0s 134ms/step - loss: 0.2737 - accuracy: 0.3629 - val_loss: 0.2689 - val_accuracy: 0.3871\n",
      "Epoch 262/500\n",
      "4/4 [==============================] - 0s 134ms/step - loss: 0.2721 - accuracy: 0.3871 - val_loss: 0.2686 - val_accuracy: 0.3871\n",
      "Epoch 263/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2684 - accuracy: 0.3871 - val_loss: 0.2684 - val_accuracy: 0.3871\n",
      "Epoch 264/500\n",
      "4/4 [==============================] - 0s 134ms/step - loss: 0.2658 - accuracy: 0.3952 - val_loss: 0.2682 - val_accuracy: 0.3952\n",
      "Epoch 265/500\n",
      "4/4 [==============================] - 0s 134ms/step - loss: 0.2651 - accuracy: 0.3952 - val_loss: 0.2679 - val_accuracy: 0.3952\n",
      "Epoch 266/500\n",
      "4/4 [==============================] - 0s 134ms/step - loss: 0.2654 - accuracy: 0.4032 - val_loss: 0.2676 - val_accuracy: 0.3952\n",
      "Epoch 267/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2729 - accuracy: 0.3952 - val_loss: 0.2674 - val_accuracy: 0.3952\n",
      "Epoch 268/500\n",
      "4/4 [==============================] - 1s 142ms/step - loss: 0.2688 - accuracy: 0.4032 - val_loss: 0.2671 - val_accuracy: 0.3952\n",
      "Epoch 269/500\n",
      "4/4 [==============================] - 0s 134ms/step - loss: 0.2680 - accuracy: 0.4194 - val_loss: 0.2668 - val_accuracy: 0.3952\n",
      "Epoch 270/500\n",
      "4/4 [==============================] - 0s 136ms/step - loss: 0.2621 - accuracy: 0.4113 - val_loss: 0.2665 - val_accuracy: 0.3952\n",
      "Epoch 271/500\n",
      "4/4 [==============================] - 0s 136ms/step - loss: 0.2695 - accuracy: 0.3952 - val_loss: 0.2663 - val_accuracy: 0.3952\n",
      "Epoch 272/500\n",
      "4/4 [==============================] - 0s 134ms/step - loss: 0.2665 - accuracy: 0.3952 - val_loss: 0.2660 - val_accuracy: 0.3952\n",
      "Epoch 273/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2615 - accuracy: 0.3871 - val_loss: 0.2657 - val_accuracy: 0.3952\n",
      "Epoch 274/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2639 - accuracy: 0.3952 - val_loss: 0.2653 - val_accuracy: 0.3952\n",
      "Epoch 275/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2742 - accuracy: 0.3952 - val_loss: 0.2651 - val_accuracy: 0.3952\n",
      "Epoch 276/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2640 - accuracy: 0.4194 - val_loss: 0.2648 - val_accuracy: 0.3952\n",
      "Epoch 277/500\n",
      "4/4 [==============================] - 0s 136ms/step - loss: 0.2663 - accuracy: 0.4032 - val_loss: 0.2646 - val_accuracy: 0.3952\n",
      "Epoch 278/500\n",
      "4/4 [==============================] - 0s 134ms/step - loss: 0.2665 - accuracy: 0.4032 - val_loss: 0.2643 - val_accuracy: 0.3952\n",
      "Epoch 279/500\n",
      "4/4 [==============================] - 0s 136ms/step - loss: 0.2618 - accuracy: 0.4032 - val_loss: 0.2640 - val_accuracy: 0.3952\n",
      "Epoch 280/500\n",
      "4/4 [==============================] - 1s 137ms/step - loss: 0.2640 - accuracy: 0.3871 - val_loss: 0.2638 - val_accuracy: 0.3952\n",
      "Epoch 281/500\n",
      "4/4 [==============================] - 1s 137ms/step - loss: 0.2639 - accuracy: 0.4032 - val_loss: 0.2635 - val_accuracy: 0.3952\n",
      "Epoch 282/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2684 - accuracy: 0.4032 - val_loss: 0.2631 - val_accuracy: 0.3952\n",
      "Epoch 283/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2583 - accuracy: 0.4113 - val_loss: 0.2628 - val_accuracy: 0.3952\n",
      "Epoch 284/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2638 - accuracy: 0.4032 - val_loss: 0.2625 - val_accuracy: 0.3952\n",
      "Epoch 285/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2601 - accuracy: 0.3952 - val_loss: 0.2623 - val_accuracy: 0.3952\n",
      "Epoch 286/500\n",
      "4/4 [==============================] - 0s 136ms/step - loss: 0.2610 - accuracy: 0.4194 - val_loss: 0.2620 - val_accuracy: 0.3952\n",
      "Epoch 287/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 1s 136ms/step - loss: 0.2612 - accuracy: 0.4032 - val_loss: 0.2617 - val_accuracy: 0.3871\n",
      "Epoch 288/500\n",
      "4/4 [==============================] - 1s 136ms/step - loss: 0.2638 - accuracy: 0.3871 - val_loss: 0.2614 - val_accuracy: 0.3952\n",
      "Epoch 289/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2637 - accuracy: 0.3952 - val_loss: 0.2611 - val_accuracy: 0.3952\n",
      "Epoch 290/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2644 - accuracy: 0.3790 - val_loss: 0.2608 - val_accuracy: 0.3952\n",
      "Epoch 291/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2585 - accuracy: 0.4113 - val_loss: 0.2605 - val_accuracy: 0.3952\n",
      "Epoch 292/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2642 - accuracy: 0.4032 - val_loss: 0.2603 - val_accuracy: 0.3952\n",
      "Epoch 293/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2584 - accuracy: 0.3871 - val_loss: 0.2601 - val_accuracy: 0.3952\n",
      "Epoch 294/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2599 - accuracy: 0.4194 - val_loss: 0.2599 - val_accuracy: 0.3952\n",
      "Epoch 295/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2640 - accuracy: 0.3710 - val_loss: 0.2596 - val_accuracy: 0.3952\n",
      "Epoch 296/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2550 - accuracy: 0.4113 - val_loss: 0.2592 - val_accuracy: 0.3952\n",
      "Epoch 297/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2571 - accuracy: 0.4194 - val_loss: 0.2589 - val_accuracy: 0.3952\n",
      "Epoch 298/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2608 - accuracy: 0.3952 - val_loss: 0.2586 - val_accuracy: 0.3952\n",
      "Epoch 299/500\n",
      "4/4 [==============================] - 0s 134ms/step - loss: 0.2488 - accuracy: 0.4194 - val_loss: 0.2583 - val_accuracy: 0.3952\n",
      "Epoch 300/500\n",
      "4/4 [==============================] - 1s 137ms/step - loss: 0.2603 - accuracy: 0.4194 - val_loss: 0.2580 - val_accuracy: 0.3952\n",
      "Epoch 301/500\n",
      "4/4 [==============================] - 0s 136ms/step - loss: 0.2590 - accuracy: 0.4032 - val_loss: 0.2578 - val_accuracy: 0.3952\n",
      "Epoch 302/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2599 - accuracy: 0.3952 - val_loss: 0.2575 - val_accuracy: 0.3952\n",
      "Epoch 303/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2545 - accuracy: 0.4113 - val_loss: 0.2571 - val_accuracy: 0.3952\n",
      "Epoch 304/500\n",
      "4/4 [==============================] - 1s 140ms/step - loss: 0.2546 - accuracy: 0.4113 - val_loss: 0.2568 - val_accuracy: 0.3952\n",
      "Epoch 305/500\n",
      "4/4 [==============================] - 1s 138ms/step - loss: 0.2569 - accuracy: 0.4032 - val_loss: 0.2566 - val_accuracy: 0.3952\n",
      "Epoch 306/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2541 - accuracy: 0.4194 - val_loss: 0.2562 - val_accuracy: 0.3952\n",
      "Epoch 307/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2583 - accuracy: 0.3952 - val_loss: 0.2559 - val_accuracy: 0.3952\n",
      "Epoch 308/500\n",
      "4/4 [==============================] - 0s 136ms/step - loss: 0.2531 - accuracy: 0.4032 - val_loss: 0.2556 - val_accuracy: 0.3952\n",
      "Epoch 309/500\n",
      "4/4 [==============================] - 0s 136ms/step - loss: 0.2522 - accuracy: 0.4274 - val_loss: 0.2553 - val_accuracy: 0.3952\n",
      "Epoch 310/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2576 - accuracy: 0.4032 - val_loss: 0.2549 - val_accuracy: 0.3952\n",
      "Epoch 311/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2531 - accuracy: 0.4194 - val_loss: 0.2546 - val_accuracy: 0.3952\n",
      "Epoch 312/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2525 - accuracy: 0.4032 - val_loss: 0.2542 - val_accuracy: 0.4032\n",
      "Epoch 313/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2547 - accuracy: 0.4113 - val_loss: 0.2539 - val_accuracy: 0.4032\n",
      "Epoch 314/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2534 - accuracy: 0.4032 - val_loss: 0.2536 - val_accuracy: 0.4032\n",
      "Epoch 315/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2532 - accuracy: 0.3952 - val_loss: 0.2533 - val_accuracy: 0.4032\n",
      "Epoch 316/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2563 - accuracy: 0.4113 - val_loss: 0.2529 - val_accuracy: 0.4032\n",
      "Epoch 317/500\n",
      "4/4 [==============================] - 0s 136ms/step - loss: 0.2552 - accuracy: 0.3952 - val_loss: 0.2525 - val_accuracy: 0.4032\n",
      "Epoch 318/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2513 - accuracy: 0.4032 - val_loss: 0.2522 - val_accuracy: 0.4032\n",
      "Epoch 319/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2561 - accuracy: 0.3871 - val_loss: 0.2520 - val_accuracy: 0.4032\n",
      "Epoch 320/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2538 - accuracy: 0.4032 - val_loss: 0.2518 - val_accuracy: 0.4032\n",
      "Epoch 321/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2519 - accuracy: 0.4113 - val_loss: 0.2515 - val_accuracy: 0.4032\n",
      "Epoch 322/500\n",
      "4/4 [==============================] - 0s 136ms/step - loss: 0.2549 - accuracy: 0.4032 - val_loss: 0.2513 - val_accuracy: 0.4032\n",
      "Epoch 323/500\n",
      "4/4 [==============================] - 0s 136ms/step - loss: 0.2546 - accuracy: 0.4032 - val_loss: 0.2510 - val_accuracy: 0.4032\n",
      "Epoch 324/500\n",
      "4/4 [==============================] - 0s 136ms/step - loss: 0.2508 - accuracy: 0.4113 - val_loss: 0.2507 - val_accuracy: 0.4113\n",
      "Epoch 325/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2489 - accuracy: 0.4032 - val_loss: 0.2504 - val_accuracy: 0.4032\n",
      "Epoch 326/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2528 - accuracy: 0.4032 - val_loss: 0.2501 - val_accuracy: 0.4032\n",
      "Epoch 327/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2503 - accuracy: 0.3871 - val_loss: 0.2498 - val_accuracy: 0.4032\n",
      "Epoch 328/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2454 - accuracy: 0.4032 - val_loss: 0.2496 - val_accuracy: 0.4113\n",
      "Epoch 329/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2463 - accuracy: 0.4194 - val_loss: 0.2493 - val_accuracy: 0.4113\n",
      "Epoch 330/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2435 - accuracy: 0.4274 - val_loss: 0.2491 - val_accuracy: 0.4274\n",
      "Epoch 331/500\n",
      "4/4 [==============================] - 1s 148ms/step - loss: 0.2454 - accuracy: 0.4032 - val_loss: 0.2489 - val_accuracy: 0.4274\n",
      "Epoch 332/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2423 - accuracy: 0.4274 - val_loss: 0.2486 - val_accuracy: 0.4274\n",
      "Epoch 333/500\n",
      "4/4 [==============================] - 0s 136ms/step - loss: 0.2440 - accuracy: 0.4274 - val_loss: 0.2483 - val_accuracy: 0.4274\n",
      "Epoch 334/500\n",
      "4/4 [==============================] - 1s 137ms/step - loss: 0.2501 - accuracy: 0.4113 - val_loss: 0.2480 - val_accuracy: 0.4274\n",
      "Epoch 335/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2467 - accuracy: 0.4113 - val_loss: 0.2476 - val_accuracy: 0.4113\n",
      "Epoch 336/500\n",
      "4/4 [==============================] - 0s 134ms/step - loss: 0.2449 - accuracy: 0.4274 - val_loss: 0.2473 - val_accuracy: 0.4032\n",
      "Epoch 337/500\n",
      "4/4 [==============================] - 1s 138ms/step - loss: 0.2406 - accuracy: 0.4194 - val_loss: 0.2469 - val_accuracy: 0.4032\n",
      "Epoch 338/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2478 - accuracy: 0.3952 - val_loss: 0.2466 - val_accuracy: 0.4032\n",
      "Epoch 339/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2499 - accuracy: 0.4194 - val_loss: 0.2463 - val_accuracy: 0.4032\n",
      "Epoch 340/500\n",
      "4/4 [==============================] - 0s 136ms/step - loss: 0.2484 - accuracy: 0.4113 - val_loss: 0.2459 - val_accuracy: 0.4032\n",
      "Epoch 341/500\n",
      "4/4 [==============================] - 1s 136ms/step - loss: 0.2499 - accuracy: 0.3952 - val_loss: 0.2456 - val_accuracy: 0.4032\n",
      "Epoch 342/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2476 - accuracy: 0.4032 - val_loss: 0.2453 - val_accuracy: 0.4032\n",
      "Epoch 343/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2462 - accuracy: 0.4113 - val_loss: 0.2450 - val_accuracy: 0.4113\n",
      "Epoch 344/500\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2408 - accuracy: 0.4194 - val_loss: 0.2448 - val_accuracy: 0.4274\n",
      "Epoch 345/500\n",
      "4/4 [==============================] - 0s 136ms/step - loss: 0.2455 - accuracy: 0.4194 - val_loss: 0.2445 - val_accuracy: 0.4274\n",
      "Epoch 346/500\n",
      "4/4 [==============================] - 1s 137ms/step - loss: 0.2456 - accuracy: 0.4113 - val_loss: 0.2441 - val_accuracy: 0.4274\n",
      "Epoch 347/500\n",
      "4/4 [==============================] - 1s 136ms/step - loss: 0.2451 - accuracy: 0.4274 - val_loss: 0.2438 - val_accuracy: 0.4274\n",
      "Epoch 348/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2430 - accuracy: 0.4113 - val_loss: 0.2434 - val_accuracy: 0.4194\n",
      "Epoch 349/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2429 - accuracy: 0.4113 - val_loss: 0.2431 - val_accuracy: 0.4274\n",
      "Epoch 350/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2407 - accuracy: 0.4516 - val_loss: 0.2427 - val_accuracy: 0.4274\n",
      "Epoch 351/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2358 - accuracy: 0.4194 - val_loss: 0.2423 - val_accuracy: 0.4274\n",
      "Epoch 352/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2392 - accuracy: 0.4355 - val_loss: 0.2418 - val_accuracy: 0.4194\n",
      "Epoch 353/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2425 - accuracy: 0.4113 - val_loss: 0.2415 - val_accuracy: 0.4113\n",
      "Epoch 354/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2353 - accuracy: 0.4113 - val_loss: 0.2412 - val_accuracy: 0.4032\n",
      "Epoch 355/500\n",
      "4/4 [==============================] - 1s 137ms/step - loss: 0.2381 - accuracy: 0.4113 - val_loss: 0.2409 - val_accuracy: 0.4032\n",
      "Epoch 356/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2404 - accuracy: 0.3952 - val_loss: 0.2407 - val_accuracy: 0.4113\n",
      "Epoch 357/500\n",
      "4/4 [==============================] - 1s 137ms/step - loss: 0.2396 - accuracy: 0.4113 - val_loss: 0.2404 - val_accuracy: 0.4194\n",
      "Epoch 358/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2457 - accuracy: 0.3952 - val_loss: 0.2401 - val_accuracy: 0.4194\n",
      "Epoch 359/500\n",
      "4/4 [==============================] - 0s 136ms/step - loss: 0.2354 - accuracy: 0.4194 - val_loss: 0.2399 - val_accuracy: 0.4274\n",
      "Epoch 360/500\n",
      "4/4 [==============================] - 1s 137ms/step - loss: 0.2348 - accuracy: 0.4274 - val_loss: 0.2396 - val_accuracy: 0.4274\n",
      "Epoch 361/500\n",
      "4/4 [==============================] - 1s 138ms/step - loss: 0.2386 - accuracy: 0.4113 - val_loss: 0.2394 - val_accuracy: 0.4274\n",
      "Epoch 362/500\n",
      "4/4 [==============================] - 1s 141ms/step - loss: 0.2412 - accuracy: 0.4274 - val_loss: 0.2390 - val_accuracy: 0.4274\n",
      "Epoch 363/500\n",
      "4/4 [==============================] - 1s 139ms/step - loss: 0.2430 - accuracy: 0.3952 - val_loss: 0.2388 - val_accuracy: 0.4274\n",
      "Epoch 364/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2358 - accuracy: 0.4274 - val_loss: 0.2385 - val_accuracy: 0.4274\n",
      "Epoch 365/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2344 - accuracy: 0.4274 - val_loss: 0.2383 - val_accuracy: 0.4274\n",
      "Epoch 366/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2359 - accuracy: 0.4355 - val_loss: 0.2380 - val_accuracy: 0.4274\n",
      "Epoch 367/500\n",
      "4/4 [==============================] - 0s 136ms/step - loss: 0.2408 - accuracy: 0.4032 - val_loss: 0.2377 - val_accuracy: 0.4274\n",
      "Epoch 368/500\n",
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      "Epoch 369/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2439 - accuracy: 0.3952 - val_loss: 0.2371 - val_accuracy: 0.4274\n",
      "Epoch 370/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2378 - accuracy: 0.4032 - val_loss: 0.2368 - val_accuracy: 0.4194\n",
      "Epoch 371/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2364 - accuracy: 0.4194 - val_loss: 0.2365 - val_accuracy: 0.4194\n",
      "Epoch 372/500\n",
      "4/4 [==============================] - 1s 141ms/step - loss: 0.2332 - accuracy: 0.4194 - val_loss: 0.2363 - val_accuracy: 0.4194\n",
      "Epoch 373/500\n",
      "4/4 [==============================] - 1s 138ms/step - loss: 0.2371 - accuracy: 0.4194 - val_loss: 0.2360 - val_accuracy: 0.4194\n",
      "Epoch 374/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2315 - accuracy: 0.4274 - val_loss: 0.2358 - val_accuracy: 0.4194\n",
      "Epoch 375/500\n",
      "4/4 [==============================] - 1s 137ms/step - loss: 0.2317 - accuracy: 0.4435 - val_loss: 0.2355 - val_accuracy: 0.4194\n",
      "Epoch 376/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2334 - accuracy: 0.4355 - val_loss: 0.2353 - val_accuracy: 0.4274\n",
      "Epoch 377/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2289 - accuracy: 0.4435 - val_loss: 0.2349 - val_accuracy: 0.4274\n",
      "Epoch 378/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2378 - accuracy: 0.4113 - val_loss: 0.2346 - val_accuracy: 0.4274\n",
      "Epoch 379/500\n",
      "4/4 [==============================] - 0s 136ms/step - loss: 0.2369 - accuracy: 0.4113 - val_loss: 0.2344 - val_accuracy: 0.4274\n",
      "Epoch 380/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2350 - accuracy: 0.3790 - val_loss: 0.2341 - val_accuracy: 0.4274\n",
      "Epoch 381/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2384 - accuracy: 0.3952 - val_loss: 0.2337 - val_accuracy: 0.4274\n",
      "Epoch 382/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2302 - accuracy: 0.4274 - val_loss: 0.2335 - val_accuracy: 0.4274\n",
      "Epoch 383/500\n",
      "4/4 [==============================] - 1s 137ms/step - loss: 0.2324 - accuracy: 0.4355 - val_loss: 0.2331 - val_accuracy: 0.4355\n",
      "Epoch 384/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2335 - accuracy: 0.4194 - val_loss: 0.2328 - val_accuracy: 0.4355\n",
      "Epoch 385/500\n",
      "4/4 [==============================] - 1s 137ms/step - loss: 0.2356 - accuracy: 0.4274 - val_loss: 0.2324 - val_accuracy: 0.4274\n",
      "Epoch 386/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2330 - accuracy: 0.4274 - val_loss: 0.2321 - val_accuracy: 0.4274\n",
      "Epoch 387/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2309 - accuracy: 0.4194 - val_loss: 0.2318 - val_accuracy: 0.4274\n",
      "Epoch 388/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2284 - accuracy: 0.4194 - val_loss: 0.2315 - val_accuracy: 0.4274\n",
      "Epoch 389/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2239 - accuracy: 0.4274 - val_loss: 0.2314 - val_accuracy: 0.4274\n",
      "Epoch 390/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2302 - accuracy: 0.4113 - val_loss: 0.2311 - val_accuracy: 0.4355\n",
      "Epoch 391/500\n",
      "4/4 [==============================] - 0s 136ms/step - loss: 0.2356 - accuracy: 0.4194 - val_loss: 0.2308 - val_accuracy: 0.4274\n",
      "Epoch 392/500\n",
      "4/4 [==============================] - 1s 141ms/step - loss: 0.2328 - accuracy: 0.4113 - val_loss: 0.2304 - val_accuracy: 0.4274\n",
      "Epoch 393/500\n",
      "4/4 [==============================] - 0s 136ms/step - loss: 0.2337 - accuracy: 0.4113 - val_loss: 0.2300 - val_accuracy: 0.4274\n",
      "Epoch 394/500\n",
      "4/4 [==============================] - 1s 136ms/step - loss: 0.2334 - accuracy: 0.4194 - val_loss: 0.2298 - val_accuracy: 0.4274\n",
      "Epoch 395/500\n",
      "4/4 [==============================] - 1s 137ms/step - loss: 0.2269 - accuracy: 0.4435 - val_loss: 0.2295 - val_accuracy: 0.4274\n",
      "Epoch 396/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2266 - accuracy: 0.4274 - val_loss: 0.2293 - val_accuracy: 0.4274\n",
      "Epoch 397/500\n",
      "4/4 [==============================] - 0s 136ms/step - loss: 0.2289 - accuracy: 0.4194 - val_loss: 0.2290 - val_accuracy: 0.4274\n",
      "Epoch 398/500\n",
      "4/4 [==============================] - 1s 136ms/step - loss: 0.2296 - accuracy: 0.4274 - val_loss: 0.2288 - val_accuracy: 0.4274\n",
      "Epoch 399/500\n",
      "4/4 [==============================] - 1s 137ms/step - loss: 0.2260 - accuracy: 0.4194 - val_loss: 0.2286 - val_accuracy: 0.4274\n",
      "Epoch 400/500\n",
      "4/4 [==============================] - 1s 138ms/step - loss: 0.2291 - accuracy: 0.4194 - val_loss: 0.2284 - val_accuracy: 0.4274\n",
      "Epoch 401/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 1s 138ms/step - loss: 0.2257 - accuracy: 0.4032 - val_loss: 0.2282 - val_accuracy: 0.4274\n",
      "Epoch 402/500\n",
      "4/4 [==============================] - 1s 137ms/step - loss: 0.2299 - accuracy: 0.4194 - val_loss: 0.2279 - val_accuracy: 0.4355\n",
      "Epoch 403/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2320 - accuracy: 0.4032 - val_loss: 0.2278 - val_accuracy: 0.4274\n",
      "Epoch 404/500\n",
      "4/4 [==============================] - 0s 134ms/step - loss: 0.2232 - accuracy: 0.4194 - val_loss: 0.2275 - val_accuracy: 0.4274\n",
      "Epoch 405/500\n",
      "4/4 [==============================] - 0s 136ms/step - loss: 0.2297 - accuracy: 0.4274 - val_loss: 0.2271 - val_accuracy: 0.4355\n",
      "Epoch 406/500\n",
      "4/4 [==============================] - 1s 139ms/step - loss: 0.2313 - accuracy: 0.4113 - val_loss: 0.2268 - val_accuracy: 0.4355\n",
      "Epoch 407/500\n",
      "4/4 [==============================] - 0s 136ms/step - loss: 0.2297 - accuracy: 0.4274 - val_loss: 0.2267 - val_accuracy: 0.4355\n",
      "Epoch 408/500\n",
      "4/4 [==============================] - 1s 141ms/step - loss: 0.2230 - accuracy: 0.4274 - val_loss: 0.2266 - val_accuracy: 0.4355\n",
      "Epoch 409/500\n",
      "4/4 [==============================] - 1s 136ms/step - loss: 0.2227 - accuracy: 0.4274 - val_loss: 0.2265 - val_accuracy: 0.4355\n",
      "Epoch 410/500\n",
      "4/4 [==============================] - 1s 136ms/step - loss: 0.2239 - accuracy: 0.4355 - val_loss: 0.2263 - val_accuracy: 0.4435\n",
      "Epoch 411/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2249 - accuracy: 0.4274 - val_loss: 0.2261 - val_accuracy: 0.4435\n",
      "Epoch 412/500\n",
      "4/4 [==============================] - 1s 146ms/step - loss: 0.2242 - accuracy: 0.4355 - val_loss: 0.2259 - val_accuracy: 0.4274\n",
      "Epoch 413/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2235 - accuracy: 0.4274 - val_loss: 0.2256 - val_accuracy: 0.4274\n",
      "Epoch 414/500\n",
      "4/4 [==============================] - 1s 137ms/step - loss: 0.2130 - accuracy: 0.4435 - val_loss: 0.2253 - val_accuracy: 0.4274\n",
      "Epoch 415/500\n",
      "4/4 [==============================] - 0s 136ms/step - loss: 0.2233 - accuracy: 0.4194 - val_loss: 0.2251 - val_accuracy: 0.4274\n",
      "Epoch 416/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2246 - accuracy: 0.4274 - val_loss: 0.2247 - val_accuracy: 0.4435\n",
      "Epoch 417/500\n",
      "4/4 [==============================] - 1s 142ms/step - loss: 0.2214 - accuracy: 0.4355 - val_loss: 0.2245 - val_accuracy: 0.4355\n",
      "Epoch 418/500\n",
      "4/4 [==============================] - 1s 136ms/step - loss: 0.2327 - accuracy: 0.4032 - val_loss: 0.2242 - val_accuracy: 0.4355\n",
      "Epoch 419/500\n",
      "4/4 [==============================] - 1s 138ms/step - loss: 0.2223 - accuracy: 0.4032 - val_loss: 0.2239 - val_accuracy: 0.4274\n",
      "Epoch 420/500\n",
      "4/4 [==============================] - 1s 141ms/step - loss: 0.2270 - accuracy: 0.4032 - val_loss: 0.2236 - val_accuracy: 0.4274\n",
      "Epoch 421/500\n",
      "4/4 [==============================] - 1s 135ms/step - loss: 0.2167 - accuracy: 0.4274 - val_loss: 0.2233 - val_accuracy: 0.4274\n",
      "Epoch 422/500\n",
      "4/4 [==============================] - 1s 148ms/step - loss: 0.2226 - accuracy: 0.4274 - val_loss: 0.2231 - val_accuracy: 0.4274\n",
      "Epoch 423/500\n",
      "4/4 [==============================] - 1s 138ms/step - loss: 0.2241 - accuracy: 0.4194 - val_loss: 0.2228 - val_accuracy: 0.4355\n",
      "Epoch 424/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2250 - accuracy: 0.4274 - val_loss: 0.2224 - val_accuracy: 0.4355\n",
      "Epoch 425/500\n",
      "4/4 [==============================] - 0s 136ms/step - loss: 0.2194 - accuracy: 0.4274 - val_loss: 0.2221 - val_accuracy: 0.4355\n",
      "Epoch 426/500\n",
      "4/4 [==============================] - 0s 136ms/step - loss: 0.2211 - accuracy: 0.4274 - val_loss: 0.2219 - val_accuracy: 0.4355\n",
      "Epoch 427/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2219 - accuracy: 0.4274 - val_loss: 0.2216 - val_accuracy: 0.4355\n",
      "Epoch 428/500\n",
      "4/4 [==============================] - 0s 137ms/step - loss: 0.2225 - accuracy: 0.4194 - val_loss: 0.2213 - val_accuracy: 0.4435\n",
      "Epoch 429/500\n",
      "4/4 [==============================] - 0s 134ms/step - loss: 0.2219 - accuracy: 0.4355 - val_loss: 0.2211 - val_accuracy: 0.4355\n",
      "Epoch 430/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2205 - accuracy: 0.4355 - val_loss: 0.2210 - val_accuracy: 0.4355\n",
      "Epoch 431/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2200 - accuracy: 0.4355 - val_loss: 0.2208 - val_accuracy: 0.4355\n",
      "Epoch 432/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2246 - accuracy: 0.4113 - val_loss: 0.2207 - val_accuracy: 0.4274\n",
      "Epoch 433/500\n",
      "4/4 [==============================] - 1s 138ms/step - loss: 0.2209 - accuracy: 0.4355 - val_loss: 0.2204 - val_accuracy: 0.4355\n",
      "Epoch 434/500\n",
      "4/4 [==============================] - 1s 137ms/step - loss: 0.2229 - accuracy: 0.4435 - val_loss: 0.2199 - val_accuracy: 0.4355\n",
      "Epoch 435/500\n",
      "4/4 [==============================] - 1s 147ms/step - loss: 0.2176 - accuracy: 0.4355 - val_loss: 0.2197 - val_accuracy: 0.4355\n",
      "Epoch 436/500\n",
      "4/4 [==============================] - 1s 150ms/step - loss: 0.2196 - accuracy: 0.4274 - val_loss: 0.2195 - val_accuracy: 0.4355\n",
      "Epoch 437/500\n",
      "4/4 [==============================] - 1s 146ms/step - loss: 0.2175 - accuracy: 0.4194 - val_loss: 0.2193 - val_accuracy: 0.4435\n",
      "Epoch 438/500\n",
      "4/4 [==============================] - 1s 148ms/step - loss: 0.2248 - accuracy: 0.4113 - val_loss: 0.2190 - val_accuracy: 0.4435\n",
      "Epoch 439/500\n",
      "4/4 [==============================] - 1s 138ms/step - loss: 0.2199 - accuracy: 0.4194 - val_loss: 0.2188 - val_accuracy: 0.4435\n",
      "Epoch 440/500\n",
      "4/4 [==============================] - 1s 139ms/step - loss: 0.2175 - accuracy: 0.4435 - val_loss: 0.2186 - val_accuracy: 0.4435\n",
      "Epoch 441/500\n",
      "4/4 [==============================] - 1s 147ms/step - loss: 0.2172 - accuracy: 0.4435 - val_loss: 0.2184 - val_accuracy: 0.4435\n",
      "Epoch 442/500\n",
      "4/4 [==============================] - 1s 138ms/step - loss: 0.2175 - accuracy: 0.4274 - val_loss: 0.2183 - val_accuracy: 0.4355\n",
      "Epoch 443/500\n",
      "4/4 [==============================] - 1s 144ms/step - loss: 0.2173 - accuracy: 0.4274 - val_loss: 0.2181 - val_accuracy: 0.4355\n",
      "Epoch 444/500\n",
      "4/4 [==============================] - 1s 141ms/step - loss: 0.2146 - accuracy: 0.4435 - val_loss: 0.2179 - val_accuracy: 0.4355\n",
      "Epoch 445/500\n",
      "4/4 [==============================] - 1s 138ms/step - loss: 0.2179 - accuracy: 0.4274 - val_loss: 0.2178 - val_accuracy: 0.4355\n",
      "Epoch 446/500\n",
      "4/4 [==============================] - 1s 140ms/step - loss: 0.2177 - accuracy: 0.4274 - val_loss: 0.2174 - val_accuracy: 0.4355\n",
      "Epoch 447/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2179 - accuracy: 0.4274 - val_loss: 0.2172 - val_accuracy: 0.4355\n",
      "Epoch 448/500\n",
      "4/4 [==============================] - 1s 137ms/step - loss: 0.2223 - accuracy: 0.4113 - val_loss: 0.2170 - val_accuracy: 0.4355\n",
      "Epoch 449/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2160 - accuracy: 0.4194 - val_loss: 0.2168 - val_accuracy: 0.4355\n",
      "Epoch 450/500\n",
      "4/4 [==============================] - 1s 137ms/step - loss: 0.2203 - accuracy: 0.4194 - val_loss: 0.2166 - val_accuracy: 0.4355\n",
      "Epoch 451/500\n",
      "4/4 [==============================] - 0s 136ms/step - loss: 0.2139 - accuracy: 0.4355 - val_loss: 0.2163 - val_accuracy: 0.4355\n",
      "Epoch 452/500\n",
      "4/4 [==============================] - 1s 140ms/step - loss: 0.2214 - accuracy: 0.4194 - val_loss: 0.2160 - val_accuracy: 0.4435\n",
      "Epoch 453/500\n",
      "4/4 [==============================] - 1s 143ms/step - loss: 0.2176 - accuracy: 0.4274 - val_loss: 0.2158 - val_accuracy: 0.4435\n",
      "Epoch 454/500\n",
      "4/4 [==============================] - 1s 141ms/step - loss: 0.2134 - accuracy: 0.4274 - val_loss: 0.2156 - val_accuracy: 0.4355\n",
      "Epoch 455/500\n",
      "4/4 [==============================] - 1s 137ms/step - loss: 0.2111 - accuracy: 0.4355 - val_loss: 0.2156 - val_accuracy: 0.4355\n",
      "Epoch 456/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2182 - accuracy: 0.4274 - val_loss: 0.2155 - val_accuracy: 0.4355\n",
      "Epoch 457/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2166 - accuracy: 0.4516 - val_loss: 0.2154 - val_accuracy: 0.4355\n",
      "Epoch 458/500\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 1s 136ms/step - loss: 0.2189 - accuracy: 0.4274 - val_loss: 0.2152 - val_accuracy: 0.4355\n",
      "Epoch 459/500\n",
      "4/4 [==============================] - 0s 134ms/step - loss: 0.2157 - accuracy: 0.4597 - val_loss: 0.2151 - val_accuracy: 0.4355\n",
      "Epoch 460/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2211 - accuracy: 0.4274 - val_loss: 0.2148 - val_accuracy: 0.4355\n",
      "Epoch 461/500\n",
      "4/4 [==============================] - 0s 136ms/step - loss: 0.2170 - accuracy: 0.4032 - val_loss: 0.2147 - val_accuracy: 0.4355\n",
      "Epoch 462/500\n",
      "4/4 [==============================] - 1s 136ms/step - loss: 0.2127 - accuracy: 0.4355 - val_loss: 0.2145 - val_accuracy: 0.4355\n",
      "Epoch 463/500\n",
      "4/4 [==============================] - 1s 141ms/step - loss: 0.2074 - accuracy: 0.4435 - val_loss: 0.2143 - val_accuracy: 0.4355\n",
      "Epoch 464/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2127 - accuracy: 0.4355 - val_loss: 0.2141 - val_accuracy: 0.4355\n",
      "Epoch 465/500\n",
      "4/4 [==============================] - 0s 134ms/step - loss: 0.2140 - accuracy: 0.4435 - val_loss: 0.2138 - val_accuracy: 0.4355\n",
      "Epoch 466/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2173 - accuracy: 0.4435 - val_loss: 0.2135 - val_accuracy: 0.4355\n",
      "Epoch 467/500\n",
      "4/4 [==============================] - 1s 141ms/step - loss: 0.2100 - accuracy: 0.4274 - val_loss: 0.2132 - val_accuracy: 0.4355\n",
      "Epoch 468/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2140 - accuracy: 0.4274 - val_loss: 0.2130 - val_accuracy: 0.4355\n",
      "Epoch 469/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2149 - accuracy: 0.4516 - val_loss: 0.2128 - val_accuracy: 0.4355\n",
      "Epoch 470/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2149 - accuracy: 0.4355 - val_loss: 0.2124 - val_accuracy: 0.4355\n",
      "Epoch 471/500\n",
      "4/4 [==============================] - 0s 134ms/step - loss: 0.2109 - accuracy: 0.4274 - val_loss: 0.2121 - val_accuracy: 0.4355\n",
      "Epoch 472/500\n",
      "4/4 [==============================] - 0s 134ms/step - loss: 0.2127 - accuracy: 0.4274 - val_loss: 0.2119 - val_accuracy: 0.4355\n",
      "Epoch 473/500\n",
      "4/4 [==============================] - 0s 134ms/step - loss: 0.2072 - accuracy: 0.4355 - val_loss: 0.2116 - val_accuracy: 0.4355\n",
      "Epoch 474/500\n",
      "4/4 [==============================] - 0s 134ms/step - loss: 0.2089 - accuracy: 0.4274 - val_loss: 0.2115 - val_accuracy: 0.4355\n",
      "Epoch 475/500\n",
      "4/4 [==============================] - 0s 134ms/step - loss: 0.2054 - accuracy: 0.4435 - val_loss: 0.2113 - val_accuracy: 0.4355\n",
      "Epoch 476/500\n",
      "4/4 [==============================] - 1s 136ms/step - loss: 0.2110 - accuracy: 0.4516 - val_loss: 0.2111 - val_accuracy: 0.4355\n",
      "Epoch 477/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2100 - accuracy: 0.4194 - val_loss: 0.2108 - val_accuracy: 0.4355\n",
      "Epoch 478/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2125 - accuracy: 0.4355 - val_loss: 0.2106 - val_accuracy: 0.4355\n",
      "Epoch 479/500\n",
      "4/4 [==============================] - 0s 134ms/step - loss: 0.2073 - accuracy: 0.4516 - val_loss: 0.2103 - val_accuracy: 0.4355\n",
      "Epoch 480/500\n",
      "4/4 [==============================] - 0s 134ms/step - loss: 0.2145 - accuracy: 0.4274 - val_loss: 0.2101 - val_accuracy: 0.4355\n",
      "Epoch 481/500\n",
      "4/4 [==============================] - 0s 134ms/step - loss: 0.2121 - accuracy: 0.4194 - val_loss: 0.2099 - val_accuracy: 0.4355\n",
      "Epoch 482/500\n",
      "4/4 [==============================] - 0s 134ms/step - loss: 0.2135 - accuracy: 0.4274 - val_loss: 0.2096 - val_accuracy: 0.4435\n",
      "Epoch 483/500\n",
      "4/4 [==============================] - 0s 134ms/step - loss: 0.2088 - accuracy: 0.4355 - val_loss: 0.2094 - val_accuracy: 0.4355\n",
      "Epoch 484/500\n",
      "4/4 [==============================] - 0s 134ms/step - loss: 0.2100 - accuracy: 0.4355 - val_loss: 0.2091 - val_accuracy: 0.4355\n",
      "Epoch 485/500\n",
      "4/4 [==============================] - 0s 134ms/step - loss: 0.2073 - accuracy: 0.4355 - val_loss: 0.2089 - val_accuracy: 0.4355\n",
      "Epoch 486/500\n",
      "4/4 [==============================] - 0s 136ms/step - loss: 0.2074 - accuracy: 0.4355 - val_loss: 0.2085 - val_accuracy: 0.4355\n",
      "Epoch 487/500\n",
      "4/4 [==============================] - 1s 140ms/step - loss: 0.2036 - accuracy: 0.4435 - val_loss: 0.2082 - val_accuracy: 0.4355\n",
      "Epoch 488/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2053 - accuracy: 0.4274 - val_loss: 0.2079 - val_accuracy: 0.4355\n",
      "Epoch 489/500\n",
      "4/4 [==============================] - 1s 139ms/step - loss: 0.2047 - accuracy: 0.4435 - val_loss: 0.2076 - val_accuracy: 0.4355\n",
      "Epoch 490/500\n",
      "4/4 [==============================] - 1s 136ms/step - loss: 0.2052 - accuracy: 0.4435 - val_loss: 0.2074 - val_accuracy: 0.4355\n",
      "Epoch 491/500\n",
      "4/4 [==============================] - 1s 136ms/step - loss: 0.2118 - accuracy: 0.4274 - val_loss: 0.2071 - val_accuracy: 0.4355\n",
      "Epoch 492/500\n",
      "4/4 [==============================] - 1s 138ms/step - loss: 0.2106 - accuracy: 0.4194 - val_loss: 0.2069 - val_accuracy: 0.4355\n",
      "Epoch 493/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2106 - accuracy: 0.4194 - val_loss: 0.2069 - val_accuracy: 0.4355\n",
      "Epoch 494/500\n",
      "4/4 [==============================] - 1s 142ms/step - loss: 0.2091 - accuracy: 0.4194 - val_loss: 0.2066 - val_accuracy: 0.4355\n",
      "Epoch 495/500\n",
      "4/4 [==============================] - 1s 136ms/step - loss: 0.2059 - accuracy: 0.4516 - val_loss: 0.2064 - val_accuracy: 0.4355\n",
      "Epoch 496/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2115 - accuracy: 0.4435 - val_loss: 0.2062 - val_accuracy: 0.4355\n",
      "Epoch 497/500\n",
      "4/4 [==============================] - 0s 134ms/step - loss: 0.1991 - accuracy: 0.4516 - val_loss: 0.2061 - val_accuracy: 0.4355\n",
      "Epoch 498/500\n",
      "4/4 [==============================] - 1s 139ms/step - loss: 0.2030 - accuracy: 0.4435 - val_loss: 0.2058 - val_accuracy: 0.4355\n",
      "Epoch 499/500\n",
      "4/4 [==============================] - 0s 135ms/step - loss: 0.2065 - accuracy: 0.4274 - val_loss: 0.2057 - val_accuracy: 0.4355\n",
      "Epoch 500/500\n",
      "4/4 [==============================] - 1s 139ms/step - loss: 0.2076 - accuracy: 0.4274 - val_loss: 0.2054 - val_accuracy: 0.4355\n"
     ]
    }
   ],
   "source": [
    "model_history = model.fit(x_train,y_train,epochs=500,validation_data=(x_val, y_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AgQMHUqNGDWW7Q4cOdOzYkR9//BGAkydPkpyczOjRo4mOjlbGtWzZkl69einjHA4HS5cupX///rqxRq7frfHjx2v2de7cGbvdzuHDhwGIjIwE4Pvvv6eoqMine77UEeJHoFCjRg3dXxT//vsvgwYNIiIigvDwcOLi4pRfkJmZmWXOW7t2bc22LITOnj3r97ny+fK5p06dIj8/XzfrwtdMjCNHjii/jOQ4nq5duwLu9yfHfXhaDzhjMxITE7FarZpxjRo18mk9AEOHDiUgIICFCxcCUFBQwDfffEPfvn01QnLBggW0bNmS4OBgYmJiiIuL44cffvDpc1Ej/5Js2LChZn9cXJxbQKjD4eCNN96gYcOGmM1mYmNjiYuLY9u2bX5fV3396tWrExYWptkvZyDK65Mp63vhjU8//ZR69ephNpvZt28f+/btIykpidDQUI0Y2L9/P9WrV9c8pFzZv38/RqORpk2blnldf9DLLMrPz2fKlClKTJT8vmdkZGje9/3799O8eXOv8xuNRoYPH87SpUvJy8sDnK7A4OBgRVx74/vvv+eqq64iODiY6Oho4uLieO+998r9+XvCZDIxYcIEMjIy+Pvvv4FS4VVYWOg2Xo4TkseEhIS4uW3VY9UizhsdOnSgZ8+emp/u3bu7jXP9/wPOAG45Lkv+Huv9LmjSpAlpaWnk5uZy+vRpsrKyyvwcZcr6Hdu1a1cGDx7M1KlTiY2NZcCAAcybN0/3PbxcEOJHoKD3iyAjI4OuXbuydetWpk2bxnfffcfKlSt5+eWXAXxKV/aUVSS5BLJW9Lm+YLfb6dWrFz/88AOPPfYYS5cuZeXKlUpgruv9na8MqWrVqtGrVy++/vprioqK+O6778jOztbEYnz66aeMHj2apKQk5syZw08//cTKlSvp0aNHpaaRT58+nYkTJ9KlSxc+/fRTVqxYwcqVK2nWrNl5S18v7/ciKyuL7777joMHD9KwYUPlp2nTpuTl5bFw4cIK+275gmugvIze/8UHHniAF198kVtvvZUvv/ySn3/+mZUrVxITE1Ou933kyJHk5OSwdOlSJfvtxhtvJCIiwut569ev56abbiI4OJh3332XH3/8kZUrV3L77bdXyntXq1YtwBloDxAdHY3ZbObkyZNuY+V9shU4MTERu93OqVOnNONsNhvp6ekaa/HFTFn/HwwGA1999RUbN25kwoQJHD9+nDvvvJO2bduSk5NzPpd6wSACngVeWbt2Lenp6SxZsoQuXboo+w8ePFiFqyqlWrVqBAcH6xYF9FYoUGb79u38999/LFiwQFNbZOXKleVeU506dVi9ejU5OTka68+ePXv8mmf48OH89NNPLF++nIULFxIeHk7//v2V41999RX169dnyZIlGpO3npvGlzUD7N27l/r16yv7T58+7WZN+eqrr+jevTtz5szR7M/IyCA2NlbZ9sftU6dOHVatWqUEqsrIblV5fefKkiVLKCgo4L333tOsFZyfz9NPP82GDRu49tprSUpKYsWKFZw5c8aj9ScpKQmHw8HOnTu9BphHRUW5ZfvZbDbdB7gnvvrqK0aNGsVrr72m7CsoKHCbNykpSTfDyJXmzZvTpk0bPvvsM2rWrMmRI0d8Kv739ddfExwczIoVKzSup3nz5rmNrYiq7bIrU7a4Go1GWrRooVvVeNOmTdSvX1/5DsmfyV9//UW/fv2UcX/99RcOh8PvpICy2Lt3r9u+//77Twlilr/Her8Ldu/eTWxsLBaLhZCQEMLDw336HP3hqquu4qqrruLFF19k4cKFDB8+nC+++OKCLfNQmQjLj8Ar8l8U6r/obDYb7777blUtSUNAQAA9e/Zk6dKlnDhxQtm/b98+XZ+83vmgvT9JknjzzTfLvaZ+/fpRXFzMe++9p+yz2+0+V5WVGThwIKGhobz77rssX76cm2++WYlp8LT2TZs2sXHjRr/X3LNnT4KCgpg9e7ZmvlmzZrmNDQgIcPsLf/HixRw/flyzT65N40uKf79+/bDb7bz99tua/W+88QYGg8Hn+K2y+PTTT6lfvz733HMPQ4YM0fxMmjQJq9WquL4GDx6MJElMnTrVbR75/gcOHIjRaGTatGlu1hf1e5SUlKSJ3wL44IMPPFp+9NB732fPnu02x+DBg9m6dSvffPONx3XLjBgxgp9//plZs2YRExPj0/scEBCAwWDQXPfQoUO6lZwtFovPJR5cU7PBGWM4a9YsYmNjadu2rbJ/yJAh/PnnnxoBtGfPHn755ReN265Hjx5ER0dr/i8CvPfee4SGhiqZaRXF0qVLNf8PNm/ezKZNm5T3NTExkdatW7NgwQLN+7Jjxw5+/vlnRaAZjUYGDhzId999pyvy/LWwnT171u0cWfhdrq4vYfkReKVTp05ERUUxatQopfXCJ598cl5dA2Xx3HPP8fPPP3PNNddw7733Kg/R5s2bl9laoXHjxiQlJTFp0iSOHz9OeHg4X3/9tU+xI57o378/11xzDY8//jiHDh2iadOmLFmyxO94CKvVysCBA5W4H9f04xtvvJElS5YwaNAgbrjhBg4ePMj7779P06ZN/TZly/WKZsyYwY033ki/fv34559/WL58uZuF5MYbb2TatGmMGTOGTp06sX37dj777DONxQicD/zIyEjef/99wsLCsFgsdOzYUTeepX///nTv3p2nnnqKQ4cO0apVK37++We+/fZbHn74YZ9SpMvixIkTrFmzxi2oWsZsNtO7d28WL17MW2+9Rffu3RkxYgRvvfUWe/fupU+fPjgcDtavX0/37t2ZMGECDRo04KmnnuL555+nc+fO3HzzzZjNZv7880+qV6+u1Mu56667lCrDvXr1YuvWraxYscLtvfXGjTfeyCeffEJERARNmzZl48aNrFq1yi3d+n//+x9fffUVt9xyi+LaOHPmDMuWLeP999+nVatWytjbb7+dyZMn880333Dvvff6VHzyhhtu4PXXX6dPnz7cfvvtnDp1infeeYcGDRq4tVxp27Ytq1at4vXXX6d69erUq1ePjh076s77zjvvKEG+tWvX5uTJk8ydO5cjR47wySefaOIR77vvPj788ENuuOEGJk2aRFBQEK+//jrx8fE8+uijyriQkBCef/557r//fm655RZ69+7N+vXr+fTTT3nxxRe9xnOpWb58uW5yR6dOnTTf+wYNGnDttddy7733UlhYqIjKyZMnK2NeffVV+vbty9VXX83YsWOVVPeIiAhNLajp06fz888/07VrV8aPH0+TJk04efIkixcv5rffflOCmH1hwYIFvPvuuwwaNIikpCSys7P58MMPCQ8P11jELivOY2aZ4ALBU6p7s2bNdMdv2LBBuuqqq6SQkBCpevXq0uTJk5VU2TVr1ijjPKW6v/rqq25z4pL66ynV/f7773c71zU9WJIkafXq1VKbNm0kk8kkJSUlSR999JH06KOPSsHBwR7ehVJ27twp9ezZU7JarVJsbKw0btw4JXVanaY9atQoyWKxuJ2vt/b09HRpxIgRUnh4uBQRESGNGDFC+ueff3xOdZf54YcfJEBKTEzUTaWePn26VKdOHclsNktt2rSRvv/+e7fPQZLKTnWXJEmy2+3S1KlTpcTERCkkJETq1q2btGPHDrf3u6CgQHr00UeVcddcc420ceNGqWvXrm5p0t9++63UtGlTpeyAfO96a8zOzpYeeeQRqXr16lJQUJDUsGFD6dVXX9Wk8Mr34uv3Qs1rr70mAdLq1as9jpk/f74ESN9++60kSc5yAq+++qrUuHFjyWQySXFxcVLfvn2lv//+W3Pe3LlzpTZt2khms1mKioqSunbtKq1cuVI5brfbpccee0yKjY2VQkNDpd69e0v79u3zmOqul5J89uxZacyYMVJsbKxktVql3r17S7t379a97/T0dGnChAlSjRo1JJPJJNWsWVMaNWqUlJaW5jZvv379JED6/fffPb4vrsyZM0dq2LChZDabpcaNG0vz5s3T/X+we/duqUuXLlJISIhbyQRXfv75Z6lXr15SQkKCFBQUJEVGRkrXX3+9x8/r6NGj0pAhQ6Tw8HDJarVKN954o7R3717dsR988IHUqFEj5ffDG2+84fa90sNbqrv6+6z+Xffaa69JtWrVksxms9S5c2dp69atbvOuWrVKuuaaa6SQkBApPDxc6t+/v7Rz5063cYcPH5ZGjhwpxcXFSWazWapfv750//33KyUuPH1f1qxZo/n9vGXLFmnYsGFS7dq1JbPZLFWrVk268cYbpb/++qvM9+BSxSBJF9Cf8AJBBTJw4ED+/fdfXT+8QCBwMmjQILZv3+5TjJxAn0OHDlGvXj1effVVJk2aVNXLEfiAiPkRXBK41uvYu3cvP/74I926dauaBQkEFwEnT57khx9+YMSIEVW9FIHgvCJifgSXBPXr12f06NHUr1+fw4cP895772EymTS+doFA4OTgwYNs2LCBjz76iKCgIO6+++6qXpJAcF4R4kdwSdCnTx8+//xzUlJSMJvNXH311UyfPl236JhAcLmzbt06xowZQ+3atVmwYAEJCQlVvSSB4LwiYn4EAoFAIBBcVoiYH4FAIBAIBJcVQvwIBAKBQCC4rBAxPzo4HA5OnDhBWFhYhZRnFwgEAoFAUPlIkkR2djbVq1fHaPRs3xHiR4cTJ04ozfQEAoFAIBBcXBw9epSaNWt6PC7Ejw5yU7yjR48SHh5exasRCAQCgUDgC1lZWdSqVUvTIFkPIX50kF1d4eHhQvwIBAKBQHCRUVbIigh4FggEAoFAcFkhxI9AIBAIBILLCiF+BAKBQCAQXFaImJ9zwG63U1RUVNXLEAgqnKCgIAICAqp6GQKBQFApCPFTDiRJIiUlhYyMjKpeikBQaURGRpKQkCBqXQkEgksOIX7KgSx8qlWrRmhoqHg4CC4pJEkiLy+PU6dOAZCYmFjFKxIIBIKKRYgfP7Hb7YrwiYmJqerlCASVQkhICACnTp2iWrVqwgUmEAguKUTAs5/IMT6hoaFVvBKBoHKRv+Mirk0gEFxqCPFTToSrS3CpI77jAoHgUkWIH4FAIBAIBJcVQvwIyk3dunWZNWuWz+PXrl2LwWAQWXICgUAgqFKE+LkMMBgMXn+ee+65cs37559/Mn78eJ/Hd+rUiZMnTxIREVGu65WHxo0bYzabSUlJOW/XFAgEAsGFjRA/lwEnT55UfmbNmkV4eLhm36RJk5SxkiRRXFzs07xxcXF+BX6bTKbzWjfmt99+Iz8/nyFDhrBgwYLzck1viMBhgUBQFeQV5VX1Ei44hPi5DEhISFB+IiIiMBgMyvbu3bsJCwtj+fLltG3bFrPZzG+//cb+/fsZMGAA8fHxWK1W2rdvz6pVqzTzurq9DAYDH330EYMGDSI0NJSGDRuybNky5bir22v+/PlERkayYsUKmjRpgtVqpU+fPpw8eVI5p7i4mAcffJDIyEhiYmJ47LHHGDVqFAMHDizzvufMmcPtt9/OiBEjmDt3rtvxY8eOMWzYMKKjo7FYLLRr145NmzYpx7/77jvat29PcHAwsbGxDBo0SHOvS5cu1cwXGRnJ/PnzATh06BAGg4FFixbRtWtXgoOD+eyzz0hPT2fYsGHUqFGD0NBQWrRoweeff66Zx+Fw8Morr9CgQQPMZjO1a9fmxRdfBKBHjx5MmDBBM/706dOYTCZWr15d5nsiEAguLx5c/iCW6Ra2nNxS1Uu5oBDi5xyRJIlcW26V/EiSVGH38fjjj/PSSy+xa9cuWrZsSU5ODv369WP16tX8888/9OnTh/79+3PkyBGv80ydOpVbb72Vbdu20a9fP4YPH86ZM2c8js/Ly2PmzJl88skn/Prrrxw5ckRjiXr55Zf57LPPmDdvHhs2bCArK8tNdOiRnZ3N4sWLueOOO+jVqxeZmZmsX79eOZ6Tk0PXrl05fvw4y5YtY+vWrUyePBmHwwHADz/8wKBBg+jXrx///PMPq1evpkOHDmVe15XHH3+chx56iF27dtG7d28KCgpo27YtP/zwAzt27GD8+PGMGDGCzZs3K+c88cQTvPTSSzzzzDPs3LmThQsXEh8fD8Bdd93FwoULKSwsVMZ/+umn1KhRgx49evi9PoFAcGkze/NsAJ5d+2wVr+TCQhQ5PEfyivKwzrBWybVznsjBYrJUyFzTpk2jV69eynZ0dDStWrVStp9//nm++eYbli1b5mZ5UDN69GiGDRsGwPTp03nrrbfYvHkzffr00R1fVFTE+++/T1JSEgATJkxg2rRpyvHZs2fzxBNPKFaXt99+mx9//LHM+/niiy9o2LAhzZo1A2Do0KHMmTOHzp07A7Bw4UJOnz7Nn3/+SXR0NAANGjRQzn/xxRcZOnQoU6dOVfap3w9fefjhh7n55ps1+9Ti7oEHHmDFihV8+eWXdOjQgezsbN58803efvttRo0aBUBSUhLXXnstADfffDMTJkzg22+/5dZbbwWcFrTRo0eL1HSBQCDwEWH5EQDQrl07zXZOTg6TJk2iSZMmREZGYrVa2bVrV5mWn5YtWyqvLRYL4eHhSpsEPUJDQxXhA85WCvL4zMxMUlNTNRaXgIAA2rZtW+b9zJ07lzvuuEPZvuOOO1i8eDHZ2dkAJCcn06ZNG0X4uJKcnMx1111X5nXKwvV9tdvtPP/887Ro0YLo6GisVisrVqxQ3tddu3ZRWFjo8drBwcEaN96WLVvYsWMHo0ePPue1CgSCS5eK9BRcCgjLzzkSGhRKzhM5VXbtisJi0VqQJk2axMqVK5k5cyYNGjQgJCSEIUOGYLPZvM4TFBSk2TYYDIorydfx5/qfdOfOnfzxxx9s3ryZxx57TNlvt9v54osvGDdunNK+wRNlHddbp15As+v7+uqrr/Lmm28ya9YsWrRogcVi4eGHH1be17KuC07XV+vWrTl27Bjz5s2jR48e1KlTp8zzBAKBQOBEWH7OEYPBgMVkqZKfynRzbNiwgdGjRzNo0CBatGhBQkIChw4dqrTr6REREUF8fDx//vmnss9ut7Nli/fAvTlz5tClSxe2bt1KcnKy8jNx4kTmzJkDOC1UycnJHuORWrZs6TWAOC4uThOYvXfvXvLyys6o2LBhAwMGDOCOO+6gVatW1K9fn//++0853rBhQ0JCQrxeu0WLFrRr144PP/yQhQsXcuedd5Z5XYFAcHkj3OJahPgR6NKwYUOWLFlCcnIyW7du5fbbb/dqwaksHnjgAWbMmMG3337Lnj17eOihhzh79qzH/8hFRUV88sknDBs2jObNm2t+7rrrLjZt2sS///7LsGHDSEhIYODAgWzYsIEDBw7w9ddfs3HjRgCeffZZPv/8c5599ll27drF9u3befnll5Xr9OjRg7fffpt//vmHv/76i3vuucfNiqVHw4YNWblyJb///ju7du3i7rvvJjU1VTkeHBzMY489xuTJk/n444/Zv38/f/zxhyLaZO666y5eeuklJEnSZKEJBAKBoGyE+BHo8vrrrxMVFUWnTp3o378/vXv35sorrzzv63jssccYNmwYI0eO5Oqrr8ZqtdK7d2+Cg4N1xy9btoz09HRdQdCkSROaNGnCnDlzMJlM/Pzzz1SrVo1+/frRokULXnrpJaV7ebdu3Vi8eDHLli2jdevW9OjRQ5OR9dprr1GrVi06d+7M7bffzqRJk3yqefT0009z5ZVX0rt3b7p166YIMDXPPPMMjz76KFOmTKFJkybcdtttbnFTw4YNIzAwkGHDhnl8LwQCgUDgAamKefvtt6U6depIZrNZ6tChg7Rp0yafzvv8888lQBowYIBm/6hRoyRA89O7d2+/1pSZmSkBUmZmptux/Px8aefOnVJ+fr5fcwoqBrvdLl1xxRXS008/XdVLqVIOHjwoGY1G6e+//660a4jvukBw8cNzSDyHdOPCG6t6KecFb89vNVVq+Vm0aBETJ07k2WefZcuWLbRq1YrevXt7zQ4CZwG5SZMmKWnLrsiF8uQf1yJygouHw4cP8+GHH/Lff/+xfft27r33Xg4ePMjtt99e1UurEoqKikhJSeHpp5/mqquuqhJrnEDgC8eyjpFflF/Vy7hskSSJ/Wf2+zS22FHMgbMHdI8dOHuAYkcxdofd45iyOJ51nLyiPPaf2U+xw7cOApVNlYqf119/nXHjxjFmzBiaNm3K+++/T2hoqG41Xhm73c7w4cOZOnUq9evX1x1jNps1VY2joqIq6xYElYzRaGT+/Pm0b9+ea665hu3bt7Nq1SqaNGlS1UurEjZs2EBiYiJ//vkn77//flUvRyDQZU/aHmq9UYum7zat6qVctkxbN40GsxuUPRAY9vUwkt5KYsmuJZr9y/YsI+mtJIYvGc5TvzxF0ltJ/Li37Dprao5kHqHmGzWxTLfQYHYDxi4b69f5lUWViR+bzcbff/9Nz549SxdjNNKzZ08l6FSPadOmUa1aNcaO9fwGrl27lmrVqtGoUSPuvfde0tPTK3TtgvNHrVq12LBhA5mZmWRlZfH777/TpUuXql5WldGtWzckSWLPnj20aNGiqpcjEOiybI+zrc2hjENVu5DLmOfWPefz2K92fgXAKxte0eyfus5Z5PXLf79k5+mdgFPY+sOag2s02x9v/div8yuLKqvzk5aWht1uV8r2y8THx7N7927dc3777TfmzJlDcnKyx3n79OnDzTffTL169di/fz9PPvkkffv2ZePGjUowqyuFhYWadgFZWVn+35BAIBAIAJFWfbFS5NDWKjuTX1oKJMfmrGdXUFzg15zRIfqFZKuai6bIYXZ2NiNGjODDDz8kNjbW47ihQ4cqr1u0aEHLli1JSkpi7dq1HqvmzpgxQ9PGQCAQCATlx4AQPxcjrvE4Z/PPKq/LK36CAy/MbNQqc3vFxsYSEBCgqXECkJqaSkJCgtv4/fv3c+jQIfr3709gYCCBgYF8/PHHLFu2jMDAQPbv1w/sql+/PrGxsezbt8/jWp544gkyMzOVn6NHj57bzQkEAsFljNEgqqhcjNgdds12ZmGm8jq3KBfwX/y4WpPKM0dlUGXfUJPJRNu2bTWVbB0OB6tXr+bqq692G9+4cWO2b9+uqdh700030b17d5KTk6lVq5budY4dO0Z6ejqJiYke12I2mwkPD9f8CAQCgaB8CLfXxYm3TCzZ8lNoL/Q4Rg+b3b0lUmpOqs7I80uVur0mTpzIqFGjaNeuHR06dGDWrFnk5uYyZswYAEaOHEmNGjWYMWMGwcHBNG/eXHN+ZGQkgLI/JyeHqVOnMnjwYBISEti/fz+TJ0+mQYMG9O7d+7zem0AgEFyuCLfXhYerVUcPX8SPv1YbXfGTm0qdyKrtR1il4ue2227j9OnTTJkyhZSUFFq3bs1PP/2kBEEfOXIEo9F341RAQADbtm1jwYIFZGRkUL16da6//nqef/55zGZzZd2GQCAQCFSoLT92h50Ao36yieD8oed+ckUtfhxSaTuj4MBgcm3lc3vpiZ+UnBS/5qgMqjzgecKECUyYMEH32Nq1a72eO3/+fM12SEgIK1asqKCVCVzp1q0brVu3ZtasWQDUrVuXhx9+mIcfftjjOQaDgW+++cathYO/VNQ8AoGg8lFbfoocRUL8XAAU2f0TP+l5pSViLEEW0vOd2xVi+bkA3F4iKu0yoH///vTp00f32Pr16zEYDGzbts3vef/880/Gjx9/rsvT8Nxzz9G6dWu3/SdPnqRv374Vei1P5OfnEx0dTWxsrKYEgkAg8A215Ufv4Xcu5Npy+eXgL5zIPsHGo55rwpVFQXEBvxz8xe/1/fcffPYZHDjgtI6sPbSW1IwsfvkFisrWF5w5A+vWgSSV7pMkiV8P/0p6Xjq5ubBkifMasz9K574ZG1i6zpmws+HIBtLy0nTn3bnTec7hw/rXle9za8pWDpw9wJqDa1j872KyC7OVMWrxo7bOZNtKxxQUF3DoEKgrztgddtYcXMOp3FN8vfNrVh1YhVRyg8LyI6gyxo4dy+DBgzl27Bg1a9bUHJs3bx7t2rWjZcuWfs8bFxdXUUssE70MwMri66+/plmzZkiSxNKlS7ntttvO27VdkSQJu91OYKD4ryq4eNBYfnywOPjD7UtuV4ooAmwcu5Gral7l9zz3fH8PC7Yu4IEOD/BW37d8Osduh/btISsLEhPhqaXvMWH5BGJX/EDaxn5Mngwvv+x9jq5dYccO+OorGDzYue/bPd8yaNEgGsc2ZsDRXao5YoBreC8ol4Ubv+f27/sTFxrHqf9pW0Dl50PbtlBQAFdcAXt06hDa7DZSc1Jp/X+tte9D23uU12rxcyq39BpqAVNoL6RePefrY8egRg14fePrTF41WTPvijtWcH3S9bqff2qusPwIzgM33ngjcXFxbm7CnJwcFi9ezNixY0lPT2fYsGHUqFGD0NBQWrRoUWZPtLp16youMIC9e/fSpUsXgoODadq0KStXrnQ757HHHuOKK64gNDSU+vXr88wzz1BU8ufS/PnzmTp1Klu3bsVgMGAwGJQ1GwwGli5dqsyzfft2evToQUhICDExMYwfP56cnBzl+OjRoxk4cCAzZ84kMTGRmJgY7r//fuVa3pgzZw533HEHd9xxB3PmzHE7/u+//3LjjTcSHh5OWFgYnTt31pRamDt3Ls2aNcNsNpOYmKi4dQ8dOoTBYNAU6czIyMBgMCgu3rVr12IwGFi+fDlt27bFbDbz22+/sX//fgYMGEB8fDxWq5X27duzatUqzboKCwt57LHHqFWrFmazmQYNGjBnzhwkSaJBgwbMnDlTMz45ORmDweC1DIRAUB7U8SIVbflRCx+A3478Vq55FmxdAMDszbN9Puf0aafwATh5Ej78ax4AaRv7AfDKK57OLGXHDue/n31Wuu/zHc7ftbvTdrPTWUiZpk0hsMFaCCiEIgvvr/7BuYa8025zHj/uFD4Ae/dCsU7css1uY3eaewHh/878p7xWi5+sQv1iv7l5pYHT8lpnbpzpNu7g2YPKdWW61HFW51dbkqoK8efkOSJJkJdXNdcODQVfMkoDAwMZOXIk8+fP56mnnlJM0osXL8ZutzNs2DBycnJo27Ytjz32GOHh4fzwww+MGDGCpKQkOnToUOY1HA4HN998M/Hx8WzatInMzEzdWKCwsDDmz59P9erV2b59O+PGjSMsLIzJkydz2223sWPHDn766SflwR4REeE2R25uLr179+bqq6/mzz//5NSpU9x1111MmDBBI/DWrFlDYmIia9asYd++fdx22220bt2acePGebyP/fv3s3HjRpYsWYIkSTzyyCMcPnyYOnWcmQnHjx+nS5cudOvWjV9++YXw8HA2bNhAcclvm/fee4+JEyfy0ksv0bdvXzIzM9mwYUOZ758rjz/+ODNnzqR+/fpERUVx9OhR+vXrx4svvojZbObjjz+mf//+7Nmzh9q1awPO7MiNGzfy1ltv0apVKw4ePEhaWhoGg4E777yTefPmMWnSJOUa8+bNo0uXLjRo4Fv/H4HAV9TBtRUtflw5nxWEXcrSYSsIqpB5JZUPTL7G9OkwbEc/imf9DWlNyDwTDCFlr0uSnCLNFZvdpmRsqVHH9qjFj95YgJyzFuV1fr7nsXJdIPnzH9tmLG0T2/Lr4V89zn0+EeLnHMnLA6u1aq6dkwMWS9njAO68805effVV1q1bR7du3QDnw2/w4MFEREQQERGheTA+8MADrFixgi+//NIn8bNq1Sp2797NihUrqF69OgDTp093i9N5+umnldd169Zl0qRJfPHFF0yePJmQkBCsViuBgYFe3VwLFy6koKCAjz/+GEvJG/D222/Tv39/Xn75ZSVbMCoqirfffpuAgAAaN27MDTfcwOrVq72Kn7lz59K3b1+lGW7v3r2ZN28ezz33HADvvPMOERERfPHFFwQFOX/xXXHFFcr5L7zwAo8++igPPfSQsq99+/Zlvn+uTJs2jV69einb0dHRtGrVStl+/vnn+eabb1i2bBkTJkzgv//+48svv2TlypVKvzx149/Ro0czZcoUNm/eTIcOHSgqKmLhwoVu1iCBoCJQuzoqW/yEm89fXbYUl1AVW77Jr/N9iQlKSZEAA/HxEgXJBWBNgbQmZKWHQk1P53jfhhK3l467SR1DpBatsnhxJedM6QNPFl15Re4WAFngyJ9/kDEIi8n5+1rOHKtKhNvrMqFx48Z06tSJuXPnArBv3z7Wr1+vNIi12+08//zztGjRgujoaKxWKytWrODIkSM+zb9r1y5q1aqlCB9At1jlokWLuOaaa0hISMBqtfL000/7fA31tVq1aqUIH4BrrrkGh8PBHpWzu1mzZpp+bomJiZw6pfWVq7Hb7SxYsIA77rhD2XfHHXcwf/58HA6nGT85OZnOnTsrwkfNqVOnOHHihMc2Kv7Qrl07zXZOTg6TJk2iSZMmREZGYrVa2bVrl/LeJScnExAQQNeuXXXnq169OjfccIPy+X/33XcUFhZyyy23nPNaBQJX1BaEyhY/vtSvqShcLT9F+f6VUFFbZNRCSHmPpNJrRMcVISGBxbkj96znv7Jd1+W6LV9DL8tKLX5sdpvy2XmyzuSdDfN6HRlX8WMKMGE1Wb3OfT4Rlp9zJDTUaYGpqmv7w9ixY3nggQd45513mDdvHklJScrD8tVXX+XNN99k1qxZtGjRAovFwsMPP4zNVnG/uDZu3Mjw4cOZOnUqvXv3Viwor732WoVdQ42rQDEYDIqI0WPFihUcP37cLcDZbrezevVqevXqRUiIB7szeD0GKDWr1CZuTzFIFheT3qRJk1i5ciUzZ86kQYMGhISEMGTIEOXzKevaAHfddRcjRozgjTfeYN68edx2222E+vslEgh84Hy6vSp7fjWuFpXiAjMEAYH5UFz2/0H1+WohpAQXF0RQWOgMS4iMKck0tToVRv7ZUgtXsaOYQGPp49t1XSdPSrhS5CjSzbJyrdica8slIjjCo0ApyIj0eF01F7r4EZafc8RgcLqequLH3wryt956K0ajkYULF/Lxxx9z5513KvE/GzZsYMCAAdxxxx20atWK+vXr899//5UxYylNmjTh6NGjnDx5Utn3xx9/aMb8/vvv1KlTh6eeeop27drRsGFDDrvkZZpMJux273/JNWnShK1bt5KbW2o63bBhA0ajkUaNGvm8ZlfmzJnD0KFDNS1UkpOTGTp0qBL43LJlS9avX68rWsLCwqhbt66mZYsaOTtO/R6pg5+9sWHDBkaPHs2gQYNo0aIFCQkJHDp0SDneokULHA4H69at8zhHv379sFgsvPfee/z000/ceeedPl1bIPAXtdvLl+J658L5FD9ulp+CEstPUOnvogIvZXDU56uFg+KOynW67MPDwRBUMpHVObAwM0oZ7+o2cl3XyRT336Ge3F6uyMLEk2uqMDNSc131H3NqZLeZ/PkL8SOoMqxWK7fddhtPPPEEJ0+eZPTo0cqxhg0bsnLlSn7//Xd27drF3Xff7dZ01hs9e/bkiiuuYNSoUWzdupX169fz1FNPacY0bNiQI0eO8MUXX7B//37eeustvvnmG82YunXrcvDgQZKTk0lLS9OtszN8+HCCg4MZNWoUO3bsYM2aNTzwwAOMGDFCiffxl9OnT/Pdd98xatQomjdvrvkZOXIkS5cu5cyZM0yYMIGsrCyGDh3KX3/9xd69e/nkk08Ud9tzzz3Ha6+9xltvvcXevXvZsmULs2c7s0lCQkK46qqreOmll9i1axfr1q3TxEB5o2HDhixZsoTk5GS2bt3K7bffrrFi1a1bl1GjRnHnnXeydOlSDh48yNq1a/nyyy+VMQEBAYwePZonnniChg0b6rolBYKKoDLdXmqLR2XM7w3XX4nFsvgxFnsc4+l8p3AoeS27o3Kcv7/i41XFBEvcXjaV6HAVD/K8YSUeqRMp7hZuf8WPJ4FSlFUqwlJTtc1P9eZRW34sQSUxPx7iic4nQvxcZowdO5azZ8/Su3dvTXzO008/zZVXXknv3r3p1q0bCQkJflVTNhqNfPPNN+Tn59OhQwfuuusuXnzxRc2Ym266iUceeYQJEybQunVrfv/9d5555hnNmMGDB9OnTx+6d+9OXFycbrp9aGgoK1as4MyZM7Rv354hQ4Zw3XXX8fbbb/v3ZqiQg6f14nWuu+46QkJC+PTTT4mJieGXX34hJyeHrl270rZtWz788EPFxTZq1ChmzZrFu+++S7NmzbjxxhvZu3evMtfcuXMpLi6mbdu2PPzww7zwwgs+re/1118nKiqKTp060b9/f3r37s2VV16pGfPee+8xZMgQ7rvvPho3bsy4ceM01jFwfv42m03pnycQVAaV5fZySA63/lOVbVlS4+b2KgwuWUSpm9qb+FGfX1DgTJvPseWUioEcZ6JHQoJK/JRYfuRj4C5M5HnlnIiTJ/XFjy/FBeW15BR5Ej+xmut6mvNCd3sZJE82q8uYrKwsIiIiyMzMdOvwXlBQwMGDB6lXrx7BwcFVtEKBoHysX7+e6667jqNHj5ZpJRPfdUF5eeDHB3j7T+cfI8OaD+P13q+TYPWtUOmBswfYeHQjw1oMw2jQ/n2ea8vFOkMb+Ptqr1eZ1MmZqZqVW8hDL/1NfWsz7h8XQbQqC37VKmecZKdOzm3D1NK4AelZ7WNw8WJo1gxq1oSPP4bskrI0s2c76/sEBjkoLjLCgDHQej5MtSPbEm4ebOerxQEYDE7Lzuefl1ZdXr4c1q8vvU7noX/QsXs6M1cugLNJcKwj7BnIkCEw5e3ttHy/JZy4Ej74G4LPwjXOQkL3d5hAjbAayjwvv1pE5tkg7r8f3nkHYmqmk97IPZPTHBhMYRntKcZdOR67ZGfRv4vI1RMpW+5yrhUIMhfR5rbv2Xx8s9uwOhF1uLvdPXz575ckp/xD3wb9aJPQmum/TQcMvNjjBfr2NdKmjdfl+I2357caEfAsEFwGFBYWcvr0aZ577jluueWWcrsHBQJfUFtnPt/xOX+d+Iv/HvAthjDpLeeD1WAwcHuL2zXH9CwGasvSiBe+Y9lLQwDIPQsvveTcf+oUyJUjioshIACMBqOmGKPMypVw663O188/Dy7GaeccUTvgVEuwWaEoBLUTZcnXAaxdC927w6ZNMHy453td/8VVrP8C4AbN/lq1VIHI4cec/xZEweoZALzjFlbotDw3bHUaiCP9WAwcm+F2PV+a9XyozP2Ut2EAFBUGsfnjQcAgt2OHgSeXANwK3Mry1bAcAOe6nloNsbFUuPjxFSF+BILLgM8//5yxY8fSunVrPv7446pejuASx9UVtffMXg8jPbP5+Ga/xc+v/xxXXquKrnO8dDdpac6YmkBjoHKuuvP8RlW7MHmODh2gRQun5emLU09DaosS8WNxCiAX9u93ih/5/Bo1QG6vGB0Nc9PuIn1rR9g6EuwlcUPWk9DwB2rERPLww0M4ori9TkH/cU6rUAnXJ/WmVkQtACTJwdx/5kL1v2nVcxgNhyxn737vrsY2iVeSYI3nl4Nr3CxBTeOasfP0v17Pv793P97Z/QTGo11pENUQo8HgVj06zBzOrc1uZdWBVRzOOESn2tfQOKYxc/9xJo/c3nI4jRqVnSFXWQjxIxBcBowePVoT4C4QVCYVEYcTFRzltk+OR4m3xDO0+VDe3PSmRvwUZJZWhFfH3qSXFjEmNdVd/OQW5eoWS5RLkN19N9x5J2w5uYcvPpgFP77pPGCzloofUy40+wL+GatcW/63Sxf46KPSeT98+WuoMwcOXAcZJcVIa/4BA8ZhjWlE7dpD+O+ASpS0/cj5U8LYIYu4tZlT/GQV5jD3JWfhVqNxGA0GfsHefcvd7kXN5meKCDQGUu/NZziUcUhzLKFeD3Ye/MXr+T1v+4Z3Fn1M3e6/sedBp8IzTO2sGWOxJvDRo7fS//M3Ofzf94y9aQ53tmnCoukPk1uUy/MP9qB+VH296c8LIuBZIBAIBBWKa1Cyr6gtO1Eh7uJHPm41WTEFOKsrq8WPLbM0yEcdXKz3Wu3yUl9XnWC6bZvzX9lLrAT3mkrG26ylwc5B2UpmlnwN+V/XgvXK9aylCzNHZAClae8FXmJz1Gno6te+vO8GDAQYnFYuOQBZzdaUrWXOIQumeItn97lewDOgVHmu6qBnIX7KiYgTF1zqiO+4oLyUt5O7ugKx/LBUoyd+1NdyZJdmIrmmlatfS5JEYXGpylE/iNVF4NNKih/L4kVZn6lEcBRZtJafEjHjavlRh9ipqygbwkovFhXnFAkZBRkUFBd4FT/q9apf59pyyxRApgCTUt9NTj1Xk56f7rbPlSOZTpOYtyD2XFsuDsnhJn5kwVXVLS6E+PETOaU5r6q6mQoE5wn5O67XykMg8EZ53V7qOjR6Akp+0FtMFl3LjzodPCurtPGmq/gpdhQ7W0e4zOs6VkYWL8r61JYfRfzkKNWY5Tn0LD/qawVHlNbIiY4tUu7pVO4pjThzxZP4ybHlaNpV6KEWlWrLj2wNciXC7N5c+nCmM33Nm+VHQiK/KN+j+Klqy4+I+fGTgIAAIiMjlR5RoaGhiooWCC4FJEkiLy+PU6dOERkZqemPJhD4QnktP+qaMXoCSrYW6Lm9HA5JqZAsk5oKdeu6u71crSpqK4Rey4Zq1VzW51H8pGjm0LP8yA99c4CZ4MhMSvQZYTG5VLNU41jWMVJzUpU1BhmD3N4Lb+KnrFo+nsRPncg6HDh7wG18VEiUWyFD2e1VVvmC3KJcTWNT9TWF+LkIkTuOe2uSKRBc7ERGRirfdYHAH8ob86N2e+kVR9SN+XE4x6WcyYGikhLHIWmQH6uIH1fLj6v48Wb5iY4GU4leUCw/cjsLm8X5I++z+G75sZgsmCMzlP3h0fnYrQkcyzpGSk6KssaY0Bg3QaOukKx+nW3LLu0T5gG1+JHjb8BZl0dX/ARHcYhDmn2HM0osP1Z9y09oUCh5RXnk2HLcY36CLoyYHyF+yoHBYCAxMZFq1ap5bEwpEFzMBAUFCYuPoNxUhNurLPEjWxLkcbsOnQHCICgHog5Afqxb4LH82pP4kSR3y4/aalO25ce5/sxMyM0tbV6qnkNtvQqMzFL2R0QXYCpxI6Xmllp+YkNj3cSPJ8vPkcwj2CXvvRGDAkrd2NYgreVHj4jgUreXbIWS44I8ub2sJqtH8aPE/FRxiwshfs6BgIAA8YAQCARVit1h57WNr9G1Tlc61uxY9gleyC8sZuADGwjMqceJ7BMA9GjWguvu+Ju9Of/wYMcH3dz8e/c6qwoXFjofvpF1DnHkRB/YOQQMdrhyDiQmu11r7lyJud/vIMwcxpm8dOIscfywNx54F4BfT9bls2MQFFRadFB+YOYfvYIln3eHA++y+bd63Lsadh0qsWJYUxT308PPHeC7H2rx3z4D8uMuORkeezgMdryrrOWZP2J5I2QrTaNbYrNp7y8/+CAr9v3HD3t/YHvqdudOWfycaQDbRpTuC87AGFiEoziI6wYfwuGoi8Eg8d7O5zn9t7Oh8dZUZzaV1WSFkFKhEhVrI8LkNBGl5KRgwLmO2NDSIG6ZdYfX8djKx3i006Ma8fPmpjfdxrriye1V3VqdQGOgm9XOHGBWXseGxnIyp7Qxsye3l9LDy5aruEBdxc+9P9xLs7hmdK7TWXeOykaIH4FAILiImZ88n8dWPQa4t2nwl0feX8bPH95cslUbgOTv4fU9H0HrT2gU24g+Dfpozpk+HebPR3VObaBL6YDTTWG0tmdeaiqMHWsAWpTsqVvy773KmNV/wer/c77u3x9CQiCr0Gkp+XPBLRz6+wqgOYeA91cBlIiEqINOyw9wKLk+c5K195iWBp/Pj9Rca0/Jvxtx5xC/0Oezu7Q75RT1gmg4ek3pPgOExZ8m83h1Nq1w3pMUfpjnf3vWbV6ryUpAYmlmlTUMQgJLLD85qUqqv7qNhcy+M/t45fdXiAiO0A1I9oZa/CSGJSqv463x1Ius51aQUj2+mqWaRvyo3V4DGg3g2z3fcn3S9YqlKrMw083yk2gtveautF1C/AgEAoHAf7af2l5hc23ZX9KEKmoftPoYdg2G1FaQHwPAttRtbuJHTgcfOFBi6c+nIa+adtJMp4iSJEmxGh0r6diAOQOuft19If+MhcxSN4wSu1PiFstLc66HNnOoXy+QUa1GsSN1O4t3fwHNvgRzJoQfh6IQOtTo4Ow9Vf1vXu/xf2Qfr8nx7ON88Nf/uV22Z/1edK7Tmeuug3//hbsXPwYtP1GOD2s+jJub3ExwYDB/Nd7F1n8LWHngZ3Kl084eX8DVD8+mecYTzNxQ0lurobPgYJuENoQGhbLh6AbAaR0JrXEGhg4AyymCA7srlpSU3BRCgpzVjxOsCfxw+w/8feJvrk+6nn9S/mHh9oWsP7KetLw03SytzXdtZmvqVtpVb8ffJ/5m5saZSgVmtZgZ33Y8QcYgDAYDY1qP4dra1/LDfz9wZeKV5BfnUy+yHjN+K22TUTeyrmK5Aq3ba/7A+Sz+dzGDmw7mtq9uA5xZa67i53/X/I84Sxx5RXm0TWzr/tmfJ4T4EQgEAgEAWdkllqNqO6Db85Bdwyl+bJ5rs+SUeF1uHlLE0i274UiJ+LGkOrOvSjKwihylqdxKUHHUAed1VIQGhZJ38kpd8SNbFHLPlrhrrn6NWu2rMWX0KD7ZmszipdNLJ7rGKT7MtTvDEWc30e43pNE6oSa/HTnIB/O01wXoeZ2Fx651WiKuuQbuPvmK5viY1mPoleRsEnbjFSXnfPw/Vh8sbbZlrbOPe+48zcwg7fw96/ckOiRaET9K0HbjrwEIDuyrWFJSc1IVC0lwYDD9GvajX8N+AHSs2ZHTuadZf2Q9ubZccoK0gcPd6najfY32tK/RHoDWCa2ZmzxXOa4WP+HmcB666iFl+8rEK7ky8UrNfHKMkCnARK3wWqX3abJqAqYjgyMZ19ZZaVoWcak5qaXZXiXzxIbGKo1oqxJR50cgEAgEAOTmlMS7yDEt6sBe9DN0ZPETEJyvBPwCEF9iIbCFgS1UE2SsBBVbXVKrKIkJcdmvFA3MSYViE7lZcj+s0oerXoA0oLFUyOv3VECwrAwkvYrIwYHBmm2b3aY7f7wlXmMpsZqsmANL42mCA4OV4+qAZ9f51evIKcpxCxzWi8NRz6FXPNIb6lgdtZvLW40f+VhKToqb5edCQYgfgUAguIiRA2MrgtxcWfyUPFD9ED9Gc56mXQPR+yCwpBhsTrxGECiWH6tLahWy+NHuV+rm5KZCrtOyFBjkgOCzZYofOU5IvX5P4qesGjm+ih+lI7uKBGuCRphYgiwEB5SeGxwYXOr2UqW6qwOOXdeRY8tx+0z0RMk5iR9jaYq6em5vNX7UIk6IH4FAIBBc0OTnlTwSZNGjrmcDnCk443ZObskQg6m0zg1QknVVsp0br2/5sXiw/FjcLT9F9iJn9eIc54M1MsYGRkl5uPqSXi+77eTqya7CQp1qb3e4p4yr3TwyausNeLH8WOM1lhOryaoRJeYAs3I8qzCLjIIMQN/yI68j15brJn7KsvzIJQJ8RW35Uc/tqcaPeg0pOSnK5yLEj0AgEAgqDHWbhnPtx1aYVxIG6sHtpWcZkS0/kqrODeB8LYuYnARNuwZvlh9LkMVtf2oqnM5zFs0x5lUHnO0ggDItP5q1ulh+XGvbqMWPXpsIXctPgLvlxxe3V2hQqEaUBAcGE2GOUASZ3ELCq9vL5u720rP8qEXeeXF7lYw7lnVM2SfEj0AgEAgqhfJWVpaR1NWKwU38qCswyyjiJyhbK1pU9XY8u73c57OYLG77U1JKhVeYrSEAsdWc9ypbFsolfiK04kct7tRCSLmlc4j5SbAmUM1SmglXaC90Ez8Gg0ERDnIV5bLEj5vbS8ciU1ExPxrLjxfxI487mnW03NetbIT4EQgEgksEb53AyyLHllPaqkGx/Ki6l+MuCGw2kIvcOwKzte4qS2rZbi8fY35SU0uFV6itLgCxcU63lDfLj6tby1X8qKsXg/MasvXM1cplwEBIYIjbNVzFSZG9SPdziAmN0VRXzrHluIkfKBUOcj8tXbeXqkWEv26v8oofi8miEW9665KRhVFeUWkD8AtN/IhUd4FAIPCDU7mneGr1U4xvO15JJ5Z54dcXiA2N5Z5293g8/5UNr2AOMGtSjGW+3f0tqw+u5vXer/PCry8QHRLNjlM7OJ13mseveVxTwfm9P99T2gzIjPhmBNO6T6NlfEu/7ys1J1XbqkH9b8n+rMIsHlr+EO1rtOeOlnco8T4Ady6/DazRpTssp1SWnwRFEHz2GezeLY/xLeZn098FDL3ZCoXfk5HhTMWOq+YUKd7ET7w1niOZR5Ttx1c/Ts3wmh6DiQvthTyz5hmyC7M5ln1Mc8xisug2sZZFQHRINGfyzzgDnnU6shsNWltDji3HLdsL3C0qrjFFUGr5OZx5WHGPKfdcwQHPsmBzjVFSu1tdiQ2NxWgw4pAcyj5PXeOrCiF+BAKBwA8eWfEIC7cv5KN/PtJUVN53Zh/PrHkGgLvb3q37oMwqzOKxVY9hNBi5u93dbn89D1w0EHA+yP/vb20RvkBjIItvWQyAQ3Jw34/3AdA7qbcy5ts93/Lj3h+xPVO2C8iVtLw0lfjRd3sBvLX5LQDuaHmH4vIioBCbIQcsqqBja4oq5qfU8vPII6qLRhzFFWuQFczaOJai/GCKdjjr78hd0Bs2skOaZ/Fza7NbySzI1IgfgDu+uYPnuj4HOONukqKS2H92v3L8xfUvuq0JPFs6aobXBKBlfEvWHlqrcXtZgizkFuVqqjDXiajD4czDDGg0QFMtWRY5ruIlNCjU7Zp67jeZOEuc275zifmRK0zLLkK5v1ePej08nhNgDOCKmCuUwoo1wmro/n+oSoT4EQgEAj/YdXqX7n51AcD84nzdh5Y8xiE53NweajYec2+0kFmQqbxWuxPO5GszsMrbVDTHlgNFJZYbt2wv94dtYXEhOTlm7bigQrivGUhGMOXzwHW3MvtHSiw/6dhspc0+GdETzO6p8/KDvcXzg5nX92t+2fcrkz+fS5ylGoObDKZVfEsSq4XQtFM+j79bKnrkHlKTrp5Ez/o96VynM9mF2fy8/2c+3/E5y/ctV64hB+LGW+LZOHYj21K3UTuiNle8fYXH98dTSYGxV46lflR9TAEmN/HTu0FvHuzwIFfElM771/i/2Ja6je51u/Phlg+V/a5uLxk9S46r+Hnt+tfoWb8ngcZAXXFzLpafka1GUiuiFp1qdQLgwEMHOJRxiA41Ong9b/nw5aw9tBaAa2pd49c1zwdC/AgEAoEfePqrW+3WyLHl6IofdSxIji1Ht2klaGvTqMfrvdarKVMenDE/zlYU7m4v9xTv1NxUcnNdxgNU26m87NmiBbMBcuMptJ/g1Cnn/sBAieJ6v+iuQ07jNsbup21b2B10FNosoFX9nrw3orTi8vEs50Pc1fITZg6jdwOnNSw0KJQRrUaQnp+uET87Tu8AnG6xOEsc19V39h6TLTV6eHLzmAJM9G7Qm+SUZGUd6gKFXet21YyPDY1VrCZ6MT+uAct6AcyuKfdDmg6hdkRt3fW5XsffVPeggCCuT7pe2a4ZXlOxdnmjbmRdRrce7de1zici4FkgEAj8wJP4Ubtd9NpAgFb8eBoDkF2Y7bZP/VBWn6s3tjw4xU/Jvelle7k8+1NzUkvdXiZ3Cw5AYkLJI6bE7SVneUXFFoFRX0zI76/8fsr3LQf5ysgWjGJHMZJUWu9H7+Hueu7WFGfVZ1crizd3UlnI61EXOXRNg3fFW8AzOONkYkJi3M5zjVVSByKXdZ0LLfC4qhDiRyAQCPxA/YD0ZIHx1CbBlzFQmumjxpPlR29secgtytXJ9pKvY4QibaZTSk4K6RkFLuO0VE8secQUWcnIKlayvKJjPcckyUJFFjPyvboKE/VDvMhRhM3huZKw67n5xc7IIVeXkjfxU1YlbbX48daaQo1axOgFPMdZ4ggwugcKu8bPlHUdIX7cEeJHIBAIyom67o2rS0sPX8aAfr0eT+JHrgR8rmQX5rhnewWVxhbJ6e4yqbmpnEgvcc+Z9K1YURFBBJid93z6lFGx/ETEeHbVyQJEjl3yRfzY7DavbRQ8iRpXy49eBWdfka9b5ChNddfL1FKjPi4LIbWby1sLCX9QX0eIHydC/AgEAoEfqN1P6ro3GpeWh7gRX8Z4vK7K1aU+V51OfC6czS5AeSSUiJkQU7DHoOfUnFROni1xuXmw/AQZgwiJdFqm0k4FKuInPCZfdzzouL1s3t1e8lhv4seTqHGNpzkXt5fsbvPH8hNoLA271XN7hZnCyryuL73dhOXHHSF+BAKBwA/UVhd1IbxztfyU1ZoityhXETpldR8vD5lZKmtTicUnNjRWN90dnPeeesYpTEIt+gIs0BhIaJTz/LNpJsXtFR7tWfi5ih9Plp8AY4Dy4LfZbUq2l6+Wn+DAYDdxURExPw7JoQi2ssSP63rAN8GjxhdrlRA/7gjxIxAIBH6gFh5qt5e6sF15xI8vrSnkFPeyxE95enxlyOInKBeMTjETGxrr1txUJjU3lfRM5/1ER7g/5AMMARgMBqyy+DltViw/lig/xE+RvvgBbZyNv26vBGuCW+xMRYgfgGyb0yLmj/iRXVP+1sPxZc2abK8A/7K9LlWE+BEIBJcUc/+Zy4AvBmhq4VQkaveTr5afhdsX0u+zfhqxJM/z94m/6fVJL/449keZ1160YxFtP2jLqKWjvI7rOr8rhzIO8cKvLzB66WhFDB04e4DrP7melftXMvmtP4ht+Rc7DpxGkmD5S6OdJweV3l9MaIxXy096hlPwRYZ5rpoSFuP8HNbt3M2KrckAfHLgFY/jZUtGji2HMd+OUd5LPQuHLDju/eHe0mwvnYe72r0ko1c/x9W1psZbRWP1WqC0VEFZ4kftsnKtAO0r3tYsIyw/7gjxIxAILinGLhvLsj3LeO/P9yplfrWwUbeX8JbGPnzJcJbvW85z655zm6f/5/1ZdWAVXeZ3KfPar/z+CltObilz3Poj67n3h3t5Zs0zLNi6gE3HNwEw7rtxrDywkus/vZ5XH7qK9O3tGHz3bvbvh/R99Z0nx+5W5mkW1wyCnTE7CYFNNddIz0/nbIZTEERFBpAUlaQ5Lhf2q5XofNjmnw0jI60k8Lak51f1sOpua1c/zOcnz2fDkQ3OU3QsHPWjnGv+ce+PStd3vYe7XKVYTeuE1m779K7RNrEtAFO7TXU7pkYtumTx45qS7kqrhFa6+29ucjMAkzpN8njuHS3vAOC5bs95vYbrOoT4cSKKHAoEgksS175XFYWnrCtfYn7UliJ5jLrFQVmczPZ97H/p/ymvZSvY3vS9buPSU0JKG40CDO+nvHy116skN8tk3RGoFdAW9bAcWw5Fp50P0sQEA8vv2cqp3FNYTVa2n9rOlYnOHlz92lzJ90CsozlpOSXBvCU9v7rW6crz3Z+nwewGyryuAkQWEnrCZN3odUS+HAmU1jvyFPB8YuIJTAEm0vPTSc1J1fRJ83RtgM9u/gwJiUYxjdyOqTEajAQaAyl2FPvs9ooNjeXYI8fcrFqfD/6cg2cP0ijW8zXnDZjH052f1lSP9oSw/LgjxI9AILgkKU/ciy9zqoWNOuvKk/jxlI0lnxtuDtet6KyH/FD1BbX1ydt7IWFQYnGotUHTVysoIIhm9WJZBxRlaYvt5dpysZ11VrGuXd2ExWShnqkegKbvU0KC07UjZdaCgijnzpKeX8GBwSRFJ2EOMCs1kFyFgFyTR0+YRARHYAowYbPblPfc08M9MSwRcLryPAkGT9fwNeXcFGCi2FHss9sLoEa4u1XKFGDyKnzA6cora4yMED/uCLeXQCAQ+IjNbsMu2ZVtX4ocuvbech0THRKte/xcUa9BrpmjBNMWad0xiuVHp8t6fElojC0zym3+/Axnw856tTzHncjnnzlU0hLBaIOQs0DpQ1kd5OvJVeQptkV+mJclfnxB7xr+BEHL15atUP4EPFcmQvy4I8SPQCC4ZFC3mKiMLtKu7ixPbi+1RUjt6tI7Nyo4Svf4ueKpHYZzR2mwb7GttP6O7I5Sk1Bi9CjIjNTsL7QXUlxiDWpUJwJPyOdLjpJKxdZU5DhfPXGgF5wMnkWIXF9HFnjn8nDXu4ZejzZPyGvxx/JzPhDixx0hfgQCwSWDr66e8uIqftTX8+T2Umd4ac4tESeVZflRI69HWVdOqRsnPyO81PJjTXXLOpItNwUZ4dpJCy1KBliTup7vId41qUplXdITB55Eqyfx4/owr2jx408WlrrKM5Rd4fl8oV6HL0URLweE+BEIBJcMatGhtgJVFK5VmX0JeC7L8uNLkTpf0pm9kWPLwWa3lbrgckoVSVFWVKnlx5LqJgBky03uWRfxI1uPgnJJiPbsGrJYwKo+rLIu+WMZ8fQ+uYodf7uW+3INX3Fdy4Vo+amoiuAXO0L8CASCSwa1OKmMKsjlcXupW2DonStXJvZGbGisX+vUu9ap3FOlO1RuLwoiOXio5IFoTXETP7LlJvesRdvZvURABYanYzR6tyZorD/W0vdDju/xxUp3Piw/5yKc9K59oYgfdRyVED9OhPgRCASXDJ4ysSp6ftcgW/Ac8OzJ7SWP8cVCda7iJ7coV7uOHG320o7tJeLF6m75kYVLsS0QClSxPSVzBEeW3VU+QX25clp+PMXeVKT4OVcuVPGj7gxfVrHGywWR6i4QXIbc/8P9/Hv6X1aOWKkUZ/vtyG/ctewu3u73Nj3r9zzva5q8cjLrDq8jJDCEf1L+YWybsbze+3W/5tCrwZOak0qvT3oxuvVoJl49kU+2fsKM32bw7dBvaRjT0Kd5i+xF9PqkF3vPOOvkxFviOZp1lNyiXCRJwmAwaCw/21O3E/tKLD3r92TRv4t055TjhXwRPzGhpWnmIYEhSvq3G/uuh6XzoUgrFJ6fAS8YDCBlOHcUax/KkiSLnxRCgyI1x0JCIDwcsrKAN46CocRyYHc+6MO89OmS0Vh+dGJ+ygpODw0K9Rh7U5Hi51yD5F2vXVaRw6pAWH6cVLnl55133qFu3boEBwfTsWNHNm/e7NN5X3zxBQaDgYEDB2r2S5LElClTSExMJCQkhJ49e7J3r3thL4Hgcubdv95l3eF1/Lz/Z2VfjwU92JO+h16f9KqSNb36+6tsPr6ZdYfXkVWYxUdbPvJ7Dj3x89uR39h+ajvzkucBMHLpSHal7eKeH+7xed7fjvzGusPrOJF9AoCa4c60bYfkUESPWvxISKTnp3sUPur1+SJ+1K0YrCYr3ep20x0XsGsY5CRCYYTbj1QQXrptNwMODKGqNPzIg4TEnOH1652Cc9LVpdWFu8jFp21hpXMUhwBQt+XRMtevnG8ohpqlbTw8WUb6X9Ffsx0ZHOlx7ooUP51rdyY4MFgRWuOvHO/X+e2rt1deVw+rrtQWuhBok9AGo8FI1zpdq3opFwRVavlZtGgREydO5P3336djx47MmjWL3r17s2fPHqpVq+bxvEOHDjFp0iQ6d+7sduyVV17hrbfeYsGCBdSrV49nnnmG3r17s3PnToKDLwwTpEBQlajjK9Ly0pTXcobKhUK2LZv8onxCgkJ8PkedfeWa4eTqfpJrsfiCq6WlflR9Nh7bqMwfEhSiiJ+P+n9EQXEBE5ZPUMY/ee2T/H7sd9YeWqtZnyRJPomfOhF1lNdWk5XVI1cz75953PXdXQBcVfMqfr7jZ4b9aeUHoPXwL0mOflJ3rp5JvZjadSoREQaa1IklcGIDJAcQfoywkAi61+tO1uNZhJlLu4t/+y28s+JHHlz+oHaywAJ69R8L3OJ1/Q89BA2v2cYNi7uDSnB5Ej9Lhy7lhV9f4Nm1zwL6fbhkKlL8hJnDSJ+cjinARH5Rvt+NTt+94V0mXzMZu2SnRliNC8btBfDnuD8ptBf6lbp/KVOllp/XX3+dcePGMWbMGJo2bcr7779PaGgoc+fO9XiO3W5n+PDhTJ06lfr162uOSZLErFmzePrppxkwYAAtW7bk448/5sSJEyxdurSS70YguDhQdw/PLCw7XqMq8RQs7Am9mB/537S8NJ86p+vhWqgw0ZpISGCIZn5Z/MSGxpIUre1zVTeyrtsDXEKioLjAJ/FTN7Ku8tpismA0GDWusHhLPGHmMFJTnW6bulfkQMx+3Z+E2jl0al2NZvXiMBoNtGoS5jwWVKg87NXCB8BohLr17cocYYmnnK8jjvtc/TiprlkjfMCz+DEajFSzlP4B7O0aro1Mz7VreWhQKIHGQMLMYX67wQwGA/Wi6tEguoFfov18EGAMEMJHRZWJH5vNxt9//03PnqWxBUajkZ49e7Jx40aP502bNo1q1aoxduxYt2MHDx4kJSVFM2dERAQdO3b0OmdhYSFZWVmaH4HgUkUdmJtRkFF1C1HhKdvHU5q4J/TcXvK/EhKnc08rx/15sLmuI8GaoAgFef7CYuf7ag40u1kMrCarrvUix5bjk8WtTqTW8gPaeBJ5brleT81EzwLANW1evVZvKfVqoaIOwI63erbKeLqOjFx/Ru/zV4/3do0LKeBZcPFQZeInLS0Nu91OvEsFrPj4eFJS9H/h/fbbb8yZM4cPP/xQ97h8nj9zAsyYMYOIiAjlp1atWv7cikBwUaGOTblQxI8n64enTClP6KW6azKvVJYkf4oguq4j3hrvJn7k9zU4MNjtQW8xWXStF3L9nbJQu73kv97VYiTBmoAkwamSbPb6tTz/he+2NpXg8ebmURfKU1udfLX86M3tzS2kHp9g8XwNtdgxYCDAEOBxrEAgU+UBz76SnZ3NiBEj+PDDD4mNPbe0T1eeeOIJMjMzlZ+jR8sO4BMILlbU4ud03mkvI88f6jWpORe3l/xaHQfkr5jytI54S7xSEE9P/OhZV/SsF7lFuT6Jn1oRpX+QyXWB1MIh3hpPRgbYSqZqWNtzuwk9q5SnY2rU14sJ0brcfEGvgKA38aN+D321/JgCTJXS1kRw6VFlAc+xsbEEBASQmqr9pZKamkpCgrvK379/P4cOHaJ//9IsAIfDmbIXGBjInj17lPNSU1NJTCyNsk9NTaV169Ye12I2mzGbL7yURIGgMlALDX/dSpWFJ/FzLm6vguIC7A67T9WWy8Kb20sWV94sP1aT9ZwsP2qRIN+Pq+VHNm5HRkLtGM8JIxUhfsrj9tLr2eUt1V3j9vIx4Fm4vAS+UmWWH5PJRNu2bVm9erWyz+FwsHr1aq6++mq38Y0bN2b79u0kJycrPzfddBPdu3cnOTmZWrVqUa9ePRISEjRzZmVlsWnTJt05BYLLEbXQKK8lpKJxFT9N45oC5XB7uTTwzC3KJadI3+3lD26WH3/dXkEWjzE//rbh0BM/8ZZ4pUVFfLx3seDN7eWtvYM6xkgWP5Ygi98ZUWp8dnt5ca0J8SMoD1Wa6j5x4kRGjRpFu3bt6NChA7NmzSI3N5cxY8YAMHLkSGrUqMGMGTMIDg6mefPmmvMjIyMBNPsffvhhXnjhBRo2bKikulevXt2tHpDg0kSSJCQkv5oRng8cjpIiceXA7rBjtQQgGyftdmf2jSfrvt1h11R0LSwupNhRrDzY5MBcKNsSYnfYMRgMGDBgMBiUufOK8jQP7TBTmOaa3uYzGow4JAcBxgDyi5wp5OogbIAW1Vqw8/ROjehwvS+9+1OETkEYBOWRVZhVpuXHITnIKswiwhyha4FwOCR2H00Be6krKaAwBpOtGuRHcPRUNmfPSpw96wAisOWGYjOGQn7peHt+GMGGSGVfeLiBrIIcTp7OpzAnBIq9u2oyMlDOzco0Ol1cuaXXCLEnsueAc2x8vFOcyO+zK94Cnq1BnoWM+v+U7Pby1erjCW/xOWoh5tXtZRTiR+A/VSp+brvtNk6fPs2UKVNISUmhdevW/PTTT0rA8pEjRzAa/XuITZ48mdzcXMaPH09GRgbXXnstP/30k6jxcxngkBxcPedqJEnij7v+uGAEUFERtGkD//5b3hkCCLEU8+emQBo0gFatoFYtWLnSfeTIb0ay+uBqdt63k4jgCHac2sHVc64mryiP9294n3Ftx2msLHL6t55LYuhXQ1l3eB2mABMNohsws9dMus7vSoPoBmxL3YZdsitjG8U0Ytu927w+fGx2Gy3ea4EBA6m5qdSOqM2u07swGozMuG6GZmyD6AZAqVh5a9NbPPXLU6wasYqONTsq4/ak7aH9h+3JseUwu+9sp9D5YTb8OQEiDtMsoCNX1r1CGe9qwSl2FNP2g7ZsS93GkKZDWHzLYrd1J7b7E9s/2vNiXwb4DIAnXoYnAMgAoNXL8qgMZbzrvrzoo5AfwsjnY4GyXXFRL5eemwpEPQNQR9nX9uXSsQkJzrTmuNA4XUtXed1eauRO9L4GO3tCFmdBxiAK0Fr/1JYmddq7K+rU9nNNcxdcPlR5e4sJEyYwYcIE3WNr1671eu78+fPd9hkMBqZNm8a0adMqYHWCi4kjmUfYfNxZITyzIJOokKgqXpGTw4fPRfg4yc8N5NdfITMT9uxx/hQWgmuo2nf/fUdGQQa703bTsWZH1h1ap1g+ftr/k5v4kZDIteUSEawNkJUkSVOd+EjmEe745g6ybdn8k/KP2/r2pO/hWNYx6kfVdzsms/n4Zv5L/0/ZVmearTq4Snl9ZeKVXFfvOl5c/6Ly8H7op4cAeGD5A2weV1oFfv2R9WTbspX7S89Lh/9udB7MrEPW0VocjDiojJcrNMscyzrGttRtAJpq1zK2Ijunktt6vKfyUnymcjJKg4JADosc0nQIy/YsU0SwjF4mmt5rV5Kik7iq5lWEm8O5rv51VLNUY1DjQX6t7/FrHueTbZ9QzVKNQGOgInK/v/17Bn85mNl9ZytjE8MSubrm1YQEhWgCrF1RC+4Lqaig4MKmysWPQFBRqAvRqa0SVY0ciFqvHuze7d+55udN8OPbsGU8KSlwRakRg9RUqF27dLuguEARFLJLSu3mkV+7xtcUFBcQgVb86HVEVwsXgJm9ZvJAxweo/lp10vPTPQYty3jrmC2vu1FMI/4a95fSQ8vVTeX6l73r/Z3OTdM27cxJICUnWdk8nHHY4/m5ttI+XTL/HT0DUhzgICPLTrApUHP8/h/u56MtpaU30h87U1oocHoYthJ3XsHThcp5CQkSZ8+WzNHwe7jtZt33ZFCTm/l88Oea67muz3XbaITAkt/qb/d7WxETo78dzcdbPwbKb/kxGoz8fufvgPOPzJRHU/zOrJrRcwbTr5uubMvnd6nThVOTTmnvxWBkw50bNOP0UIufMFOYx3ECgRohfgSXDOoCdnI68IWAHIiamAgmf0MSAosg7IQyj1zHRd5Wi59TuaUH5cJ5areHHDzsGl+jJ1r03CWu1ZEjgyMxBZiUv7bLEj/eXGKZBc5K08GBwRgMBsWdkmPLIa8oTxnn+nBWB0Sn5qRy+owN7Kq//nPjNfd7NEtbxkJ9vl2yU2gv1FgPdh8+C8RhsKQTERbntu7a0QnOzwhnLE20tXR9hsAiMDiPmc2lD++EBANnz5ZshJ1QznfFbDZoziuZtYxtl6MlokEdy+Nq3fHH7aUWIeVNKfd0nt5+X66h/l6dS/C14PLiwgiKEAgqAPUD298MmspEnYVTLqypyjzqyhAuVSI0D3L5/jXip+S1nuXHFV9SwuWHqCwW1IHUenizxsltNuS5wkxhymu1q8o1WFd9f0ezjlKQEa6dOEf7pqsFXLGj2E3kuWaL7TvidKmZIjJ0160OxHUNypXQL6Ko+R5YPWefebOU+Ys3gaPJ9vJS4flCRS1+vLntBAI1QvwILhnUD+wLSfzIbq/yi58UZR51oXLXouV696/el2PLIdeW6yZ2XC1BACezT5a9LLnNQknl37IsP94+E9ntpa77Ilt/dp7eqYxz7ZfkJtLULi+9bRUFxQVu57u6+w4dc2ajWaL0U/XUAb++Bv9qypiVfLZ6wfkVKX7UPZ0qIuD5QkJYfgTlQYgfwSWDnuXjQkC20OjU7vQNi4+WHx3Ll2udnNTcVF3Lj2urhyOZR8pclvyg8dXt5e0z8VS7BmBrylZln6t1yc09lxvvcdvV7VZoL3R7f1zFz7GTTktReIz+vanr6bjW1jF4cElpRHDJZ6vnEtTLwCsv6hIBl7T48ZKqLxCoEeJHcMlwqbu9/LX8SJLkZtlIyUnRFT+uzTUPZ2oDg3WXVYHiR0bdP0p2I21NLRU/ruLEH8uPayZaQXEBKbna89W9wQBSU5wCJiZWPy5H4/bS6dque46O20uvz1hFpm2r55e70cv4mu11oSLcXoLyIMSP4JLhQnd7navlJz8f9u8v3V1WzE9WYZbi0moV30oZ42o9KSgucNvni/iR40MqUvxoWjaUNLOUU9FBK07yi/LJKnS6o1rGt3TuLInxadUKzXaAIUDTHFReb1mWn/Q0p8Uk3sNnpxY8vqZZ67m99N6binR7qXENIr7YLT/q9+liXL+gahDZXoKLjjP5Z4gwR7hV+z0flp/TuaeJs7hn/XhDbflJz0snMjhSWbtDcrDr9C4SrAlKppM6PgMAcy4GUy6SzcLevaW7d+7NJ3mrmZTsFCQkdu80QUoLAA7uDmNd1llIaUFokIWo4A6Q4uCnDSdIy7Mp4wA2/11AwKkCzb4fTx0DR+m2Hkf/i6LoJNhONIaU0+zbFcqveRlYzVYCjYFIksTZgrNEmCPIseWw90SI5hp65Ec0ZJusdVJbQkoL9qYAOM9LsVXj6zV7SbAmOIVPSgsCA4KIt3SHFAnSGgNO8bN1K07LT0oLQkxhmMLbQUpp8HReUAiH0yMgq3RNa/5IJ+foIepE1EFC4vhhpyWqRqL+r0rXGCQ1ntxeYeps7BJhq2clqki3l7esKbVgcLUKXQyImB9BeRDiR3BRsTttN03eaUK3ut1YM2qN5lhlx/y8tektHvrpId7o/QYPX/WwT+dIUqnlJzPgP2JfbcSARgNYOnQpAPd+fy8fbPlAaUVgCjCR92Sem7Azhp3Cnl5Ps+/f5BDatAaoXrLnNeXYtPflV9vIA9aWbH2gjHhWefWMaqyMNqldn97Kee8AMOV91xEGILrkdQRwneYaenxX8uPk/pKfUvYBQ5QyMWHANooB12LXLUsMQRRZ4f1t5CjzvqCMKQCOuZz3guYeDEB7AOrUKNuqExkcqdmOt8brxk6FqrWtOdvjfBXp9vImCtQZXhUpuM4XQvwIysPF900XXNbM/WcuAGsPrXU7pq4H4xrDUhHIVYYfWfGIz+InOxsKSrxBi4+8DcC3e75Vjm9J2QKUlvm32W1k27LdHqQhVy8gd93dSA4Dzdvks+PIMUhv6PG6YeYwjIYAMgsyCAoIwmoKI6sw05lurh+KUiZGoxGHo7RXlDPexUBmYQYFRQUEBQRRZC8iwBhAbGgcqS7xOAHGAOwObbp7SFCI0t8LINQUSpjJma5ul+xkFJzF4XDo9qiymq3kFOYQGBBEuDlMqRWUVMvKrbeGsGDln2z/zVlJOTAgkJDAELIL9cVGUICJIpVgNgeacUgOZ72oqAOMHdBS9zyA169/nUX/LuLBjg9q9i+9bSljl43lpZ4vafb36gXdrytmT9AiTqgMMnNvmsudy+4sXVMFur3uuvIuvtr5FTc1usntWEhQCGPbjCXblk3dyLoVds3zhSbm5yJM1RdUDUL8CC4q9B6CMuoaLhdKzI/s8rJaodB4xu24a10Z0L9HS9f3sXWcgc1u47buz7NjzTNer/vUdS8RGhTKgz89yAClZ1Uc45aN46N/PvJp7Xe1uUsZ+3LPl5l8zWTML5hL0+ifdaqoO7+dyLzkeSRG1OZI5hFqRNTm8MOHMUxN1MyXGF6TY1mltpZ1o9dhd9jp8XEPZd/9nf7HK71eKdkKAJzdw/ed2UfD2VqxN+zKcXy45UP6XtGfZcOWAVp35JAnlrN9rdPC1a1+T8a0HsPwJcPd7jMkMISBjQfy+Y7PlX1tal6FzW5jy8kt/Hj7jyREe7YoPHL1Izxy9SNu+9sktmHL3Vvc9gcFwS+rAoHhGKbeoewf02YMzas1p8NHHYCKtcJYTVZ+u/M3j8c/usm378SFiLD8CMqDED+Ciwq9rBiZC1H8qIOd9VpG6O2T166+HwlJ2a9u4+EJ2YIELunYfnThTopOUl57e6jIgb6ysPFU7ND1M7GarG5jPQUN611//1ln9LdrlpXeOfGWeI/j4q3xbvOn5KS4BXSfD9SCRzTp9A0hfgTlQWR7CS4qPKUPS5KkqSBcGeLHUwCrN9TBzv6KH7UwUGdS+Sp+5Bgo9UPfny7cSVHu4kfvPZDFgWyx8pT1pSd+1Knt6rlc0Xuo7TuzD/As6NTnJFgTPN57gjVBt21GfrHTHee6xspEI34qKdvrUkOIH0F5EOJHcFHhye3l2jqhMsSPt95UnlBXd1YLHbn3mDfxoxYR6tdnC866naM3h1zDRv3Q92T90KOapZryWraC6IlPc4BWHPgqfixBFjex40n8uGXAAUcznX26PIkadfxHvCXeo0iKt8S7xYrkF+crveKqyvJzMQYfVwWizo+gPAjxI7io8OT2cm26WRnipzwPQXV1Z3WNmtyiXGx2m25gtp74Ud/P2fyyxU+Ro6jU8mPVt/y4BlW74mv9F9f3pdBeqPs5uYoiq8nqs/jRa/8gCzFf3F4J1gSiQ6J1BYWe5QdQ3IbC7XVho36fhOVH4CtC/AguKjy5vc6H+CmP+0Pt9lKLFrnPlh564keNz26vkrpHGsuPSgiVR/x4c3up0esX5mq1s5h8t/x4w5PlRxPzY43HaDBqrFnKMUu8xmLgauGrTPHjei3h9vIf4fYSlAchfgQXFR7dXo4L2+0VV82hKcKYY8vRdXlBqUtMT0CAb+KnsLhQqXjtqf9UWfejfpDI4kBPfOqJA3X6uicCjYFu57q60HzBl5gf+b71rETx1niNeFaqRZdQVeJHuL38R6S6C3xF/O8SVApnz8Lvv0OjRtCgQcXNq3anFBdDcjK0aeNu+SmyF7F1KyQlOdPMy6LYUcz6w+vJLcqlRlgN2iS2cRvj+hA8fRo2bXK+btcOTpxw/qiRKzJbo3MoTi1dY64t16MLLzklmQbRDdhwZIPucV/Ez+m804oAVIuDcHO48rqsdhTerCFq9Cxivxz8pcw1grvYqUjLj6ZVRskYvbEJ1gRN5/jaEbX568RfHtdYkXi1/Ai3l0/IfyyAiPkR+I4QP4JKYeBA+PVXMJmc1o+oqIqZV215eOIJmDkTnnkG7p+sFT//bqzJ45OhUyfYoK8hNMz8fSZPrH5C2f5r3F+0rd5WM0b9EJQkie7dDfz7r2/rDgpP12zn2HI8uvDGfz+e8d+P9ziXJ4uQmqNZzmDgcHO4RgSo2xxEBkd67N7eKr6VxnIiz9Ektomm0aj6mJohi4eUuUZwF07+ip/gwGDCTGG6x9RCIjbUWS8o0ZroNi7BmsDxrOOl2xatQKpMy0+T2CZsOFr6BRWWH/9Rf4fKY50VXJ6I/12CSkHuz2SzOZtxtmtXMfOq3V4zZzr/ff55uHuSVvz8+V1rwGl98oVDGYc02wfOHnATP+pfrHmFNnbudLcIREW5W7oaN4ZaTVJhU+k+b+KnPDx2zWPsSd9D87jmvLD+BdLy0gD9uJ55A+ax8sBK2ldvzyMrSovzXVv7Wm5oeAN/HPuD165/jUBjINO6TeNswVmlI/riWxbzv5X/48nOTyrnlUccfNTfWVQv0BhIoDFQsdxFh0R7PGfJrUv4dPunXBF9BS9tcFZNjrfEe+xb1TSuKfe3v5+a4TWVdiH3tLuH9Px0OtTowHf/fUeD6AZ0qNGBNglt2HB0A/2v6M+/p7WKtjJT3T+9+VMe/ulhJnWaBGgFT3lKK1yOtIpvxX3t7rsoq1MLqg4hfgQVTmEhZGSUbrt2Hz8XfM32krDrjvOE2nQO+i4h9UPw8Ik8JMmM0QhGo9MFB3DzzfCRTrHcVQe08T25RbkVKn76X9Gfl2q/xEdbnBeXg6v1AkBHtx7N6NajlVYhAF/f+jU3N7nZbewzXbWVpBvGNFT6ksn4K35mXDeDsVeOVbbNAWbl8/NWh2hQk0EMajKI2ZtmK/u8jTcYDLzd723NvvY12ivrVwu4QGMgXwz5AoDp66dr9lemBaZuZF3N+6kRP16akQpKMRgMvHPDO1W9DMFFhgh4FlQ4rmKnIsWPp4BnV/GjHlfkQ5sv15TzslxLh487xVFcHFSvXro/3kMZHdfgZm8Bz+VBFiByhpCcpu1rivq5BIr6K35cXRPq996XCtSuWVwVjZ6773whLD8CwflBiB9BheMqduSMp4rA11T34uLSB8fp02XP6yp+9Cw/auvQkRPOYOKEBOePTIIHQ4RrWru3VPfyIFulXIWFN1GjjmE6lxRhfwOCXdeozszTK2boiqZ+j8Wz5ae8qN+zygx21kN2zwkEgspFuL0EFU5lWn4Ut5dDq9uL7Frxk5tR+gBLSdFaZ/RwFU964kc95sRJp1stPh7Mquejr5Yfb9le5UG2ULgKC18tP+cifs7V8uMvl43lR7i9BIJKQ4gfQYXjaumpSMuPgxJ3Vl6sZn9GptYdlne2NKXbF/HlS8yP2jp08mRJdeFyip8cW47imjMajF671fvCxSR+zrV4nzqd2Z92Hb5SleJHXclauL0EgspDuL0EFY4sNiwW7XZFoFhLcrQPvVOp2gdFQWap+PFFfMnCRn7wleX2OqVqW+GL20tP/MjtLmJCYspeYBmUR/yoA7jPpT5KRVl+fHF5gXvbioqmKsWPGmH5EQgqDyF+BBWOLDZatXL+W6HiR475ydWKn9OnVV/lQgvFBaUPLV+uL7u05JoxZbm90k47YzPi4yEionSMJ8uPuq8XaAOe5Ro054In8eMt5kftdqsoy0+EOcLLSCeexI8v50Llu73UQrBKxY+w/AgElYZwewn84ljWMbanbqdPgz6av0wLiwtZvm853et2JzXV+RBr1cpZZ6e8bq+0NFi61JlRVb8+ZGfDiW2NYPO9cEJbOGj519GQca9zI19bK+ann4s5mPcvzeKaEhgQROPGEBLitEw1bQrffAOHfu4LOXGEXrMVOElhcWm2l8PhHHN2/TAoSSFPTnaKre15K4mwNQZqAZBrPIGhwMIvB3+hb8O+HM08yqoDq1h5YCUAUcFRnC04S0puCj9t/QmAmNBzt/zIgbn+WH7UbjxfrS6611ZZkOpE1mFb6jav4z2Jn7J6jcmcT8tPZdb4KQth+REIKg8hfgR+UesN50N+6W1LGdB4gLL/sVWP8eamN+lWtxv2lDUAtCxpkZSR4az9Y/bzOTJpEixY4Lr3Sb2hfLswAXhX99i6NYGsW9NK99hXX8GttwI8DECh6VVoMllj+fnpJxgyBOBFZZ9sx1mw/0XIjwW+AqDpu03oUKMDqw6sYnKnySzft5ztp7Yr5yVYEzhbcJYf9/6o2XeuyIGy/ogfdUFBva7pvmI1WTFgwGAwUCei/OLnmlrX+HS9cHO4cr1L2e2l14RVIBBUDEL8CMrF6oOrNeJnzj9zAFh7aC1XlLiZGjeGoCBnnZ1Tp6BWLf+u8d9/Hg6YMyBpJQQWQIPlsLcfXWpez6+H16kGSXDFD1Q7M5BTaU53VVBAEKFHBpKZWTpq0ya0ZNYEoMBeKn7kdRgjj+Ko/kfp2MhDUGc9IJHYdz4nI5aQVZjFqgOrAOd74pqaXy+qHrvSdmn2jWg5gq92fuV2m58O+pRNxzex+fhmNh13XagW2Urg2g/KWyxP64TWTO8xnTqRdbzOXRbh5nBm9ZlFcGCwplVD76TejGg5gr9P/s0XO77gZM5JwF38bL5rM59t/4xp3af5dD2rycpbfd8iwBBQKV281a7CcxGF5WXRkEX8l/4fV9e8+rxfWyC4XBDiR1AhqONHUlXBwPHxcOyY0/Xlr/jx6C5L3CKba5y0/Jyn7ljBr5/e6ja0esI2TqUkAxBqjuDKnzNYs6b0+FZtmyqMRc5AabXlR15HYNPvsF1/P20T2/L3yb815xl6PgXZ2q6mVpNVaTMhUz+yvmZ74c0LqRFWw23dL3R/geEthzO85XAe+emRMsWPjD+WH4AnOj/h9bivPNjxQQBNQ9B5A+aRGJbI8JbD2Xx8syJ+XAVa+xrtaV+jvV/Xm9Bhwjmu2DNqwWh3+FcpvCK4tZn791ggEFQsIuBZUC48BmMWmRXLijoTyt+gZ0nyco5VeyDAEOBWp0dGLWIkJLeAZFn8BEU65zTY3MWPvA6HxamCokLcu7SecBE+4HyIugZOJ0UnabbjrfG6rhX1Pn+Cev0VPxWN+nvhKTbnQm8+qV6fXTr/4kcgEFQ+QvwIKgQlOLMkC8tkcmZByWLD36DnnBzIy/Nw0KIVP0EBQb6JH0lyS0WXhU1QbEl38yLnX/3qgGd57ZLFabnwNTA3ODDY7eGZFOUifizxukG16n3+1LLxJ9urMlB/DuogavU9XOjiR41r/SeBQHBpIMSPoGLJcaqLhAQwGMpv+fE63qpVUqYAU7ktPzKBMYedYwotbufJa7GHHgcg0hzpZXGqOXUaYsaExhASGKJsJ1gTyrT8+BPUW9WWn2Kp9HNQt2q4mCw/ajx9rwQCwcWNED+CiqWk+KAsMuR/K1b8aA+qxY9r9eCyLD8yxphDADgKQ9zOU9ZScl09t5ceeUXupiuryapJYY4Kibqk3F6exIL6Hi4m8SPcXgLBpYkQP4KKpcTy4yp+/HV7yeMjI3UO6lh+5MBU13o1ahHjkBweLT+G6P0A2AuDNec5HGrx47yur26v9Lx096W7iBGjwXhpWX48iZ+L1O0lLD8CwaWJED+CiqUk5ke2sJyr26uVXnke15gfY2nMT0hQiOaYOnZHQt/yExQEhB8DoKjAGWsji58zZ8Au//FvOQX4IX7y3cWPXgyOXudw9b640Difrgfulq9zaVtRHjyJBeH2EggEFxIi1V1wzhQWgm39A5AVDgevA9wtPzt3wuOPu597LOsYmQWZBBgDCAkMoU5kHQqKC/hudTpQg8aNYd06l5O8uL1cG4Sq6+xIkn7MT1RsEQWmDABs+c4Hc0FxAcuWwXffSYCBsMgisgOc14gKLnV7mQPMFNoLXad0zmW3ue2zmqxu3dz1YoPUlh/X1HBvuI69UCw/6oJ959rY9HwixI9AcGkixI/gnFmyBGzLp2v21amj/TctDV5+We/smiU/aoIBZ+2bunV1TimxwMiYA83KQyo2NJZTuad0TnIKo2o6RXPzLbtxBDrz820Fzgdzfm4QgwdDcbEzPic7uLRKc7i5tGlqUnQSO0/v1L2eHiFBIQxuOphPt31K07imgH4bA0+VhWuG1+RYltNK1a1uN9YeWquxEsluNNlydb6zva6ueTXL9ixz268WP1XZMsJXYkJiSM9P57p611X1UgQCQSUgxI/gnDl0qORFtW2QtJIXb3yU22937qpbF+bPh20eOh68vvE1zfbEqx/lrU1vUewoAnMmd989jZeO3kDmkRoQchajqRBHgPavcbXbq0lsEx6/5nESwxLp9Ukvt+sFBsL338P6vVt4efX7cKYBIVevJqtE/BTkBYLDQH5mNMXFEGi2Udz2TWi2yHm+MZAwc5gyX1JU2eInKSqJ6ddNx2qyYjQYmd13Nq3iW3Fbs9uUMatGrOL1P15X2l64ip+/xv3FH8f+4FjWMV7a8BIAL133Er8f/Z2bGt2kGfvt0G9ZsW8FPer10GRcnQ8eueoRQgJDuD7pes1+i8nCsqHLKHIUacTjhcpf4/9iya4ljG87vqqXIhAIKgEhfgTnjBLM3PBH6PUETz75qOb4qFGez3196iTN9qtTHuGTmS9wOu80AFFR03DUXA/VsgFwuM3gtJzI4ifQGMiIViMAd5eU7AK74QZI2fIPZH4IwFljkBLoDEBRKIUZTtdWWNxZzl4/WTkUZAzSuJKqWaphNVmVDu16BAcGa6r2RgZHMqmT9r6vq3+dpueXq/hpW70tbau3Zfr66Zp5Hrn6EbfrXZ90vZv4OF+YA808dNVDusf6N+p/nldTfupG1mXi1ROrehkCgaCSEAHPgnPGNRvqXCiyF7nFqbhWSdY7RxY/akuHq4BQxwOl5pbGDRU5inAE5KBIqyILxVnOpp8hkZnqKQgKCNK4kixBljKzsXxtjqkOBPbkGlK/NxdT4LBAIBBcSAjxIygX6kBixfJj9S+lyzXwF5xCxLW3UpHDe5Vdm92msfzIuIoOTf+xHJe1GiUwlfRqt1mVrDVzhFb8BBoDNQLEYrKUWYG5POLH0zlC/AgEAsG5I8SP4Jwpr+XHNTMLnNk16ge8Xrq4KxrxY/AiftSCLVdnrUEq8VNSr8gceVY7xMXtZQowVZjlR50FJcSPQCAQVB5C/AjKhbqBpSJ+LP5ZfvQsOkX2Is3chzMO+zSPnuXHW1aRm+UHwOSM2zEUhSmVqoPCz2iGBAUEudXOKcvy42t2k1qceRI/apebED8CgUBQPoT4EZwThYVwVjaO+Gn50WsaWeQo0gQPH84sW/zY7DalDYE3t5ealBznWtUp2LL4CSqOUiw/ruIn0BjoVpenrPYTvlp+1JYwXyw//tT/EQgEAkEpQvwIfEYvRueUXFInwAYhZ92Oe0OvgFyxo1gjfg5lHCpzHl9jftTIAc8t41uW7pTFjz1KifkJCD+tOU+vQF9Fub3U4sdTIUDh9hIIBIJzR4gfgc+4NnmUJInpP84FwGA5jeyt+t/P/+NsftlCyJPbK7coV9n+38r/lTmPv+KnsLiQjIIMAFrFl/bPMJicjUht6x+EtEYA/JnxneZcPWuLnttLbVEKDvBf/OgVPoTSdhUGDAQYzm8NH4FAILhUEOJH4DNy81CZNYfW8P66JQAYwkpjaGZunMnDKx4ucz5f3F6+4K/4kYOojQYjjWIaKfuNkSedazjcDoqsYCyCSK3bzdXldW3ta2kY09DtGonWxDLX4coVMVeUOaaapRqBxkDirfEeBZJAIBAIvCOKHAp8xtXyczr3tBIbY3BpOZGcklzmfHpuL5vdRl5Rnl/rKnYUK320NAHPOg1DAfKL8gFnB3h1tebQPtN4eewonv/lZU7mnID4rWBJ05wru6P2P7ifnad30rN+TwB+uP0HJv08iV1puwCoF1WPralbnevwMeC5QXQDVo9c7TWAOjokmtUjV18UVZIFAoHgQqXKLT/vvPMOdevWJTg4mI4dO7J582aPY5csWUK7du2IjIzEYrHQunVrPvnkE82Y0aNHYzAYND99+vSp7Nu4LHC1/JgDzUpsDGHa7Cm9Zp2u6Lm9MgtK6+rc0fIOn9cmCxpfLD9y0URzgFkbQxOeyb33Qp0+S+Gqt6Cea0fVUrdX/aj63HjFjcr+fg37US+qnrLdIKpBmevQo0e9HjSr1szrmC51utA6obXPcwoEAoFAS5VafhYtWsTEiRN5//336dixI7NmzaJ3797s2bOHajodKKOjo3nqqado3LgxJpOJ77//njFjxlCtWjV69+6tjOvTpw/z5s1Tts3mC7+R4sWAq+UnODBYSQm3h5zQHPMlHkXP7XW2wBkrZMBAXGicz2vLL3aKH28VnmXklhfBgcG62VPeOqF7E3Xq+6kdUbvMdQgEAoGgaqhSy8/rr7/OuHHjGDNmDE2bNuX9998nNDSUuXPn6o7v1q0bgwYNokmTJiQlJfHQQw/RsmVLfvvtN804s9lMQkKC8hMVFXU+bueSx9XyYwowKW4vrCc1x3yx/Oi5veRAZIvJQpgpzO24J2RXWVmWH7vDrlh+3MSPsWzx4ykLC7T3o84AE+JHIBAILiyqTPzYbDb+/vtvevbsWboYo5GePXuycePGMs+XJInVq1ezZ88eunTpojm2du1aqlWrRqNGjbj33ntJT/deJbiwsJCsrCzNj8AdteVHQnJmJ8luL2vFuL1k8WM1Wd2KCXrDV/Fjs9s04kddNFA+16v48VJbR/3+qGv/CPEjEAgEFxZVJn7S0tKw2+3Ex2uDO+Pj40lJ8VwsLzMzE6vVislk4oYbbmD27Nn06tVLOd6nTx8+/vhjVq9ezcsvv8y6devo27cvdrvd45wzZswgIiJC+alVq9a53+AliNryY3fYnduK5Uf7mflSgE/X7VWSIm81WTUipCwBIbu9/BU/em4vtSByxZuoE5YfgUAguDi46LK9wsLCSE5OJicnh9WrVzNx4kTq169Pt27dABg6dKgytkWLFrRs2ZKkpCTWrl3LddddpzvnE088wcSJE5XtrKwsIYB0UFs27JLd+bAviflxbW1xzm6vIItGmMSExHA8+7jHufQsP3rZXkWOIgqLnTE/5kBtwLNcxLEi3F7qjC29HmYCgUAgqDqqTPzExsYSEBBAaqr2oZmamkpCgueKuUajkQYNnJk0rVu3ZteuXcyYMUMRP67Ur1+f2NhY9u3b51H8mM1mERTtA2rLj0NykJfvgMJI5w4Xt5dPAc96bq/CDOd0Lpaf2NBYv8WPT24vlWtNDoQur9tLLX7UqejqDDaBQCAQVD1VJn5MJhNt27Zl9erVDBw4EACHw8Hq1auZMGGCz/M4HA4KCws9Hj927Bjp6ekkJiZ6HCPwzIkTcO+9kJ4OBcXxcGI9AN8uimdNQIxzUEAhBGdozlOLEJvdxvjvxtOrfi+GtxwOwIp9Kxi+ZLjb9eYnzwdKYn5U7qfY0Fiv69x5eqfbdfXEz1UfXaX09QoODNa0iJBrBVWE20tdgFC2ZgkEAoHgwqBK3V4TJ05k1KhRtGvXjg4dOjBr1ixyc3MZM2YMACNHjqRGjRrMmDEDcMbmtGvXjqSkJAoLC/nxxx/55JNPeO+99wDIyclh6tSpDB48mISEBPbv38/kyZNp0KCBJhVe4DuLF8OyZfJWMHAtAKdKfgCI+xdcig2rRcKC5AUs2Or8kcVPn8+8115KikrSWGBqhtfEFGBSBIonyhI/R7OOejwuu8O8WX689dO6r9193PPDPUrhw1rhtTiadZQbrrjB65oFAoFAcH7xW/zUrVuXO++8k9GjR1O7du2yT/DCbbfdxunTp5kyZQopKSm0bt2an376SQmCPnLkCEZjaUx2bm4u9913H8eOHSMkJITGjRvz6aefcttttwEQEBDAtm3bWLBgARkZGVSvXp3rr7+e559/Xri1ysnJkgz2/v2h1+BjPLj8AQC61OlKm4Q2vLlpFtT63e08tQhJy0tzO14Wr17/KnvT9yrbkcGR7LxvJ9tStzEveR7f/fed7nnq63oTMeAufmR3mPq8wU0G8/fJv5UGq9Ygz3OOazuONoltaFGtBQDb7t3GwbMHaZPYxus6BAKBQHB+8Vv8PPzww8yfP59p06bRvXt3xo4dy6BBg8otLiZMmODRzbV27VrN9gsvvMALL7zgca6QkBBWrFhRrnUI9JFDsq6+Gnr0zYRDSwGo0TyENklRkLVU9zx1sUFfgp/V9G3Ql9CgUE08TnBgMEnRSSRFJ7Fiv+fP2B/x4xoQLcf8qK/brno7TuedLhU/XuY0Gox0qNFB2Y4MjhTCRyAQCC5A/E51f/jhh0lOTmbz5s00adKEBx54gMTERCZMmMCWLVsqY42CKkSuOpCQoM1aUrK9PKAWIWohJGdUeUPuhaUWGmqh4i2YWn3MX8uPfH/q86wmq8bVVdacAoFAILjwKXednyuvvJK33nqLEydO8Oyzz/LRRx/Rvn17Wrduzdy5c316yAkufGTLT3y8S6q7w7v4MaiCgNRCSHYteUMWJZo0dEq/T2ox5Yr6WmUVSfRUf8dV/KjT24X4EQgEgoufcgc8FxUV8c033zBv3jxWrlzJVVddxdixYzl27BhPPvkkq1atYuHChRW5VkEVoLb8aIoclmH5UQsltTUmtyiXkKAQr9eURUloUKiyT30tb260c4n5kVFne1mCLBrLjz9VpwUCgUBwYeK3+NmyZQvz5s3j888/x2g0MnLkSN544w0aN26sjBk0aBDt27ev0IUKzj8OB5wqSemKj4fjLpYf10anatRiRf06x5ZDZHCk1+sGBzhFidFQaphUCy9vbq+KED/C7SUQCASXNn6Ln/bt29OrVy/ee+89Bg4cSFCQe9G3evXqaSotCy5O0tNB7gpSrRocSfHd8qM+pnZ15dhyyLXler2unihRF0T05vZS19fxVq8H9CtAg1bghAaFCvEjEAgElxh+i58DBw5Qp04dr2MsFgvz5s0r96IEFwZyvE9MDAQF+Rfz40n85NpyybHleL2unvjxdi016oKC5XZ7qVxbRoNR6/YqQ1AJBAKB4MLH74DnU6dOsWnTJrf9mzZt4q+//qqQRQnOH78f/Z2OH3Xkj2N/aPbn58OVVzpfy91G/In58Wr5KfJu+ZGzvTzNp9cQVUZdU0gdM6SHJ/Hjep6w/AgEAsGlhd/i5/777+fo0aNu+48fP879999fIYsSnD+unXstm49vpseCHpr9f/0FRSUaQw7fqgjLT44txy/Lj1wtedyV43Tnfrjjw5pzb2p0k/Jazz3WO6m00rd8nf+78f8AeLrz04DT2hMT4mzd0SqhlRA/AoFAcInht9tr586dXCmbBFS0adOGnTt3VsiiBOcPOYU8vzhfsz+nRJ8YjfDRR87Xro1NfRU/cvFAcGZ7+SN+fhr+E2l5acRbS7ukq+d+vffrTL5mMrGhsWQUZBBnidOds3pYdf4a9xff//e9UiRRvs74tuPpf0V/EqylDXWPPHKEwuJCws3hItVdIBAILjH8tvyYzWa3TuwAJ0+eJDCwSluFCSoQWfxccw0ElBhQNJaf8xTwHGAM0Agf0AY/GwwGEsMSCQoI8ih8wNm0NDEsURPPo3avJYYlaoKlQ4NCiQqJUtYgI1LdBQKB4OLHb/Fz/fXX88QTT5CZmansy8jI4Mknn6RXr14VujhB1ZFbok+sKkOHJubnPLm99FCLH1/Ra1pa1nWU66lijITlRyAQCC5+/DbVzJw5ky5dulCnTh3atHH2LUpOTiY+Pp5PPvmkwhcoqBpky49G/FSA5ceXbC9PKeh6c/uK7Horj/hRd5Iva20CgUAguPDxW/zUqFGDbdu28dlnn7F161ZCQkIYM2YMw4YN0635I7g4ULejgFLxY1F5eVwtP+ptV9TH1DE/FWH5KY/4kQXMuYoftWtMIBAIBBcn5QrSsVgsjB8/vqLXIqhCXDOjdN1eFRXzU0aqe5luLy+p7mWhrtPjs/hx2MoeJBAIBIKLhnJHKO/cuZMjR45gs2kfDDfddJOHMwQXMq79snTdXhUQ8+NvtldZc/uLp07x3lBbfgQCgUBw8VOuCs+DBg1i+/btGAwGpXu77A6w2z27QgQXLr6IH4fkUF6fi+WnKgKeZdTix1uDVDVC/AgEAsGlhd/ZXg899BD16tXj1KlThIaG8u+///Lrr7/Srl071q5dWwlLFJwPPIkfTcyPa5FDyX/x8/Wur3lz05te11KZlh91qrq3xqxqhPgRCASCSwu/xc/GjRuZNm0asbGxGI1GjEYj1157LTNmzODBBx+sjDUKKgn1Q91V/JSZ6q6y/IxsNZLQoFCaxTVTjmuKHBaXBjz7gl57CzWv9nqV4MBgnu36bJlzLbl1CeYAM58P/tw5d4CZq2teTZPYJjSIbuDTel667iUsQRae6fKMT+MFAoFAcGHjt9vLbrcTFhYGQGxsLCdOnKBRo0bUqVOHPXv2VPgCBZWHuthggEEb8Fxmqrsq5qddYjs+6v8RQQFBbD6+mY4fddS1/Gy4cwMNoxs6r12US7036wHwZp83CTeHM+bbMUDZlp+W8S3JejyLoICyswsHNRlE9hPZyliDwcBvd/6GJEleu8OraVatGWcfO+vT9QQCgUBw4eO3+GnevDlbt26lXr16dOzYkVdeeQWTycQHH3xA/fr1K2ONgkpCHXujjucBHwKeVZafQGOgIgxk4aInfiKDI5UqzMGFpQJHkiRN8LEvWVj+CBHXsUaDEfzMWBfCRyAQCC4d/BY/Tz/9NLklPpFp06Zx44030rlzZ2JiYli0aFGFL1BQeajFjzouB0rdXl5jflTiR0Z+rSd+1KJG/dou2TX1c3xNQRcIBAKBoDz4LX569y7tit2gQQN2797NmTNniIqKEgXgLjLU9XZcxU9Zlh91Y1M98aMWSnriR32Oa8FEIX4EAoFAUJn4FfBcVFREYGAgO3bs0OyPjo4WwuciRG35KXIUaVxfvrS3kAWLN8tPsaNYOU8tatTfF7tk18wtWkgIBAKBoDLxS/wEBQVRu3ZtUcvnEsG13o46K0vX7eWhyKE38aOe05OocbX8+BqILBAIBAJBefA71f2pp57iySef5MyZM5WxHsF5xFX8yO6poiIoLNEsvrS38CZ+1O40TynsrpYfgUAgEAgqE79jft5++2327dtH9erVqVOnDha1aQDYsmVLhS1OUHE4JAc2u03jelKnukOpUMlV7falvYUn8SNJEtm2bGW/p4rKxY7icypcKBAIBAKBP/gtfgYOHFgJyxBUNrcuvpXv/vuOww8fJsGaAHi2/Jw+mw+EYAgoxmRSBSb7YfkBZxxRy/daAt7jeLwJI4FAIBAIKhq/nzjPPlt2VV3BhcfXu74GYN4/83ii8xOAe9uGQrvT17V213agA1JwOjZ7FKYAE+Cf5QcgPS9dsfwMaz7MbU1Tu01l0b+LeKDDA5gCTLzz5zv0qt/rXG9VIBAIBAKviD+3LzPS8tKU166FDRXLT2pJKJg1hVO5RdQMrwl4tvyoA5TV4ke2LAUYAvjwpg/d1jKl6xSmdJ2ibP9z9z/luieBQCAQCPzB74Bno9FIQECAxx/BhU1aftni5/DxkmhnSyqpOanKcV8sP+o2GbL4EXV7BAKBQHAh4bfl55tvvtFsFxUV8c8//7BgwQKmTp1aYQsTVA6+WH6OnSxy7rCmkpJTpDteQqLI4Tzmye0lxI9AIBAILkT8Fj8DBgxw2zdkyBCaNWvGokWLGDt2bIUsTFA5nM49rbyWkDTHZPGTmlJSgNCaQmpuqfhxTUeXY4bUgsdoKDUmyhWkhfgRCAQCwYWE324vT1x11VWsXr26oqYTVBLeLD9yQcL0tBIxY0klJSdFOa52e6nHq8WPwWBQtuVUeiF+BAKBQHAhUSEBz/n5+bz11lvUqFGjIqYTVCLp+enKa09ur6y0EOcOawqpOZ4tP3J2mGuaeqAxkGJHseL28lTcUCAQCASCqsBv8ePawFSSJLKzswkNDeXTTz+t0MUJKp6swizltSfxk5sR5txhTSU1t7T4oKvlR8/tpd4WMT8CgUAguBDxW/y88cYbGvFjNBqJi4ujY8eOREVFVejiBJXD0cyjHMk8Ql5RnmZ/QXEBJ9KyKTzR0LnDUhrw7JAc7Enfoxmv5/ZSb+9O2w0I8SMQCASCCwu/xc/o0aMrYRmCykSStIHNtWfV1h1XaC+kwZXHgcbOHaqA58dWPsaK/Ss04/WyvdTbb//5NiDEj0AgEAguLPwOeJ43bx6LFy9227948WIWLFhQIYsSVCyu7i1P5BUWkn/MafUxNfsRrKcV19XMjTM9nqeu7QPuYkiIH4FAIBBcSPgtfmbMmEFsbKzb/mrVqjF9+vQKWZSgYvG1aeip0xJIAWCws/xbZ8Na2bXlDVex4xob5K2vl0AgEAgE5xu/xc+RI0eoV6+e2/46depw5MiRClmUoGJxzdLyxPETJSIpNI1qYTFAaRC0N1zFz5n8M5ptYfkRCAQCwYWE3+KnWrVqbNu2zW3/1q1biYmJqZBFCSoWV0uMJ1JPlbywphJhjgDKJ35cxZYQPwKBQCC4kPBb/AwbNowHH3yQNWvWYLfbsdvt/PLLLzz00EMMHTq0MtYoOEd8dXudPuWM3QkMSyMkyFnrp8hRhENyEBkc6fE8V/HjihA/AoFAILiQ8Dvb6/nnn+fQoUNcd911BAY6T3c4HIwcOVLE/FyglOX2kosSZqSZAAiKOKuJ0yksLiQyOJKMggyP53tDiB+BQCAQXEj4LX5MJhOLFi3ihRdeIDk5mZCQEFq0aEGdOnUqY32CCqAst1dIYAjZtmyy0p1BzsERGRrBUlBcgNVk9Xh+WeJHBDwLBAKB4EKi3O0tGjZsSMOGDStyLYJKoiy3V2hQKNm2bPJLKjuHRmUTaAzEaDDikBwUFBdQZC/yeL6w/AgEAoHgYsLvmJ/Bgwfz8ssvu+1/5ZVXuOWWWypkUYKKpSy3V7AjFnbejD2lKQDW6FwMBoMiWgqKC7wGPgvxIxAIBIKLCb/Fz6+//kq/fv3c9vft25dff/21QhYlqFjKcntl/zQZvvwaUlsBEB6j7clVUFygNDHVw2jw/jUS4kcgEAgEFxJ+i5+cnBxMJpPb/qCgILKysnTOEFQ1stsrzBTG892fZ3Tr0drjqSXuy2rb4coPqN3yEFAaq1NoL1QsP9fUuoYEa4JybnRItKbXG8BPw3/SVH0WXd0FAoFAcCHht/hp0aIFixYtctv/xRdf0LRp0wpZlKBikd1eAcYAnu7yNJM7TdYezy6p2N3rf3DT3YSHOtPc9dxen938Gc3iminnxlvi3a7Xu0FvvhjyhbItLD8CgUAguJDwO+D5mWee4eabb2b//v306NEDgNWrV7Nw4UK++uqrCl+g4NyR3V5ybI6rm6ooq6Q4pTXF+U9JZpcsWvKL8hXxExwYTLy1VPCorUBq1NlhQvwIBAKB4ELCb8tP//79Wbp0Kfv27eO+++7j0Ucf5fjx4/zyyy80aNDA7wW888471K1bl+DgYDp27MjmzZs9jl2yZAnt2rUjMjISi8VC69at+eSTTzRjJEliypQpJCYmEhISQs+ePdm7d6/f67qUkN1esisqwKhqROowYstyVnPGmgqAJagk5b1EtGTbspXhwYHBGmuPWgipEeJHIBAIBBcqfosfgBtuuIENGzaQm5vLgQMHuPXWW5k0aRKtWrXya55FixYxceJEnn32WbZs2UKrVq3o3bs3p06d0h0fHR3NU089xcaNG9m2bRtjxoxhzJgxrFixQhnzyiuv8NZbb/H++++zadMmLBYLvXv3pqCg7DYNlyqy20vX8pMX62xmigNCTwPulp/MgkxleHBgsMbao+f2Us+hnkcgEAgEgguBcokfcGZ9jRo1iurVq/Paa6/Ro0cP/vjjD7/meP311xk3bhxjxoyhadOmvP/++4SGhjJ37lzd8d26dWPQoEE0adKEpKQkHnroIVq2bMlvv/0GOK0+s2bN4umnn2bAgAG0bNmSjz/+mBMnTrB06dLy3upFj+z2ki0+GvGTUyJeQtMgwDlOFi5yoLK6srMpwKQRPJ7cXrL1CESRQ4FAIBBcWPglflJSUnjppZdo2LAht9xyC+Hh4RQWFrJ06VJeeukl2rdv7/NcNpuNv//+m549e5YuxmikZ8+ebNy4sczzJUli9erV7Nmzhy5dugBw8OBBUlJSNHNGRETQsWNHn+a8VHF1e2nET26JkClxeYG75UcWP+YAMwaDwW/Lj8j2EggEAsGFhM/ip3///jRq1Iht27Yxa9YsTpw4wezZs8t94bS0NOx2O/Hx2odnfHw8KSkpHs/LzMzEarViMpm44YYbmD17Nr169QJQzvN3zsLCQrKysjQ/lxJe3V45JULGWvr+WEzamB9Z/Mjb/gY8CwQCgUBwIeFzttfy5ct58MEHuffee6u0rUVYWBjJycnk5OSwevVqJk6cSP369enWrdv/t3fv8VGU9/7AP7ub3c0mIcmGkBsECRJBkIAmEKLS/iopAW9g4RRpWgL2J3I98oOqUA8ElJ4gpdRaPPGAgrZYQrVFKUIUI1Ch4WIAA4KIFgwCm0AwV3Lf5/fHZiczewkJJDub7Of9eu2rszPPTp4ZPK/5nOf7zMxN7zMrKwsrVqzouE56gar6Kvzt1N/w8B0Puy97NfgD+5bZlgPdj/y8cvgVxXd54AkPCHf59wP0AdJyW98qT0RE5AltHvnZv38/KisrkZiYiOTkZKxbtw5Xr1696T8cHh4OnU6H4uJixfri4mJERbkeTQBspbEBAwZg+PDhWLRoESZPnoysrCwAkH7X3n0uWbIE5eXl0ufChQs3e1heY/6u+Zj+/nQ8vOVh92Wv4xnAteYgG9JyzI7hx/57+/deAb2kthGBES7/vvyOMncBiYiISA1tDj+jRo3Chg0bcPnyZTz11FPIyclBTEwMrFYrdu/ejcrKyhvvRMZgMCAxMRF5eXnSOqvViry8PKSkpLR5P1arFXV1tlcvxMXFISoqSrHPiooKHDp0qNV9Go1GBAcHKz5d3ebCzQCAg98ddF/2+v72lh+MXCct2sON40Rl+9wdnVaHv07+K9Y/vB63hd7mtg/vTXkP//Pg/2BwLz78koiIvEe7H3IYGBiIJ554Ak888QTOnDmDN954A6tWrcLixYvx4x//GNu3b2/zvhYuXIiMjAwkJSVh5MiRePnll1FdXY0ZM2YAAKZNm4bevXtLIztZWVlISkrC7bffjrq6OuzcuRN//vOfkZ2dDQDQaDRYsGABVq5cifj4eMTFxWHp0qWIiYnBxIkT23uoXZoQQlp2LHtJr55ovtNr0n8ext+CL0nt7WUtx1vU5d//Y8iNX2I7YdCEm+g5ERFR52p3+JEbOHAgVq9ejaysLPzjH/9we4u6O1OmTMGVK1ewbNkyWCwWDB8+HLm5udKE5aKiImi1LYNT1dXVmDNnDr777juYTCYMGjQImzdvxpQpU6Q2zz77LKqrqzFz5kyUlZXh/vvvR25uLvz9fetZMwIt4cdt2at5snNIz1rImsNsMgNoPfwQERF1VbcUfux0Oh0mTpx4U6Mr8+bNw7x581xu27t3r+L7ypUrsXLlylb3p9Fo8MILL+CFF15od1+6E6uwSstuy17Nt7mHhtcCV1p+a9/uGHbko0lERERd1U0/5JC6Drd3ezWXvUJ71kltNWh5Q7tj+CmvKwcREVFXx/DjA1yWvaxa4LptYrO5V0v4CfEPkZYdJzzLn/RMRETUVTH8+ACXZS/ZO71CzA1S21D/UGm5vqlesR+GHyIi6g4YfnyAy7KX/cnOAVeh17eUuuThx7HM5RiGiIiIuiKGHy+38+xOrC9YD8AWPv770//GscvH2rUPx7LX+tf8gD3NE8KDLIrXXcjDD0d6iIioO2L48XIP/eUhPLXjKRRcKsDqA6vx/CfP457197RrH/Ky11dfAf85XwecaX4Gj/kctBqt9FTnR+94VPpdcu9kxX7G3j72Fo6EiIjIO3TIre7U+U5fPY28c3k3buiCvOxVVNS8MtACJL8CDN0CDdagcFYh9pzfg2nDpkm/+3nCzwEAQyOHouBSAX5y509u6RiIiIi8AcNPF1F6vRRXr9/cu9TsZS8/rR+k155FfAH8wPbkbK1GizhzHOLMcYrf6bQ6ZAzPAADcE92+0SYiIiJvxbKXF5M/VLC0phSl10tvaj/2spdOo5PCjyaoRNoun/NDRETU3fGq58XsIzYAcK3m2k2P/MjLXhaLbZ0mqFjazvBDRES+hFc9L9ZgbXn+TmlNqeJ7e7gqe2mCWt5nwfBDRES+hFc9LyYf+bnZkhfguuyl7cHwQ0REvolXPS/W0KQc+bEz6Azt2o9U9tK0lL20PTjnh4iIfBOvel5MXub6qvQraTnMFNau/bgqe/kFt4Qphh8iIvIlvNXdi8nLXlX1VdKy/UnNAPDBVx9g19e7sDZtLfy0fnh297MYETPCtrFkMLB3OTbvGgZcS8KuHbdJ4UfXo2XyNMMPERH5EoYfLyYve8nJQ9HDWx4GAAwIG4D+5v74Xf7vWhoemg+c+g98DQC4A982rzabAQSVAs2v6mL4ISIiX8Krnhdzd3eXPPzY/fv7f8NSZVGurIwBAAxNOwJMmI60hTnYtAnYvx/Q+bU8Q4jhh4iIfAmvel6sLSM/8rbyhyICkN7c3n/USeDut5Aw7iimTwcGD1YGHoYfIiLyJbzqeTFXIcfdepdtqyMBAPrmyc3yuUIMP0RE5Kt41fNi7Sl7NVgbICAb+REAqmzhxz652U/bMsWL4YeIiHwVr3pezF3Zy/7QQkVbq0PZqzYEaPK3LTe/ykKn5cgPERERr3perLWyl+P8Hqeg1FzygrEcjbpKAMqyl7sSGBERUXfHq54Xa+1dXlZhVXx3CkrNk50RWIy6pjoALHsREREBDD9ezV3ZC3AOO05zfprn+yDIgpqGGgAsexEREQEMP17NXdnL1bYdZ3Zg7lMGIO9F2wp72SuoGLWNtQA48kNERATwCc9erbWyl1MwKhkCHPu/tuX/s6Kl7BVkkcpe7ub5aDSajukwERFRF8D/l9+LtafshbqQluXqiJayV2Axy15EREQyvOp5sfaUvVAd0bJcFcmyFxERkRu86nmxdpW97CM9gK3k1Y6yF8MPERH5El71vFi7yl72sAPYRn1uUPZyVwIjIiLq7njV82LtGvmplo38VEaz7EVEROQG7/byYu2a8yMf+Sm9A2gy2pYDi1HXZHsdBsteREREDD9erbWyV5NoUj7lWT7np3iY7X+NZYC+Do3NzXi3FxEREcteXu1GZa8mq+wFp/Kyl6U5/ARZFL9h2YuIiIjhRxVr89di2GvDcKX6SqvtblT2kt7unvs74PvbZVub/1mb3+Zux7IXERERw48qFn20CIXFhcjan9Vquxvd7SWN/Byf3rLB73rLct9PFb8x6AzSMsMPERH5Ks75UVF9U32r2+1lL4PO4NRWMfJTH2T73/nxtlJXVSSgqwdCLih+E2QIkpYZfoiIyFcx/KjoRqHDXvbqYeiB0ppSp21N1iagUQ9Ym0d0Aq4CxirbxwV5+HFXAiMiIurueNVT0Y1Ch73s1cPYQ1pnL11JIz8NgS0/0Fe3uj95+JG/zJThh4iIfAmveiq6YfhpLnvJQ0ug3hZ2pJEfe8lLWw/4uZ8jBACBhkCX6xl+iIjIl/Cqp6K2lr0C9AHSOnsQkkZ+6psDjcF1qUtOHqLa0w8iIqLuhFc9FbW17CW/S0sRfuQjP20IP/ZRo/b2g4iIqDvhVU9FbS176bV6aZ3zyE/bwo9BZ4Bep3e5jeGHiIh8Ca96KrpR6LC/kFQeWkx6EwCgydrkMPLT9snO7e0HERFRd8KrnopaCx1LP1mKrV9sBaAse9lHgZzu9rrByA/DDxERkQ2f86Oi1kLHyk9XSsupcak49/05DIkYgrLaMgDtn/PjON9HA43LZSIiou6O/y+/hwkhpOW2jrgEGgJxYvYJbJ28VXo5aaO10fZW9+bwExcR4fS7EGOItMyRHyIiIhte9TxMeiUFlE9ZlrMKq+K7XquXHkooDz/yW90NJudn/MSGxErLjuGHDzkkIiJfxaueh8lfVioPIHLXaq4pvtsDj3zZsexlNDm/J6xPcB9p2d0DDgGGHyIi8i286nmY/fZ1wH3osFRZFN/ld3s5j/w0h5+ARqf99OnREn5aK3u5C2FERETdEcOPh9mf2gy4L3sVVxUrvsuf8+M08tN8t5e/yUX4kY38GHXGm+80ERFRN6J6+Hn11VfRr18/+Pv7Izk5GYcPH3bbdsOGDRg9ejTMZjPMZjNSU1Od2k+fPh0ajUbxGTduXGcfRpvJy17uOI78uC173WDkJyooSlquaay5qf4SERF1N6qGn61bt2LhwoXIzMzE0aNHMWzYMKSlpaGkpMRl+71792Lq1KnYs2cP8vPzERsbi7Fjx+LixYuKduPGjcPly5elz5YtWzxxOG0iL3s5Tmy2K65WjvzI2/lpXM/5MQU0wZH8+UBV9Td+/QUREZEvUDX8rF27Fk8++SRmzJiBwYMH47XXXkNAQAA2btzosv3bb7+NOXPmYPjw4Rg0aBBef/11WK1W5OXlKdoZjUZERUVJH7PZ7InDaRN52Ut+51dJCVBYaPucKNQAlqFAZSQA5QRo+8jP2Wtnca7sXEv4CXQOP/K5Qo7hh8/2ISIiX6XaQw7r6+tRUFCAJUuWSOu0Wi1SU1ORn5/fpn1cv34dDQ0NCAsLU6zfu3cvIiIiYDab8cADD2DlypXo2bOn2/3U1dWhrq5O+l5RUdHOo2k7ednLPqLzzTfAoEFAo5SL/p/to2kCZiYqRot0Wts8oU3HN2HT8U1A/REAgCnAClxX/i1X7wQjIiLydaqN/Fy9ehVNTU2IjIxUrI+MjITFYnHzK6XnnnsOMTExSE1NldaNGzcOf/rTn5CXl4eXXnoJ+/btw/jx49HU5DwyYpeVlYWQkBDpExsb67btrZIHmSarrU8FBbbgo9cDkZGAMaQM0NUCQofba6YifWi69Bv5/B8AwPVwAECPEOe5RH5aP2ydvBX3RN+Dl9Ne7vBjISIi6opUn/B8s1atWoWcnBxs27YN/v7+0vrHH38cjz76KIYOHYqJEydix44dOHLkCPbu3et2X0uWLEF5ebn0uXDhQqf1W172so/8FDdP8ZkwAbBYgHGvTQcSNgMApsc/p3hGjyL8CABVtknN5l4tI1d2ep0ePx3yUxTMLEB8z/iOPRAiIqIuSrXwEx4eDp1Oh+Ji5eTe4uJiREVFufmVzZo1a7Bq1Sp89NFHSEhIaLVt//79ER4ejq+//tptG6PRiODgYMWns8jLXvY5P/aBLvsgWKO1EQiyKLbZKcJPXTDQZAt+YeHOIz/yshcRERHZqBZ+DAYDEhMTFZOV7ZOXU1JS3P5u9erVePHFF5Gbm4ukpKQb/p3vvvsOpaWliI6O7pB+3ypXZS97/rNnvgZrAxBUrNhmpwg/Vba0pPWvRkCA8wRmpxIZERERqVv2WrhwITZs2IC33noLp0+fxuzZs1FdXY0ZM2YAAKZNm6aYEP3SSy9h6dKl2LhxI/r16weLxQKLxYKqKtudTFVVVXjmmWdw8OBBnD9/Hnl5eZgwYQIGDBiAtLQ0VY7RkasJz/aAYx/5aWhqAAJtK1sd+WkueRlCSl2O8sjv9iIiIiIbVYcGpkyZgitXrmDZsmWwWCwYPnw4cnNzpUnQRUVF0Gpb8ll2djbq6+sxefJkxX4yMzOxfPly6HQ6FBYW4q233kJZWRliYmIwduxYvPjiizAaveMJx65udbcHHOXIj22l48hPbWNty5dq23kyBH/vMuiw7EVERORM9brIvHnzMG/ePJfbHCcpnz9/vtV9mUwmfPjhhx3Us87h6iGHjiM/tjk/1xTb7Eqvl7Z8aS57GULKXJa4Wit78X1eRETkq7rs3V5dlWLCs7UJQrgpezWP/FRUADWyN1OU1sjDj22oyD/ke5a9iIiI2kj1kR9f41j2KisD6utt36XwY20AjBXQG5rQUK9DcbHtOUBffgl8lX8HcOUhW8PLiQAAY2g59Drnp1iz7EVEROSM4cfDHMte9teYBQcD9scVNVobAQ0Q1qsexRdNOHUKmDQJqK0FgNVO+zSFlkGvjXBaz5EfIiIiZww/HuZY9rre/EqKoCDnNmG9GlF8Edi/3xZ8jEZg0JA6FJUXQavRofT6VSCwBNEjDsNPO9jpb/FWdyIiIme8OnqY4xOe7SUvQ8sL2KXRofBetv8tLLStT0gADh82AojH24Vv4+fbfg4ACAiZ0O67vfhiUyIi8lWc8OxhiocciiaX4ccekMIjbLfCf/65bb38NWgGXcsPtBotJzwTERG1EcOPhzm+3sLlyE9zm/AI263w331nWy9/64c8/Oi0Omg1zv+ULHsRERE5Y/jxsPaUvSKaw4+du5EfnUbn8rk9rZa9+JwfIiLyUQw/Hub4bq/Wyl7ysOP43XHkx9UcHpa9iIiInDH8eJjju70amr+6Kns5vtzebdnLzcgPy15ERETOeHX0MKcJz81f9XrnNlFRykAjH/mRj+q4HfnhQw6JiIiccOTHwxRPeJaVvfR6ge1ntqO4qlhqExOl/Odp78iPTqvrwJ4TERF1Dxz58TDHspc9/Fy6fh4TciYgpkeMtD00xA9BQUBVle17a+En1D+0M7tNRETUbXDkx8PcPefHUlMEALhUeUnartf54X//F5g8GVi3zvYKDDvHCc8DwgZgzY/XIDwgvHMPgIiIqIvjyI+HubvVXevX4NRWr9XjZz8DfvYz5/04jvwAwKJ7F+FS5SWsPbi2YztNRETUjXDkx8Mc3+1lv9tL49fo1La1W9UdR37sBESb+sHXWxARka9i+PEwd2Uvjc45/NhHdFyR38klDzJCtC38EBER+SqGHw9zX/ZShh8/rV+rT2GWj/w0iSbFPomIiMg9hh8Pc/eEZ8c5Pzd6Ro8i/Fhbwk9by15ERES+iuHHw9zd6u5Y9rrRqyncjfyw7EVERNQ6hh8PUzzkUD7nx2Hk50avppBPcpaP/LDsRURE1DqGHw/adnobtn6xVfouL3s1aWoUbdvzagrFyA/LXkRERK1i+PGgD7/5UPFd/mLTBk21Ylt73sguDz/3RN/Tpt/cG3tvm/dPRETUnfAhhx6U2j8VgfpAHLMcw57zexRlrwYow0973sguL3XNGD4DNQ01+MFtP2j1N8/c+wx6GHogbUBa2w+AiIioG2D48aDJgydj8uDJ+PjfH2PP+T2KCc91qFK0bVfZSzbnR6fVYX7y/Bv+xuhnxNOjnm7z3yAiIuouWPZSgVZjO+3yOT/1olLR5mbLXkRERNQ6hh8V2J/cLC971TuM/LSn7CUf+SEiIqLWMfyowD7yIy97QVevaHOzd3sRERFR6xh+VGB/Ro/8xaZO4ac9ZS+O/BAREbUZw48K7GUv5chP+x5yKMeRHyIiorZj+FGBNOFZNufnlspeHPkhIiJqM4YfFcjLXm7DD+/2IiIi6hQMPypoy4TntpS9egX0AgA8HP9wh/aPiIioO+NDDlXg6lb3myl7Fc4uxL8u/AuPDny0o7tIRETUbTH8qMBe9pK/2+tmyl5RQVH4yZ0/6ejuERERdWsse6nA1ROeoW1AmClMatOeu72IiIio7Rh+VOD6Vvd69AnuI7WxByQiIiLqWLzCqsDdre69e/SW2pTVlnm+Y0RERD6A4UcF9jk/jU3K8GPSm6Q2pddLVegZERFR98fwowKp7NWkgRD2lfUw6AxSm2s111ToGRERUffH8KMCqezVqGtZ6RB+Sms48kNERNQZeEuRB/32t8D77wM9zD2BQb1g1cve56Wrh0HLkR8iIqLOxpEfD/rmG+DAASB3hz9wZgJEXQAAQKO1AroGGHQGzLxnJgDg1/f/Ws2uEhERdVsMPx701FNAYGDzl7pgoCoSABBorgI0tgcb/vHBPyL/l/lY8aMV6nWUiIioG2P48aC77wZ+8YvmL/WBQLUt/JhCKwEABp0BBp0Bo/qM4kMOiYiIOgnDj4dJIz/1QUBVFADAFFoBAIoJz0RERNQ5GH48LCioeaE+SCp7+YeUAWD4ISIi8gSGHw9Thh/byI+R4YeIiMhjOLHEw6Tw0xAIWG1vbjeE2m5rZ/ghIiLqfAw/HqaY89NkCz9+PWwPNGT4ISIi6nyql71effVV9OvXD/7+/khOTsbhw4fdtt2wYQNGjx4Ns9kMs9mM1NRUp/ZCCCxbtgzR0dEwmUxITU3F2bNnO/sw2sxV2csv+CoAhh8iIiJPUDX8bN26FQsXLkRmZiaOHj2KYcOGIS0tDSUlJS7b7927F1OnTsWePXuQn5+P2NhYjB07FhcvXpTarF69Gq+88gpee+01HDp0CIGBgUhLS0Ntba2nDqtVLeGn5Vb3A9f+BgDQa/Uq9YqIiMh3aISQXq3pccnJyRgxYgTWrVsHALBarYiNjcX8+fOxePHiG/6+qakJZrMZ69atw7Rp0yCEQExMDBYtWoRf/epXAIDy8nJERkbizTffxOOPP96mflVUVCAkJATl5eUIDg6++QN04V//Au67D0BIEVDe17bymXAgsBSbH9uM9IT0Dv17REREvqKt12/VRn7q6+tRUFCA1NTUls5otUhNTUV+fn6b9nH9+nU0NDQgLCwMAHDu3DlYLBbFPkNCQpCcnNzqPuvq6lBRUaH4dBZp5McefLQNgIkTnomIiDxFtfBz9epVNDU1ITIyUrE+MjISFoulTft47rnnEBMTI4Ud++/au8+srCyEhIRIn9jY2PYcSrtI4ccusATQ2gbfGH6IiIg6n+oTnm/WqlWrkJOTg23btsHf3/+W9rVkyRKUl5dLnwsXLnRQL51Jd3vZBbWEMoYfIiKizqfare7h4eHQ6XQoLi5WrC8uLkZUVFSrv12zZg1WrVqFjz/+GAkJCdJ6+++Ki4sRHR2t2Ofw4cPd7s9oNMJoNN7EUbSf88hPy/Ez/BAREXU+1UZ+DAYDEhMTkZeXJ62zWq3Iy8tDSkqK29+tXr0aL774InJzc5GUlKTYFhcXh6ioKMU+KyoqcOjQoVb36UkmE6DRyFZw5IeIiMijVH3I4cKFC5GRkYGkpCSMHDkSL7/8MqqrqzFjxgwAwLRp09C7d29kZWUBAF566SUsW7YMf/nLX9CvXz9pHk9QUBCCgoKg0WiwYMECrFy5EvHx8YiLi8PSpUsRExODiRMnqnWYClotEBAAVFc3rwhqGfnR63irOxERUWdTNfxMmTIFV65cwbJly2CxWDB8+HDk5uZKE5aLioqg1bYMTmVnZ6O+vh6TJ09W7CczMxPLly8HADz77LOorq7GzJkzUVZWhvvvvx+5ubm3PC+oIwUFycMPR36IiIg8SdXn/HirznzODwAMGAB8803zl0mPA0O3AgBOzD6BuyLu6vC/R0RE5Au8/jk/vkxxx1cQJzwTERF5EsOPCsaMaV4ILAaijkvrGX6IiIg6H8OPCtauBa5eFbj4nQ5zf9DyOguGHyIios6n6oRnX9azpwZAOIKNLTVJhh8iIqLOx5EflRl1LQ9XZPghIiLqfAw/KpM/20ev5XN+iIiIOhvDj8rkoz0c+SEiIup8DD8qkwcenVanYk+IiIh8A8OPyjjaQ0RE5FkMPypj+CEiIvIshh+VBeoDb9yIiIiIOgyf86OyCYMmYETMCIzqM0rtrhAREfkEhh+V+fv54/CTh9XuBhERkc9g2YuIiIh8CsMPERER+RSGHyIiIvIpDD9ERETkUxh+iIiIyKcw/BAREZFPYfghIiIin8LwQ0RERD6F4YeIiIh8CsMPERER+RSGHyIiIvIpDD9ERETkUxh+iIiIyKcw/BAREZFP8VO7A95ICAEAqKioULknRERE1Fb267b9Ou4Ow48LlZWVAIDY2FiVe0JERETtVVlZiZCQELfbNeJG8cgHWa1WXLp0CT169IBGo+mw/VZUVCA2NhYXLlxAcHBwh+2XlHiePYPn2XN4rj2D59kzOvM8CyFQWVmJmJgYaLXuZ/Zw5McFrVaLPn36dNr+g4OD+X9YHsDz7Bk8z57Dc+0ZPM+e0VnnubURHztOeCYiIiKfwvBDREREPoXhx4OMRiMyMzNhNBrV7kq3xvPsGTzPnsNz7Rk8z57hDeeZE56JiIjIp3Dkh4iIiHwKww8RERH5FIYfIiIi8ikMP0RERORTGH485NVXX0W/fv3g7++P5ORkHD58WO0udSn//Oc/8cgjjyAmJgYajQbvvfeeYrsQAsuWLUN0dDRMJhNSU1Nx9uxZRZtr164hPT0dwcHBCA0NxS9/+UtUVVV58Ci8X1ZWFkaMGIEePXogIiICEydOxJkzZxRtamtrMXfuXPTs2RNBQUGYNGkSiouLFW2Kiorw0EMPISAgABEREXjmmWfQ2NjoyUPxetnZ2UhISJAe9JaSkoJdu3ZJ23meO8eqVaug0WiwYMECaR3P9a1bvnw5NBqN4jNo0CBpu9edY0GdLicnRxgMBrFx40bxxRdfiCeffFKEhoaK4uJitbvWZezcuVM8//zz4u9//7sAILZt26bYvmrVKhESEiLee+898fnnn4tHH31UxMXFiZqaGqnNuHHjxLBhw8TBgwfFp59+KgYMGCCmTp3q4SPxbmlpaWLTpk3i5MmT4vjx4+LBBx8Uffv2FVVVVVKbWbNmidjYWJGXlyc+++wzMWrUKHHvvfdK2xsbG8Vdd90lUlNTxbFjx8TOnTtFeHi4WLJkiRqH5LW2b98uPvjgA/HVV1+JM2fOiF//+tdCr9eLkydPCiF4njvD4cOHRb9+/URCQoJ4+umnpfU817cuMzNTDBkyRFy+fFn6XLlyRdrubeeY4ccDRo4cKebOnSt9b2pqEjExMSIrK0vFXnVdjuHHarWKqKgo8dvf/lZaV1ZWJoxGo9iyZYsQQohTp04JAOLIkSNSm127dgmNRiMuXrzosb53NSUlJQKA2LdvnxDCdl71er145513pDanT58WAER+fr4QwhZUtVqtsFgsUpvs7GwRHBws6urqPHsAXYzZbBavv/46z3MnqKysFPHx8WL37t3ihz/8oRR+eK47RmZmphg2bJjLbd54jln26mT19fUoKChAamqqtE6r1SI1NRX5+fkq9qz7OHfuHCwWi+Ich4SEIDk5WTrH+fn5CA0NRVJSktQmNTUVWq0Whw4d8nifu4ry8nIAQFhYGACgoKAADQ0NinM9aNAg9O3bV3Guhw4disjISKlNWloaKioq8MUXX3iw911HU1MTcnJyUF1djZSUFJ7nTjB37lw89NBDinMK8L/pjnT27FnExMSgf//+SE9PR1FREQDvPMd8sWknu3r1KpqamhT/oAAQGRmJL7/8UqVedS8WiwUAXJ5j+zaLxYKIiAjFdj8/P4SFhUltSMlqtWLBggW47777cNdddwGwnUeDwYDQ0FBFW8dz7erfwr6NWpw4cQIpKSmora1FUFAQtm3bhsGDB+P48eM8zx0oJycHR48exZEjR5y28b/pjpGcnIw333wTAwcOxOXLl7FixQqMHj0aJ0+e9MpzzPBDRC7NnTsXJ0+exP79+9XuSrc1cOBAHD9+HOXl5Xj33XeRkZGBffv2qd2tbuXChQt4+umnsXv3bvj7+6vdnW5r/Pjx0nJCQgKSk5Nx22234a9//StMJpOKPXONZa9OFh4eDp1O5zSrvbi4GFFRUSr1qnuxn8fWznFUVBRKSkoU2xsbG3Ht2jX+O7gwb9487NixA3v27EGfPn2k9VFRUaivr0dZWZmiveO5dvVvYd9GLQwGAwYMGIDExERkZWVh2LBh+MMf/sDz3IEKCgpQUlKCe+65B35+fvDz88O+ffvwyiuvwM/PD5GRkTzXnSA0NBR33HEHvv76a6/875nhp5MZDAYkJiYiLy9PWme1WpGXl4eUlBQVe9Z9xMXFISoqSnGOKyoqcOjQIekcp6SkoKysDAUFBVKbTz75BFarFcnJyR7vs7cSQmDevHnYtm0bPvnkE8TFxSm2JyYmQq/XK871mTNnUFRUpDjXJ06cUITN3bt3Izg4GIMHD/bMgXRRVqsVdXV1PM8daMyYMThx4gSOHz8ufZKSkpCeni4t81x3vKqqKnzzzTeIjo72zv+eO3wKNTnJyckRRqNRvPnmm+LUqVNi5syZIjQ0VDGrnVpXWVkpjh07Jo4dOyYAiLVr14pjx46Jb7/9Vghhu9U9NDRUvP/++6KwsFBMmDDB5a3ud999tzh06JDYv3+/iI+P563uDmbPni1CQkLE3r17FbesXr9+XWoza9Ys0bdvX/HJJ5+Izz77TKSkpIiUlBRpu/2W1bFjx4rjx4+L3Nxc0atXL94W7GDx4sVi37594ty5c6KwsFAsXrxYaDQa8dFHHwkheJ47k/xuLyF4rjvCokWLxN69e8W5c+fEgQMHRGpqqggPDxclJSVCCO87xww/HvLHP/5R9O3bVxgMBjFy5Ehx8OBBtbvUpezZs0cAcPpkZGQIIWy3uy9dulRERkYKo9EoxowZI86cOaPYR2lpqZg6daoICgoSwcHBYsaMGaKyslKFo/Fers4xALFp0yapTU1NjZgzZ44wm80iICBAPPbYY+Ly5cuK/Zw/f16MHz9emEwmER4eLhYtWiQaGho8fDTe7YknnhC33XabMBgMolevXmLMmDFS8BGC57kzOYYfnutbN2XKFBEdHS0MBoPo3bu3mDJlivj666+l7d52jjVCCNHx40lERERE3olzfoiIiMinMPwQERGRT2H4ISIiIp/C8ENEREQ+heGHiIiIfArDDxEREfkUhh8iIiLyKQw/REQuaDQavPfee2p3g4g6AcMPEXmd6dOnQ6PROH3GjRundteIqBvwU7sDRESujBs3Dps2bVKsMxqNKvWGiLoTjvwQkVcyGo2IiopSfMxmMwBbSSo7Oxvjx4+HyWRC//798e677yp+f+LECTzwwAMwmUzo2bMnZs6ciaqqKkWbjRs3YsiQITAajYiOjsa8efMU269evYrHHnsMAQEBiI+Px/bt26Vt33//PdLT09GrVy+YTCbEx8c7hTUi8k4MP0TUJS1duhSTJk3C559/jvT0dDz++OM4ffo0AKC6uhppaWkwm804cuQI3nnnHXz88ceKcJOdnY25c+di5syZOHHiBLZv344BAwYo/saKFSvw05/+FIWFhXjwwQeRnp6Oa9euSX//1KlT2LVrF06fPo3s7GyEh4d77gQQ0c3rlNelEhHdgoyMDKHT6URgYKDi85vf/EYIYXv7/KxZsxS/SU5OFrNnzxZCCLF+/XphNptFVVWVtP2DDz4QWq1WWCwWIYQQMTEx4vnnn3fbBwDiv/7rv6TvVVVVAoDYtWuXEEKIRx55RMyYMaNjDpiIPIpzfojIK/3oRz9Cdna2Yl1YWJi0nJKSotiWkpKC48ePAwBOnz6NYcOGITAwUNp+3333wWq14syZM9BoNLh06RLGjBnTah8SEhKk5cDAQAQHB6OkpAQAMHv2bEyaNAlHjx7F2LFjMXHiRNx77703daxE5FkMP0TklQIDA53KUB3FZDK1qZ1er1d812g0sFqtAIDx48fj22+/xc6dO7F7926MGTMGc+fOxZo1azq8v0TUsTjnh4i6pIMHDzp9v/POOwEAd955Jz7//HNUV1dL2w8cOACtVouBAweiR48e6NevH/Ly8m6pD7169UJGRgY2b96Ml19+GevXr7+l/RGRZ3Dkh4i8Ul1dHSwWi2Kdn5+fNKn4nXfeQVJSEu6//368/fbbOHz4MN544w0AQHp6OjIzM5GRkYHly5fjypUrmD9/Pn7xi18gMjISALB8+XLMmjULERERGD9+PCorK3HgwAHMnz+/Tf1btmwZEhMTMWTIENTV1WHHjh1S+CIi78bwQ0ReKTc3F9HR0Yp1AwcOxJdffgnAdidWTk4O5syZg+joaGzZsgWDBw8GAAQEBODDDz/E008/jREjRiAgIACTJk3C2rVrpX1lZGSgtrYWv//97/GrX/0K4eHhmDx5cpv7ZzAYsGTJEpw/fx4mkwmjR49GTk5OBxw5EXU2jRBCqN0JIqL20Gg02LZtGyZOnKh2V4ioC+KcHyIiIvIpDD9ERETkUzjnh4i6HFbriehWcOSHiIiIfArDDxEREfkUhh8iIiLyKQw/RERE5FMYfoiIiMinMPwQERGRT2H4ISIiIp/C8ENEREQ+heGHiIiIfMr/B7LrRebBLiqsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "acc_train=model_history.history['accuracy']\n",
    "acc_val=model_history.history['val_accuracy']\n",
    "epochs=range(1,501)\n",
    "plt.plot(epochs,acc_train,'g',label='Training Accuracy')\n",
    "plt.plot(epochs,acc_val,'b',label='Validation Accuracy')\n",
    "plt.title(\"Training and Validation Accuracy at 500 Epochs\")\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss_train=model_history.history['loss']\n",
    "loss_val=model_history.history['val_loss']\n",
    "epochs=range(1,31)\n",
    "plt.plot(epochs,loss_train,'g',label='Training Loss')\n",
    "plt.plot(epochs,loss_val,'b',label='Validation Loss')\n",
    "plt.title(\"Training and Validation Loss at 30 Epochs\")\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 3 of 3). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: /var/folders/rq/fttfpph53671nf5_9s4qnwlm0000gn/T/tmpxb9wf4s1/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: /var/folders/rq/fttfpph53671nf5_9s4qnwlm0000gn/T/tmpxb9wf4s1/assets\n",
      "2023-02-19 11:15:19.996629: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:362] Ignored output_format.\n",
      "2023-02-19 11:15:19.996768: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:365] Ignored drop_control_dependency.\n",
      "2023-02-19 11:15:19.999737: I tensorflow/cc/saved_model/reader.cc:45] Reading SavedModel from: /var/folders/rq/fttfpph53671nf5_9s4qnwlm0000gn/T/tmpxb9wf4s1\n",
      "2023-02-19 11:15:20.000753: I tensorflow/cc/saved_model/reader.cc:89] Reading meta graph with tags { serve }\n",
      "2023-02-19 11:15:20.000759: I tensorflow/cc/saved_model/reader.cc:130] Reading SavedModel debug info (if present) from: /var/folders/rq/fttfpph53671nf5_9s4qnwlm0000gn/T/tmpxb9wf4s1\n",
      "2023-02-19 11:15:20.006216: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:357] MLIR V1 optimization pass is not enabled\n",
      "2023-02-19 11:15:20.010340: I tensorflow/cc/saved_model/loader.cc:229] Restoring SavedModel bundle.\n",
      "2023-02-19 11:15:20.109383: I tensorflow/cc/saved_model/loader.cc:213] Running initialization op on SavedModel bundle at path: /var/folders/rq/fttfpph53671nf5_9s4qnwlm0000gn/T/tmpxb9wf4s1\n",
      "2023-02-19 11:15:20.114948: I tensorflow/cc/saved_model/loader.cc:305] SavedModel load for tags { serve }; Status: success: OK. Took 115447 microseconds.\n",
      "2023-02-19 11:15:20.147607: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:268] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n"
     ]
    }
   ],
   "source": [
    "converter = tf.lite.TFLiteConverter.from_keras_model(model)\n",
    "tflite_model = converter.convert()\n",
    "\n",
    "with open(\"model.tflite\", 'wb') as f:\n",
    "  f.write(tflite_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}