main.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logic Based FizzBuzz Function [Software 1.0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. \n",
    "import pandas as pd\n",
    "\n",
    "def fizzbuzz(n):\n",
    "    \n",
    "    # Logic Explanation\n",
    "    if n % 3 == 0 and n % 5 == 0:\n",
    "        return 'FizzBuzz'\n",
    "    elif n % 3 == 0:\n",
    "        return 'Fizz'\n",
    "    elif n % 5 == 0:\n",
    "        return 'Buzz'\n",
    "    else:\n",
    "        return 'Other'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Training and Testing Datasets in CSV Format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def createInputCSV(start,end,filename):\n",
    "    \n",
    "    # List is a collection(datasets) which is ordered and changeable and allows duplicate values.\n",
    "    inputData   = []\n",
    "    outputData  = []\n",
    "    \n",
    "    # Training data is used by ML Algorithm in training the model.\n",
    "    for i in range(start,end):\n",
    "        inputData.append(i)\n",
    "        outputData.append(fizzbuzz(i))\n",
    "    \n",
    "    # DataFrame is a 2-dimensional labeled data structure with columns of potentially different types\n",
    "    # we have two labels;one for input (number) and other for label (fizzbuzz values)\n",
    "    dataset = {}\n",
    "    dataset[\"input\"]  = inputData\n",
    "    dataset[\"label\"] = outputData\n",
    "    \n",
    "    # Writing to csv\n",
    "    pd.DataFrame(dataset).to_csv(filename)\n",
    "    \n",
    "    print(filename, \"Created!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Processing Input and Label Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cannot operate on label data directly so input variables and output variables have to be numeric.\n",
    "def processData(dataset):\n",
    "       \n",
    "    data   = dataset['input'].values\n",
    "    labels = dataset['label'].values\n",
    "    \n",
    "    processedData  = encodeData(data)\n",
    "    processedLabel = encodeLabel(labels)\n",
    "    \n",
    "    return processedData, processedLabel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    " # To Convert the input to vector of activations \n",
    "def encodeData(data):\n",
    "    \n",
    "    processedData = []\n",
    "     # The training data set ranges from 101 to 1000 which needs atmost 10 binary digits to represent the values.\n",
    "    # Creates a list containing the last bit of datainstance after it got shifted d times to the right and bitwise AND with 01.\n",
    "    for dataInstance in data:    \n",
    "        processedData.append([dataInstance >> d & 1 for d in range(10)])\n",
    "     \n",
    "    \n",
    "    return np.array(processedData)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Definition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.utils import np_utils\n",
    "# One-hot encoding to convert label data to integers\n",
    "def encodeLabel(labels):\n",
    "    \n",
    "    processedLabel = []\n",
    "    \n",
    "    for labelInstance in labels:\n",
    "        if(labelInstance == \"FizzBuzz\"):\n",
    "            # Fizzbuzz\n",
    "            processedLabel.append([3])\n",
    "        elif(labelInstance == \"Fizz\"):\n",
    "            # Fizz\n",
    "            processedLabel.append([1])\n",
    "        elif(labelInstance == \"Buzz\"):\n",
    "            # Buzz\n",
    "            processedLabel.append([2])\n",
    "        else:\n",
    "            # Other\n",
    "            processedLabel.append([0])\n",
    "    #Converts a class vector (integers) to binary class matrix.\n",
    "    return np_utils.to_categorical(np.array(processedLabel),4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Activation, Dropout\n",
    "from keras.callbacks import EarlyStopping, TensorBoard\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "input_size = 10\n",
    "drop_out = 0.1\n",
    "first_dense_layer_nodes  = 512\n",
    "second_dense_layer_nodes = 4\n",
    "\n",
    "def get_model():\n",
    "    \n",
    "    # We need a model to train the dataset.To build it on stack of layers with simplicity,we use sequential model.   \n",
    "    model = Sequential()    \n",
    "   \n",
    "    # Dense layer acts as hidden layer/output layer in model creation.\n",
    "    # Activation is used to provide non linearity to the model and must be differentiable for backpropagation.\n",
    "    #The input_dim specifies the shape of the input the model should expect,using bias\n",
    "    model.add(Dense(first_dense_layer_nodes, use_bias=True,input_dim=input_size))\n",
    "    \n",
    "    #'relu' as ativation function to minimise vanishing gradient problem\n",
    "    model.add(Activation('relu'))\n",
    "    \n",
    "    # drop out to avoid overfitting of models by randomly dropping few neurons\n",
    "    model.add(Dropout(drop_out))\n",
    "    \n",
    "    # Output layer\n",
    "    model.add(Dense(second_dense_layer_nodes))\n",
    "    \n",
    "    # Used in multiple classification logistic regression model.\n",
    "    # Softmax function is returns the probabilities of each class where target class has the highest probability.\n",
    "    model.add(Activation('softmax'))\n",
    "    \n",
    "    # prints a summary representation of the model\n",
    "    model.summary()\n",
    "    \n",
    "    # configuring the learning process to train the model\n",
    "    # multi class classification problem, we use categorical_crossentropy as loss function\n",
    "    # We judge the performance of your model by recoding the Metric values at the end of each epoch on the training dataset\n",
    "    model.compile(optimizer='adadelta',\n",
    "                  loss='categorical_crossentropy',\n",
    "                  metrics=['accuracy'])\n",
    "   \n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color='blue'>Creating Training and Testing Datafiles</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training.csv Created!\n",
      "testing.csv Created!\n"
     ]
    }
   ],
   "source": [
    "# Create datafiles\n",
    "createInputCSV(101,1001,'training.csv')\n",
    "createInputCSV(1,101,'testing.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color='blue'>Creating Model</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_1 (Dense)              (None, 512)               5632      \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 4)                 2052      \n",
      "_________________________________________________________________\n",
      "activation_2 (Activation)    (None, 4)                 0         \n",
      "=================================================================\n",
      "Total params: 7,684\n",
      "Trainable params: 7,684\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "#(10 input values + one bias ) * number of nodes= 11*512\n",
    "# (no of nodes in the hidden layer+ one bias) * no of output nodes = 513*4 \n",
    "model = get_model()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color = blue>Run Model</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 810 samples, validate on 90 samples\n",
      "Epoch 1/1500\n",
      "810/810 [==============================] - 0s 297us/step - loss: 1.2970 - acc: 0.4494 - val_loss: 1.1676 - val_acc: 0.5333\n",
      "Epoch 2/1500\n",
      "810/810 [==============================] - 0s 31us/step - loss: 1.1755 - acc: 0.5333 - val_loss: 1.1423 - val_acc: 0.5333\n",
      "Epoch 3/1500\n",
      "810/810 [==============================] - 0s 28us/step - loss: 1.1535 - acc: 0.5333 - val_loss: 1.1474 - val_acc: 0.5333\n",
      "Epoch 4/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 1.1494 - acc: 0.5333 - val_loss: 1.1505 - val_acc: 0.5333\n",
      "Epoch 5/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 1.1472 - acc: 0.5333 - val_loss: 1.1562 - val_acc: 0.5333\n",
      "Epoch 6/1500\n",
      "810/810 [==============================] - 0s 28us/step - loss: 1.1510 - acc: 0.5333 - val_loss: 1.1542 - val_acc: 0.5333\n",
      "Epoch 7/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 1.1428 - acc: 0.5333 - val_loss: 1.1646 - val_acc: 0.5333\n",
      "Epoch 8/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 1.1431 - acc: 0.5333 - val_loss: 1.1661 - val_acc: 0.5333\n",
      "Epoch 9/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 1.1405 - acc: 0.5333 - val_loss: 1.1501 - val_acc: 0.5333\n",
      "Epoch 10/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 1.1437 - acc: 0.5333 - val_loss: 1.1458 - val_acc: 0.5333\n",
      "Epoch 11/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 1.1351 - acc: 0.5333 - val_loss: 1.1502 - val_acc: 0.5333\n",
      "Epoch 12/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 1.1354 - acc: 0.5333 - val_loss: 1.1453 - val_acc: 0.5333\n",
      "Epoch 13/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 1.1334 - acc: 0.5333 - val_loss: 1.1649 - val_acc: 0.5333\n",
      "Epoch 14/1500\n",
      "810/810 [==============================] - 0s 48us/step - loss: 1.1373 - acc: 0.5333 - val_loss: 1.1456 - val_acc: 0.5333\n",
      "Epoch 15/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 1.1328 - acc: 0.5333 - val_loss: 1.1463 - val_acc: 0.5333\n",
      "Epoch 16/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 1.1286 - acc: 0.5333 - val_loss: 1.1422 - val_acc: 0.5333\n",
      "Epoch 17/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 1.1296 - acc: 0.5333 - val_loss: 1.1414 - val_acc: 0.5333\n",
      "Epoch 18/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 1.1313 - acc: 0.5333 - val_loss: 1.1418 - val_acc: 0.5333\n",
      "Epoch 19/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 1.1261 - acc: 0.5333 - val_loss: 1.1540 - val_acc: 0.5333\n",
      "Epoch 20/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 1.1242 - acc: 0.5333 - val_loss: 1.1419 - val_acc: 0.5333\n",
      "Epoch 21/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 1.1233 - acc: 0.5333 - val_loss: 1.1484 - val_acc: 0.5333\n",
      "Epoch 22/1500\n",
      "810/810 [==============================] - 0s 47us/step - loss: 1.1249 - acc: 0.5333 - val_loss: 1.1496 - val_acc: 0.5333\n",
      "Epoch 23/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 1.1200 - acc: 0.5333 - val_loss: 1.1395 - val_acc: 0.5333\n",
      "Epoch 24/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 1.1177 - acc: 0.5333 - val_loss: 1.1449 - val_acc: 0.5333\n",
      "Epoch 25/1500\n",
      "810/810 [==============================] - 0s 32us/step - loss: 1.1113 - acc: 0.5333 - val_loss: 1.1409 - val_acc: 0.5333\n",
      "Epoch 26/1500\n",
      "810/810 [==============================] - 0s 11us/step - loss: 1.1144 - acc: 0.5333 - val_loss: 1.1452 - val_acc: 0.5333\n",
      "Epoch 27/1500\n",
      "810/810 [==============================] - 0s 50us/step - loss: 1.1122 - acc: 0.5333 - val_loss: 1.1456 - val_acc: 0.5333\n",
      "Epoch 28/1500\n",
      "810/810 [==============================] - 0s 43us/step - loss: 1.1124 - acc: 0.5333 - val_loss: 1.1514 - val_acc: 0.5333\n",
      "Epoch 29/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 1.1054 - acc: 0.5333 - val_loss: 1.1463 - val_acc: 0.5333\n",
      "Epoch 30/1500\n",
      "810/810 [==============================] - 0s 77us/step - loss: 1.1049 - acc: 0.5333 - val_loss: 1.1362 - val_acc: 0.5333\n",
      "Epoch 31/1500\n",
      "810/810 [==============================] - 0s 26us/step - loss: 1.1050 - acc: 0.5333 - val_loss: 1.1453 - val_acc: 0.5333\n",
      "Epoch 32/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 1.0959 - acc: 0.5333 - val_loss: 1.1368 - val_acc: 0.5333\n",
      "Epoch 33/1500\n",
      "810/810 [==============================] - 0s 20us/step - loss: 1.0991 - acc: 0.5333 - val_loss: 1.1349 - val_acc: 0.5333\n",
      "Epoch 34/1500\n",
      "810/810 [==============================] - 0s 34us/step - loss: 1.0936 - acc: 0.5333 - val_loss: 1.1328 - val_acc: 0.5333\n",
      "Epoch 35/1500\n",
      "810/810 [==============================] - 0s 30us/step - loss: 1.0914 - acc: 0.5333 - val_loss: 1.1354 - val_acc: 0.5333\n",
      "Epoch 36/1500\n",
      "810/810 [==============================] - 0s 14us/step - loss: 1.0872 - acc: 0.5333 - val_loss: 1.1329 - val_acc: 0.5333\n",
      "Epoch 37/1500\n",
      "810/810 [==============================] - 0s 44us/step - loss: 1.0863 - acc: 0.5333 - val_loss: 1.1329 - val_acc: 0.5333\n",
      "Epoch 38/1500\n",
      "810/810 [==============================] - 0s 38us/step - loss: 1.0845 - acc: 0.5333 - val_loss: 1.1301 - val_acc: 0.5333\n",
      "Epoch 39/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 1.0790 - acc: 0.5333 - val_loss: 1.1475 - val_acc: 0.5333\n",
      "Epoch 40/1500\n",
      "810/810 [==============================] - 0s 75us/step - loss: 1.0827 - acc: 0.5333 - val_loss: 1.1280 - val_acc: 0.5333\n",
      "Epoch 41/1500\n",
      "810/810 [==============================] - 0s 30us/step - loss: 1.0745 - acc: 0.5370 - val_loss: 1.1259 - val_acc: 0.5333\n",
      "Epoch 42/1500\n",
      "810/810 [==============================] - 0s 33us/step - loss: 1.0704 - acc: 0.5333 - val_loss: 1.1258 - val_acc: 0.5333\n",
      "Epoch 43/1500\n",
      "810/810 [==============================] - 0s 65us/step - loss: 1.0713 - acc: 0.5358 - val_loss: 1.1241 - val_acc: 0.5333\n",
      "Epoch 44/1500\n",
      "810/810 [==============================] - 0s 43us/step - loss: 1.0633 - acc: 0.5346 - val_loss: 1.1170 - val_acc: 0.5333\n",
      "Epoch 45/1500\n",
      "810/810 [==============================] - 0s 71us/step - loss: 1.0628 - acc: 0.5346 - val_loss: 1.1188 - val_acc: 0.5333\n",
      "Epoch 46/1500\n",
      "810/810 [==============================] - 0s 36us/step - loss: 1.0617 - acc: 0.5358 - val_loss: 1.1209 - val_acc: 0.5333\n",
      "Epoch 47/1500\n",
      "810/810 [==============================] - 0s 34us/step - loss: 1.0558 - acc: 0.5395 - val_loss: 1.1201 - val_acc: 0.5333\n",
      "Epoch 48/1500\n",
      "810/810 [==============================] - 0s 37us/step - loss: 1.0508 - acc: 0.5346 - val_loss: 1.1122 - val_acc: 0.5333\n",
      "Epoch 49/1500\n",
      "810/810 [==============================] - 0s 36us/step - loss: 1.0506 - acc: 0.5358 - val_loss: 1.1083 - val_acc: 0.5333\n",
      "Epoch 50/1500\n",
      "810/810 [==============================] - 0s 42us/step - loss: 1.0441 - acc: 0.5407 - val_loss: 1.1297 - val_acc: 0.5333\n",
      "Epoch 51/1500\n",
      "810/810 [==============================] - 0s 48us/step - loss: 1.0434 - acc: 0.5395 - val_loss: 1.1193 - val_acc: 0.5333\n",
      "Epoch 52/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 1.0419 - acc: 0.5358 - val_loss: 1.1239 - val_acc: 0.5333\n",
      "Epoch 53/1500\n",
      "810/810 [==============================] - 0s 52us/step - loss: 1.0310 - acc: 0.5370 - val_loss: 1.1071 - val_acc: 0.5333\n",
      "Epoch 54/1500\n",
      "810/810 [==============================] - 0s 30us/step - loss: 1.0303 - acc: 0.5444 - val_loss: 1.1023 - val_acc: 0.5333\n",
      "Epoch 55/1500\n",
      "810/810 [==============================] - 0s 32us/step - loss: 1.0246 - acc: 0.5519 - val_loss: 1.0945 - val_acc: 0.5333\n",
      "Epoch 56/1500\n",
      "810/810 [==============================] - 0s 50us/step - loss: 1.0250 - acc: 0.5432 - val_loss: 1.0960 - val_acc: 0.5333\n",
      "Epoch 57/1500\n",
      "810/810 [==============================] - 0s 32us/step - loss: 1.0182 - acc: 0.5580 - val_loss: 1.0939 - val_acc: 0.5333\n",
      "Epoch 58/1500\n",
      "810/810 [==============================] - 0s 31us/step - loss: 1.0155 - acc: 0.5494 - val_loss: 1.0915 - val_acc: 0.5333\n",
      "Epoch 59/1500\n",
      "810/810 [==============================] - 0s 36us/step - loss: 1.0169 - acc: 0.5568 - val_loss: 1.0964 - val_acc: 0.5333\n",
      "Epoch 60/1500\n",
      "810/810 [==============================] - 0s 33us/step - loss: 1.0045 - acc: 0.5481 - val_loss: 1.0798 - val_acc: 0.5333\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 61/1500\n",
      "810/810 [==============================] - 0s 38us/step - loss: 1.0019 - acc: 0.5617 - val_loss: 1.0832 - val_acc: 0.5444\n",
      "Epoch 62/1500\n",
      "810/810 [==============================] - 0s 38us/step - loss: 0.9962 - acc: 0.5630 - val_loss: 1.0737 - val_acc: 0.5333\n",
      "Epoch 63/1500\n",
      "810/810 [==============================] - 0s 31us/step - loss: 0.9909 - acc: 0.5630 - val_loss: 1.0730 - val_acc: 0.5444\n",
      "Epoch 64/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.9919 - acc: 0.5790 - val_loss: 1.0672 - val_acc: 0.5333\n",
      "Epoch 65/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.9829 - acc: 0.5778 - val_loss: 1.0699 - val_acc: 0.5333\n",
      "Epoch 66/1500\n",
      "810/810 [==============================] - 0s 42us/step - loss: 0.9806 - acc: 0.5840 - val_loss: 1.0714 - val_acc: 0.5333\n",
      "Epoch 67/1500\n",
      "810/810 [==============================] - 0s 33us/step - loss: 0.9811 - acc: 0.5580 - val_loss: 1.0659 - val_acc: 0.5333\n",
      "Epoch 68/1500\n",
      "810/810 [==============================] - 0s 42us/step - loss: 0.9733 - acc: 0.5691 - val_loss: 1.0529 - val_acc: 0.5556\n",
      "Epoch 69/1500\n",
      "810/810 [==============================] - 0s 46us/step - loss: 0.9639 - acc: 0.5963 - val_loss: 1.0641 - val_acc: 0.5333\n",
      "Epoch 70/1500\n",
      "810/810 [==============================] - 0s 34us/step - loss: 0.9626 - acc: 0.5728 - val_loss: 1.0536 - val_acc: 0.5333\n",
      "Epoch 71/1500\n",
      "810/810 [==============================] - 0s 36us/step - loss: 0.9562 - acc: 0.5802 - val_loss: 1.0626 - val_acc: 0.5556\n",
      "Epoch 72/1500\n",
      "810/810 [==============================] - 0s 55us/step - loss: 0.9536 - acc: 0.5914 - val_loss: 1.0610 - val_acc: 0.5444\n",
      "Epoch 73/1500\n",
      "810/810 [==============================] - 0s 36us/step - loss: 0.9519 - acc: 0.5889 - val_loss: 1.0455 - val_acc: 0.5333\n",
      "Epoch 74/1500\n",
      "810/810 [==============================] - 0s 33us/step - loss: 0.9419 - acc: 0.5951 - val_loss: 1.0458 - val_acc: 0.5667\n",
      "Epoch 75/1500\n",
      "810/810 [==============================] - 0s 37us/step - loss: 0.9357 - acc: 0.6025 - val_loss: 1.0410 - val_acc: 0.5333\n",
      "Epoch 76/1500\n",
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      "Epoch 112/1500\n",
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      "Epoch 114/1500\n",
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      "Epoch 116/1500\n",
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      "Epoch 117/1500\n",
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      "Epoch 119/1500\n",
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      "Epoch 120/1500\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 121/1500\n",
      "810/810 [==============================] - 0s 32us/step - loss: 0.7149 - acc: 0.7407 - val_loss: 0.9035 - val_acc: 0.5333\n",
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      "Epoch 136/1500\n",
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      "Epoch 137/1500\n",
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      "Epoch 140/1500\n",
      "810/810 [==============================] - 0s 32us/step - loss: 0.6448 - acc: 0.7753 - val_loss: 0.8005 - val_acc: 0.7222\n",
      "Epoch 141/1500\n",
      "810/810 [==============================] - 0s 36us/step - loss: 0.6267 - acc: 0.8062 - val_loss: 0.7991 - val_acc: 0.6333\n",
      "Epoch 142/1500\n",
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      "Epoch 151/1500\n",
      "810/810 [==============================] - ETA: 0s - loss: 0.6046 - acc: 0.828 - 0s 39us/step - loss: 0.5926 - acc: 0.8198 - val_loss: 0.8226 - val_acc: 0.6000\n",
      "Epoch 152/1500\n",
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      "Epoch 180/1500\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "810/810 [==============================] - 0s 33us/step - loss: 0.5104 - acc: 0.8519 - val_loss: 0.7800 - val_acc: 0.5778\n",
      "Epoch 181/1500\n",
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      "Epoch 195/1500\n",
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      "Epoch 198/1500\n",
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      "Epoch 199/1500\n",
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      "Epoch 200/1500\n",
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      "Epoch 201/1500\n",
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      "Epoch 229/1500\n",
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      "Epoch 230/1500\n",
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      "Epoch 231/1500\n",
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      "Epoch 232/1500\n",
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      "Epoch 233/1500\n",
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      "Epoch 234/1500\n",
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      "Epoch 235/1500\n",
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      "Epoch 236/1500\n",
      "810/810 [==============================] - 0s 30us/step - loss: 0.3706 - acc: 0.9099 - val_loss: 0.5609 - val_acc: 0.8889\n",
      "Epoch 237/1500\n",
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      "Epoch 238/1500\n",
      "810/810 [==============================] - 0s 30us/step - loss: 0.3773 - acc: 0.8951 - val_loss: 0.5628 - val_acc: 0.7778\n",
      "Epoch 239/1500\n",
      "810/810 [==============================] - 0s 30us/step - loss: 0.3764 - acc: 0.9136 - val_loss: 0.5650 - val_acc: 0.7889\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 240/1500\n",
      "810/810 [==============================] - 0s 32us/step - loss: 0.3455 - acc: 0.9309 - val_loss: 0.5652 - val_acc: 0.8222\n",
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      "Epoch 247/1500\n",
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      "Epoch 250/1500\n",
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      "Epoch 254/1500\n",
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      "Epoch 255/1500\n",
      "810/810 [==============================] - 0s 31us/step - loss: 0.3435 - acc: 0.9370 - val_loss: 0.5147 - val_acc: 0.8444\n",
      "Epoch 256/1500\n",
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      "Epoch 257/1500\n",
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      "Epoch 258/1500\n",
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      "Epoch 259/1500\n",
      "810/810 [==============================] - 0s 30us/step - loss: 0.3252 - acc: 0.9358 - val_loss: 0.5506 - val_acc: 0.7778\n",
      "Epoch 260/1500\n",
      "810/810 [==============================] - 0s 33us/step - loss: 0.3191 - acc: 0.9346 - val_loss: 0.5045 - val_acc: 0.8778\n",
      "Epoch 261/1500\n",
      "810/810 [==============================] - 0s 33us/step - loss: 0.3185 - acc: 0.9370 - val_loss: 0.5426 - val_acc: 0.7889\n",
      "Epoch 262/1500\n",
      "810/810 [==============================] - 0s 34us/step - loss: 0.3037 - acc: 0.9420 - val_loss: 0.5129 - val_acc: 0.8444\n",
      "Epoch 263/1500\n",
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      "Epoch 264/1500\n",
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      "Epoch 268/1500\n",
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      "Epoch 285/1500\n",
      "810/810 [==============================] - 0s 28us/step - loss: 0.3058 - acc: 0.9420 - val_loss: 0.5130 - val_acc: 0.8000\n",
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      "Epoch 296/1500\n",
      "810/810 [==============================] - ETA: 0s - loss: 0.2942 - acc: 0.921 - 0s 41us/step - loss: 0.2717 - acc: 0.9519 - val_loss: 0.4519 - val_acc: 0.8667\n",
      "Epoch 297/1500\n",
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      "Epoch 298/1500\n",
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      "Epoch 299/1500\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "810/810 [==============================] - 0s 42us/step - loss: 0.2803 - acc: 0.9432 - val_loss: 0.5339 - val_acc: 0.7333\n",
      "Epoch 300/1500\n",
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      "Epoch 301/1500\n",
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      "Epoch 304/1500\n",
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      "Epoch 305/1500\n",
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      "Epoch 306/1500\n",
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      "Epoch 309/1500\n",
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      "Epoch 310/1500\n",
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      "Epoch 312/1500\n",
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      "Epoch 313/1500\n",
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      "Epoch 314/1500\n",
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      "Epoch 315/1500\n",
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      "Epoch 316/1500\n",
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      "Epoch 317/1500\n",
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      "Epoch 318/1500\n",
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      "Epoch 319/1500\n",
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      "Epoch 320/1500\n",
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      "Epoch 321/1500\n",
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      "Epoch 322/1500\n",
      "810/810 [==============================] - 0s 32us/step - loss: 0.2543 - acc: 0.9494 - val_loss: 0.4681 - val_acc: 0.8000\n",
      "Epoch 323/1500\n",
      "810/810 [==============================] - 0s 36us/step - loss: 0.2413 - acc: 0.9630 - val_loss: 0.5963 - val_acc: 0.6556\n",
      "Epoch 324/1500\n",
      "810/810 [==============================] - 0s 31us/step - loss: 0.2541 - acc: 0.9407 - val_loss: 0.4694 - val_acc: 0.8111\n",
      "Epoch 325/1500\n",
      "810/810 [==============================] - 0s 65us/step - loss: 0.2459 - acc: 0.9457 - val_loss: 0.4654 - val_acc: 0.8111\n",
      "Epoch 326/1500\n",
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      "Epoch 338/1500\n",
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      "Epoch 339/1500\n",
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      "Epoch 343/1500\n",
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      "Epoch 344/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.2149 - acc: 0.9691 - val_loss: 0.3922 - val_acc: 0.8778\n",
      "Epoch 345/1500\n",
      "810/810 [==============================] - 0s 35us/step - loss: 0.2305 - acc: 0.9506 - val_loss: 0.4879 - val_acc: 0.7889\n",
      "Epoch 346/1500\n",
      "810/810 [==============================] - 0s 31us/step - loss: 0.2121 - acc: 0.9605 - val_loss: 0.5163 - val_acc: 0.7333\n",
      "Epoch 347/1500\n",
      "810/810 [==============================] - 0s 31us/step - loss: 0.2360 - acc: 0.9469 - val_loss: 0.4979 - val_acc: 0.7667\n",
      "Epoch 348/1500\n",
      "810/810 [==============================] - 0s 33us/step - loss: 0.2280 - acc: 0.9519 - val_loss: 0.3886 - val_acc: 0.9444\n",
      "Epoch 349/1500\n",
      "810/810 [==============================] - 0s 30us/step - loss: 0.2270 - acc: 0.9568 - val_loss: 0.4690 - val_acc: 0.7778\n",
      "Epoch 350/1500\n",
      "810/810 [==============================] - 0s 33us/step - loss: 0.2199 - acc: 0.9531 - val_loss: 0.4785 - val_acc: 0.7889\n",
      "Epoch 351/1500\n",
      "810/810 [==============================] - 0s 28us/step - loss: 0.2272 - acc: 0.9494 - val_loss: 0.3789 - val_acc: 0.9000\n",
      "Epoch 352/1500\n",
      "810/810 [==============================] - 0s 26us/step - loss: 0.2054 - acc: 0.9630 - val_loss: 0.4246 - val_acc: 0.8333\n",
      "Epoch 353/1500\n",
      "810/810 [==============================] - 0s 31us/step - loss: 0.2196 - acc: 0.9543 - val_loss: 0.4265 - val_acc: 0.8333\n",
      "Epoch 354/1500\n",
      "810/810 [==============================] - 0s 57us/step - loss: 0.2240 - acc: 0.9506 - val_loss: 0.3742 - val_acc: 0.9111\n",
      "Epoch 355/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.2054 - acc: 0.9654 - val_loss: 0.4055 - val_acc: 0.8556\n",
      "Epoch 356/1500\n",
      "810/810 [==============================] - 0s 24us/step - loss: 0.1982 - acc: 0.9679 - val_loss: 0.3666 - val_acc: 0.9556\n",
      "Epoch 357/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.2147 - acc: 0.9556 - val_loss: 0.3963 - val_acc: 0.8556\n",
      "Epoch 358/1500\n",
      "810/810 [==============================] - 0s 53us/step - loss: 0.2058 - acc: 0.9605 - val_loss: 0.3886 - val_acc: 0.8667\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 359/1500\n",
      "810/810 [==============================] - 0s 26us/step - loss: 0.2086 - acc: 0.9630 - val_loss: 0.3905 - val_acc: 0.8556\n",
      "Epoch 360/1500\n",
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      "Epoch 361/1500\n",
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      "Epoch 362/1500\n",
      "810/810 [==============================] - 0s 58us/step - loss: 0.2064 - acc: 0.9630 - val_loss: 0.3791 - val_acc: 0.8889\n",
      "Epoch 363/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.2114 - acc: 0.9642 - val_loss: 0.3696 - val_acc: 0.9222\n",
      "Epoch 364/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.2110 - acc: 0.9679 - val_loss: 0.3689 - val_acc: 0.9111\n",
      "Epoch 365/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.2022 - acc: 0.9593 - val_loss: 0.4265 - val_acc: 0.8111\n",
      "Epoch 366/1500\n",
      "810/810 [==============================] - 0s 43us/step - loss: 0.1793 - acc: 0.9778 - val_loss: 0.4095 - val_acc: 0.8333\n",
      "Epoch 367/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.2025 - acc: 0.9617 - val_loss: 0.4274 - val_acc: 0.8111\n",
      "Epoch 368/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.2095 - acc: 0.9617 - val_loss: 0.3762 - val_acc: 0.8778\n",
      "Epoch 369/1500\n",
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      "Epoch 370/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.1919 - acc: 0.9691 - val_loss: 0.4226 - val_acc: 0.8111\n",
      "Epoch 371/1500\n",
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      "Epoch 372/1500\n",
      "810/810 [==============================] - 0s 24us/step - loss: 0.2021 - acc: 0.9605 - val_loss: 0.3982 - val_acc: 0.8333\n",
      "Epoch 373/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.1946 - acc: 0.9704 - val_loss: 0.3502 - val_acc: 0.9444\n",
      "Epoch 374/1500\n",
      "810/810 [==============================] - 0s 45us/step - loss: 0.1979 - acc: 0.9704 - val_loss: 0.5863 - val_acc: 0.6667\n",
      "Epoch 375/1500\n",
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      "Epoch 376/1500\n",
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      "Epoch 377/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.1740 - acc: 0.9753 - val_loss: 0.3585 - val_acc: 0.9111\n",
      "Epoch 378/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.1970 - acc: 0.9642 - val_loss: 0.3565 - val_acc: 0.9222\n",
      "Epoch 379/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.1880 - acc: 0.9704 - val_loss: 0.3550 - val_acc: 0.9222\n",
      "Epoch 380/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.1923 - acc: 0.9691 - val_loss: 0.4715 - val_acc: 0.7556\n",
      "Epoch 381/1500\n",
      "810/810 [==============================] - ETA: 0s - loss: 0.2065 - acc: 0.945 - 0s 38us/step - loss: 0.2113 - acc: 0.9531 - val_loss: 0.4137 - val_acc: 0.8111\n",
      "Epoch 382/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.1923 - acc: 0.9617 - val_loss: 0.3566 - val_acc: 0.9000\n",
      "Epoch 383/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.1791 - acc: 0.9728 - val_loss: 0.3515 - val_acc: 0.9222\n",
      "Epoch 384/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.1867 - acc: 0.9679 - val_loss: 0.3402 - val_acc: 0.9444\n",
      "Epoch 385/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.1901 - acc: 0.9691 - val_loss: 0.3885 - val_acc: 0.8444\n",
      "Epoch 386/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.1873 - acc: 0.9679 - val_loss: 0.3953 - val_acc: 0.8444\n",
      "Epoch 387/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.1845 - acc: 0.9728 - val_loss: 0.3639 - val_acc: 0.8778\n",
      "Epoch 388/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.1770 - acc: 0.9765 - val_loss: 0.4004 - val_acc: 0.8222\n",
      "Epoch 389/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.1996 - acc: 0.9605 - val_loss: 0.3960 - val_acc: 0.8444\n",
      "Epoch 390/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.1864 - acc: 0.9605 - val_loss: 0.3753 - val_acc: 0.8667\n",
      "Epoch 391/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.1731 - acc: 0.9704 - val_loss: 0.4237 - val_acc: 0.7889\n",
      "Epoch 392/1500\n",
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      "Epoch 400/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.1804 - acc: 0.9667 - val_loss: 0.3623 - val_acc: 0.8667\n",
      "Epoch 401/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.1728 - acc: 0.9716 - val_loss: 0.3916 - val_acc: 0.8667\n",
      "Epoch 402/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.1782 - acc: 0.9667 - val_loss: 0.4950 - val_acc: 0.7667\n",
      "Epoch 403/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.1922 - acc: 0.9630 - val_loss: 0.3545 - val_acc: 0.8889\n",
      "Epoch 404/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.1853 - acc: 0.9667 - val_loss: 0.3337 - val_acc: 0.9000\n",
      "Epoch 405/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.1852 - acc: 0.9642 - val_loss: 0.3534 - val_acc: 0.8778\n",
      "Epoch 406/1500\n",
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      "Epoch 407/1500\n",
      "810/810 [==============================] - 0s 30us/step - loss: 0.1764 - acc: 0.9667 - val_loss: 0.4001 - val_acc: 0.8444\n",
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      "810/810 [==============================] - 0s 58us/step - loss: 0.1793 - acc: 0.9605 - val_loss: 0.3714 - val_acc: 0.8556\n",
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      "Epoch 410/1500\n",
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      "810/810 [==============================] - 0s 30us/step - loss: 0.1854 - acc: 0.9556 - val_loss: 0.3358 - val_acc: 0.9000\n",
      "Epoch 412/1500\n",
      "810/810 [==============================] - 0s 28us/step - loss: 0.1644 - acc: 0.9679 - val_loss: 0.3515 - val_acc: 0.9111\n",
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      "Epoch 414/1500\n",
      "810/810 [==============================] - 0s 28us/step - loss: 0.1647 - acc: 0.9691 - val_loss: 0.3654 - val_acc: 0.8667\n",
      "Epoch 415/1500\n",
      "810/810 [==============================] - 0s 25us/step - loss: 0.1807 - acc: 0.9716 - val_loss: 0.4002 - val_acc: 0.8222\n",
      "Epoch 416/1500\n",
      "810/810 [==============================] - 0s 49us/step - loss: 0.1749 - acc: 0.9642 - val_loss: 0.3631 - val_acc: 0.8667\n",
      "Epoch 417/1500\n",
      "810/810 [==============================] - 0s 32us/step - loss: 0.1773 - acc: 0.9667 - val_loss: 0.4210 - val_acc: 0.8111\n",
      "Epoch 418/1500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "810/810 [==============================] - 0s 11us/step - loss: 0.1605 - acc: 0.9753 - val_loss: 0.4116 - val_acc: 0.8222\n",
      "Epoch 419/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.1692 - acc: 0.9691 - val_loss: 0.3491 - val_acc: 0.8667\n",
      "Epoch 420/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.1619 - acc: 0.9716 - val_loss: 0.3222 - val_acc: 0.9333\n",
      "Epoch 421/1500\n",
      "810/810 [==============================] - 0s 58us/step - loss: 0.1688 - acc: 0.9679 - val_loss: 0.4273 - val_acc: 0.7667\n",
      "Epoch 422/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.1746 - acc: 0.9679 - val_loss: 0.3812 - val_acc: 0.8444\n",
      "Epoch 423/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.1661 - acc: 0.9679 - val_loss: 0.4444 - val_acc: 0.7778\n",
      "Epoch 424/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.1705 - acc: 0.9765 - val_loss: 0.3402 - val_acc: 0.8889\n",
      "Epoch 425/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.1574 - acc: 0.9741 - val_loss: 0.3938 - val_acc: 0.8222\n",
      "Epoch 426/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.1639 - acc: 0.9679 - val_loss: 0.3678 - val_acc: 0.8667\n",
      "Epoch 427/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.1616 - acc: 0.9741 - val_loss: 0.3183 - val_acc: 0.9111\n",
      "Epoch 428/1500\n",
      "810/810 [==============================] - 0s 38us/step - loss: 0.1817 - acc: 0.9642 - val_loss: 0.3346 - val_acc: 0.8889\n",
      "Epoch 429/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.1709 - acc: 0.9716 - val_loss: 0.4208 - val_acc: 0.7667\n",
      "Epoch 430/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.1621 - acc: 0.9716 - val_loss: 0.3007 - val_acc: 0.9556\n",
      "Epoch 431/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.1541 - acc: 0.9802 - val_loss: 0.2988 - val_acc: 0.9556\n",
      "Epoch 432/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.1510 - acc: 0.9716 - val_loss: 0.3130 - val_acc: 0.9222\n",
      "Epoch 433/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.1590 - acc: 0.9741 - val_loss: 0.3390 - val_acc: 0.9222\n",
      "Epoch 434/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.1430 - acc: 0.9753 - val_loss: 0.3863 - val_acc: 0.8444\n",
      "Epoch 435/1500\n",
      "810/810 [==============================] - 0s 28us/step - loss: 0.1611 - acc: 0.9704 - val_loss: 0.3651 - val_acc: 0.8667\n",
      "Epoch 436/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.1801 - acc: 0.9580 - val_loss: 0.3340 - val_acc: 0.8889\n",
      "Epoch 437/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.1583 - acc: 0.9753 - val_loss: 0.3075 - val_acc: 0.9556\n",
      "Epoch 438/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.1590 - acc: 0.9679 - val_loss: 0.2987 - val_acc: 0.9667\n",
      "Epoch 439/1500\n",
      "810/810 [==============================] - 0s 35us/step - loss: 0.1516 - acc: 0.9765 - val_loss: 0.4766 - val_acc: 0.7333\n",
      "Epoch 440/1500\n",
      "810/810 [==============================] - 0s 58us/step - loss: 0.1700 - acc: 0.9667 - val_loss: 0.4149 - val_acc: 0.7889\n",
      "Epoch 441/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.1546 - acc: 0.9790 - val_loss: 0.3092 - val_acc: 0.9444\n",
      "Epoch 442/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.1589 - acc: 0.9704 - val_loss: 0.3075 - val_acc: 0.9444\n",
      "Epoch 443/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.1464 - acc: 0.9753 - val_loss: 0.3033 - val_acc: 0.9333\n",
      "Epoch 444/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.1522 - acc: 0.9679 - val_loss: 0.3661 - val_acc: 0.8556\n",
      "Epoch 445/1500\n",
      "810/810 [==============================] - 0s 58us/step - loss: 0.1729 - acc: 0.9630 - val_loss: 0.4248 - val_acc: 0.7889\n",
      "Epoch 446/1500\n",
      "810/810 [==============================] - 0s 12us/step - loss: 0.1598 - acc: 0.9728 - val_loss: 0.4481 - val_acc: 0.7667\n",
      "Epoch 447/1500\n",
      "810/810 [==============================] - 0s 75us/step - loss: 0.1465 - acc: 0.9679 - val_loss: 0.3074 - val_acc: 0.9333\n",
      "Epoch 448/1500\n",
      "810/810 [==============================] - 0s 33us/step - loss: 0.1563 - acc: 0.9728 - val_loss: 0.5369 - val_acc: 0.7000\n",
      "Epoch 449/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.1713 - acc: 0.9568 - val_loss: 0.3958 - val_acc: 0.8333\n",
      "Epoch 450/1500\n",
      "810/810 [==============================] - 0s 49us/step - loss: 0.1575 - acc: 0.9630 - val_loss: 0.2971 - val_acc: 0.9444\n",
      "Epoch 451/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.1471 - acc: 0.9778 - val_loss: 0.2968 - val_acc: 0.9444\n",
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      "810/810 [==============================] - 0s 19us/step - loss: 0.1522 - acc: 0.9654 - val_loss: 0.2940 - val_acc: 0.9333\n",
      "Epoch 456/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.1430 - acc: 0.9778 - val_loss: 0.3447 - val_acc: 0.8667\n",
      "Epoch 457/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.1457 - acc: 0.9716 - val_loss: 0.3040 - val_acc: 0.9333\n",
      "Epoch 458/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.1474 - acc: 0.9704 - val_loss: 0.2916 - val_acc: 0.9667\n",
      "Epoch 459/1500\n",
      "810/810 [==============================] - 0s 37us/step - loss: 0.1507 - acc: 0.9704 - val_loss: 0.3225 - val_acc: 0.9333\n",
      "Epoch 460/1500\n",
      "810/810 [==============================] - 0s 34us/step - loss: 0.1450 - acc: 0.9691 - val_loss: 0.2964 - val_acc: 0.9444\n",
      "Epoch 461/1500\n",
      "810/810 [==============================] - 0s 24us/step - loss: 0.1499 - acc: 0.9827 - val_loss: 0.3229 - val_acc: 0.8889\n",
      "Epoch 462/1500\n",
      "810/810 [==============================] - 0s 49us/step - loss: 0.1461 - acc: 0.9765 - val_loss: 0.2828 - val_acc: 0.9667\n",
      "Epoch 463/1500\n",
      "810/810 [==============================] - 0s 34us/step - loss: 0.1491 - acc: 0.9716 - val_loss: 0.3156 - val_acc: 0.9222\n",
      "Epoch 464/1500\n",
      "810/810 [==============================] - 0s 31us/step - loss: 0.1381 - acc: 0.9790 - val_loss: 0.3783 - val_acc: 0.8333\n",
      "Epoch 465/1500\n",
      "810/810 [==============================] - 0s 34us/step - loss: 0.1371 - acc: 0.9753 - val_loss: 0.3443 - val_acc: 0.8667\n",
      "Epoch 466/1500\n",
      "810/810 [==============================] - 0s 30us/step - loss: 0.1321 - acc: 0.9864 - val_loss: 0.2946 - val_acc: 0.9444\n",
      "Epoch 467/1500\n",
      "810/810 [==============================] - 0s 31us/step - loss: 0.1400 - acc: 0.9704 - val_loss: 0.3389 - val_acc: 0.8667\n",
      "Epoch 468/1500\n",
      "810/810 [==============================] - 0s 43us/step - loss: 0.1523 - acc: 0.9704 - val_loss: 0.3064 - val_acc: 0.9111\n",
      "Epoch 469/1500\n",
      "810/810 [==============================] - 0s 36us/step - loss: 0.1527 - acc: 0.9679 - val_loss: 0.3336 - val_acc: 0.8778\n",
      "Epoch 470/1500\n",
      "810/810 [==============================] - 0s 36us/step - loss: 0.1618 - acc: 0.9716 - val_loss: 0.3042 - val_acc: 0.9222\n",
      "Epoch 471/1500\n",
      "810/810 [==============================] - 0s 34us/step - loss: 0.1440 - acc: 0.9753 - val_loss: 0.3812 - val_acc: 0.8111\n",
      "Epoch 472/1500\n",
      "810/810 [==============================] - 0s 28us/step - loss: 0.1451 - acc: 0.9790 - val_loss: 0.3616 - val_acc: 0.8444\n",
      "Epoch 473/1500\n",
      "810/810 [==============================] - 0s 28us/step - loss: 0.1406 - acc: 0.9765 - val_loss: 0.3407 - val_acc: 0.8667\n",
      "Epoch 474/1500\n",
      "810/810 [==============================] - 0s 31us/step - loss: 0.1338 - acc: 0.9728 - val_loss: 0.4383 - val_acc: 0.8000\n",
      "Epoch 475/1500\n",
      "810/810 [==============================] - 0s 36us/step - loss: 0.1484 - acc: 0.9679 - val_loss: 0.3024 - val_acc: 0.9111\n",
      "Epoch 476/1500\n",
      "810/810 [==============================] - 0s 33us/step - loss: 0.1329 - acc: 0.9827 - val_loss: 0.4999 - val_acc: 0.7222\n",
      "Epoch 477/1500\n",
      "810/810 [==============================] - 0s 34us/step - loss: 0.1411 - acc: 0.9654 - val_loss: 0.3589 - val_acc: 0.8667\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 478/1500\n",
      "810/810 [==============================] - 0s 36us/step - loss: 0.1446 - acc: 0.9741 - val_loss: 0.2879 - val_acc: 0.9556\n",
      "Epoch 479/1500\n",
      "810/810 [==============================] - 0s 37us/step - loss: 0.1569 - acc: 0.9654 - val_loss: 0.4179 - val_acc: 0.7667\n",
      "Epoch 480/1500\n",
      "810/810 [==============================] - 0s 34us/step - loss: 0.1360 - acc: 0.9778 - val_loss: 0.3824 - val_acc: 0.8444\n",
      "Epoch 481/1500\n",
      "810/810 [==============================] - 0s 36us/step - loss: 0.1384 - acc: 0.9802 - val_loss: 0.2778 - val_acc: 0.9556\n",
      "Epoch 482/1500\n",
      "810/810 [==============================] - 0s 34us/step - loss: 0.1345 - acc: 0.9778 - val_loss: 0.3394 - val_acc: 0.8667\n",
      "Epoch 483/1500\n",
      "810/810 [==============================] - 0s 38us/step - loss: 0.1501 - acc: 0.9778 - val_loss: 0.3059 - val_acc: 0.9000\n",
      "Epoch 484/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.1495 - acc: 0.9605 - val_loss: 0.3483 - val_acc: 0.8667\n",
      "Epoch 485/1500\n",
      "810/810 [==============================] - 0s 57us/step - loss: 0.1510 - acc: 0.9605 - val_loss: 0.3355 - val_acc: 0.8556\n",
      "Epoch 486/1500\n",
      "810/810 [==============================] - 0s 36us/step - loss: 0.1378 - acc: 0.9704 - val_loss: 0.3283 - val_acc: 0.8667\n",
      "Epoch 487/1500\n",
      "810/810 [==============================] - 0s 33us/step - loss: 0.1546 - acc: 0.9691 - val_loss: 0.3435 - val_acc: 0.8667\n",
      "Epoch 488/1500\n",
      "810/810 [==============================] - 0s 31us/step - loss: 0.1413 - acc: 0.9679 - val_loss: 0.3150 - val_acc: 0.8889\n",
      "Epoch 489/1500\n",
      "810/810 [==============================] - 0s 32us/step - loss: 0.1394 - acc: 0.9753 - val_loss: 0.2753 - val_acc: 0.9444\n",
      "Epoch 490/1500\n",
      "810/810 [==============================] - 0s 31us/step - loss: 0.1330 - acc: 0.9778 - val_loss: 0.3361 - val_acc: 0.8778\n",
      "Epoch 491/1500\n",
      "810/810 [==============================] - 0s 31us/step - loss: 0.1290 - acc: 0.9790 - val_loss: 0.4382 - val_acc: 0.8000\n",
      "Epoch 492/1500\n",
      "810/810 [==============================] - 0s 34us/step - loss: 0.1329 - acc: 0.9778 - val_loss: 0.3197 - val_acc: 0.8778\n",
      "Epoch 493/1500\n",
      "810/810 [==============================] - 0s 77us/step - loss: 0.1404 - acc: 0.9778 - val_loss: 0.2860 - val_acc: 0.9333\n",
      "Epoch 494/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.1337 - acc: 0.9704 - val_loss: 0.3911 - val_acc: 0.8000\n",
      "Epoch 495/1500\n",
      "810/810 [==============================] - 0s 37us/step - loss: 0.1456 - acc: 0.9691 - val_loss: 0.3284 - val_acc: 0.9000\n",
      "Epoch 496/1500\n",
      "810/810 [==============================] - 0s 34us/step - loss: 0.1304 - acc: 0.9753 - val_loss: 0.3134 - val_acc: 0.9111\n",
      "Epoch 497/1500\n",
      "810/810 [==============================] - 0s 27us/step - loss: 0.1403 - acc: 0.9765 - val_loss: 0.3126 - val_acc: 0.8889\n",
      "Epoch 498/1500\n",
      "810/810 [==============================] - 0s 30us/step - loss: 0.1240 - acc: 0.9778 - val_loss: 0.3033 - val_acc: 0.9000\n",
      "Epoch 499/1500\n",
      "810/810 [==============================] - 0s 30us/step - loss: 0.1264 - acc: 0.9778 - val_loss: 0.3218 - val_acc: 0.8667\n",
      "Epoch 500/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.1397 - acc: 0.9679 - val_loss: 0.2715 - val_acc: 0.9444\n",
      "Epoch 501/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.1287 - acc: 0.9765 - val_loss: 0.3410 - val_acc: 0.8556\n",
      "Epoch 502/1500\n",
      "810/810 [==============================] - 0s 23us/step - loss: 0.1121 - acc: 0.9852 - val_loss: 0.2674 - val_acc: 0.9556\n",
      "Epoch 503/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.1464 - acc: 0.9704 - val_loss: 0.3042 - val_acc: 0.8889\n",
      "Epoch 504/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.1278 - acc: 0.9840 - val_loss: 0.4170 - val_acc: 0.7889\n",
      "Epoch 505/1500\n",
      "810/810 [==============================] - 0s 36us/step - loss: 0.1255 - acc: 0.9704 - val_loss: 0.3191 - val_acc: 0.8889\n",
      "Epoch 506/1500\n",
      "810/810 [==============================] - 0s 34us/step - loss: 0.1261 - acc: 0.9827 - val_loss: 0.3407 - val_acc: 0.8444\n",
      "Epoch 507/1500\n",
      "810/810 [==============================] - 0s 32us/step - loss: 0.1269 - acc: 0.9852 - val_loss: 0.2835 - val_acc: 0.9222\n",
      "Epoch 508/1500\n",
      "810/810 [==============================] - 0s 30us/step - loss: 0.1210 - acc: 0.9815 - val_loss: 0.2796 - val_acc: 0.9444\n",
      "Epoch 509/1500\n",
      "810/810 [==============================] - 0s 36us/step - loss: 0.1368 - acc: 0.9728 - val_loss: 0.2803 - val_acc: 0.9222\n",
      "Epoch 510/1500\n",
      "810/810 [==============================] - 0s 49us/step - loss: 0.1396 - acc: 0.9728 - val_loss: 0.2745 - val_acc: 0.9667\n",
      "Epoch 511/1500\n",
      "810/810 [==============================] - 0s 41us/step - loss: 0.1441 - acc: 0.9716 - val_loss: 0.3930 - val_acc: 0.8333\n",
      "Epoch 512/1500\n",
      "810/810 [==============================] - 0s 34us/step - loss: 0.1404 - acc: 0.9679 - val_loss: 0.4793 - val_acc: 0.7556\n",
      "Epoch 513/1500\n",
      "810/810 [==============================] - 0s 30us/step - loss: 0.1337 - acc: 0.9654 - val_loss: 0.2899 - val_acc: 0.9000\n",
      "Epoch 514/1500\n",
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      "Epoch 515/1500\n",
      "810/810 [==============================] - 0s 31us/step - loss: 0.1268 - acc: 0.9728 - val_loss: 0.3597 - val_acc: 0.8556\n",
      "Epoch 516/1500\n",
      "810/810 [==============================] - 0s 31us/step - loss: 0.1298 - acc: 0.9802 - val_loss: 0.3313 - val_acc: 0.8667\n",
      "Epoch 517/1500\n",
      "810/810 [==============================] - 0s 28us/step - loss: 0.1227 - acc: 0.9840 - val_loss: 0.3559 - val_acc: 0.8444\n",
      "Epoch 518/1500\n",
      "810/810 [==============================] - 0s 34us/step - loss: 0.1349 - acc: 0.9753 - val_loss: 0.3851 - val_acc: 0.8222\n",
      "Epoch 519/1500\n",
      "810/810 [==============================] - 0s 34us/step - loss: 0.1386 - acc: 0.9667 - val_loss: 0.2736 - val_acc: 0.9444\n",
      "Epoch 520/1500\n",
      "810/810 [==============================] - 0s 32us/step - loss: 0.1260 - acc: 0.9728 - val_loss: 0.3096 - val_acc: 0.8889\n",
      "Epoch 521/1500\n",
      "810/810 [==============================] - 0s 33us/step - loss: 0.1188 - acc: 0.9815 - val_loss: 0.3943 - val_acc: 0.8111\n",
      "Epoch 522/1500\n",
      "810/810 [==============================] - 0s 32us/step - loss: 0.1314 - acc: 0.9802 - val_loss: 0.2886 - val_acc: 0.9222\n",
      "Epoch 523/1500\n",
      "810/810 [==============================] - 0s 46us/step - loss: 0.1190 - acc: 0.9790 - val_loss: 0.2906 - val_acc: 0.9111\n",
      "Epoch 524/1500\n",
      "810/810 [==============================] - 0s 31us/step - loss: 0.1328 - acc: 0.9741 - val_loss: 0.3430 - val_acc: 0.8556\n",
      "Epoch 525/1500\n",
      "810/810 [==============================] - 0s 33us/step - loss: 0.1178 - acc: 0.9790 - val_loss: 0.2859 - val_acc: 0.9111\n",
      "Epoch 526/1500\n",
      "810/810 [==============================] - 0s 38us/step - loss: 0.1221 - acc: 0.9827 - val_loss: 0.2887 - val_acc: 0.8889\n",
      "Epoch 527/1500\n",
      "810/810 [==============================] - 0s 47us/step - loss: 0.1135 - acc: 0.9827 - val_loss: 0.3380 - val_acc: 0.8889\n",
      "Epoch 528/1500\n",
      "810/810 [==============================] - 0s 32us/step - loss: 0.1250 - acc: 0.9704 - val_loss: 0.2837 - val_acc: 0.9333\n",
      "Epoch 529/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.1164 - acc: 0.9852 - val_loss: 0.2792 - val_acc: 0.9222\n",
      "Epoch 530/1500\n",
      "810/810 [==============================] - 0s 38us/step - loss: 0.1385 - acc: 0.9617 - val_loss: 0.3534 - val_acc: 0.8667\n",
      "Epoch 531/1500\n",
      "810/810 [==============================] - 0s 46us/step - loss: 0.1338 - acc: 0.9765 - val_loss: 0.2516 - val_acc: 0.9667\n",
      "Epoch 532/1500\n",
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      "Epoch 533/1500\n",
      "810/810 [==============================] - 0s 31us/step - loss: 0.1226 - acc: 0.9741 - val_loss: 0.2797 - val_acc: 0.9333\n",
      "Epoch 534/1500\n",
      "810/810 [==============================] - 0s 34us/step - loss: 0.1424 - acc: 0.9728 - val_loss: 0.3457 - val_acc: 0.8889\n",
      "Epoch 535/1500\n",
      "810/810 [==============================] - 0s 37us/step - loss: 0.1321 - acc: 0.9716 - val_loss: 0.2767 - val_acc: 0.9333\n",
      "Epoch 536/1500\n",
      "810/810 [==============================] - 0s 31us/step - loss: 0.1195 - acc: 0.9741 - val_loss: 0.2579 - val_acc: 0.9444\n",
      "Epoch 537/1500\n",
      "810/810 [==============================] - 0s 31us/step - loss: 0.1188 - acc: 0.9728 - val_loss: 0.2793 - val_acc: 0.9111\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 538/1500\n",
      "810/810 [==============================] - 0s 24us/step - loss: 0.1288 - acc: 0.9716 - val_loss: 0.4006 - val_acc: 0.7889\n",
      "Epoch 539/1500\n",
      "810/810 [==============================] - 0s 22us/step - loss: 0.1251 - acc: 0.9716 - val_loss: 0.2502 - val_acc: 0.9556\n",
      "Epoch 540/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.1250 - acc: 0.9802 - val_loss: 0.2662 - val_acc: 0.9444\n",
      "Epoch 541/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.1202 - acc: 0.9716 - val_loss: 0.2721 - val_acc: 0.9444\n",
      "Epoch 542/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.1187 - acc: 0.9790 - val_loss: 0.2653 - val_acc: 0.9444\n",
      "Epoch 543/1500\n",
      "810/810 [==============================] - 0s 37us/step - loss: 0.1158 - acc: 0.9790 - val_loss: 0.3632 - val_acc: 0.8444\n",
      "Epoch 544/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.1150 - acc: 0.9815 - val_loss: 0.3255 - val_acc: 0.9000\n",
      "Epoch 545/1500\n",
      "810/810 [==============================] - 0s 60us/step - loss: 0.1245 - acc: 0.9753 - val_loss: 0.2988 - val_acc: 0.9222\n",
      "Epoch 546/1500\n",
      "810/810 [==============================] - 0s 31us/step - loss: 0.1065 - acc: 0.9815 - val_loss: 0.3189 - val_acc: 0.8889\n",
      "Epoch 547/1500\n",
      "810/810 [==============================] - 0s 33us/step - loss: 0.1205 - acc: 0.9765 - val_loss: 0.3243 - val_acc: 0.9000\n",
      "Epoch 548/1500\n",
      "810/810 [==============================] - 0s 28us/step - loss: 0.1231 - acc: 0.9704 - val_loss: 0.4625 - val_acc: 0.7333\n",
      "Epoch 549/1500\n",
      "810/810 [==============================] - 0s 31us/step - loss: 0.1206 - acc: 0.9778 - val_loss: 0.2657 - val_acc: 0.9444\n",
      "Epoch 550/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.1306 - acc: 0.9728 - val_loss: 0.2791 - val_acc: 0.9111\n",
      "Epoch 551/1500\n",
      "810/810 [==============================] - 0s 32us/step - loss: 0.1103 - acc: 0.9852 - val_loss: 0.4048 - val_acc: 0.8000\n",
      "Epoch 552/1500\n",
      "810/810 [==============================] - 0s 30us/step - loss: 0.1287 - acc: 0.9741 - val_loss: 0.2829 - val_acc: 0.9000\n",
      "Epoch 553/1500\n",
      "810/810 [==============================] - 0s 31us/step - loss: 0.1297 - acc: 0.9741 - val_loss: 0.3075 - val_acc: 0.9000\n",
      "Epoch 554/1500\n",
      "810/810 [==============================] - 0s 31us/step - loss: 0.1122 - acc: 0.9815 - val_loss: 0.3477 - val_acc: 0.8667\n",
      "Epoch 555/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.1171 - acc: 0.9765 - val_loss: 0.2496 - val_acc: 0.9556\n",
      "Epoch 556/1500\n",
      "810/810 [==============================] - 0s 33us/step - loss: 0.1164 - acc: 0.9753 - val_loss: 0.2534 - val_acc: 0.9556\n",
      "Epoch 557/1500\n",
      "810/810 [==============================] - 0s 30us/step - loss: 0.1043 - acc: 0.9889 - val_loss: 0.2789 - val_acc: 0.9222\n",
      "Epoch 558/1500\n",
      "810/810 [==============================] - 0s 32us/step - loss: 0.1077 - acc: 0.9827 - val_loss: 0.3298 - val_acc: 0.8667\n",
      "Epoch 559/1500\n",
      "810/810 [==============================] - 0s 32us/step - loss: 0.1106 - acc: 0.9802 - val_loss: 0.3111 - val_acc: 0.9000\n",
      "Epoch 560/1500\n",
      "810/810 [==============================] - 0s 34us/step - loss: 0.1089 - acc: 0.9753 - val_loss: 0.3041 - val_acc: 0.8778\n",
      "Epoch 561/1500\n",
      "810/810 [==============================] - 0s 28us/step - loss: 0.1053 - acc: 0.9827 - val_loss: 0.3725 - val_acc: 0.8000\n",
      "Epoch 562/1500\n",
      "810/810 [==============================] - 0s 34us/step - loss: 0.1182 - acc: 0.9728 - val_loss: 0.2673 - val_acc: 0.9333\n",
      "Epoch 563/1500\n",
      "810/810 [==============================] - 0s 31us/step - loss: 0.1086 - acc: 0.9790 - val_loss: 0.2738 - val_acc: 0.9222\n",
      "Epoch 564/1500\n",
      "810/810 [==============================] - 0s 27us/step - loss: 0.1138 - acc: 0.9840 - val_loss: 0.3220 - val_acc: 0.8778\n",
      "Epoch 565/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.1198 - acc: 0.9753 - val_loss: 0.2916 - val_acc: 0.8889\n",
      "Epoch 566/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.1015 - acc: 0.9840 - val_loss: 0.2905 - val_acc: 0.9111\n",
      "Epoch 567/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.1027 - acc: 0.9827 - val_loss: 0.3042 - val_acc: 0.8667\n",
      "Epoch 568/1500\n",
      "810/810 [==============================] - 0s 41us/step - loss: 0.1163 - acc: 0.9741 - val_loss: 0.2448 - val_acc: 0.9444\n",
      "Epoch 569/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.1155 - acc: 0.9840 - val_loss: 0.2885 - val_acc: 0.9000\n",
      "Epoch 570/1500\n",
      "810/810 [==============================] - 0s 31us/step - loss: 0.1043 - acc: 0.9827 - val_loss: 0.2609 - val_acc: 0.9444\n",
      "Epoch 571/1500\n",
      "810/810 [==============================] - 0s 20us/step - loss: 0.1107 - acc: 0.9741 - val_loss: 0.3170 - val_acc: 0.8778\n",
      "Epoch 572/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.1105 - acc: 0.9765 - val_loss: 0.3076 - val_acc: 0.8778\n",
      "Epoch 573/1500\n",
      "810/810 [==============================] - 0s 34us/step - loss: 0.1093 - acc: 0.9741 - val_loss: 0.2992 - val_acc: 0.8778\n",
      "Epoch 574/1500\n",
      "810/810 [==============================] - 0s 33us/step - loss: 0.1179 - acc: 0.9765 - val_loss: 0.2526 - val_acc: 0.9556\n",
      "Epoch 575/1500\n",
      "810/810 [==============================] - 0s 30us/step - loss: 0.1196 - acc: 0.9716 - val_loss: 0.3658 - val_acc: 0.8444\n",
      "Epoch 576/1500\n",
      "810/810 [==============================] - 0s 42us/step - loss: 0.1148 - acc: 0.9802 - val_loss: 0.4415 - val_acc: 0.7444\n",
      "Epoch 577/1500\n",
      "810/810 [==============================] - 0s 31us/step - loss: 0.1113 - acc: 0.9765 - val_loss: 0.2646 - val_acc: 0.9333\n",
      "Epoch 578/1500\n",
      "810/810 [==============================] - 0s 31us/step - loss: 0.1050 - acc: 0.9802 - val_loss: 0.3974 - val_acc: 0.7778\n",
      "Epoch 579/1500\n",
      "810/810 [==============================] - 0s 43us/step - loss: 0.1121 - acc: 0.9753 - val_loss: 0.2750 - val_acc: 0.9111\n",
      "Epoch 580/1500\n",
      "810/810 [==============================] - 0s 37us/step - loss: 0.1185 - acc: 0.9753 - val_loss: 0.2511 - val_acc: 0.9333\n",
      "Epoch 581/1500\n",
      "810/810 [==============================] - 0s 42us/step - loss: 0.1108 - acc: 0.9753 - val_loss: 0.3201 - val_acc: 0.8778\n",
      "Epoch 582/1500\n",
      "810/810 [==============================] - 0s 32us/step - loss: 0.1269 - acc: 0.9654 - val_loss: 0.2430 - val_acc: 0.9333\n",
      "Epoch 583/1500\n",
      "810/810 [==============================] - 0s 36us/step - loss: 0.1246 - acc: 0.9704 - val_loss: 0.3309 - val_acc: 0.8556\n",
      "Epoch 584/1500\n",
      "810/810 [==============================] - 0s 36us/step - loss: 0.1046 - acc: 0.9728 - val_loss: 0.2822 - val_acc: 0.9222\n",
      "Epoch 585/1500\n",
      "810/810 [==============================] - 0s 33us/step - loss: 0.1093 - acc: 0.9852 - val_loss: 0.2504 - val_acc: 0.9444\n",
      "Epoch 586/1500\n",
      "810/810 [==============================] - 0s 38us/step - loss: 0.1046 - acc: 0.9778 - val_loss: 0.3373 - val_acc: 0.8333\n",
      "Epoch 587/1500\n",
      "810/810 [==============================] - 0s 34us/step - loss: 0.1137 - acc: 0.9704 - val_loss: 0.2965 - val_acc: 0.9000\n",
      "Epoch 588/1500\n",
      "810/810 [==============================] - 0s 32us/step - loss: 0.1193 - acc: 0.9704 - val_loss: 0.4849 - val_acc: 0.7333\n",
      "Epoch 589/1500\n",
      "810/810 [==============================] - 0s 36us/step - loss: 0.1155 - acc: 0.9741 - val_loss: 0.2847 - val_acc: 0.9222\n",
      "Epoch 590/1500\n",
      "810/810 [==============================] - 0s 34us/step - loss: 0.0976 - acc: 0.9802 - val_loss: 0.2809 - val_acc: 0.9111\n",
      "Epoch 591/1500\n",
      "810/810 [==============================] - 0s 33us/step - loss: 0.1228 - acc: 0.9741 - val_loss: 0.2472 - val_acc: 0.9444\n",
      "Epoch 592/1500\n",
      "810/810 [==============================] - 0s 30us/step - loss: 0.0919 - acc: 0.9852 - val_loss: 0.3374 - val_acc: 0.8556\n",
      "Epoch 593/1500\n",
      "810/810 [==============================] - 0s 36us/step - loss: 0.1250 - acc: 0.9667 - val_loss: 0.3297 - val_acc: 0.8444\n",
      "Epoch 594/1500\n",
      "810/810 [==============================] - 0s 34us/step - loss: 0.1150 - acc: 0.9815 - val_loss: 0.3206 - val_acc: 0.8667\n",
      "Epoch 595/1500\n",
      "810/810 [==============================] - 0s 38us/step - loss: 0.1052 - acc: 0.9840 - val_loss: 0.2930 - val_acc: 0.9000\n",
      "Epoch 596/1500\n",
      "810/810 [==============================] - 0s 36us/step - loss: 0.1111 - acc: 0.9741 - val_loss: 0.3705 - val_acc: 0.8333\n",
      "Epoch 597/1500\n",
      "810/810 [==============================] - 0s 38us/step - loss: 0.1083 - acc: 0.9753 - val_loss: 0.3085 - val_acc: 0.8889\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 598/1500\n",
      "810/810 [==============================] - 0s 30us/step - loss: 0.1018 - acc: 0.9778 - val_loss: 0.2555 - val_acc: 0.9444\n",
      "Epoch 599/1500\n",
      "810/810 [==============================] - 0s 50us/step - loss: 0.1037 - acc: 0.9827 - val_loss: 0.2499 - val_acc: 0.9556\n",
      "Epoch 600/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.1004 - acc: 0.9852 - val_loss: 0.2381 - val_acc: 0.9556\n",
      "Epoch 601/1500\n",
      "810/810 [==============================] - 0s 58us/step - loss: 0.1246 - acc: 0.9691 - val_loss: 0.3254 - val_acc: 0.8667\n",
      "Epoch 602/1500\n",
      "810/810 [==============================] - 0s 41us/step - loss: 0.1176 - acc: 0.9667 - val_loss: 0.2419 - val_acc: 0.9556\n",
      "Epoch 603/1500\n",
      "810/810 [==============================] - 0s 32us/step - loss: 0.1014 - acc: 0.9802 - val_loss: 0.2860 - val_acc: 0.9111\n",
      "Epoch 604/1500\n",
      "810/810 [==============================] - 0s 31us/step - loss: 0.1079 - acc: 0.9815 - val_loss: 0.2862 - val_acc: 0.9000\n",
      "Epoch 605/1500\n",
      "810/810 [==============================] - 0s 32us/step - loss: 0.1027 - acc: 0.9790 - val_loss: 0.2821 - val_acc: 0.9000\n",
      "Epoch 606/1500\n",
      "810/810 [==============================] - 0s 32us/step - loss: 0.1055 - acc: 0.9827 - val_loss: 0.2709 - val_acc: 0.9222\n",
      "Epoch 607/1500\n",
      "810/810 [==============================] - 0s 32us/step - loss: 0.1014 - acc: 0.9765 - val_loss: 0.2131 - val_acc: 0.9667\n",
      "Epoch 608/1500\n",
      "810/810 [==============================] - 0s 32us/step - loss: 0.1024 - acc: 0.9778 - val_loss: 0.2253 - val_acc: 0.9556\n",
      "Epoch 609/1500\n",
      "810/810 [==============================] - 0s 37us/step - loss: 0.1039 - acc: 0.9802 - val_loss: 0.3008 - val_acc: 0.8778\n",
      "Epoch 610/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.1037 - acc: 0.9827 - val_loss: 0.3484 - val_acc: 0.8222\n",
      "Epoch 611/1500\n",
      "810/810 [==============================] - 0s 31us/step - loss: 0.1024 - acc: 0.9827 - val_loss: 0.2332 - val_acc: 0.9444\n",
      "Epoch 612/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.1160 - acc: 0.9753 - val_loss: 0.2801 - val_acc: 0.9222\n",
      "Epoch 613/1500\n",
      "810/810 [==============================] - 0s 25us/step - loss: 0.1053 - acc: 0.9765 - val_loss: 0.2761 - val_acc: 0.9222\n",
      "Epoch 614/1500\n",
      "810/810 [==============================] - 0s 49us/step - loss: 0.0891 - acc: 0.9889 - val_loss: 0.2577 - val_acc: 0.9222\n",
      "Epoch 615/1500\n",
      "810/810 [==============================] - 0s 33us/step - loss: 0.0996 - acc: 0.9877 - val_loss: 0.2434 - val_acc: 0.9333\n",
      "Epoch 616/1500\n",
      "810/810 [==============================] - 0s 54us/step - loss: 0.1028 - acc: 0.9827 - val_loss: 0.2333 - val_acc: 0.9667\n",
      "Epoch 617/1500\n",
      "810/810 [==============================] - 0s 34us/step - loss: 0.0936 - acc: 0.9852 - val_loss: 0.2977 - val_acc: 0.9000\n",
      "Epoch 618/1500\n",
      "810/810 [==============================] - 0s 30us/step - loss: 0.1019 - acc: 0.9778 - val_loss: 0.2553 - val_acc: 0.9556\n",
      "Epoch 619/1500\n",
      "810/810 [==============================] - 0s 31us/step - loss: 0.1056 - acc: 0.9790 - val_loss: 0.3809 - val_acc: 0.8222\n",
      "Epoch 620/1500\n",
      "810/810 [==============================] - 0s 32us/step - loss: 0.0944 - acc: 0.9864 - val_loss: 0.3085 - val_acc: 0.8778\n",
      "Epoch 621/1500\n",
      "810/810 [==============================] - 0s 33us/step - loss: 0.0942 - acc: 0.9840 - val_loss: 0.3201 - val_acc: 0.8778\n",
      "Epoch 622/1500\n",
      "810/810 [==============================] - 0s 34us/step - loss: 0.1151 - acc: 0.9716 - val_loss: 0.2758 - val_acc: 0.9111\n",
      "Epoch 623/1500\n",
      "810/810 [==============================] - 0s 27us/step - loss: 0.1075 - acc: 0.9840 - val_loss: 0.2692 - val_acc: 0.9000\n",
      "Epoch 624/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0846 - acc: 0.9877 - val_loss: 0.3134 - val_acc: 0.8889\n",
      "Epoch 625/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.1108 - acc: 0.9753 - val_loss: 0.2464 - val_acc: 0.9222\n",
      "Epoch 626/1500\n",
      "810/810 [==============================] - 0s 32us/step - loss: 0.1005 - acc: 0.9852 - val_loss: 0.2523 - val_acc: 0.9333\n",
      "Epoch 627/1500\n",
      "810/810 [==============================] - 0s 33us/step - loss: 0.0932 - acc: 0.9827 - val_loss: 0.3413 - val_acc: 0.8444\n",
      "Epoch 628/1500\n",
      "810/810 [==============================] - 0s 31us/step - loss: 0.1078 - acc: 0.9778 - val_loss: 0.2636 - val_acc: 0.9111\n",
      "Epoch 629/1500\n",
      "810/810 [==============================] - 0s 31us/step - loss: 0.1046 - acc: 0.9802 - val_loss: 0.2434 - val_acc: 0.9444\n",
      "Epoch 630/1500\n",
      "810/810 [==============================] - 0s 32us/step - loss: 0.1050 - acc: 0.9765 - val_loss: 0.3438 - val_acc: 0.8333\n",
      "Epoch 631/1500\n",
      "810/810 [==============================] - 0s 32us/step - loss: 0.1005 - acc: 0.9741 - val_loss: 0.2436 - val_acc: 0.9333\n",
      "Epoch 632/1500\n",
      "810/810 [==============================] - 0s 33us/step - loss: 0.1080 - acc: 0.9753 - val_loss: 0.3742 - val_acc: 0.8444\n",
      "Epoch 633/1500\n",
      "810/810 [==============================] - 0s 33us/step - loss: 0.1062 - acc: 0.9790 - val_loss: 0.2698 - val_acc: 0.9222\n",
      "Epoch 634/1500\n",
      "810/810 [==============================] - 0s 37us/step - loss: 0.0941 - acc: 0.9815 - val_loss: 0.3315 - val_acc: 0.8333\n",
      "Epoch 635/1500\n",
      "810/810 [==============================] - 0s 38us/step - loss: 0.0927 - acc: 0.9827 - val_loss: 0.2132 - val_acc: 0.9556\n",
      "Epoch 636/1500\n",
      "810/810 [==============================] - 0s 36us/step - loss: 0.0894 - acc: 0.9840 - val_loss: 0.3194 - val_acc: 0.8889\n",
      "Epoch 637/1500\n",
      "810/810 [==============================] - 0s 20us/step - loss: 0.1076 - acc: 0.9716 - val_loss: 0.2940 - val_acc: 0.8889\n",
      "Epoch 638/1500\n",
      "810/810 [==============================] - 0s 38us/step - loss: 0.0920 - acc: 0.9852 - val_loss: 0.2401 - val_acc: 0.9444\n",
      "Epoch 639/1500\n",
      "810/810 [==============================] - 0s 33us/step - loss: 0.0977 - acc: 0.9827 - val_loss: 0.3075 - val_acc: 0.8889\n",
      "Epoch 640/1500\n",
      "810/810 [==============================] - 0s 28us/step - loss: 0.0932 - acc: 0.9827 - val_loss: 0.3287 - val_acc: 0.8556\n",
      "Epoch 641/1500\n",
      "810/810 [==============================] - 0s 38us/step - loss: 0.0894 - acc: 0.9864 - val_loss: 0.3217 - val_acc: 0.8667\n",
      "Epoch 642/1500\n",
      "810/810 [==============================] - 0s 32us/step - loss: 0.0919 - acc: 0.9802 - val_loss: 0.3521 - val_acc: 0.8556\n",
      "Epoch 643/1500\n",
      "810/810 [==============================] - 0s 33us/step - loss: 0.0940 - acc: 0.9802 - val_loss: 0.2266 - val_acc: 0.9556\n",
      "Epoch 644/1500\n",
      "810/810 [==============================] - 0s 36us/step - loss: 0.0810 - acc: 0.9914 - val_loss: 0.2268 - val_acc: 0.9556\n",
      "Epoch 645/1500\n",
      "810/810 [==============================] - 0s 33us/step - loss: 0.0943 - acc: 0.9864 - val_loss: 0.2399 - val_acc: 0.9556\n",
      "Epoch 646/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.1083 - acc: 0.9753 - val_loss: 0.2449 - val_acc: 0.9444\n",
      "Epoch 647/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.0889 - acc: 0.9852 - val_loss: 0.2458 - val_acc: 0.9556\n",
      "Epoch 648/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0968 - acc: 0.9815 - val_loss: 0.2236 - val_acc: 0.9556\n",
      "Epoch 649/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.0905 - acc: 0.9802 - val_loss: 0.2455 - val_acc: 0.9333\n",
      "Epoch 650/1500\n",
      "810/810 [==============================] - 0s 32us/step - loss: 0.1036 - acc: 0.9753 - val_loss: 0.2340 - val_acc: 0.9556\n",
      "Epoch 651/1500\n",
      "810/810 [==============================] - 0s 32us/step - loss: 0.1060 - acc: 0.9704 - val_loss: 0.2495 - val_acc: 0.9444\n",
      "Epoch 652/1500\n",
      "810/810 [==============================] - 0s 62us/step - loss: 0.0954 - acc: 0.9877 - val_loss: 0.2533 - val_acc: 0.9444\n",
      "Epoch 653/1500\n",
      "810/810 [==============================] - 0s 34us/step - loss: 0.0873 - acc: 0.9840 - val_loss: 0.2712 - val_acc: 0.9111\n",
      "Epoch 654/1500\n",
      "810/810 [==============================] - 0s 36us/step - loss: 0.0912 - acc: 0.9840 - val_loss: 0.2509 - val_acc: 0.9222\n",
      "Epoch 655/1500\n",
      "810/810 [==============================] - 0s 32us/step - loss: 0.0883 - acc: 0.9901 - val_loss: 0.2672 - val_acc: 0.9222\n",
      "Epoch 656/1500\n",
      "810/810 [==============================] - 0s 32us/step - loss: 0.1027 - acc: 0.9790 - val_loss: 0.4411 - val_acc: 0.7556\n",
      "Epoch 657/1500\n",
      "810/810 [==============================] - 0s 32us/step - loss: 0.0822 - acc: 0.9914 - val_loss: 0.2503 - val_acc: 0.9556\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 658/1500\n",
      "810/810 [==============================] - 0s 28us/step - loss: 0.1105 - acc: 0.9741 - val_loss: 0.2427 - val_acc: 0.9333\n",
      "Epoch 659/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.0941 - acc: 0.9765 - val_loss: 0.2501 - val_acc: 0.9333\n",
      "Epoch 660/1500\n",
      "810/810 [==============================] - 0s 32us/step - loss: 0.1099 - acc: 0.9716 - val_loss: 0.3420 - val_acc: 0.8667\n",
      "Epoch 661/1500\n",
      "810/810 [==============================] - 0s 32us/step - loss: 0.0952 - acc: 0.9840 - val_loss: 0.2064 - val_acc: 0.9444\n",
      "Epoch 662/1500\n",
      "810/810 [==============================] - 0s 33us/step - loss: 0.0956 - acc: 0.9815 - val_loss: 0.3431 - val_acc: 0.8556\n",
      "Epoch 663/1500\n",
      "810/810 [==============================] - 0s 30us/step - loss: 0.0824 - acc: 0.9864 - val_loss: 0.4401 - val_acc: 0.8000\n",
      "Epoch 664/1500\n",
      "810/810 [==============================] - 0s 33us/step - loss: 0.0902 - acc: 0.9815 - val_loss: 0.2534 - val_acc: 0.9222\n",
      "Epoch 665/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0864 - acc: 0.9827 - val_loss: 0.2484 - val_acc: 0.9222\n",
      "Epoch 666/1500\n",
      "810/810 [==============================] - 0s 28us/step - loss: 0.1032 - acc: 0.9778 - val_loss: 0.2494 - val_acc: 0.9444\n",
      "Epoch 667/1500\n",
      "810/810 [==============================] - 0s 47us/step - loss: 0.1004 - acc: 0.9765 - val_loss: 0.2531 - val_acc: 0.9333\n",
      "Epoch 668/1500\n",
      "810/810 [==============================] - 0s 10us/step - loss: 0.0911 - acc: 0.9790 - val_loss: 0.2337 - val_acc: 0.9556\n",
      "Epoch 669/1500\n",
      "810/810 [==============================] - 0s 37us/step - loss: 0.1053 - acc: 0.9827 - val_loss: 0.2743 - val_acc: 0.9000\n",
      "Epoch 670/1500\n",
      "810/810 [==============================] - 0s 29us/step - loss: 0.0866 - acc: 0.9889 - val_loss: 0.3134 - val_acc: 0.9000\n",
      "Epoch 671/1500\n",
      "810/810 [==============================] - 0s 31us/step - loss: 0.1017 - acc: 0.9778 - val_loss: 0.3493 - val_acc: 0.8444\n",
      "Epoch 672/1500\n",
      "810/810 [==============================] - 0s 30us/step - loss: 0.0850 - acc: 0.9901 - val_loss: 0.3101 - val_acc: 0.8778\n",
      "Epoch 673/1500\n",
      "810/810 [==============================] - 0s 36us/step - loss: 0.0953 - acc: 0.9827 - val_loss: 0.2639 - val_acc: 0.9000\n",
      "Epoch 674/1500\n",
      "810/810 [==============================] - 0s 38us/step - loss: 0.0813 - acc: 0.9852 - val_loss: 0.2689 - val_acc: 0.9000\n",
      "Epoch 675/1500\n",
      "810/810 [==============================] - 0s 54us/step - loss: 0.0992 - acc: 0.9815 - val_loss: 0.2105 - val_acc: 0.9556\n",
      "Epoch 676/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0812 - acc: 0.9864 - val_loss: 0.2598 - val_acc: 0.9111\n",
      "Epoch 677/1500\n",
      "810/810 [==============================] - 0s 31us/step - loss: 0.0832 - acc: 0.9827 - val_loss: 0.2391 - val_acc: 0.9444\n",
      "Epoch 678/1500\n",
      "810/810 [==============================] - 0s 30us/step - loss: 0.0832 - acc: 0.9815 - val_loss: 0.2857 - val_acc: 0.8667\n",
      "Epoch 679/1500\n",
      "810/810 [==============================] - 0s 35us/step - loss: 0.0963 - acc: 0.9778 - val_loss: 0.2595 - val_acc: 0.9111\n",
      "Epoch 680/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.1035 - acc: 0.9802 - val_loss: 0.2498 - val_acc: 0.9444\n",
      "Epoch 681/1500\n",
      "810/810 [==============================] - 0s 29us/step - loss: 0.0820 - acc: 0.9864 - val_loss: 0.2417 - val_acc: 0.9333\n",
      "Epoch 682/1500\n",
      "810/810 [==============================] - 0s 31us/step - loss: 0.0856 - acc: 0.9852 - val_loss: 0.2566 - val_acc: 0.9333\n",
      "Epoch 683/1500\n",
      "810/810 [==============================] - 0s 33us/step - loss: 0.0974 - acc: 0.9753 - val_loss: 0.2728 - val_acc: 0.9000\n",
      "Epoch 684/1500\n",
      "810/810 [==============================] - 0s 30us/step - loss: 0.0950 - acc: 0.9827 - val_loss: 0.2953 - val_acc: 0.8778\n",
      "Epoch 685/1500\n",
      "810/810 [==============================] - 0s 31us/step - loss: 0.0917 - acc: 0.9815 - val_loss: 0.3544 - val_acc: 0.8444\n",
      "Epoch 686/1500\n",
      "810/810 [==============================] - 0s 31us/step - loss: 0.0924 - acc: 0.9790 - val_loss: 0.2812 - val_acc: 0.8889\n",
      "Epoch 687/1500\n",
      "810/810 [==============================] - 0s 52us/step - loss: 0.0872 - acc: 0.9889 - val_loss: 0.2063 - val_acc: 0.9556\n",
      "Epoch 688/1500\n",
      "810/810 [==============================] - 0s 33us/step - loss: 0.0874 - acc: 0.9840 - val_loss: 0.3156 - val_acc: 0.8667\n",
      "Epoch 689/1500\n",
      "810/810 [==============================] - 0s 30us/step - loss: 0.1011 - acc: 0.9790 - val_loss: 0.4853 - val_acc: 0.7111\n",
      "Epoch 690/1500\n",
      "810/810 [==============================] - 0s 36us/step - loss: 0.1055 - acc: 0.9741 - val_loss: 0.3199 - val_acc: 0.8778\n",
      "Epoch 691/1500\n",
      "810/810 [==============================] - 0s 33us/step - loss: 0.0969 - acc: 0.9802 - val_loss: 0.2166 - val_acc: 0.9444\n",
      "Epoch 692/1500\n",
      "810/810 [==============================] - 0s 31us/step - loss: 0.0994 - acc: 0.9827 - val_loss: 0.2780 - val_acc: 0.8778\n",
      "Epoch 693/1500\n",
      "810/810 [==============================] - 0s 58us/step - loss: 0.0930 - acc: 0.9802 - val_loss: 0.3273 - val_acc: 0.8556\n",
      "Epoch 694/1500\n",
      "810/810 [==============================] - 0s 30us/step - loss: 0.0896 - acc: 0.9852 - val_loss: 0.2110 - val_acc: 0.9556\n",
      "Epoch 695/1500\n",
      "810/810 [==============================] - 0s 28us/step - loss: 0.0890 - acc: 0.9827 - val_loss: 0.2460 - val_acc: 0.9000\n",
      "Epoch 696/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0970 - acc: 0.9765 - val_loss: 0.2384 - val_acc: 0.9333\n",
      "Epoch 697/1500\n",
      "810/810 [==============================] - 0s 16us/step - loss: 0.0785 - acc: 0.9877 - val_loss: 0.3368 - val_acc: 0.8222\n",
      "Epoch 698/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0957 - acc: 0.9840 - val_loss: 0.2909 - val_acc: 0.9000\n",
      "Epoch 699/1500\n",
      "810/810 [==============================] - 0s 41us/step - loss: 0.0861 - acc: 0.9840 - val_loss: 0.2509 - val_acc: 0.9333\n",
      "Epoch 700/1500\n",
      "810/810 [==============================] - 0s 33us/step - loss: 0.0873 - acc: 0.9815 - val_loss: 0.2733 - val_acc: 0.8889\n",
      "Epoch 701/1500\n",
      "810/810 [==============================] - 0s 34us/step - loss: 0.0875 - acc: 0.9802 - val_loss: 0.3157 - val_acc: 0.8667\n",
      "Epoch 702/1500\n",
      "810/810 [==============================] - 0s 58us/step - loss: 0.0926 - acc: 0.9815 - val_loss: 0.2252 - val_acc: 0.9444\n",
      "Epoch 703/1500\n",
      "810/810 [==============================] - 0s 31us/step - loss: 0.0874 - acc: 0.9877 - val_loss: 0.2454 - val_acc: 0.9222\n",
      "Epoch 704/1500\n",
      "810/810 [==============================] - 0s 36us/step - loss: 0.0903 - acc: 0.9815 - val_loss: 0.3067 - val_acc: 0.8667\n",
      "Epoch 705/1500\n",
      "810/810 [==============================] - 0s 33us/step - loss: 0.0941 - acc: 0.9765 - val_loss: 0.2732 - val_acc: 0.9000\n",
      "Epoch 706/1500\n",
      "810/810 [==============================] - 0s 33us/step - loss: 0.0902 - acc: 0.9827 - val_loss: 0.2685 - val_acc: 0.8889\n",
      "Epoch 707/1500\n",
      "810/810 [==============================] - 0s 30us/step - loss: 0.0732 - acc: 0.9864 - val_loss: 0.2175 - val_acc: 0.9556\n",
      "Epoch 708/1500\n",
      "810/810 [==============================] - 0s 30us/step - loss: 0.0876 - acc: 0.9790 - val_loss: 0.2386 - val_acc: 0.9333\n",
      "Epoch 709/1500\n",
      "810/810 [==============================] - 0s 46us/step - loss: 0.0919 - acc: 0.9778 - val_loss: 0.2409 - val_acc: 0.9222\n",
      "Epoch 710/1500\n",
      "810/810 [==============================] - 0s 32us/step - loss: 0.0837 - acc: 0.9840 - val_loss: 0.3906 - val_acc: 0.7778\n",
      "Epoch 711/1500\n",
      "810/810 [==============================] - 0s 30us/step - loss: 0.0848 - acc: 0.9852 - val_loss: 0.2088 - val_acc: 0.9556\n",
      "Epoch 712/1500\n",
      "810/810 [==============================] - 0s 32us/step - loss: 0.0773 - acc: 0.9864 - val_loss: 0.2166 - val_acc: 0.9444\n",
      "Epoch 713/1500\n",
      "810/810 [==============================] - 0s 32us/step - loss: 0.0807 - acc: 0.9864 - val_loss: 0.2115 - val_acc: 0.9444\n",
      "Epoch 714/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0839 - acc: 0.9815 - val_loss: 0.1980 - val_acc: 0.9556\n",
      "Epoch 715/1500\n",
      "810/810 [==============================] - 0s 31us/step - loss: 0.0825 - acc: 0.9877 - val_loss: 0.2073 - val_acc: 0.9444\n",
      "Epoch 716/1500\n",
      "810/810 [==============================] - 0s 30us/step - loss: 0.0875 - acc: 0.9827 - val_loss: 0.2636 - val_acc: 0.9333\n",
      "Epoch 717/1500\n",
      "810/810 [==============================] - 0s 42us/step - loss: 0.0753 - acc: 0.9889 - val_loss: 0.2359 - val_acc: 0.9333\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 718/1500\n",
      "810/810 [==============================] - 0s 55us/step - loss: 0.0876 - acc: 0.9840 - val_loss: 0.2397 - val_acc: 0.9222\n",
      "Epoch 719/1500\n",
      "810/810 [==============================] - 0s 31us/step - loss: 0.0799 - acc: 0.9877 - val_loss: 0.2492 - val_acc: 0.9111\n",
      "Epoch 720/1500\n",
      "810/810 [==============================] - 0s 64us/step - loss: 0.0843 - acc: 0.9765 - val_loss: 0.2355 - val_acc: 0.9333\n",
      "Epoch 721/1500\n",
      "810/810 [==============================] - 0s 31us/step - loss: 0.0864 - acc: 0.9864 - val_loss: 0.2272 - val_acc: 0.9444\n",
      "Epoch 722/1500\n",
      "810/810 [==============================] - 0s 58us/step - loss: 0.0832 - acc: 0.9802 - val_loss: 0.2613 - val_acc: 0.9111\n",
      "Epoch 723/1500\n",
      "810/810 [==============================] - 0s 47us/step - loss: 0.0844 - acc: 0.9840 - val_loss: 0.2589 - val_acc: 0.9111\n",
      "Epoch 724/1500\n",
      "810/810 [==============================] - 0s 31us/step - loss: 0.0913 - acc: 0.9840 - val_loss: 0.1952 - val_acc: 0.9556\n",
      "Epoch 725/1500\n",
      "810/810 [==============================] - 0s 31us/step - loss: 0.0948 - acc: 0.9778 - val_loss: 0.4394 - val_acc: 0.8111\n",
      "Epoch 726/1500\n",
      "810/810 [==============================] - 0s 28us/step - loss: 0.0794 - acc: 0.9852 - val_loss: 0.2384 - val_acc: 0.9444\n",
      "Epoch 727/1500\n",
      "810/810 [==============================] - 0s 30us/step - loss: 0.0765 - acc: 0.9864 - val_loss: 0.2503 - val_acc: 0.9333\n",
      "Epoch 728/1500\n",
      "810/810 [==============================] - 0s 31us/step - loss: 0.0861 - acc: 0.9815 - val_loss: 0.2551 - val_acc: 0.9222\n",
      "Epoch 729/1500\n",
      "810/810 [==============================] - 0s 31us/step - loss: 0.0759 - acc: 0.9889 - val_loss: 0.2075 - val_acc: 0.9444\n",
      "Epoch 730/1500\n",
      "810/810 [==============================] - 0s 33us/step - loss: 0.0817 - acc: 0.9802 - val_loss: 0.2780 - val_acc: 0.8778\n",
      "Epoch 731/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0916 - acc: 0.9790 - val_loss: 0.2440 - val_acc: 0.9222\n",
      "Epoch 732/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0836 - acc: 0.9840 - val_loss: 0.2121 - val_acc: 0.9444\n",
      "Epoch 733/1500\n",
      "810/810 [==============================] - 0s 37us/step - loss: 0.0804 - acc: 0.9901 - val_loss: 0.2205 - val_acc: 0.9444\n",
      "Epoch 734/1500\n",
      "810/810 [==============================] - 0s 31us/step - loss: 0.0842 - acc: 0.9778 - val_loss: 0.4617 - val_acc: 0.7444\n",
      "Epoch 735/1500\n",
      "810/810 [==============================] - 0s 28us/step - loss: 0.0819 - acc: 0.9840 - val_loss: 0.2373 - val_acc: 0.9333\n",
      "Epoch 736/1500\n",
      "810/810 [==============================] - 0s 42us/step - loss: 0.0899 - acc: 0.9753 - val_loss: 0.2026 - val_acc: 0.9556\n",
      "Epoch 737/1500\n",
      "810/810 [==============================] - 0s 30us/step - loss: 0.0787 - acc: 0.9889 - val_loss: 0.2279 - val_acc: 0.9222\n",
      "Epoch 738/1500\n",
      "810/810 [==============================] - 0s 29us/step - loss: 0.0808 - acc: 0.9852 - val_loss: 0.2359 - val_acc: 0.9333\n",
      "Epoch 739/1500\n",
      "810/810 [==============================] - 0s 43us/step - loss: 0.0885 - acc: 0.9802 - val_loss: 0.2497 - val_acc: 0.9333\n",
      "Epoch 740/1500\n",
      "810/810 [==============================] - 0s 41us/step - loss: 0.0837 - acc: 0.9802 - val_loss: 0.2788 - val_acc: 0.9000\n",
      "Epoch 741/1500\n",
      "810/810 [==============================] - 0s 30us/step - loss: 0.0873 - acc: 0.9790 - val_loss: 0.2388 - val_acc: 0.9333\n",
      "Epoch 742/1500\n",
      "810/810 [==============================] - 0s 60us/step - loss: 0.0912 - acc: 0.9852 - val_loss: 0.2262 - val_acc: 0.9444\n",
      "Epoch 743/1500\n",
      "810/810 [==============================] - 0s 36us/step - loss: 0.0770 - acc: 0.9889 - val_loss: 0.3036 - val_acc: 0.8778\n",
      "Epoch 744/1500\n",
      "810/810 [==============================] - 0s 42us/step - loss: 0.0789 - acc: 0.9790 - val_loss: 0.2670 - val_acc: 0.9222\n",
      "Epoch 745/1500\n",
      "810/810 [==============================] - 0s 42us/step - loss: 0.0790 - acc: 0.9815 - val_loss: 0.2159 - val_acc: 0.9444\n",
      "Epoch 746/1500\n",
      "810/810 [==============================] - 0s 69us/step - loss: 0.0875 - acc: 0.9840 - val_loss: 0.1925 - val_acc: 0.9556\n",
      "Epoch 747/1500\n",
      "810/810 [==============================] - 0s 36us/step - loss: 0.0871 - acc: 0.9827 - val_loss: 0.3053 - val_acc: 0.8889\n",
      "Epoch 748/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0914 - acc: 0.9815 - val_loss: 0.2200 - val_acc: 0.9333\n",
      "Epoch 749/1500\n",
      "810/810 [==============================] - 0s 34us/step - loss: 0.0792 - acc: 0.9827 - val_loss: 0.2663 - val_acc: 0.9000\n",
      "Epoch 750/1500\n",
      "810/810 [==============================] - 0s 37us/step - loss: 0.0796 - acc: 0.9877 - val_loss: 0.3610 - val_acc: 0.8333\n",
      "Epoch 751/1500\n",
      "810/810 [==============================] - 0s 36us/step - loss: 0.0788 - acc: 0.9815 - val_loss: 0.2369 - val_acc: 0.9333\n",
      "Epoch 752/1500\n",
      "810/810 [==============================] - 0s 32us/step - loss: 0.0952 - acc: 0.9753 - val_loss: 0.3156 - val_acc: 0.8667\n",
      "Epoch 753/1500\n",
      "810/810 [==============================] - 0s 32us/step - loss: 0.0857 - acc: 0.9827 - val_loss: 0.3713 - val_acc: 0.8333\n",
      "Epoch 754/1500\n",
      "810/810 [==============================] - 0s 33us/step - loss: 0.0883 - acc: 0.9765 - val_loss: 0.2263 - val_acc: 0.9333\n",
      "Epoch 755/1500\n",
      "810/810 [==============================] - 0s 34us/step - loss: 0.0762 - acc: 0.9864 - val_loss: 0.2042 - val_acc: 0.9444\n",
      "Epoch 756/1500\n",
      "810/810 [==============================] - 0s 36us/step - loss: 0.0724 - acc: 0.9840 - val_loss: 0.1832 - val_acc: 0.9556\n",
      "Epoch 757/1500\n",
      "810/810 [==============================] - 0s 30us/step - loss: 0.0919 - acc: 0.9827 - val_loss: 0.2325 - val_acc: 0.9333\n",
      "Epoch 758/1500\n",
      "810/810 [==============================] - 0s 38us/step - loss: 0.1005 - acc: 0.9778 - val_loss: 0.2325 - val_acc: 0.9333\n",
      "Epoch 759/1500\n",
      "810/810 [==============================] - 0s 37us/step - loss: 0.0868 - acc: 0.9815 - val_loss: 0.2720 - val_acc: 0.9000\n",
      "Epoch 760/1500\n",
      "810/810 [==============================] - 0s 36us/step - loss: 0.0790 - acc: 0.9877 - val_loss: 0.2410 - val_acc: 0.9222\n",
      "Epoch 761/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0756 - acc: 0.9852 - val_loss: 0.2036 - val_acc: 0.9444\n",
      "Epoch 762/1500\n",
      "810/810 [==============================] - 0s 33us/step - loss: 0.0722 - acc: 0.9864 - val_loss: 0.2786 - val_acc: 0.9000\n",
      "Epoch 763/1500\n",
      "810/810 [==============================] - 0s 54us/step - loss: 0.0737 - acc: 0.9840 - val_loss: 0.2568 - val_acc: 0.9222\n",
      "Epoch 764/1500\n",
      "810/810 [==============================] - 0s 34us/step - loss: 0.0837 - acc: 0.9778 - val_loss: 0.2293 - val_acc: 0.9333\n",
      "Epoch 765/1500\n",
      "810/810 [==============================] - 0s 30us/step - loss: 0.0816 - acc: 0.9840 - val_loss: 0.3018 - val_acc: 0.8444\n",
      "Epoch 766/1500\n",
      "810/810 [==============================] - 0s 33us/step - loss: 0.0758 - acc: 0.9840 - val_loss: 0.2326 - val_acc: 0.9333\n",
      "Epoch 767/1500\n",
      "810/810 [==============================] - 0s 33us/step - loss: 0.0870 - acc: 0.9840 - val_loss: 0.2269 - val_acc: 0.9333\n",
      "Epoch 768/1500\n",
      "810/810 [==============================] - 0s 36us/step - loss: 0.0714 - acc: 0.9877 - val_loss: 0.2892 - val_acc: 0.8889\n",
      "Epoch 769/1500\n",
      "810/810 [==============================] - 0s 32us/step - loss: 0.0683 - acc: 0.9901 - val_loss: 0.2363 - val_acc: 0.9333\n",
      "Epoch 770/1500\n",
      "810/810 [==============================] - 0s 32us/step - loss: 0.0898 - acc: 0.9827 - val_loss: 0.3173 - val_acc: 0.8556\n",
      "Epoch 771/1500\n",
      "810/810 [==============================] - 0s 32us/step - loss: 0.0809 - acc: 0.9864 - val_loss: 0.2299 - val_acc: 0.9333\n",
      "Epoch 772/1500\n",
      "810/810 [==============================] - 0s 41us/step - loss: 0.0784 - acc: 0.9877 - val_loss: 0.3626 - val_acc: 0.8333\n",
      "Epoch 773/1500\n",
      "810/810 [==============================] - 0s 38us/step - loss: 0.0726 - acc: 0.9852 - val_loss: 0.1905 - val_acc: 0.9444\n",
      "Epoch 774/1500\n",
      "810/810 [==============================] - 0s 38us/step - loss: 0.0751 - acc: 0.9840 - val_loss: 0.1696 - val_acc: 0.9778\n",
      "Epoch 775/1500\n",
      "810/810 [==============================] - 0s 68us/step - loss: 0.0754 - acc: 0.9864 - val_loss: 0.3040 - val_acc: 0.8667\n",
      "Epoch 776/1500\n",
      "810/810 [==============================] - 0s 30us/step - loss: 0.0680 - acc: 0.9877 - val_loss: 0.2244 - val_acc: 0.9444\n",
      "Epoch 777/1500\n",
      "810/810 [==============================] - 0s 63us/step - loss: 0.0798 - acc: 0.9815 - val_loss: 0.2725 - val_acc: 0.8889\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 778/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0879 - acc: 0.9840 - val_loss: 0.2254 - val_acc: 0.9556\n",
      "Epoch 779/1500\n",
      "810/810 [==============================] - 0s 41us/step - loss: 0.0832 - acc: 0.9741 - val_loss: 0.1801 - val_acc: 0.9667\n",
      "Epoch 780/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0815 - acc: 0.9877 - val_loss: 0.2244 - val_acc: 0.9111\n",
      "Epoch 781/1500\n",
      "810/810 [==============================] - 0s 31us/step - loss: 0.0744 - acc: 0.9852 - val_loss: 0.2841 - val_acc: 0.8778\n",
      "Epoch 782/1500\n",
      "810/810 [==============================] - 0s 32us/step - loss: 0.0817 - acc: 0.9852 - val_loss: 0.1949 - val_acc: 0.9444\n",
      "Epoch 783/1500\n",
      "810/810 [==============================] - 0s 59us/step - loss: 0.0989 - acc: 0.9765 - val_loss: 0.2371 - val_acc: 0.9222\n",
      "Epoch 784/1500\n",
      "810/810 [==============================] - 0s 47us/step - loss: 0.0677 - acc: 0.9889 - val_loss: 0.2192 - val_acc: 0.9444\n",
      "Epoch 785/1500\n",
      "810/810 [==============================] - 0s 42us/step - loss: 0.0724 - acc: 0.9901 - val_loss: 0.2686 - val_acc: 0.9000\n",
      "Epoch 786/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0826 - acc: 0.9778 - val_loss: 0.2249 - val_acc: 0.9556\n",
      "Epoch 787/1500\n",
      "810/810 [==============================] - 0s 32us/step - loss: 0.0714 - acc: 0.9889 - val_loss: 0.2684 - val_acc: 0.8778\n",
      "Epoch 788/1500\n",
      "810/810 [==============================] - 0s 38us/step - loss: 0.0634 - acc: 0.9864 - val_loss: 0.3560 - val_acc: 0.8222\n",
      "Epoch 789/1500\n",
      "810/810 [==============================] - 0s 15us/step - loss: 0.0624 - acc: 0.9938 - val_loss: 0.2571 - val_acc: 0.9222\n",
      "Epoch 790/1500\n",
      "810/810 [==============================] - 0s 40us/step - loss: 0.0931 - acc: 0.9753 - val_loss: 0.2141 - val_acc: 0.9444\n",
      "Epoch 791/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0775 - acc: 0.9852 - val_loss: 0.2065 - val_acc: 0.9444\n",
      "Epoch 792/1500\n",
      "810/810 [==============================] - 0s 43us/step - loss: 0.0766 - acc: 0.9790 - val_loss: 0.4155 - val_acc: 0.7667\n",
      "Epoch 793/1500\n",
      "810/810 [==============================] - 0s 38us/step - loss: 0.0735 - acc: 0.9877 - val_loss: 0.2092 - val_acc: 0.9333\n",
      "Epoch 794/1500\n",
      "810/810 [==============================] - 0s 48us/step - loss: 0.0747 - acc: 0.9790 - val_loss: 0.2248 - val_acc: 0.9333\n",
      "Epoch 795/1500\n",
      "810/810 [==============================] - 0s 52us/step - loss: 0.1010 - acc: 0.9716 - val_loss: 0.1893 - val_acc: 0.9444\n",
      "Epoch 796/1500\n",
      "810/810 [==============================] - 0s 38us/step - loss: 0.0660 - acc: 0.9889 - val_loss: 0.1912 - val_acc: 0.9556\n",
      "Epoch 797/1500\n",
      "810/810 [==============================] - 0s 34us/step - loss: 0.0728 - acc: 0.9827 - val_loss: 0.1857 - val_acc: 0.9556\n",
      "Epoch 798/1500\n",
      "810/810 [==============================] - 0s 63us/step - loss: 0.0796 - acc: 0.9827 - val_loss: 0.1843 - val_acc: 0.9556\n",
      "Epoch 799/1500\n",
      "810/810 [==============================] - 0s 32us/step - loss: 0.0668 - acc: 0.9901 - val_loss: 0.1836 - val_acc: 0.9556\n",
      "Epoch 800/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.0825 - acc: 0.9815 - val_loss: 0.2314 - val_acc: 0.9222\n",
      "Epoch 801/1500\n",
      "810/810 [==============================] - 0s 23us/step - loss: 0.0671 - acc: 0.9827 - val_loss: 0.1686 - val_acc: 0.9667\n",
      "Epoch 802/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0754 - acc: 0.9864 - val_loss: 0.2274 - val_acc: 0.9444\n",
      "Epoch 803/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.0692 - acc: 0.9815 - val_loss: 0.1856 - val_acc: 0.9556\n",
      "Epoch 804/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0890 - acc: 0.9790 - val_loss: 0.3784 - val_acc: 0.8222\n",
      "Epoch 805/1500\n",
      "810/810 [==============================] - 0s 50us/step - loss: 0.0710 - acc: 0.9840 - val_loss: 0.2448 - val_acc: 0.9333\n",
      "Epoch 806/1500\n",
      "810/810 [==============================] - 0s 33us/step - loss: 0.0773 - acc: 0.9901 - val_loss: 0.2232 - val_acc: 0.9556\n",
      "Epoch 807/1500\n",
      "810/810 [==============================] - 0s 36us/step - loss: 0.0707 - acc: 0.9852 - val_loss: 0.2256 - val_acc: 0.9333\n",
      "Epoch 808/1500\n",
      "810/810 [==============================] - 0s 33us/step - loss: 0.0758 - acc: 0.9815 - val_loss: 0.2007 - val_acc: 0.9556\n",
      "Epoch 809/1500\n",
      "810/810 [==============================] - 0s 34us/step - loss: 0.0702 - acc: 0.9840 - val_loss: 0.2243 - val_acc: 0.9222\n",
      "Epoch 810/1500\n",
      "810/810 [==============================] - 0s 32us/step - loss: 0.0721 - acc: 0.9827 - val_loss: 0.2232 - val_acc: 0.9444\n",
      "Epoch 811/1500\n",
      "810/810 [==============================] - 0s 32us/step - loss: 0.0690 - acc: 0.9901 - val_loss: 0.3113 - val_acc: 0.8667\n",
      "Epoch 812/1500\n",
      "810/810 [==============================] - 0s 33us/step - loss: 0.0896 - acc: 0.9790 - val_loss: 0.2068 - val_acc: 0.9444\n",
      "Epoch 813/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0772 - acc: 0.9864 - val_loss: 0.2406 - val_acc: 0.9111\n",
      "Epoch 814/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.0818 - acc: 0.9815 - val_loss: 0.2527 - val_acc: 0.9222\n",
      "Epoch 815/1500\n",
      "810/810 [==============================] - 0s 36us/step - loss: 0.0683 - acc: 0.9852 - val_loss: 0.1963 - val_acc: 0.9444\n",
      "Epoch 816/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0685 - acc: 0.9889 - val_loss: 0.3145 - val_acc: 0.8667\n",
      "Epoch 817/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.0657 - acc: 0.9926 - val_loss: 0.2425 - val_acc: 0.9222\n",
      "Epoch 818/1500\n",
      "810/810 [==============================] - 0s 37us/step - loss: 0.0695 - acc: 0.9914 - val_loss: 0.2582 - val_acc: 0.9111\n",
      "Epoch 819/1500\n",
      "810/810 [==============================] - 0s 32us/step - loss: 0.0625 - acc: 0.9901 - val_loss: 0.2526 - val_acc: 0.8889\n",
      "Epoch 820/1500\n",
      "810/810 [==============================] - 0s 33us/step - loss: 0.0726 - acc: 0.9901 - val_loss: 0.1944 - val_acc: 0.9556\n",
      "Epoch 821/1500\n",
      "810/810 [==============================] - 0s 34us/step - loss: 0.0746 - acc: 0.9877 - val_loss: 0.3070 - val_acc: 0.8667\n",
      "Epoch 822/1500\n",
      "810/810 [==============================] - 0s 31us/step - loss: 0.0691 - acc: 0.9852 - val_loss: 0.2699 - val_acc: 0.8889\n",
      "Epoch 823/1500\n",
      "810/810 [==============================] - 0s 45us/step - loss: 0.0714 - acc: 0.9877 - val_loss: 0.1776 - val_acc: 0.9667\n",
      "Epoch 824/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.0755 - acc: 0.9815 - val_loss: 0.2237 - val_acc: 0.9444\n",
      "Epoch 825/1500\n",
      "810/810 [==============================] - 0s 58us/step - loss: 0.0700 - acc: 0.9864 - val_loss: 0.2542 - val_acc: 0.9444\n",
      "Epoch 826/1500\n",
      "810/810 [==============================] - 0s 32us/step - loss: 0.0652 - acc: 0.9914 - val_loss: 0.2190 - val_acc: 0.9222\n",
      "Epoch 827/1500\n",
      "810/810 [==============================] - 0s 32us/step - loss: 0.0893 - acc: 0.9716 - val_loss: 0.3338 - val_acc: 0.8222\n",
      "Epoch 828/1500\n",
      "810/810 [==============================] - 0s 36us/step - loss: 0.0647 - acc: 0.9852 - val_loss: 0.2653 - val_acc: 0.9222\n",
      "Epoch 829/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.0748 - acc: 0.9877 - val_loss: 0.2038 - val_acc: 0.9444\n",
      "Epoch 830/1500\n",
      "810/810 [==============================] - 0s 30us/step - loss: 0.0728 - acc: 0.9852 - val_loss: 0.2718 - val_acc: 0.8889\n",
      "Epoch 831/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0783 - acc: 0.9852 - val_loss: 0.2155 - val_acc: 0.9444\n",
      "Epoch 832/1500\n",
      "810/810 [==============================] - 0s 32us/step - loss: 0.0736 - acc: 0.9840 - val_loss: 0.2957 - val_acc: 0.8667\n",
      "Epoch 833/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0724 - acc: 0.9901 - val_loss: 0.2136 - val_acc: 0.9444\n",
      "Epoch 834/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0841 - acc: 0.9790 - val_loss: 0.2086 - val_acc: 0.9444\n",
      "Epoch 835/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0710 - acc: 0.9877 - val_loss: 0.2921 - val_acc: 0.8778\n",
      "Epoch 836/1500\n",
      "810/810 [==============================] - 0s 29us/step - loss: 0.0640 - acc: 0.9914 - val_loss: 0.2044 - val_acc: 0.9444\n",
      "Epoch 837/1500\n",
      "810/810 [==============================] - 0s 46us/step - loss: 0.0765 - acc: 0.9840 - val_loss: 0.1961 - val_acc: 0.9556\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 838/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0753 - acc: 0.9864 - val_loss: 0.3502 - val_acc: 0.8222\n",
      "Epoch 839/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0728 - acc: 0.9889 - val_loss: 0.2060 - val_acc: 0.9333\n",
      "Epoch 840/1500\n",
      "810/810 [==============================] - 0s 36us/step - loss: 0.0734 - acc: 0.9827 - val_loss: 0.2909 - val_acc: 0.8778\n",
      "Epoch 841/1500\n",
      "810/810 [==============================] - 0s 28us/step - loss: 0.0712 - acc: 0.9840 - val_loss: 0.1897 - val_acc: 0.9444\n",
      "Epoch 842/1500\n",
      "810/810 [==============================] - 0s 37us/step - loss: 0.0879 - acc: 0.9802 - val_loss: 0.1922 - val_acc: 0.9444\n",
      "Epoch 843/1500\n",
      "810/810 [==============================] - 0s 46us/step - loss: 0.0624 - acc: 0.9877 - val_loss: 0.1950 - val_acc: 0.9444\n",
      "Epoch 844/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.0688 - acc: 0.9877 - val_loss: 0.2727 - val_acc: 0.8667\n",
      "Epoch 845/1500\n",
      "810/810 [==============================] - 0s 48us/step - loss: 0.0686 - acc: 0.9889 - val_loss: 0.2422 - val_acc: 0.9111\n",
      "Epoch 846/1500\n",
      "810/810 [==============================] - 0s 40us/step - loss: 0.0666 - acc: 0.9889 - val_loss: 0.2944 - val_acc: 0.8778\n",
      "Epoch 847/1500\n",
      "810/810 [==============================] - 0s 23us/step - loss: 0.0725 - acc: 0.9840 - val_loss: 0.2897 - val_acc: 0.8889\n",
      "Epoch 848/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.0701 - acc: 0.9827 - val_loss: 0.2004 - val_acc: 0.9444\n",
      "Epoch 849/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0828 - acc: 0.9790 - val_loss: 0.2400 - val_acc: 0.9000\n",
      "Epoch 850/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.0710 - acc: 0.9852 - val_loss: 0.2563 - val_acc: 0.9222\n",
      "Epoch 851/1500\n",
      "810/810 [==============================] - 0s 42us/step - loss: 0.0651 - acc: 0.9889 - val_loss: 0.2133 - val_acc: 0.9222\n",
      "Epoch 852/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.0701 - acc: 0.9864 - val_loss: 0.2235 - val_acc: 0.9333\n",
      "Epoch 853/1500\n",
      "810/810 [==============================] - 0s 38us/step - loss: 0.0787 - acc: 0.9827 - val_loss: 0.1778 - val_acc: 0.9444\n",
      "Epoch 854/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0655 - acc: 0.9864 - val_loss: 0.2232 - val_acc: 0.9333\n",
      "Epoch 855/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.0726 - acc: 0.9815 - val_loss: 0.3965 - val_acc: 0.8333\n",
      "Epoch 856/1500\n",
      "810/810 [==============================] - 0s 44us/step - loss: 0.0806 - acc: 0.9827 - val_loss: 0.2761 - val_acc: 0.9222\n",
      "Epoch 857/1500\n",
      "810/810 [==============================] - 0s 28us/step - loss: 0.0701 - acc: 0.9827 - val_loss: 0.3219 - val_acc: 0.8556\n",
      "Epoch 858/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.0697 - acc: 0.9864 - val_loss: 0.2309 - val_acc: 0.9333\n",
      "Epoch 859/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0675 - acc: 0.9901 - val_loss: 0.2176 - val_acc: 0.9444\n",
      "Epoch 860/1500\n",
      "810/810 [==============================] - 0s 46us/step - loss: 0.0598 - acc: 0.9926 - val_loss: 0.2985 - val_acc: 0.8889\n",
      "Epoch 861/1500\n",
      "810/810 [==============================] - 0s 33us/step - loss: 0.0775 - acc: 0.9802 - val_loss: 0.2225 - val_acc: 0.9444\n",
      "Epoch 862/1500\n",
      "810/810 [==============================] - 0s 38us/step - loss: 0.0650 - acc: 0.9840 - val_loss: 0.1991 - val_acc: 0.9444\n",
      "Epoch 863/1500\n",
      "810/810 [==============================] - 0s 37us/step - loss: 0.0680 - acc: 0.9827 - val_loss: 0.2389 - val_acc: 0.9111\n",
      "Epoch 864/1500\n",
      "810/810 [==============================] - 0s 33us/step - loss: 0.0723 - acc: 0.9827 - val_loss: 0.3399 - val_acc: 0.8222\n",
      "Epoch 865/1500\n",
      "810/810 [==============================] - 0s 42us/step - loss: 0.0601 - acc: 0.9889 - val_loss: 0.2258 - val_acc: 0.9222\n",
      "Epoch 866/1500\n",
      "810/810 [==============================] - 0s 31us/step - loss: 0.0666 - acc: 0.9877 - val_loss: 0.3763 - val_acc: 0.7889\n",
      "Epoch 867/1500\n",
      "810/810 [==============================] - 0s 31us/step - loss: 0.0819 - acc: 0.9827 - val_loss: 0.2331 - val_acc: 0.9111\n",
      "Epoch 868/1500\n",
      "810/810 [==============================] - 0s 31us/step - loss: 0.0764 - acc: 0.9827 - val_loss: 0.2306 - val_acc: 0.9333\n",
      "Epoch 869/1500\n",
      "810/810 [==============================] - 0s 31us/step - loss: 0.0601 - acc: 0.9926 - val_loss: 0.2465 - val_acc: 0.9444\n",
      "Epoch 870/1500\n",
      "810/810 [==============================] - 0s 33us/step - loss: 0.0730 - acc: 0.9827 - val_loss: 0.2095 - val_acc: 0.9444\n",
      "Epoch 871/1500\n",
      "810/810 [==============================] - 0s 32us/step - loss: 0.0737 - acc: 0.9827 - val_loss: 0.2609 - val_acc: 0.9000\n",
      "Epoch 872/1500\n",
      "810/810 [==============================] - 0s 33us/step - loss: 0.0747 - acc: 0.9778 - val_loss: 0.2562 - val_acc: 0.9222\n",
      "Epoch 873/1500\n",
      "810/810 [==============================] - 0s 33us/step - loss: 0.0717 - acc: 0.9901 - val_loss: 0.1891 - val_acc: 0.9556\n",
      "Epoch 874/1500\n",
      "810/810 [==============================] - 0s 36us/step - loss: 0.0806 - acc: 0.9765 - val_loss: 0.2473 - val_acc: 0.9111\n",
      "Epoch 875/1500\n",
      "810/810 [==============================] - 0s 41us/step - loss: 0.0722 - acc: 0.9815 - val_loss: 0.2687 - val_acc: 0.9111\n",
      "Epoch 876/1500\n",
      "810/810 [==============================] - 0s 30us/step - loss: 0.0569 - acc: 0.9914 - val_loss: 0.2986 - val_acc: 0.8778\n",
      "Epoch 877/1500\n",
      "810/810 [==============================] - 0s 28us/step - loss: 0.0659 - acc: 0.9938 - val_loss: 0.1710 - val_acc: 0.9556\n",
      "Epoch 878/1500\n",
      "810/810 [==============================] - 0s 30us/step - loss: 0.0593 - acc: 0.9864 - val_loss: 0.2831 - val_acc: 0.9000\n",
      "Epoch 879/1500\n",
      "810/810 [==============================] - 0s 28us/step - loss: 0.0674 - acc: 0.9815 - val_loss: 0.2699 - val_acc: 0.9000\n",
      "Epoch 880/1500\n",
      "810/810 [==============================] - 0s 30us/step - loss: 0.0772 - acc: 0.9864 - val_loss: 0.2139 - val_acc: 0.9444\n",
      "Epoch 881/1500\n",
      "810/810 [==============================] - 0s 30us/step - loss: 0.0591 - acc: 0.9852 - val_loss: 0.2018 - val_acc: 0.9444\n",
      "Epoch 882/1500\n",
      "810/810 [==============================] - 0s 28us/step - loss: 0.0625 - acc: 0.9889 - val_loss: 0.1746 - val_acc: 0.9444\n",
      "Epoch 883/1500\n",
      "810/810 [==============================] - 0s 28us/step - loss: 0.0714 - acc: 0.9864 - val_loss: 0.1842 - val_acc: 0.9444\n",
      "Epoch 884/1500\n",
      "810/810 [==============================] - 0s 30us/step - loss: 0.0742 - acc: 0.9926 - val_loss: 0.1613 - val_acc: 0.9556\n",
      "Epoch 885/1500\n",
      "810/810 [==============================] - 0s 30us/step - loss: 0.0692 - acc: 0.9815 - val_loss: 0.1806 - val_acc: 0.9444\n",
      "Epoch 886/1500\n",
      "810/810 [==============================] - 0s 42us/step - loss: 0.0609 - acc: 0.9877 - val_loss: 0.2305 - val_acc: 0.9333\n",
      "Epoch 887/1500\n",
      "810/810 [==============================] - 0s 28us/step - loss: 0.0587 - acc: 0.9914 - val_loss: 0.2438 - val_acc: 0.9333\n",
      "Epoch 888/1500\n",
      "810/810 [==============================] - 0s 30us/step - loss: 0.0673 - acc: 0.9840 - val_loss: 0.3500 - val_acc: 0.8333\n",
      "Epoch 889/1500\n",
      "810/810 [==============================] - 0s 46us/step - loss: 0.0724 - acc: 0.9827 - val_loss: 0.2143 - val_acc: 0.9444\n",
      "Epoch 890/1500\n",
      "810/810 [==============================] - 0s 73us/step - loss: 0.0857 - acc: 0.9815 - val_loss: 0.4026 - val_acc: 0.7889\n",
      "Epoch 891/1500\n",
      "810/810 [==============================] - 0s 30us/step - loss: 0.0691 - acc: 0.9877 - val_loss: 0.1516 - val_acc: 0.9778\n",
      "Epoch 892/1500\n",
      "810/810 [==============================] - 0s 46us/step - loss: 0.0648 - acc: 0.9852 - val_loss: 0.1573 - val_acc: 0.9556\n",
      "Epoch 893/1500\n",
      "810/810 [==============================] - 0s 42us/step - loss: 0.0728 - acc: 0.9864 - val_loss: 0.1803 - val_acc: 0.9444\n",
      "Epoch 894/1500\n",
      "810/810 [==============================] - 0s 42us/step - loss: 0.0685 - acc: 0.9827 - val_loss: 0.2028 - val_acc: 0.9333\n",
      "Epoch 895/1500\n",
      "810/810 [==============================] - 0s 52us/step - loss: 0.0758 - acc: 0.9815 - val_loss: 0.2849 - val_acc: 0.8667\n",
      "Epoch 896/1500\n",
      "810/810 [==============================] - 0s 28us/step - loss: 0.0663 - acc: 0.9877 - val_loss: 0.2058 - val_acc: 0.9333\n",
      "Epoch 897/1500\n",
      "810/810 [==============================] - 0s 41us/step - loss: 0.0595 - acc: 0.9889 - val_loss: 0.2214 - val_acc: 0.9333\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 898/1500\n",
      "810/810 [==============================] - 0s 30us/step - loss: 0.0732 - acc: 0.9840 - val_loss: 0.1858 - val_acc: 0.9556\n",
      "Epoch 899/1500\n",
      "810/810 [==============================] - 0s 33us/step - loss: 0.0667 - acc: 0.9864 - val_loss: 0.1975 - val_acc: 0.9333\n",
      "Epoch 900/1500\n",
      "810/810 [==============================] - 0s 27us/step - loss: 0.0761 - acc: 0.9864 - val_loss: 0.2055 - val_acc: 0.9444\n",
      "Epoch 901/1500\n",
      "810/810 [==============================] - 0s 27us/step - loss: 0.0700 - acc: 0.9852 - val_loss: 0.2359 - val_acc: 0.9333\n",
      "Epoch 902/1500\n",
      "810/810 [==============================] - 0s 34us/step - loss: 0.0823 - acc: 0.9827 - val_loss: 0.2125 - val_acc: 0.9333\n",
      "Epoch 903/1500\n",
      "810/810 [==============================] - 0s 32us/step - loss: 0.0647 - acc: 0.9877 - val_loss: 0.2072 - val_acc: 0.9333\n",
      "Epoch 904/1500\n",
      "810/810 [==============================] - 0s 28us/step - loss: 0.0708 - acc: 0.9840 - val_loss: 0.2379 - val_acc: 0.9333\n",
      "Epoch 905/1500\n",
      "810/810 [==============================] - 0s 30us/step - loss: 0.0557 - acc: 0.9889 - val_loss: 0.2733 - val_acc: 0.9000\n",
      "Epoch 906/1500\n",
      "810/810 [==============================] - 0s 34us/step - loss: 0.0779 - acc: 0.9778 - val_loss: 0.2184 - val_acc: 0.9333\n",
      "Epoch 907/1500\n",
      "810/810 [==============================] - 0s 30us/step - loss: 0.0564 - acc: 0.9901 - val_loss: 0.2088 - val_acc: 0.9333\n",
      "Epoch 908/1500\n",
      "810/810 [==============================] - 0s 28us/step - loss: 0.0626 - acc: 0.9840 - val_loss: 0.2075 - val_acc: 0.9444\n",
      "Epoch 909/1500\n",
      "810/810 [==============================] - 0s 31us/step - loss: 0.0702 - acc: 0.9827 - val_loss: 0.1521 - val_acc: 0.9556\n",
      "Epoch 910/1500\n",
      "810/810 [==============================] - 0s 33us/step - loss: 0.0660 - acc: 0.9877 - val_loss: 0.1669 - val_acc: 0.9556\n",
      "Epoch 911/1500\n",
      "810/810 [==============================] - 0s 31us/step - loss: 0.0613 - acc: 0.9877 - val_loss: 0.1682 - val_acc: 0.9556\n",
      "Epoch 912/1500\n",
      "810/810 [==============================] - 0s 44us/step - loss: 0.0483 - acc: 0.9975 - val_loss: 0.2207 - val_acc: 0.9333\n",
      "Epoch 913/1500\n",
      "810/810 [==============================] - 0s 28us/step - loss: 0.0608 - acc: 0.9926 - val_loss: 0.1613 - val_acc: 0.9667\n",
      "Epoch 914/1500\n",
      "810/810 [==============================] - 0s 28us/step - loss: 0.0606 - acc: 0.9901 - val_loss: 0.2309 - val_acc: 0.9111\n",
      "Epoch 915/1500\n",
      "810/810 [==============================] - 0s 30us/step - loss: 0.0549 - acc: 0.9901 - val_loss: 0.1511 - val_acc: 0.9556\n",
      "Epoch 916/1500\n",
      "810/810 [==============================] - 0s 30us/step - loss: 0.0669 - acc: 0.9852 - val_loss: 0.1715 - val_acc: 0.9444\n",
      "Epoch 917/1500\n",
      "810/810 [==============================] - 0s 27us/step - loss: 0.0639 - acc: 0.9877 - val_loss: 0.2160 - val_acc: 0.9444\n",
      "Epoch 918/1500\n",
      "810/810 [==============================] - 0s 50us/step - loss: 0.0639 - acc: 0.9802 - val_loss: 0.3073 - val_acc: 0.8667\n",
      "Epoch 919/1500\n",
      "810/810 [==============================] - 0s 28us/step - loss: 0.0634 - acc: 0.9840 - val_loss: 0.1672 - val_acc: 0.9556\n",
      "Epoch 920/1500\n",
      "810/810 [==============================] - 0s 38us/step - loss: 0.0725 - acc: 0.9802 - val_loss: 0.2299 - val_acc: 0.9333\n",
      "Epoch 921/1500\n",
      "810/810 [==============================] - 0s 28us/step - loss: 0.0770 - acc: 0.9877 - val_loss: 0.2612 - val_acc: 0.9111\n",
      "Epoch 922/1500\n",
      "810/810 [==============================] - 0s 30us/step - loss: 0.0665 - acc: 0.9889 - val_loss: 0.1970 - val_acc: 0.9444\n",
      "Epoch 923/1500\n",
      "810/810 [==============================] - 0s 32us/step - loss: 0.0656 - acc: 0.9889 - val_loss: 0.2380 - val_acc: 0.9222\n",
      "Epoch 924/1500\n",
      "810/810 [==============================] - 0s 31us/step - loss: 0.0564 - acc: 0.9889 - val_loss: 0.2696 - val_acc: 0.9000\n",
      "Epoch 925/1500\n",
      "810/810 [==============================] - 0s 27us/step - loss: 0.0758 - acc: 0.9815 - val_loss: 0.2949 - val_acc: 0.8778\n",
      "Epoch 926/1500\n",
      "810/810 [==============================] - 0s 30us/step - loss: 0.0702 - acc: 0.9827 - val_loss: 0.2454 - val_acc: 0.9000\n",
      "Epoch 927/1500\n",
      "810/810 [==============================] - 0s 30us/step - loss: 0.0638 - acc: 0.9840 - val_loss: 0.2051 - val_acc: 0.9444\n",
      "Epoch 928/1500\n",
      "810/810 [==============================] - 0s 27us/step - loss: 0.0867 - acc: 0.9790 - val_loss: 0.2429 - val_acc: 0.9111\n",
      "Epoch 929/1500\n",
      "810/810 [==============================] - 0s 32us/step - loss: 0.0597 - acc: 0.9864 - val_loss: 0.2285 - val_acc: 0.9333\n",
      "Epoch 930/1500\n",
      "810/810 [==============================] - 0s 28us/step - loss: 0.0674 - acc: 0.9877 - val_loss: 0.2432 - val_acc: 0.9444\n",
      "Epoch 931/1500\n",
      "810/810 [==============================] - 0s 30us/step - loss: 0.0692 - acc: 0.9889 - val_loss: 0.2664 - val_acc: 0.9222\n",
      "Epoch 932/1500\n",
      "810/810 [==============================] - 0s 50us/step - loss: 0.0639 - acc: 0.9877 - val_loss: 0.2927 - val_acc: 0.8778\n",
      "Epoch 933/1500\n",
      "810/810 [==============================] - 0s 30us/step - loss: 0.0808 - acc: 0.9741 - val_loss: 0.1969 - val_acc: 0.9444\n",
      "Epoch 934/1500\n",
      "810/810 [==============================] - 0s 28us/step - loss: 0.0668 - acc: 0.9864 - val_loss: 0.2131 - val_acc: 0.9333\n",
      "Epoch 935/1500\n",
      "810/810 [==============================] - 0s 30us/step - loss: 0.0696 - acc: 0.9827 - val_loss: 0.2035 - val_acc: 0.9333\n",
      "Epoch 936/1500\n",
      "810/810 [==============================] - 0s 50us/step - loss: 0.0568 - acc: 0.9901 - val_loss: 0.1645 - val_acc: 0.9667\n",
      "Epoch 937/1500\n",
      "810/810 [==============================] - 0s 34us/step - loss: 0.0619 - acc: 0.9914 - val_loss: 0.3534 - val_acc: 0.8333\n",
      "Epoch 938/1500\n",
      "810/810 [==============================] - 0s 38us/step - loss: 0.0650 - acc: 0.9802 - val_loss: 0.3348 - val_acc: 0.8444\n",
      "Epoch 939/1500\n",
      "810/810 [==============================] - 0s 41us/step - loss: 0.0528 - acc: 0.9914 - val_loss: 0.1898 - val_acc: 0.9444\n",
      "Epoch 940/1500\n",
      "810/810 [==============================] - 0s 42us/step - loss: 0.0537 - acc: 0.9901 - val_loss: 0.2283 - val_acc: 0.9333\n",
      "Epoch 941/1500\n",
      "810/810 [==============================] - 0s 53us/step - loss: 0.0552 - acc: 0.9901 - val_loss: 0.2050 - val_acc: 0.9444\n",
      "Epoch 942/1500\n",
      "810/810 [==============================] - 0s 32us/step - loss: 0.0609 - acc: 0.9889 - val_loss: 0.1818 - val_acc: 0.9333\n",
      "Epoch 943/1500\n",
      "810/810 [==============================] - 0s 41us/step - loss: 0.0612 - acc: 0.9852 - val_loss: 0.1842 - val_acc: 0.9444\n",
      "Epoch 944/1500\n",
      "810/810 [==============================] - 0s 32us/step - loss: 0.0580 - acc: 0.9889 - val_loss: 0.2211 - val_acc: 0.9222\n",
      "Epoch 945/1500\n",
      "810/810 [==============================] - 0s 38us/step - loss: 0.0635 - acc: 0.9815 - val_loss: 0.1959 - val_acc: 0.9444\n",
      "Epoch 946/1500\n",
      "810/810 [==============================] - 0s 33us/step - loss: 0.0517 - acc: 0.9926 - val_loss: 0.1789 - val_acc: 0.9556\n",
      "Epoch 947/1500\n",
      "810/810 [==============================] - 0s 33us/step - loss: 0.0612 - acc: 0.9877 - val_loss: 0.4316 - val_acc: 0.7889\n",
      "Epoch 948/1500\n",
      "810/810 [==============================] - 0s 32us/step - loss: 0.0713 - acc: 0.9840 - val_loss: 0.2401 - val_acc: 0.9222\n",
      "Epoch 949/1500\n",
      "810/810 [==============================] - 0s 34us/step - loss: 0.0648 - acc: 0.9840 - val_loss: 0.2261 - val_acc: 0.9444\n",
      "Epoch 950/1500\n",
      "810/810 [==============================] - 0s 31us/step - loss: 0.0659 - acc: 0.9877 - val_loss: 0.2367 - val_acc: 0.9222\n",
      "Epoch 951/1500\n",
      "810/810 [==============================] - 0s 36us/step - loss: 0.0543 - acc: 0.9877 - val_loss: 0.1696 - val_acc: 0.9444\n",
      "Epoch 952/1500\n",
      "810/810 [==============================] - 0s 34us/step - loss: 0.0708 - acc: 0.9852 - val_loss: 0.1846 - val_acc: 0.9444\n",
      "Epoch 953/1500\n",
      "810/810 [==============================] - 0s 31us/step - loss: 0.0610 - acc: 0.9877 - val_loss: 0.1924 - val_acc: 0.9444\n",
      "Epoch 954/1500\n",
      "810/810 [==============================] - 0s 44us/step - loss: 0.0556 - acc: 0.9926 - val_loss: 0.2043 - val_acc: 0.9444\n",
      "Epoch 955/1500\n",
      "810/810 [==============================] - 0s 37us/step - loss: 0.0715 - acc: 0.9877 - val_loss: 0.2208 - val_acc: 0.9222\n",
      "Epoch 956/1500\n",
      "810/810 [==============================] - 0s 32us/step - loss: 0.0686 - acc: 0.9864 - val_loss: 0.2357 - val_acc: 0.9111\n",
      "Epoch 957/1500\n",
      "810/810 [==============================] - 0s 36us/step - loss: 0.0577 - acc: 0.9889 - val_loss: 0.2268 - val_acc: 0.9222\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 958/1500\n",
      "810/810 [==============================] - 0s 34us/step - loss: 0.0585 - acc: 0.9864 - val_loss: 0.1656 - val_acc: 0.9444\n",
      "Epoch 959/1500\n",
      "810/810 [==============================] - 0s 34us/step - loss: 0.0642 - acc: 0.9827 - val_loss: 0.4277 - val_acc: 0.8111\n",
      "Epoch 960/1500\n",
      "810/810 [==============================] - 0s 30us/step - loss: 0.0624 - acc: 0.9877 - val_loss: 0.2647 - val_acc: 0.9111\n",
      "Epoch 961/1500\n",
      "810/810 [==============================] - 0s 34us/step - loss: 0.0613 - acc: 0.9852 - val_loss: 0.2314 - val_acc: 0.9222\n",
      "Epoch 962/1500\n",
      "810/810 [==============================] - 0s 33us/step - loss: 0.0599 - acc: 0.9901 - val_loss: 0.1704 - val_acc: 0.9556\n",
      "Epoch 963/1500\n",
      "810/810 [==============================] - 0s 37us/step - loss: 0.0704 - acc: 0.9790 - val_loss: 0.3567 - val_acc: 0.8444\n",
      "Epoch 964/1500\n",
      "810/810 [==============================] - 0s 31us/step - loss: 0.0762 - acc: 0.9827 - val_loss: 0.2608 - val_acc: 0.8889\n",
      "Epoch 965/1500\n",
      "810/810 [==============================] - 0s 30us/step - loss: 0.0706 - acc: 0.9840 - val_loss: 0.1916 - val_acc: 0.9444\n",
      "Epoch 966/1500\n",
      "810/810 [==============================] - 0s 54us/step - loss: 0.0607 - acc: 0.9926 - val_loss: 0.2013 - val_acc: 0.9333\n",
      "Epoch 967/1500\n",
      "810/810 [==============================] - 0s 30us/step - loss: 0.0484 - acc: 0.9901 - val_loss: 0.1982 - val_acc: 0.9333\n",
      "Epoch 968/1500\n",
      "810/810 [==============================] - 0s 52us/step - loss: 0.0575 - acc: 0.9901 - val_loss: 0.1563 - val_acc: 0.9444\n",
      "Epoch 969/1500\n",
      "810/810 [==============================] - 0s 30us/step - loss: 0.0730 - acc: 0.9802 - val_loss: 0.3008 - val_acc: 0.8444\n",
      "Epoch 970/1500\n",
      "810/810 [==============================] - 0s 30us/step - loss: 0.0672 - acc: 0.9877 - val_loss: 0.2192 - val_acc: 0.9444\n",
      "Epoch 971/1500\n",
      "810/810 [==============================] - 0s 31us/step - loss: 0.0667 - acc: 0.9815 - val_loss: 0.2563 - val_acc: 0.9000\n",
      "Epoch 972/1500\n",
      "810/810 [==============================] - 0s 33us/step - loss: 0.0660 - acc: 0.9864 - val_loss: 0.1633 - val_acc: 0.9556\n",
      "Epoch 973/1500\n",
      "810/810 [==============================] - 0s 34us/step - loss: 0.0675 - acc: 0.9840 - val_loss: 0.2132 - val_acc: 0.9333\n",
      "Epoch 974/1500\n",
      "810/810 [==============================] - 0s 30us/step - loss: 0.0461 - acc: 0.9914 - val_loss: 0.2331 - val_acc: 0.9000\n",
      "Epoch 975/1500\n",
      "810/810 [==============================] - 0s 30us/step - loss: 0.0592 - acc: 0.9889 - val_loss: 0.2679 - val_acc: 0.8667\n",
      "Epoch 976/1500\n",
      "810/810 [==============================] - 0s 49us/step - loss: 0.0712 - acc: 0.9802 - val_loss: 0.2038 - val_acc: 0.9444\n",
      "Epoch 977/1500\n",
      "810/810 [==============================] - 0s 33us/step - loss: 0.0673 - acc: 0.9840 - val_loss: 0.2368 - val_acc: 0.9333\n",
      "Epoch 978/1500\n",
      "810/810 [==============================] - 0s 31us/step - loss: 0.0560 - acc: 0.9889 - val_loss: 0.2127 - val_acc: 0.9222\n",
      "Epoch 979/1500\n",
      "810/810 [==============================] - 0s 34us/step - loss: 0.0762 - acc: 0.9827 - val_loss: 0.2639 - val_acc: 0.9000\n",
      "Epoch 980/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0635 - acc: 0.9827 - val_loss: 0.2312 - val_acc: 0.9222\n",
      "Epoch 981/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0577 - acc: 0.9889 - val_loss: 0.2328 - val_acc: 0.9444\n",
      "Epoch 982/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0680 - acc: 0.9840 - val_loss: 0.1676 - val_acc: 0.9444\n",
      "Epoch 983/1500\n",
      "810/810 [==============================] - 0s 33us/step - loss: 0.0544 - acc: 0.9926 - val_loss: 0.2943 - val_acc: 0.8556\n",
      "Epoch 984/1500\n",
      "810/810 [==============================] - 0s 32us/step - loss: 0.0595 - acc: 0.9889 - val_loss: 0.2274 - val_acc: 0.9333\n",
      "Epoch 985/1500\n",
      "810/810 [==============================] - 0s 37us/step - loss: 0.0704 - acc: 0.9877 - val_loss: 0.3025 - val_acc: 0.8778\n",
      "Epoch 986/1500\n",
      "810/810 [==============================] - 0s 30us/step - loss: 0.0644 - acc: 0.9852 - val_loss: 0.2646 - val_acc: 0.8889\n",
      "Epoch 987/1500\n",
      "810/810 [==============================] - 0s 40us/step - loss: 0.0658 - acc: 0.9877 - val_loss: 0.1971 - val_acc: 0.9333\n",
      "Epoch 988/1500\n",
      "810/810 [==============================] - 0s 29us/step - loss: 0.0482 - acc: 0.9926 - val_loss: 0.1702 - val_acc: 0.9444\n",
      "Epoch 989/1500\n",
      "810/810 [==============================] - 0s 58us/step - loss: 0.0664 - acc: 0.9840 - val_loss: 0.2284 - val_acc: 0.9111\n",
      "Epoch 990/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0590 - acc: 0.9852 - val_loss: 0.2868 - val_acc: 0.9000\n",
      "Epoch 991/1500\n",
      "810/810 [==============================] - 0s 37us/step - loss: 0.0650 - acc: 0.9815 - val_loss: 0.3347 - val_acc: 0.8444\n",
      "Epoch 992/1500\n",
      "810/810 [==============================] - 0s 31us/step - loss: 0.0529 - acc: 0.9951 - val_loss: 0.3114 - val_acc: 0.8778\n",
      "Epoch 993/1500\n",
      "810/810 [==============================] - 0s 33us/step - loss: 0.0637 - acc: 0.9827 - val_loss: 0.3100 - val_acc: 0.8778\n",
      "Epoch 994/1500\n",
      "810/810 [==============================] - 0s 34us/step - loss: 0.0491 - acc: 0.9914 - val_loss: 0.1779 - val_acc: 0.9444\n",
      "Epoch 995/1500\n",
      "810/810 [==============================] - 0s 13us/step - loss: 0.0516 - acc: 0.9914 - val_loss: 0.2066 - val_acc: 0.9333\n",
      "Epoch 996/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0508 - acc: 0.9889 - val_loss: 0.2721 - val_acc: 0.8889\n",
      "Epoch 997/1500\n",
      "810/810 [==============================] - 0s 49us/step - loss: 0.0632 - acc: 0.9815 - val_loss: 0.1966 - val_acc: 0.9333\n",
      "Epoch 998/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.0610 - acc: 0.9889 - val_loss: 0.2616 - val_acc: 0.9000\n",
      "Epoch 999/1500\n",
      "810/810 [==============================] - 0s 58us/step - loss: 0.0704 - acc: 0.9815 - val_loss: 0.2110 - val_acc: 0.9333\n",
      "Epoch 1000/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0581 - acc: 0.9938 - val_loss: 0.3045 - val_acc: 0.8778\n",
      "Epoch 1001/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0583 - acc: 0.9877 - val_loss: 0.2010 - val_acc: 0.9444\n",
      "Epoch 1002/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.0618 - acc: 0.9864 - val_loss: 0.1942 - val_acc: 0.9444\n",
      "Epoch 1003/1500\n",
      "810/810 [==============================] - 0s 30us/step - loss: 0.0539 - acc: 0.9901 - val_loss: 0.2148 - val_acc: 0.9333\n",
      "Epoch 1004/1500\n",
      "810/810 [==============================] - 0s 31us/step - loss: 0.0526 - acc: 0.9889 - val_loss: 0.2052 - val_acc: 0.9444\n",
      "Epoch 1005/1500\n",
      "810/810 [==============================] - 0s 32us/step - loss: 0.0710 - acc: 0.9840 - val_loss: 0.1934 - val_acc: 0.9333\n",
      "Epoch 1006/1500\n",
      "810/810 [==============================] - 0s 28us/step - loss: 0.0604 - acc: 0.9889 - val_loss: 0.1821 - val_acc: 0.9333\n",
      "Epoch 1007/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0593 - acc: 0.9877 - val_loss: 0.1415 - val_acc: 0.9556\n",
      "Epoch 1008/1500\n",
      "810/810 [==============================] - 0s 31us/step - loss: 0.0542 - acc: 0.9938 - val_loss: 0.2034 - val_acc: 0.9333\n",
      "Epoch 1009/1500\n",
      "810/810 [==============================] - 0s 41us/step - loss: 0.0594 - acc: 0.9914 - val_loss: 0.2993 - val_acc: 0.8444\n",
      "Epoch 1010/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0720 - acc: 0.9802 - val_loss: 0.2076 - val_acc: 0.9111\n",
      "Epoch 1011/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0465 - acc: 0.9938 - val_loss: 0.1994 - val_acc: 0.9222\n",
      "Epoch 1012/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.0505 - acc: 0.9938 - val_loss: 0.2274 - val_acc: 0.9111\n",
      "Epoch 1013/1500\n",
      "810/810 [==============================] - 0s 43us/step - loss: 0.0557 - acc: 0.9852 - val_loss: 0.1621 - val_acc: 0.9556\n",
      "Epoch 1014/1500\n",
      "810/810 [==============================] - 0s 32us/step - loss: 0.0614 - acc: 0.9852 - val_loss: 0.1769 - val_acc: 0.9444\n",
      "Epoch 1015/1500\n",
      "810/810 [==============================] - 0s 32us/step - loss: 0.0585 - acc: 0.9864 - val_loss: 0.1700 - val_acc: 0.9444\n",
      "Epoch 1016/1500\n",
      "810/810 [==============================] - 0s 32us/step - loss: 0.0509 - acc: 0.9926 - val_loss: 0.2012 - val_acc: 0.9444\n",
      "Epoch 1017/1500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "810/810 [==============================] - 0s 19us/step - loss: 0.0521 - acc: 0.9901 - val_loss: 0.1573 - val_acc: 0.9556\n",
      "Epoch 1018/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.0512 - acc: 0.9827 - val_loss: 0.3123 - val_acc: 0.8889\n",
      "Epoch 1019/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.0543 - acc: 0.9877 - val_loss: 0.1789 - val_acc: 0.9444\n",
      "Epoch 1020/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0480 - acc: 0.9901 - val_loss: 0.2137 - val_acc: 0.9333\n",
      "Epoch 1021/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.0665 - acc: 0.9864 - val_loss: 0.2456 - val_acc: 0.9111\n",
      "Epoch 1022/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.0538 - acc: 0.9901 - val_loss: 0.2339 - val_acc: 0.9333\n",
      "Epoch 1023/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.0547 - acc: 0.9901 - val_loss: 0.2724 - val_acc: 0.9000\n",
      "Epoch 1024/1500\n",
      "810/810 [==============================] - 0s 31us/step - loss: 0.0546 - acc: 0.9889 - val_loss: 0.1823 - val_acc: 0.9333\n",
      "Epoch 1025/1500\n",
      "810/810 [==============================] - 0s 32us/step - loss: 0.0655 - acc: 0.9852 - val_loss: 0.2283 - val_acc: 0.9333\n",
      "Epoch 1026/1500\n",
      "810/810 [==============================] - 0s 28us/step - loss: 0.0663 - acc: 0.9815 - val_loss: 0.3787 - val_acc: 0.8333\n",
      "Epoch 1027/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.0516 - acc: 0.9914 - val_loss: 0.2645 - val_acc: 0.8778\n",
      "Epoch 1028/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.0532 - acc: 0.9889 - val_loss: 0.2194 - val_acc: 0.9444\n",
      "Epoch 1029/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0640 - acc: 0.9827 - val_loss: 0.2159 - val_acc: 0.9111\n",
      "Epoch 1030/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0541 - acc: 0.9914 - val_loss: 0.1868 - val_acc: 0.9444\n",
      "Epoch 1031/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.0618 - acc: 0.9864 - val_loss: 0.1946 - val_acc: 0.9444\n",
      "Epoch 1032/1500\n",
      "810/810 [==============================] - 0s 28us/step - loss: 0.0482 - acc: 0.9926 - val_loss: 0.1923 - val_acc: 0.9444\n",
      "Epoch 1033/1500\n",
      "810/810 [==============================] - 0s 27us/step - loss: 0.0587 - acc: 0.9864 - val_loss: 0.1768 - val_acc: 0.9556\n",
      "Epoch 1034/1500\n",
      "810/810 [==============================] - 0s 15us/step - loss: 0.0656 - acc: 0.9877 - val_loss: 0.1623 - val_acc: 0.9444\n",
      "Epoch 1035/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0572 - acc: 0.9877 - val_loss: 0.2031 - val_acc: 0.9444\n",
      "Epoch 1036/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0673 - acc: 0.9877 - val_loss: 0.1862 - val_acc: 0.9444\n",
      "Epoch 1037/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0610 - acc: 0.9815 - val_loss: 0.1635 - val_acc: 0.9444\n",
      "Epoch 1038/1500\n",
      "810/810 [==============================] - 0s 38us/step - loss: 0.0629 - acc: 0.9901 - val_loss: 0.2291 - val_acc: 0.9333\n",
      "Epoch 1039/1500\n",
      "810/810 [==============================] - 0s 38us/step - loss: 0.0597 - acc: 0.9852 - val_loss: 0.2801 - val_acc: 0.8889\n",
      "Epoch 1040/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.0766 - acc: 0.9778 - val_loss: 0.1750 - val_acc: 0.9444\n",
      "Epoch 1041/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.0605 - acc: 0.9864 - val_loss: 0.1964 - val_acc: 0.9444\n",
      "Epoch 1042/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0536 - acc: 0.9901 - val_loss: 0.1812 - val_acc: 0.9444\n",
      "Epoch 1043/1500\n",
      "810/810 [==============================] - 0s 49us/step - loss: 0.0650 - acc: 0.9827 - val_loss: 0.1977 - val_acc: 0.9444\n",
      "Epoch 1044/1500\n",
      "810/810 [==============================] - 0s 22us/step - loss: 0.0431 - acc: 0.9901 - val_loss: 0.2062 - val_acc: 0.9222\n",
      "Epoch 1045/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0608 - acc: 0.9877 - val_loss: 0.2423 - val_acc: 0.9222\n",
      "Epoch 1046/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.0556 - acc: 0.9914 - val_loss: 0.2727 - val_acc: 0.8889\n",
      "Epoch 1047/1500\n",
      "810/810 [==============================] - 0s 42us/step - loss: 0.0473 - acc: 0.9938 - val_loss: 0.2212 - val_acc: 0.9333\n",
      "Epoch 1048/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0621 - acc: 0.9840 - val_loss: 0.2043 - val_acc: 0.9333\n",
      "Epoch 1049/1500\n",
      "810/810 [==============================] - 0s 33us/step - loss: 0.0596 - acc: 0.9852 - val_loss: 0.3458 - val_acc: 0.8333\n",
      "Epoch 1050/1500\n",
      "810/810 [==============================] - 0s 26us/step - loss: 0.0660 - acc: 0.9815 - val_loss: 0.3035 - val_acc: 0.8444\n",
      "Epoch 1051/1500\n",
      "810/810 [==============================] - 0s 53us/step - loss: 0.0539 - acc: 0.9864 - val_loss: 0.1726 - val_acc: 0.9444\n",
      "Epoch 1052/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.0669 - acc: 0.9840 - val_loss: 0.1740 - val_acc: 0.9444\n",
      "Epoch 1053/1500\n",
      "810/810 [==============================] - 0s 26us/step - loss: 0.0512 - acc: 0.9901 - val_loss: 0.2953 - val_acc: 0.8333\n",
      "Epoch 1054/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.0523 - acc: 0.9840 - val_loss: 0.2007 - val_acc: 0.9556\n",
      "Epoch 1055/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0660 - acc: 0.9852 - val_loss: 0.2076 - val_acc: 0.9333\n",
      "Epoch 1056/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.0554 - acc: 0.9864 - val_loss: 0.4145 - val_acc: 0.8000\n",
      "Epoch 1057/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0685 - acc: 0.9802 - val_loss: 0.2441 - val_acc: 0.9000\n",
      "Epoch 1058/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0661 - acc: 0.9827 - val_loss: 0.2885 - val_acc: 0.8778\n",
      "Epoch 1059/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0702 - acc: 0.9790 - val_loss: 0.1967 - val_acc: 0.9333\n",
      "Epoch 1060/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0516 - acc: 0.9914 - val_loss: 0.1958 - val_acc: 0.9444\n",
      "Epoch 1061/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0458 - acc: 0.9951 - val_loss: 0.1859 - val_acc: 0.9444\n",
      "Epoch 1062/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.0815 - acc: 0.9741 - val_loss: 0.2068 - val_acc: 0.9333\n",
      "Epoch 1063/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.0547 - acc: 0.9889 - val_loss: 0.2030 - val_acc: 0.9444\n",
      "Epoch 1064/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0563 - acc: 0.9827 - val_loss: 0.2090 - val_acc: 0.9444\n",
      "Epoch 1065/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0557 - acc: 0.9864 - val_loss: 0.1929 - val_acc: 0.9333\n",
      "Epoch 1066/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0550 - acc: 0.9840 - val_loss: 0.2314 - val_acc: 0.9222\n",
      "Epoch 1067/1500\n",
      "810/810 [==============================] - 0s 31us/step - loss: 0.0719 - acc: 0.9815 - val_loss: 0.1963 - val_acc: 0.9444\n",
      "Epoch 1068/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.0547 - acc: 0.9901 - val_loss: 0.2159 - val_acc: 0.9333\n",
      "Epoch 1069/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0615 - acc: 0.9827 - val_loss: 0.2007 - val_acc: 0.9333\n",
      "Epoch 1070/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0539 - acc: 0.9877 - val_loss: 0.2229 - val_acc: 0.9222\n",
      "Epoch 1071/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0569 - acc: 0.9889 - val_loss: 0.2985 - val_acc: 0.8667\n",
      "Epoch 1072/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0545 - acc: 0.9852 - val_loss: 0.1854 - val_acc: 0.9333\n",
      "Epoch 1073/1500\n",
      "810/810 [==============================] - 0s 47us/step - loss: 0.0466 - acc: 0.9926 - val_loss: 0.2196 - val_acc: 0.9222\n",
      "Epoch 1074/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0532 - acc: 0.9889 - val_loss: 0.2453 - val_acc: 0.9111\n",
      "Epoch 1075/1500\n",
      "810/810 [==============================] - 0s 32us/step - loss: 0.0517 - acc: 0.9864 - val_loss: 0.3035 - val_acc: 0.8778\n",
      "Epoch 1076/1500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "810/810 [==============================] - 0s 39us/step - loss: 0.0462 - acc: 0.9926 - val_loss: 0.2892 - val_acc: 0.8667\n",
      "Epoch 1077/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0637 - acc: 0.9877 - val_loss: 0.2443 - val_acc: 0.9222\n",
      "Epoch 1078/1500\n",
      "810/810 [==============================] - 0s 38us/step - loss: 0.0526 - acc: 0.9914 - val_loss: 0.2674 - val_acc: 0.9111\n",
      "Epoch 1079/1500\n",
      "810/810 [==============================] - 0s 37us/step - loss: 0.0592 - acc: 0.9864 - val_loss: 0.2205 - val_acc: 0.9333\n",
      "Epoch 1080/1500\n",
      "810/810 [==============================] - 0s 36us/step - loss: 0.0645 - acc: 0.9840 - val_loss: 0.1944 - val_acc: 0.9444\n",
      "Epoch 1081/1500\n",
      "810/810 [==============================] - 0s 34us/step - loss: 0.0534 - acc: 0.9840 - val_loss: 0.1757 - val_acc: 0.9444\n",
      "Epoch 1082/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0573 - acc: 0.9877 - val_loss: 0.1761 - val_acc: 0.9444\n",
      "Epoch 1083/1500\n",
      "810/810 [==============================] - 0s 41us/step - loss: 0.0526 - acc: 0.9877 - val_loss: 0.1995 - val_acc: 0.9333\n",
      "Epoch 1084/1500\n",
      "810/810 [==============================] - 0s 37us/step - loss: 0.0498 - acc: 0.9901 - val_loss: 0.1600 - val_acc: 0.9444\n",
      "Epoch 1085/1500\n",
      "810/810 [==============================] - 0s 22us/step - loss: 0.0546 - acc: 0.9914 - val_loss: 0.2010 - val_acc: 0.9444\n",
      "Epoch 1086/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0522 - acc: 0.9926 - val_loss: 0.1943 - val_acc: 0.9444\n",
      "Epoch 1087/1500\n",
      "810/810 [==============================] - 0s 32us/step - loss: 0.0574 - acc: 0.9864 - val_loss: 0.3616 - val_acc: 0.8444\n",
      "Epoch 1088/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0546 - acc: 0.9877 - val_loss: 0.2205 - val_acc: 0.9333\n",
      "Epoch 1089/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.0486 - acc: 0.9889 - val_loss: 0.2417 - val_acc: 0.9111\n",
      "Epoch 1090/1500\n",
      "810/810 [==============================] - 0s 30us/step - loss: 0.0480 - acc: 0.9901 - val_loss: 0.1996 - val_acc: 0.9444\n",
      "Epoch 1091/1500\n",
      "810/810 [==============================] - 0s 34us/step - loss: 0.0473 - acc: 0.9951 - val_loss: 0.1893 - val_acc: 0.9444\n",
      "Epoch 1092/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.0661 - acc: 0.9815 - val_loss: 0.1874 - val_acc: 0.9444\n",
      "Epoch 1093/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.0583 - acc: 0.9852 - val_loss: 0.2721 - val_acc: 0.8889\n",
      "Epoch 1094/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.0483 - acc: 0.9914 - val_loss: 0.3285 - val_acc: 0.8444\n",
      "Epoch 1095/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0641 - acc: 0.9864 - val_loss: 0.2268 - val_acc: 0.9222\n",
      "Epoch 1096/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0541 - acc: 0.9914 - val_loss: 0.2346 - val_acc: 0.9222\n",
      "Epoch 1097/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0482 - acc: 0.9914 - val_loss: 0.2044 - val_acc: 0.9444\n",
      "Epoch 1098/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.0513 - acc: 0.9877 - val_loss: 0.1429 - val_acc: 0.9556\n",
      "Epoch 1099/1500\n",
      "810/810 [==============================] - 0s 32us/step - loss: 0.0452 - acc: 0.9914 - val_loss: 0.1592 - val_acc: 0.9444\n",
      "Epoch 1100/1500\n",
      "810/810 [==============================] - 0s 20us/step - loss: 0.0513 - acc: 0.9877 - val_loss: 0.1556 - val_acc: 0.9556\n",
      "Epoch 1101/1500\n",
      "810/810 [==============================] - 0s 26us/step - loss: 0.0475 - acc: 0.9938 - val_loss: 0.1805 - val_acc: 0.9444\n",
      "Epoch 1102/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0550 - acc: 0.9877 - val_loss: 0.2039 - val_acc: 0.9333\n",
      "Epoch 1103/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.0578 - acc: 0.9802 - val_loss: 0.3485 - val_acc: 0.8333\n",
      "Epoch 1104/1500\n",
      "810/810 [==============================] - 0s 20us/step - loss: 0.0571 - acc: 0.9852 - val_loss: 0.2061 - val_acc: 0.9333\n",
      "Epoch 1105/1500\n",
      "810/810 [==============================] - 0s 38us/step - loss: 0.0690 - acc: 0.9827 - val_loss: 0.2474 - val_acc: 0.9111\n",
      "Epoch 1106/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.0530 - acc: 0.9877 - val_loss: 0.1998 - val_acc: 0.9333\n",
      "Epoch 1107/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.0620 - acc: 0.9827 - val_loss: 0.2702 - val_acc: 0.9111\n",
      "Epoch 1108/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.0469 - acc: 0.9901 - val_loss: 0.2435 - val_acc: 0.9111\n",
      "Epoch 1109/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.0548 - acc: 0.9877 - val_loss: 0.1736 - val_acc: 0.9444\n",
      "Epoch 1110/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0589 - acc: 0.9840 - val_loss: 0.2231 - val_acc: 0.9444\n",
      "Epoch 1111/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0473 - acc: 0.9938 - val_loss: 0.1893 - val_acc: 0.9444\n",
      "Epoch 1112/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0515 - acc: 0.9877 - val_loss: 0.2076 - val_acc: 0.9444\n",
      "Epoch 1113/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0662 - acc: 0.9864 - val_loss: 0.1616 - val_acc: 0.9444\n",
      "Epoch 1114/1500\n",
      "810/810 [==============================] - 0s 30us/step - loss: 0.0483 - acc: 0.9914 - val_loss: 0.1820 - val_acc: 0.9444\n",
      "Epoch 1115/1500\n",
      "810/810 [==============================] - 0s 38us/step - loss: 0.0447 - acc: 0.9951 - val_loss: 0.2025 - val_acc: 0.9333\n",
      "Epoch 1116/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.0422 - acc: 0.9926 - val_loss: 0.1787 - val_acc: 0.9444\n",
      "Epoch 1117/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.0540 - acc: 0.9901 - val_loss: 0.2043 - val_acc: 0.9222\n",
      "Epoch 1118/1500\n",
      "810/810 [==============================] - 0s 49us/step - loss: 0.0592 - acc: 0.9852 - val_loss: 0.2405 - val_acc: 0.9222\n",
      "Epoch 1119/1500\n",
      "810/810 [==============================] - 0s 25us/step - loss: 0.0587 - acc: 0.9864 - val_loss: 0.2734 - val_acc: 0.8889\n",
      "Epoch 1120/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.0518 - acc: 0.9864 - val_loss: 0.2753 - val_acc: 0.8889\n",
      "Epoch 1121/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.0463 - acc: 0.9864 - val_loss: 0.2403 - val_acc: 0.9000\n",
      "Epoch 1122/1500\n",
      "810/810 [==============================] - 0s 21us/step - loss: 0.0569 - acc: 0.9914 - val_loss: 0.2390 - val_acc: 0.9111\n",
      "Epoch 1123/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0431 - acc: 0.9914 - val_loss: 0.2873 - val_acc: 0.9000\n",
      "Epoch 1124/1500\n",
      "810/810 [==============================] - 0s 38us/step - loss: 0.0520 - acc: 0.9852 - val_loss: 0.2918 - val_acc: 0.8667\n",
      "Epoch 1125/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.0559 - acc: 0.9864 - val_loss: 0.1783 - val_acc: 0.9556\n",
      "Epoch 1126/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.0601 - acc: 0.9840 - val_loss: 0.2042 - val_acc: 0.9333\n",
      "Epoch 1127/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0534 - acc: 0.9864 - val_loss: 0.2354 - val_acc: 0.9111\n",
      "Epoch 1128/1500\n",
      "810/810 [==============================] - 0s 57us/step - loss: 0.0583 - acc: 0.9889 - val_loss: 0.1805 - val_acc: 0.9444\n",
      "Epoch 1129/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0595 - acc: 0.9889 - val_loss: 0.1896 - val_acc: 0.9222\n",
      "Epoch 1130/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.0620 - acc: 0.9864 - val_loss: 0.2879 - val_acc: 0.8778\n",
      "Epoch 1131/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.0566 - acc: 0.9889 - val_loss: 0.1784 - val_acc: 0.9444\n",
      "Epoch 1132/1500\n",
      "810/810 [==============================] - 0s 38us/step - loss: 0.0462 - acc: 0.9901 - val_loss: 0.2083 - val_acc: 0.9333\n",
      "Epoch 1133/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0477 - acc: 0.9877 - val_loss: 0.1854 - val_acc: 0.9444\n",
      "Epoch 1134/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0465 - acc: 0.9926 - val_loss: 0.1622 - val_acc: 0.9444\n",
      "Epoch 1135/1500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "810/810 [==============================] - 0s 39us/step - loss: 0.0629 - acc: 0.9852 - val_loss: 0.2091 - val_acc: 0.9333\n",
      "Epoch 1136/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.0534 - acc: 0.9864 - val_loss: 0.2957 - val_acc: 0.8444\n",
      "Epoch 1137/1500\n",
      "810/810 [==============================] - 0s 38us/step - loss: 0.0620 - acc: 0.9852 - val_loss: 0.2294 - val_acc: 0.9222\n",
      "Epoch 1138/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.0688 - acc: 0.9765 - val_loss: 0.3539 - val_acc: 0.8222\n",
      "Epoch 1139/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0478 - acc: 0.9901 - val_loss: 0.2273 - val_acc: 0.9111\n",
      "Epoch 1140/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.0644 - acc: 0.9864 - val_loss: 0.1568 - val_acc: 0.9444\n",
      "Epoch 1141/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0451 - acc: 0.9938 - val_loss: 0.1859 - val_acc: 0.9444\n",
      "Epoch 1142/1500\n",
      "810/810 [==============================] - 0s 38us/step - loss: 0.0588 - acc: 0.9840 - val_loss: 0.1846 - val_acc: 0.9444\n",
      "Epoch 1143/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.0618 - acc: 0.9852 - val_loss: 0.1846 - val_acc: 0.9444\n",
      "Epoch 1144/1500\n",
      "810/810 [==============================] - ETA: 0s - loss: 0.0502 - acc: 0.984 - 0s 19us/step - loss: 0.0444 - acc: 0.9914 - val_loss: 0.1788 - val_acc: 0.9556\n",
      "Epoch 1145/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0549 - acc: 0.9901 - val_loss: 0.2086 - val_acc: 0.9333\n",
      "Epoch 1146/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0556 - acc: 0.9914 - val_loss: 0.1874 - val_acc: 0.9444\n",
      "Epoch 1147/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.0606 - acc: 0.9840 - val_loss: 0.2197 - val_acc: 0.9222\n",
      "Epoch 1148/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.0493 - acc: 0.9901 - val_loss: 0.3906 - val_acc: 0.8000\n",
      "Epoch 1149/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0708 - acc: 0.9840 - val_loss: 0.2605 - val_acc: 0.8778\n",
      "Epoch 1150/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0639 - acc: 0.9840 - val_loss: 0.2380 - val_acc: 0.9222\n",
      "Epoch 1151/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0590 - acc: 0.9815 - val_loss: 0.1556 - val_acc: 0.9444\n",
      "Epoch 1152/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.0529 - acc: 0.9889 - val_loss: 0.1563 - val_acc: 0.9444\n",
      "Epoch 1153/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.0476 - acc: 0.9914 - val_loss: 0.2384 - val_acc: 0.9000\n",
      "Epoch 1154/1500\n",
      "810/810 [==============================] - 0s 39us/step - loss: 0.0442 - acc: 0.9889 - val_loss: 0.2745 - val_acc: 0.8778\n",
      "Epoch 1155/1500\n",
      "810/810 [==============================] - 0s 19us/step - loss: 0.0433 - acc: 0.9901 - val_loss: 0.1772 - val_acc: 0.9444\n",
      "Epoch 1156/1500\n",
      "810/810 [==============================] - 0s 47us/step - loss: 0.0450 - acc: 0.9864 - val_loss: 0.2416 - val_acc: 0.9222\n",
      "Epoch 1157/1500\n",
      "810/810 [==============================] - 0s 30us/step - loss: 0.0506 - acc: 0.9926 - val_loss: 0.2346 - val_acc: 0.9222\n",
      "Epoch 01157: early stopping\n"
     ]
    }
   ],
   "source": [
    "from keras.callbacks import ModelCheckpoint\n",
    "\n",
    "# evaluate the performance of the model on the validation dataset a end of each epoch.\n",
    "# 20% of the training data is held back for validation\n",
    "validation_data_split = 0.1\n",
    "\n",
    "# number of passes over the entire dataset\n",
    "num_epochs = 1500\n",
    "\n",
    "# number of samples propagated for one pass at a time\n",
    "model_batch_size = 128\n",
    "\n",
    "#size of batch of inputs to feed to the network for histograms computation.\n",
    "tb_batch_size = 32\n",
    "\n",
    "#number of epochs with no improvement after which training will be stopped.\n",
    "early_patience = 150\n",
    "\n",
    "#TensorBoard is a visualization tool provided with TensorFlow\n",
    "#This callback writes a log for TensorBoard, which allows you to visualize dynamic graphs of your training and test metrics, as well as activation histograms for the different layers in your model.\n",
    "tensorboard_cb   = TensorBoard(log_dir='logs', batch_size= tb_batch_size, write_graph= True)\n",
    "\n",
    "\n",
    "#min mode-training will stop when the quantity monitored has stopped decreasing\n",
    "filepath=\"weights.txt\" \n",
    "checkpoint = ModelCheckpoint(filepath, monitor='val_acc', verbose=1, save_best_only=True, mode='max')\n",
    "\n",
    "#Stop training when a monitored quantity has stopped improving.\n",
    "earlystopping_cb = EarlyStopping(monitor='val_loss', verbose=1, patience=early_patience, mode='min')\n",
    "\n",
    "# Read Dataset\n",
    "dataset = pd.read_csv('training.csv')\n",
    "\n",
    "# Process Dataset\n",
    "processedData, processedLabel = processData(dataset)\n",
    "\n",
    "#Trains the model for a given number of epochs \n",
    "history = model.fit(processedData\n",
    "                    , processedLabel\n",
    "                    , validation_split=validation_data_split\n",
    "                    , epochs=num_epochs\n",
    "                    , batch_size=model_batch_size\n",
    "                    , callbacks = [tensorboard_cb,earlystopping_cb]\n",
    "                   )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color = blue>Training and Validation Graphs</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x000001E0FD553DD8>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x000001E0FF5C1390>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x000001E0FF5E9908>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x000001E0FF60FE80>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5xOqvnFIYLVa/rm112LlbrWTrThAEQRD+QsKqFdKjXSS6JUSgV/tI2X6psDpaWIXrl+zAy45+g75ittojVS5tdAiCIAiC8AoJq1bMiO5yE9FSibASvK6yiw1+XdNksQsrZRsdgiAIgiC8Q8KqFfPU5QNwy9ge4vbp0lrUm+2pv3qzXSDVmaxYsukEuJuOzVYbx6hF6/DzPnvDZ4pYEQRBEETD8UlYMcamM8aOMcayGWNPqBzvzhjbwBjbxxjLZIxdFvihEkr0Wg3mzxiAJ2b0x9BucVh9qBD9n1mNz7acFFcI7j5VjpdWHcWOnDLVaxiMFhRXG/HMzwcBAEZHxMpGwoogCIIg/MZrLxTGmBbAYgBTAOQB2MUYW8E5Pyw57WkAyznnHzDGBgJYBSA5COMlFISHaHHPhN44WVKD/WcqAAAv/HYYvRLl9Vd1Zguyiw2wcY6+SdHifiH1B8diQ7OjaJ0iVgRBEAThP75ErEYByOac53DOTQCWApipOIcDiHH8HAuA3CWbmNRkeb1Vzrka2bbRbMPkNzZi6pubZPuF1KEA1VgRBEEQRMNh7mpvxBMYuxbAdM75nY7tmwGM5pzfLzmnE4A/AMQDiAQwmXO+R+VadwO4GwCSkpJGLl26NFDvwy0GgwFRUVFBv09LoKTWhkc31akeu3FACL45Yk8Pzh0RipR4LTaeMaO0nuPP0xZE6ID3J0diV6EFizOMYAA+nx6pei2BtjS3zQHNb3Ch+Q0uNL/Bg+Y2uLib34kTJ+7hnKd6e73XVCDEJJEMpRqbDeALzvl/GGNjAXzFGBvMOZe5THLOlwBYAgCpqak8LS3Nh9s3jvT0dDTFfVoKE8cbccU7W1BYVS/bX6FvB+AsAOCtvUZM7NceGyT9BfV6PdLS0lCxLx/IyAAHvM5bW5vbpobmN7jQ/AYXmt/gQXMbXBo7v76kAvMAdJNsd4Vrqu8OAMsBgHO+HUAYgMQGj4poMIlRobhZslJQ4OjZKtn2iZIal3MAwGQlx3WCIAiCaCi+CKtdAFIYYz0ZYyEAZgFYoTjnNIBLAYAxNgB2YVUColm4d0Jv7F8wFXdc0lPcJwip/h3theu9Feaijk42zmJ2giAIgiD8xquw4pxbANwPYA2AI7Cv/jvEGHuBMXaV47R/A7iLMbYfwHcAbuPeireIoKHRMMSG6/HMFQNdjv1470UY0CnGpf2NkO81U8SKIAiCIBqMLzVW4Jyvgt1CQbrvWcnPhwFcHNihEYHg6IvTYbFxDF6wBgAQGapDdKgOx4qqVc+XRqwsVht0Wrn2fuLHTKw7UoTdT08J3qAJgiAIopXik7AiWi9hei0A4JGpfXG00C6mosJ0qKwzq54vjViV1ZpQVGnEkK6x4r6lu84EcbQEQRAE0bohYdVGuH9SivhzVKjrr505iqykEau5SzOw7UQpdj89GYlRocEfJEEQBEG0cqhXYBskUk1YOf5fVW8R9+0+VQ4AOGcwNsWwCIIgCKLVQ8KqDZIQqXfZp9HYpZW0qF3n2Pfy70ddzqe1CQRBEAThCgmrNsjNY5Jd9pVUG7Ey86xMWGkdwir9mKtzRjB7CdYYLdQEmiAIgmiVkLBqg3SMDcOr116AR6b2xf4FU7HqwXEAgFdWH0VpjQnRjlRhtSQtqOwdaAyS31WN0YJBC9bg9T+OBeX6BEEQBBFMSFi1Ua5L7Yb7J6UgNlyPgZ1jMOvCbqioNaHUYETXhAiX8w1Gi2y7sUaiBqMFxdX1LvtLDfaI2S8Z1MebIAiCaH2QsCIAAD3aRaKq3oLiaiPG9mrncrxGIayMFmuj7jftzU0YtehPl/2CgIsI0Tbq+gRBEATRHJCwIgAAGkmr7Uen9XM5XmO04FBBpbitjFgt330GQxascUkZuiO/ok51v+CvFU7CiiAIgmiFkLAiAABTBiYhJkyHtfPGq4oag9GCy9/ZIm4rhdWzvxxEtdECQ71F+VK/qKyzpwIFY1OCIAiCaE2QsCIAAL3aRyHzuWlISbI3aV70t8Gy4zVGeepPWbwuuC9U1as7uvtKRa399ZQKJAiCIFojJKwIVW4c3UO2PXdZhmw7v6IOG8+YcbayDpe8sl4UWsJKwsvf2Yxv/zrt930rHKnAMB0JK4IgCKL1QcKKcEvX+HBcNqQjAFf39XnLMvD5IRNu/Wwn8sqd9VIrDxRg9cFCHCqowpP/O+D3PetM9shYRZ0Jc5fucymaJwiCIIiWDPUKJNyy5fFJAIANR4tx+xe7AAAPT+mLN9ZmodYhgLKKDLLXLN5wQradXWyAVsPQMzFS9R6cc7FPIQBYbPbI146cMgDA8O7xuPWi5Ma/GYIgCIJoAihiRXhlYv8O+Oy2VNw1ricm9G3v12snv7ERE19Pd3vcbOUetyWaiyAIgiBaPBSxInxiUv8kTOqfpGrq2RjMVhtCdBrZNkEQBEG0VkhYEX7RPioUGgZI7aouG9IRfdpH4Z312X5fz+KIUO0/U4GKOjMJK4IgCKJVQ6lAwi8YY1B6gLaPCsXDU/shOsw3nX7yXI34s8khpGYu3opbP9spCi0BTr2YCYIgiFYECSvCb76+Y7RsW7BIaB8V6vY1NhsH5xxGixVXL94q7heK1QVMFLEiCIIgWjE+CSvG2HTG2DHGWDZj7Ak351zHGDvMGDvEGPs2sMMkWhKXpCTitwcuwS0DQwA4GyfHRujdvqbXk6vw6ppjGPDMarFtDQCYLRy7c8vEbWXEiiAIgiBaE16FFWNMC2AxgBkABgKYzRgbqDgnBcB8ABdzzgcBmBuEsRItiMFdYjG6kw5aDcOd43oC8O6W/kH6CZc0otlmw3c7zzi3FRErWhVIEARBtCZ8iViNApDNOc/hnJsALAUwU3HOXQAWc87LAYBzXhzYYRItkUg9w4mXLkNavw4APKcC3WG22nCmrFayHZiIVUFFHX7dXxCQaxEEQRCEr/girLoAOCPZznPsk9IXQF/G2FbG2A7G2PRADZBoPVw7spts++YxPdyc6cRi5ThV5ixmN1qsHs72ndkf78AD3+3ze5XhH4cKceRsVUDGQBAEQbQ9fFnGpZaMUYYVdABSAKQB6ApgM2NsMOe8QnYhxu4GcDcAJCUlIT093d/x+o3BYGiS+7RF1Ob2sQvD8Oouu9eVreIsACA+lMEGoNLoGo3avGM3iqqc7XIKistkx49lHUe6MdfvseU5omB/btiIMJ3v+cS7V9tF3hfT1Z3iG4qNc5itQKgfY6HPbnCh+Q0uNL/Bg+Y2uDR2fn0RVnkApKGIrgCUOZY8ADs452YAJxljx2AXWrukJ3HOlwBYAgCpqak8LS2tgcP2nfT0dDTFfdoianObBkCTeAI9EiJQVFUPHD2MK0d0x9SBHXHTp3+5XCO2awqwy9lTUBMaAcDZJqdnr95IG9fL77Fp1/0Oq8WGUWMvRkJkiO8vXL3S/j4C/Jl59Pv9+H5PHnJfvtzn19BnN7jQ/AYXmt/gQXMbXBo7v76kAncBSGGM9WSMhQCYBWCF4pyfAUwEAMZYIuypwZwGj4po1dwzoTdmDOmElKRoAMDgzrEY1DlG9dwTJfJegwZF0+WG1lxpHIEhk8VzKnBHTimOFgY/9ff9njwAdtsJgiAI4vzFa8SKc25hjN0PYA0ALYDPOOeHGGMvANjNOV/hODaVMXYYgBXAo5zz0mAOnGj5XNwnEeseHo/e7aNkjZalbDl+TrZdXS8XVhY3NVKccxiMFkSHqVs8aBz381azNWvJDgDwK5LUGKycQ6OaXScIgiDOB3zyseKcr+Kc9+Wc9+acL3Lse9YhqsDtPMw5H8g5H8I5XxrMQROthz4dokVR1adDFHQauag4VlQt2zYYLdBKzjFLIjy7cstw15e7YbVxfL41F0Oe+wOFleq9C7WisPK9eJ03gc27lSJWBEEQ5zXkvE40Gb8/NA5HXvS+YDRG0hrHaLGKgmfON3ux9nARiqvr8WumvczvVKm92NxFFPmYCpRiaQLR09B7HMyvxJ5T5QEeDUEQBBFoSFgRTYZeq4Feq8Ent6Ri06MTkfPSZarnxYQ703sfbczBcysOAQAiHQakZTUmMSJVWmPCqgNncYEieqVMBf6SkY/TpU6/LDWaogG0tYE1Y1e8uwV//2BbgEdDEARBBBoSVkSTM3lgErq3i4BGw/D2rGF4/R9DZcc1inqs/24/hdUHCxEZao9knTOYxHRhQUUdHv8hE9VGCzLOVEiuYf+/0WxDqcGIh5ZmYM63e2TXVdZvmS1NkApsxq7SBqOFiucJgiCCjC92CwQRNGYOs3vN9k2KgsFowQ0f/4WT52pczrvna6coemnlEdQ7IlFFVfVoFxWCaqNFtsJQjFhZbdjl6EWoVQi2OrO8sL0pGkArm043FfVmKwYvWIPbL07GgisHNcsYCIIg2gIUsSJaBBd0jcOYnu3QLjIET18+wOO5x4qqccqR1jMYLaKIypUIMqFgft6yDOxzRLK6xkfIrlNnkgurYKQCq+vNqKx1Np1uruJ1o9n+3n5w2D4QBEEQwYEiVkSLQaNh2PPMFADAwpVHfHqNwWhFrUMgSa0ahFRgRa0ZH210WKopXA5cIlZ+FLqX1djTkbHh6nYPAhcuWod6s/O6lgD1QmwozZiJJAiCaBNQxIpo1Rjqzag12QXV6kOFeGmVXZAp67QAeeE45xwfbZJ72LqLWG0+XoIvtp6U7Rvx4lqMeelPr+OTiiqg+SJWQm2XjZQVQRBEUCFhRbRI1swdj3mT+3o9r8ZolUWelmzKgcVqk3lhCZTXmnDjJzvQ+8lV+C3zLL7967TsuLTGymy14avtubBYbbj505147tfDLtdTRrx8oSksHdQQBB0JK4IgiOBCqUCiRdKvYzT6dYxG57gwfP3Xadw7obesgB0AwvQalNYYXdrefLEtV/Waf510NnjemFUiO3aooFJ2na+2n8ILvx2W7Xv3z+N44NIUmZt7Vb0ZMW7c39XwJ2JVUhu4mi+nsPL9NXOX7gMH8Pas4QEbB0EQxPkORayIFs0/Urvhl/suxoS+7WX7Nz82ERf1TsSJEtcVhAtXHkGozvNHu6rOLNu+/J0tshoroV6rrMYk7vvP2iwAQHGVUdwn/dkXfBVWW46fw6Ob6vDrfmW/84YhpAL9cZf/OaMAv2QE5v4EQRBtBRJWRKsgPESLR6f1w6e3pmL/gqnolhCB3FKnqApRCClv8iGvvM5lX6VEbOm09lSiWcUeobjaaUTqT8E74LuwOny2EgBwIL/Sr+u7va/V/4jV+cDP+/Lx1P8ONPcwfOZUaQ0mvZ4u+4wRBNG6IGFFtBrum9gHlw5IElfiSVcBxilW56l5YUk5fLbKZV+5JDolFL+LKwol1JmcYuqydzZj8/ESl3OUCLX0vvpYCQKoMe2aP996ElmOXozNUbye/MRKvLL6aJPdT425yzLwjaKWriXz+dZc5JyrwW/7zzb3UAiCaCAkrIhWy8e3pCLC0eamvNYuijrFhvn8esaAGYM7ittltU5h5W6F4JmyWllkCwDeW5/t9V6CnrHaOG78ZAfedKQV3SEIIKayutEXOOd4/tfDmP7WJsd9bbJxNBUfpJ9o2hueJ7SxwCJBnFeQsCJaLcO6xSFzwVQs+ttg/HzfxfjXhF64e3wvn19/8v8ux9RBSeL26TJnL8Gf9qobaY57dQPu+3avyzh8xWLj2Jpdirf/PO7xPEEAqSxu9Akh4mUTBV3DrtNQGmu2arVx/OePY7IoIuE/Z8pqsfogRb8IoikhYUW0anRaDW4c3QODOsdi/owBfj/QpSv6/sopRVSoDl3iwpHrpWGzFGl04UBeJf48UuT2XE81VmarDb/uL0CpwSj29FPz4/IFZcqxqf2z6htgRSFlY1Yx3l2fjQWOBtxEw5jx9mbc8/Ve7ycSBBEwSFgR5xVDu9qjR5GOFKHA2nnjkbVwhri9+bGJAIBoibA6UVKD/h2jcUHXWL/uaZSIiCvf24I7/rvb7bmefKy2ZJ/DA9/tw9xlGWKkqaERK6XD+/d7zjTsQg1EaYzqL8KigMYKtLaOwWjxfhJBEAGFfKyI84rRvdph/4KpiA3XI7u4GrtzyzGub3t0iQsHALx8zRCEh2jRLcHeNzA6TP5PICk2DL3bR+HdZ0WmAAAgAElEQVT3g4U+39OosjKQc65aH2XzIKwEC4jiKqPbGiubjUPjUFs2G0evJ1dhTlpvPDa9v+w8qYCz2Tg+35rr25txoOyjqKS4qh7to0Pd1oA1VhA5U6G+K8tlu06je0IkxvZu16h7B4L0Y8XQahjGpbTH8aJq1JisPqWMGxigFKmuN+PkuRpc0NX39DRBEIGFIlbEeYewarBPh2jMGtVdFFUAMGtUd8wc1kXcjlGsJmwXGYIeCfJmzd5QE1bKAncBk4dUpdDzUKthot+UhjGZ95RV8nOtQ7x8slnebgcALJL7lBj889oCgMvf2ez22JGzVRj10p/4dqf71XaCsFJzwPcFMWLnx1+ox388gNkf71A95o9/VyC47fNduPnTnQCAKW9uwtWLtzbJfe/8725c9d5Wl5S4J0FPBJ5TpTVBaepOtA5IWBFtGmXEKiEyBD3aOYVVVKj3oO6K/QXYfqJUtu9AfiUe/yHT5Vx3kZwaowU1jrSNXsucdgtM7j0lrZX62NHrUPDckiI9T1qU7ys5HuwqsosNAIBtivcsRUgF6hosrBq3KlJJc/VobCgNFYL7TlcAcH2/zdVKqS1SXmPChNfS8ewv8vrA5bvO0EKCNgIJK6JNExXiKqx6JkaK22qiRYnVxnHHf3fJ9t386U4s2+1a1yRNsQkPzxqjBYMWrMHClfYG0loNEyNTVhuXffO12DhWZp7FG38cE1cW6jQMZ8pq8fgPmeK5ZsmDtKDC1QzVHw4qTEqFKytnxmK14ZwjOlbvaPuj1zbsT4yNN654X0mbERaO6VIKq9YmLFszgr+e0t/usR8zaSFBG8Gnv3qMsemMsWOMsWzG2BMezruWMcYZY6mBGyJBBA+NhuGaEc7UYEJkCNpFhYrbVqtvD6RaLzVJaucJUZ0KRdrQxp3F2xabTfZQtNo47vt2L96ReGfptRo88v1+LNt9BntOlbuMu6q+cQXMV7y7BUVVTidw7iaatGDFIaQuXIc6k1WMzPkiTD3h66u9CYe2IiyE+VIKSWsQU6EGowXJT6zEz/vyg3aP1oTwz6K19jsvqqrHpP+k40wDIt2EHa/CijGmBbAYwAwAAwHMZowNVDkvGsCDAP4K9CAJIpi8cd0wfHXHKMwY3BGjeibIjgktbaYOTEJiVIjH62ScqfB6L6lAqTbaBVV1vVxYmSw2sdGz2cplD0mrjbukJ3Va5hLhkbbiqQnAyrDCSqmwsv9fKXpWHrCnOWpNFjEyp/OnSEqC8H72ni5HdnG11/PrvBTLt5WIFXMXsfLxC0JDECKiizd4N8ptCVTVm6nmzAM/7MlDTklNq+pY0NLw5a/eKADZnPMczrkJwFIAM1XOexHAqwCoyRXR6hiX0h4f3DQSHaLtzu0Zz07Brqcmiw+ox2f0R7jCwkGJLwXKH21ytsgx1FuwK7cMM96WF4qbrDYxYlVnsmLON3vEY+uOFLksoddpNOI4hcyb9MFqaGTECrA/jJQos3TSaEm9Y/x6HyNWS3eext/ed86foAvzyusw+Y1NXl9fa3K+x1KVYv3WErFijWpi5MTFx6y1hk9UMFqsPoltNcprTLjguT/wlheD3rZMgLLvbRpf7Ba6AJAWi+QBGC09gTE2HEA3zvlvjLFH3F2IMXY3gLsBICkpCenp6X4P2F8MBkOT3Kct0hbm1uz4pn80Yxfq6wL7neGN/23DbzmugqWyugan8+1RgLUHTqOwxvlQfEylID6/og75jqjBvr37UH1Si1NVzgjOwexc2fkN+Z1t3b0f1nz7n4vDBXYRs/5wAf67ogw9YuyC02K2v5eNW7bhWJn9/haTEenp6eCc4/Y1tbi+Xwhm9NS7XP+J1TWysR3Ok8+LtzEX1zqFxJVv/YmXLpGv7Ny0ZQviQuXfI5vi8yu9vi/3ysuzi8Ks7GykW91HDPYVW9A3XotIvdKOwz4PW7ZuQ0KY8/2qvf9AkV9tv2dNbY3sPQZrfj/ONGJrgQXvTYpAVIh/KiDfYB/r9zuyMUJfEPCxAc7PYl19ver7D8ScBPOzm5Nj73Zw+vRppKf7bjvTlGSVWxETwtAxMjif6cbOry/CSu2TK/6lZ4xpALwJ4DZvF+KcLwGwBABSU1N5WlqaT4NsDOnp6WiK+7RF2sLcvh2Xj/c3nMBlk8dhfXkmftybh7vH90JkiA5vrvPc788baqIKALT6UITFRANnS2Siyhd69BuMN9cfx81j+wDYDwAIj00Ezjj/QHr7ndWbrcDq1bJ9Sd17I+3ingCA4t1ngMxMVJuABdvqkfvy5QCAkM1rAbMJw0deCOPJMuDgQURFRmBI6li8tuYYgFoszzLhldunuN509UrZ2Ip2nQYOHvB5zEcLq4BN9shfgYE7z3dcd/SYsegUGy57jafPr83GwVgjViVK34/ivXliU/Vh4NRJ9EjuhbS0PqrnFFbW47b/+xPjUhLx1R2y77jQrV8Nk9WKC0eNsXu1Oe49ZsxF6OhHH01/yCqqBrZuQmREJNLSJoj7g/X34em/1gOwYGjqaNGPzleOFVYDWzYhKlI+1kBy8lwNsCkdoaGh8vfvx+fAG8H823uUnQCyjqJrt25ISxsQlHs0ltuesM+l8Lcn0DR2fn2Re3kAukm2uwKQSv1oAIMBpDPGcgGMAbCCCtiJ84GZw7pgzbzxYIxh0d8G47cHLsGTlw3AQ5NTRPd2KfMm98WgzjGNuqfJapPVNPlD+rFi7M+rxEurjoj7Sr302+Oc46VVR5CZZ68R6//MapdzpD371Hy7AKcIMVpsMDvOYQx4dsUhLN1lD3p7kylCys7kZ02QN0NTpRO9Jzi3G6+++NsRl2NqKdFg4MkDSVgYoGajIcyvq91C8D2VlDO8/rQZvx8IvL2AUEcofY9nymrFhRue4BAWXgR8WCLCuGwtIP2aVVSN+7/d65enFmUCG48vwmoXgBTGWE/GWAiAWQBWCAc555Wc80TOeTLnPBnADgBXcc7d9/UgiFZImF6LwV2c7W66JUTgh3vGYqikBc4lKe0wsJNdWA3v7r/79W0XJcNkscmK3P1BeGCUSYSQspHxnlNluPnTv5x1XGYrlmzKwbUfbHd7XamYMropFBfubbRYRSNULWM4K7F78GafINpFuBFvqw8W4pJX1rs8KLwJK6uNg3OOxRuyvYpWodD9s61y49XVB8/iguf+8GmRgoC/flQ2ic2Gt3M8zaTLqkA316szWfHv5ftFm4yG4O4tfnnYhHu/Cby9gGCNJhUu417dgL9/sM3ra50LL4InH4RxtQBdhX8v34/fMs/iyNkqv1/b1Ka65xNehRXn3ALgfgBrABwBsJxzfogx9gJj7KpgD5AgWjKpyQn4+b6LkdIhCgDQLjIUs0d3BwCMT2nvc6ha6G0YptfCYLSgvFYeGekQHar2MhfOVbtGp8oUwuqWT3di8/Fz4mquGqNdlJisNrffbGXCyl3ESjhudhbfMwYUVzsf2t6EVWmNCR+kn3B7j6f+dwB55XWoUMyPL6sCs4sNeG3NMdz3reeHvcnNvbdknwMAMbLnC/6uRhQiS+YGruJjKtEctW3ALqreWHsMP+7Nw1se0tpbs8/Z031uaOqFAcJnqDErPZsiYtUSZIlThPr+mobaRRRX1eO4h89JW8KnXoGc81UAVin2Pevm3LTGD4sgWg+MMfxy/8XYd7oCyYmRSEYkshbO8HlF3Ov/GIpL+3dARZ0Z3/51yuX41IFJ+NvwLj59+1eLPJTVyoVVjSO6IzzEpXYM7vy4fBJWzHlcECdZRQbVc4SxxoXroZOYiL7w6yGsOVSEEW6ifcLDVPlt2t2YBKQP/4paz6lRd+JSeKD788CRjstd/0iBg/mVqKqz/y4sHlI3nlzpnSszFasCVZ6sd325WxSLIVr3K15v/MTuoKP2JWHd4SKcdURXmyqFJLxtdwLYE8EIwlisNpTXmtHe8eVHFFZu7vX2uuO4cmgn9GofFfjBKHFMVlOkJce+vB5WGw9K3dOxwmr06xgd8OsGC3JeJ4gAEBGiw8V9EsXtEJ1GfPB9fvuF4v6pA5MQFapDjKOVzoOXpuDakV0R73B8P2dwPvSFb5uJ0aFiz70QrQa5L18Od51i1PoCuvubWmeyP5ik9g3uWu7UmSxYvusMzBIrCCVCesVosXoVJ9X1ZqQuXIeFK4/IridEz4qq5O9jz6kyAE7BoRRS3h6yFptNFBtq397XHy0SDRHd9XMUplz6kOr39O94bsUh1fMBedrUxu2i7bU1R1GpiLgZjBZc8e4WrNhf4Biv+wehEM1S/Qi487FS+RAIogoAwvQNexTc+eVuPPPzwQa91hOLN2Qj/Vix6jHhM+Sp76Y73lh7DEDgWiUBwOt/ZOHCRetEwe4tgvfmuizc9EnT2D0K79IXXSVYlgj/jv2VYp7ed3ZxtdvfpzdWHTiLaW9tCkq9XrAgYUUQQWZivw6YN7kvvr9nLJbckoqDz0/DnIn2FV8l1XIBUVztrP/pFWv/59mzXaT4R06o24oIUQ82n6v2vVbmyve2AJBHqcrdRHN+zijAYz9mIuWp3/Fbpusy9cMFVSh0RC7+OlnmNoJUZ7biq+25ohv8F9ty0ffp38XjoTr7e1ZGlf7uqP8SBIe/wspq42Idlloh9z+/2I0r3t2ieq3iqnos33UG/91ujyYKzw+rjcNoseGLbbmy86XRNGmdlsVmw8H8SizecALpWfKHjNLE1VOxsViIr6IN3DmveyveD9N79mjzhUDGRF5bcwy3fb5L9ZgorCReb76y7oh93gMZXdt50t4z82ihPQ3mFLHuZ0TpRRcs1OrR1FhzqBADn12Dg/mVYkQwkFGuyW9scvv79MZRR33YsVaUZiRhRRBNwEOTU3BhstPVferAJABAao942Xk92jn7FAoeRR1jw5Bfbq+HGuAojG/ncIFXusHX+PGQAewRKulDffpbmz2cbSevXN570GK14bJ3nK/7fGuux2jCM78cwsUvr1c9FuqInKi9D4vVJv6xF5zpBYwq95O2WLnv27141OEBpnQhF6J0lY7WQsr6phs/+QuP/ej0DxMe6KU16iJWKmoWbzghGT8X692UCwqUD1pBCC1aeRivrj4qOya46nsSB8rogbeHZEMjVoEgu9hgt8vwEeHBL4hPac2cp4Jrqdt6IGuseibaU3o5JTWy+3gKXPlTl7b9RCl25LhveO4JsRODl+ie0ER+58myBt2nKWkNRfU+1VgRBBFYerWPwsHnp4lF6wLPXD4Qfx/RBSN7JODN5euwv8SKYd3iEBmqw97T5Zg7OQWAvafhqdJavDt7BE6UGPB0A9Mx5bUm1W/PkwckYd2RIp+uoSaCGlL/AgBaDy1wFq48IooebxGraW9ukn3DPVPmFINWzlFcXY96i/1aVYpejcqH0CmFrcErq49i9qhueGud3b1bWUvnLjpksTmFlbI/pDJidaqsBqsPnsXHm+0Rr8em93e5vmqNlVDYrRiDt0LvkAY2yw4Ek9/YCMB3TyJlxEqaYjJbOUJ06qpJKvYDKaw6OfzBhOiz1U0doBR/Cu9nf7wDQMM8m3ytR4sMtf8dMhgtiHS0zGqMftl7uhx7T5XjznG9Gn4RN7SGLgoUsSKIZiIqVOfycAwP0WJkD3tka3gHHXJfvhzdEiKQEBmC924YgbgIe4Sqs8PsstZkEeuvBIZILCG8Mfb/1uOB7/a57Bf+0PrCJ5tzXPZJ2+hMcUTnfKHO5D5FIk25PfDtPpwqdUYIlA8OT2kDqw0YtehPvLjDLraESJUwj9JrVdaZVSNDG44V41tHL7XYcLmTvNmNZ5RVKqyUNVaKtkM7cspwz9fqixUE4ac2Lne9Ar31xnP3oPf0uuaKHAjaW/Bqk47CU2RGKsYDabcgeGMJKWYhFehpdppKHDAxYuX5foKYqjFaAjIz17y/DQtXuvrABYLW0PeThBVBtEJemDkIt12UjHEp7dE1Xu4oPlKRXmwI7mq4/jXe9Rvou+tdm+9KLR7+MbKrz/et9rGvYX5FHR77IRP1Zit6PbkKb//puwu+1fEAzDc4IlYO089wR52RNLJx/UfbVevF5i3bL/7cp4N8dZe7hscWm00irDynApVc+e4WrDlkd88XhZXHGiv5mL09jNw9eMe9usHta7ytxJRSXmNCeY0Je06V44P0E95f4AEhYnXCkXqT6jtPtWRSwRzIiJUwt8LnRph6T7qzIeLA22pWgYP5lWKtpsbXiJXj37vBaGl0bVWwBLcghklYEQQRFNpFheK5qwYhRKfBuJT2Mhf4vkmNX5YsFJErae+jn5bU4sHX1wBAjYeIlZIzZbU4VWpP09WbfX/IK/8wCxErocm29CEkFCR7QunN5S5iJauxUkSsvL3vA/mV+NdXe/DHoUKfXOTd+ViZrTYYjBYXw0h39g75EnNX5UILox9zPvzFtRj+4lr8/YNteGX1UZwzGPHGH8dUIzfeojnS+VY+xD3V9knr8vzRVT/vy8faw+7T4sLvQ/i/GLGSjC0QYkNIPRdW1qPK5P56V7y7BZf+x55eFcSIyeq59lIQYDVGi0/O8bUmi9vooFSkKz9X3iKnajiXAgjzHPwuAo2FhBVBnAdIe6YN6+b0gBLSgg9P6SsWvvtCcjvXHmztIkMQHyEvlv/3lL6ybcHIVBqxSozyQ1gZfS++L6isx7S3Nvl8voDygSAIK6GA25MZpi/XcycMrB5qrJSpQHfc/dUeSSrQQ42VG2F15393Y/CCNZjxtnyRgvI9zFy8FZ9tOSnbd+GidbLteot/CyWkPP/rYbyzPhsbs1yX4HsrtJbq2F8y5CtURUsNG8cvGfmyh7AsauNHyGrusgzc9aX7RiJS0SrcW4naR+J4UTUe+X6/qlBYmXkWyU+slPnSCRGrMf/3Jx5c76z7W3+0CAfzK2WvFyK/QtrUW8RKaCFVY7J6jAidKq1BZl4FBj67RvQ3UyL9/Smjmu6+dPhDQ81zmxISVgRxnnDr2B4Y2CkGfZOi8OLMQdBqGJb/ayy+vXM07pvYB5/cam/f2SUu3O01LhvSEVufmIRbL0rGm9cPlR37Y954JETKhVWfDlG4uE87cVsQeFJhFRshr0HyhPBACHETMZs2yPd6LXcoo1sGh5jTOwq4n/3FvS+VkrgIvUtfQ4/F67XqqcBqP5bfm21C8br7c9xFrDZmlXi8psD+MxV44bfDLudJIy/uPM8A75EJoZauvMYpMF/49TCSn1iJTyWCrrrejMy8Clz34XaxzZM0YjV3WYb8fTgWJCzffQYPLc3AN446OEBZY9Uw5v+UiblL7TWJO0+W2cWbwi1fNLGVvE5NbD/w3T78sCfPpR4wr7xWXOko7X/oTlBIrUKUKAv93SGIobWHixwN09VTmRNeS8dV720F4H4FofTzr7yvp2jrOYMRy3adlu0rrqoXSw2ELxJUvE4QRJPx/MzBWPXQOOi0Gtw8NhknXroM4SFaXNQnEVoNQ5e4cPz2wCX44d6xeOqyAUjpECUTSk/M6I9FVw9Bl7hwMMbwt+Hy2qjIUB3iFcKqqt6MMT2dwkpYISUlyk29lho1Rgv6JUUja+EM1eP+RN2UTOjb3mXfa2uOotYhanTuXFc9EB8R4vrwcBuxssmK1202jjfXZqGoql600/CFWg8izJ2PlbeHkbQ3o6dzjRYbDuRVoriq3mP6Vc2QVIrwwJfWlgmeX8KDHQD+9dUebDxWgp25ZXjnT3sqTOtBUQoRkexiu+O/NJIj/T1pmH01YXaxf9HJ73aewc+OKNl1H23HQ0szXCJWas7ramk1nVZdKHy9wykupO2gGmKIKm2M7gm1qNlXO06h2EvPUjVxbfIUsfLwHia+lo7HfzyAwsp6zPlmDx5auk/8Pfp6jZYCCSuCaEMM7hKLTrHhuGt8L6x9eAL2PjMFANA3KQr3TOjtIpz++89R4s+hOg3iHdEnxoCJ/dpjxpBO0ErsBpIlPlxd4sKxff4kaCSC5dVrL8AlEod6JXVmq+hlpYY0rShNebpDWit22ZCOeO7KgbLjizecwE977X5XWUUGrPNQS6NGbLje5Q+9uxoQs5WjzOGsX1VvxuGzVXj7z+N48Lt9OK2wdPDEWUcTaU/u4bVGCz6XmZNyfL3DtV2S9Dhgf1AuXOkaqRKoM1lx5XtbMOXNTR4jVlKxoFZfJIiK6nqzyzEp0obXVh8idcLvQhCw3+/Jw18ODyi53QLDbZ/vwuQ3/E8lK3HWWAnO/q41VmpiVbAWUYrgEMm/pyJJw3BfBIVyroUrKUXZB+knsFASkVRGXQXUhI2UyjrX3588FWhVHFO/T2WtWYza1putWHWgEL9kFMj+djhXXwa2di0YkLAiiDbOzicvxf/mXKx6bELf9nh0Wj8kxYSCMSbaPcwc2hmf3z4KMWF66CXeUz3aRWCBQ7xoNQydYuVpx+tSu+HrO0fjqztGwR3KP9ZPXub0cOoc54yIJcV4r90K0WrQz1HMH6rTIirMNS0pTcXcKaml8aXXY1yEXFhxzvGxiv0EYBeN1UYL2kWGgHPg5Dn7qrbCqnqxCN8XTpTYH3ae7BY+2HgCz//qfHBabdyj15nJasPu3DJMeXMjPt+a6/a8d9bbo0aVdWbkOuwu1JA+/DzV7HhLgdaarPjP2iyv1xHv5XhwSxdP/HnUXsflrdj+9wNncd2H290+rKskIlB6jkWMWDmK19VSgWriUuOMWFmsNvF1eomnmFRwbztR6nVFpVTA/bQ3T0z9KqOqr6w+ik8cKdeVmWfFaKCSUC+O/ErbEECe7lNGrHbnlok2KQKfbz2JoS/8IW5LRaA07euMCDqP7z1dgeQnVmLvaWfKtCVABqEE0cbpEOOavpNy38Q+uM/Rgic2XI8/5o1Hd0mx/MzhnbHI4SnUOS4cF/dJxPDu8TJ/p49vSZXZQoxLcablEiJDZDVZSpExvm97vLTK7j7ey+Fyrdcyn1bHVRstYo2XvTjdj6LlyX1laSk14sL1sodWdrEBy3fnqZ4rrKrrHBeO0hqT6B9WWFnvV5rnt0x7zzTG7JGZzcdLMHNYF8dKLUcRskKwVNR5Xqpvsdpw7Yfbvd5bKroeWprh9jyp5YRapEVoNq0cpyeECJmn1Womqw21JgtKJT03TziiLtKVcWqfAqHJeZ3Zqmo3clwiwM9KIkkWRSrQGbFyvlat5kzqmzbixbXoEBOGeyf0lhWsCyIkTK9BvdmGVxQu/EoRKBWfDy93WoJ4qrGSOtcr8ZZCnvbWJhfj0lqz83eqFLPCHEtf8+cR+QIG6VilES/hsy2Neq10/FvYnHUOI7o33mYmUJCwIgjCL5R2Dh2iw3Bs4XSkHysRG1Er03SeTELjI/QyYSUUzX98SypqjBaE6ZzfmrvEh+PTW1MxtFuc7MHhCaHhNcAQHSb/k9clLlxmKSDlH6ldUVZjkhVTK4kI1cmEg6fUiVCvMmVgEg5Ian+Eb/XDu8dh3+kK1deqwRjw7+UZ2HCsBCO6x2P+TwfEaJ+0mTcArzVcgV5pJfXRkj4odRoGi42LDaAFgeULR85WYUdOKcxWDsbUi6t/3pePL7fLU57C/Eof8p7EmaHeIgoraVpX6FcJyGu3hHNcaqwkMSs1gSJERFcfLERVvQVV9Qb8+3v5Z1poH5UQEYKCStd6J5cOBG4Eeq2fra4EPKV7BTjnsrS0dIWrN5sHwFV4S9+TtI5P+BxJ51IQof4YGjcFlAokCKLRhOq0mDaoY4Ne++7sEaKpaUJkiFg0P2VgEq4e3kXWIFiv1eDSAUlIjAoVUwKPTe8nHv/yn64pxqhQp/mhMn2oNFeVEhGiQ2cPKygBe6pR+iBYd8TVPkDgGcdqw1E9E3BBV1d3/FE9E1z2eYKBocTxYPl0y0lRrKjhTjwKBLogWPrwkxaoK9N5K/a7NvR2x4mSGsxasgMWm83tgohdua4pISHqIRUdniKEVRJh4K7o+1CB0wdMEB/Ce1MrXldLBQo1Vl95qH0rdXzhSFD0BBXHp4gIuYviqtVC+YIvJrDKRQxSk19fvM5KFX0zTTJh5RRmJoWABZzWKMK/8ZZCyxoNQRBthmV3j0F2iQEDO8fg27tG4+ZPduJRiUgSiI/Uo29SFG4c3UO2P1xv//OV2iMBP825CJzDRbC8M3s4aowW/JxRgE6xYejTQR5tU1vFCAAf3DgCUaE6eCuzCtVpUF1vwaTX05HjqJmKCdNhVM8EmcjqGBOGQkfEql1kiGrEpFu8q3cYAKR0iMJxlUgYY0BUiD3NKW33o0ZWkeci5EAIK2laSmrf4Kubvq9YrByRoTrV+iyl8SngfFBLH/KeVjRKhaC7iM3bkpokwXtNGVGR/obV7Jv8WYWq9I8TUBaHu1s44c613Vvxty8Rq8o6s2iuCwD7JAsO3Akzo8WKUEckutQgN56Vil7p/YWVq1JxLhj4bjpegusv7OZxQUdTQhErgiCahdG92oliKVSnxfJ7xuLCZNeoTahOiz/mTcCtFyXL9r90zWA8dGkKUnvEY0T3eIzsEQ+9VoOtT0xCx5gwbHw0DVcN7YxZF3bD6rnjxDRlWj9nfVd3ySpGwOlALdhQXH9hd3x+24V49doLAAB3XtJTdr5QaCyIKgD4z3XD8PEtqZg8wJn+/P2hceLP8ZEhkD7/erWPxJSBSW4bId8zobe4GrJXonO8Ns4RFebbd2M1wSHFl3o1f5DWWPlTmO8LNSaLX6mfOsfDWVpsLxUk9WYr3lvvFErSlYr1PkRsBCEmRqwcYsVkseHnffmw2Tg+2uRadO5r6xgNc+1H6Ry70oDTe8RKKqbUGqjLr+88rhRh0Y4okTIaJi2El67qlCL4l5mtNpcuBMt3nVG9vyD+1T6rqw4U4n/78t2/kSaGhBVBEK2SDtFhmDelr2xJNmCvm9rx5KXo4RBNjDH07+j0v3r7+uF4bHo/fDYtAu0U9hL/nmqPmAmrH8NDtJjYvyBRqNUAACAASURBVAOuS+2G3Jcvx0OTU8Rztz4xSbaCS+CCrrFgjImGrAAQFabDNcO72K8drhcfUvem9ca6eROw5OaRLu9D4LIhncRakl7tnX0JrTb/oh5qD+duCeGIDNGKK+cagxAtePfP4xj/2gZxf2ae/OEa5sFOwx0pkn6MpQaT6upOdwgRpd0Ss02pIPl6xym8/oez16RQI7R89xlc8/5Wr9evdZid7jlVbu+1JxE3c5dl4EB+pepKS2+mnQJRoTpVw1zOuUwgZuZVuI9YScSPtN7Km0+VEHG6/qPtLqtK37txBADPaca33aw2FOZMqK2cN7kv/jXB3od05YGz4nnS35PTgFX9PSpd+JsTElYEQbQpYiP0mJPWBxrGxNqMXu0jseXxiZiT1hvr/z0B/Tqq91uUPuC6xIWLtS+CX9a8yX2RpLLKUqdheOXaC7Dn6cnQaTViumjKwCRoNAyMMagFrML0GoSHaMUWQ1ILCIuj79/w7nH4/PYLvb7v564a6LLvtWuHYnAX13ovYdXnw4qWRZ7ILjZg3+ly0R5B4H2FRYCaGPWEso6t1mT1WBunRIhASQv46yXiQmjmLJ5vtKC63ozHfshEUZU8TSXlot52Y1xp1Gfesgz8pIicHHYTLfS1iXV0mF41mmm2ctk1rnpvq0efKAFpqtNb7Z0QMfrrZJnMxR6wF9QDDavfeup/dpEmrJTt1zEa16d2c3t/wBlldBddfe+G4X6PI1j49AlnjE1njB1jjGUzxp5QOf4wY+wwYyyTMfYnY6yH2nUIgiBaEsK33/4do9E1PgKMMVlUSInUswsAZjvSjDufmow5ab3Fb91KGGPQazVo50jpCWmgaEnRrVbj+uc4xhGZWX7PWKy4/2LEOawjQrQaHC82YPuJUkSF6jCxXwdZI241hneLx+bHJkpWSQI9EyPR1VHbJR3LpscmYv+zU3H/xD6YK4nSKfnlPrn/2boj3g1WtX463IfpNWKEQyBOEX27Z0JvHH1xuurrDUYLOOcyH6o6yQP7u51ywVBSbcS8Zd5XnAqpaWlz6rWHi8SVnV3jw9EtIVy2AlSKN6uJy4d0AgBEh+lUxegH6SdcaqyU2wKlNSYxLSetefO2WtRosalaRUSH6sTPYlmNUfWcEd3tK4MNKu9ze04pzFabGIltHx2iGpWrMVmh1TCk9ogXI23u3mOkHx0ego1XYcUY0wJYDGAGgIEAZjPGlF999gFI5ZxfAOAHAK8GeqAEQRCBRrCOuGm0b98FhXTdDaO7AwB0Wg36d4xBbLgej03vL1vB6AnBoV5a9Cuk9UZ0j8PNY3o4fravluwQHYYLusbhycsG4K3rh4mvsdi4aCHRzs3KMYFOcWHolhCB9o5G2e/MHo6kmDD0TIxwvBe54ImN0EOjYeJDXXhQCnx080gMVdhqRCtSdLeMdZ1XZQrsfodHmjvCQ7QudgFxEXqsmTte3B7TK8Ht3Nu43bbAYLSIYsCT2ejXO07hr5OlHscEyF391RjZIx51JqtLg2QBb5EeoY7MnbB6c10WjpyVt+SRXlP4PQsCbatj1ahU0OV5EFaM2Wvzrlrs2ocwPjIEHWPDwBhQUFEvthKSppuHOz677sTb0l1nRFuQxKhQVWG15lAhwnQa8TNQb7binq/tXlgRIfLft7tUenPgS8RqFIBsznkO59wEYCmAmdITOOcbOOdCheIOAF1BEATRwhnePR7HFk7HRR7a7CjJXjQDi64e7NO516Wq/yl847ph+PCmEWK0CACGOFJyd43rhRevHowv/zkKbygaYUeH6XH18C746JaR4r4O0fbUY7hEWNx2UTKuHdkV6Y+kifuEVVgdHSshIx0PpmRHQbyyiFhAiDDFKVam9XekS6XiqVyxdL5dpKs7vlJYjUx2Gjv+5x9DlacjIkQnCithQUBceAj6dYwWV4EKvlNvzxrm8nrAHkni3C7ihBSekuR2EXh8en+crax3u5JRSMklxYTKXMHViA7ToazGhKNn1fsRnlXxpfrwphHiz8J7igrVuS10X3OoULb95TandYMQ1esUG4bYcD0KK+ux+mAhZi521o3tz6twESgCoToNfss8i4P5rqnM+MgQ6LUaJEWHIbvEgDcc6V9BzAH2LgyAvam0Gs/8fBCPOHy7EqNCEap1HcfJczWICtMhIkSLOpNVFhH1Jx3c1PgSO+sC4IxkOw/AaA/n3wHgd7UDjLG7AdwNAElJSUhPT/dtlI3AYDA0yX3aIjS3wYXmN7g0xfxelghcNj1S9T5hANLT5c7uX0yPBEqPift3uqnHZQDuGhKCjw+Y0MFciPT0EtnxtBj7du7BXXhqdBgqjNw5hlp7+mXLnkxoi/Q4V+mMBr10STjqrVw23lO5dsFVVS6P4mTu2YmTIQwTYzgmTovAvPQ6/JGRKzuntCAX/eI1mNknBP0TNPjnmlq0CwOKJM/aE0cO4LmxYYjQM7SrzsbAdhocLrWhcxRDgYHDYqzD3ReEYlWOCTWV9qhLwekTSE8/jdHxZmTmAfnHMpB+SgPXajFAx4DVu+3zWXDqBCornKKJwW6LcF1fPS7rxVBW795TCgBS4oB/DY2AXgMcyMz0eG550VnYuN0+IEQDmDyUVGkZcN+wUISdc34ezhXaa7VqKstwpLJM3J8UwVBUaxdam4/LvctWS4QWN9knubAgD8xmwVc7Trl4Zm0+fg4Xddbhb33C8eNxE3actX8WHr8wDO/vr4e70nZeV4309HREaUyi+zkAVBuc9WrFp7IBAGu228XTpG46rD+jLlh3bd8Co5vaKZ3NhOryUpRWWXHwoKRNU71csAXy33Jj/zb4IqzUZLnqDDDGbgKQCmCC2nHO+RIASwAgNTWVp6Wl+TbKRpCeno6muE9bhOY2uND8BpfWPr8TOMesczXoLakJWxR+CoM7x8pSdGmK10X3LMPWD7bjxqlj0KdDNAxGC57bvgYAcMMVk1zuc2Z7LnD0ENolJgJF9ojBQ5em4IrJKTLfoIgdf+KkIgrTvWdvvHCLs+7s46QiDOkSizWHCrFghd0wddLFo2V1baljLfh5Xz72na7Aj3vzcPO4vrh7fG88CeDhZRlAQT4GDRiAtJFdkQZgvo3L0kDxm/6QRd+6JERgZ6H9ITxq2GCcMJ0BSksQIUkx/uvKi9C7fZS90D3d2bdOynWpXfHslYPEBQ+cc1jjs8VojZJB/Xph5Um7UJo0oKNM9CiJCdfj4esn2zdWrwQAXDi4L37LOYS4hETUmCwASnHTmO546rKBmPPxOpysDUGuw8ri+asGifMp0DWpHbIrStCvd0/8lqM+RgC4cswA/GNUdxSsO44dZ7Nw85geuPfqwbAlZMtaOs2f0R8JkSF49IdM9OnWEWlpw7CiKAPZkmL9D24bi2ve3wYASBszAu9lbIcpoj2AAtx3xSi8khCBCxetcxlDWlqafUHH2lUuxzq1i0PPjtHYVnAaW0vDANi/GCQlxuNEZansGoGisX8bfEkF5gGQlut3BeDyPYoxNhnAUwCu4py7X0pBEARBNBrGmExUAcCNo3u41D0pGdkjAbkvXy6apQpCoaObnpFdHCmXQolomjelr4sZ47s3jICSS1LkKdYpA5PQMTYMt16ULBbRK20gokJ1uGlMDzw8tS++/Oco3D2+t3hM7Ru9srZmxf2XYMnNzlSptMYnJkwv1rJ1T4jA05cPQJe4cNEfLDJEB3cZvrG928kcvhlj+PtIZ6pXWSsm1JuNSk6QpcgA4Nu75Ekftfoiwby2ut7Z+3DWhd0RHqLFrYNC8fxMZzparSG58NlIig3DK38fov6mAPRxWFnodczxvuz7/3lxT3RPiMDHt6Qi9+XL8a8JvTHC0SHh+gvtkqBPkvzzN6J7PN67YThuHdtDTHMLJp7dEsLdph0B94sazDYuprmFhQD/d80Qn+sZmwNfIla7AKQwxnoCyAcwC8AN0hMYY8MBfARgOue88YYoBEEQRJPx578nuDWh7OlofH2mvA7PXjHQbfuQkT3ikb1oBj7efBKvrD6KAQkamX+YklmjumPJphzEuLlvl7hwdFG0FJo+uCP+ty8fQ1VaAgl0S4hAN0mT8HC9VnRpT4oNE4VY94QI3DmuF+4c54yoaTQMUSHqru5qBeQJkrqzBy9NQWJUCEprTPhpbz6qHIXkg7vEuiwM6KMQxGrCKtEhxqqNZlwzogteWnVUrFsC5Gax7aNdRfGj0/phUOcYXD2si/19hepdGi5HhGjFBRJKS4fwEC02KVaa9m4fJWug3C/J1Zbkigs644oLOourAY8WViNMr0H7qFDV/o4PXipfdZrczv77E9KcVXVmmRv75AFJmD2qOzZlOdPfwqKEloJXYcU5tzDG7gewBoAWwGec80OMsRcA7OacrwDwGoAoAN87vsWc5pxfFcRxEwRBEAFCGfmSIhQJj+6ZgH8qnOeV6LQaXNInEa8A6BzlOSHyxPT+eHhKX798raYN6oishTNUhYiSDtGhSIgMwZvXD8OMtzcDAHokRIju+lKRIkVNVAFApIqglK7qDNFpcNvF9vn599R++N++PADAxP7tXXoYxiuMaUN1rtEXwUvs0v5JuHt8b9xxSS9ZVCdBcg2lKP7fnIsQptfimhHOiJqy3dPL1wzBtSO7itdMTU7A2F7t8PcRvq89m9C3PV6YOQjP/nLI5ViEJKIkWJkwZvdGK6sx4Yttubh8SCeZV5pUtGUXV2PyG5tQUWsSRerr/xiK/2fvvOPkqso+/jszW7PJpveekE56IJSEDEVFVAJKCR0svFJE6ouAYkBQfEEFFEFQ1CDSFRFpQjIJnRSSkEB63fS62ZrdnbnvH+eeueeee26bsju7eb6fz3xm5pZzzz33zj2/eZ7nPOcc00ooBg9cd8pRuCrmPbK0uQmU+MEwjNcAvKYsu1P6fFqW60UQBEHkAYXRCN679eTUtDp+jO3XEX++4hg0Vjg7W5lIhKEkEt6dE0RUAcAndzi7pYJoJOVWG9BFL6x03PKVEZgxrLt2XY8OxfjG+D6O5WdN6Itx/TphaPf2GNGzA+av2YPHLp6EjqWFDjH5zUl9U59fuuoE1Dcm0K19MT79yZdSokl1lclutQ7K1EYi27yMOopu1rEDbN8n9O+EZ648TnuObhREI7jkuIFaYSW7aMukul536jC8uXIn/vLBJozv7255FK7EYwd3SU2MXa6ZwqlLWZFN4OYD+ZNRiyAIgshL+rlMEO3GySN6IL7jc/8Nm4m3bzwpNT3KajPmZ3Qf904dsALCB3Rph2s8cm3pBBxgj4HrUV7iSKYKAOP7d8LT351qEx6TB1rpJ1TLllq+oGd5CV659kTsqKzH/zy1GMN7Oi2QjDEsuOVktC8pCDXXoh+MMdxz1tGYOMA9tm+Y4jL88uie+NNlU3DyiB6u+5QURvHfG05C386leGvlLsxdtRtjJaubcPf2cIkNbElIWBEEQRBtGhGoDwB3nzUGn2456Eh4Knjg3PFoTCRx9sS+eHbhVvz0G86pgLLBqp+djqiUgDVTxvXrhHH97O40lQEu7s9Mufg47wS7d505xvadMYZTpUnK3RCC7KyJfTFzQh+bmPzhqcNw1sQ+2jivloaEFUEQBHHEcPbEfjh7onsc0TnSSL/Xfzg9Z/XI51Ft2WJU73J0Ki3UxqeFRR2FWloU9Rwc0ZKQsCIIgiCIVsjSO7+kHWmXL+RSmOYzJKwIgiAIohWiTjNE5AfZce4SBEEQBEEQJKwIgiAIgiCyBQkrgiAIgiCILEHCiiAIgiAIIkuQsCIIgiAIgsgSzGihsZqMsT0ANjfDoQYA2NIMxzkSobbNLdS+uYXaN7dQ++YOatvc4ta+Aw3D0M9tJNFiwqq5YIztCdIQRHiobXMLtW9uofbNLdS+uYPaNrdk2r5HgivwYEtXoA1DbZtbqH1zC7VvbqH2zR3Utrklo/Y9EoRVZUtXoA1DbZtbqH1zC7VvbqH2zR3Utrklo/Y9EoTV4y1dgTYMtW1uofbNLdS+uYXaN3dQ2+aWjNq3zcdYEQRBEARBNBdHgsWKIAiCIAiiWSBhRRAEQRAEkSVIWBEEQRAEQWQJElYEQRAEQRBZgoQVQRAEQRBEliBhRRAEQRAEkSVIWBEEQRAEQWQJElYEQRAEQRBZgoQVQRAEQRBEliBhRRAEQRAEkSVIWBEEQRAEQWQJElYEQRAEQRBZgoQVQRAEQRBEliBhRRAEQRAEkSVIWBEEQRAEQWQJElYEQRAEQRBZgoQVQRAEQRBEliBhRRAEQRAEkSVIWBEEQRAEQWQJElYEQRAEQRBZgoQVQRAEQRBEliBhRRAEQRAEkSV8hRVj7EnG2G7G2AqX9Ywx9jBjbB1jbDljbFL2q0kQBEEQBJH/BLFY/QXA6R7rvwpgmPm6EsCjmVeLIAiCIAii9eErrAzDWABgv8cmMwHMMTgfAejEGOudrQoSBEEQBEG0FrIRY9UXwFbpe4W5jCAIgiAI4oiiIAtlMM0yQ7shY1eCuwtRWlo6uX///lk4vDfJZBKRCMXo5wJq29xC7ZtbqH1zC7Vv7qC2zS1u7btmzZq9hmF099s/G8KqAoCskPoB2K7b0DCMxwE8DgBTpkwxFi1alIXDexOPxxGLxXJ+nCMRatvcQu2bW6h9cwu1b+6gts0tbu3LGNscZP9sSN5XAFxqjg48DkClYRg7slAuQRAEQRBEq8LXYsUYewZADEA3xlgFgJ8CKAQAwzAeA/AagDMArANQC+CKXFWWIAiCIAgin/EVVoZhXOCz3gBwTdZqRBAEQRAE0UrJRoxV1mhsbERFRQXq6+uzVmbHjh3xxRdfZK28lqSkpAT9+vVDYWFhS1eFIAiCIAgNeSWsKioq0KFDBwwaNAiM6QYbhqeqqgodOnTISlktiWEY2LdvHyoqKjB48OCWrg5BEEcS/74eOPqbwOCTWrommbPyn8CulcApP27pmhBtlLwar1lfX4+uXbtmTVS1JRhj6Nq1a1ateQRBEL4kE8DiPwN//UZL1yQ7vHA5sOD+lq4F0YbJK2EFgESVB9Q2BEE0O411LV0DgmhV5J2wIggiCxgGUF/pvU1TA+80G2qBRGN2j99Yx8v3ItHIj93SqO3k125HGkJYFZSY1/Ww/z71lfwe9CKZAA5XZV6/dPG75w9X8zr6kYvfD9GqIWGVAe3bt2/pKhCEnrk/A+4b4C0S/ngqcG8v4Oe9s+/mubcX8NiJ3tv85Wv82C3Jhjhvpw1x/n35C/z7zs9aslb5RaMpfqPF/Lo+cqz39gc2A/cNQN9tr3lv9+YdwC/6+QvwXOEl6gwD+EVf4JUf+Jfz897AnLOyVy+i1UPCiiDaIh/+nr97dVo7l1uft3yY/TrsXeO9fuvH2T9mWHaYbbDmTfP9Df6+u22MJM4KQlgVFPH3A5u8t6/kU8d23/Oe93bLn+Pvhw+lX7dMqD/ovk5YoJY+HayszT7nShxR5NWoQBuv/ygr/xpLE01A1DzNXmOBr97nuu2tt96KgQMH4uqrrwYAzJ49G4wxLFiwAAcOHEBjYyPuuecezJw50/e41dXVmDlzpna/OXPm4IEHHgBjDOPGjcNTTz2FXbt24fvf/z42bNgAAHj00UdxwgknZHj2xBFLk4iL8XHHHOmUmdN+Ve/i70mzQ43k76Ox2RHCys+1J4gWAwAiSR9LVFEZULefC6uybhlUMCwMgAHUewi6xppmqw3R9iCLlcSsWbPw3HPPpb4///zzuOKKK/DPf/4TS5Yswbx583DTTTfBCPCAKSkp0e63cuVK3HvvvZg7dy6WLVuGhx56CABw3XXXYcaMGVi2bBmWLFmCMWPG5Ow8iRxQux/4NOC/Wz8ObAK++Hf4/T57EVgyx74saGfoxrq3gVWvAav+471dQw2w6M+ZHW/JHKDugH7dR49512HrQmDrJ8GOs34u8MZt6LkzDkSifFn1bv4uLBUr/+m+/+f/Ag5u4Z8PbgXevgt47RbgjduBT57gbdBQCyz8k709KrcBK17in2v2AUufsZdrGMCiJ3lbAsDSv/P7SqbuIG+nXZ/ze+STJ4Bk0lq/+nVg7zr7Pjs/A9bPs743NfD9Ek3WsjVv8TJ1iBir2r325dsWA5s/sNdt7r3A27MBAOVV63hbzfs5UL3Hvu+m91OWLYfAaahxtl0Y9m8EPn+F123bYr6sYhGwybQqFZebx60E9q3n97fMxnf59RQc3Op+b8ttKG+/4h/6uh3abt0Dgk//5rzOQdm6ENjyEf+8ZA5QWeHedg01zvtFx5I5/Fpmg40LgHd+5rwn3fji3/z6ubHrc36v5jn5+7fMw7IUhroQeawmTpyI3bt3Y/v27dizZw86d+6M3r1744YbbsCCBQsQiUSwbds27Nq1C7169fIsyzAM3H777Y795s6di3POOQfduvF/aF26dAEAzJ07F3Pm8E4xGo2iY8eOGZw10ez843tchPQ7Bug+PLOyHp0GNFQBsX+F2++l7/D38dJkCYbPQ9SPv33L+jzbI17rzTv4kPxO/YGjTgt/nP0beDzLZy8AlymismYv8Mat3nX402n+dRQ8dTYAYBQA9LuLLxPxNkJYffEKULUT6KD5nT9/KVDaGbh1E7DiReC9X9vX953EBcX7D3FLzGjTwv2nLwOHKoDRZwEvXs47nYHHA50H8fXr5wKv3gDsWAaccB3w8lXAkJOBS1+2yv7PTfyYMu17WMd4ZpazHR6bZl+24H5gwf8BJR2BcefxZX8/17mfwG2AwROn2PdZ/TovV+b5S/n7pveBKyRh/JczrM9qHOB/bgKWPQP0GAUMTMNq/9g0oKHa+j67kscTis+lnYDDlVwoPnU2YCTs5/32bGDbIuv7g0fz984DgaGn2I/VqGmbx6ZxN+PR33Su+/NX+R+nUWcC0UIuOP51DS/3Eg8x74a473+wxB4P1mss0F+JhXvnZ8DHjwId+wMjTteXt2slL2fVa8CFz4avj4qI3Xz/IeDOvd7bAsBzF3OL509269c/ejx/D/I7b0HyV1i1EOeccw5efPFF7Ny5E7NmzcLTTz+NPXv2YPHixSgsLMSgQYMC5ZJy288wDEqb0Bap2snfEwFGTPnRkOFIKdvw+GZyBQprxuFq7+3ciJizCWz71LkukcPgZhHfIyxXSWl0l5coFZY1ncWieo9ldaqWOohDFeY+DcAhc556eYSdEHe1+yyBd2ib/ri2ZSGtC3vM+DHh7vS7ZjrxoENY8XRU7XBfp8ZY7VjG3wtLgx1XpcHjfAwDKO8LHNzMLUuGOepPDhlxO48ajTDQpaIQsVuGAajPehGflkxwYSV+n35xa36oFqpokXObqu32+ulgphNr/4bM6qPWKRli1GQ2nqEtDLkCFWbNmoVnn30WL774Is455xxUVlaiR48eKCwsxLx587B58+ZA5bjtd+qpp+L555/Hvn37AAD79+9PLX/00UcBAIlEAocOtVBAJ9H6kTvCTF2BQREPZBjBhqg7MOupE5VpleeCah0RAoiZwkoeNi+WyajtmdQIq5rdQLFpJdcFZicarPaylSc+M0noKeeuExtC1Aa91lVmPFmJ6RKrrPDePmgeq8otQPue+nVRj2m41GsixKSu/dNBFr91B3hsF2C5IgHrOjXW8+unQyfYvESnlzA3lOvamGHiZ4cY8bgXRDyhDiHIspEGo2aP/zbp0lIjSQNCwkphzJgxqKqqQt++fdG7d29cdNFFWLRoEaZMmYKnn34aI0eODFSO235jxozBHXfcgRkzZmD8+PG48cYbAQAPPfQQ5s2bh7Fjx2Ly5MlYuXJlzs6RSJM1bwK/Hm09BP/0FeCD3/HPolNz69z+MIPHNwjqDwEPjAA2e4zGS9eNJz/sHz0eeNmcI72+EvjtFOD1W/X7/d9QK17DjUQj8NB4ewxY5Tbu+hJ11pX/1Nnu2a7X/hf47WTre/yX/P2N24CXvmcXL3L7bv4AmN0R+NUofblPng58/Adg9yrgl4N4fIsqIoS1puIT4JGp7vmI3v01H1IvC51nL3J2kADw6o2WsFrwK+CPp/FYGEGi0RJWv58K/GoksGeNdW4b5wO/m2Ker1n+J0/w+02IApmDW3lcyv1HWcseGMGvtdw2yQTwu2P5uQKW4JAFxn/vBF76rr18nXiYLYUqzJkJ/PVMXo+O/Z3bAsCeVXyfXw4Clj1nX/f2XcAzF/L1sztaQmvp08B9A/n9tPQZ4JHjeIzNQxO4VfC/P+Xb/+4Yfh33rAF+c7Tz2L8ZbX2e/0tg3X/554PSeQuXpZfIfPUGHrv17EX8OA9NAB6eYG8TuV3u7sK/PzCcPyd+KU1F9utR/BqJ61y1nV+r/Rus32hjHfDwJB4ft+5t4OGJ/NmzbQmO/+By+7HU3GKPx6zru/TvfFvxG43fx8MNHhoP3D/Muh4b3wV+O4l/bqjhrt17+/B9H5vO69KgBPXP/z/+3JjdkZ/PJ08Avx4D/OEkpxVOxHbNmcnv1fuP4vuJ56Ic+/XgOL7u7m7AF68CFYt5uYKXr+L1/vVo3l5v3IZ8glyBGj77zBqN2K1bN3z4ob7zq652Nzl77XfZZZfhsssusy3r2bMn/vWvkDE1RPPyxo/4v+lD24CuQ4GtH/HXCdf677tjKX8d+z3+veIToHonf9DLMTQSTNdpB0G2MNRXAkv/Bpz1CBcW+9by11d/6dyvdi+vj1usRzLJ3VQHNnHxMMqMn/hcum8NA1j4hHPf9XP566RbnOv+caXd3bfVFHcfmSkjZkhCLdEAFPBRZ5j3c/4uXBwqWz7kr8lXcEvF6teAnkrHe1iyluxZBfSQOmG5/d+5y7ls1atAt2HcsnLy7Tx3GMDdHoWmAGqsASoWAvOlmNFEg91FVLUD+OgRax4+2YIjxPVrN/P3Hpo/dpVbgUV/sgeXV+/kL5kDm4C9q63vwj0jXF8symNhAOBbf5Tq6+PGETnAugwBWM+KywAAIABJREFUeo+3xyep1B0A/vk/9mUNNcBqzcCELR9yt9Xm94GPH+PLFtwPHNgIrH0LeP9BvmzvGv6b3PqJXSQKqncB/Y/j95UoB7Bvu+ldc5mHOxMAXr46vKu+ehfw1h32ZfWVABRLXdV24MNHrN/o2POA/euBt3/Kr8H+Dfxcd61EcYPiEtZZRg9t43FhL19lLTv6WzzPmHyNNr8PjD/fnn6loYoLOjE6UqzbuQIYMNXabvXr1n1XvRNY9ix3eR+q4AMDZGp281G4G+JAn0mWReu1m/lzUf4DddD0DCUb+XOj19G8zGgR//2seBFoqresm/vWAqf/wtkGLQRZrAgiG4iOMmj8nPjnp7NAiCLTtlhpXDcNNXZrSzrutWSTZVVxO8906lynjIhS3Wvyd/kfM/N4fMmuAiEMxENZRnVDyXE0ujZSl4lYmZNuti9X6yZGogFmHZT2q96jt3aqI7jkNBDdRwIjv84tL0FGlakdnWgXITDcYpqCCvzKCneLlYx6z7u504QVRm5zcf1U11djrV5UAVwwztKM2D2obN/U4FymEiZWSD5+UPZIwlcIlnZdLRdr1U59zKEYdSlPLH1I+cPRfSRwzpPA5Mvty4Uos1mGk3rrnfpbVdtcdiEK4fQl8w/Hwa3Ws2nM2XxwgozOrQ5w8ST2O/O31vIgMwC0EGSxypDPPvsMl1xyiW1ZcXExPv44D5IfEtklm/FKKWHlnr3f02JlGMDetfoRiKq5HuCd6kEpPlB+gAcl2SR1soy7XroPhy2eI6iw8urAVDEhP3Ab67jbq0NvKw5JZt963jZysLToiLTCSvmnX7vP+izORa6rGp+STOhzXjUp4rZEElZNDU7htXE+72xUDlVwV2tqX6kzYRGg0wBuOWvfw7mvyrK/27/vWcXbSwQqyzFEO5Zz0VnaKXiy1EQDr48fhe2UeCWX39WeVWa5kpgR+6lupoZa93uqoISLk4JS+3VRLU8r/2Gl2ejQR28JTWfqmo797L89L8Q5A3yEHgC062bdY5ve1cd6ibiwImkE/Lq39b/HTpL4LSjl13nrQmtAhUAXxL9+LrdMdhvOLUZqHJVsERXreplW4vXv8H0AvYh3E1YHt1ojU+Wg/KYM49JyCAmrDBk7diyWLl3a0tUg8oWgwiKQxcpDWH38GHdNfudtoP8x9nW6wNM5Z9pHlIlhy2FINlkdS/VO4JFjgEtfsW8TVHw+qImFSZWhWoVki1U1n1Jl2Jf1lgARIyJgUcu6ES1ydoyqxUo+tvgs11WOqRHb6OqhE7eCRIPDYIWGaneLixwjJF9DFuEdHGC5srxQc3PN/yV/6fjDdP/ydHQLkGqkqB0QJv+mfP1FjiMxclDQWOccQSkoKOYW1oJip+CVES7KaDEw7lzLLSqTjnu+fY/gwkoWKsL9XFJuPSc+eNh7v2Lpj9q7D/CXSnk/63NZd2DbEittg4xuJOcnj/PX1x+0XNfdRtgFVapOpsWtu2mZikuuusJ2/De89i3r9+MmrKq2c4tkpADoKsUR5rGwyjtXYJDkm0cq1DatAK37SCO2xL/OdIWVSIape2Dr4i3cEm96oRsBpwqT/evt26UbF6Yex/ZdKlOIxrVv6S1WKkXt7a5A1X3gNZeiYfinMkgmrHrcuhmYacaFqcJKPk5CY7EC7NYyN2SXH2PAxEvctw2D6CQzggUrp9D9nteii71R27ex1j2NQEEJf592vX79tYuAq6REp9NvAk79abg6iuPoXKElGeYkbKrX/2G5ZiFw2mz+OYAFPEVRO+tzWVf3dms6DEz5NnDF685169629vvSXcANmsFWNbt5GhXd+Re1A475LjB4Bk9/AeifnaPO5L/fxjp+3/QeB0z9Pl9XpViP86h/zCthVVJSgn379pGA0GAYBvbt24eSkpKWrgrhhe7hoBMbmboCRWcjOnX5N+M1VUcYVOubkXTGtrAobK6cIHms/H7fahsmNLmegGDTzhSVWWIqEnUKQ6956pIJdytSapsm6xqUdrKmZlHdNbIgSjTCabLS7KNDDlBnEZ57aUAWpr6KFoUXPCpdBgeLMZQ79iAkm5xCVLVWNNa63/dijkO3VBCdBwM9pRFnnQcFE+0CET/XsR9Q3sd9fbo01umtOWKwAGA9T4oDCKsCqQ8RUzrpSDTwuvca51xXtdNq75JOljiSqdnDRZXud1rYjt8rPUZZfzp0z7vSzvx32FhjuQ+7j+Dv6kCDIL+fZiKvXIH9+vVDRUUF9uzJXv6L+vr6NiNGSkpK0K9fP/8NieyxZzWw7h3g+Ku9t0ulW0jwTvSD3/KRYtFCu1BY8ZI5tYbZAXnk9/EMXhfrmEZYLfqTd129qN4NzPsFEPuRPohcjVFSOyA/y9jCPwITL/XeZu8a4L0Hre+yWJMfnkE6v8ISHtsB8Oug1t/N/QDwa+mX4ynZZO84Ck3RsPgv9u02zrdivNwsVl7uQ4HNqmXeQ536Az6D2Xxh0fQCs9OhQko9ESn0P64QVvLvQZ1Hdt7P3d1tQki4CXH1PgqbmLSgGDgM9yB1N4tVpMD7/hM01OpjuyJR65zEZNFFAWYZkeOU2nnM0Zho4NvqxFrVTmsUbEm5XlDvWMZFq+53Ktq4uJyPzN31uT0OUd6uqY6n2+ho9n1u7fnRo8CM/3U/n2Ykr4RVYWEhBg8e7L9hCOLxOCZOnJjVMokjiCdO5UGuU7+PQFnMk03AvHu5gOg5Bhh7jv2f2Ivf5u+9zVgdD6uUt8XKXJeyWEmdzt61/vV0Y9cK/hr2ZefQfp0rMFJgF3Wy8CnTBFT/5yY+5NsVxst4W3LFyFYl26jAAMJKziCtE4Yq3UdZmcmNpD7btkxTvb0ehR7WmM6DeSyKmm5BECQpo+yaFGUEGYnnR6QgvcBsHd/8I586RczTd9L/Oqe6EZSU+7tAVQufbhCCVwyTSM/hNopUvRZhLWqycDvjAXt82sl3uN9z0aJgwqqxVi8+GXOKRS+LlfidyharQdOcgxqsHSwRdtw13Bor4r5ESgXA29VZ0lHf7sI6OuhEYAF4Xrzx5zu3E3VNNlphE26/sWwmEs6QvHIFEkTeIUYO+f2rFg/nZMKankVMc6P7wQsB4vEw8LZYmfsxjbDKxjQ2DAEtVsqDXXbRtOuiL/uARyeom4pDFlZy+UEsVvJDXScMZdr3Aq6Wcs8lE/7TuTTW2evhZe2YcgV/d7NYBXHh2sS2ZLFSKVXa/iSff/KRCLI2/dG4c4FvSVbTY77jvm1BAG9CMmFvry5DwtVH3KNB3Xte4liHEG6RCI8B+po5d2RhGbegdHTxMujudR2Nde73rfr784jZtI4r7TPmLJ9tzWfZ6T93v45ers7jr9H/iRC/kyExM/axTv8slH9P4rPuD9WJ1wMn50+SUBJWBBEE+cHmFSNkJPjQbkCaakTzwBBxBZ7CKk2LVTaIFDrrpnOlRZQYK5GVPlrkXqd9HjPd61yjsiVHnvrDK49Vqn5SJ5JMeM9DFonaOwEjmV1hJUSEq7BKc2JZcb/pjiXw63CDxKuFQT4/ITx0BBEXycZg19qvLvI5ep1vWFdgVAirAvvxBG7CKoioBPg96CqsVDdmyDg5vzrI18ftWnkFzLu1syqYGmr11jubm93cJ6K5F7ymTGoBSFgRbYsdy/hUCNuznAJDFhRG0i6utnzM3WeAabEyf1bCxfHgeGd5IhZJfZgsfSb10SGsHhrPs5QDlliLRHnOoXtdAnN9cQk2rtwK/J/ilk82OefoeuFyPm2LQAxnjxZbwkwVoi95WDB0D0jZkiMPl1/+nHNbL/xcgeo/4RevAN6e7V1mY53+4a9DrEs06sW5VyC9FrMMXeBwgdIJ+gmrbM3LJ5A7/KiHsAoiLprqreseKUxj9JdI3ivVKeLREQuLlVf8kUzK1WiWL4SVEOk6lzjgvEZuNFS7W8zV6+YlMLoOdS7zs+IVBBBWOqGTWucirIqlWLDCdjz7+yPHetdPXBfdvep1PVuAQMKKMXY6Y2w1Y2wdY+xHmvUDGWPvMMaWM8bijDGKsCZahlWv8ffVmiHCmWDLSpywP9xXvCRtl7CS2Yk0C4c1lghRniqePnwk9dHhCjywyRITSckVuObNYOegw82asPkD5zI3YSKfn8iQXCBZrMJY03QPSNn9FzrbsiQc/VyBagchx2e50Vhrf9B36GXPfi0jW6x07ZiuxarvJODcvwIXSfehw2LlM1os6xYrWcR4lO1lzcrG9oDeYuUlQEQH/v13gQtfsJaPv9D6POEiqU5KcHzKYmXee73HAV+623kcL8EpU7UrmCvw3L96n9fZj7mvc0MWUzoRJreJICa55NT6jJ4JnPWoNXoW4H84xB9TABhxhnRMzTXTWS+jeRUu7i+sGGNRAI8A+CqA0QAuYIyNVjZ7AMAcwzDGAbgbQP5M2kMcYeQoVYfNFZi0iwU1jkeIiyA5nVR3m9Rx2CxWaqebElbM3tmE/efm9nDXPciTCf8A50YpQ7KofxhhpftXLIsQ3XQ9QQlrsQpCU71TOIyaqd82ZbFqcLYji4S3WMnifsxZwLDTLAGl1snPOhImvUAQ5N+EV9lB3WGp7YsR6DcuJ8FMCauAdRLCqrwPcNSp1vKx0qALeQBGKsZKsVjJ6AZsBBWzTXVWvKZXGcO+5P77H3pKevm0/Fy1cvsI+kuWJ/UcT/whMEERY6r7cuy5+v29YuVaocXqWADrDMPYYBhGA4BnAahPjtEAzDHNmKdZTxDNg99cdnvXOafD2LaED9/e9bl7uUkPYbVbSo5nSAHPQUapeAirSLKBT4QKOIf9G5KbTRZWQQNiU8dz2V7neti10n9UXcpiVczTTlQsDjdaR/fPc8kc63NY65wcU1Wzl7ttVUTwbTpxPI21TkuXWzlCRGx8l98zUcmFFDZgOix+55ZJDJMOufPzKju0xSqgEJNdTdoYKy+LleTOlete0klfD/E55QJUXIFqOWEQ+7lZT9VzcrNYpSs8/GKXdOtloaSKIG1OK8V9LrutdcJK9wcoz2KsgkjmvgDkLHkVAKYq2ywD8C0ADwE4G0AHxlhXwzBs42gZY1cCuBIAevbsiXg8nma1g1NdXd0sxzkSyce2HbR5EwYB2LhpEzZr6haLc80fj/3LsUxdDgAzwMBg4OMP38e4unqUAli0cCFq2+1AKsf0xgWp7VeuWI6u+zaiF4DdO7fj83gcMY/6btu6GWuleo47VAMxnmvQ2ieBpeuweNIDKGiqgYjUisfjmHTwAMoBLFv6KUrqd8FMmYdGI4Iwj5j6JgO6rmr7tq1wpDp8+ftYPfzq1LF0VB3Ygw4Aag43oayhGvjjKVgw/TkEzetdW98Ih8SQXYF7As5bpyP+cwCAgQgYLGF8qKgnyg8fQk1dPRb6XC+VhppDqE8UY4l0DUtrdzgekACwaOU6TAGAFS8CABpZEQpxGElE0GQUIKQkRlVVFRYr9/i0RBMKwH+bsvNvxedfwGMSIWzfuRuRnqeg1665IWthUVtXh0/M+hQ0HsI0c3l8/nzXNt1XWQNN6L0r67udhl4734FbxNi+LpPQdf8S7DHKIVJfbkl2x4Z4HJ0OfAYxIVF9UyJ134tn2JD+Z2HA1pcRf/9jmygSdf9k2SoIW8zi5Ssx2fy8Z/9BdAewv7IKy+Nx9Ny5GqMANDU14T2z7KLDB6Cmca2pqXE9D0FtSU+0q9vhnPzYrHdJ3U4cJ76/+z7AWKq+Ne36o6yWd917DhzESuleiUllxODOii/WYO9e536Czz5fjX2747Z1i5at5Pc5gKXLV+Lg1khq3cLFn6KmvT3X3fjqenSWvi9b8UXqWbd63frU82bn7r1YFY+jvHIVlImrsGb9RmyvjyNbZNq3BRFWur/+qi32ZgC/Y4xdDp6VYhsAR4i/YRiPA3gcAKZMmWLEYrEwdU2LeDyO5jjOkUhetm3iXWAzMHjwEAyeEXOuj/M3W73j1sfY9Gl2q4m5burkicDaEqAemDJ5Ip8fSzM925iRI4EvVgO7gB7du6JHLGYrX6Vvn17oK9dle2/gAA+871zDR89NPno4dx0tl+q+pgyoAsaPGwsc7Ais4esKS9oB1QHyIZmUlJUDh525mvr07AFopgobMXRw6lg6OpQUANVAWXlnoJZb2U46/lhtW+lo16ETUOcy51uWYIWlPJMzAPzvRpR/8jgQ/wXKytrzth30H+AvXwtUVhFLoKhjZ/v9tH8j8Ilz2ynHTQcaz0/FyRV++afAazcjUliMog7dgH0+0+codOjQ3vn7+6AASADt27e3zcd39JijgW8dAO7uDB19+vXnOZgMA9i5HHh8hn8FvvEw8O/rUl/blZZa9ak7CLzPP8Y8fgNde/YGnJpBz4SLMHTmQ8DvjgFcBmt2PeMOYMgp6P7aTcDej4GTf4wBJ92MAYwBm4u4CQBASWl74PA+q34AMGMGYBiIqRZIs+7HnvQlwMxtOvmEk4El/HP3Hj2BvUCXrt14Wcv3AKuAgoJCq+zqPcCH9mLLysr4ecRus+bRu/MAz4NnzvHXrnMvoE7zQxT1PrgVMI2wsZNPttW37JbPgOcuBla9iu49emufeV7XBgCOHj8JGO7cTzB2wiTgqJht3ZQTZgBmCrMJk6YAA49PrTvm2Kk827rMjn7AweWpr+MnTk4960aMHJ163vTq0xe9YjGgogPwqbmxmdNs+MgxGD45hmyRad8WxD5ZAUBOlNIPgG3ab8MwthuG8U3DMCYCuMNclmYkJkFkgtD8AabWAJwjjNQZ7VP5qaSRXKor0FaeHLyemSswRdNhOP7LpILfDXtAd1hXoNv2bokLvdIVAJYrUI7dCpJRPFWfZjDpy8do10VyNwg3coh4o6Y6p7vDNRFlhE/RIRABvJFo5vPJ+cEi/qO3GOPbBL2HvLYL6voKc7+KyZS9YFH7ORSVWfv4Ba+L83dDzgwuXy9x/VOuKjEKEc5tdMh5oNTjF/tkUveK05ITiKb7u/K7Prr1ttAE5bi6+qrPPLe0GGr7AtbvKc9cgUHu/oUAhjHGBjPGigDMAmCb0p4x1o2x1C/pNgBPZreaRJsimQReuQ7YucJ/26DsWgn8+3p7UHeguijC5qDp9V7+AvDxH6zlCXlUoOEurOTg9WwJq8OHnMcTIw7XzwX++xNredgHjFt6AFdh5RNjJebWkx+4YQLOwwrDdHB72KemCQoRD6NOaeO1fyRiP7ZI4hkpyHw+OYFbKgLfGKuAo/hkvLYLGgwfZjRi6t7wCF4XxxXtLN+v6ZyjjBxXJV+vVEyVR/C6V/t7iSff0Zw+7ZxqjzR/V77CSvO8kuO5gvzpUFO4yNdGvmbis1ymEFZ5Nr+w7xPEMIwmANcCeBPAFwCeNwxjJWPsbsbYmeZmMQCrGWNrAPQEcG+O6ku0BSq3AEv+Cjx7QfbK/Pv5wOI/Wx17YGGlCAgRy/CP7wKv/y9Sfzu9gtdt5SWsmKAgowLVbXQPsvpKzYTI5n4fPWLvPIIO4RZ87df6KVHcRv8FtT7JQfGNmn1ky83wr1qfsx1ErUMN5FWFlfzgdpu4V0ats6vFKipZUtpLw/QLw1ms+pgRJmE6E7VOZzxg/y6fs1dnPf0m67PXEPegVr8wowJLNW7MISfrjyvaWb6PIxkKK/mZUijVW+3w1XQLtmUaHMJKuq7yOZd2AU75iX1T3Xl86W7g0lfs69XtzpvDXbkAcPFL7tfLV1hp/sh5DRLQ1feYb9ufAW6jN3XB66J9/BL5NjOBnmKGYbxmGMZwwzCGGoZxr7nsTsMwXjE/v2gYxjBzm+8ahhE22QxxJJGLfxfiR+dnUVFRR7+5WVfUdAtu/5qTTVaZQeYBU7fRdTSqsGpqcBd2YS1W5X2BMx/2r5egenewcmWBp2vTWWYi1G4jgAufDVZmtlA7OdFmqRGl0vrjrtJnNj/9l9bnwBYrSVgVd7CESaRAPwGtG+NnBd/WrU7Hfs9ZN91nlROuk7ZTO8kQo+COv5a/+7m6ZNqbiTbl58dZj9q3US00ySwKKzdSow49LFaerkDFKiWfn/ynZ/BJwEk3+5d74g+BIWaMHFMseILRM4HJl/HPR50GnPADfd38/qDqhJdXO+vqe9Rp/BlQ3NG5j1+6BWH1zSQNSw6gzOtEftF02HKPJRr5q+mw3b2mmo4b65z/UJsa9NuqqALC7Z9PogEpMVWzx4qjUjGSltswmfD/wTfV292Mrq5AedqYWnc3Y+gYq0J9J+MqrHYFK1c+D11bic7EIRA14ibXpB7UmmMXttP/EZA7w8AxVlGrgyssleJfQroCU+5br6mVlHV+7Rl0uhdbwkhVUEqdsJ+LStxffq4uGV0mdDdRq3MF2lxMAS3aQRAWFjVBaNB0Cw4rsyyspHxcftYhbd2ENc3nD5fb9ZJH5OrQCiuPe8nz3ir03j8lXNuIxYogcoPm4XZPD+C5S/jnn3Xjr3t6APf2Atb+F3j2QuCe7tb225fydXtX8+/iQTr/PuC3k+zb6kgoAkIVAangdWm75y4G/qhJjCe2E/+S92/gdfPii38Dvz/O+q57wKkWq4Zqd+GTa2FVpR+h5EAWVjpXYIEpDlRXqBAEYV2aYVA7VdHpqPMvAmbuIp2wkiwtQS1WTAqqLiixjhspCOcKDOJm664kxRCnLCfOdCszSOfXoY93Z+0n5MTvtChE/q7UlCzS9VDdkeI8ug7j750HOdcB2bWaM6XD18ZYeVwzLxHaS06SoXleBhVWfpnJ3crxuy/9xJ7b9dGWVeTcXyuspPbtYybQ0IUztCAkrIgWwOehtvo/+uUb4sCaN+zLNr9v/y5bqNREoDp8LVbmw0yNOXITGHJ2chHv5ce+tfb9VRrr7MLq0I7suQIjYYWVSwZoRz18XIHiX77jfIWw8jmPaTdany97FRgcID1ACqWDEg90nStw9Jn6Tli2tASNsYpErXaRzy9S4D+Xn1qOK2Zdp/4PcIX0WxF1+p8FwNWaJKlB3WQsClzzCfD997yvkZ9FSPxGdOd93lPWZ+GiGnoK0HOMc1uHRcQ8z9Fn8vOfdJm0Tm63NIXVDSuBH5o5G25aA1y72JmAVHd9vISmW3uP+BrQS5pndMoV/P26pfjg+Ce9900d16yLXyybTvB0GwH0Guu9XzZcgQIRl8miwA+XA9evUPYXozulthx3HnDF685s7i0MCSui9aD7EavB1KFjrFRh5eK6c5sEVcVIBIutct1fI5gaamDrCCq3uLsCw2ayjhbqH3aqJU9QsydYubbgdY2ZXjzIHUH5AYXVQCnd4uDpwJizg9ULcP7xF8cS103uZApLoe2E3bJDA+6igkXs7g5xvEiB9+TNunKgr5atTgOPd+5T1hXoMVKzfcAYq0iEW8PKumY2glOkCNG5AgdL6WSFZbP/cc7tAKfVTL52A48P554MQsd+lhWsQ0+g21GSu03JvA6fY6fSMijrxG+g32S7iBDH7TIYDcVdlWO5YZbl91zQ1W+ALs2tgq5cr8muva5BKi6uCeg8EOjU36XdlPIHnpBd124WIGFF5A9unblA9yA/rCTD1MUEVCx2L9MRvO7iq//kCeDgFu/6Ady1F1bcAcB7D/Lpa4JYrN5/CKh1JvUEEL6zi0T1/3q3aCZhDoNcDzlthXxcwN3y5hcT4nDnheg0HcHrwmKlcQUCegGjc1G4lS9vl3J3FFr3XqTAOV+aF0Hiz9T2yUW6hUyElfid6abykdszdU2k+ssWxDAWkXTmggyCarHSdfJeHb9rnZV9dG0V1DLoZ7HS1iGAWNHdK7ZRfWm4Am1xcXK6BeZclu3Jw7MECSui5VAfCn7JJ3VWDNVipRNWfzzFvUxVyKjCStRRdTm6UbEwvREqb/8UeOosLjTUB2hjnb0z2bHMXbxpLT0BkioKitNIVDnxYk09pE53zyrnehGIHLtNWWGep2MS4VL70HoWBfpMBMadb30PDAPK+2FrPzNbjPjXnbJYKY9FnfiTt9n0nvs623JZWEWBLmbM0PSbglus+k+VyvcwWantESZ43a0tVfHnN7GzzFGncXde7DYeqH/Md/jyAZIl6tQ7uQCQ6yoskSO/4VJv9Ty9rG3KPdV5EDD1+/51H3A8MOpM9/VuMVZBrSiRKDBwGjBKnKPLfKfpzCcpnm9+MYtBfz8TLwF6jAa+/mAwF3aQuQIFM27l750HyhVzbpdpPrJmID9rRbRt3AJHm/yElc4VWG3/3ugzikVFjZ1yiKI0TMxqncJgJPjDauTXgM9eMOtU427ZUXHLhKwTnN9+i7/LD6fvvQP8bopz21FnAl+YuXGmXgV8LA1zn/kI8OnfnMf0orAEmO0xOYMa9PpjM7Zrtin8WAS4Mm6tD/OAZQy4cSXWx+N8SomoErweRFjJHUb9IaV8af+rP7IGJ0SkUYGRKFDayWqDtW9711luq5Uvm/XyElYB474EQawAdyizEoSxWF38kvU59iP+rl7/6Tfxl/wb7jVWc59I5x3GMqd28iJWyo9vv+G93jXdgs+zQ47pu+I/zuWOWMA0souLPwu+rkDNNdcJw5m/sz6LmC8vHMl4PQTc6DO9nwmpeskWsfy0DeVnrYgjBOWHm5awCmCx8kKNhwoz/UouSCb4PzL5X5lqsfJC9/B1+7eqSx7o9uDr0Nv6HCSOK91RfeI8ww4PzyR+JuUKdMu8rml7+fo0KWKcubhC5FGB6vmFGR3nda6p9lO38enkg6ZbkMnVNCKZpNrwjA+TziubowLd0i144Wkhcsuen8afvJSwypEr0I8wrkAtcltoXIF5CgkrIvu89F0eM6Qy9x7g9VudyxtqgIcnARvnW8t+d6xzuzekfcWDMUiMFaCvD6CPsfKL9colRoI/mOWHx55VwD+vDLa/TtC4uWwiyj9twP3B5zfPmmP7dB9+AYPXw7q67Bvbv8pBs4AmxkrT0Xl24C6B4CwqxYkoHU46wevZdAXagrwOkCg8AAAgAElEQVRzMM9fGPzunTCWOtd1uUi3oASvpxVXBcvlmo7rT0U83/zctl7B9ZngCF7PguRozhx3aZL/NSRaH5+9wGOGVBbcD3z8mHP5zs+A/evNKWRMRF4qN4R1QR2l5hZ8rqsP4IyxampQYr1CPICHnx58WzeaGsyA8jSFiW7aBzfrUWoqjgAWq0gEOP9pPlWGzpp04QtK2R6Plll/d1/nVw+38tXtL3gu+L5eowIB6C1WEX6M8RcCV853rkvVS5k3TR4JKKN2ogNPtKYc8at/kG1880q5/Jm45GX3fXIlrIKOdDvxh85VQQVvNnEErwe4Pl5WwWnXA7HbgcmXZ1w1KzdbyD8qfGHmx89USBkaty9ZrAgiBH6uQBnxwDikxH14pTpIamJl1BirRIO9HmFcBmdrRGNYGmv5gzndEUxCFMqZwV0tVhpXoNtxWRQY9XU+VYbuwTb8y/Z5zcQ25X3t25V04vFjbqTtClQ6qhGn6zN1A85/4kJ4homxYlF+jLMftZIUptZJ+8vigzF3q5hqseo+0ppyRHdsP4KOVBS4DRwZerJ+OZBDYRWwQ5+kaZ8wwevZIpVuIUSMVaTA/TwLS4HYreEGB7jhJuR19VHJsxQGKXI1ujOLkLAisotqAdq/kXeWuqByxoDqPVawdyhh1cTLrD8YfB/dpMiqEEs02N2JQfNXAf5iIAiHtrmnQAiCCL4vkjKDu8ZYhbFYyRawAOeZyrWkClM/oRrUFagGLevq7XYstwShbukWQrqNZBGjdo4Jl45OjYEJ4tLS1stlRJlfJ+k39ZOOXAmroISd7DhnHbKSjyqQxaqZxIH44+ibeT1HFqusQhYr4kjlsDRCauO7wMMTgGXPANVSxm65Q3jgKOBv3xIrgh/HSAAf/DZc3XTWLFU4JRrDB8ALwoohXWqD7Z/yB7RO9AShkzlUWbY0+FqsAsRYeSX989texk+kDJrO30UqgqDl6x62ssiX3bQOi5WaZFJ5LI7WDbX3ivORylfFR9ch/H3QNPtyddi65+i2NIKjg04xI9PvGO99WlxY6aZ4CRq8nsV6yPnIgGAxViO/Dhx1Kv+sm+Q7LG5TumRisQqLePYAPEVFNuiomYKJYqyIIw556LnIX7Rtsd3lFjR1gBfJhF3EBd3Hb1nicPozpQex5MhxIef9hef3UZEtVlO+A5w3J3gd+k7i00Ec+z2pXm4xVprgdbdOWz43v/P85hP2h5+cXdtPWJ3wA+D6z5Q50jQEiSGSj/Wlu4ELn9eXpQoEtayzHgO+fK972V6oZfedzK/PlG/blxeVATd+AYyeqa+DV/10OFyBfokkFWvxzWuBy/4d7hjNZeHwanpPV2COgtfFNE8depp1EO3g0h43fgF8/TfAqbP5FDkdfOYTvXUzf7lxywae1kNHKsbKRzjp7uewrsCrPgBuWc8/X/JPfg9lStehzjx55AokjjjqXfKQ2KxF5o84kyHPRpKLtTD/tLQWK40rUJ2IOShusTUy7Xtan0u7AD1G6csRZRUUA/00IyS96tB5oOKOUoWVMjItSIxV0ClPAN5RRFyElV+HxqJApwH+4iFI4kFZwEcKpHgvpcNQ20ctu6BI0/kFvHd19eo8UN9plfexRHAgYZXFPFaqK7B9D/+Rii0eg6OzWLVAl3ZoG38v78Pf/dq6vA//cxIt0FtkVEo78ZcbZV3tMZUyKYuVz58h7VRVIa9vcXugzIxrLCzl91A2UK1x5Aok2iSHq4B5vwBLNgLv/hqo2sWXb13IUy0AzpwxsmVIdHiZ5Iya+zOgocqaSywIckf74e/5u2xJixTy724jC/2QOxq3eqn5gnTWMSZZrCLRcKZvXdyJKvKEwBAuDHl0oGuMVaH+s7YOSh4u2c3lJ6bVIeuu6wO4AuXrzRis+COXUYHqMbwIanUNKz6CuJGC/GMP7QoMEd/Y4ij3kNxWLWHNEANoxO8qbOb1XKK6Kd3QTSqfD/UHpESqzP6ex5CwIsLz/sPA/PswfM3vgXfuAl6+ii+fd6+VJkF1P8mWoVQOKhdXXpDkkoueBD59mmfwDoos7t68jbst5XoVtuMWq3RcgdNutH/XBcoDylD8An1Qf0TKY8XCCivx8PGwWH3zce6SEh1BQTGf5Pbcv7o/gMPksZIFmpG0/5v2EyS6+tvWuwgvXYf6rT/a9+s6DOg5Fjjjfvt2Dlegpqyhp/C4r6/eD/Q8GugyxP0cMsFPWMrrdCLVcBGPuvLO+TPQbQTQ9SjguKvdjzf2XODL97ivb27OfBjoNc6yEMn4/VZK0piyyY+v/ZrHo3U9KlgdmpPT7wN6jAF6jvbe7pjv8KlqTrtLWphFAfPV+72nBWpj0JQ2RHjMjrqsZiv/LkbzuVl6GLMLDdG5ugWJl3WzzOue9SgJZ7FS3X6HVWFVys8hrMVqwkXAaUqeLLcRjuoIPDVrN2C3+MhuwUBo/tWpwmHQicD35kq7MOA7b/LPbvmMwmTmto1qNEK6AgNarIJkXh95hn2/whLgqvec26kWON2x23UBrlvCP08NmKw1HcIIKy+CpFs4+pv85YcsUPOBoafwVwo5uamPxerSV4DHZ2Q38/qQGfyVqk6AdAvNxYDjgKsDTKjeZQhw9Yf8c7QQePP27FqGpl6Zwe/GZYqfPCaPpDXRajB95yX1u/l3MerMFgguPbgMQ8kh5fNQCxMYHGR6lVS5ihXJYbEq5YIorLDSjkhzSdOgCiutxUqxUoWJG9F1zGHayK1j8huurZYhxIqRVILX/SxWQV2BQdItaPbTobZvS8Zw6LLhO7YJ4goMGWOVDVrKRRPGFZhqu2wOC1TIJ4tVOrjNVdjStAIXoKCV3wFEVln5Mp/kts4nN5T54ChqNAPV1bnWAKc4srkCfTpXNzeaSmMNFw1B4yp+M8b+vb7SHmMlXIFhg9fDxHWok92WaZJYylOfwLA/qHUpGmz7Boix8tzf5eEVapJjKV1EMsFH9giaM8ZKt58XIveX2FYePt5cBLJYSfeGAxdXYL51ktlEdsv6is5maIdWMGrNmxyKznQQiX6zkZaimSBXIGHxgTmFxr51QL8p7tup2cpTHbf8g1R+nDZXoM8PV5cWQbtdEzdbR6JAIuA+MvWVdpFXWArA4EHxYQhj4VBdal/5OT+HhbK7xUCqAzAUYfXdt4FHNPmFOg8GDmzM3GLlWu8QyU8jUSsOy0gCp80GOg/iUxZlzWIV0srkJ6wuegnoPtzclgEXPAv0nuC9Ty5Qz+97c52iNq3M6zkSFFe8waej+tc1jlVLJt6HScfN0Oyk8D/vOp8pl74SbMQcAFz2KvArce18rnNzWD3yKXg9HdRg8ZbmmO/wScrHX9DSNQkMCSvCQjwQ/ISN+hAUHbdXp5kLixXARV26/xAPH9IIK/hb7FQysVgVtwcmXGgXVmosmNxZdFSmiAGA8n6Wu033UA8yGMAPW0oGnwdupMDa3jD4/THpUnMuyAwtVm7Zl32vgU+dhyn5xEZ81ae8XKEE7/edrNkkSILQZnIFDjzeNQHtoY6j/IOmAaD3OOeyIQEEmaCDlMIkH8RAPtShLRGJOnNZ5TkkrAgL0Tn5CRs1S7OwWCW9XIFSmbtWepevm9PPjWhR+jExS+bYH4JiItwlfw1XTpjjM0VYye8CVbjKnaI2EWbS0g26IclBEpf6ESbGiikxVoBdaHnhmG9NLTuHrsB8Iu0pbVIbBS8vU1pb2wLZDV5Xyafg9bTIM4tVK6QV/iKInCHHxXihBmbrYqxUy4Rc5ivXepcfymJVmL6w2vQusHGB9V1YrNySnLqh61hGfl2/rW5ePlVYJZukZ7KhiAgGDFEmxjWScFg65Ie6Og9dOoSJsYpIMVaqsJIzwuvwdQW6pGPwq19r6fyD5Orxut+P0eSRA0hYpfCKT8vWIdxcgcx/qqZ8IF+D11sRgX4RjLHTGWOrGWPrGGM/0qwfwBibxxj7lDG2nDF2hq4cIs9J/RPOgitQ/UcYRiwFjbEC9K7AG78AisuDlyGQE1lOvzn4frqO7rS7gDt2em8rBKkav5RsgmuMFYsAF78E/FjKlGwknYLDFmMVck63n+zV1Fuuo58rUEq3IO4JxnidT/+l975+8SmuowLzILYmm6Sbef0rP+ftrFoYSVhxWvI++Mle4NqFLXf8wJDFKlN8fxGMsSiARwB8FcBoABcwxlTH+Y8BPG8YxkQAswD8PtsVJZqBwDFWqitQuH1c0i0EKVMmbIyVKmxKu/g/FMQIMBl5Cg91UlwvdPE9hSX6KUFsiTaFsFL2TzS6jwpkZvJQWSwZCTgsVl55rPzQuQ5DWawK7MHrgoIi/9QRfvOspcS/4i4mV6C5jumFdC47ydbWts2GOtm3x8wG+QRZrDImyC/iWADrDMPYYBhGA4BnAcxUtjEACBNBRwDbs1dFotlQrQxuqBarlHUlYLoFPzK1WAXJxt6us3OZ7DIL0xHpHpZu7jfdnHuqkHHEWDH9Z4FssYJGmGRj5vpoiOB1prFYhcXPYqWWm0keq3zEs75pdHg5PX/qgO3kWbqC0LT2+rc8QZ64fQFslb5XAJiqbDMbwFuMsR8AKAOgDLHhMMauBHAlAPTs2RPxeDxkdcNTXV3dLMdpC4w9cBBdAXy2bCn2bXMPeB66eQPkaTG3btmE9fE4jq2tgRn+DcMwsHbtWgwHsG37dhys/QxjNGXpMIxk4Ef1zj370amxEULGbBp4HjbF4zixqQleIdtVTYVQbVabt+2CyFy0fsNGBI2G2LR5KzaZ91jMXPbuh58gUdAu9V2wbNlyjDc/i/uy6PA+nCBt01SzF1s2bMQQAFs2b8aG+fNT5cTnL0iJDrGssbEBdVXVKAeweMkiVK2rQnH9Xhxvrl+zfiOGS+UH+T10PfrHGLvCmsbk0+UrULmFf2bJ9hje6xQ0FpbjUPko7DXLO7GRt/n7H36E4sN7MQWAkUxgfoDjpc7P3LbHrs8hzOIrxvwodYyyUXeg9463sG7RFwBbldq/6PABWxvG1evx3ntIFLSDTC6fDeK48Xgc3UffgsLGSmwPcKxh27ahL4B1GzaiolG/fWntNkwFUFtXi098yhT1eP/Dj9BY5DGRbwa0q9mCYwHU1NZioVSf5nz2xsx3v+Ol6lpTbatrNimp24njANQdrsfHOTpGLts22jQKw3rGsA7HoClP+87+Qy5Hbbu+2Jen7RtEWOn6OFXSXgDgL4Zh/IoxdjyApxhjRxuG/W+lYRiPA3gcAKZMmWLEYrE0qhyOeDyO5jhOm2D7o8B+YOyYkcComPt2Na9yeW3Sv09v9I/FgGUlgDlDC2PA8GHDgLVA3z590HfACODzYNVgun9M3UfxGd63fGhb3Ktvf6B+HXB4HzBwGgZd8QQGAcCHBYCHkaxDl55A9XrbsoFDhwOmeBg6dCiwIVh9Bw0ZikEzYvxLnL9Nj53GXTJx+7bjJ0wElvPPqfuyaicgnVZBoh5DhgwBNgIDBvTHgFgsVU7sZClw3VxWGI2isLwcqAImT5wE9D+GTwz7EV8/fORoYK21W7DfQwyQhNXESVP49BiCU77k3OVj3uYnTpvOj7+YX8tAx4srdftsL/AFgDHfxNHn3qZsfDkcGY5q9traMFWOWe70k2Y43Ls5fTbE5XrwYwx32dRGzavAduCoo4bhqONj+m32rQc+AdqVlvrX36zHiSdO0yejzQa7VwELgbJ2Zbb6NOuzN87ffI/nUtessn8j8DFQWhLg+qRJztv2tDPQK3elZ4FYTkvPtH2D2IcrAJuBoh+crr7vAHgeAAzD+BBACYAc/YqJnKPGUKmoowJFTJRrElAjuHvPzaXDIvqYkqgUt5Op+052yYUZjq1zs7ilOPByyciZhcOcizwqUJd5O0xyTzfCuhNT55+pWyHg/r7B623JFZgH5bV6cunuIlfakU6QX9tCAMMYY4MZY0XgwemvKNtsAXAqADDGRoELqz0gWg8b5gNr3uCfm3yElRoDJOKnVDGl28YPt+DO3uP08TpyjJVtX5+HW4femrKkRJphYoN0osM1RkizXIyq7Gtmuy9sB9uoQD+SCatcXeBpNvJYBRF6fSZZxwsrxDIdht5mgtfdpqTRbRpG/LfxOKigk7EXmwEAvcbmri4iWW/v8d7bEW0W36efYRhNjLFrAbwJIArgScMwVjLG7gawyDCMVwDcBOAJxtgN4E+Gyw0jlxnYiKyz/h3rs5/FylVYeYiRoCP9IgX643/9N8Bfv6HZXsqZFKQzmnAxsPRvQP9juVh768fWOpsACXH7dghhNNfVsbQz8J23gR6jgMqtQElH4LMXgpfpZ7GSJ0FOlyDZ5c/7K3e1FHcAaveHK/+7bwMHt0jHCyEsgbaTxypFtoVQGxdWP1wG1GrShKh07At8+02glybbe7Zo3wP49ltAr6Nzdwwirwn0t9IwjNcAvKYsu1P6/DmAE7NbNaJZkTtOX2GlrBeZ0lV3n/wvOagr0K2DLCzVd7KRAqvufp1/38lSUs4oMCRmXy+nJQhjsSrXTDPjiksH19+c/6/HKGVFAGEhjwrUzfNV4jNxcxCCCJPiDtZ5hLVYtevCX9YBw+3f1kYFBiJDd3VbokNP+9Q2XsixgrligDq+iziSaOO/NiIwsivFkU4BQEMNsOIf+vVuFitZCAWOsfK4JXViR7ZYyeegzfETtSxnLOo8lk1YBasuAP38fW4E7uDCCAtp0madxaokjWSpKmHz72TD/Qgg8IXwrV9rsdjkqJ5tXVgRRB5BvzaCY7NYHXauf/VG4MUrgO1LPYLX03AFlnaxf9dZOmK3uZcfKdBnG9cRKbCsa5Go08phC173OJfxF9q/d5TGdow9D+g4wH3fsB1cEFfYKT/xdp1lxWIVUlhlmjsrbEyQXL9TfqxZ31qEVQCE63najcH3yamwoqgPgpChSZgJjp/Far+Ze6CpXuMKFIHpygPW5gp0CV4vKLZ/Vy0Pp94JTL/JLF4jduRklLbOQ+c2DGOxMo8141ZgvjQNy+xK4OBWYNnf+fdrPrELsm894TwuPyCvU9AOPowQmHY9sOZNUXFzf+nc0pneRyWsxaq5M0zLGd1PusW5vi0Jq6Iyfh+GoTksVm2pjQkiA8hiRXDkB68uxkpYqSKFGldgwv6uw22dmgrAYRmRH9Y6sRQJHrweiUqTAkednb9N5HmMzpIFWNAOKyX+gnY+YS02HjFW2RBWYTvmbKR4AMKNfGtTZPm8yRVIEM0GWawIjvzgbdK4AhOmxSla4C6s3DrBRU+6H1edLFZ1Icn18gte9x1yH7XqyiIai5UkBoR4Ui1q6nZBBZA6SbIf4rhB5/kTI/90ItNvfr4ghLZYZfpoOUKtH7my+uR0rkDz3ihs570dQRwh0N8YgmML/Na43IQVS5cOIUi6BdfjKpYNtQOXOwQhrHqMkeKqotZkxzrR0n8qMPlyq2zDS1hJIur4a7gLcupVzjJlsaOb8NaTgB3cpMuwecA5wEk3u29z9h94mgYAmPkIELsdGCAmslGOc8IPQtZT4rTZQKeBflvZycb8hET2yKXFqtswHud3/lO5OwZBtCJIWBEcuSPUCSThCjQMj1GBISZPFqijxxyWEY0r8JQfA+3MxP6RAis4W3YjChF2yk+sAPlIgWWx0rkCZcFUWMrju3QTOtu2K3Ou9yKwxaoIG4dc4piGxcb4WVZ6g/bdgdit7paxSZeFq6fMtBvCWzyyYSUDQIHRWSKXwoox/gego2OiIYI4IiFh1Rap3Q801Ibbh/lYrETyRiPhPiowaEoFGVXceLoCk9Y2YnkkaqUT0GVeVxOIijJ0wetB3V2yGCwMmPFZ0FwBvo7jtDLXGgVCZxdqT4JoNkhYtUX+bzDwh5PC7aMTMIK1/7W7+xyuQCGsAk5bI+MXvG5zBcqpEpj1WVisdMJIFlCRKHcNAkDnQc5jBRVWcp0KNBYtHYYSDF+e43/3qmhsqY61f4aJEo/U4PUj9bwJog1AgRBtlX1rw20f8RBWO5dbn5NJK5A9tayJdwS5cAW6WayEBYZFraDZsu7O8llEysweAU64Dhj1DaDrUKBams7y+hVA/cHw9Q/s8pKE1U2rmyHQVxFSLSGsbvicT9eTFq3cwnLLhjTbvJWfN0EQJKwIEy9XoDzBqZHQW6x07sMg/7odwkq9JTXB69FCyWJVwLPCA/pEmJGIXfxEIlxUAXYR16k/sOuQf30zhoWbWzDtw+SBKzBMRvq2RlnXNHckSxVBtHbIFdjaOVwNvHkHsOm9zMrRudwALmYW/cm+TjcqUOcGDBJzFWpUoEuMVb2ZLFHO1yS73lKiUbXiqO6yZkhq2Vz5hBzn1sp+6mEnYSYIgsgTWtnTlnCwbRHw4e+Af1+fWTk2MSV93voJsG+d9T2ZcIooQ7MMCBZz5Wex0mVTj0SREkmRAp4aofNgYNSZzm2ZZvSftmzNd8GpP3WpfAjUGKuckweuwIxIo77DvsJHgbYFsnW9zvwt0HtCdsoiCCIQ5Aps7QirUDrxQTJuwkqNm3ILXtdZp9TRgzpUIeWV/0iIk0iB1e+yCNBjFPDDpS7la+YElNd5fRdMvxF45y73eoWhpUYFtjaLVTpc9HxL1yB7ZMtSN+lS/iIIotk4Ap62bZxsPYDlcuTPqqtOG2PVpA9cT8di5TWaTRZWssXKCzkRqJ/YaA7R09LpFlqdwDrSXIGtzbJIEIRKa3vKEirCulSzB5h/f+blqJ9VK45uguZkQi/w5t7jf1x1ypZArkAlxsoLeZJm3TrXY+WKZu44y3qYhxXn1ko67mJzih7dSE+CIIg8hlyBrR1ZBM27B5hxS3rlyK48m7BSbpHGOs2+TeklBwWcHadX5nVb8LqUbkGHHNMkRmjV7lOKDhG8/t137OLxiteDJQe9Ms7b7C9f0x8zl5z3FNB3knlcZn/PdwbP4PFBR38rs3KuXQQc2JydOhEEQQSAhFVrJ535+fzK8Yqx0k3QnEykl8MKcM5BFzRBqK8rUFi3IkDHAfzzoe32TbxyZqn0m2L/PvAE921l+kw0q9PcwesARkvB/K3NYsVYdmKDug3jL4IgiGaCXIGtHVVYVSzy36e+Eti9yr0cYX3atx6o2WvfrqleUweX4PUgdFaElZdrLxVjVSi5An1uYRblOaoA4NAOZV0LBHi3mMWotcZYHakcabFlBNF2IItVa0cVVn881X+fOWcB25cAsyv15YjPv53k3FcnrNyC14NQ3sf+3XNUoG5KmwDB6yLOaNy53tsGndImE1pK2LgF8HsRdoJpInNai6uWIAhXSFi1dtJxBW5fwt+TScviI8rp2N+7TFdXYJouSb8EobageNmd5hNjJZcXiQC3b/ef1y+noqcFXIEyLA2L1a2bclIVgiCItgz5BVo7mcRYNdZK5ZgWp4ISH2GlBK9HCt3zWAUhjDsuVS/mb7FSY5qKygKMIGyDowJTh00l/gq+T0ERfxEEQRCBIWHV2klHWAkrz0ePSuVI8/B55cZSLVYFxaYrME2B5xAzHh2/bZoav3QLUub1tOuSA1osxqmVjQokCIJopQR6yjPGTmeMrWaMrWOM/Uiz/jeMsaXmaw1jLMM04ERg0kkQKlxi86Q8U3L8kpdIUpODRgszC14Pk6Tz4heBCRcBpZ1hjQoM4ApMty65oKWETToWK6L5mX4TMOIMYMKFLV0TgiDSxDfGijEWBfAIgC8BqACwkDH2imEYn4ttDMO4Qdr+BwAm5qCuhA4vEZRM6IVFQTHQWKMvh/kJKyVBaLQYOFyVfvB6GFdg38n8Je/nZ5EKI2TacvB6S6R7IMLTvgdwwTMtXQuCIDIgyFP2WADrDMPYYBhGA4BnAcz02P4CAPRkaC7CiCBBQbFzWTJhudhCCaui5nMFarfzsdjlnSuwhSxGKeHcMocnCII4UgjSk/QFsFX6XmEuc8AYGwhgMIC5mVeNCIaHsEhoRvABemFlJLkI8RNW6sTKYq4/NxHnh8MVGFDcCIHi5gpNx0LTloPXxXXqNa5ljk8QBHGEECTdgq4ncOvNZwF40TD0fiHG2JUArgSAnj17Ih6PB6ljRlRXVzfLcVqKXjs+x0iXde+9Ox9NheWO5cccTkBkKBJtM2TzJvQzgKqqKiRqm7B83lzEpH3WD7kUQzfMwY5tFegtLa8+3IT2AJYs+hiarFcpPp3wCxQf3ovRX/zKtvzDjz7G8dL3HTt32spfvWYNdlTHHeVNrq5BBwCLFy1EVbkzpG+GYYABWPDe+0hGNULSJGa+x+NxsGQCM6Tv2UQc5933P0CioF2gfeR7t2TqY4gkm1CbQb06Tvg5qtsPQqIN/x7C0NafDS0NtW/uoLbNLZm2bxBhVQGgv/S9H4DtLtvOAnCNW0GGYTwO4HEAmDJlihGLxYLVMgPi8Tia4zgtxpItwGr9qmnHH8djNlRWdQJquREy1TYN7wA7CtCxY2egoBix6dOA+dYuQ084C9gwB717dAN2Wsvbl3cCajZj0rgxwKfu1Zx41tXA5/8CvrAvP/6EE4GPrO+9e/e2lT9i+DCMmBJzFri6A1ANTJ48yYq7kpkPwABOmnGyd8qAOH+LxWI8r9cC6Xs2MY8zffpJ1gTDfrtk/d7NZlmtnzb/bGhhqH1zB7Vtbsm0fYP4PhYCGMYYG8wYKwIXT6+oGzHGRgDoDODDtGtD6NmzGti5Qr/O023XpF+ekJZv/hA4uNV0BUoxVuq+UVOcOEYFmtagJmW5jsptzmUZuwL9tgvh3vObHicbULoDgiCINo1vT2IYRhOAawG8CW5veN4wjJWMsbsZY9Isr7gAwLOGkc74f8KTR44FHjtRvy4dYSVPI/Pn04EHj1aEleEc5SeEVVDBpWOQ5hzCpFuQmXoVf+8yWL8+dru+/JYm3+pDEARBZJVAU9oYhvEagNeUZXcq32dnr1pEYPzSLegoMeOuCsustAuGOb0NY2YmdVVAmdvIQSMAACAASURBVMHPbsuDCKve4/n8hLM7Wsv8RgW66fTx5/OXGzNu4a+8gyxWBEEQbRn6+9za8TIQugkrsVwWSQ5XoGqxchn9J0YYBhFWOtJ1BbZW2vr5EQRBHOHQU741Mu8XwHsP8s9BXIEbFwDPXWyJMLGP7O5LNAaLsVr3X3MBsy9PW1ipCULbuEWHhBVBEESbJpArkMgz5t/H36ddH0xY/f18PuFyQzVQ3MHaR7ZKNdZ5Cyt1smPGuFATwkqdQzAoaScIzRJn/wHo0Kv5jtfWhSNBEMQRDgmr1k4QYVVQwoVV/SEurFKCSnIjJhulBKG6GCslZYEQYCmLVZoJQlUh1dwWnfGzmvd4ZLEiCIJo09BTvrXjJawaqvk8foWl/Ht9pfs+ySa7xar+kH29iLFKwezLD2lSKQQh3VGBrZW2fn4EQRBHOGSxau14Cau/fI2/dz2Kvx82xZIuMX7CFFYRcxLmP0y3r3dYrEyBIILXP3g4XL1T5fi5AttI9o4hMWBDvIUrQRAEQeQaElatnSCTHxcEsVjJwesaMaPGWKnB6+lypIwKnPUMUL3TfzuCIAiiVdNGe7EjiCDCKuUKNC1WujQMiUZuhWJMX2Ykav8uBFDWhVUbdZUVtQO6DGnpWhAEQRA5hoRVa8YwgI8e89+usIS/L3vG3E8jnDbMAxpqrBgrFd2oQMByBaZLW7VQEQRBEEck1Ku1Zja9B9Ts9t+uqAN/37GMv7tZuWr3cqGjG+HHouoC/uZwEQbgzN9KxUi34LAvt7zQ6ncsELutZetAEARBtFpIWLVmGmuDbZcwc0zp8lepsIgV5C7j5gpMR1hNulQqR3L9XfSC0xXY3FNPfve/QOxHzXtMgiAIos1Awqo1EyS+CrCC1oUlyms/FgHqDqgL3TOkO9IwhMRRLt2SBEEQROuFerHWTFBrTsVC/i6mndGlWxCwiCbruuoGBLI2KtCtXIIgCIJohZCwak0kVUtTSDdZooGLMT9XoGOZRlgJ/dNnEjDjVu/jfvst7/U/WAKcN8csl4QVQRAE0XohYdWaUC1NoeOPDG6N8tpPJ6x0FiuxXUExcPLt3ocdMNV7fdehwOiZ7scnCIIgiFYC9WL5xMEtQO1+9/Wqiy5ojJVMosHHFaixGOksVsJklXUhRBYrgiAIovVCwiqfeHAs8PBE9/WqsEpnupdEAxdkboJIt1wEqI+/UNqO2d+zBbkCCYIgiFYMTWmTb9QfdF/nsFilI6waeYxVtAhoqrevu30H8KaUw6n3eJ77qrwv/37mb4FlfzdXMuXd5OZ1QFEZr2skGr6O5AokCIIgWjHUi+U7sjBRg84zcQXqRvMVtbMLmw59+Hun/vw9KulwsZ1qYSpqx18l5VxgFbcPWUGyWBEEQRCtFxJW+Y4snlRh5TW6T0VkX286zMt0yz8lC6sCU3x17KfZTrFYRTOc2sZRLkEQBEG0PkhY5TuyeFJH9CU1U8+4UVLO3xONPG2DmxCShVWTmbFduAJlvvVH7O16jDWx8CX/AMZ8EyhsF7xOOsTcg+V9gUHTgbHnZFYeQRAEQTQjFGOV78hxVckmuwVLN6efG2XdgEPbrOD1IBarmj38XSes+h2DFWN/jJhwDw6axl+ZUmwKwLLuwOWvZl4eQRAEQTQjZLHKdwzFYiVbsEQm9SC062bu0+geYwXYUytUC2HVR7Ndjm4dYVlzjIAkCIIgiPyHhFVL8sBw4K/f8N7G5gpMOC1YQSntxN9TFis3YSXFOInYqs4DNdvlSlh15O9hrHEEQRAEkScE6h0ZY6czxlYzxtYxxn7kss15jLHPGWMrGWN/121DKFTvAjYu4J8d09WYOGKsZIuVi/g4/2nuSpMpNoPXEw28zMIS/b6yYDr7UeDif7gEr+uShmaBYiGsQljjCIIgCCJP8BVWjLEogEcAfBXAaAAXMMZGK9sMA3AbgBMNwxgD4Poc1LVt4xaI7uUKdNtn1NeBTgPsy2RhZSTcg8xlYdWuG3DUqS7b5Wj0HrkCCYIgiFZMEIvVsQDWGYaxwTCMBgDPApipbPM9AI8YhnEAAAzD2J3darYREo3Amjf163Z/bn2u2cff6w4AG+Zby42EPXj9wGb3Y3Xsb/8ugsLrDvBXEGHlFuAO5E5YFZXxd3IFEgRBEK2QIKMC+wLYKn2vAKDOqjscABhj7wOIAphtGMYbakGMsSsBXAkAPXv2RDweT6PK4aiurm6W4wRh0ManMWjz81g27i4c6DIBMXN5PB5HLG5p1ZpHTsLCYx/BpMW3oLxqTWr5koUfoa50M05MLfir9jjxeBxHVSbQD8Dhoq4obtiHpftLMAEA/vE9AMChnetRrtlv0NYKDBLf333fEUsl1zmTtj2661R02/exY/9I4jBOArC+x1ewNU+uW0uRT/duW4TaN7dQ++YOatvckmn7BhFWOtOEOk9JAYBh4P1uPwDvMsaONgzDNj+LYRiPA3gcAKZMmWLEYrGw9Q1NPB5HcxwnEPufAQCMH9IDmBAD4nxxLGZ9BoCy2gpz2Rrb7pPGjgK6jwQ+8D5MLBYDmuYD24DiKRcCX74HExpqgWU/SW1TXuBMLhqLxQD2CWAawmInn+IsXKpzRm1r7qfd+9RKDAUwNL2S2wx5de+2Qah9cwu1b+6gts0tmbZvEFdgBQDZr9QPwHbNNv8yDKPRMIyNAFaDCy1CRrjW0nVzNdQGz7ZeIILTTV1c1M5KuQAAtfv1+3m5/wiCIAiC8CSIsFoIYBhjbDBjrAjALACvKNu8DOBkAGCMdQN3DW7IZkXbBEK0NNQAz17kve1+TfM11tqD2b0QGcxlISaP7nMLDndLw0AQBEEQhC++rkDDMJoYY9cCeBM8fupJwzBWMsbuBrDIMIxXzHVfZox9DiAB4BbDMPblsuKtkojZ3BviwFqXIHbB23c5lzXWeVusTr4DaNeVf04JK8k6dtzVwMp/ALX7gDPuBx6POcsgYUUQBEEQaRNoShvDMF4D8Jqy7E7pswHgRvNFuCFES+Kw/7Zl3ZzLGmvtowJVBs8ABpjjCoR1TLZMjT+fvzzrSK5AgiAIgkgXyryeawwDqK/kn4XFqilA8suyHs5lu1YAlVudywVyCoRImvFcZLEiCIIgiLQhYZVrPvo9cN8AoHKbFLweQFited25bMkcYI6aQswFncUq0H4krAiCIAgiXUhY5Zp1b/P3XSutaWCCuAL3rc/suMI6FthiZVq7SFgRBEEQRNqQsMo1wqVXvcuyHjXW2bcx1LRgAA4fyuy4EVPEBbVYMUVY5WqSZYIgCIJowwQKXifSwDCAxX/m+aMAoGa3NUKv7qB9289fzv7xI2FdgUJYmftF6NYgCIIgiLCQWSJXbIgDr94ALHqSf6/ZByRMkVN3wL7tC5f7lzdUkwUdsNyLANB9hPU5SIzVmLOBITGzHMVi5SasTvwh0PNov9oSBEEQxBEJCatc0VBj/95Ya1msgib5lLnkn9bn7iOtz+f8CZhdyV8lHa3lQWKszv3L/7N33/FVVHkfxz8nPaQRAoRAgFClKS0WetAVEVewsCIqNtRdu/s8y+qjq6Iu9oK4NkTsgigqrKIoSASkSK+hiZTQEhIIqaTc8/wRuBJISCB3cknyfb9evrwzc+ac3/0Ryc8zM2fgqneKPx+99Hd0/auyCquLn4Q7fzml0EVERGoLXe+pKsffV1UZPsesNeV+dc3xbY780ZZ3KdB9L9XxlwJ9S20uIiIiZVNh5Wlf3wXbf4ED20ruL8gBXw+l+9h+ynqKLyC0+N+BYSfv62hhdcKlQC0UKiIicqp0KdDTVn5yYlEFxTNWFV364NLnS24f/4ReRWasYuNh4LMw+LWTj3V0ZuroGOXdYyUiIiJl0m/PqlKQA0XlzB4d1axHyW1z3GW5Y4uesgorY+CCO8sf6/hLgUcLLRVWIiIip0wzVp5waDfsWQX5OWW3Ofbm9fIcf3/T8dvHvs/v6M3mp+voGlpHCyyXq/QxRUREpFyalvCEl9sX/7usJRHgyKXAUm4k969TXHQd6/hLfydcCjx2xqqShdXR8ztdWfzv4Mjif3cYXLl+RUREaiEVVpXlOmbphN9+KrtdfhkzVoHhpRdWD+2EnP0wruuJhZUnZ6z8AmHU1j+WagiJgn9sgTr1KteviIhILaRLgZWVuadi7QpySn/5cqlP7RkICoegukc2T3KPlSee3guJKvmkYWgDXQoUERE5DTV3xurAdpg1mg4pKZD6gXPj5B0sv83RdvvWn7i/tOUSjs5QHb3/yecklwZVAImIiJwxam5hVZgHe9cQmpMDrn3OjmV8wLqgXsviG9n9gyH2XNj8A0S1hvpnQdqW4jax50JGMkQ0gcy9cN4dkPgMZOyEK96ElZ9CZPPifoPrQquLoNd9xdvDPoHVk+HCRyFpenFfdaKc/W4iIiJSYTW3sGpwFty7lF8TE0lISPB2NCfX9fo/Pne57o/PPr4w4ss/ttv/ufgfKH6FjYiIiJxRdI+ViIiIiIeosBIRERHxEBVWIiIiIh6iwkpERETEQ1RYiYiIiHiIsUfXSqrqgY1JBbZXwVDNgB1VME5tpNw6S/l1lvLrLOXXOcqts8rKb3NrbYPyTvZaYVVVjDGpFUmEnDrl1lnKr7OUX2cpv85Rbp1V2fzWhkuBFVwaXU6Dcuss5ddZyq+zlF/nKLfOqlR+a0NhpZU0naPcOkv5dZby6yzl1znKrbMqld/aUFiN93YANZhy6yzl11nKr7OUX+cot86qVH5r/D1WIiIiIlWlNsxYiYiIiFQJFVYiIiIiHqLCSkRERMRDVFiJiIiIeIgKKxEREREPUWElIiIi4iEqrEREREQ8RIWViIiIiIeosBIRERHxEBVWIiIiIh6iwkpERETEQ1RYiYiIiHiICisRERERD1FhJSIiIuIhKqxEREREPESFlYiIiIiHqLASERER8RAVViIiIiIeosJKRERExENUWImIiIh4iAorEREREQ9RYSUiIiLiISqsRERERDxEhZWIiIiIh6iwEhEREfEQFVYiIiIiHuLnrYHr169v4+LiHB8nOzubkJAQx8epjZRbZym/zlJ+naX8Oke5dVZZ+V22bNl+a22D8s73WmEVFxfH0qVLHR8nMTGRhIQEx8epjZRbZym/zlJ+naX8Oke5dVZZ+TXGbK/I+boUKCIiIuIhKqxEREREPESFlYiIiIiHeO0eKxEREam+CgoKSE5OJi8vz9uheFRoaCgFBQX4+/uf1vk1trDKKcjht4O/kV2U7e1QREREapzk5GTCwsKIi4vDGOPtcDzCWktycjLJycm0aNHitPqosZcCtx/aznUzruO3w795OxQREZEaJy8vj6ioqBpTVAEYY4iIiKjULFyNLawCfQMBKLAFXo5ERESkZqpJRdVRlf1ONbawCvANAKDQFno5EhEREaktamxhpRkrERGRmi00NNTbIZygxhZWmrESERGRqlZjCyvNWImIiNQO1lpGjRpFp06dOPvss/nss88A2LNnD3379qVLly506tSJefPmUVRUxM033+xu+8orr3g0lhq73MLRGSsVViIiIs567tfn2JC+waN9tqvXjgfPe7BCbb/88ktWrlzJqlWr2L9/P+eeey59+/bl008/5ZJLLuGRRx6hqKiInJwcVq5cya5du1i7di0ABw8e9GjcNXbGysf44O/jr0uBIiIiNdz8+fMZPnw4vr6+REdH069fP5YsWcK5557Le++9x+jRo1mzZg1hYWG0bNmSrVu3cu+99/L9998THh7u0VjKnbEyxkwE/gykWGs7lXL8euBoSZkF3GmtXeXRKE9ToG+gZqxEREQcVtGZJadYa0vd37dvX+bOncu3337LiBEjGDVqFDfeeCOrVq1i5syZvP7660yZMoWJEyd6LJaKzFi9Dww8yfHfgX7W2nOAp4DxHojLIwJ8AzRjJSIiUsP17duXzz77jKKiIlJTU5k7dy7nnXce27dvp2HDhtx+++2MHDmS5cuXs3//flwuF1dffTVPPfUUy5cv92gs5c5YWWvnGmPiTnJ8wTGbi4DYyoflGZqxEhERqfmuvPJKFi5cSOfOnTHG8Pzzz9OoUSM++OADXnjhBfz9/QkNDeXDDz9k165d3HLLLbhcLgCeeeYZj8bi6ZvXRwLfebjP0xboG0hBoQorERGRmigrKwsoXi39hRde4IUXXihx/KabbuKmm2464TxPz1Idy2OFlTGmP8WFVe+TtLkDuAMgOjqaxMRETw1fqvzcfPLIc3yc2iorK0u5dZDy6yzl11nKr3POlNxGRESQmZnp7TA8rqioiLy8068dPFJYGWPOASYAl1pr08pqZ60dz5F7sOLj421CQoInhi/TO9++Q0FWAU6PU1slJiYqtw5Sfp2l/DpL+XXOmZLbpKQkwsLCvB2Gx2VmZhIUFETXrl1P6/xKL7dgjGkGfAmMsNZuqmx/nhToF8hhe9jbYYiIiNRIZT2NV51V9jtVZLmFSUACUN8Ykww8DvgfGfwt4DEgCnjjyBuhC6218ZWKykOahTVjfcp6rLU18g3cIiIi3hIUFERaWhpRUVE15nestZaMjAyCgoJOu4+KPBU4vJzjtwG3nXYEDmpdtzXZrmx+Tv6Z82POJ9gv2NshiYiI1AixsbEkJyeTmprq7VA8Kjs7m86dO5/2+TX2lTYAF8RcAMC9P91LwzoNubHDjQxvN9z9uhsRERE5Pf7+/rRo0cLbYXhcYmIi/v7+p31+jX2lDUDryNY8EP0AACk5Kby49EX6TO7Do788Sk5BTo28NiwiIiLeU6MLK4BWQa1YdeMqJl02iaigKHIKc/h6y9f0mdyHG2bcwLzkeWTlZ3k7TBEREakBavSlwKN8jA+d6ndizjVzSM1NJXFnIpM2TGL1/tXcNfsuOkV14uWEl4kJjfF2qCIiIlKN1fgZq2MZY2hYpyHXnHUNXw35iplXz+SattewNm0tA6YO4OrpV7M3e6+3wxQREZFqqlYVVsdrHNqYR3s8yr97/Zt29dqx6cAmLv7iYh795VEKXHoVjoiIiJyaWl1YHTWk9RA+v/xz3h/4Pn7Gj6+3fM0j8x4hvyjf26GJiIhINaLC6hjdo7vzxeAvaBrWlO+2fcewb4bx8rKXyS7I9nZoIiIiUg2osDpOq7qtmHHVDJ7t8yz7cvbx3tr3eGLhE7o0KCIiIuVSYVWGy1pextTLpxIRGMF3v3/HnT/eqeJKRERETkqF1UnEhMYw+y+z6RHTg8V7FzPwi4Hsydrj7bBERETkDKXCqhyBvoG8dfFbvNDvBbIKsrj5+5v5PeN3b4clIiIiZyAVVhXgY3wYGDeQZ/s8y+7s3dw681a+3/a9t8MSERGRM4wKq1PQv1l/Phj4AeEB4Twy7xHSctO8HZKIiIicQcotrIwxE40xKcaYtWUcN8aYccaYLcaY1caYbp4P88zRLbob/zz3n+S78rlk6iWsTFnp7ZBERETkDFGRGav3gYEnOX4p0ObIP3cAb1Y+rDNbrya9eCXhFQ4XHebm729mwpoJ3g5JREREzgDlFlbW2rlA+kmaDAE+tMUWAXWNMTX+bcZ/av4nPhn0CUW2iP+s+A85BTneDklERES8zBP3WDUBdh6znXxkX413ToNzmDBgAkW2iIfnP4y11tshiYiIiBeZihQDxpg44BtrbadSjn0LPGOtnX9kezbwT2vtslLa3kHx5UKio6O7T548uVLBV0RWVhahoaGOjvFDxg/89+B/ifGP4aGYh/AxteOZgKrIbW2m/DpL+XWW8usc5dZZZeW3f//+y6y18eWd7+eBGJKBpsdsxwK7S2torR0PjAeIj4+3CQkJHhj+5BITE3F6nL62L3t/2MuSvUv4rPAznu79NFHBUY6OeSaoitzWZsqvs5RfZym/zlFunVXZ/HpiamU6cOORpwMvADKstbVqeXIf48P4i8czqMUgFuxewIfrP/R2SCIiIuIF5c5YGWMmAQlAfWNMMvA44A9grX0LmAEMArYAOcAtTgV7JvPz8eO5vs9R4Crg4/UfMzBuIO2j2ns7LBEREalC5RZW1trh5Ry3wN0ei6iau7PznSzas4gH5jzASwkv0an+CbeliYiISA1VO+6yrkJtItvw5p/epMBVwB0/3EFuYa63QxIREZEqosLKAZ0bdOa5vs+RWZDJlI1TvB2OiIiIVBFPPBUopYiPjqdfbD9eW/Eav2f8zl/O+gsdozp6OywRERFxkGasHGKMYXTP0RwuOszUzVP5R+I/vB2SiIiIOEyFlYPqB9fn7T+9DUBGfgYu6/JyRCIiIuIkFVYO69mkJ8/2eZbM/Exu+f4WDuYd9HZIIiIi4hAVVlVgUItBXNn6SpanLOf+OfezLWObt0MSERERB6iwqgLGGJ7s9SQPnfcQSelJvLLsFW+HJCIiIg5QYVWFrm9/PRc2u5DV+1czZeMUMg5neDskERER8SAVVlUsPjqe/bn7eWrRU3qnoIiISA2jwqqKDWk9hCtaXwHAkr1LKH4jkIiIiNQEKqyqmL+PP0/1eoq48DhWpKzg0w2fejskERER8RAVVl4ypvcYAJ799VneW/uel6MRERERT1Bh5SXnNDiHH4f+CMDLy14mPS/dyxGJiIhIZamw8qJGIY249qxrAXhw7oN8kvSJlyMSERGRyqhQYWWMGWiM2WiM2WKMeaiU482MMXOMMSuMMauNMYM8H2rNNOrcUYT6h7JozyKe/fVZsvKzvB2SiIiInKZyCytjjC/wOnAp0AEYbozpcFyzfwFTrLVdgWuBNzwdaE0V4BvAP8/9p3v77tl3k1+U78WIRERE5HRVZMbqPGCLtXartTYfmAwMOa6NBcKPfI4AdnsuxJrvyjZXsuamNdzV+S6WpyznvE/O0wubRUREqiFT3jpKxpihwEBr7W1HtkcA51tr7zmmTQzwAxAJhAB/stYuK6WvO4A7AKKjo7tPnjzZU9+jTFlZWYSGhjo+jie4rIu/7/g7Llzc3fBu2gW383ZIJ1WdclsdKb/OUn6dpfw6R7l1Vln57d+//zJrbXx55/tVYAxTyr7jq7HhwPvW2peMMT2Aj4wxnawtOe1irR0PjAeIj4+3CQkJFRi+chITE6mKcTxldu5sLv/qciamT2Rswlh6Nunp7ZDKVN1yW90ov85Sfp2l/DpHuXVWZfNbkUuByUDTY7ZjOfFS30hgCoC1diEQBNQ/7ahqsfrB9fn88s9pFNKIv876KxvTN3o7JBEREamgihRWS4A2xpgWxpgAim9On35cmx3ARQDGmPYUF1apngy0NokNi+X2s28HYMziMV6ORkRERCqq3MLKWlsI3APMBJIofvpvnTHmSWPM4CPN/he43RizCpgE3Gz1ErxK+XPLP3Np3KXFr71J0mtvREREqoOK3GOFtXYGMOO4fY8d83k90MuzodVuxhiubXct3237jmd+fYa4iDh6Nj5z77cSERERrbx+RuvcoDODWhSvtfro/EfJOJzh5YhERETkZFRYncF8fXx5ru9zTLxkIim5KXy64VNyCnK8HZaIiIiUQYVVNdCtYTcCfAJ4Y+UbPL/keW+HIyIiImVQYVUN+Pr48mSvJwGYunkqP+34ycsRiYiISGlUWFUTl7W8jP9c+B8A7p9zv155IyIicgZSYVWN9I3tS6/GxQ9fvr3qbS9HIyIiIsdTYVWNGGPclwTfWPUGz/36nJcjEhERkWOpsKpmGtZpyJM9i4urj5M+5oJPL2BbxjbvBiUiIiKACqtq6YrWVzB18FQAsguy+dusv1HgKvByVCIiIqLCqhoyxtA2si2je4wGYFfWLsYs0jsFRUREvE2FVTV2ddur+WTQJ9QNrMvUzVO5ctqVWp1dRETEi1RYVXPnNDiHF/u9CMCWg1u4evrV7Mna4+WoREREaicVVjXA+THnM/GSibzY70X25exj9o7Z3g5JRESkVlJhVUOc2+hcLom7hOg60Szcs5Bfdv3i7ZBERERqnQoVVsaYgcaYjcaYLcaYh8poc40xZr0xZp0x5lPPhikV1aNxD+Ymz+Vvs/7G2v1rsdZ6OyQREZFao9zCyhjjC7wOXAp0AIYbYzoc16YN8H9AL2ttR+ABB2KVCri1063uz8O/Hc7036Z7MRoREZHapSIzVucBW6y1W621+cBkYMhxbW4HXrfWHgCw1qZ4NkypqBYRLZhx5Qz39r9++RdzdszxYkQiIiK1hynvUpExZigw0Fp725HtEcD51tp7jmnzNbAJ6AX4AqOttd+X0tcdwB0A0dHR3SdPnuyp71GmrKwsQkNDHR/nTJNZlMnqnNVMTp+MwXBd1HV0CO5AuG+4x8aorbmtKsqvs5RfZym/zlFunVVWfvv377/MWhtf3vl+FRjDlLLv+GrMD2gDJACxwDxjTCdr7cESJ1k7HhgPEB8fbxMSEiowfOUkJiZSFeOciS7ncq49eC33/nQvn6R9Qr2genx31XfU8a/jkf5rc26rgvLrLOXXWcqvc5RbZ1U2vxW5FJgMND1mOxbYXUqbadbaAmvt78BGigst8bJWdVsxdfBUhrcbTnpeOm+tfot317yLy7q8HZqIiEiNU5HCagnQxhjTwhgTAFwLHH9H9NdAfwBjTH2gLbDVk4HK6Qv2C+ah84of5nxv7XuMXT6Wh+c/rCcGRUREPKzcwspaWwjcA8wEkoAp1tp1xpgnjTGDjzSbCaQZY9YDc4BR1to0p4KWU+djfBjadqh7+9ut3/LTzp+8GJGIiEjNU6F1rKy1M6y1ba21ray1Y47se8xaO/3IZ2ut/R9rbQdr7dnWWufvSpdT9niPx1lz0xqWj1iOn48fq1JXATBnxxzSclUHi4iIVJZWXq+F/H38aRHRgmlbpjE3eS73zbmPh+aVuu6riIiInAIVVrVUx6iOpOelc/fsuwHYfmi7lyMSERGp/lRY1VKPXvAoN3W4yb3tsi5WpqzkyYVPsiF95aStcAAAIABJREFUgxcjExERqb5UWNVSAb4B/G/8/7q39+XsY8R3I/h80+e8s/od0vPSvRidiIhI9aTCqhYzxrBw+ELeuOgN977oOtH8sP0H+n3Wz4uRiYiIVE8qrGq50IBQejXpxWM9HmPB8AUMiBvgPnYw7yBFriIvRiciIlK9qLASfIwPf2n7F8ICwriq9VXu/X0+60OXj7rw1eavvBidiIhI9aHCSkpoHdmap3o9VWLfmMVjWJmy0ksRiYiIVB8qrOQE/Zv2d39uXbc1dQPrMuK7EQydPpSDeQdPcqaIiEjtpsJKThARGMHC4Qu5r+t9vND3BXehtfHARiZvnExeYR5rUtew8/BOvW9QRETkGH7eDkDOTKEBodx+zu0AhAeGu/e/vvJ1Xl/5unvbZ4MP17e/vsrjExERORNpxkrK1aVBFwAubn7xCcem/za9qsMRERE5Y2nGSsrVJ7YPPw79kUYhjViXto5Xlr3C4j2LAViftp5NBzax5cAWLm1xKcYYL0crIiLiPSqspEIahTQCit8xOGHABLYe3EriokReS32Nq6dfDcAnGz7hw4Ef4uvj681QRUREvKZClwKNMQONMRuNMVuMMQ+dpN1QY4w1xsR7LkQ5E7Ws25KWQS35eNDHBPsFA7A6dTVdPurCs78+i8u6vByhiIhI1Su3sDLG+AKvA5cCHYDhxpgOpbQLA+4DFns6SDlzdYzqyOy/zC6x75OkT5i2ZZqXIhIREfGeisxYnQdssdZutdbmA5OBIaW0ewp4HsjzYHxSDYQFhAHQJrINEwZMAOCxBY9x/bfXM23LNL7f9r03wxMREakyprx1iIwxQ4GB1trbjmyPAM631t5zTJuuwL+stVcbYxKBf1hrl5bS1x3AHQDR0dHdJ0+e7LEvUpasrCxCQ0MdH6c2Oja32UXZBPgE4G/8WZWzigmpE0q0fS72Oer41vFGmNWWfnadpfw6S/l1jnLrrLLy279//2XW2nJvdarIzeulPeblrsaMMT7AK8DN5XVkrR0PjAeIj4+3CQkJFRi+chITE6mKcWqjsnLb1/bltzm/MWfnHPe+B5MfdH+eOngqbSPbVkWI1Zp+dp2l/DpL+XWOcuusyua3IpcCk4Gmx2zHAruP2Q4DOgGJxphtwAXAdN3AXnv5GB/GXTiOFSNWEBced8Lx+366j5UpK7nl+1v4bMNnVR+giIiIQyoyY7UEaGOMaQHsAq4Frjt60FqbAdQ/un2yS4FSu/j5+PHBpR/w4boPCQ0I5dXlrwKwK2sXI74bAcCG9A0MazfMm2GKiIh4TLkzVtbaQuAeYCaQBEyx1q4zxjxpjBnsdIBSvdULqscD3R/gtrNvY8HwBdzQ/oYSx7MKsnhi4RNsPrDZSxGKiIh4ToUWCLXWzgBmHLfvsTLaJlQ+LKmJwgLCePC8B7mqzVXM+H0GablpfLXlK77Y9AVfbPqCoW2H8tdz/srKlJWc2+hc6gXV00ruIiJSrWjldalybSLbcH/k/Xy1+Su+2vIV93e7nw3pG9wF1rG6NuzK2xe/7V6EVERE5Eymwkq8ZnCrwTQPb0636G5k5mcyc9tM9zF/H38KXAWsSFnBuv3riG+kZyFEROTMV6FX2og4wdfHl27R3YDiy4T1g4ufgXj4/IdZdsMyfhz6IwDzd83n1eWvknE4w2uxioiIVIRmrOSM8ULfF0jNTWVg3ECMMTQKaUR8dDzvrn0XgKV7l/JEzyeIi4ijyBbh7+Pv5YhFRERKUmElZ4zSLve92O9FEqYkALAydSVDpg0pcezNlW9yZZsrSctLI8g3iM0HNnN/t/uJi4iroqhFRET+oMJKzmhRwVF0jOrIurR19Ivtx+YDm9mdXbw+7T9+/gcALy59scQ5nep3YuTZI6s8VhERERVWcsb74NIPKHQVEuIfwqQNk3h68dMnbT92+Vha1W1FQtOEqglQRETkCN28Lme8QN9AQvxDABjQfABx4XFMvmwyPw79kaU3LKVzg84nnPP4gsdZvm85K1NWsidrDxPWTKC8F46LiIhUlmaspFqJCo7iv1f+t8S+P7f8M6tSVwHwXJ/nmLJpCsv2LeOm728q0e7CphfSsm5LMvMzyS3M5aLPL+KattfwyAWP4GP0/xgiIlJ5Kqyk2ht21jB6N+lNbFgsAL1jezNm0Rhm/F7iZQEMmTaEYWcNY+rmqfgcmaydsmkK3aK7cVnLy6o8bhERqXlUWEm1Z4xxF1UA4QHhPNvnWc5pcE7xC5/bj2DA1AEAfLbxsxPO33FoB4WuQl5d/io7Du2gc8PO3NLxFr1OR0RETpkKK6mRjDFc3/569/aY3mPIL8rn9ZWvc16j80rMZr2x6g02HdjErB2zAPhp5098tfkrejfpzcizR7oXLhURESmPbiyRWmFwq8EMbTuUOdfM4eneT9MyoiXt67Xn/YHvA7iLqqO2HdrGx0kf039KfyZvmOzen5mficu6yC7IZtqWaRS5iqrya4iIyBlOM1ZS6/j6+DLtimm4rAsf48OL/V7kHz//g5YRLXmmzzNEBUXxcdLHvL/ufQDGLB7DhDUTGN5uOK8uf5WuDbuyPGU5UFyQ3dD+Bro27EqAb4AXv5WIiJwJKlRYGWMGAq8CvsAEa+2zxx3/H+A2oBBIBW611m73cKwiHnX0ScBL4i6he3R3fI0vkUGRANzZ+U6C/YK5ovUVjJw5kuSsZMYuHwvgLqoAEncmkrgzEYBpQ6bRsm7Lqv0SIiJyRim3sDLG+AKvAxcDycASY8x0a+36Y5qtAOKttTnGmDuB54FhTgQs4oTj76Oq41+Hu7rcBcB3V39HSk4KF31+0Un7GDJtCBGBEfRt0pcQ/xCubHMlKTkpzN81n+bhzRnRYQQAyZnJzPh9BreffbtukBcRqWEqMmN1HrDFWrsVwBgzGRgCuAsra+2cY9ovAm7wZJAi3tawTkPmXzufVamrCPYLZsrGKfRs3JOY0Bhu/+F2d7uMwxn8d2vxOluTN04u0cfVba6mjn8dRv08irVpa7mo2UW0qtuKXVm7qBtYlwfnPkjG4Qw+GvRRlX43ERHxHFPeatTGmKHAQGvtbUe2RwDnW2vvKaP9f4C91tp/l3LsDuAOgOjo6O6TJ08+vonHZWVlERoa6vg4tZFyCy7rYl7mPJblLOP3w7/TI7QHKQUphPmGsTJn5QntY/xj2FOwB4Dh9YbTJaQLD+58kKYBTdmZvxOAcc3GYYxh88HNhNYJJSYgpkq/U22hn19nKb/OUW6dVVZ++/fvv8xaG1/e+RUprP4CXHJcYXWetfbeUtreANwD9LPWHj5Zv/Hx8Xbp0qXlxVdpiYmJJCQkOD5ObaTc/iG3MJdVqau4IOYCAJbsXcKtM291Hz8r8iw2HthYob5ev+h1OjfoTO/JvQEY238sOQU5LN6zmAe6P0B4QPgJN8pn5mfia3yp41/HQ9+o5tPPr7OUX+cot84qK7/GmAoVVhVZbiEZaHrMdiywu5QB/wQ8Agwur6gSqWmC/YLdRRVAy4iSN7FP/vNk9/Ew/zAiAiPK7Ovu2Xe7iyqAB+Y8wMPzH2bab9PoP6U/3T/uzvjV48nKz+LXPb8CcMnUSxgybQgAy/ct5+wPzmbnoeIZMGstablpnvmiIiJyUhW5x2oJ0MYY0wLYBVwLXHdsA2NMV+Btii8Zpng8SpFqJio4iid6PkFmfiah/qH4+fgxosMIFu1ZxAv9XiAqOIrVqavpWL8jTyx4gqT0JAC6NuzKipQV7n6mXzGdwV8PPqH/11a8xmsrXgOKb7zPzM8kMz+T3Vm7mbRhEgD/Xvxv6gbW5YKYC3hswWNc3PxiVqaspG9sX0adO8r9YmsREfGccgsra22hMeYeYCbFyy1MtNauM8Y8CSy11k4HXgBCgc+PPOW0w1p74m8DkVrkqjZXldjuG9uXucPmUjewLsYY2tVrB8CUy6ew9eBWkrOS6dG4B7/u+ZVpv03j/PzzaRHRgmf7PMtD8x4qc5z9ufvdn0cvGM3CPQsBWLB7AYB7lfkft/8IwNTNU2kT2abEyvRHLd+3nJeWvsT4AeMJ8Q/hcNFhthzYQsf6HYHi2S9ATzOKiJShQutYWWtnADOO2/fYMZ//5OG4RGqko+tkHa9l3ZbuNbB6NelFrya9SExMBKBfbD96xPTg5o43ExUcRVRwFI/98hgtIlowtO1QBn89mCahTWge3txdTJXn2V+Ll6LbfGAzFku/2H50iOrATd/fBMCKlBUE+QYxeeNkZm6bydiEsfy691c+3fAp1551LY9c8MgJfeYX5eNrfPH18T3VtIiI1BhaeV3kDBcaEMr4AeNL7HvjT2+4Py+7YRkBvgEkZyZz6ZeXAnBF6yvw8/Fj4e6F7MraVWq/R4srgC83f8mlLS51b985684SbR9IfMD9efLGydzX7T6e/fVZpv82nVf7v0p4QDi3zLyF/k37M+7CccxNnsv7695ndI/RNAtvRsbhDAJ9AwnyC3L3szNzJ1M3TSU9L52HzntIN96LSI2gwkqkmjv6hGBsWCyLrlvEEwuf4K7OdxETGoO1ll92/8LZ9c9m26FtxIbGkpaXxq7MXczaMYv46Hg2pG/g0w2f8t3v39E2si1/av4n3lj5xknH7Dmpp/vz/XPud3+es3MOG9M3cvfsuwF49JdHubvL3Yz8YSR9mvShZ+OeNA9vTpeGXRj05SD3ec3Cm3Fzx5vx8/njryRrLRvSNxAZFElUUBT+vv4eyZeIiJNUWInUICH+ITzf93n3tjGG3k2KnzDs3KAzUHxjfdvItvRv1t/drk9sH0YvGM393e6nb2xfooKieGrRUww7axhx4XHM3jGbR3s8SlpuGgt3L+SdNe+UGcPQ/w4F4Pr21/NJ0ieM/GEkAPN2zWPernkAdGvYrcQ5ry5/lQlrJnBf1/vo3aQ3s3fMZs7OOe4b+W8/+3bu63afu31p93pZazHGkJKTwqifR3Frp1vp17QfLuti6uap7M3ey52d78TPx49pW6aRfjidBBJOLcEiIuVQYSUi9G7Smx+H/uguVK456xquOesa9/EbOhS/TKFlREu6R3cnPjqebtHd2JO9h7/P+Tu/ZfwGgJ+PH4WuQkL8Q7im7TV8kvRJif1HHX3fYrBfMLmFuQBkF2TzzK/PlBrfO2veITIokuvaXccDcx4gMTmRdvXa0aVBF27ocAPfbv2Wj9d/zJO9nuTviX8HYOfCnVyecjkT105099Mmsg0Dmg/gX7/8C4AbXTfi6+OLy7p4b+17DGwxkCahTQBISksiIz+jxDIaIiLlUWElIkDFn/TzMT70bFJ8KbBFRAu+vuJrxi0fxztr3mHSZZOICoqi0FVITGgMz/d9nnMbnUvdwLp8uflL1uxfw9dbvgaKF02ddNkksguymbRhEhc1v4ifd/7MuBXjSh33+SXP8/ySP2bjNqRvYEP6hhKvDjpaVAGk5qaWKKoARv08iq8bf+3eHvbNMJ7q9RQ7MncwdvlYZm6byaTLJrE8ZXmJBV6vbnM1TcOasu3QNm5ofwM/7fiJK9tcSaOQRuQV5vH8kue57ezbSEpLolP9TjSs05Cbv7+Z5SnLebX/q1zY7EIOFx0mKS2JtpFtS9xPdjDvIHWD6rq3N6ZvpG5gXaJDoiv05yEiZ5ZyV153ilZer/6UW2dVp/wWuYpYl7aOcxqcU25bay3peelEBUeVerygqIDLv76cu7vcTURgBAG+AezM3MmTC590twnyDSKvKM+9PTBuIFe2vpL/+fl/6BTViZZ1W7rX8/I0fx9/ClwFACy6bhE/7fiJh+c/7D7eu0lvUnNSS6y0P7b/WB6Y88cDAKPiR5GRn0HbyLb84+d/cH+3+7mw2YVgcS/0OvGSiRzKP0RceByt6rYCoNBVWOI+tONZa8ktzK3wgwB5hXkcLjpMqH8o/zfv/7iizRX0bNzzpOdYa/lg3Qf0b9af5uHN//iOy8YS6BfInZ2LH3yoTj+/1Y1y66zKrryuwkpOm3LrLOW3pN1Zu2kU0ohCVyEu6+KDdR9wUbOLCPEPISa0+H2KWflZhPiHYLGk5KQQ5BvEYwseY87O4vfEzx02l/fWvUdeYR65e3OJbBJJgauAZuHN+Dn5Z37Z9UuZ41/f/noSdyaWeMoy1D+UrIKs0/5OPsYHl3WV265dvXZsSN8AQP+m/QnxD+GpXk9RZItYlbKKqOAoQv1DeXj+wySlJ/H1kK+ZtGESzcKa0TSsKXuy9+BrfFmybwlD2w5l3f51rE9bz87Mnfy691fu73Y/ry5/lYbBDZl9zWwAkjOT2XZoG9Za+sT2wVrLqtRVRARGMPjrwXSM6sjkP0/m+m+vZ1DLQe6nTNfctAaAid9PpPf5vWkb2Zacghw+3/Q517W/Dn+fkg8hrE5dTYGrgO7R3U/43sv3LefsBmefcE5l5Bfl4+/jf9prsbmsi/25+2lYp6HHYjpV+rvBWSqsyqEfQOcot85Sfj1n6qapxITEuC9hwon53Zu9l2v+ew3D2w2ne3R39mTvcd+LNab3GAa3GkxOQQ7P/vosX235is4NOrMqdVWJcW7ueDPvr3vfvX1J3CXM3DazQjFGBkZy4PABOjfozEPnPcT9P91PSm7Vvsgi0DeQcf3H8fjCx9mbvde9/6e//MSiPYtKzMwB3NPlHv6z8j8l9o3sNBI/Hz/eXv02AKtvXM3bq9/m9ZWvEx4Qzrxr5+Fj/nib2tkfnA3ArKGzmPbbNLYf2s6/e/2bVamrGPHdCO7qfBe3nXMboxeM5rr21+GDD1sObqFbdDfqBdVz9/Xq8le5of0NxITE8OXmL+nfrD+RgZHkFeUR7BcMQFpuGglTEnj0gkdL3EM4ecNk6gXVI75RPBEBEaWuxVbgKsDfx583V77JG6ve4Ierf+CnnT9xVZur3P1Xlar6u2Hd/nVszdjK5a0ud3ysM4kKq3Lol5NzlFtnKb/Oqkh+l+xdQh3/OnSM6ljq8YKiAm6eeTO3drqVi5pdBEB6XjrZ+dm8tOwlxvQew8xtM1m7fy33dr0XH+PDkr1LeGPVG2w+sJneTXoTXSeaqOAo7ulyD7uydhEWUPwuSWst8R/Hk+/K59NBn9IopBGj5o5i2b5lpcbSuUFnOjfozIfrPyzz+9QLqkd6XjoRgREUFBWQU5hT4nhFZ9Aq4z8X/oddWbuYtGESeUV5JQq40nRrWFxAzdoxq8T+ekH1KHQVcrjoMAlNE9wF7NHv0KdJH9pGtuXdte8yosMI6vjVoVl4Mx6Z/8fitn1j+zI3eW6Jfq9ofQX3db2PBnUauPc9s/gZftz+I31j+zJ189QS517a4tIST+IC7Dy0k5TcFEL9QylwFbA/dz/9YvthjOGVZa+wdN9SnujxBDN+n1F86dTAzzt/5t+L/s3dXe9mSKshbEjfcMKldWstK1NXcnDdQdqf15603DQahzZmbvJchrQeUsE/AViXto5rv7mWz/78GR2iOpxwvMBVwI5DO7hi2hUAzBs2j7m75nJ5y8tPe6bv6EMqp1OEHso/RHhAuHs7pyCH9WnriW9Ubo1zWlRYlUO/nJyj3DpL+XVWdcjvofxDGAxhAWFA8Q37Y5eP5eV+L5OUXnyj/FebvyI+Op7Wka0B+O3gb/y04yc61u9Ij5geGGPYl72PhnUasj93PxvSN9C1YVdcuDiYd5CIwAj+nvh3+jftT3SdaJ5f8jyDWgzio6SPKHQVMjBuIIk7E8krymPYWcOICorijVUl1zk7v9H5LN672KPf/dgnRk9FgE8A+a78So0dGRjJSwkvMWfnHBbuXsiWg1tO2v7cRucyKn4UxhiW7VtWYvHdo3rE9KBZeDM+2/hZif0Gg+Xkv4f7NOnD3V3v5vlfn2d5ynIGhA/g18O/cvDwQdpEtmHzgc1MvmwyEYERxIbFMm3LNBbvWUzz8OYs2rOIcxudS/Pw5izbt4zPN31eou9n+jzDOfXP4ZH5j7AydWWpf5bnNDiH1amreaHvCwxsMRCALzZ9QXZBNvWC6pWY0VqZspIJaybQNrIt17a71n3JdODUgezK2sWsobPw9fHlwbkP8lSvp2gU0giDwRjjvkdwd9ZuAnwD8PXx5Z3V7zB181R6Ne7F3zr/jQ5RHRi9YDT/3fpfxl88nh6Ne5w0d6dDhVU5qsNfntWVcuss5ddZyu/JFboKWbh7IT0b98TH+DBrxyx6xPQgNCCUHYd28HvG70xcO5GNBzbyw9AfyC/KZ+LaidQPrs++7H1EpEdw+4DbySnMYU/2HiIDI9lycAtjFo8pfhih55N0qt+JpxY9xYG8A7w38D3qB9fnqYVPEREYwX3d7uOR+Y8w/bfpjOw0kkviLuG9de9hMCQ0TWDZvmUnFCkAryS8woHDB0o87AB/FDCj4kexYM8CYkJi+GLTFwCM7jGa0QtHl5mLBsENqBdUr8QDCQC+xpciW3TKuT3dorEi7u16r/sF7U7oEdODC5tdyJjFY9z7BrcazG8Hf6NJaBN2Zu50v1S+c4POvH3x27y6/NVSHyYZ0HwAa/avwWJpU7cNOYU57hnZyMBI/Hz8SM1NLTOWpmFN+eLyLzh4+CCNQhqVuMRcGSqsyqG/PJ2j3DpL+XWW8lt51loKXYWlrop/svwevV+pIv0X2aJSn4R0WRe/7PqFXk16sTp1NW0i25CUlkT36O5kFmQyesFoujbsSouIFrSu25rcwlxWpKzgitZXuH8BL9y9kHb12hEZFMm85HncNfsuRnQYwdK9S93FwTsD3uGCmAvIzM/k8QWP80C3B0jLSyMuPI7IoEheX/k6n2/8nLS8NNrXa+8+7+jL0+PC4zi30bmEB4QzIG4Ah4sO88O2H/g46WP+es5fiQqO4unFTxPqH0q36G4UuYr4ZfcvPNDtAcYuH3vC936g2wO8ueJNDtvDtK7butzZtOMNaTWEgS0G8vPOn0ssVXK8S1tcyne/fwf8cQm5qlT0exkM/Zr2IyktiV5NevFEzyc8Mn5lCyutYyUiIqfFGHNarxqq6FN+xhj8TOm/pnyMD31i+wDQpWEXAPc9N+EB4byc8PIJ57SIaFFi+9jLSH1i+7ifaNxyYAtTNk3hn+f+013UhQWEuftsFt7Mfd7dXe7m9rNv5+DhgzSs05DsgmySM5M5q95ZXNbyslJjbxrWlJzCHG7seCNZ+Vm8sfIN3rr4rRL38hW6CtmbvZdhZw0jpzCHWdtn0atJL86POZ+W+1vSOr41MSEx+Bgfbp15KwmxCZwfc37xDOK2H7jt7Nvo2rArWw5uISwgjLnJc/E1vlzd9mqgeFmQnVk7+WXXL5zT4BzG9BpDWEAY69PW07tJb1JyUpifPJ/RPUczIG4AULysypjFY/hq81c0Dm3MhAETiAiM4Jut3xAeGE5mfibTt0wnNiwWfx9/Zu2YRWZ+Jh2jOvLRoI+Yu3Mu7659l7CAMPIK81iespyejXvSOLQxX2z6gmC/YOYOm0ugbyB3zr6TDWkb+Hv3v5OWl8Yry15x52ZM7zE0DWvK4j2LeX3l6/j5+HFVm6tKzbU3aMZKTpty6yzl11nKr7OUX+d4Krc5BcWXaY+uk3a8o6+JqowiV1GpT1lC8cMhzcKaER0Szc7MndTxq1Pm+nZFriKKbBH5RfmEBoS69yelJREWEEZsWGyl4jxWZWesKnRB0hgz0Biz0RizxRjzUCnHA40xnx05vtgYE1eRfkVERMQ76vjXKbOogoq/jeFkyiqqoPim/6NvGGga1rTMoupoPwG+ASWKKoD2Ue09WlR5QrmFlTHGF3gduBToAAw3xhz/fOZI4IC1tjXwCvCcpwMVEREROdNVZMbqPGCLtXartTYfmAwcv2DGEOCDI5+/AC4ynih1RURERKqRihRWTYCdx2wnH9lXahtrbSGQAZQ9pyciIiJSA1XkqcDSZp6Ov+O9Im0wxtwB3AEQHR1NYmJiBYavnKysrCoZpzZSbp2l/DpL+XWW8usc5dZZlc1vRQqrZKDpMduxwO4y2iQbY/yACOCERS+steOB8VD8VGBVPDGiJ1Oco9w6S/l1lvLrLOXXOcqtsyqb34oUVkuANsaYFsAu4FrguuPaTAduAhYCQ4GfbDnrOCxbtmy/MWb7qYd8ypoBO6pgnNpIuXWW8uss5ddZyq9zlFtnlZXf5hU5uULrWBljBgFjAV9gorV2jDHmSWCptXa6MSYI+AjoSvFM1bXW2q0V/AKOMsakWmsblN9STpVy6yzl11nKr7OUX+cot86qbH4rtPK6tXYGMOO4fY8d8zkP+MvpBuGwg94OoAZTbp2l/DpL+XWW8usc5dZZlcqvZ95YeGbL8HYANZhy6yzl11nKr7OUX+cot86qVH5rQ2E13tsB1GDKrbOUX2cpv85Sfp2j3DqrUvn12rsCRURERGqa2jBjJSIiIlIlVFiJiIiIeIgKKxEREREPUWElIiIi4iEqrEREREQ8RIWViIiIiIeosBIRERHxEBVWIiIiIh6iwkpERETEQ1RYiYiIiHiICisRERERD1FhJSIiIuIhKqxEREREPESFlYiIiIiHqLASERER8RAVViIiIiIeosJKRERExENUWImIiIh4iAorEREREQ9RYSUiIiLiISqsRERERDxEhZWIiIiIh6iwEhEREfEQFVYiIiIiHqLCSkRERMRDVFiJiIiIeIiftwauX7++jYuLc3yc7OxsQkJCHB+nNlJunaX8Okv5dZby6xzl1lll5XfZsmX7rbUNyjvfa4VVXFwcS5cudXycxMREEhISHB+nNlJunaX8Okv5dZby6xzl1lll5dcYs70i5+tSoIiIiIiHlFtYGWMmGmNSjDFryzhujDHjjDFbjDGrjTHdPB+miIiIyJmvIjNW7wMDT3L8UqDNkX/uAN6sfFgiIiIi1U+591hZa+caY+JO0mQI8KG11gKLjDF1jTEx1to9pxpMQUEBycnJ5OXlneqpZYqIiCD36JwmAAAgAElEQVQpKclj/Z2KoKAgYmNj8ff398r4IiIiUrVMcT1UTqPiwuoba22nUo59AzxrrZ1/ZHs28KC19oQ7040xd1A8q0V0dHT3yZMnlzgeGhpKdHQ0ERERGGNO/duUoqioCF9fX4/0dSqstWRkZLBv3z6ysrKqfPyqkJWVRWhoqLfDqLGUX2cpv86qzvn12Z9G8K+LyR44EHzOvFuRq3Nuq4Oy8tu/f/9l1tr4cjuw1pb7DxAHrC3j2LdA72O2ZwPdy+uze/fu9njr16+3LpfrhP2VcejQIY/2dypcLpddv36918Z32pw5c7wdQo2m/DpL+a28/N27bcqr46yrqOiEY2XlN3fdOluUk1PpsV0ul81evtz9O8Plctmc1avdxw9M/dIe+vHH4jj37bP5u3ZVuO8dd91t15/VzmYtXHjKcRVmZtm8TZvc27kbN9qirKwSbfL37LH5ycmlnu9yuWzOihXW5XLZ3PXrbdbixXZ9+w42f/dua621B//7jf1l3GvWWmsL0tPt3meetXufe96mvvW2Pbwz2ebv3Vtqv/m7d5d6rDAz0+597nlbkJZWcv+hQ3ZTn7720OzZJfvZtcse3rbN5iYllYx5zVrrys+3W/9yjd3/zjslvn/qm2/98edUWGj3jR1rD06bZjf3v9DmbdlSarzWWpu74Y/cpX34kc1ZtarUdod+/NHm/fZbmf2cqrJ+doGltgI1kyeWW0gGmh6zHQvsPt3OPDVTdSaoSd9FxNtylq8guGsXR/+7yl29msB27fAJCKhQe1tQwJZLLqH+HX8l8tphALhyctg+4kby1q2jdeIc/Bs1crcvTE/n4OdfENA0lvBBg07oL2f5CoI7n4M5hVn2vKQk/Js2wzf0j3V3CvbswRa5ODRjBiE9e2IP5xHctStFGRkU7d9PYJs25fZblJVF6rhxRN12G34NGrjzbq2lMCWV9IkTSf/gAwDC/3wZAc2bk7t6NdkLFxJ20Z8ASHv3XYK7dKEwdT9g8W/UiG3XDsc3IoJGTz5JUPt2/DboMuI+/YSgDh1IHfcaWT//TFDHjkQOu4bgzp3J27iRovR0Qnr0IHfdOoLatcP4+pL68iukvfMOzSa+i2+9emT/8gspL7xIg7//nYI9uzk4+TMA2i5dwpa+/QDwaxxDy6+/xjc8HIDcNWsIbNUKnzp1cGVnc+DzzzEBAdjDhwHY/X8P0/y9iQTExZH9668UZWQQ1q8f+PuTs2QJQR06sv/NN0h/dyJ+0dHUHXYNGV9Po2DHDtrMmws+Pvw+eAhBZ59Nk5dexJWbS+aPs8iYNo2CnTtp+vZbBLRsyfbrrqfR44/h1yiGQzNmkD5xIpEjRnDgo4/cfx5b+l9I1G0jSZvwLpFAyuE88tYnkb1ggbtN6iuvABB5ww3kJSUR1j+BQzO+I2/9enebs1atJPXVcYT1TyD19TfIWbQIAP+YGILP7kTOsmVEjRzJoW++oTAlhT3/9zChC34h/b33CGjRguS773H35RcTQ+vZs9jz6KNkTP2SsEsHkrd6NXmrV5Py4kslfp7CLxuELSxk75NPkrNwkXv/7n8+SKMnn2D79TdQ74brqTdyJLnLlpE1dx4Hp0whfNAggrt0Yd/TT5for+m7E8j88Uf3n3PdYcOIeWJ0uT/XVcETlwIvA+4BBgHnA+OsteeV12d8fLw9fh2rpKQk2rdvX6HAKyozM5OwsDCP9nkqnPhOZwqtpeKsqsyvLSgg5aWXqXfLzfhHR5/6+YWF5K5cSXD37uStXk1Qp04nLQ6KsrLJ376N4I4dT9rv4a2/4xMSQv62bey46SYa/uN/ibrttgrFlLdxE34NG+AXGVncT2gIfg0aQEEBOStWsiQ3p0R+8zZs4PcrrsSnTh1inh7D4U2byZo/H5/AQJq8Oha/evXcbQv27uXwpk2kv/+B+xdbq++/I2fZcvY88oi7XZNXXgYfX4oOHiRy2DVsOv8CijIyAKg7/FrqDhnC4d+34crNoU7Xrvx+5VXUG3kr0aNGkbNkCekffYwrJ4fGzzzNlksG0vB//4f8bduJvHYYaePHkzFtunusgFataP7B+2QvXMjuUf88IR9Rd/6Ng59Ooigjg3ZJ6znw0Uekf/AhzT/5mLTx46l3441sHTwE33r1iPvsM1JefJFD//0vUFyQ2IICIv/yF9Lf/wBXTk7Jzo2B436XZF12GaHffktw587krloFUFzA5ORgAgPdxYu7i+BgbG5uiX1hF19M5o8/AlD/vnvZP+614ry+No5d997n/l5pb75V2o9AqQLbt+dwUhIhvXuTPX8+AMFdu5K7YkWZ59S79VbSJ04EIOaZZ8hZuoSMqV9WeMzqwAQEYPPzAQgbOBDjYzg04zuP9R8QF0f+tm0l9vmEhODKzvZI/03GvkL4wJM9Z1dxJ1nHqkKXAsstrIwxk4AEoD6wD3gc8Aew1r5liv835j8UPzmYA9xiS7m/6ngqrKo/FVbOqor8FqSksP/NNwk5/wJ2PfAA4YMupcnLLwOQ8c23YC0Rl/8Z63JhTnKvScorY0l7+233dv277qTBfcW/+LaPuBH/xjFE3jAC/8YxHPr2W/Y9/QwA7Vavwhw3O5S7Zg0Fu/cQ0qsXm+KL/w47+n/vJjCQsIsuLPEXftjFf6LJuHHubWMM1lo2tO+AX0wMbeb8RFK74v8GA+LiCIiLIysxkQP33UerjIMExMVRsGcvGV99RWFqapnfsd36dbiys9n1P/9L9rx5pbbxb9KEgl273NtBnTqRt7Z4pZqYMWNKFF1l8Y2IIPjceLJmzf5jp48PuFzlnlsdRN1+G/VuuYXNPXt5O5STipv6BakvvUT2goUVO6GMP6OA1q2wBQUUbN9xSuP7RkVRlJZ2Succq+4113BwypQS+wLbtOHw5s0nNvb3h4KCCvcd0Lw5/k0au3MTPvhyQnv3JnvhIvJ37CB32TIAov/1L1yZh0h9ddwJfYT07EnDB//JjhtvAh8ffCMjyd+6FSj+7ya4W1ey5847oRjzaxxD4e4/no1rNHo0WT//TOMXXigxa1sZlS2sKvJU4PByjlvg7vL6qU6uuOIKdu7cSV5eHvfffz933HEH33//PQ8//DBFRUXUr1+f2bNnk5WVxb333svSpUsxxvD4449z9dVXezt8OUPkLF9BYJvW+ISGnvTyVXlFS3lsURHG15e0CRNIefEl2q1dg/HzwxYUsPfpp4m6+WYCmjcHIH/HDmxhYfFfYtu2kTlrFgcnTebgpOIHSWxhEbmrV3Pou+9Jf+89AA5v3EDGN9/S5OWXqdOtK/nb/7+9O4+Pqrz+OP452QkJSxIIuyI7WmVz3wIqoj+XlmoV61oVrTtUi1pcqq1LtVatWEVtbasWrVakFXeJ1q1Sl6ogKiD7vpOQfc7vjzuMSUhIIDOZLN/368WLuXeemTlzuMyceZ7nPncxax+YQvoB+9Nh7Fi2vvEG66dOrRLTlldeJefSS1k6/iK2zZ4NUKV3ZbuKLVtIyskBYPnEiVUKpuQ9ekVubx8S8ZKSHX5Fb33tdcpXrGDJheMpXbiQ3Ouvo92JJwJQvnIlS37yk0jb0kWLIh/UHe+/nw27kOd5g/em3fHH1VpUAVWKKiBSVAH1KqoAKjZvrlpUQY1f2B3POIONTz1FQrt2hLZsqddz10dqv76UfDOfNkOGkH7wQSSkp5PSsxdrH/g9pfMXNPj5O/74xyRlZdHv3Xf45tDDIvt7PvIISy+8EAh6hdbc81sq1q4j59JLsaRE1t53P0mdO1O+Zg0A7b//fTZPnw5A20MPxUMVVYaYACwtjcxRI+l4xhksPvMs2p98EptfmEHWOeew8emnyTjiCDr8cCzb/vsRW2bOjPz7db52Em323pueDz/MvO/tCwQ9gml7D2bLjKAXL+Poo6r8O7UZMoRejz3KV0OHkX7ggWQeNYp2J55IYvv2WEICax98kIp169j41N8AyBw9mq2vvhqJv/Ddd4PnGTGc3EmTSO3Xj/I1a1gw+thITGvuuBOADT+/hv1HjaJs9Ro2PvEEW197jd7/eI6UPfdk7e8foM2QIbQ7djQpe/RizV130/aww+g8cQJpgwez4trrInkD6PHQH0jOzeXbH4wlITOTThOuYvUtt9b4b5c5Zgxdf/WrSAFT/PXXbPtwNh1/dCqWkkL7k05i07PPUvTRR/Sd9SbJXbsG8f71CSo2BP/bOv1sImkDB5Jx+OEA9M2fhaWlYWaULllC6bffknFkMHTrkypYef31bH5hBtkXXUTaPnuTccQRbHrm72x65hnK16+nw49OjQzDNxX1GgqMhbp6rFbddhslX85r8OuUV1SQFB6SSB00kC7XX1/nYzZs2EBWVhZFRUXsv//+vPHGG4wYMYK3336b3r17R+6fNGkSJSUl3HvvvQBs3LiRjh071vqeWhr1WNUuVFTEV0ODtXKTu3en7xuvs+WVV9n03LN0/eUvSerSBSoq2PzCDFb+4hf0e+9dkrKy8LIy1t53H+sffYzVf3iQvJEjge+KJwjmvqy+9VY6TfwZm59/nnUPP0yXG25gZfjYTh08iPQhQ9n41FORePr9+20SO3SIfEls12Hc6ZGiCqDdiSdS+P77VKxbt8N7Su3Xl97Tp7Pw5JMjX7A7+1Lv/fw/+PYHY+vMVY8Hfg9mVeZuNFTlIaR6SUqC8nIAut19NyuuvrrWptsLj7R99qHThKvYMnNmlWGh9BEjyDz+uMiXU+rgQWSfd14wPBceMuvxhwdpe9BBrLr1VxR9/HGk2MsYOZKCWbMAaHf88WyZOZPcyZNJysmh6LPPIsNRSV260OfVVyAUIiEtLdIjl3ncGLa+9DL93vk36/7wEAVvvUXZsmUA5Fx+GWVLlhAqLiHnpxeT0qsXXw0bHonbkpPJvvACUnrvxYprrqHvm2+Q3K1b5H4vL2fePt+rmrZKhc52A+fOAXf+e8019Dv0UNoddxwVW7ey5s47yb3uumA4NixUXMxXQ4YGj/tyLltffpnSxUvIufgi3J2tr7xKxpFHQGIiq264gY5nnU2bffYmVFpKQkoKRV/MISEtldS+fYPnKynhq/2GBLk88ki6//5+LDERS0wkVFJCQmoqXloaDHtVVATvO/L/qpCvR4yg86RJZJ93biTGoi/mUPS/T8nMyyMpN5eS+fNZ/8c/kjZwEGt+8xs6nHoqmWOOJW3wYJI6dgyG05KSavyx5KEQKyffQIdTTyG1Xz+2ffghbQ89FNwpyH+LzNHH7PA4Ly2FSu8B4O3334989lYUFFD00UeRYqS6UHExCWlpVWLY9uFslpx7Lns+8zRt9g0+E0KlpZFcFX3+OWt/d29kmHvgnC/w0lIS2rSp8TWqxOsetE1NjewrXbaM8tWrSR8+fCePrNmGp55i9S230u03d9L+pJOqvk5JSZX3Fi0xHwqMlaZcWN188808//zzACxatIirr76aefPm8eSTT1ZpN3z4cKZNm0a/nUwEVWHV8lT/QK5J6bJlLDj6mMh2jwensOySoGO37eGHk7pXbzb8+S+R+7POO4+k7KwqEz63HXYYwx99hLKVK5k/chTdfns3iZmZLLvyqshcFEtOxnehC3+3JSdjCQlkX3AB66ZMofM1V7Pmrrt3+WnaDBtG+Zo1kS/7GtsMHx4ZStguMTub7PPPp3z16siE6bR996X4s8+qtsvKivwyrs324gOCXhIvKSb9oINZe89vaXv44WSOHIlXVDBv72BKafuxY8md9HOWXXkV5atWkXvdtSy96GLajBjOnk88QaikhFU33UybYUNZdeNNJGRmMmD2h5FiJ7V/f/aa8QIF77xLar++JOXkRI4dD4XAnXVTHqTNkP3IOOII5h91NG2GD6PbnXdS8MYbZOTlBT2QFRVsffNNMo86CkIhLOm7AYfSJUsIFRaSOnAgVFRE7nN3tsycSfmq1WSf/xOq2/LKq2x97TVyLr6IlF69gn9ns0jxUd380ceSnJtLzqWXkDZwIAnt2rH2d/fS9rDDWHLOOQAMmhesG1jfz4ftedr+uIbysjIK3n2XtgccQEJ6+q49tqICEhLqdYJE8ddf8+1JJ9Pr8T/R9qCDdjfc3RKNz14vK8PqWGNxxaRr2fzCC1H7t9kdHgpR8OabZIwa1aCe/V3RYguraNnVOVb5+flMnjyZV199lfT0dPLy8pg4cSLPPPMMTzzxRJW2w4YN45lnnqFv+NdSTVRYtTyLxp1ByYIFDPjwP5F92z75hI1//Svd7rwTS05m0Rk/pujjjxv8Wrk3TKZ47tx6T5RN6tKF8nXrIr0v9ZKcTMdTT8XLStn092cByDr/J2x47I+RJhl5eRTk50e2+7zyMovPOZfyVavIOufsKkVip6uuApy1994HVJ1bNPDLueDOvMHfTVrPOvdcSr7+ilBxCV5eTs+H/kCosJAFx4ym4xln0GnCVVWGU7fNnk1q//4ktm8fLPFSUUH56tWsfWAKXSb/go1//ztr7riT5O7dCRUVkZiZiaUkU/LNfPZ8ehpt9tsv8mXe5+WXSNlzzxrTsuXlVyhfv46sH/8YCH/pmkEoxKpf/5rsc86p8lgvK2Pe9/al01VXBT0u5eWsuesu2v3f/0V6BerDQyEwa5JnFbt7rXFtevbZyHAQ1P/zYfM//4WXFNPhlFOiGWqL1lo/extLzOdYtTabN2+mY8eOpKenM2/ePD744ANKSkp46623+Pbbb6sMBY4ePZoHHnhgp0OB0nwUzZlD0Ucfk5F3ZPDrPczdqdi0iTV33U352rVVzh5yd7b860VWXHMNACm99yKpS269i6rKZ0xF9lXqsVl9669qfWy7448nVFhISu/ebHj8cQD65c9i6U8viQwnkZREUk4O5atW7fjaQ4fS5YbJJLRrR0qPHrg7KXvuSXKvXrQ9+JAqhVWbYcMihVX2+PEk9+rFXi9Mp2T+fFIHDCS0rYhOEyeQFD7+3Z11Ux/Bt22j/YknsP6RR0g/8MDgS9mMPq+9yoJjRpPQti25107a8c1lZdX6Kzl9//0jt80MkpJI7t6dbrcHp2On9Armk3U880w6/vgMzIwtL73Eip9PihRCOZdcwuonniCp0lDXDvkdc2yV7UgPZUICXW+6aYf2lpzMwC/nRgoPS0oi97rran3+2jTWr/LdsbNib3cLo/YnnrC74Yg0SSqsqhkzZgwPPfQQ++67LwMGDOCggw6iU6dOTJ06lbFjxxIKhejcuTOvvfYakydP5tJLL2WfffYhMTGRm266ibFj655TIo2vdNky1tx5J6n9+tPpisur3BcqLMTLylj0w+CLYfWdd9Lv7bdYcd11dLr8Clb8/OeUfvvtDs+54cknSe3XL1JUAaybMmWncVSeQ5M9fjydJlzFvEGDq7TZ88mgZ/STceNI++TTqo8/+iiSu3Wjw9ixpA0cGNmf2rcPiR06AJDQJphz0O3OO2h/8slsfXMWy664okovVre77iLj8MMij4HgSzP7/PMj2wPnfEHhe++RkJFBaOvW4LkzM+k8cQIQnL22fc5E11tvqRKnmdEvfxalCxdiKSn0ebnqhPPE9u2BYFg02jJG5tHr8T99V8gB7U86qcr8jE5XXM6c7+1T7/Wq6qsp9jKJSONSYVVNamoqL71U89odxx13XJXtjIwM/hye7yFNR0VBAVtfeYXMo49m+YQJZF98cXBKL8EZZBkjR5LcrSuJHTtS9On/WHzGGdWeoIKNTz5F4dv/pvDt784AyzzmaLa+9npku7bepKROnWh75BFsfvY5uv/uHpZPmAjAgM/+R0JKCotOH0fRp5+SOqA/ZkanCRMIbd3C+kcfq/I8my+6iKF5eWybPZvFZ50NQPqw4WT/5LwdXrNyb0GbIUPZMvMlknKDhSkzR41k0BefR4a/+s/+kMR6DI9bYmLkzJ2KggLaf//7ZIQn09dHYrt2tBkypNb79nzuWVL79Kn389WXmdVvzouKIBGJARVW0uxVFBRAKETZypWk7rUXq++4g83PPkfRZ59T+N77O6xDs+jUUwGCU8lTUmt6Skj6bmJ628MOI+eSS2iz374Uf/45yyZMpHxlzdcY7/zzn0cKn26/Cgqv5RMmQkJCpHdk+4TRtP79Aci5aDwQnK7tFTueVp++//7BGXannErmUaPqzEfHs86kzZD9dpjXs8dTT1I8b169iqrqEjMy6HbH7bv8uJ2pa3FQEZHmSIWVNCsVW7aw9KKL6fLLmyOFyaIfnkLp4sUAZF9wPiVfBwvgbXr66Z0+V/U1b2qTvv/+pA8LTglvM2QIXW/5JUsvHL9Du+0To6vr++YbWKVTgrvefhtbXpxJSrWTHjKPPrrWGNIGDWLQnC9qvb8yM6txsnT6sGGkDxtWr+cQEZHd03RnSYpU42VlbHzqbxR98knkjDMgUlQBrH/0sSqn4Lf/wQ/qfN52J5xAcs+eVfZtv3QGQPr+VU8CyTj8cDqeEayb2/maq+n661/Te/rzNRZVAMndulW5HEpKjx7kXDRe83FERFqgJtdjtbPTeZubeC1l0VyVr1uHJSVVmVANsO6hhyhbvhxLTWNjeMmL4rlzWfiDsaTtPbimpwKg08SJZP/kPDaH1yRLaBtcK277YozbrzGWfuABdL76Zyz60Wl4RUWVy0i0O+GEGgum3MmT6XjmWaTu1buhb1tERFqQJlVYpaWlsX79erKzs5t9ceXurF+/nrQYrArbUs0/ZjReVMTAL+dS+N57eFkZqb17R3qnKi9mV75qFeWrVlHyZc2n5Hc49RRyxl9YZd+Aj/7Lqttuo3TRInpMeYC2hxxCQno6HU4+GUtJod/bb1H4wQes+d3vSB8yBDByr7u2xue3hAQVVSIisoMmVVj16NGDZcuWsXYnF0LdVcXFxXErbtLS0ujRo0dcXrs52r6aeNEnn7Du/t8T2lZIyTfzv7u/rIykrl1J6tRphxW3q8sY+d0k7x4PTiGpU2cAOl1+OSk9epIxciSWkEDOhVWLr7YHHUTvOuZmiYiI1KZJFVbJycn07h3dXoD8/HyGDh0a1eeUhivfuJGijz9m5Q03kjEyj6yzzorct+VfL1K2cuV31yCrdB23Xo8+AgkJrPzF5MginD2nPszS8RdFHp/arx+Zo75bFiBz1HdFVmJmJllnf/daIiIi0dSkCitpmSrPmytdupTSxUtYesEFkfs3P/ePKpdsKV28mPJKvZbdbr+dtoceAhCZBL7nU09WuRZbZYnZ2bF5IyIiInXQWYESU0X/+x9fDx8RuWzLgmNGVymqanzMZ59BpYn/STnZJGVlVTmzDohcniQpO5tej/+J3tOfJ2PUKLpM/kV034SIiEg9qcdKou7bH55Cyh696H7PPWybPZvQtm0sOu10cidPrvOx6QcftMP6Ukk5OTW27fXnP1Py1TwsOTmy0nbPB3d+SRkREZFYUmElUVc8Zw7Fc+aQOWYMa+7+bWT/6l/VfkFhgKRuXWl37LE7Fla5uTW2T87tTHJu54YHLCIiEiUaCpQGcfcq63VVFBRGbi+/4so6H5/Stw97/O0pADIOPZQOp522Q5vEdu2iEKmIiEjs1auwMrMxZvaVmc03sx0W9jGzPczsDTP7zMzyzUxrDLQSS849j6/3PyCyve2D92tslxieH5V5zDFV9ie0SSd96FB6/OFBcq+/HjPD0tOBYFXz7vf+LkaRi4iIRF+dhZWZJQJTgOOAwcA4M6u+3PXdwF/cfV/gFiC6V2uVJqfo009JXLuWbf/5D6GCAgC2vvEGyy67vEq7TlddRZ+XX6JiwwYgKJYGzvmC7vfeC0BCmzYAZI4cGbnd56WX2PO5Z8k+/3zajRnTWG9JRESkweozx+oAYL67LwQws2nAycDcSm0GAxPCt2cB06MZpDQ9i04fR+Up5WsffDByfb3knj1J7NiR4s8+I/uC87GkJLJ/ejEb/vQ4yT17Br1SSYnAd4VVZZo7JSIizZXVdT07MzsFGOPuF4S3zwIOdPfLKrV5CviPu99nZmOB54Acd19f7bnGA+MBcnNzh0+bNi2qb6YmBQUFZGRkxPx1Wo3ycqy8nM5XTai1SeFRR1H4f8eTuG495b161tgm7cMPaf/HP1E8fDibL9z58gutlY7d2FJ+Y0v5jR3lNrZqy+/IkSM/cvcRdT2+Pj1WNV20r3o1djXwgJmdC7wNLAfKd3iQ+1RgKsCIESM8Ly+vHi/fMPn5+TTG67QWi88+h20ffrjTNntfeAFthgzZaZuy/v2Z/8c/MeCqK0nff/9ohthi6NiNLeU3tpTf2FFuY6uh+a1PYbUMqNzt0ANYUbmBu68AxgKYWQbwQ3ffvNtRSdyFiorYPH06HU47DUtIoGz1GpKyOtZZVA38/LMqF0uuTXK3bgyaV/MFlEVERJqr+hRWs4F+ZtaboCfqdOCMyg3MLAfY4O4h4Drgj9EOVBrXuocfZv1DD5PYvj3J3buz6LTTSR04sMa2XW66kTb77Ufa4OrnNIiIiLQudRZW7l5uZpcBrwCJwB/dfY6Z3QL8191nAHnA7WbmBEOBl8YwZmkEXlwCQOnSZZHr9pXMm1dj24yjjiK5syabi4iI1GvldXefCcystu/GSrefBZ6NbmjSWCq2bmXx2efQ6coryMzLo3TJEhLSg7P1NjzxVzIOP2Knj0/SRY9FREQAXdJGgNJFiyj58kuWXfxT+r71FgtGHxu5r2LtOjb/4x+1PjYjLw9LTGyMMEVERJo8XdJGqNi8JXK7bOmSnbZtf/LJVbZzLruslpYiIiKtj3qsWrlQcTHb/js7sl0yf0HkdkLbtoQKg2v/dTxjHCXzF5D1k5/Q4bQfUb5mLXM//pi0vTVhXUREZDsVVq1U4QcfUPzFF2z76GMKZs2K7F91882R2yl9+5DSvQcZI/Nod8IJmFVd0qw4LXWHfSIiIq2ZCqtWau0DD1D034922qbNvvvR5RfXN4gaQPoAABmfSURBVFJEIiIizZ/mWLVSNS3iOeCTj+l+332R7awfn7FDGxEREamdCqtWKlRQuMO+hDZtaHfsaBLS0wFI3mOPxg5LRESkWdNQYCu1fVJ6TXq/MJ2KjRs1f0pERGQXqbBqRby8HMywxMQdCqtOV14RuZ3Ssyf07Fn94SIiIlIHFVatyILjjscSEujzysuECgvJHD2axPbtyf3F9VhqarzDExERafZUWLUS7k7Z0qWR26HCQlL67EXnK6+Mc2QiIiIthwqrVqBs9Wq2vPjdpR6Xnn8+uJPYtm0coxIREWl5dFZgC7X5n/+idNkyANb+7l7W/OY3kfsK33sfgMQsXTxZREQkmtRj1QJ5aSkrrrkGgEHzvqRi06Yd2uRccgnt/u/4xg5NRESkRVNh1cK4O8Vz50a2V91yCwX5+VXaJGZn0+mKyxs5MhERkZZPQ4EtzJZ/vcii08dFtjc+9bcd2iRkaG6ViIhILKiwamGKv/hih339//MBqQMHApC29950ufHGxg5LRESkVajXUKCZjQHuAxKBR939jmr39wL+DHQIt7nW3Wfu8EQSc6GS4irbiTk5JLZvz55PPkGoqIiknJw4RSYiItLy1dljZWaJwBTgOGAwMM7MBldrNhl4xt2HAqcDD0Y7UKmfsmXLq2wndewIQELbtiqqREREYqw+PVYHAPPdfSGAmU0DTgbmVmrjQLvw7fbAimgGKXUr/vprCt99j4pNm7C0NNoMGQIVFXSeNCneoYmIiLQa5u47b2B2CjDG3S8Ib58FHOjul1Vq0xV4FegItAWOdvePaniu8cB4gNzc3OHTpk2L1vuoVUFBARkZGTF/nXjrNPFnJGzbRkVWFmV79WbzBRfE/DVbS27jRfmNLeU3tpTf2FFuY6u2/I4cOfIjdx9R1+Pr02NlNeyrXo2NAx5399+a2cHAX81sH3cPVXmQ+1RgKsCIESM8Ly+vHi/fMPn5+TTG68Tbl2VlACRu2ED2UUcxVLlt9pTf2FJ+Y0v5jR3lNrYamt/6FFbLgJ6Vtnuw41Df+cAYAHd/38zSgBxgzW5HJvWyZPx4tn3wHwgXVhDMpxIREZHGV5/lFmYD/cyst5mlEExOn1GtzRLgKAAzGwSkAWujGajUrPDtf+OlpVX2JaiLWEREJC7qLKzcvRy4DHgF+JLg7L85ZnaLmZ0UbvYz4EIz+x/wN+Bcr2vyljSIu+OhUI33qcdKREQkPuq1jlV4TaqZ1fbdWOn2XODQ6IYmO7NuyoOse+CBGu/z4qJGjkZERERAK683S1teeqnWogqgoqCgEaMRERGR7VRYNUObX3yx1vs6jDud7PPPb8RoREREZDsVVi1Az0cfBSB10CC63nQTSVlZcY5IRESkdarXHCtpOspWr6bg9Teq7Ms47FD2evFfJHXqFKeoREREBFRYNTtrfvvbGven9unTyJGIiIhIdSqsmomCt94iMTuH8hUrq+zvetttcYpIREREqlNh1UwsvejiHfal9u9Ph7E/iEM0IiIiUhMVVs1A9bVWO02cSPvvn0yiFgIVERFpUlRYNQOhautSpfbvR3LnznGKRkRERGqj5RaagfJ166pspw0aHKdIREREZGdUWDUDJV99HbmdOmgQSZ21rIKIiEhTpMKqGVj/2GMA5F53LXs9/w/MLM4RiYiISE1UWDVxZatWUfz553S+5mqyzjkn3uGIiIjITqiwauLKli8HILX/gDhHIiIiInVRYdWEeSjEimt+DkBStq7/JyIi0tRpuYUmbN7gvSO3E3VhZRERkSZPPVZNVGjbtirbKqxERESavnoVVmY2xsy+MrP5ZnZtDff/zsw+Df/52sw2RT/U1qV02bIq2wkpKXGKREREROqrzqFAM0sEpgDHAMuA2WY2w93nbm/j7hMqtb8cGBqDWFuVskqFVedrro5jJCIiIlJf9ZljdQAw390XApjZNOBkYG4t7ccBN0UnvNarZMECAPrOepPkrl3jHI2IiIjUR32GArsDSyttLwvv24GZ7QH0Bt5seGitV+EH/2Htb+8BILFduzhHIyIiIvVVnx6rmpb59lrang486+4VNT6R2XhgPEBubi75+fn1ibFBCgoKGuV1oqnt9BfICN9++8MPoYmutN4cc9ucKL+xpfzGlvIbO8ptbDU0v/UprJYBPStt9wBW1NL2dODS2p7I3acCUwFGjBjheXl59YuyAfLz82mM14kWd2fexT+NbOeNHBnHaHauueW2uVF+Y0v5jS3lN3aU29hqaH7rMxQ4G+hnZr3NLIWgeJpRvZGZDQA6Au/vdjRC2eLF8Q5BREREdlOdhZW7lwOXAa8AXwLPuPscM7vFzE6q1HQcMM3daxsmlJ2oKChk3UMPU7ZqVbxDERERkd1Ur5XX3X0mMLPavhurbd8cvbBanxXXXEPBrFnxDkNEREQaQJe0aQK2zpq1Q1HV7oQTSNmrd5wiEhERkd2hwqoJ2PLSSzvs63bXb7AmejagiIiI1EzXCmwCQlsLIreTOnemx4NTVFSJiIg0Q+qxagIqNm6M3O77+muYrgsoIiLSLKnHKs48FKJ844bItooqERGR5ks9VnGy+LzzKJn3FYnt21O2eAnpBxxA7i+uj3dYIiIi0gAqrOJk2/sfAN8NAyZ16kTagAHxDElEREQaSIVVE9D++98n67xz4x2GiIiINJAKqzhLSE+n2x23xzsMERERiQJNXo8za9Mm3iGIiIhIlKiwamQVBYV4aWlkO2XPPeMXjIiIiESVhgIb2dcjRtBmyJDIdo/f3x/HaERERCSa1GMVB0WffgpAzqWXkpSVFedoREREJFpUWDUiD4Wq7fD4BCIiIiIxocKqEYW2bauyXbZyZZwiERERkVjQHKtGECoupnzVKtb/6fEq+9MGDYxPQCIiIhITKqwawfKrJlCQnx/Z7nztJDJHjSK5R4/4BSUiIiJRp8KqEVQuqgBS+/QhpVev+AQjIiIiMVOvOVZmNsbMvjKz+WZ2bS1tfmRmc81sjpk9Fd0wW5aEjIx4hyAiIiIxUGePlZklAlOAY4BlwGwzm+Hucyu16QdcBxzq7hvNrHOsAm6WEhOhoiKymdSxYxyDERERkVipz1DgAcB8d18IYGbTgJOBuZXaXAhMcfeNAO6+JtqBNkdLLhwPFRUkpKURKiyM7E/eY484RiUiIiKxYl7HWkpmdgowxt0vCG+fBRzo7pdVajMd+Bo4FEgEbnb3l2t4rvHAeIDc3Nzh06ZNi9b7qFVBQQEZjTn0VlFB8jffUNa/P7mXXAqAJySwLe9Iig88ECyB8l49Gy+eGGr03LYyym9sKb+xpfzGjnIbW7Xld+TIkR+5+4i6Hl+fHiurYV/1aiwJ6AfkAT2Af5vZPu6+qcqD3KcCUwFGjBjheXl59Xj5hsnPz6cxXme7Nff8jvVTp9Ll5ptYFd5noRCDzjyTtocc0mhxNIbGzm1ro/zGlvIbW8pv7Ci3sdXQ/NZn8voyoHIXSw9gRQ1tXnD3Mnf/FviKoNBqdUoWLABg1c2/rLK/zYg6i1wRERFp5upTWM0G+plZbzNLAU4HZlRrMx0YCWBmOUB/YGE0A20OvLwcql+2Bsg691wSUlLiEJGIiIg0pjoLK3cvBy4DXgG+BJ5x9zlmdouZnRRu9gqw3szmArOAa9x9fayCbqoWjTuDglmzItsZRx4JQFJOdrxCEhERkUZUrwVC3X0mMLPavhsr3XZgYvhPq1Tw73co/vzzKvu63HgD6x7tSsdx4+IUlYiIiDQmrbweJZtnVB8dheTu3el6001xiEZERETioV4rr0vdKrZsrrKd1KVLnCIRERGReFFhFSXlq79bEzWhXTv2mvFCHKMRERGReFBhFQUl33xDybx5JKSnA9Bmn31IbNcuzlGJiIhIY1Nh1QAVBYWUrVnD8ok/A6DNsGEAJGbrLEAREZHWSJPXG2DJuedS/MUXAGRfeAFtDz+cwnfeiSyzICIiIq2LCqvdVLJwYaSoguAMwLYHHECf118npUf3OEYmIiIi8aKhwN205Jxzq2xbcrCyuooqERGR1kuF1W4qX7s2cjttv33JGDUyjtGIiIhIU6ChwF1Q9PkXlC5cQMn8BZF9OZdfRqdLL41jVCIiItJUqLCqw9Y336Rg1izaHn44y6+4ssp93e+9l8zRx8QpMhEREWlqWnRhtem5f9DhySdZ8sSTu/0che+8EzzX35+tsj+ld28yjjwCS9BoqoiIiARadmH1/D9IXriQiuTdf5spvXtTsWkT7Y4/noy8IyEUIv3gg7GkJBVVIiIiUkWLLqwoK6dsr970fvrpeEciIiIirUCL7nLxsjJITIx3GCIiItJKtOzCqrwcT1BhJSIiIo2jxRdW6rESERGRxlKvwsrMxpjZV2Y238yureH+c81srZl9Gv5zQfRD3XVeXo6rsBIREZFGUufkdTNLBKYAxwDLgNlmNsPd51Zr+rS7XxaDGHebl2uOlYiIiDSe+vRYHQDMd/eF7l4KTANOjm1YUVKmHisRERFpPPUprLoDSyttLwvvq+6HZvaZmT1rZj2jEl0DaY6ViIiINKb6rGNlNezzatv/BP7m7iVmdjHwZ2DUDk9kNh4YD5Cbm0t+fv6uRbuLOhUXUxoKxfx1WquCggLlNoaU39hSfmNL+Y0d5Ta2Gprf+hRWy4DKPVA9gBWVG7j7+kqbjwB31vRE7j4VmAowYsQIz8vL25VYd9k8IDktjYNj/DqtVX5+PrH+N2zNlN/YUn5jS/mNHeU2thqa3/oMBc4G+plZbzNLAU4HZlRuYGZdK22eBHy52xFFkYYCRUREpDHV2WPl7uVmdhnwCpAI/NHd55jZLcB/3X0GcIWZnQSUAxuAc2MYc724O5SV4UkqrERERKRx1Otage4+E5hZbd+NlW5fB1wX3dAaqKIi+Fs9ViIiItJIWuzK615eHvytwkpEREQaSYsvrNRjJSIiIo2l5RZWZWXB3yqsREREpJG02MIK9ViJiIhII2uxhZXmWImIiEhja/GFlXqsREREpLG03MKqTIWViIiINK6WW1iVhyevJ6iwEhERkcbRYgsrTV4XERGRxtZiC6u0wYMZOHcOJft+L96hiIiISCvRYgsrAEtIgIQW/RZFRESkCVHVISIiIhIlKqxEREREokSFlYiIiEiUqLASERERiRIVViIiIiJRYu4enxc2WwssboSX6gUsaYTXaY2U29hSfmNL+Y0t5Td2lNvYqi2/e7h7p7oeHLfCqrGY2dr6JEJ2nXIbW8pvbCm/saX8xo5yG1sNzW9rGArcFO8AWjDlNraU39hSfmNL+Y0d5Ta2GpTf1lBYbY53AC2Ychtbym9sKb+xpfzGjnIbWw3Kb2sorKbGO4AWTLmNLeU3tpTf2FJ+Y0e5ja0G5bfFz7ESERERaSytocdKREREpFG0iMLKzJLiHYOIiIhIsx4KDBdUdwDJwD/d/fU4h9SimNmPgB7Ae+7+QbzjaUnM7AdANvCmuy+MdzwtjY7d2NLxKy2BmZnHoAhqtj1WZmbA/UBX4ENgkpldamap8Y2s+TOzRDO7EZgU3vWImY2NZ0wthZklm9n9wC+A/sAfzeyo8H0W1+BaAB27saXjt3GY2V5m1iPecbREZjbAzL4HEIuiCqA5D6FlAkOAY919q5mtA44HTgWeiGtkzZy7V5jZAOBn7p5vZouAy8zsS3f/Ms7hNWvuXmZmOcCZ7j7PzM4G7jOzEe5eHO/4mjsdu7Gl4ze2zCyF4Iy0Q4DlZvZX4G/uXhSr3pXWIjzC9TBwGLDSzP4JPOPuS6Od22bbY+XuW4BFwLnhXe8CnwAHm1mXOIXVbJnZ2WZ2pJl1CO9aDXQ0syR3/wcwF/iRfpXuOjP7oZkNMbMEM8sCyoFUM0t0978A3wJXhds22/+T8aJjN7Z0/Daq/YAMd+8PTAaOAM4ys2QVVQ22B0FuBwA/BToBl5hZm2jntrn/J3geGGJmXd29APgcKCUYHpQ6WKCrmc0CzgF+DEwxswxgHfA9ICPc/PfAWEBFaz2Ec7uHmc0GLiEYOrkZ2EJwjB7j7hXh5pOBCWaW5u6huATcDJlZFzPLR8du1On4bTxm1qNS0Z8I9A33oLwLvAwMBA6PW4DNWHhINT28mQbsHy5SvwRmAG2BH0b7dZt7YfUOsJ5wr5W7fwTsD7SJY0zNQvjXphMMqS5396MIPkA3AfcBDwKHAvuaWbq7fwV8STDUKjthZu3Cue0OzA7ndjKQBdwA3AKcF/7iSnb3/wH5wAnxirk5MbNu4eGoTGCZjt3oMrOM8PHbDfiPjt/YMLNeZvYm8BTwuJn1BhYCbwNjws1eJShm99H84foLdxi8TTAt6IXwnKqvgJeAs8PN/kcwyrVfpd7uqGjWhZW7rwSmA8eZ2almtidQTNBVLTUwsyQzuw24zcyOBAYAFQDuXg5cDpxIUBQ8BZwe3ibc7j+NHnQzYmaXAm+b2WCCs9K2954uAH5D0HPiwDTgWmDf8P3JBP/RpRbhoajbgA+AfQjmWAI6dqOh0mfD82Z2JnAy0C58t47fKKg2HP1T4AN3PwJYBdxF0IOyEhhuZjnuvoEg94e5e4mGs2tXLTenEfyoPQR4E7gGOJBgytABZtbd3QuBZQSf00XRjKVZF1YA7v4ecDtwHEG36XR3/zC+UTVN4ULqI6AjMB+4FSgDRprZAQDhrvxfAne5+58JfjGdbWafEJzs8Hk8Ym/qKv2nziQo7scDzwEjzGyou5e7+xLgLwRfSLcD3wA3mNkXwFZgaeNH3qycRTAssp+75wMvAofp2G04M+tIUIx2AO4Fvk9QiB5tZkN0/EZN5dEUJyiocPdJBMXpIQS9KO0JhrcBXgCyK/WES81SKt1OI8gn7n47wcjW/sAKgjmYPwu3e4Pgh1g7oqg5nxUY4e4vmdnrwU1Xb1XtQsDd7v5XADMbCvQGbgT+QPArKYGgIBhpZj3dfbqZfQCka72a2rm7h3OXC0wBRgGjgesI1lo71swSCXpb+gCp7n6Pmb0ApOiMtZ0LF679gPvdfaOZHUwwKf1R4G7gCB27DZIB7OnuPwIws9OB5cCvCYb+TtLxu/ssWJLil8A3Zva6uz9JUIyGwgXTFoIh7CuB8wiG/6ZacCLWSQTFVWF8om/azGw0MAFYambvhE+oWERwgkWv8A+Cp4GLCI7fx4CZZnYPcCxBj9bWaMbU7HustnP3MhVVdfoIeCb8AQlBt2gvd38cSDSzy8O/+nsAZe6+FMDdV+mLaefMLCGcu3UEH4CvAmcS/Orf18zOCE/2TQfSwt3QuPsCfSnVLfxLPQcYa2aXAw8ADxH80hxiwWn/oGN3t4Tztc3MHg//SD2E4EdBGXComZ2u43f3WHAm5a8IegL/ApwWnjLwPMGPr54A7v4KQa/Lj8IjMacR9Are5O7XVzpZoNULn1yRZGY/B24j+DzIB443s1HApwR53Td8IsAHBPXOMe6+mGA+4CzgBne/PNpLhbSIHiupH3ffVm3XMcBn4dvnARea2b8I5l3p6um7oNLZUN8j+ABNAa4nGF55EBhnZt8HhhP0sMium0JQTKW4+3Az60fwi/Mjgrk+MwgWrXwkfiE2a6cCPyCYz3N0uJdlP4IvrB9YsNDqCIK5QLIT4d7T7Z8L3QiGoZ8Pr7O2HHifoMiaA5xiZqFwgTqNYBgQd58Tvl8qqZTbcjNbCoxz92/CZwQPA7Ld/U0zWwAcDBQQHMP/JDipBXf/lmCZkJhoMT1WUn8WrE69fdhqRnj3VoJC4A4gz9314bl7/kdQSOUDGwm69O929xMJzlDJc/c/xC+8Zu0b4Gtg+5yqbwhO/Z9OMDn1HmCkjt3d4+5rCfK5Lrz9BkFR8CzBD68ngCN1/O6cmZ1HMCn6lvCuAoIv+BwAd/8aeIbgB9ivCIZh7zCzCQTTMnQSQC0q5fZX4V3TgQXhs1MLCI7X9uH7/kwwnP1bM7uWIN/5jRGnCqvWKUQwsW8dQVfpvwhOow65+zvuvjyu0TVvCUBn4Irw2T4fAVcAuPuM7UNUsuvC3fXXEgxb/9DMBhGc+VfmgTd17DbYfKCHmR1kZp0JitgEd9+m47du4V6Tk4E7CYalBrj7IuBjgi/27SYR9LJmEawRNg3Yi6D3Rde8rUG13I4xs77uXuTuIQ+uCJBCMGn9AwB3X+HuDxIUqynA6e7+QqPEqpMMWiczOwh4L/znT+7+WJxDahEsWMW3KHzbgM7uvjrOYbUoZnYYwckBJwCPuLuG/qLEzNIIlgE4keAHwv3urmkBu2D7hGkzuwPo7e6nmVlbggnVJ7n7+xZcXuUPwK3hydVSD9Vy29Pdf1zpvs7AX9x9jJl1Bw5x97/HJU4VVq2TBRf4PAu4x91L4h1PS2PB5VR0MkUMWbDIrSb0xoAFi1Uuc/eyeMfSXIXP6JsB/NLdXwxPWD+eYGi1V/j2cR6sVSW7oFJuJ7v7q+F9BxL0Ck4jWDT8UXefEp683qiFjgorERGRGDCziwguWH14ePs4YCTB2knXamh194Vze4a7HxnevorgxIrHgF/HM7cqrERERKJs+xIsZvYswUKgIYJ11z5v7B6UlqaG3G4gmNQ+z93fjm90mrwuIiISdeEv/nSCuWqnAfPd/TMVVQ1XLbenA+vdfWpTKKpA61iJiIjEyiUEZwQeo7msUddkc6uhQBERkRiodEUGibKmnFsVViIiIiJRojlWIiIiIlGiwkpEREQkSlRYiYiIiESJCisRERGRKFFhJSIiIhIlKqxEREREouT/AYJ6vXPp9giSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x1080 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "df = pd.DataFrame(history.history)\n",
    "df.plot(subplots=True, grid=True, figsize=(10,15))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <font color = blue>Testing Accuracy [Software 2.0]</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def decodeLabel(encodedLabel):\n",
    "    if encodedLabel == 0:\n",
    "        return \"Other\"\n",
    "    elif encodedLabel == 1:\n",
    "        return \"Fizz\"\n",
    "    elif encodedLabel == 2:\n",
    "        return \"Buzz\"\n",
    "    elif encodedLabel == 3:\n",
    "        return \"FizzBuzz\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Errors: 0  Correct :100\n",
      "Testing Accuracy: 100.0\n"
     ]
    }
   ],
   "source": [
    "wrong   = 0\n",
    "right   = 0\n",
    "testData = pd.read_csv('testing.csv')\n",
    "processedTestData  = encodeData(testData['input'].values)\n",
    "processedTestLabel = encodeLabel(testData['label'].values)\n",
    "predictedTestLabel = []\n",
    "\n",
    "for i,j in zip(processedTestData,processedTestLabel):\n",
    "    \n",
    "    # Generates output predictions for the input samples.\n",
    "    y = model.predict(np.array(i).reshape(-1,10))\n",
    "    \n",
    "    # regression prediction(argmax) on nparrays of prediction(y)\n",
    "    predictedTestLabel.append(decodeLabel(y.argmax()))\n",
    "    \n",
    "    #compare the true and predicted values\n",
    "    if j.argmax() == y.argmax():\n",
    "        right = right + 1\n",
    "    else:\n",
    "        wrong = wrong + 1\n",
    "\n",
    "print(\"Errors: \" + str(wrong), \" Correct :\" + str(right))\n",
    "\n",
    "print(\"Testing Accuracy: \" + str(right/(right+wrong)*100))\n",
    "\n",
    "#UBID and personNumber \n",
    "testDataInput = testData['input'].tolist()\n",
    "testDataLabel = testData['label'].tolist()\n",
    "\n",
    "testDataInput.insert(0, \"UBID\")\n",
    "testDataLabel.insert(0, \"saranya\")\n",
    "\n",
    "testDataInput.insert(1, \"personNumber\")\n",
    "testDataLabel.insert(1, \"50248926\")\n",
    "\n",
    "predictedTestLabel.insert(0, \"\")\n",
    "predictedTestLabel.insert(1, \"\")\n",
    "\n",
    "output = {}\n",
    "output[\"input\"] = testDataInput\n",
    "output[\"label\"] = testDataLabel\n",
    "\n",
    "output[\"predicted_label\"] = predictedTestLabel\n",
    "\n",
    "opdf = pd.DataFrame(output)\n",
    "opdf.to_csv('output.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "celltoolbar": "Attachments",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}