Model_03-Adam-k=10-amsgrad-1e-3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Mandatory imports\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import log_loss\n",
    "from sklearn.model_selection import StratifiedKFold, StratifiedShuffleSplit\n",
    "from os.path import join as opj\n",
    "from matplotlib import pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import pylab\n",
    "plt.rcParams['figure.figsize'] = 10, 10\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_json(\"input/train.json\")\n",
    "target_train=train['is_iceberg']\n",
    "test = pd.read_json(\"input/test.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_train=train['is_iceberg']\n",
    "test['inc_angle']=pd.to_numeric(test['inc_angle'], errors='coerce')\n",
    "train['inc_angle']=pd.to_numeric(train['inc_angle'], errors='coerce')#We have only 133 NAs.\n",
    "train['inc_angle']=train['inc_angle'].fillna(method='pad')\n",
    "X_angle=train['inc_angle']\n",
    "test['inc_angle']=pd.to_numeric(test['inc_angle'], errors='coerce')\n",
    "X_test_angle=test['inc_angle']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Generate the training data\n",
    "X_band_1=np.array([np.array(band).astype(np.float32).reshape(75, 75) for band in train[\"band_1\"]])\n",
    "X_band_2=np.array([np.array(band).astype(np.float32).reshape(75, 75) for band in train[\"band_2\"]])\n",
    "X_band_3=(X_band_1+X_band_2)/2\n",
    "#X_band_3=np.array([np.full((75, 75), angel).astype(np.float32) for angel in train[\"inc_angle\"]])\n",
    "X_train = np.concatenate([X_band_1[:, :, :, np.newaxis]\n",
    "                          , X_band_2[:, :, :, np.newaxis]\n",
    "                         , X_band_3[:, :, :, np.newaxis]], axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_band_test_1=np.array([np.array(band).astype(np.float32).reshape(75, 75) for band in test[\"band_1\"]])\n",
    "X_band_test_2=np.array([np.array(band).astype(np.float32).reshape(75, 75) for band in test[\"band_2\"]])\n",
    "X_band_test_3=(X_band_test_1+X_band_test_2)/2\n",
    "#X_band_test_3=np.array([np.full((75, 75), angel).astype(np.float32) for angel in test[\"inc_angle\"]])\n",
    "X_test = np.concatenate([X_band_test_1[:, :, :, np.newaxis]\n",
    "                          , X_band_test_2[:, :, :, np.newaxis]\n",
    "                         , X_band_test_3[:, :, :, np.newaxis]], axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "#Import Keras.\n",
    "from matplotlib import pyplot\n",
    "from keras.optimizers import RMSprop\n",
    "# from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D, MaxPooling2D, Dense, Dropout, Input, Flatten, Activation\n",
    "from keras.layers import GlobalMaxPooling2D\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "from keras.layers.merge import Concatenate\n",
    "from keras.models import Model\n",
    "from keras import initializers\n",
    "from keras.optimizers import Adam\n",
    "from keras.optimizers import rmsprop\n",
    "from keras.layers.advanced_activations import LeakyReLU, PReLU\n",
    "from keras.optimizers import SGD\n",
    "from keras.optimizers import Adadelta\n",
    "from keras.callbacks import ModelCheckpoint, Callback, EarlyStopping\n",
    "\n",
    "from keras.datasets import cifar10\n",
    "from keras.applications.inception_v3 import InceptionV3\n",
    "from keras.applications.vgg16 import VGG16\n",
    "from keras.applications.xception import Xception\n",
    "from keras.applications.mobilenet import MobileNet\n",
    "from keras.applications.vgg19 import VGG19\n",
    "from keras.layers import Concatenate, Dense, LSTM, Input, concatenate\n",
    "from keras.preprocessing import image\n",
    "from keras.applications.vgg16 import preprocess_input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Data Aug for multi-input\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "batch_size=64\n",
    "# Define the image transformations here\n",
    "gen = ImageDataGenerator(horizontal_flip = True,\n",
    "                         vertical_flip = True,\n",
    "                         width_shift_range = 0.,\n",
    "                         height_shift_range = 0.,\n",
    "                         channel_shift_range=0,\n",
    "                         zoom_range = 0.2,\n",
    "                         rotation_range = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here is the function that merges our two generators\n",
    "# We use the exact same generator with the same random seed for both the y and angle arrays\n",
    "def gen_flow_for_two_inputs(X1, X2, y):\n",
    "    genX1 = gen.flow(X1,y,  batch_size=batch_size,seed=55)\n",
    "    genX2 = gen.flow(X1,X2, batch_size=batch_size,seed=55)\n",
    "    while True:\n",
    "            X1i = genX1.next()\n",
    "            X2i = genX2.next()\n",
    "            #Assert arrays are equal - this was for peace of mind, but slows down training\n",
    "            #np.testing.assert_array_equal(X1i[0],X2i[0])\n",
    "            yield [X1i[0], X2i[1]], X1i[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Finally create generator\n",
    "def get_callbacks(filepath, patience=2):\n",
    "    es = EarlyStopping('val_loss', patience=10, mode=\"min\")\n",
    "    msave = ModelCheckpoint(filepath, save_best_only=True)\n",
    "    return [es, msave]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getVggAngleModel():\n",
    "    input_2 = Input(shape=[1], name=\"angle\")\n",
    "    angle_layer = Dense(1, )(input_2)\n",
    "    base_model = VGG16(weights='imagenet', include_top=False, \n",
    "                 input_shape=X_train.shape[1:], classes=1)\n",
    "    x = base_model.get_layer('block5_pool').output\n",
    "    \n",
    "\n",
    "    x = GlobalMaxPooling2D()(x)\n",
    "    merge_one = concatenate([x, angle_layer])\n",
    "    merge_one = Dense(512, activation='relu', name='fc2')(merge_one)\n",
    "    merge_one = Dropout(0.3)(merge_one)\n",
    "    merge_one = Dense(512, activation='relu', name='fc3')(merge_one)\n",
    "    merge_one = Dropout(0.3)(merge_one)\n",
    "    \n",
    "    predictions = Dense(1, activation='sigmoid')(merge_one)\n",
    "    \n",
    "    model = Model(inputs=[base_model.input, input_2], outputs=predictions)\n",
    "    \n",
    "    adam = Adam(lr=1e-3, beta_1=0.9, beta_2=0.999, epsilon=1.0, decay=1e-6, amsgrad=True)\n",
    "    model.compile(loss='binary_crossentropy',\n",
    "                    optimizer=adam,\n",
    "                    metrics=['accuracy'])\n",
    "    \n",
    "#     sgd = SGD(lr=1e-3, decay=1e-6, momentum=0.9, nesterov=True)\n",
    "#     model.compile(loss='binary_crossentropy',\n",
    "#                   optimizer=sgd,\n",
    "#                   metrics=['accuracy'])\n",
    "    return model\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "===================FOLD= 0\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 19s 790ms/step - loss: 1.1837 - acc: 0.5066 - val_loss: 0.6445 - val_acc: 0.6481\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 14s 578ms/step - loss: 1.0665 - acc: 0.5131 - val_loss: 0.6004 - val_acc: 0.7099\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 14s 583ms/step - loss: 1.0049 - acc: 0.5509 - val_loss: 0.5630 - val_acc: 0.7037\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 14s 585ms/step - loss: 0.9712 - acc: 0.5681 - val_loss: 0.5106 - val_acc: 0.7407\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 14s 585ms/step - loss: 0.9068 - acc: 0.5980 - val_loss: 0.4565 - val_acc: 0.7778\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 14s 588ms/step - loss: 0.8624 - acc: 0.6170 - val_loss: 0.4399 - val_acc: 0.7778\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.7697 - acc: 0.6585 - val_loss: 0.3931 - val_acc: 0.7901\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.7139 - acc: 0.6696 - val_loss: 0.3716 - val_acc: 0.8086\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.6326 - acc: 0.7276 - val_loss: 0.3386 - val_acc: 0.8395\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.5504 - acc: 0.7401 - val_loss: 0.3852 - val_acc: 0.8210\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.5279 - acc: 0.7583 - val_loss: 0.3156 - val_acc: 0.8272\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.4971 - acc: 0.7753 - val_loss: 0.3102 - val_acc: 0.8395\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 14s 591ms/step - loss: 0.5001 - acc: 0.7892 - val_loss: 0.2919 - val_acc: 0.8580\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 12s 512ms/step - loss: 0.4199 - acc: 0.8260 - val_loss: 0.3084 - val_acc: 0.8765\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.3847 - acc: 0.8292 - val_loss: 0.3268 - val_acc: 0.8395\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.4099 - acc: 0.8314 - val_loss: 0.3356 - val_acc: 0.8519\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.3657 - acc: 0.8423 - val_loss: 0.2667 - val_acc: 0.8704\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.3706 - acc: 0.8336 - val_loss: 0.2951 - val_acc: 0.8704\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.3946 - acc: 0.8400 - val_loss: 0.2676 - val_acc: 0.8827\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.3453 - acc: 0.8578 - val_loss: 0.2667 - val_acc: 0.8704\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.3547 - acc: 0.8576 - val_loss: 0.2769 - val_acc: 0.8889\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.3363 - acc: 0.8623 - val_loss: 0.2615 - val_acc: 0.9074\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 13s 526ms/step - loss: 0.3550 - acc: 0.8504 - val_loss: 0.2744 - val_acc: 0.8704\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.3848 - acc: 0.8409 - val_loss: 0.2689 - val_acc: 0.8951\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.3036 - acc: 0.8582 - val_loss: 0.2409 - val_acc: 0.8951\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 14s 591ms/step - loss: 0.3280 - acc: 0.8725 - val_loss: 0.2395 - val_acc: 0.9074\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2985 - acc: 0.8723 - val_loss: 0.2476 - val_acc: 0.8889\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2974 - acc: 0.8726 - val_loss: 0.2506 - val_acc: 0.9012\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2873 - acc: 0.8782 - val_loss: 0.2606 - val_acc: 0.8951\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2939 - acc: 0.8812 - val_loss: 0.2447 - val_acc: 0.9198\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2956 - acc: 0.8805 - val_loss: 0.2453 - val_acc: 0.9136\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 14s 588ms/step - loss: 0.2864 - acc: 0.8801 - val_loss: 0.2388 - val_acc: 0.8951\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 14s 591ms/step - loss: 0.2549 - acc: 0.8900 - val_loss: 0.2379 - val_acc: 0.9136\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2667 - acc: 0.8849 - val_loss: 0.2793 - val_acc: 0.8889\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 14s 588ms/step - loss: 0.3014 - acc: 0.8824 - val_loss: 0.2288 - val_acc: 0.9012\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.2589 - acc: 0.8865 - val_loss: 0.2573 - val_acc: 0.9074\n",
      "Epoch 37/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2856 - acc: 0.8799 - val_loss: 0.2399 - val_acc: 0.8951\n",
      "Epoch 38/100\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.2653 - acc: 0.8889 - val_loss: 0.2450 - val_acc: 0.9136\n",
      "Epoch 39/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.2826 - acc: 0.8887 - val_loss: 0.2279 - val_acc: 0.9012\n",
      "Epoch 40/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.2495 - acc: 0.8910 - val_loss: 0.2267 - val_acc: 0.8951\n",
      "Epoch 41/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.2507 - acc: 0.8869 - val_loss: 0.2245 - val_acc: 0.9012\n",
      "Epoch 42/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2407 - acc: 0.8968 - val_loss: 0.2770 - val_acc: 0.8765\n",
      "Epoch 43/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.2333 - acc: 0.9000 - val_loss: 0.2255 - val_acc: 0.9136\n",
      "Epoch 44/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.2529 - acc: 0.8951 - val_loss: 0.2279 - val_acc: 0.9198\n",
      "Epoch 45/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.2252 - acc: 0.9059 - val_loss: 0.2224 - val_acc: 0.9074\n",
      "Epoch 46/100\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.2361 - acc: 0.9110 - val_loss: 0.2278 - val_acc: 0.9012\n",
      "Epoch 47/100\n",
      "24/24 [==============================] - 14s 592ms/step - loss: 0.2290 - acc: 0.9123 - val_loss: 0.2116 - val_acc: 0.9198\n",
      "Epoch 48/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2218 - acc: 0.9104 - val_loss: 0.2643 - val_acc: 0.8765\n",
      "Epoch 49/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2343 - acc: 0.9047 - val_loss: 0.2246 - val_acc: 0.9136\n",
      "Epoch 50/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2243 - acc: 0.9054 - val_loss: 0.2324 - val_acc: 0.9198\n",
      "Epoch 51/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.2307 - acc: 0.9106 - val_loss: 0.2265 - val_acc: 0.9198\n",
      "Epoch 52/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2227 - acc: 0.9046 - val_loss: 0.2152 - val_acc: 0.9136\n",
      "Epoch 53/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.1990 - acc: 0.9178 - val_loss: 0.2095 - val_acc: 0.9198\n",
      "Epoch 54/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2763 - acc: 0.8841 - val_loss: 0.2331 - val_acc: 0.9074\n",
      "Epoch 55/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.2130 - acc: 0.9164 - val_loss: 0.2252 - val_acc: 0.9259\n",
      "Epoch 56/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2326 - acc: 0.9040 - val_loss: 0.2171 - val_acc: 0.9136\n",
      "Epoch 57/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2017 - acc: 0.9163 - val_loss: 0.2231 - val_acc: 0.9136\n",
      "Epoch 58/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2301 - acc: 0.9101 - val_loss: 0.2307 - val_acc: 0.9198\n",
      "Epoch 59/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1884 - acc: 0.9298 - val_loss: 0.2245 - val_acc: 0.9136\n",
      "Epoch 60/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2071 - acc: 0.9143 - val_loss: 0.2160 - val_acc: 0.9074\n",
      "Epoch 61/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1920 - acc: 0.9215 - val_loss: 0.2192 - val_acc: 0.9136\n",
      "Epoch 62/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.1982 - acc: 0.9153 - val_loss: 0.2076 - val_acc: 0.9136\n",
      "Epoch 63/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1996 - acc: 0.9188 - val_loss: 0.2342 - val_acc: 0.9074\n",
      "Epoch 64/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2039 - acc: 0.9119 - val_loss: 0.2205 - val_acc: 0.9198\n",
      "Epoch 65/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2061 - acc: 0.9143 - val_loss: 0.2553 - val_acc: 0.9012\n",
      "Epoch 66/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2202 - acc: 0.8968 - val_loss: 0.2534 - val_acc: 0.9012\n",
      "Epoch 67/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2197 - acc: 0.9106 - val_loss: 0.2255 - val_acc: 0.9198\n",
      "Epoch 68/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1974 - acc: 0.9304 - val_loss: 0.2097 - val_acc: 0.9198\n",
      "Epoch 69/100\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.1872 - acc: 0.9185 - val_loss: 0.2108 - val_acc: 0.9198\n",
      "Epoch 70/100\n",
      "24/24 [==============================] - 12s 512ms/step - loss: 0.1817 - acc: 0.9181 - val_loss: 0.2152 - val_acc: 0.9074\n",
      "Epoch 71/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1766 - acc: 0.9274 - val_loss: 0.2160 - val_acc: 0.9198\n",
      "Epoch 72/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1871 - acc: 0.9192 - val_loss: 0.2322 - val_acc: 0.9012\n",
      "Train loss: 0.130273383571\n",
      "Train accuracy: 0.947295423024\n",
      "Test loss: 0.207577140026\n",
      "Test accuracy: 0.913580246914\n",
      "\n",
      "===================FOLD= 1\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 17s 727ms/step - loss: 0.8319 - acc: 0.5273 - val_loss: 0.7005 - val_acc: 0.5093\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 14s 592ms/step - loss: 0.7757 - acc: 0.5481 - val_loss: 0.6522 - val_acc: 0.5404\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.7473 - acc: 0.5393 - val_loss: 0.6226 - val_acc: 0.6149\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 14s 591ms/step - loss: 0.7057 - acc: 0.5579 - val_loss: 0.5990 - val_acc: 0.6273\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.6634 - acc: 0.6035 - val_loss: 0.5580 - val_acc: 0.6957\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.6454 - acc: 0.6242 - val_loss: 0.5183 - val_acc: 0.7516\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.5972 - acc: 0.6437 - val_loss: 0.4834 - val_acc: 0.7702\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 14s 592ms/step - loss: 0.5804 - acc: 0.6781 - val_loss: 0.4363 - val_acc: 0.8012\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.5138 - acc: 0.7217 - val_loss: 0.3995 - val_acc: 0.8323\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 14s 591ms/step - loss: 0.4835 - acc: 0.7498 - val_loss: 0.3730 - val_acc: 0.8261\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.4538 - acc: 0.7743 - val_loss: 0.3567 - val_acc: 0.8199\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.4289 - acc: 0.7861 - val_loss: 0.3329 - val_acc: 0.8385\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.4217 - acc: 0.7969 - val_loss: 0.3368 - val_acc: 0.8261\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 14s 591ms/step - loss: 0.4183 - acc: 0.7914 - val_loss: 0.2999 - val_acc: 0.8447\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.3705 - acc: 0.8347 - val_loss: 0.2976 - val_acc: 0.8385\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.3709 - acc: 0.8206 - val_loss: 0.3045 - val_acc: 0.8385\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.3271 - acc: 0.8489 - val_loss: 0.3478 - val_acc: 0.8323\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.3441 - acc: 0.8365 - val_loss: 0.2745 - val_acc: 0.8820\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.3203 - acc: 0.8485 - val_loss: 0.3294 - val_acc: 0.8261\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.3284 - acc: 0.8493 - val_loss: 0.2651 - val_acc: 0.8882\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.2957 - acc: 0.8571 - val_loss: 0.2785 - val_acc: 0.8509\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.3289 - acc: 0.8517 - val_loss: 0.2539 - val_acc: 0.8882\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 12s 520ms/step - loss: 0.2941 - acc: 0.8567 - val_loss: 0.2549 - val_acc: 0.8634\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.2889 - acc: 0.8760 - val_loss: 0.2904 - val_acc: 0.8323\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2859 - acc: 0.8795 - val_loss: 0.2594 - val_acc: 0.8634\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.2889 - acc: 0.8705 - val_loss: 0.2426 - val_acc: 0.8758\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 14s 588ms/step - loss: 0.2879 - acc: 0.8741 - val_loss: 0.2360 - val_acc: 0.8944\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2691 - acc: 0.8771 - val_loss: 0.2404 - val_acc: 0.8696\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 14s 591ms/step - loss: 0.2633 - acc: 0.8840 - val_loss: 0.2340 - val_acc: 0.8882\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 14s 593ms/step - loss: 0.2735 - acc: 0.8816 - val_loss: 0.2315 - val_acc: 0.8634\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.2746 - acc: 0.8872 - val_loss: 0.2438 - val_acc: 0.8758\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.2913 - acc: 0.8657 - val_loss: 0.2297 - val_acc: 0.8820\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2564 - acc: 0.8921 - val_loss: 0.2499 - val_acc: 0.8634\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.2615 - acc: 0.8889 - val_loss: 0.2384 - val_acc: 0.8758\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 14s 588ms/step - loss: 0.2755 - acc: 0.8702 - val_loss: 0.2259 - val_acc: 0.9006\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.2355 - acc: 0.9028 - val_loss: 0.2264 - val_acc: 0.8882\n",
      "Epoch 37/100\n",
      "24/24 [==============================] - 14s 592ms/step - loss: 0.2433 - acc: 0.8942 - val_loss: 0.2150 - val_acc: 0.8820\n",
      "Epoch 38/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2456 - acc: 0.9075 - val_loss: 0.2179 - val_acc: 0.8944\n",
      "Epoch 39/100\n",
      "24/24 [==============================] - 14s 588ms/step - loss: 0.2413 - acc: 0.8932 - val_loss: 0.2145 - val_acc: 0.8820\n",
      "Epoch 40/100\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.2395 - acc: 0.8939 - val_loss: 0.2153 - val_acc: 0.8882\n",
      "Epoch 41/100\n",
      "24/24 [==============================] - 12s 512ms/step - loss: 0.2476 - acc: 0.8909 - val_loss: 0.2189 - val_acc: 0.9006\n",
      "Epoch 42/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2606 - acc: 0.8877 - val_loss: 0.2176 - val_acc: 0.8944\n",
      "Epoch 43/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.2288 - acc: 0.9118 - val_loss: 0.2103 - val_acc: 0.8882\n",
      "Epoch 44/100\n",
      "24/24 [==============================] - 12s 512ms/step - loss: 0.2510 - acc: 0.8891 - val_loss: 0.2289 - val_acc: 0.9193\n",
      "Epoch 45/100\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.2418 - acc: 0.8897 - val_loss: 0.2819 - val_acc: 0.8509\n",
      "Epoch 46/100\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.2330 - acc: 0.8941 - val_loss: 0.2106 - val_acc: 0.9006\n",
      "Epoch 47/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.2153 - acc: 0.9087 - val_loss: 0.2064 - val_acc: 0.9006\n",
      "Epoch 48/100\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.2300 - acc: 0.9001 - val_loss: 0.2409 - val_acc: 0.8820\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 49/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.2253 - acc: 0.9048 - val_loss: 0.2075 - val_acc: 0.9006\n",
      "Epoch 50/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2274 - acc: 0.9111 - val_loss: 0.2066 - val_acc: 0.9006\n",
      "Epoch 51/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.2255 - acc: 0.8989 - val_loss: 0.2037 - val_acc: 0.9068\n",
      "Epoch 52/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2278 - acc: 0.9010 - val_loss: 0.2147 - val_acc: 0.8944\n",
      "Epoch 53/100\n",
      "24/24 [==============================] - 14s 591ms/step - loss: 0.2300 - acc: 0.9027 - val_loss: 0.1995 - val_acc: 0.9068\n",
      "Epoch 54/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.2239 - acc: 0.9158 - val_loss: 0.1853 - val_acc: 0.9255\n",
      "Epoch 55/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2115 - acc: 0.9096 - val_loss: 0.2043 - val_acc: 0.9006\n",
      "Epoch 56/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2204 - acc: 0.9057 - val_loss: 0.2083 - val_acc: 0.9068\n",
      "Epoch 57/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2192 - acc: 0.9139 - val_loss: 0.2045 - val_acc: 0.8882\n",
      "Epoch 58/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2068 - acc: 0.9145 - val_loss: 0.1942 - val_acc: 0.9255\n",
      "Epoch 59/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2074 - acc: 0.9195 - val_loss: 0.2083 - val_acc: 0.9006\n",
      "Epoch 60/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2039 - acc: 0.9100 - val_loss: 0.2218 - val_acc: 0.9193\n",
      "Epoch 61/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2376 - acc: 0.9038 - val_loss: 0.1976 - val_acc: 0.9255\n",
      "Epoch 62/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.2061 - acc: 0.9141 - val_loss: 0.2189 - val_acc: 0.8944\n",
      "Epoch 63/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2148 - acc: 0.9110 - val_loss: 0.1934 - val_acc: 0.9006\n",
      "Epoch 64/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2032 - acc: 0.9156 - val_loss: 0.2369 - val_acc: 0.8758\n",
      "Train loss: 0.17204344128\n",
      "Train accuracy: 0.929313929314\n",
      "Test loss: 0.185285735008\n",
      "Test accuracy: 0.925465838509\n",
      "\n",
      "===================FOLD= 2\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 15s 640ms/step - loss: 1.0593 - acc: 0.5240 - val_loss: 0.6747 - val_acc: 0.5155\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 14s 588ms/step - loss: 0.9683 - acc: 0.5439 - val_loss: 0.6272 - val_acc: 0.5652\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.8844 - acc: 0.5518 - val_loss: 0.5987 - val_acc: 0.6149\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 14s 588ms/step - loss: 0.8474 - acc: 0.5690 - val_loss: 0.5695 - val_acc: 0.6335\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.7930 - acc: 0.6175 - val_loss: 0.5200 - val_acc: 0.7640\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.7935 - acc: 0.6018 - val_loss: 0.5239 - val_acc: 0.6957\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.6812 - acc: 0.6427 - val_loss: 0.4580 - val_acc: 0.7640\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 15s 626ms/step - loss: 0.6251 - acc: 0.7028 - val_loss: 0.4166 - val_acc: 0.7950\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 14s 594ms/step - loss: 0.5992 - acc: 0.7186 - val_loss: 0.3824 - val_acc: 0.7950\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.5064 - acc: 0.7633 - val_loss: 0.3488 - val_acc: 0.8385\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.5009 - acc: 0.7702 - val_loss: 0.3335 - val_acc: 0.8509\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.4862 - acc: 0.7898 - val_loss: 0.3135 - val_acc: 0.8385\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.4440 - acc: 0.7892 - val_loss: 0.3081 - val_acc: 0.8509\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.4253 - acc: 0.8086 - val_loss: 0.2804 - val_acc: 0.8634\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.4203 - acc: 0.8220 - val_loss: 0.2916 - val_acc: 0.8509\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.3999 - acc: 0.8274 - val_loss: 0.2563 - val_acc: 0.9006\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.4070 - acc: 0.8173 - val_loss: 0.2972 - val_acc: 0.8696\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.3913 - acc: 0.8246 - val_loss: 0.2540 - val_acc: 0.8882\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.3681 - acc: 0.8331 - val_loss: 0.2366 - val_acc: 0.9006\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.3851 - acc: 0.8259 - val_loss: 0.2717 - val_acc: 0.8882\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.3524 - acc: 0.8507 - val_loss: 0.2585 - val_acc: 0.8944\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.3342 - acc: 0.8555 - val_loss: 0.2322 - val_acc: 0.9006\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.3487 - acc: 0.8459 - val_loss: 0.2351 - val_acc: 0.8944\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.3130 - acc: 0.8672 - val_loss: 0.2496 - val_acc: 0.9006\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.3196 - acc: 0.8610 - val_loss: 0.2281 - val_acc: 0.9193\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 12s 513ms/step - loss: 0.3183 - acc: 0.8552 - val_loss: 0.2314 - val_acc: 0.9193\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.2982 - acc: 0.8720 - val_loss: 0.2213 - val_acc: 0.9130\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.2933 - acc: 0.8723 - val_loss: 0.2155 - val_acc: 0.9193\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.3016 - acc: 0.8723 - val_loss: 0.2292 - val_acc: 0.9130\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.2576 - acc: 0.8825 - val_loss: 0.2092 - val_acc: 0.9130\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2902 - acc: 0.8747 - val_loss: 0.2159 - val_acc: 0.8944\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2825 - acc: 0.8714 - val_loss: 0.2200 - val_acc: 0.9006\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2871 - acc: 0.8724 - val_loss: 0.2755 - val_acc: 0.8944\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.2948 - acc: 0.8847 - val_loss: 0.2083 - val_acc: 0.9130\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 12s 512ms/step - loss: 0.2903 - acc: 0.8714 - val_loss: 0.3162 - val_acc: 0.8634\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.2615 - acc: 0.8818 - val_loss: 0.2021 - val_acc: 0.9193\n",
      "Epoch 37/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2766 - acc: 0.8873 - val_loss: 0.2323 - val_acc: 0.9130\n",
      "Epoch 38/100\n",
      "24/24 [==============================] - 14s 588ms/step - loss: 0.2576 - acc: 0.8826 - val_loss: 0.2001 - val_acc: 0.9193\n",
      "Epoch 39/100\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.2582 - acc: 0.8878 - val_loss: 0.2018 - val_acc: 0.9317\n",
      "Epoch 40/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2746 - acc: 0.8872 - val_loss: 0.2255 - val_acc: 0.9130\n",
      "Epoch 41/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2574 - acc: 0.8939 - val_loss: 0.2032 - val_acc: 0.9068\n",
      "Epoch 42/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2386 - acc: 0.8941 - val_loss: 0.2296 - val_acc: 0.9068\n",
      "Epoch 43/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2387 - acc: 0.8928 - val_loss: 0.2278 - val_acc: 0.9130\n",
      "Epoch 44/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2458 - acc: 0.8962 - val_loss: 0.2079 - val_acc: 0.9255\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 45/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.2378 - acc: 0.9064 - val_loss: 0.1990 - val_acc: 0.9317\n",
      "Epoch 46/100\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.2277 - acc: 0.9085 - val_loss: 0.2038 - val_acc: 0.9317\n",
      "Epoch 47/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.2472 - acc: 0.8959 - val_loss: 0.2011 - val_acc: 0.9317\n",
      "Epoch 48/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.2411 - acc: 0.8990 - val_loss: 0.1978 - val_acc: 0.9317\n",
      "Epoch 49/100\n",
      "24/24 [==============================] - 14s 592ms/step - loss: 0.2245 - acc: 0.9105 - val_loss: 0.1968 - val_acc: 0.9379\n",
      "Epoch 50/100\n",
      "24/24 [==============================] - 14s 588ms/step - loss: 0.2492 - acc: 0.8956 - val_loss: 0.1929 - val_acc: 0.9379\n",
      "Epoch 51/100\n",
      "24/24 [==============================] - 12s 512ms/step - loss: 0.2251 - acc: 0.9097 - val_loss: 0.1972 - val_acc: 0.9379\n",
      "Epoch 52/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2302 - acc: 0.9081 - val_loss: 0.1996 - val_acc: 0.9193\n",
      "Epoch 53/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2092 - acc: 0.9157 - val_loss: 0.2028 - val_acc: 0.9317\n",
      "Epoch 54/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2157 - acc: 0.9074 - val_loss: 0.2070 - val_acc: 0.9379\n",
      "Epoch 55/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2272 - acc: 0.9126 - val_loss: 0.2054 - val_acc: 0.9317\n",
      "Epoch 56/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2154 - acc: 0.9162 - val_loss: 0.2307 - val_acc: 0.9317\n",
      "Epoch 57/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2135 - acc: 0.9095 - val_loss: 0.2205 - val_acc: 0.9193\n",
      "Epoch 58/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1944 - acc: 0.9188 - val_loss: 0.2035 - val_acc: 0.9317\n",
      "Epoch 59/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2082 - acc: 0.9081 - val_loss: 0.2658 - val_acc: 0.8820\n",
      "Epoch 60/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.1770 - acc: 0.9209 - val_loss: 0.1908 - val_acc: 0.9379\n",
      "Epoch 61/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2259 - acc: 0.9031 - val_loss: 0.2458 - val_acc: 0.8882\n",
      "Epoch 62/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1936 - acc: 0.9204 - val_loss: 0.2482 - val_acc: 0.8944\n",
      "Epoch 63/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2216 - acc: 0.9008 - val_loss: 0.2046 - val_acc: 0.9379\n",
      "Epoch 64/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2117 - acc: 0.9166 - val_loss: 0.2062 - val_acc: 0.9441\n",
      "Epoch 65/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1907 - acc: 0.9201 - val_loss: 0.2183 - val_acc: 0.9193\n",
      "Epoch 66/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1862 - acc: 0.9309 - val_loss: 0.2017 - val_acc: 0.9317\n",
      "Epoch 67/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2191 - acc: 0.9041 - val_loss: 0.2068 - val_acc: 0.9317\n",
      "Epoch 68/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1844 - acc: 0.9280 - val_loss: 0.2247 - val_acc: 0.9193\n",
      "Epoch 69/100\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.2163 - acc: 0.9108 - val_loss: 0.2146 - val_acc: 0.9255\n",
      "Epoch 70/100\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.1989 - acc: 0.9255 - val_loss: 0.2037 - val_acc: 0.9441\n",
      "Train loss: 0.150518599962\n",
      "Train accuracy: 0.942480942481\n",
      "Test loss: 0.190766409687\n",
      "Test accuracy: 0.937888198758\n",
      "\n",
      "===================FOLD= 3\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 17s 712ms/step - loss: 0.9458 - acc: 0.4731 - val_loss: 0.7295 - val_acc: 0.5188\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 14s 588ms/step - loss: 0.7929 - acc: 0.5213 - val_loss: 0.6559 - val_acc: 0.6062\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.7567 - acc: 0.5212 - val_loss: 0.6170 - val_acc: 0.6438\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 14s 588ms/step - loss: 0.6905 - acc: 0.5738 - val_loss: 0.5844 - val_acc: 0.7188\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.6436 - acc: 0.6051 - val_loss: 0.5484 - val_acc: 0.7750\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.6315 - acc: 0.6350 - val_loss: 0.5113 - val_acc: 0.7875\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.5778 - acc: 0.6821 - val_loss: 0.4701 - val_acc: 0.8000\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 14s 588ms/step - loss: 0.5608 - acc: 0.6967 - val_loss: 0.4272 - val_acc: 0.8000\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 14s 591ms/step - loss: 0.5035 - acc: 0.7314 - val_loss: 0.3913 - val_acc: 0.8375\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 14s 588ms/step - loss: 0.4744 - acc: 0.7601 - val_loss: 0.3573 - val_acc: 0.8375\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 14s 591ms/step - loss: 0.4353 - acc: 0.7731 - val_loss: 0.3349 - val_acc: 0.8438\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.4065 - acc: 0.7967 - val_loss: 0.3210 - val_acc: 0.8500\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.3992 - acc: 0.8096 - val_loss: 0.3048 - val_acc: 0.8562\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.3698 - acc: 0.8168 - val_loss: 0.2933 - val_acc: 0.8500\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 14s 588ms/step - loss: 0.3672 - acc: 0.8298 - val_loss: 0.2825 - val_acc: 0.8500\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 14s 592ms/step - loss: 0.3443 - acc: 0.8386 - val_loss: 0.2822 - val_acc: 0.8625\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.3327 - acc: 0.8391 - val_loss: 0.2866 - val_acc: 0.8562\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.3304 - acc: 0.8469 - val_loss: 0.3053 - val_acc: 0.8375\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 14s 588ms/step - loss: 0.3322 - acc: 0.8463 - val_loss: 0.2676 - val_acc: 0.8750\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 14s 597ms/step - loss: 0.3192 - acc: 0.8562 - val_loss: 0.2655 - val_acc: 0.8500\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.2842 - acc: 0.8743 - val_loss: 0.2686 - val_acc: 0.8500\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.3003 - acc: 0.8710 - val_loss: 0.2591 - val_acc: 0.8812\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.3032 - acc: 0.8623 - val_loss: 0.2674 - val_acc: 0.8625\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2830 - acc: 0.8710 - val_loss: 0.2706 - val_acc: 0.8625\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 14s 588ms/step - loss: 0.2991 - acc: 0.8689 - val_loss: 0.2534 - val_acc: 0.8625\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2861 - acc: 0.8699 - val_loss: 0.2735 - val_acc: 0.8562\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 14s 591ms/step - loss: 0.2686 - acc: 0.8806 - val_loss: 0.2513 - val_acc: 0.8875\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2570 - acc: 0.8868 - val_loss: 0.2573 - val_acc: 0.8938\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.2743 - acc: 0.8871 - val_loss: 0.2569 - val_acc: 0.8625\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.2764 - acc: 0.8824 - val_loss: 0.2590 - val_acc: 0.8688\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2565 - acc: 0.8870 - val_loss: 0.2544 - val_acc: 0.8750\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2466 - acc: 0.8977 - val_loss: 0.2567 - val_acc: 0.8812\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 12s 514ms/step - loss: 0.2576 - acc: 0.8837 - val_loss: 0.2614 - val_acc: 0.8938\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.2485 - acc: 0.8927 - val_loss: 0.2747 - val_acc: 0.8688\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 35/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.2431 - acc: 0.8992 - val_loss: 0.2603 - val_acc: 0.8875\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2451 - acc: 0.8870 - val_loss: 0.2879 - val_acc: 0.8625\n",
      "Epoch 37/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.2418 - acc: 0.8852 - val_loss: 0.2466 - val_acc: 0.8875\n",
      "Epoch 38/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.2455 - acc: 0.9046 - val_loss: 0.2537 - val_acc: 0.9000\n",
      "Epoch 39/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.2218 - acc: 0.9015 - val_loss: 0.2656 - val_acc: 0.8688\n",
      "Epoch 40/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2443 - acc: 0.8945 - val_loss: 0.2472 - val_acc: 0.8938\n",
      "Epoch 41/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2102 - acc: 0.9098 - val_loss: 0.2490 - val_acc: 0.9000\n",
      "Epoch 42/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2348 - acc: 0.9051 - val_loss: 0.2723 - val_acc: 0.8812\n",
      "Epoch 43/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2320 - acc: 0.9000 - val_loss: 0.2604 - val_acc: 0.8812\n",
      "Epoch 44/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2182 - acc: 0.9039 - val_loss: 0.2896 - val_acc: 0.9000\n",
      "Epoch 45/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2312 - acc: 0.8984 - val_loss: 0.2575 - val_acc: 0.8938\n",
      "Epoch 46/100\n",
      "24/24 [==============================] - 12s 502ms/step - loss: 0.2285 - acc: 0.9009 - val_loss: 0.2678 - val_acc: 0.8750\n",
      "Epoch 47/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.2101 - acc: 0.9091 - val_loss: 0.2511 - val_acc: 0.8938\n",
      "Train loss: 0.201247669406\n",
      "Train accuracy: 0.915512465374\n",
      "Test loss: 0.246574309468\n",
      "Test accuracy: 0.8875\n",
      "\n",
      "===================FOLD= 4\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 16s 674ms/step - loss: 1.0179 - acc: 0.4694 - val_loss: 0.7165 - val_acc: 0.4875\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 14s 591ms/step - loss: 0.8062 - acc: 0.5041 - val_loss: 0.6881 - val_acc: 0.5625\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.7533 - acc: 0.5476 - val_loss: 0.6677 - val_acc: 0.5750\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.7130 - acc: 0.5446 - val_loss: 0.6429 - val_acc: 0.6188\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 14s 593ms/step - loss: 0.6720 - acc: 0.5990 - val_loss: 0.6147 - val_acc: 0.6687\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 14s 593ms/step - loss: 0.6406 - acc: 0.6178 - val_loss: 0.5824 - val_acc: 0.7000\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 14s 591ms/step - loss: 0.6241 - acc: 0.6239 - val_loss: 0.5376 - val_acc: 0.7688\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.5757 - acc: 0.6818 - val_loss: 0.4889 - val_acc: 0.8375\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.5633 - acc: 0.6892 - val_loss: 0.4412 - val_acc: 0.8375\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.5007 - acc: 0.7469 - val_loss: 0.3894 - val_acc: 0.8812\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.4726 - acc: 0.7679 - val_loss: 0.3474 - val_acc: 0.8938\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.4508 - acc: 0.7881 - val_loss: 0.3266 - val_acc: 0.8938\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.4123 - acc: 0.7984 - val_loss: 0.2952 - val_acc: 0.9000\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 14s 592ms/step - loss: 0.3993 - acc: 0.8127 - val_loss: 0.2875 - val_acc: 0.8875\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 14s 591ms/step - loss: 0.3872 - acc: 0.8124 - val_loss: 0.2846 - val_acc: 0.8812\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.3863 - acc: 0.8114 - val_loss: 0.2219 - val_acc: 0.9250\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.3826 - acc: 0.8200 - val_loss: 0.2420 - val_acc: 0.9000\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.3291 - acc: 0.8424 - val_loss: 0.2169 - val_acc: 0.9313\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.3458 - acc: 0.8407 - val_loss: 0.2083 - val_acc: 0.9313\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 12s 512ms/step - loss: 0.3182 - acc: 0.8612 - val_loss: 0.2129 - val_acc: 0.9187\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 14s 591ms/step - loss: 0.3387 - acc: 0.8404 - val_loss: 0.1884 - val_acc: 0.9625\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.3119 - acc: 0.8549 - val_loss: 0.2502 - val_acc: 0.8812\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.3218 - acc: 0.8550 - val_loss: 0.2450 - val_acc: 0.8813\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 12s 513ms/step - loss: 0.3185 - acc: 0.8502 - val_loss: 0.2291 - val_acc: 0.8938\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.2964 - acc: 0.8609 - val_loss: 0.1601 - val_acc: 0.9625\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 12s 513ms/step - loss: 0.2988 - acc: 0.8678 - val_loss: 0.1908 - val_acc: 0.9125\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.3148 - acc: 0.8588 - val_loss: 0.2150 - val_acc: 0.9000\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2677 - acc: 0.8731 - val_loss: 0.2211 - val_acc: 0.9000\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.3029 - acc: 0.8645 - val_loss: 0.1477 - val_acc: 0.9625\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 12s 513ms/step - loss: 0.2750 - acc: 0.8759 - val_loss: 0.1803 - val_acc: 0.9187\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2760 - acc: 0.8702 - val_loss: 0.1832 - val_acc: 0.9250\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2454 - acc: 0.8891 - val_loss: 0.1562 - val_acc: 0.9563\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2827 - acc: 0.8803 - val_loss: 0.1505 - val_acc: 0.9625\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2746 - acc: 0.8743 - val_loss: 0.2271 - val_acc: 0.8875\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2935 - acc: 0.8622 - val_loss: 0.1566 - val_acc: 0.9563\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2696 - acc: 0.8813 - val_loss: 0.1497 - val_acc: 0.9500\n",
      "Epoch 37/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.2781 - acc: 0.8780 - val_loss: 0.1914 - val_acc: 0.9500\n",
      "Epoch 38/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2569 - acc: 0.8806 - val_loss: 0.1559 - val_acc: 0.9688\n",
      "Epoch 39/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.2546 - acc: 0.8834 - val_loss: 0.1371 - val_acc: 0.9688\n",
      "Epoch 40/100\n",
      "24/24 [==============================] - 12s 512ms/step - loss: 0.2776 - acc: 0.8705 - val_loss: 0.1542 - val_acc: 0.9688\n",
      "Epoch 41/100\n",
      "24/24 [==============================] - 14s 591ms/step - loss: 0.2413 - acc: 0.8995 - val_loss: 0.1283 - val_acc: 0.9625\n",
      "Epoch 42/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2522 - acc: 0.8883 - val_loss: 0.1701 - val_acc: 0.9500\n",
      "Epoch 43/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.2376 - acc: 0.8919 - val_loss: 0.1722 - val_acc: 0.9437\n",
      "Epoch 44/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2456 - acc: 0.8853 - val_loss: 0.1691 - val_acc: 0.9250\n",
      "Epoch 45/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.2354 - acc: 0.8949 - val_loss: 0.1383 - val_acc: 0.9563\n",
      "Epoch 46/100\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.2384 - acc: 0.8954 - val_loss: 0.1840 - val_acc: 0.9250\n",
      "Epoch 47/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.2370 - acc: 0.8933 - val_loss: 0.1353 - val_acc: 0.9563\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 48/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2228 - acc: 0.8997 - val_loss: 0.1296 - val_acc: 0.9563\n",
      "Epoch 49/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2281 - acc: 0.8954 - val_loss: 0.2054 - val_acc: 0.9062\n",
      "Epoch 50/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.2322 - acc: 0.9005 - val_loss: 0.1762 - val_acc: 0.9375\n",
      "Epoch 51/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2005 - acc: 0.9059 - val_loss: 0.1708 - val_acc: 0.9500\n",
      "Train loss: 0.194840183549\n",
      "Train accuracy: 0.916897506925\n",
      "Test loss: 0.1282716766\n",
      "Test accuracy: 0.9625\n",
      "\n",
      "===================FOLD= 5\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 16s 677ms/step - loss: 1.6661 - acc: 0.4811 - val_loss: 0.7522 - val_acc: 0.5625\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 1.0146 - acc: 0.5404 - val_loss: 0.6118 - val_acc: 0.7000\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.9622 - acc: 0.5541 - val_loss: 0.5886 - val_acc: 0.6312\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.8533 - acc: 0.5927 - val_loss: 0.5332 - val_acc: 0.7312\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 14s 592ms/step - loss: 0.8800 - acc: 0.5734 - val_loss: 0.4830 - val_acc: 0.7750\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.7847 - acc: 0.6165 - val_loss: 0.4248 - val_acc: 0.7812\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 14s 593ms/step - loss: 0.6914 - acc: 0.6777 - val_loss: 0.3760 - val_acc: 0.8125\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.6407 - acc: 0.6950 - val_loss: 0.3552 - val_acc: 0.8125\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 14s 591ms/step - loss: 0.5921 - acc: 0.7158 - val_loss: 0.3335 - val_acc: 0.8438\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 12s 512ms/step - loss: 0.5522 - acc: 0.7578 - val_loss: 0.3393 - val_acc: 0.8313\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 12s 512ms/step - loss: 0.5121 - acc: 0.7751 - val_loss: 0.3529 - val_acc: 0.8375\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 14s 592ms/step - loss: 0.4713 - acc: 0.7779 - val_loss: 0.2996 - val_acc: 0.8500\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 14s 602ms/step - loss: 0.4556 - acc: 0.7941 - val_loss: 0.2764 - val_acc: 0.8688\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 14s 593ms/step - loss: 0.4756 - acc: 0.7757 - val_loss: 0.2604 - val_acc: 0.8750\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.4260 - acc: 0.8080 - val_loss: 0.2507 - val_acc: 0.8812\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 14s 591ms/step - loss: 0.4173 - acc: 0.8209 - val_loss: 0.2369 - val_acc: 0.8938\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 12s 512ms/step - loss: 0.3997 - acc: 0.8254 - val_loss: 0.2496 - val_acc: 0.8875\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 12s 512ms/step - loss: 0.3949 - acc: 0.8292 - val_loss: 0.2653 - val_acc: 0.8938\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 12s 521ms/step - loss: 0.3985 - acc: 0.8327 - val_loss: 0.2444 - val_acc: 0.8938\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 14s 592ms/step - loss: 0.3427 - acc: 0.8484 - val_loss: 0.2148 - val_acc: 0.8938\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 12s 512ms/step - loss: 0.3372 - acc: 0.8520 - val_loss: 0.2242 - val_acc: 0.8938\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.3374 - acc: 0.8598 - val_loss: 0.2269 - val_acc: 0.9125\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 14s 584ms/step - loss: 0.3404 - acc: 0.8582 - val_loss: 0.2032 - val_acc: 0.9125\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 12s 512ms/step - loss: 0.3471 - acc: 0.8544 - val_loss: 0.2101 - val_acc: 0.9187\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.3338 - acc: 0.8484 - val_loss: 0.1975 - val_acc: 0.9000\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 14s 593ms/step - loss: 0.3147 - acc: 0.8612 - val_loss: 0.1914 - val_acc: 0.9125\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.3208 - acc: 0.8632 - val_loss: 0.1994 - val_acc: 0.8938\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2932 - acc: 0.8695 - val_loss: 0.2011 - val_acc: 0.9187\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.3103 - acc: 0.8686 - val_loss: 0.1918 - val_acc: 0.9187\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.2955 - acc: 0.8705 - val_loss: 0.1880 - val_acc: 0.9187\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 12s 512ms/step - loss: 0.2924 - acc: 0.8788 - val_loss: 0.2037 - val_acc: 0.9125\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2768 - acc: 0.8822 - val_loss: 0.1988 - val_acc: 0.9187\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.2938 - acc: 0.8687 - val_loss: 0.1741 - val_acc: 0.9125\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2755 - acc: 0.8824 - val_loss: 0.2325 - val_acc: 0.9125\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2743 - acc: 0.8818 - val_loss: 0.1826 - val_acc: 0.9187\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2699 - acc: 0.8870 - val_loss: 0.2245 - val_acc: 0.9125\n",
      "Epoch 37/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2587 - acc: 0.8967 - val_loss: 0.1977 - val_acc: 0.9250\n",
      "Epoch 38/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2844 - acc: 0.8811 - val_loss: 0.2056 - val_acc: 0.9062\n",
      "Epoch 39/100\n",
      "24/24 [==============================] - 14s 591ms/step - loss: 0.2580 - acc: 0.8894 - val_loss: 0.1619 - val_acc: 0.9313\n",
      "Epoch 40/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2598 - acc: 0.8899 - val_loss: 0.1828 - val_acc: 0.9125\n",
      "Epoch 41/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2438 - acc: 0.8989 - val_loss: 0.1790 - val_acc: 0.9187\n",
      "Epoch 42/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2652 - acc: 0.8878 - val_loss: 0.1746 - val_acc: 0.9437\n",
      "Epoch 43/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2463 - acc: 0.8918 - val_loss: 0.2023 - val_acc: 0.9187\n",
      "Epoch 44/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.2403 - acc: 0.9055 - val_loss: 0.1799 - val_acc: 0.9437\n",
      "Epoch 45/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2617 - acc: 0.8878 - val_loss: 0.1620 - val_acc: 0.9187\n",
      "Epoch 46/100\n",
      "24/24 [==============================] - 12s 503ms/step - loss: 0.2642 - acc: 0.8809 - val_loss: 0.2128 - val_acc: 0.9250\n",
      "Epoch 47/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.2254 - acc: 0.9037 - val_loss: 0.1690 - val_acc: 0.9313\n",
      "Epoch 48/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2340 - acc: 0.9013 - val_loss: 0.2368 - val_acc: 0.9062\n",
      "Epoch 49/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2283 - acc: 0.9011 - val_loss: 0.1933 - val_acc: 0.9313\n",
      "Train loss: 0.185836594191\n",
      "Train accuracy: 0.924515235457\n",
      "Test loss: 0.16185143739\n",
      "Test accuracy: 0.93125\n",
      "\n",
      "===================FOLD= 6\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 17s 703ms/step - loss: 0.9918 - acc: 0.5349 - val_loss: 0.6527 - val_acc: 0.6312\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 14s 592ms/step - loss: 1.0095 - acc: 0.5000 - val_loss: 0.6283 - val_acc: 0.6625\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 14s 588ms/step - loss: 0.8721 - acc: 0.5695 - val_loss: 0.5944 - val_acc: 0.7000\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 14s 592ms/step - loss: 0.8352 - acc: 0.5689 - val_loss: 0.5676 - val_acc: 0.6750\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.7555 - acc: 0.6039 - val_loss: 0.5484 - val_acc: 0.6813\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.7681 - acc: 0.6246 - val_loss: 0.5200 - val_acc: 0.7063\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 7/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.7023 - acc: 0.6273 - val_loss: 0.4858 - val_acc: 0.7438\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.6373 - acc: 0.6648 - val_loss: 0.4821 - val_acc: 0.7250\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.6171 - acc: 0.6904 - val_loss: 0.4637 - val_acc: 0.7562\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 14s 588ms/step - loss: 0.5604 - acc: 0.7194 - val_loss: 0.4356 - val_acc: 0.7562\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 14s 591ms/step - loss: 0.4944 - acc: 0.7560 - val_loss: 0.4267 - val_acc: 0.7812\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.4956 - acc: 0.7537 - val_loss: 0.4291 - val_acc: 0.7937\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.4460 - acc: 0.7863 - val_loss: 0.4535 - val_acc: 0.7875\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.4045 - acc: 0.8111 - val_loss: 0.4297 - val_acc: 0.7875\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.3986 - acc: 0.8153 - val_loss: 0.5147 - val_acc: 0.7937\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.4047 - acc: 0.8233 - val_loss: 0.4395 - val_acc: 0.8313\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 14s 579ms/step - loss: 0.3589 - acc: 0.8332 - val_loss: 0.4094 - val_acc: 0.8500\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 12s 512ms/step - loss: 0.3861 - acc: 0.8200 - val_loss: 0.4098 - val_acc: 0.8500\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.3946 - acc: 0.8238 - val_loss: 0.3949 - val_acc: 0.8438\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.3612 - acc: 0.8378 - val_loss: 0.3810 - val_acc: 0.8500\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 14s 591ms/step - loss: 0.3454 - acc: 0.8490 - val_loss: 0.3752 - val_acc: 0.8625\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 12s 512ms/step - loss: 0.3438 - acc: 0.8466 - val_loss: 0.3808 - val_acc: 0.8562\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.3349 - acc: 0.8416 - val_loss: 0.3903 - val_acc: 0.8437\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 12s 512ms/step - loss: 0.3111 - acc: 0.8733 - val_loss: 0.4211 - val_acc: 0.8500\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 14s 591ms/step - loss: 0.2950 - acc: 0.8665 - val_loss: 0.3676 - val_acc: 0.8750\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 14s 593ms/step - loss: 0.3275 - acc: 0.8624 - val_loss: 0.3669 - val_acc: 0.8812\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2986 - acc: 0.8606 - val_loss: 0.3695 - val_acc: 0.8625\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.2800 - acc: 0.8769 - val_loss: 0.3548 - val_acc: 0.8875\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.2953 - acc: 0.8713 - val_loss: 0.3685 - val_acc: 0.8562\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.2874 - acc: 0.8756 - val_loss: 0.3346 - val_acc: 0.8688\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 12s 513ms/step - loss: 0.2903 - acc: 0.8709 - val_loss: 0.3554 - val_acc: 0.8625\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2741 - acc: 0.8803 - val_loss: 0.3524 - val_acc: 0.8938\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.2738 - acc: 0.8772 - val_loss: 0.3323 - val_acc: 0.8812\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2402 - acc: 0.9023 - val_loss: 0.3429 - val_acc: 0.8688\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.2494 - acc: 0.8924 - val_loss: 0.3449 - val_acc: 0.8688\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.2529 - acc: 0.8917 - val_loss: 0.3474 - val_acc: 0.8812\n",
      "Epoch 37/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.2617 - acc: 0.8881 - val_loss: 0.3279 - val_acc: 0.9000\n",
      "Epoch 38/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2588 - acc: 0.8865 - val_loss: 0.3352 - val_acc: 0.8875\n",
      "Epoch 39/100\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.2488 - acc: 0.8886 - val_loss: 0.3308 - val_acc: 0.8750\n",
      "Epoch 40/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.2324 - acc: 0.9051 - val_loss: 0.3194 - val_acc: 0.9062\n",
      "Epoch 41/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2463 - acc: 0.8914 - val_loss: 0.3417 - val_acc: 0.8750\n",
      "Epoch 42/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2381 - acc: 0.8963 - val_loss: 0.3791 - val_acc: 0.8750\n",
      "Epoch 43/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2412 - acc: 0.8998 - val_loss: 0.3415 - val_acc: 0.8688\n",
      "Epoch 44/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.2349 - acc: 0.8949 - val_loss: 0.3221 - val_acc: 0.8750\n",
      "Epoch 45/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.2300 - acc: 0.9011 - val_loss: 0.3560 - val_acc: 0.8812\n",
      "Epoch 46/100\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.2309 - acc: 0.9003 - val_loss: 0.3276 - val_acc: 0.8750\n",
      "Epoch 47/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2246 - acc: 0.9088 - val_loss: 0.3327 - val_acc: 0.8812\n",
      "Epoch 48/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2339 - acc: 0.9012 - val_loss: 0.3755 - val_acc: 0.8812\n",
      "Epoch 49/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.2331 - acc: 0.9013 - val_loss: 0.3138 - val_acc: 0.8812\n",
      "Epoch 50/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.2254 - acc: 0.8998 - val_loss: 0.3351 - val_acc: 0.8812\n",
      "Epoch 51/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2122 - acc: 0.9051 - val_loss: 0.3466 - val_acc: 0.8562\n",
      "Epoch 52/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2202 - acc: 0.9076 - val_loss: 0.3465 - val_acc: 0.8875\n",
      "Epoch 53/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2383 - acc: 0.8951 - val_loss: 0.3212 - val_acc: 0.8875\n",
      "Epoch 54/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2163 - acc: 0.9138 - val_loss: 0.3369 - val_acc: 0.8812\n",
      "Epoch 55/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2023 - acc: 0.9143 - val_loss: 0.3176 - val_acc: 0.8938\n",
      "Epoch 56/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.1944 - acc: 0.9210 - val_loss: 0.2850 - val_acc: 0.8875\n",
      "Epoch 57/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2174 - acc: 0.9080 - val_loss: 0.3225 - val_acc: 0.8875\n",
      "Epoch 58/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2098 - acc: 0.9078 - val_loss: 0.3414 - val_acc: 0.8750\n",
      "Epoch 59/100\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.2230 - acc: 0.9047 - val_loss: 0.3308 - val_acc: 0.8875\n",
      "Epoch 60/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1877 - acc: 0.9164 - val_loss: 0.3370 - val_acc: 0.8750\n",
      "Epoch 61/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2043 - acc: 0.9158 - val_loss: 0.3014 - val_acc: 0.8875\n",
      "Epoch 62/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1909 - acc: 0.9150 - val_loss: 0.3351 - val_acc: 0.8750\n",
      "Epoch 63/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1862 - acc: 0.9280 - val_loss: 0.3802 - val_acc: 0.8500\n",
      "Epoch 64/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1911 - acc: 0.9197 - val_loss: 0.3206 - val_acc: 0.8812\n",
      "Epoch 65/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2073 - acc: 0.9199 - val_loss: 0.3220 - val_acc: 0.8688\n",
      "Epoch 66/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1721 - acc: 0.9210 - val_loss: 0.3401 - val_acc: 0.8812\n",
      "Train loss: 0.171554177958\n",
      "Train accuracy: 0.925207756233\n",
      "Test loss: 0.284952160716\n",
      "Test accuracy: 0.8875\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "===================FOLD= 7\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 17s 708ms/step - loss: 1.4887 - acc: 0.5182 - val_loss: 0.6854 - val_acc: 0.5375\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 14s 592ms/step - loss: 0.9897 - acc: 0.5340 - val_loss: 0.6770 - val_acc: 0.5687\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.9920 - acc: 0.5300 - val_loss: 0.6257 - val_acc: 0.6250\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.8763 - acc: 0.5852 - val_loss: 0.5750 - val_acc: 0.6562\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 14s 592ms/step - loss: 0.8043 - acc: 0.6016 - val_loss: 0.5400 - val_acc: 0.6750\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.7098 - acc: 0.6528 - val_loss: 0.4770 - val_acc: 0.7125\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 14s 591ms/step - loss: 0.6627 - acc: 0.6756 - val_loss: 0.4000 - val_acc: 0.7937\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.6067 - acc: 0.7165 - val_loss: 0.4430 - val_acc: 0.7625\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.6026 - acc: 0.7383 - val_loss: 0.3458 - val_acc: 0.8313\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.5116 - acc: 0.7759 - val_loss: 0.3551 - val_acc: 0.8125\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.4496 - acc: 0.8000 - val_loss: 0.3301 - val_acc: 0.8125\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.4781 - acc: 0.7831 - val_loss: 0.3094 - val_acc: 0.8438\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.4609 - acc: 0.7985 - val_loss: 0.3884 - val_acc: 0.8063\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.4270 - acc: 0.8221 - val_loss: 0.3363 - val_acc: 0.8438\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.3829 - acc: 0.8368 - val_loss: 0.2822 - val_acc: 0.8750\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.3857 - acc: 0.8321 - val_loss: 0.2954 - val_acc: 0.8625\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.3664 - acc: 0.8419 - val_loss: 0.2951 - val_acc: 0.8750\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.3955 - acc: 0.8169 - val_loss: 0.2833 - val_acc: 0.8812\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.3627 - acc: 0.8404 - val_loss: 0.2706 - val_acc: 0.8688\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.3613 - acc: 0.8549 - val_loss: 0.2744 - val_acc: 0.8750\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.3493 - acc: 0.8526 - val_loss: 0.2443 - val_acc: 0.8875\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.3390 - acc: 0.8565 - val_loss: 0.2590 - val_acc: 0.9000\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 14s 583ms/step - loss: 0.3223 - acc: 0.8611 - val_loss: 0.2410 - val_acc: 0.9000\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.3220 - acc: 0.8668 - val_loss: 0.2751 - val_acc: 0.8875\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.2963 - acc: 0.8712 - val_loss: 0.2643 - val_acc: 0.8750\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.3080 - acc: 0.8678 - val_loss: 0.2957 - val_acc: 0.8875\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2806 - acc: 0.8801 - val_loss: 0.2971 - val_acc: 0.8812\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.3026 - acc: 0.8733 - val_loss: 0.2687 - val_acc: 0.8812\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2797 - acc: 0.8728 - val_loss: 0.2679 - val_acc: 0.9125\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2992 - acc: 0.8713 - val_loss: 0.2793 - val_acc: 0.9000\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2723 - acc: 0.8909 - val_loss: 0.2537 - val_acc: 0.8812\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.2494 - acc: 0.9033 - val_loss: 0.2267 - val_acc: 0.9062\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2891 - acc: 0.8790 - val_loss: 0.2733 - val_acc: 0.9125\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.2670 - acc: 0.8880 - val_loss: 0.2606 - val_acc: 0.9000\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.2919 - acc: 0.8831 - val_loss: 0.2390 - val_acc: 0.9062\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2654 - acc: 0.8946 - val_loss: 0.2708 - val_acc: 0.9062\n",
      "Epoch 37/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2688 - acc: 0.8888 - val_loss: 0.2881 - val_acc: 0.8938\n",
      "Epoch 38/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2664 - acc: 0.8922 - val_loss: 0.2408 - val_acc: 0.9125\n",
      "Epoch 39/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2543 - acc: 0.8862 - val_loss: 0.2474 - val_acc: 0.8875\n",
      "Epoch 40/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2435 - acc: 0.9020 - val_loss: 0.2405 - val_acc: 0.9062\n",
      "Epoch 41/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.2366 - acc: 0.9060 - val_loss: 0.2228 - val_acc: 0.8750\n",
      "Epoch 42/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2449 - acc: 0.9000 - val_loss: 0.2425 - val_acc: 0.9062\n",
      "Epoch 43/100\n",
      "24/24 [==============================] - 14s 591ms/step - loss: 0.2379 - acc: 0.9073 - val_loss: 0.2159 - val_acc: 0.9000\n",
      "Epoch 44/100\n",
      "24/24 [==============================] - 12s 512ms/step - loss: 0.2454 - acc: 0.9003 - val_loss: 0.2646 - val_acc: 0.9000\n",
      "Epoch 45/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2423 - acc: 0.8946 - val_loss: 0.2218 - val_acc: 0.9000\n",
      "Epoch 46/100\n",
      "24/24 [==============================] - 12s 505ms/step - loss: 0.2730 - acc: 0.8840 - val_loss: 0.2177 - val_acc: 0.8938\n",
      "Epoch 47/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2392 - acc: 0.9031 - val_loss: 0.2225 - val_acc: 0.8938\n",
      "Epoch 48/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2489 - acc: 0.8928 - val_loss: 0.2497 - val_acc: 0.9000\n",
      "Epoch 49/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2257 - acc: 0.9021 - val_loss: 0.2212 - val_acc: 0.9000\n",
      "Epoch 50/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.2347 - acc: 0.9034 - val_loss: 0.2098 - val_acc: 0.8938\n",
      "Epoch 51/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.2273 - acc: 0.9055 - val_loss: 0.2250 - val_acc: 0.9062\n",
      "Epoch 52/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.2057 - acc: 0.9132 - val_loss: 0.2188 - val_acc: 0.9062\n",
      "Epoch 53/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2158 - acc: 0.9116 - val_loss: 0.2128 - val_acc: 0.9000\n",
      "Epoch 54/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2294 - acc: 0.9078 - val_loss: 0.2197 - val_acc: 0.8812\n",
      "Epoch 55/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.2120 - acc: 0.9119 - val_loss: 0.2077 - val_acc: 0.9000\n",
      "Epoch 56/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.2131 - acc: 0.9094 - val_loss: 0.2052 - val_acc: 0.8938\n",
      "Epoch 57/100\n",
      "24/24 [==============================] - 14s 591ms/step - loss: 0.2268 - acc: 0.9013 - val_loss: 0.1993 - val_acc: 0.9000\n",
      "Epoch 58/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2206 - acc: 0.9051 - val_loss: 0.2201 - val_acc: 0.9062\n",
      "Epoch 59/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.2167 - acc: 0.9134 - val_loss: 0.2401 - val_acc: 0.9125\n",
      "Epoch 60/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2017 - acc: 0.9143 - val_loss: 0.2171 - val_acc: 0.9125\n",
      "Epoch 61/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1979 - acc: 0.9226 - val_loss: 0.2583 - val_acc: 0.9000\n",
      "Epoch 62/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2144 - acc: 0.9164 - val_loss: 0.2072 - val_acc: 0.8938\n",
      "Epoch 63/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1818 - acc: 0.9233 - val_loss: 0.2296 - val_acc: 0.8938\n",
      "Epoch 64/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2073 - acc: 0.9127 - val_loss: 0.2719 - val_acc: 0.9062\n",
      "Epoch 65/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.1925 - acc: 0.9161 - val_loss: 0.1963 - val_acc: 0.9000\n",
      "Epoch 66/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2013 - acc: 0.9221 - val_loss: 0.2197 - val_acc: 0.8938\n",
      "Epoch 67/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.1850 - acc: 0.9265 - val_loss: 0.2039 - val_acc: 0.9000\n",
      "Epoch 68/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.2058 - acc: 0.9249 - val_loss: 0.2861 - val_acc: 0.8938\n",
      "Epoch 69/100\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.1921 - acc: 0.9205 - val_loss: 0.2073 - val_acc: 0.8938\n",
      "Epoch 70/100\n",
      "24/24 [==============================] - 14s 588ms/step - loss: 0.1798 - acc: 0.9261 - val_loss: 0.1916 - val_acc: 0.8875\n",
      "Epoch 71/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1818 - acc: 0.9238 - val_loss: 0.2168 - val_acc: 0.9000\n",
      "Epoch 72/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.1773 - acc: 0.9324 - val_loss: 0.2427 - val_acc: 0.9000\n",
      "Epoch 73/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1730 - acc: 0.9362 - val_loss: 0.2650 - val_acc: 0.9062\n",
      "Epoch 74/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1668 - acc: 0.9291 - val_loss: 0.2064 - val_acc: 0.8938\n",
      "Epoch 75/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1782 - acc: 0.9306 - val_loss: 0.2327 - val_acc: 0.9062\n",
      "Epoch 76/100\n",
      "24/24 [==============================] - 12s 512ms/step - loss: 0.1625 - acc: 0.9378 - val_loss: 0.2560 - val_acc: 0.9062\n",
      "Epoch 77/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1956 - acc: 0.9228 - val_loss: 0.2270 - val_acc: 0.8938\n",
      "Epoch 78/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.1745 - acc: 0.9285 - val_loss: 0.1815 - val_acc: 0.9187\n",
      "Epoch 79/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1874 - acc: 0.9236 - val_loss: 0.2202 - val_acc: 0.9062\n",
      "Epoch 80/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1849 - acc: 0.9293 - val_loss: 0.1936 - val_acc: 0.9000\n",
      "Epoch 81/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.1762 - acc: 0.9257 - val_loss: 0.2452 - val_acc: 0.9062\n",
      "Epoch 82/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1752 - acc: 0.9288 - val_loss: 0.2005 - val_acc: 0.8875\n",
      "Epoch 83/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.1596 - acc: 0.9339 - val_loss: 0.2506 - val_acc: 0.9000\n",
      "Epoch 84/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1685 - acc: 0.9417 - val_loss: 0.2169 - val_acc: 0.9062\n",
      "Epoch 85/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1823 - acc: 0.9249 - val_loss: 0.2158 - val_acc: 0.8938\n",
      "Epoch 86/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1625 - acc: 0.9396 - val_loss: 0.2427 - val_acc: 0.9187\n",
      "Epoch 87/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1842 - acc: 0.9191 - val_loss: 0.2519 - val_acc: 0.9125\n",
      "Epoch 88/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1694 - acc: 0.9324 - val_loss: 0.2322 - val_acc: 0.9062\n",
      "Train loss: 0.117983939823\n",
      "Train accuracy: 0.957063711911\n",
      "Test loss: 0.181497557461\n",
      "Test accuracy: 0.91875\n",
      "\n",
      "===================FOLD= 8\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 17s 720ms/step - loss: 0.9635 - acc: 0.5464 - val_loss: 0.6682 - val_acc: 0.5750\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.9044 - acc: 0.5420 - val_loss: 0.6302 - val_acc: 0.6438\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.8744 - acc: 0.5513 - val_loss: 0.5825 - val_acc: 0.7250\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 14s 591ms/step - loss: 0.8201 - acc: 0.5842 - val_loss: 0.5592 - val_acc: 0.6750\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.7781 - acc: 0.6059 - val_loss: 0.5025 - val_acc: 0.7438\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 14s 591ms/step - loss: 0.6598 - acc: 0.6629 - val_loss: 0.4596 - val_acc: 0.7812\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 14s 593ms/step - loss: 0.6311 - acc: 0.6888 - val_loss: 0.4408 - val_acc: 0.7875\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.5629 - acc: 0.7298 - val_loss: 0.4193 - val_acc: 0.7812\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.5330 - acc: 0.7534 - val_loss: 0.4112 - val_acc: 0.8000\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 14s 592ms/step - loss: 0.4749 - acc: 0.7834 - val_loss: 0.4099 - val_acc: 0.7750\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.4369 - acc: 0.8140 - val_loss: 0.3823 - val_acc: 0.8187\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.4381 - acc: 0.8034 - val_loss: 0.3850 - val_acc: 0.7937\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.3849 - acc: 0.8207 - val_loss: 0.3915 - val_acc: 0.8000\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.3998 - acc: 0.8283 - val_loss: 0.3708 - val_acc: 0.7812\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 14s 592ms/step - loss: 0.3654 - acc: 0.8378 - val_loss: 0.3679 - val_acc: 0.8125\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.3973 - acc: 0.8316 - val_loss: 0.3657 - val_acc: 0.8187\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.3629 - acc: 0.8433 - val_loss: 0.3937 - val_acc: 0.7937\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.3629 - acc: 0.8407 - val_loss: 0.3831 - val_acc: 0.8063\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.3549 - acc: 0.8494 - val_loss: 0.3769 - val_acc: 0.8125\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.3448 - acc: 0.8529 - val_loss: 0.3669 - val_acc: 0.8063\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.3524 - acc: 0.8570 - val_loss: 0.3548 - val_acc: 0.8187\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 14s 594ms/step - loss: 0.3077 - acc: 0.8645 - val_loss: 0.3455 - val_acc: 0.8438\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 12s 507ms/step - loss: 0.3018 - acc: 0.8709 - val_loss: 0.3600 - val_acc: 0.8250\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.3098 - acc: 0.8647 - val_loss: 0.3640 - val_acc: 0.8250\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2944 - acc: 0.8762 - val_loss: 0.3460 - val_acc: 0.8250\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.3032 - acc: 0.8708 - val_loss: 0.3730 - val_acc: 0.8187\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.3000 - acc: 0.8766 - val_loss: 0.3843 - val_acc: 0.8125\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2753 - acc: 0.8793 - val_loss: 0.3778 - val_acc: 0.8125\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2648 - acc: 0.8797 - val_loss: 0.3935 - val_acc: 0.8250\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2624 - acc: 0.8949 - val_loss: 0.3887 - val_acc: 0.8063\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2712 - acc: 0.8868 - val_loss: 0.3558 - val_acc: 0.8250\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2423 - acc: 0.8963 - val_loss: 0.3617 - val_acc: 0.8187\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train loss: 0.220631434151\n",
      "Train accuracy: 0.906509695291\n",
      "Test loss: 0.345455223322\n",
      "Test accuracy: 0.84375\n",
      "\n",
      "===================FOLD= 9\n",
      "Epoch 1/100\n",
      "24/24 [==============================] - 18s 730ms/step - loss: 1.0751 - acc: 0.5166 - val_loss: 0.6446 - val_acc: 0.5687\n",
      "Epoch 2/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.9658 - acc: 0.5448 - val_loss: 0.5933 - val_acc: 0.6562\n",
      "Epoch 3/100\n",
      "24/24 [==============================] - 14s 588ms/step - loss: 0.8938 - acc: 0.5751 - val_loss: 0.5599 - val_acc: 0.7000\n",
      "Epoch 4/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.8959 - acc: 0.5677 - val_loss: 0.5309 - val_acc: 0.7562\n",
      "Epoch 5/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.8360 - acc: 0.5925 - val_loss: 0.5010 - val_acc: 0.7312\n",
      "Epoch 6/100\n",
      "24/24 [==============================] - 14s 593ms/step - loss: 0.7810 - acc: 0.6174 - val_loss: 0.4659 - val_acc: 0.7625\n",
      "Epoch 7/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.7216 - acc: 0.6586 - val_loss: 0.4336 - val_acc: 0.8063\n",
      "Epoch 8/100\n",
      "24/24 [==============================] - 15s 622ms/step - loss: 0.6523 - acc: 0.7016 - val_loss: 0.4262 - val_acc: 0.8125\n",
      "Epoch 9/100\n",
      "24/24 [==============================] - 14s 594ms/step - loss: 0.5728 - acc: 0.7339 - val_loss: 0.4249 - val_acc: 0.7812\n",
      "Epoch 10/100\n",
      "24/24 [==============================] - 14s 592ms/step - loss: 0.6124 - acc: 0.7176 - val_loss: 0.3702 - val_acc: 0.8375\n",
      "Epoch 11/100\n",
      "24/24 [==============================] - 14s 591ms/step - loss: 0.5489 - acc: 0.7475 - val_loss: 0.3523 - val_acc: 0.8562\n",
      "Epoch 12/100\n",
      "24/24 [==============================] - 12s 512ms/step - loss: 0.4759 - acc: 0.7813 - val_loss: 0.3530 - val_acc: 0.8562\n",
      "Epoch 13/100\n",
      "24/24 [==============================] - 14s 592ms/step - loss: 0.5032 - acc: 0.7700 - val_loss: 0.3380 - val_acc: 0.8375\n",
      "Epoch 14/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.4533 - acc: 0.8086 - val_loss: 0.3031 - val_acc: 0.8688\n",
      "Epoch 15/100\n",
      "24/24 [==============================] - 14s 594ms/step - loss: 0.4370 - acc: 0.7971 - val_loss: 0.2963 - val_acc: 0.8750\n",
      "Epoch 16/100\n",
      "24/24 [==============================] - 14s 593ms/step - loss: 0.3982 - acc: 0.8178 - val_loss: 0.2690 - val_acc: 0.9000\n",
      "Epoch 17/100\n",
      "24/24 [==============================] - 12s 512ms/step - loss: 0.3958 - acc: 0.8336 - val_loss: 0.2855 - val_acc: 0.8875\n",
      "Epoch 18/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.3804 - acc: 0.8300 - val_loss: 0.2944 - val_acc: 0.8812\n",
      "Epoch 19/100\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.3701 - acc: 0.8362 - val_loss: 0.2694 - val_acc: 0.9125\n",
      "Epoch 20/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.3544 - acc: 0.8358 - val_loss: 0.2611 - val_acc: 0.9125\n",
      "Epoch 21/100\n",
      "24/24 [==============================] - 14s 593ms/step - loss: 0.3749 - acc: 0.8256 - val_loss: 0.2604 - val_acc: 0.9125\n",
      "Epoch 22/100\n",
      "24/24 [==============================] - 12s 513ms/step - loss: 0.3286 - acc: 0.8521 - val_loss: 0.2683 - val_acc: 0.9000\n",
      "Epoch 23/100\n",
      "24/24 [==============================] - 14s 584ms/step - loss: 0.3105 - acc: 0.8567 - val_loss: 0.2365 - val_acc: 0.9188\n",
      "Epoch 24/100\n",
      "24/24 [==============================] - 12s 512ms/step - loss: 0.3436 - acc: 0.8448 - val_loss: 0.2482 - val_acc: 0.9125\n",
      "Epoch 25/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.3464 - acc: 0.8443 - val_loss: 0.2399 - val_acc: 0.9187\n",
      "Epoch 26/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.3631 - acc: 0.8427 - val_loss: 0.2445 - val_acc: 0.9125\n",
      "Epoch 27/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.3114 - acc: 0.8510 - val_loss: 0.2282 - val_acc: 0.9187\n",
      "Epoch 28/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.3401 - acc: 0.8570 - val_loss: 0.2444 - val_acc: 0.9000\n",
      "Epoch 29/100\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.3093 - acc: 0.8708 - val_loss: 0.2428 - val_acc: 0.9250\n",
      "Epoch 30/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2986 - acc: 0.8658 - val_loss: 0.2460 - val_acc: 0.9000\n",
      "Epoch 31/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2750 - acc: 0.8710 - val_loss: 0.2322 - val_acc: 0.9313\n",
      "Epoch 32/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.3010 - acc: 0.8759 - val_loss: 0.2261 - val_acc: 0.9125\n",
      "Epoch 33/100\n",
      "24/24 [==============================] - 12s 513ms/step - loss: 0.2899 - acc: 0.8756 - val_loss: 0.2358 - val_acc: 0.9125\n",
      "Epoch 34/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2955 - acc: 0.8663 - val_loss: 0.2658 - val_acc: 0.8812\n",
      "Epoch 35/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.2739 - acc: 0.8821 - val_loss: 0.2248 - val_acc: 0.9250\n",
      "Epoch 36/100\n",
      "24/24 [==============================] - 14s 593ms/step - loss: 0.2863 - acc: 0.8736 - val_loss: 0.2239 - val_acc: 0.9125\n",
      "Epoch 37/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.2699 - acc: 0.8782 - val_loss: 0.2219 - val_acc: 0.9125\n",
      "Epoch 38/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2675 - acc: 0.8893 - val_loss: 0.2419 - val_acc: 0.8938\n",
      "Epoch 39/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2619 - acc: 0.8888 - val_loss: 0.2220 - val_acc: 0.9250\n",
      "Epoch 40/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.2562 - acc: 0.8906 - val_loss: 0.2192 - val_acc: 0.9187\n",
      "Epoch 41/100\n",
      "24/24 [==============================] - 14s 594ms/step - loss: 0.2495 - acc: 0.8927 - val_loss: 0.2103 - val_acc: 0.9187\n",
      "Epoch 42/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2532 - acc: 0.8936 - val_loss: 0.2160 - val_acc: 0.9187\n",
      "Epoch 43/100\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.2502 - acc: 0.8943 - val_loss: 0.2945 - val_acc: 0.8438\n",
      "Epoch 44/100\n",
      "24/24 [==============================] - 12s 512ms/step - loss: 0.2501 - acc: 0.8941 - val_loss: 0.2110 - val_acc: 0.9187\n",
      "Epoch 45/100\n",
      "24/24 [==============================] - 14s 591ms/step - loss: 0.2398 - acc: 0.8923 - val_loss: 0.1953 - val_acc: 0.9187\n",
      "Epoch 46/100\n",
      "24/24 [==============================] - 12s 506ms/step - loss: 0.2579 - acc: 0.8897 - val_loss: 0.2343 - val_acc: 0.9063\n",
      "Epoch 47/100\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.2444 - acc: 0.8972 - val_loss: 0.1983 - val_acc: 0.9187\n",
      "Epoch 48/100\n",
      "24/24 [==============================] - 12s 512ms/step - loss: 0.2464 - acc: 0.8967 - val_loss: 0.2158 - val_acc: 0.9125\n",
      "Epoch 49/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2050 - acc: 0.9083 - val_loss: 0.2159 - val_acc: 0.9125\n",
      "Epoch 50/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2546 - acc: 0.8969 - val_loss: 0.2070 - val_acc: 0.9125\n",
      "Epoch 51/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2069 - acc: 0.9138 - val_loss: 0.2039 - val_acc: 0.9313\n",
      "Epoch 52/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2346 - acc: 0.9059 - val_loss: 0.2178 - val_acc: 0.9125\n",
      "Epoch 53/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2143 - acc: 0.9210 - val_loss: 0.2439 - val_acc: 0.9000\n",
      "Epoch 54/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2672 - acc: 0.8855 - val_loss: 0.2084 - val_acc: 0.9250\n",
      "Epoch 55/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.2249 - acc: 0.9024 - val_loss: 0.1908 - val_acc: 0.9313\n",
      "Epoch 56/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2263 - acc: 0.9081 - val_loss: 0.2040 - val_acc: 0.9250\n",
      "Epoch 57/100\n",
      "24/24 [==============================] - 12s 508ms/step - loss: 0.2361 - acc: 0.9067 - val_loss: 0.2070 - val_acc: 0.9125\n",
      "Epoch 58/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2404 - acc: 0.8987 - val_loss: 0.2069 - val_acc: 0.9187\n",
      "Epoch 59/100\n",
      "24/24 [==============================] - 12s 511ms/step - loss: 0.2242 - acc: 0.9099 - val_loss: 0.2000 - val_acc: 0.9187\n",
      "Epoch 60/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2256 - acc: 0.9098 - val_loss: 0.1915 - val_acc: 0.9313\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 61/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2218 - acc: 0.9059 - val_loss: 0.2288 - val_acc: 0.9000\n",
      "Epoch 62/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2134 - acc: 0.9148 - val_loss: 0.2023 - val_acc: 0.9000\n",
      "Epoch 63/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.2053 - acc: 0.9130 - val_loss: 0.1886 - val_acc: 0.9250\n",
      "Epoch 64/100\n",
      "24/24 [==============================] - 14s 589ms/step - loss: 0.1896 - acc: 0.9186 - val_loss: 0.1841 - val_acc: 0.9187\n",
      "Epoch 65/100\n",
      "24/24 [==============================] - 12s 509ms/step - loss: 0.2064 - acc: 0.9124 - val_loss: 0.1992 - val_acc: 0.9062\n",
      "Epoch 66/100\n",
      "24/24 [==============================] - 14s 590ms/step - loss: 0.2134 - acc: 0.9153 - val_loss: 0.1816 - val_acc: 0.9313\n",
      "Epoch 67/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2136 - acc: 0.9138 - val_loss: 0.1897 - val_acc: 0.9375\n",
      "Epoch 68/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2043 - acc: 0.9151 - val_loss: 0.2052 - val_acc: 0.9187\n",
      "Epoch 69/100\n",
      "24/24 [==============================] - 12s 504ms/step - loss: 0.1950 - acc: 0.9218 - val_loss: 0.2014 - val_acc: 0.9063\n",
      "Epoch 70/100\n",
      "24/24 [==============================] - 12s 512ms/step - loss: 0.2105 - acc: 0.9081 - val_loss: 0.1959 - val_acc: 0.9125\n",
      "Epoch 71/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2023 - acc: 0.9163 - val_loss: 0.2009 - val_acc: 0.9187\n",
      "Epoch 72/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.2032 - acc: 0.9125 - val_loss: 0.2288 - val_acc: 0.9062\n",
      "Epoch 73/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1911 - acc: 0.9200 - val_loss: 0.1970 - val_acc: 0.9062\n",
      "Epoch 74/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1988 - acc: 0.9197 - val_loss: 0.2335 - val_acc: 0.9000\n",
      "Epoch 75/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1947 - acc: 0.9176 - val_loss: 0.1817 - val_acc: 0.9187\n",
      "Epoch 76/100\n",
      "24/24 [==============================] - 12s 510ms/step - loss: 0.1979 - acc: 0.9153 - val_loss: 0.2065 - val_acc: 0.9187\n",
      "Train loss: 0.149685532371\n",
      "Train accuracy: 0.935595567867\n",
      "Test loss: 0.181594520807\n",
      "Test accuracy: 0.93125\n",
      "\n",
      " Train Log Loss Validation=  0.157049181498\n",
      " Test Log Loss Validation=  0.211348751952\n"
     ]
    }
   ],
   "source": [
    "#def myAngleCV(X_train, X_angle, X_test):\n",
    "K=10\n",
    "folds = list(StratifiedKFold(n_splits=K, shuffle=True, random_state=16).split(X_train, target_train))\n",
    "y_test_pred_log = 0\n",
    "y_train_pred_log=0\n",
    "y_valid_pred_log = 0.0*target_train\n",
    "for j, (train_idx, test_idx) in enumerate(folds):\n",
    "    print('\\n===================FOLD=',j)\n",
    "    X_train_cv = X_train[train_idx]\n",
    "    y_train_cv = target_train[train_idx]\n",
    "    X_holdout = X_train[test_idx]\n",
    "    Y_holdout= target_train[test_idx]\n",
    "    \n",
    "    #Angle\n",
    "    X_angle_cv=X_angle[train_idx]\n",
    "    X_angle_hold=X_angle[test_idx]\n",
    "    #define file path and get callbacks\n",
    "    file_path = \"%s_aug_model_weights.hdf5\"%j\n",
    "    callbacks = get_callbacks(filepath=file_path, patience=5)\n",
    "    gen_flow = gen_flow_for_two_inputs(X_train_cv, X_angle_cv, y_train_cv)\n",
    "    galaxyModel= getVggAngleModel()\n",
    "    histr = galaxyModel.fit_generator(\n",
    "            gen_flow,\n",
    "            steps_per_epoch=24,\n",
    "            epochs=100,\n",
    "            shuffle=True,\n",
    "            verbose=1,\n",
    "            validation_data=([X_holdout,X_angle_hold], Y_holdout),\n",
    "            callbacks=callbacks)\n",
    "     #Getting the Best Model\n",
    "    galaxyModel.load_weights(filepath=file_path)\n",
    "    #Getting Training Score\n",
    "    score = galaxyModel.evaluate([X_train_cv,X_angle_cv], y_train_cv, verbose=0)\n",
    "    print('Train loss:', score[0])\n",
    "    print('Train accuracy:', score[1])\n",
    "    #Getting Test Score\n",
    "    score = galaxyModel.evaluate([X_holdout,X_angle_hold], Y_holdout, verbose=0)\n",
    "    print('Test loss:', score[0])\n",
    "    print('Test accuracy:', score[1])\n",
    "\n",
    "    #Getting validation Score.\n",
    "    pred_valid=galaxyModel.predict([X_holdout,X_angle_hold])\n",
    "    y_valid_pred_log[test_idx] = pred_valid.reshape(pred_valid.shape[0])\n",
    "\n",
    "    #Getting Test Scores\n",
    "    temp_test=galaxyModel.predict([X_test, X_test_angle])\n",
    "    y_test_pred_log+=temp_test.reshape(temp_test.shape[0])\n",
    "\n",
    "    #Getting Train Scores\n",
    "    temp_train=galaxyModel.predict([X_train, X_angle])\n",
    "    y_train_pred_log+=temp_train.reshape(temp_train.shape[0])\n",
    "\n",
    "y_test_pred_log=y_test_pred_log/K\n",
    "y_train_pred_log=y_train_pred_log/K\n",
    "\n",
    "print('\\n Train Log Loss Validation= ',log_loss(target_train, y_train_pred_log))\n",
    "print(' Test Log Loss Validation= ',log_loss(target_train, y_valid_pred_log))\n",
    "#return y_test_pred_log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#preds=myAngleCV(X_train, X_angle, X_test)\n",
    "preds = y_test_pred_log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Submission to kaggle\n",
    "submission = pd.DataFrame()\n",
    "submission['id']=test['id']\n",
    "submission['is_iceberg']=preds\n",
    "submission.to_csv('sub_Adam_10folds_amsgrad_1e-3.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['acc', 'val_loss', 'val_acc', 'loss'])\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYGZmDR4UzMys4S9I/ggmuSHSZAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f281190d898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEWCAYAAACJ0YulAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f271dbd1cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# list all data in history\n",
    "print(histr.history.keys())\n",
    "# summarize history for accuracy\n",
    "plt.plot(histr.history['acc'])\n",
    "plt.plot(histr.history['val_acc'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()\n",
    "# summarize history for loss\n",
    "plt.plot(histr.history['loss'])\n",
    "plt.plot(histr.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.image as mpimg\n",
    "\n",
    "min1 = np.min(X_holdout)\n",
    "max1 = np.max(X_holdout)\n",
    "diff1 = max1 - min1\n",
    "my_sum = lambda x: (x - min1)/diff1\n",
    "\n",
    "X_holdout2 = my_sum(X_holdout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13      1\n",
       "14      0\n",
       "38      1\n",
       "43      0\n",
       "44      0\n",
       "80      0\n",
       "104     1\n",
       "127     0\n",
       "137     0\n",
       "138     1\n",
       "141     1\n",
       "167     1\n",
       "195     1\n",
       "199     0\n",
       "207     1\n",
       "212     0\n",
       "213     1\n",
       "221     0\n",
       "240     0\n",
       "256     0\n",
       "267     1\n",
       "274     1\n",
       "276     1\n",
       "285     1\n",
       "290     0\n",
       "294     0\n",
       "300     1\n",
       "307     1\n",
       "311     1\n",
       "318     1\n",
       "       ..\n",
       "1363    1\n",
       "1364    0\n",
       "1373    1\n",
       "1380    1\n",
       "1394    0\n",
       "1411    0\n",
       "1435    0\n",
       "1440    1\n",
       "1445    1\n",
       "1447    1\n",
       "1460    1\n",
       "1470    0\n",
       "1488    0\n",
       "1495    0\n",
       "1504    1\n",
       "1516    0\n",
       "1525    0\n",
       "1533    0\n",
       "1537    0\n",
       "1539    0\n",
       "1541    0\n",
       "1542    0\n",
       "1549    0\n",
       "1552    0\n",
       "1559    0\n",
       "1566    0\n",
       "1567    0\n",
       "1577    0\n",
       "1590    0\n",
       "1602    0\n",
       "Name: is_iceberg, Length: 160, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_holdout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f271db2f240>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUUAAAEICAYAAADIsubvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsvXu4LdlZ1vt+VbPmfc1127t3d6eTNHLx/kQRUY4CIVFR\n4ZFwxDwqYoOBHDxe8EoCB48oihEvyHO8xJYAzeEScqIYROEY0QhRCQYSVBKCSUhOLt29d++91ppr\n3mvOGuePGnN9v7V67UvvhN6rZ4/3efrp2nNWjRo1qlbN8Y7v/d7PQghKSEhISKiR3esOJCQkJFwk\npJdiQkJCApBeigkJCQlAeikmJCQkAOmlmJCQkACkl2JCQkIC8Jx5KZrZS83so7f4PpjZp91BOw/H\nfRt30Ye7PvYThZn9mJk98gm28c1m9n2frD5dFNzpvb/JsR8ys991k+8+18zed96+ZvaNZvadd9fj\nZ9zH7zGzhZl96Nk43ybCzP69mc3M7O232/eOX4pm9jYzOzCz1h3uf89eIBcVZvYtZvbfzWxpZt/8\nTI4NIfy+EMJjv0JdSzgHIYSfCiH86pt8960hhK+WnrVn/dtCCA/f7Esz2zOzHzazsZl92Mz+6O0a\nNLOmmb2Xkw0zu2Rm/8nMrpvZoZn9FzP7HWeO+/Nm9oSZDc3su86+E8zs68zsl2Nf3mtmnxE//0Yz\nG+G/qZlVZnYpfv8CM3uLmd0ws4+a2deizc89c+wojvkfjN+//sx3czM7Xh8fQniZpK/VHeCOXopm\n9rCkz5UUJP2BOzkm4Vy8X9LXS/rX97ojnyierR+79KN6x/hHkhaSrkj6ckn/xMx+/W2O+cuSrp35\nbCTpT0i6LGlX0t+W9K/W98HMvlDSayW9XNKLJf0qSX9tfbCZfbWkV0n6Ikl9SV8s6Snp5Iekv/4v\ntv22EMJT8fDvk/TL8Rq+SNK3mtkXxGN/6syxXxz7+uPx+6898/0PSvp/7mzoTuNOZ4p/XNJPS/oe\nSaconJl1zOzvxV+nIzN7u5l1JP1k3OUwvrk/5yx9O/sLa2ZfFX9Zjs3sg2b2v93NRZnZF5nZu+Iv\n2UduMiv7E2b2cTN73Mz+Eo7NzOy1ZvaB+Gv5JjPbu5t+nEUI4bEQwo9JOr7tzmcQZ+pfjX9/Dcbq\nPWb2mfHzB83sn5vZtfhr/WfPNNU2sx+Kx/2cmb0Ebd702Hjv3mxm32dmQ0lfGe/9Y1YziPea2dfb\nLZY40NaHzOwbYr8PzOy7zawdv3tpnCW8xsyekPTduN73x1nEj5jZg2ea/f3xmXnKzP6OmWXxuE+1\nmjpdj999v5ntnDn2t96qLze5Bj7LZ5/1z4/9/I3Y/z4zm5jZ5duNzzOFmfUk/UFJfyWEMAohvF3S\nj0j6ilsc8ymS/pikv8XPQwizEML7QgiVJJO0Uv1yXP8NPCLpDSGEXwghHEj6FklfGdvMJP1VSX8+\nhPCeUOMDIYQb55zfVL9XHov/7kt6qaS/GUIoQwg/L+nNql/Q5+ERSW8OIYxvMR53xayeyUvx++N/\nX2hmV/Dd35X0WyT9L6oH7uslVZI+L36/E9/e/+UOznNV9S/AQNJXSfr29R/7M8Q49nlH9S/OnzSz\nV5zZ5wskfbqk3yPpNebrSn9G0iskfb6kByUdqP4Vvi3M7B+b2T++i/4+I5jZH5L0zaqvcaB69n49\nPpT/StLPS3qB6l/zPxd/3df4EtW/oHuSfkDSvzSz4hkc+2bV4/r9qv8AHlY9W/jdqv/I7hRfLukL\nJX2qpM+Q9E347v7YvxdLerWZvUz1H+8rJT0g6cOS3nimvS+V9FmSPjP2c/3HZPHYByX9WkkvVD12\nd9qXO8HZZ/0/xv5xPP6IpJ8IIZydmcnMXmQ1VX3RMzzvGp8haRlC+CV89vOSbjVT/L8kfaOk6Xlf\nmtl/kzRT/XL9zhDC1fjVr49t8zxXzGxf0kPxv98QJyO/bGZ/bf0DdQafK+k+Sf98fcoz/19v/4Zz\n+taT9GW6+UvvD6qeAf/kTb6/NUIIt/xP0u+UVEq6FP/9i6p/CaT6pTqV9JJzjntYNd1u4LNvlvR9\nt9rnTBv/UtLXxe2XSvroLfoZJH3aTb77B5K+/cw5fw2+/zbVv36S9F5JL8d3D8Trb9yuv3f6n2qa\n8M3P8Ji3SfrquP3/rsflzD6/TdL/d+azb5D03Rj/n8Z3maTHVT+gd3LsT575/oOSvhD//upb3SPs\n9yFJX4t//35JH8B9Xkhq4/s3qF5TW/+7H+/Jw7j3vxff/++qX0DnnfsVkt71DPry0TP7/q6zz/J5\nz8V6PCVZ/Pc7Jb3yLp+X75H0N27x/edKeuLMZ1+jmpqet/+XSvqx2/1dSWqrfpk/gs8+cGasi3jt\nD6ueGAXVy0M78bNfkvQ157T9Bknfc+azt6t+WbdV/7jdkPS+c479CtU0227S75/QOX9fqme0b7/d\neN/Jes0jkv5tcN7/A/Gzb5d0KV7AB+6gndvCzH6f6tnHZ6j+g+1K+u930c5vk/Q61b8yTUktPX19\n4SPY/rCkNdV5saQfNrMK369Ur3NcFLxQ54/5iyU9aGaH+CyX9FP498l1hxCqSA8fVP0w3/GxEQ+e\n+ezs97fC2fEnHb4WQpidOc/Pod8jM7uuekb7oVu1F1nNd6h+cWypfq4OnkFf7gohhHeY2UTSS83s\ncUmfpnrW9SuBkWrGQAx0zjJNnGV9m+qX/y0R78EPxqWRd4ea0p4913r7WD7r/LYQwqHq5YR/Gs/1\nz9CHrqQ/pHpGT3y5alb2EdU/uN+n82e7j0j63hDfdGeu70WqX/Rfc7vruxluSZ+tXht8paTPtzra\n9ISkPy/pJXEt6inVU+xPPefw8+x3xqpfdGvcj3O1VE+l/66kKyGEHUn/Rqen03eKH1D9AL4whLAt\n6fXntPNCbL9I0sfj9kck/b4Qwg7+a4cQPnYX/fiVwkd0/ph/RNIvn+n7VgiBfwAn1x1pzUOqr/1O\njj17Tx+Pxz+t7TvAzcb/vPN8XPULf93vnqR9SbwnN2vvW2N7vzGEMFBNae/0WbhT3Mxq6rF4vq9Q\nvf41u8l+nyh+SVLDzD4dn71E0i+cs++nq57B/VT8e/4Xkh6If98P36T9QvUSiWKbL8F3L5H0ZAjh\nuqT3qZ7lczzOG5svVT0LfBs/DCF8OITwxSGEyyGE36Z60vUz3MfMXqj6pfe9N+nrV0j6TyGED97k\n+9vidmuKr1A9S/p1kn5T/O/Xqp49/PFQL8Z+l6S/Hxfpc6sDKi3VnL6SD6YkvVvS58U1lG3V9GyN\n9YzumqRlnDX+nru8ri1JN0IIMzP7bEnnyRP+ipl1rY7QfZWkH4qfv17S3zSzF0uSmV02s7O/aHeF\nuHbXVj3uDTNrm1kev1sHnR6+g6a+U9JfMrPfYjU+Lfb3ZyQdxyBFJ96P32BmvxXH/hYz+1+tDm79\nOUlz1UG0Ozn2LN4k6RvMbNfMXiDpTz+D4fhTZvaQ1UGs/0M+/ufhByV9lZn9pvhsfaukd4QQPoR9\n/nLsxwslfR3a21I9uzmKffzLn2BfzsN5z7pUz3S+VPWL8WZ/xJ8wQh1s+BeS/rqZ9ayW0HyJpP/7\nnN3/h+ofgfXf81dLejJuf8TMfruZ/U6r5TodM3uNapb0jnj890p6lZn9OqsDVt+kmt4rhDBRPXZf\nb2ZbZvaQpFdL+tEzfTh3pmdmvzYe1zSzP6b67//vnzn2KyT95xDCzdjpH1/3565xm7WMH5f09875\n/JWSnlC9ztZRvWb3MUlHqhc3O3G/v676gTmU9NvjZ/8o/vv9qqe4J2sxkv6U6ht0qPqGvlFxLUXP\nYE1R9SLsh1VP6X9U0j/U09d/Xq16RvCEpK9HO5mkv6D6V+9YNU391jPH3mwN9PWSXn+LPn5PPJ7/\nfWXwdaEPSSpucuzbFNcU47+/NvZxpPpB/83x8wdVv0SeUE0Tf1qn18HerPrBPZb0LkmfiTZvd+z3\nnelTL96nQ9Vrsd+kuB53m+fqQ6p/EN8Tj31MUvdW9zle7wdUzzB+VNJDZ+79n1VNua5L+nuS8vjd\nr5f0s3Gc3i3pL+rp64R31BfdZE3xZs96/PzfxePOXf+K+7wo9u9Ft3hubrqmGPfZU70GP1a9lvlH\n8d3nShrd5Liz1/j5qoMnx3Gs/6OkzztzzF9Q/Xc6VK0OaOG7geq/22PV7OP/5LWrXvJY6pz1f9U/\n0tfiNbxd0meds88vSnrVTa7lc+KxWzf5/it1B2uK60XghHsMM/sm1Wtp//Re9+VuYWZ/UtIfDiF8\n/m32+5DqF/y/e1Y6dg9hZt8l6eMhhGca0WYb/0x1wOPJEMJ5yyYJt4GZvVXSb5f0MyGEl99y3/RS\nTLhbmNkDqinjf1G9VvWvJf3DEMI/uM1xH9Lz4KUYl0LerXoW/8v3tjcJd4rnTO5zwoVEU9I/VU2V\n/r2kt0j6x3HN+GxK1vq/u9XiPadgZt+ielnj76QX4nMLaaaYkJCQADxnZopm9nvN7H1Wp3q99l73\nJyEhYTPxnJgpRtnKL6lOJfuopP8q6Y+EEN5zs2NarVbodmtJ5Cq4DttWfr2W+W9CZi5dW0VpFcVs\nGf4VIL3i6C2F82S+v+HYLGAbx4bSj63y+Bk6cEpKDvA8qrw3q9w/znGdwvVXFftb78NxONUexrDC\nVTdu8rvKvnMgORZscw2OM/tS8TnFpp1SHGLMs/PPs8I1C202KlufFG3jnmNfXhvv/6mxu0l/M5y+\nWg9dIztvVxmvU+fj1LO4xP20U4OObXvaNs/De87ecPzXYzudzbRYLO5GR3yh8VxxIPlsSe8PUZBp\nZm9UrcO66Uux2+3qC172MknScOXpnc2j0rfb7nhUNJsn25PVQpLUaPjwtOVvmeVqdbLNP7gnzHPT\nWx1vuxW8nW7l7bSwvbo2Otkebdd/JAv8BTXn5/94tVt+ntXMr2205c9qr9052c6PfZ/JdHKy3ejX\n+7Sz4uQzm/t1Hs/92o4zb+PSsu3746+vbONvJfc/+kbu13w89/Of9NX8PrRy78u0nHtzFV6ceOHn\nfCn2vJ2jpZ9nNIZ/wMrHd39Y9ysb+PVkhd+35Xxxsj2HUVaJH8KtDF8sfOz40mkOl75Lvz5ntefn\nLHO8qH1XtVZ+nVWOcV76vVge+nU2Cx+7quH753imq2bdZssvTRp5e6vM+zKe+99QKz4r//lnflqb\niOcKfX6BTqdifTR+dgpm9moze6eZvXM+n5/9OiEhIeG2eK7MFO8IIYRHJT0qSTs7O2Gxqn/1RsFf\nkIfbvv2pmWccrib+eRV/rQ+D/zquSv/Zzkv/BW0G/125P/Nf/EXDZ0QL+axhtvB2Zk3fp7+FWUac\nIrTATBoTzE47ftwcM7KA37jqmqe95pd81pR5M6dgkXod537NBXgilwaqiU8tJpgF5phZ2spnKkXf\nx6WDGXlv4DPYYlQfO6287RvIiivbPubdofdl6k0rYAalY58R2tTHfL+BmS36vjWo+8vZIb9f9Px6\nJmNPD+e1lZhidDHj5cxODYxLUR8QMFM7Kvw5HDSRETsFlZ35AdPCxzzve9uBzKbA+cc+E8ziGssE\nM8Ki5xfRAgvpjp8+f7K7ysC9+HiuzBQ/ptP5qQ/pdN5rQkJCwicFz5WX4n+V9Olm9ilm1pT0h/Ur\n5ziSkJDwPMZzgj6HEJZm9qdV+wjmkr4rhHCeA8gJSlW6qppCdRA8+DVl72R71EDQZcUIdf3/PkK4\nReVDFUCBJ6Av89JpTTb1c3YW3naBQE91n9PHUQs0aFXTkgyL4sv++VHz8tjpLqlxF8GVyRgL8Jm3\n2USgZ9KoD+7I+82QN6PMrYGPYYmAQug63SoRJOp0/Fir/Pp7lX++asfod3Da2ai4ZOHnWfb8Gnoz\nP09u6Hvwaxs2bhK6b3k7kxh1zQICF3gmsqbvu7W7fbI9nzjFbyFyOym8HcFjteh6v5qLeM4p7n3p\n7S25ZNDfOtkeY58Sf8EFAlO24nzH2x8P8HnsSolIUDH1fhc3/Nnq7fj55/G+VJvJnp8bL0VJCiH8\nG9VWYgkJCQm/Yniu0OeEhISEZwXPmZniM0URMl1Z1BQSLE1tUOIWIoHXe04V+pHCreagQBD1Lrf8\nuOWRU5le22ml4aRZx/dfXfZ9AqjnaulRxzVtayHiaKA1eQvUHG23oQGs0N+pOQ0tMmgjl97+IEaL\n2Xa1JAX0R2Umb2+1g2jyyrfLkUe/3189cbL9oqbXAKOuMkRRdbvl0dxe07dHc9dxClR6Dp1oC4/z\nKgf1B6385cKXEuZt336grCO9ew2/PwX4YQmaXGGZYjb0KPdyC6FwzDeo94QoQeW87uN02/dtZ07N\nWxO/huMSyyS4zzsIeVOwXd3E97ZY+T7tZWwHVHut2JCkDM/K+PqRn7Oql0ny1fnneK4jzRQTEhIS\ngPRSTEhISAA2lj5L0lqTms1At1r+O9BBtLKD6PJoWVOlFaKf1i3OPc62mGbHaK1j2nSacTgcnmxf\nbjtVCodOz0bFOrrn52ni56sAZWYEsIQAvQH6nkMXfmSIlkLgqxgBbUw9gl6Yf8/o66BwmixQudXM\nz19gaeJF+e7JdncFiouo6DJGuldLLEeY09EdIVJfIlUP6WwNiM27S6QIXvcxv/Rw/2TbpohKH9d9\nH0MvTfpOMPd5e9+vjftzaWSx8jF9CssA1o59bPkYtnOMLYUAY6fPTD/dghh9BZF4haWf+eT43P3X\n4utJx5/PDAkIrbGPzyBAcRCTG0K+mXOqzbyqhISEhLtEeikmJCQkABtLnysLmrbqaX6OHONly6nC\nNHNa0x/578OTZR2VbIJethH8rBB+3O0632oEp9vHlVPJMaKPuwLdHvs+/Q7a6da0ZrGCowosU8CG\nlUMYvGjRmcb7TlrZRhS7LCBOjksMY3qU9Z3KzZH7vHOE31Lk0uqSU1w642hGGxbsgu0yCqxXBUTX\ncNFhJPaULdgSEXwsccyx3tAovExxG8L7Zunnyqf1n8IKAuxlD5HYNrgsouYNLHtoF0sDyE8PyKe+\ntPQlk3U++eTInwPmMre7WDLYxj3EPd+qKMb3rlQLiOTz852UmjEPvYPXwHwJax5gOnX6XkW6/1yw\nHbwbpJliQkJCArCxM8XMTN0YqKD/YcN/8ISMP80K/9XbW9Wztinsx0bQiTWg5WsvGHTwX/MmtHEL\nmnW2MTtp+7E50u8GsbvzFWYQ0OPNkUJn+Pyo69c5QGrjYIbUMmgJr12/7v2K/29d9nQug0tKgfMM\n2z47yjGb3EFkIMMsF2GRE69KSSqW8PmL1z8fedv9mX/fwYx80fXf8uOmz3zanOSOT9m/+j4NjDn2\nqHbqfQz3Kmv7PaGfYwF3mOWej9Ei92uuKs7sz/fi1Cy6ISFwlnGidsPHojvwNsqFP4tD6Arpv7jE\njK/f8hnnChrbal7vswJ7yJDyx8BNwMy31a6DMacMjjcIaaaYkJCQAKSXYkJCQgKwsfRZy0qrp2ri\nZli8568Aspg0Q5DEoqHsXss1bRXcRYYrpzVPzZwcMi0MBjxqkG7B8DbHAnwFfWAWSwbkQz9Po+dt\nLGAayhTGPjRoGfrCkgm84YMHL3mbUTOYIfUvh3n5CtfQNKdjY6T8sf5MgXIMe23v1/TANYMNuNQ0\n13SfekysTJSsRQL3GjBWZdAGrlPo6hPBpWbO4jUoaxD7cj3z4E5v7I1vY2mApSsY6FqC+rNfw47/\nY460wNWgbqfbRNtMVWzhvh3P8flN6s/gGYL3sY7wjLbm3n4WSzasWNIAbRho/RRLOWs33RRoSUhI\nSHgeIL0UExISEoCNpc/B7CTSOwcNm8mpwjaNV6ArzGNKG91lpkibontOgd8Vms8G0Ncl6ntYH5ox\nRCKbcKFZrCOXl5ByhaAlq8N1oVPLJqjdgr4vkUbWn6FaHijeKI7VCHVR5kdeiyQDHW1te9g+R5SZ\nZVrpnmNwj7nU2znZpvntIt6XVtPHp8povoqoKJQCGQx8q4n3ZQRdZV9w28F9LhChX6sFdgO0liie\nEqDvm0Bfqj3fPxv4vWBNnTYMb0PX+9IN0W0Gxrp0IOLzt+qiUiGuuXmM8xgUrFg9uNGk25F/0Yup\nnS2WncWSBsd8fwZnqEjZbTPZc5opJiQkJBDppZiQkJAAbCx9zoPUm8Vi30dOg1qgpNOGf95bPL2+\nBdPsmse+73gX5psQyQ4QlbRtp1Uz1NogZb6/RInVqdPWYXtNa3A9XZTMrFDiE0LuFmq3CALzcQ6B\n8463ukQq2CRSNRZXJ2Um5hlNU/0RKrkThL0cxxKuNmWJ80ehPMtm0qVHiD53gp+T1DTHNtP/PpKh\n3CsuaSUf8yIeuwWXmG3UuZlWLpieomC9Gn6eFpYPStxPOt+0qDiItWbmqCFTot+N607T26CyywFM\ni6FEmA59OWJY+Jhv7buxb4YlhnGMKIcC9XdQVjcM/FmZtb1fWby3IYm3ExISEjYfF+qlaGbfZWZX\nzex/4LM9M3urmf3P+P/dW7WRkJCQ8IngotHn75H0DyV9Lz57raSfCCG8zsxeG//9mts1ZLKTeiQV\nDFLbMGxZIG+XhTd6RU2bioZTlhmKa2yPEIlG6ccM+2QQjG/BFPRw5SHvEYTcTQiJ8xhRHJl3Fvpe\nNZAz3QBlJH8djf08LJX68dzF0ytQ72xa06o+jG2LKx5lnpo33ocAOizRxn1IJgcWoJvXli4knjX9\n+qsY3Z2U3j9GovtwjCkhJM5xL4S+5Ij+76Ik6xz3tIP6Js2y3n/MBGIMbQcC9NUxxpY1YqAgsC0X\n/s+Rnxy6WG6IdXlyPJNVEwkAMDDOkWlgDd9nTqNelH7VUz6O2Ud8+aAx8HE8jrS9msKcuPDvGxjb\n6wPkckdVfZVtZvj5Qs0UQwg/KenGmY+/RNJjcfsxSa94VjuVkJDwvMJFmymehyshhMfj9hOSrtxs\nRzN7taRXS1IXziAJCQkJd4rnwkvxBCGEYHZzyWgI4VFJj0rS/vZuWNdVmSPiWwbQwCOnJMwnXvTr\nz5djjyAuIHrNQMF6sAhrN2AjdezRyjACNUdEbwlD0SnyXCcxt7WA5RfNcffmGAJEcOeY91PU3ESN\nmgw1auZD72O33Y3XhjKpiE7zuCXGKke0ODByiijzcO5UjiU2W4jWr8tssu0KRrElqRryjVug1XaT\nmiFzRIgDtqdYMhnGiHoLyxFD8NoWouxjZ8ZqodzqqfozWDJo9PyeM7puUUWQDXkev4bpvo9h4P1c\n+j7dwGcY9wiC+Qp523BrUxET57O+Ly90kD8/n/nzv5e5UmId5c8vFtH8pOG5cFVPmtkDkhT/f/Ue\n9ychIWGD8Vx4Kf6IpEfi9iOS3nIP+5KQkLDhuFD02cx+UNJLJV0ys49K+quSXifpTWb2KkkflvTK\nO2mrtEpXm/X0/7TIFFTiBupYID+2UdX04BiWU4sDp5q251SiSafm0mnQEuVGl3P/fHfp9UKWqAdy\n4CnBKiL17K+cGo2QE3vcgQIZ+bEt+EUViDiHKfYh3e965HxtgRWGsKiCSLjCWFTm4zlBfnBj7FRy\np+FjtAcR9ByeWitQ9bxX79OiuBpPZzXz41hzZgGH627XaWALruELlBUNEC9nEIeHuDzQMaf0bf55\nwK5sa4UIfXY+xedsYzJ3GkpLr/UwFnC1ZiQ6n/p4zrDsE+AeTvF6e+R9XGz78zzDssZ2iXzvG7GU\nLnLJxeew62M4wVLD2l6sWp0v7n+u40K9FEMIf+QmX738We1IQkLC8xbPBfqckJCQ8KzhQs0UP5nI\nZOpEqkzqNUPebgZRd4B4+uD4SJI0Rx5wG1S2HZCVjLbHT0Iw20NkFdRzCU+nQEuxpdPW3UZNJTuI\nRNoMNlugrDnyk/sVRN0QL09ZEhTR0taWU1xFQfAM/dip0D9GditGgkEl8fEYSvIC7YwXyAk+5TId\nNyDAFpYAGsgZXkAMXYI+T0unyZdAa3vycVnlsNrCPW3O6vPOM9BURnPhSL0ElWwhyp5B7Hy15cst\nR5mP6ae2LvuxUew9g8X5uIsSpCPk1WP+YlvMCXfMUBSsuUTeMpY7MmxXUZ0xO/JxG7R8rPgMG6Lv\ny26Mmt8k2v9cx2ZeVUJCQsJdIr0UExISEoDNpc+V1J7UfK5aoOgRIoTDtlOVCjmvvZie24Xl0woR\n38ZVp0a0bqpgS9bU+dRz3nPqc2AQTyMPtxGLR7EoUQHBNu2dKtCavO/npwhaB4hcNxHFnZOG1+fP\nQZ8WyLHtftT7ethBJBLj2cpRJxopxNczFPeaIVd45LTRBnXfZ34J6sEZfQWLthvIw+0XfgCdoicr\nLkcgKo0CZXNE9NdB3N4xHNMbpPJ+f/IhROpzb6PoIYsKwvz7sKyRI3I+r+rnb7j0sWUxswbuZw4n\n7xkSAxYYr2rbn7kWBOFtPH/HOYqhRUf2PqL2U1Dwjq/YqNXypZb1Xc5EVcfmIM0UExISEoD0UkxI\nSEgANpY+B3m+8hS5qsV1RPRQ9MmaENBGujE57SV9gowO14jQMZW3Qk3cJcSzOYpb9XFAgI3XYazT\nSxerQcfpS4kqVkvkZB/DaqwonMo293wA7kfya4k6xdPYZtZGzjIixd1L/vmM6eeIbB/BxqqRo1jW\nCLnSmV/zHDWWu6u6zRYeyTlssunSvX+M+4ZxY171EoF12qiNUbiq7D69BnRjy9vuTSHMRiS62vbx\nLBHNL6d+PX0IrANy75cL2J7FpYTe3L//6H1wg4eQvIlaz3QP7zeQ+73yPo6xxNA8QH48np1JdJDP\nOlAtHKP4F5T0NoTlXLz/dJHfJKSZYkJCQgKQXooJCQkJwMbSZ5lkMWJYgOKFjl9yE1FEVomqBjWt\nmGF0KDQuYaM1q1gDGnnFLQ8LGqjP8Q3UUoaoeAmrqVakXqumfzY9RFEiFGy+1N0+2d5ZQbA7BWXa\ngUgd0U+h7nGIQvUeIsgLOZVjXm8DOcNTiL37wdtrfNwjziy6NS8Qfb7keeCK/SogOm92EE1t+PYC\nUXPiGCHvy0IkGIL9WQdCduzSj+bUDUT5Wd+7QjR3vO/jWSAqXZTedhs55hWc0g2P3Jr5Gu5b68BN\noLgE0M/bPEAoAAAgAElEQVTR2ZH35anCn6fLO16gKiAqvkLCAO3aJjF3mTQ4h13bcunnqbDsM4s2\ndivd1MXvOY00U0xISEgANnamaDI1YxpVEwvTC8wUSiwkz6au/VqndI13oAcc+69mN4NpKEaQv5sV\n1/AxOyjafux8jtoYTOOKMyRW+Jy0fN8uZqQT6Mp6SFszpMsdHvlsooXF+BbMSrMYSKB2b4Dp8zr4\nI0kNaBk7HZ/BTJByp8velxbS3+aYqc97/vlg7d5yzc/T68CypYX2UIaUaZOskbKEg8sSkYkFttse\nO1BzXt/rZRvBFQTO8g40mJTnYZvHTlE+dRB8ytfBJHc8j88cgnyX9y7590tvYwnNahvpj5egHxxd\n9/vMMqgFXJ16sOnpzeondo4Z4RKz0ICgk+34fe6szXEt6RQTEhISNh7ppZiQkJAAbCx9DiFoEU1f\nG0iRyvadeg2x6N9EoavWugYIdXRYoEamnHJQ0ADXHcEglLSKgZ4GgiuGGhxrc9FRBq4FnVxjgd8y\npLZdW3kpyw5SFHtwqRHdgMzpWVbW5y/gwLPdcxPadtMpGPWTQkCBqXDzmVPjJUxUC0g/K9DdtRFw\nAzrJG0cHJ9v9vvelg8e2ieWAEpT5hlyb2JjB/BXrGrNT3sP1513cT2vhXrXp2IP7iYDWauxLMLsT\n1KKBfnG4R71jM14Dgksf937nnfPdcFooU7pCAKS4zwNXUwTj2giulYdIrWw+/c8/oIZQA0GXCQyU\nuzElFBLZjUKaKSYkJCQA6aWYkJCQAGwufVY4SbXL+k4JVseoKXIZ9K0NE9nIqx4o3T3kydxpTQfO\nNG1QiNYEDiOIyi5QYtRAXxeIIi6hd1w7wqygDdwbwQ0HtE6MrIPiUVfYgRFsiRS9HM4865WEXXOa\nPF0hVazpbeRoY4VoZevI6WMJLV+nIt1HudcxyqnGiGbAeTpY9liMYLIL2tdo4dpoILuASxHcbkif\naVY7UU0rm10/jvq+SeVLDbuG2jZdlD7F9U+hMZ3jXixOpSXWn8+QHtnb8+cmXwXsC3cdlJ69Vvg5\nB3NfGhp4d9UErZ9As9mJyzTzLagQ8PchuCSxinoruvdYlqLPCQkJCRuPC/VSNLMXmtl/MLP3mNkv\nmNnXxc/3zOytZvY/4/9373VfExISNhMXjT4vJf3FEMLPmdmWpJ81s7dK+kpJPxFCeJ2ZvVbSayW9\n5lYNZZXUmkb6AZecxcLpwQruIVZA+BzF2T2U5uyyXgvKSuaoV3LKtHQMEfK+i5CXoE853HMWiETn\n0XmnMXLaE7ZAB0EZlzBfXU49skix89q0VpIWULKzxkYWI41D0ESDSLyCwDejSh3pkVkfaY7XvZ3W\nADcArkJtUNksUjka2AqRUCHlrnuIa24jPRIiaNZFadK4FW47gjtNJx57KLjYUJmNWjhHDafyNBke\nbIP6TpAiiOI1K1DfbLgWb/t9eAqZj02kDfYDjGrbTpO3Ef2f4JkzlI+d9bCUAuPedd2bLYThF30/\njul/Ffo4jwkQ4UJNqT55uFCXFUJ4PITwc3H7WNJ7Jb1A0pdIeizu9pikV9ybHiYkJGw6LtRLkTCz\nhyX9ZknvkHQlhPB4/OoJSVducsyrzeydZvZOpi4lJCQk3CkuGn2WJJlZX9I/l/TnQghDQ45lCCGY\n2bn2HCGERyU9Kkk72zthHt1uOmOnWxWizL0OuR9Eu+eIUrdAX8oJQntdbyMHfZtOIYyeOQ3OQGsC\njGhJldY/VTmi5owaM5dbU1BJuMc0IEY/6EBtjZFrBeQhRyPWJujYCLm33ZmPz17POV6BBO3JENS7\nQzNZiOQxXiu67WzHvgydmi7hDHRagA7aj+0Zli/2Sz9PhQhxCbotFxdIUWzegtC7wtIEnWmgEdeq\nyZouSACAsmCKfsF4SPMYocaVqRhh+QDP5LThtHsxdJrcbvp1si5PC0sm1QLqB8yDVmu3J6gWYNgj\nzX3fNvsVn1vbTI/ZizdTNLNC9Qvx+0MI/yJ+/KSZPRC/f0DS1Zsdn5CQkPCJ4EK9FK2eEr5B0ntD\nCH8fX/2IpEfi9iOS3vJs9y0hIeH5gYtGn3+HpK+Q9N/N7N3xs2+U9DpJbzKzV0n6sKRX3q6hyoIm\nWRRBwwKqgDhYpOXglWXMJ6XhaAEqZbDOmoAa5SzpgrZvXL/u+7AkKUxUu6AqnVg75mgLtP/QKXib\nIV9Sph3ngwZTVFqQzREJFShev1lTvwLlYCleLmG5NocYeoGo8IKfHzrFa+Uudh4jD3jeIiWuOxl2\nfGwLiIdtjKg5lhUMllr5dc/9NoxtEznWFSL0c9jF9RrteA1QATDffOD9ylCmdX7k7Q3v88vpYtAz\n5KebkRLX93eKZ6vFlRmoHxow/20gyk/6vj1GXjWmOxki/lXbOe9BqE/WLPw8TRgPZzBTXiKCrxMb\nvc00mb1QL8UQwtulmxaTffmz2ZeEhITnJy4UfU5ISEi417hQM8VPJrJKak3q6X1vx+nbHO7QpIpz\nhNKWvXpYGjfOz181in7RBl2tD8Fw8x2UJ0Voe0Hqh9okFmnJ3iEoaw63aVBM0vQMecX5LkKrAf2t\nULYS0c1mv77mcAN2aai5skDO9gR1WfIx6DPrzOy7YJsO3gtQzxs5csVX9blWfUTE4YzOHGuW4Syg\nIGC0fn7g9L3LkrS042r6mK4ibaUi4BTQxhz3lrSyBefzFgTWVReR4NL7vmf1Pqun4GreZizaEWhd\nBlq7QslU2tIxKl/hnlejp4vX8WipM/ZnZYXkgjGuYVrWz8gqbKZ3WJopJiQkJADppZiQkJAAbCx9\nlklhnc87dXpSotzoFopI5VNEN6NQdQ6mVbJAFUJBHbhgl7DaKhDZZanOOeyqZqA+1xrIFY592S6c\n14RtqmpBxyDqnsMSuwuR8Aift1ggC8sA68JIxZafs4DotwnaW0EwbgOn6YupJ9aWoMFTjAXzaQ1L\nCeOyvv7+yM/fZI55D5ZmsL8aBh+3S0uUeIUL+AFo+gzR2sGSVLVuc3XNr2G/7znrVCdcRy5xv+PX\n/1QJt29Q6fYKBaCgaJit+37Fr20SIPQGfc3GHlmf4QFcVr7PYOrnbCL6XeGaKXzvRIrfgjC7hI1Z\nBvf4XoXnOdL+fEPnVJt5VQkJCQl3ifRSTEhISAA2lj6HKnitZkTulqAHU1C5DPLIIkaCS9DLBhyR\nAyjr5NipeThyKtfsu+Uj85aPly4YFooe9WGjVUbRdBg6vQYD0wyR8oDI4haoXAM5xnS+ruZOz240\nnVZeDvX15de9f7P8/OTWDNHvTo5c8q7nRJcY27n5dTbgNzXA+sRanH2jhzxx2LK1c1pngY6CbZJK\nb11ioSu/t90nPLpeVbgXMXLdu+yUeYUCUUtEjVm4awWB+bb1zt1/hPre46FT7Cw6qFcdJAkgmt7K\n4Ax/7P1uQvR+mMFVG/Z29wfvSw++2c3C2z+q6jbHuKA+7knjkrexwvJB96h+brLN1G6nmWJCQkIC\nkV6KCQkJCcDG0meiRJCRolpaZ81QA/oo+jvtDFHkiI7Vp2iD/2O5jSgzIt4LY4TaKU4Hn69QG3rn\nuKY4XdDH5TFyklu+3Sj8nI1D2Fuxpi+8rlrya7o/g6g95i03tj0SmgcU00KUE6xbK9SJzk4VtPLr\nz684rQ4QmI9BKxtrITGoLn+xSzlNHB04TS4xbnQbbyHhvbNEVBbjNaogQo99n0HIPAvH+B4R3DaW\nWtDHDO7trLstFCWzXTwLUQkxunZ48tmq7+PZ7vr9yUDlm8g3v2972z8foL710PfhEgtc3E7y3Q8X\nfq9mp2zOIPTGM38Y86eXGzql2tDLSkhISLg7bOxMMeR2kg63yDFTws9AKFFuEjrFy616tnS8fX5d\nkjnqvAjuMQXSxg4WvqCeH/nsaG+F+iqXff9hE247MaWsogcuTFs70OCt6Ld7DRYrSNebYxa8ZGqf\nvM21ZHCIWpY5pgJTBHemCJw0D3wsug3vcBt1WWYYvCVmYhlm7fNuff3dvi/uc9ZYwrFnC2a6YeHj\nRt3nEjMeQ/qlZijbitlfHrV8TcRe6BKTo8ToBNc/R2odnY7CDLMspEIOMA8psvo5+9iuj+3haHiy\nXUGnuNfyWSODVQI7GJd+/Z2Jj3m47PeC5WnbMbjVg7HuBM/zgo5KMK3VZmb3nSDNFBMSEhKA9FJM\nSEhIADaWPpvsxClkC4veK1C56SkdHhxZIvWyYyxWY8+SRVJANwwBjUHf6U7Zh64QDLcNQ88ic+o5\njSl/S2b2NRFcwZr/cgQt431+AJcDeo87lQ8POpUymrXGK8xxzU3Qzgy/nxNSqV3n23OknxmCRJo4\nZaUpasdgnBprjRwioALjIPVh5TLF3WhjJWOrj2tDoGkBk9cK1zHq+gl24hLHeOCftZHaxpKlI7gJ\n91ELRqD1Yzj8tLBM0oTgdBXp+/3yQFQbgaN56e0tWBAFdYYajyPoJN8eyANmgvZ0jhKyjWhiay3v\n3z5eCRl0kis881ezemkmlThNSEhIeB4gvRQTEhISgI2lz6VW+rhqnVlBmoo5P9PfBlPox2LNjB7q\nYowQHWbpyxailSXMVwuUuxwgyttFjZgmhn86QknUqCVswyUGrFItOKDM0fYBSq/mx3C1Gfg5h8Eb\n2h8hWhrrfjDiGyZO3yrQd2QknjK5ZcqboNlrgDLmSDM8WHikdSdq8nZRSpUmpjcaRyfbV8xpMgK+\np/ST1KaunwNJKrDcMcidYh7v1LSyW3qDLUScV3A62ln45we53/NLiL5vgXofo+7LjblrEh+Yxrow\nI78njSZKtvZxnpWrCfrmdLe84ue8PPXroZmtdf0ZacBkdrS1ptJ+niZMkzX2ayuRQti+VI9FRu3m\nBiHNFBMSEhKAC/VSNLO2mf2Mmf28mf2Cmf21+PmnmNk7zOz9ZvZDZta8XVsJCQkJd4OLRp/nkl4W\nQhiZWSHp7Wb2Y5L+gqRvDyG80cxeL+lVkv7JrRrKLdOu1RR2BVPUZsNpXRf0uGyBEpc1LZivkDYH\nA9leE9HsDiLRqB0iOOaEBvZHSVL4kCogWjtXTVU6KM3J9LyjDKaplW/nme+zuuTnbA+9j1cgyJ4g\n+j6ONU22abzK9MiuLwfMDp3KCilkrEVjqF0yb6NUahfmp9d9LJ5a1hHyAahhwDLF/sI700NZUbvf\nr3OB+jsGjj9ACc9TMgLcul5Wt1NCmF3BgqdZIip9DCH7nn9+3PLP+87Y1fo40iW7Pv43osC/ve39\nW8BkNm/7tW0jVbFELZw5BOvVxM9foJTuXkCp1H3cx2s36muQ92/SggMR3Hg6SB4ohzXdD6vNVHFf\nqJliqLHWjxTxvyDpZZLeHD9/TNIr7kH3EhISnge4UC9FSTKz3MzeLemqpLdK+oCkwxBOfkI/KukF\nNzn21Wb2TjN7ZwnNWEJCQsKd4qLRZ4UQVpJ+k5ntSPphSb/mGRz7qKRHJWmwvR1aMUrYKX3q34II\nOOA3YYoI4WFWUxK+WKcw+ezvuzNJhWhhsYe8XSiPV8i3XZagZ6DSc0Qa7ajuyxL0dobI9nXzfvWb\nvgTAGh1FyVKevll1EYlH++0Y6W3k+B6UvTr2cw66Hv2tUPp0hVxhQ0RzCxHl5aFfxxB5w3k01M0b\n6JObt6iDPHXtwOnowMd2ftn7G9CXBtYBAqKyJSxj5nF5pIWo+QJuOLMlxPD4s9mnagF/TYZnp8KS\nwTKDWe2gvo5xdX6edAe1bQz1hFpQBbRZhhbC+K02nkU7f+7TfWi/Pg/Kuj418uh46MH1BwLv1uN1\nHy2k6POzihDCoaT/IOlzJO2YnSywPSTpY/esYwkJCRuNC/VSNLPLcYYoM+tI+t2S3qv65fhlcbdH\nJL3l3vQwISFh03HR6PMDkh4zs1z1C/tNIYQfNbP3SHqjmf0NSe+S9IbbNZQtK7WeqKNkxugezFKz\nmW93kJ87H9S/FQVKjHb2fbtLPgqx6yFyTzMwogKRSyG6twStEiKtjVaMhMJ+aoVI37acSjWhTspB\nGTOUJF3AgmoOGhxgypr16nYqOoci97mfQTC9RPQbZUqbuObWHMsBCMrPEBUd4PFbXIk1YpYQjyNq\nfYxlimHDT9Qdo2Qo+tV+wrd76Jf6sEtr0SK2RgUTXMtgo7U8P9JKii3sz1K6zPduISqez+s2DTVn\nRgXUBB93Kpvd5+Pf6vvzbFi+gL5ebU9315IKhQL1ep6u3Va75c/WEkL7KUT/q636gFMi/g3ChXop\nhhD+m6TffM7nH5T02c9+jxISEp5vuFD0OSEhIeFe40LNFD+ZCEWuKtpk5ftOPVYovTl70nNvtyiw\nniDROCLvOa2okJN7ymoLLlKLGQTGLDHaA2XMvC+ThlOcnSjCbiPHllS2gaiw5t6XsOVtV6BvYcY8\nWEaXES2O7RRwqKpgszaGXRa6rSmo2QoU8/KBt72152VDe6iR8oQ8vLxVxXxauIovIZ7PEH3em4Nu\nol+dU67Zfp5TwVdETMurrrBexmvNYadWHqBBlLsdo0ZOtuuCdeZbVwNYh3H8sXyyFQXxvFc57uf1\nBmurIGedlmtYjggY/2PUzpmOnEu3MaaNGN3O28jHb6KeDfoyxYrReswrur5vENJMMSEhIQFIL8WE\nhIQEYHPps8KJOLeJ3OcM9LmAs3CGKPJutP16EiUuOxNYWiHHNoBuCPZS3RYE47ArKxEJ7rkeXB2I\nytda47APYS7KTTZm54tmG4g4lxDeHm35dttAK53hq3VUX9Ny5hH0qo/yqTmEvMjD7aNMLC3Bmx1E\nrhHFzyFIHn3o4GR7th9iv0HlcM4MxZXCDaeSl+931+oM17lAND+goBNLtarn92getc4UdOccHzwr\nu12nzAH55iUSq6eQHwxRDWsbf3LXox9cE3nS2yh+ttXz7fHC26AY2yD2Xk1QMhZLNj2oyg+7Tp+3\n1ksZyF/Ho31KAN/GWDSjMPz9Gzqn2syrSkhISLhLpJdiQkJCArCx9DkLplYUIk/ncKRm/R8UdxKo\n2lqUOpbvvAUBcEDO7qwPWgk63IWoOiBKF0AxGbktELo+irnXWvj5t1F8a4lzrlD3eFlAAJ7DVRz0\nKcNSAmEx5zfP/DwFqCHzoOeoidQaI68XObTMg27AKbyHcdm6vHuyPW7X94gLAyvQ0YBiTfwlXyEP\nmO7YrLtcVedblzFyu65hNUUecqPwvjYR/Z4jEhtYxxrXCSc67WIc37e86p/H4lIDOKxvwdqugQJh\n1QjRbBYuQx3vAv3tDv3ZqXaRqz70SPRTk+t1e5d8CaKPxIAKz1aFZaJiXV98maLPCQkJCRuP9FJM\nSEhIADaWPktSHhlHQAGiBn4HGrtOFcZPeCT0OIqzBx3nRiPU8e3xpwT5rhVyXBdTp2HMT2X0M4DW\nNVCbeCfaftnUPwNj1UQeCsxR31gLRE7HvmTQYu41aNA4d3o0i5faANXrQDA+ogs1aNq459fTnfh5\nLqPHWwtQL9yLy223YGtG2jaa+RrEFEWmFhiABaLyS4iUF9iG2bQMIugAB/Ul8qnbi3qbxcwWEJJT\nL69D1KauwKVZRxsWZMXIx+hXbV052T55FtCniXy73Xx6brYkiVZ0uDZalGXIIV+tUHRq36nyg6Gm\n1Uv0dYnnRniGtQWH83iZG+oclmaKCQkJCUR6KSYkJCQAG0ufQwgq5zFHE/P8FWrVLiHUbcA1u7Om\nMKCa07HTugUilBmGkHmtczgsL+eIhLJMAqyZkForixFF60CMDNo5RWS3QFQ0Q+y2gki8QA5rOUdO\n9p4vD8yj47XBoqyDHGMKhqdzp48saGWgrE9BbNxFrm4BF+w2aPia4R90fExGAVRuCPsrXHOBfi0L\nRL/xc98p/PwTCLn7S7++9iQei3FbdLy9U89K37n80jDOT0E8jUJPAcJn/sVNoiphjkJUFODnoNK6\n7LR3Dvf2xhgKiiPf/1rleeX7za2T7YPgz3GrU98w1EfTPMd1Ivpv+FtY1yNPdZ8TEhISngfY2Jmi\nMpMG9QzBYBxafdzLc1YDn9oElOdcz5BYMhJZYxo2MNuY+hA2UJJSrHUC/V6GwIhh9nEdtTHKy7Ff\n+HXOkZ4I85pTTiVbh97eCm48kwENXzGbHPt19GJQJ8fvZLmNQBPsc3IYmK4OfEaywHVuoazotAkj\nWhjBDkof82ajPte8hIHt1E8UMJVuwN10hdk0JoSnglE9BJoKzODoXrTs1ftg0qYeZqRlgbFFsKyJ\nGf4cgaEcQTQ69rQRUMtjIC9feV/LBWbEMPwN0KmytKjB4UbH3s7eCH/ae765mzsjOo6Mx2Cw3J3D\nqBg1eMdIIVS3HuhKSaeYkJCQsPFIL8WEhIQEYHPp83Kl6onaRDaH9i7bdfpgSAtjKuA8LnBn+D6H\nOWr/CE43SLNaIrgQEERpgkpzabrCPruFp2INY+2YAwQa7l861eyi/slxD3QMDjMZUhGPUdOkXUFL\nB7rVjtdHnWIJKjtHgGLc9H4PQPFKUPkdjB3rmByAn/avgpLFND66GJXQzDHQsS2n9UvsT13nHgx6\nqwOYCb94/2R7Al3jcFIvAwwaTvtXLFnK7EjoFwOchAJLpmL/JQyHW9C+rmIArjHx+1N0/P7MoY1d\nzD1AsoVUvLDEsXhGC9QUoh42K/yimjGoRNPkZhN6RNzn3ti3V3GZwJQCLQkJCQkbjwv5UjSz3Mze\nZWY/Gv/9KWb2DjN7v5n9kBlcBRISEhI+ibio9PnrVNd7Xouz/rakbw8hvNHMXi/pVZL+ya0aqHLT\naL++vGLutKoJXpMhzatog7bESOd04fSt03Ja1YJmLL/GqJzTlzlKSZZCtBBlVStETp+YeprhoKyp\nz0NLp0CklasByq0i4jt1OdqJ7lCS2ogoVuhLawdLCVXdznLo13yECG418N+hnSkivgVdWpxOXW86\nNV3CPeay/JzXd+ECE+n5MSKxrJeygL3RHPdzgSc4h8avjQj1cgBT2AO/vv4uhJW79f7UseYVrmfq\nqoUcZVXbS9RLgbEw0z/pzLRAyt06RS8ElNod+vctzFlGOwitw92noHsTpjgHSOHcWvpzOVr59Tdj\n+4aypiOY4xZDuBTheppR2LiZ5PkCzhTN7CFJXyTpO+O/TdLLJL057vKYpFfcm94lJCRsOi7cS1HS\nP5D09dLJlGZf0mEIYf3T91FJLzjvQDN7tZm908zeWS7K83ZJSEhIuCUuFH02sy+WdDWE8LNm9tJn\nenwI4VFJj0rS9vZ26FX15U0bNA516smoW4ChZjNaoswbTh9YDrSCeHd52SlYDtPNZkmxsR+bw0R0\nNPVodafl/VqnwpUjp5LzQxcy97vutJKfSg90QtNGFFlICysRrWxh+WBdWnNGAXLDaVUOsjSdeFGR\nw663sd3xZQW65DRAd8cdP//1zCOq87ym2zilaE2zNL9XM0R/M4wn0+VWKD07lW/vt3yNoYSrUWcV\nKeYQyyGI4LO2jZoQLVOYD5eigKUESpy7UC6solrgANH58cj72kNKZHfm13zQhTAdywS9A2+7hZTH\nY6ROFhDv5/GZO5VmiGe1hB1UQAS9Oaqf21Btpnj7Qr0UJf0OSX/AzH6/pLbqNcXvkLRjZo04W3xI\n0sfuYR8TEhI2GBeKPocQviGE8FAI4WFJf1jSvw8hfLmk/yDpy+Juj0h6yz3qYkJCwobjos0Ub4bX\nSHqjmf0NSe+S9IbbHWCyE8ozhAj18RyC6KFffsmRiPTsEopxzEfIw0XEdTJANPcQxrLISZ23EGVF\njZZi6O10uohiRgFv09moWoiOD2G7UuC4/gIGuqBJoUKJS1DJJehj0a6pVPsYgumh09sGzlNu+5LB\nNiLLbeQYLxo+5ke5n7N57EsGFF6vzX/nIJtzONBkKExCB5wcJT77oLiMllO/NZtCsI76MusaMMfo\ndw6lwl7uywFj1IZd4X6S4pZ4RlhfpoWyqssodh+gVtCw7bnkB+ZjtT9lIgHy55EfX+7gPMixPh55\n5HwKIfeT/agQgOj7wanf2ybo+xRmwou4rENR+Cbhwr4UQwhvk/S2uP1BSZ99L/uTkJDw/MCFos8J\nCQkJ9xoXdqb4CcOktWZ1cAxjWebWQrvbQn5yFo1LVwjtLvoQzMK0tY1gJQJ3ymA4a8gJnsM4tNoD\n3QMVacUo5mrlxw3bqIUCWtMCZWbtDkYUc5bepFAXtHYaFU8V1MAZ1MCz4J8P4CZLCjU6dJo2a/j1\nbxdOPY8y1FRBiVfFHPJd5GY3QI2n+96XFVQDBeytaO/FXPWh+Xk6O55jPkHtnMdHNyRJl2awWbuM\n0qAQbEMXrQ58cEsW0llCsN8HZcYui3gdFWzeiq6L26dHnrM9RM3UHdSFQbq1Zmh9CuH9vnmbcyyf\nbI/rMVph2WGJejEtvB74POfRIs8Q+d8kpJliQkJCApBeigkJCQnAxtLnKgQtol32dtvpQ577lP/J\nEvmsGSLR0U65RP5wF9G/NkSrFSjm8pLTLcpal8h3bUKQbIgi9oZwtr5eR7qZG92DGHzY9fY6yCVm\nudPDFRyxURfmUu5lRQ3LAM31o1Aiagox+hxi4OURIsgQhhtobQ+1Qxp+Su01XaQ+RFR2FtsvQUcr\nULkRc5Z7Ps5N5AFrwiRjLEdcdkp+apnCL0P9aXTepl0cxmcxwOc+tJqxZCpcxRtGCzbUbkG9oLUL\ne4bPKiQRtPBMZrAXm0MAX+B54rIOa+EcBr/QNvZvH9fjtcLc6Fru++5kvkzCOPNWvJxsM4PPaaaY\nkJCQQKSXYkJCQgKwsfQ5mFRGSnYIGryN6kb7I8+DXaGIUz6u98+Q10vBbLZympSBjk9BMhrMSYZd\n2Hzg+++CvhVwPF69uO5jjjzYwRC0G/2aoIjRYu4Cc4qKcxTUmo/9pBRET2NIlYWwqHruTWEdhSWD\ndh9LBuhLAyJpjX28Qtsp2QzLCo1I6xoz0HSM7X1YJshyJEgjx7uBfq1AQ+2AebuI/8I1fetk6QHX\nj2p8/rUAACAASURBVHTn2ZGPbQ+Fu5poI0dONuOyBm+SEte8Kuu+lJX3qQQnZb51F+VgA57FKfKw\nDcs6AUsZGYT/GVzbqzgnKmFdxvs87SFSjUJY1TrKHTYz9znNFBMSEhKA9FJMSEhIADaYPgdNY+7s\nHCLhFSjplS0viNtEwaA81t41UB1aZ9keVN+kTBUisaDMtBTLECE85XIcQKHiPh38ZmUQj/ev+vWM\ntpFvDFrNKHcDQmbbwj6I0GaRelWgwGM4j1c91oA+2VSBcSkXWDN4wPt76YZ/XKFo9Rzb41kd0m02\nnCaHfYwz+qW2tz3EmHcR8WU96NYItB7XUSIqX96oOXGj799PKbRHLvNix6lsizWlkW8cSMMh5M5R\nA3q2FlIP/Tz7LVeAz5pUOVA87ttwBVOJ2tiG83QQ8ReocjMmkXfhGD/Nva9HB+4Gf4SxaMclKBiT\nbxTSTDEhISEBSC/FhISEBGBj6bMqqTGtqUKDRXdAGWdwfma9W4t5nuu6vJJ0gMjuftOj1jlpHaLP\nQ+T4tkGx+yhESJfjw4Urguex3nKxQvi37bRqBSFxC9e2jYAv81JZD3gkLgmA7kVxOCOh/eDHrXCd\nW4j+LukwjSj3On9ckhYvwvlRa3mJ3OYXNHbqvuI+MBI+aiFqC0svWmStQPG4ZDDued+7E9QvRvvj\n++trKhiRDshZxnUuA58hWJc1/ZwNpHWHFhzEu6D10TW7u0KN5iki8l3/80R9LAVE0NtPQTCOpYw5\nln4sg43dAktJcewaXc6NvI3ewJ/zJSzl5tdr5/UAH4FNQpopJiQkJADppZiQkJAAbCx9NklFfuvL\nmw6dEl8rnAps9Wva0IJ79e6x057xlgugW1edswbQnT5zaBmhRcQzD75/A1Zba6fw0RihRThCX5VT\n7QEsw3srP2cBR+Z1nV5Jai5hL4WofBmpZ4N2UKDAdPVGaWSVyP1tIyoqRpnhCL1k3W1ES7W2LgMd\nFiy32jj/BDZmOaLCK1iBUSRdjGEXh+bHsE4rDuqx6CGvet5HITLUlB6XTJpGjrF8PC8H1Oz2vTVb\nwDU9is2ply9hEdeGjVkOCr4KXFZ4unu5JBlUFkeVW5CVpMpxjFqHKHj10O7JdsWlnsLPv46gZ+9P\n1mEJCQkJG4+NnSk2LNNONEM16MQE/doM6Xq7SClrzevfioBAS7blmrkM5TanD6DEJhaxO3BjobHo\nCp8vEOgoMFNcB2NGhc9ItqbeyuW5nzPjDAL6war0vjRhLMvUsTnyz3pZfc4pZmHXl37++81nUOMM\nER0EegxBijk1dqj1kuNe3C+kCMYZ1w3U09lHmVLN/HoGCOgU2x6AmkOzOJ/4OadCrRdv8YyWs57Z\nNcEOlpjhL1D6dRBYjtbHtoeZ1bD0MrAVZlkD3LtiFdvE1HtuMArGPWyhbTo95ZdQyAfPVlU6CzI8\nx7stf44bMUWTesyAlMwM/aJOMlsHDm0zhYppppiQkJAAXLiZopl9SNKxpJWkZQjhs8xsT9IPSXpY\n0ockvTKEcHCzNhISEhLuFhfupRjxBSGEp/Dv10r6iRDC68zstfHfr7lVA6EKCrOaNjT6ThlWQ6eE\nOUt80ugzpkJdb7g2ax8uLcbACQINzQZ0ajAI5QJ4Ey4w45bTnVXL9+nOanrUnzg1C7vQACIAYajj\nQjJTwvx1MYVza47gycr7eNCoKTGlZ3u5X/ME19lrOGVtBSwfTJ2yzpG22ML495aoXQPto8Xx30Lg\n6KktaO2QftZBEGU+83POoMFkWdP8Kb/nq/v9moSlhzwuiSyWMBZ2D2LlWN4I0Jcy1W2Cmj5bDafY\nYwS3eL/KeE1VE65DoNcBxwVcf0CaYyihu0RdnqqPpQEEbEq0eTCtKX6JtMXtBZYPsBzUhNZx3ZNQ\nJZece4kvkfRY3H5M0ivuYV8SEhI2GBfxpRgk/Vsz+1kze3X87EoI4fG4/YSkK+cdaGavNrN3mtk7\nF1ikTkhISLhTXET6/DtDCB8zs/skvdXMfpFfhhCCsWbo6e8elfSoJA32dsJ4v768HvRwqw5qqqxI\nT54eSStypxUlor8FaoEMtp0mWQ9mrqCPOSKaC4z4AlHsJtqsIu0PoMl0rzGmLYKmL8zbCEOnkktE\nn3PQwwnoZj9GUZsdP+eIaZBIOWyOYJobWKYUUVREgnsrp9s0Ql2Ayik67BRTpOTtIs0Qyw6LFaLf\nM5gDYwEBgVi1rniRmAZUBEeIrq8NX9slaquglmmO5RWm7THlcFR5xDcrPLK+nKKs6wjnfKDepw1N\na0CZ2iUcbQKWPSrUgmlNcKFbMILd8Xt+be661v2hX8dOWd/TCvWHyn2oMJp+3xpMp4yDu6HB54s3\nUwwhfCz+/6qkH5b02ZKeNLMHJCn+/+q962FCQsIm40K9FM2sZ2Zb621Jv0fS/5D0I5Ieibs9Iukt\n96aHCQkJm46LRp+vSPphq+flDUk/EEL4cTP7r5LeZGavkvRhSa+8o9YinVtAvNsG22vmiKghvW0Z\nRdVN0jFnElogWkjBdFk5lZuBPu7LKVsblHwBt5UpXGDW9UqGKFN6fwWhMwTQqwJ0C/S51URdjkPQ\nTVD8JaKVI6sj1PsNGKiaLw2Q9k1omooxOkba3BU40a5QsKZskXMhLS1SyKN9v56tGzBEhei85PUA\njR6sZDooWYtUQN4X1tFZxGUVlCWRgbLOsdRSYPmgg/Bzvu33eco6PkiXPEa65v1xyaIBN6KqhWUC\nGBKvkGYY4LpEc+QllnhyOPZcbrnAews1ghRNcGYYiDYMb0uoM8YI5xeLuo+bGXu+YC/FEMIHJb3k\nnM+vS3r5s9+jhISE5xsuFH1OSEhIuNe4UDPFTyasCspj7iyFt8crONygdkUXebBr49gJRMKMFE+R\nn1ohKnmqPbiCGiKqBirfBA0t4SqzLiF6OXfK/ERwKl2BykB/rf7cf+OKnouUyx7yVlEesweXnnY8\n/1rwLkntHtYM2sjZHrrrSgA1pahYoHu9BSK6WHpgTnIeo/87S7+4A7gRNSB6XmJpgCHQBuri7CBa\nXcC9hy45Ae5Bu3l978YdRNyp6mqe/6cyxLMwbHh/r+DeMcf8frkLTR7p8Qj3tg9xO5cmlj2I3pFj\nvsTz15nCSejUkgnWeKAQKPV0k9gA1x+K6wOe/5MEgA0NP6eZYkJCQgKQXooJCQkJwMbSZ0kn0/sZ\nhNRLCFx3l04VSJVm8eMm6KVNnI40BxC1tmHsOjk/HncdprCjDmjy0n+TWkMcsKbSCBTudrxehi1o\nP+YU6GrLz3MgT9xd1z+RpMOl08OtzGloM5q4TuaeJ90rUBoTouvRKYEvuDyitQtELvMVKBvGkeLo\nsI7EI/rbg0XWCmPRA+0VLK2s5zuNet52F3ZZ2Qhib1iTZVl9/mILgn0YD5+O+PvpVVB+4Oc8XPq9\nuNzz8S9QX2e4qO/FdOp9GuW+bXv880SZ1tn5udRFF4kEeEYoti8hdh92631aBRQH2O5gmaKB6Psk\nqgzChsaf00wxISEhAUgvxYSEhARgY+mzBVMrWjxNMtSxAD3LIXbNDNHnyApGGeqPoN7LfOQU066j\n5kbJ/Fjff+eSR4LHsPEy/CY1ILadzep9BjtOmcMC9KX0NlqIUN7f8ohnCbq1BD3bKUFxCx+MYYwu\nsmTrbOgUcIWAL6OOS7g6twvf6WrHz9kawo4LguQ5qH8Zo+K03KpQVpRRUzGXHZZrJRykOy1vZ4r7\nP8tRetZZtRaxJG0T4fzx0sc5hwA755/NxNsrEImnXRzt5apTl1T/g0sQpLe0YssQ/TZQ9sHYGzzK\nPff6VL2WOXLvu76dxeWO2TV4pG0jau6fnlItqP10x/BNQpopJiQkJADppZiQkJAAbCx9lrnIdWvq\nUbI2BK4Z6M4czsaT4yjC3XaaMkYkMg+goPhZWSBYG2DwvH0DFAsaWIMglxHyYaRy7Wte/GjGA1G+\ntINc6grUfAJqDPakMSK0VeXX1D+s228i4jjsoBAS3NoKCt2RM85o5O7Ury2DkDpHfjLSmdWKOddL\nRKcN1DTn7zccx0rYaC1hRVYOYK+GpZEATsglhiJasN1AwafBGHT4itNK3ovVIZzc4VrdALUsn/I2\nFy0+f3GMsDRRwcIu76HIFOhrBgX6AmqG7vE5bUvqInmhAeu4WbQma1z23GgbIbJd4jovo7BaVd84\n2qltEtJMMSEhIQFIL8WEhIQEYGPpc1hWmh3U9LPfcS67gKXTEtHlAEoaou1UPgR9Q17vAvZXdMFu\nQ2G8QL7ttA+RduZ0lxHqOSpGdYc1PRkOvI0ZIpgDOHmXoN3l0ilzB1F2OkVXiGJmcOTO+nU7zREE\nuSPkbHed65Ka0RE8Q+7xDH0pQJkLCoUhCF9G4XOOtkvkDJdwoaao3RC116GPxdVr1062exjzHpZP\nGmMM0sO1wPpKA3WUW94ea3QPIPqfCKp/jHMLawOrkUeRV6yTHZ+pfM6bhfGf4HM8czMs9WSoKd3t\nQbzNGtxjXzNYMUIeo9htwboMbu9lA27byM0fLWORMyw/bBLSTDEhISEBSC/FhISEBGBz6bOCVjEa\nOVo4fWnzdwCWXhUo2UmNpgaoMajmYuVR3iMIqfOmRyhZhXkSUEQKFZUqiKrHKMZUxAjh/sJpfyan\nRgfDQz8/qHmnCxdm3NkMVGoL4fIVlcQngmAWRXLKNEdOdNn1weo3PEJaoIhWhvraxQgu1Nt+/kND\nPnk8/8h83xULe4GCd5GzzTrec+QnD1oolsWc7LZfc7HnYxo69TWdKmXcgks56HuGmtoZ7b3gpM6G\nGrgvOT6fx+fTQGl7U944OL/b+csKxBIUt2oixz7362yv2Ga9zymLMhR2O8h8bBd4VjtRzRGqRJ8T\nEhISNh4bO1O0Ilf+grq0ZZ/aRCyuL2DJMoT5bB5nIm3UM2GaXQ/DVjR8RjJu+69z5yYmpwXKXa6w\neF8g5axcN3+AcpioOTLdoe7Mj2vA2LUY+OxghRnMmKaomOXeF+J1IFiwXlCXpGNoFltY3G/zGtCX\ndtfHJf+46/QCfodRQfNkdt6DBQ0mPjIY6Ab0i8GtyQ6CBF0fo9XMx3x47HZED6x8Jl6oPnaOGVZF\nTV/F2b73q4BOcw4BZQud7yJ4dAyWMY/pinsDd9FBzOOUNvS49GehP0WqHvoym8GU9xg6zS1PF13A\n1EjRSWcFfecYs/M+A2p4hvuxrGqjSjrFhISEhI3HhXspmtmOmb3ZzH7RzN5rZp9jZntm9lYz+5/x\n/7u3bykhISHhmeMi0ufvkPTjIYQvM7OmpK6kb5T0EyGE15nZayW9VtJrbtWISWqFWCpUTllUYdEf\nPwkVaOAo1PtvocQnqkoqA2VqQqeXT6AZxIL+svRznjIFzZCuN4E7TTRxfV+fRUJ8exBgBIpb2EI6\n4/wYgRtoE3vyY7dKaDNjelkBbVwB+lgFP38HaWOs3TKfowwojFD1wu2TzVMpck+5O8s6Ky7s+zrC\ngk4/oMB08lksvI975seOQPfAJNWFZpEax3UZ2GbHx4QBGjrjFNBjGvSljT1fDyiQWngMbeyYwYm4\nuRg77WWq4sHMlx12Suge++fXwmnjnIuB73Oj8PNvzRFRjPS9gvEtVjRkoM+GdNJsvQSTX7g51ScF\nF+qqzGxb0udJeoMkhRAWIYRDSV8i6bG422OSXnFvepiQkLDpuFAvRUmfIumapO82s3eZ2XeaWU/S\nlRDC43GfJyRdOe9gM3u1mb3TzN65mC/O2yUhISHhlrho9Lkh6TMl/ZkQwjvM7DtUU+UThBCCmYXz\nDg4hPCrpUUna2d1BXUunGMOeb+/kThZ6gVHUmkKOM0Q54a6SI/2tRC2SsI20ObikZNDYNdD1DPQ5\noJxqN9LwHigLTU4Fapo97prF8CJfarVdaOxA2TJI3GisO6rqJYNmB+lkSBvrQyeZM0QKp5QJou9C\nKdO8IA33fQLWJNathLH/mF3PoQjAkkVrAQ0g0h+P5m6KmyHNrjhVX+WUgNPbidHqwDQ7zBm6uBdN\npL8drpziCnrMEhH6DIqDbc5Doj7xYOltFPieyxtcssmR55ht4dlqo4+4NhsizW8Mg+KdOvpeYt+Z\nIZ0S2kyms4ZmPZ4bGny+cDPFj0r6aAjhHfHfb1b9knzSzB6QpPj/q/eofwkJCRuOC/VSDCE8Iekj\nZvar40cvl/QeST8i6ZH42SOS3nIPupeQkPA8wEWjz5L0ZyR9f4w8f1DSV6l+eb/JzF4l6cOSXnnb\nVsy0ihHVchsR2uDUYzl1etCjU0isHbJk+cwuzFxLRhCR/oWPM0TmmogQWt/pDmuQsI5GFekzhebL\nvveP16OuC5B3QY0bc4iQQVPLFugr3FYaw/rzpwrUc8k8bTEg5bFCnRWh/koDTi5ClH0J89sVyoA2\nQfcsLjdkaPvBAgJwlIOdI22SouY2UvsqiNDboIfF/HxRfdmL27gnC6oGDmF+i3Hu7/r4H2NtYrGA\nsTCelwZS6tZppPttF1fPK0TtYT7bBH2n6H8JN6JR5WPRhcqggbTMCmL3o5j+imA+K6mqnfsz10eq\n4NFFfGt8EnHhLi+E8G5Jn3XOVy9/tvuSkJDw/MOFos8JCQkJ9xoXbqb4ScOykh3VlGd7C8VTwBWu\nj2+cbBegSlpTVVDgFujLsuGUcQ6aGG4m2IaZbRNlVTszUBzQ8E6ob0sHJUNXEEwfI5qLqqoaglY2\njpzuMVc233JKdLXhdGvwVH3s4CGnctMGXGcg+m0e++esy1I8ifKtOy6kztt+HWNQ7ADHlixS2Tad\ndkD15oj4NmGy2wdNpIFvhv7SzaVAFD/0WGIWouZ12yiTejxwp6XJkW93fVMI5qvdwTIJnIwoNm/G\nKHoTSwN0bjpsg0qjrGuBGkGrXb+fOYTkJaLMhiWjFkr15lEovmBePZIbpjAwzvH5VkweoLvOJiHN\nFBMSEhKA9FJMSEhIADaWPpvsJNK8nJxv1zVowcQVeuQs5vB2h04flnLLKe05NaQpZ0AWzWET9UWQ\ne3pf2/OAuwgvNqGZncZc7V6AYBqC7d4hwp+gg/MecnlhAXXQcCpVNbwv7SUpY91ms4LhKXK/2xCj\nL3p+/gBbMoPAmKU6WZK1CY80GvuGGHEf4zjT+XnizQaUAoiKL9oQLFOwDrF3zsgt7lcWrc5YI+Zg\nBjE4Sq/miFqPOoi+I5d6ARF8F1FcQfEwj0sJc/lnLaggdq55/yZ9jEUDyy54FGjvlYEm0/x3RRfd\nyPcr0O5q4Q1WWBq62vPn7771caLQfXOQZooJCQkJQHopJiQkJAAbS59XFjRs1lP+BqgXxcEV3JHn\noDu7ZU2rsz4Ey/j5OIAVWffA2zvKne7sgG0Xe+6s3ABPLxFxXsAyqozR7Wvm9K0DppL14LyNqHRv\n5NRnhv5uI285gyP4DELuYasOo7YRNW8eIVIMChag8KXtVWfLlyNmcPg2XFuOWiMZqP+Nbsynxfn3\nj2HjhQjquIALdI7aOWDszJseBc8t7sPBew81dWbH9ViXUODPFk6Ztyu/b4FC/hx0HLZjc9iFtVYs\nK4pxjA7iOWq4BIjHl1u+1NIDHT4Ifm3D0p+RVsf7NThVdMg3C+RNr2JSwXxJygwrMgjz+34aDR+s\n+53oc0JCQsLzAOmlmJCQkABsLH2uMmkSXaFzWEd1MeNvjFFCE2UzpzFA2gFnLkco8YmSqbstpzjM\niWXuc+PQz3Nc+LElS2LCakqxSNYQeb3LvlPTNpy0T0VQQeVY9Il5y8copSpQYr24pvgrWIEdgR51\njpHLOwEfI+Da3UXEuYIj92zqbttNULne/fX4h6uw32o5ZR22vO0j+TU8JI4/xOAYgEHbx64PIfmT\nRy7eb8SIbhOWY9tLCMOf9HsRdnw5osR5coxLQV8tFNoSlztW9fYKInYuwXQq35eF0CaYygywzwBq\ngQxi7yWC35wGzWKq+BA5/nuVLynkOXK5Wyj+Nox9rBJ9TkhISNh4pJdiQkJCArCx9DkPpkEsJjy+\nduBf9JxKUfh7rfII5aVQU7Jy5JSpROGkbt8tqlZwJM4RWZ4g+toFDSxAlWhjNUZE1yI97ganenPk\noXYW3p7BFiuj7xN+7kq4YDcbTrEMFCuPNGyMHNcW6K21UQMY5+wHF2NnyOseI1f5atdp2H7b928j\nP7l7vd6/hBv6BLnJBdLXL828L0/mSD6GRVeB/OgtWIoFFOMa9JDn/f+3dy4xkpVlGH6/U9fu6tv0\ncBEZBIwExYVgjMFIDIGYeCHqwhiIJsSQ4MIFJBpF9ya48bIwJgY0LIxKUKNxYWKQhSsUwYQIEpFI\nYBgGZuie7q7uun8uzl/zP03I4Eya6a7D9ySTOVVdder8dU7/fd7/u7yjctzscD3p5H2M35f3URzL\nodipBJakLn2a0bV9Cb5pc1wmWSivT6vBwIz1y738xvUWIsvb6JiO60LD/D3vFGhph7Z0gzxktVJn\n73fArootwpgZvmYwQlvupOOu5j1VNUcVBEFwjsSkGARBACorn70mDefLOb950dLp5+0UImpIKl4S\n5WH5GtbMziH6u72S5ctL6FR95SlISRgAnRyjhpbtyCDfF+B/tJOkdxOyu4Pjq6F1VIFk4NqQUgpy\nvAX/XhptKb9+sFlKtQZae3XQ0moMaT5GHfAQbbGGyzguJDi/s8jybIDu0MLyQS+pth7MkthSy7r5\ntWP4ax+md3Peswao/d5BS7UBaribrbw80U6R6zbU+DgPYVf99uiSPJ4RJPMYnbLnYTo12cljPmH5\nAy5IUpWJ7pM+2r+hF1kxyOPpriJ5vJ/H31vLn8OCheZ8lsSTbWYolNsFrjPmfNMPWjjng5Tsvdvk\nqzrEnWIQBAGISTEIggBUVj6b5+CZbSHKihrSNhJft1EHPI00b9YoryG7II0OoSa2j2ipo0VUE0ni\ng6W8nz6kaq2bP2ucilXriBrWh4gmQtVMIPHqGFvPcxR5gIhyp4faWiwf1NvlTieQnX103qaUay3D\n0GoDPsLwt2aN9XiSJWENtedHi7ysMErmSgV9m1CH2+Cg2YoMUdkGjpFJ+kNG4lHPPGKn8iQh66gr\n7+C8GaTiJtuCoTt4B+driOTtTXS2bqP2eysl5yOwqzGi4210Xh8hsjx8DWstaO/VQjJ+A4UEI0S0\nHVkEp0uyUe+9M0SonO3fYH7W3Ch3bpG8/dZjZleb2T/wb8PM7jazVTP7k5n9O/1/6M33FgRBcPYc\nqDtFd39G0rWSZGY1SUcl/VbSPZIedvd7zeye9PibZ9qX9cdqPVe2qtlu5L+EQ1iF+q6/CXl72ji2\nYfkvdcPohZHf1USO2dqp9fx6NKito3uJoWNKE3dTmsv7nzYaZdPWAt4e9S3cVbb4lx9eIMgT3Oqj\nk02RA0Z1WIxOG+S2cEcwRtPW8YR3Tfmwa4uw3kQz3yH8WlAtp+ZCDkYxT3OS8uoOIU9xgEDQTjN/\n/hzsdAawaR3TehalndiUkLPZOpaDHo3UEcnm0bQX7WUKfC+dU7iecOfJTksjdkDCnXodpZNFOl9D\n/BbOMeDXzftu4fpswlZ1PMjfUf+1zdPb2/MsG8Wdeh3eMan8ssmGtF2UB6KEtHYkl1NOUvmsELSq\nEgfqTvF13CzpP+7+vKTPSnogPf+ApM/t21EFQVBpDtSd4uu4VdIv0vbF7n4sbb8s6eI3eoOZ3Snp\nTkmaa7be6CVBEARnxA5irpGZNSW9JOn97n7czNbdfQU/X3P3M64rriwt+43X3yBJWtvJsqKJHL/F\nRpZqzBUbpfwsSjnmie3A4rRGXw7IrQkDIwjSsPmod/P2Ar1LWslbZpyPqUGpDb+WjaxqNIRkRjWh\nLqrlwEgDUmmE3DNPGpNliD0ESNb7eXF/sZP3R08Xg3wdQnrrJGTqSpZ+tEddS94x7JxTY64lbGoN\n40dTH23D+pUBiB10qVlFzmS9gfLLJJomkKObE0RAkF+6XM/nqreZxzbqQe7CEre2mP9Ar8P3ZT6t\nMPhO/p5HyA1szeUxN2B9Wu/k5ynxa1g+oSVsH417O2iyu5XycVnu2UEZ7Nxi/q76R3OpbDcFIP/6\n+GPa2NysnIY+qPL5k5Ied/fj6fFxM7tEktL/r+zbkQVBUGkO6qR4m7J0lqTfS7o9bd8u6Xfn/YiC\nIHhbcODWFM2sI+njkr6Cp++V9KCZ3SHpeUlfeLP9FEWhdquUGbUmkt+gsNiUtI1uJ1tJTmwXKJtC\nyR9ztgT5zE42BZYl2rCtLCaMFufXcBlj0C9l2JBlZvN4HwTL4qn8PltiVx10W3HkniGvj/ak2yfL\nyHkPEdRpFxdJajSzZNzlJ4OouSOvsXkif3fNI3mlY9diDfLcptYkCM5rgEjwheP8OSPkEloP9qDo\nADRAisD8Rj4uz8p/VynctPHQsJE/szbEZyLX70Q/L8c0GE1GDuA2Gv4yx3VpHgeQGv6OIM178Lo9\n2c/ZDJev5mX0EaP/OLUj5L1uF1mSj1E62ML1t5yueYPUF/IrHUsQ7QvzOk1/p1xKYYemKnHgJkV3\n70o6/LrnTqqMRgdBELylVHOqD4IgOEcO3J3iXmGy3JEGXUB2JrDbPIUyvnaOtK2khOiuctSuX7Bs\nD/ad8C6psbEnEoZ73Sy3+rDnbKNcbojE53HKjqbnSx2yrkA5Vw2lhcYE9B47pqBZq2WptMWk6kvK\n4P4SauLYaabbwBLE6I2jv2w6OlyG9ERknyV6EyQVT1Kz3iHK2VaQ9V1D+Vv/MJYSkCTtx7P/C0ve\nhig/NEjZIcY3XZOgt4ojEl5Hkv4Q2x3sj41tWa63NcoR6kXI553UsWgL0rwJK1dDRsIa9tGBf20D\nyzoNfP8LuBbytyIVbUS3p75EiOZvDrIe3+zlc0E72Ko2l51S7dEFQRCcJTEpBkEQgMrK54GNdbQo\no2QTyl3YbU46WTLvoIlqLamKeci3BiK1vWaWuj34aDSRYNyEleUI9aS2hcJdJF7rcD6WybCMVxqp\nLQAAA71JREFUShYvZ8k0hsVqYzUn2BboZDJijSskcxNJwAXkYWcA6Z1k1RBR2zES0FeEyC5asCzg\nO+qhDvlEA8sKXYwD0dJmJ8vD5rSTET7z1UZOGD9UoIEvEtm7SLAuYC8yRlTYkWA9PpxfNFdH5DxF\n/2sI7U6QPO6omV6CR04D55OJ/DLUSmNp5jgsXqcJ42NkCjh9c1ZxTaBj0IR1ze28zToMw7rGAux7\nKZ+3UiqGIyVjxM5AuFS7dSwlpWTwg1j4sRfEnWIQBAGISTEIggAcyNrnvcDMXpXUlXRiv4/lPHCB\nYpxVYlbGebm7X7jfB7HXVHZSlCQze8zdP7Tfx/FWE+OsFm+XcR5UQj4HQRCAmBSDIAhA1SfFn+z3\nAZwnYpzV4u0yzgNJpdcUgyAIzpaq3ykGQRCcFTEpBkEQgMpOimb2CTN7xsyeTbaolcDMLjOzR8zs\nKTP7p5ndlZ6vnDe2mdXM7Akz+0N6fKWZPZrO6a+Sl8/MY2YrZvaQmf3LzJ42s49U8XzOCpWcFJNn\n9I9Uer1cI+k2M7tmf49qzxhJ+pq7XyPpeklfTWObemNfJenh9HjWuUvS03j8XUnfd/f3SFqTdMe+\nHNXe80NJf3T390r6gMoxV/F8zgSVnBQlfVjSs+7+nLsPJP1SpXf0zOPux9z98bS9qfIX6FJVzBvb\nzI5I+rSk+9Jjk3STpIfSS2Z+jJJkZsuSPibpfkly94G7r6ti53OWqOqkeKmkF/D4xfRcpTCzKyRd\nJ+lR/Z/e2DPEDyR9QzrdvuiwpHX300Y4VTmnV0p6VdLP0lLBfcmnqGrnc2ao6qRYecxsQdKvJd3t\n7hv8mZd5VjOba2Vmt0h6xd3/vt/Hch6oS/qgpB+7+3Uq6/V3SeVZP5+zRlUnxaOSLsPjI+m5SmBm\nDZUT4s/d/Tfp6Sp5Y39U0mfM7L8qlz5uUrnutmJm00aVVTmnL0p60d0fTY8fUjlJVul8zhRVnRT/\nJumqFK1sSrpVpXf0zJPW1u6X9LS7fw8/qow3trt/y92PuPsVKs/dn939i5IekfT59LKZHuMUd39Z\n0gtmdnV66mZJT6lC53PWqGxFi5l9SuW6VE3ST939O/t8SHuCmd0g6S+SnlReb/u2ynXFByW9S8kb\n291f25eD3EPM7EZJX3f3W8zs3SrvHFclPSHpS+7eP9P7ZwEzu1ZlQKkp6TlJX1Z5w1K58zkLVHZS\nDIIgOBeqKp+DIAjOiZgUgyAIQEyKQRAEICbFIAgCEJNiEAQBiEkxCIIAxKQYBEEA/geRfNcIKvVK\n9gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f2781651da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[0], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[13])+ \",\"+\" iceberg_probability : \"+str(pred_valid[0])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f271da91400>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUQAAAEICAYAAAAncI3RAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n6DiOE/g3I1ZbO0V+oRIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f271dbcd400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[1], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[14])+ \",\"+\" iceberg_probability : \"+str(pred_valid[1])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f271da6f9b0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUUAAAEICAYAAADIsubvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f271dafa438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[2], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[38])+ \",\"+\" iceberg_probability : \"+str(pred_valid[2])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f271d9cae80>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUQAAAEICAYAAAAncI3RAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f271da69e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[3], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[43])+ \",\"+\" iceberg_probability : \"+str(pred_valid[3])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f271d9ae6d8>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUUAAAEICAYAAADIsubvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIXxQdBzHIXxQdBzHIf4H/ulx1MAd1fEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f271d9fee48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[6], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[104])+ \",\"+\" iceberg_probability : \"+str(pred_valid[6])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f271d917048>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUUAAAEICAYAAADIsubvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsvXm0JdlV3vntiBt3vm/KrMysQaIwg+nGXkwG2mAMRsYY\ncFuyTWsxNBQgrPaE8YgEbS8wpmk1tA1eHoBqBBQWIISwLIEZjBmMWcsIF0i2QTIgiRKqISsrM99w\n5ylO/xHnvv17mfdlZaWE8uXV+daqVZH3Rpw4cSJe3POdb+9vWwhBCQkJCQkVsrvdgYSEhISzhPRS\nTEhISADSSzEhISEBSC/FhISEBCC9FBMSEhKA9FJMSEhIAO6Zl6KZfZaZPXmL74OZfeRttPNw3Ld2\nB32442PfX5jZT5vZI+9nG99kZq/7QPXprOB27/0pxz5hZn/6lO8+w8x+e92+ZvYNZva9d9bjF9zH\nHzCzmZk98cE43ybCzP6RmQ1v5+/3tl+KZvZLZrZvZo3b3P+uvUDOKszsH5vZfzezhZl90ws5NoTw\neSGEx/6AupawBiGE/xRC+MOnfPetIYSvlj5oz/q3hRAePu1LM9szszfFP/z3mtmX3GLfHTN7zMyu\nxP++6YbvP83Mfs3M+mb238zsT9zw/deY2e+Z2ZGZPc7vzezvm9lvxmN/z8z+/gtpG/t9340/dmY2\nuOG/pZn98/jdl97w3Sge/0mSFEL4Rkkfe9qYELf1UjSzhyV9hqQg6c/fzjEJa/EuSV8n6d/d7Y68\nv/hg/dilH9Xbxr+UNJN0UdKXSvouMzvtJfAdktqSHpb0KZK+zMy+UqperpJ+QtK3S9qR9G2SfsLM\nduP3nyrpNZK+UNK2pNdKepOZ5bFtk/TlknYl/VlJf8PMvuh22l4hvig/4sZOhxC6q/8kXZI0lvRj\n8bsfuuH7vybpPZJ+47ZGD7jdmeKXS/pVST8g6QSFM7OWmf2T+Ot0aGa/YmYtSb8cdzmIb+4/fiN9\nu/EX1sy+0szeGX9F3mNm/8cLvaDYzheY2dviL9n7TpmVfZWZPW1mz5jZ38OxmZm92szebWbXzOwN\n8Wa+3wghPBZC+GlJ/Rd6bJypfzX+/ZcxVu8ws0+Mnz9gZj9uZs/FX+q/eUNTTTP70Xjcb5jZx6HN\nU4+N9+6NZvY6MzuS9BXx3j9mFYN4p5l9nd1iiQNtPWFmXx/7vW9m329mzfjdZ5nZk2b2KjO7LOn7\ncb3vMrPrZvYWM3vghmY/Pz4zV83s280si8d9hJn9QryXV83sh8xs54ZjP/lWfTnlGvgs3/isf2bs\n5x/F/hfi7OW+5xufFwoz60j6S5L+YQhhEEL4FUlvkfRlpxzyv6qaeY5CCE+oerF9Vfzu0yRdDiH8\nWAhhGUJ4naTnJP3F+P3Dkn4rhPDroUqH+0FJ5yVdkKQQwreFEH4jhLAIIfy2pDdL+vTbbHv1I/jP\nJX3N81z2X5J0RdJ/OuX7RyT9YLiDlL0X8lL8ofjf55rZRXz3/0r6JFUXvKdqJlRK+pPx+5349v7P\nt3GeK5L+nKQtSV8p6TtWf+wvEMPY5x1JXyDpr5rZy27Y509J+ihJf0bSq8zXlb5G0sskfaakByTt\nq/oVfl6Y2b8ys391B/19QTCz/03SN6m6xi1Vs/dr8UXwE5L+q6QHJb1E0t8ys8/F4S9V9eu6J+mH\nJf1bMytewLFvVDWuPyTpG1X9kfwhSZ8j6X9/AZfxpZI+V9WM4KMl/QN8dyn278MkvdLMPlvS/y3p\n5ZLul/ReSa+/ob2/IOmPSfrE2M/VH7nFYx+Q9D9JepGqsbvdvtwObnzW/2PsH8fjiyX9fAjhuRsP\nNrMXm9mBmb34BZ53hY+WtAgh/A4++6+6NV20G7b/yCnf3fj9T0vKzexTrZodfpWkt0u6fNMJzEwV\nw/yt22xbkv62pF8OIfy3W/RdusVLz8w+TNU9+cHnaWM9Qgi3/E/Sn5A0l3Q+/vt/SPrbcTtTNYX9\nuDXHPayKbtfw2TdJet2t9rmhjX8r6Wvj9mdJevIW/QySPvKU775T0nfccM6PwfffJum1cfudkl6C\n7+6P1197vv7e7n+SXifpm17gMb8k6avj9s+uxuWGfT5V0u/f8NnXS/p+jP+v4rtM0jOqHtzbOfaX\nb/j+PZI+F//+6lvdI+z3hKS/gn9/vqR34z7PJDXx/WtVzWxW/+7Ge/Iw7v2fxfd/TdULaN25Xybp\nbS+gL0/esO+fvvFZXvdcrMZTksV/Py7p5Xf4vPyApG+5xfefoWoGxs/+sqRfusXz928k9SR9pKR3\nS5rG785JOlD1Ei9UvXxKSd8TvzdJ3xDHfyHpqqRPPuU8/0jVy7lxm22/SNUS0zbu601/06p+LJeS\nPvyU8/7Ddde+7j6t++92ZoqPSPr3IYSr8d8/LKfQ5yU146C+3zCzzzOzX43U40DVA3r+Dtr5VDP7\nxUgDDyX9lTXtvA/b71U1k5CqAX9T/OU+UPWSXKpaqzkreJHWj/mHSXpg1ffY/2/Qyb4fX3cIoZT0\npKprf0HHRjxww2c3fn8rnDb+kvRcCGFyw3nei34PJF1TNaO9ZXtmdtHMXm9mT0Xa/zrd/rNwxwgh\nvFXSSNJnmdnHqHr5vOX9bfcUDFQxBmJLpy/T/E1Vk5nfVUVvf0TVc6AQwjVVM+2/I+lZVeuC/2H1\nvaRXqGJxHyuprmo2/JM3LmeY2d9QxWS+IIQwvc22v1PSN4cQDp/ner9M0q+EEH7vlO+/XNIdi5K3\nfClatTb4ckmfaWaX4xrP35b0cXEt6qqkidYsiqp6I9+IoaoF3hUu4VwNST+uio5fDCHsSPop3Tzd\nvh38sKoH8EUhhG1J372mnRdh+8WSno7b75P0eSGEHfzXDCE8dQf9+IPC+7R+zN8n6fdu6HsvhPD5\n2Of4uiNlfkjVtd/OsTfe02fi8Te1fRs4bfzXnedpVS/tVb87qmYdvCentfetsb0/GkLYUvVHfLvP\nwu3itHWrx+L5vkzSG2940X8g8TuSamb2Ufjs43SSth4jhHA9hPClIYRLIYSPVfUe+DV8/x9DCJ8c\nQtiLff8YfP/xkn4yhPA7IYQyhPAzqp6DT1sdb2ZfJenVqhjXiTXZ52n7JZK+He8aSfrPdrOSfupL\nz8w+XdWP2hvXfX87eL6Z4stUzZL+Z1WD8fGq1mX+k6QvjzON75P0T+MifW6VoNJQtYBaqlpvWuHt\nkv5kXEPZVkXPVqhLWh23MLPPU7XedyfoSboeQpiY2adIWhee8A/NrG2VQveVkn40fv7dkv6vuC4h\nM7vPzF56h/04gbh211Q17jUza8Z1GYpOD99GU98r6e+Z2SdZhY+M/f01Sf0oUrTi/fgjZvbJOPaT\nzOwvxgXtvyVpqkpEu51jb8QbJH29me2a2YOS/sYLGI6/bmYPWSVi/Z/y8V+HH5H0lWb28fHZ+lZJ\nbw2VSLDC34/9eJGkr0V7PVUzqcPYxxMhInfQl3VY96xL1az0L6h6Md7Z+tZtIIQwVEWHv9nMOvHF\n8FJJ/3rd/laJT+fiPf48Sa+U9C34/hPis7qlapLyvhDCz8av/4ukLzCzPxSfvc9Rtab5m/HYL1V1\nfz4nhPCeNee+VdsfreplvnrXSJUo9CYc/2mqGMKPnTIcj0j68RDCCxYzj/E8axk/I+mfrPn85aoW\nVmuSWqqmvU9JOlSlxLXift+s6oE5kPS/xM/+Zfz3u1StexxzfEl/XdW0+kDVDX294lqKXsCaoqpw\ngfeqog8/Kelf6Ob1n1eqmhFclvR1aCdTNb3/7Xj8uyV96+2sSah6oX73Lfr4A/F4/vcVwdeFnpBU\nnHLsLymuKcZ//5XYx4GqB/IT4ucPqHqJXFYlEv2qTq6DvVHVH31f0tskfSLafL5jX3dDnzrxPq2W\nGf6B4nrc8zxXT6j6QXxHPPYxSe1b3ed4ve+WdD3e04duuPd/U9Ua5zVJ/0RSHr/7WEm/Hsfp7ZL+\nrm5eJ7ytvuiUNcXTnvX4+X+Ix9ktxuPFsX8vvsVzc+qaYtxnT9Ua/FDVWuaX4LvPkDS44e/3aVX0\n/u3CunD8/kdU/S0fxmflAr6zeK2/H5+hd0r6Mnz/e6rWGwf477tvp+1b/U3js++R9K9P2b8Zx/8l\np3z/sG5jTXG1CJxwl2Fm/0DVWtr33O2+3CnM7K9K+qIQwmc+z35PqHrB/4cPSsfuIszs+yQ9HUJ4\noYo22/j/VIkTz4YQ1i2bJDwPzOwbVU12GpI6IYTlqfuml2LCncLM7ldFGf+zqvCmfyfpX4QQvvN5\njntCHwIvxbgU8nZVs/jTRIGEM4Z7Jvc54UyirorO9CX9giol81/FNeMbU7JW/91pLN49BTP7x6qW\nNb49vRDvLaSZYkJCQgJwz8wUzezPmtlvW5Xq9eq73Z+EhITNxD0xU4xhK7+jKpXsSVVhAV8cQnjH\nace0ms2w1elVx5d+jVmeH28HhJdZQOjaakw4NgtsZ75vuJ0oSuzPvsjQTlnetH+55Gc8LFv7eXba\nbxz6aMa++OeB54pYYIcc58yMY7X+lASfMY4Xhy4r1wwkxq2U92XJ07M99gXnzLFTlvl1cPfSbr6Q\nE/dkiWcoO3EzfBP7sD3D/hy6ZXbrfoe5n79W43OLNtDH/OZbWKHw85d4/vxZOOXZZhvlzffwcNjX\naDK+kzjiM417xYHkUyS9K8S4JzN7vao4rFNfiludnr7k86s889pgcfx5c7d3vL1Y+ueNEg/66mGc\n+vdhf3y8ba2675rz4T/l+Wj6MNemeNRy3386nfrHjUKSNBoMjz8r696/WtPPr4b/sfTCKa5uLT9/\nkfl2PnEBbtYf3XTY9dz71Ks1j7c72BZfprb++pfTuZ8H45Xjtdib5boRoeF9HYfZ8fZh0/s9rXl7\njTnOv/B+dRbedrvZ8n7hbTQubn6jzIZ+zzXw8zebfv15DeOJ52xofs3Nnucr1PCjfNSpzp/P+SL0\nawuXPdRu57x7kvBFeDQaHG93J+t/FO1i93h7NPZrKorqOePL3zp4tku/nnzs+8ziY/Z9P/WGtee7\n13Gv0OcHdTIV60mdTPGSJJnZK63yd3t8PP2DSh5ISEjYZNwrM8XbQgjhUUmPStKl7l7I330gSbJL\nPjvsLv2SJ5ipGchCnt/Ma5agYOPWjQYj8ThQnEWBtjGDLPBLnB36TIyYqPqF5q92G7OT2b7PDgKu\nZ3TO9y8w880xE1gufAazPDE5rmYo0wLj0CyOt6cYn2LoPzh1zFpn8lnOssbr9xPV+z77IEEbz6vP\nZ23OiH17ecXP2UGuQmPPZ8e85sEcszyf5GmZ+fULM9EicvKJef/aTWSkXkO/p2hw9+YZriQ1tnEs\nJqGTOY6dVv01LM1w9h66fs9HPsFVwwmEml0/z1M9/6KV+X1p9v15adV9vKxfjcW8A2o+8Gdygee8\nqIPt9Kux5XLBJuFemSk+pZP5qQ/pZN5rQkJCwgcE98pL8b9I+igz+3Azq0v6Iv3BOY4kJCR8COOe\noM8hhEW0IvpZSbmk7wshrHUAWSEraupeqhanR1cOjj+fyGlFawcL0DMXGrJIw0iSQ8upJBXPRRO0\nOjjFmmKRupM5DYJEIW37v2rm7edRvJhBCJqDjnfObx9vz5Ct1B6CsubrRY9p6fRtfOh0axav43xw\nnjYBO6IoMYWeM19gCaBwGtaQXw9/euugzCUEoHmjutaQ+fdzCDTWw7IDBJ164edZLHy8eriOcubX\nbGMfrzmf/niqRo3zBCi7F/xZmU78mgeZ0/pW2xvMSmSRQf1tLiDMxL7wOmc4ZwHFf8lx2cbY4jbv\nZOv/nIcl6DNWD8Zb1f3Kr/pSwwwaXo7lgxmk7V6jGtsTKvwG4Z54KUpSCOGnVFmJJSQkJPyBYTNf\n9QkJCQl3iHtmpvhCEXLTbKeiGd3gdYqs7dRjBlqVI3g4j6rcAqrc6DyUShSYW4COMk5uypEdOWfJ\nQV/moFj5ntO9RWymjfi6CZTYOdTk5hK0/gRN9WPnoD6DfZdubYbg4NVl9Lwfe2Mo21A/WzU/5xhq\n7gLUrwaxtg5VuAT1HZU3q+/5gbeX7YACY5ngRJwm46sZP4ox2hs538+gqBuE41lZnTfjmDS8vSWW\nQxboS3Pm1zPI/VnYmWGNAestTVDckFUdOMQSxAzPU+j48ko49OWdOaISWg3fp47hHKNN3sdBzdu/\npmqf9h6WMRCwvzVDfG/A30L8uynL06LF722kmWJCQkICkF6KCQkJCcDG0ue8lHorNbbudGM6hBK5\nhcBf0AOFikot1+TDSidpanfiQ7gce9uTzBW9Zg3UeAG5EEHdB4deq2cvq/av1aEyDp2qTBAkPcp9\nmwG7R0s/fyv36wxIC2Ra3k7ME++QjuP6WxNQJVDpLs45A/WqYcwNQeoMcK8tve/lotrO26Csdf5m\nI/eaFBdUmnp7LWCJY8vPU0fsNFXhPC59TJHaZwhYFlTpztA/77e9L7vz9fszb3haQlGPSQK1DgKq\nZ97XbOT0ddnGuB36RSy6ftU1jNFWiSUGxJF3+n4vrsXuzqHatwvvSxPLJAYxfTXmtqHq82ZeVUJC\nQsIdIr0UExISEoCNpc+leSBqY+z0hVRlCkWXrLZZVvvUp84ZDEpgfQQ1t+8B0Bmo4XlzzrI4xZqJ\nP0lbM78VsyhRL5mPjX4PzSlQA7dwEPxz/trNrzs13yn8OrpQmlcqLlcRSPsQc6w2lW3kQStgXPb8\n2E4XywCHvn8D6xBLRAUcfwbrLDq5LOHYUoA+16HslyeYt495v+FUcXSEHPKVIxKOW4xc8e01/H7W\ntz2Qu5whx/rEX5OfEysSGiD3/Njq7ZSpCa3ttoZYPkCQ/Lxcf/A08zGqw2tthtz2zvTmY3NS9o53\nvA7VfFNznldIM8WEhIQEYGNniiEETRfVgvSC8VSwDSyxqFxgqlhuVbOPHLF27Sd9VrHY8dlJbeC/\n/NNz/hvTwgSi2O3iH+gjPProHTOPaWQZUgsXSAXbhmMOF+ZnSAWbzH3W2G4zLw8ngjtLPXYgID3w\ncNcfjxJt5/veb/6qTtqI9cwRm0hxCyaq2fhmIYEznDBGZzFrp1ZiiN+jma1BRLma++x0q4DqAKxm\n3FebcIlBTuYWrnTOVLyGj+3RwvvCGFNDDGwDM66Vvd0Y9yov4Eaz430tj3ysOOacQY8Q43jCCJiz\nPDgDNeOMr9FxxlCb+RgO6+u9MpvxMjfOXTYizRQTEhISgPRSTEhISAA2lj4vw1L704ryNsFZh+YU\n44LrDwq7EEZi3FaO4Znsgfe+5/rx5v5F0ArEe02wewGaGHadk41BTyZPwzk1Ihs5raq34ajTd1pZ\nwr6+Dio16Xg744lz+U4bKY+w0i/HN9cGr4P2TUCfAxxrZkfe9gJpi2XfKWuOGMsGUudYg2axilmE\nKHIoONAgha+O/LyAWELGSYYRSDbEo0Hd+7g1gntNFGzOF36eMdIjnwp+fx4o3bR4NHHKnCM2kWUS\neigTMMVCyTzus13Arank4oAjQIiaIr6zufC2G/X1SwNz3DsuwxSxK7UTp0TcIwSajHVesur8t1Wf\n6B5EmikmJCQkAOmlmJCQkABsLH3OlKmrShm8NnflmOrfNXmM4X2HqCMS+dYsX29OOiWVBh2a7vjn\nrKw2ZewfaFUOWk/j0HZMy2sgVY0ph4wlrEPlzlAXZBfxg7UtVJOrrb/li6j+Lgd+3GKIam6g/cs6\nVGYopDkNZy8PsI/TzYC4QpZNbcTNlXOLdDIGUxPvSwt1SUTWj92p4u5ACR5PnRL39zCQMfqgjti9\nK1N/PljZ7hD0PWcZXFD25gj1fbZc3Z3i0WmtDI/RxDYqMtbwfEwZCoFncYYlmyZoOCtF5jAoXoJ6\n57Xq/OWhP0RLOh11kfJHrTk+Q3ZK9cZ7HWmmmJCQkACkl2JCQkICsLH0WWVQGZW2XcGF5MgpTqfn\ntK6GgvUrOpMjMLskHUH6WQ7XmQLphIvGiQovfh5QrGkdKVcNp1gsQ7nCEsrqicBclASlq0u+BZcU\n0OASVGqKtLxRdAayFlO4GLANlXXXpe3Bwul2eMYV2oJGtAM/tjFFEPKuX/Msj4HEoLdTRJrXWwi0\nR5qfkIo2XPjYduFYs1w4ZRw3EeCN+1iLUuq1hrexNYYDT9uvuY5rI8U/uuJRCfMOjFuXSO2Ds0wj\nKt00AZ7MfN82kw5O1AXyNgrEt89QPrVW8/HK53j+Mij3Maie9X8mGCsNkemwYMpl1R6jBzYJaaaY\nkJCQAJypl6KZfZ+ZXTGz38Rne2b2c2b2u/H/u3ezjwkJCZuNs0aff0DSv5D0g/js1ZJ+PoTwGjN7\ndfz3q563pdxk3YqebDVdlct2oeLObw5YJpaguhMYyizPIff5PfvH2+U5UOCWq7UsvbmAilnrM+PZ\nEWJtGKThqrnniivznQm21p+7othD7jVrl4xRo2ZFzzLQJ7A6zUCxRgdeMpYwBAm3Efgdmk7ZSlDy\n5RTnj8HJGXN/EVA/OEGN1yu+9Q6jApAHHlBCFJS5kTnFnBTxc3xfwth1r+Vj2Fz6XGIE09geKPbR\nGA48COouqFbHJZmCrkNYMlhiyaLcAmU/aWXknyMSIVCWxz0NAwTbx+eMXsrMt6dRbn/qD2N3NW6b\nKT6frZliCOGXJV2/4eOXSnosbj8m6WUf1E4lJCR8SOFMvRRPwcUQwjNx+7Kki6ftaGavNLPHzezx\n0XRy2m4JCQkJp+Ks0edbIoQQzE4pnFJ9/6ikRyXpwfMXw06rUpdPBJnSRFTr1bMQS5LOe7B/OkVp\nyz7ynB+H05RtWjSh7gl6Pyk9OJhB1Sv7puYYDT7lVMrud9WcDGb/sk+yp1tUyJ36NDPvS0brsFi2\nNYAmZ1RnwcZKPjUog9nT+lKixGTsP1bzprffGlR9me877Ryj9KaByh0iqD7vwUYN5qsBtUja5n3p\nL8AxIfJbNFfNsLyQw8B1CQNfrjq0oP6Oke+dIRifAdPLGu5YfM5K0NsClH3xgHeQ5XPLiZ+0gXK7\nhtzzJXKcWcemue0U/2o5uun7GuvJMOIB97NcBa+n4O27hmfN7H5Jiv+/cpf7k5CQsMG4F16Kb5H0\nSNx+RNKb72JfEhISNhxnij6b2Y9I+ixJ583sSUnfKOk1kt5gZq+Q9F5JL7/d9srItAvIsn1YPTGf\ntQvnrnlrDS2ApVU9R2CywT2aFk2wgAo5goBhI1YDJaGb8yruuAkbqyyH4giKOxsjV5gO38sj7wuo\n1Gjs9HE6crp31L35UdiZg2oj6Hh76OefY82A+dm00Zp1vO9Xz/v1byHP+CjWQ0HIuYqn/V4VyLHO\nsGhwEECHKWZvw97rWY8QaKNsagaFdpUr3RsjZx3jPIa2n6Hc6+CyK/HNLaemBWy3anDnbuFPbhpV\n8RnrtmBJg9Z1tDxrQq3uI9i7U1IV98FoL33/CWMUipvnRA0o6yfypLF8suxVfQynr2Td0zhTL8UQ\nwhef8tVLPqgdSUhI+JDFvUCfExISEj5oOFMzxQ8kSgVNQ0UVarP1Kll9cUp+cqRSIxQUmqKIUZ05\noV0QvhlVRv+9+f2Ba0MXi63j7QyKdg66NbSKEo3htn1Cy0WO6yKHWjh0KtlEHnSdVUihViI2XO14\nSbTzmoEz7Y74+wnVGoWOSPFGXbhqQ+VtgiCPm75/e1ntdDh2O/QO8sprYH1Uv+uH/sVu18d2OvGL\nvj7wpQSid95dyBvtGHhP+ze4fVOJ7zf8/i9QMlQo3KVtv3fNPrgnbmQzFiCbTVC4CvS5hILexTM8\nRVREgVzmEfLWJwMfl/HEr79XeIJBv1+N9Yu2L3inYNHWRGG16QPIXljlQW8me04zxYSEhAQivRQT\nEhISgI2lzwTrIXcNzsa03WpTUa3oziF9mUB1xzXU/f09D5hmwLaNnCdtIVd3VLjMfa67fbxNiqmo\nKI7mTgG7DVdfmyUCfCdOccYFAsyXoLj47Suw3QWVmsXzT04JaJ8iqJe2XEMXedUnx+17YHrWcFWW\nxa06h7AJi/eItljyOG7V9kH2d2iz5uMyH3FBwLF82Gl1GysfvXPuLVJkN/8p0HJrwfxg5p4/5Pew\nxJIFZxtL5C0XB7hfMQrczqFoGo5rD8hP/ZytORT0DgKvkRkQwG2ziR+7j2Jgvb1q+aB41sdt3kUA\nPAqU1UHfx6vbuZmx22mmmJCQkECkl2JCQkICsLn0eRFU7kc1Fo7YAUWcGLxK1W8eGcTe1OnD9dxp\nR4eB3h24esPtuguVddpw+sI80zmCYxst71cZ82O3lr5vG9WvFih+1Wq6ynkwcxpEu64JqK8hcbcJ\npbk2q6gsLdKaE6iZ+LxsktahDSjbowb2QcB4HXWKx3j6img19uLOef/MUEf6HIKKxwiMB00sW6gB\nDbr5YOYUd74DtR6O2PUYKB9AQQ9nsP9C8L61EXGABIAcBa1aCJiujbwvzx54IPkKbVzzFIr/FMo2\nA9YVUMRrBIu2IfK9GxiLue/f6fjzMo9jN0QBrwxu88Md1DRH0PsqyMA2tPBzmikmJCQkAOmlmJCQ\nkABsLH22PFOxospjV0LH1zyQtYbg3bwNRXPl5ox844BauxPmzKKmb6Pr1CRA/e2hli/zSUvkxNaD\n/z716pVaOofNls3piI1A79IpU5ZDTUcw+KxgsPXxpgag9XkMAp7DIitgeaHMnFa1WAgKRama51yK\nri+oYiM/GlSujsDjC1l1bHeMZQJQVqriJYqMzXEv6LDdvYZAerhZ56DYhrrGFmnzAIq/NZDjjpz5\nOnLSO03kZINO1iGEcyRa56FWx0uFs5waM8xT8NdZQyC1UIN7IdJnJCDUEYSOGtQllOv9RjUuC9Tx\nXp7H0gBysocBDufR+TtsaPR2mikmJCQkABs7U5S5YerwPhhkIuVtp4baGBAgWs1qBjE8cEXFMPPj\n+rLhF3zWobEoam3gnDR5bcDVJCDez6Iw0p5j1oQ6LzM4o7BaRwczqxH0pOYC1wkHIIoKg5WoAbHk\nOTjQbE/983GxfoH9+gQxmHW/oKOlt9PGbKaDUqmzsJrZYXaOtg33qoZZ9QIxgwuM+fULvv/OxPve\ngDCVYybOrss6AAAgAElEQVS4iDN4ztgPxn49GfZluVXO/DP0nbPzJUSKEg5L82w1h/S55PWmP08X\nnznexPhIzdr6P9sZUhSvB2dH96G+zxiO9Kvni4JOvfA2KFYFxIDOYzwmn59NQpopJiQkJADppZiQ\nkJAAbCx9XpalBsOKQixBxOg2c/3QDUIvdfeOtyfTalF9jhoVOUpvNrByTlcZPecUd5L5TlsoBlI2\nEBuJxfASKWXjvKKBYDIqsM6+hHtM3odBKejODEVVwhTGthCGbAxhol318Yo57dpDICFTGHOU2Kxd\ncuFgC6lgLLHKeiF9XBNWDI7j8EYzp3dXO37cFvIgM+TCzZFyeVT37XP3e+zfeOiDV9RAn4/gJBPj\nA8cz7zfvz2maAtMDc4gec5RkzRC/SMppq9hYCFofPvFRmW4jBhLnnF13Wl9A0JlhQBdIGHxGLi7u\n0KUo9pGiYDjyBrcavi+jK8Mw3pcy0eeEhISEjUd6KSYkJCQAG0ufiSbS/GYt0FcYgQ6PhroRliN+\nCz8fdZi/1ofg0kxbg0K7RModnVdYR2MKs9hOpNvD3CmQtUCNQZO7HTqzMH7PKRZv8vIZp1KdDtxr\noonq+cyvbbTw2LzaAVyC4OhzqQ6nH6ioF5ZO0/ehvlJFXkJR70fluL6mbkj1hR83DE6x+yhLs7f0\nfxioXQv1WhpXnJIvQGVXMZ6dno/Jtbmn+W0VruAGtH196OO5nWFpAs/O4ND3GW359bUn1T7NIUrp\nHvmYt/l8Ys2gxFj0GU6BzawLN6QAyozliV67uqYR0kMzmi0jjrQ39aWEaaTmm5nkl2aKCQkJCSdw\npl6KZvYiM/tFM3uHmf2WmX1t/HzPzH7OzH43/n/3+dpKSEhIuBOcNfq8kPR3Qwi/YWY9Sb9uZj8n\n6Ssk/XwI4TVm9mpJr5b0qls1VFOm3ayiByXS2TKkzi3N6etyG+laUY1DbLUmgfafSFWDmltCZZ1Q\n/Q3rKbbhNymb+T77kfoukU5Xq1F/dExBN6fPOWVuwlg3Bw0SUtRGE1daZ5EMlT0o4qCAtYKPil//\nNPd+70EhzmEWOx/6dQzh8DNF6dN6VIIZDN1uOx2eI0ib5V5tiSUGOMOciBY4Wq7f55pT1eVDFW2u\noe1LqOGzb1zq0FrkuIdj0N0agq2t789fXlZjnR1522XDKfMUaYvzBlx68BCVCLbHrdAujIBznL/Z\n8jEt43PZg4FxydAKqO9LfF6LMRyWn6k51QcMZ+qqQgjPhBB+I273Jb1T0oOSXirpsbjbY5Jednd6\nmJCQsOk4Uy9FwswelvQJkt4q6WIIYZX0dFnSxVOOeaWZPW5mjw+n43W7JCQkJNwSZ40+S5LMrCvp\nxyX9rRDCkdmJfN1gZmujRkMIj0p6VJIeOn8pNGMO8whOLjWUrSxAj+pz3y7bFT2oIah1MochKQKp\nxw3U3FjgRQy2fVT3f/CcrQBahRKaRaviZzC60aIGKovk6ynUwsUeHE6A8b5ffyhRehTB6UVUV7MJ\n6qYg0JzXc77mFGyblPkUPdIKBHuDBl6oee2Uo92JbsT8Ou5bG442aO+hprfRyvz6L+97qdQ+qPf2\nNb++CUqF1puRKuI+NOCc1N1HMDYMia3j52e/GgO/nhny6nsljIhjrdblA654T6+44j1njj2elTke\n/xbqvc5Q4nYCZ6Be19X3soV7EUuvLhgN31r/Sijx/M0PqkiNcrm+ns+9jjM3UzSzQtUL8YdCCP8m\nfvysmd0fv79f0pXTjk9ISEh4f3CmXopWTQlfK+mdIYR/iq/eIumRuP2IpDd/sPuWkJDwoYGzRp8/\nXdKXSfrvZvb2+Nk3SHqNpDeY2SskvVfSy5+3pTJIk4oW1JHDGVBTYw5FuTaFdVikgROR9nrTATSJ\n+Z8L0E0GMu/NnfodwfB2BIrTyZEfPatOFlB2tYAqOljcTDUlqQllddh3GtaZOn2cn0MOMY4toxJ7\nlPk4dFA3piunkttYzpDHbstQSnMIU9oSKcQvfo7Z5073urFU6OWJ56Or50psDfdwp+eUlaVcW3ic\nd+SUdIQA6/l5RJ4jJ1wxD5z1V/p4PgrYhWmEa5tA/eYywbaPV36EujSZB0ovok0Zyrno6AEfn93g\n18/c6zmemxlyv7moxMiBYYG6L1C02zGfvYYlk/7InxtanjH3Pn+6eoa55LNJOFMvxRDCr+j0QPmX\nfDD7kpCQ8KGJM0WfExISEu42ztRM8QOJpQUd1iqq0BmAVyD3eVE6raijnOhKLSwQ9DpCvQq4L2kM\nJ+sl5OIuckUhHGpr1/nm1SXyra85lWxE1TwgGFhgUr0Wcpahcvbg1N15VgAcoXPvPK2xjqKD+FYd\nycQA64jMkNfdQ/0Pgb5nGFs6S0/3vKELM1xUv2qnRbsuAsHYtRO/5YgQGPgYDg1KLPKAO/Bja8OF\nu12rxrw/B0/Efeu3/Bp6LoorUIEFx6Eym6MWTB32akfXK4X8CMNgUwRJQ6mnsj9BNMVeH+7sKLd7\nQhmGvdx0x/e/Nq+C/bf2obgjsmHQxtIQlkwa98VnpLaZc6rNvKqEhISEO0R6KSYkJCQAG0ufiSOI\nlQXoSbvuvGVQ+OcrFmz7rsQ9Wwe9raGsKVy1RbsolIQsR1C2t536Bjhfj3H+VYudOsqu4jzLPnK2\nUbKyBE3NLjoNnoL6L2E1VvZdCW31otIMys6A6b3MlWguK0whyzNttmCJ1ROB5/RguzmJuIXrOdr2\n4y42PACZOeHEHMWd6nVfJmiC5p1v+/IFC5DNh9UyRHvmfbpy+Nzx9k4fSizyjQvMK+wQzutb3s50\n6uNsY9+nXVTPn11dH6Q+gWrcMx//c+ZLACP5M9qeeF8GHUQucFkHRcx6WfWMTNpIOhh6/5pw9Q7I\nZV9qM1XnFdJMMSEhIQFIL8WEhIQEYGPp89KC9mOgLAOvG7BJQnyxSMhmk+q49nmnoBdGoJ3IXx4h\nb5TBy8uJU9wAhXR46HSH1Yh65rSpsVOdN4COz+FSTZrMwlmzJijrxLnPIS2ZD1H3d8fXFfKYz9tC\ngaq93Kn+CWCw6Co+QF71ofz83Zm3uQ9H8PY5Xx5YFQkbI0h7r4Hvcc1t5Dj3UWhqAlfrhj3/o82i\nU7NWRUkHyF/Pr8HhG8e15c/FUH6fbeB9LBfrVXSDij6NbuPNHly9UZTsPMqssfhagF1cd8uXFfrB\nx3wKN22wYO1O/VyTOIxtuI3nR/58TrdA0+kCH+ubh9ppIcX3NtJMMSEhIQFIL8WEhIQEYGPpcxZM\n7WirNEcO82TilCiD+pxnThWakaqQmi6glJZDJySroF9JGpNXNkG3B77/GMWqujO4XKOPKwuoYsup\nzhwKch2FuCxzCpPhN66OIOXzI7/OFoK3r6Ffu3VI9BGLAu7dWBpgXPYUlH2Kustk7IZA4u09ryQx\nwFgsY+JuDiF0BsrWBB2elT4WozlOhOUQ1qnebjg9bOF+DRd+/ZPpzfnkjYbvS1W8HHi/cwSGi1Zr\nGK853MGHJdTi2JcOPMLG52BnxvriNQbG+325Kk8AqLHuNBIMzptff1nerByPGt5e9wGn47WZD+gQ\nfnnn82gzt6Fzqs28qoSEhIQ7xMbOFFWWUpwJLTCzEMo9Tmood4mfh62y+rWeziFWYAG+zdoqmDUt\n2ohZC1BRMjq54CccMZNCHZNJjFk0OPcsEINXwvA2Q/pVjllgA+ly9Ys+U5hf9ZnKNlIRLavOWUMJ\n1qMFFt1xoXslYjNxPYYZ1F7h52xgBlMeoUYOXYXicNXhRmMLH58AQ1yKYrUcM3KWksUsf4x6OQUL\nmUCAm9djHyco8Ym0ucV73L2H1Xo68hl2vcFxcexfvXa83YW6Vo/3v4W6LPUJ6qlgtm8oGDTFlLiN\ne/6uprsBMa50QaUFz+58FT/Z9DaWSC08OvD7vwuXpGmzOj8FxE1CmikmJCQkAOmlmJCQkABsLH02\nmYpIVWYQDOZI+WrvO68YdBBj9kyVdlXbdQpYq4MCIj4rG6AWBqjRAKvkrB1C49pzKDcZsNg9j/R4\nhs8apbc9aWIRf+7bB4dXj7cvFE7rZju+T/MC0wxZ7rW6pmtjp2DkiTmo5AjXlqOOylbLKVbGRX+I\nMXMYuy7rEHK6FYWs4Z6UddQlQWnUBe5FPXeauIr7q7qOtv0ydDBzYWK49HP1DyrHmjaoLDxwtXzI\nBQiKTmo4xW3Y+tjEds/v8xIiXTd+zrKrnKUs4Rq7xKLBEs/fdOLXzPo/J1ZpsGSwQLxrLcZmbs/X\nvwb2UH8mhBMOtrGvKU4xISEhYeORXooJCQkJwMbS51JBQ6uoRQ3pXL0pyBRSqrZQdyPE0pZLqKmN\nXaeGRUHHVcQPQnHugXrVSpQ1PSF+evt1kLxV2FoxRm0NKNhTpPCxXkgGWnm17tR4D1yK6YKExTS/\nfAdlPUGr6jhlDalqBy0o+8Ai8wulA1HY9mPzqV/TakiXH+aULQMdPKp5v++XU1mijvK1tczHczhy\nip/v+7gUxRq3F3xvWGphadwF8kavI80wzBjA6ftMEafYw1LKcjyLfaWjENL5cP7hDPG1Y+at+j4P\nHvpz+VzhywQL1LGp9fz+riIaZmhuB2bLGsDYdvfmpQFL9DkhISFh83GmXopm1jSzXzOz/2pmv2Vm\n/yh+/uFm9lYze5eZ/aiZra/6npCQkPB+4qzR56mkzw4hDMyskPQrZvbTkv6OpO8IIbzezL5b0isk\nfdetGspKqTdZUSjU8dhFsDMoVnnN+eHMIg2iyom2c9BH660vPdpE2iDLoNbrruLuQ+mdgeIf7w12\nskDw8gKGn3tzlNLcQ+BvzXtcBpTBxHU0CgZKVxxqu3A1d4l6JQ2YwzJ4msHDw5wmryiluvR2ri+d\nbl6qOw0u41LGYelj2D3RP6jJ+Ck/193xfkGtPRy6r00X5WNHyD+sHTr1345UnSor1dWFQf1F8PzB\n2M/TR7TCOQSBn5h6QFGvR/PXEQPKeYNOMdOlY07nOSjuHX8Wd2br5zsT1F1ZDdfh1Kn2HsZKHRj1\n9hk1UHXSyhS8/QeOUGEVRl/E/4Kkz5b0xvj5Y5Jedhe6l5CQ8CGAM/VSlCQzy83s7ZKuSPo5Se+W\ndBDCcWXyJyU9eMqxrzSzx83s8dFsfcH4hISEhFvhrNFnhRCWkj7ezHYkvUnSx7yAYx+V9KgkPbB7\nIRQxp/NEuUkkOc9A97TjQbtlrM3CUposK8r29mlKCu4zQt70bgazVLDqBXJlT+TTxiafXjq97oy8\n7Qs5ypCC7WxPENTb8GvrQPGsgdbN4UKz6pZd9x+Tzq6fpwGXoNOCdq9k47Wfsy7Li0uvkXKh5vT5\n6X51rZ0O1FEouD0Yru7srlef6YCzAE1fHKJfJUxet7C9ch5CPZ352O/hCPVSlnAXqiFCoIMljrKD\nErcIsC8LRBnE3OcCAv4+1Pw63HPaBZYp4P07xnNGfXi5g+UTPK881zzaHdXhQHSY+fU3kTDQqPn2\n2Kr7EjZTfD57M8UVQggHkn5R0h+XtGN2fOcekvTUXetYQkLCRuNMvRTN7L44Q5SZtSR9jqR3qno5\nfmHc7RFJb747PUxISNh0nDX6fL+kx8wsV/XCfkMI4SfN7B2SXm9m3yLpbZJe+7wtZaYQKUxxyrt/\nDk+lBYKwG9G+qjuBOr0PCrYNzkpfUeiyOQJmpyXIsccRq0AZSgZ4j6OKvbfAeXCnpiPvS9Fy2j9t\nIUgcZKqAyh5ApWg7towBxr0ajHeHCLpGjZSAQOYRbKyaGKIW6GO75QHZ1+ZuR0WrrRdn90mS9hc+\nQHtyJXyA84xgCHtxa89POgXFRX9noMkNBO9P5xjHeIsmrDsC+7UCSvWy7Z93EdTOcZ4hYJvmr0e5\n09Ne5J/jvqu/yxznbPvyRTaBQo1nbopSquM5THkRhD1hIIRvqhX3YV55jiiIjP69OQLto+HtpgZv\nn6mXYgjhv0n6hDWfv0fSp3zwe5SQkPChhjNFnxMSEhLuNs7UTPEDiTKUx/miRc8p4Qgu3A3kNmeN\nm4dikaFkJqjEBI7Y7aZTwAncqRdQBWew6GJ5ysXIycwQwcGrNGiqxg3Q5By5t8MGCZFTHObQNnI4\nUrPWB5litO7SEFZUyOWeBz9nGPr1F8P16mcdSmwHymV379LaPo5Ddd6d4Ooz84TrPe9sEyqz9VFK\nFrnsVKKnc6fkYwSS2xxq8Spoe0m7Lij1bYwhg/Fx/5dHPhaG8qBjWMC14KC+jG7r5Tm/t+vNx6Q5\ncsmzQzicN/3YGf6cA67DQIkXWMopost6AWWbwetMEggYq/pRda9smYK3ExISEjYe6aWYkJCQAGws\nfV5a0FGMVG2gKFTWYnEnqLszBLjGYkg51NkSgd6Nse87hlFzfcIcZ/9igSDp8QxyJUTpVsP71Y+q\naAvUrFkwStu3c1iBNXA78xnznb1fJYprLUmV4pJAF3RsmYNfQx0naWoMcRFQf5kT3UCbLAxVQjkt\nYl+2Ct+3e5+XQ8XHml13BXuE+1I78GWK2dgp89XSi04RvfJmssp7Pt738zS7TutHiFqY45qb266W\nF1u+/xKK/wSZViu634NFV1nApRwB43BOO1HcizeDOe4BKnpAWdOi5te8XC1f9L1PC7wR5lhSqiP3\nebV3mQpXJSQkJGw+0ksxISEhAdhY+pzNg7rPVDSnhXxXO+f0oQN7JUMg8fioCqalrltDbdwhUo/n\nT+8fb08R4FoHlZpAuV1yHwy/QaG9FGKwM+oen9gGrW0g2LdJDwzz9hagVYeHh8fbLY8ZVojtDx+C\nMznYEYtFbU+938s9H7eyiRxarA3UkUNc6/r+rSFcpqO6OzBXvzHM2m54wu/BFujovlt3ZYdOd/lr\nv7v0MdqfghLD7bx9HCjv/bscEJ3A/HEEb+uaj2cDaj2fnQK1qTM//TF9DqDmDIcOqLssKMQZbNRY\niGuAqIQc+cxLWGu3tn0ci5jvTSs0A73OkNw8hPo/nlTLO7Sz2ySkmWJCQkICkF6KCQkJCcDm0mcz\nNWIN38JZkGrX4J0E12wGoq4UuBEKHjVQuId+yCzoY0cIJEZg+FHT6SPtxQKU4wIUN2tV1ljNIwSD\nB1hKQZUmx10wbxebRxPn1XQNP6h77u/eohqLJeoIj7Z9fOqw/xo1qToif7qE7dQld8Qeox5yR+sp\npk2qsehsOWnuNJxWllC5y9n6YlklnLeFQOoWlh5azS2tQysG+BusvaYLP8+gARfuJYpb3e90dPaM\n091Q9+sY1/w+9ki9Y0D0DE8UA+PHcOQukRgwaiMAfLLenXuKJZsOnhcGas/yGIRNyjxCkD5cwmdI\nLli5ujN3fpOQZooJCQkJQHopJiQkJACbS5+zTK2oKJcImK6juBAdlAdNp0r9bqRhUAUFOqyuD1s3\nAzU575SpjSzWOhTPgIDYIayruH++omFwoWbwbL3j10BVfAT6yhzWlnk74ykKRwVvx9qxcBNqWrOk\ncx2/nyzixLrTrfvcVbvZdvWdruJLBrhDOV8Vndo2j9KeXHXn8TpcwDO4XS8uu/pcA00OWwhwx/mZ\n29zqeh9XNZaHVzyaoI1rGO7750vBIw0J5NZB8PwpRZ3KJnoTz1mWKPiFPPUutvtNLDYg/l9bfv8N\ndmUF8sCXCOTPkMPcrFfHLkawc2sjeB+n6dT9GVpZrm1m6HaaKSYkJCScwMbOFEuTpvFHdPt+Txej\n0MAFbmFRfbW+3UCZ0BomjZyF5ahdgmw+lZnvs1X3GVk/9/Pslf75ELO84flqBnX+yNuowekkR/zY\nFAvwLNm6gOFoMYXQMGLtEN9n1SJndQHxkCVi42ao91rDzKec8WhHCymKPRSLzbCQvxreozpKjGJW\nvbj63PH25amn7TUKjDPOScPdRbY+NrRgCdFZtb2EWPXME+/zNnbhhnNCX+DM0x8SxiYWs/WC2SzG\nrNaw7xx/kYa4T+G4Yh8uRehJ0YMzEEQn3qOi9HnQ/Fo147MuxJcx2u759QwXVCvjACShJSEhIWHz\nkV6KCQkJCcDG0meFoOWyoi1zLGTnuGSKFAeHSJeaVFShCfrGRefTSnyWoF5LxB3OYBDaChhy0rql\n/z61G9UC/3wPFOjAqekUfQ0ZSrOiPCfLs2Z0UiE9AyNaRvGI8W19GqKCp3VLmL/u+BJAuwsxJPfz\nd8z3n4N6byNmLm9WJ6DrDeNEwzYOvOKbY5gGG8p61s3P/0zwVLx87oPxoqWXSh0eVYLNO556z/Fn\n91+634/zU6qAM04JQeVELCFiUBk/2UD63ar06QTLDqwnZDB5LZC2aW2M2wxGuBTdcHNZr6ZgvaBG\n1X7oIw2z4Td6dIQ0wx7Gf3VvN5M9p5liQkJCAnEmX4pmlpvZ28zsJ+O/P9zM3mpm7zKzHzXD1CMh\nISHhA4izSp+/VlW955Wg+P9I+o4QwuvN7LslvULSd91uY3PUVGE633zg6VpLjkRkG9cmHgN3oekx\neBni95YtmLaiiVm5XomlMachrq6NlDZFU9Iw9TYGddTIgBLdHzuV7kKLtBb4LmIJZ0OkqJ33OLx5\ndErJ+6B6KPFJ1baB89O9RlDFaaCygBLcRYxlDpPXSaxpk63PWtPkWVecc8Qp7nX8vnC86B70or5f\n51Hp139t4rR6/9o1SdJu0yn1AOl8uy3/vED84BT1d8TYRFDLGsqTjhEPW4uKfnvJuQnaQGpjxjo7\nS5x/249FVqB6MDk+kY6HpaTV/bLzuIeseYPogxHKsNZi1EA4JRbzXseZmyma2UOSvkDS98Z/m6TP\nlvTGuMtjkl52d3qXkJCw6ThzL0VJ3ynp6+R+AeckHYRwrAA8KenBdQea2SvN7HEze5wF0xMSEhJu\nF2eKPpvZn5N0JYTw62b2WS/0+BDCo5IelaQHd+4LnUn1zl8iLW1+imRWhxI7j0GwrQYoCDK7+FNS\nQ7nJEgppgaEdwaz0hC0ny2ZC3myOqj5O8FmnhvQrBF1nVEJh4DpHMPocgeHqISB6zvKk1UWNLvg1\ntF38VeOPXDjeng58MMZboNIzBCyDvjUxjsQMfK8WDWcHz1w//mxU+nkWGIt6vv6xLRA8PYTh7GDk\n1I/pigYpfrRVUewu0tlmMwZJ46ZDWc9RSnWJWijTOWgoAvwLUNkQQ+XDBEp1F6mPWPapQWXmwkw5\n9r6US9TI6XkkwCysX8rJYxT6jKa1bdT2QZR6W74EMcO1bSLO1EtR0qdL+vNm9vmSmqrWFP+ZpB0z\nq8XZ4kOSnrqLfUxISNhgnCn6HEL4+hDCQyGEhyV9kaRfCCF8qaRflPSFcbdHJL35LnUxISFhw3HW\nZoqn4VWSXm9m3yLpbZJe+7xHlFJYBcXCMWQxQVArlLiyYLBxpDBwF5mjjfoAQc0IzC1OeJ8i8Jpq\nJahMARrW6Pu5FvnNqt7I2YtyxNSWCDCfU/Kl3yxU7kMYlLZQhnTRrtopEAC8hILOfO/tS+eOt+sH\nuOhTcmHHWN8tEE2V4XO7VnH1g+CUOaO6CSWUqugc5q+HS6fJeRufI2D70pPe36O6X99utwp04LJD\nu+VUeg7VnqVxxzW/h0MEmyPFWAVCv1tYhiljjZSshiUNOtr4ruqXvtTRxnLEiWgG5JJzzJlDPUO9\nnFUe9KofkkQTnykD/XEvdotKrc7tTM2pPmA4sy/FEMIvSfqluP0eSZ9yN/uTkJDwoYHNfNUnJCQk\n3CHO7Ezx/cVSpQ6jehkoXYIGN6kos75JrO/R2PKg1gyC27hOeuvHTRbeIOt7NFA+Nc+gFo5pIwVr\nsBi0a7CrsgMEmsMoVKDP9YZT0zBETjAo8Ra9poAy5tayzEn2gJtxtTvO31t90nQosX0o3rCxMiwZ\n2ABB6DC0nTfjOMJANRtBTW2BdqMc7GgKSy1063DhVPbcvt+jIywfoESNFjFXudN0yrw4AmVe+DhP\nEWff0PoBbWb+eRsqd9n0vqyCqkta0eExnGOpp0Bkwax5wjb3GEuMxQwB8zmWhloHqPuyioVAXZqA\nPOww9u0JlPXOXqxnk3KfExISEjYf6aWYkJCQAGwsfVZmskg5wj6jkJ37TLr+mzAumR9dUYXu+ESo\ntTcNO4oD5NKOF879uk2nm2OE2zZKUBVYNpUDpz7NWOazfuSfUc2sISe2vgVZmkbS7VN+71hrBuU2\nWzF4ugGaLAzbeAhlFbm3OwjYnqN2TYB1GlVK1giZQa0dPFvVQCkPELCNbvfgHi7k6raHTj2HsB1r\ngaZPcZ9HQ99n3EDw+GF1L8rr3qcmargwJ7tAudcjWHTloPslgtrnsI7LWeI10s8Fgq7pXp4jaoF2\ndXWUjC1G3vZzmT9PzRwcn3noSDCoxTanS3/OMtQcClOspeA8g0l1jxisvklIM8WEhIQEIL0UExIS\nEoDNpc9A/aLbS/Wn4ISgJ4aiT80Y1LyAglvrNrEvHLPhO1EXFFfkqrIMKq2ZRpB6l+2bb8US5ylQ\n4jTfdVqXw5ZKUCsn5/x6algGqIE9F5c92HmFgGJFDFLOjryNBtTsKQLNSfEOhl6e9L7CbbdGfR8w\nWrqtooYnBfsKmzcEptexBDB92kuPtu7z8wjK6XAG2ysEIXegHI/i2sO1iff7QQQ1LxAYffOoVbA9\nUFYEzE9w+3PQ80l0DW9gGEilpzs+nh1YtDUPkctf+D47KA97fejLEA2svSzqUKWjQs5Sswu4rV9s\n7fh5Jn7V8/i3YhsqP6eZYkJCQgKQXooJCQkJwMbSZ8szFe3IWyCi0RpqIOctOyjGlMVc2WbNaUWG\nHF8YT+sAymYOZXUnoIgSnZYQYF1nNSgE2I6a1fkXDChf4DxobgFpkYrz4dxp2jYCdk/UdUbN5Pq5\ninouUPe3idzfVZElSRpnToHroGY5grG3UTP7ff1nj7cfqK13yl4OY8B8x8+57Hr/kO6tzni9dVX/\nmjtpT+FWdoIeUmmFpVc9hhQ0OnDJhvN4Z4ClDCix/S3W3YYqjHs3QFD/bAvWZdFM/HALtavNl0as\nA3ghN0EAACAASURBVHqPXOY6cp8Lpoe3/fM6c9+vI/ccqeqzuFTRexr3/MP8/sxQFKy+TZd2RNhv\nINJMMSEhIQFIL8WEhIQEYGPps8ogrRyNmR9ch7M01E3uU4vB2wtQ2gwc/HJwMjeHypmDbo8QDJ7B\nhboOirWcOT0ZoBrWcQwuKOiyA5doOG8bzl+vOfVsg8qG3OnODHTbLjol6tZvdsc+OPBiUdp2CbXw\nel7a6u75P+DgvAR93DXv19Xff8b7iMDr1ur8I+/r5ALUfEQCHO27EjoNrCLmmzWMRUGn7jkrY8GC\nbHmzO3V/4BeadzwPPJt5G9uHPAIWbQaKCS57rfS+N2KAdxO5+csW3MtRg3kLkQ28hgmXQNCTYoFr\ng5t3b+bnaqxY9Ye5as9Abq7TLI58CaAeVfYs28w51WZeVUJCQsIdIr0UExISEoCNpc+lSdNILXpa\nH8i8bJ7ioxUpzBIBuAPIdi3k+zaRyzwD3e62nJrWDQWqkE+aFyA8oC1X84rX7CDHeDRxOtZC0DFI\nlfaXzE+GFRns0rIDp/VH55wy9laWVsiHnoACN1GbeA5adbjv/LGBpYkcav7hkdPwAKdsgT6XMVe6\n7PlxdLLOsEygJpY1Bt7esO60cguu5mHhn2eohz0GDVdcniixpFJ0fEkhYPlCKGiVXUPuOwLPF6gZ\nHS544PW5zLdXirIhGH0BF+69zJcAelMf9AmurQCVvoLAc9LgHbRzoh51VNe5cLBEbjSMyVUif30W\nA/DDMuU+JyQkJGw8NnammEmqx4DCsfxX88SsEbOmEml2Fn+JT1jJYlHZkIrFWdPWAjNPjGyJhfkC\nM4EJ4sByBD9+hKoaKE/IZ2GUQUosel/N/Od8r+eixyFUh1oP5Sl3EEuIMpiLOJudY7bT4Eo7jE1b\nU6SKjXxGNMQ58+WJgjXe9wnMbxGTOKlX47vAvWJZ0SOUiW1DrJr0vC8NjP907P1ibOr+xMWT9q5/\n3lrNhCFcLPEEBNzDORxwBFPa8lm/X208ACXGa4GZcivEdDna2Oz7vZ30wAgyfwIaiJkczvw6O5jN\nTjGOw6WzjHPCmK9EKjCSAEZQkknBwHZUVGNR0pF4g5BmigkJCQnAmZspmtkTkvqqAiwWIYQ/ZmZ7\nkn5U0sOSnpD08hDC/mltJCQkJNwpztxLMeJPhRCu4t+vlvTzIYTXmNmr479fdasGgknzOL03Gpvu\nT9bun0FUmXYiVcKi/Gzq9GkBykrhZsx0Qqxo92F+SkrGaXodxqWrQz8i91Ki13ve7yYoWHsEJ5+5\nt3EpdwlmQsNZLJgfwSC3DNW1diZYRkDsZqNAexB0Bvchrg4GroO500CSrO0CVJ6ULAoGbQhUwwMX\nDoqJtx1Qu6aB6Lw6WCidifqIAS0ges0hmDTyWHcEbQS4FFG4aNdhxIu4wvkluAFBJGqgBsoUz85K\n1MoCBC2Ib/MRjI9bTp+H2fqlCT5DdcQpDpAuOStuPnZrwjvk598fIyD1usfmjq26hjIJLXcVL5X0\nWNx+TNLL7mJfEhISNhhn8aUYJP17M/t1M3tl/OxiCGGVCnFZ0sV1B5rZK83scTN7fDRZPyNMSEhI\nuBXOIn3+EyGEp8zsgqSfM7P/wS9DCMFsvewVQnhU0qOSdHHvvrCKfeNVzqAuZiibOenBiPNqFb82\nRnxbY8eV2iXcVWizuQQd66N+Rd5xKjeG4pyBVrURV9hZURhkpO1CfRyjDijLfdbghpJtoZQmCozU\nabFTwzVH3jgf0Y3Fr9mwvNDtOU2coZTraOjq6/YSY1tbT7MMncljTGQNtVhaqBcTSr/mJYxi6dLT\nQ/0dmqW2Ede5nyPNrg9FOarfocExQUwp69/gHpZ1LEeApraC3/MSCwhFibjK6DbTaPtYGfqkzPc9\nhNNOjmWNBZ6zqzUo0TXG4GL5oPBj+6NqLLgcMMY4n6gLA2eedlyOqp3JOdX7jzN3VSGEp+L/r0h6\nk6RPkfSsmd0vSfH/V+5eDxMSEjYZZ+qlaGYdM+uttiX9GUm/Kektkh6Juz0i6c13p4cJCQmbjrNG\nny9KelOs/VCT9MMhhJ8xs/8i6Q1m9gpJ75X08ttprAyRtsDMlGUrp00E/lJ13KloWAfK7hylJ9sT\nP24JCioE+NbgUlMiFctQ62MJet4nV4797sDRpLzitG8BatzcdSpbgqY3UIe1C5X3CBlfDI5eEbxx\n3amegUrPETC+u+O1O8KBq5KNnNzcsTf1sTA46WAYNV9U4xtwznbLr2eO7cUppTVHCGRuwGFoEJwS\nkvIVSGksj/vubXcWfq9m5vdnBAWZWi7p5hLOOFmfgc9+bGcW+4JnK4OZcIbg6YDn7EoBmtxAoidq\n9NSa/vlFhP4vjqBox0dqgVq2zT3U/0G/azO/nlFcatnM0O0z9lIMIbxH0set+fyapJd88HuUkJDw\noYYzRZ8TEhIS7jbO1EzxA4ugEHOUL8xvNlCVJAMlmS6cVtSjuln2UAsDx5WgQDnqeGgLxqagr8uF\n71/OaErrdHOM8z+5rFTc+8a4PThNQLRReeT/YI4zY4SnbeQtD/2LI/ijTDtR/YUDDXxyT/x6Xn/O\n4+rbBcYItUtqCBKfjJx6tzEuAXnW+Swqmm2otnD3aSLoejx1OjxF2xkCn/tL/xyM9AStFnLYs1iu\ns46g94DyrfnA+zrKEOhP1xkEYdeD9/eoWE/fV5tTKPgZDHGb9eefs8zRl/s6KElKX108f4yWaB7E\nIOwHPcqAqzhUvxdYaqpZNUaWsbXNQZopJiQkJADppZiQkJAAbCx9tiDVow0SRETl+B24PvXczmJA\n3hD3hcrKnFm6YnWRKzzqrqcTNQTszqEoZlBicxCbbq2iJ/sd56/dIS2nkJ/MOiN9p4zzBvNtQYM6\nyIMFV8pWacZbUDNx3BRcOqC2TJv1RfzIE7SSSw/XFlDRsZRQxGDjOdTfBdT8EZT6oyPPid7b8ntE\nOdTmsP2iEy/apEnqLFqjseyIYQlisONfLFEjpz1DsHfhF82AbRrEXm36OE7z6vwdQzA2unoa7W1l\nqLNyiJo7DRzd8zbHSAo/P/Hlg+LB+yRJfQSGC0H6YywfBBooxyB5yzdzTrWZV5WQkJBwh0gvxYSE\nhARgY+mzSikfVVSpX0Pw9JIlIZ2UsFRkL+YZZ8irtT4VRAC8ZhdByjNQDwZ+Z6ObabokFU1vdX9e\nBUQbcqNJX0agmNuoXTKHcjwmNTbk1p6wE/fNRrzWzhVvuz51ZXuWQ4oGRrCiYinRK6VT3A7GcVhD\n8DKo9yyGQaPMioag7HNcz4msXuSbF0sET2MpYXqRVmPoi7A8EfPNaYc1R842IxUMSykBijOTBEo8\nZwvQd9brUYw+aAS/olbu92qGGjJLOMOzzg9v4tW5LwcZaPJ9waMSDA7qWRyvxtT3PQKVrqGv/aWP\n1SQmNSzK9c/yvY40U0xISEgA0ksxISEhAdhc+myuOi/Bt7IOqAqcqss5clVXecsoPRlQ7rPlqaKq\nQ6EtSZOYtztFgawDKH2XPGh2nPk+oxVVATtptH3frRGU0BboGDZbcF4WnJUXAcG+KLpURCV8ekL/\ndHTmDEb2fSbBtxdYYmC4/Ki2vs1xA0sZkW5eFxR0WJ416z6enZrTwQKFlrLlejrXPPLPx20sK8BG\nqzapxiWHOp3DeX1Qw3II8przESIIoMbSXoyRBYi1Pi6JutP26ymf9lzyGlTm2cjHFqsRmm37uJxv\nuVN7H/Zy3KxhiWEa7dVImfNtJMcDLdjKr+pcrQLeNw1pppiQkJAApJdiQkJCArCx9LnMpel2Nc/v\nlq6udabIyYXxUwfO16tRmY/hQtxzmlKDqzFNrE64cCPA16AsTx5yqtSAjVMOKrnKzy2gbNKWKkdO\nNs9PanZsmyZpAVrZLBFsPfDrK2Le8BItTpl63XZa1Z2tL5y0AK1sDL2douPjf5DBQRvHriIBurgP\ny7ofl4FKZuudw07QyuyiW6oNQPfH+XqK3elXjRYYZ0ORs6wLV2/Q+hmKgnFs6yiWhvpXaooO6tU6\nzLNyynxhF47lT14/3p7v+OdcvlmioBdKOutC6Pg/2N+hX5PFpST2ezyG/Rit6GjFFouShQ31Dksz\nxYSEhAQgvRQTEhISgI2lz9k8qH2logLleTo/01kZKm4XQbPRwXiI8r5aOu3r7jg1WcIFunYAaogg\n2Rx5yMzPHcBBO8zQrxiePAHteTpzilVH7jNzVXPQbQZ+85zDlv+jjUJPivSxhhjt3y88GPgPgfY1\nm759DfnjBzVXMR/Y2dI61EGJEVOvPNbVzlG4itsL3AtamgVw5glcw0mTM/z2T4JT/14AVY1jbQzG\nRjRB47oft9VCvjnOuURS/AjPyxaSrzNwzhdlFcUv+/7Z9bnfZwap5+sFfG3V/B7Wj3ynKw3n2Hs5\n3OahoteuV/vMCx+rcoBlHyxZ2AiWd/Hj9eXj7n2kmWJCQkICsLEzRcszZVGQ6C78NzeniSpcTQRh\nZCWqdCAW0FHmCGUguyg0wlQ8gsaiO4ixYxzaAEakRVbdlv2Jz8L2UWdkuvS+0hjlpHCB64TDjCGu\nLWDWUq6MaDELe2jifV00vI1F3xfrh5haFgiUnGScWfhj1hogrm9+c7raArPqDAJV8zk/J2fh4xbS\n7zAAY6SllbnfIzrWLFEbfDWvqtdgmhvAJPDcLBF3OsFz04AAN0eJ1cOZp+udzz3etBUFjiHGqp5h\nftiEotSFuIfnhhPIvE4G4X28tvTzt6/5c7Q6lucME6Q5ggXkTZ9VD2OsZ5lt5lQxzRQTEhISgDP3\nUjSzHTN7o5n9DzN7p5n9cTPbM7OfM7Pfjf/fvdv9TEhI2EycRfr8zyT9TAjhC82sLqkt6Rsk/XwI\n4TVm9mpJr5b0qudrKIt1QmYIbGvC5HWKhXEwrONFbbqEzECHGpP1LiVh7O1Nd2DyiTyrneAL46R7\n27nTtlFMETtnLug0UH8kowCR+TkpIs1heDsDJdI1p0TNPf9tmZbVRXcQsyZQrbFQGAZOrE3wt3LL\nx7aJ8qwLpDaS4reQOrgyapl1YKCKlLvZNpZAcP28L8O6d4YlQXssWIJpQIHlk0ZM1+uiNC1MfKQZ\n4j5RWyeHU8wIClA29c+7dapEvrmI1Nsg6MxAiEsIbUKJ2QWMgFuk2wN/Fpp1jD9qwBie/5XLjcEZ\nKOAPwRoYNzzyRUz5s1PiRe91nKmZopltS/qTkl4rSSGEWQjhQNJLJT0Wd3tM0svuTg8TEhI2HWfq\npSjpwyU9J+n7zextZva9ZtaRdDGE8Ezc57Kki+sONrNXmtnjZvb4CF6ACQkJCbeLs0afa5I+UdLX\nhBDeamb/TBVVPkYIIZitj5AKITwq6VFJurR3X5jFmKwFYtNmMP9kWh6pQB7VvVruFKQ3Q0wbVMus\nAPeBEpoj3qvcRpofjEvrUHpzuPR0o/qcw0C2hIHpDGU9c5TBLLHNcDsqp3XE9YVDULJJNUaTnlN2\nOt3UsGRAp5kdKPvXsHzAuqL1baePE8T4DQ6dnofoNpSDpo5RJOVoDAW39CWIgBi/GsaZiYgl1OoW\nisc0tqE0xy6SEebXoU6PPe6P7j59jAUP3oXzjaC+5ygYtIqfXEIp5kM5R1nRbVgw5YhgDDAw5jSg\nIX9e503v73To97xcPRdw98nMx+TEs4ryteeKSkHP7azNqT4wOGtX9aSkJ0MIb43/fqOql+SzZna/\nJMX/X7lL/UtISNhwnKmXYgjhsqT3mdkfjh+9RNI7JL1F0iPxs0ckvfkudC8hIeFDAGeNPkvS10j6\noag8v0fSV6p6eb/BzF4h6b2SXv68rQRJUVULUAvpXlODyegUVCFEV5k5qF7WoRTp24dXPC3r/I6r\nuYsdKJTP+T4TmMkuznsgbzl1wndxXtHDKfkYApkFl5QGymMucT0BjGwB+kiz1rCmXgwMZZTBBNew\nvtBE0HeOfp0f+m8s68VM2+CEVDF3naqP+nGMrju9O0Rkeqfr+84PERgOCldHsH0TQdhNUHma7I5H\nrEdSjYtB8Z6AJs/G/nmNpq0Ylx2k87VQ1naxQMA0UjtXAfZNLMG0QId3kMLHdE4G2I87WA5Av1jf\nRjCWXaD0aX6tItwMhl928D2uoQ6XIM03M2h7hTP3UgwhvF3SH1vz1Us+2H1JSEj40MOZos8JCQkJ\ndxtnbqb4gUIIQWWkyhmohy3gakKNErVWppE2U+VrIkh2Nnadr95w6jPeIsV2FJfc8HQ5cY6zf/Xa\n8faDa+prtFgOE0G9jYf2fBv0eXHZy4qOQN/b+6DJ532zAcca9at96lCZay2qnKghg2WFAMNZBh5n\npferjvoetbaP1xLHrkx0A/J3d+EGxAKr8y2ozCNvo4XlAyr7GShznUa8WLJY9SVccJqeQcHVAi5K\nCIbOYKDbGPj5y1OmGzkiGspWdex4gedpsf5PksexNOtwysgKhhz4ZoY/84M6XILOVeenW1BA0Hd/\njjKxqDnTbMZ7mGq0JCQkJGw+0ksxISEhAdhY+pzXcm3t7kiSLu8/d/x5E7mtrSGoNBS4Rl7tkx86\nZViAd9VJa2EXVkJlLi66ySrp1rODfT8WP0lHc5eU83p1/gXKh9ZBjQKoFAXqWsNpUPdJD3amRVno\nrKf42SrAGor0fN/bOOxx2cE7Xmv49gA55ohjVwmFdjLwMWpRCY+muDSwrU8RyI1rG0NZPUBeeQvK\n+XzuY14vYTIMul3CcHVlMtzAOLOezND8uDnUZB36Zn8P4z/hGMECDbVOaFC8AjOxtnq+7GIIsNYz\nvkzi8QHSuIPAfD5buI4iRxJCFJTPDbAzLm2CqI2V8bIkrfIYlhua/JxmigkJCQlAeikmJCQkABtL\nn0MIWiwqPnW+50HVg6HTtzmoX6Pnwakr5bCcIZsUQb0BpR9ZbnPecOo1fObq2n7tFH6eAspxkcMa\na8XqUDJUUHCzPmg9lNUa8qNbF3e8X8+5XZgOkLcN5XQeNxeobULrqsZlUEnE8Y4v+j/GyAkXArbB\nwtQOcM2eIqg5WoDVDIovJNTms972+D6/bxfmoNuQfFmScwpn6/YOrNuwrNFYRReAXuegyVv4U9nv\n+L2g23hvhBxiEeuDnYuYTzyHmsw89enAl1RqWyiNe8nv7fAZL4OaI5gix5JJA3n1hwWWgeIyzRif\nLeCmnZ0yZ5rGMqlhuZlB3GmmmJCQkACkl2JCQkICsLH0OQ+m7rKiB7PF+iJGjY5TEjtEtO9ORb3y\nNoJaB7C5guIcEODMkpQl8oNrUEXzGgJ80ZcZZOQiqtsL0PEcgbLZ3PdtgHYvWcYIOcnZw758QBW1\nHOKaV5eHwlH1Mb6+z5cMZn1XpRtXQTGxBAHBUwXU8gylMgPUdcWljANDUbAGXKKhFHeehP0VXKgz\nBObzye4UTrENlC9D3vSKQha4b40Al3ZQ7aKOCAYULhthbNs4djnCYMBt/DDS5iYs4ia7oLotWLSB\n1o4HPv5DlFJlTnzvggf4I+5cqHCrEP3lBm0sBwz9nAUiNQxB3cd/CyHR54SEhISNR3opJiQkJAAb\nS59DWWo+qGhWCTukPcGCCqpg87wHyo4jPRxD/SuQJ5wPoBbWQHFhqUQltMkcY6APf68mqiSNYjWo\nEjbME++27jtAUSIWYtrx5YA5akPPWrAxwy0ftv389aii1qBgDrfX/2Z2zMeqNqJaDTWbybdYehBU\n0RaKZOVxqaO19M+eyj1SACKv6sh95pLFEmp9C0WcpsGVa46RYNOVxRPMUcTpBO0FfewcoEDYrl9z\n7cQl+7EdOHwPJxjg2MwAztgZVWNYd42H/iwanoscavoC93mINpvIQ+cfvMX2A66ZFmUsi57VkONP\nnr6BSDPFhISEBCC9FBMSEhKAjaXPpbmiWIdIVoIyk6oF5MquAnIhvspQZKm153nNyp1WDFs3O1lL\n0nCCIOiFc5LeGI7QoJvt2JV3d1xlFCh7Ld8+3kZcsnaC0zRaTeUzp0QZajbv1eB8HW3UwLRUg8N1\n2fb2aghkZz1qsqpsx8dl0fDz58/5ODZ2bq5rvQBlPZ/799fM74bht3zcXZ/LzVzxWt3HZTYClUYk\nwEoUnmLZAUbax/dEknL0u2BBMSjkdVv/pxV63mg/1qnul97GLm65QR1fwBmeAfvtuvdlCjW/iTri\ndVQxYyTEMBZ0G84RhYD1CCw0qIbiW7tZRdnzDZ1TbeZVJSQkJNwh0ksxISEhAdhY+mxmx/RoiRzn\nHKpkCfXRkAc8ioxk2XBlb4IA285zINawdGre5/sPMvAt+Gg1D6FuQt5bgOLOIpV+8bNQPDtumX1g\nTncyUKPJDBZRoI8ZgtDrRr3W6d6gXZ0/O0KgO5TI+v/f3tmFyHmVcfz/n3dmdnZ2upvdbBpqEm3F\nUsmNrYgoikiL4EepXoi0KBQp6IUXLSjaei/UGz8uRAit0gtRS1QULwSpvfAqGFtBbCzWamlqP9Im\nm92deefrfR8v3jM9/41LTcImM3Py/CBk3ndm3jlnzrtnznOej38hVbg7sT/c/N9cWgAoxYs6LqK5\n3VxWoyyyHey6Wjte+3Wxx1tdfbV4SHsSjC/BxmoGFiITLk5+UPN8J1rbrfjGUU2qmsufSqZVraVS\neJmLHrfU7rJRfMNQtmlqE5NUvLxdiUhYFG/+ltxPWgquJQHrKyLWtdiP7VKV9DNDyYMPdBbj+2qS\nqD6S0maUMUQnbAFkXnn7ikPyFpJ/kX+bJB8guUby9yT/Ef5f/f9XcxzHuXRmaqVoZs8CuBUASGYA\nXgLwKwAPAnjCzB4m+WA4/sbFXre+LZvncr7P+Ot3tiMb8GGTXPVClqVQab4sTpfrRKNDdUFacUO9\nISl6b3TikqdVxN+kbiOuBPb1w3vFn6NhfwcLiUfUhZ8swjJJW6TET44kz6tfxM8826tWEKuFOE5k\nIaAb9HVZkZm0e2ixkdmGNDiGNe74GTZZaTCkLuoKe1Fuz7wex6c9lGo04lBqyvepd/ZQ9GVM7oCB\nOEYmRXW6sgptilPsvBQBXpZKR3khKztxjMgCHhTnFiQmcCmvXnR2QyrV7rBO4qquJ9oqB1ej1aBx\npdmmxFWuyRjJZ7bFGTYIC8RmXdIZ5Z7QPtTlZhhkQToYaTJTK8ULuAPAP83sBQCfBvBYOP8YgM9M\nrVWO4yTNLE+KdwP4aXh80MxeDo9fAXBwtzeQ/BLJkyRP9gb5bi9xHMd5S2bKfJ5AsgngLgAPXfic\nmRnJXVfuZnYMwDEAOLR6vTVDcc1SqtrkqnGJaOK2pbjoKDha3rYlaWv/inKkdiTateVYNE1Es6Iz\nEi2OPJo1a7KR3RNTdn0h2phbIUKyrXGU0uXzebSBOlm0mVVW0w7E8w2RLaVUQWlKWtx6rXp9KfFw\nDYlNK8RYKkTuEnGPHpRtgkFD4kGlkkxdTMxM0vx6WWWfa9Wf0Ubs56Jmx0lqnY5mITK1kiGHoivx\nlqLFA3Ee9INTqZQrbpgUnJXqOr1Mtibk83tSpWh/K45nU3IU213pSLjMDavXx3bIZ243Ra9FZEhH\nEo+oTrSBxFUu9uJ3l0ux4Pouf/HFlhYHFl2WrsSGyhqjPay+C63WlBKzulL8BICnzOzVcPwqyRsA\nIPz/2tRa5jhO0szqpHgPoukMAL8BcG94fC+AX1/1FjmOc00wc+YzySUAHwPwZTn9MIDHSd4H4AUA\nn7uYa008jZQ4sVpb4rckNnBF9DUaISavJWZPcz3qYhQit6lxZU2Ja+uKDKdYmKiJGbYisWRlFs/v\nK6vrN1Sys7a7r68rBUebUulHU7u0cKyJyVOIedpYqT6zFFlR9WZ3xf2cSQykSrzWG7E/C9sSdyle\n3vaOgizioQ6xpINS0xnVyyzaJRJf2hA7uRDTVz3LahLWZMw13q8ZUgG7o2hKLjHao31odEJ8XyFp\ngQuiPwONTZTQgYUdqXtVG8dSwFaDCtX7nUvVG0rc7ai/e/xiLjo6Kls6aklcZ7i/CxlDShHglgR7\njmTLQAMeUmTmJkUz6wLYf8G5N1B5ox3Hca4os2o+O47jTIWZWynuFSTRXKjMn61hNBPLc2KeqRmk\nwbbDyiTpiwnWEi9fLtop9UG8hlYp0RS+TCQmd+qCiFklntjaJPB6SdPz4vMUnY9MNGLGUo1Hi6IW\n52P/NZBZi8jWgxmYi9xmTTzOJqZZXYKR1cTDePcqQSqrupnHwrHjldi/dqMTOhTfNhBTr5GLV1w1\nbyRtU6mvSZUeqbxTE/dre0kq94amZ4udN0+V4/hBHUknLMTEzNQBK23PJTFgScYr70c3bm85fI/y\nfWqVorHcc0tiAhfi87amevMlnVRN3wW5L2UbZit4y9uSkqi6RCwluF3PT7RUszTXVGn2ynEc5zLx\nSdFxHEdI1nw2M/QHldlWE7NSfwUKRrNi3BKzNgQwrw+lYoialbloYch5zUPNa/F8p9Sk5PiwlAKl\n2yKPiaJq90ot+q1VTbK+FE1Dk0omWvRzPIzX27wuvqYlepct8UpvB6nM2n/Em645sZISrZVm6rl8\nt+J9zqSqkGrN5FJhR72Y26PKrJS0ZjQkZ5tivmeyBTGK1i76EE+s5GHr1gPE+z86F035SbHe8kD8\nzvtyf7SlOKxuR5T6XYgkaymB8T3JJ8aSeNRDIHexP7qKTezxRdl2gcr8SARBJiZuJjeXSWTBaE3k\nblXfJUQoZGIGL4joTzGSfQrZSum1g7ZLokuqRLvlOI5zefik6DiOI9AszQJAJM8A6AJ4fdptuQqs\nw/uZEvPSz3eY2YFpN2KvSXZSBACSJ83sfdNux5XG+5kW10o/ZxU3nx3HcQSfFB3HcYTUJ8Vj027A\nVcL7mRbXSj9nkqT3FB3HcS6V1FeKjuM4l4RPio7jOEKykyLJj5N8luRzQRY1CUgeIfkkyWdI/o3k\n/eF8ktrYJDOST5P8bTi+ieSJMK4/D3o+cw3JfSSPk/w7yVMkP5jqeM4DSU6KQTP6B6i0Xo4Ch+aA\nYgAAAh1JREFUuIfk0em2as8YA/iqmR0F8AEAXwl9m2hj3wzgiXCcAvcDOCXH3wbwXTN7F4BzAO6b\nSqv2lu8D+J2ZvRvAe1D1N9XxnHmSnBQBvB/Ac2b2vJkNAfwMlXb03GNmL5vZU+HxFqo/oENIUBub\n5GEAnwLwSDgmgNsBHA8vmft+klwB8BEAjwKAmQ3NbAMJjue8kOqkeAjAi3J8OpxLCpI3ArgNwAlc\npDb2nPE9AF9HVDLdD2DD7M1qvimM600AzgD4cdgmeCToFKU4nnNBqpNi8pDsAPgFgAfMbFOfsyrO\naq5jrUjeCeA1M/vztNtyhakDeC+AH5rZbajy9XeYyimM5zyR6qT4EoAjcnw4nEsCkg1UE+JPzOyX\n4XRq2tgfAnAXyX+j2v64HdXe2z6SkwKBKYzraQCnzexEOD6OapJMbTznhlQnxT8BuDl4KpsA7kal\nHT33hH21RwGcMrPvyFNJaWOb2UNmdtjMbkQ1fn8ws88DeBLAZ8PLUujnKwBeJHlLOHUHgGeQ2HjO\nE8lmtJD8JKo9qQzAj8zsW1Nu0p5A8sMA/gjgr4h7bd9Eta/4OIC3I2hjm9nZqTRyjyH5UQBfM7M7\nSb4T1cpxDcDTAL5gZoO3ev+sQ/JWVM6kJoDnAXwR1YIlyfGcdZKdFB3HcS6HVM1nx3Gcy8InRcdx\nHMEnRcdxHMEnRcdxHMEnRcdxHMEnRcdxHMEnRcdxHOG/FcjLjwj8AoIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f271d96d4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[9], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[138])+ \",\"+\" iceberg_probability : \"+str(pred_valid[9])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f271d8f5908>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUUAAAEICAYAAADIsubvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nx3EcwSdFx3EcwSdFx3EcwSdFx3Ec4T+Ahm4b0yAE4wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f271d8d4240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[10], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[141])+ \",\"+\" iceberg_probability : \"+str(pred_valid[10])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f271d85c3c8>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUUAAAEICAYAAADIsubvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsvXm4bdlZ1vt+s1n9bs4+p+qk0ic0tlxaY4MIEhVBMfHK\nzUW5UiCYawteGxJQH6Mil6tXgfugYEmAUpqQG8REVBQRLqISKEhQSRCSkJhKqjvdblY7m3H/mGPt\n77d39j51qhLr7Fo13uc5z5l7rtmMOeZcc413vN/3fhZCUEJCQkJCh+xuNyAhISHhIiG9FBMSEhKA\n9FJMSEhIANJLMSEhIQFIL8WEhIQEIL0UExISEoBnzUvRzD7HzB6+zefBzD7+Do7z0rht8TTa8LT3\n/WhhZv/KzO7/KI/xBjP7no9Vmy4K7vTen7Pv+83sd53z2WeZ2X87a1sz+zoz+46n1+Kn3MbvNrOV\nmb3/mTjfJsLM3hv78Emf/zt+KZrZT5jZTTPr3+H2d+0FclFhZn/TzP6LmdVm9oansm8I4fNDCA/+\nD2pawhkIIfz7EMKvOeezbwghfKX0jD3rfzuE8NLzPjSzPTP7ITObmtkHzOyP3GbbN5hZZWZH+Pfy\nM7b70nhdX3lq/aeZ2U/G/R4zs68+Y9/Pjvt+PdZ9sZn9NzPbN7PHzexBM9uOn/XN7I2x7Ydm9k4z\n+3zs+yWn2juLx//0+PluPN7j8d8b2J4QwsdJ+obz+oS4o5eimb1U0mdJCpL+wJ3sk3Am3iPpayT9\ni7vdkI8Wz9SPXfpRvWP8fUkrSVclfYmkbzOz33Cb7X8ghDDBv/fxQzO7JOnrJP3iqfVXJP2IpH8o\n6bKkj5f0b05tU0r6FklvP3XO/yDpM0MIO5JeLqmQtH5pFpI+KOmzJe1I+iuS3hzfPQohfC/bK+lP\nSXqfpJ+P+3+TpJGkl0p6haQ/amZffpvrPxd3OlL8Ukk/Lem7JZ2gcGY2NLO/G9/w+2b2U2Y2lPST\ncZNb8c3+W0/Tt9O/sGb25Wb27vhL8T4z+9+fzkWZ2e8zs3eY2YGZffCcUdkfM7MPm9kjZvYXsW9m\nZq+Pw+3rZvZmM9t7Ou04jRDCgyGEfyXp8KnuG0fqX4m//zj66l1m9mlx/fPN7AfN7Akz+1Uz+6pT\nhxqY2Q/E/X7ezD4Zxzx333jv3mJm32NmB5K+LN77B61jEO82s6+x20xx4FjvN7Ovje2+aWbfZWaD\n+NnnmNnDZvY6M3tU0nfhet9jZjfM7G1m9vxTh/2C+MxcM7O/Y2ZZ3O/jzOzfxXt5zcy+18x2T+37\nm27XlnOugc/y6Wf9s2M7Pwnb3xtHN/c8Wf88VZjZWNIfkvRXQwhHIYSfkvQ2SX/0ozjs/ynp/5F0\n7dT6Py/pX8eX1DKEcBhCePepbf6CuhflL3FlCOGDIQQer1H3UlUIYRpCeEMI4f0hhDaE8MOSflXS\np5/Tvvsl/ePgKXlfqG40PQshvF/SGyX9sadywWs8lZfi98Z/n2dmV/HZ/62u4b9N0p66kVAr6XfE\nz3fj2/0/3cF5Hpf0+yVtS/pySd+0/rI/RUxjm3cl/T5Jf9LMXn1qm98p6RMk/R5JrzOfV/qzkl6t\n7hfr+ZJuqvsVflKY2T8ws3/wNNr7lGBm/4ukN6i7xm11o/fr8UXwzyX9gqQXSHqlpD9nZp+H3V8l\n6f9Vd6++T9I/M7PyKez7FnX9+r2S/pq6X+aXS/rdkv63p3AZXyLp8yR9nKRPVDcyWON5sX0vkfRa\nM/tcdV/S10i6T9IHJL3p1PH+oKTPkPRpsZ3rL4TFfZ8v6ddJepG6vrvTttwJTj/r/19sH/vjD0v6\nsRDCE6d3NrMXm9ktM3vxUzzvGp8oqQ4h/DLW/YKk240UvzC+uH/RzP7kqfa8Ql1ffvsZ+/0WSTfM\n7D9aR1P/OdttZi9R1/d/46yTmtlvN7N9dQODPyTpm8/Z7mq8rl8847OXqOvzf3z6o1PLv/GsYz8p\nQgi3/Sfpt0uqJF2Jf/+SpP8jLmeS5pI++Yz9XqqObhdY9wZJ33O7bU4d459J+uq4/DmSHr5NO4Ok\njz/ns2+W9E2nzvlr8fnflvTGuPxuSa/EZ/fF6y+erL13+k/S90h6w1Pc5yckfWVc/tfrfjm1zW+W\n9N9PrftaSd+F/v9pfJZJekTd1Mid7PuTpz5/n6TPw99febt7hO3eL+lP4O8vkPRe3OeVpAE+f6O6\nUcD670m8Jy/Fvf+9+PxPqXsBnXXuV0t6x1Noy8Ontv1dp5/ls56LdX9Ksvj3Q5Je8zSfl++W9PW3\n+fyzJD16at0fl/QT52z/69X9SOTqBjOPSPrD8bM8tvW3nH7u4t+/LOmWpN8kaaBuNPkf8PlbJf2v\nT9ZudT+8b5D0iWd8Vkr6t5L+4Tn7/tXT16buO/VPJW2pG32+V9Ly1DbH9+x2/+5kpHi/pH8TfNj7\nfXIKfSV2zHvv4DhPCjP7fDP76fgLdkvdA3rlaRznN5vZj0cauC/pT5xxnA9i+QPqHhKpG538UPzl\nvqXuJdmom6u5KHiRzu7zl0h6/rrtsf1fp5NtP77uEEIr6WF11/6U9o14/ql1pz+/Hc7rf0l6IoSw\nOHWeD6DdR5Kuq/ti3fZ4ZnbVzN5kZh+KtP97dOfPwtNGCOHtkmaSPsfMfq26L+rbPtrjnoMjdYyB\n2NY50zQhhHeFED4cQmhCCP9R3fzfF8WP/5Sk/xxC+OlzzjWX9EMhhJ+N9+ivS/ptZrZjZl8oaSuE\n8ANP1uAQwofUzU2eGPFHxvJP1P0w/plzdv9SSadFx6+KbfsVdS/m71f3bD9l3HYS27q5wddIyuP8\njiT1Je3Guaj/Immhjnb8wqndz7LfmaqbDF3jeThXX9IPqrvgt4YQKjP7Zzo5JL5TfJ+kb5X0+SGE\nhZl9sz7yi/Ai+ZzHiyV9OC5/UNIfCyH8h9MHtTjpewHwQXV9ftb6Xw0hfMJt9n3ReiE+gC9Ud+31\nHex7+p4+Evd/1+lj3wG4Lfv/rPN8WN1LW9LxHNplSR86dbw11eLxviEe75NCCDfiNMq3PoW23AnO\ns5p6UB2FflTSW0696D+W+GVJhZl9QgjhV+K6T9YZ1PMcBPn37JWSPtvMviD+vSfpU83sU0IIf0bS\nf9bJ6+XyKyV9Bt4VO5IaM/ukEMKrzjhvITzHZmbqWMFVSV8QQqhO72Bmn6nuR+stJy4ghBvqpkHW\n232DpJ+57VWfgycbKb5a3Sjp10v6lPjv10n695K+NI40vlPS37Nukj63TlDpS3pC3dwipf53Svod\ncQ5lRx09W6On7oX7hKTaOjn+9zydi1I3hL4RX4ivkHRWeMJfNbORdQrdl0ta/7p9u6S/FectZGb3\nmNlZN/QpI87dDdT1e2FmAzPL42dr0emld3Co75D0F83s063Dx8f2/oykQ+tEimG8H7/RzH4T9v10\nM/ufrRO3/pykpToR7U72PY03S/paM7tkZi/Q+b/sZ+FPm9kLrROx/rK8/8/C90v6cjP7lPhsfYOk\nt4duQn2NvxTb8SJJX43jbakbSe3HNv6lj7ItZ+GsZ13qRqV/UN2L8fT818cMIYSpOur4N8xsHF8c\nr1I34voImNmrYl9Z/H58lbrRlSR9mbrv+Pr7/pC60eBfjp9/l6Q/GO9FqY7K/lQIYT8ufyL2fZuk\nf6Tu+7UOq3lxXH6JpL8l6cfQtG+L5/7CEML8nMu9X9IPhhBOjIKtE9Qux+f28yW9Vq5sPzU8yVzG\nj0j6u2esf426X79C0lDdnN2HJO2rU+KGcbu/oe6BuSWfo/j78e/3qJv3OJ6LkfSnJT0WP/8n6obW\nX3/W/M4ZbTqeU1RHBT6gjj78sLqRwen5n9eqGxE8KulrcJxMncL23+L+75X0DefNHZ1qw7dL+vYn\nmRsKp/59GeaF3i+pPGffn9DJuZ0/Edt4JOm/SvrUuP756l4ij6oTiX5aJ+fB3qLuS38o6R2SPg3H\nfLJ9v+dUm8bxPq2nGf6K4nzckzxX71f3g/iuuO+Dkka3u8/xet8r6Ua8py88de+/St0c53VJf1dS\nHj/7DZJ+LvbTO9Upo6fnCe+oLTpnTvG8Zz2u/7dxP7tNf7w4tu/Ft3luzp1TjNvsqZuDn6qby/wj\n+OyzJB3h7++P/XSkji191W2Oe+K5i+v+pLrv+0114tyL7qTd6l6CD8c2PizpAUmX42cvifdxEdu1\n/vcl2H8Q+/eVZ5zrNeq+z7N4nz/vjG1O3LPz/q0ngRPuMszsr6ibS/uHd7stTxdRxfziEMJnP8l2\n71f3Rfu3z0jD7iLM7DslfTiE8FQVbR7jH6lTrx8LXRBywlOEdZlJL5D05hDCbUN10ksx4WnDzO5T\nRxn/k7rwpn8h6VtDCGeGWWC/9+s58FKMUyHvVDeK/9W725qEO8WzJvc54UKipy6z4VDSv1M3L/UP\n4pzx0Tn/nm4s3rMKZvY31U1r/J30Qnx2IY0UExISEoBnzUjRzH6vdcnk7zGz19/t9iQkJGwmnhUj\nxRi28svqUskelvSz6iLw33XePr1eLwyHw+6P1q+xC4XqEM6JgMzCR37eYtkQOmnov/a8vsT6UGRY\n7esznGy9xKO1ds6xcW1ZjevM/Dw5rznjRaHtcXXbttgPbc15DD+E+eYnrofXnKEt3LfFFWaxAdae\nfYxQ+4ks5/G8XW3wbdifAec5sS+6Yt32DPe25vFwncb9znsudPZzxuNnse3sh/ac6895fiyf2BfL\nJ+5RkaPtJxr8Efux3Xw+TixHLBYLrVarpxNHfKHxbHEgeYWk94To5GFmb1IXh3XuS3E4HOozf9tn\nSpLaxep4fQnTlbZ/9guq33TrG3+WtCj98zLDQ1b5E7qsPyLWtDv23Nc39w6Pl6vK1/cab8sgdMdf\nqTnz/CdeJjM/xuCaX2d/6OcZ9wd+flyzTevj5Xm/u47ZbHa8btL3OPt2xx3jVj18WVdo18Lb0jbe\n9gHaUg+975aIzR0suu9WMff9tPD2NTe9XeXu+Hi5Gvh3crr00LZR8Pvc4EVfbKP/8fQv6q7vxnnv\neN2Nanq83J/5dQ7wDNWlH4MvlGHh/VXhjTrKfX2/151rbn6dixnC81beF+MFno8jP95Ufs+PcvRX\n39tS3uPJLmWJBscfOu43wCuBz63NfZsQ+/NnfuZpxUZfeDxb6PMLdDIV62GdTPGSJJnZa83sITN7\naLVanf44ISEh4UnxbBkp3hFCCA+oCwjVpe2dMFx2v4TTbR8ptSuMGjCwM1DFutftV2GkVmPkc9Qu\nj5d3MFTIQRNrjMjU8+UhRhxLw6jo0BsTIvdpL/mopYfR6arxbSfw/LUrvpwX3q55efZvH1iYssiC\ntnIfSfX9MnV05Blq/ZGfp8QjdFT7Nku0sV/5deSYPsgxyr6+OJIkXbWt43WDPV+udjDaRN9WhR+j\nvOnLmEnQjcYvZIguH/Z8JNyP/ZXte7v39n3jg5WPVMP2xM+z9G1KjELLwvslAw3fb/w4/aobfRmm\nBnI0nLMRi12MsPv+QSW/z0eFrx82fv7eAUaCu/5dCJFW2xzfCU5Z4N42OZjSqOs3TotsEp4tV/Uh\nncxPfaFO5r0mJCQkfEzwbHkp/qykTzCzl5lZT9IX63+c40hCQsJzGM8K+hxCqM3sz6jzEcwlfWcI\n4bYOINZKxaKjNoO+U4wKVKU+cLo3AfVqL3cUY9oHv4b62mJiOiudjhSg4G3rtGoOGl6c464fSkqU\n3TakUvkU9LqgWgn6mEFEAmVrodByUn93CnU1UqkFqNmjGWg6hANOwC/nTgdvTHz9vX2nmJlBscL0\nRbvy7ceR7oXG27fMfV44K/0YGWi3Vb796rK3kf2yc+RtCTM/ZqhBfRfddfcGmILI/LnJMqfaoe9t\nqWqn5g0EvaY3PHN93fryMN6XDM8nWK+WLZ4/TJ9oy5eHmO642roAdXT91vFyO8Dz/9iBt2XYPa8Z\nPs+DH3t6y7edbPlURi8q2Nl54RvPcjwrXoqSFEL4l5L+5d1uR0JCwmbj2UKfExISEp4RPGtGik8V\nIZOaQVTX9p3iBCp32375ix1XSNdx0iNEbD+Se8zaZIg4OdA03YCHKGIWs4lTklsDp3sFjl8IlCxS\n71LeprphTB9odQ6Vs3JqNi+cGvbh67uHOLzFLmh9bFZVQ6ls/TfzaOHxc1TltxGDOITKfWje3hI6\ntyH2Tk849barXZ/eAr1vsTya+7HXMYWStJX79EUvdxoYHvP7RTB4mxJ1E6n8OvJAkuoJFHzEGhqm\nIzKcs0VcZf2w01ebeJ8XoN51XOw1kJnBkvugstktTNlc9ucv7+H8FfoWeHh2/Xj52hamOKybVrhU\nex8yYJ8vhw83+8fLk1X3Sd3W2kSkkWJCQkICkF6KCQkJCcDG0ucmBB1UHeXrQd1bzJ0GzkAxG2eB\nGg47OlEh0PjyytW3UY2gVijYT1xCqmDfKY5u+jl3D0GfAujrwI9ZxkBiqsbV2K9hf+rUcICc1D7a\nO0dgOGO3SwR1L6D+NlHRHSKFbCWnR9u73kHXV0fHyzlU4R4UdCqTzMmuCubZOoaxiyZjp/qHQ6RQ\nbmHjJS7Im6LdQ0QIoP/bEVR55i0f+rTKfFjEc+LQLQ4ObM/8mvOeHzvfg8q9g+Yysw4Bz+urmx55\nOwY1Ixi8r6al34stqObZApEFK3/Oii2/kBdVTo8/DsdfrhOq8ZwNML3SYvpgMkGqaExkyHJw/Q1C\nGikmJCQkAOmlmJCQkABsLH1uQ6ujJlLbBnm7iIe9OnN60K+RW7rVra/g4lIh9zPMQF9AnzI4k+Sw\na6ImmEEJL0HrehCus1W3TQUqpzGUSNCWCpS1KXx5BMV5moEG45B95rY2naJbjZBXjGDwIdxwnp/5\nUU4UoSRNRx8Jwdb5EIr2rvf/8FZ3zQY1lUp5BcurG8GvJ0dg+IrZ3AieD0eYJoEzUYaA6F5U8emo\nM5lhCmCEtuS46JW3ZbiDnGjkiq8wldEiCWA5X8R1frhLCMYucG3byHFmf84bp8zN1J+t/iVvyzYC\n75lvv07JH4azXwMG56YMtHsVr8GeVvXhi480UkxISEgANnakaJmpH1O2aEbD2MNsBNFh139NsxhL\n1y781z5v4OgCAWIqHzVcuY6YPZq81n7s+mUcqzmaKWIMy+5c4TK8/25h0h8jhf7QRzBMp+tzlMd4\ny+DxZqMlTFHjYkXxB/GQywHWI36RaWl0X223EA+JVLj16EiSdpEKWO5211Qg7i/HeQy/3z0E89X7\nHuu4RIxhb+J9N8i8LeXc912gLXmMCZ3ARJNCl+ClanLRQXguaqQQ9jGyKtH2Ffwvx6vuXGHonTgb\neZ9P4BTLOM1y5OefIuX0YOxtGcKWsRpAPMP9DTGuspLfnwyMYAiXoLANAWY92mWM7gYhjRQTEhIS\ngPRSTEhISAA2lj7nWa7xqItVoxuKUfaA9X02pWLQYdBzyrBy3UI5SgNM8LOSH6JMANKymj2nO0sU\n25itnL4N4VQy2trt9qtoAe8nGpcwXKXrDwxXl8Ep0Xjg228d4pYzzCzSwB5cfCpnt+r1fQqgOYSr\n+RIxiCg1YDDWtQo2/TDZrXve/8vYRROY+QbE6S3RF32IKDczv84t0FqKSAV++wNj6xDAOY/iVm8I\n017EjtYtnyFv1wgCXY5pihbxmM0UNJ2peHGKYZojJRM0PSzhAHTkfV70YT6M6zlxnTSLhQAjCGkh\nph+WLcU1uP40uG+4/+tnIc9SnGJCQkLCxiO9FBMSEhKAjaXPbdNqFWlLgXiqsgd6BCpBg9YQ065W\nNzydroUDTXbZadpoy9PJBlARMyjUDeh7fcDCJ0jLgvPOOq6yQAzkuPBzVksYpSI2L5+DssGlJwfd\ntjHqpcDkpI5xmIa0vctGBRkGqkwnzHx9D8Y0A9Qu2VrByQXtPUTM3vqIC9TvLCFu5qBypOwjxOA1\n6P/rravSPXR5fgtxos/zXLxp6Lavs7Mr29EI9qBFUCmmXbaPfJtDTA0c9X35cgaz3sPuXA2MZw/3\nEJs48Ht+WHi7nqjc9Wa39ns7Qh8dlTCzxXF6oN7HLj1IyWxBx1cIlGjgiJPFKY5aSX1OSEhI2Hik\nl2JCQkICsLH02TJTNoxuM1hfQdGsYIQ6QdBqHlXJrIKyd8WplqH+SYDDScOSqFA2wQhP0OAeam3U\nYIcW6XGG4vIMRqarSYHztAZlF+VGqwyF3EGxSMPrqJb2EdzcOtNSg+Dx+Q4U10ALGKQfIsB82YB6\nI9g9n/kJmqhcZ1tnl08V0iNrBEyP+yjZeQ0lVvt0mPHtd6+63Q4NZ+8puvt7IlKBNXIwfsiDt3GA\nAHBS74CbniP9s2Z3RRV3EDClwZo7KJlaL32qZYmkgkM4PW3L2zXZxzMK5XwK5595VL1b1NyhUe3e\npUveWCjhi/jchCbR54SEhISNx4V6KZrZd5rZ42b2X7Fuz8x+1Mx+Jf5/6XbHSEhISPhocNHo83dL\n+lZJ/xjrXi/px0II32hmr49/v+7JDhQUjk1c+wY3HMQdtzOnZItdGG3GAOb8kkdsG2prCAHYNctk\nTkG3DvxEs8solQm1sgX1HY6QqxuDnWczV1ArlP4cwCgWjPlEHrZBLRUDeX2tygUo1qA75y1jnRlf\nzhBgvERbSPFGCDxeLGDgOoIzC+ixUJuk2ur2LTClUR/6+ccwvy0xlUBVfHEV1wzl/hLuv1gjBlg/\nKw0ceEo60qJMaQ855uUVp+OH8m0+dOvR4+Wr7e7xsg2Rbx+nW8qb3le9HWQJ4FnJMGXDOiqs40LT\nmmKAmkO45kPYGk2iN0APCv7jlefG38i8//cQvL0bHZnzizWm+pjhQl1VCOEnJd04tfpVkh6Myw9K\nevUz2qiEhITnFC7US/EcXA0hPBKXH5V09bwNzey1ZvaQmT1UrT4ybS8hISHhyXDR6PNtEUIIZiSM\nH/H5A5IekKStne2wtsEaQkWegh7Vu7x8X9+LVC0DZW6hvmXQs/t9HAO5srPMKUu+Qr4zKG4PtV4W\nK6dQs6jENlSWb7hq2AydYuWgwMIPwQC2aC2C1FfM/d4DDV995FRDCZo4k0dm7xQ4P8qwhqVfQ46A\n4BUMeiFoazBk3Y+uf2uouQOYvLYj0G6gGSNXG+rvBPnWE+T2LqG0rvooWxrvI9t6BGW3h/CAAgpx\ngWSAHF+nF+0978x9G6i7bXzmbu4gGL1CPjyMZXswud2jFRoC2WvkjSOIQC2mJE7khMeEhXqEgP2t\nKzoL8wWC4YtePO65X8VnNZ4NI8XHzOw+SYr/P36X25OQkLDBeDa8FN8m6f64fL+kt97FtiQkJGw4\nLhR9NrPvl/Q5kq6Y2cOS/pqkb5T0ZjP7CkkfkPSaOzyWymj9tQAlGmRwTaaih0Ddo2ifNJ/AhbgE\nfYN78hx5sMP8bIpncKQetXCWphAKGrgWMaegQM3Af78OoQpDV1WBmi8hA8W76duzrgbzoFexdokt\naN3l2+7ueiLsibooM6esM0xNXF/cOl7ug243tK5C2/sH3b59BIMPoKCCjWuOqY4lStbaAlZbaOMc\n7WJ9G3vUpwSqSVw/8T401JNhOdyrjV8Ppz0CqHFA7vkS9z+gVG0Zg7YvrZBjjWmaFuccN/5szQco\na1rCyfsmAsyhUBdwZ58hqP2JQdd21qq5PENbpoh4gJq/zpPf0BItF+ulGEL4w+d89MpntCEJCQnP\nWTwb6HNCQkLCM4YLNVL8mKINxzmdR4bA257TwEnpVBp1pmQxqDiDalkjf7gBfRmv/BgDBC/XyE9u\nD51iHY39OJcGbm29WzmtWucqk8r2Vsh9BgXLUPqU9K3B1MC0OjheZkGlo7lT3CIGDbOIUo4pg/qa\nq9/LPqj80M9TIcd3suVBzTs92KLBmqyEQryOe6czeV05NQ6Yssgn3uclFOweqC+vkwH7dKSuByio\n9b5Ov2tf4O0eT7zdtJbrwUarRcGzFrnU+RwlWSsUHYOl2rjf3dMhAtNXeA7bMUvwgg4/4X0xG/ty\ngciBAWLwdQn55KDkW9G1vcA0yRzu7X08W0MUOcuPunNmZ8fBP+uRRooJCQkJQHopJiQkJAAbTZ+z\neUdzrkCjrWA3jdhg9RrkIQ8jVQU/aKFsZuDaGQK2ITLKjsBfQKUHoGFHAW7eoLtrcXWAGsjNELSP\nXmTIa80QJN5HwPDRxNXHmwhwni5hqRUVxe3SldXsxGn8PCvGq2OKYfcWApy3nO7PMfXQR6JRc9MD\ngkdld6016HuDIHljzjTcsw3u3Tmo6eA6Asmhvi5h19bsIJT8k7pEKdSbUgsFG+WST9SuzjBlIKj5\ntkWHc9ag/sjog2YAyzVEHAQE7y8YnAArsNkNnxoZ3XuP74sa2KGB9Tjc1vvR3q1+xPOd+y/bO16e\nG4qFgb7bushatpnycxopJiQkJADppZiQkJAAbC59ltO/EmolXbMr1ONtYbVVRUdhWmSx7m8NapJh\nfd5C/cxp3YXc20MEfkMJXND2K6qYDepOL2GRVV93VTYHBd6+7BZVDN7dhrPzCNnHAwSsF3G5KvzY\nc8S5l89zWt3OYXV1AKsvFLQStimgyk9H3qfbCKQeRPuqwDxhBKw3yEP/xcU1P8YRFH+4cNvzXDku\nEJy8bL29+wMowcuYB8zceN5/vzLl21C2l5gPwLPQ4BkZwuqthPP5Oqg/IAB83EPO+qHf23mLqmDo\n5ysTv+ftDM8IrjmgNvio9fX9OE20fDkCw685lS4msDlD3e1FnOJolHKfExISEjYe6aWYkJCQAGws\nfTZJeUzOXCEgeDFG4HXrFKuHAN9FVJ0HyMO1CkogaGeNQliLBeo490A3EdS7AMXKkNs7hPtxOe/a\ne6324xGjXQ8wZuBtBTozK/yCtigXQ63uwXZqFesKL6Gaj3GeuYuS6qGOdI6YcrEoGOgWz9ODoj1D\nQasq9ktGRjZHwPKWU+OX7bgtV4BTOB2mG9ioVbkvL0o/wfYt3Md1XWXYYQ1Q/Km+DIuwArRfCEYH\nNV4i8P6wQf1w1l2OFHuE6IAW+cY1nOFLWtSxFBvt6kDDazxzYyQpGFT0iuEFEattP97qCHZhmNaY\nlN02m6kg7YGUAAAgAElEQVQ9p5FiQkJCwgls7EgxyzINRt1orddHHRFMOq9gitrO/Ce0P6EVatwW\n8WNDdBsdY6ohxBiYmRalbz9C6lgOoYGT520c/V3eR8xeD2INgummsNppCsamoXYMj41TrnD+WYxl\no5lrg5S8nSl+PzEKOVHmkgMPjKZqfFDAFPdw5aO8Va8bIZ0o5UqxRkjn4wiXo0OUm90vkIrHe1d6\naqX2/Fy7sfRsCYGi3UM/I9avQWnYBSSYI4xCV9DZMjr/YPko7nur8RHZGHZANUrJlgXiYVE+tkEs\nbUC8Y6+EwxCe7VsoAbMe5c7wDBUYJw3N+7y97MdbP9lhQ4eKaaSYkJCQAKSXYkJCQgKwsfQ5hKAq\nGqfmckoYEO9WYaq4AiXJDjo6c23iFPiewkUHzNvrcHZ4vLzTYjIe8YPTPV9eNnB7gbiTIU5yKzqs\nnJgGRznQPibLM6aCQSRgXF3FdDnQ3RbCzDjGL7LOCE1QFmj3gFYu6M8jdAzLdwzw22sT76NLc1LC\n7mwriBUVaPq1I3f0yXCMyQgiAvqwNRep+vveG0XlDcPsgFR327SYOWmRcpjVoNUBZr78BlV+/UOk\nP7KUbY0Sp5NZt/4IGYzbiCNdsS2Ir+3jGq4v3IGHuskMD0kf4g6nROooho0MBrKIDTUIlBQrd6O6\nlmebOabazKtKSEhIeJpIL8WEhIQEYHPps6Q68gkqhH3QmmGDOh7Yt40mqpOFb3sgp8kViOUMqXAG\nLpWjFkldOU9uVqByaEsBF5InRh09YsmXbaiWPAZT0XpnZ4Ip4PyHPTjJFCiVGSlZD1LtAtQ4gzNP\nDWW1heEsy502iHdcQRUtEVdZQv3OoktOgOtMNXU6OurBEBhtpBJbglbuIe5TiHGcIpYUrFF1TKNc\nYpqiQnpmQFnTCers2ICRCL6eyiyNcw+XTnfv6XdTMsMjv4cBEQSGiIPFCjGdiD4wxoAizbGHB6DA\ndRzgOMuotA8ex3wQjHob3FuaFt+cds48LKm6SUgjxYSEhATgQr0UzexFZvbjZvYuM/tFM/vquH7P\nzH7UzH4l/n/pbrc1ISFhM3HR6HMt6S+EEH7ezLYk/ZyZ/aikL5P0YyGEbzSz10t6vaTX3e5AmWUa\nx3S0EiUyQgGXGgTbFgjCVjQuZWnSDJRlMHCaBIFSR1OnZtkIKXxQpYubfpwcDjOG9KorR11bmpKl\nLJ1isfQma8SsTXUlaQYpcgZj3RKOLT2kqFno2kKnlRLTAVXtx8i3MWcALBCMrS3fd7D09q4OnOPP\n0XnLeEjMKOjKCJHGBx48vRCC7nH+CupvNfJr29pGvZjSa/TMWwRhZ92+A1DgkcdUy9CwOQLDe1Di\nDWrsCm47s6X3y1bufTevu+vAbdaNQ1fZezVq0WCqgQp5PfUeuASFXNsemD5FSdzipvdRL544u+z9\nzJK5LaaJONW01GbS5jUu1EgxhPBICOHn4/KhpHdLeoGkV0l6MG72oKRX350WJiQkbDou1EuRMLOX\nSvpUSW+XdDWE8Ej86FFJV8/Z57Vm9pCZPbSEuJCQkJBwp7ho9FmSZGYTST8o6c+FEA5IY0MIwczO\ndLcMITwg6QFJ2t3aCav4YqyhPlJRm82cH10aOoVYG5E2KGXJnN26dvpWQWUuS5QehasI634UQzq3\nQq1FedJlpH4VcmxzluyEu06GKQCDWtvD712DdhloWIk2toz2jmBpWBsjf3sGNR9J4f2+53XXUIWr\nMRRqlDut4MJyVHdt34btDgPdSyjYVD3phiPURen1vL37U48c2K3hUsPri3Vc6CIzZsEUUNwC9Xw0\nR1A98up7uJ9zpFsbAuabqErXeEDabW/f0S2f9+FURzH2Z2hr4P25wGRCCbV4BPPjClMSq3jTK1gT\nUSln0aExcs8H8bVBI+NNwoW7KjMr1b0QvzeE8E/j6sfM7L74+X2SHr9b7UtISNhsXKiXonVDwjdK\nencI4e/ho7dJuj8u3y/prc902xISEp4buGj0+TMl/VFJ/8XM3hnXfZ2kb5T0ZjP7CkkfkPSaJztQ\nG1otozVYUaMWCqyWVkeuFs9gohri9mAdJwKdRzACZd7oEWhSH11LI9hiSEoGTjZFUG2kQTlFPqi/\nNdRPUpgaVJ7lJyvEMc9QVnUbWmO529GjCrmvrD+S0eYMFLAAfczQzwE0jHMdtDRjGdbtqOIWEJxX\nDFKG4t8LaMsckQD9s1XxEu06AGWmHddaUQ0IUu/j/hQoH1uBY9MUd7br57/Wh5ntgZ+n94QrwfWl\nSNlB9cOCOcuumvNZWaBv52jv2pxYkuqbPmVQ8DlCsHkRr39502k6p4CYBz1HNMMwlmnltNYm4UK9\nFEMIP6XzDX1f+Uy2JSEh4bmJC0WfExISEu42LtRI8WMJK3OVV2OgLpTQ/BEPjr0CenITtDKP9k5b\nmdOhfoau2oUSiHOGBUqmInj4FoKNM5Qqzahoz5zK35fFdqEuzBLq6wmFEA0w1EjpUU2GXdgE9Tpo\nB7WKNVLyEXKsEYBcHKJeCALA9zNcG39jQQnb607lZqDHOWy0BrGOTIDNFi235pDHaa9Vwl4sw/FW\nUGLzAFp97cbx4uOVPwv3ll2fbxeufmeY6pgPwZNhRVaOfXsq+NuYblmOURfnhk9JrPOc+7SQy1Bq\nF2VSm+qM8ABJOa3AoH6HBc6D+1LggVmX0jUkGtw4OPBzXvd7u3f5srcrTtO0IZU4TUhISNh4pJdi\nQkJCArCx9FlmUq+jFhlKktqe87c6R74xgnrn0ZprlqF85pgFp1BcCCVLSfcEKnMZBYBqBGQvbzll\nvjL38y/u644ToBqWoNpl+ZGFtSRpJeRS4/euF9guX2SRIlt2HxiCwd8/dPq0DWVzF9SwQd+W8Mui\n4NnuuPy9k0PxRhD6WiJdzWA5toNrQG66wdW8PUL+MlT2BvH91M0n93qYwbBCqdhZd8wWpUStZSCz\nLx4OMe0h3M8lbNnmftY9FE67dh/dwbvtG+jznD6g4szpm4DSp/MJkgcQvN5HAa6wQBEvUPL1rWYs\n+jbK185GULzxbE0aZkhvHtJIMSEhIQFIL8WEhIQEYGPpswWpjMymLpl7CyUSQdiIgT12cK5AGXp0\nz4aNl3aRh4wA3xy0MtSgtaB1vW2n5KxxPIqBzFMEzNKuaQUrKtL0BejOFMHDxQLLyMkVlyOFY/7u\nyw+RMw5H5jkU9PUUhSQtQANLrD/KvL8y1MNuUI947XqV0wwM266gMhu4bI17kcNGqwRNp1M43c6F\nIlnrvkDKsFZjOGmDpjN4ewRqPEBQ+WLfA6IHpfP6LfPladPdxyVywDPk6d+TYcoGKd4f3vZrZg3y\nAi7cy4U/T3QUg/ivXqTkOSYYFn0UOWMRM3yH1gHw7cnSahuDNFJMSEhIANJLMSEhIQHYWPqctUH9\nSBtJWWu4Q2e7Tk8Qu638ZkePyofdJbra9c8NztOzI9BB0JSAvN0RlFtaQ5U9KN47CJReS8QIxl5B\nWZ0jmXUXRZR2EaQMxqwG2/fgDn1kTtsO5t21Xr0JOzHkyc4hWw+gfiMu/oQqeYjA8B3Ud9bSjzPP\nvO9m1SJu6524PfRkckOQ8ryBCzeurUChLdS20hBB8KTk7SMeqLx4UUcsh6DdBQpKTbd8/XiGaQKo\nvDXs1dqBb3Oj8mfuUgtKvOq2GeEeNqCyR3BMbxGKsIXg9QyKv0G5p5N8jWmYeYspoXitNQLDl9i2\nmMDVXY7VOg9/M2O300gxISEhgdjYkaIFd5kpkI60HCL26wYEiz7Sqybdr2z+cZ7alEEU4Fw906ly\n/nJiZFMxlhET4DQOHWeYgI+jn/+OmqWXcp8iR5VSHTZ+DYNDOOMgZo4xexNzYWAH24+f6P5/5IUY\nSRe4BmhLM3TABDVKMgyV70UtkoYDxbEfqFn69Y2iyS9HewZj3Qzxg+znHYymS9RXmQeOSH25qnx0\nXH2Cj9rW6Y8Bo0OKSznscKyPET7iBKe1H3vC0Sm+Ztdyj02drvP7MDrrIz10CBefLVgdHa78GDlG\n/ssh2o54xD7uBQWbW213nAJ9mJ3Qufw8EwhKo0G3nGWbOabazKtKSEhIeJpIL8WEhIQEYGPpc5ub\nlrFs6BBpdleP/JIPtxA/ByPYbBCpClSE8Lg7qjSIOytg5pqDgmcFY7x8/SI4fzmE4ekA9UBCnDC/\n9x7EMbIeBtK8imt+jA9vw4EHk/FbwZenC6d4FIP6L+6UpL0jp7Qflrvb0FGoh+thHZcm+LHvZVlT\nmJWy9CdT1wYxpa1gPt0McYqgrxRoRnD96aP+TY7A0znKqtYwq70EYWpUdetnJUU5PB893NuGUzAQ\nesZIWwQPHXCKATGTvSjSMDZzsECdmZZTE7ienq+/Nts/Xr5cuDBVGtpCUyWY2M6j89IKaYusi7PE\n1JBWfs3DmOaaXHISEhISngNIL8WEhIQEYGPps5pW7X6nns2RctfAOJTpV6yBsFx19HQ+ddo1uNfp\niB3AWPVep7j9HoIJgR6U0wLSaYs0u4PWj9nU3fYF2p2jfGn2qKuCjCvbHm779tswS2WJWNSCqVa+\nfFB2x7xn7Cr3FcRX5nOktg0wfYBrXpR+DY+vnErTfLUHGkblNI+UnNfc6+M8VNORfrc/8IOv4GpU\nIuWtgBvPVn52zOT6SnNQ5nru/SxQxQxRAzlq12SQ6BvUhWnxdJ0oK7s+NGInazgA9XK//oDzF8hF\n7KNGEM2EeUy2Mcfx1xl9JzxvcJ4t+TNER6U8Ogplm5nll0aKCQkJCcSFeima2cDMfsbMfsHMftHM\n/npc/zIze7uZvcfMfsDMzjYUTEhISPgocdHo81LS54YQjsyslPRTZvavJP15Sd8UQniTmX27pK+Q\n9G23O1AIUhupUIX0q1XrlLGH8pzLxtdbVHrHPacPTGkK206BBlMGvkLFK+BegzSvg+DLSyix9wVX\nDtcK4HAO2gt1fHnZadUQ9DXfggcK2stSnmGCUqEzX1/GMpc3dpx29VGvRDAqvY6iIkuUjB1VcK+B\ne9DAznZyWUDpbKO6jDhrZVBZm4lfZ0Zll0HaqHOTu0mNVkM85qCHNVIX57G+TmD5Vt9LVQOajtqz\nkxVMaWEg3EckAvlpVSG1MarohgDwoqQi7zvmB4gswL0dYhuWoW0x3VBDfi4xDhrEWjNLKO4lVf7c\nzzkcQNk/u1zMxuBCjRRDh/XjXMZ/QdLnSnpLXP+gpFffheYlJCQ8B3ChXoqSZGa5mb1T0uOSflTS\neyXdCuF4SPCwpBecs+9rzewhM3toBf/DhISEhDvFRaPPCiE0kj7FzHYl/ZCkX/sU9n1A0gOSdGmy\nHY6VNuQ716AHAeaezci7Yi+WNiXtrBAMfNRjjjHUZDi25Ni3j9+eLS5D/bzReltG69vC0ipzf8nX\nDZKf95xWlrcwBQDFe1m5Kny4gPMPjG0VaTXLp44DqPYOzHlBH4eY3kVF2BNuPFT2hW0GEHdtvu4L\n5BIvsQFKjOb8vVv5NVQ1ctlxohJB0Mxbr0BbVzFveAgHnAZtvYzc8xlMft/Xc6edF215/ZcSdWy0\nj9K3yCe/EnOImwLP5wB1UUCTt1BDqKQhMTYKmD2hMJwjimCFUrl5DB5fwjknQ0D9ldzPGRjBcTxN\nlIK3n1GEEG5J+nFJv1XSrtnxxNQLJX3orjUsISFho3GhXopmdk8cIcrMhpJ+t6R3q3s5flHc7H5J\nb707LUxISNh0XDT6fJ+kB80sV/fCfnMI4YfN7F2S3mRmXy/pHZLe+GQHqrOgm6OorkEJHsB9dAXV\nLYdlVX1GHm6LwFjywZuwa+Kx+wgSrksYgQYowaDPW1B6F/1Y+hI1X8YDDxIvkJPdTEkfnVcG1khB\nHRN0hYY5zrn+eUQtjgVrrMI6LWftEpi5km7Tuoz9bwgCLw+QWxvznGeQNvf3vC2XYXlWo14NIwiu\nXvflw5dAuWd0MgLZeX+LGFQ+hFJeI0i6WPmxd0vnqS+HKe0MtVsK9F1v7FMc9cynL9aB6tuwjVvh\nPEvMi0+RFxBgbLsocL9mLrmPh97GEaj8rPbnZRWngXojj3wYwq6Ngd5U39cJA5tJni/YSzGE8J8l\nfeoZ698n6RXPfIsSEhKea7hQ9DkhISHhbuNCjRQ/lrAsk8VykTtTpzJHE3BfSJElLJXasluuc6iC\n0PMaqL8ZSm+uQNPmcGFmCcle49uPCudEDOpdLLvlDFQqQy7zGJRt0aA8JtrbnpMfm8NlmYHKw2hd\nNgp0DEegM3K22VcZatQsXHyVQJ8Fuks7LpYBXSvhdBgfbjutk52db9zDNes+76P5ozf9nDtQbgdn\nl6Rt41QGg9F7cNjmvQ0I0j6QK8u9JeznVn6cWwNfZlRAP+ZBN6C0LeqsNFDH+8gZrxBBMWkR7D2F\naznc1gMSE0okGITt7vkbIMc8x36rmT9bnILJ4rRHSNZhCQkJCZuP9FJMSEhIADaWPgeTqkj5qm1/\n948RD9yi0NIAKmZWRiW0RZGhI6d6/R1XgrcWcNjun03NexBZe1A/awRK92EpNa+6thRwe17VOMih\nt2u8447YAQp5AVqbV35tDIgOl51uDg5jWVeo2Rnylw2Wawa3cW059VpO/dgVKOYJR3LaqN3rbR+2\nO5Kk2T7s2qDO9x73Y2c452JCug9X76s7x8tNzYRqBJWz3Gyk0vNDP38O+y9UXtUCN3Rmfuy88Gjv\nOWzZyp6vP0TVsWuRNhc3vM+Xl7yvtod+f3JMAZ2IPsC0i5A33cC1vMJ9Vn7CKKxr00137yYl3u37\nVMY2ArkfHXYUvz0Rlb85SCPFhISEBCC9FBMSEhKAjaXPRS1dudmN76shCgBBMLMR1N+xU5L6oKNq\nLVTT8WWnY+3SqUmA23LfoOKB+jag7xAR1TvwfY9Aw6rIVPqgQywSdGMIagy6idhx9aDQWg/WaROn\nQWOojmWk3u01uHpDqa8wvRCg+BbI8SY17iPwdwpaywDzIaYv2lHXrkvB+5nK/hK55w2VcFxDhsD4\nIwRJGwpn5cg9Zt3icdmdv3cF9bfnfgwEEMgQQXCldYrJYmWLe3Dvbrmi3C+9/9sYBD1FsbAlrb2Q\nmt4fuhJf0eGbufwI5DbkU/MZzeFjtnZTD5j2YYGuGe5VhWmVQeyMTR1Rbep1JSQkJDwtpJdiQkJC\nArCx9DnkpnaroyqGQOYVaKrg/JyjrvJw1FGiAPVRKBwFFyW1yGsOcIquQbfmsJMeVNhm5BsV8N3a\nabp2B9TdRYyu8rFv26IGc7NwujNHEHCWoZbw0Jep1g4ibZrfg8+3/KTbKwRMg/a3cDW/iSJSxWWn\nlROo4ssDDwiuThSV6DrVBn68Ue3LQwRDT6HsLnHsBXLZDYHn7Qe9Znd22WlojWmFNk4JFLRc2/Jr\nni28P1/c+DHy1dnTBGPUo741dOppsIBTPFfxPC84NgAFN+TPV9cR6A7ncRblYr57g/OUmPpolqgZ\nfvz1x1QLmhdmmPbg1Eyse22bGbudRooJCQkJRHopJiQkJAAbS58bBR1kHRc4rwbwAjmhA7gT9xYd\nPZjj8xkUwss7l46XSXHmyOs9qF3F7aObBwZH6G2nmEcI6s1ikShaex2e4DW+yJzkMZTFBhsFBG/T\ntLuFinyw6trbv+SB6SUCzVuov9MxajDD7brdQu4xTrQF7l9ccqrYrHx6YKvp2j4tYfMGpbiPPPAe\nAuNvQSE1TCUIyvXWtp8zA/VfTXBNUeqd4F71YORd4fmYzp3KZoFO2b7NBM7f/b4vH8ifo1HdbV/C\n7TpnXWhQ1uaKH6OBjZfBfi0/osqM6QPY4gUMg+YxeSBH/DeTGJizv0CRr7Xz9oay5zRSTEhISCA2\ndqSYtdIolh/tIZZtP6BUJEYiQspd3Y+/FZhdznZ8FLRE/GIJ9xb+sudIpypWZ+dD1RBDZkjj21qL\nGvgprgq6zviJWCa1h1FL6Pvv3WFOlxQ4A6GNx5PmSCHrYwTTIqcrg1Fsg8FZRvccCCMBZq50iVGP\nuXPdfy9vPE5xCLVqdRmiB4Zw8+V1b+MTLqhc6XkKIcIhtdqDQW6D0VR0QTpCOidFieETPvI/GOF+\nIu5zOPaR/xLlVjn04POi+Pwx7nEJxxyOiHswls3hQDSf+fbl2J9Rq2EQO4F4BEZSxIY1uFcLiEUF\nzHHzBjG91w67hXYzx4pppJiQkJAApJdiQkJCArCx9LnNgg6iuecVjPLpWLNsUdME3Hdu3UY1hBbW\n9ljtgFbCMSZDDGIPE+bXRqDGC58wzyEATTCRv9jq9j2E4WkDEWdSQbioUBcFpUcblMosl6BeCLJc\nZhCSIn9tly4EvGTl8XgDGNWumIpGik82hX5ZgpIJJTbZdyFy3NHU120xpnPi/XaEOL08UJhAn1dO\ng3dQ32YAI9gB6Hk76Ojhhwvfb5h5305efI/vh7S5GimkhqkHNOtEzOQYXD6Pz9S8dQq8AGXfrzym\n814hNvI6HIPgZLQ2jZVOxhAe1kgjxAf9SNuXlU9HlJilKaZ+bc3E+2ItHFm2mWOqzbyqhISEhKeJ\nC/lSNLPczN5hZj8c/36Zmb3dzN5jZj9gZr0nO0ZCQkLC08FFpc9fra7e8zrA7P+S9E0hhDeZ2bdL\n+gpJ33a7A5hMA4upa+B4T9w8PF6e9Fytm0HdHcfaKSXU6RpGpS3KgM6QcjaCq8kARrRXQVmuo6bH\nNstpgmEeLDq6M4I56YgxiAdQNodOmZgKCOZ9wmS24pQAHFvGZdcXlpNewyWnRZ0VKNtGKgdltV9A\nlobJ7CFrx4Ae55GKFXAu6rUwucX0AsuHZpgOuGfp/TUjDwQYp9rCyWe56G7eEGpyjhTCI9QrmYFu\nWt+foQqdvge1tmB9mblPpRzFOEmKuJMBSrke+nkCUkWnz0O9U1DmPurrNEe4z1CX+/uYP4quOpOB\n91vRO/uVkF9H7ZjehRxLfcxw4a7OzF4o6fdJ+o74t0n6XElviZs8KOnVd6d1CQkJm44L91KU9M2S\nvkY6Lp93WdKtEI4D8h6W9IKzdjSz15rZQ2b2UIURXEJCQsKd4kLRZzP7/ZIeDyH8nJl9zlPdP4Tw\ngKQHJGlnazvkh92LcVqcHZCsBQKfWc4yUqnrW6DUoK/DG04ZtxDgXdzn6WQBnCjneZC6Jzj22I5T\nmK3VR/5WTed+zuEUgeZDlDulESuCh4eo9TEsfXkKGtiL+V8DpKSVJdLD4Bg0BB2c7vo5bQ4HFqis\niwLlMadIucR1Hqe3efzzCTW5QcByfugUsCzPDgxn23sIUu8h8L1B2dr1DEeFIPqsQWQBaHpvgmkX\nzG73F+gX1MLZqv38Le7XODoWFci9q5DCl8FkuMBvPFP4qgWehZXfz+JRV5zrS9gej99WfC6qkskF\nKKuK6ZUSr4oQpwAynZ2U8GzHhXopSvpMSX/AzL5A0kDdnOK3SNo1syKOFl8o6UN3sY0JCQkbjAtF\nn0MIXxtCeGEI4aWSvljSvwshfImkH5f0RXGz+yW99S41MSEhYcNx0UaK5+F1kt5kZl8v6R2S3vhk\nO9RZ0I1+N8wf3mJQMygbXFVWMOVseh0t6CMndToA1ULua3kd9VqmTl8WPacsv1rcPF7+n5p7/TxI\ngw0V8k+jI8kcIuOlAah55Qr6EuanrD/SH7n6zUBemqLuHvj6cq+jUg0CchmLvUKOLZ1WxmM/zwzK\n7Y3M+4VuQ7rBnGC/wGE0l50hkJnkrOrD6QdTIKPS+fai9H1XUKhLNNiQw21w0lk/CUiBP6Gyl4hU\nKFAX5WrwZ6iEKfAToM/1AEo8ysCOIz2nUl+DvhdDTAfAdShgKiE7YrlTp968dxNMmdRjTCvEKYuA\nZ38plKad+/HKLQ8eD5Fus1bPJuHCvhRDCD8h6Sfi8vskveJutichIeG5gQtFnxMSEhLuNi7sSPGj\nhclURANYUgIhwDUMfXlYOA2sq47CzEBHhBCfBuov84B1zfNmcxh7vvSKm9J+qHXqm4HXbi9hRBuD\nnUnlGHRdXYKxLVTpe+DjNYBaPgtOK2cV8rC3YVwac6uXS7/mxXVvq644TS1Q5+WE+ShIm4GmXz30\n9o537zterqH+Dg46Snggv55bGag2QEPeJYLuV0Onc/kc9wU5ydMGUxzIFV8Hx/dg81YzxxkB+/WC\nkfZ+zxk9P5n487TCNIgtvI8O432pYfNF1FTzoZpX6LflHhRqltj9gN+7EkbIBguyUHXP1JD54Ajk\nbjAFsai8Lfla5d/QIdWGXlZCQkLC00N6KSYkJCQAG0ufi2C6XHV04ghlOBFHrN4+6BMCdZvoeMy6\nLfsIeg0Iql3AOqpBHZECuaejhe882N09Xm5ZQnUbCmnMOW6WoMyg2gdLp4BXM6e1Oc5PZTuDupnj\nOufI1T1sOqo6QP72CBSQj0qOnGyq3z0c25BiC6evEwHZMDBXHRXQFnm116AmH2AKIJ+x5o5f/wBl\nWAetX/MUzuPXC6fkews8/vHw/S04aednjxnmiKQ+2PIHariPjVCqdVX5OSegr+tA7obr8EyEnrdl\njjxsQc1uoZDPe+j/SyhVizo2Oa3m4g3oI9C9xPcjYColm8EWbV3bpqHGvTlII8WEhIQEIL0UExIS\nEoCNpc/KTBYLI9XIMa4MObxwsM6gLq4LWq3mTg0HCJJd9REAnTt9o/q6zp8+jS0oxDUCiY9W2D5a\nOq0tzCTpFnJirwSn4+MGAb4jUCPQ0Ab8NSAgdwga2o9B0Iu+fz6nOk7LLVxPb+RU2qbOmUcQjuul\nr1/boklS2QMNjG7m+S6CoXGiMVzKcwRP1/soygUbL+F+9kClryIQYdqiPGtUd3OEjLdT33gOpZq5\n1Fcbn2LY3/XtW6i1l5HQHYbe9nm0Y2vgGzdAkbUV7NqqAtMOCNLnqGYcQLev+PoSpU8D8sYtPhcH\nUPPXdnuSVMA9fr7lZxqt71u2mcHbaaSYkJCQAKSXYkJCQgKwsfS5VdAiUuU+6ufutHBEhmVSC6p4\nzEq2kOsAACAASURBVHCf77WDEX+rAtuGJ+DCPKLbtNOqsI+AYYPKDBo0ApVtojv4HIG5W3veFqHW\n8xK0boViVXRQDqBhbONqBiq101Gvm5hqYEB5C/oG9qqaYjL6cwGaaCO4WUO5XyEgPsT2hhayNfLB\nS9DnHgKgWyjEGYh9QTtr0OAcUxaTseeTfzjcjMf28zcrX+6X/gRkWK5wnQXTuk8YXHu7qL6vonIf\nMO1C9VfId65QZK2Ak/mYhbfxLAxRUzsgKkGo5dzG70eJgP4aIQENpjL2Sn+e1xEcm2odlkaKCQkJ\nCUB6KSYkJCQAG0ufFSTFgNc+gmBZkHcBWkPH5Srmtva3nV7to0Zytu/K4vgeFC4qoYrCgmt5CGU5\nwDpqzjxU5037O93yIPfP7aartkSB4kt0+2Y+Ld2mbw7hQo6iV6Net3xP85G5sZLUnLCXgl0VgsTB\nTI/tpSSpBM2qQM/akT9+VSxoZQFWbHASVwE6jns1kN/bg7n3UUAOs4FulnBYL0HPdwadNdYc5x9u\nuWrcQwB8heJbDECfw94swyNnKBxF27W1ZZjBCswwN5Gz3SyqjXu+wKxKgW1a2NgJgd99RCtM6q4x\nqKWlFfKtF3u+38EUUQvRnb0NKXg7ISEhYeORXooJCQkJwMbS51ym7agZMwh51XdaNQX3CTOnB/1I\nQ2aw5doqnA8VcHsOyP8kmahbHG+MQkeoH3wNcmUPhZFGkZ70cc4eGPiwAjUENTLm3tLsuY+gasjF\ne7CaWh+GgdYFlPUGNmstph0ag+INWleApvZoXbXwPn84ON3dshgEDjq+QM7uqMVUwp73/xL93+u7\nO3Q7RYeB7Z7I551D9Y25yv3C7xXTjVvc3QVyz+2mL48u+/n35eevULN5CwkDg7zro2YEazOozO2W\nb9uA9jawqBuycBqflzkdxvEwIHi/jnZxzQRjo5H37RjfiQY+dlXMyQ+cRtggpJFiQkJCArCxI8WQ\nScs4EKoYWIf0uwHSn0qMGhRrY+QVS3CilGR29gTzBMamoxt+7Mfu9eUcjigT/oJj1Lqs1yUk/Xg0\nGZ1DaDDEo+UUkeDMMi/w24eRwhzlNqsY70chJEOq4AmBAEakGfqzwmiSIokhDq6H1L5LwY+zLnHa\nHPjxthHTqQFT2zAKYjwebktWYGQFkaiGKWx2r5v/ZrFezxK1deZI1ZtiBM0UwgyGv6Nb3uf5AKIL\nDHeHqGkziOJWvfD27dcYncOBqIRYuH3J292/iRhDjDKFejkNBKWcNXjiCL6FiEWxaAc1dODlq3G8\nTMZ8bhLSSDEhISEBuHAjRTN7v6RDdUOnOoTwGWa2J+kHJL1U0vslvSaEcPO8YyQkJCQ8XVy4l2LE\n7wwhXMPfr5f0YyGEbzSz18e/X3e7AzQKupV19GcMlxT4qp4YJ9foiTaKMWXN9CvE/SG1bArz08Y1\nFA0QwDe44TRoNobo0SB1DI4jTayBUbeoxYHlFhP9BeL0elsuEtjYqekAdG818A7YR0rfWjBqUHNl\nhDhJgYLbIWgaBKUDlCe9BPPbBYxQb5qLK88LTiWXsS1T0Ne6QHwdUhsN1LiPFDbWi6lQynO17X00\n2fU+CkgXvBlrw/RRcqUP+l73aUiLe4HnaYkaMQN8tfq4t70B0uzWzxFCCkd4VqHVKMO0y4nYUJji\nat/pe4vnZeGPggaYhukvu2WDse0MLkk3S39Wd2tM08Rr20zy/Oyhz6+S9GBcflDSq+9iWxISEjYY\nF/GlGCT9GzP7OTN7bVx3NYTwSFx+VNLVs3Y0s9ea2UNm9lAFK/+EhISEO8VFpM+/PYTwITO7V9KP\nmtkv8cMQQjCzM+XfEMIDkh6QpK3dnbCmyjnMTweHcJXZRanOPouKxOPR2BPxfUO8b3uIGWxhFlpB\n2d0eePxaASrTgOLoCI4ssQYHmRRry7TbTgHnoMBFfraTDaPJSOuZfraMJrYFpgzsnDC0J4bO6yZT\npPNBwa9Qx8T4mOH6l6xBE1XvKWrVXCm83zKqpshLa1B/R084fbQRaGIPlJV8F/R5EudPDKmSq8bP\n06IzqGZfwr01xGySvhaogXJ46+B4eR4lXZYPXccuSicpOEvZLui+i3otOxUJLYxjEeNoiLdcxlMx\nHnOnRW0X9FU5g3tOfKI2M8nvAo4UQwgfiv8/LumHJL1C0mNmdp8kxf8fv3stTEhI2GRcqJeimY3N\nbGu9LOn3SPqvkt4m6f642f2S3np3WpiQkLDpuGj0+aqkH4o0pJD0fSGEHzGzn5X0ZjP7CkkfkPSa\nJz1SCGpjGlMG5Wx12akUqeds5hLgJO843joNSpKyjF0FigPD0bylkwrUUtCqcd/54wrbwE9Uw7Y7\n1xTpYftDpCSCJfUgm7PWx+ocJ5c+anDsFK4Qr1nTdAanGSjODUTOFkHnNJmlyt/y5xbK7R7o+x7q\nkdhBR0lRLkQzBCP3DBQQ5UMFWleMYAVMhRghBwER3isELa/9WfPK9ysnfq94aBgaaQFaS5NfYojn\nRTCuLaJyPkEwfI5pFKF87mjkUyYFFH/BDej6xPvryj4CzJGKSGei9fRQr8Rzi0sYZr7e9vwaDuP5\nqYJvEi7USzGE8D5Jn3zG+uuSXvnMtyghIeG5hgtFnxMSEhLuNi7USPFjiSyYtutu+N+HQtci5LRA\nQu8OSnUeXLshSYJop0P3m9UAlBXxrZo3UFy3QatxzvwWzGqhUFagmOMoUduJ0pNn1/FooFyukBNN\nzFH5s4ZLzIA50Wu1HNR0CTpeIw96sPLlIaYVAuhUgBI+gHtLm4OywjHGIlXOEBj+wfLG8fKLjhCM\n/vy94+UCcwkZaP0AUxZz5v6C75+4vkgx7ZKryX3Qe6F2zBJ52D2MK7YwZ2Cg4XS7aaGur6l6Bueg\nBkr1rF5iW+RSQ/YdiHVU/EYPELxeoKxvgNScxSmGCoa02WVM78A0WYjECE28BtvM8O00UkxISEgA\n0ksxISEhAdhc+twG9Q86elRNEFQLGjAAlcqGMP/c7ijU0S13bZ2AMgbQpykobgN7qRIlVQIoXoO8\n6RVyZVfMvY751NsLp51UoueV050lFFpr6J3liweP+nVsX3bbqcfhXFtGJZzq7wo1Wgo8KlvBKVuD\nAN9D7gz0V95fS1hTPVZ4snjR787FTKSPz7yt08y3zW55u/dz5OfCiuxErjCmFXJMkwxQwrXa6ajn\nikKxeVtW5dkUN0N+NAP5V6j1cjT1hOoeKPZWv1P/58hlX4Kx94beVitpEefbsCLqCIazLaIPqLKX\nAwRnxzo61+ceUL7zqLe13EV/YgqqjqESDHTfJKSRYkJCQgKQXooJCQkJwMbSZ2WmEG2V6CZdotbH\nFJQoVE7PRoOOSo2u7PrxoKYySPd6cIrV3/XuvOSMRM2WU48FAolbtKuBvdS8iQHBoPRHoM8BVk9F\nA2UbOdHq+3VehgXYEMnXZY0pgXHXRlqHhbnTOtLHFWq0lNnZucoFaOWJvF22nUHQUVKnas189N0d\np9Iz0PoSgdHTOS3NfDEw35dTAnvOVdc1dVq0T1Cnl7Cepv1cPfZ7ewCVu1z69Y+DB8njNh47pQfQ\n1BY1fEZLPHNQ/wvczxLu13PcO9buyVFrpYJP2WrnIyMOViiBO0KJ1Qb3ZR3/HjZTfE4jxYSEhAQi\nvRQTEhISgM2lz2ZSVHp3QRlZ9OjawjlujeJKsxiU2oB2MZW4RvnMFvS5zOF8jSJOJ1RhHLOCHVWJ\nHNZ+VDHnjUvYVMeLidOxfg9FnKAGzpBMnQ0RPA0btfmRU6m88+E4EWg96m8dLz9SuYJNxXVi3rc9\nOHwj3Vy9wmnYauH0cIXth+s21gxYR3QAApz7UFCXyFnvg1b2YAvGolsN1N/9HHQ79leOwl5NCVs0\nlGnN4MLevwU+jLzq8/KCqSKv61mVpV+PYRrlEEHVR40v79X+nA2FMrVQrnP0RY5rbm+hrOzNbpvr\nV0DTd6AyQ9lvSd/jfUn0OSEhIeE5gPRSTEhISAA2lj6HTKpG3Tt/gJTgAIWQ+akVXK77Ua1jrdsG\nNLVH9W/gxZd6KApEqy2q30udHew9huPxOud2CiWSCmo7ATcDxWR+bA/c5kZgQSm/5TkKQGWRNvb2\n/HoM13wFNHGJXNoM52GdZIMLN+pP6R4o1POK7tzd9hn6cAk19SYCjCett3E7gHqOEZi8zVxh8Dwo\n1Ns3QXcjJc6RwJ0zZxsq/wwVpRY5IhhI0zHfwprdLBKVxzFJH1EADaZ3VmNff7n2B3SIHO+AKYg+\n2t7C36zGOXtwIV9f/daht7tlPjzackhXeVvvn4K3ExISEjYe6aWYkJCQAGwsfbY6aHCzo6d17ZRp\ngWJEyz5oYOHUY6vtFNWAokgtArBPAEokrbuqE9TQgbK6WiKottdH4PV62y1XmRtYh7W0moLKTBds\nw+8daXIFSlxm4PijbnmG6OIxcpzVw5QBnJpXcIFewDptBCpXoC0B1mUT0MZ1fm4Fyjw0UmnHIf8C\nTSWZaxC8HnCPxlB/qwYUMwak55CN2yNEGbCI2QRjCRzPMDVT477kaAunacK826ZooMJjeuPE84Qp\niz5PP4UVHGZVZnhelow2Z4TCuLumMTzydjAdscL1MKg9H3YUPNvQMdVmXlVCQkLC08TGjhSbTNof\nxF9ixJiVDepuXPeYrSlivGZRYRkXvrKPlCuOIOheI8QmYv5dhrKhWeltGV65fLzMEcQwBvm1cDep\nch/tMO5xOoXTDOIB8wVEHD+0mhVHszAc7XXtqmFsuoQdS3EEl57R2WVdbYJRzgACwCHEIIxUlktP\n/8tmsV7JFNe556PnFvVf5uirI/nodLJALB2dZDCafKw+wja+0U7Zjcrrnq9bYHQ2wbOgG65M9OFk\n05yXireCsS7S8upJdyEBqYq8P0uYEI8rP3bF1FKM8lZwnz3qYcQN95w+hBlNozMR4hGrbb+eGiPc\n4rpvY5F5nYy/3RykkWJCQkICcOFeima2a2ZvMbNfMrN3m9lvNbM9M/tRM/uV+P+lJz9SQkJCwlPH\nRaTP3yLpR0IIX2RmPUkjSV8n6cdCCN9oZq+X9HpJr7vtUUKQ4vD+qHaKOR7CseSK07MxxIub/Y5C\nLla+bhtxYjkMTDNMtN8InorVg6AB9q5i4ZRomaO+CtK7bNS18YTxLGL9+AcpLvxbVfSdks2mPk2w\nnTk9mlzyvjiIljWLmVPaGvR2CYGqMN+PRqRDxEkuYBZbbVFE8ov6cO6pg/fud/1Y3udk/8hPo951\nCEeYGsjRbwoQOkBlA9L1dsG9hzAOzmOQ4YnUS6Tf9RCz2Nvd8fMgVXJReX/lMBNm6l6AoLcWb+B1\nq6H5ea7kXhioGXrfUmhqMTVTQ1zJIVK1UGYanD+Ltyg/wD0HBV8LKpLUu+L71R/q7puFRJ//h8PM\ndiT9DklvlKQQwiqEcEvSqyQ9GDd7UNKr704LExISNh0X6qUo6WWSnpD0XWb2DjP7DjMbS7oaQngk\nbvOopKtn7WxmrzWzh8zsoao6u7JdQkJCwu1w0ehzIenTJP3ZEMLbzexb1FHlY4QQgpmdOW4PITwg\n6QFJ2t3ZCf21GkvDU8QsbiEma4Cu6EeqTNPO/Qz1OqAEXzV3ktmCQgofVlVHMFm9ieItl51WFuZU\nxaw7L7K51MI1dclSpmCPLeLxWpRMzcZ+7Db3a54jlq6dddc3QOzgFJRtCqedHcTSGVxlqhWWweUf\nr12t3Z05DXvpNaTRxdNOc6imufPnhVc11fAQSuy+T43UL/H+LEfoT3bSELSaqWuRBgfEI2agskJ8\nZY4IgiWo6eAxmMxiamYJU+I5FO181t2vBdx2+RzQ6abBA5VDid4f+vFGrberB8W7XCL9E1NCdYwE\nqPGsNHCA4nMeQJXXX6vkkvPM4GFJD4cQ3h7/fou6l+RjZnafJMX/H79L7UtISNhwXKiXYgjhUUkf\nNLNfE1e9UtK7JL1N0v1x3f2S3noXmpeQkPAcwEWjz5L0ZyV9b1Se3yfpy9W9vN9sZl8h6QOSXvNk\nBzHLNCg7KrKiWgfKuIJLzfbQaXAdFUjSK3vCKXAJyjSbuPq4NXTldIagYgZyl0jnC6Urt0eg6r1I\ngycVaA1ozwrqd2jP+V2bYpoAzig9GLRODYHakR71drxNOzBqhRCssGAtEFAztGU0dW61PwBNJfWH\nKL1mzQNQw2zu17y1431bITB5jvK1NyqfmhjNUAsG0wcDBHIvYINURFeZHlIfixlSOFFnZupGP9qF\nye7gZff5sVG2tJl5P0+QLrl4+Iakk2mgR6Ddw74fW1CwCyQP9OGY1Bj6eeH3P0NQu/DMDba6Pq32\n3YGogbuO9kH1sbqIaaMnpiU2CBfupRhCeKekzzjjo1c+021JSEh47uFC0eeEhISEu40LN1L8WCEo\nHDu4DKCWDhBJbULOJ2p9rF1gLmVQEO9xKbgcIycXdOMIOamCihkuOWVZtFAooYq3rM0R63H0aiio\nR37sFsquQRU+qQZCoUWAt+ZQFxtfnpcdVRxhv8UA9HKOnGSorwuDMw62aZe+vJsjPxi5vyybqlF3\nzBYlUxe4P9tz5vh6n6+CL+/cQl2Wwu/nwQp1XHZcxl5i3+Wo2zfAOacd+T1fIgB8yGgvONPQJYhm\nwsx376MWT3Vvx8MXLQ6I4OkFTHvLIzrdoAwpplIMUybtLnK17/VnpEYQut3qth8N/XluQM0Nx17i\nmVvGNrabyZ7TSDEhISGBSC/FhISEBGBj6bOkY1lvCMqgQxjOIthZyHNd22i1sALLMqdSFSgG66xM\ndxH4i9xXlr6Ec5ZqUKxx5nTn1qwLdj6gySfrlYBW3rNwVXYQoFbTIBfnr+fIYUawdS9aZi0rD4Yu\nB96mxQq0Cm62h0cemF1OPCc420Fg9r73OQ1nR1Di16nlDHpfIVLgYOaWX0sY25aYduiN/HgMfD4Q\nAub3QaVxrmnsrwb50MWJeiXelhmk2BrTGgdQ2VkqdoB6r31hGiZGQS8QmM9plDnW0+93iCiHPu6R\njUBxMTXQnshJdxV9FZ/vxaH3Tw+RFQzAX5QIBo954mFDh1QbelkJCQkJTw/ppZiQkJAAbDR9PlZj\nwVlX4GeHB079iue5TdNaLBzAEXkJW6obFSyvENY8mZ/tvFwjCPnGyqnKYEhLLVDCKGmW21AFUYHk\nCupllBS8oWyWWNbAb3O9jZxoQRVfuy9PWYIVtBfBu6RV2RYCw/E4YXOFLSieaC9V8Vnozt9DKdFd\nlH2l4rpqvW9n6MJbqLkzhhR/76G3sT5wGq5tX19Ep/Aa97l97Nbx8uh5buFpDZ6hvk9HrKZOzXeR\nuM6aKlT8LSrue4hizzFOmSHqv0Gd3gq24n3muDOYGup/AXsxiPKa97pjliN/DqwHZ3j0YYlEhhVr\nvmwg0kgxISEhAUgvxYSEhARgY+lzo6CDyNUa2D71zvkdKBBIvbaGYl5vAaV6MPP1B7mreRPQUQNn\nqkdO90YDz7Euek6ximtQRWPBIIOrN5ihwDCVbzvFXHzI6V6GvNka1OcI7tA1amJuNd1xMgRPUylu\n9pxqVgtXqAcQdqux98s2bMNZrGlx4DssMjhoT7rjc5rgRHAwIgEC7lUGp/AdlAq1PlTcLZQYRe53\nCRf0UbSRsz4Ctu9BaVjYv+UImB/0vJ9LBDsXiCYozI9z4+YN337ZbVNiGmUOlb9YefuG6Ixqz9sY\nZj4fcYR9M1wH86ZXUNHX924x8T6fB78nfUQZFIgv78XjnW3g9+xHGikmJCQkAOmlmJCQkABsLH22\nNiifdbTxkaUrjld6Tl8HsKOqEYRdxKhUxA6r97AfY+8+V6qvz12JrnacPvVQOCpDTiwDpnOcIIxQ\nm/pmpGqgugVsuQagUlnp7Z7f69fWQiEtob5f6vs2Uyih02mnxO+M/fMG/Gh5cLYtV4ZccrozM9h3\nhX2FPN8BKP46HjsgYP3GLmoNg9YjPVj5EHntVNwP/Tg2Rg6vOa1sWijBNyDLRgwncCzfgsqLyALD\nvZ0gWqFE4agK0xe0lztqu31vtX6fhYDtLSjYLaY6GAmAemoaIxieUz+kxLMeKHl0564OvR92j5Az\nDVwf+HWW2bpe9WYijRQTEhISgPRSTEhISAA2lj5nlmmr7OjELizAIFBqFpy2hH2nEHWvo15HPScI\nw+chABnBy3s9D+ot5qiZi9rEAjW/0bhyGxiEveu0Kov8EHHBCqCyqC2lqmAgt9PHBiqj8bcPDJNK\n76WsC0Kfr5wmDeFCPa5QRxh0/P9v71xCLLuqMPyv+6x7q1K3q7o7TUiiiRgiPTEREUURSRB8hOhA\nJEEhSCAOHCSgaOJciBMfAxFComQgamgVxYEgMQNHQU0EMTEYgyFJ59Hp7uqqurfqvs5ycPbt/aXt\nPDpUquqeXh80fc6pU+fux6l999pr7fVTuApxz6qtwK6DKT+lMNQl8PRul+brAnSJO9uoDwKWxwyw\nRkbyKbS7R8hIvTrOn9OFicuE1NNOWcYWRJ4MZrJwPGZQOyIbJgatb+w3ri1AMxxpxDqpv2yQnzGA\nl32IqAXSOpXf2/HhvATRhaY59cVHeL7V4cVO6eIWhsjejX3l3swN1HMKq5V1qFU083bMFIMgCEAM\nikEQBKCy5rMpC0bRc1fAlHWYdVrMJs4k7RVtI2P3kBLAEHxaHSKQFnuch9jvW8ChV6vnkylMpclp\nBG+vlmYQLDAVyNg9Guc6nIFA1tIU5ij0rSmARYoOzJ+kcdzYxB5XiEI1kApryo3NB3N9lhCwvAmP\n86SFLOTwyiOmWT4LWN9E9mwUjx7cYpTrP0SQ9plubsMVR0B0M/dzG9nBmd5rnMrYX8rtZnRzw2s+\nQdvWEWBf70IUDMsntUFui5XlA2ePG8n8XB8hMBzB4NxiXGD5pMCyB3W6C3iRx0hp1sBGgjr6t54+\nf9DLP2+jTRaHrzNnStEHtaKa/ud9NVM0s2vN7O/4t25md5nZqpn90cz+nf5fefOnBUEQXDj7aqbo\n7k9Juk6SzKwu6QVJv5F0t6SH3f1eM7s7nX/rDZ9VuKZb5bflAN+sTSykL1me2bQvgYjnLEEsvnl9\nA4vhiHtjhpFiJX/H9PHtb8hS0rL8TTzawrc5YtJs9P9ZSArMWlqNfO8ytrw1MLVqo2sbqP8IToJF\nOB26Kfnp6SI7goaINZwY4jjxmUKmG26n7CBp76u1PIOrwUkyi5OTpI30/FE9z+RWJ3m210DcXdHB\n7BTyrb0txBVu5M/cOowZ/AJmjeiXmfOAWWroUJpgeryNmedrNH/gOFts5PdiSznGtT/BbC49p7mc\n3z3O1PrQomHf2sF8P8u4Psp9V4cV0oTDRAiJ9GQddbA9kZO/DeztqyPusjMTprFwtOw2N0r6j7s/\nK+lzkh5M1x+U9Pk9K1UQBJVmX80Uz+EWST9Px0fc/cV0/JKkI+f7BTO7Q9Id0jlC4kEQBG+RfTko\nmllL0s2S7jn3Z+7uZufPz+Hu90m6T5J6vZ5PzuqUwKyBWecIA5syJCw9voFF9BpNLSz6T2COjZEI\n9Uwtm0k9bC1rrsPRg8/c9Hx/e1xO4JvQz2TxJjClG+38k0UsB4wgt8rPYeJabksbpMw3jJekAmkT\nzpWt7WyDnVnMdV4WEvViW+KhRjb3+tA34Xa5g5PynjWYmqMpzDds82tiqaFb5FeYErNwoWl1G0sT\nMMOZLLZI16fIKNNHPOoUT5xA1nZhCvO9DTO4na873gsuTywm6dcGs9gMITvL1DTQi2nj/bNxfja3\nbRZY4ljZRiafJSQWTu1bxzJGp4U4Trw3p6bZWbTcKt+haS0cLbvJpyU95u4vp/OXzewySUr/v7Jn\nJQuCoNLs10HxVmXTWZJ+J+m2dHybpN/ueomCILgo2Hfms5ktSvqkpK/i8r2SHjKz2yU9K+mLb/ac\nwl2D5OlrNpvnvYdbsTa3s17LQmoWg2dx+hqDLDPsZ1OS254O0fsLK8MRD6ntbO614cWemZXj8fnN\nJKcJiLJMDGVswfbBTQN4NE/Xsyl3pF6aRPQ4dyDNuT2BNGYv12Gpkeu5gaw/C0zai4Sn3SmyzUBu\ndupl2Q8X2bwfIzPN6PWWiJF1iFl96ihXB2sj9G4zye7MFBxDZ6UJudNaHwlph7lBOyt4BuIx15Sf\n04H06hDvnKfExTT764gU6PXz57+I+MlOA0scXejPYA9rm1tO8b7U0F6TaVnGcSc3bgfbEBtYvuii\nLU5M1svfL6qp1bLvBkV370s6eM61kyq90UEQBO8o+9V8DoIg2BP23Uxxp6i51ElmThfVbEF3gtuo\nYFVppGQ2IOHpGFvLnMHgMA2thy18TWxtYzA2jhs9SFtShjWZLTTYe818bxfBy8ePH89l6WbvZ3Mx\nm0QDBF4PkRS1O4Tpu1SWq0FzGKb2esFEuag/srFMurktKF9KfRsGso8QBD9JAfN2Mpvpp4fZ47mF\nwOglZHVhwtVLHNopS7ktTtZgnuL+JqRSF5LHv4VErUNkDOqi/cUVEJiQJ/snzx4vN/Pnj6FFw+jo\nSXpHagzcR6afKbbnHYEyq1axBNFBID+2c46n2XwfQLulNcZaSlpiMSx1FEx8jPd/qZtN9mKj7M+6\nR/B2EARB5YlBMQiCAFTWfDYztZLXuQ73K/cQb8F+3oap0EwaKEMkKrVaNlPW1rOU6AQBtk14P88o\nX19msDECwpub2ayZeV8laTNl4WljV860Dm8urLFLD1969pg6Kgy2LiDr2XJ4FyGhuTbbKwz5Tgas\nr47y9TqkWSmfOd7Ipm8L9TRkmHHUUzAPi6TvUmMwOhLYDuF9ppd5AWHtDtO0P8xlYfQBMx9tw7s6\nE+SpZ4tZTSwHbEHi1JCQuNXLZvJl3V4uy1Z+9iY80e0FRBmk4g6Q2HW9yQS2WKbAcszKAG04yM9m\nYHoL7VhbXjp73O/nd6RIfxd19DP3slMmle9n70BZzzqT8FaImCkGQRCAGBSDIAiAUZaySpjZwWOn\n3QAAAulJREFUCUl9Sa/udVl2gUOKelaJeannu9398F4XYqep7KAoSWb2V3f/4F6X450m6lktLpZ6\n7lfCfA6CIAAxKAZBEICqD4r37XUBdomoZ7W4WOq5L6n0mmIQBMGFUvWZYhAEwQURg2IQBAGo7KBo\nZp8ys6fM7Okki1oJzOxKM3vEzJ4ws3+a2Z3peuW0sc2sbmaPm9nv0/nVZvZo6tNfJi2fucfMDpjZ\nMTP7l5k9aWYfqWJ/zguVHBSTZvSPVGq9HJV0q5kd3dtS7RgTSV9396OSPizpa6luM23sayQ9nM7n\nnTslPYnz70r6vru/V9JpSbfvSal2nh9K+oO7v0/S+1XWuYr9ORdUclCU9CFJT7v7M+4+kvQLldrR\nc4+7v+juj6XjDZV/QJerYtrYZnaFpM9Kuj+dm6QbJB1Lt8x9HSXJzHqSPi7pAUly95G7r6li/TlP\nVHVQvFzSczh/Pl2rFGZ2laTrJT2qt6iNPUf8QNI3lXPtHpS05u6zlDJV6dOrJZ2Q9NO0VHB/0imq\nWn/ODVUdFCuPmS1J+pWku9x9nT/zMs5qbmOtzOwmSa+4+9/2uiy7QEPSByT92N2vV7lf/zWm8rz3\n57xR1UHxBUlX4vyKdK0SmFlT5YD4M3f/dbpcJW3sj0q62cz+q3Lp4waV624HzGyWELEqffq8pOfd\n/dF0fkzlIFml/pwrqjoo/kXSNclb2ZJ0i0rt6Lknra09IOlJd/8eflQZbWx3v8fdr3D3q1T23Z/c\n/UuSHpH0hXTbXNdxhru/JOk5M7s2XbpR0hOqUH/OG5Xd0WJmn1G5LlWX9BN3/84eF2lHMLOPSfqz\npH8or7d9W+W64kOS3qWkje3up/akkDuImX1C0jfc/SYze4/KmeOqpMclfdndh2/0+/OAmV2n0qHU\nkvSMpK+onLBUrj/ngcoOikEQBG+HqprPQRAEb4sYFIMgCEAMikEQBCAGxSAIAhCDYhAEAYhBMQiC\nAMSgGARBAP4H7N4d+PBccYIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f271d8adac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[11], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[167])+ \",\"+\" iceberg_probability : \"+str(pred_valid[11])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f271d83e8d0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUUAAAEICAYAAADIsubvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJIK8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f271d81d3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[12], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[195])+ \",\"+\" iceberg_probability : \"+str(pred_valid[12])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f271d7a61d0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUQAAAEICAYAAAAncI3RAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n3CA6juME3CA6juME/gW1I106bE9ivgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f271d7f3780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[13], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[199])+ \",\"+\" iceberg_probability : \"+str(pred_valid[13])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f271d706be0>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWMAAAEICAYAAACK8ZV4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\njEsGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f271d7570b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(X_holdout2[4], cmap='gray', interpolation='nearest');\n",
    "tl = \"Actual label : \" +str(Y_holdout[44])+ \",\"+\" iceberg_probability : \"+str(pred_valid[4])\n",
    "plt.title(tl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}