MS from scratch.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For MS training we have 3 datasets: train, validation and holdout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import nibabel as nib\n",
    "\n",
    "from scipy import interp\n",
    "from sklearn.utils import shuffle\n",
    "from sklearn.model_selection import GroupShuffleSplit\n",
    "from sklearn.metrics import confusion_matrix, roc_auc_score, roc_curve, auc\n",
    "from sklearn.model_selection import KFold\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import time\n",
    "import h5py\n",
    "\n",
    "# keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Input, Conv3D, Flatten, Dense, MaxPooling3D, Dropout\n",
    "from keras.regularizers import l2\n",
    "from keras.optimizers import Adam\n",
    "from keras.models import load_model\n",
    "from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
    "from keras.utils import Sequence\n",
    "import keras.backend as K\n",
    "\n",
    "from config import *\n",
    "from utils import specificity, sensitivity, balanced_accuracy, IntensityRescale, sagittal_flip, translate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Start timing\n",
    "start_time = time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from keras.backend.tensorflow_backend import set_session\n",
    "\n",
    "config = tf.ConfigProto()\n",
    "config.gpu_options.allow_growth = True\n",
    "config.gpu_options.visible_device_list = \"6\"\n",
    "set_session(tf.Session(config=config))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "zero_one_normalize = True\n",
    "dtype = np.float32\n",
    "result_dir = \"/analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/MS_from_scratch\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "!mkdir -p $result_dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# load hdf5 files and extract columns\n",
    "train_h5 = h5py.File('/analysis/share/Ritter/MS/CIS/train_dataset.h5', 'r')\n",
    "holdout_h5 = h5py.File('/analysis/share/Ritter/MS/CIS/holdout_dataset.h5', 'r')\n",
    "\n",
    "X_train, y_train = train_h5['X'], train_h5['y']\n",
    "X_holdout, y_holdout = holdout_h5['X'], holdout_h5['y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# convert data to numpy arrays\n",
    "X_train = np.array(X_train, dtype=dtype)\n",
    "y_train = np.array(y_train)\n",
    "\n",
    "X_holdout = np.array(X_holdout, dtype=dtype)\n",
    "y_holdout = np.array(y_holdout)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total datset length: 124\n",
      "Number of healthy controls: 61\n",
      "Number of MS patients: 63\n"
     ]
    }
   ],
   "source": [
    "print(\"Total datset length: {}\".format(len(y_train)))\n",
    "print(\"Number of healthy controls: {}\".format(len(np.array(y_train)[np.array(y_train)==0.])))\n",
    "print(\"Number of MS patients: {}\".format(len(np.array(y_train)[np.array(y_train)==1.])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class CISDataset(Sequence):\n",
    "    def __init__(self, X, y, transform=None, batch_size=4, z_factor=None, shuffle=True, mask=None):\n",
    "        self.X = X\n",
    "        self.y = y\n",
    "        self.transform = transform\n",
    "        self.batch_size = batch_size\n",
    "        self.z_factor = z_factor\n",
    "        self.shuffle = shuffle\n",
    "        self.mask = mask\n",
    "        \n",
    "    def __len__(self):\n",
    "        return int(np.ceil(len(self.y)/self.batch_size))\n",
    "    \n",
    "    def __getitem__(self, idx):\n",
    "        # add BET\n",
    "        image = np.expand_dims(self.X[idx * self.batch_size:(idx + 1) * self.batch_size],5)\n",
    "        #label = np.array((batch_idx['label'] == \"MS\")* 1, dtype=np.int8) \n",
    "        label = np.array(self.y[idx * self.batch_size:(idx + 1) * self.batch_size], dtype=np.int8)\n",
    "        \n",
    "        if self.mask is not None:\n",
    "            for i in range(image.shape[0]):\n",
    "                image[i] *= self.mask\n",
    "        \n",
    "        for transformation in self.transform:\n",
    "            image = transformation(image)\n",
    "        \n",
    "        return image, label\n",
    "    \n",
    "    def on_epoch_end(self):\n",
    "        if self.shuffle:\n",
    "            self.X, self.y = shuffle(self.X, self.y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mask = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "intensity = IntensityRescale(masked=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "if zero_one_normalize:\n",
    "    cis_data = CISDataset(X_train, y_train, transform=[intensity], batch_size=4)\n",
    "else:\n",
    "    cis_data = CISDataset(X_train, y_train, batch_size=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/fabiane/anaconda2/envs/postal/lib/python3.6/site-packages/ipykernel_launcher.py:16: DeprecationWarning: Both axis > a.ndim and axis < -a.ndim - 1 are deprecated and will raise an AxisError in the future.\n",
      "  app.launch_new_instance()\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.title(\"Normalized between zero and 1\")\n",
    "plt.hist(cis_data[4][0][0].flatten())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/fabiane/anaconda2/envs/postal/lib/python3.6/site-packages/ipykernel_launcher.py:16: DeprecationWarning: Both axis > a.ndim and axis < -a.ndim - 1 are deprecated and will raise an AxisError in the future.\n",
      "  app.launch_new_instance()\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.title(\"Scan image\")\n",
    "plt.imshow(np.squeeze(cis_data[4][0][0])[:,:,42], cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def SixtyFourNet(input_shape, drop_rate=0., weight_dcay=0.):\n",
    "    model = Sequential()\n",
    "    model.add(Conv3D(64, kernel_size=3, \n",
    "                     input_shape=(input_shape[1], input_shape[2], input_shape[3], 1), \n",
    "                     activation='elu', padding='valid', name='Conv_1'))\n",
    "    model.add(MaxPooling3D(pool_size=3, name='Pool_1'))\n",
    "    model.add(Dropout(drop_rate))\n",
    "    model.add(Conv3D(64, kernel_size=3, padding='valid', activation='elu', name='Conv_2'))\n",
    "\n",
    "    model.add(MaxPooling3D(pool_size=3, name='Pool_2'))\n",
    "    model.add(Dropout(drop_rate))\n",
    "\n",
    "    model.add(Conv3D(64, kernel_size=3, padding='valid', activation='elu', name='Conv_3',\n",
    "                     kernel_regularizer=l2(weight_dcay)))\n",
    "    model.add(Conv3D(64, kernel_size=3, padding='valid', activation='elu', name='Conv_4',\n",
    "                     kernel_regularizer=l2(weight_dcay)))\n",
    "    model.add(MaxPooling3D(pool_size=3, name='Pool_4'))\n",
    "    model.add(Dropout(drop_rate))\n",
    "\n",
    "    model.add(Flatten())\n",
    "    model.add(Dense(units=1, activation='sigmoid'))\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(124, 96, 114, 96)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# split training set\n",
    "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.15, random_state=77)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trial 0\n",
      "Epoch 1/200\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/fabiane/anaconda2/envs/postal/lib/python3.6/site-packages/ipykernel_launcher.py:16: DeprecationWarning: Both axis > a.ndim and axis < -a.ndim - 1 are deprecated and will raise an AxisError in the future.\n",
      "  app.launch_new_instance()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27/27 [==============================] - 8s 300ms/step - loss: 1.9472 - acc: 0.4634 - val_loss: 1.8260 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00001: val_acc improved from -inf to 0.68421, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/MS_from_scratch/weights-augm-trial0-improvement-BEST.hdf5\n",
      "Epoch 2/200\n",
      "27/27 [==============================] - 4s 150ms/step - loss: 1.7913 - acc: 0.5273 - val_loss: 1.7032 - val_acc: 0.5789\n",
      "\n",
      "Epoch 00002: val_acc did not improve from 0.68421\n",
      "Epoch 3/200\n",
      "27/27 [==============================] - 4s 164ms/step - loss: 1.6336 - acc: 0.5366 - val_loss: 1.6979 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00003: val_acc did not improve from 0.68421\n",
      "Epoch 4/200\n",
      "27/27 [==============================] - 4s 159ms/step - loss: 1.5152 - acc: 0.5932 - val_loss: 1.4125 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00004: val_acc did not improve from 0.68421\n",
      "Epoch 5/200\n",
      "27/27 [==============================] - 4s 160ms/step - loss: 1.4574 - acc: 0.5469 - val_loss: 1.3472 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00005: val_acc did not improve from 0.68421\n",
      "Epoch 6/200\n",
      "27/27 [==============================] - 4s 165ms/step - loss: 1.3807 - acc: 0.5366 - val_loss: 1.3151 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00006: val_acc did not improve from 0.68421\n",
      "Epoch 7/200\n",
      "27/27 [==============================] - 4s 155ms/step - loss: 1.2789 - acc: 0.5561 - val_loss: 1.1981 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00007: val_acc improved from 0.68421 to 0.78947, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/MS_from_scratch/weights-augm-trial0-improvement-BEST.hdf5\n",
      "Epoch 8/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 1.2146 - acc: 0.5747 - val_loss: 1.1530 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00008: val_acc did not improve from 0.78947\n",
      "Epoch 9/200\n",
      "27/27 [==============================] - 5s 168ms/step - loss: 1.1658 - acc: 0.5088 - val_loss: 1.0570 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00009: val_acc did not improve from 0.78947\n",
      "Epoch 10/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 1.0851 - acc: 0.6014 - val_loss: 1.1221 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00010: val_acc did not improve from 0.78947\n",
      "Epoch 11/200\n",
      "27/27 [==============================] - 5s 170ms/step - loss: 1.0535 - acc: 0.5376 - val_loss: 0.9445 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00011: val_acc improved from 0.78947 to 0.84211, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/MS_from_scratch/weights-augm-trial0-improvement-BEST.hdf5\n",
      "Epoch 12/200\n",
      "27/27 [==============================] - 5s 186ms/step - loss: 1.0520 - acc: 0.5190 - val_loss: 1.1214 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00012: val_acc did not improve from 0.84211\n",
      "Epoch 13/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 1.0069 - acc: 0.5190 - val_loss: 1.0230 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00013: val_acc did not improve from 0.84211\n",
      "Epoch 14/200\n",
      "27/27 [==============================] - 4s 164ms/step - loss: 0.9862 - acc: 0.4912 - val_loss: 0.9325 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00014: val_acc did not improve from 0.84211\n",
      "Epoch 15/200\n",
      "27/27 [==============================] - 4s 164ms/step - loss: 0.9081 - acc: 0.5736 - val_loss: 0.8229 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00015: val_acc did not improve from 0.84211\n",
      "Epoch 16/200\n",
      "27/27 [==============================] - 5s 176ms/step - loss: 0.9240 - acc: 0.6293 - val_loss: 1.2353 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00016: val_acc did not improve from 0.84211\n",
      "Epoch 17/200\n",
      "27/27 [==============================] - 4s 160ms/step - loss: 0.9228 - acc: 0.5283 - val_loss: 0.8909 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00017: val_acc did not improve from 0.84211\n",
      "Epoch 18/200\n",
      "27/27 [==============================] - 5s 170ms/step - loss: 0.8756 - acc: 0.5747 - val_loss: 0.8317 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00018: val_acc did not improve from 0.84211\n",
      "Epoch 19/200\n",
      "27/27 [==============================] - 5s 170ms/step - loss: 0.8356 - acc: 0.6210 - val_loss: 0.7285 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00019: val_acc did not improve from 0.84211\n",
      "Epoch 20/200\n",
      "27/27 [==============================] - 5s 176ms/step - loss: 0.8039 - acc: 0.6210 - val_loss: 1.1688 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00020: val_acc did not improve from 0.84211\n",
      "Epoch 21/200\n",
      "27/27 [==============================] - 4s 165ms/step - loss: 0.7810 - acc: 0.6478 - val_loss: 0.6856 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00021: val_acc did not improve from 0.84211\n",
      "Epoch 22/200\n",
      "27/27 [==============================] - 4s 166ms/step - loss: 0.7311 - acc: 0.6756 - val_loss: 0.6755 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00022: val_acc did not improve from 0.84211\n",
      "Epoch 23/200\n",
      "27/27 [==============================] - 5s 181ms/step - loss: 0.8193 - acc: 0.6385 - val_loss: 0.8280 - val_acc: 0.4737\n",
      "\n",
      "Epoch 00023: val_acc did not improve from 0.84211\n",
      "Epoch 24/200\n",
      "27/27 [==============================] - 5s 172ms/step - loss: 0.7786 - acc: 0.6849 - val_loss: 0.6942 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00024: val_acc did not improve from 0.84211\n",
      "Epoch 25/200\n",
      "27/27 [==============================] - 4s 165ms/step - loss: 0.7320 - acc: 0.6849 - val_loss: 0.6252 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00025: val_acc did not improve from 0.84211\n",
      "Epoch 26/200\n",
      "27/27 [==============================] - 4s 167ms/step - loss: 0.7270 - acc: 0.6385 - val_loss: 0.6284 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00026: val_acc did not improve from 0.84211\n",
      "Epoch 27/200\n",
      "27/27 [==============================] - 5s 179ms/step - loss: 0.7121 - acc: 0.6581 - val_loss: 0.6149 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00027: val_acc did not improve from 0.84211\n",
      "Epoch 28/200\n",
      "27/27 [==============================] - 4s 166ms/step - loss: 0.6687 - acc: 0.6663 - val_loss: 0.5849 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00028: val_acc did not improve from 0.84211\n",
      "Epoch 29/200\n",
      "27/27 [==============================] - 5s 172ms/step - loss: 0.6699 - acc: 0.6571 - val_loss: 0.6538 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00029: val_acc did not improve from 0.84211\n",
      "Epoch 30/200\n",
      "27/27 [==============================] - 4s 159ms/step - loss: 0.6465 - acc: 0.6673 - val_loss: 0.5707 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00030: val_acc did not improve from 0.84211\n",
      "Epoch 31/200\n",
      "27/27 [==============================] - 5s 172ms/step - loss: 0.6181 - acc: 0.6941 - val_loss: 0.5869 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00031: val_acc did not improve from 0.84211\n",
      "Epoch 32/200\n",
      "27/27 [==============================] - 4s 161ms/step - loss: 0.6830 - acc: 0.6849 - val_loss: 0.5744 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00032: val_acc did not improve from 0.84211\n",
      "Epoch 33/200\n",
      "27/27 [==============================] - 5s 167ms/step - loss: 0.6633 - acc: 0.7219 - val_loss: 0.5899 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00033: val_acc did not improve from 0.84211\n",
      "Epoch 34/200\n",
      "27/27 [==============================] - 4s 153ms/step - loss: 0.6546 - acc: 0.7219 - val_loss: 0.6688 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00034: val_acc did not improve from 0.84211\n",
      "Epoch 35/200\n",
      "27/27 [==============================] - 5s 171ms/step - loss: 0.6207 - acc: 0.7312 - val_loss: 0.7208 - val_acc: 0.5789\n",
      "\n",
      "Epoch 00035: val_acc did not improve from 0.84211\n",
      "Epoch 36/200\n",
      "27/27 [==============================] - 5s 178ms/step - loss: 0.6502 - acc: 0.7034 - val_loss: 1.1227 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00036: val_acc did not improve from 0.84211\n",
      "Epoch 37/200\n",
      "27/27 [==============================] - 4s 157ms/step - loss: 0.7164 - acc: 0.6571 - val_loss: 0.6487 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00037: val_acc did not improve from 0.84211\n",
      "Epoch 38/200\n",
      "27/27 [==============================] - 4s 165ms/step - loss: 0.6331 - acc: 0.7127 - val_loss: 0.5501 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00038: val_acc did not improve from 0.84211\n",
      "Epoch 39/200\n",
      "27/27 [==============================] - 4s 156ms/step - loss: 0.6062 - acc: 0.7219 - val_loss: 0.6058 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00039: val_acc did not improve from 0.84211\n",
      "Epoch 40/200\n",
      "27/27 [==============================] - 5s 167ms/step - loss: 0.5913 - acc: 0.7415 - val_loss: 0.5359 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00040: val_acc did not improve from 0.84211\n",
      "Epoch 41/200\n",
      "27/27 [==============================] - 4s 161ms/step - loss: 0.6452 - acc: 0.6673 - val_loss: 0.5791 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00041: val_acc did not improve from 0.84211\n",
      "Epoch 42/200\n",
      "27/27 [==============================] - 4s 161ms/step - loss: 0.5678 - acc: 0.7590 - val_loss: 0.5333 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00042: val_acc did not improve from 0.84211\n",
      "Epoch 43/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27/27 [==============================] - 5s 168ms/step - loss: 0.5500 - acc: 0.7405 - val_loss: 0.5323 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00043: val_acc did not improve from 0.84211\n",
      "Epoch 44/200\n",
      "27/27 [==============================] - 4s 156ms/step - loss: 0.5761 - acc: 0.7127 - val_loss: 0.6518 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00044: val_acc did not improve from 0.84211\n",
      "Epoch 45/200\n",
      "27/27 [==============================] - 4s 165ms/step - loss: 0.5710 - acc: 0.7683 - val_loss: 0.5300 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00045: val_acc did not improve from 0.84211\n",
      "Epoch 46/200\n",
      "27/27 [==============================] - 5s 172ms/step - loss: 0.6191 - acc: 0.6488 - val_loss: 0.5889 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00046: val_acc did not improve from 0.84211\n",
      "Epoch 47/200\n",
      "27/27 [==============================] - 5s 170ms/step - loss: 0.5572 - acc: 0.7868 - val_loss: 0.5149 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00047: val_acc did not improve from 0.84211\n",
      "Epoch 48/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 0.5627 - acc: 0.7776 - val_loss: 0.6740 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00048: val_acc did not improve from 0.84211\n",
      "Epoch 49/200\n",
      "27/27 [==============================] - 4s 166ms/step - loss: 0.6357 - acc: 0.6488 - val_loss: 0.8121 - val_acc: 0.4211\n",
      "\n",
      "Epoch 00049: val_acc did not improve from 0.84211\n",
      "Epoch 50/200\n",
      "27/27 [==============================] - 4s 156ms/step - loss: 0.5427 - acc: 0.7590 - val_loss: 0.5854 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00050: val_acc did not improve from 0.84211\n",
      "Epoch 51/200\n",
      "27/27 [==============================] - 5s 170ms/step - loss: 0.6386 - acc: 0.7230 - val_loss: 0.7321 - val_acc: 0.5789\n",
      "\n",
      "Epoch 00051: val_acc did not improve from 0.84211\n",
      "Epoch 52/200\n",
      "27/27 [==============================] - 5s 188ms/step - loss: 0.5464 - acc: 0.7590 - val_loss: 0.7151 - val_acc: 0.5789\n",
      "\n",
      "Epoch 00052: val_acc did not improve from 0.84211\n",
      "Epoch 53/200\n",
      "27/27 [==============================] - 5s 172ms/step - loss: 0.5491 - acc: 0.7683 - val_loss: 0.5808 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00053: val_acc did not improve from 0.84211\n",
      "Epoch 54/200\n",
      "27/27 [==============================] - 5s 178ms/step - loss: 0.5722 - acc: 0.7322 - val_loss: 0.8967 - val_acc: 0.4737\n",
      "\n",
      "Epoch 00054: val_acc did not improve from 0.84211\n",
      "Epoch 55/200\n",
      "27/27 [==============================] - 5s 175ms/step - loss: 0.6328 - acc: 0.7044 - val_loss: 0.6180 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00055: val_acc did not improve from 0.84211\n",
      "Epoch 56/200\n",
      "27/27 [==============================] - 5s 167ms/step - loss: 0.5484 - acc: 0.7683 - val_loss: 0.5454 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00056: val_acc did not improve from 0.84211\n",
      "Epoch 57/200\n",
      "27/27 [==============================] - 4s 167ms/step - loss: 0.5435 - acc: 0.7961 - val_loss: 0.5167 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00057: val_acc did not improve from 0.84211\n",
      "Epoch 58/200\n",
      "27/27 [==============================] - 5s 171ms/step - loss: 0.5528 - acc: 0.7508 - val_loss: 0.5711 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00058: val_acc did not improve from 0.84211\n",
      "Epoch 59/200\n",
      "27/27 [==============================] - 5s 178ms/step - loss: 0.5575 - acc: 0.7683 - val_loss: 0.5324 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00059: val_acc did not improve from 0.84211\n",
      "Epoch 60/200\n",
      "27/27 [==============================] - 4s 166ms/step - loss: 0.5125 - acc: 0.7590 - val_loss: 0.5088 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00060: val_acc did not improve from 0.84211\n",
      "Epoch 61/200\n",
      "27/27 [==============================] - 5s 171ms/step - loss: 0.5388 - acc: 0.7405 - val_loss: 0.6534 - val_acc: 0.5789\n",
      "\n",
      "Epoch 00061: val_acc did not improve from 0.84211\n",
      "Epoch 62/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 0.4847 - acc: 0.7776 - val_loss: 0.5225 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00062: val_acc did not improve from 0.84211\n",
      "Epoch 63/200\n",
      "27/27 [==============================] - 4s 163ms/step - loss: 0.5365 - acc: 0.8239 - val_loss: 0.5030 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00063: val_acc did not improve from 0.84211\n",
      "Epoch 64/200\n",
      "27/27 [==============================] - 5s 169ms/step - loss: 0.5301 - acc: 0.7776 - val_loss: 0.5073 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00064: val_acc did not improve from 0.84211\n",
      "Epoch 65/200\n",
      "27/27 [==============================] - 4s 161ms/step - loss: 0.4992 - acc: 0.8239 - val_loss: 0.6592 - val_acc: 0.5789\n",
      "\n",
      "Epoch 00065: val_acc did not improve from 0.84211\n",
      "Epoch 66/200\n",
      "27/27 [==============================] - 4s 165ms/step - loss: 0.4532 - acc: 0.8610 - val_loss: 0.6996 - val_acc: 0.5789\n",
      "\n",
      "Epoch 00066: val_acc did not improve from 0.84211\n",
      "Epoch 67/200\n",
      "27/27 [==============================] - 4s 164ms/step - loss: 0.5133 - acc: 0.7868 - val_loss: 0.5161 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00067: val_acc did not improve from 0.84211\n",
      "Epoch 68/200\n",
      "27/27 [==============================] - 4s 165ms/step - loss: 0.5565 - acc: 0.7600 - val_loss: 0.5112 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00068: val_acc did not improve from 0.84211\n",
      "Epoch 69/200\n",
      "27/27 [==============================] - 4s 163ms/step - loss: 0.5032 - acc: 0.8239 - val_loss: 0.5649 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00069: val_acc did not improve from 0.84211\n",
      "Epoch 70/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 0.5039 - acc: 0.7776 - val_loss: 0.5660 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00070: val_acc did not improve from 0.84211\n",
      "Epoch 71/200\n",
      "27/27 [==============================] - 4s 153ms/step - loss: 0.4668 - acc: 0.8239 - val_loss: 0.4942 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00071: val_acc did not improve from 0.84211\n",
      "Epoch 72/200\n",
      "27/27 [==============================] - 4s 166ms/step - loss: 0.4567 - acc: 0.8610 - val_loss: 0.4959 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00072: val_acc did not improve from 0.84211\n",
      "Epoch 73/200\n",
      "27/27 [==============================] - 4s 155ms/step - loss: 0.4898 - acc: 0.8054 - val_loss: 0.4964 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00073: val_acc did not improve from 0.84211\n",
      "Epoch 74/200\n",
      "27/27 [==============================] - 4s 159ms/step - loss: 0.4939 - acc: 0.7961 - val_loss: 0.4971 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00074: val_acc did not improve from 0.84211\n",
      "Epoch 75/200\n",
      "27/27 [==============================] - 4s 161ms/step - loss: 0.4690 - acc: 0.8239 - val_loss: 0.4933 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00075: val_acc did not improve from 0.84211\n",
      "Epoch 76/200\n",
      "27/27 [==============================] - 5s 178ms/step - loss: 0.4307 - acc: 0.8424 - val_loss: 0.5493 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00076: val_acc did not improve from 0.84211\n",
      "Epoch 77/200\n",
      "27/27 [==============================] - 4s 152ms/step - loss: 0.4114 - acc: 0.8610 - val_loss: 0.5308 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00077: val_acc did not improve from 0.84211\n",
      "Epoch 78/200\n",
      "27/27 [==============================] - 5s 173ms/step - loss: 0.4340 - acc: 0.8517 - val_loss: 0.4945 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00078: val_acc did not improve from 0.84211\n",
      "Epoch 79/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 0.4289 - acc: 0.8332 - val_loss: 0.6072 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00079: val_acc did not improve from 0.84211\n",
      "Epoch 80/200\n",
      "27/27 [==============================] - 4s 155ms/step - loss: 0.4171 - acc: 0.8610 - val_loss: 0.5092 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00080: val_acc did not improve from 0.84211\n",
      "Epoch 81/200\n",
      "27/27 [==============================] - 4s 154ms/step - loss: 0.4209 - acc: 0.8610 - val_loss: 0.4855 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00081: val_acc did not improve from 0.84211\n",
      "Epoch 82/200\n",
      "27/27 [==============================] - 4s 156ms/step - loss: 0.4330 - acc: 0.8239 - val_loss: 0.5073 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00082: val_acc improved from 0.84211 to 0.84211, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/MS_from_scratch/weights-augm-trial0-improvement-BEST.hdf5\n",
      "Epoch 83/200\n",
      "27/27 [==============================] - 4s 161ms/step - loss: 0.4045 - acc: 0.8888 - val_loss: 0.4918 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00083: val_acc did not improve from 0.84211\n",
      "Epoch 84/200\n",
      "27/27 [==============================] - 4s 158ms/step - loss: 0.4370 - acc: 0.8332 - val_loss: 0.5791 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00084: val_acc did not improve from 0.84211\n",
      "Epoch 85/200\n",
      "27/27 [==============================] - 5s 172ms/step - loss: 0.5437 - acc: 0.8342 - val_loss: 0.5314 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00085: val_acc did not improve from 0.84211\n",
      "Epoch 86/200\n",
      "27/27 [==============================] - 4s 167ms/step - loss: 0.4047 - acc: 0.9073 - val_loss: 0.6842 - val_acc: 0.5263\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 00086: val_acc did not improve from 0.84211\n",
      "Epoch 87/200\n",
      "27/27 [==============================] - 4s 151ms/step - loss: 0.3872 - acc: 0.8888 - val_loss: 0.5062 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00087: val_acc did not improve from 0.84211\n",
      "Epoch 88/200\n",
      "27/27 [==============================] - 4s 159ms/step - loss: 0.4186 - acc: 0.8332 - val_loss: 0.5015 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00088: val_acc did not improve from 0.84211\n",
      "Epoch 89/200\n",
      "27/27 [==============================] - 4s 166ms/step - loss: 0.3827 - acc: 0.8795 - val_loss: 0.4961 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00089: val_acc did not improve from 0.84211\n",
      "Epoch 90/200\n",
      "27/27 [==============================] - 5s 167ms/step - loss: 0.3791 - acc: 0.8610 - val_loss: 0.5047 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00090: val_acc did not improve from 0.84211\n",
      "Epoch 91/200\n",
      "27/27 [==============================] - 5s 170ms/step - loss: 0.3958 - acc: 0.8795 - val_loss: 0.4915 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00091: val_acc did not improve from 0.84211\n",
      "Epoch 92/200\n",
      "27/27 [==============================] - 5s 180ms/step - loss: 0.3707 - acc: 0.8702 - val_loss: 0.5611 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00092: val_acc did not improve from 0.84211\n",
      "Epoch 93/200\n",
      "27/27 [==============================] - 5s 192ms/step - loss: 0.3501 - acc: 0.9073 - val_loss: 0.5818 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00093: val_acc did not improve from 0.84211\n",
      "Epoch 94/200\n",
      "27/27 [==============================] - 5s 183ms/step - loss: 0.3410 - acc: 0.8980 - val_loss: 0.5194 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00094: val_acc did not improve from 0.84211\n",
      "Epoch 95/200\n",
      "27/27 [==============================] - 5s 177ms/step - loss: 0.3723 - acc: 0.8517 - val_loss: 0.5692 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00095: val_acc did not improve from 0.84211\n",
      "Epoch 96/200\n",
      "27/27 [==============================] - 5s 170ms/step - loss: 0.3078 - acc: 0.9073 - val_loss: 0.6238 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00096: val_acc did not improve from 0.84211\n",
      "Epoch 00096: early stopping\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 792x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trial 1\n",
      "Epoch 1/200\n",
      "27/27 [==============================] - 6s 210ms/step - loss: 1.9852 - acc: 0.4717 - val_loss: 1.7816 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00001: val_acc improved from -inf to 0.68421, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/MS_from_scratch/weights-augm-trial1-improvement-BEST.hdf5\n",
      "Epoch 2/200\n",
      "27/27 [==============================] - 4s 153ms/step - loss: 1.8134 - acc: 0.5273 - val_loss: 1.6967 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00002: val_acc did not improve from 0.68421\n",
      "Epoch 3/200\n",
      "27/27 [==============================] - 5s 168ms/step - loss: 1.6957 - acc: 0.4253 - val_loss: 1.6688 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00003: val_acc did not improve from 0.68421\n",
      "Epoch 4/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 1.5732 - acc: 0.5376 - val_loss: 1.4843 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00004: val_acc did not improve from 0.68421\n",
      "Epoch 5/200\n",
      "27/27 [==============================] - 5s 170ms/step - loss: 1.4903 - acc: 0.5098 - val_loss: 1.4032 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00005: val_acc improved from 0.68421 to 0.73684, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/MS_from_scratch/weights-augm-trial1-improvement-BEST.hdf5\n",
      "Epoch 6/200\n",
      "27/27 [==============================] - 4s 165ms/step - loss: 1.3941 - acc: 0.5551 - val_loss: 1.3800 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00006: val_acc did not improve from 0.73684\n",
      "Epoch 7/200\n",
      "27/27 [==============================] - 4s 163ms/step - loss: 1.3232 - acc: 0.4902 - val_loss: 1.3717 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00007: val_acc did not improve from 0.73684\n",
      "Epoch 8/200\n",
      "27/27 [==============================] - 4s 163ms/step - loss: 1.2481 - acc: 0.5736 - val_loss: 1.2327 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00008: val_acc did not improve from 0.73684\n",
      "Epoch 9/200\n",
      "27/27 [==============================] - 5s 168ms/step - loss: 1.1961 - acc: 0.6107 - val_loss: 1.1372 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00009: val_acc improved from 0.73684 to 0.78947, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/MS_from_scratch/weights-augm-trial1-improvement-BEST.hdf5\n",
      "Epoch 10/200\n",
      "27/27 [==============================] - 4s 159ms/step - loss: 1.1269 - acc: 0.5922 - val_loss: 1.1259 - val_acc: 0.3684\n",
      "\n",
      "Epoch 00010: val_acc did not improve from 0.78947\n",
      "Epoch 11/200\n",
      "27/27 [==============================] - 5s 173ms/step - loss: 1.0727 - acc: 0.5736 - val_loss: 1.3029 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00011: val_acc did not improve from 0.78947\n",
      "Epoch 12/200\n",
      "27/27 [==============================] - 5s 182ms/step - loss: 1.0769 - acc: 0.5829 - val_loss: 1.0989 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00012: val_acc did not improve from 0.78947\n",
      "Epoch 13/200\n",
      "27/27 [==============================] - 5s 175ms/step - loss: 1.0123 - acc: 0.5561 - val_loss: 1.0245 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00013: val_acc did not improve from 0.78947\n",
      "Epoch 14/200\n",
      "27/27 [==============================] - 5s 169ms/step - loss: 0.9941 - acc: 0.5922 - val_loss: 0.9712 - val_acc: 0.5789\n",
      "\n",
      "Epoch 00014: val_acc did not improve from 0.78947\n",
      "Epoch 15/200\n",
      "27/27 [==============================] - 5s 173ms/step - loss: 0.9476 - acc: 0.5654 - val_loss: 0.9461 - val_acc: 0.4737\n",
      "\n",
      "Epoch 00015: val_acc did not improve from 0.78947\n",
      "Epoch 16/200\n",
      "27/27 [==============================] - 5s 181ms/step - loss: 0.9033 - acc: 0.6571 - val_loss: 1.0677 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00016: val_acc did not improve from 0.78947\n",
      "Epoch 17/200\n",
      "27/27 [==============================] - 5s 190ms/step - loss: 0.8912 - acc: 0.5932 - val_loss: 0.9213 - val_acc: 0.4211\n",
      "\n",
      "Epoch 00017: val_acc did not improve from 0.78947\n",
      "Epoch 18/200\n",
      "27/27 [==============================] - 5s 187ms/step - loss: 0.8579 - acc: 0.6014 - val_loss: 1.0282 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00018: val_acc did not improve from 0.78947\n",
      "Epoch 19/200\n",
      "27/27 [==============================] - 5s 189ms/step - loss: 0.8698 - acc: 0.5366 - val_loss: 1.0070 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00019: val_acc did not improve from 0.78947\n",
      "Epoch 20/200\n",
      "27/27 [==============================] - 5s 181ms/step - loss: 0.8462 - acc: 0.6025 - val_loss: 0.8873 - val_acc: 0.3684\n",
      "\n",
      "Epoch 00020: val_acc did not improve from 0.78947\n",
      "Epoch 21/200\n",
      "27/27 [==============================] - 5s 183ms/step - loss: 0.7955 - acc: 0.6117 - val_loss: 0.7649 - val_acc: 0.5789\n",
      "\n",
      "Epoch 00021: val_acc did not improve from 0.78947\n",
      "Epoch 22/200\n",
      "27/27 [==============================] - 5s 177ms/step - loss: 0.7734 - acc: 0.6025 - val_loss: 0.7207 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00022: val_acc did not improve from 0.78947\n",
      "Epoch 23/200\n",
      "27/27 [==============================] - 5s 186ms/step - loss: 0.7353 - acc: 0.6941 - val_loss: 0.8203 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00023: val_acc did not improve from 0.78947\n",
      "Epoch 24/200\n",
      "27/27 [==============================] - 6s 237ms/step - loss: 0.7067 - acc: 0.6663 - val_loss: 0.9092 - val_acc: 0.4737\n",
      "\n",
      "Epoch 00024: val_acc did not improve from 0.78947\n",
      "Epoch 25/200\n",
      "27/27 [==============================] - 5s 185ms/step - loss: 0.6890 - acc: 0.7034 - val_loss: 0.6685 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00025: val_acc did not improve from 0.78947\n",
      "Epoch 26/200\n",
      "27/27 [==============================] - 5s 181ms/step - loss: 0.7087 - acc: 0.6663 - val_loss: 0.6332 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00026: val_acc did not improve from 0.78947\n",
      "Epoch 27/200\n",
      "27/27 [==============================] - 5s 175ms/step - loss: 0.7000 - acc: 0.6663 - val_loss: 0.6296 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00027: val_acc did not improve from 0.78947\n",
      "Epoch 28/200\n",
      "27/27 [==============================] - 5s 185ms/step - loss: 0.6481 - acc: 0.7044 - val_loss: 0.6021 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00028: val_acc did not improve from 0.78947\n",
      "Epoch 29/200\n",
      "27/27 [==============================] - 5s 183ms/step - loss: 0.6539 - acc: 0.7127 - val_loss: 0.6071 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00029: val_acc did not improve from 0.78947\n",
      "Epoch 30/200\n",
      "27/27 [==============================] - 5s 191ms/step - loss: 0.6208 - acc: 0.7683 - val_loss: 0.8429 - val_acc: 0.5263\n",
      "\n",
      "Epoch 00030: val_acc did not improve from 0.78947\n",
      "Epoch 31/200\n",
      "27/27 [==============================] - 5s 189ms/step - loss: 0.6768 - acc: 0.7405 - val_loss: 0.6496 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00031: val_acc did not improve from 0.78947\n",
      "Epoch 32/200\n",
      "27/27 [==============================] - 5s 177ms/step - loss: 0.6189 - acc: 0.7590 - val_loss: 0.7003 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00032: val_acc did not improve from 0.78947\n",
      "Epoch 33/200\n",
      "27/27 [==============================] - 5s 172ms/step - loss: 0.6406 - acc: 0.7219 - val_loss: 0.7158 - val_acc: 0.5789\n",
      "\n",
      "Epoch 00033: val_acc did not improve from 0.78947\n",
      "Epoch 34/200\n",
      "27/27 [==============================] - 5s 181ms/step - loss: 0.6615 - acc: 0.6849 - val_loss: 0.7858 - val_acc: 0.5789\n",
      "\n",
      "Epoch 00034: val_acc did not improve from 0.78947\n",
      "Epoch 35/200\n",
      "27/27 [==============================] - 5s 182ms/step - loss: 0.5856 - acc: 0.7590 - val_loss: 1.1293 - val_acc: 0.3684\n",
      "\n",
      "Epoch 00035: val_acc did not improve from 0.78947\n",
      "Epoch 36/200\n",
      "27/27 [==============================] - 5s 181ms/step - loss: 0.5551 - acc: 0.7776 - val_loss: 0.8985 - val_acc: 0.5263\n",
      "\n",
      "Epoch 00036: val_acc did not improve from 0.78947\n",
      "Epoch 37/200\n",
      "27/27 [==============================] - 5s 181ms/step - loss: 0.6250 - acc: 0.7322 - val_loss: 0.6207 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00037: val_acc did not improve from 0.78947\n",
      "Epoch 38/200\n",
      "27/27 [==============================] - 5s 185ms/step - loss: 0.5631 - acc: 0.8239 - val_loss: 0.6915 - val_acc: 0.5789\n",
      "\n",
      "Epoch 00038: val_acc did not improve from 0.78947\n",
      "Epoch 39/200\n",
      "27/27 [==============================] - 5s 181ms/step - loss: 0.6125 - acc: 0.7044 - val_loss: 0.6523 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00039: val_acc did not improve from 0.78947\n",
      "Epoch 40/200\n",
      "27/27 [==============================] - 5s 175ms/step - loss: 0.5434 - acc: 0.7590 - val_loss: 0.6080 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00040: val_acc did not improve from 0.78947\n",
      "Epoch 41/200\n",
      "27/27 [==============================] - 5s 170ms/step - loss: 0.6030 - acc: 0.7127 - val_loss: 0.8288 - val_acc: 0.5263\n",
      "\n",
      "Epoch 00041: val_acc did not improve from 0.78947\n",
      "Epoch 42/200\n",
      "27/27 [==============================] - 5s 177ms/step - loss: 0.6300 - acc: 0.6756 - val_loss: 0.6709 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00042: val_acc did not improve from 0.78947\n",
      "Epoch 43/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27/27 [==============================] - 5s 173ms/step - loss: 0.5524 - acc: 0.8054 - val_loss: 1.2148 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00043: val_acc did not improve from 0.78947\n",
      "Epoch 00043: early stopping\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 792x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trial 2\n",
      "Epoch 1/200\n",
      "27/27 [==============================] - 6s 204ms/step - loss: 1.9598 - acc: 0.5005 - val_loss: 1.8401 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00001: val_acc improved from -inf to 0.84211, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/MS_from_scratch/weights-augm-trial2-improvement-BEST.hdf5\n",
      "Epoch 2/200\n",
      "27/27 [==============================] - 4s 158ms/step - loss: 1.8075 - acc: 0.5458 - val_loss: 1.7551 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00002: val_acc did not improve from 0.84211\n",
      "Epoch 3/200\n",
      "27/27 [==============================] - 5s 178ms/step - loss: 1.6788 - acc: 0.5551 - val_loss: 1.6486 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00003: val_acc did not improve from 0.84211\n",
      "Epoch 4/200\n",
      "27/27 [==============================] - 5s 183ms/step - loss: 1.5499 - acc: 0.5829 - val_loss: 1.4865 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00004: val_acc did not improve from 0.84211\n",
      "Epoch 5/200\n",
      "27/27 [==============================] - 5s 182ms/step - loss: 1.4753 - acc: 0.4727 - val_loss: 1.4690 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00005: val_acc did not improve from 0.84211\n",
      "Epoch 6/200\n",
      "27/27 [==============================] - 5s 192ms/step - loss: 1.3614 - acc: 0.5932 - val_loss: 1.3209 - val_acc: 0.5263\n",
      "\n",
      "Epoch 00006: val_acc did not improve from 0.84211\n",
      "Epoch 7/200\n",
      "27/27 [==============================] - 5s 192ms/step - loss: 1.2709 - acc: 0.6014 - val_loss: 1.2181 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00007: val_acc did not improve from 0.84211\n",
      "Epoch 8/200\n",
      "27/27 [==============================] - 5s 182ms/step - loss: 1.1561 - acc: 0.6849 - val_loss: 1.0332 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00008: val_acc did not improve from 0.84211\n",
      "Epoch 9/200\n",
      "27/27 [==============================] - 5s 175ms/step - loss: 1.1292 - acc: 0.6571 - val_loss: 1.0795 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00009: val_acc did not improve from 0.84211\n",
      "Epoch 10/200\n",
      "27/27 [==============================] - 5s 188ms/step - loss: 1.1043 - acc: 0.5376 - val_loss: 1.0615 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00010: val_acc did not improve from 0.84211\n",
      "Epoch 11/200\n",
      "27/27 [==============================] - 5s 193ms/step - loss: 1.0197 - acc: 0.7034 - val_loss: 0.9836 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00011: val_acc did not improve from 0.84211\n",
      "Epoch 12/200\n",
      "27/27 [==============================] - 5s 199ms/step - loss: 0.9601 - acc: 0.6663 - val_loss: 0.8338 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00012: val_acc did not improve from 0.84211\n",
      "Epoch 13/200\n",
      "27/27 [==============================] - 5s 173ms/step - loss: 1.0066 - acc: 0.6107 - val_loss: 0.9341 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00013: val_acc did not improve from 0.84211\n",
      "Epoch 14/200\n",
      "27/27 [==============================] - 5s 184ms/step - loss: 0.9269 - acc: 0.6849 - val_loss: 0.9528 - val_acc: 0.5263\n",
      "\n",
      "Epoch 00014: val_acc did not improve from 0.84211\n",
      "Epoch 15/200\n",
      "27/27 [==============================] - 5s 176ms/step - loss: 0.9045 - acc: 0.6014 - val_loss: 1.0167 - val_acc: 0.4211\n",
      "\n",
      "Epoch 00015: val_acc did not improve from 0.84211\n",
      "Epoch 16/200\n",
      "27/27 [==============================] - 5s 173ms/step - loss: 0.8090 - acc: 0.7219 - val_loss: 0.7332 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00016: val_acc did not improve from 0.84211\n",
      "Epoch 17/200\n",
      "27/27 [==============================] - 5s 173ms/step - loss: 0.8385 - acc: 0.6488 - val_loss: 0.7100 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00017: val_acc did not improve from 0.84211\n",
      "Epoch 18/200\n",
      "27/27 [==============================] - 5s 184ms/step - loss: 0.8053 - acc: 0.6941 - val_loss: 0.7375 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00018: val_acc did not improve from 0.84211\n",
      "Epoch 19/200\n",
      "27/27 [==============================] - 5s 182ms/step - loss: 0.7688 - acc: 0.6941 - val_loss: 0.6997 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00019: val_acc did not improve from 0.84211\n",
      "Epoch 20/200\n",
      "27/27 [==============================] - 6s 221ms/step - loss: 0.7114 - acc: 0.7405 - val_loss: 0.7626 - val_acc: 0.5789\n",
      "\n",
      "Epoch 00020: val_acc did not improve from 0.84211\n",
      "Epoch 21/200\n",
      "27/27 [==============================] - 5s 179ms/step - loss: 0.6919 - acc: 0.7405 - val_loss: 0.6433 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00021: val_acc did not improve from 0.84211\n",
      "Epoch 22/200\n",
      "27/27 [==============================] - 5s 173ms/step - loss: 0.7287 - acc: 0.6941 - val_loss: 0.7091 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00022: val_acc did not improve from 0.84211\n",
      "Epoch 23/200\n",
      "27/27 [==============================] - 5s 172ms/step - loss: 0.7631 - acc: 0.6952 - val_loss: 0.6818 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00023: val_acc did not improve from 0.84211\n",
      "Epoch 24/200\n",
      "27/27 [==============================] - 5s 177ms/step - loss: 0.6781 - acc: 0.7590 - val_loss: 0.8180 - val_acc: 0.5789\n",
      "\n",
      "Epoch 00024: val_acc did not improve from 0.84211\n",
      "Epoch 25/200\n",
      "27/27 [==============================] - 5s 178ms/step - loss: 0.6947 - acc: 0.6849 - val_loss: 0.6172 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00025: val_acc did not improve from 0.84211\n",
      "Epoch 26/200\n",
      "27/27 [==============================] - 5s 188ms/step - loss: 0.6531 - acc: 0.7497 - val_loss: 0.6099 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00026: val_acc did not improve from 0.84211\n",
      "Epoch 27/200\n",
      "27/27 [==============================] - 5s 193ms/step - loss: 0.6380 - acc: 0.7219 - val_loss: 0.6094 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00027: val_acc did not improve from 0.84211\n",
      "Epoch 28/200\n",
      "27/27 [==============================] - 5s 181ms/step - loss: 0.6445 - acc: 0.7127 - val_loss: 0.6915 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00028: val_acc did not improve from 0.84211\n",
      "Epoch 29/200\n",
      "27/27 [==============================] - 5s 178ms/step - loss: 0.6258 - acc: 0.7044 - val_loss: 0.6596 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00029: val_acc did not improve from 0.84211\n",
      "Epoch 30/200\n",
      "27/27 [==============================] - 5s 191ms/step - loss: 0.7050 - acc: 0.7127 - val_loss: 0.6187 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00030: val_acc did not improve from 0.84211\n",
      "Epoch 31/200\n",
      "27/27 [==============================] - 5s 180ms/step - loss: 0.6169 - acc: 0.7137 - val_loss: 0.7196 - val_acc: 0.5789\n",
      "\n",
      "Epoch 00031: val_acc did not improve from 0.84211\n",
      "Epoch 32/200\n",
      "27/27 [==============================] - 5s 181ms/step - loss: 0.6702 - acc: 0.6941 - val_loss: 0.8488 - val_acc: 0.4737\n",
      "\n",
      "Epoch 00032: val_acc did not improve from 0.84211\n",
      "Epoch 33/200\n",
      "27/27 [==============================] - 5s 179ms/step - loss: 0.6400 - acc: 0.7219 - val_loss: 0.5829 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00033: val_acc did not improve from 0.84211\n",
      "Epoch 34/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 0.6508 - acc: 0.7127 - val_loss: 0.5753 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00034: val_acc did not improve from 0.84211\n",
      "Epoch 35/200\n",
      "27/27 [==============================] - 4s 156ms/step - loss: 0.6275 - acc: 0.7034 - val_loss: 0.6417 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00035: val_acc did not improve from 0.84211\n",
      "Epoch 36/200\n",
      "27/27 [==============================] - 5s 172ms/step - loss: 0.5764 - acc: 0.7961 - val_loss: 0.6516 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00036: val_acc did not improve from 0.84211\n",
      "Epoch 37/200\n",
      "27/27 [==============================] - 5s 179ms/step - loss: 0.5546 - acc: 0.7961 - val_loss: 0.6670 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00037: val_acc did not improve from 0.84211\n",
      "Epoch 38/200\n",
      "27/27 [==============================] - 5s 179ms/step - loss: 0.5663 - acc: 0.7590 - val_loss: 0.5720 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00038: val_acc did not improve from 0.84211\n",
      "Epoch 39/200\n",
      "27/27 [==============================] - 5s 190ms/step - loss: 0.6018 - acc: 0.6756 - val_loss: 0.9359 - val_acc: 0.4737\n",
      "\n",
      "Epoch 00039: val_acc did not improve from 0.84211\n",
      "Epoch 40/200\n",
      "27/27 [==============================] - 5s 185ms/step - loss: 0.6833 - acc: 0.6849 - val_loss: 0.7946 - val_acc: 0.5263\n",
      "\n",
      "Epoch 00040: val_acc did not improve from 0.84211\n",
      "Epoch 41/200\n",
      "27/27 [==============================] - 5s 177ms/step - loss: 0.5625 - acc: 0.7312 - val_loss: 0.6238 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00041: val_acc did not improve from 0.84211\n",
      "Epoch 42/200\n",
      "27/27 [==============================] - 5s 175ms/step - loss: 0.5374 - acc: 0.7961 - val_loss: 0.5569 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00042: val_acc did not improve from 0.84211\n",
      "Epoch 43/200\n",
      "27/27 [==============================] - 5s 176ms/step - loss: 0.5386 - acc: 0.7868 - val_loss: 1.0535 - val_acc: 0.4737\n",
      "\n",
      "Epoch 00043: val_acc did not improve from 0.84211\n",
      "Epoch 44/200\n",
      "27/27 [==============================] - 5s 184ms/step - loss: 0.5380 - acc: 0.7683 - val_loss: 0.7462 - val_acc: 0.5789\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 00044: val_acc did not improve from 0.84211\n",
      "Epoch 45/200\n",
      "27/27 [==============================] - 5s 186ms/step - loss: 0.5491 - acc: 0.7776 - val_loss: 0.6627 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00045: val_acc did not improve from 0.84211\n",
      "Epoch 46/200\n",
      "27/27 [==============================] - 5s 176ms/step - loss: 0.4986 - acc: 0.8146 - val_loss: 0.9084 - val_acc: 0.5263\n",
      "\n",
      "Epoch 00046: val_acc did not improve from 0.84211\n",
      "Epoch 47/200\n",
      "27/27 [==============================] - 5s 177ms/step - loss: 0.5321 - acc: 0.8146 - val_loss: 0.5938 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00047: val_acc did not improve from 0.84211\n",
      "Epoch 48/200\n",
      "27/27 [==============================] - 5s 178ms/step - loss: 0.4534 - acc: 0.8146 - val_loss: 0.6078 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00048: val_acc did not improve from 0.84211\n",
      "Epoch 49/200\n",
      "27/27 [==============================] - 5s 175ms/step - loss: 0.7045 - acc: 0.7034 - val_loss: 0.8476 - val_acc: 0.4211\n",
      "\n",
      "Epoch 00049: val_acc did not improve from 0.84211\n",
      "Epoch 50/200\n",
      "27/27 [==============================] - 5s 175ms/step - loss: 0.5784 - acc: 0.7868 - val_loss: 0.7033 - val_acc: 0.5789\n",
      "\n",
      "Epoch 00050: val_acc did not improve from 0.84211\n",
      "Epoch 51/200\n",
      "27/27 [==============================] - 5s 179ms/step - loss: 0.5784 - acc: 0.7786 - val_loss: 0.5512 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00051: val_acc did not improve from 0.84211\n",
      "Epoch 52/200\n",
      "27/27 [==============================] - 5s 188ms/step - loss: 0.5143 - acc: 0.8054 - val_loss: 0.6778 - val_acc: 0.5789\n",
      "\n",
      "Epoch 00052: val_acc did not improve from 0.84211\n",
      "Epoch 53/200\n",
      "27/27 [==============================] - 5s 173ms/step - loss: 0.5197 - acc: 0.7971 - val_loss: 0.6651 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00053: val_acc did not improve from 0.84211\n",
      "Epoch 54/200\n",
      "27/27 [==============================] - 5s 180ms/step - loss: 0.5186 - acc: 0.8146 - val_loss: 0.5651 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00054: val_acc did not improve from 0.84211\n",
      "Epoch 55/200\n",
      "27/27 [==============================] - 5s 180ms/step - loss: 0.4863 - acc: 0.8239 - val_loss: 0.5664 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00055: val_acc did not improve from 0.84211\n",
      "Epoch 56/200\n",
      "27/27 [==============================] - 5s 185ms/step - loss: 0.4836 - acc: 0.8239 - val_loss: 0.5689 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00056: val_acc did not improve from 0.84211\n",
      "Epoch 57/200\n",
      "27/27 [==============================] - 5s 188ms/step - loss: 0.5441 - acc: 0.7497 - val_loss: 0.5439 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00057: val_acc did not improve from 0.84211\n",
      "Epoch 58/200\n",
      "27/27 [==============================] - 5s 183ms/step - loss: 0.4725 - acc: 0.7776 - val_loss: 0.6477 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00058: val_acc did not improve from 0.84211\n",
      "Epoch 59/200\n",
      "27/27 [==============================] - 5s 182ms/step - loss: 0.4973 - acc: 0.8239 - val_loss: 1.1839 - val_acc: 0.2632\n",
      "\n",
      "Epoch 00059: val_acc did not improve from 0.84211\n",
      "Epoch 60/200\n",
      "27/27 [==============================] - 5s 176ms/step - loss: 0.5505 - acc: 0.7600 - val_loss: 0.6278 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00060: val_acc did not improve from 0.84211\n",
      "Epoch 61/200\n",
      "27/27 [==============================] - 5s 169ms/step - loss: 0.4740 - acc: 0.8435 - val_loss: 0.5462 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00061: val_acc did not improve from 0.84211\n",
      "Epoch 62/200\n",
      "27/27 [==============================] - 5s 175ms/step - loss: 0.4961 - acc: 0.8249 - val_loss: 0.7327 - val_acc: 0.5263\n",
      "\n",
      "Epoch 00062: val_acc did not improve from 0.84211\n",
      "Epoch 63/200\n",
      "27/27 [==============================] - 5s 181ms/step - loss: 0.5102 - acc: 0.7878 - val_loss: 0.7058 - val_acc: 0.4737\n",
      "\n",
      "Epoch 00063: val_acc did not improve from 0.84211\n",
      "Epoch 64/200\n",
      "27/27 [==============================] - 5s 190ms/step - loss: 0.4403 - acc: 0.9073 - val_loss: 0.5422 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00064: val_acc did not improve from 0.84211\n",
      "Epoch 65/200\n",
      "27/27 [==============================] - 5s 175ms/step - loss: 0.4427 - acc: 0.8239 - val_loss: 0.5560 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00065: val_acc did not improve from 0.84211\n",
      "Epoch 66/200\n",
      "27/27 [==============================] - 5s 178ms/step - loss: 0.4662 - acc: 0.8239 - val_loss: 0.6152 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00066: val_acc did not improve from 0.84211\n",
      "Epoch 67/200\n",
      "27/27 [==============================] - 4s 165ms/step - loss: 0.4187 - acc: 0.8702 - val_loss: 0.5458 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00067: val_acc did not improve from 0.84211\n",
      "Epoch 68/200\n",
      "27/27 [==============================] - 4s 165ms/step - loss: 0.4097 - acc: 0.9073 - val_loss: 0.6444 - val_acc: 0.5789\n",
      "\n",
      "Epoch 00068: val_acc did not improve from 0.84211\n",
      "Epoch 69/200\n",
      "27/27 [==============================] - 5s 171ms/step - loss: 0.4079 - acc: 0.8702 - val_loss: 0.6185 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00069: val_acc did not improve from 0.84211\n",
      "Epoch 70/200\n",
      "27/27 [==============================] - 5s 184ms/step - loss: 0.3981 - acc: 0.8980 - val_loss: 0.5674 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00070: val_acc improved from 0.84211 to 0.84211, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/MS_from_scratch/weights-augm-trial2-improvement-BEST.hdf5\n",
      "Epoch 71/200\n",
      "27/27 [==============================] - 5s 188ms/step - loss: 0.3850 - acc: 0.8991 - val_loss: 0.6088 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00071: val_acc did not improve from 0.84211\n",
      "Epoch 72/200\n",
      "27/27 [==============================] - 5s 187ms/step - loss: 0.3678 - acc: 0.9073 - val_loss: 0.7286 - val_acc: 0.5789\n",
      "\n",
      "Epoch 00072: val_acc did not improve from 0.84211\n",
      "Epoch 73/200\n",
      "27/27 [==============================] - 5s 178ms/step - loss: 0.3247 - acc: 0.9351 - val_loss: 0.6177 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00073: val_acc did not improve from 0.84211\n",
      "Epoch 74/200\n",
      "27/27 [==============================] - 5s 182ms/step - loss: 0.3277 - acc: 0.9166 - val_loss: 0.5405 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00074: val_acc did not improve from 0.84211\n",
      "Epoch 75/200\n",
      "27/27 [==============================] - 5s 183ms/step - loss: 0.3617 - acc: 0.9166 - val_loss: 0.6945 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00075: val_acc did not improve from 0.84211\n",
      "Epoch 76/200\n",
      "27/27 [==============================] - 5s 183ms/step - loss: 0.3070 - acc: 0.9259 - val_loss: 0.6126 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00076: val_acc did not improve from 0.84211\n",
      "Epoch 77/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 0.3958 - acc: 0.8610 - val_loss: 0.6761 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00077: val_acc did not improve from 0.84211\n",
      "Epoch 78/200\n",
      "27/27 [==============================] - 5s 176ms/step - loss: 0.3411 - acc: 0.9351 - val_loss: 0.5439 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00078: val_acc did not improve from 0.84211\n",
      "Epoch 79/200\n",
      "27/27 [==============================] - 5s 194ms/step - loss: 0.3098 - acc: 0.9351 - val_loss: 0.5549 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00079: val_acc did not improve from 0.84211\n",
      "Epoch 80/200\n",
      "27/27 [==============================] - 5s 188ms/step - loss: 0.2883 - acc: 0.9722 - val_loss: 0.5628 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00080: val_acc did not improve from 0.84211\n",
      "Epoch 81/200\n",
      "27/27 [==============================] - 5s 195ms/step - loss: 0.3290 - acc: 0.9166 - val_loss: 0.5525 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00081: val_acc did not improve from 0.84211\n",
      "Epoch 82/200\n",
      "27/27 [==============================] - 5s 176ms/step - loss: 0.3423 - acc: 0.9259 - val_loss: 0.5585 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00082: val_acc did not improve from 0.84211\n",
      "Epoch 83/200\n",
      "27/27 [==============================] - 5s 184ms/step - loss: 0.3742 - acc: 0.8702 - val_loss: 0.6710 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00083: val_acc did not improve from 0.84211\n",
      "Epoch 84/200\n",
      "27/27 [==============================] - 5s 191ms/step - loss: 0.4137 - acc: 0.8620 - val_loss: 0.9528 - val_acc: 0.4211\n",
      "\n",
      "Epoch 00084: val_acc did not improve from 0.84211\n",
      "Epoch 85/200\n",
      "27/27 [==============================] - 5s 176ms/step - loss: 0.3541 - acc: 0.9073 - val_loss: 0.5261 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00085: val_acc did not improve from 0.84211\n",
      "Epoch 86/200\n",
      "27/27 [==============================] - 5s 180ms/step - loss: 0.3041 - acc: 0.9166 - val_loss: 0.5651 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00086: val_acc did not improve from 0.84211\n",
      "Epoch 87/200\n",
      "27/27 [==============================] - 5s 187ms/step - loss: 0.3021 - acc: 0.9166 - val_loss: 0.5610 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00087: val_acc did not improve from 0.84211\n",
      "Epoch 88/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27/27 [==============================] - 5s 195ms/step - loss: 0.3095 - acc: 0.9444 - val_loss: 0.5379 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00088: val_acc did not improve from 0.84211\n",
      "Epoch 89/200\n",
      "27/27 [==============================] - 5s 184ms/step - loss: 0.2624 - acc: 0.9444 - val_loss: 0.5492 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00089: val_acc did not improve from 0.84211\n",
      "Epoch 90/200\n",
      "27/27 [==============================] - 5s 183ms/step - loss: 0.3089 - acc: 0.9083 - val_loss: 0.5466 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00090: val_acc did not improve from 0.84211\n",
      "Epoch 91/200\n",
      "27/27 [==============================] - 5s 180ms/step - loss: 0.2830 - acc: 0.9351 - val_loss: 0.5760 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00091: val_acc did not improve from 0.84211\n",
      "Epoch 92/200\n",
      "27/27 [==============================] - 5s 174ms/step - loss: 0.2678 - acc: 0.9537 - val_loss: 0.5673 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00092: val_acc improved from 0.84211 to 0.89474, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/MS_from_scratch/weights-augm-trial2-improvement-BEST.hdf5\n",
      "Epoch 93/200\n",
      "27/27 [==============================] - 5s 173ms/step - loss: 0.2693 - acc: 0.9629 - val_loss: 0.5465 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00093: val_acc did not improve from 0.89474\n",
      "Epoch 94/200\n",
      "27/27 [==============================] - 5s 168ms/step - loss: 0.2421 - acc: 0.9815 - val_loss: 0.5815 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00094: val_acc did not improve from 0.89474\n",
      "Epoch 95/200\n",
      "27/27 [==============================] - 5s 183ms/step - loss: 0.3004 - acc: 0.9166 - val_loss: 0.7358 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00095: val_acc did not improve from 0.89474\n",
      "Epoch 96/200\n",
      "27/27 [==============================] - 5s 169ms/step - loss: 0.3017 - acc: 0.9351 - val_loss: 0.6943 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00096: val_acc did not improve from 0.89474\n",
      "Epoch 97/200\n",
      "27/27 [==============================] - 5s 173ms/step - loss: 0.3058 - acc: 0.9351 - val_loss: 0.6004 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00097: val_acc did not improve from 0.89474\n",
      "Epoch 98/200\n",
      "27/27 [==============================] - 4s 164ms/step - loss: 0.3168 - acc: 0.9166 - val_loss: 1.0476 - val_acc: 0.4211\n",
      "\n",
      "Epoch 00098: val_acc did not improve from 0.89474\n",
      "Epoch 99/200\n",
      "27/27 [==============================] - 5s 176ms/step - loss: 0.2921 - acc: 0.9259 - val_loss: 0.5678 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00099: val_acc did not improve from 0.89474\n",
      "Epoch 100/200\n",
      "27/27 [==============================] - 5s 171ms/step - loss: 0.2757 - acc: 0.9722 - val_loss: 0.6719 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00100: val_acc did not improve from 0.89474\n",
      "Epoch 00100: early stopping\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 792x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trial 3\n",
      "Epoch 1/200\n",
      "27/27 [==============================] - 7s 253ms/step - loss: 1.9528 - acc: 0.4902 - val_loss: 1.9171 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00001: val_acc improved from -inf to 0.31579, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/MS_from_scratch/weights-augm-trial3-improvement-BEST.hdf5\n",
      "Epoch 2/200\n",
      "27/27 [==============================] - 4s 157ms/step - loss: 1.7901 - acc: 0.5366 - val_loss: 1.7108 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00002: val_acc improved from 0.31579 to 0.84211, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/MS_from_scratch/weights-augm-trial3-improvement-BEST.hdf5\n",
      "Epoch 3/200\n",
      "27/27 [==============================] - 5s 170ms/step - loss: 1.6681 - acc: 0.5088 - val_loss: 1.7695 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00003: val_acc did not improve from 0.84211\n",
      "Epoch 4/200\n",
      "27/27 [==============================] - 5s 173ms/step - loss: 1.5696 - acc: 0.4634 - val_loss: 1.5032 - val_acc: 0.3684\n",
      "\n",
      "Epoch 00004: val_acc did not improve from 0.84211\n",
      "Epoch 5/200\n",
      "27/27 [==============================] - 4s 161ms/step - loss: 1.4503 - acc: 0.5736 - val_loss: 1.3867 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00005: val_acc did not improve from 0.84211\n",
      "Epoch 6/200\n",
      "27/27 [==============================] - 5s 173ms/step - loss: 1.3497 - acc: 0.5736 - val_loss: 1.3483 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00006: val_acc did not improve from 0.84211\n",
      "Epoch 7/200\n",
      "27/27 [==============================] - 5s 176ms/step - loss: 1.2882 - acc: 0.5469 - val_loss: 1.2042 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00007: val_acc did not improve from 0.84211\n",
      "Epoch 8/200\n",
      "27/27 [==============================] - 5s 183ms/step - loss: 1.2302 - acc: 0.5005 - val_loss: 1.2171 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00008: val_acc did not improve from 0.84211\n",
      "Epoch 9/200\n",
      "27/27 [==============================] - 5s 181ms/step - loss: 1.1590 - acc: 0.5644 - val_loss: 1.1701 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00009: val_acc did not improve from 0.84211\n",
      "Epoch 10/200\n",
      "27/27 [==============================] - 5s 173ms/step - loss: 1.1099 - acc: 0.5283 - val_loss: 1.0575 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00010: val_acc did not improve from 0.84211\n",
      "Epoch 11/200\n",
      "27/27 [==============================] - 5s 180ms/step - loss: 1.0562 - acc: 0.6107 - val_loss: 0.9391 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00011: val_acc did not improve from 0.84211\n",
      "Epoch 12/200\n",
      "27/27 [==============================] - 5s 181ms/step - loss: 1.0002 - acc: 0.5654 - val_loss: 0.9483 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00012: val_acc did not improve from 0.84211\n",
      "Epoch 13/200\n",
      "27/27 [==============================] - 5s 186ms/step - loss: 0.9584 - acc: 0.6488 - val_loss: 0.8272 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00013: val_acc did not improve from 0.84211\n",
      "Epoch 14/200\n",
      "27/27 [==============================] - 5s 194ms/step - loss: 0.9641 - acc: 0.6025 - val_loss: 0.9638 - val_acc: 0.5263\n",
      "\n",
      "Epoch 00014: val_acc did not improve from 0.84211\n",
      "Epoch 15/200\n",
      "27/27 [==============================] - 5s 185ms/step - loss: 0.8733 - acc: 0.6849 - val_loss: 0.8450 - val_acc: 0.5263\n",
      "\n",
      "Epoch 00015: val_acc did not improve from 0.84211\n",
      "Epoch 16/200\n",
      "27/27 [==============================] - 5s 184ms/step - loss: 0.8510 - acc: 0.7219 - val_loss: 0.7482 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00016: val_acc did not improve from 0.84211\n",
      "Epoch 17/200\n",
      "27/27 [==============================] - 5s 186ms/step - loss: 0.8436 - acc: 0.6478 - val_loss: 0.7655 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00017: val_acc did not improve from 0.84211\n",
      "Epoch 18/200\n",
      "27/27 [==============================] - 5s 185ms/step - loss: 0.8827 - acc: 0.5747 - val_loss: 0.9026 - val_acc: 0.4211\n",
      "\n",
      "Epoch 00018: val_acc did not improve from 0.84211\n",
      "Epoch 19/200\n",
      "27/27 [==============================] - 5s 186ms/step - loss: 0.8009 - acc: 0.6488 - val_loss: 0.7535 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00019: val_acc did not improve from 0.84211\n",
      "Epoch 20/200\n",
      "27/27 [==============================] - 5s 182ms/step - loss: 0.7983 - acc: 0.6571 - val_loss: 0.6784 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00020: val_acc did not improve from 0.84211\n",
      "Epoch 21/200\n",
      "27/27 [==============================] - 5s 177ms/step - loss: 0.7400 - acc: 0.7219 - val_loss: 0.7781 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00021: val_acc did not improve from 0.84211\n",
      "Epoch 22/200\n",
      "27/27 [==============================] - 5s 175ms/step - loss: 0.7373 - acc: 0.6673 - val_loss: 0.6522 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00022: val_acc did not improve from 0.84211\n",
      "Epoch 23/200\n",
      "27/27 [==============================] - 5s 179ms/step - loss: 0.7778 - acc: 0.6571 - val_loss: 0.8492 - val_acc: 0.5263\n",
      "\n",
      "Epoch 00023: val_acc did not improve from 0.84211\n",
      "Epoch 24/200\n",
      "27/27 [==============================] - 5s 183ms/step - loss: 0.7424 - acc: 0.6117 - val_loss: 0.6869 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00024: val_acc did not improve from 0.84211\n",
      "Epoch 25/200\n",
      "27/27 [==============================] - 5s 190ms/step - loss: 0.6640 - acc: 0.7312 - val_loss: 0.5964 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00025: val_acc did not improve from 0.84211\n",
      "Epoch 26/200\n",
      "27/27 [==============================] - 5s 187ms/step - loss: 0.7190 - acc: 0.6210 - val_loss: 0.8975 - val_acc: 0.3684\n",
      "\n",
      "Epoch 00026: val_acc did not improve from 0.84211\n",
      "Epoch 27/200\n",
      "27/27 [==============================] - 5s 175ms/step - loss: 0.7086 - acc: 0.6293 - val_loss: 0.6396 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00027: val_acc did not improve from 0.84211\n",
      "Epoch 28/200\n",
      "27/27 [==============================] - 5s 183ms/step - loss: 0.6736 - acc: 0.7034 - val_loss: 0.7216 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00028: val_acc did not improve from 0.84211\n",
      "Epoch 29/200\n",
      "27/27 [==============================] - 5s 178ms/step - loss: 0.6476 - acc: 0.7034 - val_loss: 0.8658 - val_acc: 0.4737\n",
      "\n",
      "Epoch 00029: val_acc did not improve from 0.84211\n",
      "Epoch 30/200\n",
      "27/27 [==============================] - 5s 180ms/step - loss: 0.6595 - acc: 0.6488 - val_loss: 0.7531 - val_acc: 0.5789\n",
      "\n",
      "Epoch 00030: val_acc did not improve from 0.84211\n",
      "Epoch 31/200\n",
      "27/27 [==============================] - 5s 176ms/step - loss: 0.6391 - acc: 0.7219 - val_loss: 0.5895 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00031: val_acc did not improve from 0.84211\n",
      "Epoch 32/200\n",
      "27/27 [==============================] - 5s 187ms/step - loss: 0.6038 - acc: 0.7868 - val_loss: 0.7839 - val_acc: 0.5789\n",
      "\n",
      "Epoch 00032: val_acc did not improve from 0.84211\n",
      "Epoch 33/200\n",
      "27/27 [==============================] - 5s 188ms/step - loss: 0.6522 - acc: 0.7127 - val_loss: 0.7720 - val_acc: 0.5263\n",
      "\n",
      "Epoch 00033: val_acc did not improve from 0.84211\n",
      "Epoch 34/200\n",
      "27/27 [==============================] - 5s 178ms/step - loss: 0.6261 - acc: 0.6941 - val_loss: 0.5588 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00034: val_acc did not improve from 0.84211\n",
      "Epoch 35/200\n",
      "27/27 [==============================] - 5s 173ms/step - loss: 0.5946 - acc: 0.7405 - val_loss: 0.6748 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00035: val_acc did not improve from 0.84211\n",
      "Epoch 36/200\n",
      "27/27 [==============================] - 5s 176ms/step - loss: 0.6212 - acc: 0.7127 - val_loss: 1.1070 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00036: val_acc did not improve from 0.84211\n",
      "Epoch 37/200\n",
      "27/27 [==============================] - 5s 172ms/step - loss: 0.6446 - acc: 0.6756 - val_loss: 0.6233 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00037: val_acc did not improve from 0.84211\n",
      "Epoch 38/200\n",
      "27/27 [==============================] - 5s 176ms/step - loss: 0.5982 - acc: 0.7405 - val_loss: 0.5621 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00038: val_acc did not improve from 0.84211\n",
      "Epoch 39/200\n",
      "27/27 [==============================] - 4s 167ms/step - loss: 0.6248 - acc: 0.7508 - val_loss: 0.5733 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00039: val_acc did not improve from 0.84211\n",
      "Epoch 40/200\n",
      "27/27 [==============================] - 4s 158ms/step - loss: 0.6554 - acc: 0.6859 - val_loss: 0.6350 - val_acc: 0.5789\n",
      "\n",
      "Epoch 00040: val_acc did not improve from 0.84211\n",
      "Epoch 41/200\n",
      "27/27 [==============================] - 5s 167ms/step - loss: 0.5820 - acc: 0.7961 - val_loss: 0.5253 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00041: val_acc did not improve from 0.84211\n",
      "Epoch 42/200\n",
      "27/27 [==============================] - 4s 154ms/step - loss: 0.6693 - acc: 0.6663 - val_loss: 0.5449 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00042: val_acc did not improve from 0.84211\n",
      "Epoch 43/200\n",
      "27/27 [==============================] - 5s 179ms/step - loss: 0.5860 - acc: 0.7776 - val_loss: 0.5512 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00043: val_acc did not improve from 0.84211\n",
      "Epoch 44/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27/27 [==============================] - 5s 180ms/step - loss: 0.5527 - acc: 0.7683 - val_loss: 0.5200 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00044: val_acc did not improve from 0.84211\n",
      "Epoch 45/200\n",
      "27/27 [==============================] - 5s 180ms/step - loss: 0.5928 - acc: 0.7127 - val_loss: 0.5347 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00045: val_acc did not improve from 0.84211\n",
      "Epoch 46/200\n",
      "27/27 [==============================] - 5s 183ms/step - loss: 0.5631 - acc: 0.8064 - val_loss: 0.5298 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00046: val_acc did not improve from 0.84211\n",
      "Epoch 47/200\n",
      "27/27 [==============================] - 5s 176ms/step - loss: 0.5276 - acc: 0.7961 - val_loss: 0.5149 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00047: val_acc did not improve from 0.84211\n",
      "Epoch 48/200\n",
      "27/27 [==============================] - 5s 176ms/step - loss: 0.5324 - acc: 0.7497 - val_loss: 0.5024 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00048: val_acc did not improve from 0.84211\n",
      "Epoch 49/200\n",
      "27/27 [==============================] - 5s 177ms/step - loss: 0.5142 - acc: 0.8064 - val_loss: 0.4987 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00049: val_acc did not improve from 0.84211\n",
      "Epoch 50/200\n",
      "27/27 [==============================] - 5s 182ms/step - loss: 0.4581 - acc: 0.8517 - val_loss: 0.5484 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00050: val_acc did not improve from 0.84211\n",
      "Epoch 51/200\n",
      "27/27 [==============================] - 5s 179ms/step - loss: 0.4834 - acc: 0.8054 - val_loss: 0.5530 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00051: val_acc did not improve from 0.84211\n",
      "Epoch 52/200\n",
      "27/27 [==============================] - 5s 180ms/step - loss: 0.4502 - acc: 0.8424 - val_loss: 0.5100 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00052: val_acc did not improve from 0.84211\n",
      "Epoch 53/200\n",
      "27/27 [==============================] - 5s 180ms/step - loss: 0.4306 - acc: 0.8980 - val_loss: 0.4889 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00053: val_acc did not improve from 0.84211\n",
      "Epoch 54/200\n",
      "27/27 [==============================] - 5s 182ms/step - loss: 0.4447 - acc: 0.8424 - val_loss: 0.6574 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00054: val_acc did not improve from 0.84211\n",
      "Epoch 55/200\n",
      "27/27 [==============================] - 5s 182ms/step - loss: 0.4075 - acc: 0.8702 - val_loss: 0.4748 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00055: val_acc did not improve from 0.84211\n",
      "Epoch 56/200\n",
      "27/27 [==============================] - 5s 177ms/step - loss: 0.4091 - acc: 0.8610 - val_loss: 0.4844 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00056: val_acc did not improve from 0.84211\n",
      "Epoch 57/200\n",
      "27/27 [==============================] - 5s 194ms/step - loss: 0.4211 - acc: 0.8610 - val_loss: 0.4743 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00057: val_acc did not improve from 0.84211\n",
      "Epoch 58/200\n",
      "27/27 [==============================] - 5s 172ms/step - loss: 0.3811 - acc: 0.8888 - val_loss: 0.4777 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00058: val_acc did not improve from 0.84211\n",
      "Epoch 59/200\n",
      "27/27 [==============================] - 5s 178ms/step - loss: 0.3846 - acc: 0.8888 - val_loss: 0.4875 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00059: val_acc did not improve from 0.84211\n",
      "Epoch 60/200\n",
      "27/27 [==============================] - 5s 180ms/step - loss: 0.3862 - acc: 0.8795 - val_loss: 0.4689 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00060: val_acc did not improve from 0.84211\n",
      "Epoch 61/200\n",
      "27/27 [==============================] - 5s 186ms/step - loss: 0.4117 - acc: 0.8239 - val_loss: 0.5985 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00061: val_acc did not improve from 0.84211\n",
      "Epoch 62/200\n",
      "27/27 [==============================] - 5s 176ms/step - loss: 0.3799 - acc: 0.8795 - val_loss: 0.4698 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00062: val_acc did not improve from 0.84211\n",
      "Epoch 63/200\n",
      "27/27 [==============================] - 5s 179ms/step - loss: 0.3487 - acc: 0.8888 - val_loss: 0.4565 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00063: val_acc did not improve from 0.84211\n",
      "Epoch 64/200\n",
      "27/27 [==============================] - 5s 182ms/step - loss: 0.3352 - acc: 0.9259 - val_loss: 0.4607 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00064: val_acc did not improve from 0.84211\n",
      "Epoch 65/200\n",
      "27/27 [==============================] - 5s 187ms/step - loss: 0.3322 - acc: 0.8888 - val_loss: 0.4641 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00065: val_acc did not improve from 0.84211\n",
      "Epoch 66/200\n",
      "27/27 [==============================] - 5s 190ms/step - loss: 0.3461 - acc: 0.9166 - val_loss: 0.4866 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00066: val_acc did not improve from 0.84211\n",
      "Epoch 67/200\n",
      "27/27 [==============================] - 5s 191ms/step - loss: 0.3132 - acc: 0.9166 - val_loss: 0.4523 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00067: val_acc did not improve from 0.84211\n",
      "Epoch 68/200\n",
      "27/27 [==============================] - 5s 179ms/step - loss: 0.3740 - acc: 0.8702 - val_loss: 0.5135 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00068: val_acc did not improve from 0.84211\n",
      "Epoch 69/200\n",
      "27/27 [==============================] - 5s 173ms/step - loss: 0.3239 - acc: 0.8980 - val_loss: 0.4659 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00069: val_acc did not improve from 0.84211\n",
      "Epoch 70/200\n",
      "27/27 [==============================] - 5s 172ms/step - loss: 0.3414 - acc: 0.9166 - val_loss: 0.5468 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00070: val_acc did not improve from 0.84211\n",
      "Epoch 71/200\n",
      "27/27 [==============================] - 5s 174ms/step - loss: 0.2913 - acc: 0.9259 - val_loss: 0.5375 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00071: val_acc did not improve from 0.84211\n",
      "Epoch 72/200\n",
      "27/27 [==============================] - 5s 167ms/step - loss: 0.3696 - acc: 0.8795 - val_loss: 0.5299 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00072: val_acc did not improve from 0.84211\n",
      "Epoch 73/200\n",
      "27/27 [==============================] - 5s 180ms/step - loss: 0.3506 - acc: 0.8795 - val_loss: 0.4808 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00073: val_acc did not improve from 0.84211\n",
      "Epoch 74/200\n",
      "27/27 [==============================] - 5s 175ms/step - loss: 0.3230 - acc: 0.9259 - val_loss: 0.4854 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00074: val_acc did not improve from 0.84211\n",
      "Epoch 75/200\n",
      "27/27 [==============================] - 5s 180ms/step - loss: 0.2985 - acc: 0.9166 - val_loss: 0.5398 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00075: val_acc did not improve from 0.84211\n",
      "Epoch 76/200\n",
      "27/27 [==============================] - 5s 177ms/step - loss: 0.3302 - acc: 0.9073 - val_loss: 0.5618 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00076: val_acc did not improve from 0.84211\n",
      "Epoch 77/200\n",
      "27/27 [==============================] - 5s 180ms/step - loss: 0.3426 - acc: 0.9073 - val_loss: 0.4786 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00077: val_acc did not improve from 0.84211\n",
      "Epoch 78/200\n",
      "27/27 [==============================] - 5s 187ms/step - loss: 0.2965 - acc: 0.9259 - val_loss: 0.4770 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00078: val_acc did not improve from 0.84211\n",
      "Epoch 79/200\n",
      "27/27 [==============================] - 5s 190ms/step - loss: 0.2955 - acc: 0.9166 - val_loss: 0.5665 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00079: val_acc did not improve from 0.84211\n",
      "Epoch 80/200\n",
      "27/27 [==============================] - 5s 195ms/step - loss: 0.2693 - acc: 0.9537 - val_loss: 0.4871 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00080: val_acc did not improve from 0.84211\n",
      "Epoch 81/200\n",
      "27/27 [==============================] - 5s 175ms/step - loss: 0.3035 - acc: 0.9166 - val_loss: 0.5205 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00081: val_acc did not improve from 0.84211\n",
      "Epoch 82/200\n",
      "27/27 [==============================] - 5s 183ms/step - loss: 0.3042 - acc: 0.9083 - val_loss: 0.7941 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00082: val_acc did not improve from 0.84211\n",
      "Epoch 00082: early stopping\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 792x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trial 4\n",
      "Epoch 1/200\n",
      "27/27 [==============================] - 7s 259ms/step - loss: 1.9316 - acc: 0.5273 - val_loss: 1.8580 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00001: val_acc improved from -inf to 0.31579, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/MS_from_scratch/weights-augm-trial4-improvement-BEST.hdf5\n",
      "Epoch 2/200\n",
      "27/27 [==============================] - 4s 155ms/step - loss: 1.7850 - acc: 0.5180 - val_loss: 1.7723 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00002: val_acc did not improve from 0.31579\n",
      "Epoch 3/200\n",
      "27/27 [==============================] - 5s 173ms/step - loss: 1.6509 - acc: 0.5098 - val_loss: 1.6377 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00003: val_acc did not improve from 0.31579\n",
      "Epoch 4/200\n",
      "27/27 [==============================] - 5s 176ms/step - loss: 1.5512 - acc: 0.5829 - val_loss: 1.7563 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00004: val_acc did not improve from 0.31579\n",
      "Epoch 5/200\n",
      "27/27 [==============================] - 5s 180ms/step - loss: 1.4690 - acc: 0.4995 - val_loss: 1.3951 - val_acc: 0.3684\n",
      "\n",
      "Epoch 00005: val_acc improved from 0.31579 to 0.36842, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/MS_from_scratch/weights-augm-trial4-improvement-BEST.hdf5\n",
      "Epoch 6/200\n",
      "27/27 [==============================] - 5s 180ms/step - loss: 1.3485 - acc: 0.5561 - val_loss: 1.1814 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00006: val_acc improved from 0.36842 to 0.68421, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/MS_from_scratch/weights-augm-trial4-improvement-BEST.hdf5\n",
      "Epoch 7/200\n",
      "27/27 [==============================] - 5s 186ms/step - loss: 1.2992 - acc: 0.5458 - val_loss: 1.2599 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00007: val_acc did not improve from 0.68421\n",
      "Epoch 8/200\n",
      "27/27 [==============================] - 5s 174ms/step - loss: 1.2294 - acc: 0.5098 - val_loss: 1.1861 - val_acc: 0.5789\n",
      "\n",
      "Epoch 00008: val_acc did not improve from 0.68421\n",
      "Epoch 9/200\n",
      "27/27 [==============================] - 5s 178ms/step - loss: 1.1558 - acc: 0.5551 - val_loss: 1.1450 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00009: val_acc did not improve from 0.68421\n",
      "Epoch 10/200\n",
      "27/27 [==============================] - 6s 223ms/step - loss: 1.1142 - acc: 0.5561 - val_loss: 1.2206 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00010: val_acc did not improve from 0.68421\n",
      "Epoch 11/200\n",
      "27/27 [==============================] - 5s 196ms/step - loss: 1.0596 - acc: 0.5829 - val_loss: 1.0580 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00011: val_acc did not improve from 0.68421\n",
      "Epoch 12/200\n",
      "27/27 [==============================] - 5s 178ms/step - loss: 1.0111 - acc: 0.5829 - val_loss: 0.9159 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00012: val_acc improved from 0.68421 to 0.73684, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/MS_from_scratch/weights-augm-trial4-improvement-BEST.hdf5\n",
      "Epoch 13/200\n",
      "27/27 [==============================] - 5s 171ms/step - loss: 0.9665 - acc: 0.5561 - val_loss: 1.0008 - val_acc: 0.3684\n",
      "\n",
      "Epoch 00013: val_acc did not improve from 0.73684\n",
      "Epoch 14/200\n",
      "27/27 [==============================] - 5s 173ms/step - loss: 0.9738 - acc: 0.5561 - val_loss: 1.1041 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00014: val_acc did not improve from 0.73684\n",
      "Epoch 15/200\n",
      "27/27 [==============================] - 5s 194ms/step - loss: 0.9365 - acc: 0.5273 - val_loss: 0.9522 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00015: val_acc did not improve from 0.73684\n",
      "Epoch 16/200\n",
      "27/27 [==============================] - 5s 179ms/step - loss: 0.9055 - acc: 0.5376 - val_loss: 0.9264 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00016: val_acc did not improve from 0.73684\n",
      "Epoch 17/200\n",
      "27/27 [==============================] - 5s 179ms/step - loss: 0.8618 - acc: 0.6107 - val_loss: 0.7887 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00017: val_acc did not improve from 0.73684\n",
      "Epoch 18/200\n",
      "27/27 [==============================] - 5s 181ms/step - loss: 0.8548 - acc: 0.5551 - val_loss: 0.8709 - val_acc: 0.4737\n",
      "\n",
      "Epoch 00018: val_acc did not improve from 0.73684\n",
      "Epoch 19/200\n",
      "27/27 [==============================] - 5s 171ms/step - loss: 0.8464 - acc: 0.5644 - val_loss: 0.9074 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00019: val_acc did not improve from 0.73684\n",
      "Epoch 20/200\n",
      "27/27 [==============================] - 5s 176ms/step - loss: 0.8146 - acc: 0.5644 - val_loss: 0.7349 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00020: val_acc improved from 0.73684 to 0.78947, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/MS_from_scratch/weights-augm-trial4-improvement-BEST.hdf5\n",
      "Epoch 21/200\n",
      "27/27 [==============================] - 5s 178ms/step - loss: 0.7725 - acc: 0.6385 - val_loss: 0.6990 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00021: val_acc did not improve from 0.78947\n",
      "Epoch 22/200\n",
      "27/27 [==============================] - 5s 178ms/step - loss: 0.7371 - acc: 0.7034 - val_loss: 0.6721 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00022: val_acc did not improve from 0.78947\n",
      "Epoch 23/200\n",
      "27/27 [==============================] - 5s 176ms/step - loss: 0.7188 - acc: 0.6849 - val_loss: 0.6366 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00023: val_acc did not improve from 0.78947\n",
      "Epoch 24/200\n",
      "27/27 [==============================] - 5s 179ms/step - loss: 0.7356 - acc: 0.6571 - val_loss: 0.6449 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00024: val_acc did not improve from 0.78947\n",
      "Epoch 25/200\n",
      "27/27 [==============================] - 5s 192ms/step - loss: 0.7225 - acc: 0.6488 - val_loss: 0.6262 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00025: val_acc did not improve from 0.78947\n",
      "Epoch 26/200\n",
      "27/27 [==============================] - 5s 186ms/step - loss: 0.7376 - acc: 0.6303 - val_loss: 0.6220 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00026: val_acc improved from 0.78947 to 0.84211, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/MS_from_scratch/weights-augm-trial4-improvement-BEST.hdf5\n",
      "Epoch 27/200\n",
      "27/27 [==============================] - 5s 189ms/step - loss: 0.7021 - acc: 0.6663 - val_loss: 0.5937 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00027: val_acc did not improve from 0.84211\n",
      "Epoch 28/200\n",
      "27/27 [==============================] - 5s 186ms/step - loss: 0.6787 - acc: 0.6941 - val_loss: 0.5895 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00028: val_acc did not improve from 0.84211\n",
      "Epoch 29/200\n",
      "27/27 [==============================] - 5s 176ms/step - loss: 0.6576 - acc: 0.7127 - val_loss: 0.5919 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00029: val_acc did not improve from 0.84211\n",
      "Epoch 30/200\n",
      "27/27 [==============================] - 5s 188ms/step - loss: 0.6482 - acc: 0.7137 - val_loss: 0.6157 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00030: val_acc did not improve from 0.84211\n",
      "Epoch 31/200\n",
      "27/27 [==============================] - 5s 177ms/step - loss: 0.6172 - acc: 0.6663 - val_loss: 0.5651 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00031: val_acc did not improve from 0.84211\n",
      "Epoch 32/200\n",
      "27/27 [==============================] - 4s 164ms/step - loss: 0.5838 - acc: 0.7683 - val_loss: 0.5504 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00032: val_acc did not improve from 0.84211\n",
      "Epoch 33/200\n",
      "27/27 [==============================] - 4s 159ms/step - loss: 0.6861 - acc: 0.6200 - val_loss: 0.5650 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00033: val_acc did not improve from 0.84211\n",
      "Epoch 34/200\n",
      "27/27 [==============================] - 5s 181ms/step - loss: 0.6329 - acc: 0.6849 - val_loss: 0.6014 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00034: val_acc did not improve from 0.84211\n",
      "Epoch 35/200\n",
      "27/27 [==============================] - 5s 177ms/step - loss: 0.6090 - acc: 0.6941 - val_loss: 0.5618 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00035: val_acc did not improve from 0.84211\n",
      "Epoch 36/200\n",
      "27/27 [==============================] - 5s 200ms/step - loss: 0.5767 - acc: 0.7590 - val_loss: 0.5410 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00036: val_acc did not improve from 0.84211\n",
      "Epoch 37/200\n",
      "27/27 [==============================] - 5s 187ms/step - loss: 0.6624 - acc: 0.7415 - val_loss: 1.3484 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00037: val_acc did not improve from 0.84211\n",
      "Epoch 38/200\n",
      "27/27 [==============================] - 5s 190ms/step - loss: 0.6409 - acc: 0.6581 - val_loss: 0.5981 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00038: val_acc did not improve from 0.84211\n",
      "Epoch 39/200\n",
      "27/27 [==============================] - 5s 177ms/step - loss: 0.6089 - acc: 0.7868 - val_loss: 0.5935 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00039: val_acc did not improve from 0.84211\n",
      "Epoch 40/200\n",
      "27/27 [==============================] - 5s 178ms/step - loss: 0.5715 - acc: 0.7683 - val_loss: 0.5820 - val_acc: 0.7368\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 00040: val_acc did not improve from 0.84211\n",
      "Epoch 41/200\n",
      "27/27 [==============================] - 5s 176ms/step - loss: 0.6082 - acc: 0.6941 - val_loss: 0.6134 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00041: val_acc did not improve from 0.84211\n",
      "Epoch 42/200\n",
      "27/27 [==============================] - 5s 191ms/step - loss: 0.6043 - acc: 0.7405 - val_loss: 0.6401 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00042: val_acc did not improve from 0.84211\n",
      "Epoch 43/200\n",
      "27/27 [==============================] - 5s 182ms/step - loss: 0.5847 - acc: 0.7044 - val_loss: 0.5291 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00043: val_acc did not improve from 0.84211\n",
      "Epoch 44/200\n",
      "27/27 [==============================] - 5s 188ms/step - loss: 0.5839 - acc: 0.7127 - val_loss: 0.5363 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00044: val_acc did not improve from 0.84211\n",
      "Epoch 45/200\n",
      "27/27 [==============================] - 5s 187ms/step - loss: 0.5626 - acc: 0.7219 - val_loss: 0.5190 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00045: val_acc did not improve from 0.84211\n",
      "Epoch 46/200\n",
      "27/27 [==============================] - 5s 182ms/step - loss: 0.5546 - acc: 0.7600 - val_loss: 0.8716 - val_acc: 0.4737\n",
      "\n",
      "Epoch 00046: val_acc did not improve from 0.84211\n",
      "Epoch 47/200\n",
      "27/27 [==============================] - 5s 186ms/step - loss: 0.6410 - acc: 0.7034 - val_loss: 0.8966 - val_acc: 0.4737\n",
      "\n",
      "Epoch 00047: val_acc did not improve from 0.84211\n",
      "Epoch 48/200\n",
      "27/27 [==============================] - 5s 178ms/step - loss: 0.5727 - acc: 0.7405 - val_loss: 0.6602 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00048: val_acc did not improve from 0.84211\n",
      "Epoch 49/200\n",
      "27/27 [==============================] - 5s 174ms/step - loss: 0.5642 - acc: 0.7230 - val_loss: 0.5178 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00049: val_acc did not improve from 0.84211\n",
      "Epoch 50/200\n",
      "27/27 [==============================] - 5s 167ms/step - loss: 0.5671 - acc: 0.7878 - val_loss: 0.8455 - val_acc: 0.5263\n",
      "\n",
      "Epoch 00050: val_acc did not improve from 0.84211\n",
      "Epoch 51/200\n",
      "27/27 [==============================] - 5s 169ms/step - loss: 0.5944 - acc: 0.7776 - val_loss: 0.7530 - val_acc: 0.5263\n",
      "\n",
      "Epoch 00051: val_acc did not improve from 0.84211\n",
      "Epoch 52/200\n",
      "27/27 [==============================] - 4s 165ms/step - loss: 0.5565 - acc: 0.7230 - val_loss: 0.8866 - val_acc: 0.4737\n",
      "\n",
      "Epoch 00052: val_acc did not improve from 0.84211\n",
      "Epoch 53/200\n",
      "27/27 [==============================] - 5s 185ms/step - loss: 0.6666 - acc: 0.6859 - val_loss: 0.6507 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00053: val_acc did not improve from 0.84211\n",
      "Epoch 54/200\n",
      "27/27 [==============================] - 5s 190ms/step - loss: 0.5348 - acc: 0.8424 - val_loss: 0.5386 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00054: val_acc did not improve from 0.84211\n",
      "Epoch 55/200\n",
      "27/27 [==============================] - 5s 186ms/step - loss: 0.5041 - acc: 0.8054 - val_loss: 0.7035 - val_acc: 0.5789\n",
      "\n",
      "Epoch 00055: val_acc did not improve from 0.84211\n",
      "Epoch 56/200\n",
      "27/27 [==============================] - 5s 184ms/step - loss: 0.5705 - acc: 0.6941 - val_loss: 0.5199 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00056: val_acc did not improve from 0.84211\n",
      "Epoch 57/200\n",
      "27/27 [==============================] - 5s 181ms/step - loss: 0.5434 - acc: 0.7497 - val_loss: 0.6028 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00057: val_acc did not improve from 0.84211\n",
      "Epoch 58/200\n",
      "27/27 [==============================] - 5s 172ms/step - loss: 0.5523 - acc: 0.7786 - val_loss: 0.5085 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00058: val_acc did not improve from 0.84211\n",
      "Epoch 59/200\n",
      "27/27 [==============================] - 5s 168ms/step - loss: 0.5191 - acc: 0.7219 - val_loss: 0.6208 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00059: val_acc did not improve from 0.84211\n",
      "Epoch 60/200\n",
      "27/27 [==============================] - 4s 163ms/step - loss: 0.5824 - acc: 0.7044 - val_loss: 0.6722 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00060: val_acc did not improve from 0.84211\n",
      "Epoch 61/200\n",
      "27/27 [==============================] - 5s 170ms/step - loss: 0.4794 - acc: 0.8332 - val_loss: 0.6736 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00061: val_acc did not improve from 0.84211\n",
      "Epoch 62/200\n",
      "27/27 [==============================] - 5s 167ms/step - loss: 0.5114 - acc: 0.7590 - val_loss: 0.5502 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00062: val_acc did not improve from 0.84211\n",
      "Epoch 63/200\n",
      "27/27 [==============================] - 5s 187ms/step - loss: 0.4896 - acc: 0.8146 - val_loss: 0.6260 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00063: val_acc did not improve from 0.84211\n",
      "Epoch 64/200\n",
      "27/27 [==============================] - 5s 176ms/step - loss: 0.4801 - acc: 0.7961 - val_loss: 0.5674 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00064: val_acc did not improve from 0.84211\n",
      "Epoch 65/200\n",
      "27/27 [==============================] - 5s 190ms/step - loss: 0.4962 - acc: 0.8146 - val_loss: 0.5683 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00065: val_acc did not improve from 0.84211\n",
      "Epoch 66/200\n",
      "27/27 [==============================] - 5s 182ms/step - loss: 0.4960 - acc: 0.7776 - val_loss: 0.5086 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00066: val_acc improved from 0.84211 to 0.84211, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/MS_from_scratch/weights-augm-trial4-improvement-BEST.hdf5\n",
      "Epoch 67/200\n",
      "27/27 [==============================] - 5s 183ms/step - loss: 0.4725 - acc: 0.8146 - val_loss: 0.5048 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00067: val_acc did not improve from 0.84211\n",
      "Epoch 68/200\n",
      "27/27 [==============================] - 5s 184ms/step - loss: 0.4994 - acc: 0.8146 - val_loss: 0.5892 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00068: val_acc did not improve from 0.84211\n",
      "Epoch 69/200\n",
      "27/27 [==============================] - 5s 179ms/step - loss: 0.4897 - acc: 0.8249 - val_loss: 0.5972 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00069: val_acc did not improve from 0.84211\n",
      "Epoch 70/200\n",
      "27/27 [==============================] - 5s 175ms/step - loss: 0.4301 - acc: 0.8610 - val_loss: 0.5992 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00070: val_acc did not improve from 0.84211\n",
      "Epoch 71/200\n",
      "27/27 [==============================] - 5s 180ms/step - loss: 0.4267 - acc: 0.8702 - val_loss: 0.5140 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00071: val_acc did not improve from 0.84211\n",
      "Epoch 72/200\n",
      "27/27 [==============================] - 5s 185ms/step - loss: 0.4873 - acc: 0.8054 - val_loss: 0.5232 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00072: val_acc did not improve from 0.84211\n",
      "Epoch 73/200\n",
      "27/27 [==============================] - 5s 178ms/step - loss: 0.4018 - acc: 0.8517 - val_loss: 0.5004 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00073: val_acc improved from 0.84211 to 0.89474, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/MS_from_scratch/weights-augm-trial4-improvement-BEST.hdf5\n",
      "Epoch 74/200\n",
      "27/27 [==============================] - 5s 189ms/step - loss: 0.4937 - acc: 0.7868 - val_loss: 0.4991 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00074: val_acc did not improve from 0.89474\n",
      "Epoch 75/200\n",
      "27/27 [==============================] - 5s 178ms/step - loss: 0.4164 - acc: 0.8517 - val_loss: 0.4994 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00075: val_acc did not improve from 0.89474\n",
      "Epoch 76/200\n",
      "27/27 [==============================] - 5s 176ms/step - loss: 0.3963 - acc: 0.8980 - val_loss: 0.6941 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00076: val_acc did not improve from 0.89474\n",
      "Epoch 77/200\n",
      "27/27 [==============================] - 5s 188ms/step - loss: 0.5120 - acc: 0.7786 - val_loss: 0.8082 - val_acc: 0.4211\n",
      "\n",
      "Epoch 00077: val_acc did not improve from 0.89474\n",
      "Epoch 78/200\n",
      "27/27 [==============================] - 5s 191ms/step - loss: 0.4396 - acc: 0.8064 - val_loss: 0.7131 - val_acc: 0.5263\n",
      "\n",
      "Epoch 00078: val_acc did not improve from 0.89474\n",
      "Epoch 79/200\n",
      "27/27 [==============================] - 5s 192ms/step - loss: 0.4074 - acc: 0.8702 - val_loss: 0.5952 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00079: val_acc did not improve from 0.89474\n",
      "Epoch 80/200\n",
      "27/27 [==============================] - 5s 182ms/step - loss: 0.3796 - acc: 0.8888 - val_loss: 0.5576 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00080: val_acc did not improve from 0.89474\n",
      "Epoch 81/200\n",
      "27/27 [==============================] - 5s 175ms/step - loss: 0.3643 - acc: 0.9073 - val_loss: 0.5677 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00081: val_acc did not improve from 0.89474\n",
      "Epoch 82/200\n",
      "27/27 [==============================] - 5s 188ms/step - loss: 0.3974 - acc: 0.8332 - val_loss: 0.6712 - val_acc: 0.5789\n",
      "\n",
      "Epoch 00082: val_acc did not improve from 0.89474\n",
      "Epoch 83/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27/27 [==============================] - 5s 175ms/step - loss: 0.3490 - acc: 0.9073 - val_loss: 0.4940 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00083: val_acc did not improve from 0.89474\n",
      "Epoch 84/200\n",
      "27/27 [==============================] - 5s 170ms/step - loss: 0.3245 - acc: 0.9444 - val_loss: 0.5262 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00084: val_acc did not improve from 0.89474\n",
      "Epoch 85/200\n",
      "27/27 [==============================] - 5s 180ms/step - loss: 0.3595 - acc: 0.8795 - val_loss: 0.5158 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00085: val_acc did not improve from 0.89474\n",
      "Epoch 86/200\n",
      "27/27 [==============================] - 5s 179ms/step - loss: 0.3464 - acc: 0.9259 - val_loss: 0.5144 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00086: val_acc did not improve from 0.89474\n",
      "Epoch 87/200\n",
      "27/27 [==============================] - 5s 181ms/step - loss: 0.3706 - acc: 0.8888 - val_loss: 0.7589 - val_acc: 0.5263\n",
      "\n",
      "Epoch 00087: val_acc did not improve from 0.89474\n",
      "Epoch 88/200\n",
      "27/27 [==============================] - 5s 186ms/step - loss: 0.3462 - acc: 0.8888 - val_loss: 0.5382 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00088: val_acc did not improve from 0.89474\n",
      "Epoch 89/200\n",
      "27/27 [==============================] - 5s 189ms/step - loss: 0.3488 - acc: 0.9259 - val_loss: 0.6300 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00089: val_acc did not improve from 0.89474\n",
      "Epoch 90/200\n",
      "27/27 [==============================] - 5s 186ms/step - loss: 0.3495 - acc: 0.8888 - val_loss: 0.7588 - val_acc: 0.5263\n",
      "\n",
      "Epoch 00090: val_acc did not improve from 0.89474\n",
      "Epoch 91/200\n",
      "27/27 [==============================] - 5s 182ms/step - loss: 0.3877 - acc: 0.8517 - val_loss: 0.8877 - val_acc: 0.4211\n",
      "\n",
      "Epoch 00091: val_acc did not improve from 0.89474\n",
      "Epoch 92/200\n",
      "27/27 [==============================] - 5s 175ms/step - loss: 0.5505 - acc: 0.7971 - val_loss: 0.8974 - val_acc: 0.4737\n",
      "\n",
      "Epoch 00092: val_acc did not improve from 0.89474\n",
      "Epoch 93/200\n",
      "27/27 [==============================] - 5s 177ms/step - loss: 0.4074 - acc: 0.8888 - val_loss: 0.7416 - val_acc: 0.5263\n",
      "\n",
      "Epoch 00093: val_acc did not improve from 0.89474\n",
      "Epoch 94/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 0.3571 - acc: 0.9259 - val_loss: 0.5406 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00094: val_acc did not improve from 0.89474\n",
      "Epoch 95/200\n",
      "27/27 [==============================] - 5s 172ms/step - loss: 0.3150 - acc: 0.9259 - val_loss: 0.5011 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00095: val_acc did not improve from 0.89474\n",
      "Epoch 96/200\n",
      "27/27 [==============================] - 5s 173ms/step - loss: 0.3218 - acc: 0.9176 - val_loss: 0.5073 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00096: val_acc did not improve from 0.89474\n",
      "Epoch 97/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 0.3664 - acc: 0.9259 - val_loss: 0.4966 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00097: val_acc did not improve from 0.89474\n",
      "Epoch 98/200\n",
      "27/27 [==============================] - 4s 159ms/step - loss: 0.3429 - acc: 0.9166 - val_loss: 0.4939 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00098: val_acc did not improve from 0.89474\n",
      "Epoch 99/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 0.3145 - acc: 0.9166 - val_loss: 0.5364 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00099: val_acc did not improve from 0.89474\n",
      "Epoch 100/200\n",
      "27/27 [==============================] - 5s 194ms/step - loss: 0.3153 - acc: 0.9444 - val_loss: 0.5425 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00100: val_acc did not improve from 0.89474\n",
      "Epoch 101/200\n",
      "27/27 [==============================] - 5s 187ms/step - loss: 0.3457 - acc: 0.9259 - val_loss: 0.5456 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00101: val_acc did not improve from 0.89474\n",
      "Epoch 102/200\n",
      "27/27 [==============================] - 5s 181ms/step - loss: 0.3340 - acc: 0.9166 - val_loss: 0.5913 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00102: val_acc did not improve from 0.89474\n",
      "Epoch 103/200\n",
      "27/27 [==============================] - 5s 177ms/step - loss: 0.2841 - acc: 0.9259 - val_loss: 0.4976 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00103: val_acc did not improve from 0.89474\n",
      "Epoch 104/200\n",
      "27/27 [==============================] - 5s 174ms/step - loss: 0.2891 - acc: 0.9629 - val_loss: 0.6601 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00104: val_acc did not improve from 0.89474\n",
      "Epoch 105/200\n",
      "27/27 [==============================] - 5s 182ms/step - loss: 0.2995 - acc: 0.9629 - val_loss: 0.5009 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00105: val_acc did not improve from 0.89474\n",
      "Epoch 106/200\n",
      "27/27 [==============================] - 5s 186ms/step - loss: 0.2756 - acc: 0.9537 - val_loss: 0.5761 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00106: val_acc did not improve from 0.89474\n",
      "Epoch 107/200\n",
      "27/27 [==============================] - 5s 182ms/step - loss: 0.2819 - acc: 0.9629 - val_loss: 0.5206 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00107: val_acc did not improve from 0.89474\n",
      "Epoch 108/200\n",
      "27/27 [==============================] - 5s 176ms/step - loss: 0.3010 - acc: 0.9444 - val_loss: 0.5143 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00108: val_acc did not improve from 0.89474\n",
      "Epoch 109/200\n",
      "27/27 [==============================] - 5s 173ms/step - loss: 0.2439 - acc: 0.9722 - val_loss: 0.5002 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00109: val_acc did not improve from 0.89474\n",
      "Epoch 110/200\n",
      "27/27 [==============================] - 5s 182ms/step - loss: 0.2689 - acc: 0.9537 - val_loss: 0.5158 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00110: val_acc did not improve from 0.89474\n",
      "Epoch 111/200\n",
      "27/27 [==============================] - 5s 174ms/step - loss: 0.2665 - acc: 0.9722 - val_loss: 0.5850 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00111: val_acc did not improve from 0.89474\n",
      "Epoch 112/200\n",
      "27/27 [==============================] - 5s 174ms/step - loss: 0.2736 - acc: 0.9259 - val_loss: 0.5020 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00112: val_acc did not improve from 0.89474\n",
      "Epoch 113/200\n",
      "27/27 [==============================] - 5s 180ms/step - loss: 0.2720 - acc: 0.9629 - val_loss: 0.5210 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00113: val_acc did not improve from 0.89474\n",
      "Epoch 00113: early stopping\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 792x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trial 5\n",
      "Epoch 1/200\n",
      "27/27 [==============================] - 7s 258ms/step - loss: 1.9659 - acc: 0.5273 - val_loss: 1.7981 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00001: val_acc improved from -inf to 0.68421, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/MS_from_scratch/weights-augm-trial5-improvement-BEST.hdf5\n",
      "Epoch 2/200\n",
      "27/27 [==============================] - 4s 158ms/step - loss: 1.8264 - acc: 0.4624 - val_loss: 1.8930 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00002: val_acc did not improve from 0.68421\n",
      "Epoch 3/200\n",
      "27/27 [==============================] - 5s 177ms/step - loss: 1.7012 - acc: 0.4995 - val_loss: 1.7462 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00003: val_acc did not improve from 0.68421\n",
      "Epoch 4/200\n",
      "27/27 [==============================] - 5s 184ms/step - loss: 1.5864 - acc: 0.5644 - val_loss: 1.5339 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00004: val_acc did not improve from 0.68421\n",
      "Epoch 5/200\n",
      "27/27 [==============================] - 5s 184ms/step - loss: 1.5071 - acc: 0.5283 - val_loss: 1.6327 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00005: val_acc did not improve from 0.68421\n",
      "Epoch 6/200\n",
      "27/27 [==============================] - 5s 176ms/step - loss: 1.4111 - acc: 0.4727 - val_loss: 1.3868 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00006: val_acc did not improve from 0.68421\n",
      "Epoch 7/200\n",
      "27/27 [==============================] - 5s 175ms/step - loss: 1.3365 - acc: 0.5829 - val_loss: 1.3659 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00007: val_acc did not improve from 0.68421\n",
      "Epoch 8/200\n",
      "27/27 [==============================] - 5s 181ms/step - loss: 1.2658 - acc: 0.5561 - val_loss: 1.3051 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00008: val_acc did not improve from 0.68421\n",
      "Epoch 9/200\n",
      "27/27 [==============================] - 5s 181ms/step - loss: 1.2077 - acc: 0.5190 - val_loss: 1.2007 - val_acc: 0.3684\n",
      "\n",
      "Epoch 00009: val_acc did not improve from 0.68421\n",
      "Epoch 10/200\n",
      "27/27 [==============================] - 5s 180ms/step - loss: 1.1492 - acc: 0.5839 - val_loss: 1.0026 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00010: val_acc improved from 0.68421 to 0.73684, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/MS_from_scratch/weights-augm-trial5-improvement-BEST.hdf5\n",
      "Epoch 11/200\n",
      "27/27 [==============================] - 5s 168ms/step - loss: 1.1589 - acc: 0.4356 - val_loss: 1.1194 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00011: val_acc did not improve from 0.73684\n",
      "Epoch 12/200\n",
      "27/27 [==============================] - 4s 167ms/step - loss: 1.0810 - acc: 0.5458 - val_loss: 1.0819 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00012: val_acc did not improve from 0.73684\n",
      "Epoch 13/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 1.0329 - acc: 0.6303 - val_loss: 1.0390 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00013: val_acc did not improve from 0.73684\n",
      "Epoch 14/200\n",
      "27/27 [==============================] - 5s 171ms/step - loss: 0.9885 - acc: 0.5922 - val_loss: 0.8720 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00014: val_acc did not improve from 0.73684\n",
      "Epoch 15/200\n",
      "27/27 [==============================] - 5s 184ms/step - loss: 0.9683 - acc: 0.5747 - val_loss: 1.1546 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00015: val_acc did not improve from 0.73684\n",
      "Epoch 16/200\n",
      "27/27 [==============================] - 5s 187ms/step - loss: 0.9433 - acc: 0.5932 - val_loss: 0.8955 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00016: val_acc improved from 0.73684 to 0.73684, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/MS_from_scratch/weights-augm-trial5-improvement-BEST.hdf5\n",
      "Epoch 17/200\n",
      "27/27 [==============================] - 5s 173ms/step - loss: 0.8776 - acc: 0.6293 - val_loss: 0.7454 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00017: val_acc improved from 0.73684 to 0.73684, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/MS_from_scratch/weights-augm-trial5-improvement-BEST.hdf5\n",
      "Epoch 18/200\n",
      "27/27 [==============================] - 5s 192ms/step - loss: 0.9166 - acc: 0.5469 - val_loss: 0.9400 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00018: val_acc did not improve from 0.73684\n",
      "Epoch 19/200\n",
      "27/27 [==============================] - 5s 176ms/step - loss: 0.8378 - acc: 0.7127 - val_loss: 0.7876 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00019: val_acc did not improve from 0.73684\n",
      "Epoch 20/200\n",
      "27/27 [==============================] - 5s 191ms/step - loss: 0.7998 - acc: 0.6756 - val_loss: 0.7886 - val_acc: 0.5789\n",
      "\n",
      "Epoch 00020: val_acc did not improve from 0.73684\n",
      "Epoch 21/200\n",
      "27/27 [==============================] - 5s 186ms/step - loss: 0.7825 - acc: 0.6581 - val_loss: 0.8255 - val_acc: 0.5789\n",
      "\n",
      "Epoch 00021: val_acc did not improve from 0.73684\n",
      "Epoch 22/200\n",
      "27/27 [==============================] - 5s 199ms/step - loss: 0.7399 - acc: 0.7219 - val_loss: 0.6791 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00022: val_acc did not improve from 0.73684\n",
      "Epoch 23/200\n",
      "27/27 [==============================] - 5s 181ms/step - loss: 0.7519 - acc: 0.6941 - val_loss: 0.7659 - val_acc: 0.5789\n",
      "\n",
      "Epoch 00023: val_acc did not improve from 0.73684\n",
      "Epoch 24/200\n",
      "27/27 [==============================] - 5s 181ms/step - loss: 0.7190 - acc: 0.7590 - val_loss: 0.6684 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00024: val_acc did not improve from 0.73684\n",
      "Epoch 25/200\n",
      "27/27 [==============================] - 5s 177ms/step - loss: 0.6757 - acc: 0.7034 - val_loss: 0.6133 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00025: val_acc did not improve from 0.73684\n",
      "Epoch 26/200\n",
      "27/27 [==============================] - 5s 185ms/step - loss: 0.6750 - acc: 0.7230 - val_loss: 0.6534 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00026: val_acc did not improve from 0.73684\n",
      "Epoch 27/200\n",
      "27/27 [==============================] - 5s 179ms/step - loss: 0.6618 - acc: 0.7405 - val_loss: 0.7182 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00027: val_acc did not improve from 0.73684\n",
      "Epoch 28/200\n",
      "27/27 [==============================] - 5s 172ms/step - loss: 0.6610 - acc: 0.7127 - val_loss: 1.0943 - val_acc: 0.3684\n",
      "\n",
      "Epoch 00028: val_acc did not improve from 0.73684\n",
      "Epoch 29/200\n",
      "27/27 [==============================] - 5s 182ms/step - loss: 0.7871 - acc: 0.5747 - val_loss: 0.9291 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00029: val_acc did not improve from 0.73684\n",
      "Epoch 30/200\n",
      "27/27 [==============================] - 5s 185ms/step - loss: 0.7118 - acc: 0.6941 - val_loss: 0.7319 - val_acc: 0.5789\n",
      "\n",
      "Epoch 00030: val_acc did not improve from 0.73684\n",
      "Epoch 31/200\n",
      "27/27 [==============================] - 5s 176ms/step - loss: 0.6932 - acc: 0.6395 - val_loss: 0.8332 - val_acc: 0.5263\n",
      "\n",
      "Epoch 00031: val_acc did not improve from 0.73684\n",
      "Epoch 32/200\n",
      "27/27 [==============================] - 5s 183ms/step - loss: 0.6402 - acc: 0.7497 - val_loss: 0.6682 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00032: val_acc did not improve from 0.73684\n",
      "Epoch 33/200\n",
      "27/27 [==============================] - 5s 175ms/step - loss: 0.6177 - acc: 0.7497 - val_loss: 0.5728 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00033: val_acc did not improve from 0.73684\n",
      "Epoch 34/200\n",
      "27/27 [==============================] - 5s 182ms/step - loss: 0.6230 - acc: 0.7590 - val_loss: 0.6292 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00034: val_acc improved from 0.73684 to 0.78947, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/MS_from_scratch/weights-augm-trial5-improvement-BEST.hdf5\n",
      "Epoch 35/200\n",
      "27/27 [==============================] - 5s 183ms/step - loss: 0.6524 - acc: 0.7312 - val_loss: 0.5806 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00035: val_acc did not improve from 0.78947\n",
      "Epoch 36/200\n",
      "27/27 [==============================] - 5s 180ms/step - loss: 0.6171 - acc: 0.7312 - val_loss: 0.5647 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00036: val_acc did not improve from 0.78947\n",
      "Epoch 37/200\n",
      "27/27 [==============================] - 5s 179ms/step - loss: 0.5801 - acc: 0.7776 - val_loss: 0.8774 - val_acc: 0.4737\n",
      "\n",
      "Epoch 00037: val_acc did not improve from 0.78947\n",
      "Epoch 38/200\n",
      "27/27 [==============================] - 5s 181ms/step - loss: 0.5856 - acc: 0.7961 - val_loss: 0.7699 - val_acc: 0.5789\n",
      "\n",
      "Epoch 00038: val_acc did not improve from 0.78947\n",
      "Epoch 39/200\n",
      "27/27 [==============================] - 5s 180ms/step - loss: 0.6181 - acc: 0.6941 - val_loss: 0.7109 - val_acc: 0.5789\n",
      "\n",
      "Epoch 00039: val_acc did not improve from 0.78947\n",
      "Epoch 40/200\n",
      "27/27 [==============================] - 5s 176ms/step - loss: 0.5557 - acc: 0.7776 - val_loss: 0.5455 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00040: val_acc did not improve from 0.78947\n",
      "Epoch 41/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27/27 [==============================] - 5s 179ms/step - loss: 0.6558 - acc: 0.6849 - val_loss: 0.5374 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00041: val_acc improved from 0.78947 to 0.84211, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/MS_from_scratch/weights-augm-trial5-improvement-BEST.hdf5\n",
      "Epoch 42/200\n",
      "27/27 [==============================] - 5s 175ms/step - loss: 0.6123 - acc: 0.7044 - val_loss: 0.5614 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00042: val_acc did not improve from 0.84211\n",
      "Epoch 43/200\n",
      "27/27 [==============================] - 5s 174ms/step - loss: 0.6171 - acc: 0.7219 - val_loss: 0.7373 - val_acc: 0.5789\n",
      "\n",
      "Epoch 00043: val_acc did not improve from 0.84211\n",
      "Epoch 44/200\n",
      "27/27 [==============================] - 5s 183ms/step - loss: 0.6448 - acc: 0.7322 - val_loss: 0.5366 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00044: val_acc did not improve from 0.84211\n",
      "Epoch 45/200\n",
      "27/27 [==============================] - 5s 175ms/step - loss: 0.6282 - acc: 0.7415 - val_loss: 0.5407 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00045: val_acc did not improve from 0.84211\n",
      "Epoch 46/200\n",
      "27/27 [==============================] - 5s 167ms/step - loss: 0.5815 - acc: 0.7776 - val_loss: 0.6186 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00046: val_acc did not improve from 0.84211\n",
      "Epoch 47/200\n",
      "27/27 [==============================] - 5s 170ms/step - loss: 0.5506 - acc: 0.7600 - val_loss: 0.5370 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00047: val_acc did not improve from 0.84211\n",
      "Epoch 48/200\n",
      "27/27 [==============================] - 5s 173ms/step - loss: 0.5129 - acc: 0.7868 - val_loss: 0.5367 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00048: val_acc did not improve from 0.84211\n",
      "Epoch 49/200\n",
      "27/27 [==============================] - 5s 177ms/step - loss: 0.5624 - acc: 0.7600 - val_loss: 0.5765 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00049: val_acc did not improve from 0.84211\n",
      "Epoch 50/200\n",
      "27/27 [==============================] - 5s 180ms/step - loss: 0.5542 - acc: 0.7497 - val_loss: 0.6494 - val_acc: 0.5789\n",
      "\n",
      "Epoch 00050: val_acc did not improve from 0.84211\n",
      "Epoch 51/200\n",
      "27/27 [==============================] - 5s 182ms/step - loss: 0.5202 - acc: 0.7961 - val_loss: 0.5377 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00051: val_acc did not improve from 0.84211\n",
      "Epoch 52/200\n",
      "27/27 [==============================] - 5s 180ms/step - loss: 0.4936 - acc: 0.7961 - val_loss: 0.8164 - val_acc: 0.5263\n",
      "\n",
      "Epoch 00052: val_acc did not improve from 0.84211\n",
      "Epoch 53/200\n",
      "27/27 [==============================] - 5s 174ms/step - loss: 0.5398 - acc: 0.7868 - val_loss: 0.5528 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00053: val_acc did not improve from 0.84211\n",
      "Epoch 54/200\n",
      "27/27 [==============================] - 5s 179ms/step - loss: 0.5420 - acc: 0.7590 - val_loss: 0.6567 - val_acc: 0.5789\n",
      "\n",
      "Epoch 00054: val_acc did not improve from 0.84211\n",
      "Epoch 55/200\n",
      "27/27 [==============================] - 5s 167ms/step - loss: 0.4973 - acc: 0.7868 - val_loss: 0.5698 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00055: val_acc did not improve from 0.84211\n",
      "Epoch 56/200\n",
      "27/27 [==============================] - 5s 173ms/step - loss: 0.4911 - acc: 0.7878 - val_loss: 0.5376 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00056: val_acc did not improve from 0.84211\n",
      "Epoch 57/200\n",
      "27/27 [==============================] - 5s 175ms/step - loss: 0.5130 - acc: 0.7590 - val_loss: 0.6836 - val_acc: 0.5263\n",
      "\n",
      "Epoch 00057: val_acc did not improve from 0.84211\n",
      "Epoch 58/200\n",
      "27/27 [==============================] - 5s 184ms/step - loss: 0.4906 - acc: 0.8146 - val_loss: 0.8508 - val_acc: 0.4211\n",
      "\n",
      "Epoch 00058: val_acc did not improve from 0.84211\n",
      "Epoch 59/200\n",
      "27/27 [==============================] - 5s 176ms/step - loss: 0.5499 - acc: 0.7590 - val_loss: 0.6424 - val_acc: 0.5789\n",
      "\n",
      "Epoch 00059: val_acc did not improve from 0.84211\n",
      "Epoch 00059: early stopping\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 792x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trial 6\n",
      "Epoch 1/200\n",
      "27/27 [==============================] - 7s 264ms/step - loss: 1.9660 - acc: 0.4634 - val_loss: 1.7782 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00001: val_acc improved from -inf to 0.68421, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/MS_from_scratch/weights-augm-trial6-improvement-BEST.hdf5\n",
      "Epoch 2/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 1.7567 - acc: 0.5283 - val_loss: 1.9220 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00002: val_acc did not improve from 0.68421\n",
      "Epoch 3/200\n",
      "27/27 [==============================] - 5s 175ms/step - loss: 1.6444 - acc: 0.5005 - val_loss: 1.5432 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00003: val_acc improved from 0.68421 to 0.68421, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/MS_from_scratch/weights-augm-trial6-improvement-BEST.hdf5\n",
      "Epoch 4/200\n",
      "27/27 [==============================] - 5s 184ms/step - loss: 1.4935 - acc: 0.4727 - val_loss: 1.3859 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00004: val_acc improved from 0.68421 to 0.78947, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/MS_from_scratch/weights-augm-trial6-improvement-BEST.hdf5\n",
      "Epoch 5/200\n",
      "27/27 [==============================] - 5s 180ms/step - loss: 1.3814 - acc: 0.5747 - val_loss: 1.2884 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00005: val_acc did not improve from 0.78947\n",
      "Epoch 6/200\n",
      "27/27 [==============================] - 5s 185ms/step - loss: 1.3076 - acc: 0.4634 - val_loss: 1.2566 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00006: val_acc did not improve from 0.78947\n",
      "Epoch 7/200\n",
      "27/27 [==============================] - 5s 175ms/step - loss: 1.2080 - acc: 0.5736 - val_loss: 1.1164 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00007: val_acc did not improve from 0.78947\n",
      "Epoch 8/200\n",
      "27/27 [==============================] - 5s 182ms/step - loss: 1.1524 - acc: 0.4995 - val_loss: 1.1717 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00008: val_acc did not improve from 0.78947\n",
      "Epoch 9/200\n",
      "27/27 [==============================] - 5s 176ms/step - loss: 1.0708 - acc: 0.6014 - val_loss: 0.9752 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00009: val_acc did not improve from 0.78947\n",
      "Epoch 10/200\n",
      "27/27 [==============================] - 5s 173ms/step - loss: 1.0609 - acc: 0.4727 - val_loss: 1.0077 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00010: val_acc did not improve from 0.78947\n",
      "Epoch 11/200\n",
      "27/27 [==============================] - 5s 176ms/step - loss: 1.0027 - acc: 0.5098 - val_loss: 0.9779 - val_acc: 0.5789\n",
      "\n",
      "Epoch 00011: val_acc did not improve from 0.78947\n",
      "Epoch 12/200\n",
      "27/27 [==============================] - 5s 172ms/step - loss: 0.9558 - acc: 0.5551 - val_loss: 0.8919 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00012: val_acc did not improve from 0.78947\n",
      "Epoch 13/200\n",
      "27/27 [==============================] - 5s 172ms/step - loss: 0.9263 - acc: 0.6117 - val_loss: 0.8075 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00013: val_acc did not improve from 0.78947\n",
      "Epoch 14/200\n",
      "27/27 [==============================] - 5s 173ms/step - loss: 0.9026 - acc: 0.5376 - val_loss: 0.9216 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00014: val_acc did not improve from 0.78947\n",
      "Epoch 15/200\n",
      "27/27 [==============================] - 4s 164ms/step - loss: 0.8503 - acc: 0.6756 - val_loss: 0.8682 - val_acc: 0.4737\n",
      "\n",
      "Epoch 00015: val_acc did not improve from 0.78947\n",
      "Epoch 16/200\n",
      "27/27 [==============================] - 5s 169ms/step - loss: 0.8750 - acc: 0.6107 - val_loss: 0.9814 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00016: val_acc did not improve from 0.78947\n",
      "Epoch 17/200\n",
      "27/27 [==============================] - 4s 166ms/step - loss: 0.8367 - acc: 0.6014 - val_loss: 0.8047 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00017: val_acc did not improve from 0.78947\n",
      "Epoch 18/200\n",
      "27/27 [==============================] - 5s 177ms/step - loss: 0.8602 - acc: 0.4356 - val_loss: 0.8218 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00018: val_acc did not improve from 0.78947\n",
      "Epoch 19/200\n",
      "27/27 [==============================] - 5s 174ms/step - loss: 0.8092 - acc: 0.5922 - val_loss: 0.8633 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00019: val_acc did not improve from 0.78947\n",
      "Epoch 20/200\n",
      "27/27 [==============================] - 5s 181ms/step - loss: 0.8228 - acc: 0.4717 - val_loss: 0.8312 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00020: val_acc did not improve from 0.78947\n",
      "Epoch 21/200\n",
      "27/27 [==============================] - 5s 185ms/step - loss: 0.7715 - acc: 0.6200 - val_loss: 0.8520 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00021: val_acc did not improve from 0.78947\n",
      "Epoch 22/200\n",
      "27/27 [==============================] - 5s 172ms/step - loss: 0.7717 - acc: 0.5654 - val_loss: 0.7368 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00022: val_acc did not improve from 0.78947\n",
      "Epoch 23/200\n",
      "27/27 [==============================] - 5s 169ms/step - loss: 0.6995 - acc: 0.7034 - val_loss: 0.6542 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00023: val_acc did not improve from 0.78947\n",
      "Epoch 24/200\n",
      "27/27 [==============================] - 4s 166ms/step - loss: 0.7412 - acc: 0.6673 - val_loss: 0.6651 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00024: val_acc did not improve from 0.78947\n",
      "Epoch 25/200\n",
      "27/27 [==============================] - 5s 169ms/step - loss: 0.7221 - acc: 0.6571 - val_loss: 0.9572 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00025: val_acc did not improve from 0.78947\n",
      "Epoch 26/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 0.7679 - acc: 0.5839 - val_loss: 0.7239 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00026: val_acc did not improve from 0.78947\n",
      "Epoch 27/200\n",
      "27/27 [==============================] - 4s 164ms/step - loss: 0.7125 - acc: 0.6478 - val_loss: 0.6655 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00027: val_acc did not improve from 0.78947\n",
      "Epoch 28/200\n",
      "27/27 [==============================] - 5s 177ms/step - loss: 0.7018 - acc: 0.6756 - val_loss: 0.7737 - val_acc: 0.4737\n",
      "\n",
      "Epoch 00028: val_acc did not improve from 0.78947\n",
      "Epoch 29/200\n",
      "27/27 [==============================] - 5s 181ms/step - loss: 0.6650 - acc: 0.6571 - val_loss: 0.6085 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00029: val_acc improved from 0.78947 to 0.84211, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/MS_from_scratch/weights-augm-trial6-improvement-BEST.hdf5\n",
      "Epoch 30/200\n",
      "27/27 [==============================] - 5s 173ms/step - loss: 0.6512 - acc: 0.7219 - val_loss: 0.5800 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00030: val_acc did not improve from 0.84211\n",
      "Epoch 31/200\n",
      "27/27 [==============================] - 5s 183ms/step - loss: 0.6866 - acc: 0.6581 - val_loss: 0.6414 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00031: val_acc did not improve from 0.84211\n",
      "Epoch 32/200\n",
      "27/27 [==============================] - 5s 179ms/step - loss: 0.6466 - acc: 0.7137 - val_loss: 0.6883 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00032: val_acc did not improve from 0.84211\n",
      "Epoch 33/200\n",
      "27/27 [==============================] - 5s 191ms/step - loss: 0.6918 - acc: 0.7508 - val_loss: 0.6348 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00033: val_acc did not improve from 0.84211\n",
      "Epoch 34/200\n",
      "27/27 [==============================] - 5s 178ms/step - loss: 0.6121 - acc: 0.7044 - val_loss: 0.7420 - val_acc: 0.5789\n",
      "\n",
      "Epoch 00034: val_acc did not improve from 0.84211\n",
      "Epoch 35/200\n",
      "27/27 [==============================] - 5s 182ms/step - loss: 0.5936 - acc: 0.6941 - val_loss: 0.5585 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00035: val_acc did not improve from 0.84211\n",
      "Epoch 36/200\n",
      "27/27 [==============================] - 5s 178ms/step - loss: 0.6495 - acc: 0.7034 - val_loss: 0.7408 - val_acc: 0.5263\n",
      "\n",
      "Epoch 00036: val_acc did not improve from 0.84211\n",
      "Epoch 37/200\n",
      "27/27 [==============================] - 5s 188ms/step - loss: 0.6670 - acc: 0.6756 - val_loss: 0.8402 - val_acc: 0.3684\n",
      "\n",
      "Epoch 00037: val_acc did not improve from 0.84211\n",
      "Epoch 38/200\n",
      "27/27 [==============================] - 5s 179ms/step - loss: 0.6374 - acc: 0.6766 - val_loss: 0.5681 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00038: val_acc did not improve from 0.84211\n",
      "Epoch 39/200\n",
      "27/27 [==============================] - 5s 179ms/step - loss: 0.6071 - acc: 0.7044 - val_loss: 0.5477 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00039: val_acc did not improve from 0.84211\n",
      "Epoch 40/200\n",
      "27/27 [==============================] - 5s 176ms/step - loss: 0.5962 - acc: 0.7044 - val_loss: 0.6265 - val_acc: 0.5789\n",
      "\n",
      "Epoch 00040: val_acc did not improve from 0.84211\n",
      "Epoch 41/200\n",
      "27/27 [==============================] - 5s 179ms/step - loss: 0.5595 - acc: 0.7508 - val_loss: 0.5435 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00041: val_acc did not improve from 0.84211\n",
      "Epoch 42/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27/27 [==============================] - 5s 190ms/step - loss: 0.5558 - acc: 0.7415 - val_loss: 0.5332 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00042: val_acc did not improve from 0.84211\n",
      "Epoch 43/200\n",
      "27/27 [==============================] - 5s 187ms/step - loss: 0.5887 - acc: 0.6952 - val_loss: 0.5367 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00043: val_acc did not improve from 0.84211\n",
      "Epoch 44/200\n",
      "27/27 [==============================] - 5s 175ms/step - loss: 0.5995 - acc: 0.7219 - val_loss: 0.5673 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00044: val_acc did not improve from 0.84211\n",
      "Epoch 45/200\n",
      "27/27 [==============================] - 5s 180ms/step - loss: 0.5710 - acc: 0.7137 - val_loss: 0.6241 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00045: val_acc did not improve from 0.84211\n",
      "Epoch 46/200\n",
      "27/27 [==============================] - 5s 176ms/step - loss: 0.5764 - acc: 0.7776 - val_loss: 0.5836 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00046: val_acc did not improve from 0.84211\n",
      "Epoch 47/200\n",
      "27/27 [==============================] - 5s 178ms/step - loss: 0.5795 - acc: 0.7034 - val_loss: 0.5539 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00047: val_acc did not improve from 0.84211\n",
      "Epoch 48/200\n",
      "27/27 [==============================] - 5s 180ms/step - loss: 0.5538 - acc: 0.7497 - val_loss: 0.5221 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00048: val_acc did not improve from 0.84211\n",
      "Epoch 49/200\n",
      "27/27 [==============================] - 5s 182ms/step - loss: 0.5376 - acc: 0.7590 - val_loss: 0.5355 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00049: val_acc did not improve from 0.84211\n",
      "Epoch 50/200\n",
      "27/27 [==============================] - 5s 176ms/step - loss: 0.6106 - acc: 0.6941 - val_loss: 0.5302 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00050: val_acc did not improve from 0.84211\n",
      "Epoch 51/200\n",
      "27/27 [==============================] - 5s 168ms/step - loss: 0.5648 - acc: 0.7405 - val_loss: 0.5519 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00051: val_acc did not improve from 0.84211\n",
      "Epoch 52/200\n",
      "27/27 [==============================] - 5s 172ms/step - loss: 0.5416 - acc: 0.7312 - val_loss: 0.8901 - val_acc: 0.4737\n",
      "\n",
      "Epoch 00052: val_acc did not improve from 0.84211\n",
      "Epoch 53/200\n",
      "27/27 [==============================] - 4s 165ms/step - loss: 0.5836 - acc: 0.7127 - val_loss: 0.6544 - val_acc: 0.5789\n",
      "\n",
      "Epoch 00053: val_acc did not improve from 0.84211\n",
      "Epoch 54/200\n",
      "27/27 [==============================] - 4s 159ms/step - loss: 0.5791 - acc: 0.7590 - val_loss: 0.5806 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00054: val_acc did not improve from 0.84211\n",
      "Epoch 55/200\n",
      "27/27 [==============================] - 5s 173ms/step - loss: 0.5409 - acc: 0.8146 - val_loss: 0.7640 - val_acc: 0.5263\n",
      "\n",
      "Epoch 00055: val_acc did not improve from 0.84211\n",
      "Epoch 56/200\n",
      "27/27 [==============================] - 4s 165ms/step - loss: 0.5149 - acc: 0.7776 - val_loss: 0.5344 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00056: val_acc did not improve from 0.84211\n",
      "Epoch 57/200\n",
      "27/27 [==============================] - 4s 165ms/step - loss: 0.5159 - acc: 0.7776 - val_loss: 0.5761 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00057: val_acc did not improve from 0.84211\n",
      "Epoch 58/200\n",
      "27/27 [==============================] - 5s 173ms/step - loss: 0.4918 - acc: 0.8239 - val_loss: 0.6686 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00058: val_acc did not improve from 0.84211\n",
      "Epoch 59/200\n",
      "27/27 [==============================] - 5s 177ms/step - loss: 0.5045 - acc: 0.7683 - val_loss: 0.5202 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00059: val_acc did not improve from 0.84211\n",
      "Epoch 60/200\n",
      "27/27 [==============================] - 5s 179ms/step - loss: 0.5349 - acc: 0.7683 - val_loss: 0.5938 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00060: val_acc did not improve from 0.84211\n",
      "Epoch 61/200\n",
      "27/27 [==============================] - 5s 179ms/step - loss: 0.4898 - acc: 0.8054 - val_loss: 0.5313 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00061: val_acc did not improve from 0.84211\n",
      "Epoch 62/200\n",
      "27/27 [==============================] - 5s 181ms/step - loss: 0.4865 - acc: 0.8239 - val_loss: 0.6484 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00062: val_acc did not improve from 0.84211\n",
      "Epoch 63/200\n",
      "27/27 [==============================] - 5s 178ms/step - loss: 0.4914 - acc: 0.8054 - val_loss: 0.6341 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00063: val_acc did not improve from 0.84211\n",
      "Epoch 64/200\n",
      "27/27 [==============================] - 5s 179ms/step - loss: 0.5203 - acc: 0.8054 - val_loss: 0.5565 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00064: val_acc did not improve from 0.84211\n",
      "Epoch 65/200\n",
      "27/27 [==============================] - 5s 191ms/step - loss: 0.4448 - acc: 0.8517 - val_loss: 0.5671 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00065: val_acc did not improve from 0.84211\n",
      "Epoch 66/200\n",
      "27/27 [==============================] - 5s 178ms/step - loss: 0.5063 - acc: 0.8146 - val_loss: 0.6759 - val_acc: 0.5263\n",
      "\n",
      "Epoch 00066: val_acc did not improve from 0.84211\n",
      "Epoch 67/200\n",
      "27/27 [==============================] - 5s 176ms/step - loss: 0.4918 - acc: 0.8527 - val_loss: 0.7005 - val_acc: 0.5263\n",
      "\n",
      "Epoch 00067: val_acc did not improve from 0.84211\n",
      "Epoch 68/200\n",
      "27/27 [==============================] - 5s 179ms/step - loss: 0.4874 - acc: 0.8146 - val_loss: 0.5632 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00068: val_acc did not improve from 0.84211\n",
      "Epoch 69/200\n",
      "27/27 [==============================] - 5s 177ms/step - loss: 0.6332 - acc: 0.7044 - val_loss: 0.7135 - val_acc: 0.5263\n",
      "\n",
      "Epoch 00069: val_acc did not improve from 0.84211\n",
      "Epoch 70/200\n",
      "27/27 [==============================] - 5s 175ms/step - loss: 0.4729 - acc: 0.8517 - val_loss: 0.5871 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00070: val_acc did not improve from 0.84211\n",
      "Epoch 71/200\n",
      "27/27 [==============================] - 5s 175ms/step - loss: 0.4735 - acc: 0.8424 - val_loss: 0.5269 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00071: val_acc did not improve from 0.84211\n",
      "Epoch 72/200\n",
      "27/27 [==============================] - 5s 180ms/step - loss: 0.4799 - acc: 0.7868 - val_loss: 0.5555 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00072: val_acc did not improve from 0.84211\n",
      "Epoch 73/200\n",
      "27/27 [==============================] - 5s 174ms/step - loss: 0.5171 - acc: 0.7868 - val_loss: 0.5870 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00073: val_acc did not improve from 0.84211\n",
      "Epoch 74/200\n",
      "27/27 [==============================] - 5s 176ms/step - loss: 0.5076 - acc: 0.7693 - val_loss: 0.5447 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00074: val_acc did not improve from 0.84211\n",
      "Epoch 00074: early stopping\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 792x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trial 7\n",
      "Epoch 1/200\n",
      "27/27 [==============================] - 8s 283ms/step - loss: 1.9516 - acc: 0.5551 - val_loss: 1.9116 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00001: val_acc improved from -inf to 0.31579, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/MS_from_scratch/weights-augm-trial7-improvement-BEST.hdf5\n",
      "Epoch 2/200\n",
      "27/27 [==============================] - 4s 154ms/step - loss: 1.8186 - acc: 0.5458 - val_loss: 1.7896 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00002: val_acc did not improve from 0.31579\n",
      "Epoch 3/200\n",
      "27/27 [==============================] - 5s 182ms/step - loss: 1.6686 - acc: 0.5458 - val_loss: 1.6773 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00003: val_acc did not improve from 0.31579\n",
      "Epoch 4/200\n",
      "27/27 [==============================] - 5s 178ms/step - loss: 1.5391 - acc: 0.5747 - val_loss: 1.4161 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00004: val_acc improved from 0.31579 to 0.68421, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/MS_from_scratch/weights-augm-trial7-improvement-BEST.hdf5\n",
      "Epoch 5/200\n",
      "27/27 [==============================] - 5s 185ms/step - loss: 1.4526 - acc: 0.5561 - val_loss: 1.4750 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00005: val_acc did not improve from 0.68421\n",
      "Epoch 6/200\n",
      "27/27 [==============================] - 5s 178ms/step - loss: 1.3547 - acc: 0.5458 - val_loss: 1.3670 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00006: val_acc did not improve from 0.68421\n",
      "Epoch 7/200\n",
      "27/27 [==============================] - 5s 186ms/step - loss: 1.2549 - acc: 0.6385 - val_loss: 1.3836 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00007: val_acc did not improve from 0.68421\n",
      "Epoch 8/200\n",
      "27/27 [==============================] - 5s 173ms/step - loss: 1.1830 - acc: 0.5839 - val_loss: 1.2417 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00008: val_acc did not improve from 0.68421\n",
      "Epoch 9/200\n",
      "27/27 [==============================] - 5s 180ms/step - loss: 1.0791 - acc: 0.6571 - val_loss: 1.2384 - val_acc: 0.3684\n",
      "\n",
      "Epoch 00009: val_acc did not improve from 0.68421\n",
      "Epoch 10/200\n",
      "27/27 [==============================] - 5s 173ms/step - loss: 1.0973 - acc: 0.6663 - val_loss: 1.1685 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00010: val_acc did not improve from 0.68421\n",
      "Epoch 11/200\n",
      "27/27 [==============================] - 5s 175ms/step - loss: 1.0581 - acc: 0.5283 - val_loss: 1.0860 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00011: val_acc did not improve from 0.68421\n",
      "Epoch 12/200\n",
      "27/27 [==============================] - 5s 176ms/step - loss: 0.9788 - acc: 0.6107 - val_loss: 0.8938 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00012: val_acc improved from 0.68421 to 0.73684, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/MS_from_scratch/weights-augm-trial7-improvement-BEST.hdf5\n",
      "Epoch 13/200\n",
      "27/27 [==============================] - 5s 172ms/step - loss: 0.9632 - acc: 0.6117 - val_loss: 1.0585 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00013: val_acc did not improve from 0.73684\n",
      "Epoch 14/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 0.8995 - acc: 0.6571 - val_loss: 0.8043 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00014: val_acc improved from 0.73684 to 0.78947, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/MS_from_scratch/weights-augm-trial7-improvement-BEST.hdf5\n",
      "Epoch 15/200\n",
      "27/27 [==============================] - 4s 160ms/step - loss: 0.8262 - acc: 0.6941 - val_loss: 0.9058 - val_acc: 0.5789\n",
      "\n",
      "Epoch 00015: val_acc did not improve from 0.78947\n",
      "Epoch 16/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 0.8278 - acc: 0.6673 - val_loss: 0.7398 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00016: val_acc did not improve from 0.78947\n",
      "Epoch 17/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 0.8331 - acc: 0.6766 - val_loss: 0.8974 - val_acc: 0.5263\n",
      "\n",
      "Epoch 00017: val_acc did not improve from 0.78947\n",
      "Epoch 18/200\n",
      "27/27 [==============================] - 5s 176ms/step - loss: 0.8344 - acc: 0.6756 - val_loss: 0.7631 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00018: val_acc did not improve from 0.78947\n",
      "Epoch 19/200\n",
      "27/27 [==============================] - 5s 177ms/step - loss: 0.7950 - acc: 0.6756 - val_loss: 0.7358 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00019: val_acc did not improve from 0.78947\n",
      "Epoch 20/200\n",
      "27/27 [==============================] - 5s 181ms/step - loss: 0.7338 - acc: 0.7312 - val_loss: 0.6520 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00020: val_acc improved from 0.78947 to 0.84211, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/MS_from_scratch/weights-augm-trial7-improvement-BEST.hdf5\n",
      "Epoch 21/200\n",
      "27/27 [==============================] - 5s 187ms/step - loss: 0.7486 - acc: 0.7034 - val_loss: 0.8550 - val_acc: 0.5263\n",
      "\n",
      "Epoch 00021: val_acc did not improve from 0.84211\n",
      "Epoch 22/200\n",
      "27/27 [==============================] - 5s 189ms/step - loss: 0.8546 - acc: 0.5469 - val_loss: 0.9112 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00022: val_acc did not improve from 0.84211\n",
      "Epoch 23/200\n",
      "27/27 [==============================] - 5s 191ms/step - loss: 0.7577 - acc: 0.6673 - val_loss: 0.7458 - val_acc: 0.5789\n",
      "\n",
      "Epoch 00023: val_acc did not improve from 0.84211\n",
      "Epoch 24/200\n",
      "27/27 [==============================] - 5s 177ms/step - loss: 0.6829 - acc: 0.7497 - val_loss: 0.9698 - val_acc: 0.4211\n",
      "\n",
      "Epoch 00024: val_acc did not improve from 0.84211\n",
      "Epoch 25/200\n",
      "27/27 [==============================] - 5s 179ms/step - loss: 0.7058 - acc: 0.6849 - val_loss: 0.9715 - val_acc: 0.3684\n",
      "\n",
      "Epoch 00025: val_acc did not improve from 0.84211\n",
      "Epoch 26/200\n",
      "27/27 [==============================] - 5s 183ms/step - loss: 0.6820 - acc: 0.7127 - val_loss: 0.7132 - val_acc: 0.5789\n",
      "\n",
      "Epoch 00026: val_acc did not improve from 0.84211\n",
      "Epoch 27/200\n",
      "27/27 [==============================] - 5s 179ms/step - loss: 0.6679 - acc: 0.7405 - val_loss: 0.5963 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00027: val_acc did not improve from 0.84211\n",
      "Epoch 28/200\n",
      "27/27 [==============================] - 5s 176ms/step - loss: 0.6577 - acc: 0.7230 - val_loss: 0.7464 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00028: val_acc did not improve from 0.84211\n",
      "Epoch 29/200\n",
      "27/27 [==============================] - 5s 182ms/step - loss: 0.7057 - acc: 0.6663 - val_loss: 0.8170 - val_acc: 0.5263\n",
      "\n",
      "Epoch 00029: val_acc did not improve from 0.84211\n",
      "Epoch 30/200\n",
      "27/27 [==============================] - 5s 177ms/step - loss: 0.6778 - acc: 0.7127 - val_loss: 0.6588 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00030: val_acc did not improve from 0.84211\n",
      "Epoch 31/200\n",
      "27/27 [==============================] - 5s 188ms/step - loss: 0.6175 - acc: 0.7497 - val_loss: 0.6335 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00031: val_acc did not improve from 0.84211\n",
      "Epoch 32/200\n",
      "27/27 [==============================] - 5s 183ms/step - loss: 0.6277 - acc: 0.6849 - val_loss: 0.5670 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00032: val_acc did not improve from 0.84211\n",
      "Epoch 33/200\n",
      "27/27 [==============================] - 5s 180ms/step - loss: 0.6539 - acc: 0.7127 - val_loss: 0.6036 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00033: val_acc did not improve from 0.84211\n",
      "Epoch 34/200\n",
      "27/27 [==============================] - 5s 188ms/step - loss: 0.6356 - acc: 0.7312 - val_loss: 0.5756 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00034: val_acc did not improve from 0.84211\n",
      "Epoch 35/200\n",
      "27/27 [==============================] - 5s 191ms/step - loss: 0.6496 - acc: 0.7034 - val_loss: 0.7230 - val_acc: 0.5789\n",
      "\n",
      "Epoch 00035: val_acc did not improve from 0.84211\n",
      "Epoch 36/200\n",
      "27/27 [==============================] - 5s 179ms/step - loss: 0.6073 - acc: 0.7230 - val_loss: 0.6784 - val_acc: 0.5263\n",
      "\n",
      "Epoch 00036: val_acc did not improve from 0.84211\n",
      "Epoch 37/200\n",
      "27/27 [==============================] - 5s 182ms/step - loss: 0.6428 - acc: 0.6581 - val_loss: 0.5822 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00037: val_acc did not improve from 0.84211\n",
      "Epoch 38/200\n",
      "27/27 [==============================] - 5s 191ms/step - loss: 0.5877 - acc: 0.7868 - val_loss: 0.5525 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00038: val_acc did not improve from 0.84211\n",
      "Epoch 39/200\n",
      "27/27 [==============================] - 5s 183ms/step - loss: 0.5891 - acc: 0.7312 - val_loss: 0.8126 - val_acc: 0.4737\n",
      "\n",
      "Epoch 00039: val_acc did not improve from 0.84211\n",
      "Epoch 40/200\n",
      "27/27 [==============================] - 5s 193ms/step - loss: 0.5714 - acc: 0.7405 - val_loss: 0.6315 - val_acc: 0.5789\n",
      "\n",
      "Epoch 00040: val_acc did not improve from 0.84211\n",
      "Epoch 41/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27/27 [==============================] - 5s 175ms/step - loss: 0.5980 - acc: 0.7405 - val_loss: 0.6204 - val_acc: 0.5789\n",
      "\n",
      "Epoch 00041: val_acc did not improve from 0.84211\n",
      "Epoch 42/200\n",
      "27/27 [==============================] - 5s 184ms/step - loss: 0.5777 - acc: 0.7497 - val_loss: 0.6114 - val_acc: 0.5789\n",
      "\n",
      "Epoch 00042: val_acc did not improve from 0.84211\n",
      "Epoch 43/200\n",
      "27/27 [==============================] - 5s 187ms/step - loss: 0.5622 - acc: 0.7322 - val_loss: 0.5416 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00043: val_acc did not improve from 0.84211\n",
      "Epoch 44/200\n",
      "27/27 [==============================] - 5s 184ms/step - loss: 0.5977 - acc: 0.7044 - val_loss: 0.5667 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00044: val_acc did not improve from 0.84211\n",
      "Epoch 45/200\n",
      "27/27 [==============================] - 5s 178ms/step - loss: 0.5935 - acc: 0.6673 - val_loss: 0.5311 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00045: val_acc did not improve from 0.84211\n",
      "Epoch 46/200\n",
      "27/27 [==============================] - 5s 174ms/step - loss: 0.6429 - acc: 0.6117 - val_loss: 0.5599 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00046: val_acc did not improve from 0.84211\n",
      "Epoch 47/200\n",
      "27/27 [==============================] - 4s 166ms/step - loss: 0.5784 - acc: 0.6849 - val_loss: 0.6166 - val_acc: 0.5789\n",
      "\n",
      "Epoch 00047: val_acc did not improve from 0.84211\n",
      "Epoch 48/200\n",
      "27/27 [==============================] - 5s 180ms/step - loss: 0.5394 - acc: 0.7786 - val_loss: 0.6777 - val_acc: 0.5789\n",
      "\n",
      "Epoch 00048: val_acc did not improve from 0.84211\n",
      "Epoch 49/200\n",
      "27/27 [==============================] - 5s 185ms/step - loss: 0.5709 - acc: 0.7405 - val_loss: 1.2250 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00049: val_acc did not improve from 0.84211\n",
      "Epoch 50/200\n",
      "27/27 [==============================] - 5s 186ms/step - loss: 0.6220 - acc: 0.7044 - val_loss: 0.6342 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00050: val_acc did not improve from 0.84211\n",
      "Epoch 51/200\n",
      "27/27 [==============================] - 5s 188ms/step - loss: 0.5524 - acc: 0.7590 - val_loss: 0.5487 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00051: val_acc did not improve from 0.84211\n",
      "Epoch 52/200\n",
      "27/27 [==============================] - 5s 193ms/step - loss: 0.5104 - acc: 0.7683 - val_loss: 0.5996 - val_acc: 0.5789\n",
      "\n",
      "Epoch 00052: val_acc did not improve from 0.84211\n",
      "Epoch 53/200\n",
      "27/27 [==============================] - 5s 185ms/step - loss: 0.5279 - acc: 0.7868 - val_loss: 0.6322 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00053: val_acc did not improve from 0.84211\n",
      "Epoch 54/200\n",
      "27/27 [==============================] - 5s 183ms/step - loss: 0.5181 - acc: 0.8054 - val_loss: 0.5758 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00054: val_acc did not improve from 0.84211\n",
      "Epoch 55/200\n",
      "27/27 [==============================] - 5s 176ms/step - loss: 0.5067 - acc: 0.7971 - val_loss: 0.5930 - val_acc: 0.5789\n",
      "\n",
      "Epoch 00055: val_acc did not improve from 0.84211\n",
      "Epoch 56/200\n",
      "27/27 [==============================] - 5s 180ms/step - loss: 0.4926 - acc: 0.8146 - val_loss: 0.7141 - val_acc: 0.5789\n",
      "\n",
      "Epoch 00056: val_acc did not improve from 0.84211\n",
      "Epoch 57/200\n",
      "27/27 [==============================] - 5s 183ms/step - loss: 0.4650 - acc: 0.8424 - val_loss: 0.9575 - val_acc: 0.4737\n",
      "\n",
      "Epoch 00057: val_acc did not improve from 0.84211\n",
      "Epoch 58/200\n",
      "27/27 [==============================] - 5s 188ms/step - loss: 0.5199 - acc: 0.7600 - val_loss: 0.5175 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00058: val_acc did not improve from 0.84211\n",
      "Epoch 59/200\n",
      "27/27 [==============================] - 5s 186ms/step - loss: 0.5970 - acc: 0.7405 - val_loss: 0.5149 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00059: val_acc did not improve from 0.84211\n",
      "Epoch 60/200\n",
      "27/27 [==============================] - 5s 193ms/step - loss: 0.4957 - acc: 0.8146 - val_loss: 0.7185 - val_acc: 0.5263\n",
      "\n",
      "Epoch 00060: val_acc did not improve from 0.84211\n",
      "Epoch 61/200\n",
      "27/27 [==============================] - 5s 190ms/step - loss: 0.4982 - acc: 0.7600 - val_loss: 0.7764 - val_acc: 0.5263\n",
      "\n",
      "Epoch 00061: val_acc did not improve from 0.84211\n",
      "Epoch 62/200\n",
      "27/27 [==============================] - 5s 189ms/step - loss: 0.5028 - acc: 0.8239 - val_loss: 0.6508 - val_acc: 0.5789\n",
      "\n",
      "Epoch 00062: val_acc did not improve from 0.84211\n",
      "Epoch 63/200\n",
      "27/27 [==============================] - 5s 177ms/step - loss: 0.4853 - acc: 0.8332 - val_loss: 0.5389 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00063: val_acc did not improve from 0.84211\n",
      "Epoch 64/200\n",
      "27/27 [==============================] - 5s 175ms/step - loss: 0.4470 - acc: 0.8239 - val_loss: 0.5656 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00064: val_acc did not improve from 0.84211\n",
      "Epoch 65/200\n",
      "27/27 [==============================] - 5s 176ms/step - loss: 0.4723 - acc: 0.8332 - val_loss: 0.5143 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00065: val_acc did not improve from 0.84211\n",
      "Epoch 66/200\n",
      "27/27 [==============================] - 5s 171ms/step - loss: 0.5215 - acc: 0.7405 - val_loss: 0.5126 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00066: val_acc did not improve from 0.84211\n",
      "Epoch 67/200\n",
      "27/27 [==============================] - 5s 188ms/step - loss: 0.4188 - acc: 0.8702 - val_loss: 0.5545 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00067: val_acc did not improve from 0.84211\n",
      "Epoch 68/200\n",
      "27/27 [==============================] - 5s 179ms/step - loss: 0.4365 - acc: 0.8980 - val_loss: 0.5104 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00068: val_acc did not improve from 0.84211\n",
      "Epoch 69/200\n",
      "27/27 [==============================] - 5s 179ms/step - loss: 0.3939 - acc: 0.8702 - val_loss: 0.5026 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00069: val_acc did not improve from 0.84211\n",
      "Epoch 70/200\n",
      "27/27 [==============================] - 5s 185ms/step - loss: 0.4032 - acc: 0.8702 - val_loss: 0.4923 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00070: val_acc did not improve from 0.84211\n",
      "Epoch 71/200\n",
      "27/27 [==============================] - 5s 181ms/step - loss: 0.3972 - acc: 0.8795 - val_loss: 0.5003 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00071: val_acc did not improve from 0.84211\n",
      "Epoch 72/200\n",
      "27/27 [==============================] - 5s 182ms/step - loss: 0.3965 - acc: 0.8610 - val_loss: 0.7152 - val_acc: 0.5263\n",
      "\n",
      "Epoch 00072: val_acc did not improve from 0.84211\n",
      "Epoch 73/200\n",
      "27/27 [==============================] - 5s 171ms/step - loss: 0.3865 - acc: 0.8888 - val_loss: 0.5909 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00073: val_acc did not improve from 0.84211\n",
      "Epoch 74/200\n",
      "27/27 [==============================] - 5s 174ms/step - loss: 0.3680 - acc: 0.8888 - val_loss: 0.7585 - val_acc: 0.5263\n",
      "\n",
      "Epoch 00074: val_acc did not improve from 0.84211\n",
      "Epoch 75/200\n",
      "27/27 [==============================] - 5s 178ms/step - loss: 0.3761 - acc: 0.8795 - val_loss: 0.5109 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00075: val_acc did not improve from 0.84211\n",
      "Epoch 76/200\n",
      "27/27 [==============================] - 5s 176ms/step - loss: 0.3334 - acc: 0.9351 - val_loss: 0.5616 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00076: val_acc did not improve from 0.84211\n",
      "Epoch 77/200\n",
      "27/27 [==============================] - 5s 175ms/step - loss: 0.3289 - acc: 0.9351 - val_loss: 0.7665 - val_acc: 0.5263\n",
      "\n",
      "Epoch 00077: val_acc did not improve from 0.84211\n",
      "Epoch 78/200\n",
      "27/27 [==============================] - 5s 173ms/step - loss: 0.4095 - acc: 0.8702 - val_loss: 0.5412 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00078: val_acc did not improve from 0.84211\n",
      "Epoch 79/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 0.3451 - acc: 0.8980 - val_loss: 0.5107 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00079: val_acc did not improve from 0.84211\n",
      "Epoch 80/200\n",
      "27/27 [==============================] - 5s 180ms/step - loss: 0.3576 - acc: 0.8991 - val_loss: 0.6312 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00080: val_acc did not improve from 0.84211\n",
      "Epoch 81/200\n",
      "27/27 [==============================] - 5s 173ms/step - loss: 0.3281 - acc: 0.9166 - val_loss: 0.5016 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00081: val_acc did not improve from 0.84211\n",
      "Epoch 82/200\n",
      "27/27 [==============================] - 5s 177ms/step - loss: 0.3251 - acc: 0.8980 - val_loss: 0.5213 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00082: val_acc did not improve from 0.84211\n",
      "Epoch 83/200\n",
      "27/27 [==============================] - 5s 192ms/step - loss: 0.3496 - acc: 0.8980 - val_loss: 0.5538 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00083: val_acc did not improve from 0.84211\n",
      "Epoch 84/200\n",
      "27/27 [==============================] - 5s 186ms/step - loss: 0.2962 - acc: 0.9537 - val_loss: 0.5339 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00084: val_acc did not improve from 0.84211\n",
      "Epoch 85/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27/27 [==============================] - 5s 172ms/step - loss: 0.2909 - acc: 0.9351 - val_loss: 0.5195 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00085: val_acc did not improve from 0.84211\n",
      "Epoch 00085: early stopping\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 792x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trial 8\n",
      "Epoch 1/200\n",
      "27/27 [==============================] - 7s 265ms/step - loss: 1.9423 - acc: 0.4634 - val_loss: 1.9025 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00001: val_acc improved from -inf to 0.31579, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/MS_from_scratch/weights-augm-trial8-improvement-BEST.hdf5\n",
      "Epoch 2/200\n",
      "27/27 [==============================] - 4s 153ms/step - loss: 1.7789 - acc: 0.4912 - val_loss: 1.8500 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00002: val_acc did not improve from 0.31579\n",
      "Epoch 3/200\n",
      "27/27 [==============================] - 5s 173ms/step - loss: 1.6625 - acc: 0.4542 - val_loss: 1.6124 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00003: val_acc did not improve from 0.31579\n",
      "Epoch 4/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 1.5237 - acc: 0.5551 - val_loss: 1.4605 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00004: val_acc improved from 0.31579 to 0.63158, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/MS_from_scratch/weights-augm-trial8-improvement-BEST.hdf5\n",
      "Epoch 5/200\n",
      "27/27 [==============================] - 5s 180ms/step - loss: 1.3976 - acc: 0.5829 - val_loss: 1.2622 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00005: val_acc improved from 0.63158 to 0.68421, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/MS_from_scratch/weights-augm-trial8-improvement-BEST.hdf5\n",
      "Epoch 6/200\n",
      "27/27 [==============================] - 5s 184ms/step - loss: 1.3518 - acc: 0.5180 - val_loss: 1.2903 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00006: val_acc improved from 0.68421 to 0.68421, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/MS_from_scratch/weights-augm-trial8-improvement-BEST.hdf5\n",
      "Epoch 7/200\n",
      "27/27 [==============================] - 5s 170ms/step - loss: 1.2573 - acc: 0.5273 - val_loss: 1.2446 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00007: val_acc did not improve from 0.68421\n",
      "Epoch 8/200\n",
      "27/27 [==============================] - 5s 177ms/step - loss: 1.1719 - acc: 0.6200 - val_loss: 1.0864 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00008: val_acc improved from 0.68421 to 0.78947, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/MS_from_scratch/weights-augm-trial8-improvement-BEST.hdf5\n",
      "Epoch 9/200\n",
      "27/27 [==============================] - 5s 185ms/step - loss: 1.1621 - acc: 0.5283 - val_loss: 1.0299 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00009: val_acc did not improve from 0.78947\n",
      "Epoch 10/200\n",
      "27/27 [==============================] - 5s 184ms/step - loss: 1.1003 - acc: 0.4820 - val_loss: 1.0808 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00010: val_acc did not improve from 0.78947\n",
      "Epoch 11/200\n",
      "27/27 [==============================] - 5s 177ms/step - loss: 1.0230 - acc: 0.5747 - val_loss: 1.0544 - val_acc: 0.3684\n",
      "\n",
      "Epoch 00011: val_acc did not improve from 0.78947\n",
      "Epoch 12/200\n",
      "27/27 [==============================] - 5s 175ms/step - loss: 0.9812 - acc: 0.5932 - val_loss: 1.0801 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00012: val_acc did not improve from 0.78947\n",
      "Epoch 13/200\n",
      "27/27 [==============================] - 5s 181ms/step - loss: 0.9824 - acc: 0.5098 - val_loss: 0.9629 - val_acc: 0.3684\n",
      "\n",
      "Epoch 00013: val_acc did not improve from 0.78947\n",
      "Epoch 14/200\n",
      "27/27 [==============================] - 5s 183ms/step - loss: 0.9323 - acc: 0.6663 - val_loss: 0.9372 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00014: val_acc did not improve from 0.78947\n",
      "Epoch 15/200\n",
      "27/27 [==============================] - 5s 186ms/step - loss: 0.8959 - acc: 0.5922 - val_loss: 0.9633 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00015: val_acc did not improve from 0.78947\n",
      "Epoch 16/200\n",
      "27/27 [==============================] - 5s 183ms/step - loss: 0.8461 - acc: 0.6849 - val_loss: 0.7173 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00016: val_acc improved from 0.78947 to 0.84211, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/MS_from_scratch/weights-augm-trial8-improvement-BEST.hdf5\n",
      "Epoch 17/200\n",
      "27/27 [==============================] - 5s 188ms/step - loss: 0.8114 - acc: 0.6941 - val_loss: 1.3318 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00017: val_acc did not improve from 0.84211\n",
      "Epoch 18/200\n",
      "27/27 [==============================] - 5s 175ms/step - loss: 0.8147 - acc: 0.6385 - val_loss: 0.7500 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00018: val_acc did not improve from 0.84211\n",
      "Epoch 19/200\n",
      "27/27 [==============================] - 5s 182ms/step - loss: 0.7781 - acc: 0.6385 - val_loss: 0.6806 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00019: val_acc did not improve from 0.84211\n",
      "Epoch 20/200\n",
      "27/27 [==============================] - 5s 180ms/step - loss: 0.7759 - acc: 0.5839 - val_loss: 0.6475 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00020: val_acc did not improve from 0.84211\n",
      "Epoch 21/200\n",
      "27/27 [==============================] - 5s 185ms/step - loss: 0.7244 - acc: 0.7034 - val_loss: 0.9399 - val_acc: 0.3684\n",
      "\n",
      "Epoch 00021: val_acc did not improve from 0.84211\n",
      "Epoch 22/200\n",
      "27/27 [==============================] - 5s 180ms/step - loss: 0.7244 - acc: 0.6478 - val_loss: 0.7552 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00022: val_acc did not improve from 0.84211\n",
      "Epoch 23/200\n",
      "27/27 [==============================] - 5s 183ms/step - loss: 0.7346 - acc: 0.6952 - val_loss: 0.9313 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00023: val_acc did not improve from 0.84211\n",
      "Epoch 24/200\n",
      "27/27 [==============================] - 5s 188ms/step - loss: 0.7090 - acc: 0.7044 - val_loss: 0.6681 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00024: val_acc did not improve from 0.84211\n",
      "Epoch 25/200\n",
      "27/27 [==============================] - 5s 181ms/step - loss: 0.6484 - acc: 0.7683 - val_loss: 0.5852 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00025: val_acc did not improve from 0.84211\n",
      "Epoch 26/200\n",
      "27/27 [==============================] - 5s 180ms/step - loss: 0.6656 - acc: 0.7127 - val_loss: 0.6243 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00026: val_acc did not improve from 0.84211\n",
      "Epoch 27/200\n",
      "27/27 [==============================] - 4s 166ms/step - loss: 0.6310 - acc: 0.7497 - val_loss: 0.5744 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00027: val_acc did not improve from 0.84211\n",
      "Epoch 28/200\n",
      "27/27 [==============================] - 5s 174ms/step - loss: 0.7108 - acc: 0.6581 - val_loss: 0.6445 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00028: val_acc did not improve from 0.84211\n",
      "Epoch 29/200\n",
      "27/27 [==============================] - 5s 175ms/step - loss: 0.6835 - acc: 0.6766 - val_loss: 0.5914 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00029: val_acc did not improve from 0.84211\n",
      "Epoch 30/200\n",
      "27/27 [==============================] - 5s 170ms/step - loss: 0.6686 - acc: 0.6941 - val_loss: 0.6313 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00030: val_acc did not improve from 0.84211\n",
      "Epoch 31/200\n",
      "27/27 [==============================] - 5s 175ms/step - loss: 0.6968 - acc: 0.7044 - val_loss: 0.7371 - val_acc: 0.5789\n",
      "\n",
      "Epoch 00031: val_acc did not improve from 0.84211\n",
      "Epoch 32/200\n",
      "27/27 [==============================] - 4s 159ms/step - loss: 0.5923 - acc: 0.7590 - val_loss: 0.5503 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00032: val_acc did not improve from 0.84211\n",
      "Epoch 33/200\n",
      "27/27 [==============================] - 4s 161ms/step - loss: 0.6092 - acc: 0.7961 - val_loss: 0.6677 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00033: val_acc did not improve from 0.84211\n",
      "Epoch 34/200\n",
      "27/27 [==============================] - 5s 169ms/step - loss: 0.7103 - acc: 0.6571 - val_loss: 1.0206 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00034: val_acc did not improve from 0.84211\n",
      "Epoch 35/200\n",
      "27/27 [==============================] - 5s 178ms/step - loss: 0.6737 - acc: 0.7034 - val_loss: 0.6757 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00035: val_acc did not improve from 0.84211\n",
      "Epoch 36/200\n",
      "27/27 [==============================] - 5s 179ms/step - loss: 0.6173 - acc: 0.7497 - val_loss: 0.6408 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00036: val_acc did not improve from 0.84211\n",
      "Epoch 37/200\n",
      "27/27 [==============================] - 5s 179ms/step - loss: 0.5761 - acc: 0.7868 - val_loss: 0.5377 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00037: val_acc did not improve from 0.84211\n",
      "Epoch 38/200\n",
      "27/27 [==============================] - 5s 185ms/step - loss: 0.6044 - acc: 0.7405 - val_loss: 0.5351 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00038: val_acc did not improve from 0.84211\n",
      "Epoch 39/200\n",
      "27/27 [==============================] - 5s 175ms/step - loss: 0.5672 - acc: 0.7497 - val_loss: 0.5766 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00039: val_acc did not improve from 0.84211\n",
      "Epoch 40/200\n",
      "27/27 [==============================] - 5s 174ms/step - loss: 0.5372 - acc: 0.7497 - val_loss: 0.7405 - val_acc: 0.6316\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 00040: val_acc did not improve from 0.84211\n",
      "Epoch 41/200\n",
      "27/27 [==============================] - 5s 179ms/step - loss: 0.6188 - acc: 0.7219 - val_loss: 0.5302 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00041: val_acc did not improve from 0.84211\n",
      "Epoch 42/200\n",
      "27/27 [==============================] - 5s 178ms/step - loss: 0.6028 - acc: 0.6952 - val_loss: 0.6111 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00042: val_acc did not improve from 0.84211\n",
      "Epoch 43/200\n",
      "27/27 [==============================] - 5s 186ms/step - loss: 0.5337 - acc: 0.7776 - val_loss: 1.2370 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00043: val_acc did not improve from 0.84211\n",
      "Epoch 44/200\n",
      "27/27 [==============================] - 5s 182ms/step - loss: 0.6280 - acc: 0.7312 - val_loss: 0.6452 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00044: val_acc did not improve from 0.84211\n",
      "Epoch 45/200\n",
      "27/27 [==============================] - 5s 184ms/step - loss: 0.5483 - acc: 0.7405 - val_loss: 0.7447 - val_acc: 0.5789\n",
      "\n",
      "Epoch 00045: val_acc did not improve from 0.84211\n",
      "Epoch 46/200\n",
      "27/27 [==============================] - 5s 182ms/step - loss: 0.5390 - acc: 0.7868 - val_loss: 0.6194 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00046: val_acc did not improve from 0.84211\n",
      "Epoch 47/200\n",
      "27/27 [==============================] - 5s 176ms/step - loss: 0.5564 - acc: 0.7776 - val_loss: 0.6748 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00047: val_acc did not improve from 0.84211\n",
      "Epoch 48/200\n",
      "27/27 [==============================] - 5s 180ms/step - loss: 0.5666 - acc: 0.7312 - val_loss: 0.5322 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00048: val_acc did not improve from 0.84211\n",
      "Epoch 49/200\n",
      "27/27 [==============================] - 5s 178ms/step - loss: 0.5334 - acc: 0.7590 - val_loss: 0.5718 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00049: val_acc did not improve from 0.84211\n",
      "Epoch 50/200\n",
      "27/27 [==============================] - 5s 190ms/step - loss: 0.5147 - acc: 0.7868 - val_loss: 0.5835 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00050: val_acc did not improve from 0.84211\n",
      "Epoch 51/200\n",
      "27/27 [==============================] - 5s 185ms/step - loss: 0.4962 - acc: 0.7776 - val_loss: 0.5371 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00051: val_acc did not improve from 0.84211\n",
      "Epoch 52/200\n",
      "27/27 [==============================] - 5s 180ms/step - loss: 0.5147 - acc: 0.7683 - val_loss: 0.9034 - val_acc: 0.5263\n",
      "\n",
      "Epoch 00052: val_acc did not improve from 0.84211\n",
      "Epoch 53/200\n",
      "27/27 [==============================] - 5s 174ms/step - loss: 0.5695 - acc: 0.7776 - val_loss: 0.7075 - val_acc: 0.5789\n",
      "\n",
      "Epoch 00053: val_acc did not improve from 0.84211\n",
      "Epoch 54/200\n",
      "27/27 [==============================] - 5s 177ms/step - loss: 0.4744 - acc: 0.7868 - val_loss: 0.8085 - val_acc: 0.5263\n",
      "\n",
      "Epoch 00054: val_acc did not improve from 0.84211\n",
      "Epoch 55/200\n",
      "27/27 [==============================] - 5s 191ms/step - loss: 0.5993 - acc: 0.7127 - val_loss: 0.7094 - val_acc: 0.5263\n",
      "\n",
      "Epoch 00055: val_acc did not improve from 0.84211\n",
      "Epoch 56/200\n",
      "27/27 [==============================] - 5s 183ms/step - loss: 0.4980 - acc: 0.8054 - val_loss: 0.5098 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00056: val_acc did not improve from 0.84211\n",
      "Epoch 57/200\n",
      "27/27 [==============================] - 5s 182ms/step - loss: 0.5821 - acc: 0.7219 - val_loss: 0.5357 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00057: val_acc did not improve from 0.84211\n",
      "Epoch 58/200\n",
      "27/27 [==============================] - 5s 177ms/step - loss: 0.5037 - acc: 0.7786 - val_loss: 0.6463 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00058: val_acc did not improve from 0.84211\n",
      "Epoch 59/200\n",
      "27/27 [==============================] - 5s 174ms/step - loss: 0.4936 - acc: 0.7961 - val_loss: 0.8499 - val_acc: 0.5263\n",
      "\n",
      "Epoch 00059: val_acc did not improve from 0.84211\n",
      "Epoch 60/200\n",
      "27/27 [==============================] - 5s 181ms/step - loss: 0.5214 - acc: 0.7868 - val_loss: 0.6188 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00060: val_acc did not improve from 0.84211\n",
      "Epoch 61/200\n",
      "27/27 [==============================] - 5s 173ms/step - loss: 0.5032 - acc: 0.8054 - val_loss: 0.5044 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00061: val_acc did not improve from 0.84211\n",
      "Epoch 62/200\n",
      "27/27 [==============================] - 5s 177ms/step - loss: 0.5155 - acc: 0.7497 - val_loss: 0.7233 - val_acc: 0.5263\n",
      "\n",
      "Epoch 00062: val_acc did not improve from 0.84211\n",
      "Epoch 63/200\n",
      "27/27 [==============================] - 5s 176ms/step - loss: 0.5204 - acc: 0.7600 - val_loss: 0.6673 - val_acc: 0.5263\n",
      "\n",
      "Epoch 00063: val_acc did not improve from 0.84211\n",
      "Epoch 64/200\n",
      "27/27 [==============================] - 5s 189ms/step - loss: 0.4906 - acc: 0.8517 - val_loss: 0.5285 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00064: val_acc did not improve from 0.84211\n",
      "Epoch 65/200\n",
      "27/27 [==============================] - 5s 181ms/step - loss: 0.5215 - acc: 0.7600 - val_loss: 0.8486 - val_acc: 0.5263\n",
      "\n",
      "Epoch 00065: val_acc did not improve from 0.84211\n",
      "Epoch 66/200\n",
      "27/27 [==============================] - 5s 183ms/step - loss: 0.4859 - acc: 0.8054 - val_loss: 0.6629 - val_acc: 0.5263\n",
      "\n",
      "Epoch 00066: val_acc did not improve from 0.84211\n",
      "Epoch 67/200\n",
      "27/27 [==============================] - 5s 182ms/step - loss: 0.5083 - acc: 0.7868 - val_loss: 0.6393 - val_acc: 0.5263\n",
      "\n",
      "Epoch 00067: val_acc did not improve from 0.84211\n",
      "Epoch 68/200\n",
      "27/27 [==============================] - 4s 159ms/step - loss: 0.5050 - acc: 0.7600 - val_loss: 0.7115 - val_acc: 0.5263\n",
      "\n",
      "Epoch 00068: val_acc did not improve from 0.84211\n",
      "Epoch 69/200\n",
      "27/27 [==============================] - 4s 160ms/step - loss: 0.4704 - acc: 0.8332 - val_loss: 1.0406 - val_acc: 0.4211\n",
      "\n",
      "Epoch 00069: val_acc did not improve from 0.84211\n",
      "Epoch 70/200\n",
      "27/27 [==============================] - 4s 163ms/step - loss: 0.4370 - acc: 0.7868 - val_loss: 0.5083 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00070: val_acc did not improve from 0.84211\n",
      "Epoch 71/200\n",
      "27/27 [==============================] - 4s 165ms/step - loss: 0.4403 - acc: 0.8610 - val_loss: 0.5887 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00071: val_acc did not improve from 0.84211\n",
      "Epoch 72/200\n",
      "27/27 [==============================] - 5s 185ms/step - loss: 0.4965 - acc: 0.7415 - val_loss: 0.6989 - val_acc: 0.5263\n",
      "\n",
      "Epoch 00072: val_acc did not improve from 0.84211\n",
      "Epoch 73/200\n",
      "27/27 [==============================] - 5s 182ms/step - loss: 0.4048 - acc: 0.8795 - val_loss: 0.7009 - val_acc: 0.5263\n",
      "\n",
      "Epoch 00073: val_acc did not improve from 0.84211\n",
      "Epoch 74/200\n",
      "27/27 [==============================] - 5s 174ms/step - loss: 0.4591 - acc: 0.8424 - val_loss: 0.6144 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00074: val_acc did not improve from 0.84211\n",
      "Epoch 75/200\n",
      "27/27 [==============================] - 5s 183ms/step - loss: 0.4133 - acc: 0.8610 - val_loss: 0.6528 - val_acc: 0.5263\n",
      "\n",
      "Epoch 00075: val_acc did not improve from 0.84211\n",
      "Epoch 76/200\n",
      "27/27 [==============================] - 5s 187ms/step - loss: 0.4352 - acc: 0.8517 - val_loss: 0.5252 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00076: val_acc improved from 0.84211 to 0.84211, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/MS_from_scratch/weights-augm-trial8-improvement-BEST.hdf5\n",
      "Epoch 00076: early stopping\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 792x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trial 9\n",
      "Epoch 1/200\n",
      "27/27 [==============================] - 8s 306ms/step - loss: 1.9502 - acc: 0.4912 - val_loss: 1.8398 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00001: val_acc improved from -inf to 0.31579, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/MS_from_scratch/weights-augm-trial9-improvement-BEST.hdf5\n",
      "Epoch 2/200\n",
      "27/27 [==============================] - 4s 158ms/step - loss: 1.7781 - acc: 0.4542 - val_loss: 1.6819 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00002: val_acc did not improve from 0.31579\n",
      "Epoch 3/200\n",
      "27/27 [==============================] - 5s 183ms/step - loss: 1.6093 - acc: 0.5736 - val_loss: 1.7298 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00003: val_acc did not improve from 0.31579\n",
      "Epoch 4/200\n",
      "27/27 [==============================] - 5s 188ms/step - loss: 1.5147 - acc: 0.5458 - val_loss: 1.6553 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00004: val_acc did not improve from 0.31579\n",
      "Epoch 5/200\n",
      "27/27 [==============================] - 5s 178ms/step - loss: 1.4192 - acc: 0.5098 - val_loss: 1.3915 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00005: val_acc did not improve from 0.31579\n",
      "Epoch 6/200\n",
      "27/27 [==============================] - 5s 174ms/step - loss: 1.3041 - acc: 0.5644 - val_loss: 1.2780 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00006: val_acc did not improve from 0.31579\n",
      "Epoch 7/200\n",
      "27/27 [==============================] - 5s 178ms/step - loss: 1.2134 - acc: 0.5458 - val_loss: 1.2836 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00007: val_acc did not improve from 0.31579\n",
      "Epoch 8/200\n",
      "27/27 [==============================] - 5s 180ms/step - loss: 1.1333 - acc: 0.6107 - val_loss: 1.1207 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00008: val_acc improved from 0.31579 to 0.68421, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/MS_from_scratch/weights-augm-trial9-improvement-BEST.hdf5\n",
      "Epoch 9/200\n",
      "27/27 [==============================] - 5s 172ms/step - loss: 1.1049 - acc: 0.4902 - val_loss: 1.0618 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00009: val_acc improved from 0.68421 to 0.73684, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/MS_from_scratch/weights-augm-trial9-improvement-BEST.hdf5\n",
      "Epoch 10/200\n",
      "27/27 [==============================] - 5s 177ms/step - loss: 1.0349 - acc: 0.5551 - val_loss: 0.9798 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00010: val_acc did not improve from 0.73684\n",
      "Epoch 11/200\n",
      "27/27 [==============================] - 5s 185ms/step - loss: 1.0041 - acc: 0.5829 - val_loss: 1.0491 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00011: val_acc did not improve from 0.73684\n",
      "Epoch 12/200\n",
      "27/27 [==============================] - 5s 190ms/step - loss: 0.9589 - acc: 0.6663 - val_loss: 0.9465 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00012: val_acc did not improve from 0.73684\n",
      "Epoch 13/200\n",
      "27/27 [==============================] - 5s 185ms/step - loss: 0.9258 - acc: 0.5747 - val_loss: 0.8495 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00013: val_acc did not improve from 0.73684\n",
      "Epoch 14/200\n",
      "27/27 [==============================] - 5s 179ms/step - loss: 0.8432 - acc: 0.7034 - val_loss: 0.8205 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00014: val_acc did not improve from 0.73684\n",
      "Epoch 15/200\n",
      "27/27 [==============================] - 5s 181ms/step - loss: 0.8035 - acc: 0.6766 - val_loss: 0.7626 - val_acc: 0.5789\n",
      "\n",
      "Epoch 00015: val_acc did not improve from 0.73684\n",
      "Epoch 16/200\n",
      "27/27 [==============================] - 5s 173ms/step - loss: 0.8683 - acc: 0.6478 - val_loss: 0.7749 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00016: val_acc did not improve from 0.73684\n",
      "Epoch 17/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 0.8990 - acc: 0.5283 - val_loss: 0.7395 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00017: val_acc did not improve from 0.73684\n",
      "Epoch 18/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 0.7830 - acc: 0.6571 - val_loss: 1.0249 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00018: val_acc did not improve from 0.73684\n",
      "Epoch 19/200\n",
      "27/27 [==============================] - 4s 159ms/step - loss: 0.7968 - acc: 0.6478 - val_loss: 0.8835 - val_acc: 0.3684\n",
      "\n",
      "Epoch 00019: val_acc did not improve from 0.73684\n",
      "Epoch 20/200\n",
      "27/27 [==============================] - 4s 165ms/step - loss: 0.7917 - acc: 0.6200 - val_loss: 1.0612 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00020: val_acc did not improve from 0.73684\n",
      "Epoch 21/200\n",
      "27/27 [==============================] - 5s 171ms/step - loss: 0.8195 - acc: 0.5561 - val_loss: 0.8143 - val_acc: 0.4737\n",
      "\n",
      "Epoch 00021: val_acc did not improve from 0.73684\n",
      "Epoch 22/200\n",
      "27/27 [==============================] - 5s 191ms/step - loss: 0.7565 - acc: 0.6673 - val_loss: 0.6818 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00022: val_acc did not improve from 0.73684\n",
      "Epoch 23/200\n",
      "27/27 [==============================] - 6s 216ms/step - loss: 0.6905 - acc: 0.7312 - val_loss: 0.6157 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00023: val_acc did not improve from 0.73684\n",
      "Epoch 24/200\n",
      "27/27 [==============================] - 5s 186ms/step - loss: 0.7843 - acc: 0.6293 - val_loss: 0.7233 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00024: val_acc improved from 0.73684 to 0.78947, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/MS_from_scratch/weights-augm-trial9-improvement-BEST.hdf5\n",
      "Epoch 25/200\n",
      "27/27 [==============================] - 5s 178ms/step - loss: 0.7800 - acc: 0.5654 - val_loss: 0.7378 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00025: val_acc did not improve from 0.78947\n",
      "Epoch 26/200\n",
      "27/27 [==============================] - 5s 183ms/step - loss: 0.7576 - acc: 0.5561 - val_loss: 0.7909 - val_acc: 0.4737\n",
      "\n",
      "Epoch 00026: val_acc did not improve from 0.78947\n",
      "Epoch 27/200\n",
      "27/27 [==============================] - 5s 183ms/step - loss: 0.7168 - acc: 0.6210 - val_loss: 0.6027 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00027: val_acc improved from 0.78947 to 0.84211, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/MS_from_scratch/weights-augm-trial9-improvement-BEST.hdf5\n",
      "Epoch 28/200\n",
      "27/27 [==============================] - 5s 183ms/step - loss: 0.6723 - acc: 0.6849 - val_loss: 0.7749 - val_acc: 0.5263\n",
      "\n",
      "Epoch 00028: val_acc did not improve from 0.84211\n",
      "Epoch 29/200\n",
      "27/27 [==============================] - 4s 166ms/step - loss: 0.6292 - acc: 0.7405 - val_loss: 0.8138 - val_acc: 0.4737\n",
      "\n",
      "Epoch 00029: val_acc did not improve from 0.84211\n",
      "Epoch 30/200\n",
      "27/27 [==============================] - 5s 184ms/step - loss: 0.7186 - acc: 0.6303 - val_loss: 0.6814 - val_acc: 0.5789\n",
      "\n",
      "Epoch 00030: val_acc did not improve from 0.84211\n",
      "Epoch 31/200\n",
      "27/27 [==============================] - 5s 185ms/step - loss: 0.6054 - acc: 0.7312 - val_loss: 0.6408 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00031: val_acc did not improve from 0.84211\n",
      "Epoch 32/200\n",
      "27/27 [==============================] - 5s 183ms/step - loss: 0.7564 - acc: 0.6117 - val_loss: 0.6561 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00032: val_acc did not improve from 0.84211\n",
      "Epoch 33/200\n",
      "27/27 [==============================] - 5s 180ms/step - loss: 0.6613 - acc: 0.6766 - val_loss: 0.6024 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00033: val_acc did not improve from 0.84211\n",
      "Epoch 34/200\n",
      "27/27 [==============================] - 5s 179ms/step - loss: 0.7201 - acc: 0.6303 - val_loss: 0.6131 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00034: val_acc did not improve from 0.84211\n",
      "Epoch 35/200\n",
      "27/27 [==============================] - 5s 180ms/step - loss: 0.6751 - acc: 0.6385 - val_loss: 0.6537 - val_acc: 0.5789\n",
      "\n",
      "Epoch 00035: val_acc did not improve from 0.84211\n",
      "Epoch 36/200\n",
      "27/27 [==============================] - 5s 179ms/step - loss: 0.6273 - acc: 0.6581 - val_loss: 0.5612 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00036: val_acc did not improve from 0.84211\n",
      "Epoch 37/200\n",
      "27/27 [==============================] - 5s 185ms/step - loss: 0.6216 - acc: 0.7034 - val_loss: 0.6002 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00037: val_acc did not improve from 0.84211\n",
      "Epoch 38/200\n",
      "27/27 [==============================] - 5s 182ms/step - loss: 0.6231 - acc: 0.6952 - val_loss: 0.5471 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00038: val_acc did not improve from 0.84211\n",
      "Epoch 39/200\n",
      "27/27 [==============================] - 5s 192ms/step - loss: 0.5616 - acc: 0.7683 - val_loss: 0.7924 - val_acc: 0.5789\n",
      "\n",
      "Epoch 00039: val_acc did not improve from 0.84211\n",
      "Epoch 40/200\n",
      "27/27 [==============================] - 5s 181ms/step - loss: 0.5521 - acc: 0.7312 - val_loss: 0.5352 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00040: val_acc did not improve from 0.84211\n",
      "Epoch 41/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27/27 [==============================] - 5s 181ms/step - loss: 0.6083 - acc: 0.7219 - val_loss: 0.8527 - val_acc: 0.4737\n",
      "\n",
      "Epoch 00041: val_acc did not improve from 0.84211\n",
      "Epoch 42/200\n",
      "27/27 [==============================] - 5s 179ms/step - loss: 0.6024 - acc: 0.6859 - val_loss: 0.6237 - val_acc: 0.5789\n",
      "\n",
      "Epoch 00042: val_acc did not improve from 0.84211\n",
      "Epoch 43/200\n",
      "27/27 [==============================] - 5s 181ms/step - loss: 0.5686 - acc: 0.7127 - val_loss: 0.6505 - val_acc: 0.5789\n",
      "\n",
      "Epoch 00043: val_acc did not improve from 0.84211\n",
      "Epoch 44/200\n",
      "27/27 [==============================] - 5s 177ms/step - loss: 0.5866 - acc: 0.7137 - val_loss: 0.5323 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00044: val_acc did not improve from 0.84211\n",
      "Epoch 45/200\n",
      "27/27 [==============================] - 5s 192ms/step - loss: 0.5566 - acc: 0.7405 - val_loss: 0.5836 - val_acc: 0.5789\n",
      "\n",
      "Epoch 00045: val_acc did not improve from 0.84211\n",
      "Epoch 46/200\n",
      "27/27 [==============================] - 5s 186ms/step - loss: 0.6025 - acc: 0.7312 - val_loss: 0.5861 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00046: val_acc did not improve from 0.84211\n",
      "Epoch 47/200\n",
      "27/27 [==============================] - 5s 188ms/step - loss: 0.6111 - acc: 0.6952 - val_loss: 0.6612 - val_acc: 0.5789\n",
      "\n",
      "Epoch 00047: val_acc did not improve from 0.84211\n",
      "Epoch 48/200\n",
      "27/27 [==============================] - 5s 188ms/step - loss: 0.6147 - acc: 0.7127 - val_loss: 0.6416 - val_acc: 0.5789\n",
      "\n",
      "Epoch 00048: val_acc did not improve from 0.84211\n",
      "Epoch 49/200\n",
      "27/27 [==============================] - 5s 188ms/step - loss: 0.5933 - acc: 0.7322 - val_loss: 0.5663 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00049: val_acc did not improve from 0.84211\n",
      "Epoch 50/200\n",
      "27/27 [==============================] - 5s 189ms/step - loss: 0.5828 - acc: 0.7322 - val_loss: 0.5246 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00050: val_acc did not improve from 0.84211\n",
      "Epoch 51/200\n",
      "27/27 [==============================] - 5s 181ms/step - loss: 0.5803 - acc: 0.7312 - val_loss: 0.5523 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00051: val_acc did not improve from 0.84211\n",
      "Epoch 52/200\n",
      "27/27 [==============================] - 5s 171ms/step - loss: 0.5533 - acc: 0.7590 - val_loss: 0.6173 - val_acc: 0.5789\n",
      "\n",
      "Epoch 00052: val_acc did not improve from 0.84211\n",
      "Epoch 53/200\n",
      "27/27 [==============================] - 5s 175ms/step - loss: 0.5166 - acc: 0.7590 - val_loss: 0.5598 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00053: val_acc did not improve from 0.84211\n",
      "Epoch 54/200\n",
      "27/27 [==============================] - 5s 169ms/step - loss: 0.5037 - acc: 0.8146 - val_loss: 0.6207 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00054: val_acc did not improve from 0.84211\n",
      "Epoch 55/200\n",
      "27/27 [==============================] - 4s 164ms/step - loss: 0.5395 - acc: 0.7405 - val_loss: 0.6579 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00055: val_acc did not improve from 0.84211\n",
      "Epoch 56/200\n",
      "27/27 [==============================] - 5s 169ms/step - loss: 0.6000 - acc: 0.7137 - val_loss: 0.5553 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00056: val_acc did not improve from 0.84211\n",
      "Epoch 57/200\n",
      "27/27 [==============================] - 5s 179ms/step - loss: 0.5700 - acc: 0.7127 - val_loss: 0.5438 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00057: val_acc did not improve from 0.84211\n",
      "Epoch 58/200\n",
      "27/27 [==============================] - 5s 185ms/step - loss: 0.5182 - acc: 0.7776 - val_loss: 0.6149 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00058: val_acc did not improve from 0.84211\n",
      "Epoch 59/200\n",
      "27/27 [==============================] - 5s 179ms/step - loss: 0.5010 - acc: 0.7776 - val_loss: 0.6502 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00059: val_acc did not improve from 0.84211\n",
      "Epoch 60/200\n",
      "27/27 [==============================] - 5s 179ms/step - loss: 0.5966 - acc: 0.7405 - val_loss: 0.5385 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00060: val_acc did not improve from 0.84211\n",
      "Epoch 61/200\n",
      "27/27 [==============================] - 5s 184ms/step - loss: 0.5359 - acc: 0.7497 - val_loss: 0.6850 - val_acc: 0.5789\n",
      "\n",
      "Epoch 00061: val_acc did not improve from 0.84211\n",
      "Epoch 62/200\n",
      "27/27 [==============================] - 5s 175ms/step - loss: 0.5144 - acc: 0.7786 - val_loss: 0.5521 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00062: val_acc did not improve from 0.84211\n",
      "Epoch 63/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 0.5400 - acc: 0.7312 - val_loss: 0.5506 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00063: val_acc did not improve from 0.84211\n",
      "Epoch 64/200\n",
      "27/27 [==============================] - 5s 172ms/step - loss: 0.4915 - acc: 0.8239 - val_loss: 0.6292 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00064: val_acc did not improve from 0.84211\n",
      "Epoch 65/200\n",
      "27/27 [==============================] - 5s 176ms/step - loss: 0.4912 - acc: 0.7961 - val_loss: 0.5351 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00065: val_acc did not improve from 0.84211\n",
      "Epoch 00065: early stopping\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAo4AAAEICAYAAADRD3W4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvqOYd8AAAIABJREFUeJzsvXl8Y2d97/9+tNuW5H0dz75PZpIZZ5KQkA0S1lJCIAUClLWl0JbbNnAvudACF34t6a+UHUop+5aUSyAECCQECCEkJMziZDL2rBl7xra8b5JtyZL13D+ec6Sj1bJHtsae5/16+eXx0TlHz9HxyB99vpuQUqLRaDQajUaj0cyHrdQL0Gg0Go1Go9GsDLRw1Gg0Go1Go9EUhBaOGo1Go9FoNJqC0MJRo9FoNBqNRlMQWjhqNBqNRqPRaApCC0eNRqPRaDQaTUFo4ajRaDQajUajKQgtHDUlQQjRJYS4udTr0Gg0mlIhhHhECDEmhHCXei0aTaFo4ajRaDQazTIjhNgAXAdI4JXL+LyO5XouzepEC0fNBYUQ4i+FEKeEEKNCiPuFEC3GdiGE+JQQYlAIMSGEeEYIsdt47OVCiA4hRFAI0SuEeF9pr0Kj0Wjm5c3AH4BvAG8xNwohyoQQ/y6E6Dbe6x4TQpQZj10rhHhcCDEuhDgnhHirsf0RIcRfWM7xViHEY5afpRDib4QQJ4GTxrbPGOeYFEIcFEJcZ9nfLoT4gBDitPG+elAIsVYI8QUhxL9bL0II8RMhxN8vxQukuTDRwlFzwSCEeCHwceC1QDPQDdxjPPxi4HpgG1AFvA4YMR77KvBXUkofsBv49TIuW6PRaBbDm4HvGl8vEUI0Gts/AVwOXAPUAP8LiAsh1gE/Bz4H1AN7gfYFPN+rgKuAXcbPfzTOUQN8D/i/QgiP8dgdwO3AywE/8HZgGvgmcLsQwgYghKgDbgLuXsiFa1Y2WjhqLiTeCHxNSnlIShkB/jdwtRHSiQI+YAcgpJSdUsqAcVwU2CWE8Espx6SUh0qwdo1GoykIIcS1wHrg+1LKg8Bp4A2GIHs78HdSyl4p5ZyU8nHj/fCNwMNSyrullFEp5YiUciHC8eNSylEp5QyAlPI7xjliUsp/B9zAdmPfvwD+UUp5XCqeNvZ9CphAiUWA1wOPSCkHzvMl0awgtHDUXEi0oFxGAKSUIZSruEZK+Wvg88AXgAEhxJeFEH5j19egPhl3CyF+K4S4epnXrdFoNAvhLcBDUsph4+fvGdvqAA9KSKazNsf2Qjln/UEI8V4hRKcRDh8HKo3nn++5vgm8yfj3m4Bvn8eaNCsQLRw1FxJ9qE/hAAghKoBaoBdASvlZKeXlwCWokPX/NLb/UUp5C9AA3Ad8f5nXrdFoNAVh5Cu+FrhBCNEvhOgH/gG4DJWiEwY2Zzn0XI7tAFNAueXnpiz7SMsargPeb6yjWkpZhXISRQHP9R3gFiHEZcBO1Huu5iJCC0dNKXEKITzmF0rwvU0IsddoT/EvwJNSyi4hxBVCiKuEEE7Um2QYmBNCuIQQbxRCVEopo8AkMFeyK9JoNJr8vAr1HrULlWO4FyXAfofKe/wa8EkhRItRpHK18X74XeBmIcRrhRAOIUStEGKvcc524NVCiHIhxBbgHfOswQfEgCHAIYT4ECqX0eQrwMeEEFuNwsRLhRC1AFLKHlR+5LeBe83Qt+biQQtHTSl5AJixfF0H/BNwLxBAfeJ9vbGvH/gvYAwVzh5BJZED/DnQJYSYBN5FMoyi0Wg0FxpvAb4upTwrpew3v1CpOG8E7gSOoMTZKPCvgE1KeRaVkvNeY3s7yqUE+BQwCwygQsnfnWcND6IKbU6g3k/DpIayP4n6IP8Q6sP4V4Eyy+PfBPagw9QXJUJKOf9eGo1Go9FoNIAQ4npUyHqDlDJe6vVolhftOGo0Go1GoykII13o74CvaNF4caKFo0aj0Wg0mnkRQuwExlFFPJ8u8XI0JUKHqjUajUaj0Wg0BaEdR41Go9FoNBpNQZRs2HldXZ3csGFDqZ5eo9GsUg4ePDgspawv9TqWCv3eqdFoloJC3zvnFY5CiLXAt1ANRePAl6WUn0nbRwCfQbUKmAbeOt/Ytw0bNnDgwIH5nl6j0WgWhBCie/69Vi76vVOj0SwFhb53FuI4xoD3SikPCSF8wEEhxC+llB2WfV4GbDW+rgL+w/iu0Wg0Go1Go1klzJvjKKUMmO6hlDIIdAJr0na7BfiWMQz9D0CVEKK56KvVaDQajUaj0ZSMBRXHCCE2APuAJ9MeWkNq1/keMsUlQoh3CiEOCCEODA0NLWylGo1Go9FoNJqSUnBxjBDCixoF9/dSysn0h7McktHnR0r5ZeDLAPv379d9gDSaIhKNRunp6SEcDpd6KcuCx+OhtbUVp9NZ6qWUHH3vNRrNclGQcDQ6xd8LfFdK+cMsu/QAay0/twJ95788jUZTKD09Pfh8PjZs2ICqV1u9SCkZGRmhp6eHjRs3lno5JUffe41Gs1zMG6o2Kqa/CnRKKT+ZY7f7gTcLxfOACSlloIjr1Gg08xAOh6mtrV31wgFACEFtbe1F47DNh773Go1muSjEcXw+8OfAESFEu7HtA8A6ACnll4AHUK14TqHa8byt+EvVaDTzcTEIB5OL6VoL4WJ6PS6ma9VoLjTmFY5SysfInsNo3UcCf1OsRWXjq4+doaXSw8v26GJtjUaj0awCOu6Hdc8Db0OpV7L6ee634G+Buq2lXsmKZ8WMHPzek9386HBvqZeh0WhyMDIywt69e9m7dy9NTU2sWbMm8fPs7GxB53jb297G8ePHl3ilmmKj7/0iiIbh+2+Gw98p9UouDu57Nzz6iVKvYlVQspGDC2Vbo4/j/cFSL0Oj0eSgtraW9naVzfKRj3wEr9fL+973vpR9pJRIKbHZsn9m/frXv77k69QUH33vF0FsBpAwO1XqlVwcTI/AzGipV7EqWDGO49YGL10jU4Sjc6VeikajWQCnTp1i9+7dvOtd76KtrY1AIMA73/lO9u/fzyWXXMJHP/rRxL7XXnst7e3txGIxqqqquPPOO7nsssu4+uqrGRwcLOFVaBaDvvd5iEWM77rIZ8mJzqjXeWa81CtZFawYx3FLo4+4hDPDU+xs9pd6ORrNBc3/+clROvrS262eH7ta/Hz4Ty9Z1LEdHR18/etf50tf+hIAd911FzU1NcRiMV7wghdw2223sWvXrpRjJiYmuOGGG7jrrru44447+NrXvsadd9553tex2tH3foWghePyYQrGsBaOxWDFOI7bGr0AnBjQ4WqNZqWxefNmrrjiisTPd999N21tbbS1tdHZ2UlHR0fGMWVlZbzsZS8D4PLLL6erq2u5lqspIvre52DOyP3UwnHpMQXjzFhp17FKWDGO48a6Cuw2wanBUKmXotFc8CzWHVoqKioqEv8+efIkn/nMZ3jqqaeoqqriTW96U9aefC6XK/Fvu91OLBZblrWudPS9XyGYgjGqheOSYzqOM+MgJeh2TufFinEc3Q4762vLOTmghaNGs5KZnJzE5/Ph9/sJBAI8+OCDpV6SZpnQ995CTDuOy4bpOMajEJ0u7VpWASvGcQRVIHNiUIeqNZqVTFtbG7t27WL37t1s2rSJ5z//+aVekmaZ0PfewpzOcVw2rEUxM+Pgqsi9r2ZehOrdvfzs379fHjhwYEHHfOLB4/zHb0/T8dGX4HbYl2hlGs3KpLOzk507d5Z6GctKtmsWQhyUUu4v0ZKWnGzvnfrer0BO/xq+fSusvxbe9rNSr2Z184f/gF8YxVXvfhwaL6x0jvPl2d4JPvHQcb7whjYq3Iv3Awt971wxoWqArY1e5uKSM8O675VGo9FoVjA6VL18pDuOq4xHTw7xyPEhHjgSWJbnW1nCscEHoPMcNRqNRrOy0aHq5cPahmcVtuTpn1C/Qz88tDzT9VaUcNxUX4FNwEldWa3RaDSalYzu47h8zIyDsCf/vQjOjU4TiV2YA0j6xtXv0BPPjdAztvTFPytKOHqcdtbXVnBS93LUaDQazUrGFI66Hc/SMzMGla3q34twHEORGC/61G/59hPdRV5YceifnEn0ur7v8NK7jitKOAJsafBqx1Gj0awIhBAvFUIcF0KcEkJkjD4RQqwTQvxGCHFYCPGMEOLlpVinpgToUPXyER6HqnWAWJTjeCwwSTgapzNwYZpW/RNhLl9fw1Uba7j3UC9LXfS8coRjsB/Gz7Kt0UvX8BSzsXipV6TRaDQ5EULYgS8ALwN2AbcLIXal7faPwPellPuA1wNfXN5VakqGDlUvHzPjUFYNHv+ipsd0BtQIz7OjF15hbiQ2x3BoluZKD69pa+XM8BSHzy1tHufKEY7/eT385uNsbfARi0u6Ri68G6jRXMzceOONGQ2dP/3pT/PXf/3XOY/xer1LvaxSciVwSkr5nJRyFrgHuCVtHwn4jX9XAn3LuL6ioe/9ItDCcfkIG8KxrHpRoeoOw2nsHilN8/ChYISP3H+UcDQzx3JgQv0eNVd6eNmeJjxOG/ce7FnS9awc4di0BwaOsKVBvdnoymqN5sLi9ttv55577knZds8993D77beXaEUlZw1wzvJzj7HNykeANwkheoAHgPdkO5EQ4p1CiANCiANDQ0NLsdbzQt/7RWDOqo7HYG4VjlS8kJgZh7Iq8FQtKlRtOo6DwQjTs8t/r37+bIBvPN5FexYnsW9iBoDmyjJ8HicvvaSJnzzdt6SFPCtHODbuhsFjbKl1IQSc0AUyGs0FxW233cZPf/pTIhH1Cbirq4u+vj727t3LTTfdRFtbG3v27OHHP/5xiVe6bGQbiJuefHQ78A0pZSvwcuDbQoiM92Up5ZellPullPvr6+uXYKnnh773i8DqNMZmSreO1U50RuWTeqqUeFyg4zgXlxzrn6TR7wbg7Ojyu46mcO3OEmk1W/E0VXoAeHVbK5PhGL/qHFyy9czbYlwI8TXgFcCglHJ3lscrge8A64zzfUJK+fViL5SmPRCP4hk/zbqack7pAhmNJjc/vxP6jxT3nE174GV35Xy4traWK6+8kl/84hfccsst3HPPPbzuda+jrKyMH/3oR/j9foaHh3ne857HK1/5SoTIpqtWFT3AWsvPrWSGot8BvBRASvmEEMID1AGLf9fX935lYDYABxW2dvtKt5bVjOkwmo7jxMKqjrtGpghH47x2fxPfeqKbruFpdjT55z+wiHT0mcIxU7QGDOHYbAjH52+po9Hv5t6DPbx8T/OSrKcQx/EbGG9sOfgboENKeRlwI/DvQgjX+S8tjaZL1ff+I2xt8HJSz6zWaC44rCFLM1QppeQDH/gAl156KTfffDO9vb0MDAyUeKXLwh+BrUKIjcZ74uuB+9P2OQvcBCCE2Al4gAsvFl0A+t4Dv/wwfPNPk1/fugXO/C77vmZVNShXbKmZGoH7/gZmF1kfMHgMfv5+iOcoTO05CA//n8Wv79gD8OR/Lv74XJgO4yIdR9Pte+nuJmCJCmRmxtW9CU9kPDQXlxw3IqzdWdzOwMQMfo8jMWrQbhO8at8aHjkxxHAokrF/MZjXcZRSPiqE2JBvF8An1EdILzAKFD8JoHYzOMqUcGzcx29PDBGdi+O0r5xou0azbORxh5aSV73qVdxxxx0cOnSImZkZ2tra+MY3vsHQ0BAHDx7E6XSyYcMGwuHVXxAgpYwJIf4WeBCwA1+TUh4VQnwUOCClvB94L/BfQoh/QL2XvlWeby8Nfe9LQyQEv/8MVK8Hn+H0nHsK6nfCxusy908JVS/NH/gUzjwC7d+BfW+E9dcs/PjO++HJL8F17wVvQ+bjHffB45+FF/4j2OwLP//h78DAEbjqrxZ+bD7MKmprjqOUUKDr3dE3icMmuHx9NVXlTrqWokDm7BPq3uy+FbbcnPLQmWHleAqRPVQdmAjTXFmWsu32K9axrcGH9zzmVuejGGf9POpTdB/gA14npcz6kUQI8U7gnQDr1q1b2LPY7NC4CwaOsHWPl+icpHtkii0N2t7XaC4UvF4vN954I29/+9sThRETExM0NDTgdDr5zW9+Q3f3hdlEdymQUj6AKnqxbvuQ5d8dwPOXe11LwUV/7/uPABJe+q+w3QjS/dvW3PmLKaHqZXAcg/3qe2SRaV5BYw5yJJhdOM4a542FwVWx8PPPBhe/tnzMpDmO8ShEpwteY2dgki0NXtwOO+tryjm7FMIx8dpmXn+H4XjuX1/Nsf4gUsqUVI/+iTDNVZ6UYzbUVbChbhH3oECKYde9BGgHWoC9wOeFEFkTAM47wbtxN/QfYZtRWX1CV1ZrNBcct99+O08//TSvf/3rAXjjG9/IgQMH2L9/P9/97nfZsWNHiVeoWSou6nsfaFffW/Ymtzk8ud1Ea6h6ORxHUzjOLlY4GikGuULd5vbFXsvs1OLD6PkIp+U4woIqqzsDQXY2K0mzvraC7qUIVed5bTsDyvF84Y5GguEYY9PRlMcDEzOJ/MblohiO49uAu4zwyikhxBlgB/BUEc6dStMeOPRNNnsmEcJoybOn6M+i0WjOg1tvvTVlckFdXR1PPPFE1n1DIf3hbzVxUd/7vsMqRO1rSm5zenLnL8aWOcfxvIVjIP/xplu22GuJhJSYnouC3bm4c2Qj3XEEYwRhemesTEanZumfDLOzWUU219eW89Nn+piNxXE5ipgml+e1NR3PrYZh1j0yRU2FKiNJNv8uyzhuKSnGlVuTuxuB7cBzRThvJk1KJZaNdLC2upzjA5NL8jQajUaj0SyIvnZo3pu6zeHO7cDFIiQ6Ni2L45g7HFrY8fOEumeNgtXFNjQ3RVOkyIWv4XFAgKdSNQBPbJsfszBmV3MloBzHuITe8SIL/cRrm3ntnYFJdjX72VBXDqRWVg9Oqt+bpmV2HOcVjkKIu4EngO1CiB4hxDuEEO8SQrzL2OVjwDVCiCPAr4D3SymHl2S1jZeo7/1H2NHk41i/rqzWaDQaTYmJhGD4RGqYGlRBZ678xblZNQIPljfHcTGOYzwOITOcmuPvbsSS47gYzOMX64jmYmZcvc42+4JD1aZwtDqOQPEn1+VwHEdCEQYmI+xs9tNaXW4UyCSFY9+42fz7AgtVSynztv6XUvYBLy7aivLh9kH1Rhg4wo7mW3i4c4CZ2TnKXIuo4NJoViHpidOrmfMtPl5t6HtfQvqfAeQCHcewcsHCExBdhkpzU/gtxtGbHgZpTCLJ6Tieh3CUMilIi10gEx5PCkYzVF2g49gRmKTB56bWq5p/m8JxoQUyQ8EIr/jc75iYSeYnepx2vvcXz2NXi99yb1KvvdMYdbirxY/HaafZ70mprO6fNHs4rrxQ9fLStAf6j7CzyUdcovs5ajQGHo+HkZGRC++P6hIgpWRkZASPZ3k/aV+o6HtfYvqyFMYAOMvy5DjOKuEISz+vOhKCiJHatZgCFNMRgwJyHBdxLbEwmM1Yil0gY44bhAU7jh19k4nCGIB6r5tyl33BjuMzPeMMTEb4kz0tvOXqDbz56g0EwzEeOBJQ4yZDRr//2XThaDqeag3rastTejn2jadOjVkulqbJz1LSdCl0/oSdtUrzHusPcmlrVYkXpdGUntbWVnp6ergQZxkvBR6Ph9bW1lIv44JA3/sSE2jPLIwBw3HMIaTmIuBtVP9eauEYsjRdX0woOGg9fr6q6kWE3a1OW65Q+GKZGUsKRrcfEAU5jrOxOKeHQrxgR7L1kBCCdYtoyWNOuvvQK3ZRWa4Kfw52j/G7k0O872ofiUmkafemI6BGHZrFMOtrKvjVseRQqf6JGXwex5L1a8zFChSOuwHJ2ugZypx2jhlWLiOnoaI+mTOi0VxkOJ1ONm7cWOplaEqAvvclpu9wZpgajBzHHKIwFjaEDEsvHM38RlhcKNjqOBqh7mA4yvH+IPs31KSGmhdT6GMVi0sRqvYbDdltNuXyFuA4nhoMEZ2TKY4jqHD16aGFOY6nh0LUed0J0Qhw/dZ6Pv2rE0wM9VBpbswIVac6nuvryhkORQhFYnjdDgITYVqWOUwNKzVUDdgHjrCtycex/kkYfQ7+4xr4xZ0lXpxGo9FoLioiQRg+CS37Mh9zuHOHbq2h6qXOcTSFn6dqcY6eKTw9lQlX7MuPPsdr//MJBifDKhxvhpoX044nxXFciuIYS1SywLGDHYmK6tQhIxtqKzg7Ok08XnhayKnBEJvrUxtyX7+tDinh2MkTaoOnKuXaI7E5Tg2G2GUVjjXqHKbjGZgIL3uYGlaicPSvUS/wwLPsbPLR2TeBfOB/qk9sz/5wQY09NRqNRqM5L8yJMen5jaByHPOFqt1eELblcxzrti7O0Qv1Q3mtamdjHH+ga4y4hEdPDqeKvUU5jpbji+k4SqlEYplFOJpjB+ehMzCJ22FjQ22q4FtXW85sLJ4oTJl/CZJTgyG2GH0YTS5traKq3ElPt9G9MO3enBoMEYunOp5mcY5ZIKPGDWrhOD9CJApkdjT5uCryOOLUw3DZG1RuxZH/W+oVajQajeZioe+w+p41VO3JE6qOgN2Vf59iEepXz+Nfs8gcx36Vw+nywWyI2Fycp3uU+Hr0xFBqpfaFlOMYnTHaHi3ccewMTLKjyYfDniqTTCFZaIHMcGiWyXCMzfWpwtFuEzx/Sx1jA2eRCNUxxnJvOvpSC2PAIhxHp5mNxRkORZa9ohpWYo4jKOF44OvsuhZe4vwWweqd+F75ORh4Fg5+E674i4IHmGs0Go1Gs2j62sHXAr7GzMdMUShl5t+kWEQ9vhzCMdivCnfc3sVXVfua1LGRIMcHgkzPzlFZ5uSxU8PErxdJF+p8HcciVVV//OeddBw7xreBzz0xzM8P/Y63X7uR2zxVMNGTsq+Ukju+/zTHLb2hTw2GeHVb5nSZdTXJljzXbJ5/HWZhTLrjCHDD1nrmOoaZ89fjKKtKEeCdgSAep42NlpnTPo+T2goX3SNTDCRa8WjHsTCa9kBshr0H3k8jYzy88f1gd8Dlb4GBI9B3qNQr1Gg0Gs3FQKA9e5ga1MhByBRTczHVF9HhVsJxyXMc+8HbpBzDRRXHDBjHK+F56Kxy7N5x7UZGp2Z5LpCs9F1UjmORQ9WzsThf/30Xrqhy7RzeWiZmonz64RPIsuqMUPXTPRP86HAvHqeNlqoyWqrKuHF7Pa+7Ym3GuVuqynDaBV0FVlafHlLXszmLcLxuWx2NYoxxW03itTXpDEyyvcmP3Zb6gWNdbTndI9MEJkrTigdWsnAEXKcf5Mf2m3l0xqgm3PNn4CyHQ98q4eI0Go1Gc1FgFsZkC1ODEoWQ6SjOGULS7lLicqknx5iOo6tChYIX0u8zPqfa+SSOD3G4e4w6r4vbr1wHwLGuvuT+i3EcTbHo8halOKYzMMlsLM479tcA8O6XtPG/XrqdnrEZ+iJuFaq2vAY/PNSD22HjG2+/kq+8ZT9fect+vvzm/exbV51xbrtNsLa6nLOjhTmjpwZDlLtU8+50mivLWOuc5FysUr228SjEIkgp6QhMZhTmAKyvMYWj+p1pqdLCsTDqtoPNCeW1PNzyrkSTTDyVcMmtcOQHxS/p12g0Go3GSsCYGJPLccwlHE1x5fAYLXuWeFa1maPo9qrq54W4glPG1Bgz1B0JcfjcOPvWVVPvc3NJi5+TPZZ2P4sRwaZY9DYUZVb1obNjAOyoMqbdlFXx4l1NeN0O2oeAeCzh7kVic9z/dB8vvqQJv8eZ44yprDdcv0I4PRRiU30FNlv29Llm2zgnpiuYtRsh6UiIwESYiZloRisg9dwV9E3MJCqrm3Q7ngJxuODmD8Or/4u1a1o5PRRiNma0Amh7s/olPPrD0q5Ro9FoNKubfIUxkMdxnDUedxkte5bQcYyElMvoa1SOHizM1QsZotCnQt3xSJAzw1O0GW7c9dvqGRoeTu6/mLB7JAh2t6raLoLjeOjsOE1+DzXCcAU9VZS57Lx8TxNP9RtawSiQ+c2xIcano7wmSz5jLtbXVtA9Ml3QpKbTgyG21GeGqQGYi1ERG6M/XsVpw/+KzkzyiYeOA2QdbrK+thwp4amu0ZI0/4aVKhwBrnkPbLmJnc0+onOS54aNX7a1VylH8uA3S7s+jUaj0axuAnkKYyCZ45gupkwhaXcbLXuW0HE0p8b4msFthD4X4uqZrXwMx1JEpwBJ2zolaq7fWk+ZNISvs3xxhT6zIeVmurxFiRYe6h6jbb2letpox/PqtlYGo4ZDZ+Q53nuoh3qfm2u31BV8/nU15YQiMUanZvPuNxWJ0TcRzqioTu4wiEAyImo4MqTc0Y/e+yQ/PNTL39+8lctaKzMOWW9UdR/oGitJYQysZOFosKNJWbmJaighVJFM7wEYOFrClWk0Gs1FTtfv4V/WwMfqk1+f3gOzCxvZdsHSl6cwBiyOY5qjGDMdR7cxlnARjuPUCHy2DQJP59/PbP7ta7I4jlny83oPwuevhOnRvMcLGcdrm2WPIWouX19Ntd0QvuV1ixSOU2ptbt95V1UPTobpHZ9RjujMOCDArdZ65YYaXF6V90h4nNGpWX5zbJBb963JaLuTjw11qrI6Z4HM9Ch8bj/9zz4KZK+oBhKvbVXjOp7sVb8Tx7r7+Odbd/P3N29DZOkOY7bkmYnOlSRMDatAOG6qr8BpF3QGLJ+gLrsdhB2O3le6hWk0Gs3FzqlfKiFx9d+or003wvhZmBmd78gLn/AkjOSYGGPiyFVVbeY4unPmOM7FJZ986Dh94zlE5chJGD0NJx7Kv07TMfQaxS2QPRzcewiGj8O5J9OONxzLigblCgJ7Gx2Uu1SI1OWwsaVaEMGlzr8Y4RgJEcTDU32zjIyN8J67D/Oeuw/ztcfOFBQOtmJWfO9bV60cR49fjRoEbDbBFTtUMe3YyCD3t/cSi8usbXfysc6Y4HLXzzsTa/2H/27nzLAhes89CSMniR17AMheUQ0k7s2mjZs4M6lE4vtvWssbr1qf87lrK1yJ8HSLdhwXh9NuY0uDMXrQpLwG/C3qDUqj0Wg0paGvHRp2wc0fUV+7b1Pbl7oYZDnof0Z9z5XfCCoMDZk5jOb12905cxw7A5N89ten+OkzfRmPAcmWMoH2/OsMWnIUE6HqLMLRDOv2pZ0vGFBOosPOPOdLAAAgAElEQVRF3KkE0/5mV8ouG/2SoPQQwbmoHEc5G6Rr0sZzk+CITnG0d4LDZ8f46E87uPPeI8Tm4gWf6/DZMZx2wSUt/sxxg8CNe7cB0H6yi3sP9XJJiz8RuSyU9bXlXLuljpHQLEd7JzjaO8FPn+njy48aU2CM19A1+Ax2m0i4hBkY9+aavXvYub4FgP1N+Qt0hBCJXpKlaMUDK7UBeBo7m3w8fnokdaN/DUz2lmZBGo1Gc7EjpRI1O/80uc1u/FGci5ZmTcXEFFh5Q9Vu9T1dKMcsjmOOHEdzVnLvWA7HMZfQSycYUK6mp9ISqs6S45hLiJoV2UDvtJ21wGX19pRd1pTNMSI9zEXtNC7CcZwOjjMadbJ3WyuVXRF+/b4bkVLyqV+e4LO/PsXIVITP3d5Gmcs+77kOnx3nkpZKPE575rhBoLVZXUv7iS6OTG/nn16xa8HrddptfOcvrkrZdsd/t/PTZ/r48J/uwmO8hvXBTtZVl+F25Fh3sB+EjYamVv75dQI+Q0HFQRvqyukITOocx/NhR7OP/skwY9ZEVX+LFo4ajUZTKsa7YWYsNZRrN5yquVXgOAbakf41qoVMLhyG45iew5gSqs6e42i2mevNFao2hd5kD4SGcq8h2K+Kd4RIhJqzOo7m+cxKcZNQf6L45/iYChvvrE2VDl4RZtZezkhYLCpUPRWcYMZWxsY1jariPDaLEII7Xrydj91yCb86Nsibvvok49P5i1Gic3Ge6R1PVHwzM64qta24/cSx4ZidwG4T3LK3ZcHrzcar21oJhmM83DmgXkO7G298kiur8+RsBgNQUa8GmORzg9MwQ+WlGDcIq0U4GjbzMcu4ICUc+xbW6FSj0Wg0xcF0wqyhXNOBWxWO42GeDK/lk0brlKzM5zjaXUpcZgnvmsKxJ4fjGBy3TGvJF64ODSQcw7zteEwHMzQAkwHLExnNw4Fnh1XIuNGddv8iQZzlfvqnBVPTCytuCUfnkJEg1VU1uMsrM9b351dv4AtvaONIzwS3femJ3DmfqNcsHI2zz6j4VjmOaS1tbDbwVFJjm+bGbfXUed0LWm8urt5cS3Olh1892Q6hAeK7bgHgeWV5UubMxuqQ3w1OY1O9Eo6t1ReocBRCfE0IMSiEeDbPPjcKIdqFEEeFEL8t7hLnZ4fRXT0lz7GyVX3ySa8Q02g0Gs3SE2hXgxoaL0luM0PVKz3HMTwJI6f4/VQrR3oncu83X46jw2NMjkkVjlLKRMFnz9hM1gKRsZEhItJ4PfOFq80505C/qnpmPOmQmkI0MTXGCO8OxAAQ6cJzNkRTfS3C5WFgZJwHj/ZTKA93DlAmw6xtasgpbF++p5lvvv1KBibCvPqLj3NiILu4OtStGn+3rTcdx7GMUDWArayKl2728LFX7S54nfNhtwlu3beGUNdBAPo3vYaotLNDns59UDCQFPUOl/ogUYDjeMveFr7+1ivYlKvNzxJTiOP4DeCluR4UQlQBXwReKaW8BPiz4iytcOq9bhp8bg50jSU3+g37WYerNRqNZvnpOwyNu5KuG6hiEEg2wF6pGIUxR+QmBibziOCcVdXWdjweNZnF4sL2GZND1teqfoGTM7GMU0eCowxRyYhnXWZ42Yo5pxpUSNThyd7HMTwOa68EYUsK0alhNWnG28jY1CzHRg0Bmy48Z6fwlFdyzfY1eO0x3v2dg3z3ye7ca7Lww4M9VIgwTfX1eUPpV2+u5b//6mriUnLbfzzOH7syTaHD58Zp9LtVtbGUWYtjACirotY+TUtVcR27V7e1sls8RxwbnbZtnJCtrJnO40gH+8Fr6QFa4MhFt8POC3bkSZFYYuYVjlLKR4F8tt0bgB9KKc8a+w/m2XdJEELwwh0N/PbEEJGYMWLI36q+a+Go0Wg0y4uUSnykVxwnchxXeKjaEGpH4hsZDBYiHNMdR7MBuCvrdJnOPhU9e/EuJSrOjWX2C4xPjzEhK+hybc0dqo4ElRAxHUfILU5mxlVRad22pBBN9HBs5vC5MaYx1pp+fEQ18HZ7Kqj3SG7YVs8Hf/Qsn374RN52OkPBCE+e7MVOHJvHCy5f9vMb7Grxc++7r6HO6+ZNX3mSh9KczUNnx2hbV636H0an1eznLI4jnqpkTmcR2dLg5Zryc5y1tXJiTPJMfBPe0Wezp8zNRWFqKOk4gnFvzq+P5XJQjBzHbUC1EOIRIcRBIcSbc+0ohHinEOKAEOLA0FCeZN5F8KJdjYQiMZ58ztC42nHUaDSa0jDerRys9IrjRFX1yg5Vz/UeJiBrGROVjExFiOZqF5PLcUyZVZ05XcbMb7xppxKO2QpkRGScCVnBs3KT+jsXyuLZBC1TY0zcOaazmBXILfuSQtQyNeaep84l+0CmHz8bUqLP4UHEwnz5zft5TVsrn374JB+871nm4tnF44/beymLG9fm8lrOnzvPb21NOT949zXsbPbzru8c5O6nVA7hUDDCudGZZH6jKQxzOI6JnM5iIiWXcIaD0fX87Egf3a6t2MJj2VsDmvfLKurd3qLM6l5qiiEcHcDlwJ8ALwH+SQixLduOUsovSyn3Syn319fXF+Gpkzx/Sx1lTju/7DD+o3gbwOaACS0cNRqNZlnJNcPZsTpC1bPnDvFMfCPXb6tHShgO5RDCNptyFdNzHK2zqp2ZjmNHYJL1teVsb1QOXLYCGefsJBNUcGDWaBadLc8x4Rhaw6G+TEdvLqq2earUPTMLZIw51YfHXDzUMcA7b9ym8iCtBRxSJkcGGvmaTruNT/zZpbz7xs1878mz/PV3DxKOzmUs795DvVzebHQFdHmToep5wrU1FS6+95dXcf22ev73D4/wmYdPcuiskd9oVlSnjRtMYYkcR4IBymeH6WATz/ZOEqwxciizpRJY+2uaFBiqLjXFEI49wC+klFNSymHgUeCyIpx3QXicdq7bWsfDnQPKGrfZ1aesyRzNUzUajWaJEUK8VAhxXAhxSghxZ5bHP2UUFrYLIU4IIZbgr1kJ6MtSGAOW4pgVLBzDk5RNnuGo3Mir9qqJI/nzHMsyW9RYZ1VnC1UHJtnZ5Keq3Em5y57RyzE2F6c8HmSSCh4LGdG1bOFq65xpk2yuVtgo8CmrSrrEfYcTx3/skRGa/B7ece2mTMcyOq3yIF1edS3xKMTnEELw/pfu4EOv2MWDRwd481efYmImmaLQ0TdJZ2CSP9nuT64rX/FOGuUuB/9lOJufevgEH/7xUZx2we41RmV2IY5jsbuumI2/110OgLNlj/p/kPXeWEY5muRygy8witEA/MfA54UQDsAFXAV8qgjnXTAv2tXIQx0DHO2bVL88ugm4RqMpEUIIO/AF4EWoD9h/FELcL6XsMPeRUv6DZf/3AHnm160gAu2ZhTGwOopjjNnQ4fpL2WxUtQ5O5uld6HBnEY5pxTGQ2GcqEqN7dJpXt7UihKC1uoyetBzHwESYeqawl9cwNuEh1rgFRzbHMZTD1ZpOG5hhFVlNe1SBTEC1lYm4aznUO8Un/uwy1YA73RUzhY4ZZjavxfj57ddupM7n5r3fb+d5//IrPE7lV0VicZx2wY0by+APJGdVW885D6azWe9z86XfnuayVqPxN8zvOMZjSqC6i1iZHGgHYePyK6+H0x1saKyGwZ3Z3eBQFlHv8q4Is2te4SiEuBu4EagTQvQAHwacAFLKL0kpO4UQvwCeAeLAV6SUOVv3LCUv3NGATcBDHQOGcGyZfxyTRqPRLA1XAqeklM8BCCHuAW4BOnLsfzvq/XVlYxbGGH3sUkgUx6xc4TjVdYAKoHH7VTT6lRAeyFcg4/Rk9mmci6jXQoiMHMdj/UGkhJ3NyolbU1WWkeN4dmCEtSJKdW0DTECoejdVfU9lPnewH5zl4LaM1HNVqBxUKzNGR5KyavV43Xboa2cOQfesn53Nfm7dZ8xzdqcVcJgi0u1T7XvMa7EIyVde1kJzpYefPt2H1eO7tLUKv+hIHr+AXoYmQgjufNkOLm2tTNwPdU3zOI6gxGUxhWPfYajbzo271/PBl0tu2bsGRvZCx/3q/4UQyX2NqTFUWNL23L7V4ThKKW8vYJ9/A/6tKCs6D2q9bi5fX83DHQPc8aJtULkGjj+QecM0Go1m6VkDnLP83IOKyGQghFgPbAR+vQzrWlrGuozCmCzmqWPlC8fRU39kXNZyxe7t1Hrd2MR8jmNmn0ZikaT76kytvDZHDe40+hO3Vpdz6GxqBkP/gHKrmpqa4DkY8O6g6vR9quDCOskmGFDtXqx//7KFQ9PduZa9cOpXjNpq6I35+eDLd2K3GedwpYW6TeHo8iavM8v0mCs21HDFhpqM7Tz7ZPJ4h1vVJixCPL18T3PqhsQ1VWfuXGbp81jZuuDnyor5gWnLTTjsNv7y+k1qe/NeOPQtJdarNyT3DwagokGl1Zm4Ki6aHMcLihftaqQjMKmsff8a3QRco9GUimyfVnMlVb0e+IGUMrOCgKXtSFF0zChPthnO5+k4jk/P8pOn+/hxe2/i69l8DbiXAPfQ0xwXm9ndUondJqjzuhlYjHA0w/iJsYTKtewMTOL3OFhj9BhcU13GxEyUYDiZHzg8rIpA1xhzl8+4jHpUS0hUSsnkUA/SGqaG7MUx6e5cyz6YGsQXPI2tsplrt9ZZjs8RqnZ7s+Zrzos11C1E8cTTzBggUt1WE/M6i1kgEwzA1GDmBybz5/RwdXAgNYUAkq/tBT7xbtUJx5uN9gW/6hxUwhF0nqNGoykFPcBay8+tQK4EptcDd+c60VJ2pCg6fYdVQUDDrszHTOG4yOKYLz5ymvfcfZi/u6c98fXGrzyZt1dgMYlPj9Mw28N03R5shgPX6PfM38sxXUjNWYWj8d2ovO4MTLKz2a96EZIcK2cNV0+Mqg8P/qp6nHZBJxsAkVK9+8TpEYYD3QyR5vK5s4iTdMfRqIb3EOWS7dszj7c6ggnH0bc44ThrEZ7meYoRrp0ZB0+lqmxPxxqqLha5Ogk0XqL+P6RXVgf7U/MbQb0G8dgFP1lp1QnHTfVeNtdXqLY8WjhqNJrS8UdgqxBioxDChRKH96fvJITYDlQDTyzz+paGvnb1xzK9MAZUWE7YF+049o7NsK6mnF+99wZ+9d4beN+LtzExE805z7nYdHf8AYDKzVcktjX63fmrqrPlOMYiSRHtTDqOc3HJ8f5gIr8RSDiPPaPJawyNq+IWW3kVjX4PZ0N2qN2SktPf2R+kUYxxLupLfW6XV1VBRy0FN2mOY7dzE3NSCde6pnWZx6c4jkbY2u21jFhciHA08iXN5t/uIrWkMftSZmMpHMc+VRhD057U7Q43NOzMrLcIBlLbJMG8DdAvFFadcAR40a4m/vDcCJMu45O5Fo4ajWaZkVLGgL8FHgQ6ge9LKY8KIT4qhHilZdfbgXvkctlmS4mU6g9ktjC1id216AbgA5Nh1lSVsbney+Z6L8/fokKoR41JK0tNoFMJx+37rktsa/B7FpfjaLpzpsCOzdA9MsX07By7LMKxtbocSDqOUkoiIaMq2lNFc6WHvvEZFRK1hEPP9Q9SISKcmE4r/jCLVqwFLuFxVURj5KD+66/Pchoj9y/DFfPlcBwrUq6lYCJB9VrYLf0cixKqzjFuEJbGcQy0Q/0OcJVnPmbeG/O/+FwUpoezO45wwTcBX6XCsYFYXPKbXnQTcI1GUzKklA9IKbdJKTdLKf/Z2PYhKeX9ln0+IqXM6PG4Ihk7o3oCpofrrDhchY8cnIuqeckGg8FISuXs9iYfQiQnrRCdgd5DqV/FzHHvO8yAqKehKVlQ0eBzMzI1y2wsz/SYjFD1bLJQyJLj2BlQgsHqONZ5XbgdtkRLnsFghPI5Q1iUVdNcWUb/ZFiJ9WAfPPcI9B6iovd3ALSPl6VObkm0vLGIE4vIOtg9ygNH+ok1Gu2Y58vDSziGXsu1LDBU7bKI22L1MsznOLp8yh3M5zhOjRT+eyqlCkXn+r1v2avWc/Ih9Tv53CNqe7bXFhYunONx6D+S+bs/lznjvBgUo4/jBcfetdXUeV38+vgIt+gm4BqNRrM8DBitVZouzb2P3VV4qPrxz8LvPwvvO4m0OxmYDNPo9yQeLnc52FhbkRSOD/0T/PG/Us+x5nL4y+IUqzfOnGbYtw1rgNFcz1Aokggrp5DVcQwnq6otOY6dY5PYbYKtjUkhJYRgTXWyJU/3yDSVwhBrnkqaKz384mgYuWa/qsb6lmqD9D+N40/N1nByMMiOJkOMZhMnhsiSUvLPP+ukwedm8+U3wS9+DpXWNF0MV0yqULerwlLc4rVcywKLY6wtcVze7OMTF0q+immbzZgek+NDRTwOn98P17wHrrtj/uea7FNzp3M57a1GasP3Xpu6vSotDSAxOWeB86qfuQfue3fm9vd3Za8qP09WpXC02wQ3bm/glx0DyNYWhA5VazQazdJj/sHL5fSAEo6FFsd0/V6JmqFOJit3EYnFqfel5k7ubPHzTI/hHI2ehrpt8KKPqZ//8AUYS+tZuEjicYlXBpkqq03Zbjqgg0YYPYOsOY6zSZHltDqOk2yur0g2sTZYU1WWyOPsHpmikiniLh82m52mSg+zsTij1ZdR+9afQSREKBLjf9xzmKt3ruPg0VoOnx1PCsdEONQiHGfGwFPFz5/t59DZcf7/11yKu+1G2Hh1ansfSJ1X7apQPRcdZSrU7CyC4+gqkuMYHIAtN+d+3NuYW6BODytR2f37woRjopNAjv79TXvgbb9ITugB9VptuC51P1eWe1MIZ/+ghPCt/5m63VmRff/zZFUKR4CbdjTwg4M9jNrrqZ3M1W9Xo9FoNEXDzF00Cz+yUajjaIb/QIWIbaovntVxBNjV7OdnzwSYDEfxB/uVcNz+UvXg8Z/B8MmFXkVWxqZnKSeC3ZNabNLgU+vJWSDjKMvM+ZuLgMtwgmwOFTaNzdARmOTKjZm9Dlury+noU70bu0em2SymEYY4b65UYi0wGaF2w7UAHO8e5ddxwRv376em6xkOdY9x+5WGu5WtAGNmnHjVOu76+TF2NPl4zeWtYBOqqCOdlOMbUx3DRI7jQhzHYGaoegENwLOfM6TOkR4KtuJrSo5kTMccB9h3uLA+0H2H1T1s3J17n/VX5z8HLKoBeuL5W/Ylf++XmFWZ4whw7dY6nHbByUilspFXQd65RqPRXNCYbUSyVVSbFFocM3EuGUrsa0/0SkwXjmaj7GOBoNHixCIW7FnG/S2SoWCYCsI4y1P7AprrGQzmeB6HO7O9irUBuBDgKGN6OkRgIpxSGGPSWl3GyNQs07NqHGGDcwZhhCCbK9Xz908kn//UoBKFWxt87FtbxaGzY8mTubOHqs/NuDk7Os37X7Yj2ew7G+kFHLOhpAu5qBzHqcxQ9UJDtemEVJ/LjOITK77mPMLROH56BCZ65n++vjyFMQshmxs8H7EIDHbmL0grMqtWOPo8Tq7aWMuh0TLdBFyj0WiWg0KEY6HFMWaFcFkNBNoZNBy9lLFywK7mSgCO9xrhRatwdLgX3TMyndGxcWxC4imvTNleW+HCbhO5m4A7jb9BVvPC2gAcwOmhf0SF26/eXEs6Zi/HvnFVeV3vmE6kA5jCMTCRdDVPD03hcthYU13GvnVVnB6aYnzaeB2soWaTGSUcfW4HN2ydp09oeo5kJJR0IReT45itOGZu9vzum+kYehtz7+NrVPOi41mKmszjYf6xxYlOAkUYM7+Y4piBoxCP5i9IKzKrVjiCml39dNC4EZMFfGrQaDQazeJJhKrncxwLEAV9h1UY99LXwsBRhiaUw2WGhk0a/W6qy530nTujNnjThOMiW/+kMz6hzIcyX6ojaLMJ6r3uhLDNIBG+tTw+lyYcHR6GxiaoqXCxuyVVmEKyl+O5sRm6R6apEtOJKug6rxuHTRBIcxw31VVgtwna1ilnsv2ckQeaLk7mohCd4rmQg73rqhKNzXOSXsAxawlVLybHMaM4pgi9DE0ncT7HMR7LXiBjHi/smY2705nsVYUxxRBuiYr3BVy7ub5iCNcCWdXC8aadDfRLI19EV1ZrNBrN0hIrJMcxS+g2G4F2lWO39iqYm0UMduLzOChzpRaOCCHY2exnuP+s2mAVC3a3EgfxrJMcF0RoQgkvry+z8KfR72Yg1/SYRPjWkudobQAOSIeH8ckg126pyyrczF6OHX2TTMxE8cpQwnG02QSNfk+KcDw9FGJzgxJjl62twiZIzrtOL8AwWtKcDjrYt66AClxXWjsfq2NodwFiEcUxlrzRYvQyTAjHeXIcIdVdTBwfgPI6Nf0ofVRgOubjxQgV253qd3YhojnQriqn0yu0l5BVLRzX11bgqjFaCejKao1Go1laTEGUbcybid05f6ja2hfP+IPsHTmSkd9osqvZT3jUeI+3TuPI5vYtklBQCSxPRaYjmLcJeLY1WBuAAxGciFiY67dlDxM3+Nw47YLfn1I9Ld2xyZTm1s2VnkSoOhyd49zoNFvqlQCrcDvY3uTnsJnnaHcoMWsWYBhNsMdlBW3r8lTDmyQaiFtC1abYEyJ7+6FcSJmaI5nt/IshGFDr8GTeqwSmM50tzzFkzJFuuUwJs3w1EoF25UzmK4xZCAud1d3Xrv6fzFfAU0RWtXAEaNu1jai0Mzt6rtRL0Wg0mtXN3Gz+MDUUVhwzfla1iGnZB9UbwVNJfbAzI7/RZGezn5q4EXK0Oo6maCtCuHo6qFqpCLcv47EGnzt/jiMkZlGr9VgagAMTUQduoly/tS7rKWw2QUtVGQe6x3Aziz0+m9LyqLmqLFEc0zUyRVyScBwB9q2rov3sOHGzEbi1ybbhOE5Qwb61BTiO6QUc6TmK2doP5SI6rcYfZg1Vn0eBjCn88okpXx7hGAyox5v3GgUyefRDsQpjTBbSAD0ahsGOZQ1Tw0UgHF+ws5kBqhnqPVPqpWg0Gs3qJhZOEURZcbjndxwDlvCfENC8l3WREzT6sjuOO5v9NIgx4sKhimlMzHBwERzHyLTRg88qcgwa/R7GpqNEYllC4lkdx3CKwB6NCKpdczTkcFRB5TnOxuJUYjb/Tnccw0gpExXVpuMI0LaummAkxqkhS7NuU5gZjqOvqo7KcmfO50+QtTjG8po4PIWPHLROnTEpVqg6X34jzCMcjer8ljb1c65wtemMF7Oi2eUr3HEcPKpSMZaxohouAuF4+fpqBkUt4ZGzpV6KRqPRrG5isykh2KzYnTmF3P1P93Hjv/2GuZ5DqjCm4RIAZPNeNsW7aPZm/5O1pcFLs22coLM2NUxurqUIwjE6bUyncWU6jqYTOpQtzzE9x3Euplw2Q1BORWIMhQW1nvwt48zK6k1eo7CoLFU4RmJxxqajnB6cQgjYVJ8M/5oh6EPdRrjaMg9aGh1H1ra05H3+BDa7mmsdCSZDze504Vjg622KQ6uLu9ixe1aCgfwV1aBe/7LqzBzH+JxqDO5tgsZL1O9hrgKZyV7VLLyYjp/bW7hoNte1jBXVcBEIR6fdhvS14JwKJG16jUaj0RSfuUj+whhQTluOquoDXaN0jUwTOXtIFSY4lfCbqt2NS8yxTWQPGbocNja4ggyRFmpNhKrPvyVPbMb4Y+7KnMZhOoVZm4A708SrGTY31vaH50YISydVzvwFPGaBzNZKY780xxFUS55TQyFaq8tSps9srKugqtyZ7OdoEScjI2p6ypZ1OcbzZcN0LKPTgMx0HKOFOo4WB9RkMb0M0wkOzO84gtrH7PloMjUMck45jk4P1O/M3ZLHdCKLKdwson5e+tqVw76MhTFwEQhHAH/jBhrkCEfMsVQajUajKT6xcP4ejpC3OEaN1ZM4Bp9JCb8N+NQEk43R3FNgmu3jnIumFUMkQtXn1wQ8NhdHRI0/5tlC1UYIPWuBjOl6mmIqUXmuXqdHTwwRFW7KRP7wvdmSZ2OFsZ/FcWwyp8eMhzk9GGJzfeoahRBGI3BLZbUhTgYHVKh21+YFiA+3cXwky2viXIjjmOX4823HEwnOPzXGxNeU6TiaP5vCs2WvEmjZCmT6DqvCmKYiFcaA8doWmN8ZaE+mcywj8wpHIcTXhBCDQohn59nvCiHEnBDituItrzisWbcZj4jy+LPFGT2l0Wg0mixYZzDnIk9xTO/YDK1iCNfseIqL0ysbGZcVNE915jxt9dwo56J+hkOWcydC1cpx7AxM8sTpkcKuxcLo1CwVGMLPlSkcG8x51VlD1WmOY6JJuhK1j54cxufzYZungMcMVa8rM0PVSXe1xXAc+yZmeG44lJLfaNK2rppTgyEmZqIpBRhjo0NMSzdbmzMbj+fEVWGM9TMdQ0uoeSFV1dkcx0SD8kXmOJpTXwoRjt4sYwdDace37FW9HsezpLuZLaPMAqhiUOis7mhYTYxZ5jA1FOY4fgPIOwBRCGEH/hV4sAhrKjrldesBeO7UsRKvRKPRaFYxc5H5q6pzFMdIKekZm2aPMAoZrY5jMMKz8Q1Ujh3Nfs5oGE9sggFZTWdg0vJcruS6gLt+fow3fOUPfOuJrgIvSDEUilBOhJi9TOX4pVFT7sKRa3pMQjiajmM4sf3c6DRnhqeor/bPG97d1uij3udmqz+mNlhC1bVGE/CD3WOEo/GUimqTa7YoYfiDgz0pjuP0xAgzDl/+MYPpmAUciRzF9BzH8xCODrfKK1xsVXWogB6OJr4mJRSt02MSjqNxfLORv5gerpYy2QqnmLgKnNU9UJrCGChAOEopHwXmm9f3HuBeYLAYiyo6/jUATAx0E4rESrwYjUajWaWkj9LLRo7imPHpKFOzc+yxnSGKI6Uv3mAwwhG5CefIsexhUMMlGqQqVTiaItYQMv0TYWxC8KEfH+UTDx5H5uvPZ2EoGMHLDDJLfiOodjmqJU+eHEezRY2Zb2l38dsTQwA01VbPG96trnDxxw/ezPpy43hLj0K70QTc7PO4JYtwvHx9DddtreNzvz5JxFYOswiI6YIAACAASURBVFNMRWLImTHi7gL6N1oxcyQTws/yujjLCm/Hky1ULcTC8vzSKWRqjIk5PWba4kKbx5vFNYkCmTThmCiMKbJwM93g+X43A8s/McbkvHMchRBrgFuBLxWw7zuFEAeEEAeGhobO96kLp1IJxwZGeOrMwsMUGo1GoymAgoSjURyT9oexd1w5bvudXZyUrUhLkc3AZJjTjq2IeFQ5LekYf+yjZQ109FkdR1M4KrE1EAzz2v1ref0Va/n8b05x571HiM1lmVWcxlAwQoUIZ62oNmnwexgM5nMcjccs87x/d3KINVVlVPl8ypEsRMjOjIO7MsP5bK70MBxS15me42jygZfvZGImyh8DszAb4plz41SKKZwVBfRvtOJKy3FMCVW7z89xBFVlvdjimELmVJuYzeKteY7BfjU1xm60JnJ6VDg6vbJ6qUb9ubyqOGe+17DvsCqMqVxb3OcvgGIUx3waeL+Uct6ZTlLKL0sp90sp99fXzzNIvZhUNCBdXnbbz/HYSS0cNRqNZkkotAE4MmMMYM+YqtDdbTvD03Mb6beEfQcmwwz6dqgfslW4Gn/4/Q3r6AxYwnyWBuCR2Bzj01FaKj18/NV7+B8v3MJ/HzjHP96XN30fgOGQynG0e7ILMjDGDuYNVacKxzmbm8dPjXD9tjqEs0y16IkXEBELj0NZ5kSUJiPPsabCRU1F9sr2nc1+bmtr5fFzEZBxnukK4GeK8srsjcdzYhZwzGZxDB1lhQvHSA7hWGi4NhvBfrWGfFNjTExX0lpZna0HZPPezAkyfebEmEsWt85cFDqvuu/pkhTGQHGE437gHiFEF3Ab8EUhxKuKcN7iYbMhNlzLDa5jPH56uNSr0Wg0mtVJQQ3AU/MOTXqMwpjyuSBH5CZODyZz3AaDEWTlepXXl60Zs/GH31e/lnNj08kQtD3ZfHvQCCM3+j0IIbjjxdv58+et595DPYxN5W/XMxSM4LdFsGWZGmPS4PPMUxxjhqrVPkMzkmAkpqa1mAK3kDY2M+Mp+Y0mLUbV9eb67OF0k/e+eDvTQq3peHcfdfZpXN6avMdk4PKlFcek5SgW3I4nqESe3ZG6fSHTU9Ixm3cXIqiyzas2p8ZYadmnJhlZC2SWojAGLH0s8wjn6AwMdZYkTA1FEI5Syo1Syg1Syg3AD4C/llLed94rKzYbr6c51sN4f1f2cIJGo9Fozo+CGoCbwjFVrPWMzXCFswuAI/GNnBpM/uEcnIzQ4C8zWqNkacYcDIDNSXVtI9Ozc0yGDefOMrXFfN9vsIwtfMNV64jOSX7yTF/eJQ+FIvjtkayteEwa/W7Gp6OEo2nBN0dajqPhOJ6bVPttbvAmxUchbWzC4ymteEyajF6S2fIbU/ar9HDVdlUwerSrFz9TWYVoXsx5ytmKY5xlC2vHky1vNNu85sFj2dMU0jGFYyGY4WxrZXVoIHXeOSTzGB/7JDzxRfXVc2BpClMSs7otxUHRGfjjV5PP/cjHlTtdgopqAMd8Owgh7gZuBOqEED3AhwEngJRy3rzGC4aN1wNwje0oT5we4Za9a0q8II1Go1llFNQA3Mgdi6UKx97xGZ5fNgwRCLg3cHpI/eGMxyWDwbASfNV74YnPZ+ZSGmKhyXDdAhMzVJY5U0LVAxbH0WRns5+dzX7uPdjDm6/ekHPJw8EIPhHO2orHxGwCPhSMsLbGMrfYZlPOZ1qo+uzkHGBTrXNGTIFboONYvz1js9kEPFd+o5UbL90EJ6E8NonbHs4qRPPi9gJSTViBTMdxISMHs4lxlzd5bpP7/1aJ73c/lv+coX5o2lPY8zvcKk/QFI7xOUM4poWqG3dDRQMc/Ebq9k0vKOx5FkK2BuhH74Of3ZG6n7Mc1l5V/OcvgHmFo5Ty9kJPJqV863mtZilpuARZXssNdPLYyWEtHDUajabYFNQAPPs0l56xGWo9cZi101pXk5i5PDY9S3RO0uhzQ9U+5bQMHIU1bcmDDeGYnKASZkeTP2VWtZl/2OBLXd9r2tbw//2sk1ODQbY0ZA9FD4UiVBCex3E0p8eEU4UjpLaoMULVZ8Zi1Hl9aj604/wdx62NPoSAy9bOLwLLKvwAtNiMnP8FO47G62DmE1oLdRxl6h7NxTJD0OnMhrIXHKUXx8xFIfCMOu/sNLjKM48xCfbD1hcXfi2+5qRwnBpSuabpjqXDDf9w1JiUY2Czp45KLBbZGqD3HVKv+d8fAWEEih2eZMX+MnNRTI4BjDzH67jO0cnvTw4V3IZBo9FoNAVSaANwyBCOvWPT1Lji4PCwpcHL6SH1hzPFKTRDg+nh6mA/eBtpNiao9E8keyWqdUUYDEZw2gXV5amO6C1712C3Ce491JtzycOhCGVyOm9VdWO+JuBOi3A0nNYzY1G2NFQkH4fzynHc0uDljx+8mSs2FJCvaFzHXTcbTb/LFlhVbQqmYCBTTCfSAwpICYsEczuO1hy/wU4luOUcDOQpZjJbBBVSUW3ia0zmOCZa8WQJdTtcSrCbX0shGsHiOFquv+8wNF8G5TXJ5y+RaISLSTgCbLyemtggzmA3XSPT8++v0Wg0msKQssAG4JnCcTIcZTIco8o1B04Pm+u9DAYjTIajltxED1QZBTLpldXBAPiaqfe5sQkIGK19rCJ1YDJMg8+DLa3Rdb3PzQ3b6rnvcC9z8UxDIRKbY2I6gjs+kz0fz6DBl3QcM6/ZbclxVN9PjEaTYeX0AppcRGfUa5xD6NV553ntTQxx4gsbQmmhoWrTcQwNZIbvF5KvORvKHv5PL46x3u9sxVEmiakxBfRwNLHOq15ID8ilIlEcY1z/XAz6ny1ZPmM2LjLheAOg8hwfO6WrqzUajaZoxGMqzLcIx7F3TAk9v2Mu4TgCnBoMJaqhG3xuVSnbsi9VPETDKnzra8Jpt1HvcxMwHUebDWxOiIWNApvsa3t12xoCE+Gs4whHQrOUY4igPKHq6nInTrtIaSOUwNqixrju0bBIFrIUKhxnxtT3hQq9dExxMnFOfV9oqNp8HYIDWRzHtEk5+YiEcjuO8WhSfPa1g9sPFfXZ2zGZpE99KQTr9JjFHF9s0nMch4+r17IEE2JycXEJx9rNSF8LN7mP87gWjhqNRlM8LI2t85LIO0wKxx5DOHrtMXB4Ei1lTg+GkrmJpuhr2QuDHUkHL23EXHNlWap4c3ggphzHRl/28N7NOxvxeRzce6gn47HhUIRyjPPlKY4RQrClwccz5yYyH3RkFsfM4sx0HOebuDIzrr4vVOilY4qTCeN6F+w4mvOkJzLD9+mzufMxm6uq2nTdjMpiM1Tbsi97Vb1JMPV3oSC8TcnpMaEBQIC3ofDji022a4eStd7JxsUlHIVAbLye54lneeLUUNawhEaj0WgWQWKU3mIcR5U6VG6LgsPDuppynHbBqaEQA8Ew1eVO3A6jAKN5r/pDP2i0ZgmmC0cPfeMWt8vhMqqqwzkdR4/TzisubeEXz/ZnjKUdCkbwCkPQzZPXdv3WOg50jzKVPtrWWZbMXzQEZARn0nF0Fug4hg3heL6Oo9MQa4t1HK1iMV34LSRfc3YqR3GMJc9vzpgW1LJX3fuhY6pAJhsLmVNtYu3lGAxAhWVqTCmwO9X/ITPHs69dvUY1m0u3pjQuLuEIsOkGvHPjNEbOcLQvyydDjUaj0Syc/8feece3Wd37/300vFc8Es8kTmJnkr0hYUPYs6y2lF0KdNz2Qktv21/HbW/Hvd2UFmhLS8somwIhEAiEkUn2jrO94hHb8dQ8vz+OHi1LsizLKznv1ysvyc9z9OjISaSPPt9liJ6eGoCbuzcAr2zqJMlqIkHawZqExWxibE4qB+raOX7SFtBCx+u8GOHqoLy0/Mwkalq6ApqAO+2dnOxyBl4niOvnFNHpcLF8e03A8fpWG6l4RFAExxFgaXkeDpdk7cGgkLcl0efAeQSzJSHRWwUefag6To6j2aLC58aM5pja8YS4D9E7jlIqxzFcqBrUeaMwpmCmEo/SHb5AprVWtalJzIjudYAvn7G1tnc9IPsT/xzPmi1QMF2lXQwRhs5OBoqxSwCV57h63wDOy9ZoNJpTGW+ouodqT29xjMN7qKq5k6KsZITT5m1NY1RW17XavD0SAcgarYpDjBBeUCVsYWYyHXYXrTZfE3BblxJ+wa14/Jk9egRjc1J4bWtgM/CGNn/HMbJwnDt2BMlWc/fPFkuyL+fPacOBlfF56Qhjukm0wjFejiP4Xos1tfcOm7+ADhbT0eY4OjqUCAxXHANKPPmHao0CkXDh6tYaVVHdmzF8RrPvNo9wDFVRPdAYs8BdTqjdPqTC1HA6CsesEsgex7LU/by963jP6zUajSZGhBDLhBB7hRAVQohvhVlzgxBilxBipxDi6YHeY9zwhqqjdRwDcxyLRqQoseHJkRyfl8aRxnaqmjoCBZ8QvtnB4J0aQ4pqQ2PMbK5pNhxQn3CM5DgKIbhg8ijWHTpBp903/aW+1UZegmevEaqqARItZhaOy2b1/qAcen/H0WnDhjVwNOBA5ziCT7DFIkL9xV5w+D7a12I4aiEdR6OXYav6e07MhBGlkFGoGnGHq6xuDdG8uyf8p8cMGcfR08eyfo/6MjGEKqrhdBSOAKVLmenawc7KE1Q1R9nhXqPRaHqBEMIMPAJcAkwBbhZCTAlaUwY8DJwppZwKfG3ANxovnD6hFhG/+dEGVc2dFI/wjKqz+hxHt4SGNru3R6KXwlkqhOno8k368LhMvibgvpY8DlvPwhFUqNnudLP2kC/UXN9mY1SSx72M0MfR/xqHGto5dsIvD88vx9Fh76RLWgJHA/Y2xzEps8d99Igh2GIRoSaTL0+yWzueKF+Ld851pBxHj+NohGqFCD92EjxtmXrRwxF802NaKqG9bnBb8RgYfSy9bqsWjoNP6VISXW38h+UF2p7/EvzpbPifEtj/zmDvTKPRnDrMByqklAellHbgWeCqoDV3A49IKZsApJRBc9aGEZ4q6eW7T0QuPDTCop5QdYfdyYl2O0VZHnHl5zgadBN8hTN9E2SCxEJBVvcm4E576KkxwcwvzSbRYgoINTe02hmZ6BGOPYSqQQlHgA/8w9WWJK9Qbm3vwI4lcDSgd3JMFI5jYmbgpJZYMQRbrGFvw30Nm+PYk+PYGnidUNfubPIVxhgUzFQtavxnORu01sYm/NILVN6kdPdeePYHCanq9dUMvcIYOF2F49ilYLLygOVVCqtXqm9vThscWDXYO9NoNKcORcAxv58rPcf8KQfKhRAfCyHWCiGWhbqQEOIeIcRGIcTG+vohmpvtKXb5+8bjkQsPg4pjjB6OXsfRI6LG+YVyRwa30TFCdzWbu4UXR6YnIgRUt/gcUJfDRoLZRFZK5Fy+JKuZ+aXZAcKxvs1GrtWTj9lDcQzAuNxUirKSA/McLUnenL/29nZs0hroOJotIMw9VyJ3NUNyHNxG8Imz3k6NMTAEY7Dwi1Y4GsIvUqi6cqNKafDP8SucpQRebVCBjK0VHO2xhZrT833XGwqOo1EcU71FtSEaQoUxcLoKx7Q8uG8Nj815jRldf6Txuudh1NTIo4w0Go2md4TK0A+24ixAGXAOcDPwhBCimwUkpXxMSjlXSjk3Ly8v7huNCx6hYJcWGtvs4dcZoWyP41gZIBx9jmNqooVCT9i5WxudrNEqvFi9pZvLZDWbyEtLpNYvVC0dqhWPiKJo4uzyPA7Ut3vTmBpabWRbPGH1KISjEIKl5bl8cqARh8vt2ZTPcezs7MCBlTE5wW1sknuuRO5sil3oBdOXUDX4fhfBoWZjckxPIjiaUPXhD9Wtf45fpLGTEFtxS3q+r8p/KOQ4JqSrLwm124dcmBpOV+EIkFvG4tkzcUvBu7vrfMJRz7DWaDTxoRIo8fu5GKgOseZVKaVDSnkI2IsSksMPT6jajpWGtggCyBuqVusrmw3hmBKQ4wgw3uPKdQtVG7luR9eqD9ig2cQFWcm+6TGWJKTL1mN+o4ERal69r55OT3V2lsWmcvqidH6WluXRZnOy+Wizdw84u0BKVahjSSTBEnQtS2LPlchh5lTHRF+KY8BXFBN2VnUPItgIVYdyHC2JquCp+YgKzWeP851LL1AFMqHGTkLsjqPBUKiqTkxTubtGG6IhxukrHIGphRkUZSWzYmctjJrm1zleo9Fo+swGoEwIUSqESABuAl4LWvMKcC6AECIXFbo+OKC7jBcex8aGlcb2CI5jUHFMZVMHCR6X0D/HEaBsZDomgToXTMFMaNyv7geFFwsykvyEYwLCFaLAJgxlI9PIz0hi9b56rwBON9l6rKj2Z/GEXMwmwYf7PeFqv96GDlsnJmsIEWuJwnHsao5PKx7wCb8+O47BwtHI14zWcQzj4hqCsnBGYHudUGMnIbY51Qbexwzy1BgD/9/JEGvFA6e5cBRCcPHUfD6saKAze5I6qMPVGo0mDkgpncADwApgN/AvKeVOIcQPhRBXepatABqFELuAVcCDUsruA5OHAXZP5bIdCyciCsfA4piqpk4Ks5IwSSdIl094APcsHcefPj+3uzsHgR+oQS5TfmaSrzjGnIjZZeueJxkGI9T8UUWDd3RhGp1RFcYYZCZbmVmS5ctz9AhHp60Dl6MLa2Io4ZjYc3h3SDmOaYG3BmYrIKJwHCO04wFfCDuU41YYokDG6zjGUNxiONaDPTXGINEvDcDfbR0inNbCEeDiqaOwO92sbvHkDR3fObgb0mg0pwxSyjellOVSyvFSyh97jn1PSvma576UUn5dSjlFSnmGlPLZwd1x7DQ0q9CjXfYQqjaZVSGIy3AcOykakewrpvBz4/Izk7hwShgh4J/7FSQcC7OSaLM5ae1y4DQlYMURdtxgKJaW59Ha5WSlp9dvMl1R5TcGXKMsj21VLUpEe15TVcMJrDhITEzu/oCechyljLPj2NccR6MdT1COohCBIxbDYYg+axgn17h+qBy/As8EmdrtvmOxTI0xMBzHoZDfCL5/a0OwMAa0cGTu2GxyUhN4vcIO6YVaOGo0Gk0MNLacBMCUkBS5OAaUu2bkODZ1UpyV4msY3dPkGYPMEkjJUfeDwpP5mUqY1bR00eEyk4CTUVE6jgBnTchFCHh5cxUASe7OHudUB7O0PBcpUeFqz2s6VtdEIg6SklO6P6CnHEdHp/qdxc1xNKqqYxWO6YHX8ce/4Xk47K3KXTZbQp/3hqpDhGqDx06CmvySnt+7qTEGhmAcChXV4BOOQ7AwBlRF32mN2SS4cMooXt9Wg2vCFMxaOGo0Gk2vOdGiHMeJRbkcb+9BNJit4LTT5XDR0GYLdByjFY7GBJnDH3arNPY1Ae8i3WUmE0fUxTEAWSkJTC/OYusxVdxidbZDcu/cqOnFWRRkJvGj13cxa6mb0UBlfRMFOElNDSUce3Ac4zluEHzCL1YhGi5UDYEjFg0Ofwzr/+QrQK3dFjn8n5CmWuWNKO1+LqNAhZfX/RGOfKyOHV0DOTHWlRmh6rQh0MMRIovmIUCPjqMQ4i9CiDohRMjkPyHEZ4UQ2zx/PhFCzIj/NvuXi6fm02ZzcixhHNTv9VYHajQajSY6WlpVzlpeVnrPjqNZOY5Vzf6teHopHAFm3wpz7+jmMhnCsbalk1aXmQQcURfHGJxdlgtAdmoCwt7WqxxHUKbEP+5aQKLFzM9WHgGgprGZZJMTa0KIUHVPOY7tnjGGhsvaV0YvhPJlMHJybI8ffz5Mvyl0Ox1LYveRg5ufgr3LoWG/+mNJhqnXhr/+lKtg4f3hHcS5d6p/K8b1UnJh6jWxvRZLAsy5HSZfEdvj403RXJhwIYw7d7B3EpJoHMcngd8Dfw9z/hBwtpSySQhxCfAYsCA+2xsYFk/IISvFyqqmkdzudqhKvVFTB3tbGo1GM2w42aZy1jLT0mhsb0RKGb5vojkBXA5v8++irGRwNqlzoSqOwzH1avUniJHpSaoJeHMX4x0mLMLNyNTeBdiWlufx2/cqVEW3va1XVdUG4/PSeOm+xfz6T9uhHXYcruV2kzP0WEZrsq/AIxRGn8J4hVNHjIFbnov98aMXqD+hsCZ3bwDeWqNy9u5aGd31594e+fw531R/4sUVv47ftfpKRgF87oXB3kVYenQcpZSrgRMRzn9ijMsC1qJ6lQ0rEi1mrp5ZxPPHPB35dbhao9FoosbllnR2tuMUVnLSE7E73bTZnOEfYEkAl42jnnnORSOSe5/jGIEEi4nctERqW7o46VAfcxlWV6+uMbMki/QkC7npCaqQI4o51aEYlZHEf109G4Curg4ShdPXksgfo9djONoM4ThECjgiEeq1tNYOnVCwpk/EuzjmTmB5uJNDeWzWjfNK2OcahUtYAyu1NBqNRhORqqZOzG4H0pxATqoSRRHD1eYEcNlZufs4hZlJFGb65cTFQTgCFGYmUd3SSZNNfcwJV+9SkCxmEz+6ahp3nzVWOY69DFX7k5ai3MqbZuaRKBxKOHd7wqTu4V1/vJNRhoH48pvN7SXWOdKaIUfchKMQ4lyUcAzrHQ/lsVmTCzKYWpzDIVMJUjuOGo1GEzUH6ttIwAGWJHLSlChqjFQgY7Zi6+pk9b56rpldhMnk1/cvTsLR6OV4wthGL4UjwNWzijin1Gg7E7twNKbhXDFlBCanLfRrtPbgOLbWqDGLocLcQw1rUmC+pqNTFfcMB7dU0yNxEY5CiOnAE8BVw7V5LcAN80rYai/CWaMdR41Gc3pQUdeG7OOo1Yq6NhJxYLYmkuuZ8tIQ0XFMpK65DbeEa2d7spsModGbHMcIFGQmK+HY5cmzjCTKItFTo+poMMSevR2QsYWqW48PH8cu2HFsHUZhdk2P9Fk4CiFGAy8Bn5dS7uv7lgaPK2YUUiHGYO2o81WwaTQazSlKZVMHF/7qA55Zf6xP1zlQ30a61Y3Jkuh1HCNNj5FmK40n25hZksX4PI8gi7PjWJCZRKvNSU27RxTH2i3DOxovthxHwDcNp6vF83OYULVnnnVIWmuGj/CyJAW24zFG+Q6X/WsiEk07nmeANcBEIUSlEOJOIcS9Qoh7PUu+B+QAfxBCbBFCbOzH/fYrGUlWMsd6kpirtg3ybjQajaZ/OdLYgZTw3Ma+CceKujZGJEqwJJGd6glVR5ge0+4y47R3cd3sIt/BOOc45nta8nS6PdXUrh56S4bDpvpTxlJV7cVwHL3CMdTIwSQ1DcUzirEbrbXDR3h1cxyNcYDDxDHVRKTH/gRSypt7OH8XcFfcdjTIzF+4BI7Cnq1rmVl+3mBvR6PRaPqNGs88563Hmqmoa2PCyN6HY6WUVNS3kZnqBksCiRYz6YmWiKHqmlY3CTi5Ykah76AhNKwhehzGQIFneozN+JjraZJJOIzReH0JVVuDHEdzCMfRCNE7u7o7km63cu2Gi3AMznH0FvYMk/1rInLajxwMZtbkMk6ILE4c3DTYW9FoNJp+pcbTgNsk4KVNlT2ub2q38+2Xtwe4iSfa7TR3OEi3ur25ezlpCTSGCVU7XG4qW51kJ6kJLb4ThuMYn+IPowm4Dc9zxCwcjVB1H4SjOdhxDJPjCKHzHDsaQLqGj2MXnK/ZWgsmK6RkD96eNHFDC8cghBC0Z00it72CirrWwd6ORqPR9Bs1J7vITk1gaXkeL2+uwu2OXCTzzIajPL3uKP/7ti+dvaJOCatUs8sriHLSEsOGqlfvq6fNaVKhbX+8OY7xcRyNEYN22ddQtVEc04ccR5NJicdohGOo6TFGqHc4tOKB0MIxvSC2OdKaIYcWjiHIGTeLclHJ157+NHITW41GoxnG1LZ0UZCZxHWzi6lp6WLNwfBNMaSUvPhpJULAcxuOsu+4+mJ9oF6FclOEwyccUxPCFse8tKkKYUkgxRTUkNvZCcIM5t5NeAmH0QTcjtVz/VgdRyPHsQ+OI6jwrTdUHWZyDITeZ6tRXDJMHEdrMrid4PJ8frbWQPowEb2aHtHCMQQpJTNIEg7sdfv58tObcLrcg70ljUajiTvVzZ0UZCZx4ZRRpCdZeDFCuHpbZQsH6tt56OJJpCZa+OnyPYByHJOtZqw4/ELViSFzHFs6HLyz6zgF2ZkId1ARiKMrbvmNBoVZSQjD3YtVOMajHQ8oFy6i42jsM4LjOFxyHL2vxeM6Dqf8TE2PaOEYCs+c6h/M6WDV3np+8O9dfe5zptFoNEON2pNdFGQmk2Q1c/n0At7aUUt7mCjLi5sqSbSY+OzC0Txw7gTe21PHxxUNHKhvY1xeqprMEuA42rqFvt/ZfRy7y82YkVndG3I7u+Le3LokO4URGZ4QcwwNwAFfjqO1D1XVEIVwjOQ4DqOpMeD3WjzCsbVm+Lilmh7RwjEUo6ZB7kQW1T7DF5eM5am1R/jzR4cGe1cajUYTNzrtLpo7HN62NdfOLqbD7uKtHbXd1tqdbl7bWs1FU/PJSLLyhcVjKcpK5sdv7PZVYzttfjmOCbglNHcGuop7ak6SaDGRk5Heva+isytu+Y0G37lsMt+/Zpbv+rFgb1ei0dTHj0tLEthOqvshG4B7joXLcUzJCd3/cSji7zjaO5RgHi6iV9MjWjiGwmSCsx+C+t18c8x+Lj0jnx+/uZu1EfJ/NBqNZjhR06IEilF9PHfMCEZnp4QMV7+3p47mDgfXevouJlnNPLRsIrtqTlLV3KmaeDtt3jYzOWnGvOpA90y5k2kIS8KAOI4FmcmU5ud4rt+HPo59DVODynE0hGMoARgpx7FtGE2NAd9rcXRBmzE1ZhjtXxMRLRzDMfUayC3HtPrn/PIz08lNS+TR9w8M9q40Go0mLtR6ejga/Q6FEFw7u4g1Bxs50tgesPalTZXkpSeyZEKu99gV0wuZUZwJoBxHl28Gc67RBDyoQKai3uNOmhPUev8UoH7IcQR8Iq0voeq+FsZAYNPvkA3Ae8hxHE6Onb/j2KqnxpxqaOEYDpMZlj4IdTtJqniTLywawwf7njFLAgAAIABJREFU6r2VhBqNRjOcqfYKR5+I+czcEpKtZj77xDoONSjxeKLdzqq9dVw9sxCL2feRYTIJ/t+VUxmbk8Ls0SM8oepgx9En1rocLiqbOhmfl+oTc26/fEpnV9ymxgRgjkNxTDwcR//XFqoBeE85jsPJsfPPcdRTY045tHCMxLTrIGcCfPBzPju/hCSriSc+PDjYu9JoNJo+U+sJVef7CceirGSeuXshHXYX1z36CVuPNfPvrdU4XJLr5hR3u8bs0SN4/8Fzyc9I9ISqfTmOAI3tPhF0sL4dKfE5jhDoAvaXcOxrVbW9rW9zqr378Hcce5Hj6HZBW93wcuwCHEcjVD2M9q+JiBaOkTCZYelDcHwHI46+zfVzinllczX1rTG+AWk0Gs0QoaZFNf9OspoDjs8oyeKFexeRkmDm5sfX8tjqg0wpyGBSfkb4i7mdgPSKoxEpCQhBQEueA/WqOnl8XlpoF9DR6Ru7F0+E8IXGY8HW2rc51QbWHkLV3rzAIOHYbkyNGUbCKzjH0ZwAySMGd0+auKGFY09Muw6yx8MHP+eOxWNwuN08tebwYO9Ko9Fo+kRNSxf5GaGF2ri8NF760mLG5KRS1dwZ0m0MwDv1RTmJZpNgREpCQHFMRV0bQkBpbiqYPU25XY7Aa/SH4whKqAZXcUeLvT1OoWq//M1QoerkbOVsNu4PPD7cejhCYL5ma63au54ac8qghWNPmC2qwvr4dsY1r+H8SaN4au0Ruhyunh87WGz6Ozx902DvQqPRDGFqPFNjwjEyI4nnvriQH141lVvmj458MUM4+rWZCZ4ec6C+jZIRKcrh9Iaq/VxAZ2f/CUdLQh/a8cSrOCYx9H0DkwkKpkP1lsDjbcNsagwE5mu21kDaMBK9mh7RwjEapl2nemhtfYa7l5TS1OEIbFkhJVRuDKwQHEz2vAn7lsee06PRaE55alo6KciKLNQykqzcumgsyQnmiOu8AtBPEOWkJQQUx3j7PfqvGyjH0ZLUt1nVfZlTbWDtwXEEKJwFx3cE/l6G25xq8IXlHZ2qqno4uaWaHtHCMRrMVph6LexdzvxCC2cUZfLnjw75piLsegWeOB/2vTW4+zSo361uT1YN7j40Gs2QxGj+bbTi6TPOUMIxkQZPcYzLLTnU0K4qqsEXqh6IHEdQQi2WULXbDY72+DqO5sTwYduCmcoZrd/jOzbcpsaA7wuAURwznNxSTY9o4Rgt028EZxdi9+vctaSUg/Xt/GPdEXVu3WPqdigIR3sHNHn21RJ+7qxGozl9qT3ZvRVPn/CGqn1OWm6qz3GsaurE5nT7HEcjpB1QVd2fjmNibKFqe5zmVIMvfBupyXnhTHXrH65urYWU3OEzNQZ8f4+dTWBrgfRhJHo1PaKFY7QUz4URpbDtOS6fXsj5k0by/dd2svaT9+HoJ+o/yv6Vgx+ubtwPePbQfGxQt6LRaIYmNc3dW/H0CW+o2ne97NREWjod2J3uwIpq8Mtx9IRkpeznHMfE2BqA2z2N0ONRVW0IxkjCMXu8KpCpCRKOwy3Ua/w9GiaGdhxPKbRwjBYhYPoNcGg15rZafnfLLKYVZXL0rd/iNifBOQ/DyUqo293/e9nyNPx2Fric3c/V+YU4tOOo0WhCUBM0NabPGGHgoBxHgKYOOxV1QcLRElQc43aCdPdjqDoxtpxvw3GMRx9HI8cx1JxqA5MJCmYEOY41w084mq0gTNB0SP083PaviUiPwlEI8RchRJ0QYkeY80II8VshRIUQYpsQYnb8tzlEOOMGQMKOF0hJsPCXG8u40vQhr7rPpLLkMrWm4p3+30flRjhx0JfL6E/9HjBZVDFPy9H+34tGowmLEGKZEGKv5/3xWyHO3yaEqBdCbPH8uSuez1/b0sVVv/+IN7fXBB6Pe6jaEwb2D1UbTcDblHDMSU1ghGcUYbcG4Ebvwn4NVccgHG2eSWFxCVUbjmMPIefCmVC73efGtg3D4hIh1N9l02H1s66qPqWIxnF8ElgW4fwlQJnnzz3Ao33f1hAldwIUzoZt/1I/7n+eJOw8y8V87l+VuHInw/4BEI5GsnTVpu7n6veqaTcjSrXjqNEMIkIIM/AI6j1yCnCzEGJKiKXPSSlnev48Ec895KYlcKihnff31gUcr27uZESKtVvz75gJEar2jh1st3Ggvo3xI/3El7c4xiMcDeHZr6HqvjiO8cxx7OE1Fs5Se63f45kac3x4Ci9LEpysVveHm/DVRKRH4SilXA2ciLDkKuDvUrEWyBJCnLoJDdNvgNptcHwXbHgcRi/iods+w7GmTj4Ss+DoWt+31P7CaM9Q9Wn3c/V7IG8iZBbrHEeNZnCZD1RIKQ9KKe3As6j3ywHDYjZxVlkuq/c1IP3yr2tbusiPV5ga/ELVPjctx+MuNrTZqKhv84WpoXtxTH8Lx1gbgNviWBxjhOHDteIxKPArkGmvVyH84Si8LEmAVL97PTXmlCIeOY5FgL9CqfQc64YQ4h4hxEYhxMb6+vo4PPUgMO06EGZ49X5lw8+/hzljsrl98VgerRoHbgcc/KB/92AIx+ogx9HRpXJK8iZBVolyHAe7WEejOX2J9r3xOk+azwtCiJJQF+rLe+fSsjxqT3ax35NnCCrHsTBeYWrwC1UHtuMB2H+8jeYOh6+iGkKEqj2Pt8ZRzPoTawPweOY4GqI4UnEMQPY4SMxQBTLeOc/D0IsxhHL6KD015hQjHsIx1L+IkGpFSvmYlHKulHJuXl5eHJ56EEgbCePPVaItLR8mXwHA1y4spzJtOh0k497/dv89vxG6MFmV6+k/17Rxv/p2mjcJMktUuKO9of/2otFoIhHNe+O/gbFSyunASuBvoS7Ul/fOJeVq/ep9PsFZ09IZv4pq8AlAP1GUkWTBahasP6QCVt4ejuBXHBPsOPYgqmIl1gbgXuEYj6rqKIWjt0Bms59wHI6Oo+dLwHAUvZqIxEM4VgL+35KLgeo4XHfocsYN6nbuHd5cnbREC9+5cjqrXdPo2Lmi/5w+I3RRukQNvq/d7jtXv1fd5k1SoWrQBTIazeDR43ujlLJRSmkomseBOfHeRFFWMuPzUvnAIxy7HC6aOhzxK4yBkA3AhRBkpyawtbIZILLj6BWO/eQ4xtoAPJ6hakM4RqqqNiiYAbU7oMVjWA9L4eh5ncNx75qIxEM4vgbc6qmuXgi0SClrenrQsGbqNXDRf8PCLwUcvnhqPjV5Z5Fmq6X+4JYwD+4jRrLxJE8Vt3+BTN1uFUbPGa8cR9AFMhrN4LEBKBNClAohEoCbUO+XXoLywa8E+qWf19LyPNYfOkGXwxX/VjwQclY1QE5qIg6XJNlqpjAzxMi9bsUx/eU49rEBuDUOjqM1SscRfAUyB99XPw+nqTEGRtrBcCzs0UQkmnY8zwBrgIlCiEohxJ1CiHuFEPd6lrwJHAQqUN+Y7+u33Q4VLAmw+MuQlBFwWAjBRVd9DoAPlz/TP89thC4KZ6v/kP55jvV7lGi0JPocR10go9EMClJKJ/AAsAIlCP8lpdwphPihEOJKz7KvCCF2CiG2Al8BbuuPvSwtz8PmdLPu0AlqWlR6S1wdxxCzqsHXy3FcXiomk1/kfsBzHGNsAG5rUxXVpjh4LNGGqkEJR4ADq9TUGKMKfTihHcdTFktPC6SUN/dwXgL3x21Hw5yiMRNoSJ3AqOMf8eqWKq6aGbJOKHaMwpj0AiiaHeg41u+FkZPU/eQR6g1PO44azaAhpXwT9eXa/9j3/O4/DDzc3/tYWJpDgsXE6n31TC1UX3gLsvrBcQwSRbmeApmAimrwE46exzn7uY9jzA3AW+PTigd6F6oeUaoKZGwnVbHMcETnOJ6y6Mkx/cCI6ZeywLyXH7ywju2VLfG9eGut6sifmqdcx8b90NWi3hRPHFT5jaCq2DKLfTkyGo3mtCU5wcz8sdms3lfvDVXnZ/RDjmNQqxmjJU9AfqP/OqPJtbN7H8i4YklUOeGhpm1FwtYWn/xG8HMco5g5bRTIwPB17LyO4zAMs2siooVjP2CeeDEWnCxL3s09T22kvjWGb7rhaK1W+S5mi3IcQfX7aqxQb4yGcASV56iFo0ajAZaW57K/ro3NR5vISrGSnBCn5t+gnENzYre2K9meUHU3x9FkUhOugifH9NfIQUPE9Lay2t4en4pq8MtxjPI1Fnr6OQ5X4WXVjuOpihaO/UHJAkjK5MHSQzR12Lnvn59id7rjc23/gfdGHkzVpyq/EYKEY7EOVWs0GkDlOQKs2lsf38IYUEUuIXL3DFezfFQI184/fNzfjqMRHu5tuNreFp8ejuAXqo7CcQRfI/DhKrx0juMpixaO/YHZCmUXMaJyFb+4bhobDjfx/X/vjM+1W2t9byQp2SoXpnqTym8UJjVu0CCzGDoa1bdmjUZzWjNxVDqjMhJxuWV8C2NAVSyHEESXnlHAX2+fR9moEOLLbPULVff3rGqjiruXwtHWGt9QtSUZkrOiW1/k6cyUNTo+zz/QJI+AxExIivL1aoYNWjj2F+XLoKOBK7Kr+dI543l63VHWHWzs+3VPVgd+gyuaDVWbVSueEaWBoR7jDaelqu/Pq9FohjVCCJaUKdcxrs2/QYWcQ4i+JKuZcyeODP0Yc4JfcUx/5zh6rtvbUHVXMyRlxmcPQsCdb8O8u6Jbn10Kd77j6xs83Fj0ANz+pp4acwqihWN/MeEClcOzbzlfOa+M9EQLz23oY76h0wadJyC90HescDacrISjawLD1ODXBFznOWo0Gl+4Oq7jBkG9N0VT9OGPJdHnODo61fulucdGH7ER3DcyWjpb4jtnuWB674Royfz+y/vsb1KyIX/aYO9C0w9o4dhfJGfBmMWwdznJCWaunFnImztqaOl0xH7NUOOnjAKZ9npfKx4DbxNwLRw1Gg0sLctlbE4Ks0fHUQyBJ1Tdy+bdZqvf5Bhb/7mN4Mu3600TcLcLbC061KrRBKGFY38y8VJVtHLiIDfOK6HL4ea1rb2fxvhJRQO/XrkPd4vnsf7J0gUzVG4jdHcc0wvUOV0go9FogKyUBN5/8FwWT8iN74VdoYtjIhJQHNPZz8LRCFX3wnHs8rRSizYnUaM5TdDCsT8pX6Zu977FGUWZTC7I4F+9CFdLKXlkVQWf+/M6fr1yP1WVh9SJDD/hmJAKeZPV/byJgRcwW1RYW0+P0Wg0/YnTFoNw9CuOcXT139QY8AtV9yLHsUvN2NaOo0YTiBaO/Ul2qRJ1+5YjhODGucVsr2phZ3XPTcFbuxx88alP+cWKvZw3SSWXHz18QJ0Mbs9QNAsQkFPW/UJZJdpx1Gg0/YvTFn2bGYOA4piu/ptTDbGFqjs9wlE7jhpNAFo49jcTl8GRT6CzmatnFZFgMfXoOlY2dXDVIx/z7p46vnv5FB6/dS4TR6XTVHtEhXeCk7XP+jpc9wQkpHS/mJ4eo9Fo+htXDDmK/sUxzi7fiLr+wNsAvDehau04ajSh0MKxv5l4KbidULGSrJQElk3N55Ut1XQ5XGEf8siqCqqbO/nnXQu486xSTxuNXFwna3Cn53dvb5AzHs64PvTFMkvgZJVK9NZoNJr+IEwD8IgEFMf0s+MYSwPwziZ1qx1HjSYALRz7m6I5kJILe5cDcOO8Elo6HazYWRtyeWuXg1e3VHPljEIWjsvxHl9ankeePEGrpZdJ7ZnFSri2HY/5JWg0Gk1EwjQAj4h/cUx/5zhaYhGO2nHUaEKhhWN/YzKrIpmKd8DlYNG4HEqyk/nXxtDh49e2VtNhd3HLgjEBx+eXZpNvaqLK1ctmtEYTcF0gExtNh+GFO8DeMdg70WiGLmEagEckYHLMAOU49qYBeJfOcdRoQqGF40Aw5UrV2uGNb2BCcsOcEj6uaGRP7cmAZVJKnl53lMkFGcwoDhSISVYzBaZmdrf3cvyVbgLeNw68BztehNptg70TjWboEnMDcP/imH5sxxNTqLpZ7ak/nVCNZhiiheNAUHYRLPlP2PQ3+PdX+NyCErJTE/ivl3fgdkvvMlVxfZJb5pcggvMYba0ky072tqdS09IZ/XN7haOurI6Jtjp123x0cPeh0QxlnLYYGoAn+HIcHf3dxzHGdjw6TK3RdEMLx4FACDjvO7D0Idj8FCNWfp1vX1LOp0eaeM4vZP30uqMkW81cNauo+zU8U2OOyxF8uL8h+udOTFdvftpxjA1DODYdGdx9aDRDGVeMfRydfpNj+nO0XiyzqjubdZhaowmBFo4DhRBw3n/BOQ/Dln9y3bGfsHBsFj9dvoeGNhutXQ5e21rNFTMKyEiydn/8STU1pit5VO+EIyAzi3HrHMfYMIqKmg8P6jY0miGLlDE2AE/0q6ru7N92PLHMqtaOo0YTkqiEoxBimRBirxCiQgjxrRDnRwshVgkhNgshtgkhLo3/Vk8RzvkWnP0txNZn+dX0o3TYnfzkjd28ukUVxdw8f3Tox3kcx7Fjx/PR/npcfiHuSOyuOcm6E6kcPrA3ICw+oOx8Gep2D85z95X2enWrHUeNJjQuByBjDFUbxTExCM/eIIR6vt42ANeOo0bTjR6FoxDCDDwCXAJMAW4WQkwJWvYd4F9SylnATcAf4r3RU4qzH4LciRRs/jVfWjqWlzZX8Zt39zO5IIOZJWHeqFprAJg2aSJNHY4ep8/YnW5+vXIfV/zuI3bacilyVbHxQOgWQP3Knjfh+dtg1Y8H/rnjgddx1MJRowmJEf7trfCzeCbHSKlyHPu7CMWS1PsG4Npx1Gi6YYlizXygQkp5EEAI8SxwFbDLb40EMjz3M4HqeG7ylMNkVs7jC7fzwJk7eDUnjyONHXzlvAndi2IMWmsgMYNFk8cA+/lwfwPTi7OQUrL3eCt7alqxOV10OdzYnC5e2lTFntpWrp5ZyDXjriLxzTfYsG4188tuHLjX2XQEXrlX3T+2QX1AhHt9QxUjx7GlClxONf9bo9H4MMK/vQ5Ve4pjXHZA9q/jaDxfb6uqteOo0XQjmk/BIsA/Qa4SWBC05vvA20KILwOpwAWhLiSEuAe4B2D06DAh2dOFKVfDyF+Q8NEv+Pk1b/CbVQdDF8UYtNZAej65aYlMLczg1S1VHGls54N99Rw/2f3NMD8jicdvncuFU0ZBy0h4E05WfEKX43qSrOaIW6s72UVLp4OyUemhF1RvBmsK5E0MfxGnHV64XYnFhffD2kdUZXdWScTnHnBsbfDRL1XhUnByvq0NHB2QPR5OHFATeEaMCX0djeZ0xQj/9roBuCeX29ambvszxxGUMI1WOLpdYDupHUeNJgTRCMdQFlFwstzNwJNSyv8TQiwCnhJCTJNSugMeJOVjwGMAc+fOHaSEuyGCyaRcx3/dyoKO93n67hsir2+thfR8AM6bNJLfvVdBbUsXS8ryOLs8j9ljskhJsJBoMZFkNZNsNWMyef7qMouwJY9iatteVu2p45IzCiI+1X3/3MTGI03MGzuCWxeNZdm0fKxmT1ZDxwn4+1VQMBO+8Fr4i7zzPaj6FG74u2pCvvYRqFw/9ITjgXfhw/+DMYthQtD3HSNMXTxPCcfmI1o4ajTBeEPVvW0A7nEYbZ60m/52HP37RvZEl2dPySP6bz8azTAlGuFYCfh/2hfTPRR9J7AMQEq5RgiRBOQCdfHY5CnLpCtg1DR4/6cw9VpfGNTWpty5kZN8a0/WwJhFANx/7gQumVZA+ag0LOboCuMTxi5kzu41/GBzVUThePxkFxuPNHHWhFyOnujgy89sZmR6Il8+bwKfWzgGsfp/1Ztq/d7wT7brNVj3KCy4F6ZcpRLgLclQuRGmXRfVfgcMo9q8par7OSNMXTIPtj2rQu+lA7c1jWZY4A1V99Zx9Kzv8gxC6O8cR3MvHEc9p1qjCUs0qmMDUCaEKBVCJKCKX4KtpqPA+QBCiMlAElAfz42ekphMqj3PiQOw/V/QsB+WfxN+ORn+sAB2varWSekJVSvBl2Q1M6UwI2rRCCBK5lNEHdv37qO5I3yC+Du7lMv2vSum8P5/nsNfb5vH+Lw0vvvqTn75rxXI9Y9BQjq01fq+lXe7yHchfzpc+CP1s9kKRbPh2Pqo9ztgGP0tT4YQju0e4Vg4G4RJNwHXaELhDVXHUBwDKiQM/dsA3Hi+aIVjl55TrdGEo0flIaV0Ag8AK4DdqOrpnUKIHwohrvQs+wZwtxBiK/AMcJuU8vQORUfLpMuUyHrjG/D7ubDhz1B+MRTNgZfvhZptKjzsdniFY0yUzAdgutzHG9trwi5bsbOW0txUykamYTIJzp00kn/etYB7zx7PxB2/wi7NdJ37PbW4oaL7BbpOqvnOU68OdCCK50LNVnD0oh3GQGBM1InkOGYWQ0aRrqzWaEJhVCr3OlQd5Dj2u3BMij5U3annVGs04YjKspJSvimlLJdSjpdS/thz7HtSytc893dJKc+UUs6QUs6UUr7dn5s+pRACLv6JKsA47zvw9V1w3RNw0zMqv+aZm6Fmi1rryXGMifzpSJOV89KO8PKmECIJaOl0sOZAIxdNHRVQ3W0yCb51RhuXm9fymPNSvrzGUzTTsK/7Rer3qNuRQR2biucr8TvUZj4bLuLJECMZ2+qU05iSA1ljdC9HjSYUhovX61C1keNohKr7WTiaE6JvAK4dR40mLHpyzFCgdAl86SNY+iCkjVTH0kfBzc9A5wl48S51LKMw9uewJiEKZrAk+RAbjzRxtLGj25L399bhdEsumhIkUKWEt78DqSOZeeN32dCSgRMz9uN7uj9PnadLU96kwOPF89TtUAtXex3HUMLxOKTkqvZJWaO146jRhMIQjr1uAO6pqh4wxzEx+gbg2nHUaMKiheNQpmAGXP2oEo/QN8cRoGQ++e17sODk1S3dXccVO2sZmZ7IrOAm5Hteh6Nr4NyHWTJtHH/4/AIOu0exe/undMtIqNujWvVkBVUfp49S4qtyCAlHe7v63ZosKlQd/Fra6iBtlLo/YozKMx1qoXbNkKanqVt+664XQkghxNyB3F9ciLUBuDk4x3EINQDXjqNGExYtHIc6U6+G8/+fEl19yXEEKJ6LydnJDcXNPLP+KCe7HN5TXQ4X7++t58Ipo3xtfJoOw2tfgedvVw7irFsBWDwhF1NeOSknD/KPtUEuXN0utdYU4p9W8XzVCLwH3ttznIa2XjTqjRWjorpghpqVa1RSGrTX+RxgQwiHciY1mhBEOXULIUQ68BVg3cDuME44+zA5BnxFdkOpAXhnsxKa/R0+12iGIVo4DgeWfB2+us0X2omVYlUgc+/4Jo632nj4xe1ex/DjigY67C4umpoPJ6vh1fvhd3Ng6zMw5za49dWAqSmlk2ZRajrOj1/fzpZjzb7nqN8DIyeHfv6S+dBaHboQxXh4q407ntzIT5eHCIPHG0MEjl4U+LNBm59wNPo3Nh/u/31pThW8U7eklHbAmLoVzI+AnwPD0872hqpjbMdjG6B2PL1pAN7ZpN1GjSYMWjgOF+Ixqi+zGNILGN2xg/+8aCJvbK/h6fWqOGTFzlrSkywsGpcDL38Rtj0Pc++Er26Fy/63W5hc5JVjwcmM1Bbu/+cmmtrt0N6o8gLDCcdiTxQuQrh6/SEVln9jWw2tfo5ov9DiKYwp8QxC8m/JI6V6LcGOoy6Q0URPqKlbAeOhhBCzgBIp5euRLiSEuEcIsVEIsbG+foh1OutrA3BvjuNQagDerJt/azRh0MLxdEIIJd6OreeLS8extDyPH/x7FzuqWli5u47zJo0kwdkGRz6BRffBpT8PX5CTUwbAT89Oor7VxoMvbEV6C2PCCMdRZ4AliS2fvOMViMGsO9SISUCnw8W/t4ZvGxQXWipVfmPRHN/PBl0tKh8q1ShWygeTVRfIaHpDxKlbQggT8CtUO7OISCkfk1LOlVLOzcvLi+MW40DMs6qNkYMDlOPYqwbgek61RhMOLRxPN4rnQ/MRTB31/PKGGWQmW/ncn9dxot2uqqkPfwRuJ4w/L/J1cicAUEoVDy2byMrddWz6dK06F85xtCTQmTcd17H1/H5ViB6QKMfxzAm5TMpP59kN3Rtu251ufrp8T2B4PFaajylhnF6gRKG/cDR6OBrFMSazGpeoHUdN9PQ0dSsdmAa8L4Q4DCwEXht2BTIxz6oO7uPY345jLxuA61C1RhMSLRxPNzyNwKncQG5aIr+5cSYtnQ4SLCbOmZgHB95TVdFG+DYcySOUG9ewjzvOLGV+aTb7t6/HnZARsW3QdsqYJg7x6YFa2m3OgHNN7Xb21LayoDSbG+eVsK2yhZ3VgdNpHlt9gD9+cICbHlvDu7uPx/Qr8NJyDDJLVCFPRmFgqNqYU22EqkGFq/X0GE30RJy6JaVskVLmSinHSinHAmuBK6WUGwdnuzESa6jaEtTHcUg1AG/RjqNGEwYtHE83Cmao8Kynn+LiCbn88MqpfPX8MlITLXBwFYw9K7pv/7nl0LAfk0nwf5+ZwXiOsZ9i3GFmBrndkpfqi0gUTsrdB/m4oiHg/IbDKnw9vzSHa2YVkWAx8a8NvhSxww3t/Pa9Cs6fNJLyUenc/feNPLu+D0KupVIJR1D5n/5FO8a4QX/hOGKMDlX3FkdX9zZHpwlRTt0a/hih6t4W7/n3cTRZAorv+gVzIkg3uJw9r9WOo0YTFi0cTzesyWrEoV8j7s8vGsv9505QYdjGip7D1Aa5ZVC/F6SkZEQy0xOq+bQjn7+vORxy+ZqDjbzXpopMFidU8N6euoDz6w6dIMFiYkZJJlkpCSybms/Lm6vocriQUvKdV3aQaDbxP9eewTN3L2RJWR7femk7v1m5v3s/yZ5wOVX1eJZHOGYUBU6PCQ5Vg3IcOxrB1ta75zpd6TgBv5gAO14c7J0MGj1N3Qpae86wcxtBharNib0v4POfHNPf+Y3ga//TUxNwt0vtSTuOGk1ItHA8HZlwvmrofeJg4PGDq9Rt1MKxXH0z71DV1ImSjyHKAAAf4UlEQVSOFtx5k/jpW3s4WN9dXL24qZLOpDzcBbO41/Jvtu3ZGyD41h86waySLBItZgBumlfCyS4nb+2o5bWt1XxU0cBDyyYyMiOJ1EQLT3xhLtfPKeZXK/d5q8OjprUGpEs5jQCZRUpIul3q57Y6lffo7zpkjVa32nWMjupNYG+FincHeyea/sRljy3MbOQ4OjoGpl+isceemoAbfSW146jRhEQLx9ORuXeq0NC6PwUeP/AepBcqQRgNxrqG/VC3G4BLzz+PRIuZrz67BZvT5V3ablMC8PLpBZiu+SPJsotvdf2GnVWqyOVkl4Od1S0sGJfjfczCcTmMzk7hrx8f4kev72JGSRa3LPBNpLGaTfzi+unML83mV+/so80WRQjKoMUTAvcPVbudPqexrQ5S8wIbmY8Yq251gUx0VHtmrFf23PRdM4xx2no/pxoCH9Pf+Y3gE6o9FcgYgwC046jRhEQLx9ORjAKYdh1s/odvJqvbBQffV25jtCGnXNWSh4Z9XuGYPXYmP79+OturWvifN31NvN/aUUuH3cW1s4th5CQ6z/sRS83baXr3VwB8eqQJt4QFpdkq99DlxGQS3DivhK2VLTR1OPjJNdMwmwL3JoTg4Usm0dBm5/HVQQ5qJJqDhGOGx3k0CmT8ezgaGL0c41kgs+kp+OulPpE10Bz8ABpCV7j3mRrPa2rcr8LWmlMTp633c6ohsAp7IISjkbfdU6hajxvUaCKihePpyqL7wN4Gm/6ufq7erEI048+N/hqZJeoNv2GfGjWYkgNpeVw8NZ/bzxzLk58c5q0dtYAKU4/OTmHuGNVUN/3Mu/kkYTELDz0C1ZtZd/AEE03VLNz4H/CrqfD2dwC4fk4xiRYTdy0pZWphZvc9tFQya8X13DfhBI9/eJC61iiHb3gdR79Qted6gGfc4KjAx6Tmqopz/1B1Wx3sezu65wzG7YYPfg5HPobHz4NVP/EVGgwEh1bDU9fAm/8Z+zUqVkJ7Q+hz1VuUgw1Q9Wnsz6EZ2rhssbXSMfkV0wykcOwpVO11HHUDcI0mFFo4nq4UzICxS1S42uVUYWoEjOuFcDSZIGeCClXX74GRvjG8D18ymenFmTz4wlbWHWxkzcFGrp1dhDDcTCHYPvtH1MsMXM/fwfyt/8WbCQ9hPvgeFMyEDY9D4wFGZSTx0TfP45sXT+r+/G4XvHQPVG3kS+kfYXe6+e27+6Pbe8sxJXQTUtTPGR7h6HUc6yAtqNGyEMp1NELVjQfgifPh6c+o30FvOfS+ml5z2S9h+g3wwc+UgKzd3vtr9ZaWSjWDXLqUcLW39/4am/8B/7gOPvxl93Ptjep3PPtWEKaAYizNKYYzVuFo8onHgchxNFzRHkPVHsdRh6o1mpBo4Xg6s/A+VUm8+1U4sEqJydScnh/nT24ZNOyFusAZ1QkWE7+/eTYAn//LeqSEa2cVBzz0zDPK+A/7/ZiaDnFm5wd8WnCTGnF4y3MqjPXejwDIS0/EZAoRPv/w/5ToyRxN+pF3+Nz8Qp5ZfyxkYU43mo/5wtSg3AVrihJUbrdHOI7q/jijJU/1FvjzRb4PmcMf9vycwWz6u3reWZ+Da/4INz+rnM6/Xx19o+JYcHTBc59Xz3HJL5QDc6iX+z+yBv79NXX/4Pvdz9dsVrdjFsOoqTrP8VTGaet9828D43EDGqr2+79V8W73iIEOVWs0EdHC8XSmfBlkj4MPf6XmR0dbTe1Pbjk0HVbVs3mBruDonBR+cf107E4388dmMzonJeD81MIMDqfP5E7LT1lq+xXt5/xAhYPT82Hxl2Hny1AZpjvJ0XXw/k/hjBvgoh9BRyP/UX6CJIuJX6zY613mcks67a7uj2+p9LXiAeUmZhSp451NyokLJRyzRit38cnLVWuju9+DNM/End7Q3gi7X4cZN/s+0CZeAlc/Ch0NsOeN3l2vNyx/SFU8X/MozPkCWFOh4p3oH990BJ77rBLRZ34V6nb6iooMjJzNghlqWlHVp0qQR8JpU+5lV0vkdZqhhcsWu/CzDIJw9G8C/vZ34a1vBq7TjqNGExEtHE9nTCblOh7fHt2YwVD4V2D7haoNlk0r4A+fnc1Prp3W7ZwQgnMnjuS9thLqRbY3/xFQwjE1D975XvcG0p3N8OJdSvhd9n8w4QIwJ5J5ZAX3LB3P8h21LPjJSqZ+7y3Gf/tNJn/vLS745Qf84N87eW/Pcdq7HL6pMf5kFqtQtTE1JjXETOCsMeqDJ7MI7nxbOa5jz4LDH/eu0fW2Z8HtgFmfDzi82jWNjpRCX+5pvPn0b7Dpb3DW12HyFerDdNzZsP/t6PZva4VnblL/Xm5+DiZfpY4fWh24rmaL+lKSnAXF81RfvIa93a/nz44X4d0fqBC4ZvjgtMdWVQ1+jmM/jxuE7qFqewfU71Ztyfy/rHQ1q76SA7EnjWYYEpVwFEIsE0LsFUJUCCG+FWbNDUKIXUKInUKIp+O7TU2/MfMWFZKxpvjGEfYGo7IaYGSIPETg0jMKmDAyPeS5cyepyuWphZmkJ/klyyemwznfUqHofW/5jnc2wWsPQGs1XPdnSMqAxDQleve8wd1LxnLLgtGcUz6Sm+aP5qvnl/H1C8spyEzi6XVHuePJjVzw41dU77huwrFITY9p9zX/PtrYwcMvbeOAEf6efDks+BLcvtw3WnHsmdBWq3Ieo0FKJQyL58EoJbYPNbRzx5MbuPWvG/njycWqp2bT4eiu588r98PLXwp9rrNZFR2Vng3nfcd3fMIFqlI8mjzNl+9VTd8/86SaV144ExIzu4erq7eoXFXw/bvqKc9xi+dtY8+bPe9DM3QwGoDHgiEcrQPZANwjHI/vUJNkAGq2+tZ1Nmu3UaOJQI8znoQQZuAR4EKgEtgghHhNSrnLb00Z8DBwppSySQgxMvTVNEOOhFS45GeqMjaWb9g5E9RtekFMVYhnTcglNcHMWWW53U/O/gKsfRTe+X9KbG17FvYuVzl5F/wAiuf61k6+AvYtJ6VxBz+5ZlbI5+pyuNh4uIk/PfcSOEFmFhGQOZlRrNxGT2W1TM3jO6/uYPW+el7eXMVDF0/itsVjMV3y08ALj12ibo98pMRUBNxuyeGtqxhXv4cjZ/2M+sMneGfXcf7y8SESLWYeWjaRtz6+iK85XoRN/8B0/nciXi+A+r2wxePWzbsLiucEnt/wuHL+LvpvMJl9x8suVLcV70BehB6eVZtgz+tw3nd97rTJDKVL4NAHvnVGYcz8u9XP2eMgOVvlOc75QuhrNx1ReaIpuXD0E3WN3ubbagYHlz12d25AcxyNBuAe4Vi92XeueguULlX39bhBjSYi0TiO84EKKeVBKaUdeBa4KmjN3cAjUsomACllUMKTZkgz4yZY/EBsj01IhczRAYUxvSE10cJbX1vKV88v637SbIULvq9CnM/erPII594Bd6+Cs74WuHbiJSDMKm/Qn44TsPL70HqcJKsSqPfOUM7mR/WBOZeqJY/0ug8f1phZva+e+84Zz+Lxufzw9V3c8sRajp3oCHxczgSVDxlFnuN/Pr+VDS/+hjaZxCUr87j+j2v40+qDXD2ziPf+82zuO2cC9121lA9c0+lY/3ffJJtoWPOI+nBMzoZVPw48Z2+HNX+AsouhYHrguazRkDsR9veQ57jxz8qZNgShQenZyrE8cYiTXQ6k8YFc6BHwQih3NVKBzLbn1O0Vv1YukL/LrBnaxFpVDQMrHL0NwD3teKq3QOpI9YXRX0Rqx1GjiUg0U+WLgGN+P1cCC4LWlAMIIT4GzMD3pZTd3vmFEPcA9wCMHj06lv1qhiLXPNqnb+gl2SnhT066HC79X5V/OOECJSZDkZKtKnj3vA7nf1cdkxJeuQ/2LYeTNXCtmpSzKLcTgJ+tbWf+mS7viENvS56qTUhLEt9/+yjjclP52gXlWM2C5zdW8sPXd3Hxr1fzwHkTuOPMUpKsZiWMxpzpy3MM00D9nV3HeXvzfn6aspaGsVfwyIIlmIWgMCuZCSPTvOsunprPo/lXcW79D2nYupzcWZf3/Etsq4Otz8Ksz6oJN+98D46uhdEL1flPn4TOE7A0TM/Gsgth/WN8sOMwudkjuvfM7GyC7S/CjBshKejcuLMBaN/zLotXlPDncR+oN4iCGb41xfNg/4rQH8pSqjB16VL1951RrIqDZn2259etGXxibQAOvvDxgIwcDGoAXrNFpVqYE3zN6kE5jhnF3R+v0WiA6BzHUJ+CwVn0FqAMOAe4GXhCCNFNSUgpH5NSzpVSzs3LC1F4oBmejD0L8rsXv8QFIZTDNfGS8KLRYPIVqp+kkau35vdKNI6aphwtT6WvqaUSlzmJHU0Wnlrj18zbaAZeu502ywgONnTwX5dNJsFiQgjBDfNKeOtrSzhzQi4/f2svF/1qNSt21qp522PPUnmXwfO/O07Amj/Qtfy7dD5/L8+l/h8J7i4Kz7uXcyeOZGl5XoBoVC9ZcOWNd9IoMzjyzh+j+z2tf1yFDBfer8LUqXk+19HRBR//VgmzcHmsEy4Al52nnnmKa/7wCa9trQ48v+UZcHaqcZXB5JZDWj6N29+mzeak7dBG3CPGBQrMknnqNlQj8KNroekQzLhF/X1Pukz1FbV3dF+rGXrE2gAcBilUbVcOfP0elYdbOFP9vzWqqTubdfNvjSYC0QjHSsC/iqAYqA6x5lUppUNKeQjYixKSGs3AMekydbv736oQY+X3lZi8/U3lSL7zXeVutRzDPGI0Z5eP5Lfv7qep3RO6MhxHl41DXWksKcvlvEmB6brFI1J4/Na5PHXnfBItJr741Kfc9Nha3uvy5AYe+ThwTy9/EVY8jGX9H5jn3sK4LBPMuR2KgvIPgyjOzaJy9FVMb/+EVZ/uCLnG5nTx6pYqXt9YgdzwBEy8VOVYJqSqqulDq1V/xi3/VMU7SyJMiBmzmC6RxAWWbUwvyuQrz2zmNyv3K1EspQpTF8/vHuYGJfbGnUPW8bVkJJqYKA9wJCHov3/hbECEbq+05Z+QkAZTrlQ/T7pUidSDqyL+jjRDBGdfchw9jxvoWdW1nsKYwlm+lAqjQEaHqjWaiEQjHDcAZUKIUiFEAnAT8FrQmleAcwGEELmo0HUvBgdrNHEgs1h9CGx/Xk1FySiCK3+vnK+zv6mE1P53PK14ivn2pZNpszn53XueWc2Jad6Qe60rg+9ePsU36SaIJWV5LP/qEr5/xRSOnujgjjdaaJCZbPzgNT7YV0+XwwX7V8L+tzkw40EmdP6NfyxeTvKXP1F5fFHMA59y+QNYhYt1Lz/CrX9Zz/Mbj3Gyy0Fjm43fvrufs362iq8+u4W1L/8e0XmCj0fejNvtCQbMvV0VLL333/Dxr1Wo2Ej+D8H6Y+186JzKpck7+Odd87ludjG/WrmP/3huC/b9q6CxAuaFcBs9tBWdSYa7hW9PaaBYNPBGYz4Ol1/fxqQM1a6pMqiy2t4BO1+BKVcpwQsq7J+U2b+9LDXxw9nVhwbgnijCQM+qNkLThTOhwBCOW9QULXurLo7RaCLQY46jlNIphHgAWIHKX/yLlHKnEOKHwEYp5WuecxcJIXYBLuBBKWVjf25cownJ5Cvg3R+qD7I7Vvicgzm3q/GK73wX2ushfzoT89O5cV4Jf1tzmA/312M1m3jEMYJSmhkxspjyUaFbCBlYzCZuO7OUWxeNZd2hE1S/Opui5k0s+ss60qySFUnfJi25hNt2z6VsZDJfCVUAFAHrqEnYixbwpfr3+Ez95Tz4Qj3/9fIOEGB3ujlnYh53LBrNnNe/zZ7Ocj77jmDyjo/4/hVTWDAuB5Z8wzeH+pJfhBWrUkp+8uZuFiXM5cKuP0HzQf73M9MZl5fKL1bs5QuVjzIzORsx5eqwe323axJXAZed/BcAH3cUU7Clmuvm+OWKFc+FXa+qRuAmz3fWPa+rD+qZt/jWma2qOf3e5eqD3BxNKrZmUJCybw3AzQOY42g8l8uuimFSR6ovV0KoAr/qLb5+jtpx1GjCElUfRynlm1LKcinleCnljz3HvucRjUjF16WUU6SUZ0gpn+3PTWs0YZlytfqAuPgnUDTbd9ySoCq06/dAR6O3h+ODF0/i5vkllI1KozArmRarCk1PK49e5JlMgkXjc5h+1mUUiEb+ed0ofjbmU4ocR/lGyw1Utbr4+fXTfUU4vSDhoh+Q6ahjxaTlvHL/mXx+0RhumT+alV9fypO3z2ep3Ehq22HKrvk2v75xFm02B7f+ZT2bjjapOdGZJZB/BpRfHPY5lu+oZcuxZqYuvU4dWP8Ywt7G/edO4PvnjOCM1o/ZV3hVxA/3FyokR0Uh6VWqEbgt7wz++MEBnwMKKr+yqxmOrVXFNi6nKorJGg2jFwdecNJlqpjn2Npe/840A4jLoW5jbQA+kJNjhFChcadNicTCmb4vU4UzlJjU4wY1mh7RX+U1pxY54+GbRyAhRKX25CugZKESI55xg9mpCfz31Wf41rw+FTauIzm7oPfPPfYsAM50rIXGJ6H0bL572df5ms3FtKLMyI8Nx5hFcObXEB/9kpnlFzPz8it8504cghUPQ9YYzJOv4GqzhSVluVz76Cfc9beNvPSlxYy9Y4Vy8MK4jQ6Xm5+/tYfyUWlcsmQ+VF+q+j1ueRqmXcutbhcIyVf3z+Sxxo5uYyMBTrTb+eRAI40lixh9/EXIHs8Xlk7nq89uYeXu41w0NV8tLPFUeP/1ksALnP0tnwNpMP589SG/5w3v71UzBDEqlPvaAHwghCOocHXnCdXiy8ipBVUks/vfvqb72nHUaMKiRw5qTj1CiUZQ4mnZT1TFceHs0GuMApnUGHrY502ClBwVKu9qgWX/w5jctNhFo8E5D6vWNq99BVpr1bG6PfCXZWoE4Gee9IZzc9ISefL2+Ugpue2v6zlhyYO08K/lmfVHOdzYwbcumYTZJOCmp+HOlTDtGtjxIqatT2Mfcw5VpgK+9txmnK7u86ZX7KzF5ZbkTL9IHSicyWVnFFCSncwf3j+gCmxAFe58/hW48ndw8f8gz3mYqjMewDH37m7XJDENxp2jQtm9GeWoGVhcnsKymEPVHsE5EJNjQAnHqk2qMMaYbAS+Ahmjkb12HDWasGjhqDm9KJoDD1aEn5BijCGMILbCYvRzdNlhzm0wamrM2wzAkgDXPgGOTtWXsnozPHkpIOG2NwND8kBpbipPfGEeNS1d3PW3DapQJwRvbq/hf97cw8Jx2Zw7caTvNZTMg6segW/shWsfJ+nqX/PfV09j09FmHlnVfaziG9tqKM1NpWTWxao6esyZWMwm7lk6ni3Hmnl/X71v8fhzVQh90X08YbqBMzcs5hcf1ne7JqDC1c1H4fjOWH5rmoHAGN8X86xqozhmgOZCmxN9/54KQwhHY3Smdhw1mrBo4ajR/P/27j62qvqO4/j7awFry2N5qIUWKg55looFcU4DyLNOSBSQuIX4MLYpUbOp02lm1JhsxiG4kS1GHfORZW5zRDeVAJppEKiAApOHqiiI8qQgKCLKd3+cU7iDW3r7eH7Xfl7Jze059/beT3rab7/3/M75nVS9x0VnYNcwXU61+k+KjtkbcXvD5up8Boy5B95ZBA+Phpb50fWy42tdH+vsHh2YPbWMVVv2MPWh13mtcteRPX+HDzsPLNzItU+upG9RGx6cdlb6s8dz28KZU6BDKRPLujGprCsPLt7EkvVHLwy1e/9Blr67mwkDT8XyOsCNa6KmGZh8djGnd87nxvmrqdyx//9eesXmT/j1C+tpc3ILHn31PTZu33f8+/ceHx1ecNy0sRKMBhuqbqo9jq0Aj6701CblcJS8gugEmY/eipY1j6NItdQ4iqTKbQcjflnzZOPVGXBp1Dzlp7n2dn0NuSZqpDp+B676d3Q85wmMH1jE7KllfLz3AFc8vIzJf1zK4vXbmfn0SuYs2sSlg4t5esYwurTJbJjx7kkD6NExjyvnreDHj1fwwe4veHHddr457Fw0sGv0pLyCI9fBzm2Zw7wrh9Iyx7hy3nJ27ov2Tu3af5CZT62kpMMpPH/9+bTObcEdz649OqRdpXUXmPpEdHKPhOnIUHVdrxzThPM4pr5PUdnxx/12LePIhxQNVYtUS42jSLYwgymPw7VLj17lpgYTy7rxys0juGdifz7cc4Cr5lXwwtqPuX1CX+6fXLszvdvmtuRf15/PTWPO4D+bdjFq1ivMWbSR0zrl07co/dRFJQV5PDJ9CDv3HeSaP69g/8GvuWH+KvZ8cYi5Vwyme8c8bhnbh+XvfcKzqz/MOIsE4shQdV33OMYf0JpiOh44uoczdZi6StW6lnl1H3oXaQbUOIpkE7OMJg9Pldsyhx+eW8rLNw/nvsvO5Imrz+FHF/SsdnLzml5r5sheLLlpOJeUdWX7ZweZVNbthK81qKQ9v5s2mDUf7mX0rFd4rXI390wccOR62JcPKWFQSXvufX49ew8cOvJ92/YcYNbCjez78lB1Ly1Jq2oc6zxU3dR7HOP3qzqmMVXVOu1tFDkhTccj0kyc3CKHKeUlNT8xA4Vtc7l/8iBuHtubjvk1750Z3a+QO7/fnzsXrOOys4uZMuRojpNOMu6dNIBLfv8qv31pAyP6dOHJ1z9g8frtODCouB0X9i1skNzSwL5poD2OTd04FqXZ41i1TifGiJyQGkcRqbPCtpn/w5/+3VKGlBbQq7D1cY8N6NaOHwzrwWNL3+expe/TqXUrfjr8dKYN7U5xh2qmV5KafbkXHhnTeK//1efRfTYd49i6ENqmmac1ryA6sU17HEVOSI2jiDSZfl3bVvvYTWN7Y0B5aQFj+59KqxY6kqbeLAc6927c9+g5HAoH1O17+1wMh76MmramMHRGdNWi6oy6q+maWJEsZcedydhEysvLvaKiIpH3FpFvLzN7w93Lk87RWFQ7RaQxZFo79ZFeRERERDKixlFEpJGY2Tgz22BmlWZ2a5rHf2Jma8xstZm9ambpZ3QXEQmEGkcRkUZgZjnAXGA80A+YlqYxfMrdB7p7GXAfMKuJY4qI1IoaRxGRxjEUqHT3d939K2A+MDH1Ce7+WcpiPrq+oogETmdVi4g0jm7AlpTlrcA5xz7JzK4Dfga0AkameyEzmwHMAOjevXuDBxURyZT2OIqINI50l9M5bo+iu89199OBXwB3pHshd3/I3cvdvbxz584NHFNEJHNqHEVEGsdWIPVSPcXAthM8fz4wqVETiYjUkxpHEZHGsQLoZWanmVkr4HJgQeoTzKxXyuJFwKYmzCciUmuJTQBuZjuB92v5bZ2AXY0Qp6GEnC/kbBB2vpCzgfIdq4e7BzGea2YTgNlADvCou99rZncDFe6+wMzmAKOAQ8CnwEx3X1fDa37bamfI2UD56iPkbBB2viSyZVQ7E2sc68LMKkK+IkTI+ULOBmHnCzkbKJ/ULORtEHI2UL76CDkbhJ0v5GwaqhYRERGRjKhxFBEREZGMZFvj+FDSAWoQcr6Qs0HY+ULOBsonNQt5G4ScDZSvPkLOBmHnCzZbVh3jKCIiIiLJybY9jiIiIiKSEDWOIiIiIpKRrGgczWycmW0ws0ozuzWAPI+a2Q4zW5uyrsDMFprZpvi+Q4L5SsxsiZm9bWbrzOyGUDKaWa6ZLTezN+Nsd8XrTzOzZXG2v8QTJifGzHLMbJWZPRdaPjPbbGZrzGy1mVXE6xLftnGO9mb2jJmtj3//zg0lW3Ok2lmrbMHWzThH8LVTdbNe+bKmdgbfOJpZDjAXGA/0A6aZWb9kUzEPGHfMuluBRe7eC1gULyfla+Dn7t4XGAZcF//MQsh4EBjp7oOAMmCcmQ0DfgM8EGf7FLg6gWypbgDeTlkOLd8Idy9LmecrhG0LMAd4wd37AIOIfoahZGtWVDtrLeS6CdlRO1U36y57aqe7B30DzgVeTFm+DbgtgFylwNqU5Q1AUfx1EbAh6Ywp2f4JjA4tI5AHrATOIZohv0W6bZ5ArmKiP9KRwHOABZZvM9DpmHWJb1ugLfAe8Ul3IWVrjjfVznrnDLJuxjmCq52qm/XKllW1M/g9jkA3YEvK8tZ4XWgK3f0jgPi+S8J5ADCzUuAsYBmBZIyHM1YDO4CFwDvAHnf/On5K0tt4NnALcDhe7khY+Rx4yczeMLMZ8boQtm1PYCfwp3i46mEzyw8kW3Ok2llHIdbNOFfItVN1s+6yqnZmQ+NoadZpDqEMmFlr4G/Aje7+WdJ5qrj7N+5eRvQJdSjQN93TmjZVxMwuBna4+xupq9M8NcnfwfPcfTDREOR1ZnZBgllStQAGA39w97OAzwllaKV5Cu33NiuEWjch3NqpullvWVU7s6Fx3AqUpCwXA9sSynIi282sCCC+35FkGDNrSVT8nnT3v8erg8ro7nuAl4mOJ2pvZi3ih5LcxucBl5jZZmA+0bDLbMLJh7tvi+93AP8g+gcSwrbdCmx192Xx8jNExTCEbM2RamctZUPdhCBrp+pm/WRV7cyGxnEF0Cs+O6sVcDmwIOFM6SwApsdfTyc6PiYRZmbAI8Db7j4r5aHEM5pZZzNrH399CjCK6CDgJcBlSWYDcPfb3L3Y3UuJftcWu/sVoeQzs3wza1P1NTAGWEsA29bdPwa2mFnveNWFwH9DyNZMqXbWQsh1E8Kunaqb9ZN1tTPpgywzuQETgI1Ex3PcHkCep4GPgENEnxSuJjqeYxGwKb4vSDDf94iGBN4CVse3CSFkBM4EVsXZ1gK/itf3BJYDlcBfgZMD2M7DgedCyhfneDO+rav6ewhh28Y5yoCKePs+C3QIJVtzvKl21ipbsHUzzpcVtVN1s84Zs6Z26pKDIiIiIpKRbBiqFhEREZEAqHEUERERkYyocRQRERGRjKhxFBEREZGMqHEUERERkYyocRQRERGRjKhxFBEREZGM/A9Dp7tVeSj8swAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 792x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Time: 1.0h:4.0m:54.429643630981445s\n",
      "Validation final accuracies: \n",
      " [0.6842105278843328, 0.3157894736842105, 0.6842105294528761, 0.6842105294528761, 0.8421052631578947, 0.578947369989596, 0.7894736873476129, 0.8421052631578947, 0.8421052662949813, 0.6842105278843328]\n",
      "Validation final accuracies mean: 0.6947368438306607\n",
      "Validation best accuracies: \n",
      " [0.8421052662949813, 0.7894736842105263, 0.8947368421052632, 0.8421052631578947, 0.8947368421052632, 0.8421052631578947, 0.8421052631578947, 0.8421052631578947, 0.8421052662949813, 0.8421052631578947]\n",
      "Validation best accuracies mean: 0.8473684216800489\n",
      "Validation balanced accuracies: \n",
      " [0.7243589743589745, 0.5, 0.7243589743589745, 0.7243589743589745, 0.7948717948717949, 0.6474358974358975, 0.7115384615384616, 0.75, 0.7948717948717949, 0.6346153846153846]\n",
      "Validation balanced accuracies mean: 0.7006410256410257\n",
      "Validation final sensitivities: \n",
      " [0.8333333333333334, 1.0, 0.8333333333333334, 0.8333333333333334, 0.6666666666666666, 0.8333333333333334, 0.5, 0.5, 0.6666666666666666, 0.5]\n",
      "Validation final sensitivities' mean: 0.7166666666666667\n",
      "Validation final specificities: \n",
      " [0.6153846153846154, 0.0, 0.6153846153846154, 0.6153846153846154, 0.9230769230769231, 0.46153846153846156, 0.9230769230769231, 1.0, 0.9230769230769231, 0.7692307692307693]\n",
      "Validation final specificities' mean: 0.6846153846153846\n"
     ]
    }
   ],
   "source": [
    "# training args\n",
    "lr = 0.0005\n",
    "lr_decay = 0.002\n",
    "transforms = [intensity, sagittal_flip, translate]\n",
    "\n",
    "num_trials = 10\n",
    "store_models = True\n",
    "\n",
    "accuracies = []\n",
    "balanced_accuracies = []\n",
    "sensitivities = []\n",
    "specificities = []\n",
    "max_acc = []\n",
    "\n",
    "for i in range(num_trials):\n",
    "    print(\"Trial %i\" %i)\n",
    "    \n",
    "    # init model\n",
    "    model = SixtyFourNet(drop_rate=0.3, weight_dcay=0.01, input_shape=X_train.shape)\n",
    "    opti = Adam(lr=lr, decay=lr_decay)\n",
    "    model.compile(optimizer=opti, loss='binary_crossentropy', metrics=['accuracy'])\n",
    "    \n",
    "    # callbacks\n",
    "    earlystop = EarlyStopping(monitor='val_loss', min_delta=0, patience=15, verbose=1, mode='auto')\n",
    "    if store_models:\n",
    "        result_path = os.path.join(result_dir, \"weights-augm-trial%i-improvement-BEST.hdf5\" %i)\n",
    "        model_checkpoint = ModelCheckpoint(result_path, monitor='val_acc', verbose=1, save_best_only=True, mode='max', save_weights_only=True)\n",
    "        callbacks = [earlystop, model_checkpoint]\n",
    "    else:\n",
    "        callbacks = [earlystop]\n",
    "        \n",
    "    train_loader = CISDataset(X_train, y_train, transform=transforms, batch_size=b, shuffle=True)\n",
    "    val_loader = CISDataset(X_val, y_val, transform=[intensity], batch_size=4, shuffle=False)\n",
    "    \n",
    "    # Start training\n",
    "    history = model.fit_generator(train_loader,\n",
    "                        epochs=num_epochs,\n",
    "                        validation_data=val_loader,\n",
    "                        callbacks=callbacks)\n",
    "    # Plot training curves\n",
    "    plt.figure(figsize=(11, 4))\n",
    "    plt.subplot(1, 2, 1)\n",
    "    plt.plot(history.history[\"loss\"])\n",
    "    plt.plot(history.history[\"val_loss\"])\n",
    "    plt.legend([\"Train\", \"Val\"])\n",
    "    plt.title(\"Loss\")\n",
    "    plt.subplot(1, 2, 2)\n",
    "    plt.plot(history.history[\"acc\"])\n",
    "    plt.plot(history.history[\"val_acc\"])\n",
    "    plt.legend([\"Train\", \"Val\"])\n",
    "    plt.title(\"Accuracy\")\n",
    "    plt.show()\n",
    "    # Evaluation\n",
    "    y_pred = model.predict_generator(val_loader)\n",
    "    #y_true = [item for sublist in [val_loader[batch_idx][1] for batch_idx in range(len(val_loader))] for item in sublist]\n",
    "    bal_acc = balanced_accuracy(y_val, y_pred>0.5)\n",
    "    sens = sensitivity(y_val, y_pred>0.5)\n",
    "    spec = specificity(y_val, y_pred>0.5)\n",
    "    # Store results\n",
    "    accuracies.append(history.history[\"val_acc\"][-1])\n",
    "    balanced_accuracies.append(bal_acc)\n",
    "    max_acc.append(np.max(history.history[\"val_acc\"]))\n",
    "    sensitivities.append(sens)\n",
    "    specificities.append(spec)\n",
    "\n",
    "training_time = time.time() - start_time\n",
    "print(\"Training Time: {}h:{}m:{}s\".format(\n",
    "            training_time//3600, (training_time//60)%60, training_time%60))\n",
    "# Print results\n",
    "print(\"Validation final accuracies: \\n {}\".format(accuracies))\n",
    "print(\"Validation final accuracies mean: {}\".format(np.mean(accuracies)))\n",
    "print(\"Validation best accuracies: \\n {}\".format(max_acc))\n",
    "print(\"Validation best accuracies mean: {}\".format(np.mean(max_acc)))\n",
    "print(\"Validation balanced accuracies: \\n {}\".format(balanced_accuracies))\n",
    "print(\"Validation balanced accuracies mean: {}\".format(np.mean(balanced_accuracies)))\n",
    "print(\"Validation final sensitivities: \\n {}\".format(sensitivities))\n",
    "print(\"Validation final sensitivities' mean: {}\".format(np.mean(sensitivities)))\n",
    "print(\"Validation final specificities: \\n {}\".format(specificities))\n",
    "print(\"Validation final specificities' mean: {}\".format(np.mean(specificities)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "training_time = time.time() - start_time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "weights = [\"weights-augm-trial%i-improvement-BEST.hdf5\"%i for i in range(num_trials)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# load holdout set\n",
    "test_loader = CISDataset(X_holdout, y_holdout, transform=[intensity], batch_size=1, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/fabiane/anaconda2/envs/postal/lib/python3.6/site-packages/ipykernel_launcher.py:16: DeprecationWarning: Both axis > a.ndim and axis < -a.ndim - 1 are deprecated and will raise an AxisError in the future.\n",
      "  app.launch_new_instance()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model accuracy 73.91 %\n",
      "Balanced accuracy 72.31 %\n",
      "Sensitivity 84.62 %\n",
      "Specificity 60.00 %\n",
      "Area Under the Receiver Operating Curve (ROC AUC score) 86.15\n",
      "Fold 1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/fabiane/anaconda2/envs/postal/lib/python3.6/site-packages/ipykernel_launcher.py:16: DeprecationWarning: Both axis > a.ndim and axis < -a.ndim - 1 are deprecated and will raise an AxisError in the future.\n",
      "  app.launch_new_instance()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model accuracy 69.57 %\n",
      "Balanced accuracy 71.92 %\n",
      "Sensitivity 53.85 %\n",
      "Specificity 90.00 %\n",
      "Area Under the Receiver Operating Curve (ROC AUC score) 85.38\n",
      "Fold 2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/fabiane/anaconda2/envs/postal/lib/python3.6/site-packages/ipykernel_launcher.py:16: DeprecationWarning: Both axis > a.ndim and axis < -a.ndim - 1 are deprecated and will raise an AxisError in the future.\n",
      "  app.launch_new_instance()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model accuracy 78.26 %\n",
      "Balanced accuracy 76.15 %\n",
      "Sensitivity 92.31 %\n",
      "Specificity 60.00 %\n",
      "Area Under the Receiver Operating Curve (ROC AUC score) 92.31\n",
      "Fold 3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/fabiane/anaconda2/envs/postal/lib/python3.6/site-packages/ipykernel_launcher.py:16: DeprecationWarning: Both axis > a.ndim and axis < -a.ndim - 1 are deprecated and will raise an AxisError in the future.\n",
      "  app.launch_new_instance()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model accuracy 47.83 %\n",
      "Balanced accuracy 51.54 %\n",
      "Sensitivity 23.08 %\n",
      "Specificity 80.00 %\n",
      "Area Under the Receiver Operating Curve (ROC AUC score) 77.69\n",
      "Fold 4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/fabiane/anaconda2/envs/postal/lib/python3.6/site-packages/ipykernel_launcher.py:16: DeprecationWarning: Both axis > a.ndim and axis < -a.ndim - 1 are deprecated and will raise an AxisError in the future.\n",
      "  app.launch_new_instance()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model accuracy 73.91 %\n",
      "Balanced accuracy 72.31 %\n",
      "Sensitivity 84.62 %\n",
      "Specificity 60.00 %\n",
      "Area Under the Receiver Operating Curve (ROC AUC score) 85.38\n",
      "Fold 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/fabiane/anaconda2/envs/postal/lib/python3.6/site-packages/ipykernel_launcher.py:16: DeprecationWarning: Both axis > a.ndim and axis < -a.ndim - 1 are deprecated and will raise an AxisError in the future.\n",
      "  app.launch_new_instance()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model accuracy 73.91 %\n",
      "Balanced accuracy 75.77 %\n",
      "Sensitivity 61.54 %\n",
      "Specificity 90.00 %\n",
      "Area Under the Receiver Operating Curve (ROC AUC score) 84.62\n",
      "Fold 6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/fabiane/anaconda2/envs/postal/lib/python3.6/site-packages/ipykernel_launcher.py:16: DeprecationWarning: Both axis > a.ndim and axis < -a.ndim - 1 are deprecated and will raise an AxisError in the future.\n",
      "  app.launch_new_instance()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model accuracy 69.57 %\n",
      "Balanced accuracy 70.77 %\n",
      "Sensitivity 61.54 %\n",
      "Specificity 80.00 %\n",
      "Area Under the Receiver Operating Curve (ROC AUC score) 84.62\n",
      "Fold 7\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/fabiane/anaconda2/envs/postal/lib/python3.6/site-packages/ipykernel_launcher.py:16: DeprecationWarning: Both axis > a.ndim and axis < -a.ndim - 1 are deprecated and will raise an AxisError in the future.\n",
      "  app.launch_new_instance()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model accuracy 73.91 %\n",
      "Balanced accuracy 74.62 %\n",
      "Sensitivity 69.23 %\n",
      "Specificity 80.00 %\n",
      "Area Under the Receiver Operating Curve (ROC AUC score) 88.46\n",
      "Fold 8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/fabiane/anaconda2/envs/postal/lib/python3.6/site-packages/ipykernel_launcher.py:16: DeprecationWarning: Both axis > a.ndim and axis < -a.ndim - 1 are deprecated and will raise an AxisError in the future.\n",
      "  app.launch_new_instance()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model accuracy 78.26 %\n",
      "Balanced accuracy 76.15 %\n",
      "Sensitivity 92.31 %\n",
      "Specificity 60.00 %\n",
      "Area Under the Receiver Operating Curve (ROC AUC score) 84.62\n",
      "Fold 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/fabiane/anaconda2/envs/postal/lib/python3.6/site-packages/ipykernel_launcher.py:16: DeprecationWarning: Both axis > a.ndim and axis < -a.ndim - 1 are deprecated and will raise an AxisError in the future.\n",
      "  app.launch_new_instance()\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model accuracy 69.57 %\n",
      "Balanced accuracy 70.77 %\n",
      "Sensitivity 61.54 %\n",
      "Specificity 80.00 %\n",
      "Area Under the Receiver Operating Curve (ROC AUC score) 85.38\n",
      "######## Final results ########\n",
      "Accuracy mean 70.87 %\n",
      "Balanced accuracy mean 71.23 %\n",
      "Sensitivity mean 68.46 %\n",
      "Specificity mean 74.00 %\n",
      "Mean ROC AUC score 85.46\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmQAAAHwCAYAAAAIDnN0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvqOYd8AAAIABJREFUeJzs3XecVNX5x/HPs32X3tTQpIsKiAZssSCJhvgTNSb2aIwaK2rsRizYFWss0RgLURFMTGxRY2KLsWBHBFRQBASRIp2tM3N+f5zZZVh2l2ELd+6d7/v12hdzpz4zzM589znnnmvOOUREREQkODlBFyAiIiKS7RTIRERERAKmQCYiIiISMAUyERERkYApkImIiIgETIFMREREJGAKZCIBM7NjzezfQdcRNDPraWZrzSx3Cz5mLzNzZpa3pR6zJZnZDDMb0YjbRfY9aGYjzGxB0HWIbIoCmUgKM5trZmXJYPCdmU0ws9Yt+ZjOuYnOuQNa8jEyUfK1/kn1tnNuvnOutXMuHmRdQUkGw35NuQ/n3I7Oudc38TgbhdBsfQ+KZBIFMpGNjXbOtQaGAjsDvw+4nkYJsusTlY7T5tDrLSJNoUAmUg/n3HfAS/hgBoCZFZrZLWY238wWm9l9ZlaccvkhZjbVzFab2VdmNip5fjsze9DMFpnZQjO7tnpozsxOMLM3k6fvM7NbUusws2fM7Lzk6a5m9nczW2pmX5vZ2SnXG2dmT5rZY2a2Gjih9nNK1vFI8vbzzOwyM8tJqeMtM7vLzFaZ2edm9uNat23oObxlZreb2XJgnJn1NbNXzex7M1tmZhPNrH3y+o8CPYHnkt3Ii2p3bszsdTO7Jnm/a8zs32bWOaWe45PP4Xszu7x2x63W8y42s1uT119lZm+m/r8Bxyb/T5eZ2diU2+1qZu+Y2crk877bzApSLndmdqaZzQZmJ8/7g5l9k3wPfGhme6dcP9fMLk2+N9YkL+9hZm8kr/JJ8vU4Mnn9g5Lvp5Vm9raZDUm5r7lmdrGZTQPWmVle6muQrP2DZB2Lzey25E2rH2tl8rH2SH0PJm+7o5n9x8yWJ297aT2va72/D8napqT8f55ufki1KLn9N/Nd6FVm9oaZ7ZhyvxPM7I9m9mKyxrfMbBszu8PMViTfmzvXei1+b2Yzk5c/XP04ddRc7++QSKCcc/rRj36SP8Bc4CfJ092BT4E/pFx+B/As0BFoAzwH3JC8bFdgFbA//o+dbsDA5GVPA38CWgFbAe8BpyYvOwF4M3l6H+AbwJLbHYAyoGvyPj8ErgAKgD7AHOCnyeuOA6qAQ5PXLa7j+T0CPJOsvRcwCzgppY4YcC6QDxyZfD4d03wOMeAsIA8oBvolX4tCoAs+CNxR12ud3O4FOCAvuf068BUwIHl/rwM3Ji/bAVgL7JV8LW5JPvef1PP/ek/y9t2AXGDPZF3Vj/nn5GPsBFQA2ydv90Ng9+Rz6gV8Bvwu5X4d8B/8+6E4ed6vgE7J25wPfAcUJS+7EP+e2g6w5ON1Srmvfin3vQuwBNgtWfOvk69ZYcrrNxXokfLYNa8p8A5wXPJ0a2D3ul7nOt6DbYBFydqLktu71fO6NvT7kJP8Px8H9AdWADun3PbE5G0Kk/czNeWyCcCy5OtfBLwKfA0cn3wtrgVeq/Vemp58LToCbwHXJi8bASxIqane3yH96CfIn8AL0I9+Mukn+cG+FliT/NJ6BWifvMyAdUDflOvvAXydPP0n4PY67nNr/Jd8ccp5R1d/odT6MjRgPrBPcvu3wKvJ07sB82vd9++Bh5OnxwFvNPDccpN17JBy3qnA6yl1fEsyDCbPew84Ls3nML++x05e51Dg41qv9aYC2WUpl58B/Ct5+gpgUsplJUAldQSy5JdwGbBTHZdVP2b3Ws/5qHqew++Ap1K2HTByE897RfVjA18Ah9RzvdqB7F7gmlrX+QLYN+X1O7GO9291IHsDuAroXM9zri+QHZ36/9TA82rw9yHlsZbjg+zvG7iv9sma2iW3JwB/Trn8LOCzlO3BwMpaz/u0lO0Dga+Sp0ewPpA1+DukH/0E+aN5ByIbO9Q597KZ7Qs8DnQGVuK7PCXAh2ZWfV3DBx3wf52/UMf9bYvvOC1KuV0OvhO2AeecM7PJ+C/FN4BjgMdS7qerma1MuUku8L+U7Y3uM0VnfFdgXsp58/Bdo2oLnXOu1uVd03wOGzy2mW0F3Ansje+E5ODDyeb4LuV0Kb7TQ7KmmsdzzpWa2ff13EdnfJflq819HDMbANwGDMP/3+fhOyypaj/v84GTkzU6oG2yBvDvkYbqSLUt8GszOyvlvILk/db52LWcBFwNfG5mXwNXOef+mcbjplvjpn4fcM7NNbPX8AHpnpor+aHu64DDk/eTSF7UGd+VBVic8lhldWzX3tkm9bWoft/Wls7vkEggNIdMpB7Ouf/i/1KvntO1DP9FsKNzrn3yp53zOwCA/0LoW8ddfYPvLnVOuV1b59yOdVwXYBLwSzPbFv8X/d9T7ufrlPto75xr45w7MLXsBp7SMvyw3rYp5/UEFqZsd7OUb9fk5d+m+RxqP/YNyfOGOOfa4ofyrIHrb45F+CFlwM8Rww8T1mUZUE7d/zebci/wOdA/+RwuZcPnACnPIzlf7GLgCKCDc649PmBU36a+90hdvgGuq/X/XeKcm1TXY9fmnJvtnDsaP7x8E/CkmbVq6DabWeOmfh8wswPxXbNXgJtTbnsMcAjwE6AdvpMGG7+2m6NHyunq921t6fwOiQRCgUykYXcA+5vZUOdcAj/X6PZk9wcz62ZmP01e90HgN2b2YzPLSV420Dm3CPg3cKuZtU1e1jfZgduIc+5jYCnwAPCSc676r/n3gNXJydLFyQnig8xseDpPxPnlJP4KXGdmbZKB7zzWd+DAf3mfbWb5ZnY4sD3wwuY+h6Q2+OHflWbWDT9/KtVi/ByexngSGG1me5qfZH8V9XyZJ//fHgJuS07ozk1OZC9M43HaAKuBtWY2EDg9jevH8P9/eWZ2Bb5DVu0B4Boz62/eEDOrDpK1X48/A6eZ2W7J67Yys/8zszZp1I2Z/crMuiSff/V7KJ6sLUH9r/0/gW3M7HfJSfttzGy32lfa1O+D+R0wHsR3C3+N//+qDj5t8AH/e3yX7fp0ntMmnGlm3c2sIz44P1HHdZr0OyTSkhTIRBrgnFuKnwh/efKsi4EvgSnm92R8GT9BG+fce8BvgNvxXZH/sr4bdTx+uGkmftjuSeAHDTz0JHz34PGUWuLAaPxen1/jOxQP4DsM6ToLP+9nDvBm8v4fSrn8XfwE7GX4IaVfOueqhwI39zlchZ+Yvgp4HvhHrctvAC4zvwfhBZvxHHDOzUg+l8n4btka/AT4inpucgF+Mv37+DlNN5He598F+G7OGnz4qOtLPtVLwIv4nSXm4TtzqUNpt+FD8b/xQe9B/M4E4OcA/iX5ehzhnPsAP4fwbvzr/SV17DnbgFHADDNbC/wBPy+u3DlXiv+/fSv5WLun3sg5twa/M8Zo/FDubGC/eh6j3t8H4H7gGefcC8n30EnAA8kA+kjy9VmIfz9N2YznVZ/H8a/rnOTPtbWv0Ey/QyItonpPLhHJcmZ2AnCyc26voGvZXOYX712JH1r8Ouh6ZMsys7n49+7LQdci0ljqkIlIKJnZaDMrSc6LugXfAZsbbFUiIo2jQCYiYXUIfuL2t/hh1qOcWv4iElIashQREREJmDpkIiIiIgFTIBMREREJWOhW6u/cubPr1atX0GWIiIiIbNKHH364zDnXZVPXC10g69WrFx988EHQZYiIiIhskpnN2/S1NGQpIiIiEjgFMhEREZGAKZCJiIiIBEyBTERERCRgCmQiIiIiAVMgExEREQmYApmIiIhIwBTIRERERAKmQCYiIiISMAUyERERkYApkImIiIgETIFMREREJGAKZCIiIiIBUyATERERCViLBTIze8jMlpjZ9HouNzO708y+NLNpZrZLS9UiIiIikslaskM2ARjVwOU/A/onf04B7m3BWkREREQyVl5L3bFz7g0z69XAVQ4BHnHOOWCKmbU3sx845xa1VE0iIg1xDqZOhVWrgq5ERFqUc+AcHTvnMGRI0MV4LRbI0tAN+CZle0HyPAUyEQnEO+/A2Wc74vF40KWISEtJJMhZvBiXm8vuh2zFH/8YZBRaL8gqrI7zXJ1XNDsFP6xJz549W7ImEclSV/3pej56dzDfLh1G246rad95RdAliUgzKyyvoNfsuRSVlhPPzeG7pZ2Aw4IuCwg2kC0AeqRsdwe+reuKzrn7gfsBhg0bVmdoExFpipgZIwaPZu6HlZx4QhHnnLNd0CWJSHP673/hiiug+zrYdlu4+Wbo0yfoqmoEuezFs8Dxyb0tdwdWaf6YiARp6VJwzrH11rlBlyIizSWRgHvugfPPh3XrYORIeOSRjApj0IIdMjObBIwAOpvZAuBKIB/AOXcf8AJwIPAlUAr8pqVqERFJx7Jl/l8FMpEImTIFHn4YcnJgzBg47jiwumZNBasl97I8ehOXO+DMlnp8EZHNtXSpwzkFMpFI2XNPOPFE2HVXGDYs6GrqlRm7FoiIZIBlyxzg6NIl8/56FpE0OQdPPQWDB0P//v68M84ItqY0KJCJiABVlXmsW+fIz3e0a6cOmUgoVVTADTfAP/8JPXrAE09AQUHQVaVFgUxEBChdW4Jzjg4d4uTmhuMDXERSLFwIF14Is2ZBURGcempowhgokImIAFC6tphEwtGxY5ycnCB3QBeRzfbmm3D55bBmje+M3Xwz9OsXdFWbRYFMRITqDlmCTp0SWAbugSUi9Xj4Yb+sBcC++8JVV0Hr1sHW1AgKZCIiQOkaP2TZsaPWnhYJlR/8wC9pccYZcPzx/nQIKZCJiABla4txDrp0CboSEdmktWvXd8FGjYIddoCQH1oxnDFSRKSZla4tBhydOwddiYg06OmnYfRo+OKL9eeFPIyBOmQiIkD1HDIUyEQyVUUF3HQTPPus337jDdguOsecVSATEcF3yHINttpKE/pFMs6338JFF8Hnn/ulLMaOhf/7v6CralYKZCIi+En9bQpgq600k0Mko7z9Nlx2GaxeDd26+SUtBgwIuqpmp08eEcl65eVQVVFAfj506KBV+kUyxurVcMkl/t+994ZHH41kGAN1yEREWLw4DkCHDglyc/V3qkjGaNvWL/g6fz785jehXdIiHQpkIpL1Fi2KAdChQ5ycnPAcakUkkr74AubNgwMO8Nv77x9sPVuIApmIZL0lSxIAdOwY1yr9IkF69lm48UZ/ulevyA5P1kWBTESy3nffpXbIojskIpKxKivhllvgH//w24ce6gNZFlEgE5Gst3ix75ApkIkEYNEiuPhimDnTL2lxySVw8MFBV7XFKZCJSNZbulSBTCQQH30EF14Iq1ZB164wfjwMHBh0VYHQJ4+IZLVEIsH33/t5Y506JTSHTGRL6tQJqqpgzz3hsceyNoyBOmQikuVisRgrVvi1xzp3VhgTaXFlZVBUBGaw7bYwYYKfL5bl3ensfvYikvVSA1mXLgEXIxJ1s2bB0Uevn7wP0KdP1ocxUCATkSy3Zk0la9fmYDkJOnTQR6JIi3n+eb+464IF8M9/QiIRdEUZRUOWIpLVFi2qxKyYktZl5Oe3DbockeiprITbboMnn/TbBx/s96pUV2wDCmQiktX8GmRGcetScnM7BF2OSLQsXuzD1/TpkJ8PF13k1xjTzjMbUSATkazlnGPJEocZFLcuJS9PH4kizeqqq3wY22Ybv6TFDjsEXVHG0qdPxL258E1iiVjQZUhEVfz3ZVwiHnQZjebijmlvb8+6VSPp1KeM3NzcoEsSiZbf/x7uvtv/27590NVkNAWyiIslYozoMSLoMiSiPir8mF0OPxfnHM65oMvZbOXl5UyZu45W7drRqmS+FoUVaaq1a/2E/SOP9MOSPXrATTcFXVUoKJCJSJMtX76c5cuXh3JR1ZUrOwFQ3LpCgUykKb780s8Rmz8fcnPh8MODrihUFMhEpMnWrFlDUVFRKIf8Vq8uAKBV63IFMpHG+te/4Nprobwc+veH3XcPuqLQUSATkSaJxWJUVVVRUFAQdCmN8v33PoQVty4PZYdPJFBVVXDHHfDEE377wAPh0kv9SvyyWRTIRKRJKisrgy6hSZYv94FMHTKRzbR8OVxwAUybBnl5/vQvfqElLRpJgUxEmqS0tDSUQ5Xg16tcs8bIyYGi4koFMpHN0aqVH6Lcaiu/pMWgQUFXFGoKZCLSJOvWrSM/Pz/oMhplxQofwNq3T2C5aMhSZFOc88OUBQVQWAi33uqHJztoUeWm0p+DItJoziWorKwMbYeseriyY8c4hsKYSINKS/2q+1de6YMZwA9+oDDWTNQhE5FGiycSoe4qVU/o79Ahrjgm0pCvv4YLL4S5c/1Q5YIFfo0xaTbqkIlIoyVi8VDPu6rukHXoEEeJTKQe//kPHH+8D2N9+8KjjyqMtQB1yESk0arisVAf/7E6kLVvH6NKiUxkQ7EY3HknPP643x41CsaOheLiYOuKqPB+kopIoGKxGIlEItSBrHpSf4cOcZaEeOhVpEU8/rj/yc2F886DI47QkhYtKLyfpCISqMrKytBPhF8/ZBljyeqAixHJNEcdBVOnwgknwJAhQVcTeeGd/CEigSorKwu6hCZLnUMW7mgp0gycg2eegXXr/HZBAdx2m8LYFqJAJiKNsm7dOizEE/oBvv/eL9fhA5kimWSx0lI/P+yaa+Cqq9YvayFbjIYsRWSzxeNxKioqyA3xfJJYDFat8qv0t2+fCLockeDMnQsXXQRz5kBJiZ+8H+Lf7bBSIJNQqaqqIpEI35enmYX24Nt1qaioCPX6Y7B+Qn+7dglyc9GyF5KdXn0Vxo3zHbLeveHmm6FXr6CrykoKZBIalZWVzJ8/P+gyGsU5R5cuXWjfvn3QpTSLsrKy0Aey9av0hy/gizSZc3DXXfDII377gAPgsst8h0wCoUAmobFq1SrMjOIQroGTSCRYsmQJiUSCDh06hD7MhPn4ldUUyCSrmfnJ+7m58Lvf+T0qQ/65FHYKZBIK8Xic1atXU1hYGHQpjZKTk0OrVq1YtmwZ8Xiczp07hzaUVc8fa9WqFQALFuTy1FPFxGIBF7aZFi70E/oVyCSrxGJQvXbg+efDIYfADjsEW5MACmQSEmvXrsU5F+rD9JgZrVq1YuXKlSQSCbp06RLK51N7/tijj5bwxhvhDMoA3brFQntwdJG0OQd//Ss8+SQ89BC0aeOXtVAYyxgKZJLxnHMsX748tN2xVGZGSUkJq1evJpFI0Lp166BL2mylpaU1gcw5+PRTP3R54onraNs2XLvKFxU5hg8vDWUwFklbWRlcdx38619++/XXYfToQEuSjSmQScYrKysjHo9HIpDB+k5ZWVkZa9euDbqczWZmFBUVAbBkeRtWrMihXTvHL39ZFsopKFVVCXXIJLrmz4cLL4SvvvLHoLziCth//6CrkjookEnGW7FiRaiPl1if6lATZrPmbQPAoEFVoQxj4DuwUXx/ifD663DllX7y/rbb+iUt+vQJuiqph/r0ktEqKyspLS2N1BpeUZIayMIqkVCHTCJo1iy44AIfxkaO9MtbKIxlNP1ZKBlt9erVmt+TwWbN2xqAQYMqA66k8dQhk0gaMACOOQa6dIFf/UpLWoSAPoUkY8XjcVauXBmJob0oWrYsh6Ur2tBhG0efPvGgy2k055w6ZBIN06f7PScHDPDb550XbD2yWdR6kIy1bt06AHXIMtT06X7vyh12qCLs/0UKZBJqzvnlLE4+2Q9Trl4ddEXSCOqQRZxzjoqKiqDLaJTly5dr7lgG84GsMtTzx6qFdZFeEcrL4YYb4Pnn/fbee/u9KSV0FMgirqqqivnz54fyCydqB+SOmur1x6IQyNSFlVBasMAvaTF7NhQV+WNRjhoVdFXSSApkEeecX6izJAIHjL3ttSnEEuFaeDSqKkrzeG/6PhTkOl6e8w6vzQ/v/0si4cjP/4xW4fubRbLZm2/6ALZ2LfTs6Ze06Ns36KqkCRTIIq46kEVBLOG46Md7BF2GAO+8U8DbbVvRu908zhk9POhymqS0tJRevXqR99V/gi5FJH2lpT6MjRgB48ZBCI/6IRtSIIs45xzoL39pZtXDlQN7Lga2CraYZqAhSwmFRIKaPWgOOADat4fhw7WkRUToU0hENlv1HpYDeiwOuJKmqe4gh3GOpWSZmTPhl7+Ezz5bf96uuyqMRYgCWcQlEgl92UizKiszvvoqj5wc6NdtadDlNEn1GmT6HZGM5Rw89RScdJI/LuUjjwRdkbQQDVlGnHNOXzbSrGbOzCORgAEDYhQVxoIup0m0KKxktIoKuOkmePZZv3344XDuucHWJC1GgSziFMikuVUPV0ZhuYtEIqHDJklm+vZbv6TFF19AYSGMHQsHHhh0VdKC9EkUcVHay1Iyw4wZPpANHlwF3wdcTBOpQyYZqaoKTj0VFi2C7t1h/Pj1h0OSyNIcsojTHDJpTpWV8Pnn6w+ZFHbqkElGys/3Q5P77AOPPqowliX0SRRxGrKU5jRrVh5VVbDttnHatnVUHzHPOUcikQi0tsaIx+PqkElmWL0apk2Dvfby2yNHwn77aS/KLKJAFnEKZJlpzRrjk0/ygy5js733nj+U1eDBG3bHSktLKSgoCN17rbCwkMLCwqDLkGz3+edw0UWwZAk88AAMGuTPD9nvkzSNAlnEacgy8yxblsM557Rn+fLwzhioPaHfzOjWrZuG/0Q217PPwo03+vkAO+wAnToFXZEERJ+eIltQWRmMG9eW5ctz6NEjTs+e8aBL2mwdOybYY4+Kmu14PE5eXp7CmMjmqKz0x5986im/fdhhcMEFUFAQbF0SGH2CRpw6ZJkjkYCbb27LV1/l0bVrnFtuWUnbtuHfC7aqqoo2bdoEXYZIeHz3nR+inDnTB7BLLoGDDw66KglYi46ZmNkoM/vCzL40s0vquLynmb1mZh+b2TQz0yIrzUxzyDLHhAmteOedAlq1clx11epIhDHwHbLi4uKgyxAJj8pKmDcPunaFhx5SGBOgBTtkZpYL3APsDywA3jezZ51zM1OudhnwV+fcvWa2A/AC0KulaspG6pBlhn//u5C//a2YnBwYO3Y13buHb6iyPmZGgYZZRBqWSPhJ+mbQsyfceSf07g1t2wZdmWSIlhyy3BX40jk3B8DMJgOHAKmBzAHV78Z2wLctWE/WikogK1tTwKefhm/PxKVLc7jzTj+kd+aZa9l55/Cv37We78Dm54fv/0Vki1mzBq64AvbYA444wp+3007B1iQZpyUDWTfgm5TtBcButa4zDvi3mZ0FtAJ+0oL1ZJ0ordJfXg7P37U7UwraBV1Kox16aBkHHlgedBnNyjkoKSmJTOgXaXazZvlDIC1cCDNmwEEHQUlJ0FVJBmrJQFbXJ3TthHA0MME5d6uZ7QE8amaDnHMbrDBpZqcApwD07NmzRYqNoigFssWLc6kqz6OwjaN///Ad0HqHHar49a9Lgy6j2TnnaNWqVdBliGSm55+H667zc8YGDvSHQFIYk3q0ZCBbAPRI2e7OxkOSJwGjAJxz75hZEdAZWJJ6Jefc/cD9AMOGDYtOymhhUQpk1Wt2DRgQY/z4VQFXI6m0sKpILZWVcNtt8OSTfvuQQ+Dii7WkhTSoJfeyfB/ob2a9zawAOAp4ttZ15gM/BjCz7YEiYGkL1pRVohjIOnQI3+F5oqr6/aX5YyK13HSTD2MFBXDZZXD55Qpjskkt1iFzzsXMbAzwEpALPOScm2FmVwMfOOeeBc4H/mxm5+KHM09wUUoRAYvSS1kdyDp1UiDLFLFYjJwcIycnvEccEGkRJ54In33mw9gOOwRdjYREiy4M65x7Ab+URep5V6Scngn8qCVryGZRCmTff58DJOjYUYEsU1RVVZGbowNzi+AcvP46jBjhl7Xo1g0mTtSxKGWz6E/bCItSIFuxQh2yTOOcU3dMZO1af8ijCy+ESZPWn68wJptJh06KsCgFsuohS3XIMospkEk2+/JLH8S++QbatIEePTZ9G5F66NM0wqIUyPyQpQJZpojFYhQUFKgJINnrxRfhhBN8GBswAB57DPbeO+iqJMTUIYuwqAQy5zSpP9PEYjHatm3LyqALEdnSqqrg9tvhr3/12wcd5A8OXlQUbF0SegpkERaVQFZaalRUGLn5cYqLo/Gcwi6RSOiA4pKdnINPP4W8PLjoIvj5zzVfTJqFAlmERSWQVXfHittU6nMvg+iA4pJVnPPBq6DArzO2fDkMGhR0VRIhmkMWYYlENIb31geyioArEfDvq9zcXPLy9PecZAHn4JFHYOxYfxqga1eFMWl2+kSNsEQiQdln7/DJrJlBl9Ik73/Sn9I1P2br7ktY/OorQZfTLOKr1q3/cA+ZhEuQazmszsv3y1588WLQJTWPHB1xQGpZtw6uugpefdVvH3447LxzsDVJZCmQRVgikYBEnJ32Pz3oUppk9vJiStq0ol37lWw98oSgy2kWVW+/Te7uu4eyi1leXs4222xD27Ztgy5FpOXMmeOXtJg3D1q1gquvVhiTFqVAFmFh/LKvS/WQZas2lQFX0rxKS0spKirCQjYxrqSkRBP6Jdr+/W+45hooK4O+feHmm6Fnz6CrkohTIIuwRCKBEa4v+7pUr0FW0qYq4EqaTywep6SkhO7duwddioikeuUVuPRSf/pnP/On9QeIbAEKZBGWSCQisVdi9WGTotQhi8fitGnTJugyRKS2vfeGoUPhgAP8nLEofIhKKCiQRVjUlr2IUiADNOwnkimmTYPevf3hjwoK4P77QYcFky1M77gISyQSRGDEsiaQlbSORiCLx+Pk5uaQn6+9+kQC5Rw8/jicfDKMGwfV824VxiQA6pBFWBQCWVmZUVZm5OdDYXEs6HKaRWVlJcXFxaGbzC8SKaWlfuL+f/7jt7fdNth6JOspkEVYFCb1V3fHOnaMxnw48P8vRTrunUhw5s71S1p8/TWUlPju2MiRQVclWU6BLMKiMIds/UHF4wFX0jwSiQQ5OTnkabhSJBivvOIXey0thT7+yVzEAAAgAElEQVR9/JIW6o5JBtBAeYRFYR2y1A5ZFFRVVdG6devQdy5FQuvDD30YO+AAmDBBYUwyhjpkERaFQFa9BllUAlksFqN169ZE49mIhET1gcEBzj0XhgyBn/5US1pIRlGHLKKcc5EasoxCIHPOYWaaPyayJU2bBqecAqtX++38fBg1SmFMMo4CWURFIYxBtDpksViM4uJicnNzgy5FJPqcg8mT4be/hY8/hsceC7oikQZpyDKiohLIUjtki1YHXEwTVVVV0aFDh6DLEIm+sjK49lp46SW/feyxvksmksEUyCKqengs7NbvZZmAkAcy0Or8Ii1u/ny44AKYM8cfg/KKK2D//YOuSmSTFMgiKoodMr4OuJgmiMVi5OXlaXV+kZa0bBkcdxysWwe9evklLXr3DroqkbQokEVUFAJZeTmUlhp5edCmTbifT2VlJZ06dQq6DJFo69wZDjkEFi+GK6/0i76KhIQCWURFIZDVtUp/LBYL5XNzzmm4UqQlLF8OK1f6RV4BzjnHH4syAlM2JLsokEVUGENLbbWXvHDOUVFRQatWrYIsq1FKSkooLCwMugyRaPn0U7j4YsjN9XtRtmvnT4uEkAJZREUjkPkP1k6d1i95UVRURNeuXYMqSUQygXPw5JNw660Qi/mFXquqgq5KpEkUyCIqCgvDbrQGmSMSe46KSBOUl8P118MLL/jto47yw5TaYUZCToEsosIexiB1yLL6wOJOi6qKZLMFC+DCC2H2bCgqgssv94dAEokABbKIilYgWz9kqUAmksVmzfJhrGdPv6RF375BVyTSbBTIIipKgaxDh+SkfnXIRLLbyJFw9dWwzz7QunXQ1Yg0Kx3LMqISiUTo51ttsEo/4ICcHL1lRbLGypVw7rkwY8b68w48UGFMIkkdsoiKUiDTkKVIFpo5Ey66CL77zq/A/8gjWltMIk3thogKeyCrqIC1a43cXGjbdv3wqzpkIhHnHPzjH3DSST6MDRrkl7cI8eeZSDrUIYuosM8hW7Fi/fyx1AymQCYSYRUVcNNN8Oyzfvvww/2QZUFBsHWJbAEKZBEV9g7ZRmuQJYX5OYlIA5zz64l98AEUFsLYsX6+mEiWUCCLqHg8jplRVRVn3rx5QZez2T7/vC2VlSXk5a2uqT8HUyATiSozOPpoWLLEd8n69w+6IpEtSoEsopxzNeFl2223DbiazTd1ahEFBQX07t26pv5235RqyFIkShIJP3l/0CC/ve++sOeeWnVfspK+3SIq7EOWde1hCRqyFImMVav8EOXJJ8O0aevPVxiTLKUOWUQlEolNXymD1XVgcdCkfpFI+Owzv6TFokXQrh1UVgZdkUjg9O0WUVHskDnUIRMJvWee8UtaLFoEO+wAEyfCsGFBVyUSOHXIIip1DlkYVe9l2aFDvOY8Q4FMJLQqK2H8eHj6ab992GFwwQVa0kIkSYEsoqLSIasesqwegg3zcxLJasuWwcsv+wD2+9/D6NFBVySSURTIIsg5F+oOWWUlrFlj5ORAu3Z+gdswPx8RAbp29ctZtG8P220XdDUiGUeBLMKqquCdd3bl24Wtgi5ls5SX++CVukq/c44cBTKR8Egk4KGH/IHAjzrKn7fbbsHWJJLBFMgiqPqwSR9/XMArL+9Hu3bFAVfUOF27rp8/5pzTsexEwmL1arjiCnjzTb+MxciRsNVWQVclktEUyCKoOpCtXOkDTL9+MfbZpyLIkjZbTg7svvv6XeE1ZCkSErNmwYUXwsKF0LYtXHutwphIGhTIIqg6kFUP/Q0cGOPww8uCLKnJ/HNSIBPJaP/8J1x/vZ8IOnCg36uya9egqxIJBQWyCKoOZGVlPsAUFbkgy2kWmkMmkuEefRT+8Ad/+pBD4OKLtaSFyGZQIIug2oGsuDj8gSyRSKhBJpLJfvxjePxxOPVUOPTQoKsRCR0FsgiqDmQVFdEJZOqQiWSgL76AAQP8Djddu/pFXwsLg65KJJR06KQIiuKQpZlpL0uRTFG9pMVxx8Fjj60/X2FMpNHUIYug6lXtozRkCVqlXyQjrFkDV14Jb7zht8vLg61HJCIUyCKo9l6WUeiQiUgG+PJLv6TFN99AmzZwzTWw115BVyUSCQpkERTFSf2gOf0igXrxRb+mWEWFnzd2883QrVvQVYlEhuaQRVDtDllUApkimUhAEgmYPNmHsYMOgocfVhgTaWbqkEVQ7TlkkRmy1BwykWDk5PgDg0+Z4tcY0++iSLNThyyCqgNZlDpk/tBJQVchkkXef98PUSY/T9hmG7++mH4RRVqEOmQRlEgkMLNIdcj8Hpb6IhBpcc75VffvvtuHsR/+EH72s6CrEok8BbIIqj7uY1T2sqyeE6c/zEVa2Lp1MG4cvPaa3z7xRPjpTwMtSSRbKJBFUCKRIBYz4nHIzY2Tn58fdElN4pwjJ0ej6yItas4cuOACmD8fWrXyS1rss0/QVYlkDQWyCEokElRU+ACTX1AFFAVbUBM558jNzQ26DJHomjkTTjnFL/Lar59f0qJHj6CrEskqCmQRlEgkKC/3gaygoDLgappOHTKRFta/v//p0QMuvRSKwv1HnEgYKZBFUGqHLCqBLC9Pb1WRZrVsGRQUQNu2kJ8P99wDxcWarCkSELUdIsg5t75Dll8VcDVNpw6ZSDP76CM45hh/TMrqZS1KShTGRAKkb7kIiuKQpeaQiTQD52DiRDjtNFi+HMrK/I+IBE7jQBHkA5n/S9dP6g83dchEmkFpKVx9Nbz8st/+9a/hjDNAf+yIZAQFsjrMnj27ZrX7MFq0aBHz5nWhsrKEVlbJstenB11Sk1RVVVFR0opY+TrWvPpa0OU0C8vXr55sQV9/DRdeCHPn+qHJceNg5MigqxKRFGl9K5hZAdDTOfdlC9eTERKJBNttt13QZTRabm4us2d3oKCggFb5MTqP2Cnokppk3bp1bLPNNnR8/xPajNwr6HJEwue553wY69PHL2mx7bZBVyQitWxyHMjM/g/4FPhPcnuomT2Vzp2b2Sgz+8LMvjSzS+q5zhFmNtPMZpjZ45tTvGzMObfBpP7CCEzqNzMNWYo0xRln+HljEyYojIlkqHS+5a4GdgNWAjjnpgL9NnUjM8sF7gF+BuwAHG1mO9S6Tn/g98CPnHM7Ar/brOplI9WHGao+jmVU9rI07f0lkr7vv4crroBVq/x2Xh6cfLIfrhSRjJROIKtyzq2sdV46B0fcFfjSOTfHOVcJTAYOqXWd3wL3OOdWADjnlqRxv9KA6kBWPam/ID/8e1mamQKZSLo++QSOPRZeeAFuuy3oakQkTekEss/M7Aggx8x6m9kdwJQ0btcN+CZle0HyvFQDgAFm9paZTTGzUWlVLfWq7iZVd8gKI7CXJaAhS5FNcQ4mT/aHQFq2DHbZBc4+O+iqRCRN6UzqHwNcASSAfwAv4YcZN6Wulkbtzloe0B8YAXQH/mdmg2p35MzsFOAUgJ49e6bx0Nkrih0yQB0ykYaUlsJ118FLL/nt446DMWO0pIVIiKQTyH7qnLsYuLj6DDM7DB/OGrIASD06bXfg2zquM8U5VwV8bWZf4APa+6lXcs7dD9wPMGzYsHSGS7NW7TlkUZjUD+qQidSrvBx+8xv46is/R+zKK+HHPw66KhHZTOl8y11Wx3lj07jd+0D/5DBnAXAU8Gyt6zwN7AdgZp3xQ5hz0rhvqcdGk/ojMGSpSf0iDSgqgj33hN694S9/URgTCal6O2Rm9lNgFNDNzFJnhrbFD182yDkXM7Mx+CHOXOAh59wMM7sa+MA592zysgPMbCYQBy50zn3f+KcjtYcsCyNw6CQteyFSSzwOixdD165+e8wY+O1vtRelSIg1NGS5BJgOlAMzUs5fA9S5plhtzrkXgBdqnXdFymkHnJf8kWYQtWUvqp+POmQiScuXw6WXwjff+ONStm/v54opjImEWr2BzDn3MfCxmU10zpVvwZqkCdYvDFs9hyzcHTIdWFwkxbRpcMklsGQJdOwIixb5QCYioZfOpP5uZnYdfnHXouoznXMDWqwqabQodsg0XClZzzn429/8umKxGOy0E9x4I3TpEnRlItJM0glkE4BrgVvwq+7/hjTmkEkwNtrLMuRzyNQhk6xXVgbXXw8vvui3jz4azjnHr74vIpGRTuuhxDn3EoBz7ivn3GUk94yUzOOcIx6Hqiowg/y8WNAlNYk6ZJL1pk71YayoyAez889XGBOJoHR+qyvMz6j+ysxOAxYCW7VsWdJYiUSCigofYIqKHGGfC69AJllvjz18CNttN+jTJ+hqRKSFpPNNdy7QGjgb+BH++JMntmRR0ng+kPkhvuLi8K+hm0gkyFM3QLJJIgH33usn8Fc7+miFMZGI2+Q3nXPu3eTJNcBxAGbWvSWLksZL7ZBFIZBpUVjJKitWwNix8N578Pzz8I9/QEFB0FWJyBbQYIfMzIab2aHJVfQxsx3N7BHSO7i4BCCRSNRM6C8qikYgU4dMssKMGfCrX/kw1qGDPwSSwphI1qg3kJnZDcBE4FjgX2Y2FngN+AR/iCPJQFHrkIGOYykR5xz8/e9w8sl+9f3Bg/2Cr8OHB12ZiGxBDbUeDgF2cs6VmVlH/IHBd3LOfbFlSpPGSCQSlJevn9QfBQpkEmm33QaTJvnTRxwB554L+fnB1iQiW1xD33TlzrkyAOfccuBzhbHM51fpj06HTHPIJPJ23RWKi+Hqq+GiixTGRLJUQx2yPmb2j+RpA3qlbOOcO6xFK5NG8UOW/gM9CoFMBxaXSPruO9hmG396773h2Wf9vDERyVoNBbJf1Nq+uyULkeYRxSFLdcgkMhIJ+POf4eGH4b77YOhQf77CmEjWa+jg4q9syUKkefhA5gNMFDpkoEAmEbFqFVx2GbzzDuTkwKxZ6wOZiGQ9rScQMX4OWXSWvQBN6pcImDkTLr4YFi2C9u39IZB23TXoqkQkgyiQRYxfhyxlUn+4jy0OqEMmIff003DTTf4AszvuCOPHw9ZbB12ViGSYtFsPZlbYkoVI0znncM5RURGtIUt1yCS0Vq6EO+/0YewXv/DzxxTGRKQOm+yQmdmuwINAO6Cnme0EnOycO6uli5PN45wPYOtX6gdWBVhQE1UveaEOmYRW+/Zw7bWwfDkcdFDQ1YhIBktnyPJO4CDgaQDn3Cdmtl+LViWNUh3IUif1J4IsqImcc+qOSfi8/TZ8+y388pd+e889g61HREIhnUCW45ybV6tLEW+hejLCzO9nsuibRUGXsdni8ThLli9h/ordWRdrz+yyaVS07k752oqgS2uURCKBSySYvcy3+fKJxhCsRFQiAQ8+CPffD2YwZAgM0FHmRCQ96QSyb5LDls7McoGzgFktW1aw4i7OiB4jgi5js1VVVTEvNo+/sTWt8vLYtesg3pnzKfu2Duf0v1gsRk5ODt07twNgDbGAKxKpx+rVcPnl8NZbPoydeir06xd0VSISIukEstPxw5Y9gcXAy8nzJMPUNWQZZolEgrw87QgsGe6LL+DCC/0wZdu2cN11sMceQVclIiGTzrddzDl3VItXIk228aT+cAcy5xy5ublBlyFSvzfegEsugcpK2H57v7xF165BVyUiIZTOjOn3zewFM/u1mbVp8Yqk0WoHsrB3yBTIJOMNGOAPDP7zn/v5YwpjItJIm+yQOef6mtmewFHAVWY2FZjsnJvc4tXJZvHrkBGZlfoVyCQjLV/ul7PIyfEHCJ88Gbp0CboqEQm5tNYUcM697Zw7G9gFWA1MbNGqpFGcc1RWGs5BQYH/vggzLXshGefdd+Hww+GRR9afpzAmIs1gk992ZtbazI41s+eA94ClgBbWyUDOucgMV1ZTh0wyQiIBDz0EY8b4g4RPnerPExFpJulM6p8OPAeMd879r4XryVhlZWV89913NfO0MpE/sLjP2GEfroT1K/WLBGrNGrjySj+B3wxOOQVOPjn8LWgRySjpBLI+zrms/1MwHo8Ti8UoLi4OupRNyAei0SHTYZMkcLNn+yUtFizwS1pccw386EdBVyUiEVRvIDOzW51z5wN/N7ONvt2dc4e1aGUZprozlulzmqKyBlm1TH+9JeJuv92Hse22g5tv1l6UItJiGuqQPZH89+4tUUimC8vwWVTWIKsWhtdcImzcOHj0UT93rDCcR7wQkXCot/3gnHsveXJ759wrqT/A9lumvMyRyXPHUkVtUr86ZLJFLVkC99yzfsL+VlvB+ecrjIlIi0vn2+7EOs47qbkLyXSJRCIU3ZqorEFWLQyvuUTE++/DscfCww/7tcVERLaghuaQHYlfDLa3mf0j5aI2wMqWLkwaRx0ykc3knB+WvPtu3xnbbTc48MCgqxKRLNPQHLL3gO+B7sA9KeevAT5uyaIyUdg6ZFEIZGGZtychtnYtXHUVvPaa3z7xRDjtNC1pISJbXL2BzDn3NfA18PKWKydzhW0OWdiHLKtX6VcgkxazeDGcfjrMnw+tW8PVV8M++wRdlYhkqYaGLP/rnNvXzFYAqd/uBjjnXMcWry6DhKVbE5UhSx02SVpcx47QoYOfsD9+PPToEXRFIpLFGhqy3C/5b+ctUUimC0uHLCqT+nVgcWkRVVVQUeE7Yvn5fm2xkhIoKgq6MhHJcg0NWVavzt8D+NY5V2lmewFDgMfwBxnPGmGZQ7ZRh8w51q1bF4raUznnaN26ddBlSJQsXQoXX+zD2B13+HliHbOq0S8iGSydQyc9DQw3s77AI8DzwOPAQS1ZWKYJW4esOpA5B+3ataNDhw5BltUoGrKUZvPRR3DJJbB8uV9bbPFi+MEPgq5KRKRGOoEs4ZyrMrPDgDucc3eaWdbtZRm2OWSFhesDZH5+Pvn5+UGVJBIc52DiRLjzTr+kxfDhcN116oyJSMZJJ5DFzOxw4Djg0OR5WfftHpYOWV2T+jUXS7JSaalf0uKVV/z2CSf4vSr1+yAiGSidQHYicAYw3jk3x8x6A5NatqzME5YOWV3rkGnoT7LS3/7mw1hJiQ9m++236duIiARkk4HMOTfdzM4G+pnZQOBL59x1LV9aZglbhyx1L0sFMslKv/oVLFzo/+3ZM+hqREQatMlAZmZ7A48CC/FrkG1jZsc5595q6eIySSKRCEWwqatDFobOnkiTxWLw0ENwxBHQvr0fmrz00qCrygpVVVUsWLCA8vLyoEsRCUxRURHdu3dv9JztdIYsbwcOdM7NBDCz7fEBbVijHjGkwtwhUyCTyPv+e78X5ccfw4wZ8Ic/BF1RVlmwYAFt2rShV69e+ryRrOSc4/vvv2fBggX07t27UfeRTsunoDqMJR/0M6CgUY8WYmGYQ1ZVBfG4bwykBvQwdPZEGm3qVDjmGB/GunTxx6OULaq8vJxOnTpl/GekSEsxMzp16tSkLnE6HbKPzOxP+K4YwLFk4cHFwyB1lf7Uz0V9SEokOQeTJ/tFXuNx2GUXuOEG6NQp6Mqykj5nJNs19XcgnUB2GnA2cBF+DtkbwF1NetQQCsNK/fUdx1IdMomcRAIuvxxeeslvH3ccjBmjJS1EJLQa/KY2s8HAKOAp59zBzrnRzrmbnXNZN3MzDHPI6po/BvrLVSIoJwe22cYvaXHTTXDOOQpjWS43N5ehQ4cyaNAgRo8ezcqVK2sumzFjBiNHjmTAgAH079+fa665ZoPP9BdffJFhw4ax/fbbM3DgQC644IKN7v/111/n7bffrvfxn332WW688cYGa5wwYQJjxozZ6HznHGeffTb9+vVjyJAhfPTRR3XevqysjH333Zd4PF5z3u23305RURGrVq1q8HFGjBjBBx98AMDatWs59dRT6du3LzvuuCP77LMP7777boO1b0q6z2HSpEkMHjyYIUOGMGrUKJYtW1Zz2V133cV2223HjjvuyEUXXQTAp59+ygknnNCk2sKi3g6ZmV0KnAR8hD900tXOuYe2WGUBmr2wnOdefj3lHMfatevIzU2noRicb+a3Zm3pYNaUreXNDz8FHDmEY+9QkbSsWQNt2vjTZ5wBhx0G3boFW5NkhOLiYqZOnQrAr3/9a+655x7Gjh1LWVkZBx98MPfeey8HHHAApaWl/OIXv+CPf/wjZ555JtOnT2fMmDE8//zzDBw4kFgsxv3337/R/b/++uu0bt2aPffcc6PLYrEYBx98MAcffHCjan/xxReZPXs2s2fP5t133+X000+vMyA99NBDHHbYYRss9j1p0iSGDx/OU089lXZwOfnkk+nduzezZ88mJyeHOXPm8NlnnzWq9s15DrFYjHPOOYeZM2fSuXNnLrroIu6++27GjRvHa6+9xjPPPMO0adMoLCxkyZIlAAwePJgFCxYwf/58ekZ8+ZqGEsaxwBDn3Doz6wK8AGRFIIu7HEb/ZETNtnOOr776ipKSkuCKSsPU3HxalxTTs2see/1wCIlEgsoX6/+LTiQ04nG45x7417/gscf8oY9ycxXGpE577LEH06ZNA+Dxxx/nRz/6EQcccAAAJSUl3H333YwYMYIzzzyT8ePHM3bsWAYOHAhAXl4eZ5xxxgb3N3fuXO677z5yc3N57LHHuOuuu3jwwQfp2LEjH3/8MbvssguDBw/mgw8+4O677+a5557j2muvpbKykk6dOjFx4kS23nrreut95plnOP744zEzdt99d1auXMmiRYv4Qa3jrU6cOJHHH3+8Zvurr75i7dq13HzzzVx//fVpBbKvvvqKd999l4kTJ9b8sd6nTx/69Omz6Re2Aek8B+cczjnWrVtHp06dWL16Nf369QPg3nvv5ZJLLqGwsBCArbbaquZ2o0ePZvLkyTVds6hqKJBVOOfWATjnlppZ1rZZqt9EmW7jA4s7P+tPJMyWL4ff/x4+/NAPVU6dCiNHBl2VNODlmYub/T5/skP9gSZVPB7nlVde4aSTTgL8cOUPf/jDDa7Tt29f1q5dy+rVq5k+fTrnn39+g/fZq1cvTjvtNFq3bl0znPnggw8ya9YsXn75ZXJzc5kwYULN9ffaay+mTJmCmfHAAw8wfvx4br311nrvf+HChfTo0aNmu3v37ixcuHCDMFNZWcmcOXPo1atXzXmTJk3i6KOPZu+99+aLL75gyZIlGwSZusyYMYOhQ4emdUi9I488ki+++GKj88877zyOP/74zX4O+fn53HvvvQwePJhWrVrRv39/7rnnHgBmzZrF//73P8aOHUtRURG33HILw4cPB2DYsGHceOONWR3I+pjZP5KnDeibso1z7rAWrSyDhGHJC9h4DplzDlMikzCbNg0uvhiWLvV7T954I+y8c9BVySakG56aU1lZGUOHDmXu3Ln88Ic/ZP/99wca/vxu6uf64YcfXmewWbBgAUceeSSLFi2isrJyk+tS1fUHf+3ali1bRvv27Tc4b/LkyTz11FPk5ORw2GGH8be//Y0zzzyz2Z7vE088kfZ103kOVVVV3HvvvXz88cf06dOHs846ixtuuIHLLruMWCzGihUrmDJlCu+//z5HHHEEc+bMwczYaqut+Pbbbzer9jBqqOv1C+Ce5M/dtbbvafnSMksYOmS197L0HTIFMgkh5+Cvf4VTTvFhbOhQmDhRYUzqVT2HbN68eVRWVtZ0XnbccceayezV5syZQ+vWrWnTpg077rgjH374YaMes1WrVnWef9ZZZzFmzBg+/fRT/vSnP21ybaru3bvzzTff1GwvWLCArl27bnCd4uLiDe5n2rRpzJ49m/33359evXoxefJkJk3yh5nu1KkTK1as2OD2y5cvp3Pnzuy444588sknJBKJTT6/I488kqFDh27088gjjzTqOVTP8evbty9mxhFHHFGzo0T37t057LDDMDN23XVXcnJyaib8l5eXU1xcvMl6w67eQOace6Whny1ZZNDC0iGra8gy86sWqcPnn8P48f5wSMccA/fdB507B12VhEC7du248847ueWWW6iqquLYY4/lzTff5OWXXwZ8J+3ss8+uGf668MILuf7665k1axbglzi67bbbNrrfNm3asGbNmrRqWLVqFd2S8xv/8pe/bPL6Bx98MI888gjOOaZMmUK7du02mj/WoUMH4vF4TSibNGkS48aNY+7cucydO5dvv/2WhQsXMm/ePIYPH85bb73Fd999B8AHH3xARUUFPXr0oG/fvgwbNowrr7yyptEwe/ZsnnnmmY3qeuKJJ5g6depGP7WHK9N9Dt26dWPmzJksXboUgP/85z9sv/32ABx66KG8+uqrgB++rKyspHPyd37WrFkMGjRok69j2GXtvLAoqj1kGYa100TqtP32fi/K66+H886DvMzew1kyy84778xOO+3E5MmTKS4u5plnnuHaa69lu+22Y/DgwQwfPrxmWYghQ4Zwxx13cPTRR7P99tszaNAgFi1atNF9jh49mqeeeoqhQ4fyv//9r8HHHzduHIcffjh77713TahoyIEHHkifPn3o168fv/3tb/njH/9Y5/UOOOAA3nzzTcAPV/785z/f4PKf//znTJ48ma233po//OEPHHjggQwdOpTf/e53TJo0qWYS/wMPPMB3331Hv379GDx4ML/97W836mZtroaew9ChQwHo2rUrV155Jfvssw9Dhgxh6tSpXJo83uyJJ57InDlzGDRoEEcddRR/+ctfar6/XnvtNf7v//6vSfWFgYVhKC7VsGHDXO32c3O79onHuezIY2q2q6qqmDdvXsbvZfngg6148slifvObdRxxRBllZWW8/8ajHPWbcUGX1izWvPoabUbuF3QZ0lL++19/UPCddgq6EtlMn332WU2nQ1rOxx9/zG233cajjz666StHREVFBfvuuy9vvvkmeSH4w6yu3wUz+9A5t8njf6f97Mys0DlX0Yj6Qi8sobWuSf3azVIyXiIB994LDz/shyWfeALatQu6KpGMs/POO7PffvsRj8fT2ksyCubPn8+NN94YijDWVJscsjSzXc3sU2B2cnsnM8u6QyeFQXx3qfsAACAASURBVF2HTtKIpWS0FSv8IY8eftgvaXHssdC2bdBViWSsE088MWvCGED//v0ZMWJE0GVsEelEzjuBg4CnAZxzn5hZVo0bhaVDVuc6ZOqQSaaaMQMuvBCWLPELvd5wA9RaL0pEJFukM6k/xzk3r9Z58TqvGVFhCWS1hyzNTJP6JTM99xycfLIPY0OG+NX3FcZEJIul0yH7xsx2BZyZ5QJnAbNatixpjNodMpGM1amTX9LiyCPhd7+D/PygKxIRCVQ6gex0/LBlT2Ax8HLyvKwR1g4ZoElkkjnKyqB6ccc994TJk6Fv32BrEhHJEJscsnTOLXHOHeWc65z8Oco5t2xLFJcpwhbI1CGTjPO//8Ho0fDRR+vPUxiTZpSbm8vQoUMZNGgQo0ePZuXKlTWXzZgxg5EjRzJgwAD69+/PNddcs8Hn+osvvsiwYcPYfvvtGThwYM3xKlO9/vrrNavK1+XZZ5/lxhtvbLDGCRMm1Kx/lurzzz9njz32oLCwkFtuuaXe2zvnGDlyJKtXr64576mnnsLM+Pzzzzeo9aCDDtrgtieccAJPPvkk4JdyuuSSS+jfvz+DBg1i11135cUXX2yw9nTccMMN9OvXj+22246XXnqpzuu88sor7LLLLgwdOpS99tqLL7/8EvCvTZcuXWqOBvDAAw8AsHTpUkaNGtXk2sIgnb0s/2xm99f+2RLFyeapSC5KssFelgHVIgL4JS3uuw/OPRdWroRm+NAXqUv1oZOmT59Ox44daw6dVFZWxsEHH8wll1zCrFmz+OSTT3j77bdrFi6dPn06Y8aM4bHHHuOzzz5j+vTp9OnTZ6P7byiQxWKxmsdojI4dO3LnnXfWGQRTvfDCC+y00060TdkTedKkSey1115Mnjw57ce7/PLLWbRoEdOnT2f69Ok899xzaR+FoD4zZ85k8uTJzJgxg3/961+cccYZxOMbTzc//fTTmThxIlOnTuWYY47h2muvrbnsyCOPrDkawMknnwxAly5d+MEPfsBbb73VpPrCIJ1J/S8DryR/3gK2ArJqPTLnXCi6ZHWvQyYSkFWr4Oyz4YEH/JIWY8bA738fdFWSBfbYYw8WLlwIwOOPP86PfvQjDjjgAABKSkq4++67a7pZ48ePZ+zYsQwcOBCAvLw8zjjjjA3ub+7cudx3333cfvvtNSv1n3DCCZx33nnst99+XHzxxRt0v5577jl22203dt55Z37yk5+wePHiBuvdaqutGD58OPmbmEs5ceJEDjnkkJrttWvX8tZbb/Hggw+mHchKS0v585//zF133UVhYSEAW2+9NUcccURat6/PM888w1FHHUVhYSG9e/emX79+vPfeextdz8xqOnyrVq1K6wgBhx56KBMnTmxSfWGwyTlkzrkNDvduZo8C/2mxijJUpu+tmEhARYVhBsnfsYyvWSJs5ky46CL47ju/+v7118OuuwZdlWwpX7RAJ3S7n6V1tXg8ziuvvMJJJ50E+OHKH9bag7dv376sXbuW1atXM336dM4///wG77NXr16cdtpptG7duqaL9eCDDzJr1ixefvllcnNzmTBhQs3199prL6ZMmYKZ8cADDzB+/HhuvfXWzXiydXvrrbf405/+VLP99NNPM2rUKAYMGEDHjh356KOP2GWXXRq8jy+//JKePXtu0GWrz7nnnstrr7220flHHXXURt3AhQsXsvvuu9dsd+/evSYUp3rggQc48MADKS4upm3btkyZMqXmsr///e+88cYbDBgwgNtvv50ePXoAMGzYMC77f/bePa6qMu3/fy9RCYUcOeiY6ICCiGxgC3jKs6WPjwZ5RnKGbNQyQ1J8LGfMye/IoyYqjpppQo8VyHbGeRH6G63HAz6FjpondBMFhoAcShMFSWBzWL8/lqzYsoGdqLD1fr9e+2VrrXvd97UXwb72dV339XnnnSbttXTup/WtK/C7B21Ia8YSImQVFb9Ex9q0+UXHUhJJS8GjxmCA//ovpaWFRgPvvQddu7a0VYJHiZnO04OkrKwMrVZLdnY2/v7+jB07FlD+fjf05bS5X1qnT59usklrXl4ewcHBFBYWYjAYcHV1bdY6tRQVFWFnZ6ceJyQksGjRIkBxkhISEvDz83tg7zc6OtrssaY+I02tFx0dzYEDBxg0aBBRUVFEREQQExNDYGAgISEhWFtbs337dl5++WVVbLxLly4UFBT8KtstEXNqyG5KklR093ULJTr254dvWuuhtTtjYHqH5ZPUzVnQimjfHt59F6ZPhw8/FM6Y4JFQW0OWk5ODwWBQa8i8vLy4V/84KysLW1tb7Ozs8PLy4uzZs/e1ZseOHU2eX7hwIWFhYVy6dIkdO3ZQXl5+X/PfS9u2bampqQHgxo0bHD16lLlz5+Li4kJUVBR79uxBlmUcHBy4efOm0b1FRUU4Ojri5uZGbm6uWTVjixcvVovs675MbV5wdnbm6tWr6nFeXl69dOT169dJTU1l0KBBgFIzVluX5+DgoKZQ582bZ/QzKS8vx6Z2h/ZjTKMOmaS4t76A091XZ1mWe8my/HdzJpckabwkSd9JknRZkqQGqx0lSZomSZIsSVKT4pstQWPfsFoL9+6wrKmpEQ6Z4NFRUAD/+tcvx4MGwdtvK86ZQPAI6dSpE5s3b2b9+vVUVlYya9YsUlJSOHz4MKBE0sLDw3nrrbcAWLp0KatXryYjQ2mvWVNTw8aNG+vNa2dnZ3bhe3FxMd27dwfg448/fhBvCwAPDw+ysrIA2Lt3L6GhoeTk5JCdnc3Vq1dxdXUlJSUFd3d3CgoKSE9PByAnJ4fU1FS0Wi0dOnRgzpw5hIeHYzAYACgsLCQuLq7eetHR0WqRfd2Xqc0LQUFB6HQ6KioquHLlCpmZmQy8p0yhc+fOFBcXq8/60KFDqhB3YWGhOm7fvn1GAt0ZGRloNJrmPDqLoFGHTFZCQ4myLFfffZkdKrrbRPZ94D+BfkCIJEn9TIyzA8KBU7/K8keIZUTIlH/rFvQLh0zwSDhxAn7/e/h//8+4rYVA0EL0798fX19fdDodNjY2JCUlERkZiYeHB97e3gwYMEAtwPfx8WHTpk2EhITg6emJRqMxcg5qCQwMJDExUS3qb4yVK1cyffp0hg8fjqOjY5P2/vDDDzg7O7Nx40YiIyNxdnY2am1Ry8SJEzl27BigpCsnT55sdH3q1Kns3r0ba2tr4uLieOWVV9BqtUybNo2YmBg6deoEQGRkJE5OTvTr1w+NRsOkSZNwcnJq0s7G8PLyYsaMGfTr14/x48fz/vvvq59BEyZMoKCggLZt27Jz506mTp2Kr68vn376KVFRUQBs3rwZLy8vfH192bx5s1FNXnJyMhMnTmyWfZaA1JSzIUnSB8BOWZZ/1V9aSZKGACtlWf6Pu8d/ApBlec094zah7OT8L+C/ZFk+c+9cdQkICJDvDT8/aCL37Oad4JfU45s3b1JUVNSqQ6Z6fVuWLv0NXl6VrF9fjMFg4KmnniLlf2OZFrq8pc17INw+mozdmCdKRrV1U1Oj7KDcuRNkGYYPV5wyIQ7+xJGenm4U0RA8HAoLCwkNDeXQoSdrX92IESNISkqic+fOLW1Kk5j6XZAk6awsy01mABuMkEmSVFvwPww4fTf1eE6SpPOSJJnjnHUHrtY5zrt7ru4a/YEesiz/f2bMJ2iEe1OWIkImeKiUlCiSRx/ebUn4+uuwYYNwxgSCh0i3bt2YN2+eyejZ48r169eJiIiwCGesuTS2y/I04AdMus+5TRVdqeE4SZLaANHA7CYnkqRXgVcBevbseZ/m3D+1OxZbM6Z6kLVpY06bOYHgV/L990qj14ICxQFbvRrqbHcXCAQPj+b2C7M0nJycmDTpft0Qy6Ixh0wCkGX5+/ucOw/oUefYGai7b9UO0ADH7jo7vwX2SZIUdG/aUpblD4EPQUlZ3qc9940sy1RUQFVV63XKbt1SnK+6Rf1t295PVxOBoAns7JSixX79lJYW3bq1tEUCgUBg8TT2ie0kSVJEQxdlWa6/DcWYrwF3SZJcgXxgJqAWZsmyXAyo1Y6SJB3DjBqyliA11Yq33nqGqqrWH3F66qlf/ltEyAQPDIMB2rZVOu536aLIIfXoIXZRCgQCwQOiMYfMCrDlPuUQZVmukiQpDPji7lwfybKcJknSX4Ezsizvu595W4L0dCsMBon27cHauvXuuLS2lhk06BdVK+GQCR4IhYVKC4tRo+CPf1TOCWFwgUAgeKA05pAVyrL81+ZMLsvyAeDAPef+0sDYUc1Z62FSXi4jSTBt2h1efvlOS5tjFpbQO01gAZw8CX/+s1LEX1ICL71kHIYVCAQCwQOhsRCK+DS/S8XdoFNrjo7diyRJIkImuH9qaiA2FhYuVByxZ5+FTz4Rzpig1WJlZYVWq0Wj0RAYGMitW7fUa2lpaYwZM4Y+ffrg7u7OqlWrjPpLHjx4kICAADw9Penbt6+qV1mXY8eOqV3lTbFv3z6THezrUleAvC7x8fH4+Pjg4+PDs88+S2pqqsn7ZVlmzJgxRrssExMTkSSJb7/91sjWF154weje2bNns3fvXgAqKytZtmwZ7u7uaDQaBg4cyMGDzdcfXbNmDW5ubnh4ePDFF1+YHHP06FH8/PzQaDS8/PLLVFVVAQ0/A4PBwIgRI9RxjzONfWI/98isaOXcbWZsceUyIkImuC9u34YlS+CDD5TjV1+FTZtESwtBq6ZWOkmv12Nvb69KJ5WVlREUFMSyZcvIyMggNTWVEydOsG3bNgD0ej1hYWHExcWRnp6OXq+nV69e9eZvzCGrqqpS17gfXF1d+b//+z8uXrzIihUrePXVV02OO3DgAL6+vkbC4AkJCQwbNgydTmf2eitWrKCwsBC9Xo9er2f//v1mqxA0xDfffINOpyMtLY3PP/+cBQsWUF1dbTSmpqaGl19+GZ1Oh16v53e/+52qZNDQM2jfvj3PPfcce/bsaZZ9lkCDDpksy0WP0pDWTG2ErF07y4mQgaghE9wn69bBV18pDtjf/qY4ZOL/JYEFMWTIEPLz8wHYvXs3Q4cOZdy4cQB06NCBrVu3qtGsdevWsXz5cvr27QsoepELFiwwmi87O5vt27cTHR2tduqfPXs2ERERjB49mrffftso+rV//34GDRpE//79ef755/nxxx8btffZZ59V+2wNHjyYvLw8k+Pi4+N58cUX1ePS0lKOHz9ObGys2Q7ZnTt32LlzJ1u2bFG1I7t27drsdhpJSUnMnDkTa2trXF1dcXNz4/Tp00Zjbty4gbW1NX369AFg7Nix/POf/wQafwaTJk0iPj6+WfZZAqIvghkYDEqkyZJSliAiZIL7JDwcioth2TK4RxxYIDCHY1ePPfA5R/UYZda46upqjhw5wpw5cwAlXenv7280pnfv3pSWllJSUoJer2fJkiWNzuni4sL8+fOxtbVV05mxsbFkZGRw+PBhrKysjKR+hg0bxsmTJ5EkiZiYGNatW8eGDRvMsj82Npb//M//NHnt+PHj7NixQz3+7LPPGD9+PH369MHe3p5z587h5+fX6PyXL1+mZ8+eRlG2hli8eDHJycn1zs+cObNeNDA/P5/BdfoROjs7q05xLY6OjlRWVnLmzBkCAgLYu3evkSB5Lfc+A41Gw9dff92kvZaOcMjMoDZCZmkpSxEhE5iFwQBJSTB1qhIJc3KCzZtb2iqBBWOu8/QgKSsrQ6vVkp2djb+/P2PHjgUa3+DU3C+t06dPN6mIkpeXR3BwMIWFhRgMBlxdXc2aLzk5mdjYWFJSUkxeLyoqws7OTj1OSEhg0aJFgOIkJSQk4Ofn98Deb3R0tNljTckw3rueJEnodDoWL15MRUUF48aNq9cv09QzsLKyon379ty+fdvo/T9uiE9sM1AcMon27S0nQiZ2WQrM4scflZTke+8pRfwCgYVSW0OWk5ODwWBQa8i8vLy4V/84KysLW1tb7Ozs8PLy4uzZs/e1ZseOHU2eX7hwIWFhYVy6dIkdO3ZQXl7e5FwXL15k7ty5JCUl4eDgYHJM27ZtqampAZT039GjR5k7dy4uLi5ERUWxZ88eZFnGwcGBmzdvGt1bVFSEo6Mjbm5u5ObmmlUztnjxYrRabb2Xqc0Lzs7ORtGuvLw8njERYR8yZAhfffUVp0+fZsSIEbi7u5v1DCoqKnjqMd9UJBwyM6gt6reUGrJa2SThkAka5fRpmDUL9Hr47W9h6NCWtkggaDadOnVi8+bNrF+/nsrKSmbNmkVKSgqHDx8GlEhaeHg4b731FgBLly5l9erVZGRkAErh+caN9fue29nZmV34XlxcTPfuinRzbdF6Y+Tm5jJlyhQ+/fRTtb7KFB4eHmRlZQGwd+9eQkNDycnJITs7m6tXr+Lq6kpKSgru7u4UFBSQnp4OQE5ODqmpqWi1Wjp06MCcOXMIDw/HcPfDrbCwkLi4uHrrRUdHc+HChXovU5sXgoKC0Ol0VFRUcOXKFTIzMxk4cGC9cdeuXQMUB+u9995j/vz5TT6DGzdu4OTkRLt27Zp8lpaMcMjMoLaGzFKcc6FjKWgUWYZduyAsDG7dUnQo4+IUKSSB4DGgf//++Pr6otPpsLGxISkpicjISDw8PPD29mbAgAFqAb6Pjw+bNm0iJCQET09PNBoNhYWF9eYMDAwkMTFRLepvjJUrVzJ9+nSGDx+Oo6Njo2MB/vrXv3Ljxg0WLFiAVqslICDA5LiJEydy7NgxQElXTp482ej61KlT2b17N9bW1sTFxfHKK6+g1WqZNm0aMTExdOrUCYDIyEicnJzo168fGo2GSZMm4eTk1KSdjeHl5cWMGTPo168f48eP5/3331fTuRMmTKCgQFFOjIqKwtPTEx8fHwIDAxkzZkyTzyA5OZkJEyY0yz5LQDKV923NBAQEyPeGnx80kXt2806wqvLElCm3uXzZmk2bbtG3b+vvhVLbr6Vnz57s/eS/mRa6vIUtejDcPpqM3ZjRLW2GZXPnDvzlL3D3jzpz5sBrr4ldlIJmkZ6ejqenZ0ub8dhTWFhIaGgohw4damlTHilTpkxhzZo1eHh4tLQpTWLqd0GSpLOyLJv2susgivrNoKJCQpKwmBoyWZZNFpoKBFhbK41ebW1h1SoYPrylLRIIBGbSrVs35s2bR0lJiVm7JB8HDAYDkyZNsghnrLkIh8wMKiuVf++2bGn1CIdMUI/KSmjXDqysYM0aKCsDZ+eWtkogEPxKmtsvzNJo3749oaGhLW3GI0E4ZE0gy7JaQyYiZAKLo7ISoqMhN1dpZdGmDTSwg0sgEAgELYdwyMyg1iGzpF2WwiETcO2a0tz14kVo2xa++QY0mpa2SiAQCAQmEJW8TSDLMhUVtZ36W9gYMxG7LAWcPQu//73ijHXpAjExwhkTCASCVoyIkDVBTY1MVZXlpSzv7X4seEKQZaWFxZYtUFMDAwbA6tVwVyNOIBAIBK0TEUZpgqoq5XOtTRulHtoSEF36n2AOHlQEwWtqYPZs2LpVOGOCJwIrKyu0Wi0ajYbAwEBu3bqlXktLS2PMmDH06dMHd3d3Vq1aZST1c/DgQQICAvD09KRv376qXmVdjh07xokTJxpcf9++fSY72NelrgB5XZKSkvDx8VH7bzUknVRWVsbIkSOprq5Wz0VHR/PUU09RXFzc6DqjRo1SFQtKS0t57bXX6N27N15eXowYMYJTp041antTyLJMeHg4bm5u+Pj4cO7cuXpjbt++bdTx39HRUZV+ys3NZfTo0fTv3x8fHx8OHDgAwKVLl5g9e3azbLMUhEPWBBUVyi+tJQmLS5IkUpZPKv/xHzB6NGzYoDR+tZRvEQJBM6mVTtLr9djb26vSSWVlZQQFBbFs2TIyMjJITU3lxIkTbNu2DQC9Xk9YWBhxcXGkp6ej1+vp1atXvfkbc8iqqqrUNe6H5557jtTUVC5cuMBHH33E3LlzTY776KOPmDJlilGNcEJCAgMGDCAxMdHs9ebOnYu9vT2ZmZmkpaWxa9cufvrpp/uyvZaDBw+SmZlJZmYmH374Ia+//nq9MXZ2dkYd/3/3u98xZcoUQGlWO2PGDM6fP49Op2PBggUAeHt7k5eXR25ubrPsswTEp3YT1AqLW5pig4iQPUEcPQpFRcp/W1lBVBSMHNmyNgkELciQIUPIz88HYPfu3QwdOpRx48YB0KFDB7Zu3apGs9atW8fy5cvp27cvoOhF1joDtWRnZ7N9+3aio6PVTv2zZ88mIiKC0aNH8/bbbxtFpfbv38+gQYPo378/zz//PD/++GOj9tra2qp/s3/++ecG/37Hx8fz4osvqsfff/89paWlREZGkpCQYNaz+f777zl16hSRkZHqF/devXoxceJEs+5viKSkJEJDQ5EkicGDB3Pr1i2Tige1ZGZmcu3aNYbf7YUoSRIlJSWAIj1VVwczMDAQnU7XLPssAVFo1ATl5UpkzFLqx2oREbIngKoqpZXF7t0QEADbtomO+4JWwe2jyQ98TnNVOqqrqzly5Ahz5swBlHSlv7+/0ZjevXtTWlpKSUkJer2eJUuWNDqni4sL8+fPx9bWVk1nxsbGkpGRweHDh7GysmLXrl3q+GHDhnHy5EkkSSImJoZ169axYcOGRtdITEzkT3/6E9euXeNf//pXvesGg4GsrCxcXFzUcwkJCYSEhDB8+HC+++47rl27RpcuXRpdJy0tDa1Wa9ZO/ODgYL777rt65yMiIur1BsvPz6dHjx7qsbOzM/n5+XTr1s3k3AkJCQQHB6vO58qVKxk3bhxbtmzh559/VrVHAQICAli7dq2qP/q4IhyyJrDElCWICNljz08/KS0tLlxQomJjxoD4mQtaCS0hcVZWVoZWqyU7Oxt/f3/Gjh0LNF5T29y/k9OnTzfp2OTl5REcHExhYSEGgwFXV9cm55o8eTKTJ0/myy+/ZMWKFUYOCcBPP/3Eb37zG6NzOp2OxMRE2rRpw5QpU/jHP/7BG2+88cDe7549e8wea0qGsbH1dDodn376qXqckJDA7NmzWbJkCf/+97/5wx/+gF6vp02bNnTp0kXVwnycEV+nm6A2ZSkiZIJWw/nzMGuW4ow5OcHOnTBjhnDIBE80tTVkOTk5GAwGtYbMy8uLe/WPs7KysLW1xc7ODi8vL86ePXtfa3bs2NHk+YULFxIWFsalS5fYsWMH5eXlZs85YsQIvv/++3o1XTY2NkbzXLx4kczMTMaOHYuLiws6nU5NWzo4OHDz5k2j+4uKinB0dMTLy4vU1FRqamqatCU4ONioCL/29cknn9Qb6+zszNWrV9XjvLw8o7RjXVJTU6mqqjKKXMbGxqoqBEOGDKG8vFx9BuXl5djY2DRpr6UjPrWbwBJryMQuy8eYhARFDPzGDfD3h/h48PFpaasEglZDp06d2Lx5M+vXr6eyspJZs2aRkpKiRpzKysoIDw9X019Lly5l9erVZGRkAFBTU8PGjRvrzWtnZ8ft27fNsqG4uJju3bsD8PHHHzc5/vLly2qE6dy5cxgMBhzuUdTo3Lkz1dXVqlOWkJDAypUryc7OJjs7m4KCAvLz88nJyWHAgAEcP36cH374AYAzZ85QUVFBjx496N27NwEBAbz77rvqmpmZmSQlJdWza8+ePUZF+LUvU1JGQUFBfPLJJ8iyzMmTJ+nUqVOj6cqQkBCjcz179uTIkSOAItBdXl6Ok5MTABkZGWiegD6KwiFrgtoaMktKWYpdlo8xN28qLS1CQ5WaMXv7lrZIIGh19O/fH19fX3Q6HTY2NiQlJREZGYmHhwfe3t4MGDBALcD38fFh06ZNhISE4OnpiUajMVmMHhgYSGJiolrU3xgrV65k+vTpDB8+HEdHxybt/ec//4lGo0Gr1fLGG2+wZ88ek1+qx40bp7bE0Ol0TJ482ej65MmT0el0dO3alb/97W9MmDABrVbLokWLSEhIUD8XYmJi+OGHH3Bzc8Pb25t58+Y1GM0ylwkTJtCrVy/c3NyYN2+euosVQKvVGo39+9//Xs8h27BhAzt37sTX15eQkBB27dqlPoPk5ORmbzqwBCRTed/WTEBAgHxv+PlBE7lnN+8EvwRAcnIF4eEyAwZUERlZ8lDXfRDIskxZWRlubm4A7P3kv5kWuryFrXow3D6a3CK1KS1OdfUv7StqapSU5T1FygJBS5Keno6np2dLm/HYc/78eTZu3GhUe/W4U1FRwciRI0lJSbGIhuemfhckSTory3JAU/eKMEoT1KbsLSVlKXQsHzOOHlXqw27cUI7btBHOmEDwhNK/f39Gjx5t1Bj2cSc3N5e1a9dahDPWXB7/d9hMandZWkpRv9CxfEyorla67Nd+E05Kgj/+sWVtEggELc4fn7C/A+7u7ri7u7e0GY8E4ZA1gcEgAbLF1JCJCNljQFER/OlPikC4lRW8+SbcU28hEAgEgscL4ZA1wS+NYVvYEDOpqal5IkK7jy0XL8Lbb8P16+DgAGvXQv/+LW2VQCAQCB4y4pO7CSorlX/btRMRMsFD5to1paVFZaXihK1ZA2bs0BIIBAKB5SMcsiaoLeoXKUvBQ6dLF3jlFSgthfBwEJFOgUAgeGIQ1d9N8Eun/pa1w1yEQ2Zh5ObCpUu/HM+bBxERwhkTCH4lVlZWaLVaNBoNgYGB3Lp1S72WlpbGmDFj6NOnD+7u7qxatcpI6ufgwYMEBATg6elJ3759Vb3Kuhw7dowTJ040uP6+fftUwfKGqCtAboqvv/4aKysr9u7da/J6WVkZI0eONNplGR0dzVNPPUVxcXGj64waNUpVLCgtLeW1116jd+/eeHl5MWLECE6dOtWo7U0hyzLh4eG4ubnh4+PDuXPnTI5LSEjA29sbHx8fxo8fr3bjX7lyJd27d1fVAA4cOADApUuXmD17drNssxTEX30TlFw3EP/BZwB8neJCya0epJ/P4IBVdssaZgZV1VVYW1vTvq3iQdp0Es5ZqyU5SE7ESQAAIABJREFUGVauBGtrpeO+k5OQPxII7pNa6SSAl19+mffff5/ly5dTVlZGUFAQH3zwAePGjePOnTtMnTqVbdu28cYbb6DX6wkLC+Nf//oXffv2paqqig8//LDe/MeOHcPW1pZnn3223rWqqiqCgoIICgq6b/urq6t5++23+Y//+I8Gx3z00UdMmTLF6Et3QkICAwYMIDEx0WzHZe7cubi6upKZmUmbNm3IysoiPT39vm0HxanNzMwkMzOTU6dO8frrr9dz8qqqqnjzzTf55ptvcHR05K233mLr1q2sXLkSgMWLF9dzhr29vcnLyyM3N5eePXs2y8bWjnDITCHDrNcnAXD5Whn6TIkBwz2YEPS7Fjasae7cucNvf/tbbG1tAbj+05EWtkhQj+pqpct+raTK4MHQgCaeQCD49QwZMoSLFy8CsHv3boYOHcq4ceMA6NChA1u3bmXUqFG88cYbrFu3juXLl9O3b18A2rZty4IFC4zmy87OZvv27VhZWREXF8eWLVuIjY3F3t6e8+fP4+fnh7e3N2fOnGHr1q3s37+fyMhIVQIpPj6erl27Nmrzli1bmDp1Kl9//XWDY+Lj49m9e7d6/P3331NaWkpUVBSrV682yyH7/vvvOXXqFPHx8WqLpF69etGrV68m722MpKQkQkNDkSSJwYMHc+vWLQoLC43kk2RZRpZlfv75ZxwcHCgpKVGbmDdGYGAgOp1Olbt6XBEOWRP8UkPWsnb8GoSOZSumqAiWL4evv1aavIaHK0Lh4mcmeIy4cvGnpgf9Slx9zNvgUl1dzZEjR5gzZw6gpCv972mm3Lt3b0pLSykpKUGv17NkyZJG53RxcWH+/PnY2tqqEZzY2FgyMjI4fPgwVlZW7Nq1Sx0/bNgwTp48iSRJxMTEsG7dOjZs2NDg/Pn5+SQmJnL06NEGHTKDwUBWVhYuLi7quVpNyOHDh/Pdd99x7do1unTp0uh7SUtLQ6vVmlXaEhwczHfffVfvfERERD09y/z8fHr06KEeOzs7k5+fb+SQtWvXjg8++ABvb286duyIu7u7KgIPsHXrVj755BMCAgLYsGEDnTt3BiAgIIC1a9cKh+xJx2BQ/rWUXZaAaAzbWtHr4a23lN2U9vZKSws/v5a2SiB44JjrPD1IysrK0Gq1ZGdn4+/vz9ixYwElKtPQl9TmfnmdPn26SccmLy+P4OBgCgsLMRgMuLq6NjrPokWLeO+99xp1kn766Sd+85vfGJ3T6XQkJibSpk0bpkyZwj/+8Q/eeOONB/Z+9+zZY/ZYUzKM965XWVnJBx98wPnz5+nVqxcLFy5kzZo1vPPOO7z++uusWLECSZJYsWIFS5Ys4aOPPgKgS5cuFBQU/CrbLRHxyd0ESoRMsphO/SAiZK2W0lKlv5iPD8TFCWdMIHiA1NaQ5eTkYDAY1MiLl5cX9+ofZ2VlYWtri52dHV5eXpw9e/a+1uzYQKnBwoULCQsL49KlS+zYsYPy2lRLA5w5c4aZM2fi4uLC3r17WbBgAZ999lm991d3nosXL5KZmcnYsWNxcXFBp9ORkJAAgIODAzdv3jS6v6ioCEdHR7y8vEhNTaWmpqbJ9xccHKwW2dd9ffLJJ/XGOjs7c/XqVfU4Ly+vnmB5bY1f7969kSSJGTNmqBslunbtipWVFW3atGHevHmcPn1ava+8vBwbG5sm7bV0hEPWBLURMktKWYoIWSui7h+9wYNh82bYsUNpcSEQCB44nTp1YvPmzaxfv57KykpmzZpFSkoKhw8fBpRIWnh4uJr+Wrp0KatXryYjIwNQmmtv3Lix3rx2dnbcvn3bLBuKi4vp3r07AB/X1oo2wpUrV8jOziY7O5tp06axbds2Jk2aZDSmc+fOVFdXq05ZQkICK1euVO8rKCggPz+fnJwcBgwYwPHjx/nhhx8AxeGrqKigR48e9O7dm4CAAN599101qpWZmUlSUlI9u/bs2cOFCxfqve5NVwIEBQXxySefIMsyJ0+epFOnTkbpSoDu3bvzzTffcP36dQAOHTqkCnEXFhaq4xITE9FoNOpxRkaG0fHjivjkboLatheWlLIUEbJWQl4ehIYq9WK1DBliOUr1AoGF0r9/f3x9fdHpdNjY2JCUlERkZCQeHh54e3szYMAAtS2Ej48PmzZtIiQkBE9PTzQajZFzUEtgYCCJiYlotVq++uqrRtdfuXIl06dPZ/jw4Tg+wObO48aNIyUlBVDSlZMnTza6PnnyZHQ6HV27duVvf/sbEyZMQKvVsmjRIhISEtQv6zExMfzwww+4ubnh7e3NvHnz6kWzfi0TJkygV69euLm5MW/ePLZt26Ze02q1ADzzzDO8++67jBgxAh8fHy5cuMCf//xnAN566y21HUZycjLR0dHq/cnJyUycOLFZ9lkCkqm8b2smICBAvjf8/KB5a8su1i2cDcDMmWWkpbUhOrqYfv2qHuq6D4I7d+7g6uqq1iJc/+kITo7PtbBVD4bbR5OxGzO6pc0wjy+/hL/8RUlT+vpCTIwo3Bc8tqSnp6uRDsHD4/z582zcuJFPP/20pU15ZFRUVDBy5EhSUlIsQhbQ1O+CJElnZVkOaOre1v/uWhiDQfkctZQassYKWAWPgJoa2L4d7hajMmqU0mtM/EwEAkEz6d+/P6NHj6a6uvqJaQCem5vL2rVrLcIZay6P/ztsJuXlygeppdSQSZIkHLKW4tYtpaXFqVNKS4s33lBSluLnIRAIHhB//OMfW9qER4q7uzvu7u4tbcYjQThkTWBJbS9qamqEQ9ZSyLLSU+ybb6BzZ/jv/4aBA1vaKoFAIBBYCKKovwl+2WXZ+h0yoWPZgkgSLFz4S0sL4YwJBAKB4FcgImRNUFGhRJssQVxcOGSPmIoKOH0ahg9XjgcMgIAAkaIUCAQCwa9GRMiaoDZCZglF/cIhe4QUFMAf/wgREUrNWC3CGRMIBALBfSAcskaorlZekiRhCRs8ZFkWTWEfBcePw+9/D999B927KzJIAoGgRbGyskKr1aLRaAgMDOTWrVvqtbS0NMaMGUOfPn1wd3dn1apVRlI/Bw8eJCAgAE9PT/r27avqVdbl2LFjald5U+zbt4+1a9c2auOuXbvU/mf3zt2pUye1E/5f//pXk/fLssyYMWMoKSlRzyUmJiJJEt9++63RfC+88ILRvbNnz2bv3r2AImG0bNky3N3d0Wg0DBw4kIMHDzZquzmsWbMGNzc3PDw8+OKLL0yOOXLkCH5+fmi1WoYNG8bly5cB5dk4OTmpzyAmJgaA69evM378+GbbZgmIT+9GqG0K2769bBGBj5qaGhEhe5jU1MCHH8KiRVBSAiNGwKefwhOyA0ggaM3USifp9Xrs7e1V6aSysjKCgoJYtmwZGRkZpKamcuLECbVxqV6vJywsjLi4ONLT09Hr9fTq1ave/I05ZFVVVeoa98vw4cPVTvh/+ctfTI45cOAAvr6+PP300+q5hIQEhg0bhk6nM3utFStWUFhYiF6vR6/Xs3//frNVCBrim2++QafTkZaWxueff86CBQuorq6uN+71118nPj6eCxcu8NJLLxEZGaleCw4OVp/B3LlzAXBycqJbt24cP368WfZZAhYQ92k5DAZl81z79jKyLJv8n6s1UV1dLSJkD4uSEnjnHThxQklLLlgAs2cr7S0EAkGrYsiQIVy8eBGA3bt3M3ToUMaNGwdAhw4d2Lp1K6NGjeKNN95g3bp1LF++nL59+wLQtm1bFixYYDRfdnY227dvx8rKiri4OLZs2UJsbCz29vacP38ePz8/vL29OXPmDFu3bmX//v1ERkZiMBhwcHAgPj6erl27Nvt9xcfH8+qrr6rHpaWlHD9+nOTkZIKCgli5cmWTc9y5c4edO3dy5coVrO/2c+ratSszZsxolm1JSUnMnDkTa2trXF1dcXNz4/Tp0wwZMsRonCRJaoSvuLjYLIWASZMmER8fz9ChQ5tlY2tHOGSN8EuETBE3bdu2bauOQNnY2PDUU0+1tBmPJxUV8O230KkTrF4Ngwa1tEUCQavl+7Onmh70K+ntb97vXHV1NUeOHGHOnDmAkq709/c3nqt3b0pLSykpKUGv17NkyZJG53RxcWH+/PnY2tqq6czY2FgyMjI4fPgwVlZW7Nq1Sx0/bNgwTp48iSRJxMTEsG7dOjZs2NDoGv/+97/x9fXlmWeeYf369Xh5edUbc/z4cXbs2KEef/bZZ4wfP54+ffpgb2/PuXPn8PPza3Sdy5cv07NnT6MoW0MsXryY5OTkeudnzpxZLxqYn5/P4MGD1WNnZ2fy8/Pr3RsTE8OECROwsbHh6aef5uTJk+q1f/7zn3z55Zf06dOH6OhoevToAUBAQADvvPNOk/ZaOsIhawTFIZOxtpapqanB3t4eOzu7ljZL8CiRZSUi5uQEGzeCoyP89rctbZVA0Kox13l6kJSVlaHVasnOzsbf35+xY8cCjauXNLdn4/Tp001+Sc/LyyM4OJjCwkIMBgOurq6NzuPn50dOTg62trYcOHCASZMmkZmZWW9cUVGR0WdQQkICixYtAhQnKSEhAT8/vwf2fuvqSTaFKRlGU+tFR0dz4MABBg0aRFRUFBEREcTExBAYGEhISAjW1tZs376dl19+maNHjwLQpUsXCgoKfpXtlojItzTCvcLiouHqE4TBoDR3vVtYCoBGI5wxgaCVUltDlpOTg8FgUGvIvLy8uFf/OCsrC1tbW+zs7PDy8uLs2bP3tWbHjh1Nnl+4cCFhYWFcunSJHTt2UF5e3ug8Tz/9NLa2toAi0l1ZWclPP/1Ub1zbtm2pqakB4MaNGxw9epS5c+fi4uJCVFQUe/bsQZZlHBwcuHnzptG9RUVFODo64ubmRm5urlk1Y4sXL1aL7Ou+TG1ecHZ25urVq+pxXl5evXTk9evXSU1NZdDdDENwcLBal+fg4KCmUOfNm2f0MykvL8fGxqZJey0d4ZA1QkWF4ohZWwtJoieKwkKYMwcSE+Hjj+HGjZa2SCAQmEmnTp3YvHkz69evp7KyklmzZpGSksLhw4cBJZIWHh7OW2+9BcDSpUtZvXo1GRkZgLI5auPGjfXmtbOzM7vwvbi4mO7duwPw8ccfNzn+hx9+UCNMp0+fpqamBgcHh3rjPDw8yMrKAmDv3r2EhoaSk5NDdnY2V69exdXVlZSUFNzd3SkoKCA9PR2AnJwcUlNT0Wq1dOjQgTlz5hAeHo7hbl+nwsJC4uLi6q0XHR2tFtnXfZnavBAUFIROp6OiooIrV66QmZnJwHsaZHfu3Jni4mL1WR86dEgV4i4sLFTH7du3z0igOyMjA41G0+RztHSEQ9YIdXdZAqJg/kng3/+GWbMgPR2eeQZiY8HEH0aBQNB66d+/P76+vuh0OmxsbEhKSiIyMhIPDw+8vb0ZMGCA2n7Cx8eHTZs2ERISgqenJxqNxsg5qCUwMJDExES0Wi1fffVVo+uvXLmS6dOnM3z4cBwdHZu0d+/evWg0Gnx9fQkPD0en05kMAEycOJFjx44BSrpy8uTJRtenTp3K7t27sba2Ji4ujldeeQWtVsu0adOIiYmhU6dOAERGRuLk5ES/fv3QaDRMmjQJJyenJu1sDC8vL2bMmEG/fv0YP34877//vprOnTBhAgUFBbRt25adO3cydepUfH19+fTTT4mKigJg8+bNeHl54evry+bNm41q8pKTk5k4cWKz7LMEJFN539ZMQECAfG/4+UHz1pZdrFs4m+PHa5g/34C/fzXvvFOIs7OzxRXNX//pCE6Oz7W0GQ+E20eTsRsz+uFMXlMDH30EO3YodWNDh8KqVWBG4atA8KSTnp5uFNEQPBwKCwsJDQ3l0KFDLW3KI2XEiBEkJSXRuXPnljalSUz9LkiSdFaW5YCm7hVF/Y1Qm7KsjZCJlOVjzIcfKvVikgSvvaakLEVEVCAQtCK6devGvHnzKCkpMWuX5OPA9evXiYiIsAhnrLkIh6wRalOWtcLiwiF7jJk+HZKT4c034dlnW9oagUAgMElz+4VZGk5OTkyaNKmlzXgkiBBAI/yyy1L5V9SQPWacOKGkKkGpE0tIEM6YQCAQCFoE4WE0wr1F/SJC9phgMMDatRAertSM1SIcboFAIBC0ECJl2Qh1a8gaay4osCB+/BHeegvS0hQJhm7dWtoigUAgEAiEQ9YYtb382rcXfcgeC06fhj//GW7dUhyx996Dfv1a2iqBQCAQCETKsjHu9syjXbsa4ZBZMjU18D//A2FhijM2ZAjExQlnTCB4jLCyskKr1aLRaAgMDOTWrVvqtbS0NMaMGUOfPn1wd3dn1apVRlI/Bw8eJCAgAE9PT/r27avqVdbl2LFjald5U+zbt89kB/u67Nq1S+1/Zmp+rVaLl5cXI0eONDlGlmXGjBmjinMDJCYmIkkS3377rdFcL7zwgtG9s2fPZu/evQBUVlaybNky3N3d0Wg0DBw4kIMHDzZquzmsWbMGNzc3PDw8+OKLL0yOOXLkCH5+fmi1WoYNG8bly5cB5dk4OTmpagAxd1VSrl+/zvjx45ttmyUgHLJGqFtDJgr6LRhZhlOnFMds7lz4298UkXCBQPDYUCudpNfrsbe3V6WTysrKCAoKYtmyZWRkZJCamsqJEyfYtm0bAHq9nrCwMOLi4khPT0ev19OrV6968zfmkFVVValr3A+3bt1iwYIF7Nu3j7S0NP7xj3+YHHfgwAF8fX2NWl4kJCQwbNgwdDqd2eutWLGCwsJC9Ho9er2e/fv3m61C0BDffPMNOp2OtLQ0Pv/8cxYsWEB1dXW9ca+//jrx8fFcuHCBl156icjISPVacHCwqgYwd+5cQNll2a1bN44fP94s+ywBkbI0gbX1D+QejeZ6uh81d9yoyvs3pRf05F6xvA9xibbcfkz8bqndr/zftVYY3MoKVq+Gb78VuygFgieAIUOGcPHiRQB2797N0KFDGTduHAAdOnRg69atjBo1ijfeeIN169axfPly+vbtCyh6kQsWLDCaLzs7m+3bt2NlZUVcXBxbtmwhNjYWe3t7zp8/j5+fH97e3pw5c4atW7eyf/9+IiMjMRgMODg4EB8fT9euXRu0d/fu3UyZMoWePXsCipi2KeLj43n11VfV49LSUo4fP05ycjJBQUGsXLmyyWdz584ddu7cyZUrV1TtyK5duza7nUZSUhIzZ87E2toaV1dX3NzcOH36NEOGDDEaJ0mSGuErLi6up3dpikmTJhEfH8/QoUObZWNrRzhkJpHpOWYxT31ZSZsO1TzdZySdBwzG2dm5pQ0TmMvBg3DoEERFKQ6Zvb1wxgSCR0TZNw9e/9Wmn3kSZtXV1Rw5coQ5c+YASrrS39/faEzv3r0pLS2lpKQEvV7PkiVLGp3TxcWF+fPnY2trq6YzY2NjycjI4PDhw1hZWRlJ/QwbNoyTJ08iSRIxMTGsW7eODRs2NDh/RkYGlZWVjBo1itu3b/Pmm28SGhpab9zx48fZUWdn+Geffcb48ePp06cP9vb2nDt3Dj8/v0bfy+XLl+nZs6dZjWUXL15McnJyvfMzZ86sFw3Mz89n8ODB6rGzszP5+fn17o2JiWHChAnY2Njw9NNPc/LkSfXaP//5T7788kv69OlDdHQ0PXr0ACAgIIB33nmnSXstHeGQNUJtUb+1dY1IWVoKlZUQHQ1//7tynJwMzz/fsjYJBE8Y5jpPD5KysjK0Wi3Z2dn4+/szduxYgEZ3yDe3Lnj69OmqXmNd8vLyCA4OprCwEIPBgKura6PzVFVVcfbsWY4cOUJZWRlDhgxh8ODB9OnTx2hcUVERdnZ26nFCQgKLFi0CFCcpISEBPz+/B/Z+o6OjzR5rSobR1HrR0dEcOHCAQYMGERUVRUREBDExMQQGBhISEoK1tTXbt2/n5Zdf5ujRo4ASMSwoKPhVtlsiwstohNoasrZtRQ2ZRXDtmiJ79Pe/K918//xneO7x0PEUCASNU1tDlpOTg8FgUGvIvLy8uFf/OCsrC1tbW+zs7PDy8uLs2bP3tWbHjh1Nnl+4cCFhYWFcunSJHTt2UF777b4BnJ2dGT9+PB07dsTR0ZERI0aQmppab1zbtm2pudvM+saNGxw9epS5c+fi4uJCVFQUe/bsQZZlHBwcuHnzptG9RUVFODo64ubmRm5urlk1Y4sXL1aL7Ou+TG1ecHZ25urVq+pxXl5evXTk9evXSU1NZdCgQYBSM1Zbl+fg4KCmUOfNm2f0MykvL8fGxqZJey0d4WU0wi+d+kWErNVz5gzMmgUXL0LXroou5ZQpSg2ZQCB4YujUqRObN29m/fr1VFZWMmvWLFJSUjh8+DCgRNLCw8N56623AFi6dCmrV68mIyMDgJqaGjZu3FhvXjs7O7ML34uLi+nevTsAH3/8cZPjX3zxRb766iuqqqq4c+cOp06dMinW7uHhQVZWFgB79+4lNDSUnJwcsrOzuXr1Kq6urqSkpODu7k5BQQHp6ekA5OTkkJqailarpUOHDsyZM4fw8HAMd1sJFBYWEhcXV2+96Ohotci+7svU5oWgoCB0Oh0VFRVcuXKFzMxMBg4caDSmc+fOFBcXq8/60KFD6vssLCxUx+3bt8/o/WdkZKDRaJp8jpaO8DIaobbtRfv2ssmwtKCVcP48LFgAN2/CwIFKSwsvr5a2SiAQtBD9+/fH19cXnU6HjY0NSUlJREZG4uHhgbe3NwMGDFDbT/j4+LBp0yZCQkLw9PREo9EYOQe1BAYGkpiYiFar5auvvmp0/ZUrVzJ9+nSGDx+Oo6Njk/Z6enoyfvx4fHx8GDhwIHPnzjXpgEycOJFjx44BSrpy8uTJRtenTp3K7t27sba2Ji4ujldeeQWtVsu0adOIiYmh093d5ZGRkTg5OdGvXz80Gg2TJk3CycmpSTsbw8vLixkzZtCvXz/Gjx/P+++/r35uTpgwgYKCAtq2bcvOnTuZOnUqvr6+fPrpp0RFRQGwefNmvLy88PX1ZfPmzUY1ecnJyUycOLFZ9lkCkqm8b2smICBAvjf8/KBZ8eEaVr36J/7wh0ouXKhmzZrrDB1q+0SozVskNTWKDFK/fjB/vpBAEggeMenp6SYjOoIHS2FhIaGhoRw6dKilTXmkjBgxgqSkJIv4DDb1uyBJ0llZlgOaulcU9TdCbYSsbVuRsmx1ZGUpvcQcHBQH7G9/U3ZTCgQCwWNKt27dmDdvHiUlJWbtknwcuH79OhERERbhjDWXh+plSJI0XpKk7yRJuixJUr2ksyRJEZIkfSNJ0kVJko5IkvS7h2nPr0U0hm2l/O//Qmgo/OlPUNt4UDhjAoHgCWDGjBlPjDMGSmPYSZMmtbQZj4SH5mVIkmQFvA/8J9APCJEk6V6tmvNAgCzLPsBeYN3Dsud+qFtDJhyyVkBVFWzYoOyeLC9XiverqlraKoFAIBAIms3DTFkOBC7LspwFIEmSDngR+KZ2gCzLdTvOnQR+/xDt+dXU7lRu167hPjaCR8RPP8Hbb0NqKrRtCxERMH262EUpEAgEgseCh+mQdQeu1jnOAwY1Mn4O0Hx10wdI3QiZcMhakHPnYNkyKCoCJyd47z3w8WlpqwQCgUAgeGA8TIfMlAdjckunJEm/BwIAkxL3kiS9CrwKqFpfjwKDQXkLwiFrYc6cUZwxf39Ys0aRQRIIBAKB4DHiYRZG5QE96hw7A/W0DyRJeh5YDgTJslxhaiJZlj+UZTlAluWA5vZKMZeamtoImUT79ogaspZk7lxYsQK2bRPOmEAgMImVlRVarRaNRkNgYCC3bt1Sr6WlpTFmzBj69OmDu7s7q1atMpL6OXjwIAEBAXh6etK3b19Vr7Iux44dU7vKm2Lfvn0mO9jXZdeuXWr/s7pERUWpXfA1Gg1WVlYUFRXVGyfLMmPGjFHFuQESExORJIlvv/3WyNYXXnjB6N7Zs2ezd+9eACorK1m2bBnu7u5oNBoGDhzIwYPNT1CtWbMGNzc3PDw8+OKLL0yOOXLkCH5+fmi1WoYNG8bly5cByM3NZfTo0fTv3x8fHx8OHDgAwKVLl5g9e3azbbMEHqaX8TXgLkmSqyRJ7YGZwL66AyRJ6g/sQHHGrj1EW341lZUA8t36seZrngl+BdnZSj+x69eV4zZt4MUXxU5KgUDQILXSSXq9Hnt7e1U6qaysjKCgIJYtW0ZGRgapqamcOHGCbdu2AaDX6wkLCyMuLo709HT0ej29evWqN39jDllVVZW6xv2wdOlStQv+mjVrGDlyJPYmvnweOHAAX19fo12WCQkJDBs2DJ1OZ/Z6K1asoLCwEL1ej16vZ//+/WarEDTEN998g06nIy0tjc8//5wFCxZQXbsLvg6vv/468fHxXLhwgZdeeonIyEhAaVY7Y8YMzp8/j06nY8GCBQB4e3uTl5dHbm5us+yzBB6aQybLchUQBnwBpAN/l2U5TZKkv0qSFHR3WBRgC/xDkqQLkiTta2C6R05tywtra+VblHDIHhFHjigtLc6cgQ8+aGlrBAKBBTJkyBDy8/MB2L17N0OHDmXcuHEAdOjQga1bt6rRrHXr1rF8+XL69u0LKHqRtc5ALdnZ2Wzfvp3o6Gi1U//s2bOJiIhg9OjRvP3220bRr/379zNo0CD69+/P888/z48//mi27QkJCYSEhJi8Fh8fz4svvqgel5aWcvz4cWJjY812yO7cucPOnTvZsmWLqh3ZtWtXZsyYYbaNpkhKSmLmzJlYW1vj6uqKm5sbp0+frjdOkiQ1wldcXKzqXTZ0HhSVhF/jcFoqD7UxrCzLB4AD95z7S53/fv5hrt8cftGxFA7ZI6G6GrZsUWSPAMaNAxNpA4FA0Pr57rvvHvicHh4eZo2rrq7myJEjzJkzB1DSlf7+/kZjevfuTWlpKSUlJej1epYsWdLonC4uLsyfPx9bW1s1nRkbG0tGRgaHDx/GysrKSOpn2LBhnDx5EkmSiImJYd26dWzYsKFjAB8cAAAgAElEQVRJ2+/cucPnn3/O1q1bTV4/fvw4O3bsUI8/++wzxo8fT58+fbC3t+fcuXP4+fk1usbly5fp2bOnWb3MFi9eTHJycr3zM2fOrBcNzM/PZ/Dgweqxs7Oz6hTXJSYmhgkTJmBjY8PTTz/NyZMnAUVuaty4cWzZsoWff/5Z1R4FCAgIYO3atar+6OOK6NTfABUVIMvQvr1yLGrIHiI3bihNXs+dU9KSixbBzJmipYVAYKGY6zw9SMrKytBqtWRnZ+Pv78/YsWMBpe6qoS/Uzf2iPX36dJM6x3l5eQQHB1NYWIjBYMDV1dWs+fbv38/QoUNNpisBioqKsLOzU48TEhJYtGgRoDhJCQkJ+Pn5PbD3Gx0dbfZYUzKMptaLjo7mwIEDDBo0iKioKCIiIoiJiSEhIYHZs2ezZMkS/v3vf/OHP/wBvV5PmzZt6NKlCwUF9UrQHzuEl9EASkG/TPv2IkL2ULlzB/7wB8UZc3CAHTsgJEQ4YwKB4FdRW0OWk5ODwWBQa8i8vLy4V/84KysLW1tb7Ozs8PLy4uzZs/e1ZseOHU2eX7hwIWFhYVy6dIkdO3ZQXtvUsgl0Ol2D6UpQ0qk1NTUA3Lhxg6NHjzJ37lxcXFyIiopiz549yLKMg4MDN2/eNLq3qKgIR0dH3NzcyM3NNatmbPHixepmg7ovU5sXnJ2duXr1l05XeXl5RmlHUGSQUlNTGTRI6YAVHBys1uXFxsaqadMhQ4ZQXl7OTz/9BEB5eTk2NjZN2mvpCIesAWp/f0SX/odMhw4wbRr07w/x8aDVtrRFAoHAgunUqRObN29m/fr1VFZWMmvWLFJSUtQUWFlZGeHh4Wr6a+nSpaxevZqMjAwAampq2LhxY7157ezszC58Ly4upnv37gB8/PHHZt/zf//3f0Y1Yvfi4eFBVlYWAHv37iU0NJScnByys7O5evUqrq6upKSk4O7uTkFBAenp6QDk5OSQmpqKVqulQ4cOzJkzh/DwcAx3m20WFhYSV1suUofo6Gh1s0Hdl6nNC0FBQeh0OioqKrhy5QqZmZkMHDjQaEznzp0pLi5Wn/WhQ4dUIe6ePXty5MgRQBHoLi8vp7arQkZGBhqNxqznaMkIT6MBapvCtmsnHLIHTlkZ3P2FBGD2bKWA39GxxUwSCASPD/3798fX1xedToeNjQ1JSUlERkbi4eGBt7c3AwYMUAvwfXx82LRpEyEhIXh6eqLRaCgsLKw3Z2BgIImJiWpRf2OsXLmS6dOnM3z4cBzN/LuWmJjIuHHjGoy6AUycOJFjx44BSrpy8uTJRtenTp3K7t27sba2Ji4ujldeeQWtVsu0adOIiYmhU6dOgLKj0cnJiX79+qHRaJg0aRLNbSnl5eXFjBkz6NevH+PHj+f9999X07kTJkygoKCAtm3bsnPnTqZOnYqvry+ffvopUVFRAGzYsIGdO3fi6+tLSEgIu3btUjNTycnJTJw4sVn2WQKSqbxvayYgIEC+N/z8oFnx4Rpe8P0T8+YZ8PYuZ+3aYnr06NH0jYKmyc1VivVv3lQiYl26tLRFAoGgmaSnp6uRDsHDo7CwkNDQUA4dOtTSpjwyKioqGDlyJCkpKbRt2/rL3k39LkiSdFaW5YCm7hWhnwao3WUpuvQ/QJKT4fe/h6ws6NTpl7ywQCAQCJqkW7duzJs3z6gx7ONObm4ua9eutQhnrLk8/u/wPqkt6m/XTja5i0bwK6iuVrrs19ZSPPccvPuuUj8mEAgEArNpbr8wS8Pd3R13d/eWNuORIByyBvilMWyNqCFrDkVF8Oc/K41e27SBN9+El14SuygFAoFAIKiDcMgaoLYPmYiQNZOsLKWlhb09rF0LTTQtFAgEAoHgSUQ4ZA1Qt1O/qCFrBgEB8Ne/gr8/PCJheIFAIBAILA2Ri2uAuo1hRYTsV1BertSHnTr1y7nx44UzJhAIBAJBIwiHrAF+2WVZIxwyc7l6FV55Bf71L4iMhKqqlrZIIBA8IVhZWaHVatFoNAQGBnLr1i31WlpaGmPGjKFPnz64u7uzatUqI6mfgwcPEhAQgKenJ3379lX1Kuty7Ngxtau8Kfbt22eyg31d6gqQ16W4uJjAwEB8fX3x8vLif/7nf0zeX1ZWxsiRI6murlbPRUdH89RTT1FcXNzoOqNGjVIVC0pLS3nttdfo3bs3Xl5ejBgxglN1v0TfB7IsEx4ejpubGz4+Ppw7d87kuISEBLy9vfHx8WH8+PFqN/6VK1fSvXt3VQ3gwAFFBvvSpUvMnj27WbZZCsIha4DycuWXtV07IZtkFl9+qUggZWZCz56waRM8AduUBQJB66BWOkmv12Nvb69KJ5WVlREUFMSyZcvIyMggNTWVEydOsG3bNgD0ej1hYWHExcWRnp6OXq+nV69e9eZvzCGrqqpS17gf3n//ffr160dqairHjh1jyZIlahf9unz00UdMmTLFKEiQkJDAgAEDSExMNHu9uXPnYm9vT2ZmJmlpaezatUt1jO6XgwcPkpmZSWZmJh9++CGvv/56vTFVVVW8+eabJCcnc/HiRXx8fIyE1BcvXqyqAUyYMAEAb29v8vLyyM3NbZZ9loBwyBpA9CEzk5oapaVFRASUlsLo0fDpp9C7d0tbJhAInlCGDBlCfn4+ALt372bo0KGMGzcOgA4dOrB161Y1mrVu3TqWL19O3759AUUvcsGCBUbzZWdns337dqKjo9VO/bNnzyYiIoLRo0fz9ttvG0Wl9u/fz6BBg+jfvz/PP/88P/74Y6P2SpLE7du3kWWZ0tJS7O3tTfbdio+PN5JW+v777yktLSUyMpKEhASzns3333/PqVOniIyMVDsI9OrVq9md8JOSkggNDUWSJAYPHsytW7fqKR7Isowsy/z888/IskxJSUk9vUtTBAYGotPpmmWfJSBCGA1gMCgRMuGQNcG778LBg0pLi7AwJUomnpdA8ERz/acjD3xOJ8fnzBpXXV3NkSNHmDNnDqCkK/39/Y3G9O7dm9LSUkpKStDr9SxZsqTROV1cXJg/fz62trZqOjM2NpaMjAwOHz6MlZUVu3btUscPGzaMkydPIkkSMTExrFu3jg0bNjQ4f1hYGEFBQTzzzDPcvn2bPXv21Gu3ZDAYyMrKwsXFRT2XkJBASEgIw4cP57vvvuPatWt0aUL9JC0tDa1Wa1YpTnBwMN9991298xEREYSGhhqdy8/PN1K0cXZ2Jj8/n27duqnn2rVrxwcffIC3tzcdO3bE3d1djWQCbN26lU8++YSAgAA2bNhA586dAQgICGDt2rWq/ujjinDIGqCsTPlXaFk2waRJ8PXXsGoVDBjQ0tYIBIJWgLnO04OkrKwMrVZLdnY2/v7+jB07FlCiMg19qW7ul+3p06ebdGz+//buPK7qKn/8+OsIyJL71qjoSO5elquA1C81lyAzNTFzSVOzLDNTK0tn1LK+pmmWk1MzlcuYaWBRjNq4jAs2iZlLoqGUmmKimCTuIsv1/P74XG4sF7gocFnez8fjPuLz+ZzP57zv/XTh7Tnnc05SUhJDhgwhOTmZjIwMfHx8Cr3Opk2bMJvNbNu2jV9++YXQ0FC6du1KrVq1bGV+//136tSpk+u8yMhIoqOjqVatGgMHDuSLL77gueeeK7H3u3r1aofL2luGMW99mZmZ/POf/2T//v3cddddPP/888ydO5cZM2bw7LPPMnPmTJRSzJw5k5deeolly5YB0KhRI86cOVOs2CsiyTQKkN19Ly1keWgNP/30x3ZgIKxZI8mYEMKpsseQnTx5koyMDFvLi8lkIu/6x8ePH6dGjRrUrFkTk8nEvn37bqnOghYCf/7555kwYQI//vgjH330ETeKWCbuX//6FwMHDkQpRatWrfDx8eGnnL9nre8v53UOHjzI0aNHCQ0NpUWLFkRGRtq6LevXr8+FCxdynZ+amkqDBg0wmUwcOHCAmzdvFvn+hgwZYhtkn/O1YsWKfGW9vb05deqUbTspKSlfd2RcXBxgtFAqpRg8eLBtXN6dd96Ji4sL1apVY+zYsezevdt23o0bN/D09Cwy3opOErICSEJmR3q6MafY449DzsGt7u7Oi0kIIXKoXbs2ixYtYsGCBWRmZjJ8+HB27NjBli1bAKMlbeLEibbur5dffpk5c+Zw5MgRAG7evMm7776b77o1a9bkypUrDsVw6dIlmjZtCsAn2UvGFaJ58+Zs3Wp08/7222/8/PPP+R4sqFu3LhaLxZaURUREMGvWLBITE0lMTOTMmTOcPn2akydPEhwcTGxsLGfPngVg7969pKen06xZM1q2bElQUBCvvfaarVXr6NGjrFmzJl9cq1evtg2yz/nK210J0L9/f1asWIHWml27dlG7du1c3ZUATZs25fDhw6SkpACwefNm20LcOcebRUdH4+vra9s+cuRIru3KShKyAmT/Q0QSMqvTp40pLdatg+rVjQH8QghRDnXs2JGAgAAiIyPx9PRkzZo1zJ49m7Zt2+Ln50dwcLBtAL6/vz9/+9vfGDZsGO3bt8fX1zffYHQwBpZHR0fbBvUXZtasWTz66KN07dqVBg0aFBnvzJkz2blzJ35+fvTq1Yt58+bZPS8sLIwdO3YARndleHh4ruPh4eFERkZy55138t5779GnTx/MZjOTJ08mIiLCNvxmyZIlnD17llatWuHn58fYsWMdGlxfmD59+nDXXXfRqlUrxo4da3uKFcBsNgPQpEkTXnvtNbp164a/vz9xcXH89a9/BeCVV16xTYcRExPDwoULbefHxMTc9kMHFYGy1+9bngUFBem8zc8lbebHczm9ayp79mQwe3YKffs2qdpzkcXGwowZcOUKeHvD229DFVnsVQhRtISEBFtLhyg9+/fv59133+XTTz91dihlJj09nfvuu48dO3bYffK0vLH3XVBK7dNaBxV1bvl/d06Snp79lOXNqttCdvMmLF5svAC6dYPXX4eaNZ0blxBCVEEdO3akR48eWCyWKtNI8Ouvv/LWW29ViGTsdlX+d3iLZC1L4NIl+OorY0qLZ5+FUaOMn4UQQjjFmDFjnB1CmWrdujWtq0iPjCRkBchOyNzdq/BM/XXrwrx5xocREuLsaIQQQohKSxKyAmRkGDM8eHhUsWRszRpISYGnnjK2rYMxhRBCCFF6JCErQHYLWZVJyDIyjNaw7Eef77tPBu4LIYQQZUQSsgIYCZnGw6MKjJk6cwamToWEBGNKi7/8RZIxIYQQogxVgWyj+LT+o4XM07OSf0Q7d8KIEUYy1qQJLF8O/fo5OyohhCgWFxcXzGYzvr6+9OvXj4sXL9qOHTp0iJ49e9KmTRtat27N//3f/+Va6mfDhg0EBQXRvn172rVrZ1uvMqft27fbZpW3Z+3atbYFywuScwHynC5cuEB4eDj+/v507tyZ+Ph4u+drrenZsyeXL1+27YuOjkYplWtm/+3bt9O3b99c544ePZqoqCjAWMJo2rRptG7dGl9fXzp37syGDRsKjd0Rc+fOpVWrVrRt25ZNmzbZLbN161Y6deqE2WymS5cuHDt2DDA+m4YNG9pWA1iyZAkAKSkp9O7d+7ZjqwgqebZxa25ajI/FxUXj6lqJP6ING2DSJLh8Gbp0gZUroU0bZ0clhBDFlr10Unx8PPXq1bMtnZSWlkb//v2ZNm0aR44c4cCBA+zcudM2cWl8fDwTJkxg5cqVJCQkEB8fn2+WfCg8IcvKyrLVcSvmzJmD2Wzm4MGDrFixgkmTJtktt379egICAnKtcRkREUGXLl2IjIx0uL6ZM2eSnJxMfHw88fHxrFu3zuFVCApy+PBhIiMjOXToEBs3bmT8+PFYLJZ85Z599llWrVpFXFwcjz32GLNnz7YdGzJkiG01gKes45gbNmxI48aNiY2Nva34KoJKnG3cuqwsoye30i8sHhICjRrBuHHw7ruQ40suhBAV1T333MPp06cB+Oyzz7j33nsJCwsDwMvLi/fff9/WmjV//nymT59Ou3btAHB1dWX8+PG5rpeYmMiHH37IwoULbTP1jx49mhdffJEePXowderUXK1f69atIyQkhI4dO3L//ffz22+/FRrv4cOH6dXLWJC9Xbt2JCYm2j1n1apVPPzww7btq1evEhsby9KlSx1OyK5fv87ixYv5+9//jrt12bs777yTwYMHO3R+QdasWcPQoUNxd3fHx8eHVq1a5VqPMptSytbCd+nSJYdWCBgwYACrVq26rfgqAhlDZocly5hwr3r1SpiQJSZCs2bg4gL16sEXX4CXl7OjEkJUIv/9/VKJXzOsQW2HylksFrZu3cqTTz4JGN2VgYGBucq0bNmSq1evcvnyZeLj43nppZcKvWaLFi0YN24cNWrUsHVnLl26lCNHjrBlyxZcXFxYvny5rXyXLl3YtWsXSimWLFnC/Pnzeeeddwq8fkBAAF999RVdunRh9+7dnDx5kqSkJO68885c5WJjY/noo49s2//+97/p3bs3bdq0oV69evzwww906tSp0Pdy7NgxmjdvnquVrSAvvPACMTEx+fYPHTo0X2vg6dOnufvuu23b3t7etqQ4pyVLltCnTx88PT2pVasWu3btsh378ssv+d///kebNm1YuHAhzZo1AyAoKIgZM2YUGW9FJwmZHdkJmZvbzco1G/K6dTB3Ljz2GGSPY5BkTAhRwhxNnkpSWloaZrOZxMREAgMDCQ0NBYxxVwXNJXm7c0w++uijdv9GJCUlMWTIEJKTk8nIyMDHx6fQ60ybNo1JkyZhNpvx8/OjY8eOdmemT01NpWaOlVIiIiKYPHkyYCRJERERdOrUqcTeb871JItibxlGe/UtXLiQ9evXExISwttvv82LL77IkiVL6NevH8OGDcPd3Z0PP/yQUaNGsW3bNgAaNWrEmTNnihV7RVTJmn9KRpYtIaskLWQZGTBnjrHsUUaGMQN/BVvDVAghCpM9huzkyZNkZGTYxpCZTCbyrn98/PhxatSoQc2aNTGZTOzbt++W6rzjjjvs7n/++eeZMGECP/74Ix999BE3btwo9Dq1atXiX//6F3FxcaxYsYKUlBS7SZyrqys3b94E4Pz582zbto2nnnqKFi1a8Pbbb7N69Wq01tSvX58LFy7kOjc1NZUGDRrQqlUrfv31V4fGjL3wwgu2QfY5X/YeXvD29ubUqVO27aSkpHzdkSkpKRw4cIAQ60TjQ4YMsY3Lq1+/vq0LdezYsbnuyY0bN/D09Cwy3oquEmQbJe9mlgtaG+tYVviE7OxZY5LXr74yprSYMQOmT4equvqAEKJSq127NosWLWLBggVkZmYyfPhwduzYwZYtWwCjJW3ixIm88sorALz88svMmTOHI0eOAHDz5k3efffdfNetWbOmwwPfL126RNOmTQH45JNPiix/8eJFMjIyAKNLr1u3bna7FNu2bcvx48cBiIqKYuTIkZw8eZLExEROnTqFj48PO3bsoHXr1pw5c4aEhAQATp48yYEDBzCbzXh5efHkk08yceJEW53JycmsXLkyX30LFy60DbLP+bL38EL//v2JjIwkPT2dEydOcPToUTp37pyrTN26dbl06ZLts968ebNtIe7k5GRbubVr1+ZaoPvIkSP4+voW+TlWdBU82ygdRguZrvhjyHbvhuHD4fBhaNwYli6FAQOcHZUQQpSqjh07EhAQQGRkJJ6enqxZs4bZs2fTtm1b/Pz8CA4Otg3A9/f3529/+xvDhg2jffv2+Pr65koOsvXr14/o6GjboP7CzJo1i0cffZSuXbvSoEGDIuNNSEjAZDLRrl07NmzYwHvvvWe33EMPPcT27dsBo7syPDw81/FHHnmEzz77DHd3d1auXMkTTzyB2Wxm0KBBLFmyhNq1ja7k2bNn07BhQzp06ICvry8DBgygYcOGRcZZGJPJxODBg+nQoQO9e/fmgw8+sHXn9unThzNnzuDq6srixYt55JFHCAgI4NNPP+Xtt98GYNGiRZhMJgICAli0aFGuMXkxMTE89NBDtxVfRaDs9fuWZ0FBQTpv83NJe/Kvy/lhwwhatbrMJ5944FURx1lpDRMnwnffwT33wOzZULvsx3UIISq/hISEXC0aonQkJyczcuRINm/e7OxQylS3bt1Ys2YNdevWdXYoRbL3XVBK7dNaBxV1rgzqtyMrx1OWFXZhcaXgjTeMgfwjRkBFbukTQghB48aNGTt2LJcvX3boKcnKICUlhRdffLFCJGO3S/5K22GxjiFzd69gCdnRo8bA/ezJ+OrWhZEjJRkTQohKYvDgwVUmGQNjYtgBVWSojbSQ2WHJdMUYQ0bFGUO2fj28+aax5lPLlkarmBBCCCEqBEnI7LBYl05yc6sALWSZmbBwIXz+ubHdty88+qhzYxJCCCFEsUhCZkdWpvGxuLvf/sSBpercOZg6FX78Edzc4OWXITxcprQQQgghKhhJyOyw2OYhK8ctZElJ8MQTcOEC3HknvP02dOjg7KiEEMIpzp49y+TJk9mzZw/u7u60aNGCAQMGsHbtWr7++mtnhydEkSQhs8NiyZ6HrByPIWvSBNq1Mwbwz5kDdeo4OyIhhHAKrTXh4eGMGjXKtsh2XFwc69atc3JkQjiunGYbzmXJNKa9KHdPWV67Bqmpxs/VqsG8efD++5KMCSGqtJiYGNzc3Bg3bpxtn9lspmvXrly9epVBgwbRrl07hg8fbltz8Y033iA4OBhfX1+efvpp2/7u3bszdepUOnfuTJs2bWyTwFosFqZMmYKfnx/+/v78/e9/B2Dfvn3cd999BAYG8sADD9idVFYIR0gLmR1/zEPm5EByOn4cpkwxprL46CNwdZWFwYUQ5VNQIXNg/vWvMHCg8fNXXxkt/AVxcBLw+Ph4AgMD7R7bv38/hw4dokmTJtx7773ExsbSpUsXJkyYwKuvvgrA448/ztdff02/fv0AyMrKYvfu3axfv57XX3+dLVu28PHHH3PixAn279+Pq6srqampZGZm8vzzz7NmzRoaNmzI6tWrmT59OsuWLXMobiFykoTMDkuWK1qDh0c5aR3773+NSV5v3DCeNLh4ERxYjkMIIaq6zp074+3tDRitZomJiXTp0oWYmBjmz5/P9evXSU1NxWQy2RKygdaEMTAwkMTERAC2bNnCuHHjcHU1/mzWq1eP+Ph44uPjCQ0NBYxWtMaNG5fxOxSVhSRkdlisLWROT8gyM+G998A6JoI+fYx/XXp4ODcuIYQojKPL2w0c+Edr2W0wmUxERUXZPebu7m772cXFhaysLG7cuMH48ePZu3cvzZo1Y9asWdy4cSPfOdnlwRinlncIi9Yak8nEd999d9vvQQgZQ2ZH9uLiOb7HZS8lBcaNM5IxV1djeovXX5dkTAgh8ujZsyfp6eksXrzYtm/Pnj188803dstnJ18NGjTg6tWrBSZzOYWFhfHhhx/aErTU1FTatm1LSkqKLSHLzMzk0KFDt/t2RBUlCZkd2S1k7u5ObCHbuBEOHIBGjWDxYmOy1/L0gIEQQpQTSimio6PZvHkzLVu2xGQyMWvWLJo0aWK3fJ06dRg7dix+fn4MGDCA4ODgIut46qmnaN68Of7+/gQEBPDZZ59RvXp1oqKimDp1KgEBAZjNZnbu3FnSb09UESr7yZKKIigoSO91tDn8FgX32MeN332ZO/caffvWK9W6CnTz5h+JWD0nxSCEEA5ISEigffv2zg5DCKez911QSu3TWhfypItBWsjssGQZH0uZjiG7ft142ujcOWO7WjV45hlJxoQQQogqQAb125GVZSwuXmYJ2YkTxrJHiYmQnAzW+W2EEEIIUTVIC5kdZTqGbMsWGDXKSMbuusuYa0wIIYQQVYq0kNlhyXKhGuDl5VJ6lWRlGS1hq1YZ22FhMGOGTPYqhBBCVEGSkNmRleWKG6U4huzmTZgwwZirx8UFXngBhgyRpyiFEEKIKkoSMjssWdXAtRSXTqpWDe6+2+imnDcPAgJKqSIhhBBCVASSkNlhyXIFV/D0LMEWK63ht9/gT38ytkeNgvBwqF275OoQQgghRIUkg/rt+GPppBL6eK5fh+nTYfhwOHvW2KeUJGNCCCGEACQhyycrC/RNRbVq4OZWAi1kv/4Ko0cbC4RnZhrdlEIIIUqcUorHH3/ctp2VlUXDhg3p27dvqdbr4uKC2WzG19eXfv36cfHiRduxpKQkHn74YVq3bk3Lli2ZNGkSGRkZtuNnz55l6NChtGzZkg4dOtCnTx+OHDmSr460tDTuu+8+LBaLbV90dDRKKX766SfbvsTERHx9fXOdO2vWLBYsWFCs+opr48aNtG3bllatWvHWW2/ZLbNw4UJMJhO+vr4MGzYs1/qhLVq0wM/PD7PZTFBQkXOollhMBZW5ceMGnTt3JiAgAJPJxGuvvQZARkYG3bp1sy2hVZIkIcsj+3tSvXr+hWSLbds2GDECjh+HFi3gk0+MsWNCCCFK3B133EF8fDxpaWkAbN68maZNm5Z6vZ6ensTFxREfH0+9evX44IMPAGPx8YEDBzJgwACOHj3KkSNHuHr1KtOnT7cdDw8Pp3v37vzyyy8cPnyYOXPm8Ntvv+WrY9myZQwcOBAXlz+e/o+IiKBLly5ERkY6FGdx6isOi8XCc889x4YNGzh8+DAREREcPnw4V5nTp0+zaNEi9u7dS3x8PBaLJV/cMTExxMXFUdRqPNu3b2f06NG3HVNhZdzd3dm2bRsHDhwgLi6OjRs3smvXLqpXr06vXr1YvXq1g5+O4yQhyyM7Ya9enVtPyCwWWLQIXnnF6K68/35YsQJ8fEouUCGEEPk8+OCD/Oc//wGMhGXYsGG2YytXrqRz586YzWaeeeYZW2vTgAEDCAwMxGQy8fHHHwNGS1P79u0ZO3YsJpOJsLAwW6JXmHvuuYfTp08DsG3bNjw8PHjiiScAoyVt4cKFLFu2jOvXrxMTE4Obmxvjxo2znW82m+natWu+665atUJGM28AABUJSURBVIqHH37Ytn316lViY2NZunSpwwlZceorjt27d9OqVSvuuusuqlevztChQ1mzZk2+cllZWaSlpZGVlcX169cLXGu0JDgSU2FllFLUqFEDMBaNz8zMtOUEAwYMYFX2lFUlSAb155HdQubmpqlW7Rbz1SNHYOVK42nKSZPgscdkSgshRJVQQr1N+Ti6hPHQoUN544036Nu3LwcPHmTMmDF8++23JCQksHr1amJjY3Fzc2P8+PGsWrWKkSNHsmzZMurVq0daWhrBwcE88sgjABw9epSIiAgWL17M4MGD+fLLLxkxYkSBdVssFrZu3cqTTz4JwKFDhwgMDMxVplatWjRv3pxjx44RHx+f77g9GRkZHD9+nBYtWtj2/fvf/6Z37960adOGevXq8cMPP9CpU6dCr+NofQBdu3blypUr+fYvWLCA+++/P9e+06dP06xZM9u2t7c333//fa4yTZs2ZcqUKTRv3hxPT0/CwsIICwuzHVdKERYWhlKKZ555hqeffjpf3SEhIaSnp3P16lVSU1Mxm80AzJs3jwceeKDYMRVVxmKxEBgYyLFjx3juuecICQkBwNfXlz179uSL73ZJQpZHerrxXw+P2+iybN8epk0zuimL+IIIIYQoOf7+/iQmJhIREUGfPn1s+7du3cq+ffsIDg4GjDFZjRo1AmDRokVER0cDcOrUKY4ePcqf/vQnfHx8bH/0AwMDSSxgDHBaWhpms5nExEQCAwMJDQ0FjC5Ce39HCtpfkN9//506derk2hcREcHkyZMBIwmNiIigU6dOBV63uH/Pvv32W4fLaq2LrO/ChQusWbOGEydOUKdOHR599FFWrlxpS3BjY2Np0qQJ586dIzQ0lHbt2tGtW7dc18hOlrZv387y5ctZvnz5bcVUVBkXFxfi4uK4ePEi4eHhxMfH4+vri4uLC9WrV+fKlSvUrFmzwBiKSxKyPLITMje3YvwPrDV88QU0awb33GPsGziwdAIUQohyzNGWrNLUv39/pkyZwvbt2zl//jxg/PEdNWoUc+fOzVV2+/btbNmyhe+++w4vLy+6d+9uG2zu7u5uK+fi4lJgl2X2GLJLly7Rt29fPvjgAyZOnIjJZOLLL7/MVfby5cucOnWKli1bcu7cOaKioop8P56enrkGwJ8/f55t27YRHx+PUgqLxYJSivnz51O/fn0uXLiQ6/zU1FR8fHzw9vZ2qD4oXguZt7c3p06dsm0nJSXl647csmULPj4+NGzYEICBAweyc+dOW0KWXb5Ro0aEh4eze/fufAlZcTgSkyNlAOrUqUP37t3ZuHGj7YGJ9PR0PDw8bjk+e2QMWR7FHtSflgavvgrz5xtTW1y+XLoBCiGEKNSYMWN49dVX8fPzs+3r1asXUVFRnDt3DjCSlJMnT3Lp0iXq1q2Ll5cXP/30E7t27brlemvXrs2iRYtYsGABmZmZ9OrVi+vXr7NixQrA6AJ76aWXGD16NF5eXvTs2ZP09HQWL15su8aePXv45ptvcl23bt26WCwWW1IWFRXFyJEjOXnyJImJiZw6dQofHx927NhBjRo1aNy4MVu3brW9z40bN9KlSxeH6wOjhSwuLi7fK28yBhAcHMzRo0c5ceIEGRkZREZG0r9//1xlmjdvzq5du7h+/Tpaa7Zu3Ur79u0BuHbtmi35u3btGv/973/zPSmaU/fu3QttHXM0psLKpKSk2J6WTUtLY8uWLbRr1w4wEuKGDRvi5uZWaAzFJQlZHtktZDn+YVSw7CktNmwAT0+jm7JWrdIMTwghRBG8vb2ZNGlSrn0dOnRg9uzZhIWF4e/vT2hoKMnJyfTu3ZusrCz8/f2ZOXMmd9/mk/AdO3YkICCAyMhIlFJER0fzxRdf0Lp1a9q0aYOHhwdz5swBsB3fvHkzLVu2xGQyMWvWLLutNGFhYezYsQMwuivDw8NzHX/kkUf47LPPAFixYgWzZ8/GbDbTs2dPXnvtNVq2bFms+orD1dWV999/nwceeID27dszePBgTCYTAH369OHMmTOEhIQwaNAgOnXqhJ+fHzdv3rSNE/vtt9/o0qULAQEBdO7cmYceeojevXvnqyckJASz2ZzvtWnTpluKqbAyycnJ9OjRA39/f4KDgwkNDbVNnxITE5OrO7ykKHt9qOVZUFCQLuqR2NuxcycMGpZMWI+aLF9eo+CC33xjtIxduwbNm8OCBXDXXaUWlxBClFcJCQm21g5ROvbv38+7777Lp59+6uxQqryBAwcyd+5c2rZtm++Yve+CUmqf1rrIx11kDFkeDrWQrVhhTGsB0LMnvPYa3HFHqccmhBCiaurYsSM9evTAYrHkmotMlK2MjAwGDBhgNxm7XZKQ5ZE9hqzQhMzf35iobNw4ePxxmdJCCCFEqRszZoyzQ6jyqlevzsiRI0vl2pKQ5fHHtBd5Dvz+OzRoYPxsNsPatX9sCyGEEELcBhnUn0d2Qla9unWH1vDll9C/P+Scl0WSMSGEEEKUkFJNyJRSvZVSPyuljimlptk57q6UWm09/r1SqkVpxuOIP8aQKWMdpddfh7lzjb7MgwedG5wQQgghKqVS67JUSrkAHwChQBKwRym1Vmudc3XPJ4ELWutWSqmhwDxgSGnF5IjsMWQeNy7BmCnGMkju7jBjBjz4oDNDE0IIIUQlVZotZJ2BY1rr41rrDCASeDhPmYeBT6w/RwG91C2vV1QybtyA6unp1Pr8UyMZa9YMli+XZEwIIYQQpaY0E7KmwKkc20nWfXbLaK2zgEtA/VKMqUgZ17Oocekq7hlX4b77jCkuWrd2ZkhCCCGEqORK8ylLey1deWehdaQMSqmngafBWH6hNGXcdOVy3Vq4hnaDt3tBNXnuQQghhBClqzSzjSSgWY5tb+BMQWWUUq5AbSA174W01h9rrYO01kHZC5OWlilT4OiJ5gz+R6gkY0IIUUmNGTOGRo0aFbpmoj0XL17kH//4R4HHZ82axYIFCxy+XnHLi8qrNFvI9gCtlVI+wGlgKPBYnjJrgVHAd8AgYJt28lpO1ao5uI6lEEIIu06dOkV69iPrJcDd3Z1mzZoVXdBq+/btLF++vNAFqEePHs2ECROKPclndkI2fvz4Yp0nRFFKrQnIOiZsArAJSAA+11ofUkq9oZTKXnJ9KVBfKXUMeBHINzWGEEKIiiU9PR0vL68Se5VkcpetW7du1KtXr9Ay165d46GHHiIgIABfX19Wr17NtGnT+OWXXzCbzbz88ssAvPnmm7Rt25b777+fn3/+uci6Cyu/cuVKOnfujNls5plnnsFisTB16tRcrXKzZs3inXfeuYV3LcqzUp2pX2u9HlifZ9+rOX6+ATxamjEIIYSoGkJCQkhPT+fq1aukpqZiNpsBmDdvHg888ECxr7dx40aaNGnCf/7zHwAuXbpESEgI8fHxxMXFAbBv3z4iIyPZv38/WVlZdOrUicDAwAKvWVj5hIQEVq9eTWxsLG5ubowfP55Vq1YxdOhQJk+ebGuV+/zzz9m4cWOx348o32TpJCGEEJXC999/DzjWZekIPz8/pkyZwtSpU+nbty9du3blwoULucp8++23hIeH4+XlBUD//v3tXcqh8lu3bmXfvn0EBwcDkJaWRqNGjRg5ciTnzp3jzJkzpKSkULdu3VJ/wE2UPUnIhBBCCDvatGnDvn37WL9+PX/5y18ICwuzO+asuNNnFlRea82oUaOYO3duvmODBg0iKiqKs2fPMnTo0GLVJyoGeYxQCCFEpdK9e/fbbh0DOHPmDF5eXowYMYIpU6bwww8/ULNmTa5cuWIr061bN6Kjo0lLS+PKlSusW7eu0GsWVr5Xr15ERUVx7tw5AFJTUzl58iQAQ4cOJTIykqioKAYNGnTb702UP5KQCSGEqBRCQkIwm835Xps2bcpXdtiwYdxzzz38/PPPeHt7s3Tp0nxlfvzxR9sA+zfffJMZM2ZQv3597r33Xnx9fXn55Zfp1KkTQ4YMwWw288gjj9C1a1fb+X369OHMmdyzPRVWvkOHDsyePZuwsDD8/f0JDQ0lOTkZAJPJxJUrV2jatCmNGzcutA5RMSknzzJRbEFBQXrv3r3ODkMIIYRVQkIC7du3t207e9oLIZwl73cBQCm1T2sdVNS5MoZMCCFEiZLkSYjiky5LIYQQQggnk4RMCCGEEMLJJCETQghx2yraeGQhStrtfgckIRNCCHFbPDw8OH/+vCRlosrSWnP+/Hk8PDxu+RoyqF8IIcRt8fb2JikpiZSUFGeHIoTTeHh44O3tfcvnS0ImhBDitri5ueHj4+PsMISo0KTLUgghhBDCySQhE0IIIYRwMknIhBBCCCGcrMItnaSUSgFOlnI1DYDfS7kOUXxyX8ofuSflk9yX8kfuSflUFvflz1rrhkUVqnAJWVlQSu11ZN0pUbbkvpQ/ck/KJ7kv5Y/ck/KpPN0X6bIUQgghhHAySciEEEIIIZxMEjL7PnZ2AMIuuS/lj9yT8knuS/kj96R8Kjf3RcaQCSGEEEI4mbSQCSGEEEI4WZVOyJRSvZVSPyuljimlptk57q6UWm09/r1SqkXZR1n1OHBfXlRKHVZKHVRKbVVK/dkZcVYlRd2THOUGKaW0UqpcPLVUmTlyT5RSg63flUNKqc/KOsaqyIHfX82VUjFKqf3W32F9nBFnVaKUWqaUOqeUii/guFJKLbLes4NKqU5lHSNU4YRMKeUCfAA8CHQAhimlOuQp9iRwQWvdClgIzCvbKKseB+/LfiBIa+0PRAHzyzbKqsXBe4JSqiYwEfi+bCOsehy5J0qp1sBfgHu11iZgcpkHWsU4+F2ZAXyute4IDAX+UbZRVknLgd6FHH8QaG19PQ38swxiyqfKJmRAZ+CY1vq41joDiAQezlPmYeAT689RQC+llCrDGKuiIu+L1jpGa33durkL8C7jGKsaR74rAP+HkRzfKMvgqihH7slY4AOt9QUArfW5Mo6xKnLkvmiglvXn2sCZMoyvStJa/w9ILaTIw8AKbdgF1FFKNS6b6P5QlROypsCpHNtJ1n12y2its4BLQP0yia7qcuS+5PQksKFUIxJF3hOlVEegmdb667IMrApz5HvSBmijlIpVSu1SShXWQiBKhiP3ZRYwQimVBKwHni+b0EQhivt3p1S4lnWF5Yi9lq68j5w6UkaULIc/c6XUCCAIuK9UIxKF3hOlVDWMLv3RZRWQcOh74orRBdMdoxX5W6WUr9b6YinHVpU5cl+GAcu11u8ope4BPrXel5ulH54oQLn4W1+VW8iSgGY5tr3J33RsK6OUcsVoXi6s2VPcPkfuC0qp+4HpQH+tdXoZxVZVFXVPagK+wHalVCJwN7BWBvaXKkd/f63RWmdqrU8AP2MkaKL0OHJfngQ+B9Bafwd4YKynKJzHob87pa0qJ2R7gNZKKR+lVHWMwZVr85RZC4yy/jwI2KZl4rbSVuR9sXaPfYSRjMm4mNJX6D3RWl/SWjfQWrfQWrfAGNfXX2u91znhVgmO/P76N9ADQCnVAKML83iZRln1OHJffgV6ASil2mMkZCllGqXIay0w0vq05d3AJa11clkHUWW7LLXWWUqpCcAmwAVYprU+pJR6A9irtV4LLMVoTj6G0TI21HkRVw0O3pe3gRrAF9ZnLH7VWvd3WtCVnIP3RJQhB+/JJiBMKXUYsAAva63POy/qys/B+/ISsFgp9QJGt9ho+Yd+6VJKRWB03Tewjt17DXAD0Fp/iDGWrw9wDLgOPOGUOOX/AyGEEEII56rKXZZCCCGEEOWCJGRCCCGEEE4mCZkQQgghhJNJQiaEEEII4WSSkAkhhBBCOJkkZEKIEqWUsiil4nK8WhRStoVSKr4E6tyulPpZKXXAulRQ21u4xjil1Ejrz6OVUk1yHFtib0H124xzj1LK7MA5k5VSXrdbtxCifJOETAhR0tK01uYcr8Qyqne41joA+ARjrrpi0Vp/qLVeYd0cDTTJcewprfXhEonyjzj/gWNxTgYkIROikpOETAhR6qwtYd8qpX6wvv6fnTImpdRua6vaQaVUa+v+ETn2f6SUcimiuv8Brazn9lJK7VdK/aiUWqaUcrfuf0spddhazwLrvllKqSlKqUEYa6SustbpaW3ZClJKPauUmp8j5tFKqb/fYpzfkWMBY6XUP5VSe5VSh5RSr1v3TcRIDGOUUjHWfWFKqe+sn+MXSqkaRdQjhKgAJCETQpQ0zxzdldHWfeeAUK11J2AIsMjOeeOA97TWZoyEKMm6tMwQ4F7rfgswvIj6+wE/KqU8gOXAEK21H8bKJM8qpeoB4YBJa+0PzM55stY6CtiL0ZJl1lqn5TgcBQzMsT0EWH2LcfbGWN4o23StdRDgD9ynlPLXWi/CWFOvh9a6h3UJpBnA/dbPci/wYhH1CCEqgCq7dJIQotSkWZOSnNyA961jpiwY6yrm9R0wXSnlDXyltT6qlOoFBAJ7rMtkeWIkd/asUkqlAYnA80Bb4ITW+oj1+CfAc8D7wA1giVLqP8DXjr4xrXWKUuq4db27o9Y6Yq3XLU6cd2AsrdMpx/7BSqmnMX4vNwY6AAfznHu3dX+stZ7qGJ+bEKKCk4RMCFEWXgB+AwIwWuZv5C2gtf5MKfU98BCwSSn1FKCAT7TWf3GgjuE5FzRXStW3V8i63mBnjAWehwITgJ7FeC+rgcHAT0C01lorIztyOE7gAPAW8AEwUCnlA0wBgrXWF5RSyzEWnc5LAZu11sOKEa8QogKQLkshRFmoDSRrrW8Cj2O0DuWilLoLOG7tpluL0XW3FRiklGpkLVNPKfVnB+v8CWihlGpl3X4c+MY65qq21no9xoB5e086XgFqFnDdr4ABwDCM5Izixqm1zsToerzb2t1ZC7gGXFJK3Qk8WEAsu4B7s9+TUspLKWWvtVEIUcFIQiaEKAv/AEYppXZhdFdes1NmCBCvlIoD2gErrE82zgD+q5Q6CGzG6M4rktb6BvAE8IVS6kfgJvAhRnLztfV632C03uW1HPgwe1B/nuteAA4Df9Za77buK3ac1rFp7wBTtNYHgP3AIWAZRjdoto+BDUqpGK11CsYToBHWenZhfFZCiApOaa2dHYMQQgghRJUmLWRCCCGEEE4mCZkQQgghhJNJQiaEEEII4WSSkAkhhBBCOJkkZEIIIYQQTiYJmRBCCCGEk0lCJoQQQgjhZJKQCSGEEEI42f8Hi/M3g2/2mkUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "accuracies = []\n",
    "balanced_accuracies = []\n",
    "sensitivities = []\n",
    "specificities = []\n",
    "auc_scores = []\n",
    "tprs = []\n",
    "mean_fpr = np.linspace(0, 1, 100)\n",
    "\n",
    "plt.figure(figsize=(10, 8))\n",
    "\n",
    "for fold, weight in enumerate(weights):\n",
    "    print(\"Fold {}\".format(fold))\n",
    "    model = SixtyFourNet(drop_rate=0.3, weight_dcay=0.01, input_shape=X_train.shape)\n",
    "    model_dir = os.path.join(result_dir, weight)\n",
    "    model.load_weights(model_dir)\n",
    "    \n",
    "    opti = Adam(lr=lr, decay=lr_decay)\n",
    "    model.compile(optimizer=opti,\n",
    "              loss='binary_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "    \n",
    "    # Evaluate\n",
    "    res = model.evaluate_generator(test_loader)\n",
    "    y_pred = model.predict_generator(test_loader)\n",
    "    bal_acc = balanced_accuracy(y_holdout, y_pred>0.5)\n",
    "    sens = sensitivity(y_holdout, y_pred>0.5)\n",
    "    spec = specificity(y_holdout, y_pred>0.5)\n",
    "    roc_auc = roc_auc_score(y_holdout, y_pred)\n",
    "    fpr, tpr, thresholds = roc_curve(y_holdout, y_pred)   \n",
    "    # Store results\n",
    "    accuracies.append(res[1])\n",
    "    balanced_accuracies.append(bal_acc)\n",
    "    sensitivities.append(sens)\n",
    "    specificities.append(spec)\n",
    "    auc_scores.append(roc_auc)\n",
    "    # interpolate with diagonal to get comparable results\n",
    "    tprs.append(interp(mean_fpr, fpr, tpr))\n",
    "    tprs[-1][0] = 0.0 # correct lowest value after interpolation\n",
    "    # Print results\n",
    "    print(\"Model accuracy {:.2f} %\".format(res[1]*100))\n",
    "    print(\"Balanced accuracy {:.2f} %\".format(bal_acc*100))\n",
    "    print(\"Sensitivity {:.2f} %\".format(sens*100))\n",
    "    print(\"Specificity {:.2f} %\".format(spec*100))\n",
    "    print(\"Area Under the Receiver Operating Curve (ROC AUC score) {:.2f}\".format(roc_auc*100))\n",
    "    plt.plot(fpr, tpr, lw=1, alpha=0.3,\n",
    "             label='ROC trial %d (AUC = %0.2f)' % (fold, roc_auc))\n",
    "    \n",
    "print(\"######## Final results ########\")\n",
    "print(\"Accuracy mean {:.2f} %\".format(np.mean(accuracies)*100))\n",
    "print(\"Balanced accuracy mean {:.2f} %\".format(np.mean(balanced_accuracies)*100))\n",
    "print(\"Sensitivity mean {:.2f} %\".format(np.mean(sensitivities)*100))\n",
    "print(\"Specificity mean {:.2f} %\".format(np.mean(specificities)*100))\n",
    "print(\"Mean ROC AUC score {:.2f}\".format(np.mean(auc_scores)*100))\n",
    "\n",
    "# Plot ROC Curves\n",
    "plt.plot([0, 1], [0, 1], linestyle='--', lw=2, color='r',\n",
    "         label='Chance', alpha=.8)\n",
    "\n",
    "mean_tpr = np.mean(tprs, axis=0)\n",
    "mean_tpr[-1] = 1.0 # correct max value after interpolation and mean\n",
    "mean_auc = auc(mean_fpr, mean_tpr)\n",
    "#assert(mean_auc == np.mean(auc_scores))\n",
    "\n",
    "std_auc = np.std(auc_scores)\n",
    "plt.plot(mean_fpr, mean_tpr, color='b',\n",
    "         label=r'Mean ROC (AUC = %0.2f $\\pm$ %0.2f)' % (mean_auc, std_auc),\n",
    "         lw=2, alpha=.8)\n",
    "\n",
    "std_tpr = np.std(tprs, axis=0)\n",
    "tprs_upper = np.minimum(mean_tpr + std_tpr, 1)\n",
    "tprs_lower = np.maximum(mean_tpr - std_tpr, 0)\n",
    "plt.fill_between(mean_fpr, tprs_lower, tprs_upper, color='grey', alpha=.2,\n",
    "                 label=r'$\\pm$ 1 std. dev.')\n",
    "\n",
    "plt.xlim([-0.05, 1.05])\n",
    "plt.ylim([-0.05, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver operating characteristic example')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "total_time = time.time() - start_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Time: 1.0h:4.0m:54.43931269645691s\n",
      "Total time elapsed: 1.0h:6.0m:13.00552773475647s\n"
     ]
    }
   ],
   "source": [
    "print(\"Training Time: {}h:{}m:{}s\".format(\n",
    "            training_time//3600, (training_time//60)%60, training_time%60))\n",
    "print(\"Total time elapsed: {}h:{}m:{}s\".format(\n",
    "            total_time//3600, (total_time//60)%60, total_time%60))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "quit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python (postal)",
   "language": "python",
   "name": "postal"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}