Fine-tuning on MS.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For fine-tuning 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",
    "import nilearn\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.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 = \"5\"\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\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "!mkdir -p $result_dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# load hdf5 files and extract columns\n",
    "#train_h5 = h5py.File('/analysis/fabiane/data/MS/explMS/train_dataset.h5', 'r')\n",
    "#holdout_h5 = h5py.File('/analysis/fabiane/data/MS/explMS/holdout_dataset.h5', 'r')\n",
    "\n",
    "train_h5 = h5py.File('/analysis/ritter/Dataset/MS/CIS/train_dataset.h5', 'r')\n",
    "holdout_h5 = h5py.File('/analysis/ritter/Dataset/MS/CIS/holdout_dataset.h5', 'r')\n",
    "\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": 35,
   "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": 36,
   "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": 37,
   "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": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mask = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "intensity = IntensityRescale(masked=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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": 41,
   "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": {
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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": 16,
   "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": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def init_model(path, finetune=True, up_to=7):\n",
    "    model = load_model(path)\n",
    "    model.load_weights(path)\n",
    "    if finetune:\n",
    "        for layer in model.layers[:up_to]:\n",
    "            layer.trainable = False\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "Conv_1 (Conv3D)              (None, 94, 112, 94, 64)   1792      \n",
      "_________________________________________________________________\n",
      "Pool_1 (MaxPooling3D)        (None, 31, 37, 31, 64)    0         \n",
      "_________________________________________________________________\n",
      "dropout_19 (Dropout)         (None, 31, 37, 31, 64)    0         \n",
      "_________________________________________________________________\n",
      "Conv_2 (Conv3D)              (None, 29, 35, 29, 64)    110656    \n",
      "_________________________________________________________________\n",
      "Pool_2 (MaxPooling3D)        (None, 9, 11, 9, 64)      0         \n",
      "_________________________________________________________________\n",
      "dropout_20 (Dropout)         (None, 9, 11, 9, 64)      0         \n",
      "_________________________________________________________________\n",
      "Conv_3 (Conv3D)              (None, 7, 9, 7, 64)       110656    \n",
      "_________________________________________________________________\n",
      "Conv_4 (Conv3D)              (None, 5, 7, 5, 64)       110656    \n",
      "_________________________________________________________________\n",
      "Pool_4 (MaxPooling3D)        (None, 1, 2, 1, 64)       0         \n",
      "_________________________________________________________________\n",
      "dropout_21 (Dropout)         (None, 1, 2, 1, 64)       0         \n",
      "_________________________________________________________________\n",
      "flatten_7 (Flatten)          (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_7 (Dense)              (None, 1)                 129       \n",
      "=================================================================\n",
      "Total params: 333,889\n",
      "Trainable params: 333,889\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# load model weights\n",
    "#model_path = \"/analysis/share/Ritter/models/fabi/ADNI/pretraining_paper/model.h5\"\n",
    "#model_path = \"/analysis/ritter/models/fabi/ADNI/pretraining_paper/model_trial_6.h5\"\n",
    "model_path = \"/analysis/fabiane/models/ADNI/explMS/model_trial_6.h5\"\n",
    "model = init_model(model_path, finetune=False, up_to=None)\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def visualize_weights(model):\n",
    "    # Visualize weights\n",
    "    W = model.layers[0].get_weights()[0]\n",
    "    W = np.squeeze(W)[:,:,2]\n",
    "    print(\"W shape : \", W.shape)\n",
    "\n",
    "    print(\"Weights mean {}\".format(W.mean()))\n",
    "    plt.figure(figsize=(20, 5))\n",
    "    plt.title('conv1 weights')\n",
    "    plt.imshow(make_mosaic(W, 2, 2), cmap=\"gray\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy.ma as ma\n",
    "def make_mosaic(imgs, nrows, ncols, border=1):\n",
    "    \"\"\"\n",
    "    Given a set of images with all the same shape, makes a\n",
    "    mosaic with nrows and ncols\n",
    "    \"\"\"\n",
    "    nimgs = imgs.shape[0]\n",
    "    imshape = imgs.shape[1:]\n",
    "    \n",
    "    mosaic = ma.masked_all((nrows * imshape[0] + (nrows - 1) * border,\n",
    "                            ncols * imshape[1] + (ncols - 1) * border),\n",
    "                            dtype=np.float32)\n",
    "    \n",
    "    paddedh = imshape[0] + border\n",
    "    paddedw = imshape[1] + border\n",
    "    for i in range(nimgs):\n",
    "        row = int(np.floor(i / ncols))\n",
    "        col = i % ncols\n",
    "        \n",
    "        mosaic[row * paddedh:row * paddedh + imshape[0],\n",
    "               col * paddedw:col * paddedw + imshape[1]] = imgs[i]\n",
    "    return mosaic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def reset_weights(model):\n",
    "    # Note this only used for visualization below\n",
    "    session = K.get_session()\n",
    "    for layer in model.layers: \n",
    "        if hasattr(layer, 'kernel_initializer'):\n",
    "            layer.kernel.initializer.run(session=session)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pre-trained model layer 1 weights:\n",
      "W shape :  (3, 3, 64)\n",
      "Weights mean -0.0010960017098113894\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random model layer 1 weights:\n",
      "W shape :  (3, 3, 64)\n",
      "Weights mean 0.0007953853928484023\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize weights\n",
    "print(\"Pre-trained model layer 1 weights:\")\n",
    "visualize_weights(model)\n",
    "model_untrained = load_model(model_path)\n",
    "reset_weights(model_untrained)\n",
    "print(\"Random model layer 1 weights:\")\n",
    "visualize_weights(model_untrained)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'train_test_split' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-42-505b927d0faa>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# split training set\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_val\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_val\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_test_split\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.15\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m77\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'train_test_split' is not defined"
     ]
    }
   ],
   "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": 24,
   "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 297ms/step - loss: 1.0339 - acc: 0.5273 - val_loss: 0.8904 - 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/weights-augm-trial0-improvement-BEST.hdf5\n",
      "Epoch 2/200\n",
      "27/27 [==============================] - 4s 153ms/step - loss: 0.9515 - acc: 0.4810 - val_loss: 0.8901 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00002: val_acc did not improve from 0.68421\n",
      "Epoch 3/200\n",
      "27/27 [==============================] - 5s 167ms/step - loss: 0.9102 - acc: 0.5273 - val_loss: 0.8702 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00003: val_acc did not improve from 0.68421\n",
      "Epoch 4/200\n",
      "27/27 [==============================] - 4s 154ms/step - loss: 0.8622 - acc: 0.5283 - val_loss: 0.8118 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00004: val_acc improved from 0.68421 to 0.68421, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/weights-augm-trial0-improvement-BEST.hdf5\n",
      "Epoch 5/200\n",
      "27/27 [==============================] - 4s 165ms/step - loss: 0.8634 - acc: 0.5644 - val_loss: 0.9158 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00005: val_acc did not improve from 0.68421\n",
      "Epoch 6/200\n",
      "27/27 [==============================] - 4s 157ms/step - loss: 0.9139 - acc: 0.4634 - val_loss: 0.8777 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00006: val_acc did not improve from 0.68421\n",
      "Epoch 7/200\n",
      "27/27 [==============================] - 4s 161ms/step - loss: 0.8388 - acc: 0.5469 - val_loss: 0.8561 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00007: val_acc did not improve from 0.68421\n",
      "Epoch 8/200\n",
      "27/27 [==============================] - 4s 156ms/step - loss: 0.8221 - acc: 0.5736 - val_loss: 0.8959 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00008: val_acc did not improve from 0.68421\n",
      "Epoch 9/200\n",
      "27/27 [==============================] - 5s 167ms/step - loss: 0.7907 - acc: 0.5376 - val_loss: 0.7638 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00009: val_acc did not improve from 0.68421\n",
      "Epoch 10/200\n",
      "27/27 [==============================] - 4s 166ms/step - loss: 0.7500 - acc: 0.5932 - val_loss: 0.7014 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00010: val_acc improved from 0.68421 to 0.84211, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/weights-augm-trial0-improvement-BEST.hdf5\n",
      "Epoch 11/200\n",
      "27/27 [==============================] - 4s 152ms/step - loss: 0.7828 - acc: 0.5747 - val_loss: 0.7629 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00011: val_acc did not improve from 0.84211\n",
      "Epoch 12/200\n",
      "27/27 [==============================] - 5s 167ms/step - loss: 0.7119 - acc: 0.6581 - val_loss: 0.7209 - val_acc: 0.5789\n",
      "\n",
      "Epoch 00012: val_acc did not improve from 0.84211\n",
      "Epoch 13/200\n",
      "27/27 [==============================] - 4s 166ms/step - loss: 0.7052 - acc: 0.7219 - val_loss: 0.7382 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00013: val_acc did not improve from 0.84211\n",
      "Epoch 14/200\n",
      "27/27 [==============================] - 4s 155ms/step - loss: 0.6384 - acc: 0.7868 - val_loss: 0.5832 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00014: val_acc did not improve from 0.84211\n",
      "Epoch 15/200\n",
      "27/27 [==============================] - 4s 166ms/step - loss: 0.6605 - acc: 0.7312 - val_loss: 0.6395 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00015: val_acc did not improve from 0.84211\n",
      "Epoch 16/200\n",
      "27/27 [==============================] - 4s 166ms/step - loss: 0.6010 - acc: 0.7868 - val_loss: 0.5875 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00016: val_acc improved from 0.84211 to 0.89474, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/weights-augm-trial0-improvement-BEST.hdf5\n",
      "Epoch 17/200\n",
      "27/27 [==============================] - 4s 155ms/step - loss: 0.5787 - acc: 0.7878 - val_loss: 0.6410 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00017: val_acc did not improve from 0.89474\n",
      "Epoch 18/200\n",
      "27/27 [==============================] - 5s 174ms/step - loss: 0.5737 - acc: 0.7868 - val_loss: 0.5649 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00018: val_acc did not improve from 0.89474\n",
      "Epoch 19/200\n",
      "27/27 [==============================] - 4s 164ms/step - loss: 0.5130 - acc: 0.8239 - val_loss: 0.4904 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00019: val_acc did not improve from 0.89474\n",
      "Epoch 20/200\n",
      "27/27 [==============================] - 4s 160ms/step - loss: 0.4666 - acc: 0.8610 - val_loss: 0.4820 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00020: val_acc did not improve from 0.89474\n",
      "Epoch 21/200\n",
      "27/27 [==============================] - 4s 166ms/step - loss: 0.4791 - acc: 0.8424 - val_loss: 0.4389 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00021: val_acc improved from 0.89474 to 0.94737, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/weights-augm-trial0-improvement-BEST.hdf5\n",
      "Epoch 22/200\n",
      "27/27 [==============================] - 4s 164ms/step - loss: 0.4525 - acc: 0.8795 - val_loss: 0.4329 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00022: val_acc did not improve from 0.94737\n",
      "Epoch 23/200\n",
      "27/27 [==============================] - 4s 154ms/step - loss: 0.4921 - acc: 0.8517 - val_loss: 0.4711 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00023: val_acc did not improve from 0.94737\n",
      "Epoch 24/200\n",
      "27/27 [==============================] - 5s 171ms/step - loss: 0.4419 - acc: 0.8980 - val_loss: 0.4931 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00024: val_acc did not improve from 0.94737\n",
      "Epoch 25/200\n",
      "27/27 [==============================] - 4s 163ms/step - loss: 0.4255 - acc: 0.9073 - val_loss: 0.5040 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00025: val_acc did not improve from 0.94737\n",
      "Epoch 26/200\n",
      "27/27 [==============================] - 4s 154ms/step - loss: 0.4522 - acc: 0.8435 - val_loss: 0.6214 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00026: val_acc did not improve from 0.94737\n",
      "Epoch 27/200\n",
      "27/27 [==============================] - 4s 166ms/step - loss: 0.4390 - acc: 0.8702 - val_loss: 0.4831 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00027: val_acc did not improve from 0.94737\n",
      "Epoch 28/200\n",
      "27/27 [==============================] - 4s 164ms/step - loss: 0.4107 - acc: 0.8620 - val_loss: 0.4203 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00028: val_acc did not improve from 0.94737\n",
      "Epoch 29/200\n",
      "27/27 [==============================] - 4s 153ms/step - loss: 0.3799 - acc: 0.9073 - val_loss: 0.4298 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00029: val_acc did not improve from 0.94737\n",
      "Epoch 30/200\n",
      "27/27 [==============================] - 4s 164ms/step - loss: 0.3786 - acc: 0.9166 - val_loss: 0.4021 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00030: val_acc did not improve from 0.94737\n",
      "Epoch 31/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 0.4244 - acc: 0.8805 - val_loss: 0.4720 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00031: val_acc did not improve from 0.94737\n",
      "Epoch 32/200\n",
      "27/27 [==============================] - 4s 164ms/step - loss: 0.3716 - acc: 0.9073 - val_loss: 0.5261 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00032: val_acc did not improve from 0.94737\n",
      "Epoch 33/200\n",
      "27/27 [==============================] - 5s 167ms/step - loss: 0.4170 - acc: 0.9073 - val_loss: 0.4045 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00033: val_acc did not improve from 0.94737\n",
      "Epoch 34/200\n",
      "27/27 [==============================] - 5s 171ms/step - loss: 0.3628 - acc: 0.9166 - val_loss: 0.4302 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00034: val_acc did not improve from 0.94737\n",
      "Epoch 35/200\n",
      "27/27 [==============================] - 5s 169ms/step - loss: 0.3339 - acc: 0.9166 - val_loss: 0.4310 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00035: val_acc did not improve from 0.94737\n",
      "Epoch 36/200\n",
      "27/27 [==============================] - 4s 167ms/step - loss: 0.3501 - acc: 0.9166 - val_loss: 0.4322 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00036: val_acc did not improve from 0.94737\n",
      "Epoch 37/200\n",
      "27/27 [==============================] - 4s 156ms/step - loss: 0.3710 - acc: 0.9351 - val_loss: 0.4865 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00037: val_acc did not improve from 0.94737\n",
      "Epoch 38/200\n",
      "27/27 [==============================] - 4s 166ms/step - loss: 0.3279 - acc: 0.9444 - val_loss: 0.4624 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00038: val_acc did not improve from 0.94737\n",
      "Epoch 39/200\n",
      "27/27 [==============================] - 4s 164ms/step - loss: 0.3523 - acc: 0.9166 - val_loss: 0.4219 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00039: val_acc did not improve from 0.94737\n",
      "Epoch 40/200\n",
      "27/27 [==============================] - 4s 161ms/step - loss: 0.3166 - acc: 0.9629 - val_loss: 0.3990 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00040: val_acc did not improve from 0.94737\n",
      "Epoch 41/200\n",
      "27/27 [==============================] - 5s 167ms/step - loss: 0.3469 - acc: 0.9444 - val_loss: 0.3760 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00041: val_acc did not improve from 0.94737\n",
      "Epoch 42/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27/27 [==============================] - 4s 155ms/step - loss: 0.3088 - acc: 0.9722 - val_loss: 0.4180 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00042: val_acc did not improve from 0.94737\n",
      "Epoch 43/200\n",
      "27/27 [==============================] - 4s 165ms/step - loss: 0.3549 - acc: 0.9083 - val_loss: 0.4136 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00043: val_acc did not improve from 0.94737\n",
      "Epoch 44/200\n",
      "27/27 [==============================] - 4s 164ms/step - loss: 0.3003 - acc: 0.9629 - val_loss: 0.4187 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00044: val_acc did not improve from 0.94737\n",
      "Epoch 45/200\n",
      "27/27 [==============================] - 5s 171ms/step - loss: 0.3241 - acc: 0.9537 - val_loss: 0.4389 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00045: val_acc did not improve from 0.94737\n",
      "Epoch 46/200\n",
      "27/27 [==============================] - 4s 166ms/step - loss: 0.3246 - acc: 0.9166 - val_loss: 0.3800 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00046: val_acc did not improve from 0.94737\n",
      "Epoch 47/200\n",
      "27/27 [==============================] - 5s 167ms/step - loss: 0.2954 - acc: 0.9537 - val_loss: 0.4232 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00047: val_acc did not improve from 0.94737\n",
      "Epoch 48/200\n",
      "27/27 [==============================] - 5s 167ms/step - loss: 0.2835 - acc: 0.9722 - val_loss: 0.4358 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00048: val_acc did not improve from 0.94737\n",
      "Epoch 49/200\n",
      "27/27 [==============================] - 5s 171ms/step - loss: 0.2750 - acc: 0.9537 - val_loss: 0.3948 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00049: val_acc did not improve from 0.94737\n",
      "Epoch 50/200\n",
      "27/27 [==============================] - 4s 153ms/step - loss: 0.2602 - acc: 0.9722 - val_loss: 0.4379 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00050: val_acc did not improve from 0.94737\n",
      "Epoch 51/200\n",
      "27/27 [==============================] - 4s 166ms/step - loss: 0.2856 - acc: 0.9629 - val_loss: 0.3957 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00051: val_acc did not improve from 0.94737\n",
      "Epoch 52/200\n",
      "27/27 [==============================] - 4s 155ms/step - loss: 0.2757 - acc: 0.9537 - val_loss: 0.4108 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00052: val_acc did not improve from 0.94737\n",
      "Epoch 53/200\n",
      "27/27 [==============================] - 4s 163ms/step - loss: 0.3166 - acc: 0.9351 - val_loss: 0.4242 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00053: val_acc did not improve from 0.94737\n",
      "Epoch 54/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 0.2889 - acc: 0.9351 - val_loss: 0.3963 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00054: val_acc did not improve from 0.94737\n",
      "Epoch 55/200\n",
      "27/27 [==============================] - 4s 166ms/step - loss: 0.2608 - acc: 0.9722 - val_loss: 0.4063 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00055: val_acc did not improve from 0.94737\n",
      "Epoch 56/200\n",
      "27/27 [==============================] - 4s 161ms/step - loss: 0.2820 - acc: 0.9444 - val_loss: 0.4659 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00056: val_acc did not improve from 0.94737\n",
      "Epoch 00056: 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: 1.0789 - acc: 0.4912 - val_loss: 0.9427 - 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/weights-augm-trial1-improvement-BEST.hdf5\n",
      "Epoch 2/200\n",
      "27/27 [==============================] - 4s 156ms/step - loss: 0.9999 - acc: 0.3800 - val_loss: 0.8852 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00002: val_acc improved from 0.31579 to 0.73684, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/weights-augm-trial1-improvement-BEST.hdf5\n",
      "Epoch 3/200\n",
      "27/27 [==============================] - 4s 165ms/step - loss: 0.9325 - acc: 0.5654 - val_loss: 0.8774 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00003: val_acc did not improve from 0.73684\n",
      "Epoch 4/200\n",
      "27/27 [==============================] - 4s 167ms/step - loss: 0.9597 - acc: 0.4634 - val_loss: 0.8633 - val_acc: 0.3684\n",
      "\n",
      "Epoch 00004: val_acc did not improve from 0.73684\n",
      "Epoch 5/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 0.8967 - acc: 0.5180 - val_loss: 0.8261 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00005: val_acc did not improve from 0.73684\n",
      "Epoch 6/200\n",
      "27/27 [==============================] - 5s 169ms/step - loss: 0.8185 - acc: 0.5829 - val_loss: 0.8334 - val_acc: 0.4737\n",
      "\n",
      "Epoch 00006: val_acc did not improve from 0.73684\n",
      "Epoch 7/200\n",
      "27/27 [==============================] - 4s 165ms/step - loss: 0.8089 - acc: 0.5829 - val_loss: 0.8325 - val_acc: 0.4211\n",
      "\n",
      "Epoch 00007: val_acc did not improve from 0.73684\n",
      "Epoch 8/200\n",
      "27/27 [==============================] - 4s 166ms/step - loss: 0.7773 - acc: 0.6117 - val_loss: 0.7510 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00008: val_acc improved from 0.73684 to 0.73684, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/weights-augm-trial1-improvement-BEST.hdf5\n",
      "Epoch 9/200\n",
      "27/27 [==============================] - 4s 159ms/step - loss: 0.7976 - acc: 0.5747 - val_loss: 0.7390 - 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/weights-augm-trial1-improvement-BEST.hdf5\n",
      "Epoch 10/200\n",
      "27/27 [==============================] - 4s 166ms/step - loss: 0.7647 - acc: 0.6107 - val_loss: 0.6995 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00010: val_acc did not improve from 0.78947\n",
      "Epoch 11/200\n",
      "27/27 [==============================] - 5s 170ms/step - loss: 0.7623 - acc: 0.6293 - val_loss: 0.7600 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00011: val_acc did not improve from 0.78947\n",
      "Epoch 12/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 0.6956 - acc: 0.7497 - val_loss: 0.6829 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00012: val_acc did not improve from 0.78947\n",
      "Epoch 13/200\n",
      "27/27 [==============================] - 5s 168ms/step - loss: 0.6451 - acc: 0.7590 - val_loss: 0.6399 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00013: val_acc did not improve from 0.78947\n",
      "Epoch 14/200\n",
      "27/27 [==============================] - 4s 163ms/step - loss: 0.6148 - acc: 0.7590 - val_loss: 0.6568 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00014: val_acc did not improve from 0.78947\n",
      "Epoch 15/200\n",
      "27/27 [==============================] - 4s 165ms/step - loss: 0.6410 - acc: 0.7312 - val_loss: 0.6115 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00015: val_acc improved from 0.78947 to 0.84211, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/weights-augm-trial1-improvement-BEST.hdf5\n",
      "Epoch 16/200\n",
      "27/27 [==============================] - 4s 154ms/step - loss: 0.5900 - acc: 0.7508 - val_loss: 0.5291 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00016: val_acc did not improve from 0.84211\n",
      "Epoch 17/200\n",
      "27/27 [==============================] - 5s 172ms/step - loss: 0.5339 - acc: 0.8517 - val_loss: 0.5655 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00017: val_acc improved from 0.84211 to 0.89474, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/weights-augm-trial1-improvement-BEST.hdf5\n",
      "Epoch 18/200\n",
      "27/27 [==============================] - 4s 158ms/step - loss: 0.5101 - acc: 0.8239 - val_loss: 0.5163 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00018: val_acc did not improve from 0.89474\n",
      "Epoch 19/200\n",
      "27/27 [==============================] - 5s 169ms/step - loss: 0.5470 - acc: 0.8146 - val_loss: 0.4943 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00019: val_acc improved from 0.89474 to 0.94737, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/weights-augm-trial1-improvement-BEST.hdf5\n",
      "Epoch 20/200\n",
      "27/27 [==============================] - 4s 163ms/step - loss: 0.4779 - acc: 0.8980 - val_loss: 0.4857 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00020: val_acc did not improve from 0.94737\n",
      "Epoch 21/200\n",
      "27/27 [==============================] - 4s 165ms/step - loss: 0.4995 - acc: 0.8332 - val_loss: 0.4837 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00021: val_acc did not improve from 0.94737\n",
      "Epoch 22/200\n",
      "27/27 [==============================] - 5s 172ms/step - loss: 0.4514 - acc: 0.8980 - val_loss: 0.4699 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00022: val_acc did not improve from 0.94737\n",
      "Epoch 23/200\n",
      "27/27 [==============================] - 5s 167ms/step - loss: 0.4538 - acc: 0.8795 - val_loss: 0.4178 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00023: val_acc did not improve from 0.94737\n",
      "Epoch 24/200\n",
      "27/27 [==============================] - 4s 166ms/step - loss: 0.4674 - acc: 0.8424 - val_loss: 0.5137 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00024: val_acc did not improve from 0.94737\n",
      "Epoch 25/200\n",
      "27/27 [==============================] - 4s 164ms/step - loss: 0.4260 - acc: 0.9073 - val_loss: 0.4695 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00025: val_acc did not improve from 0.94737\n",
      "Epoch 26/200\n",
      "27/27 [==============================] - 4s 154ms/step - loss: 0.4431 - acc: 0.8980 - val_loss: 0.4639 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00026: val_acc did not improve from 0.94737\n",
      "Epoch 27/200\n",
      "27/27 [==============================] - 4s 165ms/step - loss: 0.3956 - acc: 0.9259 - val_loss: 0.4286 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00027: val_acc did not improve from 0.94737\n",
      "Epoch 28/200\n",
      "27/27 [==============================] - 5s 169ms/step - loss: 0.4147 - acc: 0.8888 - val_loss: 0.5060 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00028: val_acc did not improve from 0.94737\n",
      "Epoch 29/200\n",
      "27/27 [==============================] - 4s 160ms/step - loss: 0.3712 - acc: 0.9259 - val_loss: 0.4573 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00029: val_acc did not improve from 0.94737\n",
      "Epoch 30/200\n",
      "27/27 [==============================] - 4s 165ms/step - loss: 0.3661 - acc: 0.9166 - val_loss: 0.4709 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00030: val_acc did not improve from 0.94737\n",
      "Epoch 31/200\n",
      "27/27 [==============================] - 4s 164ms/step - loss: 0.3824 - acc: 0.9259 - val_loss: 0.4454 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00031: val_acc did not improve from 0.94737\n",
      "Epoch 32/200\n",
      "27/27 [==============================] - 4s 155ms/step - loss: 0.3416 - acc: 0.9259 - val_loss: 0.4097 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00032: val_acc did not improve from 0.94737\n",
      "Epoch 33/200\n",
      "27/27 [==============================] - 4s 165ms/step - loss: 0.3718 - acc: 0.9351 - val_loss: 0.4336 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00033: val_acc did not improve from 0.94737\n",
      "Epoch 34/200\n",
      "27/27 [==============================] - 4s 163ms/step - loss: 0.3595 - acc: 0.9351 - val_loss: 0.5488 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00034: val_acc did not improve from 0.94737\n",
      "Epoch 35/200\n",
      "27/27 [==============================] - 4s 163ms/step - loss: 0.3874 - acc: 0.9444 - val_loss: 0.5046 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00035: val_acc did not improve from 0.94737\n",
      "Epoch 36/200\n",
      "27/27 [==============================] - 4s 163ms/step - loss: 0.3579 - acc: 0.9083 - val_loss: 0.4200 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00036: val_acc did not improve from 0.94737\n",
      "Epoch 37/200\n",
      "27/27 [==============================] - 4s 164ms/step - loss: 0.4214 - acc: 0.8620 - val_loss: 0.4431 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00037: val_acc did not improve from 0.94737\n",
      "Epoch 38/200\n",
      "27/27 [==============================] - 4s 154ms/step - loss: 0.3658 - acc: 0.9259 - val_loss: 0.4436 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00038: val_acc did not improve from 0.94737\n",
      "Epoch 39/200\n",
      "27/27 [==============================] - 4s 163ms/step - loss: 0.3067 - acc: 0.9629 - val_loss: 0.4455 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00039: val_acc did not improve from 0.94737\n",
      "Epoch 40/200\n",
      "27/27 [==============================] - 4s 159ms/step - loss: 0.3263 - acc: 0.9629 - val_loss: 0.4007 - val_acc: 0.9474\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 00040: val_acc did not improve from 0.94737\n",
      "Epoch 41/200\n",
      "27/27 [==============================] - 5s 169ms/step - loss: 0.3142 - acc: 0.9259 - val_loss: 0.4321 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00041: val_acc did not improve from 0.94737\n",
      "Epoch 42/200\n",
      "27/27 [==============================] - 5s 179ms/step - loss: 0.2885 - acc: 0.9629 - val_loss: 0.3980 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00042: val_acc did not improve from 0.94737\n",
      "Epoch 43/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 0.3406 - acc: 0.9351 - val_loss: 0.4363 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00043: val_acc did not improve from 0.94737\n",
      "Epoch 44/200\n",
      "27/27 [==============================] - 5s 168ms/step - loss: 0.3232 - acc: 0.9444 - val_loss: 0.4228 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00044: val_acc did not improve from 0.94737\n",
      "Epoch 45/200\n",
      "27/27 [==============================] - 5s 178ms/step - loss: 0.2780 - acc: 0.9537 - val_loss: 0.4031 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00045: val_acc did not improve from 0.94737\n",
      "Epoch 46/200\n",
      "27/27 [==============================] - 4s 160ms/step - loss: 0.3392 - acc: 0.9444 - val_loss: 0.4064 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00046: val_acc did not improve from 0.94737\n",
      "Epoch 47/200\n",
      "27/27 [==============================] - 4s 164ms/step - loss: 0.3109 - acc: 0.9722 - val_loss: 0.3965 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00047: val_acc did not improve from 0.94737\n",
      "Epoch 48/200\n",
      "27/27 [==============================] - 4s 163ms/step - loss: 0.2968 - acc: 0.9815 - val_loss: 0.4018 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00048: val_acc did not improve from 0.94737\n",
      "Epoch 49/200\n",
      "27/27 [==============================] - 5s 168ms/step - loss: 0.2760 - acc: 0.9537 - val_loss: 0.4228 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00049: val_acc did not improve from 0.94737\n",
      "Epoch 50/200\n",
      "27/27 [==============================] - 4s 166ms/step - loss: 0.3163 - acc: 0.9176 - val_loss: 0.3981 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00050: val_acc did not improve from 0.94737\n",
      "Epoch 51/200\n",
      "27/27 [==============================] - 4s 156ms/step - loss: 0.3194 - acc: 0.9444 - val_loss: 0.4834 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00051: val_acc did not improve from 0.94737\n",
      "Epoch 52/200\n",
      "27/27 [==============================] - 4s 165ms/step - loss: 0.2931 - acc: 0.9629 - val_loss: 0.4357 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00052: val_acc did not improve from 0.94737\n",
      "Epoch 53/200\n",
      "27/27 [==============================] - 4s 163ms/step - loss: 0.2735 - acc: 0.9537 - val_loss: 0.4059 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00053: val_acc did not improve from 0.94737\n",
      "Epoch 54/200\n",
      "27/27 [==============================] - 5s 168ms/step - loss: 0.3059 - acc: 0.9259 - val_loss: 0.4161 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00054: val_acc did not improve from 0.94737\n",
      "Epoch 55/200\n",
      "27/27 [==============================] - 4s 165ms/step - loss: 0.2931 - acc: 0.9629 - val_loss: 0.4510 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00055: val_acc did not improve from 0.94737\n",
      "Epoch 56/200\n",
      "27/27 [==============================] - 4s 155ms/step - loss: 0.3074 - acc: 0.9444 - val_loss: 0.4807 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00056: val_acc did not improve from 0.94737\n",
      "Epoch 57/200\n",
      "27/27 [==============================] - 4s 165ms/step - loss: 0.2657 - acc: 0.9537 - val_loss: 0.4244 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00057: val_acc did not improve from 0.94737\n",
      "Epoch 58/200\n",
      "27/27 [==============================] - 4s 166ms/step - loss: 0.2878 - acc: 0.9351 - val_loss: 0.4241 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00058: val_acc did not improve from 0.94737\n",
      "Epoch 59/200\n",
      "27/27 [==============================] - 4s 155ms/step - loss: 0.2888 - acc: 0.9269 - val_loss: 0.4367 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00059: val_acc did not improve from 0.94737\n",
      "Epoch 60/200\n",
      "27/27 [==============================] - 4s 161ms/step - loss: 0.2381 - acc: 0.9722 - val_loss: 0.4525 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00060: val_acc did not improve from 0.94737\n",
      "Epoch 61/200\n",
      "27/27 [==============================] - 4s 163ms/step - loss: 0.2537 - acc: 0.9722 - val_loss: 0.4202 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00061: val_acc did not improve from 0.94737\n",
      "Epoch 62/200\n",
      "27/27 [==============================] - 4s 154ms/step - loss: 0.2755 - acc: 0.9722 - val_loss: 0.4089 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00062: val_acc did not improve from 0.94737\n",
      "Epoch 00062: 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 [==============================] - 5s 199ms/step - loss: 1.1229 - acc: 0.4542 - val_loss: 0.8734 - 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/weights-augm-trial2-improvement-BEST.hdf5\n",
      "Epoch 2/200\n",
      "27/27 [==============================] - 4s 156ms/step - loss: 0.9512 - acc: 0.4542 - val_loss: 0.9042 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00002: val_acc did not improve from 0.68421\n",
      "Epoch 3/200\n",
      "27/27 [==============================] - 4s 165ms/step - loss: 0.9730 - acc: 0.4356 - val_loss: 0.8700 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00003: val_acc did not improve from 0.68421\n",
      "Epoch 4/200\n",
      "27/27 [==============================] - 4s 159ms/step - loss: 0.8799 - acc: 0.5273 - val_loss: 0.9165 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00004: val_acc did not improve from 0.68421\n",
      "Epoch 5/200\n",
      "27/27 [==============================] - 4s 164ms/step - loss: 0.8609 - acc: 0.5561 - val_loss: 0.8017 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00005: val_acc improved from 0.68421 to 0.68421, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/weights-augm-trial2-improvement-BEST.hdf5\n",
      "Epoch 6/200\n",
      "27/27 [==============================] - 4s 159ms/step - loss: 0.8362 - acc: 0.5736 - val_loss: 0.7531 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00006: val_acc improved from 0.68421 to 0.78947, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/weights-augm-trial2-improvement-BEST.hdf5\n",
      "Epoch 7/200\n",
      "27/27 [==============================] - 4s 155ms/step - loss: 0.8412 - acc: 0.5458 - val_loss: 0.8996 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00007: val_acc did not improve from 0.78947\n",
      "Epoch 8/200\n",
      "27/27 [==============================] - 4s 158ms/step - loss: 0.8265 - acc: 0.5469 - val_loss: 0.7084 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00008: val_acc did not improve from 0.78947\n",
      "Epoch 9/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 0.8031 - acc: 0.5561 - val_loss: 0.6407 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00009: val_acc did not improve from 0.78947\n",
      "Epoch 10/200\n",
      "27/27 [==============================] - 4s 165ms/step - loss: 0.7660 - acc: 0.6293 - val_loss: 0.8029 - val_acc: 0.3684\n",
      "\n",
      "Epoch 00010: val_acc did not improve from 0.78947\n",
      "Epoch 11/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 0.7524 - acc: 0.6025 - val_loss: 0.7395 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00011: val_acc did not improve from 0.78947\n",
      "Epoch 12/200\n",
      "27/27 [==============================] - 4s 163ms/step - loss: 0.7409 - acc: 0.7034 - val_loss: 0.7329 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00012: val_acc did not improve from 0.78947\n",
      "Epoch 13/200\n",
      "27/27 [==============================] - 4s 158ms/step - loss: 0.6754 - acc: 0.6756 - val_loss: 0.6509 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00013: val_acc improved from 0.78947 to 0.84211, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/weights-augm-trial2-improvement-BEST.hdf5\n",
      "Epoch 14/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 0.6811 - acc: 0.7405 - val_loss: 0.6079 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00014: val_acc did not improve from 0.84211\n",
      "Epoch 15/200\n",
      "27/27 [==============================] - 4s 154ms/step - loss: 0.6851 - acc: 0.7405 - val_loss: 0.5931 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00015: val_acc improved from 0.84211 to 0.89474, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/weights-augm-trial2-improvement-BEST.hdf5\n",
      "Epoch 16/200\n",
      "27/27 [==============================] - 4s 163ms/step - loss: 0.6252 - acc: 0.7590 - val_loss: 0.6323 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00016: val_acc did not improve from 0.89474\n",
      "Epoch 17/200\n",
      "27/27 [==============================] - 4s 160ms/step - loss: 0.6092 - acc: 0.8332 - val_loss: 0.5766 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00017: val_acc did not improve from 0.89474\n",
      "Epoch 18/200\n",
      "27/27 [==============================] - 4s 157ms/step - loss: 0.5478 - acc: 0.8054 - val_loss: 0.5258 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00018: val_acc did not improve from 0.89474\n",
      "Epoch 19/200\n",
      "27/27 [==============================] - 5s 168ms/step - loss: 0.5296 - acc: 0.8239 - val_loss: 0.6587 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00019: val_acc did not improve from 0.89474\n",
      "Epoch 20/200\n",
      "27/27 [==============================] - 4s 161ms/step - loss: 0.5433 - acc: 0.7971 - val_loss: 0.5039 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00020: val_acc did not improve from 0.89474\n",
      "Epoch 21/200\n",
      "27/27 [==============================] - 4s 161ms/step - loss: 0.5356 - acc: 0.8146 - val_loss: 0.5078 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00021: val_acc improved from 0.89474 to 0.94737, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/weights-augm-trial2-improvement-BEST.hdf5\n",
      "Epoch 22/200\n",
      "27/27 [==============================] - 4s 159ms/step - loss: 0.4782 - acc: 0.8702 - val_loss: 0.4767 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00022: val_acc did not improve from 0.94737\n",
      "Epoch 23/200\n",
      "27/27 [==============================] - 4s 155ms/step - loss: 0.4896 - acc: 0.8610 - val_loss: 0.4856 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00023: val_acc did not improve from 0.94737\n",
      "Epoch 24/200\n",
      "27/27 [==============================] - 4s 164ms/step - loss: 0.4864 - acc: 0.8795 - val_loss: 0.4744 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00024: val_acc did not improve from 0.94737\n",
      "Epoch 25/200\n",
      "27/27 [==============================] - 4s 161ms/step - loss: 0.4853 - acc: 0.8146 - val_loss: 0.4856 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00025: val_acc did not improve from 0.94737\n",
      "Epoch 26/200\n",
      "27/27 [==============================] - 4s 161ms/step - loss: 0.4531 - acc: 0.8795 - val_loss: 0.4302 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00026: val_acc did not improve from 0.94737\n",
      "Epoch 27/200\n",
      "27/27 [==============================] - 4s 158ms/step - loss: 0.4184 - acc: 0.8980 - val_loss: 0.5196 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00027: val_acc did not improve from 0.94737\n",
      "Epoch 28/200\n",
      "27/27 [==============================] - 4s 164ms/step - loss: 0.4057 - acc: 0.8980 - val_loss: 0.4477 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00028: val_acc did not improve from 0.94737\n",
      "Epoch 29/200\n",
      "27/27 [==============================] - 4s 156ms/step - loss: 0.4030 - acc: 0.8980 - val_loss: 0.4387 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00029: val_acc did not improve from 0.94737\n",
      "Epoch 30/200\n",
      "27/27 [==============================] - 4s 167ms/step - loss: 0.4247 - acc: 0.8610 - val_loss: 0.4562 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00030: val_acc did not improve from 0.94737\n",
      "Epoch 31/200\n",
      "27/27 [==============================] - 4s 160ms/step - loss: 0.4081 - acc: 0.8888 - val_loss: 0.5234 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00031: val_acc did not improve from 0.94737\n",
      "Epoch 32/200\n",
      "27/27 [==============================] - 4s 164ms/step - loss: 0.3835 - acc: 0.9166 - val_loss: 0.4173 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00032: val_acc did not improve from 0.94737\n",
      "Epoch 33/200\n",
      "27/27 [==============================] - 4s 153ms/step - loss: 0.3737 - acc: 0.9537 - val_loss: 0.4273 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00033: val_acc did not improve from 0.94737\n",
      "Epoch 34/200\n",
      "27/27 [==============================] - 4s 166ms/step - loss: 0.3660 - acc: 0.8991 - val_loss: 0.4558 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00034: val_acc did not improve from 0.94737\n",
      "Epoch 35/200\n",
      "27/27 [==============================] - 4s 158ms/step - loss: 0.3350 - acc: 0.9351 - val_loss: 0.5161 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00035: val_acc did not improve from 0.94737\n",
      "Epoch 36/200\n",
      "27/27 [==============================] - 4s 157ms/step - loss: 0.3512 - acc: 0.9444 - val_loss: 0.4354 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00036: val_acc did not improve from 0.94737\n",
      "Epoch 37/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 0.3853 - acc: 0.9073 - val_loss: 0.4438 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00037: val_acc did not improve from 0.94737\n",
      "Epoch 38/200\n",
      "27/27 [==============================] - 4s 158ms/step - loss: 0.3688 - acc: 0.9166 - val_loss: 0.4957 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00038: val_acc did not improve from 0.94737\n",
      "Epoch 39/200\n",
      "27/27 [==============================] - 4s 159ms/step - loss: 0.3416 - acc: 0.9444 - val_loss: 0.4173 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00039: val_acc did not improve from 0.94737\n",
      "Epoch 40/200\n",
      "27/27 [==============================] - 4s 163ms/step - loss: 0.3525 - acc: 0.9166 - val_loss: 0.4156 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00040: val_acc did not improve from 0.94737\n",
      "Epoch 41/200\n",
      "27/27 [==============================] - 4s 159ms/step - loss: 0.3300 - acc: 0.9351 - val_loss: 0.4417 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00041: val_acc did not improve from 0.94737\n",
      "Epoch 42/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 0.3776 - acc: 0.9351 - val_loss: 0.4316 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00042: val_acc did not improve from 0.94737\n",
      "Epoch 43/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 0.3501 - acc: 0.9259 - val_loss: 0.4454 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00043: val_acc did not improve from 0.94737\n",
      "Epoch 44/200\n",
      "27/27 [==============================] - 4s 156ms/step - loss: 0.3532 - acc: 0.9351 - val_loss: 0.4918 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00044: val_acc did not improve from 0.94737\n",
      "Epoch 45/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 0.3104 - acc: 0.9444 - val_loss: 0.4290 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00045: val_acc did not improve from 0.94737\n",
      "Epoch 46/200\n",
      "27/27 [==============================] - 5s 169ms/step - loss: 0.3110 - acc: 0.9629 - val_loss: 0.4259 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00046: val_acc did not improve from 0.94737\n",
      "Epoch 47/200\n",
      "27/27 [==============================] - 4s 156ms/step - loss: 0.3150 - acc: 0.9351 - val_loss: 0.4231 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00047: val_acc did not improve from 0.94737\n",
      "Epoch 48/200\n",
      "27/27 [==============================] - 4s 166ms/step - loss: 0.3409 - acc: 0.9351 - val_loss: 0.5049 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00048: val_acc did not improve from 0.94737\n",
      "Epoch 49/200\n",
      "27/27 [==============================] - 4s 157ms/step - loss: 0.3729 - acc: 0.9083 - val_loss: 0.4314 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00049: val_acc did not improve from 0.94737\n",
      "Epoch 50/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 0.3149 - acc: 0.9351 - val_loss: 0.5277 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00050: val_acc did not improve from 0.94737\n",
      "Epoch 51/200\n",
      "27/27 [==============================] - 4s 160ms/step - loss: 0.3495 - acc: 0.9073 - val_loss: 0.4589 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00051: val_acc did not improve from 0.94737\n",
      "Epoch 52/200\n",
      "27/27 [==============================] - 4s 155ms/step - loss: 0.3895 - acc: 0.9083 - val_loss: 0.4285 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00052: val_acc did not improve from 0.94737\n",
      "Epoch 53/200\n",
      "27/27 [==============================] - 4s 163ms/step - loss: 0.3190 - acc: 0.9444 - val_loss: 0.5983 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00053: val_acc did not improve from 0.94737\n",
      "Epoch 54/200\n",
      "27/27 [==============================] - 4s 161ms/step - loss: 0.3281 - acc: 0.9083 - val_loss: 0.4667 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00054: val_acc did not improve from 0.94737\n",
      "Epoch 55/200\n",
      "27/27 [==============================] - 4s 154ms/step - loss: 0.3258 - acc: 0.9351 - val_loss: 0.4707 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00055: val_acc did not improve from 0.94737\n",
      "Epoch 00055: 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 [==============================] - 6s 215ms/step - loss: 0.9688 - acc: 0.5376 - val_loss: 1.0263 - 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/weights-augm-trial3-improvement-BEST.hdf5\n",
      "Epoch 2/200\n",
      "27/27 [==============================] - 4s 156ms/step - loss: 0.9790 - acc: 0.4810 - val_loss: 0.9514 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00002: val_acc did not improve from 0.31579\n",
      "Epoch 3/200\n",
      "27/27 [==============================] - 4s 163ms/step - loss: 0.9084 - acc: 0.5551 - val_loss: 0.9069 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00003: val_acc did not improve from 0.31579\n",
      "Epoch 4/200\n",
      "27/27 [==============================] - 4s 161ms/step - loss: 0.8819 - acc: 0.4912 - val_loss: 0.8681 - val_acc: 0.4737\n",
      "\n",
      "Epoch 00004: val_acc improved from 0.31579 to 0.47368, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/weights-augm-trial3-improvement-BEST.hdf5\n",
      "Epoch 5/200\n",
      "27/27 [==============================] - 4s 159ms/step - loss: 0.8943 - acc: 0.4810 - val_loss: 0.8282 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00005: val_acc improved from 0.47368 to 0.78947, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/weights-augm-trial3-improvement-BEST.hdf5\n",
      "Epoch 6/200\n",
      "27/27 [==============================] - 4s 160ms/step - loss: 0.8529 - acc: 0.6014 - val_loss: 0.8385 - val_acc: 0.4737\n",
      "\n",
      "Epoch 00006: val_acc did not improve from 0.78947\n",
      "Epoch 7/200\n",
      "27/27 [==============================] - 4s 160ms/step - loss: 0.8316 - acc: 0.5469 - val_loss: 0.8071 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00007: val_acc improved from 0.78947 to 0.84211, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/weights-augm-trial3-improvement-BEST.hdf5\n",
      "Epoch 8/200\n",
      "27/27 [==============================] - 4s 166ms/step - loss: 0.8090 - acc: 0.6293 - val_loss: 0.7953 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00008: val_acc did not improve from 0.84211\n",
      "Epoch 9/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 0.7663 - acc: 0.6673 - val_loss: 0.7799 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00009: val_acc did not improve from 0.84211\n",
      "Epoch 10/200\n",
      "27/27 [==============================] - 4s 155ms/step - loss: 0.7587 - acc: 0.5932 - val_loss: 0.7589 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00010: val_acc did not improve from 0.84211\n",
      "Epoch 11/200\n",
      "27/27 [==============================] - 4s 166ms/step - loss: 0.7543 - acc: 0.6849 - val_loss: 0.7578 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00011: val_acc did not improve from 0.84211\n",
      "Epoch 12/200\n",
      "27/27 [==============================] - 4s 160ms/step - loss: 0.7258 - acc: 0.6941 - val_loss: 0.7781 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00012: val_acc did not improve from 0.84211\n",
      "Epoch 13/200\n",
      "27/27 [==============================] - 4s 161ms/step - loss: 0.7013 - acc: 0.7034 - val_loss: 0.6424 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00013: val_acc did not improve from 0.84211\n",
      "Epoch 14/200\n",
      "27/27 [==============================] - 4s 161ms/step - loss: 0.6297 - acc: 0.7683 - val_loss: 0.5960 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00014: val_acc improved from 0.84211 to 0.89474, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/weights-augm-trial3-improvement-BEST.hdf5\n",
      "Epoch 15/200\n",
      "27/27 [==============================] - 4s 161ms/step - loss: 0.5781 - acc: 0.7868 - val_loss: 0.5893 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00015: val_acc did not improve from 0.89474\n",
      "Epoch 16/200\n",
      "27/27 [==============================] - 5s 169ms/step - loss: 0.6030 - acc: 0.7961 - val_loss: 0.6806 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00016: val_acc did not improve from 0.89474\n",
      "Epoch 17/200\n",
      "27/27 [==============================] - 4s 163ms/step - loss: 0.5459 - acc: 0.8424 - val_loss: 0.5529 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00017: val_acc improved from 0.89474 to 0.94737, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/weights-augm-trial3-improvement-BEST.hdf5\n",
      "Epoch 18/200\n",
      "27/27 [==============================] - 4s 158ms/step - loss: 0.4869 - acc: 0.8517 - val_loss: 0.5313 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00018: val_acc did not improve from 0.94737\n",
      "Epoch 19/200\n",
      "27/27 [==============================] - 4s 160ms/step - loss: 0.5522 - acc: 0.8054 - val_loss: 0.5340 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00019: val_acc did not improve from 0.94737\n",
      "Epoch 20/200\n",
      "27/27 [==============================] - 4s 165ms/step - loss: 0.5095 - acc: 0.8517 - val_loss: 0.5091 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00020: val_acc did not improve from 0.94737\n",
      "Epoch 21/200\n",
      "27/27 [==============================] - 4s 161ms/step - loss: 0.4834 - acc: 0.8424 - val_loss: 0.4611 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00021: val_acc did not improve from 0.94737\n",
      "Epoch 22/200\n",
      "27/27 [==============================] - 5s 167ms/step - loss: 0.4771 - acc: 0.8424 - val_loss: 0.4975 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00022: val_acc did not improve from 0.94737\n",
      "Epoch 23/200\n",
      "27/27 [==============================] - 4s 164ms/step - loss: 0.4596 - acc: 0.8610 - val_loss: 0.4477 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00023: val_acc did not improve from 0.94737\n",
      "Epoch 24/200\n",
      "27/27 [==============================] - 4s 156ms/step - loss: 0.5180 - acc: 0.8249 - val_loss: 0.4627 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00024: val_acc did not improve from 0.94737\n",
      "Epoch 25/200\n",
      "27/27 [==============================] - 4s 165ms/step - loss: 0.4440 - acc: 0.8702 - val_loss: 0.4845 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00025: val_acc did not improve from 0.94737\n",
      "Epoch 26/200\n",
      "27/27 [==============================] - 4s 152ms/step - loss: 0.4475 - acc: 0.9073 - val_loss: 0.4613 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00026: val_acc did not improve from 0.94737\n",
      "Epoch 27/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 0.4389 - acc: 0.8795 - val_loss: 0.4036 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00027: val_acc did not improve from 0.94737\n",
      "Epoch 28/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 0.4249 - acc: 0.9073 - val_loss: 0.4462 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00028: val_acc did not improve from 0.94737\n",
      "Epoch 29/200\n",
      "27/27 [==============================] - 4s 154ms/step - loss: 0.3947 - acc: 0.8991 - val_loss: 0.4545 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00029: val_acc did not improve from 0.94737\n",
      "Epoch 30/200\n",
      "27/27 [==============================] - 4s 165ms/step - loss: 0.3759 - acc: 0.9259 - val_loss: 0.4239 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00030: val_acc did not improve from 0.94737\n",
      "Epoch 31/200\n",
      "27/27 [==============================] - 4s 163ms/step - loss: 0.3527 - acc: 0.9351 - val_loss: 0.4228 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00031: val_acc did not improve from 0.94737\n",
      "Epoch 32/200\n",
      "27/27 [==============================] - 4s 160ms/step - loss: 0.3674 - acc: 0.9259 - val_loss: 0.3939 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00032: val_acc did not improve from 0.94737\n",
      "Epoch 33/200\n",
      "27/27 [==============================] - 5s 169ms/step - loss: 0.3546 - acc: 0.8980 - val_loss: 0.4508 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00033: val_acc did not improve from 0.94737\n",
      "Epoch 34/200\n",
      "27/27 [==============================] - 4s 161ms/step - loss: 0.4153 - acc: 0.8795 - val_loss: 0.4129 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00034: val_acc did not improve from 0.94737\n",
      "Epoch 35/200\n",
      "27/27 [==============================] - 4s 165ms/step - loss: 0.3848 - acc: 0.9166 - val_loss: 0.3977 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00035: val_acc did not improve from 0.94737\n",
      "Epoch 36/200\n",
      "27/27 [==============================] - 4s 164ms/step - loss: 0.3871 - acc: 0.9073 - val_loss: 0.4599 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00036: val_acc did not improve from 0.94737\n",
      "Epoch 37/200\n",
      "27/27 [==============================] - 4s 155ms/step - loss: 0.3531 - acc: 0.9537 - val_loss: 0.4188 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00037: val_acc did not improve from 0.94737\n",
      "Epoch 38/200\n",
      "27/27 [==============================] - 5s 170ms/step - loss: 0.3103 - acc: 0.9444 - val_loss: 0.4263 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00038: val_acc did not improve from 0.94737\n",
      "Epoch 39/200\n",
      "27/27 [==============================] - 4s 165ms/step - loss: 0.3636 - acc: 0.9166 - val_loss: 0.4013 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00039: val_acc did not improve from 0.94737\n",
      "Epoch 40/200\n",
      "27/27 [==============================] - 4s 155ms/step - loss: 0.3669 - acc: 0.9166 - val_loss: 0.4032 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00040: val_acc did not improve from 0.94737\n",
      "Epoch 41/200\n",
      "27/27 [==============================] - 5s 170ms/step - loss: 0.3575 - acc: 0.9073 - val_loss: 0.4646 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00041: val_acc did not improve from 0.94737\n",
      "Epoch 42/200\n",
      "27/27 [==============================] - 4s 160ms/step - loss: 0.3567 - acc: 0.9259 - val_loss: 0.4238 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00042: val_acc did not improve from 0.94737\n",
      "Epoch 43/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 0.3368 - acc: 0.8991 - val_loss: 0.4441 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00043: val_acc did not improve from 0.94737\n",
      "Epoch 44/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 0.3308 - acc: 0.9444 - val_loss: 0.4497 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00044: val_acc did not improve from 0.94737\n",
      "Epoch 45/200\n",
      "27/27 [==============================] - 4s 156ms/step - loss: 0.3303 - acc: 0.9259 - val_loss: 0.4196 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00045: val_acc did not improve from 0.94737\n",
      "Epoch 46/200\n",
      "27/27 [==============================] - 4s 163ms/step - loss: 0.3121 - acc: 0.9259 - val_loss: 0.4459 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00046: val_acc did not improve from 0.94737\n",
      "Epoch 47/200\n",
      "27/27 [==============================] - 4s 163ms/step - loss: 0.3137 - acc: 0.9166 - val_loss: 0.4182 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00047: val_acc did not improve from 0.94737\n",
      "Epoch 00047: 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 [==============================] - 6s 216ms/step - loss: 1.0818 - acc: 0.5190 - val_loss: 1.3649 - 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/weights-augm-trial4-improvement-BEST.hdf5\n",
      "Epoch 2/200\n",
      "27/27 [==============================] - 4s 154ms/step - loss: 0.9689 - acc: 0.4902 - val_loss: 1.0009 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00002: val_acc did not improve from 0.31579\n",
      "Epoch 3/200\n",
      "27/27 [==============================] - 4s 163ms/step - loss: 0.9444 - acc: 0.4634 - val_loss: 0.8600 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00003: val_acc improved from 0.31579 to 0.68421, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/weights-augm-trial4-improvement-BEST.hdf5\n",
      "Epoch 4/200\n",
      "27/27 [==============================] - 4s 152ms/step - loss: 0.8728 - acc: 0.5458 - val_loss: 0.9357 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00004: val_acc did not improve from 0.68421\n",
      "Epoch 5/200\n",
      "27/27 [==============================] - 4s 166ms/step - loss: 0.9009 - acc: 0.5376 - val_loss: 0.9673 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00005: val_acc did not improve from 0.68421\n",
      "Epoch 6/200\n",
      "27/27 [==============================] - 4s 164ms/step - loss: 0.9079 - acc: 0.4717 - val_loss: 0.9043 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00006: val_acc did not improve from 0.68421\n",
      "Epoch 7/200\n",
      "27/27 [==============================] - 4s 158ms/step - loss: 0.8989 - acc: 0.5098 - val_loss: 0.8086 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00007: val_acc did not improve from 0.68421\n",
      "Epoch 8/200\n",
      "27/27 [==============================] - 4s 163ms/step - loss: 0.8452 - acc: 0.4912 - val_loss: 0.8655 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00008: val_acc did not improve from 0.68421\n",
      "Epoch 9/200\n",
      "27/27 [==============================] - 4s 166ms/step - loss: 0.8069 - acc: 0.5376 - val_loss: 0.8737 - val_acc: 0.3684\n",
      "\n",
      "Epoch 00009: val_acc did not improve from 0.68421\n",
      "Epoch 10/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 0.7722 - acc: 0.5561 - val_loss: 0.7880 - val_acc: 0.4737\n",
      "\n",
      "Epoch 00010: val_acc did not improve from 0.68421\n",
      "Epoch 11/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 0.7390 - acc: 0.6941 - val_loss: 0.7459 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00011: val_acc improved from 0.68421 to 0.73684, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/weights-augm-trial4-improvement-BEST.hdf5\n",
      "Epoch 12/200\n",
      "27/27 [==============================] - 4s 160ms/step - loss: 0.7372 - acc: 0.6941 - val_loss: 0.7256 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00012: val_acc did not improve from 0.73684\n",
      "Epoch 13/200\n",
      "27/27 [==============================] - 4s 160ms/step - loss: 0.7257 - acc: 0.7034 - val_loss: 0.6773 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00013: val_acc improved from 0.73684 to 0.84211, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/weights-augm-trial4-improvement-BEST.hdf5\n",
      "Epoch 14/200\n",
      "27/27 [==============================] - 4s 161ms/step - loss: 0.6850 - acc: 0.7683 - val_loss: 0.6913 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00014: val_acc did not improve from 0.84211\n",
      "Epoch 15/200\n",
      "27/27 [==============================] - 4s 156ms/step - loss: 0.6617 - acc: 0.7683 - val_loss: 0.6177 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00015: val_acc did not improve from 0.84211\n",
      "Epoch 16/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 0.6222 - acc: 0.7868 - val_loss: 0.5976 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00016: val_acc did not improve from 0.84211\n",
      "Epoch 17/200\n",
      "27/27 [==============================] - 4s 157ms/step - loss: 0.5827 - acc: 0.7868 - val_loss: 0.5853 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00017: val_acc improved from 0.84211 to 0.89474, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/weights-augm-trial4-improvement-BEST.hdf5\n",
      "Epoch 18/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 0.5974 - acc: 0.7600 - val_loss: 0.6319 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00018: val_acc did not improve from 0.89474\n",
      "Epoch 19/200\n",
      "27/27 [==============================] - 4s 165ms/step - loss: 0.5715 - acc: 0.8146 - val_loss: 0.5778 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00019: val_acc did not improve from 0.89474\n",
      "Epoch 20/200\n",
      "27/27 [==============================] - 4s 160ms/step - loss: 0.5751 - acc: 0.7600 - val_loss: 0.5405 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00020: val_acc did not improve from 0.89474\n",
      "Epoch 21/200\n",
      "27/27 [==============================] - 4s 158ms/step - loss: 0.5350 - acc: 0.8146 - val_loss: 0.4976 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00021: val_acc did not improve from 0.89474\n",
      "Epoch 22/200\n",
      "27/27 [==============================] - 4s 160ms/step - loss: 0.5223 - acc: 0.8424 - val_loss: 0.4818 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00022: val_acc did not improve from 0.89474\n",
      "Epoch 23/200\n",
      "27/27 [==============================] - 4s 160ms/step - loss: 0.5238 - acc: 0.8527 - val_loss: 0.5054 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00023: val_acc did not improve from 0.89474\n",
      "Epoch 24/200\n",
      "27/27 [==============================] - 4s 156ms/step - loss: 0.4845 - acc: 0.8980 - val_loss: 0.5617 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00024: val_acc did not improve from 0.89474\n",
      "Epoch 25/200\n",
      "27/27 [==============================] - 5s 167ms/step - loss: 0.4838 - acc: 0.8795 - val_loss: 0.5193 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00025: val_acc did not improve from 0.89474\n",
      "Epoch 26/200\n",
      "27/27 [==============================] - 4s 163ms/step - loss: 0.4743 - acc: 0.8239 - val_loss: 0.4598 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00026: val_acc improved from 0.89474 to 0.94737, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/weights-augm-trial4-improvement-BEST.hdf5\n",
      "Epoch 27/200\n",
      "27/27 [==============================] - 4s 157ms/step - loss: 0.4368 - acc: 0.8888 - val_loss: 0.4763 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00027: val_acc did not improve from 0.94737\n",
      "Epoch 28/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 0.4135 - acc: 0.8888 - val_loss: 0.4546 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00028: val_acc did not improve from 0.94737\n",
      "Epoch 29/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 0.3826 - acc: 0.9351 - val_loss: 0.4764 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00029: val_acc did not improve from 0.94737\n",
      "Epoch 30/200\n",
      "27/27 [==============================] - 5s 167ms/step - loss: 0.4245 - acc: 0.8795 - val_loss: 0.5065 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00030: val_acc did not improve from 0.94737\n",
      "Epoch 31/200\n",
      "27/27 [==============================] - 4s 157ms/step - loss: 0.4466 - acc: 0.8888 - val_loss: 0.5157 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00031: val_acc did not improve from 0.94737\n",
      "Epoch 32/200\n",
      "27/27 [==============================] - 4s 163ms/step - loss: 0.3974 - acc: 0.9073 - val_loss: 0.4454 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00032: val_acc did not improve from 0.94737\n",
      "Epoch 33/200\n",
      "27/27 [==============================] - 4s 166ms/step - loss: 0.3982 - acc: 0.8980 - val_loss: 0.4460 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00033: val_acc did not improve from 0.94737\n",
      "Epoch 34/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 0.4529 - acc: 0.8610 - val_loss: 0.5396 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00034: val_acc did not improve from 0.94737\n",
      "Epoch 35/200\n",
      "27/27 [==============================] - 5s 168ms/step - loss: 0.3744 - acc: 0.9259 - val_loss: 0.4657 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00035: val_acc did not improve from 0.94737\n",
      "Epoch 36/200\n",
      "27/27 [==============================] - 4s 155ms/step - loss: 0.3782 - acc: 0.9259 - val_loss: 0.4780 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00036: val_acc did not improve from 0.94737\n",
      "Epoch 37/200\n",
      "27/27 [==============================] - 4s 164ms/step - loss: 0.3499 - acc: 0.9166 - val_loss: 0.4065 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00037: val_acc did not improve from 0.94737\n",
      "Epoch 38/200\n",
      "27/27 [==============================] - 5s 169ms/step - loss: 0.3629 - acc: 0.9073 - val_loss: 0.4221 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00038: val_acc did not improve from 0.94737\n",
      "Epoch 39/200\n",
      "27/27 [==============================] - 4s 158ms/step - loss: 0.3612 - acc: 0.9537 - val_loss: 0.5090 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00039: val_acc did not improve from 0.94737\n",
      "Epoch 40/200\n",
      "27/27 [==============================] - 4s 166ms/step - loss: 0.3667 - acc: 0.8980 - val_loss: 0.4947 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00040: val_acc did not improve from 0.94737\n",
      "Epoch 41/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 0.3825 - acc: 0.9073 - val_loss: 0.4362 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00041: val_acc did not improve from 0.94737\n",
      "Epoch 42/200\n",
      "27/27 [==============================] - 4s 154ms/step - loss: 0.3417 - acc: 0.9166 - val_loss: 0.4660 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00042: val_acc did not improve from 0.94737\n",
      "Epoch 43/200\n",
      "27/27 [==============================] - 4s 165ms/step - loss: 0.3532 - acc: 0.9259 - val_loss: 0.4386 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00043: val_acc did not improve from 0.94737\n",
      "Epoch 44/200\n",
      "27/27 [==============================] - 4s 163ms/step - loss: 0.3561 - acc: 0.8980 - val_loss: 0.4606 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00044: val_acc did not improve from 0.94737\n",
      "Epoch 45/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 0.3474 - acc: 0.9166 - val_loss: 0.4654 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00045: val_acc did not improve from 0.94737\n",
      "Epoch 46/200\n",
      "27/27 [==============================] - 4s 160ms/step - loss: 0.3457 - acc: 0.9176 - val_loss: 0.4191 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00046: val_acc did not improve from 0.94737\n",
      "Epoch 47/200\n",
      "27/27 [==============================] - 4s 164ms/step - loss: 0.3302 - acc: 0.9259 - val_loss: 0.4092 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00047: val_acc did not improve from 0.94737\n",
      "Epoch 48/200\n",
      "27/27 [==============================] - 4s 166ms/step - loss: 0.3101 - acc: 0.9176 - val_loss: 0.5005 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00048: val_acc did not improve from 0.94737\n",
      "Epoch 49/200\n",
      "27/27 [==============================] - 4s 159ms/step - loss: 0.2987 - acc: 0.9444 - val_loss: 0.4215 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00049: val_acc did not improve from 0.94737\n",
      "Epoch 50/200\n",
      "27/27 [==============================] - 4s 164ms/step - loss: 0.3279 - acc: 0.9351 - val_loss: 0.4579 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00050: val_acc did not improve from 0.94737\n",
      "Epoch 51/200\n",
      "27/27 [==============================] - 5s 167ms/step - loss: 0.3098 - acc: 0.9722 - val_loss: 0.4705 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00051: val_acc did not improve from 0.94737\n",
      "Epoch 52/200\n",
      "27/27 [==============================] - 4s 159ms/step - loss: 0.2949 - acc: 0.9722 - val_loss: 0.4875 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00052: val_acc did not improve from 0.94737\n",
      "Epoch 00052: early stopping\n"
     ]
    },
    {
     "data": {
      "image/png": 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7rylw7OB6henWcSmLrPmQxw84vkdgJAw/0/E1xizG+9DXBDac4JLJQwCYmzKQ+kbD1xmFbY835uR9+8BzX2RxpKSK31w8Fm8v4axR0Tx2xUTW7S/i5//ZRUNkIo1FWew/dpynr5vSNm3Q8YPWe5m0AAaOxT/zA35yYTKr7zuPp66ZzJxmQ9qu4DmBI9jqVR93dSuUUkop95K6EoIHtlyc0gXv78ilkgByTSTHD+3p2n2HnwnB0W33DYi30uW0FzjaAriaiFEcGjgXgMKt75BVUM6BwgoaGo3V+5b+oRWYerezLCNlMV6mnqvD9zJhqDX0fMawCMICfFiT5mC4uqLA6qF1MnAsrWqn19KB3JIq/vZ5JosmxDJzZFTT9sunxHHv+Um8vS2HJ74BH+p5/PwI5iQPbHsR+8/LHlAfWgcVRQyLDGLJ5KHtrxTvJ54VOGr1GKWUUqql+hqrxzF5IXh5d+sS7+84SsrgULLMEGqPOVnetzjbWtDiaAgZWsznc6iiEKpLeGoHnPv8QfY1DiPzi38x70+fM+fxtSz685fsWbfKCvLauweQHzaWYyaCK4N3NgVVPt5enJMUw5q0AowxLU9oCsw6HqrOOV7JXa9vY9JvP+bd7Uc6PNbu0Q9SMQYeWNh2nukP5idyxZQ4vi6xpt5dPKTc8UXs7YtKtOZwmkYreHYTnhU4Bg7QOY5Kuak5c+bw0Ucftdj25JNP8v3vf7/dc0JCXDfBW/Uefe9dLPsLqD1xcqFIF6XlnSDt2AmunTmcEyEJhFdkW72BnbEvZukgqCM6yZrH6IgtQNpVM5g7zhuJ15jFzPRK42+XDeeRJeMor6lny0evUCv+HI6Y2e4tVu06xicNU0ks2wB1VU3b5yYPpOBEDXta125v3qPnQGVtPU98ks78P33O6n3HGBoRyO9X7qOipuMKRlsOFPPejlzuOHektYK6FRHh0Ssm8ODNS1q2ozX7vM/ACIidBGFxkNrO/E8X8LzAUXsclXJLS5cuZfny5S22LV++nKVLl7qoRaq/6HvvYqkrwC8ERp7XrdPf35GLl8DC8bEEDRlLkKmiMK91FRhH910JgyZYQ9LtiU6C4v3WkHNrtsBpf+MQ7jh3FMnnXYPQyEK/HdxwZjyf3nsuVwTt4PPGicz/62b+58NUh8Hbezty2Rt+Ll71ldYCIRt7epu1rYerCzPANxjChrbYbIzh3e1HmP+nz/nz6gwuGDeY1T+ew1+uPYP8EzU8s7b9RT6NjYbfvr+XwWEB3DlnVLvH+Xp7MSUp3lrR3W7g2Gz+pYgVmO//DGor2r1uf/KswDEoUnsclXJT3/72t1mxYgU1NTUAHDhwgNzcXCZPnsz8+fOZMmUKEyZM4N1333VxS1Vv0/fehRobIXUVjP4W+Ph3+XRjDO/tyGV2YjQxof7EJ1ul8Hbv2NLxieUFcHhDx72NYAVAjfWOF70UZlAj/oQOHE5ksJ/VuxY+rKknM6BgJyG1+Uxf8B0WT4jlmbX7mfv4Wt7emkNjo9Ujeri4km2HShg25UKrlF/q+02Xjwn1Z2JcOGvSWlW0KUyH6ETwOhkC7cop5cpn1/PD5duJDPbjzTvO5C9Lz2BoRCBThg/gsjOG8o8vszlUVOnwZT7xSTq7jpTywMIUgvycSJEdPdpx4Oho4U7KIqivtoJHN+A5CcDBWhxTddz6wbp4cqhSbu2DByCvk4oNXTV4Aix8tN3dUVFRzJgxgw8//JAlS5awfPlyrr76agIDA3nnnXcICwujsLCQWbNmcckll7h8gvcpS9/708uRLVCR3+1h6h05pRwqruTueVb1lOHJk2ElHNu/A/h2+yemf2jNvXMmcASH6W8aC9PY3xjLzFG2VcL23rWty6zetdSVIN5ETFrME2dGcv2ZI/jt+3v58b938PKGg/zm4rFszLI6kxafMQKOX2CrMNPQNNdzTvJA/vpZBiWVtUQE2ZKaF6TDcGvoO/9ENY9/lMa/t+YQFezHo5dP4Mppw9rUgL5/QQof7s7jD6v28ewNU1vse3PzYf66JpOrpw1jiW1Vd6eik2D3W23jGdu8zxaB44jZEBBh/TzGdG/VfG/yvB5H09BxMlGllMs0H7K0D1UaY/jFL37BxIkTOf/88zly5AjHjh1zcUv7h4gsEJE0EckUkQcc7B8hIqtFZKeIrBWROEfX8QT63rvIvvfBy9fqceyG93fk4uftxYXjBgMgobFUewXRUJBOXUNj+yemrrAqudgqvWw+UExmvoPFHtG2cn4Oetfq8tLIaBzCzISTq4+betcyV1v3GHFWUwGQKcMH8M73zuJPV07iaEkVlz+zjr9+lsEZwyOsOYUpi6CyCA5vbLrc3OQYGg18YU/LU1sJpYeoj0zk75/vZ97jn/PON0e4/ZyRrPnJHK6ZMbxN0AgwODyAu+aO4sM9eazLPJni58uMAn7xzi7OGR3N7y4b7/wfRdFJVizTur63o4U73j7Wwqe0DxwP+fczz+txBGueY2CEa9uilDvroHeoL1166aXcd999bNu2jaqqKqZMmcKyZcsoKChg69at+Pr6Eh8fT3V1tUva159ExBt4GvgWkANsFpH3jDF7mx32OPCyMeYlEZkH/BG4oUc31vf+9GGMFVwlnNOtCigNjYYVO3M5LzmG8EBb3WMRqsNHMawwh20Hj7dIKdOkphz2r4Fpt4AIn6Ue47aXtiAi3DBrBD86f/TJ3r2AcNt8vlYLZOqq8CvPYX/jTG5IiDy5ffhZ1nqGdX+BglSYenOL07y8hCumxrFg/GCeWZvJP77MZumM4dbOxPPB289KJD7iLAAmxkUQGezH2tR8Lpk0BFOUgQCPbKjnpdJUzh8zkF8uGktCdKuqNw7cds5Ilm8+zMMr9rLinrPZX1DB91/dRuLAEJ6+bgq+3l3oi7MHhoXpENIsJU+hrZJO64U7KYtgxxtwcF2357L2Fs/rcQTN5aiUmwoJCWHOnDnccsstTQsjSktLGThwIL6+vqxZs4aDB52YdH9qmAFkGmOyjDG1wHJgSatjxgL2Yr5rHOz3GPreu0BBmlUesJvD1JuyizlWVsMlk1oOrwYPGUOiV27buYF29kouYxaz+0gpd7/+DWOHhHHtjOG8vP4Acx5fy8vrD1Bv77F0NJ+vKBPBUBE2kpjQZnMzvX0gaSHkbLKetzMUHuzvw08vTGHvby/kqmnDrI0BYTByjhVM21aFe3sJ546OZm16Aal5ZTz97w8AyPUZxsu3zOD5G6c7FTQCBPh688uLxpCad4I/f5bJzS9uItDPmxdumk5YgK9T12jSXnL0wgzwDWqzcIdR88EnsP2yjP3Ic3sclVJuaenSpVx++eVNw5bXXXcdF198MdOmTWPy5MmkpHSzjq7nGQocbvY8B2idU2QHcAXwFHAZECoiUcaYouYHicjtwO0Aw4cP77MG99Qp995Xl1rB2bAZvXO9E3lQng+xE7t86q6d3+B9aF3LKiPZX1hfky/qVnPe35lLoK8388e0TELtOyiZ2D1vsjH1ADjIR2iv5JIbNolb/raRAUF+vHDjdAaGBXDdrOE8/P5eHnx3D69uOMiTV5/BWAfz+Rry0/AGwoeNbXv9lEWw43VrsUzEsA5fg0/rXr6URZDxMRzbA4PHA1YVmf9uz2XBk19yf0AGBuGZH1yJr3/blDmdWTB+MDMTIvnz6gyC/Lx5844zGRIR2PmJrYUNtQLE1j2xhelW/kavVq/LLwhGzYN97zVND+jUxKu6tWCqM54VOAZpvWql3N1ll13WIuFudHQ069evd3hseXk7CXBPDY4mO7VOjvcT4K8ichPwBXAEaJNvxBjzHPAcwLRp05xIsOcap9x7/9nvYcs/4YFD4Odcr1SHVj9szUn8aWaXfqF/c+g49W/fyXRxkJh7xNkQFtvlptQ1NPLBrqN8a+ygtquAbb1hdfkZ5Jac1zIwslVyqU1cyM0vfUNVbQOvfG8mA8OscoUpg8N47baZfLz3GL98ZxcPvrubt85oNp/PNixbkL2bgUYYmTK5beNGzYOgaJh4dZdfF8kXwfs/snodbYHjnKSBJA8KZXrCAG6prkeOjehW0AhWLsbfLhnH3a9/wy8vGsP4oV2fIgBYgWFUooMex3SIa+cPlQlXQNpKeO9u5+4xZrEGjtrjqJTyIDlA8+6SOCC3+QHGmFzgcgARCQGuMMbo6j93YJ8/2FgPRZlW71dP5e+FmjIr12DSBU6dcqiokp8t+5SPJI13Aq/gz+Vz+eu1UxgXa+t5DHFQss4JX2UUcryyrs0wNdAUOI6SXNamFXDtzGa93Ae+gupSns5LYX9BOctunkHy4NAWp4sIF44bTM7xKh5ZsZfsM4aSAFbvra295Uf2UmNimJHoIOj1C4J793Qv6AkZaPUQp66AOdZ6tPAgXz6691xr/9/2Q3Ry16/bTMrgMD69rxfmGcYkw6GTC3morYSSwzD5esfHj7/CWmHt7AIZ/24GtZ3wrDmOgRGAaI+jUsoTbAZGi0iCiPgB1wDvNT9ARKJFxP45/HPghX5uo2pP7jdQZisz117lk64w5uR1nKwCUlJZy03LNjG7cQteGOZceRf1oUO58e1cDjdGWcO4TgRX/9mWw12vbWvx+N3KvYQF+HBOkoMa05EJGPHmjKCCNrWea3a/T40E8GxOPH+4fAJnj3Zwvs2V0+II9vPmlUxbG5v1rvmVZHLUd1hTT2UbvgHdT7uXsthKSXW81ZzaxkYoyui01GC/iU6C0kNWwAhWonRMx+0LHWy97848Wg939xLPChy9vK0VWlW6OEYpR9rUZD2FuftrNcbUA3cDHwH7gDeNMXtE5GERucR22BwgTUTSgUHA73twvx622HP0y2tNXQniZT3aq/DRFSeOQm25teo3bZWVa7ADNfUN3PHKVnKKq/jh0DSIGMGAhCm8eNMM6hoMN724idLKznuejDH8z4dpfJVZSNqxE00PEeH7cxPx93FQ29rHHxkQz7TgAtZlFlJT30B9QyOvrD9AyTfvsLZ+Aj+4cMLJRSntCAvw5cppw3h1Xy2NzebzNTQ0EFNzmLoBiZ22v1vsC2rSVrXcXnrYSvXTTqnBfmcPEItsFWk6KYXoLjodqhaRF4DFQL4xZryD/dcB99uelgPfM8bs6NVWNhcUqUPVSjkQEBBAUVERUVFRp3yCZWMMRUVFBAS001vhJowxq4BVrbY92Oz7t4C3enoffe/7QOpKa1iwNKd3Akf7NSYthW0vQc6WpiTUrRljeODtXWzMLubpK0Yz4MN1MP02ECFxYAjP3TCVG/65iTte3cLLt8zEz6f9PqCDRZXklVXzyKXjuWHWCOfbG51EfF4mFbUN/P3zLFbtOorvsR3c4F/M+Pm/5sI5zgV93zlzBMvWHaDAbziDbD+DzIxUkqWW0DgHC2N6Q9QoiBljvYezvndyu7sFZs1XVsdOtAXWYrXfjTkzx3EZ8Ffg5Xb2ZwPnGWOOi8hCrAnc7Vcj76lALTuolCNxcXHk5ORQUNBOCo1TTEBAAHFxHpsvu1fpe9/LivZDwT6Y+hhkremdoWr7Nc68G7a/bpXGaydw/PsXWbzzzRF+ckESi4J2W6lvmqWlmTkyiv+9ciI/XL6dR1bs5ZFL2/TpNNmYbS3QP3NkZLvHOBQ9muD9qwn0MTzxSTpxAwJ5aVIOJs2boTMudfoyI2NCmJscw7ZD0SwoTEeArNRvSAaGjXawMKa3pCyCr56w4gX7wlp3CxwjRwFy8t9GYToMGAG+3Vil3Y86DRyNMV+ISHwH+9c1e7oBawJ43wmKhHKtPKBUa76+viQkJLi6GcoF9L3vZfY5iCkXQVkOZK1tUcauWwrTrVrK0aMh4VwrSfW3Hmkzj6+qtoFnP9/PvJSB3DU3Ef7zGARFwfBZLY5bMnkoX2UU8va2HH61eIzjIWdgY1Yx0SF+jIoJ6Vp7Y5KRhlp+d14oBX5x3HRWPAHP/QLiZ58MxJx08+wENmcOZmHpV1BbScmhPQBExbcf8PbYmMXw5eNWacTJ11rbCtOtzqdgB0nNXcE3wAoU7QFt6xrVbqq35zjeCnzQy9dsKTBSE4ArpZTqO6krYfBEiBhu/SKvr7bmx/WEvVazvR7z8WyrMkor/91+hJLKOu75z4ALAAAgAElEQVQ4dyTSUAfpH1vl5hwErQvGD6aytoHN2Y5/Jxpj2JBVxIyEyK5PYbAFMFcMr+LO80YRUHbA6oXtRrLxc0ZHUxE2EoDGwgx8ijOo8A6zAuK+EjvZypW4r9lCpMIM9wvMopOsdjU2QmGm+7XPgV4LHEVkLlbgeH8Hx9wuIltEZEu3h1R0jqNSSqm+cuIYHN4EYy62ntt/kRf0cJ5jQbPeJPuwc6vV1cYYXvw6m7GxYcxIiISDX0FNabvB2pmjovDz8Wqz8tku53gVuaXVzHJUNrAzUa1qTNvb2o1k4yLCGVOs3IRfb1jHsMYcqsNGdn/VtHM3tX7O+z87uWrZHry7k+gka6V3yQGor3K/9jnQK4GjiEwEngeWtK540Jwx5jljzDRjzLSYmJju3Sww0lqZVl/bvfOVUkqp9qR/AJiTwV17peG6ouYEnMg9GRSEDoa46W3Kx63bX0T6sXJunh1v9RCmrrSqi4yc4/CyQX4+zBoZ1W7guCHL+nU8M6EbgWNQJATHNAscVzpVyaU982efSQPCzu2bGSW5BAzpo4UxzaUssoKx/Z9Zcx0rCqzcie7E3qO9/7OTz91cjwNHERkO/Ae4wRjTC0vPOhE0wPqqvY5KKaV6274VMCAeBtoCm6BIa0i1J4GjffFD86AgZZGVK7I0p2nTi19nExXsx8WThlhDl6krIXF+h4sl5ibHkFVQwaGiyjb7NmQVMyDIl9EDuzi/0c4+jGrvhe1mTWyAoKAQSv2HMsGkEyOlBA8Z0+1rOW3EbAiIsH6O9pQ37haY2dtj/yPC3drnQKeBo4i8AawHkkUkR0RuFZE7ReRO2yEPAlHAMyKyXUS29GF7T1aP0ZXVSimlelN1GWR/bgVIzYdR7QFUdzkMHG1BWKqVrelgUQWrU/O5duZwAny9raDyxFFIubjDS89NtiqxrE1v2+u4MbuImQlReHl1c0g4erQVMDf1wnY/cAQIjE1hltde27X7IUDy9oWkBVb78+33dbOhYPvPIftLCBzQt/M+e0mngaMxZqkxJtYY42uMiTPG/NMY86wx5lnb/tuMMQOMMZNtj2l92uIgLTuolFKqD2R+Cg21bQMkewDVXYXpIN4woNnK9+jRVuk729zBl9YdxFuE6+25FlNXWOd0UpowPjqYhOhg1qS2DBxzjleSc7yKmV1Nw9NcdJL1u3brS1bbB/aslzAwNgU/sSU+768ALmWRVTRk60tW8vWILuSy7A/BUVaHWGOd9fP2gDysnlU5BrTHUSmlVN9IXQlB0Vat4+aik6CysPu/dwrTIXIk+Pi13J6yCA58RXlJIf/ecphFE2MZZC/Bl7oS4s+2eqE6MSc5hnX7i6iuO1mNZmOW1dZuzW+0s/eG5W6z2trToMZ+vf4M4BLng0+A9RqiEnuWUqmv2H8u7tYb2g7PCxy1x1EppVRvq6+FjHZS3zQtkOnmcHV7aWBSFoNpYNsnb3Cipp6bZyecPL4wzemh4TnJA6mpb2R91sm1qRuziwgP9CVlcGj32gwtA5keDlNb17P9DCJHgbcz9Ud6gV8wjJxru7+bBmb2dnnA/EZwrnKMe9EeR6WUUo5UHbcWt5xxfce9Y2W5sHVZy3rR5XlQU3YyDU9z9l/shentVntpV0M9FO+HpAvb7htyBiZ0CIP3vcD/Rk5ncvo2SAfydln7U5xLfTMzIZIAXy/WpuY3zXncmF3MjITI7s9vBAgfZvXW+Ye27YXtDlf1rI1ZbM1zdNfArOnn4qbta8XzAke/IOsfsvY4KqWUam77G/DRz60hyRFntn/c13+GjX+z5hA2NyAeEs5re3zECPD2t3oBu6rkoDVv0lFQ4OXFgeGXM3L3MyRWHYSvmu0bfQGEO1eILcDXm9mjolmTVsBDxpBXVs3Bosqu1aZ2xMvb6oGNGt07Q7zBUdZK59Hf6vm1uiL5IuvfhKP31h2MPM+aQzp0qqtb4hTPCxxBq8copZRqyx7Ypa1sP3A0xpo/mLQQrl3u3HW9vK3AoztD1R3UR25sNNxXcBFH/efx5f1z8fLu/uyxOSkDWZ2aT1ZhBbtySgG6l/i7tSuX9fwazd28qnev54ygSLhna//f11mxk+CH213dCqd53hxH0OoxSiml2rIHdvtWWAGiI3m7oPTQyQTfzuruyuqmwDGxza53dxzhm0Ml/PiCJHx7EDQCzEmyimqsTStgQ1YRoQE+jIkN69E1lXLEMwPHwAE6x1EppVRLhengF2LVgc7f5/iY1BUgXtYQbFdEJ8HxA1Bf0/U2BQ9sszq6oqaeRz9IZWJcOFdMcW5IuiPDIoMYPTCEtWn51vzG+Ei8ezK/Ual2eGbgqD2OSimlmrOXlJt6k/W8VTm/JqkrYdgsCI7u2vWjk8A0QnFW185rZ0X1s5/v51hZDb+5eFzPFrA0MzdlIOv3F5FdWNE7w9RKOeCZgWNgpPY4KqWUOsk+TJ1wrq0O9Iq2xxRnw7Hd1irbrmq+stpZxkBBWptVxIeLK/n7F1lcOnkIU0d0nqfRWXOSYqhvtIboe5T4W6kOeGjgOMBKu9DeHBallFKnl6a5hKOt+YtHt7eoAw1Amm1hRrJzaW5aiEpseR9nVBZBdQnEJLfY/McP9uEtwv0LU7rejg5Mi48k2M+bEH8fxur8RtVHPDNwDIoE0wDVpa5uiVJKKXdQmH6yIom9vnPr4ep9K2DQeIhMaHt+Z/xDICyuayurmwezNuv3F7FqVx7fmzOK2PDArrejA34+Xlw/awRXTRuGTw8X2yjVHs/8lxWo1WOUUko1U5hxsqRcdGKLOtAAlBfA4Q1dX03dXFdXVrdKxdPQaHh4xV6GRgRy+7kju9+ODvz8ojE8ePHYPrm2UuCpgaO97KDmclRKKQVWkNaiRN4iOPD1yfnw6R9ai1t6UjovOskKUFtNk9pyoJi/rM7AtJ4+VZAOPoFWTyXwr82H2Xe0jJ9flEKArxvWTFbKCZ4ZOGqPo1LKA4jIAhFJE5FMEXnAwf7hIrJGRL4RkZ0i0o3Jd4r6GitVTvPVy7Y60GR8bD1PXQnhw2HwhO7fJ3o01JZbJQttqmobuOeNb/jTJ+l8tCev5fGF6Vbvp5cXpVV1PP5xGjPiI1k0Ibb7bVDKxTwzcAzSetVKKfcmIt7A08BCYCywVERajyH+CnjTGHMGcA3wTP+28hRRnG0FidHNFqEMOQNCY2Hf+1BTDvs/s3ohO6ph3Rl7YNpsuPrZz/dztLSawWEB/G7lPqrrmtW/LkxvOufPqzM4XlnLgxePRXrSBqVczDMDR+1xVEq5vxlApjEmyxhTCywHlrQ6xgD25a/hQC6q6xwsQsHLy1o9nbnamuvYUNOz+Y3QLHC0FsgcKani2c/3c/GkIfzpqknkHK/in19lW8fUVUHJIYhOIjO/nJfWHeCa6cMYPzS8Z21QysU8NHCMAER7HJVS7mwocLjZ8xzbtuYeAq4XkRxgFXCPowuJyO0iskVEthQUFPRFWz2bvUZ1VKuyfmMWQ30VfPpbq8NheDv1q50VOhj8QpsC1T+u2ocIPLAwhdmJ0VwwdhBPr8nkWFk1FO0HDESP5ncr9xLo682PL0ju+PpKeQCPCBwbGw13v76NZ9ZmWhu8vCEgXHsclVLuzNF4ZOvks0uBZcaYOOAi4BURafO5bIx5zhgzzRgzLSYmpg+a6uEKM6wFKP4hLbePOBv8w+FErlVi0NunZ/cRgZgkKExnU3YxK3Ye5Y5zRzE0wkqr88tFY6hvMDz2YWpTcLnpRDRr0wr4wfzRRIf49+z+SrkBjwgcvbyE/LIa3t9x9OTGIK0eo5RyaznAsGbP42g7FH0r8CaAMWY9EAB0sRaearOi2s7HD5IusL7v6TC1XXQSpjCd376/h9jwAO48b1TTrhFRwdx6TgL/2XaEo/t3YBAe/LqahOhgbjwrvnfur5SLeUTgCDBvzED2HS3jaGmVtSFQ61UrpdzaZmC0iCSIiB/W4pf3Wh1zCJgPICJjsAJHHYvuCmParQcNwPTvQuL5MGpe79wvdhJy4ijlR9P5+UVjCPRrmVbnrrmJxIT6U7B7DSXBCaQW1vPrxWPw8/GYX7dKdchj/iXPSxkIwJpU22eq9jgqpdyYMaYeuBv4CNiHtXp6j4g8LCKX2A77MfBdEdkBvAHcZNokA1QdOnHUSpHjqMcRYPhMuP5t8O28SktpZR3FFbUdPvJirQD01qg9XDyxbVqdEH8ffj1vMGNrd/FG2QTOS4phbvLAHr1EpdxJDyd89J/RA0OIGxDIZ6nHuHbmcKvHMX+fq5ullFLtMsaswlr00nzbg82+3wvM7u92nVJaVWfprt+v3Ms/vsx26tiVfiO4InhHu2l1FgfsxEsa+aRxOv+7eIym31GnFI8JHEWEeSkD+feWHKrrGgjQHkellFL22tEx3V+x/MJX2fzjy2wunTyEM4YP6PT4sLxLCd75ZyjPh5C2vYleaStpCInl11deS+LA0G63Syl35DGBI1jD1S+vP8j6rCLmBkZCXYVVMcBHV6oppdRpqTAd/MMgZFC3Tv94Tx6PrNzLheMG8aerJuPt5UTvYN6VsPMpSPsApt7Ycl9tJWSuxvuM65gyIqpbbVLKnXnMHEeAWSOjCPT1Zk1qPgTZ/irUXkellDp9FaRZ8xu7MRy843AJP1j+DROHhvPk1Wc4FzQCDBoPEcOtMoatZa2xckf21ipupdyMRwWOAb7ezE6M5rPUfIxWj1FKKdXRiuoOHC6u5NaXNhMd4s/zN05vszq6QyKQcjFkrYWaEy33pa60ckfGn9PlNinlCTxqqBqs4epP9x0jpybQSpCmPY5KKXV6qjlhJfdub0U1UFXbwOfp+dQ3nlysbgw8tTqD2vpGlt8+i5jQbkx3SlkEG56GzE9h3GXWtoZ6a/g66ULw9u36NZXyAB4XOM5NsaombDhqrMBRexyVUur0ZF8Y00GP4ysbDvCHValttvt5e/HSLTO6v3hl2EwIirJ6GO2B4+EN1u8kHaZWp7BOA0cReQFYDOQbY8Y72C/AU1jlsiqx8pBt6+2G2sWGBzI2NozPDh7nStAeR6WUOl05ETiu219EQnQwz90wtcX2yGA/onpSAtDbB5IWwr73ob7WqlKzbwV4+0Pi/O5fVyk358wcx2XAgg72LwRG2x63A3/rebM6Ni9lIF8cabCeaI+jUkqdngrTQbxhQILD3fUNjWw5cJyzRkUxelBoi0ePgka7lEVQUwoHv7LGv1NXwsg54K8peNSpq9PA0RjzBdBRdLYEeNlYNgARItI2nX4vmjdmIBWNfjR4+WuPo1JKna4K0yEywertc2BPbhnlNfXMHNlHaXFGzQXfICtgzNsFpYdgzOK+uZdSbqI35jgOBQ43e55j23a0F67t0KS4CCKD/SjxGkDUiT67jVJKKXdWmAHR7Sf+3phdBMCshMi+ub9voDUsnbrSqmaGWMPXSp3CeiMdj6PEVw5rrYrI7SKyRUS2FBQUdPuG3l7CnOQYdtUNxeTt6vZ1lFJKeaiGeijK7HBF9casYkZGBzMwLKDv2pGy2KqXvfHvMHwWhMT03b2UcgO9ETjmgLXA2SYOyHV0oDHmOWPMNGPMtJiYnv3nmpcykB31I6y/OGsrenQtpZRSHqbkIDTWtbswpqHRsCm7mJkj+6i30W70BdY8y5pSXU2tTgu9MVT9HnC3iCwHZgKlxpg+Hz8+Z3QMK0w8goFje2DYjL6+pVJKKVcozoJ374H66pPb7Im32wkc9x0t40RNPTMT+rjsX1AkxJ8N2Z9r4KhOC86k43kDmANEi0gO8BvAF8AY8yywCisVTyZWOp6b+6qxzYUH+jIoeQZkwfH9WxiggaNSSp2aDq63Vi6POBt8bKuhA8IhdhLETnR4yoYsa35jn/c4Apz7U4ibDpEj+/5eSrlYp4GjMWZpJ/sNcFevtagL7rzkPEr+L5g9W7/k7Dnf79rJVSXgHwZeHlV1USmlTj/VpdbXa16FwAFOnbIhq5gRUUHEhgf2YcNsEs6xHkqdBjw6aoqNCKJ8wFjCSvfxVUah8yce2QpPjIHVD/VZ25RSSvUSe+DoH+bU4Y2Nhs0HipnZV6uplTqNeXTgCDA4eQYpXod55L0d1DU0dn5CyWF4YynUVcLmF9oWqFdKKeVeqkttI0TeTh2emneC0qo6ZvVV/kalTmMeHzj6DJ2MH/VIYTovrTvQ8cHVZfD61VBXBUuegdoTsP2NfmmnUkqpbqouteY0Osmev7HPEn8rdRrz+MCRwdbE6Mtji3jq0wwKTtQ4Pq6hHt66GQpS4aqX4IzrYOg02PQcNDrRU6mUUso1uhg4bsgqIm5AIEMj+mF+o1KnGc8PHKNHg08gV8UVU13fwP98mNr2GGPgw/sh81NY/ASMmmdtn3kHFGVA1mf922allFLO60Lg2GjL36jD1Er1Dc8PHL28YdA4IkpTuWV2Av/emsM3h463PGbj32Hz83DWPTD1ppPbx14KIYOs/UoppdxTFwLHjPxyjlfW6cIYpfqI5weOYOXxytvFPfMSiQ7x54lP0k/uqyyGT35t1Q89/+GW5/n4wdSbIeNjKNrfv21WSp3yRGSBiKSJSKaIPOBg//+JyHbbI11ESlzRTrfXhcDRnr9RexyV6hunRuA4eCLUlBFSmcMtZ8fzZUYhe3Jt6Rv2vgsNtTDnfsc5G6fdDF6+sOkf/dtmpdQpTUS8gaeBhcBYYKmIjG1+jDHmXmPMZGPMZOAvwH/6v6UeoAuB48bsIoZGBBI3QOc3KtUXTo3A0V45IG8n180cQbCfN89/mW1t2/VviBoNsZMdnxs6GMZdCt+8qql5lFK9aQaQaYzJMsbUAsuBJR0cvxTQNA+tNTZCTZlTgaMxho1ZVv5GEemHxil1+jk1AseB46wi80d3Eh7oyzUzhvP+jlyOHcqAg1/DxKugow+RmXdaqXl2LO+/NiulTnVDgcPNnufYtrUhIiOABMDhSj0RuV1EtojIloKCgl5vqFurKQOMU4FjZn45RRW1/VNmUKnT1KkROPoGQEwy5O0E4JazEzDA7o9esPZPuLLj8+Om0RA7hSMfPclfV6dhVVFUSqkecfTXansfLtcAbxljGhztNMY8Z4yZZoyZFhMT02sN9Aj2qjGdBI5r0/K589WteAmcNSq6Hxqm1Onp1AgcwZrneNQKHIdGBLJ4YixxOSuoHzIdIhM6PX1F4MUMbchh0+q3eWTFPhobNXhUSvVIDjCs2fM4ILedY69Bh6kd6yRwzCoo55Zlm7npxc00NBr+eeN0hkUG9WMDlTq9+Li6Ab0mdiLsXA7l+RAykHvG1ZCYeogvgpdybienph87wc9TR/GtwFDujd3NZV9P4kR1HY9eMRFvL50no5Tqls3AaBFJAI5gBYfXtj5IRJKBAcD6/m2eh7AFjhty68mtymmxa/eRMl7ZcIAAH29+cVEKN52VgJ/PqdMfopQ7OnUCR1sFGY7uhNHnk5i3iga8+F12MivqG9v9MDHG8Ov/7sYvIBDvxHlMPrKRH877DU99lklFbT1PXn2GfhAppbrMGFMvIncDHwHewAvGmD0i8jCwxRjznu3QpcBy48o5MrWV8OrlsPAxiJ3ksmY4ZAscH/k0lz1mR4tdInD1tGH8+IJkYkL9XdE6pU47p1DgOMH6mrfDqgyz621KhpxLelYA724/wpXThjk87d3tuWzMLuYPl03A3+8CSHuXeyfWERo4ht+t3EdFzRaevX4qgX7e/fhilFKnAmPMKmBVq20Ptnr+UH+2yaHSHDi0HnI2u23gWEYwq398Hj7NRoGC/Hw0YFSqn506XWmBERAxwupxPLQOynKInHUdY2LD+MeXWQ4XvJRV1/G7lfuYFBfO1dOHQeL51o7MT7jtnJE8dsUEvswo4I8f7OvnF6OUUv2orsL6Wlvp2nY4YgscQyKiGBUTwoio4KaHBo1K9b9TJ3AEWwWZnbDzTfANRlIWcfu5CaQfK+eVDQeprW9scfgTH6dTVFHDI5eOt+Yyhg6GQRMgczUAV08fznUzR/DaxkNk5muOR6XUKaq2ouVXd1JdSiNCTJSulFbKHZxagePgSVCcBXvegTGLwS+YxROHkDI4lAff3cOMP3zKr/67i60Hj7Mnt5SX1x/gupnDmRgXcfIaifOtIRtbMvAfnT+aIF9v/rgqte39asph73vwzvfgyYmQ+Wn/vE6llOpN9p7GOvcLHE3VccpNIPExoa5uilKKU2mOI5ysIFNTBhOuAsDX24sV95zNl5mFvLPtCG9tzeHVDYfw9RYGBPnx0wtSWl4j8Xz4+knI/gJSFhEV4s9d8xJ59INUvs4sZHZiNOx+20oWnvU5NNRYaSIaG2DrSyeHu5VSylPUltu+ul/gWFN+nDKCiI8KdnVTlFKcaoGjfWV1cAyMnNO02cfbi7nJA5mbPJAT1XV8uDuPD3fncfX0YYQH+ba8xrCZ4BcCGZ9AyiIAbjornlc3HOR3K/ex4lI/vN+6xZpPOf02SF4Iw2fBBz+DHf+Cuirw1RqpSikPUmfrcXTDOY5VJ4opM8EkRGvgqJQ7OLUCx9DBEJ0EYy4Gb8cvLTTAlyunDWt3lTU+flbQmbkajAERAny9uX9BCve88Q15K59jaGAkfG8d+IecPC9lMWx5AbLWWsGkUkp5iqY5juWubYcD9RUlVo+jBo5KuYVTa46jCHx/A8z9Vc+ukzgfSg9BYUbTpsUTY1ky5DhD89dSO+32lkEjQPw54B8O+1b07N5KKdXf7IFjnfv1OJrqEk4QTNwAHclRyh2cWoEjgJc3ePXwZY2ab31ttthFRHgw4mPKTQDP1ziYx+jjB0kXQNoqaKjv2f2VUqo/ufFQtU/tCRr8QvH1PvV+XSnlifR/oiMDRlhD3s1XSRdnE5X9Phsil/Dn9YUcLa1qe17KYqgqhsMb+q+tSinVU26cjse//gRegRGdH6iU6hcaOLYn8Xw48NXJv8C/fgq8fBhz2QMYAz9cvr1NXkgSzwdvfx2uVkp5lqahavcKHE1DPcFU4hc8wNVNUUrZaODYnsT5Vqqdg1/DiTzY/hpMvo6hw0fyP9+eyKbsYh58d3fLijT+ITBqLqSutBbWKKWUJ3DTHsfCokIAgsKjXNwSpZSdBo7tGXE2+ARaw9Xrn4bGepj9AwCWTB7KXXNHsXzzYV74+kDL81IWWQtr8nb2f5uVUqo73HSO45GjeQCEDdCqMUq5Cw0c2+MbAPFnW72HW16A8VdA5Mim3T/+VjIXjhvE71fuZU1a/snzki8C8bLOU0opT9CUALzcrUZL8gusz9aoyBgXt0QpZedU4CgiC0QkTUQyReQBB/uHi8gaEflGRHaKyEW931QXSDwfSg9bH6Zn39til5eX8MRVk0kZHMYPXv+GjGO2WtbB0TD8TJ3nqJTyHE09jQbqq13alOaKiqzAMTJKA0el3EWngaOIeANPAwuBscBSERnb6rBfAW8aY84ArgGe6e2GuoS9fGDSAhg0rs3uYH8fnr9xGv6+3tz60haOV9RaO1IWQf4eq262Ukq5u+ZzG3tjnmPJIXgsoUUu3O4oPW7NcfQO0lXVSrkLZ3ocZwCZxpgsY0wtsBxY0uoYA4TZvg8HcnuviS4UNQoWPQELH2v3kCERgTz3nanklVZz75vbaWw0TaUKdbhaKeUR6no5cCzMsFKTFaT16DIVpcXWNwHhPW+TUqpXOBM4DgUON3ueY9vW3EPA9SKSA6wC7nF0IRG5XUS2iMiWgoKCbjS3n4nA9FthQHyHh00ZPoBfLx7D2rQC/v5FlnX8oAk6XK2U8gy1FRAYefL7Hl+vvOXXbjDGUFdx3HqigaNSbsOZwFEcbGs9e3opsMwYEwdcBLwiIm2ubYx5zhgzzRgzLSbm1Jqzcv2sEVw0YTCPf5zGlgPFMGYxHN4I5fmdn6yUUq5UWwkhA63ve6PsYM2Jll+74VhZDYGN5RgE/MM6P0Ep1S+cCRxzgGHNnsfRdij6VuBNAGPMeiAAOK3yJ4gIj14xkaERgdzzxjeUjbgQMLDtJVc3TSml2tfYAPVVEGz7Y74HvYRNamzX6EHgmF1YQRgVNPiG9LyMrFKq1zjzv3EzMFpEEkTED2vxy3utjjkEzAcQkTFYgaMHjEX3rrAAX56+dgpF5bX86PN6TMpi+OJxKNrv6qYppZRj9h5Ge49jb+RyrLUFjD0IQg8UVRAmlTpMrZSb6TRwNMbUA3cDHwH7sFZP7xGRh0XkEtthPwa+KyI7gDeAm4xxo2Rg/WhCXDi/uCiFz1LzeS3qHvD2gxU/cqvcaEop1cQeKAa711D1gcIKwqVSV1Qr5Wac6v83xqwyxiQZY0YZY35v2/agMeY92/d7jTGzjTGTjDGTjTEf92Wj3d2NZ8WzYNxgHlpTTPYZP4PsL6yShUqp00pnOXBtx1wlIntFZI+IvN7fbWzqFQzpi6Hq7l8ru7CCGN9qJEADR6XciU4c6QMiwmPfnsjwqCCWrE+kYvAM+OiXcOKYq5umlOonzuTAFZHRwM+B2caYccCP+r2hda16HHtjqLqpx7Gs25c4UFRBpHeVDlUr5WY0cOwj4YG+vHrrTEIC/PhOwXWY2kr48H5XN0sp1X+cyYH7XeBpY8xxAGNM/6dhsKffaVoc4/p0PI2NhoNFlYShcxyVcjcaOPahIRGBvHLbTA4wlOe8vg173oG0D1zdLKVU/3AmB24SkCQiX4vIBhFZ4OhCfZoD1x4oBkaAl2/LZODd1dTj2L3A8WhZNTX1jQQ2lmvgqJSb0cCxj42KCeGlW2bwt7rFZHuNoGHFfVBd6upmKaX6njM5cH2A0cAcrHy4z4tIm0l9fZoD1x44+gVbj97ocezh4pgDhRV40YhfvQaOSrkbH1c34HQwfmg4z944i5+9cBvLG3/D8afO4bXhj7Crfij5J2oor67nO2eO4PpZIxBx9LtGKeWBnMmBmwNsMMbUAZw8Ht4AACAASURBVNkikoYVSG7unyZyco6jb5AtcOyNdDw9G6rOLqwgBFs7NHBUyq1oj2M/mTUyijuvu5obG35JXWUpt6XdSnLuOwT5ehHs78Ov393Dd17YRG5JlaubqpTqHc7kwP0vMBdARKKxhq6z+rWV9uDOL8QWOLp+VfWBwgpifKqtJ4G6qlopd6KBYz+aP2YQz/36R4Tfu4GAkbO5r+qvvBb1Iu/cNpHfXzaerQePc+H/fcG/txzmNE2DqdQpw8kcuB8BRSKyF1gD/NQYU9SvDbX3MPoFWb2OvZnHsfZEt3LYHiiqICWiwXqiPY5KuRUNHPtZkJ8P/hGxcP1/YO4vYde/kX/M47oUbz784bmMiQ3jp2/t5Lsvb6Gsus7VzVVK9YATOXCNMeY+Y8xYY8wEY8zyfm+kfU5j01B1D+c4NjZYC2x8g8A0disQzS6sYFSoBo5KuSMNHF3FyxvO+xl85z0ozYGPf8XwqCCW3z6LXy0aw9q0Au5/a6f2PCql+lZdBfgEWp9JvRE42oe6Q2Otr11cINPQaDhcXMWIENsfzho4KuVWNHB0tYRz4KwfWKl6Dm/Cy0u47ZyR/OTCZD7YncdrGw+5uoVKqVNZbYU1TA1WL2FPA8ea1oFj1+Y55pZUUdvQSFyABo5KuSMNHN3BWfdAyCCruoyth/H2c0ZyblIMD6/Yy76j3a++oJRSHaqttHoawVog09M5jvYexzBb4FjbtR7HrQePAzDIz7Y4RgNHpdyKBo7uwD8E5v0KcjbB3v8C4OUlPHHVJMIDfbn79W1U1ta7uJFKqVNSXQX42gPH3uhxtAWK3Riq3ne0jF/9dzdjYsOIC6wFBPxCe9YepVSv0sDRXUy+DgaNh09+A/U1AESH+PPk1ZPJKqzgN+/ucXEDlVKnpNqKZj2OvTDH0R4ohg2xPXduqDqvtJpblm0mxN+HF2+ajk/tCQgIAy/9NaWUO9H/ke7CyxsueARKDsKm55o2z06M5q45ifxn60HWfvZRt1JbKKVUu2orm81xDIbGOqiv7cH1ur44prymnluW/X979x0eZZU9cPx7J71BCEkooUMIRDqhFwGRIioKumDv6FpZdXd11/JTd1fX3bWsuioi2MUuCAgiUhTpvQZCbyEBQnqZJPf3x51JnclMIGUmns/z5Enyzjvv3KvhnTP33nPuejJyrcy6tR/NGweaHbZkmloIjyOBoyfpOAo6jYaV/4Kcs+ZYcTF/aL6NlSGPMWLl79j30wf120YhRMNSkG3WNkLpyOOF7FddcaraxRrHwqJi7v9kE4mnMnnjhj7Et2xkHpDAUQiPJIGjpxnzN3PjXfFP2LMQ3h6Gzzd3Eh0eyhnVhOMr3uX573eTX1hU3y0VQjQE9pqLUDryWMW2g+dyCqouE5ZfITmmiqlqrTVPz9vJ8sRUnpvYjRFx0aUP5qVDoOwaI4SnkcDR00R3hT43w9q3YM51JsNx0kz87ltN2KDbGG7ZztcrNnLVG7+SmFy9bEUhhKik3BrH0NJjDszdcpzezy1h5b7Tzq+Xb6sCERINylLlVPXyval8vPYI91zckesHtCn/oIw4CuGRJHD0RCOfgC6XwxX/hfvWQY9rweKDf5/rsVDMxwOOkJqZxxWv/8LMnw9IkXAhxPkrW47HPvLoYKp67YEz/PGLbWgNW46cq+J6WWDxA79AkxFdxd7X32w6TniwH4+M6Vz5QQkchfBIEjh6otAomPox9L0FfPxKj0fGQkwCnZMXsmj6cIbHRvG3BbuZvepQvTVVCOHFtDaBXaWp6vKB4/7ULKZ9uJFWEUG0aBzI3lNVzHbkZ0GArYROQKjTqeqcgkKW7DrF+G4t8PNx8FYkgaMQHkkCR2/Tcyqc2k5k1l7eubkvo7tG88KiPVXfyIUQwpHCPEA7mKouXeN4Jiuf22avx9eieO/W/nSPacye5Co2JcjPNAGj/Xr5js9dujuFXGsRV/ZsWfnBokKTVCOBoxAeRwJHb9NtspkG2joHpRTPT+pBWIAv0+dsoaCwuL5bJ4TwJvaRxYpT1bbp5TxrEXd+sIFTGXnMvCWBNk2DiWsexqEzOeRZnSToFWSVFu0OcD5VPW/rCZo1CqB/+4jKD9qDTQkchfA4Ejh6m+AI6DwWtn8BRYVEhQXwz8k92HUyg5eW7K3v1gkhPEhWfiEfrz3MPmczEhUDx5JyPDlorXn48y1sOXqOV6f2onebJgB0bhZGUbHmQKqTkj35mRWmqiu/dnqulRWJqUzo3hIfi6p8jbx0810CRyE8jgSO3qjnVMg6BQeWAzA6vhnX9W/N2yv3s/bAmfptmxDCYxQVaZ78dgfztp5wfII9cCxZ4xhScnzj4TQWbk/m0TFxjOvWouQpXZqboDDxlJPp6kpT1ZVHHBfvTKagqJgrezmYpgYJHIXwYBI4eqPYMRDUBLZ+WnLoiQnxtI0I5uHPt5KRZ63HxgkhPEXjYD/6tm3CssQUxydYbWsZKxYAL8hmWWIKPhbFjQPblntKu8gQ/HwUiclOsqULyibHNHI4Vf3d1hO0iQimZysngaEEjkJ4LAkcvZFvgFnruGcB5JlP/SEBvrw0pRcn03N5Zt4u19coLoLjG2ULQyEauBFx0ew4nkFKRl7lB0umqm0jjr6BgAJrDssTU+nbtgmNg/zKPcXPx0LHqFASnSXI5GdS6BvC2yv2k+8TXCk55nRWPquSTnNFzxYo5WCaGiRwFMKDSeDorXpeB4W5sHteyaE+bZpw/8hOfLXpGPO3OZmaslvxT3hnFOz+rpYbKoSoTyNtu7Es35ta+cGKaxyVAv9QsrPS2Xkio+S5FcU1D2PvKScjjvlZHM324fnv97AgMQudn1XuA+rC7Scp1nBlzxjnjZbAUQiPJYGjt4rpC007wdY55Q4/cEksvduE8/jX2zmW5mTbsHNHYNWr5ucVL0KxZGML0VB1bRFGs0YBrEh0EDjap6r9QkqP+QeTnHoWgJFdohxes3OzMI6fyyWz4rKY4mIoyOJQlgVfi2LfOVC6CG3NLTll3pYTxDULI862VtIhCRyF8FgSOHorpaDHVDj0MxxdV3LYz8fCq1N6ozVMn7OFwiIHQeGSpwAFlzwFp7ZD4oK6a7cQok4ppRjROZqV+1KxVrwf2Ncf+pcNHEM4m3aWFo0DiWvmOLizH69UP9aaDWiSzsGgjk0Z2NVsI/j2D1sAOH4ulw2H07iiZwuqlJcOqNKyPkIIj+FW4KiUGqeUSlRKJSmlHnNyzu+UUruUUjuVUp/UbDOFQ31ugkatYPZlsObNkumgNk2D+fvV3dhwOI3Xfkoq/5xDq2DnNzD0DzD4IYjoaKatZa2jEA3WyC5RZOYVsulwWvkH7IW+7WscAe0XTGZmBiPiop2uQbSPFlZKkLFlUB/MtDCwQ1OGd+sAwKerdvPFhqPMt2V3X+Go6HdZeekQ2AgsMrYhhKdx+a9SKeUDvAGMB+KB65RS8RXOiQUeB4ZorS8CptdCW0VFYc3hnp8h9lJY9Bh8diPkmjeGib1imNQ7htd+2se6g2baieIiWPRnaNwaBj8APr5w8Z8gebtJtBFC1ChXH7qVUrcqpVKVUltsX3fWRjuGdIrE16JYVnG6uqQcT+mIY2ZxAAHFuYyMczxNDRATHkSIv0/lEUfbCGaWDmJA+wiULbt6WOsAHv96O7NXHaJn63DaNg2peMnyZLtBITyWOx/n+gNJWusDWusCYA4wscI5dwFvaK3TALTWTmo/iBoXHAFTP4Gx/4C9i+Gt4XB0PQDPXtWN1hHBTJ+zmfQcK2z+0ASJlz5bOsLQ7RqI6AArXpBRRyFqkDsfum0+01r3sn3NrI22hAX60a9dBMsrluWxZpudqHz9Sw6dzvclROUzuFOk0+tZLIrYZmGVtx60ZVAX+ATRo1V4ST3Hx0e3okNUCMkZeVzRw8U0NUjgKIQHcydwjAGOlvn9mO1YWZ2BzkqpVUqpNUqpcY4upJSappTaoJTakJrqYKG2OD9KwaD74PbFoIBZY+HraYSm7ea/U3uTkpnPM1/8il76HLQZDBddXfpcH18Ybht1TFxYb10QogFy50N3nRnZJYo9yZmcOFeaqEJBdrlpaoATOYqm/lZCA3yrvF6X5mEkJmeiy37gtE1VxzRvhr+vpaSeY4jOY/Zt/bljaHuuTWjturF56RAY7l7HhBB1yp3A0dEil4pDU75ALDACuA6YqZSq9K9eaz1Da52gtU6IinI+DSLOU6u+cPfPMOBu2D0f3hpKz+W38VLCOeL3vYnOOcPbIXex40RG+Zt992vNqONyGXUUoga586EbYLJSaptS6kullMOoqiY+dJeU5Sk7XV2QU1r8GziWlsOpfF/CfV1vItC5WRhpOVZSs/JLjmVnmqUynVo1NwfsyS35mcSEB/Hk5fGV6kI6lHdORhyF8FDuBI7HgLI3s1ZAxSKBx4C5Wmur1vogkIgJJEVdCwqHcc/DwzttWdM7uXLb77nT93tWNbqM/2wL4vLXfmHcKz/z9or9JKfn2UYd/wjJ2yDx+/rugRANhTsfur8D2mmtewA/Au87ulBNfOjuFB1KTHhQ+V1kCrJKtxvEBJW5OoBgHBQLr8C+9eDeMgkyB44nA9C1rS35xb6DTIGTvbKdkalqITyWO4HjeiBWKdVeKeUPTAXmVTjnW2AkgFIqEjN1faAmGyqqKagJDHsEpm+HK1+HbpMZdvd/WffXS/jbVd0ICTAFege9sJQbZ67la+tgisPby1pHIWqOyw/dWuszWmv7kN07QN/aaoxSihFxUaxKOk1+YZE5aM0pV4pneWIKPoGhWApznVylVOeSPatLg8KjyWY0s0s728Cqfc9qB/tVV0mmqoXwWC4DR611IXA/sBjYDXyutd6plHpWKXWl7bTFwBml1C5gGfBHrfWZ2mq0qAbfAFO255pZEBJJeLA/Nw5sy9f3DmHZoyN4YFQsR87m8PBXO/nL2fFwcis75/6HrPzC+m65EN7O5YdupVTZTJErMffYWjMyLpqcgiI2HLKV5SnILgkc86xFrEo6Q/PIpqjCXFOFoQqRoQE0DfEvt/VgymkTOAaG2EYL7dna+dUYcSwqNCOhMuIohEeqevWzjdZ6IbCwwrGnyvysgYdtX8JLtI8M4eFLO/OH0bFsPJzG15ta8/O21fTf/AKT14cR2ak3Y+KbMzo+muiwwPpurhBeRWtdqJSyf+j2AWbZP3QDG7TW84AHbR/AC4GzwK212abBnZri72Nh2Z4UhnSKNIFjqFn7uO7gWXKtRbRuHgXJmNHIgKoLcMc1DyPRtvVgeq6V7IxzFPv6YPG13S8sFrPOsaAaI472va0lcBTCI0l1VYFSioR2EfxjUg8GPfwZKqgxs8Le4njKWf7yzXaG/nMZaw/U0AByXgYc/rVmriWEh9NaL9Rad9Zad9Ra/9127Clb0IjW+nGt9UVa655a65Fa6z212Z5gf18GdIgoXedozSlZ47gsMYUAXwttmtnWUBY42bK0jM7Nwth3KpPiYs2GQ2cJJo9i/1BT6cEuILQ0GHRH3jnzXQJHITySBI6iHN9GzfC/ZgbReQf4IX4xi6YPo1V4EL//eBNHz7p+I3FpwcMwezwcWH7h1xJCVNvIuGj2p2azPDEFa24maYX+7Diezk97UhjUsSn+wfaEFtejhHHNw8gpKOJYWi5rD56lsSUXn8AKo5T+odVb4yj7VAvh0SRwFJV1ugQGP4DaOIsuaSuYeUsChUXF3PXBhgtb+3hmP+z4yvz83XSwul6AL4SoWaO6mKnpW2evJzsrg7m7znH5a79w+EwOl3SJLs2ytrr+oBhXJkFmzYEzxAQVlewWUyKgmlPVEjgK4dEkcBSOjXoKWvSCeQ/QwT+d16/vw95Tmfzhsy0UF59n1vWqV80uFZPfhbSDZo9sIUSdahcZwtf3DuadmxNo5FPAxd3a8c7NCcy+rR9T+rUpzbK2b0dYhdhokzW98XAaO46n0zzQWnldZEBo9ZJjsmx1JoMj3H+OEKLOSOAoHPP1N5nYhQXw9TSGd4rgiQnxLNl1ipeW7K3+9dKPw5ZPTIZ392ug142w6r9mxxpvdO6I+RLCC/Vp04RLOzfBUmylfYtoLo1vxsi4aLPbSzUCx7BAP2LCg/hiw1GKNUT4FZSW4LELaFS9qerTe0FZzKYEQgiPI4GjcK5pR7jsRTj8C2yYxW1D2jEloTWvL0ti7pbjFFVn5PHX10AXw+AHze9jnjMjCvMedFn2wyN9diN8MkVqXgrvZbUFhhW2HCyZqnYjcARTCPxMdgF+PooQciuPOPqHVq8A+Om90KSdKSUmhPA4bpXjEb9hvW6A7V/Cj8+gukzguau6ceB0Fg/N2cJDc7YQ5OdDSIAvIQE+tI8M4eXf9aJJiH/5a2Sfho3vQY8p0KStORYcAeNegK/ugLVvw6B767xr5y3jBJzcan4+uRVa9qrf9ghxPuxZ02UKgJf73Y01jmAKgS/dk0LPVuFYcrNKtxm0q+5U9el9ENnZ/fOFEHVKRhxF1ZSCy1+CYit8/yf8fS3MvLkf/3dFPNNHx3LjwDZcGt+MHq3C+TXpDPd9sglrUXH5a6z5HxTmwdA/lD/ebTLEjoGf/uZd075JP9p+ULDts3ptihDnzT6i6OckcHQzoSWumQkUB3SIMFPSlaaqw8xxd0bni4vgTBJEyo61QngqCRyFaxEd4OI/we7vYM9CGgf7ceuQ9kwf3Zm/Tojn+Undee263jw/qTu/7j/Dc/N3lT439xysewfir4SoCqMISsGEl8zPCx6pu/5cqH0/QKMY6Ho5bP/C7HQhhLcpmap2Fji6N+LYt20TGgX6MqZrMzMl7WiqutgKhfmOL1DWuSNQlC8jjkJ4MAkchXsGPwjR8bDwUafTTpP7tmLa8A58sPowH689bA6un2mK/w5zEhiGt4YRfzbB2NH1tdT4CjJOwpo34d2x8M091XtuYQHsXw6xl0LP6yA7Ffb/VCvNFKJWFdTMGsfWEcFs+7+x9Gzub9Yx+ztIjgH3RjBP7zPfJXAUwmNJ4Cjc4+MHV7xq1vct+4fT0/48rgsj46J4eu5O1u45aqapO10KLXo6v3bCHaZm2+rXaqHhNvaRz1nj4aWusOgxMyW29VM4neT+dY6uNaMqnS41X0ERsG1O7bT519dNUpGbIz9CVEvJGscKgZ7FB3yDSkck3WXPnK40VW373Z11jqdtFRskcBTCY0ngKNzXuj8k3A5r34ITmx2e4mNRvHpdbzo0DeT4nIcg5wwMf7Tq6waEmuBx93dw9kDNtzs3DWaNM6OluWdhxONw33q45xdT9mPLx+5fa98PphZlh4tNyaJuk2DPgtKixTUl+wwseRJ+eAJe6wMbZkORtWZfozZUp+yKqF/2EUC/4MqP+Qe7PeJYwh4Y2kcYS65VzcAxuKnUcBTCg0ngKKpn9NMQEg1zH4DMUw5PaWQp4NumbzCJpXzqP5kDQd1cX3fA3aB8zBRyTbLmwafXm9HFG76E+9aaqfGoztCohRk13PKJ++sU9y2BtoNL13H1vM4k/uyaV7Pt3rfYTPuN/xeEt4H50+GNAbDjaygudv38+pB+DP7V0bRReD6rk6xq+7HqjnTbS+5Umqp2fwtDTu+FyLjqva4Qok5J4CiqJ7AxXPEKpO6B1/qaqdTCgtLHs1LgvQkEH/6JA/2f4QXrVCb89xc+XH0I7SSrMjPPSmJ2CMU9fgebP4KcszXT1uIi+PpOOPIrXP2WWZdYUZ+bICsZ9i91fb1zRyF1t8kEt4vpCxEdaz67es8Ck4DT/y64fTFcN8fUtfvyNvjxqZp9rZqS+L0Jojd/VHPXlDqZtcdZOR4wmdY1NlVtCxzdHXGUjGohPJoEjqL64sbDvWug7SAzlfrmYFOiJnUvzLwEUhNh6id0uGw6i6cPp1/7CJ6cu5NbZq/nVEYeAFpr1hw4w8Ofb6H/35cy9pWVTNrSB6w5bPjy3+w4nl69AuMVaQ3f/9lMf4/9h9mtxpHYsRAcCZs/dH3NpCW255QJQJUyo46Hfq65kkLWXJNwEzfeXF8p8/M9v0C3a2DdTDOV7Wn2LjLfDyyvmfYdWWP+nrypVJM3sY8AOh1xPN+pagdZ1WUfdyb7jFnaIusbhfBoEjiK8xPZCW74Aq7/HHQRfDQZ3hpqgp5b55tAB2jeOJD3b+vHcxMvYt3BM4x5eSXPzd/FiH8vZ+qMNSzZeYqresfw4jU96NpjAGt9+tJ2/0dMfu0nxr2yktyC89xV5peXYP07MOh+GHSf8/N8/aHnVDNaln266mvuW2KmjSu+sfX4nfm+7fPza2tFB5abacQuE8oft/jA8D9CYa7pmycpyIaDP0P74ebvYffc879Wbhp89xDMGmtGsJ0siRAXyJoDKPANrPyYf/B5TFXbA9GKBcDdnKo+IxnVQngDCRzFhek81ow+jv4/aDcU7vzRTN+WoZTipkHtWPjgMNpFhvDuLwdp2TiIl6f0ZN1fR/P8pO78LqE1z0/qzoAbniZKpfNe34PsS8li1qqD5iLFxWYt4o/PmOC0KutnwtJnofu1cOlzrvvQ+0YoLqx6urkwHw6sMNPUSpV/rElbaDsEts4pnVq15sHG9+HNIbD4r67bUNae+SbBoO3Qyo9Fd4HO481uO56UbX1gham/N+wRaNrp/NY5ag07voLX+8OmD0zQf+8aaN2v5tsrTLDvH1r57xls2wTW0Iiju1nVJRnVMlUthCeTLQfFhfMNMLvCVNwZpoIOUaF88/vBZOYX0jjIz/FJ7YdD8x4MOvUpY7q+wZvL93NDuyzClz0GR1abcw4sgykfQeNW5Z9bZDVldtbPNEkvE/8HFjc+G0V3hZgE2PQhDLzX8Rvp4V/Nmq9ODtZJgtlO8bsHzVrJE5th7QzITjFvwOtnmgLqgY1dt6W4CBIXmQDV19/xOUMegtnjTDZ4/7tcX7Mu7F1kRpraDIaLJsHKf0FmMoQ1d+/5J7aYHYSSlkCLXmY0W7ZyrF0F2ZVrONr5BZ/HGkd74FhhjWPJVLWLEcfTe8EnwIzqCyE8low4ijplsSjnQSOYoG3wg3B6L8922seDxR8Q9v4os27yytdhysem7uKMESaYs8tKhQ8mmiBt8AO2ZBIngZcjvW80iS/HNzl+fN8S86bWfpjjxy+6yjz+0WQTALXoATfPhZvnmYSRnd+4146j6yDnNHS5zPk5bQZCq/4mMclRNrg1F+Y9YEbv3JWVYtZV/vo6fDcdTu10/7lamzJFnUbZShRNBjTscjFdrTUcXAkfXg0zLjZrGsf+A+5cKkFjXSjIdlyKB86vHE9BlilvVfGaFh+TbONqqvr0PjNabfGp3usKIeqUjDgKz3PRVfDj/9F8yb1M84HPi0Yy8Pr/0qa1bSQicinMuR7evwLGvQCtEmDOjSbgmjQTelxb/dfsNhkWPW6SZFr1rfz4vh/MVLyjRAIwo4kj/wJn98OA30OzeHNca4jqAps/hr63um5H4gJTJ9LZyCaY4HrIQ/DZDWYtYbfJpY8VFcJXd5rp7k0fmt97TnF8ncxTprbl4V/Nf7uS61vMqOldy9wbsU3eBpknTaIRmOn06HgzXT3gbif9/B5W/huOb4CQKLjkaVMjNCjc9euJmmHNqVw6x84/tPpLIfIzzaizoxH7gFCzg1RVTu+teqMAIYRHkBFH4Xl8/MyaybZDSZv6Hf+n7uGfP5cJbKLi4K6foOMlJvB55xLzZnX74vMLGgECG5mAdcdXld8wzx40C/fLlOGZv+0EC7adLH/e0Olw5WulQSOYdvW6Ho6tK91OzRmtTRme9sNNe6oSd5kZnVn1aum6Sq1h4SMmaLz0WRPofnsPbP+y8vNPbIZ3Rpps+LhxJgC/eR78cT9c/Tac3AJb3Cyrs3cxoMpnm180CY6uMbUdK9r8MXw61QSrE16C6dth2MMSNNa1gqyqp6oLsqpXDik/q/I0tV1AWNVT1dY8SDskiTFCeAEJHIVn6nEt3LaAJl2Gc9ewDizYdpLNR9JKHw9sbKajR/zFZB/ftezCpzd732hGRXbPM2+YhQXmzW7PAvO4LTBKz7Xy5y+38dTcHViL3CjG3WOKKW7uaoea1ESzc05V09R2FouZ0j+5FQ6uMMeWPw8b3zMJKkMegus/g9YD4etpsPPb0udu/9LspKMsJtie+AYM/L3ZDSck0iQVtR5gEozc2RFn72KTEBUaXXqs2yTzvezr2vu48FFoNwzu3wj97gC/INevIWpeQY7zEXT/EECbZRZuXy+z6hHMqpJjzh4wBe8lcBTC40ngKDzeXcM7EBkawPPf7ylfRNxiMbvATPkQQqOqdc0DqVk8PXcH//khsfRg2yEQ0QG+uRueCYe/RcHzMfDDX83xph0B+HjtYbILijiTXcCqJBclfMAkiHQabbKui6soL5RoC1Dj3AgcwQSkoc3MqOP6mbDinyb4HfWkedw/BG743Ezlf3WHWXO45Gnzc0xfmLbcrMWsSCkY/6IpT7TixarbkJUKxzea7PqymnY0045l11lac+GLW81o1uSZ4NPwV8oopcYppRKVUklKqceqOO8apZRWSiXUWeOqXOMYUnqOu/IzK2dU2wWEVb3GUTKqhfAaDf/OLbxeaIAv00fH8sS3O1i6O4XR8c3O6zpaa9YePMvMnw+ydM+pklm4Pm2bMDIu2gRMV71p1jP6+IPF10ybW/xMsXMgv7CI2asOMaB9BLtPZjBvywlGxEVX8ao2vW+AzxfD/mUQO9rxOXsWQMs+0Kilex3yC4QB98DSZ8x1O4+Dy18tv8YsIMxstfjh1fD5zeZYwu0w7p9VJw+17GV21Vn7FvS52SwPcCRpCaArB45gpqt/fNpM9Ue0NxnvKbvgxq/cz7b2YkopH+AN4FLgGLBeKTVPa72rwnlhwIPA2jptoDW7ihHCMoFjSKR713M1VX3uqPPn2pdxNO3k3msJVqs26QAAFcZJREFUIeqNjDgKrzClX2s6RIXwtwW7SM3Mr/bzNxw6yxWv/8LUGWvYdCSNB0bFsuqxUXSICuGpuTvIs9pGAtsMhEueghGPwfBHzZTvoHuhZW8A5m4+QWpmPveP6sT4bi1YvDO59LlV6TwegiKcrxvMOGlG7tyZpi4r4XYIDIdW/eCa2Y5H8QIbwU1fmySaK16Fy192L+N81FMmG3bR487Xuu1dBGEtoLmDkcuLrjbfd35jRh43vmdKNnVyEjg3PP2BJK31Aa11ATAHmOjgvOeAF4FqzAvXAFfleOznuH29LOcjjv4ukmNO74XGrZ1PnQshPIYEjsIr+PlYeGFSD5Iz8pg6YzXJ6e6/x6bnWvn9x5tIy7byj6u78+tjo3j40s7EhAfxt6u6cfRsLq//lOTyOsXFmhk/HyC+RSOGdopkYq+WZBcUsXR3iutG+PqbtYN7FpidUSpKXGi+d7nc7X4BJqHk/g1w20LnQQCYNaHXzHIvs9suNMoE0PuXlm4nWFZhAST95LgoOpjC6K36wab3Yd5DZt3kyGoWQ/duMUDZYbZjtmMllFK9gdZa6/l12TDAxRpH28ihtRqZ1fasakfcmaqWaWohvIIEjsJr9G8fwQe3DyA5PY8pM1ZzLM29N7X//JDImax83rqxL9cPaEOgX2mduMEdI5nUO4a3V+4nKaXqnS1+2pNCUkoWd1/cAaUUAzo0JTosgLlbjrvXgd43QFFB5Szn3HOw9VNo0t6U7qmu0CgzpV4b+t8FkXFm1LGwwkjvkdUmIaLzOOfPv2iSyZa1+MDkd2uvnZ7JQTRNydCtUsoCvAw84vJCSk1TSm1QSm1ITU298JYVF5mtK/2cBY7nMeKYn1nFVHWo86xqrc1UdaST5RBCCI8igaPwKv3bR/DhnQM4m13AlLfXcPhM1W9sW4+e48M1h7l5UDu6t3K8c8tfJnQl2N+Xv36zo3zyTQUzVh4gJjyIy7q3AMDHori8R0uWJ6aSnmt13fjmPaBZN7N1Ipg37w2z4bU+cGyD2VPb0chdffLxg/EvQNpBeKM/LHveZMCCyab2CTDZ2M50mwxNY02Jn/DWddNmz3EMKNvpVsCJMr+HAd2A5UqpQ8BAYJ6jBBmt9QytdYLWOiEqqnqJYA7ZRxKrzKrG/cBR66qTY/zDzJaUhQWVH8s4YdZbyoijEF7BrcDRozMDxW9OnzZN+PSugeQUFHLtW6tJSnE8klFYVMxfvtlOVGgAj4xxXuYjMjSAx8Z3Ye3Bs3y9yfHo4eYjaaw7dJbbh7bHz6f0n83EXi0pKCpm8Y5k1w1XCnrdACc2mfV+b18M86ebkZa7V3jO9oEVdRwFv/sAmrQzmdv/7Q3vjjXrFtsPq3pdWlgzeGCDqRX527MeiFVKtVdK+QNTgXn2B7XW6VrrSK11O611O2ANcKXWekOtt8xeq9TpGkfb/1N3p6oL80AXOU+2sQeUjqarT9sqG0gpHiG8gsvAsUxm4HggHrhOKRXv4Lz6yQwUv0ndYhozZ9ogijVc89av/LCzcuD24ZrD7DyRwdNXXERYYNVTpFMSWtOnTTh/X7ibczmVR0VmrDxAo0BfpvYrP2rWo1Vj2jYNZu5WN6ere/zOZGt/9xDknTMJLbct9PwdM+Inmi0U/7DTFGfPOwdZydD1yvpumcfSWhcC9wOLgd3A51rrnUqpZ5VS9fsfzh7AuZyqdrFNoJ19GtppOR77ftUOEmTsGdUSOArhFdwZcfTszEDxmxXXPIwv7xlETHgQ0z7cWC47Ojk9j//8sJeLO0dxWXfXpV8sFsXfr+5Oeq6VO97fwFsr9rNybyqns/I5dDqbRTuTuXFgW0ICymctK6WY2LMlq/efISXDjT/9kEgY8zeTuX3fOlMo29Omp6vSOMZkRt+7xuz40vum+m6RR9NaL9Rad9Zad9Ra/9127Cmt9TwH546ok9FGqMZUtZsjjvaAsKqsanC8zvH0XghoXL6AvBDCY7lTx9FRZuCAsieUzQxUSj3q7EJKqWnANIA2bdpUv7VCVNAuMoSv7x3MvxYlMvOXg6w7eJbXruvNKz/uw1pUzLMTL0K5GZh1bdGIJyd05Z2fD/LC93tKjgf6WfCzWLh1SDuHz7uyV0v++1MS87ed5Pah7V2/0MDfu9Uej6YUhMu/Ya9lX7voaqra3TWO9pHJ85qqtmVUe9MHKCF+w9wJHN3NDLzV1YW01jOAGQAJCQnV2ARVCOcCfH144vJ4hsZG8ugXW5nw2i8UFBbz6JjOtG1avbpwtw5pz61D2nMup4BdJzPYdSKDXScz6NU6nOiwQIfP6RQdRnyLRszdesK9wLEGFRYVs2hnMj1bhdM6oopyPEKUVRI4Ogn0fP1N4Xurm4FjyVS1i8DR0baDp/dBh5HuvY4Qot65EzhWJzMQoDkmM7BuFnkLYTMiLpqFDw3jz19uIy3Hyl3DO5z3tcKD/RncMZLBHd3bNWNir5Y8//0eDp/Jrnawer5+TTrNs/N3sSc5k64tGvHd/UPw9ZFCCcIN9sDR2ZaDYEYj3R1xtAeEVW05WPY8u7wMyDwJUbK+UQhv4c67jOdmBgpRQXRYILNv68+39w0hwNfH9RNqyOU9zTaBr/y4jx93nWLbsXMkp+dRWFRc46916HQ20z7YwPUz15KVX8idQ9uz+2QGH6w+XOOvJRooV2scwYxGurvGsWSq2sUax4pT1ZIYI4TXcTniqLUuVErZMwN9gFn2zEBgg6NF3kL81sSEB3Fx5yi+2XycbzaXZlgrBZd0iebf1/YkPNiNbf6qkJ5r5X/Lk5j9yyF8fRR/HBvHHUPbE+BrYV9KFi8t2cuEHi1o1sjxlLoQJUoCvSoCR7/gamRVu0iOKcmqrjDieOhn893TqwoIIUq4M1WN1nohsLDCsaecnDviwpslhPeZdWs/kjPySM3MJyUjj9SsfA6fyeG9VYe44vVfePvGBOJbNqr0vLTsAj5YfRhfH8WVPVtWWqtYUFjMR2sO89pP+ziXa2VS71b8aVxcuQDx2YkXcenLK3lu/i5ev75PrfdVeLkCd0YcQ9yv4+hqjaN9JLJiVvWe+dCiFzRu5d7rCCHqnVuBoxDCNR+LIiY8iJjwoHLHx3Vrzr0fbWLSm6t4YVIPrupttivOyi9k1i8HeWflAbIKCtEa/rU4kYS2TZjYO4YJ3Vvw6/7TvLgokSNncxjaKZLHxnehW0zlHXDaNg3hvhGdePnHvUzpl8qwWNe7iySn57HrZDpdmjeiZYU2iwbOrTWOIdXPqnZWF9LHF3yDytdxzEyGY+th5BPuvYYQwiNI4ChELevTpgnfPTCU+z7ZxPTPtrD12DlaNwnmjWVJnMkuYEx8Mx4dG0eQnw/ztp7g283HefLbHTz57Q4AujQP473b+nFx56gqSwvdfXEHvt1inrto+vBye3Jrrdl05BzrDp5ly9E0th5NJ9lWd9Ki4OLOUUzp14ZLukaX2xlHNFDWbBPIWapYB+wfAlkp7l0vP9OMKlqq+NsJCCs/9Z1om8TqMsG91xBCeAQJHIWoA1FhAXx85wCeX7iHWasOAjC4Y1P+ODaO3m2alJx338hO3DuiI7tOZrBoRzLtmoZwVe8YfCyua9wF+vnw7MSLuOnddby94gAPjY4lNTOfrzcd47P1Rzlw2owetW0azIAOEfRqHU5c8zBW7z/D5xuOcs9HG4kKC+Cavq24c2h7moYG1M5/DFH/CnKc13C08wuuxlR1pvNparuA0PJT1XsWQJP2EN3VvdcQQngECRyFqCN+PhaeuiKeYbGRBPhZnJb6UUpxUcvGXNSy8pS0K8Nio7i8RwveWJ7EzhPp/LQnhcJiTb92Tbh3ZCcu6RJNk5DySTqDO0by0CWxLE9MZc76I7y9Yj/fbDrO/27sQ58yQa1oQAqyq17fCLas6mpMVTurCVn2evbkmLwMOLACBt4jhb+F8DISOApRx0Z2qd2t1Z68PJ6Ve1NZf+gstw5ux9T+rekU7STb1cbXx8Lo+GaMjm/GjuPp/P7jjUx5ezVPTIjn5kFt3d59R3gJa7bz9Yh21a3j6Cyj2i6gUelU9b4foNgKXS537/pCCI8hgaMQDUyzRoH8/KdRBPpbzquWZbeYxsy/fxgPf76Fp+ftZNORNJ6f1J1gf7ldNBgF2e5NVbsdOGa5N1WdYds7Ys8CCImCVv3cu74QwmPIO4EQDVDjYL8Lfv47Nyfw5or9/OeHRHafzODKni1pHOxP4yA/woP8iAwNoGuLsPMajTyVkUd0WICMZNaXghz3pqqLrVBYYLYgrEp+JoS42G7TPlVdmA/7lkC3q6tOzhFCeCQJHIUQDlksivtGdqJnq3D++OVW/v3D3krn3DyoLc9O7Ob2NbXWvLEsiX//sJfBHZvyj6u70y6ybrZoFGUUZEOQi/Wr9hFJa7brwLEg0/UaR3tW9cGfzfldrnC/vUIIjyGBoxCiSkNjI1n9+CXkFxaRnmslI9fKuRwr3245zgerD9MpOpSbB7VzeZ2iYs0z3+3kg9WHGdopkq1HzzH2lZU8NDqWu4Z1kDJAdcnqTnKM7fGCHNdBprtT1fmZpui3fyi0H+5+e4UQHkMCRyGEWwJ8fYgO8yE6zOxY07tNE5LT83jmu120axrC8M7Oi47nWYt4+PMtLNyezLThHXhsXBdSs/J5eu5OXlyUyLwtJ3hhcg96tQ6vq+78trm1xjGk9FxX3E2OKcwzgWOn0eAnW2MK4Y3kI74Q4rz4WBSvTO1NbHQo932yiaQUx/saZ+RZuXX2OhZuT+aJCV35y2VdsVgUzRoF8tZNfXn7pr6cy7Fy9f9W8cx3O8nOL6zjnvwGFeS4UT7HFjhaXQSOhflmLaQ75XgAslMlm1oILyaBoxDivIUG+DLzlgQCfC3c8f560rILSh47lpbDh2sOc82bv7LxcBqvTu3FncM6VLrG2Iuas+Th4dw0sC2zVx1izMsrWbE3tS678duitVlrWNV2g1A6IulqxNFem9HliKMtcLT4QecxrtsphPBIMlUthLggrZoE8/ZNCVw3Yw13f7iR3m3C+WlPCvtsI5BtmwYz69Z+Ve6fHRbox7MTu3Flz5b8+att3DJrHZN6x/Dk5fGVCpaLC1SYB2j3sqrBjE5Wxe3A0fZ4+2EQWP3i9kIIzyCBoxDigvVt24QXr+nB9M+2sPloGv3bRzClX2tGdommQ2SI22V3EtpFsODBYbyxLIk3l+9nxd5U/ndDHwZ0aFrLPWig8tLh3Qqje8VF5rurwNE+Ijl/etVBYWG+7Xqupqpt15C9qYXwahI4CiFqxFW9Y+gW04jmjYMIDTj/W0ugnw+PjInjsu4t+MfC3VKu50IoH4iKq3y8ZS+IdTFdHBkLCbdDzhnXr9NmILQd7PqcQfdD92tdX08I4bGU1rpeXjghIUFv2LChXl5bCNFwKaU2aq0T6rsdtUXunUKI2uDuvVOSY4QQopYopcYppRKVUklKqcccPH6PUmq7UmqLUuoXpVR8fbRTCCHcJYGjEELUAqWUD/AGMB6IB65zEBh+orXurrXuBbwIvFTHzRRCiGqRwFEIIWpHfyBJa31Aa10AzAEmlj1Ba51R5tcQoH7WDgkhhJskOUYIIWpHDHC0zO/HgAEVT1JK3Qc8DPgDoxxdSCk1DZgG0KZNmxpvqBBCuEtGHIUQonY4qkFUaURRa/2G1roj8GfgCUcX0lrP0FonaK0ToqKc18MUQojaJoGjEELUjmNA6zK/twJOVHH+HOCqWm2REEJcIAkchRCidqwHYpVS7ZVS/sBUYF7ZE5RSsWV+nQDsq8P2CSFEtckaRyGEqAVa60Kl1P3AYsAHmKW13qmUehbYoLWeB9yvlBoNWIE04Jb6a7EQQrhWbwXAlVKpwOFqPi0SOF0LzalrDaEfDaEPIP3wNDXRj7Za6wa7EPA3fO9sCH0A6YenaQj9qKk+uHXvrLfA8XwopTY0hB0hGkI/GkIfQPrhaRpKPzxNQ/jv2hD6ANIPT9MQ+lHXfZA1jkIIIYQQwi0SOAohhBBCCLd4W+A4o74bUEMaQj8aQh9A+uFpGko/PE1D+O/aEPoA0g9P0xD6Uad98Ko1jkIIIYQQov5424ijEEIIIYSoJxI4CiGEEEIIt3hF4KiUGqeUSlRKJSmlHqvv9rhLKTVLKZWilNpR5liEUmqJUmqf7XuT+myjO5RSrZVSy5RSu5VSO5VSD9mOe1VflFKBSql1Sqmttn48YzveXim11taPz2y7fHg0pZSPUmqzUmq+7Xdv7MMhpdR2pdQWpdQG2zGv+pvydHLvrF8N4d7ZkO6bIPfOmuDxgaNSygd4AxgPxAPXKaXi67dVbnsPGFfh2GPAUq11LLDU9runKwQe0Vp3BQYC99n+H3hbX/KBUVrrnkAvYJxSaiDwT+BlWz/SgDvqsY3uegjYXeZ3b+wDwEitda8yNci87W/KY8m90yM0hHtnQ7pvgtw7L5jHB45AfyBJa31Aa10AzAEm1nOb3KK1XgmcrXB4IvC+7ef3gavqtFHnQWt9Umu9yfZzJuYfXQxe1hdtZNl+9bN9aWAU8KXtuMf3QynVCrOv8Uzb7wov60MVvOpvysPJvbOeNYR7Z0O5b4LcO2uKNwSOMcDRMr8fsx3zVs201ifB3FSA6HpuT7UopdoBvYG1eGFfbNMUW4AUYAmwHzintS60neINf1+vAH8Cim2/N8X7+gDmzecHpdRGpdQ02zGv+5vyYHLv9CDefO9sIPdNkHtnjfCtrQvXIOXgmNQQqgdKqVDgK2C61jrDfFjzLlrrIqCXUioc+Abo6ui0um2V+5RSlwMpWuuNSqkR9sMOTvXYPpQxRGt9QikVDSxRSu2p7wY1MN76d9HgePu909vvmyD3zprkDSOOx4DWZX5vBZyop7bUhFNKqRYAtu8p9dwetyil/DA3vo+11l/bDntlXwC01ueA5Zh1R+FKKfuHKE//+xoCXKmUOoSZehyF+RTtTX0AQGt9wvY9BfNm1B8v/pvyQHLv9AAN6d7pxfdNkHtnjfGGwHE9EGvLfPIHpgLz6rlNF2IecIvt51uAufXYFrfY1oG8C+zWWr9U5iGv6otSKsr2iRmlVBAwGrPmaBlwje00j+6H1vpxrXUrrXU7zL+Fn7TWN+BFfQBQSoUopcLsPwNjgB142d+Uh5N7Zz1rCPfOhnDfBLl31iittcd/AZcBezHrKv5a3+2pRrs/BU4CVsyn/zswayqWAvts3yPqu51u9GMoZvh+G7DF9nWZt/UF6AFstvVjB/CU7XgHYB2QBHwBBNR3W93szwhgvjf2wdberbavnfZ/1972N+XpX3LvrPd+eP29s6HdN21tl3vnBXzJloNCCCGEEMIt3jBVLYQQQgghPIAEjkIIIYQQwi0SOAohhBBCCLdI4CiEEEIIIdwigaMQQgghhHCLBI5CCCGEEMItEjgKIYQQQgi3/D9jAjV45nbDAgAAAABJRU5ErkJggg==\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 [==============================] - 6s 214ms/step - loss: 1.0274 - acc: 0.4634 - val_loss: 0.8589 - 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/weights-augm-trial5-improvement-BEST.hdf5\n",
      "Epoch 2/200\n",
      "27/27 [==============================] - 4s 161ms/step - loss: 0.9004 - acc: 0.5932 - val_loss: 1.0389 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00002: val_acc did not improve from 0.68421\n",
      "Epoch 3/200\n",
      "27/27 [==============================] - 4s 160ms/step - loss: 0.9850 - acc: 0.3986 - val_loss: 1.1855 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00003: val_acc did not improve from 0.68421\n",
      "Epoch 4/200\n",
      "27/27 [==============================] - 4s 165ms/step - loss: 0.9144 - acc: 0.5366 - val_loss: 0.9336 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00004: val_acc did not improve from 0.68421\n",
      "Epoch 5/200\n",
      "27/27 [==============================] - 4s 161ms/step - loss: 0.8769 - acc: 0.5098 - val_loss: 0.8686 - val_acc: 0.3684\n",
      "\n",
      "Epoch 00005: val_acc did not improve from 0.68421\n",
      "Epoch 6/200\n",
      "27/27 [==============================] - 5s 169ms/step - loss: 0.8194 - acc: 0.6200 - val_loss: 0.8202 - val_acc: 0.4211\n",
      "\n",
      "Epoch 00006: val_acc did not improve from 0.68421\n",
      "Epoch 7/200\n",
      "27/27 [==============================] - 5s 168ms/step - loss: 0.7951 - acc: 0.5829 - val_loss: 0.8033 - val_acc: 0.4211\n",
      "\n",
      "Epoch 00007: val_acc did not improve from 0.68421\n",
      "Epoch 8/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 0.8377 - acc: 0.5839 - val_loss: 0.9223 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00008: val_acc did not improve from 0.68421\n",
      "Epoch 9/200\n",
      "27/27 [==============================] - 4s 160ms/step - loss: 0.8183 - acc: 0.5839 - val_loss: 0.7464 - 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/weights-augm-trial5-improvement-BEST.hdf5\n",
      "Epoch 10/200\n",
      "27/27 [==============================] - 4s 159ms/step - loss: 0.7764 - acc: 0.6478 - val_loss: 0.8042 - val_acc: 0.3684\n",
      "\n",
      "Epoch 00010: val_acc did not improve from 0.73684\n",
      "Epoch 11/200\n",
      "27/27 [==============================] - 5s 167ms/step - loss: 0.7449 - acc: 0.6849 - val_loss: 0.7745 - val_acc: 0.5263\n",
      "\n",
      "Epoch 00011: val_acc did not improve from 0.73684\n",
      "Epoch 12/200\n",
      "27/27 [==============================] - 4s 154ms/step - loss: 0.6985 - acc: 0.6849 - val_loss: 0.7553 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00012: val_acc did not improve from 0.73684\n",
      "Epoch 13/200\n",
      "27/27 [==============================] - 4s 161ms/step - loss: 0.6552 - acc: 0.7590 - val_loss: 0.6901 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00013: val_acc improved from 0.73684 to 0.73684, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/weights-augm-trial5-improvement-BEST.hdf5\n",
      "Epoch 14/200\n",
      "27/27 [==============================] - 4s 158ms/step - loss: 0.6453 - acc: 0.7497 - val_loss: 0.6072 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00014: val_acc improved from 0.73684 to 0.84211, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/weights-augm-trial5-improvement-BEST.hdf5\n",
      "Epoch 15/200\n",
      "27/27 [==============================] - 4s 155ms/step - loss: 0.6059 - acc: 0.7415 - val_loss: 0.6342 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00015: val_acc did not improve from 0.84211\n",
      "Epoch 16/200\n",
      "27/27 [==============================] - 5s 169ms/step - loss: 0.5911 - acc: 0.8610 - val_loss: 0.5870 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00016: val_acc improved from 0.84211 to 0.89474, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/weights-augm-trial5-improvement-BEST.hdf5\n",
      "Epoch 17/200\n",
      "27/27 [==============================] - 4s 161ms/step - loss: 0.5500 - acc: 0.8610 - val_loss: 0.5217 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00017: val_acc did not improve from 0.89474\n",
      "Epoch 18/200\n",
      "27/27 [==============================] - 4s 155ms/step - loss: 0.5325 - acc: 0.8332 - val_loss: 0.5419 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00018: val_acc did not improve from 0.89474\n",
      "Epoch 19/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 0.5661 - acc: 0.7693 - val_loss: 0.4801 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00019: val_acc did not improve from 0.89474\n",
      "Epoch 20/200\n",
      "27/27 [==============================] - 4s 159ms/step - loss: 0.5239 - acc: 0.8332 - val_loss: 0.5574 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00020: val_acc did not improve from 0.89474\n",
      "Epoch 21/200\n",
      "27/27 [==============================] - 4s 159ms/step - loss: 0.5138 - acc: 0.8702 - val_loss: 0.4680 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00021: val_acc improved from 0.89474 to 0.94737, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/weights-augm-trial5-improvement-BEST.hdf5\n",
      "Epoch 22/200\n",
      "27/27 [==============================] - 4s 163ms/step - loss: 0.4871 - acc: 0.8435 - val_loss: 0.5160 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00022: val_acc did not improve from 0.94737\n",
      "Epoch 23/200\n",
      "27/27 [==============================] - 4s 165ms/step - loss: 0.4358 - acc: 0.9073 - val_loss: 0.4817 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00023: val_acc did not improve from 0.94737\n",
      "Epoch 24/200\n",
      "27/27 [==============================] - 4s 164ms/step - loss: 0.3853 - acc: 0.9166 - val_loss: 0.4439 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00024: val_acc did not improve from 0.94737\n",
      "Epoch 25/200\n",
      "27/27 [==============================] - 4s 156ms/step - loss: 0.4224 - acc: 0.8795 - val_loss: 0.5786 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00025: val_acc did not improve from 0.94737\n",
      "Epoch 26/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 0.4438 - acc: 0.8795 - val_loss: 0.5473 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00026: val_acc did not improve from 0.94737\n",
      "Epoch 27/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 0.4368 - acc: 0.8702 - val_loss: 0.5432 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00027: val_acc did not improve from 0.94737\n",
      "Epoch 28/200\n",
      "27/27 [==============================] - 4s 161ms/step - loss: 0.4352 - acc: 0.8702 - val_loss: 0.4305 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00028: val_acc did not improve from 0.94737\n",
      "Epoch 29/200\n",
      "27/27 [==============================] - 4s 165ms/step - loss: 0.4239 - acc: 0.8795 - val_loss: 0.4568 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00029: val_acc did not improve from 0.94737\n",
      "Epoch 30/200\n",
      "27/27 [==============================] - 4s 164ms/step - loss: 0.4372 - acc: 0.8795 - val_loss: 0.4774 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00030: val_acc did not improve from 0.94737\n",
      "Epoch 31/200\n",
      "27/27 [==============================] - 4s 163ms/step - loss: 0.4095 - acc: 0.8610 - val_loss: 0.4517 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00031: val_acc did not improve from 0.94737\n",
      "Epoch 32/200\n",
      "27/27 [==============================] - 4s 156ms/step - loss: 0.3964 - acc: 0.8888 - val_loss: 0.4246 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00032: val_acc did not improve from 0.94737\n",
      "Epoch 33/200\n",
      "27/27 [==============================] - 4s 163ms/step - loss: 0.3595 - acc: 0.9073 - val_loss: 0.4378 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00033: val_acc did not improve from 0.94737\n",
      "Epoch 34/200\n",
      "27/27 [==============================] - 4s 159ms/step - loss: 0.3912 - acc: 0.9073 - val_loss: 0.3934 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00034: val_acc did not improve from 0.94737\n",
      "Epoch 35/200\n",
      "27/27 [==============================] - 4s 159ms/step - loss: 0.3377 - acc: 0.9444 - val_loss: 0.4411 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00035: val_acc did not improve from 0.94737\n",
      "Epoch 36/200\n",
      "27/27 [==============================] - 4s 165ms/step - loss: 0.3711 - acc: 0.9444 - val_loss: 0.4710 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00036: val_acc did not improve from 0.94737\n",
      "Epoch 37/200\n",
      "27/27 [==============================] - 4s 156ms/step - loss: 0.3662 - acc: 0.9166 - val_loss: 0.4378 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00037: val_acc did not improve from 0.94737\n",
      "Epoch 38/200\n",
      "27/27 [==============================] - 4s 160ms/step - loss: 0.3314 - acc: 0.9259 - val_loss: 0.4310 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00038: val_acc did not improve from 0.94737\n",
      "Epoch 39/200\n",
      "27/27 [==============================] - 4s 166ms/step - loss: 0.3205 - acc: 0.9444 - val_loss: 0.4378 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00039: val_acc did not improve from 0.94737\n",
      "Epoch 40/200\n",
      "27/27 [==============================] - 4s 155ms/step - loss: 0.3466 - acc: 0.9073 - val_loss: 0.4246 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00040: val_acc did not improve from 0.94737\n",
      "Epoch 41/200\n",
      "27/27 [==============================] - 5s 167ms/step - loss: 0.3904 - acc: 0.8795 - val_loss: 0.3973 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00041: val_acc did not improve from 0.94737\n",
      "Epoch 42/200\n",
      "27/27 [==============================] - 4s 160ms/step - loss: 0.3277 - acc: 0.9444 - val_loss: 0.4326 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00042: val_acc did not improve from 0.94737\n",
      "Epoch 43/200\n",
      "27/27 [==============================] - 4s 160ms/step - loss: 0.3334 - acc: 0.9166 - val_loss: 0.4348 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00043: val_acc did not improve from 0.94737\n",
      "Epoch 44/200\n",
      "27/27 [==============================] - 4s 164ms/step - loss: 0.3461 - acc: 0.9259 - val_loss: 0.4548 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00044: val_acc did not improve from 0.94737\n",
      "Epoch 45/200\n",
      "27/27 [==============================] - 4s 163ms/step - loss: 0.2887 - acc: 0.9722 - val_loss: 0.4373 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00045: val_acc did not improve from 0.94737\n",
      "Epoch 46/200\n",
      "27/27 [==============================] - 4s 158ms/step - loss: 0.3278 - acc: 0.9166 - val_loss: 0.4080 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00046: val_acc did not improve from 0.94737\n",
      "Epoch 47/200\n",
      "27/27 [==============================] - 4s 157ms/step - loss: 0.3039 - acc: 0.9629 - val_loss: 0.4328 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00047: val_acc did not improve from 0.94737\n",
      "Epoch 48/200\n",
      "27/27 [==============================] - 4s 164ms/step - loss: 0.3027 - acc: 0.9444 - val_loss: 0.4375 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00048: val_acc did not improve from 0.94737\n",
      "Epoch 49/200\n",
      "27/27 [==============================] - 4s 159ms/step - loss: 0.3307 - acc: 0.9351 - val_loss: 0.4210 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00049: val_acc did not improve from 0.94737\n",
      "Epoch 00049: 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 [==============================] - 6s 220ms/step - loss: 1.0637 - acc: 0.5273 - val_loss: 0.8491 - 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/weights-augm-trial6-improvement-BEST.hdf5\n",
      "Epoch 2/200\n",
      "27/27 [==============================] - 4s 155ms/step - loss: 0.9590 - acc: 0.4902 - val_loss: 1.0038 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00002: val_acc did not improve from 0.68421\n",
      "Epoch 3/200\n",
      "27/27 [==============================] - 5s 170ms/step - loss: 0.9772 - acc: 0.4717 - val_loss: 0.8925 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00003: val_acc did not improve from 0.68421\n",
      "Epoch 4/200\n",
      "27/27 [==============================] - 4s 164ms/step - loss: 0.9503 - acc: 0.4449 - val_loss: 0.8741 - val_acc: 0.2632\n",
      "\n",
      "Epoch 00004: val_acc did not improve from 0.68421\n",
      "Epoch 5/200\n",
      "27/27 [==============================] - 4s 159ms/step - loss: 0.8713 - acc: 0.4717 - val_loss: 0.8573 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00005: val_acc did not improve from 0.68421\n",
      "Epoch 6/200\n",
      "27/27 [==============================] - 4s 163ms/step - loss: 0.8775 - acc: 0.5376 - val_loss: 0.8935 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00006: val_acc did not improve from 0.68421\n",
      "Epoch 7/200\n",
      "27/27 [==============================] - 4s 160ms/step - loss: 0.8741 - acc: 0.4820 - val_loss: 0.8468 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00007: val_acc did not improve from 0.68421\n",
      "Epoch 8/200\n",
      "27/27 [==============================] - 4s 156ms/step - loss: 0.8430 - acc: 0.4902 - val_loss: 0.8524 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00008: val_acc did not improve from 0.68421\n",
      "Epoch 9/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 0.8170 - acc: 0.5551 - val_loss: 0.8188 - val_acc: 0.3684\n",
      "\n",
      "Epoch 00009: val_acc did not improve from 0.68421\n",
      "Epoch 10/200\n",
      "27/27 [==============================] - 4s 161ms/step - loss: 0.7987 - acc: 0.5654 - val_loss: 0.7927 - val_acc: 0.4211\n",
      "\n",
      "Epoch 00010: val_acc did not improve from 0.68421\n",
      "Epoch 11/200\n",
      "27/27 [==============================] - 4s 153ms/step - loss: 0.7638 - acc: 0.6200 - val_loss: 0.7544 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00011: val_acc improved from 0.68421 to 0.68421, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/weights-augm-trial6-improvement-BEST.hdf5\n",
      "Epoch 12/200\n",
      "27/27 [==============================] - 4s 160ms/step - loss: 0.7462 - acc: 0.6488 - val_loss: 0.8023 - val_acc: 0.3684\n",
      "\n",
      "Epoch 00012: val_acc did not improve from 0.68421\n",
      "Epoch 13/200\n",
      "27/27 [==============================] - 4s 160ms/step - loss: 0.7076 - acc: 0.6478 - val_loss: 0.7076 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00013: val_acc improved from 0.68421 to 0.73684, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/weights-augm-trial6-improvement-BEST.hdf5\n",
      "Epoch 14/200\n",
      "27/27 [==============================] - 4s 154ms/step - loss: 0.7002 - acc: 0.6849 - val_loss: 0.8034 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00014: val_acc did not improve from 0.73684\n",
      "Epoch 15/200\n",
      "27/27 [==============================] - 4s 156ms/step - loss: 0.7199 - acc: 0.6385 - val_loss: 0.6627 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00015: val_acc improved from 0.73684 to 0.89474, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/weights-augm-trial6-improvement-BEST.hdf5\n",
      "Epoch 16/200\n",
      "27/27 [==============================] - 4s 161ms/step - loss: 0.6880 - acc: 0.7137 - val_loss: 0.7322 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00016: val_acc did not improve from 0.89474\n",
      "Epoch 17/200\n",
      "27/27 [==============================] - 4s 161ms/step - loss: 0.6583 - acc: 0.7230 - val_loss: 0.6629 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00017: val_acc did not improve from 0.89474\n",
      "Epoch 18/200\n",
      "27/27 [==============================] - 5s 169ms/step - loss: 0.5979 - acc: 0.8424 - val_loss: 0.6411 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00018: val_acc did not improve from 0.89474\n",
      "Epoch 19/200\n",
      "27/27 [==============================] - 4s 164ms/step - loss: 0.5570 - acc: 0.7868 - val_loss: 0.5610 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00019: val_acc did not improve from 0.89474\n",
      "Epoch 20/200\n",
      "27/27 [==============================] - 4s 155ms/step - loss: 0.5368 - acc: 0.8054 - val_loss: 0.5679 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00020: val_acc did not improve from 0.89474\n",
      "Epoch 21/200\n",
      "27/27 [==============================] - 4s 163ms/step - loss: 0.5157 - acc: 0.8517 - val_loss: 0.4918 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00021: val_acc did not improve from 0.89474\n",
      "Epoch 22/200\n",
      "27/27 [==============================] - 4s 157ms/step - loss: 0.4836 - acc: 0.8795 - val_loss: 0.4812 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00022: val_acc did not improve from 0.89474\n",
      "Epoch 23/200\n",
      "27/27 [==============================] - 5s 171ms/step - loss: 0.4995 - acc: 0.8517 - val_loss: 0.5108 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00023: val_acc did not improve from 0.89474\n",
      "Epoch 24/200\n",
      "27/27 [==============================] - 5s 171ms/step - loss: 0.4999 - acc: 0.8239 - val_loss: 0.4857 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00024: val_acc improved from 0.89474 to 0.94737, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/weights-augm-trial6-improvement-BEST.hdf5\n",
      "Epoch 25/200\n",
      "27/27 [==============================] - 4s 161ms/step - loss: 0.4995 - acc: 0.8610 - val_loss: 0.5157 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00025: val_acc did not improve from 0.94737\n",
      "Epoch 26/200\n",
      "27/27 [==============================] - 4s 157ms/step - loss: 0.4628 - acc: 0.8517 - val_loss: 0.5846 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00026: val_acc did not improve from 0.94737\n",
      "Epoch 27/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 0.4300 - acc: 0.8888 - val_loss: 0.4284 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00027: val_acc did not improve from 0.94737\n",
      "Epoch 28/200\n",
      "27/27 [==============================] - 4s 159ms/step - loss: 0.4647 - acc: 0.8795 - val_loss: 0.4410 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00028: val_acc did not improve from 0.94737\n",
      "Epoch 29/200\n",
      "27/27 [==============================] - 5s 167ms/step - loss: 0.3906 - acc: 0.8980 - val_loss: 0.4692 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00029: val_acc did not improve from 0.94737\n",
      "Epoch 30/200\n",
      "27/27 [==============================] - 4s 163ms/step - loss: 0.4128 - acc: 0.9166 - val_loss: 0.4516 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00030: val_acc did not improve from 0.94737\n",
      "Epoch 31/200\n",
      "27/27 [==============================] - 4s 160ms/step - loss: 0.3828 - acc: 0.9073 - val_loss: 0.4376 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00031: val_acc did not improve from 0.94737\n",
      "Epoch 32/200\n",
      "27/27 [==============================] - 5s 168ms/step - loss: 0.3692 - acc: 0.9351 - val_loss: 0.5335 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00032: val_acc did not improve from 0.94737\n",
      "Epoch 33/200\n",
      "27/27 [==============================] - 4s 164ms/step - loss: 0.3397 - acc: 0.9537 - val_loss: 0.5168 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00033: val_acc did not improve from 0.94737\n",
      "Epoch 34/200\n",
      "27/27 [==============================] - 4s 160ms/step - loss: 0.3663 - acc: 0.9351 - val_loss: 0.4288 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00034: val_acc did not improve from 0.94737\n",
      "Epoch 35/200\n",
      "27/27 [==============================] - 4s 164ms/step - loss: 0.4037 - acc: 0.9166 - val_loss: 0.4632 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00035: val_acc did not improve from 0.94737\n",
      "Epoch 36/200\n",
      "27/27 [==============================] - 4s 164ms/step - loss: 0.3660 - acc: 0.9259 - val_loss: 0.5266 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00036: val_acc did not improve from 0.94737\n",
      "Epoch 37/200\n",
      "27/27 [==============================] - 4s 161ms/step - loss: 0.3295 - acc: 0.9444 - val_loss: 0.4588 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00037: val_acc did not improve from 0.94737\n",
      "Epoch 38/200\n",
      "27/27 [==============================] - 4s 163ms/step - loss: 0.3648 - acc: 0.9166 - val_loss: 0.4449 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00038: val_acc did not improve from 0.94737\n",
      "Epoch 39/200\n",
      "27/27 [==============================] - 4s 160ms/step - loss: 0.3468 - acc: 0.8898 - val_loss: 0.5271 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00039: val_acc did not improve from 0.94737\n",
      "Epoch 40/200\n",
      "27/27 [==============================] - 4s 153ms/step - loss: 0.3669 - acc: 0.8888 - val_loss: 0.4713 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00040: val_acc did not improve from 0.94737\n",
      "Epoch 41/200\n",
      "27/27 [==============================] - 4s 163ms/step - loss: 0.3253 - acc: 0.9444 - val_loss: 0.4213 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00041: val_acc did not improve from 0.94737\n",
      "Epoch 42/200\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27/27 [==============================] - 4s 165ms/step - loss: 0.3344 - acc: 0.9259 - val_loss: 0.4708 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00042: val_acc did not improve from 0.94737\n",
      "Epoch 43/200\n",
      "27/27 [==============================] - 4s 157ms/step - loss: 0.3354 - acc: 0.9166 - val_loss: 0.4897 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00043: val_acc did not improve from 0.94737\n",
      "Epoch 44/200\n",
      "27/27 [==============================] - 5s 167ms/step - loss: 0.3166 - acc: 0.9537 - val_loss: 0.4247 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00044: val_acc did not improve from 0.94737\n",
      "Epoch 45/200\n",
      "27/27 [==============================] - 4s 160ms/step - loss: 0.3802 - acc: 0.8980 - val_loss: 0.4214 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00045: val_acc did not improve from 0.94737\n",
      "Epoch 46/200\n",
      "27/27 [==============================] - 4s 163ms/step - loss: 0.3027 - acc: 0.9351 - val_loss: 0.4677 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00046: val_acc did not improve from 0.94737\n",
      "Epoch 47/200\n",
      "27/27 [==============================] - 4s 165ms/step - loss: 0.3390 - acc: 0.9351 - val_loss: 0.4113 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00047: val_acc did not improve from 0.94737\n",
      "Epoch 48/200\n",
      "27/27 [==============================] - 4s 158ms/step - loss: 0.3050 - acc: 0.9629 - val_loss: 0.5152 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00048: val_acc did not improve from 0.94737\n",
      "Epoch 49/200\n",
      "27/27 [==============================] - 4s 160ms/step - loss: 0.3353 - acc: 0.8888 - val_loss: 0.4010 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00049: val_acc did not improve from 0.94737\n",
      "Epoch 50/200\n",
      "27/27 [==============================] - 4s 158ms/step - loss: 0.3177 - acc: 0.9351 - val_loss: 0.3831 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00050: val_acc did not improve from 0.94737\n",
      "Epoch 51/200\n",
      "27/27 [==============================] - 4s 166ms/step - loss: 0.3080 - acc: 0.9351 - val_loss: 0.3933 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00051: val_acc did not improve from 0.94737\n",
      "Epoch 52/200\n",
      "27/27 [==============================] - 4s 164ms/step - loss: 0.3173 - acc: 0.9537 - val_loss: 0.3736 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00052: val_acc did not improve from 0.94737\n",
      "Epoch 53/200\n",
      "27/27 [==============================] - 4s 155ms/step - loss: 0.2895 - acc: 0.9629 - val_loss: 0.4001 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00053: val_acc did not improve from 0.94737\n",
      "Epoch 54/200\n",
      "27/27 [==============================] - 4s 163ms/step - loss: 0.2981 - acc: 0.9444 - val_loss: 0.4467 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00054: val_acc did not improve from 0.94737\n",
      "Epoch 55/200\n",
      "27/27 [==============================] - 5s 168ms/step - loss: 0.3029 - acc: 0.9444 - val_loss: 0.4143 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00055: val_acc did not improve from 0.94737\n",
      "Epoch 56/200\n",
      "27/27 [==============================] - 4s 160ms/step - loss: 0.2783 - acc: 0.9537 - val_loss: 0.4115 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00056: val_acc did not improve from 0.94737\n",
      "Epoch 57/200\n",
      "27/27 [==============================] - 4s 158ms/step - loss: 0.2755 - acc: 0.9629 - val_loss: 0.4228 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00057: val_acc did not improve from 0.94737\n",
      "Epoch 58/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 0.3115 - acc: 0.9351 - val_loss: 0.4332 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00058: val_acc did not improve from 0.94737\n",
      "Epoch 59/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 0.2915 - acc: 0.9444 - val_loss: 0.4138 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00059: val_acc did not improve from 0.94737\n",
      "Epoch 60/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 0.2619 - acc: 0.9629 - val_loss: 0.3985 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00060: val_acc did not improve from 0.94737\n",
      "Epoch 61/200\n",
      "27/27 [==============================] - 4s 153ms/step - loss: 0.2912 - acc: 0.9537 - val_loss: 0.4448 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00061: val_acc did not improve from 0.94737\n",
      "Epoch 62/200\n",
      "27/27 [==============================] - 4s 165ms/step - loss: 0.2700 - acc: 0.9722 - val_loss: 0.4137 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00062: val_acc did not improve from 0.94737\n",
      "Epoch 63/200\n",
      "27/27 [==============================] - 4s 161ms/step - loss: 0.2537 - acc: 0.9629 - val_loss: 0.4141 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00063: val_acc did not improve from 0.94737\n",
      "Epoch 64/200\n",
      "27/27 [==============================] - 4s 156ms/step - loss: 0.2948 - acc: 0.9259 - val_loss: 0.4559 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00064: val_acc did not improve from 0.94737\n",
      "Epoch 65/200\n",
      "27/27 [==============================] - 4s 152ms/step - loss: 0.2432 - acc: 0.9629 - val_loss: 0.5136 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00065: val_acc did not improve from 0.94737\n",
      "Epoch 66/200\n",
      "27/27 [==============================] - 4s 164ms/step - loss: 0.2752 - acc: 0.9629 - val_loss: 0.4037 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00066: val_acc did not improve from 0.94737\n",
      "Epoch 67/200\n",
      "27/27 [==============================] - 4s 156ms/step - loss: 0.2753 - acc: 0.9629 - val_loss: 0.3982 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00067: val_acc did not improve from 0.94737\n",
      "Epoch 00067: 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 [==============================] - 6s 229ms/step - loss: 1.0281 - acc: 0.5190 - val_loss: 0.9419 - 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/weights-augm-trial7-improvement-BEST.hdf5\n",
      "Epoch 2/200\n",
      "27/27 [==============================] - 4s 155ms/step - loss: 0.9398 - acc: 0.5644 - val_loss: 0.8735 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00002: val_acc improved from 0.31579 to 0.68421, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/weights-augm-trial7-improvement-BEST.hdf5\n",
      "Epoch 3/200\n",
      "27/27 [==============================] - 4s 160ms/step - loss: 0.9204 - acc: 0.4810 - val_loss: 0.8962 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00003: val_acc did not improve from 0.68421\n",
      "Epoch 4/200\n",
      "27/27 [==============================] - 4s 161ms/step - loss: 0.8911 - acc: 0.5088 - val_loss: 1.0685 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00004: val_acc did not improve from 0.68421\n",
      "Epoch 5/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 0.8845 - acc: 0.5469 - val_loss: 0.8329 - val_acc: 0.3684\n",
      "\n",
      "Epoch 00005: val_acc did not improve from 0.68421\n",
      "Epoch 6/200\n",
      "27/27 [==============================] - 4s 154ms/step - loss: 0.8166 - acc: 0.6107 - val_loss: 0.8065 - 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/weights-augm-trial7-improvement-BEST.hdf5\n",
      "Epoch 7/200\n",
      "27/27 [==============================] - 4s 163ms/step - loss: 0.8439 - acc: 0.5376 - val_loss: 0.8250 - val_acc: 0.3684\n",
      "\n",
      "Epoch 00007: val_acc did not improve from 0.68421\n",
      "Epoch 8/200\n",
      "27/27 [==============================] - 4s 164ms/step - loss: 0.8019 - acc: 0.5839 - val_loss: 0.8409 - val_acc: 0.3684\n",
      "\n",
      "Epoch 00008: val_acc did not improve from 0.68421\n",
      "Epoch 9/200\n",
      "27/27 [==============================] - 4s 153ms/step - loss: 0.7983 - acc: 0.5839 - val_loss: 0.7340 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00009: val_acc improved from 0.68421 to 0.78947, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/weights-augm-trial7-improvement-BEST.hdf5\n",
      "Epoch 10/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 0.7434 - acc: 0.6663 - val_loss: 0.8825 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00010: val_acc did not improve from 0.78947\n",
      "Epoch 11/200\n",
      "27/27 [==============================] - 4s 163ms/step - loss: 0.7464 - acc: 0.6293 - val_loss: 0.7938 - val_acc: 0.4211\n",
      "\n",
      "Epoch 00011: val_acc did not improve from 0.78947\n",
      "Epoch 12/200\n",
      "27/27 [==============================] - 4s 160ms/step - loss: 0.7408 - acc: 0.6941 - val_loss: 0.7176 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00012: val_acc did not improve from 0.78947\n",
      "Epoch 13/200\n",
      "27/27 [==============================] - 4s 165ms/step - loss: 0.6911 - acc: 0.7508 - val_loss: 0.6965 - val_acc: 0.7368\n",
      "\n",
      "Epoch 00013: val_acc did not improve from 0.78947\n",
      "Epoch 14/200\n",
      "27/27 [==============================] - 4s 160ms/step - loss: 0.6390 - acc: 0.7683 - val_loss: 0.6834 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00014: val_acc did not improve from 0.78947\n",
      "Epoch 15/200\n",
      "27/27 [==============================] - 4s 164ms/step - loss: 0.6227 - acc: 0.7786 - val_loss: 0.6158 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00015: val_acc improved from 0.78947 to 0.89474, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/weights-augm-trial7-improvement-BEST.hdf5\n",
      "Epoch 16/200\n",
      "27/27 [==============================] - 4s 155ms/step - loss: 0.6057 - acc: 0.8517 - val_loss: 0.5987 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00016: val_acc did not improve from 0.89474\n",
      "Epoch 17/200\n",
      "27/27 [==============================] - 4s 163ms/step - loss: 0.5753 - acc: 0.8239 - val_loss: 0.6250 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00017: val_acc did not improve from 0.89474\n",
      "Epoch 18/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 0.5699 - acc: 0.8239 - val_loss: 0.4960 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00018: val_acc did not improve from 0.89474\n",
      "Epoch 19/200\n",
      "27/27 [==============================] - 4s 163ms/step - loss: 0.5012 - acc: 0.8054 - val_loss: 0.5382 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00019: val_acc did not improve from 0.89474\n",
      "Epoch 20/200\n",
      "27/27 [==============================] - 4s 159ms/step - loss: 0.5050 - acc: 0.8424 - val_loss: 0.5530 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00020: val_acc did not improve from 0.89474\n",
      "Epoch 21/200\n",
      "27/27 [==============================] - 4s 164ms/step - loss: 0.5436 - acc: 0.7971 - val_loss: 0.5746 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00021: val_acc did not improve from 0.89474\n",
      "Epoch 22/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 0.4738 - acc: 0.8610 - val_loss: 0.5175 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00022: val_acc did not improve from 0.89474\n",
      "Epoch 23/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 0.4547 - acc: 0.8888 - val_loss: 0.4488 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00023: val_acc improved from 0.89474 to 0.94737, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/weights-augm-trial7-improvement-BEST.hdf5\n",
      "Epoch 24/200\n",
      "27/27 [==============================] - 4s 156ms/step - loss: 0.4672 - acc: 0.8610 - val_loss: 0.5185 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00024: val_acc did not improve from 0.94737\n",
      "Epoch 25/200\n",
      "27/27 [==============================] - 5s 170ms/step - loss: 0.4571 - acc: 0.8610 - val_loss: 0.4405 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00025: val_acc did not improve from 0.94737\n",
      "Epoch 26/200\n",
      "27/27 [==============================] - 4s 156ms/step - loss: 0.5435 - acc: 0.8156 - val_loss: 0.4857 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00026: val_acc did not improve from 0.94737\n",
      "Epoch 27/200\n",
      "27/27 [==============================] - 4s 158ms/step - loss: 0.4631 - acc: 0.8795 - val_loss: 0.4335 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00027: val_acc did not improve from 0.94737\n",
      "Epoch 28/200\n",
      "27/27 [==============================] - 4s 164ms/step - loss: 0.4141 - acc: 0.9351 - val_loss: 0.4319 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00028: val_acc did not improve from 0.94737\n",
      "Epoch 29/200\n",
      "27/27 [==============================] - 4s 160ms/step - loss: 0.4345 - acc: 0.8702 - val_loss: 0.4195 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00029: val_acc did not improve from 0.94737\n",
      "Epoch 30/200\n",
      "27/27 [==============================] - 4s 160ms/step - loss: 0.3817 - acc: 0.9166 - val_loss: 0.4262 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00030: val_acc did not improve from 0.94737\n",
      "Epoch 31/200\n",
      "27/27 [==============================] - 4s 165ms/step - loss: 0.3906 - acc: 0.8888 - val_loss: 0.4744 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00031: val_acc did not improve from 0.94737\n",
      "Epoch 32/200\n",
      "27/27 [==============================] - 4s 161ms/step - loss: 0.3712 - acc: 0.9259 - val_loss: 0.4236 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00032: val_acc did not improve from 0.94737\n",
      "Epoch 33/200\n",
      "27/27 [==============================] - 4s 164ms/step - loss: 0.4027 - acc: 0.9444 - val_loss: 0.3992 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00033: val_acc did not improve from 0.94737\n",
      "Epoch 34/200\n",
      "27/27 [==============================] - 4s 154ms/step - loss: 0.3635 - acc: 0.9351 - val_loss: 0.3977 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00034: val_acc did not improve from 0.94737\n",
      "Epoch 35/200\n",
      "27/27 [==============================] - 4s 163ms/step - loss: 0.3689 - acc: 0.9351 - val_loss: 0.4018 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00035: val_acc did not improve from 0.94737\n",
      "Epoch 36/200\n",
      "27/27 [==============================] - 4s 155ms/step - loss: 0.3757 - acc: 0.9259 - val_loss: 0.4403 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00036: val_acc did not improve from 0.94737\n",
      "Epoch 37/200\n",
      "27/27 [==============================] - 4s 163ms/step - loss: 0.3498 - acc: 0.9351 - val_loss: 0.4277 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00037: val_acc did not improve from 0.94737\n",
      "Epoch 38/200\n",
      "27/27 [==============================] - 4s 160ms/step - loss: 0.3372 - acc: 0.9351 - val_loss: 0.4332 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00038: val_acc did not improve from 0.94737\n",
      "Epoch 39/200\n",
      "27/27 [==============================] - 4s 160ms/step - loss: 0.3515 - acc: 0.9166 - val_loss: 0.4186 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00039: val_acc did not improve from 0.94737\n",
      "Epoch 40/200\n",
      "27/27 [==============================] - 4s 156ms/step - loss: 0.3833 - acc: 0.8888 - val_loss: 0.3967 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00040: val_acc did not improve from 0.94737\n",
      "Epoch 41/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 0.3244 - acc: 0.9722 - val_loss: 0.4124 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00041: val_acc did not improve from 0.94737\n",
      "Epoch 42/200\n",
      "27/27 [==============================] - 4s 160ms/step - loss: 0.3027 - acc: 0.9629 - val_loss: 0.4180 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00042: val_acc did not improve from 0.94737\n",
      "Epoch 43/200\n",
      "27/27 [==============================] - 4s 155ms/step - loss: 0.3350 - acc: 0.9351 - val_loss: 0.3993 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00043: val_acc did not improve from 0.94737\n",
      "Epoch 44/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 0.3308 - acc: 0.9537 - val_loss: 0.4257 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00044: val_acc did not improve from 0.94737\n",
      "Epoch 45/200\n",
      "27/27 [==============================] - 4s 157ms/step - loss: 0.3223 - acc: 0.9259 - val_loss: 0.4016 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00045: val_acc did not improve from 0.94737\n",
      "Epoch 46/200\n",
      "27/27 [==============================] - 5s 168ms/step - loss: 0.3287 - acc: 0.9351 - val_loss: 0.4311 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00046: val_acc did not improve from 0.94737\n",
      "Epoch 47/200\n",
      "27/27 [==============================] - 4s 155ms/step - loss: 0.3483 - acc: 0.9259 - val_loss: 0.4084 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00047: val_acc did not improve from 0.94737\n",
      "Epoch 48/200\n",
      "27/27 [==============================] - 4s 163ms/step - loss: 0.3079 - acc: 0.9629 - val_loss: 0.4516 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00048: val_acc did not improve from 0.94737\n",
      "Epoch 49/200\n",
      "27/27 [==============================] - 4s 154ms/step - loss: 0.3130 - acc: 0.9444 - val_loss: 0.4394 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00049: val_acc did not improve from 0.94737\n",
      "Epoch 50/200\n",
      "27/27 [==============================] - 4s 161ms/step - loss: 0.3053 - acc: 0.9259 - val_loss: 0.4142 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00050: val_acc did not improve from 0.94737\n",
      "Epoch 51/200\n",
      "27/27 [==============================] - 4s 158ms/step - loss: 0.3256 - acc: 0.9444 - val_loss: 0.4356 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00051: val_acc did not improve from 0.94737\n",
      "Epoch 52/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 0.3091 - acc: 0.9537 - val_loss: 0.3826 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00052: val_acc did not improve from 0.94737\n",
      "Epoch 53/200\n",
      "27/27 [==============================] - 4s 154ms/step - loss: 0.2695 - acc: 0.9722 - val_loss: 0.4000 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00053: val_acc did not improve from 0.94737\n",
      "Epoch 54/200\n",
      "27/27 [==============================] - 4s 164ms/step - loss: 0.2626 - acc: 0.9629 - val_loss: 0.3890 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00054: val_acc did not improve from 0.94737\n",
      "Epoch 55/200\n",
      "27/27 [==============================] - 4s 165ms/step - loss: 0.2979 - acc: 0.9629 - val_loss: 0.3865 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00055: val_acc did not improve from 0.94737\n",
      "Epoch 56/200\n",
      "27/27 [==============================] - 4s 159ms/step - loss: 0.2691 - acc: 0.9629 - val_loss: 0.4677 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00056: val_acc did not improve from 0.94737\n",
      "Epoch 57/200\n",
      "27/27 [==============================] - 4s 163ms/step - loss: 0.3109 - acc: 0.9444 - val_loss: 0.4676 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00057: val_acc did not improve from 0.94737\n",
      "Epoch 58/200\n",
      "27/27 [==============================] - 4s 159ms/step - loss: 0.2901 - acc: 0.9351 - val_loss: 0.4896 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00058: val_acc did not improve from 0.94737\n",
      "Epoch 59/200\n",
      "27/27 [==============================] - 4s 156ms/step - loss: 0.3022 - acc: 0.9351 - val_loss: 0.4379 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00059: val_acc did not improve from 0.94737\n",
      "Epoch 60/200\n",
      "27/27 [==============================] - 4s 164ms/step - loss: 0.2798 - acc: 0.9537 - val_loss: 0.4228 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00060: val_acc did not improve from 0.94737\n",
      "Epoch 61/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 0.2692 - acc: 0.9537 - val_loss: 0.4427 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00061: val_acc did not improve from 0.94737\n",
      "Epoch 62/200\n",
      "27/27 [==============================] - 4s 154ms/step - loss: 0.3189 - acc: 0.9259 - val_loss: 0.4423 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00062: val_acc did not improve from 0.94737\n",
      "Epoch 63/200\n",
      "27/27 [==============================] - 5s 167ms/step - loss: 0.2688 - acc: 0.9537 - val_loss: 0.4053 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00063: val_acc did not improve from 0.94737\n",
      "Epoch 64/200\n",
      "27/27 [==============================] - 4s 156ms/step - loss: 0.2795 - acc: 0.9444 - val_loss: 0.4660 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00064: val_acc did not improve from 0.94737\n",
      "Epoch 65/200\n",
      "27/27 [==============================] - 4s 166ms/step - loss: 0.2458 - acc: 0.9722 - val_loss: 0.4157 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00065: val_acc did not improve from 0.94737\n",
      "Epoch 66/200\n",
      "27/27 [==============================] - 4s 164ms/step - loss: 0.2460 - acc: 0.9907 - val_loss: 0.4210 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00066: val_acc did not improve from 0.94737\n",
      "Epoch 67/200\n",
      "27/27 [==============================] - 4s 161ms/step - loss: 0.2670 - acc: 0.9629 - val_loss: 0.4300 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00067: val_acc did not improve from 0.94737\n",
      "Epoch 00067: early stopping\n"
     ]
    },
    {
     "data": {
      "image/png": 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y4eWroKmboNnTD27f3vX5MzAiAscGSzNvfZnP5OgAZo0L6r5xTTH4RYB3kKSqhRBCDKzdz0LJMTj3fuOXs6OyLDj8NjTVOg8cv37XeO+MNaB6ETAVfAnpz8HZ90DHACsvHXJ3QMp54BuOtaWFrOJaxoX44G0enLltzhw5UUp5cxNxidHtjiug6nQ1BwsqOScigmAfDw7kVZBhrWFOQjB7c8pJcPdldnxw9xeoK4GMDzi440OqGjy4dFYMYKxuvm/VNO56fT/Pbs1i3bJkqCuDA/+GuLkQOgGNZl9uBcfLagn2MTMvKbRv6fGiQ/DlS3DBg2BuWyF+YWoUV6TF8vjmTBaOD2VhcqiTv5gNUJpB7ZSr+fBwEUE+ZpZNDMdkC67NQFZGMVX1FlZOjMbNls63tLTw6VenWO5+kJ+E76Jx2jU8s/UEAFOjA1iSEmb78vI1TL8aTObO1wYw92/hUG/0+DeolHoOuAQo0lqnOjmvgEeBi4A64Nta670D2Uk3k+JX7xzm2rnjehE4FoJfJHgHQ0MltDSDaej+JxJCCDFK1RRD8RHjee4OSDm3/fnsLcZjzg5otoBbh1/q2VshbCJc8VTvrrfvVXjrNig6DFEdfv1mbwUUXPkP8A7mvje/4uXsHIKKzPzxqpmcN0QFrX/16BaiYj1Zefm8Tuei6y1c+buP2eIWyZoZ8Xxz1w6un5/AVZel8tR/D3D33ny23fyNdvP4Ommogt8nUHLwY4J8VhmBk82VabF8cOg0f9p0jOUTI5hUbhtGPP83EL+Al7Znc+/OQ3x7USK/vGgKHu59nJ13bBO8cg3k7YakZe1O3b9qGl/mVHD9szu587yJ3LY8uf2cyeyt6Mhp3FJ5C1/pSt7/7jJMIe3T4yqjmDue3cUf4mdwzRxjrua/tp3gV7sPs3P2x5i+/hf33DCeJRPCeNBWqFwpZfw7C4yHK5/p288zQHrzt/gCcGE351cCE2x/1gFPnHm32jO7mZidEMzOE2U9N64pAt8I8LIFmA2VA90dIYQTK1as6FTQ+y9/+Qu33357l+/x8/Pr8pwQw85Jh/xm9tbO5+3HmmrgVIc5fM1WI4WauKT310tc3M21tkDUdPAOZvPRIl7emcOVaXHEBnnz3RfTufftg4Ne0LqlRXOipKbd/EZHgd5mvjk/nncPnOLH/95HYqgvv7hoMmDUG7Q0t/Di9uzuL+IVQEvULMJKdrMyNbpdeR/7nMMAb3d+/O99NGdtAXdviEkjq7iG3248wvKJ4dx36dS+B40A8QuMkWEnf/8BXmbevmMxK1Oj+OOmo9zw7E4Kq2xTEKxNkLOTQ+YZ7Mgq475LpzEupPOcyiUpYUyNDuDpz7NoadFYm1t4ZssJ5iQEEznjPGhuhLzdrJgUwYd3LufsyRHQ0mL8O+zLv6MB1uOIo9b6c6VUYjdNVgMvaq01sEMpFaSUitZanxqgPgIwLymERz46RmWdhUCfLoZmtYbaorZUNRjpap+RswWSECPV2rVrWb9+PRdccEHrsfXr1/PHP/7Rhb0SYgBlbwWzL4RP6hxMaA3Z2yBxqRHUZW+BuDlt508fgKbqdr/wDxVU8s8vsvnt5dNxd1bvMCgeghKMz1pwW9txqxFQMOcWymub+PkbB5gU6c+Dl6eiFPzx/aM8s/UEHx8pIjqwLWUZG+zN/140hYiA9mnMqgYLv3/va46erm495mZS/M85E1jsMMLXUUFlPQ2WltZSPM7cvCSJ57dlU1TdwBvfW9RaVzA53I/zpkTy4vaT3LY8uTWFvOtEGc9syeLW5eNbty884TeLVF7EMq1zxjHUz5hzuO6lPZQ2fkxE/Hysyp0fv7YbT3c3/nDVjD6X7GnlFQjRM50H7hjB49/WnsWyCeHct+EQ5z/yORMi/JjUdIgHrfU8cTKac6dEcvWcOKfvV0px6/Lx/HD9PjYfLaKm0Up+RT33XToV4r0AZQSJSUvb3lT8NdSVujRwHIhV1bFArsPrPNuxTpRS65RS6Uqp9OLi4j5dZF5SCFpD+sluRh0bKqC5qS1VDbKyWoghctVVV/Huu+/S2GhsdZWdnU1BQQGzZs3inHPOIS0tjenTp/P222+7uKdC9FP2VmMUKvlsyN8LjTVt58qyoLoApl0O4ZM7Bxv21wltv/Bf253La+l5HCusoUuJS4zgocVhBW/+HrA2QOJi/t/bBymva+LP187Ey+yGp7sb91wyledvmktKhB+eZhOeZhMe7iY2HTrNhY9uYfPXbRtl7M0p56JHt7B+dy7ubqq1fW5ZHXe8spei6q4X8rSuqA7rOnMQHejNfZdO5fdXziCtw3zGW5ePp7LewmvpuVibW3jkw2OseXo7Hx4p5JqndvDYJ8b2hRurU/BUVua6H3d6jfOnRXHjTH8i6jLJD5rDE58eZ39uBb+5LJXIgDOc65ew2AjSLfVOTyuluGbuON75wRIWJYfiaTYxs/krAPwmLuN3V0zvNnC9aHo0sUHePPnZcZ76LIvx4b6cOyXSGPyKntH1v6PhPOLYC87+RpxWz9RaPw08DTBnzpw+bWY5a1wQHm4mdp0o45wpXczdsO8a4+eQqpYi4GIseu9uOP3VwH5m1HRY+VCXp0NDQ5k3bx7vv/8+q1evZv369Vx77bV4e3vz5ptvEhAQQElJCQsWLGDVqlX9HwUQwhVqS4z5jTOuhpizYMvDkLsTUs4xzrf+Ql8KhYeMRRrNVnBzbzsfOgH8235/pZ80BjYOFlQyNSbA+XUTl8C+l41rR05zuJZiY1Ui7x7I5qcXTOpUouXsSRGcPSmi3bHMomrueOVLbnphNzctTiTMz5M/f3iM6EAvXrt1IbMTgtu1vfivW/nFf77imRudF9pureEY0fWII8ANCxOdHp+dEMKchGCe2XKC9746za7sMq5Ii+VnF0zmtxuP8KcPjrElo4RjeVF8392E28ltkLzC6WfdPa0cjsIDXwXzcW0Gl86M4dKZMd32q1cSl8L2x4zFSI4jfx2kRPjxxPWzjRcvPgRe0/j9DWf3+PFmNxM3L0ni1+8eBuChK6a37UqTuNRYCGNpaFvscnKrMb8xOOGMfqwzMRAjjnmAYwXOOGDA6wN4md2YOS6w+3mO9l1jOqaqhRBDwp6uBiNNvXbtWmNf2F/+khkzZnDuueeSn59PYWFhD58kxDBjn9+YuBTGzQeTe/vRoOytxvz6sAlGsOc4z7GlGXLaz2+sbbRy5FQVYNQ27FKCk3mO2VtojkzlF+/lc1Z8ELf2UBDaLiXCn7e+v5hvL0rk+W3Z/HHTUVamRrHxh0vbBY32tj+/cDIff13Ea+m5Tj8vq6QWf093wv26WdzSg3XLxpNfUc+hgkoeuXYmf75mFlGBXjy6ZhZ/unomX+VXUm71oj4stcuUMYB3/naa3bzYXDOOUD8Pfr16Wr/71E438xydsjYZXyj6MCK4Zu44Ar3NnQuQJyw25jnmpxuvtTb6YZ/76iIDMeK4AbhDKbUemA9UDvT8Rrt5SSE89VkWtY1W50vqW0ccI425CSCpajE2dTMyOJguu+wy7rzzTvbu3Ut9fT1paWm88MILFBcXs2fPHsxmM4mJiTQ09KGOnRDDQfZWMPsYo41uZohJawsmtLYtWFgMSrUFDdlbIG62Mb+xsapdMLEvt4IWDZ7upu4Dx+AEY65j9haYf6sxvzF3Nyfir6ay3sIvL5rifH5kF7zMbty/ahrfmBxBdYOVi6ZHdTn6/+1FiXx4uJAH3jnMouSwTgs8soprGR/ue0bZg3OnRPL7K6ezYHwoCaFtI5dKKa6aHcfshGD2nCzHp2Q57HrKSBk7lMZplb0Vt/j5PDZnPgmhvgT5ePS7T+14B0GUk5RxVwq+BEtdnwJHX093nrguDXc3E16OpZQSFgLKNnd2ybCY3wi9GHFUSr0KbAcmKaXylFK3KKVuU0rZZ+puBLKATOAfQNdLKM/Q/KRQrC2aL3O6GEW0B46+4ZKqFsIF/Pz8WLFiBTfffDNr164FoLKykoiICMxmM5s3b+bkyZMu7qUQ/WCf32gvsZO4BAps8xzLT0BVftsvdL8ICHNYQONkXlp6djlKwaqZMRw+VUVzS+fZW7WNVmobrbYFN7Z5jvl7wVrPezXJxAR69VwHsQvLJoZz8YzoboM+k0nxp2tmYlKKn7y2v1Mfjxd3vaK6t0wmxbVz49sFjY6Swny5anYcKmmpsYYhb3fnRnVlUHgQEpdy/rQoJkX1fivAXklcYpvn2IsvvPaSTAl9GxVclBLGvKQOC3m9g40pQvbPHAbzG6EXgaPWeq3WOlprbdZax2mtn9VaP6m1ftJ2Xmutv6+1TtZaT9dapw9WZ9MSgnEzKXaeKHXeoKbQKIbpHWzMB3D3klS1EENs7dq17N+/nzVr1gBw3XXXkZ6ezpw5c3j55ZeZPHmyi3soRB/Vlhi1FB2DgcQl0GI10pKO8xtbzy820tPNVtv8xhTwj2o9nX6yjEmR/ixMDqXB0sLx4s4LZG771x6uf3YnOmER1JcZ8xyzt6JR/DM/hktmxrTNhxsksUHe3LdqGruyy/jP3rzW43VNVk5VNjA+rPv5jQOmu5TxyS8APXgBVeKS1tI4PcreChFTwddJUfB+XXtpW9CavQUCxxkr7V1oRO1V7efpTmpMQNfzHGuLjTS1/RuUd7CkqoUYYpdffjla69YAMSwsjO3bt5Oens4zzzzDkSNHSExMBKCmppvVpEIMF47zG+0c5zlmbzUyXWEO28/Z5zkW7DUCG4egprlFsy+ngtkJwaTGGtOqOqarqxssbD9eypc5Few12Yp/Z2+F7C1U+E+kpNmXVQOx+KMXrkyLbVdvEJzvUT2ouiuNk73VqN8YmzY41463p4x7SFc3W/o8v7FHiUuMFfT56W0paxcvLBxRgSMY8xz35VY4L2xaUwh+4W2vvYIkVS2EcBml1IVKqaNKqUyl1N1OzicopT5WSh1QSn2qlHJe8E24Vva2tvmNdp5+bfMcs7d2/oVuL7uz4wnb/Ma2oPNYYTXVjVZmJwSTHO6Hl9nEwfyqdpfcfrwUa4vG3aT4654mYyXt8c2Qu4sdegpJYb5M62ol9gCz1xvMLKrhE1spn6wSI3DsaUX1gGpNGXcojXNyK4ybB+79X6TTLXtpHMcC8M70Y35jj+zzHHc/a2y/6OI0NYzIwDGUJmsLB/KcTCa2bzdo5x0kO8cIIVxCKeUGPI6xu9ZUYK1SamqHZn/C2EBhBvAA8Luh7aXoleytxgije4cFF4m2Gn9V+Z3ntPlHGiOQh940Xjuct5fhmZMQgptJMTU6oNOI4+cZxfh6uPH9s1P47FgxFZHz4Nj7YK3nrfIkLu1hfuJAs9cbfOpzo5ZiVnGNsQ6oi7mJgyJhiW2eo8OMuLoyOH1w8AOqxKWQu6v7eY79nN/YLe9gY7tJJ/+OXGXEBY5zE42JwLuczXOsse0aYyepajHGGBs4jQ0j4GedB2RqrbO01k3AeoydthxNBT62Pd/s5LxwtdpSKDrkvARK4hJayxYnOqnxl7DYOB+SDAHRrYf3ZJcR7u/JuBBjdXBqbCCHCipb08AAWzJKWJgcyk2LE/HxcOO96uTWa+1snjwwNQr7wOxm4pYlSezOLmfPyXKOF9cSG+TdfhXwYHM2zzFnO4M6v9GuY2kcZ7K3QvgU8O16t51+SVwKaAiIg+DEgf3sfhiIcjxDKsjHg8lR/uw8UcYdjidamo0JzL4OgaNXENQfGOouCuESXl5elJaWEhoaOuqLa2utKS0txcvrDHeFGFzOdtWa36HNfuBK4FHgcsBfKRWqtW73zVgptQ5YBxAfHz9oHR4x6itg/6vG6JNd+GSYeIHz9ic+N9KI/VFq263EWWA4bgEoN2OQInxS5/OJS2DP852Cmj055cxJCG79/zQ1NpAXt58ku7SW8eF+nCyt5WRpHTcvTiLIx4M1c+N5avtp1npAtnsSUVExTIgc4JXDvXDt3HE8+nEGT39+nLzy+qGb32hnL41z6E3wsJUGOv6JsRA2dvbgXtueMv7ib8bOPc7k7IBZ1w38tROXwI6/D4v5jTACA0cw5jm+sScPa3NLW/2qujLQzU5S1TLHUYwNcXFx5OXl0dftPEcqLy8v4uKG9ZRcR9/kAAAgAElEQVTA3uyqdRfwmFLq28DnQD5g7fSmM9h1a1Q69Ca832HKqJsn/O9pMDlJpL15m5FO7i//GGM+Y0eefkawGhDj/Bf6+BXgEwZTVrUeKqpqILesnhsddlNJte368lV+JePD/fj8mPH/8LKJxpz9m5ck8s/tJ8j2nMx/alK5dPHQjjba+Xq6c8OCBB7/NBOzycTcxJCe3zTQplwKn/waPrzX4diqwZvfaOcdDEnLjOkCx9533kaZYNLKgb92wmIjtpm6que2Q2DkBI7NFqPwqacf85JCeHH7SfbnVbZVu3fcNcbOO9hY1dZsaau9JcQoZTabSUpKcnU3RJsed9XSWhcAVwAopfyAK7XWMjG7J/W2yho/O2EEDHtfgvd/DjWnjSDOkbXRCBqX3gVL7+zf9dw827YO7Gjtq12/zzcMftZ+f2X7/EbHnVomRPrh4W7iUEEVq2fF8nlGCXHB3iSGGqNqccE+XDojhhX7jGDpsxnRuMqNixJ5eksWTdYWksOHcH6j3bK7YMHttPsOZvbpsvmAuuEtsDrfsxowRp/Ng5AF8Q6Cu44N/Of208iY49hYDQ8lwK6nAVicHEaAlzt3vb6f4upGo02twz7VdlIEXAjhOruBCUqpJKWUB7AGY6etVkqpMKWU/T78C+C5Ie7jyNRQZQRzPiHg4WvUSASoyOncttJWezA0xWjbnz8OQeOru3L4jW1f4f5Izy7H093Ubm9ps5uJKVH+HMyvxNLcwvbjpSybGN5uysm6ZckAzBwX1GWx7KEQ7u/JlWnGSH/yUKeq7Tx82v/3Gar0rcnU/b+TwQgah6GRETh6+kNgrDF/AAj29eD5m+ZyurKBbz23i8p6S/vtBu1kv2ohhItora3AHcAm4Ajwmtb6kFLqAaWUPee0AjiqlDoGRAIPuqSzI01DZdu2smBsyQfOA8eKk+3bnIGaRiu/3XiEZ7ae6H6bwG7sySlnZlwQHu7tf/1Oiw3kYH4le0+WU9NoZdmE9gsspsYE8PMLJ/PT853MpRxiPzxnAjcuTCAtoX+71oiRbWQEjmCspsrdYWy5BMxOCOHJG2aTWVTNLS/sxlJp2x67Y6oaZMRRCOESWuuNWuuJtp21HrQdu1drvcH2/A2t9QRbm+9orRtd2+MRolPgaJsRUOFkO0t7MDkAgeP6XTlUN1jxcDfxjy1ZfX5/fVMzh/IrmZ3YOeCaHhtIVYOVl3fm4GZSLEzuvDL3eyuSWTJhgFfs9kNUoBe/Wp06tCuqxbAxggLHhcbNouRo66HlE8P5y7VnsTennI92H0S7e4OHw9B5a6paSvIIIcSo0TFwNHsbFTWcjjjmGDu8+J/ZvEBLcwvPbT3B/KQQvrUggXcPnCKvvK5Pn/FlTjnWFs0cJyN19gUy7x4oYNa4IAK9ZV6+GJ5GUOC4wHjM2d7u8MUzovn1Zak0VJyixhzSfq6DpKqFEGL0aagErw67pgTFQ0Vu57YVORAQ2/Xill5690ABBZUN3LY8mZuXJKGAZ7ee6NNnbDx4Cm+zGwuTO+9jPDHKD7ObokXDsgnhTt4txPAwcgLH4CTjG6VtnqOjb86LJ9m7lhMNfu23IpRUtRBCjD4dRxzBSFd3NeJ4hmlqrTVPfZbFxEg/VkwKJybIm1UzY/j37lwq6pp6/gDA2tzCxq9Oc86UCHw8Ogexnu5uTLTVZlw60fXpaCG6MnICR6WMUUcngaNSihSfOk5Z/Xlpu8McF/uNRVLVQggxejgNHOOhMrd1HnyrAQgcP88o4evT1axblty60nnd8vHUNTXzrx1O5lU68cXxUspqm7rd8WV2QjBhfh7MjAs6o/4KMZhGTuAIxjzHipNQVdDplE9TKe4BUfz900yqGyzGQTezMedRUtVCCDF6dBU4Nje11fQFo4Zj9ekzDhyf+uw4UQFerHII+iZHBbB8YjgvfJHdPtPVhQ37C/D3dGf5xK7T0D+7cDLv/GAJbibX7w4iRFdGWOBon+fYYdSx2QJ1paROmkB5nYVntjjMO/EOllS1EEKMFpYGY8/gToFjgvHomK6uzAP0GQWOX+VV8sXxUm5ektiphM6ty8dTUtPEfW8f4pktWTyzJYvntp6gqKqhXbtGazObDp7mgtSoblci+3m6Ex3o3e++CjEURs7OMQBR040K8Tk7IPWKtuO1xvZMkdHjWJkaxTNbsrhxUSIhvh62/aolVS2EEKNCg61+orMRRzACx/j5bc8dz/XDK7tO4uPhxtp5nT9j4fhQ5iYG8+/09oty3tiTx1vfX9waaH52tJjqRmu3aWohRoqRFTi6mSFujlHP0ZFD8e87z5vIpkOneeLTTP734qmyX7UQQowmrYFjh3mAgU5qOZ5h4NhkbeG9g6c5f2ok/l6dy+MopVi/biG1TW3bi2/NKOH2l/fy6MfH+OkFkwF458ApQnw9WORkNbUQI83ISlWDMc/x9FfGNoR2Rbbtn/wimRDpz+VnxfHP7SfJLKoxAkdJVQshxOjgZMRxX24FT20/Bb7h7VPVFTnG/sH+/Rvp25ZZQkWdpduRQjeTIsDL3PrnounRXD07jic+Pc6ek+XUNVn56HAhK1OjMLuNvF+5QnQ08v4Vj5sPugXydhuvqwvhg3uMNHbMLAB+fuEkfDzc+Mnr+2nxlFS1EEKMGk4Cx8c+yeR3731NrU9s58AxsP81HDfsLyDAy52lfayreO+lU4kO9OYnr+3j3f2nqLc0S5pajBojL3CMmwvKBDk7QWvYcAc01cIVzxipbCAiwItfr05lf24F+0qQVLUQQowW9vu5LXBssraw/XgJAF/XBxkleewqctoWzfT1MpZmPjh0mpWp0Z0WxfTE38vMw9fM5GRZHfe8dZDIAE/mJob0qx9CDDcjL3D0CoDIVGMHmfRnIeMDOO8BiJjcrtmlM2O4ZEY0n5xsAmsDWOoH5vqlx+G9u6HZ2nNbIYQQA6vDiOOXOeXUNjUzJTqA3RX+6AqHWo69qOFYWtPI1U9+wZc57TNTm78uorapmVWz+jdSuGB8KLcsTqKpuYVLZsRIiR0xaoy8wBGMeY65u2DTPZB8Dsz9rtNmv16disVs3Fwaa0oH5tpHNsDOJ9rmVQohhBg6HQLHzzOKcTMpHvvmWZxS4ajmRqgtstVwPNVj4Pj2vgJ2Z5fzo3/vo7axbUDgnQMFhPl5smB8/xe03HXBJL63wtiiUIjRYoQGjvPBWm9sbL/6cTA5/zGCfT24ZP4UAP61+cDAXLvqlPFYeGhgPk8IIUTvNVSCmwe4ewGwJaOEs8YFkRzuR+J4I/NUcSqzrYajfbV1F945UEC4vyc5ZXU8uPEIADWNVj4+UsTF06POaKTQy+zGzy+cTGyQ1GYUo8fIDByTlht7V69+HAKiu206PSURgE++PEajtefq/j2qyjceCw+e+WcJIYTom8YqY7RRKcpqm/gqv7J18co5i+YCsC39y16V4sktq+PLnApuXpzEd5eO55WdOWw+WsRHhwtptLbIghYhnBhZdRztfMPgh/t619ZW68u7uYrdJ8pZMuEMN4+vlhFHIYRwGYftBrdmlqA1LJto3NfjkyYBcDzjMI0pfnhCt4HjuweM+/klM6IJ9/fk06NF/PyNAySG+hIT6EVafPCg/ihCjEQjc8SxL7yNwDHUVMenR4vO/PPs+2RL4CiEEEPPIXD8/Fgxgd5mZsTZioF7+GLxCiXMcpr0ffvQyo3PTpv59GgRNY2dFzRu2F/AWfFBjAvxwcvsxp+vmUV5XRO7ssu4ZGYMJlnQIkQnYyBwNL4xTg/TfHqs+Mw+q9kKNYXGTau2qG3HGiGEEEPDFjhqrdmSUcySlLB28xDNIQlM9amgMDeT/JZgbvznl3z7+d2seXo7luaW1naZRdUcOVXFpTPa0tGpsYH86NyJKAWr+7maWojRbvQHjp6BgGJqUDOZRTXkldf1/7NqCo3i48nfMF7LqKMQQgythkrwDOBYYQ2FVY0s7Tj9KCie6X5VnB/TSGB0Mv+9fRG/uSyVg/lV/O2TzNZm7+w/hVJw8Yz28+RvX5HM5z89m2kxHfbCFkIAYyFwNJnAK4Dx/kaa4tOjZzDqaJ/fmHKe8SiBoxBCDC3biOOWDONevmxih11dguJxq8rDry4f/8hk0uKDuX5BAlekxfL45kz25VagteadAwXMTwohMsCr3duVUowL8Rmqn0aIEWf0B44A3sEEUktcsPeZBY72FdVR08EvSgJHIUS3lFIXKqWOKqUylVJ3Ozkfr5TarJT6Uil1QCl1kSv6OaLYAsfPjhWTEuFHTMdSN0HxxqYP1QXtFsbcv2oakf6e3PnvfezNKSeruFZWTQvRD2MjcPQKQtWXs2JSOF8cL+m+LE9DJWz8GZRkdj5nr+EYEAOR06QkjxCiS0opN+BxYCUwFVirlJraodk9wGta67OANcDfh7aXI4ylAawNWDwC2HWirHOaGtpvMegQOAZ4mfnT1TPJKqnluy/uwd2kWJnafTk3IURnYyNw9A6ChgpWTIygrqmZ9Oxy5+0aKuGly2HXU3D4rc7nq/KNwrM+oUbgWPy1bD0ohOjKPCBTa52ltW4C1gOrO7TRQIDteSBQMIT9G3kaqwDIrnGn0drSOU0N7cvvdCjFsygljJsWJ1JW28SSCWGE+HoMZm+FGJXGSOAYDLUlLEoJxcPN1KksT5O1BV1fbgSNpw6A2RdKnYw4Vp8C/2hQytgvu7nJeTshhIBYINfhdZ7tmKP7geuVUnnARuAHzj5IKbVOKZWulEovLj7D6hAjmW27wb1FLXib3ViQ5GQ7wKBxzp/b/PzCyVwyI5p1S8cPVi+FGNXGRuAYvxDKT+CT9QHzkkLazXP89GgR3/jN2xQ8dpERNF7zIsTNdh4QVhVAgO2+HznNeJR0tRDCOWdFAHWH12uBF7TWccBFwEtKqU73Za3101rrOVrrOeHhTkbZxgpb4Lg118K5UyPx9nDr3MbD18gKKVPb/dqBl9mNx76ZxqKUM9wMQogxamwEjnNuhvAp8N7POSfFjwxbWZ7HN2dy+wtbeEz/hvCao5Re8ixMvghCU7oJHG1zYsImgsldFsgIIbqSBzgOecXRORV9C/AagNZ6O+AFjI6IpiIXdjw5sJ/ZUAFAfoMnq7pb2BIUbwSNbuaBvb4QYowEjm5muPhhqMxhdfV6AL75j508vOkI60OfZaYpix81/4g/ZdtSF6EpUF8OdWVtn6G1LXC03azcPSBskgSOQoiu7AYmKKWSlFIeGItfNnRokwOcA6CUmoIROI6OXPTB/8D7P28dJRwQts9q9gxo3WbQqdQrYcY1A3ddIUSrsRE4AiQuhhnXErzvSRYGlpFXXsfbE95jRs021IW/J2zuFbyenktuWZ0ROEL7Ucf6cmhuBH+Hb7mR0yRwFEI4pbW2AncAm4AjGKunDymlHlBKrbI1+wnwXaXUfuBV4Nta647p7JHJ2mB7bBywj7TUGiOOcyYm4unuJE1tt+gHcM69A3ZdIUSbsRM4Apz3a5S7F/8I+zefr8hgeu7LsOB2mL+O21ekYDIp/vZJRlvgWJLR9l57DceADoFjVZ4RVAohRAda641a64la62St9YO2Y/dqrTfYnh/WWi/WWs/UWs/SWn/g2h4PoEEIHE/kGffhs2dNGLDPFEL0Ta8Cx1FTxNY/Er5xD375W4jbfh9MuhjO/w0AUYFeXDc/nv/szSe7OcyYv+g44uhYw9EuMtV4LDw8RD+AEEKMEPaAsblpwD4yO+8UFtxZMLHzohchxNDoMXAcdUVs59wCcfMgbi5c+Q8wtaU7vrciGbOb4q+fnoDgpA6BYxcjjiDpaiGE6OgMRxy/Pl1FbWNbndzaRislpcU0uvvh3l2aWggxqHoz4ji6iti6ucNNG+HmD4yyDQ4i/L24YUECb32ZT7VvApQebztZfQpQ4BfZdsw/CrxDpCSPEEJ01Dri2PfAsbi6kYv/upWL/rqFA3nGvMaPjhTiq2tx8w4ayF4KIfqoN4Hj6Cti62YGk/Mf/dblyYT4evDGSS+aSzKhpcU4UZVvBI2O5R2UkgUyQgjhjD1wtPY9VZ1RWE1zi6a4upEr/v4FT312nA37Cgh3r8fLP2SAOyqE6IveBI5jqohtmJ8n7/xgCY0B43FraeRPb3xMg6XZmOMY4GRf08hUI3CUrQeFEKKNPVXdjxHH4yW1APzne4s4d0okv3vvaz7+uohxPhaUV+BA9lII0Ue9CRzHXBHb6EBvvnP5+QDs27eHq578gubKfKe7EBCbBtZ6Y99qIYQQhjMYccwqrsHHw41Jkf48cX0aD16eSlKYLxEejeAV0PMHCCEGTW8CxzFZxNY93Cj38Mv5Zg7mV2GpyDf2qe4odrbxWLB3CHsnhBDDXOvimIY+vzWruJakMF9MJoVSiuvmJ7D5rhV4WqpBRhyFcKkeA8cxW8TWLxI8/JjqUcS5yb54WatpdhY4how3bmT5e4a+j0IIMVydweKYrJIaxof7dT7RUCmBoxAu5t6bRlrrjRiLXhyP3evw/DCweGC75mJKQWgylGRwy8zLIR8OVPpwlrN2MWkSOAohhKPWEce+paobLM3klddzxVlxHT6v0ZgWJIGjEC41tnaO6avQFCjNZH6ocQN8K6uLdrGzjSLglvqh65sQQgxn/RxxzC6tRWsYH96+XBoNVcajl5TjEcKVJHDsTugEqMjBVHESgM9Pm1trirUTmwa6GU4dGOIOCiHEMNXPEcesYmNFdXLHVHVDpfEoI45CuJQEjt0JTQE0nNwGQLU5jBe+yO7czr5ARtLVQghh6OeIY1ZxDQBJ3rVtdXRBAkchhgkJHLsTmmw8ntgCXoFcNDuFd/efoqTGuBFqrfkis4R3slqMUj2ysloIIQz93HIwq7iWqQEN+P79LPjypbYTDbZsjwSOQrhUrxbHjFn2wLG6ACKm8q2Fiby4/SQv78hhcrQ/f//0OPtzjZvZzPhJxMuIoxBCGFpHHPuWqj5eUsuFfplQ1gCZH8LsG40TMuIoxLAgI47d8QoE3wjjuX80KRF+LJsYziMfHePWl/ZQXtvEg5encu6UCF4tiICyLKgrc22fhRDC1bTu14ij1pqs4hrmqyPGgextbelqCRyFGBYkcOxJmFEInIAYAH587gSWpITx6JpZfPKT5Vw3P4G/rU2jJnQGAMf2bXFVT4UQYnhottC6M20fRhyLaxqpbrAyqWE/uHlAfRkU24JICRyFGBYkcOyJPV1tCxzPig/mX9+Zz+pZsbi7GX993h5u/Phb1wDw4YcbWyd3CyHEmOS4W0wfRhyzimsJo5Kg2iw46wbjYPZW47GhEkzuYPYZwI4KIfpKAseehKYYj7bAsSshoeFYglNI1Zn87A0pyyOEGMMcg8U+rKrOKq5lvsk2wjjrOgiKh2xbFse+a4xSA9hRIURfSeDYk9bAMbbHpub4ucwxnyD9ZBnF1X3fZksIIUaFfo841rDI/Wu0hx9Ez4TEpW3zHBurJE0txDAggWNPUs6F8x+EpOU9t41Jw9dSSjRlbP66aPD7JoQQw5FjsNiXwLGkliXuR1DxC8HNHRKXtM1zlH2qhRgWJHDsibsnLLoD3D16bmsrBH62Xw4fHSkc5I4JIYY7pdSFSqmjSqlMpdTdTs4/opTaZ/tzTCnlZGuqEchxxLEPi2PKivJIaMmFxMXGgQTbY/ZWCRyFGCakjuNAikoFk5kLQ05xa0YJDZZmvMxuru6VEMIFlFJuwOPAeUAesFsptUFrfdjeRmv9Y4f2PwDOGvKODoZ+jDg2WpuJrdwLZowUNUBwAgTGtwWO/lED31chRJ/IiONAcveEqOmkNaVjsTSyPavU1T0SQrjOPCBTa52ltW4C1gOru2m/Fnh1SHo22Pox4phTWsd8dQSLm48xv9EucYmx7Wt9OXgGDHBHhRB9JYHjQFv4ffwqvuZuzzf4WNLVQoxlsUCuw+s827FOlFIJQBLwSRfn1yml0pVS6cXFxQPe0QFnDxzNvr0ecTxeXMsC02Hqo+eCm7ntROISqCuFmkJJVQsxDEjgONCmXwWzb+I7agMNBzeitXZ1j4QQruGsbkxXN4Q1wBta62ZnJ7XWT2ut52it54SHhw9YBweNPVj0Cuh1OZ5TBTlMNOXjmdJhIWLikrbnXkED1EEhRH9J4DgYLnyI8oDJ3GN5lMxjR1zdGyGEa+QB4xxexwEFXbRdw2hJU0PbiKNnAFh7l6p2y9luvCV5WfsT9nmOICOOQgwDEjgOBrMXLVe9gBst+L/7nV7fOIUQo8puYIJSKkkp5YERHG7o2EgpNQkIBrYPcf8GTz9GHMNKd9GgvCBmVueT9lFHCRyFcDkJHAdJaPwU/h74Y6KqD8Hbt/eplpkQYuTTWluBO4BNwBHgNa31IaXUA0qpVQ5N1wLr9VDMa6nIgf+u6//9aNP/wslexLf2EUevwF59cdZaM6F+Pzl+M9rPb7STwFGIYUMCx0HkO+sK/mC5Fr56HV64BKplsYwQY4nWeqPWeqLWOllr/aDt2L1a6w0Obe7XWneq8TgoTmyBA/+Gsqy+v7elBbY/Bkf/r+e29sDUs3cjjnnl9UTrYppDJjlvMPliY+/q+Pl96LAQYjBI4DiIvjE5kr83r2Zb2sNQeBD+cTYU7HN1t4QQY5W13ni01A/ue1tHHAN6Nbp5MK8cP9VAcEiI8wbeQbD6MfAO7mVnhRCDRQLHQTQl2p/YIG+eK5sJN28CFDx3IeTsdHXXhBBjkcUW0DnWWeytprr2j91xHHHsReB4NO80AKFdBY5CiGFDAsdBpJTiwtQotmSUUBU8BdZtBjcP2PcvV3dNCDEWncmIo6W2/WO312kAkzuYfaDFYqS5u3E8vwgAs7cU+BZiuJPAcZBdND2apuYWPjpcCH4REL8Acna4ultCiLHoTEYcLX1JVTeCuxe4exivu9k9RmtN3ikjcMTDr+/9EkIMKQkcB9lZ44KIDvRi41enjAPxC6DkGNSWuLZjQoixpy/BX0d9SlU3GFuwunkar7tZIHOqsgFLfZXxQgJHIYY9CRwHmcmkWJkazefHSqhqsEDCIuOEjDoKIYbakKWq7SOOtsCxm5I8B/Mr8VP2guESOAox3EngOAQunmGkqz8+UggxZxnfwnNGT61fIcQIMWSpavuIoz1V3ch/9+bxyze/6tT0YEFVW+AoI45CDHsSOA4Be7r6/w6cNm6msbMlcBRCDL0zGXFsso009jpV7Tji2MgrO3N4ZWcOJ0raj1gezK9kfICt9rkEjkIMexI4DoHWdHVGMdUNFmOe46n9bTdiIYQYCgMy4tjbVHXbiGNTUz0H8isBeHd/++26D+ZXkmLfEEZS1UIMexI4DpGLZ0TRZG3h4yNFxjzHFivkpbu6W0KIseSM5jjW9f69HUYcMwvKabK24GU28c6BtsCxqKqBoupGEvxlxFGIkUICxyFy1rhgogK8+L+vTkHcXEDJAhkhxNA6owLg9sUxdT3WZWwdcbQFjscKigFYtyyZY4U1HD1dDcDBAmMUMs6n2Xifh2/f+yWEGFISOA4Rk0mxcnoUnx0rplr5QmQq5Hzh6m4JIcaSMxpxdHhPT4GnfcTRVo7neEEpCaE+fGthAiYF79jS1Qfzq1AKwj2tRrFwk1vf+yWEGFISOA6hi6dH02RtYdOhQmOeY+5uaLa6ultCiLHijOY4OsxttPSwQKbDiOOJwnJmJwQT5ufJ4pQwNuwvQGvNwfxKksJ88WiulTS1ECOEBI5DKC0+mEmR/vzlo2M0xc03bsSnD7i6W0KIsaJ1xLEXK6M7clxN3dPCvmZbHUfb4pjGhnrmJBj7UF86I4acsjoO5FVyML+S1JhAaKyRhTFCjBASOA4hk0lx36VTySuv51/5McZBmecohBgqrSujz2BVdcfnznQYcfTAyuyEYAAuSI3C7Kb45xfZFFQ2kBobAE01MuIoxAghgeMQW5QSxsrUKP6wvQprQHz39Rxzd8MnDw5d54QQo1trqvoMdo7p+NyZ1jmOxohjoEcLEyKMwDDQ28zyiRG8uS8fgNTYQGME09O/730SQgw5CRxd4JcXTUFr2MtkI3DUunMjSz385xb4/A/QUDX0nRRCjD7WARpx7KkIeIcRx5QQMyaTaj196czo1tvetJhAaKyWFdVCjBASOLrAuBAfbl2ezH9L46G2GEozOzfa8meoOGk8r8wd2g4KIUafZqtRPxb6N+LYVAfu3sbz7lLVWreOOFY2GcFicoi5XZNzp0TiZTYRH+JDoLdZUtVCjCASOLrI95Ynk+E7Gwvu6Ld/0P4bfEkmbPsLREwzXlfkuKaTQojRwzFY7NeIYy34hrU970qLFXQLuHuy/5RxnYRA93ZNfD3d+Z9zJnDjokTjgCyOEWLE6FXgqJS6UCl1VCmVqZS6u4s21yilDiulDimlXhnYbo4+3h5u3HzJ2fxP0/dpyd1JxQtrwNpkfFvfeJcxP+iKp4zGFTLiKIQ4Q47BYn+3HPQJNZ53l6q2f7a7F+n5NQDE+Hf+VXP7ihRuWZJk+7xa8JA5jkKMBD0GjkopN+BxYCUwFVirlJraoc0E4BfAYq31NOBHg9DXUefiGdFccPWt/N79VoIKPmP3I1dTtP0VyNoM37jHKBLu7tWWshZCjCjD6ku3fcTRzbN/BcCb6sA33HjeXTkfa6Px6O7FrpNG4Oihu6lXq7UtVS1zHIUYCdx7bsI8IFNrnQWglFoPrAYOO7T5LvC41rocQGtdNNAdHa0uOyuWC6Y9yLZXPVh84lGsmz6nJnQafnNuAaUgKF5S1UKMQA5fus8D8oDdSqkNWuvDDm0cv3SXK6UiBq1D9hFH72AjUOvz+x1T1T2POFpNHuzLr8Lqbsa9ubHr9k21gJZUtRAjRG9S1bGAY640z3bM0URgolJqm1Jqh1LqQmcfpJRap5RKV0qlFxcX96/Ho5C3hxuLb3yA2gU/plm5c1vZWmv/+ecAACAASURBVI6X2W7ygeNkcYwQI1Prl26tdRNg/9LtaOi+dNtHHL2DjcDPWTWH7ljqafYyajF2laour22ipMKoApFRaqHB0oJ282gbhXTGHsTK4hghRoTeBI7KybGOdxx3YAKwAlgLPKOUCur0Jq2f1lrP0VrPCQ8P72tfRz3fC++n+HtHOOI2mZtf2E1ZbZOMOAoxcg3Yl+4BYXEIHHULNFt6/96WFrDU8fzuYqwmL6cjjjuzSjnr1x9y3ZOfAfDoZ8aP7mb26iFwtC20kTqOQowIvQkc84BxDq/jgAInbd7WWlu01ieAoxiBpOijuMgwnv7WHE5VNnDrS+lYA8ZBXamx6lAIMZIM2JfuAcnW2ANHH2Prvz6V5LG1LWl0o057OA0cNx0qxNPdxI9WxAOwZuEEnr9pLiZ3T2MLwq40VhuPMsdRiBGhN4HjbmCCUipJKeUBrAE2dGjzFnA2gFIqDONbdNZAdnQsmZ0QzMNXz2R3djmvHLX9npF0tRAjzYB96R6QbI3VYY4j9K0kjy3orMOT6hYPGus7f5HdklHMvKQQVk42Pn/FtDjOnhQB7h5GxYiuSKpaiBGlx8BRa20F7gA2AUeA17TWh5RSDyilVtmabQJKlVKHgc3AT7XWpYPV6bHg0pkx/PSCSbx1wg0Aa5msrBZihBleX7odU9XQpxHH2hpj3mJIUDB12pPS8op25wsq6skoqmHZhPB25XgAYxV3tyOOtsBRFscIMSL0qo6j1nqj1nqi1jpZa/2g7di9WusNtudaa32n1nqq1nq61nr9YHZ6rPj+2SmsXrEQgFc+2Ep9U3Pv32xtgi8eg9qSQeqdEKI7w+5L9xmMOO48amQ8LkobT5PJk8rK9oHjlgwjfb5sYrhDOR5P22NvRxxljqMQI4HsHDPM3XjePJpNZuqLTnDjc7uoaujlhPaPfwUf/C/seX5wOyiE6NKw+tJ9BiOOX3xtLNBLiY3A7OVHXW0N2mFV9ucZJUQGeDIx0q/vI46tgaPMcRRiJJDAcbgzmXALiufS/9/eeYdHWaZ9+7xnMumNFAik06VI6N2OAqJYP8DedVdXd9Xd133X19Vd3VV317Lqoq51bdgVWRUVqdKrdAgtpAAhpBDSJsn9/XHPpExmkgkkZAau8zhyTOZpcw0Tnvzyu8qdWs26/YXc8/66ls/ZMReWvQgoyJzX7iEKguAHHKfjWFxmZ2vWQQAsQWGER0RirSljV74RfDW1miU7DzO+VzxKKTeOY1DzjqOkqgXBrxDh6A9Ep9CNfO46uwdLduZTXNaM61icA5/fZVadGX037F8JFcUnL1ZBEHyT43Qc524+gK3WITJtoURHRhFKJQt3mDKYjTnFFJfbTZoa3DiOgV46jiIcBcEfEOHoD0QnQ1EW43vFU6th2W4PJVA11fDpbeYv/qvfgr4Xg66B3QtPariCIPgg1RWAguBI89zLZQdnb8gl1Vl+aAslNDySCKu9rq5x0Y58lIJxPR2ryjRYctA8BjU/x7HyKNhCwWJt3fsRBKFDEOHoD0SnwLF8MhKCCA208lOmh4aXhU9B1lKY8gzE9YKk4abgfJekqwXhtMdeDrYQI9Kcz1sg/2glS3cdZlRSiNkQGAq2UCKtVSzfXUCFvYbFO/MZmBhFTFigOabOcXSkqq2BUNNcc8wxqW8UBD9ChKM/EJ0KQGBpDiPTY/hplxvh+NPzsOhpyLgWBk0326w26H62qXNs7fJigiCcWtjLjQvodAKrW65x/GZTHrUahnR1iEBbKNhCCNaVVNhrWbD9EGuzihjfK67+JKe7aHXWOAY3/1pVpZKmFgQ/QoSjPxDlmCFclMXYnnHszj9GXrHDLdAafngMvn8E+l8OU55rfG7P883w8MM7T27MgiD4FtUVdcIP8Mpx/O/PefTuEk6XEMcoMFsoBIZhrSnHZoW/f7eDmlpt5jc2fB1LAFgDzPOWxvFUlkpjjCD4ESIc/YFos4QXxUY4AvyUWQC1NfDf+2HJMzD0JrjydXOTbkiP881j5g8nL15BEHwPeznYvHccq2tq2ZBdxLie8VDlWGLQFlInPEcnh5J5qJSwQCuDUzo1OLGy/jXAu3E8MsNREPwGEY7+QEQCWGxQlEWfLhHEhgWaOsevfwur34CxvzZOo7vi8k6pENtL6hwF4XSnugICQiiudjiBLYzj2ZV/jAp7LQOTIs3a1AHB5h5jM/WI53Q3LuHoHnEEBjT4VVJdYeoanbQ0jqeqVGocBcGPEOHoD1isEJUIRVlYLIoxPePYtnMnes1bMOxWmPAYKNXolJyicma8upxNOcXQ8wLYu8TrLkp3vDQ/k6WemnIEQfB97OUcqlAM/csCtLK2OI5nU44Z4zWgW5QRjs6mmkDzOD7VISD7uKydXV3h4ji2MI5HUtWC4FeIcPQXolOgyCz7NbZHLGPL56N0DYz6RZNDy6qquf3t1SzbXcCna7NNnWN1BexbelwvXVxu5+/fbef1JXtO6C0IgtBxlJUdY1dhDdW1mhprcIuO48acYkJsVrrHhzvS3A7h6EhV9+pk4dNfjGba8OTGJ1ZX1ndUg/m+pspzg540xwiCXyHC0V+IToEis+zX2B6xXG1dyKGoM83YnQZorXnw4w1sPVBCUqcQFu7Ih9Sxps7oOFeRWZtViNawbn9Ro2XGBEHwD6qqa8nJP4LdYlLIdhXYouO4ObeYft0isVqUY2SOUzg60sr2MoamxmCzuvwacec4gueRPJWlECQ1joLgL4hw9BeiUqD0ANgrSK7YRh9LNt8GnN/ksBd+zOTrjQf4/aS+3Dw2nd35x9hfCqSOOe46x9V7jwBw5FgV+wrKTuRdCILQAbzw4060vZzeiZ2JCQukgsBmS1dqazWbc0sY0M05LLxpqhq7h3uBO8fRud0VraXGURD8DBGO/oKzs7okB9a9R5UKYubhQVTX1NYd8u2mAzzz/Q6uGJLI7eO7c7ZjCbBFO/NNnWP+trp0dxMWPAWvX+h216q9hUSH2gBYt7+w7d6TIAjtztqsQl6an0lsUA0JsdF0jwvjWG3zwnH34WOUVdUwIDHKbGiUqnY8VnkSjq6Oo0M4unMc7WWAllS1IPgRIhz9BadwPLwDNn1CftKF5FUG8nNOMT9nF3H3+2v55XtryEiO5i+XD0QpRY/4MBKjQ1i4Pd8MAgfPdY7bvoL9K6DsSKPNldU1bNhfxGUZiYQHBbB2X1E7vklBENqS8qoaHvhoA12jQugUWAsBwXSPD6O0JqDZcTybcx2NMU7h2ChV3VrHMbB+uyuVjnWqpTlGEPwGEY7+QrSjAH35TKgoJmL0TQDc9c4aLn3xJxbtyOeOs3rw1s3DCbaZsTxKKc7qHc/SXQXYY/tCUCTsX9702pVH4eBm833u2ka7NuWUUFldy6jusQxKjhLHURD8iG0HSigoreRvV5+JxTEAvHt8OKW1NqorPZedbMwuJijAQq/ODkFnL6sfHF43QLy1jqMb4VjlEI4yx1EQ/AYRjv5CRDdQVtizEKKSiex7HqO6x2C1KP4w+QyWPnQeD03qS3Ro4wHgZ/eOp7SymrX7S8za1VluhGP2atCOlHfuuka7nPWNw9I6MTi5E1vzjlJWVd0ub1EQhLZlcEonfnroPMb0iKsbAN4jPpwKbaOi/JjH8zblFtO3ayQBzsYXe1l9U4yzHrHKw/mtqXGsPNr4moIg+DwBHR2A4CXWgLpZjgyaARYL7902CgVYLMrjaWN6xhJgUSzckc/IlFEw/y9QXgghDVZ62L8CUBDRFXLXNzp/1d5CuseFERcexJDUaGpqNRuzixnZPbZ93qcgCG1KRLDNrDJVa4eAELrHh7GHQOwV7h3D2lrN5pwSLs3oVr+xqsxNqtpDjaSr49iccHSKT0lVC4LfII6jPxHlqHPMuAYAq0U1KxoBIoNtDEntZMbypIwCNOxf1figrOXQpT+kjYOc+lR1ba1mzb4jDEszIjMj2TyuzZI6R0HwK5wizxZMSkwoVQRS60H4ZR0p42hlNQOd9Y3O813mOHpd49hcc4ykqgXB7xDh6E/0m2pWiolJb9VpZ/eOZ3NuCYei+pt0d8M6x9oak6pOHgHdBsPRXDh6AIBd+aUUltkZlhYDQExYIGmxoazLkjrHU5rqZoY1C/6JsxEmIASb1YI1KNSjY7jJtTFG68bjeCxW4yh6TFW7Oo7NNcc4UtXiOAqC3yDC0Z8YeQdMeabVpznH8izeUw5dB0HWivqdh7ZA1VFIHgWJQ8w2R53jqr1GIA5PizGpqppqhqR0kkHgpzJHD8DzZ8LCpzo6EqEtaeA4AgSFhGHxsAzgppwSbFZF7y4RDc7V9alqMCLSY6rak+PYXHOMCEdB8BdEOJ4G9OsaSVx4oJnnmDIKclYbVwnqm2VSRkLCQFCWOuG4eu8R4sIDSYu2wUsjYd5jDE6JJv9oJdmFx7/uteCj1NbC53fC0Tz4+SNxHU8lGjiOAKGh4dhqK6ipbfoZb8oppk9CBIEBzsYYp+h0FY5uUtVaN+M4uktVO1xLaY4RBL9BhONpgMWiOKtXPIt25FOTPNLc2A/8bHbuXwnhXSA61dy848+odxz3HWFYagxqzyIozoJNnzE4ORowyw8KpxjLXoDdCyBlDBzZZWaGCieEUmqiUmq7UipTKfWQm/03KaXylVLrHV+3tUsgLo5jeHgEwVSR4/IHoNaaTbnFDOjWsL7RIe4aCsdAD8KxttpMaPB2HE+lOI6C4G+IcDxNOLtPPIVldv6TnWA2ZC0zj/uXQ/JI/rvxAP/4bjtVXQZBzloOFJWz/0i5aYzZ+JE5tiSbvuwlxGZl7b4TqHM8nAkHNp3YGxLalpw1MO9PcMalcOVrZtu2/3ZsTH6OUsoKvARMAvoBM5RS/dwc+qHWOsPx9Vq7BOMUjg7HMTIyggBVy+5Djf8f5xSVU1Rmr69vhPoVYlxT1e5WjqlzNt2N43HnOB41MVllwIcg+AsiHE8TJg/sysVnduWx+QUUBiWis5ZDSR4UZTG3JJW731/LCz9m8szmMCg7zKatZiD4iKQQ2DoH+lwMKAIyv+XMpKgTcxw/vRU+mC6pUF+hogQ+uRXCE+DSf5qxT10zYPvXHR2ZvzMCyNRa79ZaVwGzgKkdEkl1Y8exU6RZgzrrYOOVojbluDTGQL2z6E2q2tkA08hxdKSqPTmO0hgjCH6FCMfTBJvVwgvTB3Pj6FR+LOtO6c4lFGxbBMDM3fHcNi6dz345hvzI/gDM+ea/hNis9CtZYlJVo+6C5JGw/WsGp3RiS24xFfaa1gdyZA/krYfi/eZROPmsfx9mjjNfL4+Hl8dC0T7jNDrne/a92HTbHz3Ytq+dswaKs9v2mr5LItBwcfhsxzZXrlRK/ayU+kQplezuQkqpO5RSq5VSq/Pz81sfid3hBDpG6YSGGbGWfchVOJZgtSj6JjQYj+NOOHpKVdcJRy8HgFcdk/pGQfAzRDieRlgsikcv7U9M3/FE1BSxYc5MKrSN26dN5eEp/RiS0om//XIGNSqAMy27Gd8rjoDNn5pVa1LHQZ9JkLeB0XHl2Gt03Xq2rWLLF+ZRWSQV2lGsfQdKD5hlLCMTTV3r1H9B6uj6Y/pMBjTs+KbtXremGt65HP77QNtd07dxN2TV1Wb/CkjTWp8J/AC87e5CWutXtdbDtNbD4uPjWx9JdeNUtXIIyLzD9cJRa828bYfo3y2ybtlSoD4l3chxDGkhVe3OcfQwx1FmOAqCXyHC8TRDKcW5E0y27DzLOmq7DeHijLS6/ZbAYKwJA7gxvZB/Tk2FzO9h4JVgsRgXChhSYTqxF+883PoANn8B3YaYBgwRjief2lrTGNXvMpjxAVwzC679CDJmND6uS3+IToFtbZiuzl0HFcWw68f6+X2nNtlAQwcxCchteIDWukBr7bTi/g0MbZdI6hxHh6BzCLuDR+r/+Fu88zBb80q4flSqy7nuahzD6ptmGtKs41jR9PjKo5KqFgQ/Q4Tj6Uhcbwg23dGhPcY03d9tMNa89QTv+NJ0SQ682nFeL4jtScTe7xnTI5ZP1mRT62ach0ecaer+l8EZU8wMyYJdbfCGBK85ssu4PN0ymj9OKVPXuntBfefribJngXmsqYKd37fNNX2bVUAvpVS6UioQmA7MbniAUqprg6eXAlvbJRIXx9GZsj52rJSjFXYAXlm0iy6RQUzNcMmme0xVuxnJ5dZxbK45plQ6qgXBzxDheDpisTiWH8QM/nal22DjDP30PMT1gYQz6/f1mQR7FnFtRieyC8tZtrvA+9fd6vid2W+qIxXK6es67lsG/xoDmT+c3NfN22Aeuw5q+di+k01Dw64f2+a1dy+Ezv0hLB62zWmba/owWutq4B5gLkYQfqS13qyU+pNS6lLHYfcqpTYrpTYA9wI3tUswHhzHYKrYc/gYm3KK+SmzgFvGptfPb6w710NzjNtUtRvH0WIBi81zc4zUOAqCXyHC8XSl+znml0fyiKb7nCvIFGUZt1E1KNXqMxlq7UwI2kRkcAAfrtrf9HxPbP7CiNJOadAp1QjS00BAuGXZi3BoM7x7Fcz/i1n68WSQt944QPF9Wz42ZYxxptuiu7qqDPavgB7nmj8+dnznvlniFENr/bXWurfWuofW+gnHtke01rMd3/9ea91faz1Ia32u1npbuwTiwXEMUVXszj/GK4t2Ex4UwIyRKU3P9TSOx17WdDKCO8cRjJD0NABcUtWC4FeIcDxdGX47/GoNhMY03Rfft/7GP/CqxvuSR0JIDIGZc5makci3mw9QXGZv+fUK90HuWlNb56TvFDOA/EQ6d2trzFghf6LsCOyYC8NugUEzzPJ+714BpcfRLdtacteb+kWrreVjrQHQ+yLY8a1pbHHH3iXw5mR4YyK8NcU0v/zwWNPj9i83Keru55hZkVVHYc+iE3knQmuwVwCq3glsIBwX7sjn6415XDMyhchgNz8XnlLV6KZ1i+4cRzANMp6WHJTmGEHwK0Q4nq5YAyAqycM+m3EiU8dCTHrjfRYr9J4IO+YybWgCVdW1fLkhp9Eh76/IYuRffqCorIHDsOVL89i/oXC8GNAn5mj9+Dg82x+Wvug/cyE3fQq1diMcL/sXXPqCSV2/dp6ZqeiOmuoTf39aQ97P3qWpnfSZDOWFptbRlSN7YNa1ULjX/MzUVptRO0ueqU+JO9m9ECwBkDIa0s8yYmHr7KbXFNoHe5n5Y9CZPXA4j4nhis/X5WBRcPPYNM/nWoPM/30nThHpmq5u1nF0EY5aG+EojqMg+BUiHAX3/L93TNetO/pMgooiBmx8it/ELGX3T5/CwS2gNUt3Heb/vtzEwZJKlu1qUP+45QszVLpTWv22Lv3N8+Otc7SXw5o3TY3Ud38wg8WrGnR6lh2BDbPM7EBfYsMH0GWAY21wBUNugBu+hKL9ZvUWVypKzKzFEx1jU7gXKotbJxx7XgBRyfDpLcYddlJ1zIhGgJvmwI1fwS3fwq3fG1Gx4tXG19m9AJJGGJEQEAS9LzQd2y2l6P3ljwFfp7qivr4R6r5Pcmi2Swcl0jUqxP25VWWN09RQLxxdO6ubdRxdUtX2MrM8odQ4CoJfIcJRcE9INARHud/X4zzTmb3yVe4re5FHS/8EM0dT+uHtPPDuMtLjwgixWVmxxzEjrijLiLeGbiMY0dR3CuxZ6Nlpa47Nnxs3bNq7cP4fzfPXLoA1b8H70+DvveHzO+H1C+Hnj1p//RPh2GF4cQSscRnLl7/D/FsMchl/kzoaRt4Jq15rLNC0hjm/hvxtsPkzzyljb3AOXG+NcAwKh5u/hpAY+M9lxjnUGr68G/K3wlVvQEz3+uNDos172/ix+TcAI+DzNkD3s+uP6zsFyg6bukd31Naaz+zVc47vZ0NojL2icaq5geMIcMdZ3d2d5Ti3zIzfaYhTSLp2VrfGcXT+kSdd1YLgV4hwFFpPUDjcswoezqf4rvVcVf0438fdQOi2T3hb/y9vXRrD0NROLN9dAAc3w7e/N+f1c7PaWt+LjROReRzjWVa9ZgRs+lkw/n649hM4mgdf3WfWwh51F9z8rUmPfnY7LPvXib1vV9a/b4Zpu2Ph03B4O3zzOzjUYMLKhg9AWetHHDXkvIfNQO6v7qtvJFjzlkltp4wxIjl7ZdPzvCVvg0kXd+nfuvOiU4ybGJ0C711t/i03f27Ees/zmx4/8k5Tz7bmTfN87xJAQ3oD4dhrgkl/bnXTHJW1HF6/wLwOGo6dhNrPU53q8sZizuE4jksL582bhtMnoZk6Q3tZXU1k/fnOVLUnx9FFOFqDmjqOzlmeQVLjKAj+hAhH4fgJCCQqIZ2u/cdxe/ZEbrH/D+mBJSR9PJm7A77gsSO/hZmOkTNjf93YmXKSPNKMZ1n1RuvSkjlrjXM3/Lb6uq2e58Pdq+COBfDrjXDh48bJu/YTOOMSmPt7+OHRtkl/HtoKs38FX91rluZryJHdsPoNI5QDw40Aqq4yadmfPzRxRnRpes2gCLj472a+5dLn4cBG+OZ/oPu5pmzAYoPtJ7CSS94G6HxG0zSiN0QkGOexSz/jJva/Asbe5/7Y+D7GlV71OtTYjaNsC4PEBrOtgyJMo8zWr8znUWOHzHnw4fXwxkVQkguXvQy3L4DYHsfxZoVG2Csaiz+H4xhhtXNu387Nn9tsqtqT4+jyMxYQ6MZxdMwHFcdREPwKEY7CCXPdyBSsFsXZk6dj++ViiO3J6L3/IoEjbBv4O7h/K0xw02kLpuD+3D/AviXGXfOW1a8bMTJoeuPt4fFm5I+lwY+2LRiufhuG3gxLnoV5HmJxUlsDexbD7Hvh7UubdjtrbeoNgyIgoit88cv6OXkAPz5hnL2JT5nGlwMbYf4TsHcxlOQ0jbkhfSaZzvOFf4MPrzNrR1/xb5MCThtrOpyPB62NcGxNmtqV0Bi4YTZc8jxMfanxmCZXRt5l3N+ts019Y9pYIx4acsYlUJwFH99oygrevcLMjDz7IdPxnzGj8ecoHD+ujqPVZpb9tFd4PseJvaxxmhsaCEfX5pjWOI5O4Sg1joLgTwR0dACC/zOyeyxr/28CUSGOUR63zMV+aAcTZ+5jemAaf3Q38qchQ28y9Xvf/Z9JYXrq9nZSXggbPzG1dJ7qMF2xWGHKs6bzd8mzkDa+aZq1ogQWPW2ufTTPCNPaapg1wzR/OB2bnz+CfT8ZARWVBO9eaUbqXPBHs6zepk9g/IMQ2dV8DbnRDFPfPR+CouqHn3ti0tOwa76pDb1hthHDYM775ndmtZ3WunDF2VBWYBqUToTgSPN5tUTPCdApHeb/FQoyjWh3pc9k43xlzqsXzD3Pb5oWFU4cV8dRKfNv724ZwCbnltWtNFVHoKdUdYUpxbC6/GoJCGx6rPO5pKoFwa/w6s95pdREpdR2pVSmUuqhZo67SimllVLD2i5EwR+oE40AAYHYug1gSGosy3cfaflkpeCSf4KuoWb2r7nh9RXM29rMbMd175lfUMNvbV2QShlRFt8XPr+rsZNYXgTvXGbqILsNNk0fv90JV75mUtGf32UaNiqK4buHTdp18A2m6zjjOiMMc9eZVHhIDIy9t/7aF/3FjDXK22AahFoSRhFd4JoPTdNP+vj67b0nmsfjcR3rVow5QeHoLRaLqXUs2GmeN2yMcRIWC/eth99mmn/nM6aIaGwvXB1HMP/Wro6hO1qbqnZ9HTDbmqSqHTWOkqoWBL+iReGolLICLwGTgH7ADKVUPzfHRWCWzPLQJimcboxKj2XbgZLG8xw9EZMO5z+Cddf3xOz6glcW7nZ/XG2tSVMnjzLjbFpLYKgRhRXF8MUvzPXKjsB/ppoZh9McY4gGXGlSaP0uhQl/MuOEfvyzWeXlWD5c/I/6NOpFT0B4Z9PJvXsBnPXbxk5oUDhc8RpEJpnZjd6QOtox57IBnVKhc7/jq3PM22BSk61tjDkRMq41oiA01iw16I6IBBGLJwO7yzgecAjHE01Vu2mOcVdD624cjzNVLXMcBcGv8MZxHAFkaq13a62rgFmAm/ZY/gw8DXhxJxJOB0Z2j0VrWLnHC9cRqBxyKz+rPjxqe5vArIXk7txgBF5NNexfBYv+Bm9PMc0nw287/sC69DdiL/N7WPikEY2HthiHz1WsAYz5lUnPLnkGVrxinM5ug+v3h0TDlOeg9KDpPHbnhCYNhfs3Q7cTdPx6T4R9S0263kl1Fax+0zSUeCJvvXFaXZ2j9iQ4EiY9Bec/IrWKHY29rH65QScBwfVLEbZ0rqtwrEtVuxkA7tZxdDeOR2ocBcEf8abGMRFouCBxNjCy4QFKqcFAstZ6jlLqQU8XUkrdAdwBkJLiZk1U4ZRiUHIUQQEWlu8+woX9E+q2Pz5nCwXHqvj71YOwWuobLD5em8ebFbcxN/QR3lV/hff+anYoixkUDNBlIJz9P01nQraW4beZOsKFT5nC/envm/pKdygFk/9u6gQPbjZjc1zpM9HUPHYZeHxdy97SZ5IRsJnzzHKQtbXwxV1mZE9wFEz+h9nu2riSt8F0Op9sBl938l9TaIrrAHAwz71xHKvKmoo7j6lqT45jc80xUuMoCP6EN8LRXetk3TwTpZQFeBa4qaULaa1fBV4FGDZsmCwJcYoTFGBlSEonVuypX0Hmy/U5vLZkDwBdIoN5aFJfAKqqa5m5YBedk/tjvX49j7/9BfroAR4+KwZVXmjS0ulnQVhc2wSnFEx9EebYjJvY49zmj7fazFgfu5tfok68aRo5URKHQmicSVcPuNI0y2z6FMbca+YffnYbbJsDFz9jaggBjh4wbuiJdFQL/o3rAHBwNMe04Dhq7X6Oo8VqxGCTVLUnx9HDOJ6A4KaNNIIg+DTe/I/NBpIbPE8CGubEIoABwAJlXI4EYLZS6lKttcuAO+F0Y1T3WJ6bt4PiMjslFXYe/nwTw1I70atLBC8v3MWAxEimnNmNT9dmk1NUZm3KhAAAHOxJREFUzhOXD0BFdKbv6Ck8+PEGJiWOZlhaC13Zx0l1UDT/in2YafHJuJmq2BSlOj6tZrFC74uMOJz/BKz6t0mlX/hnM0bop+dNHeaehZA03AxIdy7rd7IaYwTfw21zjBeOY3UFoJuKTjDp6iap6lY4jlWl0hgjCH6IN4VHq4BeSql0pVQgMB2Y7dyptS7WWsdprdO01mnAckBEowDAqO4xaA3Ldh/mvlnrAHh2WgaPXdqfoamd+O3HP7Mpp5iX5mcyKCmKs3ub0TMTByQQYrPy6dqcdovth60Heeb7Hby3fF+7vUa70Huiqf1c9DfTgDLhz2a7xWpW0Lljgen2LsmFlf+GFTONaEgY0JFRCx1FbY0Rba6uoTeOo1MYuhOOttBWdFW7cRwrSzv+DzFBEFpNi8JRa10N3APMBbYCH2mtNyul/qSUurS9AxT8m0HJ0QQGWHj4i82szSriiSsGkhwTSmCAhZnXDiEyJICrX15GdmE5913QC4drTXhQABMHJDDn51wq7DXtEtsHK03p7uLMw+1y/Xajx3lm5E+fi80YI9d6xoQBZrzNL36CP+TBvevgF0tlXt7piqf1o71xHJ3jetw1VdlCW9FVHWSWoWy4alNVqfxMCoIf4lWro9b6a611b611D631E45tj2itZ7s59hxxGwUnwTYrQ1KiOVxayRVDErl0ULe6fZ0jg5l53VBqajUDEiM5t0/jpc+uGJLI0Ypq5m091OZxZReWsWhnPtGhNjbsL6K4zN7mr9FuBIWb+YfT32u5PsxiNUs9yrJ9py9OcXg8jqO9GcfRbaq6GccRzNKSTiRVLQh+iczIENqdywcnMjglmj9NbZoqHZLSic/vHsPrNw6vcxudjOkRR0JkMJ+tzXZ73Qp7Dev3F/FzdlGrY/p4tbnmo5f0p1bD0l1+5joGRzW/5J8gOHGKQ7cDwE9AONpC3S856MlxBOM6Ojl22CxjKQiCXyHtbEK7M214CtOGex6/1L+b+2UDrRbFZYMT+ffi3bw0P5NKew3HqmooPFbFlrwSdh4qpabWpL4evvgMbhvf3at4amo1H6/ez7iecVx8Zlf+74tNLNp5mEkDu7b+zQmCr+PJcfRmAHhVC6nqCpc/2pqb4whGWAZFmJR1URaku1lRSBAEn0aEo+DTXD0sideX7OZvc7ejFITarESG2OiTEMEFZ3RhQGIkszfk8vh/t1JeVcM95/Vs4ly6smhHPrnFFTw8pR82q4XRPWJZvDMfrXWL5wqC3+F0BV0FnTcDwOscRzdNLIGhZk33hjS3coxzP5gB9lWlZmC+IAh+hQhHwafpER/Ohj9eiEIRbLO4FXYXnNGF4ICf+cf3Oyiz1/C7i/o0KwA/WJlFbFggF5xhhvCM7xXHd1sOsq+gjLQ46fIU2g6l1ETgecAKvKa1ftLDcVcBHwPD27xGvLoZx7G22qzM5KlWtk44ulkW0hYKVd7OcXRsc6aqixyTDEQ4Ch2M3W4nOzubiorTZ9G74OBgkpKSsNlsx3W+CEfB5wkNbP7HNMBq4e9XDyI40MrMBbs4WmHnj5f0x2ZtWsJ76GgF87Yd4tZx6QQGmP3je5kRQIt35rebcNRas3LPEYanxWCxiKt5OqCUsgIvARMw83BXKaVma623uBwXAdwLrGiXQOweahydz6vLweqhu7mlVLW3NY7O5phqxyzHoizzKMJR6GCys7OJiIggLS3ttMg4aa0pKCggOzub9PT047qGNMcIpwQWi+KJywZw51ndeXd5FtNfXU5ecdM03Cdrsqmp1UwbXj/TPjU2lOSYEBbtbL8GmY9W72faq8v5ckP7zaUUfI4RQKbWerfWugqYBUx1c9yfgaeB9rE86hxHF/HndBGbq3N0jttxm6oO836Oo2tzTJ1wTG56rCCcRCoqKoiNjT0tRCOAUorY2NgTclhFOAqnDEopfj/5DF6YMZhteSVc/M8lLNqRT02tZm1WIf/4bjuvL97DiPQYesSHNzpvfK94lu8qwF5T2+ZxFZfbefrb7UB9N7dwWpAI7G/wPNuxrQ6l1GAgWWs9p7kLKaXuUEqtVkqtzs/Pb10UTnHnulZ1Q8exxXPdpapDTKraOZuxphp0TQvNMU7HcT8ERUJwtHfvQRDakdNFNDo50fcrqWrhlOOSQd3o1y2SX767lhvfXEl0iI3CMjsWZcb/PHzxGU3OOatXHO+vyGLD/qI2X+Lw+R92cqSsiskDE/h64wGyC8tI6uQm9Secari7O9dNwFZKWYBngZtaupDW+lXgVYBhw4bpFg5vjMcB4F44ji2tHIM217eFNHidZppjGjqO0SkyUkoQ/BBxHIVTkh7x4Xxx91huHZvOOX068/z0DNY8PIFPfjGGM5Oauhyje8RhUTSbri4orWTB9kNkFZRRW+vd7+6dB4/yn2V7mTEihf+dbATrZ+24jKLgU2QDDXOxSUBug+cRwABggVJqLzAKmK2UGtamUXhyDZ1C0rVOsdG5x4zoc9c84xSTzus7O6bd1jgGNT7GKRwF4TSnoKCAjIwMMjIySEhIIDExse55VVVVyxcAbr75ZrZv397OkdYjjqNwyhISaOXhKf28OjYqxMag5GgW78zn/gm93R5z36z1LHEsTxgUYKF7fDhn9Y7jF2f3IDo0sMnxWmse+2oLoYFWHrywDzFhgYzpEcuna7P5lRdjgwS/ZxXQSymVDuQA04FrnDu11sVAnPO5UmoB8GC7dVV7chyrm6txLHfvNkJ9w0zVMTPI2yvHsarBDMfx3sUvCKcwsbGxrF+/HoBHH32U8PBwHnzwwUbHaK3RWmOxuPf63nzzzXaPsyEiHAXBwfhe8bz4406Ky+xEhTYeU7A08zBLMg9zx1nd6R4XRuahUrYfPMqri3bzwYos7jmvJzeMTiPYZq0757stB1mSeZhHL+lHTJj5xXnlkCQe+HgDq/cVMrwVKfGPVu2nS1QwZ/eOb5s3K7Q7WutqpdQ9wFzMOJ43tNablVJ/Ala7W7K1XfDkONalqpupcawqM00w7qhzHB2OpSeBCo0dx/JCqDoqjqPgczz21Wa25Ja06TX7dYvkj5f0b/V5mZmZXHbZZYwbN44VK1YwZ84cHnvsMdauXUt5eTnTpk3jkUceAWDcuHG8+OKLDBgwgLi4OO666y6++eYbQkND+fLLL+ncuXMLr9Y6RDgKgoML+3Xhn/N28uwPO3j00vr/6Fprnp67nW5Rwdw/oXcjcbjtQAl//Xobf/l6G28v3cfI9BjKqmoos9ewKaeY3l3CuW5Uat3xkwYm8MiXm/hkdbbXwvHzddn87tOfsVkVb908grE941o+SfAJtNZfA1+7bHvEw7HntEsQngRdXXNMc45jmfvGGHAjHJtJVdd1VVfJKB5B8JItW7bw5ptv8vLLLwPw5JNPEhMTQ3V1Neeeey5XXXUV/fo1zqoVFxdz9tln8+STT3L//ffzxhtv8NBDD7VpXCIcBcHBgMQobhqTxltL93Je386c5XD3vt9ykPX7i3jqyoGNRCNA34RI3r5lBEt2HubZH3awcu8RQgOthAQG0L9bJL+7qC8BDeZJhgYGMHlgV/67MY8/XtqvxRmVG7OLeejTjYxIj6G4zM6d76zhwztHeVymURCaYC8zItG1NMIbx9Fe5kWq2hvHscHKMSIcBR/leJzB9qRHjx4MHz687vkHH3zA66+/TnV1Nbm5uWzZsqWJcAwJCWHSpEkADB06lMWLF7d5XCIcBaEBD03qy0+Zh3nw4w3M/fVZRIbY+Md3O0iPC+PKIUkezxvXK45xvbxzAq8amsTHa7KZu/kAlw/2fM38o5Xc8c5q4sKDmHntEKpqarniX0u56c1VfPaLMSTHSGe24AX2FlZzac5xrDrWTKrasb01jmN1BRQVm+9FOApCs4SF1f/f27lzJ88//zwrV64kOjqa6667zu0sxsDA+np7q9VKdXV1m8clXdWC0IBgm5Vnp2VQWFbFH77YyOwNOWw/eJT7J/Ru5ByeCMPTYkiJCeWDlftZsP0Qryzcxf0fruf2/6zmXwsyWbnnCKWV1dz93loKy6p45fqhxIYH0TUqhLdvGUGlvYYb31jJkWPeddwJpznVHhpcvHIcy5tJVTu256yBPYsgx9HT01yNY00VFMsMR0FoLSUlJURERBAZGUleXh5z587tsFjEcRQEFwYkRvGbCb15+tvtLNyeT7+ukVw8sGubXd9iUVw5JMmktvccASAhMpjQQCvfbzkImKyi1vD89AwGJNanpXt3ieD1m4Zz7b9X8Mz323n8soFtFpcnKuw1TVL0nvhsbTY/bjvECzMGS9e4r2CvaDr8G7xzHMsOQ5QHVzzM4bAv+Gvj7aGxbl6rQXOMzHAUhFYzZMgQ+vXrx4ABA+jevTtjx47tsFhEOAqCG+48qwcLtuWzcu8RfntRnzZfX/q28el0iw4mJSaUPgkRdeN8jhyrYs2+QlbvO0JSp1CmZiQ2OXd4WgyXDOrG52tz+J+JfYkIbnmh+tpa7fE97D9ShsWiSIxu6ix9uymPe2et54EJvbnz7B4tvs7LC3ex42Ap145MZXQPNwJCOPlUV0CAh5VfwLPjePQAFO6F4be53x+RAL9YBuVH6rcFRUJ8n6bHujbHRMlSg4LgyqOPPlr3fc+ePevG9IBZ7eWdd95xe96SJUvqvi8qKqr7fvr06UyfPr3N4xThKAhusFoUM68bwoo9RzinT9uPwAkLCuDqYU1/ecaEBTKhXxcm9OvS7PnXj07l07XZfLEuh+tHpzV77NtL9/LPeTuZ+5uziAtvXH9WYa/hqpeXUlJezZNXDmwkVL/akMuvP1yPzar429ztjO0Z18j9dGX7gaPsOFgKwH+W7RXh6CvYy907jtZAUBbPwnGv45dR2jjP1+7i3ZxULBawBNQ7jqkd55YIgnBiSI2jIHggNjyIyQO7+mTKdVBSFAMTo3hn+T609ryKjb2mlpcX7qLgWBUv/pjZZP87y/ZxsKSS1NhQ7pu1nj9+uYmq6lq+WJfDfbPWMTSlE/MeOIfY8EB+/eF6Kuw1Hl9rzs+5WJSZVfndloPkFTdTOyecPDw5jkqZ7Z5S1XuXGAcx4cy2icMaBKWHoLJEGmMEwY8R4SgIfohSiutHpbLjYGldnaQ7vtl0gLziCvomRPDein1kFdQvL1daWc3MhbsY3yuOr341jtvGpfP2sn1c/M/F/Oaj9YxMj+WtW4aTGB3C368eROahUp76dpvb19Fa89WGXMb0iOPXF/SiVmveX5HV5u9bOA48OY5gtjfnOKaMBot39a0tEhAIBTvN9yIcBcFvEeEoCH7KJYO6ERkcwDvL93k85o0le0iLDeXNm4djtSj+8f32RvuOHKviwQv7YLNaeHhKP/517RByi8oZ1zOON24aXjdncnyveG4ak8abP+1l8c78Jq+zObeEvQVlXDKoK8kxoZzXpzMfrMyistqzQymcJKo9jOMBz47j0QNG5DWXpm4t1iA4LMJREPwdEY6C4KeEBFq5elgyczcf4NDRpr/81+wrZP3+Im4em07XqBBuHZfOl+tz2ZRTTFFZFf9etJsL+3VhUHL9WJTJA7uy8g8X8PbNIwgJbOw0PTSpLz3iw3jw4w0UuowC+mpDLgEWxUX9EwC4YUwah0ur+HbTgbpjams1n6/LZmte2y7pJbRAs6u/eHAc9/1kHttSOAYE1jfSiHAUBL9FhKMg+DHXjkzBXqP5aNX+JvveWLKHyOAArhpqxqnceXYPOoXaeOrbbbyyaDelVdU8cGHTDtiwoAC3HdjBNivPTx9M4TE7v/loPbW1prZSa82cn/M4q3d8XXf4+J5xpMWG8p9lxg09XFrJzW+t4jcfbuA3H65vti5TaGM8DQAHz45jW9c3Qn1ndWAEhHRqu+sKgnBSEeEoCH5M9/hwxveK4/0VWVTX1NZtzy4s45tNecwYkUJYkEk3RwbbuOe8XizeeZh/L9rNpYO60ScholWvNyAxikcu6ceC7fm84Gi2WZtVRE5ROZcMqp91abEorh+dxpp9hby+ZA+Tnl/Mst0FXDywK9sOHGXhjqbpbqGdqG5uiLcHx9FZ32htw8EbzlmOMsNREOo455xzmgzzfu655/jlL3/p8Zzw8PD2DqtZRDgKgp9z3ahUcosruOXt1SzNPIzWmv8s24dSihvGpLkcm0JidAga+M0FvY/r9a4dmcIVgxN5bt4OFu7I56sNuQQFWLjgjMYjhK4amkSIzcqf52whKsTGl3eP5dlpGSREBvPKwt1evZY4k22AvcKzcAwIbuo4Hj0Ih3dAWhuPzLE6lkKTNLUg1DFjxgxmzZrVaNusWbOYMWNGB0XUMjLHURD8nAlndOG3F/XhjSV7uOa1FZzRNZLsI2VMHJDQZKh3UICVF68ZTE5ROWlxHtYgbgGlFE9cPpAteSXcN2sdVqU4t0/nJoPIo0JsPDSpL/uPlHH/hb3rGm1uHZfOE19vZcP+okb1la5szC7mD19s5KVrhsi63MdLbS3UVLofxwNGUJYearxtnxfzG48HZ7pchKPgq3zzEBzY2LbXTBgIk570uPuqq67i4YcfprKykqCgIPbu3Utubi4ZGRmcf/75FBYWYrfbefzxx5k6dWrbxnaciOMoCH6OxaK4+9ye/PTQeTx15UCqa2o5VlXN7eO7uz1+cEonppzZ7YReMyTQyszrhlJToyk4VsUlg9xf78YxaTw8pV+daASYPiKZiOAAXlm0y+P1C49Vcde7aygorapLtQvHgdNN9DiOJ6RpqnrvElOHmDCobWMJEMdREFyJjY1lxIgRfPvtt4BxG6dNm0ZISAiff/45a9euZf78+TzwwAM+k4GRO7IgnCIE26xMG57C/xuWTMGxqiarxLQ16XFhvHDNYD5YmcV5fTt7fV5EsI3rR6Uyc+Eu9h4+1sT5rKnV3DtrHflHK/n4rtHEhAW2deinD07h6MlxDAgxNZAN2fsTpLZxfSPUN8eIcBR8lWacwfbEma6eOnUqs2bN4o033kBrzf/+7/+yaNEiLBYLOTk5HDx4kISEhA6JsSHiOArCKYZSqt1Fo5Nz+nTmleuHNRnd0xI3jU3DZrHw78VNax2f+2EHi3ce5rGp/ZtNZQte4HQTmx0A3qDGsfQQHN7e9mlqaOA4yjrVgtCQyy67jHnz5rF27VrKy8sZMmQI7733Hvn5+axZs4b169fTpUsXKio8rPJ0khHhKAjCSadzRDBXDk3k4zXZbMktoayqGoDvtxzkhR8zmTYsmRkjxJk6YbxyHBv8MnKuT53aDsKxznFMbftrC4IfEx4ezjnnnMMtt9xS1xRTXFxM586dsdlszJ8/n337PC/0cLKRVLUgCB3C7eO78/HqbCb/czEA4UEBVNXUMjAxisem9u/g6E4R7I4lJptzHCtL4KWR5vmxwxAYDl3buL4RTHNMYLjMcBQEN8yYMYMrrriirsP62muv5ZJLLmHYsGFkZGTQt2/fDo6wHhGOgiB0CN3jw/n6vvH8nF3MoaMVHCqppKqmlnvO7UmwrY3WRz7dCQyDfpdBlIf0cP/LoXAfaMfSkPF9IP2stq9vBBhyPSQNkxmOguCGyy+/vFHzS1xcHMuWLXN7bGlp6ckKyy0iHAVB6DB6d4mgd5fWDSH3J5RSE4HnASvwmtb6SZf9dwF3AzVAKXCH1npLmwUQ0x3+39ue93cdBFe/2WYv1ywpo8yXIAh+jdQ4CoIgtANKKSvwEjAJ6AfMUEr1cznsfa31QK11BvA08MxJDlMQBKFViHAUBEFoH0YAmVrr3VrrKmAW0GiCr9a6pMHTMMA3BrUJwmmEr8xHPFmc6PsV4SgIgtA+JAL7GzzPdmxrhFLqbqXULozjeO9Jik0QBCA4OJiCgoLTRjxqrSkoKCA42EPDnBdIjaMgCEL74K4LpMlvJ631S8BLSqlrgIeBG5tcSKk7gDsAUlJkTJEgtBVJSUlkZ2eTn5/f0aGcNIKDg0lKSjru80U4CoIgtA/ZQMN25iQgt5njZwEz3e3QWr8KvAowbNiw08MaEYSTgM1mIz09vaPD8CskVS0IgtA+rAJ6KaXSlVKBwHRgdsMDlFK9Gjy9GNh5EuMTBEFoNeI4CoIgtANa62ql1D3AXMw4nje01puVUn8CVmutZwP3KKUuAOxAIW7S1IIgCL6ECEdBEIR2Qmv9NfC1y7ZHGnx/30kPShAE4QRQHdVJpJTKB1q7+GIccLgdwjlRfDUu8N3YJK7W46ux+VpcqVrr+I4Oor2Qe+dJwVfjAt+NzVfjAt+Nzdfi8ure2WHC8XhQSq3WWg/r6Dhc8dW4wHdjk7haj6/G5qtxCfX46mckcbUeX43NV+MC343NV+NqCWmOEQRBEARBELxChKMgCIIgCILgFf4mHF/t6AA84Ktxge/GJnG1Hl+NzVfjEurx1c9I4mo9vhqbr8YFvhubr8bVLH5V4ygIgiAIgiB0HP7mOAqCIAiCIAgdhAhHQRAEQRAEwSv8QjgqpSYqpbYrpTKVUg91cCxvKKUOKaU2NdgWo5T6Xim10/HYqQPiSlZKzVdKbVVKbVZK3edDsQUrpVYqpTY4YnvMsT1dKbXCEduHjmXZTjpKKatSap1Sao6vxKWU2quU2qiUWq+UWu3Y1uGfpSOOaKXUJ0qpbY6ft9G+EpvQGLl3ehWXT9475b553HHJvbOd8XnhqJSyAi8Bk4B+wAylVL8ODOktYKLLtoeAeVrrXsA8x/OTTTXwgNb6DGAUcLfj38kXYqsEztNaDwIygIlKqVHAU8CzjtgKgVs7IDaA+4CtDZ77Slznaq0zGsz58oXPEuB54FutdV9gEObfzldiExzIvdNrfPXeKffN40fune2J1tqnv4DRwNwGz38P/L6DY0oDNjV4vh3o6vi+K7DdB/7dvgQm+FpsQCiwFhiJmZgf4O5zPonxJGH+s54HzAGUj8S1F4hz2dbhnyUQCezB0VjnS7HJV5PPSu6dxxejz9075b7Zqtjk3tnOXz7vOAKJwP4Gz7Md23yJLlrrPADHY+eODEYplQYMBlbgI7E50hrrgUPA98AuoEhrXe04pKM+1+eA3wG1juexPhKXBr5TSq1RSt3h2OYLn2V3IB9405Gmek0pFeYjsQmNkXtnK/G1e6fcN48LuXe2M/4gHJWbbTJDyANKqXDgU+DXWuuSjo7Hida6RmudgflLdQRwhrvDTmZMSqkpwCGt9ZqGm90c2hE/b2O11kMwaca7lVJndUAM7ggAhgAztdaDgWP4Q2rl9MRXfpb9Al+8d8p987iQe2c74w/CMRtIbvA8CcjtoFg8cVAp1RXA8XioI4JQStkwN773tNaf+VJsTrTWRcACTC1RtFIqwLGrIz7XscClSqm9wCxM2uU5H4gLrXWu4/EQ8Dnml4YvfJbZQLbWeoXj+SeYm6EvxCY0Ru6dXuLr9065b3qP3DvbH38QjquAXo6OrUBgOjC7g2NyZTZwo+P7GzE1MicVpZQCXge2aq2f8bHY4pVS0Y7vQ4ALMEXB84GrOio2rfXvtdZJWus0zM/Vj1rrazs6LqVUmFIqwvk9cCGwCR/4LLXWB4D9Sqk+jk3nA1t8ITahCXLv9AJfvXfKfbP1yL3zJNHRRZbefAGTgR2Y+o4/dHAsHwB5gB3zF8StmPqOecBOx2NMB8Q1DpMa+BlY7/ia7COxnQmsc8S2CXjEsb07sBLIBD4Ggjrwcz0HmOMLcTlef4Pja7PzZ94XPktHHBnAasfn+QXQyVdik68mn5XcO1uOyyfvnXLfPK545N55Er5kyUFBEARBEATBK/whVS0IgiAIgiD4ACIcBUEQBEEQBK8Q4SgIgiAIgiB4hQhHQRAEQRAEwStEOAqCIAiCIAheIcJREARBEARB8AoRjoIgCIIgCIJX/H+M0wA3GiFG9gAAAABJRU5ErkJggg==\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 [==============================] - 6s 239ms/step - loss: 1.0045 - acc: 0.5098 - val_loss: 0.9906 - 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/weights-augm-trial8-improvement-BEST.hdf5\n",
      "Epoch 2/200\n",
      "27/27 [==============================] - 4s 153ms/step - loss: 0.9422 - acc: 0.4912 - val_loss: 0.8805 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00002: val_acc improved from 0.31579 to 0.68421, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/weights-augm-trial8-improvement-BEST.hdf5\n",
      "Epoch 3/200\n",
      "27/27 [==============================] - 4s 160ms/step - loss: 0.8669 - acc: 0.5829 - val_loss: 0.9292 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00003: val_acc did not improve from 0.68421\n",
      "Epoch 4/200\n",
      "27/27 [==============================] - 4s 153ms/step - loss: 0.8601 - acc: 0.5922 - val_loss: 0.8611 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00004: val_acc did not improve from 0.68421\n",
      "Epoch 5/200\n",
      "27/27 [==============================] - 4s 159ms/step - loss: 0.8845 - acc: 0.4995 - val_loss: 0.8802 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00005: val_acc did not improve from 0.68421\n",
      "Epoch 6/200\n",
      "27/27 [==============================] - 5s 171ms/step - loss: 0.8529 - acc: 0.5098 - val_loss: 0.8197 - val_acc: 0.5789\n",
      "\n",
      "Epoch 00006: val_acc did not improve from 0.68421\n",
      "Epoch 7/200\n",
      "27/27 [==============================] - 4s 159ms/step - loss: 0.8028 - acc: 0.5551 - val_loss: 0.8394 - val_acc: 0.3684\n",
      "\n",
      "Epoch 00007: val_acc did not improve from 0.68421\n",
      "Epoch 8/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 0.8180 - acc: 0.5736 - val_loss: 0.9136 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00008: val_acc did not improve from 0.68421\n",
      "Epoch 9/200\n",
      "27/27 [==============================] - 4s 153ms/step - loss: 0.8025 - acc: 0.5922 - val_loss: 0.9549 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00009: val_acc did not improve from 0.68421\n",
      "Epoch 10/200\n",
      "27/27 [==============================] - 4s 165ms/step - loss: 0.8230 - acc: 0.5098 - val_loss: 0.7509 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00010: val_acc improved from 0.68421 to 0.89474, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/weights-augm-trial8-improvement-BEST.hdf5\n",
      "Epoch 11/200\n",
      "27/27 [==============================] - 4s 158ms/step - loss: 0.7516 - acc: 0.6663 - val_loss: 0.7731 - val_acc: 0.5789\n",
      "\n",
      "Epoch 00011: val_acc did not improve from 0.89474\n",
      "Epoch 12/200\n",
      "27/27 [==============================] - 4s 157ms/step - loss: 0.7621 - acc: 0.6200 - val_loss: 0.8076 - val_acc: 0.3684\n",
      "\n",
      "Epoch 00012: val_acc did not improve from 0.89474\n",
      "Epoch 13/200\n",
      "27/27 [==============================] - 4s 166ms/step - loss: 0.6927 - acc: 0.7034 - val_loss: 0.7514 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00013: val_acc did not improve from 0.89474\n",
      "Epoch 14/200\n",
      "27/27 [==============================] - 4s 161ms/step - loss: 0.6964 - acc: 0.7034 - val_loss: 0.6212 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00014: val_acc did not improve from 0.89474\n",
      "Epoch 15/200\n",
      "27/27 [==============================] - 4s 152ms/step - loss: 0.6495 - acc: 0.7683 - val_loss: 0.6308 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00015: val_acc did not improve from 0.89474\n",
      "Epoch 16/200\n",
      "27/27 [==============================] - 4s 157ms/step - loss: 0.6415 - acc: 0.6941 - val_loss: 0.7033 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00016: val_acc did not improve from 0.89474\n",
      "Epoch 17/200\n",
      "27/27 [==============================] - 4s 164ms/step - loss: 0.6216 - acc: 0.7508 - val_loss: 0.6178 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00017: val_acc did not improve from 0.89474\n",
      "Epoch 18/200\n",
      "27/27 [==============================] - 4s 158ms/step - loss: 0.5691 - acc: 0.8146 - val_loss: 0.6427 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00018: val_acc did not improve from 0.89474\n",
      "Epoch 19/200\n",
      "27/27 [==============================] - 4s 163ms/step - loss: 0.5732 - acc: 0.8054 - val_loss: 0.5168 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00019: val_acc did not improve from 0.89474\n",
      "Epoch 20/200\n",
      "27/27 [==============================] - 4s 165ms/step - loss: 0.5093 - acc: 0.8702 - val_loss: 0.5858 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00020: val_acc did not improve from 0.89474\n",
      "Epoch 21/200\n",
      "27/27 [==============================] - 4s 159ms/step - loss: 0.5163 - acc: 0.8064 - val_loss: 0.6461 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00021: val_acc did not improve from 0.89474\n",
      "Epoch 22/200\n",
      "27/27 [==============================] - 4s 157ms/step - loss: 0.5137 - acc: 0.8239 - val_loss: 0.4877 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00022: val_acc improved from 0.89474 to 0.94737, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/weights-augm-trial8-improvement-BEST.hdf5\n",
      "Epoch 23/200\n",
      "27/27 [==============================] - 4s 163ms/step - loss: 0.6108 - acc: 0.7693 - val_loss: 0.5183 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00023: val_acc did not improve from 0.94737\n",
      "Epoch 24/200\n",
      "27/27 [==============================] - 4s 153ms/step - loss: 0.4978 - acc: 0.8702 - val_loss: 0.5073 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00024: val_acc did not improve from 0.94737\n",
      "Epoch 25/200\n",
      "27/27 [==============================] - 4s 163ms/step - loss: 0.4826 - acc: 0.8424 - val_loss: 0.4518 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00025: val_acc did not improve from 0.94737\n",
      "Epoch 26/200\n",
      "27/27 [==============================] - 4s 163ms/step - loss: 0.4377 - acc: 0.8795 - val_loss: 0.5297 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00026: val_acc did not improve from 0.94737\n",
      "Epoch 27/200\n",
      "27/27 [==============================] - 4s 157ms/step - loss: 0.4459 - acc: 0.8795 - val_loss: 0.4612 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00027: val_acc did not improve from 0.94737\n",
      "Epoch 28/200\n",
      "27/27 [==============================] - 4s 164ms/step - loss: 0.4653 - acc: 0.8805 - val_loss: 0.4709 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00028: val_acc did not improve from 0.94737\n",
      "Epoch 29/200\n",
      "27/27 [==============================] - 5s 169ms/step - loss: 0.4298 - acc: 0.8795 - val_loss: 0.5268 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00029: val_acc did not improve from 0.94737\n",
      "Epoch 30/200\n",
      "27/27 [==============================] - 4s 157ms/step - loss: 0.4123 - acc: 0.8888 - val_loss: 0.4836 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00030: val_acc did not improve from 0.94737\n",
      "Epoch 31/200\n",
      "27/27 [==============================] - 4s 164ms/step - loss: 0.4142 - acc: 0.8980 - val_loss: 0.4958 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00031: val_acc did not improve from 0.94737\n",
      "Epoch 32/200\n",
      "27/27 [==============================] - 4s 165ms/step - loss: 0.3886 - acc: 0.9166 - val_loss: 0.4668 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00032: val_acc did not improve from 0.94737\n",
      "Epoch 33/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 0.3481 - acc: 0.9444 - val_loss: 0.4373 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00033: val_acc did not improve from 0.94737\n",
      "Epoch 34/200\n",
      "27/27 [==============================] - 4s 159ms/step - loss: 0.3892 - acc: 0.9073 - val_loss: 0.4380 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00034: val_acc did not improve from 0.94737\n",
      "Epoch 35/200\n",
      "27/27 [==============================] - 5s 167ms/step - loss: 0.3529 - acc: 0.9259 - val_loss: 0.4470 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00035: val_acc did not improve from 0.94737\n",
      "Epoch 36/200\n",
      "27/27 [==============================] - 4s 165ms/step - loss: 0.3571 - acc: 0.9073 - val_loss: 0.4090 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00036: val_acc did not improve from 0.94737\n",
      "Epoch 37/200\n",
      "27/27 [==============================] - 4s 154ms/step - loss: 0.3671 - acc: 0.9073 - val_loss: 0.5368 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00037: val_acc did not improve from 0.94737\n",
      "Epoch 38/200\n",
      "27/27 [==============================] - 4s 159ms/step - loss: 0.3615 - acc: 0.9351 - val_loss: 0.3982 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00038: val_acc did not improve from 0.94737\n",
      "Epoch 39/200\n",
      "27/27 [==============================] - 5s 169ms/step - loss: 0.3569 - acc: 0.9444 - val_loss: 0.4372 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00039: val_acc did not improve from 0.94737\n",
      "Epoch 40/200\n",
      "27/27 [==============================] - 4s 159ms/step - loss: 0.2959 - acc: 0.9537 - val_loss: 0.3977 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00040: val_acc did not improve from 0.94737\n",
      "Epoch 41/200\n",
      "27/27 [==============================] - 4s 154ms/step - loss: 0.3446 - acc: 0.9351 - val_loss: 0.4433 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00041: val_acc did not improve from 0.94737\n",
      "Epoch 42/200\n",
      "27/27 [==============================] - 4s 166ms/step - loss: 0.3328 - acc: 0.9444 - val_loss: 0.4023 - val_acc: 0.9474\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Epoch 00042: val_acc did not improve from 0.94737\n",
      "Epoch 43/200\n",
      "27/27 [==============================] - 4s 163ms/step - loss: 0.3190 - acc: 0.9444 - val_loss: 0.3737 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00043: val_acc did not improve from 0.94737\n",
      "Epoch 44/200\n",
      "27/27 [==============================] - 4s 163ms/step - loss: 0.3020 - acc: 0.9722 - val_loss: 0.3877 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00044: val_acc did not improve from 0.94737\n",
      "Epoch 45/200\n",
      "27/27 [==============================] - 4s 157ms/step - loss: 0.3307 - acc: 0.9351 - val_loss: 0.4059 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00045: val_acc did not improve from 0.94737\n",
      "Epoch 46/200\n",
      "27/27 [==============================] - 4s 165ms/step - loss: 0.2789 - acc: 0.9537 - val_loss: 0.4325 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00046: val_acc did not improve from 0.94737\n",
      "Epoch 47/200\n",
      "27/27 [==============================] - 4s 160ms/step - loss: 0.3095 - acc: 0.9537 - val_loss: 0.4259 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00047: val_acc did not improve from 0.94737\n",
      "Epoch 48/200\n",
      "27/27 [==============================] - 4s 165ms/step - loss: 0.2789 - acc: 0.9537 - val_loss: 0.4127 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00048: val_acc did not improve from 0.94737\n",
      "Epoch 49/200\n",
      "27/27 [==============================] - 4s 156ms/step - loss: 0.3239 - acc: 0.9351 - val_loss: 0.3926 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00049: val_acc did not improve from 0.94737\n",
      "Epoch 50/200\n",
      "27/27 [==============================] - 4s 166ms/step - loss: 0.3268 - acc: 0.8991 - val_loss: 0.4522 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00050: val_acc did not improve from 0.94737\n",
      "Epoch 51/200\n",
      "27/27 [==============================] - 4s 160ms/step - loss: 0.2936 - acc: 0.9537 - val_loss: 0.4075 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00051: val_acc did not improve from 0.94737\n",
      "Epoch 52/200\n",
      "27/27 [==============================] - 5s 169ms/step - loss: 0.3201 - acc: 0.9259 - val_loss: 0.4400 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00052: val_acc did not improve from 0.94737\n",
      "Epoch 53/200\n",
      "27/27 [==============================] - 4s 164ms/step - loss: 0.2475 - acc: 0.9815 - val_loss: 0.4462 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00053: val_acc did not improve from 0.94737\n",
      "Epoch 54/200\n",
      "27/27 [==============================] - 4s 164ms/step - loss: 0.2899 - acc: 0.9629 - val_loss: 0.3923 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00054: val_acc did not improve from 0.94737\n",
      "Epoch 55/200\n",
      "27/27 [==============================] - 4s 157ms/step - loss: 0.3029 - acc: 0.9259 - val_loss: 0.4467 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00055: val_acc did not improve from 0.94737\n",
      "Epoch 56/200\n",
      "27/27 [==============================] - 4s 163ms/step - loss: 0.2822 - acc: 0.9629 - val_loss: 0.4246 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00056: val_acc did not improve from 0.94737\n",
      "Epoch 57/200\n",
      "27/27 [==============================] - 4s 158ms/step - loss: 0.2680 - acc: 0.9537 - val_loss: 0.4198 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00057: val_acc did not improve from 0.94737\n",
      "Epoch 58/200\n",
      "27/27 [==============================] - 4s 164ms/step - loss: 0.2808 - acc: 0.9537 - val_loss: 0.4118 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00058: val_acc did not improve from 0.94737\n",
      "Epoch 00058: early stopping\n"
     ]
    },
    {
     "data": {
      "image/png": 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xfP44VBdCeS6Mcfs/mRBCiJGqvgImXwpr13v+nJoS+F2yyVS6Cxy1hsxNMHUNXP3PluPPXQHHPug4cDz2Qcuth4FjU/4efGrO8FTUj7j1ew94/jsc+xDWfw1Kj3ccOJZkQdwCuPkDKuqa+PW7R3hxRw5xEQE8csV0lk002xFbtOZ7z+1i05EzrL95IYsmmIUkWmte3ZXHIxsPU17bfmHI/1w3h9XTx7Y6poCfv76fN77MY8cDK/kko4jvvfglNywez8Nr0rr8tRSQV1TNmr98TmJ4EK98ZzH+Pl5ur92aWcy1f9vOzy6dQnSwHz94aQ9/Xjebr8wcB8DXnvgcb4vite+e0+H77c8p44q/buWXl0zn2oUJANz49A4yCqr4/P4LmoPfgTK0JglGpQJwaWw1RwqqOFVe1/9jKNgPeTvN/drB329JCCFEP6uvBL9ulIUBgqIgdg5kfuj+fOFBqDoNKataH09ZBSXHoCzb/fOcr5ezDRqqPBrK4U9fA+CpU0nklNR69BwAoiaY29Ksjq8pPYGOmsC7+0+z6rFPeGlnDrecm8T7d5/XHDSCmQ/96NUzGR8ZyJ0v7Kagop7s4hqu//t27nt1Hymjgnni2jk8ef3c5p/nv72Qi9Ni3L7t2vnx1DfZeez9o/z4tX3MHR/Bzy71PPEzYVQwj149k/2nKvj5WwfdXqO15nfvZzA2zJ/rF43nKzPHMWlMCI99cJQmm52Kuib255WzxGU1tTuz4sOZNCaEDTtzAMgrq+XTY0VcPS/Obfa1vw2twDHarKxeFFYKwOYjA7ANWbrLfro1EjgKIYRoo6Gqe/MJnVJWQV461Ja2P+ecM5mysvXxVEcg6S7gLM+FoiMwcTXYm+D4J10Oob7Jhj72IUctKZSqMF5Oz/V8/GEJYPE2GUd3muqgMo+3Tvrz3fW7GRXix1t3LOWBS6e6XQQT4u/D/35jLrWNNtb93zYu+uOn7Mut4JEr0nj5tsVcOmMsF6fFNP8sTY1utRDF1Yy4MKaMDeXZrdkE+Xnz1+vm4OvdvRDowmkx3Hl+Ci+l5/L8tpPtzm86fIYvc8q5a0Uq/j5eeFkU9144kRPFNby2K4/tx0uwa5r7N3ZEKcXaBfHsy6vgYH4Fr6SbFeFfnxffrfH2laEVOIYlgMWH0Q25xEcG8HFGPweODdWw72VIXmEe1w6ubvRCCCEGmN0ODT3IOIIjCNSQ9VH7c8c+hDHTIbR1GZaoFAgfb8635QwmL3gAfEM6zma6eGXLAdLsGQRMvYhlE0fxyq5cz1u7eHmbsXQUODqyop+UhPKT1ZN5644lTI/rfAvh1DEh/P7rM8kuqeH8SaP58N5lXLdwfLe3EFRKcdOSRPy8LTxx7RzGhPp36/lOd6+ayHkTR/GzNw9wz0t7mvtD2u2a37+fQVJ0EF+bG9d8/aqpY5gVH87jm46xOeMM/j4WZieEd/k+V8yOxdfbwgvbc3glPZelKdHERwb2aMy9bWgFjl7eEDkBVZLJ+ZNGsyWzmPomW9fP6y0HXoXGKjj3XvNYMo5CNFu+fHm7ht5//OMfuf322zt8TnBwcF8PS4j+1VQD6J4tnBw3GwIiW+YlOtVXQu42SF3Z/jlKmYDzxKdgbWh9LvNDs8p5TBpMWGYed9IfsbrByoFP38RLaeIXrGHtggQKKxv4uDu9kyMnmHmMbmQe3gvAkvkLuG1Zssct9S6ZPpa9P7+QJ78xt8cBH5iM3Z4HLzyrhbVeFsVT35jL9y5I4e29+ax87BPe+DKPf+3L50hBFXevmoiPy++llOJHF03idEU9G3bmsiApCj9v9/MjXYUH+rI6LYYXd+SQX1HPugUJPR5zbxtagSNAdCqUHOOCyaOpb7K33oOy+oz5ttdX0p+B0VNh/DkQECFzHIVwsW7dOjZs2NDq2IYNG1i3bt0AjUiIAVBfaW57Uqq2eEHyBSbAc/1bdvxjs+K6bZnaKWWVCVhPbm05Zm00z0tZaYLLlJVQkQtFGR2+/dNbTjCvKR2rbyjEzuWCyaOJDvZrnmvX1rv7T3OkoLL1wcgJpm1QmwBVa80X6WZ9wCXLO14Y0pFQ/26sUO9EgG/XQVtX/H28uPfCSfz7++cyPiqQu1/ayw9f2cvkmBAua7MwB0xpeklKFFrT5fxGV2vnJ2DXEBXky8opHSw2GgBDL3CMSoHSEyxKDCPAx4uPDjvK1WUn4Q/TYO+LffO+p3bD6T2mX5ZSEBgtGUchXFx11VW88847NDSYrEd2djb5+fnMmjWLFStWMGfOHKZPn85bb701wCMVog85F6D0tFVb6iqTlCjY23Is80NT+o5f6P45Seea9jaupejcbaYFj3MOZPNcSPf9JctqGvnbp5lc6HcA79QV4OWNj5eFr8+L46MjZyioaL2n/L/3nea763dz/d+2c7rCZaFqVLKpzNW0zlJ+eqwYr7Lj1PtEEBjqefA0mE2KCeHV75zDw2umMTrEn/+6bGqHJfT7L55CbHgAF05zv3jHnUUTIlmYFMnN507o9nzMvjS02vGAyTjam/CvzmNpajQfHTnDw1qjDr5u+lllfgCzr+v9901/GnwCYcbV5nFQtMxxFIPXu/ebDgC9KWY6rP51h6ejoqJYsGAB//nPf1izZg0bNmzgmmuuISAggDfeeIPQ0FCKi4tZtGgRl19+eYeT2IUY0hocGTi/zufudcg5h/7Yh6Z0rbUJCCcs67gvpG8QjF9irrvoEcfzPwCLDySdZx6HxcGoKeb4Od9r9xJPfppFXNNxwlRpq5Xb18yL538+zuLVXbnceYHpbHKssIr7Xt3LtHGhZBfX8N3nd/PSbYtMCTbSubL6OASbBtZmR5cjPORbhO/olJ79uwxSXhbFDYsTuWFxYqfXTY8LM610ukEpxUu3LT6L0fWNwRPCesrRkoeSTFZMHs2p8joyCqvggGkfQPbnnc7h6JH6CvP6aV8Df8eHQWCUZByFaMO1XO0sU2ut+elPf8qMGTNYuXIlp06dorCwcIBHKkQfcZaqe5pxDB5lAkZnZvDMYag81b4NT1spK80K6nLHKujMDyFhUetxpK6EnC/MQk8Xp8rr+MfWbG6PPdHyWg6J0UEsnhDFS+m52O2aqvombntuF4G+Xvz9m/P5/ddnsie3nIf/dcg8wRk4usxzfPdAAQdOVTLFrxhLVHK3/0nE4DI0M44Axcc4P20ZALt2bWdywX4YPQ3OHISSzJbresO+l01n/nk3tRwLiobc7b33HkL0pk4yg33pq1/9Kvfccw+7d++mrq6OOXPm8Oyzz1JUVMSuXbvw8fEhMTGR+vr6rl9MiKGo4SzmODqlrDK7xNSWdtyGp63UVfD+A+b61AvhzCFY9XD71936Z7OQZvIlZrhWG7ev342XUqz02QcxM9o17167IJ67Nuzh86xint92kpOltay/eSExYf6snj6W25ZN4H8/Oc7M+HCunp1gtkF0rKy22uw8+n4GU0b5ElhV0BJYiiFr6GUcAyPNqrOSY4wJ9Wd6bBheh94EFKz+jbkmuwf7a3bmy+dh7EzTnLV5HNHmP+q+XIwjxBATHBzM8uXLuemmm5oXxVRUVDB69Gh8fHzYvHkzJ0+27382XCmlLlZKZSilMpVS97s5P14ptUkptU8p9bFSKs7d64ghpLlUfTaB40qzBeHxzaa0PHpqx3tRO0VPNC3rjn3YMtexbZYyYRH4BLWa5/jf/zrE3txy/rgmCf/T6W4D1IumxRAe6MPdL+3hvYOF/GT15OadXADuu3AS5yRH8bM3D7D/dC2EJzQ3AX/jy1NkFdXw08X+KLQEjsOAR4HjoPvwi06F4kwALpg0inlVH9EUfw4kLjWbxJ/8vPfeq74CTu+FSZe0Ph4UbfbkrC/vvfcSYhhYt24de/fuZe3atQBcd911pKenM2/ePNavX8/kyQO0VWg/U0p5AU8AqzHbya5TSrXdquL3wD+11jOAh4EO9o0TQ8bZLo4BiJsH/uFw8E2z40sH2cbaRivX/W0bmzPOONryrIQTn0DGuxAaC6OntH6Ct1+rtjwv78zlhe05fGdZMqv8D5u/aantS+L+Pl5cMTuW4upGvjJzHN9emtT6Zb0s/HndbKKDfFn71Bdsq4gg49BezvvtZh586yDTY8NYGlFhLo6SwHGo67JU7fLhtwrIA3Yqpd7WWh9yucz54fcPpdQFmA+/b/TFgAEzz9HxjenSMSWkWPL5MvIWZisFiUta5jn2xuT7vHRAQ/yC1scDHZ3fa4pNFlQIAcAVV1yBdplnHB0dzRdffOH22urqarfHh4kFQKbW+jiAUmoDsAZw/eycCtztuL8ZeLNfRyh6X30loMD3LHqUOtvyHHzdPHYTzAE883k2n2eWkF9ez9KUaHxSVpmFnEf/A3O+6f5vYMpKyNhIxsFd/OytIpakRPHDCyfCO38xC3riFrR/DnD78hTCAny49bwJbhe2RQX78cyNC/jbZ8dpyh/P+IojzE0IR1kUNy1JQp107K8tGcchz5OMY/OHn9a6EXB++LmaCmxy3N/s5nzvik6B6kKoryT1zPtYsbCherY5N34JVOV3vG9nd+XtBBTEzmt9PMiRppdejkII92IB1/3a8hzHXO0Fvua4fwUQopRq16tEKXWrUipdKZVeVNSNZsyi/zVUmWyj5SxngjmDRd9giF/U7nRFbRP/+0kWcREBzVvakXSeacvj+vwOXve9N58nOsiXP62djbdFQeYmSF5uNtpwY1SIHz9YOdHt1oBOk2JC+N3XZ3LuwkX422v5w2VxPHb1LNJiw8ycx4BI0wNZDGme/D978H34RTmW85ccQx18jazg+WzMaqTJZjflaui9cnXudjO/pO1EZ9eMoxBCtOeu5NG25cMPgWVKqS+BZcApwNruSVo/pbWep7WeN2rUqN4f6UBprIFnL4MzR3r/td+8HfZ00tdXa9hwHRx9v3fft6fbDbblbMszYTl4+7Y7/dRnWVTWW3nqG/OYnWC2tKu3BEDCYrNfdNIyty+7rzqUk5Z4bm56gY+97yDqqdnw2BSTcOlq5banXFvyOJVkSbZxmPAkcBx8H37Oljx7X4LyHBomX0FVvZX07DIYNdm0ysnuhcDRbjel6rZlajBzHEEyjmJQ0b3dimoQGwK/ax4Q7/I4Dsh3vUBrna+1vlJrPRt4wHGsov+GOMDKcyH7M8jb0buvW5EHe9Z3vjdzYw0cecf89Kb6irOb3+gUMgYufRSW/ajdqaKqBp7eks3lM8cxdVwo9zm2tHt+20m44L/gsj+2S3bUNFj5xTuH+OoTn/MH9Q3Kk7+C78QVkHw+pKyABbfC1F4qFjYHji5bD5aekMBxmPCkHY9HH37AlQBKqWDga3364ReZBMoCu54FL18mnHsNvl9s56MjhSxOjjJbAp5sv7L6eFE1P35tHw+vSWPKWA++ERYdMd8e3XXrD3QkVGukCbgYHPz9/SkpKSEqKmrYN9fWWlNSUoK/f8/3re0HO4FUpVQS5sv0WuBa1wuUUtFAqdbaDvwEeLrfRzmQbI69lRtrevd1nQFjXVnH1zjPuWbFekND1dm14nE1/2a3h5/YnEmjzc7dqyYCcE5yNEtTovnrx1ms/dH5BMfPb3X9xxlneOCNA5wqr+O6hQn8ePWFvbaFn1vhrVvy0FRvtjuM6oPNOUS/8yRwHHwfft5+ED4eyk7A5MsIDotk4YRINh05wwOXToXxS+Hwv8y32XAT89rtmvtf38/O7DL+8lEmT1w3p4s3oaVPo7uMo7efKUdIxlEMEnFxceTl5TFS5sD5+/sTFzd4u9dora1KqTuB9wAv4Gmt9UGl1MNAutb6bWA58CullAY+Be4YsAEPBFuTuW3s5UVSxxztZupKO76mzwLHypapTH0gr6yWF7bncPW8OJKig5qP//CiSXz1ic95essJvr/CVOWKqxv4xTuHeGtPPsmjgnjlO4uZn9gPizm9fc3fXmcT8PKTIK14ho0uA8dB++EXnWoCx7QrAVgxeTQP/esQJ4prSEpcYq45+TmEm5YgL6XnsuNEKbNGe/HugdPkldUSFxHY+Xvk7jCZxY7+zy67x4hBxMfHh6SkpK4vFP1Ga70R2Njm2IMu918FXu3vcQ0a1j7IOFob4fgn5n4H1ytqAAAgAElEQVSnGUdHUFl5CprqwCegd96/oQoi+u6/w8c/PAYKvndB600uZsWHc+HUMfzfp8e5ftF4Nh0u5JGNh6lpsHLXilRuPz/ZbAnYXyKTW4Jy522k7BozHHi07EtrvVFrPVFrnay1fsRx7EFH0IjW+lWtdarjmpu11g19OWjANOT2D4OJFwOwYsoYlIL//tdB6iMnm3OORuBnKuv55cbD3B+TzhtV17LEcoB/fuFBE+Lc7aZM3VHZLyhaMo5CCNFTfVGqztsBjVUQFu9ZqRp6rwsHmHY8vVSqTs8u5ZX03Oaff36RzWu78/jGovGMC28f6N574SSqG61c+IdPue/VfaSMCubdu87l7lUT+zdoBJNwKT1uFiE5M4+R8sV2OBh6Ww46nXsvzPu22dwdiI8M5JGvTueBN/dz8z9382z8YrwdK6t//vZB5tn2cFvln1Daxq3RB7h9xyzuWpFKkF8H/wQ1JWZi75xO2lEGRptJ2EIIIbqvuVTdi4HjsQ/A4mMWenzxF7DbTF/EtlwDx5Ks9s2ye6qhslcWx5RUN7Du/7bRZGu9CCzE35vbl7vP3E2KCeHqufFs3H+aR65IY938BCyWAZrvHDnB/FvUlpgA0j9ceh4PE0M3cPQJaFdauHZhAr7eFn706l5eiI7nhqr/sHnnXk4c3MHbgY+joidDYCQLir6kqv4aXknP5VtLOvgG5Fzl525hjFNQFJze00u/kBBCjDDOUrVzt5XekPmh2VovzLGms77CfcDiGjj21jxHayNY600j7bP0+u5TNNk0L96yiLiIlr91oQE+hAV0vLDll1dO57/XTMPfp58zjG1FOYLbkiyThImSMvVwMfT2qu7CVXPjeHztbF4vHQ/A/v/8jef9f4dPYBhc+zJMuRy/6lxWj6vjma3Z2O0dtPTI3W56YY2b3fGbBUabOY6Dvy2IEEIMPrZGc9tbGcfKfCg8YHZHcTaaru1ggUxdGfgEmqbUrm1junDgVAUfHip0f7Ib2w1uOlzInlz3W9ZqrXlxZw5zx0ewODmK+MjA5p/OgkYAL4sa+KARWvdyLD0uC2OGkWEXOAJ8ZeY4vnvNFVTrAL5v+ycR3g2o614xm8QnXwDA7fHZnCypZdORM+5fJHcnxMzofMJ0UDTYm1o2tRdCCOG53g4cnW14Ule1BI4dzXOsLTPXOOfieehX7x7mzhd3U1HX1P6k829BF3Mctdb88JW93LF+Nw1WW7vzO7PLOF5UwzXz4908e4gIH2/a5hUdNlO6JHAcNoZl4Ahw0Yw4rPGLsStvvNY+BzFp5kRUMkQkMq02nXFh/vx9i5sPDFsTnNrVeZkaZPcYIYQ4G729qjrzQwgZZ3b76ipwrHMEjlHJUOJZ4Giza/bklFPfZOftPafaX+AMHLvYOeZUeR1ltU2cKq/jxe057c5v2JlDsJ83l80Y69G4BiVvXzNdIOsj0HZZUT2MDNvAESD8ysew3PRuc5axWfIKLCe3cOOiWLYdL+Vgfpte5QX7wVrnvn+jq+bdY6QJuBBCdFtv9nG0WSHrY0hdaTphOOc1dhU4Rk6AyjzTkqcLGQVV1DTa8LIoXtyR2373Ig9L1QdOmQAzJtSfv2zOpLaxZaO1iromNu4/zeWzxnW6L/SQEDnB/D113hfDwrAOHIlMch/8JV8AjdWsG3uaAB8vnt6S3fp83k5z21Xg2Lx7jGQchRCi23qzHU/eDmioaNlv2aOMY3hLJqys6xZtu3PMa317aRKHTley/1SbpEO9Z6XqA6cq8LIoHrt6JsXVjTzzeXbzubf3nKK+yc66+QldjmfQc10QI4tjho3hHTh2JOk8sHgTnPcJV86J5Z19+VTUusxXyd0OobEQ1sWuFLJftRBC9FxvlqqPfWAWNE5YZh77O1Y2d7R7TF2ZWRjjbl/lDuw+WUZ0sB93nJ+Cv4+FDTtzW1/gYan6QH4FqaODOSclmpVTRvO/n2RRUdtkFsXsyGXq2FDSYntp28KB5Py39Q9rCeTFkDcyA0f/UIhbAJmbWLcggQarnTdd56vk7ug62wgyx1EIIc6Gs1TdVAN2+9m9VuYHZl66M2C0eJn77jKOWpuAMiCipSm1BwtkdueUMXd8OGEBPlwyfSxv78mnpqGlzNxSqu446NNac+BUBdPGmXHee+EkKuutPPVZFvtPVXDodCXrFsQPj/3mnYFj5ISON9IQQ87IDBwBUi6Agn2khTUybVwoL+7IMfNVKvPNZuxdLYwB8A007RxkjqMQQnSfzWWTsaazyDpWFZi5dCkrWx8PiHQfODbVmhXdARFmLmRARMvuJm2VZYPdTnF1A9kltcxJMJmzdQsSqG6w8u/9p1uurXeUrjuZ41hY2UBxdSPTHRnFKWNDuXzmOJ7eks0TmzPx97Fw+axYT3/zwc05DUAWxgwrIzdwTF5hbo9vZu2CBI4UVLEvtxw+/Z05Pv4cz17H2ctRCCFE99hcpgidTbk65wtzm3x+6+MBEe4DR+cxZ/m0o5Y8ZSfhz3Nh1zN8mWN6Ls4db54zb3wEKaOD2bDDZVV0QxV4+YKPf4dDPeCYF5kW29Ik/O5VE2m02XnvYCGXTB/bZa/GISNiPPgGw5hpAz0S0YtGbuA4dpb5Npq5iTWzxhHg40Xuu49C+tOw5AdmL2xPBEXJHEchhOgJq0vG8WwCx+oicxvWpu9hQIT7BuDtAsdk94HjsffBboWMjew6WYaPl2oO+JRSrJ0fz+6cco4WOkrUHmw3eCC/AqVMptEpKTqIq+eZOfVrh8OiGCdvP7h9Gyy+Y6BHInrRyA0cLRbz7TTrI0J9vbgvMYtL8v+CddJlsOLnnr+OZByFEKJnXEvVZ9OSxxkI+oe3Pt5VxtHZsidygmlS3VTf+jpnQ/HsLew/WcC0cWGtdmW5YnYsPl6KDTsci2QaqrpeGHOqkgnRQQT5tW6185NLpvDX6+YwP3GYLSIJjzcBpBg2Rm7gCKZcXXMG9r7AN0//gv06iTeTHjRBpaeComWOoxBC9ERvlarrysz+0F5t+h52FDg6s5DOjGNUMqCh3KUlT1M9nPgUoieCtZ7AU1ub5zc6RQX7ceG0GF7/Mo/6Jptpx+NBK57pse33sg71NwtuhsWiGDGsjfDA0dEY/K07sARG8cvQB3l+Vzezh4FRknEUQoie6K1StbMnY1uBkWbBit3W/npoPccRWi+QydlqFtGc/wB2Lz/O0Xua5ze6um5BAuW1Tdzw9A7qqss6zTgWVTVQUFnfan6jEEPNyA4cQ8fCmDTwDUFd9zIXLprFntxyjhS07D1dVNXAHz88ylOfZlFW09juJXRgNFjruOufW/joSAcb3wshhGjP1mj2M4azL1W76xMYEAHoltXOrtc3n8ell6PLPMfMTWahS+oq8sPnsdyyhznj2wen56RE89urZpBRUMXJ/AKyqiw0Wt23FnLuUuZsxSPEUDSyA0eAr/0dbvoPjJnGlbNj8fWysGFHLvnldTz09kGW/uYjHt90jF9uPMLCX23i3pf3sje3nAarjVfSc3lsqyl57DqcyT+2dr3zgBBCCAdnSxyAhrMJHEs7CRxpX66uKwNvf/AJMI8DI838SNcm4Mc+gPFLwDeIL7zmkmQpZKw13+3bXz0vng/vWcYonwb2nLFx6Z8+Y39eRbvrnCuqpw2H5t5ixBriG2H2gtGTm+9GBPlyUVoML+7IYf32k2gNV86J5bvLU2i02nluWzZv7D7Fa7vzCPT1orbRxjejzDfHtVMD+J9jpTTZ7Ph4STwuhBBdsjaY7ha1JWdfqm67oho6DxwDIlsfc23JU54DxRkw95sAvFI+ma+DyUJ2sHXeqBA/8G5gQVIivztp5eZ/7uST+85vtZjmwKlKEqMCCfUfJu12xIjkUYSjlLpYKZWhlMpUSt3v5nyCUmqzUupLpdQ+pdQlvT/U/nHTkkRC/L1ZtyCBj+9bzm+vmklSdBCTYkL4f1+dzrafruDhNdNYnTaW5769gIfWngfAgjGamkZb+71LnZrq4Lkr4fjH/ffLCCHEYGZralnZ3GelajoIHNtcH+XSkufYB+Y2ZRUFFfXsqAynIiDe7E7TEa2hoYr4mDH84ZpZFFY28Py21lWoA/kVTJP5jWKI6zJwVEp5AU8Aq4GpwDql1NQ2l/0MeFlrPRtYC/y1twfaX2YnRJD+s1U8vCaNuIjAdudD/H24YXEij149k3NTR6Ec+1VPCTWTvLcd72CF9a5/QNYmSH+mz8YuhBBDiq3BNIi2ePc842i3m0AwMLL9OWdW0ZPAMXICuiKP57YcxXbsQwhLgOhUdueY5zYmrYATn7Vv2ePUVAvaBn6hLE6O4tzUaJ7YnElVvVk5XlbTSF5ZndsV1UIMJZ5kHBcAmVrr41rrRmADsKbNNRpwTtoIA9xPBBmOHPtVh9gqmDgmmC+y3ASO1gb4/HFzP3MTWNsvshFCDD8jqVrTI9ZG0+PPN6jngWNjFWh7DzKObRa6RCajtJ31/95Ew9GPKByzFJRi98ky/LwthM9YDdY6OLnF/TjqHYsqHe147rtoEmW1TTy9JRuAg/nmfJosjBFDnCeBYyyQ6/I4z3HM1UPA9UqpPGAj8D13L6SUulUpla6USi8qKurBcAchvxCz8q62mMUTokjPLqPJ1mZF3Z71UJUPC24zH3I5WwdmrEKIfjPSqjU9Yms0n5++wT0PHNv2ZHTlH9b6Gic3GcdC73EA3DcmnUDqeeDAWO5/bR9bs0qYEReGz4TzwMvPfPl3p8ERODra8cyIC+fiaTH832fHKatp5EDzimpZGCOGNk8CR3fdSHWbx+uAZ7XWccAlwHNKqXavrbV+Sms9T2s9b9SoUd0f7WCklGP3mBIWJ0dR12RjX155y3lbE2z5A8TOg5U/Nyv5jr43cOMVQvQXqdZ0xdbgCByDej7HsW1rHVde3qYxuLuMY5vS9isnfAE4v+59tJcvkxdfyiu78jh0upI54yPANxASl7bMf2yrwbHtoEsfx3svnEhNo5UnP8li/6kK4iICiAjy7dGvKcRg4UngmAe4LleLo/2H27eBlwG01l8A/kB0bwxwSHDsV70gKQqgdbl6/ytmhd6yH5kPx6TzIONdM5FaCDGcSbWmK7Ym8O7DwBFMSdo1cGysBWt9q+utNjvP7a2kxhKMpaESlbCYH35lLm/dsYTLZozlqjlmH2lSV0HJMSjLbv8+zl6RLntVp44J4YrZsTy7NZsdJ0qlTC2GBU8Cx51AqlIqSSnliymnvN3mmhxgBYBSagomcBxGn25dcOxXHRnky+SYELYdd5RF7Db47FGImQ6pF5pjEy+CshNQfGzgxiuE6A9SremKtcGUf8+mVN0cOLpZHAMms+gaOLoJND/OKKKwqhFrWKI5kLISgLTYMP5y7RxSxziCwZRV5tZd1tGZcWyz5eDdKydi15qiqgbSpH+jGAa6DBy11lbgTuA94DBmPs5BpdTDSqnLHZfdC9yilNoLvAh8S+sRlFILioZas+3goglRpJ8spcFqg4NvQEkmnHefKWkDpF5kbo/+Z4AGK4ToJ1Kt6UqrOY59lXGMMA3CO7l+w84cRoX4ETJukjmQusr9a0UlQ/h4yPyw/bnmOY4hrQ7HRwaybkECgLTiEcOCRw3AtdYbMWUU12MPutw/BCzp3aENIY45jgCLk6N4dms2+3LLmP/ZozBqMkz+Ssu14fEwZrqZ57jk+wM0YCFEP2iu1gCnMNWaa9tc46zWPDsiqzW2RpdS9dlmHN3sVQ0mQHQtLbcJHAsq6vnoyBluW5aMJe4yM+9y1OT2rwMmAZB0rvt56m7mODrds2oiUUF+nJMc5cEvJMTgJluc9IagKLNa2trAwqRIlIKi7a/AmUNw7g/B0uafeeJFkPNF+wnbQohhQ6o1XdDapVR9loGjbwh4dbAbS0BEB6VqU9p+dVcudg3XzIuHtCth7fqWCpE7USlQU9TSfsep3n3GESA80Je7Vqbi5+3V7pwQQ40Ejr3B0cuRmmLCA305d3QD5x79pcksTrui/fUTLzaNYjtq6yCEGBa01hu11hO11sla60ccxx7UWr/tuH9Ia71Eaz1Taz1La/3+wI64H9mtgD77djwd7RrjFBAJdeWmUTi0lK0DIrDbNS+l57J4QhSJ0UGevV/kBHNbdqL18YZKRzNzCQ7F8CaBY29w7B5DbTHYmnjE+hgWWyONV/7dtIMAbHbNu/tPU1HXBLFzTLAp8xyFECOVzbERgmup2m7v/Dnu1JVBYGeBYwSgoaGi5XrH8a1ZJeSW1rF2gZt9rjsS6diruiSr9fGGSrdlaiGGGwkce4NLxpFNDxNfs5/7m25md405fqaqnm/8fTvfXb+b//0ky3wjnXiRWZlnsw7gwIUQYoBYzTatzX0c0Wbbvu6qLe0i4xjRch2YwNHLD3wCeHFnDuGBPlw0Lcbz94tMMrfOfa2d6ivdlqmFGG4kcOwNzozjnhdg659omPUt3tHnsO14CZ8dK+KSxz9jd04ZY0L9WvaynngR1JdD3o6BG7cQQgwUm9nDuSVwpGfl6i5L1c5tB8tbXd9gs/PBwULWzByHv083ysu+QRAcA6VtS9VV7VrxCDEcSeDYGwIdK+UOvAoxM/C79DdMGxfKP7Zmc8PTO4gI9OXtO5dy5Zw49uVVUNtohQnng8VHytVCiJHJ5sg4evu1ZOp60pLH48CxrOU2MJKsMzU02uzMTeyg/2NnopKh1F2pWjKOYviTwLE3+IeD8jIr+77+LPj4szRlFGW1TXx9bhxv37mUiWNCWDQhCqtds+tkmflmmrgEjo6cufBCCNHM6pjjeDYZR607DBz35ZVTUFHfsrWgM3CsNddnFJpV0JNjehDsRSa1L1U3VMkcRzEiSODYGywWWPZjuOaf5pso8P0VKbx5xxJ+e9VMAnxNGWTu+Ai8LKqlXB07F4qPyjxHIcTIY+uFwLGhynSoaLNrjM2uue5v2/n1u4fdZxwDIsgoqMbHS5Hk6WpqV5HJUF3Y0rsRzBxHKVWLEUACx96y/MeQfEHzw0Bfb2bFt25IG+znTVpsGNudWxKGjzcfepWn+nOkQggx8FxL1b7B5n53A0eX1jqujhZWUVVvZd+pClMRcr22rgwCwskoqCR5VDA+Xj34M+hsyeM6z1FWVYsRQgLHfrZoQiR788qpa7RBxHhzsPzkwA5KCCH6W3Op2scl49jNOY4dbDe4O8ccP1FcQ7UVE9C1yjhGklFQxaSelKnBJXB0zHO0Wc2KcAkcxQgggWM/W5QURZNNmw+2cEfgWCaBoxBihGkuVfv1euC466Q5rjUcyq802xHWlUFTHVjrqPcJI7+ivhcCR8c8xw72qRZiOJLAsZ/NS4zAomD78RIIiwNlkYyjEGLksbn2cexpqdp94PhlTnnzVKEDpyocu8eUNbfkKWj0B3q4MAbALxiCx7gEjo65jjLHUYwAEjj2sxB/H9Jiw9h2vNSUaEJjJeMohBh5nH0cvV0Dxx5mHANbFseUVDdworiGi9NiGB3i5wgcI0wDcMc8x5w6PwAmjjmLDGFkMpRIxlGMPBI4DoBFE6LYk1tOfZPNlKvLcwZ6SEII0b+ad47xMwtklFf3M461jsDRv2Uh4pc5Jqs4JyGCtNgwDuQ7Ase6suZAM7PKlxA/b2LDA3o+/sgJ7TOOMsdRjAASOA6AhUmRNNrs5gMuYryUqoUQI49rOx6lTNaxJ6Vq32CTtXTYlVOGt0UxIy6MtHGhZJ6pxuoX3ipwPFzhzcSYEJRSPR9/1ASoLoCGatOKB6RULUYECRwHwLzESJTC9HMMHw9Vp6GpfqCHJYQQ/ccZODqDPt+gnpWq266oPlnGtHGh+Pt4kRYbhl1DkS3IbPFaa3ro7i1WPV8Y4+RcIFN2wqVULYGjGP4kcBwAYQE+TBsXyvYTJS0teSpyB3ZQQgjRn6wui2PAETj2IOMY0FKmbrLZ2ZtXzpzxJphMiw0DIK/eD7S9eVpQbr0fk85mfiO0XlktgaMYQTwKHJVSFyulMpRSmUqp+92c/4NSao/j56hSqrz3hzq8LEyKYndOOQ0h8eaALJARQowkzsUxZx04tiyMOXK6ivomO3MSTOA4NsyfyCBfjlf7mAtKsrBbfKjDr/cyjiVZLaVqWRwjRoAuA0ellBfwBLAamAqsU0pNdb1Ga3231nqW1noW8Gfg9b4Y7HCyaEIUjVY7B2sd35bLs8kpqeXJT7KoqG3q/MmNtX0/QCGE6EuuO8eAmavY0N1SdWmrUvWuk2bVtDPjqJQiLTaMw+Xe5oLSLOq9wwB19hlHvxAIGu3IOFaBxRt8zmKxjRBDhCcZxwVAptb6uNa6EdgArOnk+nXAi70xuOFsgWOe45YCb+xefnywdQfLf7+ZX797hPU7Osk+5u+BX8fD6b39N1ghhOhtVpfFMWB6I57lHMfdOeXEhPozLsy/+VjauFAOlXuZB6UnqFAhjA7xIyLIt+2rdV9Uckup2i/ELPIRYpjzJHCMBVwn4OU5jrWjlBoPJAEfdXD+VqVUulIqvaioqLtjHVbCAn2YEhPK/3xyghNNkejSk9xy3gRSRwfz8ZFO/m2yNoHdCtmf999ghRCit9kaAWUyddD9UrXW7QLHXSfLmDM+vNVq6bTYMErsLTvTlNgCz75M7eRsyVMv+1SLkcOTwNHdVyjdwbVrgVe11jZ3J7XWT2mt52mt540aNcrTMQ5bX5k5jsggX3yiE1kxto6frJ7CRdNi2JVT1nG5Ome7uZWMoxBiKLM1OPo3Ov7EdDdwbKw2X6IdgeOZynpOldc1z290ShsXRrkObn5c0BjQ8x1j2opMMl0xqgukFY8YMTwJHPOAeJfHcUB+B9euRcrUHvvu8mQ+v/8CEpIm4+VYVX3+5FHY7JrPMt1kHe12yJXAUQgxDFgbW8rU0P0+jm12jdmdYx475zc6xUcGoP3Dmh+X2oOYFNNLQV5ksrk9vU8yjmLE8CRw3AmkKqWSlFK+mODw7bYXKaUmARHAF707xBEgYryZ5N1Qxaz4CMIDfdjsrlxdfNT0IgtPgOIMWSQjxCAnHSk6YWsbODr6OOqOClpt1JqFMM6M466TZfh6W5g2rnUAp5RicmwUtcosXCkn+OwXxjg5V1bXl0vgKEaMLgNHrbUVuBN4DzgMvKy1PqiUelgpdbnLpeuADVp7+l+9aBbu6OVYdhIvi+K81FF8cvQMdnubf8rcbeZ2/i2mJ1nhwf4dpxDCY9KRogu2hvaBIxqa6jx7vjPj6Agcd+eUMz02DD9vr3aXpsWGUeqY51hBMKljgttd0yPOwBGkFY8YMTzq46i13qi1nqi1TtZaP+I49qDW+m2Xax7SWrf7Ri084GwC7th68PzJoyiubjR7rLrK2QaB0TDtCvP49J5+HKQQopukI0VnbE2ttgrE1xHMebqy2iVwbLDa2J9Xwdw2ZWqntNgwyhzzHL2CIvH3aR9c9oh/KASNarkvxAggO8cMBuGJ5tbRBPy81FEoBR8dOdP6upxtkLAIwuIgMErmOQoxuElHis5YG8DLr+Wxb8vK565s3H+aN74wFZdfbi7gnpf30mizMych3O31aeNCmxfIhET08sJMZ9ZRMo5ihJDAcTAIjDTfth0Zx6hgP2bGhbM5w+UPRPUZsydq/EKzCnHsTMk4CjG4SUeKzrSb4+jMOHa9QOaXGw+TnWti8neO1bPjRCmpo4NZNCHK7fWJUUHUWExgFxE15uzG3ZZzgYzMcRQjhPdAD0BgAsHw8a22HTx/0mj+uOkoxdUNRAf7mWwjmIwjmMBx65/Nt3ZvPzcvKoQYYN3tSHFHn49oMLE1tilVOzOOnQeO1Q1W8srqWJ7sDWeC2PrA6i7fymJRWIIioRbGxow9m1G3JxlHMcJIxnGwiBjfnHEEM89Ra/j0qCPrmLvdlHXGzjSPx84yPczOHBqAwQohPDA4O1JUFUDl6X55q05Z2y6O8WyOY0ZBFQCjfepaNf/uSkCoydTGxcZ1b5xdiXIEji4tf4QYziRwHCycGUfHovS0cWFEB/u1lKtztkHsnJbsojOAlHmOQgxKg7Yjxdvfh7cGQXLTXTse6DLjeLTQBI4RqqZbgWPy5BnUW4KIj43v+uLuiJkJygIRib37ukIMUlKqHiwixkNTjelNFhSFxaJYPmkUHxwqxFpfjffpPbD4TpfrE8EvTAJHIQYxrfVGYGObYw+2efxQf46JmiJwP5Wyf9kaW2fpPAwcMwqqCPT1IsBaCQHuF8O4M+68G2Hh18AvsCej7Vh0CtyX1dyIXIjhTjKOg0V4grktz24+dP6k0VTUNZG551NTlk5Y3HK9UjB2hgSOQojuaaodHJsHuNs5BqCh81L1kYJKJo4JQdWXdS9Ys1i6FWh2iwSNYgSRwHGwcGkC7rQ0NRovi+LUvo8BqBo9B5trU/CxM6HggOmHJoQQnmiq9bzJdl/qsFTdceCotSajoMrsNV1b2q1StRCid0jgOFi0aQIOEBbgw/zECFTudo7ZY5n+m50k/3QjC3/5IUcKKs0CGVuD2Yqwre5Ol7Lb4J9fhSP/PotfQggx6DXVmeBxoNnadITwCTBzBTspVRdVN1BW28TE0cGmAbgEjkL0OwkcBwu/EAiIbJVxBHj06zNY6n8clbCQBy6Zwt0rJ1LXaOMPHxzteIHMobfg0UlQU+z5++d/Ccc3w/GPz+73EEIMbo21gyNwtDaCl0/LY6VMubqTwNG5onpqtBfYmyRwFGIAyOKYwaRNSx6A2KYcaKokZe5KUmabtg82rfnTpmMcWZnCZN9gEzjOutY8oaoQ/nWX+TZeeBAmLPPsvTM3mdvKjtrMCSGGPK0dQaMGu93M+xsotsbWO8eAKVd3Uqp2Bo6TwqzmgASOQvQ7yTgOJm2agAOQ42jt5mz8Ddy0JJEgXy+e+PgExEyHfMcOMlrDOz+A+krzuCzb8/fOcgSOVYOgv5sQom9Y65FgWskAACAASURBVGnevGags45t5zgC+AbRWFfFmap6t0/JKKgiOtjPtOIBU6URQvQrCRwHk4jxUJFrMgEA5Tnw5XoIjG7ZnQAID/TlhnMSeWdfPuXhU6Fgv5mjuO8lyNgIKx4Ei7fngWNdOeSlm/uDoTGwEKJvuC6KGegFMm13jgHwDSIjp4Dr/7Ydd20tMwqrmBQTDHWl5oBkHIXodxI4Dibh482HaWkWfPQI/GW+KTev+C8z/8fFzUuT8Pf2YmPxaNP/MfszePdHEL8Izvmeae/jaeB44hPT1y3xXKguNEGoEGL4cc0yDmTGUesOStXB6IYqjhZWk1XUumRts2uOFlYxaUyomYoDEjgKMQAkcBxMnCurn1oOn/4WJl8G30uHud9qd2lUsB/XLUzguZOOvmSv3Ggmm3/1r2DxMg3CPQ0cMzeBXyhMudwEkNVneuGXEUIMOo2DJHC0NZpb18UxAL5BeNvMuN47WNjqVG5pLfVNdtOKRwJHIQaMBI6DyajJpsQclQI3vQdX/R3COt5X9dbzJpCt4mhSvqZ0s/IhiEo2JyOSPAsctYasjyDpPAh3bMUlC2SEGJ4GS8bRGTh6t844Wr2D8LOb+Y3vHSxode6Ic2GMBI5CDCgJHAeTsDi45zDcsrnVYpiOjA715+vzE9lmm0x9wnmw4NaWkxGJJpisr+j8RYqPmnmVKSsgZKw5ViWBoxDDkmuwOJC7x1idGcfWcxxr8CdI1TNpTAj78irIL2+Zh5lRUIVSkDrG0cPRJxB8/Ptz1EIIJHAcfIJHd6tFxneWJXOL7Uf8JvIXrZ8XkWhu267SbsvZhid5BYSOM/dlgYwQw1OrjOMALo6xNZjbNoFjtd2PQOr55jmJAHxwqKVcfbSwioTIQAJ9vaFWmn8LMVA8ilCUUhcrpTKUUplKqfs7uOZqpdQhpdRBpdQLvTtM0ZFx4QFcOjOBl3cXUN1gbTnRHDhmd/4CWZsgKtXMrwyMBouPZByFGK5araoefKXqCpsvQdSzNDmK5FFBvH+opVx9pKCSSWNCzAPZNUaIAdNl4KiU8gKeAFYDU4F1Sqmpba5JBX4CLNFaTwN+0AdjFR24flECNY023t7jEvA5F9p0Fjg21UP256ZMDSZjGRIjGUchhqvBsjimg1J1WZMvXkozJkhz4bQYth0vpby2kfomG9kltWZhDEjgKMQA8iTjuADI1Fof11o3AhuANW2uuQV4QmtdBqC1lmW5/WhWfDhTx4by/LaTLb3P/MMcWxhmd/zEnK1grTNlaqeQsZJxFGK4GmyLY9oEjsWNZjMzP3s9F02LwWbXfHTkDFlF1djsmonOwLG+3HzGCSH6nSeBYyyQ6/I4z3HM1URgolLqc6XUNqXUxe5eSCl1q1IqXSmVXlRU1LMRi3aUUly3KIFDpyvZk1veciIiEcpOdPzEzE3mgztxScux0HGScRRiuBosi2OccxzblKoLGxy74DZUMSM2jDGhfrx3sKB5q8HmjGNDlWkhJoTod54EjsrNsbYt/b2BVGA5sA74m1IqvN2TtH5Kaz1Paz1v1KhR3R2r6MSaWbEE+XqxfntOy8Guejlmbvr/7Z15eFTl+b/vdyb7vkJCQhL2fd9BRVQU3NBqVdyrdau2tbWLttbWfrW1tbXtr7V1txYXtNYqKkpRQRFlCfsOAQKEsISQlSSTmeT9/fHOJJNkZjITEmYCz31duTLnzDnvPAOTcz7zrJA71cyHdZHQS8YOCsLpSqgUxzg893E8VOsUjvUnsFgUFw7N4POdJWw4UE5EmIW8VOe1qq4SokQ4CkIw8Ec4FgG93bazgdaxzCLgPa21XWu9F9iBEZLCKSIuMowrxmTx/oZiKmrsZmdynhlb6GkSTMVBKNnWMkwNJlRdX90871oQhNMHe63pFWuNDJFQdUuPY3GN85ZUb2ZRXzQsgzp7I2+vKaJ/ehxhVovpPWurhMj4U2mxIAhO/BGOq4EBSqk+SqkI4DpgQatj3gVmACil0jCh6z2daajQPjdMysXmaOTttUVmR3IeNDqg8mDbg/csMb/7txKOrpY84nUUhNOP+hpn/8PoEBGOzTmOVXV2jtU7PZD1ZtzgpL4pJESFcaK+wTT+bnpOS6haEIJEu8JRa+0A7gMWAduAt7TWW5RSv1ZKXe48bBFQqpTaCiwBfqy1Lu0qowXPDO2VwJicJF5b6SyScbbkKTu4k1+8u5lPt7mN8Cr80rTf6TG05SKuJuAyPUYQTj/sNUY0hscEuaralePYLBwPVdRRg7Oht9PjGG61cP6QngDNwtEVDZFQtSAEBb/6OGqtF2qtB2qt+2mtH3fue0RrvcD5WGutf6i1Hqq1HqG1nt+VRgveuWFSLntKTrBiz3G0syXPn/+9mHkr9vGPpbubD9y33OQ3qlYprAmu6THicRSEkyXkeuDanR7HiJggF8e0DVUXl9dyopVwBBOuBhjWyykUbaZQRkLVghAcZHLMacalIzNJiArj70sLuPntg9i1lVGx5VwxuhfrDpRTVWeH8gMm9zF3WtsFxOMoCJ1CSPbAtde6haqDOTmmbXHM4Yo6arRLOFY37b9oWE9e+/YkzuqfZnbYnB7HSGnHIwjBQITjaUZUuJWrx/Vm2a5jrC+qpi62F1fm2blmQm8aGjUr9hyHfV+Zg/M8CMfwaNNYVzyOgnCyhF4PXHuN8TaGx4ZGjqNbO57iijpqlHPbTTgqpZjWPw3lio40CUfxOApCMAgLtgFC53PPuf2IjbRyw6Rc4t/rD+WFjMtNJjrcyrJdJcxUy03z3Nb5jS7ipZejIHQCnnrgTmp1zEAApdRywAr8Smv9ceuFlFJ3AncC5OTkdNyiemeOo7K2EGenHEfbWdWHymuJj0sAu2oRqm6D5DgKQlARj+NpSHp8JA9cOIiMxKimXo6RYVYm9knhy13HjMcxZwpYrJ4XSJDpMYLQCYReD1xXjmN4TIiEqlsWx2QkxUBEnG/hKDmOghBURDie7iTnQU0p1FVy9oA0Ko8VQ+kuUxjjjfhM8TgKwskTej1wWxTH+BBnXY2HUPWhilp6JUaZgQS+vKFNoWrxOApCMBDheLrjbMlD+T7OHpDOBMt2s+2pMMZFQi+oPgIN9i43TxBOY0KvB26oFMc4WnoctdYcqqgjMzHaKRzbC1Ur45kUBOGUI8LxdMclHMsKGdgzjnOjdmFTUZA5yvs58ZmANuJREIQOEZI9cFsUxwQ5VK2sTekylbUOauobyGzyOLYTqo6MB4vcvgQhGEhxzOmOm3BUSnF2+A7W2QYwUYV5/9bgmh5TeQgSs0+BkYJweqK1XggsbLXvEbfHGvih86frcS+OsXsWZ3X2BuZ9vY8R2YlM7pvaqS//VcExCkqqubnB1qqi2ojYzKQoP3IcKyVMLQhBRL6yne5EJ0FUEpQVQs1xMm17WG4fxJZiH7OoXb0cpUBGEE4fGhugwdZcHNPoaJOO8vXuUmb/ZRmPL9zG00sKfK/3wgWw6nm/X77gaDV3/CufX7+/lXpbXZsejkBzqNpVAOMJmVMtCEFFhOOZgLOymgMrUWhWNQ5hWUGJ9+PdPY6CIJweuPo2uopj3PaV19Tz07c3Mvf5FTQ0akZlJ7L7qI8CFa3h4Bo4tN6vl662ObhrXj72Bo2jUVNSXtVyaozT49grKcp82a0r975YXaW04hGEICLC8UzAJRz3LQdrBLU9Rpm2PN6ISTVJ6+JxbOaTR+HrvwfbCkHoOK6cxvBo8wNQX0NRWQ0XPPUFb68t4q7pfVl0/znMHNqT4oo6TtgcnteqPwG6EWp9CDwnWmt+/O8NFJbW8NzN44gIsziFo3sPxzosCtLjIs0Agtoy7wu6chwFQQgKIhzPBJLzoGwf7F0GWeOYOCCL/MIyausbPB+vFMRntPE4Hj9Rz4P/2ciuIz7CSKcrG96ALe8E2wpB6DiuvMGIWFMcA2Cv4ePNhzlWbePfd0/hodlDiI6w0i/dVCzvPeYl19DVEseXwHPy7Bd7+GjzYR6aPZhzB/VgfG4yZZVVENYsHIsraumZEEWY1WKEY12FCa17e23JcRSEoCHC8UwgOQ8a7SaslDuNswakUd/QyKrC497Pie/VYuxgRa2dm15cyfzVB/jdx9sDe/2FP4GFPw7snMZGqDoc2Dldhb3W/FuUH2j/WEEIVTx5HO01rN1fRlZSNGNzkpsO7d/DCMcCb+Fq1/SWGh/XEGB5wTF+//F2Lh2Zye1n9QFgWv80auvqcKhm4Xi4os5UVANEpzhfo8L7a0uoWhCChgjHM4GUPs2Pc6cyqU8qEVYLy3b6ynPMhEoTqq62ObjlpVXsPFLFjEHpfLLtqPcbSmu0hk3/hg3zvXsQPLH+VfjTcCjd7f85XYVLMFYfbh6VJgjdjSbh6CyOce5bu6+csbnJLQ7NSY3BalHsLvHyd+4qXvHhcTxYXst331hH/x5x/O6qkU2zpqf2SyUCOycczYN1DlXUkZnkFLPRTlu8iVIJVQtCUBHheCbgasmjrNB7ItERVsbnJfNlgY88R6fHscZm57aXV7PpYAV/u34sf/jmKCLDLLz4pZ89io/vgdrjJrx0ZLP/Nm9513hJ173q/zldRVlh8+OKoqCZIQgnhav9jltxzLGycg5X1jEup+WUw8gwKzkpMT6Eo9MbWFtmvhy2os7ewD2vrsHuaOSZG8cRG9nc+W1EViLR1kYq7Ob2o7WmuLyWzASXxzG5ee3WNNjBUQuRiX6+aUEQOhsRjmcCCdlGNGaOavqmft7gHmw/XMUTH22nobHthZ+ETLDX8P1XPid/33H+fO1oLsq1kPrq+TzX6wPeX7uXkio/vG8H1zQ/Llzun722KihcZh6vfx0avCTonyrK9zU/rpBwtdBNaeFxNN69PcVHAdp4HAH6pcey+6i3HEenx7HB5rGR+K8WbGFjUQVPXTuavuktJ7yEWS2kRUGp6cBDWY0dm6OxrcfRk3CUOdWCEHREOJ4JWMNg6OUw+vqmXTdPyeP6STk88/lu7vhXPpV1zf3ctNZsrjIeiX2Fu3ny6lFcNqoXfP47OLyZ6Udf5T3LT/lkUevpaR4oyjc3qsQcU9XtD7uXmMkSk+4x4eGCxQG93U7H3eMoeY5Cd6WpOCamqThm/5FjRIVbGJLZNmewX3oce4+dwNHQ2HatOrc+sK0E3hur9jN/9QHum9GfmUN7ejQlOVJTZbdw4HgNh1yteFw5jjEpHtc1r+v0dEqOoyAEDRGOZwrf/CdMvKNpMyLMwm+uHMH/XTGcL3aWcOXTy9lTUs3mgxXc8MJKHl1q2mz8fmYaV43LhmO7IP9lmHA73PgOiRGNXLv5Duwf/sT3lIeD+dBrDPQ5G/Z9ZYpe2mPnIohKhAt+CbE9YO28k3zzJ0lZIST3AZR4HIXui4fimEMlpYzMTiLc2vZW0K9HHPUNjRSVeRhN6N6gu7Y5F3H9gXJ++d4WzhmYzg9mDvRqSkK4pp4wlhcc41C5cT1mJPoRqhaPoyAEHb+Eo1JqllJqh1KqQCn1oIfnb1VKlSil1jt/vt35pgpdwU2Tc3n125M4fqKeS//6JZf97Uu2HarkuvMnATA6yXnT+PRRc7OZ/lPofz77r/2EeY4LCF/9LLz2Tc+LO2xweBNkjYPcqeYGU9JORXZjI+xaBP1nmtcbdR3s/Biqgjg3u2wfpA0wE3XE4yh0V9wbgDuLY46XVzDOQ5gaaGrJ4zHP0dbW41habeOeV9fQIyGSv1w7GqtFtT3PSaRyYAmLYPnu0maPoytUHeXMX6z1UBzjel1pxyMIQaNd4aiUsgJPA7OBocBcpdRQD4e+qbUe7fx5oZPtFLqQyX1TWXDfWYzLTebOc/qy9MczuGr6ePNk5SHYvxK2vQ/Tvg9xPQAY07837/b6Ac+H32BC0OX72y58eJMJOWePh9xpZl974eqDa+BECQycZbbH3gy6wfRRDAZamxzH5DxI6i0eR6H74mFyTKS2tWjD406/dBPO9igcW4WqHQ2N3Pf6Oo6fqOeZG8eRHBvR9hw3VIONxPg4vt59jIPldYRZFGlxzkkyFqsRjx5D1S7hKB5HQQgW/ngcJwIFWus9Wut6YD4wp2vNEk41vVNimHf7JB6aPYTE6HAIjzITZKqKYfEvIK4nTLm36XilFHee3Zc3qseYHTsXtV20KN/8zhpvhFdCVvvCcedHppBnwAVmO20A5EyBdfM8Vm92ObVlxsuRlAuJIhyFbox7qDosCo0iWtkY26qi2kVSTARpcRGeW2/ZqszfKUBtGU8u2sHXe0p5/MoRDM/yo+K5wU5KQjzHquv5fGcJPROiWnoovU2PcYWqo6SqWhCChT/CMQtwv1sWOfe15iql1Eal1NtKqd6eFlJK3amUyldK5ZeU+OghKIQG8b1gy3/hwEqY8TMzccKNC4dl4Ejuz36VycGV71DdejzZwXwT3k3MMtNocqeaPEdfAnDnIiMUo928IGNugtIC2L+ieV/9CVjwPfjHWS29H52NqzCmyeN40L88TUEINepPQFiU8egphU1FkhHVSGpcpNdT+qXHsbvEQw6zrQISswHYtmc/z36xhxsn53D1uGz/bHHY6JFkvIbbDlU2N/92EZ3iRThKqFoQgo0/wtFTokrrO//7QJ7WeiTwCfCKp4W01s9prcdrrcenp6cHZqlw6knINBfvtEEw+sY2T1stij98cxRrIieRdmwVMx5/n1+8u5mdrpGERfkmv9FF7jSoPuK9qXf5ftPrcdCslvuHXQER8bD2X2b78CZ47lyzfWQTLPvjyb9XbzQJR6fHsdFuKr0Fobthr20qitFaU6MjyI737cXv1yOOgqPV6NZf9mxVENeTRmskyzfvYkxOEo9cOsx/WxrqiY2JIS/VhMybWvG48OpxlFC1IAQbf4RjEeDuQcwGit0P0FqXaq1dTf2eB8YhdH/iM83vmY+alj4emNgnhSuuvZ1I5eDenP28mX+Ai/78Bau37IKyvSa/0UV7eY6ucPfAVsIxIhZGXAVb34Xl/w+eP994GW9+D0ZdDyv+3nUTZlw9HF2hapACGaF7Yq9tKorZf7yGGh1BRnQ7wjE9jopaO8dP1Ld8oq4SR0Q8xxtjSbOc4B83jCMiLIAmHQ31YA1nav80wK0Vj4voZM+TY+oqwRphUmkEQQgK/vylrwYGKKX6KKUigOuAFg38lFKZbpuXA9s6z0QhaIy+Hs75cVsh1wqVMwWiErk1dTtfP3geSdHhrF7u7L2Y5SYc0wZAbLoP4fgxpPQzx7VmzM0muX/xL6DvdLhnufl9wS/NjeR/v+jgm2yHskITNotKMKFqkDxHodtQXF7LrS+voqisxkyOcQrHtfvLqNGRpEf5TrtwFci0yXO0VbHhaAOlDbGc09va3ErHXxw2CItkWj8jHNuGqn3kOIq3URCCSrvCUWvtAO4DFmEE4Vta6y1KqV8rpS53HvY9pdQWpdQG4HvArV1lsHAKyZkM5z1s8hN9YQ2HARfCrkWkxoQxZ3QWjQfy0cpieji6cOU5Fi5vm+doq4a9X8Cg2Z5fI2usaQg+63cw902INTcc4jPg7B/Cjg9N4/DOpmxf88jGJo+jhwpyQQhBbI5G1hSWcc+ra2mw1TSFqtfsK6NeRRFvqfd5fnNLnpZ5jo7aCnaUK2KT0khRNYEZ1dhgOiVYIzlnYBrTB6Yzzel5bCI62TT7bj3f3lYp+Y2CEGT8ii1orRdqrQdqrftprR937ntEa73A+fghrfUwrfUorfUMrXU7zfqE046Bs6CmFIryuXpcNiPYRXlsP4hsOW6M3LOgsqit+NrjnBYz8CLP6ysFs5+AyXeDpdXHdvK9JpT88UOdP56wrNDkN4J5L9HJ4nEUug190mJ56trRbDpYwd5DR9HOAre1+8qxRsaiPIwLdCcrKZqocEubljy6roIaFUN6jwzPnkFfNDjFqjWc+KhwXrltIgN6tvIixqQAunlSjAvxOApC0JHJMULn0P8CsITBzo8YlhnP2LA9rHb0bXtcnpc8xx0fQWSiqagOlPAouPAxKNkGa14O/HxvNDYYkejyOILxOkqOo9CNmDm0J989rz9VVVUcrlGcsDnYfriSqJg4E772gcWi6JsW10I42mx1hDfa6Jneg8j4tI4LxzDv1dxep8fUVUorHkEIMiIchc4hOsmIvh0foY7vIV5X82lVDruOVLU8Ln2IuSm4hGNFEbx9G6x/DYZcasLeHWHIZZB3Nix53HNSfUeoPAiNDuPNdJGUIx5Hodtx/wUDSY1sYPNRO698XUijhrj4+Obejj7o16OlcFyywRSiDcnLcuYiBvj35nB5HH00CfcmHCVULQhBR4Sj0HkMmm1GCm5+G4CN9OfttUUtj7FYIGcq7F0GS38Hfx0P2z+E6Q/CxX/o+GsrBbOeMKGtpU+cxJtwo8xZUe3J4xiMZuRCtyNUxrVaLYqsWE1jeDS//3gHAIkJSc3TZHzQLz2WorJa6uwm3/DjtbsA6JudaQSeo84vAdpEg7MBR4eFo4SqBSGYiHAUOg9X9fXXT0NEHNkDRvPuuoM0NLYSWblTTZubpb8xOY33rYYZDzWNQeswGcPNiML8F+HYrpNbC1r2cHSR1NuE9wINzwlnHKE2rtXqqGXCgCwiwywM6BFHRHQs1LcvHPv3iENr2FNygv2lNezcdxAAS3Sid4HnC79C1Sme162rNB0OBEEIGiIchc4jtR+kDTRegV5juGp8LkcqbSzb1WpK0IirYeS1cMsHcM0rJvzbWcz4OYRFw/8ePvm1yveBsjRXU0PzYwlXC+0TWuNa62tISUrmldsm8ptvjDCtefwJVTdVVlfzZv5+4pXznMj4jglHR3NxjFc8rau1FMcIQgggwlHoXFztdLLGMWNwD5Jiwnl7TatwdXwGfOM56HN2579+XA845wHTE/Jk2/OUFUJCdssbnHPM2mlbIHNwbefliAqhM65VaxOWDo9mct9UJuSlOIVjTbtpF33SYlEKdh6p4t/5RUzNdoaYIxOc1c8E6HF0hap9eBxdBTDu69prTBsfyXEUhKAiwlHoXIY4W3vmnUVkmJU5o3rxv61HqKixnzobJt1jvJiLft62D1wglO1rGaaGZu/o6ehxtFXDy7Ph4zapeELHCJ1xrQ12I7rC3Ub7hUcbcxx1Pk+NCreSnRzNG6v2c7TKxnl5TsEXmdDsGQzky0aD81rgK1RtDTNdFtzXtTkL7cTjKAhBRYSj0Llkj4fvrTPteYCrx/Wm3tHIXz/bxbyvC3nsg63c+a98bn5pVdtpFJ1FeBTM/DUc3dI839obez6HF2bCW7e0fa6ssGVhDEBMqgmFn44ex8JlRkRsXWByydpDa9i/Et6/HxY/4vvYzx4z1fMnI+S7H6EzrtXVdic8tnmfs6ejP+Hq/ulxHKuup0d8JENTnXo4KqGDoWqXx7GdDgrRSS3XdX0mpR2PIAQVzwOIBeFkSGnu3zg8K4HBGfG88OVeACLDLOSkxHCs2sb1z6/gzbum0Cct1ttKHWfoFaY90JLHYfhVbRPqD22ATx6F3Z8aIVi0yuzLHGWer6+BE0fbehyVMgUyFR2YHmOvg2emwZR7YfxtHXtfXUnBJyan01Fr5oKPvdnzcRVFsP4N2PAGHHebET7pHkjIbHt8gx1WPWcq3tMHw/SfdI39oUfTuFbgIGZc6/XuByilMrXWh5ybXTeu1SUO23gcgfoTzSFnL/RLj2PJjhKuHpeNtX6D2RkZD9o5srAjxTG+QtVgbHJf1+YUjhKqFoSgIsJR6FKUUvzzWxPZf7yG3NQY0uMisVgUu45Ucd1zK4x4vHMKOaknWVHd9oXhosfh+fNgwXeh12gjBu01xpO4/QPjLbnwcRh5DfxlNKz4B1z5jDm/3NWKp0/btb01AT+4BuJ7eRZPADsWQmkBLP8LjL217QScYKI17FpsRkce3wPrX/csHMv3w9OTjQcr72w4+wFIHwQvnA87P/IsiPd/bURj6gBY+lvoPcnMGT/N0Vo7lFKuca1W4CXXuFYg3zl563vO0a0O4DhdNa7VVT0d4fYlzTm32h+P46jeSUSEWbhuQg6sqwJLOIQ550tbIzooHH2044G286qbhKOEqgUhmIhwFLqcjMQoMhKjWuwb0DOeV789ibnPr2Du8yt4867JZCd3snjMGgdjboR1rxoPGphQXVSCETzTvt8c9hpzI+S/BBf8yhTvuHo4JuW2XTepNxxa33JfzXF4+RLInQI3/dezPRvmG49eWSHs+awpnN8p2GuN+OtoS6Pje4xYnvpdqK+GT34FpbtNpbw7XzwJjXb4zkroMdjs09p4mbcv9Cwcty80IuNbC+Gfl8B/vg13fwnxPTtmazdCa70QWNhq3yNujx8CHupyQ1z9Glt4HF3C0ff0GIBLR2ZyzoB0EmPCm1viuGbYR6d0LFQd5odwdP0dgluoWjyOghBMQsjlIZxpDMlM4NXbJ1FVZ2fu8ys4WB5AE2F/ueyv8OM98LND8Mty+HkxPLAdzn+kZa7UpLvMlJjVzjZ6TT0c89qumdjbzOWud7vhrptnQry7P/PcQ7L6qAkFT7obYtIgvxNHIzbY4aVZ8GR/4109uCbwBuUFn5rf/c83rZKUxYSi3SndDeteM+LQJRrBCIhBF8Pez5sLGFxoDTs+hL7nmor3b75ijvnP7WdavmNw8RWq9sPjqJQyohHatsQJdHqMv6Hq1utKcYwghAQiHIWgMjwrkXm3T6L8hJ2ZT33OH/+3g8q6TqzAtlggNtV44pSnIlcnqf1MK6H8l8yNtHyf8cjEprU9tqmy2tlmqMEBq56HjJEmhLfaQw/nzf8xVa1jb4axN5mwdcXBk39/ACv+bjygeWfBprdNeP7Zc4w4bXD4t0bBJ8ZrmNIXEnpBv/OMh7SxsfmYz39nwotn/bDt+YMvMYKg4JOW+49uNeHtQReb7Z5D4dKnTCHO0t927P0KgeOzOKb95LMZvwAAIABJREFUJuAtaD32LzoZasv9P7/Bjz6O4PRkljd/BiXHURBCAhGOQtAZ1TuJBd89ixmDevDXzwo45/dLePbz3U0jzjyhtaaorKbFDN2TZvJ3jCdx41vNFdWexKarl6OrJc/Oj8zj6T+BYVcar1xrz9uGNyBzNPQYAuNuNZ649iq+/aFsHyz5LQy6BG54y3hTL/mjWf+D++HVK+HEMd9r2OuMkHMPnY++3rynwmVm++h28+8y8Q7PIebek0zF+fYPW+7fvhBQzVOFXGuPvtGEvV+4AD7/PRSvaylShc7FZ3FMgMKxzpNw7Eio2g+PIxpsFWZbPI6CEBJIjqMQEvRJi+XpG8Zyz8EK/vC/Hfz2o+38feluBvaMIzc1lrzUGHqnxFBcXse6/WWsO1BOSZWNcKvi4/vPaZpucVLknQUZI0yRjFKe8xuheXqMq0Bm5bOQmAMDZ0N8Jmx6y3jrJt5hnj+y1VRsz/qd2U7OMyJt7Stwzo/a97x4Q2v48AGwWOHi35t9UYkw4dsw/nZY/xp88EN4djpc+y+T8+mJ/V8br5O7cBx0iemjt/51U8iy9LfGQzXtfs9rWKzm/W9/34TOXe9px4emRVNrsXnJH8y/w86PYMlvTPV7bDoMvxqm3tcszoXO4SSLY1pgqzJ5vi6ik9vm/PrC1cfRn1A1GFEanWwEa0Sc+awJghA0xOMohBTDsxL557cm8tZdU5g1LAOlFMt2lfCH/+3k+/PX87uPt7PraDVn90/jl5cNJTLMyuMfdlIHE6WM17FkmwmxespvBCMOldV45I5sMV65CbebpsVZ46DXGBO6duUZbpwPljDTFsjF+Nug6pCZcNNRtrwDBYvhvIfbCi2lTMHP7YtMvuJLs7x7OAs+MSHovLOa94VHwfBvwLYFsO9rU1w0+Tsm7O+NwZeY6ul9y812ZbHxJLqmCbkTHg3Tfwx3fAY/2gVXPgu502D186bC/b174VhBYP8egndOsjimBbaKll6/mAA9jg3+9nFs1SOy9esKghAUxOMohCQT+6QwsU9zb7maegdFZbWkxUWSEttcjWlvaOQ3C7ezdMdRzh3U4+RfePhVsPiXnns4urCGQUKW8TiufNb0gXS1rlEKJt4F795tikXyzjYh3v4zIc5t4sfAi8w4w9UvwpDLAreztgw+etCI1Il3ej+u1xi463PTfHvBd02By8xHWx5T8CnkTm3pjQIYfQOseRnmX288mVPu9W1T33PNv8V2ZzHMjo/M/kGX+D4vLh1GXWd+yg/AV3813th1r8HQOXDhYy09XELgNIWq3aruAyiOaYGtqm2o2l5jUh7Co7yf58KV4+hXqBqocQnHKslvFIQQwC+Po1JqllJqh1KqQCnldR6ZUupqpZRWSo3vPBMFAWIiwhjYM76FaAS4dWof+qTF8n8fbMXe0Ak5cmGRzSFmb6FqMELm8CYjCkde07KB8rArTb7fqueNeKw6ZESROxYrjLsF9iwxYi5QPnnU5GNe9pf2Q3cxKXDjf4yXc/mfYY3bVLuKIuNh9dQaKHu86b1Ye9y06YlO8v06ETGmqGb7Qmc19UJTbJM+yP/3ldTbhN3v3wRn3W/C6O5eMqFjNBXHuAnHjhTHaN3cjsdFoNNjHE7haGnH49h6Dnbr1xUEISi0KxyVUlbgaWA2MBSYq5Qa6uG4eOB7wMrONlIQvBERZuHhS4awu+QE877e1/4J/jDpLjjnx8Zr5o3E3kZwOWrN8e6ER8HYW4xwWvaU8da5F4e4GHuzCWGvCbA1z7b3zTmT72medNMeFivMfhL6nQ8f/hAKvzT7m9rweBCOSsHkuyGln2kj5A+DL4HKIhOu3vuFqab2Vc3ujbgepqfmD7Z4rmwXAsNeC6iWXj5ruPn8BVIcY6813QFat+MB/4Vjg82IxvYa4LcJVVdJqFoQQgB/PI4TgQKt9R6tdT0wH5jj4bj/A34P1HWifYLQLucN7sE5A9P50yc7Ka22tX9Ce0QlmrxBX820XaHTvLOh57C2z0+43fwuXAbDvuE5hBefYYTW2nmmgbg/7F0Gb98O2RNgxs/9O8eFNQyufslMw3nzJji+1+Q3JmSZUYCemPBt+N5a/2/YA2eZnMqPfmpCkq42PB2lo4VDQkvqa4yHsbWID48NLFTtqSVOwMLR3n6YGiAqqeW6rdsACYIQFPwRjlmA+3y1Iue+JpRSY4DeWusPfC2klLpTKZWvlMovKSkJ2FhB8IRSikcuHUJNfQNPLd7Z7vHbD1fS2Bhgg+zWuMLYrb2NLhKzjSgEGDXX+zrn/MTcEJc+0f5rFq+HN+ZCSh+4/q2OTYmJToLr3zQzht+4DvZ8bpp+d8Qr6InYVDMj/MhmIyh6T+qcdYWTw17jOeQfHh1YcUxTSxx34dgqpNweDpt/XwisYeZ1XE3AxeMoCCGBP8LR0x2l6a6rlLIAfwIeaG8hrfVzWuvxWuvx6enp7R0uCH7Tv0c8N0/J5Y1V+9laXOn1uLX7y5j152W8le9h1nQgDLsCrviH78KPCx6F838JvSd6PyZjuMk9XP2CadvjjdLd8OpVRvjd+E7LnMpASe0H1/zLzM22VXTu6ENo9jIOnGVu/kLwsde2zG90ER4dmMfR09i/Jo+jn17zhvr2W/E0rZ3UKscx0ffxgiB0Of4IxyLAvaQxGyh2244HhgNLlVKFwGRggRTICKea+88fSGxkGM987r3Y5PWV+wF4b32x12P8IjLeNLL2laeV2g/O/mH73rwZPzfrffxTz6MCKw/BvCsADTe9C4lZbY8JlL7T4dI/meKVvuee/HruDL3c9NsbeW3nrit0HPsJz8IxItBQtbMZ90nlONab9k/+4JqD3eAw70E8joIQdPwRjquBAUqpPkqpCOA6YIHrSa11hdY6TWudp7XOA1YAl2ut87vEYkHwQmJMON8c15uFmw5xtLJtqm1lnZ0PNhYTE2Flxd5Sj8cEhZgUk1O59wtT+OLO0e3w0kUmB/KGtyGtf+e97tib4XvrOt+Lk5QDPzsI/WZ07rpCx7HXeg9V159kqDoi1hS7BBKqDvNXODp7RNZ7eF1BEIJCu8JRa+0A7gMWAduAt7TWW5RSv1ZKXd7VBgpCINw0JRdHo+aNVW1D0e+tO0idvZHHrhiO1rBw06EgWOiFcd+CHsNg0c+bPUC7FsOLM832ze9B1tjg2ih0X1zFMa0Jjzn5ULVSgY0dDChU7VzX0+sKghAU/OrjqLVeqLUeqLXup7V+3LnvEa31Ag/HniveRiFY9EmLZfrAdF5buY96R3NfR601r686wLBeCVw5JovBGfG8vzGEhKM1DGb/Dir2w/L/B1//HV6/xjQhv3OJ6akoCB3Fa3FMTAeLY1qFjGNSAhSOflbLRycbb7vMqRaEkEFGDgqnHbdOzeNolY1FWw437dtYVMG2Q5VcNzEHpRSXjerFmn1lFJf7522pqLVTUWPvKpMNfc42k1KW/hYWPWSKTG5bJHObhZPHXtM5xTGe2vFAs8DzB4fNv3Y8YARpXbkZZenpdQVBOOWIcBROO6YPTCcnJYZ/fV3YtG/+6v1Eh1uZM7oXAJeOzATgw3a8jidsDp763w4m/eYTxj22mNv+uZr31h/khM3RNcZf+JhpLn72A3DNPM/hRUEIFG9V1REBhqptVab3Y+tpRdHJUFvu3xoN9gCKY5JN66jKg2ZbhKMgBB3plSGcdlgsipun5PLYh9vYUlxBbmos760v5tKRmSREmRBZbmosI7MTeX9jMXec07fNGg2NmrfyD/DH/+3kWLWNS0dmkpUUzfsbivls+1Giw63MHNqTGyfnMiEvGdVZfRCTcuAHmzpnLUFw4StUHUhxTF2F5zzD6GQ4tNG/NRpsEN7O+Er3dQHKnFOhJMdREIKOCEfhtOSb43rzx//t5F9f7WN0ThI19Q1cNzGnxTGXjszkNwu3s6/0BLmpzZ69vcdOcPe8New4UsW43GSeu3kcY3PMDeynswaTv6+M99Yf5P0NxSzYUMyQzARumZLLnNFZREe0MzdaEIJBfY3nhvGBFsfYKj3nGQZaHONvqNolHMsLzW/JcRSEoCOhauG0JDEmnCvGZPHu+oO8vHwvA3vGMTanpZfjkpEmbP2BW7h677ETXPfc15RU2/jHDWN5++4pTaIRjDdzYp8UHr9yBCt+dj6//cYItNY8+M4mJv/2Uz7ZeuTUvEFB8JfGRjNT3WOOY4zxADY2+LeWrcpzuDg62RTZOPwY+ekIsDgGmj2OEqoWhKAjwlE4bbl5Si42RyM7j1Qz11kU405WUjTjcpN5f4NpBl547ARzn1uBvUHzxh2TmT0i02cIOiYijLkTc/jo+2fz5p2TSYgO47lle7r0PQlCwDicHkVvxTFgQtn+UFfpPVQN/nkdG2wBtONxTkgq3weWMM/hdkEQTikSqhZOW4ZkJjCxTwrrD5Rz5RjP01YuG5nJr97fyidbj/Dwu5upb2jk9TsmMSjD/5CYUopJfVO5eHgmLy3fS029g5gI+dMSQCk1C/gLYAVe0Fp7HEqulLoa+DcwodPbmdl9CEdX+Npe618Y2FbleXKRu3CMz/C9RoM9sAbgABUHjWDtrFxiQXBit9spKiqiri5EBkKcAqKiosjOziY83E/Pfyvk7iac1vzxm6M4WF5LUoznG9XFIzJ59IOt3DEvn6TocF6/YzKDMzoWDpvaP41nv9jDqr3HOXdQD4/H1NQ7iAyzYrXIDfB0RyllBZ4GZmJGt65WSi3QWm9tdVw88D1gZZcY4vImeiuOAf8LZGyV3kPV4J/H0WELoKramV6iGyRMLXQJRUVFxMfHk5eX13lFjiGM1prS0lKKioro06dPh9aQULVwWtM7JYbJfVO9Pt8jIYqzB6Q3icYhmR2/OU3ISybCauGr3aUenz9hczDtic84/49LmbdiH3V2P/PKhO7KRKBAa71Ha10PzAfmeDju/4DfA13j8qh3CkdvxTHgf4GMtxzHGGdI2a9QdQCTY6zhEOH0hIpwFLqAuro6UlNTzwjRCCZClpqaelIeVhGOwhnP09ePYemPZpyUaAST8zgmJ4nlBcc8Pv/p9qOU1dixWBS/eHczU5/4jD9/spPSaj8KCoTuSBbgPvuyyLmvCaXUGKC31voDXwsppe5USuUrpfJLSkoCs6LJ4+hLOPqR49jYAPXVnZDjGEBxDECMc21pxSN0EWeKaHRxsu9XhKNwxhMfFU5iTMdyPVozrX8aWw9VcvxEfZvnFm48RHp8JIt/MJ0375zM2Jwk/vzJLi546nO2Flf6/Ro2RwMvLNvTdU3Ihc7C09VZNz2plAX4E/BAewtprZ/TWo/XWo9PT08PzAqfwjGA4pimqTFe2vFA+9NjtA5scoz72tKKRxBCAhGOgtCJTOufitbwdatw9QmbgyU7jnLx8AysFlNM88ItE/j4/rOJCrdy44sr2XG4yq/XmL/qAI99uI231xR1xVsQOo8ioLfbdjZQ7LYdDwwHliqlCoHJwAKlVOcOJve3OKY9muZFe/D8RcSZquf2PI6NDYD2P1QNbsJRPI7C6UdpaSmjR49m9OjRZGRkkJWV1bRdX9/WAeGJb33rW+zYsaOLLW1GhKMgdCIjs5OIiwxj+e6W4epPtx/F5mhs6h3pYnBGAq/fMZkwi+KGF1ZQcLTa5/qOhkaed7b8WSw9I0Od1cAApVQfpVQEcB2wwPWk1rpCa52mtc7TWucBK4DLO7+qupOKY+qcHkdPIWOl/GsC3uBMywgkVC0eR+E0JjU1lfXr17N+/XruvvtufvCDHzRtR0SYIjKtNY2NjV7XePnllxk0aNCpMlmqqgWhMwm3WpjUJ4WvWuU5frixmB7xkYzPTW5zTp+0WN64czLXPruC659fwZt3TaFPmucZ1R9uOkRRWS3DeiWwYk8pFbV2EqM7FmbXWnO0ykbPhKgOnS/4RmvtUErdByzCtON5SWu9RSn1ayBfa73A9wqdRGcVxzR5HL0IuOiU9oWjq0F4R0LVkuModDGPvr8loLQhfxjaK4FfXjYs4PMKCgq44oorOOuss1i5ciUffPABjz76KGvXrqW2tpZrr72WRx55BICzzjqLv/3tbwwfPpy0tDTuvvtuPvroI2JiYnjvvffo0cNzl4+OIh5HQehkpvZPo7C0hqIyc8OutjlYuqOEi0dkYvHShqdfehxv3DGJhkbN3OdWcOB425wzrTX/WLqbAT3i+PWcYTgaNUt3HO2wnS8tL2TSbz5l4aZD7R/sZsPrK/dzuOLM6Xl2MmitF2qtB2qt+2mtH3fue8STaNRan9vp3kbovOKYphzHRM/P++VxtJvf/rbjgeYm4BKqFs4wtm7dyu233866devIysriiSeeID8/nw0bNrB48WK2bt3a5pyKigqmT5/Ohg0bmDJlCi+99FKn2yUeR0HoZKb1N+1/vioo5ZoJMXy67Qg2RyMXj8j0ed6AnvG8dsckrn12BXf8K593vjO1RSPxpTtL2H64iievHsmY3smkx0fyv61HmDPac3NzXzQ2al75qhCAH7y5nozEqBajFb3x9e5SfvbfTdw1vS8PzR4S8OsKQaDTimPa8zgmQ+VB32s0haoDEY4SqhZODR3xDHYl/fr1Y8KECU3bb7zxBi+++CIOh4Pi4mK2bt3K0KFDW5wTHR3N7NmzARg3bhzLli3rdLvE4ygIncygnvGkxUU05Tku3HTIa5i6NYMzEvh/c8ew80gVP3l7I1o3FeHyzNLdZCZGMWd0FhaL4oIhPfh8Rwk2R+D9IJcVHGP/8Rp+ddlQMhKjuOOVfPaXti8eXvxyLwCr97ZTPSuEDk3FMT5yHP0JVddVmN/eQsaBeBw7FKr24ukUhNOU2NjmlKVdu3bxl7/8hc8++4yNGzcya9Ysj70YXXmRAFarFYej87tviHAUhE5GKcXUfml8tbuUqjo7S9oJU7dm+sB0fnzRYD7YeKipEGbt/jJW7j3O7Wf1ISLM/NnOHNqTapujTQW3P7y6Yh+psRHMnZTDy7dOoEFrbv3nKsprvFfx7Smp5tPtR0mICmPTwQppYN5dsNeAJdxzQYrFAmFR/hXH+GrHA/4JR4cUxwhCR6isrCQ+Pp6EhAQOHTrEokWLgmaLX8JRKTVLKbVDKVWglHrQw/N3K6U2KaXWK6W+VEoN9bSOIJwpTOufSkmVjWc/30O9o5FLRvoOU7fm7ul9uXhEBk98tJ0vdx3jmaW7SYwOZ+7EnKZjpvZLIybC6rG6etmuEqY/uYTNByvaPFdcXsun245wzYTeRIZZ6Zsex3M3jafoeC13zVvj1YP58vJCIqwWfn7JEOwNmnX7y/1+P46Gxg55RoVOoL7Gc2GMi/AY/4tjlNVzyBsgoZdpEF7pI2e2KVQdgMex51CI6wnpp65qVBBCjbFjxzJ06FCGDx/OHXfcwbRp04JmS7s5jn7OW31da/2M8/jLgaeAWV1gryB0C6b2SwPguS/20DMhknF+5A+6o5TiyatHsfvoCb7z2hqqbA7um9Gf2MjmP9mocCvTB6bzybYj/N+c4U0ezao6Oz95eyOHKup44K0NLPjuNCLDrE3nzV99AA1c7yZCJ/ZJ4clvjuT789fz8H838/urR7aYLlBeU8/ba4q4fHQvZg3L5MF3NrG68DhT+nkf5+iiosbO3OdXUGdv4O17ppISG0B+m3Dy2Gu8iz1wCkc/chzrKk2Y2tvUib7nmt8Fn8DYmzwf0xSqDuAzkJwHP9rp//GC0E351a9+1fS4f//+rF+/vmlbKcW8efM8nvfll182PS4vb/5Cf91113Hdddd1up3+eBzbnbeqtXavX4/FbTqCIJyJ9E6JITc1hvqGRmYP9z9M7U5sZBjP3jQOgMgwC7dOzWtzzMyhPTlSaWOjm2fxiY+2c6Syju+fP4AdR6r466cFTc/ZGxqZv2o/0wem0zulpZiYMzqL757Xn3+vKeK1lftbPDd/9QFq7Q3cNq0PiTHhDOoZz+rC9vMcq20Obnl5FQVHqykqr+VuHx5NoYtoVzhG+18c4ytc3HMYxPeCgsXej3F0oDhGEISQwh/h2O68VQCl1L1Kqd3A74HveVropOatCkI3w+V1DDRM7U5eWixv3jWFl26ZQGpc2/DeeYN7YLUoFm89DJiq59dW7ue2aX34wcyBXDU2m398vpuNReZb6Cdbj3C0ysaNk3I9vt79Fwzk3EHpPPr+FtbsM/lq9oZGXvmqkKn9UhnayxRGTMhLYe2+MhwN3pvS1tkb+PYrq9l0sIK/XT+GP35zFKsKj/PQfza1KPoRuhh7rW/hGOFvqLrSeyseMJ7I/ufD7qXQ4CUhvyOhakEQQgp/hKPPeatNO7R+WmvdD/gp8LCnhU5q3qogdDNunZrH3dP7BRymbs2QzASm9k/z+FxSTAQT81JYvPUItfUNPPjORnJTY3jgQpMP9shlQ0mLi+BH/96AzdHAayv30ysxihmDPTeEtVoUf7l2DJmJ0dzz6hqOVtXx0ebDHKqo4/az+jQdNz4vmRP1DWw95LlZbr2jke+8tpaVe4/z1DWjuHBYBpeN6sUDMwfyzrqD/O2zAo/nCV2AvcZzRbWL8Bg/i2Pa8TgCDJgJtgooWu35+Y6EqgVBCCn8EY7tzVttzXzgipMxShBOBwZlxPPg7MEdClMHwsyhPdl5pJofvrWefaU1PPGNkURHmJzGxOhwnrhqJDuPVPOjf2/ky4JjzJ2Yg9WHTYkx4Tx70ziq6hzc+9paXly2h75pscwY1Cw2J/YxTZlXeWjL09io+cFb6/ls+1Eev2JEiz6T953Xn2+MyeKPi3eyYIOvy4jQaXRWcUxdRfvTW/qeawpovIWrJVQtCN0ef4Sjz3mrAEqpAW6blwC7Os9EQRB8MXNoTwA+2nyY6yfltClYmTGoB9eMz+b9DcWEWRTXTuztaZkWDMlM4ImrRrC6sIwNRRV8a1peCwGcmRhNdnI0+YVt268s2nKYDzce4qezBnP9pJwWzyml+O1VI5iYl8KP/r3B57ztekdjQFNtBC+0F6r2O8exsn2PY1Qi9J4Eu7wIx6bJMRKqFoTuSrvCUWvtAFzzVrcBb7nmrTorqAHuU0ptUUqtB34I3NJlFguC0ILeKTEMz0ogMzGKh2YP9njMw5cOJSclhivGZNEj3r/Z1HNGZ3HvjH70TYvlqnHZbZ6fmJfC6sLjLfIVtdY88/luclNjuPOcvh7XjQyz8uxN4xicEc+d8/J5/os9bXIeC45Wc9U/vuI7r61tys8UOoj9ROdUVduq/Bv7N+ACOLwRqjx8KWjoQB9HQRBCCr/6OLY3b1Vr/X2t9TCt9Wit9Qyt9ZauNFoQhJY8e9N43r5nKvFRnm/ICVHhLP7hOfzuqpEBrfvjiwbz6QPTW4w+dDGhTwqlJ+rZc6w5P27FnuNsKKrgjrP7+gyHJ8dG8OadU5g1LIPHF27jZ//djL2hEa01874u5NK/LqOorIZnbhzHyOykgGwWWmGv9Z3j6E9xjNbN7Xjao/9M87vgk7bPuULVgUyOEYTTmHPPPbdNM+8///nPfOc73/F6TlxcXFeb5ROZVS0IpwFZST6EgRP3Xo6BoLz07ZuQZ/IcV+89Tr90cyF79ovdpMVFcLUHD2VroiOsPH39WP64eAdPL9nNvtITRIZZWLKjhHMGpvOHq0fSI8E/76jgA3/6ONa343F02KDR7t/0lowREJdh8hzH3NDyuaZQteQ4CgLA3LlzmT9/PhdddFHTvvnz5/Pkk08G0SrfiHAUBKFD9EuPJTU2glWFx7luYg7bDlWydEcJP7pwIFHh/olUi0Xx44sG0yctjofe2YhFKR69fBg3T8n1KliFAPGrOKbGeBW9/Zs3jRv0w+OoFPS/ALa/b9ryWN1uMw1SHCOEMB89CIc3de6aGSNg9hNen7766qt5+OGHsdlsREZGUlhYSHFxMaNHj+b888+nrKwMu93OY489xpw5c7yucyoR4SgIQodQSjE+L7mpEfhzX+whNsLKTZPzAl7r6nHZjMhKJCrcQm5qbCdbegbTYDeewvaKY3QDNNR7DyHbqsxvf4QjmDzH9a/CwXzImdy83+GchS6hakEAIDU1lYkTJ/Lxxx8zZ84c5s+fz7XXXkt0dDT//e9/SUhI4NixY0yePJnLL788JL5Qi3AUBKHDTMhLYdGWI6zZd5wFG4r51tQ8EmM6VvgwKMOPMKgQGK6il/ZC1a5jvQm6OudkIn9yHMHZlsdi8hzdhWODUzha5NYjhCA+PINdiStc7RKOL730Elprfvazn/HFF19gsVg4ePAgR44cISMjIyg2uuNXcYwgCIInXHmOP3xrAwq4za1JuBACuIpe2iuOcT/WE00eRz/FfXQyZE9s25anwWZa8YSA10QQQoUrrriCTz/9lLVr11JbW8vYsWN57bXXKCkpYc2aNaxfv56ePXtSV1cXbFMBEY6CIJwEw3olEBNhZV9pDXNGZ9HLjyId4RQSiMfRV4FMIDmOLgZcAIfWQ/XR5n0NdglTC0Ir4uLiOPfcc7ntttuYO3cuABUVFfTo0YPw8HCWLFnCvn37gmxlMxIvEAShw4RZLYzNSebLgmPcNd1z30YhiLjEYHvFMQCvXQVhXqrY61zCMYB0gv4z4bPH4Pnzm1+/6rD0cBQED8ydO5dvfOMbzJ8/H4AbbriByy67jPHjxzN69GgGD/bcozcYiHAUBOGk+M6MfswY3IOBPSVHMeSIiIGhV0Cij2lBOVNg1Nz2m4DHZUBSrv+vnTESptwHFQea96UPMiFsQRBacOWVV7YYhJCWlsbXX3/t8djq6upTZZZHRDgKgnBSTO2XxtR+acE2IyRRSs0C/gJYgRe01k+0ev5u4F6gAagG7tRab+00A1L6wjWv+D4mNhWufKbTXrIJiwUuerzz1xUEIahIjqMgCEIXoJSyAk8Ds4GhwFyl1NBWh72utR6htR4N/B546hSbKQiaajCFAAAF5UlEQVSCEBAiHAVBELqGiUCB1nqP1roemA+06OCrta5024wFWg7tFgShy3EPEZ8JnOz7FeEoCILQNWQBbgl+FDn3tUApda9SajfG4/i9U2SbIAhAVFQUpaWlZ4x41FpTWlpKVFTHx7lKjqMgCELX4KlZYZu7k9b6aeBppdT1wMPALW0WUupO4E6AnJycTjZTEM5csrOzKSoqoqSkJNimnDKioqLIzs7u8PkiHAVBELqGIsC9nDkbKPZx/HzgH56e0Fo/BzwHMH78+DPDNSIIp4Dw8HD69JHBBYEgoWpBEISuYTUwQCnVRykVAVwHLHA/QCk1wG3zEmDXKbRPEAQhYMTjKAiC0AVorR1KqfuARZh2PC9prbcopX4N5GutFwD3KaUuAOxAGR7C1IIgCKGECEdBEIQuQmu9EFjYat8jbo+/f8qNEgRBOAlUsCqJlFIlQKDDF9OAY11gTlfR3eyF7mdzd7MXup/N3c3eXK11erCN6Crk2hmydDebu5u90P1s7m72+nXtDJpw7AhKqXyt9fhg2+Ev3c1e6H42dzd7ofvZ3N3sFdrS3f4Pu5u90P1s7m72QvezubvZ6y9SHCMIgiAIgiD4hQhHQRAEQRAEwS+6m3B8LtgGBEh3sxe6n83dzV7ofjZ3N3uFtnS3/8PuZi90P5u7m73Q/Wzubvb6RbfKcRQEQRAEQRCCR3fzOAqCIAiCIAhBQoSjIAiCIAiC4BfdQjgqpWYppXYopQqUUg8G2x5PKKVeUkodVUptdtuXopRarJTa5fydHEwb3VFK9VZKLVFKbVNKbVFKfd+5P5RtjlJKrVJKbXDa/Khzfx+l1EqnzW86x7uFDEopq1JqnVLqA+d2qNtbqJTapJRar5TKd+4L2c+F4B25dnY+cu08dci1MzQJeeGolLICTwOzgaHAXKXU0OBa5ZF/ArNa7XsQ+FRrPQD41LkdKjiAB7TWQ4DJwL3Of9dQttkGnKe1HgWMBmYppSYDvwP+5LS5DLg9iDZ64vvANrftULcXYIbWerRbD7JQ/lwIHpBrZ5ch185Th1w7Q5CQF47ARKBAa71Ha10PzAfmBNmmNmitvwCOt9o9B3jF+fgV4IpTapQPtNaHtNZrnY+rMH+cWYS2zVprXe3cDHf+aOA84G3n/pCyWSmVDVwCvODcVoSwvT4I2c+F4BW5dnYBcu08Nci1M3TpDsIxCzjgtl3k3Ncd6Km1PgTmYgP0CLI9HlFK5QFjgJWEuM3O0MV64CiwGNgNlGutHc5DQu3z8WfgJ0CjczuV0LYXzA3lf0qpNUqpO537QvpzIXhErp1djFw7uxS5doYoYcE2wA+Uh33SQ6iTUErFAf8B7tdaV5ovdaGL1roBGK2USgL+CwzxdNiptcozSqlLgaNa6zVKqXNduz0cGhL2ujFNa12slOoBLFZKbQ+2QUKH6A6ftW6LXDu7Drl2hjbdweNYBPR2284GioNkS6AcUUplAjh/Hw2yPS1QSoVjLnyvaa3fce4OaZtdaK3LgaWYHKMkpZTrS1AofT6mAZcrpQoxYcLzMN+iQ9VeALTWxc7fRzE3mIl0k8+F0AK5dnYRcu3scuTaGcJ0B+G4GhjgrKaKAK4DFgTZJn9ZANzifHwL8F4QbWmBM1/kRWCb1vopt6dC2eZ057dllFLRwAWY/KIlwNXOw0LGZq31Q1rrbK11HuZz+5nW+gZC1F4ApVSsUire9Ri4ENhMCH8uBK/ItbMLkGtn1yPXzhBHax3yP8DFwE5MTsbPg22PFxvfAA4Bdsw3/dsxORmfArucv1OCbaebvWdh3PwbgfXOn4tD3OaRwDqnzZuBR5z7+wKrgALg30BksG31YPu5wAehbq/Ttg3Ony2uv7dQ/lzIj8//T7l2dr69cu08tbbLtTPEfmTkoCAIgiAIguAX3SFULQiCIAiCIIQAIhwFQRAEQRAEvxDhKAiCIAiCIPiFCEdBEARBEATBL0Q4CoIgCIIgCH4hwlEQBEEQBEHwCxGOgiAIgiAIgl/8f4qrsolUvAz5AAAAAElFTkSuQmCC\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 [==============================] - 7s 245ms/step - loss: 0.9800 - acc: 0.5180 - val_loss: 0.9562 - 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/weights-augm-trial9-improvement-BEST.hdf5\n",
      "Epoch 2/200\n",
      "27/27 [==============================] - 4s 155ms/step - loss: 0.9890 - acc: 0.4634 - val_loss: 0.9030 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00002: val_acc did not improve from 0.31579\n",
      "Epoch 3/200\n",
      "27/27 [==============================] - 4s 161ms/step - loss: 0.9219 - acc: 0.5273 - val_loss: 0.8394 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00003: val_acc improved from 0.31579 to 0.68421, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/weights-augm-trial9-improvement-BEST.hdf5\n",
      "Epoch 4/200\n",
      "27/27 [==============================] - 5s 171ms/step - loss: 0.8821 - acc: 0.4912 - val_loss: 0.8425 - val_acc: 0.6316\n",
      "\n",
      "Epoch 00004: val_acc did not improve from 0.68421\n",
      "Epoch 5/200\n",
      "27/27 [==============================] - 4s 158ms/step - loss: 0.8825 - acc: 0.5273 - val_loss: 0.8754 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00005: val_acc did not improve from 0.68421\n",
      "Epoch 6/200\n",
      "27/27 [==============================] - 5s 170ms/step - loss: 0.8704 - acc: 0.5005 - val_loss: 0.8815 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00006: val_acc did not improve from 0.68421\n",
      "Epoch 7/200\n",
      "27/27 [==============================] - 4s 154ms/step - loss: 0.8183 - acc: 0.5088 - val_loss: 0.7665 - 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/weights-augm-trial9-improvement-BEST.hdf5\n",
      "Epoch 8/200\n",
      "27/27 [==============================] - 5s 180ms/step - loss: 0.8030 - acc: 0.5654 - val_loss: 0.8245 - val_acc: 0.3158\n",
      "\n",
      "Epoch 00008: val_acc did not improve from 0.78947\n",
      "Epoch 9/200\n",
      "27/27 [==============================] - 4s 161ms/step - loss: 0.7826 - acc: 0.6200 - val_loss: 0.7811 - val_acc: 0.5263\n",
      "\n",
      "Epoch 00009: val_acc did not improve from 0.78947\n",
      "Epoch 10/200\n",
      "27/27 [==============================] - 5s 167ms/step - loss: 0.7626 - acc: 0.6117 - val_loss: 0.7998 - val_acc: 0.3684\n",
      "\n",
      "Epoch 00010: val_acc did not improve from 0.78947\n",
      "Epoch 11/200\n",
      "27/27 [==============================] - 5s 170ms/step - loss: 0.7122 - acc: 0.6488 - val_loss: 0.7575 - val_acc: 0.5789\n",
      "\n",
      "Epoch 00011: val_acc did not improve from 0.78947\n",
      "Epoch 12/200\n",
      "27/27 [==============================] - 4s 166ms/step - loss: 0.6820 - acc: 0.7405 - val_loss: 0.6523 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00012: val_acc improved from 0.78947 to 0.84211, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/weights-augm-trial9-improvement-BEST.hdf5\n",
      "Epoch 13/200\n",
      "27/27 [==============================] - 4s 163ms/step - loss: 0.6595 - acc: 0.7137 - val_loss: 0.5630 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00013: val_acc did not improve from 0.84211\n",
      "Epoch 14/200\n",
      "27/27 [==============================] - 4s 158ms/step - loss: 0.6377 - acc: 0.8054 - val_loss: 0.6093 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00014: val_acc improved from 0.84211 to 0.89474, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/weights-augm-trial9-improvement-BEST.hdf5\n",
      "Epoch 15/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 0.6063 - acc: 0.7600 - val_loss: 0.6124 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00015: val_acc did not improve from 0.89474\n",
      "Epoch 16/200\n",
      "27/27 [==============================] - 4s 158ms/step - loss: 0.5417 - acc: 0.8517 - val_loss: 0.5339 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00016: val_acc did not improve from 0.89474\n",
      "Epoch 17/200\n",
      "27/27 [==============================] - 5s 168ms/step - loss: 0.5190 - acc: 0.8610 - val_loss: 0.6381 - val_acc: 0.6842\n",
      "\n",
      "Epoch 00017: val_acc did not improve from 0.89474\n",
      "Epoch 18/200\n",
      "27/27 [==============================] - 4s 163ms/step - loss: 0.4929 - acc: 0.8610 - val_loss: 0.4824 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00018: val_acc improved from 0.89474 to 0.94737, saving model to /analysis/fabiane/models/MS/pretrained/rebuild_64Net/new_script/weights-augm-trial9-improvement-BEST.hdf5\n",
      "Epoch 19/200\n",
      "27/27 [==============================] - 4s 159ms/step - loss: 0.5368 - acc: 0.8610 - val_loss: 0.4462 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00019: val_acc did not improve from 0.94737\n",
      "Epoch 20/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 0.5376 - acc: 0.7683 - val_loss: 0.4503 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00020: val_acc did not improve from 0.94737\n",
      "Epoch 21/200\n",
      "27/27 [==============================] - 5s 168ms/step - loss: 0.4483 - acc: 0.8517 - val_loss: 0.4459 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00021: val_acc did not improve from 0.94737\n",
      "Epoch 22/200\n",
      "27/27 [==============================] - 4s 158ms/step - loss: 0.4515 - acc: 0.8980 - val_loss: 0.4376 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00022: val_acc did not improve from 0.94737\n",
      "Epoch 23/200\n",
      "27/27 [==============================] - 5s 170ms/step - loss: 0.4783 - acc: 0.8517 - val_loss: 0.4393 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00023: val_acc did not improve from 0.94737\n",
      "Epoch 24/200\n",
      "27/27 [==============================] - 4s 155ms/step - loss: 0.4262 - acc: 0.9073 - val_loss: 0.4796 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00024: val_acc did not improve from 0.94737\n",
      "Epoch 25/200\n",
      "27/27 [==============================] - 4s 159ms/step - loss: 0.4071 - acc: 0.9073 - val_loss: 0.4457 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00025: val_acc did not improve from 0.94737\n",
      "Epoch 26/200\n",
      "27/27 [==============================] - 4s 164ms/step - loss: 0.4102 - acc: 0.8888 - val_loss: 0.4392 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00026: val_acc did not improve from 0.94737\n",
      "Epoch 27/200\n",
      "27/27 [==============================] - 4s 154ms/step - loss: 0.3885 - acc: 0.9166 - val_loss: 0.4211 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00027: val_acc did not improve from 0.94737\n",
      "Epoch 28/200\n",
      "27/27 [==============================] - 4s 165ms/step - loss: 0.4070 - acc: 0.8888 - val_loss: 0.4920 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00028: val_acc did not improve from 0.94737\n",
      "Epoch 29/200\n",
      "27/27 [==============================] - 4s 153ms/step - loss: 0.3297 - acc: 0.9351 - val_loss: 0.4760 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00029: val_acc did not improve from 0.94737\n",
      "Epoch 30/200\n",
      "27/27 [==============================] - 4s 165ms/step - loss: 0.3600 - acc: 0.9166 - val_loss: 0.4405 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00030: val_acc did not improve from 0.94737\n",
      "Epoch 31/200\n",
      "27/27 [==============================] - 4s 157ms/step - loss: 0.3840 - acc: 0.9166 - val_loss: 0.4599 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00031: val_acc did not improve from 0.94737\n",
      "Epoch 32/200\n",
      "27/27 [==============================] - 4s 160ms/step - loss: 0.3259 - acc: 0.9722 - val_loss: 0.4578 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00032: val_acc did not improve from 0.94737\n",
      "Epoch 33/200\n",
      "27/27 [==============================] - 4s 166ms/step - loss: 0.3601 - acc: 0.9351 - val_loss: 0.4352 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00033: val_acc did not improve from 0.94737\n",
      "Epoch 34/200\n",
      "27/27 [==============================] - 4s 166ms/step - loss: 0.3272 - acc: 0.9351 - val_loss: 0.4156 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00034: val_acc did not improve from 0.94737\n",
      "Epoch 35/200\n",
      "27/27 [==============================] - 4s 158ms/step - loss: 0.3263 - acc: 0.9444 - val_loss: 0.4739 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00035: val_acc did not improve from 0.94737\n",
      "Epoch 36/200\n",
      "27/27 [==============================] - 4s 164ms/step - loss: 0.3179 - acc: 0.9444 - val_loss: 0.4817 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00036: val_acc did not improve from 0.94737\n",
      "Epoch 37/200\n",
      "27/27 [==============================] - 4s 164ms/step - loss: 0.3923 - acc: 0.8980 - val_loss: 0.4378 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00037: val_acc did not improve from 0.94737\n",
      "Epoch 38/200\n",
      "27/27 [==============================] - 4s 163ms/step - loss: 0.3919 - acc: 0.8795 - val_loss: 0.5622 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00038: val_acc did not improve from 0.94737\n",
      "Epoch 39/200\n",
      "27/27 [==============================] - 4s 166ms/step - loss: 0.3225 - acc: 0.9444 - val_loss: 0.4063 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00039: val_acc did not improve from 0.94737\n",
      "Epoch 40/200\n",
      "27/27 [==============================] - 4s 157ms/step - loss: 0.3197 - acc: 0.9259 - val_loss: 0.4692 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00040: val_acc did not improve from 0.94737\n",
      "Epoch 41/200\n",
      "27/27 [==============================] - 5s 183ms/step - loss: 0.3173 - acc: 0.9351 - val_loss: 0.4048 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00041: val_acc did not improve from 0.94737\n",
      "Epoch 42/200\n",
      "27/27 [==============================] - 4s 158ms/step - loss: 0.3003 - acc: 0.9444 - val_loss: 0.4661 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00042: val_acc did not improve from 0.94737\n",
      "Epoch 43/200\n",
      "27/27 [==============================] - 5s 185ms/step - loss: 0.2965 - acc: 0.9444 - val_loss: 0.4178 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00043: val_acc did not improve from 0.94737\n",
      "Epoch 44/200\n",
      "27/27 [==============================] - 5s 168ms/step - loss: 0.3014 - acc: 0.9537 - val_loss: 0.4908 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00044: val_acc did not improve from 0.94737\n",
      "Epoch 45/200\n",
      "27/27 [==============================] - 4s 166ms/step - loss: 0.2719 - acc: 0.9629 - val_loss: 0.4606 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00045: val_acc did not improve from 0.94737\n",
      "Epoch 46/200\n",
      "27/27 [==============================] - 4s 166ms/step - loss: 0.3053 - acc: 0.9259 - val_loss: 0.4922 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00046: val_acc did not improve from 0.94737\n",
      "Epoch 47/200\n",
      "27/27 [==============================] - 4s 155ms/step - loss: 0.2802 - acc: 0.9351 - val_loss: 0.4564 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00047: val_acc did not improve from 0.94737\n",
      "Epoch 48/200\n",
      "27/27 [==============================] - 5s 167ms/step - loss: 0.2942 - acc: 0.9259 - val_loss: 0.4371 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00048: val_acc did not improve from 0.94737\n",
      "Epoch 49/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 0.2956 - acc: 0.9537 - val_loss: 0.4056 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00049: val_acc did not improve from 0.94737\n",
      "Epoch 50/200\n",
      "27/27 [==============================] - 4s 156ms/step - loss: 0.2852 - acc: 0.9722 - val_loss: 0.5272 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00050: val_acc did not improve from 0.94737\n",
      "Epoch 51/200\n",
      "27/27 [==============================] - 4s 166ms/step - loss: 0.2967 - acc: 0.9537 - val_loss: 0.4429 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00051: val_acc did not improve from 0.94737\n",
      "Epoch 52/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 0.2867 - acc: 0.9444 - val_loss: 0.4415 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00052: val_acc did not improve from 0.94737\n",
      "Epoch 53/200\n",
      "27/27 [==============================] - 4s 159ms/step - loss: 0.2786 - acc: 0.9629 - val_loss: 0.4446 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00053: val_acc did not improve from 0.94737\n",
      "Epoch 54/200\n",
      "27/27 [==============================] - 4s 165ms/step - loss: 0.2828 - acc: 0.9259 - val_loss: 0.4267 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00054: val_acc did not improve from 0.94737\n",
      "Epoch 55/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 0.2725 - acc: 0.9537 - val_loss: 0.3919 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00055: val_acc did not improve from 0.94737\n",
      "Epoch 56/200\n",
      "27/27 [==============================] - 4s 163ms/step - loss: 0.2704 - acc: 0.9629 - val_loss: 0.4796 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00056: val_acc did not improve from 0.94737\n",
      "Epoch 57/200\n",
      "27/27 [==============================] - 4s 166ms/step - loss: 0.2489 - acc: 0.9444 - val_loss: 0.4784 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00057: val_acc did not improve from 0.94737\n",
      "Epoch 58/200\n",
      "27/27 [==============================] - 5s 169ms/step - loss: 0.2610 - acc: 0.9537 - val_loss: 0.4336 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00058: val_acc did not improve from 0.94737\n",
      "Epoch 59/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 0.2575 - acc: 0.9722 - val_loss: 0.4280 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00059: val_acc did not improve from 0.94737\n",
      "Epoch 60/200\n",
      "27/27 [==============================] - 4s 159ms/step - loss: 0.2599 - acc: 0.9444 - val_loss: 0.4303 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00060: val_acc did not improve from 0.94737\n",
      "Epoch 61/200\n",
      "27/27 [==============================] - 4s 164ms/step - loss: 0.2716 - acc: 0.9629 - val_loss: 0.4623 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00061: val_acc did not improve from 0.94737\n",
      "Epoch 62/200\n",
      "27/27 [==============================] - 4s 165ms/step - loss: 0.2463 - acc: 0.9815 - val_loss: 0.4328 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00062: val_acc did not improve from 0.94737\n",
      "Epoch 63/200\n",
      "27/27 [==============================] - 4s 163ms/step - loss: 0.2485 - acc: 0.9629 - val_loss: 0.4134 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00063: val_acc did not improve from 0.94737\n",
      "Epoch 64/200\n",
      "27/27 [==============================] - 4s 162ms/step - loss: 0.2612 - acc: 0.9259 - val_loss: 0.4644 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00064: val_acc did not improve from 0.94737\n",
      "Epoch 65/200\n",
      "27/27 [==============================] - 4s 166ms/step - loss: 0.2457 - acc: 0.9722 - val_loss: 0.4142 - val_acc: 0.8947\n",
      "\n",
      "Epoch 00065: val_acc did not improve from 0.94737\n",
      "Epoch 66/200\n",
      "27/27 [==============================] - 4s 166ms/step - loss: 0.2263 - acc: 0.9907 - val_loss: 0.5141 - val_acc: 0.8421\n",
      "\n",
      "Epoch 00066: val_acc did not improve from 0.94737\n",
      "Epoch 67/200\n",
      "27/27 [==============================] - 5s 182ms/step - loss: 0.2413 - acc: 0.9629 - val_loss: 0.5132 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00067: val_acc did not improve from 0.94737\n",
      "Epoch 68/200\n",
      "27/27 [==============================] - 5s 169ms/step - loss: 0.2331 - acc: 0.9815 - val_loss: 0.4159 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00068: val_acc did not improve from 0.94737\n",
      "Epoch 69/200\n",
      "27/27 [==============================] - 4s 163ms/step - loss: 0.2351 - acc: 0.9629 - val_loss: 0.4981 - val_acc: 0.7895\n",
      "\n",
      "Epoch 00069: val_acc did not improve from 0.94737\n",
      "Epoch 70/200\n",
      "27/27 [==============================] - 4s 158ms/step - loss: 0.2423 - acc: 0.9537 - val_loss: 0.4414 - val_acc: 0.9474\n",
      "\n",
      "Epoch 00070: val_acc did not improve from 0.94737\n",
      "Epoch 00070: 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": [
      "Training Time: 0.0h:44.0m:12.01481556892395s\n",
      "Validation final accuracies: \n",
      " [0.8421052631578947, 0.9473684210526315, 0.8947368421052632, 0.9473684210526315, 0.8947368421052632, 0.9473684210526315, 0.9473684210526315, 0.9473684210526315, 0.9473684210526315, 0.9473684210526315]\n",
      "Validation final accuracies mean: 0.9263157894736842\n",
      "Validation best accuracies: \n",
      " [0.9473684210526315, 0.9473684210526315, 0.9473684210526315, 0.9473684210526315, 0.9473684210526315, 0.9473684210526315, 0.9473684210526315, 0.9473684210526315, 0.9473684210526315, 0.9473684210526315]\n",
      "Validation best accuracies mean: 0.9473684210526315\n",
      "Validation balanced accuracies: \n",
      " [0.8397435897435898, 0.9166666666666667, 0.8782051282051282, 0.9166666666666667, 0.8333333333333333, 0.9166666666666667, 0.9166666666666667, 0.9166666666666667, 0.9166666666666667, 0.9166666666666667]\n",
      "Validation balanced accuracies mean: 0.8967948717948717\n",
      "Validation final sensitivities: \n",
      " [0.8333333333333334, 0.8333333333333334, 0.8333333333333334, 0.8333333333333334, 0.6666666666666666, 0.8333333333333334, 0.8333333333333334, 0.8333333333333334, 0.8333333333333334, 0.8333333333333334]\n",
      "Validation final sensitivities' mean: 0.8166666666666667\n",
      "Validation final specificities: \n",
      " [0.8461538461538461, 1.0, 0.9230769230769231, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
      "Validation final specificities' mean: 0.976923076923077\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 = init_model(model_path, finetune=False, up_to=None)    \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": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "training_time = time.time() - start_time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lr = 0.0005\n",
    "lr_decay = 0.002\n",
    "num_trials = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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": 45,
   "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": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rc('font', family='serif')\n",
    "#file_path = None\n",
    "image_file_dir = \"/analysis/fabiane/documents/publications/MS_CNN_VIS/\"\n",
    "file_path = os.path.join(image_file_dir, \"fine_tuning_MS_ROC_curve.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": true
   },
   "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 91.30 %\n",
      "Balanced accuracy 91.15 %\n",
      "Sensitivity 92.31 %\n",
      "Specificity 90.00 %\n",
      "Area Under the Receiver Operating Curve (ROC AUC score) 94.62\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 86.96 %\n",
      "Balanced accuracy 86.15 %\n",
      "Sensitivity 92.31 %\n",
      "Specificity 80.00 %\n",
      "Area Under the Receiver Operating Curve (ROC AUC score) 94.62\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 86.96 %\n",
      "Balanced accuracy 86.15 %\n",
      "Sensitivity 92.31 %\n",
      "Specificity 80.00 %\n",
      "Area Under the Receiver Operating Curve (ROC AUC score) 96.92\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 86.96 %\n",
      "Balanced accuracy 85.00 %\n",
      "Sensitivity 100.00 %\n",
      "Specificity 70.00 %\n",
      "Area Under the Receiver Operating Curve (ROC AUC score) 97.69\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 86.96 %\n",
      "Balanced accuracy 86.15 %\n",
      "Sensitivity 92.31 %\n",
      "Specificity 80.00 %\n",
      "Area Under the Receiver Operating Curve (ROC AUC score) 96.92\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 91.30 %\n",
      "Balanced accuracy 91.15 %\n",
      "Sensitivity 92.31 %\n",
      "Specificity 90.00 %\n",
      "Area Under the Receiver Operating Curve (ROC AUC score) 95.38\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 86.96 %\n",
      "Balanced accuracy 86.15 %\n",
      "Sensitivity 92.31 %\n",
      "Specificity 80.00 %\n",
      "Area Under the Receiver Operating Curve (ROC AUC score) 95.38\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 86.96 %\n",
      "Balanced accuracy 86.15 %\n",
      "Sensitivity 92.31 %\n",
      "Specificity 80.00 %\n",
      "Area Under the Receiver Operating Curve (ROC AUC score) 96.92\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 86.96 %\n",
      "Balanced accuracy 86.15 %\n",
      "Sensitivity 92.31 %\n",
      "Specificity 80.00 %\n",
      "Area Under the Receiver Operating Curve (ROC AUC score) 96.15\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 86.96 %\n",
      "Balanced accuracy 86.15 %\n",
      "Sensitivity 92.31 %\n",
      "Specificity 80.00 %\n",
      "Area Under the Receiver Operating Curve (ROC AUC score) 96.15\n",
      "######## Final results ########\n",
      "Accuracy mean 87.83 %\n",
      "Balanced accuracy mean 87.04 %\n",
      "Sensitivity mean 93.08 %\n",
      "Specificity mean 81.00 %\n",
      "Mean ROC AUC score 96.08\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 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=(8, 6))\n",
    "\n",
    "for fold, weight in enumerate(weights):\n",
    "    print(\"Fold {}\".format(fold))\n",
    "    model = load_model(model_path)\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', fontsize=14)\n",
    "plt.ylabel('True Positive Rate', fontsize=14)\n",
    "plt.tick_params(axis='both', labelsize=12)\n",
    "plt.title('Receiver Operating Characteristic Curve', fontsize=16)\n",
    "plt.legend(loc=\"lower right\")\n",
    "if file_path is not None:\n",
    "        plt.savefig(file_path, format='png', transparent=True, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "total_time = time.time() - start_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Time: 0.0h:44.0m:12.027180671691895s\n",
      "Total time elapsed: 0.0h:45.0m:56.69809341430664s\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": 31,
   "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
}