2000video.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use CPU only\n",
    "#import os\n",
    "#os.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\"   # see issue #152\n",
    "#os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"\""
   ]
  },
  {
   "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 matplotlib.pyplot as plt\n",
    "plt.style.use('ggplot')\n",
    "import h5py\n",
    "\n",
    "import cv2\n",
    "import sys\n",
    "import itertools\n",
    "\n",
    "\n",
    "np.random.seed(1337)\n",
    "from tensorflow import set_random_seed\n",
    "set_random_seed(1337)\n",
    "\n",
    "from utils import *\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "project_dir = '/home/rabreu/projeto_multimodal/'\n",
    "data_dir = project_dir+'data/'\n",
    "\n",
    "audioset_indices_csv = project_dir+'segments/subset_class_labels_indices.csv'\n",
    "\n",
    "## pegando dataset desbalanceado, que eu balanceei para 600 ocorrências para cada classe\n",
    "audioset_train_csv = project_dir+'segments/2000max_subset_unbalanced_train_segments.csv'\n",
    "train_sub_dir = '2000unbalanced_train/video'\n",
    "\n",
    "audioset_eval_csv = project_dir+'segments/subset_eval_segments.csv'\n",
    "eval_sub_dir = 'eval/video'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "fps_trimmed = 3 # get four frames each second\n",
    "max_frames = 32 # cut the video past this treshold\n",
    "frame_size = 32 # 32x32 pixels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Salvando dados"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Salva videos e features relativos ao vídeo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def parse_video_files(parent_dir,sub_dir,csv_file,file_ext='*.mp4'):\n",
    "    ### HARDCODED VALUE: 3 color channel for images\n",
    "    features = np.empty((0,max_frames,frame_size,frame_size,3))\n",
    "    file_labels = []\n",
    "    count  = 0\n",
    "    #for label, sub_dir in enumerate(sub_dirs):\n",
    "    for fn in natural_sort(glob.glob(os.path.join(parent_dir, sub_dir, file_ext))):\n",
    "        print('Processing Line #' + str(count))\n",
    "        # The print() calls here are just for debugging. Feel free to remove them.\n",
    "        print(fn)\n",
    "        cap = cv2.VideoCapture(fn)\n",
    "        fps = cap.get(5)\n",
    "        frames = []\n",
    "        while(cap.isOpened()):\n",
    "            frameId = cap.get(1) #current frame number\n",
    "            ret, frame = cap.read()\n",
    "            if (ret != True):\n",
    "                break\n",
    "            if (len(frames) >= max_frames):\n",
    "                break\n",
    "            if (frameId % (np.floor(fps/fps_trimmed)) == 0):\n",
    "                frame=cv2.resize(frame,(frame_size,frame_size),interpolation=cv2.INTER_AREA)\n",
    "                #gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n",
    "                frames.append(frame)\n",
    "                #plt.imshow(frame)\n",
    "                #plt.xticks([]), plt.yticks([])  # to hide tick values on X and Y axis\n",
    "                #plt.show()\n",
    "\n",
    "                #filename = imagesFolder + \"/image_\" +  str(int(frameId)) + \".jpg\"\n",
    "                #cv2.imwrite(filename, frame)\n",
    "            if cv2.waitKey(1) & 0xFF == ord('q'):\n",
    "                break\n",
    "\n",
    "        # equalize sequence length\n",
    "        if len(frames) < max_frames:\n",
    "            blank_frame = np.zeros((frame_size, frame_size,3))\n",
    "            for _ in xrange(max_frames - len(frames)):\n",
    "                frames.append(blank_frame)\n",
    "\n",
    "        cap.release()\n",
    "        cv2.destroyAllWindows()\n",
    "        frames=np.array(frames)\n",
    "        #print('Frames -->',frames.shape,'Features -->',features.shape)\n",
    "        #features = np.stack((features, frames),axis=0)\n",
    "        # insert frame sequence into the features set\n",
    "        features = np.append(features, [frames], axis=0)\n",
    "        #print('features stacked to pre-existing features array')\n",
    "        #print('features numpy array size = ' + str(features.size))\n",
    "\n",
    "        # Need to get the labels for each clip from the CSV file\n",
    "        rownum = fn.split(sub_dir)[1].split('/')[1].split('_')[0]\n",
    "        print('rownum = ' + str(rownum))\n",
    "        labels = (get_file_name_labels_from_audioset_csv(rownum,csv_file,audioset_indices_csv))\n",
    "        print('Labels = '+str(labels))\n",
    "        file_labels.append(labels)\n",
    "\n",
    "        count += 1\n",
    "\n",
    "    return np.array(features), np.array(file_labels)\n",
    "\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "rownum = 0\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rownum = 561\n",
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      "rownum = 579\n",
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      "/home/rabreu/projeto_multimodal/data/balanced_train/video/580_zlt89JkjR8c.mp4\n",
      "rownum = 580\n",
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      "Processing Line #551\n",
      "/home/rabreu/projeto_multimodal/data/balanced_train/video/581_zu53y8M2xBc.mp4\n",
      "rownum = 581\n",
      "Labels = [0]\n",
      "Features of =  (552, 32, 32, 32, 3)\n",
      "Labels of =  (552, 7)\n",
      "Saved /home/rabreu/projeto_multimodal/data/balanced_train/video/features/_x.npy\n",
      "Saved /home/rabreu/projeto_multimodal/data/balanced_train/video/features/_y.npy\n"
     ]
    }
   ],
   "source": [
    "## já que estamos aqui, vamos Extrair features do balanced dataset também\n",
    "\n",
    "features, labels = parse_video_files(data_dir,\"balanced_train/video\",audioset_train_csv,file_ext='*.mp4')\n",
    "save_dir = data_dir+\"balanced_train/video\"+\"/features/\"\n",
    "assure_path_exists(save_dir)\n",
    "save_files(save_dir,features,labels)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "/home/rabreu/projeto_multimodal/data/eval/video/554_wzjeayE_MWo.mp4\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rownum = 554\n",
      "Labels = [6]\n",
      "Processing Line #513\n",
      "/home/rabreu/projeto_multimodal/data/eval/video/555_x7RLEgi_tGU.mp4\n",
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      "/home/rabreu/projeto_multimodal/data/eval/video/569_zQPKy8HMbDc.mp4\n",
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      "/home/rabreu/projeto_multimodal/data/eval/video/570_zW_741B6ugQ.mp4\n",
      "rownum = 570\n",
      "Labels = [6]\n",
      "Processing Line #527\n",
      "/home/rabreu/projeto_multimodal/data/eval/video/571_zexkXRiD9zk.mp4\n",
      "rownum = 571\n",
      "Labels = [0]\n",
      "Processing Line #528\n",
      "/home/rabreu/projeto_multimodal/data/eval/video/572_ztfXxgXqHGM.mp4\n",
      "rownum = 572\n",
      "Labels = [6]\n",
      "Processing Line #529\n",
      "/home/rabreu/projeto_multimodal/data/eval/video/573_ztrU90Ub4Uw.mp4\n",
      "rownum = 573\n",
      "Labels = [5]\n",
      "Processing Line #530\n",
      "/home/rabreu/projeto_multimodal/data/eval/video/574_zwi96DG85AI.mp4\n",
      "rownum = 574\n",
      "Labels = [2]\n",
      "Processing Line #531\n",
      "/home/rabreu/projeto_multimodal/data/eval/video/575_zzbTaK7CXJY.mp4\n",
      "rownum = 575\n",
      "Labels = [0]\n",
      "Features of =  (532, 32, 32, 32, 3)\n",
      "Labels of =  (532, 7)\n",
      "Saved /home/rabreu/projeto_multimodal/data/eval/video/features/_x.npy\n",
      "Saved /home/rabreu/projeto_multimodal/data/eval/video/features/_y.npy\n"
     ]
    }
   ],
   "source": [
    "## só use este pedaço de código para salvar os dados. Caso tenha salvo pode pular ele\n",
    "\n",
    "features, labels = parse_video_files(data_dir,train_sub_dir,audioset_train_csv,file_ext='*.mp4')\n",
    "save_dir = data_dir+train_sub_dir+\"/2000features/\"\n",
    "assure_path_exists(save_dir)\n",
    "save_files(save_dir,features,labels)\n",
    "\n",
    "print(data_dir+eval_sub_dir)\n",
    "features, labels = parse_video_files(data_dir,eval_sub_dir,audioset_eval_csv,file_ext='*.mp4')\n",
    "save_dir = data_dir+eval_sub_dir+\"/2000features/\"\n",
    "assure_path_exists(save_dir)\n",
    "save_files(save_dir,features,labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Carregando dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10950, 32, 32, 32, 3)\n",
      "(532, 32, 32, 32, 3)\n"
     ]
    }
   ],
   "source": [
    "# Load up numpy arrays to save time if they have already been procesed.\n",
    "\n",
    "save_dir = data_dir+train_sub_dir+\"/2000features/\"\n",
    "feature_file = os.path.join(save_dir+'_x.npy')\n",
    "labels_file = os.path.join(save_dir+'_y.npy')\n",
    "train_x = np.load(feature_file)\n",
    "train_y = np.load(labels_file)\n",
    "print(train_x.shape)\n",
    "\n",
    "\n",
    "save_dir = data_dir+eval_sub_dir+\"/features/\"\n",
    "feature_file = os.path.join(save_dir+'_x.npy')\n",
    "labels_file = os.path.join(save_dir+'_y.npy')\n",
    "eval_x = np.load(feature_file)\n",
    "eval_y = np.load(labels_file)\n",
    "print(eval_x.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "fps_trimmed = 3 # get four frames each second\n",
    "max_frames = 32 # cut the video past this treshold\n",
    "frame_size = 32 # 32x32 pixels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature Dimension: (32, 32, 32, 3) Classes: 7\n"
     ]
    }
   ],
   "source": [
    "n_dim = np.empty((max_frames,frame_size,frame_size,3)).shape  # TODO: Fazer o slicing do train_x corretamente. \n",
    "n_classes = train_y.shape[1]\n",
    "\n",
    "print \"Feature Dimension:\", n_dim, \"Classes:\", n_classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Mapeando valores para o espaço entre 0 e 1... será que dá diferença?\n",
    "\n",
    "train_x = train_x.astype('float32')\n",
    "eval_x = eval_x.astype('float32')\n",
    "train_x  /= 255\n",
    "eval_x /= 255"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Time distributed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(32, 32, 32, 3)\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import Dropout\n",
    "from keras.layers import Flatten\n",
    "from keras.layers import BatchNormalization\n",
    "from keras.layers import Lambda\n",
    "from keras.layers import Input\n",
    "from keras.models import Model\n",
    "\n",
    "\n",
    "from keras.layers.merge import concatenate\n",
    "\n",
    "\n",
    "from keras.layers.convolutional import Conv2D\n",
    "from keras.layers.convolutional import MaxPooling2D\n",
    "from keras.layers.wrappers import TimeDistributed\n",
    "from keras.layers.recurrent import LSTM\n",
    "\n",
    "from keras.optimizers import SGD\n",
    "\n",
    "\n",
    "import keras.backend as K\n",
    "\n",
    "print(n_dim)\n",
    "# o modelo original não tinha padding no maxpooling. \n",
    "#Tive que inserir para comportar nosso tamanho de imagem que é de 32x32 atualmente\n",
    "\n",
    "video_input  = Input(shape=(n_dim),name='video_input')\n",
    "\n",
    "#model.add(BatchNormalization(epsilon=1e-06,input_shape=n_dim, axis = 1, momentum=0.9, weights=None)) # Normalizar no input vale a pena? talvez seja algo a testar.\n",
    "\n",
    "\n",
    "video = TimeDistributed(Conv2D(32, (3, 3), strides=(2, 2),\n",
    "    activation='relu', padding='same',data_format=\"channels_last\"), input_shape=n_dim)(video_input)\n",
    "video = TimeDistributed(BatchNormalization())(video)\n",
    "video = TimeDistributed(Conv2D(32, (3,3),\n",
    "    padding='same', activation='relu'))(video)\n",
    "video = TimeDistributed(BatchNormalization())(video)\n",
    "video = TimeDistributed(MaxPooling2D((2, 2), strides=(2, 2)))(video)\n",
    "\n",
    "video = TimeDistributed(Dropout(0.5))(video)\n",
    "\n",
    "\n",
    "\n",
    "video = TimeDistributed(Conv2D(64, (3,3),\n",
    "    padding='same', activation='relu'))(video)\n",
    "video = TimeDistributed(BatchNormalization())(video)\n",
    "video = TimeDistributed(Conv2D(64, (3,3),\n",
    "    padding='same', activation='relu'))(video)\n",
    "video = TimeDistributed(BatchNormalization())(video)\n",
    "video = TimeDistributed(MaxPooling2D((2, 2),padding=\"same\", strides=(2, 2)))(video)\n",
    "#model.add(Dropout(0.5))\n",
    "\n",
    "\n",
    "\n",
    "video = TimeDistributed(Conv2D(128, (3,3),\n",
    "    padding='same', activation='relu',kernel_initializer='uniform'))(video)\n",
    "video = TimeDistributed(BatchNormalization())(video)\n",
    "video = TimeDistributed(Conv2D(128, (3,3),\n",
    "    padding='same', activation='relu'))(video)\n",
    "video = TimeDistributed(BatchNormalization())(video)\n",
    "video = TimeDistributed(MaxPooling2D((2, 2),padding=\"same\", strides=(2, 2)))(video)\n",
    "\n",
    "\n",
    "#model.add(TimeDistributed(Conv2D(256, (3,3),\n",
    "#    padding='same', activation='relu')))\n",
    "#model.add(TimeDistributed(Conv2D(256, (3,3),\n",
    "#    padding='same', activation='relu')))\n",
    "#model.add(TimeDistributed(MaxPooling2D((2, 2),padding=\"same\", strides=(2, 2),data_format=\"channels_last\")))\n",
    "\n",
    "#model.add(TimeDistributed(BatchNormalization()))\n",
    "\n",
    "#model.add(TimeDistributed(Conv2D(512, (3,3),\n",
    "#    padding='same', activation='relu')))\n",
    "#model.add(TimeDistributed(Conv2D(512, (3,3),\n",
    "#    padding='same', activation='relu')))\n",
    "#model.add(TimeDistributed(MaxPooling2D((2, 2),padding=\"same\", strides=(2, 2))))\n",
    "\n",
    "video = TimeDistributed(Flatten())(video)\n",
    "#video = Flatten()(video) seráaa??\n",
    "\n",
    "#model.add(Dropout(0.5))\n",
    "video =  LSTM(256, return_sequences=False)(video)\n",
    "video =  Dropout(0.5)(video)\n",
    "video =  Dense(1000, activation='relu')(video)\n",
    "video =  Dropout(0.5)(video)\n",
    "\n",
    "main_output = Dense(n_classes, activation='sigmoid',name='main_output')(video)\n",
    "model = Model(inputs=[video_input], outputs=main_output)\n",
    "\n",
    "sgd = SGD(lr=0.01, momentum=0.9, nesterov=True)\n",
    "model.compile(loss='binary_crossentropy', optimizer=sgd, metrics=['binary_accuracy'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(None, 32, 512)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "video_input (InputLayer)     (None, 32, 32, 32, 3)     0         \n",
      "_________________________________________________________________\n",
      "time_distributed_79 (TimeDis (None, 32, 16, 16, 32)    896       \n",
      "_________________________________________________________________\n",
      "time_distributed_80 (TimeDis (None, 32, 16, 16, 32)    128       \n",
      "_________________________________________________________________\n",
      "time_distributed_81 (TimeDis (None, 32, 16, 16, 32)    9248      \n",
      "_________________________________________________________________\n",
      "time_distributed_82 (TimeDis (None, 32, 16, 16, 32)    128       \n",
      "_________________________________________________________________\n",
      "time_distributed_83 (TimeDis (None, 32, 8, 8, 32)      0         \n",
      "_________________________________________________________________\n",
      "time_distributed_84 (TimeDis (None, 32, 8, 8, 32)      0         \n",
      "_________________________________________________________________\n",
      "time_distributed_85 (TimeDis (None, 32, 8, 8, 64)      18496     \n",
      "_________________________________________________________________\n",
      "time_distributed_86 (TimeDis (None, 32, 8, 8, 64)      256       \n",
      "_________________________________________________________________\n",
      "time_distributed_87 (TimeDis (None, 32, 8, 8, 64)      36928     \n",
      "_________________________________________________________________\n",
      "time_distributed_88 (TimeDis (None, 32, 8, 8, 64)      256       \n",
      "_________________________________________________________________\n",
      "time_distributed_89 (TimeDis (None, 32, 4, 4, 64)      0         \n",
      "_________________________________________________________________\n",
      "time_distributed_90 (TimeDis (None, 32, 4, 4, 128)     73856     \n",
      "_________________________________________________________________\n",
      "time_distributed_91 (TimeDis (None, 32, 4, 4, 128)     512       \n",
      "_________________________________________________________________\n",
      "time_distributed_92 (TimeDis (None, 32, 4, 4, 128)     147584    \n",
      "_________________________________________________________________\n",
      "time_distributed_93 (TimeDis (None, 32, 4, 4, 128)     512       \n",
      "_________________________________________________________________\n",
      "time_distributed_94 (TimeDis (None, 32, 2, 2, 128)     0         \n",
      "_________________________________________________________________\n",
      "time_distributed_95 (TimeDis (None, 32, 512)           0         \n",
      "_________________________________________________________________\n",
      "lstm_6 (LSTM)                (None, 256)               787456    \n",
      "_________________________________________________________________\n",
      "dropout_13 (Dropout)         (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 1000)              257000    \n",
      "_________________________________________________________________\n",
      "dropout_14 (Dropout)         (None, 1000)              0         \n",
      "_________________________________________________________________\n",
      "main_output (Dense)          (None, 7)                 7007      \n",
      "=================================================================\n",
      "Total params: 1,340,263\n",
      "Trainable params: 1,339,367\n",
      "Non-trainable params: 896\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 10950 samples, validate on 532 samples\n",
      "Epoch 1/40\n",
      "10950/10950 [==============================] - 32s 3ms/step - loss: 0.4295 - binary_accuracy: 0.8276 - val_loss: 0.5076 - val_binary_accuracy: 0.8126\n",
      "Epoch 2/40\n",
      "   64/10950 [..............................] - ETA: 30s - loss: 0.4118 - binary_accuracy: 0.8147"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/rabreu/projeto_multimodal/venv/local/lib/python2.7/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n",
      "/home/rabreu/projeto_multimodal/venv/local/lib/python2.7/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10950/10950 [==============================] - 28s 3ms/step - loss: 0.4048 - binary_accuracy: 0.8328 - val_loss: 0.4280 - val_binary_accuracy: 0.8166\n",
      "Epoch 3/40\n",
      "10950/10950 [==============================] - 28s 3ms/step - loss: 0.3950 - binary_accuracy: 0.8355 - val_loss: 0.4045 - val_binary_accuracy: 0.8244\n",
      "Epoch 4/40\n",
      "10950/10950 [==============================] - 28s 3ms/step - loss: 0.3844 - binary_accuracy: 0.8389 - val_loss: 0.3936 - val_binary_accuracy: 0.8244\n",
      "Epoch 5/40\n",
      "10950/10950 [==============================] - 28s 3ms/step - loss: 0.3816 - binary_accuracy: 0.8403 - val_loss: 0.4008 - val_binary_accuracy: 0.8316\n",
      "Epoch 6/40\n",
      "10950/10950 [==============================] - 28s 3ms/step - loss: 0.3756 - binary_accuracy: 0.8422 - val_loss: 0.3892 - val_binary_accuracy: 0.8386\n",
      "Epoch 7/40\n",
      "10950/10950 [==============================] - 28s 3ms/step - loss: 0.3712 - binary_accuracy: 0.8427 - val_loss: 0.3993 - val_binary_accuracy: 0.8316\n",
      "Epoch 8/40\n",
      "10950/10950 [==============================] - 28s 3ms/step - loss: 0.3625 - binary_accuracy: 0.8470 - val_loss: 0.4301 - val_binary_accuracy: 0.8179\n",
      "Epoch 9/40\n",
      "10950/10950 [==============================] - 28s 3ms/step - loss: 0.3601 - binary_accuracy: 0.8483 - val_loss: 0.4055 - val_binary_accuracy: 0.8147\n",
      "Epoch 10/40\n",
      "10950/10950 [==============================] - 28s 3ms/step - loss: 0.3547 - binary_accuracy: 0.8502 - val_loss: 0.4191 - val_binary_accuracy: 0.8220\n",
      "Epoch 11/40\n",
      "10950/10950 [==============================] - 28s 3ms/step - loss: 0.3512 - binary_accuracy: 0.8518 - val_loss: 0.3899 - val_binary_accuracy: 0.8271\n",
      "Epoch 12/40\n",
      "10950/10950 [==============================] - 28s 3ms/step - loss: 0.3486 - binary_accuracy: 0.8520 - val_loss: 0.3933 - val_binary_accuracy: 0.8289\n",
      "Epoch 13/40\n",
      "10950/10950 [==============================] - 28s 3ms/step - loss: 0.3450 - binary_accuracy: 0.8535 - val_loss: 0.4243 - val_binary_accuracy: 0.8064\n",
      "Epoch 14/40\n",
      "10950/10950 [==============================] - 28s 3ms/step - loss: 0.3405 - binary_accuracy: 0.8569 - val_loss: 0.3947 - val_binary_accuracy: 0.8268\n",
      "Epoch 15/40\n",
      "10950/10950 [==============================] - 29s 3ms/step - loss: 0.3358 - binary_accuracy: 0.8578 - val_loss: 0.3709 - val_binary_accuracy: 0.8386\n",
      "Epoch 16/40\n",
      "10950/10950 [==============================] - 28s 3ms/step - loss: 0.3329 - binary_accuracy: 0.8588 - val_loss: 0.4372 - val_binary_accuracy: 0.8161\n",
      "Epoch 17/40\n",
      "10950/10950 [==============================] - 28s 3ms/step - loss: 0.3292 - binary_accuracy: 0.8596 - val_loss: 0.3877 - val_binary_accuracy: 0.8289\n",
      "Epoch 18/40\n",
      "10950/10950 [==============================] - 28s 3ms/step - loss: 0.3265 - binary_accuracy: 0.8604 - val_loss: 0.3780 - val_binary_accuracy: 0.8357\n",
      "Epoch 19/40\n",
      "10950/10950 [==============================] - 28s 3ms/step - loss: 0.3238 - binary_accuracy: 0.8626 - val_loss: 0.3953 - val_binary_accuracy: 0.8244\n",
      "Epoch 20/40\n",
      "10950/10950 [==============================] - 28s 3ms/step - loss: 0.3188 - binary_accuracy: 0.8653 - val_loss: 0.4082 - val_binary_accuracy: 0.8206\n",
      "Epoch 21/40\n",
      "10950/10950 [==============================] - 28s 3ms/step - loss: 0.3176 - binary_accuracy: 0.8645 - val_loss: 0.3674 - val_binary_accuracy: 0.8400\n",
      "Epoch 22/40\n",
      "10950/10950 [==============================] - 28s 3ms/step - loss: 0.3116 - binary_accuracy: 0.8674 - val_loss: 0.4366 - val_binary_accuracy: 0.8099\n",
      "Epoch 23/40\n",
      "10950/10950 [==============================] - 28s 3ms/step - loss: 0.3087 - binary_accuracy: 0.8686 - val_loss: 0.3864 - val_binary_accuracy: 0.8271\n",
      "Epoch 24/40\n",
      "10950/10950 [==============================] - 28s 3ms/step - loss: 0.3056 - binary_accuracy: 0.8700 - val_loss: 0.3503 - val_binary_accuracy: 0.8531\n",
      "Epoch 25/40\n",
      "10950/10950 [==============================] - 28s 3ms/step - loss: 0.3014 - binary_accuracy: 0.8734 - val_loss: 0.3859 - val_binary_accuracy: 0.8335\n",
      "Epoch 26/40\n",
      "10950/10950 [==============================] - 28s 3ms/step - loss: 0.2993 - binary_accuracy: 0.8724 - val_loss: 0.3926 - val_binary_accuracy: 0.8298\n",
      "Epoch 27/40\n",
      "10950/10950 [==============================] - 28s 3ms/step - loss: 0.2927 - binary_accuracy: 0.8765 - val_loss: 0.3755 - val_binary_accuracy: 0.8327\n",
      "Epoch 28/40\n",
      "10950/10950 [==============================] - 28s 3ms/step - loss: 0.2906 - binary_accuracy: 0.8777 - val_loss: 0.3988 - val_binary_accuracy: 0.8289\n",
      "Epoch 29/40\n",
      "10950/10950 [==============================] - 28s 3ms/step - loss: 0.2891 - binary_accuracy: 0.8762 - val_loss: 0.3670 - val_binary_accuracy: 0.8330\n",
      "Epoch 30/40\n",
      "10950/10950 [==============================] - 28s 3ms/step - loss: 0.2818 - binary_accuracy: 0.8810 - val_loss: 0.3948 - val_binary_accuracy: 0.8220\n",
      "Epoch 31/40\n",
      "10950/10950 [==============================] - 28s 3ms/step - loss: 0.2818 - binary_accuracy: 0.8813 - val_loss: 0.3735 - val_binary_accuracy: 0.8359\n",
      "Epoch 32/40\n",
      "10950/10950 [==============================] - 28s 3ms/step - loss: 0.2765 - binary_accuracy: 0.8819 - val_loss: 0.3853 - val_binary_accuracy: 0.8346\n",
      "Epoch 33/40\n",
      "10950/10950 [==============================] - 28s 3ms/step - loss: 0.2704 - binary_accuracy: 0.8865 - val_loss: 0.4028 - val_binary_accuracy: 0.8238\n",
      "Epoch 34/40\n",
      "10950/10950 [==============================] - 28s 3ms/step - loss: 0.2680 - binary_accuracy: 0.8873 - val_loss: 0.3711 - val_binary_accuracy: 0.8386\n",
      "Epoch 35/40\n",
      "10950/10950 [==============================] - 28s 3ms/step - loss: 0.2636 - binary_accuracy: 0.8891 - val_loss: 0.4040 - val_binary_accuracy: 0.8214\n",
      "Epoch 36/40\n",
      "10950/10950 [==============================] - 28s 3ms/step - loss: 0.2587 - binary_accuracy: 0.8914 - val_loss: 0.4005 - val_binary_accuracy: 0.8300\n",
      "Epoch 37/40\n",
      "10950/10950 [==============================] - 28s 3ms/step - loss: 0.2544 - binary_accuracy: 0.8923 - val_loss: 0.3760 - val_binary_accuracy: 0.8330\n",
      "Epoch 38/40\n",
      "10950/10950 [==============================] - 28s 3ms/step - loss: 0.2497 - binary_accuracy: 0.8947 - val_loss: 0.3878 - val_binary_accuracy: 0.8300\n",
      "Epoch 39/40\n",
      "10950/10950 [==============================] - 28s 3ms/step - loss: 0.2425 - binary_accuracy: 0.8989 - val_loss: 0.3954 - val_binary_accuracy: 0.8303\n",
      "Epoch 40/40\n",
      "10950/10950 [==============================] - 28s 3ms/step - loss: 0.2389 - binary_accuracy: 0.8996 - val_loss: 0.4120 - val_binary_accuracy: 0.8281\n"
     ]
    }
   ],
   "source": [
    "metrics = custom_metrics()\n",
    "hist = model.fit(x = train_x,\n",
    "                 y = train_y,\n",
    "                 validation_data=(eval_x,eval_y),\n",
    "                 batch_size=32,\n",
    "                 epochs = 40,\n",
    "                 callbacks=[metrics])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "History keys: ['loss', 'val_binary_accuracy', 'val_loss', 'binary_accuracy']\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f14fe2e0110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f14ec4ab650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_history(hist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f14ec3eb450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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rVsGKZdB1O6RZbcshp0bkPW3MDXPZlMrYXFUl4atORwYNJXT6JbFfv+ESaN6C0P/dUe/fR77ct1RrzLEFjdT19mRoclNt5Kz16yAchhZFqTlfUZAEK9em5nwqbVzlWio/eDupXnMxz/fpdKiqRCKqlyJJiyJCp17g27MmP5+Sa6r8pAkiU2oGtqUqQTRr5tssKjRB5Dr3+H2svuV6WNCgPhe/nO/DKdBxS+jZu9Z9pN9AZOB+uJct7ufN27yUikeTSxA5W2VWVQkFBZCiwXkiAkUtobIi7e85Z+9pI+A+m4mb4TsTuLlfNPx8a1bC7E+Rwfsjobr/+8pJ50Kz5oSfuFf/DVVSmlyCCIVCGW1biFtVJbRomdqZUVu2ijntRipVV1cTqueDSMXm1pYTfuJe6LotBVt3wX3zZcPPOeNdcOFaq5ciSfuOyHGnw9wv/FxNEVO7pIoLh3E6HXyTlUsjqVOiqKiIyspKqqqq6vwwbtGiRcZWbXPr1+G+/hJ67ECoTf3zI8Ubm6vegJv3DaxbT6jbdimINOr8ESvKqcS5Zx+G1SsJXXQ9zWe+Q8V7b+LCG5BQ8otBuQ/fhh47Il16xLW/DP217/Y66RnCX1yMHHYMcvgJKZkN2FWsJXzHDX66mMv/jLRq3eBzZpL737dQvPXGcSRqc00uQYhIXKufZbIHgvt8NuHxDxD67V+ROHpYxR9bKza8/yY0a07B7zabPb1JceVlMPcL6Dcw7b22UsF99Rnu3deQw45Ftu9Fs4pSKl6fCAv/B9vumNw5f1oI879DTjon7mMkFEKONLi9D8K98Bhu0jO4aW8ix52ODDmg3mqqWmOpqiJ8900w/zsAwnffROiyPyMRgy9zlStdjRs/zlf97bEPBRdem+2QcpbWHWSA+34uhEKwXS3jHxpAeg+AH+b60ddNkPvhW8KP3En4mjN9Ncl/Ys94m0tcVSXhx+6GrbogI/yEAc377O5fa0A7hPvwbZAQstfQhI+VjsWEzrmS0LW3QMdi3MN3EB5zDe77rxOPo3o94fvHwLdzkHOuRM65Er77ivADf8dVr0/4fJninCP84RTCf7wI99E06L49fDYDV7om26HlLE0QGeDmfQ3dtvdzL6WY9NkdNmzw366bCFdVSfjd19jwlysJ//Uq3KxpyJADkf0Oxb3zql8gJ4e5/z4By5cQOuMSJJiQsaDTlrBV8u0QLhzGTX8H+gxA2nVIOjbZcRdC196CnH0FrFxOeMw1rL79T7hVK+KMYwPuoTvgy4+QURcR2utXhPb6FTLqQvhiFu6hO3DhDUnHt8m1lv7Emnv+RnjaG37cT0POVbKU8F1/xv37dtiqC6H/u4PQOVfChmrc9LcTP195KWX24SafXJpcFVOuceEN8MO3yD4HpucCO+wCLYpwcz5BBjTuKTTcz4twUybhPpgCFeXQpQdyyvnI4AOQVq1x1dW4xfMJP3Y3oW17Ilt2jvvc4bdfwX35EbLbIGTAEKRteuqd3fdf4958ETngCGSnvpu8Jjv3xc2allw7xHdzoGQpcsyoBscooRCy94G43YfgXnmOytf/CzOnIceehuz/61pjc87hnrgPN/Nd5ISzCA399cbXQkOHES4vxz3/KLRqDade2KAOGa6ygvA9N1Px4wL/XAR22Nn/+/UfBF26x3V+F97g/6ZeeNy/95HnIQce8ct73LYnbtqbcMjRdZwlxnlfeY7yyc/D6xMJ/eb/4m4Tamw0QaTbjwugqsJ/kKeBNGsGO/XFzfksLefPFLd4AeGbr/Q9dPbYFzngcOi56yYfAlJYSGj01YRvvJzwg/8gdM3fkML62yPCU17GPfUAtGyF+2wG7ol7Yed+yJ77IrsPRrZI/hv5Ju9h/XrCj/4LOhQjx5+++Q4794N3X0uqHcJ9+Da0KEJ2H5KSWAGkqCVy7CjaH3k8JXf/FffU/bgP3iI06kKkx6bxOedwzz7i21WOMIR+fexm5wsdfjzhtWW4V5+DVm18D6okOOdwj90NPy2i/Q13sCaM/3f7bAbu+cdwzz8GW3ZG+u+F9OoNEsKvUlzzy4FzuA0bcG+9BPPmQt89CI26yE+MGXkP9j3Ev+8F32/2nmuNr6oS9+5kmu3Sj/U/LiQ85hpCo69G+u6Z1PvNZZog0sx9PxcA2WHntF1Deg/AfTELt3wJUrx12q6TLq56PeF//xOKWhL6w21Ix9qnIpHirQmdcQnh+/+Oe+EJ5MSz6jx3eOqrPjkMGEzo/GvgxwW4j9733+SfuBf35P3Qqzey5z5+2uwt2if/Pl76D/y00K/HULR5ZwTZqS8O3w4hCSQIt36dr2bbfe+0VFMWdulO6IobcTOm4v4zjvBfrkIOPgoZcfLG9+EmPYN77QX/7fuYU2s9lxx3Oqwtx73yLOHWbQj9+riE43GvT/CllOPOoMWAQcjy5UiPHeCokbiVJbjPZ/pk8c6ruDdfrPtkbdoi51zhS6ExShwyaCjO/hv33hvIKXEmiA/egrXltDnjElaHCgn/6y+E77oJOelc5KAjU9uVPcs0QaTb91/79R4SqA5JlPQe4D945nzquzU2Mm7CU7DwB0IX/77O5FBD9twX2X+Yn5Bul/5Iv9jf3MLvvY57/F7oN5DQ6Gt8aaPHjkiPHXHHjPLLdH70Pu7j93FPj8U99yhy0HBk2HEJT5XtFszDvfossvdBSN89YsfdodMv7RCHHRP/yT+fBRXlcY19SJaIIIP3x/Xd039Lf2MC7qNphEaeh1tVgvvvE77X08jRdX4Aigicej5UlOOefYRwy9abVEXVx839AvfcI7DH3siwzZOLdOiE7D8M9h+Gq6qEJT/WXDhiZiEJpsgX6FQcM1lv3LN1G2T3IT45nnh2vT3kXDiMe+NF2K4XzXbui5SUEPrdGML/vg03fiz8tABGjkbSsFplNjSNd5HD3Ly5vu40nd8qtukO7TvBnE+hAQnCVa/Hvfs6FBYi7TtCuw7QriO03aJBfffrvOY3s3GTn0d+dVhCbShizsF9/zXhh24ndMOdSPtOm7we/mCKr6bovTuhC6/d7D++iEC37ZBu28GIU3CL5+MmPYub/DzunVeQQ0Ygh47w07DXFb9zsGAe4UfvgjZb1NsFNZl2iPCHU/y/xa7949q/IaR1G+S0i3D7HET48XsI3/c3/8KAwciZl8XVLVZCBXD25biKtbgn7iVcvd63ydRzrFuxnPADt/hG5DMvq/f/jLQogh47xP3eaj3PvofgZr4Ln02HgfvVvfPsj2HJYuTcqzbGJ0UtCV14He6Fx3GvPodb+hOh83+HtG7T4NiyTRNEGrmyNf6Pad+D03odEUH6DMB9Mr1BA7Hc5Bd8Dxw21uh6oRC0bQ/tOrB6ux1xI0Y1qCpm4/Uq1hJ+6HY/WMnE37cfQJq3IDT6GsJ/uYLwuNsIXXnjxtfC09/BPXwn7NKf0MXXI82a13++rtsi512FO+IEwhOfwr34NO6tl3xp4sAjN6nacZVrYc6nuM9n4b78GFavgFCI0IXX1V/ySLAdwq1aAZ/P9MkqTUk6FtlxF0J/uB331ouw5EffuFsQ//WlsBmhC64lfP/ffOls5ruETru41sZctz7oOrtuHaGrr683MafUrv2hQzHhaW9QUE+CCL8xEdp3RPbcd5PtEgohx59BuHM33OP3EP7b1b7xeuv4lvbMVZog0umHbwCQNDVQb2LXATDtTZg/D7ZPfKVWV7IMN8nCgCGETjoHVq+E1Stwq1f5D8DVK3GrV1L54Tsw5zNCl96AbNOtQSG78Q/CiuWEfjcmqZG9sk035JQLcI/ciXv5GTjrEtys93AP3Q69ehO6+A8bu5nGfc6u21Jw4XW4+d8R/u+TuOcexb0+ATn8eN/2+cUs+GY2bKiGlq19N+N+A5G+e8SVNBNth3DT3oBwGPlV5qsOpbAQOWzzxui4j2/RgtClN+Defwv3zEOEb7zcj+I+4sTNSnRu/IPwwze+tNfAv6uE4wwVIHsfhHvlWdzKEl8VGINbPB/mfOp7e9VShRTa92Dclp0J3/dXwjdeiuz1K/9vl0AtgnN+ZUk6FGc2UcagCSKN3Pdfp22AXLRf2iE+QZJIEOFn/g1AaOS5vqdH0Ngd/SfdbsVSVvzlKsJjriZ00fXIzv2Sitd9/D7u/TeRIw2yY/IJVPY5CL7+DPfieMpaNCf8/OOww87+21sDRvXKtj0puOwG3LdzCP/3Cdx//P1hm+7IIUch/faCHXdJuK45kXYIFw7j3nvd97hqpN9ERQTZ92Bcvz19Y/BL43GzgtJE0A04/N7ruKmvIsOOR/bYJztx7nsQbpLFffAWcsSJMfdxb74IzZvX284nO/Uh9PvbfMP+jKm+G2237ZChv/aTLLbavOrJVVX5v+PPZ+I+nwWrSqBdB0KnX4JELQqVSZog0sjNm+v/MNLQ8ySatG0HPXbEzfkEjjQJHevmfAofvY+MOHWzboDRmu3Um9D1/yB8142Eb78BOeM3hPZObIyHW7WC8OP3wLY9keEjEzo2mm8UvQA37xvKn3nEJ4dLb0jJXEMA0qs3od/e7Kfqbt0mJb3EZOe+uI/iaIf4+jNYviQlYx+yTbZoj5x7FW7IgYSfuJfwrdf7dqeB+/meZLvultX3KVt1gZ364Ka9iTv8hM2+7bvS1bgPpiD7HBzX3E1SvDVy+iU4czZu+lRc0JvOPfswMvBXPsm074T7IkgIX3/u14wpagl9dkd27o975xXC/7rJJ5YTz07Z33QiNEEkwVWshWbN6uyD78IbYN43SIIfng0hvQf4LoKVa+vsuRHJVa8n/PRY3688Rt/2mNcp3prQtX8nfN8Y3EO3E172E3LUyfENXHLOjxVYV0XonCtT0ttDiloRuvh6imZOpfLQY1JeLBeRpOdPimmnvr4dYtH/oI6+927qa9C6LbLH3qm7dpZJ3z0I/flu38bz+gTcu69Bxy0JnXd1Qm0caYltn0Nwj9wJ330FvTZda8O98ypUr0cOHp7YOYta/dLrav53uKmTfcJ4/81fdtqys08C/feCnfps/Fxx+x2Km/Ck76331WeEzr4cqWMNkHTQBJGE8D//AEDoqr/U/mH040I/QG7H9I1/iCa9B/hBSnNnw27xFUvdmy/Cz4t8lUwcjbkbr9WqDaHLbsA9fi/uxfGwbAmcfkn93QTfecVP03DK+Smta5YuPWh7zuVU5egSkJF+aYf4stbBWW7NKtyn030DeQL/Lo2BtChCTjgLN2gobvILyK+PS9vI9oTi2nMf3NMP4Ka94QfgBVz1etzbk/w3+waMmJZteyKn9cSdeBZu5ntQsRbpNxA6d409RqNZM+SEM3H99yL88B2Eb7keGXYsctQpGZuwUudiSpALh2HxfJj/HeF7bsatXxd7v3l+ErSMNFDX6NkbmjfHfTglrjn63coS/+G+26Ck6jmlsBly5qXIiFNxH04hfMcNuPLSza/jnK9P/2kR7pmH/H+0A45I+HpNhXQshq22qXPiPvfBW7ChGhl6WAYjyyzpsSOh837rB8HlAClq6au8Zk3DVVZs3O5mveenbU9wOo7ar9OK0K8OI3TYMb6jRX3deXfq47ty73cI7pXn/Pxki/6XkljqoyWIRJWuhur1sOtu8PXnhB+4xXdvjC4efz837QPkokmzZsjBR+FeeQ63fp2vwqmjusU9+zBs2EDopHOTv6YIMvwkwsVb4x69i/AVo/BN2366g820bkvozEub1GjTZMjO/Wpth3DO+eqlnr2RbbpnKcL8JPsegpv2Bu6j933junO41yf6sUZ9Yg+AzEhcRa18m8Zugwg/+i/CN1/p2/+GpLcKWxNEokqWAhA6+Gg/2dlTD+Ae/ReceekmA4HcvK/TP0AuBjn2dOhQjBv/oJ8j5uLfI1tts9l+bu4XuBlTkeEjE5r0rjahIQfgtu6C+3R6TSQ+T0jEqFYRP9la+9jdCPNKXe0Q33wJS39EEuxsoFKg566+l9n7b8C+B8O3c2DB98ioi3LiS43sNsi34Tw9FumewnaxWmiCSJALEgSdtiS0216Ey0pxE5+C1m3AnIOI+GqWnxcjex+U8fhEBDnwSFznboQfuIXwX39L6PxrkF13++U9VFf7hulOWyHDjk/dtbffCdnpP+2zAAAbrElEQVR+p5Sdrymrqx3CTX0NWrVGBu4b+2CVNiKC7HMQ7r9P4Jb+RPjNib6jQJq/qSdC2rZDRl+dkWtpG0SiNiYI3x1Uhp/kq3XemIh72frX5gUD5BrQv7+hZNfdCF3/D9iiPeE7biD81ksbF653b78Mi+cTOuncRrECWFNUWzuEK1uD+3ian1wuwUF+KjVk74NAQv6L3yfTfQ+jPP1/oiWIRJUs81MZB3X7IgLmHCgvw014knCbtn4UcigE2yU+YC2VZKttCF13q59I7OmxvjrjyJNwE5+GvntAI18/orGL1Q7hPpwC1dWNctLFpkI6FkOfAX6BpoKCvO5QkbEEYYwZBtwJFADjrLVjol4/E7gVWBxsuttaOy5T8cXLlSyFTpvOOCqhEJzxG1xFuZ9aeosOGRsgVx9p2YrQRdfjJjzlR4rOeBeq1xM66bycqFPNa1HtEBsbp7ffyU8iqLJG9jkE9+XHfubgjsXZDidrMlLFZIwpAO4BDgd6AycbY2KN+PiPtXZA8JNzyQHwVUydNh9NW7OYDb16w+oVaV3/IVESChE6dhRy3m/BbUCOOAHp3DXbYeW9mqkm3NxgGdLvv4KfFmrpIQfIgMHBmhinZDuUrMpUCWIQ8J21dh6AMWY8MAKYk6Hrp4Rzzi/7GNHgG0matyB08R9w4x9E9knvDK7JCA0aihswGJrYwKvGapN2iENH4KZOhqKWyF6/ynZoeU+aNUNGnpftMLIuUwmiK7Aw4vkiIFYF+PHGmKHAN8AV1tqFMfbJnvJSqKrc2EAdi7RqjZx9eQaDSow2fOaWje0QZWv8qnH7HJQTVZNKQW41Ur8IPG2trTLGnA88CmzWT9QYMxoYDWCtpbg4ufrBwsLChI9dv7qEFcAW2/ekKMnrxiOZ2DJFY0tObbFV7Lk3a959jeYvjady/To6HHUSzTL8HhrjfcsF+RBbphLEYiBySGg3fmmMBsBaWxLxdBxwS6wTWWvHAmODp255knPvFBcXk+ixNetLlzYroiyNc/4kE1umaGzJqS02t822AFS++RL02JHV7TpBht9DY7xvuaAxx9alS3zTx2dqHMRMoJcxZntjTHNgJDAxcgdjTORw36OBrzIUW9xcyTL/oLjuKbGVildNOwSgjdMq52SkBGGtrTbGXAJMxndzfchaO9sYcyMwy1o7EbjUGHM0UA2sAM7MRGwJKVkKLVpCjAU/lEqW9B6AW7MKGTQ026EotYmMtUFYaycBk6K2/THi8XXAdZmKJxk1YyB0/IBKJTnuDCQN61go1VC51Eid+0qW1tmDSalkSMtWoMlB5SCdiykRJUvrXZJTKaWaCk0QcXIVa2FtuTZQK6XyhiaIeNXM4tpRE4RSKj9ogohXkCAkaqI+pZRqqjRBxGnjQkFaxaSUyhOaIOJVstRPcte2fbYjUUqpjNAEESdXshQ66hgIpVT+0AQRr5JlOgZCKZVXNEHEq2SpNlArpfKKJog4uKoqKF2tJQilVF7RBBGPFcEsrpoglFJ5RBNEPEqWAOg0G0qpvKIJIg4b14HQBKGUyiOaIOJRsgQKCqB9h2xHopRSGaMJIh4ly/wYiFBBtiNRSqmMSTpBGGN2MMZsl8JYclbNIDmllMoncScIY8zTxph9gsdnAbOB2caYc9IVXM7QdSCUUnkokRLEwcCs4PGVwCHAIODaVAeVS9z69bB6pTZQK6XyTiJLjja31q4zxnQFOlprpwEYY7ZOT2g5YuUycE4ThFIq7ySSID41xlwHbAu8DBAkizXpCCxnBF1cRaf5VkrlmUSqmM4B+gEtgT8E2/YGnkx1ULlk4zoQ2kitlMozcZcgrLXfA6dEbXsWeDbVQeWUkqUgIehQnO1IlFIqoxKpYsIYcxgwAGgTud1a+8dUBpVTSpZCh45IYUK3SimlGr24P/WMMXcDBpgCrE1bRDnGlSyDjtr+oJTKP4l8LT4F2M1auzBdweSkkqVIz12zHYVSSmVcIo3Uy4FV6QokF7kNG2DlcujUtHvyKqVULImUIP4JPGmM+RuwJPIFa+28+g42xgwD7gQKgHHW2jG17Hc8vuF7L2vtrFj7ZMyqEgiHQVeSU0rloURKEPcBw4FpwHcRP9/Wd6AxpgC4Bzgc6A2cbIzpHWO/tsBlwPQE4kqfoIurjoFQSuWjRLq5NmTm10HAdzUlDWPMeGAEMCdqv5uAvwNXN+BaKbNxHQhtpFZK5aGE+24aY3oAXYFFCTRYdwUi910EDI467x5Ad2vty8aYWhOEMWY0MBrAWktxcXLjEwoLC+s9tqyijHKgeKddkOYtkrpOMuKJLVs0tuRobMnR2JKTqtgS6ea6DTAeP3q6BOhkjPkQGGmt/bEhQRhjQsBtwJn17WutHQuMDZ665cuXJ3XN4uJi6js2vPB/0K4DJWtKgdKkrpOMeGLLFo0tORpbcjS25NQXW5cuXeI6T6JtEJ8BHay12wAdgE+A++M4djHQPeJ5t2BbjbZAX+BtY8z/gCHARGPMwATiSzldB0Iplc8SqWLaD9jGWrsewFpbboy5hk0/6GszE+hljNk+2H8kEdN2WGtXAxvLQ8aYt4HfZr0XU8lSZNueWQ1BKaWyJZESxEp8D6RIOxPH2AhrbTVwCTAZ+MpvsrONMTcaY45OIIaMceEwrFimJQilVN5KpARxC/CGMebfwHz8tN9nAf8Xz8HW2knApKhtMedwstYekEBc6bFmJVRX6zoQSqm8FXcJwlr7IHASviroqOD3KUGjcdOj60AopfJcQt1crbVvAW+lKZac8ss6EJoglFL5qc4EYYz5vbX25uDxjbXt1ySn+65JEDrNhlIqT9VXgugW8bh7rXs1RSVLoU1bpKhltiNRSqmsqDNBWGsvjHh8VvrDyR1+DIRWLyml8lciI6l7AyXW2iXGmDb4+ZLCwK3W2qa3gFDJMtimW/37KaVUE5XIOIingfbB438AQ/Ejnh9IdVDZ5pzzg+S0BKGUymOJ9GLazlo71xgjwHH4QXMVwA9piSybytbAuirQLq5KqTyWSAmiMlivYRCwwFq7HKgCitISWTbVrAOhPZiUUnkskRLEU/gxEG2Bu4Nte9AUSxC6DoRSSiU0kvoK4PfAhdbamgQRBq5IR2DZ5EqCFVW1ikkplccSHUn9WtTz7M62mi4ly6BlK6RVm2xHopRSWVPfSOpXrbXDgsfvAi7WftbaoWmILWvcqhJo3ynbYSilVFbVV4J4LOLxuHQGklPKSqHtFtmOQimlsqq+kdRPRTx+NP3h5IjyUthym2xHoZRSWRV3I7Ux5i5jzD5R2/YxxtyR+rCyrLwUadM221EopVRWJTIO4mQgulH6IyKWDm0KnHO+iqm1JgilVH5LJEG4GPsXJHiO3LeuCqrXa4JQSuW9RD7c3wX+YowJAQS//xRsbzrKSv1vrWJSSuW5RMZBXAa8BPxkjJkP9AB+wi8/2nSU+wQhWoJQSuW5REZSL8JPrTECuBU4Btgz2N50lGsJQimlIPH2gwKgGRCy1n4ItDTGtE59WNnjaqqYtAShlMpziXRz7Qd8AzwI/DvYvD/wUBriyp7yNf63JgilVJ5LpARxH/BHa+0uwPpg2zvAfimPKpvKy/xvrWJSSuW5RBJEH+CJ4LEDsNaWAy1THVRWlZVCi5ZIYbNsR6KUUlmVSIL4H7Bn5AZjzCDgu1QGlHXla6C1zuKqlFKJdHP9P+BlY8z9QHNjzHXABcB58RxsjBkG3Ilv6B5nrR0T9foFwMXABqAMGG2tnZNAfCnhysu0ekkppUism+tLwDBgS3zbw7bAcdFrRMRijCkA7gEOx69lfbIxpnfUbk9Za/tZawcAtwC3xRtbSpXrNBtKKQVxliCCD/iH8N/qL0riOoOA76y184LzjcePp9hYQrDWronYvzW1rD2RdmWlSPfirFxaKaVySVwJwlq7wRhzGH6J0WR0BRZGPF8EDI7eyRhzMXAl0Bw4KNaJjDGjgdFBXBQXJ/dhXlhYGPPYpWvLKCreki2SPG8q1BZbLtDYkqOxJUdjS06qYkukDeJ24M/GmBustevr3TsJ1tp7gHuMMacAfwDOiLHPWGBs8NQtX748qWsVFxcTfawLh3FlpVQWNGNdkudNhVix5QqNLTkaW3I0tuTUF1uXLl3iOk8iCeI3QGfgSmPMMiKqgKy1Peo5djHQPeJ5t2Bbbcbjx11kVsVacGFtpFZKKRJLEKMacJ2ZQC9jzPb4xDCSqHUkjDG9rLXfBk+PBL4l02pGUbfSBKGUUnEnCGvtO8lexFpbbYy5BJiM7+b6kLV2tjHmRmCWtXYicIkx5hD8KO2VxKheSrtgHiZdTU4ppRJIEMaY5vh2gZOBLsCP+Kqgm621lfUdb62dBEyK2vbHiMeXxRtL2tRMs6HdXJVSKqEqpvuAnYFLgfn4cRDX43sonZ360DLP6UR9Sim1USIJ4hhgR2vtquD5HGPMdPxUG00iQehqckop9YtE5mL6GWgVta0lflW5pqG8DESgVZNa4kIppZKSSAniceBVY8y/8APduuPnTnrMGLNxUJu19q3UhphB5WugVRskVJDtSJRSKusSSRDnB7+vj9p+QfADfmzEDg0NKmvKSnUmV6WUCiTSzXX7+vYxxnSvb59c5nSiPqWU2ijRNanrMzvF58us8jJos0W2o1BKqZyQ6gQhKT5fZpWtQbQEoZRSQOoTRHam6E6Vcm2DUEqpGqlOEI2Wq66GygodA6GUUgFNEDXWBoPkWmsbhFJKgbZB/KJmFLVWMSmlFJD6BBG9znTjoTO5KqXUJuocB2GMWUgcDc81CwZZaxfWt2/O0iompZTaRH0D5RqySFCj4nSiPqWU2kSdCaIhiwQ1OuXaBqGUUpESmYsJY8wA4FdAMREN0pEL/zRaZaVQUAgtWmY7EqWUyglxN1IbY0YD04CDgN8B/YCrgJ7pCS3DykuhTVtEGm9HLKWUSqVEejFdAwyz1h4LVAS/T8CvId3oufJSaKXVS0opVSORBLGVtfbd4HHYGBOy1r4CHJWGuDKvrFQbqJVSKkIiCWKRMaZmyu9vgBHGmF8B61IfVhaUl2oXV6WUipBII/UtwC7AD8CNwLNAc+DSNMSVeeWlyPY7ZTsKpZTKGYmUIAYAJQBB1VIHoIO19r50BJZJzjldTU4ppaIk1M0V+K8xphx4CnjSWvtNGmLKvHVVUL1eq5iUUipC3CUIa+3lQDfgIqA7MN0Y85Ex5sp0BZcx5TqKWimloiVUgrDWhoHXgdeNMf8HPAzcCtyWhtgyp2aiPq1iUkqpjRIdSd0aOBY4GTgAeAc4I85jhwF3AgXAOGvtmKjXrwTOBaqBZcDZ1tr5icSXtHKdqE8ppaIlMpL6GWAJMBp4CdjWWnuEtfaJOI4tAO4BDsdPCX6yMSZ6avBPgIHW2v74HlK3xBtbQ+lEfUoptblEShAzgaustQuSuM4g4Dtr7TwAY8x4YAQwp2YHa+2UiP0/JJMzyW4sQWiCUEqpGnEnCGttQ77RdwUi14pYBAyuY/9zgFdivRDMCTU6iIni4uKkAiosLNx4bJnbQDlQvO12SLPmSZ0vlSJjyzUaW3I0tuRobMlJVWyJdnNNO2PMKGAgsH+s1621Y4GxwVO3fPnypK5TXFxMzbHhpUugRRElq9ckda5Ui4wt12hsydHYkqOxJae+2Lp06RLXeTKVIBbju8bW6BZs24Qx5hDg98D+1tqqDMUWTLOh1UtKKRUpUwliJtArmMtpMTASOCVyB2PM7sAD+Bljl2YoLiCYyVUbqJVSahOJTLWRNGttNXAJMBn4ym+ys40xNxpjjg52uxVoAzxjjPnUGDMxE7EBWoJQSqkYMtYGYa2dBEyK2vbHiMeHZCqWzZSVIt1zs7FJKaWyJSMliJynVUxKKbWZvE8QLhyG8jJopQlCKaUi5X2CoGItuLCWIJRSKoomCB1FrZRSMWmCCBKEaAlCKaU2oQmiTEsQSikVS94nCFceTK+hCUIppTaR9wmC8jL/W6uYlFJqE5ogykpBBFq1znYkSimVUzRBlK+Blq2RUEG2I1FKqZyiCaJMR1ErpVQseZ8gXHmZNlArpVQMeZ8gdCZXpZSKTRNE2RodJKeUUjFoglirVUxKKRVLXicIV13tJ+vTEoRSSm0mrxMEa3WaDaWUqk1+Jwidh0kppWqV3wkimGZDG6mVUmpzeZ4gdKI+pZSqTV4nCKdVTEopVau8ThAbV5PTKiallNqMJoiCQmjRMtuRKKVUzsnvBFFWCq3bICLZjkQppXJOXicIp/MwKaVUrfI6QVBepu0PSilVi8JMXcgYMwy4EygAxllrx0S9PhS4A+gPjLTWPpv2oMrWwJad034ZpZRqjDJSgjDGFAD3AIcDvYGTjTG9o3ZbAJwJPJWJmAAoL0W0ikkppWLKVAliEPCdtXYegDFmPDACmFOzg7X2f8Fr4UwE5JzT1eSUUqoOmUoQXYGFEc8XAYOTOZExZjQwGsBaS3FxcVIBhaqroXo9rbfqTOskz5EuhYWFSb+vdNPYkqOxJUdjS06qYstYG0SqWGvHAmODp2758uVJnacDGwAoJ0RFkudIl+LiYpJ9X+mmsSVHY0uOxpac+mLr0qVLXOfJVC+mxUD3iOfdgm1ZEy5dDehEfUopVZtMlSBmAr2MMdvjE8NI4JQMXTsmV1ozUd8W2QxDKaVyVkZKENbaauASYDLwld9kZxtjbjTGHA1gjNnLGLMIOBF4wBgzO50xhWsShJYglFIqpoy1QVhrJwGTorb9MeLxTHzVU0bUVDHRuk2mLqmUUo1K3o6k/iVBaBWTUkrFkrcJwpWtgRZFSLNm2Q5FKaVyUt4miHDpGq1eUkqpOuRxglitM7kqpVQd8jZBuNLV0EbbH5RSqjZ5myDCZWt0oj6llKpD/iYIbYNQSqk65WWCcOGw78WkXVyVUqpWeZkgqFwL4bCOolZKqTrkZ4IoK/W/tYpJKaVqlZ8JotwnCNEqJqWUqlV+JoiaEoRWMSmlVK3yMkG48poqJk0QSilVm7xMEGiCUEqpeuVlgpBOW9Ji8FBo3TrboSilVM5qdGtSp4IMGEL7Q4bn7HqySimVC/KyBKGUUqp+miCUUkrFpAlCKaVUTJoglFJKxaQJQimlVEyaIJRSSsWkCUIppVRMmiCUUkrFJM65bMfQEI06eKWUyiKpb4fGXoKQZH+MMR815Ph0/mhsGpvGprFlILZ6NfYEoZRSKk00QSillIopnxPE2GwHUAeNLTkaW3I0tuQ0+dgaeyO1UkqpNMnnEoRSSqk6aIJQSikVU14uGGSMGQbcCRQA46y1Y7Ic0kbGmP8BpcAGoNpaOzCLsTwEDAeWWmv7Bts6Av8BtgP+Bxhr7cocie1PwHnAsmC36621k7IQW3fgMWBr/FidsdbaO3Ph3tUR25/I8r0zxhQBU4EW+M+mZ621NxhjtgfGA52Aj4DTrLXrciS2R4D9gdXBrmdaaz/NZGwRMRYAs4DF1trhqbhveVeCCG7iPcDhQG/gZGNM7+xGtZkDrbUDspkcAo8Aw6K2XQu8aa3tBbwZPM+GR9g8NoDbg3s3IBvJIVANXGWt7Q0MAS4O/sZy4d7VFhtk/95VAQdZa3cDBgDDjDFDgL8HsfUEVgLn5FBsAFdH3LesJIfAZcBXEc8bfN/yLkEAg4DvrLXzgmw6HhiR5ZhykrV2KrAiavMI4NHg8aPAMRkNKlBLbDnBWvuTtfbj4HEp/j9tV3Lg3tURW9ZZa521tix42iz4ccBBwLPB9mzdt9piywnGmG7AkcC44LmQgvuWj1VMXYGFEc8XAYOzFEssDnjNGOOAB6y1udaVbmtr7U/B45/xVRW55BJjzOn4ovZV2aj+imSM2Q7YHZhOjt27qNj2JQfuXVDC/wjoiS/pfw+sstZWB7ssIksJLTo2a+10Y8yFwM3GmD8SlAqttVVZCO8O4BqgbfC8Eym4b/lYgsh1+1lr98BXgV1sjBma7YBqY6115NC3KOA+YEd8FcBPwD+zGYwxpg3wHHC5tXZN5GvZvncxYsuJe2et3WCtHQB0w5f2d8lGHLFEx2aM6Qtch49xL6Aj8LtMx2WMqWmL+yjV587HBLEY6B7xvFuwLSdYaxcHv5cCL+D/k+SSJcaYbQCC30uzHM9G1tolwX/iMPAgWbx3xphm+A/gJ621zwebc+LexYotl+5dEM8qYAqwN9DeGFNT25H1/68RsQ0LquxcUGp4mOzct32Bo4MOLuPxVUt3koL7lo8JYibQyxizvTGmOTASmJjlmAAwxrQ2xrSteQwcBnyZ3ag2MxE4I3h8BjAhi7FsoubDN3AsWbp3Qf3vv4GvrLW3RbyU9XtXW2y5cO+MMVsaY9oHj1sCh+LbSKYAJwS7Zeu+xYrt64iEL/g6/ozfN2vtddbabtba7fCfZ29Za08lBfctL0dSG2OOwNfZFQAPWWtvznJIABhjdsCXGsC3Dz2VzdiMMU8DBwDFwBLgBuC/gAV6APPxXTUz3lhcS2wH4KtIHL4b6fkRdf6ZjG0/4F3gCyAcbL4eX9ef1XtXR2wnk+V7Z4zpj29MLcB/ebXW2huD/xfj8VU4nwCjMl3PX0dsbwFb4mdH/RS4IKIxO+OMMQcAvw26uTb4vuVlglBKKVW/fKxiUkopFQdNEEoppWLSBKGUUiomTRBKKaVi0gShlFIqpnycakOpnBJMefED0CxiagSlsk5LEEoppWLSBKGUUiomHSinVAzGmC7Av4ChQBl+Xv27goV1+uIXdDoC+BY4y1r7WXDcrviJ7wbg5765zlo7MXitJfAX/PQH7fGjmQ/Fz+r6A3AmcBPQKrheTozwV/lLE4RSUYwxIfycXROAMfiJzt4ALsRPHvd7/NQUE/CLtFwM7BQc/hXwEPAPYL9gn4HW2rnGmHuAPsCp+Om+B+Onj94GnyDGAZcG55oBDLDWRi4Ao1RGaYJQKooxZjDwjLW2R8S26/Af3PPxs3gOCbaH8CUFE+z6DNAlmBW1Zs6oucCNQDkwpKa0EXHu7fAJoru1dlGwbQZwm7V2fLrep1L10V5MSm1uW6CLMWZVxLYC/CR384lYcMpaGzbGLAK6BJsW1iSHwHz8Qi3FQBF+AZza/BzxeC3QJul3oFQKaIJQanMLgR+CtaM3EbRBdI94HsJXQf0YbOpujAlFJIkewDfAcqASvyjPJiUIpXKVJgilNjcDKDXG/A64C1gH7Aq0DF7f0xhzHH59h0vxC9p/iJ/yeS1wjTHmn/iFXI4C9gpKGg8BtxljTsNPUT4I+Dhzb0upxGg3V6WiWGs3AMPxPZF+wH/7Hwe0C3aZAJwErAROA46z1q631q7DJ4TDg2PuBU631n4dHPdbfM+lmcAK4O/o/0GVw7SRWqkEBFVMPa21o7Idi1Lppt9elFJKxaQJQimlVExaxaSUUiomLUEopZSKSROEUkqpmDRBKKWUikkThFJKqZg0QSillIrp/wFzkcKKC8a7YAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f14ec3ebf90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f14ec37e1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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yYUTqq5dAE4RSjVZw5n+gTTvkqBORnBYErvglFIzEPfEAwTnvJ+We7r/PQ9ES5PzLkLzOAMjQg2HdStzu2lO9JMW6lVC8Hjks+iotOeRwOORw3Ix/4bZsSl5sCeQqKnDzPkJGjUNC5k1LJU0QSjVCbv1a+GI2ctwpVXXT0iKXwJU3weBhuEfvxX36cWLvuXo57qV/IeOOJnD4MVXbZegIcA6Wpqaayc37CCSAjDkipvO8yTsdwX8/mpzAEm3JAti5PS29lyppglAqBRJd9+3eeNGb9vm4U6ttl9yWBK66GfoPIfjw3bgv5iTmfvv3EXz8fmjbATnv0uo7BwyF7BxvcFoKuHkfQcEIpF1sKyZKfjfk1O/Apx/j5s9NUnSJ4+Z+CLktYcRhaYtBE4RSSRb88C02XXAybteOhFzPbduC+/ht5Mjjw/btl5atCfz0Fujdn+Dffotb8GnD7zn9aVi3isCFP6lV7y85LWBgQUoaqt261bB+TUzVS6HkpG9B994En3kIty/9XckjcRUVuM8+Tmv1EjTBbq5KZRK3exfuucehbA+y8WuIolG13mu+/TJUVCAnnh7xGGndlsDVtxG85yaCD94JPXpDIAsCAcjyf/vvt7VtR7BrL6T/EBgwGGmfV/1+SxbiXn8ROWYyMjL8t1kpGIF72eJKd9fZ7bSh3LwPQQQZMyGu8yU7h8D5l3kD6F57Djn9/ARHmCAZUL0EmiCUSir30r9g107vzbbNDb9eWSnu3dfg0AlI1551Hitt2hG45g7c8096pZdgECoqwPm/y8shWEH5jm24OR8cmFKlUz70H4r0H4L0HUjwn3+B/G7I2d+PfK+hI3BuGiz7CkYlbzpq9+lHMGQ40iGv/oMjkGGjkPETcf99Hjf+WKR7rwRGmBhu7geQ2wpGHJrWODRBKJUkbt1q3DuvIGOPws39ALe1pP75leu75v9ehz27CZwc3drh0q49ctFVdR6Tn5/PprVrYPVy3MqlsHIZbuVS3Kcf4QAkQOD639Q9D9DAAsjOxi1ZgCQpQbj1a2HdKuScSxp8LTn7B7gv5xJ88NcErr4D6ZSfgAgTwy1ZgJv1LjLmCKRF+qqXQBOEUknhnCP470egZSvkvMtwX86BrQ0rQbjyctybM6BgJDJgSIIi9UjLVjB0hNcjqfJ+u3bAymWQlYUMjrzuOXg9qOg/NKntEO7Tj7x7HRpf9VIo6ZBH4Cf/R/CPtxG8+0YC19yBdOne4Os2lFuygOAfb4dOXZCzf5DucLSRWqmk+GyWNxXF1PORdu3J6ty14QlizvuwtYTAyWcmKMi6Sdv2yIhDkYMOie74oSNg1TJcWWlS4nHzPoRBw6rGXzSUDBlO4JpfQ+lugr+/Ebeh7qWAk60qOeTlE/j5nQ2qRksUTRBKJZjbv4/gs49Dz75V00wHOnfBNSBBOOdwM1/wprFOc710JFJwsNfOsWxxwq/tir+GNUXIYYldj1kGDCFw3Z1QUe4libVFCbmuW/SFN7VH8frojl+ygOADt0GnLhmTHEAThFIJ515/EUo2EjjnR0iWt1BNVn5X2NqACRgXfOrVv590Rp2zFKfVoIMgKysp4yHcPG/QnyRhQkDpPYDA9b+FrGyCd/8KV7S0QddzS78i+Oc7cO++RvDmKwhOewS3c3vk4wv95NC5K4Frf50xyQE0QSiVUG5LCe7VZ2HMEdWqZgKdusC2Ld4iN3EIznwB8vKRw49OVKgJJ7ktod/gJCWID2HAUKRzl4RfG0C69/aSROs2BP9wU9xtKW5NEcE/3QF5XQjc/ABy5PG4d14h+MtLCL5icTWm8XeFCwj+MTOTA2iCUCqh3PNPQTBIoEYDY1bnrlBRDrsif5OMeM2ipVA4HzlhKpKdk6hQk0KGjvB6QSVwPRNXshFWLYt7cFy0pEt3AtffBR07E3zgFtxXn8V0vtv4NcH7b4GWrQhcfTvSZwCBC64gcOufYNgo3Iv/JHjTpQTff92bxjs0Ofw885IDaIJQzYQr2Zj0qSDcsq9wn7znVQPV6BETqPzmG0c7hHtzBrRqgxxzUiLCTCoZOsJLhCsS1w5xoPdS8tebkLzOBK77DXTtSfBPdxB8Y3pU06S4rZsJ3nez9+Xg6turlXSkRx+yrviVl3w6dcH9/c8Eb72qenJon3nJATRBqGbAOUfwkXsI/vF2XEVFcu4RrCD4r0egY2fklLNq7c/K7+q9iKMdwq1cigwfjbRs3dAwk2/wQSCBhCZjN+8j6DsoZd1QpX1HAj+/E4YdgrOPeSvSLYxcmnC7dnjJYfdOAj+7FenRO/x1hwwncMPvCVx2gzdQsUv3jE4OoOMgVHOw8FNvQXuAdaug78CE38J9+BasXo788NqwK38FOnsJwm3dEtNgORcMwpZiiHHm0nSRVq2h78CEJYiKko2wohA544KEXC9a0qadN+nhl3MJ/vsRr+rokMMJmIuRrj2qjnNlpV7X1E0bvOTQb3Dd1xWBw75B4NAJ4IJIICvZf0qDaAlCNWnOOYIz/gVt2nnvi5Yk/h67d+H+8w8YfBASMg12qED7jpCVHXsJYvtWb0qMyhJIIyAFI2FFYUImw9v78bveNVO5nKlPRJBDxhG47UHkzAth8XyCt1xB8IWnvClP9u8j+JffwqplBC69zvu7Y7l2hicH0AShmroFn3oL3Jx5AbTrcKAkkUDumYegdBeBcy+J2AVVAgHo2Cn2NojNG73z87s1NMyUkaEjvKSWgGRc9tE70Lt/WudLkpwcAlO+TeDXf0HGHY177XmCN/2Yrbdd7Q2GvPAqZHTjKOHFKmVVTMaYycADQBbwqLX2rgjHfRt4Dhhnrc38SdtVxnLOEXzpX9C5K/KN43FfzMElOEEE53zgNUxPPQ/pO6jug/M642IsQbgSL0HQufEkCIYcBCK4wgUxfauuyW3dzP7FXyKnn5fA4OInHTsjP7gaN3EKwWmPsH/hZ8h3fkjgG5PSHVrSpKQEYYzJAh4EpgDDgXONMbUmdzHGtAN+CsxORVyqiassPZxyNpKdgwwsgA1rE7Y0ptu2Bff0X6H/EGRK7YbpmiQvP/YSREmx9ztJ/f+TQVq3hT4DGtwO4d5/3bveYemd8romGTSMwI130/mvzxI4YWq6w0mqVFUxHQ4ss9ausNbuA6YB4SazvwP4HVCWorhUI+G2b6XivpujHsDktT0845cevG94MrDA27myYSNlq67/1J9g/14CF1+NZEdRGM/rDNtKDkyrHY3NxdAhL+2zesZKho7w2iH274/rfFe6C/fmDHLHHxOxV1A6SSBAdgZOE55oqapi6gWsCXm/FhgfeoAx5lCgj7X2FWPMdZEuZIy5BLgEwFpLfn580/RmZ2fHfW6yaWy17X7vVXZ99Tlu9XLyfvsQ2b371xnb3nkfsW3lUtr9+AZad/d6nQQPO4JNIrTasIa2E09sUDylM19k54J5tPvRNbQeMbre47Ozs2nTux+79u2jc6tcAlEuHLR1+xZc9150SuIzT8Z/07KxE9j+5gw6bC2mxfDoJvsLtWvai+zes5v2511CQP8txCxRsWVEN1djTAD4A3BRfcdaax8GHvbfupKS+Oa3yc/PJ95zk01jq845R/CtV6BXP9yObWy+7WoCN95da7nNyticcwT/8Tfo3JXdIw+nNDTeHn3YveAzyo6P/29wxV8TfPwBOOgQdo89pvr1I8jPz6c0xysFbF62BAmT4MKp2LAOGTA0qc88Gf9NXbc+AGyb8yGBrrF903aluwjO+DeMOYJA34H6byEO9cXWs2fdi01VSlUV0zqgT8j73v62Su2AEcC7xpiVwBHADGPM2BTFpzLZmhXeOsTHnkLgJ/8HO7YS/POvI0/nMH+uNzXDqaZW1Y8MLICiJbFV84RwwQqCj98PWdkELvqp1zspWnn+N7oo2yFcsAK2bILOjaeLayVp295L6HG0Q7g3ZniLIn3z3CREpmKRqhLEHGCIMWYAXmI4B6jqmmCt3Q5UlYeMMe8CP9deTArAzXoXsrKRcUd5A5h++HOCf/0twcfuJXDZL6r1J68a99C5KzIhTO+SgQXwwRtQvB66RfctqlosM/8DyxcjF18T+ypk/joGUa8st3WLN+K2EXVxDSVDR+A+egtXXh5dGw3+mJK3ZnhLqvYZkOQIVX1SUoKw1pYDVwIzgUXeJrvQGHO7MaZpdwNQDeKCFbhP/gcjxyL+YDcZcwRiLobPZuGefbL6CV9GLj0AyICh3nXj6O7qVq/ATX8GOexIZPzEmM+nfR5IIPqeTCWVYyAaXwkCQApGwN4yWLUs6nPcm9NhTymBb56TxMhUtFLWBmGtfRV4tca2myMce2wqYlKNwKIvYftWAkccW21z4ISpBEs24t6cTjC/G4HjTzsw7iG/W/jSA0DPPt5i8EWFMOG4qMNw+/cTfPw+aNsO+e7lca3JINnZ0KFj1KOpnT9IrrGWIBhyMADu89nIoGH1Hu5278S99RIc+g2kt5YeMkFGNFIrFYmb9S60bgOjajdHifkBbnMx7t+P4Drns69DR6/0cOFPIlZpSCALBgzBrYhtlK+b/k9Yt4rAVTd79evxysvHbYuhBCECeY1nDEQoad8ROexI3H+fJ9ilO4FjTq7zePeGlh4yjU61oTKW21uG++xjr0onp0Wt/RLIIvDDa6HfYIKP3MPOJ/4IXbojR9RdMpABQ2FtUdRrFrgdW3FvTEeOOhEZ2cB+E7FMt1FS7M0Om5PZa0DURS6+BkaOxf3zLwQ/fDPicVWlh8O+EXUPL5V8miBUxnKfzYK9ZUiN6qVQktuSwE9ugnYdqVi/NmLbQ7VzBhZ4jb+rl0cXx5wPIBhETgw3tjM2sYymdps3NsoeTKEkJ4fA5TfAQaNxT/2J4MfvhD3OvT4dyvYQOE1LD5lEE4TKWG72u94H5OBas7JUI+3zCFxzO20vuBwZf2z9Fx7oN1QXRddQ7Wa9C30HIj37RnV8nfI6w57duLLS+o8tKW5Uk/RFIjktCFzxSygYiXviAYKz36u23+3agXv7Ja+kqKWHjKIJQmUkt2MrLPwcGT8xqrEG0rUnbc68IKrulNI+D/K7RdWTyW1YByuXRpd4olE1FmJL3fctL/dKGo20B1NN0iKXwJX/B0OG4x67j+CcD6r2uTemeyVFbXvIOJogVEZyn7zvLahSR/VSQ8iAoRBFQ7Wb/R6IIIcfnZj7+mMh6u3JtLUEXLDRVzGFktxcb6DjoGG4R+/BffqRV3p462Wv9NCrX7pDVDVoglAZyc16F/oNRnr0qffYuAwsgK0luDraA5xzXjXXsFFIx86Jua9fgqi3J1NJ41sHIhrSshWBn94MA4YSfPhugo/cA/vKEG17yEiaIFTGcevXeN1Vj4hjMFqUqmZ2rasdYkUhbNqQ2FJMx07e73oaqg+sA9F0ShCVpGVrAlfdAn0HwVefI2OPQnoloH1HJZwmCJVx3Kz3QALIuPDLdyZEn4GQnV1nO4Sb9S7ktEDGTEjYbSWnBbRtX38V0+ZiCASgU+McA1Efad3GW8P55DORsy5KdzgqAk0QqoorLyf4zqtRjw9ISgzBoFetM/wQpENe0u4jOTnQZ2DEBOHKy3Fz30dGj0datU7szfM611m1BXhVTHn5SFbmr1scL2ndlsBZFyFNNAk2BZog1AELP8M98zfc2y+lL4Zli2Bzcb2D3RJBBhbAqmW4ioraOxd+Brt2Jq73Uqi8/HpLEG5zceOdYkM1GZogVJXKb9Pu7Zdx5fGtBNbgGGa/C7ktkTEpWAR+wFDYtw/WrQwfR9t2cPCYhN9W8jrXP1iuZCPSBNsfVOOiCUJVcUWF0CIXtm3xRg+n+v779+HmfuDN1prbMun3q2yorlnN5PaUehPMjT066mmqY5KXD7t24PbvC7vb7d8P27ZoCUKlnSYIBfiL0xQtQSYcBz364F5/Me5FdeI2fy6U7k5J9RLgfQC361BrPIT77GPYvy9pYzCo7DK7LcJguc3F3m8tQag00wShPOvXQdkeGFjgzTm0tggWf5nSEIKz3oUOeTBsVEruJyIwsKDWlBtu1rvQpbs3ViIZ961vsJyfIBrrOhCq6dAEoYAD8xLJwALvm3O7DgTfmJ66+5ftgflzkXFHp7Tnjgy0cc4aAAAf4klEQVQYChvW4Xbv8uLYthkWz/em+IhjzYeoVA6Wi9AO0ejXgVBNhiYI5VlR6K270LUnktMCOe5UmD/XG7SWCssWQXk5MuKw1NzPd2DAnFfNVDXFRzwrxkUrr3KwXIQSRMlGyMo+MKhOqTTRBKEAcEVLYMDQqonx5NgpkNMC9+aM1Nx/8Zfeh+Lgg1JyvyoDhoDIgR5cs9/1pvjo3jtpt5SWraFVm8g9mUqKoVN+tbW2lUoHTRDKq95ZtxoZcKDOXdp1QCYch/v4HdzO7cmPoXC+l6BS0HsplLRsDT374ooKcV+vhtUrktc4Hapjp4jzMbmSjVq9pDKCJgjlLSrvggeqW3xywumwfx/u3deSentXuhtWLUeGjUzqfSKRgQWwYonXOB0IJGzm1jrVtXDQ5qaxDoRq/DRBNDFuTyluX2xTZVSNAxgwpNp26dHbWy7ynVci9tlPiKVfeQmqID0JggFDoXQX7t1X4aBDvPUikswbLFe7DcLt3Qs7tmkXV5URNEE0McH7bsY9+ceYznErCr3G6bbta+0LnHg67NzufbtOErf4S8jOgUHDknaPushA/757SlNTvQReCWL7Vm9hoFBb/DEQWoJQGUATRBPi9pbBymW4z2d7r6M5xzlvgFykPv/DRkGfAbg3pidt4Jwr/BIGDfNmOk2HHr2gZStokYuMTsEUH+AtPeoc7NhafXvlOhBaglAZQBNEU7JmhbcK2f59sPDT6M7Zsgm2b61ap7kmEUFO/BasXxP9NWPgdu2AtSvT1v4AIIEs5MgTkBOmIi1bpeaeVUuPVm+HcCWVJQhNECr9NEE0IW7lMu9Fbkvcpx9Hd44/zUTEEgQg446Cjp2SM3BuyQJwDilIzejpSALn/IjAGRek7oaVYyFq9mTavNGrbktBO4hS9dEE0ZSsWg4dOiGHHYn7cm50M7KuKIScFtCrf8RDJDsHmXQafPU5+yuTUIK4xfO9CQJrNJA3eVWjqas3VLuSjdC5a9V4FKXSSf8vbELcqmXQb5A3Vfae3VC4oP5zigq9c+qZtVSOmQwtcil9aVqiwvXuXzgfBg9HsnMSet2M17ottGhRu6trSbFWL6mMEdNcxsaYc4HPrbWLjDEFwCNABXC5tXZxMgJU0XF7y2DDOmTskTB8tFfN9NnHSB3rGbjy/d74g+NOqff60qYtcuQJlL33XySvC3LC6Q2eCtvt2Apfr05dz6EMIiLQMcxYiM0bkX6D0xOUUjXEWoL4NVA5R/E9wCfAe8BfEhmUioPfQC39BiMtcuHgQ3Gff4ILBiOfs3YllO+vs/0hlJx+Hrljj8I9/xTBO6/FFS1tUMjOL+GkbfxDuuV1rlbF5MpKYddO7eKqMkasCaKLtXajMaYlcBTwK+B2YHTCI1MxqWqg7jcIwKtm2r6lahK6sOdUDZCLMkG0aUfHG35L4PIbYdd2gr/9OcFpj3gfbPFYPN/rXtpMvzFLzdHU2oNJZZhYE8QmY8xgYAowx1q7F2gJJGleZBW1ygZqfzEaGTUWsrK8xW8iKVoCHTpBp/yYbiWHTiBw+1+QY0/Bvf0ywZuvxH0+O+aQXeF8GHJwSqf3zih5nbzV+ypLeVXrQGgJQmWGWBPEHcA84DHgbn/bCcAXiQxKxa6ygbqStG4LBaNwn82KOMDNrSj0JsiLY90DadWawHmXEvjF76B1G4IP3knFX++KOAFdrXtv3Qwb16V1/EPa5eVDRTns8iZDdP4gOZ1mQ2WKmBKEtfZJoAfQ21r7hr95FnBOguNSMahqoA5JEOBXMxWvh69X1z5n1w4oXh91+0MkMmgYgZvuQ878HsyfS/C313mzw9YXc6G3Wl26xz+k04GV5fykWlLsdflt1yF9QSkVot5uKMaYmkmkrMb2CKueqJQJaaAOJaPH4575m9ebqVe/6ucUVQ6QCz+COhaSnY1MOQs3eDjB39+Ae+155Izv1n3S4vleV88+/Rt8/0arajR1CfQbfGAMRLJWslMqRtGUIMqB/XX8VO5XaVKzgbqSdOzkrbn82aza56woBAkktIFYhgxHxk/Evf6fA9UlkWIunA9DRzTvRXH8EkTV0qObdR0IlVmiSRADgIF1/FTuV+lSo4E6lIyZAKtX1PrAdiuWQK++CZ97SM68EAIBgs89EfEYV7IRSjY27/YHgLYdvFX0qhJEMaI9mFQGqbeKyVq7KhWBqPjVbKAOJWOOwD33BO7zWd4CQOD1mila4s2xlGDSKR+Z8m3c9GdwhfPDjnFwhfO9Y5vr+AefBALeutNbN+NKd0HpbuisJQiVOaJpg/gHUO88z9ba79VzncnAA0AW8Ki19q4a+y8DrsAbmb0LuMRa+1V9923uqo2gDkO69oBe/bzJ+/wEwcavvak4BjS8/SHsPU86A/f+GwSnPULg/+6rXY20eL7XENuzb1Lu36j4g+WkRLu4qswTTRXTMmB5FD8RGWOygAfxxk8MB841xgyvcdgz1tqR1trRwO+BP8TwdzRfERqoQ8mYCbBsEW7HNuDAALmG9mCKeL8WuQTO/j6sXYl7/41q+5xzXsli6AidkI6QwXKVVYBaxaQySDRVTLcl4D6HA8ustSsAjDHTgNOBqhKCtXZHyPFtiKLU0hi5zcUEH7iNwEVXJeQDOlIDdSgZcwTu5Wm4Lz5Bjj4JigqhVWvo3rvB94/osCNh6MG4F/+JG3eUNy4DvG63W0tg2FnJu3djktcZvph9oI1ISxAqg8Q825oxpgVQAOQTMoLaWvt2Haf1AtaEvF8LjA9z7SuAa4AWwKQI978EuMS/J/n5sY0CrpSdnR33uQ2xZ9Gn7Fi/Bnnifjr94SkCrVo3KLbtG9eyL68zXQZHTjauc2dKuvYge+E88s44j82rlxMYejB5XWP/thpLbPsv/Tlbfv4DWr45nXY/+CkApZ9+wE6g04SJZCf4+afrv2k0IsW2u3c/du3bR27xOva2ak1+3/4p7+baGJ9bJmgOscU6m+tRwLNALtAe2AG0w/vwb3BPJmvtg8CDxpjzgJuAC8Mc8zDwsP/WlZTENwwjPz+feM9tiOByb4K7io1fU/LX3xP43pW1jokltorChdBnYL3Hu1GHs+/dV9i0fCnBVcuRyWfF9ffH9Nzad0aOOpHSV5+jbNxEpEdvgnM/hg6d2NqiNZLg55+u/6bRiBSba9ESgLIFn0GnLmzeHN1I9ERqjM8tEzTm2Hr27BnVdWKtBL4P+L21thOw0/99B/XP5roO6BPyvre/LZJpwLdijK1x2LTB65J60hm491/HffFJ3JeKNII6HBlzBJSX4175NwSDSWt/qHXfb30XWuQStI8daH8oGKmDwSpVdk3eXKzVSyrjxJoghuL1RAp1F3B1PefNAYYYYwb4VVTnADNCDzDGhC4pdirQsLmkM5TbtB66dEdOPx96DyD41J+qGo9jFkUDdZXBw6BdB9z/ZnrvEzCCOhrSviNy2jmwYB7u9RdhxzZo7uMfQuUdGLuiPZhUpok1QWzHq1oCWO/3RMoD2tZ1krW2HLgSmAks8jbZhcaY240xU/3DrjTGLDTGfI7XDlGreqlJ2LQB6doDyckh8MNrYE8pwb//OeKEenVxq/zOY9GUIAJZyOjxUFHhJagUzvcjk06Fbr1wzz/pvR/WfOdfqqV9njeiHbQHk8o4sTZSvwCcAjwDPA68gzfNxnP1nWitfRV4tca2m0Ne/zTGWBodt3cvbNsCXboDIL36IWd+D2cfw73/OnLMybFdcOUy6JAXdgR1ODJmgnefKNd/SBTJziFgfkDwT3dApy5alRJCsrOhQ0fYtgXRQXIqw8SUIKy1Pwt5fY8xZhZeI/XMRAfWJJVs8H77CQJAjv8mbv5cL0kMG4l0ja7xCPwR1H3rLz1UGTbKm977sG9Ef06ijBzrJcD87tr+UFNevvfFQROnyjAxVTEZY3oZY/Iq31trPwBmA90jn6WqbFoP+KObfRIIELjop5CVRfCx+3AVFVFdqqqBun/0k+1JTg5Zv7wHOXRCTGEngogQuOAKAlO+nfJ7Z7yOnbzfug6EyjCxtkG8iNcDKVQv4D+JCadpc8V+CSIkQYA/f9F5l8GKQtxrz0Z3sVgaqFVGk559vWm+W7dJdyhKVRNzLyZr7fzQDf77YYkLqQnbtB5at0HatKu1KzB+InL4MbiXprF/af1TUMXSQK0ym5z6HQL/d1+6w1CqlnjXpK7iv0/96J5GyBVvgC49Iu6X8y6DDp3Yfv/tuLLSui8WYwO1ylySkxP2S4NS6RZrL6bHgeeNMb8CVgCD8AbKPZrowJqkTevrnlSvTVsCF19NxR9uhsfuI3D5jREntIu5gVoppWIUawniLuCfwD14g99+77+/q66TFF7j85ZN1XowhSMFI2l30ZXw+WzcS9PCX6tqBLW2PyilkifWbq5B4G7/R8ViyyZvkFrXyFVMlVqdZti1eIE3A2vv/rW7pVY2UMfQg0kppWIVz2yuJ+JNldHVWvtNY8xYoH09s7mqYr+Lax1tEJVEBPnu5bj1awg+cT+Bbj2Q3gOq9msDtVIqFWIdB/ET4K948yQd42/eA/w6wXE1Oc4fA1FfFVMlyWlB4Mc3QsvWBP98J25nyHIZ2kCtlEqBWNsgfgac4C8XGvS3LcZbH0LVZdMGyM45MCgqCtKxs5cktm8l+NDvcOXlgDZQK6VSI9YEUbn2AxxY8S0H2JewiJoor4tr95iX2ZSBBcgFV0DhfNxzT2gDtVIqZWJtg/gfcANwZ8i2q/Am7VN12bQ+qgbqcALfmERwTRHuzemwb68/glpLEEqp5Io1QfwEeMkY8yOgnTGmENgJnJbwyJoQ55w3zfdBh8R9DTnrIty6lbj3X/c2aA8mpVSSxZogNgLj/J9+eNVNn/jdX1Uk27d63/yjbKAOR7KyCFx6PcE7r4X9+7WBWimVdFEnCGNMFrAL6Git/QSIf63M5maTN0lfNF1c6yJt2hG4/i7YvSsRUSmlVJ2ibjG11lYASwD96hqjqi6ucbZBhJKOnZBefRt8HaWUqk+sVUxPAy8bYx4A1nKgJxM6UK4Oxeu9ZSU7d0l3JEopFbVYE8Tl/u9ba2x3wMAGR9NUbdoAnfKR7Jx0R6KUUlGLdS6mAfUdY4zpba1dG39ITY/btCEh1UtKKZVKsQ6Ui0b9q900N5vWIw3owaSUUumQjAShK9KHcKW7YNdOLUEopRqdZCQIV/8hzUhVF1ctQSilGpdkJAgVwhV7CaKupUaVUioTaYJItqppvrulNw6llIqRtkEk26YN0L4j0rJ1uiNRSqmYJCNBDE/CNRstV7y+QXMwKaVUutQ7DsIYs4YoGp6ttX3932vqO7ZZ2bQBKRiZ7iiUUipm0QyU+27So2ii3P59sG2zliCUUo1SvQnCWvteKgJpkko2gnPQVROEUqrxiXUuJowxo4GjgXxCGqSttTcnMK6moTgx03wrpVQ6xNRIbYy5BPgQmAT8AhgJXAvo8mZhuE1fey90FLVSqhGKtRfT9cBka+0ZwB7/91nA/oRH1hQUb4CWraBt+3RHopRSMYs1QXS11r7vvw4aYwLW2teAbyY4ribBbdoAXbojokNDlFKNT6wJYq0xpnLK7yXA6caYo4F9iQ0rswVnvYNb9EX9B+o030qpRizWBPF7YJj/+nbgn8DbwG2JDCqTuWAQ98xDBB+/D7dvbx3HVUDJRm2gVko1WrEmiNHAZgC/aikPyLPW/jXRgWWsjetgTyls24L738zIx20pgYpyHQOhlGq0Yu7mCrxojNkNPAM8ba1dEs1JxpjJwANAFvCotfauGvuvAX4IlAObgB9Ya1fFEV9SuSL/z+3aA/fac7ijT0Zyc2sfqNN8K6UauZhKENbanwG9gR8DfYDZxph5/od7RMaYLOBBYAreXE3nGmNqztn0GTDWWjsKeA6vOivzFC2Blq0IfO8nsGMb7r3Xwh7mKmdx1TYIpVQjFXMJwlobBN4A3jDG/B/wBHA38Ic6TjscWGatXQFgjJkGnE7I8qTW2ndCjp9Fhk7x4YqWQv8hSMEIOOgQ3H+fx02cjOS2rH5g8QbIzoa8zukJVCmlGiiekdRtgDOAc4FjgfeAC+s5rRcQOonfWmB8HcdfDIT9au4P1rsEwFpLfn5+VHHXlJ2dHfO5bu9eitcW0fpb59MuP5993/sxW2+8lNafvEebM86vduy27Vso79aT/K6xrwMRT2yporHFR2OLj8YWn0TFFlOCMMY8i1dN9CnwL+BCa21Jg6Oofo/vAmOBieH2W2sfBh7237qSkvhun5+fT6znuuWLoaKCsm692VtSAvk94OAx7Hrh75SOO7ramg8Va1dBp64x3yPe2FJFY4uPxhYfjS0+9cXWs2fPqK4TawliDnCttXZ1jOetw2uzqNTb31aNMeYE4FfARGtt5D6kaeKKCr0XA4ZWbQtMPY/gb6/Dvf0KcsrZ3nHOedN8Dz04HWEqpVRCxJQgrLXxNhzPAYb4g+zWAecA54UeYIwZAzyEN5VHcZz3Sa6ipZCXj3TsVLVJBhbAyLG411/EHXcq0qo17NwOe/doF1elVKOWkjWprbXlwJXATGCRt8kuNMbcboyZ6h92N9AWeNYY87kxZkYqYouFK1pSrfRQKTD1XNi9E/fWS96Gyi6u2oNJKdWIxTMOIi7W2leBV2tsuznk9QmpiiUebucOr9romJNr7ZP+Q+CQw3FvvIibdKq3zCiAjqJWSjViKSlBNAkrvQFyMqAg7O7A1HOhdDfuzZdg03oQgfzYezAppVSm0AQRJVe0BCQA/QaF3S99B8GYI3BvTsetWg55nZGcnBRHqZRSiaMJIkquaAn07IO0bBXxmMDUc715mr6co9VLSqlGTxNEFJxzULQUCdNAHUp6D4DDvuG91gZqpVQjpwkiGps2wO6dMGBIvYcGvnmuVxXVs0+9xyqlVCZLWS+mxqxyBtdIDdShpFc/Arf9GfK7JjsspZRKKk0Q0ShaAi1yoWffqA6XHr2THJBSSiWfVjFFwRUtgX6DkKysdIeilFIpowmiHq58P6xeUW8DtVJKNTWaIOqzdiWU79cEoZRqdjRB1MMVLfVeaIJQSjUzmiDqU1QI7TpApy7pjkQppVJKE0Q9XNFSGFiAiKQ7FKWUSilNEHVwpbtgw1qkf/0D5JRSqqnRBFGXlcsAkIHa/qCUan40QdShcgQ1WoJQSjVDmiDq4IqWQPdeSOu26Q5FKaVSThNEBN4MrkuQ/lq9pJRqnjRBRLKlBHZsA21/UEo1U5ogIqlcYlRLEEqpZkoTRARuxRLIzoY+/dMdilJKpYUmiAjcyiXQZyCSretKK6WaJ00QYbiKCli5TCfoU0o1a5ogwlm/Gvbt1Qn6lFLNmiaIMNyKyiVGNUEopZovTRDhrFwKrdtC1x7pjkQppdJGE0QYbkUhDByqM7gqpZo1TRA1uLJS+Hq1Vi8ppZo9TRA1rVwGziEDCtIdiVJKpZUmiBqqZnAdoDO4KqWaN00QNbgVhdC1J9K2fbpDUUqptNIEEaJqBledoE8ppTRBVLOlBLZvhYHa/qCUUpogQrgVhYAOkFNKKdAEUV1RIWTnQO/+6Y5EKaXSThNECFe0BPoN0hlclVIKTRBVXHk5rFqu4x+UUsqXnaobGWMmAw8AWcCj1tq7auw/BrgfGAWcY619LlWxAbBuJezfp0uMKqWULyUlCGNMFvAgMAUYDpxrjBle47DVwEXAM6mIqaaqGVy1B5NSSgGpK0EcDiyz1q4AMMZMA04Hvqo8wFq70t8XTFFM1a0ohPYdoVOXtNxeKaUyTaoSRC9gTcj7tcD4eC5kjLkEuATAWkt+fn5cAWVnZ1c7t2T1crILRtCxS/oTRM3YMonGFh+NLT4aW3wSFVvK2iASxVr7MPCw/9aVlJTEdZ38/Hwqz3W7dxL8ejXB8ROJ93qJFBpbptHY4qOxxUdji099sfXs2TOq66SqF9M6oE/I+97+tsxQpCvIKaVUTakqQcwBhhhjBuAlhnOA81J073q5FUtABPrrDK5KKVUpJSUIa205cCUwE1jkbbILjTG3G2OmAhhjxhlj1gJnAw8ZYxamIjbwB8j17Iu0ap2qWyqlVMZLWRuEtfZV4NUa224OeT0Hr+oppapmcB1zRKpvrZRSGU1HUhevh907dQZXpZSqodknCFekM7gqpVQ4zT5BsKIQcltBzz71H6uUUs1Is08QbsUS6D8YCWSlOxSllMoozTpBuH17YW2RLjGqlFJhNOsEweoVUFGhU3wrpVQYzTpBOH8ENdpArZRStTTrBEHREujUBenYKd2RKKVUxmnWCcKtKNTurUopFUGzTRAV27bA5mIdIKeUUhE02wSxf4k31ZP2YFJKqfCad4LIyoK+g9IdilJKZaRmmyDKlyyE3gOQFrnpDkUppTJSs0wQLljB/mWLtIFaKaXq0CwTBOvX4vaU6vgHpZSqQ7NMEG6FP4Or9mBSSqmImmWCkHbtyT38aOgW3cLdSinVHKVsRblMIqOPoOMJp1FSUpLuUJRSKmM1yxKEUkqp+mmCUEopFZYmCKWUUmFpglBKKRWWJgillFJhaYJQSikVliYIpZRSYWmCUEopFZY459IdQ0M06uCVUiqNpL4DGnsJQuL9McbMa8j5yfzR2DQ2jU1jS0Fs9WrsCUIppVSSaIJQSikVVnNOEA+nO4A6aGzx0djio7HFp8nH1tgbqZVSSiVJcy5BKKWUqoMmCKWUUmE1ywWDjDGTgQeALOBRa+1daQ6pijFmJbATqADKrbVj0xjL48BpQLG1doS/rRPwb6A/sBIw1tqtGRLbrcCPgE3+Yb+01r6ahtj6AH8HuuGN1XnYWvtAJjy7OmK7lTQ/O2NMS+B/QC7eZ9Nz1tpbjDEDgGlAZ2AecIG1dl+GxPYkMBHY7h96kbX281TGFhJjFjAXWGetPS0Rz63ZlSD8h/ggMAUYDpxrjBme3qhqOc5aOzqdycH3JDC5xrYbgLestUOAt/z36fAktWMDuM9/dqPTkRx85cC11trhwBHAFf7/Y5nw7CLFBul/dnuBSdbaQ4DRwGRjzBHA7/zYBgNbgYszKDaA60KeW1qSg++nwKKQ9w1+bs0uQQCHA8ustSv8bDoNOD3NMWUka+3/gC01Np8OPOW/fgr4VkqD8kWILSNYa9dbaz/1X+/E+0fbiwx4dnXElnbWWmet3eW/zfF/HDAJeM7fnq7nFim2jGCM6Q2cCjzqvxcS8NyaYxVTL2BNyPu1wPg0xRKOA143xjjgIWttpnWl62atXe+/3oBXVZFJrjTGfA+vqH1tOqq/Qhlj+gNjgNlk2LOrEduRZMCz80v484DBeCX95cA2a225f8ha0pTQasZmrZ1tjLkcuNMYczN+qdBauzcN4d0PXA+08993JgHPrTmWIDLdUdbaQ/GqwK4wxhyT7oAisdY6MuhbFPBXYBBeFcB64N50BmOMaQs8D/zMWrsjdF+6n12Y2DLi2VlrK6y1o4HeeKX9YemII5yasRljRgA34sU4DugE/CLVcRljKtvi5iX62s0xQawD+oS87+1vywjW2nX+72LgP3j/SDLJRmNMDwD/d3Ga46lird3o/yMOAo+QxmdnjMnB+wB+2lr7gr85I55duNgy6dn58WwD3gEmAB2NMZW1HWn/9xoS22S/ys75pYYnSM9zOxKY6ndwmYZXtfQACXhuzTFBzAGGGGMGGGNaAOcAM9IcEwDGmDbGmHaVr4GTgAXpjaqWGcCF/usLgelpjKWayg9f3xmk6dn59b+PAYustX8I2ZX2Zxcptkx4dsaYLsaYjv7rVsCJeG0k7wBn+Yel67mFi21xSMIXvDr+lD83a+2N1tre1tr+eJ9nb1trzycBz61ZjqQ2xpyCV2eXBTxurb0zzSEBYIwZiFdqAK996Jl0xmaM+RdwLJAPbARuAV4ELNAXWIXXVTPljcURYjsWr4rE4XUjvTSkzj+VsR0FvA/MB4L+5l/i1fWn9dnVEdu5pPnZGWNG4TWmZuF9ebXW2tv9fxfT8KpwPgO+m+p6/jpiexvogjc76ufAZSGN2SlnjDkW+LnfzbXBz61ZJgillFL1a45VTEoppaKgCUIppVRYmiCUUkqFpQlCKaVUWJoglFJKhdUcp9pQKqP4U14UATkhUyMolXZaglBKKRWWJgillFJh6UA5pcIwxvQE/gQcA+zCm1f/j/7COiPwFnQ6BVgKfN9a+4V/3kF4E9+Nxpv75kZr7Qx/Xyvg13jTH3TEG818It6srkXARcAdQGv/fhkxwl81X5oglKrBGBPAm7NrOnAX3kRnbwKX400e9yu8qSmm4y3ScgUw1D99EfA4cA9wlH/MWGttoTHmQeBg4Hy86b7H400f3QMvQTwKXOVf6xNgtLU2dAEYpVJKE4RSNRhjxgPPWmv7hmy7Ee+DexXeLJ5H+NsDeCUF4x/6LNDTnxW1cs6oQuB2YDdwRGVpI+Ta/fESRB9r7Vp/2yfAH6y105L1dypVH+3FpFRt/YCexphtIduy8Ca5W0XIglPW2qAxZi3Q09+0pjI5+FbhLdSSD7TEWwAnkg0hr0uBtnH/BUolgCYIpWpbAxT5a0dX47dB9Al5H8Crgvra39THGBMISRJ9gSVACVCGtyhPtRKEUplKE4RStX0C7DTG/AL4I7APOAho5e8/zBhzJt76DlfhLWg/C2/K51LgemPMvXgLuXwTGOeXNB4H/mCMuQBvivLDgU9T92cpFRvt5qpUDdbaCuA0vJ5IRXjf/h8FOviHTAe+A2wFLgDOtNbut9buw0sIU/xz/gJ8z1q72D/v53g9l+YAW4Dfof8GVQbTRmqlYuBXMQ221n433bEolWz67UUppVRYmiCUUkqFpVVMSimlwtIShFJKqbA0QSillApLE4RSSqmwNEEopZQKSxOEUkqpsP4fRTVeXhavrM8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f14ec27fe10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_metrics(metrics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Hamming Loss:', 0.17185821697099893)\n",
      "('Total of correcly predicted:', 168)\n",
      "('Accuracy of correclty predicted:', 0.3157894736842105)\n"
     ]
    }
   ],
   "source": [
    "predictions = model.predict(eval_x)\n",
    "predictions[predictions>=0.5] = 1\n",
    "predictions[predictions<0.5] = 0\n",
    "\n",
    "from sklearn.metrics import hamming_loss\n",
    "print(\"Hamming Loss:\",hamming_loss(eval_y,predictions))\n",
    "\n",
    "# a diferença entre o ground truth e predições será toda 0 quando os dois forem iguais\n",
    "## Então buscamos desses não dão tudo 0 e tiramos o inverso disso\n",
    "predicted_diference = eval_y-predictions\n",
    "correcly_predicted = (np.where(~(predicted_diference).any(axis=1))[0])\n",
    "print(\"Exact match:\",correcly_predicted.shape[0])\n",
    "print(\"Accuracy Exact match:\", float(correcly_predicted.shape[0])/eval_y.shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Acertos e erros por label\n",
    "Tipo alguns que acertaram quase todos ou todos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Multilabel Confusion Matrix\n",
      "  TP,   FP,     TN,     FN, \n",
      "0 112\t80\t279\t61\n",
      "1 12\t16\t463\t41\n",
      "2 36\t33\t407\t56\n",
      "3 49\t112\t358\t13\n",
      "4 28\t12\t460\t32\n",
      "5 22\t30\t447\t33\n",
      "6 74\t33\t337\t88\n",
      "Σ 333\t316\t2751\t324\n",
      "\n",
      "F1 Score: 0.474009\n",
      "Recall: 0.482700\n",
      "Precision: 0.521808\n",
      "Hamming Loss: 0.171858\n"
     ]
    }
   ],
   "source": [
    "labels = [\"Wind\",\"Thunder\",\"Rain\",\"Ocean\",\"Fire\",\"Explosion\",\"Gunshot, gunfire\"]\n",
    "multilabel_confusion_matrix(eval_y,predictions,n_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('modelos_salvos/2000video_cnn.h5') \n",
    "\n",
    "np.save('modelos_salvos/2000video_metrics.npy', metrics.custom_metrics)\n",
    "np.save('modelos_salvos/2000video_history.npy', hist.history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}