7/ds_7.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sarcasm: sentiment analysis of news headlines"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We develop a model that predicts if sarcasm is present in news headlines."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We load the dataset and explore some of its statistics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import pandas as pd\n",
    "from sklearn.utils import shuffle\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from nltk.corpus import stopwords\n",
    "import re\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "from wordcloud import WordCloud\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import tensorflow as tf\n",
    "import urllib.request\n",
    "import zipfile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>article_link</th>\n",
       "      <th>headline</th>\n",
       "      <th>is_sarcastic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>54147</td>\n",
       "      <td>https://www.theonion.com/irish-wake-a-blur-181...</td>\n",
       "      <td>irish wake a blur</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5380</td>\n",
       "      <td>https://www.theonion.com/queen-elizabeth-frant...</td>\n",
       "      <td>queen elizabeth frantically trying to preserve...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>54825</td>\n",
       "      <td>https://local.theonion.com/dad-can-t-believe-l...</td>\n",
       "      <td>dad can't believe lawn didn't get him anything...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25315</td>\n",
       "      <td>https://www.huffingtonpost.com/entry/the-sec-c...</td>\n",
       "      <td>the sec, cftc and the real-time risks in today...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>52063</td>\n",
       "      <td>https://politics.theonion.com/nation-finds-sol...</td>\n",
       "      <td>nation finds solace in knowledge candidates ta...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            article_link  \\\n",
       "54147  https://www.theonion.com/irish-wake-a-blur-181...   \n",
       "5380   https://www.theonion.com/queen-elizabeth-frant...   \n",
       "54825  https://local.theonion.com/dad-can-t-believe-l...   \n",
       "25315  https://www.huffingtonpost.com/entry/the-sec-c...   \n",
       "52063  https://politics.theonion.com/nation-finds-sol...   \n",
       "\n",
       "                                                headline  is_sarcastic  \n",
       "54147                                  irish wake a blur             1  \n",
       "5380   queen elizabeth frantically trying to preserve...             1  \n",
       "54825  dad can't believe lawn didn't get him anything...             1  \n",
       "25315  the sec, cftc and the real-time risks in today...             0  \n",
       "52063  nation finds solace in knowledge candidates ta...             1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_json('sarcasm.json', lines=True)\n",
    "data = shuffle(data)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 55328 entries, 54147 to 50238\n",
      "Data columns (total 3 columns):\n",
      "article_link    55328 non-null object\n",
      "headline        55328 non-null object\n",
      "is_sarcastic    55328 non-null int64\n",
      "dtypes: int64(1), object(2)\n",
      "memory usage: 1.7+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>is_sarcastic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>55328.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>0.458321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>0.498264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       is_sarcastic\n",
       "count  55328.000000\n",
       "mean       0.458321\n",
       "std        0.498264\n",
       "min        0.000000\n",
       "25%        0.000000\n",
       "50%        0.000000\n",
       "75%        1.000000\n",
       "max        1.000000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "headlines = data.loc[:,'headline'].values\n",
    "is_sarcastic = data.loc[:,'is_sarcastic'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([29970.,     0.,     0.,     0.,     0.,     0.,     0.,     0.,\n",
       "            0., 25358.]),\n",
       " array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ]),\n",
       " <a list of 1 Patch objects>)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 10))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "ax.set_title('IS_SARCASTIC')\n",
    "ax.hist(is_sarcastic, histtype='stepfilled', color='steelblue', edgecolor='none')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45.83212839791787"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(is_sarcastic)*100/len(is_sarcastic)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the dataset is a bit unbalanced so it would be helpful to plot a confusion matrix for each model in order to asses its performance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we define some hyperparameters and we construct the datasets for training and testing. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "stops_words = set(stopwords.words('english'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "wordnet_lemmatizer = WordNetLemmatizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def nlp_preprocess(sentence):\n",
    "    sentence_ = ''\n",
    "    for word in sentence.split(' '):\n",
    "        word_lower = word.lower()\n",
    "        if word_lower not in stops_words:\n",
    "            word_lemmatized = wordnet_lemmatizer.lemmatize(word_lower)\n",
    "            word_reg = re.sub(r\"[^A-Za-z]+\", '', word_lemmatized)\n",
    "            sentence_ = sentence_ + word_reg + ' '\n",
    "    return sentence_[:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "headlines_ = []\n",
    "for headline in headlines:\n",
    "    headline_ = nlp_preprocess(headline)\n",
    "    headlines_.append(headline_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "vocab_size = 10000\n",
    "max_length = 50\n",
    "filters='''!\"#$%&()*+,-./:;<=>?@[\\\\]^_`{|}~\\t\\n'''\n",
    "oov_tok = \"<OOV>\"\n",
    "trunc_type ='post'\n",
    "padding_type ='post'\n",
    "training_size = 80*len(data)//100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenizer = tf.keras.preprocessing.text.Tokenizer(num_words=vocab_size,\n",
    "                                                  lower=True,\n",
    "                                                  split=' ',\n",
    "                                                  char_level=False,\n",
    "                                                  oov_token=oov_tok,\n",
    "                                                  filters=filters)\n",
    "tokenizer.fit_on_texts(headlines_[:training_size])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "sequences_train = tokenizer.texts_to_sequences(headlines_[:training_size])\n",
    "padded_sequences_train = tf.keras.preprocessing.sequence.pad_sequences(sequences=sequences_train,\n",
    "                                                                       maxlen=max_length,\n",
    "                                                                       dtype='int32',\n",
    "                                                                       padding=padding_type,\n",
    "                                                                       truncating=trunc_type,\n",
    "                                                                       value=0)\n",
    "\n",
    "X_train = np.array(padded_sequences_train)\n",
    "Y_train = np.reshape(np.array(is_sarcastic[:training_size]), newshape=(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "sequences_test = tokenizer.texts_to_sequences(headlines_[training_size:])\n",
    "padded_sequences_test = tf.keras.preprocessing.sequence.pad_sequences(sequences=sequences_test,\n",
    "                                                                      maxlen=max_length,\n",
    "                                                                      dtype='int32',\n",
    "                                                                      padding=padding_type,\n",
    "                                                                      truncating=trunc_type,\n",
    "                                                                      value=0)\n",
    "\n",
    "X_val = np.array(padded_sequences_test)\n",
    "Y_val = np.reshape(np.array(is_sarcastic[training_size:]), newshape=(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((44262, 50), (44262, 1), (11066, 50), (11066, 50))"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, Y_train.shape, X_val.shape, X_val.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we visualize the word frequencies of our dictionary created in the last step via a word cloud."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "word_freq = dict(tokenizer.word_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('new', 2547),\n",
       " ('trump', 2101),\n",
       " ('man', 2091),\n",
       " ('woman', 1240),\n",
       " ('say', 1092),\n",
       " ('report', 1032),\n",
       " ('get', 969),\n",
       " ('one', 902),\n",
       " ('day', 849),\n",
       " ('make', 829),\n",
       " ('time', 816),\n",
       " ('us', 808),\n",
       " ('american', 793),\n",
       " ('area', 789),\n",
       " ('year', 770),\n",
       " ('donald', 733),\n",
       " ('like', 712),\n",
       " ('life', 692),\n",
       " ('first', 664),\n",
       " ('way', 638),\n",
       " ('trumps', 632),\n",
       " ('people', 628),\n",
       " ('house', 605),\n",
       " ('show', 582),\n",
       " ('obama', 578),\n",
       " ('thing', 565),\n",
       " ('take', 556),\n",
       " ('white', 556),\n",
       " ('find', 552),\n",
       " ('back', 549),\n",
       " ('still', 546),\n",
       " ('clinton', 514),\n",
       " ('want', 509),\n",
       " ('family', 508),\n",
       " ('could', 479),\n",
       " ('know', 466),\n",
       " ('world', 465),\n",
       " ('state', 465),\n",
       " ('call', 451),\n",
       " ('school', 449),\n",
       " ('right', 445),\n",
       " ('child', 439),\n",
       " ('need', 436),\n",
       " ('study', 432),\n",
       " ('bill', 416),\n",
       " ('black', 416),\n",
       " ('would', 407),\n",
       " ('home', 406),\n",
       " ('love', 402),\n",
       " ('gop', 400)]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(word_freq.items(), key=lambda x: x[1], reverse=True)[:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<wordcloud.wordcloud.WordCloud at 0x7fd806eb1e80>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wordcloud = WordCloud(width=1920,\n",
    "                      height=1920, \n",
    "                      mask=None,\n",
    "                      max_font_size=500,\n",
    "                      colormap='viridis',\n",
    "                      max_words=200)\n",
    "\n",
    "wordcloud.fit_words(word_freq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fd80acbc588>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(15, 15))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "ax.set_xticklabels([])\n",
    "ax.set_yticklabels([])\n",
    "ax.imshow(wordcloud)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This function plots the confusion matrix of the model predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(model):    \n",
    "    cm = confusion_matrix(Y_val.flatten(), model.predict(X_val).flatten()>0.5, labels=[1.,0.])\n",
    "    fig = plt.figure(figsize=(10, 10))\n",
    "    ax = fig.add_subplot(1, 1, 1)\n",
    "    ax.set_xlabel('PREDICTED')\n",
    "    ax.set_ylabel('ACTUAL')\n",
    "    ax.set_xticklabels(2*['']+['Positive']+3*['']+['Negative']+2*[''])\n",
    "    ax.set_yticklabels(2*['']+['Positive']+3*['']+['Negative']+2*[''])\n",
    "    ax.text(0., 0., str(cm[0,0]), fontsize=40, color='white', horizontalalignment='center', verticalalignment='center')\n",
    "    ax.text(1., 0., str(cm[0,1]), fontsize=40, color='white', horizontalalignment='center', verticalalignment='center')\n",
    "    ax.text(0., 1., str(cm[1,0]), fontsize=40, color='white', horizontalalignment='center', verticalalignment='center')\n",
    "    ax.text(1., 1., str(cm[1,1]), fontsize=40, color='white', horizontalalignment='center', verticalalignment='center')\n",
    "    ax.imshow(cm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This function shows the training history of the model to see how is performing in every step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_training(model):\n",
    "    loss = model.history.history['loss']\n",
    "    val_loss = model.history.history['val_loss']\n",
    "    accuracy = model.history.history['binary_accuracy']\n",
    "    val_accuracy = model.history.history['val_binary_accuracy']\n",
    "    fig = plt.figure(figsize=(10, 10))\n",
    "    ax1 = fig.add_subplot(2, 1, 1)\n",
    "    ax1.set_title('LOSS')\n",
    "    ax1.set_xlabel('iterations')\n",
    "    ax1.set_ylabel('loss')\n",
    "    ax1.plot(loss, marker='.', color='#FF00AE', markersize=1, linestyle='-', label='train')\n",
    "    ax1.plot(val_loss, marker='.', color='#0F00FF', markersize=1, linestyle='-', label='val')\n",
    "    ax1.legend()\n",
    "    ax2 = fig.add_subplot(2, 1, 2)\n",
    "    ax2.set_title('ACCURACY')\n",
    "    ax2.set_xlabel('iterations')\n",
    "    ax2.set_ylabel('accuracy')\n",
    "    ax2.plot(accuracy, marker='.', color='#FF00AE', markersize=1, linestyle='-', label='train')\n",
    "    ax2.plot(val_accuracy, marker='.', color='#0F00FF', markersize=1, linestyle='-', label='val')\n",
    "    ax2.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we train our first model and evaluate its performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Model_1():\n",
    "    model = tf.keras.Sequential([        \n",
    "        tf.keras.layers.Embedding(input_dim=vocab_size,\n",
    "                                  output_dim=16,\n",
    "                                  input_length=X_train.shape[1]),\n",
    "        tf.keras.layers.Flatten(),\n",
    "        tf.keras.layers.Dense(units=32, \n",
    "                              activation='relu',\n",
    "                              kernel_initializer='glorot_uniform',\n",
    "                              bias_initializer='zeros'),\n",
    "        tf.keras.layers.Dropout(rate=0.5),\n",
    "        tf.keras.layers.Dense(units=16, \n",
    "                              activation='relu',\n",
    "                              kernel_initializer='glorot_uniform',\n",
    "                              bias_initializer='zeros'),\n",
    "        tf.keras.layers.Dropout(rate=0.5),\n",
    "        tf.keras.layers.Dense(units=Y_train.shape[1], \n",
    "                              activation='sigmoid',\n",
    "                              kernel_initializer='glorot_uniform',\n",
    "                              bias_initializer='zeros')\n",
    "    ])\n",
    "    optimizer = tf.keras.optimizers.Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-07)\n",
    "    callback1 = tf.keras.callbacks.ReduceLROnPlateau(monitor='loss',\n",
    "                                                     patience=10,\n",
    "                                                     min_delta=0.001,\n",
    "                                                     factor=0.1,\n",
    "                                                     min_lr=0.0001)\n",
    "    callback2 = tf.keras.callbacks.EarlyStopping(monitor='loss',\n",
    "                                                 patience=20,\n",
    "                                                 min_delta=0.001)\n",
    "    model.compile(loss='binary_crossentropy', metrics=['binary_accuracy'], optimizer=optimizer)\n",
    "    model.summary()\n",
    "    model.fit(X_train, Y_train, epochs=100, batch_size=32, callbacks=[callback1, callback2], validation_data=(X_val, Y_val))\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding (Embedding)        (None, 50, 16)            160000    \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 800)               0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 32)                25632     \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 32)                0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 16)                528       \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 16)                0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 1)                 17        \n",
      "=================================================================\n",
      "Total params: 186,177\n",
      "Trainable params: 186,177\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Train on 44262 samples, validate on 11066 samples\n",
      "Epoch 1/100\n",
      "44262/44262 [==============================] - 3s 72us/sample - loss: 0.5125 - binary_accuracy: 0.7380 - val_loss: 0.3055 - val_binary_accuracy: 0.8710\n",
      "Epoch 2/100\n",
      "44262/44262 [==============================] - 3s 59us/sample - loss: 0.2251 - binary_accuracy: 0.9275 - val_loss: 0.2239 - val_binary_accuracy: 0.9173\n",
      "Epoch 3/100\n",
      "44262/44262 [==============================] - 3s 60us/sample - loss: 0.1029 - binary_accuracy: 0.9735 - val_loss: 0.2174 - val_binary_accuracy: 0.9392\n",
      "Epoch 4/100\n",
      "44262/44262 [==============================] - 3s 57us/sample - loss: 0.0518 - binary_accuracy: 0.9882 - val_loss: 0.2642 - val_binary_accuracy: 0.9457\n",
      "Epoch 5/100\n",
      "44262/44262 [==============================] - 3s 57us/sample - loss: 0.0322 - binary_accuracy: 0.9931 - val_loss: 0.3345 - val_binary_accuracy: 0.9476\n",
      "Epoch 6/100\n",
      "44262/44262 [==============================] - 3s 57us/sample - loss: 0.0208 - binary_accuracy: 0.9956 - val_loss: 0.3690 - val_binary_accuracy: 0.9473\n",
      "Epoch 7/100\n",
      "44262/44262 [==============================] - 3s 57us/sample - loss: 0.0171 - binary_accuracy: 0.9962 - val_loss: 0.4497 - val_binary_accuracy: 0.9479\n",
      "Epoch 8/100\n",
      "44262/44262 [==============================] - 3s 60us/sample - loss: 0.0133 - binary_accuracy: 0.9968 - val_loss: 0.4846 - val_binary_accuracy: 0.9486\n",
      "Epoch 9/100\n",
      "44262/44262 [==============================] - 3s 59us/sample - loss: 0.0122 - binary_accuracy: 0.9970 - val_loss: 0.5026 - val_binary_accuracy: 0.9494\n",
      "Epoch 10/100\n",
      "44262/44262 [==============================] - 3s 58us/sample - loss: 0.0089 - binary_accuracy: 0.9978 - val_loss: 0.5264 - val_binary_accuracy: 0.9487\n",
      "Epoch 11/100\n",
      "44262/44262 [==============================] - 3s 58us/sample - loss: 0.0078 - binary_accuracy: 0.9984 - val_loss: 0.5876 - val_binary_accuracy: 0.9486\n",
      "Epoch 12/100\n",
      "44262/44262 [==============================] - 3s 57us/sample - loss: 0.0079 - binary_accuracy: 0.9979 - val_loss: 0.6119 - val_binary_accuracy: 0.9494\n",
      "Epoch 13/100\n",
      "44262/44262 [==============================] - 3s 57us/sample - loss: 0.0071 - binary_accuracy: 0.9983 - val_loss: 0.6479 - val_binary_accuracy: 0.9482\n",
      "Epoch 14/100\n",
      "44262/44262 [==============================] - 2s 56us/sample - loss: 0.0081 - binary_accuracy: 0.9980 - val_loss: 0.7045 - val_binary_accuracy: 0.9470\n",
      "Epoch 15/100\n",
      "44262/44262 [==============================] - 3s 57us/sample - loss: 0.0060 - binary_accuracy: 0.9984 - val_loss: 0.8043 - val_binary_accuracy: 0.9498\n",
      "Epoch 16/100\n",
      "44262/44262 [==============================] - 3s 58us/sample - loss: 0.0050 - binary_accuracy: 0.9989 - val_loss: 0.7697 - val_binary_accuracy: 0.9482\n",
      "Epoch 17/100\n",
      "44262/44262 [==============================] - 3s 57us/sample - loss: 0.0053 - binary_accuracy: 0.9986 - val_loss: 0.7704 - val_binary_accuracy: 0.9472\n",
      "Epoch 18/100\n",
      "44262/44262 [==============================] - 3s 57us/sample - loss: 0.0055 - binary_accuracy: 0.9986 - val_loss: 0.8551 - val_binary_accuracy: 0.9471\n",
      "Epoch 19/100\n",
      "44262/44262 [==============================] - 3s 59us/sample - loss: 0.0049 - binary_accuracy: 0.9988 - val_loss: 0.8685 - val_binary_accuracy: 0.9466\n",
      "Epoch 20/100\n",
      "44262/44262 [==============================] - 3s 59us/sample - loss: 0.0078 - binary_accuracy: 0.9980 - val_loss: 0.8394 - val_binary_accuracy: 0.9487\n",
      "Epoch 21/100\n",
      "44262/44262 [==============================] - 3s 57us/sample - loss: 0.0047 - binary_accuracy: 0.9989 - val_loss: 0.8409 - val_binary_accuracy: 0.9478\n",
      "Epoch 22/100\n",
      "44262/44262 [==============================] - 3s 57us/sample - loss: 0.0046 - binary_accuracy: 0.9988 - val_loss: 0.9988 - val_binary_accuracy: 0.9482\n",
      "Epoch 23/100\n",
      "44262/44262 [==============================] - 3s 59us/sample - loss: 0.0043 - binary_accuracy: 0.9988 - val_loss: 1.1157 - val_binary_accuracy: 0.9470\n",
      "Epoch 24/100\n",
      "44262/44262 [==============================] - 3s 58us/sample - loss: 0.0045 - binary_accuracy: 0.9987 - val_loss: 1.1499 - val_binary_accuracy: 0.9466\n",
      "Epoch 25/100\n",
      "44262/44262 [==============================] - 3s 57us/sample - loss: 0.0048 - binary_accuracy: 0.9987 - val_loss: 0.9384 - val_binary_accuracy: 0.9468\n",
      "Epoch 26/100\n",
      "44262/44262 [==============================] - 3s 58us/sample - loss: 0.0055 - binary_accuracy: 0.9987 - val_loss: 1.0437 - val_binary_accuracy: 0.9483\n",
      "Epoch 27/100\n",
      "44262/44262 [==============================] - 3s 57us/sample - loss: 0.0045 - binary_accuracy: 0.9988 - val_loss: 1.1287 - val_binary_accuracy: 0.9483\n",
      "Epoch 28/100\n",
      "44262/44262 [==============================] - 3s 57us/sample - loss: 0.0052 - binary_accuracy: 0.9989 - val_loss: 1.1531 - val_binary_accuracy: 0.9471\n",
      "Epoch 29/100\n",
      "44262/44262 [==============================] - 2s 56us/sample - loss: 0.0047 - binary_accuracy: 0.9987 - val_loss: 1.2657 - val_binary_accuracy: 0.9477\n",
      "Epoch 30/100\n",
      "44262/44262 [==============================] - 3s 58us/sample - loss: 0.0037 - binary_accuracy: 0.9990 - val_loss: 1.2380 - val_binary_accuracy: 0.9483\n",
      "Epoch 31/100\n",
      "44262/44262 [==============================] - 3s 57us/sample - loss: 0.0026 - binary_accuracy: 0.9993 - val_loss: 1.2766 - val_binary_accuracy: 0.9475\n",
      "Epoch 32/100\n",
      "44262/44262 [==============================] - 2s 56us/sample - loss: 0.0027 - binary_accuracy: 0.9993 - val_loss: 1.2985 - val_binary_accuracy: 0.9475\n",
      "Epoch 33/100\n",
      "44262/44262 [==============================] - 3s 57us/sample - loss: 0.0025 - binary_accuracy: 0.9994 - val_loss: 1.3581 - val_binary_accuracy: 0.9474\n",
      "Epoch 34/100\n",
      "44262/44262 [==============================] - 3s 57us/sample - loss: 0.0029 - binary_accuracy: 0.9992 - val_loss: 1.4222 - val_binary_accuracy: 0.9479\n",
      "Epoch 35/100\n",
      "44262/44262 [==============================] - 3s 58us/sample - loss: 0.0026 - binary_accuracy: 0.9994 - val_loss: 1.4154 - val_binary_accuracy: 0.9474\n",
      "Epoch 36/100\n",
      "44262/44262 [==============================] - 3s 57us/sample - loss: 0.0031 - binary_accuracy: 0.9992 - val_loss: 1.4279 - val_binary_accuracy: 0.9472\n",
      "Epoch 37/100\n",
      "44262/44262 [==============================] - 2s 56us/sample - loss: 0.0024 - binary_accuracy: 0.9994 - val_loss: 1.5095 - val_binary_accuracy: 0.9470\n",
      "Epoch 38/100\n",
      "44262/44262 [==============================] - 3s 57us/sample - loss: 0.0023 - binary_accuracy: 0.9995 - val_loss: 1.4818 - val_binary_accuracy: 0.9466\n",
      "Epoch 39/100\n",
      "44262/44262 [==============================] - 2s 56us/sample - loss: 0.0031 - binary_accuracy: 0.9992 - val_loss: 1.5333 - val_binary_accuracy: 0.9463\n",
      "Epoch 40/100\n",
      "44262/44262 [==============================] - 2s 56us/sample - loss: 0.0026 - binary_accuracy: 0.9993 - val_loss: 1.6061 - val_binary_accuracy: 0.9467\n",
      "Epoch 41/100\n",
      "44262/44262 [==============================] - 3s 57us/sample - loss: 0.0032 - binary_accuracy: 0.9993 - val_loss: 1.6011 - val_binary_accuracy: 0.9463\n",
      "Epoch 42/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "44262/44262 [==============================] - 2s 55us/sample - loss: 0.0026 - binary_accuracy: 0.9993 - val_loss: 1.6815 - val_binary_accuracy: 0.9463\n",
      "Epoch 43/100\n",
      "44262/44262 [==============================] - 3s 58us/sample - loss: 0.0031 - binary_accuracy: 0.9992 - val_loss: 1.6739 - val_binary_accuracy: 0.9461\n",
      "Epoch 44/100\n",
      "44262/44262 [==============================] - 3s 60us/sample - loss: 0.0025 - binary_accuracy: 0.9993 - val_loss: 1.6309 - val_binary_accuracy: 0.9463\n",
      "Epoch 45/100\n",
      "44262/44262 [==============================] - 3s 57us/sample - loss: 0.0022 - binary_accuracy: 0.9993 - val_loss: 1.6524 - val_binary_accuracy: 0.9468\n",
      "Epoch 46/100\n",
      "44262/44262 [==============================] - 3s 60us/sample - loss: 0.0022 - binary_accuracy: 0.9994 - val_loss: 1.6156 - val_binary_accuracy: 0.9470\n",
      "Epoch 47/100\n",
      "44262/44262 [==============================] - 3s 57us/sample - loss: 0.0024 - binary_accuracy: 0.9993 - val_loss: 1.6057 - val_binary_accuracy: 0.9468\n",
      "Epoch 48/100\n",
      "44262/44262 [==============================] - 2s 54us/sample - loss: 0.0027 - binary_accuracy: 0.9993 - val_loss: 1.6724 - val_binary_accuracy: 0.9469\n",
      "Epoch 49/100\n",
      "44262/44262 [==============================] - 2s 54us/sample - loss: 0.0027 - binary_accuracy: 0.9994 - val_loss: 1.7435 - val_binary_accuracy: 0.9474\n",
      "Epoch 50/100\n",
      "44262/44262 [==============================] - 2s 53us/sample - loss: 0.0021 - binary_accuracy: 0.9995 - val_loss: 1.6794 - val_binary_accuracy: 0.9468\n",
      "Epoch 51/100\n",
      "44262/44262 [==============================] - 3s 60us/sample - loss: 0.0028 - binary_accuracy: 0.9992 - val_loss: 1.7337 - val_binary_accuracy: 0.9469\n"
     ]
    }
   ],
   "source": [
    "model_1 = Model_1()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "44262/44262 [==============================] - 1s 19us/sample - loss: 6.1827e-04 - binary_accuracy: 0.9996\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.0006182736356235895, 0.9996385]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_1.evaluate(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11066/11066 [==============================] - 0s 20us/sample - loss: 1.7337 - binary_accuracy: 0.9469\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[1.7337038075257034, 0.94686425]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_1.evaluate(X_val, Y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NkU/O4uPjWbduXZFOXpxzrFu3jvj4+KBDEREpspyDx++H+/4PLugEw8b4zlVF8luRvyGgTp06JCcnk5KSEnQoERUfH0+dOipXFxGJhL/+hFt7wrcT4Moe8OJbEBsbdFRSVBX55CwuLo4GDSIwgqyIiBR56ekw8E14+C5ITYXKVaDnTUrMJLIiVq1pZgPNbI2Zzc1mfVsz22Rms0KPB8PWtTezRWa2xMzujlSMIiJSdKWm+mGEDtXSJXDe6dDvJjjh3zB1Poyc6MdUFomkSLY5GwS0P8g23zvnEkKPRwHMLBZ4DegAHAN0NbNCOuKYiIgEYedO6NQejqwCF5/t767cvDln+6alwSvPQ5vmMHc2vPIOfDoe6jfwHcbmx7B+IgcSseTMOTcZWH8Iu7YCljjnljrndgMfAR3zNTgRESmydu+G7p1h0tdQujTMn+Pvrjy6BlxzGYwZ5bfJysL5cHYbeKAfnHYm/DwfrrxWCZkUrKDv1vy3mc02sy/NrGloWW1gedg2yaFlWTKzXmaWZGZJRb3Rv4iIHFhqKvToCuPHwAtvwtjvYX6y7yT2yh4w+Ru4/EI4+nC4tZfv4T89HfbsgeeegFNb+urMAR/C0FFQs1bQr0iKoyBvCJgB1HPObTWzc4CRQEMgq98n2faD4ZzrD/QHSExMLLr9ZYiIyAGlpfkSss8/hadegmuu37vuxNb+8eQL8N1EGD4ERnwIg9+GWrWh5GGwbClcdCk88wpUqx7c6xAJrOTMObfZObc1ND0WiDOzqviSsiPCNq0DrAwgRBERKSTS031J2PAP4eGn4IZbst4uLg7O6gD9P4Df/vYlZPWOhBXL4fHn4d2PlZhJ8AIrOTOzw4G/nXPOzFrhE8V1wEagoZk1AFYAXYBuQcUpIiLRzTm482b4YCDc+SD0vStn+5UpA527QqcuMGc2NGsR2ThFcipiyZmZDQXaAlXNLBl4CIgDcM69CXQGbjSzVGAH0MX5bvxTzawPMB6IBQY65+ZFKk4RESm8nIMH7oABr8Mtd8A9D+f+GGb+LkyRaGFFaVijxMREl5SUFHQYIiJSQJ54EJ59DHr2gWde1l2VUriY2XTnXGLm5UHfrSkiInJI/vdfn5hddR08/ZISMyk6lJyJiEih8/qL8Oi9cOnlvsuMGH2bSRFS5MfWFBGRwm/jRvhtASxaADN+gXffgvMvhtcHaZxLKXqUnImISMQsXgS39IQ1f8MRdaF6Daha3XdXkfFcrTpUqwFVq8GG9T4J+23h3mTstwV+/wwlSvg7Lfve7adFihr9WYuISERM+NL31m/mh0uKD3X0unYNbN168P3LV4BGTeCsc+DoxnB0E/9ctz4smKeuL6ToUnImIiL5yjl45Tl46C5o2hyGjIRNG30yldFof/t2WJviE7WU0GPOTN+J7IP/hQ7n+1K27Br5q+sLKcqUnImISL7ZsQNu7QnDhsCFl8Br7/oqyMxKl4a69fwjg7saLr923yROpDhSciYiIvliRTJccRHMTIL7H4fb781dkqXOYEU8JWciIpJnP/8EV14M27fBh6PgnAuCjkik8FLPMCIikifvD4TzT4cyZWHCVCVmInml5ExERA5JaircdSvc3ANanwrf/AJNmgYdlUjhp+RMRERybW0KXHw2vPUy3HArjPgSKlUOOiqRokHJmYiI5MqUH+DUljDle6hSDbpdrc5gRfKTkjMREcmRjP7LzmsLh8X79mWffaXOYEXym37riIjIQW1YDzdeDeM+hws6wSvvQIUKQUclUjQpORMRkQOaMQ26XwKrV8JTL8H1N6uTWJFIUrWmiIhkyTno/yqc3cZPf/k93HCLEjORSFPJmYiI7GfzZrjlOhg5HM4+D94crLsxRQqKkjMREdnHnNlw9SWwbCk88jTc3A9iVM8iUmCUnImICH8ug2/Gw4QvYcJYqFIVPv8WWp8SdGQixY+SMxGRYmjbNvjhO/h6vE/Klvzml1c/HOJLwVsfKDETCYqSMxGRYsA5mDfHJ2ITx8HUH2D3bihVCk5uCz16w3/OhqOOhrm/qu8ykSApORMRKcKcg3FfwNOPwKzpftkxx8L1t/hk7KSTIT5+332aJxR8nCKyV8SSMzMbCJwHrHHOHZvF+suBu0KzW4EbnXOzQ+uWAVuANCDVOZcYqThFRKLRe+/Aj5OgVx847oTcd1+Rng5jRsGzj8GvM6FmLahcBd7+EP7TLjIxi0j+iOT9N4OA9gdY/wdwmnOuOfAY0D/T+tOdcwlKzESkuPllCtzaEz5+H/5zIiQ2gv8+DIsXHXzf9HTf/cXJCXDlxbB1C7z2Lsz+A0ZOhDPOinj4IpJHEUvOnHOTgfUHWP+Tc25DaHYqUCdSsYiIFBZbt8L1V0KdujB8LLz0NtQ+Ap55FE5oDG0T4dX/wcoV++6XlgbDP4TWzeDqS2HPbnjrffhlAVx+NZQs6asr1YGsSPSLljZnPYAvw+Yd8JWZOeAt51zmUrV/mFkvoBdA3bp1IxqkiEik3Xub719szKS9d0t2vw5WrYRPP4YRH8L9t8MD/XxD/ku6QYk4+N+T/o7LJk3hnaFw4SUQGxvoSxGRQ2TOucgd3Kw+8EVWbc7CtjkdeB042Tm3LrSslnNupZlVByYAN4dK4g4oMTHRJSUl5UvsIiIFbexo6NYR+t4FDz+V/XaLF8GIoT5R+32xX3ZMM7jrITj/InUYK1JYmNn0rJpvBfovbGbNgQFAx4zEDMA5tzL0vAb4DGgVTIQiIgVjzd9+uKRmCXDvowfetmEjuOdhSFoEb38ANWrCG4OgYyclZiJFQWDVmmZWF/gUuNI591vY8jJAjHNuS2i6HXCQjyoRkcLLObj5Otiy2ffKX7JkzvYzg87doFFT9UsmUpREsiuNoUBboKqZJQMPAXEAzrk3gQeBKsDr5luoZnSZUQP4LLSsBPChc25cpOIUEQna4Ldh/Bfw3xd9m7HcMFO/ZCJFTUTbnBU0tTkTkcLm98VwSgK0ag2fjle1pEhxEpVtzkREirPUVN9tRsnD4PVBSsxExIuWrjRERIqd556ApJ9h4EdQq3bQ0YhItNDvNBGRAEz/xQ+tdOnlcPFlQUcjItFEyZmISAHbtg16XeHHu3zm1aCjEZFoo2pNEZEC9kA/WLoERn8DFSsGHY2IRBuVnImIFKDxY2Dgm9DndjilbdDRiEg0UsmZiBQJa1Pg3bfg15nw7sdQIoo+3X5bCGNH+ccvU+DIo+C+x4KOSkSiVRR9fImI5N68OfDmSzDsA9i1yy97bwBce0NwMaWnw7SpexOyxYv88hbHQY8b4asxPmFT57EikhUlZyJS6KSn++rBN16Eyd9AqVLQ7Wro3gvOOw1+mlzwydmOHTDpaxgzEsZ9DilrfOndKadDr5uhwwVQ5wg/VNOcXhpuSUSyp+RMRAqNzZvhw0Hw1svwx+9Quw48/BR07wmVKvttLr0Chg7225YvXzBxLZgH55/uq1bLloV258I5HeHMDvs3+NdwSyJyMErORCTqrV7l73D8cjRs3QontoYHn4TzLoK4uH237drdN7gfORyu6hH52P5cBhe38yVi1Q+HoaPg+FaRP6+IFF1KzkQkqu3ZA+e1hSW/wVkd4J5H4LgTst8+8UQ46mhfehbp5GzN33DRWbB9O4yZ5KtbVV0pInmlrjREJKo994RPzB5/HoaNOXBiBr7asGt3mPK9r/qMlE2boFN7WL0Sho+FY5v76kqzyJ1TRIoHJWciErVmzYDnn/BDHPX5v5wnPpdd6bcd+l5k4tq+HbqcDwvnwXufQKt/R+Y8IlI8KTkTkai0axf0vhqqVoOnX87dvnWOgNP+Ax+956sa89OePXDNpTD1B3jrfTizff4eX0REyZmIRKVnH4P5c+DF/nvvxMyNrt3hr2Xw0/f5F1N6Otx0je/G4/nXNWC5iESGkjMRiTozpsELT/m+y9qfd2jHOO8i363F0MH5E5NzcHdfGDYE7n882E5uRaRoU3ImIlFl5064sTvUOByefOHQj1OmDHS8BEYNh23b8h7XM49B/1eg921w+715P56ISHaUnIlIVPnvQ7BoAbw8YP8OXHOr29W+X7TPP83bcfq/6uPq2h0ef053ZIpIZCk5E5Go8csUeOU53+N/fjS0//fJUK9B3qo2h38Id4aGX3plAMToU1NEIkwfMyISFbZv93dn1qoDjz2XP8eMiYEuV/nxN5OX537/KT/ADVdBi+Nh4Ed+rEwRkUhTciYiUeHx+31ns6++k79jYna9yjfm//j93O23bi306AI1asKGdbB4Uf7FJCJyIErORCRwP30Pb7wIPW6Etmfm77HrHwmtT/VVm87lbJ/0dH9TwtoUGDISPvhMwzKJSMGJaHJmZgPNbI2Zzc1mvZnZy2a2xMx+NbPjwtZ1N7PFoUf3SMYpIsHZts33HVa3PjzyTGTO0bW7L5WbNjVn27/6PHw1Fp74H7Q8XsMyiUjBinTJ2SDgQM16OwANQ49ewBsAZlYZeAg4EWgFPGRmlSIaqYgE4pF7/BiYr73r+yWLhI6doVSpnN0Y8PNPPqaOneG63pGJR0TkQCKanDnnJgPrD7BJR+A9500FKppZTeBsYIJzbr1zbgMwgQMneSJSCE36xvcd1utmOPm0yJ2nfHk4/2L49GPfj1p21q/z7czq1PVdeai0TESCEHSbs9pA+D1UyaFl2S3fj5n1MrMkM0tKSUmJWKAikr/S0uDmHv4OyE5dI3++blfDpo3w5eis1zsHva+Bv1fDoGFQoULkYxIRyUrQyVlWv0vdAZbvv9C5/s65ROdcYrVq1fI1OBGJnE8/9mNfPvAktDop8uc75XSoXSf7qs3XXoBxn/tuPFomRj4eEZHsBJ2cJQNHhM3XAVYeYLmIFAFpafDMo9CkKdx8e8FUH8bGwmVXwsRxsHrVvuumTYWH7/LjcV5/c+RjERE5kKCTs9HAVaG7Nk8CNjnnVgHjgXZmVil0I0C70DIRKQI+G+b7DbvroYLtcb9rd99NxvAhe5dt3ADXdvGd3776jtqZiUjwItrftZkNBdoCVc0sGX8HZhyAc+5NYCxwDrAE2A5cE1q33sweA6aFDvWoc+5ANxaISCERXmp2QaeCPXfDRpB4oq/a7HO7X9b7Gli9Esb9ABV1T7iIRIGIJmfOuQM283XOOeCmbNYNBAZGIi4RCc7I4fDbQnj342DGqex2NfzfjTB7Jkz5HsaO8v2ZHd+q4GMREcmKuZx2mV0IJCYmuqSkpKDDEJFspKVB62a+6vCnOcEkZxs3QKOa8O9T4MdJcGYH+HCkqjNFpOCZ2XTn3H63IGkYXxEpMKNGwKIFfhDxIBIz8FWX7c/3sdQ+Al5/V4mZiESXoG8IEJFiIj3dtzVr1MT3vh+ks87xd2/e/zhUqhxsLCIimankTEQKxKgRsHA+vDPUJ0ZBuvxqP15ms4Rg4xARyUqOSs7M7FYzKx/q8uIdM5thZu0iHZyIFA3hpWYXXhJ0NL4as3lLVWeKSHTKabXmtc65zfj+xqrhu7x4KmJRiUiRMvoTWDAP7ngg+FIzEZFol9PkLOP35TnAu8652WQ9xJKIyD7S0+HpR+DoxnDRpUFHIyIS/XLa5my6mX0FNADuMbNyQHrkwhKRouLzT32p2dtDVGomIpITOU3OegAJwFLn3HYzq0yoN38RkexklJo1bAQXXxZ0NCIihUNOk7N/A7Occ9vM7ArgOOClyIUlIkXB55/B/LnQ/wOVmomI5FRO25y9AWw3sxbAncCfwHsRi0pEIs45SF4OE76EAa/DF5/5Zfklo9TsqKOhU5f8O66ISFGX05KzVOecM7OOwEvOuXfMrHskAxOR/OEcrPkbFs7zpVgL5vrphfNg8+Z9t23SFHr09lWQlavk7bxfjIT5c+Ct91VqJiKSGzkaW9PMJgHjgGuBU4AUfDVns8iGlzsaW1NkrzmzoW8vWLoENqzfu7xyFWhyLBxzrH9u3BRqHA4D34RvJ/iEKi7OD3HUtTuc1cHP50Z6OpzSEnbthKnzoIS6uxYR2U9ex9a8DOiG7+9stZnVBZ7NzwBFJP/8vhguOB02bYZzLoA2p+1NxqpVz7rz1See98+/zoKhg2H4EH+nZdVq0LkbdLkKWuSw4wQWPrAAACAASURBVNYxo2Der77UTImZiEju5KjkDMDMagAnhGZ/cc6tiVhUh0glZyKwcgWc3Qa2bYWXB8C5HQ+tJ/w9e+Dr8T5R+3I07N4NjY+BChXhsHhITYXUPX67PXv2nV67BipXhZlLcl/qJiJSXOSp5MzMLsWXlH2H73z2FTO7wzk3Il+jFJE8WbcWLjoLNq6Hz7+FhOMP/VhxcdD+PP/YsB4+/Rg+GAgzp/sOZStUhNJl/HYl4nwJWcb01i0wY5rv36y5xq8UEcmVnLY5mw2clVFaZmbVgInOuRYRji9XVHImxdmWLdDxP7468ZPxcPJp+X8O53xbtmYtDlwal9PtRESKs7y2OYvJVI25jpx3wyEiEbZzJ1x+IcyeAR98FpnEDEIDhuegJCyn24mIyP5ympyNM7PxwNDQ/GXA2MiEJFK0bNnin8uVi8zxU1OhR1eY/A28+R50OD8y5xERkYKRo9Iv59wdQH+gOdAC6O+cuyuSgYkUBRs3wqktoWF16NfH30WZn9LT4ZaeMGYkPPUSdLkyf48vIiIFL8dVk865T5xz/+ecu80591kkgxIpCtLT4forYPmfEBMLg96CxEbQ5QKY/G3ee+N3Du7vBx8OgrsfhhtuyY+oRUQkaAdMzsxsi5ltzuKxxcw2H2hfkeLumcdg/BhfojXuB5j7F/S7H6ZNgQvO8J20DhkEu3Yd2vGffxJefwF63Qx3PZifkYuISJBy3M9ZYaC7NSVajPsCuoR62H/93X3vWNy5E4YNgTdf9MMpVavuh0zqcaOfzol33oDbe8OlV8CbgyFGt+eIiBQ62d2tqeRMJJ8tXQJtE6H+kTD+RyhVKuvtnINJX/vSr6/G+j7CmreEcuX9uvT0vQ8Xek5L8zcAzJ4BZ58HH3yqTl5FRAqr7JKziP7eNrP2ZrbIzJaY2d1ZrH/BzGaFHr+Z2cawdWlh60ZHMk6R/LJtG1xxsR/o+/1Ps0/MwJemtT0Tho2BaQvhvIt832Apf8POHb7HffCdux4WD2XKQsVK/pjlykO/+5SYiYgURREb9c7MYoHXgLOAZGCamY12zs3P2MY5d1vY9jcDLcMOscM5p56SpNBwDm7tCQvmwifjoF79nO/bsBEM/Cj3HbyKiEjRE8mSs1bAEufcUufcbuAjoOMBtu/K3n7URAqdN16CEUPhgSfgjHa53z+j49aD9aif0+1ERKRwimRyVhtYHjafHFq2HzOrBzQAvglbHG9mSWY21cwuzO4kZtYrtF1SSkpKfsQtkms/TIIH+vmqydv2q8AXERHJuUgmZ1n9rs/u7oMuwAjnXFrYsrqhRnLdgBfN7F9Z7eic6++cS3TOJVarVi1vEYscgpUr4JpL4cij4PVBKtESEZG8iWRylgwcETZfB1iZzbZdyFSl6ZxbGXpeCnzHvu3RRKLCrl3QvTPs2O5vAChfPuiIRESksItkcjYNaGhmDcysJD4B2++uSzNrBFQCpoQtq2Rmh4WmqwJtgPmZ9xUJ2j19YdpUeG0QND4m6GhERKQoiNjdms65VDPrA4wHYoGBzrl5ZvYokOScy0jUugIfuX07XGsCvGVm6fgE8qnwuzxFosF778DAN+GWO6Bjp6CjERGRokKd0Irk0p498NTDfvik+Hj48ntouV8XgiIiIgeWXSe0ESs5EymK/vgdrusG03+BK66FK3tAwvFBRyUiIkWJkjORHProfejXG2JLwKBhcOElQUckIiJFkYZLFjmITZug1xVww1XQrCX8MFuJmYiIRI6SM5ED+GUKnNoSPvkI7n0UvvgWjqgbdFQiIlKUKTkTyUJaGjz7OHQ4xY9lOXYy3PmAH9BcREQkktTmTCST5X/5aswp30PnrvD8G1ChQtBRiYhIcaHkTCRk5Qp48yUY+IYvOXtjMHS5UsMxiYhIwVJyJsXegnnwynMwfIhPytqeCYsWQNPmSsxERKTgKTmTYsk5+HEyvPIsjB8DpUrB1dfDTf8H9erDnNnQrEXQUYqISHGk5EyKlbQ0+PxTePlZmDENqlT1d2Fe1xsqV9m7XfOE4GIUEZHiTcmZFAtrU+DTj+HFp2FlMjT4F/zvDeja3ZeaiYiIRAslZ1Jk/b4Yxo7yj59/gvR0KHkYPPos3HSbusUQEZHopORMioz0dF9VOXYUjBnpG/UDHNsC+t0P53SEGINmCWroLyIi0UvJmRRq6enw9XifjI37HFav8iVibU6Da26ADhf4Bv4iIiKFhZIzKbTWr4PrusE3X0GZMnBmB1861u4cqFQ56OhEREQOjZIzKZRmJsFVnWHVCn+X5bAxkHhi0FGJiIjknZIzKXTeGwD9boLqNWD8jxBXUn2SiYhI0aHkTAqNHTvgjj7wwUA4/SwY8KHvp0xERKQoiQk6AJGcWPYHnN3GJ2b97ocRXyoxExGRokklZxL1vhoLva7wQy599Dm0Py/oiERERCJHJWcStdLS4MmH4LLzoE5dmDRdiZmIiBR9KjmTqLR0CdzY3ffs37W7H2pJwyyJiEhxoORMokJaGiT97DuS/XI0LJzvl/e7D+57TD36i4hI8RHR5MzM2gMvAbHAAOfcU5nWXw08C6wILXrVOTcgtK47cH9o+ePOucGRjDVHXl4CG/fAA42VLeSDrVvh2wk+GftqjB+cvEQJaH0qXNUTjjoazuqgt1pERIqXiCVnZhYLvAacBSQD08xstHNufqZNP3bO9cm0b2XgISARcMD00L4bIhVvjgz8ExZshQtqQkLFQEMpjJyDP36HIe/C7Bnw/bewaxdUqOh79W9/PvynPVTUWysiIsVYJEvOWgFLnHNLAczsI6AjkDk5y8rZwATn3PrQvhOA9sDQCMWaMxfVgtkLoXZ8oGEUFrt2wa8zYeqP8POP8MtPsOZvv65WHejR2499eVIbiIsLNlYREZFoEcnkrDawPGw+GchqgJ1OZnYq8Btwm3NueTb71s7qJGbWC+gFULdu3XwI+wDOrgEPL4Rv1sJldSJ7rkJobQr8MmVvIjZjmk/QAOofCae3gxNbQ7XqcO6FEKN7hUVERPYTyeQsq5ZCLtP858BQ59wuM7sBGAyckcN9/ULn+gP9ARITE7PcJt8kVoSKcfDVGiVnIbt3w8jh8MaLfrxL8KVgCcdDzz7QqrVPyGocHmycIiIihUUkk7Nk4Iiw+TrAyvANnHPrwmbfBp4O27dtpn2/y/cIc6tEDPynmk/OnCvWLdU3rIdB/aH/K7BqJdStD5Uqw2PPQacu6vZCRETkUEWyYmka0NDMGphZSaALMDp8AzOrGTZ7AbAgND0eaGdmlcysEtAutCx47apD8g5YuCXoSAKx5Dc/6HjTI+CRe6BxUxg+FmYugVFfw+VXKzETERHJi4iVnDnnUs2sDz6pigUGOufmmdmjQJJzbjRwi5ldAKQC64GrQ/uuN7PH8AkewKMZNwcErl11//zVGmhSPthYCohz8MMkeO1/MP4LX215yeXQ+zZo2mzvds0TgotRRESkqDDnIttMqyAlJia6pKSkyJ+o0QQ4qgyMaR35cwVs1Cfw+H2weJEfaLxHb7iuN1SvEXRkIiIihZuZTXfOJWZerhECDkW76r7Ps11pcFhs0NFEzNuvwR19fEnZnQ/AbfeoylJERCTS1JnBoWhXHbanwU/RUdMaCYPf9olZhwtg/I9wzyNKzERERAqCSs4ORduqUMJ8u7PTqwUdTb77cDD0vd732j9oGBx2WNARiYiIFB8qOTsU5eKgdWUY/3fQkeS74R/CTddA2zPhvU+UmImIiBQ0JWeHql0NmLkJ1uwKOpJ8M3I43HAVtDkNhoyEeI1SJSIiUuCUnB2qjC41Jq4JNo58MmYUXNcNTjgJPvocSpcOOiIREZHiScnZoTquIlQODeVUyH01Fq6+xA+5NGwslC0bdEQiIiLFl5KzQxVrcGb1vUM5FVLffAVXXgxNm8OIcVC+ePSrKyIiErWUnOVFu+qwaifMK5xDOU3+Frp1hIaN4bOvoGLFoCMSERERJWd5cVbGUE6F767Nn76HLudBg3/ByAl+0HIREREJnpKzvKhbGhqXLVTtzhYvgtt7w0VnQdVqMHKifxYREZHooOQsr9rVgElrYWda0JFkyzn4dgJcei6c0BjefwfOaAfpDv5eHXR0IiIiEk7JWV61qw470+GHdUFHsp8dO+C9AdC6GVzUDmYmwd0Pw9y/4MNR/tGsRdBRioiISDgN35RXp1WFuNBQTmdWDzoaAFavggGvw7tvwrq1cGwLeH0QdOqyb4//zRMCC1FERESyoeQsr8qWgDZVfHL2TLChrF8H99wGnwyF1FQ4pyPc2BdOPg3Mgo1NREREckbJWX5oVx3unQ+rd8LhwYx59Ocy6Nweli2FUqXh7Q+g/fmBhCIiIiJ5oDZn+eHsGv45oKGcfp0F7f4Na/6GUV/DmElw9nmBhCIiIiJ5pOQsF1JTIT09ixUJFaBqyUC61Jj0NZx7KsTFwfgfofUpvi2ZqjFFREQKJyVnOZSaCld28oOD75egxZjvkLaAh3Ia/iF07gBH1IevpkDjYwrs1CIiIhIhSs5yKDYWyleATz+Gm67JIgdrVx3+3gVzNkc8Fufgleeg5+VwYhsYOxlq1Y74aUVERKQA6IaAHDKDNwbB7l0w9D1fWnXvI2Eb/DOU0xpoXiFicaSnw323wxsvwkWXwpvv7ds9hoiIiBRuKjnLhZgYeGcoXHEtPPMovPBU2MrapaBpuYi2O9u1C3p09YnZDbf6WJSYiYiIFC0qOculmBh4qT/s2gmP3AOHxUPvvqGV7arD63/AjjQoFZuv5920CS6/EH74Dh59Bm7up0b/IiIiRVFES87MrL2ZLTKzJWZ2dxbr/8/M5pvZr2b2tZnVC1uXZmazQo/RkYwzt2Jj4Y3BcP7FcO9t8O5boRXtasCudPh+bb6e7/fFcHoiTPke+n8At9yhxExERKSoilhyZmaxwGtAB+AYoKuZZb6fcCaQ6JxrDoxg3z72dzjnEkKPCyIV56EqUcJXK559Ltx2A3w4GDi1CpSMybeqzW3b4PH74cRj4I/f4dlX4dLL8+XQIiIiEqUiWa3ZCljinFsKYGYfAR2B+RkbOOe+Ddt+KnBFBOPJdyVLwuAR0OV86HMtxMeX4OJTquQ5OXMORn8K990GycvhksuhW3doe2Y+BS4iIiJRK5LVmrWB5WHzyaFl2ekBfBk2H29mSWY21cwuzG4nM+sV2i4pJSUlbxEfgvh4GDLSd2nR83IYU7uB705j5Y5DOt6iBXBRO+jeGSpU8t1kvP0BnH6WqjJFRESKg0gmZ1mlEln20GpmVwCJwLNhi+s65xKBbsCLZvavrPZ1zvV3ziU65xKrVauW15gPSZkyMGwMtEyEa4bWYiLVYULuSs+2bIEH74Q2zWHGNHjmFZg03ff4LyIiIsVHJJOzZOCIsPk6wMrMG5nZmcB9wAXOuV0Zy51zK0PPS4HvgJYRjDXPypWDEV9Co6ZwOSfxzOMwM8mRvBx27sx+P+d8T/8nNIKXn4UuV8H036BXH9+uTURERIqXSH79TwMamlkDYAXQBV8K9g8zawm8BbR3zq0JW14J2O6c22VmVYE27HuzQFSqWAlGTjDOrLebJ5fU48kT9q4rWxaqVoeq1aBKtdBzVT825uwZkHA8fPAZJJ4YXPwiIiISvIglZ865VDPrA4wHYoGBzrl5ZvYokOScG42vxiwLDDffoOqv0J2ZTYC3zCwdX7r3lHNufpYnijJVqsKPsxwjm82k/JElWXfrMaxdZ6xNgXUpkLIGVibDnJmw5m/f43+/++Geh30XHSIiIlK8mSvAgbojLTEx0SUlJQUdhjdgGfScCf0ToGeDLDdJT4c5s6B5SzX2FxERKW7MbHqoff0+NHxTpPSoB6dXhTvmZXvnZkwMtDhOiZmIiIjspeQsUsygf0vYlQY3zfYt/0VEREQOQslZJB1VFh5pAiNXwSf73agqIiIish8lZ5H2f0fBcRWhz2xYvzvoaERERCTKKTmLtBIxMKAlrN0N/eYEHY2IiIhEOSVnBaFlReh3FLz7F0zMn0HRRUREpGhSclZQHmoCDcvA9bNge2rQ0YiIiEiUUnJWUErFwtvHwdJt8OCCoKMRERGRKKXkrCCdVhV61YcXlsC0DUFHIyIiIlFIyVlBe7op1IiH62bAnvSgoxEREZEoo+SsoFUsCa+3gF83w7OLg45GREREooySsyBcWAs614JHF8KiLUFHIyIiIlFEyVlQXmkBpWKg08+wKuuxN0VERKT4UXIWlMPj4d5GMG8LNJoIg//U+JsiIiKi5CxQ/RrCpyfCseXg6hnQ4Sf4c3vQUYmIiEiAlJwFyQwuqgU/nAavNIcf1kHTifDq75CuUjQREZHiSMlZNIgx6PMvmPsfaFMFbv4VTp0MC3WzgIiISHGj5Cya1C8D41rDoONg/hZo8Q08uUj9oYmIiBQjSs6ijRl0rwfzz4QLDof75kOr72C6RhQQEREpDpScRavD42H4ifBJK1i5AxK/g6PGQ88ZMGQ5rFD3GyIiIkVRiaADkIO4uDZULQkX/wJ1SsHwlTDgT7/uqDLQtiq0reafa5cKNlYRERHJMyVnhcEpVWFiG2hRAdKBXzfBd2vhuxQYkSlZO6UKVIrzg6wfXQ6OLAMlVUAqIiJSWJgrQh2fJiYmuqSkpKDDKFhpbt9kbdJa2JS6d30MUK80NCzrk7eGZUOPMlClJJQt4ZM3s8BegoiISHFkZtOdc4mZl0e05MzM2gMvAbHAAOfcU5nWHwa8BxwPrAMuc84tC627B+gBpAG3OOfGRzLWQivWoGVF/7jtKD/KwOS1cFgMLNkGi7fBb1th8VaYuh42p+5/jBLmk7SyJaBc6LlsrH8uEwu7032CVy7Ory+Xadtyoe3jY/2xYg1KxPirXiImNG971ykRFBERyVbEkjMziwVeA84CkoFpZjbaOTc/bLMewAbn3FFm1gV4GrjMzI4BugBNgVrARDM72jmXFql4iwwzOK2anz6pyr7rnIOU3T5R+yYFXvodutbxydXW1NAjDbaEppfvgLW7YNVOiIuBnfnUpUcsUKZE6BELpWP9dOlYP1+mhB93dFsaHH6YT/BKWNaJXiywehfUjt836cuqQNg5WLULjoj3x4oxX7IYa3464zkG+GO7P2Y6kJrufyKkudC084896ZCyy3eBEh/rE+J/HmHz8bEQF3aOAz1iQrE79g7nFT6f8bqyWk/YNuGvP4bQ67K908b+8zvTYUeaf2zP9Bw+vWanrzLPuGb7PMKuaYlcVKdbxsP2nc6N7N6vdLf/sozp9ND6tLDprOZjwv/mMk/H7J2G0L4Zj0zzaeztYPqf65DpmoQ/h/9t6EeNSLERyZKzVsAS59xSADP7COgIhCdnHYGHQ9MjgFfNzELLP3LO7QL+MLMloeNNiWC8RZ8ZVD/MP1pXhvMP9+3YDvSh7xzM3rS3vdv21L3J25aMZG4PzNkMLyyBGxtArVKhRMbtTWgyppN3wMcroEMN/yW+LfSlvy3VP6/d7ac37YF1u6FUrP8izdg/rehUw0sOWdizC3uGrJPwoiqGfX+g/JO44d+H+Jis12csi3E+AS8T6//nM7934Qn+9jSfYGckiRlJM2Sad7AlDUrH+BXhiXDGdEZiuiPjmDFhCTj7JuTgP1tKx4Ydh72JdEZynZa+97XExuz7QyP8x4g52JYO5WL3fs6Ff9xlLHPOn7dU7L4Je0aSnjGdlu7fm1KxYT/msvmxY+w9ZsbrCE/aw38I7E7f/5iZX0/G8UpnPh6Zfmgc5L2JDYt5W5p/bzKuSebraxnvTRqUL3Hw74otqf7HPrbvj6WM54xl6aG/mzKhv5usfjhl/O1k/C0e7Nw70nxNTpbvYWja8LVHZWPBWfY/pNLSYY+DpNMD+1EUyeSsNrA8bD4ZODG7bZxzqWa2CagSWj410761szqJmfUCegHUrVs3XwIvFswgoWLutoslVLUZt/925x7uE66cJHs3NMhdUrhPiVjYh2ZqKPGbtQmald//eJkPn+58Etmk3N4Pt3/+Gd2+H8i/bYWm5SAuNpvSktA/+8xNcHQZ2O1gV3rYI80/70yHBZvhyd/g9qN89XCay+KBH1f17WVwXT04ovS+X1jhH5bLt8Oby3wiXK/03tca/uVjwF874JXfoc+R/k7f8A/zzF+cy3fA4L/g5iOhcTn/RVEqVAIW/nxYjL8u9UvDjnSfrG9P25tkZ8wv3grvL4duR0Ct+Oyv88qd8OFyX4J7eHz2H9KrdsInK6FTLagZv+/1DU8cVu+EYSvgsjr+vOFflOFfOqt2wHvL4Zp6cESp/UtO/5kPex/Df3iE//jImE7eAR8n+9dcJ37fL4XwL4wVO0LXub4vnf3nOrBvEpK8w7+HXer4H1ThP3bCz796J0xIgZMqQ4W47OPbuBsWbYXmFfwXclb/I2b+x9bi0HYZiUVWpZLp+C/jhVugafnQl3dM1iWAW/bAgq1wXAX/+ZHVNXbA5j0+xhYVoHxc6As17Is143ibU+GXDZBY0ScDmf+2M6Y3pfoOvRuX9V/c4Qlp+PS2VH/exmX9e1jCfFvc8BLtWPOv9+cN/r0uXyKb/6fQ86bQa0moABXjsk4WYsxvN2U9nJjFMcNfz8aw96ZC3L4JR/gxN6X6JiytKoWOF3as8GRkcyrM2+z/38vE7p9EZcxvTfVNZI4q40vGs7M91TejaRjabp/Pr7C/Lwu93wu3+s/iciX2/m9mvJ6Mv6Otqf5vsUUFf/2ys2WP/8w+toJP+NJDf/OZk+GtqbB0OxxVGsrG7f+/GWc+od3q/N/17E05+56MBOdcRB7AJfh2ZhnzVwKvZNpmHlAnbP53fHL2GnBF2PJ3gE4HO+fxxx/vRKJOerpzMzf452jcLrfb5kS0v+ZIvDfFbbvCEKPem+jbrjDEmN+fhwcAJLks8pmI3a1pZv8GHnbOnR2avyeUDP43bJvxoW2mmFkJYDVQDbg7fNvw7Q50zmJ5t6aIiIgUStndrRnJDrCmAQ3NrIGZlcQ38B+daZvRQPfQdGfgm1AmORroYmaHmVkDoCHwSwRjFREREYkKEWtz5nwbsj7AeHxrpYHOuXlm9ii+GG80vrry/VCD//X4BI7QdsPwNw+kAjc53akpIiIixYA6oRUREREJQBDVmiIiIiKSS0rORERERKKIkjMRERGRKKLkTERERCSKKDkTERERiSJF6m5NM0sB/ozwaaoCayN8DskdXZPopOsSfXRNopOuS/QpqGtSzzlXLfPCIpWcFQQzS8rqtlcJjq5JdNJ1iT66JtFJ1yX6BH1NVK0pIiIiEkWUnImIiIhEESVnudc/6ABkP7om0UnXJfromkQnXZfoE+g1UZszERERkSiikjMRERGRKKLkTERERCSKKDnLITNrb2aLzGyJmd0ddDzFlZkNNLM1ZjY3bFllM5tgZotDz5WCjLG4MbMjzOxbM1tgZvPM7NbQcl2XAJlZvJn9YmazQ9flkdDyBmb2c+i6fGxmJYOOtbgxs1gzm2lmX4TmdU0CZmbLzGyOmc0ys6TQssA+w5Sc5YCZxQKvAR2AY4CuZnZMsFEVW4OA9pmW3Q187ZxrCHwdmpeCkwrc7pxrApwE3BT6/9B1CdYu4AznXAsgAWhvZicBTwMvhK7LBqBHgDEWV7cCC8LmdU2iw+nOuYSw/s0C+wxTcpYzrYAlzrmlzrndwEdAx4BjKpacc5OB9ZkWdwQGh6YHAxcWaFDFnHNulXNuRmh6C/5Lpza6LoFy3tbQbFzo4YAzgBGh5bouBczM6gDnAgNC84auSbQK7DNMyVnO1AaWh80nh5ZJdKjhnFsFPlEAqgccT7FlZvWBlsDP6LoELlR9NgtYA0wAfgc2OudSQ5vos6zgvQjcCaSH5qugaxINHPCVmU03s16hZYF9hpUoqBMVcpbFMvVBIhLGzMoCnwB9nXObfYGABMk5lwYkmFlF4DOgSVabFWxUxZeZnQescc5NN7O2GYuz2FTXpOC1cc6tNLPqwAQzWxhkMCo5y5lk4Iiw+TrAyoBikf39bWY1AULPawKOp9gxszh8YjbEOfdpaLGuS5Rwzm0EvsO3CaxoZhk/zPVZVrDaABeY2TJ885gz8CVpuiYBc86tDD2vwf+QafX/7d17fFx1ve//1yf35tJb2kJpadNCuRSBVkpBuSMq4lZQOUcQt4i66wW8oCjoVkE2HOvBnxtxq2xUZMvhIoIox8NWgRaKAtIUCqWF0tICTUvb9JK0SZsmmXx+f3zXNCvTpJ0kM5lJ8n4+HvNY97W+M2su7/muy5ccfocpnKVnMTAjuqKmBLgIeCjHZZJODwGXRv2XAn/MYVmGneicmV8BL7v7j2KTtF9yyMzGRzVmmNkI4BzC+YALgQuj2bRfBpC7f9PdJ7t7DeF3ZIG7X4L2SU6ZWYWZVSX7gfcAL5HD7zC1EJAmMzuP8A+nELjd3W/McZGGJTO7BzgTGAdsAq4F/gDcB0wB3gT+h7unXjQgWWJmpwJPAsvoPI/mW4TzzrRfcsTMjiOcxFxI+CN+n7tfb2bTCbU2Y4HngY+7+57clXR4ig5rXuXu/6R9klvR6/9gNFgE3O3uN5pZNTn6DlM4ExEREckjOqwpIiIikkcUzkRERETyiMKZiIiISB5ROBMRERHJIwpnIiIiInlE4UxEhgQzeyrq1pjZxzK87m91ty0RkWzQrTREZEiJ3z+qF8sURk0d9TS9yd0rM1E+EZEDUc2ZiAwJZtYU9c4HTjOzpWZ2ZdT4901mttjMXjSzz0bzn2lmC83sbsINdDGzP0QNHy9PNn5sZvOBEdH67opvy4KbzOwlM1tmZh+NrftxM7vfzF4xs7uilhQws/lmtiIqyw8H8jUSkcFBDZ+LyFBzDbGasyhkNbr7iWZWCvzdzP4azTsXeJu7r42GP+Xu26Lmjhab2QPufo2ZXeHufprT4gAAIABJREFUs7rZ1oeBWcDxhFYrFpvZomjabOAYQjuJfwdOMbMVwIeAo9zdk80riYjEqeZMRIa69wCfMLOlhCalqoEZ0bRnY8EM4Etm9gLwDHBobL6enArc4+4Jd98EPAGcGFt3nbt3AEuBGmAH0AL80sw+DOzq97MTkSFH4UxEhjoDvujus6LHNHdP1pw1750pnKt2DvAOdz+e0MZhWRrr7km8bcQEUOTu7YTaugeAC4A/9+qZiMiwoHAmIkPNTqAqNvwX4PNmVgxgZkeYWUU3y40Ctrv7LjM7Cjg5Nq0tuXyKRcBHo/PaxgOnA8/2VDAzqwRGufvDwFcIh0RFRLrQOWciMtS8CLRHhyfvAH5MOKT4XHRSfj2h1irVn4HPmdmLwErCoc2k24AXzew5d78kNv5B4B3AC4AD33D3jVG4604V8EczKyPUul3Zt6coIkOZbqUhIiIikkd0WFNEREQkjyiciYiIiOQRhTMRERGRPKJwJiIiIpJHFM5ERERE8ojCmYgMGlF7ldujZpji4+ea2cNm1mBm28zsWTO7LDZ9pJndbGZvmlmTma2OhsdF093MDk9Z53Vm9n+i/jPNrCNadqeZrYyvP7aMmdmaqJmm7sr/XjNbFK2j3syeMLMPmtkxZtZoZkekzP+YmX2/76+YiAxGCmciMiiYWQ1wGuF+Yh+MjX8HsIDQdNLhhOaZPg+8L5peAjxGaOfyXGAk8E5gK+Fu/ena4O6V0fJXAr8wsyNT5jkdmABMN7MT4xPM7ELgd8BvgMnAQcB3gQ+4+3Lgh8CvYg2kfxqYBHyvF2UUkSFA9zkTkUHBzL4LvJfQPuYRsYbN/wa84O6X97DcZ4AbgcPcvamHeRyY4e6rY+OuAw53949HTTv9H3efHJu+Gbjc3X8XG3c7UAqMIIS5K6LxBrwB/MTdb+qhDEXAYuCXwP3AcuCD7v7UgV4bERla1EKAiAwWnwB+RAhnz5jZQYSmmt4BfGc/y50D/LmnYNZbZlYA/BMwDoiHuXLgQuAiQjj7TzP7qru3AkcSGlK/v6f1unu7mX2KUMt3ASEMKpiJDEM6rCkiec/MTgWmAve5+xLgNeBjwBjC99hb+1m8+gDT03WImTUAuwnNNn3V3Z+PTf8wobHzvwJ/Ivz5fX+sDByoHNH6fgUcDXwrA2UWkUFI4UxEBoNLgb+6+5Zo+O5o3HagA5i4n2W3HmA6QAJIbdi8GGiLDW9w99GEc85uAc7upoz3uXu7u+8Bfh+NS5aBNMoB4XDm6+6+K415RWQIUjgTkbxmZiOA/wmcYWYbzWwj4YT844EZwNPAR/azikeB95pZxX7meZPQOHrcNMJ5Yl1Ewetq4FgzuyAq42RCWPt4rIwXAudFV4SuBNYdoJwiIoDCmYjkvwsINVszgVnR42jgScJ5aN8APmlmXzezagAzO97M7o2Wv5MQjB4ws6PMrMDMqs3sW2Z2XjTPb4Fvm9nkaPo5wAfo4Ryx6Dyy/49wtSXAPwOvEs4tS5bxCKAOuNjDlVdfBb5jZpdFt/YoMLNTzey2zLxMIjJUKJyJSL67FPi1u7/p7huTD+A/gEuAZwm1VmcDa8xsG3Ab8DDsrek6B3gFeATYES0zjnBxAcD1wFPA3wiHSv83cIm7v7Sfct0OTDGzD0Rl/Fm8fFEZb42m4e73Ax8FPgVsADYBNwB/7O8LJCJDi26lISIiIpJHVHMmIiIikkcUzkRERETyiMKZiIiISB5ROBMRERHJI0Oq+aZx48Z5TU1NroshIiIickBLlizZ4u7jU8cPqXBWU1NDbW1troshIiIickBmts+NrkGHNUVERETyisKZiIiISB5ROBMRERHJIwpnIiIiInlE4UxEREQkj2Q1nJnZ7Wa22cy6bTzYglvMbLWZvWhmb49Nu9TMVkWPS7NZThEREZF8ke2aszuAc/cz/X3AjOgxD/g5gJmNBa4FTgLmAtea2ZisllREREQkD2Q1nLn7ImDbfmY5H/iNB88Ao81sIvBe4BF33+bu24FH2H/IExERERkScn0T2knAuthwXTSup/H7MLN5hFo3pkyZkp1SimSKO7Q57GqHXQloToTurnZobodXm+GoSigrhNICKI26JQXRcPQoLgBLZ3tAW0fYZrLbnjLc1gEdDlVFMKYERheH9fdGh0NjG2xphS17oH4PrGqCwyuhqACKDAqtm25B7/4imoX593aBAuvaNet8bSy2XJfh2OvjhP2yv/6Ed//oiE1r74DXmmFaediex7ZBtK5kd+1umFERnn+hQSFRN+XRAbR2wJ4EtHrU7eja35KAtbtgQmnX/draEfpbk8MJqG+FMcX7f41w2NYGo4ugw8K+7Yg93739Ubc5AWOj90xynxZZ7BHt4817wnur3TsfbR1Rf+y92NQeylhS2LmO4oKu/YWE99roYkgQW0/KOls7oLEdyqM3S0e0P1O7CYfdCRhZ1Lnd4gIoTukWAjuj8rl1vj/2ro+u4xrawrwF1vU9uPdhna/3qKKwznjZOuKveQfsTIT9Uhi91gXR+6SAzn5L7r/izueWfJ/G92Ui+syOLIrt+264h9ewsrCzfB09lK85AZVFne/fgng3KmNBVN4d7Z2vzd7PLl37DdjaCiOLuz6H1M9hewfsSIT9nNwPyfLFhxMd0NIRXuvSwvC9Whx9v+59WNjfzQm4c87+X5ssynU46+5Z+37G7zvS/TbgNoA5c+Z0O4/kuQ6HHW3hC2Vba3hsbYWXdoQPb3MUYprbwxd3cripHZrawod8ZBFUFMGIws5HeUp/iUFdS/hg7ol+3FqiH7dkd09seEzxvuGoJNZfbPBWS/gyaukIX+67E137d0fBa2d7WPfujvBlku8qi8LzH1McAtuYYhhbAiML4c3d4dO4tTUKY9H+GgzPa7gojH5gkj8+xdFX6pZWOKg0jIfuA2RrRwhxE0rDZyf+o5oaBHa3w+pdMHVE2E578scy+sFMBqY9HeFzMKo4rDM1bCX79yTCH5Tp5SEkJUNbPMC1Rz+wze3hs1xW1H2AS65vVTMcXdUZQgoBK+ga8ne2w/IdcOyo8DrsToTvlbaO2J8aD9vcsid8FkoKuvmzkPyjQHjOG1pgYml4LsnXOTX87+kIn5/k650MKAV0DTMtHfD6LpgyImy728BMeM7b2qC6BMoKuu63ZFAqsLCfX98FNeXh+7EnuxJhvsPKobI4Vq5Y+YoM9gArm8JrWJ58L0R//BJ0DYZN7bBuN0yOnks84Mb7WxLhtTmoNJQxNfQlHy0JeLUJZlaFIFeUsl8KYvv51QY4bmQYtysBrW2xPzBRoG9uD4H0qhkwa3QfPoD9l+twVgccGhueDGyIxp+ZMv7xASuVdHIPXwpN7V0fO5P9UUja2Rb+vVeXdP33uveffOzf+6Y9YXh7e/jgbW8NXyr7U1YQAkNFEVQUhm5lIVQVhy/fSdGPQ3Mi/ADtioejqJv8ASo2KC8K60zWUCX7y6Ivild2wlFVUJzoWnMR79/dHp7T6KJQjhGFYR0jCkL/yOjLdncCntwKH5wYfRFGz6E89qgoCsutiX7oWqMv7T0dnTUkyeG1zXDnOrh4Mkws6/k1e6sF7qmDfz4UppZHP9KxWoB47cDru+B/rYRPTQ3l294K29s6u6ubYfv2sL9aO8LzmDwi/OiNK4FxpVE36h9bHN4Ph1fE/t120311J/xgFVw9A6ZV9Pxc1jaH+b5+ONRUdNZQpHbX7oKfvAZXTIcp5dF7OPZeTg6/uQt+tha+MC28Nskf1XjNW7L/jV3w49fgqsNDTWD8RyH5g5fsXxM954LUmrrYsHv4EZteHmqmuq2Rc1jdBD9cDf96ZHgvpv45iP9JWNkEbx8V/kwUF3RuP84dXmiE40cduKYknfmysc6hMt9gKKNem/TmyxHz5BdWtjZgVgP8yd3f1s209wNXAOcRTv6/xd3nRhcELAGSV28+B5zg7vs7f405c+a42tY8gOZ2WNIAS7bD8p0hEDQlQtjaEYWsvf3toUbrQMEpzuj+cECyP+GwviX8kEwtD/9Aq0tCd2xUOzO2BMYUwfo9cNJoqCgOP37d6c2HrbUj/DuePTo/vxDSNRi+LNOVD1++mXptMi1X2xWRAWNmS9x9zj7jsxnOzOweQg3YOGAT4QrMYgB3v9XMDPgPwsn+u4DL3L02WvZTwLeiVd3o7r8+0PYUzlK0d8CKnfCPbfDs9vB4aUfXsJU8fFVVFKr9q6L+5GNXOzz4FsyrCf/eK4vCoyrqVhZGNVqFoVo5U8FHRERkiMtJOBtowz6c7U7A/3sL/u9GWNMMzzWGQ3oQaqXmjoW5Y+CkMXDCaNiwOxxPV5gSEREZcD2Fs1yfcyaZsKoJbl0Lv34jnB8EcGwVfKYmBLG5Y+Cwin3D1UH7OV8pySxnJ0SKiIgMRwpng1V7R6gh+9kaeLQ+nNz9oUNg3lQYVQJzDlAjJiIiInlJ4Wyw2bAbfvF6eKxvgUNHwL8dDZ+u2f+VeyIiIjIoKJwNBu6wcEuoJfvDW+GKx/dOgJ8eD+8/ONzkUURERIYEhbPB4LqX4fqV4fYSVx4Gn50W7rckIiIiQ47CWb57cEMIZpWF8N+nwEljc10iERERySIdD8tnK3bAJ5aEk/sfPTVcdSkiIiJDmmrO8lVDK1zwTGja58GTQ1M5IiIiMuQpnOWjhMMltaGdwAWnKpiJiIgMIwpn+ejal+HhTfCz4+G0cbkujYiIiAwgnXOWb36/Hm5cCZ+eCp+bluvSiIiIyABTOMsnL0UXAJw0JtzDTHf4FxERGXYUzvLF9ugCgKoieOAkKC3MdYlEREQkB3TOWT5IOHysFt7cBY+fBpN0AYCIiMhwpXCWD76zAv68CW6dBe+sznVpREREJId0WDPXfrcevv8qzKsJzTKJiIjIsKZwlksvNcInauHkMXDLcbkujYiIiOQBhbNc+u7LsKcDrp+pCwBEREQEUDjLraWNcMY4OGd8rksiIiIieULhLFdebw7NM334EN3PTERERPZSOMuVhVtC92zVmomIiEgnhbNcWVAPE0phZlWuSyIiIiJ5ROEsF9xhYT2cNU6HNEVERKQLhbNcWNUE61t0SFNERET2oXCWCwt0vpmIiIh0T+EsFxbUw6Ej4LCKXJdERERE8ozC2UDrcHhc55uJiIhI9xTOBtryHVDfqkOaIiIi0i2Fs4G2oD50z1I4ExERkX0pnA20BfVweAVMKc91SURERCQPKZwNpITDE1tVayYiIiI9UjgbSM83QGObzjcTERGRHimcDaS955uNy205REREJG8pnA2kBfVwTBUcVJbrkoiIiEieymo4M7NzzWylma02s2u6mT7VzB4zsxfN7HEzmxybljCzpdHjoWyWc0C0dsCTOt9MRERE9q8oWys2s0Lgp8C7gTpgsZk95O4rYrP9EPiNu/+XmZ0NfB/452jabnefla3yDbjF22FXQuebiYiIyH5ls+ZsLrDa3de4eytwL3B+yjwzgcei/oXdTB86FtSDAWfofLPhZks9rK+Djo5cl0RERAaDrNWcAZOAdbHhOuCklHleAD4C/Bj4EFBlZtXuvhUoM7NaoB2Y7+5/6G4jZjYPmAcwZcqUzD6DTFpQD7NHw9iSXJck4zo6YOdOaNgO27dBY9Tdvq1zXLJ/21bY2QgHHwKjx8Co0TByVNdusr+iEgoLoaCg82EFXYcLCsL2G7Z3biu+zcbYuB2NUFQMM46EiZPg4ImhHAcfEvrHTwjb668N6+GpRfD3J8Lj1VfC+NJSmHYY1BwWutMOg+mHh+6hU6Ekz98aiQSsezOUc2w1lOnUSRGRrMhmOOuu4UhPGb4K+A8z+ySwCFhPCGMAU9x9g5lNBxaY2TJ3f22fFbrfBtwGMGfOnNT154fdCXhqG3xxelY3s6UeXl8TmuwsLgk/oiUlIZAk+5Pji4qgpQV2NUNzEzQ1he6u5s7+5GP1q1BSCjt3QGNDCDmNDZ39O3eA7+eVLy2FMWPDo7gkhJWWFmht7VxHIpH518MsBMAxY0PXHZ5fAiuWhbCWqqAAJhwEB00MwfComXDI5BDkJkXdQyZDVVXX5d54PYSwZCBbG71Lq6rg5FPhrPfA/XfDu88Lr9Xa12DRY7BrV9dtTzoUxo6DmmmhHNXjQ2AcNx7GRd3xE8JzKcjypTz1m8PrtHxZ1H0RXlkOu3d3zlNREco7thqqo+7YcaF/zFh4a33otrTA7l3h+e5OPnaHbnMztLXB4UfAxEPCa5/aHTEiu88Vwntj+zZ4Y214vL6ms7/uzVD2kpL9N4frHgLrtMND2Q/u5vmMrd5337W3h9dh72cu+ZncCS+/FPZ3U1N47+zcEcbH+xsbw2t50MEwprrz/Z78zMX7q0am36Sv2f7/FJmFR0sLtOzufv/Gx9XXw2GHQ3kFlI2A8nIYUd7ZHVEe9nVpKRQWhT9KRUXhke33uwxeyc9PYWH4ThoqTVZnM5zVAYfGhicDG+IzuPsG4MMAZlYJfMTdG2PTcPc1ZvY4MBvYJ5wNCk9vCxcEZOB8M3eoWwevvgwrX+7srlwRaqWypbIy/PAma7emTuta09XcBPfeCV/9Jsw6oesPQ/zH1R2WvQDHHt/5IXIPH6548HvxObjpBvjClTB5Sqgdiz886q57A+64Db54FRz/9s5tjh4LI0d2/VKPb7utDTZvgo0bYONbXburXoHFz8CypeEHMFVVVQhqEw4OQXPzxjB+9Bh45+nw6S/AqWfA244PPyzucMll+z7nTRtDUEs+nl8MTz4O27fAjh3dB0gIz6miMoSF/dUsmoUvrjFjobIq7MPyirBsRWUYTvYXFsI//h7K9PJLIZwljRsPM4+FS+eFH/fbfw4f/mgI2tu2wrYtobv2tdBtbNi3zCUlnT/EZSM6+9vbw/bWvQHbt4b9kmrkqFCzWTYilKW8ovMHPf4jn/xx37wpBKFEIqy/vR0SyW5yXFvY5q5dIYC9uTa85nFjxkLN9BCylvwD3nFa2Mc92bYVnnoyBJGnFoWwl6qoKATvRAe07gnztrT0vM5UlZVhX1aN7HxUF8Nzz4bgt34dvPQCNGwLgW6oMOsMaoWFUBC970eMCH8c4388k/3FxeHR0hJe89KyEJ6T3ZLSzuHS0vCen3ZY6E9dV7xbWBj+WO5pgT17Qrelpftucp1lI7puO7UM6dbYFxSE4FoUe6QG2eS6Ojo63//xz0B8uLW1hzC9uzNUNzfD+jfDd2oi0f162tvDZ7dhe/iMFBd3li21rMmwvWVz+E1x3/d7PflIJGDTW2G5ZEVCagXCnj1dX6ORsc9G6melohKaduy73eS2E4nO7e5ohP/6Xe7Cnvn+qjz6s2KzIuBV4F2EGrHFwMfcfXlsnnHANnfvMLMbgYS7f9fMxgC73H1PNM/TwPkpFxPsY86cOV5bW5uV59Mv314B81+F7e+HquJeL/703+DH/zsEh9Uru37pjq2GI4+GI6JHYSH86Pvw5W/AIZPCh6+tDdpaQ39ra+ivexMevA8+OQ9mHNX5Y5388U7+cI8oh7Wr4bjZB641SA1d/ZHu+jK93dR1trSEGqAN66NuXejfUAdrVsHaNTDvCrjwYzDzbf37h5/6XNraYOuWUCO6ZXPo1m+Gl5fBg7+Dd703BIjU4Jr8okmGheNmh/dFvDa0uTnUusRrLM3gyJkw56QQxo45NnTHT+i5jN1pbw/bfnk5zJ4TwlNRD38D4+tL1l6lhuWNb4U/H/94CqYfFrab+iPSXajbn8LC8DpNnRY+P1Omhf6a6aE7dVr4kk/3OXc3X0tLCLvx57LprRBGFy2E95wHU2rC56y8ovMzl+wvrwjvtbefGAJqZWX3P+I9lS/5Y5k8rP98bfSH5ythuz1583X42c3w+S+H2tyOjs4fstQfz7o34c5fhT9RR87cNygnQ3hpGby4FKZND7Vs3dWs7d4d/hjdcRtcfGkIVD2Fi00b4a9/gtPfFX6A22LfbfHvuYaG8P110ETAoxr7PZ0195K+ZBDuLmwVFUV/eNbB5MkhyB4oFLa1RUG4uPua2YKCMH/Ddpg6HcaP7/wzGf+8VFSGP4R33wEf+Eh4v/VUy7x9e/j+KyvrDInxbSf/7Cbaw7wLFsNxWb4s0cyWuPucfcZnK5xFGz0PuBkoBG539xvN7Hqg1t0fMrMLCVdoOuGw5uVRIHsn8J9AB+GihZvd/VcH2l7ehrN3PhG6T53Rq8VWvwrXXQN/ejC8Yd4+F06YG35MjpwZuuNSKuNyGWqGm1y9hpnax+7hS7K5KfxYbd4Yah/z8f1woOfS3h5+3Hc1w4vPwzHHdf333t0hsly9/wf7+6av8+aijPubr6Oja83XC8+H8N/W1vUPbTzwrX4VfvJD+NLXw/dvTzVxZWXhdJJlz8P0GZ2BMB4Ok9td9Qr87N/h818J5572ZN0b8POb4TOXh5rh1MCTrBVeXwcP/hYuvDiE655qsN5aD7f9BK78ZvhjGa/Rjofr0tJwekMu3jf5Pl8m5CScDbS8DGc722DM/4OrZ8CNx6S1yJZ6+MH34Nf/GT7kX/oGnHEOzD05P384RUSGg2zX1A/VACI96ymcZfOcMwH429bQ4Hka55vt2hX+Gd08P/R/ch5cfW2o3hcRkdwyy/xhrnTXmav5JDcUzrJtQT2UFMA7q3ucJZEIJ9P/r++EKunzzofr5sMRRw1gOUVERCQvKJxl24It8M6xMKL7y3EWPgLf+Xq4wurtJ8Jtd8Eppw9wGUVERCRv6O4x2bStFZ5v6LE9zZt/AB96T7jS5Ff3wKPPKJiJiIgMd6o5y6ZFW8J1qGfv22TT5k3hpP8R5XDHfeEqTBERERGFs2xaUA/lhTB37D6TbrohXMZ95+/D4UwRERERUDjLrgX1cFp1uCAgZs1q+PWt4WrM9w/dpt5FRESkD3TOWbZsaoHlO7s93+yGb4dmQL7x3RyUS0RERPKawlm2PL4ldFPub/Z8Lfz+t/CFr4b2AkVERETiFM6yZUE9jCqG2aP2jnKHa6+G6nGh+Q8RERGRVDrnLFsW1MMZ1VDUmX8X/BUWLYD5P+5sVFlEREQkTjVn2bBuF6xu7nK+WUdHqDWbOg0u+2wOyyYiIiJ5TTVn2bBw3/PN7r8ntALwi7ugtDRH5RIREZG8p5qzbHi8HkYVwTFVQLif2Q3fhuNmw0cuynHZREREJK8pnGXDyiZo6YBlOwD41c/hzdfhez+AAr3iIiIish+KCtmQ6AhXaR4/isZG+OENcOY5cNa7c10wERERyXcKZ9nQ2A6HloMZP7kJtm2F6+bnulAiIiIyGCicZUNDG4wuZuNb8NMfwYUXw6wTcl0oERERGQwUzrIhCmfzr4P2dvj2DbkukIiIiAwWCmeZ1pKAlg5ebavgzl/BZZ+Dmum5LpSIiIgMFgpnmdbYBsC/PT6BESPg69/OcXlERERkUFE4y7SGNhYzhv+7tIIvfQPGT8h1gURERGQwUTjLtIY2buYIRlcm+PxXcl0YERERGWwUzjIssbWNxxlPe4ex9rVcl0ZEREQGG4WzDFv2AjRTzJX/sodjj891aURERGSwUTjLsCdrQ1vyH/sEmOW4MCIiIjLoKJxl2KLlZcxgJxOPKs51UURERGQQUjjLoLY2eHptGacVbIERhbkujoiIiAxCCmcZtHQJNLUWcnrlNh3TFBERkT5ROMugRQtC99TqHbktiIiIiAxaCmcZ9ORCmFnZzLhxqjUTERGRvlE4y5A9e+Aff4fTKrfDaF0MICIiIn2jcJYhtf+A3bvhtKKtCmciIiLSZwpnGfLkwnANwKltmxXOREREpM+yGs7M7FwzW2lmq83smm6mTzWzx8zsRTN73Mwmx6Zdamarosel2SxnJjy5EI6bDaN37FY4ExERkT7LWjgzs0Lgp8D7gJnAxWY2M2W2HwK/cffjgOuB70fLjgWuBU4C5gLXmtmYbJW1v3bvhsVPw2mnd8DuBIxSOBMREZG+yWbN2VxgtbuvcfdW4F7g/JR5ZgKPRf0LY9PfCzzi7tvcfTvwCHBuFsvaL88+Ba2tcNqcRBihmjMRERHpo2yGs0nAuthwXTQu7gXgI1H/h4AqM6tOc1kAzGyemdWaWW19fX1GCt5bTy6EwkJ4x9EtYYTCmYiIiPRRNsNZdzf78pThq4AzzOx54AxgPdCe5rJhpPtt7j7H3eeMHz++P+Xts0ULYPYcGNneFkYonImIiEgfZTOc1QGHxoYnAxviM7j7Bnf/sLvPBv41GteYzrL5oqkJnlsMp50FNCiciYiISP9kM5wtBmaY2TQzKwEuAh6Kz2Bm48wsWYZvArdH/X8B3mNmY6ILAd4Tjcs7z/wN2tvh9LNROBMREZF+y1o4c/d24ApCqHoZuM/dl5vZ9Wb2wWi2M4GVZvYqcBBwY7TsNuDfCAFvMXB9NC7vLFoAxcVw0ikonImIiEi/FWVz5e7+MPBwyrjvxvrvB+7vYdnb6axJy1tPLoQ5J0N5OQpnIiIi0m9qIaAfGhrgheei880ghLMig/LCnJZLREREBi+Fs354ahF0dKSEs9HFoR0nERERkT5IK5yZ2QNm9v7YyftCOKRZVgYnnhyNSIYzERERkT5KN2z9HPgYsMrM5pvZUVks06Dx5EKY+84Q0ACFMxEREem3tMKZuz/q7pcAbwdeBx4xs6fM7DIzG5ZpZOsWeOmF2CFNUDgTERGRfkv7MGXUrNIngc8AzwM/JoS1R7JSsjz3tydC9/SzYyMVzkRERKSf0rqVhpn9HjgKuBP4gLu/FU36rZnVZqtw+exvC6GiAt5+Ymxko8KZiIiI9E+69zn7D3df0N0Ed5+TwfIMGk8uhJNPDTeg3Us1ZyIiItJP6R7WPNrMRicHomaVvpClMuW9TRvhlRUphzRbO2BXAkaX5KxcIiIiMvilG87+xd0bkgPuvh34l+wUKf/97fHQ7XIxQKNaBxAREZH+SzdKMRviAAAWiklEQVScFZh13lnVzAqBYVtF9ORCGDkSjpsdG6mmm0RERCQD0j3n7C/AfWZ2K+DA54A/Z61UeW7RAnjnGVAUf/UUzkRERCQD0g1nVwOfBT4PGPBX4JfZKlQ+W18Ha1bDp1PPuFM4ExERkQxIK5y5ewehlYCfZ7c4+e/JhaHb5Xwz6AxnoxTOREREpO/Svc/ZDOD7wEwg2VgR7j49S+XKW4sWwJix8LbjUiY0tIauas5ERESkH9K9IODXhFqzduAs4DeEG9IOO08uhFPPhILUV06HNUVERCQD0g1nI9z9McDc/Q13vw44+wDLDDmvr4V1b3RzSBNCOCs0qCgc8HKJiIjI0JHuBQEtZlYArDKzK4D1wITsFSs/LYraSDi9u1iabB2g844jIiIiIr2Wbs3ZV4By4EvACcDHgUuzVah89WR0vtkRR3UzUU03iYiISAYcMJxFN5z9n+7e5O517n6Zu3/E3Z8ZgPLllSXPQuseeOnFbiYqnImIiEgGHPCwprsnzOwEMzN394EoVL5a/Ao88xQce3w3ExXOREREJAPSPefseeCPZvY7oDk50t1/n5VS5anCQjjltB4mNrTBxLIeJoqIiIikJ91wNhbYStcrNB0YVuFsv1RzJiIiIhmQbgsBl2W7IIOewpmIiIhkQLotBPyaUFPWhbt/KuMlGoxaO2BXQuFMRERE+i3dw5p/ivWXAR8CNmS+OINUo1oHEBERkcxI97DmA/FhM7sHeDQrJRqMFM5EREQkQ9K9CW2qGcCUTBZkUFO7miIiIpIh6Z5ztpOu55xtBK7OSokGI4UzERERyZB0D2tWZbsgg9recFaS23KIiIjIoJfWYU0z+5CZjYoNjzazC7JXrEFGNWciIiKSIemec3atuzcmB9y9Abg2O0UahBTOREREJEPSDWfdzZfubTiGvoY2KDSoKMx1SURERGSQSzec1ZrZj8zsMDObbmb/DizJZsEGlWTrAGa5LomIiIgMcumGsy8CrcBvgfuA3cDlB1rIzM41s5VmttrMrulm+hQzW2hmz5vZi2Z2XjS+xsx2m9nS6HFr+k8pBxraYJQOaYqIiEj/pXu1ZjOwT7jaHzMrBH4KvBuoAxab2UPuviI227eB+9z952Y2E3gYqImmvebus3qzzZxpaNX5ZiIiIpIR6V6t+YiZjY4NjzGzvxxgsbnAandf4+6twL3A+SnzODAy6h/FYG0SSo2ei4iISIake1hzXHSFJgDuvh2YcIBlJgHrYsN10bi464CPm1kdodbsi7Fp06LDnU+Y2Wk9bcTM5plZrZnV1tfXp/FUskDhTERERDIk3XDWYWZ7m2sysxq6thjQne7Ojk9d5mLgDnefDJwH3GlmBcBbwBR3nw18FbjbzEbSDXe/zd3nuPuc8ePHp/VkMk7hTERERDIk3dth/CvwNzN7Iho+HZh3gGXqgENjw5PZ97Dlp4FzAdz9aTMrI9TSbQb2ROOXmNlrwBFAbZrlHVgKZyIiIpIhadWcufufgTnASsIVm18jXLG5P4uBGWY2zcxKgIuAh1LmeRN4F4CZHQ2UAfVmNj66oAAzm05oaH1NWs9ooLV1QHNC4UxEREQyIt2Gzz8DfJlQ+7UUOBl4Gji7p2Xcvd3MrgD+AhQCt7v7cjO7Hqh194cIIe8XZnYl4ZDnJ93dzex04HozawcSwOfcfVufn2U2Nap1ABEREcmcdA9rfhk4EXjG3c8ys6OA7x1oIXd/mHCif3zcd2P9K4BTulnuAeCBNMuWW2q6SURERDIo3QsCWty9BcDMSt39FeDI7BVrEFE4ExERkQxKt+asLrrP2R+AR8xsO4P1nmSZpnAmIiIiGZRuCwEfinqvM7OFhBvG/jlrpRpMFM5EREQkg9KtOdvL3Z848FzDiMKZiIiIZFC655xJT3S1poiIiGSQwll/NbSFV7Gy15WQIiIiIvtQOOuvZOsA1l1rVSIiIiK9o3DWXw1tMLok16UQERGRIULhrL/UrqaIiIhkkMJZfymciYiISAYpnPWXwpmIiIhkkMJZfymciYiISAYpnPVXQxuM0m00REREJDMUzvqjvQOa2nW1poiIiGSMwll/qHUAERERyTCFs/5Qu5oiIiKSYQpn/aFwJiIiIhmmcNYfCmciIiKSYQpn/aFwJiIiIhmmcNYfCmciIiKSYQpn/aFwJiIiIhmmcNYfDW3hFazUTWhFREQkMxTO+qOhDUYVQ4HluiQiIiIyRCic9Yfa1RQREZEMUzjrj0aFMxEREckshbP+UM2ZiIiIZJjCWX8onImIiEiGKZz1h8KZiIiIZJjCWX8onImIiEiGKZz1VXsH7GyH0SW5LomIiIgMIQpnfbWjPXRVcyYiIiIZpHDWV2q6SURERLJA4ayvkuFslJpuEhERkcxROOurvTVnOudMREREMier4czMzjWzlWa22syu6Wb6FDNbaGbPm9mLZnZebNo3o+VWmtl7s1nOPmloDV0d1hQREZEMytoxOTMrBH4KvBuoAxab2UPuviI227eB+9z952Y2E3gYqIn6LwKOAQ4BHjWzI9w9ka3y9prOORMREZEsyGbN2VxgtbuvcfdW4F7g/JR5HBgZ9Y8CNkT95wP3uvsed18LrI7Wlz8UzkRERCQLshnOJgHrYsN10bi464CPm1kdodbsi71YFgAzm2dmtWZWW19fn4lyp6ehDQyo0gUBIiIikjnZDGfWzThPGb4YuMPdJwPnAXeaWUGay4aR7re5+xx3nzN+/Ph+FbhXGtpgVDEUdFdUERERkb7JZrVPHXBobHgynYctkz4NnAvg7k+bWRkwLs1lc0tNN4mIiEgWZLPmbDEww8ymmVkJ4QT/h1LmeRN4F4CZHQ2UAfXRfBeZWamZTQNmAM9msay9p3AmIiIiWZC1mjN3bzezK4C/AIXA7e6+3MyuB2rd/SHga8AvzOxKwmHLT7q7A8vN7D5gBdAOXJ5XV2qCwpmIiIhkRVbPZnf3hwkn+sfHfTfWvwI4pYdlbwRuzGb5+qWhDQ6ryHUpREREZIhRCwF9pZozERERyQKFs75qVDgTERGRzFM464uEw452hTMRERHJOIWzvtih1gFEREQkOxTO+kJNN4mIiEiWKJz1hcKZiIiIZInCWV/sDWcluS2HiIiIDDkKZ32hmjMRERHJEoWzvlA4ExERkSxROOsLhTMRERHJEoWzvmhoAwOqstr6lYiIiAxDCmd90dAGI4uhwHJdEhERERliFM76oqFVhzRFREQkKxTO+kKNnouIiEiWKJz1hcKZiIiIZInCWV8onImIiEiWKJz1hcKZiIiIZInCWV8onImIiEiWKJz1VsJhR7vCmYiIiGSFwllv7VDrACIiIpI9Cme9paabREREJIvU/lBvKZyJiIj0W1tbG3V1dbS0tOS6KFlXVlbG5MmTKS5OLzsonPVWo8KZiIhIf9XV1VFVVUVNTQ1mQ7c5RHdn69at1NXVMW3atLSW0WHN3lLNmYiISL+1tLRQXV09pIMZgJlRXV3dqxpChbPeUjgTERHJiKEezJJ6+zwVznpL4UxERESySOGstxrawICRCmciIiKDVUNDAz/72c96vdx5551HQ0NDFkrUSeGstxraQjArGB5VsSIiIkNRT+EskUjsd7mHH36Y0aNHZ6tYgK7W7D013SQiIpJZX3kRljYeeD53aE5ARSEc6DyuWaPg5uN6nHzNNdfw2muvMWvWLIqLi6msrGTixIksXbqUFStWcMEFF7Bu3TpaWlr48pe/zLx58wCoqamhtraWpqYm3ve+93Hqqafy1FNPMWnSJP74xz8yYsSI3jzzbqnmrLcUzkRERHKjOQHLd4RuP82fP5/DDjuMpUuXctNNN/Hss89y4403smLFCgBuv/12lixZQm1tLbfccgtbt27dZx2rVq3i8ssvZ/ny5YwePZoHHnig3+UC1Zz1nsKZiIhIZu2nhqsLd3ihEY4fdeCas16aO3dul/uQ3XLLLTz44IMArFu3jlWrVlFdXd1lmWnTpjFr1iwATjjhBF5//fWMlEXhrLca2mBq/6ssRUREpJfMYFZ2zveqqKjY2//444/z6KOP8vTTT1NeXs6ZZ57Z7X3KSktL9/YXFhaye/fujJRFhzV7SzVnIiIig15VVRU7d+7sdlpjYyNjxoyhvLycV155hWeeeWZAy6aas95qaIPRJbkuhYiIiPRDdXU1p5xyCm9729sYMWIEBx100N5p5557LrfeeivHHXccRx55JCeffPKAli2r4czMzgV+DBQCv3T3+SnT/x04KxosBya4++hoWgJYFk17090/mM2ypqXDYYdqzkRERIaCu+++u9vxpaWl/Pd//3e305LnlY0bN46XXnpp7/irrroqY+XKWjgzs0Lgp8C7gTpgsZk95O4rkvO4+5Wx+b8IzI6tYre7z8pW+fpkRxs4CmciIiKSNdk852wusNrd17h7K3AvcP5+5r8YuCeL5ek/Nd0kIiIiWZbNcDYJWBcbrovG7cPMpgLTgAWx0WVmVmtmz5jZBT1txMzmRfPV1tfXZ6LcPVM4ExERkSzLZjjr7gYk3sO8FwH3u3v8rnJT3H0O8DHgZjM7rLsF3f02d5/j7nPGjx/fvxIfiMKZiIiIZFk2w1kdcGhseDKwoYd5LyLlkKa7b4i6a4DH6Xo+Wm4onImIiEiWZTOcLQZmmNk0MyshBLCHUmcysyOBMcDTsXFjzKw06h8HnAKsSF12wCmciYiISJZlLZy5eztwBfAX4GXgPndfbmbXm1n8thgXA/e6e/yQ59FArZm9ACwE5sev8swZhTMREZFhqbKycsC2ldX7nLn7w8DDKeO+mzJ8XTfLPQUcm82y9UljFM5GKpyJiIhIdqiFgN5oaIORRVCY2cZWRUREhrNrvgLLlh54PndoboaKigO3e37sLJh/c8/Tr776aqZOncoXvvAFAK677jrMjEWLFrF9+3ba2tq44YYbOP/8/d0FLDvUtmZvqF1NERGRnGluhleWh25/XXTRRfz2t7/dO3zfffdx2WWX8eCDD/Lcc8+xcOFCvva1r9H1rKuBoZqz3lA4ExERybj91XDFucOyF+DY4w9cc3Ygs2fPZvPmzWzYsIH6+nrGjBnDxIkTufLKK1m0aBEFBQWsX7+eTZs2cfDBB/dvY72kcNYbCmciIiI5YwbHZbBhxwsvvJD777+fjRs3ctFFF3HXXXdRX1/PkiVLKC4upqamhpaWlsxtME06rNkb21vDrXVzUMUpIiIimXXRRRdx7733cv/993PhhRfS2NjIhAkTKC4uZuHChbzxxhs5KZfCWW9cPh1WNcMLjbkuiYiIiPTTMcccw86dO5k0aRITJ07kkksuoba2ljlz5nDXXXdx1FFH5aRcOqzZG5+pgRPHwPGjcl0SERERyYBly5bt7R83bhxPP/10t/M1NTUNVJEUznrFDGaNznUpREREZAjTYU0RERGRPKJwJiIiIjmRi3uI5UJvn6fCmYiIiAy4srIytm7dOuQDmruzdetWysrK0l5G55yJiIjIgJs8eTJ1dXXU19fnuihZV1ZWxuTJk9OeX+FMREREBlxxcTHTpk3LdTHykg5rioiIiOQRhTMRERGRPKJwJiIiIpJHbChdJWFm9UC2G8IaB2zJ8jakd7RP8pP2S/7RPslP2i/5Z6D2yVR3H586ckiFs4FgZrXuPifX5ZBO2if5Sfsl/2if5Cftl/yT632iw5oiIiIieUThTERERCSPKJz13m25LoDsQ/skP2m/5B/tk/yk/ZJ/crpPdM6ZiIiISB5RzZmIiIhIHlE4ExEREckjCmdpMrNzzWylma02s2tyXZ7hysxuN7PNZvZSbNxYM3vEzFZF3TG5LONwY2aHmtlCM3vZzJab2Zej8dovOWRmZWb2rJm9EO2X70Xjp5nZP6L98lszK8l1WYcbMys0s+fN7E/RsPZJjpnZ62a2zMyWmlltNC5n32EKZ2kws0Lgp8D7gJnAxWY2M7elGrbuAM5NGXcN8Ji7zwAei4Zl4LQDX3P3o4GTgcujz4f2S27tAc529+OBWcC5ZnYy8APg36P9sh34dA7LOFx9GXg5Nqx9kh/OcvdZsfub5ew7TOEsPXOB1e6+xt1bgXuB83NcpmHJ3RcB21JGnw/8V9T/X8AFA1qoYc7d33L356L+nYQfnUlov+SUB03RYHH0cOBs4P5ovPbLADOzycD7gV9Gw4b2Sb7K2XeYwll6JgHrYsN10TjJDwe5+1sQggIwIcflGbbMrAaYDfwD7Zeciw6fLQU2A48ArwEN7t4ezaLvsoF3M/ANoCMarkb7JB848FczW2Jm86JxOfsOKxqoDQ1y1s043YNEJMbMKoEHgK+4+45QISC55O4JYJaZjQYeBI7ubraBLdXwZWb/BGx29yVmdmZydDezap8MvFPcfYOZTQAeMbNXclkY1Zylpw44NDY8GdiQo7LIvjaZ2USAqLs5x+UZdsysmBDM7nL330ejtV/yhLs3AI8TzgkcbWbJP+b6LhtYpwAfNLPXCafHnE2oSdM+yTF33xB1NxP+yMwlh99hCmfpWQzMiK6oKQEuAh7KcZmk00PApVH/pcAfc1iWYSc6Z+ZXwMvu/qPYJO2XHDKz8VGNGWY2AjiHcD7gQuDCaDbtlwHk7t9098nuXkP4HVng7pegfZJTZlZhZlXJfuA9wEvk8DtMLQSkyczOI/zDKQRud/cbc1ykYcnM7gHOBMYBm4BrgT8A9wFTgDeB/+HuqRcNSJaY2anAk8AyOs+j+RbhvDPtlxwxs+MIJzEXEv6I3+fu15vZdEKtzVjgeeDj7r4ndyUdnqLDmle5+z9pn+RW9Po/GA0WAXe7+41mVk2OvsMUzkRERETyiA5rioiIiOQRhTMRERGRPKJwJiIiIpJHFM5ERERE8ojCmYiIiEgeUTgTkSHBzJ6KujVm9rEMr/tb3W1LRCQbdCsNERlS4veP6sUyhVFTRz1Nb3L3ykyUT0TkQFRzJiJDgpk1Rb3zgdPMbKmZXRk1/n2TmS02sxfN7LPR/Gea2UIzu5twA13M7A9Rw8fLk40fm9l8YES0vrvi27LgJjN7ycyWmdlHY+t+3MzuN7NXzOyuqCUFzGy+ma2IyvLDgXyNRGRwUMPnIjLUXEOs5iwKWY3ufqKZlQJ/N7O/RvPOBd7m7muj4U+5+7aouaPFZvaAu19jZle4+6xutvVhYBZwPKHVisVmtiiaNhs4htBO4t+BU8xsBfAh4Ch392TzSiIicao5E5Gh7j3AJ8xsKaFJqWpgRjTt2VgwA/iSmb0APAMcGpuvJ6cC97h7wt03AU8AJ8bWXefuHcBSoAbYAbQAvzSzDwO7+v3sRGTIUTgTkaHOgC+6+6zoMc3dkzVnzXtnCueqnQO8w92PJ7RxWJbGunsSbxsxARS5ezuhtu4B4ALgz716JiIyLCicichQsxOoig3/Bfi8mRUDmNkRZlbRzXKjgO3uvsvMjgJOjk1rSy6fYhHw0ei8tvHA6cCzPRXMzCqBUe7+MPAVwiFREZEudM6ZiAw1LwLt0eHJO4AfEw4pPhedlF9PqLVK9Wfgc2b2IrCScGgz6TbgRTN7zt0viY1/EHgH8ALgwDfcfWMU7rpTBfzRzMoItW5X9u0pishQpltpiIiIiOQRHdYUERERySMKZyIiIiJ5ROFMREREJI8onImIiIjkEYUzERERkTyicCYiIiKSRxTORERERPLI/w/2bGQygyuQwQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_training(model_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(model_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that this model is performing quite well but it is a bit overfitting so we keep trying with other architectures."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we train our second model and evaluate its performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Model_2():\n",
    "    model = tf.keras.Sequential([\n",
    "        tf.keras.layers.Embedding(input_dim=vocab_size,\n",
    "                                  output_dim=64,\n",
    "                                  input_length=X_train.shape[1]),\n",
    "        tf.keras.layers.GlobalMaxPool1D(),\n",
    "        tf.keras.layers.Dense(units=64, \n",
    "                              activation='relu',\n",
    "                              kernel_initializer='glorot_uniform',\n",
    "                              bias_initializer='zeros'),\n",
    "        tf.keras.layers.Dropout(rate=0.5),\n",
    "        tf.keras.layers.Dense(units=32, \n",
    "                              activation='relu',\n",
    "                              kernel_initializer='glorot_uniform',\n",
    "                              bias_initializer='zeros'),\n",
    "        tf.keras.layers.Dropout(rate=0.5),\n",
    "        tf.keras.layers.Dense(units=16, \n",
    "                              activation='relu',\n",
    "                              kernel_initializer='glorot_uniform',\n",
    "                              bias_initializer='zeros'),\n",
    "        tf.keras.layers.Dropout(rate=0.5),\n",
    "        tf.keras.layers.Dense(units=8, \n",
    "                              activation='relu',\n",
    "                              kernel_initializer='glorot_uniform',\n",
    "                              bias_initializer='zeros'),\n",
    "        tf.keras.layers.Dropout(rate=0.5),\n",
    "        tf.keras.layers.Dense(units=Y_train.shape[1], \n",
    "                              activation='sigmoid',\n",
    "                              kernel_initializer='glorot_uniform',\n",
    "                              bias_initializer='zeros')\n",
    "    ])\n",
    "    optimizer = tf.keras.optimizers.Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-07)\n",
    "    callback1 = tf.keras.callbacks.ReduceLROnPlateau(monitor='loss',\n",
    "                                                     patience=10,\n",
    "                                                     min_delta=0.001,\n",
    "                                                     factor=0.1,\n",
    "                                                     min_lr=0.0001)\n",
    "    callback2 = tf.keras.callbacks.EarlyStopping(monitor='loss',\n",
    "                                                 patience=20,\n",
    "                                                 min_delta=0.001)\n",
    "    model.compile(loss='binary_crossentropy', metrics=['binary_accuracy'], optimizer=optimizer)\n",
    "    model.summary()\n",
    "    model.fit(X_train, Y_train, epochs=100, batch_size=32, callbacks=[callback1, callback2], validation_data=(X_val, Y_val))\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_1 (Embedding)      (None, 50, 64)            640000    \n",
      "_________________________________________________________________\n",
      "global_max_pooling1d (Global (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 64)                4160      \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 32)                2080      \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 32)                0         \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 16)                528       \n",
      "_________________________________________________________________\n",
      "dropout_4 (Dropout)          (None, 16)                0         \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, 8)                 136       \n",
      "_________________________________________________________________\n",
      "dropout_5 (Dropout)          (None, 8)                 0         \n",
      "_________________________________________________________________\n",
      "dense_7 (Dense)              (None, 1)                 9         \n",
      "=================================================================\n",
      "Total params: 646,913\n",
      "Trainable params: 646,913\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Train on 44262 samples, validate on 11066 samples\n",
      "Epoch 1/100\n",
      "44262/44262 [==============================] - 5s 121us/sample - loss: 0.5941 - binary_accuracy: 0.6615 - val_loss: 0.3767 - val_binary_accuracy: 0.8327\n",
      "Epoch 2/100\n",
      "44262/44262 [==============================] - 5s 112us/sample - loss: 0.3540 - binary_accuracy: 0.8525 - val_loss: 0.2797 - val_binary_accuracy: 0.8883\n",
      "Epoch 3/100\n",
      "44262/44262 [==============================] - 5s 106us/sample - loss: 0.2188 - binary_accuracy: 0.9127 - val_loss: 0.2438 - val_binary_accuracy: 0.9175\n",
      "Epoch 4/100\n",
      "44262/44262 [==============================] - 5s 103us/sample - loss: 0.1432 - binary_accuracy: 0.9382 - val_loss: 0.2405 - val_binary_accuracy: 0.9339\n",
      "Epoch 5/100\n",
      "44262/44262 [==============================] - 5s 110us/sample - loss: 0.0984 - binary_accuracy: 0.9575 - val_loss: 0.3341 - val_binary_accuracy: 0.9453\n",
      "Epoch 6/100\n",
      "44262/44262 [==============================] - 5s 107us/sample - loss: 0.0741 - binary_accuracy: 0.9650 - val_loss: 0.3475 - val_binary_accuracy: 0.9459\n",
      "Epoch 7/100\n",
      "44262/44262 [==============================] - 5s 105us/sample - loss: 0.0637 - binary_accuracy: 0.9674 - val_loss: 0.4582 - val_binary_accuracy: 0.9481\n",
      "Epoch 8/100\n",
      "44262/44262 [==============================] - 5s 111us/sample - loss: 0.0618 - binary_accuracy: 0.9680 - val_loss: 0.5036 - val_binary_accuracy: 0.9523\n",
      "Epoch 9/100\n",
      "44262/44262 [==============================] - 5s 105us/sample - loss: 0.0611 - binary_accuracy: 0.9668 - val_loss: 0.5979 - val_binary_accuracy: 0.9494\n",
      "Epoch 10/100\n",
      "44262/44262 [==============================] - 5s 107us/sample - loss: 0.0567 - binary_accuracy: 0.9672 - val_loss: 0.7401 - val_binary_accuracy: 0.9515\n",
      "Epoch 11/100\n",
      "44262/44262 [==============================] - 5s 106us/sample - loss: 0.0524 - binary_accuracy: 0.9694 - val_loss: 0.7584 - val_binary_accuracy: 0.9510\n",
      "Epoch 12/100\n",
      "44262/44262 [==============================] - 5s 103us/sample - loss: 0.0524 - binary_accuracy: 0.9686 - val_loss: 0.8280 - val_binary_accuracy: 0.9519\n",
      "Epoch 13/100\n",
      "44262/44262 [==============================] - 5s 104us/sample - loss: 0.0500 - binary_accuracy: 0.9697 - val_loss: 0.9860 - val_binary_accuracy: 0.9511\n",
      "Epoch 14/100\n",
      "44262/44262 [==============================] - 5s 105us/sample - loss: 0.0504 - binary_accuracy: 0.9696 - val_loss: 0.9133 - val_binary_accuracy: 0.9521\n",
      "Epoch 15/100\n",
      "44262/44262 [==============================] - 5s 108us/sample - loss: 0.0511 - binary_accuracy: 0.9699 - val_loss: 0.8081 - val_binary_accuracy: 0.9536\n",
      "Epoch 16/100\n",
      "44262/44262 [==============================] - 5s 110us/sample - loss: 0.0504 - binary_accuracy: 0.9692 - val_loss: 1.0422 - val_binary_accuracy: 0.9517\n",
      "Epoch 17/100\n",
      "44262/44262 [==============================] - 5s 108us/sample - loss: 0.0512 - binary_accuracy: 0.9694 - val_loss: 1.0811 - val_binary_accuracy: 0.9510\n",
      "Epoch 18/100\n",
      "44262/44262 [==============================] - 5s 108us/sample - loss: 0.0494 - binary_accuracy: 0.9701 - val_loss: 0.9662 - val_binary_accuracy: 0.9498\n",
      "Epoch 19/100\n",
      "44262/44262 [==============================] - 5s 109us/sample - loss: 0.0494 - binary_accuracy: 0.9710 - val_loss: 1.0473 - val_binary_accuracy: 0.9526\n",
      "Epoch 20/100\n",
      "44262/44262 [==============================] - 5s 110us/sample - loss: 0.0479 - binary_accuracy: 0.9707 - val_loss: 1.2575 - val_binary_accuracy: 0.9538\n",
      "Epoch 21/100\n",
      "44262/44262 [==============================] - 5s 106us/sample - loss: 0.0500 - binary_accuracy: 0.9693 - val_loss: 1.0615 - val_binary_accuracy: 0.9509\n",
      "Epoch 22/100\n",
      "44262/44262 [==============================] - 5s 107us/sample - loss: 0.0479 - binary_accuracy: 0.9708 - val_loss: 1.2556 - val_binary_accuracy: 0.9526\n",
      "Epoch 23/100\n",
      "44262/44262 [==============================] - 5s 109us/sample - loss: 0.0491 - binary_accuracy: 0.9696 - val_loss: 1.1591 - val_binary_accuracy: 0.9481\n",
      "Epoch 24/100\n",
      "44262/44262 [==============================] - 5s 105us/sample - loss: 0.0500 - binary_accuracy: 0.9700 - val_loss: 1.0785 - val_binary_accuracy: 0.9516\n",
      "Epoch 25/100\n",
      "44262/44262 [==============================] - 5s 108us/sample - loss: 0.0482 - binary_accuracy: 0.9702 - val_loss: 1.3100 - val_binary_accuracy: 0.9517\n",
      "Epoch 26/100\n",
      "44262/44262 [==============================] - 5s 107us/sample - loss: 0.0474 - binary_accuracy: 0.9701 - val_loss: 1.2910 - val_binary_accuracy: 0.9537\n",
      "Epoch 27/100\n",
      "44262/44262 [==============================] - 5s 107us/sample - loss: 0.0481 - binary_accuracy: 0.9693 - val_loss: 1.5772 - val_binary_accuracy: 0.9544\n",
      "Epoch 28/100\n",
      "44262/44262 [==============================] - 5s 107us/sample - loss: 0.0487 - binary_accuracy: 0.9704 - val_loss: 1.5151 - val_binary_accuracy: 0.9516\n",
      "Epoch 29/100\n",
      "44262/44262 [==============================] - 5s 108us/sample - loss: 0.0499 - binary_accuracy: 0.9687 - val_loss: 1.3293 - val_binary_accuracy: 0.9524\n",
      "Epoch 30/100\n",
      "44262/44262 [==============================] - 5s 107us/sample - loss: 0.0505 - binary_accuracy: 0.9700 - val_loss: 1.2561 - val_binary_accuracy: 0.9508\n",
      "Epoch 31/100\n",
      "44262/44262 [==============================] - 5s 110us/sample - loss: 0.0472 - binary_accuracy: 0.9704 - val_loss: 1.2372 - val_binary_accuracy: 0.9518\n",
      "Epoch 32/100\n",
      "44262/44262 [==============================] - 5s 110us/sample - loss: 0.0457 - binary_accuracy: 0.9713 - val_loss: 1.3384 - val_binary_accuracy: 0.9521\n",
      "Epoch 33/100\n",
      "44262/44262 [==============================] - 5s 115us/sample - loss: 0.0452 - binary_accuracy: 0.9720 - val_loss: 1.4496 - val_binary_accuracy: 0.9522\n",
      "Epoch 34/100\n",
      "44262/44262 [==============================] - 5s 108us/sample - loss: 0.0478 - binary_accuracy: 0.9692 - val_loss: 1.5780 - val_binary_accuracy: 0.9520\n",
      "Epoch 35/100\n",
      "44262/44262 [==============================] - 5s 109us/sample - loss: 0.0470 - binary_accuracy: 0.9700 - val_loss: 1.7613 - val_binary_accuracy: 0.9517\n",
      "Epoch 36/100\n",
      "44262/44262 [==============================] - 5s 110us/sample - loss: 0.0466 - binary_accuracy: 0.9702 - val_loss: 1.9664 - val_binary_accuracy: 0.9517\n",
      "Epoch 37/100\n",
      "44262/44262 [==============================] - 5s 108us/sample - loss: 0.0453 - binary_accuracy: 0.9702 - val_loss: 2.1142 - val_binary_accuracy: 0.9508\n",
      "Epoch 38/100\n",
      "44262/44262 [==============================] - 5s 109us/sample - loss: 0.0471 - binary_accuracy: 0.9703 - val_loss: 2.2339 - val_binary_accuracy: 0.9512\n",
      "Epoch 39/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "44262/44262 [==============================] - 5s 112us/sample - loss: 0.0466 - binary_accuracy: 0.9701 - val_loss: 2.3656 - val_binary_accuracy: 0.9512\n",
      "Epoch 40/100\n",
      "44262/44262 [==============================] - 5s 113us/sample - loss: 0.0466 - binary_accuracy: 0.9705 - val_loss: 2.4625 - val_binary_accuracy: 0.9521\n",
      "Epoch 41/100\n",
      "44262/44262 [==============================] - 5s 109us/sample - loss: 0.0466 - binary_accuracy: 0.9698 - val_loss: 2.5995 - val_binary_accuracy: 0.9516\n",
      "Epoch 42/100\n",
      "44262/44262 [==============================] - 5s 108us/sample - loss: 0.0450 - binary_accuracy: 0.9704 - val_loss: 2.7285 - val_binary_accuracy: 0.9515\n",
      "Epoch 43/100\n",
      "44262/44262 [==============================] - 5s 107us/sample - loss: 0.0467 - binary_accuracy: 0.9698 - val_loss: 2.7423 - val_binary_accuracy: 0.9521\n",
      "Epoch 44/100\n",
      "44262/44262 [==============================] - 5s 110us/sample - loss: 0.0462 - binary_accuracy: 0.9707 - val_loss: 2.7863 - val_binary_accuracy: 0.9522\n",
      "Epoch 45/100\n",
      "44262/44262 [==============================] - 5s 110us/sample - loss: 0.0456 - binary_accuracy: 0.9706 - val_loss: 2.8751 - val_binary_accuracy: 0.9521\n",
      "Epoch 46/100\n",
      "44262/44262 [==============================] - 5s 108us/sample - loss: 0.0455 - binary_accuracy: 0.9717 - val_loss: 2.9002 - val_binary_accuracy: 0.9520\n",
      "Epoch 47/100\n",
      "44262/44262 [==============================] - 5s 107us/sample - loss: 0.0457 - binary_accuracy: 0.9701 - val_loss: 2.9512 - val_binary_accuracy: 0.9521\n",
      "Epoch 48/100\n",
      "44262/44262 [==============================] - 5s 109us/sample - loss: 0.0462 - binary_accuracy: 0.9699 - val_loss: 3.0021 - val_binary_accuracy: 0.9526\n",
      "Epoch 49/100\n",
      "44262/44262 [==============================] - 5s 107us/sample - loss: 0.0476 - binary_accuracy: 0.9692 - val_loss: 3.0059 - val_binary_accuracy: 0.9529\n",
      "Epoch 50/100\n",
      "44262/44262 [==============================] - 5s 113us/sample - loss: 0.0460 - binary_accuracy: 0.9703 - val_loss: 3.0199 - val_binary_accuracy: 0.9526\n",
      "Epoch 51/100\n",
      "44262/44262 [==============================] - 4s 101us/sample - loss: 0.0470 - binary_accuracy: 0.9701 - val_loss: 3.0129 - val_binary_accuracy: 0.9525\n",
      "Epoch 52/100\n",
      "44262/44262 [==============================] - 5s 104us/sample - loss: 0.0456 - binary_accuracy: 0.9705 - val_loss: 3.0681 - val_binary_accuracy: 0.9525\n"
     ]
    }
   ],
   "source": [
    "model_2 = Model_2()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "44262/44262 [==============================] - 1s 22us/sample - loss: 8.3724e-04 - binary_accuracy: 0.9995\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.0008372424872222871, 0.99950296]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_2.evaluate(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11066/11066 [==============================] - 0s 23us/sample - loss: 3.0681 - binary_accuracy: 0.9525\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[3.068108115687003, 0.952467]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_2.evaluate(X_val, Y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_training(model_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(model_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This model is also performing quite well but it is a bit overfitting. We keep trying with other architectures."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we train our third model and evaluate its performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Model_3():\n",
    "    model = tf.keras.Sequential([\n",
    "        tf.keras.layers.Embedding(input_dim=vocab_size,\n",
    "                                  output_dim=64,\n",
    "                                  input_length=X_train.shape[1]),\n",
    "        tf.keras.layers.Conv1D(filters=128,\n",
    "                               kernel_size=3,\n",
    "                               strides=1,\n",
    "                               padding='valid',\n",
    "                               activation='relu',\n",
    "                               kernel_initializer='glorot_uniform',\n",
    "                               bias_initializer='zeros'),\n",
    "        tf.keras.layers.Dropout(rate=0.5),\n",
    "        tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(units=64,\n",
    "                                                           activation='tanh',\n",
    "                                                           recurrent_activation='sigmoid',\n",
    "                                                           kernel_initializer='glorot_uniform',\n",
    "                                                           bias_initializer='zeros',\n",
    "                                                           recurrent_initializer='zeros',\n",
    "                                                           return_sequences=False,\n",
    "                                                           return_state=False),\n",
    "                                      merge_mode='concat'),\n",
    "        tf.keras.layers.Dropout(rate=0.5),\n",
    "        tf.keras.layers.Dense(units=Y_train.shape[1], \n",
    "                              activation='sigmoid',\n",
    "                              kernel_initializer='glorot_uniform',\n",
    "                              bias_initializer='zeros')\n",
    "    ])\n",
    "    optimizer = tf.keras.optimizers.Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-07)\n",
    "    callback1 = tf.keras.callbacks.ReduceLROnPlateau(monitor='loss',\n",
    "                                                     patience=10,\n",
    "                                                     min_delta=0.001,\n",
    "                                                     factor=0.1,\n",
    "                                                     min_lr=0.0001)\n",
    "    callback2 = tf.keras.callbacks.EarlyStopping(monitor='loss',\n",
    "                                                 patience=20,\n",
    "                                                 min_delta=0.001)\n",
    "    model.compile(loss='binary_crossentropy', metrics=['binary_accuracy'], optimizer=optimizer)\n",
    "    model.summary()\n",
    "    model.fit(X_train, Y_train, epochs=100, batch_size=32, callbacks=[callback1, callback2], validation_data=(X_val, Y_val))\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_2 (Embedding)      (None, 50, 64)            640000    \n",
      "_________________________________________________________________\n",
      "conv1d (Conv1D)              (None, 48, 128)           24704     \n",
      "_________________________________________________________________\n",
      "dropout_6 (Dropout)          (None, 48, 128)           0         \n",
      "_________________________________________________________________\n",
      "bidirectional (Bidirectional (None, 128)               98816     \n",
      "_________________________________________________________________\n",
      "dropout_7 (Dropout)          (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_8 (Dense)              (None, 1)                 129       \n",
      "=================================================================\n",
      "Total params: 763,649\n",
      "Trainable params: 763,649\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Train on 44262 samples, validate on 11066 samples\n",
      "Epoch 1/100\n",
      "44262/44262 [==============================] - 59s 1ms/sample - loss: 0.4057 - binary_accuracy: 0.8097 - val_loss: 0.3011 - val_binary_accuracy: 0.8710\n",
      "Epoch 2/100\n",
      "44262/44262 [==============================] - 59s 1ms/sample - loss: 0.1871 - binary_accuracy: 0.9290 - val_loss: 0.2314 - val_binary_accuracy: 0.9130\n",
      "Epoch 3/100\n",
      "44262/44262 [==============================] - 55s 1ms/sample - loss: 0.0936 - binary_accuracy: 0.9665 - val_loss: 0.2099 - val_binary_accuracy: 0.9320\n",
      "Epoch 4/100\n",
      "44262/44262 [==============================] - 53s 1ms/sample - loss: 0.0513 - binary_accuracy: 0.9823 - val_loss: 0.2483 - val_binary_accuracy: 0.9360\n",
      "Epoch 5/100\n",
      "44262/44262 [==============================] - 53s 1ms/sample - loss: 0.0332 - binary_accuracy: 0.9888 - val_loss: 0.2579 - val_binary_accuracy: 0.9410\n",
      "Epoch 6/100\n",
      "44262/44262 [==============================] - 54s 1ms/sample - loss: 0.0248 - binary_accuracy: 0.9917 - val_loss: 0.2789 - val_binary_accuracy: 0.9437\n",
      "Epoch 7/100\n",
      "44262/44262 [==============================] - 52s 1ms/sample - loss: 0.0182 - binary_accuracy: 0.9936 - val_loss: 0.2910 - val_binary_accuracy: 0.9495\n",
      "Epoch 8/100\n",
      "44262/44262 [==============================] - 53s 1ms/sample - loss: 0.0151 - binary_accuracy: 0.9948 - val_loss: 0.2948 - val_binary_accuracy: 0.9471\n",
      "Epoch 9/100\n",
      "44262/44262 [==============================] - 53s 1ms/sample - loss: 0.0135 - binary_accuracy: 0.9954 - val_loss: 0.3035 - val_binary_accuracy: 0.9477\n",
      "Epoch 10/100\n",
      "44262/44262 [==============================] - 52s 1ms/sample - loss: 0.0116 - binary_accuracy: 0.9961 - val_loss: 0.3043 - val_binary_accuracy: 0.9481\n",
      "Epoch 11/100\n",
      "44262/44262 [==============================] - 52s 1ms/sample - loss: 0.0091 - binary_accuracy: 0.9970 - val_loss: 0.3049 - val_binary_accuracy: 0.9492\n",
      "Epoch 12/100\n",
      "44262/44262 [==============================] - 52s 1ms/sample - loss: 0.0081 - binary_accuracy: 0.9971 - val_loss: 0.3208 - val_binary_accuracy: 0.9520\n",
      "Epoch 13/100\n",
      "44262/44262 [==============================] - 52s 1ms/sample - loss: 0.0079 - binary_accuracy: 0.9973 - val_loss: 0.3220 - val_binary_accuracy: 0.9506\n",
      "Epoch 14/100\n",
      "44262/44262 [==============================] - 52s 1ms/sample - loss: 0.0055 - binary_accuracy: 0.9979 - val_loss: 0.3640 - val_binary_accuracy: 0.9511\n",
      "Epoch 15/100\n",
      "44262/44262 [==============================] - 51s 1ms/sample - loss: 0.0072 - binary_accuracy: 0.9977 - val_loss: 0.3442 - val_binary_accuracy: 0.9499\n",
      "Epoch 16/100\n",
      "44262/44262 [==============================] - 52s 1ms/sample - loss: 0.0063 - binary_accuracy: 0.9978 - val_loss: 0.3712 - val_binary_accuracy: 0.9513\n",
      "Epoch 17/100\n",
      "44262/44262 [==============================] - 51s 1ms/sample - loss: 0.0051 - binary_accuracy: 0.9984 - val_loss: 0.3550 - val_binary_accuracy: 0.9513\n",
      "Epoch 18/100\n",
      "44262/44262 [==============================] - 52s 1ms/sample - loss: 0.0057 - binary_accuracy: 0.9981 - val_loss: 0.3545 - val_binary_accuracy: 0.9508\n",
      "Epoch 19/100\n",
      "44262/44262 [==============================] - 52s 1ms/sample - loss: 0.0042 - binary_accuracy: 0.9986 - val_loss: 0.3874 - val_binary_accuracy: 0.9517\n",
      "Epoch 20/100\n",
      "44262/44262 [==============================] - 51s 1ms/sample - loss: 0.0031 - binary_accuracy: 0.9989 - val_loss: 0.3664 - val_binary_accuracy: 0.9515\n",
      "Epoch 21/100\n",
      "44262/44262 [==============================] - 51s 1ms/sample - loss: 0.0029 - binary_accuracy: 0.9989 - val_loss: 0.4104 - val_binary_accuracy: 0.9498\n",
      "Epoch 22/100\n",
      "44262/44262 [==============================] - 51s 1ms/sample - loss: 0.0033 - binary_accuracy: 0.9989 - val_loss: 0.4013 - val_binary_accuracy: 0.9494\n",
      "Epoch 23/100\n",
      "44262/44262 [==============================] - 51s 1ms/sample - loss: 0.0037 - binary_accuracy: 0.9987 - val_loss: 0.4122 - val_binary_accuracy: 0.9498\n",
      "Epoch 24/100\n",
      "44262/44262 [==============================] - 51s 1ms/sample - loss: 0.0046 - binary_accuracy: 0.9985 - val_loss: 0.4038 - val_binary_accuracy: 0.9487\n",
      "Epoch 25/100\n",
      "44262/44262 [==============================] - 51s 1ms/sample - loss: 0.0036 - binary_accuracy: 0.9988 - val_loss: 0.3868 - val_binary_accuracy: 0.9485\n",
      "Epoch 26/100\n",
      "44262/44262 [==============================] - 51s 1ms/sample - loss: 0.0027 - binary_accuracy: 0.9991 - val_loss: 0.4459 - val_binary_accuracy: 0.9494\n",
      "Epoch 27/100\n",
      "44262/44262 [==============================] - 51s 1ms/sample - loss: 0.0027 - binary_accuracy: 0.9992 - val_loss: 0.4101 - val_binary_accuracy: 0.9497\n",
      "Epoch 28/100\n",
      "44262/44262 [==============================] - 51s 1ms/sample - loss: 0.0027 - binary_accuracy: 0.9990 - val_loss: 0.4165 - val_binary_accuracy: 0.9489\n",
      "Epoch 29/100\n",
      "44262/44262 [==============================] - 51s 1ms/sample - loss: 0.0029 - binary_accuracy: 0.9988 - val_loss: 0.4193 - val_binary_accuracy: 0.9500\n",
      "Epoch 30/100\n",
      "44262/44262 [==============================] - 51s 1ms/sample - loss: 0.0028 - binary_accuracy: 0.9990 - val_loss: 0.4290 - val_binary_accuracy: 0.9493\n",
      "Epoch 31/100\n",
      "44262/44262 [==============================] - 51s 1ms/sample - loss: 0.0013 - binary_accuracy: 0.9995 - val_loss: 0.4384 - val_binary_accuracy: 0.9494\n",
      "Epoch 32/100\n",
      "44262/44262 [==============================] - 51s 1ms/sample - loss: 0.0010 - binary_accuracy: 0.9995 - val_loss: 0.4451 - val_binary_accuracy: 0.9490\n",
      "Epoch 33/100\n",
      "44262/44262 [==============================] - 51s 1ms/sample - loss: 8.3356e-04 - binary_accuracy: 0.9997 - val_loss: 0.4560 - val_binary_accuracy: 0.9489\n",
      "Epoch 34/100\n",
      "44262/44262 [==============================] - 51s 1ms/sample - loss: 9.7509e-04 - binary_accuracy: 0.9996 - val_loss: 0.4664 - val_binary_accuracy: 0.9489\n",
      "Epoch 35/100\n",
      "44262/44262 [==============================] - 50s 1ms/sample - loss: 9.2432e-04 - binary_accuracy: 0.9996 - val_loss: 0.4769 - val_binary_accuracy: 0.9493\n",
      "Epoch 36/100\n",
      "44262/44262 [==============================] - 51s 1ms/sample - loss: 7.7046e-04 - binary_accuracy: 0.9997 - val_loss: 0.4883 - val_binary_accuracy: 0.9493\n",
      "Epoch 37/100\n",
      "44262/44262 [==============================] - 50s 1ms/sample - loss: 7.5778e-04 - binary_accuracy: 0.9996 - val_loss: 0.4960 - val_binary_accuracy: 0.9498\n",
      "Epoch 38/100\n",
      "44262/44262 [==============================] - 50s 1ms/sample - loss: 7.2881e-04 - binary_accuracy: 0.9996 - val_loss: 0.5063 - val_binary_accuracy: 0.9503\n",
      "Epoch 39/100\n",
      "44262/44262 [==============================] - 50s 1ms/sample - loss: 7.3167e-04 - binary_accuracy: 0.9996 - val_loss: 0.5151 - val_binary_accuracy: 0.9500\n",
      "Epoch 40/100\n",
      "44262/44262 [==============================] - 50s 1ms/sample - loss: 7.4324e-04 - binary_accuracy: 0.9996 - val_loss: 0.5308 - val_binary_accuracy: 0.9500\n",
      "Epoch 41/100\n",
      "44262/44262 [==============================] - 50s 1ms/sample - loss: 7.1217e-04 - binary_accuracy: 0.9996 - val_loss: 0.5415 - val_binary_accuracy: 0.9495\n",
      "Epoch 42/100\n",
      "44262/44262 [==============================] - 50s 1ms/sample - loss: 7.5727e-04 - binary_accuracy: 0.9996 - val_loss: 0.5522 - val_binary_accuracy: 0.9493\n",
      "Epoch 43/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "44262/44262 [==============================] - 50s 1ms/sample - loss: 7.1218e-04 - binary_accuracy: 0.9996 - val_loss: 0.5635 - val_binary_accuracy: 0.9497\n",
      "Epoch 44/100\n",
      "44262/44262 [==============================] - 50s 1ms/sample - loss: 8.1904e-04 - binary_accuracy: 0.9996 - val_loss: 0.5692 - val_binary_accuracy: 0.9493\n",
      "Epoch 45/100\n",
      "44262/44262 [==============================] - 50s 1ms/sample - loss: 7.2667e-04 - binary_accuracy: 0.9996 - val_loss: 0.5718 - val_binary_accuracy: 0.9490\n",
      "Epoch 46/100\n",
      "44262/44262 [==============================] - 50s 1ms/sample - loss: 7.5043e-04 - binary_accuracy: 0.9996 - val_loss: 0.5785 - val_binary_accuracy: 0.9487\n",
      "Epoch 47/100\n",
      "44262/44262 [==============================] - 50s 1ms/sample - loss: 6.8987e-04 - binary_accuracy: 0.9996 - val_loss: 0.5838 - val_binary_accuracy: 0.9490\n",
      "Epoch 48/100\n",
      "44262/44262 [==============================] - 50s 1ms/sample - loss: 7.1127e-04 - binary_accuracy: 0.9996 - val_loss: 0.5796 - val_binary_accuracy: 0.9494\n",
      "Epoch 49/100\n",
      "44262/44262 [==============================] - 50s 1ms/sample - loss: 7.1798e-04 - binary_accuracy: 0.9996 - val_loss: 0.5891 - val_binary_accuracy: 0.9490\n",
      "Epoch 50/100\n",
      "44262/44262 [==============================] - 50s 1ms/sample - loss: 7.2489e-04 - binary_accuracy: 0.9996 - val_loss: 0.5947 - val_binary_accuracy: 0.9489\n",
      "Epoch 51/100\n",
      "44262/44262 [==============================] - 50s 1ms/sample - loss: 7.4210e-04 - binary_accuracy: 0.9996 - val_loss: 0.6028 - val_binary_accuracy: 0.9495\n"
     ]
    }
   ],
   "source": [
    "model_3 = Model_3()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "44262/44262 [==============================] - 9s 206us/sample - loss: 6.5484e-04 - binary_accuracy: 0.9996\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.0006548378996660205, 0.9996385]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_3.evaluate(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11066/11066 [==============================] - 2s 191us/sample - loss: 0.6028 - binary_accuracy: 0.9495\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.6027836554302891, 0.9494849]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_3.evaluate(X_val, Y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_training(model_3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(model_3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This model is performing a bit better than the last one. We keep trying with other architectures."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we train our fourth model. We will use some pretrained embeddings in an attempt to enhance performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://firebasestorage.googleapis.com/v0/b/z2tma61d2a74hya815w9x621uszb3a.appspot.com/o/glove.6B.100d.txt.zip?alt=media&token=df3a863b-bea5-4e6f-a97b-ae0a69fbf053'\n",
    "local_zip = 'glove.6B.100d.txt.zip'\n",
    "urllib.request.urlretrieve(url, local_zip)\n",
    "zip_ref = zipfile.ZipFile(local_zip, 'r')\n",
    "zip_ref.extractall()\n",
    "zip_ref.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('glove.6B.100d.txt', 'r') as f:\n",
    "    word_to_vec = {}\n",
    "    for line in f:\n",
    "        values = line.strip().split()\n",
    "        curr_word = values[0]\n",
    "        word_to_vec[curr_word] = np.array(values[1:], dtype='float32')\n",
    "        \n",
    "embedding_matrix = np.zeros(shape=(vocab_size, 100), dtype='float32')\n",
    "keys = list(tokenizer.word_index.keys())\n",
    "values = tokenizer.texts_to_sequences(list(tokenizer.word_index.keys()))\n",
    "for i in range(len(tokenizer.word_index)):\n",
    "    word = keys[i]\n",
    "    idx = values[i]\n",
    "    if word in word_to_vec:\n",
    "        vec = word_to_vec[word]\n",
    "        embedding_matrix[idx, :] = vec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Model_4():\n",
    "    model = tf.keras.Sequential([\n",
    "        tf.keras.layers.Embedding(input_dim=embedding_matrix.shape[0],\n",
    "                                  output_dim=embedding_matrix.shape[1],\n",
    "                                  input_length=X_train.shape[1]),\n",
    "        tf.keras.layers.Conv1D(filters=256,\n",
    "                               kernel_size=3,\n",
    "                               strides=1,\n",
    "                               padding='valid',\n",
    "                               activation='relu',\n",
    "                               kernel_initializer='glorot_uniform',\n",
    "                               bias_initializer='zeros'),\n",
    "        tf.keras.layers.Dropout(rate=0.5),\n",
    "        tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(units=128,\n",
    "                                                           activation='tanh',\n",
    "                                                           recurrent_activation='sigmoid',\n",
    "                                                           kernel_initializer='glorot_uniform',\n",
    "                                                           bias_initializer='zeros',\n",
    "                                                           recurrent_initializer='zeros',\n",
    "                                                           return_sequences=False,\n",
    "                                                           return_state=False),\n",
    "                                      merge_mode='concat'),\n",
    "        tf.keras.layers.Dropout(rate=0.5),\n",
    "        tf.keras.layers.Dense(units=128,\n",
    "                              activation='relu',\n",
    "                              kernel_initializer='glorot_uniform',\n",
    "                              bias_initializer='zeros'),\n",
    "        tf.keras.layers.Dropout(rate=0.5),\n",
    "        tf.keras.layers.Dense(units=Y_train.shape[1],\n",
    "                              activation='sigmoid',\n",
    "                              kernel_initializer='glorot_uniform',\n",
    "                              bias_initializer='zeros')\n",
    "    ])\n",
    "    model.layers[0].trainable = False\n",
    "    model.layers[0].set_weights([embedding_matrix])\n",
    "    optimizer = tf.keras.optimizers.Adam(learning_rate=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-07)\n",
    "    callback1 = tf.keras.callbacks.ReduceLROnPlateau(monitor='loss',\n",
    "                                                     patience=10,\n",
    "                                                     min_delta=0.001,\n",
    "                                                     factor=0.1,\n",
    "                                                     min_lr=0.0001)\n",
    "    callback2 = tf.keras.callbacks.EarlyStopping(monitor='loss',\n",
    "                                                 patience=20,\n",
    "                                                 min_delta=0.001)\n",
    "    model.compile(loss='binary_crossentropy', metrics=['binary_accuracy'], optimizer=optimizer)\n",
    "    model.summary()\n",
    "    model.fit(X_train, Y_train, epochs=100, batch_size=32, callbacks=[callback1, callback2], validation_data=(X_val, Y_val))\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_3\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding_3 (Embedding)      (None, 50, 100)           1000000   \n",
      "_________________________________________________________________\n",
      "conv1d_1 (Conv1D)            (None, 48, 256)           77056     \n",
      "_________________________________________________________________\n",
      "dropout_8 (Dropout)          (None, 48, 256)           0         \n",
      "_________________________________________________________________\n",
      "bidirectional_1 (Bidirection (None, 256)               394240    \n",
      "_________________________________________________________________\n",
      "dropout_9 (Dropout)          (None, 256)               0         \n",
      "_________________________________________________________________\n",
      "dense_9 (Dense)              (None, 128)               32896     \n",
      "_________________________________________________________________\n",
      "dropout_10 (Dropout)         (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_10 (Dense)             (None, 1)                 129       \n",
      "=================================================================\n",
      "Total params: 1,504,321\n",
      "Trainable params: 504,321\n",
      "Non-trainable params: 1,000,000\n",
      "_________________________________________________________________\n",
      "Train on 44262 samples, validate on 11066 samples\n",
      "Epoch 1/100\n",
      "44262/44262 [==============================] - 98s 2ms/sample - loss: 0.5411 - binary_accuracy: 0.7226 - val_loss: 0.4690 - val_binary_accuracy: 0.7781\n",
      "Epoch 2/100\n",
      "44262/44262 [==============================] - 96s 2ms/sample - loss: 0.4432 - binary_accuracy: 0.7926 - val_loss: 0.4005 - val_binary_accuracy: 0.8128\n",
      "Epoch 3/100\n",
      "44262/44262 [==============================] - 94s 2ms/sample - loss: 0.3894 - binary_accuracy: 0.8231 - val_loss: 0.3642 - val_binary_accuracy: 0.8357\n",
      "Epoch 4/100\n",
      "44262/44262 [==============================] - 94s 2ms/sample - loss: 0.3476 - binary_accuracy: 0.8447 - val_loss: 0.3343 - val_binary_accuracy: 0.8530\n",
      "Epoch 5/100\n",
      "44262/44262 [==============================] - 94s 2ms/sample - loss: 0.3149 - binary_accuracy: 0.8644 - val_loss: 0.3093 - val_binary_accuracy: 0.8670\n",
      "Epoch 6/100\n",
      "44262/44262 [==============================] - 94s 2ms/sample - loss: 0.2870 - binary_accuracy: 0.8756 - val_loss: 0.2903 - val_binary_accuracy: 0.8773\n",
      "Epoch 7/100\n",
      "44262/44262 [==============================] - 94s 2ms/sample - loss: 0.2625 - binary_accuracy: 0.8894 - val_loss: 0.2709 - val_binary_accuracy: 0.8869\n",
      "Epoch 8/100\n",
      "44262/44262 [==============================] - 97s 2ms/sample - loss: 0.2439 - binary_accuracy: 0.8988 - val_loss: 0.2669 - val_binary_accuracy: 0.8872\n",
      "Epoch 9/100\n",
      "44262/44262 [==============================] - 98s 2ms/sample - loss: 0.2264 - binary_accuracy: 0.9054 - val_loss: 0.2419 - val_binary_accuracy: 0.9037\n",
      "Epoch 10/100\n",
      "44262/44262 [==============================] - 95s 2ms/sample - loss: 0.2173 - binary_accuracy: 0.9116 - val_loss: 0.2331 - val_binary_accuracy: 0.9097\n",
      "Epoch 11/100\n",
      "44262/44262 [==============================] - 95s 2ms/sample - loss: 0.2044 - binary_accuracy: 0.9172 - val_loss: 0.2372 - val_binary_accuracy: 0.9058\n",
      "Epoch 12/100\n",
      "44262/44262 [==============================] - 94s 2ms/sample - loss: 0.1962 - binary_accuracy: 0.9211 - val_loss: 0.2184 - val_binary_accuracy: 0.9169\n",
      "Epoch 13/100\n",
      "44262/44262 [==============================] - 94s 2ms/sample - loss: 0.1862 - binary_accuracy: 0.9277 - val_loss: 0.2085 - val_binary_accuracy: 0.9218\n",
      "Epoch 14/100\n",
      "44262/44262 [==============================] - 95s 2ms/sample - loss: 0.1795 - binary_accuracy: 0.9278 - val_loss: 0.2042 - val_binary_accuracy: 0.9259\n",
      "Epoch 15/100\n",
      "44262/44262 [==============================] - 95s 2ms/sample - loss: 0.1735 - binary_accuracy: 0.9312 - val_loss: 0.2049 - val_binary_accuracy: 0.9231\n",
      "Epoch 16/100\n",
      "44262/44262 [==============================] - 94s 2ms/sample - loss: 0.1643 - binary_accuracy: 0.9351 - val_loss: 0.1944 - val_binary_accuracy: 0.9303\n",
      "Epoch 17/100\n",
      "44262/44262 [==============================] - 94s 2ms/sample - loss: 0.1615 - binary_accuracy: 0.9371 - val_loss: 0.1943 - val_binary_accuracy: 0.9317\n",
      "Epoch 18/100\n",
      "44262/44262 [==============================] - 94s 2ms/sample - loss: 0.1519 - binary_accuracy: 0.9405 - val_loss: 0.1939 - val_binary_accuracy: 0.9359\n",
      "Epoch 19/100\n",
      "44262/44262 [==============================] - 94s 2ms/sample - loss: 0.1486 - binary_accuracy: 0.9429 - val_loss: 0.1849 - val_binary_accuracy: 0.9318\n",
      "Epoch 20/100\n",
      "44262/44262 [==============================] - 94s 2ms/sample - loss: 0.1456 - binary_accuracy: 0.9426 - val_loss: 0.1921 - val_binary_accuracy: 0.9319\n",
      "Epoch 21/100\n",
      "44262/44262 [==============================] - 95s 2ms/sample - loss: 0.1418 - binary_accuracy: 0.9459 - val_loss: 0.1804 - val_binary_accuracy: 0.9397\n",
      "Epoch 22/100\n",
      "44262/44262 [==============================] - 94s 2ms/sample - loss: 0.1354 - binary_accuracy: 0.9493 - val_loss: 0.1780 - val_binary_accuracy: 0.9398\n",
      "Epoch 23/100\n",
      "44262/44262 [==============================] - 94s 2ms/sample - loss: 0.1369 - binary_accuracy: 0.9467 - val_loss: 0.1790 - val_binary_accuracy: 0.9390\n",
      "Epoch 24/100\n",
      "44262/44262 [==============================] - 94s 2ms/sample - loss: 0.1344 - binary_accuracy: 0.9485 - val_loss: 0.1716 - val_binary_accuracy: 0.9444\n",
      "Epoch 25/100\n",
      "44262/44262 [==============================] - 94s 2ms/sample - loss: 0.1265 - binary_accuracy: 0.9520 - val_loss: 0.1695 - val_binary_accuracy: 0.9442\n",
      "Epoch 26/100\n",
      "44262/44262 [==============================] - 95s 2ms/sample - loss: 0.1249 - binary_accuracy: 0.9524 - val_loss: 0.1858 - val_binary_accuracy: 0.9329\n",
      "Epoch 27/100\n",
      "44262/44262 [==============================] - 95s 2ms/sample - loss: 0.1232 - binary_accuracy: 0.9532 - val_loss: 0.1860 - val_binary_accuracy: 0.9408\n",
      "Epoch 28/100\n",
      "44262/44262 [==============================] - 95s 2ms/sample - loss: 0.1232 - binary_accuracy: 0.9543 - val_loss: 0.1705 - val_binary_accuracy: 0.9440\n",
      "Epoch 29/100\n",
      "44262/44262 [==============================] - 95s 2ms/sample - loss: 0.1217 - binary_accuracy: 0.9534 - val_loss: 0.1811 - val_binary_accuracy: 0.9459\n",
      "Epoch 30/100\n",
      "44262/44262 [==============================] - 94s 2ms/sample - loss: 0.1165 - binary_accuracy: 0.9563 - val_loss: 0.1788 - val_binary_accuracy: 0.9465\n",
      "Epoch 31/100\n",
      "44262/44262 [==============================] - 94s 2ms/sample - loss: 0.1193 - binary_accuracy: 0.9549 - val_loss: 0.1695 - val_binary_accuracy: 0.9485\n",
      "Epoch 32/100\n",
      "44262/44262 [==============================] - 96s 2ms/sample - loss: 0.1150 - binary_accuracy: 0.9575 - val_loss: 0.1653 - val_binary_accuracy: 0.9496\n",
      "Epoch 33/100\n",
      "44262/44262 [==============================] - 101s 2ms/sample - loss: 0.1147 - binary_accuracy: 0.9565 - val_loss: 0.1638 - val_binary_accuracy: 0.9505\n",
      "Epoch 34/100\n",
      "44262/44262 [==============================] - 101s 2ms/sample - loss: 0.1113 - binary_accuracy: 0.9578 - val_loss: 0.1742 - val_binary_accuracy: 0.9474\n",
      "Epoch 35/100\n",
      "44262/44262 [==============================] - 100s 2ms/sample - loss: 0.1076 - binary_accuracy: 0.9595 - val_loss: 0.1713 - val_binary_accuracy: 0.9492\n",
      "Epoch 36/100\n",
      "44262/44262 [==============================] - 98s 2ms/sample - loss: 0.1100 - binary_accuracy: 0.9590 - val_loss: 0.1832 - val_binary_accuracy: 0.9470\n",
      "Epoch 37/100\n",
      "44262/44262 [==============================] - 96s 2ms/sample - loss: 0.1043 - binary_accuracy: 0.9614 - val_loss: 0.1682 - val_binary_accuracy: 0.9480\n",
      "Epoch 38/100\n",
      "44262/44262 [==============================] - 98s 2ms/sample - loss: 0.1041 - binary_accuracy: 0.9615 - val_loss: 0.1633 - val_binary_accuracy: 0.9479\n",
      "Epoch 39/100\n",
      "44262/44262 [==============================] - 99s 2ms/sample - loss: 0.1032 - binary_accuracy: 0.9614 - val_loss: 0.1788 - val_binary_accuracy: 0.9478\n",
      "Epoch 40/100\n",
      "44262/44262 [==============================] - 95s 2ms/sample - loss: 0.1025 - binary_accuracy: 0.9620 - val_loss: 0.1745 - val_binary_accuracy: 0.9515\n",
      "Epoch 41/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "44262/44262 [==============================] - 99s 2ms/sample - loss: 0.1031 - binary_accuracy: 0.9617 - val_loss: 0.1676 - val_binary_accuracy: 0.9477\n",
      "Epoch 42/100\n",
      "44262/44262 [==============================] - 98s 2ms/sample - loss: 0.1015 - binary_accuracy: 0.9632 - val_loss: 0.1662 - val_binary_accuracy: 0.9533\n",
      "Epoch 43/100\n",
      "44262/44262 [==============================] - 99s 2ms/sample - loss: 0.0966 - binary_accuracy: 0.9637 - val_loss: 0.1733 - val_binary_accuracy: 0.9533\n",
      "Epoch 44/100\n",
      "44262/44262 [==============================] - 100s 2ms/sample - loss: 0.0988 - binary_accuracy: 0.9637 - val_loss: 0.1615 - val_binary_accuracy: 0.9536\n",
      "Epoch 45/100\n",
      "44262/44262 [==============================] - 96s 2ms/sample - loss: 0.0961 - binary_accuracy: 0.9646 - val_loss: 0.1637 - val_binary_accuracy: 0.9535\n",
      "Epoch 46/100\n",
      "44262/44262 [==============================] - 96s 2ms/sample - loss: 0.0989 - binary_accuracy: 0.9639 - val_loss: 0.1835 - val_binary_accuracy: 0.9470\n",
      "Epoch 47/100\n",
      "44262/44262 [==============================] - 97s 2ms/sample - loss: 0.0956 - binary_accuracy: 0.9658 - val_loss: 0.1824 - val_binary_accuracy: 0.9526\n",
      "Epoch 48/100\n",
      "44262/44262 [==============================] - 96s 2ms/sample - loss: 0.0935 - binary_accuracy: 0.9653 - val_loss: 0.1676 - val_binary_accuracy: 0.9529\n",
      "Epoch 49/100\n",
      "44262/44262 [==============================] - 97s 2ms/sample - loss: 0.0929 - binary_accuracy: 0.9660 - val_loss: 0.1686 - val_binary_accuracy: 0.9525\n",
      "Epoch 50/100\n",
      "44262/44262 [==============================] - 97s 2ms/sample - loss: 0.0932 - binary_accuracy: 0.9655 - val_loss: 0.1629 - val_binary_accuracy: 0.9551\n",
      "Epoch 51/100\n",
      "44262/44262 [==============================] - 97s 2ms/sample - loss: 0.0949 - binary_accuracy: 0.9653 - val_loss: 0.1625 - val_binary_accuracy: 0.9536\n",
      "Epoch 52/100\n",
      "44262/44262 [==============================] - 97s 2ms/sample - loss: 0.0929 - binary_accuracy: 0.9661 - val_loss: 0.1534 - val_binary_accuracy: 0.9554\n",
      "Epoch 53/100\n",
      "44262/44262 [==============================] - 97s 2ms/sample - loss: 0.0909 - binary_accuracy: 0.9681 - val_loss: 0.1736 - val_binary_accuracy: 0.9505\n",
      "Epoch 54/100\n",
      "44262/44262 [==============================] - 97s 2ms/sample - loss: 0.0913 - binary_accuracy: 0.9668 - val_loss: 0.1622 - val_binary_accuracy: 0.9541\n",
      "Epoch 55/100\n",
      "44262/44262 [==============================] - 98s 2ms/sample - loss: 0.0918 - binary_accuracy: 0.9662 - val_loss: 0.1730 - val_binary_accuracy: 0.9538\n",
      "Epoch 56/100\n",
      "44262/44262 [==============================] - 97s 2ms/sample - loss: 0.0895 - binary_accuracy: 0.9676 - val_loss: 0.1664 - val_binary_accuracy: 0.9554\n",
      "Epoch 57/100\n",
      "44262/44262 [==============================] - 97s 2ms/sample - loss: 0.0888 - binary_accuracy: 0.9673 - val_loss: 0.1710 - val_binary_accuracy: 0.9545\n",
      "Epoch 58/100\n",
      "44262/44262 [==============================] - 97s 2ms/sample - loss: 0.0851 - binary_accuracy: 0.9684 - val_loss: 0.1799 - val_binary_accuracy: 0.9536\n",
      "Epoch 59/100\n",
      "44262/44262 [==============================] - 97s 2ms/sample - loss: 0.0861 - binary_accuracy: 0.9680 - val_loss: 0.1734 - val_binary_accuracy: 0.9529\n",
      "Epoch 60/100\n",
      "44262/44262 [==============================] - 97s 2ms/sample - loss: 0.0859 - binary_accuracy: 0.9682 - val_loss: 0.1740 - val_binary_accuracy: 0.9552\n",
      "Epoch 61/100\n",
      "44262/44262 [==============================] - 97s 2ms/sample - loss: 0.0870 - binary_accuracy: 0.9674 - val_loss: 0.1562 - val_binary_accuracy: 0.9540\n",
      "Epoch 62/100\n",
      "44262/44262 [==============================] - 97s 2ms/sample - loss: 0.0844 - binary_accuracy: 0.9691 - val_loss: 0.1710 - val_binary_accuracy: 0.9555\n",
      "Epoch 63/100\n",
      "44262/44262 [==============================] - 97s 2ms/sample - loss: 0.0816 - binary_accuracy: 0.9697 - val_loss: 0.1769 - val_binary_accuracy: 0.9545\n",
      "Epoch 64/100\n",
      "44262/44262 [==============================] - 98s 2ms/sample - loss: 0.0849 - binary_accuracy: 0.9688 - val_loss: 0.1785 - val_binary_accuracy: 0.9546\n",
      "Epoch 65/100\n",
      "44262/44262 [==============================] - 97s 2ms/sample - loss: 0.0870 - binary_accuracy: 0.9682 - val_loss: 0.1832 - val_binary_accuracy: 0.9554\n",
      "Epoch 66/100\n",
      "44262/44262 [==============================] - 97s 2ms/sample - loss: 0.0860 - binary_accuracy: 0.9680 - val_loss: 0.1629 - val_binary_accuracy: 0.9536\n",
      "Epoch 67/100\n",
      "44262/44262 [==============================] - 97s 2ms/sample - loss: 0.0856 - binary_accuracy: 0.9693 - val_loss: 0.1530 - val_binary_accuracy: 0.9583\n",
      "Epoch 68/100\n",
      "44262/44262 [==============================] - 97s 2ms/sample - loss: 0.0821 - binary_accuracy: 0.9701 - val_loss: 0.1862 - val_binary_accuracy: 0.9521\n",
      "Epoch 69/100\n",
      "44262/44262 [==============================] - 97s 2ms/sample - loss: 0.0822 - binary_accuracy: 0.9702 - val_loss: 0.1745 - val_binary_accuracy: 0.9553\n",
      "Epoch 70/100\n",
      "44262/44262 [==============================] - 97s 2ms/sample - loss: 0.0801 - binary_accuracy: 0.9713 - val_loss: 0.1968 - val_binary_accuracy: 0.9538\n",
      "Epoch 71/100\n",
      "44262/44262 [==============================] - 97s 2ms/sample - loss: 0.0820 - binary_accuracy: 0.9696 - val_loss: 0.1733 - val_binary_accuracy: 0.9547\n",
      "Epoch 72/100\n",
      "44262/44262 [==============================] - 97s 2ms/sample - loss: 0.0794 - binary_accuracy: 0.9706 - val_loss: 0.1863 - val_binary_accuracy: 0.9540\n",
      "Epoch 73/100\n",
      "44262/44262 [==============================] - 97s 2ms/sample - loss: 0.0796 - binary_accuracy: 0.9719 - val_loss: 0.1853 - val_binary_accuracy: 0.9541\n",
      "Epoch 74/100\n",
      "44262/44262 [==============================] - 97s 2ms/sample - loss: 0.0837 - binary_accuracy: 0.9696 - val_loss: 0.1711 - val_binary_accuracy: 0.9573\n",
      "Epoch 75/100\n",
      "44262/44262 [==============================] - 97s 2ms/sample - loss: 0.0797 - binary_accuracy: 0.9717 - val_loss: 0.1608 - val_binary_accuracy: 0.9554\n",
      "Epoch 76/100\n",
      "44262/44262 [==============================] - 97s 2ms/sample - loss: 0.0804 - binary_accuracy: 0.9709 - val_loss: 0.1757 - val_binary_accuracy: 0.9565\n",
      "Epoch 77/100\n",
      "44262/44262 [==============================] - 97s 2ms/sample - loss: 0.0812 - binary_accuracy: 0.9712 - val_loss: 0.1728 - val_binary_accuracy: 0.9564\n",
      "Epoch 78/100\n",
      "44262/44262 [==============================] - 97s 2ms/sample - loss: 0.0806 - binary_accuracy: 0.9705 - val_loss: 0.1693 - val_binary_accuracy: 0.9554\n",
      "Epoch 79/100\n",
      "44262/44262 [==============================] - 98s 2ms/sample - loss: 0.0766 - binary_accuracy: 0.9724 - val_loss: 0.1863 - val_binary_accuracy: 0.9572\n",
      "Epoch 80/100\n",
      "44262/44262 [==============================] - 98s 2ms/sample - loss: 0.0780 - binary_accuracy: 0.9726 - val_loss: 0.1754 - val_binary_accuracy: 0.9540\n",
      "Epoch 81/100\n",
      "44262/44262 [==============================] - 98s 2ms/sample - loss: 0.0773 - binary_accuracy: 0.9723 - val_loss: 0.1804 - val_binary_accuracy: 0.9541\n",
      "Epoch 82/100\n",
      "44262/44262 [==============================] - 98s 2ms/sample - loss: 0.0760 - binary_accuracy: 0.9722 - val_loss: 0.1860 - val_binary_accuracy: 0.9570\n",
      "Epoch 83/100\n",
      "44262/44262 [==============================] - 97s 2ms/sample - loss: 0.0762 - binary_accuracy: 0.9721 - val_loss: 0.1890 - val_binary_accuracy: 0.9541\n",
      "Epoch 84/100\n",
      "44262/44262 [==============================] - 98s 2ms/sample - loss: 0.0771 - binary_accuracy: 0.9723 - val_loss: 0.1712 - val_binary_accuracy: 0.9531\n",
      "Epoch 85/100\n",
      "44262/44262 [==============================] - 98s 2ms/sample - loss: 0.0756 - binary_accuracy: 0.9732 - val_loss: 0.1893 - val_binary_accuracy: 0.9539\n",
      "Epoch 86/100\n",
      "44262/44262 [==============================] - 98s 2ms/sample - loss: 0.0720 - binary_accuracy: 0.9741 - val_loss: 0.1952 - val_binary_accuracy: 0.9550\n",
      "Epoch 87/100\n",
      "44262/44262 [==============================] - 98s 2ms/sample - loss: 0.0760 - binary_accuracy: 0.9733 - val_loss: 0.1737 - val_binary_accuracy: 0.9566\n",
      "Epoch 88/100\n",
      "44262/44262 [==============================] - 98s 2ms/sample - loss: 0.0762 - binary_accuracy: 0.9730 - val_loss: 0.1648 - val_binary_accuracy: 0.9556\n",
      "Epoch 89/100\n",
      "44262/44262 [==============================] - 98s 2ms/sample - loss: 0.0745 - binary_accuracy: 0.9735 - val_loss: 0.1868 - val_binary_accuracy: 0.9525\n",
      "Epoch 90/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "44262/44262 [==============================] - 96s 2ms/sample - loss: 0.0751 - binary_accuracy: 0.9732 - val_loss: 0.2062 - val_binary_accuracy: 0.9557\n",
      "Epoch 91/100\n",
      "44262/44262 [==============================] - 96s 2ms/sample - loss: 0.0772 - binary_accuracy: 0.9731 - val_loss: 0.1884 - val_binary_accuracy: 0.9554\n",
      "Epoch 92/100\n",
      "44262/44262 [==============================] - 96s 2ms/sample - loss: 0.0740 - binary_accuracy: 0.9734 - val_loss: 0.1792 - val_binary_accuracy: 0.9540\n",
      "Epoch 93/100\n",
      "44262/44262 [==============================] - 96s 2ms/sample - loss: 0.0747 - binary_accuracy: 0.9740 - val_loss: 0.1849 - val_binary_accuracy: 0.9539\n",
      "Epoch 94/100\n",
      "44262/44262 [==============================] - 96s 2ms/sample - loss: 0.0749 - binary_accuracy: 0.9736 - val_loss: 0.1840 - val_binary_accuracy: 0.9534\n",
      "Epoch 95/100\n",
      "44262/44262 [==============================] - 96s 2ms/sample - loss: 0.0727 - binary_accuracy: 0.9736 - val_loss: 0.1993 - val_binary_accuracy: 0.9552\n",
      "Epoch 96/100\n",
      "44262/44262 [==============================] - 97s 2ms/sample - loss: 0.0732 - binary_accuracy: 0.9742 - val_loss: 0.1682 - val_binary_accuracy: 0.9535\n",
      "Epoch 97/100\n",
      "44262/44262 [==============================] - 96s 2ms/sample - loss: 0.0642 - binary_accuracy: 0.9777 - val_loss: 0.1895 - val_binary_accuracy: 0.9543\n",
      "Epoch 98/100\n",
      "44262/44262 [==============================] - 96s 2ms/sample - loss: 0.0606 - binary_accuracy: 0.9789 - val_loss: 0.1915 - val_binary_accuracy: 0.9548\n",
      "Epoch 99/100\n",
      "44262/44262 [==============================] - 96s 2ms/sample - loss: 0.0551 - binary_accuracy: 0.9807 - val_loss: 0.1997 - val_binary_accuracy: 0.9556\n",
      "Epoch 100/100\n",
      "44262/44262 [==============================] - 98s 2ms/sample - loss: 0.0541 - binary_accuracy: 0.9814 - val_loss: 0.1933 - val_binary_accuracy: 0.9560\n"
     ]
    }
   ],
   "source": [
    "model_4 = Model_4()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "44262/44262 [==============================] - 25s 574us/sample - loss: 0.0026 - binary_accuracy: 0.9995\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.002563861348606202, 0.99954814]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_4.evaluate(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11066/11066 [==============================] - 6s 542us/sample - loss: 0.1933 - binary_accuracy: 0.9560\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.1933412130110938, 0.9559913]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_4.evaluate(X_val, Y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_training(model_4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(model_4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We see that the last model is the best one. Some overfitting is present but it could be considered negligible. Additional exploration of hyperparameters would be necessary to make a final conclusion. But this performance enhance over the other models is at expense of a higher number of parameters and longer training time. "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}