NLP_Analise_Sentimentos_v2.ipynb

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      "display_name": "Python 3",
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      "pygments_lexer": "ipython3",
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    "colab": {
      "name": "NLP_Analise_Sentimentos_v2.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/YanSym/Data-Science-Projects/blob/master/NLP_Analise_Sentimentos_v2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DZwACnseoghJ"
      },
      "source": [
        "## NLP: Natural Language Processing\n",
        "### Analise de sentimentos"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WJ-7x1zHoghM"
      },
      "source": [
        "<img src=\"https://miro.medium.com/max/960/0*xLRsbQ02J7sQpNNy\" width=600 height=350/>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UVj_v2GjoghN"
      },
      "source": [
        "### Imports"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N37zoXAgoghO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1dcfce05-55ad-449f-80ce-5480d2e342f5"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from IPython.display import Image\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from random import sample\n",
        "from numpy import array\n",
        "from numpy import asarray\n",
        "from numpy import zeros\n",
        "import re\n",
        "import nltk\n",
        "import tensorflow_hub as hub\n",
        "from sklearn.metrics import accuracy_score\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem import PorterStemmer\n",
        "from nltk.stem import LancasterStemmer\n",
        "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "from tensorflow.keras.models import load_model\n",
        "from keras.preprocessing.sequence import pad_sequences\n",
        "from keras.preprocessing.text import Tokenizer\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras.layers import Activation, Dropout, Dense, Embedding\n",
        "from tensorflow.keras.layers import Flatten, Conv1D, GlobalMaxPooling1D, LSTM, SimpleRNN, SpatialDropout1D\n",
        "from tensorflow.keras.models import Sequential\n",
        "import tensorflow_datasets as tfds\n",
        "from google.colab import drive\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n",
        "import os\n",
        "from collections import Counter\n",
        "import random\n",
        "\n",
        "!pip install transformers\n",
        "!pip install ktrain\n",
        "\n",
        "import ktrain\n",
        "from ktrain import text\n",
        "from transformers import BertTokenizer, TFBertForSequenceClassification\n",
        "from transformers import InputExample, InputFeatures\n",
        "\n",
        "#import kerastuner as kt\n",
        "#from kerastuner.tuners import BayesianOptimization\n",
        "nltk.download('stopwords')"
      ],
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rGjbEpL8oghP"
      },
      "source": [
        "### Métodos"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KSKf2Ie2oghP"
      },
      "source": [
        "TAG_RE = re.compile(r'<[^>]+>')\n",
        "list_stop_words = set(stopwords.words('english'))\n",
        "    \n",
        "# plot resultados\n",
        "def plot_results(history):\n",
        "    # acuracia\n",
        "    plt.plot(history.history['acc'])\n",
        "    plt.plot(history.history['val_acc'])\n",
        "    plt.title('model accuracy')\n",
        "    plt.ylabel('accuracy')\n",
        "    plt.xlabel('epoch')\n",
        "    plt.legend(['train','test'], loc='upper left')\n",
        "    plt.show()\n",
        "\n",
        "    # loss\n",
        "    plt.plot(history.history['loss'])\n",
        "    plt.plot(history.history['val_loss'])\n",
        "    plt.title('model loss')\n",
        "    plt.ylabel('loss')\n",
        "    plt.xlabel('epoch')\n",
        "    plt.legend(['train','test'], loc='upper left')\n",
        "    plt.show()\n",
        "\n",
        "\n",
        "# pre-processamento\n",
        "def preprocess_text(sen):\n",
        "    \n",
        "    # Removing html tags\n",
        "    sentence = TAG_RE.sub('', sen)\n",
        "\n",
        "    # Remove punctuations and numbers\n",
        "    sentence = re.sub('[^a-zA-Z]', ' ', sentence)\n",
        "\n",
        "    # Single character removal\n",
        "    sentence = re.sub(r\"\\s+[a-zA-Z]\\s+\", ' ', sentence)\n",
        "\n",
        "    # Removing multiple spaces\n",
        "    sentence = re.sub(r'\\s+', ' ', sentence)\n",
        "    \n",
        "    # lowercase\n",
        "    sentence = sentence.lower()\n",
        "    \n",
        "    # remove stopwords\n",
        "    #sentence = [w for w in sentence if w not in list_stop_words]\n",
        "\n",
        "    return sentence"
      ],
      "execution_count": 96,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b4jj_shRzNRm",
        "outputId": "bef73bf2-4c96-49d6-c81a-9bba3e29f734"
      },
      "source": [
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9kkxV3pNoghQ"
      },
      "source": [
        "### Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZAdgTUzeoghR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        },
        "outputId": "9eba46a2-becc-4e8d-8720-946f1af8ecd6"
      },
      "source": [
        "train_data, test_data = tfds.load(name=\"imdb_reviews\", split=[\"train\", \"test\"], \n",
        "                                  batch_size=-1, as_supervised=True)\n",
        "\n",
        "train_examples, train_labels = tfds.as_numpy(train_data)\n",
        "test_examples, test_labels = tfds.as_numpy(test_data)\n",
        "\n",
        "df_train = pd.DataFrame({'review':train_examples, 'sentiment':train_labels})\n",
        "df_test = pd.DataFrame({'review':test_examples, 'sentiment':test_labels})\n",
        "\n",
        "movie_reviews = pd.concat([df_train, df_test]).reset_index(drop=True)\n",
        "\n",
        "movie_reviews['review'] = movie_reviews['review'].str.decode(\"utf-8\")\n",
        "\n",
        "print (movie_reviews.isnull().values.any())\n",
        "print (movie_reviews.shape)\n",
        "\n",
        "# amostra\n",
        "movie_reviews, _ = train_test_split(movie_reviews, train_size=0.1, random_state=42, stratify=movie_reviews['sentiment'])\n",
        "print (movie_reviews.shape)\n",
        "movie_reviews.head()"
      ],
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:absl:No config specified, defaulting to first: imdb_reviews/plain_text\n",
            "INFO:absl:Load dataset info from /root/tensorflow_datasets/imdb_reviews/plain_text/1.0.0\n",
            "INFO:absl:Reusing dataset imdb_reviews (/root/tensorflow_datasets/imdb_reviews/plain_text/1.0.0)\n",
            "INFO:absl:Constructing tf.data.Dataset for split ['train', 'test'], from /root/tensorflow_datasets/imdb_reviews/plain_text/1.0.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "False\n",
            "(50000, 2)\n",
            "(5000, 2)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "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>review</th>\n",
              "      <th>sentiment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>41986</th>\n",
              "      <td>I was expecting a lot from Mr.Amitabh Bachan's...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33935</th>\n",
              "      <td>Well, it has to be said that Monster Man is a ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46975</th>\n",
              "      <td>Even though I have great interest in Biblical ...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16402</th>\n",
              "      <td>The world is made up two different types of mo...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39790</th>\n",
              "      <td>Background info - The movies Octopussy &amp; Never...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                  review  sentiment\n",
              "41986  I was expecting a lot from Mr.Amitabh Bachan's...          0\n",
              "33935  Well, it has to be said that Monster Man is a ...          1\n",
              "46975  Even though I have great interest in Biblical ...          0\n",
              "16402  The world is made up two different types of mo...          1\n",
              "39790  Background info - The movies Octopussy & Never...          1"
            ]
          },
          "metadata": {},
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dE8GegzUoghS"
      },
      "source": [
        "### Analises"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qhyZLNwNoghS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 525
        },
        "outputId": "8539cc6e-0e76-4ffb-f0f8-988adb9b9cfd"
      },
      "source": [
        "print (movie_reviews['sentiment'].value_counts(dropna=False))\n",
        "sns.countplot(x='sentiment', data=movie_reviews)"
      ],
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1    2500\n",
            "0    2500\n",
            "Name: sentiment, dtype: int64\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc8a74addd0>"
            ]
          },
          "metadata": {},
          "execution_count": 99
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 700x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-G0SMfJ7BhBA",
        "outputId": "f286d40f-f9a0-42c4-f68f-6a055fe9053e"
      },
      "source": [
        "movie_reviews['len'] = [len(sent) for sent in movie_reviews['review']]\n",
        "print ('Max len:', movie_reviews['len'].max())\n",
        "print ('Min len:', movie_reviews['len'].min())\n",
        "print ('Mean len:', movie_reviews['len'].mean())"
      ],
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Max len: 12930\n",
            "Min len: 111\n",
            "Mean len: 1327.1528\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "id": "S9vUXISLThlx",
        "outputId": "875f5e4d-997e-4b44-a689-d4ddbb41a0b7"
      },
      "source": [
        "sentences = [len(sent) for sent in movie_reviews['review']]\n",
        "plt.rcParams.update({'figure.figsize':(7,5), 'figure.dpi':100})\n",
        "plt.bar(range(1, len(sentences)+1), sentences, color = ['blue'])\n",
        "plt.gca().set(title='No. of characters in each sentence', xlabel='Number of sentence', ylabel='Number of Characters in each sentence');"
      ],
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 700x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "id": "aS70vAGX_ptn",
        "outputId": "3fae6e16-1e07-4db1-8f4e-99e0f6ff5d0f"
      },
      "source": [
        "df_positive = movie_reviews.query('sentiment == 1')\n",
        "df_negative = movie_reviews.query('sentiment == 0')\n",
        "\n",
        "positive_sentences = [len(sent) for sent in df_positive['review']]\n",
        "negative_sentences = [len(sent) for sent in df_negative['review']]\n",
        "\n",
        "plt.rcParams.update({'figure.figsize':(7,5), 'figure.dpi':100})\n",
        "plt.bar(range(1, len(positive_sentences)+1), positive_sentences, color = ['green'])\n",
        "plt.bar(range(1, len(negative_sentences)+1), negative_sentences, color = ['red'])\n",
        "plt.gca().set(title='No. of characters in each sentence',\n",
        "              xlabel='Number of sentence',\n",
        "              ylabel='Number of Characters in each sentence');"
      ],
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 700x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HC6oyBKhA4KI",
        "outputId": "2aa408dd-b206-4110-e8f7-f3da32ea3350"
      },
      "source": [
        "movie_reviews['len'] = [len(sent) for sent in movie_reviews['review']]\n",
        "movie_reviews_short = movie_reviews.query(\"len <= 200\")\n",
        "movie_reviews_lon = movie_reviews.query(\"len >= 3_000\")\n",
        "\n",
        "print(movie_reviews['sentiment'].value_counts(dropna=False, normalize=True))\n",
        "print(movie_reviews_short['sentiment'].value_counts(dropna=False, normalize=True))\n",
        "print(movie_reviews_lon['sentiment'].value_counts(dropna=False, normalize=True))"
      ],
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1    0.5\n",
            "0    0.5\n",
            "Name: sentiment, dtype: float64\n",
            "1    0.5\n",
            "0    0.5\n",
            "Name: sentiment, dtype: float64\n",
            "1    0.552\n",
            "0    0.448\n",
            "Name: sentiment, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PymZKJyNDFlA"
      },
      "source": [
        "### Palavras mais frequentes nas críticas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x8ME7nhaDSOw",
        "outputId": "08585ccd-b3f2-482b-d59b-7c31fd251317"
      },
      "source": [
        "lista_strings = ' '.join(df_positive['review'].tolist()).split(' ')\n",
        "counter_object = Counter(lista_strings)\n",
        "palavras_mais_aparecem_positivos = [valor[0] for valor in counter_object.most_common(1000)]\n",
        "\n",
        "lista_strings = ' '.join(df_negative['review'].tolist()).split(' ')\n",
        "counter_object = Counter(lista_strings)\n",
        "palavras_mais_aparecem_negativos = [valor[0] for valor in counter_object.most_common(1000)]\n",
        "\n",
        "lista_positivos = list(set(palavras_mais_aparecem_positivos).difference(set(palavras_mais_aparecem_negativos)))\n",
        "print ('Positivas:')\n",
        "print (lista_positivos)\n",
        "\n",
        "lista_negativos = list(set(palavras_mais_aparecem_negativos).difference(set(palavras_mais_aparecem_positivos)))\n",
        "print ('Negativas:')\n",
        "print (lista_negativos)\n",
        "\n",
        "print ('Palavras positivas:')\n",
        "list_positive_words = ['like', 'love', 'good', 'amazing', 'pure', 'lovely', 'best', 'enjoyable', 'talanted', 'fantastic', 'best', 'superb', 'incredible', 'happy']\n",
        "print (list_positive_words)\n",
        "\n",
        "print ('Palavras positivas:')\n",
        "list_negative_words = ['wasted', 'disappointed', 'horrible', 'fake', 'killing', 'awful', 'pathetic', 'screenplay', 'joke', 'ridiculous', 'boring', 'bad', 'silly']\n",
        "print (list_negative_words)"
      ],
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Positivas:\n",
            "['meet', 'pure', 'too.', 'Bond', 'brilliant', 'recent', 'dark', 'lovely', 'catch', 'personal', 'directors', 'moving', 'King', 'feelings', 'among', 'series.', 'War', 'enjoyable', 'talented', 'became', 'it!', 'series,', 'fantastic', 'Japanese', 'portrayal', 'elements', 'French', 'emotional', 'Best', 'Sam', 'overall', 'wonderful', 'Jane', 'portrayed', 'hilarious', 'boys', 'highly', 'drug', 'tale', 'telling', 'nature', \"He's\", \"'The\", 'popular', 'Great', 'towards', 'memorable', 'war', 'Also,', 'too,', 'Paul', 'today', 'various', 'future', 'ones', 'social', 'her,', 'crime', 'powerful', 'greatest', 'decides', 'miss', 'admit', 'musical', 'parents', 'unique', 'believable', 'deep', 'famous', 'love,', 'murder', 'changes', 'Charlie', 'realistic', 'novel', 'compared', 'control', 'power', 'beauty', 'dance', 'cinematography', 'natural', 'surprisingly', 'question', 'times,', 'sometimes', 'touching', 'herself', 'OF', 'episodes', 'fighting', 'easy', 'city', 'watch.', 'life,', 'funniest', 'heart', 'beautifully', 'romantic', 'ways', 'message', 'sees', 'animated', 'people,', 'soundtrack', 'forget', 'favorite', 'deserves', 'supporting', 'superb', 'Their', 'release', 'Ben', 'age', 'incredible', 'glad', 'history', 'character.', 'recently', 'perfectly', 'animation', 'emotions', 'Steve', 'intelligent', 'forward', 'dramatic', 'eventually', 'view', 'country', 'Dr.', 'modern', 'follows', \"who's\", 'working', 'level', 'public', 'Joe', 'atmosphere', 'documentary', 'older', 'brings', 'days', 'meets', 'solid', 'York', 'theme', 'actors,', 'Bill', 'begins', 'Oscar', 'years.', 'rare', 'William', 'Disney', 'uses', 'caught', '/>A', \"/>It's\", 'Her', 'situation', 'whom', 'features', 'keeps', 'effective', 'leaves', 'worked', 'Man', 'amazing', 'you.', 'interested', 'roles', 'great,', 'political', 'reality', 'Peter', 'leads', 'happy']\n",
            "Negativas:\n",
            "['wasted', 'twist', 'disappointed', 'fake', 'killing', 'premise', '/>And', '4', 'script,', 'write', \"weren't\", 'quickly', 'NOT', 'material', 'awful.', 'worst', 'gone', 'pathetic', 'screenplay', 'joke', 'save', 'But,', '/>What', 'blood', 'footage', '5', 'incredibly', 'none', 'First', 'filmed', 'OK', 'die', 'Now', 'plot.', 'dull', 'ridiculous', 'expecting', 'ended', 'dumb', 'unless', 'somehow', 'IMDb', \"what's\", 'sitting', 'front', 'review', 'minutes.', \"Don't\", 'remake', 'crap', 'etc.', 'cheap', 'you,', 'sad', 'Why', 'up.', 'bad,', 'Is', 'excuse', 'stupid', 'seriously', 'Maybe', 'total', 'minute', 'ask', 'rent', 'please', 'predictable', 'paid', 'sequences', 'badly', 'form', 'figure', 'waiting', 'pay', 'random', 'basically', 'waste', 'leading', 'sit', 'rating', 'cheesy', 'thrown', 'result', 'flick', 'filmmakers', 'agree', 'hours', 'barely', 'off.', 'comment', 'say,', 'is.', 'sounds', 'check', '...', 'fairly', 'worse', 'attempts', 'scary', 'dialog', 'biggest', 'space', 'mention', 'poorly', 'Unfortunately,', 'scene,', 'know,', 'better.', 'expected', 'sorry', 'editing', 'up,', 'hell', 'nor', 'boring,', 'male', 'possibly', 'avoid', 'gore', 'however', 'hot', 'scenes,', 'outside', 'bad.', 'silly', 'plain', \"I'll\", 'move', 'eye', 'redeeming', 'kills', 'failed', 'either.', 'awful', 'monster', 'Or', 'cop', 'hoping', 'straight', 'annoying', 'third', 'room', 'meant', 'laughing', 'spent', 'kept', 'doubt', 'apparently', 'entirely', 'likely', 'on,', 'problems', 'Well,', 'cinematic', 'lame', 'makers', 'Oh', 'bunch', 'reading', 'particular', 'Most', 'gay', 'These', 'explain', 'tried', 'effort', 'fails', '1', 'decided', '(or', 'mess', 'terrible', 'speak', 'horrible', '(I', 'cover']\n",
            "Palavras positivas:\n",
            "['like', 'love', 'good', 'amazing', 'pure', 'lovely', 'best', 'enjoyable', 'talanted', 'fantastic', 'best', 'superb', 'incredible', 'happy']\n",
            "Palavras positivas:\n",
            "['wasted', 'disappointed', 'horrible', 'fake', 'killing', 'awful', 'pathetic', 'screenplay', 'joke', 'ridiculous', 'boring', 'bad', 'silly']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uy_ox6e5oghT"
      },
      "source": [
        "### Pre-processamento"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kwD5YwT4oghT"
      },
      "source": [
        "X = []\n",
        "sentences = list(movie_reviews['review'])\n",
        "for sen in sentences:\n",
        "    X.append(preprocess_text(sen))\n",
        "    \n",
        "y = movie_reviews['sentiment']"
      ],
      "execution_count": 105,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "boXLjn6zoghT"
      },
      "source": [
        "### Separa dados em treino e teste"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZedNA8GyoghU"
      },
      "source": [
        "# treino e teste\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42)\n",
        "X_val_tmp, X_test_tmp, y_val_tmp, y_test_tmp = train_test_split(X_test, y_test, test_size=0.20, random_state=42)"
      ],
      "execution_count": 106,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UzfZQB_xCubf"
      },
      "source": [
        "### Modelo Dummy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WgeekUuWCqle",
        "outputId": "c78e86e1-96e6-4d74-90d3-318fce112d9e"
      },
      "source": [
        "list_positive_words = ['like', 'love', 'good', 'amazing', 'pure', 'lovely', 'best', 'enjoyable', 'talanted', 'fantastic', 'best', 'superb', 'incredible', 'happy']\n",
        "list_negative_words = ['wasted', 'disappointed', 'horrible', 'fake', 'killing', 'awful', 'pathetic', 'screenplay', 'joke', 'ridiculous', 'boring', 'bad', 'silly']\n",
        "\n",
        "def modelo_dummy(texto):\n",
        "  texto = texto.split(' ')\n",
        "  if any(x in list_positive_words for x in texto):\n",
        "    return 1\n",
        "  elif any(x in list_negative_words for x in texto):\n",
        "    return 0\n",
        "  else:\n",
        "    return random.randint(0, 1)\n",
        "\n",
        "df_train = pd.DataFrame(X_train, columns=['review'])\n",
        "df_test = pd.DataFrame(X_test, columns=['review'])\n",
        "\n",
        "df_train['prediction_dummy'] = df_train['review'].apply(modelo_dummy)\n",
        "df_test['prediction_dummy'] = df_test['review'].apply(modelo_dummy)\n",
        "\n",
        "score_train = round(accuracy_score(df_train['prediction_dummy'], y_train), 4)\n",
        "score_test = round(accuracy_score(df_test['prediction_dummy'], y_test), 4)\n",
        "\n",
        "# results\n",
        "print(\"Train Accuracy:\", score_train)\n",
        "print(\"Test Accuracy:\", score_test)"
      ],
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Accuracy: 0.535\n",
            "Test Accuracy: 0.526\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r9kCqyAUoghU"
      },
      "source": [
        "### Tokenizer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hy5jmYUwoghU"
      },
      "source": [
        "tokenizer = Tokenizer(num_words=5000)\n",
        "tokenizer.fit_on_texts(X_train)\n",
        "\n",
        "X_train = tokenizer.texts_to_sequences(X_train)\n",
        "X_test = tokenizer.texts_to_sequences(X_test)\n",
        "X_val_tmp = tokenizer.texts_to_sequences(X_val_tmp)\n",
        "X_test_tmp = tokenizer.texts_to_sequences(X_test_tmp)\n",
        "\n",
        "# Adding 1 because of reserved 0 index\n",
        "vocab_size = len(tokenizer.word_index) + 1\n",
        "maxlen = 100\n",
        "\n",
        "X_train = pad_sequences(X_train, padding='post', maxlen=maxlen)\n",
        "X_test = pad_sequences(X_test, padding='post', maxlen=maxlen)\n",
        "X_val_tmp = pad_sequences(X_val_tmp, padding='post', maxlen=maxlen)\n",
        "X_test_tmp = pad_sequences(X_test_tmp, padding='post', maxlen=maxlen)"
      ],
      "execution_count": 108,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ykUbYIovoghU"
      },
      "source": [
        "## Modelos com Bag of Words"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k0KxrSGkoghV"
      },
      "source": [
        "### Bag of Words"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fF2vAM3ToghV"
      },
      "source": [
        "<img src=\"https://i.ibb.co/j5Dj80T/bad.png\" width=400 height=200/>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "eORAi9NAoghV"
      },
      "source": [
        "X_train_str = []\n",
        "for lista in X_train:\n",
        "    lista = ' '.join([str(x) for x in lista])\n",
        "    X_train_str.append(lista)\n",
        "    \n",
        "X_test_str = []\n",
        "for lista in X_test:\n",
        "    lista = ' '.join([str(x) for x in lista])\n",
        "    X_test_str.append(lista)\n",
        "    \n",
        "CountVec = CountVectorizer(ngram_range=(1,1), stop_words='english')\n",
        "\n",
        "#transform train\n",
        "Count_data_train = CountVec.fit_transform(X_train_str)\n",
        " \n",
        "#create dataframe\n",
        "df_train_str=pd.DataFrame(Count_data_train.toarray(), columns=CountVec.get_feature_names())\n",
        "\n",
        "\n",
        "#transform test\n",
        "Count_data_test = CountVec.transform(X_test_str)\n",
        " \n",
        "#create dataframe\n",
        "df_test_str=pd.DataFrame(Count_data_test.toarray(), columns=CountVec.get_feature_names())"
      ],
      "execution_count": 109,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J6Q_GZ2QoghV"
      },
      "source": [
        "### BoW (Bag of Words) + Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "osV_SJK_oghV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "374b926a-00b5-40c7-c63b-7d168993bd32"
      },
      "source": [
        "classifier = LogisticRegression(solver='lbfgs', max_iter=1000)\n",
        "classifier.fit(df_train_str, y_train)\n",
        "score_train = round(classifier.score(df_train_str, y_train), 4)\n",
        "score_test = round(classifier.score(df_test_str, y_test), 4)\n",
        "\n",
        "# results\n",
        "print(\"Train Accuracy:\", score_train)\n",
        "print(\"Test Accuracy:\", score_test)\n",
        "\n",
        "# score medio\n",
        "y_pred = classifier.predict_proba(df_train_str)[:, 1]"
      ],
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Accuracy: 0.9988\n",
            "Test Accuracy: 0.799\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4uf14PgMoghW"
      },
      "source": [
        "### Score test"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7CRWfiS5oghW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145
        },
        "outputId": "b614fdef-1525-43fc-a787-36836e60b811"
      },
      "source": [
        "def score_list(list_instances):\n",
        "    \n",
        "    list_scores = []\n",
        "    \n",
        "    for text in list_instances:\n",
        "        instance = tokenizer.texts_to_sequences([text])\n",
        "        instance = pad_sequences(instance, padding='post', maxlen=maxlen)   \n",
        "        instance = ' '.join([str(x) for x in instance])\n",
        "        \n",
        "        #transform test\n",
        "        instance = CountVec.transform([instance])\n",
        "\n",
        "        #create dataframe\n",
        "        instance=pd.DataFrame(instance.toarray(), columns=CountVec.get_feature_names())\n",
        "\n",
        "        score = classifier.predict_proba(instance)[:, 1][0]\n",
        "        print (score)\n",
        "        \n",
        "        if (score >= 0.5):\n",
        "            flag = 'Positivo'\n",
        "        else:\n",
        "            flag = 'Negativo'\n",
        "            \n",
        "        list_scores.append([text, score, flag])\n",
        "        \n",
        "    df = pd.DataFrame(list_scores, columns=['text', 'score', 'rating'])\n",
        "    display(df)\n",
        "        \n",
        "        \n",
        "list_instances = ['The movie was amazing, I loved it, very good',\n",
        "                  'I hate this movie, it is very bad and terrible']\n",
        "# score list\n",
        "score_list(list_instances)"
      ],
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9393853698669685\n",
            "0.09259102304322094\n"
          ]
        },
        {
          "output_type": "display_data",
          "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>text</th>\n",
              "      <th>score</th>\n",
              "      <th>rating</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>The movie was amazing, I loved it, very good</td>\n",
              "      <td>0.939385</td>\n",
              "      <td>Positivo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>I hate this movie, it is very bad and terrible</td>\n",
              "      <td>0.092591</td>\n",
              "      <td>Negativo</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                             text     score    rating\n",
              "0    The movie was amazing, I loved it, very good  0.939385  Positivo\n",
              "1  I hate this movie, it is very bad and terrible  0.092591  Negativo"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8R8l6bAxoghW"
      },
      "source": [
        "### BoW (Bag of Words) + Naive Bayes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hj_uXu7qoghW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f6530551-962d-40b2-bb3f-e323e80ebbf4"
      },
      "source": [
        "classifier = GaussianNB()\n",
        "classifier.fit(df_train_str, y_train)\n",
        "score_train = round(classifier.score(df_train_str, y_train), 4)\n",
        "score_test = round(classifier.score(df_test_str, y_test), 4)\n",
        "\n",
        "# results\n",
        "print(\"Train Accuracy:\", score_train)\n",
        "print(\"Test Accuracy:\", score_test)"
      ],
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Accuracy: 0.8908\n",
            "Test Accuracy: 0.688\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3gDJQrxqoghX"
      },
      "source": [
        "### BoW (Bag of Words) + Random Forest"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DXrvtC8IoghX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9a715ba6-5100-4b4c-f47e-54a55c83a1e7"
      },
      "source": [
        "classifier = RandomForestClassifier(max_depth=5)\n",
        "classifier.fit(df_train_str, y_train)\n",
        "score_train = round(classifier.score(df_train_str, y_train), 4)\n",
        "score_test = round(classifier.score(df_test_str, y_test), 4)\n",
        "\n",
        "# results\n",
        "print(\"Train Accuracy:\", score_train)\n",
        "print(\"Test Accuracy:\", score_test)"
      ],
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Accuracy: 0.8212\n",
            "Test Accuracy: 0.773\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hLfBiETGoghX"
      },
      "source": [
        "## Modelos com tf-idf"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YvnYFPyioghX"
      },
      "source": [
        "<img src=\"https://miro.medium.com/max/1000/1*KZrjbKHcsWt-zzUj2oRk3w.jpeg\" width=400 height=200/>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jQz9h3f5oghX"
      },
      "source": [
        "<img src=\"https://i.ytimg.com/vi/vZAXpvHhQow/maxresdefault.jpg\" width=600 height=290/>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J4AhPqz_oghX"
      },
      "source": [
        "X_train_str = []\n",
        "for lista in X_train:\n",
        "    lista = ' '.join([str(x) for x in lista])\n",
        "    X_train_str.append(lista)\n",
        "    \n",
        "X_test_str = []\n",
        "for lista in X_test:\n",
        "    lista = ' '.join([str(x) for x in lista])\n",
        "    X_test_str.append(lista)\n",
        "    \n",
        "vectorizer = TfidfVectorizer()\n",
        "\n",
        "#transform train\n",
        "Count_data_train = CountVec.fit_transform(X_train_str)\n",
        " \n",
        "#create dataframe\n",
        "df_train_str=pd.DataFrame(Count_data_train.toarray(), columns=CountVec.get_feature_names())\n",
        "\n",
        "\n",
        "#transform test\n",
        "Count_data_test = CountVec.transform(X_test_str)\n",
        " \n",
        "#create dataframe\n",
        "df_test_str=pd.DataFrame(Count_data_test.toarray(), columns=CountVec.get_feature_names())"
      ],
      "execution_count": 114,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hymJsgfHoghX"
      },
      "source": [
        "### Tf-Idf (Bag of Words) + Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "okRB6PXgoghY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d3dec2e7-24ae-441a-9c55-f406ae4ca5b3"
      },
      "source": [
        "classifier = LogisticRegression(solver='lbfgs', max_iter=1000)\n",
        "classifier.fit(df_train_str, y_train)\n",
        "score_train = round(classifier.score(df_train_str, y_train), 4)\n",
        "score_test = round(classifier.score(df_test_str, y_test), 4)\n",
        "\n",
        "# results\n",
        "print(\"Train Accuracy:\", score_train)\n",
        "print(\"Test Accuracy:\", score_test)"
      ],
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Accuracy: 0.9988\n",
            "Test Accuracy: 0.799\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xpVEbaHhoghY"
      },
      "source": [
        "### Tf-Idf (Bag of Words) + Naive Bayes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VTntWcDXoghY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "531d438e-cdcf-4e82-d080-4f11ee8efe32"
      },
      "source": [
        "classifier = GaussianNB()\n",
        "classifier.fit(df_train_str, y_train)\n",
        "score_train = round(classifier.score(df_train_str, y_train), 4)\n",
        "score_test = round(classifier.score(df_test_str, y_test), 4)\n",
        "\n",
        "# results\n",
        "print(\"Train Accuracy:\", score_train)\n",
        "print(\"Test Accuracy:\", score_test)"
      ],
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Accuracy: 0.8908\n",
            "Test Accuracy: 0.688\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-cw1Z4FToghY"
      },
      "source": [
        "### Tf-Idf (Bag of Words) + Decision Tree"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QUUO5nUDoghY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a456e9ea-75b3-45a7-8c7b-7a27cd5252c0"
      },
      "source": [
        "classifier = DecisionTreeClassifier(max_depth=5)\n",
        "classifier.fit(df_train_str, y_train)\n",
        "score_train = round(classifier.score(df_train_str, y_train), 4)\n",
        "score_test = round(classifier.score(df_test_str, y_test), 4)\n",
        "\n",
        "# results\n",
        "print(\"Train Accuracy:\", score_train)\n",
        "print(\"Test Accuracy:\", score_test)"
      ],
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Accuracy: 0.6738\n",
            "Test Accuracy: 0.632\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iiqOH62RoghZ"
      },
      "source": [
        "### Tf-Idf (Bag of Words) + Random Forest (max_depth=3)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j48MVYvsoghZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "16e496d8-4975-48f2-a3e2-93cfb7a39907"
      },
      "source": [
        "classifier = RandomForestClassifier(max_depth=3)\n",
        "classifier.fit(df_train_str, y_train)\n",
        "score_train = round(classifier.score(df_train_str, y_train), 4)\n",
        "score_test = round(classifier.score(df_test_str, y_test), 4)\n",
        "\n",
        "# results\n",
        "print(\"Train Accuracy:\", score_train)\n",
        "print(\"Test Accuracy:\", score_test)"
      ],
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Accuracy: 0.8048\n",
            "Test Accuracy: 0.746\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nyDANC48oghZ"
      },
      "source": [
        "### Tf-Idf (Bag of Words) + Random Forest (max_depth=5)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Iglhv6ERoghZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4b40963c-90ed-49f1-98e6-c79e03016e63"
      },
      "source": [
        "classifier = RandomForestClassifier(max_depth=5)\n",
        "classifier.fit(df_train_str, y_train)\n",
        "score_train = round(classifier.score(df_train_str, y_train), 4)\n",
        "score_test = round(classifier.score(df_test_str, y_test), 4)\n",
        "\n",
        "# results\n",
        "print(\"Train Accuracy:\", score_train)\n",
        "print(\"Test Accuracy:\", score_test)"
      ],
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Accuracy: 0.8415\n",
            "Test Accuracy: 0.756\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1eCHOfRfoghZ"
      },
      "source": [
        "### Tf-Idf (Bag of Words) + Random Forest (max_depth=7)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_K5hAVyDogha",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "093edd9b-6be7-4409-fee5-2b70842ab765"
      },
      "source": [
        "classifier = RandomForestClassifier(max_depth=7)\n",
        "classifier.fit(df_train_str, y_train)\n",
        "score_train = round(classifier.score(df_train_str, y_train), 4)\n",
        "score_test = round(classifier.score(df_test_str, y_test), 4)\n",
        "\n",
        "# results\n",
        "print(\"Train Accuracy:\", score_train)\n",
        "print(\"Test Accuracy:\", score_test)"
      ],
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Accuracy: 0.8725\n",
            "Test Accuracy: 0.781\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kJWAi0Vkogha"
      },
      "source": [
        "### Tf-Idf (Bag of Words) + Random Forest (max_depth=15)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BLu_Qk4oogha",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a4480388-2678-4624-9b5b-3d7b04c4ac03"
      },
      "source": [
        "classifier = RandomForestClassifier(max_depth=15)\n",
        "classifier.fit(df_train_str, y_train)\n",
        "score_train = round(classifier.score(df_train_str, y_train), 4)\n",
        "score_test = round(classifier.score(df_test_str, y_test), 4)\n",
        "\n",
        "# results\n",
        "print(\"Train Accuracy:\", score_train)\n",
        "print(\"Test Accuracy:\", score_test)"
      ],
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Accuracy: 0.9552\n",
            "Test Accuracy: 0.795\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eeT7Zw8Iogha"
      },
      "source": [
        "## Modelos com Embedding"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3YBUccOtogha"
      },
      "source": [
        "### Regressão Logística"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YQaX6Swroghb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9548004e-68e2-490b-e76f-e58bc5342384"
      },
      "source": [
        "classifier = LogisticRegression()\n",
        "classifier.fit(X_train, y_train)\n",
        "score_train = round(classifier.score(X_train, y_train), 4)\n",
        "score_test = round(classifier.score(X_test, y_test), 4)\n",
        "\n",
        "# results\n",
        "print(\"Train Accuracy:\", score_train)\n",
        "print(\"Test Accuracy:\", score_test)"
      ],
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Accuracy: 0.5638\n",
            "Test Accuracy: 0.53\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "biEhN8fyoghb"
      },
      "source": [
        "### Naive Bayes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PaM6BXEOoghb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "330c9b8f-9e4d-4241-9f60-00f08a970b7a"
      },
      "source": [
        "classifier = GaussianNB()\n",
        "classifier.fit(X_train, y_train)\n",
        "score_train = round(classifier.score(X_train, y_train), 4)\n",
        "score_test = round(classifier.score(X_test, y_test), 4)\n",
        "\n",
        "# results\n",
        "print(\"Train Accuracy:\", score_train)\n",
        "print(\"Test Accuracy:\", score_test)"
      ],
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Accuracy: 0.563\n",
            "Test Accuracy: 0.523\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zMZDLI6poghc"
      },
      "source": [
        "### Árvore de Decisão"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j4qiL4Jkoghc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0b2effbc-b3f9-4e73-bcdc-4b89ab273827"
      },
      "source": [
        "classifier = DecisionTreeClassifier(max_depth=5)\n",
        "classifier.fit(X_train, y_train)\n",
        "score_train = round(classifier.score(X_train, y_train), 4)\n",
        "score_test = round(classifier.score(X_test, y_test), 4)\n",
        "\n",
        "# results\n",
        "print(\"Train Accuracy:\", score_train)\n",
        "print(\"Test Accuracy:\", score_test)"
      ],
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Accuracy: 0.5868\n",
            "Test Accuracy: 0.514\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "50DNCXVDoghc"
      },
      "source": [
        "### Random Forest"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UACORUtCoghc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b4dd6bcb-d109-419c-8fa6-c0e7e904ab34"
      },
      "source": [
        "classifier = RandomForestClassifier(max_depth=5)\n",
        "classifier.fit(X_train, y_train)\n",
        "score_train = round(classifier.score(X_train, y_train), 4)\n",
        "score_test = round(classifier.score(X_test, y_test), 4)\n",
        "\n",
        "# results\n",
        "print(\"Train Accuracy:\", score_train)\n",
        "print(\"Test Accuracy:\", score_test)"
      ],
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Accuracy: 0.8298\n",
            "Test Accuracy: 0.546\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7CGydqRAoghc"
      },
      "source": [
        "### Modelo simples com Redes Neurais v1 (sem Early Stopping)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KbTqlqO0oghc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "5b0e89e6-680f-45e4-b2fb-7ffd69c8b501"
      },
      "source": [
        "model = Sequential()\n",
        "model.add(Dense(32, activation='relu'))\n",
        "model.add(Dropout(0.4))\n",
        "model.add(Dense(8, activation='relu'))\n",
        "model.add(Dense(1, activation='sigmoid'))\n",
        "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['acc'])\n",
        "history = model.fit(X_train, y_train, batch_size=128, epochs=50, verbose=1, validation_split=0.2)\n",
        "print(model.summary())\n",
        "score_train = model.evaluate(X_train, y_train, verbose=1)\n",
        "score_test = model.evaluate(X_test, y_test, verbose=1)\n",
        "\n",
        "# results\n",
        "print (\"Train Accuracy:\", score_train[1])\n",
        "print(\"Test Accuracy:\", score_test[1])\n",
        "plot_results(history)"
      ],
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "25/25 [==============================] - 3s 12ms/step - loss: 329.1710 - acc: 0.4888 - val_loss: 158.7962 - val_acc: 0.5188\n",
            "Epoch 2/50\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 170.5098 - acc: 0.4997 - val_loss: 102.4936 - val_acc: 0.5450\n",
            "Epoch 3/50\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 117.5611 - acc: 0.5234 - val_loss: 75.6611 - val_acc: 0.5275\n",
            "Epoch 4/50\n",
            "25/25 [==============================] - 0s 5ms/step - loss: 86.5933 - acc: 0.5031 - val_loss: 57.4147 - val_acc: 0.5100\n",
            "Epoch 5/50\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 64.0062 - acc: 0.5116 - val_loss: 43.0416 - val_acc: 0.4950\n",
            "Epoch 6/50\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 48.8169 - acc: 0.4994 - val_loss: 32.0265 - val_acc: 0.4863\n",
            "Epoch 7/50\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 35.1956 - acc: 0.5041 - val_loss: 24.0388 - val_acc: 0.5075\n",
            "Epoch 8/50\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 25.1047 - acc: 0.5222 - val_loss: 18.6859 - val_acc: 0.4913\n",
            "Epoch 9/50\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 19.3261 - acc: 0.5119 - val_loss: 14.0056 - val_acc: 0.4762\n",
            "Epoch 10/50\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 15.0407 - acc: 0.5059 - val_loss: 10.4609 - val_acc: 0.4888\n",
            "Epoch 11/50\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 10.6359 - acc: 0.5122 - val_loss: 7.8762 - val_acc: 0.4825\n",
            "Epoch 12/50\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 8.8075 - acc: 0.5172 - val_loss: 5.8610 - val_acc: 0.4712\n",
            "Epoch 13/50\n",
            "25/25 [==============================] - 0s 5ms/step - loss: 7.6834 - acc: 0.5106 - val_loss: 4.4702 - val_acc: 0.4762\n",
            "Epoch 14/50\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 6.2334 - acc: 0.5106 - val_loss: 3.5010 - val_acc: 0.4762\n",
            "Epoch 15/50\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 5.0175 - acc: 0.5181 - val_loss: 2.8800 - val_acc: 0.4837\n",
            "Epoch 16/50\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 4.2618 - acc: 0.5081 - val_loss: 2.4832 - val_acc: 0.4900\n",
            "Epoch 17/50\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 3.5281 - acc: 0.5166 - val_loss: 2.2477 - val_acc: 0.4888\n",
            "Epoch 18/50\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 3.2090 - acc: 0.5166 - val_loss: 2.0919 - val_acc: 0.4963\n",
            "Epoch 19/50\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 2.8217 - acc: 0.5109 - val_loss: 1.8811 - val_acc: 0.4988\n",
            "Epoch 20/50\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 2.3352 - acc: 0.5134 - val_loss: 1.7167 - val_acc: 0.5000\n",
            "Epoch 21/50\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 2.3690 - acc: 0.5047 - val_loss: 1.6187 - val_acc: 0.4950\n",
            "Epoch 22/50\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 1.9003 - acc: 0.5103 - val_loss: 1.5586 - val_acc: 0.4925\n",
            "Epoch 23/50\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 1.8768 - acc: 0.5084 - val_loss: 1.4783 - val_acc: 0.4925\n",
            "Epoch 24/50\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 1.8185 - acc: 0.5141 - val_loss: 1.3994 - val_acc: 0.4938\n",
            "Epoch 25/50\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 1.6988 - acc: 0.5081 - val_loss: 1.3240 - val_acc: 0.4938\n",
            "Epoch 26/50\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 1.6561 - acc: 0.5097 - val_loss: 1.2627 - val_acc: 0.4875\n",
            "Epoch 27/50\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 1.5678 - acc: 0.5084 - val_loss: 1.2415 - val_acc: 0.4863\n",
            "Epoch 28/50\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 1.3690 - acc: 0.5063 - val_loss: 1.2140 - val_acc: 0.4900\n",
            "Epoch 29/50\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 1.2666 - acc: 0.5138 - val_loss: 1.1934 - val_acc: 0.4888\n",
            "Epoch 30/50\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 1.5252 - acc: 0.5113 - val_loss: 1.1753 - val_acc: 0.4913\n",
            "Epoch 31/50\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 1.2424 - acc: 0.5134 - val_loss: 1.1167 - val_acc: 0.4888\n",
            "Epoch 32/50\n",
            "25/25 [==============================] - 0s 5ms/step - loss: 1.2291 - acc: 0.5113 - val_loss: 1.0736 - val_acc: 0.4925\n",
            "Epoch 33/50\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 1.1111 - acc: 0.5153 - val_loss: 1.0497 - val_acc: 0.4938\n",
            "Epoch 34/50\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 1.1533 - acc: 0.5138 - val_loss: 1.0367 - val_acc: 0.4950\n",
            "Epoch 35/50\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 1.2178 - acc: 0.5122 - val_loss: 1.0375 - val_acc: 0.4913\n",
            "Epoch 36/50\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 1.3641 - acc: 0.5047 - val_loss: 1.0123 - val_acc: 0.4950\n",
            "Epoch 37/50\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 1.0086 - acc: 0.5116 - val_loss: 1.0026 - val_acc: 0.4913\n",
            "Epoch 38/50\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 1.0349 - acc: 0.5041 - val_loss: 0.9870 - val_acc: 0.4950\n",
            "Epoch 39/50\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 1.0592 - acc: 0.5103 - val_loss: 0.9702 - val_acc: 0.4938\n",
            "Epoch 40/50\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.9823 - acc: 0.5097 - val_loss: 0.9463 - val_acc: 0.4913\n",
            "Epoch 41/50\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.9994 - acc: 0.5116 - val_loss: 0.9348 - val_acc: 0.4963\n",
            "Epoch 42/50\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 1.0433 - acc: 0.5175 - val_loss: 0.9250 - val_acc: 0.4925\n",
            "Epoch 43/50\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 1.0126 - acc: 0.5072 - val_loss: 0.9128 - val_acc: 0.4975\n",
            "Epoch 44/50\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.9645 - acc: 0.5066 - val_loss: 0.9148 - val_acc: 0.4988\n",
            "Epoch 45/50\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.9215 - acc: 0.5113 - val_loss: 0.9168 - val_acc: 0.4950\n",
            "Epoch 46/50\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.9523 - acc: 0.5094 - val_loss: 0.9025 - val_acc: 0.4938\n",
            "Epoch 47/50\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.9879 - acc: 0.5066 - val_loss: 0.8954 - val_acc: 0.4963\n",
            "Epoch 48/50\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.8695 - acc: 0.5125 - val_loss: 0.8896 - val_acc: 0.4925\n",
            "Epoch 49/50\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.9127 - acc: 0.5119 - val_loss: 0.8787 - val_acc: 0.4938\n",
            "Epoch 50/50\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.9169 - acc: 0.5056 - val_loss: 0.8795 - val_acc: 0.4925\n",
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense (Dense)                (None, 32)                3232      \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 32)                0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 8)                 264       \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 1)                 9         \n",
            "=================================================================\n",
            "Total params: 3,505\n",
            "Trainable params: 3,505\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n",
            "125/125 [==============================] - 0s 2ms/step - loss: 0.7346 - acc: 0.5060\n",
            "32/32 [==============================] - 0s 2ms/step - loss: 0.8095 - acc: 0.4890\n",
            "Train Accuracy: 0.5059999823570251\n",
            "Test Accuracy: 0.48899999260902405\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 700x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 700x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P4k-K2stoghc"
      },
      "source": [
        "### Modelo simples com Redes Neurais  (Early Stopping)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Le2FTx38oghd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "7e43aef6-9853-4895-da14-13c631c62bae"
      },
      "source": [
        "model = Sequential()\n",
        "model.add(Dense(64, activation='relu'))\n",
        "model.add(Dense(8, activation='relu'))\n",
        "model.add(Dense(1, activation='sigmoid'))\n",
        "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['acc'])\n",
        "\n",
        "es = EarlyStopping(monitor='val_acc', mode='min', patience=5, verbose=1)\n",
        "mc = ModelCheckpoint('best_model.h5', monitor='val_loss', mode='min', verbose=1, save_best_only=True)\n",
        "\n",
        "history = model.fit(X_train, y_train, validation_data=(X_val_tmp, y_val_tmp), batch_size=128, epochs=30, verbose=1, callbacks=[es, mc])\n",
        "print(model.summary())\n",
        "score_train = model.evaluate(X_train, y_train, verbose=1)\n",
        "score_test = model.evaluate(X_test, y_test, verbose=1)\n",
        "\n",
        "# results\n",
        "print (\"Train Accuracy:\", score_train[1])\n",
        "print(\"Test Accuracy:\", score_test[1])\n",
        "plot_results(history)"
      ],
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "32/32 [==============================] - 1s 9ms/step - loss: 177.9399 - acc: 0.4940 - val_loss: 96.3685 - val_acc: 0.5063\n",
            "\n",
            "Epoch 00001: val_loss improved from inf to 96.36848, saving model to best_model.h5\n",
            "Epoch 2/30\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 74.2347 - acc: 0.5145 - val_loss: 55.7390 - val_acc: 0.5175\n",
            "\n",
            "Epoch 00002: val_loss improved from 96.36848 to 55.73899, saving model to best_model.h5\n",
            "Epoch 3/30\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 37.9803 - acc: 0.5123 - val_loss: 18.5245 - val_acc: 0.5300\n",
            "\n",
            "Epoch 00003: val_loss improved from 55.73899 to 18.52451, saving model to best_model.h5\n",
            "Epoch 4/30\n",
            "32/32 [==============================] - 0s 5ms/step - loss: 8.5866 - acc: 0.5077 - val_loss: 3.6194 - val_acc: 0.5175\n",
            "\n",
            "Epoch 00004: val_loss improved from 18.52451 to 3.61944, saving model to best_model.h5\n",
            "Epoch 5/30\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 1.8530 - acc: 0.5065 - val_loss: 2.0891 - val_acc: 0.4812\n",
            "\n",
            "Epoch 00005: val_loss improved from 3.61944 to 2.08914, saving model to best_model.h5\n",
            "Epoch 6/30\n",
            "32/32 [==============================] - 0s 5ms/step - loss: 1.1872 - acc: 0.5002 - val_loss: 1.7009 - val_acc: 0.4800\n",
            "\n",
            "Epoch 00006: val_loss improved from 2.08914 to 1.70093, saving model to best_model.h5\n",
            "Epoch 7/30\n",
            "32/32 [==============================] - 0s 5ms/step - loss: 0.9588 - acc: 0.5027 - val_loss: 1.4087 - val_acc: 0.5238\n",
            "\n",
            "Epoch 00007: val_loss improved from 1.70093 to 1.40871, saving model to best_model.h5\n",
            "Epoch 8/30\n",
            "32/32 [==============================] - 0s 5ms/step - loss: 0.8381 - acc: 0.5075 - val_loss: 1.3480 - val_acc: 0.5200\n",
            "\n",
            "Epoch 00008: val_loss improved from 1.40871 to 1.34803, saving model to best_model.h5\n",
            "Epoch 9/30\n",
            "32/32 [==============================] - 0s 5ms/step - loss: 0.7945 - acc: 0.5080 - val_loss: 1.2975 - val_acc: 0.5238\n",
            "\n",
            "Epoch 00009: val_loss improved from 1.34803 to 1.29749, saving model to best_model.h5\n",
            "Epoch 10/30\n",
            "32/32 [==============================] - 0s 5ms/step - loss: 0.7719 - acc: 0.5107 - val_loss: 1.2551 - val_acc: 0.5238\n",
            "\n",
            "Epoch 00010: val_loss improved from 1.29749 to 1.25513, saving model to best_model.h5\n",
            "Epoch 11/30\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 0.7496 - acc: 0.5088 - val_loss: 1.2232 - val_acc: 0.5125\n",
            "\n",
            "Epoch 00011: val_loss improved from 1.25513 to 1.22320, saving model to best_model.h5\n",
            "Epoch 00011: early stopping\n",
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_3 (Dense)              (None, 64)                6464      \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 8)                 520       \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 1)                 9         \n",
            "=================================================================\n",
            "Total params: 6,993\n",
            "Trainable params: 6,993\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n",
            "125/125 [==============================] - 1s 5ms/step - loss: 0.7350 - acc: 0.5125\n",
            "32/32 [==============================] - 0s 2ms/step - loss: 1.1162 - acc: 0.5090\n",
            "Train Accuracy: 0.512499988079071\n",
            "Test Accuracy: 0.5090000033378601\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 700x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 700x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g7Nw0eTvoghd"
      },
      "source": [
        "### Simple Model Embedding layer (sem Glove)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hkqM2mucoghd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "e8bb9ce5-7444-4cf5-9450-4dee05a67fee"
      },
      "source": [
        "model = Sequential()\n",
        "embedding_layer = Embedding(vocab_size, 100, input_length=maxlen)\n",
        "model.add(embedding_layer)\n",
        "model.add(Flatten())\n",
        "model.add(Dense(1, activation='sigmoid'))\n",
        "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['acc'])\n",
        "print(model.summary())\n",
        "\n",
        "es = EarlyStopping(monitor='val_acc', mode='min', patience=5, verbose=1)\n",
        "mc = ModelCheckpoint('best_model.h5', monitor='val_loss', mode='min', verbose=1, save_best_only=True)\n",
        "\n",
        "history = model.fit(X_train, y_train, batch_size=128, epochs=20, verbose=1, callbacks=[es, mc], validation_split=0.2)\n",
        "print(model.summary())\n",
        "score_train = model.evaluate(X_train, y_train, verbose=1)\n",
        "score_test = model.evaluate(X_test, y_test, verbose=1)\n",
        "\n",
        "# results\n",
        "print (\"Train Accuracy:\", score_train[1])\n",
        "print(\"Test Accuracy:\", score_test[1])\n",
        "plot_results(history)"
      ],
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding (Embedding)        (None, 100, 100)          3516900   \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 10000)             0         \n",
            "_________________________________________________________________\n",
            "dense_6 (Dense)              (None, 1)                 10001     \n",
            "=================================================================\n",
            "Total params: 3,526,901\n",
            "Trainable params: 3,526,901\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n",
            "Epoch 1/20\n",
            "25/25 [==============================] - 1s 15ms/step - loss: 0.6905 - acc: 0.5253 - val_loss: 0.6855 - val_acc: 0.5850\n",
            "\n",
            "Epoch 00001: val_loss improved from inf to 0.68546, saving model to best_model.h5\n",
            "Epoch 2/20\n",
            "25/25 [==============================] - 0s 10ms/step - loss: 0.6317 - acc: 0.9087 - val_loss: 0.6678 - val_acc: 0.6675\n",
            "\n",
            "Epoch 00002: val_loss improved from 0.68546 to 0.66777, saving model to best_model.h5\n",
            "Epoch 3/20\n",
            "25/25 [==============================] - 0s 9ms/step - loss: 0.5376 - acc: 0.9459 - val_loss: 0.6229 - val_acc: 0.7150\n",
            "\n",
            "Epoch 00003: val_loss improved from 0.66777 to 0.62295, saving model to best_model.h5\n",
            "Epoch 4/20\n",
            "25/25 [==============================] - 0s 9ms/step - loss: 0.3973 - acc: 0.9666 - val_loss: 0.5579 - val_acc: 0.7400\n",
            "\n",
            "Epoch 00004: val_loss improved from 0.62295 to 0.55787, saving model to best_model.h5\n",
            "Epoch 5/20\n",
            "25/25 [==============================] - 0s 9ms/step - loss: 0.2626 - acc: 0.9828 - val_loss: 0.5063 - val_acc: 0.7638\n",
            "\n",
            "Epoch 00005: val_loss improved from 0.55787 to 0.50630, saving model to best_model.h5\n",
            "Epoch 6/20\n",
            "25/25 [==============================] - 0s 10ms/step - loss: 0.1669 - acc: 0.9928 - val_loss: 0.4730 - val_acc: 0.7812\n",
            "\n",
            "Epoch 00006: val_loss improved from 0.50630 to 0.47303, saving model to best_model.h5\n",
            "Epoch 00006: early stopping\n",
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding (Embedding)        (None, 100, 100)          3516900   \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 10000)             0         \n",
            "_________________________________________________________________\n",
            "dense_6 (Dense)              (None, 1)                 10001     \n",
            "=================================================================\n",
            "Total params: 3,526,901\n",
            "Trainable params: 3,526,901\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.1940 - acc: 0.9545\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.4667 - acc: 0.7830\n",
            "Train Accuracy: 0.9545000195503235\n",
            "Test Accuracy: 0.7829999923706055\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 700x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 700x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GcPIY6npoghd"
      },
      "source": [
        "### Glove"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-PkizEZ6oghd"
      },
      "source": [
        "embeddings_dictionary = dict()\n",
        "glove_file = open('/content/gdrive/MyDrive/glove.6B.100d.txt', encoding=\"utf8\")\n",
        "\n",
        "for line in glove_file:\n",
        "    records = line.split()\n",
        "    word = records[0]\n",
        "    vector_dimensions = asarray(records[1:], dtype='float32')\n",
        "    embeddings_dictionary [word] = vector_dimensions\n",
        "glove_file.close()\n",
        "\n",
        "embedding_matrix = zeros((vocab_size, 100))\n",
        "for word, index in tokenizer.word_index.items():\n",
        "    embedding_vector = embeddings_dictionary.get(word)\n",
        "    if embedding_vector is not None:\n",
        "        embedding_matrix[index] = embedding_vector"
      ],
      "execution_count": 129,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TP2rsDf7oghd"
      },
      "source": [
        "### Simple Model Glove (sem Early Stopping)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6bJ1VASdoghd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "577756cf-f46b-4348-9bbb-d6269c6f16b1"
      },
      "source": [
        "model = Sequential()\n",
        "embedding_layer = Embedding(vocab_size, 100, weights=[embedding_matrix], input_length=maxlen, trainable=False)\n",
        "model.add(embedding_layer)\n",
        "model.add(Flatten())\n",
        "model.add(Dense(1, activation='sigmoid'))\n",
        "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['acc'])\n",
        "print(model.summary())\n",
        "\n",
        "history = model.fit(X_train, y_train, batch_size=128, epochs=20, verbose=1, validation_split=0.2)\n",
        "print(model.summary())\n",
        "score_train = model.evaluate(X_train, y_train, verbose=1)\n",
        "score_test = model.evaluate(X_test, y_test, verbose=1)\n",
        "\n",
        "# results\n",
        "print (\"Train Accuracy:\", score_train[1])\n",
        "print(\"Test Accuracy:\", score_test[1])\n",
        "plot_results(history)"
      ],
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_3\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding_1 (Embedding)      (None, 100, 100)          3516900   \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 10000)             0         \n",
            "_________________________________________________________________\n",
            "dense_7 (Dense)              (None, 1)                 10001     \n",
            "=================================================================\n",
            "Total params: 3,526,901\n",
            "Trainable params: 10,001\n",
            "Non-trainable params: 3,516,900\n",
            "_________________________________________________________________\n",
            "None\n",
            "Epoch 1/20\n",
            "25/25 [==============================] - 1s 10ms/step - loss: 0.6958 - acc: 0.5684 - val_loss: 0.6780 - val_acc: 0.5913\n",
            "Epoch 2/20\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.5367 - acc: 0.7453 - val_loss: 0.6642 - val_acc: 0.6275\n",
            "Epoch 3/20\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.4450 - acc: 0.8350 - val_loss: 0.6603 - val_acc: 0.6225\n",
            "Epoch 4/20\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.3904 - acc: 0.8684 - val_loss: 0.6697 - val_acc: 0.6388\n",
            "Epoch 5/20\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.3484 - acc: 0.8981 - val_loss: 0.6893 - val_acc: 0.6162\n",
            "Epoch 6/20\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.3225 - acc: 0.9003 - val_loss: 0.6707 - val_acc: 0.6450\n",
            "Epoch 7/20\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.2776 - acc: 0.9409 - val_loss: 0.6885 - val_acc: 0.6463\n",
            "Epoch 8/20\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.2519 - acc: 0.9547 - val_loss: 0.6957 - val_acc: 0.6375\n",
            "Epoch 9/20\n",
            "25/25 [==============================] - 0s 3ms/step - loss: 0.2315 - acc: 0.9625 - val_loss: 0.6899 - val_acc: 0.6513\n",
            "Epoch 10/20\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.2095 - acc: 0.9744 - val_loss: 0.7079 - val_acc: 0.6538\n",
            "Epoch 11/20\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.1930 - acc: 0.9828 - val_loss: 0.7033 - val_acc: 0.6525\n",
            "Epoch 12/20\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.1791 - acc: 0.9872 - val_loss: 0.7124 - val_acc: 0.6525\n",
            "Epoch 13/20\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.1659 - acc: 0.9884 - val_loss: 0.7348 - val_acc: 0.6388\n",
            "Epoch 14/20\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.1553 - acc: 0.9928 - val_loss: 0.7308 - val_acc: 0.6500\n",
            "Epoch 15/20\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.1430 - acc: 0.9962 - val_loss: 0.7475 - val_acc: 0.6475\n",
            "Epoch 16/20\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.1342 - acc: 0.9959 - val_loss: 0.7540 - val_acc: 0.6538\n",
            "Epoch 17/20\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.1249 - acc: 0.9981 - val_loss: 0.7642 - val_acc: 0.6438\n",
            "Epoch 18/20\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.1196 - acc: 0.9987 - val_loss: 0.7601 - val_acc: 0.6575\n",
            "Epoch 19/20\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.1112 - acc: 0.9987 - val_loss: 0.7695 - val_acc: 0.6500\n",
            "Epoch 20/20\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.1039 - acc: 0.9997 - val_loss: 0.7818 - val_acc: 0.6500\n",
            "Model: \"sequential_3\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding_1 (Embedding)      (None, 100, 100)          3516900   \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 10000)             0         \n",
            "_________________________________________________________________\n",
            "dense_7 (Dense)              (None, 1)                 10001     \n",
            "=================================================================\n",
            "Total params: 3,526,901\n",
            "Trainable params: 10,001\n",
            "Non-trainable params: 3,516,900\n",
            "_________________________________________________________________\n",
            "None\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.2338 - acc: 0.9300\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6870 - acc: 0.6880\n",
            "Train Accuracy: 0.9300000071525574\n",
            "Test Accuracy: 0.6880000233650208\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 700x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 700x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ETaakV5Eoghe"
      },
      "source": [
        "### Simple Model Glove (com Early Stopping)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ArrmlHgtoghe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "127e6ee2-fc96-45e1-bd07-ea987c2238b3"
      },
      "source": [
        "model = Sequential()\n",
        "embedding_layer = Embedding(vocab_size, 100, weights=[embedding_matrix], input_length=maxlen, trainable=False)\n",
        "model.add(embedding_layer)\n",
        "model.add(Flatten())\n",
        "model.add(Dense(1, activation='sigmoid'))\n",
        "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['acc'])\n",
        "print(model.summary())\n",
        "\n",
        "es = EarlyStopping(monitor='val_loss', mode='min', patience=5, verbose=1)\n",
        "mc = ModelCheckpoint('best_model.h5', monitor='val_loss', mode='min', verbose=1, save_best_only=True)\n",
        "\n",
        "history = model.fit(X_train, y_train, batch_size=128, epochs=30, verbose=1, callbacks=[es, mc], validation_split=0.2)\n",
        "print(model.summary())\n",
        "score_train = model.evaluate(X_train, y_train, verbose=1)\n",
        "score_test = model.evaluate(X_test, y_test, verbose=1)\n",
        "\n",
        "# results\n",
        "print (\"Train Accuracy:\", score_train[1])\n",
        "print(\"Test Accuracy:\", score_test[1])\n",
        "plot_results(history)"
      ],
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_4\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding_2 (Embedding)      (None, 100, 100)          3516900   \n",
            "_________________________________________________________________\n",
            "flatten_2 (Flatten)          (None, 10000)             0         \n",
            "_________________________________________________________________\n",
            "dense_8 (Dense)              (None, 1)                 10001     \n",
            "=================================================================\n",
            "Total params: 3,526,901\n",
            "Trainable params: 10,001\n",
            "Non-trainable params: 3,516,900\n",
            "_________________________________________________________________\n",
            "None\n",
            "Epoch 1/30\n",
            "25/25 [==============================] - 1s 10ms/step - loss: 0.7647 - acc: 0.5353 - val_loss: 0.7012 - val_acc: 0.5625\n",
            "\n",
            "Epoch 00001: val_loss improved from inf to 0.70118, saving model to best_model.h5\n",
            "Epoch 2/30\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.5904 - acc: 0.6759 - val_loss: 0.6930 - val_acc: 0.5987\n",
            "\n",
            "Epoch 00002: val_loss improved from 0.70118 to 0.69301, saving model to best_model.h5\n",
            "Epoch 3/30\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.4878 - acc: 0.7825 - val_loss: 0.6654 - val_acc: 0.6137\n",
            "\n",
            "Epoch 00003: val_loss improved from 0.69301 to 0.66536, saving model to best_model.h5\n",
            "Epoch 4/30\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.4303 - acc: 0.8406 - val_loss: 0.6647 - val_acc: 0.6112\n",
            "\n",
            "Epoch 00004: val_loss improved from 0.66536 to 0.66468, saving model to best_model.h5\n",
            "Epoch 5/30\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.3880 - acc: 0.8706 - val_loss: 0.6650 - val_acc: 0.6275\n",
            "\n",
            "Epoch 00005: val_loss did not improve from 0.66468\n",
            "Epoch 6/30\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.3495 - acc: 0.8953 - val_loss: 0.6879 - val_acc: 0.6125\n",
            "\n",
            "Epoch 00006: val_loss did not improve from 0.66468\n",
            "Epoch 7/30\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.3101 - acc: 0.9300 - val_loss: 0.6698 - val_acc: 0.6300\n",
            "\n",
            "Epoch 00007: val_loss did not improve from 0.66468\n",
            "Epoch 8/30\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.2839 - acc: 0.9431 - val_loss: 0.6755 - val_acc: 0.6288\n",
            "\n",
            "Epoch 00008: val_loss did not improve from 0.66468\n",
            "Epoch 9/30\n",
            "25/25 [==============================] - 0s 4ms/step - loss: 0.2588 - acc: 0.9566 - val_loss: 0.6911 - val_acc: 0.6325\n",
            "\n",
            "Epoch 00009: val_loss did not improve from 0.66468\n",
            "Epoch 00009: early stopping\n",
            "Model: \"sequential_4\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding_2 (Embedding)      (None, 100, 100)          3516900   \n",
            "_________________________________________________________________\n",
            "flatten_2 (Flatten)          (None, 10000)             0         \n",
            "_________________________________________________________________\n",
            "dense_8 (Dense)              (None, 1)                 10001     \n",
            "=================================================================\n",
            "Total params: 3,526,901\n",
            "Trainable params: 10,001\n",
            "Non-trainable params: 3,516,900\n",
            "_________________________________________________________________\n",
            "None\n",
            "125/125 [==============================] - 0s 2ms/step - loss: 0.3327 - acc: 0.8980\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.6236 - acc: 0.6710\n",
            "Train Accuracy: 0.8980000019073486\n",
            "Test Accuracy: 0.6710000038146973\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 700x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 700x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F_-sz8CZoghe"
      },
      "source": [
        "### CNN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ma7JjcTsoghe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "d70389b6-a722-4235-810c-3b88468d931e"
      },
      "source": [
        "model = Sequential()\n",
        "\n",
        "embedding_layer = Embedding(vocab_size, 100, weights=[embedding_matrix], input_length=maxlen, trainable=False)\n",
        "model.add(embedding_layer)\n",
        "\n",
        "model.add(Conv1D(128, 5, activation='relu'))\n",
        "model.add(GlobalMaxPooling1D())\n",
        "model.add(Dense(1, activation='sigmoid'))\n",
        "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['acc'])\n",
        "print(model.summary())\n",
        "\n",
        "history = model.fit(X_train, y_train, batch_size=128, epochs=50, verbose=1, validation_split=0.2)\n",
        "print(model.summary())\n",
        "score_train = model.evaluate(X_train, y_train, verbose=1)\n",
        "score_test = model.evaluate(X_test, y_test, verbose=1)\n",
        "\n",
        "# results\n",
        "print (\"Train Accuracy:\", score_train[1])\n",
        "print(\"Test Accuracy:\", score_test[1])\n",
        "plot_results(history)"
      ],
      "execution_count": 132,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_5\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding_3 (Embedding)      (None, 100, 100)          3516900   \n",
            "_________________________________________________________________\n",
            "conv1d (Conv1D)              (None, 96, 128)           64128     \n",
            "_________________________________________________________________\n",
            "global_max_pooling1d (Global (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_9 (Dense)              (None, 1)                 129       \n",
            "=================================================================\n",
            "Total params: 3,581,157\n",
            "Trainable params: 64,257\n",
            "Non-trainable params: 3,516,900\n",
            "_________________________________________________________________\n",
            "None\n",
            "Epoch 1/50\n",
            "25/25 [==============================] - 28s 20ms/step - loss: 0.6851 - acc: 0.5625 - val_loss: 0.6522 - val_acc: 0.6087\n",
            "Epoch 2/50\n",
            "25/25 [==============================] - 0s 10ms/step - loss: 0.5559 - acc: 0.7469 - val_loss: 0.5859 - val_acc: 0.7113\n",
            "Epoch 3/50\n",
            "25/25 [==============================] - 0s 11ms/step - loss: 0.4815 - acc: 0.8159 - val_loss: 0.5644 - val_acc: 0.7063\n",
            "Epoch 4/50\n",
            "25/25 [==============================] - 0s 10ms/step - loss: 0.4214 - acc: 0.8587 - val_loss: 0.5219 - val_acc: 0.7538\n",
            "Epoch 5/50\n",
            "25/25 [==============================] - 0s 10ms/step - loss: 0.3832 - acc: 0.8669 - val_loss: 0.5104 - val_acc: 0.7462\n",
            "Epoch 6/50\n",
            "25/25 [==============================] - 0s 10ms/step - loss: 0.3341 - acc: 0.9047 - val_loss: 0.4781 - val_acc: 0.7800\n",
            "Epoch 7/50\n",
            "25/25 [==============================] - 0s 9ms/step - loss: 0.2940 - acc: 0.9284 - val_loss: 0.4721 - val_acc: 0.7675\n",
            "Epoch 8/50\n",
            "25/25 [==============================] - 0s 9ms/step - loss: 0.2680 - acc: 0.9316 - val_loss: 0.4623 - val_acc: 0.7837\n",
            "Epoch 9/50\n",
            "25/25 [==============================] - 0s 9ms/step - loss: 0.2305 - acc: 0.9591 - val_loss: 0.4457 - val_acc: 0.7862\n",
            "Epoch 10/50\n",
            "25/25 [==============================] - 0s 10ms/step - loss: 0.2056 - acc: 0.9706 - val_loss: 0.4388 - val_acc: 0.7887\n",
            "Epoch 11/50\n",
            "25/25 [==============================] - 0s 9ms/step - loss: 0.1852 - acc: 0.9781 - val_loss: 0.4350 - val_acc: 0.7912\n",
            "Epoch 12/50\n",
            "25/25 [==============================] - 0s 9ms/step - loss: 0.1622 - acc: 0.9847 - val_loss: 0.4332 - val_acc: 0.7887\n",
            "Epoch 13/50\n",
            "25/25 [==============================] - 0s 9ms/step - loss: 0.1439 - acc: 0.9925 - val_loss: 0.4299 - val_acc: 0.7937\n",
            "Epoch 14/50\n",
            "25/25 [==============================] - 0s 9ms/step - loss: 0.1268 - acc: 0.9972 - val_loss: 0.4282 - val_acc: 0.7862\n",
            "Epoch 15/50\n",
            "25/25 [==============================] - 0s 10ms/step - loss: 0.1152 - acc: 0.9950 - val_loss: 0.4332 - val_acc: 0.7887\n",
            "Epoch 16/50\n",
            "25/25 [==============================] - 0s 9ms/step - loss: 0.1028 - acc: 0.9981 - val_loss: 0.4298 - val_acc: 0.7875\n",
            "Epoch 17/50\n",
            "25/25 [==============================] - 0s 9ms/step - loss: 0.0911 - acc: 0.9994 - val_loss: 0.4356 - val_acc: 0.7925\n",
            "Epoch 18/50\n",
            "25/25 [==============================] - 0s 9ms/step - loss: 0.0814 - acc: 0.9997 - val_loss: 0.4397 - val_acc: 0.7912\n",
            "Epoch 19/50\n",
            "25/25 [==============================] - 0s 9ms/step - loss: 0.0697 - acc: 1.0000 - val_loss: 0.4341 - val_acc: 0.7912\n",
            "Epoch 20/50\n",
            "25/25 [==============================] - 0s 9ms/step - loss: 0.0640 - acc: 1.0000 - val_loss: 0.4381 - val_acc: 0.7887\n",
            "Epoch 21/50\n",
            "25/25 [==============================] - 0s 9ms/step - loss: 0.0572 - acc: 1.0000 - val_loss: 0.4453 - val_acc: 0.7925\n",
            "Epoch 22/50\n",
            "25/25 [==============================] - 0s 9ms/step - loss: 0.0517 - acc: 1.0000 - val_loss: 0.4414 - val_acc: 0.7875\n",
            "Epoch 23/50\n",
            "25/25 [==============================] - 0s 9ms/step - loss: 0.0468 - acc: 1.0000 - val_loss: 0.4433 - val_acc: 0.7912\n",
            "Epoch 24/50\n",
            "25/25 [==============================] - 0s 9ms/step - loss: 0.0417 - acc: 1.0000 - val_loss: 0.4463 - val_acc: 0.7912\n",
            "Epoch 25/50\n",
            "25/25 [==============================] - 0s 10ms/step - loss: 0.0383 - acc: 1.0000 - val_loss: 0.4520 - val_acc: 0.7950\n",
            "Epoch 26/50\n",
            "25/25 [==============================] - 0s 10ms/step - loss: 0.0344 - acc: 1.0000 - val_loss: 0.4521 - val_acc: 0.7875\n",
            "Epoch 27/50\n",
            "25/25 [==============================] - 0s 10ms/step - loss: 0.0313 - acc: 1.0000 - val_loss: 0.4539 - val_acc: 0.7900\n",
            "Epoch 28/50\n",
            "25/25 [==============================] - 0s 10ms/step - loss: 0.0288 - acc: 1.0000 - val_loss: 0.4575 - val_acc: 0.7900\n",
            "Epoch 29/50\n",
            "25/25 [==============================] - 0s 10ms/step - loss: 0.0268 - acc: 1.0000 - val_loss: 0.4637 - val_acc: 0.7925\n",
            "Epoch 30/50\n",
            "25/25 [==============================] - 0s 9ms/step - loss: 0.0249 - acc: 1.0000 - val_loss: 0.4628 - val_acc: 0.7887\n",
            "Epoch 31/50\n",
            "25/25 [==============================] - 0s 11ms/step - loss: 0.0234 - acc: 1.0000 - val_loss: 0.4653 - val_acc: 0.7912\n",
            "Epoch 32/50\n",
            "25/25 [==============================] - 0s 10ms/step - loss: 0.0211 - acc: 1.0000 - val_loss: 0.4671 - val_acc: 0.7887\n",
            "Epoch 33/50\n",
            "25/25 [==============================] - 0s 10ms/step - loss: 0.0197 - acc: 1.0000 - val_loss: 0.4714 - val_acc: 0.7887\n",
            "Epoch 34/50\n",
            "25/25 [==============================] - 0s 10ms/step - loss: 0.0182 - acc: 1.0000 - val_loss: 0.4731 - val_acc: 0.7912\n",
            "Epoch 35/50\n",
            "25/25 [==============================] - 0s 9ms/step - loss: 0.0169 - acc: 1.0000 - val_loss: 0.4760 - val_acc: 0.7900\n",
            "Epoch 36/50\n",
            "25/25 [==============================] - 0s 11ms/step - loss: 0.0161 - acc: 1.0000 - val_loss: 0.4788 - val_acc: 0.7900\n",
            "Epoch 37/50\n",
            "25/25 [==============================] - 0s 10ms/step - loss: 0.0149 - acc: 1.0000 - val_loss: 0.4820 - val_acc: 0.7887\n",
            "Epoch 38/50\n",
            "25/25 [==============================] - 0s 9ms/step - loss: 0.0140 - acc: 1.0000 - val_loss: 0.4869 - val_acc: 0.7887\n",
            "Epoch 39/50\n",
            "25/25 [==============================] - 0s 10ms/step - loss: 0.0131 - acc: 1.0000 - val_loss: 0.4880 - val_acc: 0.7887\n",
            "Epoch 40/50\n",
            "25/25 [==============================] - 0s 9ms/step - loss: 0.0123 - acc: 1.0000 - val_loss: 0.4892 - val_acc: 0.7875\n",
            "Epoch 41/50\n",
            "25/25 [==============================] - 0s 9ms/step - loss: 0.0116 - acc: 1.0000 - val_loss: 0.4940 - val_acc: 0.7900\n",
            "Epoch 42/50\n",
            "25/25 [==============================] - 0s 10ms/step - loss: 0.0111 - acc: 1.0000 - val_loss: 0.4954 - val_acc: 0.7887\n",
            "Epoch 43/50\n",
            "25/25 [==============================] - 0s 9ms/step - loss: 0.0104 - acc: 1.0000 - val_loss: 0.4967 - val_acc: 0.7900\n",
            "Epoch 44/50\n",
            "25/25 [==============================] - 0s 9ms/step - loss: 0.0099 - acc: 1.0000 - val_loss: 0.5014 - val_acc: 0.7925\n",
            "Epoch 45/50\n",
            "25/25 [==============================] - 0s 10ms/step - loss: 0.0095 - acc: 1.0000 - val_loss: 0.5029 - val_acc: 0.7887\n",
            "Epoch 46/50\n",
            "25/25 [==============================] - 0s 11ms/step - loss: 0.0089 - acc: 1.0000 - val_loss: 0.5054 - val_acc: 0.7875\n",
            "Epoch 47/50\n",
            "25/25 [==============================] - 0s 11ms/step - loss: 0.0084 - acc: 1.0000 - val_loss: 0.5067 - val_acc: 0.7887\n",
            "Epoch 48/50\n",
            "25/25 [==============================] - 0s 10ms/step - loss: 0.0081 - acc: 1.0000 - val_loss: 0.5086 - val_acc: 0.7887\n",
            "Epoch 49/50\n",
            "25/25 [==============================] - 0s 10ms/step - loss: 0.0076 - acc: 1.0000 - val_loss: 0.5103 - val_acc: 0.7887\n",
            "Epoch 50/50\n",
            "25/25 [==============================] - 0s 10ms/step - loss: 0.0073 - acc: 1.0000 - val_loss: 0.5131 - val_acc: 0.7887\n",
            "Model: \"sequential_5\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding_3 (Embedding)      (None, 100, 100)          3516900   \n",
            "_________________________________________________________________\n",
            "conv1d (Conv1D)              (None, 96, 128)           64128     \n",
            "_________________________________________________________________\n",
            "global_max_pooling1d (Global (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_9 (Dense)              (None, 1)                 129       \n",
            "=================================================================\n",
            "Total params: 3,581,157\n",
            "Trainable params: 64,257\n",
            "Non-trainable params: 3,516,900\n",
            "_________________________________________________________________\n",
            "None\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.1081 - acc: 0.9578\n",
            "32/32 [==============================] - 0s 4ms/step - loss: 0.4759 - acc: 0.8230\n",
            "Train Accuracy: 0.9577500224113464\n",
            "Test Accuracy: 0.8230000138282776\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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n+j19oIiI+MHvecoWAx0OfHHOLejgmLkxKypBXX/99Xz2s59l+vTp1NbWsnDhwnb3u++++7jqqqs47rjjGDx4MDfccAMVFRV9XG1i2lfdwD3PrubRt7YCkGJwwqRiLpw7gvmHDY2oRTUlxRg1MIdRA3OiXa6IiCQYc/1s7iEzKwAOHDhwgIKCgla31dXVsXHjRsaNG0dWVpY/BSaJZHotm5sdf35rK3f/czXlwTFflxw5kuvOmEJJYWI/NxERib2KigoKCwsBCp1zHbaE+N19KRLXPthxgJuffJ93t5QDMLUkn9s/PoMjx2p+NhERiS6FMpF2VNY1ct/za3n49U00O2/M2HXzp/DZY8eQpjFfIiISAwplImGcc/x92Q5uf3rVwTnCzps1jJvPna6uShERiSmFMpGg97cf4M5nVvH6hr0AjBucy60fO4wTJ2mBdhERiT2FMun3Vu+q4L7n1vLcSm9Z1cy0FL5yykS+ePJ4MtM0R5iIiPQNhTLpt9aXVnL/C+t4evlOAMzg43NGcN0ZkzVFhYiI9DmFMul3NpVV8+CL6/jbe9sJLU157qxhfP30SUwcku9vcSIi0m8plEm/sXVfDT9+aR2PvbOdQDCNzZ8+lK+fMZlpwwq6OFpERCS2FMok6ZVW1vHgC+v481tbaQx4YeyUKcVcd8YUZo4s9Lk6ERERj0KZJK2GpmZ++/pGfvTieqrqmwA4cdJgrj19MkeMGeBzdSIiIq0plCWJefPmMWfOHB544IGo3N+CBQsoLy/nySefjMr99bWX15Ry21Mr+bCsGoDZIwv59jnTOHr8IJ8rExERaZ9CmSSVTWXV3PaPlby4uhSAwXkZfPOsqVx0+EhSUszn6kRERDqm9WKSwIIFC1iyZAkPPvggZoaZsWnTJt5//33OPvts8vLyGDp0KFdccQVlZWUHj/vrX//KzJkzyc7OZtCgQZx++ulUV1dzyy238PDDD/O3v/3t4P0tXrzYvyfYDdX1Tdzzz9XMv/8VXlxdSlqK8YUTx/HS9fO45MhRCmQiIhL31FLWFeegscafx07P8SbP6sKDDz7I2rVrmTFjBrfeeqt3aHo6Rx11FJ///Oe5//77qa2t5YYbbuCSSy7hpZdeYufOnVx22WV8//vf54ILLqCyspJXX30V5xzXX389q1atoqKigoULFwIwcGB8LsDtnOPJ97Zz97Or2V3hLYt00uRivnvedCYOyfO5OhERke5TKOtKYw3cOdyfx/72DsjI7XK3wsJCMjIyyMnJoaSkBIDbb7+duXPncueddx7c7ze/+Q2jRo1i7dq1VFVV0dTUxIUXXsiYMWMAmDlz5sF9s7Ozqa+vP3h/8WhjWTXX/2UZb2/eD8DogTl897zpnDZtCNaNMCsiIhJPFMqS1LJly3j55ZfJyzu0tWjDhg3Mnz+f0047jZkzZ3LmmWcyf/58LrroIgYMSIyzEpdtLefK377JvuoGcjJSueaUiXzuhHFkpWtZJBERSUwKZV1Jz/FarPx67AhVVVVx/vnnc8899xxy27Bhw0hNTeX555/n9ddf57nnnuPHP/4xN910E0uXLmXcuHG9qTrmFq8p5cu/f4faxgAzRxTyv585gmGF2X6XJSIi0isKZV0x61YXot8yMjIIBAIHrx9++OE89thjjB07lrS09n/MZsbxxx/P8ccfz3e/+13GjBnDE088wXXXXXfI/cWLx97exg2PLaep2XHipMH8/NNHkJept7GIiCQ+nX2ZJMaOHcvSpUvZtGkTZWVlXHPNNezbt4/LLruMN998kw0bNrBo0SKuvPJKAoEAS5cu5c477+Stt95iy5YtPP744+zZs4dp06YdvL/ly5ezZs0aysrKaGxs9PX5Oef4xZINfOMvy2hqdnxsznB+/dmPKJCJiEjSUChLEtdffz2pqalMnz6d4uJiGhoaeO211wgEAsyfP5+ZM2dy7bXXUlRUREpKCgUFBbzyyiucc845TJ48mZtvvpkf/vCHnH322QB84QtfYMqUKRx55JEUFxfz2muv+fbcmpsdt/1jFXc/uxqAz58wjvsvmUNGmt6+IiKSPMw553cNfcrMCoADBw4coKCg9SLUdXV1bNy4kXHjxpGVleVPgUkiWq9lfVOA6/+ynKeWeeP6bjpnGl84aXy0yhQREYm5iooKCgsLAQqdcxUd7ae+H4lblXWNXP37t3lt/V7SUox7L57Nx+eO8LssERGRmFAok7i0p7KeBQvf4IMdFeRkpPKLTx/BSZOL/S5LREQkZhTKJO5s3VfD5b9aypZ9NQzKzWDhlR9h1sgiv8sSERGJKYUyiTu3/P0DtuyrYfTAHB656ijGDo7/KUlERER6S6evtaO/nfwQC5G+hmt2VfLi6lLMYOGVH1EgExGRfkOhLEx6ejoANTU+LUCeREKvYeg17a5fLtkAwFmHlTChWAuKi4hI/6HuyzCpqakUFRVRWloKQE5Ojha27iHnHDU1NZSWllJUVERqavfXoty2v4a/B6e+uPrkCbEqUUREJC4plLVRUlICcDCYSWSKiooOvpbd9atXN9LU7DhuwiBmj9LAfhER6V8UytowM4YNG8aQIUN8X1ooUaWnp/eohQxgX3UDj765FYAvz1MrmYiI9D8KZR1ITU3tcbCQyD38+iZqGwMcNryAEyYO9rscERGRPqeB/uK7moYmHv73JsBrJdM4PhER6Y8UysR3j765lfKaRsYMyuHsGcP8LkdERMQXCmXiq8ZAM796dSMAXzxpPKkpaiUTEZH+SaFMfPXUsh1sL69lcF4mnzh8pN/liIiI+EahTHzT3Oz4RXCy2KtOGEtWuk6sEBGR/kuhTHzz8ppS1u6uIi8zjcuPHuN3OSIiIr5SKBPf/Hyx10p2+TGjKczu2XJMIiIiyUahTHzx1qZ9vLV5PxmpKXzu+HF+lyMiIuI7hTLxRWgs2SeOGMGQgiyfqxEREfGfQpn0uTW7KnlhVSlm8IUTx/tdjoiISFxQKJM+98tXvFaysw4rYXxxns/ViIiIxAeFMulT28tr+ft7OwC4+mQtPC4iIhKiUCZ96levfkhTs+O4CYOYParI73JERETihkKZ9Jn91Q386Y2tgLfwuIiIiLRI87sASW7lNQ28uWk/b2zcy5K1e6htDHDY8AJOmDjY79JERETiikKZRNWeynre2LiPNzbuZenGfazeVdnq9ozUFL551lTMtPC4iIhIOIUy6bWt+2r42eL1LN24jw/3VB9y+4TiXI4aN4hjxg/k6HGDKCnUvGQiIiJtKZRJrzQFmlmw8A02BMOYGUwtKeDocQM5atxAPjJ2IMX5mT5XKSIiEv8UyqRX/vr2NjbsqWZATjrfv2g2R40dSGGO1rEUERHpKYUyiVhtQ4D7X1gLwFdOncQZ04f6XJGIiEji0pQYErHfvLaR3RX1jByQzaePGe13OSIiIglNoUwisr+6gV8s9pZLun7+FDLTUn2uSEREJLEplElEfvLyeirrm5g2rICPzh7udzkiIiIJT6FMemzrvhp+9+/NAHzr7KmkpGjOMRERkd5SKJMeu//5tTQEmjl+4iBOmqSZ+UVERKJBoUx6ZOWOCp54bzsAN2hmfhERkahRKJMeueefq3EOzps1jFkji/wuR0REJGkolEm3vb6+jCVr95CWYvz3mVP8LkdERCSpKJRJtzjnuPufqwG4/OjRjBmU63NFIiIiyUWhTLrl6RU7Wb7tALkZqXz1tEl+lyMiIpJ0FMqkS42BZn6waA0AXzxpAoPztMC4iIhItCmUSZf+9MYWNu+tYXBeBp8/cZzf5YiIiCQlhTLpVFV9Ew++uA6Ar502idxMrWEvIiISCwpl0qlfvfohZVUNjB2UwyeP0qLjIiIisaJQJh3aU1nPQ698CMB/nzmV9FS9XURERGLF909ZM7vGzDaZWZ2ZLTWzozrZN93MvmtmG4L7LzOzs/qy3v7kpy+vp7ohwOyRhZwzs8TvckRERJKar6HMzC4F7gO+BxwOLAMWmdmQDg65HfgS8FVgOvAL4Akzm9sH5fYr9U0BHntnGwDfmD9FyymJiIjEmN8tZdcBDznnFjrnVgJXAzXAVR3sfwVwp3PuGefch865nwPPAN/om3L7j1fXllFZ18TQgkxOmKhFx0VERGLNt1BmZhnAEcALoW3Ouebg9WM7OCwTqGuzrRY4IRY19mf/WL4DgHNmDiMlRa1kIiIiseZnS9lgIBXY3Wb7bqCjAUyLgOvMbJKZpZjZGcCFwLCOHsTMMs2sIHQB8qNQe1Krawzw/Ervx3LerOE+VyMiItI/+N192VNfA9YBq4EG4CfAQqC5k2NuBA6EXbbFuMaEt3hNKdUNAUYUZXP46CK/yxEREekX/AxlZUAAGNpm+1BgV3sHOOf2OOc+DuQCY4CpQBXwYSePcxdQGHYZ2buyk99Ty3cCcO6sYRrgLyIi0kd8C2XOuQbgbeC00DYzSwle/3cXx9Y557YDacAngL91sm+9c64idAEqo1F/sqppaOKlVaUAnDerw15hERERiTK/18y5D3jYzN4C3gCuxWsFWwhgZo8A251zNwavHw2MAN4Lfr0FL1h+v88rT1IvriqltjHA6IE5zBxR6Hc5IiIi/Yavocw596iZFQO34g3ufw84yzkXGvw/mtbjxbLw5iobj9dt+QxwhXOuvO+qTm5Pq+tSRETEF363lOGc+wnegP32bpvX5voSvEljJQaq6pt4eY26LkVERPyQaGdfSgy9sHI39U3NjB+cy/RhBX6XIyIi0q8olMlBoQljz1PXpYiISJ9TKBMADtQ2smTtHgDOm60JY0VERPqaQpkA8NwHu2gMOCYPzWPyUC16ICIi0tcUygSAfwTPutSySiIiIv5QKBP2Vzfw2voywJsKQ0RERPqeQpmw6INdNDU7pg0rYEJxnt/liIiI9EsKZRLWdalWMhEREb8olPVzZVX1vL7B67o8X+PJREREfKNQ1s89+/4umh3MGlnI6EE5fpcjIiLSbymU9XP/WNYyYayIiIj4R6GsH9tdUccbm/YBcM5MhTIRERE/KZT1Y8+u2IlzMHd0ESMHqOtSRETETwpl/ZgmjBUREYkfCmX91I7yWt7avB8zOFddlyIiIr5TKOunnlnhtZJ9ZMxASgqzfK5GREREFMr6qadCXZez1UomIiISDxTK+qGt+2pYtrWcFIOzZyiUiYiIxAOFsn4oNMD/mPGDKM7P9LkaERERAUjzuwDpO845/r5sB798ZQMA52rCWBERkbihUNZPbNlbw01PruDVdd46l9OHFfCxOSN8rkpERERCFMqSXFOgmV//ayP3v7CWusZmMtJS+K9TJ/LFkyaQkabeaxERkXihUJbElm8r51uPrWDlzgoAjhk/kDsvmMn44jyfKxMREZG2FMqSUHV9E/c9v5aFr22k2UFhdjo3nTuNi48YiZn5XZ6IiIi0Q6Esyby8upSbn3yf7eW1AHxsznC+c950BufpLEsREZF4plCWRO56ZhW/fOVDAEYOyOb2j89g3pQhPlclIiIi3aFQliTKaxr41b82AvCFE8fx9TMmk5OhH6+IiEii0Kd2kli8Zg+BZseUofncdO50v8sRERGRHtKcCEnihVW7AThtmrorRUREEpFCWRJoDDSzZO0eQKFMREQkUSmUJYE3N+2jsq6JgbkZzBk1wO9yREREJAIKZUngxVWlAJwyZQipKZqHTEREJBEplCWBl1Z7oex0dV2KiIgkLIWyBLdhTxUby6pJTzVOmDTY73JEREQkQgplCe7F4FmXx4wfRH5Wus/ViIiISKQUyhLcC8HxZKdNVdeliIhIIlMoS2DlNQ28vXk/AKdNG+pzNSIiItIbCmUJbMlabxb/yUPzGDUwx+9yREREpBcUyhLYwa5LtZKJiIgkPIWyBNUYaGbJGk2FISIikiwUyhLUW5v2U6FZ/EVERJKGQlmCCk2FMW9KsWbxFxERSQIKZQnqxYOz+Gs8mYiISDJQKEtAH4bN4n+iZvEXERFJCgplCSi0APnR4zSLv4iISLJQKEtALwTHk52msy5FRESShkJZgjlQ08hboVn8p2o8mYiISLJQKEswi9eWEmh2TBqSx+hBmsVfREQkWSiUJZgXNYu/iIhIUlIoSyBNgU5rlw0AACAASURBVGYWaxZ/ERGRpKRQlkDe2uzN4j8gJ525ozWLv4iISDJRKEsgoVn8T5kyRLP4i4iIJBmFsgSi8WQiIiLJS6EsQXy4p4oPg7P4nzRZs/iLiIgkG4WyBPHSas3iLyIikswUyhJEaBb/U6fqrEsREZFkpFCWAA7UNvLmJm8W/9M1nkxERCQpKZQlgCVr92gWfxERkSQXUSgzs1OiXYh0LDQVxqmaMFZERCRpRdpS9k8z22BmN5vZqKhWJK00NzuWrN0DqOtSREQkmUUaykYAPwEuAj40s0VmdomZZUSvNAHYtr+W8ppGMlJTmDOqyO9yREREJEYiCmXOuTLn3P3OuTnA0cBa4GfADjP7kZnNjmaR/dnKnRUATBqaR3qqhgCKiIgkq15/yjvn3gHuwms5ywOuAt42s1fN7LDe3n9/Fwpl04cV+FyJiIiIxFLEoczM0s3sIjN7BtgMnAl8BRgKTAxu+0tUquzHVgVD2TSFMhERkaSWFslBZvZj4DLAgN8B33TOvR+2S7WZXQ/s6H2J/dvKHcGWsuEKZSIiIsksolAGTAe+CjzunKvvYJ8yQFNn9MKBmka2l9cCaikTERFJdhGFMufcad3YpwlYEsn9i2fVLq+VbERRNoXZWu9SREQkmUU6eeyNZnZVO9uvMrMbel+WgLouRURE+pNIB/p/CVjdzvYPgKt7ckdmdo2ZbTKzOjNbamZHdbH/tWa2xsxqzWyrmd1vZlk9ecxEoUH+IiIi/UekoawE2NnO9j3AsO7eiZldCtwHfA84HFgGLDKzdtcTMrNPAXcH958GfA64FLizJ8UnCk2HISIi0n9EGsq2Ase3s/14enbG5XXAQ865hc65lXitbDV4c5215zjgNefcH51zm5xzzwH/B3TaupaIGgPNrNtdBSiUiYiI9AeRhrKHgAfM7EozGxO8XAXcH7ytS8ElmY4AXghtc841B68f28FhrwNHhLo4zWw8cA7wTCePk2lmBaELkN+d+vy2YU8VDYFm8jPTGDkg2+9yREREJMYinRLjB8AgvKWVQutd1gH3OOfu6uZ9DAZSgd1ttu8GprZ3gHPuj2Y2GPiXmRle/b9wznXWfXkj8D/drCluhAb5TxtWQEqK+VyNiIiIxFqka18659wNQDFwDDAbGOicuzWaxbVlZvOAbwP/D28M2oXAuWb2nU4OuwsoDLuMjGWN0dIyyD8hGvZERESklyJtKQPAOVcFvBnh4WVAAG9ZpnBDgV0dHHMb8Dvn3K+C11eYWS7wv2Z2R7D7s22N9cDBCW69Brb4d3CQv6bDEBER6RciDmVmdiRwCTCali5MAJxzF3Z1vHOuwczeBk4DngzeZ0rw+k86OCwHaBu8AqGSul18nHPOsWpnJaDpMET6VH0lbHgZ1j0HZWvhsAvgI5+HVE3eLCKxF+nal58EHgEWAfOB54DJeK1cT/Tgru4DHjazt4A3gGuBXGBh8HEeAbY7524M7v8UcJ2ZvQssxVv4/DbgKedcoO2dJ6rdFfXsq24gNcWYPFTdl3GlYge89wdY9zwMnwsn3wA5A/2uSiLlHOxd74WwtYtg8+vQ3Nhy+9al8PZv4ay7YYJWjROR2Iq0pezbwNedcz81s0rga8BG4Je0P39Zu5xzj5pZMXAr3txn7wFnOedCg/9H07pl7HbABb+OwJsX7SngpgifR1wKjSebUJxLVnqqz9UIgUbvA/udR2D98xDqJd+6FJY/CqfcBEdcCam9Gg0QHXUV8P5jsOz/oLoM8ksgb6h3yR8KeSVhX0sgewD0ZZf+3g0tAahmL0z/KMy5HAqG910NTfWw6V8tdezf2Pr2geNh0pleTa89AHtWw+8+DlPPgzPvgAFj+67WcFWlsHM57HwPdi6D0lVQNMqrdfJ8r26RSFSVeu+pg++t1TBgTMt7y6/3fD9kzrmeH2RWDRzmnNtkZnuBec65FWY2DXjJOdftCWT7WnBajAMHDhygoCA+uwZ/+vJ6frBoDR+bM5wHPznX73IST3Oz90G7c5l32bUcytZB4SgYNrvlMnhy50GqbD28+wi8939QXdqyffRxMO18ePf3UPqBt23oDDj7Hhh7QmyfW3uc8wLiO4/AB09AY033j03NgMx8utX7n1UAJbNav4a5gzs/pqkBNr/mBaB1z3mtUm1ZCkyaD3OvgMln9qyrcP/mlvve8V5LYO5MQxU01bVcT0mHscd7H0CT5sPgiS231e6HxXfDGw+BC0BqJhz/NTjhWsjI7X6dPeEcVGxvef+GLpVd/L87aJL3+k06w3uPpmV0vn91WfBDeHnw92QFpGe3/vkOnQGZedF7buB1Ee9a0fq51VdC3pA2/zS0+ZpbHB//+CQy5+DAttav/a7lXb+3Bk/2fjcmnwmjj1V3fgQqKiooLCwEKHTOVXS0X6ShbBtwdjCILQfucs79n5kdC/zTOVcYaeGxlgih7Jo/vMPTK3Zy49lT+dLJE/wuJ741B7zA1faPTH2H7/kWaVkw9LDWH0IDxsHaf3oBZ/NrLfvmFsOcT3nBYfAkb1ugCd5eCC/dDnXl3rbDLoAzbvNaMGKtao/XIvbu77zxTyGDp8DhV3jdq1W7oXI3VO069Gvt/t7XUDCy9es3bLbX8hZqhfpwsReCQlLSYMxxXgDKKoT3/ghbXm+5PXeI9zof/hkY1M57P9DoBdC1i7zH2NPeam/dkFfihZfJZ8L4ecFg2onSVfDsDbBxSfB5j4D5t8FhF7bf0tjcDPs2hLU+LPfuI1B/6L5tBZqgsbqdGwwGTWx5nYdMhz2rvNdiy7+huall14x8mDAvGDTP8MJq25BXsb3rWjDv/d4qjM/yWli7o2bfob+b7QXzbjHILOje6OHUjLAW4pKOv6bH0RyQgSbY2+ZvWdk675+hzsJq/lDIKoKG6uDv+672f98rd0PlDqg70M6Dmxe8Dr63psLuD2Dtc957K3x0UGaB15Ufem/ltbsAj7QR61D2R+At59x9wekovgr8DTgDeKc7A/39kgih7NR7F/NhWTWPXHUUJ00u9ruc+NHU4H0Itfoj/z401R66b2pm68BVPAXKt4QduxwaKjt/PEuBiWd4AWfyWR3/d1izzwtmby/0PvzSsuGEr8Px/9X+H33noHxzSx2hVgro5A9viffHL7cYNr0G7zwMa55p+SBOz/ECwuGfgVFHda9Lsqne+yPe0F4AaEflLu9DNfQadvfDNXdI8L/s+TD+FO9DJlzZOi9YvvdHqN7Tsn3M8d7zGXOc1924dpE3CL8+7EPFUmHU0d59jzvJex26kprhdfX1tNvWOVj1FDx3k/deCtV45h3efbZ6X65oHUZ7ylJhyLTut1rVHWg5QWHdc61fx86Eh7ySWdBY26Z1roMFWtJz6DoduY5bbQtGtH5uOYO992JHYaK6tHutoD2Vlu39nkdLSqrXetxpiCrxWllLV7UOqx39LesOS20dnDqtMQ2K2763Duv4vVVbDhteCr63noeasta35w9v8xzDw3DYtkBDMDDu7vhr1W5orGu/jr7wxcVQPDkmdx3rUDYQyHLO7QieMflNvCWQ1gG3O+ei8C94bMR7KKtpaOKw/1mEc/DmTadTnJ/pd0n+aKjx/lPb+V5LENi9svUg7JD0XO+/99AHSyiEddbEfrCL873WH0K1+6FoNMz9jNdiUzii+zXvWuG1poRa2ApHw5m3ey0a4eM1di7r4L/VCIw4wgsuh114aNiJtboK2P1+69dvz2ovvIw4vGU8SslsSOnGB1+gMdhK+bvWY/fayhnkheXJ82HCqd1vtYmWxlp4/cfw6n2df4imZUPJjDahqhs/IzMvtKRnRVZfczPsfNf7AF27CHa8431oF09t+T0J1dPVe6btOLady7x/KHpiwLjWrWwlsyGvh/9sNge8MYh13WgBB+/n0lkrceXuyANQLGXkQcnMsH8mp7ZpAdt9aJgJtdKHju+wdTDYPTxwfO/eWzveaWkJ3/ledJ53vLjmDe+zIwZiFsrMLA34FLAobEB+woj3UPbOlv1c+LPXKc7P5M2bTve7nL5RV3HoGJOyNe1/KGcVhv2Bn+OFsEETvP9Qe8s5r9Ure0D3QkRH9/HB4/DcdzrvHkpJh6HTw1opZnvjZTr6EKkq9f4ANzd69c261OtKLZkRWZ2x0lDj/UecXdS7+zmw3Ws5e/cRr1WqZFZwvNSZXuCLxs+7t8q3wvPf9X7eGfmHduMOmhgfY6BqyyEtM3pddbX7u/9PRVZR798LseCcN8QhGl344QKN3u/qwd/ddkJU7T5v36yiNu+ZOV5g6unfnsY6L7BmFUZ//F9Xqvd6Ib2r1q/wFv2uupRjNVazO/KHdz0WM0KxbimrAaY553r4L5P/4j2U/f4/m7n5yfc5eXIxD1+VdOuse7/Eu9qMbdn3Yfv75hYf+kFXNKZvzxaMVEM1/OsBr0UFWreYDJvtdR/09JffOe9DJDO//wy0bW72WjT8/EPdlYZqr1Us0iAv/UtTvXdiQ86gxPhb1lvNzd7frbSMrsduJrHuhrJI/417A5gDJFwoi3erknkm/yU/gJfvwJvVpI1DBozPgvxhiftHKyMXTr0JTv4mYNFpMTHrf3OipaTEdyCD+K9P4ktapnfpL1JSIHeQ31UkjEg/KX4G3Gdmo4C3gVYjhZ1zy3tbWH+18uCal0kWyj54El6+3ft+4PjWAaxkdvL+0vaXFi0REem1SEPZn4JffxS2zeGdjuOAOBjwkXgCzY7VweWVpsdLKGtq6H0fe+lqePL/ed8f91WYf3vv6xIREUkykYaycVGtQgDYvLea2sYAWekpjBscB10iT30Nlv8FLvylN1lqJOoOwKOXe/MujTsJTrslqiWKiIgki4hCWSIO8E8Eoa7LKSUFpKb4PJaqao83Y31zE/z5s/CJX8GMHk4/19wMT3zZm8+qYCRctDA+zkYTERGJQ5EuSP6Zzm53zj0SWTn928FB/sPi4AyV5Y96gSwl3ZuG4bHPead7z760+/fxrx/Cmqe9iVwv/V3XS/KIiIj0Y5E2WzzY5no6kAM0ADWAQlkEVu4IhTKfx5M5B+/9wfv+rLu8aSve/R088SUvoM39dNf3se4FeOkO7/tzf+jNLSUiIiIdirT78pAptM1sEvBz4Ae9Laq/WhUa5O/3dBg73oXSld7akDMvhiM/5y0j89av4W/XeJODHnlVx8fv2+i1rOHgiCu9ZYpERESkU1Gb7dA5tw74Foe2okk37K2qZ1eFt+bXlBKfQ9m7v/e+Tj3Pm407JcVr7Tr6y972f3wdlv6y/WMbauDRK7ylP0YcCWff0zc1i4iIJLhoT0HdBAyP8n32C6FWsrGDcsjL9HEwfGMdvP9X7/vwbkozryvzuP/yrj/7TXjtR62Pdc47Y3P3Cm82/kse6V+TJIqIiPRCpAP9P9p2EzAM+ArwWm+L6o9Wxcuksav/4U1jUTgKxp3c+jYzOONWL2i98gN4/jteV+ZJ13u3L/0lrPizt/jxxQ/3bDFvERGRfi7SJpkn21x3wB7gJeAbvaqon1q5M04G+Ye6Lmdf1v5afmZw6s3eGLOX74CXbvPOyhx3Ejx3k7fPmXfA2OP7rmYREZEkEOlAf628G2VxseZl+Vb4cLH3/ZxPdb7vyd/0lhB64RZYcje89oA3hcbMi+Hoq2NdqYiISNJRuIoD9U0B1pdWAT53Xy77E+Bg7IkwsBuLNpzwdTjzTu/7pjoYOgPOfzBxFxEXERHxUaRjyh4D3nDO3dNm+zeBjzjnLo5Gcf3Fut1VNDU7inLSGVaY5U8Rzc3wXrDrcs7l3T/u2GsgZxCsfhrm3wYZcbA8lIiISAKKtKXsJOCZdrY/G7xNeiA0nmxaSQHmVyvTltdh/ybIyIfpbc/j6MLsT3oz9g8YG4vKRERE+oVIQ1ke3uz9bTUCPo9UTzwHZ/L3czzZu8EZ/GdcoNYuERERH0QaylYA7S2C+ElgZeTl9E++T4dRXwkrgyfUzunGEkoiIiISdZFOiXEb8LiZTcCbBgPgNOAyQOPJesA55/90GB88AY01MGgSjDrKnxpERET6uUinxHjKzD4OfBu4CKgFlgOnO+eWRLG+pLe9vJbKuibSU42JQ/L8KSLUdTn3cp05KSIi4pOI1/Nxzj0NPB3FWvql0HiyiUPyyUjzYYaSsnWw9T9gKTDrk33/+CIiIgJEOKbMzD5iZke3s/1oMzuy92X1H6E1L33runwv2Eo28XQoGOZPDSIiIhLxQP+fAqPa2T4ieJt008qdBwCYNiy/7x880BScMJbWi4+LiIhIn4s0lE0H3mln+7vB26SbVkZzeaWdy+H9x6CpvdlK2rHhJajcCdkDYfLZvX98ERERiVikY8rqgaHAh222DwOaelVRP1JR18jWfbVAFLovmwPw+09Adal3FuVZd8Ok0zs/JjSD/6xLIC2jd48vIiIivRJpS9lzwF1mVhjaYGZFwJ3A89EorD9YHRxPNrwwi6KcXoaiHe95gQxg7zr4wyfgj5+EvRva3796L6wOLsqgrksRERHfRRrKrscbU7bZzF42s5eBjUAJ8I1oFZfsVkWz6/LD4HRxE06FY78CKWmw9ln42THwwi1QX9V6/xV/geZGKJkFJTN7//giIiLSKxGFMufcdmAW8E28GfzfBr4GzHTObY1eecktNB1GVGby37DY+zr1XDjzDvjyv2HCaRBogH/dDz85Epb/GZzz9gt1XaqVTEREJC70Zp6yajP7F7AFCPW9nW1mOOf+HpXqktyqXVGayb++CrYu9b4ff4r3tXgyfPoxWPtP+Oe3vMXGH/8CvPkrOPyzsGsFpGbATC3AICIiEg8iCmVmNh54ApgJOMCCX0NSe19acgs0O1bv8saU9bqlbPPrXldk0WgYOL5luxlMOdsLav/5KbzyQy+8hQLclHMgZ2DvHltERESiItIxZQ/ijSEbAtQAM4CTgbeAeVGpLMntr2mgoakZgFEDc3p3ZxuC48nGn9L+MknpWXDiN+Crb8HMS1q2H/6Z3j2uiIiIRE2k3ZfHAqc658rMrBkIOOf+ZWY3Aj8C5katwiRVXtMIQEFWGqkpvVxv8sOXva8TTul8v4Lh8ImH4JgvQ1UpTDytd48rIiIiURNpKEsFKoPflwHDgTXAZmBKFOpKegdqvQleez0VRsUO2LMaMBh3cveOGXF47x5TREREoi7SUPY+MBuvC3Mp8E0zawC+yKETyko7DtR6LWVFOem9u6MPF3tfh8/V+DAREZEEFmkoux3IDX7/XeAfwKvAXuDSKNSV9ELdl4XZvQxlG7rZdSkiIiJxLaJQ5pxbFPb9emCqmQ0E9jvnXMdHSkhUQplzLS1l4xXKREREElnE85S15ZzbF6376g/Ko9F9ufsDb2ml9BwYdVSUKhMRERE/RDolhvTSgZrgQP/sXgz0D02FMeZ4SMuMQlUiIiLiF4Uyn0RloH93p8IQERGRuKdQ5pNQ92VBpGPKGuu8mfxB48lERESSgEKZT0ID/YsiDWVb/wNNdZBXAkOmRbEyERER8YNCmU9aui8jHFMWPhVGe0sriYiISEJRKPNJeWigf6RjykLjydR1KSIikhQUynzQ3OxaWsoi6b6sLoOdy7zvx8+LWl0iIiLiH4UyH1Q1NNEcnGI3ooH+oQljhxwG+UOjVpeIiIj4R6HMBweCg/yz0lPISk/t+R1oKgwREZGko1Dmg5YzLyMY5O8cbFjsfa/xZCIiIklDocwH5bW9GOS/dz1UbIPUDBhzXJQrExEREb8olPmgV4uRh6bCGH0MZOREsSoRERHxk0KZD3q1xJKmwhAREUlKCmU+CIWyHreUBRph46ve9xrkLyIiklQUynzQMnFsDwf6b3sLGioheyCUzI5BZSIiIuIXhTIfRDym7GDX5cmQoh+diIhIMtEnuw/KIx1TtkHjyURERJKVQpkPWpZY6kH3Zd0B2P62973Gk4mIiCQdhTIfhGb071FL2cZXwQVg0EQoGh2jykRERMQvCmU+CE0eW5gJNNR076ANL3lf1XUpIiKSlNL8LqA/Kq9pJJUAUx4/Eyq2wNTz4PDPwLhOBvBrvUsREZGkplDWx+oaA9Q3NTPS9pK+f7238YPHvUvRaJh7Bcy5HApHtBy0fzPs+xAsFcae4E/hIiIiElPqvuxjoekwxqSUeRvyh8NHvgCZhVC+BV6+Ax6YAX+4GFY95U0YG2olG3kkZBX6VLmIiIjEklrK+ljozMtJGfvAAUOmwrn3wvzbYOXf4Z1HYPO/YN1z3iW3GDLzvYM1nkxERCRpKZT1sdBs/uPS9kIjLWdSpmfD7Eu9S9l6ePd38N4foboUqvd4+2g8mYiISNKKi+5LM7vGzDaZWZ2ZLTWzozrZd7GZuXYuT/dlzZEKTRw7OjXYfdne9BaDJ8IZ34PrVsIn/+idCDD3Chj5kT6sVERERPqS7y1lZnYpcB9wNbAUuBZYZGZTnHOl7RxyIRA+6+ogYBnwl1jXGg2hOcqGhZ5a0ZiOd05Nh6nnehcRERFJavHQUnYd8JBzbqFzbiVeOKsBrmpvZ+fcPufcrtAFOCO4f0KEstAcZUMCoVCmiWBFRETE51BmZhnAEcALoW3Ouebg9WO7eTefA/7knKuOfoXRd6C2kTSaKGoKjhNTKBMRERH8774cDKQCu9ts3w1M7erg4NizGXjBrKN9MoHMsE35PS8zesprGimxfaTQDKmZkDvEz3JEREQkTsRD92VvfA5Y4Zx7o5N9bgQOhF229UVhHSmvbWSUhVrJRnU8g7+IiIj0K34ngjIgAAxts30osKuzA80sF/gk8OsuHuMuoDDsMjKiSqPkQE0jI01dlyIiItKar6HMOdcAvA2cFtpmZinB6//u4vCL8bolf9/FY9Q75ypCF6Cyd1X3TnltAyOtk+kwREREpF/ye0wZeNNhPGxmbwFv4E2JkQssBDCzR4Dtzrkb2xz3OeBJ59zeviy2t8rVUiYiIiLt8D2UOeceNbNi4FagBHgPOMs5Fxr8PxpoDj/GzKYAJwDz+7LWaDhQGx7KOpmjTERERPoV30MZgHPuJ8BPOrhtXjvb1gAW47KirinQTGVdEyMy1X0pIiIirfk90L9fqahrIo0mhhHscVVLmYiIiAQplPWh8poGSmwfqeYgLQvyNEeZiIiIeBTK+lCrOcoKR4ElXA+siIiIxIhCWR9qPchf48lERESkhUJZH/ImjtUgfxERETmUQlkfKq9pYKSVelcUykRERCSMQlkfKq9VS5mIiIi0T6GsD7WezV/TYYiIiEgLhbI+VF1TQwn7vCtqKRMREZEwCmV9KKVyB6nmaErJ1BxlIiIi0opCWR/KrN4GQF3ucM1RJiIiIq0olPWhvLqdADTlj/K5EhEREYk3CmV9aEC9F8qcxpOJiIhIGwplfcQ5x+Cm3QCkDdCZlyIiItKaQlkfqW4IMDw4HUZm8Vh/ixEREZG4o1DWR7zZ/L1QljFonM/ViIiISLxRKOsjByqrNUeZiIiIdEihrI/U7t1KqjnqydAcZSIiInIIhbI+0rR3MwB7UodqjjIRERE5hEJZXynfAsD+jBKfCxEREZF4pFDWR1IrvVBWmTXc50pEREQkHimU9ZGsqu0A1OaM8LkSERERiUcKZX0kt3YHAI35I32uREREROKRQlkfKWrQEksiIiLSMYWyvtDUQFFTGQApWmJJRERE2qFQ1hcqtpFCM3UunZwBOvtSREREDqVQ1heC02Fsc8UU5mT4XIyIiIjEI4WyvhAWyoqyFcpERETkUAplfaBp7yYAtrnBFOak+1uMiIiIxCWFsj7QtM9bYmk7xeRnpvlcjYiIiMQjhbI+4PZ73Zf70ktISdG6lyIiInIohbI+kFqxFYCKTC2xJCIiIu1TKIu1pgbSa3YBUJ2rJZZERESkfQplsVaxDcNR59KxnGK/qxEREZE4pVAWa+HTYeRqOgwRERFpn0JZrLWao0zTYYiIiEj7FMpiLRjKtrpiChXKREREpAMKZbGmJZZERESkGxTKYk3dlyIiItINCmWxdjCUDaZISyyJiIhIBxTKYqmpASp2AMGWMoUyERER6YBCWSxVbAMctWSwlwIKszWmTERERNqnUBZLoa7L5mLAdPaliIiIdEihLJbCxpMBCmUiIiLSIYWyWAo78zI3I5WMNL3cIiIi0j6lhFjavxkIDfLXeDIRERHpmEJZLIVPHKuuSxEREemEQlkshY0pUygTERGRziiUxUpTPVTuBDRHmYiIiHRNoSxWDnhzlDWmZLKXAoUyERER6ZRCWawEuy7LM4bhzVGmgf4iIiLSMYWyWAmGsrK0oQBqKRMREZFOKZTFSjCU7bIhABRpoL+IiIh0QqEsVsKmwwDN5i8iIiKdUyiLlWAo2xgILrGk7ksRERHphEJZrARD2fqGQQAUaaC/iIiIdEKhLBbC5ihbU1cEaKC/iIiIdE6hLBaCc5S59Bx2B/IAhTIRERHpnEJZLAS7LpvyRwJGeqqRnZ7qb00iIiIS1xTKYiEYyupyRwJQmJ2BmflZkYiIiMQ5hbJYCIayquzhgLouRUREpGsKZbEQDGUHMoYBmjhWREREuqZQFgvBULY3XUssiYiISPcolMVCaImlFC+UFailTERERLqgUBZtYXOUhZZY0sSxIiIi0hWFsmgLzlFGeg67GnMAdV+KiIhI19L8LiDp5A2FTz8GteWUL2sCFMpERESkawpl0ZaZBxNPB6D83/8BoFBjykRERKQLvndfmtk1ZrbJzOrMbKmZHdXF/kVm9lMz22lm9Wa21szO6at6e+JAbSMARTkaUyYiIiKd87WlzMwuBe4DrgaWAtcCi8xsinOutJ39M4DngVLgImA7MAYo77OieyAUytRSJiIiIl3xu/vyOuAh59xCADO7GjgXuAq4u539rwIGAsc55xqD2zb1QZ0RKa9pADR5rIiIiHTNt+7LYKvXEcALoW3OuebgXrQW4wAAC5NJREFU9WM7OOyjwL+Bn5rZbjN738y+bWYdrvZtZplmVhC6APnRexYda2hqprohAGigv4iIiHTNzzFlg4FUYHeb7buBkg6OGY/XbZkKnAPcBnwDuLmTx7kROBB22RZ5yd0X6ro0g/wshTIRERHpnO8D/XsoBW882Redc2875x4F7sAbk9aRu4DC/9/e/cdKVt51HH9/dllYCuxSK8uKSFPLDwmQkC7okpqCAlLFxjZiRf+omMa0tAY1NYY1tiKNNq11gci2SaO1EEmFpAZDmqaW2hqLWywQWNtSkAgFyoKFzd5d2v29X/84Z5Zhsiv3yj1zzt77fiUnM/OcMzPPvd/M3c+e5znPjG0nd95LXgplK5YvY+mSTOMtJUnSYazPOWXPA/uAEyfaTwSePcRzNgN7qmrfWNvDwOokR1bV7sknVNUuYNfocTKdgDSzo+mKk/wlSdJs9HamrA1Q9wMXj9qSLGkfbzzE0+4BTm2PGzkd2HywQNanrT8cLYdhKJMkSa+s7+HL9cDvJPmtJGcCnwSOAUZXY96a5CNjx3+S5urLm5KcnuRy4I+BDVPu9ysahTLPlEmSpNnodUmMqro9yQnA9TST+x8E3lpVo8n/pwD7x45/KsllwA3AJpp1ym4CPjrVjs/CVheOlSRJc9D3OmVU1c3AzYfYd9FB2jYCazvu1qt2YDV/z5RJkqRZ6Hv4csGaGS0c65wySZI0C4ayjmz1K5YkSdIcGMo64kR/SZI0F4ayjjjRX5IkzYWhrCPOKZMkSXNhKOuIV19KkqS5MJR1YP/+OhDKnFMmSZJmw1DWge279rK/mvsrDGWSJGkWDGUdmGmvvDx62VKWL1vac28kSdLhwFDWga07nOQvSZLmxlDWAeeTSZKkuTKUdcCFYyVJ0lwZyjrw0sKxhjJJkjQ7hrIOHFg49mhX85ckSbNjKOvAaPjSM2WSJGm2DGUdGA1frjSUSZKkWTKUdeClr1hy+FKSJM2OoawDM159KUmS5shQ1gEXj5UkSXNlKOuA65RJkqS5MpR1wHXKJEnSXBnK5tnOPfvYvXc/AMe/xon+kiRpdgxl82w0dLl0STjmyKU990aSJB0ujui7AwvNEUvDuy54PXv2FUn67o4kSTpMpKr67sNUJVkBzMzMzLBixYq+uyNJkha4bdu2sXLlSoCVVbXtUMc5fClJkjQAhjJJkqQBMJRJkiQNgKFMkiRpAAxlkiRJA2AokyRJGgBDmSRJ0gAYyiRJkgbAUCZJkjQAhjJJkqQBMJRJkiQNgKFMkiRpAAxlkiRJA2AokyRJGoAj+u5AX7Zt29Z3FyRJ0iIw28yRquq4K8OS5MeBp/vuhyRJWnROrqrvHWrnYgxlAU4Ctnf4NsfRBL+TO34fzZ41GRbrMTzWZHisybC82nocBzxT/0fwWnTDl+0v45ApdT40uQ+A7VXlOOkAWJNhsR7DY02Gx5oMyzzU4xWf40R/SZKkATCUSZIkDYChrBu7gD9rbzUM1mRYrMfwWJPhsSbD0nk9Ft1Ef0mSpCHyTJkkSdIAGMokSZIGwFAmSZI0AIYySZKkATCUzbMk70/yRJKdSe5N8tN992mxSPKWJHcleSZJJXn7xP4kuT7J5iQ7ktyd5LS++rvQJVmX5BtJtif5nyR3Jjlj4pjlSTYkeSHJi0k+l+TEvvq80CW5OsmmJNvabWOSXxzbbz16lOTa9m/XjWNt1mSKklzX1mB8+87Y/k7rYSibR0l+HVhPc8nsm4CHgC8mWdVrxxaPY2h+5+8/xP4/Aq4B3gv8DPADmvosn073Fp0LgQ3AWuBSYBnwz0mOGTvmBuBtwK+1x58E/OOU+7mYPA1cC6wBzgP+BfinJGe1+61HT5KcD7wH2DSxy5pM37eAHxvbfnZsX7f1qCq3edqAe4Gbxx4voflKp2v77tti24AC3j72OMBm4A/H2lYCO4Er++7vYtiAE9q6vGXs978buGLsmJ9qj1nbd38XywZsAd5tPXqtwbHAo8AlwFeBG9t2azL9WlwHPHiIfZ3XwzNl8yTJkTT/+7x71FZV+9vHF/TVLx3wBmA1L6/PDE2Qtj7TsbK93dLerqE5ezZek+8AT2JNOpdkaZIrac4wb8R69GkD8Pmqunui3Zr047R2Gsx/J7ktySlte+f1WHRfSN6hHwWWAs9NtD9Hk6TVr9Xt7cHqsxp1KskS4Ebgnqr6Ztu8GthdVVsnDrcmHUpyDk0IWw68CLyjqr6d5Fysx9S1wfhNwPkH2e1nZPruBa4CHqEZuvxT4N+SnM0U6mEokzQNG4CzefncDPXjEeBcmjOXVwC3JLmw3y4tTkl+ArgJuLSqdvbdH0FVfWHs4aYk9wLfBd4J7Oj6/R2+nD/PA/uAyaswTgSenX53NGFUA+szZUluBn4Z+Lmqenps17PAkUmOn3iKNelQVe2uqseq6v6qWkdzcczvYT36sAZYBTyQZG+SvTSTx69p7z+HNelVe1bsUeBUpvAZMZTNk6raDdwPXDxqa4dsLqYZKlC/Hqf50IzXZwXNVZjWpwPtEiQ3A+8Afr6qHp845H5gDy+vyRnAKViTaVoCHIX16MOXgXNozlyOtvuA28buW5MeJTkWeCPNhWKdf0Ycvpxf62mGAu4D/gP4fZpJtH/Xa68WifbDc+pY0xvaeTJbqurJdu2fP0nyXzQh7cPAM8Cd0+/torAB+E3gV4DtSUZzLmaqakdVzST5W2B9ki3ANuCvgY1V9fV+urywJfkI8AWaicnH0dTnIuAy6zF9VbUd+OZ4W5IfAC+M5l5ak+lK8nHgLpohy5NolrjaB3x2Gp8RQ9k8qqrbk5wAXE8z6e9B4K1VNTm5XN04D/jK2OP17e0tNBM3P0YTkj8FHA98jaY+zuXoxtXt7Vcn2n8b+Ex7/w+A/cDnaM7WfBF43xT6tlitAm6lmcA8Q7Mm1mVV9aV2v/UYHmsyXScDnwVeB3yf5t+JtVX1/XZ/p/VIu86GJEmSeuScMkmSpAEwlEmSJA2AoUySJGkADGWSJEkDYCiTJEkaAEOZJEnSABjKJEmSBsBQJkkdSnJRkjrI9+VJ0ssYyiRJkgbAUCZJkjQAhjJJC1qSJUnWJXk8yY4kDyW5ot03Glq8PMmmJDuTfD3J2ROv8atJvpVkV5InknxgYv9RST6a5Kn2mMeSvHuiK2uS3Jfkh0n+PckZHf/okg4zhjJJC9064F3Ae4GzgBuAv09y4dgxfwl8ADif5kuI70qyDCDJGuAO4B+Ac4DrgA8nuWrs+bcCvwFcA5wJvAd4caIff96+x3nAXuDT8/UDSloY/EJySQtWkqOALcAlVbVxrP1vgNcAnwK+AlxZVbe3+34EeBq4qqruSHIbcEJV/cLY8z8GXF5VZyU5HXgEuLSq7j5IHy5q3+OSqvpy2/ZLwOeBo6tqZwc/uqTDkGfKJC1kp9KEry8leXG00Zw5e+PYcQcCW1VtoQlZZ7ZNZwL3TLzuPcBpSZYC5wL7gH99hb5sGru/ub1dNYefRdICd0TfHZCkDh3b3l4OfG9i3y5eHsz+v3bM8rg9Y/dHQxT+x1jSAf5BkLSQfZsmfJ1SVY9NbE+NHbd2dCfJa4HTgYfbpoeBN0+87puBR6tqH/CfNH9LL0SSXgXPlElasKpqe5KPAzckWQJ8DVhJE6q2Ad9tD/1QkheA52gm5D8P3Nnu+yvgG0k+CNwOXAD8LvC+9j2eSHIL8Okk1wAPAa8HVlXVHVP4MSUtEIYySQvdB2muqFwH/CSwFXgA+AteGi24FrgJOA14EHhbVe0GqKoHkrwTuL59rc3Ah6rqM2PvcXX7ep8AXgc82T6WpFnz6ktJi9bYlZGvraqtPXdH0iLnnDJJkqQBMJRJkiQNgMOXkiRJA+CZMkmSpAEwlEmSJA2AoUySJGkADGWSJEkDYCiTJEkaAEOZJEnSABjKJEmSBsBQJkmSNACGMkmSpAH4X8mKXH1MdB9SAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 700x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 700x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "czKzwIvsoghe"
      },
      "source": [
        "### CNN Early Stopping"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lyi30YN7oghf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "34e08495-092f-411b-ab5e-be24b45a2753"
      },
      "source": [
        "model = Sequential()\n",
        "\n",
        "embedding_layer = Embedding(vocab_size, 100, weights=[embedding_matrix], input_length=maxlen, trainable=False)\n",
        "model.add(embedding_layer)\n",
        "\n",
        "model.add(Conv1D(128, 5, activation='relu'))\n",
        "model.add(GlobalMaxPooling1D())\n",
        "model.add(Dense(1, activation='sigmoid'))\n",
        "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['acc'])\n",
        "\n",
        "es = EarlyStopping(monitor='val_loss', mode='min', patience=5, verbose=1)\n",
        "mc = ModelCheckpoint('best_model.h5', monitor='val_loss', mode='min', verbose=1, save_best_only=True)\n",
        "\n",
        "history = model.fit(X_train, y_train, batch_size=128, epochs=30, verbose=1, callbacks=[es, mc], validation_split=0.2)\n",
        "print(model.summary())\n",
        "score_train = model.evaluate(X_train, y_train, verbose=1)\n",
        "score_test = model.evaluate(X_test, y_test, verbose=1)\n",
        "\n",
        "# results\n",
        "print (\"Train Accuracy:\", score_train[1])\n",
        "print(\"Test Accuracy:\", score_test[1])\n",
        "plot_results(history)"
      ],
      "execution_count": 133,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "25/25 [==============================] - 1s 18ms/step - loss: 0.6984 - acc: 0.5444 - val_loss: 0.6554 - val_acc: 0.5938\n",
            "\n",
            "Epoch 00001: val_loss improved from inf to 0.65544, saving model to best_model.h5\n",
            "Epoch 2/30\n",
            "25/25 [==============================] - 0s 11ms/step - loss: 0.5519 - acc: 0.7412 - val_loss: 0.5938 - val_acc: 0.6925\n",
            "\n",
            "Epoch 00002: val_loss improved from 0.65544 to 0.59380, saving model to best_model.h5\n",
            "Epoch 3/30\n",
            "25/25 [==============================] - 0s 12ms/step - loss: 0.4780 - acc: 0.8156 - val_loss: 0.5581 - val_acc: 0.7262\n",
            "\n",
            "Epoch 00003: val_loss improved from 0.59380 to 0.55807, saving model to best_model.h5\n",
            "Epoch 4/30\n",
            "25/25 [==============================] - 0s 11ms/step - loss: 0.4153 - acc: 0.8678 - val_loss: 0.5294 - val_acc: 0.7462\n",
            "\n",
            "Epoch 00004: val_loss improved from 0.55807 to 0.52939, saving model to best_model.h5\n",
            "Epoch 5/30\n",
            "25/25 [==============================] - 0s 9ms/step - loss: 0.3653 - acc: 0.8919 - val_loss: 0.5009 - val_acc: 0.7563\n",
            "\n",
            "Epoch 00005: val_loss improved from 0.52939 to 0.50094, saving model to best_model.h5\n",
            "Epoch 6/30\n",
            "25/25 [==============================] - 0s 9ms/step - loss: 0.3210 - acc: 0.9169 - val_loss: 0.4825 - val_acc: 0.7625\n",
            "\n",
            "Epoch 00006: val_loss improved from 0.50094 to 0.48253, saving model to best_model.h5\n",
            "Epoch 7/30\n",
            "25/25 [==============================] - 0s 9ms/step - loss: 0.2876 - acc: 0.9312 - val_loss: 0.4786 - val_acc: 0.7575\n",
            "\n",
            "Epoch 00007: val_loss improved from 0.48253 to 0.47862, saving model to best_model.h5\n",
            "Epoch 8/30\n",
            "25/25 [==============================] - 0s 9ms/step - loss: 0.2636 - acc: 0.9334 - val_loss: 0.4730 - val_acc: 0.7675\n",
            "\n",
            "Epoch 00008: val_loss improved from 0.47862 to 0.47299, saving model to best_model.h5\n",
            "Epoch 9/30\n",
            "25/25 [==============================] - 0s 9ms/step - loss: 0.2311 - acc: 0.9494 - val_loss: 0.4538 - val_acc: 0.7750\n",
            "\n",
            "Epoch 00009: val_loss improved from 0.47299 to 0.45380, saving model to best_model.h5\n",
            "Epoch 10/30\n",
            "25/25 [==============================] - 0s 9ms/step - loss: 0.1971 - acc: 0.9728 - val_loss: 0.4562 - val_acc: 0.7725\n",
            "\n",
            "Epoch 00010: val_loss did not improve from 0.45380\n",
            "Epoch 11/30\n",
            "25/25 [==============================] - 0s 9ms/step - loss: 0.1841 - acc: 0.9734 - val_loss: 0.4513 - val_acc: 0.7750\n",
            "\n",
            "Epoch 00011: val_loss improved from 0.45380 to 0.45133, saving model to best_model.h5\n",
            "Epoch 12/30\n",
            "25/25 [==============================] - 0s 9ms/step - loss: 0.1545 - acc: 0.9881 - val_loss: 0.4356 - val_acc: 0.7775\n",
            "\n",
            "Epoch 00012: val_loss improved from 0.45133 to 0.43560, saving model to best_model.h5\n",
            "Epoch 13/30\n",
            "25/25 [==============================] - 0s 9ms/step - loss: 0.1367 - acc: 0.9947 - val_loss: 0.4350 - val_acc: 0.7788\n",
            "\n",
            "Epoch 00013: val_loss improved from 0.43560 to 0.43497, saving model to best_model.h5\n",
            "Epoch 14/30\n",
            "25/25 [==============================] - 0s 11ms/step - loss: 0.1212 - acc: 0.9978 - val_loss: 0.4326 - val_acc: 0.7788\n",
            "\n",
            "Epoch 00014: val_loss improved from 0.43497 to 0.43257, saving model to best_model.h5\n",
            "Epoch 15/30\n",
            "25/25 [==============================] - 0s 10ms/step - loss: 0.1079 - acc: 0.9978 - val_loss: 0.4383 - val_acc: 0.7887\n",
            "\n",
            "Epoch 00015: val_loss did not improve from 0.43257\n",
            "Epoch 16/30\n",
            "25/25 [==============================] - 0s 9ms/step - loss: 0.0955 - acc: 0.9991 - val_loss: 0.4314 - val_acc: 0.7837\n",
            "\n",
            "Epoch 00016: val_loss improved from 0.43257 to 0.43144, saving model to best_model.h5\n",
            "Epoch 17/30\n",
            "25/25 [==============================] - 0s 9ms/step - loss: 0.0852 - acc: 1.0000 - val_loss: 0.4338 - val_acc: 0.7850\n",
            "\n",
            "Epoch 00017: val_loss did not improve from 0.43144\n",
            "Epoch 18/30\n",
            "25/25 [==============================] - 0s 11ms/step - loss: 0.0757 - acc: 0.9997 - val_loss: 0.4368 - val_acc: 0.7887\n",
            "\n",
            "Epoch 00018: val_loss did not improve from 0.43144\n",
            "Epoch 19/30\n",
            "25/25 [==============================] - 0s 9ms/step - loss: 0.0678 - acc: 1.0000 - val_loss: 0.4389 - val_acc: 0.7900\n",
            "\n",
            "Epoch 00019: val_loss did not improve from 0.43144\n",
            "Epoch 20/30\n",
            "25/25 [==============================] - 0s 9ms/step - loss: 0.0611 - acc: 1.0000 - val_loss: 0.4371 - val_acc: 0.7850\n",
            "\n",
            "Epoch 00020: val_loss did not improve from 0.43144\n",
            "Epoch 21/30\n",
            "25/25 [==============================] - 0s 10ms/step - loss: 0.0559 - acc: 1.0000 - val_loss: 0.4377 - val_acc: 0.7862\n",
            "\n",
            "Epoch 00021: val_loss did not improve from 0.43144\n",
            "Epoch 00021: early stopping\n",
            "Model: \"sequential_6\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding_4 (Embedding)      (None, 100, 100)          3516900   \n",
            "_________________________________________________________________\n",
            "conv1d_1 (Conv1D)            (None, 96, 128)           64128     \n",
            "_________________________________________________________________\n",
            "global_max_pooling1d_1 (Glob (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_10 (Dense)             (None, 1)                 129       \n",
            "=================================================================\n",
            "Total params: 3,581,157\n",
            "Trainable params: 64,257\n",
            "Non-trainable params: 3,516,900\n",
            "_________________________________________________________________\n",
            "None\n",
            "125/125 [==============================] - 0s 3ms/step - loss: 0.1271 - acc: 0.9572\n",
            "32/32 [==============================] - 0s 3ms/step - loss: 0.4185 - acc: 0.8090\n",
            "Train Accuracy: 0.9572499990463257\n",
            "Test Accuracy: 0.8090000152587891\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 700x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 700x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AsJ9ilZ6oghf"
      },
      "source": [
        "### RNN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RF-2_TQuoghf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "72461fe5-c384-46fd-c272-81e30872112b"
      },
      "source": [
        "model = Sequential()\n",
        "embedding_layer = Embedding(vocab_size, 100, weights=[embedding_matrix], input_length=maxlen, trainable=False)\n",
        "model.add(embedding_layer)\n",
        "model.add(SimpleRNN(128, dropout=0.2, recurrent_dropout=0.2))\n",
        "model.add(Dense(1, activation='sigmoid'))\n",
        "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['acc'])\n",
        "print(model.summary())\n",
        "\n",
        "es = EarlyStopping(monitor='val_loss', mode='min', patience=5, verbose=1)\n",
        "mc = ModelCheckpoint('best_model.h5', monitor='val_loss', mode='min', verbose=1, save_best_only=True)\n",
        "\n",
        "history = model.fit(X_train, y_train, batch_size=128, epochs=30, verbose=1, callbacks=[es, mc], validation_split=0.2)\n",
        "print(model.summary())\n",
        "score_train = model.evaluate(X_train, y_train, verbose=1)\n",
        "score_test = model.evaluate(X_test, y_test, verbose=1)\n",
        "\n",
        "# results\n",
        "print (\"Train Accuracy:\", score_train[1])\n",
        "print(\"Test Accuracy:\", score_test[1])\n",
        "plot_results(history)"
      ],
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_7\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding_5 (Embedding)      (None, 100, 100)          3516900   \n",
            "_________________________________________________________________\n",
            "simple_rnn (SimpleRNN)       (None, 128)               29312     \n",
            "_________________________________________________________________\n",
            "dense_11 (Dense)             (None, 1)                 129       \n",
            "=================================================================\n",
            "Total params: 3,546,341\n",
            "Trainable params: 29,441\n",
            "Non-trainable params: 3,516,900\n",
            "_________________________________________________________________\n",
            "None\n",
            "Epoch 1/30\n",
            "25/25 [==============================] - 4s 107ms/step - loss: 0.7223 - acc: 0.5231 - val_loss: 0.6987 - val_acc: 0.5300\n",
            "\n",
            "Epoch 00001: val_loss improved from inf to 0.69874, saving model to best_model.h5\n",
            "Epoch 2/30\n",
            "25/25 [==============================] - 2s 96ms/step - loss: 0.6837 - acc: 0.5584 - val_loss: 0.7007 - val_acc: 0.5325\n",
            "\n",
            "Epoch 00002: val_loss did not improve from 0.69874\n",
            "Epoch 3/30\n",
            "25/25 [==============================] - 2s 96ms/step - loss: 0.6721 - acc: 0.5791 - val_loss: 0.6783 - val_acc: 0.5962\n",
            "\n",
            "Epoch 00003: val_loss improved from 0.69874 to 0.67826, saving model to best_model.h5\n",
            "Epoch 4/30\n",
            "25/25 [==============================] - 2s 95ms/step - loss: 0.6613 - acc: 0.5975 - val_loss: 0.6773 - val_acc: 0.6050\n",
            "\n",
            "Epoch 00004: val_loss improved from 0.67826 to 0.67731, saving model to best_model.h5\n",
            "Epoch 5/30\n",
            "25/25 [==============================] - 2s 98ms/step - loss: 0.6538 - acc: 0.6078 - val_loss: 0.7253 - val_acc: 0.5375\n",
            "\n",
            "Epoch 00005: val_loss did not improve from 0.67731\n",
            "Epoch 6/30\n",
            "25/25 [==============================] - 2s 96ms/step - loss: 0.6494 - acc: 0.6187 - val_loss: 0.6729 - val_acc: 0.6137\n",
            "\n",
            "Epoch 00006: val_loss improved from 0.67731 to 0.67294, saving model to best_model.h5\n",
            "Epoch 7/30\n",
            "25/25 [==============================] - 2s 97ms/step - loss: 0.6368 - acc: 0.6294 - val_loss: 0.6897 - val_acc: 0.5900\n",
            "\n",
            "Epoch 00007: val_loss did not improve from 0.67294\n",
            "Epoch 8/30\n",
            "25/25 [==============================] - 2s 96ms/step - loss: 0.6331 - acc: 0.6450 - val_loss: 0.6592 - val_acc: 0.6413\n",
            "\n",
            "Epoch 00008: val_loss improved from 0.67294 to 0.65919, saving model to best_model.h5\n",
            "Epoch 9/30\n",
            "25/25 [==============================] - 2s 97ms/step - loss: 0.6379 - acc: 0.6316 - val_loss: 0.7267 - val_acc: 0.5200\n",
            "\n",
            "Epoch 00009: val_loss did not improve from 0.65919\n",
            "Epoch 10/30\n",
            "25/25 [==============================] - 2s 99ms/step - loss: 0.6698 - acc: 0.5797 - val_loss: 0.7017 - val_acc: 0.5412\n",
            "\n",
            "Epoch 00010: val_loss did not improve from 0.65919\n",
            "Epoch 11/30\n",
            "25/25 [==============================] - 2s 96ms/step - loss: 0.6540 - acc: 0.6066 - val_loss: 0.6904 - val_acc: 0.5800\n",
            "\n",
            "Epoch 00011: val_loss did not improve from 0.65919\n",
            "Epoch 12/30\n",
            "25/25 [==============================] - 2s 96ms/step - loss: 0.6495 - acc: 0.6084 - val_loss: 0.7006 - val_acc: 0.5587\n",
            "\n",
            "Epoch 00012: val_loss did not improve from 0.65919\n",
            "Epoch 13/30\n",
            "25/25 [==============================] - 2s 96ms/step - loss: 0.6380 - acc: 0.6175 - val_loss: 0.6776 - val_acc: 0.5925\n",
            "\n",
            "Epoch 00013: val_loss did not improve from 0.65919\n",
            "Epoch 00013: early stopping\n",
            "Model: \"sequential_7\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding_5 (Embedding)      (None, 100, 100)          3516900   \n",
            "_________________________________________________________________\n",
            "simple_rnn (SimpleRNN)       (None, 128)               29312     \n",
            "_________________________________________________________________\n",
            "dense_11 (Dense)             (None, 1)                 129       \n",
            "=================================================================\n",
            "Total params: 3,546,341\n",
            "Trainable params: 29,441\n",
            "Non-trainable params: 3,516,900\n",
            "_________________________________________________________________\n",
            "None\n",
            "125/125 [==============================] - 2s 11ms/step - loss: 0.6152 - acc: 0.6572\n",
            "32/32 [==============================] - 0s 11ms/step - loss: 0.6653 - acc: 0.6020\n",
            "Train Accuracy: 0.6572499871253967\n",
            "Test Accuracy: 0.6019999980926514\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 700x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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PBwq9HaXZzXSUNjb6SnDFw9Z50gUtYkqc3QEopc4HHgCuAhYCNwCfKKX6a60LmnjL2UCC39edgRXAG96vU4ARwJ3e1zsCM4H3gFGh+AzUVsLdbcsLFXCQmRwH98ed5jfPVsjIyCAhIYGUlBS6du0KwF133cURRxzB3Xff7Tvv2WefpVevXmzYsIHy8nLq6uo4++yzOeSQQwAYMmSI79zk5GSqq6t91xMO0tT8NouvQUH+MRRhUrwF6qrAndByR6m/9G4w5BxY8QrMewTOfS6kIQoRKSKh4nYj8JTW+jmt9RpMAlcJXN7UyVrrvVrr3dYDOMl7/hve75dorU/SWr+utV6vtV4AXAuMVEr1DssncogVK1bw5ZdfkpaW5nsMGGCGKTZt2sSwYcM44YQTGDJkCOeeey5PPfUU+/btszlqEbDqMti90hz3aiJx6z4CUFC8FcoLwxqaiFGF681zVp5ZT7C1rKVB1rxr/r4KEQNsrbgppRKAkcA91mtaa49S6nOgiTGcJl0BvKq1bmlBnwxAA8XNxJEIJPq91KGV9zbiU0z1q408Hs3a3WV4tKZfThpJ8S20wDd33wCUl5dz+umn87e//e0n3+vWrRtut5vPPvuMefPm8emnn/Lwww/zpz/9iYULF9KnT5+A7i1stH0JaA9k9IaMHj/9flK66ewrXGeGS/ufGv4YRWwpXGueD9ZR2ljXIdBnAvz4NSx8Ak75a/BjEyLC2F1xywLcQH6j1/OBg46/eefCHQ483cI5ScDfgFe01qXNnPYHoMTvsf2gkR94EzNk2caHKymNlLR0dHwKpZ6Etl9DqTaFmZCQQH19Q+v8iBEjWL16Nbm5ufTt2/eAR2pqqvXnx9FHH83tt9/OsmXLSEhI4O23327yesIhmlq/rTFrWRBpUBDhYFXcWju/zZ+1DdbSF2B/SfBiEiJC2Z24BeoKYJXWelFT31RKxQOvY6aUXd3Cde7BVOWsR88gx9msDmFcFiQ3N5eFCxeyefNmioqKmD59Onv37uXCCy9k8eLFbNq0iU8++YTLLruM+vp6Fi5cyN13382SJUvYunUrb731FoWFhQwcONB3vZUrV7J+/XqKioqora0N+WcQQdDS/DZLjxHmWRoURDgUtLPiBtD3RNOJWlMG3/47uHEJEYHsTtyKgHqgS6PXuwAt7iCslEoFLgCeaeb7VtJ2CHBSC9U2tNbVWutS6wGUtf4jBMZK3Cqr66nzeEJ6rxkzZuB2uxk0aBDZ2dnU1NQwd+5c6uvrOfnkkxkyZAg33HADmZmZuFwu0tPTmT17NpMnTyYvL48///nP3H///Zx6qhk6mzZtGv3792fUqFFkZ2czd+7ckMYvgqC+rqGK1lRHqcXXoPCt6fgTIlQ8noPvUdoSpRrmui34F9TLL5Aiutk6x01rXaOUWgqcALwDoJRyeb9+5CBvPxczL+2lxt/wS9r6AcdprfcEM+5gSoxzkxjnprqunvL9dWSmJBz8Te2Ul5fH/Pnzf/L6W2+91eT5AwcO5OOPP272etnZ2Xz66adBi0+EQf4qqK2ApIyW18vKGWw2n68uhT3fQ3Ze+GIUsaVkq+nMdyeYrazaY8h58MUdULrdNCoMOSe4MQoRQeyuuIFZCmSaUupSpdRA4HEgFXgOwLsG2z1NvO8K4J3GSZk3aXsTs/THxYBbKdXV+whdVhQA2UVBhI01v63XGHC18L+/Ow66DzfHMlwqQsma39a5X9s6Sv3FJ8GR08zx/EfatMalEE5je+KmtX4NmAHcASwHhgOTtNZWw0JvoJv/e5RS/YHxND1M2gM4AzNPbTmwy+8RkSvE+s9z0/IDR4RSa+a3WWQHBREOgcxv83fkFRCXBDuXwZZ5gcclRISyfQFeAK31IzQzNKq1ntjEa+sxDQdNnb+5ue9FqpTEONxKUefxUFVbT0pCRPxnEdFG65Z3TGjMN89NEjcRQlbFrT3z2/ylZsGwC2Hpc6bqlnt04LEJEYFsr7gJcClFmgyXilDb9yOU55u5RN1HHPx8a0mQ/NVQWxXa2ETsau8abk0Ze415Xv8RFH0f+PWEiECSuLVTsIc0rU3nYylxk2HhMNu60Dx3P8LMCTqYjJ6QmgOeOti1IrSxidjUnj1KW5KdB3mTAA0LHgv8ekJEIEnc2ig+3iRYlZWVQb2ub1mQmjpq62Nj+QXrz9D6MxUh1pb5bWCWWegpC/GKECrZZrqcXfHQKUi7sYy71jwv/w9U7g3ONYWIIDKZqo3cbjeZmZkUFBQAkJKSgmrjDgbNSaCe6rp69paUkxHCZUHsprWmsrKSgoICMjMzcbvbuNWXaB9fR2krEzcwDQrrP5QN50Vo+PYo7QfuIP0Clzseug0zVeLFz8CEm4JzXSEihCRu7dC1q9mNy0regqW0qpbS/XWUF7nplBq9iZslMzPT92cpQqxiDxR5/5HsNab175MGBRFKwZzfZlHKVN3emgaLnoSjr4e4xIO/TwiHkMStHZRSdOvWjZycnKBu87R6Rwm3fbSM1MQ43rr6KOLc0TuSHR8fL5W2cNrmnd+W1R9SO7f+fd1HAAqKt0J5IaRlhyQ8EaMC2aO0JYOnwOe3QekOWPUGHPGL4F5fCBtJ4hYAt9sd1ORjeJ9EKutd7CiqZnV+FWMObcM/sEK0pK3z2yxJ6aYaUrjOVN36nxr82ETsCtYabo2542HMlfDZX2D+ozD8YlOJEyIKRG9Jx4HcLsWEPFPR+HJ9oc3RiKhiVdxas35bY7IQrwgFrYO3hltTRlwKCWlQsAY2fRH86wthE0ncIsxxA3IA+HJdcOfPiRhWW2U2i4e2V9ygIXGTeW4imA7oKD00+NdPzoQjLjHH8x8N/vWFsIkkbhFmQl42LgXr88vYUSyLnoog2LkMPLWQ1gU65rb9/b4GhW/NultCBINvj9K+wesobWzsVaBcsGmWWUhaiCggiVuEyUxJYETvjoBU3USQ+M9va888n5zBEJcM1aWwR1ajF0ESqvlt/jrmwsAzzLFU3USUkMQtAlnDpV+tl8RNBEFb9idtijsOug83xzJcKoIllPPb/B11nXle+TqU7Q7tvYQIA0ncItBx/U3iNvf7Peyvrbc5GuFoHk/DVlftmd9mkQYFEWyhWMOtKT1HmbULPbWw6KnQ3kuIMJDELQIN7NaBrulJVNXWs/BH2bJFBKBwLVSXQHwqdBnS/uvIQrwimPw7SoO9hltTrG2wljwDNRWhv58QISSJWwRSSnHcAO+yIDLPTQTCt83VkWbIs72silv+atOlKkQgSrZDTTm44kLTUdrYgNOgYx+o2mf2MBXCwSRxi1ATvcOls9YVoLW2ORrhWIHOb7Nk9ILUHPDUmT0ghQiEf0dpXBi293O5Yew15njBY+CRKSjCuSRxi1Dj+2YR71Zs3VvJD0VS2hft5Ku4tWF/0qYo1TBcKvPcRKDCNb/N3/CLICkD9v4AGz4O332FCDJJ3CJUamIcY/qYLa9kuFS0S8l2KNkKyt2QdAXCtxDv0sCvJWJb4TrzHI75bZbENBh1uTme90j47itEkEniFsF8uyjIsiCiPaxqW9chkNgh8OtJg4IIlgIrcQtjxQ1g9JVmp4at8+QXEOFYkrhFsOP6mwaFRT/upby6zuZohOMEa36bpfsIQEHxViiXvXRFO4V6j9KWpHeDIeeYY6m6CYeSxC2CHZqdRm7nFGrrNXM2FtkdjnCabVbiFsD6bf6S0hsqJFJ1E+1VugNqyrwdpYeF//7jppvnNe+aX0KEcBhJ3CKc1V0quyiINtlf0rA3Y7ASN5CFeEXgrPltnQ4LT0dpY12HQJ8JoOth4RPhv78QAZLELcId7zfPTZYFEa22fTFoj9mrsUPX4F3X16AgiZtoJ2t+W84A+2KwtsFa+oL5JUcIB5HELcKN7tOJ5Hg3+aXVrNlVanc4wimCPb/N4mtQ+NZspyVEW/k6Sm1M3PqeaO5fUwbf/tu+OIRoB0ncIlxSvJuj+2YB8NV6mRAuWmlrkOe3WXIGQ1wyVJfCnu+De20RGyIhcVPKb0Hef0G9NH8J55DEzQGs7a9myXpuojXqaxvmoAW74uaOg+7DzbEMl4q2OmCPUhsTN4Ch50NqNpRuhzXv2BuLEG0giZsDWA0Ky7buY19Fjc3RiIi3ayXUVUFyJ8jKC/71pUFBtFfpTlOtVW6z3ZWd4pPgyGnmeP4jJqkUwgEkcXOAHpnJDOjaAY+G2RtluFQcxNb55rn3WDMkFGzSoCDayxom7WxTR2ljR14BcUmwcxlsmWd3NEK0iiRuDmFV3WT7K3FQVuIW6P6kzbEaFPJXQ21VaO4holMkzG/zl5oFwy4wx/MftTcWIVpJEjeHsJYF+XpDIfUeKemLZmgduo5SS0YvSM0BTx3sWhGae4joFGmJG8BY74K86z+EPZvsjUWIVpDEzSFG9M4kPSmOfZW1LN9WbHc4IlLt2QSVReBObGgiCDal/JYFkf0eRRtEwhpujWXnQd4kQEvVTTiCJG4OEed2cWye6S6V4VLRLGuYtMdIiEsM3X2kQUG0VSR1lDY27lrzvPw/ULnX3liEOAhJ3BzkuP4NuygI0aRQrd/WmK/iJombaKWyXVBdEhkdpY3ljoeuQ0039uJn7I5GiBZJ4uYgE/pnoxSs3llKful+u8MRkWhbiOe3WbofASizSXe5dDqLVvDtUXpoaKvB7aFUwzZYi56Eump74xGiBZK4OUhWWiJDe2YCsum8aEJ5YcNuBr2ODO29kjIa1oiTqptojUic3+Zv8BRI7wEVBbDqDbujEaJZkrg5zPHe4VLZRUH8hFVtyxkEyR1Dfz9ruFTmuYnWiMSOUn/ueBhzpTme/6gsyCsiliRuDmNtfzVnYxE1dbLJt/ATrvltFlmIV7RFpCduACMuhYQ0KFgDm76wOxohmiSJm8Mc3j2DrLREKmrqWbxZup+EH9+OCSGe32bxNSgsA4/8EiFaoLUzErfkTDjiEnMsS4OICCWJm8O4XIqJ/WVZENFITWXDYrjhqrjlDIa4ZNMpaM2tE6IpZbthfwkoF2T1szualo29ysS5aZbZHUSICCOJmwNZuyjMkgYFYdmx1OxkkN7D7GwQDu64hkV+ZbhUtCSSO0ob65gLA88wx1J1ExFIEjcHGt8viziX4ofCCrbsqbA7HBEJrPltvcaEZmP55shCvKI1nDBM6s9akHfl66ZaKEQEkcTNgdKT4hmVa7oGZbhUAOGf32aRBgXRGk5L3HodaX4J8tTCoqfsjkaIA0ji5lANuyjI4qcxz1MP2xaZ43DNb7NYDQr5q6G2Krz3Fs7hW8NtoL1xtIVVdVvyDNTIyIaIHJK4OZQ1z23+D3uoqqm3ORphq/zVUFMGCR2gy+Dw3jujF6TmmPl1VnOEEP4O6Cjtb28sbTHgNDPfrWqf2cNUiAghiZtD9c1Jo0dmMjV1HuZtKrI7HGEn3/y20eByh/feSvktC7I0vPcWzlCeD/uLTadm5wjvKPXncsPYa8zxgsdMZVuICCCJm0MppXyL8cqm8zEuXPuTNkcaFERLrGpbxz4Qn2RvLG01/GKzvdveH2DDx3ZHIwQgiZujWcOlX64rRMv2LLFJa9hiNSaEeX6bxVdxk8RNNMGJ89ssiWkw6nJzPO8Re2MRwksSNwcbd2gWiXEudhRXsbGg3O5whB1KtkHZTnDFNVS+wq37EYCC4q1mo3sh/Dlxfpu/0VeCKx62zpPpACIiSOLmYMkJbsYd1hmQTedjljW/rdswSEixJ4akDMjKM8dSdRON+RI3B1bcANK7weE/N8dSdRMRQBI3h/MtCyKJW2yya/22xqzhUpnnJvxpDQVrzbFTK24AR3mXBlnzrqksC2EjSdwczkrclmzZR0lVrc3RiLCzKm52zW+zyEK8oinlBQ0dpZG+R2lLug6BPhNA18PCJ+yORsQ4SdwcrnfnFA7LTqXeo5mzUZYFiSlV+xqqGb1sTtx8DQrLwOOxNxYROXwdpbkQn2xrKAE76jrzvPQF2F9ibywipkniFgV8m87LcGls2bYY0NC5L6Rl2xtLzmCIS4bqEtjzvb2xiMjh9Plt/g47AbL6m8Wuv/233dGIGCaJWxSwhku/3lCAxyPLgsQMa36b3dU2AHccdB9ujp/ATXkAACAASURBVGW4VFic3lHqz+WCcdPN8YJ/QX2dvfGImCWJWxQYlduJtMQ4isprWLVDSvgxI1Lmt1lkIV7RmJPXcGvK0PMhNRtKt8Oad+yORsQoSdyiQEKci/F9swDZRSFm1FU3rClld0epRRoUhD+toTAKOkr9xSfBkdPM8fxHzGcUIswkcYsSDbsoSOIWE3Yuh/pqSMmCzofZHY1hNSjkr4baKntjEfarKDQNNKiGdf6iwZFXQFwS7FwGW+bZHY2IQZK4RYmJ/c3k9BXbSygsq7Y5GhFyW/22uVLK3lgsGb0gNQc8dbBrhd3RCLtFU0epv9QsGHaBOZ7/qL2xiJgkiVuUyElP4vAe6QB8vUG2HYp62xaa50gZJgWTQPqWBZGtgWJetM1v8zfW26Sw/kPYs8neWETMkcQtivh2UZB5btHN44m8xgSLNCgISzR1lDaWnQd5kwANs+6UtQtFWEniFkWO885zm72hkNp6+UEStfZshKq9Zt20rkPtjuZA0qAgLNG0hltTxt9odoRY/TZ8cKMkbyJsJHGLIsN6ZtIpNYGy/XV8u2Wf3eGIULHmt/UcBXEJ9sbSWI8RgDL7OZbLkH1Mi+aKG0DvMXDWvwAFS5+DD2dIl6kIi4hI3JRS05VSm5VS+5VSC5VSo1s49yullG7i8YHfOUopdYdSapdSqkop9blSysEb5bWO26WYkGeaFGYFOlyqNZTtlu7ASBSpw6QASRkNHYRSdYtd5YVQuYeo6yhtbNj5cNbjgIIlz8CHN0nyJkLO9sRNKXU+8ABwOzACWAF8opTKaeYtZwPd/B6HA/XAG37n3AxcD1wFjAEqvNdMCsVniCRWd+lX69pQ7ajcC5vnwKKn4P3fwrOT4G+HwP394cnjoF42r48o/h2lkchqUJB5brHL11F6CCSk2BtLqA2/EM58FFCw+Cn4+BZJ3tpifwl8eDMsfNLuSBwjzu4AgBuBp7TWzwEopa4CTgMuB+5tfLLWeq//10qpC4BKvImbUkoBNwB3aa3f9b72SyAfOAt4NWSfJAJMyMvGpWB9fhk7iqvokenXhl9dDoXroWCN2Zzcei7f3fwFC9fCildgxC9DH7w4uLLdsG+zmVvTs9nCtL16jITlL0vFLZZF+/y2xo64GLQH3rsWFnqHTyfdEzlL9USqst3w0jmQv8p8nTseugyyNyYHsDVxU0olACOBe6zXtNYepdTnQGvXObgCeFVrXeH9ug/QFfjc75olSqmF3mv+JHFTSiUCiX4vdWjL54gkmSkJjO6Vxr5ta/hx1vP0yChsSNL2bW7hjb3NRuE5AyFnkHne+Cl8cTvM/gcMuxDc8WH7HKIZ1jBpzmBISrc3lub4lgRZZiZsu2wv7Itwi/b5bU0ZcYlJ3v53PSx83PxydcpfJXlrzp5N8OIUKN7S8No398E5z9oXk0PYXXHLAtyYapi/fGDAwd7snQt3OCZ5s3T1u0bja3alaX8Abj3Y/SKOp94kYwVrD6igvVy0AXdiPaxs4j2pOeY3Gis5yxlkfrgmNpGrdjoUFjxmJppL1S0yRPL8NkvOINPxWl0Ce743SyeI2BLNa7i1ZOSlJnl7/wZY8Kj5peWkOyV5a2zHt/DyuVBZBB37wEl3wOuXwHdvwcQ/QFbUT0kPiN2JW6CuAFZprRcFeJ17MPPsLB2A7QFeM3i0hrJdfkOcVpK2Dup+2jzgBkp1MhvpzbAR44jralXSBppVv1srIQWOvgE+/ZNU3SJFpM9vA/N3pNsw2LbADJdK4hZ7YrHiZhl1mUnePrgR5j1sKm8n3i7Jm2XTLHjtEqgpNz8nLn4T0nIg71TY8BF8cz9M+ZfdUUY0uxO3IkxjQZdGr3cBWph4BUqpVOAC4C+NvmW9rwuwq9E1lzd1La11NeDbJ0rZ+T9Y5d4D558VrDGP/SVNn+9OND8cuzQMc+rsAZz86Hp2l1XzwsDRvk7Tdhl1Ocx9UKpukaC6DHZ7y6iRtGNCU3qOMonb9iUw/CK7oxHhVFFkKikoyIrBxA3MfqbaY5YImTvTJG8n3CrJ26o34e2rwFMLfSbA+S81TPmYcJNJ3Fa+DhN+D5362BtrBLM1cdNa1yillgInAO8AKKVc3q8fOcjbz8XMS3up0es/YpK3E/AmakqpdEx36eNBCz5QNRXmt9KCtZC/5uCNAsptNhPPaTTM2akPuNwHngocN3AfryzaxpfrCgJL3KTqFjm2LzH/GGT0howedkfTMlmIN3ZZ1bbM3tHfUdqS0dPMaMlHN8Gcf5rk7fj/i93kbcHjpuMWYPAUmPIExPlNLe8xEg47ATZ9AXMegDMetidOB7C74gZmiPIFpdQSYBGmIzQVsLpM/w3s0Fr/odH7rgDe0Vrv8X9Ra62VUg8Cf1ZKbcQkcncCO/Emh7bb8An853ygmZbxzN4HJmc5A6FzP4hv/WomE/vn8MqibcxaV8Ctpw8KrIooVbfI4NufNIKHSS1Wg0L+arMWYDRtMi5aVrDWPMfa/LamjPm1+WXr49+bIUDlhuP/ZHdU4aU1fHGHScYARl8Jk+5tumlpws0mcVv+Chx7M2T2Cm+sDmF74qa1fk0plQ3cgWkeWA5M0lpbzQW9gQP2ElFK9QfGAyc3c9m/Y5K/J4FMYI73mvuD/wnaoWMfQJtGAf/kzGoUCEK34Pi+WcS7FVv3VvJDUQWHZae1/2JSdYsMvvltY+yNozUyepm/3xUFsGulM2IWwVG43jzH4vy2poy9CnQ9fPJHmP13M0Iy8Ra7owqP+jp4/zewzDswdvz/wTG/a77q2Hss5B4Dm78xxYLT7g9frA5ie+IGoLV+hGaGRrXWE5t4bT1mRLC562nM3LfG898iQ+fD4KZNbWsUaKPUxDjG9OnMnO+L+HJdQWCJG0jVzW71dbBtsTmO9PltYH4w9xwF6z80w6WSuMWOWFvDrTXGTTeVp0//BF/dAyiY+Hu7owqtmkp48zLY8LEZJv7Zg6br9mAm3GwSt29fhGNmQHq30MfqMLLAkh1c7pAmbRZr0/kvA93+ChqqbmCqbrKbQnjlr4LaCkjMcM4/iNY8N9lBIbbEckdpS4661iwNAvDV3fD1P+yNJ5Qq98KLZ5mkLS4Jzn+5dUkbmIpbr7FQXw3zHgptnA4liVsUO867/dWiH/dSXl0X+AVHXQ6p2Q1VNxE+vvXbxjhnQVtpUIg9FXugwrvdniRuP3X09XDibeb4y7vMvLdoU7IDnjvVzMlNyoBL3oEBk1v/fqVMhynAkufMvrfiAA75F0C0x6HZaeR2TqG2XjNnY1HgF5Sqm32csH5bYz1GAMok+vLDNzYc0FGaam8skWr8b+EE7yyeL+4wHafRonA9PHOy+XvQoRtc9jEc0o6pHYedAN1HmHVK50t3aWOSuEW5if3NcOlXwRguBW/VLUeqbuGktV/FzQHz2yxJGZDlXXxXqm6xodDbUeqU4Xy7HPM7OP7P5vjz28xab063bRE8ewqUbjerIFzxafv3HVXKzHUDWPS0GXoVPpK4Rbnj/ea5mZ6NACWkwHi/qltdTeDXFC3btxnK88GdYH4LdRJrWRCZ5xYbpKO09Y69CY7zLg3y2V/MLgtOteETeOEMqNoHPUbB5Z+Yqmsg8iZB1yFmbu+Cx4ITZ5SQxC3Kje7TieR4N/ml1azZVRqci468TKpu4WRV27oNb9NafhHBN89tqb1xiPCQNdzaZsLNMMG7NMinf4b5j9obT3ss/w+8cqEZ1ux7Elz6HqR2Dvy6SpnkFmDhE1BVHPg1o4QkblEuKd7N0X1NB+uX64I0XOpfdfvmPqm6hZoT57dZrIrbjm/B42n5XOF8UnFru4m3mMVmwaz1tsAh+3RqDXMehHeuNuvUDb0ALnwluHMbB5xuht2rS2HRU8G7rsNJ4hYDjhtguku/XB/ECeJSdQsfJ85vs+QMMssBVJfAnu/tjkaEUuVes+AyxO4epe2hFBz3R7NmGZhdFhY+aW9MB+PxmArh57ear4+6Ds56PPgLs7tccKz3z2XBo2a/ZiGJWyw4ztugsGzrPvZVBKk6JlW38KjYA0XeKkYvBy5i6443Q7wgDQrRzuoozegNiQEu+B1rlDLNCuN/a77+6KbIrTDV1cDbV8J875r5J90JJ98VumWKBk+Bzn3N/LnFz4TmHg4jiVsM6J6ZzICuHfBomL1Rqm6OYu1PmtU/OPNG7CANCrHBN79tgL1xOJVScMKtcPRvzNcfzoi8RKW6HF65AFa9Dq44s1H80deH9p4ut+nCBZMs1lSG9n4OIIlbjLCWBZkVrHluIFW3cNhmDZM6cH6bRRbijQ0yvy1wSsGJt5uhR4APbjSL0EaCiiJ44XSzCXx8Clz4Kgy7IDz3HnIuZB5iFnde+nx47hnBJHGLEdayIF9vKKTeE4RlQSxSdQutrVGQuFkVt/zVUFtlbywidGQNt+BQygw/jrvWfP3+DbD0BXtj2rfFrNG281tI7gSX/g/6nRS++7vj4ZgbzfHcmVC7P3z3jkCSuMWIEb0zSU+Ko7iyluXbgthWLVW30KmtMt2Y4OzELaOXSe49dbBrpd3RiFDxVdxkqDRgSpl5Y2OuNl//73qz6boddn9ndkPY8735f/nyTxp+GQunYRdBeg8o3w3LbPqziBCSuMWIOLeLY/O83aXBHC4FqbqFys5l4KmFtC7QsY/d0bSfUn7LgshwaVSq3GsWiQbIzrM3lmihFEy6B0Zfab5+7zpY9nJ4Y9g8F56bbJKlnEFmNwS7/vvGJTRsuTjnwZguEkjiFkOs7tIvg7X9lUWqbqHhv36bUvbGEqge3h0fpEEhOlnVtoxekNjB3liiiVJw6t/gyGmAhnenw/Iw/XK89n14cYpZyqf3OLjsQ0jvHp57N2fEJeYX2dLtMV0kkMQthkzon41SsHpnKfmlQZ4jIFW34HPy+m2N9ZCKW1TzzW+TYdKgUwom/wNGXQFos+DtitdCe88lz8Hrl0B9NfSfDJe8DckdQ3vP1ohPhqO8XaxzHoD6OnvjsYkkbjEkKy2RoT0zgSBuOm9JSGlYg0iqboHzeBqWAnHy/DZLjxGAMol9eRCXpBGRQTpKQ0spmHyf+QUZDe9cBSvfCP59tIav/24aIrQHRvwSznvRJEyRYtRlkNLZ7OG8KgR/Bg4giVuMOT4Uy4JYRl1mytjFW2HFf4J//VhSuA72l0B8KnQZYnc0gUvKgCzv3BipukUf2aM09FwuOO0BGHGpSare/jWsejN41/fUw4c3wZd/NV8fexOc/hC444J3j2BISG3ouP3mPhN3jJHELcZY21/N2VhETV2Q946MT26YPDr7fqm6BcKa39ZzVOT94GwvWYg3eklHaXi4XPCzB+GIS0zy9tY0+O6/gV+3rhrevAwWPwUoOPXvZieHSJ1bO3oaJGWaTtfVb9sdTdhJ4hZjDu+eQVZaIhU19SzevDf4N7CqbiVSdQtINM1vs/gW4l1qbxwiuKr2ma5DkKHScHC5TCVs+C9M8vbfaYElL/tL4aWfw5p3wRUP5zwLY64MXryhkNgBxl5jjmffZ6aWxBBJ3GKMy6WY2D9Ey4KAVN2CJRoW3m3MtyTItzH3gzaqWdW29J7SURouLhec8bBZ20zXw5tXmMSrrcry4fnJsPkbSEiDX7wJh58d/HhDYcyVkJhuGmPWvW93NGEliVsMsnZRmBXsBgWLVN0CU7Ld/Nkptz0LXYZKziCISzLLC+z53u5oRLDIHqX2cLngzEdg6AXe5O1yWPu/1r9/zyZ49mTYvQpSs2HqB3DoxFBFG3zJmTD61+Z49j9MY0WMkMQtBo3vl0WcS/FDYQVb9lQE/wZSdQuMVW3rOiS6KhjueOg23BxLg0L0kPlt9nG54azHYMh5ZmeSN6bCug8O/r6dy80WVvs2Q8dcsxtC9+EhDjYExl5jGrh2r4QNn9gdTdhI4haD0pPiGZVr1uQJyXApSNUtENG0DEhj0qAQfWQNN3u53DDlX3D4OSZ5e/1SWP9R8+dv+hKeP81s2N51CFz+KXQ+LHzxBlNqZzjyCnM8++8xU3WTxC1GNeyiEKI1taTq1n7+OyZEG1+DgiRuUUMqbvZzuWHKEzD4bLNN3muXwPqPf3red2/By+dCTTnkHgNTP4QOXcIfbzAddR3EJZump02z7I4mLCRxi1HWPLf5P+yhsiZEq09L1a3t9pdA/mpz3CsKEzer4pa/Gmqr7I1FBK6qGMp2mWPpKLWXOw7OfgoGnWWSt9cvgQ2fNnx/4RNmHpynFgadCb/4LySl2xdvsKTlwMip5jhG5rpJ4haj+uak0SMzmZo6D/M37QnNTaTq1nbbF5sW/465kN7N7miCL6OX2RrNUwe7VtodjQiUr6O0R3QkAU7njoOfP20Ss/oaeO1i2PgZfHEnfHQzoM2+p+c8B3GJdkcbPEdfD+4EM1qxeY7d0YScJG4xSinV0F0aqnluIFW3torG9dv8KSXDpdFE5rdFHnc8/PwZGHi6Sd5ePtfsMABw3J/Nvqcut70xBlt6d7MoMZi5blFOErcYZu2i8NX6QnSoysvxyQ17mErV7eCicf22xnp6EzdpUHA+md8Wmdzx8PNnYcDPAA3KBafPhAk3Re5uCIEafwO44uDH2bB1od3RhJQkbjFs3KFZJMa52FFcxYb88tDdaORUqbq1Rn1tQzITjfPbLD2shXijNHGLgTk2PrKGW+SKSzBDoqfcA798r2EeWLTK7A3DLjTHUV51k8QthiUnuBl3WGcAvgzVYrwgVbfW2rUS6qoguWPDhuzRqMcIQEHxVigPUVezXb65H+7qYn7rjwVScYtscQkw7hroc4zdkYTHMTeahcu//zyqt9aTxC3GWcuChHSeGxxYdVv+cmjv5VTWMiC9xppV0aNVUkZDYhpNVbftS2DWXVBfDfMftTua0KsqhrKd5lg6SkUk6HQoDDnXHM++z95YQiiK/3UQrWElbku37KOkqjZ0N/Kvun0jVbcmRfP6bY1F20K8tfvhnatNRzCYTr5oqyY2VrTBPHfobpJxISLBMb8DFKz/0GznFYUkcYtxvTuncFh2KvUezTcbQ/wPja/qtk2qbo1pHf0dpf58naVRMpzx1d0mkUnNgZzBZu/I7960O6rQkvltIhJl58HgKeZ49j/sjSVEJHETvmVBvlwX4sRNqm7N27MJKovAnejMPQPbype4fQsej72xBGrbYpj3sDk+/UGzBA7AilfsiykcZH6biFTHzjDPa96DgnX2xhICkrgJ33Dp1xsK8HhC3BEnVbembfNW23qMjK6FMZvTZTDEJUF1Cez53u5o2q92P7x7jRkiHXIeDDjNbDvkioddKyB/jd0Rho6s4SYiVZfBDUuhfBN9c90kcROMyu1EWmIcReU1rNpREtqbSdWtab75bWPsjSNc3PHQzVtZdHKDwpd/NUOkaV3g1L+Z11I7Q94p5njlq/bFFmpScROR7NibzPN3/zUjGlFEEjdBQpyL8X2zgBAvC2KRqttPxdL8NovTGxS2LYb5j5jjnz0IKZ0avjfsAvO88nXw1Ic/tlDbXwKlO8yxdJSKSNR9OPQ7xVTDv7nf7miCShI3AfjPcwtD4iZVtwOVFzYMF/YabW8s4eTkra9qqxq6SIeeDwMmH/j9fieb9fjKdsGPX9sTYygVWh2l3SA5095YhGjOhJvN84pXYd8We2MJIkncBAAT+5vtr1ZsL6GwrDr0Nxw5FdK6StUNGua35Qwy/9jHCqvilr/aJEJO8uVfYc9GUzmedO9Pvx+XCIf/3ByviMLhUpnfJpyg5yg49DjT5T3nn3ZHEzSSuAkActKTOLxHOgBfbwjD+lNSdWsQC/uTNiWjF6Rmg6fO7BrhFNsWwTzvEOnpMw8cIvVnbb+z9n9QXRae2MJF5rcJp7CqbstfhpId9sYSJJK4CR+ruzQs89wARl4qVTfwa0yIofltYDa7dtq+pdYQKRqGXgD9T23+3B4joXNfqK00yVs0kTXchFMcchQcMh7qa2DuTLujCQpJ3ITPcd55brM3FFJbH4a1taTqBjWVZtkIgF4x0lHqr6d3nptTGhRm3WXmI6Z1hVObGCL1p1RDk0K0rekmFTfhJBO8HabfvgBl+fbGEgSSuAmfYT0z6ZSaQNn+Or7dsi88N431qtuOpWaosEN3yOxtdzTh56SK29aFDXuQnj6zdfMRh55vnn/8Boq3hS62cNpfCqXbzbF0lAon6DMBeo6Guv0w7yG7owmYJG7Cx+1STMgzTQqzwjVcGutVN//5bUrZG4sdeowAFBRvjey9PWurzEK7aDN3rf+k1r0vszfkHmPet+r1UEYYPtYepWldY6uZRjiXUg1z3ZY8CxVF9sYTIEncxAGs7tKvQr39lb8Dqm4vhe++kSBW57dZkjIgK88cR3LVzRoi7dANJt3Ttvf6hktfNXvSOp3MbxNO1PdE6H6EmXNqVc4dShI3cYAJedm4FKzPL2NHcZiWaPCvus2Ooaqbp950KELsdZT6s5YFidQN57cuaPsQqb+BZ0BcsqlU7fw2+PGFW6F370eZ3yacRKmG3RQWPQWVe+2NJwCSuIkDZKYkMKK3+YcpLIvxWqyqW+n22Km65a+GmjJI6GD21otVPUaY50hsUKiphHesIdKLGrayaoukdBj4M3McDWu6SeImnKr/ZOhyuPm5u/AJu6NpN0ncxE8cF85dFCyxWHXbttA89zoSXG57Y7GTr0HhW/CEoZu5LWbdBXs3tW+I1J81XLrqTef/3ZaOUuFUSsGxM8zxwsdNo40DSeImfsJaz23upiL214Zxn8VYq7rF+vw2S5fBEJcE1SUNW39Fgi3zYcFj5vj0hwLb2qnPRPN3u2ovfP9ZUMKzRXWZmYsK0lEqnGngmZDV3+y3u+hJu6NpF0ncxE8M7NaBrulJ7K/1sOCHPeG7cSxV3bQ2iQHE9vw2AHc8dBtujiOlQaGmsqGLdPjFkHdyYNdzx8HQc82xk9d0s/YoTevS/I4RQkQyl6uh6jb/UagutzeedpDETfyEUorjBni7S9eHeYkGaw/TaK+6lWyDsp3gimvYbD2WWQ0KkTLPbdadsPcHs77eKXcH55rWFljrP3buxGjZo1REg8FnQ6dDTQV8ybN2R9NmkriJJk30Dpd+9N0u9pSHYdN5S3wSHHOjOY7mqpu1flu3YZCQam8skcBKXiOh4rZlHix43ByfEeAQqb8ug6HrEPDUwuq3gnPNcJPGBBEN3HFwzO/M8byHzTqNDiKJm2jSsf2y6ZGZTH5pNb94ZhHFlWFMoEbEwFw3md92IKvilr/a3h+iNZXw7nTMEOkvoN9Jwb2+VXVzandpgTdxkzXchNMNPd8skF1RAEtfsDuaNpHETTQpOcHNi1eMJistkbW7Srn02UWU7a8Nz81joeq21eoojcH9SZuS0QtSs832X7tW2hfHF3f4DZH+NfjXP/wcUG7YvhiKIqgRo7Wko1REC3d8w5zquTOhLowjSwGSxE0069DsNF7+1Rg6psSzYnsJlz+/mMqauvDcPJqrblX7oGCNOY71xgSLUvbvW7plHiz8lzkO5hCpvw5doO8J5nilw6pu1eVQstUcS+ImosHwiyG9h5lvvMw5/85I4iZa1L9rB168YgwdkuJYvHkfv3phSXiWCInmqtu2xYCGTodBWo7d0USOnt55bnY0KNRUNCy0e0QIhkj9+bbAei3y1q1rSZG32paaIx2lIjrEJcLRvzHHcx6E+jCNKgWoXYmbUupSpdRpfl//XSlVrJSap5Q6JHjhiUhweI8MXrh8NKkJbuZt2sPVLy2lpi4M/+BEa9VN5rc1zc6K2xd3wL4fzW/fweoibU7/yZCYbqpXW+eF9l7BJPPbRDQa8Uvzy0jJVsfMPW1vxe2PQBWAUmocMB24GSgC/hmc0EQkGdG7I89OPZKkeBdfri/kule+pbY+xMnbT6puzpmD0CKro1SGSQ/UYwSgoHgrlIdxGZrNcw8cIk3KCO394pNh8Fnm2ElruklHqYhG8clw1HXm+Jv7oT5M04EC0N7ErRdgzaw9C/iv1vpJ4A/AMcEITESeMYd25qlfjiLB7eKT1fn87vUV1Ht0aG/qX3Vz0ByEZtVVN2ymLhW3AyVlQFaeOQ5X1a2mwrvQLnDEJdD3xPDc1+ouXf2u6WR1AkncRLQadTkkdzJV9+/+a3c0B9XexK0c6Ow9Phmw9nDZDyQHGpSIXMf0y+axi0cQ51K8t2Int/x3JZ5QJm/+VbdvHnB+1W3ncqivhpQs6HyY3dFEHt96bkvDc7/Pb4d9m71DpCHoIm1Or7GQeYjZ7Hr9h+G7byAkcRPRKjENxk03x9/cB54wbvXYDu1N3D4DnlZKPQ3kAdZPnsHA5iDEJSLYiYO68NCFR+BS8MbS7dz63mq0DmHyNuJSs8l3NFTdtvkNkyplbyyRKJwNCpvnwKInzHE4hkj9uVx+TQoOGC6tLjdD2AA5A+2NRYhQGP1r8zOgaAOsedfuaFrU3sRtOjAfyAZ+rrW2NrQcCbTpp5BSarpSarNSar9SaqFSavRBzs9USj2qlNqllKpWSm1QSk32+75bKXWnUupHpVSVUmqTUur/lJJ/JYNp8pBu3H/eMJSCFxds4e4P14YueYtPgvFRUnWT+W0t8zUofBvajsuaCu9Cu5jJyeEaIvU39HzzvGkWlO0O//3bosi7R2lqtnSUiuiUlA5jrjbHs++L6I7vdiVuWutirfW1WusztdYf+71+q9a61eMNSqnzgQeA24ERwArgE6VUk2skKKUSMNW+XOAcoD8wDdjhd9rvgauBa4GB3q9vBq5r9QcUrTLliJ7cM2UIAE998yP//GxD6G424pfOr7p5PH6Jm8xva1KXwRCXBNUlsCeEC9R+fpt3iLQnnBzGIVJ/nQ8zCzBrD6x6w54YWkuGSUUsGHsVJHSAgtURPYWhvcuBTFJKjff7erpSarlS6j9KqY5tuNSNwFNaW/CQVAAAIABJREFU6+e01muAq4BK4PJmzr8c6AScpbWeq7XerLX+Wmu9wu+co4B3tdYfeL//JvAp0GIlT7TPBaN7c9vpgwB4aNb3PPpliP6xjYaq256NZlPjuGToOtTuaCKTOx66DTfHoWpQ+PEbWPSkOT7jIfObtl18w6URvgyBJG4iFiR3hNHTzPHsv0MopwAFoL1Dpf8A0gGUUkOA+zHz3PpgKmgH5a2ejQQ+t17TWnu8XzdXjjgDM0T7qFIqXyn1nVLqj0opt98584ATlFJ53vsMA8YDH7X+44m2mHp0H2451fxA/8cn63lmzo+huZHTq27W+m09R0Fcgr2xRDJr39JQzHOrLvcbIr20YRcDuwyeAu4EyP8Odq+yN5aWyBpuIlaMmw7xKbBrBWz87ODn26C9iVsfwLtnDz8H3tda/xEz9+3UVl4jC3AD+Y1ezwe6NvOeQzFDpG5gMnAn8Dvgz37n3Au8CqxTStUCy4AHtdYvNxeIUipRKZVuPYAOrfwMwuuqCYdxw4n9ALjz/TW8tGBL8G/i9KqbzG9rHV9naQgSt89vg+ItZm/Uk+8K/vXbKrkj9Pf+yIzkqptU3ESsSM0yy4NAxFbd2pu41QAp3uMTMUORAHvxVuJCxAUUAL/WWi/VWr8G/BUzxGo5D7gYuAgzb+5SYIZS6tIWrvsHoMTvsT0EsUe935zQjysnHArAn9/5jjeXhuCP0clVNytx6yWJW4usxC1/NdRWBe+6P86GxU+ZY7uHSP1Za7qteiMyF/+sqTDJLkC2dJSKGHDU9Wau7fbF8MNXdkfzE+1N3OYADyil/g8zd+wD7+t5tD7pKQLqgS6NXu8CNNditQvYoLX2X2RlLdDVO/QKZhj3Xq31q1rrVVrrFzG7OfyhhVjuATL8Hj1b+RmEH6UUt0wawNSjcgG4+c0V/G/FzuDexKlVt7LdZnFHFPQ60u5oIltmb9O96KmDXSuDc03/IdKRl8FhxwfnusHQ90RI6Qzl+RH5j4SvozQlC1I7t3yuENGgQxczlQJg9j/sjaUJ7U3crgXqMMOWV2utra7OU4GPm32XH611DbAU8E0yUUq5vF/Pb+Ztc4G+3vMsecAu7/XAVAIb9/HW08Jn1VpXa61LrQdQ1prPIH5KKcWtpw/iwtG98Gi44bXlfLo6yEsdOLHqZlXbuhwe3vXCnEip4O9b+vmtZh2yjN5w8p3BuWawuONhyLnmOBLXdPPNb5Nqm4ghR//GzD/dMtdsixdB2rscyFat9c+01sO01s/4vf5brfX1bbjUA8A076b1A4HHgVTgOQCl1L+VUvf4nf84pqt0plIqz7vR/R+BR/3O+R/wJ6XUaUqpXKXUFEz36tvt+ayi7ZRS3HXWEKYc0YN6j+ba/yzjq/UFwbuBE6tuMr+tbYK5EO8PX8Pip83xmQ9DYgROYbW6S9e9D/tL7Y2lMd/8tv72xiFEOGX0gOEXm+MIq7q1t+JmLXT7c6XUn72PKY26Ow/KO0dtBnAHsBwYDkzSWlsNC72Bbn7nbwNOAY4EVgIPATMxDQmW64A3gccww6j3AU8A/9f2Tynay+1S/OOcoUwe0pWaeg9XvriUeZuKgncDp1XdrI5SSdxaJ1gVt+pyeO9aczzqcjh0YmDXC5Vuw83E/7r9kbdquzQmiFg1/rfgioMfvgzPbi6tpNqz2r1Sqi9m+Y8ewHrvy/2BbcBpWutNQYvQBt7O0pKSkhLS0yNkArND1dR5uOblpXy+toCUBDf/vnw0o3KDtPL6wifho5tAuSEtx3QDpWSZ+VGp2WY+jnWckmW+n5oNCanh3W6quhzu7Q26Hn67xvwmJ1q2vwTuPQTQMON7SMtu33XevxGWPGOGSK+ZF5nVNsucf5qu10PGw2UfHPT0sJk5zCxWfOn70OcYu6MRIrzemQ7LX4J+p8DFr4f0VqWlpWRkZABkeKdtNSmundd/CNgEjNVa7wVQSnUGXvJ+77R2XldEmYQ4F49cNIJp/17CNxuLuOy5xbw8bQxDe2YGfvERvzT/KBeug7Jd5tEacUnehM4/0fNL7FKzzWRx6/X45MDi3LHEJG0ZvSRpa62kDMjKg6L1ZsP5/pPafo0fvjZ/PwDOfCSykzaAIeeZTe+3zIF9W6DjIXZHBDWVJhaQOW4iNh1zI6z4D2z8BHYuh+7D7Y6o3YnbBPySNgCt9R6l1C2YBgIhfJLi3Tx5ySgufW4Ri37cyyXPLOLVX49lYLcAq5nxSXD1PNOxWVEIFUVQWeQ9LoSKPQ3HlUVQXgh1VWY4qmSbebRGQlpDUudfufN/thLAlM4/XVxX5re1T4+R3sRtSdsTt+oyeNcaIr0CDp0Q/PiCLaOHifOHr2Dl6zDhJrsj8naUau8vMll2RyNE+HU+DA4/B1a9bua6XdDskrBh097ErZqmF6lNw6zxJsQBkhPcPDv1SC55ZiHLthbzi6cX8tqV4+ibkxbYhV1u8w9eaytZNRUmwasoakjorKSv8XNlEdTXQE25eezb3Lp7JGUcmOTt9i5pIYlb2/QcaX7Tbc/cks/+AiVbzdIiJ90R/NhCZdiFJnFb8QocOyO8Q/pN8c1vk2qbiGHHzjDrLK57H/LXQJdBtobT3sTtfeBJpdQVwCLva2OAfwHvBSMwEX3SEuN4/rLRXPTUAlbvLOXipxfw+pXjOKRzaviCSEg1j9YMQ2kN1aUHJnq+ZM8/0bOSwD1mSHR/iXk03iT9kKND85mila9B4VvweMDVyl6qH76CJc+a4zMfhcQAfzkIpwE/g/hU2LvJJKx2r/knHaVCmL//R11rGnSy+tkdTbsTt+uBFzDrrdV6X4sH3gVuCEJcIkplJMfz4hVjuPDJBazPL+Oipxby2pVj6dkx5eBvDjelTPUsKcOUyw/G44H9xY0qd96ErmOuzBFqqy6DzXzEam8SnJ138PdUl8G715njI38FfY4NbYzBlpgGg84wFbcVr9ifuMkabkIYkbBFnld713Er1lqfiVn89hzvI09rPUVrXRzMAEX06ZSawEu/GsOhWansKK7i4qcXkl+63+6wAudyQUon89tZ7tEw+CwYPe3/27vv8KrL+//jz/tkQhYjZBAS9ibsKW7FARVXi4ALB9q6vlb7batV+7PWqv3WXWvrwsW0WsFRFXCzdwh7J0AGBEggIfv+/XFOICJgIMn5nPF6XFcuTk4+55x3zkXOeZ17vD9w7u+P9umSugsJc7fJgLq3BfniYc8UaVu48NHGq60x1fxfyXzf+R6FGnET8Tl1HnEzxjzzE4ecZzzrMay199WnKAl8rWIimDxxCGP+tYAdBSWMf3Uh028fRnx0hNOliS9pMxCyF7qnDfuOP/mxW76CZZPcl/1tirS2dmdBbAoU7YKNn7tH4JxQXnJ0XafWuIn4jFMZcetXxy/n98qKX0iOa8KUW4eSHBfJlj3FXPfaIg6UaG+L1JLS3/3vrmUnP660CGbVTJFO9O9+Y64Q6D3GfXnVNOfqKNgEWGjSQjtKRXxInUfcrLXnNWYhEpxSWzRlysShjPnXAtbnHuSGNxbz7q1DiI0Mc7o08QU1GxTyMqHi8Il76s1+2N3epVlbuPD/eau6xtN7rLsh76bP3W1tnDi5e+31bU7vbhWRI077lFciDaV9fBSTbx1Ci6hwMnYWctOkJRSXVTpdlviCZmnu1irVlZCTcfxjtnwJy950X/bnKdLaErpB637u3zvzfWdq0Po2EZ+k4CY+oUtiDO/cMpjYyFCW7djPrW8tpbSiyumyxGnGnPy8paVFR3eRDr7Nv6dIj9VnnPvfVVOdeXz1cBPxSQpu4jN6to7j7VuGEB0RyoKtBdz+zjLKKhXegl6bAe5/j9eI94uHoGinu93Khf/Pi0V5Qa+r3Se43r0c9mz46eMbmkbcRHySgpv4lL6pzXhjwiCahIXwzcY93D1lBRVV1U6XJU460Yjb5rmw/C335ctfcjdWDiRR8dBphPuytzcpVByGfdvcl9XDTcSnKLiJzxncvgWv3jCQ8FAXX6zN474Zq6iqtk6XJU6p2Vl6IMt9vllwn5li1j3uy4Nvh3ZnOlNbY6vp6ZYx3d3g2Vv21uwobe5eYygiPkPBTXzSmZ3j+ed1/QkLMXy0aje/ez+DaoW34BQZB/GesybUtAU5MkXaHi78o3O1NbYul7h//6JdsP077z1u7fVt2lEq4lMU3MRnnd8tkRfH9SPEZfj3sp08MisTaxXeglLt6dLNc2D52+7vA3GKtLawSOh5lfuyN6dLtb5NxGcpuIlPu6RXMs+M6YMx8O7CLP78yTqFt2BUs0Fh69dHp0iH/NJ9arFAV7O7dO1MKC/2zmPqHKUiPkvBTXze5X1TeOqq3gC8/v02nv5io8MVidfVjLjtXOKeNmzeHi54xNmavCV1sPv3rSiGdR975zE14ibisxTcxC+MGZTKny7vCcDfv9rM37/c5HBF4lWJPSE00vONgSv+EdhTpLUZ492ebhWlsN+zo1Q93ER8joKb+I0bhrXjwZHdAPjbFxt57butDlckXhMSBm0GuS8P+SW0PcPZeryt5tylW7+Got2N+1gFm8BWu3eURic07mOJyClTcBO/ctvZHblvhHuH4Z8/Wcc7C3c4XJF4zain4dL/gxGPOl2J97VoD2lnABYyZjTuY9Wsb2vVTTtKRXyQgpv4nbvP78Qd53YE4OEPM5mxNNvhisQrWnWFIbdBaITTlTijpqfbqqnQmBt09tQKbiLicxTcxO8YY/jfi7ty0/B2APzu/QymL8lytiiRxtbzCgiJcAernFWN9zgKbiI+TcFN/JIxhkd+1oMbhrXFWvjd+6t5e8F2p8sSaTyRcdBtlPtyY/Z0qwluCQpuIr5IwU38ljGGR0f35NYz2wPwyMw1vPqtNixIAKvZXbr6PaiqaPj7ryiFfZ6/IY24ifgkBTfxa8YY/jCqO3ed1wmAxz9dx4tz1SpEAlTH893nDi3ZC5vnNvz9F2x27yiNbAbRiQ1//yJSbwpu4veMMfzm4q7c79lt+vTsjfzt8w06w4IEnpBQSPe0BmmMnm57tKNUxNcpuEnAuPuCzvxhpLth6N+/2szjOj2WBKKa3aUb/guH9zfsfWt9m4jPU3CTgDLx7A5HzrDw2vfbeGTmGqqrFd4kgCSlQ0JPqCqDNR827H3nr3P/q/VtIj5LwU0Czg3D2vHkVekYA+8s3MHvP8igSuFNAoUxtXq6NfDu0j0b3P8quNVJYUmFPhiK1ym4SUAaOziNZ8b0wWVgxtKd3D9jJZVV1U6XJdIw0n8BxgXZC4/uAq2vyjLtKD0Fy7P20//Ps7lr6nItyRCvUnCTgHVlvza8OK4/oS7Dhyt3c8+0FZRXKrxJAIhNhg7nuS+vmt4w91mwGWyVu19cTFLD3GcAm7wwi6pqy6erc5m8SA3AxXsU3CSgjeqdzMvXDSA8xMWnq3O5Y/IySiuqnC5LpP5qero11Cmwaq9v047SkyqtqOLzNblHvv/zJ2vZnH/IwYokmCi4ScAb0SORV28cSESoiznr8pn49lIOlyu8iZ/rNgrCo+HADshaWP/70/q2OvtyfT6HyipJadaEszrHU1pRzb3TNaIv3qHgJkHhnC6tmHTTIJqGh/Ddpr3c9OZiissqnS5L5PSFN4UeV7gvN0RPtz3aUVpXM1fuAmB039b87Rd9aN40jMxdRTw7Z6PDlUkwUHCToHFGx3jevnkw0RGhLNy6j+tfX0RRaSOcNkjEW2p2l675ECoO1+++akbc1MPtpAoPV/DV+j0AjO7TmsTYSJ64qjcA//xmCwu3FjhZngQBBTcJKgPbtWDyrUOIaxLG8qwDXPfaIg6UlDtdlsjpaTsc4lKhrNDdkPd0VZZBwRb3ZY24ndTnmbmUV1XTJTGabkkxAFzSK4lrBqZiLdw3fSWFJfpAKI1HwU2CTp/UZkydOJQWUeFk7Cxk7CsL2XuozOmyRE6dywW9r3Ffrk9Pt4It7h2lEXEQk9wwtQWomavc06SX903B1NrE8chlPWjXsim7C0t5aGamWoRIo1Fwk6DUo3Us028bSquYCNbnHmTsKwvJLyp1uiyRU1czXbp5DhzKP737OLK+rat2lJ5EflEp87e4p0JH92n9g59FRYTy7DV9CXEZPlq1mw896+BEGpqCmwStzokxTL9tKMlxkWzOP8SYfy1g94F6rhMS8bb4zpAy0D1itvrfp3cfWt9WJx9l5GAt9E9rRmqLpj/6eb+05vzPBZ0BeOTDNWTvK/F2iRIEFNwkqHVoFc2M24fRpnkTtheUMOZfC/RiK/7nyCmwTnN3qc5RWiezVh6dJj2RO87tyIC2zTlYVsl9M1bqdHvS4BTcJOiltmjKjNuH0T4+ip37D/OLfy5g6x410xQ/0utqcIVBbgbkrTn126uH20/atreYVTsLCXEZRqafeB1gaIiL567pS3REKEu27+flrzd7sUoJBgpuIkDrZk2YfttQOidEk1tUyph/LWRj3kGnyxKpm6YtoMvF7sunukmhshz2aUfpT5m1cjcAwzvF0yom4qTHprZoyqOjewLw3JxNrMo+0Oj1SfBQcBPxSIiNZNptQ+meHMveQ2WMfWUha3YXOl2WSN3UnAIrYwZUn8KZQfZtgepKiIiF2NY/fXwQstYe3U3ap27P0VX9U/hZ72Qqqy33Tl+pht/SYBTcRGppGR3B1IlD6NMmjn3F5Yx7ZSEr9WlZ/EHni6BJcziUC1u/rvvt8rWj9Kes2V3E1j3FRIS6uKhnYp1uY4zh8SvSaR0Xyba9xfz5k7WNXKUECwU3kWM0axrOO7cOYUDb5hSVVnLda4tYsn2f02WJnFxoOPT6ufvyqUyXan3bT6o5xdWF3ROJiQyr8+3imobx9Ji+GANTF2f/4MT0IqdLwU3kOGIjw3j75sEM69CSQ2WV3PD6YuZv3ut0WSInVzNduu4jKKvjGk2do/Skqqots1a517eN7nvqU8nDOrbktrM7APD79zPIU79IqScFN5ETiIoIZdJNgzi7SysOV1Rx05tL+HrDaTY4FfGGlP7QsjNUHoa1s+p2G/VwO6nF2/aRV1RGTGQo53ZtdVr3cf+IrvRsHcv+kgp+894qqtUiROpBwU3kJCLDQnj1hgFc2D2Bsspqbnt7GV9oukN8lTGn1tOtshwKPO0qNOJ2XDWjbSN7JRMRGnJa9xEe6uL5sX2JCHXx3aa9vDl/ewNWKMFGwU3kJ0SEhvCPawcwMj2J8qpq7pi8nE8ycpwuS+T4as5duv07OJB18mP3bXXvKA2PgdgTN5UNVuWV1Xy62v23fvlpTJPW1ikhhodGdQfgyc/Wsz63qN71SXBScBOpg/BQFy+M7ceV/VKorLbcPXU5Hyzf6XRZIj/WLBXaneW+nDHj5MfqHKUn9e3GPRQeriAhJoIhHVrW+/6uG9qW87slUF5Zzb3TVlJacQptW0Q8FNxE6ig0xMXfftGHsYNSqbZw/3urmLr4J0Y0RJxQs0lh1TSwJ1lPpfVtJzXTM016WZ/WhLjqH2yNMTx1dW9aRoWzPvcg//f5hnrfpwQfBTeRUxDiMvzlynRuGNYWa+GBD1bzltariK/pMRpCm0DBJti1/MTH6RylJ1RcVsnste71rPWdJq2tVUwEf/15bwBe/34b323a02D3LcFBwU3kFLlchkdH9zyyxf+Ps9bwr2+2OFyVSC0RMdD9Mvflk21SONLDrXvj1+RnZq/No7SimvbxUaSnxDXofV/QPZHrh7YF4DfvrWJ/cXmD3r8ENgU3kdNgjOGBS7txz/mdAHjiv+t5Ye4m7MmmpUS8qWZ3aea/3btHj1VVUWtHaVfv1eUnapruju7TGtMI6/8eHNmdjq2iyCsq4/cfZOi1Q+pMwU3kNBljuO+irvzmoi4APDN7I3/7YoNegMU3dDgXopPg8H7Y9MWPf75vK1RXQHg0xLXxdnU+reBQGd9ucjfcPp2mu3XRJDyE58f2IyzE8PmaPGYszW6Ux5HAo+AmUk93nd/5yDb/l77awp8/WafwJs5zhUDvMe7Lx5su1TlKT+jTzFyqqi3pKXF0bBXdaI/TKyWO+y9yj3Y++tFatu0tbrTHksCh4CbSAG49qwOPXd4TcC84fnhmprqji/Nqdpdu/BxKjjnfrta3ndAszzRpQ25KOJGJZ3VgaIcWlJRXce/0lVRUVTf6Y4p/U3ATaSDXD2vHX6/ujTHw7sIsfvd+BlUKb+KkxB6Q1Ns9JZr5/g9/VruHmxyxc38JS7bvxxj4We/GD24hLsMzY/oSGxnKquwDvDh3U6M/pvg3BTeRBjRmUCrPjulLiMvw3rKd3DdjJZX6BC1Oqt3TrbYjPdw04lbbR6vcZ0oY2r4lSXGRXnnM1s2a8PiV6QD8/avNLN2+7yduIcFMwU2kgV3RL4W/j+tHqMswc+Vu7pqygvJKhTdxSPrPwYTArqWw1zOaU1Vx9LJG3H5gphenSWu7rE9rruqXQrWFe6ev5GBphVcfX/yHgptII7g0PZl/XjeA8BAXn63J5bZ3lqpXkzgjOgE6XeC+XDPqtm9brR2lqc7V5mM25B5kfe5BwkIMl/ZK9vrjP3p5T9o0b8LO/Yf546w1Xn988Q8KbiKN5MIeibx240Aiw1x8vWEPI579hs8ydXJ6cUBNT7eM6VBdfXR9W3wX7SitZdYq92jbuV0TiGsa5vXHj4kM47lr+uIy8MHyXXycsdvrNYjvczy4GWPuNMZsN8aUGmMWGWMG/8TxzYwxLxljcowxZcaYjcaYkccck2KMedcYU2CMOWyMWW2MGdi4v4nIj53dpRUzbh9G54Ro9h4q55fvLueuKcspOFTmdGkSTLqOhIhYKMyGHfO0vu04rLXMXOkOSt6eJq1tYLsW3HWeu7H3gx+sZveBw47VIr7J0eBmjLkGeAZ4FOgPrAI+N8YknOD4cGA20A74OdAVmAjsqnVMc2AeUAFcCvQA7gf2N9bvIXIyvds04+N7zuTO8zoS4jJ8nJHDRc9+yycZGn0TLwlrAj2vcF9eNe2HPdwEgOVZB9i5/zBR4SFc0C3R0VruvqAzfVKbUVRayX0zVmp3uvyA0yNu9wGvWmsnWWvXAr8ESoCbT3D8zUAL4Apr7Txr7XZr7TfW2lW1jvkdkG2tvclau9hau81a+4W1VieTFMdEhIbwvxd348M7htMtKYaC4nLunLKcX727jD0HNfomXlCzu3Tth5Cz0n1ZPdyOqOnddnHPJJqEhzhaS1iIi+ev6UvT8BAWbt3Hq99tdbQe8S2OBTfP6NkAYE7Nddbaas/3w05ws9HAAuAlY0yeMSbTGPOgMSbkmGOWGmPeM8bkG2NWGGMm/kQtEcaY2JovIKY+v5vIiaS3iWPWXWdyzwWdCXUZ/puZy0XPfsPMlbt0tgVpXKlDoVlbKD/kPt0VaMTNo7Kqmo89I+CNdYqrU9UuPoo/XtYDgKe/2EDmrkKHKxJf4eSIWzwQAuQdc30ekHSC23TAPUUaAowEHsM9DfrQMcf8CtgEXAy8DLxgjLnxJLU8ABTW+tp5Kr+IyKkID3Vx34guzLxrOD2SY9lfUsH/TFvJbe8sI7+o1OnyJFC5XEc3KQCERWlHqce8LQUUFJfTMiqc4Z3inS7niDEDU7m4ZyIVVZb/mbaCw+VVTpckPsDpqdJT5QLygdustcustdOBx3FPsdY+Zrm19kFr7Qpr7SvAq8ccc6wngLhaXzrjsjS6nq3jmHnXcO4b0YWwEMPstXmMePZbPli+U6Nv0jh6X3P0cqsu7jAnR3q3jeqdTFiI7zwnxhievKo3CTERbNlTzF8+Xed0SeIDnPwfuheoAo5dBZoI5J7gNjnARmtt7Y8d64Akz9RrzTFrj7ndOiDtRIVYa8ustUU1X8DBOv4OIvUSFuLings689HdZ9IrJZbCwxXcN2MVt761lNxCjb5JA2vZEVKHuC9rfRsApRVVfJ7pfstxcjfpiTSPCufpMX0AeGfhDr5cf+wklQQbx4KbtbYcWAZcUHOdMcbl+X7BCW42D+jkOa5GFyDHc381xxy7cKMLsKMh6hZpDN2SYvnPHcP534u7Eh7iYu76fEY8+w0zlmZr9E0a1rm/d691qz1tGsTmrsunuLyKNs2b0D+tudPlHNdZnVtx8/D2APz23xnsVTuhoOb0mPAzwERjzI3GmO6416NFAZMAjDFvG2OeqHX8y7h3lT5vjOlijBkFPAi8VOuYZ4Ghnk0LnYwx44HbjjlGxOeEhbi487xOfHzPmfRJbcbB0kp+++8MJkxaol5O0nA6ng/3ZkCHc5yuxCfUTJOO7tMa48PNiH97SVe6JcWw91A5v/13hj7QBTFHg5tnjdpvgD8BK4G+wCXW2pqx4DQgudbx2bg3HAwCMoAXgOeBJ2sdswS4EhgHZAIPA/daayc39u8j0hC6JMbw/i+H8ftLuxEe6uKbjXu46Nlvmbo4Sy/WIg2osKSCrzfsAeDyvikOV3NykWEhPDe2L+GhLr5cn8+7i7KcLkkcYvRG8GOeliCFhYWFxMbGOl2OBLHN+Yf47b9XsTzrAABndY7niavSadO8qcOVifi/6Uuy+N37q+mWFMNn957tdDl18sb32/jTx2uJCHXxyT1n0ilB3asCRVFREXFxcQBxnvX2x+X0VKmInESnhGje++UZPDSqOxGhLr7btJeLn/2WdxfuoFrd1EXqpeYUV77Su60uJpzRjrM6x1NWWc3/TFtJeWW10yWJlym4ifi4EJfh1rM68Nm9ZzOoXXOKy6t46MNMrn1tEdn7SpwuT8Qv5RWVsmBrAQCX9faf4OZyGZ7+RR+aNw1jze4inp69wemSxMsU3ET8RPv4KKbfNow/XtaDyDAXC7YWcPFz3/LW/O0afRM5RR+t2o21MKBtc1Jb+NfSg4TYSJ68ujcAr3y7lQVbChyuSLxJwU3Ej7hchpuGt+fze89mSPsWlJRX8cdZaxj76kK27y2MjAEVAAAgAElEQVR2ujwRvzFrlXua1Bd7t9XFxT2TGDsoFWvhvhkrKSypcLok8RIFNxE/1LZlFFMnDuWxy3vSNDyExdv2ccnz3/L699uo0uibyElt3XOIjJ2FhLgMI9OTf/oGPurhn/WgXcum5BSW8ocPV2vXeZBQcBPxUy6X4fph7fj83rM5o2NLSiuqeezjtVzzrwVs3XPI6fJEfFbNaNuZneKJj45wuJrTFxURynNj+xHiMnyckcN/VuxyuiTxAgU3ET+X2qIpk28dwl+uTCc6IpSlO/Zz6fPf8eq3WzX6JnIMay2zVvr3NGltfVObce8FnQF4ZOYabVgKAgpuIgHAGMP4IWl8/uuzj7QKePzTdfz8n/PZnK9T74rUyNxVxNa9xUSEurioZ5LT5TSIO87rxMC2zTlUVsmvp6+kskotQgKZgptIAElp1oS3bx7MX6/uTUxEKCuyDjDyhe95+estejEX4egpri7skUh0RKjD1TSMEJfh2Wv6Hhlx/8fXW5wuSRqRgptIgDHGMGZQKl/cdzbndm1FeWU1T322nqtfns+GXI2+SfCqqrZ8lOGZJu3j/9OktaW2aMpjV/QE4Pm5m1iRtd/hiqSxKLiJBKjkuCZMmjCIv/2iD7GRoazaWchlL37P37/cRIVG3yQILd62j7yiMmIjQzmnayuny2lwV/RN4bI+ramqtvx6+kqKyyqdLkkagYKbSAAzxvDzAW2Yfd85XNg9gfKqav72xUau/Mc81uWc8FR4IgFp1ir3NOnI9GQiQkMcrqbhGWP48xW9aB0XyfaCEv700VqnS5JGoOAmEgQSYyN59YaBPHdNX5o1DSNzVxGj//49z83ZqHMdSlAoq6zi09W5gH+dm/RUxTUJ45lr+mIMTF+azWeZuU6XJA1MwU0kSBhjuKJfCl/8+mwu7plIRZXluTmbuPyleWTuKnS6PJFG9e3GvRQeriAxNoIh7Vs6XU6jGtqhJbef3RGA33+QQV5RqcMVSUNScBMJMgkxkfzzugG8OK4fzZuGsS6niCtemsdfP1vPnoNlTpcn0ihqdpNe1rs1IS7jcDWN774RXeiVEsuBkgqufnk+//pmCwWH9PcdCIxOkfFjxphYoLCwsJDY2FinyxFpNHsPlfHHmWv4ZHUOAKEuw4geiYwdnMaZneKD4g1OAt+hskoG/nk2pRXVfHTXmaS3iXO6JK/YnH+Isa8sYO+hcgDCQ1xc0iuJ8UPSGNK+Bcbo79uXFBUVERcXBxBnrT3hImQFt+NQcJNg81lmLv/8Zgsrsw8cuS6lWRPGDExlzKA2JMc1cbA6kfr5z4qd/Hr6KjrERzH3/nOCKrCUlFfy0ardTF6URcbOo0siOraKYvyQtlzdP4VmTcMdrFBqKLjVg4KbBKv1uUVMW5zNB8t3UlTqbiXgMnBOl1aMHZzG+d0SCAvRCgvxLxMmLebrDXu498LO3HthF6fLcUzmrkImL8pi5spdlJRXARAR6mJUejLXDk2jf1rzoAq1vkbBrR4U3CTYlVZU8VlmLlMXZ7Fo274j17eKieAXA9pwzaBU2raMcrBCkbopOFTG4L/Mpara8uX959ChVbTTJTnuYGkFM1e6R+FqtwXqmhjD+CFpXNk/hdjIMAcrDE4KbvWg4CZy1NY9h5i+NJv3l+08slYG4IyOLRk7OI2LeyYGZE8sCQzvLNjOwzPX0LtNHLPuOtPpcnyKtZaV2QeYsiiLjzJ2U1rhbg3UJCyEy/okM35IW/q0idMonJcouNWDgpvIj5VXVjN3XR7TlmTz7aY91Lx0NGsaxlX92jBucCqdE2OcLVLkGD9/eT5Ld+znoVHdufWsDk6X47MKD1fw4YpdTF60g415h45c37N1LOOHpHF535SAOberr1JwqwcFN5GT27m/hBlLd/Le0mxyCo/2iBrQtjljB6UyqncyTcP1Ii/Oyt5Xwll//QpjYOEDF5AYG+l0ST7PWsuyHfuZvCiLT1bnHGnQHRUewuX9Uhg/OI1eKcGxK9fbFNzqQcFNpG6qqi3fbMxn2uJs5q7Pp6ra/XoSExHK6L6tGacXeXHQP77ezF8/28AZHVsyZeJQp8vxO/uLy3l/+U6mLMpi697iI9f3SW3GtYPT+FkffUBrSApu9aDgJnLq8otKeW/ZTqYvySZrX8mR63ulxHLNoDQu79taC57Fqy557lvW5x7kqavTuWZQmtPl+C1rLQu37mPK4iw+y8yhouroB7Sr+qcwfkhbuiZpmUR9KbjVg4KbyOmrrrYs3FrA1CXZfJ6ZS3nV0QXPo3onM3ZQKgPaqu2ANK71uUVc8tx3hIe4WPKHC4lrqg8NDWHvoTL+vWwnUxdnsaPg6Ae0AW2bM35wGqN6JxMZps1Kp0PBrR4U3EQaxr7icj5YvpNpS7LZnH90wXPnhGiuGZTKVf3b0CJKzT+l4f31s/X84+stXNQjkVduGOh0OQGnutoyb8tepizK4ou1eUeWScQ1CePq/m0YPySNTglqvXIqFNzqQcFNpGFZa1metZ+pi7P5uFbbgfAQFxf1TGTc4DSGdWiJS6fYkgZgreXMp75i14HDvDS+P6N6JztdUkDLLyplxtJspi7OZteBw0euH9K+BeOHpHFJryS1DKoDBbd6UHATaTxFpRXMWrmbaUuyyNx19LUprUVTrhmUyi8GtCFBu/+kHpbt2MfVLy8gKjyEZQ+P0NSdl1RVW77duIfJi7L4cn0enkE4WkSF84sBbRg3OI128WrcfSIKbvWg4CbiHZm7Cpm2JIuZK3ZzsMx9iq0Ql+H8bgmMG5zKOV0SdKJ7OWWPzMzk7QU7uKp/Cs+M6et0OUFp94HDTF+SzfQl2eQWHW0ZdGaneMYPSWNEj0SdPu8YCm71oOAm4l0l5ZV8kpHD9CXZLN2x/8j1SbGRjBnYhjGDUmnTvKmDFYq/qKiqZuhf5lJQXM5bNw/mnC6tnC4pqFVWVfPl+nymLM7im41HG3fHR0cwZqB7FC61hf62QcGtXhTcRJyzKe8g05a4T3S/v6QCAGPgrM6tuHZIGhf1SNSOVDmhrzfkM2HSElpGhbPowQsI1aiOz8jeV+IehVuazZ6DZYD7b/vszq2449yODOnQ0uEKnaXgVg8KbiLOK6us4vM1eUxfksW8zQVHrr/ngs7cN6KLg5WJL7tv+ko+WLGLG4e15dHLezldjhxHRVU1c9bmMWVxFt9t2gtAqMvw8nUDGNEj0eHqnKPgVg8KbiK+ZUdBMW/N38Eb87YB8MRV6YwbrIaq8kOHy6sY+OfZFJdX8f6vzmBA2+ZOlyQ/YfveYp76bD3/zcwlLMTwyvUDOa9bgtNlOaKuwU1jyCLi89q2jOKRy3pw9/mdAHjow0zmrstzuCrxNXPX51FcXkWb5k3on9bM6XKkDtrFR/HiuH6MSk+mospy+7vL+HbjHqfL8mkKbiLiN+4b0YWfD2hDVbXlrikrWJl9wOmSxIfMXLkbgMv7ttY6SD8SGuLiubF9ubhnIuWV1Ux8eynzNu91uiyfpeAmIn7DGMMTV6VzTpdWHK6o4uY3l7C91smvJXgVllTw9YZ8AC7vm+JwNXKqwkJcvDiuPxd2T6Cssppb3lrCwq0FP33DIKTgJiJ+JSzExT+u7U96Shz7isu5cdJi9h4qc7oscdh/PSc/75YUQ5dEnfDcH4WHunjp2v6c27UVpRXV3PzmEpZu3+d0WT5HwU1E/E5URChvTBhEaosm7Cgo4ZY3l1BSXul0WeKgo9OkGm3zZxGhIfzzugGc1TmekvIqJkxawvKs/T99wyCi4CYifqlVTARv3TSY5k3DWLWzkDsnL6eyqtrpssQBuYWlLNzmnla7rI/OS+rvIsNCeOX6gQzr0JJDZZXc+PpiMnZqPWsNBTcR8VsdWkXz+oRBRIa5+GrDHv7wn0zU4ij4fJyxG2thULvmOsNGgGgSHsLrEwYyuF0LDpZVct1ri8jcVeh0WT5BwU1E/Fr/tOa8OK4/LgPTl2bz/NxNTpckXlYzTTpa06QBpWl4KG/cNIgBbZtTVFrJda8vYl3OCdubBQ0FNxHxeyN6JPLYFe4u+c/N2cS0xVkOVyTesmXPIVbvKiTUZRiVrmnSQBMdEcqbNw2iT2ozDpRUcN1ri9iUd9Dpshyl4CYiAeHaIW256zx3g94/fJjJV+vzHa5IvGGWZ7TtzM7xtIgKd7gaaQwxkWG8ffNgeqXEUlBczrhXF7E5/5DTZTlGwU1EAsb9F3Xh6v7uBr13TF7OKjXoDWjWWj5adbTprgSuuCZhvHvLELonx7L3UBnjX13ItiDt4ajgJiIBwxjDk1enc7Ya9AaFzF1FbN1bTGSYixE9kpwuRxpZs6bhTL51CF0TY8g/6A5vWQUlTpfldQpuIhJQahr01kyrqEFv4Jq5chcAF3ZPJDoi1OFqxBtaRIUzeeIQOiVEk1NYyrhXF7Jzf3CFNwU3EQk40Z4GvW2aq0FvoKqqtnyUoaa7wSg+OoIptw6hQ3wUuw4cZtyrC9l94LDTZXmNgpuIBKSEmEjeuvlog967pqxQg94AsmhbAXlFZcQ1CeOcLq2cLke8LCE2kikTh9K2ZVOy9x1m/KsLyS0sdbosr1BwE5GA1bFVNK/dOIiIUBdfrs/noQ/VoDdQ1OwmHZmeRHio3sqCUVKcO7y1ad6E7QUljH91IfkHAz+86X+7iAS0AW2b8+K4frgMTFuSzQtzNztdktRTWWUVn67OAWB0H02TBrOUZk2YOnEoKc2asHVvMde+uijg17QquIlIwLuoZxJ/utzdoPfZORuZsSTb4YqkPr7ZsIei0kqSYiMZ3L6F0+WIw1JbNGXKxCEkxUayKf8Q1722iH3F5U6X1WgU3EQkKFw3tC13ntcRgAf+s1oNev3YTE/vtsv6JBPiMg5XI76gbcsopt42lISYCNbnHuS61xZxoCQww5uCm4gEjd9c1JWr+qeoQa8fO1RWyZy1eYB2k8oPtY+PYsrEocRHh7M2p4jrX19M4eEKp8tqcApuIhI0jDE8dXVvzuocf6RB744CNej1J1+syaWsspoOraLo2TrW6XLEx3RKiGbKxKG0iApn9a5CbnxjMQdLAyu8KbiJSFAJC3Hx8nUD6Nna06D3jcUUBPhi5kAy07Ob9PI+KRijaVL5sS6JMbx7yxCaNQ1jZfYBJkxaQnFZ4PRxVHATkaATHRHKpJsGHWkjcPNbS9Wg1w/sPVTG95v3AjBa5yaVk+jROpZ3bxlCbGQoy3bs56YAasKt4CYiQammQW+zpmGsyj7A3WrQ6/M+XZ1DVbWlT5s42sdHOV2O+LheKXG8c8sQYiJCWbxtH7e+tZTD5VVOl1VvCm4iErQ6tormdU+D3rnr83l4phr0+rKaadLR2pQgddQntRlv3jyYqPAQ5m8p4LZ3llJa4d/hTcFNRILagLbNecHToHfq4mxe/FINen1R9r4Slu3YjzFwWe9kp8sRPzKgbXPevHkwTcND+G7TXn717jLKKv03vCm4iUjQu7hnEo96GvQ+M3sjM5aqQa+vmeXp3XZGx5YkxEY6XI34m0HtWvDGhEFEhrn4asMe7py8gvJK/1waoeAmIgJcP7Qtd5zradD7wWq+2qAGvb5kVq3dpCKnY2iHlkeWRsxZl8c9U1dQ4YfrWhXcREQ8/vfiow1675y8nIydatDrC9bnFrEh7yDhIS4u7pXkdDnix4Z3iueVGwYSHuLiszW53Dt9pd9tSvKJ4GaMudMYs90YU2qMWWSMGfwTxzczxrxkjMkxxpQZYzYaY0ae4NjfG2OsMea5xqleRAKFMYYnr3I36C0pdzfozSoocbqsoFezKeG8bq2IaxLmcDXi787p0op/Xt+fsBDDJxk53P/eKqqq/WdTkuPBzRhzDfAM8CjQH1gFfG6MSTjB8eHAbKAd8HOgKzAR2HWcYwcBtwMZjVG7iASe8NCjDXr3Hirnxklq0Ouk6mp7dJpUu0mlgZzfLZGXxvcn1GWYuXI3v/13BtV+Et4cD27AfcCr1tpJ1tq1wC+BEuDmExx/M9ACuMJaO89au91a+421dlXtg4wx0cBk3KFuf+OVLyKBJjoilEkTBpHSrAnb9hZzS4D0f/JHy7P2s+vAYaIjQjm/23E/z4uclot6JvHiuH6EuAzvL9/Jg/9Z7RfhzdHg5hk9GwDMqbnOWlvt+X7YCW42GlgAvGSMyTPGZBpjHjTGhBxz3EvAJ9baOT++ix/VEWGMia35AmJO5/cRkcCREHu0Qe/K7APcPXW5362FCQQ106QX90wiMuzYl3mR+rk0PZlnr+mLy8C0Jdk8Msv3ezk6PeIWD4QAecdcnwecaAVqB9xTpCHASOAx4H7goZoDjDFjcU+7PlDHOh4ACmt97azj7UQkgHVKiOa1GwZ6dqHl8/DMNT7/oh5IKqqq+WR1DgCX6xRX0khG92nN02P6YAy8uzCLRz9a69N/504Ht9PhAvKB26y1y6y104HHcU+xYoxJBZ4HrrXWltbxPp8A4mp9tWnwqkXELw1s14Lnx/bDGJi6OIu/q0Gv13y/eS/7isuJjw7njI4tnS5HAtiV/drw1NW9AXhz/nYe/2Sdz4Y3p4PbXqAKSDzm+kQg9wS3yQE2WmtrLzhZByTVmnpNAJYbYyqNMZXAOcA9nu9/NNZurS2z1hbVfAEH6/driUgguaRXEo+O7gnA07M38p4a9HpFzaaEn/VuTWiI029XEujGDEzliavSAXjt+2389fMNPhneHP1LsNaWA8uAC2quM8a4PN8vOMHN5gGdPMfV6ALkeO5vLpAO9K31tRT3RoW+xwQ+EZE6uWFYO37ladD7+w9W87Ua9Daqw+VVfL7G/fl9tKZJxUvGDU7jscvdH9Je/noLz87e6HBFP+YLH2GeASYaY240xnQHXgaigEkAxpi3jTFP1Dr+Zdy7Sp83xnQxxowCHsS9GQFr7UFrbWbtL6AYKPBcFhE5Lb+9uCtX9XM36L1j8nJW7yx0uqSANWddHiXlVaS2aEK/1GZOlyNB5Pph7XjkZz0AeOHLzbwwd5PDFf2Q48HNs0btN8CfgJW4R8gusdbWbFhIA5JrHZ8NXAwMwt2f7QXca9qe9GLZIhKEjDE8eXVvzuzkbtB705uL1aC3kcysdYorY4zD1UiwufnM9jw4shvgPn/xP772nbWtxhfnb53maQlSWFhYSGxsrNPliIiPOVhawTX/WsjanCLax0fx/q/OoEVUuNNlBYwDJeUMenwOFVWW2b8+m86J6tAkznjpq8383+cbAPjDyO5MPLtDoz1WUVERcXFxAHGe9fbH5fiIm4iIv4mJDOPNm2o36F2iBr0NoLiskncWbOeql+dTUWXpnhyr0CaOuvO8Tvz6wi4APP7pOpZnOd/PP9TpAkRE/JG7Qe8grn55ASuyDnD31BX887r+2v14GrL3lfDW/O1MX5rNwdJKwH32it9e0tXhykTgfy7sTGV1NaEuF/3TmjtdjqZKj0dTpSJSV0u37+Pa1xZRVlnNtUPS+PMVvbQmqw6stSzYWsCkeduZsy6Pmreidi2bMuGMdlw9oA0xkTqhvASPuk6VasRNRKQeahr0/mryMiYvyqJ1sybceV4np8vyWaUVVXy4Yhdvzt/O+tyjLTPP6hzPzcPbc06XVrhcCr4iJ6IRt+PQiJuInKq35m/nj7PWADC4fQtG9kri0vRkEmMjHa7MN+w+cJh3Fu5g6uIsDpRUANAkLISrB6Qw4Yx2dErQWjYJbnUdcVNwOw4FNxE5Hc/O3sjztXo+GQMD2zbn0l7JXJqeRHJcEwer8z5rLct27GfSvO18tiaXqmr3+02b5k24cVg7xgxMJa6ppkNFQMGtXhTcROR07TpwmP+uzuHT1Tkszzrwg5/1T2vGyPRkRqYn07pZ4Ia4ssoqPl6Vw6T528jcdfT9Z2iHFtw0vD0Xdk8kRNOhIj+g4FYPCm4i0hB2HzjMZ5m5fLo6h6U7fthGoG9qM0alJ3NJryRSWzR1qMKGlV9UyruLspiyaAd7D5UDEBHq4oq+KUwY3o7uyXo9FTkRBbd6UHATkYaWW1jKZ5k5fLo6lyU79lH7pbdPm7gjI3H+GOJWZh/gzXnb+GR1DhVV7l8sKTaS64e1ZdzgNDUnFqkDBbd6UHATkcaUX1TKZ2vcI3GLt+2jutbLcHpKHJemJzEqPZm2LaOcK/InVFRV8+nqHN6cv50VtaaEB7Rtzk3D23FxzyTC1NNOpM4U3OpBwU1EvGXPwTI+94S4hVsLfhDieiTHMqq3eySufbxvhLiCQ2VMWZTFu4t2kFdUBkBYiOGy3q2ZMLwdvdvohPAip0PBrR4U3ETECXsPlfHFmjw+XZ3Dgq0FR3ZhAnRLimFUejKXpifTKSHa67Wt2V3Im/O2M3PVbsorqwGIj47guqFpjB+SRkKM2p6I1IeCWz0ouImI0/YVl/PFmlw+zcxl/ua9VNYKcV0TYzxr4pIa9VyelVXVzF6bx6T521m8bd+R63u3ieOm4e0Yld6a8FBNh4o0BAW3elBwExFfsr+4nNlr8/g0M4fvN/0wxHVOiObS9GRGpSfTJTG6QU63daCknGlLsnlnwQ52HTgMQKjLcGl6MhPOaEf/tGY6rZdIA1NwqwcFNxHxVYUlFcxe555O/W7TniO7OAE6too6sju1W1LMKYerjXkHmTRvO/9ZsZPSCvd0aIuocMYPTuO6oW1JitN0qEhjUXCrBwU3EfEHhYcrmLsuj09X5/Ltxj2UV1Uf+Vn7+ChGpidxaa9keraOPWGIq6q2fLU+n0nztzFvc8GR67snx3LT8HaM7tOayLCQRv9dRIKdgls9KLiJiL85WFrB3HX5fLo6h6837jmygQCgbcum7pG4Xsn0SnGHuKLSCt5bupO35m8na18JAC4DF/VI4qbh7RjcvoWmQ0W8SMGtHhTcRMSfHSqr5Mv1+XyakcNXG/IpqxXiUls0oW9qc75cl0dxeRUAsZGhjBucxvXD2tKmuf81ABYJBApu9aDgJiKBoriskq82uEfivlyff2TtGrg3NkwY3o4r+6XQNDzUwSpFRMGtHhTcRCQQlZRX8vWGPWTsLOTMTvEM79RS06EiPkLBrR4U3ERERMSb6hrc1DlRRERExE8ouImIiIj4CQU3ERERET+h4CYiIiLiJxTcRERERPyEgpuIiIiIn1BwExEREfETCm4iIiIifkLBTURERMRPKLiJiIiI+AkFNxERERE/oeAmIiIi4icU3ERERET8hIKbiIiIiJ8IdboAX1ZUVOR0CSIiIhIE6po5jLW2kUvxP8aYFGCn03WIiIhI0Gljrd11oh8quB2HMcYArYGDjfgwMbjDYZtGfpxAp+exYeh5bBh6HhuOnsuGoeexYXjreYwBdtuThDNNlR6H5wk7YdptCO5sCMBBa63mZE+TnseGoeexYeh5bDh6LhuGnseG4cXn8SfvW5sTRERERPyEgpuIiIiIn1Bwc04Z8KjnXzl9eh4bhp7HhqHnseHouWwYeh4bhs88j9qcICIiIuInNOImIiIi4icU3ERERET8hIKbiIiIiJ9QcBMRERHxEwpuDjDG3GmM2W6MKTXGLDLGDHa6Jn9jjHnAGLPEGHPQGJNvjPnQGNPV6br8nTHm98YYa4x5zula/I0xJsUY864xpsAYc9gYs9oYM9DpuvyJMSbEGPOYMWab5zncYox52NTqfirHZ4w52xjzkTFmt+dv+Ipjfm6MMX8yxuR4nts5xpjOTtXrq072PBpjwowxT3n+tos9x7xtjGntzRoV3LzMGHMN8AzubcX9gVXA58aYBEcL8z/nAC8BQ4ERQBjwhTEmytGq/JgxZhBwO5DhdC3+xhjTHJgHVACXAj2A+4H9Ttblh34H/Aq4C+ju+f63wN1OFuUnonC/n9x5gp//FrgH+CUwBCjG/d4T6Z3y/MbJnsemuN+3H/P8exXQFZjltepQOxCvM8YsApZYa+/yfO8CsoEXrbVPOlqcHzPGtALygXOstd86XY+/McZEA8uBO4CHgJXW2nudrcp/GGOeBIZba89yuhZ/Zoz5GMiz1t5S67r3gcPW2uucq8y/GGMscKW19kPP9wbYDTxtrf2b57o4IA+YYK2d5lixPuzY5/EExwwCFgNtrbVZ3qhLI25eZIwJBwYAc2qus9ZWe74f5lRdASLO8+8+R6vwXy8Bn1hr5/zkkXI8o4Glxpj3PFP3K4wxE50uyg/NBy4wxnQBMMb0Ac4E/utoVf6vPZDED997CoFF6L2nvuIACxzw1gPqJPPeFQ+E4P6UU1se0M375QQGz6jlc8A8a22m0/X4G2PMWNzD/oOcrsWPdcA9xfcM8Bfcz+ULxphya+1bjlbmX54EYoH1xpgq3K+Xf7DWTna2LL+X5Pn3eO89Schp8UwzPwVMbeQTz/+AgpsEgpeAXrg/mcspMMakAs8DI6y1pU7X48dcwFJr7YOe71cYY3rhXk+k4FZ3Y4BrgfHAGqAv8JwxZrcCsPgSY0wYMAMwuD+0eY2mSr1rL1AFJB5zfSKQ6/1y/J8x5u/Az4DzrLU7na7HDw0AEoDlxphKY0wl7o0f93i+D3G2PL+RA6w95rp1QJoDtfiz/wOetNZOs9autta+AzwLPOBwXf6u5v1F7z0NoFZoa4v7Q6/XRttAwc2rrLXlwDLggprrPNN8FwALnKrLH3m2tv8duBI431q7zema/NRcIB33yEbN11JgMtDXWlvlYG3+ZB7u3WW1dQF2OFCLP2sKVB9zXRV6r6qvbbgDWu33nljcu0v13nMKaoW2zsCF1toCb9egqVLvewZ4yxizFPdOlHtxbz+e5GhV/ucl3NMplwMHjTE16zQKrbWHnSvLv1hrDwI/WBdojCkGCrRe8JQ8C8w3xjyI+0V9MHCb50vq7iPgD8aYLNxTpf2A+4A3HK3KD3h2hneqdVV7Y0xfYJ+1NsvTm/EhY8wm3EHuMdw7TU+4YzIYnYLYSqUAAAO1SURBVOx5xD2y/m/ca4J/BoTUeu/Z5xmcafwa1Q7E+4wxdwH/i3tR6ErgHmvtImer8i+ebdrHc5O19k1v1hJojDFfo3Ygp8wY8zPgCdyfxLcBz1hrX3W2Kv9ijInBHSiuxD2FvxuYCvzJW2+K/soYcy7w1XF+9Ja1doKnJcijuD9MNAO+B+6w1m70XpW+72TPI/D/cP9tH8951tqvG6eqH1JwExEREfETWjcgIiIi4icU3ERERET8hIKbiIiIiJ9QcBMRERHxEwpuIiIiIn5CwU1ERETETyi4iYiIiPgJBTcREYcZY841xlhjTDOnaxER36bgJiIiIuInFNxERERE/ISCm4gEPWOMyxjzgDFmmzHmsDFmlTHm556f1UxjjjLGZBhjSo0xC40xvY65j6uNMWuMMWXGmO3GmPuP+XmEMeYpY0y255jNxphbjillgDFmqTGmxBgz3xjTtZF/dRHxMwpuIiLwAHAD8EugJ/As8K4x5pxax/wfcD8wCNgDfGSMCQMwxgwAZgDTgHTcJ6N+zBgzodbt3wbGAfcA3YHbgUPH1PG45zEGApXAGw31C4pIYNBJ5kUkqBljIoB9wIXW2gW1rn8NaAq8AnwFjLXWTvf8rAWwE5hgrZ1hjJkMtLLWXlTr9n8FRllrexpjugAbgBHW2jnHqeFcz2NcaK2d67luJPAJ0MRaW9oIv7qI+CGNuIlIsOuEO6DNNsYcqvnCPQLXsdZxR0KdtXYf7iDW3XNVd2DeMfc7D+hsjAkB+gJVwDc/UUtGrcs5nn8TTuF3EZEAF+p0ASIiDov2/DsK2HXMz8r4YXg7XYfreFxFrcs10yH6gC0iR+gFQUSC3VrcAS3NWrv5mK/sWscNrblgjGkOdAHWea5aBww/5n6HAxuttVXAatyvt+cgIlIPGnETkaBmrT1ojPkb8KwxxgV8D8ThDl5FwA7PoY8YYwqAPNybCPYCH3p+9jSwxBjzMDAdGAbcBdzheYztxpi3gDeMMfcAq4C2QIK1doYXfk0RCRAKbiIi8DDunaIPAB2AA8By4C8cnZn4PfA80BlYCVxmrS0HsNYuN8aMAf7kua8c4BFr7Zu1HuNXnvv7B9ASyPJ8LyJSZ9pVKiJyErV2fDa31h5wuBwRCXJa4yYiIiLiJxTcRERERPyEpkpFRERE/IRG3ERERET8hIKbiIiIiJ9QcBMRERHxEwpuIiIiIn5CwU1ERETETyi4iYiIiPgJBTcRERERP6HgJiIiIuInFNxERERE/MT/BxP+rtTGDwg3AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 700x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WC192u1ooghf"
      },
      "source": [
        "### LSTM"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uPQM7lFWoghf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "79ff5dc0-6eed-4aca-f919-79a4520b962e"
      },
      "source": [
        "model = Sequential()\n",
        "embedding_layer = Embedding(vocab_size, 100, weights=[embedding_matrix], input_length=maxlen, trainable=False)\n",
        "model.add(embedding_layer)\n",
        "model.add(LSTM(128, dropout=0.4, recurrent_dropout=0.4))\n",
        "model.add(Dense(1, activation='sigmoid'))\n",
        "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['acc'])\n",
        "print(model.summary())\n",
        "\n",
        "history = model.fit(X_train, y_train, batch_size=128, epochs=15, verbose=1, validation_split=0.2)\n",
        "print(model.summary())\n",
        "score_train = model.evaluate(X_train, y_train, verbose=1)\n",
        "score_test = model.evaluate(X_test, y_test, verbose=1)\n",
        "\n",
        "# results\n",
        "print (\"Train Accuracy:\", score_train[1])\n",
        "print(\"Test Accuracy:\", score_test[1])\n",
        "plot_results(history)"
      ],
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_8\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding_6 (Embedding)      (None, 100, 100)          3516900   \n",
            "_________________________________________________________________\n",
            "lstm (LSTM)                  (None, 128)               117248    \n",
            "_________________________________________________________________\n",
            "dense_12 (Dense)             (None, 1)                 129       \n",
            "=================================================================\n",
            "Total params: 3,634,277\n",
            "Trainable params: 117,377\n",
            "Non-trainable params: 3,516,900\n",
            "_________________________________________________________________\n",
            "None\n",
            "Epoch 1/15\n",
            "25/25 [==============================] - 8s 249ms/step - loss: 0.6815 - acc: 0.5534 - val_loss: 0.6563 - val_acc: 0.6300\n",
            "Epoch 2/15\n",
            "25/25 [==============================] - 6s 227ms/step - loss: 0.6396 - acc: 0.6347 - val_loss: 0.6521 - val_acc: 0.5987\n",
            "Epoch 3/15\n",
            "25/25 [==============================] - 6s 224ms/step - loss: 0.6038 - acc: 0.6828 - val_loss: 0.6101 - val_acc: 0.6800\n",
            "Epoch 4/15\n",
            "25/25 [==============================] - 6s 223ms/step - loss: 0.5643 - acc: 0.7212 - val_loss: 0.6465 - val_acc: 0.6413\n",
            "Epoch 5/15\n",
            "25/25 [==============================] - 6s 221ms/step - loss: 0.5780 - acc: 0.6994 - val_loss: 0.5667 - val_acc: 0.7050\n",
            "Epoch 6/15\n",
            "25/25 [==============================] - 6s 226ms/step - loss: 0.5607 - acc: 0.7169 - val_loss: 0.5787 - val_acc: 0.7225\n",
            "Epoch 7/15\n",
            "25/25 [==============================] - 6s 221ms/step - loss: 0.5297 - acc: 0.7391 - val_loss: 0.5559 - val_acc: 0.7225\n",
            "Epoch 8/15\n",
            "25/25 [==============================] - 6s 223ms/step - loss: 0.5191 - acc: 0.7500 - val_loss: 0.5865 - val_acc: 0.7075\n",
            "Epoch 9/15\n",
            "25/25 [==============================] - 6s 227ms/step - loss: 0.5087 - acc: 0.7538 - val_loss: 0.6182 - val_acc: 0.7138\n",
            "Epoch 10/15\n",
            "25/25 [==============================] - 6s 226ms/step - loss: 0.5104 - acc: 0.7466 - val_loss: 0.5045 - val_acc: 0.7387\n",
            "Epoch 11/15\n",
            "25/25 [==============================] - 6s 223ms/step - loss: 0.4933 - acc: 0.7581 - val_loss: 0.5069 - val_acc: 0.7500\n",
            "Epoch 12/15\n",
            "25/25 [==============================] - 6s 224ms/step - loss: 0.4916 - acc: 0.7656 - val_loss: 0.6500 - val_acc: 0.7075\n",
            "Epoch 13/15\n",
            "25/25 [==============================] - 6s 222ms/step - loss: 0.4784 - acc: 0.7675 - val_loss: 0.5713 - val_acc: 0.7500\n",
            "Epoch 14/15\n",
            "25/25 [==============================] - 6s 222ms/step - loss: 0.4997 - acc: 0.7597 - val_loss: 0.5265 - val_acc: 0.7538\n",
            "Epoch 15/15\n",
            "25/25 [==============================] - 6s 222ms/step - loss: 0.4743 - acc: 0.7837 - val_loss: 0.5363 - val_acc: 0.7650\n",
            "Model: \"sequential_8\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding_6 (Embedding)      (None, 100, 100)          3516900   \n",
            "_________________________________________________________________\n",
            "lstm (LSTM)                  (None, 128)               117248    \n",
            "_________________________________________________________________\n",
            "dense_12 (Dense)             (None, 1)                 129       \n",
            "=================================================================\n",
            "Total params: 3,634,277\n",
            "Trainable params: 117,377\n",
            "Non-trainable params: 3,516,900\n",
            "_________________________________________________________________\n",
            "None\n",
            "125/125 [==============================] - 5s 41ms/step - loss: 0.4685 - acc: 0.7925\n",
            "32/32 [==============================] - 1s 43ms/step - loss: 0.4850 - acc: 0.7900\n",
            "Train Accuracy: 0.7925000190734863\n",
            "Test Accuracy: 0.7900000214576721\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 700x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 700x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lupBrgYgoghg"
      },
      "source": [
        "### LSTM com Early Stopping"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s7cc-H9Loghg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "f5ea1a93-8dc5-4410-b826-49c7fcd6a699"
      },
      "source": [
        "model = Sequential()\n",
        "embedding_layer = Embedding(vocab_size, 100, weights=[embedding_matrix], input_length=maxlen, trainable=False)\n",
        "model.add(embedding_layer)\n",
        "model.add(LSTM(128, dropout=0.4, recurrent_dropout=0.4))\n",
        "model.add(Dense(1, activation='sigmoid'))\n",
        "model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['acc'])\n",
        "print(model.summary())\n",
        "\n",
        "es = EarlyStopping(monitor='val_loss', mode='min', patience=10, verbose=1)\n",
        "mc = ModelCheckpoint('best_model.h5', monitor='val_loss', mode='min', verbose=1, save_best_only=True)\n",
        "\n",
        "history = model.fit(X_train, y_train, batch_size=128, epochs=100, verbose=1, callbacks=[es, mc], validation_split=0.2)\n",
        "\n",
        "# load a saved model (best model)\n",
        "model = load_model('best_model.h5')\n",
        "print(model.summary())\n",
        "\n",
        "score_train = model.evaluate(X_train, y_train, verbose=1)\n",
        "score_test = model.evaluate(X_test, y_test, verbose=1)\n",
        "\n",
        "# results\n",
        "print (\"Train Accuracy:\", score_train[1])\n",
        "print(\"Test Accuracy:\", score_test[1])\n",
        "plot_results(history)"
      ],
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_9\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding_7 (Embedding)      (None, 100, 100)          3516900   \n",
            "_________________________________________________________________\n",
            "lstm_1 (LSTM)                (None, 128)               117248    \n",
            "_________________________________________________________________\n",
            "dense_13 (Dense)             (None, 1)                 129       \n",
            "=================================================================\n",
            "Total params: 3,634,277\n",
            "Trainable params: 117,377\n",
            "Non-trainable params: 3,516,900\n",
            "_________________________________________________________________\n",
            "None\n",
            "Epoch 1/100\n",
            "25/25 [==============================] - 8s 235ms/step - loss: 0.6822 - acc: 0.5609 - val_loss: 0.6691 - val_acc: 0.6125\n",
            "\n",
            "Epoch 00001: val_loss improved from inf to 0.66906, saving model to best_model.h5\n",
            "Epoch 2/100\n",
            "25/25 [==============================] - 6s 222ms/step - loss: 0.6371 - acc: 0.6297 - val_loss: 0.6106 - val_acc: 0.6637\n",
            "\n",
            "Epoch 00002: val_loss improved from 0.66906 to 0.61064, saving model to best_model.h5\n",
            "Epoch 3/100\n",
            "25/25 [==============================] - 6s 222ms/step - loss: 0.6015 - acc: 0.6687 - val_loss: 0.6172 - val_acc: 0.6787\n",
            "\n",
            "Epoch 00003: val_loss did not improve from 0.61064\n",
            "Epoch 4/100\n",
            "25/25 [==============================] - 6s 222ms/step - loss: 0.5727 - acc: 0.7050 - val_loss: 0.7028 - val_acc: 0.6925\n",
            "\n",
            "Epoch 00004: val_loss did not improve from 0.61064\n",
            "Epoch 5/100\n",
            "25/25 [==============================] - 6s 222ms/step - loss: 0.5537 - acc: 0.7266 - val_loss: 0.5520 - val_acc: 0.7287\n",
            "\n",
            "Epoch 00005: val_loss improved from 0.61064 to 0.55201, saving model to best_model.h5\n",
            "Epoch 6/100\n",
            "25/25 [==============================] - 5s 220ms/step - loss: 0.5340 - acc: 0.7375 - val_loss: 0.5371 - val_acc: 0.7300\n",
            "\n",
            "Epoch 00006: val_loss improved from 0.55201 to 0.53707, saving model to best_model.h5\n",
            "Epoch 7/100\n",
            "25/25 [==============================] - 6s 221ms/step - loss: 0.5179 - acc: 0.7447 - val_loss: 0.5858 - val_acc: 0.7375\n",
            "\n",
            "Epoch 00007: val_loss did not improve from 0.53707\n",
            "Epoch 8/100\n",
            "25/25 [==============================] - 6s 224ms/step - loss: 0.5028 - acc: 0.7663 - val_loss: 0.5062 - val_acc: 0.7513\n",
            "\n",
            "Epoch 00008: val_loss improved from 0.53707 to 0.50619, saving model to best_model.h5\n",
            "Epoch 9/100\n",
            "25/25 [==============================] - 6s 223ms/step - loss: 0.5198 - acc: 0.7381 - val_loss: 0.5339 - val_acc: 0.7462\n",
            "\n",
            "Epoch 00009: val_loss did not improve from 0.50619\n",
            "Epoch 10/100\n",
            "25/25 [==============================] - 6s 222ms/step - loss: 0.4864 - acc: 0.7709 - val_loss: 0.5617 - val_acc: 0.7113\n",
            "\n",
            "Epoch 00010: val_loss did not improve from 0.50619\n",
            "Epoch 11/100\n",
            "25/25 [==============================] - 6s 222ms/step - loss: 0.4798 - acc: 0.7741 - val_loss: 0.5128 - val_acc: 0.7613\n",
            "\n",
            "Epoch 00011: val_loss did not improve from 0.50619\n",
            "Epoch 12/100\n",
            "25/25 [==============================] - 5s 217ms/step - loss: 0.4811 - acc: 0.7731 - val_loss: 0.5668 - val_acc: 0.7538\n",
            "\n",
            "Epoch 00012: val_loss did not improve from 0.50619\n",
            "Epoch 13/100\n",
            "25/25 [==============================] - 5s 219ms/step - loss: 0.4555 - acc: 0.7866 - val_loss: 0.4919 - val_acc: 0.7650\n",
            "\n",
            "Epoch 00013: val_loss improved from 0.50619 to 0.49188, saving model to best_model.h5\n",
            "Epoch 14/100\n",
            "25/25 [==============================] - 5s 219ms/step - loss: 0.4515 - acc: 0.7819 - val_loss: 0.5470 - val_acc: 0.7450\n",
            "\n",
            "Epoch 00014: val_loss did not improve from 0.49188\n",
            "Epoch 15/100\n",
            "25/25 [==============================] - 5s 219ms/step - loss: 0.4742 - acc: 0.7797 - val_loss: 0.5233 - val_acc: 0.7312\n",
            "\n",
            "Epoch 00015: val_loss did not improve from 0.49188\n",
            "Epoch 16/100\n",
            "25/25 [==============================] - 5s 219ms/step - loss: 0.4457 - acc: 0.7931 - val_loss: 0.4903 - val_acc: 0.7700\n",
            "\n",
            "Epoch 00016: val_loss improved from 0.49188 to 0.49031, saving model to best_model.h5\n",
            "Epoch 17/100\n",
            "25/25 [==============================] - 5s 219ms/step - loss: 0.4511 - acc: 0.7841 - val_loss: 0.4721 - val_acc: 0.7775\n",
            "\n",
            "Epoch 00017: val_loss improved from 0.49031 to 0.47205, saving model to best_model.h5\n",
            "Epoch 18/100\n",
            "25/25 [==============================] - 5s 218ms/step - loss: 0.4320 - acc: 0.7925 - val_loss: 0.4478 - val_acc: 0.7900\n",
            "\n",
            "Epoch 00018: val_loss improved from 0.47205 to 0.44777, saving model to best_model.h5\n",
            "Epoch 19/100\n",
            "25/25 [==============================] - 5s 219ms/step - loss: 0.4162 - acc: 0.8094 - val_loss: 0.4423 - val_acc: 0.7962\n",
            "\n",
            "Epoch 00019: val_loss improved from 0.44777 to 0.44232, saving model to best_model.h5\n",
            "Epoch 20/100\n",
            "25/25 [==============================] - 5s 219ms/step - loss: 0.4147 - acc: 0.8072 - val_loss: 0.4634 - val_acc: 0.7825\n",
            "\n",
            "Epoch 00020: val_loss did not improve from 0.44232\n",
            "Epoch 21/100\n",
            "25/25 [==============================] - 5s 218ms/step - loss: 0.4056 - acc: 0.8059 - val_loss: 0.5040 - val_acc: 0.7513\n",
            "\n",
            "Epoch 00021: val_loss did not improve from 0.44232\n",
            "Epoch 22/100\n",
            "25/25 [==============================] - 5s 219ms/step - loss: 0.4058 - acc: 0.8100 - val_loss: 0.4410 - val_acc: 0.7738\n",
            "\n",
            "Epoch 00022: val_loss improved from 0.44232 to 0.44097, saving model to best_model.h5\n",
            "Epoch 23/100\n",
            "25/25 [==============================] - 5s 220ms/step - loss: 0.4183 - acc: 0.8034 - val_loss: 0.4399 - val_acc: 0.7825\n",
            "\n",
            "Epoch 00023: val_loss improved from 0.44097 to 0.43987, saving model to best_model.h5\n",
            "Epoch 24/100\n",
            "25/25 [==============================] - 5s 219ms/step - loss: 0.3788 - acc: 0.8209 - val_loss: 0.4919 - val_acc: 0.7575\n",
            "\n",
            "Epoch 00024: val_loss did not improve from 0.43987\n",
            "Epoch 25/100\n",
            "25/25 [==============================] - 5s 217ms/step - loss: 0.3816 - acc: 0.8213 - val_loss: 0.4330 - val_acc: 0.8025\n",
            "\n",
            "Epoch 00025: val_loss improved from 0.43987 to 0.43300, saving model to best_model.h5\n",
            "Epoch 26/100\n",
            "25/25 [==============================] - 6s 223ms/step - loss: 0.3769 - acc: 0.8319 - val_loss: 0.4362 - val_acc: 0.7975\n",
            "\n",
            "Epoch 00026: val_loss did not improve from 0.43300\n",
            "Epoch 27/100\n",
            "25/25 [==============================] - 6s 222ms/step - loss: 0.3933 - acc: 0.8300 - val_loss: 0.4410 - val_acc: 0.7900\n",
            "\n",
            "Epoch 00027: val_loss did not improve from 0.43300\n",
            "Epoch 28/100\n",
            "25/25 [==============================] - 5s 219ms/step - loss: 0.3565 - acc: 0.8400 - val_loss: 0.4480 - val_acc: 0.7962\n",
            "\n",
            "Epoch 00028: val_loss did not improve from 0.43300\n",
            "Epoch 29/100\n",
            "25/25 [==============================] - 5s 220ms/step - loss: 0.3516 - acc: 0.8416 - val_loss: 0.4979 - val_acc: 0.7962\n",
            "\n",
            "Epoch 00029: val_loss did not improve from 0.43300\n",
            "Epoch 30/100\n",
            "25/25 [==============================] - 5s 217ms/step - loss: 0.3534 - acc: 0.8450 - val_loss: 0.4493 - val_acc: 0.7937\n",
            "\n",
            "Epoch 00030: val_loss did not improve from 0.43300\n",
            "Epoch 31/100\n",
            "25/25 [==============================] - 5s 220ms/step - loss: 0.3450 - acc: 0.8406 - val_loss: 0.5015 - val_acc: 0.7850\n",
            "\n",
            "Epoch 00031: val_loss did not improve from 0.43300\n",
            "Epoch 32/100\n",
            "25/25 [==============================] - 5s 221ms/step - loss: 0.3471 - acc: 0.8475 - val_loss: 0.4576 - val_acc: 0.7937\n",
            "\n",
            "Epoch 00032: val_loss did not improve from 0.43300\n",
            "Epoch 33/100\n",
            "25/25 [==============================] - 5s 219ms/step - loss: 0.3306 - acc: 0.8531 - val_loss: 0.4386 - val_acc: 0.7950\n",
            "\n",
            "Epoch 00033: val_loss did not improve from 0.43300\n",
            "Epoch 34/100\n",
            "25/25 [==============================] - 5s 219ms/step - loss: 0.3155 - acc: 0.8569 - val_loss: 0.4665 - val_acc: 0.7937\n",
            "\n",
            "Epoch 00034: val_loss did not improve from 0.43300\n",
            "Epoch 35/100\n",
            "25/25 [==============================] - 6s 222ms/step - loss: 0.3137 - acc: 0.8547 - val_loss: 0.4272 - val_acc: 0.7912\n",
            "\n",
            "Epoch 00035: val_loss improved from 0.43300 to 0.42715, saving model to best_model.h5\n",
            "Epoch 36/100\n",
            "25/25 [==============================] - 5s 218ms/step - loss: 0.3238 - acc: 0.8591 - val_loss: 0.4914 - val_acc: 0.8000\n",
            "\n",
            "Epoch 00036: val_loss did not improve from 0.42715\n",
            "Epoch 37/100\n",
            "25/25 [==============================] - 5s 220ms/step - loss: 0.2920 - acc: 0.8712 - val_loss: 0.4843 - val_acc: 0.7912\n",
            "\n",
            "Epoch 00037: val_loss did not improve from 0.42715\n",
            "Epoch 38/100\n",
            "25/25 [==============================] - 5s 221ms/step - loss: 0.2945 - acc: 0.8672 - val_loss: 0.5051 - val_acc: 0.7950\n",
            "\n",
            "Epoch 00038: val_loss did not improve from 0.42715\n",
            "Epoch 39/100\n",
            "25/25 [==============================] - 5s 220ms/step - loss: 0.2889 - acc: 0.8747 - val_loss: 0.5108 - val_acc: 0.7887\n",
            "\n",
            "Epoch 00039: val_loss did not improve from 0.42715\n",
            "Epoch 40/100\n",
            "25/25 [==============================] - 5s 221ms/step - loss: 0.2753 - acc: 0.8803 - val_loss: 0.5126 - val_acc: 0.7887\n",
            "\n",
            "Epoch 00040: val_loss did not improve from 0.42715\n",
            "Epoch 41/100\n",
            "25/25 [==============================] - 6s 222ms/step - loss: 0.2926 - acc: 0.8822 - val_loss: 0.4994 - val_acc: 0.8037\n",
            "\n",
            "Epoch 00041: val_loss did not improve from 0.42715\n",
            "Epoch 42/100\n",
            "25/25 [==============================] - 6s 221ms/step - loss: 0.2661 - acc: 0.8828 - val_loss: 0.4875 - val_acc: 0.7962\n",
            "\n",
            "Epoch 00042: val_loss did not improve from 0.42715\n",
            "Epoch 43/100\n",
            "25/25 [==============================] - 5s 219ms/step - loss: 0.2722 - acc: 0.8894 - val_loss: 0.4780 - val_acc: 0.7925\n",
            "\n",
            "Epoch 00043: val_loss did not improve from 0.42715\n",
            "Epoch 44/100\n",
            "25/25 [==============================] - 6s 221ms/step - loss: 0.2505 - acc: 0.8919 - val_loss: 0.5643 - val_acc: 0.7850\n",
            "\n",
            "Epoch 00044: val_loss did not improve from 0.42715\n",
            "Epoch 45/100\n",
            "25/25 [==============================] - 5s 220ms/step - loss: 0.2480 - acc: 0.8922 - val_loss: 0.5475 - val_acc: 0.7650\n",
            "\n",
            "Epoch 00045: val_loss did not improve from 0.42715\n",
            "Epoch 00045: early stopping\n",
            "Model: \"sequential_9\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding_7 (Embedding)      (None, 100, 100)          3516900   \n",
            "_________________________________________________________________\n",
            "lstm_1 (LSTM)                (None, 128)               117248    \n",
            "_________________________________________________________________\n",
            "dense_13 (Dense)             (None, 1)                 129       \n",
            "=================================================================\n",
            "Total params: 3,634,277\n",
            "Trainable params: 117,377\n",
            "Non-trainable params: 3,516,900\n",
            "_________________________________________________________________\n",
            "None\n",
            "125/125 [==============================] - 5s 41ms/step - loss: 0.2818 - acc: 0.8913\n",
            "32/32 [==============================] - 2s 41ms/step - loss: 0.4162 - acc: 0.8110\n",
            "Train Accuracy: 0.8912500143051147\n",
            "Test Accuracy: 0.8109999895095825\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 700x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 700x500 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fMtb0eSzoghg"
      },
      "source": [
        "### Prediction (sample)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "tSYSJj9Qoghg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "07e6c932-b931-4272-8b8a-62dd07e18fc9"
      },
      "source": [
        "instance = sample(X, 1)\n",
        "print (instance)\n",
        "instance = tokenizer.texts_to_sequences(instance)\n",
        "\n",
        "instance = pad_sequences(instance, padding='post', maxlen=maxlen)\n",
        "print (instance)\n",
        "\n",
        "score = model.predict(instance)[0][0]\n",
        "print (score)\n",
        "if (score >= 0.5):\n",
        "    print ('Positivo')\n",
        "else:\n",
        "    print ('Negativo')"
      ],
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['i have recently watched this film and have decided to comment on it the best way to watch this film is to not expect what you have seen in the past by miyazaki miyazaki is well known for his work on on spirited away and howl moving castle well for western viewers anyway both of them films were kind of similar to each other but at the same time completely different however ponyo is whole different type of story and animation all together the story follows ponyo fish that has the face girl after ponyo runs away from her home at the bottom of the sea she find whole new world she never knew was out there and new trouble as well when she almost caught by fishing boat she was rescued by five year old boy known as sousuke the story then follows the two of them and the pure friendship between boy and fish can ponyo really stay with sousuke forever feel the movie was inspired by the little mermaid and at the same time similar to tonari no totoro the movie is very short and you have very little time to learn about the characters in this movie but the characters fish and little boy so how much are you expecting to learn about them the film is set over about what seems to be days think this is why the movie is so short really enjoyed watching this movie and hope you all enjoy this movie as well']\n",
            "[[   1  874 2058  181  402    2 1743   45 2227   62  816   12 1256  216\n",
            "     1   13   10 1673   28    1  116 4740    2   27    1  165   53  762\n",
            "     4   55    1   13    5   51  341    2   17   23   51  116   53    4\n",
            "   784   40    1   96    7    8   13   15    1   96 1743    2  116  402\n",
            "    34   80   69   22   17 1029    4  784   40   88    1   16    5  258\n",
            "   124   40   43  183    4   24  524   98    8    5  135    1   13    5\n",
            "    34  341   62  533  151    8   13    2  380   17   26  375    8   13\n",
            "    11   66]]\n",
            "0.995329\n",
            "Positivo\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1zdYUPmOoghg"
      },
      "source": [
        "### Prediction (tests)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZSCpDpysoghg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        },
        "outputId": "8fd49610-8851-44f8-c18c-e03e8c4f5660"
      },
      "source": [
        "def score_list(list_instances):\n",
        "    \n",
        "    list_scores = []\n",
        "    \n",
        "    for text in list_instances:\n",
        "        instance = tokenizer.texts_to_sequences([text])\n",
        "        instance = pad_sequences(instance, padding='post', maxlen=maxlen)\n",
        "\n",
        "        score = model.predict(instance)[0][0]\n",
        "        \n",
        "        if (score >= 0.5):\n",
        "            flag = 'Positivo'\n",
        "        else:\n",
        "            flag = 'Negativo'\n",
        "            \n",
        "        list_scores.append([text, score, flag])\n",
        "        \n",
        "    df = pd.DataFrame(list_scores, columns=['text', 'score', 'rating'])\n",
        "    display(df)\n",
        "        \n",
        "        \n",
        "list_instances = ['The movie was amazing, I loved it, very good',\n",
        "                  'I hate this movie, it is very bad and terrible']\n",
        "\n",
        "# score list\n",
        "score_list(list_instances)"
      ],
      "execution_count": 138,
      "outputs": [
        {
          "output_type": "display_data",
          "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>text</th>\n",
              "      <th>score</th>\n",
              "      <th>rating</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>The movie was amazing, I loved it, very good</td>\n",
              "      <td>0.992776</td>\n",
              "      <td>Positivo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>I hate this movie, it is very bad and terrible</td>\n",
              "      <td>0.020271</td>\n",
              "      <td>Negativo</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                             text     score    rating\n",
              "0    The movie was amazing, I loved it, very good  0.992776  Positivo\n",
              "1  I hate this movie, it is very bad and terrible  0.020271  Negativo"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rAiiXW85gR46"
      },
      "source": [
        "Modelo TensorFlow Hub"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qHhnJ12pQ4BI",
        "outputId": "a421aa82-c374-43a9-8bb4-11d28ab16196"
      },
      "source": [
        "# Split the training set into 60% and 40% to end up with 15,000 examples\n",
        "# for training, 10,000 examples for validation and 25,000 examples for testing.\n",
        "train_data, validation_data, test_data = tfds.load(\n",
        "    name=\"imdb_reviews\", \n",
        "    split=('train[:60%]', 'train[60%:]', 'test'),\n",
        "    as_supervised=True)\n",
        "\n",
        "embedding = \"https://tfhub.dev/google/nnlm-en-dim50/2\"\n",
        "hub_layer = hub.KerasLayer(embedding, input_shape=[], dtype=tf.string, trainable=True)\n",
        "\n",
        "model = tf.keras.Sequential()\n",
        "model.add(hub_layer)\n",
        "model.add(tf.keras.layers.Dense(16, activation='relu'))\n",
        "model.add(tf.keras.layers.Dense(1))\n",
        "\n",
        "model.summary()\n",
        "\n",
        "model.compile(optimizer='adam',\n",
        "              loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "history = model.fit(train_data.shuffle(10000).batch(512),\n",
        "                    epochs=10,\n",
        "                    validation_data=validation_data.batch(512),\n",
        "                    verbose=1)\n",
        "\n",
        "results = model.evaluate(test_data.batch(512), verbose=2)\n",
        "\n",
        "for name, value in zip(model.metrics_names, results):\n",
        "  print(\"%s: %.3f\" % (name, value))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:absl:No config specified, defaulting to first: imdb_reviews/plain_text\n",
            "INFO:absl:Load dataset info from /root/tensorflow_datasets/imdb_reviews/plain_text/1.0.0\n",
            "INFO:absl:Reusing dataset imdb_reviews (/root/tensorflow_datasets/imdb_reviews/plain_text/1.0.0)\n",
            "INFO:absl:Constructing tf.data.Dataset for split ('train[:60%]', 'train[60%:]', 'test'), from /root/tensorflow_datasets/imdb_reviews/plain_text/1.0.0\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_11\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "keras_layer_1 (KerasLayer)   (None, 50)                48190600  \n",
            "_________________________________________________________________\n",
            "dense_16 (Dense)             (None, 16)                816       \n",
            "_________________________________________________________________\n",
            "dense_17 (Dense)             (None, 1)                 17        \n",
            "=================================================================\n",
            "Total params: 48,191,433\n",
            "Trainable params: 48,191,433\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Epoch 1/10\n",
            "30/30 [==============================] - 5s 126ms/step - loss: 0.6694 - accuracy: 0.5249 - val_loss: 0.6196 - val_accuracy: 0.5789\n",
            "Epoch 2/10\n",
            "30/30 [==============================] - 4s 118ms/step - loss: 0.5560 - accuracy: 0.6675 - val_loss: 0.5180 - val_accuracy: 0.7253\n",
            "Epoch 3/10\n",
            "30/30 [==============================] - 5s 120ms/step - loss: 0.4282 - accuracy: 0.8065 - val_loss: 0.4179 - val_accuracy: 0.8001\n",
            "Epoch 4/10\n",
            "30/30 [==============================] - 5s 118ms/step - loss: 0.3135 - accuracy: 0.8799 - val_loss: 0.3555 - val_accuracy: 0.8456\n",
            "Epoch 5/10\n",
            "30/30 [==============================] - 5s 120ms/step - loss: 0.2328 - accuracy: 0.9169 - val_loss: 0.3248 - val_accuracy: 0.8539\n",
            "Epoch 6/10\n",
            "30/30 [==============================] - 5s 120ms/step - loss: 0.1749 - accuracy: 0.9439 - val_loss: 0.3102 - val_accuracy: 0.8677\n",
            "Epoch 7/10\n",
            "30/30 [==============================] - 4s 117ms/step - loss: 0.1312 - accuracy: 0.9633 - val_loss: 0.3051 - val_accuracy: 0.8691\n",
            "Epoch 8/10\n",
            "30/30 [==============================] - 4s 117ms/step - loss: 0.0979 - accuracy: 0.9755 - val_loss: 0.3074 - val_accuracy: 0.8725\n",
            "Epoch 9/10\n",
            "30/30 [==============================] - 4s 118ms/step - loss: 0.0726 - accuracy: 0.9848 - val_loss: 0.3132 - val_accuracy: 0.8706\n",
            "Epoch 10/10\n",
            "30/30 [==============================] - 5s 119ms/step - loss: 0.0539 - accuracy: 0.9905 - val_loss: 0.3222 - val_accuracy: 0.8697\n",
            "49/49 - 3s - loss: 0.3453 - accuracy: 0.8533\n",
            "loss: 0.345\n",
            "accuracy: 0.853\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ABA3VfZ8gAUs"
      },
      "source": [
        "Modelo Bert"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6C-gS9PUvLpT",
        "outputId": "45232af9-a7c8-470e-caa4-c2baffd4cca0"
      },
      "source": [
        "%tensorflow_version 2.x\n",
        "device_name = tf.test.gpu_device_name()\n",
        "if device_name != '/device:GPU:0':\n",
        "  raise SystemError('GPU device not found')\n",
        "print('Found GPU at: {}'.format(device_name))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found GPU at: /device:GPU:0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "id": "cGmIr_BehMji",
        "outputId": "86488fe1-2de0-4e36-9a27-08ebca48884e"
      },
      "source": [
        "dataset = tf.keras.utils.get_file(fname = 'aclImdb_v1.tar.gz',\n",
        "                                  origin = 'http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz',\n",
        "                                  extract = True)\n",
        "\n",
        "IMDB_DATADIR = os.path.join(os.path.dirname(dataset), 'aclImdb')\n",
        "\n",
        "(x_train, y_train), (x_test, y_test), preproc = text.texts_from_folder(datadir = IMDB_DATADIR,\n",
        "                                                                       classes = ['pos', 'neg'],\n",
        "                                                                       maxlen = 200,\n",
        "                                                                       train_test_names = ['train', 'test'],\n",
        "                                                                       preprocess_mode = 'bert')\n",
        "\n",
        "model = text.text_classifier(name = 'bert',\n",
        "                             train_data = (x_train, y_train),\n",
        "                             preproc = preproc)\n",
        "\n",
        "learner = ktrain.get_learner(model = model,\n",
        "                             train_data = (x_train, y_train),\n",
        "                             val_data = (x_test, y_test),\n",
        "                             batch_size = 6)\n",
        "\n",
        "learner.fit_onecycle(lr = 2e-5,\n",
        "                     epochs = 1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "detected encoding: utf-8\n",
            "preprocessing train...\n",
            "language: en\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "done."
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": [
            "Is Multi-Label? False\n",
            "preprocessing test...\n",
            "language: en\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "done."
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": [
            "Is Multi-Label? False\n",
            "maxlen is 200\n",
            "done.\n",
            "\n",
            "\n",
            "begin training using onecycle policy with max lr of 2e-05...\n",
            "4167/4167 [==============================] - 3306s 787ms/step - loss: 0.2959 - accuracy: 0.8736 - val_loss: 0.2191 - val_accuracy: 0.9107\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fe1cb29bb90>"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YMgKVsjUhc0q"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}