NLP_Naive_Bayes_vs_Deep_Learning.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "NLP - Naive Bayes vs Deep Learning.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZmzIzY0LpGyT",
        "colab_type": "text"
      },
      "source": [
        "https://realpython.com/python-keras-text-classification/\n",
        "\n",
        "Check this out\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y-kFsjXA4Dp0",
        "colab_type": "text"
      },
      "source": [
        "# Using NLP to newsgroup documents classification\n",
        "\n",
        "We'll compare Naive Bayes and Deep Learning models used for the classification of [newsgroup texts](http://qwone.com/~jason/20Newsgroups/). \n",
        "\n",
        "What we'll be doing: \n",
        "1. Multinomial Naive Bayes model\n",
        "2. Deep Learning model\n",
        "3. Deep Learning model with pre-trained embedded layer\n",
        "\n",
        "We'll also check the accuracy of the models and some other metrics as well as ploting a confusion matrix.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4nJa3LrtOgE9",
        "colab_type": "text"
      },
      "source": [
        "# Multinomial Naive Bayes"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VXx0Wx5aQOJf",
        "colab_type": "text"
      },
      "source": [
        "## Import Libs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9LEH7QhxOgl5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "import re\n",
        "\n",
        "from sklearn import datasets\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
        "from sklearn.pipeline import Pipeline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x85x58CBQ2b2",
        "colab_type": "text"
      },
      "source": [
        "## Load the Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DeQZFW20OhRM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# load dataset\n",
        "twenty = datasets.fetch_20newsgroups(shuffle = False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "04MZ7FgKOhbS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "outputId": "cabadf7f-31c7-4446-9a4b-06816407d44e"
      },
      "source": [
        "# display labels\n",
        "for trg, trg_name in zip(set(twenty.target), twenty.target_names):\n",
        "    print(f\"{trg}  ==>  {trg_name}\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0  ==>  alt.atheism\n",
            "1  ==>  comp.graphics\n",
            "2  ==>  comp.os.ms-windows.misc\n",
            "3  ==>  comp.sys.ibm.pc.hardware\n",
            "4  ==>  comp.sys.mac.hardware\n",
            "5  ==>  comp.windows.x\n",
            "6  ==>  misc.forsale\n",
            "7  ==>  rec.autos\n",
            "8  ==>  rec.motorcycles\n",
            "9  ==>  rec.sport.baseball\n",
            "10  ==>  rec.sport.hockey\n",
            "11  ==>  sci.crypt\n",
            "12  ==>  sci.electronics\n",
            "13  ==>  sci.med\n",
            "14  ==>  sci.space\n",
            "15  ==>  soc.religion.christian\n",
            "16  ==>  talk.politics.guns\n",
            "17  ==>  talk.politics.mideast\n",
            "18  ==>  talk.politics.misc\n",
            "19  ==>  talk.religion.misc\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fCnPLD2eW55a",
        "colab_type": "text"
      },
      "source": [
        "## Transform Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XBuC824xW5bX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# train test split\n",
        "X_train, X_test, y_train, y_test = train_test_split(twenty.data, twenty.target, test_size=0.3, random_state=10)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Oi9DFvrp72Ft",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "2178af12-9f1a-40c7-f1c6-09f3480af1fb"
      },
      "source": [
        "print(len(X_train))\n",
        "print(len(X_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "7919\n",
            "3395\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H-nhw9prXORp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 486
        },
        "outputId": "e0400675-bdd4-4930-b390-c5e3328531d8"
      },
      "source": [
        "i = 4\n",
        "print(f\"Target: {y_train[i]} ==> {twenty.target_names[y_train[i]]}\")\n",
        "print('-------------------------------------------------------------')\n",
        "print(X_train[i])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Target: 3 ==> comp.sys.ibm.pc.hardware\n",
            "-------------------------------------------------------------\n",
            "From: spring@diku.dk (Jesper Honig Spring)\n",
            "Subject: COMPAQ and standard SIMM RAM (HELP)\n",
            "Organization: Department of Computer Science, U of Copenhagen\n",
            "Lines: 19\n",
            "\n",
            "\n",
            "Hello,\n",
            "\n",
            "\n",
            "Can anyone out there tell me if it is possible to put ordinary standard\n",
            "SIMM RAM chips (70 ns) in a COMPAQ PROLINEA 4/50 or do COMPAQ require \n",
            "special COMPAQ RAM chips. \n",
            "\n",
            "Please (also) email me.\n",
            "\n",
            "\n",
            "Thanks in advance.\n",
            "\n",
            "Jesper\n",
            "-- \n",
            "-------------------------------------------------------------------------------\n",
            "jesper honig spring, spring@diku.dk |        IF ANIMALS BELIEVED IN GOD       \n",
            "university of copenhagen, denmark   |         THE DEVIL WOULD BE A MAN\n",
            "-------------------------------------------------------------------------------\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gAQQCzn74J12",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# english stop words\n",
        "stop_words = frozenset([\"'d\", \"'ll\", \"'re\", \"'s\", \"'ve\", 'a', 'about', 'above', 'after', 'again', 'against', 'all', 'am', 'an', 'and', 'any', 'are', 'as', 'at', 'be', \n",
        "'because', 'been', 'before', 'being', 'below', 'between', 'both', 'but', 'by', 'can', 'could', 'did', 'do', 'does', 'doing', 'don', 'down', \n",
        "'during', 'each', 'few', 'for', 'from', 'further', 'had', 'has', 'have', 'having', 'he', \"he'd\", \"he'll\", \"he's\", 'her', 'here', \"here's\", \n",
        "'hers', 'herself', 'him', 'himself', 'his', 'how', \"how's\", 'i', \"i'd\", \"i'll\", \"i'm\", \"i've\", 'if', 'in', 'into', 'is', 'it', \"it's\", \n",
        "'its', 'itself', 'just', \"let's\", 'me', 'more', 'most', 'my', 'myself', 'no', 'nor', 'not', 'now', 'of', 'off', 'on', 'once', 'only', \n",
        "'or', 'other', 'ought', 'our', 'ours', 'ourselves', 'out', 'over', 'own', 's', 'same', 'she', \"she'd\", \"she'll\", \"she's\", 'should', 'so', \n",
        "'some', 'such', 't', 'than', 'that', \"that's\", 'the', 'their', 'theirs', 'them', 'themselves', 'then', 'there', \"there's\", 'these', \n",
        "'they', \"they'd\", \"they'll\", \"they're\", \"they've\", 'this', 'those', 'through', 'to', 'too', 'under', 'until', 'up', 'very', 'was', \n",
        "'we', \"we'd\", \"we'll\", \"we're\", \"we've\", 'were', 'what', \"what's\", 'when', \"when's\", 'where', \"where's\", 'which', 'while', 'who', \n",
        "\"who's\", 'whom', 'why', \"why's\", 'will', 'with', 'would', 'you', \"you'd\", \"you'll\", \"you're\", \"you've\", 'your', 'yours', 'yourself', 'yourselves'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "os6ESEYPePje",
        "colab": {}
      },
      "source": [
        "def transform_sentences(sentences_array, stop_words):\n",
        "    # transforms the sentences removing: \n",
        "    # stop_words, multiple blank spaces and e-mail addresses\n",
        "    tmp = \"\"\n",
        "    sentences = []\n",
        "    for sentence in sentences_array:\n",
        "        # to lower case\n",
        "        tmp = sentence.lower()\n",
        "        # remove email addresses\n",
        "        tmp = re.sub(r\"\\S*@\\S*\", \" \", tmp)\n",
        "        # remove e-mail common words\n",
        "        tmp = tmp.replace(\"from:\", \"\")\n",
        "        tmp = tmp.replace(\"re:\", \"\")\n",
        "        tmp = tmp.replace(\"subject:\", \"\")\n",
        "        tmp = tmp.replace(\"lines:\", \"\")\n",
        "        tmp = tmp.replace(\"organization:\", \"\")\n",
        "        # remove single chars on the text\n",
        "        tmp = re.sub(rf\"(\\W)\\w(\\W)\", \" \", tmp)\n",
        "\n",
        "        # for each stop_word\n",
        "        for stop_word in stop_words:\n",
        "            # remove stop word\n",
        "            tmp = re.sub(rf\"(\\W){stop_word}(\\W)\", \" \", tmp)\n",
        "\n",
        "        # convert multiple spaces to single ones\n",
        "        tmp = re.sub(r\"\\s+\", \" \", tmp)\n",
        "        # append result to sentences\n",
        "        sentences.append(tmp)\n",
        "    return sentences"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "mqyhqDi3ePkK",
        "colab": {}
      },
      "source": [
        "# pre-process the training and testing set\n",
        "X_train = transform_sentences(X_train, stop_words=stop_words)\n",
        "X_test = transform_sentences(X_test, stop_words=stop_words)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QxKYnfDv9410",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Pipeline: vectorizer => transformer\n",
        "text_clf = Pipeline([('vect', CountVectorizer(stop_words=stop_words)),\n",
        "                     ('tfidf', TfidfTransformer())\n",
        "])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9OROa_t7-Puv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "77cd3c59-a972-49d5-824e-a252eeec3749"
      },
      "source": [
        "# prepare the data to input the model\n",
        "X_train_tf = text_clf.fit_transform(X_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/feature_extraction/text.py:385: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['let', 'll', 're', 've'] not in stop_words.\n",
            "  'stop_words.' % sorted(inconsistent))\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wRP49sto_JdL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "acc07b78-ed1e-46fd-ebe5-a7c40c9f0ec4"
      },
      "source": [
        "print(X_train_tf.shape)\n",
        "print(f\"There are {X_train_tf.shape[0]} sentences in the training set.\")\n",
        "print(f\"There are {X_train_tf.shape[1]} unique words in the training set.\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(7919, 86878)\n",
            "There are 7919 sentences in the training set.\n",
            "There are 86878 unique words in the training set.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bHsIsNaEB4qo",
        "colab_type": "text"
      },
      "source": [
        "## Create the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RIjlrGNGOhxg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# create and fit the Multinomial Naive Bayes model\n",
        "model = MultinomialNB().fit(X_train_tf, y_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W12PtqMVOh41",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# transform the test dataset\n",
        "X_test_tf = text_clf.transform(X_test)\n",
        "\n",
        "# predict\n",
        "y_hat = model.predict(X_test_tf)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "binVIui4OiAG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        },
        "outputId": "7fbceb46-d7f9-43b9-e4a6-7e1a4dba67f6"
      },
      "source": [
        "i = 0\n",
        "print(f\"Predicted: {y_hat[i]} ==> {twenty.target_names[y_hat[i]]}\")\n",
        "print(f\"Original : {y_test[i]} ==> {twenty.target_names[y_test[i]]}\")\n",
        "print(\"--------------------------------------------------------------\")\n",
        "print(X_test[i])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Predicted: 11 ==> sci.crypt\n",
            "Original : 11 ==> sci.crypt\n",
            "--------------------------------------------------------------\n",
            " (rob defriesse) des code shipped canada? article d.: fripp.1993apr22.125402.27561 reply cadre technologies inc. 13 nntp-posting-host: 192 200.19 someone canada asked send public domain des file encryption code legal send thanx. -- eschew obfuscation rob defriesse mail: cadre technologies inc. phone: (401) 351-5950 222 richmond st. fax: (401) 351-7380 providence, ri 02903 speak employer. \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z9idiN4CYhFg",
        "colab_type": "text"
      },
      "source": [
        "## Make predictions on new data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TGvrP2UaOiHb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        },
        "outputId": "7290165b-28e5-4d71-da6b-c81c44f10530"
      },
      "source": [
        "# custom phrases\n",
        "docs_new = ['God is love', \n",
        "            'OpenGL on the GPU is fast', \n",
        "            \"You can not go on without our new product. There's a promotion for you right now!!\", \n",
        "            'You should read the BIBLE! It is the greatest book of all time.']\n",
        "\n",
        "X_new_tfidf = text_clf.transform(docs_new)\n",
        "\n",
        "predicted = model.predict(X_new_tfidf)\n",
        "\n",
        "for doc, category in zip(docs_new, predicted):\n",
        "    print(f'{doc} => {twenty.target_names[category]}')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "God is love => soc.religion.christian\n",
            "OpenGL on the GPU is fast => rec.autos\n",
            "You can not go on without our new product. There's a promotion for you right now!! => sci.crypt\n",
            "You should read the BIBLE! It is the greatest book of all time. => soc.religion.christian\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QAt-1gcuCCav",
        "colab_type": "text"
      },
      "source": [
        "## Model Performance"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9LI5TcgHOiO9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c9f5c6d1-1394-4c65-8651-ca9178a8355d"
      },
      "source": [
        "# check if the model performs well on unseen data\n",
        "accuracy_score = accuracy_score(y_test, y_hat)\n",
        "print(accuracy_score)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.8674521354933726\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "DBk1TQyPZP-4",
        "colab": {}
      },
      "source": [
        "# confusion matrix\n",
        "cm = confusion_matrix(y_test, y_hat)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "hqvyhAbqZP_T",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 486
        },
        "outputId": "479815c9-20f3-4083-f25b-bea8fa66fed6"
      },
      "source": [
        "# text report showing the main classification metrics\n",
        "print(classification_report(y_test, y_hat))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.89      0.77      0.82       149\n",
            "           1       0.94      0.75      0.84       196\n",
            "           2       0.77      0.90      0.83       156\n",
            "           3       0.73      0.86      0.79       161\n",
            "           4       0.93      0.88      0.90       172\n",
            "           5       0.93      0.86      0.89       175\n",
            "           6       0.83      0.85      0.84       149\n",
            "           7       0.87      0.93      0.90       168\n",
            "           8       0.97      0.93      0.95       181\n",
            "           9       0.98      0.97      0.98       193\n",
            "          10       0.95      0.98      0.97       189\n",
            "          11       0.76      0.98      0.85       171\n",
            "          12       0.92      0.77      0.84       184\n",
            "          13       0.96      0.97      0.97       164\n",
            "          14       0.88      0.98      0.93       167\n",
            "          15       0.67      0.97      0.79       197\n",
            "          16       0.85      0.96      0.90       174\n",
            "          17       0.90      0.99      0.94       180\n",
            "          18       0.96      0.68      0.80       139\n",
            "          19       0.95      0.14      0.24       130\n",
            "\n",
            "    accuracy                           0.87      3395\n",
            "   macro avg       0.88      0.86      0.85      3395\n",
            "weighted avg       0.88      0.87      0.86      3395\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Cxxl_iBEZP_e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 643
        },
        "outputId": "7472a699-351b-4a24-9e3d-50f386862010"
      },
      "source": [
        "# plot the confusion matrix\n",
        "plt.rcParams.update({'figure.figsize': (10, 10), 'figure.dpi': 80})\n",
        "plt.figure(figsize=(10, 10))\n",
        "sns.heatmap(cm, annot=True, fmt=\".0f\", linewidths=.5, square = True, cmap = 'Blues_r');\n",
        "plt.ylabel('Actual label');\n",
        "plt.xlabel('Predicted label');\n",
        "all_sample_title = 'Accuracy Score: {:f}'.format(accuracy_score)\n",
        "plt.title(all_sample_title, size = 15);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x800 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fENHXQRGCgTg",
        "colab_type": "text"
      },
      "source": [
        "# Using Tensorflow and Keras"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CVzyaCwxw6u1",
        "colab_type": "text"
      },
      "source": [
        "## Import Libs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VyN5VoNpBeiT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tensorflow as tf\n",
        "import csv\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import re\n",
        "from math import floor\n",
        "\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
        "from sklearn import datasets\n",
        "import sklearn as sk\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.model_selection import train_test_split\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4FzLpfqCcCsl",
        "colab_type": "text"
      },
      "source": [
        "## Load Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "mYo_0pkTzJb7",
        "colab": {}
      },
      "source": [
        "# load the dataset\n",
        "twenty = datasets.fetch_20newsgroups(shuffle = False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "mxhsob-EzJcW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "outputId": "35e27e82-ecb4-40ea-9610-38ea12952f91"
      },
      "source": [
        "# display the labels\n",
        "for trg, trg_name in zip(set(twenty.target), twenty.target_names):\n",
        "    print(f\"{trg}  ==>  {trg_name}\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0  ==>  alt.atheism\n",
            "1  ==>  comp.graphics\n",
            "2  ==>  comp.os.ms-windows.misc\n",
            "3  ==>  comp.sys.ibm.pc.hardware\n",
            "4  ==>  comp.sys.mac.hardware\n",
            "5  ==>  comp.windows.x\n",
            "6  ==>  misc.forsale\n",
            "7  ==>  rec.autos\n",
            "8  ==>  rec.motorcycles\n",
            "9  ==>  rec.sport.baseball\n",
            "10  ==>  rec.sport.hockey\n",
            "11  ==>  sci.crypt\n",
            "12  ==>  sci.electronics\n",
            "13  ==>  sci.med\n",
            "14  ==>  sci.space\n",
            "15  ==>  soc.religion.christian\n",
            "16  ==>  talk.politics.guns\n",
            "17  ==>  talk.politics.mideast\n",
            "18  ==>  talk.politics.misc\n",
            "19  ==>  talk.religion.misc\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "x5M3ybjqzJck"
      },
      "source": [
        "## Transform Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "xaYLoFvGzJcn",
        "colab": {}
      },
      "source": [
        "# train test split\n",
        "X_train, X_test, y_train, y_test = train_test_split(twenty.data, twenty.target, test_size=0.15, random_state=10)\n",
        "\n",
        "# train validation split\n",
        "X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.1, random_state=10)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "IgejCocHzJct",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "5545ea9a-e852-4118-9f49-25b3a06687ef"
      },
      "source": [
        "# size of train, test and validation datasets\n",
        "print(len(X_train))\n",
        "print(len(y_val))\n",
        "print(len(X_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "8654\n",
            "962\n",
            "1698\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "qARvm7Z4zJc0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 434
        },
        "outputId": "0deff789-656c-4fbf-8952-d1785e4e134d"
      },
      "source": [
        "i = 4\n",
        "print(f\"Target: {y_train[i]} ==> {twenty.target_names[y_train[i]]}\")\n",
        "print('-------------------------------------------------------------')\n",
        "print(X_train[i])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Target: 1 ==> comp.graphics\n",
            "-------------------------------------------------------------\n",
            "Organization: City University of New York\n",
            "From: <NT3QC@CUNYVM.BITNET>\n",
            "Subject: RE: Hot new 3D software\n",
            "Lines: 16\n",
            "\n",
            "I don't think speed has been determined, since it has never run on Intel chips.\n",
            "But on the Amiga's Motorola Chips, it was one of the fastest true 'Ray Tracers'\n",
            "\n",
            "I don't think Impulse would port it over and not take speed into consideration.\n",
            "\n",
            "In terms of features, and learning curve... ALL that you stated for 3DS is also\n",
            "true for Imagine, and lots more... But I'll have to admit that after 3 years of\n",
            "use on the Amiga, the learning curve is very steep. This is due ONLY to the\n",
            "manual. It is realy BAD. However, there is a lot of after market support for\n",
            "this product, including regular 'Tips' articles in many magazines such as \"AVID\n",
            "and a great book by Steve Worley called \"Understanding Imagine 2.0\" This book i\n",
            "is not just recommened, IT IS A MUST!\n",
            "\n",
            "I think an important consideration should be price......\n",
            "$3000 for 3DS (Not including \"tool\" packages)\n",
            "Under $500 for Imagine complete.\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "F9ufFemDzJc6",
        "colab": {}
      },
      "source": [
        "# english stop words\n",
        "stop_words = frozenset([\"'d\", \"'ll\", \"'re\", \"'s\", \"'ve\", 'a', 'about', 'above', 'after', 'again', 'against', 'all', 'am', 'an', 'and', 'any', 'are', 'as', 'at', 'be', \n",
        "'because', 'been', 'before', 'being', 'below', 'between', 'both', 'but', 'by', 'can', 'could', 'did', 'do', 'does', 'doing', 'don', 'down', \n",
        "'during', 'each', 'few', 'for', 'from', 'further', 'had', 'has', 'have', 'having', 'he', \"he'd\", \"he'll\", \"he's\", 'her', 'here', \"here's\", \n",
        "'hers', 'herself', 'him', 'himself', 'his', 'how', \"how's\", 'i', \"i'd\", \"i'll\", \"i'm\", \"i've\", 'if', 'in', 'into', 'is', 'it', \"it's\", \n",
        "'its', 'itself', 'just', \"let's\", 'me', 'more', 'most', 'my', 'myself', 'no', 'nor', 'not', 'now', 'of', 'off', 'on', 'once', 'only', \n",
        "'or', 'other', 'ought', 'our', 'ours', 'ourselves', 'out', 'over', 'own', 's', 'same', 'she', \"she'd\", \"she'll\", \"she's\", 'should', 'so', \n",
        "'some', 'such', 't', 'than', 'that', \"that's\", 'the', 'their', 'theirs', 'them', 'themselves', 'then', 'there', \"there's\", 'these', \n",
        "'they', \"they'd\", \"they'll\", \"they're\", \"they've\", 'this', 'those', 'through', 'to', 'too', 'under', 'until', 'up', 'very', 'was', \n",
        "'we', \"we'd\", \"we'll\", \"we're\", \"we've\", 'were', 'what', \"what's\", 'when', \"when's\", 'where', \"where's\", 'which', 'while', 'who', \n",
        "\"who's\", 'whom', 'why', \"why's\", 'will', 'with', 'would', 'you', \"you'd\", \"you'll\", \"you're\", \"you've\", 'your', 'yours', 'yourself', 'yourselves'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T5RczlZn4e9J",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "outputId": "5c089369-5072-4429-be57-98d4e8beb63d"
      },
      "source": [
        "# take a look at our data\n",
        "print(X_train[0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "From: glover@tafs2.mitre.org (Graham K. Glover)\n",
            "Subject: The Cold War: Who REALLY Won?\n",
            "Nntp-Posting-Host: gglover-mac.mitre.org\n",
            "Organization: The MITRE Corporation, McLean, VA\n",
            "Lines: 13\n",
            "\n",
            "If one reasons that the United States of America at one time represented \n",
            "and protected freedom << individual liberty and personal responsibility >> \n",
            "(and I do, in fact, think that this is true) and that totalitarianism << \n",
            "absolute government control and tyranny >> represents freedom's opposite \n",
            "(which it does), did the USA really win the cold war?\n",
            "\n",
            "Standard disclaimers ALWAYS apply!\n",
            "\n",
            "----------------\n",
            "Graham K. Glover\n",
            "----------------\n",
            "\n",
            "UNMUTUAL\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AtVwb53Hx0we",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def transform_sentences(sentences_array, stop_words):\n",
        "    # transforms the sentences removing: \n",
        "    # stop_words, multiple blank spaces and e-mail addresses\n",
        "    tmp = \"\"\n",
        "    sentences = []\n",
        "    for sentence in sentences_array:\n",
        "        # to lower case\n",
        "        tmp = sentence.lower()\n",
        "        # remove email addresses\n",
        "        tmp = re.sub(r\"\\S*@\\S*\", \" \", tmp)\n",
        "        # remove e-mail common words\n",
        "        tmp = tmp.replace(\"from:\", \"\")\n",
        "        tmp = tmp.replace(\"re:\", \"\")\n",
        "        tmp = tmp.replace(\"subject:\", \"\")\n",
        "        tmp = tmp.replace(\"lines:\", \"\")\n",
        "        tmp = tmp.replace(\"organization:\", \"\")\n",
        "        # remove single chars on the text\n",
        "        tmp = re.sub(rf\"(\\W)\\w(\\W)\", \" \", tmp)\n",
        "\n",
        "        # for each stop_word\n",
        "        for stop_word in stop_words:\n",
        "            # remove stop word\n",
        "            tmp = re.sub(rf\"(\\W){stop_word}(\\W)\", \" \", tmp)\n",
        "\n",
        "        # convert multiple spaces to single ones\n",
        "        tmp = re.sub(r\"\\s+\", \" \", tmp)\n",
        "        # append result to sentences\n",
        "        sentences.append(tmp)\n",
        "    return sentences"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yQ7_he2Dqkj2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# pre-process the training and testing set\n",
        "X_train = transform_sentences(X_train, stop_words=stop_words)\n",
        "X_val = transform_sentences(X_val, stop_words=stop_words)\n",
        "X_test = transform_sentences(X_test, stop_words=stop_words)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uTk1222C461O",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "2522c27c-8562-439e-98fb-292a1b07ecbb"
      },
      "source": [
        "print(len(X_train))\n",
        "print(len(X_val))\n",
        "print(len(X_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "8654\n",
            "962\n",
            "1698\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eY2mtGHTPHmp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "40ee040b-3829-461e-e823-627f32447cf1"
      },
      "source": [
        "i = 0\n",
        "print(X_train[i])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            " (graham glover) cold war: really won? nntp-posting-host: gglover-mac.mitre.org mitre corporation, mclean, va 13 one reasons united states america one time represented protected freedom << individual liberty personal responsibility >> fact, think true) totalitarianism << absolute government control tyranny >> represents freedom opposite , usa really win cold war? standard disclaimers always apply! ---------------- graham glover ---------------- unmutual \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OpMgastJpGSQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "c8cbbf8d-8859-4a38-cccf-12b71fb5ec7f"
      },
      "source": [
        "count_word = 0\n",
        "for sentence in X_train:\n",
        "    # count the words on training set\n",
        "    count_word += len(sentence.split())\n",
        "\n",
        "# mean number of words on training set\n",
        "max_words = floor(count_word/len(X_train))\n",
        "print(f\"max_words in the model: {max_words}\")\n",
        "\n",
        "# the maximum number of words to keep, based on word frequency\n",
        "# Only the most common `num_words-1` words will be kept\n",
        "vocab_size = 20000\n",
        "# number of dimentions on the embedding layer\n",
        "embedding_dim = 16\n",
        "# truncating type of the sentences\n",
        "trunc_type='post'\n",
        "# padding type of sequences of words\n",
        "padding_type='post'\n",
        "# out-of-value token\n",
        "oov_tok = \"<OOV>\""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "max_words in the model: 164\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VivzIB_ttLh5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# vectorize a text corpus, by turning each text into a sequence of \n",
        "# integers (each integer being the index of a token in a dictionary)\n",
        "tokenizer = Tokenizer(num_words = vocab_size, oov_token=oov_tok)\n",
        "tokenizer.fit_on_texts(X_train)\n",
        "word_index = tokenizer.word_index"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dSas-hPtCNNd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "outputId": "aae35f5f-f901-495a-c781-9c13e7bff398"
      },
      "source": [
        "# top 20 words and it's tokens\n",
        "i=0\n",
        "for word in word_index:\n",
        "    print(f\"{word} : {word_index[word]}\")\n",
        "    i+=1\n",
        "    if i==20: \n",
        "        break"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<OOV> : 1\n",
            "'ax : 2\n",
            "one : 3\n",
            "writes : 4\n",
            "' : 5\n",
            "article : 6\n",
            "people : 7\n",
            "like : 8\n",
            "university : 9\n",
            "posting : 10\n",
            "know : 11\n",
            "host : 12\n",
            "get : 13\n",
            "max : 14\n",
            "nntp : 15\n",
            "m : 16\n",
            "think : 17\n",
            "also : 18\n",
            "use : 19\n",
            "new : 20\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W_hgQkTZ52d7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "db143fc2-cfdd-4bdb-ee96-983c287604de"
      },
      "source": [
        "print(f\"Total of tokens: {len(word_index)}\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Total of tokens: 89357\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tof6Q045Drht",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Transforms each text in texts to a sequence of integers.\n",
        "train_sequences = tokenizer.texts_to_sequences(X_train)\n",
        "val_sequences = tokenizer.texts_to_sequences(X_val)\n",
        "test_sequences = tokenizer.texts_to_sequences(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2rc4wFtDLqOV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "af5a390b-0dc6-45ef-9697-2261c36c81f1"
      },
      "source": [
        "print(len(train_sequences))\n",
        "print(train_sequences[0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "8654\n",
            "[2416, 19129, 1669, 431, 56, 247, 15, 10, 12, 1, 272, 4897, 2073, 4897, 653, 7770, 1616, 214, 3, 975, 785, 369, 949, 3, 21, 6975, 2260, 932, 903, 2953, 678, 1591, 137, 17, 125, 1, 1716, 83, 236, 8838, 3041, 932, 3042, 84, 56, 287, 1669, 431, 305, 3810, 248, 1210, 2416, 19129, 1]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I1LyN3r5LnDH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Pads sequences to the same length. (required to input the model)\n",
        "train_padded = pad_sequences(train_sequences, padding=padding_type, maxlen=max_words)\n",
        "val_padded = pad_sequences(val_sequences, padding=padding_type, maxlen=max_words)\n",
        "test_padded = pad_sequences(test_sequences, padding=padding_type, maxlen=max_words)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OcmuE0eVMBAH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "outputId": "689e9acc-2359-4e67-dd6c-27cff734ed97"
      },
      "source": [
        "print(len(train_padded))\n",
        "print(train_padded[0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "8654\n",
            "[ 2416 19129  1669   431    56   247    15    10    12     1   272  4897\n",
            "  2073  4897   653  7770  1616   214     3   975   785   369   949     3\n",
            "    21  6975  2260   932   903  2953   678  1591   137    17   125     1\n",
            "  1716    83   236  8838  3041   932  3042    84    56   287  1669   431\n",
            "   305  3810   248  1210  2416 19129     1     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0     0     0     0     0\n",
            "     0     0     0     0     0     0     0     0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sGlBfQAiLopl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# inserts a dimension of size 1 at the end of the array (required shape to fit the keras model training)\n",
        "y_train = tf.expand_dims(y_train, -1)\n",
        "y_val = tf.expand_dims(y_val, -1)\n",
        "y_test = tf.expand_dims(y_test, -1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z6-6BvG-3139",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        },
        "outputId": "a972bc05-d4bd-4c7f-e33c-69485b7afbf3"
      },
      "source": [
        "print(train_padded.shape)\n",
        "print(y_train.shape)\n",
        "\n",
        "print(val_padded.shape)\n",
        "print(y_val.shape)\n",
        "\n",
        "print(test_padded.shape)\n",
        "print(y_test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(8654, 164)\n",
            "(8654, 1)\n",
            "(962, 164)\n",
            "(962, 1)\n",
            "(1698, 164)\n",
            "(1698, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EJjUBiSUiG9X",
        "colab_type": "text"
      },
      "source": [
        "## Create the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rQ23sibp37-s",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# create the model\n",
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Embedding(vocab_size, embedding_dim, input_length=train_padded.shape[1]),\n",
        "    tf.keras.layers.Dropout(0.2),\n",
        "    tf.keras.layers.Conv1D(128, 5, activation='relu'),\n",
        "    tf.keras.layers.GlobalAveragePooling1D(),\n",
        "    tf.keras.layers.Dropout(0.5),\n",
        "    tf.keras.layers.Dense(100, activation='relu'),\n",
        "    tf.keras.layers.Dropout(0.2),\n",
        "    tf.keras.layers.Dense(20, activation='softmax')\n",
        "])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sebNlVwi8zna",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 434
        },
        "outputId": "43f41d85-841f-45a6-b0c8-4011e6d2202e"
      },
      "source": [
        "# compile model\n",
        "model.compile(loss='sparse_categorical_crossentropy',optimizer='adam',metrics=['accuracy'])\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_20\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding_22 (Embedding)     (None, 164, 16)           320000    \n",
            "_________________________________________________________________\n",
            "dropout_20 (Dropout)         (None, 164, 16)           0         \n",
            "_________________________________________________________________\n",
            "conv1d_47 (Conv1D)           (None, 160, 128)          10368     \n",
            "_________________________________________________________________\n",
            "global_average_pooling1d_29  (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dropout_21 (Dropout)         (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_40 (Dense)             (None, 100)               12900     \n",
            "_________________________________________________________________\n",
            "dropout_22 (Dropout)         (None, 100)               0         \n",
            "_________________________________________________________________\n",
            "dense_41 (Dense)             (None, 20)                2020      \n",
            "=================================================================\n",
            "Total params: 345,288\n",
            "Trainable params: 345,288\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eaelAOWmDNNE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "b905a75c-df4f-4d17-9285-d701551af9ed"
      },
      "source": [
        "num_epochs = 40\n",
        "history = model.fit(train_padded, y_train, epochs=num_epochs, \n",
        "                    validation_data=(val_padded, y_val), verbose=2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/40\n",
            "271/271 - 3s - loss: 2.7875 - accuracy: 0.0937 - val_loss: 2.3589 - val_accuracy: 0.1861\n",
            "Epoch 2/40\n",
            "271/271 - 3s - loss: 2.2230 - accuracy: 0.1992 - val_loss: 1.9670 - val_accuracy: 0.3472\n",
            "Epoch 3/40\n",
            "271/271 - 3s - loss: 1.7697 - accuracy: 0.3453 - val_loss: 1.4975 - val_accuracy: 0.5031\n",
            "Epoch 4/40\n",
            "271/271 - 3s - loss: 1.3226 - accuracy: 0.5225 - val_loss: 1.1460 - val_accuracy: 0.6518\n",
            "Epoch 5/40\n",
            "271/271 - 2s - loss: 0.9732 - accuracy: 0.6578 - val_loss: 0.9721 - val_accuracy: 0.6954\n",
            "Epoch 6/40\n",
            "271/271 - 3s - loss: 0.7452 - accuracy: 0.7490 - val_loss: 0.8645 - val_accuracy: 0.7453\n",
            "Epoch 7/40\n",
            "271/271 - 3s - loss: 0.5763 - accuracy: 0.8121 - val_loss: 0.8605 - val_accuracy: 0.7547\n",
            "Epoch 8/40\n",
            "271/271 - 3s - loss: 0.4485 - accuracy: 0.8545 - val_loss: 0.8480 - val_accuracy: 0.7807\n",
            "Epoch 9/40\n",
            "271/271 - 3s - loss: 0.3813 - accuracy: 0.8825 - val_loss: 0.8804 - val_accuracy: 0.7713\n",
            "Epoch 10/40\n",
            "271/271 - 2s - loss: 0.3105 - accuracy: 0.9040 - val_loss: 0.9005 - val_accuracy: 0.7879\n",
            "Epoch 11/40\n",
            "271/271 - 3s - loss: 0.2804 - accuracy: 0.9138 - val_loss: 0.9079 - val_accuracy: 0.7911\n",
            "Epoch 12/40\n",
            "271/271 - 3s - loss: 0.2350 - accuracy: 0.9292 - val_loss: 0.9462 - val_accuracy: 0.7859\n",
            "Epoch 13/40\n",
            "271/271 - 3s - loss: 0.2063 - accuracy: 0.9390 - val_loss: 0.9779 - val_accuracy: 0.7931\n",
            "Epoch 14/40\n",
            "271/271 - 3s - loss: 0.1744 - accuracy: 0.9519 - val_loss: 1.0266 - val_accuracy: 0.7859\n",
            "Epoch 15/40\n",
            "271/271 - 3s - loss: 0.1676 - accuracy: 0.9524 - val_loss: 1.0438 - val_accuracy: 0.7879\n",
            "Epoch 16/40\n",
            "271/271 - 3s - loss: 0.1524 - accuracy: 0.9570 - val_loss: 1.0507 - val_accuracy: 0.7963\n",
            "Epoch 17/40\n",
            "271/271 - 3s - loss: 0.1383 - accuracy: 0.9607 - val_loss: 1.0933 - val_accuracy: 0.8035\n",
            "Epoch 18/40\n",
            "271/271 - 2s - loss: 0.1218 - accuracy: 0.9645 - val_loss: 1.1461 - val_accuracy: 0.7963\n",
            "Epoch 19/40\n",
            "271/271 - 3s - loss: 0.1101 - accuracy: 0.9693 - val_loss: 1.1760 - val_accuracy: 0.7931\n",
            "Epoch 20/40\n",
            "271/271 - 3s - loss: 0.1096 - accuracy: 0.9674 - val_loss: 1.1773 - val_accuracy: 0.7921\n",
            "Epoch 21/40\n",
            "271/271 - 3s - loss: 0.1085 - accuracy: 0.9718 - val_loss: 1.1773 - val_accuracy: 0.7942\n",
            "Epoch 22/40\n",
            "271/271 - 3s - loss: 0.0986 - accuracy: 0.9741 - val_loss: 1.1631 - val_accuracy: 0.8025\n",
            "Epoch 23/40\n",
            "271/271 - 3s - loss: 0.0891 - accuracy: 0.9757 - val_loss: 1.2017 - val_accuracy: 0.7973\n",
            "Epoch 24/40\n",
            "271/271 - 3s - loss: 0.0934 - accuracy: 0.9737 - val_loss: 1.2810 - val_accuracy: 0.7859\n",
            "Epoch 25/40\n",
            "271/271 - 3s - loss: 0.0781 - accuracy: 0.9777 - val_loss: 1.2553 - val_accuracy: 0.7859\n",
            "Epoch 26/40\n",
            "271/271 - 3s - loss: 0.0785 - accuracy: 0.9767 - val_loss: 1.3540 - val_accuracy: 0.7817\n",
            "Epoch 27/40\n",
            "271/271 - 3s - loss: 0.0808 - accuracy: 0.9765 - val_loss: 1.3210 - val_accuracy: 0.7848\n",
            "Epoch 28/40\n",
            "271/271 - 3s - loss: 0.0676 - accuracy: 0.9817 - val_loss: 1.3133 - val_accuracy: 0.7890\n",
            "Epoch 29/40\n",
            "271/271 - 3s - loss: 0.0649 - accuracy: 0.9799 - val_loss: 1.3654 - val_accuracy: 0.7848\n",
            "Epoch 30/40\n",
            "271/271 - 3s - loss: 0.0618 - accuracy: 0.9821 - val_loss: 1.4333 - val_accuracy: 0.7848\n",
            "Epoch 31/40\n",
            "271/271 - 2s - loss: 0.0643 - accuracy: 0.9814 - val_loss: 1.4343 - val_accuracy: 0.7900\n",
            "Epoch 32/40\n",
            "271/271 - 3s - loss: 0.0594 - accuracy: 0.9830 - val_loss: 1.4117 - val_accuracy: 0.7942\n",
            "Epoch 33/40\n",
            "271/271 - 2s - loss: 0.0611 - accuracy: 0.9847 - val_loss: 1.4783 - val_accuracy: 0.7921\n",
            "Epoch 34/40\n",
            "271/271 - 3s - loss: 0.0595 - accuracy: 0.9843 - val_loss: 1.4783 - val_accuracy: 0.8035\n",
            "Epoch 35/40\n",
            "271/271 - 2s - loss: 0.0553 - accuracy: 0.9843 - val_loss: 1.4881 - val_accuracy: 0.7963\n",
            "Epoch 36/40\n",
            "271/271 - 3s - loss: 0.0555 - accuracy: 0.9839 - val_loss: 1.4602 - val_accuracy: 0.7890\n",
            "Epoch 37/40\n",
            "271/271 - 3s - loss: 0.0599 - accuracy: 0.9841 - val_loss: 1.4989 - val_accuracy: 0.7879\n",
            "Epoch 38/40\n",
            "271/271 - 3s - loss: 0.0454 - accuracy: 0.9881 - val_loss: 1.5302 - val_accuracy: 0.7890\n",
            "Epoch 39/40\n",
            "271/271 - 3s - loss: 0.0478 - accuracy: 0.9873 - val_loss: 1.5902 - val_accuracy: 0.7827\n",
            "Epoch 40/40\n",
            "271/271 - 3s - loss: 0.0486 - accuracy: 0.9865 - val_loss: 1.5324 - val_accuracy: 0.7921\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kSPijPxciKtX",
        "colab_type": "text"
      },
      "source": [
        "## Check how the model performs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BX97-bAVDNET",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "fcc25875-39bd-450b-fcef-50dcae171348"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "def plot_graphs(history, string):\n",
        "  plt.plot(history.history[string])\n",
        "  plt.plot(history.history['val_'+string])\n",
        "  plt.xlabel(\"Epochs\")\n",
        "  plt.ylabel(string)\n",
        "  plt.legend([string, 'val_'+string])\n",
        "  plt.show()\n",
        "  \n",
        "plot_graphs(history, \"accuracy\")\n",
        "plot_graphs(history, \"loss\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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+3+DBg3nxxRf5t3/7N+rr63G5XNxxxx2ce+65gNM6+sQTT3DNNdd091soImFgg4mpt5eMsdayp7aForLOs4w3l9fT4j14q15sjGFQbAyJce4DgmpSfAyDYt0MinPhMqZTN6rBOF2sxvk6TPDrce7rOOfLqkZWl9Swu7ajhyolwc1xYzKYOdIJnseOGExaYuwBtaUkxJKSEMuozKQuff3+gKW22UtCrCsk3bnGGOLchji3i5Ref3aR3mVsKNYW6EPDhw+3O3fu7HTM7/dTVFTEhAkT1PoWYX7/+9+zceNG/vSnPx3yHP38RHqfzx/oGB/YaYKKh9pmH3ubPZ0nrgTHEdY1ewlYS2ZyHFnJ8WQlOy102cHL/Y9lJMUdMB6tqqG1fXZxYZkTOotK66lv9XU6Ly81ITjRI5n0xDiaPX4aPT6aPX6aPH6aPL7gZcf1tnMOFWC7wxgYn5PcHjxnjhzMuOxktR6K9AJjzC5r7fCD3aeWUekzU6ZMwRjDa6+9Fu5SRKJGi9dPRX0r5fUtlNe1Ur7f9bK6li6NRWwTG2NIG+SMCxyVmUj6IKcVsLLRQ1VDK1sqGo4Y/AYnxpKVHE9Kgpvt1U1UNng63Z+eGMvkoakUBJe1KchLYUJOykFbHLsqELA0e/00e/3tS+8ELFgswZsEgpNhbPB4wHYs12Mt5KTGk5rQ8xpEpGcURiPcY4891mlMZpsHHnigfcZ6f7F+/fpwlyDS77R1Z2/cU8eG3XV8WdlI2T5hs7bZe8jHxrtd5KTGMzorieNSMhicFOdMRhkU2z5RpS14th0bFBtz2C55ay1NHj+VDa3BDw+VDa1U7XNZEbxve3UTwwcncvrE3PalbSbkJZOdHN/r3f4ulyEp3k1SvF7WRPobddNLxNHPT/oLnz/Qq0ukeP0Bissb2LC7zgmfwY+9TZ0DZ3K8m5zg5Jac1ARyUuLJTY0nJ8W5npMaT3ZKQpeXExIRCbUB102vf77RQT9HiSTl9S18sauWtTtr+WJXLet21VJW10pSXAwZyQeuL5mRFBdcwqdjKZ+2lkl3jIvaZm97a+eG4GVxeQMef0cXeGJcDBPzUpg8NJXJQ9KYNCSF8bkpJKv1T0SiSFT+R3NmSRq8Xq9a1vohr9fb/jMUCYeK+tb2wNkWPkvrOmZYu12GgrwUZo0aTH2Ls6RPRX0rm0rru7S+Y0q8+6CTd04an8WkISlMHpLG5KGpjMpI1OQZEYl6URtG09PTKSsrY9iwYQo1/Yi1lrKyMtLT0/Vzk5Cz1lLZ4GH97lrW7XTC57pdteyp7Rw8J+SmcMqEbI4Znsa0YWkU5KWQEHvwN7rNHn/HmpLBNSerGtrWnnQuaxq95KUlMHlIKpOHpjJpSCoZSXF99WWLiESUqBwzChAIBCgpKenyjkYSORISEhg1ahQul3b4kN5hrWV3bQvF5Q1sLqtnS0UDm8saKK5o6DQeM8ZlGJ+TzNRhaUwbnsbU4elMPEzwFBGRrhlwY0bB2RN9zJgxBAIB+nvgHkiMMQqh0mP+gGV7dZMTOsvrKS5voLi8gS3lDTR6Ou+5nZ4YS352Mvk5yUwaksrU4WlMHpKq4Cki0seiNoy2UbARiT7NHj9bKhqCH43OZXkDWysb8ew3ZjMnJZ4ZI9Od4JmbQn52MuNzk8lMitNQEBGRCBD1YVRE+qe28ZxtobO4PBg8yxvYtbe507nGwLD0QZw4LpP8nGTG56QwLsdp9UwbpEXMRUQimcKoiIRdbbOX9btrWb+rjqLgmM4tFY0HLOge73YxNjuZY0emk5+TzLhs52NMVhKD4tS9LiLSHymMikifqm708MWuWr4Ihs8vdtdSUtXU6Zys5DgK8lKCYTPJaeXMTmZY+iAtdSQiEmUURkUkZNoWiv9iVx1f7Kpl/e66Tl3sxsDYrCQumDGUY4alMWVoGhPzUhisZY5ERAYMhVEROajPd+xl3a5afP4AXr/FGwjg81vndsC2H/cFj+97vaHVx8Y9dZTXt7Y/X9uySRfPHM7UYakcMyyNSUNStZe4iMgAp1cBEenky8pGfvvqJl5bX9rj54iLcTE+N5nTJuYwZVgaxwQXdteySSIisj+FUREBoKqhlfvf2sxfPtmOL2BZMCWXK44fRYLbhTvGRWyMwe0KXsa4cLsMsTEu3DGGWJdz2XZd4zpFRKSrFEZFBrhmj58/f/Ql//XuFhpafRw7Mp1fLJzEnNEZ4S5NREQGAIVRkQHKH7C8sGon97xRRGldC6MyE/ndJdP42jF5WgxeRET6jMKoyAD0XlEFv3llI5tK6xmcGMuvzp3MN48fRZxbO5aJiEjfUhgVGUDW767l7lc38cHmSuLdLv7PV8bxf74yjtQE7VIkIiLhoTAqMgDs2tvMPW8U8uLqXQBcPHM4/3bmBIamDwpzZSIiMtApjIpEsfK6Fv780Tb+/NGXeHwB5o/P4pavTWTK0LRwlyYiIgIojIpEHWstK76sZsnHJbz2RSm+gGViXgo/XziJUyZkh7s8ERGRThRGRaJEY6uPF1fv4umPS9hUWg/AifmZLDphFF+dnEeM1v4UEZEIpDAq0s8Vlzfw9MclPL9yJ/WtPlLi3Vw1bzRXnjCK/JzkcJcnIiJyWAqjIv2Qzx/gXxvLWPJxCR8VVwFQkJvCormjuPDYYdrvXURE+g29Yon0IxX1rfx1xXaeWbGdPbUtuF2Gc6YNYfHc0cwZPViL1YuISL+jMCrSD2zYXcd/v7eFV7/Yg9dvyU2N50dnTODy40aQk5oQ7vJERER6TGFUJIK1eP3c/9ZmHn5/K/6AZe7YTBbPHcUZk3OJjdFuSSIi0v8pjIpEqJUl1fz0ubVsqWjkmGGp3H3RNI4ZpvVBRUQkuiiMikSYJo+P371WyJPLtxEb4+KnZxVw7fyxuNUSKiIiUUhhVCSCfFRcyS0vrGVHdTOzRg3mtxdP0/JMIiIS1RRGRSJAXYuXu5Zu5K+f7mBQbAy/Oncyi+eO1kL1IiIS9RRGRcLsXxvK+Pf/WUdZXSsn5mdy90XTGJGRGO6yRERE+oTCqEiYVDW0cvv/buDlz3eTkuDmtxdP5bLZI7RWqIiIDCgKoyJ9zFrL/67dw20vr6e60cMZk3K484Kp5KVpvVARERl4FEZF+lBZXQv//uIX/GtjGRlJcfy/b8zgvOlD1RoqIiIDlsKoSB/YUtHAkuUlPLdyJw2tPs6dPpTbzp1MZnJ8uEsTEREJK4VRkRDx+QP8a2M5Sz7exkfFVQAU5Kbwb2dO4MwpeWGuTkREJDIojIr0sor6Vv726Xae+WQ7u2tbcLsM50wbwqITRnHcmAx1yYuIiOxDYVSkF1hrWVlSw1PLS3j1iz14/Zbc1Hh+dMYELj9uBDmpmpwkIiJyMAqjIkehyePjpTW7eWp5CRv31AEwd2wmi+eO4ozJucRqC08REZHDUhgV6YGtFQ0s+diZkFTf4iM53s3iuaNYdMIoxuemhLs8ERGRfkNhVKQb/AHL7f+7nqeWlwAwITeZn541mguPHUZyvP6cREREukuvniJd1OL186O/reHVL0o5bnQGN585geM1IUlEROSoKIyKdEF9i5drn1rJ8q1VnDNtCPdcNp14d0y4yxIREen3FEZFjqCivpWrHl/B+t11LJ47il+dO4UYl1pDRUREeoPCqMhh7KhuYtGfPmFbVRM/OmMCPzg9X93yIiIivUhhVOQQNu6pY/GfV1DZ0MqdFxzDlSeMCndJIiIiUUdhVOQgVnxZzXee/JRWb4AHr5jJwqlDwl2SiIhIVFIYFdnPmxvKuOGZVbhdhieunsO8/KxwlyQiIhK1FEZF9vH3T3dwywtrGZwYxxNXH8fU4WnhLklERCSqKYyK4Owt//D7W7n71U0MHzyIJd85njFZSeEuS0REJOopjMqAFwhY7nplI499+CUT81J48tvHkZuaEO6yREREBgSFURnQvP4AP3tuLS+s3sWc0YN57FtzSBsUG+6yREREBgyFURmwmj1+rv/LSt4prOCMSTn88YqZJMRqVyUREZG+pDAqA1KTx8fiP63gs5IaLpk1nLsvmoo7xhXuskRERAYchVEZcAIBy81/+5zPSmr4zkljuPXsSdpVSUREJEzUFCQDzu/fKOS19aWcM22IgqiIiEiYKYzKgPLcyp089O4Wpo9I5/eXTlcQFRERCTOFURkwPt1Wzc9fWMvQtAQeXTxLk5VEREQigMKoDAjbq5r43pKVxMa4eOxbc8hJ0TqiIiIikUATmCTq1bV4+faTn1LT5OHRRbOZPDQ13CWJiIhIkFpGJar5/AFueGY1xeUN/OJrkzhjcm64SxIREZF9KIxKVLvjnxt4v6iCr88ewXfnjwl3OSIiIrIfhVGJWk8t38aTy0s4YWwGd1xwjGbOi4iIRCCFUYlK7xdVcPv/bmB0ZiL/feUs4tz6VRcREYlEeoWWqLO5rJ7v/2UVSXEx/OmqOaQnxoW7JBERETkEzaaXqFLd6OE7T35Gs9fPk98+jnHZyeEuSURERA5DYVSiRqvPz3VLVrK9uom7LpzKiflZ4S5JREREjkDd9BIVrLX84oUvWLGtmu+cNIYrjh8Z7pJERESkCxRGJSr893tbeX7VTk6bmMMvFk4KdzkiIiLSRQqj0u+99kUpv31tExPzUrj/8mOJcWkJJxERkf5CYVT6tQ276/jR39aQlRzHY9+aTXK8hkGLiIj0Jwqj0m9Za/nFi+vw+gM8vGg2wwcnhrskERER6SaFUem3lq7bw5ode7nyhFHMGjU43OWIiIhIDyiMSr/U6vPz29c2kRLv5genjw93OSIiItJDCqPSLy1ZXsKO6ma+f1o+GUnaYUlERKS/UhiVfmdvk4f739rMsPRBXDVvdLjLERERkaOgMCr9zgNvF1PX4uOnZxWQEBsT7nJERETkKCiMSr9SUtXIU8u3MX14GudOGxruckREROQoKYxKv/K71wrx+i2/WDgJlxa3FxER6fcURqXfWFlSzdJ1ezhzci7Hj80MdzkiIiLSCxRGpV+w1nLn0o24XYZbvjYx3OWIiIhIL1EYlX7hlXWlrN6+l28eP5Kx2cnhLkdERER6icKoRDwtcC8iIhK9FEYl4i1ZXsL26iauPzWfzOT4cJcjIiIivUhhVCLa3iYPD7xdzLD0QVx94uhwlyMiIiK9TGFUItof3y6mttnLTxZogXsREZFopDAqEWt7VRNPLt/GtOFpnDddC9yLiIhEI4VRiVi/fX2TFrgXERGJcgqjEpFWltSwdO0evjo5lxO0wL2IiEjUUhiViGOt5a5XNhKjBe5FRESinsKoRJzXvihlZUkN3zx+JOO0wL2IiEhUUxiViOLxBbj7tU0kx7u5SQvci4iIRD2FUYkoSz4uoaSqietPHacF7kVERAYAhVGJGLVNXu5/azND0xL49oljwl2OiIiI9AGFUYkYf3xns7PA/Vla4F5ERGSgUBiViLC9qoknl5UwdVga508fFu5yREREpI8ojEpE+N3rm/D4A1rgXkREZIBRGJWw21nTxNJ1ezi1IJu547TAvYiIyECiMCph9+KqXVgLV54wKtyliIiISB9TGJWwstby/KqdZCXHcfKE7HCXIyIiIn1MYVTCamVJDduqmrhgxjBiY/TrKCIiMtCE/NXfGDPeGLPMGFNkjPnUGDPlIOe4jDH3GmM2GGPWGmPeMcbkh7o2Cb/nV+0E4OJZw8NcifRrTdVQshy8zeGuRER6S+0uKF0H1oa7Egkxdx98joeBR6y1TxhjLgGeAObsd855wInAdGut1xhzK3AXcFkf1Cdh0uL188/P9zBlaCqThqSGuxzpb6q/hMJXYNMrsH05WD/EJsK402Di2TB+ASRpQhw+D2x5Cza/ASlDYPgcGDYLEvQ3JxGqshg+vBc+/6vzd50yFAq+5vxdj54P7rhwVyi9LKRh1BiTA8wGzgweeh74ozEm31pbvM+pFogHEowxPiAV2BnK2iT8Xl9fSn2rj4tnqlVUusBa2L06GECXQvkG57h7EEw4C4ZMg63vBe//JxgXjJwLBQth4kLIGBve+vtSIOAE9HX/gA3/A801+51gIGcSDJ8Nw49zAmrWBHBpqIyEUdkG+OAeWP8C2IATPIdMh8JX4bM/OR/xqQd6WhAAACAASURBVJB/hhNM88+AQenhrlp6gbEhbP42xswCnrHWFuxzbAVwi7X27X2OuYB7gWuAemAXcIq1tuFIn2P48OF2507l1v5o0Z8+YfmWKj75xenah14OzueBbe87rZ+Fr0L9bud4YiZMCLaUjP0KxCV2PKaxEoped0Jp8VvgC3bdZ09yQunEs2HIsdEXvKyFsvWw7u+w7nmoC/5fzJkMUy+FyedDUxXs/BR2rICdn3WcAxCfBsNnOeF0RLD1dNDg8HwtMrDsXgPv/6fzJhKckDn/xzBqrnPbWqgscu7f9Ars+sw57nLD6JOg4GznbztNDRuRzBizy1p70B9SpITR43C65S8B6oC7gaHW2isP8pw3Aze33U5LSxu2d+/ekH0NEhqltS3Mu/stTp+Uy6OLZ4e7HIkkzXuh+F9O6+fmN8FT7xzPGOe84BScDSOOA1cXtoz1NMHWd6FwKRS+Bk2VzvGUIU63X8HZMGY+uPvxm6GaEvjiOVj7D6jY6BxLGwFTL3FCaO4Bw/Q71O3uHE53rwZ/a8f9WQVOq+m4U2HyBRDTFyO7ZMDYscIJoZvfcG4XnA0n/5vzRuhw6kudN6eFrzi9IW2/s0OmdwTT3GPAaAOVSBLOMJoDFAMZ1lqfMcYAe4CT9u2mN8b8Edhtrb0reHsK8Ia19oj7QqpltH966N1ifvdaIQ8vmsWCKXnhLkfCraXOCZ/r/gFfvgcBn3N82Oxga+Y5Tjfy0by4BPxO8Nq01HkRqwr+C4pLgaEzYPAoGDwaBo8JXo52WmAj8QWtsRLWv+h8v3Z84hwblAFTLnQC6Ijje9by6/NA2TrY8anzvdq5AvZud+4bPBpO+hFMv0Jj9qTnrIVtHzoh9Mv3AANTLnBaQvOO6f7ztTY4Y6I3LXV6RFqCjVPpI50hOgULYdQ8iInt1S9Dui9sYTT4yd8FnthnAtMt1trZ+51zM7AQWGit9RhjfgacZq1dcKTnVxjtf6y1nHHve1Q3evjkF2cQ546y7lLpGl+r0wK69u9Q9Br4WsAV63S7TzzbablMCeEblYqijhbT8g3QWnfgObFJHcF0/4/0kRCbEJrarAVPAzSUO8GzsRwaK6ChwumiLH6rY8JWwUKYdhmMPTU0IbG+FFY9BcsfdF7oU4fBiT+EmYsgdlDvfz6JHNY6v4Pl653xnM3VkJTd+SM5xxnOcaSeCmud39v3/xN2fAwmxnnjNP9myC44/GO7yu91xkpvesX52257I5WQDhMWOH8r+adDfErvfD7plnCH0QKcGfSZOF3wV1tr1xljHgNetta+bIyJB/4InAR4gVLgOmvt1iM9v8Jo/7N6ew0XPrSMq+aN5rbzDtOFKD3naYKt73TMNM87xum+mnBmeMcBBgJQ8pEzrnHDS9BS6xwfdZLTrTz5fEjM6Pu6rHUm+ewtgZptB37s3eGEv/0lZUNcMsQlOcEwLukQ1xOd89quxyZCa70TMBsrgqGzoiNwNlZ0jHXdn4lxXlCnXuYE9vjkkH1bOmmth0//BMv/6NSXnAvzboRZV/ddDRI6nkYo39QRPNsu24a2HI5xQWKWE0yTsiApJxhUg4HVuGDFI84wEFcszLjcaWUP5aTCtjHUbRMa93zuHI+JgzGnBIf8LAztG95IZa3T8xGXdPhhPL0srGE01BRG+59b/2cdT3+8nX/eeBLHDEsLdzmhY63zot0eakqgdofTHTx8Dgyd2bsv4o2VTgvjpqWw5Z2OMJM20vm8WCfIjD6xY1xV+sje+/yHYi2UrnW6lNc93zEJKXcqTLsUjrk48ice+H3OZJ+2n2Pbz7RuN3gbnRdyTxN4m5wWTRvo/udwxe7zAr7fi3lS8EU+OccZDxrOGcSeJlj1JHz0/6B+jzM8YO71cNy1kBDFf8/RIuCH6q1OUCvf0HFZ/SXOwjZBsUnOigu5U5yPnMnOG5Cmys5vng72ZqptnPe+YuJh1rdg3g8gfUSffbntanc640w3LYVtH+wzFGhWcMWNc5wW2kgcltNbmmucnqiVTzg/8ykXwqVP9NmnVxiViNHi9XPcr//F0PRBvHrTfEx//8P3NjtdQQdrTavZ5oSTQzEuyJnizFwePseZxZw5rnv/DKu2dIyB3PGJE4KMC0bO6+jqzhjjvFi0Dfjf8k7HgP/cqR0zzPOm9e4/4uqtTvhc9w+oLHSOpY9yuuamXuK80EUja50hCG3B1NO0X2ANXsandHRzJmU5XYn96e/B1wqrn4YP74Pa7c5s/OO/Byf8n/C0bodDawOUb3RCeWY+ZI2PvLGJDeXB8b+fOmOBd6/q/H/JuJzacyZ3Dp7po3q+4oS3uXM4bamFsadETitkS60zOXLTUmeoUNsQnYyxTjAdNrNzT0b79WBvR0xc9/5WrXWGIR30f0Gj8/NIG+E0UPT2JEFrYfvHTgDd8D9OHQlpMO0bzpsDtYz2DoXR/mXp2j18/5lV/PvCSVxzcj9c99FaWL0EVv/FCZsNpQee43I7/1gONs4wdagTIHeu6Hhx2Pc5Bg3uCKbDZx+4OHkgALtWOuOhNr3SEfJikyD/tGBX/ILDhwFPI2x523l80asda1CmjQjOMF/oLJdyuBdVn+cwLSPlzuSg3auccxMzYcpFzrjG4XP6V+CSI/N7ndaWD+6B6i3OC/ec78DcG52W3Wjg9zlf276tiWXrnWEd+3LFOhPtcqdA7mTnzWbuZGecbV/83vu9zo5F7eFzReca45Jh6LHOrPO24JlVELqxz/2Bz+O0lLZtoNHWc3M4Jubgw3Bi4jpCp6ch+IY0GDq70lsSl+L87x37FefjaFpqm6rh82dh5ZMdrxMj58Ksq5zhUGEY760wKhHj6sdX8P7mSpb//DRyUvrZP8DGSnj5RuefVnyq0wpysMCZMrTr726tdbqPdgaX1tmxwhnbFPAGT2hbnDwY4gpfhYYy566knI5dScac0rMXFL/PmUzQNuC/ZptzPD4Nxn/VeWFtrAhOoGnrnivvGOt5KAlpzkL0Uy91/qlGWmuR9L6A35nh//7vnSWm3IPg2CudlvmeGDTYaaHLnth3YclaZ8JW+7jJDVD2hTPZbd8lr1xuyBwfDJyTnTeZlZs7wmrtjs7Pm5DWEUzbWiBzJh39sIa6PR2rHrQtzeVr6bg/c7yzDFrb5gY5k7q2JNpAZS3sWeP8H/Q0BVstG7t+3dfq/K7GBsNp2/jwQ44lD153J0BFobMM3c5PO8anJ+cFg+kpzv/4tCMsMGStMyZ/5RPOmHy/x/k7mn45zPwW5EwM6bfvSBRGJSKU17Uw9+63OWVCNn++av8dYSPc5jfhf653gtjUS2Hh70M3bs/b4oyxPNji5FkTguObznaWPerNhdutdboc21pd21o22wzKCHYpZ3fuXm4f37jPffsuQi8DSyDgTBh5/z+d3+OjtX83cttlT7uRrXXe0NUcZLJaxSZnxvi+UocFP2dbS+cU543o4dambal1/pbaWlDLNzjhtnW/N3FpI3o+obCper9NC1KdnpThc5wAOmzWwBkuEU1a6qBkmbPs1dZ3O3aaA+f//9ivOB+jT+p4M9NYCWueccZyty1ZN+okpxV00rkR0/KtMCoR4ZH3t3DXK5t46JszWTh1SLjL6RpvM7z5S2cmaHwqnH2vM+mmr9Xtdt5197SVqUefc09w1nSO09Wu1k3pDmudVkXvIVYFONJjG0o7z+qu3srBJ9hMdhY4bwupiRlOK9WhVkaoKTn4SgWDMpyQ2Sn0Tu691SeshbpdnQNq+UanO7cn2rrc28Jn1gS1ekaj+lL48n1ncf+t7zi/Q+C8SRs60xmHW/S605uWmAkzrnBaQbPGh7fug1AYlbCz1nLWfR9QWtfCin8/nXh3L/3T9HmgarPzwtHbY7L2rIUXrnFaS0bOg4se7pvZ5yJyIE+j87dY1jYD/BBLD8WnHdgCCc54zvaNDUY7Lavtw2tGaSUAiXzWOnMOtr7jtJpu+8BphR9zijMZaeI5Eb2b3OHCqPZ2kz7xxa46CsvqWXTCqN4Lon4v/PVyZzZk5njnj3H6FZCUeXTPGwg4aym+9R+AhdN/6SzyrVYHkfCJS3K6nvffKrKh3GmBbRvjuXe701p0wFjuIfoblv7NGMjKdz6Ou8YZp91SGxXDMRRGpU88v8ppvb54Vi+tJxkIwEvfd4LoiBOgsgjeuNUJkBPPccbKjJ7f/TFltTvhxeucd5yZ+XDRo84yHyISmZJzIPk0GHdauCsR6VuumKgIoqAwKn3A4wvw0ppd5OckM314L3WF/euXsPZvMOFr8PWnndmHG//XmUW4/gXnI2MszFwMM77pvGAdyRcvwD9/6LzTnP1tOPNOpzVGREREQkZhVELu7U3l1DR5ufbkcb2zyP1H98OyB5wW0Uv+HFxGye0spD71EqgsdmYVrnkG/nUbvH2nM/t81lUw5isHtpa21MGrP3XWZEvMgsv/6iyZJCIiIiGnCUwSct998jPe3lTGsltOJy/tKJeY+Pyv8OL3IHsSXP3K4bsofB5nmaKVTziDvcGZtDBzsbP+YUqeszPFC9c6M2/zvwrnPwgpuUdXo4iIiHSi2fQSNpUNrZxw11vMy8/iqW8fd3RPtvlNePYbzkLA33njyAsA76t6K6xa4mxf2Fju7KAxap6zQHBMnNMlP+e72h1IREQkBDSbXsLmpTW78QUslxztxKWdn8HfFzv7eS96oXtBFJzxo2f8Ck79hbOL0connC0x846Bix4L+84UIiIiA5XCqITUcyt3kpLg5szJR9H1XVEEf7nUWWPtin84+/X2VEwsTD7P+WitdxbO7s1djERERKRb9CosIbN+dy0b99RxzrShJMT2cH2/ut3w9EXODPfLnoIRvbiNaHyKgqiIiEiYqWVUQub5lc62ZZfM6maXepvmGnj6YqjdARf8F0w4sxerExERkUigZiEJCa/fWVt0TFYSM0f2YG9nbzM8e7mzo8oZtzv77YqIiEjUURiVkHi3sIKqRg8XzxzW/bVF/T547tuwfTmc8H048abQFCkiIiJhpzAqIfH8yp0YAxfO7OYsemth6Y+g8BWYeqmz5JKWWxIREYlaCqPS62oaPby1qYx54zIZlj6oew9+59ew6ilnn+nzH9IEIxERkSinV3rpdS9/vhuvvwdri37yCLz/nzB0Jly2BNxxoSlQREREIoZm00uve27lTpLiYlgwJa9rD7AW1v3D2R8+Yxx88x8QnxzaIkVERCQiKIxKryosrWfdrloumz2cxLgj/Ho118Davzu7IZVvcLb5XPQiJGX1Sa0iIiISfgqj0queX7UTgIsPNXHJWtjxiRNA178IvhaIT4U518C8G2DwqL4rVkRERMJOYVR6jc8f4MXVuxiZkcic0Rmd72yqhs//6oTQykLn2IjjYea3YMoFEJfU5/WKiIhI+CmMSq/5oLiSivpWfnTGBFwu47SClnzkBNANL4O/FRLS4PjrnBCaOzncJYuIiEiYKYxKr3l3UzkA546Pg2UPOCG0qti5c+Q8mHUVTD4PYru53JOIiIhELYVR6TXrN2/lkcSHGfPkJxDwwqAMmHsDzFwM2QXhLk9EREQikMKo9Iry+hYu3fsnznR/CKPnO62gE8+B2IRwlyYiIiIRTGFUesXqL9ZzYcwHVGTMIvuqf4a7HBEREekntAOT9Iqkz/6LOOPHzP9RuEsRERGRfkRhVI5eYxWzql6m2DWarBnnhLsaERER6UcURuWo1b73IINo5dPhV4Ex4S5HRERE+hGFUTk6rQ0krH6UbYFcUo69ONzViIiISD+jMCpHZ+UTxHvreMR/Dsfn54a7GhEREelnNJtees7Xil3+R6pIZ23mQrJT4sNdkYiIiPQzahmVnlv7N0z9Hh7xfo3Z+UPCXY2IiIj0Qwqj0jMBP3x4H63uFJ7xn868cZnhrkhERET6IYVR6ZmNL0P1Ft5OOY8mk8jxYxVGRUREpPsURqX7rIUP7sW6B/G7vacydVgaaYNiw12ViIiI9EMKo9J9W96C0rVUF3ydL5sTmTsuK9wViYiISD+lMCrd9+F94HLzRvplABovKiIiIj2mMCrds+NT2PYBTL2MN3fFERtjmD16cLirEhERkX5KYVS658N7AfDOvZFPtlZx7IjBJMZpuVoRERHpGYVR6bqyDVD4Ckw8h3WeITR6/MxVF72IiIgcBYVR6bqP7nMuT7qZ5VuqAI0XFRERkaOjMCpdU1MC656DMSfD8Fl8VFxJQqyLGSPTw12ZiIiI9GMKo9I1yx4A64eTfkSL189nJTXMGZ1BvDsm3JWJiIhIP6YwKkfWUA6rl8CQGTD2VFZtr8HjC2i8qIiIiBw1hVE5so//C3wtMP9mMKZ9vOiJWuxeREREjpLCqBxeSy18+hhkjoeJ5wKwbEsVKQlupgxNDXNxIiIi0t8pjMrhffZnaK2DE28Cl4uGVh+f79jL8WMyccfo10dERESOjtKEHJq3GZY/BKnDYNrXAfh0WzW+gNWSTiIiItIrFEbl0Nb8BRrLYe4N4I4D6FhfNF9hVERERI6ewqgcnN8HH90PgzJg1rfaDy/bUklmUhwTclLCWJyIiIhEC4VRObj1L8LeEjj+OohLAmBvk4f1u+s4YVwmLpcJc4EiIiISDRRG5UDWwod/gNgkOO6a9sMfb63CWm0BKiIiIr1HYVQOVPQ6lK+H2VdDYkb74WXt+9FrfVERERHpHQqj0pm18ME94IqFud/vdNeyLVUMSUtgdGZimIoTERGRaKMwKp0Vvgo7V8DMRZA6tP1weV0LxeUNzBuXhTEaLyoiIiK9Q2FUOvh98NbtEJsIp/ys013Lt7Z10Wu8qIiIiPQehVHp8PmzULHJ6Z5Pyet017JiJ4zOVRgVERGRXqQwKg5vM7xzl7Ou6LwfHHD3sq2VjMlKYmj6oDAUJyIiItFKYVQcnzwM9bvhlJ9CQmqnu3ZUN7GjulmtoiIiItLrFEYFmqrhw3shfSTM/vYBd7dvAaowKiIiIr3MHe4CJAJ8+AdoqYWFvwd3/AF3f7SlEoATxiqMioiISO9Sy+hAV7vT6aLPnQrHXHLA3dZalm2pYmJeClnJBwZVERERkaOhMDrQvfMb8LfCV28D14G/DlsqGqiob9V4UREREQkJhdGBrGwDfP4MjDkZxp1+0FO0BaiIiIiEksLoQPbWf4ANwBm3wSF2VVpWXIXLwPFjMw56v4iIiMjRUBgdqEqWQdGrMOVCGDbroKcEApblW6uYOjyd1ITYPi5QREREBgKF0YHIWnjzV+Byw2n/95CnbdhTR22zV0s6iYiISMgojA5Em5bCzhUw6yrIHHfI07S+qIiIiISawuhA4/fBW7dDbBKc/NPDnrpsSyWxMYbZozReVEREREJDYXSgWfMXqCyCeTdASu4hT/P6A6z4sppjRw5mUFxMHxYoIiIiA4nC6EDiaYJ3fwOJWTD3hsOeunbnXho9fnXRi4iISEgpjA4kn/w31O+BU34KCamHPXVZsdYXFRERkdBTGB0omqrhw/sgfRTMuvqIpy/bUsWg2BhmjEjvg+JERERkoFIYHSg+uAdaa+H0X4I77rCntnj9rNxew5wxGcS59SsiIiIioaOkMRDs3Q4rHoG8aTDloiOevqqkBo8voPGiIiIiEnIKowPBO78Bvwe+eju4jvwjX6b1RUVERKSPKIxGu7L18PmzMPYrMO60Lj1k2ZZKUhLcTBmaFtLSRERERBRGo92/bgcsnHFbl073Byzrd9dx7MjBxLhMKCsTERERURiNats+hM2vwzEXw9Bju/SQkqpGWn0BJualhLg4EREREYXR6GUtvPkrcLnhtFu7/LDC0noAJuQqjIqIiEjoKYxGqy/fg12fOWuKZozt8sMKy5wwqpZRERER6QsKo9Fq9xrncuol3XpYYWk9LgP5OckhKEpERESkM4XRaFW52bnMmtCthxWW1TM6M4mE2JgQFCUiIiLSmcJotKoshMQsSMzo8kNavH62VTZSoC56ERER6SMKo9HIWqgsguyCbj2suLyBgNXkJREREek7CqPRqKEcWmoha3y3HtY2k16Tl0RERKSvKIxGo8pC5zKrey2jRcGZ9BMURkVERKSPKIxGo8oi57Kbk5c2ldYT53YxOjMpBEWJiIiIHEhhNBpVBMNodvfCaFFZPeNzkrUNqIiIiPQZhdFoVFkEsYmQOrzLD6lt8rKntkUz6UVERKRPKYxGo8oiyMwHV9d/vG07LxVoJr2IiIj0IYXRaNNaD3W7erTYPaCWUREREelTCqPRpm3npW6uMVpYWgcojIqIiEjfUhiNNu3bgHZvjdGi0gZSE9zkpSaEoCgRERGRg1MYjTY9WGPUWsum0joK8lIwRjPpRUREpO8ojEabyiIwLsgc1+WHlNW1UtfiUxe9iIiI9DmF0WhTUQSDR4M7vssP2dQ2XlQz6UVERKSPKYxGE78Xqrf0eBvQgrzUUFQlIiIickgKo9GkZhsEfN2evLSpVGuMioiISHgojEaTiuDkpW4u61RUVk9eagJpibEhKEpERETk0BRGo0llcE/6bix47w9YNpc1aPKSiIiIhIXCaDRpD6Nd76YvqWqk1RdQGBUREZGwUBiNJpVFkJQDgwZ3+SGFGi8qIiIiYaQwGi2sdZZ16uZ40fbJS2oZFRERkTBQGI0W9aXgqe/+NqBl9bgM5Ockh6gwERERkUNTGI0WPdgGFJxu+tGZSSTExoSgKBEREZHDUxiNFpWbncvsrs+kb/H62VbVqC56ERERCRuF0WjRtsZoN5Z1Ki5vIGBhgiYviYiISJgojEaLyiKITYLUYV1+SNtM+olqGRUREZEwCXkYNcaMN8YsM8YUGWM+NcZMOcR5U40x7xpjNgY/Lgp1bVGlssiZvGRMlx9SGNyTfoLCqIiIiIRJX7SMPgw8Yq2dAPwWeGL/E4wxicBLwK3W2knAMcAHfVBbdGiphfo93V7WqbC0nji3i9GZSSEqTEREROTwQhpGjTE5wGzg6eCh54ERxpj8/U69AvjYWvshgLXWb62tCGVtUaWy2Lns5rJOhaX1jM9JJsbV9dZUERERkd4U6pbREcAea60PwFprge3AyP3Omwy0GmP+aYxZY4x5yhiTfbAnNMbcbIzZ2fbR0NAQ0i+gX+jBsk61TV5K61o0k15ERETCKlImMLmBM4DvAccCu4D/OtiJ1tp7rbXD2z6Sk7VYe8ee9F2fSd82XlTbgIqIiEg4hTqM7gCGGGPcAMYYg9Mqun2/87YD71hrdwVbT58GTghxbdGjoghMDGSM7fJD2sOoWkZFREQkjEIaRq215cAq4MrgoYuBndba4v1O/TswxxiTGry9EPg8lLVFlcoiyBgD7rguP6SwtA5QGBUREZHw6otu+u8B3zPGFAG3AFcDGGMeM8acB2Ct3Q7cBSwzxqwFTgOu64Pa+j+fB6q39mgb0NQEN3mpCSEqTEREROTI3KH+BNbaQmDuQY5/d7/bS4Aloa4n6tR8CdbfrZn01loKS+uZmJeK6ca6pCIiIiK9LVImMElPtW0D2o01RkvrWqhr8TEhT5O/REREJLwURvu79pn0XQ+jbduAFuSlHuFMERERkdBSGO3v2sPo/vsIHFp7GNWyTiIiIhJmCqP9XUUhpAyBhLQuP0RrjIqIiEikUBjtz6yFys092gY0LzWBtMTYEBUmIiIi0jUKo/1Z3S7wNnZrvKg/YNlc3qD1RUVERCQiKIz2Zz3YBnRbVSMeX0BhVERERCJCl8OoMUbBNdJUBMNodtfDaJEmL4mIiEgE6U7A3GaM+YUxJjtk1Uj39KBldFOp9qQXERGRyNGdMPpVIAf4whizxBhzfIhqkq6qLIK4FGc2fRcVldXjMpCfowXvRUREJPy6HEattYXW2h8CY4APgL8bYz41xlxhtKdkeFQWOTPpu/HtLyytZ3RmEgmxMSEsTERERKRrujUONBg6zwS+DjQAzwKXAy/0fmlyWM17oaGsW9uAtnj9bKtqVBe9iIiIRAx3V080xvwcuBZYD/zWWvtG8K57jTGbQ1GcHEZl8FvejTVGi8sbCFiYoMlLIiIiEiG6HEaBocACa23RQe77Ri/VI11VWehcdmON0bbJSxPVMioiIiIRojvd9P8JbG+7YYwZZIwZAWCtXdnbhckRVATDaDe66YuC24BOUBgVERGRCNGdMPpcF49JX6jcDC43DB7d5YdsKq0n3u1idGZS6OoSERER6YbuhNE4a21L2w1rbTMQ3/slSZdUFkLGOIjp+v7yRaX1jM9NJsalxQ9EREQkMnQnjFpjTE7bDWNMHqBUEw6+VqjZ1q3JS7VNXkrrWjR5SURERCJKdyYw3Q8sN8YsCd6+Eri990uSI6raAjbQrfGihWWavCQiIiKRp8th1Fr7uDHmS2Bh8NDV1toPQlOWHFYPtgEtLK0DtKyTiIiIRJbutIxirX0XeDcklUjX9SSMtreMpoaiIhEREZEe6c6i94OAG4EZQELbcWvtRSGoSw6nPYx2fcxoYWk9qQluclM150xEREQiR3cmMD0KjAbmAe8Ao4CSENQkR1JRCKnDIL5rXe7WWgpL65mYl4rpxj72IiIiIqHWnTA63Vp7PVBnrX0A+AowKyRVyaEFAlBV3K1W0dK6FupafEzISw5hYSIiIiLd150w2hy89Bljkqy19UB2CGqSw6nbCd6mbm0DWhjcBrRA40VFREQkwnRnAlO1MWYw8ArwujGmEtgZmrLkkNrGi2Z3ZyZ9MIxqJr2IiIhEmO6E0bOttX5jzP8FrgAGA0+Fpiw5pIqeLOukMCoiIiKRqUth1BgTA7wOnGGttcBfQlqVHFploXPZnW76snryUhNIS+z61qEiIiIifaFLY0attX4g0RjTnTGmEgqVmyE+DZJzjnwu4PMH2FzeQIF2XhIREZEI1J1u+k+BfxpjngYa2g5aa1/u9ark0CoKnfGiXVyiqaS6CY8voDAqG4QyQAAAIABJREFUIiIiEak7YXRa8PKafY5ZQGG0rzRVQ1MlTFjQ5YdovKiIiIhEsu7sTX9qKAuRLujRnvRtyzopjIqIiEjk6c52oCcf7Li19v3eK0cOq4dh1GUgP0cL3ouIiEjk6U43/T37XE8ACoAvgJm9WpEcWkVwJn1212fSF5XVMzoriYTYmBAVJSIiItJz3emmn7PvbWPMccBVvV2QHEblZoiJg/RRXTq9xetnW1UjC6bkhbgwERERkZ7p8VJN1toVwNxerEWOpLIQMsZBTNfeQxSXNxCwGi8qIiIikas7Y0an7XMzBjge0CrqfcXbAjUlMOncLj9kk2bSi4iISITrzpjRl/a57gM2A9/q3XLkkKqKAdvt8aKgllERERGJXN0ZMzomlIXIEfRgG9BNpfXEu12MykwKUVEiIiIiR6fLY0aNMecaY9L3uT3YGHN2aMqSA1Rudi6zxnf5IYWldYzPTSbG1bXdmkRERET6WncmMN1h7f9v7+6D47rO+47/HmDxSoAACJISX0BSIuUoenElWW5rJ7bTTJO47kxGsdwmncieqpNW/SOpxqqnk75MXE+caZw/PEmnM4kdp+M4nsx4Wqd27ahqOpMqcayklQRSchpHxAIiF5QoAtgFSCwW7/v0j90FIYoE9969d+/d3e9nBrPEYi95eLle/XzOeZ7jS7u+X5L0yxGPB7dSa+tUZxhdKm3oyrV1vYv9ogAAIMUaqaZ3VQqZ0AwLU9LIhNRb35J77eSle9kvCgAAUixIGF02s/fXvjGzH5K0HP2Q8A7lbSk/FejkpdcXViRJpw9x8hIAAEivINX0/0rSfzOzv65+f4+kn4p+SHiHq7PS1lqgMJorlCRJJ8cH4xoVAABAw4JU0/+5mf2grje6f+GGPaSIy3z1TPpDwcPo8THCKAAASK8g1fTvlbTl7s+6+7OSymb2aHxDw46FahgNMDM6Wyjpjv19nEkPAABSLcie0S9IKu36viTpt6IdDm4qRI/RXKGkEweYFQUAAOkWJIx2uft27Rt331KwPacIa2FK6h+V9h2s6+XX1ja1WNrUBGEUAACkXJAwumFmO00uzexdkjajHxLeYf61yjGgVl/z+tnqflFmRgEAQNoFmdn8jKQ/M7P/Ickk/bikfxLLqHDdyoK0Wgi8X1QijAIAgPQLUk3/h2b2w5J+TJJL+oy7z8Q2MlSEKF7KEUYBAECLqDuMmtlhSZ+U9JCkfkn/1Mzk7o/ENTjo+jGgh4IVL0mEUQAAkH5B9oz+jqSLkg5K+rSkNyX9YRyDwi4LU5XHOs+kl6RcYVV9mS4dGu6LaVAAAADRCBJGJ9z9c5LW3f1bkj4q6e/GMyzsWHhN6u6TRk/Wfclsta2T1VnwBAAAkJRA1fTVxzUzG5e0pcosKeK0MCWNn5a66mtev112XVqkxygAAGgNQcLo+WoI/aqk/yPpJUkvxzIqVGyuSUs5afxM3Ze8dW1Nm9tOj1EAANASglTTP1H95W+Y2UuSxiQ9F8uoULH4uiQPtl80T/ESAABoHaFOUHL370Y9ENxErXgpwMwoPUYBAEArCbJMj2bL18JokEr6ahgdJ4wCAID0I4ymWX668jh+uu5LamF0YowwCgAA0o8wmmYLU9LguDR4oO5LcoWSDg33aaC3vup7AACAJBFG0yw/FWiJXrreYxQAAKAVEEbTqlSQVhelg/UXLxXXt5Rf2SCMAgCAlkEYTasGKunpMQoAAFoFYTStGqmkJ4wCAIAWQRhNq3y28hig4T09RgEAQKshjKbVwpRkXdLYqbovYWYUAAC0GsJoWuWnpdGTUqav7ktyhZJ6M106PFz/NQAAAEkijKZReVsqzARaopcqYXRibEBdXRbTwAAAAKJFGE2jpZy0vR6okr5cdl0qrLJEDwAAWgphNI12jgGtP4xeWV7TxnaZMAoAAFoKYTSNam2dAizT5/L0GAUAAK2HMJpGIRreU0kPAABaEWE0jfJZqWefNHyk7kt2eoyOE0YBAEDrIIymUT4rjZ+WrP6q+NrM6MQYYRQAALQOwmjabKxI194I1dbp4FCv9vVlYhoYAABA9AijabNTSR80jK5SvAQAAFoOYTRt8sGLl0obW1oorlO8BAAAWg5hNG0WspXHg/WH0dnCqiQq6QEAQOshjKZNvhpGQ7R1YpkeAAC0GsJo2uSnpKE7pb7hui+hxygAAGhVhNE0ca8s0wespJ8ljAIAgBZFGE2T4py0sRxoiV6qzIz2dnfpjv39MQ0MAAAgHoTRNAlRSS9VwujxsQF1d9XfJB8AACANCKNpUiteCrBMXy67ZgslipcAAEBLIoymyULwmdH54rrWt8rsFwUAAC2JMJom+azU1SONnqz7EirpAQBAKyOMpkk+Kx24S+qu/3z5XJ4eowAAoHURRtNie1NavBDiTHpmRgEAQOsijKbF4gWpvCWNnw502ezO6UsDMQwKAAAgXoTRtAhRSS9VZkYP7OvVcH9PDIMCAACIF2E0LXYq6YOHUfaLAgCAVkUYTYsQDe9XN7Y1t7zOflEAANCyCKNpkZ+W+kekfQfrvuTSYq14if2iAACgNRFG02JhqrJEb/Uf6UklPQAAaHWE0TRYuyqtzIU6k16ixygAAGhdhNE02KmkDxdGmRkFAACtijCaBgvVMBqwkn62UFKmy3RkhD2jAACgNRFG0yBEJb1UmRk9Pjag7q7695kCAACkCWE0DWrL9AFOX3J3eowCAICWRxhNg4WsNDIh9dS/3D5fXNfaZpn9ogAAoKURRpNWLldmRgMu0c9SvAQAANoAYTRpy29KW6uhzqSXCKMAAKC1EUaTthCyeCm/KokeowAAoLURRpO2U7wUssfoOGEUAAC0LsJo0nYa3gfvMTo62KP9/T0xDAoAAKA5CKNJW5iSMv3S/uOBLssVSuwXBQAALY8wmrR8VjpwWuqq/59ibXNbb11bY78oAABoeYTRJG2uSUu5wGfSX1qsFC8xMwoAAFodYTRJhRlJTo9RAADQsQijSdqppKfHKAAA6EyE0STlqz1GaXgPAAA6FGE0SQu1mdHTgS7LFUrq7jIdGemPYVAAAADNQxhNUj4rDR6UBsYCXTZbKOnY6IAy3fzzAQCA1hZ7mjGze8zsBTM7b2Yvmtn9e7zWzOyPzWwp7nGlQn4q8BK9u9NjFAAAtI1mTK19QdIX3f1dkj4n6ct7vPaTkqabMKbkreSl1cXAS/T5lQ2VNrbpMQoAANpCrGHUzA5LelTSV6tPfV3ShJm9o5dRdcb0MUm/GueYUoNKegAAgNhnRickXXb3LUlyd5eUk3Ri94vMrEfSb0t6StL2Xr+hmT1jZpdqX8ViMZ6Rxy1kJT09RgEAQDtJSwXMpyX9gbt//3YvdPfPu/vx2tfQ0FAThheDhWoYDdjwPpcnjAIAgPYRdxidlXTEzDJSpUBJlVnR3A2v+5CkXzCzC5L+TNJ+M7tgZodiHl9y8lnJuqWxuwJdxjI9AABoJ7GGUXefkzQp6YnqU49LuuTu2Rte9wF3P+nupyT9sKRr7n7K3efjHF+i8llp7KSU6Q10Wa5Q0v7+jEYGe2IaGAAAQPM0Y5n+KUlPmdl5Sb8o6UlJMrMvmdlPNuHPT5/yduVc+oBL9FJlz+iJcWZFAQBAe8jE/Qe4+2uS3neT53/uFq+/IGk05mElayknbW8ErqRf39rW5WtreuhEe98eAADQOdJSwNRZam2dDgabGX1jcVXuoscoAABoG4TRJOz0GA1YSU/xEgAAaDOE0STstHWixygAAOhshNEk5Kek3iFp+M5AlzEzCgAA2g1hNAn56cqZ9GaBLssVSuoy6ejoQEwDAwAAaC7CaLNtrEjX3gi8RC9JucKqjo4OqKebfzYAANAeSDXNFrJ4yd0rPUZZogcAAG2EMNpsO22dgs2MLpY2VVzfIowCAIC2QhhttoXG2jrRYxQAALQTwmiz5WttnU4HuoxKegAA0I4Io82Wz0rDR6S+4UCX0WMUAAC0I8JoM7lXlukDLtFLUi5PGAUAAO2HMNpMxSvSxnK4MFooabgvo9HBnhgGBgAAkAzCaDOFrKSXKmF04sCgLGCjfAAAgDQjjDZTyDPpN7bKunx1lSV6AADQdgijzbTT8D5YJf2bS6squ3RinDAKAADaC2G0mfJZqatHGj0Z6DJ6jAIAgHZFGG2mhSnpwN1SdybQZfQYBQAA7Yow2izbm9LihVCV9PQYBQAA7Yow2iyLFyTflg6Ga+tkJh0bHYh+XAAAAAkijDZLyEp6qRJGj44MqDfDPxcAAGgvpJtm2amkDzYz6u7K5UuaOMCsKAAAaD+E0WbJV2dGAza8v7q6qeX1LfaLAgCAtkQYbZaFrNQ/Kg2OB7qMSnoAANDOCKPNks9WlugDHudJj1EAANDOCKPNsHZVWpkLfSa9xMwoAABoT4TRZgh5DKhEj1EAANDeCKPNkJ+uPIZoeJ8rlLSvt1sH9vVGPCgAAIDkEUabIWRbJ0mamV/RqYP7ZAH3mgIAALQCwmgz1GZGD9wd6LLltU1dvrqmew4PxTAoAACA5BFGm6EwLQ0flXr3Bbpsen5FknSGMAoAANoUYTRu7pWZ0RDFS9m5oiTCKAAAaF+E0bitLEjr1wIv0Uu7w+hw1KMCAABIBcJo3ArhK+mzc8vKdJlOjtPWCQAAtCfCaNwa6DGanSvq1MF96unmnwkAALQnUk7cQvYYXdvcVq5Q0plD7BcFAADtizAat3xWsi5p7FSgyy7kV1R26Z47CKMAAKB9EUbjVpiRRo5Lmb5Al01doZIeAAC0P8JonMrlShgNVbxUCaOnWaYHAABtjDAap+XL0mZJOhCieGm+KDPCKAAAaG+E0TjttHUKHkan54o6Pjaggd7uiAcFAACQHoTROO20dQq2TL+1XdbM/AqV9AAAoO0RRuNUa+sU8PSl2cVVbWyXKV4CAABtjzAap8KM1JWRRk8Guowz6QEAQKcgjMYpn630F+3OBLqMM+kBAECnIIzGpbwtFV4PVUk/NbcsiZlRAADQ/gijcVnKSeXNUD1Gp+eKOjTcp5GBnhgGBgAAkB6E0bjstHUKVrzk7pqmkh4AAHQIwmhc8jOVx4Azo29dW1NxfYsz6QEAQEcgjMal1mM04J5RzqQHAACdhDAal8K0lOmX9h8LdNlOJT3L9AAAoAMQRuOSz1aa3XcFu8XZeWZGAQBA5yCMxmFro1JNH/DkJUnKXilqf39Gh4b7YhgYAABAuhBG47B0UfJyqLZO2fmizhwekpnFMDAAAIB0IYzGoVa8NB6seKmwsqHCygZL9AAAoGMQRuOQr/YYDVhJXyteuodjQAEAQIcgjMZhZ2Y02DI9x4ACAIBOQxiNQ2Fa6h2Shg4HumynrRNhFAAAdAjCaBzyM5X9ogGLkLJzRfX3dOnY6EBMAwMAAEgXwmjUNkrStUuB94tK0vRcUacPDamri0p6AADQGQijUVt8vfIYsJK+uL6lN6+usUQPAAA6CmE0aiGLl6Y5BhQAAHQgwmjUGmzrxMwoAADoJITRqBWqYTTgMn3tTPp77iCMAgCAzkEYjVp+WhoYkwYPBLps6kpRmS7TyfF9MQ0MAAAgfQijUctPh6ukny/q5Pigerr5JwEAAJ2D5BOltWvSylzg4qX1rW1dzK+wXxQAAHQcwmiUQu4XvbBQUtk5kx4AAHQewmiU8uHCKGfSAwCATkUYjRJtnQAAAAIhjEYpbFunahi9+xCV9AAAoLMQRqOUz0pDd0h9wfZ+ZueKOj42oMHeTEwDAwAASCfCaJRCtHXaLrtmFqikBwAAnYkwGpVSQVpbCrxEP1soaWOrzJn0AACgIxFGo5LPVh5D7hdlZhQAAHQiwmhUwlbScyY9AADoYITRqOxU0gc7fWnqSnVm9BAN7wEAQOchjEaltkx/4K5Al2Xnizo41KeRwZ4YBgUAAJBuhNGo5KelkQmpZ6DuS9xd03NFnTlMf1EAANCZCKNRcK+2dbo70GVXrq2ruL7FmfQAAKBjEUajULwiba5wJj0AAEBAhNEo5MMVL9HWCQAAdDrCaBR2ipfoMQoAABAEYTQKIds6ZeeKGu7P6PBwXwyDAgAASD/CaBTy05J1S2MnA12WnSvqzOEhmVlMAwMAAEg3wmgU8tPS6Ampu/5eoYsrG8qvbHAmPQAA6GiE0UaVy1JhJvgS/Tz7RQEAAAijjbp2SdpeD97W6Qpn0gMAABBGG9VoWyfOpAcAAB2MMNqonbZOwU5fys4X1Zfp0rGx+o8PBQAAaDeE0UYVZiqPAZfpp+eKuvvQkLq7qKQHAACdizDaqPy01N0rjUzUfcnK+pbeWFrVPRQvAQCADkcYbVQ+K43dJXV1133JNJX0AAAAkgijjdnelJYuciY9AABASITRRizlpPKWNB6weKkaRlmmBwAAnY4w2ohaW6cDAXuMzhXV3WU6Ob4vhkEBAAC0DsJoIwrheoxOzxV1cnxQvRluPwAA6GykoUbUeowGaOu0sVXWxUKJM+kBAABEGG1MflrqGZSGj9R9yYX8irbLzjGgAAAAIow2Jj9d2S9q9Teur51JTyU9AAAAYTS8zTXp6mzoSnrOpAcAACCMhrd4QZIH7zFabXh/+jCV9AAAAITRsGrFSwHbOmXnijo2OqDB3kwMgwIAAGgthNGwQrR12i67pueL7BcFAACoIoyGFaKt06XFkja2yoRRAACAKsJoWPkZqW9EGhyv+xLOpAcAAHg7wmhYhenKrGiAtk6cSQ8AAPB2hNEw1ovS8uVAS/RS5Ux6iZlRAACAGsJoGIWZymPQtk5zRR0c6tXoYG8MgwIAAGg9hNEwQrR1cndNzxV1mjPpAQAAdhBGw9hp61T/6Utzy+taXt/iTHoAAIBdCKNh5KvL9AFmRnfOpGdmFAAAYAdhNIx8Vho8KA2M1n1Jdm5ZknTmMGfSAwAA1MQeRs3sHjN7wczOm9mLZnb/TV7zo2b2f83sr8zs/5nZr5lZeoNyYTr0mfRU0gMAAFzXjMD3BUlfdPd3SfqcpC/f5DWLkn7G3e+T9B5J75f0iSaMLbjVRamUD9zWKTtX1HBfRnfs74tpYAAAAK0n1jBqZoclPSrpq9Wnvi5pwszeNq3o7mfdfab66zVJ5ySdinNsoe3sF62/eEmqhNHTh4dkAZrkAwAAtLu4Z0YnJF129y1JcneXlJN04lYXmNmdkj4m6dsxjy2cnUr6+pfpl0obWihusEQPAABwg1TtyzSz/ZK+JenX3P2lW7zmGTO7VPsqFovNHWStx2iAZfrX3qoUL3EMKAAAwNvFHUZnJR0xs4wkWWWN+oQqs6NvY2bDkp6T9E13//ytfkN3/7y7H699DQ01OeDlqzOjAZbpz84uSZLefbz+6nsAAIBOEGsYdfc5SZOSnqg+9bikS+6e3f06MxtSJYg+5+6fjXNMDStMS8NHpd59dV8yeXFR3V2mvzExEuPAAAAAWk8zlumfkvSUmZ2X9IuSnpQkM/uSmf1k9TVPS/qbkj5qZueqX/+2CWMLxr0yMxpgid7dNZlb0r13DmuwNxPj4AAAAFpP7OnI3V+T9L6bPP9zu379K5J+Je6xNGxlQVq/FiiMXlpc1UJxXR958M4YBwYAANCaUlXAlHq14qUAx4C+fHFRkvTIibE4RgQAANDSCKNBhGjrNJkjjAIAANwKYTSIWiV9gGX6ydyiDg71auLAQEyDAgAAaF2E0SDyWcm6pLFTdb28tLGl719e1sMnxjh5CQAA4CYo7w7ioZ+Vjj8qZeo7X/7VS1e1XXaW6AEAAG6BMBrED3y48lWn6/tFaXYPAABwMyzTx2jy4pIyXcbJSwAAALdAGI2Ju+tsblH3Hd2vgd7upIcDAACQSoTRmFzMl5Rf2WC/KAAAwB4IozGp7Rd9mP2iAAAAt0QYjQnN7gEAAG6PMBqTyYtLOjTcp+NjNLsHAAC4FcJoDFbWt/TXb13TIydGaXYPAACwB8JoDF65tKSys0QPAABwO4TRGJzNLUmSHjlJGAUAANgLYTQGkxcX1dNtevDYSNJDAQAASDXCaMTcXZO5Rd13dET9PTS7BwAA2AthNGKvL6xosbTJefQAAAB1IIxGbLK2X5TiJQAAgNsijEZsp9k9xUsAAAC3RRiN2OTFRd2xv09HR/qTHgoAAEDqEUYjVFzf0vkry3rkxBjN7gEAAOpAGI3QK7OVZvfvYYkeAACgLoTRCE1erOwXfZjiJQAAgLoQRiM0mVtUb3eXHji2P+mhAAAAtATCaETKZddkbkn3H9uvvgzN7gEAAOpBGI3IzMKKrq5u0l8UAAAgAMJoRHb6ixJGAQAA6kYYjcjZnWb3HAMKAABQL8JoRCYvLunISL+OjAwkPRQAAICWQRiNwLW1TZ2fW+YIUAAAgIAIoxF4ZXZJ7uwXBQAACIowGoHJi0uSpEdOsF8UAAAgCMJoBF7OLao306X7j44kPRQAAICWQhhtULnsOptb1IPHRtSb4XYCAAAEQXpq0PR8UctrWyzRAwAAhEAYbRDN7gEAAMIjjDZop3iJtk4AAACBEUYbNJlb1LHRAd2xvz/poQAAALQcwmgDrq5uamquyKwoAABASITRBpybpb8oAABAIwijDXj5IsVLAAAAjSCMNuBsblF9mS794JH9SQ8FAACgJRFGQyqXXedyS3r3cZrdAwAAhEWKCmlqrqjl9S2W6AEAABpAGA2p1uz+YcIoAABAaITRkCZrxUsnqaQHAAAIizAa0mRuURMHBnR4mGb3AAAAYRFGQ1gqbWh6foX9ogAAAA0ijIZwNldrdk8YBQAAaARhNIRa8RJhFAAAoDGE0RAmc4vq7+nSvUeGkx4KAABASyOMBrS90+x+VD3d3D4AAIBGkKYCOn9lWSsb23rPSZboAQAAGkUYDYj9ogAAANEhjAY0ebFSSf/wCZrdAwAANIowGtDZ3KJOjg/q4FBf0kMBAABoeYTRAAorG5pZoNk9AABAVAijAZzd2S/KEj0AAEAUCKMB1IqXHmZmFAAAIBKE0QDO5pY02Nute++k2T0AAEAUMkkPoJX89ice1esLK8rQ7B4AACASpKoA9vVl9MCxkaSHAQAA0DYIowAAAEgMYRQAAACJIYwCAAAgMYRRAAAAJIYwCgAAgMQQRgEAAJAYwigAAAASQxgFAABAYgijAAAASAxhFAAAAIkhjAIAACAxhFEAAAAkhjAKAACAxBBGAQAAkBjCKAAAABJDGAUAAEBiCKMAAABIDGEUAAAAiSGMAgAAIDGEUQAAACSGMAoAAIDEEEYBAACQGMIoAAAAEkMYBQAAQGIIowAAAEiMuXvSY2iIma1Lmm/iHzkkqdjEP6/VcH9uj3u0N+7P3rg/e+P+3B73aG/cn72FvT+H3L3vZj9o+TDabGZ2yd2PJz2OtOL+3B73aG/cn71xf/bG/bk97tHeuD97i+P+sEwPAACAxBBGAQAAkBjCaHCfT3oAKcf9uT3u0d64P3vj/uyN+3N73KO9cX/2Fvn9Yc8oAAAAEsPMKAAAABJDGAUAAEBiCKN1MrN7zOwFMztvZi+a2f1JjyltzOyCmb1mZueqXz+d9JiSZGb/sXpP3Mwe2vU87yXteX94H0kys34z+0b1ffKKmf0vMztT/dlhM3vOzKbM7C/N7INJj7fZbnN/njez13e9hz6Z9HiTYmZ/ZGavVu/Dd8zs4erzfA5pz/vD59AuZvZk9bP6ser30X4GuTtfdXxJ+mNJ/7j6649JejHpMaXtS9IFSQ8lPY60fEn6oKTjN94X3ku3vT+8jyr3oV/SR3R9b//PS3q++uv/LOnfV3/9XkmXJPUkPeYU3Z/nJT2W9BjT8CVpdNevf0rSK9Vf8zm09/3hc+j6fTkl6QVJf17731XUn0HMjNbBzA5LelTSV6tPfV3SRO3/hQM34+5/6u6Xdj/He+m6m90fXOfua+7+rFc/7SX9hSr/UZCkfyjpt6qve1HSm5I+1PRBJug29wdV7r6069sRSc7n0HU3uz9JjSWNzKxL0pck/YKk9V0/ivQziDBanwlJl919S5KqH345SScSHVU6fcXMvmdmv2Nmh5IeTArxXqoP76N3elrSN81sXJUZiLd2/eyCeA89Lembu77/1ep76GtmdndSg0oDM/uKmc1K+mVJHxefQ29zk/tTw+eQ9Iyk77r7y7Un4vgMIowiSh9093dLekTSgqTfTXg8aE28j25gZv9G0hlJ/zrpsaTRTe7Px939XknvlvQdSd9Oamxp4O6fcPcJSf9O0ueSHk/a3OL+dPznkJk9IOlxSZ+N+88ijNZnVtIRM8tIkpmZKv8PIJfoqFLG3XPVx01Jvy7pA8mOKJV4L90G76O3M7NPSfqopL/n7iV3z0vaMrM7d73slDr0PXTj/ZEkd5+tPrq7/ydJd1dnczqau/+upL+jyv4+PoduULs/ZjbO55Ckyt/5lKQpM7sg6W9L+qIqS/SRfgYRRuvg7nOSJiU9UX3qcUmX3D2b3KjSxcz2mdnorqf+kaSzSY0nrXgv7Y330duZ2TOq3IMfu2Fv23+R9M+rr3mvpGOS/qT5I0zWze6PmWXM7I5dr3lc0pVqiO8oZjZqZkd3ff+YpLwkPoe05/1Z43NIcvffdPcj7n7K3U+psi/7n7n7byrizyBOYKqTmf2ApC9LGpd0TdKT7v69RAeVItU9WV+X1C3JJM1IetrdLyQ5riSZ2Rck/X1Jd6ryAbfs7md4L1Xc7P5I+nHxPpIkmdlxVWbSZ1S5N5K07u5/qxq2fk/SXZI2JP28u//vZEaajFvdH0k/qsp/FPsklVVZYn3G3V9JYpxJMrOTqoSGAVXuxbykT7n7OT6Hbn1/VLkffA7dwMyel/Tr7v6NqD+DCKMAAABIDMv0AAAASAxhFAAAAIkhjAIAACAxhFEAAAAkhjAKAACAxGSSHgAAtINqU+h1Sau7nv54lO1yzOyUpHPuPnqblwJAyyDT7lc+AAACDklEQVSMAkB0ftrdzyU9CABoJSzTA0CMzMzN7LNmdtbMzpvZz+762U+Y2aSZvWpmf2Jm9+362ZNmds7MXjGzl6qzorWffcbMXjazrJl9pPrcgJl9zcz+qnrNHzXz7wkAYTEzCgDR+ZqZ7V6mf1/10d394epJZS+Z2XcllST9vqQfcffvVUPqfzWz+yV9SNIvSXq/u182s8Hq73NY0oikV93902b2YUm/IelZSR+WNOru90mSmR2I+e8KAJHgBCYAiEB1z+hjNy7Tm5lLOuXuF6vff0PSH0halPQv3f1Hdr12SdIDkp6WtOruv3TD73VK0vclDbq7m9mIpLy7Z6pB93lJ31blOMxn3X1ZAJByLNMDQPM1Mguw7tdnEbZVOT9b7j4j6T5Jz0n6IUl/aWZjDY0SAJqAMAoA8XtS2pnZ/ICk70j6C0kPmtkD1Z/9jKQ3ql/fkvSEmR2p/mxw11L9TZnZcVW2A/x3SZ+SZJIm4vjLAECU2DMKANG5cc/oJ6uP3WZ2VtI+Sf/C3S9IUnWf6FfMLKPKsv0/qM56/qmZfUbS/6wu829I+tht/uwHJf0HMzNVPtt/z91fjeovBgBxYc8oAMSoGibH3H0p6bEAQBqxTA8AAIDEMDMKAACAxDAzCgAAgMQQRgEAAJAYwigAAAASQxgFAABAYgijAAAASAxhFAAAAIkhjAIAACAx/x+zenCI2iM8HAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ouV-vPdcDM8Y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# make prediction on the test dataset\n",
        "y_hat = model.predict(test_padded)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y2pX5TrQh81Y",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "db9867f8-440f-4664-990f-2223915bab05"
      },
      "source": [
        "print(y_hat.shape)\n",
        "print(y_test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1698, 20)\n",
            "(1698, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MlihtbHmm925",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        },
        "outputId": "cb620273-6f58-4787-efa3-670bcb4ed1d7"
      },
      "source": [
        "# result of softmax output layer (probabilities)\n",
        "y_hat[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([8.13644013e-11, 2.52522051e-01, 4.25022226e-05, 4.70035288e-10,\n",
              "       8.98344332e-09, 1.22294425e-10, 8.65720115e-18, 2.42283822e-16,\n",
              "       8.55790092e-12, 1.94225753e-13, 1.13482906e-13, 7.43737459e-01,\n",
              "       6.26668480e-05, 2.78507615e-03, 7.07581115e-04, 5.72950411e-12,\n",
              "       3.55332418e-07, 5.80143169e-05, 8.42650697e-05, 1.34926662e-12],\n",
              "      dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 205
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GpLirqMQwy9j",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# transform the softmax output to sparse categorical\n",
        "y_hat = tf.argmax(y_hat, axis=1)\n",
        "y_hat = tf.expand_dims(y_hat, -1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q0LUd2sEw6vW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        },
        "outputId": "91a641a6-6742-4547-99e5-c1954fd8bb1e"
      },
      "source": [
        "print(y_hat.shape)\n",
        "print(y_hat[0])\n",
        "print(y_hat)\n",
        "print('-----------------------------------------------------------------------')\n",
        "\n",
        "print(y_test.shape)\n",
        "print(y_test[0])\n",
        "print(y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1698, 1)\n",
            "tf.Tensor([11], shape=(1,), dtype=int64)\n",
            "tf.Tensor(\n",
            "[[11]\n",
            " [ 5]\n",
            " [ 0]\n",
            " ...\n",
            " [ 6]\n",
            " [ 8]\n",
            " [ 8]], shape=(1698, 1), dtype=int64)\n",
            "-----------------------------------------------------------------------\n",
            "(1698, 1)\n",
            "tf.Tensor([11], shape=(1,), dtype=int64)\n",
            "tf.Tensor(\n",
            "[[11]\n",
            " [19]\n",
            " [16]\n",
            " ...\n",
            " [ 6]\n",
            " [ 8]\n",
            " [ 8]], shape=(1698, 1), dtype=int64)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dCDzc7QiDM0z",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d9d5cfd5-13a3-4958-ce9a-353268ad89e1"
      },
      "source": [
        "# check if the model performs well on unseen data\n",
        "accuracy_score = sk.metrics.accuracy_score(y_test, y_hat)\n",
        "print(accuracy_score)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.7997644287396938\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JFILHZH42AT8",
        "colab_type": "text"
      },
      "source": [
        "## Let's try using adding an already trained embedded layer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "reTGTZ882O1S",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 208
        },
        "outputId": "1e80d35c-2e06-4b65-9f90-91b087822109"
      },
      "source": [
        "# we'll use a 100 dimension pre trained embedded layer \n",
        "embedding_dim = 100\n",
        "\n",
        "# Note this is the 100 dimension version of GloVe from Stanford\n",
        "# Already unzipped and hosted on laurence moroney site to make this notebook easier\n",
        "!wget --no-check-certificate \\\n",
        "    https://storage.googleapis.com/laurencemoroney-blog.appspot.com/glove.6B.100d.txt \\\n",
        "    -O /tmp/glove.6B.100d.txt\n",
        "embeddings_index = {};\n",
        "with open('/tmp/glove.6B.100d.txt') as f:\n",
        "    for line in f:\n",
        "        values = line.split();\n",
        "        word = values[0];\n",
        "        coefs = np.asarray(values[1:], dtype='float32');\n",
        "        embeddings_index[word] = coefs;"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-07-24 14:56:20--  https://storage.googleapis.com/laurencemoroney-blog.appspot.com/glove.6B.100d.txt\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 172.217.212.128, 172.217.214.128, 108.177.111.128, ...\n",
            "Connecting to storage.googleapis.com (storage.googleapis.com)|172.217.212.128|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 347116733 (331M) [text/plain]\n",
            "Saving to: ‘/tmp/glove.6B.100d.txt’\n",
            "\n",
            "/tmp/glove.6B.100d. 100%[===================>] 331.04M   153MB/s    in 2.2s    \n",
            "\n",
            "2020-07-24 14:56:22 (153 MB/s) - ‘/tmp/glove.6B.100d.txt’ saved [347116733/347116733]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IWGQ1cqH4NZD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "ea77ee64-c47a-40a4-d223-fd1a823e0099"
      },
      "source": [
        "print(len(embeddings_index))\n",
        "print(embeddings_index['fell'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "400000\n",
            "[ 1.1723    0.47077   0.24895   0.34277   0.15616  -0.64096   0.29827\n",
            " -0.41819  -0.35123  -0.45461   0.64912   0.21499  -0.12391  -0.24777\n",
            "  0.75844  -0.66743  -0.84218   0.1259   -0.033644  0.18011   1.4764\n",
            "  0.11518   0.75285   1.3225    0.52252  -0.36595  -0.38948  -0.35271\n",
            " -0.078046 -0.18693   0.28865  -0.32948  -0.16373   0.066645 -0.31936\n",
            "  0.78904   0.51626  -0.17687   0.32629  -0.1747   -0.057353 -0.73643\n",
            " -0.14736  -0.50068   0.1074    0.40757  -0.035597  0.80876   0.38344\n",
            " -1.6169   -0.022249 -0.041174  0.43792   1.4448   -0.36316  -1.8444\n",
            " -0.92259   0.11691   1.7746    1.1205    0.48343  -0.25156  -0.7128\n",
            "  0.5216   -0.72338   0.12475   0.21331   0.4404    0.65213   0.34467\n",
            " -0.60703   0.32183   0.13004   0.4413   -0.43109  -0.08948  -0.56405\n",
            "  0.58261  -0.71475   0.35147   0.23263   0.88021  -0.57649  -0.11717\n",
            " -0.92284  -0.988    -0.090227 -0.072176  0.49901   0.79342   0.23638\n",
            "  0.074772 -0.2732    0.475    -1.2078    0.53425   0.58641   0.070467\n",
            "  0.096335 -0.2843  ]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tvcgr3In5BAr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b70c2f3a-7bcc-4932-d654-08b9201c4d7c"
      },
      "source": [
        "# there are more words on the 'word_index' than our desired vocabulary size\n",
        "# The Tokenizer stores everything in the word_index during fit_on_texts. \n",
        "# Then, when calling the texts_to_sequences method, only the top num_words are considered.\n",
        "print(len(word_index))\n",
        "#print(word_index)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "89357\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wlZazaDihs4m",
        "colab_type": "text"
      },
      "source": [
        "## Creating our embedded layer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wfp2VljC395v",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# create an embedding matrix for the first 'vocab_size' words\n",
        "# on the 'word_index' created by the Tokenizer\n",
        "# 'vocab_size + 1' the 'plus one' is the zero used for the padding\n",
        "# it's embedded values are all zeroes\n",
        "embeddings_matrix = np.zeros((vocab_size+1, embedding_dim));\n",
        "wi_c = 0\n",
        "for word, i in word_index.items():\n",
        "    # stop after the number of words 'vocab_size'\n",
        "    # the first value will not be iterated, thus it is zero\n",
        "    if wi_c == vocab_size:\n",
        "        break\n",
        "    wi_c += 1\n",
        "\n",
        "    # get only the needed words to the embedded layer\n",
        "    embedding_vector = embeddings_index.get(word);\n",
        "    if embedding_vector is not None:\n",
        "        embeddings_matrix[i] = embedding_vector;\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eqNlhQ9V2OyT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 260
        },
        "outputId": "547c8900-df73-48c0-cbb4-bbda05c94302"
      },
      "source": [
        "print(embeddings_matrix.shape)\n",
        "print(embeddings_matrix)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(20001, 100)\n",
            "[[ 0.00000000e+00  0.00000000e+00  0.00000000e+00 ...  0.00000000e+00\n",
            "   0.00000000e+00  0.00000000e+00]\n",
            " [ 0.00000000e+00  0.00000000e+00  0.00000000e+00 ...  0.00000000e+00\n",
            "   0.00000000e+00  0.00000000e+00]\n",
            " [ 0.00000000e+00  0.00000000e+00  0.00000000e+00 ...  0.00000000e+00\n",
            "   0.00000000e+00  0.00000000e+00]\n",
            " ...\n",
            " [ 6.66700006e-01  4.27980006e-01  1.80710003e-01 ...  1.37889996e-01\n",
            "  -1.14070000e-02 -7.40620017e-01]\n",
            " [-2.51549989e-01 -4.06970009e-02  5.60880005e-01 ... -8.91839992e-03\n",
            "  -5.57090007e-02  5.29079974e-01]\n",
            " [-4.84600008e-01  4.18020010e-01  5.99720001e-01 ...  4.32740003e-01\n",
            "   6.18770003e-01 -6.25170011e-04]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vtBLcHqKh1ZL",
        "colab_type": "text"
      },
      "source": [
        "## Create new model with pre-trained embedded layer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "IXVlLs7d2IaE",
        "colab": {}
      },
      "source": [
        "# create the model\n",
        "model = tf.keras.Sequential([\n",
        "    tf.keras.layers.Embedding(vocab_size+1, embedding_dim, input_length=train_padded.shape[1], weights=[embeddings_matrix], trainable=False),\n",
        "    tf.keras.layers.Dropout(0.2),\n",
        "    tf.keras.layers.Conv1D(128, 5, activation='relu'),\n",
        "    tf.keras.layers.GlobalAveragePooling1D(),\n",
        "    tf.keras.layers.Dropout(0.35),\n",
        "    tf.keras.layers.Dense(100, activation='relu'),\n",
        "    tf.keras.layers.Dropout(0.2),\n",
        "    tf.keras.layers.Dense(20, activation='softmax')\n",
        "])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "3ugqBWpS2Iat",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 434
        },
        "outputId": "c39f571e-5a74-49f3-cbe9-97dbcd7b2fa1"
      },
      "source": [
        "# compile model\n",
        "model.compile(loss='sparse_categorical_crossentropy',optimizer='adam',metrics=['accuracy'])\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_27\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding_29 (Embedding)     (None, 164, 100)          2000100   \n",
            "_________________________________________________________________\n",
            "dropout_41 (Dropout)         (None, 164, 100)          0         \n",
            "_________________________________________________________________\n",
            "conv1d_59 (Conv1D)           (None, 160, 128)          64128     \n",
            "_________________________________________________________________\n",
            "global_average_pooling1d_33  (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dropout_42 (Dropout)         (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_54 (Dense)             (None, 100)               12900     \n",
            "_________________________________________________________________\n",
            "dropout_43 (Dropout)         (None, 100)               0         \n",
            "_________________________________________________________________\n",
            "dense_55 (Dense)             (None, 20)                2020      \n",
            "=================================================================\n",
            "Total params: 2,079,148\n",
            "Trainable params: 79,048\n",
            "Non-trainable params: 2,000,100\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "KFBs1h1W2Ia3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "e0806ae7-7b05-4a79-857a-daa10858a4fb"
      },
      "source": [
        "num_epochs = 150\n",
        "history = model.fit(train_padded, y_train, epochs=num_epochs, \n",
        "                    validation_data=(val_padded, y_val), verbose=2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/150\n",
            "271/271 - 2s - loss: 2.2905 - accuracy: 0.2716 - val_loss: 1.5760 - val_accuracy: 0.5353\n",
            "Epoch 2/150\n",
            "271/271 - 2s - loss: 1.6597 - accuracy: 0.4508 - val_loss: 1.3418 - val_accuracy: 0.5852\n",
            "Epoch 3/150\n",
            "271/271 - 2s - loss: 1.4607 - accuracy: 0.5133 - val_loss: 1.2195 - val_accuracy: 0.6133\n",
            "Epoch 4/150\n",
            "271/271 - 2s - loss: 1.3505 - accuracy: 0.5488 - val_loss: 1.1510 - val_accuracy: 0.6518\n",
            "Epoch 5/150\n",
            "271/271 - 2s - loss: 1.2857 - accuracy: 0.5759 - val_loss: 1.0934 - val_accuracy: 0.6549\n",
            "Epoch 6/150\n",
            "271/271 - 2s - loss: 1.2233 - accuracy: 0.5941 - val_loss: 1.0556 - val_accuracy: 0.6590\n",
            "Epoch 7/150\n",
            "271/271 - 2s - loss: 1.1878 - accuracy: 0.6070 - val_loss: 1.0245 - val_accuracy: 0.6736\n",
            "Epoch 8/150\n",
            "271/271 - 2s - loss: 1.1477 - accuracy: 0.6212 - val_loss: 1.0089 - val_accuracy: 0.6881\n",
            "Epoch 9/150\n",
            "271/271 - 2s - loss: 1.1122 - accuracy: 0.6362 - val_loss: 0.9711 - val_accuracy: 0.6913\n",
            "Epoch 10/150\n",
            "271/271 - 2s - loss: 1.0821 - accuracy: 0.6455 - val_loss: 0.9491 - val_accuracy: 0.7037\n",
            "Epoch 11/150\n",
            "271/271 - 2s - loss: 1.0706 - accuracy: 0.6499 - val_loss: 0.9420 - val_accuracy: 0.6954\n",
            "Epoch 12/150\n",
            "271/271 - 2s - loss: 1.0343 - accuracy: 0.6569 - val_loss: 0.9284 - val_accuracy: 0.7027\n",
            "Epoch 13/150\n",
            "271/271 - 2s - loss: 1.0294 - accuracy: 0.6613 - val_loss: 0.9078 - val_accuracy: 0.7183\n",
            "Epoch 14/150\n",
            "271/271 - 2s - loss: 0.9956 - accuracy: 0.6754 - val_loss: 0.8943 - val_accuracy: 0.7162\n",
            "Epoch 15/150\n",
            "271/271 - 2s - loss: 0.9891 - accuracy: 0.6801 - val_loss: 0.8868 - val_accuracy: 0.7277\n",
            "Epoch 16/150\n",
            "271/271 - 2s - loss: 0.9599 - accuracy: 0.6842 - val_loss: 0.8532 - val_accuracy: 0.7287\n",
            "Epoch 17/150\n",
            "271/271 - 2s - loss: 0.9559 - accuracy: 0.6918 - val_loss: 0.8698 - val_accuracy: 0.7380\n",
            "Epoch 18/150\n",
            "271/271 - 2s - loss: 0.9398 - accuracy: 0.6915 - val_loss: 0.8410 - val_accuracy: 0.7349\n",
            "Epoch 19/150\n",
            "271/271 - 2s - loss: 0.9130 - accuracy: 0.7006 - val_loss: 0.8306 - val_accuracy: 0.7391\n",
            "Epoch 20/150\n",
            "271/271 - 2s - loss: 0.9069 - accuracy: 0.7088 - val_loss: 0.8271 - val_accuracy: 0.7328\n",
            "Epoch 21/150\n",
            "271/271 - 2s - loss: 0.9030 - accuracy: 0.7117 - val_loss: 0.8186 - val_accuracy: 0.7412\n",
            "Epoch 22/150\n",
            "271/271 - 2s - loss: 0.8684 - accuracy: 0.7146 - val_loss: 0.8100 - val_accuracy: 0.7443\n",
            "Epoch 23/150\n",
            "271/271 - 2s - loss: 0.8710 - accuracy: 0.7152 - val_loss: 0.7989 - val_accuracy: 0.7443\n",
            "Epoch 24/150\n",
            "271/271 - 2s - loss: 0.8476 - accuracy: 0.7205 - val_loss: 0.7971 - val_accuracy: 0.7547\n",
            "Epoch 25/150\n",
            "271/271 - 2s - loss: 0.8474 - accuracy: 0.7254 - val_loss: 0.8013 - val_accuracy: 0.7391\n",
            "Epoch 26/150\n",
            "271/271 - 2s - loss: 0.8350 - accuracy: 0.7192 - val_loss: 0.7866 - val_accuracy: 0.7578\n",
            "Epoch 27/150\n",
            "271/271 - 2s - loss: 0.8210 - accuracy: 0.7340 - val_loss: 0.7902 - val_accuracy: 0.7526\n",
            "Epoch 28/150\n",
            "271/271 - 2s - loss: 0.8123 - accuracy: 0.7348 - val_loss: 0.7761 - val_accuracy: 0.7588\n",
            "Epoch 29/150\n",
            "271/271 - 2s - loss: 0.8051 - accuracy: 0.7348 - val_loss: 0.7680 - val_accuracy: 0.7578\n",
            "Epoch 30/150\n",
            "271/271 - 2s - loss: 0.7905 - accuracy: 0.7395 - val_loss: 0.7618 - val_accuracy: 0.7640\n",
            "Epoch 31/150\n",
            "271/271 - 2s - loss: 0.7924 - accuracy: 0.7423 - val_loss: 0.7660 - val_accuracy: 0.7672\n",
            "Epoch 32/150\n",
            "271/271 - 2s - loss: 0.7680 - accuracy: 0.7446 - val_loss: 0.7517 - val_accuracy: 0.7723\n",
            "Epoch 33/150\n",
            "271/271 - 2s - loss: 0.7661 - accuracy: 0.7464 - val_loss: 0.7516 - val_accuracy: 0.7692\n",
            "Epoch 34/150\n",
            "271/271 - 2s - loss: 0.7638 - accuracy: 0.7484 - val_loss: 0.7337 - val_accuracy: 0.7755\n",
            "Epoch 35/150\n",
            "271/271 - 2s - loss: 0.7473 - accuracy: 0.7573 - val_loss: 0.7572 - val_accuracy: 0.7640\n",
            "Epoch 36/150\n",
            "271/271 - 2s - loss: 0.7417 - accuracy: 0.7523 - val_loss: 0.7261 - val_accuracy: 0.7661\n",
            "Epoch 37/150\n",
            "271/271 - 2s - loss: 0.7358 - accuracy: 0.7563 - val_loss: 0.7200 - val_accuracy: 0.7744\n",
            "Epoch 38/150\n",
            "271/271 - 2s - loss: 0.7318 - accuracy: 0.7529 - val_loss: 0.7303 - val_accuracy: 0.7734\n",
            "Epoch 39/150\n",
            "271/271 - 2s - loss: 0.7213 - accuracy: 0.7593 - val_loss: 0.7250 - val_accuracy: 0.7817\n",
            "Epoch 40/150\n",
            "271/271 - 2s - loss: 0.7196 - accuracy: 0.7623 - val_loss: 0.7233 - val_accuracy: 0.7838\n",
            "Epoch 41/150\n",
            "271/271 - 2s - loss: 0.7004 - accuracy: 0.7688 - val_loss: 0.7155 - val_accuracy: 0.7838\n",
            "Epoch 42/150\n",
            "271/271 - 2s - loss: 0.6932 - accuracy: 0.7702 - val_loss: 0.7226 - val_accuracy: 0.7786\n",
            "Epoch 43/150\n",
            "271/271 - 2s - loss: 0.6955 - accuracy: 0.7719 - val_loss: 0.7178 - val_accuracy: 0.7796\n",
            "Epoch 44/150\n",
            "271/271 - 2s - loss: 0.6773 - accuracy: 0.7719 - val_loss: 0.7185 - val_accuracy: 0.7827\n",
            "Epoch 45/150\n",
            "271/271 - 2s - loss: 0.6767 - accuracy: 0.7718 - val_loss: 0.7146 - val_accuracy: 0.7807\n",
            "Epoch 46/150\n",
            "271/271 - 2s - loss: 0.6725 - accuracy: 0.7779 - val_loss: 0.7168 - val_accuracy: 0.7744\n",
            "Epoch 47/150\n",
            "271/271 - 2s - loss: 0.6553 - accuracy: 0.7840 - val_loss: 0.7005 - val_accuracy: 0.7807\n",
            "Epoch 48/150\n",
            "271/271 - 2s - loss: 0.6502 - accuracy: 0.7816 - val_loss: 0.7043 - val_accuracy: 0.7900\n",
            "Epoch 49/150\n",
            "271/271 - 2s - loss: 0.6447 - accuracy: 0.7861 - val_loss: 0.6806 - val_accuracy: 0.7859\n",
            "Epoch 50/150\n",
            "271/271 - 2s - loss: 0.6552 - accuracy: 0.7844 - val_loss: 0.6940 - val_accuracy: 0.7890\n",
            "Epoch 51/150\n",
            "271/271 - 2s - loss: 0.6250 - accuracy: 0.7895 - val_loss: 0.7023 - val_accuracy: 0.7859\n",
            "Epoch 52/150\n",
            "271/271 - 2s - loss: 0.6411 - accuracy: 0.7858 - val_loss: 0.6880 - val_accuracy: 0.7983\n",
            "Epoch 53/150\n",
            "271/271 - 2s - loss: 0.6363 - accuracy: 0.7881 - val_loss: 0.6918 - val_accuracy: 0.7952\n",
            "Epoch 54/150\n",
            "271/271 - 2s - loss: 0.6186 - accuracy: 0.7903 - val_loss: 0.6908 - val_accuracy: 0.8004\n",
            "Epoch 55/150\n",
            "271/271 - 2s - loss: 0.6169 - accuracy: 0.7965 - val_loss: 0.6788 - val_accuracy: 0.7952\n",
            "Epoch 56/150\n",
            "271/271 - 2s - loss: 0.5957 - accuracy: 0.7976 - val_loss: 0.6684 - val_accuracy: 0.7983\n",
            "Epoch 57/150\n",
            "271/271 - 2s - loss: 0.6008 - accuracy: 0.7973 - val_loss: 0.6651 - val_accuracy: 0.8035\n",
            "Epoch 58/150\n",
            "271/271 - 2s - loss: 0.5958 - accuracy: 0.8017 - val_loss: 0.6755 - val_accuracy: 0.8015\n",
            "Epoch 59/150\n",
            "271/271 - 2s - loss: 0.5950 - accuracy: 0.7995 - val_loss: 0.6739 - val_accuracy: 0.7963\n",
            "Epoch 60/150\n",
            "271/271 - 2s - loss: 0.6025 - accuracy: 0.7972 - val_loss: 0.6663 - val_accuracy: 0.8004\n",
            "Epoch 61/150\n",
            "271/271 - 2s - loss: 0.5868 - accuracy: 0.8034 - val_loss: 0.6631 - val_accuracy: 0.8098\n",
            "Epoch 62/150\n",
            "271/271 - 2s - loss: 0.5782 - accuracy: 0.8066 - val_loss: 0.6678 - val_accuracy: 0.7994\n",
            "Epoch 63/150\n",
            "271/271 - 2s - loss: 0.5735 - accuracy: 0.8097 - val_loss: 0.6531 - val_accuracy: 0.8108\n",
            "Epoch 64/150\n",
            "271/271 - 2s - loss: 0.5677 - accuracy: 0.8107 - val_loss: 0.6541 - val_accuracy: 0.8108\n",
            "Epoch 65/150\n",
            "271/271 - 2s - loss: 0.5619 - accuracy: 0.8100 - val_loss: 0.6573 - val_accuracy: 0.8015\n",
            "Epoch 66/150\n",
            "271/271 - 2s - loss: 0.5585 - accuracy: 0.8200 - val_loss: 0.6513 - val_accuracy: 0.8077\n",
            "Epoch 67/150\n",
            "271/271 - 2s - loss: 0.5394 - accuracy: 0.8214 - val_loss: 0.6449 - val_accuracy: 0.8067\n",
            "Epoch 68/150\n",
            "271/271 - 2s - loss: 0.5481 - accuracy: 0.8214 - val_loss: 0.6444 - val_accuracy: 0.8077\n",
            "Epoch 69/150\n",
            "271/271 - 2s - loss: 0.5505 - accuracy: 0.8153 - val_loss: 0.6391 - val_accuracy: 0.8056\n",
            "Epoch 70/150\n",
            "271/271 - 2s - loss: 0.5393 - accuracy: 0.8155 - val_loss: 0.6396 - val_accuracy: 0.8139\n",
            "Epoch 71/150\n",
            "271/271 - 2s - loss: 0.5304 - accuracy: 0.8246 - val_loss: 0.6403 - val_accuracy: 0.8067\n",
            "Epoch 72/150\n",
            "271/271 - 2s - loss: 0.5230 - accuracy: 0.8215 - val_loss: 0.6294 - val_accuracy: 0.8087\n",
            "Epoch 73/150\n",
            "271/271 - 2s - loss: 0.5218 - accuracy: 0.8284 - val_loss: 0.6414 - val_accuracy: 0.8098\n",
            "Epoch 74/150\n",
            "271/271 - 2s - loss: 0.5268 - accuracy: 0.8232 - val_loss: 0.6309 - val_accuracy: 0.8160\n",
            "Epoch 75/150\n",
            "271/271 - 2s - loss: 0.5203 - accuracy: 0.8297 - val_loss: 0.6344 - val_accuracy: 0.8129\n",
            "Epoch 76/150\n",
            "271/271 - 2s - loss: 0.5076 - accuracy: 0.8304 - val_loss: 0.6255 - val_accuracy: 0.8191\n",
            "Epoch 77/150\n",
            "271/271 - 2s - loss: 0.5156 - accuracy: 0.8256 - val_loss: 0.6319 - val_accuracy: 0.8150\n",
            "Epoch 78/150\n",
            "271/271 - 2s - loss: 0.4929 - accuracy: 0.8378 - val_loss: 0.6213 - val_accuracy: 0.8170\n",
            "Epoch 79/150\n",
            "271/271 - 2s - loss: 0.4911 - accuracy: 0.8337 - val_loss: 0.6257 - val_accuracy: 0.8119\n",
            "Epoch 80/150\n",
            "271/271 - 2s - loss: 0.5063 - accuracy: 0.8304 - val_loss: 0.6414 - val_accuracy: 0.8087\n",
            "Epoch 81/150\n",
            "271/271 - 2s - loss: 0.5015 - accuracy: 0.8348 - val_loss: 0.6319 - val_accuracy: 0.8181\n",
            "Epoch 82/150\n",
            "271/271 - 2s - loss: 0.4824 - accuracy: 0.8400 - val_loss: 0.6255 - val_accuracy: 0.8191\n",
            "Epoch 83/150\n",
            "271/271 - 2s - loss: 0.4842 - accuracy: 0.8391 - val_loss: 0.6195 - val_accuracy: 0.8170\n",
            "Epoch 84/150\n",
            "271/271 - 2s - loss: 0.4797 - accuracy: 0.8356 - val_loss: 0.6059 - val_accuracy: 0.8222\n",
            "Epoch 85/150\n",
            "271/271 - 2s - loss: 0.4754 - accuracy: 0.8423 - val_loss: 0.6201 - val_accuracy: 0.8212\n",
            "Epoch 86/150\n",
            "271/271 - 2s - loss: 0.4731 - accuracy: 0.8418 - val_loss: 0.6112 - val_accuracy: 0.8264\n",
            "Epoch 87/150\n",
            "271/271 - 2s - loss: 0.4683 - accuracy: 0.8445 - val_loss: 0.6110 - val_accuracy: 0.8295\n",
            "Epoch 88/150\n",
            "271/271 - 2s - loss: 0.4451 - accuracy: 0.8492 - val_loss: 0.6144 - val_accuracy: 0.8254\n",
            "Epoch 89/150\n",
            "271/271 - 2s - loss: 0.4639 - accuracy: 0.8433 - val_loss: 0.6015 - val_accuracy: 0.8264\n",
            "Epoch 90/150\n",
            "271/271 - 2s - loss: 0.4502 - accuracy: 0.8479 - val_loss: 0.6130 - val_accuracy: 0.8202\n",
            "Epoch 91/150\n",
            "271/271 - 2s - loss: 0.4615 - accuracy: 0.8489 - val_loss: 0.6016 - val_accuracy: 0.8181\n",
            "Epoch 92/150\n",
            "271/271 - 2s - loss: 0.4461 - accuracy: 0.8495 - val_loss: 0.6198 - val_accuracy: 0.8285\n",
            "Epoch 93/150\n",
            "271/271 - 2s - loss: 0.4415 - accuracy: 0.8500 - val_loss: 0.6041 - val_accuracy: 0.8285\n",
            "Epoch 94/150\n",
            "271/271 - 2s - loss: 0.4402 - accuracy: 0.8487 - val_loss: 0.6009 - val_accuracy: 0.8264\n",
            "Epoch 95/150\n",
            "271/271 - 2s - loss: 0.4291 - accuracy: 0.8544 - val_loss: 0.6011 - val_accuracy: 0.8285\n",
            "Epoch 96/150\n",
            "271/271 - 2s - loss: 0.4297 - accuracy: 0.8582 - val_loss: 0.5967 - val_accuracy: 0.8368\n",
            "Epoch 97/150\n",
            "271/271 - 2s - loss: 0.4324 - accuracy: 0.8545 - val_loss: 0.5981 - val_accuracy: 0.8254\n",
            "Epoch 98/150\n",
            "271/271 - 2s - loss: 0.4289 - accuracy: 0.8556 - val_loss: 0.5966 - val_accuracy: 0.8264\n",
            "Epoch 99/150\n",
            "271/271 - 2s - loss: 0.4382 - accuracy: 0.8550 - val_loss: 0.6051 - val_accuracy: 0.8191\n",
            "Epoch 100/150\n",
            "271/271 - 2s - loss: 0.4178 - accuracy: 0.8624 - val_loss: 0.6084 - val_accuracy: 0.8274\n",
            "Epoch 101/150\n",
            "271/271 - 2s - loss: 0.4242 - accuracy: 0.8616 - val_loss: 0.5860 - val_accuracy: 0.8295\n",
            "Epoch 102/150\n",
            "271/271 - 2s - loss: 0.4142 - accuracy: 0.8598 - val_loss: 0.5913 - val_accuracy: 0.8337\n",
            "Epoch 103/150\n",
            "271/271 - 2s - loss: 0.4113 - accuracy: 0.8653 - val_loss: 0.5809 - val_accuracy: 0.8316\n",
            "Epoch 104/150\n",
            "271/271 - 2s - loss: 0.4167 - accuracy: 0.8590 - val_loss: 0.5929 - val_accuracy: 0.8316\n",
            "Epoch 105/150\n",
            "271/271 - 2s - loss: 0.3929 - accuracy: 0.8702 - val_loss: 0.5903 - val_accuracy: 0.8337\n",
            "Epoch 106/150\n",
            "271/271 - 2s - loss: 0.4009 - accuracy: 0.8705 - val_loss: 0.5888 - val_accuracy: 0.8358\n",
            "Epoch 107/150\n",
            "271/271 - 2s - loss: 0.3930 - accuracy: 0.8668 - val_loss: 0.5860 - val_accuracy: 0.8316\n",
            "Epoch 108/150\n",
            "271/271 - 2s - loss: 0.4009 - accuracy: 0.8643 - val_loss: 0.5880 - val_accuracy: 0.8306\n",
            "Epoch 109/150\n",
            "271/271 - 2s - loss: 0.3953 - accuracy: 0.8699 - val_loss: 0.5821 - val_accuracy: 0.8389\n",
            "Epoch 110/150\n",
            "271/271 - 2s - loss: 0.3740 - accuracy: 0.8724 - val_loss: 0.5793 - val_accuracy: 0.8337\n",
            "Epoch 111/150\n",
            "271/271 - 2s - loss: 0.3878 - accuracy: 0.8673 - val_loss: 0.5821 - val_accuracy: 0.8337\n",
            "Epoch 112/150\n",
            "271/271 - 2s - loss: 0.3849 - accuracy: 0.8734 - val_loss: 0.5663 - val_accuracy: 0.8358\n",
            "Epoch 113/150\n",
            "271/271 - 2s - loss: 0.3852 - accuracy: 0.8694 - val_loss: 0.5752 - val_accuracy: 0.8295\n",
            "Epoch 114/150\n",
            "271/271 - 2s - loss: 0.3788 - accuracy: 0.8753 - val_loss: 0.5762 - val_accuracy: 0.8347\n",
            "Epoch 115/150\n",
            "271/271 - 2s - loss: 0.3774 - accuracy: 0.8738 - val_loss: 0.5696 - val_accuracy: 0.8399\n",
            "Epoch 116/150\n",
            "271/271 - 2s - loss: 0.3761 - accuracy: 0.8727 - val_loss: 0.5773 - val_accuracy: 0.8358\n",
            "Epoch 117/150\n",
            "271/271 - 2s - loss: 0.3618 - accuracy: 0.8786 - val_loss: 0.5831 - val_accuracy: 0.8358\n",
            "Epoch 118/150\n",
            "271/271 - 2s - loss: 0.3702 - accuracy: 0.8752 - val_loss: 0.5829 - val_accuracy: 0.8410\n",
            "Epoch 119/150\n",
            "271/271 - 2s - loss: 0.3845 - accuracy: 0.8697 - val_loss: 0.5720 - val_accuracy: 0.8430\n",
            "Epoch 120/150\n",
            "271/271 - 2s - loss: 0.3795 - accuracy: 0.8755 - val_loss: 0.5833 - val_accuracy: 0.8326\n",
            "Epoch 121/150\n",
            "271/271 - 2s - loss: 0.3644 - accuracy: 0.8820 - val_loss: 0.5825 - val_accuracy: 0.8358\n",
            "Epoch 122/150\n",
            "271/271 - 2s - loss: 0.3574 - accuracy: 0.8818 - val_loss: 0.5834 - val_accuracy: 0.8389\n",
            "Epoch 123/150\n",
            "271/271 - 2s - loss: 0.3636 - accuracy: 0.8786 - val_loss: 0.5856 - val_accuracy: 0.8389\n",
            "Epoch 124/150\n",
            "271/271 - 2s - loss: 0.3552 - accuracy: 0.8802 - val_loss: 0.5847 - val_accuracy: 0.8337\n",
            "Epoch 125/150\n",
            "271/271 - 2s - loss: 0.3565 - accuracy: 0.8834 - val_loss: 0.5847 - val_accuracy: 0.8347\n",
            "Epoch 126/150\n",
            "271/271 - 2s - loss: 0.3560 - accuracy: 0.8831 - val_loss: 0.5886 - val_accuracy: 0.8389\n",
            "Epoch 127/150\n",
            "271/271 - 2s - loss: 0.3504 - accuracy: 0.8819 - val_loss: 0.5739 - val_accuracy: 0.8410\n",
            "Epoch 128/150\n",
            "271/271 - 2s - loss: 0.3403 - accuracy: 0.8861 - val_loss: 0.5909 - val_accuracy: 0.8472\n",
            "Epoch 129/150\n",
            "271/271 - 2s - loss: 0.3479 - accuracy: 0.8875 - val_loss: 0.5840 - val_accuracy: 0.8368\n",
            "Epoch 130/150\n",
            "271/271 - 2s - loss: 0.3404 - accuracy: 0.8848 - val_loss: 0.5803 - val_accuracy: 0.8389\n",
            "Epoch 131/150\n",
            "271/271 - 2s - loss: 0.3486 - accuracy: 0.8843 - val_loss: 0.5811 - val_accuracy: 0.8347\n",
            "Epoch 132/150\n",
            "271/271 - 2s - loss: 0.3394 - accuracy: 0.8894 - val_loss: 0.5878 - val_accuracy: 0.8378\n",
            "Epoch 133/150\n",
            "271/271 - 2s - loss: 0.3383 - accuracy: 0.8873 - val_loss: 0.6131 - val_accuracy: 0.8347\n",
            "Epoch 134/150\n",
            "271/271 - 2s - loss: 0.3333 - accuracy: 0.8898 - val_loss: 0.5891 - val_accuracy: 0.8441\n",
            "Epoch 135/150\n",
            "271/271 - 2s - loss: 0.3277 - accuracy: 0.8911 - val_loss: 0.5857 - val_accuracy: 0.8358\n",
            "Epoch 136/150\n",
            "271/271 - 2s - loss: 0.3333 - accuracy: 0.8876 - val_loss: 0.5772 - val_accuracy: 0.8451\n",
            "Epoch 137/150\n",
            "271/271 - 2s - loss: 0.3390 - accuracy: 0.8909 - val_loss: 0.5682 - val_accuracy: 0.8410\n",
            "Epoch 138/150\n",
            "271/271 - 2s - loss: 0.3338 - accuracy: 0.8903 - val_loss: 0.5699 - val_accuracy: 0.8451\n",
            "Epoch 139/150\n",
            "271/271 - 2s - loss: 0.3052 - accuracy: 0.8962 - val_loss: 0.5754 - val_accuracy: 0.8399\n",
            "Epoch 140/150\n",
            "271/271 - 2s - loss: 0.3234 - accuracy: 0.8925 - val_loss: 0.5827 - val_accuracy: 0.8420\n",
            "Epoch 141/150\n",
            "271/271 - 2s - loss: 0.3284 - accuracy: 0.8884 - val_loss: 0.5759 - val_accuracy: 0.8493\n",
            "Epoch 142/150\n",
            "271/271 - 2s - loss: 0.3191 - accuracy: 0.8946 - val_loss: 0.5861 - val_accuracy: 0.8410\n",
            "Epoch 143/150\n",
            "271/271 - 2s - loss: 0.3244 - accuracy: 0.8931 - val_loss: 0.5907 - val_accuracy: 0.8347\n",
            "Epoch 144/150\n",
            "271/271 - 2s - loss: 0.3247 - accuracy: 0.8921 - val_loss: 0.5757 - val_accuracy: 0.8462\n",
            "Epoch 145/150\n",
            "271/271 - 2s - loss: 0.3110 - accuracy: 0.8932 - val_loss: 0.5694 - val_accuracy: 0.8451\n",
            "Epoch 146/150\n",
            "271/271 - 2s - loss: 0.3248 - accuracy: 0.8944 - val_loss: 0.5652 - val_accuracy: 0.8451\n",
            "Epoch 147/150\n",
            "271/271 - 2s - loss: 0.3000 - accuracy: 0.8981 - val_loss: 0.5773 - val_accuracy: 0.8472\n",
            "Epoch 148/150\n",
            "271/271 - 2s - loss: 0.3146 - accuracy: 0.8931 - val_loss: 0.5707 - val_accuracy: 0.8472\n",
            "Epoch 149/150\n",
            "271/271 - 2s - loss: 0.3225 - accuracy: 0.8931 - val_loss: 0.5696 - val_accuracy: 0.8430\n",
            "Epoch 150/150\n",
            "271/271 - 2s - loss: 0.3155 - accuracy: 0.8930 - val_loss: 0.5681 - val_accuracy: 0.8441\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SGtPCEAoh85d",
        "colab_type": "text"
      },
      "source": [
        "## Check how the model performs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "3xQ92N-B2Ia_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "62ba85c7-8737-4dc5-f56a-01efbcf88215"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def plot_graphs(history, string):\n",
        "  plt.plot(history.history[string])\n",
        "  plt.plot(history.history['val_'+string])\n",
        "  plt.xlabel(\"Epochs\")\n",
        "  plt.ylabel(string)\n",
        "  plt.legend([string, 'val_'+string])\n",
        "  plt.show()\n",
        "  \n",
        "plot_graphs(history, \"accuracy\")\n",
        "plot_graphs(history, \"loss\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "TWXMNZUS4RXy",
        "colab": {}
      },
      "source": [
        "# predict on test dataset\n",
        "y_hat = model.predict(test_padded)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "erYSKNwL4RYb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "97e68f35-c014-4644-c683-63d26fe947ae"
      },
      "source": [
        "print(y_hat.shape)\n",
        "print(y_test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1698, 20)\n",
            "(1698, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "WTmpwAk04RYp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        },
        "outputId": "026042a7-95cc-4745-9734-4a43df87bee8"
      },
      "source": [
        "# result of softmax output layer (probabilities)\n",
        "y_hat[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([6.2157380e-07, 3.6178266e-03, 1.8171255e-03, 2.0800896e-03,\n",
              "       6.2363782e-05, 6.9949143e-03, 4.0384988e-05, 4.5608240e-06,\n",
              "       1.5237625e-05, 5.7125595e-07, 2.4893725e-06, 9.8401654e-01,\n",
              "       6.7503680e-04, 2.0626654e-05, 3.7990143e-05, 1.2252261e-08,\n",
              "       5.6575873e-04, 8.5459033e-06, 1.4375124e-05, 2.4909272e-05],\n",
              "      dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 280
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "lAYzwACS4RYx",
        "colab": {}
      },
      "source": [
        "# transform the softmax output to sparse categorical\n",
        "y_hat = tf.argmax(y_hat, axis=1)\n",
        "y_hat = tf.expand_dims(y_hat, -1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "yP4i9W_m4RY4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        },
        "outputId": "c4bb8878-8f39-47e5-c2c2-4dd12a106000"
      },
      "source": [
        "print(y_hat.shape)\n",
        "print(y_hat[0])\n",
        "print(y_hat)\n",
        "print('-----------------------------------------------------------------------')\n",
        "\n",
        "print(y_test.shape)\n",
        "print(y_test[0])\n",
        "print(y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1698, 1)\n",
            "tf.Tensor([11], shape=(1,), dtype=int64)\n",
            "tf.Tensor(\n",
            "[[11]\n",
            " [ 6]\n",
            " [16]\n",
            " ...\n",
            " [ 6]\n",
            " [ 8]\n",
            " [ 8]], shape=(1698, 1), dtype=int64)\n",
            "-----------------------------------------------------------------------\n",
            "(1698, 1)\n",
            "tf.Tensor([11], shape=(1,), dtype=int64)\n",
            "tf.Tensor(\n",
            "[[11]\n",
            " [19]\n",
            " [16]\n",
            " ...\n",
            " [ 6]\n",
            " [ 8]\n",
            " [ 8]], shape=(1698, 1), dtype=int64)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "jLqt8boY4RZD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "29ceed46-8a7c-46c0-a536-7691bb2d4f19"
      },
      "source": [
        "# check if the model performs well on unseen data\n",
        "from sklearn.metrics import accuracy_score\n",
        "acc_score = accuracy_score(y_test, y_hat)\n",
        "print(acc_score)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.8180212014134276\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-hfM4ekBHJP8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# confusion matrix\n",
        "cm = confusion_matrix(y_test, y_hat)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "KRMQ6P79Gjph",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 486
        },
        "outputId": "15f58f09-deaf-4c4d-83e6-5250433a5cd4"
      },
      "source": [
        "# text report showing the main classification metrics\n",
        "print(classification_report(y_test, y_hat))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.84      0.76      0.80        76\n",
            "           1       0.76      0.86      0.80       105\n",
            "           2       0.69      0.80      0.74        83\n",
            "           3       0.59      0.72      0.65        81\n",
            "           4       0.89      0.63      0.74        89\n",
            "           5       0.72      0.71      0.72        84\n",
            "           6       0.78      0.79      0.78        78\n",
            "           7       0.75      0.91      0.83        70\n",
            "           8       0.99      0.84      0.91        86\n",
            "           9       0.94      0.93      0.93        96\n",
            "          10       0.95      0.93      0.94        76\n",
            "          11       0.90      0.89      0.89       100\n",
            "          12       0.72      0.65      0.68        85\n",
            "          13       0.90      0.93      0.92        82\n",
            "          14       0.90      0.90      0.90        82\n",
            "          15       0.80      0.88      0.84        97\n",
            "          16       0.89      0.86      0.87        91\n",
            "          17       0.89      0.92      0.91        99\n",
            "          18       0.77      0.74      0.76        78\n",
            "          19       0.73      0.62      0.67        60\n",
            "\n",
            "    accuracy                           0.82      1698\n",
            "   macro avg       0.82      0.81      0.81      1698\n",
            "weighted avg       0.82      0.82      0.82      1698\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "T2bQynRwGjqE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 643
        },
        "outputId": "a3e52659-79e5-48f0-94e6-9b7b94d52713"
      },
      "source": [
        "# plot the confusion matrix\n",
        "plt.rcParams.update({'figure.figsize': (10, 10), 'figure.dpi': 80})\n",
        "plt.figure(figsize=(10, 10))\n",
        "sns.heatmap(cm, annot=True, fmt=\".0f\", linewidths=.5, square = True, cmap = 'Blues_r');\n",
        "plt.ylabel('Actual label');\n",
        "plt.xlabel('Predicted label');\n",
        "all_sample_title = f'Accuracy Score: {acc_score}'\n",
        "plt.title(all_sample_title, size = 15);"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x800 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5r_-_5TFhgxN",
        "colab_type": "text"
      },
      "source": [
        "## Quick test on new data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SKYsXJEtcNm_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        },
        "outputId": "8fde9ca4-b7e9-492e-9306-d20f10577f0a"
      },
      "source": [
        "# custom phrases\n",
        "docs_new = ['God is love', \n",
        "            'OpenGL on the GPU is fast', \n",
        "            \"You can not go on without our new product. There's a promotion for you right now!!\", \n",
        "            'You should read the BIBLE! It is the greatest book of all time.']\n",
        "\n",
        "\n",
        "# Transforms each text in texts to a sequence of integers.\n",
        "docs_sequences = tokenizer.texts_to_sequences(docs_new)\n",
        "\n",
        "# Pads sequences to the same length.\n",
        "docs_padded = pad_sequences(docs_sequences, padding=padding_type, maxlen=max_words)\n",
        "\n",
        "# make prediction on the new dataset\n",
        "predicted = model.predict(docs_padded)\n",
        "\n",
        "# transform the softmax output to sparse categorical\n",
        "predicted = tf.argmax(predicted, axis=1)\n",
        "\n",
        "for doc, category in zip(docs_new, predicted):\n",
        "    print(f'{doc} => {twenty.target_names[category]}')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "God is love => comp.windows.x\n",
            "OpenGL on the GPU is fast => comp.windows.x\n",
            "You can not go on without our new product. There's a promotion for you right now!! => comp.windows.x\n",
            "You should read the BIBLE! It is the greatest book of all time. => comp.windows.x\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_Uy4IeMwlL3T",
        "colab_type": "text"
      },
      "source": [
        "# Conclusion\n",
        "\n",
        "The Naive-Bayes model was very easy and quick to create and it performed very well even on our first try.\n",
        "\n",
        "The Deep Learning model performed well too, nonetheless it wasn't the best performer model. Altough the accuracy of this model wasn't as good as the previous model, we could check that even when the prediction was not accurate, the wrong predictions were classified as to a class close to the correct answer. That's due to the power of the pre-trained embedding in NN that captures the relative fields in which the words are usually inserted into.\n",
        "\n",
        "- The **Naive-Bayes is very quick to implement**. The model itself does not require tunning it's hiperparameters (not in most cases). On the other hand, **usually the data must be well pre-processed** before being fed to the model, in this case the pre-processing is very simple.\n",
        "\n",
        "- The **Deep Learning model requires a lot of hiperparameter** tunning and also testing and **finding a proper model architecture** to perform better (was not the focus on this notebook). A bigger dataset always happen to help deep learning models as well. That would take a lot more of our time.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jjIjyE_MCJAI",
        "colab_type": "text"
      },
      "source": [
        "# References\n",
        "\n",
        "- Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014. [GloVe: Global Vectors for Word Representation](https://nlp.stanford.edu/pubs/glove.pdf)\n",
        "- https://nlp.stanford.edu/projects/glove/\n",
        "- https://www.tensorflow.org/tutorials/text/word_embeddings\n",
        "- https://github.com/keras-team/keras/issues/7551\n",
        "- https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/text/Tokenizer\n",
        "- https://www.analyticsvidhya.com/blog/2017/09/naive-bayes-explained/\n",
        "- https://scikit-learn.org/stable/modules/naive_bayes.html\n",
        "- https://gist.github.com/sebleier/554280\n",
        "- https://machinelearningmastery.com/prepare-text-data-machine-learning-scikit-learn/\n",
        "- https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfTransformer.html\n",
        "- http://qwone.com/~jason/20Newsgroups/\n"
      ]
    }
  ]
}