NLP_Emotion_Detection.ipynb

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      "name": "NLP - Emotion Detection.ipynb",
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      "source": [
        "# NLP - Emotion Detection\n",
        "\n",
        "Author: Adhang Muntaha Muhammad\n",
        "\n",
        "[![LinkedIn](https://img.shields.io/badge/linkedin-0077B5?style=for-the-badge&logo=linkedin&logoColor=white&link=https://www.linkedin.com/in/adhangmuntaha/)](https://www.linkedin.com/in/adhangmuntaha/)\n",
        "[![GitHub](https://img.shields.io/badge/github-121011?style=for-the-badge&logo=github&logoColor=white&link=https://github.com/adhang)](https://github.com/adhang)\n",
        "[![Kaggle](https://img.shields.io/badge/kaggle-20BEFF?style=for-the-badge&logo=kaggle&logoColor=white&link=https://www.kaggle.com/adhang)](https://www.kaggle.com/adhang)\n",
        "[![Tableau](https://img.shields.io/badge/tableau-E97627?style=for-the-badge&logo=tableau&logoColor=white&link=https://public.tableau.com/app/profile/adhang)](https://public.tableau.com/app/profile/adhang)\n",
        "___"
      ],
      "metadata": {
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    },
    {
      "cell_type": "markdown",
      "source": [
        "# Libraries"
      ],
      "metadata": {
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      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Update"
      ],
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      }
    },
    {
      "cell_type": "code",
      "source": [
        "# !pip install --upgrade pandas\n",
        "# !pip install --upgrade numpy\n",
        "# !pip install --upgrade scikit-learn\n",
        "# !pip install --upgrade matplotlib\n",
        "# !pip install --upgrade seaborn"
      ],
      "metadata": {
        "id": "QrpP9i9lMK2F"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Import"
      ],
      "metadata": {
        "id": "lf9sZy5qL9lb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "import string\n",
        "from shutil import unpack_archive\n",
        "import os\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "plt.style.use('fivethirtyeight')\n",
        "sns.set_style('white')\n",
        "sns.set_context('notebook', font_scale=1.5, rc={'lines.linewidth':1.5})\n",
        "\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from tensorflow.keras.preprocessing.text import Tokenizer\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "\n",
        "from tensorflow.keras import layers\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "\n",
        "import nltk\n",
        "nltk.download('stopwords')\n",
        "nltk.download('wordnet')\n",
        "nltk.download('omw-1.4')\n",
        "\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem import WordNetLemmatizer"
      ],
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        "id": "ktp4mSGwC2Js",
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "outputId": "63a56eb3-1da8-4cd4-c9da-d4be0f0914ee"
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      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/wordnet.zip.\n",
            "[nltk_data] Downloading package omw-1.4 to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/omw-1.4.zip.\n"
          ]
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    {
      "cell_type": "markdown",
      "source": [
        "# Dataset Overview"
      ],
      "metadata": {
        "id": "nZSPd8BpDTV1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Reading Datasets"
      ],
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      }
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      "execution_count": 3,
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        "data_train = pd.read_csv('/content/drive/MyDrive/Learning Course/DTS PROA - ML with Tensorflow/Exercise/Week 6 - NLP/train.txt',\n",
        "                         names=['text','emotion'], sep=';')\n",
        "data_val = pd.read_csv('/content/drive/MyDrive/Learning Course/DTS PROA - ML with Tensorflow/Exercise/Week 6 - NLP/val.txt',\n",
        "                         names=['text','emotion'], sep=';')\n",
        "data_test = pd.read_csv('/content/drive/MyDrive/Learning Course/DTS PROA - ML with Tensorflow/Exercise/Week 6 - NLP/test.txt',\n",
        "                         names=['text','emotion'], sep=';')"
      ]
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              "                                                text  emotion\n",
              "0                            i didnt feel humiliated  sadness\n",
              "1  i can go from feeling so hopeless to so damned...  sadness\n",
              "2   im grabbing a minute to post i feel greedy wrong    anger\n",
              "3  i am ever feeling nostalgic about the fireplac...     love\n",
              "4                               i am feeling grouchy    anger"
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              "                                                text  emotion\n",
              "0  im feeling quite sad and sorry for myself but ...  sadness\n",
              "1  i feel like i am still looking at a blank canv...  sadness\n",
              "2                     i feel like a faithful servant     love\n",
              "3                  i am just feeling cranky and blue    anger\n",
              "4  i can have for a treat or if i am feeling festive      joy"
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              "      <td>i feel like a faithful servant</td>\n",
              "      <td>love</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>i am just feeling cranky and blue</td>\n",
              "      <td>anger</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>i can have for a treat or if i am feeling festive</td>\n",
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              "                                                text  emotion\n",
              "0  im feeling rather rotten so im not very ambiti...  sadness\n",
              "1          im updating my blog because i feel shitty  sadness\n",
              "2  i never make her separate from me because i do...  sadness\n",
              "3  i left with my bouquet of red and yellow tulip...      joy\n",
              "4    i was feeling a little vain when i did this one  sadness"
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>text</th>\n",
              "      <th>emotion</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>im feeling rather rotten so im not very ambiti...</td>\n",
              "      <td>sadness</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>im updating my blog because i feel shitty</td>\n",
              "      <td>sadness</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>i never make her separate from me because i do...</td>\n",
              "      <td>sadness</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>i left with my bouquet of red and yellow tulip...</td>\n",
              "      <td>joy</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>i was feeling a little vain when i did this one</td>\n",
              "      <td>sadness</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f07d2a78-49ec-4de7-aa36-07633b06578b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f07d2a78-49ec-4de7-aa36-07633b06578b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f07d2a78-49ec-4de7-aa36-07633b06578b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Cleansing\n",
        "\n",
        "- Remove non-alphabetical characters\n",
        "- Convert the text to lower case\n",
        "- Remove stopwords"
      ],
      "metadata": {
        "id": "AOp6R4YkDjbA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "stop = stopwords.words('english')\n",
        "wordnet_lemmatizer = WordNetLemmatizer()"
      ],
      "metadata": {
        "id": "OuXsTIFqA_ll"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def clean(text):\n",
        "  global stop\n",
        "  global word_lemmatizer\n",
        "\n",
        "  # remove non-alphabetical characters\n",
        "  text = re.sub(r'[^a-zA-Z ]', '', text)\n",
        "\n",
        "  # lower case\n",
        "  text = text.lower()\n",
        "\n",
        "  # remove stopwords\n",
        "  text = ' '.join(word for word in text.split() if word not in stop)\n",
        "\n",
        "  # lemmatization\n",
        "  text = ''.join(wordnet_lemmatizer.lemmatize(word) for word in text)\n",
        "\n",
        "  return text"
      ],
      "metadata": {
        "id": "k8zLaki2144m"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = data_train['text'].apply(clean)\n",
        "y_train = data_train['emotion']\n",
        "\n",
        "X_test = data_test['text'].apply(clean)\n",
        "y_test = data_test['emotion']\n",
        "\n",
        "X_val = data_val['text'].apply(clean)\n",
        "y_val = data_val['emotion']"
      ],
      "metadata": {
        "id": "igOly243DpUG"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_train['text'].head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9ajYWmvaDzYv",
        "outputId": "1bfc79bd-4026-4129-fb92-f121a4075219"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0                              i didnt feel humiliated\n",
              "1    i can go from feeling so hopeless to so damned...\n",
              "2     im grabbing a minute to post i feel greedy wrong\n",
              "3    i am ever feeling nostalgic about the fireplac...\n",
              "4                                 i am feeling grouchy\n",
              "Name: text, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iAtvSafLDtrt",
        "outputId": "06fa812c-09b3-47a6-8d45-ee8f3cc50ee8"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0                                didnt feel humiliated\n",
              "1    go feeling hopeless damned hopeful around some...\n",
              "2            im grabbing minute post feel greedy wrong\n",
              "3    ever feeling nostalgic fireplace know still pr...\n",
              "4                                      feeling grouchy\n",
              "Name: text, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Class Distribution"
      ],
      "metadata": {
        "id": "R10Z0Wh0Ec6f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "colors = ['gray', 'orangered', 'hotpink', 'gold', 'teal', 'cornflowerblue']\n",
        "_, ax = plt.subplots(1,3,figsize=(20,6))\n",
        "# _, ax = plt.subplots(3,1,figsize=(6,12))\n",
        "\n",
        "tmp = y_train.value_counts(normalize=True)\n",
        "tmp.sort_index(inplace=True)\n",
        "sns.barplot(x=tmp.index, y=tmp.values, palette=colors, saturation=1, ax=ax[0])\n",
        "ax[0].set_title('Training Data - Class Count', pad=20)\n",
        "\n",
        "tmp = y_val.value_counts(normalize=True)\n",
        "tmp.sort_index(inplace=True)\n",
        "sns.barplot(x=tmp.index, y=tmp.values, palette=colors, saturation=1, ax=ax[1])\n",
        "ax[1].set_title('Validation Data - Class Count', pad=20)\n",
        "\n",
        "tmp = y_test.value_counts(normalize=True)\n",
        "tmp.sort_index(inplace=True)\n",
        "sns.barplot(x=tmp.index, y=tmp.values, palette=colors, saturation=1, ax=ax[2])\n",
        "ax[2].set_title('Testing Data - Class Count', pad=20)\n",
        "\n",
        "for i in range(0,3):\n",
        "  ax[i].set_xticklabels(ax[i].get_xticklabels(), rotation=90)\n",
        "  ax[i].bar_label(ax[i].containers[0], padding=3, fmt='%.2f%%')\n",
        "  ax[i].set_yticklabels([])\n",
        "\n",
        "sns.despine(left=True, bottom=True)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "ncv0GJVWH4EP",
        "outputId": "cb6852e3-1ea1-4cac-800a-f86e3d440ada"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x432 with 3 Axes>"
            ],
            "image/png": 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NNVaoUMHYqVMn4507d0ztBw4cMFaoUCHdd0RGeSM1NdXYsWNH4wsvvGAW372+YzL6bvvyyy+NXl5exqioKLO+abl1woQJ6fZv3ry5MTk52dR+/vx5Y+XKlY29e/dO957/lXaNhgwZkmmfy5cvG728vIwtW7Y0tWXlz2HXrl0Z/pvIaMxaPs8pPXr0MHp5eRkPHDhg8T4Z/TkmJiYaDQZDur59+/Y1ent7m32Wa9asaXz33Xfv+R5hYWFGLy8v4z///GNxXGnmzJlj9PLyMs6YMcPifbLyWbvX5/jVV181duzY0azNy8vLWKFCBeMff/xh1t61a1djpUqVjDdu3DC1ZfTvUQB5S0b3ths2bDB6eXkZFyxYYNb39u3bxpYtWxpfffVV03dk9+7djdWrVzfevn37nu+T0b/r03Dvehf3rndx78q96+OKaSVsiLu7u1q1apWu3cnJyTSX0p07d3T16lUlJCSobt26kpThozEZee2118xWjLWzs1OtWrV08eJFs0f57uW/iwDUrl1bknTq1ClT2w8//CAHBwfT46Jp3njjjQwfZ3wQBQoU0I0bN0yv7ezsTIsSpaam6tq1a0pISDDFuW/fPouPnT9/fkl3H1+5fv26EhISVKFCBRUsWDBLx7FU2nk86GIwWfm8FChQQPHx8dqzZ0+mx0v7M9u8ebOSk5OzFEvaOf37V+372bhxo+zt7U0j7NK88sorqlixojZv3iyDwZClOP7N09NTgYGBZm0ZfY4B5A2vv/66UlNTtWLFClNbbGys/vjjD9WvX980GjKncuW/bd68WUajUW+//bbZHMCVK1dWvXr10vVPyxvS3ekCLl++rCtXrqhevXq6ceOG/vrrryzHkGbjxo0qXLiw2rZta9betm1bFS5cWJs2bUq3z5tvvmk2urZo0aIqW7asTp48me04/i0tX/07D+fUn0NO5nNL5VQednFxMY08TklJ0ZUrV5SQkKCXXnpJBoNBBw4cMPUtUKCAjh8/rpiYmEyPl5aH169fn24qsPvJzjll57OWFdWqVdPzzz9v1la7dm3duXMn3SO4AB490dHReuKJJ9SgQQMlJCSY/rt27Zrq16+vuLg4Ux4qWLCgkpKStHXrVhmNxhyPhXtX7l2zgntX5GVMK2FDSpcunekCNHPnztWCBQt0/PjxdF8uV69etfj4/+Xu7i5JunLlikVfgv89RtojC1euXDG1nT17Vp6enumO5+TkpFKlSunatWsWxWuJjBbtW7t2rWbOnKnDhw+bzT0kWX6tJOnnn3/WlClT9Oeff6ZLLJYc59/zAEl3F0tIu94ZSTsPSwv192Lp5+Xjjz9W9+7d1aFDB3l6esrX11evvPKKGjZsaCooNGnSRNHR0frmm280a9YsPf/883rppZfUpEkTlSxZ8p5xZOec0j4/GT3GVb58eR0+fFiXL1/Wk08+afEx/+1+fw8A5C0BAQEqVKiQli1bpvfee0+StHTpUkkyTSmRJidy5b+dOXNGkvTss8+m21auXDn99NNPZm03b97U5MmTtW7dOv3999/p9nmQ/Hf27Fn5+PikW/gtX758euaZZ3To0KF0+2T2fZdTBbjMbgxz6s/hQfJ5amqqab7KNC4uLve80c+pPHznzh1NmzZNK1eu1KlTp9IVPP79OQgNDVW/fv3UtGlTlS5dWrVq1dKrr76q+vXrm+Y87tChgzZv3qwhQ4Zo3LhxevHFF/Xyyy8rKCjovlNFZDcPZ/WzlhXkYeDxFhsbq5s3b5qKexm5dOmSypYtq5CQEP3666/q3r273N3d5evrq//9739q3LjxAxcdJe5ds3Ic7l25d0XeRnHYhri6umbYPnPmTI0aNUovvfSSOnXqJE9PTzk6Oio+Pl6fffaZxb+y3mvl8wc9xsP4pfd+zp49q5s3b6p69eqmtg0bNqh3796qWrWqQkNDVbx4cTk7Oys1NVXvvvuuxXHu27dP77zzjsqUKaM+ffqoVKlSppFAvXv3tug4L730ktlrX19fRUZGZtr/ueeekyQdOnTINI9UdmTl81K9enVt3LhRP/30k3755Rf98ssvWr16tSIiIjRv3jy5u7vLyclJM2fO1L59+7R9+3bt2bNHkyZN0uTJkzV+/Ph7xpp2TocPH872+dzLveaEzGx0VU78PQCQe5ydnRUUFKR58+bp999/1/PPP6/o6GgVK1ZML7/8sqlfTuXKB9GnTx9t3bpVbdq0Uc2aNeXu7i4HBwdt27ZNs2bNeqCRI9mR0YJqOeno0aOSZDbnbU79OTxoPv/7779Nc1qmadmypUaNGpXpPs8995w2bNigQ4cOqVKlShbFmZFRo0YpMjJSgYGB6tatmwoXLixHR0cdPHhQ48aNM/scNGjQQFu2bNG2bdv066+/aufOnVqyZIlq1KihmTNnysnJSR4eHlqyZIn27NmjnTt36tdff9XIkSMVHh6uadOmmf07KKNzkvTABd3MkIcB/JfRaFThwoU1fvz4TPukfTc988wzWrt2rX7++Wf9/PPP2r17t/r3769JkyZp7ty5KlOmzAPFwr0r967cu+JxQXEYWrlypUqWLKlvv/3W7Ebvxx9/tGJUmStZsqR+/vln3bx50+wX2Nu3b+vs2bMqVKhQjrxP2urvfn5+praVK1fK2dlZc+bMMSu2x8bGptv/Xl/Oq1evVmpqqr799luzX+tu3bpl8a/HM2fONHt9v/N+5ZVX5OzsrJUrV+r999/P9kI7Wf28PPHEE2rYsKEaNmwo6e4vt0OHDtWSJUv07rvvmvpVrVpVVatWlXT3prtFixaaOHHiPRNs6dKlValSJf3++++KjY21aOGI0qVLa/v27bp27Vq6axYbG6sCBQqYfvVP+4U2o1/Dz549a3pEKTseZDEiADnr9ddf17x587Rs2TJdvXpVFy9eVLdu3cy+4x5Grkz7/v/rr7/S3aD+N69cu3ZNW7duVfPmzc0WHJOknTt3pjt2Vr9jSpcurRMnTujOnTtmIzrv3LmjkydPZjiy5GHLLA9b+udwr2uQlXyekaeeeipdHvb09LznPgEBAfr666+1ZMkStW7dOtt5YOXKlapZs6YmTJhg1p7Z45/u7u5q3ry5mjdvLqPRqHHjxum7777T5s2b1bhxY0l3bw5r1aqlWrVqSZKOHDmi1q1bKyIiQtOmTcs0lhdeeEFPPfWUNm3apMuXL1u0IFFWPmv/zsP/nrosOTlZFy9e1NNPP33f98sMeRh4ND399NM6efKknn/+eYueTHVycpKfn58pl2zbtk3vvfeeZs6cqUGDBj3scLl3/T/cu3LviryNOYche3t72dnZmf0ydOfOHX377bdWjCpz9evXV2pqqubMmWPWvmjRIl2/fj1H3mPdunWaPn26PD091aFDB1O7g4OD7OzszEblGI1GRUREpDtG2rxMGX05Z/YL3dSpUy0e+VW3bl2z/3x8fO7Z/8knn9Q777yjuLg4ffHFF0pJSUnX58aNGwoLC7vncbLyefnvI7fS3bk0pf9/XTLqU6xYMRUuXNiiR5T69u0r6e5jQP99XEm6++jvrFmzdPz4cUl3R1EZDIZ0N7vbtm3ToUOHzB61feaZZySlL7ysXr1aFy5cuG9s93KvzweA3FW5cmVVrFhRa9eu1dy5c2VnZ6fXX3/drM/DyJX169eXnZ2dZs6cqdTUVFP7wYMH033vpH0v/XcUx4ULF0w3hP+W1e+YtLkb/3usRYsWKSEhQQ0aNLDoODll9uzZWrVqlSpUqGA2F15W/hzul4ctzecZcXZ2TpeH77fyuLe3t5o3b669e/dq/PjxGY7IuXjxor788st7Hsfe3j7dvrdu3dKsWbPM2tLml/w3Ozs706jle+XhZ599Vs7Ozvf9/Dg5Oemjjz7SzZs31bt3b7O5LtMkJyfryy+/NG3LymctszycEyPl0z4fPDYLPFpatGghg8GQ6XflP//8Y/r/jL7f/vsdKN0tCF69evWhjJTk3vUu7l3v4t4VeRUjh6FGjRpp/Pjx6tq1q/z9/XXjxg2tXr063VxwecUbb7yhBQsWaOLEiTp9+rSqVKmio0eP6vvvv9fTTz+dpcVU4uPjtXLlSkl3b17OnTunHTt2aN++fXr66acVHh5u9gtdw4YNtX79enXu3FktWrTQnTt3tGnTJiUmJqY7toeHh55++mmtWbNGpUuXVpEiReTq6qr69eurQYMGmjVrlrp27aq2bdvK0dFRO3bs0NGjRy0adZNdPXv21MWLF7V48WL99ttvatKkicqUKaPbt2/ryJEj+v777+Xo6KjQ0NBMj5GVz0tgYKCqVaumqlWrytPTUxcvXtSiRYvk6OioJk2aSJIiIiK0Y8cOvfLKKypVqpSMRqN++OEH/fXXX2a/zmamXr16Gjp0qIYMGaJGjRqpSZMmqlixovLly6dTp05pw4YNOn36tFavXi3p7mO/y5cv17fffqu4uDjVqFFDp0+f1rx581SkSBF9/PHHpmM/++yzqlu3rhYuXCij0aiKFSvq8OHD2rRpU5Y/a/+VtljOuHHj1LRpUzk7O+u5556Tl5dXto8JIPtef/11DRs2TNu3b5evr2+6kbIPI1eWK1dOHTp0UFRUlDp37qyAgABdunRJc+fOlbe3t9mj+gUKFFC9evUUHR0tFxcXValSRXFxcVq4cKFKlSqVrsCV1e+Yd999V99//72GDh2qQ4cOmb7vlixZorJly1r0fZwdJ0+eNOXhpKQknT59Wlu3btXx48dVuXJlTZkyxewaZ+XPoXz58nriiSc0b948ubi4qFChQipcuLDq1KmTpXyek4YMGaJr167p22+/1bZt2xQQEKASJUro1q1b2rdvnzZu3HjfPNCwYUMtXLhQH330kerWrat//vlHS5cuTTd3482bN/XSSy+pfv36qlSpkgoXLqyzZ89q/vz5cnNz06uvvipJGjBggM6fP6+XXnpJJUqUUFJSktatW6ebN2+qefPm9z2n119/XefPn9fkyZMVEBCgoKA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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Preparation"
      ],
      "metadata": {
        "id": "OGacQ7jst8ih"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Label Encoding\n",
        "\n",
        "Make sure to fit on train data, then transform on all data."
      ],
      "metadata": {
        "id": "TdoXzTg7VJVI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "le = LabelEncoder()\n",
        "\n",
        "le.fit(y_train)\n",
        "\n",
        "y_train_encode = le.transform(y_train)\n",
        "y_val_encode = le.transform(y_val)\n",
        "y_test_encode = le.transform(y_test)"
      ],
      "metadata": {
        "id": "KqIovSkmJygd"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(y_train[:5].values)\n",
        "print(y_train_encode[:5])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "__yRH96xVWbt",
        "outputId": "77e02bc6-649d-4bc3-cf38-0615aea1be3f"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['sadness' 'sadness' 'anger' 'love' 'anger']\n",
            "[4 4 0 3 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(y_val[:5].values)\n",
        "print(y_val_encode[:5])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rDoXI9xkVXgE",
        "outputId": "1fc024f6-86c8-40d6-b386-fa606e682645"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['sadness' 'sadness' 'love' 'anger' 'joy']\n",
            "[4 4 3 0 2]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(y_test[:5].values)\n",
        "print(y_test_encode[:5])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qtIuy2NcVY41",
        "outputId": "7bf1d75f-24ea-42e0-be4b-2e8fd8763e04"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['sadness' 'sadness' 'sadness' 'joy' 'sadness']\n",
            "[4 4 4 2 4]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Tokenization\n",
        "\n",
        "Always fit only on train data to avoid over-optimistic and data leakage"
      ],
      "metadata": {
        "id": "2E0cPMA2Xkk1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Tokenize words\n",
        "tokenizer = Tokenizer()\n",
        "# tokenizer.fit_on_texts(pd.concat([X_train, X_test], axis=0))\n",
        "tokenizer.fit_on_texts(X_train)\n",
        "\n",
        "sequences_train = tokenizer.texts_to_sequences(X_train)\n",
        "sequences_test = tokenizer.texts_to_sequences(X_test)\n",
        "sequences_val = tokenizer.texts_to_sequences(X_val)\n",
        "\n",
        "X_train_sequence = pad_sequences(sequences_train, maxlen=256, truncating='pre')\n",
        "X_test_sequence = pad_sequences(sequences_test, maxlen=256, truncating='pre')\n",
        "X_val_sequence = pad_sequences(sequences_val, maxlen=256, truncating='pre')\n",
        "\n",
        "vocabSize = len(tokenizer.index_word) + 1\n",
        "print(f'Vocabulary size = {vocabSize}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NG0KYuzVVlbx",
        "outputId": "625d10ae-c75a-4f86-8b86-9a508d2b9b3b"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Vocabulary size = 15065\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Read GloVe Embedding"
      ],
      "metadata": {
        "id": "RtuaU0oNYu8F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# !wget http://nlp.stanford.edu/data/glove.6B.zip"
      ],
      "metadata": {
        "id": "OZxCLW1-3AAA"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "glove_dir = '/content/drive/MyDrive/Learning Course/DTS PROA - ML with Tensorflow/Exercise/GloVe/'\n",
        "\n",
        "# unpack_archive('glove.6B.zip', glove_dir)\n",
        "# # os.remove('glove.6B.zip')"
      ],
      "metadata": {
        "id": "9Aovgnmc3RJ9"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Read GloVE embeddings\n",
        "# path_to_glove_file = '/content/drive/MyDrive/Learning Course/DTS PROA - ML with Tensorflow/Exercise/Week 6 - NLP/glove.6B.200d.txt'\n",
        "\n",
        "path_to_glove_file = os.path.join(glove_dir, 'glove.6B.300d.txt')\n",
        "\n",
        "num_tokens = vocabSize\n",
        "embedding_dim = 300\n",
        "hits = 0\n",
        "misses = 0\n",
        "embeddings_index = {}\n",
        "\n",
        "# Read word vectors\n",
        "with open(path_to_glove_file) as f:\n",
        "    for line in f:\n",
        "        word, coefs = line.split(maxsplit=1)\n",
        "        coefs = np.fromstring(coefs, 'f', sep=' ')\n",
        "        embeddings_index[word] = coefs\n",
        "print(f'Found {len(embeddings_index)} word vectors.')\n",
        "\n",
        "# Assign word vectors to our dictionary/vocabulary\n",
        "embedding_matrix = np.zeros((num_tokens, embedding_dim))\n",
        "for word, i in tokenizer.word_index.items():\n",
        "    embedding_vector = embeddings_index.get(word)\n",
        "    if embedding_vector is not None:\n",
        "        # Words not found in embedding index will be all-zeros.\n",
        "        # This includes the representation for 'padding' and 'OOV'\n",
        "        embedding_matrix[i] = embedding_vector\n",
        "        hits += 1\n",
        "    else:\n",
        "        misses += 1\n",
        "\n",
        "print(f'Converted {hits} words ({misses} misses)')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lNnSwQ1eYS5-",
        "outputId": "f89744ef-dbf7-4619-f9fd-1a7e2846f3c8"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 400000 word vectors.\n",
            "Converted 14065 words (999 misses)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "embedding_layer = layers.Embedding(\n",
        "    num_tokens,\n",
        "    embedding_dim,\n",
        "    embeddings_initializer=tf.keras.initializers.Constant(embedding_matrix),\n",
        "    trainable=False,\n",
        ")"
      ],
      "metadata": {
        "id": "SX-0dZM7qwv4"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Training Viz Function"
      ],
      "metadata": {
        "id": "QAvUAp4ruMez"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_history(history, metrics='loss', val_metrics='val_loss', \n",
        "                 ax=None, show=True, **kwargs):\n",
        "  if ax == None:\n",
        "    _, ax = plt.subplots(figsize=(8,6))\n",
        "\n",
        "  epochs = range(1, len(history.history[metrics])+1)\n",
        "\n",
        "  sns.lineplot(x=epochs, y=history.history[metrics],\n",
        "               label=f'training {metrics}', ax=ax, **kwargs)\n",
        "  sns.lineplot(x=epochs, y=history.history[val_metrics], \n",
        "               label=f'validation {metrics}', ax=ax, **kwargs)\n",
        "\n",
        "  ax.set_ylabel(metrics)\n",
        "  ax.set_xlabel('epochs')\n",
        "  ax.set_xlim(1, len(epochs))\n",
        "\n",
        "  ax.legend(loc='lower center', \n",
        "          bbox_to_anchor=(0.5, 1), \n",
        "          title='', ncol=2, frameon=False)\n",
        "\n",
        "  sns.despine()\n",
        "  if show:\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "8hxC-Y2wuPsU"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model Default (Keras Tutorial)"
      ],
      "metadata": {
        "id": "VEZLoP38t-PU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model Architecture"
      ],
      "metadata": {
        "id": "BbI2UJsuaD4K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "int_sequences_input = layers.Input(shape=(None,), dtype='int64')\n",
        "embedded_sequences = embedding_layer(int_sequences_input)\n",
        "x = layers.Conv1D(128, 5, activation='relu')(embedded_sequences)\n",
        "x = layers.MaxPooling1D(5)(x)\n",
        "x = layers.Conv1D(128, 5, activation='relu')(x)\n",
        "x = layers.MaxPooling1D(5)(x)\n",
        "x = layers.Conv1D(128, 5, activation='relu')(x)\n",
        "x = layers.GlobalMaxPooling1D()(x)\n",
        "x = layers.Dense(128, activation='relu')(x)\n",
        "x = layers.Dropout(0.2)(x)\n",
        "preds = layers.Dense(6, activation='softmax')(x)\n",
        "model = tf.keras.Model(int_sequences_input, preds)\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NDethfZfrJrC",
        "outputId": "152f9acf-90df-4edc-b191-cd39f2b9f7e0"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, None)]            0         \n",
            "                                                                 \n",
            " embedding (Embedding)       (None, None, 300)         4519500   \n",
            "                                                                 \n",
            " conv1d (Conv1D)             (None, None, 128)         192128    \n",
            "                                                                 \n",
            " max_pooling1d (MaxPooling1D  (None, None, 128)        0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv1d_1 (Conv1D)           (None, None, 128)         82048     \n",
            "                                                                 \n",
            " max_pooling1d_1 (MaxPooling  (None, None, 128)        0         \n",
            " 1D)                                                             \n",
            "                                                                 \n",
            " conv1d_2 (Conv1D)           (None, None, 128)         82048     \n",
            "                                                                 \n",
            " global_max_pooling1d (Globa  (None, 128)              0         \n",
            " lMaxPooling1D)                                                  \n",
            "                                                                 \n",
            " dense (Dense)               (None, 128)               16512     \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 128)               0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 6)                 774       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 4,893,010\n",
            "Trainable params: 373,510\n",
            "Non-trainable params: 4,519,500\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Compile"
      ],
      "metadata": {
        "id": "OHkWMTYcrxA0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "adam = Adam(learning_rate=0.005)\n",
        "\n",
        "model.compile(loss='sparse_categorical_crossentropy', \n",
        "              optimizer=adam, \n",
        "              metrics=['accuracy']\n",
        "              )"
      ],
      "metadata": {
        "id": "lms-Hcq5rtTo"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Callbacks"
      ],
      "metadata": {
        "id": "tn4CfyxBs1qH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "callbacks = [\n",
        "  tf.keras.callbacks.EarlyStopping(\n",
        "    monitor='val_loss',\n",
        "    min_delta=1e-3,\n",
        "    patience=3,\n",
        "    restore_best_weights=True,\n",
        "    verbose=1,\n",
        "  )\n",
        "]"
      ],
      "metadata": {
        "id": "jqta0_GCs2dJ"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Fit"
      ],
      "metadata": {
        "id": "SGesemM9r_wz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(X_train_sequence, y_train_encode, \n",
        "                    batch_size=128, epochs=20, \n",
        "                    validation_data=(X_val_sequence, y_val_encode),\n",
        "                    callbacks=callbacks)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZZTHjLiLr_PQ",
        "outputId": "c8b25fa0-af58-4f80-b169-867444876121"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "125/125 [==============================] - 16s 18ms/step - loss: 1.5814 - accuracy: 0.3330 - val_loss: 1.5412 - val_accuracy: 0.3440\n",
            "Epoch 2/20\n",
            "125/125 [==============================] - 2s 15ms/step - loss: 1.5050 - accuracy: 0.3776 - val_loss: 1.5082 - val_accuracy: 0.3945\n",
            "Epoch 3/20\n",
            "125/125 [==============================] - 2s 15ms/step - loss: 1.4338 - accuracy: 0.3972 - val_loss: 1.4818 - val_accuracy: 0.4090\n",
            "Epoch 4/20\n",
            "125/125 [==============================] - 2s 15ms/step - loss: 1.3650 - accuracy: 0.4263 - val_loss: 1.4943 - val_accuracy: 0.3705\n",
            "Epoch 5/20\n",
            "125/125 [==============================] - 2s 15ms/step - loss: 1.3074 - accuracy: 0.4503 - val_loss: 1.5168 - val_accuracy: 0.3715\n",
            "Epoch 6/20\n",
            "125/125 [==============================] - ETA: 0s - loss: 1.2489 - accuracy: 0.4776Restoring model weights from the end of the best epoch: 3.\n",
            "125/125 [==============================] - 2s 15ms/step - loss: 1.2489 - accuracy: 0.4776 - val_loss: 1.5424 - val_accuracy: 0.4150\n",
            "Epoch 6: early stopping\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## History"
      ],
      "metadata": {
        "id": "rw2pQ5OVslJd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plot_history(history, metrics='loss', val_metrics='val_loss')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        },
        "id": "Vfxmg_1DsqRo",
        "outputId": "7c61a862-ae80-4ff2-a6f2-67a6c59df87f"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_history(history, metrics='accuracy', val_metrics='val_accuracy')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        },
        "id": "KgkcUCOVsrwc",
        "outputId": "8f233599-2715-4ff0-b54a-e31948d2bcdc"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Evaluate"
      ],
      "metadata": {
        "id": "5hRgcFXvxZ96"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "eval = model.evaluate(X_test_sequence, y_test_encode)\n",
        "\n",
        "print(f'loss\\t: {eval[0]:.3f}')\n",
        "print(f'acc\\t: {eval[1]:.3f}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5388e707-421e-462a-df0f-817e9744fe69",
        "id": "UQ1-TbgExZ99"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "63/63 [==============================] - 0s 6ms/step - loss: 1.4622 - accuracy: 0.4065\n",
            "loss\t: 1.462\n",
            "acc\t: 0.407\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model with LSTM"
      ],
      "metadata": {
        "id": "hVfkcdIRuWto"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model Architecture"
      ],
      "metadata": {
        "id": "RvExaMIOuWtp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "int_sequences_input = layers.Input(shape=(None,), dtype='int64')\n",
        "embedded_sequences = embedding_layer(int_sequences_input)\n",
        "x = layers.Bidirectional(layers.LSTM(64))(embedded_sequences)\n",
        "x = layers.Dense(128, activation='relu')(x)\n",
        "x = layers.Dropout(0.2)(x)\n",
        "preds = layers.Dense(6, activation='softmax')(x)\n",
        "model = tf.keras.Model(int_sequences_input, preds)\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "77287cad-3685-49e9-f231-63c1734ce3b7",
        "id": "BbQM4mhcuWtp"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_2 (InputLayer)        [(None, None)]            0         \n",
            "                                                                 \n",
            " embedding (Embedding)       (None, None, 300)         4519500   \n",
            "                                                                 \n",
            " bidirectional (Bidirectiona  (None, 128)              186880    \n",
            " l)                                                              \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 128)               16512     \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 128)               0         \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 6)                 774       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 4,723,666\n",
            "Trainable params: 204,166\n",
            "Non-trainable params: 4,519,500\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Compile"
      ],
      "metadata": {
        "id": "JNbHl2pMuWtp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "adam = Adam(learning_rate=0.005)\n",
        "\n",
        "model.compile(loss='sparse_categorical_crossentropy', \n",
        "              optimizer=adam, \n",
        "              metrics=['accuracy']\n",
        "              )"
      ],
      "metadata": {
        "id": "NHH39YGzuWtq"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Callbacks"
      ],
      "metadata": {
        "id": "JRPhJFGluWtq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "callbacks = [\n",
        "  tf.keras.callbacks.EarlyStopping(\n",
        "    monitor='val_loss',\n",
        "    min_delta=1e-3,\n",
        "    patience=3,\n",
        "    restore_best_weights=True,\n",
        "    verbose=1,\n",
        "  )\n",
        "]"
      ],
      "metadata": {
        "id": "a53zuQS5uWtq"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Fit"
      ],
      "metadata": {
        "id": "JnDxjmP_uWtr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(X_train_sequence, y_train_encode, \n",
        "                    batch_size=128, epochs=20, \n",
        "                    validation_data=(X_val_sequence, y_val_encode),\n",
        "                    callbacks=callbacks)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6bdbdb72-86ea-4268-f155-ee19b3d2d622",
        "id": "lqkjyae1uWtr"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "125/125 [==============================] - 9s 42ms/step - loss: 0.8676 - accuracy: 0.6833 - val_loss: 0.3688 - val_accuracy: 0.8725\n",
            "Epoch 2/20\n",
            "125/125 [==============================] - 5s 37ms/step - loss: 0.2640 - accuracy: 0.9007 - val_loss: 0.2365 - val_accuracy: 0.9060\n",
            "Epoch 3/20\n",
            "125/125 [==============================] - 5s 38ms/step - loss: 0.1494 - accuracy: 0.9381 - val_loss: 0.1828 - val_accuracy: 0.9190\n",
            "Epoch 4/20\n",
            "125/125 [==============================] - 4s 32ms/step - loss: 0.1011 - accuracy: 0.9562 - val_loss: 0.1862 - val_accuracy: 0.9230\n",
            "Epoch 5/20\n",
            "125/125 [==============================] - 4s 31ms/step - loss: 0.0755 - accuracy: 0.9696 - val_loss: 0.1994 - val_accuracy: 0.9200\n",
            "Epoch 6/20\n",
            "125/125 [==============================] - ETA: 0s - loss: 0.0620 - accuracy: 0.9743Restoring model weights from the end of the best epoch: 3.\n",
            "125/125 [==============================] - 4s 32ms/step - loss: 0.0620 - accuracy: 0.9743 - val_loss: 0.2194 - val_accuracy: 0.9210\n",
            "Epoch 6: early stopping\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## History"
      ],
      "metadata": {
        "id": "6PGPdKgnuWts"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plot_history(history, metrics='loss', val_metrics='val_loss')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        },
        "outputId": "843f8261-30e7-4c12-fd80-22eb68bb66cb",
        "id": "PLTvJl4duWts"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_history(history, metrics='accuracy', val_metrics='val_accuracy')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        },
        "outputId": "0b028876-b0d0-44f8-8592-cf57cf306b04",
        "id": "hde6-LYeuWts"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Evaluate"
      ],
      "metadata": {
        "id": "ZDLMH4fcyfyT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "eval = model.evaluate(X_test_sequence, y_test_encode)\n",
        "\n",
        "print(f'loss\\t: {eval[0]:.3f}')\n",
        "print(f'acc\\t: {eval[1]:.3f}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "772c82de-ae2e-44aa-ae08-35cbf6c7b4db",
        "id": "RTKbrmrZyfyW"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "63/63 [==============================] - 1s 12ms/step - loss: 0.1747 - accuracy: 0.9230\n",
            "loss\t: 0.175\n",
            "acc\t: 0.923\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model with GRU"
      ],
      "metadata": {
        "id": "58s4IVX6wlxa"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model Architecture"
      ],
      "metadata": {
        "id": "8f45ZXFDwlxb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "int_sequences_input = layers.Input(shape=(None,), dtype='int64')\n",
        "embedded_sequences = embedding_layer(int_sequences_input)\n",
        "x = layers.Bidirectional(layers.GRU(64))(embedded_sequences)\n",
        "x = layers.Dense(128, activation='relu')(x)\n",
        "x = layers.Dropout(0.2)(x)\n",
        "preds = layers.Dense(6, activation='softmax')(x)\n",
        "model = tf.keras.Model(int_sequences_input, preds)\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9025150c-eac7-4689-f2b9-b5f119667bb3",
        "id": "OS-xmvtgwlxb"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_3 (InputLayer)        [(None, None)]            0         \n",
            "                                                                 \n",
            " embedding (Embedding)       (None, None, 300)         4519500   \n",
            "                                                                 \n",
            " bidirectional_1 (Bidirectio  (None, 128)              140544    \n",
            " nal)                                                            \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 128)               16512     \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 128)               0         \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 6)                 774       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 4,677,330\n",
            "Trainable params: 157,830\n",
            "Non-trainable params: 4,519,500\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Compile"
      ],
      "metadata": {
        "id": "x5tr6d0-wlxc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "adam = Adam(learning_rate=0.005)\n",
        "\n",
        "model.compile(loss='sparse_categorical_crossentropy', \n",
        "              optimizer=adam, \n",
        "              metrics=['accuracy']\n",
        "              )"
      ],
      "metadata": {
        "id": "ICI5Uh4Lwlxc"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Callbacks"
      ],
      "metadata": {
        "id": "NPlos2oDwlxd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "callbacks = [\n",
        "  tf.keras.callbacks.EarlyStopping(\n",
        "    monitor='val_loss',\n",
        "    min_delta=1e-3,\n",
        "    patience=3,\n",
        "    restore_best_weights=True,\n",
        "    verbose=1,\n",
        "  )\n",
        "]"
      ],
      "metadata": {
        "id": "NrJBL2sCwlxd"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Fit"
      ],
      "metadata": {
        "id": "rnWw9uwnwlxd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(X_train_sequence, y_train_encode, \n",
        "                    batch_size=128, epochs=20, \n",
        "                    validation_data=(X_val_sequence, y_val_encode),\n",
        "                    callbacks=callbacks)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6882a4d4-7c5d-477e-bb26-e30cd70715d3",
        "id": "xq-gbaMKwlxe"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "125/125 [==============================] - 7s 33ms/step - loss: 0.7146 - accuracy: 0.7389 - val_loss: 0.2297 - val_accuracy: 0.9085\n",
            "Epoch 2/20\n",
            "125/125 [==============================] - 3s 26ms/step - loss: 0.1898 - accuracy: 0.9217 - val_loss: 0.1781 - val_accuracy: 0.9260\n",
            "Epoch 3/20\n",
            "125/125 [==============================] - 3s 26ms/step - loss: 0.1253 - accuracy: 0.9436 - val_loss: 0.1510 - val_accuracy: 0.9320\n",
            "Epoch 4/20\n",
            "125/125 [==============================] - 3s 26ms/step - loss: 0.0947 - accuracy: 0.9571 - val_loss: 0.1571 - val_accuracy: 0.9325\n",
            "Epoch 5/20\n",
            "125/125 [==============================] - 3s 26ms/step - loss: 0.0767 - accuracy: 0.9655 - val_loss: 0.1733 - val_accuracy: 0.9265\n",
            "Epoch 6/20\n",
            "124/125 [============================>.] - ETA: 0s - loss: 0.0675 - accuracy: 0.9702Restoring model weights from the end of the best epoch: 3.\n",
            "125/125 [==============================] - 3s 26ms/step - loss: 0.0676 - accuracy: 0.9701 - val_loss: 0.1713 - val_accuracy: 0.9335\n",
            "Epoch 6: early stopping\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## History"
      ],
      "metadata": {
        "id": "Cd95sjbHwlxe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plot_history(history, metrics='loss', val_metrics='val_loss')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        },
        "outputId": "b0118c61-c27f-4700-d0fb-858143bfa63e",
        "id": "QTFFWRHPwlxe"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_history(history, metrics='accuracy', val_metrics='val_accuracy')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        },
        "outputId": "12481302-e650-45d8-bfb7-c3b96f47e6ac",
        "id": "w3pDni74wlxf"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Evaluate"
      ],
      "metadata": {
        "id": "FiK0AcOzyhpt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "eval = model.evaluate(X_test_sequence, y_test_encode)\n",
        "\n",
        "print(f'loss\\t: {eval[0]:.3f}')\n",
        "print(f'acc\\t: {eval[1]:.3f}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "667e6a9d-8feb-4928-a6d6-b2508db8ca3f",
        "id": "Lg14Ik5Xyhpv"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "63/63 [==============================] - 1s 11ms/step - loss: 0.1620 - accuracy: 0.9245\n",
            "loss\t: 0.162\n",
            "acc\t: 0.924\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model with Conv1D"
      ],
      "metadata": {
        "id": "OtYywokTw4d3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model Architecture"
      ],
      "metadata": {
        "id": "2R3UU7OLw4eA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "int_sequences_input = layers.Input(shape=(None,), dtype='int64')\n",
        "embedded_sequences = embedding_layer(int_sequences_input)\n",
        "x = layers.Conv1D(128, 5, activation='relu')(embedded_sequences)\n",
        "x = layers.GlobalAveragePooling1D()(x)\n",
        "x = layers.Dense(128, activation='relu')(x)\n",
        "x = layers.Dropout(0.2)(x)\n",
        "preds = layers.Dense(6, activation='softmax')(x)\n",
        "model = tf.keras.Model(int_sequences_input, preds)\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ed3d8620-8944-4716-e066-e7df91154085",
        "id": "AuC72QaAw4eB"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_3\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_4 (InputLayer)        [(None, None)]            0         \n",
            "                                                                 \n",
            " embedding (Embedding)       (None, None, 300)         4519500   \n",
            "                                                                 \n",
            " conv1d_3 (Conv1D)           (None, None, 128)         192128    \n",
            "                                                                 \n",
            " global_average_pooling1d (G  (None, 128)              0         \n",
            " lobalAveragePooling1D)                                          \n",
            "                                                                 \n",
            " dense_6 (Dense)             (None, 128)               16512     \n",
            "                                                                 \n",
            " dropout_3 (Dropout)         (None, 128)               0         \n",
            "                                                                 \n",
            " dense_7 (Dense)             (None, 6)                 774       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 4,728,914\n",
            "Trainable params: 209,414\n",
            "Non-trainable params: 4,519,500\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Compile"
      ],
      "metadata": {
        "id": "7wLL_71ww4eF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "adam = Adam(learning_rate=0.005)\n",
        "\n",
        "model.compile(loss='sparse_categorical_crossentropy', \n",
        "              optimizer=adam, \n",
        "              metrics=['accuracy']\n",
        "              )"
      ],
      "metadata": {
        "id": "NUJqewACw4eG"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Callbacks"
      ],
      "metadata": {
        "id": "cKpYTgB0w4eG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "callbacks = [\n",
        "  tf.keras.callbacks.EarlyStopping(\n",
        "    monitor='val_loss',\n",
        "    min_delta=1e-3,\n",
        "    patience=3,\n",
        "    restore_best_weights=True,\n",
        "    verbose=1,\n",
        "  )\n",
        "]"
      ],
      "metadata": {
        "id": "GAraj1Tew4eG"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Fit"
      ],
      "metadata": {
        "id": "43ZLzU0kw4eG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(X_train_sequence, y_train_encode, \n",
        "                    batch_size=128, epochs=20, \n",
        "                    validation_data=(X_val_sequence, y_val_encode),\n",
        "                    callbacks=callbacks)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "473aeef8-078c-43b4-be81-3c5e0dd3688f",
        "id": "aiXoBp29w4eG"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "125/125 [==============================] - 2s 14ms/step - loss: 1.3715 - accuracy: 0.4812 - val_loss: 1.0934 - val_accuracy: 0.6310\n",
            "Epoch 2/20\n",
            "125/125 [==============================] - 2s 12ms/step - loss: 0.9021 - accuracy: 0.6808 - val_loss: 0.7428 - val_accuracy: 0.7500\n",
            "Epoch 3/20\n",
            "125/125 [==============================] - 1s 12ms/step - loss: 0.6447 - accuracy: 0.7747 - val_loss: 0.6078 - val_accuracy: 0.7810\n",
            "Epoch 4/20\n",
            "125/125 [==============================] - 2s 13ms/step - loss: 0.4806 - accuracy: 0.8343 - val_loss: 0.5035 - val_accuracy: 0.8240\n",
            "Epoch 5/20\n",
            "125/125 [==============================] - 2s 12ms/step - loss: 0.3450 - accuracy: 0.8810 - val_loss: 0.4766 - val_accuracy: 0.8265\n",
            "Epoch 6/20\n",
            "125/125 [==============================] - 2s 12ms/step - loss: 0.2524 - accuracy: 0.9167 - val_loss: 0.4311 - val_accuracy: 0.8435\n",
            "Epoch 7/20\n",
            "125/125 [==============================] - 1s 12ms/step - loss: 0.1737 - accuracy: 0.9435 - val_loss: 0.4623 - val_accuracy: 0.8480\n",
            "Epoch 8/20\n",
            "125/125 [==============================] - 2s 12ms/step - loss: 0.1306 - accuracy: 0.9586 - val_loss: 0.4917 - val_accuracy: 0.8505\n",
            "Epoch 9/20\n",
            "121/125 [============================>.] - ETA: 0s - loss: 0.0998 - accuracy: 0.9681Restoring model weights from the end of the best epoch: 6.\n",
            "125/125 [==============================] - 1s 12ms/step - loss: 0.0992 - accuracy: 0.9682 - val_loss: 0.4744 - val_accuracy: 0.8490\n",
            "Epoch 9: early stopping\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## History"
      ],
      "metadata": {
        "id": "SkolsbGEw4eH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plot_history(history, metrics='loss', val_metrics='val_loss')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        },
        "outputId": "9c269ec6-f5b7-4110-bcda-aa8fac8e1410",
        "id": "mrwvKPZmw4eH"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_history(history, metrics='accuracy', val_metrics='val_accuracy')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        },
        "outputId": "83d74056-c2de-4433-9f6b-feee73723603",
        "id": "o9D7reRLw4eH"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Evaluate"
      ],
      "metadata": {
        "id": "MOew_W7Vyink"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "eval = model.evaluate(X_test_sequence, y_test_encode)\n",
        "\n",
        "print(f'loss\\t: {eval[0]:.3f}')\n",
        "print(f'acc\\t: {eval[1]:.3f}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e9301731-6aad-4fb0-a268-33e5ce41f25b",
        "id": "k0afEkWNyinl"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "63/63 [==============================] - 0s 4ms/step - loss: 0.4485 - accuracy: 0.8545\n",
            "loss\t: 0.449\n",
            "acc\t: 0.854\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model Experiment"
      ],
      "metadata": {
        "id": "xJWEAkvfx0IF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model Architecture"
      ],
      "metadata": {
        "id": "ZXpB7FKgx0IR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "int_sequences_input = layers.Input(shape=(None,), dtype='int64')\n",
        "embedded_sequences = embedding_layer(int_sequences_input)\n",
        "x = layers.Bidirectional(layers.LSTM(64, return_sequences=True))(embedded_sequences)\n",
        "x = layers.Bidirectional(layers.GRU(64))(embedded_sequences)\n",
        "x = layers.Dense(128, activation='relu')(x)\n",
        "x = layers.Dense(64, activation='relu')(x)\n",
        "x = layers.Dropout(0.2)(x)\n",
        "preds = layers.Dense(6, activation='softmax')(x)\n",
        "\n",
        "model = tf.keras.Model(int_sequences_input, preds)\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "63a3e16d-daf6-4367-9291-0617a57be9ea",
        "id": "O2XkV9uqx0IR"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_5\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_6 (InputLayer)        [(None, None)]            0         \n",
            "                                                                 \n",
            " embedding (Embedding)       (None, None, 300)         4519500   \n",
            "                                                                 \n",
            " bidirectional_4 (Bidirectio  (None, 128)              140544    \n",
            " nal)                                                            \n",
            "                                                                 \n",
            " dense_11 (Dense)            (None, 128)               16512     \n",
            "                                                                 \n",
            " dense_12 (Dense)            (None, 64)                8256      \n",
            "                                                                 \n",
            " dropout_5 (Dropout)         (None, 64)                0         \n",
            "                                                                 \n",
            " dense_13 (Dense)            (None, 6)                 390       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 4,685,202\n",
            "Trainable params: 165,702\n",
            "Non-trainable params: 4,519,500\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Compile"
      ],
      "metadata": {
        "id": "TN6ZjhtPx0IS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "adam = Adam(learning_rate=0.0025)\n",
        "\n",
        "model.compile(loss='sparse_categorical_crossentropy', \n",
        "              optimizer=adam, \n",
        "              metrics=['accuracy']\n",
        "              )"
      ],
      "metadata": {
        "id": "R_mVYpclx0IT"
      },
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Callbacks"
      ],
      "metadata": {
        "id": "DebFULxqx0IT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "callbacks = [\n",
        "  tf.keras.callbacks.EarlyStopping(\n",
        "    monitor='val_loss',\n",
        "    min_delta=1e-3,\n",
        "    patience=3,\n",
        "    restore_best_weights=True,\n",
        "    verbose=1,\n",
        "  )\n",
        "]"
      ],
      "metadata": {
        "id": "uTxJwatGx0IT"
      },
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Fit"
      ],
      "metadata": {
        "id": "u1FhYWR2x0IU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(X_train_sequence, y_train_encode, \n",
        "                    batch_size=128, epochs=20, \n",
        "                    validation_data=(X_val_sequence, y_val_encode),\n",
        "                    callbacks=callbacks)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0f5a8eea-7fbb-45e3-80b0-f483a64188da",
        "id": "Y-tWhBnox0IU"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "125/125 [==============================] - 7s 36ms/step - loss: 0.9472 - accuracy: 0.6586 - val_loss: 0.3440 - val_accuracy: 0.8760\n",
            "Epoch 2/20\n",
            "125/125 [==============================] - 4s 32ms/step - loss: 0.2819 - accuracy: 0.8949 - val_loss: 0.2122 - val_accuracy: 0.9140\n",
            "Epoch 3/20\n",
            "125/125 [==============================] - 3s 26ms/step - loss: 0.1701 - accuracy: 0.9298 - val_loss: 0.1612 - val_accuracy: 0.9295\n",
            "Epoch 4/20\n",
            "125/125 [==============================] - 3s 26ms/step - loss: 0.1222 - accuracy: 0.9453 - val_loss: 0.1911 - val_accuracy: 0.9230\n",
            "Epoch 5/20\n",
            "125/125 [==============================] - 3s 26ms/step - loss: 0.1000 - accuracy: 0.9541 - val_loss: 0.1571 - val_accuracy: 0.9285\n",
            "Epoch 6/20\n",
            "125/125 [==============================] - 3s 26ms/step - loss: 0.0841 - accuracy: 0.9606 - val_loss: 0.1711 - val_accuracy: 0.9290\n",
            "Epoch 7/20\n",
            "125/125 [==============================] - 3s 26ms/step - loss: 0.0741 - accuracy: 0.9646 - val_loss: 0.2244 - val_accuracy: 0.9205\n",
            "Epoch 8/20\n",
            "125/125 [==============================] - ETA: 0s - loss: 0.0644 - accuracy: 0.9702Restoring model weights from the end of the best epoch: 5.\n",
            "125/125 [==============================] - 3s 26ms/step - loss: 0.0644 - accuracy: 0.9702 - val_loss: 0.2065 - val_accuracy: 0.9260\n",
            "Epoch 8: early stopping\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## History"
      ],
      "metadata": {
        "id": "ly1jv8L7x0IV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plot_history(history, metrics='loss', val_metrics='val_loss')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        },
        "outputId": "d1b1238c-f6fe-4196-c77c-2b0c6cb2a390",
        "id": "fcVT_tVlx0IV"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_history(history, metrics='accuracy', val_metrics='val_accuracy')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        },
        "outputId": "6c649c2e-853b-4f03-9ff4-b9f6dc47a87e",
        "id": "EGt2DotDx0IV"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Evaluate"
      ],
      "metadata": {
        "id": "7SXDuGfDJYjE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "eval = model.evaluate(X_test_sequence, y_test_encode)\n",
        "\n",
        "print(f'loss\\t: {eval[0]:.3f}')\n",
        "print(f'acc\\t: {eval[1]:.3f}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "29a6e4d1-6d5c-491e-fde9-848e5a99359a",
        "id": "xdhB7DjOywhK"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "63/63 [==============================] - 1s 12ms/step - loss: 0.1616 - accuracy: 0.9270\n",
            "loss\t: 0.162\n",
            "acc\t: 0.927\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# References\n",
        "\n",
        "Keras Documentation. \"Using pre-trained word embeddings\". [source](https://keras.io/examples/nlp/pretrained_word_embeddings/)"
      ],
      "metadata": {
        "id": "RYWQh_vUtfXy"
      }
    }
  ]
}