POS_tagging.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/TheOnesThatWereAbroad/Assignment1/blob/main/POS_tagging.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tc8x9pA00Sfd"
   },
   "source": [
    "# Assignment 1 - POS tagging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!git clone https://c9699cfe58c4d4109c1407f6ac2a52494cff8d40@github.com/TheOnesThatWereAbroad/Assignment1.git\n",
    "%cd Assignment1\n",
    "!pip install keras-tuner tensorflow-addons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "jHhrkDf2kqzg"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import random\n",
    "import glob\n",
    "from urllib import request\n",
    "import zipfile\n",
    "\n",
    "import tensorflow as tf\n",
    "import tensorflow_addons as tfa\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "import tensorflow.keras.backend as K\n",
    "from tensorflow.keras.models import model_from_json\n",
    "import keras_tuner as kt\n",
    "import sklearn\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, f1_score, classification_report\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jx2qo48Ikqzi"
   },
   "source": [
    "## 1. Build a dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IN34sX55kqzi"
   },
   "source": [
    "### 1.1 Dataset preparation\n",
    "For this experiment, the [Dependency Parsed Treebank](https://www.nltk.org/nltk_data/) dataset is used.\n",
    "Notice that each document is splitted into sentences, so the dimensionality of the data is more than 199 (total number of documents in the dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DataInput:\n",
    "    \"\"\"\n",
    "    This class is used to load the data from the data_url and create the train, dev and test datasets.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(\n",
    "        self,\n",
    "        data_url,\n",
    "        train_size,\n",
    "        dev_size,\n",
    "        dataset_folder=\"data\",\n",
    "        split_into_sentences=True,\n",
    "        shuffle=False,\n",
    "    ):\n",
    "        \"\"\"\n",
    "        Constructor for DataInput class that loads the data from the data_url and creates the train, dev and test datasets.\n",
    "        The dataset splitted is available through the instance variables self.train, self.dev, self.test\n",
    "\n",
    "        Parameters\n",
    "        ----------\n",
    "        data_url : URL of the dataset\n",
    "        train_size : percentage of the dataset that will be used for training\n",
    "        dev_size : percentage of the dataset that will be used for development\n",
    "        dataset_folder : folder where the dataset will be downloaded\n",
    "        split_into_sentences : boolean indicating if each document in the dataset should be splitted into sentences or not\n",
    "        shuffle : boolean indicating if the dataset should be shuffled or not before splitting\n",
    "        \"\"\"\n",
    "        docs = self.import_data(data_url, dataset_folder)\n",
    "        X, y = self.parse_dataset(docs, split_into_sentences)\n",
    "        self.max_size = self.get_max_size(X)\n",
    "        self.train, self.dev, self.test = self.train_dev_test_split(\n",
    "            X,\n",
    "            y,\n",
    "            train_size,\n",
    "            dev_size,\n",
    "            path_store=os.path.join(dataset_folder, \"split\"),\n",
    "            shuffle=shuffle,\n",
    "        )\n",
    "\n",
    "    def import_data(self, data_url, dataset_folder):\n",
    "        \"\"\"\n",
    "        Import dataset from URL.\n",
    "\n",
    "        Parameters\n",
    "        ----------\n",
    "        data_url : URL of the dataset\n",
    "        dataset_folder : folder where the dataset will be downloaded\n",
    "\n",
    "        Returns\n",
    "        -------\n",
    "        list of document paths\n",
    "        \"\"\"\n",
    "        # create dataset folder if it does not exist\n",
    "        if not os.path.exists(dataset_folder):\n",
    "            os.makedirs(dataset_folder)\n",
    "\n",
    "        # extract the dataset if it is not extracted\n",
    "        if not glob.glob(os.path.join(dataset_folder, \"**/*.dp\"), recursive=True):\n",
    "\n",
    "            # download the dataset if it does not exist\n",
    "            dataset_zip = os.path.join(dataset_folder, data_url.split(\"/\")[-1])\n",
    "            if not os.path.exists(dataset_zip):\n",
    "                print(\"Downloading the dataset...\")\n",
    "                request.urlretrieve(data_url, dataset_zip)\n",
    "                print(\"Successful download!\")\n",
    "\n",
    "            # extract the dataset\n",
    "            print(\"Extracting the dataset...\")\n",
    "            with zipfile.ZipFile(dataset_zip, \"r\") as zip_ref:\n",
    "                zip_ref.extractall(dataset_folder)\n",
    "                print(\"Successfully extracted the dataset!\")\n",
    "            os.remove(dataset_zip)\n",
    "\n",
    "        dataset_extracted_dir = os.path.join(\n",
    "            dataset_folder, os.listdir(dataset_folder)[0]\n",
    "        )\n",
    "        docs = [\n",
    "            os.path.join(dataset_extracted_dir, doc)\n",
    "            for doc in sorted(os.listdir(dataset_extracted_dir))\n",
    "        ]\n",
    "        return docs\n",
    "\n",
    "    def parse_dataset(self, docs, split_into_sentences):\n",
    "        \"\"\"\n",
    "        Parse the dependency treebank dataset. This takes into account if the dataset should be splitted into sentences or not (according to self.split_into_sentences).\n",
    "\n",
    "        Parameters\n",
    "        ----------\n",
    "        docs : list of document paths\n",
    "        split_into_sentences : boolean indicating if each document in the dataset should be splitted into sentences or not\n",
    "\n",
    "        Returns\n",
    "        -------\n",
    "        pair where the first element is a list of lists representing tokens in each document/sentence, the second is a list of lists representing POS tag of tokens in each document/sentence\n",
    "        \"\"\"\n",
    "        X = []\n",
    "        y = []\n",
    "        for doc in docs:\n",
    "            if split_into_sentences:\n",
    "                # if split_into_sentences is True, then we split the document into sentences considering as new sentence all the tokens after an empty line in the document\n",
    "                with open(doc, mode=\"r\", encoding=\"utf-8\") as text_file:\n",
    "                    sentence_text = []\n",
    "                    sentence_tags = []\n",
    "\n",
    "                    # read the doc and extract informations\n",
    "                    for line in text_file.readlines():\n",
    "                        if line.strip() == \"\":\n",
    "                            # if the line is empty, then we add the sentence to the list of sentences\n",
    "                            X.append(np.array(sentence_text))\n",
    "                            y.append(np.array(sentence_tags))\n",
    "                            sentence_text = []\n",
    "                            sentence_tags = []\n",
    "                        else:\n",
    "                            # if the line is not empty, then we add the token to the sentence\n",
    "                            token, tag, number = line.split(\"\\t\")\n",
    "                            sentence_text.append(token)\n",
    "                            sentence_tags.append(tag)\n",
    "                    X.append(np.array(sentence_text))\n",
    "                    y.append(np.array(sentence_tags))\n",
    "            else:\n",
    "                # otherwise we consider the whole document as a single data point\n",
    "                np_doc = np.loadtxt(doc, str, delimiter=\"\\t\", usecols=(0, 1))\n",
    "                X.append(np_doc[:, 0])\n",
    "                y.append(np_doc[:, 1])\n",
    "        return np.array(X, dtype=object), np.array(y, dtype=object)\n",
    "\n",
    "    def get_max_size(self, X):\n",
    "        \"\"\"\n",
    "        Get the maximum size of the dataset.\n",
    "\n",
    "        Parameters\n",
    "        ----------\n",
    "        X : list of lists of tokens\n",
    "\n",
    "        Returns\n",
    "        -------\n",
    "        int\n",
    "        \"\"\"\n",
    "        return max([len(x) for x in X])\n",
    "\n",
    "    def train_dev_test_split(\n",
    "        self, X, y, train_size, dev_size, path_store=None, shuffle=False\n",
    "    ):\n",
    "        \"\"\"\n",
    "        Split dataset into train, validation and test.\n",
    "\n",
    "        Parameters\n",
    "        ----------\n",
    "        X : list of lists of tokens in each document/sentence\n",
    "        y : list of lists of POS tags in each document/sentence\n",
    "        train_size : percentage of the dataset used for training\n",
    "        dev_size : percentage of the dataset used for validation (note that test size is 1-train_size-dev_size)\n",
    "        shuffle : boolean indicating if the dataset should be shuffled before splitting\n",
    "        path_store : path where the split datasets will be stored. If None, then the split datasets will not be stored.\n",
    "\n",
    "        Returns\n",
    "        -------\n",
    "        triple where the first element is the train-set, the second element is the dev-set and the third element is the test-set\n",
    "        \"\"\"\n",
    "        # shuffle the dataset\n",
    "        if shuffle:\n",
    "            dataset = list(zip(X, y))\n",
    "            random.shuffle(dataset)\n",
    "            X, y = zip(*dataset)\n",
    "            X, y = np.array(X), np.array(y)\n",
    "\n",
    "        # create folder where the split datasets will be stored if it does not exist\n",
    "        if path_store is not None and not os.path.exists(path_store):\n",
    "            os.makedirs(os.path.join(path_store, \"train\"))\n",
    "            os.makedirs(os.path.join(path_store, \"dev\"))\n",
    "            os.makedirs(os.path.join(path_store, \"test\"))\n",
    "\n",
    "        # build the train set\n",
    "        train_size = int(np.ceil(train_size * len(X)))\n",
    "        train_set = (X[:train_size], y[:train_size])\n",
    "        if path_store is not None:\n",
    "            np.savetxt(\n",
    "                os.path.join(path_store, \"train\", \"X_train.txt\"), train_set[0], fmt=\"%s\"\n",
    "            )\n",
    "            np.savetxt(\n",
    "                os.path.join(path_store, \"train\", \"y_train.txt\"), train_set[1], fmt=\"%s\"\n",
    "            )\n",
    "        print(\"Train set size:\", len(train_set[0]))\n",
    "\n",
    "        # build the dev set\n",
    "        dev_size = int(np.ceil(dev_size * len(X)))\n",
    "        dev_set = (\n",
    "            X[train_size : train_size + dev_size],\n",
    "            y[train_size : train_size + dev_size],\n",
    "        )\n",
    "        if path_store is not None:\n",
    "            np.savetxt(\n",
    "                os.path.join(path_store, \"dev\", \"X_dev.txt\"), dev_set[0], fmt=\"%s\"\n",
    "            )\n",
    "            np.savetxt(\n",
    "                os.path.join(path_store, \"dev\", \"y_dev.txt\"), dev_set[1], fmt=\"%s\"\n",
    "            )\n",
    "        print(\"Dev set size:\", len(dev_set[0]))\n",
    "\n",
    "        # build the test set\n",
    "        test_set = (X[train_size + dev_size :], y[train_size + dev_size :])\n",
    "        if path_store is not None:\n",
    "            np.savetxt(\n",
    "                os.path.join(path_store, \"test\", \"X_test.txt\"), test_set[0], fmt=\"%s\"\n",
    "            )\n",
    "            np.savetxt(\n",
    "                os.path.join(path_store, \"test\", \"y_test.txt\"), test_set[1], fmt=\"%s\"\n",
    "            )\n",
    "        print(\"Test set size:\", len(test_set[0]))\n",
    "        return train_set, dev_set, test_set\n",
    "\n",
    "    def preprocessing(self, set=\"train\", to_lower=True):\n",
    "        \"\"\"\n",
    "        Preprocess the dataset.\n",
    "\n",
    "        Parameters\n",
    "        ----------\n",
    "        set : the set to preprocess (it can be train, dev or test)\n",
    "        to_lower : boolean indicating if the dataset should be converted to lowercase\n",
    "        \"\"\"\n",
    "        # lower the tokens in a given set\n",
    "        if set == \"train\" or set == \"dev\" or set == \"test\":\n",
    "            for doc in getattr(self, set)[0]:\n",
    "                if to_lower:\n",
    "                    for i in range(len(doc)):\n",
    "                        doc[i] = doc[i].lower()\n",
    "        else:\n",
    "            raise ValueError(\"Invalid set name. It should be train, dev or test.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "eRQ_hwv9mHtK",
    "outputId": "a140a3ef-f843-4aba-a64e-f10e72f3c866"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set size: 1957\n",
      "Dev set size: 979\n",
      "Test set size: 978\n"
     ]
    }
   ],
   "source": [
    "# download the dataset and split it into train, dev and test sets\n",
    "dataset = DataInput(\n",
    "        data_url=\"https://raw.githubusercontent.com/nltk/nltk_data/gh-pages/packages/corpora/dependency_treebank.zip\",\n",
    "        train_size=0.50,\n",
    "        dev_size=0.25,\n",
    "        dataset_folder=os.path.join(os.getcwd(), \"dataset\"),\n",
    "        split_into_sentences=True,\n",
    "        shuffle=False\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SGQatFzWkqzk"
   },
   "source": [
    "Pre-processing is always an important step with which start. There are a lot of pre-processing steps that we can consider, but for this experiment the only pre-processing operation performed is:\n",
    "- **to lower**, in part-of-speech tagging scenario casing of the input tokens is crucial to find the correspondent token in the embedding vocabulary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "YbVR94oskqzk"
   },
   "outputs": [],
   "source": [
    "# do preprocessing for train, validation and test sets\n",
    "dataset.preprocessing(\"train\", to_lower=True)\n",
    "dataset.preprocessing(\"dev\", to_lower=True)\n",
    "dataset.preprocessing(\"test\", to_lower=True)\n",
    "\n",
    "# separate inputs and targets\n",
    "X_train, y_train = dataset.train\n",
    "X_dev, y_dev = dataset.dev\n",
    "X_test, y_test = dataset.test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "lxfgDx0jrnqV",
    "outputId": "5e7475da-c1e0-43f8-f0ec-e881d526e5f6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Example of input document:  too often now , a single court decision becomes the precedent for other , less compelling cases .\n",
      "Training set shape:  (1957,)\n"
     ]
    }
   ],
   "source": [
    "X_test_sentences = X_test\n",
    "print(\"Example of input document: \", ' '.join(X_test_sentences[1]))\n",
    "print(\"Training set shape: \", X_train.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IPv-M1xEmHtM"
   },
   "source": [
    "### 1.2 Dataset analysis\n",
    "Let's take a look at the dataset, to inspect the distribution of the POS tags"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "Oh9Jg3ePkqzm"
   },
   "outputs": [],
   "source": [
    "def get_occurences(data, ordered=False, exclude=[]):\n",
    "    \"\"\"\n",
    "    Get the occurences of each token in the data.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    data : list of np arrays, representing the data to get the occurences from\n",
    "    ordered : bool, if True, the occurences will be returned in descending order\n",
    "    exclude : list of strings, tokens that should not be included in the occurences\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    occurences : dict, mapping each token to the number of occurences\n",
    "    \"\"\"\n",
    "    tokens, counts = np.unique(np.concatenate(data), return_counts=True)\n",
    "    occurences = dict(zip(tokens, counts))\n",
    "\n",
    "    if len(exclude) > 0:\n",
    "        for token in exclude:\n",
    "            if token in occurences:\n",
    "                del occurences[token]\n",
    "\n",
    "    if ordered:\n",
    "        occurences = dict(sorted(occurences.items(), key=lambda x: x[1], reverse=True))\n",
    "    return occurences\n",
    "\n",
    "# get the occurences of each POS tag in the three sets \n",
    "train_tags_occ = get_occurences(y_train, ordered=True, exclude=[])\n",
    "dev_tags_occ = get_occurences(y_dev, exclude=[])\n",
    "test_tags_occ = get_occurences(y_test, exclude=[])\n",
    "\n",
    "# build a matrix of the occurences of size (n_tags, 3)\n",
    "tags_occ = np.array([[v, dev_tags_occ[k], test_tags_occ[k]] if k in dev_tags_occ and k in test_tags_occ else [v, dev_tags_occ[k], 0] if k in dev_tags_occ else [v, 0, test_tags_occ[k]] if k in test_tags_occ else [v, 0, 0] for k, v in train_tags_occ.items()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 530
    },
    "id": "aZh5QAJIkqzn",
    "outputId": "556398d9-7751-49d5-845f-0644ee869d20"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1656x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot bar chart of the occurences of each POS tag\n",
    "fig, ax = plt.subplots(1, 1, figsize=(23, 8))\n",
    "tags = list(train_tags_occ.keys())\n",
    "ax.bar(tags, tags_occ[:, 1], width=0.3, align='edge', label='dev')\n",
    "ax.bar(tags, tags_occ[:, 2], width=-0.6, align='edge', label='test')\n",
    "ax.bar(tags, tags_occ[:, 0], width=-0.3, align='edge', label='train')\n",
    "ax.set_xticks(tags)\n",
    "ax.set_xlabel('Tags')\n",
    "ax.set_ylabel('Occurences')\n",
    "ax.set_title(\"Occurences of POS tags\")\n",
    "ax.legend()\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "QYCqD_Zpkqzo",
    "outputId": "045b1806-bede-45c5-aee3-64968514b9a8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The following tags (from the train set) are not in the dev set: {'SYM'}\n",
      "The following tags (from the train set) are not in the test set: {'UH', 'FW', 'SYM', 'LS'}\n"
     ]
    }
   ],
   "source": [
    "# analyze the difference between the presence of the POS tags in the train, dev and test sets\n",
    "train_tags_not_dev = set(train_tags_occ.keys()) - set(dev_tags_occ.keys())\n",
    "train_tags_not_test = set(train_tags_occ.keys()) - set(test_tags_occ.keys())\n",
    "dev_tags_not_train = set(dev_tags_occ.keys()) - set(train_tags_occ.keys())\n",
    "test_tags_not_train = set(test_tags_occ.keys()) - set(train_tags_occ.keys())\n",
    "\n",
    "if(len(train_tags_not_dev) > 0):\n",
    "    print(\"The following tags (from the train set) are not in the dev set:\", train_tags_not_dev)\n",
    "if(len(train_tags_not_test) > 0):\n",
    "    print(\"The following tags (from the train set) are not in the test set:\", train_tags_not_test)\n",
    "if(len(dev_tags_not_train) > 0):\n",
    "    print(\"The following tags (from the dev set) are not in the train set:\", dev_tags_not_train)\n",
    "if(len(test_tags_not_train) > 0):\n",
    "    print(\"The following tags (from the test set) are not in the train set:\", test_tags_not_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mkIYSNxukqzo"
   },
   "source": [
    "### 1.3 GloVe embeddings vectorization\n",
    "The next step consists in downloading a pre-trained embedding model, namely GloVe. It is a techniques that tries to encoded global semantic properties based on the co-occurrence matrix.\n",
    "Three different dimensional space versions are available: 50, 100, 200. In this experiment the version with vectors of length 100 is used.\n",
    "\n",
    "From the choosen embedding, a starting vocabulary is built.\n",
    "Then this vocabulary is enriched with the embeddings computed for the OOV words."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "class NotAdaptedError(Exception):\n",
    "    pass\n",
    "\n",
    "\n",
    "class TextVectorizer:\n",
    "    def __init__(\n",
    "        self,\n",
    "        glove_url=\"http://nlp.stanford.edu/data/glove.6B.zip\",\n",
    "        embedding_dim=100,\n",
    "        embedding_folder=\"glove\",\n",
    "        max_size=300,\n",
    "    ):\n",
    "        \"\"\"\n",
    "        This class parses the GloVe embeddings, the input documents are expected\n",
    "        to be in the form of a list of lists.\n",
    "        [[\"word1\", \"word2\", ...], [\"word1\", \"word2\", ...], ...]\n",
    "\n",
    "        Parameters\n",
    "        ----------\n",
    "        glove_url : The url of the GloVe embeddings.\n",
    "        embedding_dim : The dimension of the embeddings (pick one of 50, 100, 200, 300).\n",
    "        embedding_folder : folder where the embedding will be downloaded\n",
    "        max_size : The maximum size of the documents.\n",
    "        \"\"\"\n",
    "        self.embedding_dim = embedding_dim\n",
    "        self.download_glove_if_needed(\n",
    "            glove_url=glove_url, embedding_folder=embedding_folder\n",
    "        )\n",
    "        self.max_size = max_size\n",
    "\n",
    "        # create the vocabulary\n",
    "        self.vocabulary = self.parse_glove(embedding_folder)\n",
    "\n",
    "    def download_glove_if_needed(self, glove_url, embedding_folder):\n",
    "        \"\"\"\n",
    "        Downloads the glove embeddings from the internet\n",
    "\n",
    "        Parameters\n",
    "        ----------\n",
    "        glove_url : The url of the GloVe embeddings.\n",
    "        embedding_folder: folder where the embedding will be downloaded\n",
    "        \"\"\"\n",
    "        # create embedding folder if it does not exist\n",
    "        if not os.path.exists(embedding_folder):\n",
    "            os.makedirs(embedding_folder)\n",
    "\n",
    "        # extract the embedding if it is not extracted\n",
    "        if not glob.glob(\n",
    "            os.path.join(embedding_folder, \"**/glove*.txt\"), recursive=True\n",
    "        ):\n",
    "\n",
    "            # download the embedding if it does not exist\n",
    "            embedding_zip = os.path.join(embedding_folder, glove_url.split(\"/\")[-1])\n",
    "            if not os.path.exists(embedding_zip):\n",
    "                print(\"Downloading the GloVe embeddings...\")\n",
    "                request.urlretrieve(glove_url, embedding_zip)\n",
    "                print(\"Successful download!\")\n",
    "\n",
    "            # extract the embedding\n",
    "            print(\"Extracting the embeddings...\")\n",
    "            with zipfile.ZipFile(embedding_zip, \"r\") as zip_ref:\n",
    "                zip_ref.extractall(embedding_folder)\n",
    "                print(\"Successfully extracted the embeddings!\")\n",
    "            os.remove(embedding_zip)\n",
    "\n",
    "    def parse_glove(self, embedding_folder):\n",
    "        \"\"\"\n",
    "        Parses the GloVe embeddings from their files, filling the vocabulary.\n",
    "\n",
    "        Parameters\n",
    "        ----------\n",
    "        embedding_folder : folder where the embedding files are stored\n",
    "\n",
    "        Returns\n",
    "        -------\n",
    "        dictionary representing the vocabulary from the embeddings\n",
    "        \"\"\"\n",
    "        vocabulary = {\"<pad>\": np.zeros(self.embedding_dim)}\n",
    "        embedding_file = os.path.join(\n",
    "            embedding_folder, \"glove.6B.\" + str(self.embedding_dim) + \"d.txt\"\n",
    "        )\n",
    "        with open(embedding_file, encoding=\"utf8\") as f:\n",
    "            for line in f:\n",
    "                word, coefs = line.split(maxsplit=1)\n",
    "                coefs = np.fromstring(coefs, \"f\", sep=\" \")\n",
    "                vocabulary[word] = coefs\n",
    "        return vocabulary\n",
    "\n",
    "    def adapt(self, documents):\n",
    "        \"\"\"\n",
    "        Computes the OOV words for a single data split, and adds them to the vocabulary.\n",
    "\n",
    "        Parameters\n",
    "        ----------\n",
    "        documents : The data split (might be training set, validation set, or test set).\n",
    "        \"\"\"\n",
    "        # create a set containing words from the documents in a given data split\n",
    "        words = {word for doc in documents for word in doc}\n",
    "        oov_words = words - self.vocabulary.keys()\n",
    "\n",
    "        # add the OOV words to the vocabulary giving them a random encoding\n",
    "        for word in oov_words:\n",
    "            self.vocabulary[word] = np.random.uniform(-1, 1, size=self.embedding_dim)\n",
    "        print(f\"Generated embeddings for {len(oov_words)} OOV words.\")\n",
    "\n",
    "    def transform(self, documents):\n",
    "        \"\"\"\n",
    "        Transform the data into the input structure for the training. This method should be used always after the adapt method.\n",
    "\n",
    "        Parameters\n",
    "        ----------\n",
    "        documents : The data split (might be training set, validation set, or test set).\n",
    "\n",
    "        Returns\n",
    "        -------\n",
    "        Array of shape (number of documents, number of words, embedding dimension)\n",
    "        \"\"\"\n",
    "        result = []\n",
    "        for document in documents:\n",
    "            result.append(\n",
    "                np.vstack(\n",
    "                    (\n",
    "                        self._transform_document(document),\n",
    "                        np.zeros((self.max_size - len(document), self.embedding_dim)),\n",
    "                    )\n",
    "                )\n",
    "            )\n",
    "        return np.array(result)\n",
    "\n",
    "    def _transform_document(self, document):\n",
    "        \"\"\"\n",
    "        Transforms a single document to the GloVe embedding\n",
    "\n",
    "        Parameters\n",
    "        ----------\n",
    "        document : The document to be transformed.\n",
    "\n",
    "        Returns\n",
    "        -------\n",
    "        Numpy array of shape (number of words, embedding dimension)\n",
    "        \"\"\"\n",
    "        try:\n",
    "            return np.array([self.vocabulary[word] for word in document])\n",
    "        except KeyError:\n",
    "            raise NotAdaptedError(\n",
    "                f\"The whole document is not in the vocabulary. Please adapt the vocabulary first.\"\n",
    "            )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "MxU2LR1Bkqzp",
    "outputId": "428fcf88-124e-4e49-a2cb-2fd6e16ac514"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parsing the training set...\n",
      "Generated embeddings for 358 OOV words.\n",
      "\n",
      "Parsing the validation set...\n",
      "Generated embeddings for 131 OOV words.\n",
      "\n",
      "Parsing the test set...\n",
      "Generated embeddings for 187 OOV words.\n"
     ]
    }
   ],
   "source": [
    "# initialize the vectorizer for the input tokens to convert them to embedding vectors\n",
    "# and build the vocabulary V1 from the glove embeddings\n",
    "text_vectorizer = TextVectorizer(\n",
    "    glove_url=\"http://nlp.stanford.edu/data/glove.6B.zip\",\n",
    "    embedding_dim=100,\n",
    "    embedding_folder=os.path.join(os.getcwd(), \"glove\"),\n",
    "    max_size=dataset.max_size\n",
    ")\n",
    "\n",
    "# compute embeddings for terms (OOV1) in the training set that are out of vocabulary V1 and add them: V2=V1+OOV1\n",
    "print(\"Parsing the training set...\")\n",
    "text_vectorizer.adapt(X_train)\n",
    "# use the vocabulary V2 to convert the training set inputs into embedding vectors\n",
    "X_train = text_vectorizer.transform(X_train)\n",
    "\n",
    "# compute embeddings for terms (OOV2) in the validation set that are out of vocabulary V2 and add them: V3=V2+OOV2\n",
    "print(\"\\nParsing the validation set...\")\n",
    "text_vectorizer.adapt(X_dev)\n",
    "# use the vocabulary V3 to convert the validation set inputs into embedding vectors\n",
    "X_dev = text_vectorizer.transform(X_dev)\n",
    "\n",
    "# compute embeddings for terms (OOV3) in the test set that are out of vocabulary V3 and add them: V4=V3+OOV3\n",
    "print(\"\\nParsing the test set...\")\n",
    "text_vectorizer.adapt(X_test)\n",
    "# use the vocabulary V4 to convert the test set inputs into embedding vectors\n",
    "X_test = text_vectorizer.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "NwDnKWdckqzp",
    "outputId": "3bdc5ada-1348-4f96-9509-8e578aef07f4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input shape: (1957, 249, 100)\n"
     ]
    }
   ],
   "source": [
    "print(f\"Input shape: {X_train.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tiDdPTFrkqzp"
   },
   "source": [
    "### 1.4 One-hot encoding of the targets\n",
    "Each POS tag in each set is converted using the one-hot representation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "class TargetVectorizer:\n",
    "    def __init__(self, max_size):\n",
    "        \"\"\"\n",
    "        This class one-hot encodes the target documents, containing the POS tags.\n",
    "        \"\"\"\n",
    "        self.vectorizer = LabelBinarizer()\n",
    "        self.max_size = max_size\n",
    "\n",
    "    def adapt(self, targets):\n",
    "        \"\"\"\n",
    "        Fits the vectorizer for the classes.\n",
    "\n",
    "        Parameters\n",
    "        ----------\n",
    "        targets : The target tags for the dataset split given (it is a list of lists).\n",
    "        \"\"\"\n",
    "        self.vectorizer.fit(\n",
    "            [target for doc_targets in targets for target in doc_targets]\n",
    "        )\n",
    "\n",
    "    def transform(self, targets):\n",
    "        \"\"\"\n",
    "        Performs the one-hot encoding for the dataset Ys, returning a list of encoded document tags.\n",
    "\n",
    "        Parameters\n",
    "        ----------\n",
    "        targets : The target tags for the dataset split given (it is a list of lists).\n",
    "\n",
    "        Returns\n",
    "        -------\n",
    "        List of shape (number of documents, numbero of tokens, number of classes)\n",
    "        \"\"\"\n",
    "        if self.vectorizer.classes_.shape[0] == 0:\n",
    "            raise NotAdaptedError(\n",
    "                \"The target vectorizer has not been adapted yet. Please adapt it first.\"\n",
    "            )\n",
    "\n",
    "        result = []\n",
    "        for document in targets:\n",
    "            transformed = self.vectorizer.transform(document)\n",
    "            pad_size = (self.max_size - len(document), transformed.shape[1])\n",
    "            result.append(\n",
    "                np.vstack((self.vectorizer.transform(document), np.zeros(pad_size)))\n",
    "            )\n",
    "        return np.array(result)\n",
    "\n",
    "    def inverse_transform(self, targets):\n",
    "        \"\"\"\n",
    "        Performs the inverse one-hot encoding for the dataset Ys, returning a list of decoded document tags.\n",
    "        Parameters\n",
    "        ----------\n",
    "        targets : The target tags for the dataset split given, already in one-hot encoding form.\n",
    "        Returns\n",
    "        -------\n",
    "        List of decoded document tags\n",
    "        \"\"\"\n",
    "        if self.vectorizer.classes_.shape[0] == 0:\n",
    "            raise NotAdaptedError(\n",
    "                \"The target vectorizer has not been adapted yet. Please adapt it first.\"\n",
    "            )\n",
    "        return np.array(\n",
    "            [\n",
    "                self.vectorizer.inverse_transform(document[document.any(1)])\n",
    "                for document in targets\n",
    "            ]\n",
    "            , dtype=object\n",
    "        )\n",
    "\n",
    "    def inverse_transform_probabilities(self, targets):\n",
    "        \"\"\"\n",
    "        Performs the inverse one-hot encoding for a prediction, given the matrix of probabilities.\n",
    "\n",
    "        Parameters\n",
    "        ----------\n",
    "        targets : The predicted probabilities for targets.\n",
    "\n",
    "        Returns\n",
    "        -------\n",
    "        List of decoded document tags\n",
    "        \"\"\"\n",
    "        n_classes = targets.shape[2]\n",
    "        targets = np.array(\n",
    "            [document[np.std(document, axis=1) > 0.05] for document in targets], dtype=object\n",
    "        )\n",
    "        y_pred = np.zeros((len(targets), max([len(document) for document in targets]), n_classes))\n",
    "        for i, document in enumerate(targets):\n",
    "            for j, word in enumerate(document):\n",
    "                y_pred[i, j, np.argmax(word)] = 1\n",
    "\n",
    "        return self.inverse_transform(y_pred)\n",
    "\n",
    "    def get_classes(self):\n",
    "        \"\"\"\n",
    "        Returns the classes of the target vectorizer.\n",
    "\n",
    "        Returns\n",
    "        -------\n",
    "        List of classes\n",
    "        \"\"\"\n",
    "        return self.vectorizer.classes_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "C37qdVk7kqzq"
   },
   "outputs": [],
   "source": [
    "# initialize the vectorizer for the target tags to convert them into one-hot representation\n",
    "target_vectorizer = TargetVectorizer(max_size=dataset.max_size)\n",
    "\n",
    "# adapt the target vectorizer with only the training set: we do not consider possible targets that are not seen in training set but they are in the dev/test set\n",
    "target_vectorizer.adapt(y_train)  \n",
    "\n",
    "# convert the targets into one-hot representation for each splitting set\n",
    "y_train = target_vectorizer.transform(y_train)\n",
    "y_dev = target_vectorizer.transform(y_dev)\n",
    "y_test = target_vectorizer.transform(y_test)\n",
    "\n",
    "n_classes = y_train[0].shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "qnH7e4okkqzq",
    "outputId": "067add6b-be52-4989-8fc6-5489c0c6f068"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Target shape: (1957, 249, 45)\n",
      "Number of classes for one-hot encoded targets: 45\n"
     ]
    }
   ],
   "source": [
    "print(f\"Target shape: {y_train.shape}\")\n",
    "print(f\"Number of classes for one-hot encoded targets: {n_classes}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "khILeN7fkqzq"
   },
   "source": [
    "## 2. Training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "GZfQ0dECkqzr"
   },
   "source": [
    "### 2.1 Models definition\n",
    "We are now ready to define our neural network architectures! \n",
    "Four variants will be evaluated:\n",
    "- Bidirectional LSMT + Dense layer\n",
    "- Bidirectional GRU + Dense layer\n",
    "- Two Bidirectional LSMT + Dense layer\n",
    "- Bidirectional LSMT + Two Dense layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "l057_W6_87Fo"
   },
   "outputs": [],
   "source": [
    "tfa_f1 = tfa.metrics.F1Score(\n",
    "    num_classes= n_classes,\n",
    "    name = 'f1_score',\n",
    "    )\n",
    "punctuation_indices = [0, 1, 2, 3, 4, 5, 6,7, 16, 30, 43]\n",
    "\n",
    "def f1_with_tfa(y_true, y_pred):\n",
    "    \"\"\"\n",
    "    Computes the F1 score using TensorFlow.\n",
    "    The punctuation is ignored.\n",
    "    \"\"\"\n",
    "    y_true = tf.reshape(y_true, (-1, n_classes))\n",
    "    punctuation = tf.gather(y_true, punctuation_indices, axis=-1)\n",
    "    y_mask = tf.cast(tf.logical_not(tf.reduce_any(tf.cast(punctuation, tf.bool), axis=-1)), tf.float32)\n",
    "    y_pred = tf.reshape(y_pred, (-1, n_classes))\n",
    "    tfa_f1.update_state(y_true, y_pred, sample_weight=y_mask)\n",
    "    result = tfa_f1.result()\n",
    "    K.batch_set_value([(v, np.zeros(v.shape.as_list())) for v in tfa_f1.variables])\n",
    "    return K.sum(result)/(n_classes-len(punctuation_indices))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "BFmjvEd_3ZUK"
   },
   "outputs": [],
   "source": [
    "def build_bilstm_model(hp):\n",
    "    \"\"\"\n",
    "    Build th Bidirectional LSMT + Dense layer model\n",
    "    \"\"\"\n",
    "    model = keras.Sequential()\n",
    "    model.add(layers.Masking(name='padding_masking', mask_value=0., input_shape=X_train.shape[1:]))\n",
    "    model.add(layers.Bidirectional(layers.LSTM(hp.Choice('units', [64, 128, 256]), return_sequences=True, kernel_regularizer=keras.regularizers.l2(0.01)), name=\"bilstm_1\"))\n",
    "    model.add(layers.Dropout(hp.Choice('dropout', [0.0, 0.1, 0.2, 0.3, 0.4, 0.5])))\n",
    "    model.add(layers.Dense(n_classes, activation=\"softmax\", name=\"dense_1\"))\n",
    "    model.compile(loss=\"categorical_crossentropy\", \n",
    "                  optimizer=keras.optimizers.Adam(hp.Choice('learning_rate', values=[1e-2, 1e-3, 1e-4])),\n",
    "                  metrics=[f1_with_tfa])\n",
    "    return model\n",
    "\n",
    "def build_bigru_model(hp):\n",
    "    \"\"\"\n",
    "    Build th Bidirectional GRU + Dense layer model\n",
    "    \"\"\"\n",
    "    model = keras.Sequential()\n",
    "    model.add(layers.Masking(name='padding_masking', mask_value=0., input_shape=X_train.shape[1:]))\n",
    "    model.add(layers.Bidirectional(layers.GRU(hp.Choice('units', [64, 128, 256]), return_sequences=True, kernel_regularizer=keras.regularizers.l2(0.01)), name=\"gru_1\"))\n",
    "    model.add(layers.Dropout(hp.Choice('dropout', [0.0, 0.1, 0.2, 0.3, 0.4, 0.5])))\n",
    "    model.add(layers.Dense(n_classes, activation=\"softmax\", name=\"dense_1\"))\n",
    "    model.compile(loss=\"categorical_crossentropy\", \n",
    "                  optimizer=keras.optimizers.Adam(hp.Choice('learning_rate', values=[1e-2, 1e-3, 1e-4])),\n",
    "                  metrics=[f1_with_tfa])\n",
    "    return model\n",
    "\n",
    "def build_two_bilstm_model(hp):\n",
    "    \"\"\"\n",
    "    Build the Two Bidirectional LSTM + Dense layer model\n",
    "    \"\"\"\n",
    "    model = keras.Sequential()\n",
    "    model.add(layers.Masking(name='padding_masking', mask_value=0., input_shape=X_train.shape[1:]))\n",
    "    model.add(layers.Bidirectional(layers.LSTM(hp.Choice('units1', [64, 128, 256]), return_sequences=True, kernel_regularizer=keras.regularizers.l2(0.01)), name=\"bilstm_1\"))\n",
    "    model.add(layers.Bidirectional(layers.LSTM(hp.Choice('units2', [64, 128, 256]), return_sequences=True, kernel_regularizer=keras.regularizers.l2(0.01)), name=\"bilstm_2\"))\n",
    "    model.add(layers.Dropout(hp.Choice('dropout', [0.0, 0.1, 0.2, 0.3, 0.4, 0.5])))\n",
    "    model.add(layers.Dense(n_classes, activation=\"softmax\", name=\"dense_1\"))\n",
    "    model.compile(loss=\"categorical_crossentropy\", \n",
    "                  optimizer=keras.optimizers.Adam(hp.Choice('learning_rate', values=[1e-2, 1e-3, 1e-4])),\n",
    "                  metrics=[f1_with_tfa])\n",
    "    return model\n",
    "\n",
    "def build_bilstm_two_dense_model(hp):\n",
    "    model = keras.Sequential()\n",
    "    model.add(layers.Masking(name='padding_masking', mask_value=0., input_shape=X_train.shape[1:]))\n",
    "    model.add(layers.Bidirectional(layers.LSTM(hp.Choice('units1', [64, 128, 256]), return_sequences=True, kernel_regularizer=keras.regularizers.l2(0.01)), name=\"bilstm_1\"))\n",
    "    model.add(layers.Dropout(hp.Choice('dropout1', [0.0, 0.1, 0.2, 0.3, 0.4, 0.5])))\n",
    "    model.add(layers.Dense(hp.Choice('units2', [64, 128, 256]), activation=\"relu\", name=\"dense_1\", kernel_regularizer=keras.regularizers.l2(0.01)))\n",
    "    model.add(layers.Dropout(hp.Choice('dropout2', [0.0, 0.1, 0.2, 0.3, 0.4, 0.5])))\n",
    "    model.add(layers.Dense(n_classes, activation=\"softmax\", name=\"dense_2\"))\n",
    "    model.compile(loss=\"categorical_crossentropy\", \n",
    "                  optimizer=keras.optimizers.Adam(hp.Choice('learning_rate', values=[1e-2, 1e-3, 1e-4])),\n",
    "                  metrics=[f1_with_tfa])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "L0R3goy83ZUK"
   },
   "source": [
    "### 2.2 Hyperparameter tuning\n",
    "Let's do a tuning of the hyperparameters for each variant of the architecture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "n5sOwkYf3ZUK"
   },
   "outputs": [],
   "source": [
    "epochs_tuning = 100\n",
    "batch_size_tuning = 64"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3sErnjwr3ZUL"
   },
   "source": [
    "Model 1: Bidirectional LSMT + Dense layer variant tuning\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "b3vU8lwL3ZUL",
    "outputId": "70b39dea-36b4-4c67-f6ed-904b887f50c4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trial 51 Complete [00h 00m 12s]\n",
      "val_f1_with_tfa: 0.01914656162261963\n",
      "\n",
      "Best val_f1_with_tfa So Far: 0.34006720781326294\n",
      "Total elapsed time: 00h 10m 54s\n",
      "INFO:tensorflow:Oracle triggered exit\n",
      "The hyperparameter search is complete.\n",
      "The optimal number of units in the LSTM layer is 256.\n",
      "The optimal rate for Dropout layer is 0.1.\n",
      "The optimal learning rate for the optimizer is 0.01.\n"
     ]
    }
   ],
   "source": [
    "# tune the hyperparameters of the first model\n",
    "tuner_bilstm = kt.Hyperband(build_bilstm_model,\n",
    "                     objective=kt.Objective(\"val_f1_with_tfa\", direction=\"max\"),\n",
    "                     max_epochs=epochs_tuning,\n",
    "                     #overwrite=True,\n",
    "                     directory=\"tuner\",\n",
    "                     project_name=\"tuner_bilstm\")\n",
    "tuner_bilstm.search(X_train, y_train, epochs=epochs_tuning, batch_size=batch_size_tuning, validation_data=(X_dev, y_dev))\n",
    "best_hps_bilstm = tuner_bilstm.get_best_hyperparameters()[0]\n",
    "\n",
    "print(f\"The hyperparameter search is complete.\\n\" \n",
    "      f\"The optimal number of units in the LSTM layer is {best_hps_bilstm.get('units')}.\\n\" \n",
    "      f\"The optimal rate for Dropout layer is {best_hps_bilstm.get('dropout')}.\\n\" \n",
    "      f\"The optimal learning rate for the optimizer is {best_hps_bilstm.get('learning_rate')}.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vPb8yMfz3ZUL"
   },
   "source": [
    "Model 2: Bidirectional GRU + Dense layer variant tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "h_tLVIkM3ZUL",
    "outputId": "48f180a8-7c0f-4845-c927-8e0cd8505e95"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trial 52 Complete [00h 00m 13s]\n",
      "val_f1_with_tfa: 0.017159314826130867\n",
      "\n",
      "Best val_f1_with_tfa So Far: 0.46531569957733154\n",
      "Total elapsed time: 00h 11m 38s\n",
      "INFO:tensorflow:Oracle triggered exit\n",
      "The hyperparameter search is complete.\n",
      "The optimal number of units in the GRU layer is 256.\n",
      "The optimal rate for Dropout layer is 0.4.\n",
      "The optimal learning rate for the optimizer is 0.01.\n"
     ]
    }
   ],
   "source": [
    "# tune the hyperparameters of the second model\n",
    "tuner_bigru = kt.Hyperband(build_bigru_model,\n",
    "                     objective=kt.Objective(\"val_f1_with_tfa\", direction=\"max\"),\n",
    "                     max_epochs=epochs_tuning,\n",
    "                     #overwrite=True,\n",
    "                     directory=\"tuner\",\n",
    "                     project_name=\"tuner_bigru\")\n",
    "tuner_bigru.search(X_train, y_train, epochs=epochs_tuning, batch_size=batch_size_tuning, validation_data=(X_dev, y_dev))\n",
    "best_hps_bigru = tuner_bigru.get_best_hyperparameters()[0]\n",
    "\n",
    "print(f\"The hyperparameter search is complete.\\n\" \n",
    "      f\"The optimal number of units in the GRU layer is {best_hps_bigru.get('units')}.\\n\" \n",
    "      f\"The optimal rate for Dropout layer is {best_hps_bigru.get('dropout')}.\\n\" \n",
    "      f\"The optimal learning rate for the optimizer is {best_hps_bigru.get('learning_rate')}.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lIp3D6c-3ZUM"
   },
   "source": [
    "Model 3: Two Bidirectional LSMT + Dense layer variant tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "VXxWaco93ZUM",
    "outputId": "ae82257e-b209-490d-982d-4f3fb5156747"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trial 216 Complete [00h 00m 47s]\n",
      "val_f1_with_tfa: 0.01919066533446312\n",
      "\n",
      "Best val_f1_with_tfa So Far: 0.4894739091396332\n",
      "Total elapsed time: 02h 00m 23s\n",
      "INFO:tensorflow:Oracle triggered exit\n",
      "The hyperparameter search is complete.\n",
      "The optimal number of units in the first LSTM layer is 128.\n",
      "The optimal number of units in the second LSTM layer is 256.\n",
      "The optimal rate for Dropout layer is 0.5.\n",
      "The optimal learning rate for the optimizer is 0.001.\n"
     ]
    }
   ],
   "source": [
    "# tune the hyperparameters of the third model\n",
    "tuner_two_bilstm = kt.Hyperband(build_two_bilstm_model,\n",
    "                     objective=kt.Objective(\"val_f1_with_tfa\", direction=\"max\"),\n",
    "                     max_epochs=epochs_tuning,\n",
    "                     #overwrite=True,\n",
    "                     directory=\"tuner\",\n",
    "                     project_name=\"tuner_two_bilstm\")\n",
    "tuner_two_bilstm.search(X_train, y_train, epochs=epochs_tuning, batch_size=batch_size_tuning, validation_data=(X_dev, y_dev))\n",
    "best_hps_two_bilstm = tuner_two_bilstm.get_best_hyperparameters()[0]\n",
    "\n",
    "print(f\"The hyperparameter search is complete.\\n\" \n",
    "      f\"The optimal number of units in the first LSTM layer is {best_hps_two_bilstm.get('units1')}.\\n\" \n",
    "      f\"The optimal number of units in the second LSTM layer is {best_hps_two_bilstm.get('units2')}.\\n\" \n",
    "      f\"The optimal rate for Dropout layer is {best_hps_two_bilstm.get('dropout')}.\\n\" \n",
    "      f\"The optimal learning rate for the optimizer is {best_hps_two_bilstm.get('learning_rate')}.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kMNWw_i33ZUM"
   },
   "source": [
    "Model 4: Bidirectional LSMT + two Dense layer variant tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "pHSGHizM3ZUM",
    "outputId": "d3efa4d9-56cf-47af-8b2e-4fed4214c5a7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trial 254 Complete [00h 03m 13s]\n",
      "val_f1_with_tfa: 0.4473048448562622\n",
      "\n",
      "Best val_f1_with_tfa So Far: 0.48067712783813477\n",
      "Total elapsed time: 01h 51m 28s\n",
      "INFO:tensorflow:Oracle triggered exit\n",
      "The hyperparameter search is complete.\n",
      "The optimal number of units in the LSTM layer is 64.\n",
      "The optimal rate for the first Dropout layer is 0.1.\n",
      "The optimal number of units in first densely-connected layer is 256.\n",
      "The optimal rate for the second Dropout layer is 0.1.\n",
      "The optimal learning rate for the optimizer is 0.01.\n"
     ]
    }
   ],
   "source": [
    "# tune the hyperparameters of the fourth model\n",
    "tuner_bilstm_two_dense = kt.Hyperband(build_bilstm_two_dense_model,\n",
    "                     objective=kt.Objective(\"val_f1_with_tfa\", direction=\"max\"),\n",
    "                     max_epochs=epochs_tuning,\n",
    "                     #overwrite=True,\n",
    "                     directory=\"tuner\",\n",
    "                     project_name=\"tuner_bilstm_two_dense\")\n",
    "tuner_bilstm_two_dense.search(X_train, y_train, epochs=epochs_tuning, batch_size=batch_size_tuning, validation_data=(X_dev, y_dev))\n",
    "best_hps_bilstm_two_dense = tuner_bilstm_two_dense.get_best_hyperparameters()[0]\n",
    "\n",
    "print(f\"The hyperparameter search is complete.\\n\" \n",
    "      f\"The optimal number of units in the LSTM layer is {best_hps_bilstm_two_dense.get('units1')}.\\n\" \n",
    "      f\"The optimal rate for the first Dropout layer is {best_hps_bilstm_two_dense.get('dropout1')}.\\n\" \n",
    "      f\"The optimal number of units in first densely-connected layer is {best_hps_bilstm_two_dense.get('units2')}.\\n\" \n",
    "      f\"The optimal rate for the second Dropout layer is {best_hps_bilstm_two_dense.get('dropout2')}.\\n\"\n",
    "      f\"The optimal learning rate for the optimizer is {best_hps_bilstm_two_dense.get('learning_rate')}.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kWdZM3rskqzr"
   },
   "source": [
    "### 2.3 Model training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "HGdlAMOu3ZUN"
   },
   "outputs": [],
   "source": [
    "epochs_training = 100\n",
    "batch_size_training = 64\n",
    "callbacks_training = [keras.callbacks.EarlyStopping(monitor='val_f1_with_tfa', patience=10, mode=\"max\", restore_best_weights=True)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "1CkJaP7_3ZUN"
   },
   "outputs": [],
   "source": [
    "def create_model(layers_info, compile_info):\n",
    "    \"\"\"\n",
    "    Create a Keras model given a list of layer information\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    layers_info : a list of dictionaries, one for each layer\n",
    "    compile_info : dictionary containing compile information\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    model: the built keras sequential model\n",
    "    \"\"\"\n",
    "\n",
    "    model = keras.Sequential()\n",
    "    for info in layers_info:\n",
    "        layer = info['layer_name'](**{key: value for key, value in info.items() if key != 'layer_name'})\n",
    "        model.add(layer)\n",
    "\n",
    "    # Compile\n",
    "    model.compile(**compile_info)\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XNvSu_g23ZUN"
   },
   "source": [
    "Model 1: Bidirectional LSMT + Dense layer variant training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 252
    },
    "id": "f57Z0qk03ZUO",
    "outputId": "e17c9bba-93fb-4308-9859-7b3965572775"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " padding_masking (Masking)   (None, 249, 100)          0         \n",
      "                                                                 \n",
      " bilstm_1 (Bidirectional)    (None, 249, 512)          731136    \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 249, 512)          0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 249, 45)           23085     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 754,221\n",
      "Trainable params: 754,221\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Epoch 1/100\n",
      "31/31 [==============================] - 11s 122ms/step - loss: 0.7244 - categorical_accuracy: 0.2321 - f1_with_tfa: 0.0358 - val_loss: 0.2882 - val_categorical_accuracy: 0.4145 - val_f1_with_tfa: 0.0856\n",
      "Epoch 2/100\n",
      "31/31 [==============================] - 1s 43ms/step - loss: 0.1992 - categorical_accuracy: 0.5511 - f1_with_tfa: 0.2225 - val_loss: 0.1678 - val_categorical_accuracy: 0.6117 - val_f1_with_tfa: 0.2860\n",
      "Epoch 3/100\n",
      "31/31 [==============================] - 1s 44ms/step - loss: 0.1378 - categorical_accuracy: 0.7032 - f1_with_tfa: 0.3888 - val_loss: 0.1273 - val_categorical_accuracy: 0.7216 - val_f1_with_tfa: 0.4181\n",
      "Epoch 4/100\n",
      "31/31 [==============================] - 1s 43ms/step - loss: 0.1157 - categorical_accuracy: 0.7613 - f1_with_tfa: 0.4607 - val_loss: 0.1173 - val_categorical_accuracy: 0.7606 - val_f1_with_tfa: 0.4632\n",
      "Epoch 5/100\n",
      "31/31 [==============================] - 1s 46ms/step - loss: 0.1051 - categorical_accuracy: 0.7866 - f1_with_tfa: 0.4949 - val_loss: 0.1037 - val_categorical_accuracy: 0.7837 - val_f1_with_tfa: 0.4919\n",
      "Epoch 6/100\n",
      "31/31 [==============================] - 1s 47ms/step - loss: 0.0981 - categorical_accuracy: 0.8014 - f1_with_tfa: 0.5182 - val_loss: 0.1002 - val_categorical_accuracy: 0.7984 - val_f1_with_tfa: 0.5156\n",
      "Epoch 7/100\n",
      "31/31 [==============================] - 1s 43ms/step - loss: 0.0921 - categorical_accuracy: 0.8134 - f1_with_tfa: 0.5321 - val_loss: 0.1000 - val_categorical_accuracy: 0.7751 - val_f1_with_tfa: 0.5208\n",
      "Epoch 8/100\n",
      "31/31 [==============================] - 1s 43ms/step - loss: 0.0903 - categorical_accuracy: 0.8147 - f1_with_tfa: 0.5415 - val_loss: 0.0912 - val_categorical_accuracy: 0.8141 - val_f1_with_tfa: 0.5649\n",
      "Epoch 9/100\n",
      "31/31 [==============================] - 1s 48ms/step - loss: 0.0856 - categorical_accuracy: 0.8241 - f1_with_tfa: 0.5651 - val_loss: 0.0877 - val_categorical_accuracy: 0.8095 - val_f1_with_tfa: 0.5382\n",
      "Epoch 10/100\n",
      "31/31 [==============================] - 1s 46ms/step - loss: 0.0825 - categorical_accuracy: 0.8330 - f1_with_tfa: 0.5733 - val_loss: 0.0882 - val_categorical_accuracy: 0.8174 - val_f1_with_tfa: 0.5655\n",
      "Epoch 11/100\n",
      "31/31 [==============================] - 1s 43ms/step - loss: 0.0814 - categorical_accuracy: 0.8331 - f1_with_tfa: 0.5813 - val_loss: 0.0842 - val_categorical_accuracy: 0.8168 - val_f1_with_tfa: 0.5535\n",
      "Epoch 12/100\n",
      "31/31 [==============================] - 1s 44ms/step - loss: 0.0783 - categorical_accuracy: 0.8376 - f1_with_tfa: 0.5893 - val_loss: 0.0822 - val_categorical_accuracy: 0.8255 - val_f1_with_tfa: 0.5730\n",
      "Epoch 13/100\n",
      "31/31 [==============================] - 1s 45ms/step - loss: 0.0762 - categorical_accuracy: 0.8436 - f1_with_tfa: 0.5978 - val_loss: 0.0783 - val_categorical_accuracy: 0.8377 - val_f1_with_tfa: 0.5876\n",
      "Epoch 14/100\n",
      "31/31 [==============================] - 1s 44ms/step - loss: 0.0754 - categorical_accuracy: 0.8454 - f1_with_tfa: 0.6024 - val_loss: 0.0785 - val_categorical_accuracy: 0.8341 - val_f1_with_tfa: 0.5885\n",
      "Epoch 15/100\n",
      "31/31 [==============================] - 1s 48ms/step - loss: 0.0738 - categorical_accuracy: 0.8468 - f1_with_tfa: 0.6078 - val_loss: 0.0786 - val_categorical_accuracy: 0.8417 - val_f1_with_tfa: 0.5876\n",
      "Epoch 16/100\n",
      "31/31 [==============================] - 1s 46ms/step - loss: 0.0734 - categorical_accuracy: 0.8487 - f1_with_tfa: 0.6114 - val_loss: 0.0795 - val_categorical_accuracy: 0.8296 - val_f1_with_tfa: 0.5826\n",
      "Epoch 17/100\n",
      "31/31 [==============================] - 2s 50ms/step - loss: 0.0717 - categorical_accuracy: 0.8528 - f1_with_tfa: 0.6168 - val_loss: 0.0785 - val_categorical_accuracy: 0.8288 - val_f1_with_tfa: 0.5966\n",
      "Epoch 18/100\n",
      "31/31 [==============================] - 1s 48ms/step - loss: 0.0719 - categorical_accuracy: 0.8514 - f1_with_tfa: 0.6180 - val_loss: 0.0772 - val_categorical_accuracy: 0.8360 - val_f1_with_tfa: 0.5871\n",
      "Epoch 19/100\n",
      "31/31 [==============================] - 1s 48ms/step - loss: 0.0699 - categorical_accuracy: 0.8587 - f1_with_tfa: 0.6280 - val_loss: 0.0760 - val_categorical_accuracy: 0.8412 - val_f1_with_tfa: 0.6040\n",
      "Epoch 20/100\n",
      "31/31 [==============================] - 1s 49ms/step - loss: 0.0680 - categorical_accuracy: 0.8622 - f1_with_tfa: 0.6357 - val_loss: 0.0811 - val_categorical_accuracy: 0.8154 - val_f1_with_tfa: 0.5827\n",
      "Epoch 21/100\n",
      "31/31 [==============================] - 2s 49ms/step - loss: 0.0691 - categorical_accuracy: 0.8592 - f1_with_tfa: 0.6289 - val_loss: 0.0754 - val_categorical_accuracy: 0.8371 - val_f1_with_tfa: 0.6033\n",
      "Epoch 22/100\n",
      "31/31 [==============================] - 1s 47ms/step - loss: 0.0671 - categorical_accuracy: 0.8662 - f1_with_tfa: 0.6334 - val_loss: 0.0741 - val_categorical_accuracy: 0.8425 - val_f1_with_tfa: 0.6023\n",
      "Epoch 23/100\n",
      "31/31 [==============================] - 1s 48ms/step - loss: 0.0661 - categorical_accuracy: 0.8677 - f1_with_tfa: 0.6455 - val_loss: 0.0761 - val_categorical_accuracy: 0.8341 - val_f1_with_tfa: 0.6081\n",
      "Epoch 24/100\n",
      "31/31 [==============================] - 1s 49ms/step - loss: 0.0654 - categorical_accuracy: 0.8707 - f1_with_tfa: 0.6510 - val_loss: 0.0765 - val_categorical_accuracy: 0.8358 - val_f1_with_tfa: 0.6004\n",
      "Epoch 25/100\n",
      "31/31 [==============================] - 1s 45ms/step - loss: 0.0656 - categorical_accuracy: 0.8712 - f1_with_tfa: 0.6598 - val_loss: 0.0738 - val_categorical_accuracy: 0.8457 - val_f1_with_tfa: 0.6183\n",
      "Epoch 26/100\n",
      "31/31 [==============================] - 1s 46ms/step - loss: 0.0634 - categorical_accuracy: 0.8757 - f1_with_tfa: 0.6656 - val_loss: 0.0761 - val_categorical_accuracy: 0.8376 - val_f1_with_tfa: 0.6143\n",
      "Epoch 27/100\n",
      "31/31 [==============================] - 1s 47ms/step - loss: 0.0653 - categorical_accuracy: 0.8748 - f1_with_tfa: 0.6642 - val_loss: 0.0788 - val_categorical_accuracy: 0.8357 - val_f1_with_tfa: 0.6092\n",
      "Epoch 28/100\n",
      "31/31 [==============================] - 2s 61ms/step - loss: 0.0630 - categorical_accuracy: 0.8811 - f1_with_tfa: 0.6704 - val_loss: 0.0772 - val_categorical_accuracy: 0.8382 - val_f1_with_tfa: 0.5978\n",
      "Epoch 29/100\n",
      "31/31 [==============================] - 2s 59ms/step - loss: 0.0626 - categorical_accuracy: 0.8825 - f1_with_tfa: 0.6674 - val_loss: 0.0780 - val_categorical_accuracy: 0.8291 - val_f1_with_tfa: 0.6027\n",
      "Epoch 30/100\n",
      "31/31 [==============================] - 1s 48ms/step - loss: 0.0618 - categorical_accuracy: 0.8860 - f1_with_tfa: 0.6838 - val_loss: 0.0809 - val_categorical_accuracy: 0.8236 - val_f1_with_tfa: 0.6017\n",
      "Epoch 31/100\n",
      "31/31 [==============================] - 2s 49ms/step - loss: 0.0616 - categorical_accuracy: 0.8877 - f1_with_tfa: 0.6788 - val_loss: 0.0778 - val_categorical_accuracy: 0.8359 - val_f1_with_tfa: 0.6160\n",
      "Epoch 32/100\n",
      "31/31 [==============================] - 2s 55ms/step - loss: 0.0612 - categorical_accuracy: 0.8879 - f1_with_tfa: 0.6814 - val_loss: 0.0788 - val_categorical_accuracy: 0.8384 - val_f1_with_tfa: 0.6075\n",
      "Epoch 33/100\n",
      "31/31 [==============================] - 2s 52ms/step - loss: 0.0607 - categorical_accuracy: 0.8912 - f1_with_tfa: 0.6864 - val_loss: 0.0823 - val_categorical_accuracy: 0.8241 - val_f1_with_tfa: 0.5979\n",
      "Epoch 34/100\n",
      "31/31 [==============================] - 2s 52ms/step - loss: 0.0599 - categorical_accuracy: 0.8945 - f1_with_tfa: 0.6933 - val_loss: 0.0796 - val_categorical_accuracy: 0.8341 - val_f1_with_tfa: 0.6068\n",
      "Epoch 35/100\n",
      "31/31 [==============================] - 2s 59ms/step - loss: 0.0598 - categorical_accuracy: 0.8941 - f1_with_tfa: 0.6763 - val_loss: 0.0841 - val_categorical_accuracy: 0.8239 - val_f1_with_tfa: 0.5679\n"
     ]
    }
   ],
   "source": [
    "# first model    \n",
    "layers_info_bilstm = [\n",
    "    {\n",
    "        \"layer_name\": layers.Masking,\n",
    "        \"name\": \"padding_masking\",\n",
    "        \"mask_value\": 0.,\n",
    "        \"input_shape\": X_train.shape[1:]\n",
    "    },\n",
    "    {\n",
    "        'layer_name': layers.Bidirectional,\n",
    "        \"layer\": layers.LSTM(best_hps_bilstm.get('units'), return_sequences=True, kernel_regularizer=keras.regularizers.l2(0.01)), # units = 256\n",
    "        \"name\": \"bilstm_1\",\n",
    "    },\n",
    "    {\n",
    "        \"layer_name\": layers.Dropout,\n",
    "        \"rate\": best_hps_bilstm.get('dropout'), # dropout = 0.1\n",
    "        \"name\": \"dropout_1\"\n",
    "    },\n",
    "    {\n",
    "        \"layer_name\": layers.Dense,\n",
    "        \"units\": n_classes,\n",
    "        \"activation\": \"softmax\",\n",
    "        \"name\": \"dense_1\"\n",
    "    }\n",
    "]\n",
    "\n",
    "compile_info_bilstm = {\n",
    "    'optimizer': keras.optimizers.Adam(learning_rate=best_hps_bilstm.get('learning_rate')), # learning_rate = 0.01\n",
    "    'loss': 'categorical_crossentropy',\n",
    "    'metrics' : ['categorical_accuracy', f1_with_tfa]   \n",
    "}\n",
    "\n",
    "model_bilstm = create_model(layers_info_bilstm, compile_info_bilstm)\n",
    "model_bilstm.summary()\n",
    "\n",
    "# start training\n",
    "history_bilstm = model_bilstm.fit(X_train, y_train, epochs=epochs_training, batch_size=batch_size_training, validation_data=(X_dev, y_dev), callbacks=callbacks_training)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "V2QWE-m03ZUO"
   },
   "source": [
    "Model 2: Bidirectional GRU + Dense layer variant training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "nXZNNrlO3ZUO",
    "outputId": "b5530ff9-59cf-41f0-f033-db639d234482"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " padding_masking (Masking)   (None, 249, 100)          0         \n",
      "                                                                 \n",
      " bigru_1 (Bidirectional)     (None, 249, 512)          549888    \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 249, 512)          0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 249, 45)           23085     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 572,973\n",
      "Trainable params: 572,973\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Epoch 1/100\n",
      "31/31 [==============================] - 10s 129ms/step - loss: 0.6618 - categorical_accuracy: 0.4852 - f1_with_tfa: 0.1880 - val_loss: 0.2050 - val_categorical_accuracy: 0.6642 - val_f1_with_tfa: 0.3600\n",
      "Epoch 2/100\n",
      "31/31 [==============================] - 2s 56ms/step - loss: 0.1460 - categorical_accuracy: 0.7303 - f1_with_tfa: 0.4211 - val_loss: 0.1217 - val_categorical_accuracy: 0.7566 - val_f1_with_tfa: 0.4707\n",
      "Epoch 3/100\n",
      "31/31 [==============================] - 2s 54ms/step - loss: 0.1140 - categorical_accuracy: 0.7708 - f1_with_tfa: 0.4824 - val_loss: 0.1100 - val_categorical_accuracy: 0.7682 - val_f1_with_tfa: 0.4887\n",
      "Epoch 4/100\n",
      "31/31 [==============================] - 2s 55ms/step - loss: 0.1090 - categorical_accuracy: 0.7825 - f1_with_tfa: 0.5029 - val_loss: 0.1004 - val_categorical_accuracy: 0.8048 - val_f1_with_tfa: 0.5350\n",
      "Epoch 5/100\n",
      "31/31 [==============================] - 2s 50ms/step - loss: 0.0989 - categorical_accuracy: 0.8040 - f1_with_tfa: 0.5394 - val_loss: 0.0974 - val_categorical_accuracy: 0.8012 - val_f1_with_tfa: 0.5365\n",
      "Epoch 6/100\n",
      "31/31 [==============================] - 2s 54ms/step - loss: 0.0934 - categorical_accuracy: 0.8142 - f1_with_tfa: 0.5540 - val_loss: 0.1000 - val_categorical_accuracy: 0.7904 - val_f1_with_tfa: 0.5391\n",
      "Epoch 7/100\n",
      "31/31 [==============================] - 2s 50ms/step - loss: 0.0946 - categorical_accuracy: 0.8121 - f1_with_tfa: 0.5524 - val_loss: 0.0965 - val_categorical_accuracy: 0.8029 - val_f1_with_tfa: 0.5285\n",
      "Epoch 8/100\n",
      "31/31 [==============================] - 2s 52ms/step - loss: 0.0897 - categorical_accuracy: 0.8235 - f1_with_tfa: 0.5669 - val_loss: 0.0877 - val_categorical_accuracy: 0.8268 - val_f1_with_tfa: 0.5648\n",
      "Epoch 9/100\n",
      "31/31 [==============================] - 2s 53ms/step - loss: 0.0885 - categorical_accuracy: 0.8224 - f1_with_tfa: 0.5752 - val_loss: 0.0937 - val_categorical_accuracy: 0.8071 - val_f1_with_tfa: 0.5488\n",
      "Epoch 10/100\n",
      "31/31 [==============================] - 2s 51ms/step - loss: 0.0880 - categorical_accuracy: 0.8263 - f1_with_tfa: 0.5770 - val_loss: 0.0869 - val_categorical_accuracy: 0.8233 - val_f1_with_tfa: 0.5808\n",
      "Epoch 11/100\n",
      "31/31 [==============================] - 2s 53ms/step - loss: 0.0843 - categorical_accuracy: 0.8330 - f1_with_tfa: 0.5956 - val_loss: 0.0840 - val_categorical_accuracy: 0.8355 - val_f1_with_tfa: 0.6001\n",
      "Epoch 12/100\n",
      "31/31 [==============================] - 2s 51ms/step - loss: 0.0832 - categorical_accuracy: 0.8349 - f1_with_tfa: 0.5937 - val_loss: 0.0896 - val_categorical_accuracy: 0.8183 - val_f1_with_tfa: 0.5569\n",
      "Epoch 13/100\n",
      "31/31 [==============================] - 2s 53ms/step - loss: 0.0850 - categorical_accuracy: 0.8312 - f1_with_tfa: 0.5890 - val_loss: 0.0859 - val_categorical_accuracy: 0.8325 - val_f1_with_tfa: 0.5899\n",
      "Epoch 14/100\n",
      "31/31 [==============================] - 2s 53ms/step - loss: 0.0836 - categorical_accuracy: 0.8379 - f1_with_tfa: 0.6022 - val_loss: 0.0856 - val_categorical_accuracy: 0.8283 - val_f1_with_tfa: 0.5944\n",
      "Epoch 15/100\n",
      "31/31 [==============================] - 2s 54ms/step - loss: 0.0822 - categorical_accuracy: 0.8391 - f1_with_tfa: 0.6122 - val_loss: 0.0854 - val_categorical_accuracy: 0.8279 - val_f1_with_tfa: 0.5984\n",
      "Epoch 16/100\n",
      "31/31 [==============================] - 2s 52ms/step - loss: 0.0805 - categorical_accuracy: 0.8427 - f1_with_tfa: 0.6103 - val_loss: 0.0880 - val_categorical_accuracy: 0.8209 - val_f1_with_tfa: 0.5799\n",
      "Epoch 17/100\n",
      "31/31 [==============================] - 2s 52ms/step - loss: 0.0818 - categorical_accuracy: 0.8384 - f1_with_tfa: 0.6077 - val_loss: 0.0881 - val_categorical_accuracy: 0.8312 - val_f1_with_tfa: 0.6029\n",
      "Epoch 18/100\n",
      "31/31 [==============================] - 2s 52ms/step - loss: 0.0820 - categorical_accuracy: 0.8419 - f1_with_tfa: 0.6152 - val_loss: 0.0816 - val_categorical_accuracy: 0.8362 - val_f1_with_tfa: 0.6117\n",
      "Epoch 19/100\n",
      "31/31 [==============================] - 2s 53ms/step - loss: 0.0776 - categorical_accuracy: 0.8497 - f1_with_tfa: 0.6334 - val_loss: 0.0839 - val_categorical_accuracy: 0.8338 - val_f1_with_tfa: 0.5983\n",
      "Epoch 20/100\n",
      "31/31 [==============================] - 2s 56ms/step - loss: 0.0794 - categorical_accuracy: 0.8471 - f1_with_tfa: 0.6025 - val_loss: 0.0858 - val_categorical_accuracy: 0.8392 - val_f1_with_tfa: 0.6032\n",
      "Epoch 21/100\n",
      "31/31 [==============================] - 2s 60ms/step - loss: 0.0790 - categorical_accuracy: 0.8480 - f1_with_tfa: 0.6088 - val_loss: 0.0838 - val_categorical_accuracy: 0.8338 - val_f1_with_tfa: 0.5823\n",
      "Epoch 22/100\n",
      "31/31 [==============================] - 2s 58ms/step - loss: 0.0753 - categorical_accuracy: 0.8576 - f1_with_tfa: 0.6171 - val_loss: 0.0819 - val_categorical_accuracy: 0.8374 - val_f1_with_tfa: 0.5731\n",
      "Epoch 23/100\n",
      "31/31 [==============================] - 2s 52ms/step - loss: 0.0771 - categorical_accuracy: 0.8537 - f1_with_tfa: 0.6203 - val_loss: 0.0846 - val_categorical_accuracy: 0.8367 - val_f1_with_tfa: 0.5807\n",
      "Epoch 24/100\n",
      "31/31 [==============================] - 2s 53ms/step - loss: 0.0777 - categorical_accuracy: 0.8556 - f1_with_tfa: 0.6193 - val_loss: 0.0892 - val_categorical_accuracy: 0.8269 - val_f1_with_tfa: 0.5785\n",
      "Epoch 25/100\n",
      "31/31 [==============================] - 2s 53ms/step - loss: 0.0784 - categorical_accuracy: 0.8560 - f1_with_tfa: 0.6155 - val_loss: 0.0870 - val_categorical_accuracy: 0.8274 - val_f1_with_tfa: 0.5690\n",
      "Epoch 26/100\n",
      "31/31 [==============================] - 2s 60ms/step - loss: 0.0765 - categorical_accuracy: 0.8588 - f1_with_tfa: 0.6285 - val_loss: 0.0839 - val_categorical_accuracy: 0.8412 - val_f1_with_tfa: 0.5948\n",
      "Epoch 27/100\n",
      "31/31 [==============================] - 2s 60ms/step - loss: 0.0732 - categorical_accuracy: 0.8664 - f1_with_tfa: 0.6344 - val_loss: 0.0847 - val_categorical_accuracy: 0.8367 - val_f1_with_tfa: 0.6050\n",
      "Epoch 28/100\n",
      "31/31 [==============================] - 2s 61ms/step - loss: 0.0747 - categorical_accuracy: 0.8633 - f1_with_tfa: 0.6376 - val_loss: 0.0853 - val_categorical_accuracy: 0.8416 - val_f1_with_tfa: 0.5981\n"
     ]
    }
   ],
   "source": [
    "# second model    \n",
    "layers_info_bigru = [\n",
    "    {\n",
    "        \"layer_name\": layers.Masking,\n",
    "        \"name\": \"padding_masking\",\n",
    "        \"mask_value\": 0.,\n",
    "        \"input_shape\": X_train.shape[1:]\n",
    "    },\n",
    "    {\n",
    "        'layer_name': layers.Bidirectional,\n",
    "        \"layer\": layers.GRU(best_hps_bigru.get('units'), return_sequences=True, kernel_regularizer=keras.regularizers.l2(0.01)),  # units = 256\n",
    "        \"name\": \"bigru_1\",\n",
    "    },\n",
    "    {\n",
    "        \"layer_name\": layers.Dropout,\n",
    "        \"rate\": best_hps_bigru.get('dropout'), # dropout = 0.4\n",
    "        \"name\": \"dropout_1\"\n",
    "    },\n",
    "    {\n",
    "        \"layer_name\": layers.Dense,\n",
    "        \"units\": n_classes,\n",
    "        \"activation\": \"softmax\",\n",
    "        \"name\": \"dense_1\"\n",
    "    }\n",
    "]\n",
    "\n",
    "compile_info_bigru = {\n",
    "    'optimizer': keras.optimizers.Adam(learning_rate=best_hps_bigru.get('learning_rate')), # learning_rate = 0.01\n",
    "    'loss': 'categorical_crossentropy',\n",
    "    'metrics' : ['categorical_accuracy', f1_with_tfa]\n",
    "}\n",
    "\n",
    "model_bigru = create_model(layers_info_bigru, compile_info_bigru)\n",
    "model_bigru.summary()\n",
    "\n",
    "# start training\n",
    "history_bigru = model_bigru.fit(X_train, y_train, epochs=epochs_training, batch_size=batch_size_training, validation_data=(X_dev, y_dev),  callbacks=callbacks_training)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "P52dWFq63ZUO"
   },
   "source": [
    "Model 3: Two Bidirectional LSMT + Dense layer variant training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "gzTLBLdC3ZUO",
    "outputId": "5fa4e8a9-a816-41ff-a9b5-58897d70ac02"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_3\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " padding_masking (Masking)   (None, 249, 100)          0         \n",
      "                                                                 \n",
      " bilstm_1 (Bidirectional)    (None, 249, 256)          234496    \n",
      "                                                                 \n",
      " bilstm_2 (Bidirectional)    (None, 249, 512)          1050624   \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 249, 512)          0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 249, 45)           23085     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1,308,205\n",
      "Trainable params: 1,308,205\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Epoch 1/100\n",
      "31/31 [==============================] - 24s 306ms/step - loss: 6.8303 - categorical_accuracy: 0.1426 - f1_with_tfa: 0.0196 - val_loss: 3.0520 - val_categorical_accuracy: 0.1673 - val_f1_with_tfa: 0.0164\n",
      "Epoch 2/100\n",
      "31/31 [==============================] - 3s 95ms/step - loss: 1.6840 - categorical_accuracy: 0.1695 - f1_with_tfa: 0.0229 - val_loss: 0.7936 - val_categorical_accuracy: 0.1833 - val_f1_with_tfa: 0.0166\n",
      "Epoch 3/100\n",
      "31/31 [==============================] - 3s 86ms/step - loss: 0.5299 - categorical_accuracy: 0.1827 - f1_with_tfa: 0.0260 - val_loss: 0.3784 - val_categorical_accuracy: 0.1534 - val_f1_with_tfa: 0.0143\n",
      "Epoch 4/100\n",
      "31/31 [==============================] - 2s 70ms/step - loss: 0.3211 - categorical_accuracy: 0.1866 - f1_with_tfa: 0.0270 - val_loss: 0.2895 - val_categorical_accuracy: 0.1809 - val_f1_with_tfa: 0.0230\n",
      "Epoch 5/100\n",
      "31/31 [==============================] - 2s 73ms/step - loss: 0.2761 - categorical_accuracy: 0.2009 - f1_with_tfa: 0.0324 - val_loss: 0.2680 - val_categorical_accuracy: 0.2053 - val_f1_with_tfa: 0.0364\n",
      "Epoch 6/100\n",
      "31/31 [==============================] - 3s 85ms/step - loss: 0.2702 - categorical_accuracy: 0.2051 - f1_with_tfa: 0.0351 - val_loss: 0.2624 - val_categorical_accuracy: 0.2175 - val_f1_with_tfa: 0.0433\n",
      "Epoch 7/100\n",
      "31/31 [==============================] - 3s 86ms/step - loss: 0.2563 - categorical_accuracy: 0.2275 - f1_with_tfa: 0.0496 - val_loss: 0.2498 - val_categorical_accuracy: 0.2487 - val_f1_with_tfa: 0.0603\n",
      "Epoch 8/100\n",
      "31/31 [==============================] - 2s 79ms/step - loss: 0.2451 - categorical_accuracy: 0.2578 - f1_with_tfa: 0.0681 - val_loss: 0.2357 - val_categorical_accuracy: 0.3048 - val_f1_with_tfa: 0.0857\n",
      "Epoch 9/100\n",
      "31/31 [==============================] - 2s 79ms/step - loss: 0.2265 - categorical_accuracy: 0.3350 - f1_with_tfa: 0.0976 - val_loss: 0.2149 - val_categorical_accuracy: 0.3812 - val_f1_with_tfa: 0.1012\n",
      "Epoch 10/100\n",
      "31/31 [==============================] - 3s 85ms/step - loss: 0.2062 - categorical_accuracy: 0.4105 - f1_with_tfa: 0.1303 - val_loss: 0.1970 - val_categorical_accuracy: 0.4407 - val_f1_with_tfa: 0.1348\n",
      "Epoch 11/100\n",
      "31/31 [==============================] - 2s 81ms/step - loss: 0.1904 - categorical_accuracy: 0.4564 - f1_with_tfa: 0.1552 - val_loss: 0.1825 - val_categorical_accuracy: 0.4745 - val_f1_with_tfa: 0.1634\n",
      "Epoch 12/100\n",
      "31/31 [==============================] - 3s 86ms/step - loss: 0.1790 - categorical_accuracy: 0.4814 - f1_with_tfa: 0.1771 - val_loss: 0.1751 - val_categorical_accuracy: 0.5059 - val_f1_with_tfa: 0.1972\n",
      "Epoch 13/100\n",
      "31/31 [==============================] - 3s 84ms/step - loss: 0.1739 - categorical_accuracy: 0.5002 - f1_with_tfa: 0.1904 - val_loss: 0.1720 - val_categorical_accuracy: 0.5099 - val_f1_with_tfa: 0.1867\n",
      "Epoch 14/100\n",
      "31/31 [==============================] - 3s 87ms/step - loss: 0.1663 - categorical_accuracy: 0.5219 - f1_with_tfa: 0.2097 - val_loss: 0.1629 - val_categorical_accuracy: 0.5313 - val_f1_with_tfa: 0.2125\n",
      "Epoch 15/100\n",
      "31/31 [==============================] - 3s 85ms/step - loss: 0.1602 - categorical_accuracy: 0.5381 - f1_with_tfa: 0.2266 - val_loss: 0.1600 - val_categorical_accuracy: 0.5439 - val_f1_with_tfa: 0.2273\n",
      "Epoch 16/100\n",
      "31/31 [==============================] - 3s 91ms/step - loss: 0.1557 - categorical_accuracy: 0.5558 - f1_with_tfa: 0.2410 - val_loss: 0.1568 - val_categorical_accuracy: 0.5443 - val_f1_with_tfa: 0.2330\n",
      "Epoch 17/100\n",
      "31/31 [==============================] - 2s 77ms/step - loss: 0.1506 - categorical_accuracy: 0.5695 - f1_with_tfa: 0.2566 - val_loss: 0.1497 - val_categorical_accuracy: 0.5766 - val_f1_with_tfa: 0.2686\n",
      "Epoch 18/100\n",
      "31/31 [==============================] - 2s 80ms/step - loss: 0.1453 - categorical_accuracy: 0.5863 - f1_with_tfa: 0.2762 - val_loss: 0.1453 - val_categorical_accuracy: 0.5828 - val_f1_with_tfa: 0.2752\n",
      "Epoch 19/100\n",
      "31/31 [==============================] - 3s 84ms/step - loss: 0.1423 - categorical_accuracy: 0.5929 - f1_with_tfa: 0.2824 - val_loss: 0.1413 - val_categorical_accuracy: 0.5998 - val_f1_with_tfa: 0.2885\n",
      "Epoch 20/100\n",
      "31/31 [==============================] - 3s 93ms/step - loss: 0.1370 - categorical_accuracy: 0.6127 - f1_with_tfa: 0.3040 - val_loss: 0.1385 - val_categorical_accuracy: 0.6049 - val_f1_with_tfa: 0.2974\n",
      "Epoch 21/100\n",
      "31/31 [==============================] - 3s 97ms/step - loss: 0.1329 - categorical_accuracy: 0.6278 - f1_with_tfa: 0.3234 - val_loss: 0.1321 - val_categorical_accuracy: 0.6250 - val_f1_with_tfa: 0.3268\n",
      "Epoch 22/100\n",
      "31/31 [==============================] - 3s 93ms/step - loss: 0.1301 - categorical_accuracy: 0.6342 - f1_with_tfa: 0.3329 - val_loss: 0.1309 - val_categorical_accuracy: 0.6301 - val_f1_with_tfa: 0.3299\n",
      "Epoch 23/100\n",
      "31/31 [==============================] - 3s 93ms/step - loss: 0.1263 - categorical_accuracy: 0.6435 - f1_with_tfa: 0.3478 - val_loss: 0.1259 - val_categorical_accuracy: 0.6488 - val_f1_with_tfa: 0.3543\n",
      "Epoch 24/100\n",
      "31/31 [==============================] - 3s 101ms/step - loss: 0.1210 - categorical_accuracy: 0.6625 - f1_with_tfa: 0.3678 - val_loss: 0.1236 - val_categorical_accuracy: 0.6521 - val_f1_with_tfa: 0.3630\n",
      "Epoch 25/100\n",
      "31/31 [==============================] - 3s 98ms/step - loss: 0.1210 - categorical_accuracy: 0.6632 - f1_with_tfa: 0.3723 - val_loss: 0.1224 - val_categorical_accuracy: 0.6575 - val_f1_with_tfa: 0.3717\n",
      "Epoch 26/100\n",
      "31/31 [==============================] - 3s 92ms/step - loss: 0.1154 - categorical_accuracy: 0.6806 - f1_with_tfa: 0.3924 - val_loss: 0.1185 - val_categorical_accuracy: 0.6668 - val_f1_with_tfa: 0.3787\n",
      "Epoch 27/100\n",
      "31/31 [==============================] - 3s 107ms/step - loss: 0.1136 - categorical_accuracy: 0.6855 - f1_with_tfa: 0.4019 - val_loss: 0.1174 - val_categorical_accuracy: 0.6706 - val_f1_with_tfa: 0.3928\n",
      "Epoch 28/100\n",
      "31/31 [==============================] - 3s 90ms/step - loss: 0.1115 - categorical_accuracy: 0.6930 - f1_with_tfa: 0.4073 - val_loss: 0.1151 - val_categorical_accuracy: 0.6766 - val_f1_with_tfa: 0.3969\n",
      "Epoch 29/100\n",
      "31/31 [==============================] - 3s 97ms/step - loss: 0.1087 - categorical_accuracy: 0.6997 - f1_with_tfa: 0.4196 - val_loss: 0.1161 - val_categorical_accuracy: 0.6754 - val_f1_with_tfa: 0.3920\n",
      "Epoch 30/100\n",
      "31/31 [==============================] - 3s 104ms/step - loss: 0.1073 - categorical_accuracy: 0.7031 - f1_with_tfa: 0.4249 - val_loss: 0.1094 - val_categorical_accuracy: 0.6937 - val_f1_with_tfa: 0.4191\n",
      "Epoch 31/100\n",
      "31/31 [==============================] - 3s 102ms/step - loss: 0.1047 - categorical_accuracy: 0.7112 - f1_with_tfa: 0.4330 - val_loss: 0.1082 - val_categorical_accuracy: 0.7008 - val_f1_with_tfa: 0.4242\n",
      "Epoch 32/100\n",
      "31/31 [==============================] - 3s 90ms/step - loss: 0.1030 - categorical_accuracy: 0.7150 - f1_with_tfa: 0.4370 - val_loss: 0.1130 - val_categorical_accuracy: 0.6840 - val_f1_with_tfa: 0.4025\n",
      "Epoch 33/100\n",
      "31/31 [==============================] - 3s 104ms/step - loss: 0.1036 - categorical_accuracy: 0.7138 - f1_with_tfa: 0.4378 - val_loss: 0.1065 - val_categorical_accuracy: 0.7042 - val_f1_with_tfa: 0.4344\n",
      "Epoch 34/100\n",
      "31/31 [==============================] - 3s 92ms/step - loss: 0.1005 - categorical_accuracy: 0.7238 - f1_with_tfa: 0.4460 - val_loss: 0.1063 - val_categorical_accuracy: 0.7070 - val_f1_with_tfa: 0.4358\n",
      "Epoch 35/100\n",
      "31/31 [==============================] - 3s 93ms/step - loss: 0.1002 - categorical_accuracy: 0.7220 - f1_with_tfa: 0.4470 - val_loss: 0.1060 - val_categorical_accuracy: 0.7002 - val_f1_with_tfa: 0.4338\n",
      "Epoch 36/100\n",
      "31/31 [==============================] - 3s 103ms/step - loss: 0.0976 - categorical_accuracy: 0.7293 - f1_with_tfa: 0.4516 - val_loss: 0.1048 - val_categorical_accuracy: 0.7090 - val_f1_with_tfa: 0.4386\n",
      "Epoch 37/100\n",
      "31/31 [==============================] - 3s 100ms/step - loss: 0.0974 - categorical_accuracy: 0.7300 - f1_with_tfa: 0.4551 - val_loss: 0.1050 - val_categorical_accuracy: 0.7009 - val_f1_with_tfa: 0.4315\n",
      "Epoch 38/100\n",
      "31/31 [==============================] - 3s 102ms/step - loss: 0.0961 - categorical_accuracy: 0.7354 - f1_with_tfa: 0.4612 - val_loss: 0.1020 - val_categorical_accuracy: 0.7130 - val_f1_with_tfa: 0.4384\n",
      "Epoch 39/100\n",
      "31/31 [==============================] - 3s 91ms/step - loss: 0.0969 - categorical_accuracy: 0.7302 - f1_with_tfa: 0.4527 - val_loss: 0.1030 - val_categorical_accuracy: 0.7109 - val_f1_with_tfa: 0.4493\n",
      "Epoch 40/100\n",
      "31/31 [==============================] - 3s 99ms/step - loss: 0.0954 - categorical_accuracy: 0.7347 - f1_with_tfa: 0.4573 - val_loss: 0.0993 - val_categorical_accuracy: 0.7205 - val_f1_with_tfa: 0.4511\n",
      "Epoch 41/100\n",
      "31/31 [==============================] - 3s 101ms/step - loss: 0.0928 - categorical_accuracy: 0.7427 - f1_with_tfa: 0.4640 - val_loss: 0.0997 - val_categorical_accuracy: 0.7214 - val_f1_with_tfa: 0.4523\n",
      "Epoch 42/100\n",
      "31/31 [==============================] - 3s 103ms/step - loss: 0.0923 - categorical_accuracy: 0.7432 - f1_with_tfa: 0.4731 - val_loss: 0.0969 - val_categorical_accuracy: 0.7271 - val_f1_with_tfa: 0.4562\n",
      "Epoch 43/100\n",
      "31/31 [==============================] - 3s 93ms/step - loss: 0.0913 - categorical_accuracy: 0.7462 - f1_with_tfa: 0.4735 - val_loss: 0.0987 - val_categorical_accuracy: 0.7213 - val_f1_with_tfa: 0.4561\n",
      "Epoch 44/100\n",
      "31/31 [==============================] - 3s 87ms/step - loss: 0.0900 - categorical_accuracy: 0.7493 - f1_with_tfa: 0.4739 - val_loss: 0.0991 - val_categorical_accuracy: 0.7160 - val_f1_with_tfa: 0.4548\n",
      "Epoch 45/100\n",
      "31/31 [==============================] - 3s 104ms/step - loss: 0.0898 - categorical_accuracy: 0.7509 - f1_with_tfa: 0.4802 - val_loss: 0.0951 - val_categorical_accuracy: 0.7319 - val_f1_with_tfa: 0.4542\n",
      "Epoch 46/100\n",
      "31/31 [==============================] - 3s 105ms/step - loss: 0.0886 - categorical_accuracy: 0.7535 - f1_with_tfa: 0.4784 - val_loss: 0.0953 - val_categorical_accuracy: 0.7325 - val_f1_with_tfa: 0.4598\n",
      "Epoch 47/100\n",
      "31/31 [==============================] - 3s 103ms/step - loss: 0.0877 - categorical_accuracy: 0.7564 - f1_with_tfa: 0.4851 - val_loss: 0.1011 - val_categorical_accuracy: 0.7112 - val_f1_with_tfa: 0.4213\n",
      "Epoch 48/100\n",
      "31/31 [==============================] - 3s 102ms/step - loss: 0.0889 - categorical_accuracy: 0.7500 - f1_with_tfa: 0.4759 - val_loss: 0.0950 - val_categorical_accuracy: 0.7314 - val_f1_with_tfa: 0.4658\n",
      "Epoch 49/100\n",
      "31/31 [==============================] - 3s 91ms/step - loss: 0.0852 - categorical_accuracy: 0.7640 - f1_with_tfa: 0.4936 - val_loss: 0.0930 - val_categorical_accuracy: 0.7384 - val_f1_with_tfa: 0.4738\n",
      "Epoch 50/100\n",
      "31/31 [==============================] - 3s 92ms/step - loss: 0.0861 - categorical_accuracy: 0.7624 - f1_with_tfa: 0.4908 - val_loss: 0.0932 - val_categorical_accuracy: 0.7394 - val_f1_with_tfa: 0.4709\n",
      "Epoch 51/100\n",
      "31/31 [==============================] - 3s 95ms/step - loss: 0.0852 - categorical_accuracy: 0.7621 - f1_with_tfa: 0.4945 - val_loss: 0.0931 - val_categorical_accuracy: 0.7390 - val_f1_with_tfa: 0.4693\n",
      "Epoch 52/100\n",
      "31/31 [==============================] - 3s 93ms/step - loss: 0.0841 - categorical_accuracy: 0.7682 - f1_with_tfa: 0.5013 - val_loss: 0.0917 - val_categorical_accuracy: 0.7425 - val_f1_with_tfa: 0.4744\n",
      "Epoch 53/100\n",
      "31/31 [==============================] - 3s 91ms/step - loss: 0.0835 - categorical_accuracy: 0.7652 - f1_with_tfa: 0.4979 - val_loss: 0.0911 - val_categorical_accuracy: 0.7450 - val_f1_with_tfa: 0.4802\n",
      "Epoch 54/100\n",
      "31/31 [==============================] - 3s 97ms/step - loss: 0.0834 - categorical_accuracy: 0.7662 - f1_with_tfa: 0.4999 - val_loss: 0.0927 - val_categorical_accuracy: 0.7384 - val_f1_with_tfa: 0.4749\n",
      "Epoch 55/100\n",
      "31/31 [==============================] - 3s 95ms/step - loss: 0.0813 - categorical_accuracy: 0.7734 - f1_with_tfa: 0.5059 - val_loss: 0.0936 - val_categorical_accuracy: 0.7347 - val_f1_with_tfa: 0.4716\n",
      "Epoch 56/100\n",
      "31/31 [==============================] - 3s 94ms/step - loss: 0.0819 - categorical_accuracy: 0.7732 - f1_with_tfa: 0.5085 - val_loss: 0.0928 - val_categorical_accuracy: 0.7403 - val_f1_with_tfa: 0.4771\n",
      "Epoch 57/100\n",
      "31/31 [==============================] - 3s 95ms/step - loss: 0.0811 - categorical_accuracy: 0.7761 - f1_with_tfa: 0.5159 - val_loss: 0.0915 - val_categorical_accuracy: 0.7403 - val_f1_with_tfa: 0.4787\n",
      "Epoch 58/100\n",
      "31/31 [==============================] - 3s 93ms/step - loss: 0.0804 - categorical_accuracy: 0.7764 - f1_with_tfa: 0.5158 - val_loss: 0.0943 - val_categorical_accuracy: 0.7312 - val_f1_with_tfa: 0.4690\n",
      "Epoch 59/100\n",
      "31/31 [==============================] - 3s 96ms/step - loss: 0.0802 - categorical_accuracy: 0.7754 - f1_with_tfa: 0.5168 - val_loss: 0.0943 - val_categorical_accuracy: 0.7345 - val_f1_with_tfa: 0.4570\n",
      "Epoch 60/100\n",
      "31/31 [==============================] - 3s 93ms/step - loss: 0.0787 - categorical_accuracy: 0.7797 - f1_with_tfa: 0.5208 - val_loss: 0.0895 - val_categorical_accuracy: 0.7480 - val_f1_with_tfa: 0.4803\n",
      "Epoch 61/100\n",
      "31/31 [==============================] - 3s 94ms/step - loss: 0.0780 - categorical_accuracy: 0.7841 - f1_with_tfa: 0.5248 - val_loss: 0.0887 - val_categorical_accuracy: 0.7511 - val_f1_with_tfa: 0.4970\n",
      "Epoch 62/100\n",
      "31/31 [==============================] - 3s 92ms/step - loss: 0.0763 - categorical_accuracy: 0.7874 - f1_with_tfa: 0.5265 - val_loss: 0.0899 - val_categorical_accuracy: 0.7478 - val_f1_with_tfa: 0.4849\n",
      "Epoch 63/100\n",
      "31/31 [==============================] - 3s 93ms/step - loss: 0.0773 - categorical_accuracy: 0.7834 - f1_with_tfa: 0.5237 - val_loss: 0.0906 - val_categorical_accuracy: 0.7428 - val_f1_with_tfa: 0.4842\n",
      "Epoch 64/100\n",
      "31/31 [==============================] - 3s 94ms/step - loss: 0.0765 - categorical_accuracy: 0.7866 - f1_with_tfa: 0.5288 - val_loss: 0.0900 - val_categorical_accuracy: 0.7456 - val_f1_with_tfa: 0.4875\n",
      "Epoch 65/100\n",
      "31/31 [==============================] - 3s 92ms/step - loss: 0.0766 - categorical_accuracy: 0.7864 - f1_with_tfa: 0.5283 - val_loss: 0.0919 - val_categorical_accuracy: 0.7425 - val_f1_with_tfa: 0.4809\n",
      "Epoch 66/100\n",
      "31/31 [==============================] - 3s 95ms/step - loss: 0.0759 - categorical_accuracy: 0.7879 - f1_with_tfa: 0.5282 - val_loss: 0.0881 - val_categorical_accuracy: 0.7556 - val_f1_with_tfa: 0.4981\n",
      "Epoch 67/100\n",
      "31/31 [==============================] - 3s 98ms/step - loss: 0.0751 - categorical_accuracy: 0.7919 - f1_with_tfa: 0.5357 - val_loss: 0.0910 - val_categorical_accuracy: 0.7412 - val_f1_with_tfa: 0.4871\n",
      "Epoch 68/100\n",
      "31/31 [==============================] - 3s 90ms/step - loss: 0.0745 - categorical_accuracy: 0.7937 - f1_with_tfa: 0.5399 - val_loss: 0.0902 - val_categorical_accuracy: 0.7464 - val_f1_with_tfa: 0.4861\n",
      "Epoch 69/100\n",
      "31/31 [==============================] - 3s 94ms/step - loss: 0.0740 - categorical_accuracy: 0.7935 - f1_with_tfa: 0.5366 - val_loss: 0.0887 - val_categorical_accuracy: 0.7524 - val_f1_with_tfa: 0.4950\n",
      "Epoch 70/100\n",
      "31/31 [==============================] - 3s 92ms/step - loss: 0.0735 - categorical_accuracy: 0.7947 - f1_with_tfa: 0.5395 - val_loss: 0.0884 - val_categorical_accuracy: 0.7495 - val_f1_with_tfa: 0.4949\n",
      "Epoch 71/100\n",
      "31/31 [==============================] - 3s 96ms/step - loss: 0.0726 - categorical_accuracy: 0.7987 - f1_with_tfa: 0.5463 - val_loss: 0.0870 - val_categorical_accuracy: 0.7537 - val_f1_with_tfa: 0.4978\n",
      "Epoch 72/100\n",
      "31/31 [==============================] - 3s 94ms/step - loss: 0.0714 - categorical_accuracy: 0.8013 - f1_with_tfa: 0.5512 - val_loss: 0.0876 - val_categorical_accuracy: 0.7532 - val_f1_with_tfa: 0.4960\n",
      "Epoch 73/100\n",
      "31/31 [==============================] - 3s 94ms/step - loss: 0.0716 - categorical_accuracy: 0.8018 - f1_with_tfa: 0.5526 - val_loss: 0.0897 - val_categorical_accuracy: 0.7484 - val_f1_with_tfa: 0.4887\n",
      "Epoch 74/100\n",
      "31/31 [==============================] - 3s 95ms/step - loss: 0.0720 - categorical_accuracy: 0.7998 - f1_with_tfa: 0.5504 - val_loss: 0.0918 - val_categorical_accuracy: 0.7364 - val_f1_with_tfa: 0.4844\n",
      "Epoch 75/100\n",
      "31/31 [==============================] - 3s 93ms/step - loss: 0.0712 - categorical_accuracy: 0.8029 - f1_with_tfa: 0.5567 - val_loss: 0.0889 - val_categorical_accuracy: 0.7507 - val_f1_with_tfa: 0.4926\n",
      "Epoch 76/100\n",
      "31/31 [==============================] - 3s 93ms/step - loss: 0.0711 - categorical_accuracy: 0.8034 - f1_with_tfa: 0.5487 - val_loss: 0.0868 - val_categorical_accuracy: 0.7552 - val_f1_with_tfa: 0.4997\n",
      "Epoch 77/100\n",
      "31/31 [==============================] - 3s 111ms/step - loss: 0.0698 - categorical_accuracy: 0.8065 - f1_with_tfa: 0.5605 - val_loss: 0.0904 - val_categorical_accuracy: 0.7463 - val_f1_with_tfa: 0.4926\n",
      "Epoch 78/100\n",
      "31/31 [==============================] - 4s 117ms/step - loss: 0.0683 - categorical_accuracy: 0.8118 - f1_with_tfa: 0.5671 - val_loss: 0.0864 - val_categorical_accuracy: 0.7543 - val_f1_with_tfa: 0.5010\n",
      "Epoch 79/100\n",
      "31/31 [==============================] - 3s 103ms/step - loss: 0.0672 - categorical_accuracy: 0.8153 - f1_with_tfa: 0.5684 - val_loss: 0.0891 - val_categorical_accuracy: 0.7499 - val_f1_with_tfa: 0.4944\n",
      "Epoch 80/100\n",
      "31/31 [==============================] - 3s 106ms/step - loss: 0.0682 - categorical_accuracy: 0.8105 - f1_with_tfa: 0.5640 - val_loss: 0.0881 - val_categorical_accuracy: 0.7522 - val_f1_with_tfa: 0.5010\n",
      "Epoch 81/100\n",
      "31/31 [==============================] - 3s 101ms/step - loss: 0.0679 - categorical_accuracy: 0.8131 - f1_with_tfa: 0.5644 - val_loss: 0.0880 - val_categorical_accuracy: 0.7540 - val_f1_with_tfa: 0.4998\n",
      "Epoch 82/100\n",
      "31/31 [==============================] - 3s 103ms/step - loss: 0.0669 - categorical_accuracy: 0.8176 - f1_with_tfa: 0.5648 - val_loss: 0.0871 - val_categorical_accuracy: 0.7552 - val_f1_with_tfa: 0.5039\n",
      "Epoch 83/100\n",
      "31/31 [==============================] - 3s 101ms/step - loss: 0.0661 - categorical_accuracy: 0.8164 - f1_with_tfa: 0.5699 - val_loss: 0.0872 - val_categorical_accuracy: 0.7580 - val_f1_with_tfa: 0.5067\n",
      "Epoch 84/100\n",
      "31/31 [==============================] - 3s 101ms/step - loss: 0.0654 - categorical_accuracy: 0.8192 - f1_with_tfa: 0.5738 - val_loss: 0.0897 - val_categorical_accuracy: 0.7488 - val_f1_with_tfa: 0.4964\n",
      "Epoch 85/100\n",
      "31/31 [==============================] - 3s 99ms/step - loss: 0.0681 - categorical_accuracy: 0.8103 - f1_with_tfa: 0.5682 - val_loss: 0.0911 - val_categorical_accuracy: 0.7474 - val_f1_with_tfa: 0.4901\n",
      "Epoch 86/100\n",
      "31/31 [==============================] - 3s 106ms/step - loss: 0.0657 - categorical_accuracy: 0.8213 - f1_with_tfa: 0.5772 - val_loss: 0.0880 - val_categorical_accuracy: 0.7550 - val_f1_with_tfa: 0.5032\n",
      "Epoch 87/100\n",
      "31/31 [==============================] - 3s 103ms/step - loss: 0.0639 - categorical_accuracy: 0.8239 - f1_with_tfa: 0.5821 - val_loss: 0.0886 - val_categorical_accuracy: 0.7515 - val_f1_with_tfa: 0.4966\n",
      "Epoch 88/100\n",
      "31/31 [==============================] - 3s 99ms/step - loss: 0.0637 - categorical_accuracy: 0.8253 - f1_with_tfa: 0.5891 - val_loss: 0.0894 - val_categorical_accuracy: 0.7521 - val_f1_with_tfa: 0.4920\n",
      "Epoch 89/100\n",
      "31/31 [==============================] - 3s 101ms/step - loss: 0.0632 - categorical_accuracy: 0.8275 - f1_with_tfa: 0.5836 - val_loss: 0.0887 - val_categorical_accuracy: 0.7571 - val_f1_with_tfa: 0.5041\n",
      "Epoch 90/100\n",
      "31/31 [==============================] - 3s 104ms/step - loss: 0.0620 - categorical_accuracy: 0.8311 - f1_with_tfa: 0.5853 - val_loss: 0.0889 - val_categorical_accuracy: 0.7564 - val_f1_with_tfa: 0.5094\n",
      "Epoch 91/100\n",
      "31/31 [==============================] - 3s 107ms/step - loss: 0.0618 - categorical_accuracy: 0.8310 - f1_with_tfa: 0.5893 - val_loss: 0.0897 - val_categorical_accuracy: 0.7516 - val_f1_with_tfa: 0.5015\n",
      "Epoch 92/100\n",
      "31/31 [==============================] - 3s 101ms/step - loss: 0.0624 - categorical_accuracy: 0.8290 - f1_with_tfa: 0.5848 - val_loss: 0.0908 - val_categorical_accuracy: 0.7488 - val_f1_with_tfa: 0.5024\n",
      "Epoch 93/100\n",
      "31/31 [==============================] - 3s 104ms/step - loss: 0.0625 - categorical_accuracy: 0.8302 - f1_with_tfa: 0.5844 - val_loss: 0.0902 - val_categorical_accuracy: 0.7495 - val_f1_with_tfa: 0.5070\n",
      "Epoch 94/100\n",
      "31/31 [==============================] - 3s 102ms/step - loss: 0.0614 - categorical_accuracy: 0.8334 - f1_with_tfa: 0.5925 - val_loss: 0.0891 - val_categorical_accuracy: 0.7555 - val_f1_with_tfa: 0.5019\n",
      "Epoch 95/100\n",
      "31/31 [==============================] - 3s 101ms/step - loss: 0.0612 - categorical_accuracy: 0.8323 - f1_with_tfa: 0.5880 - val_loss: 0.0910 - val_categorical_accuracy: 0.7488 - val_f1_with_tfa: 0.5032\n",
      "Epoch 96/100\n",
      "31/31 [==============================] - 3s 103ms/step - loss: 0.0599 - categorical_accuracy: 0.8371 - f1_with_tfa: 0.6068 - val_loss: 0.0912 - val_categorical_accuracy: 0.7503 - val_f1_with_tfa: 0.4946\n",
      "Epoch 97/100\n",
      "31/31 [==============================] - 3s 104ms/step - loss: 0.0588 - categorical_accuracy: 0.8409 - f1_with_tfa: 0.6001 - val_loss: 0.0912 - val_categorical_accuracy: 0.7494 - val_f1_with_tfa: 0.4964\n",
      "Epoch 98/100\n",
      "31/31 [==============================] - 3s 101ms/step - loss: 0.0588 - categorical_accuracy: 0.8400 - f1_with_tfa: 0.5981 - val_loss: 0.0952 - val_categorical_accuracy: 0.7439 - val_f1_with_tfa: 0.4931\n",
      "Epoch 99/100\n",
      "31/31 [==============================] - 3s 104ms/step - loss: 0.0592 - categorical_accuracy: 0.8395 - f1_with_tfa: 0.6001 - val_loss: 0.0904 - val_categorical_accuracy: 0.7561 - val_f1_with_tfa: 0.5040\n",
      "Epoch 100/100\n",
      "31/31 [==============================] - 3s 105ms/step - loss: 0.0573 - categorical_accuracy: 0.8448 - f1_with_tfa: 0.6101 - val_loss: 0.0910 - val_categorical_accuracy: 0.7511 - val_f1_with_tfa: 0.5037\n"
     ]
    }
   ],
   "source": [
    "# third model    \n",
    "layers_info_two_bilstm = [\n",
    "    {\n",
    "        \"layer_name\": layers.Masking,\n",
    "        \"name\": \"padding_masking\",\n",
    "        \"mask_value\": 0.,\n",
    "        \"input_shape\": X_train.shape[1:]\n",
    "    },\n",
    "    {\n",
    "        'layer_name': layers.Bidirectional,\n",
    "        \"layer\": layers.LSTM(best_hps_two_bilstm.get('units1'), return_sequences=True, kernel_regularizer=keras.regularizers.l2(0.01)), # units = 128\n",
    "        \"name\": \"bilstm_1\",\n",
    "    },\n",
    "    {\n",
    "        'layer_name': layers.Bidirectional,\n",
    "        \"layer\": layers.LSTM(best_hps_two_bilstm.get('units2'), return_sequences=True, kernel_regularizer=keras.regularizers.l2(0.01)), # units = 256\n",
    "        \"name\": \"bilstm_2\",\n",
    "    },\n",
    "    {\n",
    "        \"layer_name\": layers.Dropout,\n",
    "        \"rate\": best_hps_two_bilstm.get('dropout'), # dropout = 0.5\n",
    "        \"name\": \"dropout_1\"\n",
    "    },\n",
    "    {\n",
    "        \"layer_name\": layers.Dense,\n",
    "        \"units\": n_classes,\n",
    "        \"activation\": \"softmax\",\n",
    "        \"name\": \"dense_1\"\n",
    "    }\n",
    "]\n",
    "\n",
    "compile_info_two_bilstm = {\n",
    "    'optimizer': keras.optimizers.Adam(learning_rate=best_hps_two_bilstm.get('learning_rate')), # learning rate = 0.001\n",
    "    'loss': 'categorical_crossentropy',\n",
    "    'metrics' : ['categorical_accuracy', f1_with_tfa]\n",
    "}\n",
    "\n",
    "model_two_bilstm = create_model(layers_info_two_bilstm, compile_info_two_bilstm)\n",
    "model_two_bilstm.summary()\n",
    "\n",
    "# start training\n",
    "history_two_bilstm = model_two_bilstm.fit(X_train, y_train, epochs=epochs_training, batch_size=batch_size_training, validation_data=(X_dev, y_dev), callbacks=callbacks_training)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BDri7iKq3ZUP"
   },
   "source": [
    "Model 4: Bidirectional LSMT + two Dense layer variant training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "OrxBTqOQ3ZUP",
    "outputId": "acd4cd39-4184-4cec-a568-219464177d8b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_4\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " padding_masking (Masking)   (None, 249, 100)          0         \n",
      "                                                                 \n",
      " bilstm_1 (Bidirectional)    (None, 249, 128)          84480     \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 249, 128)          0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 249, 256)          33024     \n",
      "                                                                 \n",
      " dropout_2 (Dropout)         (None, 249, 256)          0         \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 249, 45)           11565     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 129,069\n",
      "Trainable params: 129,069\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Epoch 1/100\n",
      "31/31 [==============================] - 14s 210ms/step - loss: 0.9841 - categorical_accuracy: 0.1484 - f1_with_tfa: 0.0081 - val_loss: 0.3296 - val_categorical_accuracy: 0.1791 - val_f1_with_tfa: 0.0163\n",
      "Epoch 2/100\n",
      "31/31 [==============================] - 2s 57ms/step - loss: 0.2962 - categorical_accuracy: 0.1933 - f1_with_tfa: 0.0209 - val_loss: 0.2811 - val_categorical_accuracy: 0.2254 - val_f1_with_tfa: 0.0318\n",
      "Epoch 3/100\n",
      "31/31 [==============================] - 2s 60ms/step - loss: 0.2680 - categorical_accuracy: 0.2478 - f1_with_tfa: 0.0316 - val_loss: 0.2544 - val_categorical_accuracy: 0.2778 - val_f1_with_tfa: 0.0316\n",
      "Epoch 4/100\n",
      "31/31 [==============================] - 2s 58ms/step - loss: 0.2496 - categorical_accuracy: 0.2917 - f1_with_tfa: 0.0434 - val_loss: 0.2431 - val_categorical_accuracy: 0.3226 - val_f1_with_tfa: 0.0488\n",
      "Epoch 5/100\n",
      "31/31 [==============================] - 2s 63ms/step - loss: 0.2381 - categorical_accuracy: 0.3263 - f1_with_tfa: 0.0544 - val_loss: 0.2343 - val_categorical_accuracy: 0.3554 - val_f1_with_tfa: 0.0661\n",
      "Epoch 6/100\n",
      "31/31 [==============================] - 2s 62ms/step - loss: 0.2267 - categorical_accuracy: 0.3755 - f1_with_tfa: 0.0623 - val_loss: 0.2226 - val_categorical_accuracy: 0.4334 - val_f1_with_tfa: 0.0801\n",
      "Epoch 7/100\n",
      "31/31 [==============================] - 2s 62ms/step - loss: 0.2195 - categorical_accuracy: 0.4367 - f1_with_tfa: 0.0703 - val_loss: 0.2119 - val_categorical_accuracy: 0.4555 - val_f1_with_tfa: 0.0833\n",
      "Epoch 8/100\n",
      "31/31 [==============================] - 2s 57ms/step - loss: 0.2056 - categorical_accuracy: 0.4743 - f1_with_tfa: 0.0895 - val_loss: 0.2000 - val_categorical_accuracy: 0.4703 - val_f1_with_tfa: 0.0976\n",
      "Epoch 9/100\n",
      "31/31 [==============================] - 2s 60ms/step - loss: 0.1949 - categorical_accuracy: 0.5074 - f1_with_tfa: 0.1228 - val_loss: 0.1883 - val_categorical_accuracy: 0.5221 - val_f1_with_tfa: 0.1460\n",
      "Epoch 10/100\n",
      "31/31 [==============================] - 2s 58ms/step - loss: 0.1838 - categorical_accuracy: 0.5402 - f1_with_tfa: 0.1576 - val_loss: 0.1777 - val_categorical_accuracy: 0.5610 - val_f1_with_tfa: 0.1939\n",
      "Epoch 11/100\n",
      "31/31 [==============================] - 2s 63ms/step - loss: 0.1776 - categorical_accuracy: 0.5703 - f1_with_tfa: 0.2052 - val_loss: 0.1758 - val_categorical_accuracy: 0.5652 - val_f1_with_tfa: 0.2322\n",
      "Epoch 12/100\n",
      "31/31 [==============================] - 2s 58ms/step - loss: 0.1689 - categorical_accuracy: 0.6027 - f1_with_tfa: 0.2394 - val_loss: 0.1663 - val_categorical_accuracy: 0.6185 - val_f1_with_tfa: 0.2813\n",
      "Epoch 13/100\n",
      "31/31 [==============================] - 2s 59ms/step - loss: 0.1619 - categorical_accuracy: 0.6272 - f1_with_tfa: 0.2700 - val_loss: 0.1587 - val_categorical_accuracy: 0.6498 - val_f1_with_tfa: 0.3136\n",
      "Epoch 14/100\n",
      "31/31 [==============================] - 2s 63ms/step - loss: 0.1600 - categorical_accuracy: 0.6385 - f1_with_tfa: 0.2863 - val_loss: 0.1528 - val_categorical_accuracy: 0.6657 - val_f1_with_tfa: 0.3281\n",
      "Epoch 15/100\n",
      "31/31 [==============================] - 2s 59ms/step - loss: 0.1545 - categorical_accuracy: 0.6594 - f1_with_tfa: 0.3063 - val_loss: 0.1513 - val_categorical_accuracy: 0.6672 - val_f1_with_tfa: 0.3289\n",
      "Epoch 16/100\n",
      "31/31 [==============================] - 2s 61ms/step - loss: 0.1514 - categorical_accuracy: 0.6647 - f1_with_tfa: 0.3130 - val_loss: 0.1466 - val_categorical_accuracy: 0.6837 - val_f1_with_tfa: 0.3371\n",
      "Epoch 17/100\n",
      "31/31 [==============================] - 2s 58ms/step - loss: 0.1468 - categorical_accuracy: 0.6770 - f1_with_tfa: 0.3274 - val_loss: 0.1464 - val_categorical_accuracy: 0.6861 - val_f1_with_tfa: 0.3591\n",
      "Epoch 18/100\n",
      "31/31 [==============================] - 2s 60ms/step - loss: 0.1446 - categorical_accuracy: 0.6861 - f1_with_tfa: 0.3340 - val_loss: 0.1494 - val_categorical_accuracy: 0.6495 - val_f1_with_tfa: 0.3331\n",
      "Epoch 19/100\n",
      "31/31 [==============================] - 2s 58ms/step - loss: 0.1436 - categorical_accuracy: 0.6885 - f1_with_tfa: 0.3384 - val_loss: 0.1454 - val_categorical_accuracy: 0.6830 - val_f1_with_tfa: 0.3618\n",
      "Epoch 20/100\n",
      "31/31 [==============================] - 2s 62ms/step - loss: 0.1411 - categorical_accuracy: 0.6941 - f1_with_tfa: 0.3432 - val_loss: 0.1374 - val_categorical_accuracy: 0.7080 - val_f1_with_tfa: 0.3711\n",
      "Epoch 21/100\n",
      "31/31 [==============================] - 2s 58ms/step - loss: 0.1403 - categorical_accuracy: 0.6949 - f1_with_tfa: 0.3436 - val_loss: 0.1412 - val_categorical_accuracy: 0.7013 - val_f1_with_tfa: 0.3663\n",
      "Epoch 22/100\n",
      "31/31 [==============================] - 2s 64ms/step - loss: 0.1374 - categorical_accuracy: 0.7021 - f1_with_tfa: 0.3514 - val_loss: 0.1358 - val_categorical_accuracy: 0.7041 - val_f1_with_tfa: 0.3708\n",
      "Epoch 23/100\n",
      "31/31 [==============================] - 2s 60ms/step - loss: 0.1366 - categorical_accuracy: 0.7061 - f1_with_tfa: 0.3541 - val_loss: 0.1374 - val_categorical_accuracy: 0.6925 - val_f1_with_tfa: 0.3582\n",
      "Epoch 24/100\n",
      "31/31 [==============================] - 2s 61ms/step - loss: 0.1362 - categorical_accuracy: 0.7066 - f1_with_tfa: 0.3513 - val_loss: 0.1352 - val_categorical_accuracy: 0.7127 - val_f1_with_tfa: 0.3728\n",
      "Epoch 25/100\n",
      "31/31 [==============================] - 2s 58ms/step - loss: 0.1342 - categorical_accuracy: 0.7127 - f1_with_tfa: 0.3586 - val_loss: 0.1368 - val_categorical_accuracy: 0.6941 - val_f1_with_tfa: 0.3690\n",
      "Epoch 26/100\n",
      "31/31 [==============================] - 2s 57ms/step - loss: 0.1322 - categorical_accuracy: 0.7204 - f1_with_tfa: 0.3612 - val_loss: 0.1301 - val_categorical_accuracy: 0.7233 - val_f1_with_tfa: 0.3977\n",
      "Epoch 27/100\n",
      "31/31 [==============================] - 2s 63ms/step - loss: 0.1300 - categorical_accuracy: 0.7223 - f1_with_tfa: 0.3623 - val_loss: 0.1304 - val_categorical_accuracy: 0.7305 - val_f1_with_tfa: 0.3973\n",
      "Epoch 28/100\n",
      "31/31 [==============================] - 2s 66ms/step - loss: 0.1303 - categorical_accuracy: 0.7243 - f1_with_tfa: 0.3644 - val_loss: 0.1276 - val_categorical_accuracy: 0.7324 - val_f1_with_tfa: 0.3981\n",
      "Epoch 29/100\n",
      "31/31 [==============================] - 2s 60ms/step - loss: 0.1271 - categorical_accuracy: 0.7324 - f1_with_tfa: 0.3736 - val_loss: 0.1271 - val_categorical_accuracy: 0.7333 - val_f1_with_tfa: 0.4008\n",
      "Epoch 30/100\n",
      "31/31 [==============================] - 2s 58ms/step - loss: 0.1276 - categorical_accuracy: 0.7307 - f1_with_tfa: 0.3662 - val_loss: 0.1326 - val_categorical_accuracy: 0.7076 - val_f1_with_tfa: 0.3792\n",
      "Epoch 31/100\n",
      "31/31 [==============================] - 2s 61ms/step - loss: 0.1277 - categorical_accuracy: 0.7339 - f1_with_tfa: 0.3726 - val_loss: 0.1250 - val_categorical_accuracy: 0.7425 - val_f1_with_tfa: 0.4159\n",
      "Epoch 32/100\n",
      "31/31 [==============================] - 2s 57ms/step - loss: 0.1248 - categorical_accuracy: 0.7406 - f1_with_tfa: 0.3794 - val_loss: 0.1284 - val_categorical_accuracy: 0.7269 - val_f1_with_tfa: 0.3983\n",
      "Epoch 33/100\n",
      "31/31 [==============================] - 2s 62ms/step - loss: 0.1251 - categorical_accuracy: 0.7407 - f1_with_tfa: 0.3756 - val_loss: 0.1208 - val_categorical_accuracy: 0.7531 - val_f1_with_tfa: 0.4185\n",
      "Epoch 34/100\n",
      "31/31 [==============================] - 2s 61ms/step - loss: 0.1217 - categorical_accuracy: 0.7484 - f1_with_tfa: 0.3845 - val_loss: 0.1220 - val_categorical_accuracy: 0.7422 - val_f1_with_tfa: 0.4207\n",
      "Epoch 35/100\n",
      "31/31 [==============================] - 2s 62ms/step - loss: 0.1212 - categorical_accuracy: 0.7498 - f1_with_tfa: 0.3824 - val_loss: 0.1235 - val_categorical_accuracy: 0.7424 - val_f1_with_tfa: 0.4155\n",
      "Epoch 36/100\n",
      "31/31 [==============================] - 2s 58ms/step - loss: 0.1228 - categorical_accuracy: 0.7454 - f1_with_tfa: 0.3783 - val_loss: 0.1235 - val_categorical_accuracy: 0.7417 - val_f1_with_tfa: 0.4066\n",
      "Epoch 37/100\n",
      "31/31 [==============================] - 2s 60ms/step - loss: 0.1215 - categorical_accuracy: 0.7494 - f1_with_tfa: 0.3805 - val_loss: 0.1199 - val_categorical_accuracy: 0.7592 - val_f1_with_tfa: 0.4296\n",
      "Epoch 38/100\n",
      "31/31 [==============================] - 2s 57ms/step - loss: 0.1183 - categorical_accuracy: 0.7589 - f1_with_tfa: 0.3910 - val_loss: 0.1200 - val_categorical_accuracy: 0.7513 - val_f1_with_tfa: 0.4295\n",
      "Epoch 39/100\n",
      "31/31 [==============================] - 2s 60ms/step - loss: 0.1198 - categorical_accuracy: 0.7530 - f1_with_tfa: 0.3859 - val_loss: 0.1187 - val_categorical_accuracy: 0.7568 - val_f1_with_tfa: 0.4285\n",
      "Epoch 40/100\n",
      "31/31 [==============================] - 2s 60ms/step - loss: 0.1183 - categorical_accuracy: 0.7579 - f1_with_tfa: 0.3835 - val_loss: 0.1199 - val_categorical_accuracy: 0.7434 - val_f1_with_tfa: 0.4279\n",
      "Epoch 41/100\n",
      "31/31 [==============================] - 2s 60ms/step - loss: 0.1180 - categorical_accuracy: 0.7591 - f1_with_tfa: 0.3875 - val_loss: 0.1189 - val_categorical_accuracy: 0.7548 - val_f1_with_tfa: 0.4225\n",
      "Epoch 42/100\n",
      "31/31 [==============================] - 2s 58ms/step - loss: 0.1160 - categorical_accuracy: 0.7638 - f1_with_tfa: 0.3897 - val_loss: 0.1187 - val_categorical_accuracy: 0.7449 - val_f1_with_tfa: 0.4199\n",
      "Epoch 43/100\n",
      "31/31 [==============================] - 2s 59ms/step - loss: 0.1164 - categorical_accuracy: 0.7632 - f1_with_tfa: 0.3932 - val_loss: 0.1206 - val_categorical_accuracy: 0.7530 - val_f1_with_tfa: 0.4324\n",
      "Epoch 44/100\n",
      "31/31 [==============================] - 2s 63ms/step - loss: 0.1159 - categorical_accuracy: 0.7645 - f1_with_tfa: 0.3925 - val_loss: 0.1163 - val_categorical_accuracy: 0.7576 - val_f1_with_tfa: 0.4323\n",
      "Epoch 45/100\n",
      "31/31 [==============================] - 2s 56ms/step - loss: 0.1133 - categorical_accuracy: 0.7712 - f1_with_tfa: 0.3979 - val_loss: 0.1155 - val_categorical_accuracy: 0.7571 - val_f1_with_tfa: 0.4306\n",
      "Epoch 46/100\n",
      "31/31 [==============================] - 2s 63ms/step - loss: 0.1147 - categorical_accuracy: 0.7678 - f1_with_tfa: 0.3932 - val_loss: 0.1157 - val_categorical_accuracy: 0.7661 - val_f1_with_tfa: 0.4359\n",
      "Epoch 47/100\n",
      "31/31 [==============================] - 2s 57ms/step - loss: 0.1128 - categorical_accuracy: 0.7735 - f1_with_tfa: 0.3919 - val_loss: 0.1164 - val_categorical_accuracy: 0.7517 - val_f1_with_tfa: 0.4376\n",
      "Epoch 48/100\n",
      "31/31 [==============================] - 2s 59ms/step - loss: 0.1136 - categorical_accuracy: 0.7722 - f1_with_tfa: 0.3971 - val_loss: 0.1154 - val_categorical_accuracy: 0.7638 - val_f1_with_tfa: 0.4432\n",
      "Epoch 49/100\n",
      "31/31 [==============================] - 2s 58ms/step - loss: 0.1118 - categorical_accuracy: 0.7738 - f1_with_tfa: 0.4021 - val_loss: 0.1138 - val_categorical_accuracy: 0.7665 - val_f1_with_tfa: 0.4348\n",
      "Epoch 50/100\n",
      "31/31 [==============================] - 2s 60ms/step - loss: 0.1116 - categorical_accuracy: 0.7759 - f1_with_tfa: 0.3962 - val_loss: 0.1125 - val_categorical_accuracy: 0.7776 - val_f1_with_tfa: 0.4425\n",
      "Epoch 51/100\n",
      "31/31 [==============================] - 2s 63ms/step - loss: 0.1117 - categorical_accuracy: 0.7769 - f1_with_tfa: 0.4002 - val_loss: 0.1163 - val_categorical_accuracy: 0.7596 - val_f1_with_tfa: 0.4402\n",
      "Epoch 52/100\n",
      "31/31 [==============================] - 2s 59ms/step - loss: 0.1101 - categorical_accuracy: 0.7809 - f1_with_tfa: 0.4015 - val_loss: 0.1117 - val_categorical_accuracy: 0.7741 - val_f1_with_tfa: 0.4524\n",
      "Epoch 53/100\n",
      "31/31 [==============================] - 2s 50ms/step - loss: 0.1102 - categorical_accuracy: 0.7782 - f1_with_tfa: 0.4059 - val_loss: 0.1207 - val_categorical_accuracy: 0.7408 - val_f1_with_tfa: 0.4116\n",
      "Epoch 54/100\n",
      "31/31 [==============================] - 1s 49ms/step - loss: 0.1118 - categorical_accuracy: 0.7764 - f1_with_tfa: 0.4020 - val_loss: 0.1142 - val_categorical_accuracy: 0.7651 - val_f1_with_tfa: 0.4417\n",
      "Epoch 55/100\n",
      "31/31 [==============================] - 2s 50ms/step - loss: 0.1090 - categorical_accuracy: 0.7834 - f1_with_tfa: 0.4116 - val_loss: 0.1114 - val_categorical_accuracy: 0.7715 - val_f1_with_tfa: 0.4392\n",
      "Epoch 56/100\n",
      "31/31 [==============================] - 2s 52ms/step - loss: 0.1072 - categorical_accuracy: 0.7900 - f1_with_tfa: 0.4120 - val_loss: 0.1146 - val_categorical_accuracy: 0.7543 - val_f1_with_tfa: 0.4317\n",
      "Epoch 57/100\n",
      "31/31 [==============================] - 2s 51ms/step - loss: 0.1090 - categorical_accuracy: 0.7841 - f1_with_tfa: 0.4127 - val_loss: 0.1103 - val_categorical_accuracy: 0.7774 - val_f1_with_tfa: 0.4595\n",
      "Epoch 58/100\n",
      "31/31 [==============================] - 2s 55ms/step - loss: 0.1069 - categorical_accuracy: 0.7865 - f1_with_tfa: 0.4134 - val_loss: 0.1143 - val_categorical_accuracy: 0.7640 - val_f1_with_tfa: 0.4385\n",
      "Epoch 59/100\n",
      "31/31 [==============================] - 2s 53ms/step - loss: 0.1075 - categorical_accuracy: 0.7893 - f1_with_tfa: 0.4146 - val_loss: 0.1103 - val_categorical_accuracy: 0.7785 - val_f1_with_tfa: 0.4589\n",
      "Epoch 60/100\n",
      "31/31 [==============================] - 2s 52ms/step - loss: 0.1081 - categorical_accuracy: 0.7871 - f1_with_tfa: 0.4098 - val_loss: 0.1118 - val_categorical_accuracy: 0.7703 - val_f1_with_tfa: 0.4521\n",
      "Epoch 61/100\n",
      "31/31 [==============================] - 2s 53ms/step - loss: 0.1077 - categorical_accuracy: 0.7879 - f1_with_tfa: 0.4148 - val_loss: 0.1126 - val_categorical_accuracy: 0.7673 - val_f1_with_tfa: 0.4586\n",
      "Epoch 62/100\n",
      "31/31 [==============================] - 2s 53ms/step - loss: 0.1058 - categorical_accuracy: 0.7923 - f1_with_tfa: 0.4272 - val_loss: 0.1123 - val_categorical_accuracy: 0.7617 - val_f1_with_tfa: 0.4483\n",
      "Epoch 63/100\n",
      "31/31 [==============================] - 2s 49ms/step - loss: 0.1065 - categorical_accuracy: 0.7895 - f1_with_tfa: 0.4143 - val_loss: 0.1158 - val_categorical_accuracy: 0.7615 - val_f1_with_tfa: 0.4471\n",
      "Epoch 64/100\n",
      "31/31 [==============================] - 2s 53ms/step - loss: 0.1078 - categorical_accuracy: 0.7891 - f1_with_tfa: 0.4164 - val_loss: 0.1123 - val_categorical_accuracy: 0.7732 - val_f1_with_tfa: 0.4509\n",
      "Epoch 65/100\n",
      "31/31 [==============================] - 2s 52ms/step - loss: 0.1056 - categorical_accuracy: 0.7934 - f1_with_tfa: 0.4218 - val_loss: 0.1135 - val_categorical_accuracy: 0.7707 - val_f1_with_tfa: 0.4516\n",
      "Epoch 66/100\n",
      "31/31 [==============================] - 1s 46ms/step - loss: 0.1057 - categorical_accuracy: 0.7912 - f1_with_tfa: 0.4163 - val_loss: 0.1096 - val_categorical_accuracy: 0.7772 - val_f1_with_tfa: 0.4495\n",
      "Epoch 67/100\n",
      "31/31 [==============================] - 1s 48ms/step - loss: 0.1045 - categorical_accuracy: 0.7974 - f1_with_tfa: 0.4246 - val_loss: 0.1075 - val_categorical_accuracy: 0.7857 - val_f1_with_tfa: 0.4678\n",
      "Epoch 68/100\n",
      "31/31 [==============================] - 1s 48ms/step - loss: 0.1037 - categorical_accuracy: 0.7983 - f1_with_tfa: 0.4222 - val_loss: 0.1102 - val_categorical_accuracy: 0.7765 - val_f1_with_tfa: 0.4573\n",
      "Epoch 69/100\n",
      "31/31 [==============================] - 1s 48ms/step - loss: 0.1051 - categorical_accuracy: 0.7946 - f1_with_tfa: 0.4266 - val_loss: 0.1138 - val_categorical_accuracy: 0.7640 - val_f1_with_tfa: 0.4500\n",
      "Epoch 70/100\n",
      "31/31 [==============================] - 2s 49ms/step - loss: 0.1041 - categorical_accuracy: 0.7989 - f1_with_tfa: 0.4199 - val_loss: 0.1114 - val_categorical_accuracy: 0.7699 - val_f1_with_tfa: 0.4554\n",
      "Epoch 71/100\n",
      "31/31 [==============================] - 1s 48ms/step - loss: 0.1032 - categorical_accuracy: 0.7989 - f1_with_tfa: 0.4287 - val_loss: 0.1097 - val_categorical_accuracy: 0.7729 - val_f1_with_tfa: 0.4565\n",
      "Epoch 72/100\n",
      "31/31 [==============================] - 2s 50ms/step - loss: 0.1021 - categorical_accuracy: 0.8021 - f1_with_tfa: 0.4294 - val_loss: 0.1074 - val_categorical_accuracy: 0.7890 - val_f1_with_tfa: 0.4683\n",
      "Epoch 73/100\n",
      "31/31 [==============================] - 1s 47ms/step - loss: 0.1026 - categorical_accuracy: 0.8016 - f1_with_tfa: 0.4328 - val_loss: 0.1099 - val_categorical_accuracy: 0.7761 - val_f1_with_tfa: 0.4538\n",
      "Epoch 74/100\n",
      "31/31 [==============================] - 1s 47ms/step - loss: 0.1034 - categorical_accuracy: 0.7998 - f1_with_tfa: 0.4191 - val_loss: 0.1115 - val_categorical_accuracy: 0.7737 - val_f1_with_tfa: 0.4556\n",
      "Epoch 75/100\n",
      "31/31 [==============================] - 1s 47ms/step - loss: 0.1037 - categorical_accuracy: 0.7992 - f1_with_tfa: 0.4305 - val_loss: 0.1080 - val_categorical_accuracy: 0.7907 - val_f1_with_tfa: 0.4719\n",
      "Epoch 76/100\n",
      "31/31 [==============================] - 1s 46ms/step - loss: 0.1008 - categorical_accuracy: 0.8065 - f1_with_tfa: 0.4307 - val_loss: 0.1107 - val_categorical_accuracy: 0.7647 - val_f1_with_tfa: 0.4582\n",
      "Epoch 77/100\n",
      "31/31 [==============================] - 1s 47ms/step - loss: 0.1013 - categorical_accuracy: 0.8054 - f1_with_tfa: 0.4334 - val_loss: 0.1123 - val_categorical_accuracy: 0.7676 - val_f1_with_tfa: 0.4682\n",
      "Epoch 78/100\n",
      "31/31 [==============================] - 1s 46ms/step - loss: 0.1014 - categorical_accuracy: 0.8054 - f1_with_tfa: 0.4317 - val_loss: 0.1090 - val_categorical_accuracy: 0.7804 - val_f1_with_tfa: 0.4670\n",
      "Epoch 79/100\n",
      "31/31 [==============================] - 2s 50ms/step - loss: 0.1002 - categorical_accuracy: 0.8072 - f1_with_tfa: 0.4365 - val_loss: 0.1072 - val_categorical_accuracy: 0.7848 - val_f1_with_tfa: 0.4583\n",
      "Epoch 80/100\n",
      "31/31 [==============================] - 2s 50ms/step - loss: 0.1006 - categorical_accuracy: 0.8052 - f1_with_tfa: 0.4348 - val_loss: 0.1104 - val_categorical_accuracy: 0.7762 - val_f1_with_tfa: 0.4757\n",
      "Epoch 81/100\n",
      "31/31 [==============================] - 2s 49ms/step - loss: 0.1037 - categorical_accuracy: 0.8008 - f1_with_tfa: 0.4273 - val_loss: 0.1088 - val_categorical_accuracy: 0.7845 - val_f1_with_tfa: 0.4690\n",
      "Epoch 82/100\n",
      "31/31 [==============================] - 1s 49ms/step - loss: 0.1013 - categorical_accuracy: 0.8084 - f1_with_tfa: 0.4401 - val_loss: 0.1124 - val_categorical_accuracy: 0.7688 - val_f1_with_tfa: 0.4503\n",
      "Epoch 83/100\n",
      "31/31 [==============================] - 1s 47ms/step - loss: 0.1007 - categorical_accuracy: 0.8053 - f1_with_tfa: 0.4328 - val_loss: 0.1087 - val_categorical_accuracy: 0.7769 - val_f1_with_tfa: 0.4650\n",
      "Epoch 84/100\n",
      "31/31 [==============================] - 1s 47ms/step - loss: 0.1010 - categorical_accuracy: 0.8060 - f1_with_tfa: 0.4362 - val_loss: 0.1084 - val_categorical_accuracy: 0.7833 - val_f1_with_tfa: 0.4625\n",
      "Epoch 85/100\n",
      "31/31 [==============================] - 2s 49ms/step - loss: 0.0996 - categorical_accuracy: 0.8103 - f1_with_tfa: 0.4384 - val_loss: 0.1079 - val_categorical_accuracy: 0.7771 - val_f1_with_tfa: 0.4635\n",
      "Epoch 86/100\n",
      "31/31 [==============================] - 1s 45ms/step - loss: 0.0983 - categorical_accuracy: 0.8127 - f1_with_tfa: 0.4442 - val_loss: 0.1056 - val_categorical_accuracy: 0.7882 - val_f1_with_tfa: 0.4748\n",
      "Epoch 87/100\n",
      "31/31 [==============================] - 2s 50ms/step - loss: 0.0982 - categorical_accuracy: 0.8132 - f1_with_tfa: 0.4400 - val_loss: 0.1075 - val_categorical_accuracy: 0.7849 - val_f1_with_tfa: 0.4788\n",
      "Epoch 88/100\n",
      "31/31 [==============================] - 1s 49ms/step - loss: 0.1013 - categorical_accuracy: 0.8069 - f1_with_tfa: 0.4358 - val_loss: 0.1127 - val_categorical_accuracy: 0.7709 - val_f1_with_tfa: 0.4595\n",
      "Epoch 89/100\n",
      "31/31 [==============================] - 1s 48ms/step - loss: 0.0998 - categorical_accuracy: 0.8103 - f1_with_tfa: 0.4424 - val_loss: 0.1056 - val_categorical_accuracy: 0.7872 - val_f1_with_tfa: 0.4735\n",
      "Epoch 90/100\n",
      "31/31 [==============================] - 1s 46ms/step - loss: 0.0988 - categorical_accuracy: 0.8114 - f1_with_tfa: 0.4366 - val_loss: 0.1061 - val_categorical_accuracy: 0.7907 - val_f1_with_tfa: 0.4840\n",
      "Epoch 91/100\n",
      "31/31 [==============================] - 1s 47ms/step - loss: 0.0990 - categorical_accuracy: 0.8129 - f1_with_tfa: 0.4458 - val_loss: 0.1054 - val_categorical_accuracy: 0.7891 - val_f1_with_tfa: 0.4783\n",
      "Epoch 92/100\n",
      "31/31 [==============================] - 1s 48ms/step - loss: 0.0970 - categorical_accuracy: 0.8132 - f1_with_tfa: 0.4471 - val_loss: 0.1089 - val_categorical_accuracy: 0.7708 - val_f1_with_tfa: 0.4653\n",
      "Epoch 93/100\n",
      "31/31 [==============================] - 1s 48ms/step - loss: 0.1019 - categorical_accuracy: 0.8048 - f1_with_tfa: 0.4397 - val_loss: 0.1082 - val_categorical_accuracy: 0.7818 - val_f1_with_tfa: 0.4658\n",
      "Epoch 94/100\n",
      "31/31 [==============================] - 1s 48ms/step - loss: 0.0992 - categorical_accuracy: 0.8085 - f1_with_tfa: 0.4472 - val_loss: 0.1068 - val_categorical_accuracy: 0.7824 - val_f1_with_tfa: 0.4751\n",
      "Epoch 95/100\n",
      "31/31 [==============================] - 2s 60ms/step - loss: 0.0985 - categorical_accuracy: 0.8133 - f1_with_tfa: 0.4430 - val_loss: 0.1091 - val_categorical_accuracy: 0.7760 - val_f1_with_tfa: 0.4765\n",
      "Epoch 96/100\n",
      "31/31 [==============================] - 2s 55ms/step - loss: 0.0976 - categorical_accuracy: 0.8179 - f1_with_tfa: 0.4475 - val_loss: 0.1057 - val_categorical_accuracy: 0.7908 - val_f1_with_tfa: 0.4865\n",
      "Epoch 97/100\n",
      "31/31 [==============================] - 2s 56ms/step - loss: 0.0961 - categorical_accuracy: 0.8185 - f1_with_tfa: 0.4464 - val_loss: 0.1051 - val_categorical_accuracy: 0.7894 - val_f1_with_tfa: 0.4816\n",
      "Epoch 98/100\n",
      "31/31 [==============================] - 2s 55ms/step - loss: 0.0963 - categorical_accuracy: 0.8189 - f1_with_tfa: 0.4521 - val_loss: 0.1054 - val_categorical_accuracy: 0.7909 - val_f1_with_tfa: 0.4800\n",
      "Epoch 99/100\n",
      "31/31 [==============================] - 2s 51ms/step - loss: 0.0957 - categorical_accuracy: 0.8204 - f1_with_tfa: 0.4587 - val_loss: 0.1062 - val_categorical_accuracy: 0.7781 - val_f1_with_tfa: 0.4623\n",
      "Epoch 100/100\n",
      "31/31 [==============================] - 1s 48ms/step - loss: 0.0975 - categorical_accuracy: 0.8145 - f1_with_tfa: 0.4485 - val_loss: 0.1064 - val_categorical_accuracy: 0.7905 - val_f1_with_tfa: 0.4764\n"
     ]
    }
   ],
   "source": [
    "# fourth model    \n",
    "layers_info_bilstm_two_dense = [\n",
    "    {\n",
    "        \"layer_name\": layers.Masking,\n",
    "        \"name\": \"padding_masking\",\n",
    "        \"mask_value\": 0.,\n",
    "        \"input_shape\": X_train.shape[1:]\n",
    "    },\n",
    "    {\n",
    "        'layer_name': layers.Bidirectional,\n",
    "        \"layer\": layers.LSTM(64, return_sequences=True, kernel_regularizer=keras.regularizers.l2(0.01)), # units = 64\n",
    "        \"name\": \"bilstm_1\",\n",
    "    },\n",
    "    {\n",
    "        \"layer_name\": layers.Dropout,\n",
    "        \"rate\": 0.1, # dropout = 0.1\n",
    "        \"name\": \"dropout_1\"\n",
    "    },\n",
    "    {\n",
    "        \"layer_name\": layers.Dense,\n",
    "        \"units\": 256,  # units = 256\n",
    "        \"activation\": \"relu\",\n",
    "        \"kernel_regularizer\": keras.regularizers.l2(0.01),\n",
    "        \"name\": \"dense_1\"\n",
    "    },\n",
    "    {\n",
    "        \"layer_name\": layers.Dropout,\n",
    "        \"rate\": 0.1,  # dropout = 0.1\n",
    "        \"name\": \"dropout_2\"\n",
    "    },\n",
    "    {\n",
    "        \"layer_name\": layers.Dense,\n",
    "        \"units\": n_classes,\n",
    "        \"activation\": \"softmax\",\n",
    "        \"name\": \"dense_2\"\n",
    "    }\n",
    "]\n",
    "\n",
    "compile_info_bilstm_two_dense = {\n",
    "    'optimizer': keras.optimizers.Adam(learning_rate=0.01),   # learning_rate = 0.01\n",
    "    'loss': 'categorical_crossentropy',\n",
    "    'metrics' : ['categorical_accuracy', f1_with_tfa]\n",
    "}\n",
    "\n",
    "model_bilstm_two_dense = create_model(layers_info_bilstm_two_dense, compile_info_bilstm_two_dense)\n",
    "model_bilstm_two_dense.summary()\n",
    "\n",
    "# start training\n",
    "history_bilstm_two_dense = model_bilstm_two_dense.fit(X_train, y_train, epochs=epochs_training, batch_size=batch_size_training, validation_data=(X_dev, y_dev), callbacks=callbacks_training)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2NnTW1ct3ZUP"
   },
   "source": [
    "#### 2.3.1 Store all the trained models\n",
    "Store structure, weights and history of the models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Rp38wlcW3ZUP"
   },
   "outputs": [],
   "source": [
    "# store models weights\n",
    "path_model_weights = os.path.join(os.getcwd(), 'models', 'weights')\n",
    "model_bilstm.save_weights(os.path.join(path_model_weights, 'weights_bilstm.h5'))\n",
    "model_bigru.save_weights(os.path.join(path_model_weights, 'weights_bigru.h5'))\n",
    "model_two_bilstm.save_weights(os.path.join(path_model_weights, 'weights_two_bilstm.h5'))\n",
    "model_bilstm_two_dense.save_weights(os.path.join(path_model_weights, 'weights_bilstm_two_dense.h5'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "OSpyVgHIVS0Q"
   },
   "outputs": [],
   "source": [
    "# convert the history.history dict to a pandas DataFrame: \n",
    "path_model_weights = os.path.join(os.getcwd(), 'models', 'history')\n",
    "    \n",
    "df_history_bilstm = pd.DataFrame(history_bilstm.history) \n",
    "with open(os.path.join(path_model_weights, \"history_bilstm.csv\"), mode=\"w\") as file:\n",
    "    df_history_bilstm.to_csv(file)\n",
    "\n",
    "df_history_bigru = pd.DataFrame(history_bigru.history)\n",
    "with open(os.path.join(path_model_weights, \"history_bigru.csv\"), mode=\"w\") as file:\n",
    "    df_history_bigru.to_csv(file)\n",
    "\n",
    "df_history_two_bilstm = pd.DataFrame(history_two_bilstm.history)\n",
    "with open(os.path.join(path_model_weights, \"history_two_bilstm.csv\"), mode=\"w\") as file:\n",
    "    df_history_two_bilstm.to_csv(file)\n",
    "\n",
    "df_history_bilstm_two_dense = pd.DataFrame(history_bilstm_two_dense.history)\n",
    "with open(os.path.join(path_model_weights, \"history_bilstm_two_dense.csv\"), mode=\"w\") as file:\n",
    "    df_history_bilstm_two_dense.to_csv(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "Id-_Zvn7VS0Q"
   },
   "outputs": [],
   "source": [
    "path_model_structure = os.path.join(os.getcwd(), 'models', 'structure')\n",
    "with open(os.path.join(path_model_structure, \"model_bilstm.json\"), mode=\"w\") as file:\n",
    "    file.write(model_bilstm.to_json())\n",
    "\n",
    "with open(os.path.join(path_model_structure, \"model_bigru.json\"), mode=\"w\") as file:\n",
    "    file.write(model_bigru.to_json())\n",
    "\n",
    "with open(os.path.join(path_model_structure, \"model_two_bilstm.json\"), mode=\"w\") as file:\n",
    "    file.write(model_two_bilstm.to_json())\n",
    "\n",
    "with open(os.path.join(path_model_structure, \"model_bilstm_two_dense.json\"), mode=\"w\") as file:\n",
    "    file.write(model_bilstm_two_dense.to_json())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qQy5JgGT3ZUQ"
   },
   "source": [
    "#### 2.3.2 Load all the pre-trained models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "B6LQewzw-Ek1"
   },
   "outputs": [],
   "source": [
    "# load models structure\n",
    "path_model_structure = os.path.join(os.getcwd(), 'models', 'structure')\n",
    "with open(os.path.join(path_model_structure, 'model_bilstm.json'), 'r') as json_file:\n",
    "    model_bilstm_json = json_file.read()    \n",
    "    model_bilstm = model_from_json(model_bilstm_json)\n",
    "\n",
    "with open(os.path.join(path_model_structure, 'model_bigru.json'), 'r') as json_file:\n",
    "    model_bigru_json = json_file.read()    \n",
    "    model_bigru = model_from_json(model_bigru_json)\n",
    "\n",
    "with open(os.path.join(path_model_structure, 'model_two_bilstm.json'), 'r') as json_file:\n",
    "    model_two_bilstm_json = json_file.read()    \n",
    "    model_two_bilstm = model_from_json(model_two_bilstm_json)\n",
    "\n",
    "with open(os.path.join(path_model_structure, 'model_bilstm_two_dense.json'), 'r') as json_file:\n",
    "    model_bilstm_two_dense_json = json_file.read()    \n",
    "    model_bilstm_two_dense = model_from_json(model_bilstm_two_dense_json)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "_puUbUNv3ZUQ"
   },
   "outputs": [],
   "source": [
    "# load the weights\n",
    "path_model_weights = os.path.join(os.getcwd(), 'models', 'weights')\n",
    "model_bilstm.load_weights(os.path.join(path_model_weights, 'weights_bilstm.h5'))\n",
    "model_bigru.load_weights(os.path.join(path_model_weights, 'weights_bigru.h5'))\n",
    "model_two_bilstm.load_weights(os.path.join(path_model_weights, 'weights_two_bilstm.h5'))\n",
    "model_bilstm_two_dense.load_weights(os.path.join(path_model_weights, 'weights_bilstm_two_dense.h5'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "AVIa_P03-Ek2"
   },
   "outputs": [],
   "source": [
    "# Restore history\n",
    "cols = ['loss','categorical_accuracy','f1_with_tfa','val_loss','val_categorical_accuracy','val_f1_with_tfa']\n",
    "path_model_history = os.path.join(os.getcwd(), 'models', 'history')\n",
    "history_bilstm = pd.read_csv(os.path.join(path_model_history, \"history_bilstm.csv\"), usecols=cols)\n",
    "history_bigru = pd.read_csv(os.path.join(path_model_history, \"history_bigru.csv\"), usecols=cols)\n",
    "history_two_bilstm = pd.read_csv(os.path.join(path_model_history, \"history_two_bilstm.csv\"), usecols=cols)\n",
    "history_bilstm_two_dense = pd.read_csv(os.path.join(path_model_history, \"history_bilstm_two_dense.csv\"), usecols=cols)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ab9ETsbC2Q82"
   },
   "source": [
    "### 2.4 History of the training\n",
    "Plot metrics evolution for each epoch during the training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "id": "25YBLK3E2R6m"
   },
   "outputs": [],
   "source": [
    "def plot_history(models_history, keys, model_names=[], labels=(\"epochs\", \"metrics\"), y_scale=\"linear\", figsize=(10,5), cmap='rainbow'):\n",
    "    \"\"\"\n",
    "    Plot the history of the metrics in the history dictionary for each model.\n",
    "        :param models_history: array of dictionary of the metric history for each model\n",
    "        :param keys: list of keys of the metrics to plot\n",
    "        :param model_names: list of names of the models\n",
    "        :param labels: list of labels of the axes\n",
    "        :param figsize: size of the figure\n",
    "        :param cmap: color map used for the plot\n",
    "    \"\"\"\n",
    "\n",
    "    # maps each model to a distinct RGB color\n",
    "    cmap = plt.cm.get_cmap(cmap, len(models_history))\n",
    "\n",
    "    \n",
    "    fig = plt.figure(figsize=figsize)\n",
    "\n",
    "    # for each model trained\n",
    "    for i, history in enumerate(models_history):\n",
    "        # take all pairs of training and val metrics\n",
    "        for j in range(0, len(keys), 2):\n",
    "            metric, val_metric = keys[j], keys[j+1]\n",
    "            \n",
    "            plt.plot(history[metric], label=f\"{model_names[i]} {metric}\", linestyle=\"solid\", color=cmap(i))\n",
    "            plt.plot(history[val_metric],  label=f\"{model_names[i]} {val_metric}\", linestyle=\"--\",  color=cmap(i))\n",
    "        \n",
    "    plt.xlabel(labels[0])\n",
    "    plt.ylabel(labels[1])\n",
    "    plt.yscale(y_scale)\n",
    "\n",
    "    # Adding legend\n",
    "    plt.legend(\n",
    "          title =\"Legend\",\n",
    "          loc =\"best\",\n",
    "          bbox_to_anchor=(1.0,0.6))\n",
    "    plt.title(\"Training history\")\n",
    "    plt.grid(linestyle='--', linewidth=1)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 899
    },
    "id": "Ca9TMoj42VgT",
    "outputId": "d47666bc-dae8-4eb5-caf6-09d98faa1074"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "models_history = [history_bilstm, history_bigru, history_two_bilstm, history_bilstm_two_dense]\n",
    "model_names = [\"BiLSTM\", \"BiGRU\", \"2 BiLSTM\", \"BiLSTM + 2 Dense\"]\n",
    "plot_history(models_history, keys=['loss', 'val_loss'], model_names=model_names, labels=(\"epochs\", \"loss\"), y_scale=\"log\", figsize=(15,7),  cmap='bwr')\n",
    "plot_history(models_history, keys=['categorical_accuracy', 'val_categorical_accuracy'], model_names=model_names, labels=(\"epochs\", \"accuracy\"), figsize=(15,7),  cmap='bwr')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZmIWLatvkqzt"
   },
   "source": [
    "## 3. Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WvbuyGp7AhdN"
   },
   "source": [
    "The two best models according to validation set are **BiLSTM** and **BiGRU**. Therefore the test set will be used with both of them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Q8P40fDtB5eU"
   },
   "source": [
    "The *classification report* is a summary of the metrics (precision, recall, f1_score) computed for each class and globally (micro, macro and weighted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "theVB92PXka6"
   },
   "outputs": [],
   "source": [
    "def get_flattened_comparison(model, X_test, y_test):\n",
    "\n",
    "    raw_y_pred = model.predict(X_test)\n",
    "\n",
    "    # converting indexes to labels\n",
    "    y_true_label = target_vectorizer.inverse_transform(y_test)\n",
    "    y_pred_label = target_vectorizer.inverse_transform_probabilities(raw_y_pred)\n",
    "\n",
    "    y_true_flat = []\n",
    "    y_pred_flat = []\n",
    "    for document_predicted, document_true in zip(y_pred_label, y_true_label):\n",
    "        for word_predicted, word_true in zip (document_predicted, document_true):\n",
    "            y_pred_flat.append(word_predicted)\n",
    "            y_true_flat.append(word_true)\n",
    "    return y_true_flat, y_pred_flat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "8Keo7Bin_8-6",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def show_classification_report(model, X_test, y_test, sorted_by=None, ascending=True):\n",
    "  \"\"\"\n",
    "  Show classification report using only classes in names\n",
    "  \"\"\"\n",
    "  y_true, y_pred = get_flattened_comparison(model, X_test, y_test)\n",
    "  report =  classification_report(y_true, y_pred, output_dict=True, zero_division=0)\n",
    "  \n",
    "  # global performance\n",
    "  keys = ['weighted avg', 'accuracy', 'macro avg']\n",
    "  global_dict = {key: report[key] for key in keys}\n",
    "  for key in keys:\n",
    "    del report[key]\n",
    "\n",
    "  df = pd.DataFrame.from_dict(report).transpose().round(2)\n",
    "  if sorted_by:\n",
    "    df.sort_values(by=sorted_by, ascending=ascending, inplace=True)\n",
    "  print(df)\n",
    "\n",
    "  print(\"\\n\\n\")\n",
    "\n",
    "  df_glob = pd.DataFrame.from_dict(global_dict).transpose().round(2)\n",
    "  print(df_glob)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "C6eu5_AhChls"
   },
   "source": [
    "The *confusion matrix* can be very useful to understand main errors committed by the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "id": "Q71LkwAPkqzt"
   },
   "outputs": [],
   "source": [
    "def evaluate_model(model, X_test, y_test, labels):\n",
    "    \"\"\"\n",
    "    given a trained model and a test set returns the f-score and the confusion matrix\n",
    "    taking into account only classes in labels\n",
    "    \"\"\"\n",
    "    # show confusion matrix\n",
    "    y_true_flat, y_pred_flat = get_flattened_comparison(model, X_test, y_test)\n",
    "    conf_matrix = confusion_matrix(y_true_flat, y_pred_flat, labels=labels)\n",
    "    disp = ConfusionMatrixDisplay(conf_matrix, display_labels=labels)\n",
    "    fig, ax = plt.subplots(figsize=(12,14))\n",
    "    disp.plot(ax=ax)\n",
    "    # f1 score\n",
    "    print(\"F1 score:\\n-------------------------------\")\n",
    "    print(sklearn.metrics.f1_score(y_true_flat, y_pred_flat, labels=labels, average='macro', zero_division=0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eKkXStMxCu88"
   },
   "source": [
    "It is not interesting evaluating *punctuation classes*, therefore they are not taken into account in the final evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "id": "ua2c1KdCkqzt"
   },
   "outputs": [],
   "source": [
    "punctuation_indexes = [0, 1, 2, 3, 4, 5, 6, 7, 17, 44]\n",
    "classes = target_vectorizer.get_classes()\n",
    "valid_labels = list(set(range(len(classes))) - set(punctuation_indexes))\n",
    "meaningful_classes = classes[valid_labels]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rZAdUCrBsj59"
   },
   "source": [
    "BiLSTM and BiGRU model have been created using the best hyperparameter found by the tuner"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RWfh0QL8DJH4"
   },
   "source": [
    "### BiLSTM Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 756
    },
    "id": "f5t_osxmBoxc",
    "outputId": "2fcf3cf4-450c-47fe-ff84-209175eddc0b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 score:\n",
      "-------------------------------\n",
      "0.6245671834024557\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x1008 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# confusion matrix and global f1 score\n",
    "evaluate_model(model_bilstm, X_test, y_test, meaningful_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "qiLRnJ-3_8-9",
    "outputId": "1943cb29-03f1-435b-bbca-4f5c870911d0",
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       precision  recall  f1-score  support\n",
      "``          0.99    0.99      0.99     98.0\n",
      ",           0.98    0.98      0.98   1171.0\n",
      "''          0.96    0.99      0.97     95.0\n",
      "PRP$        0.95    0.99      0.97    148.0\n",
      "TO          0.97    0.97      0.97    560.0\n",
      ".           0.94    0.99      0.97    907.0\n",
      "$           0.95    0.97      0.96    270.0\n",
      "POS         0.96    0.96      0.96    244.0\n",
      "DT          0.94    0.94      0.94   1988.0\n",
      "MD          0.91    0.96      0.93    245.0\n",
      ":           0.96    0.86      0.91     84.0\n",
      "CC          0.94    0.87      0.90    541.0\n",
      "IN          0.87    0.92      0.90   2460.0\n",
      "PRP         0.95    0.85      0.90    265.0\n",
      "VB          0.86    0.90      0.88    602.0\n",
      "VBD         0.83    0.88      0.86    964.0\n",
      "CD          0.86    0.85      0.86   1271.0\n",
      "WDT         0.76    0.93      0.84    124.0\n",
      "VBZ         0.75    0.89      0.81    390.0\n",
      "NNS         0.75    0.79      0.77   1452.0\n",
      "NNP         0.78    0.76      0.77   2225.0\n",
      "NN          0.74    0.79      0.77   3563.0\n",
      "WP          0.87    0.67      0.75     30.0\n",
      "VBP         0.74    0.77      0.75    196.0\n",
      "EX          0.69    0.64      0.67     14.0\n",
      "VBN         0.70    0.63      0.66    548.0\n",
      "JJ          0.68    0.59      0.63   1405.0\n",
      "RB          0.66    0.54      0.60    599.0\n",
      "RBS         0.75    0.50      0.60      6.0\n",
      "VBG         0.59    0.61      0.60    321.0\n",
      "JJR         0.59    0.51      0.55    108.0\n",
      "RP          0.44    0.51      0.48     37.0\n",
      "RBR         0.40    0.42      0.41     19.0\n",
      "JJS         0.85    0.25      0.39     44.0\n",
      "WRB         0.75    0.18      0.29     34.0\n",
      "WP$         0.00    0.00      0.00      5.0\n",
      "NNPS        0.00    0.00      0.00     83.0\n",
      "-LRB-       0.00    0.00      0.00     34.0\n",
      "-RRB-       0.00    0.00      0.00     34.0\n",
      "PDT         0.00    0.00      0.00      9.0\n",
      "#           0.00    0.00      0.00      3.0\n",
      "\n",
      "\n",
      "\n",
      "              precision  recall  f1-score   support\n",
      "weighted avg       0.82    0.83      0.82  23196.00\n",
      "accuracy           0.83    0.83      0.83      0.83\n",
      "macro avg          0.69    0.65      0.66  23196.00\n"
     ]
    }
   ],
   "source": [
    "# classification report using only meaningful classes\n",
    "show_classification_report(model_bilstm, X_test, y_test, sorted_by=['f1-score'], ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Result examples of BiLSTM model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original sentence:  ['but' 'courts' 'quickly' 'tumbled' 'down' 'a' 'slippery' 'slope' '.']\n",
      "Original POS tagging:  ['CC' 'NNS' 'RB' 'VBD' 'IN' 'DT' 'JJ' 'NN' '.']\n",
      "Predicted POS tagging:  ['IN' 'NNS' 'RB' 'VBD' 'RP' 'DT' 'JJ' 'NN' '.']\n"
     ]
    }
   ],
   "source": [
    "i_test = 10  # change this to see the prediction for a different sentence\n",
    "print(\"Original sentence: \", X_test_sentences[i_test])\n",
    "print(\"Original POS tagging: \",target_vectorizer.inverse_transform([y_test[i_test]])[0])\n",
    "\n",
    "raw_y_pred = model_bilstm.predict(np.array([X_test[i_test]]))\n",
    "\n",
    "print(\"Predicted POS tagging: \",target_vectorizer.inverse_transform_probabilities(raw_y_pred)[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sx-hTiJODO27"
   },
   "source": [
    "### BiGRU Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "akvx1C1aD2_8",
    "outputId": "dfc265ab-ce76-4476-dea0-eb7bd7d19bb7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "F1 score:\n",
      "-------------------------------\n",
      "0.6062472113054655\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# confusion matrix and global f1 score\n",
    "evaluate_model(model_bigru, X_test, y_test, meaningful_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "0hf5iZbkD3kL",
    "outputId": "8ddf4817-0258-4273-a9a4-1f6355f0e841"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       precision  recall  f1-score  support\n",
      "``          1.00    1.00      1.00     98.0\n",
      "''          1.00    0.98      0.99     95.0\n",
      "PRP$        0.98    0.98      0.98    148.0\n",
      ",           0.97    0.97      0.97   1171.0\n",
      ".           0.92    1.00      0.96    889.0\n",
      "POS         0.96    0.96      0.96    244.0\n",
      "$           0.94    0.94      0.94    270.0\n",
      "TO          0.94    0.94      0.94    560.0\n",
      "DT          0.93    0.94      0.93   1988.0\n",
      ":           0.92    0.92      0.92     84.0\n",
      "CC          0.90    0.95      0.92    541.0\n",
      "MD          0.91    0.92      0.92    245.0\n",
      "IN          0.92    0.87      0.90   2460.0\n",
      "WP          0.96    0.80      0.87     30.0\n",
      "PRP         0.81    0.92      0.86    265.0\n",
      "CD          0.90    0.80      0.85   1270.0\n",
      "VB          0.83    0.87      0.85    602.0\n",
      "WDT         0.87    0.81      0.84    124.0\n",
      "VBZ         0.83    0.85      0.84    390.0\n",
      "VBD         0.86    0.82      0.84    964.0\n",
      "NNP         0.70    0.82      0.76   2225.0\n",
      "NNS         0.67    0.82      0.74   1451.0\n",
      "NN          0.74    0.73      0.73   3562.0\n",
      "VBP         0.58    0.80      0.67    196.0\n",
      "VBN         0.65    0.68      0.66    548.0\n",
      "VBG         0.67    0.56      0.61    321.0\n",
      "JJ          0.62    0.57      0.59   1404.0\n",
      "RB          0.66    0.52      0.59    599.0\n",
      "EX          0.62    0.57      0.59     14.0\n",
      "WRB         0.78    0.41      0.54     34.0\n",
      "JJR         0.71    0.37      0.48    109.0\n",
      "RP          0.37    0.54      0.44     37.0\n",
      "RBR         0.42    0.42      0.42     19.0\n",
      "PDT         1.00    0.11      0.20      9.0\n",
      "JJS         1.00    0.05      0.09     44.0\n",
      "NNPS        0.33    0.04      0.07     83.0\n",
      "RBS         0.00    0.00      0.00      6.0\n",
      "-RRB-       0.00    0.00      0.00     34.0\n",
      "-LRB-       0.00    0.00      0.00     34.0\n",
      "WP$         0.00    0.00      0.00      5.0\n",
      "LS          0.00    0.00      0.00      0.0\n",
      "#           0.00    0.00      0.00      3.0\n",
      "\n",
      "\n",
      "\n",
      "              precision  recall  f1-score   support\n",
      "weighted avg       0.81    0.81      0.80  23175.00\n",
      "accuracy           0.81    0.81      0.81      0.81\n",
      "macro avg          0.69    0.62      0.63  23175.00\n"
     ]
    }
   ],
   "source": [
    "# classification report using only meaningful classes\n",
    "show_classification_report(model_bigru, X_test, y_test, sorted_by=['f1-score'], ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Tb4izTq5DVab"
   },
   "source": [
    "#### Result examples of BiGRU model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "id": "sFaG0yb4kqzu",
    "outputId": "157b8786-fce4-4166-9766-a4aa064595fb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original sentence:  ['but' 'courts' 'quickly' 'tumbled' 'down' 'a' 'slippery' 'slope' '.']\n",
      "Original POS tagging:  ['CC' 'NNS' 'RB' 'VBD' 'IN' 'DT' 'JJ' 'NN' '.']\n",
      "Predicted POS tagging:  ['CC' 'NNS' 'RB' 'VBD' 'RP' 'DT' 'JJ' 'NN' '.']\n"
     ]
    }
   ],
   "source": [
    "i_test = 10  # change this to see the prediction for a different sentence\n",
    "print(\"Original sentence: \", X_test_sentences[i_test])\n",
    "print(\"Original POS tagging: \",target_vectorizer.inverse_transform([y_test[i_test]])[0])\n",
    "\n",
    "raw_y_pred = model_bigru.predict(np.array([X_test[i_test]]))\n",
    "\n",
    "print(\"Predicted POS tagging: \",target_vectorizer.inverse_transform_probabilities(raw_y_pred)[0])"
   ]
  }
 ],
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