RMDL_of_elmoMinor(1).ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "RMDL of elmoMinor",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3dAw1xhlSZjk"
      },
      "source": [
        "# Elmo"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pv51hxXAWLqy"
      },
      "source": [
        "## Import Packages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YWD2PY8jfudF",
        "outputId": "3a9aba05-d0c3-4bd3-8fd2-8af964cd0ff3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!pip install tensorflow==2.3\n",
        "!pip install \"tensorflow_hub>=0.6.0\"\n",
        "!pip3 install tensorflow_text==2.3"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: tensorflow==2.3 in /usr/local/lib/python3.6/dist-packages (2.3.0)\n",
            "Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.3) (0.10.0)\n",
            "Requirement already satisfied: keras-preprocessing<1.2,>=1.1.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.3) (1.1.2)\n",
            "Requirement already satisfied: h5py<2.11.0,>=2.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.3) (2.10.0)\n",
            "Requirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.3) (1.15.0)\n",
            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.3) (3.3.0)\n",
            "Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.3) (0.35.1)\n",
            "Requirement already satisfied: gast==0.3.3 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.3) (0.3.3)\n",
            "Requirement already satisfied: tensorboard<3,>=2.3.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.3) (2.3.0)\n",
            "Requirement already satisfied: astunparse==1.6.3 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.3) (1.6.3)\n",
            "Requirement already satisfied: google-pasta>=0.1.8 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.3) (0.2.0)\n",
            "Requirement already satisfied: tensorflow-estimator<2.4.0,>=2.3.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.3) (2.3.0)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.3) (1.1.0)\n",
            "Requirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.3) (1.12.1)\n",
            "Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.3) (1.33.1)\n",
            "Requirement already satisfied: protobuf>=3.9.2 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.3) (3.12.4)\n",
            "Requirement already satisfied: numpy<1.19.0,>=1.16.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.3) (1.18.5)\n",
            "Requirement already satisfied: scipy==1.4.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow==2.3) (1.4.1)\n",
            "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard<3,>=2.3.0->tensorflow==2.3) (2.23.0)\n",
            "Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard<3,>=2.3.0->tensorflow==2.3) (50.3.2)\n",
            "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.6/dist-packages (from tensorboard<3,>=2.3.0->tensorflow==2.3) (0.4.1)\n",
            "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard<3,>=2.3.0->tensorflow==2.3) (1.7.0)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tensorboard<3,>=2.3.0->tensorflow==2.3) (1.0.1)\n",
            "Requirement already satisfied: google-auth<2,>=1.6.3 in /usr/local/lib/python3.6/dist-packages (from tensorboard<3,>=2.3.0->tensorflow==2.3) (1.17.2)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard<3,>=2.3.0->tensorflow==2.3) (3.3.2)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<3,>=2.3.0->tensorflow==2.3) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<3,>=2.3.0->tensorflow==2.3) (2020.6.20)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<3,>=2.3.0->tensorflow==2.3) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<3,>=2.3.0->tensorflow==2.3) (3.0.4)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard<3,>=2.3.0->tensorflow==2.3) (1.3.0)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard<3,>=2.3.0->tensorflow==2.3) (0.2.8)\n",
            "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard<3,>=2.3.0->tensorflow==2.3) (4.1.1)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4; python_version >= \"3\" in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard<3,>=2.3.0->tensorflow==2.3) (4.6)\n",
            "Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from markdown>=2.6.8->tensorboard<3,>=2.3.0->tensorflow==2.3) (2.0.0)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<3,>=2.3.0->tensorflow==2.3) (3.1.0)\n",
            "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.6/dist-packages (from pyasn1-modules>=0.2.1->google-auth<2,>=1.6.3->tensorboard<3,>=2.3.0->tensorflow==2.3) (0.4.8)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard<3,>=2.3.0->tensorflow==2.3) (3.3.1)\n",
            "Requirement already satisfied: tensorflow_hub>=0.6.0 in /usr/local/lib/python3.6/dist-packages (0.9.0)\n",
            "Requirement already satisfied: numpy>=1.12.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow_hub>=0.6.0) (1.18.5)\n",
            "Requirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow_hub>=0.6.0) (1.15.0)\n",
            "Requirement already satisfied: protobuf>=3.8.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow_hub>=0.6.0) (3.12.4)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.8.0->tensorflow_hub>=0.6.0) (50.3.2)\n",
            "Collecting tensorflow_text==2.3\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/28/b2/2dbd90b93913afd07e6101b8b84327c401c394e60141c1e98590038060b3/tensorflow_text-2.3.0-cp36-cp36m-manylinux1_x86_64.whl (2.6MB)\n",
            "\u001b[K     |████████████████████████████████| 2.6MB 10.6MB/s \n",
            "\u001b[?25hRequirement already satisfied: tensorflow<2.4,>=2.3.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow_text==2.3) (2.3.0)\n",
            "Requirement already satisfied: scipy==1.4.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow<2.4,>=2.3.0->tensorflow_text==2.3) (1.4.1)\n",
            "Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow<2.4,>=2.3.0->tensorflow_text==2.3) (0.10.0)\n",
            "Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tensorflow<2.4,>=2.3.0->tensorflow_text==2.3) (0.35.1)\n",
            "Requirement already satisfied: google-pasta>=0.1.8 in /usr/local/lib/python3.6/dist-packages (from tensorflow<2.4,>=2.3.0->tensorflow_text==2.3) (0.2.0)\n",
            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.6/dist-packages (from tensorflow<2.4,>=2.3.0->tensorflow_text==2.3) (3.3.0)\n",
            "Requirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow<2.4,>=2.3.0->tensorflow_text==2.3) (1.15.0)\n",
            "Requirement already satisfied: gast==0.3.3 in /usr/local/lib/python3.6/dist-packages (from tensorflow<2.4,>=2.3.0->tensorflow_text==2.3) (0.3.3)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow<2.4,>=2.3.0->tensorflow_text==2.3) (1.1.0)\n",
            "Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow<2.4,>=2.3.0->tensorflow_text==2.3) (1.33.1)\n",
            "Requirement already satisfied: astunparse==1.6.3 in /usr/local/lib/python3.6/dist-packages (from tensorflow<2.4,>=2.3.0->tensorflow_text==2.3) (1.6.3)\n",
            "Requirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow<2.4,>=2.3.0->tensorflow_text==2.3) (1.12.1)\n",
            "Requirement already satisfied: numpy<1.19.0,>=1.16.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow<2.4,>=2.3.0->tensorflow_text==2.3) (1.18.5)\n",
            "Requirement already satisfied: h5py<2.11.0,>=2.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow<2.4,>=2.3.0->tensorflow_text==2.3) (2.10.0)\n",
            "Requirement already satisfied: tensorboard<3,>=2.3.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow<2.4,>=2.3.0->tensorflow_text==2.3) (2.3.0)\n",
            "Requirement already satisfied: tensorflow-estimator<2.4.0,>=2.3.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow<2.4,>=2.3.0->tensorflow_text==2.3) (2.3.0)\n",
            "Requirement already satisfied: keras-preprocessing<1.2,>=1.1.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow<2.4,>=2.3.0->tensorflow_text==2.3) (1.1.2)\n",
            "Requirement already satisfied: protobuf>=3.9.2 in /usr/local/lib/python3.6/dist-packages (from tensorflow<2.4,>=2.3.0->tensorflow_text==2.3) (3.12.4)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tensorboard<3,>=2.3.0->tensorflow<2.4,>=2.3.0->tensorflow_text==2.3) (1.0.1)\n",
            "Requirement already satisfied: google-auth<2,>=1.6.3 in /usr/local/lib/python3.6/dist-packages (from tensorboard<3,>=2.3.0->tensorflow<2.4,>=2.3.0->tensorflow_text==2.3) (1.17.2)\n",
            "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard<3,>=2.3.0->tensorflow<2.4,>=2.3.0->tensorflow_text==2.3) (2.23.0)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard<3,>=2.3.0->tensorflow<2.4,>=2.3.0->tensorflow_text==2.3) (3.3.2)\n",
            "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard<3,>=2.3.0->tensorflow<2.4,>=2.3.0->tensorflow_text==2.3) (1.7.0)\n",
            "Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard<3,>=2.3.0->tensorflow<2.4,>=2.3.0->tensorflow_text==2.3) (50.3.2)\n",
            "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.6/dist-packages (from tensorboard<3,>=2.3.0->tensorflow<2.4,>=2.3.0->tensorflow_text==2.3) (0.4.1)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4; python_version >= \"3\" in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard<3,>=2.3.0->tensorflow<2.4,>=2.3.0->tensorflow_text==2.3) (4.6)\n",
            "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard<3,>=2.3.0->tensorflow<2.4,>=2.3.0->tensorflow_text==2.3) (4.1.1)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tensorboard<3,>=2.3.0->tensorflow<2.4,>=2.3.0->tensorflow_text==2.3) (0.2.8)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<3,>=2.3.0->tensorflow<2.4,>=2.3.0->tensorflow_text==2.3) (2020.6.20)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<3,>=2.3.0->tensorflow<2.4,>=2.3.0->tensorflow_text==2.3) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<3,>=2.3.0->tensorflow<2.4,>=2.3.0->tensorflow_text==2.3) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tensorboard<3,>=2.3.0->tensorflow<2.4,>=2.3.0->tensorflow_text==2.3) (1.24.3)\n",
            "Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from markdown>=2.6.8->tensorboard<3,>=2.3.0->tensorflow<2.4,>=2.3.0->tensorflow_text==2.3) (2.0.0)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard<3,>=2.3.0->tensorflow<2.4,>=2.3.0->tensorflow_text==2.3) (1.3.0)\n",
            "Requirement already satisfied: pyasn1>=0.1.3 in /usr/local/lib/python3.6/dist-packages (from rsa<5,>=3.1.4; python_version >= \"3\"->google-auth<2,>=1.6.3->tensorboard<3,>=2.3.0->tensorflow<2.4,>=2.3.0->tensorflow_text==2.3) (0.4.8)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata; python_version < \"3.8\"->markdown>=2.6.8->tensorboard<3,>=2.3.0->tensorflow<2.4,>=2.3.0->tensorflow_text==2.3) (3.3.1)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<3,>=2.3.0->tensorflow<2.4,>=2.3.0->tensorflow_text==2.3) (3.1.0)\n",
            "Installing collected packages: tensorflow-text\n",
            "Successfully installed tensorflow-text-2.3.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E12entNbSBuj",
        "outputId": "87b2c511-3e1e-43d7-c716-b8f267a08fa2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "#Load packages\n",
        "import tensorflow as tf \n",
        "from tensorflow.keras.preprocessing.text import Tokenizer \n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences \n",
        "from sklearn import metrics, preprocessing,model_selection \n",
        "from sklearn.metrics import accuracy_score \n",
        "from tensorflow import keras\n",
        "from tensorflow.keras.layers import Input, Lambda, Dense \n",
        "from tensorflow.keras.models import Model \n",
        "import tensorflow.keras.backend as K \n",
        "import matplotlib.pyplot as pit \n",
        "import seaborn as sns\n",
        "import numpy as np \n",
        "import string \n",
        "import pandas as pd \n",
        "import re \n",
        "import spacy \n",
        "from nltk.corpus import stopwords \n",
        "from sklearn.feature_extraction.stop_words import ENGLISH_STOP_WORDS \n",
        "from spacy.lang.en import English \n",
        "spacy.load('en') \n",
        "parser = English()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:144: FutureWarning: The sklearn.feature_extraction.stop_words module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.feature_extraction.text. Anything that cannot be imported from sklearn.feature_extraction.text is now part of the private API.\n",
            "  warnings.warn(message, FutureWarning)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pHthsbyGsVXL"
      },
      "source": [
        "K= tf.compat.v1.keras.backend.get_session()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LrP8jJaDQNql",
        "outputId": "607fc09b-4533-44f3-fed2-9b9a003b1282",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZnZd4aZwY8cM"
      },
      "source": [
        "##load Elmo"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jE_okjTsZAhH",
        "outputId": "7be957ae-f2d3-4219-c0d9-1ffb7ebbf747",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# get elmo from tensorflow hub \n",
        "import tensorflow_hub as hub \n",
        "import tensorflow as tf \n",
        "\n",
        "embed = hub.load('https://tfhub.dev/google/nnlm-en-dim128/1')\n",
        "embed.signatures['default'](tf.convert_to_tensor(['my text', 'batch']))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'default': <tf.Tensor: shape=(2, 128), dtype=float32, numpy=\n",
              " array([[ 0.06817301,  0.12772666,  0.04918525, -0.03550394, -0.04533052,\n",
              "          0.04868454,  0.00771219, -0.02631581, -0.16468817,  0.1887698 ,\n",
              "          0.01009648, -0.18591057,  0.05160484,  0.13291915,  0.01235215,\n",
              "         -0.00492161, -0.06701647, -0.01161232, -0.01099506,  0.21438217,\n",
              "          0.04877856, -0.10143851, -0.12461483, -0.00835736, -0.01975276,\n",
              "         -0.0518438 ,  0.0135941 ,  0.04147662, -0.14309719, -0.10322613,\n",
              "         -0.08090361, -0.01536523, -0.11944003, -0.04822929,  0.13167971,\n",
              "         -0.06830869, -0.04490855,  0.03839394,  0.0882614 ,  0.01578063,\n",
              "         -0.16467947, -0.09377561,  0.01380978, -0.04058216,  0.03929568,\n",
              "          0.09588426, -0.15572111, -0.15270787, -0.04349912, -0.0844278 ,\n",
              "          0.00472539,  0.01002582,  0.03455054,  0.0139303 , -0.11444485,\n",
              "         -0.07043104,  0.03757556, -0.00822732,  0.07750841, -0.02362585,\n",
              "          0.11321948, -0.02998928,  0.19748685,  0.16866075,  0.05818968,\n",
              "         -0.08720152,  0.02429257, -0.08923671,  0.04004676, -0.11712038,\n",
              "         -0.04423016,  0.00084125,  0.09887315, -0.10869319, -0.02063273,\n",
              "         -0.0078976 ,  0.03247038, -0.053636  , -0.06318824,  0.09211446,\n",
              "          0.06257474, -0.04052477, -0.02375481,  0.06692737,  0.01396037,\n",
              "         -0.00387887, -0.11007597, -0.02819042,  0.02156279, -0.03867229,\n",
              "          0.09522782,  0.03073994,  0.1777497 ,  0.07217226,  0.2552764 ,\n",
              "          0.00558206, -0.13917604, -0.01151034, -0.00699278, -0.00058617,\n",
              "         -0.12016475, -0.04845744,  0.00108416,  0.01308661, -0.08460812,\n",
              "          0.04146604, -0.02392333,  0.0211766 ,  0.01239127,  0.09751251,\n",
              "          0.08847063, -0.04759594, -0.14307027, -0.05373308,  0.06285188,\n",
              "         -0.06437098,  0.07693451, -0.10320926, -0.13538882,  0.02908451,\n",
              "         -0.01237198,  0.05219781, -0.02561041,  0.06948396,  0.05050331,\n",
              "         -0.0589957 ,  0.14204101,  0.05099661],\n",
              "        [ 0.01706688,  0.16787235,  0.09149991, -0.14499378, -0.08243445,\n",
              "          0.12357763,  0.01297838, -0.00048092,  0.0466181 ,  0.1043538 ,\n",
              "          0.02300307,  0.03475228, -0.04275631,  0.21015036, -0.02800887,\n",
              "         -0.05067778, -0.00960012, -0.01759498,  0.01835109, -0.07776673,\n",
              "         -0.03645713,  0.03972793, -0.03069391, -0.03422287, -0.16668513,\n",
              "         -0.04430129,  0.01050955, -0.04430391, -0.05041046,  0.01521919,\n",
              "         -0.00071418, -0.00097364,  0.19556542,  0.0457493 , -0.11583306,\n",
              "         -0.04204868,  0.04599042, -0.06314116, -0.10379948,  0.0802775 ,\n",
              "         -0.00482234, -0.02366483,  0.01422982, -0.1438511 ,  0.00452225,\n",
              "          0.00247407,  0.02109903,  0.05930688,  0.08789889, -0.09902695,\n",
              "          0.14681002, -0.09353893,  0.10695235,  0.00454453, -0.03457799,\n",
              "          0.05088221, -0.01846903, -0.10245369,  0.14374888, -0.10189284,\n",
              "          0.15101384, -0.00152795,  0.09687524,  0.06957269,  0.06327613,\n",
              "          0.03616752, -0.0828315 , -0.09079228, -0.01129711,  0.1017618 ,\n",
              "          0.0306166 , -0.01996683, -0.03714248, -0.00299824,  0.01350647,\n",
              "          0.00259201, -0.03050259,  0.01793307,  0.01617317,  0.07043888,\n",
              "          0.03073978, -0.02132967, -0.02019092,  0.02026299,  0.01462819,\n",
              "          0.01552583,  0.01370566, -0.11604404,  0.07736313,  0.00742614,\n",
              "         -0.19387761, -0.06397852, -0.00174941, -0.00899995, -0.00136152,\n",
              "         -0.12590362, -0.15958397,  0.07374638, -0.03661045,  0.18323964,\n",
              "          0.16642828, -0.01950032, -0.00106799, -0.20730937, -0.06857546,\n",
              "          0.11397488,  0.06187399,  0.22359262, -0.05364588, -0.14494267,\n",
              "         -0.03669563, -0.06828848, -0.12812346, -0.06980857,  0.24724436,\n",
              "         -0.07156715, -0.08053041, -0.06844967, -0.12146654,  0.1684804 ,\n",
              "          0.00748511, -0.18130283,  0.03431722, -0.14918843, -0.03616229,\n",
              "         -0.05937109, -0.00597944, -0.03736918]], dtype=float32)>}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ETeG5NvTZBJI"
      },
      "source": [
        "## Pre-process Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KTTh1xSIZIzR",
        "outputId": "1f74eea8-a0d0-4aa1-af53-6ea24f0bf3ec",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# Stop words and special characters \n",
        "import nltk \n",
        "nltk.download('stopwords') \n",
        "\n",
        "STOPLIST = set(stopwords.words('english') + list(ENGLISH_STOP_WORDS)) \n",
        "SYMBOLS = \" \".join(string.punctuation).split(\" \") + [\"-\",\"...\",\"”\",\"''\"]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cwN6tjzCa7Hl"
      },
      "source": [
        "#Data Cleaner and tokenizer \n",
        "def tokenizeText(text): \n",
        "  text = text.strip().replace(\"\\n\", \" \").replace(\"\\r\", \" \") \n",
        "  text = text.lower() \n",
        "  \n",
        "  tokens = parser(text) \n",
        "  #lematization\n",
        "  lemmas = [] \n",
        "  for tok in tokens:\n",
        "     lemmas.append(tok.lemma_.lower().strip() if tok.lemma_ != \"-PRON-\" else tok.lower_)\n",
        "  tokens = lemmas \n",
        "  #reomve stop words and special charaters \n",
        "  tokens = [tok for tok in tokens if tok.lower() not in STOPLIST] \n",
        "  tokens = [tok for tok in tokens if tok not in SYMBOLS] \n",
        "  \n",
        "  tokens = [tok for tok in tokens if len(tok) >= 3]\n",
        "  \n",
        "  #remove remaining tokens that are not alphabetic \n",
        "  tokens = [tok for tok in tokens if tok.isalpha()] \n",
        "  \n",
        "  tokens = list(set(tokens)) \n",
        "  \n",
        "  return ' '.join(tokens[:]) "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KtKqA7R2b3ih"
      },
      "source": [
        "def encode(le_enc, labels): \n",
        "  enc = le_enc.transform(labels) \n",
        "  return keras.utils.to_categorical(enc) \n",
        "\n",
        "def decode(le_enc, one_hot):\n",
        "   dec = np.argmax(one_hot, axis =1) \n",
        "   return le_enc.inverse_transform(dec) "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J9pw-LmCSOgc"
      },
      "source": [
        "# load the dataset \n",
        "import pandas as pd\n",
        "trainDF_Sheet_1 = pd.read_csv(\"/content/drive/My Drive/data/data_train.csv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DzdZNMeSSQHP",
        "outputId": "5b08376d-b6d9-457e-820a-98dbaaa902f8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        }
      },
      "source": [
        "trainDF_Sheet_1.head(2) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>review</th>\n",
              "      <th>is_bad_review</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>I am so angry that i made this post available...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>No Negative No real complaints the hotel was g...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                              review  is_bad_review\n",
              "0   I am so angry that i made this post available...              1\n",
              "1  No Negative No real complaints the hotel was g...              0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u8adxxLUST8_",
        "outputId": "9ffcaa74-85d4-42f4-a9be-68f336f3c6cf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "trainDF_Sheet_1.shape "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(515738, 2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3X1XPGCYSUSs",
        "outputId": "65f23d59-a3bc-4022-b7eb-c8447c250758",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "trainDF_Sheet_1['review'].unique() "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([' I am so angry that i made this post available via all possible sites i use when planing my trips so no one will make the mistake of booking this place I made my booking via booking com We stayed for 6 nights in this hotel from 11 to 17 July Upon arrival we were placed in a small room on the 2nd floor of the hotel It turned out that this was not the room we booked I had specially reserved the 2 level duplex room so that we would have a big windows and high ceilings The room itself was ok if you don t mind the broken window that can not be closed hello rain and a mini fridge that contained some sort of a bio weapon at least i guessed so by the smell of it I intimately asked to change the room and after explaining 2 times that i booked a duplex btw it costs the same as a simple double but got way more volume due to the high ceiling was offered a room but only the next day SO i had to check out the next day before 11 o clock in order to get the room i waned to Not the best way to begin your holiday So we had to wait till 13 00 in order to check in my new room what a wonderful waist of my time The room 023 i got was just as i wanted to peaceful internal garden view big window We were tired from waiting the room so we placed our belongings and rushed to the city In the evening it turned out that there was a constant noise in the room i guess it was made by vibrating vent tubes or something it was constant and annoying as hell AND it did not stop even at 2 am making it hard to fall asleep for me and my wife I have an audio recording that i can not attach here but if you want i can send it via e mail The next day the technician came but was not able to determine the cause of the disturbing sound so i was offered to change the room once again the hotel was fully booked and they had only 1 room left the one that was smaller but seems newer  Only the park outside of the hotel was beautiful ',\n",
              "       'No Negative No real complaints the hotel was great great location surroundings rooms amenities and service Two recommendations however firstly the staff upon check in are very confusing regarding deposit payments and the staff offer you upon checkout to refund your original payment and you can make a new one Bit confusing Secondly the on site restaurant is a bit lacking very well thought out and excellent quality food for anyone of a vegetarian or vegan background but even a wrap or toasted sandwich option would be great Aside from those minor minor things fantastic spot and will be back when i return to Amsterdam ',\n",
              "       ' Rooms are nice but for elderly a bit difficult as most rooms are two story with narrow steps So ask for single level Inside the rooms are very very basic just tea coffee and boiler and no bar empty fridge  Location was good and staff were ok It is cute hotel the breakfast range is nice Will go back ',\n",
              "       ...,\n",
              "       ' The ac was useless It was a hot week in vienna and it only gave more hot airNo Positive',\n",
              "       'No Negative The rooms are enormous and really comfortable I believe that a family of 5 members could be more than comfy in these spaces ',\n",
              "       ' I was in 3rd floor It didn t work Free Wife  staff was very kind '],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TWwr5eOxSUpV",
        "outputId": "85e36f5e-69f4-4516-b288-cfa80f0d7c8a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "trainDF_Sheet_1['review'].value_counts() "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "No Negative Location                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   1009\n",
              " Nothing Everything                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     940\n",
              "No Negative Everything                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  593\n",
              "No Negative Great location                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              256\n",
              "No Negative Everything                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  204\n",
              "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       ... \n",
              " Two gripes no tea bags in room not everyone is a nesspresso lover The Hungarian Waitress in the Radio Bar poured our bottle of Lauren Perrier Champagne to empty in half an hour Way too fast and unclassy obviously this was to make us buy another bottle which we did but thereafter we took charge of our own refilling Waitress apart Radio Bar is fantastic  Hotel Staff fabulous from meet and greet to first floor front desk Internal architecture is just awesome wow                                                                                                                                                                                                                                                                                                                                          1\n",
              " 1 I booked junior suite with a kitchen as found in the Declaration of booking com when I arrived and I entered the the suite There was no kitchen and Microwave just water heater which has an exciting story kettle 2 when I started to use the kettle after a minute all suite shutdown no electricity when i asked reception help they sent guy he said it s your fault you put a lot of water in kettle i was surprised hahahaha any way he fixed the problem 3 the worst wifi as usual for good wifi 8 hour 4 the bathroom frozen you can not take a shower hahahaha under zero degree sure 5 Parking don t think about it if you have SUV 6 refrigerator it has fungus inside lol i paid more than 250 night Worst hotel inhabited since my visit to Europe and France in particular during the year nothing       1\n",
              " No gym Staff                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             1\n",
              " The coffee machine was too complicated and I didn t like the tea bags either The bed was really comfortable                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              1\n",
              " Coffee during the breakfast was quite bad Size of the room though typical for central London leaves some space to dream of more space  The location is really great The hotel has the needed facilities including 24 hour fitness room Great bar with the right coffee to serve Friendly staff willing to help                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           1\n",
              "Name: review, Length: 499288, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pbzQwIIKTE4I"
      },
      "source": [
        "# model parameters\n",
        "\n",
        "vocab_size = 1000\n",
        "embedding_dim = 256\n",
        "max_length = 512\n",
        "trunc_type='post'\n",
        "padding_type='post'\n",
        "oov_tok = \"<00V>\"\n",
        "training_portion = .7"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ThxjnKWgTFT3"
      },
      "source": [
        "# train test split\n",
        "# ---------- \n",
        "              \n",
        "# proportion of training dataset\n",
        "train_size = int(trainDF_Sheet_1.shape[0] * training_portion)\n",
        "\n",
        "# training dataset\n",
        "train_sentences = trainDF_Sheet_1['review'][:train_size]\n",
        "train_labels = trainDF_Sheet_1['is_bad_review'][:train_size]\n",
        "\n",
        "# validation dataset\n",
        "validation_sentences = trainDF_Sheet_1['review'][train_size: ]\n",
        "validation_labels = trainDF_Sheet_1['is_bad_review'][train_size: ]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bS_C32UnXnQF",
        "outputId": "2e364d0b-8cdd-48cb-dc77-bac135e0f4c0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from nltk.tokenize import word_tokenize\n",
        "nltk.download('punkt')\n",
        "def padd(sentence):\n",
        "    arr=word_tokenize(sentence)\n",
        "    for i in range(1000-len(arr)):\n",
        "        arr.append('null')\n",
        "    return arr[:1000]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w7MYGF5rbPeA"
      },
      "source": [
        "y_test = trainDF_Sheet_1[\"is_bad_review\"]\n",
        "train_sentences = trainDF_Sheet_1[\"review\"]\n",
        "train_data=[padd(each_sentence) for each_sentence in train_sentences[:1000]]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9eGcYHAFcTdn"
      },
      "source": [
        "# for i in range(0,len(train_sentences),10000):\n",
        "training_embed=([embed.signatures['default'](tf.convert_to_tensor(each_sentence))['default'] for each_sentence in train_data])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fMpB3CJpNGoe",
        "outputId": "92be836a-8fde-4f27-9d9d-b87dae0c09ff",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "len(training_embed[0][0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "128"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UBr6i0LAaLtS"
      },
      "source": [
        "## With RMDL"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nfQXTtHmaPF1",
        "outputId": "eb5b4e79-3d0e-45d5-c83c-a3a5540eef97",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# model initialization\n",
        "\n",
        "model = tf.keras.Sequential([\n",
        "  tf.keras.layers.Conv1D(128, 5, activation='relu'),\n",
        "  tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(32)),\n",
        "  tf.keras.layers.Dense(24, activation='relu'),\n",
        "  tf.keras.layers.Dense(1, activation='sigmoid' )\n",
        "])\n",
        "# compile model\n",
        "model.compile(loss='binary_crossentropy',\n",
        "    optimizer='adam',\n",
        "    metrics=['accuracy'])\n",
        "\n",
        "model.build(input_shape=[len(training_embed),1000,128])\n",
        "\n",
        "# model summary\n",
        "model. summary ()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv1d (Conv1D)              (1000, 996, 128)          82048     \n",
            "_________________________________________________________________\n",
            "bidirectional (Bidirectional (1000, 64)                41216     \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (1000, 24)                1560      \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (1000, 1)                 25        \n",
            "=================================================================\n",
            "Total params: 124,849\n",
            "Trainable params: 124,849\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rQcR6g6umpeG",
        "outputId": "1caabbc2-2654-46ad-d351-ccbd976ae5f8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "num_epochs = 8\n",
        "\n",
        "history = model.fit(np.array(training_embed), np.array(y_test[:1000]),\n",
        "   epochs=num_epochs,verbose=1)\n",
        "\n",
        "model.save_weights('/content/drive/My Drive/response-rmdl-elmo-model.h5')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/8\n",
            "32/32 [==============================] - 2s 67ms/step - loss: 0.3084 - accuracy: 0.9260\n",
            "Epoch 2/8\n",
            "32/32 [==============================] - 2s 65ms/step - loss: 0.1939 - accuracy: 0.9520\n",
            "Epoch 3/8\n",
            "32/32 [==============================] - 2s 65ms/step - loss: 0.1877 - accuracy: 0.9520\n",
            "Epoch 4/8\n",
            "32/32 [==============================] - 2s 66ms/step - loss: 0.1812 - accuracy: 0.9520\n",
            "Epoch 5/8\n",
            "32/32 [==============================] - 2s 65ms/step - loss: 0.1687 - accuracy: 0.9520\n",
            "Epoch 6/8\n",
            "32/32 [==============================] - 2s 66ms/step - loss: 0.1466 - accuracy: 0.9520\n",
            "Epoch 7/8\n",
            "32/32 [==============================] - 2s 65ms/step - loss: 0.1142 - accuracy: 0.9520\n",
            "Epoch 8/8\n",
            "32/32 [==============================] - 2s 65ms/step - loss: 0.0843 - accuracy: 0.9560\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lZWcogO4LaQe"
      },
      "source": [
        "model.load_weights('/content/drive/My Drive/response-rmdl-elmo-model.h5')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qtl5wPAjLeQD"
      },
      "source": [
        "y_test = (y_test[:1000])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7BwNKtEQLo4P"
      },
      "source": [
        "prediction = model.predict(np.array(training_embed))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9gMb_hQzLsZu"
      },
      "source": [
        "y_pred = (prediction > 0.5)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AqMxXaOZmtH1",
        "outputId": "182c52c4-54cb-4b47-eec4-ade59146f775",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from sklearn.metrics import f1_score, precision_score, recall_score, confusion_matrix,accuracy_score\n",
        "print(\"F1-score: {0}\".format(f1_score(y_pred, y_test)))\n",
        "print(\"Precision: \",precision_score(y_pred, y_test))\n",
        "print(\"Recall: \",recall_score(y_pred, y_test))\n",
        "print(\"Accuracy: \",accuracy_score(y_pred,y_test))\n",
        "print(\"Confusion matrix: \")\n",
        "confusion_matrix(y_pred, y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "F1-score: 0.7435897435897435\n",
            "Precision:  0.6041666666666666\n",
            "Recall:  0.9666666666666667\n",
            "Accuracy:  0.98\n",
            "Confusion matrix: \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[951,  19],\n",
              "       [  1,  29]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fs_bqt8InQPZ",
        "outputId": "be74adda-ead6-4454-db48-9712777746b5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        }
      },
      "source": [
        "from matplotlib.pyplot import *\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# accuracy and loss\n",
        "plt.figure(figsize=(10, 5))\n",
        "\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.plot(history.history['accuracy'], label='Training Accuracy')\n",
        "#plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.plot(history.history['loss'], label='Training Loss')\n",
        "#plt.plot(history.history['val_loss'], label='Validation Loss')\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}