is_dnn/labworks/LW4/LR4.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OYAvCUKEaAFU",
        "outputId": "73187a69-9816-406f-e2f2-0485e4781d62"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found GPU at: /device:GPU:0\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "device_name = tf.test.gpu_device_name()\n",
        "if device_name != '/device:GPU:0':\n",
        " raise SystemError('GPU device not found')\n",
        "print('Found GPU at: {}'.format(device_name))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "import os\n",
        "os.chdir('/content/drive/MyDrive/Colab Notebooks/is_lab4')\n",
        "\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "from tensorflow.keras.models import Sequential\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "from sklearn.metrics import ConfusionMatrixDisplay"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uPkd-u_ha6eY",
        "outputId": "2361583e-948b-4c38-e980-8583c72a1c09"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# загрузка датасета\n",
        "from keras.datasets import imdb\n",
        "vocabulary_size = 5000\n",
        "index_from = 3\n",
        "(X_train, y_train), (X_test, y_test) = imdb.load_data(path=\"imdb.npz\",\n",
        "                                                      num_words=vocabulary_size,\n",
        "                                                      skip_top=0,\n",
        "                                                      maxlen=None,\n",
        "                                                      seed=7,\n",
        "                                                      start_char=1,\n",
        "                                                      oov_char=2,\n",
        "                                                      index_from=index_from\n",
        "                                                      )\n",
        "print('Shape of X train:', X_train.shape)\n",
        "print('Shape of y train:', y_train.shape)\n",
        "print('Shape of X test:', X_test.shape)\n",
        "print('Shape of y test:', y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6WUs3rs0bz2o",
        "outputId": "47357f47-f051-4f2e-af60-70bcd2ae19a4"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/imdb.npz\n",
            "\u001b[1m17464789/17464789\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 0us/step\n",
            "Shape of X train: (25000,)\n",
            "Shape of y train: (25000,)\n",
            "Shape of X test: (25000,)\n",
            "Shape of y test: (25000,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# создание словаря для перевода индексов в слова\n",
        "# заргузка словаря \"слово:индекс\"\n",
        "word_to_id = imdb.get_word_index()\n",
        "# уточнение словаря\n",
        "word_to_id = {key:(value + index_from) for key,value in word_to_id.items()}\n",
        "word_to_id[\"<PAD>\"] = 0\n",
        "word_to_id[\"<START>\"] = 1\n",
        "word_to_id[\"<UNK>\"] = 2\n",
        "word_to_id[\"<UNUSED>\"] = 3\n",
        "# создание обратного словаря \"индекс:слово\"\n",
        "id_to_word = {value:key for key,value in word_to_id.items()}"
      ],
      "metadata": {
        "id": "MXQiPqDBcXR-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d808649b-852d-48ff-d736-13cfa8f2b660"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/imdb_word_index.json\n",
            "\u001b[1m1641221/1641221\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 1us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Вывод отзыва из обучающего множества в виде списка индексов слов\n",
        "some_number = 192\n",
        "review_indices = X_train[some_number]\n",
        "print(\"Список индексов слов:\")\n",
        "print(review_indices)\n",
        "\n",
        "#Преобразование списка индексов в текст\n",
        "review_as_text = ' '.join(id_to_word[id] for id in X_train[some_number])\n",
        "print(\"\\nОтзыв в виде текста:\")\n",
        "print(review_as_text)\n",
        "\n"
      ],
      "metadata": {
        "id": "UsL3hvkkcZvZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3d158ce8-44e7-461c-a70a-ff78ccd9fc15"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Список индексов слов:\n",
            "[1, 225, 164, 433, 74, 2753, 35, 2188, 20, 5, 397, 35, 298, 20, 585, 305, 10, 10, 45, 64, 61, 652, 21, 6, 52, 708, 9, 2, 725, 4, 2, 7, 1451, 1089, 105, 17, 230, 17, 2, 9, 1947, 4, 3238, 11, 135, 422, 26, 6, 2, 1021, 378, 1780, 224, 472, 36, 26, 379, 724, 2607, 387, 178, 1582, 4, 771, 36, 2, 112, 2, 34, 6, 2, 10, 10, 300, 103, 6, 2, 2, 8, 516, 25, 30, 252, 8, 376, 90, 51, 1455, 335, 4010, 33, 54, 25, 2440, 90, 125, 10, 10, 241, 1559, 4, 609, 46, 7, 4, 2, 11, 3826, 2, 5, 11, 1011, 7, 4193, 7, 4684, 2, 3498, 90, 8, 3534, 2, 7, 4779, 10, 10, 342, 92, 1414, 979, 4, 568, 44, 4, 2, 5, 331, 2237, 18, 57, 684, 52, 282, 15, 4, 1788, 71, 2, 34, 90, 10, 10, 470, 137, 269, 8, 1090, 387, 129, 761, 46, 7, 129, 1682, 17, 76, 17, 614, 8, 2, 15, 4, 2, 2, 41, 10, 10, 457, 103, 397, 339, 39, 294, 8, 169, 4, 2, 103, 2, 129, 322, 30, 252, 8, 2222, 98, 245, 17, 515, 17, 614, 38, 25, 70, 393, 90, 31, 23, 31, 57, 213, 11, 112, 2, 208, 10, 10, 150, 474, 115, 535, 15, 101, 415, 62, 30, 2, 8, 231, 6, 171, 2497, 467, 134, 2, 2, 21, 4, 105, 11, 135, 422, 26, 38, 2, 5, 97, 38, 111, 1297, 2497, 15, 45, 2620, 1167, 18, 4, 529, 8, 459, 44, 68, 4369, 237, 36, 26, 1484, 7, 68, 205, 399, 14, 1098, 4, 2, 7, 4, 436, 22, 10, 10, 11, 420, 25, 71, 1535, 4, 2, 161, 570, 19, 2, 2, 105, 237, 36, 533, 26, 1348, 2, 2, 18, 487, 14, 2, 36, 872, 8, 97, 1186, 38, 2, 2270, 15, 32, 281, 7, 635, 271, 46, 4, 2054, 10, 10, 300, 4, 2, 1098, 6, 1002, 1004, 6, 568, 1709, 475, 137, 4, 2311, 9, 2359, 57, 53, 74, 747, 2194, 245, 10, 10, 241, 4, 2, 2, 11, 32, 2580, 7, 2, 4983, 11, 3826, 2, 5, 187, 3400, 7, 84, 246, 57, 31, 85, 74, 4, 1021, 378, 186, 8, 1495, 27, 1032, 2003, 10, 10, 342, 4, 2, 2, 35, 1755, 1166, 7, 567, 15, 62, 28, 556, 101, 406, 112, 10, 10, 470, 4, 836, 139, 69, 57, 1546, 1689, 11, 192, 49, 139, 71, 1504, 1677, 2, 39, 298, 102, 10, 10, 4, 64, 1123, 9, 4, 2, 751, 4, 130, 63, 16, 6, 184, 1770, 136, 237, 12, 16, 2, 725, 4, 322, 45, 99, 78, 4, 1057, 1477, 12, 56, 19, 35, 2, 379, 277, 15, 266, 46, 7, 317, 1845, 10, 10, 371, 4, 2, 496, 4, 231, 7, 135, 422, 144, 30, 3013, 7, 533, 128, 246, 36, 144, 43, 847, 8, 2642, 5, 193, 2, 19, 84, 37, 97, 102, 19, 6, 729, 2, 18, 489, 5, 1663]\n",
            "\n",
            "Отзыв в виде текста:\n",
            "<START> there's nothing worse than renting an asian movie and getting an american movie experience instead br br it's only my opinion but a good thriller is <UNK> upon the <UNK> of likable intelligent characters as far as <UNK> is concerned the protagonists in say yes are a <UNK> married couple nicely done unfortunately they are stupid beyond belief let us count the ways they <UNK> being <UNK> by a <UNK> br br 1 after a <UNK> <UNK> to kill you be sure to tell him what hotel you're staying at when you drop him off br br 2 beat the hell out of the <UNK> in broad <UNK> and in front of dozens of witnesses <UNK> allowing him to press <UNK> of assault br br 3 don't bother telling the police about the <UNK> and simply assume for no apparently good reason that the cops were <UNK> by him br br 4 while trying to escape let your lady out of your sight as much as possible to <UNK> that the <UNK> <UNK> her br br 5 after getting help from someone to find the <UNK> after <UNK> your wife be sure to send them away as soon as possible so you can face him one on one no point in being <UNK> right br br now i'd never expect that any person would be <UNK> to making a few mistakes under these <UNK> <UNK> but the characters in say yes are so <UNK> and make so many unbelievable mistakes that it's effectively impossible for the viewer to care about their safety since they are victims of their own doing this kills the <UNK> of the entire film br br in case you were wondering the <UNK> didn't stop with <UNK> <UNK> characters since they themselves are surely <UNK> <UNK> for writing this <UNK> they decided to make situations so <UNK> unrealistic that all sense of reality goes out the window br br 1 the <UNK> kills a cop inside a police station – while the protagonist is asleep no more than ten feet away br br 2 the <UNK> <UNK> in all sorts of <UNK> activities in broad <UNK> and around tons of people yet no one other than the married couple seems to notice his odd behavior br br 3 the <UNK> <UNK> an absurd amount of violence that would have killed any human being br br 4 the suspense scenes had no imagination whatsoever in fact some scenes were direct rip <UNK> from american movies br br the only positive is the <UNK> near the end which was a pretty brutal scene since it was <UNK> upon the wife it's too bad the filmmakers followed it up with an <UNK> stupid ending that comes out of left field br br truly the <UNK> behind the making of say yes should be ashamed of themselves better yet they should just move to california and take <UNK> with people who make movies with a similar <UNK> for quality and intelligence\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Вывод длины отзыва\n",
        "max_review_length = len(max(X_train, key=len))\n",
        "print(f\"\\nМаксимальная длина отзыва: {max_review_length}\")\n",
        "min_review_length = len(min(X_train, key=len))\n",
        "print(f\"\\nМинимальная длина отзыва: {min_review_length}\")\n",
        "review_length = len(review_indices)\n",
        "print(f\"\\nДлина отзыва: {review_length}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j_BZb_zZitog",
        "outputId": "b768c6c1-58e0-49d4-92c4-f24ac8dd90e5"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Максимальная длина отзыва: 2494\n",
            "\n",
            "Минимальная длина отзыва: 11\n",
            "\n",
            "Длина отзыва: 502\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Вывод метки и названия класса\n",
        "class_label = y_train[some_number]\n",
        "class_name = \"Positive\" if class_label == 1 else \"Negative\"\n",
        "print(f\"\\nМетка класса: {class_label} - {class_name}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qoKxmrsjiu4r",
        "outputId": "14bb4c59-f44b-4956-af1c-c1ed863a811a"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Метка класса: 0 - Negative\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# предобработка данных\n",
        "from tensorflow.keras.utils import pad_sequences\n",
        "max_words = 500\n",
        "X_train = pad_sequences(X_train, maxlen=max_words, value=0, padding='pre', truncating='post')\n",
        "X_test = pad_sequences(X_test, maxlen=max_words, value=0, padding='pre', truncating='post')"
      ],
      "metadata": {
        "id": "_hg4IJrifshd"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Вывод отзыва из обучающего множества в виде списка индексов слов\n",
        "some_number = 192\n",
        "review_indices = X_train[some_number]\n",
        "print(\"Список индексов слов:\")\n",
        "print(review_indices)\n",
        "\n",
        "#Преобразование списка индексов в текст\n",
        "review_as_text = ' '.join(id_to_word[id] for id in X_train[some_number])\n",
        "print(\"\\nОтзыв в виде текста:\")\n",
        "print(review_as_text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uKQxrcKFgOHM",
        "outputId": "bbabf061-13cc-4a10-a523-1fc595defd64"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Список индексов слов:\n",
            "[   1  225  164  433   74 2753   35 2188   20    5  397   35  298   20\n",
            "  585  305   10   10   45   64   61  652   21    6   52  708    9    2\n",
            "  725    4    2    7 1451 1089  105   17  230   17    2    9 1947    4\n",
            " 3238   11  135  422   26    6    2 1021  378 1780  224  472   36   26\n",
            "  379  724 2607  387  178 1582    4  771   36    2  112    2   34    6\n",
            "    2   10   10  300  103    6    2    2    8  516   25   30  252    8\n",
            "  376   90   51 1455  335 4010   33   54   25 2440   90  125   10   10\n",
            "  241 1559    4  609   46    7    4    2   11 3826    2    5   11 1011\n",
            "    7 4193    7 4684    2 3498   90    8 3534    2    7 4779   10   10\n",
            "  342   92 1414  979    4  568   44    4    2    5  331 2237   18   57\n",
            "  684   52  282   15    4 1788   71    2   34   90   10   10  470  137\n",
            "  269    8 1090  387  129  761   46    7  129 1682   17   76   17  614\n",
            "    8    2   15    4    2    2   41   10   10  457  103  397  339   39\n",
            "  294    8  169    4    2  103    2  129  322   30  252    8 2222   98\n",
            "  245   17  515   17  614   38   25   70  393   90   31   23   31   57\n",
            "  213   11  112    2  208   10   10  150  474  115  535   15  101  415\n",
            "   62   30    2    8  231    6  171 2497  467  134    2    2   21    4\n",
            "  105   11  135  422   26   38    2    5   97   38  111 1297 2497   15\n",
            "   45 2620 1167   18    4  529    8  459   44   68 4369  237   36   26\n",
            " 1484    7   68  205  399   14 1098    4    2    7    4  436   22   10\n",
            "   10   11  420   25   71 1535    4    2  161  570   19    2    2  105\n",
            "  237   36  533   26 1348    2    2   18  487   14    2   36  872    8\n",
            "   97 1186   38    2 2270   15   32  281    7  635  271   46    4 2054\n",
            "   10   10  300    4    2 1098    6 1002 1004    6  568 1709  475  137\n",
            "    4 2311    9 2359   57   53   74  747 2194  245   10   10  241    4\n",
            "    2    2   11   32 2580    7    2 4983   11 3826    2    5  187 3400\n",
            "    7   84  246   57   31   85   74    4 1021  378  186    8 1495   27\n",
            " 1032 2003   10   10  342    4    2    2   35 1755 1166    7  567   15\n",
            "   62   28  556  101  406  112   10   10  470    4  836  139   69   57\n",
            " 1546 1689   11  192   49  139   71 1504 1677    2   39  298  102   10\n",
            "   10    4   64 1123    9    4    2  751    4  130   63   16    6  184\n",
            " 1770  136  237   12   16    2  725    4  322   45   99   78    4 1057\n",
            " 1477   12   56   19   35    2  379  277   15  266   46    7  317 1845\n",
            "   10   10  371    4    2  496    4  231    7  135  422  144   30 3013\n",
            "    7  533  128  246   36  144   43  847    8 2642    5  193    2   19\n",
            "   84   37   97  102   19    6  729    2   18  489]\n",
            "\n",
            "Отзыв в виде текста:\n",
            "<START> there's nothing worse than renting an asian movie and getting an american movie experience instead br br it's only my opinion but a good thriller is <UNK> upon the <UNK> of likable intelligent characters as far as <UNK> is concerned the protagonists in say yes are a <UNK> married couple nicely done unfortunately they are stupid beyond belief let us count the ways they <UNK> being <UNK> by a <UNK> br br 1 after a <UNK> <UNK> to kill you be sure to tell him what hotel you're staying at when you drop him off br br 2 beat the hell out of the <UNK> in broad <UNK> and in front of dozens of witnesses <UNK> allowing him to press <UNK> of assault br br 3 don't bother telling the police about the <UNK> and simply assume for no apparently good reason that the cops were <UNK> by him br br 4 while trying to escape let your lady out of your sight as much as possible to <UNK> that the <UNK> <UNK> her br br 5 after getting help from someone to find the <UNK> after <UNK> your wife be sure to send them away as soon as possible so you can face him one on one no point in being <UNK> right br br now i'd never expect that any person would be <UNK> to making a few mistakes under these <UNK> <UNK> but the characters in say yes are so <UNK> and make so many unbelievable mistakes that it's effectively impossible for the viewer to care about their safety since they are victims of their own doing this kills the <UNK> of the entire film br br in case you were wondering the <UNK> didn't stop with <UNK> <UNK> characters since they themselves are surely <UNK> <UNK> for writing this <UNK> they decided to make situations so <UNK> unrealistic that all sense of reality goes out the window br br 1 the <UNK> kills a cop inside a police station – while the protagonist is asleep no more than ten feet away br br 2 the <UNK> <UNK> in all sorts of <UNK> activities in broad <UNK> and around tons of people yet no one other than the married couple seems to notice his odd behavior br br 3 the <UNK> <UNK> an absurd amount of violence that would have killed any human being br br 4 the suspense scenes had no imagination whatsoever in fact some scenes were direct rip <UNK> from american movies br br the only positive is the <UNK> near the end which was a pretty brutal scene since it was <UNK> upon the wife it's too bad the filmmakers followed it up with an <UNK> stupid ending that comes out of left field br br truly the <UNK> behind the making of say yes should be ashamed of themselves better yet they should just move to california and take <UNK> with people who make movies with a similar <UNK> for quality\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Вывод длины отзыва\n",
        "max_review_length = len(max(X_train, key=len))\n",
        "print(f\"\\nМаксимальная длина отзыва: {max_review_length}\")\n",
        "min_review_length = len(min(X_train, key=len))\n",
        "print(f\"\\nМинимальная длина отзыва: {min_review_length}\")\n",
        "review_length = len(review_indices)\n",
        "print(f\"\\nДлина отзыва: {review_length}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mWnmL-NmqeBS",
        "outputId": "6de9583a-734e-4739-8c95-6c9b7b14429e"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Максимальная длина отзыва: 500\n",
            "\n",
            "Минимальная длина отзыва: 500\n",
            "\n",
            "Длина отзыва: 500\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# вывод данных\n",
        "print('X train: \\n',X_train)\n",
        "print('X train: \\n',X_test)\n",
        "\n",
        "# вывод размерностей\n",
        "print('Shape of X train:', X_train.shape)\n",
        "print('Shape of X test:', X_test.shape)"
      ],
      "metadata": {
        "id": "S6Oz1bkPqrIc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9584fd3c-41eb-4b45-9c21-7eb55551c8cc"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X train: \n",
            " [[   0    0    0 ...  104  545    7]\n",
            " [   0    0    0 ...    2  262  372]\n",
            " [   0    0    0 ...  758   10   10]\n",
            " ...\n",
            " [   0    0    0 ...    2   27  375]\n",
            " [   0    0    0 ...   11  111  531]\n",
            " [   0    0    0 ...  152 1833   12]]\n",
            "X train: \n",
            " [[   0    0    0 ...    2  126 3849]\n",
            " [   0    0    0 ...   25 1833   12]\n",
            " [   0    0    0 ...  129  249 4262]\n",
            " ...\n",
            " [   0    0    0 ...    2   24 1178]\n",
            " [   0    0    0 ...   61  278  145]\n",
            " [   0    0    0 ...   12    5  358]]\n",
            "Shape of X train: (25000, 500)\n",
            "Shape of X test: (25000, 500)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "model.add(layers.Embedding(input_dim=vocabulary_size, output_dim=32, input_length=max_words, input_shape=(max_words,)))\n",
        "model.add(layers.LSTM(76))\n",
        "model.add(layers.Dropout(0.3))\n",
        "model.add(layers.Dense(1, activation='sigmoid'))\n",
        "\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "id": "5mni99gaIjVu",
        "outputId": "b98022c4-9974-4901-9212-63b7a13771cb"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/embedding.py:97: UserWarning: Argument `input_length` is deprecated. Just remove it.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.12/dist-packages/keras/src/layers/core/embedding.py:100: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(**kwargs)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential_1\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_1\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
              "│ embedding_1 (\u001b[38;5;33mEmbedding\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m500\u001b[0m, \u001b[38;5;34m32\u001b[0m)        │       \u001b[38;5;34m160,000\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ lstm_1 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m76\u001b[0m)             │        \u001b[38;5;34m33,136\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m76\u001b[0m)             │             \u001b[38;5;34m0\u001b[0m │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │            \u001b[38;5;34m77\u001b[0m │\n",
              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
              "│ embedding_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Embedding</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">500</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)        │       <span style=\"color: #00af00; text-decoration-color: #00af00\">160,000</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ lstm_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">76</span>)             │        <span style=\"color: #00af00; text-decoration-color: #00af00\">33,136</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dropout_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">76</span>)             │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
              "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">77</span> │\n",
              "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m193,213\u001b[0m (754.74 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">193,213</span> (754.74 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m193,213\u001b[0m (754.74 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">193,213</span> (754.74 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size = 64\n",
        "epochs = 5\n",
        "model.compile(loss=\"binary_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])\n",
        "model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OW7miFG1IReh",
        "outputId": "668ba65f-33c7-4580-e52e-e41683d4bab9"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 23ms/step - accuracy: 0.6543 - loss: 0.5985 - val_accuracy: 0.8430 - val_loss: 0.3728\n",
            "Epoch 2/5\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 23ms/step - accuracy: 0.8699 - loss: 0.3238 - val_accuracy: 0.8742 - val_loss: 0.3080\n",
            "Epoch 3/5\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 21ms/step - accuracy: 0.8501 - loss: 0.3290 - val_accuracy: 0.8256 - val_loss: 0.3999\n",
            "Epoch 4/5\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 23ms/step - accuracy: 0.8745 - loss: 0.3161 - val_accuracy: 0.8616 - val_loss: 0.3363\n",
            "Epoch 5/5\n",
            "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 21ms/step - accuracy: 0.9138 - loss: 0.2272 - val_accuracy: 0.8718 - val_loss: 0.3244\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.history.History at 0x7ac149df4800>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_loss, test_acc = model.evaluate(X_test, y_test)\n",
        "print(f\"\\nTest accuracy: {test_acc}\")\n",
        "print(f\"\\nTest loss: {test_loss}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ObHtCELvLShK",
        "outputId": "4c992534-4284-4e22-a346-f705577e3752"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 11ms/step - accuracy: 0.8669 - loss: 0.3332\n",
            "\n",
            "Test accuracy: 0.8670799732208252\n",
            "\n",
            "Test loss: 0.3307616412639618\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_score = model.predict(X_test)\n",
        "y_pred = [1 if y_score[i,0]>=0.5 else 0 for i in range(len(y_score))]\n",
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(y_test, y_pred, labels = [0, 1], target_names=['Negative', 'Positive']))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j113-7jxLlvN",
        "outputId": "0bee8b19-a223-4035-b6d5-40a3dcc0a01e"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 7ms/step\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    Negative       0.86      0.88      0.87     12500\n",
            "    Positive       0.88      0.86      0.87     12500\n",
            "\n",
            "    accuracy                           0.87     25000\n",
            "   macro avg       0.87      0.87      0.87     25000\n",
            "weighted avg       0.87      0.87      0.87     25000\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import roc_curve, auc\n",
        "import matplotlib.pyplot as plt\n",
        "fpr, tpr, thresholds = roc_curve(y_test, y_score)\n",
        "plt.plot(fpr, tpr)\n",
        "plt.grid()\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('ROC')\n",
        "plt.show()\n",
        "print('Area under ROC is', auc(fpr, tpr))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "collapsed": true,
        "id": "VNEb-prSLr0s",
        "outputId": "b51fc65a-5e17-4d56-e1b2-d7e8c7435f59"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Area under ROC is 0.936850272\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import roc_auc_score\n",
        "print('AUC ROC:', roc_auc_score(y_test, y_score))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AdLEblyULyUH",
        "outputId": "7357c246-4db4-495c-a9b8-60917b37e1e2"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "AUC ROC: 0.936850272\n"
          ]
        }
      ]
    }
  ]
}