FastText + BiLSTM.ipynb

{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"id":"-FkJTs6kNQdI","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1663215477874,"user_tz":-420,"elapsed":2277,"user":{"displayName":"Hyperlogy Cloud","userId":"15340550436473201616"}},"outputId":"1be7ce01-cacb-4817-de11-8ccb06364253"},"outputs":[{"output_type":"stream","name":"stdout","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"]}],"source":["from google.colab import drive\n","drive.mount('/content/drive')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"gC_FjnOTcXHM","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1663215483991,"user_tz":-420,"elapsed":6121,"user":{"displayName":"Hyperlogy Cloud","userId":"15340550436473201616"}},"outputId":"bf843961-5149-4ce8-fd30-757e7d2124e4"},"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Requirement already satisfied: gensim==4.1.2 in /usr/local/lib/python3.7/dist-packages (4.1.2)\n","Requirement already satisfied: numpy>=1.17.0 in /usr/local/lib/python3.7/dist-packages (from gensim==4.1.2) (1.21.6)\n","Requirement already satisfied: scipy>=0.18.1 in /usr/local/lib/python3.7/dist-packages (from gensim==4.1.2) (1.7.3)\n","Requirement already satisfied: smart-open>=1.8.1 in /usr/local/lib/python3.7/dist-packages (from gensim==4.1.2) (5.2.1)\n","Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Requirement already satisfied: underthesea in /root/.local/lib/python3.7/site-packages (1.3.4)\n","Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from underthesea) (1.1.0)\n","Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from underthesea) (2.23.0)\n","Requirement already satisfied: unidecode in /root/.local/lib/python3.7/site-packages (from underthesea) (1.3.4)\n","Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from underthesea) (1.0.2)\n","Requirement already satisfied: underthesea-core==0.0.4_alpha.10 in /root/.local/lib/python3.7/site-packages (from underthesea) (0.0.4a10)\n","Requirement already satisfied: PyYAML in /usr/local/lib/python3.7/dist-packages (from underthesea) (6.0)\n","Requirement already satisfied: python-crfsuite>=0.9.6 in /root/.local/lib/python3.7/site-packages (from underthesea) (0.9.8)\n","Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from underthesea) (4.64.1)\n","Requirement already satisfied: nltk in /usr/local/lib/python3.7/dist-packages (from underthesea) (3.7)\n","Requirement already satisfied: Click>=6.0 in /usr/local/lib/python3.7/dist-packages (from underthesea) (7.1.2)\n","Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.7/dist-packages (from nltk->underthesea) (2022.6.2)\n","Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->underthesea) (2.10)\n","Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->underthesea) (3.0.4)\n","Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->underthesea) (1.24.3)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->underthesea) (2022.6.15)\n","Requirement already satisfied: scipy>=1.1.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->underthesea) (1.7.3)\n","Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->underthesea) (3.1.0)\n","Requirement already satisfied: numpy>=1.14.6 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->underthesea) (1.21.6)\n"]}],"source":["# !pip install transformers==2.1.0\n","!pip install gensim==4.1.2\n","!pip3 install --user underthesea\n","\n","# !pip install pyvi"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"f82YXfOvXH1i"},"outputs":[],"source":["#from pyvi import ViTokenizer, ViPosTagger\n","import regex as re\n","from underthesea import word_tokenize\n","import numpy as np\n","import pandas as pd\n","import gensim\n","import sklearn\n","import tensorflow as tf\n","from sklearn import svm\n","from sklearn.model_selection import train_test_split\n","#from sklearn.decomposition import TruncatedSVD\n","from sklearn.metrics import classification_report\n","from sklearn.feature_extraction.text import TfidfVectorizer\n","from tensorflow.keras.layers import *\n","from keras.layers import *\n","from keras.preprocessing.text import Tokenizer\n","from keras.models import Model\n","from keras.metrics import sparse_categorical_accuracy\n","from tensorflow.keras.optimizers import Adam\n","# Chia tập train/test\n","from sklearn.model_selection import train_test_split\n","from sklearn.preprocessing import LabelEncoder\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"jpZ60BlfMwyF","scrolled":true,"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1663215492503,"user_tz":-420,"elapsed":4534,"user":{"displayName":"Hyperlogy Cloud","userId":"15340550436473201616"}},"outputId":"0fc343c4-53c5-46cc-bdf9-5a8e0b32317b"},"outputs":[{"output_type":"stream","name":"stdout","text":["__label__thể_thao 2611\n","__label__âm_nhạc 2595\n","__label__nhịp_sống 2613\n","__label__công_nghệ 2595\n","__label__thời_sự 2607\n","__label__thế_giới 2602\n","__label__thời_trang 2596\n","__label__du_lịch 2593\n","__label__sống_trẻ 2602\n","__label__giáo_dục 2603\n","__label__kinh_doanh 2597\n","__label__pháp_luật 2592\n","__label__giải_trí 2604\n","__label__phim_ảnh 2596\n","__label__xe_360 2602\n","__label__ẩm_thực 2482\n","__label__xuất_bản 2599\n","__label__sức_khỏe 2589\n"]}],"source":["# Thống kê số lượng data theo nhãn\n","count = {}\n","for line in open('/content/drive/MyDrive/KhanhMD/Data/news_categories.csv'):\n","    key = line.split()[0]\n","    count[key] = count.get(key, 0) + 1\n","\n","for key in count:\n","    print(key, count[key])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ClZ0AZOoMwyL"},"outputs":[],"source":["# Thống kê các word xuất hiện ở tất cả các nhãn\n","total_label = 18\n","vocab = {}\n","label_vocab = {}\n","for line in open('/content/drive/MyDrive/KhanhMD/Data/news_categories.csv'):\n","    words = line.split()\n","    # lưu ý từ đầu tiên là nhãn\n","    label = words[0]\n","    if label not in label_vocab:\n","        label_vocab[label] = {}\n","    for word in words[1:]:\n","        label_vocab[label][word] = label_vocab[label].get(word, 0) + 1\n","        if word not in vocab:\n","            vocab[word] = set()\n","        vocab[word].add(label)\n","\n","# count = {}\n","# for word in vocab:\n","#     if len(vocab[word]) == total_label:\n","#         count[word] = min([label_vocab[x][word] for x in label_vocab])\n","        \n","# sorted_count = sorted(count, key=count.get, reverse=True)\n","# for word in sorted_count[:100]:\n","#     print(word, count[word])\n","text = []\n","label = []\n","\n","for line in open('/content/drive/MyDrive/KhanhMD/Data/news_categories.csv'):\n","    words = line.strip().split()\n","    label.append(words[0])\n","    text.append(' '.join(words[1:]))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"HL8l1uWQlFfb"},"outputs":[],"source":["label_encoder = LabelEncoder()\n","label_encoder.fit(label)\n","encode_label = label_encoder.transform(label)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"OC9HK9aLxhH_"},"outputs":[],"source":["import pandas as pd\n","df = pd.DataFrame(text, columns=[\"text\"])\n","df.to_csv('text.csv', index=False)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"_iRnrv9myHhV","colab":{"base_uri":"https://localhost:8080/","height":363},"executionInfo":{"status":"ok","timestamp":1663215530572,"user_tz":-420,"elapsed":3807,"user":{"displayName":"Hyperlogy Cloud","userId":"15340550436473201616"}},"outputId":"a99a2a04-26f8-43d9-d0c2-6a4a9f0ce836"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["                                                text\n","0  sanchez không dự trận khai màn nh anh cùng ars...\n","1  những sao việt hóa_thân thành phó nháy trong m...\n","2  vợ phải quỳ gối mới được nói_chuyện cười zing ...\n","3  lumia 520 sẽ có giá 3 9 triệu đồng tại việt_na...\n","4  hà_nội t t chốt danh_sách dự afc cup thể_thao ...\n","5  vụ xe điên gây tai_nạn ở sài_gòn tài_xế bị tâm...\n","6  lính nga nhảy_dù xuống tảng băng trôi trên bắc...\n","7  ngắm vĩnh_thụy đầy chất chơi thời_trang zing v...\n","8  bố_mẹ dừng tàu lượn siêu_tốc vì lo cho con_gái...\n","9  cụ bà 84 tuổi đặc_trị bệnh chó dại thời_sự zin..."],"text/html":["\n","  <div id=\"df-761bf888-531d-4e81-86bf-18a07f1ed602\">\n","    <div class=\"colab-df-container\">\n","      <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>text</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>sanchez không dự trận khai màn nh anh cùng ars...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>những sao việt hóa_thân thành phó nháy trong m...</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>vợ phải quỳ gối mới được nói_chuyện cười zing ...</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>lumia 520 sẽ có giá 3 9 triệu đồng tại việt_na...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>hà_nội t t chốt danh_sách dự afc cup thể_thao ...</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>vụ xe điên gây tai_nạn ở sài_gòn tài_xế bị tâm...</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>lính nga nhảy_dù xuống tảng băng trôi trên bắc...</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>ngắm vĩnh_thụy đầy chất chơi thời_trang zing v...</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>bố_mẹ dừng tàu lượn siêu_tốc vì lo cho con_gái...</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>cụ bà 84 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index=False)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"SBRXbZMO1cLh","colab":{"base_uri":"https://localhost:8080/","height":363},"executionInfo":{"status":"ok","timestamp":1663215541974,"user_tz":-420,"elapsed":3603,"user":{"displayName":"Hyperlogy Cloud","userId":"15340550436473201616"}},"outputId":"4ece774a-2909-410b-ef9c-fb8ec16d55a5"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["   label                                               text\n","0     11  sanchez không dự trận khai màn nh anh cùng ars...\n","1     16  những sao việt hóa_thân thành phó nháy trong m...\n","2      5  vợ phải quỳ gối mới được nói_chuyện cười zing ...\n","3      0  lumia 520 sẽ có giá 3 9 triệu đồng tại việt_na...\n","4     11  hà_nội t t chốt danh_sách dự afc cup thể_thao ...\n","5     12  vụ xe điên gây tai_nạn ở sài_gòn tài_xế bị tâm...\n","6     10  lính nga nhảy_dù xuống tảng băng trôi trên bắc...\n","7     13  ngắm vĩnh_thụy đầy chất chơi thời_trang zing v...\n","8      1  bố_mẹ dừng tàu lượn siêu_tốc vì lo cho con_gái...\n","9     12  cụ bà 84 tuổi đặc_trị bệnh chó dại thời_sự zin..."],"text/html":["\n","  <div id=\"df-85666105-b2a2-46f5-8677-a5cc2d87f568\">\n","    <div class=\"colab-df-container\">\n","      <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>label</th>\n","      <th>text</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>11</td>\n","      <td>sanchez không dự trận khai màn nh anh cùng ars...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>16</td>\n","      <td>những sao việt hóa_thân thành phó nháy trong m...</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>5</td>\n","      <td>vợ phải quỳ gối mới được nói_chuyện cười zing ...</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0</td>\n","      <td>lumia 520 sẽ có giá 3 9 triệu đồng tại việt_na...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>11</td>\n","      <td>hà_nội t t chốt danh_sách dự afc cup thể_thao ...</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>12</td>\n","      <td>vụ xe điên gây tai_nạn ở sài_gòn tài_xế bị tâm...</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>10</td>\n","      <td>lính nga nhảy_dù xuống tảng băng trôi trên bắc...</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>13</td>\n","      <td>ngắm vĩnh_thụy đầy chất chơi thời_trang zing v...</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>1</td>\n","      <td>bố_mẹ dừng tàu lượn siêu_tốc vì lo cho con_gái...</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>12</td>\n","      <td>cụ bà 84 tuổi đặc_trị bệnh chó dại thời_sự zin...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-85666105-b2a2-46f5-8677-a5cc2d87f568')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":12}],"source":["c = pd.read_csv(\"data.csv\")\n","c[:10]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"MtpQEZvT6LHm","colab":{"base_uri":"https://localhost:8080/","height":363},"executionInfo":{"status":"ok","timestamp":1663215541975,"user_tz":-420,"elapsed":13,"user":{"displayName":"Hyperlogy Cloud","userId":"15340550436473201616"}},"outputId":"ad532521-b11d-48bb-dc2c-9f1fed8d40a1"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["   label                                               text\n","0     11  sanchez không dự trận khai màn nh anh cùng ars...\n","1     16  những sao việt hóa_thân thành phó nháy trong m...\n","2      5  vợ phải quỳ gối mới được nói_chuyện cười zing ...\n","3      0  lumia 520 sẽ có giá 3 9 triệu đồng tại việt_na...\n","4     11  hà_nội t t chốt danh_sách dự afc cup thể_thao ...\n","5     12  vụ xe điên gây tai_nạn ở sài_gòn tài_xế bị tâm...\n","6     10  lính nga nhảy_dù xuống tảng băng trôi trên bắc...\n","7     13  ngắm vĩnh_thụy đầy chất chơi thời_trang zing v...\n","8      1  bố_mẹ dừng tàu lượn siêu_tốc vì lo cho con_gái...\n","9     12  cụ bà 84 tuổi đặc_trị bệnh chó dại thời_sự zin..."],"text/html":["\n","  <div id=\"df-7c41b5db-c17b-4080-9ad8-6e3115dec6ad\">\n","    <div class=\"colab-df-container\">\n","      <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>label</th>\n","      <th>text</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>11</td>\n","      <td>sanchez không dự trận khai màn nh anh cùng ars...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>16</td>\n","      <td>những sao việt hóa_thân thành phó nháy trong m...</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>5</td>\n","      <td>vợ phải quỳ gối mới được nói_chuyện cười zing ...</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0</td>\n","      <td>lumia 520 sẽ có giá 3 9 triệu đồng tại việt_na...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>11</td>\n","      <td>hà_nội t t chốt danh_sách dự afc cup thể_thao ...</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>12</td>\n","      <td>vụ xe điên gây tai_nạn ở sài_gòn tài_xế bị tâm...</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>10</td>\n","      <td>lính nga nhảy_dù xuống tảng băng trôi trên bắc...</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>13</td>\n","      <td>ngắm vĩnh_thụy đầy chất chơi thời_trang zing v...</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>1</td>\n","      <td>bố_mẹ dừng tàu lượn siêu_tốc vì lo cho con_gái...</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>12</td>\n","      <td>cụ bà 84 tuổi đặc_trị bệnh chó dại thời_sự zin...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" 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document.querySelector('#df-7c41b5db-c17b-4080-9ad8-6e3115dec6ad button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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Cloud","userId":"15340550436473201616"}},"outputId":"c7b3ae1a-6c4e-4e3e-b86b-1772effa94f4"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["(46678, 2)"]},"metadata":{},"execution_count":14}],"source":["df.shape"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"31QMWZxATuL2"},"outputs":[],"source":["#tok = Tokenizer()\n","# lst_corpus = []\n","# for string in df['text']:\n","#    lst_words = string.split()\n","#    lst_grams = [\" \".join(lst_words[i:i+1]) for i in range(0, len(lst_words), 1)]\n","#    lst_corpus.append(lst_grams)\n","\n","# print(len(lst_corpus))\n"]},{"cell_type":"code","source":["lst_corpus = [[word for word in document.split()] for document in text]"],"metadata":{"id":"ZC-Dg7o46Z-h"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["len(lst_corpus)"],"metadata":{"id":"bY5EMHPQ6gji","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1663215546308,"user_tz":-420,"elapsed":15,"user":{"displayName":"Hyperlogy Cloud","userId":"15340550436473201616"}},"outputId":"d29b2d5f-0ef3-4172-f968-87a1644e7eab"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["46678"]},"metadata":{},"execution_count":17}]},{"cell_type":"code","execution_count":null,"metadata":{"id":"1vD8KDIpftsA"},"outputs":[],"source":["w2v = gensim.models.fasttext.FastText(lst_corpus,\n","                                   min_n=2,\n","                                   bucket= 500000,\n","                                   vector_size=100,\n","                                   window=5,\n","                                   min_count=1)\n","words = set(w2v.wv.index_to_key)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"DeqGmN9iEKnQ","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1663217876446,"user_tz":-420,"elapsed":1006375,"user":{"displayName":"Hyperlogy Cloud","userId":"15340550436473201616"}},"outputId":"3302119b-3aba-44aa-e5b3-84443369cd00"},"outputs":[{"output_type":"stream","name":"stderr","text":["WARNING:gensim.models.word2vec:Effective 'alpha' higher than previous training cycles\n"]},{"output_type":"execute_result","data":{"text/plain":["(227649413, 256614670)"]},"metadata":{},"execution_count":20}],"source":["w2v.train(lst_corpus,epochs=10,total_examples=len(lst_corpus))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"IIwClV-vFR6J","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1663217876447,"user_tz":-420,"elapsed":8,"user":{"displayName":"Hyperlogy Cloud","userId":"15340550436473201616"}},"outputId":"9b281fab-0164-4c74-9f92-6e2405c3e97e"},"outputs":[{"output_type":"stream","name":"stdout","text":["The total number of words are :  233702\n"]}],"source":["vocab=w2v.wv.key_to_index\n","print(\"The total number of words are : \",len(vocab))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"mE8KGmHuFws2"},"outputs":[],"source":["vocab=list(vocab.keys())"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"kz-ZFxs5F30S","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1663217876969,"user_tz":-420,"elapsed":528,"user":{"displayName":"Hyperlogy Cloud","userId":"15340550436473201616"}},"outputId":"b5c09dda-3283-4f24-b398-02cce6fbc2d5"},"outputs":[{"output_type":"stream","name":"stdout","text":["233702\n"]}],"source":["word_vec_dict={}\n","for word in vocab:\n","  word_vec_dict[word]=w2v.wv.get_vector(word)\n","print(len(word_vec_dict))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"cCWOvanPC1ay"},"outputs":[],"source":["# maxi=-1\n","# for i,rev in enumerate(df['text']):\n","#     tokens=rev.split()\n","#     if(len(tokens)>maxi):\n","#         maxi=len(tokens)\n","# print(maxi)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"anYVoP3fC-qR"},"outputs":[],"source":["tok = Tokenizer()\n","tok.fit_on_texts(df['text'])\n","vocab_size = len(tok.word_index) + 1\n","encd_rev = tok.texts_to_sequences(df['text'])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"R9xiq0k9DNTZ"},"outputs":[],"source":["max_rev_len=700  # max lenght of a review\n","vocab_size = len(tok.word_index) + 1  # total no of words\n","embed_dim=100"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"2F2D86cHDSEJ","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1663217932159,"user_tz":-420,"elapsed":1900,"user":{"displayName":"Hyperlogy Cloud","userId":"15340550436473201616"}},"outputId":"effbb65f-806c-42c5-9e33-55ab748b30ea"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["(46678, 700)"]},"metadata":{},"execution_count":27}],"source":["from keras.preprocessing.sequence import pad_sequences\n","pad_rev= pad_sequences(encd_rev, maxlen=max_rev_len, padding='post')\n","pad_rev.shape "]},{"cell_type":"code","execution_count":null,"metadata":{"id":"H-DKX6nvk8Es","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1663217932160,"user_tz":-420,"elapsed":14,"user":{"displayName":"Hyperlogy Cloud","userId":"15340550436473201616"}},"outputId":"beaa1080-16bb-42cb-cc46-ebcd1ea08849"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[4729,   10,  226, ...,    0,    0,    0],\n","       [   1, 3335,   95, ...,   53,  439,   58],\n","       [ 490,   54, 2801, ...,    0,    0,    0],\n","       ...,\n","       [ 354,  328,  376, ...,  120,  260,   55],\n","       [2610,  298,  692, ...,    0,    0,    0],\n","       [  39, 1143, 7507, ...,    0,    0,    0]], dtype=int32)"]},"metadata":{},"execution_count":28}],"source":["pad_rev"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Qj9VGNpuDZgj"},"outputs":[],"source":["embed_matrix=np.zeros(shape=(vocab_size,embed_dim))\n","for word,i in tok.word_index.items():\n","  embed_vector=word_vec_dict.get(word)\n","  if embed_vector is not None:  # word is in the vocabulary learned by the w2v model\n","    embed_matrix[i]=embed_vector"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"uHP5NYggdU8k","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1663217932162,"user_tz":-420,"elapsed":14,"user":{"displayName":"Hyperlogy Cloud","userId":"15340550436473201616"}},"outputId":"04e1a06f-0d47-4bee-bc60-a460b29fb8cd"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["(93363, 100)"]},"metadata":{},"execution_count":30}],"source":["embed_matrix.shape"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"_Gi8_Y-JrGkC","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1663217932162,"user_tz":-420,"elapsed":11,"user":{"displayName":"Hyperlogy Cloud","userId":"15340550436473201616"}},"outputId":"4714c1a1-3e72-49ce-cbc7-488e906a7030"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["<function sklearn.model_selection._split.train_test_split(*arrays, test_size=None, train_size=None, random_state=None, shuffle=True, stratify=None)>"]},"metadata":{},"execution_count":31}],"source":["import keras\n","import keras.utils\n","from keras import utils as np_utils\n","import tensorflow as tf\n","from tensorflow.keras.layers import Dense, LSTM\n","from tensorflow.keras.models import Model\n","from tensorflow.keras.models import Sequential\n","from keras.utils import np_utils\n","sklearn.model_selection.train_test_split"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"sjJiKsExyCjX"},"outputs":[],"source":["# !pip install np_utils\n","from keras.utils.np_utils import to_categorical"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"M_X1MI6Kx02g"},"outputs":[],"source":["# Y=keras.utils.to_categorical(df['label'])\n","# Y.shape\n","from keras import utils as np_utils\n","Y=keras.utils.np_utils.to_categorical(df['label'])  # one hot target as required by NN."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"sgZDxOc4G20t"},"outputs":[],"source":["X_train, X_test, y_train, y_test = train_test_split(pad_rev, Y, test_size=0.1, random_state=42)\n","\n","X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2, random_state=42) # 0.25 x 0.8 = 0.2\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"3h7JpbWYHVmj"},"outputs":[],"source":["from keras.initializers import Constant\n","from keras.layers import ReLU\n","from keras.layers import Dropout\n","model=Sequential()\n","model.add(Embedding(input_dim=vocab_size,output_dim=embed_dim,input_length=max_rev_len,embeddings_initializer=Constant(embed_matrix)))\n","model.add(Bidirectional(LSTM(128)))\n","model.add(Flatten())\n","model.add(Dense(64,activation='relu',kernel_regularizer='l2'))\n","model.add(Dropout(0.2))\n","\n","#model.add(Dense(1024,activation='relu'))\n","# model.add(Dense(64,activation='relu',kernel_regularizer='l2'))\n","# model.add(Dropout(0.20))\n","# model.add(Dense(32,activation='relu',kernel_regularizer='l2'))\n","#model.add(Dense(32,activation='relu',kernel_regularizer='l2'))\n","# model.add(Dense(1,activation='relu'))\n","# model.add(Dense(16,activation='relu'))\n","# model.add(Dropout(0.20))\n","model.add(Dense(18,activation='softmax'))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"rpKcH9CQHawH","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1663217936752,"user_tz":-420,"elapsed":23,"user":{"displayName":"Hyperlogy Cloud","userId":"15340550436473201616"}},"outputId":"8c0b2eed-64d2-4a66-97ee-3f241bf9c4ee"},"outputs":[{"output_type":"stream","name":"stdout","text":["Model: \"sequential\"\n","_________________________________________________________________\n"," Layer (type)                Output Shape              Param #   \n","=================================================================\n"," embedding (Embedding)       (None, 700, 100)          9336300   \n","                                                                 \n"," bidirectional (Bidirectiona  (None, 256)              234496    \n"," l)                                                              \n","                                                                 \n"," flatten (Flatten)           (None, 256)               0         \n","                                                                 \n"," dense (Dense)               (None, 64)                16448     \n","                                                                 \n"," dropout (Dropout)           (None, 64)                0         \n","                                                                 \n"," dense_1 (Dense)             (None, 18)                1170      \n","                                                                 \n","=================================================================\n","Total params: 9,588,414\n","Trainable params: 9,588,414\n","Non-trainable params: 0\n","_________________________________________________________________\n"]}],"source":["model.summary()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"jFxSFaqDHo6c"},"outputs":[],"source":["from tensorflow import keras\n","from keras import optimizers"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"3LNCiHkgHd4U","executionInfo":{"status":"ok","timestamp":1663217936753,"user_tz":-420,"elapsed":13,"user":{"displayName":"Hyperlogy Cloud","userId":"15340550436473201616"}},"colab":{"base_uri":"https://localhost:8080/"},"outputId":"544f816e-66e6-4b15-d112-3d1c20ad52d6"},"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/rmsprop.py:130: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n","  super(RMSprop, self).__init__(name, **kwargs)\n"]}],"source":["model.compile(optimizer=keras.optimizers.RMSprop(lr=0.0001),loss='categorical_crossentropy',metrics=['accuracy'])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"rAj6DJQ0HrGc"},"outputs":[],"source":["epochs=30\n","batch_size=64"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"GOvYg3Pi0CtY"},"outputs":[],"source":["from keras.callbacks import EarlyStopping\n","from keras.callbacks import ModelCheckpoint\n","es = EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=5)\n","mc = ModelCheckpoint('best_model.h5', monitor='val_loss', mode='min', verbose=1, save_best_only=True)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"1LIq8H34qX60","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1663219582913,"user_tz":-420,"elapsed":619985,"user":{"displayName":"Hyperlogy Cloud","userId":"15340550436473201616"}},"outputId":"effa1853-590d-4ba0-919a-caf9b5d41c63"},"outputs":[{"output_type":"stream","name":"stdout","text":["Epoch 1/30\n","526/526 [==============================] - ETA: 0s - loss: 3.4330 - accuracy: 0.2194\n","Epoch 1: val_loss improved from inf to 2.82831, saving model to best_model.h5\n","526/526 [==============================] - 63s 105ms/step - loss: 3.4330 - accuracy: 0.2194 - val_loss: 2.8283 - val_accuracy: 0.4010\n","Epoch 2/30\n","526/526 [==============================] - ETA: 0s - loss: 2.2995 - accuracy: 0.4979\n","Epoch 2: val_loss improved from 2.82831 to 1.73811, saving model to best_model.h5\n","526/526 [==============================] - 54s 103ms/step - loss: 2.2995 - accuracy: 0.4979 - val_loss: 1.7381 - val_accuracy: 0.6638\n","Epoch 3/30\n","526/526 [==============================] - ETA: 0s - loss: 1.6082 - accuracy: 0.6709\n","Epoch 3: val_loss improved from 1.73811 to 1.33249, saving model to best_model.h5\n","526/526 [==============================] - 56s 106ms/step - loss: 1.6082 - accuracy: 0.6709 - val_loss: 1.3325 - val_accuracy: 0.7489\n","Epoch 4/30\n","526/526 [==============================] - ETA: 0s - loss: 1.3031 - accuracy: 0.7385\n","Epoch 4: val_loss improved from 1.33249 to 1.15143, saving model to best_model.h5\n","526/526 [==============================] - 54s 104ms/step - loss: 1.3031 - accuracy: 0.7385 - val_loss: 1.1514 - val_accuracy: 0.7785\n","Epoch 5/30\n","526/526 [==============================] - ETA: 0s - loss: 1.1105 - accuracy: 0.7772\n","Epoch 5: val_loss improved from 1.15143 to 0.98585, saving model to best_model.h5\n","526/526 [==============================] - 54s 103ms/step - loss: 1.1105 - accuracy: 0.7772 - val_loss: 0.9858 - val_accuracy: 0.8105\n","Epoch 6/30\n","526/526 [==============================] - ETA: 0s - loss: 0.9707 - accuracy: 0.8074\n","Epoch 6: val_loss improved from 0.98585 to 0.90726, saving model to best_model.h5\n","526/526 [==============================] - 54s 103ms/step - loss: 0.9707 - accuracy: 0.8074 - val_loss: 0.9073 - val_accuracy: 0.8208\n","Epoch 7/30\n","526/526 [==============================] - ETA: 0s - loss: 0.8724 - accuracy: 0.8262\n","Epoch 7: val_loss improved from 0.90726 to 0.81104, saving model to best_model.h5\n","526/526 [==============================] - 54s 102ms/step - loss: 0.8724 - accuracy: 0.8262 - val_loss: 0.8110 - val_accuracy: 0.8384\n","Epoch 8/30\n","526/526 [==============================] - ETA: 0s - loss: 0.7943 - accuracy: 0.8408\n","Epoch 8: val_loss improved from 0.81104 to 0.76592, saving model to best_model.h5\n","526/526 [==============================] - 56s 106ms/step - loss: 0.7943 - accuracy: 0.8408 - val_loss: 0.7659 - val_accuracy: 0.8366\n","Epoch 9/30\n","526/526 [==============================] - ETA: 0s - loss: 0.7228 - accuracy: 0.8526\n","Epoch 9: val_loss improved from 0.76592 to 0.72487, saving model to best_model.h5\n","526/526 [==============================] - 55s 104ms/step - loss: 0.7228 - accuracy: 0.8526 - val_loss: 0.7249 - val_accuracy: 0.8461\n","Epoch 10/30\n","526/526 [==============================] - ETA: 0s - loss: 0.6686 - accuracy: 0.8627\n","Epoch 10: val_loss improved from 0.72487 to 0.66814, saving model to best_model.h5\n","526/526 [==============================] - 56s 107ms/step - loss: 0.6686 - accuracy: 0.8627 - val_loss: 0.6681 - val_accuracy: 0.8543\n","Epoch 11/30\n","526/526 [==============================] - ETA: 0s - loss: 0.6162 - accuracy: 0.8727\n","Epoch 11: val_loss improved from 0.66814 to 0.63808, saving model to best_model.h5\n","526/526 [==============================] - 56s 107ms/step - loss: 0.6162 - accuracy: 0.8727 - val_loss: 0.6381 - val_accuracy: 0.8590\n","Epoch 12/30\n","526/526 [==============================] - ETA: 0s - loss: 0.5906 - accuracy: 0.8756\n","Epoch 12: val_loss improved from 0.63808 to 0.60898, saving model to best_model.h5\n","526/526 [==============================] - 55s 104ms/step - loss: 0.5906 - accuracy: 0.8756 - val_loss: 0.6090 - val_accuracy: 0.8641\n","Epoch 13/30\n","526/526 [==============================] - ETA: 0s - loss: 0.5531 - accuracy: 0.8825\n","Epoch 13: val_loss improved from 0.60898 to 0.57569, saving model to best_model.h5\n","526/526 [==============================] - 56s 106ms/step - loss: 0.5531 - accuracy: 0.8825 - val_loss: 0.5757 - val_accuracy: 0.8713\n","Epoch 14/30\n","526/526 [==============================] - ETA: 0s - loss: 0.5265 - accuracy: 0.8868\n","Epoch 14: val_loss improved from 0.57569 to 0.55708, saving model to best_model.h5\n","526/526 [==============================] - 55s 104ms/step - loss: 0.5265 - accuracy: 0.8868 - val_loss: 0.5571 - val_accuracy: 0.8705\n","Epoch 15/30\n","526/526 [==============================] - ETA: 0s - loss: 0.5004 - accuracy: 0.8905\n","Epoch 15: val_loss improved from 0.55708 to 0.54161, saving model to best_model.h5\n","526/526 [==============================] - 56s 106ms/step - loss: 0.5004 - accuracy: 0.8905 - val_loss: 0.5416 - val_accuracy: 0.8760\n","Epoch 16/30\n","526/526 [==============================] - ETA: 0s - loss: 0.4772 - accuracy: 0.8974\n","Epoch 16: val_loss improved from 0.54161 to 0.52540, saving model to best_model.h5\n","526/526 [==============================] - 54s 103ms/step - loss: 0.4772 - accuracy: 0.8974 - val_loss: 0.5254 - val_accuracy: 0.8786\n","Epoch 17/30\n","526/526 [==============================] - ETA: 0s - loss: 0.4559 - accuracy: 0.9008\n","Epoch 17: val_loss improved from 0.52540 to 0.51734, saving model to best_model.h5\n","526/526 [==============================] - 55s 105ms/step - loss: 0.4559 - accuracy: 0.9008 - val_loss: 0.5173 - val_accuracy: 0.8794\n","Epoch 18/30\n","526/526 [==============================] - ETA: 0s - loss: 0.4327 - accuracy: 0.9063\n","Epoch 18: val_loss improved from 0.51734 to 0.49268, saving model to best_model.h5\n","526/526 [==============================] - 56s 106ms/step - loss: 0.4327 - accuracy: 0.9063 - val_loss: 0.4927 - val_accuracy: 0.8862\n","Epoch 19/30\n","526/526 [==============================] - ETA: 0s - loss: 0.4149 - accuracy: 0.9081\n","Epoch 19: val_loss improved from 0.49268 to 0.47909, saving model to best_model.h5\n","526/526 [==============================] - 54s 104ms/step - loss: 0.4149 - accuracy: 0.9081 - val_loss: 0.4791 - val_accuracy: 0.8851\n","Epoch 20/30\n","526/526 [==============================] - ETA: 0s - loss: 0.3979 - accuracy: 0.9131\n","Epoch 20: val_loss improved from 0.47909 to 0.47421, saving model to best_model.h5\n","526/526 [==============================] - 55s 105ms/step - loss: 0.3979 - accuracy: 0.9131 - val_loss: 0.4742 - val_accuracy: 0.8897\n","Epoch 21/30\n","526/526 [==============================] - ETA: 0s - loss: 0.3822 - accuracy: 0.9150\n","Epoch 21: val_loss improved from 0.47421 to 0.47232, saving model to best_model.h5\n","526/526 [==============================] - 54s 103ms/step - loss: 0.3822 - accuracy: 0.9150 - val_loss: 0.4723 - val_accuracy: 0.8884\n","Epoch 22/30\n","526/526 [==============================] - ETA: 0s - loss: 0.3695 - accuracy: 0.9191\n","Epoch 22: val_loss did not improve from 0.47232\n","526/526 [==============================] - 47s 89ms/step - loss: 0.3695 - accuracy: 0.9191 - val_loss: 0.4761 - val_accuracy: 0.8868\n","Epoch 23/30\n","526/526 [==============================] - ETA: 0s - loss: 0.3523 - accuracy: 0.9224\n","Epoch 23: val_loss improved from 0.47232 to 0.45034, saving model to best_model.h5\n","526/526 [==============================] - 55s 105ms/step - loss: 0.3523 - accuracy: 0.9224 - val_loss: 0.4503 - val_accuracy: 0.8938\n","Epoch 24/30\n","526/526 [==============================] - ETA: 0s - loss: 0.3366 - accuracy: 0.9267\n","Epoch 24: val_loss did not improve from 0.45034\n","526/526 [==============================] - 47s 89ms/step - loss: 0.3366 - accuracy: 0.9267 - val_loss: 0.4512 - val_accuracy: 0.8917\n","Epoch 25/30\n","526/526 [==============================] - ETA: 0s - loss: 0.3283 - accuracy: 0.9273\n","Epoch 25: val_loss improved from 0.45034 to 0.43704, saving model to best_model.h5\n","526/526 [==============================] - 54s 103ms/step - loss: 0.3283 - accuracy: 0.9273 - val_loss: 0.4370 - val_accuracy: 0.8966\n","Epoch 26/30\n","526/526 [==============================] - ETA: 0s - loss: 0.3165 - accuracy: 0.9305\n","Epoch 26: val_loss improved from 0.43704 to 0.43389, saving model to best_model.h5\n","526/526 [==============================] - 55s 105ms/step - loss: 0.3165 - accuracy: 0.9305 - val_loss: 0.4339 - val_accuracy: 0.8955\n","Epoch 27/30\n","526/526 [==============================] - ETA: 0s - loss: 0.3023 - accuracy: 0.9336\n","Epoch 27: val_loss did not improve from 0.43389\n","526/526 [==============================] - 47s 89ms/step - loss: 0.3023 - accuracy: 0.9336 - val_loss: 0.4373 - val_accuracy: 0.8931\n","Epoch 28/30\n","526/526 [==============================] - ETA: 0s - loss: 0.2921 - accuracy: 0.9368\n","Epoch 28: val_loss improved from 0.43389 to 0.42760, saving model to best_model.h5\n","526/526 [==============================] - 55s 105ms/step - loss: 0.2921 - accuracy: 0.9368 - val_loss: 0.4276 - val_accuracy: 0.8979\n","Epoch 29/30\n","526/526 [==============================] - ETA: 0s - loss: 0.2792 - accuracy: 0.9403\n","Epoch 29: val_loss improved from 0.42760 to 0.42544, saving model to best_model.h5\n","526/526 [==============================] - 54s 103ms/step - loss: 0.2792 - accuracy: 0.9403 - val_loss: 0.4254 - val_accuracy: 0.8981\n","Epoch 30/30\n","526/526 [==============================] - ETA: 0s - loss: 0.2680 - accuracy: 0.9441\n","Epoch 30: val_loss improved from 0.42544 to 0.42477, saving model to best_model.h5\n","526/526 [==============================] - 55s 105ms/step - loss: 0.2680 - accuracy: 0.9441 - val_loss: 0.4248 - val_accuracy: 0.8960\n"]},{"output_type":"execute_result","data":{"text/plain":["<keras.callbacks.History at 0x7fab936a7e10>"]},"metadata":{},"execution_count":41}],"source":["model.fit(X_train,y_train,epochs=epochs,batch_size=batch_size,validation_data=(X_val,y_val),callbacks=[es, mc])"]},{"cell_type":"code","source":["modelx = keras.models.load_model('best_model.h5')"],"metadata":{"id":"tN9c8rnbSo3-"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["y_pred_one_hot_encoded = (modelx.predict(X_test)> 0.5).astype(\"float\")\n","#y_pred_test = np.array(tf.argmax(y_pred_one_hot_encoded, axis=1))\n"],"metadata":{"id":"g6XLvBJVxKYK"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["print(classification_report(y_test, y_pred_one_hot_encoded))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"POoNxzfx02F7","executionInfo":{"status":"ok","timestamp":1663219591527,"user_tz":-420,"elapsed":3,"user":{"displayName":"Hyperlogy Cloud","userId":"15340550436473201616"}},"outputId":"454f4461-fb21-498f-9c97-e412e2392064"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["              precision    recall  f1-score   support\n","\n","           0       0.98      0.91      0.94       268\n","           1       0.94      0.93      0.94       267\n","           2       0.92      0.80      0.85       270\n","           3       0.85      0.79      0.82       228\n","           4       0.91      0.81      0.86       270\n","           5       0.90      0.85      0.87       252\n","           6       0.88      0.91      0.89       250\n","           7       0.95      0.95      0.95       270\n","           8       0.79      0.71      0.75       249\n","           9       0.91      0.91      0.91       252\n","          10       0.94      0.90      0.92       258\n","          11       0.98      0.94      0.96       264\n","          12       0.92      0.84      0.88       241\n","          13       0.96      0.92      0.94       277\n","          14       0.98      0.97      0.97       262\n","          15       0.93      0.94      0.94       247\n","          16       0.95      0.87      0.91       290\n","          17       0.95      0.96      0.95       253\n","\n","   micro avg       0.93      0.88      0.90      4668\n","   macro avg       0.92      0.88      0.90      4668\n","weighted avg       0.93      0.88      0.90      4668\n"," samples avg       0.88      0.88      0.88      4668\n","\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in samples with no predicted labels. Use `zero_division` parameter to control this behavior.\n","  _warn_prf(average, modifier, msg_start, len(result))\n"]}]},{"cell_type":"code","execution_count":null,"metadata":{"id":"EAgjG6hD_QSg","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1663219594486,"user_tz":-420,"elapsed":2960,"user":{"displayName":"Hyperlogy Cloud","userId":"15340550436473201616"}},"outputId":"05220416-28f9-4b09-96ef-67662c17f42d"},"outputs":[{"output_type":"stream","name":"stdout","text":["73/73 [==============================] - 3s 33ms/step - loss: 0.4050 - accuracy: 0.9002\n"]},{"output_type":"execute_result","data":{"text/plain":["[0.40503913164138794, 0.9001713991165161]"]},"metadata":{},"execution_count":45}],"source":["modelx.evaluate(X_test, y_test, batch_size=64)"]}],"metadata":{"accelerator":"GPU","colab":{"collapsed_sections":[],"machine_shape":"hm","provenance":[{"file_id":"1XBy3TPMb3IuEVkCxtliYpWUgdo13EH1e","timestamp":1661843950562},{"file_id":"1yasZMzwARi7_MLSZBhiwcK-cz-0OSUUU","timestamp":1661308164140},{"file_id":"1M7C_zjI7-q22B-9ymuYvGy-QHmzwBELS","timestamp":1660274269126},{"file_id":"1nc8hybaV8wPECAkoW9Fiay_diQFlCBgm","timestamp":1660270521743},{"file_id":"1i1XkgHsjFhlwfnEtteSvJCZ61q_IXH_8","timestamp":1659406730650}]},"gpuClass":"standard","kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.3"}},"nbformat":4,"nbformat_minor":0}