bert-exposes-fake-tweets.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"},"colab":{"name":"bert-exposes-fake-tweets.ipynb","provenance":[]},"accelerator":"GPU"},"cells":[{"cell_type":"code","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"id":"pl8Lk0dgBn-f","executionInfo":{"status":"ok","timestamp":1620931774086,"user_tz":-60,"elapsed":758,"user":{"displayName":"高元天","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiDILTHk-n-9OOyEEzccJQYWOAbWcR4kOXBgDNV=s64","userId":"09826960303655324698"}}},"source":["# This Python 3 environment comes with many helpful analytics libraries installed\n","# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n","# For example, here's several helpful packages to load\n","\n","import numpy as np # linear algebra\n","import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n","\n","# Input data files are available in the read-only \"../input/\" directory\n","# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n","\n","import os\n","for dirname, _, filenames in os.walk('/kaggle/input'):\n","    for filename in filenames:\n","        print(os.path.join(dirname, filename))\n","\n","# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n","# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"],"execution_count":1,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"KK4aicZHBn-l"},"source":["In this notebook I will fine-tune BERT with PyTorch framework to perform a classification task by using Natural Language Processing with Disaster Tweets dataset. \n","In this notebook, I will:\n","\n","    Load the Disaster Tweets dataset\n","    Pre-processing of tweets\n","    Load a BERT model from TensorFlow Hub\n","    Build a BertForSequenceClassification model\n","    Train the model and fine-tuning BERT\n","    Perform a tweet classification for submission\n"]},{"cell_type":"code","metadata":{"trusted":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"WguRRs9ABn-m","executionInfo":{"status":"ok","timestamp":1620931784873,"user_tz":-60,"elapsed":8449,"user":{"displayName":"高元天","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiDILTHk-n-9OOyEEzccJQYWOAbWcR4kOXBgDNV=s64","userId":"09826960303655324698"}},"outputId":"e2d52547-7a87-45e9-c96c-012698c03742"},"source":["!pip install transformers"],"execution_count":2,"outputs":[{"output_type":"stream","text":["Collecting transformers\n","\u001b[?25l  Downloading https://files.pythonhosted.org/packages/b0/9e/5b80becd952d5f7250eaf8fc64b957077b12ccfe73e9c03d37146ab29712/transformers-4.6.0-py3-none-any.whl (2.3MB)\n","\u001b[K     |████████████████████████████████| 2.3MB 4.2MB/s \n","\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers) (3.0.12)\n","Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from transformers) (4.0.1)\n","Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers) (2.23.0)\n","Collecting sacremoses\n","\u001b[?25l  Downloading https://files.pythonhosted.org/packages/75/ee/67241dc87f266093c533a2d4d3d69438e57d7a90abb216fa076e7d475d4a/sacremoses-0.0.45-py3-none-any.whl (895kB)\n","\u001b[K     |████████████████████████████████| 901kB 19.0MB/s \n","\u001b[?25hCollecting huggingface-hub==0.0.8\n","  Downloading 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/usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->transformers) (3.4.1)\n","Requirement already satisfied: typing-extensions>=3.6.4; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->transformers) (3.7.4.3)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2020.12.5)\n","Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2.10)\n","Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (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->transformers) (1.24.3)\n","Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (7.1.2)\n","Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.0.1)\n","Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.15.0)\n","Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->transformers) (2.4.7)\n","Installing collected packages: sacremoses, huggingface-hub, tokenizers, transformers\n","Successfully installed huggingface-hub-0.0.8 sacremoses-0.0.45 tokenizers-0.10.2 transformers-4.6.0\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"trusted":true,"id":"g4-AybT-Bn-m","executionInfo":{"status":"ok","timestamp":1620931791006,"user_tz":-60,"elapsed":6128,"user":{"displayName":"高元天","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiDILTHk-n-9OOyEEzccJQYWOAbWcR4kOXBgDNV=s64","userId":"09826960303655324698"}}},"source":["#import tensorflow as tf\n","import torch\n","from torch.utils.data import TensorDataset, random_split\n","from torch.utils.data import DataLoader, RandomSampler, SequentialSampler\n","from transformers import BertTokenizer, get_linear_schedule_with_warmup\n","from transformers import BertForSequenceClassification, AdamW, BertConfig\n","import torch.nn.functional as F\n","\n","\n","import pandas as pd\n","import numpy as np\n","import time\n","import datetime\n","import random, re\n","import seaborn as sns\n","import matplotlib.pyplot as plt"],"execution_count":3,"outputs":[]},{"cell_type":"code","metadata":{"trusted":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"H0GQ07HsBn-m","executionInfo":{"status":"ok","timestamp":1620931793901,"user_tz":-60,"elapsed":1320,"user":{"displayName":"高元天","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiDILTHk-n-9OOyEEzccJQYWOAbWcR4kOXBgDNV=s64","userId":"09826960303655324698"}},"outputId":"b7c2c546-9342-4f99-f84d-9c43bfb8f85d"},"source":["# Check device \n","# Get the GPU device name if available.\n","if torch.cuda.is_available():    \n","\n","    # Tell PyTorch to use the GPU.    \n","    device = torch.device(\"cuda\")\n","\n","    print('There are %d GPU(s) available. {}'.format(torch.cuda.device_count()))\n","\n","    print('We will use the GPU: {}'.format(torch.cuda.get_device_name(0)))\n","\n","# If we dont have GPU but a CPU, training will take place on CPU instead\n","else:\n","    print('No GPU available, using the CPU instead.')\n","    device = torch.device(\"cpu\")\n","    \n","# Set the seed value all over the place to make this reproducible.\n","seed_val = 42\n","\n","random.seed(seed_val)\n","np.random.seed(seed_val)\n","torch.manual_seed(seed_val)\n","torch.cuda.manual_seed_all(seed_val)"],"execution_count":4,"outputs":[{"output_type":"stream","text":["There are %d GPU(s) available. 1\n","We will use the GPU: Tesla V100-SXM2-16GB\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"trusted":true,"colab":{"base_uri":"https://localhost:8080/","height":204},"id":"gYMJByuZBn-n","executionInfo":{"status":"ok","timestamp":1620933021019,"user_tz":-60,"elapsed":687,"user":{"displayName":"高元天","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiDILTHk-n-9OOyEEzccJQYWOAbWcR4kOXBgDNV=s64","userId":"09826960303655324698"}},"outputId":"10248f18-b85b-4e1d-90d6-b2e5cc51ff07"},"source":["# Read the data\n","train = pd.read_csv(r'/content/drive/MyDrive/DM/data/nlp-getting-started/train.csv')\n","test = pd.read_csv(r'/content/drive/MyDrive/DM/data/nlp-getting-started/test.csv')\n","pd.set_option('display.max_colwidth', 150)\n","train.head()"],"execution_count":68,"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>id</th>\n","      <th>keyword</th>\n","      <th>location</th>\n","      <th>text</th>\n","      <th>target</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>Our Deeds are the Reason of this #earthquake May ALLAH Forgive us all</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>4</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>Forest fire near La Ronge Sask. Canada</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>5</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>All residents asked to 'shelter in place' are being notified by officers. No other evacuation or shelter in place orders are expected</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>6</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>13,000 people receive #wildfires evacuation orders in California</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>7</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>Just got sent this photo from Ruby #Alaska as smoke from #wildfires pours into a school</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   id  ... target\n","0   1  ...      1\n","1   4  ...      1\n","2   5  ...      1\n","3   6  ...      1\n","4   7  ...      1\n","\n","[5 rows x 5 columns]"]},"metadata":{"tags":[]},"execution_count":68}]},{"cell_type":"code","metadata":{"trusted":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"_0PBiwDgBn-n","executionInfo":{"status":"ok","timestamp":1620931835880,"user_tz":-60,"elapsed":1140,"user":{"displayName":"高元天","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiDILTHk-n-9OOyEEzccJQYWOAbWcR4kOXBgDNV=s64","userId":"09826960303655324698"}},"outputId":"e0a9c782-4df7-4630-b031-00ac09f7577a"},"source":["train.info()"],"execution_count":8,"outputs":[{"output_type":"stream","text":["<class 'pandas.core.frame.DataFrame'>\n","RangeIndex: 7613 entries, 0 to 7612\n","Data columns (total 5 columns):\n"," #   Column    Non-Null Count  Dtype \n","---  ------    --------------  ----- \n"," 0   id        7613 non-null   int64 \n"," 1   keyword   7552 non-null   object\n"," 2   location  5080 non-null   object\n"," 3   text      7613 non-null   object\n"," 4   target    7613 non-null   int64 \n","dtypes: int64(2), object(3)\n","memory usage: 297.5+ KB\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"trusted":true,"colab":{"base_uri":"https://localhost:8080/"},"id":"yS7JfWO1Bn-n","executionInfo":{"status":"ok","timestamp":1620931837370,"user_tz":-60,"elapsed":796,"user":{"displayName":"高元天","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiDILTHk-n-9OOyEEzccJQYWOAbWcR4kOXBgDNV=s64","userId":"09826960303655324698"}},"outputId":"1eb005ce-b9e1-469f-84d6-d877f4e3f265"},"source":["# Report the number of sentences.\n","print('Number of training sentences: {:,}\\n'.format(train.shape[0]))\n","print('Number of test sentences: {:,}\\n'.format(test.shape[0]))"],"execution_count":9,"outputs":[{"output_type":"stream","text":["Number of training sentences: 7,613\n","\n","Number of test sentences: 3,263\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"trusted":true,"colab":{"base_uri":"https://localhost:8080/","height":204},"id":"H87Awbl5Bn-o","executionInfo":{"status":"ok","timestamp":1620933025018,"user_tz":-60,"elapsed":715,"user":{"displayName":"高元天","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiDILTHk-n-9OOyEEzccJQYWOAbWcR4kOXBgDNV=s64","userId":"09826960303655324698"}},"outputId":"b11fd640-5b92-4087-f858-47a814ce7df5"},"source":["def clean_text(text):\n","    text = text.lower() #lowercase\n","    \n","    text = re.sub(r'[!]+','!',text)\n","    text = re.sub(r'[?]+','?',text)\n","    text = re.sub(r'[.]+','.',text)\n","    text = re.sub(r\"'\",\"\",text)\n","    #text = re.sub('\\s+', '', text)#.strip() # Remove and double spaces\n","    text = re.sub(r'&amp;?',r'and', text) # replace & -> and\n","    text = re.sub(r\"https?:\\/\\/t.co\\/[A-Za-z0-9]+\", \"\", text) # Remove URLs\n","    text = re.sub(r'[:\"$%&\\*+,-/:;<=>@\\\\^_`{|}~]+','',text) #remove some puncts (except . ! # ?)\n","    \n","    return text\n","\n","train['text'] = train['text'].apply(clean_text)\n","test['text'] = test['text'].apply(clean_text)\n","train.head()"],"execution_count":69,"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>id</th>\n","      <th>keyword</th>\n","      <th>location</th>\n","      <th>text</th>\n","      <th>target</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>our deeds are the reason of this #earthquake may allah forgive us all</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>4</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>forest fire near la ronge sask canada</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>5</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>all residents asked to shelter in place are being notified by officers no other evacuation or shelter in place orders are expected</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>6</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>13000 people receive #wildfires evacuation orders in california</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>7</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","      <td>just got sent this photo from ruby #alaska as smoke from #wildfires pours into a school</td>\n","      <td>1</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   id  ... target\n","0   1  ...      1\n","1   4  ...      1\n","2   5  ...      1\n","3   6  ...      1\n","4   7  ...      1\n","\n","[5 rows x 5 columns]"]},"metadata":{"tags":[]},"execution_count":69}]},{"cell_type":"code","metadata":{"trusted":true,"id":"LYghTn3EBn-y","executionInfo":{"status":"ok","timestamp":1620933038308,"user_tz":-60,"elapsed":720,"user":{"displayName":"高元天","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiDILTHk-n-9OOyEEzccJQYWOAbWcR4kOXBgDNV=s64","userId":"09826960303655324698"}}},"source":["from sklearn.model_selection import train_test_split\n","\n","train, val = train_test_split(train, test_size = 0.2, random_state = 42, stratify = train['target'])"],"execution_count":71,"outputs":[]},{"cell_type":"code","metadata":{"trusted":true,"id":"byhrFvLaBn-y","executionInfo":{"status":"ok","timestamp":1620933044132,"user_tz":-60,"elapsed":787,"user":{"displayName":"高元天","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiDILTHk-n-9OOyEEzccJQYWOAbWcR4kOXBgDNV=s64","userId":"09826960303655324698"}}},"source":["# importing libraries\n","import tensorflow as tf\n","from tensorflow.keras.layers import (Embedding,\n","                                     LSTM,\n","                                     Dense,\n","                                     Dropout,\n","                                     GlobalMaxPool1D,\n","                                     BatchNormalization)\n","from tensorflow.keras.models import Sequential\n","from tensorflow.keras.preprocessing.sequence import pad_sequences\n","from tensorflow.keras.preprocessing.text import one_hot"],"execution_count":72,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":35},"id":"W8iGqByZFQTC","executionInfo":{"status":"ok","timestamp":1620933046991,"user_tz":-60,"elapsed":689,"user":{"displayName":"高元天","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiDILTHk-n-9OOyEEzccJQYWOAbWcR4kOXBgDNV=s64","userId":"09826960303655324698"}},"outputId":"3a2fb73b-5f3b-456c-e4bc-1cd6e1fa75d6"},"source":["train['text'][0]"],"execution_count":73,"outputs":[{"output_type":"execute_result","data":{"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"},"text/plain":["'our deeds are the reason of this #earthquake may allah forgive us all'"]},"metadata":{"tags":[]},"execution_count":73}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"1pkxRlFzEsai","executionInfo":{"status":"ok","timestamp":1620933051236,"user_tz":-60,"elapsed":801,"user":{"displayName":"高元天","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiDILTHk-n-9OOyEEzccJQYWOAbWcR4kOXBgDNV=s64","userId":"09826960303655324698"}},"outputId":"12520f67-621f-483b-a0f2-13ac29f7680c"},"source":["VOC_SIZE = 30000\n","onehot_repr_train = [one_hot(words, VOC_SIZE) for words in train['text']]\n","onehot_repr_val = [one_hot(words, VOC_SIZE) for words in val['text']]\n","print(onehot_repr_train[3000])\n","SENT_LENGTH = 15\n","embedded_docs_train = pad_sequences(onehot_repr_train, padding=\"post\", maxlen=SENT_LENGTH)\n","embedded_docs_val = pad_sequences(onehot_repr_val, padding=\"post\", maxlen=SENT_LENGTH)"],"execution_count":74,"outputs":[{"output_type":"stream","text":["[22359, 15904, 2856, 14995, 8493, 15503, 2999, 13138, 2993, 5961]\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"Z2r51cFUFayX","executionInfo":{"status":"ok","timestamp":1620933077392,"user_tz":-60,"elapsed":686,"user":{"displayName":"高元天","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiDILTHk-n-9OOyEEzccJQYWOAbWcR4kOXBgDNV=s64","userId":"09826960303655324698"}}},"source":["def create_model():\n","  VECT_FEATURES = 32\n","  model = Sequential()\n","  model.add(Embedding(VOC_SIZE,\n","                      VECT_FEATURES,\n","                      input_length=SENT_LENGTH))\n","  model.add(LSTM(100, return_sequences = True))\n","  model.add(GlobalMaxPool1D())\n","  model.add(BatchNormalization())\n","  model.add(Dropout(0.5))\n","  model.add(Dense(10, activation=\"relu\"))\n","  model.add(Dropout(0.2))\n","  model.add(Dense(1, activation = \"sigmoid\"))\n","  return model"],"execution_count":78,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"957ooTocFfk9","executionInfo":{"status":"ok","timestamp":1620933080125,"user_tz":-60,"elapsed":1217,"user":{"displayName":"高元天","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiDILTHk-n-9OOyEEzccJQYWOAbWcR4kOXBgDNV=s64","userId":"09826960303655324698"}},"outputId":"9bd185a0-8fc7-43a2-8fb0-4e3b76d601f7"},"source":["model = create_model()\n","model.compile(optimizer = \"adam\", loss = \"binary_crossentropy\", metrics = [\"accuracy\"]) # compiling\n","model.summary() #summary"],"execution_count":79,"outputs":[{"output_type":"stream","text":["Model: \"sequential_3\"\n","_________________________________________________________________\n","Layer (type)                 Output Shape              Param #   \n","=================================================================\n","embedding_3 (Embedding)      (None, 15, 32)            960000    \n","_________________________________________________________________\n","lstm_3 (LSTM)                (None, 15, 100)           53200     \n","_________________________________________________________________\n","global_max_pooling1d_3 (Glob (None, 100)               0         \n","_________________________________________________________________\n","batch_normalization_3 (Batch (None, 100)               400       \n","_________________________________________________________________\n","dropout_6 (Dropout)          (None, 100)               0         \n","_________________________________________________________________\n","dense_6 (Dense)              (None, 10)                1010      \n","_________________________________________________________________\n","dropout_7 (Dropout)          (None, 10)                0         \n","_________________________________________________________________\n","dense_7 (Dense)              (None, 1)                 11        \n","=================================================================\n","Total params: 1,014,621\n","Trainable params: 1,014,421\n","Non-trainable params: 200\n","_________________________________________________________________\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"q6Dn-e6XFjoZ","executionInfo":{"status":"ok","timestamp":1620934865070,"user_tz":-60,"elapsed":7281,"user":{"displayName":"高元天","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiDILTHk-n-9OOyEEzccJQYWOAbWcR4kOXBgDNV=s64","userId":"09826960303655324698"}},"outputId":"af5a9497-3917-48cf-c72a-bc927e5eb5a0"},"source":["history = model.fit(embedded_docs_train, train['target'], validation_data=\n","                    (embedded_docs_val, val['target']),epochs=3, batch_size=32)"],"execution_count":92,"outputs":[{"output_type":"stream","text":["Epoch 1/3\n","153/153 [==============================] - 2s 14ms/step - loss: 0.0236 - accuracy: 0.9875 - val_loss: 3.2400 - val_accuracy: 0.7488\n","Epoch 2/3\n","153/153 [==============================] - 2s 14ms/step - loss: 0.0219 - accuracy: 0.9881 - val_loss: 3.2718 - val_accuracy: 0.7570\n","Epoch 3/3\n","153/153 [==============================] - 2s 14ms/step - loss: 0.0226 - accuracy: 0.9867 - val_loss: 3.5383 - val_accuracy: 0.7586\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":621},"id":"506DiehQFsTi","executionInfo":{"status":"ok","timestamp":1620934869747,"user_tz":-60,"elapsed":1819,"user":{"displayName":"高元天","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiDILTHk-n-9OOyEEzccJQYWOAbWcR4kOXBgDNV=s64","userId":"09826960303655324698"}},"outputId":"8867755f-ccad-4442-e05d-3bf33c0bdeb1"},"source":["import matplotlib.pyplot as plt\n","fig,(ax1,ax2) = plt.subplots(2,2,figsize=(15,10))\n","ax1[0].plot(history.history[\"accuracy\"])\n","ax1[0].set_title(\"Train_Accuracy\")\n","ax1[0].set_xlabel(\"Epochs\")\n","ax1[0].set_ylabel(\"Accuracy\")\n","\n","ax1[1].plot(history.history[\"val_accuracy\"])\n","ax1[1].set_title(\"Val_Accuracy\")\n","ax1[1].set_xlabel(\"Epochs\")\n","ax1[1].set_ylabel(\"Accuracy\")\n","\n","ax2[0].plot(history.history[\"loss\"])\n","ax2[0].set_title(\"Train_Loss\")\n","ax2[0].set_xlabel(\"Epochs\")\n","ax2[0].set_ylabel(\"Loss\")\n","\n","ax2[1].plot(history.history[\"val_loss\"])\n","ax2[1].set_title(\"Val_Loss\")\n","ax2[1].set_xlabel(\"Epochs\")\n","ax2[1].set_ylabel(\"Loss\")\n","plt.show()"],"execution_count":93,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1080x720 with 4 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"j0f0DIWKJma9","executionInfo":{"status":"ok","timestamp":1620933262507,"user_tz":-60,"elapsed":2519,"user":{"displayName":"高元天","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiDILTHk-n-9OOyEEzccJQYWOAbWcR4kOXBgDNV=s64","userId":"09826960303655324698"}},"outputId":"713e80b6-569f-48a7-9a3c-5ae3649ad74f"},"source":["import numpy as np\n","\n","data_test = pd.read_csv(r'/content/drive/My Drive/DM/data/processed_test.txt',sep = ',',\n","                         encoding = 'utf-8', header=None)\n","\n","data_answer = pd.read_csv(r'/content/drive/My Drive/DM/data/answer.csv',sep = ',',\n","                         encoding = 'utf-8', header=None, index_col=0)\n","\n","VOC_SIZE = 30000\n","\n","print(data_test[2])\n","test = np.array(data_test[2],dtype = str)\n","onehot_repr = [one_hot(words, VOC_SIZE) for words in test]\n","\n","SENT_LENGTH = 15\n","embedded_docs = pad_sequences(onehot_repr, padding=\"post\", maxlen=SENT_LENGTH)\n","embedded_docs\n","\n","pred = model.predict(embedded_docs)\n","data_answer = np.array(data_answer[1][1:],dtype=int)"],"execution_count":83,"outputs":[{"output_type":"stream","text":["0                                                                                happen terribl car crash\n","1                                                           heard earthquak differ citi stay safe everyon\n","2                                                      forest fire spot pond gees flee across street save\n","3                                                                          apocalyps light spokan wildfir\n","4                                                                      typhoon soudelor kill china taiwan\n","                                                      ...                                                \n","3258                                                        earthquak safeti los angel safeti fasten xrwn\n","3259    storm wors last hurrican cityand other hardest hit yard look like bomb around still without power\n","3260                                                                            green line derail chicago\n","3261                                                                  meg issu hazard weather outlook hwo\n","3262                                                      cityofcalgari activ municip emerg plan yycstorm\n","Name: 2, Length: 3263, dtype: object\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"n2pfXVZSJsIL","executionInfo":{"status":"ok","timestamp":1620933272963,"user_tz":-60,"elapsed":687,"user":{"displayName":"高元天","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiDILTHk-n-9OOyEEzccJQYWOAbWcR4kOXBgDNV=s64","userId":"09826960303655324698"}},"outputId":"61ef8bc2-c326-4d05-ce87-6e84a73b664b"},"source":["correct=0\n","sums=0\n","for i in range(len(pred)):\n","  if (pred[i] < 0.5):\n","    pred[i] = 0\n","  else:\n","    pred[i] = 1\n","  if (pred[i] == data_answer[i]):\n","    correct+=1\n","  sums+=1\n","    \n","print(correct/sums)"],"execution_count":84,"outputs":[{"output_type":"stream","text":["0.6840330983757279\n"],"name":"stdout"}]}]}