0506-session-hw.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","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\nimport numpy as np # linear algebra\nimport 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\nimport os\nfor 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","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2022-05-05T14:15:21.835402Z","iopub.execute_input":"2022-05-05T14:15:21.835808Z","iopub.status.idle":"2022-05-05T14:15:21.869341Z","shell.execute_reply.started":"2022-05-05T14:15:21.835708Z","shell.execute_reply":"2022-05-05T14:15:21.868481Z"},"trusted":true},"execution_count":1,"outputs":[]},{"cell_type":"markdown","source":"## Loading Necessary libraries🌱","metadata":{}},{"cell_type":"code","source":"import nltk\nfrom nltk.corpus import stopwords\nfrom nltk.stem.porter import PorterStemmer\nfrom wordcloud import WordCloud,STOPWORDS\nfrom nltk.stem import WordNetLemmatizer\nfrom nltk.tokenize import word_tokenize,sent_tokenize\nfrom bs4 import BeautifulSoup\nimport re,string,unicodedata\nfrom keras.preprocessing import text, sequence\nfrom sklearn.metrics import classification_report,confusion_matrix,accuracy_score\nfrom sklearn.model_selection import train_test_split\nfrom string import punctuation\nimport keras\nfrom keras.models import Sequential\nimport tensorflow as tf\n\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\nfrom tensorflow.keras.preprocessing.text import Tokenizer\nfrom tensorflow.keras.preprocessing.sequence import pad_sequences\n\nimport tensorflow as tf","metadata":{"execution":{"iopub.status.busy":"2022-05-05T14:15:21.895783Z","iopub.execute_input":"2022-05-05T14:15:21.896060Z","iopub.status.idle":"2022-05-05T14:15:31.660574Z","shell.execute_reply.started":"2022-05-05T14:15:21.896027Z","shell.execute_reply":"2022-05-05T14:15:31.659765Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"markdown","source":"## Reading the dataset 🌱","metadata":{}},{"cell_type":"code","source":"train_data = pd.read_csv('/kaggle/input/covid-19-nlp-text-classification/Corona_NLP_train.csv',\n                        encoding='latin_1')\ntest_data = pd.read_csv(\"/kaggle/input/covid-19-nlp-text-classification/Corona_NLP_test.csv\",encoding='latin_1')","metadata":{"execution":{"iopub.status.busy":"2022-05-05T14:15:31.662098Z","iopub.execute_input":"2022-05-05T14:15:31.662554Z","iopub.status.idle":"2022-05-05T14:15:32.011716Z","shell.execute_reply.started":"2022-05-05T14:15:31.662518Z","shell.execute_reply":"2022-05-05T14:15:32.010803Z"},"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"code","source":"train_data.head()","metadata":{"execution":{"iopub.status.busy":"2022-05-05T14:16:31.617687Z","iopub.execute_input":"2022-05-05T14:16:31.618011Z","iopub.status.idle":"2022-05-05T14:16:31.647632Z","shell.execute_reply.started":"2022-05-05T14:16:31.617974Z","shell.execute_reply":"2022-05-05T14:16:31.646430Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"code","source":"test_data.head()","metadata":{"execution":{"iopub.status.busy":"2022-05-05T14:16:38.097909Z","iopub.execute_input":"2022-05-05T14:16:38.098913Z","iopub.status.idle":"2022-05-05T14:16:38.110924Z","shell.execute_reply.started":"2022-05-05T14:16:38.098834Z","shell.execute_reply":"2022-05-05T14:16:38.110135Z"},"trusted":true},"execution_count":5,"outputs":[]},{"cell_type":"markdown","source":"## Check for any null value/s 🌱","metadata":{}},{"cell_type":"code","source":"# check for null value in train_data\nsns.heatmap(train_data.isnull());","metadata":{"execution":{"iopub.status.busy":"2022-05-05T14:16:50.829883Z","iopub.execute_input":"2022-05-05T14:16:50.830216Z","iopub.status.idle":"2022-05-05T14:16:51.634238Z","shell.execute_reply.started":"2022-05-05T14:16:50.830167Z","shell.execute_reply":"2022-05-05T14:16:51.633501Z"},"trusted":true},"execution_count":6,"outputs":[]},{"cell_type":"code","source":"# check for null values in test data\nsns.heatmap(test_data.isnull());","metadata":{"execution":{"iopub.status.busy":"2022-05-05T14:18:20.051980Z","iopub.execute_input":"2022-05-05T14:18:20.053431Z","iopub.status.idle":"2022-05-05T14:18:20.485996Z","shell.execute_reply.started":"2022-05-05T14:18:20.053361Z","shell.execute_reply":"2022-05-05T14:18:20.484757Z"},"trusted":true},"execution_count":7,"outputs":[]},{"cell_type":"markdown","source":"## drop duplicate and 'NA' value/s 🌱","metadata":{}},{"cell_type":"code","source":"train_data.drop_duplicates(inplace= True)\ntest_data.drop_duplicates(inplace=True)","metadata":{"execution":{"iopub.status.busy":"2022-05-05T14:18:43.676714Z","iopub.execute_input":"2022-05-05T14:18:43.677028Z","iopub.status.idle":"2022-05-05T14:18:43.765592Z","shell.execute_reply.started":"2022-05-05T14:18:43.676996Z","shell.execute_reply":"2022-05-05T14:18:43.764569Z"},"trusted":true},"execution_count":8,"outputs":[]},{"cell_type":"code","source":"train_data.dropna(inplace=True)\ntest_data.dropna(inplace=True)","metadata":{"execution":{"iopub.status.busy":"2022-05-05T14:18:43.805512Z","iopub.execute_input":"2022-05-05T14:18:43.806058Z","iopub.status.idle":"2022-05-05T14:18:43.845033Z","shell.execute_reply.started":"2022-05-05T14:18:43.805998Z","shell.execute_reply":"2022-05-05T14:18:43.844227Z"},"trusted":true},"execution_count":9,"outputs":[]},{"cell_type":"markdown","source":"## Making checkpoint 🌱","metadata":{}},{"cell_type":"code","source":"# copy the dataset into new data\ntrain_df = train_data.copy()\ntest_df = test_data.copy()","metadata":{"execution":{"iopub.status.busy":"2022-05-05T14:19:01.626625Z","iopub.execute_input":"2022-05-05T14:19:01.627491Z","iopub.status.idle":"2022-05-05T14:19:01.634568Z","shell.execute_reply.started":"2022-05-05T14:19:01.627441Z","shell.execute_reply":"2022-05-05T14:19:01.633738Z"},"trusted":true},"execution_count":10,"outputs":[]},{"cell_type":"code","source":"train_df.head()","metadata":{"execution":{"iopub.status.busy":"2022-05-05T14:19:01.745676Z","iopub.execute_input":"2022-05-05T14:19:01.745949Z","iopub.status.idle":"2022-05-05T14:19:01.758980Z","shell.execute_reply.started":"2022-05-05T14:19:01.745921Z","shell.execute_reply":"2022-05-05T14:19:01.758033Z"},"trusted":true},"execution_count":11,"outputs":[]},{"cell_type":"markdown","source":"## check if any null value exists? 🌱","metadata":{}},{"cell_type":"code","source":"print(train_df.isnull().sum())\nprint(\"*\"*50)\nprint(test_df.isnull().sum())","metadata":{"execution":{"iopub.status.busy":"2022-05-05T14:19:31.271885Z","iopub.execute_input":"2022-05-05T14:19:31.272224Z","iopub.status.idle":"2022-05-05T14:19:31.304769Z","shell.execute_reply.started":"2022-05-05T14:19:31.272169Z","shell.execute_reply":"2022-05-05T14:19:31.303508Z"},"trusted":true},"execution_count":12,"outputs":[]},{"cell_type":"code","source":"train_data.columns","metadata":{"execution":{"iopub.status.busy":"2022-05-05T14:19:31.381831Z","iopub.execute_input":"2022-05-05T14:19:31.382732Z","iopub.status.idle":"2022-05-05T14:19:31.389530Z","shell.execute_reply.started":"2022-05-05T14:19:31.382689Z","shell.execute_reply":"2022-05-05T14:19:31.388251Z"},"trusted":true},"execution_count":13,"outputs":[]},{"cell_type":"markdown","source":"## performing small EDA 🌱","metadata":{}},{"cell_type":"code","source":"sns.set_style(\"whitegrid\")\nsns.set(rc={'figure.figsize':(11,4)})","metadata":{"execution":{"iopub.status.busy":"2022-05-05T14:19:38.714011Z","iopub.execute_input":"2022-05-05T14:19:38.714354Z","iopub.status.idle":"2022-05-05T14:19:38.720959Z","shell.execute_reply.started":"2022-05-05T14:19:38.714320Z","shell.execute_reply":"2022-05-05T14:19:38.719899Z"},"trusted":true},"execution_count":14,"outputs":[]},{"cell_type":"code","source":"sns.countplot(train_df['Sentiment'])","metadata":{"execution":{"iopub.status.busy":"2022-05-05T14:19:41.602599Z","iopub.execute_input":"2022-05-05T14:19:41.603626Z","iopub.status.idle":"2022-05-05T14:19:42.194547Z","shell.execute_reply.started":"2022-05-05T14:19:41.603580Z","shell.execute_reply":"2022-05-05T14:19:42.193443Z"},"trusted":true},"execution_count":15,"outputs":[]},{"cell_type":"code","source":"train_df.shape, test_df.shape","metadata":{"execution":{"iopub.status.busy":"2022-05-05T14:19:54.267351Z","iopub.execute_input":"2022-05-05T14:19:54.267685Z","iopub.status.idle":"2022-05-05T14:19:54.275374Z","shell.execute_reply.started":"2022-05-05T14:19:54.267652Z","shell.execute_reply":"2022-05-05T14:19:54.274502Z"},"trusted":true},"execution_count":16,"outputs":[]},{"cell_type":"code","source":"def change_sen(sentiment):\n    if sentiment == \"Extremely Positive\":\n        return 'positive'\n    elif sentiment == \"Extremely Negative\":\n        return 'negative'\n    elif sentiment == \"Positive\":\n        return 'positive'\n    elif sentiment == \"Negative\":\n        return 'negative'\n    else:\n        return 'netural'","metadata":{"execution":{"iopub.status.busy":"2022-05-05T14:19:54.411955Z","iopub.execute_input":"2022-05-05T14:19:54.412857Z","iopub.status.idle":"2022-05-05T14:19:54.418811Z","shell.execute_reply.started":"2022-05-05T14:19:54.412807Z","shell.execute_reply":"2022-05-05T14:19:54.418260Z"},"trusted":true},"execution_count":17,"outputs":[]},{"cell_type":"markdown","source":"## Applying the change_sen function/method 🌱","metadata":{}},{"cell_type":"code","source":"train_df['Sentiment'] = train_df['Sentiment'].apply(lambda x: change_sen(x))\ntest_df['Sentiment'] = test_df['Sentiment'].apply(lambda x: change_sen(x))","metadata":{"execution":{"iopub.status.busy":"2022-05-05T14:20:20.693767Z","iopub.execute_input":"2022-05-05T14:20:20.694035Z","iopub.status.idle":"2022-05-05T14:20:20.718383Z","shell.execute_reply.started":"2022-05-05T14:20:20.694008Z","shell.execute_reply":"2022-05-05T14:20:20.717447Z"},"trusted":true},"execution_count":18,"outputs":[]},{"cell_type":"code","source":"sns.countplot(train_df['Sentiment'])","metadata":{"execution":{"iopub.status.busy":"2022-05-05T14:20:20.807324Z","iopub.execute_input":"2022-05-05T14:20:20.807776Z","iopub.status.idle":"2022-05-05T14:20:21.070993Z","shell.execute_reply.started":"2022-05-05T14:20:20.807745Z","shell.execute_reply":"2022-05-05T14:20:21.070264Z"},"trusted":true},"execution_count":19,"outputs":[]},{"cell_type":"code","source":"sns.countplot(test_df['Sentiment'])","metadata":{"execution":{"iopub.status.busy":"2022-05-05T14:20:21.072545Z","iopub.execute_input":"2022-05-05T14:20:21.072927Z","iopub.status.idle":"2022-05-05T14:20:21.296104Z","shell.execute_reply.started":"2022-05-05T14:20:21.072895Z","shell.execute_reply":"2022-05-05T14:20:21.294849Z"},"trusted":true},"execution_count":20,"outputs":[]},{"cell_type":"markdown","source":"## Now time for \"Data Clearning and processing\" 🌱","metadata":{}},{"cell_type":"code","source":"# load stop words\nstop_word = stopwords.words('english')","metadata":{"execution":{"iopub.status.busy":"2022-05-05T14:21:16.408568Z","iopub.execute_input":"2022-05-05T14:21:16.409069Z","iopub.status.idle":"2022-05-05T14:21:16.420802Z","shell.execute_reply.started":"2022-05-05T14:21:16.409035Z","shell.execute_reply":"2022-05-05T14:21:16.419911Z"},"trusted":true},"execution_count":24,"outputs":[]},{"cell_type":"code","source":"def clean(text):\n\n    #     remove urls\n    text = re.sub(r'http\\S+', \" \", text)\n\n    #     remove mentions\n    text = re.sub(r'@\\w+',' ',text)\n\n    #     remove hastags\n    text = re.sub(r'#\\w+', ' ', text)\n\n    #     remove digits\n    text = re.sub(r'\\d+', ' ', text)\n\n    #     remove html tags\n    text = re.sub('r<.*?>',' ', text)\n    \n    #     remove stop words \n    text = text.split()\n    text = \" \".join([word for word in text if not word in stop_word])\n    \n      \n    return text","metadata":{"execution":{"iopub.status.busy":"2022-05-05T14:21:16.558175Z","iopub.execute_input":"2022-05-05T14:21:16.558582Z","iopub.status.idle":"2022-05-05T14:21:16.564803Z","shell.execute_reply.started":"2022-05-05T14:21:16.558552Z","shell.execute_reply":"2022-05-05T14:21:16.564221Z"},"trusted":true},"execution_count":25,"outputs":[]},{"cell_type":"code","source":"train_df['OriginalTweet'] = train_df['OriginalTweet'].apply(lambda x: clean(x))\ntest_df['OriginalTweet'] = test_df['OriginalTweet'].apply(lambda x: clean(x))","metadata":{"execution":{"iopub.status.busy":"2022-05-05T14:21:16.749000Z","iopub.execute_input":"2022-05-05T14:21:16.749425Z","iopub.status.idle":"2022-05-05T14:21:19.284399Z","shell.execute_reply.started":"2022-05-05T14:21:16.749390Z","shell.execute_reply":"2022-05-05T14:21:19.283376Z"},"trusted":true},"execution_count":26,"outputs":[]},{"cell_type":"code","source":"train_df.head()","metadata":{"execution":{"iopub.status.busy":"2022-05-05T14:21:21.749519Z","iopub.execute_input":"2022-05-05T14:21:21.749882Z","iopub.status.idle":"2022-05-05T14:21:21.764293Z","shell.execute_reply.started":"2022-05-05T14:21:21.749849Z","shell.execute_reply":"2022-05-05T14:21:21.763344Z"},"trusted":true},"execution_count":27,"outputs":[]},{"cell_type":"markdown","source":"## We only need \"OriginalTweet\" and \"Sentiment\" 🌱","metadata":{}},{"cell_type":"code","source":"df_train = train_df.iloc[:,4:]\ndf_test = test_df.iloc[:,4:]","metadata":{"execution":{"iopub.status.busy":"2022-05-05T14:21:56.924446Z","iopub.execute_input":"2022-05-05T14:21:56.925597Z","iopub.status.idle":"2022-05-05T14:21:56.933753Z","shell.execute_reply.started":"2022-05-05T14:21:56.925542Z","shell.execute_reply":"2022-05-05T14:21:56.932709Z"},"trusted":true},"execution_count":28,"outputs":[]},{"cell_type":"code","source":"df_train.head()","metadata":{"execution":{"iopub.status.busy":"2022-05-05T14:21:59.304126Z","iopub.execute_input":"2022-05-05T14:21:59.305043Z","iopub.status.idle":"2022-05-05T14:21:59.315012Z","shell.execute_reply.started":"2022-05-05T14:21:59.304986Z","shell.execute_reply":"2022-05-05T14:21:59.314106Z"},"trusted":true},"execution_count":29,"outputs":[]},{"cell_type":"markdown","source":"### now mapping the sentiment 🌱\n- 0: Netural\n- 1: Positive\n- 2: Negative","metadata":{}},{"cell_type":"code","source":"l = {\"netural\":0, \"positive\":1,\"negative\":2}","metadata":{"execution":{"iopub.status.busy":"2022-05-05T14:22:33.696913Z","iopub.execute_input":"2022-05-05T14:22:33.697238Z","iopub.status.idle":"2022-05-05T14:22:33.701995Z","shell.execute_reply.started":"2022-05-05T14:22:33.697180Z","shell.execute_reply":"2022-05-05T14:22:33.700909Z"},"trusted":true},"execution_count":30,"outputs":[]},{"cell_type":"code","source":"df_train['Sentiment'] = df_train['Sentiment'].map(l)\ndf_test['Sentiment']  = df_test['Sentiment'].map(l)","metadata":{"execution":{"iopub.status.busy":"2022-05-05T14:22:37.855337Z","iopub.execute_input":"2022-05-05T14:22:37.855612Z","iopub.status.idle":"2022-05-05T14:22:37.867003Z","shell.execute_reply.started":"2022-05-05T14:22:37.855584Z","shell.execute_reply":"2022-05-05T14:22:37.866296Z"},"trusted":true},"execution_count":31,"outputs":[]},{"cell_type":"code","source":"df_train.head()","metadata":{"execution":{"iopub.status.busy":"2022-05-05T14:22:41.474542Z","iopub.execute_input":"2022-05-05T14:22:41.475227Z","iopub.status.idle":"2022-05-05T14:22:41.484437Z","shell.execute_reply.started":"2022-05-05T14:22:41.475164Z","shell.execute_reply":"2022-05-05T14:22:41.483702Z"},"trusted":true},"execution_count":32,"outputs":[]},{"cell_type":"code","source":"x_train = df_train['OriginalTweet'].copy()\nx_test = df_test['OriginalTweet'].copy()\n\ny_train = df_train['Sentiment'].copy()\ny_test = df_test['Sentiment'].copy()","metadata":{"execution":{"iopub.status.busy":"2022-05-05T14:29:24.582621Z","iopub.execute_input":"2022-05-05T14:29:24.582960Z","iopub.status.idle":"2022-05-05T14:29:24.590368Z","shell.execute_reply.started":"2022-05-05T14:29:24.582928Z","shell.execute_reply":"2022-05-05T14:29:24.589479Z"},"trusted":true},"execution_count":53,"outputs":[]},{"cell_type":"code","source":"x_train.shape, y_train.shape,x_test.shape, y_test.shape","metadata":{"execution":{"iopub.status.busy":"2022-05-05T14:29:26.075421Z","iopub.execute_input":"2022-05-05T14:29:26.076254Z","iopub.status.idle":"2022-05-05T14:29:26.082385Z","shell.execute_reply.started":"2022-05-05T14:29:26.076214Z","shell.execute_reply":"2022-05-05T14:29:26.081621Z"},"trusted":true},"execution_count":54,"outputs":[]},{"cell_type":"markdown","source":"### Maxiumn lenght of sequence 🌱","metadata":{}},{"cell_type":"code","source":"max_len = np.max(x_train.apply(lambda x :len(x)))\nmax_len","metadata":{"execution":{"iopub.status.busy":"2022-05-05T14:29:30.733345Z","iopub.execute_input":"2022-05-05T14:29:30.733855Z","iopub.status.idle":"2022-05-05T14:29:30.765329Z","shell.execute_reply.started":"2022-05-05T14:29:30.733820Z","shell.execute_reply":"2022-05-05T14:29:30.764629Z"},"trusted":true},"execution_count":55,"outputs":[]},{"cell_type":"markdown","source":"### Tokenizer initlization 🌱","metadata":{}},{"cell_type":"code","source":"tokenizer = Tokenizer()","metadata":{"execution":{"iopub.status.busy":"2022-05-05T14:24:29.594926Z","iopub.execute_input":"2022-05-05T14:24:29.595253Z","iopub.status.idle":"2022-05-05T14:24:29.599774Z","shell.execute_reply.started":"2022-05-05T14:24:29.595221Z","shell.execute_reply":"2022-05-05T14:24:29.598874Z"},"trusted":true},"execution_count":39,"outputs":[]},{"cell_type":"code","source":"\nx_train.shape","metadata":{"execution":{"iopub.status.busy":"2022-05-05T14:29:45.028933Z","iopub.execute_input":"2022-05-05T14:29:45.030059Z","iopub.status.idle":"2022-05-05T14:29:45.037041Z","shell.execute_reply.started":"2022-05-05T14:29:45.030004Z","shell.execute_reply":"2022-05-05T14:29:45.035671Z"},"trusted":true},"execution_count":56,"outputs":[]},{"cell_type":"code","source":"tokenizer.fit_on_texts(x_train)\nvocab_length = len(tokenizer.word_index) + 1\n\nx_train = tokenizer.texts_to_sequences(x_train)\nx_test = tokenizer.texts_to_sequences(x_test)\n\nx_train = pad_sequences(x_train, maxlen=max_len, padding='post')\nx_test = pad_sequences(x_test, maxlen=max_len, padding='post')","metadata":{"execution":{"iopub.status.busy":"2022-05-05T14:29:47.674094Z","iopub.execute_input":"2022-05-05T14:29:47.675031Z","iopub.status.idle":"2022-05-05T14:29:49.986135Z","shell.execute_reply.started":"2022-05-05T14:29:47.674989Z","shell.execute_reply":"2022-05-05T14:29:49.985004Z"},"trusted":true},"execution_count":57,"outputs":[]},{"cell_type":"code","source":"print(\"Vocab length:\", vocab_length)\nprint(\"Max sequence length:\", max_len)","metadata":{"execution":{"iopub.status.busy":"2022-05-05T14:29:58.252918Z","iopub.execute_input":"2022-05-05T14:29:58.253545Z","iopub.status.idle":"2022-05-05T14:29:58.261476Z","shell.execute_reply.started":"2022-05-05T14:29:58.253492Z","shell.execute_reply":"2022-05-05T14:29:58.260356Z"},"trusted":true},"execution_count":58,"outputs":[]},{"cell_type":"code","source":"embedding_dim = 16","metadata":{"execution":{"iopub.status.busy":"2022-05-05T14:30:02.339846Z","iopub.execute_input":"2022-05-05T14:30:02.341935Z","iopub.status.idle":"2022-05-05T14:30:02.347144Z","shell.execute_reply.started":"2022-05-05T14:30:02.341857Z","shell.execute_reply":"2022-05-05T14:30:02.345729Z"},"trusted":true},"execution_count":59,"outputs":[]},{"cell_type":"markdown","source":"## Model Creation 🌱","metadata":{}},{"cell_type":"code","source":"model = tf.keras.Sequential([\n    tf.keras.layers.Embedding(vocab_length, embedding_dim, input_length=max_len),\n    tf.keras.layers.Bidirectional(tf.keras.layers.GRU(256, return_sequences=True)),\n    tf.keras.layers.GlobalAveragePooling1D(),\n    tf.keras.layers.Dense(64, activation='relu'),\n    tf.keras.layers.Dropout(0.4),\n    tf.keras.layers.Dense(3, activation='softmax')\n])\n# opt = tf.keras.optimizers.Adam(learning_rate=0.01)\nmodel.compile(loss='categorical_crossentropy',optimizer=\"adam\",metrics=['accuracy'])","metadata":{"execution":{"iopub.status.busy":"2022-05-05T14:30:19.621762Z","iopub.execute_input":"2022-05-05T14:30:19.622038Z","iopub.status.idle":"2022-05-05T14:30:20.371648Z","shell.execute_reply.started":"2022-05-05T14:30:19.622010Z","shell.execute_reply":"2022-05-05T14:30:20.370532Z"},"trusted":true},"execution_count":60,"outputs":[]},{"cell_type":"code","source":"print(model.summary())","metadata":{"execution":{"iopub.status.busy":"2022-05-05T14:30:23.123540Z","iopub.execute_input":"2022-05-05T14:30:23.123818Z","iopub.status.idle":"2022-05-05T14:30:23.133162Z","shell.execute_reply.started":"2022-05-05T14:30:23.123789Z","shell.execute_reply":"2022-05-05T14:30:23.132316Z"},"trusted":true},"execution_count":61,"outputs":[]},{"cell_type":"code","source":"tf.keras.utils.plot_model(model)","metadata":{"execution":{"iopub.status.busy":"2022-05-05T14:30:30.677065Z","iopub.execute_input":"2022-05-05T14:30:30.678244Z","iopub.status.idle":"2022-05-05T14:30:31.711959Z","shell.execute_reply.started":"2022-05-05T14:30:30.678152Z","shell.execute_reply":"2022-05-05T14:30:31.710686Z"},"trusted":true},"execution_count":62,"outputs":[]},{"cell_type":"code","source":"x_train.shape, x_test.shape, y_train.shape, y_test.shape","metadata":{"execution":{"iopub.status.busy":"2022-05-05T14:30:43.968585Z","iopub.execute_input":"2022-05-05T14:30:43.968967Z","iopub.status.idle":"2022-05-05T14:30:43.977366Z","shell.execute_reply.started":"2022-05-05T14:30:43.968924Z","shell.execute_reply":"2022-05-05T14:30:43.976441Z"},"trusted":true},"execution_count":63,"outputs":[]},{"cell_type":"code","source":"from tensorflow.keras.utils import to_categorical\n\ny_train = to_categorical(y_train, 3)\ny_test = to_categorical(y_test, 3)","metadata":{"execution":{"iopub.status.busy":"2022-05-05T14:31:27.373711Z","iopub.execute_input":"2022-05-05T14:31:27.374177Z","iopub.status.idle":"2022-05-05T14:31:27.382040Z","shell.execute_reply.started":"2022-05-05T14:31:27.374142Z","shell.execute_reply":"2022-05-05T14:31:27.381266Z"},"trusted":true},"execution_count":65,"outputs":[]},{"cell_type":"markdown","source":"## Model training 🌱","metadata":{}},{"cell_type":"code","source":"num_epochs = 10\nhistory = model.fit(x_train, y_train, epochs=num_epochs, \n                    validation_data=(x_test, y_test))","metadata":{"execution":{"iopub.status.busy":"2022-05-05T14:31:41.989681Z","iopub.execute_input":"2022-05-05T14:31:41.990167Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"acc = history.history['accuracy']\nval_acc = history.history['val_accuracy']\n\nloss = history.history['loss']\nval_loss = history.history['val_loss']","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Model Accuracy and loss 🌱","metadata":{}},{"cell_type":"code","source":"print(f\"Accuracy on training data is:- {acc[-1]*100} %\")\nprint(f\"Loss {loss[-1]*100}\")\n\nprint(f\"Accuracy on validation data is:- {val_acc[-1]*100} %\")\nprint(f\"Loss {val_loss[-1]*100}\")","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Plotting 🌱","metadata":{}},{"cell_type":"code","source":"epochs = range(len(acc))\n\nplt.plot(epochs, acc,'b',label='training acc')\nplt.plot(epochs, val_acc, 'r', label='validation acc')\nplt.legend()\nplt.show()\n\n\nplt.plot(epochs, loss,'b',label='training loss')\nplt.plot(epochs, val_loss, 'r', label='validation loss')\nplt.legend()\nplt.show()","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"pred = model.predict_classes(x_test)","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Confusion Matrix 🌱","metadata":{}},{"cell_type":"code","source":"cm = confusion_matrix(np.argmax(y_test,1),pred)\ncm","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sns.heatmap(cm,annot=True)","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Classification Report 🌱","metadata":{}},{"cell_type":"code","source":"print(classification_report(np.argmax(y_test,1),pred))","metadata":{},"execution_count":null,"outputs":[]}]}