Explain how LSTM is used for Classification in keras

Recipe Objective.

Explain how LSTM is used for Classification?

LSTM is mainly used for text classification so, we will take the example of it.

We will create a LSTM model for text classification

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Step 1- Loading the text.

First we will load the text from our drive. pharma_train=pd.read_csv('/content/drive/My Drive/Python/pharma/train.csv') pharma_train

Step 2- Preprocessing of text.

MAX_WORDS = 10000 MAX_LENGTH = 150 # This is fixed. EMBEDDING_DIM = 100 tokenizer = Tokenizer(num_words=MAX_NB_WORDS, filters='!"#$%&()*+,-./:;<=>?@', lower=True) tokenizer.fit_on_texts(parma_train['job_discription'].values) word_index = tokenizer.word_index print('tokens' % len(word_index)) X = tokenizer.texts_to_sequences(df['job_discription'].values) X = pad_sequences(X, maxlen=MAX_LENGTH) Y = pd.get_dummies(pharma_train['job_type']).values

Step 3- Splitting the dataset

We will split the dataset into training and testing

X_train, X_test, Y_train, Y_test = train_test_split(X,Y, test_size = 0.10, random_state = 42)

Step 4- Creating a LSTM model.

we will create a LSTM model and pass our dataset through it.

model = Sequential() model.add(Embedding(MAX_WORDS, EMBEDDING_DIM, input_length=X.shape[1])) model.add(SpatialDropout1D(0.2)) model.add(LSTM(50)) model.add(Dense(32, activation='softmax')) model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) history = model.fit(X_train, Y_train, epochs=10, batch_size=50,validation_split=0.1)