Explain how LSTM is used for Classification?
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Explain how LSTM is used for Classification?

Explain how LSTM is used for Classification?

This recipe explains how LSTM is used for Classification

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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

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)

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