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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
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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