LSTM-01

# LSTM_01
model = Sequential()
model.add(Embedding(vocab_length, embedding_dim, weights=[embedding_matrix], input_length=max_len, trainable=False))
model.add(CuDNNLSTM(64))
model.add(Dropout(0.5))
model.add(Dense(1, activation='sigmoid'))

model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])


# Define early stopping callback
early_stop = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=10)

# Train the model
history = model.fit(X_train, y_train, epochs=30, batch_size=64, validation_data=(X_test, y_test), callbacks=[early_stop])

# Evaluate the model
test_loss, test_acc = model.evaluate(X_test, y_test)
print('Test Loss:', test_loss)
print('Test Accuracy:', test_acc)

# Plot the training and validation accuracy
plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.title('Model Accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'], loc='upper right')
plt.show()

# Plot the training and validation loss
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('Model Loss')
plt.ylabel('Loss')
plt.xlabel('Epoch')
plt.legend(['Train', 'Test'], loc='upper right')
plt.show()

y_pred_prob = model.predict(X_test)
y_pred = (y_pred_prob >= 0.5).astype(int)
print(classification_report(y_test, y_pred))