Training and Validation
Training my model felt like nurturing a seedling into a flourishing tree. With a blend of determination and patience, I fed my model with the nourishment of labelled data, monitoring its growth and fine-tuning its parameters along the way. Validation became my compass, guiding me towards the optimal configuration for achieving peak performance.
# Train model
model.fit(X_train, y_train, epochs=10, batch_size=32, validation_data=(X_test, y_test))
# Evaluate model
loss, accuracy = model.evaluate(X_test, y_test)
print(“Test Accuracy: {:.2f}%”.format(accuracy * 100))
My Journey of Building a Sentiment Analysis Model with Python and TensorFlow
Embarking on the journey of building a sentiment analysis model has been an exciting and fulfilling experience for me. In this article, I’m thrilled to share the process I followed to create a sentiment analysis model using Python and TensorFlow, hoping it inspires others in their own AI endeavours.