TensorFlow TensorFlow Deployment and TFLite 1 β Questions and Answers
Question 1: What is TensorFlow Lite (TFLite) designed for?
- Deploying models on mobile and embedded devices (Correct answer)
- Training models faster on desktop GPUs
- Serving models in cloud environments
- Visualizing model architectures
Correct answer: Deploying models on mobile and embedded devices
TFLite is a lightweight runtime optimized for on-device inference on mobile phones, microcontrollers, and IoT devices.
Question 2: What is quantization in TFLite model optimization?
- Reducing model weight precision (e.g., float32 to int8) to decrease size and improve speed (Correct answer)
- Counting model parameters
- Splitting a model into multiple files
- Converting weights to binary format
Correct answer: Reducing model weight precision (e.g., float32 to int8) to decrease size and improve speed
Quantization reduces the numerical precision of model weights and activations, significantly reducing model size and inference latency.
Question 3: Which class is used to convert a Keras model to TFLite format?
- tf.lite.TFLiteConverter (Correct answer)
- tf.keras.TFLiteExporter
- tf.lite.Converter
- tf.convert.TFLite
Correct answer: tf.lite.TFLiteConverter
tf.lite.TFLiteConverter converts TensorFlow models (SavedModel or Keras model) into the TFLite FlatBuffer format.
Question 4: What is the TensorFlow SavedModel format?
- A directory containing model architecture, weights, and computation graph (Correct answer)
- A single .h5 weight file
- A compressed ZIP archive of model files
- A JSON configuration file
Correct answer: A directory containing model architecture, weights, and computation graph
SavedModel is TensorFlow's standard serialization format that saves the complete model including computation graph, weights, and metadata.
Question 5: What does TensorFlow Serving provide?
- A production-grade server for serving TensorFlow models via REST and gRPC APIs (Correct answer)
- A web dashboard for monitoring models
- A training cluster manager
- A dataset hosting service
Correct answer: A production-grade server for serving TensorFlow models via REST and gRPC APIs
TensorFlow Serving is a flexible, high-performance serving system for machine learning models designed for production environments.
Question 6: Which TFLite optimization reduces model size with minimal accuracy loss using magnitude-based pruning?
- Weight pruning (Correct answer)
- Quantization
- Clustering
- Knowledge distillation
Correct answer: Weight pruning
Weight pruning removes weights close to zero, creating sparse models that can be more efficiently stored and executed.
TensorFlow TensorFlow Deployment and TFLite 1
What is TensorFlow Lite (TFLite) designed for?