TensorFlow tf.data Pipelines 2 β Questions and Answers
Question 1: Which tf.data method is used to read TFRecord files?
- tf.data.TFRecordDataset() (Correct answer)
- tf.data.RecordDataset()
- tf.data.BinaryDataset()
- tf.data.FileDataset()
Correct answer: tf.data.TFRecordDataset()
TFRecordDataset reads TFRecord binary files, the recommended format for large-scale TensorFlow datasets.
Question 2: What does the num_parallel_calls parameter in dataset.map() do?
- Parallelizes the map transformation across multiple CPU threads (Correct answer)
- Sets the number of GPUs to use
- Controls batch size
- Limits dataset size
Correct answer: Parallelizes the map transformation across multiple CPU threads
num_parallel_calls allows multiple map function calls to run simultaneously, improving throughput on multi-core systems.
Question 3: Which tf.data method caches dataset elements to memory or disk?
- dataset.cache() (Correct answer)
- dataset.store()
- dataset.persist()
- dataset.save()
Correct answer: dataset.cache()
dataset.cache() saves elements after the first epoch so subsequent epochs read from cache instead of re-processing.
Question 4: What does tf.io.parse_single_example() do?
- Parses a single serialized tf.train.Example protobuf (Correct answer)
- Reads a single CSV row
- Decodes a JPEG image
- Parses a JSON string
Correct answer: Parses a single serialized tf.train.Example protobuf
tf.io.parse_single_example() deserializes a TFRecord's tf.train.Example into tensors using a feature description dict.
Question 5: Which method creates a dataset from a list of filenames?
- tf.data.Dataset.list_files() (Correct answer)
- tf.data.Dataset.from_files()
- tf.data.Dataset.glob()
- tf.data.Dataset.file_list()
Correct answer: tf.data.Dataset.list_files()
tf.data.Dataset.list_files() creates a dataset of file path strings matching a glob pattern.
Question 6: What is the purpose of tf.data.experimental.AUTOTUNE?
- Lets TensorFlow automatically tune pipeline parameters like buffer sizes (Correct answer)
- Enables experimental model features
- Tunes learning rate automatically
- Auto-selects the best GPU
Correct answer: Lets TensorFlow automatically tune pipeline parameters like buffer sizes
AUTOTUNE allows the tf.data runtime to dynamically choose optimal values for buffer sizes and parallelism.
TensorFlow tf.data Pipelines 2
Which tf.data method is used to read TFRecord files?