TFRecords is a binary format used in TensorFlow to store datasets efficiently. It's particularly useful for large-scale machine learning projects where data needs to be serialized and deserialized quickly.

📌 Why Use TFRecords?

  • Compact Storage: Reduces file size compared to plain text formats like CSV
  • Fast I/O: Optimized for reading/writing large datasets
  • Scalability: Works well with distributed systems
  • 💡 Integration: Seamlessly used with TensorFlow's tf.data API

🧰 How to Create TFRecords

  1. Prepare Data: Organize your dataset in a structured format (e.g., NumPy arrays)
  2. Serialize Features: Use tf.train.Example or tf.train.SequenceExample
  3. Write File: Save the serialized data using tf.io.TFRecordWriter
import tensorflow as tf  

def serialize_example(feature0, feature1):  
    feature = {  
        'feature0': tf.train.Feature(int64_list=tf.train.Int64List(value=[feature0])),  
        'feature1': tf.train.Feature(float_list=tf.train.FloatList(value=[feature1]))  
    }  
    example = tf.train.Example(features=tf.train.Features(feature=feature))  
    return example.SerializeToString()

🧪 Example Use Case

For image classification tasks, TFRecords can store:

  • Image raw bytes
  • Label information
  • Metadata (e.g., image dimensions)

💡 Tip: Use tf.io.parse_example to decode data when reading from TFRecords.

🌐 Further Reading

Check out our TensorFlow Programming Guide to learn more about working with datasets in TensorFlow.

TFRecords Introduction
TFRecords Workflow