TensorFlow Patterns

What problem does it solve?

This Skill provides expert patterns and best practices for building, optimizing, and deploying machine learning models using TensorFlow and Keras, addressing common challenges in model architecture, data pipelines, and deployment.

Core Features & Use Cases

• Keras Functional API: Demonstrates building complex, multi-input models with proper layer usage.
• Optimized tf.data Pipelines: Illustrates the correct order of operations for efficient data loading and preprocessing.
• Custom Layers & Losses: Shows how to implement custom components like attention layers and focal loss.
• SavedModel Export: Guides on exporting models with serving signatures for TensorFlow Serving.
• TFLite Quantization: Explains post-training quantization for model size reduction and performance improvement.
• Use Case: Develop a robust image classification model by leveraging the Keras Functional API, optimizing its data pipeline with tf.data, and then quantizing it to TFLite for efficient mobile deployment.

Quick Start

Show me how to build a Keras functional API model for multi-input classification.