graph LR
Build_Environment_Setup["Build & Environment Setup"]
Core_Integration_Registration["Core Integration & Registration"]
Fundamental_ML_Building_Blocks["Fundamental ML Building Blocks"]
Keras_Model_Extensions["Keras Model Extensions"]
Specialized_ML_Modules["Specialized ML Modules"]
Build_Environment_Setup -- "Configures Environment" --> Core_Integration_Registration
Core_Integration_Registration -- "Integrates Components" --> Fundamental_ML_Building_Blocks
Core_Integration_Registration -- "Integrates Components" --> Keras_Model_Extensions
Core_Integration_Registration -- "Integrates Components" --> Specialized_ML_Modules
Keras_Model_Extensions -- "Utilizes & Evaluates" --> Fundamental_ML_Building_Blocks
Fundamental_ML_Building_Blocks -- "Updates Parameters Of" --> Keras_Model_Extensions
Specialized_ML_Modules -- "Provides Specialized Layers/Data" --> Keras_Model_Extensions
click Build_Environment_Setup href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/addons/Build_Environment_Setup.md" "Details"
click Fundamental_ML_Building_Blocks href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/addons/Fundamental_ML_Building_Blocks.md" "Details"
click Specialized_ML_Modules href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/addons/Specialized_ML_Modules.md" "Details"
The TensorFlow Addons project is structured around extending TensorFlow's core capabilities with specialized functionalities. The Build & Environment Setup component forms the foundation, managing the compilation and configuration necessary for the entire library, especially for custom C++/CUDA operations. This setup enables the Core Integration & Registration component, which provides the essential utilities and mechanisms for making custom components discoverable and usable within the TensorFlow ecosystem. Building upon this core, Fundamental ML Building Blocks offers a suite of advanced machine learning primitives like custom activations, losses, metrics, and optimizers. These blocks are extensively utilized by Keras Model Extensions, which introduces specialized neural network layers and callbacks to enhance Keras models. Finally, Specialized ML Modules provides domain-specific functionalities for image processing, natural language processing, and sequential data, often leveraging and extending the capabilities provided by the other components. This modular design ensures extensibility and clear separation of concerns, facilitating both development and integration into larger machine learning workflows.
Build & Environment Setup [Expand]
Manages the project's build processes, including dependency resolution (especially for CUDA/GPU components) and environment configuration. It handles the compilation of custom C++/CUDA operations, ensuring compatibility and performance.
Related Classes/Methods:
Provides foundational utilities and mechanisms for integrating custom components within the TensorFlow ecosystem, ensuring they are discoverable and usable by TensorFlow's core APIs. This component also includes general-purpose utility functions used across the library for seamless operation.
Related Classes/Methods:
Fundamental ML Building Blocks [Expand]
Implements fundamental machine learning building blocks such as specialized activation functions, custom loss functions, performance metrics, and advanced optimization algorithms. These components extend TensorFlow's core capabilities for model training and evaluation.
Related Classes/Methods:
tensorflow_addons/activations/tensorflow_addons/losses/tensorflow_addons/metrics/tensorflow_addons/optimizers/
Contains a variety of unique and specialized neural network layers that extend Keras's built-in capabilities, such as Conditional Random Field (CRF) layers, unpooling, and various normalization techniques, providing advanced model building blocks. It also includes custom callback functions for monitoring and controlling the training process.
Related Classes/Methods:
Specialized ML Modules [Expand]
Offers domain-specific functionalities for image processing, natural language processing, and sequential data handling. This includes image transformations, text-specific operations (like CRF for sequence labeling), and custom Recurrent Neural Network (RNN) cells and sequence-to-sequence components.
Related Classes/Methods: