graph LR
Hopfield_Layer["Hopfield Layer"]
Hopfield_Core_Logic["Hopfield Core Logic"]
Transformer_Integration_Layers["Transformer Integration Layers"]
Auxiliary_Data_Generators["Auxiliary Data Generators"]
Hopfield_Layer -- "Delegates Computation To" --> Hopfield_Core_Logic
Hopfield_Layer -- "Processes Input From" --> Auxiliary_Data_Generators
Hopfield_Core_Logic -- "Provides Core Functionality To" --> Hopfield_Layer
Transformer_Integration_Layers -- "Incorporates" --> Hopfield_Layer
Transformer_Integration_Layers -- "Processes Input From" --> Auxiliary_Data_Generators
Auxiliary_Data_Generators -- "Provides Data To" --> Hopfield_Layer
Auxiliary_Data_Generators -- "Provides Data To" --> Transformer_Integration_Layers
click Hopfield_Layer href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/hopfield-layers/Hopfield_Layer.md" "Details"
click Hopfield_Core_Logic href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/hopfield-layers/Hopfield_Core_Logic.md" "Details"
click Transformer_Integration_Layers href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/hopfield-layers/Transformer_Integration_Layers.md" "Details"
The hopfield-layers project is structured around a core Hopfield Layer that provides a PyTorch-compatible interface for associative memory. This layer offloads its complex mathematical operations to the Hopfield Core Logic, ensuring a clean and efficient implementation. For integration into modern deep learning models, particularly Transformers, the project offers Transformer Integration Layers that seamlessly embed the Hopfield Layer within standard encoder and decoder blocks. The entire system can be tested and demonstrated using synthetic data generated by the Auxiliary Data Generators, which supply inputs to both the standalone Hopfield Layer and the Transformer Integration Layers. This modular design promotes reusability, clear component responsibilities, and ease of integration into diverse neural network architectures.
Hopfield Layer [Expand]
The primary user-facing PyTorch module that encapsulates the Hopfield associative memory mechanism. It handles input/output normalization, parameter initialization, and orchestrates the core association process, designed as a drop-in replacement or enhancement for standard PyTorch layers.
Related Classes/Methods:
Hopfield Core Logic [Expand]
Encapsulates the fundamental mathematical and computational operations of the Hopfield association mechanism. This internal component performs low-level projections (query, key, value), attention calculations, and iterative updates, forming the heart of the associative memory behavior.
Related Classes/Methods:
Transformer Integration Layers [Expand]
Specialized PyTorch modules (HopfieldEncoderLayer, HopfieldDecoderLayer) designed for seamless embedding of the Hopfield Layer into Transformer-based neural network architectures. These layers manage typical Transformer block functionalities while leveraging Hopfield mechanisms for attention.
Related Classes/Methods:
Utility classes for generating specific synthetic data patterns (e.g., bit patterns, latch sequences). These datasets are primarily used for testing, demonstrating the capabilities of the Hopfield layers, and providing examples for users.
Related Classes/Methods: