graph LR
Keras_Core_API_Model_Layer_Definition_["Keras Core API (Model & Layer Definition)"]
Backend_Abstraction_Operations["Backend Abstraction & Operations"]
Training_Evaluation_Orchestrator["Training & Evaluation Orchestrator"]
Data_Preprocessing_Pipeline["Data & Preprocessing Pipeline"]
Model_Persistence_Deployment["Model Persistence & Deployment"]
Keras_Core_API_Model_Layer_Definition_ -- "Translates Operations" --> Backend_Abstraction_Operations
Keras_Core_API_Model_Layer_Definition_ -- "Utilizes Primitives" --> Backend_Abstraction_Operations
Training_Evaluation_Orchestrator -- "Executes Model" --> Keras_Core_API_Model_Layer_Definition_
Training_Evaluation_Orchestrator -- "Updates Model Weights" --> Keras_Core_API_Model_Layer_Definition_
Data_Preprocessing_Pipeline -- "Provides Processed Data" --> Training_Evaluation_Orchestrator
Training_Evaluation_Orchestrator -- "Delegates Computations" --> Backend_Abstraction_Operations
Model_Persistence_Deployment -- "Persists Model State" --> Keras_Core_API_Model_Layer_Definition_
Model_Persistence_Deployment -- "Restores Model State" --> Keras_Core_API_Model_Layer_Definition_
Keras_Core_API_Model_Layer_Definition_ -- "Integrates Preprocessing Layers" --> Data_Preprocessing_Pipeline
click Keras_Core_API_Model_Layer_Definition_ href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/keras/Keras_Core_API_Model_Layer_Definition_.md" "Details"
click Backend_Abstraction_Operations href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/keras/Backend_Abstraction_Operations.md" "Details"
click Training_Evaluation_Orchestrator href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/keras/Training_Evaluation_Orchestrator.md" "Details"
click Data_Preprocessing_Pipeline href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/keras/Data_Preprocessing_Pipeline.md" "Details"
click Model_Persistence_Deployment href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/keras/Model_Persistence_Deployment.md" "Details"
The Keras architecture is designed for flexibility and backend agnosticism, centered around the Keras Core API (Model & Layer Definition) where users construct neural networks. All underlying tensor operations from these models and layers are routed through the Backend Abstraction & Operations component, which translates them into calls compatible with various deep learning frameworks (e.g., TensorFlow, JAX, PyTorch). The Training & Evaluation Orchestrator drives the learning process, consuming data prepared by the Data & Preprocessing Pipeline, applying optimization strategies, and monitoring performance. This orchestrator heavily relies on the Backend Abstraction & Operations for efficient computation. Finally, the Model Persistence & Deployment component ensures that trained models can be saved and loaded, facilitating deployment and continued development. This layered approach provides a consistent high-level API while allowing for powerful, backend-specific optimizations.
Keras Core API (Model & Layer Definition) [Expand]
The primary interface for users to define, build, and interact with neural network models and layers. It includes the foundational Model and Layer classes, along with a rich library of standard layers and pre-built application models.
Related Classes/Methods:
Backend Abstraction & Operations [Expand]
Provides a unified, backend-agnostic API for fundamental tensor operations and computational primitives. It translates Keras's high-level operations into specific calls for the underlying deep learning frameworks (TensorFlow, JAX, PyTorch, OpenVINO), ensuring seamless execution across different backends.
Related Classes/Methods:
Training & Evaluation Orchestrator [Expand]
Manages the entire deep learning workflow, including training, evaluation, and prediction. It orchestrates data flow, applies optimization algorithms, computes losses, updates metrics, and integrates callbacks for monitoring and control.
Related Classes/Methods:
Data & Preprocessing Pipeline [Expand]
Offers layers for data preprocessing and augmentation, which can be integrated directly into Keras models. It also includes general-purpose utilities for handling various data formats and common data-related tasks.
Related Classes/Methods:
Model Persistence & Deployment [Expand]
Handles the serialization and deserialization of Keras models, enabling their architecture, learned weights, and optimizer states to be persistently stored and retrieved for deployment or resuming training.
Related Classes/Methods: