on_boarding.md

graph LR
    Training_Loop_Orchestrator["Training Loop Orchestrator"]
    Data_Loader_Preprocessor["Data Loader & Preprocessor"]
    Neural_Network_Model["Neural Network Model"]
    Loss_Function_Module["Loss Function Module"]
    SAM_Optimizer_Core["SAM Optimizer Core"]
    PyTorch_Base_Optimizer["PyTorch Base Optimizer"]
    Training_Loop_Orchestrator -- "Requests Batches From" --> Data_Loader_Preprocessor
    Data_Loader_Preprocessor -- "Provides Batches To" --> Training_Loop_Orchestrator
    Training_Loop_Orchestrator -- "Feeds Data To" --> Neural_Network_Model
    Neural_Network_Model -- "Generates Predictions For" --> Loss_Function_Module
    Loss_Function_Module -- "Calculates Loss For" --> Training_Loop_Orchestrator
    Training_Loop_Orchestrator -- "Triggers Optimization Steps In" --> SAM_Optimizer_Core
    Neural_Network_Model -- "Provides Gradients To" --> SAM_Optimizer_Core
    SAM_Optimizer_Core -- "Updates Parameters Of" --> Neural_Network_Model
    SAM_Optimizer_Core -- "Delegates Final Update To" --> PyTorch_Base_Optimizer
    PyTorch_Base_Optimizer -- "Receives Update Call From" --> SAM_Optimizer_Core
    click Training_Loop_Orchestrator href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/sam/Training_Loop_Orchestrator.md" "Details"
    click Neural_Network_Model href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/sam/Neural_Network_Model.md" "Details"
    click SAM_Optimizer_Core href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/sam/SAM_Optimizer_Core.md" "Details"

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Details

The project implements a Sharpness-Aware Minimization (SAM) training pipeline. The Training Loop Orchestrator acts as the central coordinator, managing the flow of data from the Data Loader & Preprocessor to the Neural Network Model. It leverages the Loss Function Module to quantify prediction errors. The core optimization is handled by the SAM Optimizer Core, which interacts with the Neural Network Model to obtain gradients and delegates the final parameter updates to a PyTorch Base Optimizer. This modular design ensures clear separation of concerns, facilitating both understanding and potential modifications.

Training Loop Orchestrator [Expand]

The central control unit managing the entire training process, iterating over epochs and batches, and coordinating interactions between all other components. This component is primarily embodied by the train.py script.

Related Classes/Methods:

• train.py

Data Loader & Preprocessor

Responsible for loading, transforming, and batching datasets for training and evaluation.

Related Classes/Methods:

• cifar.py

Neural Network Model [Expand]

Defines the neural network architecture whose parameters are optimized; performs forward passes and generates gradients during backward passes.

Related Classes/Methods:

• wide_res_net.py

Loss Function Module

Provides the differentiable loss function used to quantify the error between model predictions and true labels.

Related Classes/Methods:

• smooth_cross_entropy.py

SAM Optimizer Core [Expand]

Implements the Sharpness-Aware Minimization (SAM) algorithm, orchestrating a two-step gradient update process to find flatter minima.

Related Classes/Methods:

• sam.py

PyTorch Base Optimizer

Represents the underlying standard PyTorch optimizer (e.g., SGD, Adam) that the SAM optimizer wraps and delegates the final parameter update to. This is a conceptual component representing the torch.optim.Optimizer class and its subclasses, which are external to this project.

Related Classes/Methods:

FAQ