NeuZephyr
Simple DL Framework
nz::opt Namespace Reference

Contains optimization algorithms for training deep learning models. More...

Classes

class  AdaDelta
 AdaDelta optimizer for deep learning models. More...
 
class  AdaGrad
 AdaGrad optimizer for deep learning models. More...
 
class  Adam
 Adam optimizer for deep learning models. More...
 
class  Momentum
 Momentum optimizer for deep learning models. More...
 
class  NAdam
 NAdam optimizer for deep learning models. More...
 
class  Optimizer
 Base class for optimization algorithms in deep learning. More...
 
class  RMSprop
 RMSprop optimizer for deep learning models. More...
 
class  SGD
 Stochastic Gradient Descent (SGD) optimizer for deep learning models. More...
 

Detailed Description

Contains optimization algorithms for training deep learning models.

The nz::opt namespace includes a collection of optimization algorithms designed to update model parameters during the training of deep learning models. These optimizers aim to minimize the loss function by adjusting the learning rate dynamically or incorporating momentum terms to improve convergence.

Key components in this namespace:

  • SGD (Stochastic Gradient Descent): A basic optimization method that updates model parameters in the direction of the negative gradient, with a fixed learning rate.
  • Momentum: Enhances SGD by introducing a momentum term that helps accelerate convergence and reduces oscillations.
  • AdaGrad: An optimizer that adjusts learning rates based on the historical gradients, allowing it to handle sparse data more effectively.
  • RMSprop: A modification of AdaGrad that uses a moving average of squared gradients to stabilize the learning rate, leading to more consistent updates.
  • Adam (Adaptive Moment Estimation): Combines the benefits of momentum and RMSprop, providing adaptive learning rates for each parameter using first and second moment estimates.
  • NAdam (Nesterov-accelerated Adam): An improvement over Adam by incorporating Nesterov momentum, which helps achieve faster convergence.
  • AdaDelta: A variant of AdaGrad that maintains a constant learning rate by using a running average of squared updates, avoiding the diminishing learning rate problem.

These optimizers are designed to work efficiently in high-performance computing environments, utilizing GPU-based tensor operations to accelerate training. The algorithms in this namespace can be easily extended to support additional optimization strategies in the future.

This namespace plays a critical role in the optimization of deep learning models by providing a set of tools to adaptively adjust model parameters during training, improving the overall performance and stability of the training process.

Note
The optimizers in this namespace rely on tensor-based operations for efficient computation. Ensure that proper memory management and error handling are applied when using these algorithms.
Author
Mgepahmge(https://github.com/Mgepahmge)
Date
2024/12/07