What is OptKit?
OptKit is an open-source Python library that provides a toolkit for optimization algorithms targeting machine learning and deep learning models. It aims to make it easier for developers and researchers to test different optimization techniques when training neural networks.
OptKit includes Python implementations of several popular optimization methods such as stochastic gradient descent, RMSProp, Adam, Adadelta, Adagrad, Nadam and more. It provides a simple unified API to switch between different optimizers with just a function call.
Some of the key features of OptKit are:
- Modular architecture makes it easy to add new optimization algorithms
- Hyperparameter tuning capabilities to find best settings for each optimizer
- Utilities to visualize loss over training iterations to compare optimization methods
- Support for TensorFlow and PyTorch models
- Options for batch, mini-batch and stochastic gradient descent
- Pure Python implementation makes it easy to use and modify the code
By providing these optimization building blocks for machine learning, OptKit makes it more convenient for developers and researchers to experiment with neural network training. The goal is to advance research into better optimization techniques for deep learning.