Photon: Machine Learning Framework

A end-to-end Machine Learning ramework that extends the functionality of other frameworks such as TensorFlow & Keras. Photon ML is built to apply neural network and ensemble modeling techniques for deep learning financial algorithms. The framework supports the entire lifecycle of a machine learning project including data preparation, model development, training, monitoring, evaluation and deployment.

Key Features of Photon ML:

• Streamlines the development and implementation of end-to-end Machine Learning systems.
• Custom object-oriented API with built-in subclassing of Keras and TensorFlow APIs.
• Built-in custom modules such as Models, Layers, Optimizers and Loss Functions.
• Highly customizable interface to extend built-in modules for specific algorithms/networks.
• Detailed logging and analysis of model parameters to increase interpretability and optimization.
• Works natively with TensorFlow distributed strategies.
• Real-time data preprocessing; dataset splitting, normalization, scaling, aggregation & resampling.
• Custom batching, padding and masking of data.
• Designed to be model/algorithm agnostic and to work natively with container services.
• Natively shares input & output between multiple networks to streamline deep ensemble learning.
• Interface for saving, serializing and loading entire networks including learned & hyper parameters.
• Custom dynamic learning rate scheduling.

Photon ML Examples: https://github.com/sequenzia/photon_examples

A Collection of Algorthims/Models designed with Photon ML: https://github.com/sequenzia/dyson