OrcaNet: A training organizer for Deep Learning in KM3NeT

OrcaNet is a deep learning framework based on Keras in order to simplify the training process of neural networks for astroparticle physics. It incorporates automated logging, plotting and validating during the training, as well as saving and continuing the training process. Additionally, it features easy management of multiple neural network inputs and the use of training data which is split over multiple files.

In this sense, it tackles many challenges that are usually found in astroparticle physics, like huge datasets.

OrcaNet is a part of the Deep Learning efforts for the neutrino telescope KM3NeT. Find more information about KM3NeT on http://www.km3net.org

OrcaNet is currently being developed at the official KM3NeT gitlab (https://git.km3net.de/ml/OrcaNet).

However, there’s also a github mirror that can be found at https://github.com/ViaFerrata/OrcaNet.

OrcaNet can be installed via pip by running:

pip install orcanet

By default, orcanet will install tensorflow (the cpu version). For training with graphics cards, tensorflow-gpu is required, which needs to be installed manually via:

pip install tensorflow-gpu

• OrcaNet overview
  • Step 1: Setting up the Organizer
  • Step 2: Working with the model
  • Building models with the model builder
• Input toml files
  • For the Organizer
  • For the model builder
• Learning rate
  • Float
  • Tuple
  • Function
  • String
• Modifiers
  • Label modifier
  • Sample modifier
  • Dataset modifier
• Working example
  • The data
  • The model
  • Training and results
• Contributing
  • Contact Us
  • Filing Bugs or Feature Requests
• API Reference
  • orcanet
  • orcanet_contrib

Indices and tables

• Index

• Module Index

• Search Page