Neural Network Graph

Library to build, differentiate, and optimize computational graphs. Supports differentially private optimization via mixins.

Install dependencies with:
pip install -r requirements.txt

Folder	Name
Environments	Classes for wrapping data
Neural_Network	Node implementations for building a computational graph
Tests	Example networks, compatible with pytest

• Optimizers
  • Gradient Descent
  • Momentum
  • Nesterov Accelerated Gradient
  • Adagrad
  • RMSProp
  • Adam
  • Adamax
  • Nadam
  • Quickprop
• Selectable functions
  • Basis / Activation
    • Regression (softplus, tanh, etc.)
    • Classification (use softmax on final layer)
  • Cost (sum squared and cross entropy)
• Network Configuration
  • Construct flexible graphs of vector functions
  • Multiple layers
  • Set number of nodes for each layer
• Optimizer Configuration
  • Batch size (set to one for stochastic descent)
  • Set hyperparameters for each layer, or broadcast one
    • Learning rate
    • Parameters specific to convergence algorithm
  • Multiprocessed graphing

This is a rewrite of a more rigid 'caterpillar' network: https://github.com/Shoeboxam/Neural_Network