ffnn - a very simple feedforward neural network library

Not useful yet, still a WIP.

Getting started

Create a network using the NetBuilder struct:

use ffnn::NetBuilder;
use ffnn::activations::RELU;

let net = NetBuilder::new(3).layer(2, RELU).init();

Predict on a set of inputs using Network::predict:

use nalgbebra::dvector;

net.predict(dvector![0.0, 0.0, 0.0]);

TODO

• Add documentation
• Backward propagation
• Batched backprop
• Adam optimizer
• Softmax activation
• Dropout
• Genetic algorithm utils?
• Multiple cost functions
  • Binary cross entropy
  • Categorical cross entropy
  • Mean squared error
• Maybe multiple optimization methods (not just gradient descent)
• Thread pool to increase performance
• Use GPU

Create some examples

• MNIST digits
• XOR function
• Sine wave

Done

• Forward propagation
• Add bias nodes
• Move initialization to the builder
• Multiple activation functions

References

• nalgebra docs
• The Math behind Neural Networks - Backpropagation
• Why Initialize a Neural Network with Random Weights?
• A Step by Step Backpropagation Example
• How to Implement Backpropagation from Scratch in Python
• Loss and Loss Functions
• Toy-Neural-Network-JS