Neural-Network

A simple neural network written in rust.

About

This implementation of a neural network using gradient-descent is completely written from ground up using rust. It is possible to specify the shape of the network, as well as the learning-rate of the network. Additionally, you can choose from one of many predefined datasets, for example the XOR- and CIRCLE Datasets, which represent the relative functions inside the union-square. As well as more complicated datasets like the RGB_DONUT, which represents a donut-like shape with a rainbow like color transition.

Below, you can see a training process, where the network is trying to learn the color-values of the RGB_DONUT dataset.

Features

The following features are currently implemented:

• Optimizers
  1. Adam
  2. RMSProp
  3. SGD
• Loss Functions
  1. Quadratic
• Activation Functions
  1. Sigmoid
  2. ReLU
• Layers
  1. Dense
• Plotting
  1. Plotting the cost-history during training
  2. Plotting the final predictions inside, either in grayscale or RGB

Usage

The process of creating and training the neural network is pretty straightforwards:

Example Training Process

Below, you can see how the network learns:

Learning Animation

https://user-images.githubusercontent.com/54124311/195410077-7a02b075-0269-4ff2-965f-97f224ab2cf1.mp4

Final Result

Cool training results

RGB_DONUT

Big Network

Small Network

XOR_PROBLEM