https://github.com/jbahire/backpropnn
Backpropagation implementation using Julia
https://github.com/jbahire/backpropnn
artificial-intelligence artificial-neural-networks backpropagation-learning-algorithm machine-learning
Last synced: 25 days ago
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Backpropagation implementation using Julia
- Host: GitHub
- URL: https://github.com/jbahire/backpropnn
- Owner: JBAhire
- License: mit
- Created: 2018-01-12T18:32:38.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2018-07-02T09:45:41.000Z (almost 7 years ago)
- Last Synced: 2025-02-12T12:16:33.791Z (3 months ago)
- Topics: artificial-intelligence, artificial-neural-networks, backpropagation-learning-algorithm, machine-learning
- Language: Julia
- Size: 10.7 KB
- Stars: 2
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# BackPropNN
# Install
## Stable
```julia
Pkg.add("BackpropNeuralNet")
```
## Source
```julia
Pkg.clone(https://github.com/JBAhire/BackPropNN)
```
# Usage
To initialize a network of 2 inputs, 1 hidden layer with 3 neurons, and 2 outputs:
```julia
using BackpropNeuralNet
net = init_network([2, 3, 2])
# To train the network use the form `train(network, input, output)`:
train(net, [0.15, 0.7],[0.1, 0.9])
# To evaluate an input use the form `net_eval(network, inputs)`
net_eval(net, [0.15, 0.7])
```
# History
This is a Julia implementation of a neural network based on Sergio Fierens [ruby version](https://github.com/SergioFierens/ai4r).