https://github.com/rileynwong/backprop-neural-net
Implementation of a multi-layer backpropagating neural network algorithm trained with the generalized delta learning rule
https://github.com/rileynwong/backprop-neural-net
backpropagation delta-rule machine-learning neural-network
Last synced: 3 months ago
JSON representation
Implementation of a multi-layer backpropagating neural network algorithm trained with the generalized delta learning rule
- Host: GitHub
- URL: https://github.com/rileynwong/backprop-neural-net
- Owner: rileynwong
- Created: 2016-04-17T05:17:57.000Z (about 9 years ago)
- Default Branch: master
- Last Pushed: 2016-05-23T08:35:31.000Z (about 9 years ago)
- Last Synced: 2025-01-27T23:19:11.403Z (5 months ago)
- Topics: backpropagation, delta-rule, machine-learning, neural-network
- Language: Python
- Homepage:
- Size: 25.4 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Multi-Layer Backpropagating Neural Network with Generalized Delta Learning Rule
Implementation of a multi-layer back propagating neural network algorithm using the generalized delta rule. Assumes a simple fully connected feedforward network with 1 hidden layer.
### Setup
Before running the network, ensure your parameters and data sets are in place. Place the following files in the main directory:
- `param.txt`
- `in.txt`
- `teach.txt`
`param.txt` should contain 6 lines, each with a single value. The first 3 lines respectively specify, in integers, the number of input, hidden, and output units. The next 3 lines respectively specify, as real values, the learning constant, the momentum constant, and the error criterion.
`in.txt` should contain the input patterns, with one pattern per line. Each pattern should be a sequence of values, separated by single spaces.
`teach.txt` should contain the teaching patterns. Each pattern should be a sequence of values, separated by single spaces.
By default, the paramaters and data sets used are for the iris data set.
### Run
To run the neural network, navigate to the main directory and run the following command:
`python main.py`
Alternatively, run:
`make`
(will clean up *.pyc files before running the main program)
This will launch the text-based user interface with the following options:
```
1 - Train network with current settings.
2 - Give the network a pattern and see the predicted output.
3 – View network settings.
4 - Change network settings.
5 – Exit.
```
Enter the number for the option you want. Note: The network cannot predict output without being trained first.
### File Descriptions
Necessary files for running the main interface:
- `main.py` contains the code for the text-based user interface
- `backprop.py` contains the code for the backpropagating neural network algorithm implementation
- `neuron.py` contains the code for the implementation of individual neurons
- `parse.py` contains the code for parsing text files containing network parameters, input patterns, and target values
- `param.txt` see Setup
- `in.txt` see Setup
- `teach.txt` see Setup
Helpful but not strictly necessary:
- `test.py` contains the code for testing various parameter values or data sets
- `Makefile` contains Make rules for convenient cleanup, running, and testing of the network
- `data/*` directory containing sample data sets, necessary for running tests in test.py