"NodeNet" is a project aims to bulid neural network. It's oriention is to collect plenty of types of neural networks and provide manager to organize and test it, and the neural network libary can work without needs of manager isolatedly. It is a project belongs to NOOXY. There still lots of miles to complete it. Article about it might be established some day. Visit us www.nooxy.tk.
- Python package: NumPy
- Python package: Matplotlib
- Python package: CuPy(Optional)
to install Python packages
#!/bin/bash sudo python3 -m pip install numpy matplotlib
to use Nvdia GPU (example: ArchLinux)
#!/bin/bash sudo pacman -S cuda # Install CUDA sudo vim /etc/profile # In vim add /opt/cuda/bin to your path # finally sudo python3 -m pip install cupy # if something wrong try source /etc/profile # or sudo reboot now
node manager's executable files. They are compiled by C++ side source code in main directory, and it's build target is mainly macos(Darwin) now.
Source code file for node manager.
There include two type of launguages
python. They are completely seperated(work independently). However, it's functions act similarly.
there are source for
matrix(mathematical lib for ann),
ann(artificial neuron network library) and
node manager(main program).
We are contructing now. Not yet to be describe. For launching manager
If you need Nvidia GPU support(for really large size of neural network) simply change all
import numpy as np
import cupy as np
Test the nerual network for finding good models etc.
A directory to test some functions that not yet to be added to mainline.
- we stop this project temporary
- Normalization ability [x]
- CuPy compatible [v]
- More types of neural network training method, listed below.
- Validation graph and fucntion [v]
- Validation to stop training [x]
- Ability to start a project to determine best model automatically [x]
- Plotting [v]
- Costomizable activation function, cost function [x]
- Save learning profile [v]
- Merge validation data to one file [x]
- Mini batch training [x]
- Apps API [x]
- more and more to be added
Training method list
- backpropagation vanilla[v]
- Levenberg-Marquardt backpropagation [x]
- Backpropagation with classical momentum [v]
- Backpropagation with Nesterov momentum [v]
- RMSprop [v]
- Adagrad [v]
- Adadelta [x]
- Adam [v]
- Resilient Backpropagation [x]
- Scaled Conjugate Gradient [x]
- Deep feed forward [v]
- Convolutional neural network [x]
- We are planning to design neural network with network protocol feature.
Input layer 8 neurons, Output layer 8 neurons. 2 hidden layer. Training Graph. backpropagation. Input layer 8 neurons, Output layer 8 neurons. 2 hidden layer. Training Graph. backpropagation. NodePy execution NodePy execution NodePy execution