A simple sequential neural network python extension
SeqNN is a simple single threaded sequential neural network python extension written in c++ and compiled with SWIG.
- Modular layers
- 2 Dimensional Convolutional
- 2 Dimensional Max/Min Pool
- Fully Connected Dense Layer
- Regularization
- Weight decay
- Soft weight sharing for the 2D Convolutional layer
- Early Stopping
- Momentum Gradient Descent
- Clone the repo
git clone https://github.com/BrettCleary/DigitCNN
OR
- Install with PyPi
pip install SeqNN
SeqNN.py is the python wrapper for the C++ library.
Example usage with a subset of the MNIST dataset is given in DigitCNN.py. Here is a sample output:
Error Rate (%) after training 2 number of epochs is 22.8
Error Rate (%) after training 4 number of epochs is 14.4
Error Rate (%) after training 6 number of epochs is 12.8
Error Rate (%) after training 8 number of epochs is 12.0
Error Rate (%) after training 10 number of epochs is 10.8
Error Rate (%) after training 12 number of epochs is 10.8
Error Rate (%) after training 14 number of epochs is 10.0
Error Rate (%) after training 16 number of epochs is 10.8
Error Rate (%) after training 18 number of epochs is 10.4
Error Rate (%) after training 20 number of epochs is 9.6
The error rate for test dataset is 6.4
Any contributions you make are greatly appreciated.
- Fork the Project
- Create your Feature Branch (
git checkout -b feature/AmazingFeature
) - Commit your Changes (
git commit -m 'Add some AmazingFeature'
) - Push to the Branch (
git push origin feature/AmazingFeature
) - Open a Pull Request
Distributed under the MIT License. See LICENSE
for more information.
Project Link: https://github.com/BrettCleary/SeqNN
PyPi Link: https://pypi.org/project/SeqNN/0.0.3/