nianet 1.0.0

Designing and constructing neural network topologies using nature-inspired algorithms

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Designing and constructing neural network topologies using nature-inspired algorithms

Description 📝

The proposed method NiaNet attempts to pick hyperparameters and AE architecture that will result in a successful encoding and decoding (minimal difference between input and output). NiaNet uses the collection of algorithms available in the library NiaPy to navigate efficiently in waste search-space.

What it can do? 👀

• Construct novel AE's architecture using nature-inspired algorithms.
• It can be utilized for any kind of dataset, which has numerical values.

Installation ✅

Installing NiaNet with pip3:

pip3 install nianet

Documentation 📘

The paper referring to this source code is currently being published. The link will be posted here once it is available.

Examples

Usage examples can be found here.

Getting started 🔨

Create your own example:

In examples folder create the Python file based on the existing evolve_for_diabetes_dataset.py.

Change dataset:

Change the dataset import function as follows:

from sklearn.datasets import load_diabetes
dataset = load_diabetes()

Specify the search space:

Set the boundaries of your search space with autoencoder.py.

The following dimensions can be modified:

• Topology shape (symmetrical, asymmetrical)
• Size of input, hidden and output layers
• Number of hidden layers
• Number of neurons in hidden layers
• Activation functions
• Number of epochs
• Learning rate
• Optimizer

You can run the NiaNet script once your setup is complete.

Running NiaNet script:

python evolve_for_diabetes_dataset.py

HELP ⚠️

[e-mail]

Acknowledgments 🎓

• NiaNet was developed under the supervision of doc. dr Iztok Fister ml. at University of Maribor.

• This code is a fork of NiaPy. I am grateful that the authors chose to open-source their work for future use.

License

This package is distributed under the MIT License. This license can be found online at http://www.opensource.org/licenses/MIT.

Disclaimer

This framework is provided as-is, and there are no guarantees that it fits your purposes or that it is bug-free. Use it at your own risk!