Authors: Francisco Erivaldo Fernandes Junior and Gary G. Yen

This code can be used to replicate the results from the following paper:

F. E. Fernandes Junior and G. G. Yen, “Particle swarm optimization of deep neural networks architectures for image classification,” Swarm and Evolutionary Computation, vol. 49, pp. 62–74, Sep. 2019.

@article{fernandes_junior_particle_2019,
	title = {Particle swarm optimization of deep neural networks architectures for image classification},
	volume = {49},
	issn = {22106502},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S2210650218309246},
	doi = {10.1016/j.swevo.2019.05.010},
	language = {en},
	urldate = {2019-07-06},
	journal = {Swarm and Evolutionary Computation},
	author = {Fernandes Junior, Francisco Erivaldo and Yen, Gary G.},
	month = sep,
	year = {2019},
	pages = {62--74},
}

To run this code, you will need the following packages installed on you machine:

• Python 3.7;
• Tensorflow 2.2.0;
• Keras 2.3.1;
• Numpy 1.16.4;
• Matplotplib 3.1.0.

Note1: If your system has all these packages installed, the code presented here should be able to run on Windows, macOS, or Linux.

1. First, clone this repository:

   git clone https://github.com/feferna/psoCNN.git
   

2. Download the following datasets and extract them to their corresponding folders inside the datasets folder:

   1. Convex: http://www.iro.umontreal.ca/~lisa/icml2007data/convex.zip
   2. Rectangles: http://www.iro.umontreal.ca/~lisa/icml2007data/rectangles.zip
   3. Rectangles with Background Images: http://www.iro.umontreal.ca/~lisa/icml2007data/rectangles_images.zip
   4. MNIST with Background Images: http://www.iro.umontreal.ca/~lisa/icml2007data/mnist_background_images.zip
   5. MNIST with Random Noise as Background: http://www.iro.umontreal.ca/~lisa/icml2007data/mnist_background_random.zip
   6. MNIST with Rotated Digits: http://www.iro.umontreal.ca/~lisa/icml2007data/mnist_rotation_new.zip
   7. MNIST with Rotated Digits and Background Images: http://www.iro.umontreal.ca/~lisa/icml2007data/mnist_rotation_back_image_new.zip

3. Now, you can test the algorithm by running the main.py file:

   python main.py
   

   or

   python3 main.py
   

Note2: The algorithm's parameters can modified in the file main.py.

Note3: due to our limited resources, we cannot provide any support to the code in this repository.