! WIP !

This is still a work in progress, and does not offer a graphical user interface for now.

Introduction

The Amygdala corresponds to the part of the brain that is responsible for the processing of memory and the making of decisions, kind of like a neural network...

This project aims to demonstrate how deep neural networks (a perceptron in this example) work through a simple, clean, and easy to understand code structure, in object oriented to avoid the matrix headaches. A (soon to come) visualisation GUI also helps understanding how the data is processed within such structure, and parameters can be adjusted in order to understand from the basics, to more advanced calculations. Of course, everything can be greatly optimized with the use of matrices, but the goal is not to have a fast and very precise Deep Neural Network, but rather to offer a clear overview of the implementation of one.

It is probably much slower than your quickly written TensorFlow NN, but guarenties satisfying results :) (and even your kids can read the code !)

Deep learning

This project implements a Deep Neural Network, or a multi-layer perceptron, with a few hidden layers.

Optimization algorithms

• Back-propagation

Data set

This Neural Network is trained and tested on Google's Quick, Draw! data set. The .pny Numpy files are not included in this repo, therefore you have to download the ones you want to use from here (I recommend using about 3-4 classes/files).

How to use

You need python 3.6 to run this application, as well as the Numpy dependency. Of course, you also need a data set, which should be placed in datasets/ as separate .npy files. Then, change the main function of network.py to load your input data and set the hidden layers (or none, that's your choice):

if __name__ == "__main__":
	random.seed()
	# Using npz files from https://console.cloud.google.com/storage/browser/quickdraw_dataset/full/numpy_bitmap/
	neuralNetwork = Network(nbPixels = 28*28, samplesPerClass = 10000, nbClasses = 3, learningRate = 0.35)
	neuralNetwork.add_hidden_layer(64)
	neuralNetwork.add_hidden_layer(32)
	print("[*] Loading data sets")
	neuralNetwork.set_inputs({
		'sword': np.load('datasets/full_numpy_bitmap_sword.npy'),
		'skull': np.load('datasets/full_numpy_bitmap_skull.npy'),
		'skateboard': np.load('datasets/full_numpy_bitmap_skateboard.npy')
	})
	neuralNetwork.train()
	neuralNetwork.test()

Note that you can set a different learning rate or a different amount of samples per class (which will be divided in 80% training and 20% testing), and that you have to set the right number of classes (corresponding to the number of numpy files).

Finally, simply run it as an executable:

./network.py

Todo

• Write code base
• Write the feed forward algorithm
• Write the backpropagation algorithm
• Fix the backpropagation algorithm for more than one hidden layers
• Improve the speeeeeeed
• Code the GUI

Resources

I found the following resources very helpful to understand the concepts and inner workings of neural networks:

• 3Blue1Brown's video
• A Step by Step Backpropagation Example
• A Deep Learning Tutorial: From Perceptrons to Deep Networks

Author

Made by Theo Morales theo.morales.fr@gmail.com for the fun, and because of a failed exam in Optimization and Data Analytics and a re-exam comming soon... Feel free to share <3