Binary_Neural_Network

Overview This is an implementation of a two-layer neural network. The training method is stochastic (online) gradient descent with momentum. It computes XOR for the given input. It uses two activation functions, one for each layer. One is a tanh function and the other is the sigmoid function. It uses cross-entropy as it's loss function. This is all done in less than 100 lines of code.

What does it do?

Code view 1.

In this neural network i use the python library called "Numpy" I use this because it is my favorite library to work with when working with Mathematical Code

import numpy as np
import time

Code view 2.

This part of the code is where i get the output of what it have calculated for me

print "============================================"
print ""
print "XOR FORUDSEELSE:"
print ""
print x
print predict(x, *params)
print ""
print "============================================"

Code view 3.

This is the part where i define the Sigmoid function that i use in the Neural Network

def sigmoid(x):
    return 1.0/(1.0 + np.exp(-x))

Usage

The way to use this Neural network is either to run it through python cmd or you can install Anaconda / Miniconda

and after simply just type this to make it run

python Binary.py

Afterwards you can see it have run 100 different simulations which are called "Generations" because after each generation you can see it have been getting better in "Loss" section and that it because at every Generation we have trained it to be faster over time

And then at last you can see it have spit out something that looks kinda like this

[0100101110]
[1011010001]

Now your neural network have converted all the zeros to ones and that is because we told it to take all the zeros place and replace it with a one and the other way around.