Machine Learning – results

So, I reached the point where my Neural Network , after a couple of thousand training iterations, is capable of correctly identifying and separating different categories of input into their corresponding classes.

More specifically, as “targets”, I’ve chosen a number of continuous sampled waveforms, each one 1000 samples long, wheras as “noise”, I’ve simpy used equally many samples of random noise.  One could imagine that the targets represent sonar clips of submarines, sea life or something else, and the noise samples just represent the background noise in the marine environment.

Anyways, a visual illustration of the 100 target samples is below:

sinus-100.png

Next image illustrates the noise, equally 100 samples, each with size of 1000:

noise-100.png

I put these data as the training data to my neural network, in this case a 1000 x 100 x 10 x 1 network, and after a couple of hundred learning iterations the results are as below:

learning_run

So, the network is capable of providing a perfect separation between the two classes of training data, as can be seen from the upper graph. The lower graph shows how the network fairly quickly converges towards a solution: the y-axis shows the cumulative error over all the training data per iteration. As can be seen, the network is able to adjust its 101.010 (1000 x 100 + 100 x 10 + 10 x 1) weights towards a convergence.

To test the network after the training, I pass in one single target waveform, randomly chosen, and similarly, one single noise input.  The below graph shows that the network is capable of correctly classifying these two inputs.

sample_test

I’ve done this experiment using my very modest laptop – despite my very limited number crunching and data processing power, I’m able to obtain good results. Imagine then what the Big Guys, the Google’s, Facebook’s, Amazon’s and others are able to do with this technology, with their immensely more powerful machinery….

Artificial intelligence is already changing socities and human life, and it will continue doing so in an ever increasing pace. Humans will need to think carefully about how to deal with this “Pandora’s Box” – a good intro to the potential dangers of Super-AI can be found here:

About swdevperestroika

High tech industry veteran, avid hacker reluctantly transformed to mgmt consultant.
This entry was posted in AI, development, Machine Learning, Neural networks, Society, Technology and tagged , , , , , , , . Bookmark the permalink.

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