Why Neural Networks work

One popular explanation of the fact, that artificial neural networks can do what they can do, goes along these lines:

  1. A brain is capable to do these things.
  2. An artificial neural network is a simulation of a brain.
  3. Therefor an artificial neural network can do these things, too.

20160222_091412.jpgAdmittedly, most things in computer science that “work” in the sense, that they produce useful output for the real world, are implementations of theoretical models that have been build by other sciences, so this is kind of a valid explanation. I don’t like it anyway.

My first problem with it is this: it’s not quite true, that an artificial neural network (ANN) is a simulation of a brain. To be fair, some  come impressively close. But in the context of this blog we unambitiously restrain ourselves to the level of sophistication that we find in most real world ANNs, which are radical (radical!) simplifications of even the simplest natural neural networks.

Second: it does not help most people to understand, why the ANN is capable of doing useful work. Unless you already understand the brain, it won’t help you much, when I tell you, how we are going to map gray matter to mathematical concepts. (And if you already understand the brain: Welcome to the blog. You can skip the rest of this post, if you want.)

I want to come from the other side, and approach the topic as an engineering problem. Buckle up. We are going to manufacture a special purpose classification machine, and then (in a later post) we will generalize it and see if the result has any similarity to what the neuro sciences know about the brain.

As a basic motivation, let’s assume, that your boss has found a webcam that shows a stock market chart (like this), and came up with this brilliant idea: he will become insanely rich with a new software, that reads the chart and outputs some kind of likelihood, that the market is in an upward trend. Your boss calls this likelihood “Zuversicht”, and we are going to stick with this term for a while, because we like German words, and because the corresponding English word (“confidence”) already has a certain meaning in statistics, and we want to prevent confusion resulting from ambiguous terminology.

Ok, now our input is an image from a webcam, so we have a two dimensional array of pixel colors. To make it easier, you convert the image to grayscale, so you only have to think about the pixels’ brightness and ignore hue and saturation. You look at some examples of upwards trends and can’t help but to observe, that the lines tend to start in the lower left corner and zigzag their way to the upper right corner.


Breaking down the image in quadrants, you notice, that in these cases the average pixel brightness in Q2 and Q3 is higher than in Q1 and Q4. With this insight you write the following lines of code and declare your job done.

double zuversichtUptrend(double[][] image){
  int imgLenth = image.size();
  int imgWidth = image[0].size();
  double averageBrightnessQ1 = Math.avg(Arrays.subarray(image,0,imgLenght/2,0,imgHeight/2);
  return averageBrightnessQ2 + averageBrightnessQ3  


It works great on the test data, your boss is happy and his boss gives him a raise. But a few weeks later, he tells you, that he’s not happy anymore. He has not become insanely rich!

What went wrong?

Apparently, your program has mis-classified the trend on several occasions. So you have a look on the chart images for these days, and see two major flaws of your approach:


  1. On some days, the chart went almost flat or turned back to negative. The chart was just low enough in the early hours to run through Q3 and just high enough in the later hours to run mostly through Q2.
  2. On other days the chart went clearly down, but your software’s Zuversicht value was very high. The reason turns out to be, that the overall brightness of the picture was high on those days, illuminating Q2 and Q3 without the chart line covering much space in them.

So you start the second iteration of your engineering endeavor.

To solve problem 1, you obviously need a higher resolution. Let’s try 8×8! This partition conveniently allows us to identify each field with a chessboard notation.


A perfect upward trend, wich your boss defines as a straight line from the lower left to the upper right, will result in the fields A1, B2, …, H8 lit up and the other fields remain dark. The flat chart from problem 1 will rather lite up the fields A4, B4, .., G5, H5. Great, but what about all the other possible charts that show a trend that goes upward in a non steady, somewhat chaotic fashion? This is, after all, rather the norm then the exception.


Let’s add some fuzziness to the system.  The intuition is like this: For each field you guess the probability that the full chart shows an overall upward trend if the particular field is lit. For example, if the lower right corner (H1) is lit, the probability of an overall positive trend is zero. If the field left to it (G1) is lit, the probability is close to zero, but there is still a possibility, that the chart makes a radical upward turn in the remaining 1/8 of the chart. The closer you get to the perfect upward trend, the higher the probability becomes.


You call the resulting 8×8 numbers a “weight matrix”. You can use it as a filter for the actual chart images by doing the following:

  1. For each field of the loaded picture you multiply the actual average brightness with the corresponding value in the probability matrix. The product will be a high value, when the average brightness is high and the value in the probability matrix is high. Otherwise it is a low value. You repeat this step for each field, 64 times altogether
  2. You add up all the  products.

The closer the actual chart zigzags around the ideal chart, the higher the sum will be. But even when the actual chart goes astray: if it remains on a positive trajectory, we will get a relatively high result in this calculation.

So far so good. Let’s  look at the second problem: the tide lifts all boats and the ceiling light lights up all pixels. When someone turns on the light in the trading room, all pixels in the Webcam picture become brighter. Even areas that are not trespassed by the line chart seem brighter, which renders our filtering result worthless.

Let’s add a preprocessing step to fix this.  If there was no line chart in the picture, all pixels would have approximately the same brightness and they were supposed to be black (brightness zero). If in this case, you would subtract the overall average brightness from each fields measured brightness, the result would be all black fields. Subtracting the overall average brightness normalizes the picture to what it is needed for our further processing.

Now add the line chart to your consideration. Because it covers only a very small fraction of the image, it does not change the overall average brightness too much. The light noise that illuminated the dark parts of the image, also made the bright parts (the lines of the chart) brighter. So if we subtract the average overall brightness from the bright pixels, we also normalize those parts of the image to what is expected as an input for the next processing step.

Great, now you know what to do to solve problem 2. Question is: How do you do it. Wouldn’t it be great if you could implement both processing steps in a unified way. In other words: is it possible to define a weight matrix in such a way that when we apply it to the input data, the average overall brightness subtraction of your preprocessing step is executed. Turns out: it is possible.

Imagine the following weight matrix for field A1:

  • Value at position A1: 1-1/64
  • Value at all other positions: -1/64

Please convince yourself, that this Matrix will do the average subtraction for position A1. Of course, this works just as well for all other positions.


Hmmm, interesting, you just solved two seemingly totally different problems with the same approach. It feels a little odd to define a huge matrix for a calculation that could easily be done procedurally, but you have a feeling, that there might be a systematical advantage in a unified way to tackle problems in this project. Also, of course, you know, that vector (and with it matrix-) calculations are the strongpoint of GPU data processing as well as highly optimized Software packages like Matlab (“Matrix Lab”!) and Octave. You feel that after your initial success, your boss might become greedy, which will ultimately put more load on your software. Having some strong performance afterburners like these in your arsenal, might come out handy later.

Your overall process has three steps now:

  1. You create an 8×8 matrix from the image data as input data layer. (To facilitate vector operations, you “flatten” this matrix to a vector of lenght 64, but that’s an implementation detail).
  2. For each field you apply the corresponding preprocessing 8×8 weight matrix to whole input layer 8×8 matrix. The result is a new 8×8 matrix, which you call the “hidden layer“. (And in the real world, you would do this again with “flattened” vectors and a large 64×64 weight matrix representing all fields. This is mathematically equivalent and can be well parallelized. Again: just an implementation detail)
  3. You apply the classification weight matrix an the hidden layer and get the Zuversicht value as output.


There you go: without thinking much about neurons, synapses and ganglia, you have handcrafted your first artificial neural network. Your new software is actually what people call a Feedforward Neural Network with a linear activation function. When you define a threshold value for the Zuversicht output, you also have a binary linear classifier.

Your neural network is still far from being perfect. You will eventually get there, but not today. Lets just mention a few things that you would need to think about before going into production:

  • It is not able to learn! It works because you were able to provide a “model” (that is the weights in the weight matrices). This is good enough for now, but for the future we prefer to let the computer do the work of figuring out the model data.
  • It is not well protected from eccentric input data. Imagine what happens, if a camera error or a data transfer problem produces for a single pixel a value of  325212498434 instead of a value in the expected range between 0 and 1.
  • It will still fail to make your boss immeasurably rich, because it does not predict anything. It only classifies a chart as close enough to your bosses definition of a perfect chart. This is, what he wanted, so it is partly his fault. But we nevertheless can do better.

Even with these shortcomings, you hopefully have built up some comprehension as to how a neural network is able to recognize a pattern. We have seen that, even without actively imitating nature, we get to a similar result, when we just work our way to the best solution in a straightforward manner.

A little heads-up: In the next post, we will build the software to convert the collected Bitcoin price and market data to a format suited as an input data layer for a neural network like this. If your data collector from the previous post is not running yet, please start it soon to have some data to play with next time.




One thought on “Why Neural Networks work

  1. Pingback: Data Re-Coding 1 | notes on personal data science

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s