Last week, I posted a list of goals for my neural network for that weekend. I’ve managed most of them except for the storing of the network.
Didn’t do it last weekend because I was just plain exhausted. After writing the post, I basically went to sleep for the rest of the weekend.
demo (Firefox only)
The demo is not straightforward. Usually, I create a demo which is fully automatic. In this case, you need to work at it. In this case, you need to enter an image URL (JPG-only for now), a name (or names) for a neuron which the photo should answer “yes” to, and similar for “no”, then click “add image”.
The network then trains against that image and will alert you when it’s done. The first one should complete pretty much instantly. Subsequent ones are slower to learn (in some cases, /very/ slow) as they need to fit into the network without breaking the existing learned responses.
I’ve put a collection of images here. The way I do it is to insert one image from Dandelion, enter “dandelion” for yes and “grass,foxtail” for no. Then when that’s finished, take an image from Foxtail and adapt the neuron names, then take one from Grass, and similar. Over time, given enough inputs, I think the network would train well enough to learn new images almost as fast as the first one.
Onto the problems… I spent all weekend on this. First problem is with the sheer number of inputs – with an image of 160×120 size, there are 19200 pixels. With RGB channels, that’s 57600!
So the first solution was to read in the RGB channels and change them to greyscale – a simple calculation – that cut the inputs by two thirds.
setTimout replacements for some
Another JS problem had to do with the
<canvas>. There is no “getPixel()” yet for Canvas, so I get to rely on getImageData which returns raw data. According to the WhatWG specs, you cannot read the pixels of a Canvas object which has ever held an image from an external server. So, I needed to create a simple Proxy script to handle that. (just thought of a potential DOS for that – will fix in a few minutes)
Another problem had to do with the network topology. Up until now, I was using networks where there were only inputs and outputs – no hidden neurons. The problem is that when you train a network to recognise something in a photo based on the actual image values, you are relying on the exact pixels, and possibly losing out on quicker tricks such as combining inputs from neurons which say “is there a circle” or “are there parallel lines”. Also, networks where all neurons can read from all inputs are slow. A related problem here is that it is fundamentally impossible to know exactly what shortcut neurons would give you the right answer (otherwise there would be no need for a neural network!).
So, I needed to separate the inputs from the outputs with a bunch of hidden neurons. The way I did this was to have a few rules – inputs connect to nothing, hidden neurons connect to everything, outputs connect to everything except inputs.
The hope with this is that the outputs would read from the hidden neurons, which would pick up on some subtle undefinable traits they spot in the inputs. So the outputs would be almost like a very simple network “overlaid” on the more complex hidden neurons.
Problem is – how do you train these hidden units in a meaningful way? I’m still working on this… Right now, it’s based on “expectation” – the output neuron, when it produces a result, is either right or wrong. If the neuron is then to be trained, then it tells the hidden units that it relies on what output it expected from those units in order to come to the correct conclusion. The hidden units wait until there have been a number of these corrections pointed out to them, then it adjusts accordingly. (two thoughts just occurred to me – 1; try adjusting the hidden neurons after every ‘n’ corrections (instead of at arbitrary points), and 2; only correct a hidden unit if the output neuron’s result was incorrect.)
Another thing was that there is no way of knowing how many hidden units are actually required to have the network produce the right results, so the way I get around this is to train the network, and every (hidden units * 10) cycles, if a solution has still not been found, add another hidden network. This means that at the beginning of training, hidden units will be added pretty regularly, but as the network matures, new neurons will be added at rarer periods.
I’ve caught the ANN bug. After spending the weekend on this, I still have more ideas to work on which I’ll probably do during the week’s evenings. Some ideas:
- Store successful networks after training.
- Have a number of tests installed from “boot” instead of needing them to be entered manually.
- Disable the test entry form when in training mode.
- Work some more on the hidden unit training methodology – it’s still way off.
- Allow already-inputed training sets to be edited (for example, if you want to add “rose” to the “no” list of “grass”).
I think that a good network will have a solid number of hidden neurons which do not change very often. The outputs will rely on feedback from each other as well as those hidden units. For example, “foxtail” could be gleaned from a situation where the “dandelion” is a definite no, and “grass” is a vague yes, along with some input from the hidden units approximating to “whitish blobs at the top” (over-simplification, yes, but I think that’s approximately it).
update It turns out the network learns very very well given just two neurons to learn – I’ve managed to get the network to learn almost every image in “dandelions” and “grass” with only 4 hidden units and 2 output neurons.