one for the bias neuron, we use only one bias neuron because the information needed for every other neuron is stored in the weight of the link connecting it with the bias.Īll of them will have properties for storing the activation value of the neuron (output) and the backpropagated gradient that must act on the links reaching it.one for every type of layer-neurons (input, hidden and output), in this way it iis simpler to provide different behaviours for them, and.We will define several breeds of agents in order to ease the model: Outputs List with the binary output in the trainingĮpoch-error error in every epoch during training Inputs List with the binary inputs in the training Global variables needed to operate de model are (together with those from the interface controls) globals [ĭata-list List of pairs to train the network With this in mind, let's see the data structures to be used in the implementation. Back-Propagate: Adjust the weights in some way to reduce this error.Compute the error between the expected value and the obtained one.Propagate: Compute the value of the network for the sample.Take every sample from the the samples dataset.Starting from a random value of weights.We will train the network to compute a function from a sample-set of pairs $(input\ output)$ with the correct return that the network must provide (computing with the function we are trying to learn), and as usual in this learning model:
NETLOGO OF CODE
Even (it returns \(1\) if there are an even number of \(1\)'s).Īgain, this affects only auxiliary procedures, not the main ones for the learning process, and it is easy to add more functions, or adapt the code to work with different kinds of functions.Majority (it returns \(1\) if the input has more 1's than \(0\)'s), and.The NetLogo model comes with two example boolean functions: Indeed, our networks will return a value in the interval \(\), that we can transform adequately in a boolean value (for example, with an appropriate step function). All these restrictions can be easily adapted to fix other requirements, ans then the main learning process has not to be changed, only the setup. That is, neurons from input layer will take only boolean values (\(0\)/\(1\)) and the return will be a boolean string. As a first approach, we will use them for computing boolean functions, The model we will prepare here is a slightly variant of that coming with the NetLogo official distribution. Although it lacks some good features (for example, we can't control the learning process when we need several hidden layers to model more complex problems because the bad composition of Gradient Descent Method for tuning weights), it can solve a lot of interesting problems and offers a very visual way to understand the fundamentals of this area of Machine Learning.Īs you can find a lot of good resources out there about the basics of ANN (better than I can write here) we will focus this post only in the implementation in NetLogo of a more or less flexible multilayer perceptron network, hoping that the use of agents and links in the model can help the reader to understand the central ideas of how it works. indeed, they are the basement of the new DNN methods and it is still appearing in some phases of them. The Multilayer Perceptron is one of the main ANN structures in use today. my excuses for those of you awaiting for something about Deep Neural Networks (DNN, as those used in AlphaGo from Google, or CaptionBot from Microsoft), maybe in a later post I will try to extract the main features of some convolutional neural network and test it on a very simple NetLogo model, but I am afraid that we will need too many computational resources to obtain anything of interest with this tool. We will restrict ourselves to the more common and classical ones, the Multilayer Perceptron Network. As a way to continue with AI algorithms implemented in NetLogo, in this post we will see how we can make a simple model to investigate about Artificial Neural Networks (ANN).
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