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Part I OverviewPart 2 Learning Through Back PropagationPart III The InterfacesPart IV Implementation ClassesPart v Actually Doing Something XORPart v ConclusionBasically each neuron in our brain accepts input from many other neurons and then provides a resulting output This is precisely what we will be replicating in code Each neuron class will have a structure similar to diagram 1 where there is a body of attributes and one output Each neuron can have multiple inputs and the neurons will be grouped as in diagram 2 Neurons will be grouped in layers While processing a signal (we&#39ll call it a “pulse”) the signal will start at the top layer flowing through and being modified by each neuron in that layer Each neuron will modify the strength of the pulse After the modification has been completed the “pulse” will travel to the next layer and be modified again Now that you have the details let&#39s take a step back and see how a large number of cells create a “neural net” or a network of neurons For a neural net to work we need at least three groupings of neurons The top layer is used by the neural net to perceive the environment and So now for the magic making the network learn In order for our neural net to have the ability to learn after our signal travels from the top of our net to the bottom we have to update how each neuron will affect the next pulse that travels the network This is done by a process called back propagation Basically we figure out a figure representing the level of error that our network produced This is arrived at by comparing the expected output of the net to the actual output Let&#39s say we have an error in one of the cells in the output layer Each neuron will keep track of the neurons sending the pulse through and adjust the importance of the output of each of these parent neurons that contributed to the final output of the error cell Next each of these neurons will have an error value calculated and the adjustment will “back propagate” meaning that we will perform the same process to the next layer of neurons (the ones that send the pulse to our hidden layer) Conceptually First we will build the basic interfaces and then implement them In developing scalable code our interfaces can be the most crucial part of the project because they determine how the implementation will fall into place and ultimately the success of our project First we need an interface to define signals traveling through the neurons of our network And we need an interface to define the input of a neuron which is composed of the output from many other neurons For this we&#39ll use a generic dictionary where the key is a signal and the output is a class defining the “weight” of that signal We could have used a double to represent the weight of each INeuronSignal in the INeuronReceptor but we are going to be performing a technique called batch updating after a number of back propagations which will help our network learn more efficiently As a result we need a class to store not only the weight of the signal but also the amount of adjustment we&#39ll be applying when updating O Still with me? I hope so because now we start getting to the fun part implementing the neural net 1) The Neuron The neuron has member variables used in implementing the interfaces The two interesting things to highlight are the Sigmoid() static function and the Pulse() method The Sigmoid() function uses a Sigmoid curve to squash the output of the neuron to values between 0 and 1 Note This means our entire network runs with values x where 0 < x < 1 or alternatively x (01) (x is between 0 and 1) B) The Neural Layer The NeuralLayer class is basically a collection of neurons responsible for passing a Pulse() or ApplyLearning() command through to it&#39s member neurons This is implemented by wrapping a List and passing the IList methods and properties through The only implementation we really worry about are the following two methods Last but definitely not least is the NeuralNet The most interesting things to note in the NeuralNet class are the m_learning We want to train a neural net to perform an XOR operation on two bits We&#39ll build a neural net with two input neurons two hidden neurons and one output neuron We want to train the net to perform the following operation The problem is that we are dealing with fuzzy Boolean numbers Our entire neural net runs with the double data type having values between 0 and 1 and we need to get crisp values out of the net Not to worry we just have to fuzzify our input and defuzzify out output For the inputs instead of using the value 1 we&#39ll use a "big" number (like 09) and for the value 0 we&#39ll substitute a "small" number (like 01) We&#39ll use these same values for expected values during training After training for our output we&#39ll say anything 05 and above is a 1 and anything below 05 is a 0 We&#39ll create a button for training our neural net initialize it and run through iterations of 100 training sessions for each application of learning It would be interesting to see how ma On a final note another application (often used in gaming AI) consists of having multiple output neurons each with an associated action After observation of the environment and pulsing the network the node with the highest output value is determined to be the “winner” and the associated action is taken This is called the “winner take all” approach I hope you enjoyed this article It is meant to an introduction as there are many aspects to neural net programming that we did not get into Efficient training of neural net is a huge subject to cover by itself But I imagine you are pretty beat by this time Until next time.

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