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For example, try the following and verify that it works in a simple case - this creates a net with 64 inputs, 10 outputs, and it contains one trivial training sample: First, make sure that the input data filename is correct at line 361 The contents of trainingData.txt should be formatted as shown in Comment #4 above. That assert at line 390 is triggered if your input data file isnвЂ™t formatted exactly as the program expects. And as you can see in, the rand() function as implemented in todayвЂ™s compilers is way more than sufficient for our needs. Even the old Turbo C implementation would have sufficed. WeвЂ™re only using rand() to initialize weights so that the infant neural net begins its walk toward a solution somewhere in the midst of the huge solution space instead of always starting out at the same remote corner of the solution space. (The drand48() function is not standard in C++98 nor C++11.) In C++-11, you could use new cryptographic quality random number generators if you needed random bits that would survive intense statistical analysis. Besides, thatвЂ™s our only choice if weвЂ™re sticking with standard C++ features. I appreciate your comments about the deficiencies of rand(), but itвЂ™s not a problem here. But you can certainly abstract it differently and implement it in several alternative ways, all mathematically equivalent and all quite correct. TheyвЂ™re more alike than different, and many loops are simplified by including the bias neuron in the loops. The bias is no more or less important than the other input signals that get summed, and its weighting is recalculated during training in the same way as the other neurons. A neuron is just an integrator, and the bias input carries what you could think of as a DC component that goes with the signals that are on the other inputs. The reason the bias neuron is not a private member of the neuron class is so that the backpropagation loop can adjust its weight the same way it adjusts all the other weights. The bias input is slightly special, but only because its input is constant when running a trained simulator. YouвЂ™re absolutely correct that showVectorVals() could take its string argument by reference to const. Hi Dov, Thanks very much for the thoughtful feedback it deserves a thoughtful reply.

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I hope these comments serve to make your code even better. The abstraction should be that you link each neuron with the location where its input values are.ДЄny way you slice it, youвЂ™ve done a real service to anyone getting started in neural networking. Similarly, I donвЂ™t see that you gain anything by making a bunch of bogus neurons on the input. Sure itвЂ™s slower and bigger, but itвЂ™s not the speed, itвЂ™s the lack of clarity that bothers me.

Bias seems to me to be intrinsic to the neuron, so it seems to me that having a private member with the bias number is the way to go, not creating an extra bogus Neuron that is always one, and then weighting it.

The other nitpicky things are not serious, but I donвЂ™t see why you abstracted certain things the way you did.

Even better, use boost, but if youвЂ™re trying to keep things simple I can understand why your wouldnвЂ™t. drand48() is the ANSI standard uniformly distibuted u. On Turbo C way back when, I actually saw an implementation that cycled after 137 numbers! Hopefully no implementation is as bad today, but rand() is not designed for floating point applications. The rand function has no specified implementation, so itвЂ™s highly nonstandard.

This is a nice intro to neural networks, and itвЂ™s certainly making my task of getting something going in that area easier.

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The finished neural net source code is also available for download here (see the video for instructions on what to do with it): This tutorial does not cover or require exception handling, memory management, STL iterators, inheritance, threads, or graphical input or output.

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YouвЂ™ll see how to teach your neural net to solve a simple task, then you can supply your own training data to train your net to do wonderful and amazing things.Д«esides showing how a neural net works, we also discuss: YouвЂ™ll end up with a classic back propagation model with adjustable gradient descent learning and adjustable momentum. If youвЂ™re a beginning to intermediate C++ programmer, this tutorial will guide you through the analysis, design, and coding of a command line console program that implements a neural net in C++. Neural Net in C++ Tutorial from David Miller on Vimeo.

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Released today! Our newest video programming tutorial, A Neural Net Simulator in C++, is now available for your viewing pleasure at the following link:

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