Compiling the mlp programs
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Compiling the MLP programs. Create a folder to work in Download ALL .c files to this folder MLPInit.c MLPTrain.c MLPClass.c MLPLib.c MLPReadWrite.c Use Visual Studio .NET command line compiler E.g: cl MLPInit.c cl mlpinit.c will also work. Running the MLP programs.

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Compiling the mlp programs
Compiling the MLP programs

  • Create a folder to work in

  • Download ALL .c files to this folder

    • MLPInit.c

    • MLPTrain.c

    • MLPClass.c

    • MLPLib.c

    • MLPReadWrite.c

  • Use Visual Studio .NET command line compiler

  • E.g:

    • cl MLPInit.c

    • cl mlpinit.c will also work


Running the mlp programs
Running the MLP programs

  • Start with MLPInit

  • Make sure mlpconfig is in your current folder

  • If it’s called mlpconfig.txt, then

    • EITHER: Change its name to mlpconfig (ren mlpconfig.txt mlpconfig),

    • OR: edit MLPLib.c, change

      • if ((config=fopen("mlpconfig","r"))==NULL) , to

      • if ((config=fopen("mlpconfig.txt","r"))==NULL)

  • Run program from command line, e.g:

    • MLPInit 3 5 10 init-3x5x10

    • This will create an MLP with 3 input units, 5 hidden units and 10 output units and save it in init-3x5x10


Running the mlp programs1
Running the MLP programs

  • Similarly

    • run MLPTrain to train your MLP

    • run MLPClass to use your MLP to classify data

  • E.G 1

    • MLPTrain init pbData_m_f1f2_train.txt 100 trmlp

    • This will

      • Use the data in file init to define the initial MLP

      • Use pbData_m_f1f2_train.txt as training data

      • Run 100 iterations of the Error Back Propagation algorithm

      • Put the results in trmlp


Running the mlp programs2
Running the MLP programs

  • Similarly

    • run MLPClass to use your MLP to classify data

  • E.G 2

    • MLPClass trmlp pbData_m_f1f2_test.txt

    • This will

      • Use the data in file trmlp to define the MLP

      • Use pbData_m_f1f2_test.txt as test data

      • Print out the error rate


Running the mlp programs3
Running the MLP Programs

  • Make sure mlpconfig is in your folder!

  • Example mlpconfig

    • <KSCALE> controls the slope of the sigmoid function (see slide 11 from last week)

    • <LRATE> controls the learning rate

    • <MOMENTUM> is the momentum – it controls the ‘stability’ of the learning process

    • <TRMODE> can either be BATCH or SEQUENTIAL

<KSCALE> 1.0

<LRATE> 1.0

<MOMENTUM> 0.0

<TRMODE> BATCH


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