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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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