Want Help on Neural Network

I have developed some code for the implementation of a two layer neural network structure in Labview. The network is supposed to read the training sets of data from a file and should train itself, but it is not working may be because of some error. The network can simulate successfully but is unable to train itself properly. I require this network for the implementaion of a very novel project
I have marked the whole program with appropriate descriptive tags(see trainig.vi). If some one can tryout and find the error it will be of great help to me. I will then be able to post the correct network for the benefit of others.
Attachments:
data1.txt ‏6 KB
our net.zip ‏75 KB

I have two suggestions for improving your code and increasing your possibility of troubleshooting it accurately.
The first suggestion is to not use sequence structures (flat or stacked). If you need to make a part of your code happen after another part of code, consider using a state machine architecture as described here:
http://zone.ni.com/devzone/cda/tut/p/id/3024
Additionally, instead of using variables (local or global) transport your data using wires. This way you can be sure to conform to LabVIEW's data flow.
Both of these things will make your code easier to read and debug.
Best of luck!

Similar Messages

Neural Network Issue

Please help
I am trying to run a Neural Network for my company. I have a data set that I already used to train a Logistic Regression function using SAS EG, but I wanted to see if I could better predict using a Neural Networkin SQL SSAS, given that my outcome (equal
to 1) is a rare event.
Using the same data set, I created a data mining structure in SQL SSAS similar to the one used to train my logistic regression model in SAS EG. In SQL SSAS, I set a Holdout Seed, so that if I left off at the end of the day I could work with the exact same
model the next day.
However, when I ran the model 'the next day' I got different results, my score was different, my classification matrix was different, etc. And not just a little different, very different.
Based on further investigation, I found I had included some variables that had the potential to cause separation (as determined by SAS Logistic procedure). If I removed these variables, I could recreate my model 'the next day'. However, if I did
not have SAS EG, I would not have known which variables were problematic without going through quite a bit of work and testing. In SQL SSAS, there is no warning in the log to tell me which variables were causing my issue.
So my question:
In SQL SSAS
Is there a way to train a Neural Network Model and have it identify any variables that are causing a potential problem in the model?
Or is there a way to extend the training duration to make sure I acheive similar results each time I run the model?
Any help would be greatly appreciated
~S

Hi TJ,
A lot of us are still looking at Azure for answers on this one. The problem is ongoing for many. While workarounds are available depending on context, it's nothing to do with the configuration of your servers, but rather to an unresolved problem at
the Azure end.
Alexander

Using threads in a neural network

Hello,
I've written a neural network and I'm wondering how I could use threads in it's execution to 1) increase (more precisely achieve!) learning speed and 2) print out the current error value for the network so that I can see how it is working without using the de-bugger. Basically, i've read the Concurrency tutorial but I'm having trouble getting my head around how I can apply it to my network (must be one of those days!)
I'll give a brief explanation of how i've implemented the NN to see if anybody can shed any light on how I should proceed (i.e. whether it can be threaded, what parts to thread etc.)
The network consists of classes:
Neuron - stores input values to be put into the network and performs the activation functions (just a mathematical operation)
WeightMatrix - contains random weights in a 2-D array with methods for accessing and changing those weights based on output error
Layer - simply an array that stores a collection of neurons
InputPattern - stores the values in an array and target value of a pattern (e.g. for logical AND i would store in pattern[0] = 1; pattern [1] = 1; target = 1;)
PatternSet - set of InputPatterns stored so that they can be input into the network for learning
NeuralNetwork - the main class that I want to thread. This class contains multiple layers and multiple WeightMatrices (that connects the neurons in each layer). The learn algorithm then uses the methods of the previous classes to generate neuron inputs and ouputs and error values given a specific input. It uses a loop that iterates through as follows:
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        InputPattern currentPattern = null;
        double netError=0f;
        float previousError=0f;
        float outputValue = 0f;
        float sum=0f;
        float wcv=0f;
        float output1=0f;
        float output2=0f;
        float currentError=0f;
        float multiply=0f;
        float outputError = 0f;
        float weight = 0f;
        int count;
        int setPosition=0;
        int setSize = p.getSetSize();
        Neuron outputNeuron = layers[getNumberOfLayers()-1].getNeuron(0);
        //execute learning loop and repeat until an acceptable error value is obtained
        do
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            currentPattern = p.getPattern(setPosition);
            for (int i=0; i<currentPattern.getPatternSize(); i++)
                layers[0].getNeuron(i).setNeuronInput(currentPattern.getValue(i));
            currentError = layers[getNumberOfLayers()-1].getNeuron(0).getOutputError();
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                layers[getNumberOfLayers()-1].getNeuron(a).setTarget(currentPattern.getTarget());
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            for (int i=0; i<getNumberOfLayers()-1; i++)
                for (int j=0; j<layers[i+1].getNumberOfNeurons(); j++)
                    sum =0f;
                    count=0;
                    for (int k=0; k<layers.getNumberOfNeurons(); k++)
weight = weights[i].getWeight(k,j);
outputValue = layers[i].getNeuron(count).getOutput();
multiply = layers[i].getNeuron(count).getOutput() * (weights[i].getWeight(k,j));
//add values
sum = sum + multiply;
count++;
//check that all weighted neuron outputs have been completed
if (count == layers[i].getNumberOfNeurons())
//pass results to neuron
layers[i+1].getNeuron(j).setNeuronInput(sum);
//activate neuron
layers[i+1].getNeuron(j).neuronActivation();
//calculate output error of neuron for given input
layers[i+1].getNeuron(j).calculateOutputError();
//check that output layer has been reached and all neurons have been summed together
if (i == getNumberOfLayers()-2 && count == layers[i].getNumberOfNeurons())
outputError = layers[i+1].getNeuron(j).getOutputError();
netError = layers[i+1].getNeuron(j).getNetError();
for (int a=getNumberOfLayers()-1; a>0; a--)
for (int b=0; b<layers[a-1].getNumberOfNeurons(); b++)
for (int c=0; c<layers[a].getNumberOfNeurons(); c++)
output1 = layers[a-1].getNeuron(b).getOutput();
output2 = layers[a].getNeuron(c).getOutput();
wcv = learningRate * (outputError) * output1 * output2 * (1-output2);
weights[a-1].changeWeight(wcv, b, c);
learningCycle++;
if (setPosition < setSize-1)
setPosition++;
else
setPosition=0;
while (netError > acceptableError && learningCycle < 1000000000);
return currentError;
}At the moment the net doesn't seem to learn to an acceptable degree of accuracy, so I was looking to use threads to monitor it's error value change while I left it running just to ensure that it is working as intended (which it seems to be based on NetBeans debugger output). For the moment, all I'm aiming for is an output of the netError value of the NN at a particular time - would this be possible given my current implementation?
Thanks for the help,
Nick

For huge NN and a really multi-core CPU (reporting to OS as a multiple CPU's) one may benefit from having:
- an example pump
- a separate threads for calculation of forward and backwards propagation with in/out queues.
Example pump pumps one forward example to each forward processing thread. It waits for them to complete. Then it reads their output and finds errors to backpropagate. It pumps errors to back-propagation threads. They finds weights correction but does not update weight matrix only are pushing them to the output temporary arrays. The example pump takes those corrections, combines them an updates weights.
Redo from start.
The rule of thumb for high-performance is - avoid locks. If must access data which are changing, make a copy of them in bulk operation, prepare bulk result and read-write in bulk operations.
In this example a whole bunch of weight matrixes and states of neurons are such a kind of data. Each thread should use separate copy and the teaching pump should combine them together. This makes one to split data in two blocks - non-changing, common for all threads (the geometry of NN and weights) and changing, separate for each thread (weight correction, in/out of neurons).
Avoid "new", "clone" and etc. for the preference of System.arraycopy on existing data.
Regards,
Tomasz Sztejka.

Neural network backpropagation

hi
ive created a backpropagation neural network but it doesnt seem to work. i have looked over the code many times but cant seem to debug it. can any1 help me?? i can send my code if needed

ehsanmasaud wrote:
hi
ive created a backpropagation neural network but it doesnt seem to work. i have looked over the code many times but cant seem to debug it. can any1 help me?? i can send my code if neededIf you want help on these forums, you're going to have to provide an [SSCCE |http://sscce.org] and ask a specific question. Narrow it down to a couple of lines that are making it "not work" (and define what "doesn't seem to work" means exactly).
See also: [How To Ask Questions The Smart Way|http://catb.org/~esr/faqs/smart-questions.html]

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Pahu

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Trouble Setting Neural Network Parameter

I am trying to create a neural network mining model using the DMX code below:
ALTER MINING STRUCTURE [Application]
ADD MINING MODEL [Neural Net]
Person_ID,
Applied_Flag PREDICT,
[system_entry_method_level_1],
[system_entry_method_level_2],
[system_entry_time_period]
) USING MICROSOFT_NEURAL_NETWORK (MAXIMUM_INPUT_ATTRIBUTES = 300, MAXIMUM_STATES=300 )
WITH DRILLTHROUGH
but it is giving me this error:
Error (Data mining): The 'MAXIMUM_INPUT_ATTRIBUTES' data mining parameter is not valid for the 'Neural Net' model.
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https://social.msdn.microsoft.com/forums/sqlserver/en-US/9f0cdecd-2e23-48da-aeb3-6ea2cd32ae2b/help-with-setting-algorithm-paramteres that said that the problem was that I was using standard edition instead of enterprise edition.
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Nevermind, it turned out to be a simple solution. I just needed to reboot the server after the edition upgrade (after which I discovered that I needed to remove the "WITH DRILLTHROUGH" clause because neural network models
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Settings of neural network

Hi all,
we are developping an algae reactor which is controlled by a computer with LabView and a neural network. I have found the *.vi's and tried to get this thing running. We have the following in- and outputs
Inputs
pH-Value
concentration
efficiency
Outputs
Light on/off
CO2 on/off
The concentration is measured with a laser/fotodiode and the efficiency is measured with two CO2-sensors(what goes in and what comes out). The data is captured by a NI USB 6008.
Now my question is: How many hidden layers should I use?
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2
4
less than 6
Kind regards
Simon, Zurich University of Applied Sciences

Hi Simon,
I guess nobody of the National instruments support staff can help you how exactly to implement your neural network. But we can support you with all data acquisition and LabVIEW programming issues.
Maybe this helps you a bit:
Implementing Neural Networks with LabVIEW - An Introduction
Best Regards,
Andreas S
Systems Engineer

TargetInvocationException with Multiclass neural network

Hi all,
Wondering if anyone is having difficulties with multi class neural networks - I have a custom multiclass neural network with the following definition script:
input test [101];
hidden H [101] from test all;
hidden J [101] from H all;
output Result [101] softmax from J all;
I'm running it through the sweep parameters module and my dataset has the first column as a label (0 - 100) and the next 101 numbers are the input numbers.
The training does occur since I get this on the log:
[ModuleOutput] Iter:160/160, MeanErr=5.598084(0.00%), 1480.53M WeightUpdates/sec
[ModuleOutput] Done!
[ModuleOutput] Iter:150/160, MeanErr=5.600375(0.00%), 1480.48M WeightUpdates/sec
[ModuleOutput] Estimated Post-training MeanError = 5.598006
[ModuleOutput] ___________________________________________________________________
[ModuleOutput] Iter:151/160, MeanErr=5.600346(0.00%), 1475.91M WeightUpdates/sec
[ModuleOutput] Iter:152/160, MeanErr=5.600317(0.00%), 1483.43M WeightUpdates/sec
[ModuleOutput] Iter:153/160, MeanErr=5.600285(0.00%), 1477.52M WeightUpdates/sec
[ModuleOutput] Iter:154/160, MeanErr=5.600252(0.00%), 1476.20M WeightUpdates/sec
[ModuleOutput] Iter:155/160, MeanErr=5.600217(0.00%), 1482.20M WeightUpdates/sec
[ModuleOutput] Iter:156/160, MeanErr=5.600180(0.00%), 1484.14M WeightUpdates/sec
[ModuleOutput] Iter:157/160, MeanErr=5.600141(0.00%), 1477.28M WeightUpdates/sec
[ModuleOutput] Iter:158/160, MeanErr=5.600099(0.00%), 1483.68M WeightUpdates/sec
[ModuleOutput] Iter:159/160, MeanErr=5.600055(0.00%), 1483.56M WeightUpdates/sec
[ModuleOutput] Iter:160/160, MeanErr=5.600007(0.00%), 1453.19M WeightUpdates/sec
[ModuleOutput] Done!
[ModuleOutput] Estimated Post-training MeanError = 5.600238
[ModuleOutput] ___________________________________________________________________
[ModuleOutput] DllModuleHost Stop: 1 : DllModuleMethod::Execute. Duration: 00:05:20.1489353
[ModuleOutput] DllModuleHost Error: 1 : Program::Main encountered fatal exception: Microsoft.Analytics.Exceptions.ErrorMapping+ModuleException: Error 0000: Internal error ---> System.Reflection.TargetInvocationException: Exception has been thrown by the target of an invocation. ---> System.AggregateException: One or more errors occurred. ---> System.ArgumentException: Right hand side shape must match region being assigned to
Module finished after a runtime of 00:05:20.3363329 with exit code -2
Module failed due to negative exit code of -2
But something seems to break after a few sweeps.
Regards,
Jarrel

Hi Jarrel,
Sorry for the trouble, this is actually a known defect with multiclass neural networks, defect #3533885. I've increased the priority of the defect so that it will be addressed sooner. If you need a workaround for this issue I could help
you, please let me know. Probably changing the random seed or the number of folds in cross validation within parameter sweep would fix this issue.
Thank you, Ilya

Neural network

Hi guys,
I have written a neural network with a standard back-propagation learning algroithm which aims to learn a XOR logic function. However, it doesn't seem to work as expected. If i present all patterns to it (0,0, 1,1, 0,1, 1,0) the weights stay pretty static, however if i just train it with 0,0 and 1,1 it seems to work (after alot of epochs, about 300).
I have included my code below;
Does anybody have any idea why its not working?
/* Generated by Together */
import java.lang.*;
public class NN
//weights
private static double _w1 = 0.5;
private static double _w2 = 0.9;
private static double _w3 = 0.4;
private static double _w4 = 1.0;
private static double _w5 = -1.2;
private static double _w6 = 1.1;
//thresholds
private static double _t1 = 0.8;
private static double _t2 = -0.1;
private static double _t3 = 0.3;
//neuron outputs
private static double _n1 = 0;
private static double _n2 = 0;
private static double _n3 = 0;
private static int[][] inputs = new int[4][2];
private static int[] desired = new int[4];
public NN()
public static void main (String[] args)
    inputs[0][0] = 1;
    inputs[0][1] = 1;
    inputs[1][0] = 0;
    inputs[1][1] = 0;
    inputs[2][0] = 1;
    inputs[2][1] = 0;
    inputs[3][0] = 0;
    inputs[3][1] = 1;
    desired[0] = 0;
    desired[1] = 0;
    desired[2] = 1;
    desired[3] = 1;
    int y = 0;
    while(y <= 1000)
      for (int x = 0; x < 2; x++) {
        double actual = feedforward(inputs[x][0], inputs[x][1]);
        updateWeights(inputs[x][0], inputs[x][1], desired[x], actual);
        System.out.println("Input 1 - " + inputs[x][0] + " Input 2 - " + inputs[x][1] + " Desired - " + desired[x]);
        printWeights(desired[x]);
      y++;
public static void printWeights(double desired)
    System.out.println("Actual - " + _n3);
    System.out.println("Desired - " + desired);
    System.out.println("Weight 1 - " + _w1);
    System.out.println("Weight 2 - " + _w2);
    System.out.println("Weight 3 - " + _w3);
    System.out.println("Weight 4 - " + _w4);
    System.out.println("Weight 5 - " + _w5);
    System.out.println("Weight 6 - " + _w6);
    System.out.println("True Neuron 1 - " + _t1);
    System.out.println("True Neuron 2 - " + _t2);
    System.out.println("True Neuron 3 - " + _t3);
public static double activation(double input)
    double actResult = 1 / (1 + (Math.exp(-(input))));
    return actResult;
public static void updateWeights(int inputOne, int inputTwo, double desired, double actual)
    double error = desired - actual;
    System.out.println("Error - " + error);
    double _eg3 = _n3 * (1 - _n3) * error;
    double _eg1 = _n1 * (1 - _n1) * _eg3 * _w5;
    double _eg2 = _n2 * (1 - _n2) * _eg3 * _w6;
    double learningRate = 0.1;
    //update hidden layer bias
    double _ct1 = learningRate * _eg1;
    double _ct2 = learningRate * _eg2;
    //update output layer bias
    double _ct3 = learningRate * _eg3;
    //update hidden layer weights
    double _cw1 = learningRate * inputOne * _eg1;
    double _cw2 = learningRate * inputTwo * _eg1;
    double _cw3 = learningRate * inputOne * _eg2;
    double _cw4 = learningRate * inputTwo * _eg2;
    //update output layyer weights
    double _cw5 = learningRate * _n1 * _eg3;
    double _cw6 = learningRate * _n2 * _eg3;
    //update weights
    _w1 = _w1 + _cw1;
    _w2 = _w2 + _cw2;
    _w3 = _w3 + _cw3;
    _w4 = _w4 + _cw4;
    _w5 = _w5 + _cw5;
    _w6 = _w6 + _cw6;
    //update true neuron weights
    _t1 = _t1 + _ct1;
    _t2 = _t2 + _ct2;
    _t3 = _t3 + _ct3;
public static double feedforward(int inputOne, int inputTwo)
    double y1 = (inputOne * _w1) + (inputTwo * _w3) - (1 * _t1);
    double y2 = (inputOne * _w2) + (inputTwo * _w4) - (1 * _t2);
    _n1 = activation(y1);
    _n2 = activation(y2);
    double y3 = (_n1 * _w5) + (_n2 * _w6) - (1 * _t3);
    _n3 = activation(y3);
    return _n3;
}Any help or if you know of any specific NN forums that will be a great help.
Many thanks
Alex

Nothing is wrong with scaning:) I just need to do this usgin a neural network.
Well i doesn't need to look inside a file, it was just an eg. U can put the 0 and 1 into the code or whatever. It is important that the network should train.

Neural network: is there any toolkit?

Is there any toolkit in order to use neural networks with labview? (I am not an expert about neural networks, I have just been said today to try to solve a problem using neural networks, I even don't know where to start from...well..I am starting from labview!).
Solved!
Go to Solution.

if you want to just use it and have it simple use this one: https://decibel.ni.com/content/docs/DOC-41891
Best regards, Piotr
Certified TestStand Architect
Certified LabVIEW Architect

Neural Network in LabVIEW

I've searched the forums for any support for neural networks in LabIVEW, but found only one post and the original author seems to have disappeared:
http://forums.ni.com/ni/board/message?board.id=170&message.id=157334&query.id=59698#M157334
Has anyone seen an implemention of any type of neural network in LabVIEW?
Thanks,
Derek

Hi Derek
I downloaded the code at the time of the post and still had it kicking about on my hard disk. Just for interest, you know...
Way over my head
Hope this helps you out.
David
Attachments:
aNETka_ver_1-0.zip ‏1223 KB

I do not want to switch wireless networks

No, Airport Utility, I do not want to switch wireless networks to get my Time Capsule mounting again. This is because if I switch wireless networks, the network will go spinning its little time wheel of nothingness forever and ultimately report that it cannot find the Time Capsule it switched for. On the other hand, if I choose not to switch, it insists that there's nothing to be done with the Time Capsule that's right up top, all by itself, in the Airport wireless devices in the network.
All I want is for my Time Capsule to stop having these fits where it decides it won't join the network that already exists, that is already quite happy, and that it does not need to be the base station for. What is the problem that keeps making it go off into these fits where it won't join the network and won't let any network join it? And why does this keep happening? And what will stop it short of throwing the Time Capsule out the window?

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Thanks for your help.

Programming an neural network (or circuit, or whatever)

Hi, I want to know which of two options is a standard when coding neural networks, or (for non-AI people), what seems like a better method for program any kind of feed-forward network, like a virtual circuit.
As I see it, I have two ways of making a network iterate through and finding the end result:
1) I set the inputs, and then run each layer consecutively: I run the input layer, which sets the inputs of the hidden layer, I run the hidden layer(s), which sets the inputs of the output layer, I run the output layer, and finally query the network to see what the final values of the output layer are.
2) I ask the output layer what it's values are. By asking any neuron what it's value is, it checks to see what it's inputs are, and thus asks the neurons below it what their values are. This trickles down to the input layer, at which point the input neurons give their answer and this is fed back up.
The first method seems more intuitive, and seems more realistic: each neuron is fed a value and this iterates up towards the top. The second method, however, is one that we used when designing virtual circuits in a college programming class, and seems perhaps to be a little more elegant.
Obviously, I think that both give the same final result(?), but it would be good to know if there is some standard, or reason to pick one over the other.
Thanks for any recommendations!

Anyway, id rule against the 2nd method. It would be
hard to implement
that type of "pull" structure with a ANN because what
happens at the
input node?Well, basically I was thinking about doing something like the code below, although I hadn't thought about the details (such as whether I ought to use the 'isInput' flag):
public float getOutput() {
float totalInput;
if (isInput)
     totalInput = somePreSetInput;
else {
    for ( all the neurons connected to this one )
      totalInput += connectedNeuron.getOutput();
float output = activationFunction(totalInput);
return output;
It seems that this shouldn't be too hard to set up, but I don't know whether it might lead to errors.
what type of trigger function are you using?Assuming a trigger function is an activation function, I currently have it so it can be set to either sigmoid, tanh, linear, or step. The default is sigmoid, shifted to the right so a total input of zero will give an output close to zero (as opposed to 0.5, as a regular sigmoid would).

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