Hello Mahesan, Thank you for pointing that out. This was actually before the latest built,
This was happening because of following code snippent in view-model.jag var actual = testResultDataPointsSample[i].predictedVsActual.actual; var predicted = testResultDataPointsSample[i].predictedVsActual.predicted; var labeledPredicted = labelPredicted(predicted, 0.5); if(actual == labeledPredicted) { predictedVsActualPoint[2] = 'Correct'; } else { predictedVsActualPoint[2] = 'Incorrect'; } where it should compare *actual == predicted* for deeplearning. But it is fixed in the latest commit as CD mentioned. So it is working properly at the moment. I've attached a screenshot of new version On Wed, Jul 15, 2015 at 7:54 PM, Sinnathamby Mahesan <sinnatha...@wso2.com> wrote: > Hi Thushan > thank you for sending the attachments. > I am just wondering why I see many red-dots in the graphs: > For example, for iris data set, oaccroding to the table only 3 were found > incorrectly predicted > whereas the scatter diagram shows many reds as well as greens. > Enlighten me if the way I see is wrong. > :-) > Regards > Mahesan > > On 13 July 2015 at 07:14, Thushan Ganegedara <thu...@gmail.com> wrote: > >> Hi all, >> >> I have integrated H-2-O deeplearning to WSO2-ml successfully. Following >> are the stats on 2 tests conducted (screenshots attached). >> >> Iris dataset - 93.62% Accuracy >> MNIST (Small) dataset - 94.94% Accuracy >> >> However, there were few unusual issues that I had to spend lot of time to >> identify. >> >> *FrameSplitter does not work for any value other than 0.5. Any value >> other than 0.5, the following error is returned* >> (Frame splitter is used to split trainingData to train and valid sets) >> barrier onExCompletion for >> hex.deeplearning.DeepLearning$DeepLearningDriver@25e994ae >> java.lang.RuntimeException: java.lang.RuntimeException: >> java.lang.NullPointerException >> at >> hex.deeplearning.DeepLearning$DeepLearningDriver.trainModel(DeepLearning.java:382) >> >> *DeepLearningModel.score(double[] vec) method doesn't work. * >> The predictions obtained with score(Frame f) and score(double[] v) is >> shown below. >> >> *Actual, score(Frame f), score(double[] v)* >> 0.0, 0.0, 1.0 >> 1.0, 1.0, 2.0 >> 2.0, 2.0, 2.0 >> 2.0, 1.0, 2.0 >> 1.0, 1.0, 2.0 >> >> As you can see, score(double[] v) is quite poor. >> >> After fixing above issues, everything seems to be working fine at the >> moment. >> >> However, the I've a concern regarding the following method in >> view-model.jag -> function >> drawPredictedVsActualChart(testResultDataPointsSample) >> >> var actual = testResultDataPointsSample[i].predictedVsActual.actual; >> var predicted = >> testResultDataPointsSample[i].predictedVsActual.predicted; >> var labeledPredicted = labelPredicted(predicted, 0.5); >> >> if(actual == labeledPredicted) { >> predictedVsActualPoint[2] = 'Correct'; >> } >> else { >> predictedVsActualPoint[2] = 'Incorrect'; >> } >> >> why does it compare the *actual and labeledPredicted* where it should be >> comparing *actual and predicted*? >> >> Also, the *Actual vs Predicted graph for MNIST show the axis in "Meters" >> *(mnist.png) which doesn't make sense. I'm still looking into this. >> >> Thank you >> >> >> >> -- >> Regards, >> >> Thushan Ganegedara >> School of IT >> University of Sydney, Australia >> >> _______________________________________________ >> Dev mailing list >> Dev@wso2.org >> http://wso2.org/cgi-bin/mailman/listinfo/dev >> >> > > > -- > ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ > Sinnathamby Mahesan > > > > ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ > ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ > -- Regards, Thushan Ganegedara School of IT University of Sydney, Australia
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