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
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