Thanks Thushan for the update. On Mon, Jul 27, 2015 at 6:01 AM, Thushan Ganegedara <thu...@gmail.com> wrote:
> Hi all, > > I'm doing some tests with several datasets and most of them seemed to be > working fine. Somehow, I stumbled upon the leaf dataset ( > https://archive.ics.uci.edu/ml/datasets/Leaf), which does not seem to be > working well for. However, the dataset works fine with other algorithms > (e.g. Logistic Regression L-BFGS) Therefore, I suspect this is due to some > sort of malformed data format. I'm right now looking into that. > > Furthermore, I am thinking of starting with the D3 visualization on the > parameter setting stage. Should we be moving forward with that idea? > +1 > > Finally, I would like to remind that, we haven't decided a date for code > review. Should we do that? > Yes, let's have it this week, if you are ok. > > Thank you > > On Wed, Jul 22, 2015 at 11:13 PM, Thushan Ganegedara <thu...@gmail.com> > wrote: > >> Hi, >> >> Apologies about the late reply. >> >> Notes of the Demonstration >> >> Time duration: approximately 30 mins >> >> The demonstration was to demonstrate the implemented deeplearning feature >> of WSO2-ML. The demo started first explaining the dataset used (i.e. >> MNIST). The dataset is a CSV file with approximately 30000 rows and 784 >> features. >> >> Next the dataset was loaded to WSO2-ml. Here a concern was raised >> regarding selecting the type of data in the Preprocessing Phase (i.e. >> Categorical vs Numerical) The suggestion was that there should be a UI >> feature to change the data type for all the variables at once (very useful >> for large amounts of features). >> >> Next the deeplearning algorithm for MNIST dataset was demonstrated and >> was able to achieve an appx 95% accuracy. Regarding the deeplearning >> algorithms, H-2-O doesn't seem to have different deeplearning algorithms at >> the moment, but a general deep network + classifier (probably autoencoder). >> So the idea was to ask H-2-O team whether they are planning to implement >> different networks in the future. >> >> Also, it was suggested to add a visualization feature in parameter >> setting stage to provide a summarized visualization of the network to the >> user. >> >> Furthermore, another suggestion was to test the deep network on real >> world datasets and see how it performs. For this datasets from Kaggle will >> be used. >> >> >> About progress. >> >> I'm currently testing the algorithm against different datasets. and I'll >> provide a detailed report on that in the recent future. >> >> Thank you >> >> >> >> On Wed, Jul 22, 2015 at 2:00 PM, Nirmal Fernando <nir...@wso2.com> wrote: >> >>> @Thushan how are you progressing? Could you please send the notes of our >>> last review? >>> >>> On Thu, Jul 16, 2015 at 10:43 AM, CD Athuraliya <chathur...@wso2.com> >>> wrote: >>> >>>> >>>> >>>> On Mon, Jul 13, 2015 at 11:12 AM, Thushan Ganegedara <thu...@gmail.com> >>>> wrote: >>>> >>>>> Hello CD, >>>>> >>>>> Yes, it seems to be working fine now. But why does it show the axes in >>>>> meters? Is this a d3 specific thing? >>>>> >>>> >>>> I think *m* stands for *Milli* here. >>>> >>>>> >>>>> On Mon, Jul 13, 2015 at 3:17 PM, Thushan Ganegedara <thu...@gmail.com> >>>>> wrote: >>>>> >>>>>> Hi all, >>>>>> >>>>>> Thank you very much for pointing out. I'll get the latest update and >>>>>> see. >>>>>> >>>>>> On Mon, Jul 13, 2015 at 3:03 PM, CD Athuraliya <chathur...@wso2.com> >>>>>> wrote: >>>>>> >>>>>>> Hi Thushan, >>>>>>> >>>>>>> That method has been updated. Please get the latest. You might have >>>>>>> to define your own case depending on predicted values. >>>>>>> >>>>>>> CD Athuraliya >>>>>>> Sent from my mobile device >>>>>>> On Jul 13, 2015 10:24 AM, "Nirmal Fernando" <nir...@wso2.com> wrote: >>>>>>> >>>>>>>> Great work Thushan! On the UI issues, @CD could help you. AFAIK >>>>>>>> actual keeps the pointer to the actual label and predicted is the >>>>>>>> probability and predictedLabel is after rounding it using a threshold. >>>>>>>> >>>>>>>> On Mon, Jul 13, 2015 at 7:14 AM, 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 >>>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> >>>>>>>> -- >>>>>>>> >>>>>>>> Thanks & regards, >>>>>>>> Nirmal >>>>>>>> >>>>>>>> Associate Technical Lead - Data Technologies Team, WSO2 Inc. >>>>>>>> Mobile: +94715779733 >>>>>>>> Blog: http://nirmalfdo.blogspot.com/ >>>>>>>> >>>>>>>> >>>>>>>> >>>>>> >>>>>> >>>>>> -- >>>>>> Regards, >>>>>> >>>>>> Thushan Ganegedara >>>>>> School of IT >>>>>> University of Sydney, Australia >>>>>> >>>>> >>>>> >>>>> >>>>> -- >>>>> Regards, >>>>> >>>>> Thushan Ganegedara >>>>> School of IT >>>>> University of Sydney, Australia >>>>> >>>> >>>> >>>> >>>> -- >>>> *CD Athuraliya* >>>> Software Engineer >>>> WSO2, Inc. >>>> lean . enterprise . middleware >>>> Mobile: +94 716288847 <94716288847> >>>> LinkedIn <http://lk.linkedin.com/in/cdathuraliya> | Twitter >>>> <https://twitter.com/cdathuraliya> | Blog >>>> <http://cdathuraliya.tumblr.com/> >>>> >>> >>> >>> >>> -- >>> >>> Thanks & regards, >>> Nirmal >>> >>> Associate Technical Lead - Data Technologies Team, WSO2 Inc. >>> Mobile: +94715779733 >>> Blog: http://nirmalfdo.blogspot.com/ >>> >>> >>> >> >> >> -- >> Regards, >> >> Thushan Ganegedara >> School of IT >> University of Sydney, Australia >> > > > > -- > Regards, > > Thushan Ganegedara > School of IT > University of Sydney, Australia > -- Thanks & regards, Nirmal Associate Technical Lead - Data Technologies Team, WSO2 Inc. Mobile: +94715779733 Blog: http://nirmalfdo.blogspot.com/
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