Hello,

I was able to get the DeepLearning4J example working (
https://github.com/deeplearning4j/dl4j-0.0.3.3-examples) after some
extensive building and dependency updates. I haven't still run anything as
I need to bring them to produce proper outputs, but I was able to compile
WSO2-ML without any issues while including the deeplearning4j MNIST example.

Just for the record I had to follow the following procedure in order to get
this working

Download *Canova *from (https://github.com/deeplearning4j/Canova.git) and
build the project and install dependency via mvn install:install-file
However, in order to compile successfully, I had to skip the Tests and
Javadoc check because otherwise I cannot compile it (figured out this after
a discussion with one of the contributors)

Download *nd4j *from (https://github.com/deeplearning4j/nd4j) and build it
and install it via mvn install:install-file. Because the any of the
releases do not have, do not contain the (nd4j.linalg.dataset.api.iterator)

Download *DeepLearning4J *from (
https://github.com/deeplearning4j/deeplearning4j) and build it and install
it via mvn install:install-file. Because the any of the releases do not
have org.deepleaning4j.nn.conf.override package

After the process I have following versions

   - Canova - 0.0.0.2-SNAPSHOT
   - nd4j - 0.0.3.5.5.3-SNAPSHOT
   - dl4j - 0.0.3.3.3.alpha1-SNAPSHOT


In these conditions it compiles without any issues.

So I guess we have 2 major options

   - continue with the locally built dependencies or
   - switch to a previous release which supports this example (
   http://deeplearning4j.org/mnist-tutorial.html)


Thank you


On Fri, Mar 27, 2015 at 7:03 AM, Thushan Ganegedara <[email protected]>
wrote:

> We can try to maybe use a previous release corresponding to the example at
> http://deeplearning4j.org/mnist-tutorial.html (may be <= 0.0.3.2)
> However, as the explain this in there github issue
> https://github.com/deeplearning4j/deeplearning4j/issues/173 using it
> reduces the generality of the deepLearning. But I think it can get the work
> done.
>
>
>
> On Thu, Mar 26, 2015 at 11:04 PM, Nirmal Fernando <[email protected]> wrote:
>
>> Can't we use a released version and samples corresponds to a released
>> version?
>>
>> On Thu, Mar 26, 2015 at 5:29 PM, Thushan Ganegedara <[email protected]>
>> wrote:
>>
>>> Dear Devs,
>>>
>>> For the past few days, I've been try to add deeplearning4j dependency
>>> with maven and was able to do it successfully.
>>>
>>> However, the example provided in
>>> http://deeplearning4j.org/mnist-tutorial.html uses a class called *DBN 
>>> *which
>>> is no longer available in the latest deeplearning4j releases.
>>>
>>> Furthermore, they have other examples available at
>>> https://github.com/deeplearning4j/dl4j-0.0.3.3-examples which again
>>> uses a class (i.e. org.deeplearning4j.nn.conf.override.
>>> *ClassifierOverride*) which is not available in any of the releases,
>>> but only the source. I asked about this from the contributors and they told
>>> me to build from the source.
>>>
>>> But when I tried that, I was unable to get it to compile, as Canova
>>> (0.0.0.2) cannot find one of it's dependencies (
>>> *openjfx-78-backport-native*) and it's not mentioned in Canova's
>>> pom.xml Therefore I tried including it in the pom.xml and compiling it. But
>>> it's still giving me some problems.
>>>
>>> ​So by looking at all these information, deeplearning4j seems a little
>>> bit unstable at the moment.
>>>
>>> I would highly appreciate your thoughts about this and if there are any
>>> alternative suggestions.
>>>
>>> Thank you​
>>>
>>>
>>> --
>>> Regards,
>>>
>>> Thushan Ganegedara
>>> School of IT
>>> University of Sydney, Australia
>>>
>>
>>
>>
>> --
>>
>> Thanks & regards,
>> Nirmal
>>
>> Senior Software Engineer- Platform 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
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