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https://issues.apache.org/jira/browse/HAMA-681?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13675370#comment-13675370
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Edward J. Yoon commented on HAMA-681:
-------------------------------------
I'm still see errors like:
{code}
edward@udanax:~/workspace/hama-trunk$ mvn install -projects ml
....
-------------------------------------------------------------------------------
Test set: org.apache.hama.ml.perception.TestSmallMultiLayerPerceptron
-------------------------------------------------------------------------------
Tests run: 4, Failures: 1, Errors: 0, Skipped: 0, Time elapsed: 36.445 sec <<<
FAILURE!
testWriteReadMLP(org.apache.hama.ml.perception.TestSmallMultiLayerPerceptron)
Time elapsed: 0.003 sec <<< FAILURE!
java.lang.AssertionError: expected:<SmallMLP> but was:<null>
at org.junit.Assert.fail(Assert.java:91)
at org.junit.Assert.failNotEquals(Assert.java:645)
at org.junit.Assert.assertEquals(Assert.java:126)
at org.junit.Assert.assertEquals(Assert.java:145)
at
org.apache.hama.ml.perception.TestSmallMultiLayerPerceptron.testWriteReadMLP(TestSmallMultiLayerPerceptron.java:72)
at sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)
at
sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:57)
at
sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)
at java.lang.reflect.Method.invoke(Method.java:601)
at
org.junit.runners.model.FrameworkMethod$1.runReflectiveCall(FrameworkMethod.java:44)
at
org.junit.internal.runners.model.ReflectiveCallable.run(ReflectiveCallable.java:15)
at
org.junit.runners.model.FrameworkMethod.invokeExplosively(FrameworkMethod.java:41)
at
org.junit.internal.runners.statements.InvokeMethod.evaluate(InvokeMethod.java:20)
at
org.junit.runners.BlockJUnit4ClassRunner.runChild(BlockJUnit4ClassRunner.java:76)
at
org.junit.runners.BlockJUnit4ClassRunner.runChild(BlockJUnit4ClassRunner.java:50)
at org.junit.runners.ParentRunner$3.run(ParentRunner.java:193)
at
org.junit.experimental.ParallelComputer$1$1.call(ParallelComputer.java:46)
at java.util.concurrent.FutureTask$Sync.innerRun(FutureTask.java:334)
at java.util.concurrent.FutureTask.run(FutureTask.java:166)
at
java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1145)
at
java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:615)
at java.lang.Thread.run(Thread.java:722)
{code}
I would recommend to use the /tmp directory instead of
"src/test/resources/perception".
> Multi Layer Perceptron
> -----------------------
>
> Key: HAMA-681
> URL: https://issues.apache.org/jira/browse/HAMA-681
> Project: Hama
> Issue Type: New Feature
> Components: machine learning
> Reporter: Christian Herta
> Assignee: Yexi Jiang
> Labels: patch, perceptron
> Attachments: HAMA-681.patch, HAMA-681.patch, perception.patch
>
>
> Implementation of a Multilayer Perceptron (Neural Network)
> - Learning by Backpropagation
> - Distributed Learning
> The implementation should be the basis for the long range goals:
> - more efficent learning (Adagrad, L-BFGS)
> - High efficient distributed Learning
> - Autoencoder - Sparse (denoising) Autoencoder
> - Deep Learning
>
> ---
> Due to the overhead of Map-Reduce(MR) MR didn't seem to be the best strategy
> to distribute the learning of MLPs.
> Therefore the current implementation of the MLP (see MAHOUT-976) should be
> migrated to Hama. First all dependencies to Mahout (Matrix-Library) must be
> removed to get a standalone MLP Implementation. Then the Hama BSP programming
> model should be used to realize distributed learning.
> Different strategies of efficient synchronized weight updates has to be
> evaluated.
> Resources:
> Videos:
> - http://www.youtube.com/watch?v=ZmNOAtZIgIk
> - http://techtalks.tv/talks/57639/
> MLP and Deep Learning Tutorial:
> - http://www.stanford.edu/class/cs294a/
> Scientific Papers:
> - Google's "Brain" project:
> http://research.google.com/archive/large_deep_networks_nips2012.html
> - Neural Networks and BSP: http://ipdps.cc.gatech.edu/1998/biosp3/bispp4.pdf
> - http://jmlr.csail.mit.edu/papers/volume11/vincent10a/vincent10a.pdf
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