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https://issues.apache.org/jira/browse/SPARK-5575?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14997300#comment-14997300
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Alexander Ulanov commented on SPARK-5575:
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Hi Narine,
Thank you for your observation. It seems that such information is useful to
know. Indeed, LBFGS in Spark does not print any information during the
execution. ANN uses Spark's LBFGS. You might want to add the needed output to
the LBFGS code
https://github.com/apache/spark/blob/master/mllib/src/main/scala/org/apache/spark/mllib/optimization/LBFGS.scala#L185.
Best regards, Alexander
> Artificial neural networks for MLlib deep learning
> --------------------------------------------------
>
> Key: SPARK-5575
> URL: https://issues.apache.org/jira/browse/SPARK-5575
> Project: Spark
> Issue Type: Umbrella
> Components: MLlib
> Affects Versions: 1.2.0
> Reporter: Alexander Ulanov
>
> Goal: Implement various types of artificial neural networks
> Motivation: deep learning trend
> Requirements:
> 1) Basic abstractions such as Neuron, Layer, Error, Regularization, Forward
> and Backpropagation etc. should be implemented as traits or interfaces, so
> they can be easily extended or reused
> 2) Implement complex abstractions, such as feed forward and recurrent networks
> 3) Implement multilayer perceptron (MLP), convolutional networks (LeNet),
> autoencoder (sparse and denoising), stacked autoencoder, restricted
> boltzmann machines (RBM), deep belief networks (DBN) etc.
> 4) Implement or reuse supporting constucts, such as classifiers, normalizers,
> poolers, etc.
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