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Posted to issues@spark.apache.org by "yuhao yang (JIRA)" <ji...@apache.org> on 2015/08/13 07:45:46 UTC
[jira] [Commented] (SPARK-5575) Artificial neural networks for
MLlib deep learning
[ https://issues.apache.org/jira/browse/SPARK-5575?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14694735#comment-14694735 ]
yuhao yang commented on SPARK-5575:
-----------------------------------
Hi [~avulanov] and [~mengxr], I'm refactoring the CNN implementation on https://github.com/hhbyyh/mCNN towards the MLP interface and architecture in Spark. I'll further
1. investigate on other popular library like caffe and torch for interface and function requirement.
2. perform initial benchmark and test on Mnist (maybe imagenet).
3. collect feedback from customers and community.
I'll hold an alpha version in my repository and hope it can become a Spark Package, which can later be reviewed by community. Meanwhile, for anyone with interest, trial ans suggestions are welcome. Perhaps we can reopen Spark-9273 for CNN-specific discussion.
> 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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