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Posted to issues@spark.apache.org by "Weide Zhang (JIRA)" <ji...@apache.org> on 2015/10/02 03:18:26 UTC
[jira] [Comment Edited] (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=14940670#comment-14940670 ]
Weide Zhang edited comment on SPARK-5575 at 10/2/15 1:17 AM:
-------------------------------------------------------------
Hi Alexander,
The features I am looking to add/have include :
1. more activation function such as ReLU, LeakyReLU, max pooling
2. support simultaneous testing and training phase similar to what caffe does
3. scalability change (including support larger model, parameter server, this is long term)
so far i haven't made any of the change yet. if other people already have made the change to the current spark, i will be happy to take that as well.
was (Author: weidezhang):
Hi Alexander,
The features I am looking to add include :
1. more activation function such as ReLU, LeakyReLU, max pooling
2. support simultaneous testing and training phase similar to what caffe does
3. scalability change (including support larger model, parameter server, this is long term)
> 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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