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Alexander Ulanov commented on SPARK-9273: ----------------------------------------- Hi Yuhao. Sounds good! Thanks for refactoring the code to support ANN internal interface. Also, I was able to run your example. It shows increasing accuracy while training however it is not very fast. There is a good explanation how to use matrices multiplication in convolution: http://cs231n.github.io/convolutional-networks/. Basically, one needs to roll all image patches (regions that will be convolved) the into vectors and stack them together in a matrix. The weights of convolutional layer also should be rolled into vectors and stacked. Multiplying two mentioned matrices provides the convolution result that can be unrolled to 3d matrix, however it would not be necessary for this implementation. We can discuss it offline if you wish. Besides the optimization, there are few more things to be done. It includes unit tests for new layers, gradient test, representing pooling layer as functional layer, and performance comparison with the other implementation of CNN. You can take a look at the tests I've added for MLP https://issues.apache.org/jira/browse/SPARK-11262 and MLP benchmark at https://github.com/avulanov/ann-benchmark. A separate branch/repo for these developments might be a good thing to do. I'll be happy to help you with this. > Add Convolutional Neural network to Spark MLlib > ----------------------------------------------- > > Key: SPARK-9273 > URL: https://issues.apache.org/jira/browse/SPARK-9273 > Project: Spark > Issue Type: New Feature > Components: MLlib > Reporter: yuhao yang > > Add Convolutional Neural network to Spark MLlib -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org