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https://issues.apache.org/jira/browse/SPARK-9273?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14737948#comment-14737948
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Alexander Ulanov edited comment on SPARK-9273 at 9/10/15 1:18 AM:
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Hi Yuhao! I have few comments regarding the interface and the optimization of
your implementation. There are two options of optimizing convolutions: using
matrix-matrix multiplication and using FFTs. The latter seems a bit more
complicated since we don't have optimized parallel FFT in Spark. It also has to
support batch data processing. Instead, if one uses matrix-matrix
multiplication for convolution, then it can take advantage of native BLAS and
batch computations can be supported straightforward. Another benefit is that we
would not need to change the current Layer's input/ouptput type (matrix) to
tensor. We can store the unwrapped inputs/outputs as vectors within the
input/output matrix. Does it make sense to you?
was (Author: avulanov):
Hi Yuhao! I have few comments regarding the interface and the optimization of
your implementation. There are two options of optimizing convolutions: using
matrix-matrix multiplication and using FFTs. The latter seems a bit more
complicated since we don't have optimized parallel FFT in Spark. It also has to
support batch data processing. Instead, if one uses matrix-matrix
multiplication for convolution, then it can take advantage of native BLAS and
batch computations can be supported straightforward. Another benefit is that we
would not need to change the current Layer's input/ouptput type (matrix) to
tensor. We can store the unwrapped inputs/outputs as vectors within the
input/output matrix. Do you think that it is reasonable?
> 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
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