GitHub user avulanov opened a pull request:
https://github.com/apache/spark/pull/9229
[SPARK-11262][ML] Unit test for gradient, loss layers, memory management
for multilayer perceptron
1.Implement LossFunction trait and implement squared error and cross entropy
loss with it
2.Implement unit test for gradient and loss
3.Implement InPlace trait and in-place layer evaluation
4.Refactor interface for ActivationFunction
5.Update of Layer and LayerModel interfaces
6.Fix random weights assignment
7.Implement memory allocation by MLP model instead of individual layers
These features decreased the memory usage and increased flexibility of
internal API.
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/avulanov/spark mlp-refactoring
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/spark/pull/9229.patch
To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:
This closes #9229
----
commit d47310b4a0e0229f96c2aded7c5fd9158ef26983
Author: Alexander Ulanov <[email protected]>
Date: 2015-09-01T23:21:35Z
Refactor Multilayer perceptron (MLP)
1.Implement LossFunction trait and implement squared error and cross entropy
loss with it
2.Implement unit test for gradient and loss
3.Implement InPlace trait and in-place layer evaluation
4.Refactor interface for ActivationFunction
5.Update of Layer and LayerModel interfaces
6.Fix random weights assignment
7.Implement memory allocation by MLP model instead of individual layers
These features decreased the memory usage and increased flexibility of
internal API.
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