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Posted to dev@hama.apache.org by "Edward J. Yoon (JIRA)" <ji...@apache.org> on 2015/07/03 02:14:04 UTC
[jira] [Commented] (HAMA-961) Parameter Server for large scale MLP
[ https://issues.apache.org/jira/browse/HAMA-961?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14612692#comment-14612692 ]
Edward J. Yoon commented on HAMA-961:
-------------------------------------
Here's new neuron-centric programming interface I propose. I'll start the refactoring first.
{code}
public class ThreeLayerANNExample {
/**
* User-defined sigmoid actiavation function
*/
public static class Sigmoid extends ActivationFunction {
@Override
public double apply(double input) {
return 1.0 / (1 + Math.exp(-input));
}
@Override
public double applyDerivative(double input) {
return input * (1 - input);
}
}
/**
* User-defined cost function
*/
public static class CrossEntropy extends CostFunction {
@Override
public double apply(double target, double actual) {
double adjustedTarget = (target == 0 ? 0.000001 : target);
adjustedTarget = (target == 1.0 ? 0.999999 : target);
double adjustedActual = (actual == 0 ? 0.000001 : actual);
adjustedActual = (actual == 1 ? 0.999999 : actual);
return -adjustedTarget * Math.log(adjustedActual) - (1 - adjustedTarget)
* Math.log(1 - adjustedActual);
}
@Override
public double derivative(double target, double actual) {
double adjustedTarget = target;
double adjustedActual = actual;
if (adjustedActual == 1) {
adjustedActual = 0.999;
} else if (actual == 0) {
adjustedActual = 0.001;
}
if (adjustedTarget == 1) {
adjustedTarget = 0.999;
} else if (adjustedTarget == 0) {
adjustedTarget = 0.001;
}
return -adjustedTarget / adjustedActual + (1 - adjustedTarget)
/ (1 - adjustedActual);
}
}
public static void main(String[] args) throws Exception {
ANNJob ann = new ANNJob();
// set learning rate and momentum weight
ann.setLearningRate(0.1);
ann.setMomemtumWeight(0.1);
// initialize the topology of the model and set the activation function and parallel degree.
ann.addLayer(featureDimension, Sigmoid.class, numOfTasks);
ann.addLayer(featureDimension, Sigmoid.class, numOfTasks);
ann.addLayer(labelDimension, Sigmoid.class, numOfTasks);
// set the cost function to evaluate the error
ann.setCostFunction(CrossEntropy.class);
ann.setMaxIteration(50);
ann.setBatchSize(500);
ann.setInputPath(path);
...
}
}
{code}
> Parameter Server for large scale MLP
> ------------------------------------
>
> Key: HAMA-961
> URL: https://issues.apache.org/jira/browse/HAMA-961
> Project: Hama
> Issue Type: Improvement
> Components: machine learning
> Affects Versions: 0.7.0
> Reporter: Edward J. Yoon
> Assignee: Edward J. Yoon
> Fix For: 0.8.0
>
>
> I've recently started to review the MLP source codes closely, and I'm thinking about some improvement and API refactoring e.g., APIs for user-defined neuron and synapse models, data structure, ..., etc.
> This issue is one of them, and related to train large models. I'm considering distributed parameter server (http://parameterserver.org) for managing parameters.
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