Author: tommaso
Date: Wed Jun 26 20:59:08 2013
New Revision: 1497094
URL: http://svn.apache.org/r1497094
Log:
backprop testing improved
Modified:
labs/yay/trunk/core/src/test/java/org/apache/yay/core/BackPropagationLearningStrategyTest.java
labs/yay/trunk/core/src/test/java/org/apache/yay/core/BasicPerceptronTest.java
Modified:
labs/yay/trunk/core/src/test/java/org/apache/yay/core/BackPropagationLearningStrategyTest.java
URL:
http://svn.apache.org/viewvc/labs/yay/trunk/core/src/test/java/org/apache/yay/core/BackPropagationLearningStrategyTest.java?rev=1497094&r1=1497093&r2=1497094&view=diff
==============================================================================
---
labs/yay/trunk/core/src/test/java/org/apache/yay/core/BackPropagationLearningStrategyTest.java
(original)
+++
labs/yay/trunk/core/src/test/java/org/apache/yay/core/BackPropagationLearningStrategyTest.java
Wed Jun 26 20:59:08 2013
@@ -50,7 +50,25 @@ public class BackPropagationLearningStra
assertNotNull(learntWeights);
for (int i = 0; i < learntWeights.length; i++) {
- assertFalse("weights have not been changed at level " + i + 1,
learntWeights[i].equals(initialWeights[i]));
+ assertFalse("weights have not been changed",
learntWeights[i].equals(initialWeights[i]));
+ }
+ }
+
+ @Test
+ public void testLearningWitgRandomNetworkAndRandomSettings() throws
Exception {
+ BackPropagationLearningStrategy backPropagationLearningStrategy = new
BackPropagationLearningStrategy(Math.random(),
+ Math.random(), new FeedForwardStrategy(Math.random() >= 0.5d ? new
TanhFunction() : new SigmoidFunction()),
+ new LogisticRegressionCostFunction(Math.random()));
+
+ RealMatrix[] initialWeights = createRandomWeights();
+
+ Collection<TrainingExample<Double, Double>> samples = createSamples(1000,
initialWeights[0].getColumnDimension() - 1);
+ TrainingSet<Double, Double> trainingSet = new TrainingSet<Double,
Double>(samples);
+ RealMatrix[] learntWeights =
backPropagationLearningStrategy.learnWeights(initialWeights, trainingSet);
+ assertNotNull(learntWeights);
+
+ for (int i = 0; i < learntWeights.length; i++) {
+ assertFalse("weights have not been changed",
learntWeights[i].equals(initialWeights[i]));
}
}
@@ -113,7 +131,7 @@ public class BackPropagationLearningStra
initialWeights[1] = new Array2DRowRealMatrix(new double[][]{{0d, 0d, 0d,
0d}, {1d, 0.5d, 1d, 0.5d}, {1d, 0.1d, 8d, 0.1d}, {1d, 0.1d, 8d, 0.2d}}); // 4 x
4
initialWeights[2] = new Array2DRowRealMatrix(new double[][]{{1d, 2d, 0.3d,
0.5d}}); // 1 x 4
- Collection<TrainingExample<Double, Double>> samples = createSamples(1000,
2);
+ Collection<TrainingExample<Double, Double>> samples = createSamples(10000,
2);
TrainingSet<Double, Double> trainingSet = new TrainingSet<Double,
Double>(samples);
RealMatrix[] learntWeights =
backPropagationLearningStrategy.learnWeights(initialWeights, trainingSet);
assertNotNull(learntWeights);
@@ -135,7 +153,7 @@ public class BackPropagationLearningStra
initialWeights[1] = new Array2DRowRealMatrix(new double[][]{{0d, 0d, 0d,
0d}, {1d, 0.5d, 1d, 0.5d}, {1d, 0.1d, 8d, 0.1d}, {1d, 0.1d, 8d, 0.2d}});
initialWeights[2] = new Array2DRowRealMatrix(new double[][]{{1d, 2d, 0.3d,
0.5d}});
- Collection<TrainingExample<Double, Double>> samples = createSamples(100,
2);
+ Collection<TrainingExample<Double, Double>> samples = createSamples(10000,
2);
TrainingSet<Double, Double> trainingSet = new TrainingSet<Double,
Double>(samples);
RealMatrix[] learntWeights =
backPropagationLearningStrategy.learnWeights(initialWeights, trainingSet);
assertNotNull(learntWeights);
@@ -157,7 +175,7 @@ public class BackPropagationLearningStra
initialWeights[1] = new Array2DRowRealMatrix(new double[][]{{0d, 0d, 0d,
0d}, {1d, Math.random(), Math.random(), Math.random()}, {1d, Math.random(),
Math.random(), Math.random()}, {1d, Math.random(), Math.random(),
Math.random()}});
initialWeights[2] = new Array2DRowRealMatrix(new double[][]{{1d,
Math.random(), Math.random(), Math.random()}});
- Collection<TrainingExample<Double, Double>> samples =
createSamples(500000, 2);
+ Collection<TrainingExample<Double, Double>> samples =
createSamples(1000000, 2);
TrainingSet<Double, Double> trainingSet = new TrainingSet<Double,
Double>(samples);
RealMatrix[] learntWeights =
backPropagationLearningStrategy.learnWeights(initialWeights, trainingSet);
assertNotNull(learntWeights);
Modified:
labs/yay/trunk/core/src/test/java/org/apache/yay/core/BasicPerceptronTest.java
URL:
http://svn.apache.org/viewvc/labs/yay/trunk/core/src/test/java/org/apache/yay/core/BasicPerceptronTest.java?rev=1497094&r1=1497093&r2=1497094&view=diff
==============================================================================
---
labs/yay/trunk/core/src/test/java/org/apache/yay/core/BasicPerceptronTest.java
(original)
+++
labs/yay/trunk/core/src/test/java/org/apache/yay/core/BasicPerceptronTest.java
Wed Jun 26 20:59:08 2013
@@ -25,7 +25,6 @@ import java.util.LinkedList;
import org.apache.yay.Feature;
import org.apache.yay.TrainingExample;
import org.apache.yay.TrainingSet;
-import org.apache.yay.core.BasicPerceptron;
import org.junit.Before;
import org.junit.Test;
@@ -40,12 +39,12 @@ public class BasicPerceptronTest {
@Before
public void setUp() throws Exception {
- Collection<TrainingExample<Double, Double>> samples = new
LinkedList<TrainingExample<Double, Double>>() ;
- samples.add(createTrainingExample(1d, 4d, 5d, 6d));
- samples.add(createTrainingExample(1d, 5d, 6d, 0.5d));
- samples.add(createTrainingExample(0.1d, 9d, 4d, 1.9d));
- samples.add(createTrainingExample(0.11d, 4d, 2.6d, 9.5d));
- dataset = new TrainingSet<Double,Double>(samples);
+ Collection<TrainingExample<Double, Double>> samples = new
LinkedList<TrainingExample<Double, Double>>() ;
+ samples.add(createTrainingExample(1d, 4d, 5d, 6d));
+ samples.add(createTrainingExample(1d, 5d, 6d, 0.5d));
+ samples.add(createTrainingExample(0.1d, 9d, 4d, 1.9d));
+ samples.add(createTrainingExample(0.11d, 4d, 2.6d, 9.5d));
+ dataset = new TrainingSet<Double,Double>(samples);
}
@Test
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